{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `bqplot` Interactive Demo\n",
    "\n",
    "Plotting in JupyterLite\n",
    "\n",
    "`bqplot` can be installed in this deployment (it provides the bqplot federated\n",
    "extension), but you will need to make your own deployment to have access to other\n",
    "interactive widgets libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q bqplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bqplot import *\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "n = 100\n",
    "\n",
    "x = list(range(n))\n",
    "y = np.cumsum(np.random.randn(n)) + 100.0\n",
    "\n",
    "sc_x = LinearScale()\n",
    "sc_y = LinearScale()\n",
    "\n",
    "lines = Lines(x=x, y=y, 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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.colors = [\"green\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.fill = \"bottom\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.marker = \"circle\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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(x=x, y=y, 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=\"bars\")\n",
    "\n",
    "Figure(marks=[bars], axes=[ax_x, ax_y], title=\"Bars\", animation_duration=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bars.y = np.cumsum(np.random.randn(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots which use Nested Buffers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bqplot import *\n",
    "\n",
    "np.random.seed(0)\n",
    "y1 = np.cumsum(np.random.randn(150)) + 100.0\n",
    "y2 = np.cumsum(np.random.randn(150)) + 100.0\n",
    "y3 = np.cumsum(np.random.randn(150)) + 100.0\n",
    "y4 = np.cumsum(np.random.randn(150)) + 100.0\n",
    "\n",
    "sc_x = LinearScale()\n",
    "sc_y = LinearScale()\n",
    "\n",
    "lines = Lines(x=np.arange(len(y1)), y=[y1, y2, y3, y4], 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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}