jupyter-collection/scientific-computing-1/Lecture-4-Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib - 2D and 3D plotting in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "J.R. Johansson (jrjohansson at gmail.com)\n",
    "\n",
    "The latest version of this [IPython notebook](http://ipython.org/notebook.html) lecture is available at [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n",
    "\n",
    "The other notebooks in this lecture series are indexed at [http://jrjohansson.github.io](http://jrjohansson.github.io)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is an excellent 2D and 3D graphics library for generating scientific figures. Some of the many advantages of this library include:\n",
    "\n",
    "* Easy to get started\n",
    "* Support for $\\LaTeX$ formatted labels and texts\n",
    "* Great control of every element in a figure, including figure size and DPI. \n",
    "* High-quality output in many formats, including PNG, PDF, SVG, EPS, and PGF.\n",
    "* GUI for interactively exploring figures *and* support for headless generation of figure files (useful for batch jobs).\n",
    "\n",
    "One of the key features of matplotlib that I would like to emphasize, and that I think makes matplotlib highly suitable for generating figures for scientific publications is that all aspects of the figure can be controlled *programmatically*. This is important for reproducibility and convenient when one needs to regenerate the figure with updated data or change its appearance. \n",
    "\n",
    "More information at the Matplotlib web page: http://matplotlib.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get started using Matplotlib in a Python program, either include the symbols from the `pylab` module (the easy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MATLAB-like API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to get started with plotting using matplotlib is often to use the MATLAB-like API provided by matplotlib. \n",
    "\n",
    "It is designed to be compatible with MATLAB's plotting functions, so it is easy to get started with if you are familiar with MATLAB.\n",
    "\n",
    "To use this API from matplotlib, we need to include the symbols in the `pylab` module: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple figure with MATLAB-like plotting API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 10)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6fa20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "plot(x, y, 'r')\n",
    "xlabel('x')\n",
    "ylabel('y')\n",
    "title('title')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the plotting related functions in MATLAB are covered by the `pylab` module. For example, subplot and color/symbol selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7001780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(1,2,1)\n",
    "plot(x, y, 'r--')\n",
    "subplot(1,2,2)\n",
    "plot(y, x, 'g*-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The good thing about the pylab MATLAB-style API is that it is easy to get started with if you are familiar with MATLAB, and it has a minimum of coding overhead for simple plots. \n",
    "\n",
    "However, I'd encourage not using the MATLAB compatible API for anything but the simplest figures.\n",
    "\n",
    "Instead, I recommend learning and using matplotlib's object-oriented plotting API. It is remarkably powerful. For advanced figures with subplots, insets and other components it is very nice to work with. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The matplotlib object-oriented API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main idea with object-oriented programming is to have objects that one can apply functions and actions on, and no object or program states should be global (such as the MATLAB-like API). The real advantage of this approach becomes apparent when more than one figure is created, or when a figure contains more than one subplot. \n",
    "\n",
    "To use the object-oriented API we start out very much like in the previous example, but instead of creating a new global figure instance we store a reference to the newly created figure instance in the `fig` variable, and from it we create a new axis instance `axes` using the `add_axes` method in the `Figure` class instance `fig`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although a little bit more code is involved, the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEgCAYAAACq+TSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4VFX+x/H3CU2KILgx9CaiIIHQRIGVKFWkiKIURQQWK2LZdXGxgLoq6GJ3UVE04FJWUBGkCoSSH71KWxQlEjABDL0ISc7vjxsiJWVIZuZO+byeZx7CzJ25Hy4z8805555zjbUWERGRnES4HUBERAKbCoWIiORKhUJERHKlQiEiIrlSoRARkVypUIiISK5UKEQugjGmijHmsDHG5LJNhjGmpj9zifiSCoVIHowxPxtjbgaw1u6y1pa2mROQjDELjTH9z3uKJidJSFGhEPG+HFsbIsFIhUIkF8aYcUBVYEZml9NTmV1LEcaYfwJ/Bt7LfOydbJ5f1BjzL2NMojHmV2PMv40xxfz97xApCBUKkVxYa+8FfgFutdaWBv5LZteStfZZYAkwKLM7anA2LzESqAXUz/yzEvC8P7KLeIsKhYhn8tudNBB4wlp7yFp7DBgB9PJeLBHfK+x2AJFQZYyJBEoAa846SSoCjWFIkFGhEMlbbmcx5fbYfuA4cK219lfvRhLxH3U9ieQtGTgzL8Jwbosg5azHzpF5Cu0Y4K3M1gXGmErGmHY+zCridSoUInkbATxnjEkF7uDcVsTbwJ3GmN+MMW9l3nf240OAH4HlxpiDwFygth8yi3iN8eWFi4wxlYFxQBSQAXxkrX3XGDMMZ5Bvb+amQ621s30WRERE8s3XhaI8UN5au94YUwpYA3QFegBHrLVv+GznIiLiFT4dzLbWJuP072KtPWqM2YpzHjnozA8RkaDgtzEKY0x1IAZYkXnXIGPMemPMx8aYMv7KISIiF8enXU9ZO3G6neKBl6y10zLPANlvrbWZyyBUsNYOyOZ5WlxNRMTHrLW59vD4vEVhjCkMTAHGW2unZYbaZ/+oUGOApjk931qr21m3YcOGuZ4h0G46JjomOiYXeUtJwVatip061aPvcX90PY0Ftlhr3z5zR+Yg9xm3A5v8kENERNLSoEcPuOceuP12j57i08FsY0wL4G7ge2PMOpzzy4cCvY0xMTinzO4EHvBlDhERyTRkCBQrBi++6PFTfH3WUwJQKJuHNGcin2JjY92OEHB0TC6kY3IhHRNgwgT4+mtYtQoKZffVnD2/DGbnlzHGBnI+EZGgsWEDtGkD8+dD/fpZdxtjsG4PZouIiMtSU53xiHffPadIeEotChGRUJaeDh07Qr16MGrUBQ+rRSEiEu6ee84502nkyHy/hK5HISISqr780hnAXrUKCuf/615dTyIioWjLFmjVCmbPhsaNc9xMXU8iIuHo0CHo1g1efz3XIuEptShEREJJRgbcdhtUrQrvvZfn5p60KDRGISISSv75T+d02ClTvPaSKhQiIqFixgz46CNYvRqKFvXay2qMQjxWr149Fi9e7HYMj3Ts2JHx48fn+Hi/fv14/vnn/ZhIxMd++AH694cvvoDy5fPe/iKoUIjHNm3axI033ujXfSYmJhIREUFGRkaO27zwwgvce++959w3c+ZM+vTpA0BcXBx//vOffZpTxFVHjzrjEi+9BDfc4PWXV6GQgJWeno619sxgW75f58xriIQka6FfP6dA3H+/T3ahQiEeq1GjBgsWLACc3+J79OhB3759KV26NNHR0axduzZr25EjR1K5cmVKly5NnTp1WLhwIeB8aY8YMYJatWoRGRlJz549OXjwIPBH62Hs2LFUq1aN1q1b06pVK6y1XHbZZZQuXZoVK1ack2nOnDm88sorTJ48mUsvvZSGDRsCcNNNNzF27Fi2bdvGQw89xLJly7j00kspV65ctv+2GTNm0LBhQ8qWLUvLli35/vvvvX78RHzitdfgl1+cM5x89AuRCoXk2/Tp0+nduzeHDh2ic+fOPPLIIwBs376d999/nzVr1nD48GHmzJlD9erVAXjnnXf45ptvWLJkCXv27KFs2bI8/PDD57zu4sWL2bZtG3PmzGHx4sUYYzh8+DCHDx+mWbNm52zbvn17hg4dSo8ePThy5Ajr1q075/FrrrmGDz74gBtuuIEjR46Qmpp6wb9j3bp1DBgwgDFjxpCamsoDDzxAly5dOH36tBePlogPzJsHb78NU6fCJZf4bDcqFJJvLVu2pH379hhj6NOnDxs3bgSgUKFCnDp1ik2bNpGWlkbVqlWpUaMGAB9++CEvv/wyFSpUoEiRIjz//PNMmTIlawzCGMMLL7xA8eLFKVasWNa+fDmfZsyYMTz44IM0adIk699SrFgxli9f7rN9ihTYzz9Dnz4wcSJUruzTXalQSL6VP+vMihIlSnDy5EkyMjK48soreeuttxg+fDhRUVH07t2b5ORkwOle6tatG+XKlaNcuXLUrVuXIkWKkJKSkvValX38pj9fYmIio0aNyspUtmxZkpKS2LNnj19ziHjs+HFn5vXQoc4yHT6mQiE+0bNnT5YsWUJiYiIAQ4YMAaBq1arMmjWL1NRUUlNTOXDgAMeOHaNChQpZzz174NmTQei8tsnr8SpVqvDMM8+ck+no0aP06NEjz32L+J21zqB1dDQ8+qhfdqlCIV5zpnto+/btLFy4kFOnTlG0aFGKFy9ORITzVnvggQcYOnQov/zyCwD79u3jm2++ueA1zoiMjCQiIoIdO3bkuN+oqCh27tyZY/dUVFQUSUlJOY45DBw4kA8++ICVK1cCcOzYMWbOnMmxY8c8/JeL+NE778DmzfDhhz4bvD6fCoV4zNPf3H///XeefvppIiMjqVixIvv27ePVV18F4LHHHqNr1660a9eOMmXK0Lx586wv6Oz2Ubx4cZ555hlatGhBuXLlztn2jDvvvBNrLZdffjlNmjS54HVuvvlmrr32WsqXL88VV1xxwfMbN27MmDFjGDRoEOXKlaN27drExcV5eFRE/GjRInj1VfjqKyhRwm+71aKAHqpevTplypQhIiKCIkWKZPuFJSLiM7t2QbNmEBcHbdt67WW1KKAXRUREEB8fT9myZd2OIiLh5uRJuOMOePxxrxYJT6nryUPW2lyXkRAR8Qlr4ZFHoHp1eOopVyKoUHjIGEPbtm1p2rQpY8aMcTuOiISLDz+EFStg7Fi/DV6fT11PHkpISKBChQrs27ePtm3bUqdOHVq2bJn1uNYSkmAUKGOAkoNly2DYMEhIgFKlXIuhFoWHzpznHxkZSbdu3bIdzLbWBsRt2LBhrmcIpByBlMVfOZKPJDNh4wT6f92fqm9WpeKoitz71b2MWz+O3Yd3Y60KRMD79Ve4806nJVGrlqtR1KLwwPHjx8nIyKBUqVIcO3aMuXPnMmzYMLdjiWQ5euooixMX891P3zH/5/n8cugXWlVrRZuabXiqxVNcffnVavUGk1OnnCLxwANw661up1Gh8ERKSgrdunXDGENaWhp333037dq1czuWhLHT6adZuXtlVmFY++tamlZqSpsabfio00c0rtiYwhH6eAetJ56Ayy+HZ55xOwmgQuGRGjVqsH79erdjeCw2NtbtCEDg5IDAyZLfHNZaNu/bnFUYFicu5sqyV9KmZhuevfFZWlZtSYki/puAJT702Wcwf74zgB0RGKMDmnDnJQW9uI7I+XYd2sX8n+dnFYcSRUrQpkYb2tRsw001buJPJf5UoNfXezYArV4NHTs6M7Dr1PHLLj2ZcKdC4SX60ElBHThxgPid8Xz303d89/N3pJ5IpXWN1rSp2YbWNVpTo2wNr+5P79kAs3cvNG0Kb74Jt9/ut92qUPiRPnRysU6mneT/dv2fUxh++o5t+7fRomqLrOJQP6o+EcZ3XQ96zwaQtDRnxnXz5vDyy37dtQqFH+lDJ3lJz0hnffL6rBbD8qTlRF8RnVUYrq98PcUKF8v7hbxE79kA8te/OivCfvstFCrk112rUPiRPnSSnX3H9jHrx1nM2D6D+T/PJ6pkFG1qOuMMraq1oswlZVzLpvdsgJg4EZ59Flatghyu6e5LKhR+pA+dgHN20pZ9W5i+fTrTt09n095NtKnZhk5XdaJ9rfZ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x72d0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
