11_widgets_and_more.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Widgets and more...\n",
    "## Wittle widgets\n",
    "\n",
    "<img src=\"images/logos.3.600.wide.png\" width=\"600\" align='right'>\n",
    "\n",
    "### Chalmer Lowe\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objectives\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the end of this lesson, students will be able to:\n",
    "\n",
    "* Insert widgets into their Notebooks\n",
    "* Nest widgets\n",
    "* Capture outputs from widgets\n",
    "* Install new modules into the current Notebook environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# You want more Jupyter magic?\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with Ipython Magics...\n",
    "\n",
    "IPython magics are a series of quick commands that you can use to simplify some of your workflow tasks.\n",
    "\n",
    "1. Type **%quickref** in any empty cell\n",
    "1. Press the **Enter** key\n",
    "\n",
    "Let's go through some of these together in a Code Console.\n",
    "\n",
    "1. Open a Code Console associated with this notebook.\n",
    "1. Right click on any cell in the notebook\n",
    "1. Click on **New Console for Notebook**\n",
    "\n",
    "Some suggested items to test...\n",
    "\n",
    "1. Get help on any magic\n",
    "1. Use %timeit\n",
    "1. Use %%timeit\n",
    "1. Use a system command (`ls` OR `dir` are good candidates)\n",
    "1. Store the results of a system command using Python variable assignment \n",
    "1. Use %recall\n",
    "\n",
    "Some commands will work better in your notebook...\n",
    "\n",
    "1. Open an empty cell **in your notebook, not the console**\n",
    "1. Type `%load sample_files/sample.py` \n",
    "1. Press **Enter**\n",
    "1. Execute the cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How about widgets?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing ipywidgets\n",
    "\n",
    "Open a command prompt and stop your `jupyter lab` server, by pressing:\n",
    "\n",
    "```bash\n",
    "Ctrl+C\n",
    "```\n",
    "This should return you to your command prompt.\n",
    "\n",
    "On the command prompt type the following to ensure that you are in the desired virtual environment and to install `nodejs` and the package manager `npm`:\n",
    "\n",
    "```Bash\n",
    "$ conda activate mylab\n",
    "(mylab) $ conda install -y -c conda-forge nodejs\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From there, install `ipywidgets` and a `labextension`\n",
    "\n",
    "```\n",
    "(mylab) $ conda install -y -c conda-forge ipywidgets\n",
    "(mylab) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import IntSlider, interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40167cb41be3426eba751470b497d220",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(f, x=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c4793dc7c204ebabe7f9964695a328d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=15, max=50, min=-20)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.IntSlider(min=-20,max=50,step=1,value=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27f09c386b18411781417baa930f2d85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatText(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "791b911ee2194a2d919efb3c88b583ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from traitlets import link\n",
    "from ipywidgets import FloatText, FloatSlider\n",
    "\n",
    "a = FloatText()\n",
    "b = FloatSlider()\n",
    "display(a,b)\n",
    "\n",
    "mylink = link((a, 'value'), (b, 'value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "400c3c67185f44f4a2f8f51f73ec80d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=7, continuous_update=False, description='Test:', max=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.IntSlider(\n",
    "    value=7,\n",
    "    min=0,\n",
    "    max=10,\n",
    "    step=1,\n",
    "    description='Test:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d',\n",
    "    slider_color='purple'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5999b688734047f18064b15a1d7455fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatRangeSlider(value=(5.0, 7.5), continuous_update=False, description='Test:', max=10.0, readout_format='.1f…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.FloatRangeSlider(\n",
    "    value=[5, 7.5],\n",
    "    min=0,\n",
    "    max=10.0,\n",
    "    step=0.1,\n",
    "    description='Test:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    "    slider_color='white',\n",
    "    color='black'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61aae3b95ae5401b923f92fee27c6ae3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=74, bar_style='danger', description='Loading:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.IntProgress(\n",
    "    value=74,\n",
    "    min=0,\n",
    "    max=100,\n",
    "    step=1,\n",
    "    description='Loading:',\n",
    "    bar_style='danger', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    orientation='horizontal'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54b6846461e34dcb9d4a311c29f253a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=74, bar_style='danger', description='Loading:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pb = widgets.IntProgress(\n",
    "    value=74,\n",
