tutorials/easyvvuq_fusion_SC_dask_tutorial.ipynb

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   "source": [
    "# Run the fusion EasyVVUQ campaign\n",
    "\n",
    "Run an EasyVVUQ campaign to analyze the sensitivity of the temperature\n",
    "profile predicted by a simplified model of heat conduction in a\n",
    "tokamak plasma.\n",
    "\n",
    "This is done with SC."
   ]
  },
  {
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   "execution_count": 1,
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   "source": [
    "# import packages that we will use\n",
    "\n",
    "import os\n",
    "import easyvvuq as uq\n",
    "import chaospy as cp\n",
    "import pickle\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "if not os.getenv(\"DISPLAY\"): matplotlib.use('Agg')\n",
    "import matplotlib.pylab as plt\n",
    "from IPython.display import display\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# we need fipy -- install if not already available\n",
    "\n",
    "try:\n",
    "    import fipy\n",
    "except ModuleNotFoundError:\n",
    "    ! pip install future\n",
    "    ! pip install fipy\n",
    "    import fipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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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'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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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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   "cell_type": "code",
   "execution_count": 5,
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    "# 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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   "execution_count": 6,
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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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   "cell_type": "code",
   "execution_count": 7,
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   "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",
    "    Inputs:\n",
    "        sc_order: order of the sc expansion\n",
    "        local: if using Dask, whether to use the local option (True)\n",
    "        dask: whether to use dask (True)\n",
    "        batch_size: for the non Dask option, number of cases to run in parallel (16)\n",
    "    Outputs:\n",
    "        results_df: Pandas dataFrame containing inputs to and output from the model\n",
    "        results: Results of the sc analysis\n",
    "        times: Information about the elapsed time for the various phases of the calculation\n",
    "        sc_order: sc_order \n",
    "        count: number of sc samples\n",
    "    \"\"\"\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.')        \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",
    "    my_campaign.set_sampler(uq.sampling.SCSampler(vary=define_vary(), polynomial_order=sc_order))\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",
    "    results = my_campaign.analyse(qoi_cols=[\"te\", \"ne\", \"rho\", \"rho_norm\"])\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"
   ]
  },
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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": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:18:30.251249Z",
     "start_time": "2021-06-04T20:25:34.380415Z"
    },
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 1 = 0.283\n",
      "Number of samples = 1024\n",
      "Time for phase 2 = 2.021\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1024/1024 [01:23<00:00, 12.25it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 3 = 83.878\n",
      "Time for phase 4 = 0.447\n",
      "Time for phase 5 = 14.969\n",
      "Time for phase 6 = 0.031\n",
      "Time for phase 1 = 0.024\n",
      "Number of samples = 59049\n",
      "Time for phase 2 = 107.914\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 59049/59049 [1:20:58<00:00, 12.15it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 3 = 4881.222\n",
      "Time for phase 4 = 27.570\n",
      "Time for phase 5 = 10180.643\n",
      "Time for phase 6 = 4.975\n",
      "Time for phase 1 = 0.068\n",
      "Number of samples = 1048576\n",
      "Time for phase 2 = 1678.196\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1048576/1048576 [28:06:49<00:00, 10.36it/s]    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for phase 3 = 101575.420\n",
      "Time for phase 4 = 882.496\n",
      "Time for phase 5 = 3476360.413\n",
      "Time for phase 6 = 175.156\n"
     ]
    }
   ],
   "source": [
    "# Calculate the stochastic collocation expansion for a range of orders\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    local = False        # if True, use local cores; if False, use SLURM\n",
    "    dask = False         # if True, use DASK; if False, use a fall-back non-DASK option\n",
    "\n",
    "    R = {}\n",
    "    for sc_order in range(1, 4):\n",
    "        R[sc_order] = {}\n",
    "        (R[sc_order]['results_df'], \n",
    "         R[sc_order]['results'], \n",
    "         R[sc_order]['times'], \n",
    "         R[sc_order]['order'], \n",
    "         R[sc_order]['number_of_samples']) = run_sc_case(sc_order=sc_order, \n",
    "                                                           local=local, dask=dask, \n",
    "                                                           batch_size=7, use_files=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:20:48.322921Z",
     "start_time": "2021-07-16T11:18:30.253551Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# save the results\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    pickle.dump(R, open('collected_results.pickle','bw'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:20:52.573368Z",
     "start_time": "2021-07-16T11:20:48.326442Z"
    },
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Phase 1</th>\n",
       "      <th>Phase 2</th>\n",
       "      <th>Phase 3</th>\n",
       "      <th>Phase 4</th>\n",
       "      <th>Phase 5</th>\n",
       "      <th>Phase 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.016301e+02</td>\n",
       "      <td>0.283412</td>\n",
       "      <td>2.021353</td>\n",
       "      <td>83.878372</td>\n",
       "      <td>0.446589</td>\n",
       "      <td>1.496874e+01</td>\n",
       "      <td>0.030772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.520235e+04</td>\n",
       "      <td>0.023786</td>\n",
       "      <td>107.913591</td>\n",
       "      <td>4881.221638</td>\n",
       "      <td>27.570202</td>\n",
       "      <td>1.018064e+04</td>\n",
       "      <td>4.974859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.580672e+06</td>\n",
