{
 "cells": [
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   "source": [
    "# Dimension adaptive sampling tutorial\n",
    "\n",
    "Here, briefly describe the concept behind dimension-adaptive sparse grids, starting from a standard Stochastic Collocation (SC) campaign. Following this, a dimension adaptive EasyVVUQ script using a simple analytic test function is presented. We will assume you are familiar with the basics of EasyVVUQ.\n",
    "\n",
    "## Standard SC\n",
    "\n",
    "In a standard EasyVVUQ Campaign, a Stochastic Collocation sampler object might be created via::\n",
    "\n",
    "```python\n",
    "sampler = uq.sampling.SCSampler(vary=vary, polynomial_order=2)\n",
    "```\n",
    "Here the specified `polynomial_order`, and the number of inputs in `vary`, determine the\n",
    "number of samples, which increases exponentially fast with an increasing amount of inputs. This\n",
    "is the so-called *curse of dimensionality*. \n",
    "\n",
    "Basically, by setting `polynomial_order=2` we create a sampling plan through a single tensor product of one-dimensional quadrature nodes with order 3 for every input. It is this tensor product construction that leads to the exponential rise in cost. So if we have 2 inputs `x1` and `x2`, and our one-dimensional quadrature rule of order 2 produces 5 points, we obtain a total of 25 points in the `(x1, x2)` domain. Likewise, if `vary` contains 3 inputs, we would need to evaluate the computational model 125 times, and 10 inputs would require `5**10 = 9765625` model evaluations. For this reason, a standard SC campaign is rarely used beyond 6 or 7 inputs.\n",
    "\n",
    "## Sparse SC\n",
    "\n",
    "Sparse grids on the other hand, do not create a single tensor product, but build the sampling plan from the ground up by using a *linear combination of tensor products involving 1D quadrature rules of* ***different*** *orders*. \n",
    "\n",
    "For two inputs, we might for instance consider using 1D quadrature rules of order [0, 0], [0, 1] and [1, 0], where:\n",
    "\n",
    "    * [0, 0]: a single point in the 2D domain (x1, x2)\n",
    "    * [0, 1]: a line of 3 points with constant x1\n",
    "    * [1, 0]: a line of 3 points with constant x2\n",
    "\n",
    "In the case of sparse grids it is common to select a *nested* quadrature rule. This means that the quadrature\n",
    "rule of order p contains all points of the same rule of order p-1. When taking the linear combinations, a nested rule ensures that many points will conincide, which yields efficient sampling \n",
    "plans, especially in higher dimensions. If our nested 1D rule of order 1 and 2 generates the points [0.5] and [0, 0.5, 1] we obtain a sampling plan consisting of\n",
    "\n",
    "    * [0, 0]: [0.5, 0.5]\n",
    "    * [0, 1]: [0.5, 0.0], [0.5, 0.5], [0.5, 1.0]\n",
    "    * [1, 0]: [0.0, 0.5], [0.5, 0.5], [1.0, 0.5],\n",
    "\n",
    "which gives a total of 5 unique points, compared to a corresponding standard SC campaign with [1, 1], which would generate 9 unique points (`[0, 0.5, 1] x [0, 0.5, 1.0]`). Note that sparse grids do **not** circumvent the curse of dimensionality, although they can postpone its effect to higher dimensions.\n",
    "\n",
    "## Dimension-adaptive SC\n",
    "\n",
    "What we described above is an *isotropic* sparse grid, since the multi indices `[0, 0], [1, 0], [0,1]` result in a sampling plan where both inputs end up with the same number of samples. However, in practice model parameters are rarely equally important. The idea behind dimension-adaptive sampling is to build the sampling plan in an iterative fashion, find out which (combination of) parameters are important as we go, and then place more samples along those directions. This results in a anisotropic sampling plan, where the important inputs get relatively high number of samples. To find out which directions are important we need an appropriate error measure, and we need to split the quadrature order multi indices in an *accepted* and an *admissible* set. The accepted set is initialized to `[0, 0]` in 2D, i.e. we start with just a single code evaluation. Without going into detail, we can think of the admissible set as the candidate refinement directions, from which we must add a single entry to the accepted set at every iteration.\n",
    "\n",
    "In our 2D example, at the 1st iteration the candidate set consists of `[1, 0]` and `[0, 1]`. That is, we can either refine only `x1` or only `x2`. We must select the multi index which generates the highest error when added to the accepted set. There are a variety of error measures, the two main ones in EasyVVUQ are:\n",
    "\n",
    "1. the hierarchical surplus error, and\n",
    "2. a variance-based error.\n",
    "\n",
    "Roughly speaking, the surplus is an interpolation based error, which measures the difference between the code output and the corresponding SC polynomial surrogate, when evaluated at new sample locations. The variance-based error selects the direction in which the variance in the output changes the most. For more information we refer to the references below.\n",
    "\n",
    "Assume that `[1, 0]` generated the highest error, and so it is added to the accepted set, now consisting of `[0, 0]` and `[1, 0]`. This means that `x1` has more points than `x2`. Also, adding a multi index to the accepted set means that the admissible set changes. In this case, since `[1, 0]` has been accepted, `[2, 0]` has become admissible. Note that the new entry `[2, 0]` also requires new evaluations of the code, and so a new ensemble must be submitted. Again, if we use a nested rule, the grid of `[2, 0]` will have a partial overlap with the accepted points, so we only have to evaluate the code at the new points, *not* all points of `[2, 0]`.\n",
    "\n",
    "Thus, the admissible set now consists of `[0, 1]` and `[2, 0]`. Hence, we now have to option of refining `x1` again (to second order), or refining `x2` to first order. Assume the latter happens. As both `x1` and `x2` have been refined to 1st order, `[1, 1]` has become admissible. If accepted, this multi index results in a *simultaneous* refinement of both `x1` and `x2`. Note that `[1, 1]` represents a tensor product, and that therefore it is not the same as `[1, 0]` and `[0, 1]` taken together. We added this example to show that the algoritmn is not limited to one-at-a-time refinement.\n",
