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Matthias Bussonnier
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custom display objects
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‎5b - Custom Display Logic Exercises.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Custom Display Logic Exercises"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from IPython.display import display\n",
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"from IPython.display import (\n",
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" display_png, display_html, display_latex,\n",
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" display_javascript, display_svg\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Circle class with custom display methods"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Write a simple `MyCircle` Python class. Here is a skeleton to get you started:\n",
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"\n",
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"```python\n",
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"class MyCircle(object):\n",
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" def __init__(self, center=(0.0,0.0), radius=1.0, color='blue'):\n",
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" self.center = center\n",
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" self.radius = radius\n",
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" self.color = color\n",
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"```\n",
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"\n",
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"Now add special display methods to this class for the following representations (remember to wrap them in Python strings):\n",
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"\n",
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"For HTML:\n",
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"\n",
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" ○\n",
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"\n",
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"For SVG:\n",
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"\n",
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" <svg width=\"100px\" height=\"100px\">\n",
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" <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"white\"/>\n",
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" </svg>\n",
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"\n",
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"For LaTeX (wrap with `$` and use a raw Python string):\n",
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"\n",
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" \\bigcirc\n",
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"\n",
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"For JavaScript:\n",
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"\n",
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" alert('I am a circle!');\n",
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"\n",
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"After you write the class, create an instance and then use `display_html`, `display_svg`, `display_latex` and `display_javascript` to display those representations."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Solution"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here is the solution to the simple `MyCircle` class:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%load soln/mycircle.py"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now create an instance and use the display methods:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"c = MyCircle()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display(c)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_html(c)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_svg(c)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_latex(c)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_javascript(c)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PNG formatter for `MyCircle`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's assume that the `MyCircle` class has already been defined and add a PNG representation using a formatter display function. Here is a function that converts a `MyCircle` instance to raw PNG data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from IPython.core.pylabtools import print_figure\n",
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"\n",
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"def circle_to_png(circle):\n",
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" \"\"\"Render AnotherCircle to png data using matplotlib\"\"\"\n",
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" fig, ax = plt.subplots()\n",
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" patch = plt.Circle(circle.center,\n",
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" radius=circle.radius,\n",
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" fc=circle.color,\n",
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" )\n",
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" ax.add_patch(patch)\n",
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" plt.axis('scaled')\n",
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" data = print_figure(fig, 'png')\n",
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" # We MUST close the figure, otherwise IPython's display machinery\n",
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" # will pick it up and send it as output, resulting in a double display\n",
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" plt.close(fig)\n",
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" return data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now use the IPython API to get the PNG formatter (`image/png`) and call the `for_type` method to register `circle_to_png` as the display function for `MyCircle`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%load soln/mycircle_png.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_png(c)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PNG formatter for NumPy arrays"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this exercise, you will register a display formatter function that generates a PNG representation of a 2d NumPy array. Here is the function that uses the [Python Imaging Library (PIL)](http://www.pythonware.com/products/pil/) to generate the raw PNG data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"from io import BytesIO\n",
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"import numpy as np\n",
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"\n",
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"def ndarray_to_png(x):\n",
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" if len(x.shape) != 2: return\n",
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" x = np.asarray(Image.fromarray(x).resize((500, 500)))\n",
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" x = (x - x.min()) / (x.max() - x.min())\n",
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" img = Image.fromarray((x*256).astype('uint8'))\n",
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" img_buffer = BytesIO()\n",
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" img.save(img_buffer, format='png')\n",
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" return img_buffer.getvalue()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Use the `for_type` method of the PNG formatter to register `ndarray_to_png` as the display function for `np.ndarray`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%load soln/ndarray_png.py"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now create a few NumPy arrays and display them. Notice that their default representation in the Notebook is PNG rather than text."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a = np.random.rand(100,100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can still display the plain text representation using the `display_pretty` function."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from IPython.display import display_pretty"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"display_pretty(a)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"b = np.linspace(0,100.0, 100**2).reshape((100,100))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"b"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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