{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is the \"grandfather\" data visualisation tool for data in Python - it offers unparalleled control over graphs and diagrams for data, and lets us annotate and customise figures to our heart's content. Matplotlib is built upon for other important modules we'll use later, such as Seaborn, which is more used for statistical visualisation.\n",
    "\n",
    "All the documentation for Matplotlib can be found [here](http://matplotlib.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recap - Functional Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create quick and dirty graphs using the functional method as we've seen before. This method is simpler but won't allow us to customise our plots as much.\n",
    "\n",
    "First, we need to import our modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we're running in a notebook, we can run the following line of code to save us from writing plt.show() to give us our graphs. Spyder automatically shows plots, so we don't need this line of code and everything will still work as normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then graph any data we want using the plt.plot command. Here we will use the <i>np.linspace</i> function to generate a numpy array called x with 101 equally spaced points between 0 and 10, and another numpy array called y that is simply x squared. (Remember numpy arrays can be operated on and the operation will apply element-wise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0,10,101)\n",
    "y = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then plot the graph of x versus y by using the plt.plot() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eb758f7358>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wTk/dMCScqOhFWrl3thbx3YVr6ZQYw4LpYzQ5WRhS0Yu0Ys9n7WPOSxsY3LUtT940ii7t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\n3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75453da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! From here we could add more plots, linestyles, x labels, y labels, titles and more using various methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1eb759992b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HxHG21d+BnnqWObqHda4Grk5bUSLZoqUOnroGnv8dFA+FKdfDfp9VZ02ScbrC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75922ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,x**2,x,x**3)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"Graph of x squared and x cubed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create multiple plots on one output. These are called subplots, and to do this we use the plt.subplot() function - this takes 3 arguments - the number of rows, the number of columns, and then the plot number we're refering to. This may seem like an ugly way to do things, and we'll see a better way to do it later with the object orientated method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eb75a65128>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTNmYVnFpMUsOL2F4u+HU86tndBzh5uSAKTfzn80nOJCczacPdaVRXVllY1Yb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\nQghhctLohRDC5KTRCyGEyf0fVcfYwBTe+9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb759d9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,x**2)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x**2,x,'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Orientated - There has to be a better way!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem with the functional method is that it's a bit of a mess: Commands can be a bit hard to work with and understand at a glance, and problems often happen because of ambiguity in code. Python works on an \"object orientetated\" philosophy so it makes sense for us to use a similar methodology for graphing. The object orientated way of creating plots works by creating figure objects and calling methods on it.\n",
    "\n",
    "To start, we need to create a figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7594e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figures are a blank canvas for us to work on. To add axis, we use the add.axes() method. We can always look at our figure by just running it as a line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1b7v7ou5+c3e/Ock/JPkbwd3VIv8t+GaS91bVwap6RZJ3J/nx8JzrZpF9fSxb7x6kqt6Y\nrZv1Pzo65f60p3at9Eq33UJyJRbc108leX2SL25fkZ1qNz7f1YJ7yxlaZF+7+8dV9Z0kDyZ5JsmX\nu9u/RPY8Fvx9/UySr1TVQ9n6pO2N3e1fHtpFVX0tyeVJzq2qE0k+neRlyQtrlztSAcAQd6QCgCGi\nCwBDRBcAhoguAAwRXQAYIroAMER0AWCI6ALAkP8BSZeS8XcpYIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7594e710>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes = fig.add_axes([0,0,1,1])\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Techinically, the \"add_axes\" method takes in one arguement - a list. This list has 4 elements - the x position with respect to the left of the axis (in percent), the y position with respect to the bottom of the axis (also in percent) - so here, 0,0 just means we don't want white space - and width and height (so here, 1,1 means width 1, height 1).\n",