    "\n",
    "To conclude, every time a multi index is accepted, new indices become admissible, and the cycle repeats.\n",
    "\n",
    "## References\n",
    "\n",
    "Our description of the method here was rather limited, so for more information and applications of this (and similar) methods, see the following references:\n",
    "\n",
    "* T. Gerstner  and M. Griebel. \"Dimension–adaptive tensor–product quadrature.\" Computing 71.1 (2003): 65-87.\n",
    "* W. Edeling , H. Arabnejad , R. Sinclair, D. Suleimenova, K. Gopalakrishnan, B. Bosak, D. Groen, I. Mahmood, D. Crommelin, and Peter V Coveney, \"The Impact of Uncertainty on Predictions of the CovidSim Epidemiological Code\", Nature Computational Science, 1 (2), 2021.\n",
    "* D. Loukrezis, U. Römer, and H. De Gersem. \"Assessing the performance of Leja and Clenshaw-Curtis collocation for computational electromagnetics with random input data\". International Journal for Uncertainty Quantification , 9(1), 2019.\n",
    "* J.D. Jakeman, M.S. Eldred, G. Geraci, and A. Gorodetsky. \"Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis\". Numerical Methods in Engineering , 121(6):1314-1343, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:12.765233Z",
     "start_time": "2021-07-28T07:14:00.969965Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# import packages that we will use\n",
    "\n",
    "import os\n",
    "import easyvvuq as uq\n",
    "import chaospy as cp\n",
    "import pickle\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "if not os.getenv(\"DISPLAY\"): matplotlib.use('Agg')\n",
    "import matplotlib.pylab as plt\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "#%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.783395Z",
     "start_time": "2021-07-28T07:15:12.768522Z"
    },
    "code_folding": [
     0
    ]
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   "outputs": [],
   "source": [
    "# we need fipy -- install if not already available\n",
    "\n",
    "try:\n",
    "    import fipy\n",
    "except ModuleNotFoundError:\n",
    "    ! pip install future\n",
    "    ! pip install fipy\n",
    "    import fipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.791865Z",
     "start_time": "2021-07-28T07:15:15.787001Z"
    },
    "code_folding": [
     0
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   "source": [
    "# routine to write out (if needed) the fusion .template file\n",
    "\n",
    "def write_template(params):\n",
    "    str = \"\"\n",
    "    first = True\n",
    "    for k in params.keys():\n",
    "        if first:\n",
    "            str += '{\"%s\": \"$%s\"' % (k,k) ; first = False\n",
    "        else:\n",
    "            str += ', \"%s\": \"$%s\"' % (k,k)\n",
    "    str += '}'\n",
    "    print(str, file=open('fusion.template','w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.829622Z",
     "start_time": "2021-07-28T07:15:15.794481Z"
    },
    "code_folding": [
     0
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   "outputs": [],
   "source": [
    "# define parameters of the fusion model\n",
    "def define_params():\n",
    "    return {\n",
    "        \"Qe_tot\":   {\"type\": \"float\",   \"min\": 1.0e6, \"max\": 50.0e6, \"default\": 2e6},\n",
    "        \"H0\":       {\"type\": \"float\",   \"min\": 0.00,  \"max\": 1.0,    \"default\": 0},\n",
    "        \"Hw\":       {\"type\": \"float\",   \"min\": 0.01,  \"max\": 100.0,  \"default\": 0.1},\n",
    "        \"Te_bc\":    {\"type\": \"float\",   \"min\": 10.0,  \"max\": 1000.0, \"default\": 100},\n",
    "        \"chi\":      {\"type\": \"float\",   \"min\": 0.01,  \"max\": 100.0,  \"default\": 1},\n",
    "        \"a0\":       {\"type\": \"float\",   \"min\": 0.2,   \"max\": 10.0,   \"default\": 1},\n",
    "        \"R0\":       {\"type\": \"float\",   \"min\": 0.5,   \"max\": 20.0,   \"default\": 3},\n",
    "        \"E0\":       {\"type\": \"float\",   \"min\": 1.0,   \"max\": 10.0,   \"default\": 1.5},\n",
    "        \"b_pos\":    {\"type\": \"float\",   \"min\": 0.95,  \"max\": 0.99,   \"default\": 0.98},\n",
    "        \"b_height\": {\"type\": \"float\",   \"min\": 3e19,  \"max\": 10e19,  \"default\": 6e19},\n",
    "        \"b_sol\":    {\"type\": \"float\",   \"min\": 2e18,  \"max\": 3e19,   \"default\": 2e19},\n",
    "        \"b_width\":  {\"type\": \"float\",   \"min\": 0.005, \"max\": 0.025,  \"default\": 0.01},\n",
    "        \"b_slope\":  {\"type\": \"float\",   \"min\": 0.0,   \"max\": 0.05,   \"default\": 0.01},\n",
    "        \"nr\":       {\"type\": \"integer\", \"min\": 10,    \"max\": 1000,   \"default\": 100},\n",
    "        \"dt\":       {\"type\": \"float\",   \"min\": 1e-3,  \"max\": 1e3,    \"default\": 100},\n",
    "        \"out_file\": {\"type\": \"string\",  \"default\": \"output.csv\"}\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.875454Z",
     "start_time": "2021-07-28T07:15:15.831921Z"
    },
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     28
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   "outputs": [],
   "source": [
    "# define varying quantities\n",
    "def define_vary():\n",
    "    vary_all = {\n",
    "        \"Qe_tot\":   cp.Uniform(1.8e6, 2.2e6),\n",
    "        \"H0\":       cp.Uniform(0.0,   0.2),\n",
    "        \"Hw\":       cp.Uniform(0.1,   0.5),\n",
    "        \"chi\":      cp.Uniform(0.8,   1.2),\n",
    "        \"Te_bc\":    cp.Uniform(80.0,  120.0),\n",
    "        \"a0\":       cp.Uniform(0.9,   1.1),\n",
    "        \"R0\":       cp.Uniform(2.7,   3.3),\n",
    "        \"E0\":       cp.Uniform(1.4,   1.6),\n",
    "        \"b_pos\":    cp.Uniform(0.95,  0.99),\n",
    "        \"b_height\": cp.Uniform(5e19,  7e19),\n",
    "        \"b_sol\":    cp.Uniform(1e19,  3e19),\n",
    "        \"b_width\":  cp.Uniform(0.015, 0.025),\n",
    "        \"b_slope\":  cp.Uniform(0.005, 0.020)\n",
    "    }\n",
    "    vary_2 =  {\n",
    "        \"Qe_tot\":   cp.Uniform(1.8e6, 2.2e6),\n",
