Matplotlib_1_Basic_Plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BASIC PLOTTING WITH MATPLOTLIB\n",
    "\n",
    "![architecture](mpl_arch.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More details: http://www.aosabook.org/en/matplotlib.html\n",
    "10 simple rules for better figures: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833#s11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:47:27.796744Z",
     "start_time": "2018-11-11T05:47:27.419983Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook #interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:47:58.643522Z",
     "start_time": "2018-11-11T05:47:58.636137Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nbAgg'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.get_backend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:51:33.895287Z",
     "start_time": "2018-11-11T05:51:33.888048Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:51:35.376897Z",
     "start_time": "2018-11-11T05:51:35.229586Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112961128>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:51:17.439920Z",
     "start_time": "2018-11-11T05:51:17.432188Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline #not interactive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the plot function, which generates a series of lines which get rendered against an axis object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T05:51:27.559231Z",
     "start_time": "2018-11-11T05:51:27.393836Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1139e0a58>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(3,2,'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is approach 1 using scripting layer\n",
    "we've just called one function on a module named plot. So what's going on? You see, the pie plot scripting interface is managing a lot of objects for you. It keeps track of the latest figure, of sub plots and of the axis objects. Moreover, it actually hides some of these behind methods of its own. So, the pY plot module itself has a function which is called plot. But it redirects calls to this function to the current axis object. \n",
    "- Approach 2: vebose- matplotlib object API. But I think it's more accurate to think of it as directly interfacing with the artist layer instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T06:04:34.251856Z",
     "start_time": "2018-11-11T06:04:34.123283Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.backends.backend_agg import FigureCanvasAgg\n",
    "from matplotlib.figure import Figure\n",
    "\n",
    "#create new figure\n",
    "fig = Figure()\n",
    "#associate with backend\n",
    "canvas = FigureCanvasAgg(fig)\n",
    "# add a subplot; 111 means we want just one plot\n",
    "ax = fig.add_subplot(111)\n",
    "#return value for the subplot is the axes object, which contains method for plotting\n",
    "ax.plot(3,2,'.')\n",
    "# The backend used for jupyter notebook cant render directly, so we save the fing to png \n",
    "canvas.print_png('mpl_test.png')\n",
    "# we do a html cell magic and see the rendered image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T06:05:11.358497Z",
     "start_time": "2018-11-11T06:05:11.349066Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src='mpl_test.png'/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src='mpl_test.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T06:08:07.003983Z",