    "\n",
    "Now if we want to plot something on these axis, we call the \"plot\" method with respect to the axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bh6Dq9Q8J7U2lzVLJkodv0joctyIJV4gJqjC6Em5ySPKYrq8v7sAv0JuI2EDK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\nXCGEEKIPSOAKIYQQfUACVwghhOgD/x/tsfjv5F7I7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75495438>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes.plot(x,y)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add x labels, y lables, and more using the following methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V1fz2X2t4ZUUOI1IS+NvkISTHt/A6lkjIUVGLSINbnV3EDS+uYEdhGTeN7c11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\ntYiISBBTUYuIiASx/wNQrTNeaaXHzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7594e710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes.set_xlabel('X')\n",
    "axes.set_ylabel('Y')\n",
    "axes.set_title('x vs x squared')\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see why the object orientated way of creating graphs is so much more powerful:\n",
    "\n",
    "To start, we can create graphs within graphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1eb75c43358>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FvCOrGIBAf19G9ezE7THBnNUzmB6d/D3unJtIY9uUWsgjy5I4t1dnfju2l9NxfhYVtYib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\nAOdB2W+7Uwc8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75b93780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0,0,1,1])\n",
    "axes2 = fig.add_axes([0.1,0.6,0.4,0.3]) #Creating axes within our axes\n",
    "\n",
    "axes1.plot(x,y)\n",
    "axes2.plot(y,x)\n",
    "\n",
    "axes1.set_xlabel('X')\n",
    "axes1.set_ylabel('Y')\n",
    "axes1.set_title('x vs x squared')\n",
    "\n",
    "axes2.set_xlabel('X')\n",
    "axes2.set_ylabel('Y')\n",
    "axes2.set_title('Inverse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create subplots as before using a shortcut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75ba5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This combines the \"fig\" and \"axes\" call together assuming we want the large plot by using <i>tuple unpacking</i>. We can add arguements to the subplot method to create more plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QOyBdDHMtnZWqGnsbXmUymdTKipdmazGSHKmqybLrmmst0jy59s5iSWqcjUCSGmcjkKTG\n2QgkqXE2AklqnI1AkhpnI5CkxtkIJKlxNgJJapyNQJIaZyOQpMbZCCSpcTYCSWqcjUCSGmcjkKTG\n9WoESXYmeTrJapK7z7H8piTfS3K0+/pk33WlsZhraWrdJ5Ql2QTcA9wCnAQOJzlUVU+tGfq1qvr1\ni1xXWipzLZ3V54hgB7BaVSeq6mXgILC75/vPs660SOZa6vRpBFuBZ2emT3bz1rohyRNJHk7y7gtc\nlyR7k6wkWTl9+nSPzZLmYq6lzlAni/8FuLqqrgP+Avjihb5BVe2vqklVTbZs2TLQZklzMddqQp9G\ncAq4amb6ym7e/6mq71fVf3avHwLelOTyPutKIzHXUqdPIzgMbE+yLclmYA9waHZAkp9Kku71ju59\nX+izrjQScy111r1qqKrOJLkLeATYBByoquNJ9nXL7wVuA347yRngv4A9VVXAOddd0L5IvZlr6axM\nc72xTCaTWllZGXsz9AaV5EhVTZZd11xrkebJtXcWS1LjbASS1DgbgSQ1zkYgSY2zEUhS42wEktQ4\nG4EkNc5GIEmNsxFIUuNsBJLUOBuBJDXORiBJjbMRSFLjbASS1DgbgSQ1zkYgSY3r1QiS7EzydJLV\nJHefY/lHkzyR5MkkjyW5fmbZt7v5R5P4VA5tGOZamlr3UZVJNgH3ALcAJ4HDSQ5V1VMzw74FfLCq\nXkyyC9gPvG9m+c1V9fyA2y3NxVxLZ/U5ItgBrFbViap6GTgI7J4dUFWPVdWL3eTjwJXDbqY0OHMt\ndfo0gq3AszPTJ7t553Mn8PDMdAFfTXIkyd7zrZRkb5KVJCunT5/usVnSXMy11Fn3R0MXIsnNTD8w\nN87MvrGqTiV5O/CVJP9aVY+uXbeq9jM99GYymdSQ2yXNw1zrja7PEcEp4KqZ6Su7eT8iyXXAfcDu\nqnrhlflVdar78zngQaaH5NLYzLXU6dMIDgPbk2xLshnYAxyaHZDkauALwG9V1Tdn5l+a5LJXXgMf\nAo4NtfHSHMy11Fn3R0NVdSbJXcAjwCbgQFUdT7KvW34v8EngJ4G/SgJwpqomwBXAg928S4DPVdWX\nFrIn0gUw19JZqdp4P7acTCa1suKl2VqMJEe6/9CXylxrkebJtXcWS1LjbASS1DgbgSQ1zkYgSY2z\nEUhS42wEktQ4G4EkNc5GIEmNsxFIUuNsBJLUOBuBJDXORiBJjbMRSFLjbASS1DgbgSQ1zkYgSY3r\n1QiS7EzydJLVJHefY3mSfLpb/kSS9/RdVxqLuZam1m0ESTYB9wC7gGuB25Ncu2bYLmB797UX+MwF\nrCstnbmWzupzRLADWK2qE1X1MnAQ2L1mzG7gszX1OPDWJO/oua40BnMtddZ9eD2wFXh2Zvok8L4e\nY7b2XBeAJHuZftcF8N9JjvXYtqFdDjzfUN0xa4+5z++krVxDm//Ore3zOy92xT6NYCmqaj+wHyDJ\nyhgPF2+t7pi1x97nZdXaCLkes7b7vNy6F7tun0ZwCrhqZvrKbl6fMW/qsa40BnMtdfqcIzgMbE+y\nLclmYA9waM2YQ8DHuqss3g98r6q+03NdaQzmWuqse0RQVWeS3AU8AmwCDlTV8ST7uuX3Ag8BtwKr\nwA+AO15r3R7btf9idmYArdUds/ao+9xYrses7T6/DuqmqobcEEnS64x3FktS42wEktS40RrBPLf3\nL6H2R7uaTyZ5LMn1y6g7M+6XkpxJctsQdfvWTnJTkqNJjif5h2XUTfKWJH+T5Btd3TsGqnsgyXPn\nu25/5HwtpPZYue5Te2bcoNkeK9d9ai8i2wvLdVUt/YvpCbZngJ8FNgPfAK5dM+ZW4GEgwPuBf15i\n7RuAt3Wvdw1Ru0/dmXF/x/RE5W1L3Oe3Ak8BV3fTb19S3T8E/qx7vQX4LrB5gNofAN4DHDvP8jHz\nNXjtsXI9ZrbHyvWY2V5Ursc6Ipjn9v6F166qx6rqxW7ycabXiS+8bud3gM8Dzw1Q80Jq/wbwhar6\nd4CqGqJ+n7oFXJYkwI8z/bCcmbdwVT3avdf5jJavBdUeK9e9aneGzvZYue