    "\n",
    "# main figure\n",
    "axes1.plot(x, y, 'r')\n",
    "axes1.set_xlabel('x')\n",
    "axes1.set_ylabel('y')\n",
    "axes1.set_title('title')\n",
    "\n",
    "# insert\n",
    "axes2.plot(y, x, 'g')\n",
    "axes2.set_xlabel('y')\n",
    "axes2.set_ylabel('x')\n",
    "axes2.set_title('insert title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we don't care about being explicit about where our plot axes are placed in the figure canvas, then we can use one of the many axis layout managers in matplotlib. My favorite is `subplots`, which can be used like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8065710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8498390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'r')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was easy, but it isn't so pretty with overlapping figure axes and labels, right?\n",
    "\n",
    "We can deal with that by using the `fig.tight_layout` method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x892eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'r')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure size, aspect ratio and DPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib allows the aspect ratio, DPI and figure size to be specified when the `Figure` object is created, using the `figsize` and `dpi` keyword arguments. `figsize` is a tuple of the width and height of the figure in inches, and `dpi` is the dots-per-inch (pixel per inch). To create an 800x400 pixel, 100 dots-per-inch figure, we can do: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8065320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same arguments can also be passed to layout managers, such as the `subplots` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x807b278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12,3))\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save a figure to a file we can use the `savefig` method in the `Figure` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can also optionally specify the DPI and choose between different output formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\", dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What formats are available and which ones should be used for best quality?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. For scientific papers, I recommend using PDF whenever possible. (LaTeX documents compiled with `pdflatex` can include PDFs using the `includegraphics` command). In some cases, PGF can also be good alternative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legends, labels and titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have covered the basics of how to create a figure canvas and add axes instances to the canvas, let's look at how decorate a figure with titles, axis labels, and legends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure titles**\n",
    "\n",
    "A title can be added to each axis instance in a figure. To set the title, use the `set_title` method in the axes instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.set_title(\"title\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Axis labels**\n",
    "\n",
    "Similarly, with the methods `set_xlabel` and `set_ylabel`, we can set the labels of the X and Y axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Legends**\n",
    "\n",
    "Legends for curves in a figure can be added in two ways. One method is to use the `legend` method of the axis object and pass a list/tuple of legend texts for the previously defined curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.legend([\"curve1\", \"curve2\", \"curve3\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method described above follows the MATLAB API. It is somewhat prone to errors and unflexible if curves are added to or removed from the figure (resulting in a wrongly labelled curve).\n",
    "\n",
    "A better method is to use the `label=\"label text\"` keyword argument when plots or other objects are added to the figure, and then using the `legend` method without arguments to add the legend to the figure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.plot(x, x**2, label=\"curve1\")\n",
    "ax.plot(x, x**3, label=\"curve2\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage with this method is that if curves are added or removed from the figure, the legend is automatically updated accordingly.\n",
    "\n",
    "The `legend` function takes an optional keyword argument `loc` that can be used to specify where in the figure the legend is to be drawn. The allowed values of `loc` are numerical codes for the various places the legend can be drawn. See http://matplotlib.org/users/legend_guide.html#legend-location for details. Some of the most common `loc` values are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x3dfc1d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
    "ax.legend(loc=1) # upper right corner\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.legend(loc=3) # lower left corner\n",
    "ax.legend(loc=4) # lower right corner\n",
    "# .. many more options are available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure shows how to use the figure title, axis labels and legends described above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6feab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x**2, label=\"y = x**2\")\n",
    "ax.plot(x, x**3, label=\"y = x**3\")\n",
    "ax.legend(loc=2); # upper left corner\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting text: LaTeX, fontsize, font family"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above is functional, but it does not (yet) satisfy the criteria for a figure used in a publication. First and foremost, we need to have LaTeX formatted text, and second, we need to be able to adjust the font size to appear right in a publication.\n",
    "\n",
    "Matplotlib has great support for LaTeX. All we need to do is to use dollar signs encapsulate LaTeX in any text (legend, title, label, etc.). For example, `\"$y=x^3$\"`.\n",
    "\n",
    "But here we can run into a slightly subtle problem with LaTeX code and Python text strings. In LaTeX, we frequently use the backslash in commands, for example `\\alpha` to produce the symbol $\\alpha$. But the backslash already has a meaning in Python strings (the escape code character). To avoid Python messing up our latex code, we need to use \"raw\" text strings. Raw text strings are prepended with an '`r`', like `r\"\\alpha\"` or `r'\\alpha'` instead of `\"\\alpha\"` or `'\\alpha'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x706e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n",
    "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.set_xlabel(r'$\\alpha$', fontsize=18)\n",
    "ax.set_ylabel(r'$y$', fontsize=18)\n",
    "ax.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change the global font size and font family, which applies to all text elements in a figure (tick labels, axis labels and titles, legends, etc.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Update the matplotlib configuration parameters:\n",
    "matplotlib.rcParams.update({'font.size': 18, 'font.family': 'serif'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x702a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n",
    "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.set_xlabel(r'$\\alpha$')\n",
    "ax.set_ylabel(r'$y$')\n",
    "ax.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good choice of global fonts are the STIX fonts: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Update the matplotlib configuration parameters:\n",
    "matplotlib.rcParams.update({'font.size': 18, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c84908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n",
    "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.set_xlabel(r'$\\alpha$')\n",
    "ax.set_ylabel(r'$y$')\n",
