{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f6af50b",
   "metadata": {},
   "source": [
    "# Simplex Stochastic Collocation tutorial\n",
    "\n",
    "Standard Stochastic Collocation (SC) uses **global** polynomials to construct a surrogate model $\\tilde{f}$ of some computational model $f$:\n",
    "\n",
    "\\begin{align}\n",
    "f(\\boldsymbol\\xi) = f(\\xi_1,\\cdots, \\xi_d) \\approx \\tilde{f}(\\xi_1,\\cdots, \\xi_d) := \\sum_{j_1=1}^{m_{l_1}}\\cdots\\sum_{j_d = 1}^{m_{l_d}}f\\left(\\xi_{j_1}, \\cdots, \\xi_{j_d}\\right) a_{j_1}(\\xi_1)\\otimes\\cdots\\otimes a_{j_d}(\\xi_d)\n",
    "\\end{align}\n",
    "\n",
    "Here $a_{j_i}(\\xi_i)$ are (typically) 1D Lagrange interpolation polynomials, which span the entire domain of $\\xi_i$ (i.e. they are global interpolation polynomials).\n",
    "\n",
    "Challenges exist if the number of parameters $d$ is high, which is the well-known **curse of dimensionality**. The SC method can be made dimension-adaptive however, to achieve must better scalability. Click [here](https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/dimension_adaptive_tutorial.ipynb) for a practical EasyVVUQ tutorial on how to use the dimension-adaptive SC sampler. The maths behind the dimension-adaptivity are described [here](https://www.researchgate.net/publication/359296270_Adaptive_sparse-grid_tutorial).\n",
    "\n",
    "However, this tutorial is on the 2nd main challenge of SC (and PCE) methods, namely the **assumed regularity of the code output**. If discontinuities or high gradient are present in the stochastic input space $\\boldsymbol\\xi$, the quality of the surrogate $\\tilde f$ deteriorates. To see this, run the example below which creates a SC surrogate for a 2D discontinuous function. We will assume you are familiar with the basic EasyVVUQ building blocks. If not, you can look at the [basic tutorial](https://github.com/UCL-CCS/EasyVVUQ/blob/dev/tutorials/basic_tutorial.ipynb).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6cc65c",
   "metadata": {},
   "source": [
    "## Install EasyVVUQ\n",
    "\n",
    "If you have not installed EasyVVUQ, uncomment the line below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98f2122c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install EasyVVUQ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088c72bf",
   "metadata": {},
   "source": [
    "## Standard SC on a discontinuous function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64f9eb32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import chaospy as cp\n",
    "import numpy as np\n",
    "import easyvvuq as uq\n",
    "import matplotlib.pyplot as plt\n",
    "from easyvvuq.actions import CreateRunDirectory, Encode, Decode, ExecuteLocal, Actions\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b8e899",
   "metadata": {},
   "source": [
    "This is the function we consider, it is located in `discont_model.py` as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e81a53fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x, n_xi = 2):\n",
    "    \"\"\"\n",
    "    Discontinuous test function\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : array\n",
    "        input parameters\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        f(x)\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    if x[1] <= -0.6 * x[0] + 0.8:\n",
    "        return np.sum(x[0:n_xi]) - 1\n",
    "    else:\n",
    "        return x[0] ** 3 + np.cos(np.sum(x[1:n_xi] ** 2)) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b286420",
   "metadata": {},
   "source": [
    "This is what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db8e085f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx = np.linspace(0,1,20)\n",
    "X1, X2 = np.meshgrid(xx, xx)\n",
    "\n",
    "F = np.zeros([20,20])\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        F[i, j] = f(np.array([xx[j], xx[i]]))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d', xlabel=r'$\\xi_1$', ylabel=r'$\\xi_2$')\n",
    "surf = ax.plot_surface(X1, X2, F, linewidth=0, antialiased=False, cmap=cm.coolwarm)  \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7641674",
   "metadata": {},
   "source": [
    "This is a standard EasyVVUQ SC campaign, where we prescribe a standard uniform distribution for inputs $\\xi_1$ and $\\xi_2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d031aeff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.972725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.294315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>1.675251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             f\n",
       "             0\n",
       "mean  0.972725\n",
       "std   1.294315\n",
       "var   1.675251"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_campaign(n_xi = 2):\n",
    "    ##################################\n",
    "    # Define (total) parameter space #\n",
    "    ##################################\n",
    "\n",
    "    # Define parameter space\n",
    "    params = {}\n",
    "    for i in range(5):\n",
    "        params['xi_%d' % (i + 1)] = {'type': 'float', 'default': 0.5}\n",
    "    params['n_xi'] = {'type': 'integer', 'default': 2}\n",
    "\n",
    "    ###########################\n",
    "    # Set up a fresh campaign #\n",
    "    ###########################\n",
    "\n",
    "    encoder = uq.encoders.GenericEncoder(\n",
    "        template_fname= os.path.abspath('discont_model.template'),\n",
    "        delimiter='$',\n",
    "        target_filename='input.csv')\n",
    "\n",
    "    execute = ExecuteLocal('{}/discont_model.py'.format(os.getcwd()))\n",
    "\n",
    "    output_columns = [\"f\"]\n",
    "\n",
    "    decoder = uq.decoders.SimpleCSV(\n",
    "        target_filename='output.csv',\n",
    "        output_columns=output_columns)\n",
    "\n",
    "    actions = Actions(CreateRunDirectory(root='/tmp', flatten=True), Encode(encoder), execute, \n",
    "                      Decode(decoder))\n",
    "\n",
    "    campaign = uq.Campaign(name='standard_SC', work_dir='/tmp', params=params, actions=actions)\n",
    "    \n",
    "    return campaign\n",
    "\n",
    "# get the main Campaign object\n",
    "campaign = get_campaign()\n",
    "\n",
    "#######################\n",
    "# Specify input space #\n",
    "#######################\n",
    "\n",
    "vary = {\n",
    "    \"xi_1\": cp.Uniform(0.0, 1.0),\n",
    "    \"xi_2\": cp.Uniform(0.0, 1.0)\n",
    "}\n",
    "\n",
    "##################\n",
    "# Select sampler #\n",
    "##################\n",
    "\n",
    "sampler = uq.sampling.SCSampler(vary=vary, polynomial_order=6)\n",
    "\n",
    "# associate the sampler with the campaign\n",
    "campaign.set_sampler(sampler)\n",
    "\n",
    "###############################\n",
    "# execute the defined actions #\n",
    "###############################\n",
    "\n",
    "campaign.execute().collate()\n",
    "\n",
    "# get EasyVVUQ data frame\n",
    "data_frame = campaign.get_collation_result()\n",
    "\n",
    "############################\n",
    "# Post-processing analysis #\n",
    "############################\n",
    "\n",
    "analysis = uq.analysis.SCAnalysis(sampler=sampler, qoi_cols=[\"f\"])\n",
    "results = analysis.analyse(data_frame=data_frame)\n",
    "results.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3b3a50",
