diff --git a/examples/bisego.ipynb b/examples/bisego.ipynb
new file mode 100644
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@@ -0,0 +1,4327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "61c212aa7b3ad82f",
+   "metadata": {},
+   "source": [
+    "# Unconstrained bi-objective optimization - Bi-SEGO\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "036d66a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from smt_optim.acquisition_strategies.bisego import BiSEGO\n",
+    "from smt_optim.acquisition_strategies import MFSEGO\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from smt_optim.core import Problem\n",
+    "from smt_optim.surrogate_models.smt import SmtGPX,SmtAutoModel\n",
+    "from smt_optim.core import ObjectiveConfig, DriverConfig\n",
+    "from smt_optim.core import Driver\n",
+    "\n",
+    "from smt_optim.benchmarks.registry import list_problems, get_problem\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pymoo.core.problem import ElementwiseProblem\n",
+    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
+    "from pymoo.operators.crossover.sbx import SBX\n",
+    "from pymoo.operators.mutation.pm import PM\n",
+    "from pymoo.operators.sampling.rnd import FloatRandomSampling\n",
+    "from pymoo.optimize import minimize\n",
+    "from pymoo.termination import get_termination\n",
+    "from pymoo.indicators.igd_plus import IGDPlus\n",
+    "from pymoo.indicators.hv import HV\n",
+    "\n",
+    "from smt_optim.benchmarks.base import PymooWrapper\n",
+    "\n",
+    "from smt_optim.acquisition_strategies.mosego import MOSEGO\n",
+    "from smt_optim.acquisition_functions import init_ehvi_2o\n",
+    "\n",
+    "from smt_optim.utils.inject_doe import InjectData\n",
+    "from smt.sampling_methods import LHS\n",
+    "import smt.design_space as ds\n",
+    "\n",
+    "from datetime import datetime\n",
+    "import pickle\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9b327bf",
+   "metadata": {},
+   "source": [
+    "### Parameters for optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "617f2e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_accuracy=100 # Precision on composite acquisition function\n",
+    "seed=421 # Seed for generating the initial DoE\n",
+    "budget_factor=20 # Budget: budget_factor * dim\n",
+    "init_factor=2 # Initial DoE size: init_factor * dim + 1\n",
+    "min_factor=2 # Initial calls to determine min(f1): min_factor * dim + 1 (same for min(f2))\n",
+    "max_so_iter_factor=2 # Max calls to a single-objective subproblem: max_so_iter_factor * dim + 1\n",
+    "soformulation_naive=\"Normalized\"\n",
+    "soformulation_composite=\"Product\"\n",
+    "multi_start_factor=10 # Number of multistart calls for acquisition function optimization: multi_start_factor * dim\n",
+    "test_number=0\n",
+    "surrogate=SmtAutoModel\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7417beb2adb18659",
+   "metadata": {},
+   "source": [
+    "### Importing a test function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "060a70fb",
+   "metadata": {},
+   "source": [
+    "Here, we are using the analytical test function ZDT1 in 2 dimensions as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "af1aa64bb3136a1e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T20:01:26.591042814Z",
+     "start_time": "2026-04-14T20:01:26.530371991Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bproblem = get_problem(\"ZDT1\")\n",
+    "bproblem.set_dim(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74cd636a",
+   "metadata": {},
+   "source": [
+    "### Initialize the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d54ba287c875095d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T20:01:27.683572510Z",
+     "start_time": "2026-04-14T20:01:26.592497043Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name=bproblem.name\n",
+    "num_dim=bproblem.num_dim\n",
+    "num_obj=bproblem.num_obj\n",
+    "num_cstr=bproblem.num_cstr\n",
+    "bounds=bproblem.bounds\n",
+    "objective=bproblem.objective\n",
+    "\n",
+    "assert(num_obj==2)\n",
+    "assert(num_cstr==0)\n",
+    "\n",
+    "f1=objective[0]\n",
+    "f2=objective[1]\n",
+    "\n",
+    "max_budget=budget_factor*num_dim\n",
+    "n_init=init_factor*num_dim+1\n",
+    "n_min=min_factor*num_dim+1\n",
+    "n_so=max_so_iter_factor*num_dim+1\n",
+    "\n",
+    "obj_config1 = ObjectiveConfig(\n",
+    "    [f1],\n",
+    "    type=\"minimize\",\n",
+    "    surrogate=surrogate,\n",
+    ")\n",
+    "\n",
+    "obj_config2 = ObjectiveConfig(\n",
+    "    [f2],\n",
+    "    type=\"minimize\",\n",
+    "    surrogate=surrogate,\n",
+    ")\n",
+    "\n",
+    "prob_definition = Problem(\n",
+    "    obj_configs=[obj_config1,obj_config2],\n",
+    "    design_space=bounds,\n",
+    "    costs=[1,1]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b67f608",
+   "metadata": {},
+   "source": [
+    "Initialize the DoE using Latin Hypercube Sampling:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "77a438eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "float_vars = []\n",
+    "for idx in range(bounds.shape[0]):\n",
+    "    float_vars.append(\n",
+    "        ds.FloatVariable(bounds[idx, 0], bounds[idx, 1])\n",
+    "    )\n",
+    "design_space = ds.DesignSpace(float_vars)\n",
+    "\n",
+    "F=lambda x: (f1(x),f2(x))\n",
+    "sampler = LHS(xlimits=design_space.get_unfolded_num_bounds(),\n",
+    "                            criterion=\"ese\",\n",
+    "                            seed=seed, )\n",
+    "doe = sampler(n_init)\n",
+    "D=[x for x in doe]\n",
+    "Y=[F(x) for x in D]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "963d6560",
+   "metadata": {},
+   "source": [
+    "Initialize the driver and run the optimization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4da3d085",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "             0              0    3.78341e+00    0.00000e+00            nan            nan            nan\n",
+      "Min(f1) phase\n",
+      "             1              1    5.09267e+00    0.00000e+00              1          0.563          0.834\n",
+      "             2              2    5.09267e+00    0.00000e+00              1          0.578          1.272\n",
+      "             3              3    5.09267e+00    0.00000e+00              1          0.553          1.133\n",
+      "             4              4    5.09267e+00    0.00000e+00              1          0.441          1.025\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/src/smt_optim/core/driver.py:470: UserWarning: Infill point [1.e-10 1.e+00] already in training data. L2 = 0.0\n",
+      "  warnings.warn(\n",
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             5              5    5.09267e+00    0.00000e+00              1          0.475          1.530\n",
+      "Min(f2) phase\n",
+      "             6              6    5.21103e+00    3.05335e-01              1          0.443          1.909\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             7              7    5.21103e+00    3.43916e-01              1          0.417          0.663\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             8              8    5.21103e+00    2.99306e-01              1          0.440          1.370\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n",
+      "/home/mauganto/Bureau/biEGO/smt-optim/src/smt_optim/core/driver.py:470: UserWarning: Infill point [1.e+00 1.e-10] already in training data. L2 = 0.0\n",
+      "  warnings.warn(\n",
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             9              9    5.21103e+00    3.04055e-01              1          0.466          0.769\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            10             10    5.21103e+00    3.04055e-01              1          0.488          1.337\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Pareto front is of length 6\n",
+      "Bi-objective phase with r = (np.float64(1.3066270718840642), np.float64(-0.9170751820834083))\n",
+      "            11             11    5.21103e+00    3.04055e-01              1          0.472          2.199\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            12             12    5.21103e+00    3.04055e-01              1          0.443          2.243\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            13             13    5.21103e+00    3.04055e-01              1          0.440          2.201\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            14             14    5.30194e+00    2.79899e-01              1          0.404          2.542\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Pareto front is of length 6\n",
+      "Bi-objective phase with r = (np.float64(1.2432146940630817), np.float64(-0.9686452545381867))\n",
+      "            15             15    5.30194e+00    2.79899e-01              1          0.453          2.207\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            16             16    5.30194e+00    2.79899e-01              1          0.401          2.194\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            17             17    5.30194e+00    2.79899e-01              1          0.438          2.201\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            18             18    5.30194e+00    2.79899e-01              1          0.478          2.243\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            19             19    5.30194e+00    2.79899e-01              1          0.464          2.234\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            20             20    5.33439e+00    2.50609e-01              1          0.441          2.599\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Pareto front is of length 7\n",
+      "Bi-objective phase with r = (np.float64(1.289147941276782), np.float64(-0.9557820695652569))\n",
+      "            21             21    5.33439e+00    2.50609e-01              1          0.439          2.208\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            22             22    5.33439e+00    2.50609e-01              1          0.453          2.198\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            23             23    5.33439e+00    2.50609e-01              1          0.453          2.197\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            24             24    5.33439e+00    2.50609e-01              1          0.407          2.207\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            25             25    5.33439e+00    2.50609e-01              1          0.482          2.208\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            26             26    5.33439e+00    2.50609e-01              1          0.478          2.207\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            27             27    5.33439e+00    2.50609e-01              1          0.422          2.267\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            28             28    5.33439e+00    2.50609e-01              1          0.467          2.350\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            29             29    5.33439e+00    2.50609e-01              1          0.473          2.308\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            30             30    5.33439e+00    2.50609e-01              1          0.436          2.320\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            31             31    5.33439e+00    2.50609e-01              1          0.469          2.246\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            32             32    5.33439e+00    2.50609e-01              1          0.471          2.257\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            33             33    5.33439e+00    2.50609e-01              1          0.467          2.253\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            34             34    5.33439e+00    2.50609e-01              1          0.464          2.255\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mauganto/Bureau/biEGO/smt-optim/.venv/lib/python3.14/site-packages/smt/surrogate_models/krg_based/krg_based.py:679: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            35             35    5.33439e+00    2.50609e-01              1          0.467          2.249\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt_config = DriverConfig(\n",
+    "    max_iter = max_budget - n_init,\n",
+    "    max_budget = max_budget,\n",
+    "    nt_init = n_init+2*n_min,\n",
+    "    verbose = True,\n",
+    "    scaling = True,\n",
+    "    seed=seed,\n",
+    ")\n",
+    "\n",
+    "strategy_kwargs = {\n",
+    "    \"n_multi_start\":multi_start_factor*num_dim,\n",
+    "    \"n_accuracy\":n_accuracy,\n",
+    "    \"so_formulation\":soformulation_composite,\n",
+    "    \"single_objective_max_calls\":n_so,\n",
+    "    \"init_calls\":n_min,\n",
+    "    \"naive\":False,\n",
+    "    \"verbose\":True,\n",
+    "}\n",
+    "\n",
+    "xt_init = np.vstack([np.atleast_1d(x) for x in D])\n",
+    "yt_init = np.vstack([np.atleast_1d(y) for y in Y])\n",
+    "\n",
+    "driver = Driver(prob_definition, opt_config, strategy=BiSEGO, strategy_kwargs=strategy_kwargs)\n",
+    "\n",
+    "InjectData(driver.state, xt_init, yt_init)\n",
+    "\n",
+    "state = driver.optimize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d8b0759b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_DoE(state):\n",
+    "    # Gets the DoE in the format (D,Y) where D is the list of evaluation points and Y is the list of objective function values\n",
+    "    Y=state.dataset.export_as_dict()[\"obj\"]\n",
+    "    D=state.dataset.export_as_dict()[\"x\"]\n",
+    "    return (D,Y)\n",
+    "\n",
+    "def dominates(p,q):\n",
+    "    # Returns True if point p strictly dominates point q, else returns False\n",
+    "    return (p[0]<q[0] and p[1]<=q[1]) or (p[0]<=q[0] and p[1]<q[1])\n",
+    "\n",
+    "def get_Pareto_front(D,Y):\n",
+    "    # Given a DoE (D,Y), returns the list of indices of non-dominated points, sorted by ascending value of f1\n",
+    "    t=len(D)\n",
+    "    front=[]\n",
+    "    for i in range(t):\n",
+    "        if all([not dominates(q,Y[i]) for q in Y]):\n",
+    "            front.append((Y[i][0],i))\n",
+    "    front.sort()\n",
+    "    return [p[1] for p in front]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d5b701",
+   "metadata": {},
+   "source": [
+    "Obtain the true Pareto front using pymoo with high budget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8b364356",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==========================================================\n",
+      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
+      "==========================================================\n",
+      "     1 |     1000 |     17 |             - |             -\n",
+      "     2 |     1250 |     18 |  0.0120425576 |             f\n",
+      "     3 |     1500 |     26 |  0.0147107720 |             f\n",
+      "     4 |     1750 |     26 |  0.0031005586 |         ideal\n",
+      "     5 |     2000 |     32 |  0.0052941786 |         ideal\n",
+      "     6 |     2250 |     35 |  0.1504788162 |         nadir\n",
+      "     7 |     2500 |     44 |  0.8488294957 |         nadir\n",
+      "     8 |     2750 |     45 |  0.6028072980 |         nadir\n",
+      "     9 |     3000 |     50 |  1.3195495157 |         nadir\n",
+      "    10 |     3250 |     64 |  0.0062469853 |             f\n",
+      "    11 |     3500 |     79 |  0.7033701841 |         nadir\n",
+      "    12 |     3750 |     85 |  4.2727835614 |         nadir\n",
+      "    13 |     4000 |     98 |  0.6164142689 |         nadir\n",