    "ax.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, alternatively, we can request that matplotlib uses LaTeX to render the text elements in the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({'font.size': 18, 'text.usetex': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x96ba940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n",
    "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.set_xlabel(r'$\\alpha$')\n",
    "ax.set_ylabel(r'$y$')\n",
    "ax.set_title('title');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# restore\n",
    "matplotlib.rcParams.update({'font.size': 12, 'font.family': 'sans', 'text.usetex': False})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting colors, linewidths, linetypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where `'b'` means blue, `'g'` means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, 'b.-' means a blue line with dots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x96df0b8>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MATLAB style line color and style \n",
    "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
    "ax.plot(x, x**3, 'g--') # green dashed line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x6fbc048>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x984b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x+1, color=\"red\", alpha=0.5) # half-transparent red\n",
    "ax.plot(x, x+2, color=\"#1155dd\")        # RGB hex code for a bluish color\n",
    "ax.plot(x, x+3, color=\"#15cc55\")        # RGB hex code for a greenish color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Line and marker styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the line width, we can use the `linewidth` or `lw` keyword argument. The line style can be selected using the `linestyle` or `ls` keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x891f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "ax.plot(x, x+1, color=\"blue\", linewidth=0.25)\n",
    "ax.plot(x, x+2, color=\"blue\", linewidth=0.50)\n",
    "ax.plot(x, x+3, color=\"blue\", linewidth=1.00)\n",
    "ax.plot(x, x+4, color=\"blue\", linewidth=2.00)\n",
    "\n",
    "# possible linestype options ‘-‘, ‘--’, ‘-.’, ‘:’, ‘steps’\n",
    "ax.plot(x, x+5, color=\"red\", lw=2, linestyle='-')\n",
    "ax.plot(x, x+6, color=\"red\", lw=2, ls='-.')\n",
    "ax.plot(x, x+7, color=\"red\", lw=2, ls=':')\n",
    "\n",
    "# custom dash\n",
    "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
    "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
    "\n",
    "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
    "ax.plot(x, x+ 9, color=\"green\", lw=2, ls='--', marker='+')\n",
    "ax.plot(x, x+10, color=\"green\", lw=2, ls='--', marker='o')\n",
    "ax.plot(x, x+11, color=\"green\", lw=2, ls='--', marker='s')\n",
    "ax.plot(x, x+12, color=\"green\", lw=2, ls='--', marker='1')\n",
    "\n",
    "# marker size and color\n",
    "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
    "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
    "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
    "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
    "        markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control over axis appearance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The appearance of the axes is an important aspect of a figure that we often need to modify to make a publication quality graphics. We need to be able to control where the ticks and labels are placed, modify the font size and possibly the labels used on the axes. In this section we will look at controlling those properties in a matplotlib figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we might want to configure is the ranges of the axes. We can do this using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatically getting \"tightly fitted\" axes ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cb3750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "axes[0].plot(x, x**2, x, x**3)\n",
    "axes[0].set_title(\"default axes ranges\")\n",
    "\n",
    "axes[1].plot(x, x**2, x, x**3)\n",
    "axes[1].axis('tight')\n",
    "axes[1].set_title(\"tight axes\")\n",
    "\n",
    "axes[2].plot(x, x**2, x, x**3)\n",
    "axes[2].set_ylim([0, 60])\n",
    "axes[2].set_xlim([2, 5])\n",
    "axes[2].set_title(\"custom axes range\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logarithmic scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact only one application of a more general transformation system in Matplotlib. Each of the axes' scales are set seperately using `set_xscale` and `set_yscale` methods which accept one parameter (with the value \"log\" in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108836e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n",
    "      \n",
    "axes[0].plot(x, x**2, x, np.exp(x))\n",
    "axes[0].set_title(\"Normal scale\")\n",
    "\n",
    "axes[1].plot(x, x**2, x, np.exp(x))\n",
    "axes[1].set_yscale(\"log\")\n",
    "axes[1].set_title(\"Logarithmic scale (y)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Placement of ticks and custom tick labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explicitly determine where we want the axis ticks with `set_xticks` and `set_yticks`, which both take a list of values for where on the axis the ticks are to be placed. We can also use the `set_xticklabels` and `set_yticklabels` methods to provide a list of custom text labels for each tick location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x10a3ae610>,\n",
       " <matplotlib.text.Text at 0x10a3aedd0>,\n",
       " <matplotlib.text.Text at 0x10a3fe110>,\n",
       " <matplotlib.text.Text at 0x10a3fe750>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a35d810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "\n",
    "ax.plot(x, x**2, x, x**3, lw=2)\n",
    "\n",
    "ax.set_xticks([1, 2, 3, 4, 5])\n",
    "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$'], fontsize=18)\n",
    "\n",
    "yticks = [0, 50, 100, 150]\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18); # use LaTeX formatted labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of more advanced methods for controlling major and minor tick placement in matplotlib figures, such as automatic placement according to different policies. See http://matplotlib.org/api/ticker_api.html for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scientific notation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With large numbers on axes, it is often better use scientific notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1086350d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "      \n",
    "ax.plot(x, x**2, x, np.exp(x))\n",
    "ax.set_title(\"scientific notation\")\n",
    "\n",
    "ax.set_yticks([0, 50, 100, 150])\n",
    "\n",
    "from matplotlib import ticker\n",
    "formatter = ticker.ScalarFormatter(useMathText=True)\n",
    "formatter.set_scientific(True) \n",
    "formatter.set_powerlimits((-1,1)) \n",
    "ax.yaxis.set_major_formatter(formatter) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axis number and axis label spacing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e5aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distance between x and y axis and the numbers on the axes\n",
    "matplotlib.rcParams['xtick.major.pad'] = 5\n",
    "matplotlib.rcParams['ytick.major.pad'] = 5\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "      \n",
    "ax.plot(x, x**2, x, np.exp(x))\n",
    "ax.set_yticks([0, 50, 100, 150])\n",
    "\n",
    "ax.set_title(\"label and axis spacing\")\n",
    "\n",
    "# padding between axis label and axis numbers\n",
    "ax.xaxis.labelpad = 5\n",
    "ax.yaxis.labelpad = 5\n",
    "\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# restore defaults\n",
    "matplotlib.rcParams['xtick.major.pad'] = 3\n",
    "matplotlib.rcParams['ytick.major.pad'] = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Axis position adjustments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, when saving figures the labels are sometimes clipped, and it can be necessary to adjust the positions of axes a little bit. This can be done using `subplots_adjust`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f68910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "      \n",
