00 - Available Libraries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebooks is to run simple tests on the libraries that are expected to be available on this server."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earth Engine\n",
    "\n",
    "## Earth Engine Python API\n",
    "\n",
    "\"The Earth Engine Python API is a client library that facilitates interacting with the Earth Engine servers using the Python programming language.\" \n",
    "\n",
    "* Homepage: https://earthengine.google.com/\n",
    "* Docs: https://developers.google.com/earth-engine/\n",
    "* Source code: https://github.com/google/earthengine-api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.140\n"
     ]
    }
   ],
   "source": [
    "import ee\n",
    "print(ee.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This notebook does not attempt to run a test on the library, because it requires authentication. Authentication will be covered in another notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Project packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Notebook\n",
    "\n",
    "The core library for Jupyter Interactive Notebooks.\n",
    "\n",
    "* Docs: https://jupyter-notebook.readthedocs.io/\n",
    "* Source code: https://github.com/jupyter/notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.4.1\n"
     ]
    }
   ],
   "source": [
    "import notebook\n",
    "print(notebook.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JupyterHub\n",
    "\n",
    "\"JupyterHub, a multi-user Hub, spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server.\"\n",
    "\n",
    "* Docs: https://jupyterhub.readthedocs.io\n",
    "* Source code: https://github.com/jupyterhub/jupyterhub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8.1\n"
     ]
    }
   ],
   "source": [
    "import jupyterhub\n",
    "print(jupyterhub.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JupyterLab\n",
    "\n",
    "\"JupyterLab computational environment.\"\n",
    "\n",
    "* Docs: http://jupyterlab.readthedocs.io\n",
    "* Source code: https://github.com/jupyterlab/jupyterlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.32.1\n"
     ]
    }
   ],
   "source": [
    "import jupyterlab\n",
    "print(jupyterlab.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Notebook Widgets\n",
    "\n",
    "\"Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox, etc.\"\n",
    "\n",
    "* Homepage: http://jupyter.org/widgets\n",
    "* Docs: https://ipywidgets.readthedocs.io\n",
    "* Source code: https://github.com/jupyter-widgets/ipywidgets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.2.1\n"
     ]
    }
   ],
   "source": [
    "import ipywidgets\n",
    "print(ipywidgets.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "526c194afb6b45eeab7559a011ee7292",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_slider= ipywidgets.widgets.IntSlider()\n",
    "display(my_slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_slider.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ipyleaflet\n",
    "\n",
    "ipyleaflet is a Jupyter Widget for interactive mapping, based on the [Leaflet](https://leafletjs.com/) Javascript library.\n",
    "\n",
    "* Source code: https://github.com/jupyter-widgets/ipyleaflet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8.1\n"
     ]
    }
   ],
   "source": [
    "import ipyleaflet\n",
    "print(ipyleaflet.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f6c4c89edac4d97960d79c63cc959d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_map = ipyleaflet.Map(center=(53.35, -6.2), zoom=11)\n",
    "my_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bqplot\n",
    "\n",
    "\"bqplot is a Grammar of Graphics-based interactive plotting framework for the Jupyter notebook.\" \n",
    "\n",
    "* Docs: https://bqplot.readthedocs.io/en/stable/\n",
    "* Source code: https://github.com/bloomberg/bqplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10.1\n"
     ]
    }
   ],
   "source": [
    "import bqplot\n",
    "print(bqplot.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1a63c5ec025449e84678f6a56aea4a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(grid_lines='none', label='Test X', scale=OrdinalScale(), tick_format='0.0f'), Axis(label='Te…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "size = 100\n",
    "x_data = range(size)\n",
    "np.random.seed(0)\n",
    "y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
    "y_data_2 = np.cumsum(np.random.randn(size))\n",
    "y_data_3 = np.cumsum(np.random.randn(size) * 100.)\n",
    "\n",
    "sc_ord = bqplot.OrdinalScale()\n",
    "sc_y = bqplot.LinearScale()\n",
    "sc_y_2 = bqplot.LinearScale()\n",
    "\n",
    "ord_ax = bqplot.Axis(label='Test X', scale=sc_ord, tick_format='0.0f', grid_lines='none')\n",
    "y_ax = bqplot.Axis(label='Test Y', scale=sc_y, \n",
    "            orientation='vertical', tick_format='0.2f', \n",
    "            grid_lines='solid')\n",
    "y_ax_2 = bqplot.Axis(label='Test Y 2', scale=sc_y_2, \n",
    "              orientation='vertical', side='right', \n",
    "              tick_format='0.0f', grid_lines='solid')\n",
    "\n",
    "line_chart  = bqplot.Lines(x=x_data[:10], y = [y_data[:10], y_data_2[:10] * 100, y_data_3[:10]],\n",