+      "    14 |     4250 |    106 |  0.0028406672 |         nadir\n",
+      "    15 |     4500 |    121 |  0.0009706262 |             f\n",
+      "    16 |     4750 |    134 |  0.0017912732 |             f\n",
+      "    17 |     5000 |    156 |  0.0023394607 |             f\n",
+      "    18 |     5250 |    161 |  0.0848709455 |         nadir\n",
+      "    19 |     5500 |    179 |  0.0006343212 |             f\n",
+      "    20 |     5750 |    200 |  0.2191947257 |         nadir\n",
+      "    21 |     6000 |    228 |  0.0005645002 |             f\n",
+      "    22 |     6250 |    261 |  0.1714892787 |         nadir\n",
+      "    23 |     6500 |    287 |  0.0003064572 |             f\n",
+      "    24 |     6750 |    314 |  0.0005509557 |             f\n",
+      "    25 |     7000 |    347 |  2.3880070727 |         nadir\n",
+      "    26 |     7250 |    411 |  0.0004294819 |             f\n",
+      "    27 |     7500 |    464 |  0.0007247957 |             f\n",
+      "    28 |     7750 |    516 |  0.0085076603 |         nadir\n",
+      "    29 |     8000 |    571 |  0.0002759916 |             f\n",
+      "    30 |     8250 |    637 |  0.0004818762 |             f\n",
+      "    31 |     8500 |    711 |  0.0006097193 |             f\n",
+      "    32 |     8750 |    777 |  0.0007414370 |             f\n",
+      "    33 |     9000 |    833 |  0.0008244870 |             f\n",
+      "    34 |     9250 |    896 |  0.0009180603 |             f\n",
+      "    35 |     9500 |    980 |  0.0009810692 |             f\n",
+      "    36 |     9750 |   1000 |  0.0126589980 |         nadir\n",
+      "    37 |    10000 |   1000 |  0.0000801468 |             f\n",
+      "    38 |    10250 |   1000 |  0.0001543658 |             f\n",
+      "    39 |    10500 |   1000 |  0.0002303114 |             f\n",
+      "    40 |    10750 |   1000 |  0.0002809446 |             f\n",
+      "    41 |    11000 |   1000 |  0.0003469688 |             f\n",
+      "    42 |    11250 |   1000 |  0.0003791574 |             f\n",
+      "    43 |    11500 |   1000 |  0.0004219727 |             f\n",
+      "    44 |    11750 |   1000 |  0.0004399800 |             f\n",
+      "    45 |    12000 |   1000 |  0.0004590590 |             f\n",
+      "    46 |    12250 |   1000 |  0.0004904766 |             f\n",
+      "    47 |    12500 |   1000 |  0.0005249224 |             f\n",
+      "    48 |    12750 |   1000 |  0.0005339727 |             f\n",
+      "    49 |    13000 |   1000 |  0.0005569164 |             f\n",
+      "    50 |    13250 |   1000 |  0.0005696112 |             f\n",
+      "    51 |    13500 |   1000 |  0.0005847185 |             f\n",
+      "    52 |    13750 |   1000 |  0.0006064527 |             f\n",
+      "    53 |    14000 |   1000 |  0.0006222090 |             f\n",
+      "    54 |    14250 |   1000 |  0.0006213558 |             f\n",
+      "    55 |    14500 |   1000 |  0.0006230743 |             f\n",
+      "    56 |    14750 |   1000 |  0.0006239858 |             f\n",
+      "    57 |    15000 |   1000 |  0.0006354642 |             f\n",
+      "    58 |    15250 |   1000 |  0.0006402679 |             f\n",
+      "    59 |    15500 |   1000 |  0.0006460170 |             f\n",
+      "    60 |    15750 |   1000 |  0.0053034367 |         nadir\n",
+      "    61 |    16000 |   1000 |  0.0000462655 |             f\n",
+      "    62 |    16250 |   1000 |  0.0000800221 |             f\n",
+      "    63 |    16500 |   1000 |  0.0001076620 |             f\n",
+      "    64 |    16750 |   1000 |  0.0001256034 |             f\n",
+      "    65 |    17000 |   1000 |  0.0001547601 |             f\n",
+      "    66 |    17250 |   1000 |  0.0001761829 |             f\n",
+      "    67 |    17500 |   1000 |  0.0001977369 |             f\n",
+      "    68 |    17750 |   1000 |  0.0002124869 |             f\n",
+      "    69 |    18000 |   1000 |  0.0002252295 |             f\n",
+      "    70 |    18250 |   1000 |  0.0002403949 |             f\n",
+      "    71 |    18500 |   1000 |  0.0002594367 |             f\n",
+      "    72 |    18750 |   1000 |  0.0002739403 |             f\n",
+      "    73 |    19000 |   1000 |  0.0002846716 |             f\n",
+      "    74 |    19250 |   1000 |  0.0002992582 |             f\n",
+      "    75 |    19500 |   1000 |  0.0003061636 |             f\n",
+      "    76 |    19750 |   1000 |  0.0003126112 |             f\n",
+      "    77 |    20000 |   1000 |  0.0003191980 |             f\n",
+      "    78 |    20250 |   1000 |  0.0003230195 |             f\n",
+      "    79 |    20500 |   1000 |  0.0003333829 |             f\n",
+      "    80 |    20750 |   1000 |  0.0003341254 |             f\n",
+      "    81 |    21000 |   1000 |  0.0003397833 |             f\n",
+      "    82 |    21250 |   1000 |  0.0003467655 |             f\n",
+      "    83 |    21500 |   1000 |  0.6560381085 |         nadir\n",
+      "    84 |    21750 |   1000 |  0.0000276872 |             f\n",
+      "    85 |    22000 |   1000 |  0.0000536988 |             f\n",
+      "    86 |    22250 |   1000 |  0.0000696723 |             f\n",
+      "    87 |    22500 |   1000 |  0.0000920788 |             f\n",
+      "    88 |    22750 |   1000 |  0.0001134370 |             f\n",
+      "    89 |    23000 |   1000 |  1.9029810868 |         nadir\n",
+      "    90 |    23250 |   1000 |  0.0000357155 |             f\n",
+      "    91 |    23500 |   1000 |  0.0000762004 |             f\n",
+      "    92 |    23750 |   1000 |  0.0001106900 |             f\n",
+      "    93 |    24000 |   1000 |  0.0001362975 |             f\n",
+      "    94 |    24250 |   1000 |  0.0001617707 |             f\n",
+      "    95 |    24500 |   1000 |  0.0001879978 |             f\n",
+      "    96 |    24750 |   1000 |  0.0002108084 |             f\n",
+      "    97 |    25000 |   1000 |  0.0002205578 |             f\n",
+      "    98 |    25250 |   1000 |  0.0002299795 |             f\n",
+      "    99 |    25500 |   1000 |  0.0002441685 |             f\n",
+      "   100 |    25750 |   1000 |  0.0002635736 |             f\n",
+      "   101 |    26000 |   1000 |  0.0002750781 |             f\n",
+      "   102 |    26250 |   1000 |  0.0002874086 |             f\n",
+      "   103 |    26500 |   1000 |  0.0002932636 |             f\n",
+      "   104 |    26750 |   1000 |  0.0003101024 |             f\n",
+      "   105 |    27000 |   1000 |  0.0003153042 |             f\n",
+      "   106 |    27250 |   1000 |  0.0003280560 |             f\n",
+      "   107 |    27500 |   1000 |  0.0003301288 |             f\n",
+      "   108 |    27750 |   1000 |  0.0003320823 |             f\n",
+      "   109 |    28000 |   1000 |  0.0003401928 |             f\n",
+      "   110 |    28250 |   1000 |  0.0003473696 |             f\n",
+      "   111 |    28500 |   1000 |  0.0003517220 |             f\n",
+      "   112 |    28750 |   1000 |  0.0003575765 |             f\n",
+      "   113 |    29000 |   1000 |  0.0071240522 |         nadir\n",
+      "   114 |    29250 |   1000 |  0.0000417740 |             f\n",
+      "   115 |    29500 |   1000 |  0.0000814439 |             f\n",
+      "   116 |    29750 |   1000 |  0.0001132954 |             f\n",
+      "   117 |    30000 |   1000 |  0.0001299481 |             f\n",
+      "   118 |    30250 |   1000 |  0.0001512318 |             f\n",
+      "   119 |    30500 |   1000 |  0.0001645993 |             f\n",
+      "   120 |    30750 |   1000 |  0.0001846982 |             f\n",
+      "   121 |    31000 |   1000 |  0.0271815053 |         nadir\n",
+      "   122 |    31250 |   1000 |  0.0000353952 |             f\n",
+      "   123 |    31500 |   1000 |  0.0000739494 |             f\n",
+      "   124 |    31750 |   1000 |  0.0001019696 |             f\n",
+      "   125 |    32000 |   1000 |  0.0001234248 |             f\n",
+      "   126 |    32250 |   1000 |  0.0001437298 |             f\n",
+      "   127 |    32500 |   1000 |  0.0001691324 |             f\n",
+      "   128 |    32750 |   1000 |  0.0001924487 |             f\n",
+      "   129 |    33000 |   1000 |  0.0002050934 |             f\n",
+      "   130 |    33250 |   1000 |  0.0002227493 |             f\n",
+      "   131 |    33500 |   1000 |  0.0156150698 |         nadir\n",
+      "   132 |    33750 |   1000 |  0.0000344370 |             f\n",
+      "   133 |    34000 |   1000 |  0.0000686771 |             f\n",
+      "   134 |    34250 |   1000 |  0.0000928005 |             f\n",
+      "   135 |    34500 |   1000 |  0.0001247791 |             f\n",
+      "   136 |    34750 |   1000 |  0.0001496451 |             f\n",
+      "   137 |    35000 |   1000 |  0.0001662561 |             f\n",
+      "   138 |    35250 |   1000 |  0.0001813611 |             f\n",
+      "   139 |    35500 |   1000 |  0.0001954404 |             f\n",
+      "   140 |    35750 |   1000 |  0.0002153096 |             f\n",
+      "   141 |    36000 |   1000 |  0.0002349791 |             f\n",
+      "   142 |    36250 |   1000 |  0.0002505186 |             f\n",
+      "   143 |    36500 |   1000 |  0.0002570691 |             f\n",
+      "   144 |    36750 |   1000 |  0.0002665874 |             f\n",
+      "   145 |    37000 |   1000 |  0.0002794871 |             f\n",
+      "   146 |    37250 |   1000 |  0.0002894251 |             f\n",
+      "   147 |    37500 |   1000 |  0.0002977679 |             f\n",
+      "   148 |    37750 |   1000 |  0.0003068712 |             f\n",
+      "   149 |    38000 |   1000 |  0.0003123638 |             f\n",
+      "   150 |    38250 |   1000 |  0.0003145234 |             f\n",
+      "   151 |    38500 |   1000 |  0.0003179797 |             f\n",
+      "   152 |    38750 |   1000 |  0.0003236341 |             f\n",
+      "   153 |    39000 |   1000 |  0.0003289688 |             f\n",
+      "   154 |    39250 |   1000 |  0.0003396171 |             f\n",
+      "   155 |    39500 |   1000 |  0.0003430605 |             f\n",
+      "   156 |    39750 |   1000 |  0.0003442432 |             f\n",
+      "   157 |    40000 |   1000 |  0.2746050374 |         nadir\n",
+      "   158 |    40250 |   1000 |  0.1079524658 |         nadir\n",
+      "   159 |    40500 |   1000 |  0.0000324839 |             f\n",
+      "   160 |    40750 |   1000 |  0.0000610567 |             f\n",
+      "   161 |    41000 |   1000 |  0.0000858390 |             f\n",
+      "   162 |    41250 |   1000 |  0.0001068846 |             f\n",
+      "   163 |    41500 |   1000 |  0.2319306957 |         nadir\n",
+      "   164 |    41750 |   1000 |  0.0000373151 |             f\n",
+      "   165 |    42000 |   1000 |  0.0000650884 |             f\n",
+      "   166 |    42250 |   1000 |  0.0000894910 |             f\n",
+      "   167 |    42500 |   1000 |  0.0001139623 |             f\n",
+      "   168 |    42750 |   1000 |  0.0001348486 |             f\n",
+      "   169 |    43000 |   1000 |  0.0001628615 |             f\n",
+      "   170 |    43250 |   1000 |  0.0001841665 |             f\n",
+      "   171 |    43500 |   1000 |  0.0001990228 |             f\n",
+      "   172 |    43750 |   1000 |  0.0002183627 |             f\n",
+      "   173 |    44000 |   1000 |  0.0002283689 |             f\n",
+      "   174 |    44250 |   1000 |  0.0002392906 |             f\n",
+      "   175 |    44500 |   1000 |  0.0002518729 |             f\n",
+      "   176 |    44750 |   1000 |  0.0002600179 |             f\n",
+      "   177 |    45000 |   1000 |  0.0002692000 |             f\n",
+      "   178 |    45250 |   1000 |  0.0002822995 |             f\n",
+      "   179 |    45500 |   1000 |  0.0002894969 |             f\n",
+      "   180 |    45750 |   1000 |  0.0002983682 |             f\n",
+      "   181 |    46000 |   1000 |  0.0003089147 |             f\n",
+      "   182 |    46250 |   1000 |  0.0003102073 |             f\n",
+      "   183 |    46500 |   1000 |  0.0003172140 |             f\n",
+      "   184 |    46750 |   1000 |  0.0003241871 |             f\n",
+      "   185 |    47000 |   1000 |  0.0003284743 |             f\n",
+      "   186 |    47250 |   1000 |  0.0003369888 |             f\n",
+      "   187 |    47500 |   1000 |  0.0003392818 |             f\n",
+      "   188 |    47750 |   1000 |  0.0003445446 |             f\n",
+      "   189 |    48000 |   1000 |  0.0003555706 |             f\n",
+      "   190 |    48250 |   1000 |  0.0003521243 |             f\n",
+      "   191 |    48500 |   1000 |  0.0106246096 |         nadir\n",
+      "   192 |    48750 |   1000 |  0.0000403811 |             f\n",
+      "   193 |    49000 |   1000 |  0.0000743472 |             f\n",
+      "   194 |    49250 |   1000 |  0.0001093824 |             f\n",
+      "   195 |    49500 |   1000 |  0.0001362435 |             f\n",
+      "   196 |    49750 |   1000 |  0.0001543945 |             f\n",
+      "   197 |    50000 |   1000 |  0.0001736013 |             f\n",
+      "   198 |    50250 |   1000 |  0.0001930700 |             f\n",
+      "   199 |    50500 |   1000 |  0.0002126345 |             f\n",
+      "   200 |    50750 |   1000 |  0.0002259231 |             f\n",
+      "   201 |    51000 |   1000 |  0.0002364093 |             f\n",
+      "   202 |    51250 |   1000 |  0.0002527907 |             f\n",
+      "   203 |    51500 |   1000 |  0.0002641589 |             f\n",
+      "   204 |    51750 |   1000 |  0.0002750762 |             f\n",
+      "   205 |    52000 |   1000 |  0.0002906849 |             f\n",
+      "   206 |    52250 |   1000 |  0.0002985173 |             f\n",
+      "   207 |    52500 |   1000 |  0.0003067170 |             f\n",
+      "   208 |    52750 |   1000 |  0.0003125947 |             f\n",
+      "   209 |    53000 |   1000 |  0.0003196451 |             f\n",
+      "   210 |    53250 |   1000 |  0.0003182191 |             f\n",
+      "   211 |    53500 |   1000 |  0.0003232456 |             f\n",
+      "   212 |    53750 |   1000 |  0.0003332424 |             f\n",
+      "   213 |    54000 |   1000 |  0.0003364988 |             f\n",
+      "   214 |    54250 |   1000 |  0.0003437425 |             f\n",
+      "   215 |    54500 |   1000 |  0.0003508777 |             f\n",
+      "   216 |    54750 |   1000 |  0.0003546776 |             f\n",
+      "   217 |    55000 |   1000 |  0.0003584288 |             f\n",
+      "   218 |    55250 |   1000 |  0.0003573855 |             f\n",
+      "   219 |    55500 |   1000 |  0.0003621073 |             f\n",
+      "   220 |    55750 |   1000 |  0.0003682090 |             f\n",
+      "   221 |    56000 |   1000 |  0.0003699439 |             f\n",
+      "   222 |    56250 |   1000 |  0.0003813540 |             f\n",
+      "   223 |    56500 |   1000 |  0.0003868346 |             f\n",
+      "   224 |    56750 |   1000 |  0.0003924066 |             f\n",
+      "   225 |    57000 |   1000 |  0.0003933536 |             f\n",
+      "   226 |    57250 |   1000 |  0.0003953652 |             f\n",
+      "   227 |    57500 |   1000 |  0.0003964461 |             f\n",
+      "   228 |    57750 |   1000 |  0.0003979797 |             f\n",
+      "   229 |    58000 |   1000 |  0.0003991501 |             f\n",
+      "   230 |    58250 |   1000 |  0.0004004280 |             f\n",
+      "   231 |    58500 |   1000 |  0.0004014974 |             f\n",
+      "   232 |    58750 |   1000 |  0.0004056109 |             f\n",
+      "   233 |    59000 |   1000 |  0.0004102317 |             f\n",
+      "   234 |    59250 |   1000 |  0.0004156880 |             f\n",
+      "   235 |    59500 |   1000 |  0.0004202007 |             f\n",
+      "   236 |    59750 |   1000 |  0.0004184126 |             f\n",
+      "   237 |    60000 |   1000 |  0.0004153783 |             f\n",
+      "   238 |    60250 |   1000 |  0.0004156013 |             f\n",