       "      <td>0.068411</td>\n",
       "      <td>1678.195918</td>\n",
       "      <td>101575.420001</td>\n",
       "      <td>882.496385</td>\n",
       "      <td>3.476360e+06</td>\n",
       "      <td>175.155989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total   Phase 1      Phase 2        Phase 3     Phase 4  \\\n",
       "1  1.016301e+02  0.283412     2.021353      83.878372    0.446589   \n",
       "2  1.520235e+04  0.023786   107.913591    4881.221638   27.570202   \n",
       "3  3.580672e+06  0.068411  1678.195918  101575.420001  882.496385   \n",
       "\n",
       "        Phase 5     Phase 6  \n",
       "1  1.496874e+01    0.030772  \n",
       "2  1.018064e+04    4.974859  \n",
       "3  3.476360e+06  175.155989  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# produce a table of the time taken for various phases\n",
    "# the phases are:\n",
    "#   1: creation of campaign\n",
    "#   2: creation of samples\n",
    "#   3: running the cases\n",
    "#   4: calculation of statistics including Sobols\n",
    "#   5: returning of analysed results\n",
    "#   6: saving campaign and pickled results\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    Timings = pd.DataFrame(np.array([R[r]['times'] for r in list(R.keys())]), \n",
    "                 columns=['Total', 'Phase 1', 'Phase 2', 'Phase 3', 'Phase 4', 'Phase 5', 'Phase 6'], \n",
    "                 index=[R[r]['order'] for r in list(R.keys())])\n",
    "    Timings.to_csv(open('Timings.csv', 'w'))\n",
    "    display(Timings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:21:05.954235Z",
     "start_time": "2021-07-16T11:20:52.578061Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the convergence of the mean and standard deviation to that of the highest order\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    last = -1\n",
    "    O = [R[r]['order'] for r in list(R.keys())]\n",
    "    if len(O[0:last]) > 0:\n",
    "        plt.figure()\n",
    "        plt.semilogy([o for o in O[0:last]],\n",
    "                     [np.sqrt(np.mean((R[o]['results'].describe('te', 'mean') - \n",
    "                                       R[O[last]]['results'].describe('te', 'mean'))**2)) for o in O[0:last]],\n",
    "                     'o-', label='mean')\n",
    "        plt.semilogy([o for o in O[0:last]],\n",
    "                     [np.sqrt(np.mean((R[o]['results'].describe('te', 'std') - \n",
    "                                       R[O[last]]['results'].describe('te', 'std'))**2)) for o in O[0:last]],\n",
    "                     'o-', label='std')\n",
    "        plt.xlabel('SC order')\n",
    "        plt.ylabel('RMSerror compared to order=%s' % (O[last]))\n",
    "        plt.legend(loc=0)\n",
    "        plt.savefig('Convergence_mean_std.png')\n",
    "        plt.savefig('Convergence_mean_std.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:21:07.272635Z",
     "start_time": "2021-07-16T11:21:05.956222Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NQUpJl7srYoFEqtF7j9Ax3BHZz2a0UZZaFldIWOGqoCilCKO2sH/XFjvTHlw0n5hvQjEe8QIyGLJAdGskVkAMqeaYDCxHxApZyAIipSTQ2TnafXX4cHQAVTh9d2T8o6REpe+OoNfTO6YF0jIY7SVm0kyRWpBYF1ZpaqmqBVkgKKE4AQdeep6XHrqf/s4OUjKzOONjV7PijHNmaIXTQwYlgW73qAD6KAFxmUPBc0dcOu+CFpBAAF9j46jgeVz6rtmMuaICa1Vl1PqoqsKYo3z2Ixn0DcaNtK3t0a2R4wPHI7UgBmGgOKV4lAVS5irDZrTN8hUoJsOiEIqpZj0deOl5nrnrp/i90VoKo9nC+df965wVi7GICEhM8Fx3Yw2DP1ZALCNqQEIWiGXhCkjQ48F75Ei89VFTE5++63JhqVwa776qrMSQkjKLK5+buP3uyFyQWBdWfV99XC1IobNwlICUp5WrWpA5ynRjFLnoU+4A3pBStk20/2wzWYvirhs/RX9H+6jtKZnZXPfz3yRzabNCnIC0xrix2kcISJolLn03nM6rWRZuj6JAT09M+m5UQIIDA5F9jPn5o91XS5ao9N0x8AV8HOs7FtfS5EjvEep66/AFfZH9cu25oyYTVqRVqFqQWWY6WU8fAX4AvIAezD4D+LKU8k8zsM6kMFmh+OHHLo7MtB5J2SkbKF27ntK168kqLl1QrgkZlAS6whbIYNSN1T4E/uj7YUizxLiuHJF2JgtVQKSU+JubRwXPPUePRtN3jUbMZaXx1kdVFaaCApW+Owb+oJ/jA8dHdeU92nuUYX90omOmNXNMCyTTmrmg/vbmKtMRineA94WtCCFENvB/UspTZmSlSWCyQvGTz16Nv69r1HZhtpCWlU13k56e6UhLp3TNOkrXrqdkzTqc6QszJVMGJf4ud3wAPSwggbEExBHXD0szL1AB8fnw1tWNsj5i03c1ux1z2H1VGRaQSpW+Ow5BGaR5sDluHkjYEhnwRa06l8Wli0fI8ggLSK5dtYVJJtMquIstrhNCaOijUBdMwd1H/9/PWF//d0wy2ibBJ4zsKnkfD//PjfR1tHNs7y6O7dnNsb27cffrcwmyiksj1kbRilWYLAu7iZsMSPxdwyOKCAd1F1asgKRbotlXOQtfQAIDg3gP14xO3+3piexjyMrSg+eVMRbI0go0mwr2joWUkrahNj0TKyadt7a3lh5PT2Q/h8kRJyBhS6TQWahqQabAdITiB8Ba4MHQpo8Ce6WU/y/pq0wSkxWKJV95iqX91Wzpfp2UwAD9Bic70jdRk1LFF95bxaqCVFYVppKXagUpaaur5dje3Rzbs4vjB/cR8PsxGI0ULl9JyZr1lK1dT05Z+aJxQYwSkJAl4u+IERABhnTriAC6A2O2bUEKiJSSQEfHKOtjVPpuSfEo60Ol705Ml7tLF41wQ8WQBdI+HI0zWg1WlriWRNuZhCyQ4pRiVQsyAdMNZl9OfMHdYyd4yawyWaHY+v3nON4zPGq7URMEpIyELzIcZlbmp7KqIJWVBamsKnBRlGKg5dB+6kLC0VFfB4A1JZXS1aeELI51pGYtnAFIiSIDEn/ncDSAHk7nHUtAcqNdeE05dkw5NoRpAQpIIICvoWGUgHiPHYtP311agTXW+qiqVOm7J6DX08vR3qNRCyQkIM2DzZF9jJqRstSyqAUSEpKy1DLMBpWcMB2L4r+llP9+om1zickKxeO7jvO/jz3HGhpwCC+D0sxeirnhsnN538pcDrb0sa+pj33H+9jX3Et1ywDegP5HbTMZWJGfwqoCFysLUlnqDGJuPULT/nc4tnc3g9167CM9vzDipipeuQbLIq4YloEg/k633r4k1o3VPgzBGAHJsEaKCCNWSPbCFJCg2x0aHhVjfVRX42+LJhhqLhfWyso48VDpuydmyDcUSd+NtUQa+htG1YKEBWSJa0nkfjHVgkxHKN6WUm4YsW3PXOweG2ayQrFnzx4ee+LPyEA0RiEMRi679BLWrh19mV5/kMNtA+xr6mV/sy4iB5r66PforzdqgqU5Tlbmp7DcOkRmbx2BhkM0H9yH3+tBaBr5lcspC1kbeRVVaIaF9+E3WWQgiL9jOK4CPWKBxAiIMcM6KoBuyrYjTAvP1efv7g6JRryABAejTf+MBfmjrA/LkiUIlb47IW6/W2/rHm6oGLJE6vvq8YfilQJBgbNgVBpvuascp9k5y1eQfCYtFEKIG4B/AcqBIzFPpQCvSCk/MRMLnQ5TLbi7/fbb6e3tHbXd4XBw0003YbFYTniMYFDS0D2kWx5NvaH7Ptr7o0V8pWkmNlj6KHE3Ym07zFDzMZASi91B8ao1lK7dQOnadaTl5isXQwzSH4y4sGLdWLqAhHYSYMy0YcyxI/KsdOd68RqCCA1YgO+l9Pv1m8+H9PshfB+DMBrBaEKYjAiTCWE0IgyGcd8Pq9VKUVERJtPibsfhC/io76+PpvHG1IJ4g97Ifjn2nMg8kNhYSJo1bfYWP02mIhQuIB34HvCVmKf6pZSjc0nnEJO1KG699dZxnxNCkJ+fT0lJCcXFxZSUlJAyCVO/rd/N/pBo7A+JSF3nEADWgJsVsoUVwRYyeo4iBnsASM3OoSwkGsWrT8HmVK6FsZD+WAtkMNLKpGulIKM8h3S7CyEEwqiBSUMYNUTs/QITEBkMIr1egm430u1Betz6z75ooZvQNITFgrBa0axWhMWCZrWCwUBnZyf9/f0sWbJkFq9i7hIIBiK1IEd6o+m8tb21cbUgGdaMaAZWOJ03rWJe1IIsihYeYSYrFONZFHa7nVNPPZX6+nqOHz+OP/SNLT09nZKSksgtKytrUr8A/W4fB1v62Xc8annUtPbh8PRQPNzIEk8jhcNNGAMeQJBavISqDadSvm4DBVXLMRgX9ze+E3HgwAGqKirBL/XeV/4g0hdExlShAxHBICwcYRGZ43/Mk0UGAgQ9HmRIQIIet/5zIDobRRiNYLFwuLWVouNNKn13EgRlkJbBllEurNqeWvp90QFdqebUUS6sirSKOVULooRiAvbs2cP27dvxxXzzMplMXHzxxZEYhd/vp6Wlhfr6+shtaEi3DGw2W0Q0iouLKSgowDjJ9EavP0h1a3/E6jhwvJv22sNk9x+jeLiRPE8rGpKgwYyleCnFq9dz6pZNFJcvmTO/ZHOFAwcOsGLFilHbpZQRwYgIx0QCMtIKWUDvs5RSd13FWh5uD4eOHcN04436TkJgLimJ63tlqarCXFqiu7AUEyKlpGO4Y1QQvba3li531CljN9qjlehhEXFVUOAswKCd3PdZCcUJ2LNnD88++yy9vb24XC7OO++8MQPZYaSUdHZ2xglHV5f+n28wGCgsLIwTD9sUvpkFg5JjXUN6zKOulYZ9e/HVHyS77xjpft0CGjY58eUtJbNqDctPPZV1VUXkpCzswr8TMZ5QjIcMSl08wgIStkIWoYAc2L+fcpttVPDcW18fTd+1WLBUVIwSEGNO9oJ6L2aSLndXnHCEhaRtOJrlZjFYWOJaomdgxcRCilOKMWnxXoWnap/iJ2//hJbBFvIcedy84WY+WP7BSa9LCcVJYGBggIaGhohwNDc3Ewz9ceXk5MQJR1pa2pT+qKSUtPd7eHtfLYfe3kl3zT4sbUcwh2Ynt5sz6Ugtw1q2nNKVq1lZnMWqglRKMuxo2uL4I56sUIzHZAUkzo01TwVkvPcu6HbjOXwk2vcqnL7bHi1yM7hccX2vdAGpxOBceNlBM0Wfty8