PlotAnimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with animation\n",
    "\n",
    "An animated plot is just a series of plots that are linked together to work as a movie.\n",
    "\n",
    "Matplotlib come with a rich collection of tools to allow us to make movies. In this notebook, we will give a simple example, to get you started. Read online to get many more examples and details.\n",
    "\n",
    "First we load in a bunch of modules that do the work in creating the animation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from numpy import *\n",
    "from matplotlib.pyplot import *\n",
    "from IPython.display import clear_output\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a simple for loop to do the animation.\n",
    "\n",
    "In this loop, for each index i, we plot a shifted sine function, $\\sin(x - i/10)$. We then show the plot, pause sleep for a fraction of a second using the \"sleep\" command, then clear the output and graph the next step in the animation. \n",
    "\n",
    "Here is the code. Run it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 20\n",
    "x = linspace(-pi,pi)\n",
    "for i in range(n):\n",
    "    plot(x,sin(x-i/10))\n",
    "    show()\n",
    "    sleep(0.1)\n",
    "    clear_output(wait=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This idea for animation came from a blog by Allen Downey.\n",
    "[https://www.allendowney.com/blog/2019/07/25/matplotlib-animation-in-jupyter/](https://www.allendowney.com/blog/2019/07/25/matplotlib-animation-in-jupyter/)\n",
    "\n",
    "A sample of his code is here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAACeNJREFUeJzt3N+L5XUdx/Hnq9lVWyvs1427S2sQlQRlDGIKXWjQT/KmCwODvNmbMo1AtJv+gRC7iGAxvVHyYvMiQrLox0U3W+Mq1ToVYqWbhluQhZG71ruLmWAzd853dr5fv3PePB8g7MyePb5Y5rnfc86c+aSqkNTTa+YeIGk6Bi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSY3umuNO3vGmlDh3cO/r9/vYX+0a/T2kZ/ZMXOF0vZtHtJgn80MG9/Ozhg6Pf74cvfd/o9ykto2P1w0G38yG61JiBS40ZuNSYgUuNGbjUmIFLjQ0KPMlHkvwmyRNJbp96lKRxLAw8yQrwdeCjwOXAp5NcPvUwSTs35Ap+JfBEVT1ZVaeBB4Drp50laQxDAt8PPH3Wxyc3P/c/khxOspZk7dRf/jXWPkk7MCTwV3q/6/8dxVpVR6pqtapW3/rmlZ0vk7RjQwI/CZz9xvIDwDPTzJE0piGB/xx4R5LLklwA3AB8Z9pZksaw8KfJquqlJJ8HHgZWgHuq6sTkyyTt2KAfF62qh4CHJt4iaWS+k01qzMClxgxcaszApcYMXGpskkMXf/uLfZMckPjwM4+Nfp/gYY7qyyu41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNTYJKeqTmWq00+nOK3Vk1q1G3gFlxozcKkxA5caM3CpMQOXGjNwqbGFgSc5mOTHSdaTnEhyy6sxTNLODfk++EvAl6rqeJLXA48k+UFVPT7xNkk7tPAKXlXPVtXxzV//HVgH9k89TNLObes5eJJDwBXAsSnGSBrX4LeqJnkd8G3g1qr62yv8/mHgMMBF7BttoKTzN+gKnmQvG3HfX1UPvtJtqupIVa1W1epeLhxzo6TzNORV9ADfBNar6s7pJ0kay5Ar+DXAZ4Brkzy2+d/HJt4laQQLn4NX1U+BvApbJI3Md7JJjRm41JiBS40ZuNSYgUuNLdWhi1OZ4oDEKQ5yBA9z1PZ4BZcaM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGvNU1YlMdfqpp7VqO7yCS40ZuNSYgUuNGbjUmIFLjRm41JiBS40NDjzJSpJHk3x3ykGSxrOdK/gtwPpUQySNb1DgSQ4AHwfunnaOpDENvYLfBdwG/PtcN0hyOMlakrUzvDjKOEk7szDwJJ8AnquqR7a6XVUdqarVqlrdy4WjDZR0/oZcwa8BPpnk98ADwLVJ7pt0laRRLAy8qu6oqgNVdQi4AfhRVd04+TJJO+b3waXGtvXz4FX1E+AnkyyRNDqv4FJjBi41ZuBSYwYuNWbgUmOeqrpkPK1V2+EVXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzFNVBXhaa1dewaXGDFxqzMClxgxcaszApcYMXGpsUOBJLklyNMmvk6wn+cDUwyTt3NDvg38N+F5VfSrJBcC+CTdJGsnCwJO8Afgg8FmAqjoNnJ52lqQxDHmI/nbgFHBvkkeT3J3k4ol3SRrBkMD3AO8HvlFVVwAvALe//EZJDidZS7J2hhdHninpfAwJ/CRwsqqObX58lI3g/0dVHamq1apa3cuFY26UdJ4WBl5VfwKeTvLOzU9dBzw+6SpJoxj6KvrNwP2br6A/Cdw03SRJYxkUeFU9BqxOvEXSyHwnm9SYgUuNGbjUmIFLjRm41JiBS415qqomtUyntXY8qdUruNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNeeiiltIUByROcZAjzHuYo1dwqTEDlxozcKkxA5caM3CpMQOXGjNwqbFBgSf5YpITSX6V5FtJLpp6mKSdWxh4kv3AF4DVqnoPsALcMPUwSTs39CH6HuC1SfYA+4BnppskaSwLA6+qPwJfBZ4CngWer6rvv/x2SQ4nWUuydoYXx18qaduGPER/I3A9cBlwKXBxkhtffruqOlJVq1W1upcLx18qaduGPET/EPC7qjpVVWeAB4Grp50laQxDAn8KuCrJviQBrgPWp50laQxDnoMfA44Cx4Ffbv6ZIxPvkjSCQT8PXlVfAb4y8RZJI/OdbFJjBi41ZuBSYwYuNWbgUmOeqiptmur00ylOa73yw/8YdDuv4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSY6mq8e80OQX8YcBN3wL8efQB01mmvcu0FZZr727Y+raqeuuiG00S+FBJ1qpqdbYB27RMe5dpKyzX3mXa6kN0qTEDlxqbO/AjM///t2uZ9i7TVliuvUuzddbn4JKmNfcVXNKEZgs8yUeS/CbJE0lun2vHIkkOJvlxkvUkJ5LcMvemIZKsJHk0yXfn3rKVJJckOZrk15t/xx+Ye9NWknxx8+vgV0m+leSiuTdtZZbAk6wAXwc+ClwOfDrJ5XNsGeAl4EtV9W7gKuBzu3jr2W4B1uceMcDXgO9V1buA97KLNyfZD3wBWK2q9wArwA3zrtraXFfwK4EnqurJqjoNPABcP9OWLVXVs1V1fPPXf2fjC3D/vKu2luQA8HHg7rm3bCXJG4APAt8EqKrTVfXXeVcttAd4bZI9wD7gmZn3bGmuwPcDT5/18Ul2eTQASQ4BVwDH5l2y0F3AbcC/5x6ywNuBU8C9m08n7k5y8dyjzqWq/gh8FXgKeBZ4vqq+P++qrc0VeF7hc7v65fwkrwO+DdxaVX+be8+5JPkE8FxVPTL3lgH2AO8HvlFVVwAvALv59Zg3svFI8zLgUuDiJDfOu2prcwV+Ejh41scH2MUPdZLsZSPu+6vqwbn3LHAN8Mkkv2fjqc+1Se6bd9I5nQROVtV/HxEdZSP43epDwO+q6lRVnQEeBK6eedOW5gr858A7klyW5AI2Xqj4zkxbtpQkbDxHXK+qO+fes0hV3VFVB6rqEBt/rz+qql15lamqPwFPJ3nn5qeuAx6fcdIiTwFXJdm3+XVxHbv4RUHYeIj0qquql5J8HniYjVci76mqE3NsGeAa4DPAL5M8tvm5L1fVQzNu6uRm4P7Nf+ifBG6aec85VdWxJEeB42x8d+VRdvm72nwnm9SY72STGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqbH/AAcfJCfPJk4KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10\n",
    "a = zeros((n, n))\n",
    "figure()\n",
    "\n",
    "for i in range(n):\n",
    "    imshow(a)\n",
    "    show()\n",
    "    a[i, i] = 1\n",
    "    sleep(0.1)\n",
    "    clear_output(wait=True)\n",
    "imshow(a);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The hard way\n",
    "\n",
    "Smoother annimations are possible. However, you need to have the appropriate software tools installed, which is not always the case. \n",
    "\n",
    "We we show this method anyway, in case you need to try something else.\n",
    "\n",
    "First, install a few tools here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animate tools\n",
    "\n",
    "First, we create a figure object using the subplot command, and define two functions \"init\" and \"animate\". Init() sets up the y-data in the plot for us, for the initial frame in the animation. Animate() defines the data for the series of plots we want to display.\n",
    "\n",
    "In this case, we plot translates of the sine function. Animate(i) uses the variable i to count frames, and that i value determines how much we translate the sine function by.\n",
    "\n",
    "In this example, i will range from 0 to 99, giving a full cycle of the sine function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots()\n",
    "\n",
    "x = linspace(0, 2*pi, 100)\n",
    "line, = ax.plot(x, sin(x));\n",
    "\n",
    "\n",
    "def init():  # only required for blitting to give a clean slate.\n",
    "    line.set_ydata([np.nan] * len(x))\n",
    "    return line,\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    line.set_ydata(sin(x + 2*pi*i / 100))  # update the data.\n",
    "    return line,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we call the animation function as follows, making use of the figure, init and animate functions defined above.\n",
    "\n",
    "This creates a new data object call \"ani\" which holds the movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ani = animation.FuncAnimation(\n",
    "    fig, animate, init_func=init, interval=20, blit=True, save_count=100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we display the movie using the HTML() command from the module IPython.display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2. Saving the movie\n",
    "A simple \"save\" command turns the animation into an mp4 movie file, that you can use elsewhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ani.save(\"movie.mp4\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3. Playing the movie\n",
    "Now that the movie is saved, we can just play it directly in html. This is a useful trick in case you have a movie that takes a long time to create. You can create and save, and let the next user just play the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"movie.mp4\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<iframe src=\"movie.mp4\"></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)"
   ]
  },
  {
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}