    "        \"Te_bc\":    cp.Uniform(80.0,  120.0)\n",
    "    }\n",
    "    vary_5 =  {\n",
    "        \"Qe_tot\":   cp.Uniform(1.8e6, 2.2e6),\n",
    "        \"H0\":       cp.Uniform(0.0,   0.2),\n",
    "        \"Hw\":       cp.Uniform(0.1,   0.5),\n",
    "        \"chi\":      cp.Uniform(0.8,   1.2),\n",
    "        \"Te_bc\":    cp.Uniform(80.0,  120.0)\n",
    "    }\n",
    "    vary_10 = {\n",
    "        \"Qe_tot\":   cp.Uniform(1.8e6, 2.2e6),\n",
    "        \"H0\":       cp.Uniform(0.0,   0.2),\n",
    "        \"Hw\":       cp.Uniform(0.1,   0.5),\n",
    "        \"chi\":      cp.Uniform(0.8,   1.2),\n",
    "        \"Te_bc\":    cp.Uniform(80.0,  120.0),\n",
    "        \"b_pos\":    cp.Uniform(0.95,  0.99),\n",
    "        \"b_height\": cp.Uniform(5e19,  7e19),\n",
    "        \"b_sol\":    cp.Uniform(1e19,  3e19),\n",
    "        \"b_width\":  cp.Uniform(0.015, 0.025),\n",
    "        \"b_slope\":  cp.Uniform(0.005, 0.020)\n",
    "    }\n",
    "    return vary_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.893653Z",
     "start_time": "2021-07-28T07:15:15.877392Z"
    },
    "code_folding": [
     0
    ]
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   "outputs": [],
   "source": [
    "# define a model to run the fusion code directly from python, expecting a dictionary and returning a dictionary\n",
    "def run_fusion_model(input):\n",
    "    import json\n",
    "    import fusion\n",
    "    qois = [\"te\", \"ne\", \"rho\", \"rho_norm\"]\n",
    "    del input['out_file']\n",
    "    return {q: v for q,v in zip(qois, [t.tolist() for t in fusion.solve_Te(**input, plots=False, output=False)])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:15:15.922179Z",
     "start_time": "2021-07-28T07:15:15.895535Z"
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   "source": [
    "# routines for plotting the results\n",
    "\n",
    "def plot_Te(results, title=None):\n",
    "    # plot the calculated Te: mean, with std deviation, 1, 10, 90 and 99%\n",
    "    plt.figure()\n",
    "    rho = results.describe('rho', 'mean')\n",
    "    plt.plot(rho, results.describe('te', 'mean'), 'b-', label='Mean')\n",
    "    plt.plot(rho, results.describe('te', 'mean')-results.describe('te', 'std'), 'b--', label='+1 std deviation')\n",
    "    plt.plot(rho, results.describe('te', 'mean')+results.describe('te', 'std'), 'b--')\n",
    "    plt.fill_between(rho, results.describe('te', 'mean')-results.describe('te', 'std'), results.describe('te', 'mean')+results.describe('te', 'std'), color='b', alpha=0.2)\n",
    "    try:\n",
    "        plt.plot(rho, results.describe('te', '10%'), 'b:', label='10 and 90 percentiles')\n",
    "        plt.plot(rho, results.describe('te', '90%'), 'b:')\n",
    "        plt.fill_between(rho, results.describe('te', '10%'), results.describe('te', '90%'), color='b', alpha=0.1)\n",
    "        plt.fill_between(rho, results.describe('te', '1%'), results.describe('te', '99%'), color='b', alpha=0.05)\n",
    "    except:\n",
    "        print('Problem with some of the percentiles')\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('Te [$eV$]')\n",
    "    if not title is None: plt.title(title)\n",
    "    plt.savefig('Te.png')\n",
    "    plt.savefig('Te.pdf')\n",
    "\n",
    "def plot_ne(results, title=None):\n",
    "    # plot the calculated ne: mean, with std deviation, 1, 10, 90 and 99%\n",
    "    plt.figure()\n",
    "    rho = results.describe('rho', 'mean')\n",
    "    plt.plot(rho, results.describe('ne', 'mean'), 'b-', label='Mean')\n",
    "    plt.plot(rho, results.describe('ne', 'mean')-results.describe('ne', 'std'), 'b--', label='+1 std deviation')\n",
    "    plt.plot(rho, results.describe('ne', 'mean')+results.describe('ne', 'std'), 'b--')\n",
    "    plt.fill_between(rho, results.describe('ne', 'mean')-results.describe('ne', 'std'), results.describe('ne', 'mean')+results.describe('ne', 'std'), color='b', alpha=0.2)\n",
    "    try:\n",
    "        plt.plot(rho, results.describe('ne', '10%'), 'b:', label='10 and 90 percentiles')\n",
    "        plt.plot(rho, results.describe('ne', '90%'), 'b:')\n",
    "        plt.fill_between(rho, results.describe('ne', '10%'), results.describe('ne', '90%'), color='b', alpha=0.1)\n",
    "        plt.fill_between(rho, results.describe('ne', '1%'), results.describe('ne', '99%'), color='b', alpha=0.05)\n",
    "    except:\n",
    "        print('Problem with some of the percentiles')\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('ne [$m^{-3}$]')\n",
    "    if not title is None: plt.title(title)\n",
    "    plt.savefig('ne.png')\n",
    "    plt.savefig('ne.pdf')\n",
    "\n",
    "def plot_sobols_first(results, title=None, field='te'):\n",
    "    # plot the first Sobol results\n",
    "    plt.figure()\n",
    "    rho = results.describe('rho', 'mean')\n",
    "    for k in results.sobols_first()[field].keys(): plt.plot(rho, results.sobols_first()[field][k], label=k)\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('sobols_first')\n",
    "    if not title is None: plt.title(field + ': ' + title)\n",
    "    plt.savefig('sobols_first_%s.png' % (field))\n",
    "    plt.savefig('sobols_first_%s.pdf' % (field))\n",
    "\n",
    "def plot_sobols_second(results, title=None, field='te'):\n",
    "    # plot the second Sobol results\n",
    "    plt.figure()\n",
    "    rho = results.describe('rho', 'mean')\n",
    "    for k1 in results.sobols_second()[field].keys():\n",
    "        for k2 in results.sobols_second()[field][k1].keys():\n",
    "            plt.plot(rho, results.sobols_second()[field][k1][k2], label=k1+'/'+k2)\n",
    "    plt.legend(loc=0, ncol=2)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('sobols_second')\n",
    "    if not title is None: plt.title(field + ': ' + title)\n",
    "    plt.savefig('sobols_second_%s.png' % (field))\n",
    "    plt.savefig('sobols_second_%s.pdf' % (field))\n",
    "\n",
    "def plot_sobols_total(results, title=None, field='te'):\n",
    "    # plot the total Sobol results\n",
    "    plt.figure()\n",
    "    rho = results.describe('rho', 'mean')\n",
