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timkpaine · Jul 27, 2018 · 72dc4e0 · 72dc4e0
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diff --git a/experimental/DataShaderExample.ipynb b/experimental/DataShaderExample.ipynb
diff --git a/experimental/ipympl.ipynb b/experimental/ipympl.ipynb
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+{
+ "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": 3,
+   "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": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7da8f712c59e40a78067301f5a5c75c2",
+       "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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a7c53ea26d9404e81f721cd7fa2af54",
+       "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",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09b306227930436c927d73f4abe4fc25",
+       "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### "
+   ]
+  },
+  {
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}