   "metadata": {},
   "source": [
    "The displayed output stats do not immediately hint at a problem. However, when we plot the surrogate $\\tilde f$ we find:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81de8c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################\n",
    "# Plot the surrogate samples #\n",
    "##############################\n",
    "def plot_surrogate(analysis):\n",
    "    \n",
    "    if len(vary) == 2:\n",
    "        #generate n_mc samples from the input distributions\n",
    "        n_mc = 20\n",
    "        xx = np.linspace(0, 1, n_mc)\n",
    "        X, Y = np.meshgrid(xx, xx)\n",
    "        F = np.zeros([n_mc, n_mc])\n",
    "\n",
    "        # evaluate the surrogate at these values\n",
    "        for i in range(n_mc):\n",
    "            for j in range(n_mc):\n",
    "                xi_mc = np.array([X[i, j], Y[i, j]])\n",
    "                F[i, j] = analysis.surrogate('f', xi_mc)\n",
    "\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111, projection='3d', xlabel=r'$\\xi_1$', ylabel=r'$\\xi_2$')\n",
    "        surf = ax.plot_surface(X, Y, F, linewidth=0, antialiased=False, cmap=cm.coolwarm)  \n",
    "        plt.tight_layout()\n",
    "\n",
    "    elif len(vary) == 1:\n",
    "        \n",
    "        n_mc = 100\n",
    "        xx = np.linspace(0, 1, n_mc)\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111, xlabel=r'$\\xi$', ylabel=r'$\\tilde{f}$')\n",
    "        \n",
    "        F = np.zeros([n_mc])\n",
    "        for i in range(n_mc):\n",
    "            F[i] = analysis.surrogate('f', xx[i])\n",
    "        ax.plot(xx, F)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "plot_surrogate(analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb683f4b",
   "metadata": {},
   "source": [
    "Clearly, $\\tilde f$ contains many \"unphysical\" oscillations. This is Runge phenomenon, caused by trying to model a function $f$ with highly **localized & abrupt** changes, by a function $\\tilde f$ based on **global & smooth** polynomials. In other words, for this function our choice of interpolation polynomials (the Lagrange polynomials $a_{j_i}(x_i)$ shown above) is not good. A localized polynomial basis is required, that is able to adapt the local behavior of $f$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c135b44",
   "metadata": {},
   "source": [
    "## Simplex stochastic collocation \n",
    "\n",
    "The Simplex Stochastic Collocation (SSC) method differs from traditional collocation methods (SC/PCE), as it employs the Delaunay triangulation to discretize the probability space into simplex elements, rather than relying on the more common tensor product of one-dimensional abscissas. Using a multi-element technique has the advantage that local mesh adaptation can be performed, such that only regions of interest are refined. Since the SSC method discretizes the parameter space $\\Xi$ into $n_e$ disjoint simplices $\\Xi = \\Xi_1 \\cup \\cdots \\cup \\Xi_{n_e}$,\n",
    "the local surrogate function $w_j$ (for inputs $\\boldsymbol\\xi\\in\\Xi_j$) is given by the expansion\n",
    "\n",
    "\\begin{align} \n",
    " w_j(\\boldsymbol\\xi) = \\sum^{N_j}_{l = 0} c_{j,l}\\Psi_{j, l}(\\boldsymbol\\xi).\n",
    "\\end{align}\n",
    "\n",
    "The $N_j+1$ number of points needed for $n_\\xi$-dimensional interpolation ($n_\\xi$ is the number of random inputs $\\xi_i$, $i=1,\\cdots,n_\\xi$) of polynomial order $p_j$ is given by \n",
    "\n",
    "\\begin{align} \\label{eq:Np1_j}\n",
    " N_j + 1 = \\frac{(n_\\xi+p_j)!}{n_\\xi!p_j!}.\n",
    "\\end{align}\n",
    "\n",
    "The interpolation basis functions $\\Psi_{j, l}$ are simple monomials, and the coefficients $c_{j, l}$ are found by solving a Vandermonde system, see the references below.\n",
    "\n",
    "The SSC method automatically reduces the\n",
    "polynomial order if a stencil $S_j$ crosses a discontinuity. It achieves this by\n",
    "enforcing the so-called **Local Extremum Conserving (LEC)** limiter to all simplices $\\Xi_j$ in all $S_j$. The LEC condition is given by\n",
    "\n",
    "\\begin{align}\n",
    "\\boxed{\n",
    " \\min_{\\boldsymbol\\xi\\in\\Xi_j} w_j(\\boldsymbol\\xi) = \\min{\\bf v}_j \\wedge \\max_{\\boldsymbol\\xi\\in\\Xi_j}\n",
    "w_j(\\boldsymbol\\xi) = \\max{\\bf v}_j},\n",
    "\\end{align}\n",
    "\n",
    "where ${\\bf v}_j = \\{v_{k_{j,0}},\\cdots,{v_{k_{j,n_\\xi}}}\\}$ are the code samples at\n",
    "the vertices of $\\Xi_j$. If $w_j$ violates the LEC condition above in one of its\n",
    "$\\Xi_j\\in S_j$, the polynomial order $p_j$ of that stencil is reduced, usually\n",
    "by 1. Since polynomial overshoots occur when trying to interpolate a discontinuity, $p_j$ is reduced\n",
    "the most in discontinuous regions. Interpolating a function on a simplex with\n",
    "$p_j = 1$ and ${\\bf v}_j$ located at its vertices always satisfies\n",
    "the LEC condition. This ensures that $w(\\boldsymbol\\xi)$ is\n",
    "able to represent discontinuities without suffering from the Runge phenomenon.\n",
    "In practice, the LEC condition is enforced for all $\\Xi_j $ in all $S_j$ via random\n",
    "sampling of the $w_j$. If for a given $w_j$ the LEC condition is violated for any of the randomly placed\n",
    "samples $\\boldsymbol\\xi_j$, the polynomial order of the corresponding stencil is reduced. \n",
    "\n",
    "Which $N_j+1$ points are used for $w_j$ is determined by the interpolation stencil $S_j$. The stencil can be constructed based on the nearest-neighbour principle. In this case the first $n_\\xi+1$ points are the vertices \n",
    "$\\{\\boldsymbol\\xi_{k_{j,0}},\\cdots,\\boldsymbol\\xi_{k_{j,n_\\xi}}\\}$ of the simplex $\\Xi_j$, which would suffice for $p_j = 1$. For higher degree interpolation, neighbouring points $\\boldsymbol\\xi_k$ are added based on their proximity to the center of simplex\n",
    "$\\Xi_j$, i.e. based on their ranking according to\n",
    "\n",
    "\\begin{align} \\label{eq:center_j}\n",
    " \\Vert \\boldsymbol\\xi_k - \\boldsymbol\\xi_{\\mathrm{center},j} \\rVert_2,\n",
    "\\end{align}\n",
    "\n",
    "where those $\\boldsymbol\\xi_k$ of the current simplex $\\Xi_j$ are excluded.\n",
    "The simplex center $\\boldsymbol\\xi_{\\mathrm{center},j}$ is defined as\n",
    "\n",
    "\\begin{align}\n",
    "  \\boldsymbol\\xi_{\\mathrm{center},j} := \\frac{1}{n_\\xi+1}\\sum^{d}_{l = 0}\\boldsymbol\\xi_{k_{j,l}}.\n",
    "\\end{align}\n",
    "\n",
    "The nearest neighbor stencil leads to a $p_j$\n",
    "distribution that can increase quite slowly when moving away from a\n",
    "discontinuity. An example of this behavior can be found in the figure\n",
    "below, which shows the Delaunay triangulation with a discontinuity\n",
    "running through the domain (dotted line). An alternative to nearest-neighbor\n",
    "stencils are stencils created according to the **Essentially Non-Oscillatory (ENO)**\n",
    "principle [1]. The idea behind ENO stencils is to have\n",
    "higher degree interpolation stencils up to a thin layer of simplices containing\n",
    "the discontinuity. For a given simplex $\\Xi_j$, its ENO stencil is created by\n",
    "locating all the nearest-neighbor stencils that contain $\\Xi_j$, and\n",
    "subsequently selecting the one with the highest $p_j$. This leads to a Delaunay\n",