    "    for k in results.sobols_total()[field].keys(): plt.plot(rho, results.sobols_total()[field][k], label=k)\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('sobols_total')\n",
    "    if not title is None: plt.title(field + ': ' + title)\n",
    "    plt.savefig('sobols_total_%s.png' % (field))\n",
    "    plt.savefig('sobols_total_%s.pdf' % (field))\n",
    "\n",
    "def plot_distribution(results, results_df, title=None):\n",
    "    te_dist = results.raw_data['output_distributions']['te']\n",
    "    rho_norm = results.describe('rho_norm', 'mean')\n",
    "    for i in [np.maximum(0, int(i-1)) \n",
    "              for i in np.linspace(0,1,5) * rho_norm.shape]:\n",
    "        plt.figure()\n",
    "        pdf_raw_samples = cp.GaussianKDE(results_df.te[i])\n",
    "        pdf_kde_samples = cp.GaussianKDE(te_dist.samples[i])\n",
    "        plt.hist(results_df.te[i], density=True, bins=50, label='histogram of raw samples', alpha=0.25)\n",
    "        if hasattr(te_dist, 'samples'):\n",
    "            plt.hist(te_dist.samples[i], density=True, bins=50, label='histogram of kde samples', alpha=0.25)\n",
    "\n",
    "        plt.plot(np.linspace(pdf_raw_samples.lower, pdf_raw_samples.upper), pdf_raw_samples.pdf(np.linspace(pdf_raw_samples.lower, pdf_raw_samples.upper)), label='PDF (raw samples)')\n",
    "        plt.plot(np.linspace(pdf_kde_samples.lower, pdf_kde_samples.upper), pdf_kde_samples.pdf(np.linspace(pdf_kde_samples.lower, pdf_kde_samples.upper)), label='PDF (kde samples)')\n",
    "\n",
    "        plt.legend(loc=0)\n",
    "        plt.xlabel('Te [$eV$]')\n",
    "        if title is None:\n",
    "            plt.title('Distributions for rho_norm = %0.4f' % (rho_norm[i]))\n",
    "        else:\n",
    "            plt.title('%s\\nDistributions for rho_norm = %0.4f' % (title, rho_norm[i]))\n",
    "        plt.savefig('distribution_function_rho_norm=%0.4f.png' % (rho_norm[i]))\n",
    "        plt.savefig('distribution_function_rho_norm=%0.4f.pdf' % (rho_norm[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:52.826350Z",
     "start_time": "2021-07-28T07:15:15.925442Z"
    },
    "code_folding": [
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [01:29<00:00, 11.21it/s]\n"
     ]
    }
   ],
   "source": [
    "# prepare the test data\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    test_campaign = uq.Campaign(name='fusion_pce.')        \n",
    "\n",
    "    # Add the app (automatically set as current app)\n",
    "    test_campaign.add_app(name=\"fusion\", params=define_params(), \n",
    "                          actions=uq.actions.Actions(uq.actions.ExecutePython(run_fusion_model)))\n",
    "\n",
    "    # Associate a sampler with the campaign\n",
    "    test_campaign.set_sampler(uq.sampling.quasirandom.LHCSampler(vary=define_vary(), count=100))\n",
    "\n",
    "    # Perform the actions\n",
    "    test_campaign.execute(nsamples=1000).collate(progress_bar=True)\n",
    "\n",
    "    # Collate the results\n",
    "    test_df = test_campaign.get_collation_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:52.832816Z",
     "start_time": "2021-07-28T07:16:52.828698Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# calculate the SC surrogates\n",
    "def test_surrogate():\n",
    "    test_points = np.array(test_df[test_campaign.get_active_sampler().vary.get_keys()])\n",
    "    test_results = test_df['te'].values\n",
    "    test_predictions = np.array([analysis.surrogate('te', tp) for tp in np.array(test_points)])\n",
    "    frms = np.sqrt(((test_predictions - test_results)**2).mean(axis=0)) / test_results.mean(axis=0)\n",
    "    return frms.mean(), frms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:54.948167Z",
     "start_time": "2021-07-28T07:16:52.834504Z"
    },
    "code_folding": [
     0,
     8,
     31,
     48,
     61,
     91
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 1 = 0.025\n",
      "Number of samples = 1\n",
      "Time for phase 2 = 0.039\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 3 = 0.591\n",
      "Time for phase 4 = 0.033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/toks/scratch/dpc/GIT/EasyVVUQ/env/lib/python3.8/site-packages/easyvvuq-0.9.3+138.gcde288ec-py3.8.egg/easyvvuq/analysis/sc_analysis.py:1097: RuntimeWarning: invalid value encountered in true_divide\n",
      "  S_u[u] = D_u[u] / D\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 5 = 0.412\n",
      "Time for phase 6 = 0.980\n"
     ]
    }
   ],
   "source": [
    "# routine to run a SC campaign\n",
    "\n",
    "#def run_sc_case(sc_order=2, local=True, dask=True, batch_size=os.cpu_count(), use_files=True):\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    sc_order=2; local=True; dask=False; batch_size=os.cpu_count(); use_files=True\n",
    "\n",
    "    if dask:\n",
    "        if local:\n",
    "            print('Running locally')\n",
    "            import multiprocessing.popen_spawn_posix\n",
    "            from dask.distributed import Client, LocalCluster\n",
    "            cluster = LocalCluster(threads_per_worker=1)\n",
    "            client = Client(cluster) # processes=True, threads_per_worker=1)\n",
    "        else:\n",
    "            print('Running using SLURM')\n",
    "            from dask.distributed import Client\n",
    "            from dask_jobqueue import SLURMCluster\n",
    "            cluster = SLURMCluster(\n",
    "                job_extra=['--qos=p.tok.openmp.2h', '--mail-type=end', '--mail-user=dpc@rzg.mpg.de', '-t 2:00:00'], \n",
    "                queue='p.tok.openmp', \n",
    "                cores=8, \n",
    "                memory='8 GB',\n",
    "                processes=8)\n",
    "            cluster.scale(32)\n",
    "            print(cluster)\n",
    "            print(cluster.job_script())\n",
    "            client = Client(cluster)\n",
    "        print(client)\n",
    "\n",
    "    else:\n",
    "        import concurrent.futures\n",
    "#        client = concurrent.futures.ProcessPoolExecutor(max_workers=batch_size)\n",
    "        client = concurrent.futures.ThreadPoolExecutor(max_workers=batch_size)\n",
    "#        client = None\n",
    "    \n",
    "    times = np.zeros(7)\n",
