{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a design of experiments with discrete and continuous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we present how to create a design of experiments when one (or several) of the marginals are discrete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import openturns as ot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the first marginal of the distribution, we select a univariate discrete distribution. Some of them, like the `Bernoulli` or `Geometric` distributions, are implemented in the library as classes. In this example however, we pick the `UserDefined` distribution that assigns equal weights to the values -2, -1, 1 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<TABLE><TR><TD></TD><TH>v0</TH></TR>\n",
       "<TR><TD>0</TD><TD>-2.0</TD></TR>\n",
       "<TR><TD>1</TD><TD>-1.0</TD></TR>\n",
       "<TR><TD>2</TD><TD>1.0</TD></TR>\n",
       "<TR><TD>3</TD><TD>2.0</TD></TR>\n",
       "</TABLE>"
      ],
      "text/plain": [
       "class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=4 dimension=1 data=[[-2],[-1],[1],[2]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = ot.Sample([-2., -1., 1., 2.],1)\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X0 = ot.UserDefined(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second marginal, we pick a Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = ot.Normal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the multivariate distribution from its marginals and an independent copula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "distribution = ot.ComposedDistribution([X0,X1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 100\n",
    "experiment = ot.MonteCarloExperiment(distribution, size)\n",
    "sample = experiment.generate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "class=Graph name=MonteCarloExperiment implementation=class=GraphImplementation name=MonteCarloExperiment title=MonteCarloExperiment xTitle=x0 yTitle=x1 axes=ON grid=ON legendposition= legendFontSize=1 drawables=[class=Drawable name=Unnamed implementation=class=Cloud name=Unnamed derived from class=DrawableImplementation name=Unnamed legend= data=class=Sample name=ComposedDistribution implementation=class=SampleImplementation name=ComposedDistribution size=100 dimension=2 description=[v0,X0] data=[[-2,0.00413162],[-2,2.6562],[-1,0.270297],[2,0.449078],[-2,1.32742],[1,0.546244],[-1,0.30214],[-2,-0.60771],[-1,0.0909411],[-2,-0.865828],[-1,1.19839],[2,-0.0361958],[-2,-0.980263],[-2,-1.00308],[-2,-0.250602],[-2,-0.204413],[2,-1.1613],[2,-1.10178],[1,-0.792623],[2,-1.45138],[-1,0.098328],[-1,-0.735837],[2,1.48025],[1,0.719079],[2,-1.80012],[1,-0.520973],[1,-0.0252317],[2,0.899794],[-2,0.257652],[2,-0.59333],[2,0.245849],[-2,-0.487845],[-2,0.23423],[-2,0.682265],[-1,-0.29389],[2,0.860012],[2,-2.68674],[-2,1.51149],[1,1.02765],[-1,-1.23384],[-1,0.127792],[-1,0.945496],[-1,1.02385],[-1,-0.069534],[-2,0.477728],[1,0.545467],[2,-0.56378],[-1,-0.617868],[2,-0.445931],[-1,-0.564637],[-2,-2.01027],[-1,0.0328794],[-1,2.51464],[-1,-0.901581],[-1,-1.51696],[-1,-1.29938],[-2,0.230372],[-1,-3.09737],[2,0.01323],[-1,-1.25743],[-2,1.02776],[1,-0.766431],[-1,0.217512],[2,1.04533],[2,0.331569],[-2,-0.488205],[2,-0.465482],[-2,0.332084],[2,-0.167726],[2,3.01263],[-2,0.94204],[-1,0.61189],[-2,0.611715],[1,-1.5375],[-1,-2.4067],[2,0.662936],[1,-0.65616],[1,-0.751611],[-1,0.438177],[2,-0.455335],[-2,1.86038],[-2,0.219721],[-2,1.72546],[2,-0.543405],[2,-0.736749],[-2,-0.508206],[1,-2.25867],[-2,-0.5964],[-2,-0.31468],[-1,-1.78274],[2,-0.684734],[-1,0.0611157],[-2,0.87372],[-1,-1.46295],[2,-0.318786],[1,1.26314],[-2,-0.426726],[2,-1.89234],[1,-0.514391],[-1,0.647229]] color=blue