+      "   239 |    60500 |   1000 |  0.0004134429 |             f\n",
+      "   240 |    60750 |   1000 |  0.0004116148 |             f\n",
+      "   241 |    61000 |   1000 |  0.0004062444 |             f\n",
+      "   242 |    61250 |   1000 |  0.0004047039 |             f\n",
+      "   243 |    61500 |   1000 |  0.0004051856 |             f\n",
+      "   244 |    61750 |   1000 |  0.0004083974 |             f\n",
+      "   245 |    62000 |   1000 |  0.0004178345 |             f\n",
+      "   246 |    62250 |   1000 |  0.0004185390 |             f\n",
+      "   247 |    62500 |   1000 |  0.0004198341 |             f\n",
+      "   248 |    62750 |   1000 |  0.0004150320 |             f\n",
+      "   249 |    63000 |   1000 |  0.0004168754 |             f\n",
+      "   250 |    63250 |   1000 |  0.0004125767 |             f\n",
+      "   251 |    63500 |   1000 |  0.0004134903 |             f\n",
+      "   252 |    63750 |   1000 |  0.0004214788 |             f\n",
+      "   253 |    64000 |   1000 |  0.0004212220 |             f\n",
+      "   254 |    64250 |   1000 |  0.0004223413 |             f\n",
+      "   255 |    64500 |   1000 |  0.0004282734 |             f\n",
+      "   256 |    64750 |   1000 |  0.0004229550 |             f\n",
+      "   257 |    65000 |   1000 |  0.0004204131 |             f\n",
+      "   258 |    65250 |   1000 |  0.0004222722 |             f\n",
+      "   259 |    65500 |   1000 |  0.0004230530 |             f\n",
+      "   260 |    65750 |   1000 |  0.0004237200 |             f\n",
+      "   261 |    66000 |   1000 |  0.0004238687 |             f\n",
+      "   262 |    66250 |   1000 |  0.0004282212 |             f\n",
+      "   263 |    66500 |   1000 |  0.0004236448 |             f\n",
+      "   264 |    66750 |   1000 |  0.0004283570 |             f\n",
+      "   265 |    67000 |   1000 |  0.0004319986 |             f\n",
+      "   266 |    67250 |   1000 |  0.0004292933 |             f\n",
+      "   267 |    67500 |   1000 |  0.0004348504 |             f\n",
+      "   268 |    67750 |   1000 |  0.0004435489 |             f\n",
+      "   269 |    68000 |   1000 |  0.0004438570 |             f\n",
+      "   270 |    68250 |   1000 |  0.0004413398 |             f\n",
+      "   271 |    68500 |   1000 |  0.0004357140 |             f\n",
+      "   272 |    68750 |   1000 |  0.0004354174 |             f\n",
+      "   273 |    69000 |   1000 |  0.0004348386 |             f\n",
+      "   274 |    69250 |   1000 |  0.0004355358 |             f\n",
+      "   275 |    69500 |   1000 |  0.0004305667 |             f\n",
+      "   276 |    69750 |   1000 |  0.0004288138 |             f\n",
+      "   277 |    70000 |   1000 |  0.0004314957 |             f\n",
+      "   278 |    70250 |   1000 |  0.0004351078 |             f\n",
+      "   279 |    70500 |   1000 |  0.0004374857 |             f\n",
+      "   280 |    70750 |   1000 |  0.0004439968 |             f\n",
+      "   281 |    71000 |   1000 |  0.0004401689 |             f\n",
+      "   282 |    71250 |   1000 |  0.0004382736 |             f\n",
+      "   283 |    71500 |   1000 |  0.0004426882 |             f\n",
+      "   284 |    71750 |   1000 |  0.0004472792 |             f\n",
+      "   285 |    72000 |   1000 |  0.0004520400 |             f\n",
+      "   286 |    72250 |   1000 |  0.0004501556 |             f\n",
+      "   287 |    72500 |   1000 |  0.0004518169 |             f\n",
+      "   288 |    72750 |   1000 |  0.0004489540 |             f\n",
+      "   289 |    73000 |   1000 |  0.0004523308 |             f\n",
+      "   290 |    73250 |   1000 |  0.0004527392 |             f\n",
+      "   291 |    73500 |   1000 |  0.0004544836 |             f\n",
+      "   292 |    73750 |   1000 |  0.0004539348 |             f\n",
+      "   293 |    74000 |   1000 |  0.0004503726 |             f\n",
+      "   294 |    74250 |   1000 |  0.0004426137 |             f\n",
+      "   295 |    74500 |   1000 |  0.0004361125 |             f\n",
+      "   296 |    74750 |   1000 |  0.0004362516 |             f\n",
+      "   297 |    75000 |   1000 |  0.0004345883 |             f\n",
+      "   298 |    75250 |   1000 |  0.0004308850 |             f\n",
+      "   299 |    75500 |   1000 |  0.0004266916 |             f\n",
+      "   300 |    75750 |   1000 |  0.0004304663 |             f\n",
+      "   301 |    76000 |   1000 |  0.0004283145 |             f\n",
+      "   302 |    76250 |   1000 |  0.0004291010 |             f\n",
+      "   303 |    76500 |   1000 |  0.0004313644 |             f\n",
+      "   304 |    76750 |   1000 |  0.0004347628 |             f\n",
+      "   305 |    77000 |   1000 |  0.0004277731 |             f\n",
+      "   306 |    77250 |   1000 |  0.0004255706 |             f\n",
+      "   307 |    77500 |   1000 |  0.0004290983 |             f\n",
+      "   308 |    77750 |   1000 |  0.0004341708 |             f\n",
+      "   309 |    78000 |   1000 |  0.0004352523 |             f\n",
+      "   310 |    78250 |   1000 |  0.0004310314 |             f\n",
+      "   311 |    78500 |   1000 |  0.0004338648 |             f\n",
+      "   312 |    78750 |   1000 |  0.0004352704 |             f\n",
+      "   313 |    79000 |   1000 |  0.0004339723 |             f\n",
+      "   314 |    79250 |   1000 |  0.0004351611 |             f\n",
+      "   315 |    79500 |   1000 |  0.0004371754 |             f\n",
+      "   316 |    79750 |   1000 |  0.0004341384 |             f\n",
+      "   317 |    80000 |   1000 |  0.0004314430 |             f\n",
+      "   318 |    80250 |   1000 |  0.0004341238 |             f\n",
+      "   319 |    80500 |   1000 |  0.0004356670 |             f\n",
+      "   320 |    80750 |   1000 |  0.0004335375 |             f\n",
+      "   321 |    81000 |   1000 |  0.0004393329 |             f\n",
+      "   322 |    81250 |   1000 |  0.0004397872 |             f\n",
+      "   323 |    81500 |   1000 |  0.0004421992 |             f\n",
+      "   324 |    81750 |   1000 |  0.0004439718 |             f\n",
+      "   325 |    82000 |   1000 |  0.0004448124 |             f\n",
+      "   326 |    82250 |   1000 |  0.0004417036 |             f\n",
+      "   327 |    82500 |   1000 |  0.0004320943 |             f\n",
+      "   328 |    82750 |   1000 |  0.0004337068 |             f\n",
+      "   329 |    83000 |   1000 |  0.0004357297 |             f\n",
+      "   330 |    83250 |   1000 |  0.0004404737 |             f\n",
+      "   331 |    83500 |   1000 |  0.0004417322 |             f\n",
+      "   332 |    83750 |   1000 |  0.0004443732 |             f\n",
+      "   333 |    84000 |   1000 |  0.0004429759 |             f\n",
+      "   334 |    84250 |   1000 |  0.0004435054 |             f\n",
+      "   335 |    84500 |   1000 |  0.0004395406 |             f\n",
+      "   336 |    84750 |   1000 |  0.0004378334 |             f\n",
+      "   337 |    85000 |   1000 |  0.0004373004 |             f\n",
+      "   338 |    85250 |   1000 |  0.0004374749 |             f\n",
+      "   339 |    85500 |   1000 |  0.0004393070 |             f\n",
+      "   340 |    85750 |   1000 |  0.0004404659 |             f\n",
+      "   341 |    86000 |   1000 |  0.0004379140 |             f\n",
+      "   342 |    86250 |   1000 |  0.0004429573 |             f\n",
+      "   343 |    86500 |   1000 |  0.0004371843 |             f\n",
+      "   344 |    86750 |   1000 |  0.0004350823 |             f\n",
+      "   345 |    87000 |   1000 |  0.0004295448 |             f\n",
+      "   346 |    87250 |   1000 |  0.0004274140 |             f\n",
+      "   347 |    87500 |   1000 |  0.0004263274 |             f\n",
+      "   348 |    87750 |   1000 |  0.0004259730 |             f\n",
+      "   349 |    88000 |   1000 |  0.0004248795 |             f\n",
+      "   350 |    88250 |   1000 |  0.0004281104 |             f\n",
+      "   351 |    88500 |   1000 |  0.0004296496 |             f\n",
+      "   352 |    88750 |   1000 |  0.0004333505 |             f\n",
+      "   353 |    89000 |   1000 |  0.0004384516 |             f\n",
+      "   354 |    89250 |   1000 |  0.0004382178 |             f\n",
+      "   355 |    89500 |   1000 |  0.0004432181 |             f\n",
+      "   356 |    89750 |   1000 |  0.0004397416 |             f\n",
+      "   357 |    90000 |   1000 |  0.0004404766 |             f\n",
+      "   358 |    90250 |   1000 |  0.0004417394 |             f\n",
+      "   359 |    90500 |   1000 |  0.0004386149 |             f\n",
+      "   360 |    90750 |   1000 |  0.0004397995 |             f\n",
+      "   361 |    91000 |   1000 |  0.0004405598 |             f\n",
+      "   362 |    91250 |   1000 |  0.0004396773 |             f\n",
+      "   363 |    91500 |   1000 |  0.0004374876 |             f\n",
+      "   364 |    91750 |   1000 |  0.0004325685 |             f\n",
+      "   365 |    92000 |   1000 |  0.0004311498 |             f\n",
+      "   366 |    92250 |   1000 |  0.0004292333 |             f\n",
+      "   367 |    92500 |   1000 |  0.0004303996 |             f\n",
+      "   368 |    92750 |   1000 |  0.0004319746 |             f\n",
+      "   369 |    93000 |   1000 |  0.0004378588 |             f\n",
+      "   370 |    93250 |   1000 |  0.0004389100 |             f\n",
+      "   371 |    93500 |   1000 |  0.0004387923 |             f\n",
+      "   372 |    93750 |   1000 |  0.0004367349 |             f\n",
+      "   373 |    94000 |   1000 |  0.0004389818 |             f\n",
+      "   374 |    94250 |   1000 |  0.0004383869 |             f\n",
+      "   375 |    94500 |   1000 |  0.0004305909 |             f\n",
+      "   376 |    94750 |   1000 |  0.0004267414 |             f\n",
+      "   377 |    95000 |   1000 |  0.0004267623 |             f\n",
+      "   378 |    95250 |   1000 |  0.0004240088 |             f\n",
+      "   379 |    95500 |   1000 |  0.0004199093 |             f\n",
+      "   380 |    95750 |   1000 |  0.0004201345 |             f\n",
+      "   381 |    96000 |   1000 |  0.0004171803 |             f\n",
+      "   382 |    96250 |   1000 |  0.0004238545 |             f\n",
+      "   383 |    96500 |   1000 |  0.0004226909 |             f\n",
+      "   384 |    96750 |   1000 |  0.0004239325 |             f\n",
+      "   385 |    97000 |   1000 |  0.0004270702 |             f\n",
+      "   386 |    97250 |   1000 |  0.0004260667 |             f\n",
+      "   387 |    97500 |   1000 |  0.0004254848 |             f\n",
+      "   388 |    97750 |   1000 |  0.0004303510 |             f\n",
+      "   389 |    98000 |   1000 |  0.0004289632 |             f\n",
+      "   390 |    98250 |   1000 |  0.0004266858 |             f\n",
+      "   391 |    98500 |   1000 |  0.0004240873 |             f\n",
+      "   392 |    98750 |   1000 |  0.0004240835 |             f\n",
+      "   393 |    99000 |   1000 |  0.0004217732 |             f\n",
+      "   394 |    99250 |   1000 |  0.0004274624 |             f\n",
+      "   395 |    99500 |   1000 |  0.0004302126 |             f\n",
+      "   396 |    99750 |   1000 |  0.0004323712 |             f\n",
+      "   397 |   100000 |   1000 |  0.0004361781 |             f\n",
+      "   398 |   100250 |   1000 |  0.0004295911 |             f\n",
+      "   399 |   100500 |   1000 |  0.0004294234 |             f\n",
+      "   400 |   100750 |   1000 |  0.0004288894 |             f\n",
+      "   401 |   101000 |   1000 |  0.0004303006 |             f\n",
+      "   402 |   101250 |   1000 |  0.0004317504 |             f\n",
+      "   403 |   101500 |   1000 |  0.0004331515 |             f\n",
+      "   404 |   101750 |   1000 |  0.0004344101 |             f\n",
+      "   405 |   102000 |   1000 |  0.0004335555 |             f\n",
+      "   406 |   102250 |   1000 |  0.0004388329 |             f\n",
+      "   407 |   102500 |   1000 |  0.0004390617 |             f\n",
+      "   408 |   102750 |   1000 |  0.0004417281 |             f\n",
+      "   409 |   103000 |   1000 |  0.0004413965 |             f\n",
+      "   410 |   103250 |   1000 |  0.0004404538 |             f\n",
+      "   411 |   103500 |   1000 |  0.0004372096 |             f\n",
+      "   412 |   103750 |   1000 |  0.0004373995 |             f\n",
+      "   413 |   104000 |   1000 |  0.0004452308 |             f\n",
+      "   414 |   104250 |   1000 |  0.0004426199 |             f\n",
+      "   415 |   104500 |   1000 |  0.0004380007 |             f\n",
+      "   416 |   104750 |   1000 |  0.0004379978 |             f\n",
+      "   417 |   105000 |   1000 |  0.0004421939 |             f\n",
+      "   418 |   105250 |   1000 |  0.0004379907 |             f\n",
+      "   419 |   105500 |   1000 |  0.0004400523 |             f\n",
+      "   420 |   105750 |   1000 |  0.0004334390 |             f\n",
+      "   421 |   106000 |   1000 |  0.0004345806 |             f\n",
+      "   422 |   106250 |   1000 |  0.0004370478 |             f\n",
+      "   423 |   106500 |   1000 |  0.0004364205 |             f\n",
+      "   424 |   106750 |   1000 |  0.0004385114 |             f\n",
+      "   425 |   107000 |   1000 |  0.0004383491 |             f\n",
+      "   426 |   107250 |   1000 |  0.0004382202 |             f\n",
+      "   427 |   107500 |   1000 |  0.0004399156 |             f\n",
+      "   428 |   107750 |   1000 |  0.0004417915 |             f\n",
+      "   429 |   108000 |   1000 |  0.0004423325 |             f\n",
+      "   430 |   108250 |   1000 |  0.0004411592 |             f\n",
+      "   431 |   108500 |   1000 |  0.0004413792 |             f\n",
+      "   432 |   108750 |   1000 |  0.0004387329 |             f\n",
+      "   433 |   109000 |   1000 |  0.0004335760 |             f\n",
+      "   434 |   109250 |   1000 |  0.0004323218 |             f\n",
+      "   435 |   109500 |   1000 |  0.0004318545 |             f\n",
+      "   436 |   109750 |   1000 |  0.0004350461 |             f\n",
+      "   437 |   110000 |   1000 |  0.0004372490 |             f\n",
+      "   438 |   110250 |   1000 |  0.0004380946 |             f\n",
+      "   439 |   110500 |   1000 |  0.0004323671 |             f\n",
+      "   440 |   110750 |   1000 |  0.0004342355 |             f\n",
+      "   441 |   111000 |   1000 |  0.0004298149 |             f\n",
+      "   442 |   111250 |   1000 |  0.0004269938 |             f\n",
+      "   443 |   111500 |   1000 |  0.0004258855 |             f\n",
+      "   444 |   111750 |   1000 |  0.0004234677 |             f\n",
+      "   445 |   112000 |   1000 |  0.0004221376 |             f\n",
+      "   446 |   112250 |   1000 |  0.0004229949 |             f\n",
+      "   447 |   112500 |   1000 |  0.0004322959 |             f\n",
+      "   448 |   112750 |   1000 |  0.0004330502 |             f\n",
+      "   449 |   113000 |   1000 |  0.0004313469 |             f\n",
+      "   450 |   113250 |   1000 |  0.0004394090 |             f\n",
+      "   451 |   113500 |   1000 |  0.0004405117 |             f\n",
+      "   452 |   113750 |   1000 |  0.0004397451 |             f\n",
+      "   453 |   114000 |   1000 |  0.0004410781 |             f\n",
+      "   454 |   114250 |   1000 |  0.0004418181 |             f\n",
+      "   455 |   114500 |   1000 |  0.0004388654 |             f\n",
+      "   456 |   114750 |   1000 |  0.0004385390 |             f\n",
+      "   457 |   115000 |   1000 |  0.0004432718 |             f\n",
+      "   458 |   115250 |   1000 |  0.0004398144 |             f\n",
+      "   459 |   115500 |   1000 |  0.0004362629 |             f\n",
+      "   460 |   115750 |   1000 |  0.0004376938 |             f\n",
+      "   461 |   116000 |   1000 |  0.0004382186 |             f\n",
+      "   462 |   116250 |   1000 |  0.0004454191 |             f\n",
