{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 31/03/2015 \n",
      "\n",
      "CPython 3.4.3\n",
      "IPython 3.0.0\n",
      "\n",
      "matplotlib 1.4.3\n",
      "numpy 1.9.1\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Formatting II: gridlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Generating-some-sample-data](#Generating-some-sample-data)\n",
    "- [Default grid](#Default-grid)\n",
    "- [Vertical and horizontal grids](#Vertical-and-horizontal-grids)\n",
    "    - [Vertical grid](#Vertical-grid)\n",
    "    - [Horizontal grid](#Horizontal-grid)\n",
    "- [Controlling the gridline style](#Controlling-the-gridline-style)\n",
    "    - [Changing the tick frequency](#Changing-the-tick-frequency)\n",
    "    - [Changing the tick color and linestyle](#Changing-the-tick-color-and-linestyle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating some sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eUXY1cF9VPd9d/PQMg4uhJlZVTVfVi93qN4At3XJzfWHowrOqeh44feFZM6rqRFV9u1v+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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065c1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "data = np.random.normal(0, 20, 1000) \n",
    "\n",
    "# fixed bin size\n",
    "bins = np.arange(-100, 100, 5) # fixed bin size\n",
    "\n",
    "plt.xlim([min(data)-5, max(data)+5])\n",
    "\n",
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Default grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lXHtkak2PG4qfN7KmDISafaZyxico4wBAAxD2NcdRUzZq9pkYn+Ug7AGgAQj7mmMeczZq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\nAxD2ANAAhD0ANMD/A0Hf/AGvx+/PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065c16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or alternatively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lXHtkak2PG4qfN7KmDISafaZyxico4wBAAxD2NcdRUzZq9pkYn+Ug7AGgAQj7mmMeczZq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\nAxD2ANAAhD0ANMD/A0Hf/AGvx+/PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068c8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "ax = plt.gca()\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vertical and horizontal grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertical grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vjIh525dJOrvMY2f7No/y6t8e1wvPRmafifpsr3m/Y3vW87Vq9rYt6eOSjkfEvr67Dkva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\nDwAdQLMHgA6g2QNAB/w/iinSQYv+tTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b20c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "ax = plt.gca()\n",
    "ax.xaxis.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Horizontal grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d6ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "ax = plt.gca()\n",
    "ax.yaxis.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Controlling the gridline style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the tick frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SHEyyzBkckqTV0PXI/nngX1bV3wHeAPyLJK8G9gKHquoK4OG2LUlaY53CvqpOVNWRdvrb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\nDHtJGgDDXpIGwLCXpAH4/45j/sWLi1wcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106aaa8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# major ticks every 10                                     \n",
    "major_ticks = np.arange(-100, 101, 10)                                              \n",
    "\n",
    "ax = plt.gca()\n",
    "ax.yaxis.grid()\n",
    "\n",
    "ax.set_yticks(major_ticks)                                                       \n",
    "                                    \n",
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the tick color and linestyle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kNcCwl6QGGPaS1ADDXpIa4Nk4DRh+Ct3Mu2HrM4u3v7ab/vMJtG6c2p3senDwOv7e1xPD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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065c1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['grid.linestyle'] = '-'\n",
    "rcParams['grid.color'] = 'blue'\n",
    "rcParams['grid.linewidth'] = 0.2\n",
    "\n",
    "plt.grid()\n",
    "plt.hist(data, bins=bins, alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}