LinearInterpolate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numba as nb\n",
    "\n",
    "from orderedTableSearch import locate, locate_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#simple linterpolation subroutine with numba\n",
    "#xgrid ans ygrid should be increasing, and the codes do not check it.\n",
    "\n",
    "@nb.jit(nopython=True)\n",
    "def linearinterp(x, x0, x1, y0, y1):\n",
    "\n",
    "    return (y1 - y0)/(x1 - x0)*(x - x0) + y0\n",
    "\n",
    "@nb.jit(nopython=True)\n",
    "def interp_point(x, xgrid, ygrid):\n",
    "    #ind = locate_grid(x, xgrid)\n",
    "    ind = locate(x, xgrid)\n",
    "    return linearinterp(x, xgrid[ind], xgrid[ind+1], ygrid[ind], ygrid[ind+1])\n",
    "\n",
    "@nb.jit(nopython=True)\n",
    "def interp_array(x, xgrid, ygrid):\n",
    "    #x is supposed to be array-like\n",
    "    ind = locate_grid(x, xgrid)\n",
    "    M = len(x)\n",
    "    ans = np.empty(M) #\n",
    "    \n",
    "    for i in range(M):\n",
    "        ix = ind[i]\n",
    "        ans[i] = linearinterp(x[i], xgrid[ix],  xgrid[ix+1], ygrid[ix],  ygrid[ix+1])\n",
    "    \n",
    "    return ans\n",
    "\n",
    "@nb.generated_jit(nopython=True)\n",
    "def interp(x, xgrid, ygrid):\n",
    "    if isinstance(x, nb.types.Float) or isinstance(x, nb.types.Integer) :\n",
    "        return lambda x, xgrid, ygrid: interp_point(x, xgrid, ygrid)\n",
    "    else:\n",
    "        return lambda x, xgrid, ygrid: interp_array(x, xgrid, ygrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0.7988240718841553 seconds\n"
     ]
    },
    {
     "data": {
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weR30LIbQysbAIJJgfq30+GMggtfCUAC8AFziUy0iIulnwaPwzI8hUgclo+Ds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\nS4FBRERE4lJgEBERkbgUGERERCSu/wczjsKQU3yZZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b7a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#testcode\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    %matplotlib inline\n",
    "    import matplotlib as mpl\n",
    "    mpl.rc(\"savefig\",dpi=100)\n",
    "    from matplotlib import pyplot as plt\n",
    "    import time\n",
    "    \n",
    "    xg = np.linspace(0, 5, 10)\n",
    "    yg = np.exp(xg)\n",
    "    \n",
    "    plt.plot(xg, yg, 'o')\n",
    "    \n",
    "    xvals = np.linspace(0,5, 1000)\n",
    "    \n",
    "    t1 = time.time()\n",
    "    \n",
    "    #xinp = interp_array(xvals, xg, yg)\n",
    "    xinp = interp(xvals, xg, yg)\n",
    "    \n",
    "    t2 = time.time()\n",
    "    print(' {} seconds'.format(t2 - t1))\n",
    "    \n",
    "    plt.plot(xvals, xinp, '--')"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}