5be/BsLyrXYzWC8926\nf6FjFlV71p1MO+zC6ybZCnyE7pebDajPPv888LYkf5/kSJKPLanuXwLvAv4DeBL43ar64QC1h9i2\nRb3vImqPletetReU7bFy3bf2GNm+qGxtmF8xsREluZnpB+bGJZX8c+APquqH028iluoS4L3ArwA/\nBvxTkser6psLrvurwFHgl4GfA76S5GtV9f0F123WCLmG8bI9Vq7hdZTtsRrBPLf3L6M2Sa4D7gN2\nVdULS6o7AQ52H5TLgVuTnKmqLy6h9knghap6CXgpyaPA9cA8H5g+de8A/rSmP+BcTfIt4Brg63PU\nHWrbFvW+i6g9Vq771l5EtsfKdd/aY2T74rI1xImTizjhcQlwAtjG2RMt714z5tf40ZMeX19i7auZ\n3k16wzL3ec34+xnuZHGffX4X8Lfd2DcDx4BfWELdzwB/0r2+ogvt5QPt989w/pNqY+Zr8Npj5XrM\nbI+V67GzvYhcDxaGi9iZW5l25WeAP+rm7QP2da/D9OEfzzD9+dpkibXvA15kelh3FFhZRt01Ywf5\nsFxIbeATTK+wOAb83pL+rn8a+HL3b3wM+M2B6j4AfAf4H6bfFd65gfK1kNpj5XrMbI+V67Gyvahc\n+ysmJKlx3lksSY2zEUhS42wEktQ4G4EkNc5GIEmNsxFIUuNsBJLUuP8PTaaW5WOCrzYAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75ce0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that all the numbers are a little bit squished together - we can fix this using the <i>plt.tight_layout()</i> function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75e99390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! For now, to learn how to access these axes let's work with a 2x1 subplot grid.\n",
    "\n",
    "If we call the \"axes\" object after using the subplot command, we can see that \"axes\" is just a list of axes objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VCpLUhJ+0laQmLHxJasLCl6QmLHxJasLCl6QmLHxJasLCl6Qm/hdmHqOclqRawgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7608d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001EB75B213C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001EB760ED940>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes #Notice the square brackets? It's a list (technically a numpy array)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means we can iterate over it, but more importantly, we can select what axes we want by using indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z258H5kygT3ctXyajhGjqkdTo9y9XalSkrd7bdYQ7F31IcXkV9848g3kzRmj9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7608d080>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes[0].plot(x,x)\n",
    "axes[1].plot(x,x**2)\n",
    "\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we don't want to use two axes and we'd rather use one set of axes with two lines. We already know how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eb75ec1160>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ENRB9JiAijU5ZVRlj/zWW0qpSXhj2AunN0v0uKWYpBESkUXHOMWHeBJZvW84TFz6hD4Ib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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75c2f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x,x)\n",
    "ax.plot(x,x**2)\n",
    "ax.plot(x,x**3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How can we tell at a glance which plot is which? A legend would be helpful here - to do this we need to edit out code a little bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb75e15940>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mA3j2qmdpEtbE7rBULVbuZweJSCsR+VpEdorIDhEZb9U3FJFlIrLP6kZ6jTNZ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\nAOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75e69ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x,x, label='Linear')\n",
    "ax.plot(x,x**2, label='Squared')\n",
    "ax.plot(x,x**3, label='Cubed')\n",
    "\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we need to label our plots within their respective methods, and then call the legend method. A similar method is used to change the color and linestyles of these plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb775811d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gfXtnY1OhqyK9gwR4BVhjjHki6Km5wFjv8lhgTlD5GBGJFZHWQFvgu/K+v1Kh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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75e2d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x,x, label='Linear', color = \"purple\", lw=3,ls=':')\n",
    "ax.plot(x,x**2, label='Squared', color = \"pink\", lw =3,ls='-.')\n",
    "ax.plot(x,x**3, label='Cubed', color = \"blue\", lw=3,ls='--')\n",
    "\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are hundreds of different plot options out there, as well as the option for 3d plots, contour plots, log scaling and more! To check these out, take a look at the documentation [here](http://matplotlib.org/)\n",
    "\n",
    "Finally, it's worth noting that we can use the object orientated method for scatter graphs, boxplots and histograms.\n",
    "\n",
    "To start, let's take a scatter plot looking at a (albeit fake) positive correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1eb7773d4a8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R7pSLmY2Z2REz+0He98LgCz38lJQ4F2UpEMxbnZ8Ec6A4RaRcbpX0jKS/LOC9MODSHn4m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\nA0BNENABoCb+H9yfJS7UBZaGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb776e3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "\n",