    "triangulation which captures the discontinuity better than its nearest-neighbor\n",
    "counterpart.\n",
    "\n",
    "![](./images/NN_ENO.png)\n",
    "\n",
    "Like dimension-adaptive sampling, the SSC method iteratively refines the sampling plan. Which simplex element is refined is based on a geometrical refinement measure, given by;\n",
    "\n",
    "\\begin{align} \\label{eq:ref_meas}\n",
    " \\bar{e}_j := \\bar{\\Omega}_j\\bar{\\Xi}_j^{2O_j}.\n",
    "\\end{align}\n",
    "\n",
    "It contains the probability and the volume of simplex $\\Xi_j$, i.e.\n",
    "\n",
    "\\begin{align} \\label{eq:prob_and_vol}\n",
    " \\bar{\\Omega}_j =\n",
    "\\int\\limits_{\\Xi_j}f_{\\boldsymbol\\xi}(\\boldsymbol\\xi)d\\boldsymbol\\xi\\;\\;\\;\\mathrm{and}\\;\\;\\;\n",
    " \\bar{\\Xi}_j = \\int\\limits_{\\Xi_j}d\\boldsymbol\\xi,\n",
    "\\end{align}\n",
    "where $\\bar{\\Xi} = \\sum^{n_e}_{j=1}\\bar{\\Xi}_j$. The probability $\\bar{\\Omega}_j$ can be computed by Monte-Carlo sampling and $\\bar{\\Xi}_j$ via the relation\n",
    "\n",
    "\\begin{align} \\label{eq:vol_j}\n",
    " \\bar{\\Xi}_j = \\frac{1}{n_\\xi!}\\lvert\\mathrm{det}\\left(D\\right)\\rvert,\\;\\;\\;D =\n",
    "\\left[\\boldsymbol\\xi_{k_{j,1}}-\\boldsymbol\\xi_{k_{j,0}}\\;\\;\\;\\;\\boldsymbol\\xi_{k_{j,2}}-\\boldsymbol\\xi_{k_{j,0}}\n",
    "\\;\\;\\;\\;\\cdots\\;\\;\\;\\; \\boldsymbol\\xi_{k_{j,n_\\xi+1}}-\\boldsymbol\\xi_{k_{j,0}}\\right]\n",
    "\\in\\mathbb{R}^{n_\\xi \\times n_\\xi}.\n",
    "\\end{align}\n",
    "\n",
    "Finally, the order of convergence $O_j$ is given by\n",
    "\n",
    "\\begin{align} \\label{eq:order_conv}\n",
    " O_j = \\frac{p_j+1}{n_\\xi}.\n",
    "\\end{align}\n",
    "\n",
    "The simplex with the highest $\\bar{e}_j$ is selected for refinement. To ensure a good spread of the sample points, only randomly-selected points inside a simplex sub-element $\\Xi_{sub_j}$ are allowed. The vertices of this sub-element are defined as\n",
    "\n",
    "\\begin{align} \\label{eq:sub_simplex}\n",
    " \\boldsymbol\\xi_{sub_{j,l}} := \\frac{1}{n_\\xi}\\sum^{n_\\xi}_{\\substack{l^*=0 \\\\ l^*\\neq l}}\n",
    "\\boldsymbol\\xi_{k_{j,l^*}},\n",
    "\\end{align}\n",
    "\n",
    "see the figure below for a two-dimensional example. **The SSC algorithm can be partially parallelized** by selecting the $N<n_e$ simplices with the $N$ largest $\\bar{e}_j$ for refinement. \n",
    "\n",
    "Note that $\\bar{e}_j$ is probabilistically weighted through $\\Omega_j$ and that it assigns\n",
    "high value to those simplices with low $p_j$ since in general $\\bar{\\Xi}_j\\ll1$. Effectively this means that\n",
    "$\\bar{e}_j$ is a solution-dependent refinement measure which refines simplices near discontinuities since the SSC method automatically reduces the polynomial order (via LEC) if a stencil $S_j$ crosses a discontinuity.\n",
    "\n",
    "This is the overall overview of the SSC method. For more information consult the articles below and the references therein.\n",
    "\n",
    "**References**\n",
    "\n",
    "[1] Witteveen, J. A. S., & Iaccarino, G. (2013). Simplex stochastic collocation with ENO-type stencil selection for robust uncertainty quantification. Journal of Computational Physics, 239, 1-21.\n",
    "\n",
    "[2] Edeling, W. N., Dwight, R. P., & Cinnella, P. (2016). Simplex-stochastic collocation method with improved scalability. Journal of Computational Physics, 310, 301-328.\n",
    "\n",
    "## Current limitations\n",
    "\n",
    "* We do not recommend **running the SSC sampler using more than (approximately) 5 inputs variables**.\n",
    "* At this point, **only scalar output variables are supported**.\n",
    "\n",
    "Both limitations will be addressed in a later release of EasyVVUQ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8a5670",
   "metadata": {},
   "source": [
    "## Simplex Stochastic Collocation on a discontinuous function\n",
    "\n",
    "We generate the EasyVVUQ campaign in the exact same way as in the SC case;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca51efe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "campaign = get_campaign()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b32e880",
   "metadata": {},
   "source": [
    "Selecting the SSC sampler is also very similar to the SC case;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "791ce8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "# Select sampler #\n",
    "##################\n",
    "\n",
    "sampler = uq.sampling.SSCSampler(vary=vary, max_polynomial_order=4)\n",
    "\n",
    "# Associate the sampler with the campaign\n",
    "campaign.set_sampler(sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bc9d5b",
   "metadata": {},
   "source": [
    "Here, `max_polynomial_order = 4` limits the maximum polynomial order of the individual interpolation stencils to 4. \n",
    "\n",
    "Now we run all actions in the defined `actions` object, including running the code on the initial sampling plan:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6eaaf6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################\n",
    "# execute the defined actions #\n",
    "###############################\n",
    "\n",
    "campaign.execute().collate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121bab12",
   "metadata": {},
   "source": [
    "Now we set the following flags;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b10d01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of samples used in the LEC check \n",
    "n_mc_LEC = 50\n",
    "\n",
    "# max number of LEC evaluations that can be run in parallel\n",
    "max_LEC_jobs = 2\n",
    "\n",
    "# max number of ENO stencils that can be computed in parallel\n",
    "max_ENO_jobs = 2\n",
    "\n",
    "# max number of functions evaluations that can be run in parallel\n",
    "max_fjobs = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9017b98",
   "metadata": {},
   "source": [
    "Here;\n",
    "\n",
    "* `n_mc_LEC = 50`: sets the number of times *each* local surrogate $w_j$ is evaluated in the LEC check to 50, with $j=1,\\cdots,n_e$. Here $n_e$ is the number of simplex elements.\n",
    "\n",
    "* `max_LEC_jobs = 2`: sets the number of LEC evaluations that are done in parallel to 4. Each simplex element can be given the LEC check independent of the others. The [multiprocessing](https://docs.python.org/3/library/multiprocessing.html) library is used for this.\n",
    "\n",
    "* `max_ENO_jobs = 2`: sets the number of ENO stencils that are computed in parallel to 2. Again, the [multiprocessing](https://docs.python.org/3/library/multiprocessing.html) library is used for this.\n",
    "\n",
    "* `max_fjobs = 4`: sets the number of new code evaluations per iteration of the SSC algorithm to 4. This means that the 4 simplex elements with the largest refinement metric $\\bar{e}_j$ will get refined. The `max_f_jobs` code evaluations can be done in parallel, and on supercomputers, using the VECMA/SEAVEA tools [FabSim3](https://github.com/djgroen/FabSim3) (see [this recent article](https://www.sciencedirect.com/science/article/pii/S0010465522003150) for a detailed overview of FabSim3) or [QCG-PilotJob](https://easyvvuq.readthedocs.io/en/nbsphinx/notebooks/easyvvuq_qcgpj_tutorial.html). In this tutorial the executing will be done sequentially on the localhost."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea9916f",