    "ax.plot(x, x**2, x, np.exp(x))\n",
    "ax.set_yticks([0, 50, 100, 150])\n",
    "\n",
    "ax.set_title(\"title\")\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "\n",
    "fig.subplots_adjust(left=0.15, right=.9, bottom=0.1, top=0.9);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axis grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `grid` method in the axis object, we can turn on and off grid lines. We can also customize the appearance of the grid lines using the same keyword arguments as the `plot` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAADMCAYAAACxx+0TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4FFX28PHvTcISwpKwI4KBoMgiBlREQWWEqCMoiBsqaBBRR0QFZ9QZN9zl9yJuMLIoqKgDqCCiqCAYFAERIcgmECCEfUkICdnTfd8/qjtkJZ30UtWV83mefqCqq7vPsfH06epb9yqtNUIIIYQQwnshZgcghBBCCGEX0lgJIYQQQviINFZCCCGEED4ijZUQQgghhI9IYyWEEEII4SPSWAkhhBBC+Ig0VkIIIYQQPnLGxkop9bBSap1SKlcpNauCY55TSjmVUleX2j9BKXXcdXvdl0ELIYQnpIYJIQItrJL7DwAvAdcC4aXvVErFALcAB0vtfwAYBHRz7VqqlNqjtZ7mdcRCCOE5qWFCiIA64xkrrfUCrfVCILWCQyYDTwIFpfbfA0zUWh/UWh8EJgLxXsYqhBBVIjVMCBFono6xUmV2KHUrkKu1/q6c4zsDG4tt/wl0qXp4QgjhE1LDhBABUdlPgW4lFhRUSjUAXgH6V3B8feBkse0M1z4hhDCD1DAhREB42liV/rY3HpittU6p4JhTQMNi241c+0o+qVKyArQQNZDWuswZJD/zeQ1TqqWGusX2RLpuycDeckI4B4guZ78cL8fL8dY/vhHG/997K69fWutKbxiDP2cV294AHAMOuW6FGGMY/uW6/1fgvmLHjwRWlfO82i6ef/55s0PwCbvkobXkYlWu/+89qj2+uvmjhkn9sia75GKXPLS2Vy6e1K8znrFSSoUCtTDObIUqpeoADqAfp892KeB3YCzgHqvwMTBOKbXYdf844O0zdnhCCOFjUsOEEIFW2U+BzwLPFdseBozXWr9Y/CCllAM4obXOBtBaT1NKtQc2uQ6ZobWe7qOYLSk5OdnsEHzCLnmA5CIAqWFCiAA7Y2OltR6PMRbhjLTW7crZ9yTGZcw1QmxsrNkh+IRd8gDJRUgN81Tfvn3NDsFn7JKLXfIAe+XiCWX8ZGjSiyulzXx9IUTgKaXMGLzuc1K/hKh5PKlfslagEEJUyzlmB+Azdvql2S652CUPsFcunpDGykcSEhLMDsEn7JIHSC7C36LNDsBn7PTBZ5dc7JIH2CsXT0hjJYQQQgjhI9JY+YhdBufZJQ+QXIQQQgSeNFZCCCGEED4ijZWP2GUMjF3yAMlFCCFE4EljJYQQ1ZJsdgA+Ex1tdgS+Y5dc7JIH2CsXT8g8VkKIgJJ5rIQQwUrmsRJCCCGECCBprHzELmNg7JIHSC5CCCECTxorIUTAyE9nQohg5Wn9kjFWQoiA2HZsG3d8eQcb/7FRxlgJIYKKUzu57pPrWHr3UhljJYQwn1M7uf+b+9l4ZKPZofiQrBVoRXbJxS55gD1ymfHHDJbuXurRsWdsrJRSDyul1imlcpVSs4rt76WUWqqUSlVKHVVKzVNKtSz12AlKqeOu2+vVyiSI2GUMjF3yAMnFSt5f/z4rU1bSIqJFQF/XvzUs2u/xB4odPvjc7JKLXfKA4M/lYOZBnvjxCY+Pr+yM1QHgJWBmqf2RwFSMr2znAJlA8aL1ADAI6Oa63eDaJ4SoYQ5lHuKJpUZReufv7wT65aWGCSG88uj3j5KRl8GAcwd4dPwZGyut9QKt9UIgtdT+77XWX2qtT2mtc4ApQO9ih9wDTNRaH9RaHwQmAvFVyCPo2GUtN7vkAZKLVTz6/aOczDvJgHMHcGvnWwP62lLDhBDe+Hr713yx9QsiakXw3wH/9egxno6xqmyg6ZXA5mLbnYHigyn+BLp4+FpCCJtYtH0Rn2/9vKgoKWXamHWpYUKIKsnIy2D04tEAvHL1K7Rt1Najx3naWFV46YtSqhvwLPCvYrvrAyeLx+faZ1vBPgbGzS55gORitsy8TB5a/BBQtaLkJ1LDhBBV8szyZ9ifsZ9LzrqEh3s+7PHjwjw8rtxve0qpDsBi4BGt9a/F7joFNCy23ci1r4z4+HiiXQsJRUZGEhsbW/Szh/vDRLYDt52YmGipeLzZTkxMtFQ8NW07/q149m/bz3lNzyP1u1Tip8ZjIj/UsFTi48cXbcXG9iU2ti/R0eWvjZacXP4gXiscf/gwuHt3K8TjzfFhFXyqBUv87uNLP8bseLw5PjraWvF4cvxv+39j8trJhCSH0COzB4+Pe4n09LLHl8ejeayUUi8BZ2utRxTbdw6QALymtZ5e6vhfgVla6/dd2yOBkVrry0sdJ/PACGFDaw+spdf7vQhRIay7fx2xLWOL7jNjrUB/1DCpX0LYU4GjgIumX8Smo5t4sveTvN7/9EXBXq8VqJQKVUrVxTizFaqUquPa1xpYDkwuXZBcPgbGKaXOch07DviwaqkJIYJRgaOAUYtGodE8ftnjJZqqQJMaJoSoqjdWv8Gmo5toH9We5656rsqPr2yM1bNANvAkMAzIAZ4BRgLtgPFKqUzXLcP9IK31NGARsAlj0OeiCoqXbQTjGJjy2CUPkFzMMmn1JP488iftItvxfN/nzQ5HapgQwmNJaUm8sOIFAKYOmEq9WvWq/BxnHGOltR4PjK/g7hcreeyTGMVMCFFDJKUlMX7FeACmDZxWraLkS1LDhBCe0lrz4DcPkluYy/Buw4mLiavW88hagUIIn9BaEzc7jmV7ljG823A+vunjco8zY4yVP0j9EsJePkr8iPiF8TQJb8K20dtoFtGszDFej7ESQghPzf5zNsv2LKNJeBPeuOYNs8MJAFkr0Irskotd8oDgyOVY1jHGLRkHwJvXvlluU+Upaax8JJjGwJyJXfIAySWQjmUdY9wPRlGadO0kr4pS8Ig2OwCfCYYPPk/ZJRe75AHBkcu4JeNIy0mjf/v+DOs2zKvnksZKCOG1x5c8TmpOKv3a9WN4t+FmhyOEEB5bsmsJn/z5CXXD6jJ1wFSvV4iQxspHgnktt+LskgdILoGydNdSZv852yhKA70vSkIIESjZBdk8+M2DAIy/ajwxjWO8fk5prIQQ1ZZdkM2D3xpF6fmrnqdD4w4mRySEEJ4bnzCePel76NaiG+MuG+eT55TGykesPgbGU3bJAySXQHgh4QV2n9hNtxbdePyyx80ORwghPLbh0AYmrZ6EQjHjhhnUCq3lk+eVxkoIUS2JhxN5Y/UbPi9KwSPZ7AB8pry11IKVXXKxSx5gzVwcTgejFo3CoR2M6TmGnq17+uy5ZR4rIUSVOZwOen3Qi3UH1/FIz0d4++9ve/xYmcdKCGG2t9a8xdgfxtKmYRu2PLSFBnUaePQ4mcdKCOEXk9dOZt3BdZzd8Gxevvpls8MRQgiP7U3fyzPLnwFgyvVTPG6qPCWNlY9YdQxMVdklD5Bc/CXlZApPL38agP9e/1+fFyUhhPAXrTWjF48mqyCLWzvfyg0db/D5a0hjJYTwmNaah759iKyCLG7pfItfipIQQvjLvC3z+HbntzSq04i3r/N8CENVyBgrIYTH5m2Zx+1f3E6jOo3YNnobrRq0qvJzyBgrIYQZTuSc4Pwp53M06yjTBk7j/ovur/JzyBgrIYTPnMg5wSPfPQLAhP4TqtVU2YusFWhFdsnFLnmAdXJ5YukTHM06yhVtr+C+Hvf57XXO2FgppR5WSq1TSuUqpWaVuq+fUuovpVSWUmq5UqptqfsnKKWOu26v+yN4K7HSGBhv2CUPkFx87ckfn+RI1hH6tO3DqItGmR2OR/xbw6L9GnsgWeWDzxfskotd8gBr5LIieQXvb3if2qG1mTZwGiHKf+eVKnvmA8BLwMziO5VSTYEvgaeBKGAdMLfY/Q8Ag4BurtsNrn1CiCD0896fmbF+BrVCajF94HS/FiUfkxomRA2XW5jLA98Y//v+p89/6NSsk19f74zVUWu9QGu9EEgtddcQYLPW+kutdT4wHrhQKXWe6/57gIla64Na64PARCDep5FbjJXXcqsKu+QBkouv5BXmcf8iYyzCf67wf1HyJalhQojXfnmN7anbOb/p+TzV5ym/v56nXztLD9TqAmx0b2its4Ek136AzsX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d8d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n",
    "\n",
    "# default grid appearance\n",
    "axes[0].plot(x, x**2, x, x**3, lw=2)\n",
    "axes[0].grid(True)\n",
    "\n",
    "# custom grid appearance\n",
    "axes[1].plot(x, x**2, x, x**3, lw=2)\n",
    "axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axis spines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change the properties of axis spines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a410bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,2))\n",
    "\n",
    "ax.spines['bottom'].set_color('blue')\n",
    "ax.spines['top'].set_color('blue')\n",
    "\n",
    "ax.spines['left'].set_color('red')\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "# turn off axis spine to the right\n",
    "ax.spines['right'].set_color(\"none\")\n",
    "ax.yaxis.tick_left() # only ticks on the left side"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Twin axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to have dual x or y axes in a figure; for example, when plotting curves with different units together. Matplotlib supports this with the `twinx` and `twiny` functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e34f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(x, x**2, lw=2, color=\"blue\")\n",
    "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n",
    "for label in ax1.get_yticklabels():\n",
    "    label.set_color(\"blue\")\n",
    "    \n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(x, x**3, lw=2, color=\"red\")\n",
    "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n",
    "for label in ax2.get_yticklabels():\n",
    "    label.set_color(\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axes where x and y is zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a47a110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data',0)) # set position of x spine to x=0\n",
    "\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.spines['left'].set_position(('data',0))   # set position of y spine to y=0\n",
    "\n",
    "xx = np.linspace(-0.75, 1., 100)\n",
    "ax.plot(xx, xx**3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other 2D plot styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the regular `plot` method, there are a number of other functions for generating different kind of plots. See the matplotlib plot gallery for a complete list of available plot types: http://matplotlib.org/gallery.html. Some of the more useful ones are show below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = np.array([0,1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa437d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n",
    "\n",
    "axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx)))\n",
    "axes[0].set_title(\"scatter\")\n",
    "\n",
    "axes[1].step(n, n**2, lw=2)\n",
    "axes[1].set_title(\"step\")\n",
    "\n",
    "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n",
    "axes[2].set_title(\"bar\")\n",
    "\n",
    "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n",
    "axes[3].set_title(\"fill_between\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa714d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# polar plot using add_axes and polar projection\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)\n",
    "t = np.linspace(0, 2 * np.pi, 100)\n",
    "ax.plot(t, t, color='blue', lw=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10880b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A histogram\n",
