MatPlotLib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#the following command is only for jupyter notebooks. In case you are using another python\n",
    "#editor you should use the command: plt.show() at the end of all your plotting scripts\n",
    "#in order to have the figure pop up in a new window\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an numpy array x. Set y as square of x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps: Add Canvas Figure, Add set of axes to figure, add plot to axes and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'This is the title')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAFACAYAAADOC2nHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA87MzKrS/wdaTP3kqWa63gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x85acb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "#use add_axes() method and use [] for left, bottom, width and height =fractions of figure\n",
    "axes = fig.add_axes([0.1 ,0.1, 0.9, 0.9]) \n",
    "\n",
    "#Plot and set labels\n",
    "axes.plot(x, y, 'r-')  #r for red colour , - for continuous line\n",
    "axes.set_xlabel('X Axis')\n",
    "axes.set_ylabel('Y Axis')\n",
    "axes.set_title('This is the title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new figure with 2 plots one inside the other. Use as an example x**2 and x**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = x**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x86d49e8>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEJCAYAAAB8Pye7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x873cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure()\n",
    "\n",
    "axes1 = fig2.add_axes([0.1 , 0.1 , 0.8 , 0.8])\n",
    "axes2 = fig2.add_axes([0.2 , 0.5 , 0.3 , 0.3])\n",
    "\n",
    "axes1.plot(x, y, 'r')\n",
    "axes2.plot(x, z, 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save figure as an image on your working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig2.savefig('figure2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save figure as an image with dpi=200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig2.savefig('figure2.jpg', dpi=200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}