   "metadata": {},
   "source": [
    "To refine the sampling plan, we need an `SSCAnalysis` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2248ae85",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = uq.analysis.SSCAnalysis(sampler = sampler, qoi_cols=['f'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ad8788",
   "metadata": {},
   "source": [
    "Here we update the surrogate (i.e. check the LEC condition and compute the ENO stencils), using the initial code samples (contained in the `data_frame`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0384166f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 1\n",
      "Checking LEC condition of 4 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get code samples\n",
    "data_frame = campaign.get_collation_result()\n",
    "# update surrogate by checking LEC condition and computing ENO stencils\n",
    "analysis.update_surrogate('f', data_frame, n_mc_LEC=n_mc_LEC, max_LEC_jobs=max_LEC_jobs,\n",
    "                         max_ENO_jobs=max_ENO_jobs)\n",
    "# plot the sampling plan\n",
    "analysis.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7321e4b",
   "metadata": {},
   "source": [
    "This shows the initial sampling plan, consisting of 4 simplices ($n_e = 4$) and 5 code samples ($n_s=5$). The code samples are located at the points of the simplices, Hence the initial sampling plan is created by placing one code sample in the middle, and one on each corner of the hypercube (in this case the square). Hence in the case of $n_\\xi$ random inputs, the number of initial code samples is given by:\n",
    "\n",
    "\\begin{align*}\n",
    "n_s = 2^{n_\\xi} + 1\n",
    "\\end{align*}\n",
    "\n",
    "The exponential increase the number of (initial) code samples is already an indication that the curse of dimensionality is present in the SSC method. As such, this method does not scale well to high $n_\\xi$. Means to extend it to higher dimensions are discussed in reference [2] above.\n",
    "\n",
    "For now, we stick with $n_\\xi=2$. Note from the figure above that all elements are linear ($p_j=1$), since $n_s=5$ is not suffient to build higher-order interpolation stencils in 2D. Let us now execute our first refinement iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84bb143c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def refine(campaign, analysis, n_refinements, max_fjobs, max_LEC_jobs = 4, n_mc_LEC = 50, max_ENO_jobs = 4):\n",
    "    \"\"\"\n",
    "    Refine the SSC sampling plan with max_fjobs new samples.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    campaign : easyvvuq.campaign.Campaign\n",
    "        The EasyVVUQ campaign of the SSC sampler.\n",
    "    analysis : easyvvuq.analysis.ssc_analysis.SSCAnalysis\n",
    "        The SSC analysis object, used to refine the sampling plan.\n",
    "    n_refinements : int\n",
    "        The number of refinement iterations.\n",
    "    max_fjobs : int\n",
    "        The number of new code evaluations per refinement iteration.\n",
    "    max_LEC_jobs : int\n",
    "        The number of LEC checks to perform in parallel.\n",
    "    n_mc_LEC : int\n",
    "        The number of surrogate Monte Carlo samples used in each LEC check.\n",
    "    max_ENO_jobs : int\n",
    "        The number of ENO stencils to compute in parallel.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    None.\n",
    "\n",
    "    \"\"\"\n",
    "    for i in range(n_refinements):\n",
    "        # get code samples\n",
    "        data_frame = campaign.get_collation_result()\n",
    "        # update surrogate by checking LEC condition and computing ENO stencils\n",
    "        analysis.update_surrogate('f', data_frame, n_mc_LEC=n_mc_LEC, max_LEC_jobs=max_LEC_jobs,\n",
    "                                 max_ENO_jobs=max_ENO_jobs)\n",
    "        # refine the max_jobs simplices with the highest refinement measure\n",
    "        analysis.adapt_locally(max_fjobs)\n",
    "        # run the new code samples\n",
    "        campaign.execute().collate()\n",
    "\n",
    "    # do not forget to update the surrogate after the last code evaluations\n",
    "    data_frame = campaign.get_collation_result()\n",
    "    analysis.update_surrogate('f', data_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "217455c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 1\n",
      "Checking LEC condition of 4 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 8 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 2 2 2 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fix random seed\n",
    "# np.random.seed(42)\n",
    "# refine the sampling plan once\n",
    "refine(campaign, analysis, 1, max_fjobs, n_mc_LEC)\n",
    "analysis.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd166b39",
   "metadata": {},
   "source": [
    "We can see that new code samples have been added, and that some simplices now have quadratic interpolation stencils, unless the LEC check reduced them back to 1. The refinement within the subsimplex is random, so if you rerun the analysis you will not get the exact same sampling plan, unless the random seed is fixed (uncomment the `np.random.seed` line above).\n",
    "\n",
    "Let us now run several consecutive refinements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4eb2477",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 8 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 2 2 2 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Edge already refined, selecting another sample.\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 3\n",
      "Checking LEC condition of 16 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 2 2 2 2 2 2 2 1 2 1 1 2 2 2]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 4\n",
      "Checking LEC condition of 23 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 2 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 29 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 2 1 1 2 2 3 1 1 1 2 1 1 1 3 2 1 3 3 3 3 1 2 2 2 3 3 3]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 34 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 1 1 2 3 1 1 2 2 2 2 2 1 1 1 3 2 1 3 3 3 3 2 1 1 1 2 2 1 1 3 3 3]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 42 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 3 1 1 1 2 3 3 3 3 3 3 3 2 2 1 2 3 3 2 1 3 3 3 1 1 2 3 1 3 1 3 3 1 1 3\n",
      " 3 3 3 3 3]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 50 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 3 3 2 2 2 2 2 2 2 2 1 3 3 1 1 3 3 3 3 3 1 1 3 3 3 1 1 2 1 1 3 3 3 1 3 1\n",
      " 1 2 1 1 2 1 3 3 3 3 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 57 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 1 1 1 1 1 3 3 3 1 1 1\n",
      " 3 3 3 1 1 2 1 1 3 3 3 3 1 1 3 3 3 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 65 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 1 3 1 1 2 3 3 1 1 3 3 3 3\n",
      " 3 1 1 1 1 1 2 2 2 1 1 3 1 1 3 3 3 3 3 3 3 1 1 1 1 3 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 70 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 1 3 3 3 3 1 3 3 3 3 3 3 1 2 3 1 3 3 3 3 1 1 1 3 1 1 3 3 3 3 3 3 3 3 3\n",
