{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "import numpy as np\n",
    "from bqplot import pyplot as plt\n",
    "import bqplot\n",
    "from bqplot import Tooltip\n",
    "\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dashboard Experiment\n",
    "Cleaning imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo uses voila to render a notebook to a custom HTML page using gridstack.js for the layout of each output. In the cell metadata you can change the default cell with and height (in grid units between 1 and 12) by specifying.\n",
    " * `grid_row`\n",
    " * `grid_columns`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Toy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000\n",
    "x = np.linspace(0.0, 10.0, n)\n",
    "y = np.cumsum(np.random.randn(n)*10).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_1 = plt.figure(title='Scatter')\n",
    "def_tt = Tooltip(fields=['x'], formats=[''])\n",
    "scatter_1 = plt.scatter(x,y, tooltip=def_tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['red', 'blue', 'yellow', 'green', 'white', 'black', 'purple', 'orange', 'brown', 'pink']\n",
    "for i in range(10):\n",
    "    inst_color = colors[i]\n",
    "    time.sleep(1)\n",
    "    scatter_1.colors=[inst_color]\n",
    "    scatter_1.default_opacities = [float(i/20) + 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter_1.colors=['red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter_1.opacity = [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter_1.default_opacities=[1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.2, 0.1, 0.1, 0.01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5873f09228f04f459b0af843a0975e48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(scale=LinearScale(), side='bottom'), Axis(orientation='vertical', scale=LinearScale(), side=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
