{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Optimal LHS vs LHS\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from __future__ import annotations\n\nimport matplotlib.pyplot as plt\nfrom gemseo.algos.doe.doe_factory import DOEFactory\n\nn_samples = 30\nn_parameters = 2\n\nfactory = DOEFactory()\n\nlhs = factory.create(\"OT_LHS\")\nsamples = lhs(n_samples, n_parameters)\nsamples2 = lhs(n_samples, n_parameters)\n\nolhs = factory.create(\"OT_OPT_LHS\")\no_samples = olhs(n_samples, n_parameters)\n\nolhs = factory.create(\"OT_OPT_LHS\")\no_a_samples = olhs(n_samples, n_parameters, annealing=False)\n\n_, ax = plt.subplots(2, 2)\nax[0, 0].plot(samples[:, 0], samples[:, 1], \"o\")\nax[0, 0].set_title(\"A first standard LHS\")\nax[0, 1].plot(samples2[:, 0], samples[:, 1], \"o\")\nax[0, 1].set_title(\"A second standard LHS\")\nax[1, 0].plot(o_samples[:, 0], o_samples[:, 1], \"o\")\nax[1, 0].set_title(\"An LHS optimized with simulated annealing\")\nax[1, 1].plot(o_a_samples[:, 0], o_a_samples[:, 1], \"o\")\nax[1, 1].set_title(\"An LHS optimized with Monte Carlo\")\nplt.show()"
      ]
    }
  ],
  "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": "3.9.13"
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