amueller/scipy_interpolation_weirdness.ipnb

## scipy_interpolation_weirdness.ipnb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy\n",
      "from scipy.interpolate import interp1d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scipy.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.13.3'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [  8.,   8.,   8.,  10.,  10.,  10.,  12.,  12.,  12.,  14.,  14.]\n",
      "y = [ 21.   ,  22.375,  22.375,  22.375,  22.375,  22.375,  22.375,\n",
      "        22.375,  22.375,  23.5  ,  25.   ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy\n",
      "from scipy.interpolate import interp1d\n",
      "inp = np.linspace(8, 14, 30)\n",
      "f_linear = interp1d(x, y, kind=\"linear\")\n",
      "pred_linear = f_linear(inp)\n",
      "pred_x_linear = f_linear(x)\n",
      "f_slinear = interp1d(x, y, kind=\"slinear\")\n",
      "pred_slinear = f_slinear(inp)\n",
      "pred_x_slinear = f_slinear(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py:416: RuntimeWarning: divide by zero encountered in true_divide\n",
        "  slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n",
        "/usr/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py:419: RuntimeWarning: invalid value encountered in multiply\n",
        "  y_new = slope*(x_new - x_lo)[:, None] + y_lo\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(inp, pred_linear, label=\"linear\")\n",
      "plt.plot(inp, pred_slinear, label=\"slinear\")\n",
      "plt.plot(x, pred_x_slinear, \"x\", label=\"slinear on x\")\n",
      "plt.plot(x, pred_x_linear, \"x\", label=\"linear on x\")\n",
      "\n",
      "plt.plot(x, y, \"x\", label=\"input\")\n",
      "plt.legend(loc=\"best\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.legend.Legend at 0x7fcc4bce0350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3tjZC4aWA3FM+1KcmIrI7uacc3p7eaGprskp5Qx7ifFqTiNydNUeoDH2Isz+ciNycNfvF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\niMiJMcSJiJwYQ5yIyIkxxImInNj/AZlUXcFLGG9ZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fcc581cf5d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pred_linear"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([         nan,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.45258621,  22.56896552,  22.68534483,  22.80172414,\n",
        "        22.91810345,  23.03448276,  23.15086207,  23.26724138,\n",
        "        23.38362069,  23.5       ])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pred_slinear"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([  0.        ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.375     ,  22.375     ,  22.375     ,  22.375     ,\n",
        "        22.45258621,  22.56896552,  22.68534483,  22.80172414,\n",
        "        22.91810345,  23.03448276,  23.15086207,  23.26724138,\n",
        "        23.38362069,   0.        ])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pred_x_slinear"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import scipy\n",
	"from scipy.interpolate import interp1d"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"scipy.__version__"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 2,
	"text": [
	"'0.13.3'"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"x = [ 8., 8., 8., 10., 10., 10., 12., 12., 12., 14., 14.]\n",
	"y = [ 21. , 22.375, 22.375, 22.375, 22.375, 22.375, 22.375,\n",
	" 22.375, 22.375, 23.5 , 25. ]"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import numpy as np\n",
	"import scipy\n",
	"from scipy.interpolate import interp1d\n",
	"inp = np.linspace(8, 14, 30)\n",
	"f_linear = interp1d(x, y, kind=\"linear\")\n",
	"pred_linear = f_linear(inp)\n",
	"pred_x_linear = f_linear(x)\n",
	"f_slinear = interp1d(x, y, kind=\"slinear\")\n",
	"pred_slinear = f_slinear(inp)\n",
	"pred_x_slinear = f_slinear(x)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stderr",
	"text": [
	"/usr/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py:416: RuntimeWarning: divide by zero encountered in true_divide\n",
	" slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n",
	"/usr/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py:419: RuntimeWarning: invalid value encountered in multiply\n",
	" y_new = slope*(x_new - x_lo)[:, None] + y_lo\n"
	]
	}
	],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"plt.plot(inp, pred_linear, label=\"linear\")\n",
	"plt.plot(inp, pred_slinear, label=\"slinear\")\n",
	"plt.plot(x, pred_x_slinear, \"x\", label=\"slinear on x\")\n",
	"plt.plot(x, pred_x_linear, \"x\", label=\"linear on x\")\n",
	"\n",
	"plt.plot(x, y, \"x\", label=\"input\")\n",
	"plt.legend(loc=\"best\")"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 5,
	"text": [
	"<matplotlib.legend.Legend at 0x7fcc4bce0350>"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
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3tjZC4aWA3FM+1KcmIrI7uacc3p7eaGprskp5Qx7ifFqTiNydNUeoDH2Isz+ciNycNfvF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\niMiJMcSJiJwYQ5yIyIkxxImInNj/AZlUXcFLGG9ZAAAAAElFTkSuQmCC\n",
	"text": [
	"<matplotlib.figure.Figure at 0x7fcc581cf5d0>"
	]
	}
	],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"pred_linear"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 6,
	"text": [
	"array([ nan, 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.45258621, 22.56896552, 22.68534483, 22.80172414,\n",
	" 22.91810345, 23.03448276, 23.15086207, 23.26724138,\n",
	" 23.38362069, 23.5 ])"
	]
	}
	],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"pred_slinear"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 7,
	"text": [
	"array([ 0. , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.375 , 22.375 , 22.375 , 22.375 ,\n",
	" 22.45258621, 22.56896552, 22.68534483, 22.80172414,\n",
	" 22.91810345, 23.03448276, 23.15086207, 23.26724138,\n",
	" 23.38362069, 0. ])"
	]
	}
	],
	"prompt_number": 7
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"pred_x_slinear"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 8,
	"text": [
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	{
	"cell_type": "code",
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	"language": "python",
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	],
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	}
	]
	}