    "n = np.random.randn(100000)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12,4))\n",
    "\n",
    "axes[0].hist(n)\n",
    "axes[0].set_title(\"Default histogram\")\n",
    "axes[0].set_xlim((min(n), max(n)))\n",
    "\n",
    "axes[1].hist(n, cumulative=True, bins=50)\n",
    "axes[1].set_title(\"Cumulative detailed histogram\")\n",
    "axes[1].set_xlim((min(n), max(n)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotating text in matplotlib figures can be done using the `text` function. It supports LaTeX formatting just like axis label texts and titles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d95550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(xx, xx**2, xx, xx**3)\n",
    "\n",
    "ax.text(0.15, 0.2, r\"$y=x^2$\", fontsize=20, color=\"blue\")\n",
    "ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=20, color=\"green\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures with multiple subplots and insets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes can be added to a matplotlib Figure canvas manually using `fig.add_axes` or using a sub-figure layout manager such as `subplots`, `subplot2grid`, or `gridspec`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d95950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### subplot2grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEWCAYAAAApTuNLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X/sHHW97/Hnq9+2yA+pBQLh0FRDgNDWE0C5kR7gUGg8RY9UQOPBW7Ac9BJiUKghh4NSW4qpqSHhEiIaww8LAserkdRQSa+W1gsccpselSo2IN5UfpXyqy2Fggp93z9mvnW67HdnZr+zu7O7r0cyaXf2s/N97+x757Mz85n3KCIwMzPrtQm9DsDMzAzcIZmZWU24QzIzs1pwh2RmZrXgDsnMzGrBHZKZmdWCOyQzM6uF3A5J0uWSNkp6S9IdOW0XSdoqaaek2yRNri5UMzMbZEX2kJ4Drgdub9VI0jzgauAs4P3A0cB14w3QzMyGQ26HFBH3RcQq4JWcpguBWyNic0TsAJYBF48/RDMzGwZlziEp5/mZwGOZx5uAIyRNLR2VmZkNnTIdUl7Ru4OAnZnHr6X/vrdURGZmNpQmlmibt4f0OnBw5vGU9N9d+yxEcjVXM7MBFhF5/UVTVe4hPQ6cmHl8ArAtIra/a0ERngpMS5Ys6XkM/TR5fXldeV31fhqPIsO+RyS9h2RvakTSfpJGmjS9E/i8pBnpeaPFQMth4mZmZqOK7CEtBnaTDOm+EHgT+Jqk6ZJ2SZoGEBFrgG8B64AtwB+BJZ0I2szMBk/uOaSIWAosHePpfQYsRMSNwI3jjsoAmDNnTq9D6CteX8V5XRXnddU9Gu8xv9J/UIpu/00zM+sOSUQXBjWYmZl1TJFBDYdIuk/S65K2SPpsi7aLJT0jaYekdZJmVhuumZkNqiJ7SN8G3gIOBxYA32nW0UiaD1wGnA4cAjwK3FVdqGZmNshadkiSDgTOBxZHxO6IeARYBVzUpPks4OGI2BIRe4C7ScoJmZmZ5crbQzoOeDsinsrMe4yk82m0Fpgt6VhJk0iKrT5QTZhmZjbo8oZ9H8TfatKN2kWT+nQRsUHSSuAJ4B3gaWBuFUGamdngy+uQGuvTQVKjbldjQ0mXk3RA04AXSA7rPShpVkS8mW27dOnSvf+fM2eOx/mbmfWp9evXs379+kqW1fI6pPQc0qvArNHDdpLuAp6JiK82tL0fWBMRN2fmbQfmRsSvMvN8HZKZ2YDq2HVIEfEG8BNgmaQDJJ0GnEPz0XObgM9IOlzSBEkXkeyBPdWkrZmZ2T6KDPv+IrA/8CLwA+CyiNjcWMsO+AbJ+aNNwHbgCuBTEdF4DsrMzOxdXDrIzMwq49JBZmbW96ouHXS0pPslvSbpJUkrqg3XzMwGVZWlgyYDPwd+ARwBHEVyzsnMzCxXO8O+VwLPR8Q1DW0vBRZExBkt/6DPIZmZDaxOnkMqUzroFOBPkn6WHq5bJ+mD7QRlZmbDJ69DKlw6iKRCwwXATcCRwGpgVVrXzszMrKW8Dqlw6SBgN/BQRKyJiLcj4gbgUOD48YdpZmaDLq+W3ZPAREnHZA7bnQD8rknbTcCpow8kjXkM0bXszMwGQ9dq2QFIuhcI4AvAh4D7gdkRsbmh3XHAr4H5wHrgyyRVHmZExNuZdh7UYGY2oDp9YWyh0kER8SRwIfBdkpF55wDzs52RmZnZWFw6yMzMKuPSQWZm1vfcIZmZWS1UWssu85q1kvZIcodnZmaF5A37hn1r2Z0ErJb0WET8vlljSQvS5fpEkZmZFVZZLbv0uSnABuBzwKPAxIjY09DGgxrMzAZUXWrZASwHbgG2tROMmZkNr8pq2Uk6GZgN3FxNaGZmNkzyziEVqmWXDl64BbgyIvZkqgY13W1z6SAzs8HQtdJBY5xDugt4JiK+mmn3PuAVkmoOACPAYSSH7j4dEY9k2vockpnZgBrPOaQqa9kdnnk4nWRww1HAyxHx10w7d0hmZgOqLrXsXhydgJdJOrFt2c7IzMxsLK5lZ2ZmlXEtOzMz63vukMzMrBYKdUhF69lJWihpo6Sdkp6RtELSSLUhm5nZICq6h5StZ7cA+I6kmU3a7Q9cARwKfASYC1xVQZxmZjbgigz7LlXPruG1i4AzI2J+Zp4HNZiZDahOD2ooW88u6wzgd+0EZmZmw6XI7ScK17PLknQJyYW0l7QXmpmZDZMiHVKhenZZks4lqfw9NyJebXzetezMzAZD12rZQfF6dpn2ZwN3Ah+PiI1Nnvc5JDOzAdXRWnbpHyhaz+4s4EfAJyPi4TGW5Q7JzGxAdaNSQ6F6dsC1JOeWHkjn75K0up3AzMxsuLiWnZmZVca17MzMrO/ldkhFywalbRdJ2pqWDrpN0uRqwzUzs0FVZA+pUNkgSfOAq4GzgPcDRwPXVRfq8KlqKOWw8PoqzuuqOK+r7mnZIaVDvs8HFkfE7vRW5KuAi5o0XwjcGhGbI2IHsAy4uOJ4h4q/COV4fRXndVWc11X35O0hlSkbNDN9btQm4AhJU8cXopmZDYO8DqlM2aCDgJ2Zx6Ova1liyMzMDHKGfUs6CXg4Ig7MzLsK+MdsBe90/m+Ab0TEj9PHh5Fct3RoRGzPtPOYbzOzAdbusO+8WnZPAhMlHZM5bHcCzSt4Pw6cCPw4025btjMaT6BmZjbYitSyK1o2aB7wfZJRdi8A9wH/2azenZmZWaMiw74LlQ2KiDXAt4B1wBbgj8CSjkRtZmYDp+ulg8zMzJqpvHSQKzuUU3R9SVooaWO6rp6RtELSSLfj7aUyuZV5zVpJeyQNVZmskt/DoyXdL+k1SS9JWtHNWOug5PpanH4Hd0ha16xQwKCSdHm6HXpL0h05bUtv3zvxJXVlh3IKrS+Sw6ZXAIcCHwHmAld1K8iaKLquAJC0gGTgzjAeBij6PZwM/Bz4BXAEcBTJoflhU3R9zQcuA04HDgEeBe7qYpy99hxwPXB7q0Ztb98jorIJOBD4M3BMZt5K4JtN2t5DMkx89PGZwNYq46n7VGZ9NXntIuCnvX4PdV1XJHc1foKk894DTOj1e6jjugIuBX7Z65j7aH1dA/ww83gW8Gav30MP1tn1wB0tnm9r+171HpIrO5RTZn01OoPmw+8HVdl1tRy4BdjW6cBqqMy6OgX4k6SfpYfr1kn6YFeirI8y62stMFvSsZImkZRMe6ALMdZN3uU7bW3fq+6QXNmhnDLray9Jl5AMwb+hQ3HVUeF1JelkYDZwcxfiqqMyeTUNuAC4CTgSWA2sSje2w6Lw+oqIDSR7T08Au4FPAV/pdIA1lHcYvK3te9Ud0uvAwQ3zppB8uHltp6T/Nms7qMqsLwAknUvy6/9jEfFqB2Orm0LrKh28cAtwZUTsyT7V2fBqpUxe7QYeiog1EfF2RNxAcp7y+A7HWCeF15eky0nO304D9iMpIv2gpP07HWTN5H2f2tq+V90h7a3skJmXV9kh2+5dlR0GXJn1haSzge8Bn4iIx7sQX50UXVcHAx8GfihpK7Ahnf+spFM7H2YtlMmrTdkHkoap4x5VZn2dDdwbEc9HxJ6IWAlMBWZ0Ic46ydtDam/73oGTXfeSnNA6ADgN2AHMaNJuHrCV5IOcCqwHlvf6ZF0PTg4WXV9nAa8Ap/U65j5YV4dnppNJBjUcCUzq9Xuo4bo6DniD5Ff/CMlgmT8AE3v9Hmq6vpYDD6W5NYHkVjy7gIN7/R66tJ5GgPcA3wTuJNlLHGnSrq3teycCnkpSNuh1kooNF6Tzp6cf3LRM20UkZYZ2ArcN0waj7PoCHgT+ks4bnVb3Ov46rquG13wAeIchGmVXdl0B56Wd0M40z961IR70qcT38ADg1sx2ayPwT72Ov4vraSnJD7zs9PWqtu+u1GBmZrUwVFevm5lZfblDMjOzWnCHZGZmteAOyczMasEdkpmZ1YI7JDMzqwV3SGZmVgvukMzMrBbcIZmZWS24QzIzs1pwh2RmZrXgDsnMzGrBHZKZmdWCOyQzM6uF3A5J0uWSNkp6S9IdOW0XSdoqaaek2yRNri5UMxsk3rZYoyJ7SM8B1wO3t2okaR5wNcmdTd8PHA1cN94AzWxgedti+8jtkCLivohYRXL77FYWArdGxOaI2AEsAy4ef4hmNoi8bbF
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a33e490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n",
    "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n",
    "ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n",
    "ax4 = plt.subplot2grid((3,3), (2,0))\n",
    "ax5 = plt.subplot2grid((3,3), (2,1))\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2ce5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "gs = gridspec.GridSpec(2, 3, height_ratios=[2,1], width_ratios=[1,2,1])\n",
    "for g in gs:\n",
    "    ax = fig.add_subplot(g)\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### add_axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manually adding axes with `add_axes` is useful for adding insets to figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b619090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(xx, xx**2, xx, xx**3)\n",
    "fig.tight_layout()\n",
    "\n",
    "# inset\n",
    "inset_ax = fig.add_axes([0.2, 0.55, 0.35, 0.35]) # X, Y, width, height\n",
    "\n",
    "inset_ax.plot(xx, xx**2, xx, xx**3)\n",
    "inset_ax.set_title('zoom near origin')\n",
    "\n",
    "# set axis range\n",
    "inset_ax.set_xlim(-.2, .2)\n",
    "inset_ax.set_ylim(-.005, .01)\n",
    "\n",
    "# set axis tick locations\n",
    "inset_ax.set_yticks([0, 0.005, 0.01])\n",
    "inset_ax.set_xticks([-0.1,0,.1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colormap and contour figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colormaps and contour figures are useful for plotting functions of two variables. In most of these functions we will use a colormap to encode one dimension of the data. There are a number of predefined colormaps. It is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see: http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alpha = 0.7\n",
    "phi_ext = 2 * np.pi * 0.5\n",
    "\n",
    "def flux_qubit_potential(phi_m, phi_p):\n",
    "    return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phi_m = np.linspace(0, 2*np.pi, 100)\n",
    "phi_p = np.linspace(0, 2*np.pi, 100)\n",
    "X,Y = np.meshgrid(phi_p, phi_m)\n",
    "Z = flux_qubit_potential(X, Y).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pcolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b55f9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n",
    "cb = fig.colorbar(p, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b29ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "im = ax.imshow(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n",
    "im.set_interpolation('bilinear')\n",
    "\n",