yDz22FqRpsCmyj1EzUppSGrFAet29PHL4EbyBaKDdarBy65ZbJy0WUwlm9wPjqoiUMnVSKziJzJXBRV6vl+PHj0eEo7GxEY9Hz4JKSUmJi3Pk5uaiTbGSOxgMcPTgId5+9Q2a9r2Dr6kWIQP4hYEmSz71tiI6XWXklZWzstAVKRiszEnBbFx4KaWTFYrHdx3nB08foqlnmII0G1++YBkfWl847v5jCYj0B+M68TY2H+fmr3+Jg9UHCQQDvP/C93PbbbdhddgSFpC6ujp27NjBxz/+8Qn32717N01NTXzgAx9I7IInYLLvnb+7e5T14ampiUvfNRUUjLI+LEvKVPruJBjyDXG072hkHkjYEmnobyAog2O+Jt+RzzNXPDOp80ynjuLbQAvwW/TK7H8GUqSU/zOpFZxE5opQjCQYDNLW1hbnrurr0/tGmc3mSFZVcXExRUVFmKf4h+R1D9N44F1qd7/N4d27GGzRm9Z5TXbqrYUctRTRYCvCZ0mlMteptygp0AVkRX4qjnk+l2IyH3aP7zrOVx/dy7AvGti1mQx87/I1E4rFWIQFJOgNsPmsrXzuk5/h6o98goDHx7/8+004HA5+9O3/0a2NkYH0MSyQF154gdtuu40nn3xywvPee++97Ny5k5/+9KeTWu9YJMMak1LiO940yvrwHD0K4RRekwlLWdkoATEVFsxLS2y28AQ8nPa70yKFg7EIBHs+uWdSx5uOULwupdx0om1zibkqFGPR09MTJxxtoUrc2LTcsHhMJi03lv6uDur3vsOxPbs4tnc3Q709AEhXDh2uMt4VeRwiB59mQghYkulgRUFqnIBkOU9cSzJXiP2w+9b2fexv6ht33131PZEq+1jMBo31JWljvmZlQSrfvHjVuMd89tln+da3vsWLL74I6ALS29XDkspyDr9ziP/6n+/xj5dfxOPxcP0nP8tnP3EtCOIERJg0tpy9jQMHD7JkyRI++clPcsMNN3DDDTewc+dOjEYjP/rRj9i6dStLly5leHiYwsJCvvrVr/LRj350Eu9WPMly242F9HrxHK2LF4/qanxNUbeK5nBErY5IFXolxvT0GVnTQuD8P50f16YkTDItikS+OgaEEP8MPITuiroSCEz8EkWipKWlkZaWFgmcDw8P09jYGBGOnTt38tprrwGQkZERV8+RaFpuSkYWq846j1VnnadnYtTXUbdnF8f27MJ0YDfZPi/nGQzYiioYzl5KnbmAt+r9PLUn+suXm2qJiIbe78pFcUbibpS5ylgiMdH2RNi3bx+nnnpq5LHQBGlZ6ZSVlXH3Q/eSnpfJzt1v4R52s23bNi64+P2UFZUifUGC3iAM69+6v/Olb3L7L+/gid8/gjBq3P7DnyD9Qd55ezeHDldzwQUXUF1dzbe//e2kWRQziTCbsS6rwrqsKm57YGBglPuq/+mn6fnDHyL7GLOzR7uvllboNSCLnJs33MytO27FHYpTgh6juHnDzUk7RyJC8XHgJ6GbBF4JbVtQVL/ewqtPHGGgy4Mzw8LmSyuo2pR30tdhs9morKyksrIS0NNym5ubI8Jx6NAhdu/eDeh1HmHRKCkpIT8//4RpuUIIskuXkF26hNMuvhy/18vxg/sjbdQHd/6FEqDK4SRvxRq0wiraXWUcHDCxr6mPf1S3Ewi100ixGiOiEe6wW5HtxGSYO3GPib75w/gNIQvTbDz8uc1TOqeUckwBlVLyzDPPUF9fz5/+pM/96u3tpfZ4HUtXRT88wy4szWnWiwUNGkFvkJdfepl/+dTn8LcOUZFaRElBEfvf3ENw2Ke7vHxBhFHMO/E2OJ3YN6zHvmF9ZJuUEn9b+yjro/uBB5ChOB+aFk3fjbE+zCWLK303HLBORtbTeJxQKKSUdcClSTvjHKT69Rae//1B/F79W+RAl4fnf38QYFbEIhaj0UhxcTHFxcVs3bp1zLTcQ4cORfYNp+WGX3OitFyj2Uzp2nWUrl0H/wxDvT0cezfqphrY+SoAq3Lz+cDadeRvOoXhrCVUdwcirUoeeOMYbp/+3pmNGstyU0Juq1RWFrhYkZ+C3Tw34x5fvmDZmDGKL1+wbMrHXLVqFY888kjctr6+PlpbW6moqODOO+/kggsuGPf1QhMIswGDzYgwapiy9P9DYTFgSLNgSLeGMq8E0isJDgcIuv34WwdBCF0sIim8Bl1s5pmACCEw5eZgys3Beca2yHYZCOA9Vh8vIIcO0f/3v0emVAqrNZq+G+u+yl646bsfLP9gUoVhJInEKKqA/wVypZSrhRBrgUuklP/fjK0q/vwrgJuBLOBZKeX/nug1k41R3PcfrzDQ5Rm13ZZi4qP/eTr2VPOc/gUbGBiIE46WlpYx03JLSkpwuVwJX4uUkq6mRl009uyiYf+7+NzDCKGRt7Qy0g03p7yK+h53pMo8LCA9Q3oBoxBQnuWIdNgNxz4yHDOT9TLTWU8nQkrJaaedxk033cTVV19NIBDg+uuvp6ysjOzsbP7yl7/wxz/+EZPJRHV1NYWFhTgcjlHHeeutt/jiF7/IP/7xDwB+9KMfsW/fPu6++26qq6t53/veR3V1Ndv/vJ0/P/FnfvPLu+MKCol1n8UJiCGazmuIF5CZjFHMJMHh4Wj6bkhA3DXVBNo7IvsY0tJGpe5aKqswOEe/94uV6QSz/wF8GfillHJ9aNu7UsrVCZz0HuAioC12fyHEheiuLAPwaynl9xM4lgb8Skr56RPtO1mh+Nn1z034vC3FRFZxCllFTrKKnWQVpZCWO3frEkam5TY0NOD16jnWqampcXGOyaTlBvw+mmsOhYRjNy1HapAyiNlmo2jlGkrXrKfslPWk5+sfsk29bvYdj3bY3d/UF+fmyXdZI1ZHOPZRlD79uMdc+LBraGjgxhtv5MCBA7S3t/PRj36UX/7ylwSDQf7zP/+T7du3I6UkOzubxx9/HJfLNeoYPp+PCy+8kI6ODq655hpuuOEGrr/+et56661IMPucc86hq6uLCy64AJ/PFxfMlsHRlejSF4ibRkioF1ZYOA4draaqsAJDuhUxR3+/J4O/uxvPoXj3laemhmCoqwKAqbBwlPVhWbIEsQibI05HKN6UUp4mhNgVIxS7pZTrEjjpmcAAcH9YKIQQBqAaeB/QCLyJHiA3oDcgjOVaKWWbEOIS9MaEP5VSPnCi8ybTojj1/WV0NA7Q0dBPV9MgwdAfmdGkkVGoC0d2kZOs4hQyChyYrXPPxRIMBmltbY2zOvr79R40sWm5JSUlFBYWJpyW6x4YoGHfHur2vM2xvbvpbW0BICUzO+TOWk/J6lOwp0Y/BLsHvSHh6I2Ix5H2gUgXcZfNFBkOtapQtzzKsxwYJxH3mAtCEcuOHTu48sorefTRR+OC3LPFKAHxBXRXVkByqP4w6X8eQJg0vX17TngaoT4XxJBmmfcCIoNBfE1NcWNrPTXVeI7WxafvLlkSJx7WqiqMBQs7fXc6QvFX4F+BP0opNwghrgA+LaV8f4InLgOejBGKzcCtUsoLQo+/CiClHCkSYx3rKSnlmI44IcR1wHUAJSUlpx47diyR5QFQ/afHef5ZC34ZTQE1Cg/nnOeh6ooPRbYF/EG6W4boaOyno2Egcu8ZCv1yCXBl28gqSglZHk6yi1Owu+aW60pKSW9v75hpuZqmjeqW60ywsranpTkSFK/f9w6eQd1nnlNWTulafURswbKVGEd8Uxv2BqLDoZr62N/Uy8GWfjyhAjaLUWN5XkrE8lhVkMryvFRs44xQnWtCMV+QQcmBffspHc7Ux9mG5oEE+6IVvxEBGTGRcEEIiNeL5+jROAFx11Tjb4pm/2lOpy4eIwTEkJY2ewtPItMRinLgLmAL0A0cBf5ZSpnQJ/EYQnEFcKGU8jOhx1cBm6SU/zrO688GLgcswB4p5c9OdM5J11Hcvprq5jJeHfgEA8EsnFoHm52/oyr9HbjkDshZCRnlYBhtikopGej20NHQH7I8dAHp64imqtlSTLrbKiIgKaTl2tDmUHbQ8PBwXPuR48ePEwh1Fw2n5YZvmZknbpccDARorT2sWxt7dtNcc5BgIIDRbKFoxSpK1+gWR1ZJ2ZjH8geC1HYM6pbH8Wjso8+ti7ImoCI7WiwYjn2k2c3zTij27t3LVVddFbfNYrHw+uuvn/S1jPXeBYf9IdGImUjYOkSwP0ZAzFELxJTriFghhjTLnPqSNBUC/f3xw6Oqq3HX1BCM6ThtzMkZ7b6qmH/pu1MSipCb6PtSyi8LIRyAJqXsH/cFYx+jjHih+CfgghFCcbqU8vOTOe5ETFoobk1jgm4lOpoJsiohZwVkr4Cc5bqApJfBGB0ePUM+Oo8P0N4wQGfjAB2NA3Q2DRD0h0YvmjQyCxwxsY8UMgvnjutqZFpufX09w8N6fMFut8cVAiaSlusdHqJh/7u6xfHOLrqa9GpxR1o6JWvWRYTDmZ4x7jGklDR2D0esjnDso7k3KsqFaTb++7wMKpctx2YyYDUZMBnmV8bPbDIZkQ0O+aJz0EOzQHytgwT7Y+ZfmA0Yc2zx42xz7Rhc81tA9PTdtpBwRIPn3sNHkKF4IJqGubQ0LnhurarCVFw8Z9N3p2NRPCelPHcaJy4jSa6nBM41pQl33L4aehtGb08tgisfgLaD0H4A2kK3nhhjymgNCchKyF6uC0nOCnCVwIggcSAQpKdliI6GftrD1kdD/7xwXUkp6ejoiBOO7u5uID4tt6SkhKKiohOm5fZ1tFO/dzd1e3ZRv3c3w/169XRWcake31iznqIVqzEl8I2sc8ATEY19TX18uBwyCqPDd4yawGoyYDMbIuJhmadN+2aaZFhjowSkdVC3QAZGCEhu2AKJurEMc+B3fTpIvx9vfX1MAWE17upqfPUN8em7S5eOdl9Ncq7NTDAdofghUAn8EYh0+pJSPprgicuIFwojejD7POA4ejD741LKfQldSQJM2qLY8wfYfhP4YgqvTDa4+A5Y+5HR+3sGoOPQCAE5CH2NMa93QHbVaAFJLYwbRRlxXYUC5uH7sVxXmUXRzKv0XPusu676+/tHdcsN/z7l5ubGxTnSJvDhymCQtmNHI2m4xw/tJ+DzYTAaKVi2ktI16yg7ZQM5ZeWIBDK0Dhw4QNWy5bh9Ady+AMO+AMPeAG5/MLI+TYTEw6RFRMRqNMzZTLaTxUy67QKDvsgc9NiJhHECYjGMCqAbc+0Y5niK+okIDg3hOVIbFzx3V9cQ6IhJ301PHyUe5qWVJzV9dzpC8ZsxNksp5bUJnPRB4Gz0GohW4JtSyruFEB8Afoye6XSPlPK7J7yCBJiyRQG6WDz7behtBFcRnPeNsUViIty90H5IF472g9C2XxeQgZboPpZUyF422oXlzI0TEM+wP+SyCgfOx3FdhdxWupA4Z9V15fF4RnXLHZmWG77l5OSMm5br87hD1eK7OfbO27TX1wFgTUmlZPUpunCsXU9qds6Yrx+3VbaUeHzBqHiEhCRcaS4QWExaxOoIi8hkMq7mO7MR3wkM+kKiMRjnxholILn2UCDdEbFCtHkuIP6urlHWh6fmMDI2fbeoaLSAlJXFpe/2bt9O2+0/xt/cjDE/n5wv3ILr4osnvR41j2I2GeoKCceB6H3bARiKfpvAmha1OmIFxJEV2WVM11VjP55Bf2QfV7YtEjAP3zvSZuePKRAIjOqWG07LtVgscd1yJ0rLHezppn7vbo6FXFWD3fqAqPT8woibqnjVWix2OzC5DzspJd5AELc3wLAvGBEPX0yxmtmg6RaHSXdd2UwGjCeIezidTgYGBiKPYzu8ejwerr76at566y0yMzN5+OGHKSsrS2i9M81cSgQIDHhjLJCQK6ttkGDM77uwGkYF0E0581tAZDCI7/jxuNoPd3U13qN1EB5fazJhKS/HUlmJDAQYePbZaGwE3b2V/51vT1ospmNRWIFPA6uAiMM4EYtitphzQjEeA+0h11XI+giLiLsnuo89K0ZAlkfv7XrQd6TrqrNxgPbGAfrao240q9MUZ3nMlutKSjmqW257aPBNbFpuWDzGSsuVUtLZWM+xPbs5tncXDfv34vd4EJpGfuVyStesI2XZalavWZP4B8UY1qRv1RXxbitfEI8/2ubDqGlYTdq4cY+JhOLnP/85e/bs4Re/+AUPPfQQjz32GA8//PA03tnkMZeEYjwCA96QaMS7sYJDsQJijMY+YuIgWsr8FZCg14v36NE48fBU1+BvHt05FsBYUEDlc89O6hzTEYo/AgfRGwF+G30exQEpZfJaEyaJabme5gpSQn/LGAJyELwxCWfOvKjVESsgVn2elHfYT8fxgbh6jzjXlVEjs3D2XVdDQ0Nx3XJj03IzMzPj4hxjpeX6fT6aqw/o1sY7u2g9epht//IlyoqLsNhsmG12zDY7BpNp7A+IScSnAkEZEQ/dAhkv7mFgWXE2bV09WE0GNCHihOKCCy7g1ltvZfPmzfj9fvLy8mhvb58TH2DzQSjGIyIgMQF0f9sIAbEZIwH0qAXiQEsZ5/djHnBgxcpIoDwOIVhxYP+kjjUdodglpVwvhNgjpVwrhDABT08nE2qmmTcWxWSQEvqOR91W4RhI+yHwRf2ZpBaFBCTGhZW9HMyOqOsqJnDe3jCG6yqmVUlWsRPHScyF9/v9NDU1xbUfGSstt6SkhLy8vFFpucP9fdQcOUJpYQHe4WHsO76PqfsQCA1N02/CYEAQup7GNyEwuiofgwWKTht7kXlr4P161xk97qG7rdwR6yPA2pIMKpevBPQGd3093Vz4gYv4yZ138p5T1/G3v/2NoqIiACoqKnj99dfJysoa+3wnkfksFGMhpSQ44AsJSLSI0Nc6hBweISAxritjKA6iOee+gNScex7+mJkeYZJpUSTy9TEcUeoRQqxGn3ZXNqmzK6aPELpbxFUEle+Lbg8G9XTd2NhH+wE4+lL8B2BaKYacFWTmrCAzewXLti6HrOVIo5XBHk+c5dHeOMCRXdFZyFaHKZKuG7Y+0vLsGGbAdWU0GiNCoF9ecFS33IMHD0b2LSoqiu+Wm5KKyWLFlZ2LlBKZkoIcMCMDAYKBAMGAH3wghC4YhoCHMT8GxhKPMdCEwGY2YosJr0gpsdlsvPbmWyELJMgDv72fve+8TW37AMO+AIfb+gnYBrGZDAQl+IMLL1Y4FxBCYEgxY0gxw9K0yPaogMQH0If2dMQJiGY3xriuHJGf55KA5HzhFpq//g2kO5opKaxWcr5wS9LOkYhQ3CWESAe+DvwZcIZ+nnPEuJ5meyknD02DjCX6bVlMV5VgALqOjnZhHX4WgiHtFxoifQnOnBU4c1ZQVrwcTl0Bmafi9WmjXFd7XzhOINRWw2DUyChwxAfOC52Ybcl1XWmaRnZ2NtnZ2ZE+Sf39/XHC8dJLL8Wl5W7atImhoSHMZjPGD/4gciwpJT6PG+/QEJ7hYXyeYbIeuQjjYMvoE7uK4VNPTWnN4Q+QNHtUPQrTbRx3mCnLclBSVERXWzN5BYV09g/T09NDq8dIV1NfKGiuRYLmZlXvMSPECoh1aXR6npSSYL8vznXlax1i6J0OpDv6e6LZjSHXlSMuDmJwnvw54OGAdTKynsYjkXkUvw79+A+gPGlnngGklNuB7Rs3bvzsbK9l1tEMkLVUv62I+YUJ+KDzyGgBOfRXkKFgrWbEnFFBQc5yCnJWwoblcOEKAq5T6Gn36q6rkPvq6O4ODrwSDaalZttCTRJnznWVkpLCqlWrWLVKH0o0Mi3X6/XS09MDgMFgwGw2R24mixWz1YYTvc2I/+yvIZ/+MsIf/TYmDVaGT7sZ0d+nxzdOUHWeKJoQpFpNXHH5h3jm8T9w+YXn8sADD3DOuedSmG5nOBT3GHD7IzOQDSJaLBhO2bWE4h6K5COEwJBqxpBqxlo5UkC80eyrsAWyuw3pjiY5aA4jxhzHqED6TAuI6+KLkyoMIznhX4AQIhO4FdiK3ufiJeA7UsrOGVuVYuYwmEIxjOV6HlsYvwc6auJdWC17Yf+fCbc3MRjMZGZWkpmznGU5K2DlCmT2cga1AjqOD8VZHyfTdWWxWCgvL6e8XP8es3//frKysvB6vXi9XjweTyTOIYSIF45TP4GwWOHZbyN7G5EpBQyffguDRecRbGsFwGixYLHZMdtsmKy2hNuyj8enP/1prrrqKpYuXUpGRgYPPfRQ3EzyaNwjFPvwBuga9BIMV/YKgdWojao2NyzyYsGZRBcQC4ZUy2gB6fPGpPAO4m8bYmhXG9ITKyCmEQH0UCW6Y360Mk8kmP134EXgd6FN/wycLaV87wyvbcosyGD2bOEdgo7q0TGQnvroPkYrZFXF1YF4Xcvo7E+j4/hgJHDeeXww4rrSjILMAmd84LwoOa6rkQFZKSWBQCAiHF6vF78/6oc2mUxx4mEwGPRePh4PnuEhvMND+NzuyIhTk9WGxa5nVBnNJyfQL6XE6w9GCgXDKbv+YLTewxIWjxgLZLJjaRdaMHu2kFIS6POOysDytQ7FC4jTFFOJ7oj8PFsCMp2sp7eklKeO2LZzrIPNNgsiPXa+4BnQM65ie2C1H9Qzs8KYHKEq9JWQs5xg5gq6Kaejx0ZHY1RA3DEVuKlZ1rhGiVlFTpzpk/swTuTDLhAI4PP5IhaHzxddg8FgwGKxjBIOn3s4FN8Ywh8qbtIMBsw2u25x2G0YjCfvD1xKiT8oIy6rcOqu1x8VD5NBi7isbCYDVrMBs2H8uIcSiplFSkmg16tnYIXcV2E31igBGRFAN+Xa0exj/34N7mqj7+k6Aj0eDGkWUi8ow7F+7M4FEzEdobgN2An8IbTpCmCVlPKbk17FSUJZFLPIcE+MgMTEQAZao/tYUiO1HzJ7BYO2ZXR4i+noMEZatffGFAxaHMZIvCM8JGoi19VUPuyCwWBEOMK3SH2EpsW7q0wmgoEA3pC14RkaIhiq/TCazZHaDbNt+m6qqeAPBnF7g3Hi4fEF4+MeMS4rm8mAxaShCaGEYpbQBcQTUwcSFRHpjRGQFFOc5WHKteNrHaT3yaP6+NsQwqSRdnnlpMViOkLRDziA8Co0os0BpZQydVIrOQkooZiDDHVF3VZtB6M/D8WEumzpodqPFXjTV9IZrKRjOJeONvSq86ZBAr7xXVeZRU4sNmNSPuyklPj9/jjhCBcCCiFGuasCPl9EOLzDwzFuKmtEOEyW2WutHQxK3P7YYkG97mNk3KO9sZbDXldkOJTDMjfa3i9WZDBGQNri4yDSG5zwtYY0C/lfOX1S51O9nhRzk4H2+PYlYQFxR4fC4MiG7OUEs1bRY1lNh6+Mjv50Olp8tDeMdl2tusTBsqrlGM0aRrMBLUnzKEbGOWLdVUajMeKuMhmNccLh8+g1GZpBw2y1Y7brwjFy0t/JRkqJxx+Ma1VSd7iaa5/Qs9iEgCVZjtBo2uh0wcyYwLtidpBBSaDHg69tiM57x2+8XfT9MyZ13OkU3CGEWIteZBfZP9E24wrFhDizwXkWlJ8V3RZuYzJCQLQ9vyPDO0AGUAWQko9cs5yh1HW0i5V0uAvo6LERCEgGe6MFc0ITumiYDBHxMJomX59gMBiw2WyRWRvBYDBOOIaGhhgcHIzsazabsaZn4jQYCMYIh3tQ7wFlMJlC2VQhN9VJHmYjQqm3VpOBtNA2T4eNHV85l/1N0amCu+p7eHJPNAU6L9UaEY2VoQmDRek2Ve9xEhGawJhhxZhhxZBmIdAzukDUkJY8QU/E9XQPsBbYR9T9lFCb8ZONCmYvcKTUG/fFubBCbUz80ZjGgfc/yrKKJfiFHT8W/EEjfr/A75PRnjgCXThMIeEIWx/TSDGVUo6KcwRDWUmxabkGTUP6fHjdITdVaB+T1RoRDpPVOisfvOO57XqGvOxv6osZENXL4bYBwgXlqVZjRDTC42krsh2LqkX7bDG4q42eR2tmPUaxX0q5clJnm2WU62mREW5jEhKQA/ZNrCjOAr+b2BG30mAhoDnwCxv+oBl/0IDfp/vvwxiM2mjxmILryul00t/fH3FX3Xvvvbz11lt85zvfiewTjnMYBAR9PnzuYXyhNgxC0zCHmhpaJmpqmGQmE99x+wIcbOnX55qHLJCDzX14QllXZqPG8ryUkOWhC8iKvFRs5rk5BnQ+M9NZT4m4nl4VQqyUUk6uDaFCcbKIbWOy/ANw4IBeUCilXkjod4PfjfANY/QPYfR1ExYQaYAn23dyR81DtLrbybHm8JmK6zk3+/zI4afquhJCYDQaMRqN2O12bDYbubm5cWm5YVcVgNFkwWJ3oiGRIeHwDA7SDxiMRt1FZdfdVAbD7AeZrSYD64rTWFecFtnmDwQ52jEYsTr2NfXxl70tPPiGPmpYE1Ce7dTFIyb2ke44+a0vFhKO9TlTEoZESeS37T50sWgBPIBAdz2tnbFVKRTJQAgwWfVbLDIYEZC/1D7Ft/f/EneoCWCru5UfHvgeDtHGhYUX4MeODwt+v5FhTyBqoIzlujJpCc34WLlyJUeOHCEYDJKbm8szzzzDpk2bOP/88/nhD38YGWKkma2Y7E40IOj34h4aiMwWN1ks0TRcqzWhEbEnA6NBozI3hcrcFD60vhDQXXLHe4Zj4h59vHm0iyd2RzueFrisEatjZSj+UZim4h5zhUSE4h7gKmAv0RiFQjEv+O83/puDXQfHfX5P+x68QW/cNnfQy637f82jTS+F+l+F1EGCRFCZWsnNy2/CL814hw24B6PZT5pRw2TSGB4e5pS1p+gfdAK6urq45JJLMBgMVFVVsX//fo4ePcqpp57K66+/zhlnnEFLS0tkRsWYabkWO0aDAQ1J0O9jsLeHwZ5uPf4RM3vDaJ5bw3mEEBSl2ylKt3P+qrzI9q5Bb0g8eiOxj+cOtkbiHml2U8jqiFoe5dlO1apkFkhEKOqllH+e8ZUoFLPASJGIbvfFWCKhILgMIGQQoyGIU+uAgBepQVAz4MeqB8+lFb/HhNVq4+/bXwJ019UfHn2Ad/buYnjAy5YtW/nHP/5BXV0dX/3qV/nVr37FWWedxWmnnRap0TCZTDgcDiA+Ldfj8YQm7WlgtWPQNDTA5/fi6eoE2YFmMGCx26NDm5LU1DDZZDjMbKvMYltldA7HkNcfinv0sT/kurrv1WORanOrSWNZXmok62pVgYvleSlYTSruMZMk8ht0UAjxALAd3fUEqPRYxfzg30//9wmfP/9P59M8OHqUZL4jn99c+JuJDx4MIPxuDD43Br8bi38YfD0gfAiCpBsaIwIiAz4CviD9nW5OWb6R+35/D23tLXzppq/y39//H579v+c4Y9vYOe8nSsv1+XxIzQhWI5oQSCTDbg/Dg4MQDGI0m0MtRuxJaWo4k9jNRjaUpLOhJNp4zxcIUts+GBM072X7O0088Lreb8ygCSqyHawqcMVZIK5x2l0oJk8iQmFDF4jzY7ZJYM4JxaKcR6GYFjdvuJlbd9yKOxBtM241WLl5QwKTfjUDmB36LZagH4SGKT0Hk98Nvn6chh6sopcMYz1nn1rCTf/2GqUlZWhBjRVVq7jrrrv43d0P03F8ANMJsq40TcNqtWK16hbPmGm5RhOE+k75kfiGhqC/D03KSLW4xWbHOIvV4oliMmgsy0thWV4Kl2/Qt0kpaewejojH/qY+Xj3SyWO7or3GCtNscW6rlQWp5LtmJ+14vpPIPIpPnYyFJAM1j0IxWT5Y/kEAfvL2T2gZbCHPkcfNG26ObJ8SWujPyhEz2tRVhHA0Ycwqw+UfpqQon22nrSLLdIz3vqeSx7f/kY0r8wgG+/G7LXiGo3+aQhOjUnZjs65iazRgnG65evSdAKE25v399Pf2oEFktrjFZp/6NZ9khBAUZ9gpzrBz4er8yPaOAU9cseD+5j7+fqA1Uj6T4TBHrI5w3ceSLIeKe5yAROooioA7ic6jeBm4WUrZOPPLmxqqjmJxM68a20mpWyB+N/iGQ/dugn4PgYBBLxiUFnzSQkCakTGDW6PiceKsq4najyCDiEAAgkEajh+n7+AeytZuoHjVWiz2+SMe4zHo8XOwJSQex/vY19xLdcsA3oAe97CZDCzPT4mzPqpyF2fcYzp1FL8BHgD+KfT4E6Ft7xv3FQqFIjGE0IdJGUxgSYls1qREC/ow+dyhOpAhpK+LgC+AP2jCLy34/Wa8PgvuwegHmmYAk9mA0WyM1H5oRjFmnGOsbrkBzcArR4/z2r6DGIZ/RV5WFpVr1rLklA3kL6066W1GkoHDYuTU0gxOLc2IbPMFgtS0DoSyrXT31RO7mvjda3rcw6gJluY4I1bHynzdAnHZFmfcIxGLYreUct2Jts0llEWxuJlXFsVkkRIC3ojlgd9NwOfF75NRAZFmAkQL2IQAo0no4mExjFkwGO6Wu2/fPg4fPkxdbS0DQ0P6k8EAhuFBzD4PBXm5LF+7jqXrTyUtr2BB+fuDQUlD91DUbRVyYbX1R/soFWfYWJUfalNSqItITsrcj/MkynQsig4hxCeAB0OPrwTUGFSFYjYQAowW/WZ1AWAADFJiCXgj7qugb5CA16/3uJJmfD4zbq8FORDjujJKvWjQYtIFxKQPbLriiisA6O3tpaGhgdojhzl6+DDd/QMccQc58vpbaP94CRtBCvPzWHHKBpafdjo2Z8pYK543aJqgNNNBaaaDD6yJxj3a+z1xQfN9Tb38bV9L5PlMh3lEn6tUyjId0+obNtdIxKIoAX4KbEaPUexAj1Ecm/nlTQ1lUSxuFrRFMVli2phI3zABrw+/L4jfr+GXZvzSTDDm++KxxsO0vdxOVmk62ZXFZJWmkZKpZwq53W4aGxupPrCf2poauvr6IxW4wuvBaRAUFhSw8pR1rNh4Oibzwm3LMeDxc6C5j33Ho32uatr68QX0z1OH2cCK/GiV+aoCF5W5TizGue26U/MoFIsGJRQJENPGJODx4Pf68fskh+oaOfhADd2BAvQZZWA2eslK95BVYCGrPJesZUvIKEwFTdLU1MS+XW9Re/gwnb39BMKZWAE/TpOBooJCVpyyjhXr1mOa5fkbM43XH6S6tZ/9zVHLY39TH4OhCXUmg2BpTkpcseCK/BRSrHPnfZlO99j70C2IntDjdOCHqs24Yq6ihGLqHNi/nxUZAXzHD9B5uJGOxn462jU6BrPo9Jfil3rthkaAdGcfWdlBsopTyaosInNZBT39nex54zVdOPr68YebF0qJ02SkqLCAFaeso3L5CuwLIKPqRASDkmNdQ3Eddvc39dIxEO0IUJppjwhH2ALJSbFOcNSZYzpCsUtKuf5E2+YSyqJY3MxFobjmmmu46KKLIv7/ME1NTdx000386U9/mqWVxTPue+cdIth2kL7DNbTXttLR7KWjy0LHcD5DwWg2kdPUQ1baIFn5RrJLs5AZNmoa6jl6pFYXDrMFhG6p2E1GigoLWbZmDUuWlJOenr5ggsInoq3PHddhd19TH/VdQ5Hns5yWOMtjVUEqJRn2GY97TCeYrQkh0qWU3aEDZST4OoViXtC7fTttt/8Yf3Mzxvx8cr5wC66LLz4p5y4oKJgzIjEhZjta0QbSijaQdjZUhrd7+hk6eoCO6qN0HOuhozVIR28qx9qzkHsMgBezSCXPUciqDC+a002bd4DmvgG6e/qodruprtPDnRajLhxLly+ntLSU3NxcDPMwHTcRclKt5KRaOWd5tDV4n9vHgZgOu/uaennlcAf+UJdEp8UYSdMNWx6VOSmYjRqP7zrOD54+RFPPMAVpNr58wbJI995kkIhFcTXwVeBP6MHsjwDflVL+NmmrSDLKoljcTMai6N2+neavfwPpjrbwEFYr+d/59rTE4v777+e2225DCMHatWsxGAykpqayc+dOWlpa+J//+R+uuOIK6urquOiii3j33XenfK5kkixrzNfbSdeBg3QcPk5H4xAdHUY6BjPxS308p4Yfl7EBi/EYQ1oPnUE/A8JAwO5EmvR9DJpGYWEh5RUVFBcXU1RUhMWyuOZ1u30BaloH4jrsHmjuYygU9zAbNLJTzLT0eQjEDOCymQx87/I1kxaLaQWzhRArgXPRZ1E8O9eHGCmhWNzEfti1/Nd/4Tkwfpvx4XfeQXpHd5AVZjO2U04Z8zWWFcvJ+4//GPeY+/bt4/LLL+eVV14hKyuLrq4uvvjFLzI4OMjDDz/MwYMHueSSS/R6hQUqFGMRDEr66ptpP1BDx9EOOpp9dPTYGfI5AZDBAUyBfQSpY8DoxWuxEbA7CVpsIPR+V7m5OZSWllFSUkJJSQkpKfM7JXcqBIKSus7ocKh7X6mLTBWMpTDNxitfOXdSx56O64mQMMxpcVAopsJYIjHR9kR47rnnuOKKK8jK0ns9ZWToPvwPfehDaJrGypUraW1tnfLx5yuaJkgrKyCtrCDqugKGej101DTSUVNPR8MKOtpWEOx3YB3uIthXTzB4FJ+xF7/dQftAL63Nzbz++usApKe5KCkto7i4mJKSErKysuZ0d9xkoHfLdVKR7eSSUwq46x+1Y+7X1DM85vapoGINigXNRN/8AWrOPQ9/U9Oo7caCAkp/e/+UzimlHDMoG+s2WYhp6VPF7rJQsrGCko0VkW0+b4Cu4wO6gNS20NbQT1tbJ77+RgK+egKmAQJ2JwP9Pbzb2ck777wDgM2kUZyfTUnFMkqWLKWgoADjHJ3HkSwK0mwcH0MUCtJsSTvHwn4HFYoTkPOFW8aMUeR84ZYpH/O8887jsssu4wtf+AKZmZl0dXUlYaWLC5PZQO4SF7lLXMAqAGRQ0ts+TEdDH837amjYv4eutmO4jx9BGr0E7E789nRqhzxU17fC8y9iQFKYAiX5mRQvqaJ41enYUzMmPvk848sXLOOrj+5l2BeIbLOZDHz5gmVJO4cSCsWiJhywTmbW06pVq/ja177GWWedhcFgYP36OZtJPq8QmiAt105arp2lG/MAfdDTYK+HuncOc+StnbTUvMNAy34CmiRgcxJ05NLkdlLf1wXVr8HTr5EhhilL8VKal0ZJeSVppWsQ2VV6W5R5SDhgPStZT0KIfqKj5EEPZMvwvZQyNWmrSDIqmL24mYt1FPOFhfDeBfx+Gg8c4NCrb9Kwbzc9LUeRQhCwpRJ0FhCwpRCwSqQWGq8a9JMvO6lw9lKeayO3pBxD7krIXgGZFXpn30XCpIPZUsrFl06gUCjmPQajkdI1ayhdswYA9+AADfv2cOydXRx9Zxd9x/br33idWQQdhfgsTo5Z7BwdzoU60I76SA+8QZF8kqWGWqpy/Fhyl0LOCv2WvQIylug93RcJCbmehBCnELbz4EUp5Z6ZW5JCoVAkD6vDSeXpW6g8fQsAvW0tHNuzm2N7dlH/7ju4WwewALbsUkgtYlhY6cFHp9HCO6IY2sHWYiIr0EMRj1KlHaDY0oAxZ4kuGjnLIWclZC+HtFJYgFlXJxQKIcTNwGeJzsj+vRDiLinlnTO6svg1OIAXgW9KKZ88WedVKBQLD1dOHmvfeyFr33shwWCA1trDunDs3UXTodch4CfFZCZzyTI0VzFD0krP8AANgQANopRXKcXgteKot5J9bJDSwH7KDI+TbTqKzRKA7GWjBcRVpLeIn6ckYlF8GtgkpRwEEEL8N/Aq+njUCRFC3ANcBLRJKVfHbL8Q+Al6K/1fSym/f4JD/TvwhwTWqlAoFAmjaQbyly4jf+ky3nP5R/G6h2nc/y7H9uzi2N7dtFXvBcDpSmP5qlOw5JbS7zPT2tFJV18rR6SPIxQjgksweV04PHZyBz2UHD1KjvwrWcaf4zK0IixOXTiyl8e7sFLy5oWAJCIUAgjEPA6EtiXCveizLCIJ6UIIA/Az9FGqjcCbQog/o4vG90a8/lpgLXqx3+y0U1QoFIsGs9VG+YbTKN9wGgD9nR0c2xt1Uw3t+AcAmUUlLF+zjoyKSoakhaNHG2lqbqTb3UA3cFA6MfnOx+h2YQumkOeT5HmOk1X3DlnybjKMDRiFVx8+FbY6YgXEmT2L78JoEp2Z/boQ4jF0gbgUuDuRg0spXxRClI3YfDpwWEpZCyCEeAi4VEr5PXTrIw4hxDmAA1gJDAsh/iKlHF2vrlAoFEkmJTOL1We/l9VnvxcZDNJeXxexNvb+39/w//XPaAYjhctWcOba9WQvXYbbYOTYsQaOHqmjvbORYSnpAqoDDozeLZi8F2L2uchMgWxDB1nNh8k69iZZ8hFsWp9+YntmjIDEuLDs49SA7PkDPPtt6G3U3VznfQPWfiRp78MJhUJK+SMhxAvAttCmT0kpd03jnIVAQ8zjRmDTBOf/GoAQ4hqgYzyREEJcB1wHUFJSMo3lKRQKxWiEppFTVk5OWTmnXfJhfF4Pxw/ujwjHyw/pjhOrM4WS1aewbe06CpZfRL/XR319PfX19TQ0NNDvbQagT7Ny3OtCDFZi9G7E6HfgdAqy0gbIEo1kte0n69hzuIK/RohQpYIzN2p1hAWkbT/87Sv6GFyA3gbYfpP+c5LEYjIFdwIIkrjbaaLjjOSE/QyklPee4Pm7gLtAr6OY0soUi5Lq11t49YkjDHR5cGZY2HxpBVWb8qZ8vM7OTs477zwAWlpaMBgMZGfrroQ33ngD8wlGhJ599tncdtttbNw4Kp1dMYcwmS2UrV1P2Vq9oHKotyfkptID49WvvQxAen4BJWvW856167jisg/ROzAYEY76+nr6DHrfL6PBhN+UQb8nlcONeRi9lQh5OSazIDMrSJaziyxTHVmdu8g89gDGQG9kLdVDZ/DqwCcYCGbh1DrY7PwdVc9+++QJhRDiG8A/AY+gf8j/RgjxRynl/zfFczYCxTGPi4DRzXamQMyEu2QcTrEIqH69hed/fxC/VzdUB7o8PP97vdvsVMUiMzOT3bt3A3DrrbfidDr50pe+lJT1KuYudlcaK7adzYptZyOlpOt4Q8Ta2P+PZ3nnmacQmkb+0mWUrl3H5jXrufyyy+jr748Tjra2GkgHIQTpKVlYDRn0DTtpa3ARHD4VOBUhPkNatomsDDfy6Csc9byHAPoXkIFgDs/3/Qvwv1Ql6doSmUdxAFgvpXSHHtuAt6WUCZVvhmIUT4aznoQQRqAaOA84DrwJfFxKuW+qFzESVZm9uImtLn7pD9V0NAyMu2/r0V4C/tF/AwajCPUZGk1WsZMzPpLYn2BYKM455xy++MUvMjAwQFZWFvfeey/5+fljvubss89m3bp1vPHGG/T19XHPPfdw+umnMzAwwOc//3l27tyJEIJvfvObfPjDH05oHYmyECqz5yIBv4/m6kPU7dnFsb27aD1yGCmDmG12iletpXTtOkrXrCc9vwC3201jY2NEOI4fP47f7wcgzZVGZmoeNpEOA04GmiWD3V7c1lYGnXUEDR60gAXHQBlZfhOf/OkVJ1hZPNNpM16HnnEU7ppmAY4keNIHgbOBLCFEI3odxN1CiH8FnkbPdLonWSIxHYuiueUJao/chtvTjNWST3nFl8jPuzQZy1LMYcYSiYm2TwUpJZ///Od54oknyM7O5uGHH+ZrX/sa99xzz7ivGRwcZMeOHbz44otce+21vPvuu3znO9/B5XKxd6+estnd3Z20NSpmFoPRRNHK1RStXM22j12Fe2CA+n3vcOwdXTiO7HwNgNTsHErXrKN07Xq2nH4a5513Hn6/n+bm5hir4xjDw7rVa0+34xGCgGkIQnGMoNFDf2oN9FWOu57JMq5QCCHuRI8deIB9Qoi/hx6/D3g5kYNLKa8cZ/tfgL9MerUnPt92YPvGjRs/O5nXNbc8wcGDXyMY1INBbk8TBw9+DUCJxTznRN/87/uPVxjo8oza7sywcNm/bUjKGjweD++++y7ve9/7AAgEAuNaE2GuvFL/0znzzDPp6+ujp6eH//u//+Ohhx6K7JOenp6U9SlOPlank6pNW6natBWAnpZm3drYs4vq115h73PPgBDkLlkasTY2nX46W7duRUpJZ2dnRDh273onIhIRtCBDrmNJW+9EFkXYd/MW8FjM9heSdvY5Qu2R2yIiESYYHKb2yG1KKBY4my+tiItRABjNGpsvrZjgVZNDSsmqVat49dVXE37NyHkWQohx51wo5j9pefmsy8tn3fkfIBgI0HKkJhTf2MXO7Y/yxuN/xGixULxiNaVr11O6Zh3r169nw4YNkXjYSALCPeb2qTBRU8D7wj8LIcwQiYscklL6kraCJDJV15Pb0zzO9iY6Ol8gPe10DAZ7ElaomGuEA9bJzHoaicViob29nVdffZXNmzfj8/morq5m1apV477m4Ycf5pxzzuHll1/G5XLhcrk4//zz+elPf8qPf/xjQHc9Kati4aEZDBRULaegajmbr7gSz9AQjQf2UveOHhg/ev+vAXCkZ1C6Zh0Wg4YnMLpqwG5NXtv0RLKezgbuQ49VCKBYCPFJKeWLSVtFkpiq68lqycftGTvx6p13Po0QJlyuDWRkbCUjfSupqWvQC8wVC4GqTXlJFYaRaJrGn/70J2666SZ6e3vx+/3ccsstEwpFeno6W7ZsiQSzAf7zP/+TG2+8kdWrV2MwGPjmN7/J5ZdfPmPrVswNLHY7FaduouJUvdysr6NNT8N9Zxe1u3aCMEJ+aXw322AAc1tj0taQSNbTW+hZSYdCj6uAB6WUpyZtFUlmsllPI2MUAJpmY1nVrViseXR1vUx31w76B/SYu9GYSnr6ZjLSt5KRsRWbrVS5BOYQKnNn6qj3bn4hg0F+dOUleFMz8GYXIk1mhM+Luf045v5u/u2h7ZM63nSynkxhkQCQUlYLIRbUJI9wHGK8rKfMDL0o3evtpLv7Vbq6XqGr62Xa258GwGotDInGNtLTN2M2L6xRiwqFYm4iNI2UrGz6O9ox98WP3E3JSl6/qESEYqcQ4m7gt6HHn0APcM85ppMem5936QkD12ZzJrm5F5GbexFSSoaH6yKi0db+V5qa/wAIUlJWkpG+jYyMrbhcGzEY5ueIRcXMcuONN/LKK6/Ebbv55pv51Kc+NUsrUsxHzvjY1Txz10/xe6PZe0azhTM+dnXSzpGI68kC3Ije60mgz4X4uZRydE7hHGEqBXePtHTxvdpmjnt8FFpMfLU8nw/nJW4ZBIN++vv36sLR/Qq9vbuQ0oemWUhzbdTjGxnbcDpXIMTCG2wyl1Duk6mj3rv5yYGXnuelh+6nv7ODlMwszvjY1aw445xJH2c819MJhWLEQTKAork+4W6yQvFISxdfOtTAcDD6Xtg0wW3LiiclFrH4/YP09LxBV7ducQwO1gBgMmWQnr6ZzIxtpKdvxWZL3gB0hY76sJs66r1b3Ew5RhHqHHtJaN/dQLsQ4h9Syi8me5Gzxfdqm+NEAmA4KPl6zXGWO21U2CxYDZOzAoxGB1lZ55CVpau6x9MWsTa6ul6hre0pAGy2spC1sZX0tM2YTKnJuSiFQqFIEonEKFxSyj4hxGeA30gpvymEmJMWxVRjFMc9Y5eFdPkDnPfmITSg1GamymGlym6lymGl0m6l0m7BYUwsTdZiySE//zLy8y9DSsngYE1ENFpaHuP48d8DGqmpa0NpuNtwudahaRN3GlUoFIqZJhGhMAoh8oGPAF+b4fVMi6nWURRaTDSOIRa5ZiPfWlpI9ZCbmkEP1UNunuvsxxfjriuymiLiUeWwssxupdJhJXUCARFC4HRW4XRWUVL8KYJBL71979DV9TJdXa9QV/e/1NX9DIPBTlra6WRkbCMjfQsOR5VKw1UoFCedRITi2+gN/F6WUr4phCgHamZ2WSeXr5bnjxmj+EZFAR/Kja989QUldcO6aFQP6reaIQ87ejpwx7w+z2yiymEZZYVkmke/5ZpmJj3tNNLTTqOi/Av4fH309LxGZ9crdHe/Qk3NCwCYzTlkZGyJ1G9YLLkz84YsMpIVCIylrq6Oiy66iHfffTdJq1QoZo9EJtz9EfhjzONaILm9jWeZcMA6kawnkyaodOhWwwdj0pQDUtLg9lI96ObQoDtihTzQ3MVQTHl9psmoC0hIPJaFhCTbbIxYCyZTKtnZ55OdfT4AbndTJA23s/NFWloeB8DhqIzUb6SlnYbR6Jyhd2jhcuCl5+NSC/s72nnmrp8CTFssFIqFwqSynuYLc2keRVBKmjy+iPURFpBDQ8P0+aMC4jIaQuIRb4UUWExx7iYpgwwMHIy4qXp63yQY9CCEEVfq+khgPCVlLZo2mQGGC4fYzJ3n772LtmO14+7bXH2IgH+029FgNJFftWzM1+SUlnPONddNuIa6ujouvPBCNm3axK5du6iqquL+++/Hbh/dM6ysrIyPfvSjPP/88wA88MADLF26lGPHjnHttdfS3t5OdnY2v/nNbygpKeGPf/wj3/rWtzAYDLhcLl58MXnddFTW0+JmOpXZ84a5OOFOE4Iiq5kiq5lzM6MZTVJK2rx+3QIZCruw3Pyto4/fN0crLB0GjcqwgESskAqKSlZQWnodgYCH3t6ddHXvoKvrZWqP/oTaoz/GYHCSnv4eMjK2kZmxDZutTMU3xmAskZho+2Q4dOgQd999N1u3buXaa6/l5z//+biT7lJTU3njjTe4//77ueWWW3jyySf513/9V66++mo++clPcs8993DTTTfx+OOP8+1vf5unn36awsJCenp6pr1OheJEJFJwt0RKefRE2+YSc8mimAodXj81MTGQsBXS4o1+eFk1wdJwEN1uiQTTCwyD9Pe+HrE43G69MZjFkq8HxTO2kpG+BbM5c7Yub8aZzLfiu278FP0d7aO2p2Rlc93PfjPlNdTV1XHmmWdSX18PwHPPPccdd9zB448/PmrfsrIynnvuOcrLy/H5fOTl5dHZ2UlWVhbNzc2YTCZ8Ph/5+fl0dHRw/fXXc+TIET7ykY9w+eWXk5mZvP9LZVEsbqZjUTwCjJzg8idgzjYFnO9kmY1kmZ1sTouPOfT6/NQMeeKskNd7Bni0NTrpzCQE5fZyquwrqcr7PGWmAbJ9e7AOvEB7+9M0N+vhJqdzZSgwrsc3DAbrSb3GucJMtj8Ya6ZEIvuOt194+y9+8Qtef/11nnrqKdatW8fu3buTKhYKxUgmmnC3HFgFuIQQsb2MU9FHoypOMi6TkY0uIxtdjrjtg/6ALiAxVsi7A0M82d6Dbi9WYBAVlFlvYEmKl0LZSLZ3NxkN/yCv/n5sGrhcp0b6U6WkrFw0bdTDAetkZz0B1NfXR2ZQPPjgg2zbtm3cfR9++GG+8pWv8PDDD7N582YAtmzZwkMPPcRVV13F73//+8jrjxw5wqZNm9i0aRPbt2+noaFBCYViRpnIolgGXASkARfHbO8HJlWnoJhZHEYD61LtrEuND5QOB4LUDnviXFjVg4IXhovwyyLgIoSQ5GtDFPQ3kNt9kMLan1Bq6GF1eglFmaeRkbENm614di7sJLHijHNmJMNpxYoV3HfffXzuc5+jsrKSG264Ydx9PR4PmzZtIhgM8uCDDwJwxx13cO211/KDH/wgEswG+PKXv0xNTQ1SSs477zxOOeWUpK9doYglkRjFZill4jMc5wDzPUYx03iDQY4Oe0cIiJsjQ268Mb8OmbKdQhopMfax3OlkTUYZG3LXk22b223U55ufvaysjJ07d5KVlTXbS5l3750iuUwnRnGZEGIfMAz8DTgFuEVK+bskr3HazMWsp7mIWdNYFqrhiMUflNRHakGGOdBn5OCAg797TDzZa4Je4Gg96WIf5WYfy1JSWZ1WyDKnkyqHlSyTUWVWKRQLkESE4nwp5f8TQlwGNAL/BDwPzDmhmGoLD4WOUROU2y2U2y1cmO0C9PGgQSlpGB5id/tB9nbVcXBwgDqPjUfdVh7oaAVaAUg3QpXDEVcHUuWwkGc2KQEBLrvsMo4ejU8W/O///m/q6upmZ0EKRYIkNOEudP8B9BGoXeqPfnGhCUGp3UFp6alcWqonu/n9/XR1vc6hzpd4t6ueWo+B474imvqW8HhfCf0yOqwpxaBFWpiE03ir7BaKrGa0RfS79Nhjj832EhSKKZGIUGwXQhxEdz39ixAiG3DP7LIUcx2jMYWcnPeSk/NeziDUZqR7B11dr9DZ+QM6/X6OU0S7eT3t5lM4Hijm2U4PD7VEiwltmkZlTCFh+L7UZsawiAREoZjrJNLr6StCiP8G+qSUASHEEDDxzFDFosNqLaAg/woK8q/Q24wMVtPV9TLdXa/Q3fMngkE3QhgQKe+h134Obea1NAQzqBnysaNngD/F1IJYNEGFLVpEGLZEltjMmDU1HVChONkk1MJDStkd8/OgEGIL0DJjq1LMa4TQSHEuJ8W5nNKSzxAMeujpfZvuLn3+RrDlu6QgqTI4uSJ9ExnFWzG5tnJc5oUKCvWakLf7hni8rSdyXKOAJTbLiBiIdUqDpRQKReJMtdfT3UBJMheiWLhomoWM9M1kpG+mouJL+Hw9dHe/prcZ6X6Fjo5nAbBY8libvpWzM7aSXrIVi7mcwUCAI0MeagbdVIeq0g8OuPlrey/hloojB0tdEAgyFAhg0TTlwlIoksBEldl/Hu8pQJWBKqaMyZRGTs6F5ORcCMDwcENINHbQ3vEszS2PAOB0LicjfSuFGVtZlX06BkO0fsMTDFI75OFQqJlidcxgqdNcUDOot+QwaQKrJrBqGhZNw6oJLJqGUYsKyOCuNvqeriPQ48GQZiH1gjIc63OmdY2TmUcx2TqKnTt3cv/993PHHXdM6fz33nsv559/PgUFBQmdT6GYyKI4A/gEMDBiuwBOn7EVKRYdNlsxhYVXUlh4JVIG6O/fR1fXDrq6X6ah8bfUN9yNEGZcrvVkZmwjPWMrqSmrWeG0scJpizuWLyg5cOAAJTYznqDEHQziDkoGfH5ia0uNIQGxvNsF24+CT7dPAj0eeh7V53JNVyxmio0bN7Jx46iaqIS59957Wb16tRIKRcJMJBSvAUNSyn+MfEIIcWjmljR1VMHd/EcIA6mpa0lNXUtZ2fUEAsP09Oykq/tlurp2cKT2h1D7Q4xGF+npm0PdcLdit5cCugVh0gRpJv1Xu2f7EWxNg4BEAkEJQWToHmTjACIQ351A+oJ0/6manteb0BBoQv92FP7XXOAg7eKKE16L3+/nk5/85AnnUQDceeedbN++HZ/Pxx//+EeWL1/O4OAgn//859m7dy9+v59bb72VSy+9lBdeeIHbbruNJ598kvb2dj7+8Y/T2dnJaaedxt/+9jfeeustAAKBAJ/97GfZsWMHhYWFPPHEEzz11FPs3LmTf/7nf8Zms/Hqq69is9nGXJNCEWbcCKCU8v1SyufHee7MmVvS1JFSbpdSXudyuWZ7KYokYTDYyMw8g8qlX2XT6ds5Y9vrrFp5O9nZ59PX9w6HDn2dV187l1d2nM2Bg/9Ba9tfkDI4xpEEAoFBCExCd0PZNG2USISRAYk3ZJEMBfTbcDCIJxhkOBCkzx/AGwwyUQucQ4cOcd1117Fnzx5SU1P5+c9/Pu6+WVlZvP3229xwww3cdtttAHz3u9/l3HPP5c033+T555/ny1/+MoODg3Gv+9a3vsW5557L22+/zWWXXRZpaw5QU1PDjTfeyL59+0hLS+ORRx7hiiuuYOPGjfz+979n9+7dSiQUCbGgBhcpFj5mcxZ5eZeQl3cJUkqGh+vo7HqZrq6XaW19iqamh8lI/yWDgxYMBifO9+diMNgRYuzvRM3ff4NAj2fUdmOahbzrT8EdEouIGysgCUgJQ/prNEEk9qHHQQRWg4aUkuLiYrZu3QrAJz7xCe64445xBxddfrneoPnUU0/l0UcfBeCZZ57hz3/+c0Q43G53nBAAvPzyy5FCvgsvvJD09OiM9yVLlrBu3brIcVUFuGKqKKFQzFuEENjtS7Dbl1BcdBXBoJ/+/j3U1/sAgdfbgdfbDkJgNDgwGJwYjU40zRppKZJ6QRk9j9YgfVErRJg0Ui8ow6RpmDRIIb7lui8o8QSDcQLSHwjS7QtE9mkadBMAjg17sGoaA/6A7uqScsx2JhaLXsluMBjw+/0Q2veRRx5h2bL4kaytra2RnyeyaMLHDB93eHh44jdUoRgHlXyuWDBomhGXawNGYyoORwUpKSuw2UoxmzIIBn14PC0MDh5mYOAAw8P1eL1d2E5JI+3ySgxpoQ/qNAtpl1dOGMg2aQKn0UCW2USh1UyF3coqp41VThtLQ61J0k0GmhoaeGXHq7R4fNz7+weoPG0TeweGOTg4TN2whxaPlx6fPxQ7Gf2Bf8EFF3DnnXdGxGDXrl2j9tm2bRt/+MMfAN0C6e7uHrXPSFJSUujv70/kLVUogBNYFEKfXnOflPITJ2k9CkXSEMKAyZSKyaTPKg8Gffj9AwQCA/j9A/h8vQBolWbSVxRhMDgwGp1THtpk1ARGzYAD6LeYWbFiBS/+8SG+/4WbKF+6lC/8641oJiOeoGQ4EKTXpwuAT0r2Dw7TZRvmuNuLNyjp9vn5t//4D776xS+ydu1apJSUlZXx5JNPxp3zm9/8JldeeSUPP/wwZ511Fvn5+aSkpDAwMDJZMco111zD9ddfr4LZioRJZB7F08DFUkrvyVnS9FHzKBY3icxUkFISDHoiwhEIDEaC4AaDHYPRidHgxGCwjRvfmC5BKWNSeENurEAQj5QQ82dpDtV+ROIgBv3eIAQejweDwYDRaOTVV1/lhhtuYPfu3VNek5pHsbiZzjyKOuCVUAFeJOVCSvmj5C1PoTi5CCEwGKyhWeFZSBkkEBjCHxgg4B/A62nDSxtCaBgMjohwaJolaS3TNSGwGQS2Ee1HgjKaceWOiYcM+GRcLYhJEzTXHuHmqz8BwSAWs4X//eVdSVmbQhFLIkLRFLppQMrMLkehmB2E0DAa9WA3FggGA7qLKiQcfnc/HkAII0ajMyYwbjrhscOMN4/iggsuiNumCYHVIEb1r5JjCEhxxVIeeGlHnIDsGxjWs69GZGMZhVBzQRRTIpHusd8CEEKk6A/l+M5PhWKBoGkGNM2FyaTX5ASDXvx+XTj8/n58vp7QfpYY4XBMGN+Y7jwKIQQWg8Bi0IitFJJS4g27sQJRN1a3z08wRkAMgpDrKl5ATEpAFCfghEIhhFgN/BbICD3uAK6WUu6b4bUpFHMGTTNjNmdgJiMU33BH4hteXxd4OwGBwWCPCIce35j5D2AhBBYhsGiQaowKlZQSX1wcROIJBOn1+emKERAtLCCaoN8f4P86+xblYCnF+CTieroL+GK4SlsIcTbwK2DLzC1LoZi76PENGwaDDciOxjf8/QQCA3g8+nhYPb7hjAiHpplP6jd3IQRmITCPqAWRUuIfISDhWpAef4Cr99QCarCUIkoiQuGIbeUhpXxBCOGYwTXFERKm7wD7gIeklC+crHMrFIkQF98AgkF/JAU3EBjA7e4DQNNMI4RjdupdhdDdTSYNnCOKCbGY+PP6kkhL9+pB94SDpWLH26rBUguXRH5Ta4UQX0d3P4HeUfboBPtHEELcA1wEtEkpV8dsvxD4CWAAfi2l/P4Eh5HoHWytQGMi51UoZhNNM6JpaZhMaSE3lTciHH5/Lz6f/qGrZ12FhcMxY2m4k0ETgtPTnJye5ozb3u8PhNq5u+MGSz3R1hPJ5FWDpRYuiQjFtcC3gEdDj18EPpXg8e8FfgrcH94QKuL7GfA+9A/+N0Optwbge2Oc+yUp5T+EELnAj4B/TvDcCkVC7Nmzh2effZbe3l5cLhfnnXcea9eundYxY+dBGAwWDAYLZnMmUkoCgeGIcHi9nXi9HSBC8Q1Dyqg2I7HceuutOJ3OcXtGzRQpRgMbUh1sSI13JgwFghwJC0iCg6Uqw/d2Cw7j1IobFSeXiQYX/VZKeRV64PqmqRxcSvmiEKJsxObTgcNSytrQeR4CLpVSfg/d+hiPbsAy3pNCiOuA6wBKStTwPUVi7NmzJ9LeG6C3t5ft27cDTFssxkIIgdFox2i0Y7HkIGUAf2BIT8H19+Pxt+Dx6FXl4doNXTjMSV9LMrAbNNak2FmTEt8+PTxYqnqEFfJcZz++mFzeIqspzvoIC4jLpNrQzSUm+t84VQhRClwrhLifcDP+EFLKrimesxBoiHncCGwab2chxOXABUAaunUyJlLKu9AD72zcuHHicnPFouGvf/0rLS3jj3dvbGwkEAjEbfP5fDzxxBORuQ4jycvL4/3vf/8Jz53IPAohDHz9P7/Ln//8Z4xGI+9733v53ve/ztHag3zuc9fS0dFFZmY6v/jF9ylbUkUg4EbKsWdazCUsmjbuYKlj7mj8I2yF7OjpwB2Ty5tnNlHlGBEHsVvJNCsBmQ0metd/AfwNKAfeIl4oZGj7VBgrXWLcD3Yp5aNE3V4TH1gNLlJMkpEicaLtk+HQoUPcfffdbN26lWuvvZaf//zno1xGXV1dPPbYYxw8eBAhBD09PZhNaXzpS//FNddcz1VXfYxf3/1L/t//+x4PPHA7fn8vHo+PwcHDMfGN8duozzVMmmCp3cpSu5UPZEe3B6Skwe2NERDdCnmguYuhQLSzb6bJqAvIiEysHLNR1YLMIOMKhZTyDuAOIcT/SilvSOI5G4HimMdF6JXf00ZKuR3YvnHjxs8m43iK+c+Jvvnffvvt9Pb2jtrucrn41KcSDcWNTSLzKFJTU7FarXzmM5/hgx/8IBddpHtfX331VR599FEMBhPXfupf+Np/fAencwUmUzpGowm9jXp7qI26NqKNevLajJwsDEJQZrNQZrNwfla0nFBKyXGPj+rIbHRdQB5v66HXHxVzl9EQEo14K6TQYpp378VcJJHK7GSKBMCbQKUQYglwHPgY8PEkn0OhSIjzzjsvLkYBYDKZOO+886Z97JEfUGN9YBmNRt544w2effZZHnroIX7605/y3HPPjXksITQ0zYzR6MThqNDjG/7BmIyq5lB8Y+ptRuYaQgiKrGaKrGbOzUyNbJdS0ub1x1gf+v3fOvr4fXPUK+4waCHRsMTFQoqtqhZkMsyow08I8SBwNpAlhGgEvimlvFsI8a/A0+iZTvckq8pbuZ4UkyUcsE521hNAfX09r776Kps3b+bBBx9k27Zto/YZGBhgaGiID3zgA7znPe8h/Lu7ZcsWHnroIa666ip+//vfj/na0W3UvTFt1KfWZmS+IIQg12Ii12LijIz4FnQdXn/E+gjfv9g1wB9aorUg1pALTHdfWSICUma1YNSUgIxkRoVCSnnlONv/AvxlBs6nXE+KSbN27doZyXBasWIF9913H5/73OeorKzkhhtGG+f9/f1ceumluN1upJTcfvvtANxxxx1ce+21/OAHPyA7O5vf/OY3JzxfuM0IJ2wzYotpM2JfcK6ZLLORLLOTzSNqQXp9fmrChYQhAXmjd4BHY4oJTUJQbrfEubGq7FbK7RYsi7iYcMJ5FKGah6ellO89eUuaPmoexeJGzVQYTbTNSHj+hj4WNdpmxIHBkEJ1de2ie+8G/QFdQIbc1MSISN2wN5JlYxBQZrXEpPHqP1fYrdgXUDHhlOZRSCkDQoghIYRLSjk64jfHUK4nhWJsxm4zMjiizUgzbk8H+w/cS0b6VjIytmA2Z83uwk8CDqOBdal21qXGpx0PB4LUDntGZGK5+XtnL/6Qggig2GqOycCKWiHOBVRMmIjryQ3sFUL8nfjBRVMqwptJlOtJMZdJdB7FyUBvMxLbRl2f9qeJPtrb/05z858AcDpXkJG+hYyMbaSlnRZqhLg4sBm0yCz0WLzBIEeHo6m80ThIP94YD02BZWQxoYVKh5X0eVhMmMiKnwrdFArFNJjuPIqZRNMsmM0WzOYMzjzjTfr799HV9QpdXS/T0Phb6hvuRggzaa4NZGRsIyNjKykpqxZEYHyymDWNZQ4ryxzWuO3+oKQ+XAsSk4n126ZOhoPRWpBss3GUgFQ5rGSZ5m4tyAlnZgMIIcxAVejhISmlb6L9Z4sY19Nna2pqZns5illCxSimzljvXSAwTE/Pm3R1vUxX9w4GBg4AYDSmkZG+mfSMLWRmbMNmU61zxiIoJY1ub1xH3rAV0h9TTJhhMsRVoetCYiHPfPJqQcaLUZxQKEJtvu9Dn50t0IvlPimlfDHpq0wSKpi9uFFCMXUSee883g66u3bQ1a1bHB6P3iLFZi0hPUN3U2Wkb8ZkSjsJK56/SClp8fr0Plgh66Nm0M2hQTfdMcWEKQYt0kgx1gqJHSz1SEsX36tt5rjHR6HFxFfL8/lwXsak1zSlYHaIHwLnSykPhQ5UBTwInDrpVSgUinmPxZxFXt4l5OVdgpSSoaHakGi8QmvrkzQ1PQQIUlJWR9xUaa4NaNq4PT0XJUII8i1m8i1mzoqpBZFS0uHzx/XCqh5081xXHw+1RIsJw4OlrELwdv9QJMDe6PHxpUN6O72piMVYJCIUprBIhC6iWggxf0s9FYoRNLc8Qe2R23B7mrFa8imv+BL5eZfO9rLmBUIIHI4KHI4KiouuJhj00de/JxTfeIX6+l9x7Nj/omlW0tJOIyNjKxnp23A6l82b/lQnGyEE2WYT2WYTW9Pjiwm7ff5QCm9UQF7q7mdkZ7LhoOR7tc0nVSjeEkLcTXRw0T+jNwmcc6j0WMVkaW55goMHv0YwqNcVuD1NHDz4NYBpiUXsPIrp8o1vfIMzzzyT9743vpzphRde4LbbbuPJJ5/khRdewGw2s2WLPqH4mmuu4aKLLuKKK66Y9vkng6aZSHOdSprrVMqX3ITf3093zxsR4Th8WJ9RZjJlkpGxhYx03eKwWvNP6jrnK+km46jBUvnP7x5z3+Oe5IWSExGK64EbgZvQYxQvAj9P2gqSiEqPVYykuvo79IeCr2PR27sLKb1x24LBYQ4c+ApNTQ+P+ZoU5wqqqr6e1HVOxLe//e0T7vPCCy/gdDojQjFXMBpTyM46j+wsvXeW291Md/cOXTi6X6G1VZ/9YbeXh6yNraSnvwejMWWiwypiKLSYaBxDFAotyXP8TGj7Cd02fEtK+SMp5eVSysuklLdLKT1JW4FCMYuMFIkTbZ8M4XkUa9eu5YorrmBoaGjUPm+88QaXX345AE888QQ2mw2v14vb7aa8XO/kf8011/CnP+l1DX/7299Yvnw527Zt49FH9e77dXV1/OIXv+D2229n3bp1vPTSSwC8+OKLbNmyhfLy8sjrZxurNZ/8/A+zatWP2Lb1NTad/hcql/4HNlsxTU1/Ys/e63nxpVPZ+dY/UVv7Y3p6dhIMzskkyznDV8vzsY3oT2XTBF8tT56VdqLK7KAQ4h0hRImUsj5pZ1UoThIn+ub/yitn4PaM7nJvtRRw6oYHpnXuROZRbNiwgV27dgHw0ksvsXr1at588038fj+bNsXP83K73Xz2s5/lueeeY+nSpXz0ox8FoKysjOuvvz5uROrdd99Nc3MzL7/8MgcPHuSSSy456W6oEyGEwOlchtO5jJKSTxMMeujt3RWxNo7W/YyjdXdiMDhIT9tERsZW0jO24rAvnbP1BrNBOA6RjKyn8UjE9ZQP7BNCvEF8ZfYlSVuFQjFLlFd8KS5GAaBpNsorpj+TOpF5FEajkaVLl3LgwAHeeOMNvvjFL/Liiy8SCAQ444wz4vY9ePAgS5YsobKyMnLMu+66a9zzf+hDH0LTNFauXElra+u0r2em0TQL6envIT39PVTwb/h8vXR3v0ZX98t0db1CR6feft1iySMjfQvpIVeVxZJ9giMvfD6cl5FUYRhJIkLxrRk7e5JRwWzFZAkHrGci6ymReRQAZ5xxBn/9618xmUy8973v5ZprriEQCHDbbbed8JgTYbFE01ETKayda5hMLnJyLiAnR29xMjzcGCr6e4WOzudpbtFdb07HMl00MraSnnY6BsPcHxU735hQKEIxip9JKVefpPVMCxXMVkyF/LxLZyQdNpF5FABnnnkmV199NVdffTXZ2dl0dnbS0tLCqlWr4vZbvnw5R48e5ciRI1RUVPDggw9GnktJSaGvry/p1zCXsNmKKCz8GIWFH0PKIP0D+yNtRo4f/x0NDfcghAmXa0MkMJ6aumZRthlJNipGoVDMEInMowDYtGkTra2tnHnmmYA+HyMnJ2eU9WC1Wrnrrrv44Ac/SFZWFtu2bYuk31588cVcccUVPPHEE9x5550ze2FzACE0UlNWk5qymrLSzxEIuOnp3UlX18t0d+2gtvZH1PIjjMZU0tM3h7rhbsVmK1XxjSmQSAuP54DTgHkTo1AtPBY3qoXH1Fko753X20l396sRiyOcsGC1FoZEYxsZGVswmdJneaVzi+m08Jg3MQqFQqEAMJszyc29iNzci5BSMjxcFxGN1ra/0NT8B/Q2IysjRX8u10YMBtVmZCxOKBRSyn8IIUqBSinl/wkh7OizrhUKxSSYS/MoFhNCCOz2JdjtSygq+gTBoJ/+/r2Rbrj1DXdzrP6XaJqFNNdpesV4xjaczhWqzUiIRFxPnwWuAzKklBVCiErgF1LK807GAieDajOugIXjPpkNFuN75/cP0tPzRqQb7uCg/tlhMmWQnr6ZzIxtpKdvxWYrnOWVzjzTcT3dCJwOvA4gpawRQuQkeX1JQWU9KRSKyWI0OsjKOoesrHMA8HjaQkV/L9PVtYO2Nn1um81WFoltpKdtxmRKnc1ln1QSEQqPlNIbzhQQQhiB+ZeUrVAoFAlgseSQn38Z+fmXIaVkcLAm0ka9peVRjh//HaCRmnpKpLGhy7UOTTPP9tJnjESE4h9CiP8AbEKI9wH/Amyf2WUpFArF7KO3GanC6ayipPhTBINeevve0eMbXa9QV/e/1NX9DIPBTlra6aGhTVtwOKoWVBpuIkLxFeDTwF7gc8BfgF/P5KIUipNJsqaDKRY+mmYmPe000tNOo6L8C/h8ffT0vEZn1yt0d79CTc0LAJjNOSFrQ6/fsFhyZ3fh0ySRrKcg8CshxH3AKuC4nI/9ABSKMXikpYsvHWpgOKj/SidrOth051HceuutcU3+FHMTkymV7Ozzyc4+HwC3uymShtvZ+SItLY8D4HBURuo30tJOw2h0TnDUuce4QiGE+AVwp5RynxDCBbwKBIAMIcSXpJQPjvdahWKu8PWaRt4dGB73+bd6h/CO+N4zHJR84WADv2vuHPM1q502vlNZlNR1KhYGVmsBBQX/REHBPyFlkIGBgxE31fGmB2lovBchjLhS1+ttRjK2kpKyFk1LxLkze0yUJHyGlHJf6OdPAdVSyjXos7L/34yvTKE4CYwUiRNtnwyJzKMA+MpXvsLKlStZu3btmBbE7t27ec973sPatWu57LLL6O7uBuDss8/mlltuYcuWLaxevZo33ngDgMHBQa699lpOO+001q9fzxNPPDHta1FMHiE0UlJWUlp6HevX38eZZ+xi/br7KSn5DIHgMLVHf8LOt/6JF186lXf2fI6Gxt8yNHR0TjZwnEjGYie3vA/4I4CUsmUhBWkUC5sTffPfuGPfmNPBiiwmHltfOa1zJzKPoquri8cee4yDBw8ihKCnp2fUca6++mruvPNOzjrrLL7xjW/wrW99ix//+MeALgo7duzgxRdf5Nprr+Xdd9/lu9/9Lueeey733HMPPT09nH766bz3ve/F4XBM63oU08NgsESsCCq+jNfbRXfPaxGLo6Pj/wCwWPJDabhbyUjfgtmcOcsrn9ii6BFCXCSEWA9sBf4GkfRY28lY3GQRQlwshLirt7d3tpeimCfM5HSwkfMoXn755VH7pKamYrVa+cxnPsOjjz6K3R7fIru3t5eenh7OOussAD75yU/y4osvRp6/8sorAb0DbV9fHz09PTzzzDN8//vfZ926dZx99tm43W7q61VPz7mG2ZxBbs4HWLH8v9iy+QU2v+c5li37Dqmpp9De/jT79t3CSy+fzutvXEzN4e/T2fkSgYB7VtY6kUXxOeAOIA+4RUrZEtp+HvDUTC9sKqiCO8VkmcnpYInMozAajbzxxhs8++yzPPTQQ/z0pz/lueeem9Y5pJQ88sgjLFu2bGoLV5x09DYjpdjtpRQVfhwpA/T1v0t31yt0dr1MQ8O91Nf/Ck0z43JtjGRTpaSsQgiN5pYnZmSmSphxhUJKWQ1cOMb2p4Gnk7YChWKWmanpYInMoxgYGGBoaIgPfOADvOc972Hk0C2Xy0V6ejovvfQSZ5xxBr/97W8j1gXAww8/zDnnnMPLL7+My+XC5XJxwQUXcOedd3LnnXcihGDXrl2sX78+6denmDmEMOBKPQVX6imUlf0LgcAQPT1vRjKqjtT+gCO1P8BoTMNmK2VgYD9S6i5Ut6eJgwe/BpA0sZgo6+mOiV4opbwpKStQKBYoicyj6O/v59JLL8XtdiOl5Pbbbx+1z3333cf111/P0NAQ5eXl/OY3v4k8l56ezpYtW+jr6+Oee+4B4Otf/zq33HILa9euRUpJWVkZTz755MxdqGLGMRjsZGaeRWam/iXB42mnu3sHXV0v09zyOBCM2z8YHKb2yG1JE4pxmwIKIbzAu8AfgCYgzsaVUt6XlBXMAGoexeJmsTS2O/vss7ntttvYuHFUD7cps1jeu4XEs88tZeyuSoLzzj08qWNNpSlgPvBPwEcBP/Aw8IiUsntSZ1YoFArFjGG15EcGM43cnizGzXqSUnZKKX8hpTwHuAZIA/YJIa5K2tkVikXEZZddxrp16+JuTz899XDfCy+8kFRrQjE/Ka/4EpoWn4iqaTbKK5JX1X/CckAhxAbgSvRair8CbyXt7ArFDCGlnHNN2R577LHZXsKEzMVCL8WJCcchZiXrSQjxLeAi4ADwEPBVKaU/aWdWKGYIq9VKZ2cnmZmZc04s5ipSSjo7O7FarbO9FMUUyM+7NKnCMJKJLIqvA7XAKaHbf4X+6AQgpZRrZ2xVCsU0KCoqorGxkfb29tleyrzCarVSVKR6WClGM5FQLDlpq1AokojJZGLJEvXrq1Aki4kK7o6NtV0IYQA+Boz5vEKhUCgWFuNmPQkhUoUQXxVC/FQIcb7Q+Ty6O+ojJ2+JCoVCoZhNJnI9/RboRp9D8Rngy4AZuFRKuXvml6ZQKBSKucBEldl7Q/Mnwu6mDqBEStl/Etc3JYQQ7UzdNZaFfq2LCXXNiwN1zQuf6V5vqZQye+TGiSyKSJN+KWVACHF0PogEwFgXmihCiJ1jlbAvZNQ1Lw7UNS98Zup6JxKKU4QQfeHzA7bQ43B6bGqyF6NQKBSKucdEWU+Gk7kQhUKhUMxNJppwt1i5a7YXMAuoa14cqGte+MzI9Y4bzFYoFAqFApRFoVAoFIoToIRCoVAoFBOyKIVCCHGPEKJNCPHuOM8LIcQdQojDQog9oVbr85oErvmfQ9e6RwixQwhxysleY7I50TXH7HeaECIghLjiZK1tpkjkmoUQZwshdgsh9gkh/nEy1zcTJPC77RJCbBdCvBO65k+d7DUmEyFEsRDieSHEgdD13DzGPkn9DFuUQgHcC1w4wfPvBypDt+uA/z0Ja5pp7mXiaz4KnBXqCvwdFkYQ8F4mvuZwMel/A1OfIDS3uJcJrlkIkQb8HLhESrkKfYrlfOdeJv5/vhHYL6U8BTgb+KEQwnwS1jVT+IF/k1KuAN4D3CiEWDlin6R+hi1KoZBSvgh0TbDLpcD9Uuc1IE0Ikby5grPAia5ZSrkjZszta8C87zedwP8zwOeBR4C2mV/RzJPANX8ceFRKWR/af95fdwLXLIEUoc9JcIb2nbezdaSUzVLKt0M/96PPDCocsVtSP8MWpVAkQCHQEPO4kdH/EQuZT6NPM1zQCCEKgcuAX8z2Wk4iVUC6EOIFIcRbQoirZ3tBJ4GfAiuAJmAvcLOUMji7S0oOQogyYD3w+oinkvoZdsJRqIuUscaiLYo8YiHEOehCsW2213IS+DHw76EWNbO9lpOFETgVOA+wAa8KIV6TUlbP7rJmlAuA3cC5QAXwdyHES1LKvglfNccRQjjRreFbxriWpH6GKaEYm0agOOZxEfq3kQWNEGIt8Gvg/VLKztlez0lgI/BQSCSygA8IIfxSysdndVUzSyPQIaUcBAaFEC+iT7BcyELxKeD7Ui8aOyyEOAosB96Y3WVNHSGECV0kfi+lfHSMXZL6GaZcT2PzZ+DqUObAe4BeKWXzbC9qJhFClACPAlct8G+XEaSUS6SUZVLKMuBPwL8scJEAeAI4QwhhFELYgU3oPu6FTD26BYUQIhdYhj5XZ14SirXcDRyQUv5onN2S+hm2KC0KIcSD6NkPWUKIRuCbgAlASvkL4C/AB4DDwBD6N5J5TQLX/A0gE/h56Bu2f7533UzgmhccJ7pmKeUBIcTfgD1AEPi1lHLC9OG5TgL/z98B7hVC7EV3yfy7lHI+tx7fClwF7BVC7A5t+w+gBGbmM0y18FAoFArFhCjXk0KhUCgmRAmFQqFQKCZECYVCoVAoJkQJhUKhUCgmRAmFQqFQKCZECYVCMQFCiK+FOnTuCXVc3RTabhJCfF8IUSOEeFcI8YYQ4v1JOufZQognk3EshSIZLMo6CoUiEYQQm4GLgA1SSo8QIgsIdx39DpAPrA49lwucNcXzGKSUgWms0yilnLdN7hRzHyUUCsX45KO3u/AAhIu0QhXNnwWWxDzXCvxh5AGEEOcBt6H/rb0J3BASljrgHuB84KdCiB703lMdwNsxr3cAdwJrQse4VUr5hBDiGuCDgBVwoPcxUihmBOV6UijG5xmgWAhRLYT4uRAibDEsBepP1FROCGFFn5XwUSll+IP+hphd3FLKbcDjwK+Ai4EzgLyYfb4GPCelPA04B/hBSDwANgOflFIqkVDMKEooFIpxkFIOoHdavQ5oBx4OfZNPlGXA0ZjeWfcBZ8Y8/3Dofnlov5pQ47rfxexzPvCVUKuGF9AtiJLQc3+XUp5o3oZCMW2U60mhmIBQ7OAF4IVQr6BPoruYSoQQKaHBMeNxot7lg7GnmuAYH5ZSHorbqAfVB8d+iUKRXJRFoVCMgxBimRCiMmbTOuCYlHIIvXvnHeGRmkKIfCHEJ0Yc4iBQJoRYGnp8FTDWjOqDwBIhREXo8ZUxzz0NfD7UMRQhxPrpXJNCMRWUUCgU4+ME7hNC7BdC7AFWAreGnvtPdHfUfiHEu+hxhvbYF0sp3ehdO/8YskaCjDFNL7TfdcBTQoiXgWMxT38HvRPqntB5vpO0q1MoEkR1j1UoFArFhCiLQqFQKBQTooRCoVAoFBOihEKhUCgUE6KEQqFQKBQTooRCoVAoFBOihEKhUCgUE6KEQqFQKBQT8v8DzZ6wL1VhHwsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the convergence of the first sobol to that of the highest order\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    last = -1\n",
    "    O = [R[r]['order'] for r in list(R.keys())]\n",
    "    if len(O[0:last]) > 0:\n",
    "        plt.figure()\n",
    "        O = [R[r]['order'] for r in list(R.keys())]\n",
    "        for v in list(R[O[last]]['results'].sobols_first('te').keys()):\n",
    "            plt.semilogy([o for o in O[0:last]],\n",
    "                         [np.sqrt(np.mean((R[o]['results'].sobols_first('te')[v] - \n",
    "                                           R[O[last]]['results'].sobols_first('te')[v])**2)) for o in O[0:last]],\n",
    "                         'o-',\n",
    "                         label=v)\n",
    "        plt.xlabel('SC order')\n",
    "        plt.ylabel('RMSerror for 1st sobol compared to order=%s' % (O[last]))\n",
    "        plt.legend(loc=0)\n",
    "        plt.savefig('Convergence_sobol_first.png')\n",
    "        plt.savefig('Convergence_sobol_first.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:21:12.363744Z",
     "start_time": "2021-07-16T11:21:07.278208Z"
    },
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem with some of the percentiles\n",
      "Problem with some of the percentiles\n",
      "Problem with sobols_second\n",
      "Problem with sobols_total\n",
      "Problem with distribution\n",
      "Problem with sobols_second\n",
      "Problem with sobols_total\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a standard set of graphs for the highest order case\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    last = -1\n",
    "    title = 'fusion test case with SC order = %i' % list(R.values())[last]['order']\n",
    "    plot_Te(list(R.values())[last]['results'], title=title,)\n",
    "    plot_ne(list(R.values())[last]['results'], title=title)\n",
    "    plot_sobols_first(list(R.values())[last]['results'], title=title)\n",
    "    try:\n",
    "        plot_sobols_second(list(R.values())[last]['results'], title=title)\n",
    "    except:\n",
    "        print('Problem with sobols_second')\n",
    "    try:\n",
    "        plot_sobols_total(list(R.values())[last]['results'], title=title)\n",
    "    except:\n",
    "        print('Problem with sobols_total')\n",
    "    try:\n",
    "        plot_distribution(list(R.values())[last]['results'], list(R.values())[last]['results_df'], title=title)\n",
    "    except:\n",
    "        print('Problem with distribution')\n",
    "    plot_sobols_first(list(R.values())[last]['results'], title=title, field='ne')\n",
    "    try:\n",
    "        plot_sobols_second(list(R.values())[last]['results'], title=title, field='ne')\n",
    "    except:\n",
    "        print('Problem with sobols_second')\n",
    "    try:\n",
    "        plot_sobols_total(list(R.values())[last]['results'], title=title, field='ne')\n",
    "    except:\n",
    "        print('Problem with sobols_total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-16T11:23:21.604268Z",
     "start_time": "2021-07-16T11:21:12.365988Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [01:56<00:00,  8.60it/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": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T12:46:20.388796Z",
     "start_time": "2021-07-16T11:23:21.606413Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# calculate the SC surrogates\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    test_points = test_df[test_campaign.get_active_sampler().vary.get_keys()]\n",
    "    test_results = test_df['te'].values\n",
    "    test_predictions = {}\n",
    "    for i in list(R.keys()):\n",
    "        test_predictions[i] = np.squeeze(np.array(R[i]['results'].surrogate()(test_points)['te']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T12:46:21.334748Z",
     "start_time": "2021-07-18T12:46:20.391462Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the performance of the SC surrogates\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    for i in list(R.keys()):\n",
    "        plt.semilogy(R[i]['results'].describe('rho', 'mean'), \n",
    "                 np.sqrt(((test_predictions[i] - test_results)**2).mean(axis=0)) / test_results.mean(axis=0), \n",
    "                 label='SC order %s with %s samples' % (R[i]['order'], R[i]['number_of_samples']))\n",
    "    plt.xlabel('rho [m]') ; plt.ylabel('fractional RMS for predicted Te') ; plt.legend(loc=0)\n",
    "    plt.title('Performance of the SC surrogate')\n",
    "    plt.savefig('SC_surrogate.png')\n",
    "    plt.savefig('SC_surrogate.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-18T12:46:21.873529Z",
     "start_time": "2021-07-18T12:46:21.336591Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the convergence of the surrogate based on 1000 random points\n",
    "if __name__ == '__main__':\n",
    "    _o = []\n",
    "    _RMS = []\n",
    "    for r in R.values():\n",
    "        _RMS.append((np.sqrt((((test_predictions[r['order']] - test_results) / test_results)**2).mean())))\n",
    "        _o.append(r['order'])\n",
    "\n",
    "    plt.figure()\n",
    "    plt.semilogy(_o, _RMS, 'o-')\n",
    "    plt.xlabel('SC order')\n",
    "    plt.ylabel('fractional RMS error for the SC surrogate')\n",
    "    plt.legend(loc=0)\n",
    "    plt.savefig('Convergence_SC_surrogate.png')\n",
    "    plt.savefig('Convergence_SC_surrogate.pdf')"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "executable": " /usr/bin/env python",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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