mimischi/Creating animations with matplotlib.ipynb

## Creating animations with matplotlib.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Intro"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Date"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "2018-02-09 13:21:37 "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Motivation"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This notebook gives a very short example of how to create animations with `matplotlib`.\n\n_Note: The actual animation is not shown on GitHub. You need to download this notebook and run it yourself._"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Preparation"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Imports"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Here we import all used functions."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt       # Import plotting function from matplotlib\nimport numpy as np                    # Import numpy library\n\nfrom matplotlib import animation      # Import the animation module\nfrom IPython.display import HTML      # This lets us display the movie inside the notebook\n\n# Show matplotlib plots inside the notebook\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Generate some random dataset. Here we will create 100 frames with `x` and `y` coordinates between 0 and 1."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "data = np.random.rand(100, 2)\nprint(data)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[[ 0.84935839  0.41132654]\n [ 0.17555485  0.83193251]\n [ 0.9041901   0.33336556]\n [ 0.66627739  0.9697452 ]\n [ 0.68894658  0.19471348]\n [ 0.5824735   0.43533082]\n [ 0.77584939  0.96997069]\n [ 0.99819386  0.69803432]\n [ 0.04497476  0.96115146]\n [ 0.44732827  0.30672021]\n [ 0.07366294  0.39889686]\n [ 0.1050148   0.82347483]\n [ 0.42828094  0.43914305]\n [ 0.83196074  0.1003399 ]\n [ 0.43683157  0.5452606 ]\n [ 0.25974223  0.00798196]\n [ 0.58171241  0.48308596]\n [ 0.62347011  0.69856815]\n [ 0.56211277  0.29894896]\n [ 0.5856637   0.0693734 ]\n [ 0.33947753  0.30665868]\n [ 0.02589199  0.43554655]\n [ 0.89573104  0.02009607]\n [ 0.21748702  0.98488227]\n [ 0.2802664   0.07599353]\n [ 0.23189868  0.49844154]\n [ 0.59673699  0.53443779]\n [ 0.56355137  0.51063918]\n [ 0.55400403  0.66150198]\n [ 0.90784856  0.78373507]\n [ 0.31718708  0.64245593]\n [ 0.06426488  0.23624865]\n [ 0.0592509   0.36209249]\n [ 0.37581173  0.5209664 ]\n [ 0.17956789  0.78170761]\n [ 0.80258249  0.48890597]\n [ 0.5391004   0.14467206]\n [ 0.39185846  0.09808844]\n [ 0.13852318  0.8215773 ]\n [ 0.79873734  0.70523252]\n [ 0.8103653   0.34806973]\n [ 0.99470576  0.27437718]\n [ 0.92179415  0.13738557]\n [ 0.38024554  0.46579156]\n [ 0.40338737  0.76948153]\n [ 0.34763809  0.10219539]\n [ 0.44530691  0.81812966]\n [ 0.78899606  0.97497443]\n [ 0.8700494   0.31174178]\n [ 0.39558853  0.92966228]\n [ 0.5911712   0.02537511]\n [ 0.72451843  0.05206693]\n [ 0.45746891  0.14190012]\n [ 0.2904503   0.14017864]\n [ 0.12388151  0.81178593]\n [ 0.47265654  0.81887764]\n [ 0.55180375  0.47532112]\n [ 0.74239521  0.67982179]\n [ 0.07656544  0.50494024]\n [ 0.00637194  0.38960763]\n [ 0.35108909  0.82791854]\n [ 0.35577826  0.40288718]\n [ 0.90665843  0.29839659]\n [ 0.18894738  0.3803263 ]\n [ 0.50333826  0.33111219]\n [ 0.53450222  0.85155252]\n [ 0.28741516  0.10860943]\n [ 0.74741944  0.01024013]\n [ 0.26776894  0.97507509]\n [ 0.77138122  0.62222033]\n [ 0.44194717  0.24246568]\n [ 0.59886391  0.80809996]\n [ 0.66070404  0.77490463]\n [ 0.13340956  0.98162543]\n [ 0.14637542  0.32778983]\n [ 0.69660278  0.44324269]\n [ 0.15735863  0.8261729 ]\n [ 0.91424622  0.37276521]\n [ 0.62762719  0.48127716]\n [ 0.0453711   0.69968361]\n [ 0.19989325  0.05302128]\n [ 0.0044854   0.09485372]\n [ 0.21715782  0.99341994]\n [ 0.03988137  0.41690575]\n [ 0.94286975  0.02289669]\n [ 0.3700532   0.69106681]\n [ 0.17866589  0.28813123]\n [ 0.45412182  0.39545006]\n [ 0.37115219  0.98907273]\n [ 0.54849835  0.48575784]\n [ 0.88815911  0.74567918]\n [ 0.85206677  0.85792666]\n [ 0.75931338  0.5980223 ]\n [ 0.34132905  0.20951348]\n [ 0.9390265   0.68108024]\n [ 0.05445835  0.29556837]\n [ 0.6830691   0.56062966]\n [ 0.85594116  0.48693533]\n [ 0.65989809  0.98917631]\n [ 0.10953349  0.04577174]]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Animating"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The following shows a minimal animation function. It is composed of two separate functions: `init` and `animate`."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "# Generate figure object\nfig, ax = plt.subplots()\n\n# You need to set the limits of your axes to the correct region.\nax.set_xlim(-0.1, 1.1)\nax.set_ylim(-0.1, 1.1)\n\n# Create an empty scatter plot.\n# In every frame we are going to add data into it\nscatter = ax.scatter([], [])\n\n# initialization function: this will plot the background of each frame\ndef init():\n    return (scatter)\n\n\n# animation function: This loops over your data.\n# It receives the integer `i`, which you can use to plot the different frames.\ndef animate(i):\n    scatter.set_offsets(data[i])\n    return (scatter)\n\n# The actual matplotlib function that does the animation.\nanim = animation.FuncAnimation(\n    fig,             # The figure object that we generate on top\n    animate,         # The animate function to call in every frame\n    init_func=init,  # The init function\n    frames=100,      # Number of frames to use\n    interval=1000,   # Milliseconds to wait between each frame.\n    repeat=True      # Repeat the animation after it finished.\n)\n\n# Display the animation as HTML5 video.\n# You should be able to save it via right click.\nHTML(anim.to_html5_video())",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
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HJ05x375EA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