    "cb = fig.colorbar(im, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEECAYAAAAmiP8hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXeYHWd59/+ZOb3v2aZt6r3LkizLkmXLBRuwAYNtnNiAKcEvgSTkzQskkJdgQl4gjQDJLyEkEFpMi02oLrjIkmwVy+pWrytt3z29TX1+f4xWlhXJmjnn7GpXOp/r2sve1TzPmXPOzHfu537uIgkhqFGjRo0aVy7y5T6BGjVq1KgxstSEvkaNGjWucGpCX6NGjRpXODWhr1GjRo0rnJrQ16hRo8YVTk3oa9SoUeMKpyb0NWrUqHGFc0mhlyTpDyRJ2iZJUkmSpP+4xLH/W5KkHkmS0pIkfUuSJG/1TrVGjRo1apSDHYu+C/gC8O03OkiSpDuAPwVuASYD04DPV3qCNWrUqFGjMi4p9EKInwkhfg4MXeLQh4B/F0LsF0KkgL8E3l/5KdaoUaNGjUpw4qOXLvHv84Bd5/y+G5ggSVLc8VnVqFGjRo2q4UToL1UUJwykz/k9c+a/EUdnVKNGjRo1qko1LfocED3n99iZ/2YdnVGNGjVq1KgqbgfHXsqifxVYAvzXmd8XA31CiOS5B0mSVCuXWaNGjRplIIS4lMF9QeyEV7okSfJjPRRckiT5JElyXeDQ7wEfkiRp7hm//GeBC4ZjCiFqP0Lwuc99ztZx62+5k0JXd1mvkV33OIUdL9g+3hjsxEj32zq2L1MgU1RtHbvu6ACbTyYcfRYP//sWNh8ZuOzfU7V/iqrOtZ99wtFncSpZ4AfbT9maX9UNOoeyto41S3n00/ttn7va20ni0a+U/d7X3/xWit09to+3e49cDT+VYMd181mggBU6+R6gCPy5JEmTJEnKSpLUcUa8nwL+BngeOAEcBT5X0dnVAEAYBpLrQs9WG0gymA4uEkkGmxeVLEm2L0CvS0bRTfvnAdSFvCRyqqMx44FETqE+7HM0RjFMfG57nlYhBJJk0/ATwvrO7WKaSHL5eZaSLCNMZ9dBjcq5pOtGCPEI8MhF/vl1G61CiH8A/qHis6rxOoRhILudeNleQ3K5EKbuYIAMwt6NKEv2nyEBj4uMotg/D6Al5qc3XXQ0ZjzQmy7REvM7GlPUDAJuew97Uwhkuwt8YYIT4TZ1KNfoACS3G6EbZY+vUR61EgiXkbVr19o6TmgaUplCj8sNhkOht2lxyZKEadOiD3ld5JWLn8eFPouJDUFODORtzT+eODGQZ2JD6KL/fqHPIq8ahLz2BNYwre/GFsJ0ZNELQ0eSKxB6jxtT02wfb/ceqfHG1IT+MmL3IjZVDdlXXjUJyeVB6A6E3uUC057F5ZJlDJsPhajfQ9qh0M9pjXKgO/M/Dx7nHOjOMKf14lHHF/os0iWNmN9ja35DmLhsWulOhVvoOrjtnceFkL3emtBfBmpCP8YRQmCqKrK3TKH3eBC6/RtLkt22XT0uWUK36bup83tIFzVHm0ozWiKcShTecCUwHtl7OsW89tilDzyHZFGjLmBvVWcYArdd341pgBMLXVeRKhB6l8+HWSqVPb5GedSEfoxjFkvIfl/ZG2CSx4fQHNxYDlw9bllGN+xZ9D63TMDjIlWyL9p+j4sFHTFeOZ6wPWask8yrnBoqsKCjztG4wbxKY8jeBq5umrhdNq8XQweXfeEWqoLkdbaRfC6uYACjWBP60aYm9GMcPZ/HFQiUPV7yeBGqg01QlwcMeysAj0vCMO2HfjWFvAzknG3IrpzRyIuHBhyNGctsPjLI0qn1eGxG0ADkVB1TCMI2ffSaE4ve0KyHu02EpiB5nG0kn4srGMTIX3n7LmOdmtCPcfRsDk+k/CoSki+AUBxErrg8oNtzsUiShEuW0Gxa9a1RP90ZZ9bcbQtaeGZvr+2Vw1jnyd093LagxdGY7kyJ1qjfVsikEALNMPDaDcXUVSS3fbegqRSRfOULvTsSRsvmyh5fozxqQj/G0bNZ3JFw2eMlX9CR0EuybPlsbbpvvG6XbaFvj/o57TBcclJDiLZ4gE2HBx2NG4skcgrbTyS4eW6zo3Fd6RLtUXviapgCkGxvxmJo4EDohVJE9gVtH38+nmgUPXPlbbCPdWpCP8ZRkyk8cWf+3HORAyHMosOlsscLuj0Xi9dtPxGqLeZnqKBR1JzFUd+/cjLf2XDM0ZixyH++dII7FrYSthk9M8zxRIEpcXviquhOEqtM0B0KfTGPFLh4aOilcMeiaKn0pQ+sUVVqQj/G0ZJJvPHyKz1LZQi95PEjNHtC73O7UGwmwLhlmUl1AY4lCo7O582LWulLl8b1pmymqPHY1lN88KZpjsalitaDsSVibwNU0Q18NhOr0FRwe+1n0QJmMYdcgdB76+OoieSlD6xRVWpCP8ZRBxN4GyoQeo8PEM42ZD0+UO25WIaF3u6G7MzGEIcGnPlo3S6Zh2+ZwdeeOoDppJzDGOLfnj/CrfNbaLNpmQ9zcCDHjMaQbTFWNAOfx67Ql6zv2gGW0JfvSvQ21KMOjd8H9nilJvRjHGVgEF9zU9njJUlCDkYxC/b9opInYDsk0yVLeGTZtlU/szFMZ6ro2H1z15J2DBOe2N3taNxY4PhAjl/t6OJjb5rpeOy+/izzmu1txptCoOgGfpsWvVCLSF5nEV1mLoMcdpYDcC6+piaUgfG/3zLeqAn9GKfU11eR0API4RhG1oFf1BsAtWjbSg94XRRVe8Ltc8tMqw+yv99ZmwJZlvjUXXP56pMHSRXGT6Ez0xR88eev8qG10x0XMuvNllB1kw6bdXFKmhVtI9sMrXQq9ELXEWqpIh+9v6UZpbev7PE1yqMm9GOcUncv/va2iuZwReowc/b9opLbY9U/sbkhG/C6Kaj2E6GWtMXY0Z12XHp18aQ4dyxq5Qv/vbfisq2jxQ9eOoGim/zu9VMcj93RnWZJW8y226ao6gS99mLihRCg5MFBBI2ZSyGHY0hOql2eh7exAT2bw6hlx44qNaEfwwghKJ7uJlCh0MuROGbGmV9U8oUQir1NU7/bhW4K22GWHTE/siRxIum8MuUf3T6LvlSJb6076njsaLP5yCDfXX+ML9+/GJftcpIWBdXg8GCehS3RSx+Mda3kHQg9ugqS7CiG3sgmcEUqawEtyTL+1haKp8efC248UxP6MYyWTAFUFF4J4Io1YKQdboD5w1Cyt2kqSRIhr5u8Yi+jVpIkVkyMs6XT+aac1+3iK+9ZyuPbTvOL7acdjx8tDnSn+cxPdvG3D1zjeAMWYNvpFHObwwRtZsMquoEsSXhslj4QpRyS39mmqpEaQo41OBpzIYJTJlE42VnxPDXsUxP6MUz++AlCUyc7Cn+7EK66JoyUszICkj+MKGZtu0hCfjc5Rbd9/NzmMFnF4GTSWaglQHPUzz89tJyvP3WQDQf6HY8faTqH8vzR917hz98xn6VT6h2PL6gGu3rSrJho33rOKTphn9v+tVLKWQ9zB5jpQVx1jY7GXIjglEnkjx2veJ4a9qkJ/Rgmd/Aw4ZnTK57HFbeE3pFfezjszqaf3u92Wa3mbCZPyZLEjdMaeP7ooO2a9ucyrTnMP7xnGY88vodf7uhyPH6k2HsqxYf/fQsfuXUmt853VupgmA0nhpg/IWK7LLEpBHlFI+yzd7wQAlHMIAWcldbQE/244s6yei9EeOYMcofGvuvtSuKyCL2p2bf8rmayBw4RnjOr4nmkM3HPomg/fl2SJKRAFGEzLFOSJCJ+D5mS/YiYWY0h/G4Xu8qsOb9wYh3f/L0VfPO5w3ztqYNn0v8vH0/s6uYPv7eNT799Pu+6dmJZc/RmSxwZzLPKwUogr2j4PW77FSuVArg8jvzzAEayrypCH5k7m9yBQxXPczUgDAOjVMJUK4s0K7NtUWU8v+wGqz2e34c7EsEdCeOJRvA2NuJrbsLX3EigrY3g5IkEJk3EEy2/qNd4JrPnVdrvfWfF80iShKt+AvpQL96g/c9SCkYx0/0Qs3dzR/weTifzGKa9xheSJHHrzEZ+vKuLGY0hIj7nl+P05gjf+8gq/uxHO/n9/9jKX927mGa
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b545210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "cnt = ax.contour(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use 3D graphics in matplotlib, we first need to create an instance of the `Axes3D` class. 3D axes can be added to a matplotlib figure canvas in exactly the same way as 2D axes; or, more conveniently, by passing a `projection='3d'` keyword argument to the `add_axes` or `add_subplot` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d.axes3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Surface plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAFdCAYAAABfObGNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuUHPV55/2te99HoxsCIQmBQIAEAiPLxAJC/Dq+hCV2wuZ437wn3uTYJg52dk18nATWwLFNbBPWl90Yx17HeYk3BjsGHCd+IbIXYi4SIBASuiAhCd0QQveZ6VtVd93eP2qe6l9VV/W9Z7pnfp9z5kgz01NdVd39/H7P7fsIruuCw+FwOBwOh8PhzD7E6T4BDofD4XA4HA6HMz1wZ4DD4XA4HA6Hw5mlcGeAw+FwOBwOh8OZpXBngMPhcDgcDofDmaVwZ4DD4XA4HA6Hw5mlcGeAw+FwOBwOh8OZpchNfs91RzkcDmf6Eab7BAYYvk5xOJypYkbaYp4Z4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBwOBwOh8PhcGYp3BngcDgcDofD4XBmKdwZ4HA4HA6Hw+FwZincGeBMCa7rTvcpcDgcDmdI4GsGhzN1cGeA03ccx4Gu6zBNE47jTPfpcDgcDmeAqVarMAwDtm1zp4DDmQKEJh80/inkdIzrurAsC5ZlwTAMCIIAwHMONE2DqqqQJAmCIPi/43A4kfAPSDx8nZoh0JpRqVRQrVYhCAJc10UymYSiKP56weFMIzPyDShP9wlwZiaUDahUKkgmk74Rd10Xuq5DEASYpgkAEAQBiqJAlmVIkgRRFLnB53A4nFmE67owDAOlUgmpVMpfM0qlEgRBQKVSgSAIkGXZDySJIi9u4HB6AXcGOD3FdV3Ytg3TNGHbNmzbBgDYth2I6rDOAeClhavVKgD4Bp+cAx4N4nA4nJmL67qoVqv+muE4DhzHgSRJAILrhWmafiBJkiSoqgpZlnkQicPpAl4mxOkZjuP4fQGCIMCyLJTLZX/D7zgORFH0y4TiIjuu6/pfhCRJgewBN/qcWQZ/w8fD16khxnEcVKtVuK4Lx3FQKBQCv3ddF5qmQZblgN0PrxOCIEBVVV5OxOk3M/KNxZ0BTtew2QAAvhEuFoswTRPpdNp/LJUPSZIE27YhCIIf/Y/rHyCDT04GUHMO6O94upgzw5mRC1CP4OvUkOI4DiqVCgBAFEXoug5d15FOp317Xy6X/SCSJEmBclIWWiMIRVH8ABJfHzg9ZEbaYl4mxOkKSu+S4aaMANV5UhqXmsEo7ZtIJAB4iwGlhqlMiAw92z8gCIJv0MnoG4bhn4coipBlORAV4pEhDofDGUwsy4Jpmr6dLpfL/johyzJM0/TtfyKR8NcW27ZRqVT8NYItEaL1hcqJ2HWHnANeTsTh1MOdAU5HUDbAsiy4rusbV13XYRgGUqkUBEGAruuxx2CzAnRMOi5lGlzXDWQOWOeAPRfXdVGpVJDP5/2FgzclczgczmDBqsyRTS6VSrBtG+l0GqVSKfLvyKYriuIHhEipDkAgaxB2DBzHQT6fhyiK/jFofeDrAofDnQFOB1DUhcp8RFGEbdu+Ec/lcpAkCZZlRf49NYKFjTCbAVAUBQD8RjKKBlHfQVRpER1XkiQ/Y8E2JYdLi/giwOFwOFNHeO1wXRfFYhGiKCKXy7U8h4YNJGma5jsGpmnCMIy6ciJ2bREEIbA2UEaZsgYczmyEOwOctmCbvWgzbRgGdF1HMpmEpmk93WSLouiXAAEIZA6oPIktKSLYyBD7d6yDwpuSORwOZ2oIl5RSs7Cqqkgmk4GATruIoghVVf3niSonogxyOGtA64Ku6/5x+JrAmW1wZ4DTEuHULjV0lUolOI7jZwP6DSs7SudFmQPa6JdKpdjMAXs91MzMNiWzkqY8SsThcDjdEw4iWZaFYrGIZDLp94+xdLMJjysnYmVL2c0+PRc5C1R2xB6HlxNxZjrcGeA0xbZtlMtl3yBSmrVUKkHTNGQymWkzlGy62HVdlEolJBIJ3zlgm5Jpg9+oKblSqQTULXhTMofD4XQO1fWT/a1UKiiXy0in0340v1+E1wey92w5UbgJmQiXmvKZBpyZDHcGOLFQNsA0TRSLRcydO9fPBliWhWw260foo+g05dstjZqSKTrVSlMy4A1DI+eA5iOwcnV8QeBwOJxoqPxG13Xkcjl/Kn2ztaNfULQfqC8RAuBnhqPUiSiTTGtKMpnkMw04MwbuDHAiCQ8QA7xoSqlUgqIoGBkZGQoDGNeUTOliakpm5UzDk5IJanSjciTelMzhcDj1hMtKKWtr2zZyudxAlGCyJadsORGtCVRKxGbEAW/9oNkHlUrFP46qqry8lDO0cGeAEyBqgBhFyUulEtLptL+pHlaoVIho1JQc3uSzf8veq/AwNN6AxuFwZiNhxSDA20C7rotcLte2TZyKDHNY5prtQ6MmZNZxABDIGpim6a+ZfKYBZxjhzgDHJ5wNYBu9AAxMRKfXNGpKptpSMui2bQNAy8PQeFMyh8OZLUQpBpHkdCu9ZdNVWhqGgj7UhBwuJwK8Eqi4mQaGYfjrBm9C5gwD3BngRGYDAG8iZKVSQSqV8icK94JBMfhxRA1Do3rRqKbkZopFUU3JfBgah8OZSYSDSbZto1AoQNM0fxLwMBIuJ7JtG4Zh+E5PXDkRUN+EzGcacAYV7gzMcsKRHDLiVB8/MjICURRjp0I2YtA3/a3CRn5IBq8XTcn0e1poeFMyh8MZRljpUFEUfbW5dDoNSZL8zfCwww4uS6VSseVE7BoQnmlAZaW8pJQzSHBnYJbCpj6naoDYTKFRUzLdU2pKZhuTo5qSqd40rGbBZg/4a8DhcAYVUpwju6jrOgzD8BWDqLRyJhJVTkSZAwC+DWezx6Io+lnjYrEYUKnj5USc6YI7A7OQcIOXKIqwbduP/k/VALGZBLsoAK03JVOEyLZtP6oW15TMOhccDocznYQVgwCvtNSyrMAaMlMyxM1gs7yqqvoBIrL/VEoUzhpQVj6unIjPt+FMBdwZmGWEJ0EC8IfAJBIJJBKJSMNDBr1TozTbjFlUUzLJmVJTMjkQNBCH/q5RUzKVI4XTy7Pt/nI4nOkjyhEoFotwXRfZbHbWByzYvjNVVf37Zdt2oJwIgL+uhsuJLMtCuVwODDvjmWJOv+DOwCyBegNKpRJSqRREUfSVHhzH6esQmGq1Cl3XAxvf2RApYiHngGAVi2iRoJ83a0p2XZc3JXM4nGnBdd1AeYvruigUCpBlGalUqie2R9d1mKbpN+32ar2YrnWHVRVis8Yk2mEYRmRwh66dgkHscXiWmNNLuDMwC6BsAFvbSY6BpmktSb51iuu60HXdj45QOQw7Cn42bmDDikVk7KlkiNLFbGkRb0rmcDjTCWU2qdeMxCY0TYvNKneCZVlQFMWfT1CpVGDbdlfDHQfFBrL2mQQ7qOk6rpyICKsTsVkDbuc53cCdgRlMOJVL9YmlUgmmaSKTybQ8QKzduk+aVgx4PQg0tVdRFJTLZf95KToSp8YzW6BNPEV9gMZNyeF7FNWUTFEnAH4/A0WUZtO95XA43ROlGFQoFJBKpaBpWsO/bWXtoIZaAEin035Pm2VZfjApTs5zWCG7r6pqbDkRXWeUOhFJXtOxZFmGpmncxnPahjsDM5SoAWK0OXRdFyMjI30xFpQJCM8nCPcbkOEKb3xJl7/Rxne20Kgpme5Ro6Zkgl6TarXqlyrxpmQOh9MqbFZZFEU/O5DNZpsGlFqx25RhkGU50IdAf092jm3MJTlPsmG0YR5mwuVEjuPAsqxA5jiunIgeR68Tra8z4b5w+g93BmYYzQaIAd4kyH4Qnk8gCELkfIKoxYE2vkSrG9/ZRFxTMjspme4
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b08b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,6))\n",
