tutorials/simplex_stochastic_collocation_tutorial.ipynb

{
 "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": 121,
   "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": 122,
   "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": 123,
   "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": 124,
   "id": "db8e085f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 125,
   "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": 125,
     "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": 126,
   "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, label=r'$\\tilde{f}$')\n",
    "        ax.plot(analysis.sampler.tri.points, analysis.samples['f'], 'o',label=r'$f$')\n",
    "        plt.legend()\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 polynomial order if a stencil $S_j$ crosses a discontinuity. It achieves this by 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": 127,
   "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": 128,
   "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": 129,
   "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": 130,
   "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": 131,
   "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": 132,
   "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": 133,
   "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": 134,
   "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 = [2 2 2 1 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": [
    "# 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": 135,
   "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 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",
      "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 16 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 1 1 1 1 1 1 1 1 1 1 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",
      "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 1 1 1 2 2 2 1 2 1 1 2 1 1 1 2 2 2 1 1 2 2]\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 30 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 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 set by hand to = 4\n",
      "Checking LEC condition of 36 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 2 3 2 3 3 3 2 2 3 3 3 3 3 1 2 1 1 1 1 2 2 2 2 1 1 1 2 2 2 1 1 3 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",
      "refining edge\n",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 41 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 2 3 2 3 3 3 2 1 3 3 3 3 1 1 1 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 2 1 1 2\n",
      " 2 2 3 2]\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 49 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 1 1 3 3 3 2 1 3 3 3 2 2 2 1 1 3 3 3 3 3 1 1 1 3 2\n",
      " 2 2 1 1 1 1 1 2 1 1 2 2]\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 57 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 1 4 3 3 4 4 3 1 4 1 3 3 4 4 3 1 2 2 1 2 2 2 2 1 1 2 1 2 1 1 1 3 1 3 3\n",
      " 3 3 3 4 4 4 4 4 4 2 2 2 4 4 1 1 1 1 3 3]\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 = [4 4 4 3 4 4 1 1 3 1 1 3 3 3 4 4 2 2 2 2 3 1 4 4 4 3 3 3 3 3 4 4 4 3 3 1 1\n",
      " 1 3 3 1 3 3 1 2 2 2 4 4 2 2 2 2 1 2 1 2 1 1 3 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 72 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [3 3 3 3 3 3 1 3 1 1 4 4 4 1 3 2 3 3 2 1 3 3 3 4 4 4 4 4 3 3 3 4 4 3 3 1 4\n",
      " 4 1 1 2 2 2 3 3 3 3 1 1 2 2 3 3 3 3 3 1 4 1 1 4 4 4 1 1 1 1 1 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",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 78 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 3 4 4 4 4 4 4 3 1 1 4 4 4 2 3 3 4 1 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 1 1 2\n",
      " 2 2 4 4 3 3 1 1 1 3 3 3 3 3 3 4 4 3 3 4 4 4 1 1 1 4 1 1 4 4 4 1 1 1 1 3 3\n",
      " 1 1 1 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 86 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 3 4 4 4 4 4 4 3 1 1 3 4 4 2 4 4 3 3 4 1 1 4 4 4 4 4 4 4 4 4 4 4 3 3 1 2\n",
      " 2 1 3 3 3 1 1 1 3 3 3 3 4 4 4 4 4 4 4 1 3 1 1 3 1 4 3 3 1 1 1 2 1 1 1 1 1\n",
      " 4 4 4 3 1 1 1 1 1 4 4 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 94 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 2 4 4 4 4 4 4 2 4 3 4 4 4 4 4 2 4 4 3 2 2 1 1 4 1 4 4 4 1 4 4 4 4 4 4 4\n",
      " 4 3 1 2 2 1 3 3 3 3 3 1 1 3 1 1 1 2 1 1 1 4 3 3 3 3 1 1 1 3 1 1 3 4 1 1 1\n",
      " 3 3 4 4 4 4 1 1 1 3 3 1 1 1 3 4 1 1 4 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 102 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 2 4 4 3 4 4 4 1 1 4 1 4 4 4 1 4 4 4 4 4 4\n",
      " 4 4 3 1 2 2 1 3 3 3 3 1 1 3 3 1 1 2 3 2 1 3 4 3 3 1 1 1 1 3 3 3 1 1 4 4 1\n",
      " 1 1 3 3 4 4 4 4 3 2 1 1 1 1 3 3 3 3 1 1 3 1 4 1 4 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",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 109 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 3 4 4 1 1 4 4 4 4 1 4 4 4 4 4 4 4 4 1 2\n",
      " 2 1 3 3 3 3 1 4 1 3 1 3 1 1 4 4 4 3 4 4 1 1 3 4 3 3 3 3 1 1 1 1 3 4 4 1 1\n",