      " 3 3 3 1 1 1 1 3 3 1 3 1 1 3 3 3 3 1 1 3 3 1 1 1 3 1 1 3 3 3 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 77 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 4 3 3 3 3 1 3 3 3 3 3 3 2 2 4 4 4 4 4 3 4 3 2 3 4 4 4 3 2 2 4 1 4 4 1 1\n",
      " 1 4 4 4 4 2 2 1 1 1 3 3 3 1 1 3 4 4 3 1 4 4 1 1 3 1 4 4 1 1 1 3 1 1 3 3 3\n",
      " 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 85 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 4 3 3 1 3 3 3 3 3 3 3 2 2 4 4 4 4 4 3 4 3 2 3 2 4 4 4 2 2 2 4 4 1 1 4 1\n",
      " 4 4 4 4 2 2 1 1 1 3 3 3 1 1 2 4 4 2 1 4 4 1 1 3 1 1 1 3 1 1 4 3 1 2 1 1 1\n",
      " 1 3 3 3 1 3 1 3 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 93 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 3 4 4 4 4 4 4 4 2 2 4 4 4 4 4 3 4 3 2 3 4 4 4 4 4 4 4 2 2 2 4 4 1 1 4\n",
      " 1 4 4 4 4 2 2 1 1 1 3 3 3 1 1 2 4 4 2 4 4 1 1 3 1 1 1 1 3 4 4 1 2 4 1 1 3\n",
      " 4 3 4 2 2 1 2 1 1 1 1 2 2 3 2 2 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 101 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 3 3 4 4 4 4 4 4 4 1 2 2 2 4 3 4 4 3 3 3 2 4 4 4 4 4 4 4 2 4 1 2 4 4 1\n",
      " 1 4 4 4 4 4 4 2 2 3 1 2 4 4 4 2 1 1 1 1 3 4 4 4 2 4 4 1 2 1 3 1 1 1 3 4 3\n",
      " 4 2 2 2 1 1 1 1 2 2 1 1 2 2 1 1 1 4 4 3 1 1 2 1 1 3 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 108 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 3 3 4 4 4 4 4 4 4 4 1 3 3 3 3 3 4 4 4 4 2 4 4 4 4 4 4 4 1 1 3 3 3 3 3\n",
      " 4 4 1 2 1 1 1 1 1 1 4 4 4 4 4 4 1 3 4 4 4 2 2 4 4 3 2 2 2 3 1 1 1 1 1 4 1\n",
      " 1 2 2 1 3 4 4 4 1 1 3 1 2 1 2 1 1 3 4 1 4 1 4 4 3 1 2 1 1 1 2 1 3 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 116 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 3 3 4 4 4 4 4 4 4 4 1 3 3 3 3 3 4 4 4 4 4 1 4 4 4 4 4 4 4 3 3 3 3 3 1\n",
      " 4 1 1 4 4 2 3 1 1 1 4 4 4 4 4 4 1 1 1 1 3 1 1 1 1 4 4 4 3 2 3 3 3 2 2 1 1\n",
      " 2 3 2 4 2 1 3 2 1 3 4 4 4 3 3 1 1 2 1 3 3 3 4 1 4 1 1 3 3 3 4 4 3 1 2 1 1\n",
      " 1 2 1 3 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 100000 samples.\n",
      "% of simplices with no samples = 0.0\n"
     ]
    }
   ],
   "source": [
    "refine(campaign, analysis, 15, max_fjobs, n_mc_LEC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37531f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c8ad54",
   "metadata": {},
   "source": [
    "Where the discontinuity lies should now be clear through a relatively thin line of simplices with $p_j=1$ or $p_j=2$. Simplices with higher 4-th order interpolation stencils should appear away from the discontinutity, where the function is smooth.\n",
    "\n",
    "We now plot the SSC surrogate;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c306eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_surrogate(analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d8e98c",
   "metadata": {},
   "source": [
    "This should now be a much better approximation to the real function than the SC surrogate, free of the Runge phenomenom. \n",
    "\n",
    "We can now compute the mean and variance with confidence. The SSC method uses simple Monte Carlo to compute these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb80df60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Computing mean and variance...\n",
      "done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.919233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.261427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>1.591198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             f\n",
       "             0\n",
       "mean  0.919233\n",
       "std   1.261427\n",
       "var   1.591198"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campaign.apply_analysis(analysis)\n",
    "results = campaign.get_last_analysis()\n",
    "results.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e3ba27",
   "metadata": {},
   "source": [
    "If you want to manually check the points and simplex elements, you can run the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5bf4d756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.        ]\n",
      " [0.         1.        ]\n",
      " [1.         0.        ]\n",
      " [1.         1.        ]\n",
      " [0.5        0.5       ]\n",
      " [1.         0.50966174]\n",
      " [0.43453103 0.        ]\n",
      " [0.4319555  1.        ]\n",
      " [0.         0.51602691]\n",
      " [0.7601685  0.2398315 ]\n",
      " [0.24317632 0.33646657]\n",
      " [0.77224903 0.77224903]\n",
      " [0.2373937  0.23411025]\n",
      " [0.36152623 0.7390783 ]\n",
      " [0.23153546 0.77544468]\n",
      " [0.56671892 0.78204021]\n",
      " [0.76997971 1.        ]\n",
      " [0.78599151 0.60057807]\n",
      " [0.7798546  0.4285229 ]\n",
      " [1.         0.29844448]\n",
      " [1.         0.72449598]\n",
      " [0.54540988 0.27624598]\n",
      " [0.67830031 0.        ]\n",
      " [0.         0.76218645]\n",
      " [0.22274859 1.        ]\n",
      " [0.12156254 0.6163367 ]\n",
      " [0.37800386 0.45044833]\n",
      " [0.9012071  0.0987929 ]\n",
      " [0.64832744 0.65174955]\n",
      " [0.33761294 0.59785378]\n",
      " [0.49028617 0.68643761]\n",
      " [0.13301208 0.87099786]\n",
      " [0.8300928  0.31330418]\n",
      " [0.59575597 0.37629292]\n",
      " [0.21832013 0.55501963]\n",
      " [0.6535986  0.51917827]\n",
      " [0.         0.26829404]\n",
      " [0.75457567 0.09832791]\n",
      " [0.11794801 0.76226158]\n",
      " [0.91783557 0.28072862]\n",
      " [0.70556828 0.37105076]\n",
      " [0.80937733 0.        ]\n",
      " [0.         0.90053679]\n",
      " [0.         0.65418696]\n",
      " [0.41503932 0.63118902]\n",
      " [1.         0.15573821]\n",
      " [0.84517317 0.40943596]\n",
      " [0.52359566 0.59363101]\n",
      " [0.84007167 0.2008237 ]\n",
      " [0.24292245 0.64015948]\n",
      " [0.20616366 0.65789453]\n",
      " [0.570652   0.46099052]\n",
      " [0.4059138  0.54070083]\n",
      " [0.51746895 0.43162138]\n",
      " [0.31686135 0.54449129]\n",
      " [0.05327118 0.80576529]\n",
      " [0.11516881 0.68551431]\n",
      " [0.2701981  0.71389454]\n",
      " [0.9158463  0.19885348]\n",
      " [0.6558705  0.41800222]\n",
      " [0.30477229 0.64083317]\n",
      " [0.77373098 0.31402591]\n",
      " [0.16920594 0.71960076]\n",
      " [1.         0.23818   ]\n",
      " [0.7637165  0.38737273]\n",
      " [0.17626025 0.78362143]\n",
      " [0.42926806 0.57977337]\n",
      " [0.49083028 0.53876496]\n",
      " [0.87578255 0.2716848 ]]\n",
      "[[22 21  6]\n",
      " [15 16  7]\n",
      " [22 37 21]\n",
      " [41 37 22]\n",
      " [26 10 21]\n",
      " [36 10  8]\n",
      " [21 12  6]\n",
      " [10 12 21]\n",
      " [ 6 12  0]\n",
      " [12 36  0]\n",
      " [36 12 10]\n",
      " [13 15  7]\n",
      " [45 63 58]\n",
      " [27 41  2]\n",
      " [45 27  2]\n",
      " [27 45 58]\n",
      " [27 37 41]\n",
      " [37  9 21]\n",
      " [66 47 44]\n",
      " [47 30 44]\n",
      " [13 30 15]\n",
      " [30 13 44]\n",
      " [17 46  5]\n",
      " [46 19  5]\n",
      " [30 28 15]\n",
      " [28 30 47]\n",
      " [20 17  5]\n",
      " [33 53 21]\n",
      " [53 26 21]\n",
      " [26 53  4]\n",
      " [52 26  4]\n",
      " [48  9 37]\n",
      " [48 27 58]\n",
      " [27 48 37]\n",
      " [40 33 21]\n",
      " [ 9 40 21]\n",
      " [47 67  4]\n",
      " [66 67 47]\n",
      " [67 52  4]\n",
      " [52 67 66]\n",
      " [46 18 64]\n",