    "\n",
    "# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "\n",
    "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n",
    "\n",
    "# surface_plot with color grading and color bar\n",
    "ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0, antialiased=False)\n",
    "cb = fig.colorbar(p, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wire-frame plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b078990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "\n",
    "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coutour plots with projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc019d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "ax = fig.add_subplot(1,1,1, projection='3d')\n",
    "\n",
    "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n",
    "cset = ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n",
    "cset = ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n",
    "cset = ax.contour(X, Y, Z, zdir='y', offset=3*np.pi, cmap=matplotlib.cm.coolwarm)\n",
    "\n",
    "ax.set_xlim3d(-np.pi, 2*np.pi);\n",
    "ax.set_ylim3d(0, 3*np.pi);\n",
    "ax.set_zlim3d(-np.pi, 2*np.pi);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change the view angle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the perspective of a 3D plot using the `view_init` method, which takes two arguments: `elevation` and `azimuth` angle (in degrees):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c859810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = fig.add_subplot(1,2,1, projection='3d')\n",
    "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n",
    "ax.view_init(30, 45)\n",
    "\n",
    "ax = fig.add_subplot(1,2,2, projection='3d')\n",
    "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n",
    "ax.view_init(70, 30)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib also includes a simple API for generating animations for sequences of figures. With the `FuncAnimation` function we can generate a movie file from sequences of figures. The function takes the following arguments: `fig`, a figure canvas, `func`, a function that we provide which updates the figure, `init_func`, a function we provide to setup the figure, `frame`, the number of frames to generate, and `blit`, which tells the animation function to only update parts of the frame which have changed (for smoother animations):\n",
    "\n",
    "    def init():\n",
    "        # setup figure\n",
    "\n",
    "    def update(frame_counter):\n",
    "        # update figure for new frame\n",
    "\n",
    "    anim = animation.FuncAnimation(fig, update, init_func=init, frames=200, blit=True)\n",
    "\n",
    "    anim.save('animation.mp4', fps=30) # fps = frames per second\n",
    "\n",
    "To use the animation features in matplotlib we first need to import the module `matplotlib.animation`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# solve the ode problem of the double compound pendulum again\n",
    "\n",
    "from scipy.integrate import odeint\n",
    "from numpy import cos, sin\n",
    "\n",
    "g = 9.82; L = 0.5; m = 0.1\n",
    "\n",
    "def dx(x, t):\n",
    "    x1, x2, x3, x4 = x[0], x[1], x[2], x[3]\n",
    "    \n",
    "    dx1 = 6.0/(m*L**2) * (2 * x3 - 3 * cos(x1-x2) * x4)/(16 - 9 * cos(x1-x2)**2)\n",
    "    dx2 = 6.0/(m*L**2) * (8 * x4 - 3 * cos(x1-x2) * x3)/(16 - 9 * cos(x1-x2)**2)\n",
    "    dx3 = -0.5 * m * L**2 * ( dx1 * dx2 * sin(x1-x2) + 3 * (g/L) * sin(x1))\n",
    "    dx4 = -0.5 * m * L**2 * (-dx1 * dx2 * sin(x1-x2) + (g/L) * sin(x2))\n",
    "    return [dx1, dx2, dx3, dx4]\n",
    "\n",
    "x0 = [np.pi/2, np.pi/2, 0, 0]  # initial state\n",
    "t = np.linspace(0, 10, 250) # time coordinates\n",
    "x = odeint(dx, x0, t)    # solve the ODE problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate an animation that shows the positions of the pendulums as a function of time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "ax.set_ylim([-1.5, 0.5])\n",
    "ax.set_xlim([1, -1])\n",
    "\n",
    "pendulum1, = ax.plot([], [], color=\"red\", lw=2)\n",
    "pendulum2, = ax.plot([], [], color=\"blue\", lw=2)\n",
    "\n",
    "def init():\n",
    "    pendulum1.set_data([], [])\n",
    "    pendulum2.set_data([], [])\n",
    "    return pendulum1, pendulum2\n",
    "\n",
    "def update(n): \n",
    "    # n = frame counter\n",
    "    # calculate the positions of the pendulums\n",
    "    x1 = + L * sin(x[n, 0])\n",
    "    y1 = - L * cos(x[n, 0])\n",
    "    x2 = x1 + L * sin(x[n, 1])\n",
    "    y2 = y1 - L * cos(x[n, 1])\n",
    "    \n",
    "    \n",
    "    # update the line data\n",
    "    pendulum1.set_data([0 ,x1], [0 ,y1])\n",
    "    pendulum2.set_data([x1,x2], [y1,y2])\n",
    "    \n",
    "    return pendulum1, pendulum2\n",
    "\n",
    "anim = animation.FuncAnimation(fig, update, init_func=init, frames=len(t), blit=True)\n",
    "\n",
    "# anim.save can be called in a few different ways, some which might or might not work\n",
    "# on different platforms and with different versions of matplotlib and video encoders\n",
    "#anim.save('animation.mp4', fps=20, extra_args=['-vcodec', 'libx264'], writer=animation.FFMpegWriter())\n",
    "#anim.save('animation.mp4', fps=20, extra_args=['-vcodec', 'libx264'])\n",
    "anim.save('animation.mp4', fps=20, writer=\"ffmpeg\", codec=\"libx264\")\n",
    "#anim.save('animation.mp4', fps=20, writer=\"avconv\", codec=\"libx264\")\n",
    "\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: To generate the movie file we need to have either `ffmpeg` or `avconv` installed. Install it on Ubuntu using:\n",
    "\n",
    "    $ sudo apt-get install ffmpeg\n",
    "\n",
    "or (newer versions)\n",
    "\n",
    "    $ sudo apt-get install libav-tools\n",
    "\n",
    "On MacOSX, try: \n",
    "\n",
    "    $ sudo port install ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "bwAAABpkYXRhAAAAAQAAAABMYXZmNTYuMS4w\n",
       "\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display animation using HTML element\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML(\"\"\"\n",
    "    <video alt=\"test\" controls>\n",
    "        <source src=\"animation.mp4\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\")\n",
    "\n",
    "# OR use Video class\n",
    "\n",
    "#from IPython.display import Video\n",
    "#Video(\"animation.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib has a number of \"backends\" which are responsible for rendering graphs. The different backends are able to generate graphics with different formats and display/event loops. There is a distinction between noninteractive backends (such as 'agg', 'svg', 'pdf', etc.) that are only used to generate image files (e.g. with the `savefig` function), and interactive backends (such as Qt4Agg, GTK, MaxOSX) that can display a GUI window for interactively exploring figures. \n",
    "\n",
    "A list of available backends are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'GTK', u'GTKAgg', u'GTKCairo', u'MacOSX', u'Qt4Agg', u'Qt5Agg', u'TkAgg', u'WX', u'WXAgg', u'CocoaAgg', u'GTK3Cairo', u'GTK3Agg', u'WebAgg', u'nbAgg', u'agg', u'cairo', u'emf', u'gdk', u'pdf', u'pgf', u'ps', u'svg', u'template']\n"
     ]
    }
   ],
   "source": [
    "print(matplotlib.rcsetup.all_backends)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default backend, called `agg`, is based on a library for raster graphics which is great for generating raster formats like PNG.\n",
    "\n",
    "Normally we don't need to bother with changing the default backend; but sometimes it can be useful to switch to, for example, PDF or GTKCairo (if you are using Linux) to produce high-quality vector graphics instead of raster based graphics. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generating SVG with the svg backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# RESTART THE NOTEBOOK: the matplotlib backend can only be selected before pylab is imported!\n",
    "# (e.g. Kernel > Restart)\n",
    "# \n",
    "import matplotlib\n",
    "matplotlib.use('svg')\n",
    "import matplotlib.pylab as plt\n",
    "import numpy\n",
    "from IPython.display import Image, SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Now we are using the svg backend to produce SVG vector graphics\n",