+      "   463 |   116500 |   1000 |  0.0004410351 |             f\n",
+      "   464 |   116750 |   1000 |  0.0004436012 |             f\n",
+      "   465 |   117000 |   1000 |  0.0004461193 |             f\n",
+      "   466 |   117250 |   1000 |  0.0004451085 |             f\n",
+      "   467 |   117500 |   1000 |  0.0004424254 |             f\n",
+      "   468 |   117750 |   1000 |  0.0004409921 |             f\n",
+      "   469 |   118000 |   1000 |  0.0004400739 |             f\n",
+      "   470 |   118250 |   1000 |  0.0004395960 |             f\n",
+      "   471 |   118500 |   1000 |  0.0004457634 |             f\n",
+      "   472 |   118750 |   1000 |  0.0004482012 |             f\n",
+      "   473 |   119000 |   1000 |  0.0004454327 |             f\n",
+      "   474 |   119250 |   1000 |  0.0004449783 |             f\n",
+      "   475 |   119500 |   1000 |  0.0004431928 |             f\n",
+      "   476 |   119750 |   1000 |  0.0004512749 |             f\n",
+      "   477 |   120000 |   1000 |  0.0004530142 |             f\n",
+      "   478 |   120250 |   1000 |  0.0004522449 |             f\n",
+      "   479 |   120500 |   1000 |  0.0004503810 |             f\n",
+      "   480 |   120750 |   1000 |  0.0004503612 |             f\n",
+      "   481 |   121000 |   1000 |  0.0004562045 |             f\n",
+      "   482 |   121250 |   1000 |  0.0004480650 |             f\n",
+      "   483 |   121500 |   1000 |  0.0004438167 |             f\n",
+      "   484 |   121750 |   1000 |  0.0004411358 |             f\n",
+      "   485 |   122000 |   1000 |  0.0004422567 |             f\n",
+      "   486 |   122250 |   1000 |  0.0004372845 |             f\n",
+      "   487 |   122500 |   1000 |  0.0004364857 |             f\n",
+      "   488 |   122750 |   1000 |  0.0004329400 |             f\n",
+      "   489 |   123000 |   1000 |  0.0004392644 |             f\n",
+      "   490 |   123250 |   1000 |  0.0004414693 |             f\n",
+      "   491 |   123500 |   1000 |  0.0004445389 |             f\n",
+      "   492 |   123750 |   1000 |  0.0004427692 |             f\n",
+      "   493 |   124000 |   1000 |  0.0004397126 |             f\n",
+      "   494 |   124250 |   1000 |  0.0004391174 |             f\n",
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+      "   496 |   124750 |   1000 |  0.0004326307 |             f\n",
+      "   497 |   125000 |   1000 |  0.0004348183 |             f\n",
+      "   498 |   125250 |   1000 |  0.0004321476 |             f\n",
+      "   499 |   125500 |   1000 |  0.0004356887 |             f\n",
+      "   500 |   125750 |   1000 |  0.0004372321 |             f\n",
+      "   501 |   126000 |   1000 |  0.0004322933 |             f\n",
+      "   502 |   126250 |   1000 |  0.0004349898 |             f\n",
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+      "   504 |   126750 |   1000 |  0.0004399964 |             f\n",
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+      "   506 |   127250 |   1000 |  0.0004423172 |             f\n",
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+      "   508 |   127750 |   1000 |  0.0004413004 |             f\n",
+      "   509 |   128000 |   1000 |  0.0004426965 |             f\n",
+      "   510 |   128250 |   1000 |  0.0004402300 |             f\n",
+      "   511 |   128500 |   1000 |  0.0004389007 |             f\n",
+      "   512 |   128750 |   1000 |  0.0004398737 |             f\n",
+      "   513 |   129000 |   1000 |  0.0004399885 |             f\n",
+      "   514 |   129250 |   1000 |  0.0004403513 |             f\n",
+      "   515 |   129500 |   1000 |  0.0004359103 |             f\n",
+      "   516 |   129750 |   1000 |  0.0004344896 |             f\n",
+      "   517 |   130000 |   1000 |  0.0004337184 |             f\n",
+      "   518 |   130250 |   1000 |  0.0004346685 |             f\n",
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+      "   521 |   131000 |   1000 |  0.0004400786 |             f\n",
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+      "   523 |   131500 |   1000 |  0.0004380528 |             f\n",
+      "   524 |   131750 |   1000 |  0.0004397862 |             f\n",
+      "   525 |   132000 |   1000 |  0.0004427407 |             f\n",
+      "   526 |   132250 |   1000 |  0.0004506785 |             f\n",
+      "   527 |   132500 |   1000 |  0.0004490412 |             f\n",
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+      "   529 |   133000 |   1000 |  0.0004494720 |             f\n",
+      "   530 |   133250 |   1000 |  0.0004488366 |             f\n",
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+      "   535 |   134500 |   1000 |  0.0004375378 |             f\n",
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+      "   537 |   135000 |   1000 |  0.0004374104 |             f\n",
+      "   538 |   135250 |   1000 |  0.0004435526 |             f\n",
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+      "   540 |   135750 |   1000 |  0.0004392406 |             f\n",
+      "   541 |   136000 |   1000 |  0.0004363974 |             f\n",
+      "   542 |   136250 |   1000 |  0.0004396154 |             f\n",
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+      "   545 |   137000 |   1000 |  0.0004399100 |             f\n",
+      "   546 |   137250 |   1000 |  0.0004336395 |             f\n",
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+      "   548 |   137750 |   1000 |  0.0004380461 |             f\n",
+      "   549 |   138000 |   1000 |  0.0004374286 |             f\n",
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+      "   556 |   139750 |   1000 |  0.0004398204 |             f\n",
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+      "   569 |   143000 |   1000 |  0.0004366139 |             f\n",
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+      "   755 |   189500 |   1000 |  0.0004483832 |             f\n",
+      "   756 |   189750 |   1000 |  0.0004465159 |             f\n",
+      "   757 |   190000 |   1000 |  0.0004416227 |             f\n",
+      "   758 |   190250 |   1000 |  0.0004425867 |             f\n",
+      "   759 |   190500 |   1000 |  0.0004452064 |             f\n",
+      "   760 |   190750 |   1000 |  0.0004461825 |             f\n",
+      "   761 |   191000 |   1000 |  0.0004481740 |             f\n",
+      "   762 |   191250 |   1000 |  0.0004539252 |             f\n",
+      "   763 |   191500 |   1000 |  0.0004512063 |             f\n",
+      "   764 |   191750 |   1000 |  0.0004519204 |             f\n",
+      "   765 |   192000 |   1000 |  0.0004496275 |             f\n",
+      "   766 |   192250 |   1000 |  0.0004463875 |             f\n",
+      "   767 |   192500 |   1000 |  0.0004474983 |             f\n",
+      "   768 |   192750 |   1000 |  0.0004472933 |             f\n",
+      "   769 |   193000 |   1000 |  0.0004459646 |             f\n",
+      "   770 |   193250 |   1000 |  0.0004492652 |             f\n",
+      "   771 |   193500 |   1000 |  0.0004447473 |             f\n",
+      "   772 |   193750 |   1000 |  0.0004429396 |             f\n",
+      "   773 |   194000 |   1000 |  0.0004430470 |             f\n",
+      "   774 |   194250 |   1000 |  0.0004458880 |             f\n",
+      "   775 |   194500 |   1000 |  0.0004435387 |             f\n",
+      "   776 |   194750 |   1000 |  0.0004442391 |             f\n",
+      "   777 |   195000 |   1000 |  0.0004411294 |             f\n",
+      "   778 |   195250 |   1000 |  0.0004447863 |             f\n",
+      "   779 |   195500 |   1000 |  0.0004447562 |             f\n",
+      "   780 |   195750 |   1000 |  0.0004479105 |             f\n",
+      "   781 |   196000 |   1000 |  0.0004466828 |             f\n",
+      "   782 |   196250 |   1000 |  0.0004491434 |             f\n",
+      "   783 |   196500 |   1000 |  0.0004489823 |             f\n",
+      "   784 |   196750 |   1000 |  0.0004498815 |             f\n",
+      "   785 |   197000 |   1000 |  0.0004526272 |             f\n",
+      "   786 |   197250 |   1000 |  0.0004540192 |             f\n",
+      "   787 |   197500 |   1000 |  0.0004536405 |             f\n",
+      "   788 |   197750 |   1000 |  0.0004589999 |             f\n",
+      "   789 |   198000 |   1000 |  0.0004563477 |             f\n",
+      "   790 |   198250 |   1000 |  0.0004532138 |             f\n",
+      "   791 |   198500 |   1000 |  0.0004589117 |             f\n",
+      "   792 |   198750 |   1000 |  0.0004538285 |             f\n",
+      "   793 |   199000 |   1000 |  0.0004549564 |             f\n",
+      "   794 |   199250 |   1000 |  0.0004504469 |             f\n",
+      "   795 |   199500 |   1000 |  0.0004533267 |             f\n",
+      "   796 |   199750 |   1000 |  0.0004557502 |             f\n",
+      "   797 |   200000 |   1000 |  0.0004569746 |             f\n",
+      "   798 |   200250 |   1000 |  0.0004523761 |             f\n",
+      "   799 |   200500 |   1000 |  0.0004469307 |             f\n",
+      "   800 |   200750 |   1000 |  0.0004426413 |             f\n",
+      "   801 |   201000 |   1000 |  0.0004468153 |             f\n",
+      "   802 |   201250 |   1000 |  0.0004436816 |             f\n",
+      "   803 |   201500 |   1000 |  0.0004432781 |             f\n",
+      "   804 |   201750 |   1000 |  0.0004443097 |             f\n",
+      "   805 |   202000 |   1000 |  0.0004428490 |             f\n",
+      "   806 |   202250 |   1000 |  0.0004436296 |             f\n",
+      "   807 |   202500 |   1000 |  0.0004462273 |             f\n",
+      "   808 |   202750 |   1000 |  0.0004517382 |             f\n",
+      "   809 |   203000 |   1000 |  0.0004445966 |             f\n",
+      "   810 |   203250 |   1000 |  0.0004376644 |             f\n",
+      "   811 |   203500 |   1000 |  0.0004453173 |             f\n",
+      "   812 |   203750 |   1000 |  0.0004431711 |             f\n",
+      "   813 |   204000 |   1000 |  0.0004416475 |             f\n",
+      "   814 |   204250 |   1000 |  0.0004356068 |             f\n",
+      "   815 |   204500 |   1000 |  0.0004386314 |             f\n",
+      "   816 |   204750 |   1000 |  0.0004366067 |             f\n",
+      "   817 |   205000 |   1000 |  0.0004416618 |             f\n",
+      "   818 |   205250 |   1000 |  0.0004381413 |             f\n",
+      "   819 |   205500 |   1000 |  0.0004416755 |             f\n",
+      "   820 |   205750 |   1000 |  0.0004423460 |             f\n",
+      "   821 |   206000 |   1000 |  0.0004495733 |             f\n",
+      "   822 |   206250 |   1000 |  0.0004492423 |             f\n",
+      "   823 |   206500 |   1000 |  0.0004439123 |             f\n",
+      "   824 |   206750 |   1000 |  0.0004471177 |             f\n",
+      "   825 |   207000 |   1000 |  0.0004508336 |             f\n",
+      "   826 |   207250 |   1000 |  0.0004462143 |             f\n",
+      "   827 |   207500 |   1000 |  0.0004351624 |             f\n",
+      "   828 |   207750 |   1000 |  0.0004390606 |             f\n",
+      "   829 |   208000 |   1000 |  0.0004451853 |             f\n",
+      "   830 |   208250 |   1000 |  0.0004496615 |             f\n",
+      "   831 |   208500 |   1000 |  0.0004489599 |             f\n",
+      "   832 |   208750 |   1000 |  0.0004426477 |             f\n",
+      "   833 |   209000 |   1000 |  0.0004442560 |             f\n",
+      "   834 |   209250 |   1000 |  0.0004477333 |             f\n",
+      "   835 |   209500 |   1000 |  0.0004499719 |             f\n",
+      "   836 |   209750 |   1000 |  0.0004466881 |             f\n",
+      "   837 |   210000 |   1000 |  0.0004422985 |             f\n",
+      "   838 |   210250 |   1000 |  0.0004403674 |             f\n",
+      "   839 |   210500 |   1000 |  0.0004377549 |             f\n",
+      "   840 |   210750 |   1000 |  0.0004384067 |             f\n",
+      "   841 |   211000 |   1000 |  0.0004418919 |             f\n",
+      "   842 |   211250 |   1000 |  0.0004342033 |             f\n",
+      "   843 |   211500 |   1000 |  0.0004285590 |             f\n",
+      "   844 |   211750 |   1000 |  0.0004243761 |             f\n",
+      "   845 |   212000 |   1000 |  0.0004241011 |             f\n",
+      "   846 |   212250 |   1000 |  0.0004289795 |             f\n",
+      "   847 |   212500 |   1000 |  0.0004349860 |             f\n",
+      "   848 |   212750 |   1000 |  0.0004301363 |             f\n",
+      "   849 |   213000 |   1000 |  0.0004323988 |             f\n",
+      "   850 |   213250 |   1000 |  0.0004361378 |             f\n",
+      "   851 |   213500 |   1000 |  0.0004386183 |             f\n",
+      "   852 |   213750 |   1000 |  0.0004374234 |             f\n",
+      "   853 |   214000 |   1000 |  0.0004421224 |             f\n",
+      "   854 |   214250 |   1000 |  0.0004373505 |             f\n",
+      "   855 |   214500 |   1000 |  0.0004363114 |             f\n",
+      "   856 |   214750 |   1000 |  0.0004389284 |             f\n",
+      "   857 |   215000 |   1000 |  0.0004357375 |             f\n",
+      "   858 |   215250 |   1000 |  0.0004384278 |             f\n",
+      "   859 |   215500 |   1000 |  0.0004364405 |             f\n",
+      "   860 |   215750 |   1000 |  0.0004362509 |             f\n",
+      "   861 |   216000 |   1000 |  0.0004376521 |             f\n",
+      "   862 |   216250 |   1000 |  0.0004354521 |             f\n",
+      "   863 |   216500 |   1000 |  0.0004288769 |             f\n",
+      "   864 |   216750 |   1000 |  0.0004275538 |             f\n",
+      "   865 |   217000 |   1000 |  0.0004267611 |             f\n",
+      "   866 |   217250 |   1000 |  0.0004268853 |             f\n",
+      "   867 |   217500 |   1000 |  0.0004305199 |             f\n",
+      "   868 |   217750 |   1000 |  0.0004305769 |             f\n",
+      "   869 |   218000 |   1000 |  0.0004345961 |             f\n",
+      "   870 |   218250 |   1000 |  0.0004389112 |             f\n",
+      "   871 |   218500 |   1000 |  0.0004412603 |             f\n",
+      "   872 |   218750 |   1000 |  0.0004421915 |             f\n",
+      "   873 |   219000 |   1000 |  0.0004447628 |             f\n",
+      "   874 |   219250 |   1000 |  0.0004468740 |             f\n",
+      "   875 |   219500 |   1000 |  0.0004459639 |             f\n",
+      "   876 |   219750 |   1000 |  0.0004429554 |             f\n",
+      "   877 |   220000 |   1000 |  0.0004438812 |             f\n",
+      "   878 |   220250 |   1000 |  0.0004368103 |             f\n",
+      "   879 |   220500 |   1000 |  0.0004349741 |             f\n",
+      "   880 |   220750 |   1000 |  0.0004312281 |             f\n",
+      "   881 |   221000 |   1000 |  0.0004329297 |             f\n",
+      "   882 |   221250 |   1000 |  0.0004383651 |             f\n",
+      "   883 |   221500 |   1000 |  0.0004402801 |             f\n",
+      "   884 |   221750 |   1000 |  0.0004432239 |             f\n",
+      "   885 |   222000 |   1000 |  0.0004412382 |             f\n",
+      "   886 |   222250 |   1000 |  0.0004463797 |             f\n",
+      "   887 |   222500 |   1000 |  0.0004465276 |             f\n",
+      "   888 |   222750 |   1000 |  0.0004461946 |             f\n",
+      "   889 |   223000 |   1000 |  0.0004468457 |             f\n",
+      "   890 |   223250 |   1000 |  0.0004430105 |             f\n",
+      "   891 |   223500 |   1000 |  0.0004416809 |             f\n",
+      "   892 |   223750 |   1000 |  0.0004343527 |             f\n",
+      "   893 |   224000 |   1000 |  0.0004393391 |             f\n",
+      "   894 |   224250 |   1000 |  0.0004363276 |             f\n",
+      "   895 |   224500 |   1000 |  0.0004373459 |             f\n",