    "\n",
    "    time_start = time.time()\n",
    "    time_start_whole = time_start\n",
    "    # Set up a fresh campaign called \"fusion_sc.\"\n",
    "    my_campaign = uq.Campaign(name='fusion_sc_adaptive.')        \n",
    "\n",
    "    # Define parameter space\n",
    "    params = define_params()\n",
    "\n",
    "    # Create an encoder and decoder for sc test app\n",
    "    if use_files:\n",
    "        encoder = uq.encoders.GenericEncoder(template_fname='fusion.template',\n",
    "                                             delimiter='$',\n",
    "                                             target_filename='fusion_in.json')\n",
    "\n",
    "\n",
    "        decoder = uq.decoders.SimpleCSV(target_filename=\"output.csv\",\n",
    "                                        output_columns=[\"te\", \"ne\", \"rho\", \"rho_norm\"])\n",
    "\n",
    "        execute = uq.actions.ExecuteLocal('python3 %s/fusion_model.py fusion_in.json' % (os.getcwd()))\n",
    "\n",
    "        actions = uq.actions.Actions(uq.actions.CreateRunDirectory('/tmp'), \n",
    "                                     uq.actions.Encode(encoder), execute, uq.actions.Decode(decoder))\n",
    "    else:\n",
    "        actions = uq.actions.Actions(uq.actions.ExecutePython(run_fusion_model))\n",
    "\n",
    "\n",
    "    # Add the app (automatically set as current app)\n",
    "    my_campaign.add_app(name=\"fusion\", params=params, actions=actions)\n",
    "\n",
    "    time_end = time.time()\n",
    "    times[1] = time_end-time_start\n",
    "    print('Time for phase 1 = %.3f' % (times[1]))\n",
    "\n",
    "    time_start = time.time()\n",
    "    # Associate a sampler with the campaign\n",
    "    sampler = uq.sampling.SCSampler(vary=define_vary(), polynomial_order=1,\n",
    "                                quadrature_rule=\"C\",\n",
    "                                sparse=True, growth=True,\n",
    "                                midpoint_level1=True,\n",
    "                                dimension_adaptive=True)\n",
    "    my_campaign.set_sampler(sampler)\n",
    "    my_campaign.draw_samples()\n",
    "    print('Number of samples = %s' % my_campaign.get_active_sampler().count)\n",
    "\n",
    "    time_end = time.time()\n",
    "    times[2] = time_end-time_start\n",
    "    print('Time for phase 2 = %.3f' % (times[2]))\n",
    "\n",
    "    time_start = time.time()\n",
    "    # Perform the actions\n",
    "    my_campaign.execute(pool=client).collate(progress_bar=True)\n",
    "\n",
    "    if dask:\n",
    "        client.close()\n",
    "        client.shutdown()\n",
    "\n",
    "    time_end = time.time()\n",
    "    times[3] = time_end-time_start\n",
    "    print('Time for phase 3 = %.3f' % (times[3]))\n",
    "\n",
    "    time_start = time.time()\n",
    "    # Collate the results\n",
    "    results_df = my_campaign.get_collation_result()\n",
    "\n",
    "    time_end = time.time()\n",
    "    times[4] = time_end-time_start\n",
    "    print('Time for phase 4 = %.3f' % (times[4]))\n",
    "\n",
    "    time_start = time.time()\n",
    "    # Post-processing analysis\n",
    "    qoi_cols = [\"te\", \"ne\", \"rho\", \"rho_norm\"]\n",
    "    results = my_campaign.analyse(qoi_cols=qoi_cols)\n",
    "\n",
    "    time_end = time.time()\n",
    "    times[5] = time_end-time_start\n",
    "    print('Time for phase 5 = %.3f' % (times[5]))\n",
    "\n",
    "    time_start = time.time()\n",
    "    # Save the results\n",
    "    pickle.dump(results, open('fusion_results.pickle','bw'))\n",
    "    time_end = time.time()\n",
    "    times[6] = time_end-time_start\n",
    "    print('Time for phase 6 = %.3f' % (times[6]))\n",
    "\n",
    "    times[0] = time_end - time_start_whole\n",
    "\n",
    "    #return results_df, results, times, sc_order, my_campaign.get_active_sampler().count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:55.156476Z",
     "start_time": "2021-07-28T07:16:54.950416Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/toks/scratch/dpc/GIT/EasyVVUQ/env/lib/python3.8/site-packages/easyvvuq-0.9.3+138.gcde288ec-py3.8.egg/easyvvuq/analysis/sc_analysis.py:1097: RuntimeWarning: invalid value encountered in true_divide\n",
      "  S_u[u] = D_u[u] / D\n"
     ]
    }
   ],
   "source": [
    "analysis = uq.analysis.SCAnalysis(sampler=sampler, qoi_cols=qoi_cols)\n",
    "my_campaign.apply_analysis(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:55.160818Z",
     "start_time": "2021-07-28T07:16:55.158196Z"
    }
   },
   "outputs": [],
   "source": [
    "S = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:55.279185Z",
     "start_time": "2021-07-28T07:16:55.162485Z"
    }
   },
   "outputs": [],
   "source": [
    "frms_mean, frms = test_surrogate()\n",
    "S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:55.287432Z",
     "start_time": "2021-07-28T07:16:55.280993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.l_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A standard SC (or PCE) campaign would be over at this point. Except we have thus far only sampled a single point in the stochastic domain. To show this, we define the following function to plot 2D slices of the *accepted* points in the 20 dimensional input space. The `analysis.l_norm` array contains the accepted multi indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:55.319574Z",
     "start_time": "2021-07-28T07:16:55.288862Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def plot_te(analysis):\n",
    "    te_mean, te_std = analysis.get_moments('te')[0], np.sqrt(analysis.get_moments('te')[1])\n",
    "    rho_mean, rho_std = analysis.get_moments('rho')[0], np.sqrt(analysis.get_moments('rho')[1])\n",
    "    plt.figure()\n",
    "    rho = rho_mean\n",
    "    plt.plot(rho, te_mean, 'b-', label='Mean')\n",
    "    plt.plot(rho, te_mean-te_std, 'b--', label='+1 std deviation')\n",
    "    plt.plot(rho, te_mean+te_std, 'b--')\n",
    "    plt.fill_between(rho, te_mean-te_std, te_mean+te_std, color='b', alpha=0.2)\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('rho [$m$]')\n",
    "    plt.ylabel('Te [$eV$]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:56.495141Z",
