2D-LatinHypercubeSample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from pyDOE import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.25, 0.95],\n",
       "       [0.35, 0.25],\n",
       "       [0.45, 0.35],\n",
       "       [0.95, 0.75],\n",
       "       [0.15, 0.85],\n",
       "       [0.05, 0.55],\n",
       "       [0.65, 0.65],\n",
       "       [0.85, 0.15],\n",
       "       [0.75, 0.45],\n",
       "       [0.55, 0.05]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = lhs(2, n, criterion='center')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12848d7d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 10])\n",
    "plt.xticks(numpy.arange(0, 1, 1/float(n)))\n",
    "plt.yticks(numpy.arange(0, 1, 1/float(n)))\n",
    "plt.grid(True)\n",
    "plt.scatter(p[:, 0], p[:, 1], c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}