      " [18 46 17]\n",
      " [18 40 64]\n",
      " [68 48 58]\n",
      " [51 47  4]\n",
      " [53 51  4]\n",
      " [51 53 33]\n",
      " [16 11  3]\n",
      " [11 20  3]\n",
      " [20 11 17]\n",
      " [11 28 17]\n",
      " [11 16 15]\n",
      " [28 11 15]\n",
      " [46 39 19]\n",
      " [19 39 63]\n",
      " [63 39 58]\n",
      " [39 68 58]\n",
      " [32 46 64]\n",
      " [32 39 46]\n",
      " [39 32 68]\n",
      " [48 32  9]\n",
      " [68 32 48]\n",
      " [35 18 17]\n",
      " [28 35 17]\n",
      " [35 28 47]\n",
      " [51 35 47]\n",
      " [31 24  1]\n",
      " [13 60 44]\n",
      " [57 60 13]\n",
      " [25 43  8]\n",
      " [23 43 56]\n",
      " [43 25 56]\n",
      " [61 32 64]\n",
      " [32 61  9]\n",
      " [40 61 64]\n",
      " [61 40  9]\n",
      " [18 59 40]\n",
      " [35 59 18]\n",
      " [40 59 33]\n",
      " [59 51 33]\n",
      " [59 35 51]\n",
      " [42 31  1]\n",
      " [38 23 56]\n",
      " [31 14 24]\n",
      " [24 14  7]\n",
      " [14 13  7]\n",
      " [14 57 13]\n",
      " [60 29 44]\n",
      " [29 66 44]\n",
      " [29 52 66]\n",
      " [55 42 23]\n",
      " [42 55 31]\n",
      " [38 55 23]\n",
      " [55 38 31]\n",
      " [38 65 31]\n",
      " [65 14 31]\n",
      " [52 54 26]\n",
      " [29 54 52]\n",
      " [26 54 10]\n",
      " [54 29 60]\n",
      " [62 38 56]\n",
      " [62 65 38]\n",
      " [14 62 57]\n",
      " [65 62 14]\n",
      " [54 34 10]\n",
      " [10 34  8]\n",
      " [34 25  8]\n",
      " [49 54 60]\n",
      " [49 34 54]\n",
      " [49 60 57]\n",
      " [62 50 57]\n",
      " [50 49 57]\n",
      " [25 50 56]\n",
      " [50 62 56]\n",
      " [34 50 25]\n",
      " [49 50 34]]\n",
      "[[[0.67830031 0.        ]\n",
      "  [0.54540988 0.27624598]\n",
      "  [0.43453103 0.        ]]\n",
      "\n",
      " [[0.56671892 0.78204021]\n",
      "  [0.76997971 1.        ]\n",
      "  [0.4319555  1.        ]]\n",
      "\n",
      " [[0.67830031 0.        ]\n",
      "  [0.75457567 0.09832791]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.80937733 0.        ]\n",
      "  [0.75457567 0.09832791]\n",
      "  [0.67830031 0.        ]]\n",
      "\n",
      " [[0.37800386 0.45044833]\n",
      "  [0.24317632 0.33646657]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.         0.26829404]\n",
      "  [0.24317632 0.33646657]\n",
      "  [0.         0.51602691]]\n",
      "\n",
      " [[0.54540988 0.27624598]\n",
      "  [0.2373937  0.23411025]\n",
      "  [0.43453103 0.        ]]\n",
      "\n",
      " [[0.24317632 0.33646657]\n",
      "  [0.2373937  0.23411025]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.43453103 0.        ]\n",
      "  [0.2373937  0.23411025]\n",
      "  [0.         0.        ]]\n",
      "\n",
      " [[0.2373937  0.23411025]\n",
      "  [0.         0.26829404]\n",
      "  [0.         0.        ]]\n",
      "\n",
      " [[0.         0.26829404]\n",
      "  [0.2373937  0.23411025]\n",
      "  [0.24317632 0.33646657]]\n",
      "\n",
      " [[0.36152623 0.7390783 ]\n",
      "  [0.56671892 0.78204021]\n",
      "  [0.4319555  1.        ]]\n",
      "\n",
      " [[1.         0.15573821]\n",
      "  [1.         0.23818   ]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.9012071  0.0987929 ]\n",
      "  [0.80937733 0.        ]\n",
      "  [1.         0.        ]]\n",
      "\n",
      " [[1.         0.15573821]\n",
      "  [0.9012071  0.0987929 ]\n",
      "  [1.         0.        ]]\n",
      "\n",
      " [[0.9012071  0.0987929 ]\n",
      "  [1.         0.15573821]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.9012071  0.0987929 ]\n",
      "  [0.75457567 0.09832791]\n",
      "  [0.80937733 0.        ]]\n",
      "\n",
      " [[0.75457567 0.09832791]\n",
      "  [0.7601685  0.2398315 ]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.42926806 0.57977337]\n",
      "  [0.52359566 0.59363101]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.52359566 0.59363101]\n",
      "  [0.49028617 0.68643761]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.36152623 0.7390783 ]\n",
      "  [0.49028617 0.68643761]\n",
      "  [0.56671892 0.78204021]]\n",
      "\n",
      " [[0.49028617 0.68643761]\n",
      "  [0.36152623 0.7390783 ]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.78599151 0.60057807]\n",
      "  [0.84517317 0.40943596]\n",
      "  [1.         0.50966174]]\n",
      "\n",
      " [[0.84517317 0.40943596]\n",
      "  [1.         0.29844448]\n",
      "  [1.         0.50966174]]\n",
      "\n",
      " [[0.49028617 0.68643761]\n",
      "  [0.64832744 0.65174955]\n",
      "  [0.56671892 0.78204021]]\n",
      "\n",
      " [[0.64832744 0.65174955]\n",
      "  [0.49028617 0.68643761]\n",
      "  [0.52359566 0.59363101]]\n",
      "\n",
      " [[1.         0.72449598]\n",
      "  [0.78599151 0.60057807]\n",
      "  [1.         0.50966174]]\n",
      "\n",
      " [[0.59575597 0.37629292]\n",
      "  [0.51746895 0.43162138]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.51746895 0.43162138]\n",
      "  [0.37800386 0.45044833]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.37800386 0.45044833]\n",
      "  [0.51746895 0.43162138]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.4059138  0.54070083]\n",
      "  [0.37800386 0.45044833]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.84007167 0.2008237 ]\n",
      "  [0.7601685  0.2398315 ]\n",
      "  [0.75457567 0.09832791]]\n",
      "\n",
      " [[0.84007167 0.2008237 ]\n",
      "  [0.9012071  0.0987929 ]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.9012071  0.0987929 ]\n",
      "  [0.84007167 0.2008237 ]\n",
      "  [0.75457567 0.09832791]]\n",
      "\n",
      " [[0.70556828 0.37105076]\n",
      "  [0.59575597 0.37629292]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.7601685  0.2398315 ]\n",
      "  [0.70556828 0.37105076]\n",
      "  [0.54540988 0.27624598]]\n",
      "\n",
      " [[0.52359566 0.59363101]\n",
      "  [0.49083028 0.53876496]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.42926806 0.57977337]\n",
      "  [0.49083028 0.53876496]\n",
      "  [0.52359566 0.59363101]]\n",
      "\n",
      " [[0.49083028 0.53876496]\n",
      "  [0.4059138  0.54070083]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.4059138  0.54070083]\n",
      "  [0.49083028 0.53876496]\n",
      "  [0.42926806 0.57977337]]\n",
      "\n",
      " [[0.84517317 0.40943596]\n",
      "  [0.7798546  0.4285229 ]\n",
      "  [0.7637165  0.38737273]]\n",
      "\n",
      " [[0.7798546  0.4285229 ]\n",
      "  [0.84517317 0.40943596]\n",
      "  [0.78599151 0.60057807]]\n",
      "\n",
      " [[0.7798546  0.4285229 ]\n",
      "  [0.70556828 0.37105076]\n",
      "  [0.7637165  0.38737273]]\n",
      "\n",
      " [[0.87578255 0.2716848 ]\n",
      "  [0.84007167 0.2008237 ]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.570652   0.46099052]\n",
      "  [0.52359566 0.59363101]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.51746895 0.43162138]\n",
      "  [0.570652   0.46099052]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.570652   0.46099052]\n",
      "  [0.51746895 0.43162138]\n",
      "  [0.59575597 0.37629292]]\n",
      "\n",
      " [[0.76997971 1.        ]\n",
      "  [0.77224903 0.77224903]\n",
      "  [1.         1.        ]]\n",
