lesson 3/plotting-introduction-exercise-additional-tips.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting with Matplotlib"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preparations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy as sp\n",
      "import sympy\n",
      "\n",
      "# the convienient Matplotib plotting interface pyplot (the tidy/right way)\n",
      "# use this for building scripts\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# for using the matplotlib API directly (the hard and verbose way)\n",
      "# use this when building applications, and/or backends\n",
      "import matplotlib as mpl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scaling: getting font sizes right for publications"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# some sample data\n",
      "x = np.arange(0,10,0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page_width_cm = 13\n",
      "dpi = 200\n",
      "inch = 2.54 # inch in cm\n",
      "\n",
      "plt.rc('font', family='serif')\n",
      "plt.rc('xtick', labelsize=12) # tick labels\n",
      "plt.rc('ytick', labelsize=20) # tick labels\n",
      "plt.rc('axes', labelsize=20)  # axes labels\n",
      "# If you don\u2019t need LaTeX, don\u2019t use it. It is slower to plot, and text\n",
      "# looks just fine without. If you need it, e.g. for symbols, then use it.\n",
      "plt.rc('text', usetex=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# chose the backend: 'gtk', 'inline', 'osx', 'qt', 'qt4', 'tk', 'wx'\n",
      "%matplotlib qt\n",
      "# create a figure instance, note that figure size is given in inches!\n",
      "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,6))\n",
      "# set the big title (note aligment relative to figure)\n",
      "fig.suptitle(\"suptitle 16, figure alignment\", fontsize=16)\n",
      "# add axes to plot manually\n",
      "# left, bottom, width, height (range 0 to 1)\n",
      "ax = fig.add_axes([0.1, 0.1, 0.5, 0.8])\n",
      "# actual plotting\n",
      "ax.plot(x, x**2, label=\"label 12\")\n",
      "# set axes title (note aligment relative to axes)\n",
      "ax.set_title(\"title 14, axes alignment\", fontsize=14)\n",
      "# axes labels\n",
      "ax.set_xlabel('xlabel 12')\n",
      "ax.set_ylabel(r'$y_{\\alpha}$ 12', fontsize=8)\n",
      "# legend\n",
      "ax.legend(fontsize=12, loc=\"best\")\n",
      "fig.savefig('figure-%03i.png' % dpi, dpi=dpi)\n",
      "fig.savefig('figure.svg')\n",
      "fig.savefig('figure.eps')\n",
      "\n",
      "# figure scope\n",
      "y_ = 0.7\n",
      "ax.annotate('',  xy=(0.0, y_), xycoords='figure fraction',\n",
      "             xytext=(0.1, y_), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))\n",
      "ax.annotate('',  xy=(0.1, y_), xycoords='figure fraction',\n",
      "             xytext=(0.6, y_), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))\n",
      "ax.annotate('',  xy=(0.6, y_), xycoords='figure fraction',\n",
      "             xytext=(1.0, y_), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))\n",
      "\n",
      "x_ = 0.15\n",
      "ax.annotate('',  xy=(x_, 0), xycoords='figure fraction',\n",
      "             xytext=(x_, 0.1), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))\n",
      "ax.annotate('',  xy=(x_, 0.1), xycoords='figure fraction',\n",
      "             xytext=(x_, 0.9), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))\n",
      "ax.annotate('',  xy=(x_, 0.9), xycoords='figure fraction',\n",
      "             xytext=(x_, 1.0), textcoords='figure fraction',\n",
      "             arrowprops=dict(arrowstyle=\"<->\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Annotation at 0x6a57e90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stext = u'$\\\\sigma = \\\\frac{33 \\\\cdot 10^3 \\\\Omega}{5 \\\\epsilon^5}$'\n",
      "# bbox is a dictionary of matplotlib.patches.Rectangle properties:\n",
      "# http://matplotlib.org/api/artist_api.html#matplotlib.patches.Rectangle\n",
      "bbox = dict(boxstyle=\"round\", alpha=0.5, edgecolor=(1., 0.5, 0.5),\n",
      "            facecolor=(1., 0.8, 0.8),)\n",
      "ax.text(6, 40, stext, fontsize=24, verticalalignment='bottom',\n",
      "         horizontalalignment='center', bbox=bbox)\n",
      "# we have to update the figure\n",
      "ax.plot(x, x**2, 'ks', label=\"label 12\", alpha=0.3)\n",
      "fig.canvas.draw()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# what is the current backend?\n",
      "%matplotlib\n",
      "fig = plt.figure(1, figsize=(8,6))\n",
      "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
      "ax.plot(x, x**2, label=\"label 12\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using matplotlib backend: Qt4Agg\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x5588450>]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# switch to inline plotting\n",
      "%matplotlib inline\n",
      "fig = plt.figure(1, figsize=(8,6))\n",
      "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
      "ax.plot(x, x**2, label=\"label 12\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x5662ed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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aOVtNBPn9fnV2dsrtdstxHOXn56u0tDSaIQAA4mjSJKmrS3rhBZJ/solaAdDS0iKfz6ep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\npCEKAAAA0tD/B24AZAZ3mHHSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4b4e450>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib qt\n",
      "fig = plt.figure(1, figsize=(8,6))\n",
      "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
      "ax.plot(x, x**2, label=\"label 12\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[<matplotlib.lines.Line2D at 0x5589710>]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(5):\n",
      "    plt.figure(i)\n",
      "    plt.plot(x, i*x**2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def serialplots():\n",
      "    for i in range(5):\n",
      "        plt.figure(i)\n",
      "        plt.plot(x, i*x**2)\n",
      "        plt.show()\n",
      "\n",
      "def serialplots2():\n",
      "    for i in range(5):\n",
      "        plt.figure(i)\n",
      "        plt.plot(x, i*x**2)\n",
      "    plt.show()\n",
      "\n",
      "def serialplots3():\n",
      "    for i in range(5):\n",
      "        figure(i)\n",
      "        plot(x, i*x**2)\n",
      "        show()\n",
      "\n",
      "def serialplots4():\n",
      "    for i in range(5):\n",
      "        figure(i)\n",
      "        plot(x, i*x**2)\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib qt\n",
      "serialplots4()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}