{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from cem_gridtools.cem_gridtools import NN\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define input data\n",
    "x = np.array([0, 1,   0.4], dtype='d')\n",
    "y = np.array([0, 0.2, 0.8], dtype='d')\n",
    "z = np.array([1, 2, 3], dtype='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NN interface\n",
    "I = NN(x,y,z,\"nn_sibson\")\n",
    "xo = 0.5\n",
    "yo = 0.2\n",
    "V = I.interp(xo, yo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0e22776ba8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,s=500,c=z,label='Source')\n",
    "plt.scatter(xo, yo, s=500, c=V, marker='s', label='Interpolated data')\n",
    "plt.colorbar()\n",
    "plt.legend(markerscale=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or the points to interpolate onto may be a vector\n",
    "xo = np.array([0.5, 0.5, 0.5, 0.5, 0.5], dtype='d')\n",
    "yo = np.array([0, 0.15, 0.3, 0.6, 0.7], dtype='d')\n",
    "V = I.interp(xo, yo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0e2206d588>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,s=500,c=z,label='Source')\n",
    "plt.scatter(xo, yo, s=500, c=V, marker='s',label='Interpolated data')\n",
    "plt.colorbar()\n",
    "plt.legend(markerscale=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}