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    "# Dimension adaptive sampling tutorial applied to the fusion example\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 the fusion example is presented. We will assume you are familiar with the basics of EasyVVUQ.\n",
    "\n",
    "The explanation below is a copy of that in dimension_adaptive_tutorial.ipynb and describes a simpler 2D case.  The fusion example has 10 varying quantities.\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."
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    "# import packages that we will use\n",
    "\n",
    "%matplotlib inline\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"
   ]
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    "# 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"
   ]
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    "# 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'))"
   ]
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    "# 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",
    "    }"
   ]
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   "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"
   ]
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   "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)])}"
   ]
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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": {
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     "shell.execute_reply.started": "2025-07-18T15:04:57.770826Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 1000/1000 [00:19<00:00, 51.40it/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
    ],
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:18.932577Z",
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     "shell.execute_reply": "2025-07-18T15:05:18.935031Z",
     "shell.execute_reply.started": "2025-07-18T15:05:18.932567Z"
    }
   },
   "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"
    },
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     0,
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    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:18.935676Z",
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     "shell.execute_reply.started": "2025-07-18T15:05:18.935669Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 1 = 0.007\n",
      "Number of samples = 1\n",
      "Time for phase 2 = 0.012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Time for phase 3 = 3.248\n",
      "Time for phase 4 = 0.007\n",
      "Time for phase 5 = 0.110\n",
      "Time for phase 6 = 0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.329359Z",
     "iopub.status.busy": "2025-07-18T15:05:22.329198Z",
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     "shell.execute_reply": "2025-07-18T15:05:22.448938Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.329342Z"
    }
   },
   "outputs": [],
   "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"
    },
    "execution": {
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     "shell.execute_reply.started": "2025-07-18T15:05:22.449609Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.451920Z",
     "iopub.status.busy": "2025-07-18T15:05:22.451846Z",
     "iopub.status.idle": "2025-07-18T15:05:22.480532Z",
     "shell.execute_reply": "2025-07-18T15:05:22.480051Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.451912Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.481126Z",
     "iopub.status.busy": "2025-07-18T15:05:22.481027Z",
     "iopub.status.idle": "2025-07-18T15:05:22.487992Z",
     "shell.execute_reply": "2025-07-18T15:05:22.487637Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.481118Z"
    }
   },
   "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
    ],
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.488551Z",
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     "shell.execute_reply": "2025-07-18T15:05:22.490855Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.488543Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.491604Z",
     "iopub.status.busy": "2025-07-18T15:05:22.491518Z",
     "iopub.status.idle": "2025-07-18T15:05:22.930033Z",
     "shell.execute_reply": "2025-07-18T15:05:22.929772Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.491597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [],
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.930634Z",
     "iopub.status.busy": "2025-07-18T15:05:22.930533Z",
     "iopub.status.idle": "2025-07-18T15:05:22.934739Z",
     "shell.execute_reply": "2025-07-18T15:05:22.934497Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.930625Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:22.935267Z",
     "iopub.status.busy": "2025-07-18T15:05:22.935188Z",
     "iopub.status.idle": "2025-07-18T15:05:23.284823Z",
     "shell.execute_reply": "2025-07-18T15:05:23.284500Z",
     "shell.execute_reply.started": "2025-07-18T15:05:22.935259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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
    ],
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:23.285398Z",
     "iopub.status.busy": "2025-07-18T15:05:23.285312Z",
     "iopub.status.idle": "2025-07-18T15:05:23.287798Z",
     "shell.execute_reply": "2025-07-18T15:05:23.287531Z",
     "shell.execute_reply.started": "2025-07-18T15:05:23.285389Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:23.288246Z",
     "iopub.status.busy": "2025-07-18T15:05:23.288172Z",
     "iopub.status.idle": "2025-07-18T15:05:32.021828Z",
     "shell.execute_reply": "2025-07-18T15:05:32.021207Z",
     "shell.execute_reply.started": "2025-07-18T15:05:23.288238Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|██████████████▏                                                                                                                                                                                                                                                                            | 1/20 [00:04<01:24,  4.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:08<00:00,  2.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:32.023416Z",
     "iopub.status.busy": "2025-07-18T15:05:32.023205Z",
     "iopub.status.idle": "2025-07-18T15:05:32.114140Z",
     "shell.execute_reply": "2025-07-18T15:05:32.113720Z",
     "shell.execute_reply.started": "2025-07-18T15:05:32.023400Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:32.114844Z",
     "iopub.status.busy": "2025-07-18T15:05:32.114758Z",
     "iopub.status.idle": "2025-07-18T15:05:32.118015Z",
     "shell.execute_reply": "2025-07-18T15:05:32.117731Z",
     "shell.execute_reply.started": "2025-07-18T15:05:32.114836Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:32.118437Z",
     "iopub.status.busy": "2025-07-18T15:05:32.118358Z",
     "iopub.status.idle": "2025-07-18T15:05:32.233659Z",
     "shell.execute_reply": "2025-07-18T15:05:32.233051Z",
     "shell.execute_reply.started": "2025-07-18T15:05:32.118429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:32.255619Z",
     "iopub.status.busy": "2025-07-18T15:05:32.255469Z",
     "iopub.status.idle": "2025-07-18T15:05:32.508594Z",
     "shell.execute_reply": "2025-07-18T15:05:32.508322Z",
     "shell.execute_reply.started": "2025-07-18T15:05:32.255607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:32.509246Z",
     "iopub.status.busy": "2025-07-18T15:05:32.509155Z",
     "iopub.status.idle": "2025-07-18T15:05:35.602527Z",
     "shell.execute_reply": "2025-07-18T15:05:35.602250Z",
     "shell.execute_reply.started": "2025-07-18T15:05:32.509238Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:35.603160Z",
     "iopub.status.busy": "2025-07-18T15:05:35.603073Z",
     "iopub.status.idle": "2025-07-18T15:05:35.754519Z",
     "shell.execute_reply": "2025-07-18T15:05:35.754235Z",
     "shell.execute_reply.started": "2025-07-18T15:05:35.603150Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:35.754898Z",
     "iopub.status.busy": "2025-07-18T15:05:35.754819Z",
     "iopub.status.idle": "2025-07-18T15:05:35.840388Z",
     "shell.execute_reply": "2025-07-18T15:05:35.839154Z",