    "                   scales={'x': sc_ord, 'y': sc_y},\n",
    "                   labels=['Line1', 'Line2', 'Line3'], \n",
    "                   display_legend=True)\n",
    "\n",
    "bar_chart = bqplot.Bars(x=x_data[:10], \n",
    "                 y=[y_data[:10], y_data_2[:10] * 100, y_data_3[:10]], \n",
    "                 scales={'x': sc_ord, 'y': sc_y_2},\n",
    "                 labels=['Bar1', 'Bar2', 'Bar3'],\n",
    "                 display_legend=True)\n",
    "\n",
    "# the line does not have a Y value set. So only the bars will be displayed\n",
    "bqplot.Figure(axes=[ord_ax, y_ax],  marks=[bar_chart, line_chart], legend_location = 'bottom-left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JupyterLab Sidecar\n",
    "\n",
    "\"A sidecar output widget for JupyterLab\" \n",
    "\n",
    "* Source code: https://github.com/jupyter-widgets/jupyterlab-sidecar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.0.dev\n"
     ]
    }
   ],
   "source": [
    "import sidecar\n",
    "print(sidecar.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import IntSlider\n",
    "\n",
    "sc = sidecar.Sidecar(title='Sidecar Output')\n",
    "sl = IntSlider(description='Some slider')\n",
    "with sc:\n",
    "    display(sl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ipythonblocks\n",
    "\n",
    "\"Practice Python with colored grids in the IPython Notebook\"\n",
    "\n",
    "* Homepage: http://www.ipythonblocks.org\n",
    "* Source code: https://github.com/jiffyclub/ipythonblocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7.1\n"
     ]
    }
   ],
   "source": [
    "import ipythonblocks\n",
    "print(ipythonblocks.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 4]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 5]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 6]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 7]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 8]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [5, 9]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td></tr><tr><td title=\"Index: [6, 0]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 1]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 2]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 3]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 4]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 5]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 6]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 7]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 8]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [6, 9]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td></tr><tr><td title=\"Index: [7, 0]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 1]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 2]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 3]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 4]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 5]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 6]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 7]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 8]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [7, 9]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td></tr><tr><td title=\"Index: [8, 0]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 1]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 2]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 3]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 4]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 5]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 6]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 7]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 8]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [8, 9]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td></tr><tr><td title=\"Index: [9, 0]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 1]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 2]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 3]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 4]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 5]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 6]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 7]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 8]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td><td title=\"Index: [9, 9]&#10;Color: (123, 234, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 234, 123);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<ipythonblocks.ipythonblocks.BlockGrid at 0x7fed0b96bcf8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = ipythonblocks.BlockGrid(10, 10, fill=(123, 234, 123))\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Palettable\n",
    "\n",
    "\"Palettable is a library of color palettes for Python.\" \n",
    "\n",
    "* Docs: https://jiffyclub.github.io/palettable/\n",
    "* Source code: https://github.com/jiffyclub/palettable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1.1\n"
     ]
    }
   ],
   "source": [
    "import palettable\n",