+      "   896 |   224750 |   1000 |  0.0004351006 |             f\n",
+      "   897 |   225000 |   1000 |  0.0004334228 |             f\n",
+      "   898 |   225250 |   1000 |  0.0004285561 |             f\n",
+      "   899 |   225500 |   1000 |  0.0004248057 |             f\n",
+      "   900 |   225750 |   1000 |  0.0004272336 |             f\n",
+      "   901 |   226000 |   1000 |  0.0004325941 |             f\n",
+      "   902 |   226250 |   1000 |  0.0004386157 |             f\n",
+      "   903 |   226500 |   1000 |  0.0004399316 |             f\n",
+      "   904 |   226750 |   1000 |  0.0004368522 |             f\n",
+      "   905 |   227000 |   1000 |  0.0004363993 |             f\n",
+      "   906 |   227250 |   1000 |  0.0004367515 |             f\n",
+      "   907 |   227500 |   1000 |  0.0004312362 |             f\n",
+      "   908 |   227750 |   1000 |  0.0004317686 |             f\n",
+      "   909 |   228000 |   1000 |  0.0004278627 |             f\n",
+      "   910 |   228250 |   1000 |  0.0004307990 |             f\n",
+      "   911 |   228500 |   1000 |  0.0004256816 |             f\n",
+      "   912 |   228750 |   1000 |  0.0004254109 |             f\n",
+      "   913 |   229000 |   1000 |  0.0004256142 |             f\n",
+      "   914 |   229250 |   1000 |  0.0004258150 |             f\n",
+      "   915 |   229500 |   1000 |  0.0004308359 |             f\n",
+      "   916 |   229750 |   1000 |  0.0004307658 |             f\n",
+      "   917 |   230000 |   1000 |  0.0004280213 |             f\n",
+      "   918 |   230250 |   1000 |  0.0004268820 |             f\n",
+      "   919 |   230500 |   1000 |  0.0004296017 |             f\n",
+      "   920 |   230750 |   1000 |  0.0004321065 |             f\n",
+      "   921 |   231000 |   1000 |  0.0004319072 |             f\n",
+      "   922 |   231250 |   1000 |  0.0004264982 |             f\n",
+      "   923 |   231500 |   1000 |  0.0004245913 |             f\n",
+      "   924 |   231750 |   1000 |  0.0004262582 |             f\n",
+      "   925 |   232000 |   1000 |  0.0004261919 |             f\n",
+      "   926 |   232250 |   1000 |  0.0004265030 |             f\n",
+      "   927 |   232500 |   1000 |  0.0004244479 |             f\n",
+      "   928 |   232750 |   1000 |  0.0004268013 |             f\n",
+      "   929 |   233000 |   1000 |  0.0004230899 |             f\n",
+      "   930 |   233250 |   1000 |  0.0004264242 |             f\n",
+      "   931 |   233500 |   1000 |  0.0004279397 |             f\n",
+      "   932 |   233750 |   1000 |  0.0004254360 |             f\n",
+      "   933 |   234000 |   1000 |  0.0004261358 |             f\n",
+      "   934 |   234250 |   1000 |  0.0004283916 |             f\n",
+      "   935 |   234500 |   1000 |  0.0004310644 |             f\n",
+      "   936 |   234750 |   1000 |  0.0004306489 |             f\n",
+      "   937 |   235000 |   1000 |  0.0004349697 |             f\n",
+      "   938 |   235250 |   1000 |  0.0004373821 |             f\n",
+      "   939 |   235500 |   1000 |  0.0004373588 |             f\n",
+      "   940 |   235750 |   1000 |  0.0004382359 |             f\n",
+      "   941 |   236000 |   1000 |  0.0004347358 |             f\n",
+      "   942 |   236250 |   1000 |  0.0004378228 |             f\n",
+      "   943 |   236500 |   1000 |  0.0004415141 |             f\n",
+      "   944 |   236750 |   1000 |  0.0004382070 |             f\n",
+      "   945 |   237000 |   1000 |  0.0004344650 |             f\n",
+      "   946 |   237250 |   1000 |  0.0004393500 |             f\n",
+      "   947 |   237500 |   1000 |  0.0004329584 |             f\n",
+      "   948 |   237750 |   1000 |  0.0004324799 |             f\n",
+      "   949 |   238000 |   1000 |  0.0004269250 |             f\n",
+      "   950 |   238250 |   1000 |  0.0004241546 |             f\n",
+      "   951 |   238500 |   1000 |  0.0004329828 |             f\n",
+      "   952 |   238750 |   1000 |  0.0004293425 |             f\n",
+      "   953 |   239000 |   1000 |  0.0004337746 |             f\n",
+      "   954 |   239250 |   1000 |  0.0004355549 |             f\n",
+      "   955 |   239500 |   1000 |  0.0004292254 |             f\n",
+      "   956 |   239750 |   1000 |  0.0004317353 |             f\n",
+      "   957 |   240000 |   1000 |  0.0004271114 |             f\n",
+      "   958 |   240250 |   1000 |  0.0004257726 |             f\n",
+      "   959 |   240500 |   1000 |  0.0004249169 |             f\n",
+      "   960 |   240750 |   1000 |  0.0004265418 |             f\n",
+      "   961 |   241000 |   1000 |  0.0004289623 |             f\n",
+      "   962 |   241250 |   1000 |  0.0004289997 |             f\n",
+      "   963 |   241500 |   1000 |  0.0004309725 |             f\n",
+      "   964 |   241750 |   1000 |  0.0004296292 |             f\n",
+      "   965 |   242000 |   1000 |  0.0004282817 |             f\n",
+      "   966 |   242250 |   1000 |  0.0004276226 |             f\n",
+      "   967 |   242500 |   1000 |  0.0004293581 |             f\n",
+      "   968 |   242750 |   1000 |  0.0004334098 |             f\n",
+      "   969 |   243000 |   1000 |  0.0004341562 |             f\n",
+      "   970 |   243250 |   1000 |  0.0004303308 |             f\n",
+      "   971 |   243500 |   1000 |  0.0004296283 |             f\n",
+      "   972 |   243750 |   1000 |  0.0004316753 |             f\n",
+      "   973 |   244000 |   1000 |  0.0004285051 |             f\n",
+      "   974 |   244250 |   1000 |  0.0004294176 |             f\n",
+      "   975 |   244500 |   1000 |  0.0004323332 |             f\n",
+      "   976 |   244750 |   1000 |  0.0004246550 |             f\n",
+      "   977 |   245000 |   1000 |  0.0004221032 |             f\n",
+      "   978 |   245250 |   1000 |  0.0004232213 |             f\n",
+      "   979 |   245500 |   1000 |  0.0004312099 |             f\n",
+      "   980 |   245750 |   1000 |  0.0004303929 |             f\n",
+      "   981 |   246000 |   1000 |  0.0004349814 |             f\n",
+      "   982 |   246250 |   1000 |  0.0004340276 |             f\n",
+      "   983 |   246500 |   1000 |  0.0004319755 |             f\n",
+      "   984 |   246750 |   1000 |  0.0004350116 |             f\n",
+      "   985 |   247000 |   1000 |  0.0004371571 |             f\n",
+      "   986 |   247250 |   1000 |  0.0004397833 |             f\n",
+      "   987 |   247500 |   1000 |  0.0004501623 |             f\n",
+      "   988 |   247750 |   1000 |  0.0004514033 |             f\n",
+      "   989 |   248000 |   1000 |  0.0004465536 |             f\n",
+      "   990 |   248250 |   1000 |  0.0004484204 |             f\n",
+      "   991 |   248500 |   1000 |  0.0004467946 |             f\n",
+      "   992 |   248750 |   1000 |  0.0004455412 |             f\n",
+      "   993 |   249000 |   1000 |  0.0004464286 |             f\n",
+      "   994 |   249250 |   1000 |  0.0004496437 |             f\n",
+      "   995 |   249500 |   1000 |  0.0004488119 |             f\n",
+      "   996 |   249750 |   1000 |  0.0004456306 |             f\n",
+      "   997 |   250000 |   1000 |  0.0004461934 |             f\n",
+      "   998 |   250250 |   1000 |  0.0004431488 |             f\n",
+      "   999 |   250500 |   1000 |  0.0004460343 |             f\n",
+      "  1000 |   250750 |   1000 |  0.0004356015 |             f\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_benchmark_pymoo(bproblem):\n",
+    "    # Runs pymoo with high budget on a given benchmark problem. Returns a list of the Pareto optimal points and a list of the corresponding points in the objective space\n",
+    "\n",
+    "    pymoo_problem=PymooWrapper(bproblem)\n",
+    "    algorithm = NSGA2(\n",
+    "        pop_size=1000,\n",
+    "        n_offsprings=250,\n",
+    "        sampling=FloatRandomSampling(),\n",
+    "        crossover=SBX(prob=0.9, eta=15),\n",
+    "        mutation=PM(eta=20),\n",
+    "        eliminate_duplicates=True\n",
+    "    )\n",
+    "    termination = get_termination(\"n_gen\", 1000)\n",
+    "    res = minimize(pymoo_problem,\n",
+    "                algorithm,\n",
+    "                termination,\n",
+    "                seed=1,\n",
+    "                save_history=True,\n",
+    "                verbose=True)\n",
+    "\n",
+    "    X = res.X\n",
+    "    F = res.F\n",
+    "    return (X,F)\n",
+    "\n",
+    "X_true,F_true=run_benchmark_pymoo(bproblem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "31cdf7a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iISFB737d+1Pm94isatWqJVxdXQ3e5+npKZo0aZJjOSIiIgQAMX/+fFOLLl3bI0eOSNueP38uypcvL+rVqyf7e7x//75wc3MTb7zxhrRt3Lhxetc3u9f5jRs3hJubm/j0009l23fu3CkAiA4dOhg8buvWrQKASV85/W0bev59+/Y1+RhrYLOUBaWmpkrV57Vq1ULjxo1Rr149/P7771AqlcjIyMDPP/+M/v37IyQkBIGBgRg0aBAAbY1MZkIIvPPOO9Jte3t7nD9/HlFRURg8eLBUw6HTp08fXL16VdY8ZcjOnTuRnp6OoUOHyrbXq1cPTZo0wdatW016rr169cLs2bNlXwEBAdL9Pj4+sv8U7O3tsX37dmg0Gr1zt2rVCtWrV9c7d9myZfU69AUGBuL8+fMmldGQrVu3olGjRnpt2B9++CHS09NlzV4A0Lt3b9l/N8WLF0evXr2wa9cukzprP3z4EHPmzEH37t0RHBws1RAJIXDx4sUcj+/bt6+szVvXjKO7BvHx8di/fz/69++v10egT58+SEhIwPHjxwEA27dvR2BgIKpWrSrto1AopNfgy7h9+zaOHz+Ot99+W9YU4OTkhPfeew8XL17Ev//+KztG15QIINtak5y4urrizTfflG3L+vr45ZdfYG9vjw8++EC2X0BAAOrWrYvffvstx/Oo1Wq89957sm1DhgyBQqHQe822a9cOZcqUkW7b29tj69atcHR0lP09A9pmDzs7O73HaN26NXx8fKTbdnZ2GDx4MK5fv44zZ84YLOOJEyfw77//4oMPPpBqgHX69OmDM2fO6DUx9O/fX3a7SZMmuHbtml7n1SZNmuDChQt6TanGnD59Gu+//z6cnZ2xbdu2lx6t6ejomOcDIl7WiRMnEBcXhyFDhsj+Hj08PPDWW29h3759eiPXMv9dZfc6nzZtGpRKJSZPnizbrnuPMtbfpmXLloiIiDDpq3Xr1iY9z+joaAwePBjly5fHggULTDrGWtgsZUFOTk5S04STkxMqVKgAT09P6f5u3bohMjIS48ePx9tvvy01QwQHB+Pp06eyx7Kzs9NrXrl+/ToAbXv47t27pTcbIYQ0LDYuLk4WMrLS9Rcx1PnPz88Pp0+fNum55tSh2NfXN9fnPnXqlGxbxYoV9fbz8PB4pZFRsbGxBtuvdWXK+gFgqAy+vr54/vw54uPjs20Cu3fvHurXrw83NzeMHj0alSpVQpEiRXDy5El8/PHHer9zQ7Ke38nJCUWLFpWuge41sWPHDpw8eVL2mtC92erm6FCpVGjWrJnB5/Oycvqd6s5bvXr1PDlfZoauvYeHB1JSUvD06VMULVoU169fh1KplEZ3CSGkr+vXryMjIyPH87i4uKBUqVJ620qXLq33ejH2uvf29tYLHc7OzvD09DT5NQdor6Wh16/udbBs2TJs2rRJeo6AtpkL0L4OMoemrNdP1wRhaHtaWhqSkpLg7u6ud+7MEhIS0K1bN6SmpmLNmjWv1Ffk3r17eTqNxavI6XWuVqtx584d2dQIhn6PWWVkZGDLli3o06cP3NzcZPfp3tONXcOSJUvm6aCOK1eu4PXXX4eTkxP27t0rC+n5EcONBWUdLZXZ6dOnsXPnTixdulT2X2RcXJzB/4gcHBz00r6uj0OvXr3Qpk0bg+epVatWtmXUPUbWTsyAdujty3bEy8rQ42Q+d9Z+PYbO/Sr/1Rvj7Oxs9LkD+uVOTk42ed+sVq5cidjYWFy7dk3WVyRrB8/sGLsGuteM7pp27NhR1s8kM12fGhcXl2yfz8vI6fWkO29mefUay+71kfn6uLq6Gh0xY8r0Bc+ePUN6erpeOElKSjLpuRl7zRl7jJd5zel+D2+//TaaN29ucJ8aNWrIbhu7fjm95oxJT09Hr169oFKp8Mknn7zSxHMqlQoqlcqkjriWkNvXuVKpNNqfJbM7d+4gKSnJYL83XT9LYzU3edHnRicmJgYhISHIyMjAgQMH9F4r+RHDTT6h++8pa4fIXbt2mfwYjRo1QpEiRRAfH59jYtfNJ6FWq2XbdZ0KIyIiZBNDpaam4uTJk9L95pD53JlH2jx+/BjR0dEvNXeKseeZXRlOnjyJ1NRU6Q1LV6bMZdQ5dOgQxo4dq7fNz89P+m/e0dHR4PkfPHgApVKp9x9cbn7nOalZsyZKliyJ2NjYHF8TjRo1wvHjx5GRkSH7AHuVOXqqV68ONzc3REREYPz48bL7IiIi4OTk9NLD3HP7uzUkKCgIe/bsQdmyZVGlSpWXegyNRoOjR4/KRnadOnUKz549M+nvpXHjxti7dy8uXbokq8E6ffo0nj59qvcYR44cgUajkc17dOjQIdjb2xv9L75Zs2awt7fHvXv3rDZFw5gxY/D333+jbdu2evPS5NasWbMAAMOGDcuLor2yhg0bws7ODhEREejdu7fsvoiICJQrV05vmLspdO9BWSfQTEtLw9y5c1GiRAmjNZ0PHjww+W83uwk6Y2NjERoaitTUVBw4cAC1a9c2rfBWxj43+USdOnVQpEgR/PTTT9Kb9ZkzZ7Bx40aT5xFwd3fH5MmTsWzZMqxcuVL2n9TNmzdlbba6P4is/R1CQkLQuHFjzJgxQ+rzoVarMXbsWNy7d0/vAyovdenSBQEBAZg8eTJiYmIAaP/bGzlyJFJTU/Hpp5/m+jG9vb2hVCr1nqcxn332GR48eICxY8dKv4eLFy/iiy++QKNGjfRqxK5fvy7rE/HDDz8gMjJSNmOqr68vMjIypKYBnaZNm0KtVuOnn36Stv3yyy95OmW+g4MDvvzyS2zatAnz58+XBYE7d+5g4sSJ0rZx48YhLi4On3/+ufTaOXr0KCIjI1/p/GPHjsUff/yBNWvWSNu3bt2KDRs2YOTIkS89+k4XCk393RoyYsQI+Pr6on///rLfj0ajwbZt20zqY+bm5oaFCxdK/6A8fvwYH330ETw9PWX9h4wZNmwY3N3d8eGHH0r/5T98+BAjRoxA6dKl9ZaRcHZ2ltU0/fXXX1i+fDkGDx5sdKZcT09PfPLJJ/j222+xdu1a2X3Xrl3D9OnTcyznq1ixYgX+97//oVKlSli3bt1Lz41y584djBo1CkuWLMH48eOtPomejpeXFwYOHIgVK1bIpk/47rvvcOTIEZNrULIqVaoUatWqhdWrV0tTPDx9+hSDBg1CXFxcts16edHnJj4+HqGhoUhKSsKff/5ZIObbkli6B3NhZWi0VFa//vqrcHd3F+XKlRMBAQGibt264sqVK0KpVIpx48ZJ++U0+mbhwoWibNmywsPDQ9SvX1+UK1dOVK5cWSxevFjaR61Wi5CQEOHs7CwaNWokWrRoIU6cOCGEECI+Pl506tRJODg4iJo1a4qSJUsKLy8vsX79+hyfp2601MqVK43uU7NmTdGxY0eD9926dUuEhoYKBwcHUbt2bVG8eHFRoUIF8dtvv8n2a9KkiQgODtY7fvz48SLry3rKlClCqVSKOnXqiBYtWuiNSslq3bp1wsvLS5QsWVIa2dWpUyfZKBfdaKkVK1aIgQMHisqVK4uKFSsKR0dH8X//93+yx0tMTBQBAQGiePHiomnTpqJFixbizp07QgghxowZI+zs7ERAQICoUKGC6NWrlzQKIvMoIGOjpQyNcihatKgYMWKEbNuqVatEhQoVRPHixUW9evWEj4+P8PHxEbNnz5btt2zZMuHq6irKlCkjAgICRJs2baQRWC87WkqtVovPP/9cODs7i4oVK4pKlSoJJycnMW7cONkoEd3oIN21McXw4cOFvb29qF+/vmjRooXYsGGDEEI7AqhBgwZ6+4eFhQkAIjk5WdqmUqlEt27dhKOjo/D39xe1a9cWrq6uokePHuL8+fPZnn/w4MHCw8NDnDx5UlStWlW89tprwsXFRfj7+4vTp09L++lGS02dOtXg4xw7dkzUqlVLuLi4iNdee004OzuLevXqiX/++Ue2n+69ICwsTFSoUEFUq1ZN2NnZiX79+slGSGYdLSWEduTb119/LUqXLi1Kly4t6tevL8qWLSuqVasmli9fLu2nGy2VdcSloWsnhBCzZs0SAPRG42UWHBwsAAg/Pz+9UZSZv/bs2SOEeDFaqlatWqJFixaiadOmonLlyqJIkSKiTZs2YufOnUbPJUTOo6VCQkJEixYtRO3atfXKdf369Wwf29BoKSGESElJER988IFwcHAQ/v7+onz58sLV1VVvJN24ceOEUqnM9hyZnTx5UlSoUEEULVpU1K9fX/j5+Yl58+aJYsWKyT4XzOG9996TRrUZ+n3l9PdhTQohTOziTq/k/PnzSEpKMthhM7PU1FRcvnwZzs7O0qiVQ4cOoXz58lJtS0xMDO7cuWO07RzQtn9fuXIFiYmJ8PHxMTr7cUxMDO7evQu1Wo3atWvLOgTeu3cPMTExKFasGKpXr27S9P+PHj3C+fPnUbVqVaMdzqKiouDk5JRtu+3du3dx69YtuLu7o2rVqtI6Mjpnz56FQqHQqyK9efMmVCqVXtW77rk8f/4cFSpUyLajL6D9z/3SpUt48uQJ/Pz89DoupqSk4PTp06hWrRpKly6N2NhYqcOgoZlLhRC4fPkyHjx4AI1Gg0aNGklt7g8fPkRMTAy8vLxQrlw5JCYmIjo6GrVq1ZIe6+zZs7Czs5P6TD158gT//PMPAgIC9P5bP3LkCDw9PfWaOAHtf+kPHz7Mtpo8JSUFFy9eRPHixVG5cuVsz5WZbr/q1avrdbAFtK/tS5cuQQiBatWq6fVnuX37Nq5fv44mTZro9V/Jzt27d3Hz5k2kp6ejUqVKKFeuHM6dOwe1Wo06derI9lWpVLh58yaaN2+u93pOSkrClStXUKRIEVSuXFlvxKEhQ4YMwbZt23D//n2kp6fj4sWLUCqVqFGjhuw1K4TAoUOHcnztxcTEICEhAWXLljXY4TTze8GDBw9w/fp1g3/fDx48wMWLF1GvXj2966zRaHDlyhUkJyejQoUKen+nt27dwq1bt9CiRQvZczB27WJjY3Hjxg00a9bMaI1MdHS0SX23dH9Pd+7ckfU9c3Jygru7O/z8/Ex6bRw/fhxFihQxWtNw+PBh2SSLmdWvXz/bfl+6a1unTh2DtY6615G9vT0CAgKk5lOdGzduIC4uDi1atMjxeeikp6fj8uXLEEKgevXqEELg2LFjqFy5Mry8vEx+nNy6fPlytgM0jF2D/IDhhojoJWUON0SUf7DPDREREdkUhhsiIiKyKWyWIiJ6SVevXsWDBw+kta2IKH9guCEiIiKbwmYpIiIisikMN0RERGRTCuXyCxqNBrdv34arq6ve/ClERESUPwkhkJycjHLlymU791qhDDe3b9+WrX5LREREBYdKpYK3t7fR+wtluNHNqKhSqfSWkSciIqL8KSkpCT4+PjnOjFwow42uKcrNzY3hhoiIqIDJqUsJOxQTERGRTWG4ISIiIpvCcENEREQ2pVD2uSEiKgjUajXS09OtXQwii3FwcIBSqXzlx2G4ISLKZ4QQuHv3Lh4/fmztohBZXPHixVG2bNlXmoeO4YaIKJ/RBZsyZcrAxcWFk41SoSCEQEpKChISEgAAXl5eL/1YDDdERPmIWq2Wgo2Hh4e1i0NkUc7OzgCAhIQElClT5qWbqNihmIgoH9H1sXFxcbFySYisQ/faf5X+Zvki3Agh8OzZM2g0mlwdl9v9iYgKCjZFUWGVF699q4abe/fuYfbs2ahUqRKcnZ3x999/m3TcrFmz4OnpCQcHB9SuXRsHDhwwc0lzplYDBw8Ca9dqv6vV1i4RERHltVOnTuGjjz4qtOcvKKza52bJkiV4+vQpfvrpJwQHB5t0zPfff48vv/wSW7duRdOmTTF37lx06tQJ58+fh5+fn5lLbNiWLcDo0UBs7Itt3t7At98C3btbpUhERFaRmJiITZs24dy5c7Czs0PVqlXRo0cPlCpVytpFyxMxMTH4+eef8d133wEATpw4gV9//RXz589/pce9ePEivvnmG+m2s7MzfH190b9/f5QtW9bo+Y1JSkrCmjVrcPnyZbi7uyM4OBitW7c2er7Mpk6dKltcOjk5GVu2bMHZs2eRkZEBX19ftG7dGnXr1tU7NiEhARs2bMC///4LBwcHNGrUCD169ICjo6OplyJPWLXm5v/+7/8wZ84cVKpUyeRj5s2bh8GDB6NNmzYoVqwYpk2bhlKlSuH77783Y0mN27IF6NlTHmwAIC5Ou33LFqsUi4jI4nbv3g0/Pz+sXr0aXl5e8PHxwdmzZ9GkSRP8+eef1i5enmjYsCEWLlwo3b527RpWr179yo8bFxeH5cuXo1q1amjatCmqVq2KPXv2oHr16rh8+bLR8xty/fp1+Pv7Y/369fDx8YGjoyNmzZqFzp07Gz1f5q+iRYtK++3Zswe+vr748ccfUapUKfj7+yMuLg7vv/8+3nnnHdl5t2/fjsqVK2PXrl3w9/dHmTJlMHv2bLz22mu4fv36K1+j3ChQo6UePHiAK1euyGp5FAoFgoODceTIEYuXR63W1tgIoX+fEIBCAYwZA3TtCuTBnERERCZTq4GICODOHcDLCwgKMu/70NWrV9GjRw+MGDECX331ley+lJQUPHz4ULqdlJSEVatW4d9//0WZMmXQv39/VKlSRbr/8OHD2LZtG95//31s27YNKpUKjRs3Rv/+/XH16lX88ssvSEpKQrt27fDGG2/oHTd8+HBs2LABKpUKgYGB6N27t6w8OZ0f0Aa18PBwKBQKtGnTBm3atAGg/Rw6ceIEBgwYgIsXL2LZsmV48uQJhgwZAgDo1KkTunXrhvT0dKxbtw4nTpxA8eLF0b59ezRr1izH69i3b194e3sDAD788EOULVsWmzZtwqRJk/TOb8y8efNQpkwZHDhwAHZ22jqMSZMmyUKSofNldfXqVXTv3h3Dhw/H3Llz9e6/cOGC9POVK1fQp08fjB07FjNnzpS2jxs3Dm3btkW3bt0QFRWVJxP0mSJfdCg2VXx8PACgdOnSsu1lypSR7jMkLS0NSUlJsq+8EBEhr7GZjilIRjFMxxQA2oCjUmn3IyKylC1bAF9foHVroF8/7XdfX/PWJC9atAguLi6YNm2a3n0uLi7SB2hycjIaNGiANWvWoFKlSvj333/x2muvyf5BvXz5MhYtWoT27dsjLS0NJUqUwNChQ9G7d2907NgRTk5OcHFxQbdu3bBt2za944KCgpCcnIxSpUph2LBhGDdunLSPKef/+uuvMXDgQBQtWhSlS5fG119/jXnz5gF40SwEAO7u7vD394eDg4NU6+Hj44PU1FQEBwdj2bJl8PX1hUKhQJcuXbBo0aJcXdMnT57g2bNnKFmypLQt8/mNuX//PlxdXaVgo1O1atVcnX/hwoUoWrQopk+fbvD+GjVqSD8vWrQIRYsWxeeffy7bx8HBAXPmzEF0dDR2796dq/O/igJVc6OTdZSURqPJtnf1rFmzjP5yXsWdO/LbEzAbjsjABMzGVIQZ3Y+IyFx0TeVZa5R1TeWbNpmnL+CRI0dQr169HIewf/PNN0hPT8fff/8NJycnAMDbb7+Njz/+GEePHpX2S01NxaZNm6R+HcnJyViwYAEuXLiA6tWrA9AOSlm5ciW6desmO27BggXo0aMHAKBZs2bo2LEjRo4cCT8/P5POv3nzZowfP14KRePGjUNs1r4HAMqVK4fWrVtj8+bNUs0NAMycORMZGRmIiIiQaiqaN2+O7t27Y/DgwdJcLoZ89tlncHFxwbNnzxAZGYn+/fvrNf/kpE+fPnjzzTfRuXNndO/eHU2aNJEFEUPn03FwcMCSJUsAAEePHkXdunVNmpbg6NGjqF+/vsF9GzduDCcnJxw5cgQdO3bM1XN5WQUq3OhmK9TNXqiTkJAg63CV1cSJEzF27FjpdlJSkqyz1MuXR37bHhmy78b2IyIyB2s2lSclJcHf3z/H/Q4ePIg333xTChaANlx06NABqamp0gd/yZIlZR1Wq1SpAi8vLynY6LadOnVK9vj29vaysPP666/D1dUVhw4dgp+fn0nnr1GjBlavXo3XXnsNQUFBKFKkiNGmG0N+//13pKenY8SIERBCSNOdpKSk4PLly6hTp47RY+vVq4cSJUogLS0NALBr1y58+OGHBjvvGtOtWzf89ddfWLp0KSZPnozbt2/D29sbs2fPRv/+/Q2eT8fe/kUsMPQ7Xb9+Pfbt2yfdXrhwIZydnbP9/SsUCnh4eORZq4kp8n24ycjIgEajgaOjI0qUKIEaNWogPDwcPXv2BKCttQkPD8e7775r9DGcnJxkL+S8EhSkHRUVF/ffG8d/26XvCu39QUF5fmoiIj1Zm8qzytxU3qpV3p67TJkyuHXrVo77JSQk6I2cKlWqFIQQuHfvHipUqAAAerUbdnZ2Breps8y7Ubx4cb1+HR4eHtI/xaac/9tvv8XcuXPxySef4MqVKwgJCcHcuXMREBCQ4/MDtM1CNWvWRMOGDWXbW7Zsme0/4oC8D8yIESPw5ptvYtSoUYgw0r9h5MiRePbsGQBtM5HuH/mWLVuiZcuWAIAbN25g6tSpGDhwIAICAlC/fn2D58uqTJkyUKlUsm0VK1ZE06ZN8c8//2Dx4sX4+uuv4ezsbHBfnfT0dMTHx6NMmTLZPve8ZNU+N2q1Gs+ePZMS6vPnz/Hs2TNkZLyo+Rg2bJjsFzF+/HisWLECmzdvxu3btzF27Fg8efIEH374ocXLr1Rqh3sD2iCTme72ggXsTExElmFqE7g5msrbtGmDU6dO4U4OD+7j44ObN2/Ktt28eRP29vYoV67cK5fj4cOHePr0qXQ7PT0dt2/flmrrTTl/sWLFMH36dJw5cwY3btxAkSJF0LdvX4PnM9Qlonz58ihSpAiGDBmi9+Xp6Zmr5xMQECDruJtVo0aNpP4+xsKXr68vli5dCiFErgbfhIaG4tSpU7h79660rWnTphgyZIjUwVqnTZs2OHHihMHf/86dO6FWq/WOMSerhptff/0VxYsXR82aNeHk5IQuXbqgePHimD17trSPg4ODrNZl4MCB+OabbzBx4kQEBATg5MmT2LdvX66qDPNS9+7aNuzy5eXbvb3N17ZNRGSIqU3g5mgqHzFiBEqWLIkPPvhAFi4A4Ny5c4iKigIA9OzZExs3bpT6sKSnp+O7775Dly5dZE0iL0uj0cg67i5btgz29vYIDQ01+fxbtmyRaoTKlCmDoKAgPHjwwOD5SpYsieTkZNlSAW+//TY2b94sCxJCCGzevDlXz0WtViM8PBy1atUyus+gQYOk4NS+fXsAwI4dO/RCxqFDhyCEyFWn4hEjRqB48eJ47733cP/+fb2yZTZ8+HCUKFECI0aMkCosAO2Q808//RRdunRB06ZNTT73q7Jqs9SAAQOyHc4GQOrYlNnw4cMxfPhwcxUr17p317ZhZ76aMTGssSEiy8raVJ6VOZvKS5YsiQMHDmDAgAGoWLEigoOD4ejoiMuXL0Oj0eCnn34CALz33nvYuXMn6tWrh9atWyM6Ohrp6ek5jgAylbu7O9auXYv9+/fD3t4ef/75J5YsWSKNODLl/IcPH8Ynn3yC+vXrIz09HeHh4ViwYIHB8zVt2hQlS5ZESEgIqlWrhk6dOmHIkCE4f/48WrdujeDgYLi5uSEqKgohISFSR2djdB1809LScPz4caSmpmLnzp25ugaJiYkIDAyEp6cnKlWqhISEBPz999/46KOP0LZtW4Pny2zkyJGoW7cuPDw8EB4ejnfeeQdVqlRBgwYNUL58eSQkJODYsWN49913pWM9PDywf/9+9OnTB5UrV0ZwcDBSUlLw559/okOHDvjxxx9z9RxelUIIQ38Cti0pKQnu7u5ITEyEm5tb3j1w5urJwndZiSgPPHv2DDExMfDz80ORIkVyfbxutBQgfxvSvT1Zokb53LlzOHfuHJRKJfz9/VGnTh295pvjx4/j0qVLKFOmDFq3bi2rob9y5QpOnjwpawq6dOkSoqOj0atXL2lbdHQ0rl69ijfffBMAsGrVKkyePBnXrl3D0aNHoVKp0KRJE4MdXbM7P6CtcTh69CiUSiWaNm0q9ZW5ceMGIiIiZP+YP378GAcPHsSDBw9Qt25dNGjQAIC2uevYsWOwt7dHgwYNULFiRaPX7Pbt2/jjjz+k246OjqhQoQKaN28um93X0PkNSU9Px6lTp3Dt2jUUL14cderUkbVwZD1fZm3bttUr66VLl6QZin18fFCzZk3ZEHUdIQROnjwpm6E4NxP1Atn/DZj6+c1ww3BDRPnIq4YbwPCSMD4+2j6AttxUrgs3hoZtU8GRF+GmQE3iV6BMmWLtEhBRIdW9O3DjBhAeDvz6q/Z7TIxtBxuizBhu8lJg4IufM3WKJiKyNKVSO9y7b1/t98LQB7BFixYGlwmgwiffz3NToEREvGiaysjIfl8iIspT/v7+Jk0kSLaPNTdERERkUxhuiIiIyKYw3BAREZFNYbghIiIim8JwQ0RERDaF4YaIiAqEuLg47Nixw+bOZW1nz57Ftm3bcPLkSWsXJc9wKDgREeWpq1evStPv169fH6VKlcr1Y8TGxiIqKgqdO3eWth05cgRDhgzB48eP87C0huV0Lt16TYB2ZfDSpUujVq1aBpckyGvx8fE4dOgQuufBrIzjxo3Dr7/+imbNmiE0NBQNGzbMgxIaFxUVhbS0NLMvoslwk9fs7V/McTNlChAWZt3yEBFZyM2bNzFgwABER0ejWbNmSElJwfHjx/HOO+9g/vz5eus3ZScyMhIjR46UrUbt7e2Nrl27mqPouXb27Fn06tULHTp0gLOzM27evIkLFy7gm2++wbBhw8x67qioKLz11lvIyIP51L7//nts3LgRHTp0yIOS5Wzp0qW4f/8+Nm3aZNbzMNzktQkTgBkztD/Pns1wQ0SFQlJSElq3bg1/f3/cvHlTWvfnypUrCA0NxePHj/Hrr78C0Iag8+fPo127djh//jxUKhUaNGggLU6pW3X6+fPn0odgQEAAfHx8ZKtqZ36cCxcuQKVSoW7duihXrhzUajWOHz+OpKQkNGrUSFaj8uDBA4SHhwMAihQpAn9/f1SrVu2lnvfSpUulBSm/+OILjBo1Cp07d0Z0dDSePHkCOzs7lC9fHnXq1NFbJ2nTpk1o2bIlUlNTcfbsWfj7+6N69eoAgHv37uHkyZMoVqwY6tatC1dXVwDAo0ePEBkZCSGEdG2qVKmCunXrAgCuX7+O8+fPo2TJkmjSpAns7Q1/zN+7dw979+5FSkoKzpw5g5SUFAQFBUnXrWvXrjh16hRUKhVCQ0Ph5uYGjUaDkydPIj4+XlZWnZMnT0IIgVq1auHs2bN48uQJGjVqJL0Wzp8/j+vXryM5OVkqe8uWLVGmTJmXuvbZYbjJa2FhL8INZykmokJiyZIliIuLw8GDB2ULGvr7+2POnDno168fPvnkE9SvXx/h4eEYO3YsatSogbS0NNjb2+PMmTP4+eef0bNnT8THx+PIkSNIS0vDunXrAAA9e/aEvb29rKlI9zh+fn5wdnZGeno6/vnnH/zvf//D4sWLUaxYMaSkpODGjRv466+/ULNmTQDaD3bd46ampuLo0aMICQnB+vXrYWf38l1Re/TogalTp+LcuXPYuXMn7ty5A41Gg3///RdPnz7Fjh078Nprr0n79+rVC506dUJ0dDTq1KmDgQMHonr16pg7dy5mzpyJhg0bIjU1FVevXsUvv/yCtm3b4tGjR/jrr78ghJCeQ/v27VG3bl2MGjUKK1euRJMmTXD9+nU4OTlh165d8PPz0ytrQkKCFDD27duHU6dOoUqVKrhx4wb69OmDVq1aISEhAZUqVULDhg2RlpaG9u3b486dO6hRowaOHz+Ojh07YvXq1VD+t7bHokWLEB0djdTUVJQrVw53797FgwcPcPDgQVSrVg3R0dG4du2a7PdarVo1s4QbiEIoMTFRABCJiYnmOYF2TXDtFxFRLqSmpooLFy6I1NRUaxclV1q3bi0CAwMN3peWlibs7OzEl19+KYQQYuXKlQKAmDx5srTP7NmzhYeHh/S+vHbtWuHh4SF7nI0bNwp3d3fptu5xVq1aJW3r1auXACDWrVsnbevQoYMYNGiQ0bI/fPhQVKxYUfz8889Gz5XVvn37BAChUqmkbdu2bRMAxD///KO3/0cffSRCQkJk2wCIZs2aiZSUFGnb7t27hYeHh7h69aq0bfny5cLT01Pab9euXUKpVMoea8eOHcLBwUFERUUJIYR49uyZaNWqlejYsaPR55CcnCwAiCNHjkjbtm7dKgCIadOmyfZ97733RL169URycrIQQoirV68KV1dX8cMPP0j7DBo0SBQpUkScO3dOCCGEWq0WrVq1EoMHD5b2GTp0qOjRo4fRMgmR/d+AqZ/fHC1FRESv7O7du6hQoYLB+xwdHVG2bFncuXNH2qZQKPDpp59Kt8eMGYOUlBTs27cvV+d1cXHBwIEDpdstWrRAiRIl0Lt3b2lb8+bNcenSJdlxGo0Gp06dwo4dO/Dnn3/Cz88PR48ezdW5AeCPP/7Apk2bMH/+fAwbNgxt27aVamdu3ryJffv2YfPmzShZsiSOHTumd/zQoUPh7Ows3V6xYgVq1aqF6OhobN26FVu2bIGzszPi4+Nx7tw5o+VYu3YtOnXqJDVPOTk5Yfz48di5cyeSk5Nz/bzGjBkju71u3TqMGTMGxYoVAwBUrlwZ/fr1w9q1a2X7tW7dWqohs7OzQ6tWrfSuvSWwWcrc2KmYiKxlyhRg/nzg44/N/j5UtGhRPHjwwOB9Qgg8fPhQ+mAEAA8PD1nzlZOTE8qVK4dbt27l6rwlSpSAQrdg8X+Pk3XEkpOTE549eybdvnr1Kt544w1kZGSgevXqKFasGG7duvVSo7q2b98OFxcXeHh4YObMmRgwYAAA4N1338XGjRvRsGFDlCxZEo8fP8bTp0/x9OlTFC1aVDq+XLlysseLiYlBUlISfvnlF9n2Hj16ZNtkduPGDTRv3ly2rUqVKtJ9tWvXNvk5ubi4wN3dXbqdkJCAlJQUVKpUSe/xs4bRnK69pTDcmIODA5Cerv15xgyGGyKyjvnzgadPtd/N/D7UqFEjbNmyBWlpaXqjos6cOYNnz56hcePG0rakpCRoNBrZB/bjx48tMpR66tSpqF27NrZu3Spt69WrF4QQuX6szB2Kdfbt24cNGzbgypUrUnjZuXMnwsPD9c6ROZgBgKurK6pUqSJ1vjaVh4eH3rD1hw8fSvflRtYylShRAkql0uDj5/axLYXNUuYwfry1S0BEpK2xKVoUGDvW7KcaPnw4Hj58iHnz5sm2azQaTJw4Ef7+/ujYsaO0/fnz59i9e7d0OyIiAo8ePZLmPylWrBjS0tLMUlZdp1idx48fS6On8urxS5QoIauV2bJli0nHtmvXDr///rusCQ/QNvvpFCtWDGq1WjYUPDAwELt27ZJdsy1btsDPz0+vdii3HBwc0KhRI1kY1Gg02L59O1q0aJGrxzLn7zUz1tyYQ+YRU0RE1hIWZrGa41q1amHFihUYMmSINDw7JSUFq1evxs2bN7F79244ODhI+zs5OWHo0KEYM2YM7O3tMWvWLLz33nvSkOzXXnsNz549w7Rp01CrVi0EBATkWVk7d+6ML774Ah4eHnBxccHixYvzZM4YndatWyMxMRHvvvsuWrdujQMHDmDbtm0mHTtq1Cjs2LEDTZo0wahRo+Dh4YGoqCjs3r0bV65cAaAdFu/s7IxJkyahcePGqFKlCkaNGoXly5cjNDQU77zzDi5evIiFCxdi48aNefKcvv76a4SGhsLBwQGNGzfGhg0b8OjRI0yaNClXj9OgQQMsX74cy5YtQ8mSJc02FJw1N0RElCfefvttXLx4EdWqVcOePXtw9OhR9OvXDxcvXpQ6meqUKlUKe/bswf3793HixAlMnToVS5Yske6vUKEC/vjjD9y9exfr169HdHS03iR+vr6+stogQNvRtX379rJt1apVQ9u2baXbY8aMwcKFC3H27FmcOnUKU6dOxVdffYVmzZpJ++Q0YaCnpyd69OgBFxcXvft8fHxw5MgRuLq6Ys+ePahWrRr27t2LHj16yOad6dGjBzw9PWXHOjs7Izw8HGFhYbh06RIOHz6MgIAAnDlzRtrHw8MDe/fuxZMnT7B+/XqcOnUKLi4uOHbsGDp27Ijw8HA8f/4cf//9d7bPwd7eHj169JA1LZUvXx5vvvmm3r4tWrTAiRMn4OLigv3796Np06Y4ffo0SpcuLe3TqFEjWdMjAFSvXh2hoaHS7d69e+Orr77C0aNHsW7dOsTHxxst36tQiJdpZCzgkpKS4O7ujsTERFmHtjyVuc1y8mT2uyEikzx79gwxMTHw8/PTm/TNVqxatQqTJ09GbGystYtC+VB2fwOmfn6z5sZcAgNf/DxnjvXKQUREVMgw3JhLRMSL2hvOVExEJDHUnESUl9ih2Jx0LX6Fr+WPiMioVq1aoVWrVtYuBtkw1txYSlCQtUtARERUKDDcmFPmTsWRkdYrBxERUSHCcGNOn39u7RIQUQFVCAeyEgHIm9c+w405ZR3+PWWKdcpBRAWGbqK7lJQUK5eEyDp0r/3Mkz7mFjsUW9KXX3K+GyLKllKpRPHixZGQkABAu4hh1rV+iGyREAIpKSlISEhA8eLFoVQqX/qxGG7MLTDwRX8bjca6ZSGiAqFs2bIAIAUcosKkePHi0t/Ay2K4MbfM890QEZlAoVDAy8sLZcqUQXp6urWLQ2QxDg4Or1Rjo8NwY2lTprBpiohMolQq8+SNnqiwYYdiS8i8FMPMmdYrBxERUSHAcGMJEREvfubwTiIiIrNiuLEGDgknIiIyG4YbS8ncNDVjhvXKQUREZOMYbiwlc9MUERERmQ3DjbVwIU0iIiKzYLixpMxNU1xIk4iIyCwYbiyJTVNERERmx3BjafaZ5k3kqCkiIqI8x3BjaRMmvPiZE/oRERHlOYYbS8u89IIQrL0hIiLKYww31sDlGIiIiMyG4cYasi7HwNobIiKiPMNwYy12mS49a2+IiIjyDMONtUya9OJn1t4QERHlGYUQhW+Z6qSkJLi7uyMxMRFubm7WK4id3YtVwhUKQKOxXlmIiIjyOVM/v1lzY02ff/7i58KXMYmIiMyC4caawsK0NTY6Pj7WKwsREZGNYLixtsy1N7Gx1isHERGRjWC4sbbMk/oBXC2ciIjoFTHc5AcODi9+5mrhREREr4ThJj8YP15+m7U3REREL83q4Wb37t3o2LEjGjZsiHfffRc3btzIdv/nz59jwYIFeP3119GoUSP06NEDe/bssUxhzSUsDJg8+cVt1t4QERG9NKuGm927d6Nz584ICgrCN998g8ePHyMwMBCPHz82eszHH3+MOXPm4P3338f//vc/1K1bFx06dMDevXstV3BzyNr3hiOniIiIXopVJ/Fr0qQJqlatitWrVwMA0tLSULZsWYwfPx4TJkwweIyfnx8GDhyI6dOnS9tee+01tGnTBvPmzTPpvPlmEr+sgoLktTac+4aIiEiS7yfxe/LkCU6cOIEOHTpI25ycnNCmTRuEh4cbPa5FixaIiIhAamoqAODff/9FTEwMAjOvtF1QZV5QE2DfGyIiopdgtXATFxcHIQS8vLxk2728vKBSqYwet3z5cnh6esLT0xNVqlRBgwYNsGDBAnTv3t3oMWlpaUhKSpJ95VuZQxr73hAREeWa1cJNRkYGAMDR0VG23cnJCenp6UaPmzVrFv7++28sXboUv/76KyZMmICxY8fi8OHD2R7j7u4uffnk5/4sERHyWYtZe0NERJQrVutzc/fuXXh5eWHHjh3o3LmztP29997DxYsXceTIEb1jkpKSULJkSSxfvhyDBg2Stnfr1g2pqalGR02lpaUhLS1N9jg+Pj75r8+NzpQpwIwZL26z7w0REVH+73NTtmxZlCtXDseOHZNtP3LkCBo0aGDwmNTUVKjVapQqVUq2vXTp0tk2NTk5OcHNzU32la9x5BQREdFLs+pQ8KFDh+LHH3/E9evXAQA///wzLl++jCFDhkj7TJ06FW+++SYAwNPTEzVq1MDChQvx9OlTAMClS5ewZcsWhISEWP4JmFPmvjexsdraHCIiIsqRVcPNpEmT0KFDB1SvXh3lypXDRx99hBUrVqBu3brSPnFxcbhy5Yp0e8OGDXj06BE8PT1RqVIl1KlTB126dMEUW/vwj4gAvL1f3J4503plISIiKkCsOs+NzqNHj3D//n1UqFABTk5Osvtu376NlJQUVKlSRbb98ePHePDgAby9vfWOyUm+nefGkMydiydP1m+yIiIiKiRM/fzOF+HG0gpUuLGze9Gh2MEBeP7cuuUhIiKyknzfoZhM9PnnL35OT+fQcCIiohww3OR3hhbVtLX+RURERHmIzVL5vVlKJ3PfG4UC0GisVxYiIiIrYLOUrck8NFwINk8REREZwXBTUEREaDsX63DdKSIiIoMYbgqSSZPktzlzMRERkR6Gm4IkLIwzFxMREeWA4aagyTpzceYFNomIiIjhpkBSqeS3WXtDREQkYbgpqLLW3jDgEBERAWC4KbhUKsDe/sVtLqxJREQEgOGmYJsw4cXPQnD0FBERERhuCjaOniIiItLDcFPQGRo9xYBDRESFGMONLVCpAAeHF7fnzLFeWYiIiKyM4cZWjB//4uf0dK49RUREhRbDja0IC5OPnoqMZPMUEREVSgw3tiTz6CkA+PJL65SDiIjIihhubElYGDB58ovbGg1rb4iIqNBhuLE1WYeHz5jB/jdERFSoMNzYoogI+W32vyEiokKE4cZWZa69ATj/DRERFRoMN7YqIkLe/wbg+lNERFQoMNzYsqwdjIVg/xsiIrJ5DDe2LmsHY/a/ISIiG8dwUxhk7WDMEVRERGTDGG4Ki6wdjCMjrVMOIiIiM2O4KSwMdTBm7Q0REdkghpvCJGsH48hIBhwiIrI5DDeFTVgYoFC8uM2AQ0RENobhpjD6/HP5bY6gIiIiG8JwUxhlbZ4CuII4ERHZDIabwsrQCuI+PtYrDxERUR5huCnMsk7wFxvL/jdERFTgMdwUdhERgLf3i9uRkazBISKiAo3hhgCVijU4RERkMxhuSMtQDQ4DDhERFUAMN/SCSqU/Bw6HiBMRUQHDcENyWefAmTGDAYeIiAoUhhuSMzQHDgMOEREVIAohhLB2ISwtKSkJ7u7uSExMhJubm7WLkz8FBclXDlcotHPhEBERWYmpn9+suSHDIiLkI6iE4BBxIiIqEBhuyLisASc2lgGHiIjyPYYbyl7WIeIMOERElM8x3FDOVCr9gMM5cIiIKJ9iuCHTZA04XKaBiIjyKYYbMh2XaSAiogKA4YZyJyJCfps1OERElM8w3FDuZZ3kj52MiYgoH2G4odwzNIsxm6iIiCifYLihlxMWpp3Yj52MiYgon2G4oVfDTsZERJTPMNzQq8s60R9rcIiIyIoYbihvqFTaxTV1WINDRERWwnBDeefzz+W3WYNDRERWoBBCCGsW4MaNG1i5ciXi4+NRu3ZtDB48GEWKFMn2GCEEfvvtNxw4cAAuLi4YOHAgqlevbvI5TV0ynV6SUgloNC9uBwbqz49DRESUS6Z+flu15ubcuXOoU6cOLly4AH9/f3z//fdo1aoV0tPTjR6Tnp6Orl27YuTIkfD09ISnpycGDBiA6OhoC5acsjVpkvx2ZCSbqIiIyGKsWnPTsWNHPH/+HPv27QMAxMfHo2LFili8eDEGDx5s8Jg5c+bgyy+/xPnz5+H9XyfWtLQ0PH36FCVLljTpvKy5sZCgIG2w0WENDhERvYJ8X3OjCzW9e/eWtnl6eiIkJAS//fab0eOWLl2KAQMGSMEGAJycnEwONmRBERHyYeKswSEiIguwt9aJb926hfT0dPj6+sq2+/r6IsLIf/eJiYmIiYlBkyZN8OOPP+LUqVMoV64c+vbtiypVqhg9V1paGtLS0qTbSUlJefIcyAQREfIaHF0nY5XKuuUiIiKbZbWam9TUVABA0aJFZdtdXV2l+7JKTk4GAEybNg0HDhxA7dq1cfXqVdSsWRPh4eFGzzVr1iy4u7tLXz4cwWNZWWtwOEyciIjMyGrhRtdW9vjxY9n2hw8fwt3d3eAxuu3VqlXDr7/+iuHDh+Onn35Cx44dMXXqVKPnmjhxIhITE6UvFWsNLM/QRH92dsCUKdYrExER2SSrhRsfHx+4u7vj/Pnzsu3nzp1DrVq1DB7j6uqKihUr6t1fs2ZNxMbGGj2Xk5MT3NzcZF9kBVmXahACmDGDAYeIiPKU1cKNnZ0devfujRUrVuDJkycAgGPHjuHYsWPo16+ftN/KlSsxOdMK1AMGDMCePXukPjTp6enYvXs3GjVqZNknQC8naxMVoA04bKYiIqI8YtV5bmbNmoUiRYqgdu3a6NKlC9q2bYtRo0ahXbt20j6HDh3Ctm3bpNsTJ05EmTJlEBAQgLfeegsBAQFIS0vDvHnzrPAM6KUYCjgcSUVERHnE6jMUZ2Rk4O+//5ZmKM7a5HT48GHcu3cPXbt2lbYJIXD06FHcvHkTFSpUQJMmTaBUKk0+J+e5ySemTNHW2mTGuXCIiMgIUz+/rR5urIHhJh+ZMgX48ksu10BERDnK95P4EQEAwsIAtVq+ojgX3CQiolfAcEP5w+efywNObKx2AU6OpCIiolxiuKH8ISxM2zSVeS4cjYZDxYmIKNcYbih/yToXDgDMnGmdshARUYHEcEP5T9ah4kJwNmMiIjIZww3lT4YCDif7IyIiEzDcUP5lbLI/jqQiIqJsMNxQ/hYRAUyerG2W0uFIKiIiygbDDeV/urlwDI2kYjMVERFlwXBDBYehkVSRkazBISIiGYYbKlh0zVSZsQaHiIgyYbihgicsTD/gcFVxIiL6D8MNFUy6gJN1TSrOh0NEVOhxVXCuCl7wBQVpg42OQiGtMq5Wa1uy7twBvLy0uyqVVionERG9Eq4KToWHoQn/FArcCwiCry/QujXQr5/2u68vsGWLtQpKRESWwHBDtsHAhH+lLkXiRqwS0/GimSouDujZkwGHiMiWMdyQ7fgv4AgAAoACgBIaTMEMKeDoGmHHjNE2WRERke1huCHbEhGBv8IFIqANOYA25EzBDPwF7WgqIbRT5kREWK2URERkRgw3ZHPu3AGCEaEXcIIQiUdwk+1HRES2h+GGbI6Xl/Z75oCja6ZyRzLUUGA6pkj7ERGRbeFQcA4FtzlqtXZUVFzciz42j+AGdyRDNyuOGgogQ8Nh4UREBYjZhoKvX78ePXv2xODBg3Ho0CG9+xs2bJjbhyTKU0ol8O232p91c/yVQBJuwVtqprKDgNKBE/4REdmiXIWbn376Cf3790dKSgrOnj2Lli1b4uuvv5btc+rUqTwtINHL6N4d2LQJKF/+xTZfqHDMIVBqooIQXJeKiMgG5SrczJs3D6tWrcIff/yBEydOYOXKlZgyZQpmzpxprvIRvbTu3YEbN4DwcODXX7XfG6VGQGFoZXEu20BEZDNy1eemaNGiSEhIQNGiRaVtBw4cQOfOnTFhwgRMmTIFCoUC+b0bD/vcEKZMAWbOfNEpRycwkGPEiYjyKbP0uSlXrhzuZBk/GxISgj/++ANz5szBjBkzXq60RJYWFqZdf8pQLQ6bqYiICrRchZvu3btj27ZtetuDg4OlgENUoBhYtgGRkdqeyAw5REQFUq7CzdixY/H48WOD97Vs2RK7d+9Gjx498qJcRJYTEQFMnqztd5NZZCT74RARFUAm97mZNm0apk2bBgC4dOkSqlevbs5ymRX73JBRQUHaUJMZ++EQEeULed7n5osvvpA6CgcEBLx6CYnyI10tjr39i22RkdrJc1iLQ0RUIJgcbsqXL4/NmzdLHYrv3r1r9IuoQAsLA9LT5X1xNBrOiUNEVECY3Cz1448/YsSIEXj+/HmO+3IoONkMQ81UgLZ2JyzM8uUhIirETP38ztU8N0+fPoVKpUJAQACio6ON7lerVq3cldbCGG4oVxhwiIjyBbOEG51Vq1bhnXfeeZXyWRXDDb0UHx8gNla+zdsbUKmsUx4iokLGbAtnAijQwYbopalU+p2NY2O5dAMRUT7zUuGGqNDSdTb29n6xjQtwEhHlKww3RC9DpeLMxkRE+RTDDdHL0s2Jo1DIt3NmYyIiq2K4IXoVxhbgZDMVEZHVMNwQ5QVdLU5mbKYiIrIKhhuivBIWZryZigGHiMhiGG6I8pKumcpQLQ6HjBMRWQTDDZE5GKrF4ZBxIiKLYLghMhdjnY1Zi0NEZFYMN0TmZmjIOGtxiIjMhuGGyBKyq8VxcGAtDhFRHmK4IbKkiAj9gJORwVocIqI8xHBDZGkREdpmKc6LQ0RkFgw3RNYSFqZfiwNoQ46bm+XLQ0RkIxhuiKxJV4uTNeQkJwNKJfviEBG9BIYbovzA0PINGo22Lw4DDhFRrjDcEOUXYWGGa3FmzOC8OEREucBwQ5TfGBpRpZsXh8PGiYhyxHBDlB8Z64vDYeNERDliuCHKz3R9cezt5du50jgRkVFWDzdpaWn47bff8OOPP+LYsWO5OvbatWtYtGgRIiMjzVQ6onwgLAxITzc8uzHnxSEi0mPVcJOQkIB69eph/Pjx+PPPP9G+fXsMGTLEpGPT0tLQo0cPjB8/Hps2bTJzSYnyAV0tjl2WP9vISMDHxzplIiLKh6wabiZMmAAHBwecOnUKa9euxYEDB7By5Ur89ttvOR47duxYNG/eHP7+/hYoKVE+ERYGqNX6C3HGxrIWh4joP1YLNxqNBps2bcI777wDZ2dnAEDdunXRvHlzrF+/Pttjt27digMHDuDrr7+2RFGJ8h/dQpze3vLtbKoiIrJeuFGpVEhOTkZAQIBse0BAAC5cuJDtcR9++CHWrFkDFxcXk86VlpaGpKQk2ReRTVCpjDdVcYZjIiqkrBZukpOTAQDFixeXbS9RooTR8KFWq9GvXz98/PHHqF+/vsnnmjVrFtzd3aUvH/ZPIFuia6rK2uFYN8Mxa3GIqJCxWrjRNUXpQo5OUlKS0RqZNWvWIDo6Gs7Ozli0aBEWLVqE+/fv48yZM1i0aBGEEAaPmzhxIhITE6UvlUqVt0+GKD/IrsMxZzgmokLEauGmQoUKcHR0RExMjGz79evXjXYS9vX1xdtvv43Lly/j0qVLuHTpEtLS0vDo0SNcunTJaLhxcnKCm5ub7IvIJhmrxdHNcMxaHCIqBBTCWCKwgG7duuHx48cIDw+HQqFAbGwsKleujGXLlmHQoEEAgPDwcNy9exd9+/Y1+Bh169ZFq1atsGDBApPPm5SUBHd3dyQmJjLokG0LCtLW3GQ1ebI2CBERFSCmfn5bdSj4nDlzEB0djU6dOuGLL75ASEgImjdvjv79+0v7rFmzBjNnzrRiKYkKMF1TVeZh44C2FocdjonIRlk13FSrVg3nzp1DcHAwEhMTMWnSJOzduxf2maaaDwkJQb9+/Yw+Rp8+fRDEqnYi44wNG9d1OGZ/HCKyMVZtlrIWNktRoTVlCjBzprYPTlZsqiKifK5ANEsRkYXpanEMjaqaMQNwdGQtDhEVeAw3RIVR5mUcMktP14YcBhwiKsAYbogKs7AwbcDJ1M8NgDbgcBkHIiqgGG6ICruwMG2NzeTJgIOD/L7ISO021uQQUQHCcENEWmFhwPPn+hMAZmRoa3K4bAkRFRAMN0Qkp5sbx95e3uk4NpZNVURUIDDcEJE+XVOVWq0/P05kpF7IUauBgweBtWu139Vqi5aWiEiG4YaIsqdS6TdVAVLIudhrCnx9gdatgX79tN99fYEtWyxdUCIiLYYbIspZRIR24r+sQ8cBVN80A2ti5U1VcXFAz54MOERkHQw3RGQ63dBxALo5jhUAghAJDRT4C9qQo5sAecwYNlERkeUx3BBR7oSFAULg5oDJUEMBAW3A0YWczAFHpdJW+hARWRLDDRG9lCPtw2APDcIwWa8WJwN2mA7t3Dh37litiERUSDHcENFL8fLSfp+KMIRBXoujhMAUzIAGCvTpx1XHiciyGG6I6KUEBWlHiSsU2oBjDw0iEAgByJqqFBBcr4qILIrhhoheilIJfPut9meFQvs9GBGwg8CMTDU5Et16VQw5RGRmDDdE9NK6dwc2bQLKl5dv/8EnDNs3a6AwND/OjBmAoyNDDhGZjUIIIXLezbYkJSXB3d0diYmJcHNzs3ZxiAo8tVo7KurOHW1fnKAgbc2OxMdHu3yDIZMna0dgERHlwNTPb4Ybhhsiy5gyBZg9W7sQZ1aBgRwzTkQ5MvXzm81SRGQZuvWqJk9+0UlHx8B6VUREL4vhhogsKywM0GgYcojIbBhuiMg6cgo5SiU7HRPRS2G4ISLr0oWcrCOrNBrtyCofH+uUi4gKLIYbIsofdCuPZw05sbFsqiKiXGG4IaL8RRdyvL3l29kfh4hMxHBDRPmTSqXtj2OX5W0qMlK7jf1xiMgIhhsiyr/CwrQzBGbtdCzEi+UcWJNDRFkw3BBR/mes0zHA5ioi0sNwQ0QFh7FOxwCbq4hIwnBDRAWPsZCja65iLQ5RocZwQ0QFl7GQo2uqYnMVUaHEcENEBR+bq4goE4YbIrIdERHakVX29vLtbK4iKlQYbojItnD1caJCj+GGiGyTKauPs7mKyCYx3BCRbcsu5OiaqxwcGHKIbAjDDREVDtlNBJiRoQ05DDhENoHhhogKF93IKkMdj2fMAJRKhhyiAo7hhogKp8wdjzOHHI1GG3J8fKxXNiJ6JQw3RFS46UJO1uaq2FhOBEhUQDHcEBEBL5qrvL317+NEgEQFCsMNEVFmKlX261axJoco32O4ISIyRDfbcdbh4wAnAyTK5xhuiIiM0Q0fz27dKoWCnY+J8hmGGyIiU2S3OGdsLPvkEOUjDDdERLlhLORk7pOTpSZHrQYOHgTWrtV+V6stVlqiQonhhojoZeRUk/Nfn5wtWwBfX6B1a6BfP+13X19gyxZLF5io8GC4ISJ6FbqOxw4OeneJyEh07aHE+7Hy5qq4OKBnTwYcInNhuCEielVhYcDz57KaHAFAAUAJDaZgBtRQYDq0IUcI7WFjxrCJisgcGG6IiPLSf81VibUDIfAi5NgBmIIZ0ECBvxAEIbRT6kREWLe4RLaI4YaIyAx2TYyAHQRuwVsWchQAghAJDRTIgB1KfccRVkR5jeGGiMgMvLy0332hgh0EIhCoF3KUEKi5dQYnAyTKYww3RERmEBSkXaZKN8FxMLQ1OWGYDDUU+K/bDRQA164iymMMN0REZqBUAt9+q/058woOUxEGB4UGMzAZApnu4NpVRHmG4YaIyEy6dwc2bQLKl5dv9/YGam4Og0Josl/WgSGH6KUohNANSiw8kpKS4O7ujsTERLi5uVm7OERk49Rq7aioO3e0fXGCgrQ1OzJBQdpQY4iDAzB+vHbIOVEhZurnt70Fy2SUSqVCfHw8qlatalLYUKvVuHz5Muzt7eHn5wd7+3zxNIiIDFIqgVatcthJNybcUMhJT9c2WQEMOEQmsGqz1LNnz9CjRw9Uq1YNAwYMQNmyZbFw4cJsj5k5cybKly+PHj164PXXX4evry9+//13C5WYiMjMslvWQdcnh52PibJl1XAzffp0HD9+HNeuXcPFixfx66+/4qOPPsKxY8cM7q9Wq5GamooLFy7gwoULiImJwZAhQ9C7d2/cvXvXwqUnIjIjXcgxtLRDNot0EpGVw83KlSsxZMgQeP03IUS3bt1Qq1YtrFy50uD+SqUSM2bMQMmSJaVtH374IVJSUnD69GmLlJmIyKJ0SztMniwfdqWjW6STIYdIYrVwc/v2bcTHx6NBgway7Y0bN0ZUVJTJj3PixAkAQOXKlY3uk5aWhqSkJNkXEVGBEhYGaDQmrUROVNhZLdw8fPgQAODh4SHb7uHhId2Xk/v372PUqFF46623UK1aNaP7zZo1C+7u7tKXD//DIaKCLHOTVVa6YeR8n6NCzGrhxuG/NuRnz57JtqempsLR0THH4xMTE/HGG2+gbNmy+PHHH7Pdd+LEiUhMTJS+VCrVyxeciCi/CAvLuSZHqWTnYyp0rBZufHx8YGdnh7i4ONn2uLg4VKhQIdtjk5KS8Prrr0OpVGL37t1wdXXNdn8nJye4ubnJvoiIbEZ2I6w0mhedjxlyqJCwWrhxcXFB8+bNsWPHDmnb06dPsX//frRt21badvXqVVkfHF2wAYC9e/fC3d3dcoUmIsrPsgs5AJd3oELDqjMU//XXX2jbti3GjRuHZs2aYeHChYiJicE///yDYsWKAQCGDBmCo0eP4ty5c0hPT0erVq1w9epVrFixQhZs/P394enpadJ5OUMxERUKU6a8mPzPkMDAF5MHEhUApn5+W3UoeHBwMMLDw3Hz5k18++23qFmzJiIjI6VgA2hDS/369QEAKSkpUCgU8Pf3x6xZszBhwgTpSzdqioiI/qPrk2NsGLmu8zEnBSQbw7WlWHNDRIXJlCnAzJna0JMVa3IonysQNTdERGRhuvlysluNnDU5VMAx3BARFUaZ58rJ2mSlW96BIYcKKIYbIqLCTFeTY2hCQIYcKqAYboiIKPvOx5kX6uQwcioAGG6IiOiFzDU5dgY+InT9chhyKB9juCEiIn1hYYBabXxSQHY+pnyM4YaIiLKXufNx1tocNllRPsRwQ0REptHV5nBSQMrnGG6IiCh3dP1yjDVZcZQVWRnDDRERvbzsFutkkxVZCcMNERG9upxWJOcoK7IghhsiIso7upBjbM4cXchRKNhkRWbDcENEROaR3TpWAPvlkNkw3BARkXllHkqeFfvlkBkw3BARkWVkt8QDoG2ysrMDHBxYm0OvhOGGiIgsK/NQcm9v+X1CABkZrM2hV8JwQ0RE1qNSvajNcXDQv1/XAdnHx/JlowKL4YaIiKwvLAx4/tx4yImN1YYcNlmRCRhuiIgo/9CFHGNz5rDJikzAcENERPmTqRMDssmKsmC4ISKi/C3zUHJ7e/37dU1WSiWbrAgAww0RERUUYWFAerrx2hyN5kWTFUNOocZwQ0REBU/m2hxDc+boQo6jI4NOIcRwQ0REBZduzhxjISc9nbU5hRDDDRERFXyZJwbMbi0rdkAuFBhuiIjItmS3lhWg7YBsZ8cmKxvGcENERLYp81pWRYsCrq4v7hPiRZMVVya3OQw3RERk28LCgCdPgKQkw8PJdSuTszbHZjDcEBFR4aEbTm6oAzJrc2wGww0RERU+mTsgG1rPSlebw2UeCiSGGyIiKtwyL9ppaDi5bpkHLtpZYDDcEBERATkPJ+einQUGww0REVFWpi7ayb45+RLDDRERkTGZ58zJ2i8HYN+cfIrhhoiIKCe6fjmm1OYw5Fgdww0REVFuZK7NyTpnDvAi5CiVbLKyEoYbIiKil6GbM8dYbY5Gw8kBrYThhoiI6FXpanMMBZ3MkwNy4U6LYLghIiLKSxERLzogG5o3JzaWIcfMGG6IiIjymq4DskZjfHJAXchh35w8x3BDRERkTjlNDqjrm8Ogk2cYboiIiCwl80grQ7U5mTshM+S8NIYbIiIiS9PV5hgLOZknB+TcObnGcENERGQtmZusOEFgnmG4ISIiyi9yarbiCuUmYbghIiLKb3JqtuIK5dliuCEiIsqvchppBbyozVEoWJvzH4YbIiKigiDzLMje3ob3mTFDO5y8kDdbMdwQEREVNCqV8aCj0ZjUbKVWAwcPAmvXar+r1WYvtcUw3BARERVkKtWLFcqz64ScKeRs2QL4+gKtWwP9+mm/+/pqt9sChhsiIqKCTrdCua4TsoOD/j7/hRyhUKBTD0e8HytvtoqLA3r2tI2Aw3BDRERkS3TrWhnphKwA4Ih0TMEMZMAO06ENOUJo7x8zpuA3UTHcEBER2aosnZAFgP8yDBQAlBCYghlQQ4E0OGKamAKVSntYQcZwQ0REVBioVFj3q0AYJkMNOynoKKANA7raHA0UaNTDx7plfUUMN0RERIWElxcwFWGw/y/eRCAQAoAGL4KOAoDLw9gCvUp5vgk3Go3GIscQEREVVkFB2pHjukFVwYiAHQSUmYKOLuQAeLFKuUJRoFYqt3q4mTVrFjw9PeHg4IDatWvjwIEDZjmGiIiosFMqgW+/1f6cddR4K0UElAqBSz1zWKk8m5Dz/DmwYAEwapT2+/PneVp8k1k13Hz//ff48ssvsWbNGiQmJqJ79+7o1KkTYmJi8vQYIiIi0ureHdi0CShfXr7d21u7PWBjWPZDynUhR6EAfF70zfnsM8DFBfj4Y2DRIu13FxftdktTCKEb/GV5VatWRYcOHbBgwQIAgBACFStWRN++fTFnzpw8OyarpKQkuLu7IzExEW5ubnnxVIiIiAoUtVo7KurOHW1fnKAgbc2OQVOmAF9+qQ09BggAEQhEMAwPs/r0U+Crr169zKZ+flut5ubBgwe4cuUKgoODpW0KhQLBwcE4cuRInh1DRERE+pRKoFUroG9f7XejwQbQzp2jVmtrbSZP1rtbASAIkdBAIZs7R2fePMs2UVkt3MTHxwMASpcuLdtepkwZ6b68OAYA0tLSkJSUJPsiIiKilxAW9mLunEB5J+TMc+f8hRfLPajVwP/+Z7kiWr1DcdYRTxqNBgpDHZle4ZhZs2bB3d1d+vLxKdjj94mIiPKFiAh8NFI3d45CFnSCECnb9do1yxXLauHGy8sLAJCQkCDbnpCQgLJly+bZMQAwceJEJCYmSl8qlepVik5ERET/qVxZN3eORjZ3zt8I0tvPUqwWbkqUKIEaNWogPDxc2qbRaBAeHo4WLVpI2zIyMvD8v4Y6U4/JysnJCW5ubrIvIiIienXDh8v76+jmzmmFv6VtSqV2P0uxarPU+PHjsWLFCmzevBm3b9/G2LFj8eTJE3z44YfSPsOGDUP9+vVzdQwRERFZhqMjMHZs9vuMHavdz1LsLXcqfQMHDsSTJ08wceJExMfHo3bt2ti3bx+8vb2lfRwcHODk5JSrY4iIiMhydMO8582TryiuVGqDTV4MA88Nq85zYy2c54aIiCjvPX+uHRV17Zq2j83w4XlbY2Pq57dVa26IiIjIdjg6AmPGWLsU+WAoOBEREVFeYrghIiIim8JwQ0RERDaF4YaIiIhsCsMNERER2RSGGyIiIrIpDDdERERkUxhuiIiIyKYUykn8dJMyJyUlWbkkREREZCrd53ZOiysUynCTnJwMAPDx8bFySYiIiCi3kpOT4e7ubvT+Qrm2lEajwe3bt+Hq6gqFQpFnj5uUlAQfHx+oVCquWWVGvM6Ww2ttGbzOlsHrbBnmvM5CCCQnJ6NcuXKwszPes6ZQ1tzY2dmZdRVxNzc3/uFYAK+z5fBaWwavs2XwOluGua5zdjU2OuxQTERERDaF4YaIiIhsCsNNHnJycsLUqVPh5ORk7aLYNF5ny+G1tgxeZ8vgdbaM/HCdC2WHYiIiIrJdrLkhIiIim8JwQ0RERDaF4YaIiIhsCsNNLj169AgnT55EXFycWY8p7FJTU3Hq1ClcvXrV5GPu37+PqKgoPHr0yIwlsy1qtRpnz55FdHQ0NBpNro69cuUKIiMj8eTJEzOVzrb8+++/OH36NNLS0kw+JjU1FVFRUYiPjzdjyWyLSqXCyZMnTV5eRwiB69ev49SpU0hISDBz6WxHYmIiDh06hDt37ph8zL1793DixAnLXGdBJps9e7YoUqSICAgIEEWKFBH9+vUTz58/z/NjCrstW7YId3d3UaVKFeHu7i6aN28u7t27Z3T/48ePi1atWolSpUqJunXrCmdnZzFo0CCRlpZmwVIXPKdPnxYVK1YU5cuXF2XLlhWVK1cW0dHRJh17/fp1UaJECQFAnDhxwswlLdhu374tGjRoIEqUKCEqVaokPDw8xB9//JHjcV999ZUoVqyYqFWrlqhUqZIYPHiwSE9Pt0CJC6bU1FTRvXt34ezsLKpXry6cnZ3Fd999l+0xZ86cEdWrVxeenp6ifv36wsXFRXTv3l2kpKRYqNQFT0xMjHj//fdF2bJlhVKpFAsXLjTpuE8++UQ4OTmJGjVqCCcnJzFq1Cih0WjMVk6GGxPt379f2NnZif379wshtL/gUqVKiZkzZ+bpMYWdSqUSzs7OYt68eUIIIZKTk0WdOnVEr169jB6zZs0acfDgQen2tWvXROnSpcWUKVPMXt6C6vnz56JSpUpi0KBBQgghNBqN6NWrl6hevbpQq9U5HtukSRMxfvx4hhsTvPHGG6J58+YiNTVVCCHE1KlThZubW7aBfdGiRcLFxUVERERI21atWiWePHli9vIWVBMmTBDe3t7i9u3bQgghtm7dKgCIo0ePGj2mefPmol27dlJovHnzpnB3dxdz5861SJkLot27d4ulS5eK5ORk4e7ublK4+eWXX4Szs7M4deqUEEIbKl1cXMTy5cvNVk6GGxP169dPBAYGyraNGTNGVK5cOU+PKey++uorUaJECdl/qKtWrRL29vbi0aNHJj/O22+/LVq3bm2GEtqGvXv3CgDi6tWr0rZ//vlHAJB9oBry6aefij59+ojo6GiGmxzExcUJhUIhtm3bJm178uSJcHZ2FkuWLDF4THp6uihTpoz47LPPLFVMm+Dp6SmmTZsm21arVi0xdOhQo8f4+/uLyZMny7bVrFmT195EpoabkJAQ0bNnT9m2Pn36iBYtWpiraIJ9bkwUFRWFBg0ayLY1btwY165dk1YZz4tjCruoqCi89tprsLd/sexZ48aNkZGRgejoaJMeQ6PR4PTp06hSpYq5ilngRUVFwd3dHZUrV5a21alTB46OjoiKijJ63N69e7FhwwYsWbLEEsUs8P755x8IIWTvA0WLFkVAQIDR6xwdHY2EhAR07twZCQkJOH36NPuR5eD27duIj483+H6b3ev5iy++wMqVK7F8+XLs378fn332GVJSUjB8+HBzF7lQMfZZmN3v5lUVyoUzX8bDhw/h4eEh26a7/fDhQ7i6uubJMYVdTtfMFF988QVu3LiBLVu25Hn5bIWh6wxor7Wx6xwfH4933nkHa9euRfHixREbG2vuYhZ4umtp6DVt7Drfvn0bALBp0yasW7cOXl5euHTpEt5++218//33UCqV5i10AfQy1xkAQkJC0LhxY0ycOBHe3t64fv06JkyYgAoVKpi1vIWJEAKPHz82+LtJSUlBWlqaWWYyZrgxkYODA549eybblpqaCgBwdHTMs2MKu1e9Zt9//z1mzZqFjRs3olq1amYpoy0wdJ0B7bU2dp1HjRqF+vXrQ6lUIjIyEjExMQCAM2fOwNXVldfbAAcHBwDAs2fP4OzsLG1PTU1F6dKlsz3m3LlziImJgbOzMy5evIjGjRujVq1aGD16tPkLXsBkvs6ZZfd6FkKgffv2qFixImJjY+Ho6AiVSiXVFE+ePNns5S4MFAoF7O3tjb6v6353eY3NUiaqWLGi3lDuuLg4ODk5oUyZMnl2TGFn7JoByPG/qR9++AGjR4/GunXr0KVLF7OV0RZUrFgR9+/fx/Pnz6VtT58+RWJiotHrXKJECTx+/BgTJkzAhAkTMH/+fADAwoULsXbtWouUu6CpWLEiABh8TRu7zr6+vgCAQYMGSYEoICAALVu2REREhPkKW4D5+PjAzs4uV9f59u3bOH36NN5//30pAPn4+KBLly7YsWOH2ctcmFSoUMHg78bb2xt2duaJIQw3Jmrbti327Nkj+zDYvn07QkJCpGri+/fvIzIyEunp6SYfQ3Jt27bF2bNncfPmTWnb9u3bUb58eQQEBADQJv7IyEg8fvxY2ufHH3/EyJEjsXbtWrz55puWLnaBExoaivT0dOzevVvatmPHDtjZ2SEkJETadvjwYan5aenSpYiMjJS+fv75ZwDaaz9t2jSLlr+gaNCgAUqWLCn7sIyOjkZMTAzatm0rbTtz5gwuX74MAKhatSr8/PwMfhgYq+0p7FxcXNC8eXPZdX769Cn2798vu85Xr16V+nmULFkSdnZ2es2rKpWK1/kV3b59G5GRkdLttm3b4vfff4f4bylLIQR27Ngh+93kObN1VbYxDx48EN7e3qJz585ix44dYvTo0cLJyUkcP35c2mft2rUCgLhz547Jx5CcWq0WLVq0EPXq1RObN28Wc+fOFfb29uKnn36S9rl48aIAIHbt2iWE0F53hUIhxo4dKyIiIqQv3bBDMuzDDz8UZcuWFT///LNYuXKl8PDwEGPHjpXt4+TkJGbNmmXweI6WMs3ixYtFkSJFxLfffis2bNggAgICxOuvvy7bp0GDBqJ///7S7U2bNkkjUXbv3i3ef/994eLiIs6fP2/p4hcYBw8eFA4ODmLChAli+/btok2bNqJy5coiOTlZ2mfw4MGiZs2a0u2hQ4eKUqVKiSVLlog9e/aIcePGCYVCYdI8RIVVcnKy9B5brFgx8fHHH4uIiAhx8eJFaZ/58+eLzPEiJiZGlChRQvTv31/s2LFDDBo0SLi5uYkrV66YrZzsc2OikiVL4siRI5g9ezYWLFiAcuXKISIiAo0aNZL2KV26NFq0aCFVcZpyDMnZ2dlh165dmDt3LpYsWQI3Nzds3rxZ1szk4uKCFi1aoESJEgCA69evo3nz5jh27BiOHTsm7VexYkWsWbPG4s+hoFi4cCFq1KiBX3/9FQqFAjNmzMAHH3wg26dFixbw8fExeHzRokXRokULdozPwfDhw1G6dGmsWbMGKSkp6NevH8aOHSvbp27dulITFgD06NED7u7u+PHHH7Fjxw5UrVoVZ86c4QjAbAQHByM8PByLFy/G8ePHUbt2baxevRrFihWT9vH395fVpC9evBhNmjTBnj17sHnzZlSsWBGHDx9G06ZNrfEUCoS4uDhMmDABgHaE5fHjx3H8+HG0bt0aYWFhAIDy5cujRYsW0jG+vr44evQo5s6di/nz58PPzw9Hjx416+tZIcR/9URERERENoB9boiIiMimMNwQERGRTWG4ISIiIpvCcENEREQ2heGGiIiIbArDDREREdkUhhsiIiKyKZzEj4gKtCtXruDcuXNwcXFBu3btrF0cIsoHWHNDRAXWihUr0KhRI/zyyy8IDw+XtqvVauzduxcbN260YumIyFo4QzERFVjNmjVDly5dMHHiRGnbvHnz8N1330GpVCIuLg7Pnj2zYgmJyBpYc0NEBU56ejrWrVuHmJgYXLt2DevWrcPFixcBAEWKFEFERIQs8BBR4cI+N0RU4KSnp2Pbtm14+vQpzpw5gydPnsDFxQUBAQEYPny4tYtHRFbGcENEBY6LiwvWrVuHKlWqYPDgwRg2bJi1i0RE+QibpYiIiMimMNwQERGRTWG4ISIiIpvCcENEREQ2hR2KicimHDlyBDdv3sSJEyeg0Wiwbt06AEDbtm3h4eFh5dIRkSUw3BBRgdWpUydUqVJFtu3EiRM4fPgwAKB79+7Ytm0bAKBhw4YMN0SFBGcoJiIiIpvCPjdERERkUxhuiIiIyKYw3BAREZFNYbghIiIim8JwQ0RERDaF4YaIiIhsCsMNERER2RSGGyIiIrIpDDdERERkUxhuiIiIyKYw3BAREZFNYbghIiIim/L/MR9Asx+O7wIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax=plt.subplots(1,1)\n",
+    "\n",
+    "D,Y=get_DoE(state)\n",
+    "\n",
+    "\n",
+    "pareto_points = [(D[i],Y[i]) for i in get_Pareto_front(D,Y)]\n",
+    "ax.scatter([p[1][0] for p in pareto_points],[p[1][1] for p in pareto_points],color=\"blue\",label=\"Composite Bi-SEGO\")\n",
+    "\n",
+    "pareto_points_pymoo = [(X_true[i],F_true[i]) for i in range(len(X_true))]\n",
+    "ax.scatter([p[1][0] for p in pareto_points_pymoo],[p[1][1] for p in pareto_points_pymoo],marker=\".\",s=5,color=\"red\",label=\"Optimal Pareto front\")\n",
+    "\n",
+    "ax.legend(loc=\"best\")\n",
+    "ax.set_xlabel(\"f1\")\n",
+    "ax.set_ylabel(\"f2\")\n",
+    "ax.title.set_text(\"Pareto Front obtained for the problem ZDT1 for $d = 2$\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "ax.set_xlabel(\"f1\")\n",
+    "ax.set_ylabel(\"f2\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62918660",
+   "metadata": {},
+   "source": [
+    "### Comparison of composite and naive versions with Pymoo with same budget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8423bd6",
+   "metadata": {},
+   "source": [
+    "We want to compare both versions of Bi-SEGO with pymoo on a low budget on the ZDT analytical test cases. We want to use the same initial DoE for all 3 algorithms and do the pre-processing of min(f1) and min(f2) beforehand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7043c72",
+   "metadata": {},
+   "source": [
+    "Initialize the list of problems and parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f43ad760",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_problems=list_problems(num_obj=[2,2],tags=[\"zdt\"])\n",
+    "\n",
+    "surrogate=SmtGPX # Choice of surrogate\n",
+    "n_accuracy=100 # Precision on composite acquisition function\n",
+    "seed=420 # Seed for generating the initial DoE\n",
+    "budget_factor=20 # Budget: budget_factor * dim\n",
+    "init_factor=2 # Initial DoE size: init_factor * dim + 1\n",
+    "min_factor=2 # Initial calls to determine min(f1): min_factor * dim + 1 (same for min(f2))\n",
+    "max_so_iter_factor=2 # Max calls to a single-objective subproblem: max_so_iter_factor * dim + 1\n",
+    "soformulation_naive=\"Normalized\"\n",
+    "soformulation_composite=\"Product\"\n",
+    "multi_start_factor=10 # Number of multistart calls for acquisition function optimization: multi_start_factor * dim\n",
+    "\n",
+    "parameters={\n",
+    "    \"surrogate\":surrogate,\n",
+    "    \"n_accuracy\":n_accuracy,\n",
+    "    \"seed\":seed,\n",
+    "    \"budget_factor\":budget_factor,\n",
+    "    \"init_factor\":init_factor,\n",
+    "    \"min_factor\":min_factor,\n",
+    "    \"max_so_iter_factor\":max_so_iter_factor,\n",
+    "    \"soformulation_composite\":soformulation_composite,\n",
+    "    \"soformulation_naive\":soformulation_naive,\n",
+    "    \"multi_start_factor\":multi_start_factor,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32a4bfac",
+   "metadata": {},
+   "source": [
+    "Function for running composite or naive Bi-SEGO on a Benchmark problem with a specific input DoE:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b5289325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_benchmark_bisego(bproblem,D,Y,naive=False):\n",
+    "    name=bproblem.name\n",
+    "    print(f\"Running {\"naive\" if naive else \"composite\"} BiSEGO on benchmark problem\",name)\n",
+    "    num_dim=bproblem.num_dim\n",
+    "    num_obj=bproblem.num_obj\n",
+    "    num_cstr=bproblem.num_cstr\n",
+    "    bounds=bproblem.bounds\n",
+    "    objective=bproblem.objective\n",
+    "\n",
+    "    assert(num_obj==2)\n",
+    "    assert(num_cstr==0)\n",
+    "\n",
+    "    f1=objective[0]\n",
+    "    f2=objective[1]\n",
+    "\n",
+    "    max_budget=budget_factor*num_dim\n",
+    "    n_init=init_factor*num_dim+1\n",
+    "    n_min=min_factor*num_dim+1\n",
+    "    n_so=max_so_iter_factor*num_dim+1\n",
+    "\n",
+    "    obj_config1 = ObjectiveConfig(\n",
+    "        [f1],\n",
+    "        type=\"minimize\",\n",
+    "        surrogate=surrogate,\n",
+    "    )\n",
+    "\n",
+    "    obj_config2 = ObjectiveConfig(\n",
+    "        [f2],\n",
+    "        type=\"minimize\",\n",
+    "        surrogate=surrogate,\n",
+    "    )\n",
+    "\n",
+    "    prob_definition = Problem(\n",
+    "        obj_configs=[obj_config1,obj_config2],\n",
+    "        design_space=bounds,            # problem bounds\n",
+    "        costs=[1,1]\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    opt_config = DriverConfig(\n",
+    "        max_iter = max_budget - n_init-2*n_min,\n",
+    "        max_budget = max_budget,\n",
+    "        nt_init = n_init+2*n_min,\n",
+    "        verbose = True,\n",
+    "        scaling = True,\n",
+    "        seed=seed,\n",
+    "    )\n",
+    "\n",
+    "    strategy_kwargs = {\n",
+    "        \"n_multi_start\":multi_start_factor*num_dim,\n",
+    "        \"n_accuracy\":n_accuracy,\n",
+    "        \"so_formulation\":soformulation_composite,\n",
+    "        \"single_objective_max_calls\":n_so,\n",
+    "        \"init_calls\":0,\n",
+    "        \"naive\":naive,\n",
+    "    }\n",
+    "\n",
+    "    xt_init = np.vstack([np.atleast_1d(x) for x in D])\n",
+    "    yt_init = np.vstack([np.atleast_1d(y) for y in Y])\n",
+    "\n",
+    "    driver = Driver(prob_definition, opt_config, strategy=BiSEGO, strategy_kwargs=strategy_kwargs)\n",
+    "\n",
+    "    InjectData(driver.state, xt_init, yt_init)\n",
+    "\n",
+    "    state = driver.optimize()\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6efcda62",
+   "metadata": {},
+   "source": [
+    "Function for running MO-SEGO with same budget as comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "11b8cec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_benchmark_mosego(bproblem,D,Y):\n",
+    "    name=bproblem.name\n",
+    "    print(f\"Running MO-SEGO on benchmark problem\",name)\n",
+    "    num_dim=bproblem.num_dim\n",
+    "    num_obj=bproblem.num_obj\n",
+    "    num_cstr=bproblem.num_cstr\n",
+    "\n",
+    "    assert(num_obj==2)\n",
+    "    assert(num_cstr==0)\n",
+    "    max_budget=budget_factor*num_dim\n",
+    "    n_init=init_factor*num_dim+1\n",
+    "    n_min=min_factor*num_dim+1\n",
+    "    \n",
+    "    # initialize the first objective configuration\n",
+    "    obj1_config = ObjectiveConfig(\n",
+    "        [bproblem.f1],\n",
+    "        type=\"minimize\",\n",
+    "        surrogate=surrogate,\n",
+    "    )\n",
+    "\n",
+    "    # initialize the second objective configuration\n",
+    "    obj2_config = ObjectiveConfig(\n",
+    "        [bproblem.f2],\n",
+    "        type=\"minimize\",\n",
+    "        surrogate=surrogate,\n",
+    "    )\n",
+    "\n",
+    "    # initialize the problem configuration\n",
+    "    prob_definition = Problem(\n",
+    "        obj_configs=[obj1_config, obj2_config],\n",
+    "        design_space=bproblem.bounds,\n",
+    "    )\n",
+    "\n",
+    "    # initialize the driver\n",
+    "    opt_config = DriverConfig(\n",
+    "        max_iter = max_budget - n_init-2*n_min,\n",
+    "        max_budget = max_budget,\n",
+    "        nt_init = n_init+2*n_min,\n",
+    "        verbose = True,\n",
+    "        scaling = True,\n",
+    "        seed=0,\n",
+    "    )\n",
+    "\n",
+    "    xt_init = np.vstack([np.atleast_1d(x) for x in D])\n",
+    "    yt_init = np.vstack([np.atleast_1d(y) for y in Y])\n",
+    "\n",