      " 1 4 4 4 4 4 4 4 1 1 1 1 3 1 1 1 1 1 3 3 4 4 4 1 4 1 4 2 2 4 1 1 2 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",
      "Loading samples...\n",
      "done\n",
      "Max. polynomial order set by hand to = 4\n",
      "Checking LEC condition of 115 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 1 1 4 4 4 4 1 4 4 4 4 4 4 4 4 1 3 2\n",
      " 4 4 1 4 1 3 1 4 1 1 4 4 4 1 2 3 4 4 2 1 3 1 2 3 2 4 4 4 3 3 1 1 4 4 4 2 2\n",
      " 1 1 4 4 4 4 4 4 2 2 4 1 2 4 1 1 3 4 1 1 1 2 1 4 4 4 4 4 4 1 4 2 4 2 2 4 1\n",
      " 1 2 3 3]\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": 136,
   "id": "37531f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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": 137,
   "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": 138,
   "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>0.896950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.248267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>1.558171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             f\n",
       "             0\n",
       "mean  0.896950\n",
       "std   1.248267\n",
       "var   1.558171"
      ]
     },
     "execution_count": 138,
     "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": 139,
   "id": "5bf4d756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0.81906525 0.4056478 ]\n",
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      "[[[0.28215246 0.28742651]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "  [0.         1.        ]]\n",
      "\n",
      " [[0.10976166 0.68566957]\n",
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      "\n",
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      "\n",
      " [[0.48552849 0.57878535]\n",
      "  [0.45025676 0.56347211]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.45025676 0.56347211]\n",
      "  [0.37822795 0.55006621]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.45924229 0.63530335]\n",
      "  [0.45025676 0.56347211]\n",
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      "\n",
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      "\n",
      " [[0.38019495 0.42948316]\n",
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      "\n",
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      "\n",
      " [[0.56985068 0.50294115]\n",
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      "\n",
      " [[0.56551612 0.64490633]\n",
      "  [0.6395195  0.52793074]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "  [0.36370968 0.65979093]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " [[0.26227833 0.63063678]\n",
      "  [0.36370968 0.65979093]\n",
      "  [0.32664753 0.72276204]]\n",
      "\n",
      " [[0.26227833 0.63063678]\n",
      "  [0.22186137 0.70675523]\n",
      "  [0.19270426 0.67340211]]\n",
      "\n",
      " [[0.60793171 0.42116799]\n",
      "  [0.68433183 0.43493004]\n",
      "  [0.6395195  0.52793074]]\n",
      "\n",
      " [[0.6395195  0.52793074]\n",
      "  [0.68433183 0.43493004]\n",
      "  [0.72686922 0.61002265]]\n",
      "\n",
      " [[0.68433183 0.43493004]\n",
      "  [0.84094386 0.52917209]\n",
      "  [0.72686922 0.61002265]]\n",
      "\n",
      " [[0.38019495 0.42948316]\n",
      "  [0.56502743 0.36974661]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.56502743 0.36974661]\n",
      "  [0.60793171 0.42116799]\n",
      "  [0.5        0.5       ]]\n",
      "\n",
      " [[0.60437879 0.24234286]\n",
      "  [0.56502743 0.36974661]\n",
      "  [0.28215246 0.28742651]]\n",
      "\n",
      " [[0.56502743 0.36974661]\n",
      "  [0.38019495 0.42948316]\n",
      "  [0.28215246 0.28742651]]\n",
      "\n",
      " [[0.87832922 0.34153458]\n",
      "  [0.85621934 0.2776845 ]\n",
      "  [0.9300334  0.26332102]]\n",
      "\n",
      " [[0.93788858 0.37820213]\n",
      "  [0.87832922 0.34153458]\n",
      "  [0.9300334  0.26332102]]\n",
      "\n",
      " [[0.87832922 0.34153458]\n",
      "  [0.93788858 0.37820213]\n",
      "  [0.81906525 0.4056478 ]]\n",
      "\n",
      " [[0.37623423 0.60986567]\n",
      "  [0.31876619 0.60327407]\n",
      "  [0.37822795 0.55006621]]\n",
      "\n",
      " [[0.37623423 0.60986567]\n",
      "  [0.36370968 0.65979093]\n",
      "  [0.31876619 0.60327407]]\n",
      "\n",
      " [[0.36370968 0.65979093]\n",
      "  [0.37623423 0.60986567]\n",
      "  [0.45924229 0.63530335]]\n",
      "\n",
      " [[0.37623423 0.60986567]\n",
      "  [0.45025676 0.56347211]\n",
      "  [0.45924229 0.63530335]]\n",
      "\n",
      " [[0.45025676 0.56347211]\n",
      "  [0.37623423 0.60986567]\n",
      "  [0.37822795 0.55006621]]\n",
      "\n",
      " [[0.13067784 0.72687984]\n",
      "  [0.16586081 0.72951474]\n",
      "  [0.20274601 0.79725399]]\n",
      "\n",
      " [[0.16586081 0.72951474]\n",
      "  [0.22186137 0.70675523]\n",
      "  [0.20274601 0.79725399]]\n",
      "\n",
      " [[0.22186137 0.70675523]\n",
      "  [0.16586081 0.72951474]\n",