    "print(palettable.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fed06cfa6a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed08dc1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import LogNorm\n",
    "from palettable.colorbrewer.sequential import YlGnBu_9\n",
    "\n",
    "#normal distribution center at x=0 and y=5\n",
    "x = np.random.randn(100000)\n",
    "y = np.random.randn(100000)+5\n",
    "\n",
    "plt.hist2d(x, y, bins=40, norm=LogNorm(), cmap=YlGnBu_9.mpl_colormap)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Altair\n",
    "\n",
    "\"Altair is a declarative statistical visualization library for Python.\" \n",
    "\n",
    "* Docs: https://altair-viz.github.io/\n",
    "* Source code: https://github.com/altair-viz/altair\n",
    "* Tutorials: https://github.com/altair-viz/altair_notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import altair as alt\n",
    "print(alt.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "values": [
         {
          "Acceleration": 12,
          "Cylinders": 8,
          "Displacement": 307,
          "Horsepower": 130,
          "Miles_per_Gallon": 18,
          "Name": "chevrolet chevelle malibu",
          "Origin": "USA",
          "Weight_in_lbs": 3504,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 11.5,
          "Cylinders": 8,
          "Displacement": 350,
          "Horsepower": 165,
          "Miles_per_Gallon": 15,
          "Name": "buick skylark 320",
          "Origin": "USA",
          "Weight_in_lbs": 3693,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 11,
          "Cylinders": 8,
          "Displacement": 318,
          "Horsepower": 150,
          "Miles_per_Gallon": 18,
          "Name": "plymouth satellite",
          "Origin": "USA",
          "Weight_in_lbs": 3436,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 12,
          "Cylinders": 8,
          "Displacement": 304,
          "Horsepower": 150,
          "Miles_per_Gallon": 16,
          "Name": "amc rebel sst",
          "Origin": "USA",
          "Weight_in_lbs": 3433,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10.5,
          "Cylinders": 8,
          "Displacement": 302,
          "Horsepower": 140,
          "Miles_per_Gallon": 17,
          "Name": "ford torino",
          "Origin": "USA",
          "Weight_in_lbs": 3449,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10,
          "Cylinders": 8,
          "Displacement": 429,
          "Horsepower": 198,
          "Miles_per_Gallon": 15,
          "Name": "ford galaxie 500",
          "Origin": "USA",
          "Weight_in_lbs": 4341,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 9,
          "Cylinders": 8,
          "Displacement": 454,
          "Horsepower": 220,
          "Miles_per_Gallon": 14,
          "Name": "chevrolet impala",
          "Origin": "USA",
          "Weight_in_lbs": 4354,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 8.5,
          "Cylinders": 8,
          "Displacement": 440,
          "Horsepower": 215,
          "Miles_per_Gallon": 14,
          "Name": "plymouth fury iii",
          "Origin": "USA",
          "Weight_in_lbs": 4312,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10,
          "Cylinders": 8,
          "Displacement": 455,
          "Horsepower": 225,
          "Miles_per_Gallon": 14,
          "Name": "pontiac catalina",
          "Origin": "USA",
          "Weight_in_lbs": 4425,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 8.5,
          "Cylinders": 8,
          "Displacement": 390,
          "Horsepower": 190,
          "Miles_per_Gallon": 15,
          "Name": "amc ambassador dpl",
          "Origin": "USA",
          "Weight_in_lbs": 3850,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 17.5,
          "Cylinders": 4,
          "Displacement": 133,
          "Horsepower": 115,
          "Miles_per_Gallon": null,
          "Name": "citroen ds-21 pallas",
          "Origin": "Europe",
          "Weight_in_lbs": 3090,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 11.5,
          "Cylinders": 8,
          "Displacement": 350,
          "Horsepower": 165,
          "Miles_per_Gallon": null,
          "Name": "chevrolet chevelle concours (sw)",
          "Origin": "USA",
          "Weight_in_lbs": 4142,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 11,
          "Cylinders": 8,
          "Displacement": 351,
          "Horsepower": 153,
          "Miles_per_Gallon": null,
          "Name": "ford torino (sw)",
          "Origin": "USA",
          "Weight_in_lbs": 4034,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10.5,
          "Cylinders": 8,
          "Displacement": 383,
          "Horsepower": 175,
          "Miles_per_Gallon": null,
          "Name": "plymouth satellite (sw)",
          "Origin": "USA",
          "Weight_in_lbs": 4166,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 11,
          "Cylinders": 8,
          "Displacement": 360,
          "Horsepower": 175,
          "Miles_per_Gallon": null,
          "Name": "amc rebel sst (sw)",
          "Origin": "USA",
          "Weight_in_lbs": 3850,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10,
          "Cylinders": 8,
          "Displacement": 383,
          "Horsepower": 170,
          "Miles_per_Gallon": 15,