      "\n",
      " [[0.77224903 0.77224903]\n",
      "  [1.         0.72449598]\n",
      "  [1.         1.        ]]\n",
      "\n",
      " [[1.         0.72449598]\n",
      "  [0.77224903 0.77224903]\n",
      "  [0.78599151 0.60057807]]\n",
      "\n",
      " [[0.77224903 0.77224903]\n",
      "  [0.64832744 0.65174955]\n",
      "  [0.78599151 0.60057807]]\n",
      "\n",
      " [[0.77224903 0.77224903]\n",
      "  [0.76997971 1.        ]\n",
      "  [0.56671892 0.78204021]]\n",
      "\n",
      " [[0.64832744 0.65174955]\n",
      "  [0.77224903 0.77224903]\n",
      "  [0.56671892 0.78204021]]\n",
      "\n",
      " [[0.84517317 0.40943596]\n",
      "  [0.91783557 0.28072862]\n",
      "  [1.         0.29844448]]\n",
      "\n",
      " [[1.         0.29844448]\n",
      "  [0.91783557 0.28072862]\n",
      "  [1.         0.23818   ]]\n",
      "\n",
      " [[1.         0.23818   ]\n",
      "  [0.91783557 0.28072862]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.91783557 0.28072862]\n",
      "  [0.87578255 0.2716848 ]\n",
      "  [0.9158463  0.19885348]]\n",
      "\n",
      " [[0.8300928  0.31330418]\n",
      "  [0.84517317 0.40943596]\n",
      "  [0.7637165  0.38737273]]\n",
      "\n",
      " [[0.8300928  0.31330418]\n",
      "  [0.91783557 0.28072862]\n",
      "  [0.84517317 0.40943596]]\n",
      "\n",
      " [[0.91783557 0.28072862]\n",
      "  [0.8300928  0.31330418]\n",
      "  [0.87578255 0.2716848 ]]\n",
      "\n",
      " [[0.84007167 0.2008237 ]\n",
      "  [0.8300928  0.31330418]\n",
      "  [0.7601685  0.2398315 ]]\n",
      "\n",
      " [[0.87578255 0.2716848 ]\n",
      "  [0.8300928  0.31330418]\n",
      "  [0.84007167 0.2008237 ]]\n",
      "\n",
      " [[0.6535986  0.51917827]\n",
      "  [0.7798546  0.4285229 ]\n",
      "  [0.78599151 0.60057807]]\n",
      "\n",
      " [[0.64832744 0.65174955]\n",
      "  [0.6535986  0.51917827]\n",
      "  [0.78599151 0.60057807]]\n",
      "\n",
      " [[0.6535986  0.51917827]\n",
      "  [0.64832744 0.65174955]\n",
      "  [0.52359566 0.59363101]]\n",
      "\n",
      " [[0.570652   0.46099052]\n",
      "  [0.6535986  0.51917827]\n",
      "  [0.52359566 0.59363101]]\n",
      "\n",
      " [[0.13301208 0.87099786]\n",
      "  [0.22274859 1.        ]\n",
      "  [0.         1.        ]]\n",
      "\n",
      " [[0.36152623 0.7390783 ]\n",
      "  [0.30477229 0.64083317]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.2701981  0.71389454]\n",
      "  [0.30477229 0.64083317]\n",
      "  [0.36152623 0.7390783 ]]\n",
      "\n",
      " [[0.12156254 0.6163367 ]\n",
      "  [0.         0.65418696]\n",
      "  [0.         0.51602691]]\n",
      "\n",
      " [[0.         0.76218645]\n",
      "  [0.         0.65418696]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.         0.65418696]\n",
      "  [0.12156254 0.6163367 ]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.77373098 0.31402591]\n",
      "  [0.8300928  0.31330418]\n",
      "  [0.7637165  0.38737273]]\n",
      "\n",
      " [[0.8300928  0.31330418]\n",
      "  [0.77373098 0.31402591]\n",
      "  [0.7601685  0.2398315 ]]\n",
      "\n",
      " [[0.70556828 0.37105076]\n",
      "  [0.77373098 0.31402591]\n",
      "  [0.7637165  0.38737273]]\n",
      "\n",
      " [[0.77373098 0.31402591]\n",
      "  [0.70556828 0.37105076]\n",
      "  [0.7601685  0.2398315 ]]\n",
      "\n",
      " [[0.7798546  0.4285229 ]\n",
      "  [0.6558705  0.41800222]\n",
      "  [0.70556828 0.37105076]]\n",
      "\n",
      " [[0.6535986  0.51917827]\n",
      "  [0.6558705  0.41800222]\n",
      "  [0.7798546  0.4285229 ]]\n",
      "\n",
      " [[0.70556828 0.37105076]\n",
      "  [0.6558705  0.41800222]\n",
      "  [0.59575597 0.37629292]]\n",
      "\n",
      " [[0.6558705  0.41800222]\n",
      "  [0.570652   0.46099052]\n",
      "  [0.59575597 0.37629292]]\n",
      "\n",
      " [[0.6558705  0.41800222]\n",
      "  [0.6535986  0.51917827]\n",
      "  [0.570652   0.46099052]]\n",
      "\n",
      " [[0.         0.90053679]\n",
      "  [0.13301208 0.87099786]\n",
      "  [0.         1.        ]]\n",
      "\n",
      " [[0.11794801 0.76226158]\n",
      "  [0.         0.76218645]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.13301208 0.87099786]\n",
      "  [0.23153546 0.77544468]\n",
      "  [0.22274859 1.        ]]\n",
      "\n",
      " [[0.22274859 1.        ]\n",
      "  [0.23153546 0.77544468]\n",
      "  [0.4319555  1.        ]]\n",
      "\n",
      " [[0.23153546 0.77544468]\n",
      "  [0.36152623 0.7390783 ]\n",
      "  [0.4319555  1.        ]]\n",
      "\n",
      " [[0.23153546 0.77544468]\n",
      "  [0.2701981  0.71389454]\n",
      "  [0.36152623 0.7390783 ]]\n",
      "\n",
      " [[0.30477229 0.64083317]\n",
      "  [0.33761294 0.59785378]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.33761294 0.59785378]\n",
      "  [0.42926806 0.57977337]\n",
      "  [0.41503932 0.63118902]]\n",
      "\n",
      " [[0.33761294 0.59785378]\n",
      "  [0.4059138  0.54070083]\n",
      "  [0.42926806 0.57977337]]\n",
      "\n",
      " [[0.05327118 0.80576529]\n",
      "  [0.         0.90053679]\n",
      "  [0.         0.76218645]]\n",
      "\n",
      " [[0.         0.90053679]\n",
      "  [0.05327118 0.80576529]\n",
      "  [0.13301208 0.87099786]]\n",
      "\n",
      " [[0.11794801 0.76226158]\n",
      "  [0.05327118 0.80576529]\n",
      "  [0.         0.76218645]]\n",
      "\n",
      " [[0.05327118 0.80576529]\n",
      "  [0.11794801 0.76226158]\n",
      "  [0.13301208 0.87099786]]\n",
      "\n",
      " [[0.11794801 0.76226158]\n",
      "  [0.17626025 0.78362143]\n",
      "  [0.13301208 0.87099786]]\n",
      "\n",
      " [[0.17626025 0.78362143]\n",
      "  [0.23153546 0.77544468]\n",
      "  [0.13301208 0.87099786]]\n",
      "\n",
      " [[0.4059138  0.54070083]\n",
      "  [0.31686135 0.54449129]\n",
      "  [0.37800386 0.45044833]]\n",
      "\n",
      " [[0.33761294 0.59785378]\n",
      "  [0.31686135 0.54449129]\n",
      "  [0.4059138  0.54070083]]\n",
      "\n",
      " [[0.37800386 0.45044833]\n",
      "  [0.31686135 0.54449129]\n",
      "  [0.24317632 0.33646657]]\n",
      "\n",
      " [[0.31686135 0.54449129]\n",
      "  [0.33761294 0.59785378]\n",
      "  [0.30477229 0.64083317]]\n",
      "\n",
      " [[0.16920594 0.71960076]\n",
      "  [0.11794801 0.76226158]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.16920594 0.71960076]\n",
      "  [0.17626025 0.78362143]\n",
      "  [0.11794801 0.76226158]]\n",
      "\n",
      " [[0.23153546 0.77544468]\n",
      "  [0.16920594 0.71960076]\n",
      "  [0.2701981  0.71389454]]\n",
      "\n",
      " [[0.17626025 0.78362143]\n",
      "  [0.16920594 0.71960076]\n",
      "  [0.23153546 0.77544468]]\n",
      "\n",
      " [[0.31686135 0.54449129]\n",
      "  [0.21832013 0.55501963]\n",
      "  [0.24317632 0.33646657]]\n",
      "\n",
      " [[0.24317632 0.33646657]\n",
      "  [0.21832013 0.55501963]\n",
      "  [0.         0.51602691]]\n",
      "\n",
      " [[0.21832013 0.55501963]\n",
      "  [0.12156254 0.6163367 ]\n",
      "  [0.         0.51602691]]\n",
      "\n",
      " [[0.24292245 0.64015948]\n",
      "  [0.31686135 0.54449129]\n",
      "  [0.30477229 0.64083317]]\n",
      "\n",
      " [[0.24292245 0.64015948]\n",
