ipympl.ipynb

{
 "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": 1,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5273a11b53744a1940545237f95786a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing matplotlib interactions with a simple plot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(np.sin(np.linspace(0, 20, 100)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f44adb1bc4d4423a553e5755954899c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "fig.canvas.layout.max_width = '800px'\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8363a08d76dc4d5e8ce6f1b47a85b601",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A more complex example from the matplotlib gallery\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.toolbar_position = 'right'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.canvas.toolbar_visible = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "002cf7ad72c04039b5b278c5b9536ab4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatSlider(value=1.0, max=2.0, min=0.02, orientation='vertical'), FigureCanvasNbAgg()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# When using the `widget` backend from ipympl,\n",
    "# fig.canvas is a proper Jupyter interactive widget, which can be embedded in\n",
    "# Layout classes like HBox and Vbox.\n",
    "\n",
    "# One can bound figure attributes to other widget values.\n",
    "\n",
    "from ipywidgets import HBox, FloatSlider\n",
    "\n",
    "plt.ioff()\n",
    "plt.clf()\n",
    "\n",
    "slider = FloatSlider(\n",
    "    orientation='vertical',\n",
    "    value=1.0,\n",
    "    min=0.02,\n",
    "    max=2.0\n",
    ")\n",
    "\n",
    "fig = plt.figure(3)\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",
    "    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",
    "HBox([slider, fig.canvas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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