     "shell.execute_reply.started": "2025-07-18T15:05:35.754889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CbIXLM9jwFoOQlStXOsEGa3j69u2L/v37O8OLU7pdnUEMV2sx2IkrT548zkvWCDHj89lnnzkF5jziulQxOoMi1jIxs8MsDmuiWEPFozl/4FqwuHVHzIL5iwIjSXH8GefeXo7Z4C9Q1h3NnGlHbSIS+jJnDvx1L4UZE17idku5i8x94YorrkCdOnWcS79+/ZyAaN68eTE1Qazj8cTsS9yi4qWXWSXAz2FtEmt48rNOIZ7PufHGG53jNH5/lxpqyOCQwVPBggWROnVqJ2OUENYp8XiLBdFu8BW3UDy+7zG++8/basNiVI/bdnecBoqO0iQg+IfK2LFWc7RzJ8C1d99/H+h7JSJyMR4RsSCaL/lkz9d5YUAQH66oevPNN53rsDuLNUDMcLB2yJ3xw2JpBhMHDx50PsYsFjvMevXq5RRss+bnUlkbYtDF2hwGFjwy45Hgc889d1HAkzt3bqeLjB/nVGhmsljztGvXrljXY5brpZdecrI/7p7R+LCuiAXW/P74dZs3b35RBoffI3eg/vXXX873GB9+r/weWV/F7/21115zbpcZqUBSYCQBw7Uh7MwsUwY4ehTgHygffRToeyUiEhuPwlhAzcwPgyG+zgu7z+LD7BCf4NnuzqwIu8UmTpyI66+/3vk4n/hZnMzMCI+zGHAVKlTI6QzjxGoWN/Nz4hZSx8XMDo+9Tpw4gSpVqjidbAxsPLHImwEKb5/ZKt4fLmZnjZFnBonZMt7GmjVrEuxGczGAyZEjhzP0kt1o7HpjZsoTO9IY+PGIzj2yi6tx48ZOlx6P5PjYvPPOO05hONv9A0m70rykXWm+x2PpF15gJ4S9/fLL/MUR6HslIpeiXWkSjLQrTcJCunQ2KduthWRwyInZIiIiKU3F1xIU2H3SqxdQr55NzWY7P4/YWKQtIiKSUpQxkqDBqfEcBsmWftYE8mitffuLp9+KiIj4izJGEnS4W40jRFq1st1qXCXy3XecDRLoeyYiIuFOGSMJ2uCIR2s8Slu0iEsJAT+uxhEREXEoMJKgxe5PLqBlpoiLZ2vWBPbuDfS9EhFPamyWcPt5VGAkQY1jP95/30YYrF/PPTo2EFJEAstd03C5HWAiKcn9eYy7RsQbqjGSoHfNNcCHHwKdOgFbt1rt0fz5gb5XIpGNAwo5yNBd5slBgtxhJRKoTBGDIv488ueSP59JpcBIQkLBgsAHH3ACLdCuHcAl0MnY8SgiPpAvXz7npT83nYt4g0GR+3OZVAqMJGTwZ/3ddwGu3Vm9mn8h2BGbZh2JBAYzRFxeygWlZ86cCfTdkQiXNm3aZGWKXAqMJOTkzg38/Tfw1lu2a43zjqJ3M4pIAPDJyBdPSCLBQMXXEpKyZwe+/BL46y/rVtu0KdD3SEREwoECIwlJ/OOUrfzFigEHDgC1aik4EhGR5FNgJCGdNWLNUdGiLP604Oj33wN9r0REJJQpMJKwC464RkRERCQpFBhJyMuRw4KjIkWAffuAN98M9D0SEZFQpcBIwio4evhhoF49YNeuQN8jEREJRWrXl7DBmUZdu9qy2TVrgHPngPTpbf6RiIhIYihjJGEZIHH4Y/PmwE03WUu/iIhIYigwkrB0xRXAtm3A9u3ALbcAe/YE+h6JiEgoUGAkYTsdmzVHefMCW7ZYcKR1TiIicjkKjCRs5c9vwVGuXNbCX7u21R+JiIgkRIGRhLWCBS044ryj9euB228H/v030PdKRESClQIjCXuFC9v6kP/9D9i4EVi6NND3SEREgpXa9SUiFC8OjBoF7NwJpE0L/POPzT4SERHxpMBIIsZ11wElS1qH2urVFhjxfZx1JCIiQjpKk4iSKpUVZa9aBdx6K3D//cCZM4G+VyIiEiwUGElEBkfp0gHHjgHTpgEtWtiUbBEREQVGEpGqVgWGDbNBkJMm2Y618+cDfa9ERCTQFBhJxKpVCxg0CEidGvj4Y6BLF1slIiIikUuBkUS0OnWAvn3teO2dd4Ann1RwJCISyRQYScS76y6gd297fc4cDYAUEYlkatcXgXWncQBkoUK2W61sWSvQFhGRyBIUGaMhQ4agcuXKyJo1K/LmzYvGjRtj06ZNsa5z6623IlWqVLEujz76aKzr7NixAw0bNkSmTJmc2+nVqxfOnj0b6zrz58/HjTfeiPTp06N48eIYN25cinyPEvzq1QOuuQbYvh1Ytw745ZdA3yMREYnIwOjHH39E165dsXTpUsyePRtnzpxBvXr1cIz91B46dOiAPXv2xFyGDx8e87Fz5845QdHp06exePFijB8/3gl6+rKAJNq2bduc69SuXRurVq1C9+7d0b59e8yaNStFv18JXpyKnS8fwB+typWB998P9D0SEZGIO0qbOXNmrLcZ0DDjs2LFCtRi61A0ZoLy8VkrHt9//z3Wr1+POXPm4Morr0SFChUwcOBA9O7dG/3790e6dOkwZswYFC1aFK+++qrzOaVKlcLChQvx+uuvo379+n7+LiVU8AjNbd3v1Ik/d0Dz5oG+VyIiEjEZo7j+ja5+zZkzZ6z3f/rpp8idOzfKlCmDPn364Pjx4zEfW7JkCcqWLesERS4GO0eOHME6notEX6cO25A88Dp8v4iLHWq9egH33GMBUuvWwNdfB/peiYhIxGSMPJ0/f9454qpevboTALmaN2+OwoULo0CBAlizZo2TCWId0tfRz1h79+6NFRSR+zY/dqnrMHg6ceIEMmbMeNH9OXXqlHNx8boSGcHRc88BJ08CPGlt1gyYMgVo0CDQ90xERCIqMGKt0W+//eYccXnq2LFjzOvMDOXPnx+33347tmzZgmtYMevHwvABAwb47fYleHHwI//XMy6ePx+47z5gxgzgttsCfc9ERCQiAqNu3bph2rRpWLBgAQoWLHjJ61blTgcAmzdvdgIj1h4tX7481nX27dvnvHTrkvjSfZ/ndf73v//Fmy0iHtn17NkzVsbo6quvhq+x7IldUMxUZM1qdS5cV8G7lTnzhUuWLLYVPnt2KxQW/+L/gyFDAP4I8MT1hx+A2rXt/5OIiISfoAiMoqKi8Nhjj2Hy5MlOOz0LpC+HXWXEzBHdfPPNeOmll7B//36ncJvY4cagp3Tp0jHXmcE/+T3wOnx/QtjWz4u/ffQRsGaNd5/DAIpBUu7c1knlXviQMHbjyzRp/HWPIwcD0FdesaCIp7tbtwLFiik4EhEJR6miGJUEWJcuXTBhwgR88803KFmyZMz7s2XL5mRyeFzGjzdo0AC5cuVyaox69OjhZJXY6u+267MTjTVIbONnPVGrVq2cdvzBgwfHtOuzbonHdY888gjmzZuHxx9/HNOnT090VxozRrxfLBBn0OUrb7wBLFsGnDhhwczp08CZM/Y2pxawzvy//+xy+HDiFp7yCZ0BUuHCAGPNEiWA664DChSwYyLx3tGj/Bmwx5RZvVKlAn2PRETEl8/fQREYcVhjfMaOHYu2bdti586daNmypVN7xNlGPMq699578fzzz8f65rZv347OnTs7WafMmTOjTZs2GDp0KK7geUg0foxBFVv7GVi98MILztdILH8FRvTTT1bsG6cZ7yIMivjk/M8/dtm/n4XlPBa0l7t3A7t2WW1MfHgkxyCJ2Y/y5e3CzJMkzo4d1rXG5kn+P2OwKSIiwS2kAqNQEgyBUWIweGKQxCnOf/5pay44THzzZstExcVVGAyQWLpVpYpv7kM4Z406dLDHkk2OixbZxGwREQleCowiPDBKCDekMFDasMFqmlavtpqZuJhRYpBUvTpQoYIVIcsFPM5s394ey6uusuCIx2siIhKcFBj5SagHRvHhsdzatdYVxzqn33+P/fFs2YAaNYBbbmEBu3XKCXDwoAVHPLZkUMQJE5dpphQRkQBRYOQn4RgYxXXoEMDJB2xP55N99CByBxv0uKXljjuAatU0MoD1Xe3aAXv22HEaH68EttaIiEgAKTDyk0gIjOIevfG4jc1/vPz114WP8du//XagYUOrT4rU9nUGRQyOzp0DvvnGjh9FRCS4KDDyk0gLjDzxJ2XjRi79tTUZPEpyFSkCNG5sQVIkdrjxOI2PB4vYWZOVJ0+g75GIiHhSYOQnkRwYeWJ25NdfgenTOSTT7jexSJuToR980AKESMsisROQx418PFiXxQnlIiISeAqM/ESB0cU4dPL7723J6vr1F97PWZ1cvlqvngULkYLB4qBBQLlywLx5duQoIiKh8fyt+ceSbNzfxgWrXGvy6afAvfdaIMS5SVzCetddwLvvWot7JLj2WiBDBmDFCgsKGTiKiEhoUMbIS8oYJQ6DIGaQJk2yidzEYIFBU8uWNhgxnLEWq1MnW+fCYmxm1DJlCvS9EhGJXEd0lOYfCoy872rj8tVx4yyD5NYh3Xkn8MgjtsstXP32G/cA2p47zoD67jvNgBIRCRQdpUlQYBBUty7wySfAyJFAxYoWLE2dCtx/P/Dii7FHAIQT7qLj98xMGUcd8EjRLVIXEZHgpMBIUgS70zg1+513gA8/tOMldrZ9+63VJ730knV0hRvOd3KDo9Sp7WhNRESCl47SvKSjNN/hGhIGSkuX2tucos02/4cfDr82dy6c5feXKxdwww3qVBMRSWk6SpOgV7Ys8NZbwPvvA5UqAWfOWFfbPfcAY8eG17FT8eI2/JFF6StXAq++Cpw+Heh7JSIicSkwkoDjIMjRo+3IqUQJO256+23rYGMt0vnzCJvjxKuuAoYMAZ56ymqsGAyKiEjwUGAkQVWDxCLtgQOBAgWAAwdsDlKbNsCqVQib75NrU3is5hagKzgSEQkeCowkqLBAma38X34JPPEEkDkzsGED0L490KcPsHs3Qh4Lz19+2Tr2WHz+wAMKjkREgoUCIwlK6dIBrVoBkydb1xoDJu5kYxDBKdqhXn/EPWpucPTNN0DTpjbGQEREAkuBkQQ1dug9+6wVZVeuDJw6ZYERA4kFCxDSatYEhg+34IgBYOvWgb5HIiKiwEhCZv/YqFHA0KFA3rw2FLJnT6B7d2DXLoSsWrWAYcNszhGL0LVXTUQksBQYSUgVLtepY/VHLMhmpmXhQssecWhkqNbpcF0Ia43Ykbd6tYIjEZFAUmAkIYfLWB97DPjsM6BKFTteYzapRYvQ7V7jkSFb+dmJN2MG0Llz6AZ6IiKhTIGRhKwiRWzeEdv7c+QAtm617rVBgzjhFCGHBeY8JmQ33pgxlglTcCQikrIUGEnIH6+57f2NG9v7pkyx7rW5cxFy0qcHevW6UJDNFSkKjkREUo4CIwkL2bIBzz9v60WYSfr7b6B3bwsyeDwVajVHLMhmcMQgj0MgtT5ERCRlKDCSsMLOLrb2t2sHpEkD/PCDZY8YYITSumQGR55DIJs0UXAkIpISFBhJ2OFxFIuXGSBdf711ebHuiAXbe/cipOYccdks14dMmwY8/XSg75GISPhTYCRhixvt2cbPWUcMlpYutZqdr74KnewR14cwOCpZ0kYVhGJRuYhIKFFgJGGNx2ktWwITJgDlywPHj9t2+y5dQmfvWrVqwEcfAefO2ZwjBkfnzwf6XomIhCcFRhIRChe2VSJPPmnZo59/Bpo1C53aIwZ4nHN06BDQvz9Qt64FeSIi4lsKjCRiMLh46CEbDMkibQYWrD3i3KBQ6FzjaAKuDhk9Gpg3z8YUHDsW6HslIhJeFBhJxLn6auCdd6z2KF06YPFiqz367rvgzx7lzg2MHGkBEpfo1qunFSIiIr6kwEgiuvbok0+A0qWBo0eBF14A+vQBDh9GULvhBpv4zdUoDOpUlC0i4jsKjCSiFStmnWuPPmrB0pw5Vnu0aBGCGgvJuR8uc2Zg2TKgdu3gD+hEREKBAiOJeByiyB1r48bZ1OyDB63uiN1rJ04gaJUpYzvVsmYFVq60+y8iIsmjwEgkWqlSdrTGAm3ivKMWLYB16xDU95nddm3aAGXLWlAnIiJJp8BIxAOLmtnSz2OqK68EduwAHnnEdrCdPYugdO21NtX75Elg1Spg2zZg375A3ysRkdCkwEgkHlWqABMn2rwgDlbkkVXHjsCuXQha+fJZEXbjxjYxO5jvq4hIsFJgJJKA//0PGDwYePFFK3JeswZo3tz2lgVrWz+HV3If3JYtFhz9+Weg75GISGhRYCRymaGKDRpY9oht8hwKycnTzz5rLf7BmDVil13+/HYMyODojz8Cfa9EREKHAiORRChQwI7TOne2tv7Zs61Im91gwRoccZAl98HVqAFs2BDoeyUiEhoUGIkkEgOidu2ADz4ACha0IyvOP2KhdrAVZufJYwXjRYsC+/cDNWvaAloREbk0BUYiSZgf9OmnQKNGtuWe2ZkOHYKv2DlXLuC996xr7dQpYM+e4K2NEhEJFgqMRJKAxdj9+tkQyCxZgLVrbebRzJkIKtmz25yj114DTp+2Vn4FRyIiCVNgJJIMbOdnYTZXdHDT/fPPA337BtfWe07GvvFGe/nbb8Bnn1mNlIiIXEyBkUgysQPsnXdszlHq1MCMGbagNtgKnjl+4O+/bWDlXXcB334b6HskIhJ8giIwGjJkCCpXroysWbMib968aNy4MTZt2hTrOidPnkTXrl2RK1cuZMmSBU2aNMG+OON9d+zYgYYNGyJTpkzO7fTq1Qtn41TFzp8/HzfeeCPSp0+P4sWLY5wWTImP9q0xMOKxFSdm79wJPPywrRhhHVKwuO46oFIlO1Zr0gT4/PNA3yMRkeASFIHRjz/+6AQ9S5cuxezZs3HmzBnUq1cPxzzOI3r06IGpU6di0qRJzvV3796N++67L+bj586dc4Ki06dPY/HixRg/frwT9PTluUa0bdu2OdepXbs2Vq1ahe7du6N9+/aYNWtWin/PEp4qVAAmTLBt94zJR4wAuncHDh1CUEiXDnjlFaBOHbt/HFg5dmyg75WISPBIFRUVfKWYBw4ccDI+DIBq1aqFf//9F3ny5MGECRNw//33O9fZuHEjSpUqhSVLluCmm27Cd999h7vuussJmK7kn+zg3Jkx6N27t3N76dKlc16fPn06fmOhRbRmzZrh8OHDmJnIqtkjR44gW7Zszn36H88mfOinn2zfVc6cPr1ZCQD+q/r6ayt6ZkcYO8Q4QbtqVQQFrjkZONCmeNObb9q+NRGRcJXY5++gyBjFxTtNOaMjhBUrVjhZpDr8Mzfaddddh0KFCjmBEfFl2bJlY4Iiql+/vvNArItej87reN6Gex33NuJz6tQp5zY8LyKJmZjNo6qPPgKKFbPanm7dgLffDo6ZR5zJxK66Bx6wtx9/HPjqq0DfKxGRwAu6wOj8+fPOEVf16tVRhgNjwEF6e52MT3b2HntgEMSPudfxDIrcj7sfu9R1GOycOHEiwfonRpju5WqOExZJpGuuseCIp77MIvHYirVInCkUDMHb008DrVvbuhP+aLP2SEQkkgVdYMRaIx51fcae4iDQp08fJ4PlXnayqlbECxky2G61oUNt5pG7jHbevOAIjpgt4lEaB1RyHhP3wQVTwbiISMQGRt26dcO0adPwww8/oCB3LkTLly+fU1TNWiBP7Erjx9zrxO1Sc9++3HV41pgxY8Z47xO71/hxz4tIUvAUlxOzmQjlAlpma4YNsxqkQOOP/1VXAX/+CbRpYy39rEMSEYk0QREYsf6bQdHkyZMxb948FOWCJw8VK1ZE2rRpMXfu3Jj3sZ2f7fk333yz8zZfrl27Fvu5GCoaO9wYyJQuXTrmOp634V7HvQ0Rf2PwwR1mPL6iSZOsrX/79uAYOfDff1Y0Pn480LSpjtZEJPKkDpbjs08++cTpOuMsI9YC8eLW/bC2p127dujZs6eTTWIx9sMPP+wENOxII7b3MwBq1aoVVq9e7bTgP//8885tM+tDjz76KLZu3Yqnn37a6WobNWoUvvjiC2cUgEhKBiDu8VWOHMDvv9tASA6GDDRmswYNsvvIYuy77wYSKL8TEQlLQdGun4qFDvEYO3Ys2rZtGzPg8cknn8TEiROdTjF2kzGwcY/JaPv27ejcubMzxDFz5sxo06YNhg4diiv4Wz4aP8ZAaP369c5x3QsvvBDzNRJD7friSwcOAC+8APzyi73NxbQ8YkvgZDfFLFoE9OplGaPq1S1o0ymyiISyxD5/B0VgFEoUGImvsZbnww+B996zomeeJHM5bfHigb1fK1cCTzxhGSN2rXG/GucxiYiEopCeYyQSSThTqEMHYNQoIHduTmi3AujJk63FP1C4eJY74Pj7Y/VqLZ4VkcigwEgkSHCH2cSJQLVq1qn20kvA888DHptxUhz7Fj74AHjyScsWxWnqFBEJOwqMRIIIi7G5X41TsplJ4hq/Vq3YhRm4+8SjPXao8cjv11+BZcuA6GHyIiJhR4GRSJBJnRpgPwCPsTiofccOa+n/8svAHq3lycN1PcC99wI1agDLlwfuvoiI+IsCI5EgVaGCDYSsWdO6wzg5mxO0OWsokMFRtmwAZ63edhsQZyyYiEjIU2AkEsS4HvC114Du3e1ojQXQPFrbuDEw94dB0bvvWmE2a58aNLAicRGRcKHASCTIccwXB0ByYjbHdnFdXyCP1jJlAt5660Im64EHbDmuiEg40ByjIJpjxCc/7qricD9mB3hJmxZIlw7InNkKcl1LlgCHDtl1+UTF5aS8jnvh+xKYmykhjDU+AwYACxbY23XrAs89Z///U9rZs3ZfvvvO3h43zsYMiIiE8vP3hZHQEnCcFfPbb/F/jMFO48YXinPZQr1qVfzXZUD0448WUPG6o0cDGzbYsQyPQnjh6+yA4oXD+xiESfDj/7tXXwUmTLCVIjxa4/9b1h9dd13K3hcOlGdgxN8v/Hnj3mdmkPhzJyISqhQYBRGugti1y/4Sdy98ouFMGwYu119/4f2s8eAKuOPH7cKCXL5k3QezRXybU5R5XU4wZtCVUBDFlnA+mfGJjq3ia9bYoEHOreFLFty6l1KlFEQFGv+ftWgBlC8PPPOM/cw88gjQsyfQpEnKZgoZeD/1lH19ZrPWrrWfU/5sKmMpIqFIR2lBdJSWXJwzwwuDIwY6DIr49sKFNk3577+BgwftCI6v88LAa+RIux6v37u3BUbx4RMdMxR80uMRH9dYbN5sdS/589vFfZ0PjZ4Y/Y/BSP/+tk4mGI7Wdu+2QJxzlz75RNkjEQkeOkqLQG5dUtwnI25IvxQ3s3TmjO3r2roV2LsX2LMn9ksGUcTAi9flotGEBv1lzXohE0UrVljwVagQkDevZRrEN0dr7FpjWz8DXAauDEq4a61kyZS9L8w4MmgePtx+VhiAf/ONHQOLiIQKZYzCKGOUEtzjPQZG339v9S3skuIQQh7pMIj65x87gmMXlfvTxQ3ybv0Ug6WrrwaKFLGpynxZv74yTMnFTF+fPra2g48x13jcd1/KP64MmHkszCPgypWBmTO1GFlEQuf5W4GRlyI9MLoc/jTxeIdBEo/U+OTIC5+kmTX66y/LHHliAfgXX9iTOTMOH39sAdi111rWg8dzCpoSh4MXebTG41NiwMmhkCmdtWGQ9vjjVuvG/4ccBHnVVSl7H0REPCkw8hMFRsnDIOn33y3TxGM4Dirk8V+nTheCKM7oYf2Tiw8zO654KVcOuPXWQH4HwY9Ho6zvefvtC8eXPN4qXjxl78eWLUDnznakxqCIwVFKH++JiPgtMPr222/hrbp16yIjB+2EEQVG/sGfQgZF7KxjZxyP3Rg48cnVM8PE4IjDBfljxezSlCl2FMct8Cr0jY3jHJgt2r/fHqtevYB77knZ7BuLsRkcMVPIDjrWPomIhEVglNrLatlUqVLhjz/+QLFixRBOFBilLAZKv/wC/PyzHcUx88A1FHw/j+y4HoMZEgZFbBPnGAPOZeKesQwZAn3vg+NorW9fYPFie5uPHeuQUvLvFdacTZoE3HUXUKaMBbI6GhWRsAiM9uzZgyu57jsRsmbNitWrVyswEp9zRxL88YdlRLjlnQGAJ44T4BTmRx8N1L0MHgwcx4+3QZ98nf8kORAypf9pHjlil8KFrUj/zjtT9uuLSGQ7ksjn70Sngdq0aYMzbEVKpJYtWypwEL9gTRJ/tCpWtJEArGH59VebCM3p4BxEyR9VXo8dc5zdxBEEnO8zY4ZlMCIJk72s22JgxG5BPhatWwPTp6fs/eD/M85XatcOaNjQJneLiAQbr4qvc+fOjUGDBqFTp07OUVkkUsYo+PEnmhOY3WnerLHhnJ9Ro+zj/NFlTVLt2lbIzaOdSMEg8vnnLctGrDli7VFKHTsy28ds1eTJ9jaP9V56SUdrIhKiXWlDhgxxLjweGzlyJGpyvXaEUWAUevgTzholjgFwByB64tHOsGEp37UVKAxOOLX83XftseFYBAYrfBxSAr/mO+/YnCviOhG+zSBWRCRkjtKoT58+2LRpE2644QbUrl0bDz30EP5iu4lIEGM2okoVmwzN8QA8Snr5ZYBxPZ+M+SPMuUkHDgAnT9qxHEcKhOsgCx4xduhg7fwcvMhaLRaxc2BnSv3/YO0Xu9R4zMcg7d57gRMnUubri4hcSpLnGK1YsQLdu3fHr7/+imeeeQa9evVCevYEhzlljMIL6424Z6xs2QtTu594wsYEcP4Pu7h4KVAAYYn1Vyxg534zuv9+oEcPa+9PCXPm2FR01oRxdQ1XiIiIhPSAx88//9wJjFhz9Oqrr+Je/ukXxhQYhS/+S2A9EguT58+/sBuO2P7PdvM6dQKzoNWfmC3jsRozN+6sKB6tFSyYMl+f4xhYGM9aIwZmuXKlzNcVkchyJCUnX586dQqvvPIKhg0bhqpVq2I2CznClAKjyMD2/wkTrGh7yZILx2o1atjS1nBcgstZR8zecD4UV4j06wfcdlvKfG0O92S2jgXzzN5xTUwEJKBFJJwCo9OnT2Pjxo347bffYi7Lli3DwYMHcS7uMqwwosAo8vz5J/DBB8DEiZZN4hM3n8BZE/Pjj0CjRuGT5di7147WuOuMHnrIdp5xLlRKHe1x4vkrr9j+PAaiIiJBGxgNGDAgJgjasmULzp4963yRMmXKoFy5cihbtqzzslq1aghXCowiF/+lsDibLe9cksvVJZ99ZgXcbP3nMRAnb4d66zmP1liYzS4+4kRxHq1xKXBKeOwxy9IxY8Ss3X33pczXFZHw5pfAiAGQG/y4LwuxQjWCKDAS4r8aLmrlsRp3krk4E+mBB6weKaU32vvaggVA//42rZo/6ny9Vi3/f10Gn5ytxOCIR5bsJuzSxf9fV0TCW4rWGEUSBUYSF2ckvfEG8NVX9qROPFqbNi3ljqD8uQSWQxh5vEVs6+/a1f8zh5i1YjH21Kn2Nlv7Bw8O/WyciITZHCMRuVjlypY9Yrs/j5yYNbr5ZgsqWDPDJ/n160NzLhLHFHAQI2uNiMdrHTsC+/b59+sy8OLyW64PIT6urO/yYiuRiEiSJCkwYrTVsWNHFC9eHKVKlXKWy4pEuuzZgd69bQYSi7VZb8RVGyzQ5pN6y5a2242BUihh1uvJJ206OI8HWZjdvLl1sfkTs0OdO1vGikdqLILn8mARkaALjLp27Yq1a9di+PDh2L59O05Ej6zt0aMH3nrrLV/fR5GQwifxvHktc1S9umU/WEjMVSSc18P9ZBwDcPw4Qsrtt9v95pwjtvSzW41F2v4O9Jo0AfhrpVs3YPXqyFsCLCIhEBh99913GDVqFO677z6k4X6BaPXr18f48eN9ef9EQhqDop49gZ07rU6G83l4DPX667ZhfsyY0FqFwaGPzIaxwJzGjrXCaK5T8SeudLnmGisEX7EC6N794p13IiIBC4xYr501a9aL3n/ttdfiDy5eEpFY8uThEmarO2KhNhe2Hj1qKzB4PBRK9UfMfvHIkMXQPFrjOhEerS1d6v+jNdY8TZpkj+FNNwGLFvn3a4pI5ElSYHTnnXfiU+bU4zh27JizGkRE4seaIx5BsQ7po49s0jSzSjt2WCaJT/ickRQK6tWz7+Haa+14i/OH3nkH8Pd8V2areJzH6eQ83mOgJCLiK0lq19+xYwcqVark1Bq9/PLLTr1R/vz50apVKxw6dAhz585FuFK7vvgaW/w5cZrHaixwZo3SnXcCDz9sdUqhcP9ffRWYPNnerlQJGDQIyJ3bv1/z6aetAJx/i738shWIi4gEpF2fQx0XL17sXI4fP44qVaoge/bs+PHHH519aSLiXRaJARC3y3OA4vnzwPTpwIMPAs8/D2zfjqC//ywqHzgQyJjRlsK2aGHznfz5NVmnxZ3V/NPuqacsYxXG24hEJIUke8Ajs0erV69G2rRpnQWyOVhdGsaUMRJ/Y90Mp0zPmWNvM4NUv74FScG+WJUt9e7IAt7vDh2ARx4BPHo0fIq/vcaNs+44fg2ORmAnoIiI3ydfr1mzxlkJkjqRa8XXrVuHkiVL4gp/j8hNYQqMJKUsW2bBEAOkMmVssSr/7vBXkOHLY67hw4Fvv73QUcZskj8X7X73HbB/v2XZuOQ3Wzb/fS0RCU0+D4zYlr93717kYXtNIvCLrlq1CsWKFUM4UWAkgQiQWNycKRPw9982/fnLL4G2bYErr0TQ4koUTqxmoMSgiCs+WH/kLzyCZNdfliz2WPFrlijhv68nIuH5/J3odA7jpxdeeAGZ+BsnEU6fPp3YmxaRS6ha1V4yIGKRNo+q2Ik1ZYoNP2SRtj+zMUnFRbqlS9v8pq1bbd4R14nw/voj68Vk9lVXARs32hJaBmTctVajhu+/loiEr0RnjG699VavW/EnTJjgdKuFE2WMJNDmz7fgaPnyC4XInCPEtSPMlgQbDrDk0Zq7EJaBHo/Wcub0z9c7dMgKsTkAMl06q0Fyd72JSOQ64uujNDEKjCQY8F/tjBnAs8/a7jLizFUWOzNICkYMjHi0duqUtfKzpd9fR2vMFjFTtXChvc1jPO5c05g1kch1xJ/t+iISWHyC50qRVauYmbV1GZykvXYt8N9/CEqNGtlASJYdHjxoR2vvveefFntm0ThbyV1dwnEC7I7jcaSIyKUoY+QlZYwkGDG4eP99mwjNbAmPrxgosVPrlluCK1MS92jN311rDBxHjLDibO5Y4/wjEYk8R3SU5h8KjCTYcdEq5wnxWI11SGxfZ0BQvjyCumuNR2uVK/vna3G+EWcdMSjinKNgLFYXEf8KqaO0BQsWoFGjRihQoIBT4D2F7TYe2rZt67zf83LHHXfEug5XkbRo0cL5ZjmFu127dvgvzpkCZzHVrFkTGTJkwNVXX43h/LNVJMzw3zvnHjEA4EBIHq+1a2fToYNpija71j7+2I4BOYaAR2v+2rXGrNnEiZY549JbtvVzP52ISFAGRlw+W758ebzNP+kSwEBoz549MZeJ/C3ngUERh0rOnj0b06ZNc4KtjuwN9ogU69Wrh8KFC2PFihXOjrf+/fvj3Xff9ev3JhIIbF1/7TWbQM31HHyb3WysuRkyxDq3gkHRosD48UDjxlZQzpojBkisQfI1jggoUMCO1FiMzRlHWkArIj4LjH766Se0bNkSN998M/766y/nfR9//DEWum0gXrjzzjsxaNAg3MvFRwlInz498uXLF3PxXD2yYcMGzJw5E++//76zlqRGjRoYOXIkPvvsM+zmn4YAPv30U2e20ocffojrr78ezZo1w+OPP47X+OwhEqY41+eTT4BffwXq1LGg4KuvbEBksByis1CaE755lMYxaStWWHv90qX++Xo8Rlu92rrjOCl78ODgeSxEJEQDo6+++gr169dHxowZ8euvv+IUf8MAzrndYP6W8YP58+cjb968zpqRzp0742/m3qMtWbLEOT6r5NH7W6dOHWd9yTKODY6+Tq1atZCOg02i8XvYtGkT/uFY4QTwe2O2yfMiEmrKlQNmzwZmzbK9a8zQ8CiJp80HDljAFGg8HefRGjM5/CfJWURMIp8969uvw+M0/j10//0XOtY4RVwzaUUkyYERsztjxozBe++95yyPdVWvXh0reYDvYzxG++ijjzB37lwMGzYMP/74o5NlOhddjMBVJQyaPHFHW86cOZ2Pude5Ms7+BPdt9zrxGTJkiFOs5V5YmyQSqurVA2bOtJobTqU+dsyKtFu1skxNoBUuDHz4oU30ZhZn7FigUyeb+O3rYzXOOerZ044ZOUaAGbVgOWIUkRALjJhlYfYlLgYOhw8fhq/x2Ovuu+9G2bJl0bhxY6eG6Oeff3aySP7Wp08fJxPmXnbu3On3rynibzyyYms/h0IyO8Mp0QxAnnwSCPSPOI/WOIyRtVCZM9uxF+ukFizw/dfiMEzOO8qYkeUBNpWb4wREJHIlKTBijc/mzZsvej/ri1JiaSy/Ru7cuWPuA+/Pfg5s8XD27FmnU40fc6+zb9++WNdx33avk1BtEzvdPC8i4aJmTSvQ5lESMydsa2eBNo+aAn1qXLcuawMts/Xvv5bdYRDj6yGNfAyYpWLSmZk0ntKr5kgkciUpMOrQoQOeeOIJp36HrfMscGZx81NPPeXU//jbrl27nBojdw8bC8CZqWK3mWvevHk4f/68U4ztXoedamc8fquyg401S56F3CKRhifKPLJigfatt1pND4ci3nOPtbUHUsGCwAcfXFhzwmZUTrDetcu3X+faa4EvvrBibD4O/JuLAzJFJPIkKTB65pln0Lx5c9x+++3OrCAeq7Vv3x6dOnXCY6yY9BJvY9WqVc6Ftm3b5ry+Y8cO52O9evXC0qVL8eeffzp1Rvfccw+KFy/uFE9TqVKlnDokBmzLly/HokWL0K1bN+cIjrORiPeXhdecb8S2/s8//xxvvPEGevLPUBFxCrR/+AH49lsLFNhKz78jjh8P7P1iGSP/mTKLlS0bu1DtaI2F5L7EBbzsWGNS+JdfgIoVgccf989cJREJXl5Nvn7xxRedrFAmFiiAXRynneMsBi+lS5dGliSu9matUO3atS96f5s2bTB69Ginrojdb8wKMdDhPKKBAwfGKqbmsRmDoalTpzrdaE2aNMGbb74Z6z5xwGPXrl2d+iQexTGI68015V7Q5GuJBMwasWuNxdl8yaMsbqlnHVKRIoG7XyzCZmt/9N9Q4IQP1kWxLsmXuKC3b98LBeucd6R/7iKhzS8rQdKkSeMMV4zbARZJFBhJpGHNDTMnPF5jNxdrkDg7NVA//gzaOAiSdUH87cWyRhZqc4K2L33/PdC/v7XxlyxpwVIKlFCKSCitBNFaNZHIw+OlF16wYmgeK332mc1B4pBIX88YSowrrgBYysgZR7xvW7cCrVsDX3/t26JpZoq4mDdnTuva45g0f3TGiUiI1xix2FpEIgtb+5lB4eLX4sWtY40LYFkUzXqcQKhSxYqxb77Zplhztizb/H1ZNM2OOHbGseaKYw1uv90CQhEJX14HRiVKlHAGJ17qIiLhqWFDYP164OWX7SiN2ZpAZY6Iv27eeAN44gk75pszxwqzuTjXV/Lksa49lkFy7hMLwDUlWyR8eVVjxKLmESNGOGd0l8Ki6XClGiMRw0WvLIS++24LFDgkkatFGDzw9ZT222+23oOrGxkk8biNR2ycz+QL/E3J4Zf8HjlG4PrrAW4Y8tgyJCKRVnzNwCi+9RuRRIGRSGysO9qzx2b/9OoFbN8OdO9uNTopffLO3W88UuOxn3vc9uKLQO7cvvsazI5xvtOaNbbbjceLPGoUkQgsvlZ9kYjExewMMyisPWKAxKW0zNy0b29FyymJ0zleesmKxdnCv3w58NBDwKJFvi3+5mxZDp7k1HAGX9On++72RSSw1JUmIj7B4OiPPywoYlDCHWdcTstAxQ8rFBPEv984tZvZHLdomjVIHBDpq9ogDp1kYFSmjBV7N2pkxej6FSkSYYERV2xE8jGaiFwaa4sGDbJMEVv6WY8zebINYmQNUEri5G4OpWza1N7mHKaHH7ajPl8VfnOeEoMiBkTsiGOXnpbQioQ2H5UliohcUKiQBURz51r9TebMlkVK6fUi6dNb3ZO7ToQBW8uWwNSpvsnuMHPECdmcvs0ib854ql4dOHnSF/deRAJBgZGI+M1tt1nr/OzZNhOI84/Y2fX667beI6XUqmUzjzikkRmdAQPsyM8XM494dMc6ppEjrSOPtVYpeXQoIr7lVVeaqCtNJLkt/sOGAa+8YtkcHm2xDomvp1QH3UcfAWPG2Ossoh44EKhQwTe3v2/fhUwUM2X58lm2TETCtCtNRCQ52DbP7Ao7uTitmgEKd6/9+GPKFC6zg47BGAunr7rKuui49421Qr4YUsm91m4wtHIlUKMG0KGDBkKKhBIFRiKSom68EVi61KZJs5eDM4FYo/PYY74rjL4cdpNx1UeDBlYg/s47QKdOFij5Ao/UduywzjzuW7vllpQ9OhSRpFNgJCIpjnU5bdvaHCC20nM2EIMlDmNkoJJSM4/49XiUxgwPgxhms2bN8s3t33or8OqrQKZM9r3dcAOwbJlvbltE/EeBkYgEDIOTESOsQJsLWrt1s+JsFmmfOZMyx2t33mnZo3LlbHI2i7L79QOOHfNN0TfnKbFLjxkjvs1jOxEJXgqMRCTgWKjMBbAsxGaAwpocDkxkfQ6HRqbEcMp337Wvx7Z7TrLmTCJfLKMtXNiCI9Yb8ftiTRNnPYlIcFJgJCJBg91pbHfnglbOQFq1CmjRwjrZmEXyJx7nsc6IARK71biMlmtNfFGYzaM6zlJi4MUsWcmS/v9+RCRp1K7vJbXri6QMFmKzIJvDGIkDGvn23XdbVsefON+IGSu33ohZLNYisZMtuf7+22YpMUBiAMjfwAzERMS/1K4vIiGNR1DffmvBCXee/fuvHUG1bg1s2+bfr82uMu54cwuz16yxozUesSX3T8lcuSzA4rEaxwYUKQK88Yb2rIkECwVGIhLU6tUD1q0DBg+2LAszSSyM9sXcocQUZnPHWvny9jVZlP3ss8k/BmNXHkcV/PKLBUjdu1vg5YuCbxFJHh2leUlHaSKBwzlD8+dbloVdXszssEC6WjWrEfIXBmFcSMt6I07M5iBHrhXhipHk4G9fTuJ++20bU8C1Kd98Y3VWIuJbOkoTkbDDWhzOGqpc2QZFcrp0z56WbVmxwn9fl0EXC7F59MXWe67+6NzZjsCSM9WamaM2bYBRo4Ds2YH164GKFe0IUUQCQ4GRiIQcBiqsQSpQwIqyt261jrI+fYD9+/0/Mfveey3bwzZ8BjabNyfvdpl54pJbd9HuPfcAS5b46l6LiDcUGIlIyGrZ0qZnc/8ZO9Vmzwbuu8/WjfhrP1nGjDYEklOtc+SwOUucv/TJJ8mb2p0nj2WkmjSxqdlp06rmSCQQVGPkJdUYiQQnFjI/+uiFIzUetXEmkT+x9Z6dcj/9ZG/ziI8F2lwkmxwnT1rmK2dOy4r9848t3hWRpFONkYhEFB5H/fyzLW3NnRuoXx/YtctWi/gLW+85uJGdahky2Ndv1gyYOTN57fe8rauvtmM1ZsU4NXvkSLX0i6QEZYy8pIyRSPDjzrNTp6z2aPduYMEC4PBh4JFHLOjwhx07gL59gd9+s7fr1gWeecZqoJKTOWLQxftPDzwAfPihjS0QEf88fysw8pICI5HQwZqf338HbrrJBkRydhC72Liwlh1h/m7rZ90Qg6Wbb05+Sz8713ibHHY5ebJNzRaRxNNRmohEPBZkcy/ZO+/YtGnW7TCLww42Fm37q62fxd+ctXTggK0x4XoRrgFJTks/vwce3bHYm7VMDJZExPcUGIlIWGNg0bSpBRQMiriolvOPOA/p5ZdtL5qvse2eXWqsN6Ivv7RZS6tXJ/02K1QAPvvMisoZZHE0AYuyRcS3dJTmJR2liYQ21h0xizNjhgVNLGrmUZu/LFsGvPiiDYVkBovZn44drR0/KXicxuwRM2G1agGlSiWvjkkkUhxRjZF/KDASCQ8MjDj3iPvQjh+3eiAWbbOjzdeYlWJ2il+TWCfElSIlSiSvnolrUbjklgXfbOt/8EGf3WWRsKPAyE8UGImEFwYtzCItXWqZpDvuALp1s3oeX5s715bhshCc9UjMHLVunbw9bzwW7NrVxhJwTcnrr9txoYjEpuJrEZFE4CLacuWAgwctuJg61VZ+cPUHszK+xG64L76wIzDeNjvNWKz9559Jv03ed9ZQ0ejRdizoj8JykUihjJGXlDESCV+cYM3sy9q19jb3sfXq5fsaJP7WnT7djte49oMZHn5dFmuzDikpOOuIU7eZAWOwx0GXOloTuUAZIxERL9WsCfz6K/D227YHbft2O1bjbjRfYtH3XXcBn39uqz44jJITtLnShNO6k4JZKHatsSOOwRGzSF26aFq2iLcUGImIeEiTxgIK1h1x3hHfZkE2j9qSsyQ2PtypxiCMYwS4nNYdI8D2/qQENFdeaZOxuUaEeFzHtSIikng6SvOSjtJEIguP1TJlskwOg6O//rKMDHex+XJ6Nm+fbf0MjoiZpBdeAPLnT9rtMfPFJbT8NXXddXa8prZ+iWRH1JXmHwqMRCIT95Yxi9SwoRVLs+j56act6PAVZqRYnM3ZSjxeYyt+9+5A48ZJD8L+/tu64HgcWLWqFWhr15pEoiOqMRIR8R0uny1e3I6p+PqaNUCrVsDAgb6bQM3CaxZgT5wIlC9vhdkvvWRjBDizKCk4doCBHDvVOI2bk7OTM4FbJNwpMBIRSaR06SwQ4mJatvQz3/7NN5bR8WV7f6FCwLvvWraIHWucscRiai6PTUqO/7bbrJaJR2tcjcLMEbNSOi8QuZgCIxERL119NfD118APP9iWe2Z2OFiRk7R9hUXfzE4x4OKxnZs9Ypfcnj3e3x4Xz/KYjqMHeEz3+ONAo0Z21CYiFygwEhFJoltvtWOpt94C6tSxRa8szuagSAYfvlCkCPDeexeyR9y9xuwRO9e87ZLLnt0yRU88YdO2OUupXTvf3E+RcKHASEQkmZkdDmdktujmm62LjEXa99wDjBhh+9d8lT2aMMFqj7jbbehQGyvg7dwjFnGzNmrsWFtEyyNB1h9xOa2IqCvNa+pKE5FLYRbnlVeA3r0vZGl4/MVjKwY4ycUAhkdizFIxK8VCcAZmnHLt7e3ztz9HDxw+bHVN8+dboFSsWPLvp0iwCamutAULFqBRo0YoUKAAUqVKhSlTpsT6OGO3vn37In/+/MiYMSPq1KmDP1hB6OHQoUNo0aKF881mz54d7dq1w39x/lRbs2YNatasiQwZMuDqq6/G8OHDU+T7E5HIwc4ytvHz1xgDDAYdgwZZxoezhZKLwQ+HQHJqdsWKNkbg1VeBDh2837nG7BGfH5jl4v196inLSLGuSSRSBUVgdOzYMZQvXx5vs20iHgxg3nzzTYwZMwbLli1D5syZUb9+fZzkb4RoDIrWrVuH2bNnY9q0aU6w1ZGrqz0ixXr16qFw4cJYsWIFXn75ZfTv3x/vsvVDRMTHeJS2YQMweLANV+TfcgxeOOXaF8dWBQvaTCLeHgdQcnxA8+bAuHHed8elTWsDJblOhH9PMojjRVOzJRIF3VEaM0aTJ09GY/a/RmeLmEl68skn8RT/nAGHlf2LK6+8EuPGjUOzZs2wYcMGlC5dGj///DMqVarkXGfmzJlo0KABdu3a5Xz+6NGj8dxzz2Hv3r1Ix55b8BfKM052auPGjYm+fzpKExFv7d9vy2g5R+iWW+yYja3zSV0YGxdnHLFjbckSe5tDJzk1mzVE3mBA9c47wPjxdiTIJbqcqcTaKZFQF1JHaZeybds2J5jh8ZmL31jVqlWxJPq3AF/y+MwNiojXT506tZNhcq9Tq1atmKCImHXatGkT/vHVdDYRkXjkzWvBxi+/AC+/bN1lO3daRmnq1OTvYOPOtTffBPr3t6Mx/q3XurXNLvKmO46daqxXYnDE+8wlulysy/ssEimCPjBiUETMEHni2+7H+DIv/xV7uOKKK5AzZ85Y14nvNjy/RnxOnTrlRJmeFxGRpLjhBqsL4iyhsmWBMWOAAQOsS2zVquTdNuuF7rrLCrNvv92O69h5xuM1b2+b95O3w79HeTsM3HzRXScSCoI+MAq0IUOGOBkq98KibRERX6wX4SJa7kPbtAlo396O2DgHKTly5waGDWNtpq0DYdaHt833eRPccJ8aRwIwe8QjueXLgd27LdMVXAUYIhEWGOVjjhjAvn37Yr2fb7sf48v9PMT3cPbsWadTzfM68d2G59eIT58+fZzzSPeyk78VRESQ/AwP64A2b7aMEeuN5s4F7r/fhjAmN0PDNSCTJgF3321v83UOhvzpJ+9uhxkuFnpzaOWcOTbEskkTTcyW8BX0gVHRokWdwGUuf2NE43EWa4dujq4I5MvDhw873WauefPm4fz5804tknsddqqd4b/uaOxgK1myJHLkyJHg10+fPr1TpOV5ERHxFf5d9tFHVn9Uo4YFIKxH8kXLPH9d9e0LjBoFXHUV/xgEevTgH3zeBTYM4piJ2rGDzS+2s61MGd+uQBEJFkERGHHe0KpVq5yLW3DN13fs2OF0qXXv3h2DBg3Ct99+i7Vr16J169ZOp5nbuVaqVCnccccd6NChA5YvX45FixahW7duTscar0fNmzd3Cq8534ht/Z9//jneeOMN9OzZM6Dfu4iIW9ezYAHw1VdAtWo2EJKByIkTyc8esRWfc4/czBQDmgcesAW43hyL3Xkn8OGHFmSxNLNePRteyUncIuEiKNr158+fj9q1a1/0/jZt2jgt+byL/fr1c2YOMTNUo0YNjBo1CiVKlIi5Lo/NGAxNnTrV6UZr0qSJM/soCw/KPQY8du3a1Wnrz507Nx577DH0dsfTJpLa9UUkJXAiNQc2bttme9I4hJHZnqJFk3e77ITjwEnWNRGbeZ991iZfJxZHyLFTjYEV8Vcx15Xw2E0kWCX2+TsoAqNQosBIRFIKfzvPmmXdZuwOY7bnvvuATp2AS1QAJGpeEQMZFlaznZ9TTFigzYwShz0mFuuVXnwR4MQTTuPmnCZfzWYS8bWwmWMkIhKpWNtzxx3Ab79ZBxvb5r/80qZqsy7JmxlFcecVcc4Rj9d4zHb6tNUhtWgBrF6d+NvhjCMWdbOqgUd//Fwe++nPbQllyhh5SRkjEQkU1gbxOG3duguF2x98wJlsSb9NPgN89x3w2mu2143BGLNSjz1mLfuJxeCKdUdcf8IlusxydemiDJIED2WMRETCTN26thPNnUzN4zQGJB5rI73GQKhBA8tEMevDQIkF4GzJZ3t+Yv905nEcx7z9+KMd/zGwYnH2rl1Jv28igaCMkZeUMRKRYHDsGLBli9UeMfhgZxhrfNq1swAlqTg2gItv2RFH7JBjjwo70RKDx32sX+LRHIM2/prkXCbWLzEIEwkUFV/7iQIjEQkm/A1+4ADAHdsff2z1QxwSyWLq7NmTdpusXRo3zi6cq8Tdbh06AC1b2u0nBrvpnnsO+P13e5uDJt99N3nHfiLJocDITxQYiUgwYuEz2/rnz7e3uWqE2SNOu2ZgkxQcFzBkCODOzr3mGhsOyenXie1+e/99C7D4OnfERe/+FklxCoz8RIGRiAQzFlI/+aTNKyJmaFjvw+62pOAzxPTpwOuv29RrYhcaBzsmNiO1cSPQr58VY7OeqVgxq0kSSUkKjPxEgZGIBDvWHbFbjetAuAaE83MHDrTltUnFjjXWCrlDHRkUPfGEdZ8lpnaIzzSsi+IqEnbTLV5sgyHdXW4i/qbAyE8UGIlIqGBB9rBh3BVpR1n8bc+aIV6KF0/abXJzE4/XWPjtrjJhcXZib49BG4/mHn/c7hNnJzHgSs7ASpHEUGDkJwqMRCTU8Lc8MzVbt1od0tKldqTVubNlb5I6OZvF1BwVkCYN91FagXamTIkr7n7rLRswyS42HvexFonZJxF/0RwjERFx8Kgrd26gfHl76dYNcZDjm2/aXrakTM7m7CMe0zELxI44LqadO/fys49YDM46KAZWHAPA4z7OUGLX26FDyfpWRZJNGSMvKWMkIqFu0SILTJYts7c5rZodbAxsktLBtnChLZX96y97m0d3vXolbjEtM07MHn3xhWWP+Dls8U9qJ51IQpQxEhGReFWvbm3zzPiwNogZoxEj7HgsKWrUsGMxzk7iElreNscEjBlz+ancLAjnDCZmjwoWtAwUxwRwOKRIIChj5CVljEQknLBe6L33gNGjgRdftPfxuI11QNmyeT+tmhOzmT1y5xUVKAD07Anccsvlb4tf88QJGwvA2icGbEeO2MBKTc2W5FLxtZ8oMBKRcMRnAgYhzNYwuGHXGDvF+LJsWe9va948W0zL+iF3tQiP1xKzroQ1SzyW4ziAnTttbhIDt6QUiou4dJQmIiKJxowMM0TlygFZsgC7dwO//go8/LDVIzFg8ua2br/djuratrVibc4tevBBC3Aud7zGLrf8+YHbbrPXp0wBSpUCxo9P/FJbkaRSxshLyhiJSCTYvt3Wf7gt9alTWzt9x47eZ24YVL3yio0JIH4+j9dYT3S5IzJ3arY7N6luXTv6K1w4id+YRKwjOkrzDwVGIhJJ1q61I7BZs+xtFld/9BFw7bXe3Q6faX74wY7X9u6191WpYrddtOjl66A+/BAYO9aGU3JW0s8/A6VLJ/Gbkoh0REdpIiKSXKwvmjkTWLAAqFrVutgYHHFFCIOdxP5pzcwQj8Z4vMbuNe5KW74caNbMOuL++y/hz+VRHDNV7Jq7/nq7DwyQONlbxNeUMfKSMkYiEqn4bLF/v+0827bN6pBeeMG6xjgs0pvZQ7t2WfaIARflymXLbjmRm8d2CeGx3sGDVqfEmijWIk2bZoXaWkwrl6KMkYiI+BSzPlzfUayYDXFct85qkV59Fbj3Xlswy2OvxODMIgZGzBZxqCNXlvTvDzzyiN1uQhg05c1r3W3MGrEw/OmngQoVLtQwiSSHAiMREfEa63wGDrSVIgyWmEni2+w8mz3bMjveDIfkWADe5m+/AW3aAAMGWGbocmtObroJ4B//GzbYSIBu3bxfcSLiSUdpXtJRmohIbDzWYtaInWesPaKSJa1Y2pvjLQZCI0faHjfKnNlWlTz0kNU1JYRfc+hQYM6cC0MlOXWb+9dEXDpKExGRFMG1Hs89Z8dqzzxjAQ2Xwx44YNOsE4sZIGaKGFCxyJq1TMxIcb3ITz8lXOidPbsFRm+8YaMAWPt0993A66/77FuUCKKMkZeUMRIRuXzmhxOvmUniBGteGOx07mwDJBODR3EzZlgGifVHxGMzzj9ijVNC+DXffts+9+OPrZMuTx6tFBFojpG/KDASEUkcPrswa8SaIbb8uwtsGSBdd13iboNZI84wYqs+i605CbtJE2vfZ6YoIVxvwnZ+3gcOg5w4EWjeHChTxjffm4QeBUZ+osBIRMQ7bO1//nkrsuYeNLr1VuDRR20mUWLb+3lUxiGRxF+/DI44KoBzjhLCpbRTpwLDh9v1OFCSIwYyZvTBNyYhRYGRnygwEhFJGq73ePZZ233GZx4eb3H+EVePJNYvv1ih9x9/2NvMBvXoYZmohI7L9uwBBg8Gliyxt4sUseLs+vV98E1JyFDxtYiIBBUen339tS2nbdjQgiMWbvO4zc0kXU6lSsAnn1gwlSOHFXx3727DITdvjv9zOASStUpDhtggSe5uu+MOGy3AoEnEkzJGXlLGSETEN7jvLGtWC4xYsM0ZRswIcWUIW+4vh2tEWH/E+iHWH3H4Y+PGdkSXM2fCNUsMkhigscCbK09Wrbr0tG0JDzpK8xMFRiIivsWghpmbunWB33+3WiDOIOIMI7bfJ6b+iG398+bZ2xwX8PDDNv8ooTUl69fbQEoWZLOYm51u/DwJXwqM/ESBkYiIf3BvGuchLVxobzNA4jwirglJTIDEIzrOLmLQ4x6hde1qtUTx1R8xY8Q5S8xY8df5smUWoA0aZHvYJLwoMPITBUYiIv7FzA87xxYvtrc59Zot/q1bX/5zGexwNABnGXGWEnFYJAu0uU8tPnwWZNapRQtr8eeKE+5w42BJzT8KHyq+FhGRkHTbbcCiRbZzjUMdedTGIm0OerzcDjbWCjVoAHz1ldUasS2fS2lZt8Rlszt3Xvw5DH64lJbTs1nbxICKx3C8H+ykk8iijJGXlDESEUlZ8+dbWz470P75xwImHn+1bXv5IzYWdb/zDvDNNxZU8XiOs48YKMU3IJJHax98YJ1vp09btuqpp2wOE5fcSujSUZqfKDASEQkMBi1stWcWiYtj3RokFlqznuhS2MrPAm33eC5LFgusmjWzbFRczCyxvX/5cstCMTirWdM/35ekDAVGfqLASEQksL7/HujXD1i61N5mgHTXXRYgcXntpbDAmhO02f1GrCfq0gW4886LW/b57Dh3ro0RaNkSKFrUMlecpn2pdSQSnBQY+YkCIxGR4DBnjgVIbhaIgQ1XfjzwQOIW1I4efaFAu0QJ4PHHLRsVn6NHgUOHbAdbt242VJJTvHW8FjpUfC0iImGtTh0r0mYXG4+5GPCw5oh1RZyknVChNgMoZphYoM0gh/OLmEHi67xs2nTx53AQJQu02fHGIZEvvWSTvCdPtsyShA9ljLykjJGISHDikRfnD23datmdd9+1jjYWWl97bcKfx3olFlxPmgScPWtdalwZwhEBcSdw8xlz1ixr52cA5gZob70FlCzp3+9PkkdHaX6iwEhEJLgxGOKQR+5VY6BDtWpZgFS6dMKfx1lGo0ZZDROxI43HchwwGbemiHVG7Hb77DP7GrwuZy/xIsFJR2kiIhKRGKSUL2+F1uxaYwaIU7U5IJJHZdyNFp+CBYHBg4GPPwaqVLEAa8IE4J57bCcbgyEX5yOxzujzz4HKle26HCXAi4Q2ZYy8pIyRiEjoHbENGGD1QKw9oj59bEdaQvjMyMCKLf5uB1uuXECHDraolp1wntf96SegUCErxmb32t69QI4cFqBJcFDGSEREBECZMlY/tGGDtd2zDolrQnbvttlIrEeKW6jNLBM71DjokctmOQaAk7c5HfvBB23IpPs5vC6P6ooUscBo9WqgVSvgxhutTomfJ6FDGSMvKWMkIhLa/vvPdqJxkjaXxnJVCKdcs5aobt3Y2SAXj8q+/tqKtBlIEbvSuKSWAZTnTrV//7UMFY/viPVJDK64oiS+25aUoeJrP1FgJCISHvjsx+wOW/0ZLBEnaLdpAzRqBKRPf/HnsFWfdUfMJPF1qljRapfKlo19XR7FDR9uARgxSzVyJFC7tr+/M4mIo7T+/fsjVapUsS7XMVyPdvLkSXTt2hW5cuVClixZ0KRJE+xzJ3dF27FjBxo2bIhMmTIhb9686NWrF866LQsiIhJRmOWpUIHPDcBzz1lNEDNIPC5jYPTRRxeCHxdnHrHOaMoUoHlzIF06YMUKm7rds6etHnFVrWrF2T162AoSLrPlYlp3IKUEp5AJjOj666/Hnj17Yi4LFy6M+ViPHj0wdepUTJo0CT/++CN2796N++67L+bj586dc4Ki06dPY/HixRg/fjzGjRuHvn37Bui7ERGRYMCAaNAga9cfNszWhPC4jIXXXAkS37kKP4eBEI/X2PnGoZE8OnvoIWvZ520Rj85atLBAit1tN9xgBeAMwFijpDOb4BMyR2nMGE2ZMgWr4umzZFosT548mDBhAu7n2mQAGzduRKlSpbBkyRLcdNNN+O6773DXXXc5AdOV/KkHMGbMGPTu3RsHDhxAOob9iaCjNBGR8MZ6I7bnc0bRM89YPRILtjkRm9Ov4w59JC635YoRBlKUJo11r7VrB+TNG/u22dLPmiVmkR57zDrkGDzF3dUmvhV2R2n0xx9/oECBAihWrBhatGjhHI3RihUrcObMGdTh+NFoPGYrVKiQExgRX5YtWzYmKKL69es7D9Q65jcTcOrUKec6nhcREQlf/DuZhdLz51u3GVvuGdA8/7wFO3zpeWRG7EhjtokzkKpVs6wQV47cey/w+usX5hvxtvk0lCePDYhkpxznK/HYLfrpSgIsZAKjqlWrOkdfM2fOxOjRo7Ft2zbUrFkTR48exd69e52MT/Y4o0kZBPFjxJeeQZH7cfdjCRkyZIgTYbqXq/nngoiIRAS2319zjS2ZZds/j7+4L61ZM1s6u3Jl7OOwUqXsCI7rSBhQcRzAp5/aMRozSlxGSyzsZu1Rx45AhgzAL79YQMWjuJ07A/btSigFRnfeeSceeOABlCtXzsn0zJgxA4cPH8YXX3zh16/bp08fJ+3mXnbqJ1ZEJOIwOGIN0dKltoCWhdssomZg07atDZH0xBlG778PvPGGtfXzOI6t/u4Ubb7N7BE/n4MnuZuNt8njOwZhL74YqO9UQiYwiovZoRIlSmDz5s3Ily+fU1TNQMkTu9L4MeLLuF1q7tvudeKTPn165yzS8yIiIpGJR15Tp9oRGIc4MrhhNQaPyk6ejH1dBjrVq9vxGtv2ixVjnYvtY2OAxJZ/fg6P1Vj8PXasZaX4PgZaWi8SGCEbGP3333/YsmUL8ufPj4oVKyJt2rSY61a9gUVym5wapJtvvtl5my/Xrl2L/fv3x1xn9uzZTqBT+lJbBUVEROIoWdLa+Vl0zRqiW24B+Lc5DxX4NoMctySVARLb9CdOtEGPrMhg0DNihNUssaWfNUwMivh5DJKYlWJ2igEYe47cYZHifyHTlfbUU0+hUaNGKFy4sNNZ1q9fP6dDbf369U5HWufOnZ3jNdYhMdh5jKX+YKpzcUy7foUKFZzi7eHDhzt1Ra1atUL79u0xmFsDE0ldaSIiEhefSRkY/fqrHYux64y1Qyy+5rwjDo50cXze9OnAe+/ZTjViySs72Dg/iUtwiUMn3TUkP/9sQdTLLwPFiwfmewx1YdeVtmvXLjz00EMoWbIkHnzwQWeQ49KlS52giF5//XWnHZ+DHWvVquUcj33NARPR0qRJg2nTpjkvmT1q2bIlWrdujRd1kCsiIsnErBBnG3GK9ttvW50Qj8SYJeKx2bPPcozMhdlGfB9rizgOgO38rOzg3+hcbPvttxY8sZ2fARU/zlZ+zkLiAQfnJ8WpHJFIzBgFC2WMRETkcvjMylok1hYtWnTh/W7Lvyd2rjFI4jGau3C2YEGgfXvLPjGQ+v134JVXrAuOcua0fWydOl3IMMmlaVeanygwEhERbyxfztEvwPffc7CwLZVlYMOsD1+684WZYfryS2D8+AuF14UKWYBUv74NjWStEWuY3AZpjgaIrhyRy1Bg5CcKjEREJCkY7LDImutCWFvUuzewezfQtCnApQ3uKL4TJwBOomFx97//2vsKF7YdbXXrWjZq0iRg2jTg1VetEJwZJs5Y4twliZ8CIz9RYCQiIsnBZ90tW4AaNay2yB34yE40FmozCCIusGWAxLZ+N0DihG1mkBggse6InW/MPPHpqHNnGyfA7JRmEV9MgZGfKDASERFfYOfauHHWtr9+fezZR8wOXX/9he40N0ByRwB4Bkj8nNmzgeees4+xG45TtVnYraepCxQY+YkCIxER8SU+CzOwYaG2O46vVy8r0mYmycUAiTOPuGIkvgCJwRWP1twp3GzaZuM1P84C7kh3RIGRfygwEhERf2FQw8nYLVsChw7ZMlqO42NQdN99VofE17k6ZMKECwESj9/cAInLb1mUzfol4ugAdsblzo2IdkSBkX8oMBIREX/jMzMDo+3brWX/wAHLHjVsaHVIzBS5GSQGSG4NErvYOCjy9tutQJt72TgQktdj3RGP2SLVEQVG/qHASEREUgoHPTK44TJatw6JuO2qRQsrtnaLtHnE5hkgPfKIFXhzExaDKo4G4GXkSKBv38gr0D6iwMg/FBiJiEhK4zP1nDk25JH1SO4zN1v9WY9EDJCYJeLSWjdAYhs/AyRmnXjs9tZb1ubvFmj36QNkzYqIcCTcVoKIiIhEKnaesX5o1ixbLMvjMs4sqlLFhj3yWI2B0Z132sRtDn1kPRJnJrEA+4EHgIULbRUJu904TJJt/ddcA7zzjmWmxChj5CVljEREJBgcPWoBDYus//rLOtLY1XbbbXbMxqCHk7SZQWK9EnH32sMPAxkzAqNHXyjQ5g42TtSuVw9hS0dpfqLASEREgg0zRhwQ+eOPF97HzBALtTkX6ZtvbJK2u4stXz6gdWvLMnEFCT//oYdsrpK7oiTcKDDyEwVGIiISzHvZWIc0ZYoNkHTnGbHOqFEjW1bLQOjgQfvYlVcCzZpZxqlBA6BYMeti4+fyKY7F2uFCNUYiIiIRhjVH7FBjm//TTwO5clmrP1eQsK6IxdoMmp56yuYacSUJO95Yf/THH1ag/csvlmniURw72CKt/kgZIy8pYyQiIqHi1Ck7HuOxGl9nt9q6ddaZxoJsLrP1zCDlz2/BEzNLDK7ouutsYCSLv0OZjtL8RIGRiIiEImaMONOIARGP3IizjO6/3yZscw7S39E1SAUKAOXK2cRsFnnTvffaXjfOSApFCoz8RIGRiIiEMh6rsQ6J3WosvibOMrr7bhsB8PXXFwIkzkFiFmnFCuD8eetm+/57GxwZalRjJCIiIhdh7ZDbqj90qGWNmBVixuiHH4CvvrI5SNmy2Rykn38GrrrKMkWcjcSuNXdHWzhSYCQiIhKBmDTp3RvYts2W0nK9SJMmwD//2KTsTz6xgZGZM9sQyR07gBw5gHnzgKVLgU2bbLWIm10KFzpK85KO0kREJFydOmWF2AyCWLT97ruWUeKRGrvV3BEA1aoBefNahxu729jZxjlInNAdrHSUJiIiIl5Jn96Ozdj2zwJsN1u0ZInVIZUpY8HP4sXAt9/a+xhIcdI2s0vuJO1QpsBIREREYkmTBujXzwY/Dhhg2SGuFfntNyvQZr0Ri7FZm8Sao9SpbY8bxwLwWC6UKTASERGReGXLZnVEPFrjLKPCha2TjStFhg2zKdmnT1uQlDYtcPiwHak99xxC1hWBvgMiIiIS/Edsjz0GdOkCTJhgq0IyZACKFrVMETvaOCeJmD3iMRyP2Fh/FGoUGImIiEiij9hatbLXT5ywozbOQ2JQdMUVtj6E2SN2u23caMEUu9bY8caAKRSEyN0UERGRYJIxox2ltW4NlCp1Yacagycet731lrX916wJNGxoNUqhQIGRiIiIJNn991tR9vvv2xEa14sQu9c4I4lvz5wJVKhgwyKDnQIjERERSZbUqYF27YCtW4HBg61om1MSWYvkYts/V4lMnIigpsBIREREfFak3acP8OefQI8ewOefA23aWP0RsYOteXMLnoJ1vLQmX3tJk69FREQSh8doe/bY4ll2tHGytotB04MPIsVo8rWIiIgEVJo0tk6kenUgS5bYH3vlFcssBRsFRiIiIuJXJUsCv/9uhdouFmJXrGhzkLi4NlgoMBIRERG/y5kTmDTJVoa4J1ls4eeONXasHTiAoKDASERERFJM06bAH38Adeva26x05soRLqjduzfQ906BkYiIiKSwvHntCO2NN4CbbrL37d8PXHedtfUHkgIjERERSXGpUgGPPw4sXAi0b2/v+/dfO1bjupFAUWAkIiIiAe1ce+8926vm1h198UXg7o+WyIqIiEjAjRgBFC0KbNkCNGsWuPuhwEhERESCYq1Ijx6Bvhc6ShMRERGJocBIREREJJoCIxEREZFoCoxEREREoikwEhEREYmmwEhEREQkkgOjt99+G0WKFEGGDBlQtWpVLF++PNB3SURERIJAxAVGn3/+OXr27Il+/fph5cqVKF++POrXr4/9XNIiIiIiES3iAqPXXnsNHTp0wMMPP4zSpUtjzJgxyJQpEz788MNA3zUREREJsIgKjE6fPo0VK1agTp06Me9LnTq18/aSJUsCet9EREQk8CJqJcjBgwdx7tw5XHnllbHez7c3btwY7+ecOnXKubiOHDni9/spIiIigRFRGaOkGDJkCLJlyxZzufrqqwN9l0RERMRPIiowyp07N9KkSYN9+/bFej/fzpcvX7yf06dPH/z7778xl507d6bQvRUREZGUFlFHaenSpUPFihUxd+5cNG7c2Hnf+fPnnbe7desW7+ekT5/eubiioqKclzpSExERCR3u87b7PJ6QiAqMiK36bdq0QaVKlVClShWMGDECx44dc7rUEuPo0aPOSx2piYiIhB4+j7M0JiERFxg1bdoUBw4cQN++fbF3715UqFABM2fOvKggOyEFChRwjtOyZs2KVKlS+SSCZZDF2/zf//6X7NsLV3qcEk+PVeLocUocPU6Jp8cquB8nZooYFPF5/FJSRV0upyR+/wFh5Mr6Jf1DSpgep8TTY5U4epwSR49T4umxCo/HKaKKr0VEREQuRYGRiIiISDQFRgHGjjfubfPsfJOL6XFKPD1WiaPHKXH0OCWeHqvweJxUYyQiIiISTRkjERERkWgKjERERESiKTASERERiabASERERCSaAqMU8Pbbb6NIkSLIkCEDqlatiuXLl1/y+pMmTcJ1113nXL9s2bKYMWMGIoE3j9N7772HmjVrIkeOHM6lTp06l31cI/lnyvXZZ585E9vdXYHhztvH6fDhw+jatSvy58/vdMyUKFEiIv79efs4cZVSyZIlkTFjRmeCcY8ePXDy5EmEswULFqBRo0bO1GT+G5oyZcplP2f+/Pm48cYbnZ+l4sWLY9y4cYgEC7x8rL7++mvUrVsXefLkcQY+3nzzzZg1axYChl1p4j+fffZZVLp06aI+/PDDqHXr1kV16NAhKnv27FH79u2L9/qLFi2KSpMmTdTw4cOj1q9fH/X8889HpU2bNmrt2rVR4czbx6l58+ZRb7/9dtSvv/4atWHDhqi2bdtGZcuWLWrXrl1R4c7bx8q1bdu2qKuuuiqqZs2aUffcc09UuPP2cTp16lRUpUqVoho0aBC1cOFC5/GaP39+1KpVq6LCmbeP06effhqVPn165yUfo1mzZkXlz58/qkePHlHhbMaMGVHPPfdc1Ndff81O7qjJkydf8vpbt26NypQpU1TPnj2d3+UjR450frfPnDkzKtzN8PKxeuKJJ6KGDRsWtXz58qjff/89qk+fPs7z3sqVK6MCQYGRn1WpUiWqa9euMW+fO3cuqkCBAlFDhgyJ9/oPPvhgVMOGDWO9r2rVqlGdOnWKCmfePk5xnT17Nipr1qxR48ePjwp3SXms+PhUq1Yt6v33349q06ZNRARG3j5Oo0ePjipWrFjU6dOnoyKJt48Tr3vbbbfFeh+f/KtXrx4VKRLzZP/0009HXX/99bHe17Rp06j69etHRRIk4rGKT+nSpaMGDBgQFQg6SvOj06dPY8WKFc4xjyt16tTO20uWLIn3c/h+z+tT/fr1E7x+pD5OcR0/fhxnzpxBzpw5Ec6S+li9+OKLyJs3L9q1a4dIkJTH6dtvv3VS+DxK41LpMmXKYPDgwTh37hzCVVIep2rVqjmf4x63bd261TlubNCgQYrd71AQib/LfeX8+fPOstdA/T6/IiBfNUIcPHjQ+aXKX7Ke+PbGjRvj/Zy9e/fGe32+P1wl5XGKq3fv3s55dtxfROEmKY/VwoUL8cEHH2DVqlWIFEl5nPgEP2/ePLRo0cJ5ot+8eTO6dOniBNyc0huOkvI4NW/e3Pm8GjVqONvKz549i0cffRTPPvtsCt3r0JDQ73IuUD1x4oRTnyXxe+WVV/Dff//hwQcfRCAoYyQhb+jQoU5R8eTJk53iUbmAf3W1atXKKVbPnTt3oO9O0P+Vyqzau+++i4oVK6Jp06Z47rnnMGbMmEDftaDCgmJm0kaNGoWVK1c6hbPTp0/HwIEDA33XJAxMmDABAwYMwBdffOH8ewwEZYz8iE9EadKkwb59+2K9n2/ny5cv3s/h+725fqQ+Tp5/WTAwmjNnDsqVK4dw5+1jtWXLFvz5559Oh4hnAEBXXHEFNm3ahGuuuQbhJik/U+xES5s2rfN5rlKlSjl/+fPIKV26dAg3SXmcXnjhBSfYbt++vfM2O2ePHTuGjh07OoEkj+Ik4d/l7LpStih+/AOXP1fszA5k9l8/wX7EX6T8y3Pu3LmxnpT4NmsZ4sP3e16fZs+eneD1I/VxouHDhzt/pc6cOROVKlVCJPD2seLYh7Vr1zrHaO7l7rvvRu3atZ3X2WodjpLyM1W9enXn+MwNHOn33393AqZwDIqS+jixni9u8OMGk1q9Gdm/y5Nj4sSJePjhh52XDRs2REAFpOQ7grAVlq2t48aNc1o2O3bs6LTC7t271/l4q1atop555plY7fpXXHFF1CuvvOK0offr1y9i2vW9eZyGDh3qtBh/+eWXUXv27Im5HD16NCrceftYxRUpXWnePk47duxwOhu7desWtWnTpqhp06ZF5c2bN2rQoEFR4czbx4m/k/g4TZw40WlJ//7776OuueYap6M2nPF3C8eD8MKnztdee815ffv27c7H+RjxsYrbrt+rVy/ndznHi0RKu/5RLx8rjn7g8x4fI8/f54cPHw7I/VdglAI4v6JQoULOEzlbY5cuXRrzsVtuucV5ovL0xRdfRJUoUcK5Pts9p0+fHhUJvHmcChcu7PyDi3vhL+1I4O3PVCQGRkl5nBYvXuyMx2CgwNb9l156yRl1EO68eZzOnDkT1b9/fycYypAhQ9TVV18d1aVLl6h//vknKpz98MMP8f7OcR8bvuRjFfdzKlSo4Dyu/HkaO3ZsVCT4wcvHiq9f6vopLRX/E9iclYiIiEhwUI2RiIiISDQFRiIiIiLRFBiJiIiIRFNgJCIiIhJNgZGIiIhINAVGIiIiItEUGImIiIhEU2AkIiIiEk2BkYiIiEg0BUYiEtJuvfVWdO/e3S+3mypVKufChbvJ0bZt25jbmjJlis/uo4j4ngIjEZEEdOjQAXv27EGZMmWSdTtvvPGGczsiEvyuCPQdEBFJqtOnT/v19jNlyoR8+fIl+3ayZcvmXEQk+CljJCIhg8db3bp1c47OcufOjfr16zvvP3/+PJ5++mnkzJnTCWT69+8f6/NOnTqFxx9/HHnz5kWGDBlQo0YN/Pzzz15//T///NM5Dvvqq69Qq1YtZMyYEZUrV8aOHTvw008/4aabbnKCqdtvvx2HDx/22fctIilHgZGIhJTx48cjXbp0WLRoEcaMGRPzvsyZM2PZsmUYPnw4XnzxRcyePTvmcxg0MZjh9VauXInixYs7QdWhQ4e8+tqrV692Xo4ePRqDBw/G4sWLsW/fPrRs2RJDhw7FW2+9hR9++MG53tixY338nYtIStBRmoiElGuvvdYJfjyVK1cO/fr1i/k4A5S5c+eibt26OHbsmBPIjBs3Dnfeeadznffee88JnD744AP06tUr0V+bRdjMSn3++efIlSuX875bbrkFCxcuxLp165xsETGLtHfvXh9+1yKSUpQxEpGQUrFixYvex8DIU/78+bF//37n9S1btuDMmTOoXr16zMfTpk2LKlWqYMOGDV59bWaC7r333pigiHiM1rRp05igyH1f0aJFvbptEQkOCoxEJKTwyCwuBjqeWAfEuiNfY8aoatWqFwVLrC1ynTx5Eps2bUL58uV9/vVFxP8UGIlIWLvmmmtiapJczCCx+Lp06dKJvp0jR444xdc33HBDzPu2bduGf//9N9b71q5di6ioKJQtW9aH34WIpBTVGIlI2GeYOnfu7NQSsT6oUKFCTo3S8ePH0a5du0TfDjNDadKkiTXTyK05Kly4cKz3MRjLkiWLz78XEfE/BUYiEvbYMcajtVatWuHo0aOoVKkSZs2ahRw5cngVGJUsWdJp9/d8n2e2yH2fjtFEQleqKOZ8RUTkoplJFSpUwIgRI3x2m6x9mjx5Mho3buyz2xQR31KNkYhIAkaNGuUcibFuKDkeffRRHa2JhAhljERE4vHXX3/hxIkTzuusS2IBd1JxdACLt91RAvF11olIcFBgJCIiIhJNR2kiIiIi0RQYiYiIiERTYCQiIiISTYGRiIiISDQFRiIiIiLRFBiJiIiIRFNgJCIiIhJNgZGIiIhINAVGIiIiItEUGImIiIjA/B8LKSroAj4VwAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:35.843709Z",