     "start_time": "2021-07-28T07:16:55.321244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:56.508336Z",
     "start_time": "2021-07-28T07:16:56.496774Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def plot_grid_2D():\n",
    "\n",
    "    labels = list(my_campaign.get_active_sampler().vary.get_keys())\n",
    "    values = list(my_campaign.get_active_sampler().vary.get_values())\n",
    "    L = (len(labels)+1)//2\n",
    "    C = int(np.ceil(np.sqrt((10+1)//2)))\n",
    "    R = int(np.ceil(L / C))\n",
    "\n",
    "    fig = plt.figure(figsize=[12,12/C*R])\n",
    "    \n",
    "    ax=[]\n",
    "    ic=0\n",
    "    for i in range(L-1):\n",
    "        xd = values[ic*2].upper[0] - values[ic*2].lower[0]\n",
    "        yd = values[ic*2+1].upper[0] - values[ic*2+1].lower[0]\n",
    "        ax.append(fig.add_subplot(R, C, ic+1,\n",
    "                                  xlim=[values[ic*2].lower[0] - xd/10, values[ic*2].upper[0] + xd/10],\n",
    "                                  ylim=[values[ic*2+1].lower[0] - yd/10, values[ic*2+1].upper[0] + yd/10], \n",
    "                                  xlabel=labels[ic*2], ylabel=labels[ic*2+1])\n",
    "                 )\n",
    "        ic += 1\n",
    "\n",
    "    xd = values[ic*2].upper[0] - values[ic*2].lower[0]\n",
    "    yd = values[ic*2+1].upper[0] - values[ic*2+1].lower[0]\n",
    "    ax.append(fig.add_subplot(R, C, ic+1,\n",
    "                              xlim=[values[-2].lower[0] - xd/10, values[-2].upper[0] + xd/10], \n",
    "                              ylim=[values[-1].lower[0] - yd/10, values[-1].upper[0] + yd/10], \n",
    "                              xlabel=labels[-2], ylabel=labels[-1])\n",
    "             )\n",
    "\n",
    "    accepted_grid = sampler.generate_grid(analysis.l_norm)\n",
    "\n",
    "    ic=0\n",
    "    for i in range(L-1):\n",
    "        ax[i].plot(accepted_grid[:,ic*2], accepted_grid[:,ic*2+1], 'o', alpha=0.25)\n",
    "        ic += 1\n",
    "    ax[-1].plot(accepted_grid[:,-2], accepted_grid[:,-1], 'o', alpha=0.25)\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:57.448223Z",
     "start_time": "2021-07-28T07:16:56.509834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grid_2D()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To refine the sampling plan, we need to:\n",
    "\n",
    "* Compute the candidate directions of the admissible set. This is done in the `look_ahead` subroutine.\n",
    "* Run the ensemble of the new points. This is done exactly the same as before.\n",
    "* Accept the direction with the highest error. This is done in the `adapt_dimension` subroutine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:16:57.454383Z",
     "start_time": "2021-07-28T07:16:57.450263Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def refine_sampling_plan(number_of_refinements):\n",
    "        \"\"\"\n",
    "        Refine the sampling plan.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        number_of_refinements (int)\n",
    "           The number of refinement iterations that must be performed.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        None. The new accepted indices are stored in analysis.l_norm and the admissible indices\n",
    "        in sampler.admissible_idx.\n",
    "        \"\"\"\n",
    "        for i in range(number_of_refinements):\n",
    "            # compute the admissible indices\n",
    "            sampler.look_ahead(analysis.l_norm)\n",
    "\n",
    "            # run the ensemble\n",
    "            my_campaign.execute().collate(progress_bar=True)\n",
    "\n",
    "            # accept one of the multi indices of the new admissible set\n",
    "            data_frame = my_campaign.get_collation_result()\n",
    "            analysis.adapt_dimension('te', data_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the subroutine above uses the surplus error by default. To select the variance-based error use `analysis.adapt_dimension('f', data_frame, method='var')` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:02.461632Z",
     "start_time": "2021-07-28T07:16:57.456206Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:02<00:00,  9.90it/s]\n"
     ]
    }
   ],
   "source": [
    "# refine the sampling plan once and then do the analysis to see the results.\n",
    "refine_sampling_plan(1)\n",
    "my_campaign.apply_analysis(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:02.899907Z",
     "start_time": "2021-07-28T07:17:02.464451Z"
    }
   },
   "outputs": [],
   "source": [
    "frms_mean, frms = test_surrogate()\n",
    "S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:02.906543Z",
     "start_time": "2021-07-28T07:17:02.902104Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 2, 1, 1, 1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.l_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:03.145205Z",
     "start_time": "2021-07-28T07:17:02.911832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:03.996821Z",
     "start_time": "2021-07-28T07:17:03.147125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the 2D slices again. Note that the most important input (Hw) got refined.\n",
    "plot_grid_2D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:06.643327Z",
     "start_time": "2021-07-28T07:17:03.998400Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00,  3.00it/s]\n"
     ]
    }
   ],
   "source": [
    "# repeat\n",
    "refine_sampling_plan(1)\n",
    "my_campaign.apply_analysis(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:07.379725Z",
     "start_time": "2021-07-28T07:17:06.645258Z"
    }
   },
   "outputs": [],
   "source": [
    "frms_mean, frms = test_surrogate()\n",
    "S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:07.597607Z",
     "start_time": "2021-07-28T07:17:07.382795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:08.446110Z",
     "start_time": "2021-07-28T07:17:07.599407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now chi got refined. \n",
    "plot_grid_2D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:10.756335Z",
