{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Matplotlib Jupyter Widget Backend\n",
    "\n",
    "Enabling interaction with matplotlib charts in the Jupyter notebook and JupyterLab\n",
    "\n",
    "https://github.com/matplotlib/jupyter-matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enabling the `widget` backend.\n",
    "# This requires jupyter-matplotlib a.k.a. ipympl.\n",
    "# ipympl can be install via pip or conda.\n",
    "%matplotlib widget\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing matplotlib interactions with a simple plot\n",
    "fig = plt.figure()\n",
    "plt.plot(np.sin(np.linspace(0, 20, 100)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.toolbar_visible = False\n",
    "fig.canvas.header_visible = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.footer_visible = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.resizable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.capture_scroll = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Grab some test data.\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "\n",
    "# Plot a basic wireframe.\n",
    "ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A more complex example from the matplotlib gallery\n",
    "np.random.seed(0)\n",
    "\n",
    "n_bins = 10\n",
    "x = np.random.randn(1000, 3)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "ax0, ax1, ax2, ax3 = axes.flatten()\n",
    "\n",
    "colors = ['red', 'tan', 'lime']\n",
    "ax0.hist(x, n_bins, density=1, histtype='bar', color=colors, label=colors)\n",
    "ax0.legend(prop={'size': 10})\n",
    "ax0.set_title('bars with legend')\n",
    "\n",
    "ax1.hist(x, n_bins, density=1, histtype='bar', stacked=True)\n",
    "ax1.set_title('stacked bar')\n",
    "\n",
    "ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)\n",
    "ax2.set_title('stack step (unfilled)')\n",
    "\n",
    "# Make a multiple-histogram of data-sets with different length.\n",
    "x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]\n",
    "ax3.hist(x_multi, n_bins, histtype='bar')\n",
    "ax3.set_title('different sample sizes')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.toolbar_position = 'right'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.toolbar_visible = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactions with other widgets and layouting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When using the `widget` backend from ipympl,\n",
    "# fig.canvas is a proper Jupyter interactive widget, which can be embedded in\n",
    "# an ipywidgets layout. See https://ipywidgets.readthedocs.io/en/stable/examples/Layout%20Templates.html\n",
    "\n",
    "# One can bound figure attributes to other widget values.\n",
    "from ipywidgets import AppLayout, FloatSlider\n",
    "\n",
    "plt.ioff()\n",
    "\n",
    "slider = FloatSlider(\n",
    "    orientation='horizontal',\n",
    "    description='Factor:',\n",
    "    value=1.0,\n",
    "    min=0.02,\n",
    "    max=2.0\n",
    ")\n",
    "\n",
    "slider.layout.margin = '0px 30% 0px 30%'\n",
    "slider.layout.width = '40%'\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.layout.min_height = '400px'\n",
    "plt.title('Plotting: y=sin({} * x)'.format(slider.value))\n",
    "\n",
    "x = np.linspace(0, 20, 500)\n",
    "\n",
    "lines = plt.plot(x, np.sin(slider.value * x))\n",
    "\n",
    "def update_lines(change):\n",
    "    plt.title('Plotting: y=sin({} * x)'.format(change.new))\n",
    "    lines[0].set_data(x, np.sin(change.new * x))\n",
    "    fig.canvas.draw()\n",
    "    fig.canvas.flush_events()\n",
    "\n",
    "slider.observe(update_lines, names='value')\n",
    "\n",
    "AppLayout(\n",
    "    center=fig.canvas,\n",
    "    footer=slider,\n",
    "    pane_heights=[0, 6, 1]\n",
    ")"
   ]
  }
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