{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# `bqplot` Interactive Demo\n",
    "\n",
    "Plotting in JupyterLite\n",
    "\n",
    "`bqplot` can be installed in this deployment (it provides the bqplot federated extension), but you will need to make your own deployment to have access to other interactive widgets libraries."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import piplite\n",
    "\n",
    "await piplite.install('bqplot')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from bqplot import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "n = 100\n",
    "\n",
    "x = list(range(n))\n",
    "y = np.cumsum(np.random.randn(n)) + 100.\n",
    "\n",
    "sc_x = LinearScale()\n",
    "sc_y = LinearScale()\n",
    "\n",
    "lines = Lines(\n",
    "    x=x, y=y,\n",
    "    scales={'x': sc_x, 'y': sc_y}\n",
    ")\n",
    "ax_x = Axis(scale=sc_x, label='Index')\n",
    "ax_y = Axis(scale=sc_y, orientation='vertical', label='lines')\n",
    "\n",
    "Figure(marks=[lines], axes=[ax_x, ax_y], title='Lines')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "lines.colors = ['green']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "lines.fill = 'bottom'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "lines.marker = 'circle'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "n = 100\n",
    "\n",
    "x = list(range(n))\n",
    "y = np.cumsum(np.random.randn(n))\n",
    "\n",
    "sc_x = LinearScale()\n",
    "sc_y = LinearScale()\n",
    "\n",
    "bars = Bars(\n",
    "    x=x, y=y,\n",
    "    scales={'x': sc_x, 'y': sc_y}\n",
    ")\n",
    "ax_x = Axis(scale=sc_x, label='Index')\n",
    "ax_y = Axis(scale=sc_y, orientation='vertical', label='bars')\n",
    "\n",
    "Figure(marks=[bars], axes=[ax_x, ax_y], title='Bars', animation_duration=1000)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "bars.y = np.cumsum(np.random.randn(n))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plots which use Nested Buffers"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from bqplot import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(0)\n",
    "y1 = np.cumsum(np.random.randn(150)) + 100.\n",
    "y2 = np.cumsum(np.random.randn(150)) + 100.\n",
    "y3 = np.cumsum(np.random.randn(150)) + 100.\n",
    "y4 = np.cumsum(np.random.randn(150)) + 100.\n",
    "\n",
    "sc_x = LinearScale()\n",
    "sc_y = LinearScale()\n",
    "\n",
    "lines = Lines(x=np.arange(len(y1)), y=[y1, y2, y3, y4],\n",
    "             scales={'x': sc_x, 'y': sc_y})\n",
    "ax_x = Axis(scale=sc_x, label='Index')\n",
    "ax_y = Axis(scale=sc_y, orientation='vertical', label='lines')\n",
    "\n",
    "Figure(marks=[lines], axes=[ax_x, ax_y], title='Lines')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}