     "start_time": "2018-11-11T06:08:06.862740Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 6, 0, 10]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot, grab the axes, and set the x and y limits. \n",
    "plt.figure()\n",
    "plt.plot(3,2,'o')\n",
    "ax = plt.gca() #get current axes\n",
    "ax.axis([0,6,0,10]) #[min-x,max-x,min-y,max-y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T06:09:58.865739Z",
     "start_time": "2018-11-11T06:09:58.728653Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115e72e48>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(1.5,1.5,'o')\n",
    "plt.plot(2,2,'o')\n",
    "plt.plot(2.5,2.5,'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-11T06:11:30.213776Z",
     "start_time": "2018-11-11T06:11:30.059706Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.spines.Spine at 0x115e7cc88>,\n",
       " <matplotlib.spines.Spine at 0x115e7cc18>,\n",
       " <matplotlib.spines.Spine at 0x115d72c88>,\n",
       " <matplotlib.spines.Spine at 0x115de4da0>,\n",
       " <matplotlib.axis.XAxis at 0x115de4eb8>,\n",
       " <matplotlib.axis.YAxis at 0x115dc8518>,\n",
       " Text(0.5,1,''),\n",
       " Text(0,1,''),\n",
       " Text(1,1,''),\n",
       " <matplotlib.patches.Rectangle at 0x115e015c0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADYBJREFUeJzt3HGI33d9x/Hny8ROprWO5QRJou1Y\nuhrKoO7oOoRZ0Y20fyT/FEmguEppwK0OZhE6HCr1rylDELJptolT0Fr9Qw+J5A9X6RAjudJZmpTA\nLTpzROhZu/5TtGZ774/fT++4XHLf3v3uLt77+YDA7/v7fX6/e+fD3TO/fH/3+6WqkCRtf6/a6gEk\nSZvD4EtSEwZfkpow+JLUhMGXpCYMviQ1sWrwk3wuyXNJnrnC7Uny6SRzSZ5O8rbJjylJWq8hz/A/\nDxy4yu13AfvGf44C/7T+sSRJk7Zq8KvqCeBnV1lyCPhCjZwC3pDkTZMaUJI0GTsn8Bi7gQtLjufH\n1/1k+cIkRxn9L4DXvva1f3TLLbdM4MtLUh9PPvnkT6tqai33nUTws8J1K35eQ1UdB44DTE9P1+zs\n7AS+vCT1keS/13rfSfyWzjywd8nxHuDiBB5XkjRBkwj+DPDe8W/r3AG8WFWXnc6RJG2tVU/pJPky\ncCewK8k88FHg1QBV9RngBHA3MAe8BLxvo4aVJK3dqsGvqiOr3F7AX01sIknShvCdtpLUhMGXpCYM\nviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMG\nX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmD\nL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJDiQ5l2QuycMr3P7mJI8neSrJ00nunvyo\nkqT1WDX4SXYAx4C7gP3AkST7ly37O+CxqroNOAz846QHlSStz5Bn+LcDc1V1vqpeBh4FDi1bU8Dr\nx5dvAC5ObkRJ0iQMCf5u4MKS4/nxdUt9DLg3yTxwAvjASg+U5GiS2SSzCwsLaxhXkrRWQ4KfFa6r\nZcdHgM9X1R7gbuCLSS577Ko6XlXTVTU9NTX1yqeVJK3ZkODPA3uXHO/h8lM29wOPAVTV94DXALsm\nMaAkaTKGBP80sC/JTUmuY/Si7MyyNT8G3gWQ5K2Mgu85G0m6hqwa/Kq6BDwInASeZfTbOGeSPJLk\n4HjZQ8ADSX4AfBm4r6qWn/aRJG2hnUMWVdUJRi/GLr3uI0sunwXePtnRJEmT5DttJakJgy9JTRh8\nSWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+\nJDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZf\nkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwFda8J8nZJGeSfGmyY0qS1mvnaguS\n7ACOAX8GzAOnk8xU1dkla/YBfwu8vapeSPLGjRpYkrQ2Q57h3w7MVdX5qnoZeBQ4tGzNA8CxqnoB\noKqem+yYkqT1GhL83cCFJcfz4+uWuhm4Ocl3k5xKcmClB0pyNMlsktmFhYW1TSxJWpMhwc8K19Wy\n453APuBO4AjwL0necNmdqo5X1XRVTU9NTb3SWSVJ6zAk+PPA3iXHe4CLK6z5RlX9sqp+CJxj9A+A\nJOkaMST4p4F9SW5Kch1wGJhZtubrwDsBkuxidIrn/CQHlSStz6rBr6pLwIPASeBZ4LGqOpPkkSQH\nx8tOAs8nOQs8Dnyoqp7fqKElSa9cqpafjt8c09PTNTs7uyVfW5J+UyV5sqqm13Jf32krSU0YfElq\nwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1\nYfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5Ka\nMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn+RAknNJ5pI8fJV19ySpJNOTG1GSNAmr\nBj/JDuAYcBewHziSZP8K664H/hr4/qSHlCSt35Bn+LcDc1V1vqpeBh4FDq2w7uPAJ4CfT3A+SdKE\nDAn+buDCkuP58XW/luQ2YG9VffNqD5TkaJLZJLMLCwuveFhJ0toNCX5WuK5+fWPyKuBTwEOrPVBV\nHa+q6aqanpqaGj6lJGndhgR/Hti75HgPcHHJ8fXArcB3kvwIuAOY8YVbSbq2DAn+aWBfkpuSXAcc\nBmZ+dWNVvVhVu6rqxqq6ETgFHKyq2Q2ZWJK0JqsGv6ouAQ8CJ4Fngceq6kySR5Ic3OgBJUmTsXPI\noqo6AZxYdt1HrrD2zvWPJUmaNN9pK0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMv\nSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGX\npCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqYlDw\nkxxIci7JXJKHV7j9g0nOJnk6ybeTvGXyo0qS1mPV4CfZARwD7gL2A0eS7F+27Clguqr+EPga8IlJ\nDypJWp8hz/BvB+aq6nxVvQw8ChxauqCqHq+ql8aHp4A9kx1TkrReQ4K/G7iw5Hh+fN2V3A98a6Ub\nkhxNMptkdmFhYfiUkqR1GxL8rHBdrbgwuReYBj650u1VdbyqpqtqempqaviUkqR12zlgzTywd8nx\nHuDi8kVJ3g18GHhHVf1iMuNJkiZlyDP808C+JDcluQ44DMwsXZDkNuCzwMGqem7yY0qS1mvV4FfV\nJeBB4CTwLPBYVZ1J8kiSg+NlnwReB3w1yX8mmbnCw0mStsiQUzpU1QngxLLrPrLk8rsnPJckacJ8\np60kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1IT\nBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJ\ngy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwCrf/VpKv\njG//fpIbJz2oJGl9Vg1+kh3AMeAuYD9wJMn+ZcvuB16oqt8HPgX8/aQHlSStz5Bn+LcDc1V1vqpe\nBh4FDi1bcwj4t/HlrwHvSpLJjSlJWq+dA9bsBi4sOZ4H/vhKa6rqUpIXgd8Ffrp0UZKjwNHx4S+S\nPLOWobehXSzbq8bci0XuxSL3YtEfrPWOQ4K/0jP1WsMaquo4cBwgyWxVTQ/4+tuee7HIvVjkXixy\nLxYlmV3rfYec0pkH9i453gNcvNKaJDuBG4CfrXUoSdLkDQn+aWBfkpuSXAccBmaWrZkB/mJ8+R7g\n36vqsmf4kqSts+opnfE5+QeBk8AO4HNVdSbJI8BsVc0A/wp8Mckco2f2hwd87ePrmHu7cS8WuReL\n3ItF7sWiNe9FfCIuST34TltJasLgS1ITGx58P5Zh0YC9+GCSs0meTvLtJG/Zijk3w2p7sWTdPUkq\nybb9lbwhe5HkPePvjTNJvrTZM26WAT8jb07yeJKnxj8nd2/FnBstyeeSPHel9ypl5NPjfXo6ydsG\nPXBVbdgfRi/y/hfwe8B1wA+A/cvW/CXwmfHlw8BXNnKmrfozcC/eCfz2+PL7O+/FeN31wBPAKWB6\nq+fewu+LfcBTwO+Mj9+41XNv4V4cB94/vrwf+NFWz71Be/GnwNuAZ65w+93Atxi9B+oO4PtDHnej\nn+H7sQyLVt2Lqnq8ql4aH55i9J6H7WjI9wXAx4FPAD/fzOE22ZC9eAA4VlUvAFTVc5s842YZshcF\nvH58+QYuf0/QtlBVT3D19zIdAr5QI6eANyR502qPu9HBX+ljGXZfaU1VXQJ+9bEM282QvVjqfkb/\ngm9Hq+5FktuAvVX1zc0cbAsM+b64Gbg5yXeTnEpyYNOm21xD9uJjwL1J5oETwAc2Z7RrzivtCTDs\noxXWY2Ify7ANDP57JrkXmAbesaETbZ2r7kWSVzH61NX7NmugLTTk+2Ino9M6dzL6X99/JLm1qv5n\ng2fbbEP24gjw+ar6hyR/wuj9P7dW1f9t/HjXlDV1c6Of4fuxDIuG7AVJ3g18GDhYVb/YpNk222p7\ncT1wK/CdJD9idI5yZpu+cDv0Z+QbVfXLqvohcI7RPwDbzZC9uB94DKCqvge8htEHq3UzqCfLbXTw\n/ViGRavuxfg0xmcZxX67nqeFVfaiql6sql1VdWNV3cjo9YyDVbXmD426hg35Gfk6oxf0SbKL0Sme\n85s65eYYshc/Bt4FkOStjIK/sKlTXhtmgPeOf1vnDuDFqvrJanfa0FM6tXEfy/AbZ+BefBJ4HfDV\n8evWP66qg1s29AYZuBctDNyLk8CfJzkL/C/woap6fuum3hgD9+Ih4J+T/A2jUxj3bccniEm+zOgU\n3q7x6xUfBV4NUFWfYfT6xd3AHPAS8L5Bj7sN90qStALfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGX\npCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get child objects of axes\n",
    "ax=plt.gca()\n",
    "ax.get_children()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that there's actually three line 2D objects contained in this axis. These are our data points. The number of spines, which are actual renderings of the borders of the frame, including tic markers, two axis objects, and a bunch of text, which are the labels for the chart. There's even a rectangle, which is the background for the axis. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "-  One, pyplot is going to retrieve the current figure with the function gcf and then get the current axis with the function gca. \n",
    "- Pyplot is keeping track of the axis objects for you\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": "3.6.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}