     "start_time": "2021-07-28T07:17:08.448185Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [00:01<00:00,  5.94it/s]\n"
     ]
    }
   ],
   "source": [
    "# again\n",
    "refine_sampling_plan(1)\n",
    "my_campaign.apply_analysis(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:11.652020Z",
     "start_time": "2021-07-28T07:17:10.758848Z"
    }
   },
   "outputs": [],
   "source": [
    "frms_mean, frms = test_surrogate()\n",
    "S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:11.863091Z",
     "start_time": "2021-07-28T07:17:11.654203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T07:17:12.928893Z",
     "start_time": "2021-07-28T07:17:11.865069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now H0 got refined to first order\n",
    "plot_grid_2D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:40.932128Z",
     "start_time": "2021-07-28T07:17:12.931191Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  8.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14/14 [00:01<00:00,  9.05it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18/18 [00:01<00:00,  9.85it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:01<00:00,  7.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:01<00:00,  6.80it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [00:02<00:00,  9.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00,  9.12it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26/26 [00:02<00:00,  9.70it/s]\n",
      "  0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:01<00:00,  7.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:02<00:00,  9.00it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/100\n"
     ]
    },
    {
     "name": "stderr",
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     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00,  9.62it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00,  9.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 56/56 [00:06<00:00,  8.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00,  9.70it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00, 10.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 32/32 [00:03<00:00, 10.60it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 44/44 [00:04<00:00,  9.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00,  9.97it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 52/52 [00:06<00:00,  8.32it/s]\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# perform 100 refinements\n",
    "for i in range(100):\n",
    "    print('%s/%s' % (i,100))\n",
    "    refine_sampling_plan(1)\n",
    "    my_campaign.apply_analysis(analysis)\n",
    "    frms_mean, frms = test_surrogate()\n",
    "    S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:42.421323Z",
     "start_time": "2021-07-28T12:46:40.935488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:43.295538Z",
     "start_time": "2021-07-28T12:46:42.423195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the slices again. \n",
    "plot_grid_2D()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post processing\n",
    "\n",
    "There are a number of post-processing step we can take. Below we show the 'adaptation table' which displays which multi indices were refined at every iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:43.784983Z",
     "start_time": "2021-07-28T12:46:43.297595Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.adaptation_table()\n",
    "plt.savefig('Adaptation_table_DASC.png')\n",
    "plt.savefig('Adaptation_table_DASC.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:43.844322Z",
     "start_time": "2021-07-28T12:46:43.787037Z"
    }
   },
   "outputs": [],
   "source": [
    "analysis.adaptation_table?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also make a histogram which visualises the adaptation. This displays only a first-order information, i.e. only the maximum quadrature order per input. It therefore does not display that certain inputs were refined simultaneously:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:44.023963Z",
     "start_time": "2021-07-28T12:46:43.846068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.adaptation_histogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a list of the error magnitudes associated to the multi indices that were selected use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:44.663885Z",
     "start_time": "2021-07-28T12:46:44.025762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x149f1f153b20>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(analysis.get_adaptation_errors())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows a nice decrease of the error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute the mean and variance of the code output we use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:45.670374Z",
     "start_time": "2021-07-28T12:46:44.665898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8663242668726445"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.get_uncertainty_amplification('te')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:46:47.144460Z",
     "start_time": "2021-07-28T12:46:45.672191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the estimates are fair, although not yet fully converged in this case. Note however that these results are computed only with the accepted set of multi indices. At the end, we can merge the accepted and admissible set (thereby using all samples), and recompute the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T12:50:11.208183Z",
     "start_time": "2021-07-28T12:46:47.146385Z"
    }
   },
   "outputs": [],
   "source": [
    "analysis.merge_accepted_and_admissible()\n",
    "df = my_campaign.get_collation_result()\n",
    "results = analysis.analyse(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:41.720248Z",
     "start_time": "2021-07-28T12:50:11.211305Z"
    }
   },
   "outputs": [],
   "source": [
    "frms_mean, frms = test_surrogate()\n",
    "S.append([frms_mean, frms])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:44.358797Z",
     "start_time": "2021-07-28T13:01:41.722953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_te(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:46.268234Z",