    "    min=0,\n",
    "    max=100,\n",
    "    step=1,\n",
    "    description='Loading:',\n",
    "    bar_style='danger', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    orientation='horizontal'\n",
    ")\n",
    "\n",
    "import time\n",
    "\n",
    "display(pb)\n",
    "\n",
    "for i in range(100):\n",
    "    pb.value = i\n",
    "    \n",
    "    time.sleep(0.1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42f5c01cf54b41419b38d0b8a4600d24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Number:', index=1, options={'One': 1, 'Two': 2, 'Three': 3}, value=2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.Dropdown(\n",
    "    options={'One': 1, 'Two': 2, 'Three': 3},\n",
    "    value=2,\n",
    "    description='Number:',\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61791a493607474199e9354b0b3e9539",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatText(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0602cfc28d524f969209096367b3071b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = widgets.FloatText()\n",
    "b = widgets.FloatSlider()\n",
    "display(a,b)\n",
    "\n",
    "mylink = widgets.jslink((a, 'value'), (b, 'value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9513c314a5dd490c88301f552f65c45a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Textarea(value='Hello World', description='String:', placeholder='Type something')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.Textarea(\n",
    "    value='Hello World',\n",
    "    placeholder='Type something',\n",
    "    description='String:',\n",
    "    disabled=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e84634821f5492c967f448838e23913",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DatePicker(value=None, description='Pick a Date')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.DatePicker(\n",
    "    description='Pick a Date',\n",
    "    disabled=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc7c8b3b2c2b4196aab673b915a7ed25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ColorPicker(value='blue', description='Pick a color')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.ColorPicker(\n",
    "    concise=False,\n",
    "    description='Pick a color',\n",
    "    value='blue',\n",
    "    disabled=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "421fe82d86d54dd5bf59cedd117a0f33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Text(value='', description='P0'), Text(value='', description='P1'), Text(value='', description='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab_contents = ['P0', 'P1', 'P2', 'P3', 'P4']\n",
    "children = [widgets.Text(description=name) for name in tab_contents]\n",
    "tab = widgets.Tab()\n",
    "tab.children = children\n",
    "for i, tab_name in enumerate(tab_contents):\n",
    "    tab.set_title(i, 'Tab ' + tab_name)\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a2da7397be74578a6c5b030cf824941",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(IntSlider(value=0), Text(value='')), _titles={'0': 'Slider', '1': 'Text'})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accordion = widgets.Accordion(children=[widgets.IntSlider(), widgets.Text()])\n",
    "accordion.set_title(0, 'Slider')\n",
    "accordion.set_title(1, 'Text')\n",
    "accordion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c147fc2b56248b68f0ed2bdae6ffcd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(IntSlider(value=0, description='a'), IntSlider(value=0, description='b'), IntSli…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = widgets.IntSlider(description='a')\n",
    "b = widgets.IntSlider(description='b')\n",
    "c = widgets.IntSlider(description='c')\n",
    "def f(a, b, c):\n",
    "    print('{}*{}*{}={}'.format(a, b, c, a*b*c))\n",
    "\n",
    "out = widgets.interactive_output(f, {'a': a, 'b': b, 'c': c})\n",
    "\n",
    "widgets.HBox([widgets.VBox([a, b, c]), out])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Need to install a package on the fly?\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to install a package in the current Jupyter kernel space, but it is somewhat dicey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.5\n",
      "  latest version: 4.5.11\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# install a conda package in the current Jupyter kernel\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} astropy\n",
    "\n",
    "# install a conda package in the current Jupyter kernel\n",
    "# import sys\n",
    "# !conda install --yes --prefix {sys.prefix} bokeh\n",
    "\n",
    "# Install a pip package in the current Jupyter kernel\n",
    "# import sys\n",
    "# !{sys.executable} -m pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.4'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "astropy.version.version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you complete this exercise, please put your green post-it on your monitor. \n",
    "\n",
    "If you want to continue on at your own-pace, please feel free to do so.\n",
    "\n",
    "<img src='./images/green_sticky.300px.png' width='200' style='float:left'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like you are done here! **Congrats**.\n",
    "\n",
    "Please proceed to the next lesson."
   ]
  }
 ],
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