    "\n",
    "randomData = 0.4 * np.random.randn(101) + 0.5 #Nice way to fake a correlation - 0.4 is the slope, 0.5 is the intercept.\n",
    "\n",
    "axes.scatter(x,randomData + x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's look at how to create a histogram representing a normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  25.,   55.,  138.,  207.,  230.,  185.,  108.,   35.,   14.,    4.]),\n",
       " array([-2.56993424, -1.97147985, -1.37302546, -0.77457106, -0.17611667,\n",
       "         0.42233772,  1.02079212,  1.61924651,  2.2177009 ,  2.8161553 ,\n",
       "         3.41460969]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npOYMvSQ1Z+glqTlDL0nNGXpJau5/AAZJyfySdWfrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb778228d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "\n",
    "data = np.random.randn(1001)\n",
    "\n",
    "axes.hist(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, let's look at boxplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1eb7793e5f8>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77961048>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77979358>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1eb77949fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77952828>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77969940>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77969b00>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb7797fc18>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77986ac8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1eb77958898>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb7796fb70>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77990b38>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1eb779529e8>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb7796f358>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77986c88>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1eb7793ef60>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb779497b8>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb779618d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77961a90>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb77979ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x1eb7797fa58>]}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AyRDsAJAMwQ4AyRDsAJAMwQ4AyfwftCLwGdkzh+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb778c5f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "\n",
    "\n",
    "data = [np.random.normal(0,1,100),np.random.normal(0,2,100),np.random.normal(0,3,100)]\n",
    "\n",
    "axes.boxplot(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the boxplot and histogram methods also output a lot of other useful information about the plots to access later if we want to. If we don't want these, we can just run fig again in another cell, or omit %matplotlib inline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AyRDsAJAMwQ4AyRDsAJAMwQ4AyfwftCLwGdkzh+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb778c5f98>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Worked Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to create a graph that looks at what happens to the graph of e<sup>x</sup>, 2<sup>x</sup>, 3<sup>x</sup>, x<sup>2</sup> and x<sup>3</sup>. Soon we will be able to import data so we're not working the mathematical functions all the time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb77a383c8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvURKSgqvvfZa//Ynb29voqOj+48jXLRoEa2trUyfPh2AX/7yl1gsFlJSUkhOTuaXv/yl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\nMCdOnODVV1/lRz/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4iMbK3iFpP09zg8nfAV1NI3a1dB9JxEKIIZNZm8mU0CkEeAXo2m7f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb75fe08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(0,10,101)\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0,0,1,1])\n",
    "\n",
    "axes.plot(x, np.exp(x), label = \"Exponential\")\n",
    "axes.plot(x, 2**x, label = \"2 Power\")\n",
    "axes.plot(x, 3**x, label = \"3 Power\")\n",
    "axes.plot(x, x**2, label = \"Squared\")\n",
    "axes.plot(x, x**3, label = \"Cubed\")\n",
    "\n",
    "axes.set_ylim((0,1000))\n",
    "axes.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try graphing the sin(), cos() and tan() functions on one graph, adding a legend, title and axis. Then, try using the subplot command to put them all on different plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Resources"
   ]
  },
  {
   "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}