customized_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualistation with matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import matplotlib modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib has many different backends, usually its fine to stick to the default. But sometimes it is necessary to change the backend (before importing the plotting module ```pyplot```). The full list of available backends reads:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[u'pgf', u'cairo', u'MacOSX', u'CocoaAgg', u'gdk', u'ps', u'GTKAgg', u'nbAgg', u'GTK', u'Qt5Agg', u'template', u'emf', u'GTK3Cairo', u'GTK3Agg', u'WX', u'Qt4Agg', u'TkAgg', u'agg', u'svg', u'GTKCairo', u'WXAgg', u'WebAgg', u'pdf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matplotlib.use('MacOSX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working from within a jupyter notebook, we can choose *matplotlib* backend with a magic function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib --list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "#% matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we import ```pyplot``` the main plotting module of *matplotlib*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. The default plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some data (normal distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.random.normal(loc=5., scale=2., size=(10000,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plot a simple histogram of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.hist(data, range=(-5,15), bins=20, normed=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Adding more to the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are going to fit a Gaussian profile to our function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "mu,sigma = norm.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plot it on top of our histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.hist(data, range=(-5,15), bins=20, normed=True)\n",
    "ax.plot(np.arange(-5,15,0.1),norm.pdf(np.arange(-5,15,0.1),mu,sigma))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's make a bit easier to read (colors, legends, axis lables, etc..)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.set_title('My fancy plot')\n",
    "ax.hist(data, range=(-5,15), bins=20, normed=True, label='data', color='0.75', lw=0.2)\n",
    "ax.plot(np.arange(-5,15,0.1),norm.pdf(np.arange(-5,15,0.1),mu,sigma), label='fit', color='r')\n",
    "ax.set_xlabel(r'Some variable $\\varphi$')\n",
    "ax.set_ylabel(r'Probability density')\n",
    "ax.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. More changes to the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our plot looks already quite good, but some of the features don't give any extra information so why not removing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.set_title('My fancy plot')\n",
    "ax.hist(data, range=(-5,15), bins=20, normed=True, label='data', color='0.75', lw=0.2)\n",
    "ax.plot(np.arange(-5,15,0.1),norm.pdf(np.arange(-5,15,0.1),mu,sigma), label='fit', color='r')\n",
    "ax.set_xlabel(r'Some variable $\\varphi$')\n",
    "ax.legend(loc=2, frameon=False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.tick_params(axis='x', which='both', bottom='on', top='off')\n",
    "ax.tick_params(axis='y', which='both', left='off', right='off', labelleft='off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Saving the plot as a PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.set_title('My fancy plot')\n",
    "ax.hist(data, range=(-5,15), bins=20, normed=True, label='data', color='0.75', lw=0.2)\n",
    "ax.plot(np.arange(-5,15,0.1),norm.pdf(np.arange(-5,15,0.1),mu,sigma), label='fit', color='r')\n",
    "ax.set_xlabel(r'Some variable $\\varphi$')\n",
    "ax.legend(loc=2, frameon=False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.tick_params(axis='x', which='both', bottom='on', top='off')\n",
    "ax.tick_params(axis='y', which='both', left='off', right='off', labelleft='off')\n",
    "fig.savefig('my_fancy_plot.pdf', format='pdf', bbox_inches='tight')"
   ]
  }
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