fillStyle=solid lineStyle=solid pointStyle=fsquare lineWidth=1]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = ot.Graph(\"MonteCarloExperiment\", \"x0\", \"x1\", True, \"\")\n",
    "cloud = ot.Cloud(sample, \"blue\", \"fsquare\", \"\")\n",
    "graph.add(cloud)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any other type of design of experiments can be generated based on this distribution. The following example shows a LHS experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 100\n",
    "alwaysShuffle = True\n",
    "randomShift = True\n",
    "experiment = ot.LHSExperiment(distribution, size, alwaysShuffle, randomShift)\n",
    "sample = experiment.generate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "class=Graph name=LHSExperiment implementation=class=GraphImplementation name=LHSExperiment title=LHSExperiment xTitle=x0 yTitle=x1 axes=ON grid=ON legendposition= legendFontSize=1 drawables=[class=Drawable name=Unnamed implementation=class=Cloud name=Unnamed derived from class=DrawableImplementation name=Unnamed legend= data=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=100 dimension=2 description=[v0,X0] data=[[-2,-0.144538],[-2,1.64684],[-1,0.858947],[1,0.432335],[1,0.0192448],[1,-1.79546],[1,-0.151671],[1,2.76631],[-2,-0.102719],[1,1.11333],[2,0.733422],[-2,-1.91152],[-2,-0.0476581],[2,-0.763789],[-2,1.19289],[2,0.354632],[-2,0.280359],[1,-0.532477],[1,-0.385362],[-1,1.01351],[1,-0.223299],[-2,0.785818],[-1,0.162757],[-2,-0.931728],[2,-1.42655],[-2,-0.436668],[1,-1.10313],[-1,0.220441],[1,-0.240703],[2,-1.01459],[1,-1.38302],[1,-1.21767],[-2,-0.460092],[1,-1.27976],[1,-0.885904],[2,-2.26494],[2,0.235766],[1,0.0255401],[-2,0.672287],[1,-0.0563505],[2,-0.682073],[1,-0.284564],[-1,0.106995],[-2,2.27718],[2,0.989895],[-1,1.15629],[-2,1.8733],[2,-0.0939797],[-1,1.28079],[-2,-0.376741],[-2,-1.05097],[2,0.326082],[-2,0.886154],[1,1.28172],[-2,1.5561],[1,-1.3317],[-2,-0.0208517],[-2,0.146488],[-1,-0.265896],[-1,-0.500241],[1,0.185144],[2,-0.6578],[-1,2.02226],[2,0.83133],[2,-0.356116],[-2,0.363158],[-2,-0.469253],[-1,1.41854],[2,-1.13798],[-1,0.770154],[2,-0.729431],[2,-0.634263],[2,0.266101],[-2,0.939142],[1,-0.838944],[-2,-0.985592],[2,-0.20121],[-1,-0.851032],[-1,-0.601432],[-1,1.06248],[2,-0.312884],[2,0.555072],[2,0.531389],[-1,0.620669],[-2,0.398444],[-1,0.604487],[1,0.488432],[1,1.53299],[1,-1.53573],[-1,-1.58077],[-1,0.705901],[-1,-1.69308],[-1,1.38549],[-1,0.0771083],[-1,0.517319],[2,-0.773623],[-1,0.0590901],[2,-2.68632],[2,0.460697],[-1,-0.554135]] color=blue fillStyle=solid lineStyle=solid pointStyle=fsquare lineWidth=1]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = ot.Graph(\"LHSExperiment\", \"x0\", \"x1\", True, \"\")\n",
    "cloud = ot.Cloud(sample, \"blue\", \"fsquare\", \"\")\n",
    "graph.add(cloud)\n",
    "graph"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}