          "Name": "dodge challenger se",
          "Origin": "USA",
          "Weight_in_lbs": 3563,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 8,
          "Cylinders": 8,
          "Displacement": 340,
          "Horsepower": 160,
          "Miles_per_Gallon": 14,
          "Name": "plymouth 'cuda 340",
          "Origin": "USA",
          "Weight_in_lbs": 3609,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 8,
          "Cylinders": 8,
          "Displacement": 302,
          "Horsepower": 140,
          "Miles_per_Gallon": null,
          "Name": "ford mustang boss 302",
          "Origin": "USA",
          "Weight_in_lbs": 3353,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 9.5,
          "Cylinders": 8,
          "Displacement": 400,
          "Horsepower": 150,
          "Miles_per_Gallon": 15,
          "Name": "chevrolet monte carlo",
          "Origin": "USA",
          "Weight_in_lbs": 3761,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 10,
          "Cylinders": 8,
          "Displacement": 455,
          "Horsepower": 225,
          "Miles_per_Gallon": 14,
          "Name": "buick estate wagon (sw)",
          "Origin": "USA",
          "Weight_in_lbs": 3086,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 15,
          "Cylinders": 4,
          "Displacement": 113,
          "Horsepower": 95,
          "Miles_per_Gallon": 24,
          "Name": "toyota corona mark ii",
          "Origin": "Japan",
          "Weight_in_lbs": 2372,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 15.5,
          "Cylinders": 6,
          "Displacement": 198,
          "Horsepower": 95,
          "Miles_per_Gallon": 22,
          "Name": "plymouth duster",
          "Origin": "USA",
          "Weight_in_lbs": 2833,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 15.5,
          "Cylinders": 6,
          "Displacement": 199,
          "Horsepower": 97,
          "Miles_per_Gallon": 18,
          "Name": "amc hornet",
          "Origin": "USA",
          "Weight_in_lbs": 2774,
          "Year": "1970-01-01"
         },
         {
          "Acceleration": 16,
          "Cylinders": 6,
          "Displacement": 200,
          "Horsepower": 85,
          "Miles_per_Gallon": 21,
          "Name": "ford maverick",
          "Origin": "USA",
          "Weight_in_lbs": 2587,
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          "Horsepower": 68,
          "Miles_per_Gallon": 29.5,
          "Name": "honda Accelerationord lx",
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          "Horsepower": 115,
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          "Name": "chevrolet caprice classic",
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          "Horsepower": 129,
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          "Horsepower": 138,
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          "Name": "dodge st. regis",
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         },
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          "Horsepower": 155,
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         {
          "Acceleration": 14.3,
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          "Horsepower": 142,
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          "Name": "ford country squire (sw)",
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         },
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          "Horsepower": 125,
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         },
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          "Horsepower": 150,
          "Miles_per_Gallon": 18.5,
          "Name": "chrysler lebaron town @ country (sw)",
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         },
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          "Miles_per_Gallon": 31.9,
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         },
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          "Horsepower": 65,
          "Miles_per_Gallon": 34.1,
          "Name": "maxda glc deluxe",
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         },
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          "Horsepower": 80,
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         },
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          "Miles_per_Gallon": 27.4,
          "Name": "amc spirit dl",
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         },
         {
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          "Displacement": 183,
          "Horsepower": 77,
          "Miles_per_Gallon": 25.4,
          "Name": "mercedes benz 300d",
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          "Year": "1979-01-01"
         },
         {
          "Acceleration": 17.4,
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          "Horsepower": 125,
          "Miles_per_Gallon": 23,
          "Name": "cadillac eldorado",
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          "Year": "1979-01-01"
         },
         {
          "Acceleration": 24.8,
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          "Horsepower": 71,