     "iopub.status.busy": "2025-07-18T15:05:35.842270Z",
     "iopub.status.idle": "2025-07-18T15:05:36.101106Z",
     "shell.execute_reply": "2025-07-18T15:05:36.100844Z",
     "shell.execute_reply.started": "2025-07-18T15:05:35.843675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:36.101777Z",
     "iopub.status.busy": "2025-07-18T15:05:36.101636Z",
     "iopub.status.idle": "2025-07-18T15:05:39.450580Z",
     "shell.execute_reply": "2025-07-18T15:05:39.450244Z",
     "shell.execute_reply.started": "2025-07-18T15:05:36.101765Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:02<00:00,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:39.451331Z",
     "iopub.status.busy": "2025-07-18T15:05:39.451219Z",
     "iopub.status.idle": "2025-07-18T15:05:39.666100Z",
     "shell.execute_reply": "2025-07-18T15:05:39.665837Z",
     "shell.execute_reply.started": "2025-07-18T15:05:39.451318Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:39.666478Z",
     "iopub.status.busy": "2025-07-18T15:05:39.666405Z",
     "iopub.status.idle": "2025-07-18T15:05:39.738682Z",
     "shell.execute_reply": "2025-07-18T15:05:39.737836Z",
     "shell.execute_reply.started": "2025-07-18T15:05:39.666470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:39.741876Z",
     "iopub.status.busy": "2025-07-18T15:05:39.740597Z",
     "iopub.status.idle": "2025-07-18T15:05:40.041248Z",
     "shell.execute_reply": "2025-07-18T15:05:40.040981Z",
     "shell.execute_reply.started": "2025-07-18T15:05:39.741847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T15:05:40.041818Z",
     "iopub.status.busy": "2025-07-18T15:05:40.041732Z",
     "iopub.status.idle": "2025-07-18T17:06:37.324735Z",
     "shell.execute_reply": "2025-07-18T17:06:37.323468Z",
     "shell.execute_reply.started": "2025-07-18T15:05:40.041810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00,  2.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|████████████████████▏                                                                                                                                                                                                                                                                      | 1/14 [01:10<15:11, 70.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [02:23<00:00, 10.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.141e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18/18 [02:30<00:00,  8.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.859e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22/22 [02:31<00:00,  6.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [02:30<00:00,  9.43s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  4%|██████████▉                                                                                                                                                                                                                                                                                | 1/26 [01:17<32:11, 77.24s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                             | 13/26 [02:38<02:19, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 26/26 [03:56<00:00,  9.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [01:15<00:00,  9.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [02:31<00:00,  6.32s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  3%|█████████▍                                                                                                                                                                                                                                                                                 | 1/30 [01:15<36:27, 75.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                              | 20/30 [01:27<00:28,  2.85s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [01:30<00:00,  3.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  3%|████████▎                                                                                                                                                                                                                                                                                  | 1/34 [00:07<04:19,  7.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                      | 14/34 [00:14<00:18,  1.08it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                           | 23/34 [00:14<00:04,  2.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:19<00:00,  1.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 24%|███████████████████████████████████████████████████████████████████                                                                                                                                                                                                                        | 9/38 [00:05<00:13,  2.20it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 22/38 [00:11<00:06,  2.60it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 27/38 [00:17<00:06,  1.62it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 38/38 [00:19<00:00,  1.93it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
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      "Volume = 88.8264396098042 m^3\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:07<00:00,  2.16it/s]"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                       | 13/28 [00:10<00:10,  1.49it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:12<00:00,  2.21it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:07<00:00,  2.02it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|█████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                                                                    | 13/36 [00:09<00:14,  1.54it/s]"
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36/36 [00:15<00:00,  2.39it/s]"
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                 | 21/32 [00:15<00:05,  1.94it/s]"
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     "name": "stdout",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:19<00:00,  1.66it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:08<00:00,  1.94it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                     | 23/36 [00:10<00:04,  3.07it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36/36 [00:15<00:00,  2.35it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                                                       | 13/32 [00:10<00:13,  1.44it/s]"
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:14<00:00,  2.22it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  2%|███████                                                                                                                                                                                                                                                                                    | 1/40 [00:06<04:00,  6.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                             | 20/40 [00:11<00:08,  2.44it/s]"