     "start_time": "2021-07-28T13:01:44.360662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the slices again. \n",
    "plot_grid_2D()\n",
    "plt.savefig('Grid_DASC.png')\n",
    "plt.savefig('Grid_DASC.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This improved our estimates. Note however, that if we would refine again from this point, the new admissble set will be very large, since we added *all* previous admissible indices to the accepted set. This opens up a wide range of possible new candidate directions, making the corresponding ensemble very large.\n",
    "\n",
    "Thus if we are still not happy about the result, we first have to undo the merging via `analysis.undo_merge()`, before refining again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:46.273104Z",
     "start_time": "2021-07-28T13:01:46.270259Z"
    }
   },
   "outputs": [],
   "source": [
    "# This will undo the merge, and reproduce the old results\n",
    "#analysis.undo_merge()\n",
    "#df = campaign.get_collation_result()\n",
    "#results = analysis.analyse(df)\n",
    "#print('Mean = %.4e' % results.describe('f', 'mean'))\n",
    "#print('Standard deviation = %.4e' % results.describe('f', 'std'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display the Sobol sensitivity indices via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.423100Z",
     "start_time": "2021-07-28T13:01:46.274701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sobols = []\n",
    "# retrieve the Sobol indices from the results object\n",
    "params = list(sampler.vary.get_keys())\n",
    "for param in params:\n",
    "    sobols.append(results._get_sobols_first('te', param))\n",
    "sobols = np.array(sobols).mean(axis=1)\n",
    "# make a bar chart\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, title='First-order Sobol indices (mean over rho)')\n",
    "ax.bar(range(len(sobols)), height=np.array(sobols).flatten())\n",
    "ax.set_xticks(range(len(sobols)))\n",
    "ax.set_xticklabels(params)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.savefig('Average_Sobol_DASC.png')\n",
    "plt.savefig('Average_Sobol_DASC.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.429977Z",
     "start_time": "2021-07-28T13:01:47.425043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Qe_tot': 0.0826519003066042,\n",
       " 'H0': 0.01976287997465273,\n",
       " 'Hw': 0.21007714324359764,\n",
       " 'chi': 0.34090854895957406,\n",
       " 'Te_bc': 0.02555370033111004,\n",
       " 'b_pos': 0.0608031439755366,\n",
       " 'b_height': 0.20208219034474545,\n",
       " 'b_sol': 0.030533812380147896,\n",
       " 'b_width': 0.0008424155090723398,\n",
       " 'b_slope': 0.0017000632610264885}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(params, sobols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.714171Z",
     "start_time": "2021-07-28T13:01:47.431782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.plot_stat_convergence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:50.155890Z",
     "start_time": "2021-07-28T13:01:47.716263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00612372, 0.01837117, 0.03061862, 0.04286607, 0.05511352,\n",
       "       0.06736097, 0.07960842, 0.09185587, 0.10410331, 0.11635076,\n",
       "       0.12859821, 0.14084566, 0.15309311, 0.16534056, 0.17758801,\n",
       "       0.18983546, 0.2020829 , 0.21433035, 0.2265778 , 0.23882525,\n",
       "       0.2510727 , 0.26332015, 0.2755676 , 0.28781504, 0.30006249,\n",
       "       0.31230994, 0.32455739, 0.33680484, 0.34905229, 0.36129974,\n",
       "       0.37354719, 0.38579463, 0.39804208, 0.41028953, 0.42253698,\n",
       "       0.43478443, 0.44703188, 0.45927933, 0.47152678, 0.48377422,\n",
       "       0.49602167, 0.50826912, 0.52051657, 0.53276402, 0.54501147,\n",
       "       0.55725892, 0.56950637, 0.58175381, 0.59400126, 0.60624871,\n",
       "       0.61849616, 0.63074361, 0.64299106, 0.65523851, 0.66748595,\n",
       "       0.6797334 , 0.69198085, 0.7042283 , 0.71647575, 0.7287232 ,\n",
       "       0.74097065, 0.7532181 , 0.76546554, 0.77771299, 0.78996044,\n",
       "       0.80220789, 0.81445534, 0.82670279, 0.83895024, 0.85119769,\n",
       "       0.86344513, 0.87569258, 0.88794003, 0.90018748, 0.91243493,\n",
       "       0.92468238, 0.93692983, 0.94917728, 0.96142472, 0.97367217,\n",
       "       0.98591962, 0.99816707, 1.01041452, 1.02266197, 1.03490942,\n",
       "       1.04715687, 1.05940431, 1.07165176, 1.08389921, 1.09614666,\n",
       "       1.10839411, 1.12064156, 1.13288901, 1.14513645, 1.1573839 ,\n",
       "       1.16963135, 1.1818788 , 1.19412625, 1.2063737 , 1.21862115])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te_mean, te_std = analysis.get_moments('te')[0], np.sqrt(analysis.get_moments('te')[1])\n",
    "rho_mean, rho_std = analysis.get_moments('rho')[0], np.sqrt(analysis.get_moments('rho')[1])\n",
    "\n",
    "rho_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:50.937953Z",
     "start_time": "2021-07-28T13:01:50.157716Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the performance of the SC surrogates\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    for i in range(len(S)):\n",
    "        plt.semilogy(rho_mean, S[i][1])\n",
    "    plt.xlabel('rho [m]') ; plt.ylabel('fractional RMS for predicted Te') #; plt.legend(loc=0, ncol=5)\n",
    "    plt.title('Performance of the SC surrogate')\n",
    "    plt.show()\n",
    "#    plt.savefig('SC_surrogate.png')\n",
    "#    plt.savefig('SC_surrogate.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:52.048722Z",
     "start_time": "2021-07-28T13:01:50.939512Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the convergence of the surrogate based on 1000 random points\n",
    "if __name__ == '__main__':\n",
    "    plt.figure()\n",
    "    plt.semilogy([s[0] for s in S], 'o-')\n",
    "    plt.xlabel('element number')\n",
    "    plt.ylabel('fractional RMS error for the SC surrogate')\n",
    "    plt.legend(loc=0)\n",
    "    plt.savefig('Convergence_DASC_surrogate.png')\n",
    "    plt.savefig('Convergence_DASC_surrogate.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}