          "Miles_per_Gallon": 27.2,
          "Name": "peugeot 504",
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          "Year": "1979-01-01"
         },
         {
          "Acceleration": 22.2,
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          "Horsepower": 90,
          "Miles_per_Gallon": 23.9,
          "Name": "oldsmobile cutlass salon brougham",
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          "Year": "1979-01-01"
         },
         {
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          "Horsepower": 70,
          "Miles_per_Gallon": 34.2,
          "Name": "plymouth horizon",
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          "Year": "1979-01-01"
         },
         {
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          "Displacement": 105,
          "Horsepower": 70,
          "Miles_per_Gallon": 34.5,
          "Name": "plymouth horizon tc3",
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          "Year": "1979-01-01"
         },
         {
          "Acceleration": 19.2,
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          "Horsepower": 65,
          "Miles_per_Gallon": 31.8,
          "Name": "datsun 210",
          "Origin": "Japan",
          "Weight_in_lbs": 2020,
          "Year": "1979-01-01"
         },
         {
          "Acceleration": 14.7,
          "Cylinders": 4,
          "Displacement": 91,
          "Horsepower": 69,
          "Miles_per_Gallon": 37.3,
          "Name": "fiat strada custom",
          "Origin": "Europe",
          "Weight_in_lbs": 2130,
          "Year": "1979-01-01"
         },
         {
          "Acceleration": 16,
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          "Horsepower": 90,
          "Miles_per_Gallon": 28.4,
          "Name": "buick skylark limited",
          "Origin": "USA",
          "Weight_in_lbs": 2670,
          "Year": "1979-01-01"
         },
         {
          "Acceleration": 11.3,
          "Cylinders": 6,
          "Displacement": 173,
          "Horsepower": 115,
          "Miles_per_Gallon": 28.8,
          "Name": "chevrolet citation",
          "Origin": "USA",
          "Weight_in_lbs": 2595,
          "Year": "1979-01-01"
         },
         {
          "Acceleration": 12.9,
          "Cylinders": 6,
          "Displacement": 173,
          "Horsepower": 115,
          "Miles_per_Gallon": 26.8,
          "Name": "oldsmobile omega brougham",
          "Origin": "USA",
          "Weight_in_lbs": 2700,
          "Year": "1979-01-01"
         },
         {
          "Acceleration": 13.2,
          "Cylinders": 4,
          "Displacement": 151,
          "Horsepower": 90,
          "Miles_per_Gallon": 33.5,
          "Name": "pontiac phoenix",
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load a simple dataset as a pandas DataFrame\n",
    "from vega_datasets import data\n",
    "cars = data.cars()\n",
    "\n",
    "alt.Chart(cars).mark_point().encode(\n",
    "    x='Horsepower',\n",
    "    y='Miles_per_Gallon',\n",
    "    color='Origin',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PILLOW\n",
    "\n",
    "\"Pillow is the friendly PIL fork by Alex Clark and Contributors. PIL is the Python Imaging Library by Fredrik Lundh and Contributors.\"\n",
    "\n",
    "* Docs: http://pillow.readthedocs.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1.0\n"
     ]
    }
   ],
   "source": [
    "import PIL\n",
    "print(PIL.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## imageio\n",
    "\n",
    "\"Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, video, volumetric data, and scientific formats.\"\n",
    "\n",
    "* Home: https://imageio.github.io/\n",
    "* Docs: http://imageio.readthedocs.io/\n",
    "* Source Code: https://github.com/imageio/imageio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning\n",
    "\n",
    "## Tensorflow\n",
    "\n",
    "\"Computation using data flow graphs for scalable machine learning\"\n",
    "\n",
    "Homepage: https://www.tensorflow.org/\n",
    "Docs: https://www.tensorflow.org/api_docs/python/\n",
    "Source code: https://github.com/tensorflow/tensorflow\n",
    "Tutorials: https://www.tensorflow.org/tutorials/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/2\n",
      "60000/60000 [==============================] - 14s 227us/step - loss: 0.2461 - acc: 0.9250 - val_loss: 0.1085 - val_acc: 0.9658\n",
      "Epoch 2/2\n",
      "60000/60000 [==============================] - 13s 223us/step - loss: 0.1004 - acc: 0.9696 - val_loss: 0.0818 - val_acc: 0.9753\n",
      "Test loss: 0.0818361439583\n",
      "Test accuracy: 0.9753\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.keras.datasets import mnist\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense, Dropout\n",
    "from tensorflow.python.keras.optimizers import RMSprop\n",
    "from tensorflow.python import keras\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 2\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.reshape(60000, 784)\n",
    "x_test = x_test.reshape(10000, 784)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=RMSprop(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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