    "#\n",
    "fig, ax = plt.subplots()\n",
    "t = numpy.linspace(0, 10, 100)\n",
    "ax.plot(t, numpy.cos(t)*numpy.sin(t))\n",
    "plt.savefig(\"test.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# Show the produced SVG file. \n",
    "#\n",
    "SVG(filename=\"test.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The IPython notebook inline backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we use IPython notebook it is convenient to use a matplotlib backend that outputs the graphics embedded in the notebook file. To activate this backend, somewhere in the beginning on the notebook, we add:\n",
    "\n",
    "    %matplotlib inline\n",
    "\n",
    "It is also possible to activate inline matplotlib plotting with:\n",
    "\n",
    "    %pylab inline\n",
    "\n",
    "The difference is that `%pylab inline` imports a number of packages into the global address space (scipy, numpy), while `%matplotlib inline` only sets up inline plotting. In new notebooks created for IPython 1.0+, I would recommend using `%matplotlib inline`, since it is tidier and you have more control over which packages are imported and how. Commonly, scipy and numpy are imported separately with:\n",
    "\n",
    "    import numpy as np\n",
    "    import scipy as sp\n",
    "    import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inline backend has a number of configuration options that can be set by using the IPython magic command `%config` to update settings in `InlineBackend`. For example, we can switch to SVG figures or higher resolution figures with either:\n",
    "\n",
    "    %config InlineBackend.figure_format='svg'\n",
    "     \n",
    "or:\n",
    "\n",
    "    %config InlineBackend.figure_format='retina'\n",
    "    \n",
    "For more information, type:\n",
    "\n",
    "    %config InlineBackend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='svg'\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (http://matplotlib.org/) -->\n<svg height=\"256pt\" version=\"1.1\" viewBox=\"0 0 381 256\" width=\"381pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"\nM0 256.117\nL381.344 256.117\nL381.344 0\nL0 0\nL0 256.117\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"\nM33.8625 235.239\nL368.663 235.239\nL368.663 12.0391\nL33.8625 12.0391\nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"line2d_1\">\n    <path clip-path=\"url(#p33057e3db6)\" d=\"\nM33.8625 123.639\nL37.2443 104.979\nL40.6261 87.0774\nL44.008 70.6631\nL47.3898 56.4035\nL50.7716 44.8787\nL54.1534 36.5573\nL57.5352 31.7779\nL60.917 30.7347\nL64.2989 33.4704\nL67.6807 39.8735\nL71.0625 49.6836\nL74.4443 62.5018\nL77.8261 77.8066\nL81.208 94.9756\nL84.5898 113.31\nL87.9716 132.065\nL91.3534 150.478\nL94.7352 167.798\nL98.117 183.323\nL101.499 196.42\nL104.881 206.556\nL108.263 213.321\nL111.644 216.437\nL115.026 215.779\nL118.408 211.374\nL121.79 203.399\nL125.172 192.181\nL128.553 178.175\nL131.935 161.951\nL135.317 144.168\nL138.699 125.551\nL142.081 106.856\nL145.463 88.843\nL148.844 72.2456\nL152.226 57.7386\nL155.608 45.912\nL158.99 37.2469\nL162.372 32.0956\nL165.753 30.6677\nL169.135 33.0213\nL172.517 39.0606\nL175.899 48.54\nL179.281 61.074\nL182.663 76.1527\nL186.044 93.1629\nL189.426 111.413\nL192.808 130.16\nL196.19 148.641\nL199.572 166.106\nL202.953 181.844\nL206.335 195.214\nL209.717 205.673\nL213.099 212.795\nL216.481 216.291\nL219.863 216.019\nL223.244 211.989\nL226.626 204.366\nL230.008 193.459\nL233.39 179.712\nL236.772 163.685\nL240.153 146.029\nL243.535 127.462\nL246.917 108.74\nL250.299 90.6233\nL253.681 73.8499\nL257.063 59.1016\nL260.444 46.9782\nL263.826 37.9729\nL267.208 32.452\nL270.59 30.6399\nL273.972 32.6105\nL277.353 38.2835\nL280.735 47.4282\nL284.117 59.6727\nL287.499 74.5189\nL290.881 91.3631\nL294.263 109.52\nL297.644 128.251\nL301.026 146.795\nL304.408 164.396\nL307.79 180.341\nL311.172 193.978\nL314.553 204.755\nL317.935 212.233\nL321.317 216.107\nL324.699 216.22\nL328.081 212.567\nL331.463 205.298\nL334.844 194.707\nL338.226 181.226\nL341.608 165.402\nL344.99 147.88\nL348.372 129.371\nL351.753 110.63\nL355.135 92.4176\nL358.517 75.4752\nL361.899 60.4918\nL365.281 48.0768\nL368.663 38.7352\" style=\"fill:none;stroke:#0000ff;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"\nM33.8625 12.0391\nL368.663 12.0391\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"\nM368.663 235.239\nL368.663 12.0391\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"\nM33.8625 235.239\nL368.663 235.239\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"\nM33.8625 235.239\nL33.8625 12.0391\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_2\">\n      <defs>\n       <path d=\"\nM0 0\nL0 -4\" id=\"m93b0483c22\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"33.8625\" xlink:href=\"#m93b0483c22\" y=\"235.2390625\"/>\n      </g>\n     </g>\n     <g id=\"line2d_3\">\n      <defs>\n       <path d=\"\nM0 0\nL0 4\" id=\"m741efc42ff\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"33.8625\" xlink:href=\"#m741efc42ff\" y=\"12.0390625\"/>\n      </g>\n    
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085c4890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Now we are using the SVG vector graphics displaced inline in the notebook\n",
    "#\n",
    "fig, ax = plt.subplots()\n",
    "t = numpy.linspace(0, 10, 100)\n",
    "ax.plot(t, numpy.cos(t)*numpy.sin(t))\n",
    "plt.savefig(\"test.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive backend (this makes more sense in a python script file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "# RESTART THE NOTEBOOK: the matplotlib backend can only be selected before pylab is imported!\n",
    "# (e.g. Kernel > Restart)\n",
    "# \n",
    "import matplotlib\n",
    "matplotlib.use('Qt4Agg') # or for example MacOSX\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Now, open an interactive plot window with the Qt4Agg backend\n",
    "fig, ax = plt.subplots()\n",
    "t = np.linspace(0, 10, 100)\n",
    "ax.plot(t, np.cos(t) * np.sin(t))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when we use an interactive backend, we must call `plt.show()` to make the figure appear on the screen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* http://www.matplotlib.org - The project web page for matplotlib.\n",
    "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
    "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n",
    "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n",
    "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]"
        },
        {
         "module": "IPython",
         "version": "3.2.1"
        },
        {
         "module": "OS",
         "version": "Darwin 14.1.0 x86_64 i386 64bit"
        },
        {
         "module": "numpy",
         "version": "1.9.2"
        },
        {
         "module": "scipy",
         "version": "0.16.0"
        },
        {
         "module": "matplotlib",
         "version": "1.4.3"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]</td></tr><tr><td>IPython</td><td>3.2.1</td></tr><tr><td>OS</td><td>Darwin 14.1.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.9.2</td></tr><tr><td>scipy</td><td>0.16.0</td></tr><tr><td>matplotlib</td><td>1.4.3</td></tr><tr><td colspan='2'>Sat Aug 15 11:30:23 2015 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n",
       "IPython & 3.2.1 \\\\ \\hline\n",
       "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.9.2 \\\\ \\hline\n",
       "scipy & 0.16.0 \\\\ \\hline\n",
       "matplotlib & 1.4.3 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:30:23 2015 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n",
       "IPython 3.2.1\n",
       "OS Darwin 14.1.0 x86_64 i386 64bit\n",
       "numpy 1.9.2\n",
       "scipy 0.16.0\n",
       "matplotlib 1.4.3\n",
       "Sat Aug 15 11:30:23 2015 JST"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%reload_ext version_information\n",
    "%version_information numpy, scipy, matplotlib"
   ]
  }
 ],
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   "display_name": "Python 3.8.10 64-bit",
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   "file_extension": ".py",
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