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 35/40 [00:16<00:01,  3.01it/s]"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:19<00:00,  2.07it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 22%|███████████████████████████████████████████████████████████████▋                                                                                                                                                                                                                           | 9/40 [00:05<00:13,  2.26it/s]"
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                           | 18/40 [00:11<00:11,  1.95it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                      | 30/40 [00:17<00:04,  2.25it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:19<00:00,  2.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:03<00:00,  2.03it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:10<00:00,  2.20it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                       | 13/28 [00:11<00:11,  1.32it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:14<00:00,  1.94it/s]"
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:07<00:00,  2.12it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                      | 21/40 [00:10<00:06,  2.74it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                         | 25/40 [00:15<00:08,  1.70it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:17<00:00,  2.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  2%|██████▍                                                                                                                                                                                                                                                                                    | 1/44 [00:04<03:27,  4.82s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                      | 23/44 [00:09<00:06,  3.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 28/44 [00:14<00:07,  2.15it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44/44 [00:18<00:00,  2.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:08<00:00,  1.99it/s]"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                         | 10/16 [00:05<00:02,  2.78it/s]"
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|██████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                                            | 16/48 [00:10<00:19,  1.68it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                               | 29/48 [00:15<00:09,  1.95it/s]"
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48/48 [00:20<00:00,  2.38it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00,  1.12it/s]"
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:09<00:00,  1.67it/s]"
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
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      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:10<00:00,  1.47it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  2%|█████                                                                                                                                                                                                                                                                                      | 1/56 [00:04<04:01,  4.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|█████████████████████████████████████████████████████████████████▍                                                                                                                                                                                                                        | 13/56 [00:09<00:26,  1.64it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 25/56 [00:13<00:16,  1.85it/s]"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 37/56 [00:18<00:08,  2.15it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:22<00:00,  2.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:07<00:00,  2.03it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:08<00:00,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                                                       | 13/32 [00:10<00:12,  1.47it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:13<00:00,  2.36it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███████████████████████████████████████████████████████████████████████████████████▎                                                                                                                                                                                                      | 13/44 [00:09<00:19,  1.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                         | 25/44 [00:14<00:09,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44/44 [00:18<00:00,  2.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  6%|█████████████████▋                                                                                                                                                                                                                                                                         | 1/16 [01:20<20:00, 80.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [02:35<00:00,  9.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  2%|█████▍                                                                                                                                                                                                                                                                                   | 1/52 [01:16<1:05:11, 76.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██████████████████████████████████████████████████████████████████████▌                                                                                                                                                                                                                   | 13/52 [02:33<06:40, 10.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                                  | 25/52 [03:49<03:36,  8.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                 | 37/52 [05:05<01:48,  7.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 1.800e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.200e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 52/52 [06:20<00:00,  7.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n",
      "Volume = 88.8264396098042 m^3\n",
      "Heating power = 2.000e+06 W\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:37.327171Z",
     "iopub.status.busy": "2025-07-18T17:06:37.326742Z",
     "iopub.status.idle": "2025-07-18T17:06:37.806144Z",
     "shell.execute_reply": "2025-07-18T17:06:37.805065Z",
     "shell.execute_reply.started": "2025-07-18T17:06:37.327153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:37.812457Z",
     "iopub.status.busy": "2025-07-18T17:06:37.811375Z",
     "iopub.status.idle": "2025-07-18T17:06:38.048660Z",
     "shell.execute_reply": "2025-07-18T17:06:38.048294Z",
     "shell.execute_reply.started": "2025-07-18T17:06:37.812431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:38.049271Z",
     "iopub.status.busy": "2025-07-18T17:06:38.049191Z",
     "iopub.status.idle": "2025-07-18T17:06:41.029048Z",
     "shell.execute_reply": "2025-07-18T17:06:41.027621Z",
     "shell.execute_reply.started": "2025-07-18T17:06:38.049263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.030548Z",
     "iopub.status.busy": "2025-07-18T17:06:41.030186Z",
     "iopub.status.idle": "2025-07-18T17:06:41.072097Z",
     "shell.execute_reply": "2025-07-18T17:06:41.071791Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.030517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[31mSignature:\u001b[39m analysis.adaptation_table(**kwargs)\n",
       "\u001b[31mDocstring:\u001b[39m\n",
       "Plots a color-coded table of the quadrature-order refinement.\n",
       "Shows in what order the parameters were refined, and unlike\n",
       "adaptation_histogram, this also shows higher-order refinements.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "**kwargs: can contain kwarg 'order' to specify the order in which\n",
       "the variables on the x axis are plotted (e.g. in order of decreasing\n",
       "1st order Sobol index).\n",
