Matplotlib_Parametrized_Curve2D_easy.ipynb

Matplotlib: Parametrized Curve in 2D with Colormap (The Easy Way)

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Matplotlib: Parametrized Curve with Colormap (The Easy Way)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "deg=u\"\\u00b0\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x=np.arange(0,6*np.pi,0.1)#time is actually our x axis"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t=np.arange(1,650)#temperature will be our hidden dimension we want to see in our 2D Plot\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y=1-np.exp(-1.0/2*x)#some function, just something to work with\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = plt.subplot2grid((15,15),(0, 0),rowspan=10,colspan=10)\n",
      "\n",
      "samples=range(1,len(x))\n",
      "\n",
      "#decrese this value if you want bigger color jumps (usable if you need a lot of subplots)\n",
      "#the plot shows a complete walk through the cm\n",
      "\n",
      "colormap = plt.cm.YlOrRd\n",
      "plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 1, len(samples))])\n",
      "\n",
      "n=1\n",
      "labels=[]\n",
      "for i in range (0,len(t),1):\n",
      "    n=n+1\n",
      "    ax1.plot(x[i:n],y[i:n])\n",
      "    \n",
      "    labels.append(r'$time = %s $' % t[i])\n",
      "plt.grid(True)\n",
      "#plt.legend(loc='best')\n",
      "#ax2 = plt.subplot2grid((3,3),(1, 1))\n",
      "plt.xlabel('time in oven [s]')\n",
      "plt.ylabel('rel. el. Resistivity [E]')\n",
      "plt.ylim(ymax=1.1)\n",
      "plt.title('Heating of Metals')\n",
      "a = np.linspace(0, 1, 256).reshape(1,-1)\n",
      "a = np.vstack((a,a))\n",
      "\n",
      "ax2 = plt.subplot2grid((15,15),(0, 11),colspan=5)\n",
      "ax2.imshow(a, aspect='auto', cmap=plt.cm.YlOrRd, origin='lower')\n",
      "ax2.text(0,-2,'T'+'['+deg+'C]='+str(t[0]))\n",
      "ax2.text(175,-2,'T'+'['+deg+'C]='+str(t[-1]))\n",
      "ax2.axis('off')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MmTN4enpaLVwGg4GFCxcyePBgTCYTEydOJDw8nCVLlgDw8MMPExYWxpAhQ+je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\njTfe4MCBA7r6/QnnaLAFpjY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       "text": [
        "<matplotlib.figure.Figure at 0x32814d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext version_information\n",
      "%reload_ext version_information\n",
      "\n",
      "%version_information numpy, scipy, matplotlib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.6 |Anaconda 1.9.1 (64-bit)| (default, Jan 17 2014, 10:13:17) [GCC 4.1.2 20080704 (Red Hat 4.1.2-54)]</td></tr><tr><td>IPython</td><td>2.0.0-wakari</td></tr><tr><td>OS</td><td>posix [linux2]</td></tr><tr><td>numpy</td><td>1.8.1</td></tr><tr><td>scipy</td><td>0.13.3</td></tr><tr><td>matplotlib</td><td>1.3.1</td></tr><tr><td colspan='2'>Thu Jul 10 00:17:48 2014 MSD</td></tr></table>"
       ],
       "json": [
        "{\"Software versions\": [{\"version\": \"2.7.6 |Anaconda 1.9.1 (64-bit)| (default, Jan 17 2014, 10:13:17) [GCC 4.1.2 20080704 (Red Hat 4.1.2-54)]\", \"module\": \"Python\"}, {\"version\": \"2.0.0-wakari\", \"module\": \"IPython\"}, {\"version\": \"posix [linux2]\", \"module\": \"OS\"}, {\"version\": \"1.8.1\", \"module\": \"numpy\"}, {\"version\": \"0.13.3\", \"module\": \"scipy\"}, {\"version\": \"1.3.1\", \"module\": \"matplotlib\"}]}"
       ],
       "latex": [
        "\\begin{tabular}{|l|l|}\\hline\n",
        "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
        "Python & 2.7.6 |Anaconda 1.9.1 (64-bit)| (default, Jan 17 2014, 10:13:17) [GCC 4.1.2 20080704 (Red Hat 4.1.2-54)] \\\\ \\hline\n",
        "IPython & 2.0.0-wakari \\\\ \\hline\n",
        "OS & posix [linux2] \\\\ \\hline\n",
        "numpy & 1.8.1 \\\\ \\hline\n",
        "scipy & 0.13.3 \\\\ \\hline\n",
        "matplotlib & 1.3.1 \\\\ \\hline\n",
        "\\hline \\multicolumn{2}{|l|}{Thu Jul 10 00:17:48 2014 MSD} \\\\ \\hline\n",
        "\\end{tabular}\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Software versions\n",
        "Python 2.7.6 |Anaconda 1.9.1 (64-bit)| (default, Jan 17 2014, 10:13:17) [GCC 4.1.2 20080704 (Red Hat 4.1.2-54)]\n",
        "IPython 2.0.0-wakari\n",
        "OS posix [linux2]\n",
        "numpy 1.8.1\n",
        "scipy 0.13.3\n",
        "matplotlib 1.3.1\n",
        "<tr><td colspan='2'>Thu Jul 10 00:17:48 2014 MSD</td></tr>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}