      "  [0.21832013 0.55501963]\n",
      "  [0.31686135 0.54449129]]\n",
      "\n",
      " [[0.24292245 0.64015948]\n",
      "  [0.30477229 0.64083317]\n",
      "  [0.2701981  0.71389454]]\n",
      "\n",
      " [[0.16920594 0.71960076]\n",
      "  [0.20616366 0.65789453]\n",
      "  [0.2701981  0.71389454]]\n",
      "\n",
      " [[0.20616366 0.65789453]\n",
      "  [0.24292245 0.64015948]\n",
      "  [0.2701981  0.71389454]]\n",
      "\n",
      " [[0.12156254 0.6163367 ]\n",
      "  [0.20616366 0.65789453]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.20616366 0.65789453]\n",
      "  [0.16920594 0.71960076]\n",
      "  [0.11516881 0.68551431]]\n",
      "\n",
      " [[0.21832013 0.55501963]\n",
      "  [0.20616366 0.65789453]\n",
      "  [0.12156254 0.6163367 ]]\n",
      "\n",
      " [[0.24292245 0.64015948]\n",
      "  [0.20616366 0.65789453]\n",
      "  [0.21832013 0.55501963]]]\n"
     ]
    }
   ],
   "source": [
    "tri = sampler.get_Delaunay()\n",
    "# indiviual points\n",
    "print(tri.points)\n",
    "# simplex indices\n",
    "print(tri.simplices)\n",
    "# simplex points\n",
    "print(tri.points[tri.simplices])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0607c787",
   "metadata": {},
   "source": [
    "## One dimensional problems\n",
    "\n",
    "For 2 or more random inputs the `SSCSampler` uses the SciPy Delaunay triangulation. This does not work if there is only 1 random input. You can still use the `SSCSampler` though, as under the hood the Delaunay triangulation (`sampler.tri`) is replaced by a 1D grid that mimics the SciPy Delaunay properties and subroutines that are expected by the `SSCSampler`.\n",
    "\n",
    "This is illustrated below, where we remove $\\xi_2$ as a random variable. The rest is unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2a35000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 2 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 4\n",
      "Checking LEC condition of 4 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 8 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 1 3 3 3]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 12 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 1 3 4 4 4 4 4]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 16 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 4 1 3 4 4 4 4 4 4 4]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 20 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000 samples.\n",
      "% of simplices with no samples = 0.0\n"
     ]
    }
   ],
   "source": [
    "campaign = get_campaign(n_xi = 1)\n",
    "\n",
    "# redefine vary as 1D, this fixes xi_2 to its default value defined in params\n",
    "vary = {'xi_1' : cp.Uniform(0, 1)}\n",
    "\n",
    "##################\n",
    "# Select sampler #\n",
    "##################\n",
    "\n",
    "sampler = uq.sampling.SSCSampler(vary=vary, max_polynomial_order=4)\n",
    "\n",
    "# Associate the sampler with the campaign\n",
    "campaign.set_sampler(sampler)\n",
    "\n",
    "###############################\n",
    "# execute the defined actions #\n",
    "###############################\n",
    "\n",
    "campaign.execute().collate()\n",
    "\n",
    "analysis = uq.analysis.SSCAnalysis(sampler = sampler, qoi_cols=['f'])\n",
    "\n",
    "refine(campaign, analysis, 5, max_fjobs, n_mc_LEC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eedb0851",
   "metadata": {},
   "source": [
    "A plot of the 1D grid and the polynomial order of each element. We can already see where the LEC condition lowered the polynomial order to avoid oscillations at the discontinuity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba26cfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a453ab",
   "metadata": {},
   "source": [
    "The 1D surrogate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ef61838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_surrogate(analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972f68cd",
   "metadata": {},
   "source": [
    "## Higher dimensional problems\n",
    "\n",
    "As mentioned, the curse of dimensionality is present in the SSC method. Not only due to the exponential increase in the number of code evaluations, but **also in the cost of building the surrogate [2]**. This is related to the exponential increase in the number of simplex elements in the Delaunay triangulation. If you are running this one a node with a large number of cores, you can increase `max_LEC_jobs` and `max_ENO_jobs` to get some speedup.\n",
    "\n",
    "This can be seen by executing the 5D example below. You will note that checking the LEC condition and computing the ENO stencils takes considerably longer. Methods for extending the SSC sampler to dimensions higher than 5 are discussed in reference [2] above (e.g. HDMR expansions). These will be implemented in a later stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470f70cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 210 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 418 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2\n",
      " 2 2 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2\n",
      " 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2\n",
      " 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Edge already refined, selecting another sample.\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 630 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Edge already refined, selecting another sample.\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 818 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "Edge already refined, selecting another sample.\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 2\n",
      "Checking LEC condition of 1045 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [1 1 1 ... 1 1 1]\n",
      "Computing simplex probabilities...\n",
      "done, used 10000000 samples.\n",
      "% of simplices with no samples = 0.0\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "refining edge\n",
      "Edge already refined, selecting another sample.\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order allowed by n_s = 3\n",
      "Checking LEC condition of 1399 stencils...\n"
     ]
    }
   ],
   "source": [
    "n_xi = 5\n",
    "campaign = get_campaign(n_xi = n_xi)\n",
    "\n",
    "# redefine vary\n",
    "for i in range(n_xi):\n",
    "    vary['xi_%d' % (i + 1)] =  cp.Uniform(0, 1)\n",
    "\n",
    "##################\n",
    "# Select sampler #\n",
    "##################\n",
    "\n",
    "sampler = uq.sampling.SSCSampler(vary=vary, max_polynomial_order=4)\n",
    "\n",
    "# Associate the sampler with the campaign\n",
    "campaign.set_sampler(sampler)\n",
    "\n",
    "###############################\n",
    "# execute the defined actions #\n",
    "###############################\n",
    "\n",
    "campaign.execute().collate()\n",
    "\n",
    "analysis = uq.analysis.SSCAnalysis(sampler = sampler, qoi_cols=['f'])\n",
    "\n",
    "max_fjobs = 5\n",
    "\n",
    "# increase these a little to speed things up if you have available cores\n",
    "max_LEC_jobs = 8\n",
    "max_ENO_jobs = 8\n",
    "\n",
    "refine(campaign, analysis, 5, max_fjobs, max_LEC_jobs=max_LEC_jobs, max_ENO_jobs=max_ENO_jobs)"
   ]
  }
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