       "\n",
       "Returns\n",
       "-------\n",
       "None.\n",
       "\u001b[31mFile:\u001b[39m      /Volumes/UserData/dpc/GIT/EasyVVUQ/env_3.12/lib/python3.12/site-packages/easyvvuq/analysis/sc_analysis.py\n",
       "\u001b[31mType:\u001b[39m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.072595Z",
     "iopub.status.busy": "2025-07-18T17:06:41.072479Z",
     "iopub.status.idle": "2025-07-18T17:06:41.129722Z",
     "shell.execute_reply": "2025-07-18T17:06:41.129403Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.072583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.130774Z",
     "iopub.status.busy": "2025-07-18T17:06:41.130696Z",
     "iopub.status.idle": "2025-07-18T17:06:41.214985Z",
     "shell.execute_reply": "2025-07-18T17:06:41.214741Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.130767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17a867d40>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.215364Z",
     "iopub.status.busy": "2025-07-18T17:06:41.215280Z",
     "iopub.status.idle": "2025-07-18T17:06:41.426169Z",
     "shell.execute_reply": "2025-07-18T17:06:41.425922Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.215356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------\n",
      "Mean CV input = 21.3848 %\n",
      "Mean CV output = 18.5262 %\n",
      "Uncertainty amplification factor = 0.1853/0.2138 = 0.8663\n",
      "-----------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8663242668726164"
      ]
     },
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.426694Z",
     "iopub.status.busy": "2025-07-18T17:06:41.426599Z",
     "iopub.status.idle": "2025-07-18T17:06:41.925432Z",
     "shell.execute_reply": "2025-07-18T17:06:41.925105Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.426686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:06:41.926215Z",
     "iopub.status.busy": "2025-07-18T17:06:41.926115Z",
     "iopub.status.idle": "2025-07-18T17:08:41.982515Z",
     "shell.execute_reply": "2025-07-18T17:08:41.981926Z",
     "shell.execute_reply.started": "2025-07-18T17:06:41.926207Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:08:41.983965Z",
     "iopub.status.busy": "2025-07-18T17:08:41.983529Z",
     "iopub.status.idle": "2025-07-18T17:11:34.010973Z",
     "shell.execute_reply": "2025-07-18T17:11:34.010100Z",
     "shell.execute_reply.started": "2025-07-18T17:08:41.983936Z"
    }
   },
   "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:34.014380Z",
     "iopub.status.busy": "2025-07-18T17:11:34.012731Z",
     "iopub.status.idle": "2025-07-18T17:11:34.857864Z",
     "shell.execute_reply": "2025-07-18T17:11:34.857485Z",
     "shell.execute_reply.started": "2025-07-18T17:11:34.014356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:34.859327Z",
     "iopub.status.busy": "2025-07-18T17:11:34.858877Z",
     "iopub.status.idle": "2025-07-18T17:11:35.300713Z",
     "shell.execute_reply": "2025-07-18T17:11:35.300393Z",
     "shell.execute_reply.started": "2025-07-18T17:11:34.859313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:35.301381Z",
     "iopub.status.busy": "2025-07-18T17:11:35.301244Z",
     "iopub.status.idle": "2025-07-18T17:11:35.303455Z",
     "shell.execute_reply": "2025-07-18T17:11:35.303150Z",
     "shell.execute_reply.started": "2025-07-18T17:11:35.301369Z"
    }
   },
   "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": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.714171Z",
     "start_time": "2021-07-28T13:01:47.431782Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:35.303951Z",
     "iopub.status.busy": "2025-07-18T17:11:35.303850Z",
     "iopub.status.idle": "2025-07-18T17:11:35.379696Z",
     "shell.execute_reply": "2025-07-18T17:11:35.379401Z",
     "shell.execute_reply.started": "2025-07-18T17:11:35.303942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.plot_stat_convergence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display the Sobol sensitivity indices via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.423100Z",
     "start_time": "2021-07-28T13:01:46.274701Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:35.380210Z",
     "iopub.status.busy": "2025-07-18T17:11:35.380113Z",
     "iopub.status.idle": "2025-07-18T17:11:35.509208Z",
     "shell.execute_reply": "2025-07-18T17:11:35.508971Z",
     "shell.execute_reply.started": "2025-07-18T17:11:35.380201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:47.429977Z",
     "start_time": "2021-07-28T13:01:47.425043Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:35.509796Z",
     "iopub.status.busy": "2025-07-18T17:11:35.509716Z",
     "iopub.status.idle": "2025-07-18T17:11:35.512843Z",
     "shell.execute_reply": "2025-07-18T17:11:35.512558Z",
     "shell.execute_reply.started": "2025-07-18T17:11:35.509788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Qe_tot': 0.0826519003065851,\n",
       " 'H0': 0.019762879974652064,\n",
       " 'Hw': 0.21007714324357513,\n",
       " 'chi': 0.34090854895953654,\n",
       " 'Te_bc': 0.025553700331106294,\n",
       " 'b_pos': 0.06080314397550473,\n",
       " 'b_height': 0.20208219034487726,\n",
       " 'b_sol': 0.030533812380133626,\n",
       " 'b_width': 0.000842415509072117,\n",
       " 'b_slope': 0.0017000632610276914}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(params, sobols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:50.155890Z",
     "start_time": "2021-07-28T13:01:47.716263Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:35.513370Z",
     "iopub.status.busy": "2025-07-18T17:11:35.513289Z",
     "iopub.status.idle": "2025-07-18T17:11:36.260205Z",
     "shell.execute_reply": "2025-07-18T17:11:36.259962Z",
     "shell.execute_reply.started": "2025-07-18T17:11:35.513362Z"
    }
   },
   "outputs": [],
   "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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-18T14:51:27.162750Z",
     "iopub.status.busy": "2025-07-18T14:51:27.160666Z",
     "iopub.status.idle": "2025-07-18T14:51:27.280042Z",
     "shell.execute_reply": "2025-07-18T14:51:27.279646Z",
     "shell.execute_reply.started": "2025-07-18T14:51:27.162671Z"
    }
   },
   "source": [
    "We can also plot the Sobol first order indices as a function of rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:36.261066Z",
     "iopub.status.busy": "2025-07-18T17:11:36.260973Z",
     "iopub.status.idle": "2025-07-18T17:11:36.343221Z",
     "shell.execute_reply": "2025-07-18T17:11:36.342950Z",
     "shell.execute_reply.started": "2025-07-18T17:11:36.261058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the Sobol first order indices as a function of rho\n",
    "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",
    "fig = plt.figure()\n",
    "plt.plot(rho_mean, np.array(sobols).T, label=params)\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('rho [m]')\n",
    "plt.ylabel('First order Sobol indices');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:50.937953Z",
     "start_time": "2021-07-28T13:01:50.157716Z"
    },
    "code_folding": [],
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:36.343878Z",
     "iopub.status.busy": "2025-07-18T17:11:36.343748Z",
     "iopub.status.idle": "2025-07-18T17:11:36.473036Z",
     "shell.execute_reply": "2025-07-18T17:11:36.472695Z",
     "shell.execute_reply.started": "2025-07-18T17:11:36.343869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-28T13:01:52.048722Z",
     "start_time": "2021-07-28T13:01:50.939512Z"
    },
    "code_folding": [],
    "execution": {
     "iopub.execute_input": "2025-07-18T17:11:36.473495Z",
     "iopub.status.busy": "2025-07-18T17:11:36.473416Z",
     "iopub.status.idle": "2025-07-18T17:11:36.618527Z",
     "shell.execute_reply": "2025-07-18T17:11:36.618157Z",
     "shell.execute_reply.started": "2025-07-18T17:11:36.473487Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6f/rn14629n60j16dc99dtk7bs4000ctx/T/ipykernel_27273/1120173477.py:7: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend(loc=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.11"
  },
  "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,
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