      "  [0.19270426 0.67340211]]\n",
      "\n",
      " [[0.16586081 0.72951474]\n",
      "  [0.10976166 0.68566957]\n",
      "  [0.19270426 0.67340211]]\n",
      "\n",
      " [[0.16586081 0.72951474]\n",
      "  [0.13067784 0.72687984]\n",
      "  [0.10976166 0.68566957]]\n",
      "\n",
      " [[0.25939326 0.72319204]\n",
      "  [0.26227833 0.63063678]\n",
      "  [0.32664753 0.72276204]]\n",
      "\n",
      " [[0.25939326 0.72319204]\n",
      "  [0.22186137 0.70675523]\n",
      "  [0.26227833 0.63063678]]\n",
      "\n",
      " [[0.30835918 0.79230879]\n",
      "  [0.25939326 0.72319204]\n",
      "  [0.32664753 0.72276204]]\n",
      "\n",
      " [[0.25939326 0.72319204]\n",
      "  [0.30835918 0.79230879]\n",
      "  [0.20274601 0.79725399]]\n",
      "\n",
      " [[0.22186137 0.70675523]\n",
      "  [0.25939326 0.72319204]\n",
      "  [0.20274601 0.79725399]]\n",
      "\n",
      " [[0.84094386 0.52917209]\n",
      "  [0.73774888 0.39453742]\n",
      "  [0.81906525 0.4056478 ]]\n",
      "\n",
      " [[0.68433183 0.43493004]\n",
      "  [0.73774888 0.39453742]\n",
      "  [0.84094386 0.52917209]]\n",
      "\n",
      " [[0.73774888 0.39453742]\n",
      "  [0.80857201 0.33809961]\n",
      "  [0.81906525 0.4056478 ]]\n",
      "\n",
      " [[0.85621934 0.2776845 ]\n",
      "  [0.80857201 0.33809961]\n",
      "  [0.76189818 0.24795324]]\n",
      "\n",
      " [[0.80857201 0.33809961]\n",
      "  [0.87832922 0.34153458]\n",
      "  [0.81906525 0.4056478 ]]\n",
      "\n",
      " [[0.87832922 0.34153458]\n",
      "  [0.80857201 0.33809961]\n",
      "  [0.85621934 0.2776845 ]]\n",
      "\n",
      " [[0.56502743 0.36974661]\n",
      "  [0.66056549 0.37288498]\n",
      "  [0.60793171 0.42116799]]\n",
      "\n",
      " [[0.66056549 0.37288498]\n",
      "  [0.68433183 0.43493004]\n",
      "  [0.60793171 0.42116799]]\n",
      "\n",
      " [[0.66056549 0.37288498]\n",
      "  [0.73774888 0.39453742]\n",
      "  [0.68433183 0.43493004]]\n",
      "\n",
      " [[0.66056549 0.37288498]\n",
      "  [0.56502743 0.36974661]\n",
      "  [0.60437879 0.24234286]]\n",
      "\n",
      " [[0.80857201 0.33809961]\n",
      "  [0.73339237 0.32661225]\n",
      "  [0.76189818 0.24795324]]\n",
      "\n",
      " [[0.73339237 0.32661225]\n",
      "  [0.80857201 0.33809961]\n",
      "  [0.73774888 0.39453742]]\n",
      "\n",
      " [[0.66056549 0.37288498]\n",
      "  [0.73339237 0.32661225]\n",
      "  [0.73774888 0.39453742]]\n",
      "\n",
      " [[0.73339237 0.32661225]\n",
      "  [0.60437879 0.24234286]\n",
      "  [0.76189818 0.24795324]]\n",
      "\n",
      " [[0.73339237 0.32661225]\n",
      "  [0.66056549 0.37288498]\n",
      "  [0.60437879 0.24234286]]]\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": 140,
   "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 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 12 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 4 4 3 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 16 stencils...\n",
      "done.\n",
      "Computing ENO stencils...\n",
      "done.\n",
      "Updated polynomials orders = [4 4 4 4 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",
      "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 2 1 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": 141,
   "id": "ba26cfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 142,
   "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": [
    "%matplotlib inline\n",
    "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": 143,
   "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 1 1 1 1 1 1 1 1 1 1 1 1 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 2 2 2 2 1 1 1 1 2 2 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 2 2 1 1 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 1 1 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 2 1 1 1 1 2 1 1 1 1 1 1 1 2 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 2 1 1 1 1 1 1 2 1 1 1 1 1 2 1\n",
      " 2 1 2 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 1 2 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 2 2 2 2 1 2 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 2 1 1 2 1 1 2 2 2 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]\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 535 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]\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 694 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]\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 916 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 1 1 1 1 1 1 1 1 1 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]\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",
      "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 = 3\n",
      "Checking LEC condition of 1044 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"
     ]
    }
   ],
   "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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