+    "    driver = Driver(prob_definition, opt_config, MOSEGO, strategy_kwargs={\"acq_init\": init_ehvi_2o,})\n",
+    "\n",
+    "    InjectData(driver.state, xt_init, yt_init)\n",
+    "\n",
+    "    # starts the optimization process\n",
+    "    state = driver.optimize()\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc70793",
+   "metadata": {},
+   "source": [
+    "Function for running pymoo on a benchmark problem with a low budget and a specific input DoE:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b5c4b05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_benchmark_pymoo_with_budget(bproblem,D,Y):\n",
+    "    num_dim=bproblem.num_dim\n",
+    "    n_init=init_factor*num_dim+1\n",
+    "    n_i=min_factor*num_dim+1\n",
+    "    budget=budget_factor*num_dim-n_init-2*n_i\n",
+    "    pymoo_problem=PymooWrapper(bproblem)\n",
+    "\n",
+    "    X = np.array([x.tolist() for x in D])\n",
+    "\n",
+    "    all_evaluated_X=[]\n",
+    "    all_evaluated_F=[]\n",
+    "\n",
+    "\n",
+    "    algorithm = NSGA2(\n",
+    "        pop_size=budget_factor,\n",
+    "        n_offsprings=1,\n",
+    "        sampling=X,\n",
+    "        crossover=SBX(prob=0.9, eta=15),\n",
+    "        mutation=PM(eta=20),\n",
+    "        eliminate_duplicates=True,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    termination = get_termination(\"n_gen\", 2*budget)\n",
+    "    res = minimize(pymoo_problem,\n",
+    "                algorithm,\n",
+    "                termination,\n",
+    "                seed=1,\n",
+    "                save_history=True,\n",
+    "                verbose=True)\n",
+    "    \n",
+    "    for algorithm_state in res.history:\n",
+    "        offspring = algorithm_state.off\n",
+    "        if offspring is not None and len(offspring) > 0:\n",
+    "            all_evaluated_X.append(offspring.get(\"X\"))\n",
+    "            all_evaluated_F.append(offspring.get(\"F\"))\n",
+    "\n",
+    "    historical_X = np.vstack(all_evaluated_X)\n",
+    "    historical_F = np.vstack(all_evaluated_F)\n",
+    "\n",
+    "    return (historical_X, historical_F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85a5fe64",
+   "metadata": {},
+   "source": [
+    "Function to run a simple EGO subsolver on a problem with a specific input DoE, for solving min(f1) and min(f2):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e88b4ce6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def SimpleEGO(f,bounds,D,Y,max_iter,strategy=MFSEGO,surrogate=SmtAutoModel,strat_kwargs=None):\n",
+    "    #Runs a default implementation of EGO on a single-objective problem\n",
+    "    obj_config = ObjectiveConfig(\n",
+    "        [f],\n",
+    "        type=\"minimize\",\n",
+    "        surrogate=surrogate,\n",
+    "    )\n",
+    "\n",
+    "    prob_definition = Problem(\n",
+    "        obj_configs=[obj_config],\n",
+    "        design_space=bounds,            # problem bounds\n",
+    "    )\n",
+    "\n",
+    "    xt_init = np.vstack([np.atleast_1d(x) for x in D])\n",
+    "    yt_init = np.vstack([np.atleast_1d(y) for y in Y])\n",
+    "\n",
+    "    opt_config = DriverConfig(\n",
+    "        max_iter = max_iter,\n",
+    "        nt_init = 0,\n",
+    "        verbose = True,\n",
+    "        scaling = True,\n",
+    "        seed=42,\n",
+    "    )\n",
+    "\n",
+    "    driver = Driver(prob_definition, opt_config, strategy=strategy,strategy_kwargs=strat_kwargs)\n",
+    "    \n",
+    "    InjectData(driver.state, xt_init, yt_init)\n",
+    "\n",
+    "    state = driver.optimize()\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "107bc39a",
+   "metadata": {},
+   "source": [
+    "Function to initialize and run all Benchmarks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b9df186a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_all_benchmarks(bproblem):\n",
+    "    #Initialization\n",
+    "    name=bproblem.name\n",
+    "    num_dim=bproblem.num_dim\n",
+    "    print(\"Running all benchmarks on problem\",name,\"with dimension\",num_dim)\n",
+    "    bounds=bproblem.bounds\n",
+    "    objective=bproblem.objective\n",
+    "\n",
+    "    f1=objective[0]\n",
+    "    f2=objective[1]\n",
+    "\n",
+    "    n_init=init_factor*num_dim+1\n",
+    "\n",
+    "    float_vars = []\n",
+    "    for idx in range(bounds.shape[0]):\n",
+    "        float_vars.append(\n",
+    "            ds.FloatVariable(bounds[idx, 0], bounds[idx, 1])\n",
+    "        )\n",
+    "    design_space = ds.DesignSpace(float_vars)\n",
+    "\n",
+    "    F=lambda x: (f1(x),f2(x))\n",
+    "    #init DoE using LHS with given seed\n",
+    "    sampler = LHS(xlimits=design_space.get_unfolded_num_bounds(),\n",
+    "                                criterion=\"ese\",\n",
+    "                                seed=seed, )\n",
+    "    doe = sampler(n_init)\n",
+    "    D=[x for x in doe]\n",
+    "    Y=[F(x) for x in D]\n",
+    "    Ni0=min_factor*num_dim+1\n",
+    "    n_multistart=multi_start_factor*num_dim\n",
+    "    initial_budget=Ni0*2+n_init\n",
+    "\n",
+    "    #run min f1 and min f2\n",
+    "    #Initialization\n",
+    "\n",
+    "    def F_eval(x):\n",
+    "        #This function is a substitute for F that automatically updates the global DoE when running the EGO subsolver\n",
+    "        y=F(x)\n",
+    "        D.append(x)\n",
+    "        Y.append(y)\n",
+    "        return y\n",
+    "\n",
+    "    #Coordinate applications of f\n",
+    "    fa=lambda x:F_eval(x)[0]\n",
+    "    fb=lambda x:F_eval(x)[1]\n",
+    "\n",
+    "    #Run EGO on the problems min(f1(x)) and min(f2(x))\n",
+    "    print(\"Min(f1) phase\")\n",
+    "    state=SimpleEGO(fa,bounds,D,[y[0] for y in Y],Ni0,MFSEGO,strat_kwargs={\"n_start\":n_multistart})\n",
+    "    print(\"Min(f2) phase\")\n",
+    "    state=SimpleEGO(fb,bounds,D,[y[1] for y in Y],Ni0,MFSEGO,strat_kwargs={\"n_start\":n_multistart})\n",
+    "\n",
+    "    #D and Y are initialized\n",
+    "\n",
+    "    #bisego benchmark\n",
+    "    state_bisego_composite = run_benchmark_bisego(bproblem,D[:initial_budget],Y[:initial_budget],naive=False)\n",
+    "\n",
+    "    #naive benchmark\n",
+    "    state_bisego_naive = run_benchmark_bisego(bproblem,D[:initial_budget],Y[:initial_budget],naive=True)\n",
+    "    \n",
+    "    #mosego benchmark\n",
+    "    state_mosego = run_benchmark_mosego(bproblem,D[:initial_budget],Y[:initial_budget])\n",
+    "\n",
+    "    #pymoo_benchmark\n",
+    "    D_pymoo,Y_pymoo = run_benchmark_pymoo_with_budget(bproblem,D[:initial_budget],Y[:initial_budget])\n",
+    "\n",
+    "    #True Front\n",
+    "    X,F = run_benchmark_pymoo(bproblem)\n",
+    "\n",
+    "    return state_bisego_composite,state_bisego_naive,state_mosego,X,F,D_pymoo,Y_pymoo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da9917ef",
+   "metadata": {},
+   "source": [
+    "Run Benchmark and save optimization data in output files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a2dfe82c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running all benchmarks on problem ZDT3 with dimension 10\n",
+      "Min(f1) phase\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "             0              0    3.70203e-02      0.000e+00            nan            nan            nan\n",
+      "             1              1    1.00000e-10      0.000e+00              1          1.872          3.144\n",
+      "             2              2    1.00000e-10      0.000e+00              1          1.806          1.755\n",
+      "             3              3    1.00000e-10      0.000e+00              1          1.737          1.507\n",
+      "             4              4    1.00000e-10      0.000e+00              1          1.888          1.082\n",
+      "             5              5    1.00000e-10      0.000e+00              1          2.363          1.116\n",
+      "             6              6    1.00000e-10      0.000e+00              1          2.440          0.999\n",
+      "             7              7    1.00000e-10      0.000e+00              1          2.330          1.072\n",
+      "             8              8    1.00000e-10      0.000e+00              1          2.489          0.843\n",
+      "             9              9    1.00000e-10      0.000e+00              1          2.430          0.841\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "            10             10    1.00000e-10      0.000e+00              1          2.498          0.826\n",
+      "            11             11    1.00000e-10      0.000e+00              1          2.466          0.839\n",
+      "            12             12    1.00000e-10      0.000e+00              1          2.506          0.871\n",
+      "            13             13    1.00000e-10      0.000e+00              1          2.567          0.740\n",
+      "            14             14    1.00000e-10      0.000e+00              1          2.411          0.817\n",
+      "            15             15    1.00000e-10      0.000e+00              1          2.618          0.791\n",
+      "            16             16    1.00000e-10      0.000e+00              1          2.297          0.909\n",
+      "            17             17    1.00000e-10      0.000e+00              1          2.550          0.809\n",
+      "            18             18    1.00000e-10      0.000e+00              1          2.504          0.812\n",
+      "            19             19    1.00000e-10      0.000e+00              1          2.584          0.779\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "            20             20    1.00000e-10      0.000e+00              1          2.656          0.860\n",
+      "            21             21    1.00000e-10      0.000e+00              1          2.351          0.804\n",
+      "Min(f2) phase\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "             0              0    2.29820e+00      0.000e+00            nan            nan            nan\n",
+      "             1              1    5.84750e-01      0.000e+00              1          1.302         10.155\n",
+      "             2              2    5.84750e-01      0.000e+00              1          1.067          9.243\n",
+      "             3              3    4.44714e-01      0.000e+00              1          1.270          9.278\n",
+      "             4              4    4.08170e-01      0.000e+00              1          1.134          7.135\n",
+      "             5              5    2.07625e-01      0.000e+00              1          1.313          3.818\n",
+      "             6              6    2.07625e-01      0.000e+00              1          1.196          7.231\n",
+      "             7              7    2.07625e-01      0.000e+00              1          1.063          6.385\n",
+      "             8              8    2.07625e-01      0.000e+00              1          1.181          7.966\n",
+      "             9              9   -1.24187e-01      0.000e+00              1          1.129          6.869\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "            10             10   -1.24187e-01      0.000e+00              1          1.066          4.934\n",
+      "            11             11   -1.24187e-01      0.000e+00              1          1.177          5.624\n",
+      "            12             12   -1.24187e-01      0.000e+00              1          1.164          4.637\n",
+      "            13             13   -1.24187e-01      0.000e+00              1          1.252          8.274\n",
+      "            14             14   -1.24187e-01      0.000e+00              1          1.121          5.416\n",
+      "            15             15   -1.24187e-01      0.000e+00              1          1.087          5.526\n",
+      "            16             16   -7.34076e-01      0.000e+00              1          1.101          5.086\n",
+      "            17             17   -7.34076e-01      0.000e+00              1          1.110          7.274\n",
+      "            18             18   -7.34076e-01      0.000e+00              1          1.128          5.434\n",
+      "            19             19   -7.34076e-01      0.000e+00              1          1.120          5.066\n",
+      "          iter         budget           fmin           rscv       fidelity        gp_time       acq_time\n",
+      "            20             20   -7.34076e-01      0.000e+00              1          1.420          4.436\n",
+      "            21             21   -7.73366e-01      0.000e+00              1          1.369          4.948\n",
+      "Running composite BiSEGO on benchmark problem ZDT3\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "             0              0    6.23665e+00    4.50364e-01            nan            nan            nan\n",
+      "             1              1    7.15288e+00    2.82525e-01              1          0.179         48.421\n",
+      "             2              2    7.15288e+00    2.82525e-01              1          0.070         46.283\n",
+      "             3              3    7.15288e+00    2.82525e-01              1          0.082         45.718\n",
+      "             4              4    7.15288e+00    2.82525e-01              1          0.072         46.475\n",
+      "             5              5    7.15288e+00    2.82525e-01              1          0.083         44.907\n",
+      "             6              6    7.15288e+00    2.82525e-01              1          0.075         44.756\n",
+      "             7              7    7.15288e+00    2.82525e-01              1          0.187         44.653\n",
+      "             8              8    7.15288e+00    2.82525e-01              1          0.247         44.629\n",
+      "             9              9    7.15288e+00    2.82525e-01              1          0.075         44.627\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            10             10    7.15288e+00    2.82525e-01              1          0.085         45.415\n",
+      "            11             11    7.15288e+00    2.82525e-01              1          0.097         46.236\n",
+      "            12             12    7.15288e+00    2.82525e-01              1          0.101         45.480\n",
+      "            13             13    7.15288e+00    2.82525e-01              1          0.103         45.948\n",
+      "            14             14    7.15288e+00    2.82525e-01              1          0.108         45.742\n",
+      "            15             15    7.15288e+00    2.82525e-01              1          0.108         44.322\n",
+      "            16             16    7.15288e+00    2.82525e-01              1          0.103         45.444\n",
+      "            17             17    7.15288e+00    2.82525e-01              1          0.110         45.237\n",
+      "            18             18    7.15288e+00    2.82525e-01              1          0.100         44.326\n",
+      "            19             19    7.15288e+00    2.82525e-01              1          0.106         45.148\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            20             20    7.15288e+00    2.82525e-01              1          0.114         44.287\n",
+      "            21             21    7.15288e+00    2.82525e-01              1          0.114         44.126\n",
+      "            22             22    7.15288e+00    2.82525e-01              1          0.113         45.196\n",
+      "            23             23    7.15288e+00    2.82525e-01              1          0.115         43.643\n",
+      "            24             24    7.15288e+00    2.82525e-01              1          0.123         44.505\n",
+      "            25             25    7.15288e+00    2.82525e-01              1          0.127         43.611\n",
+      "            26             26    7.15288e+00    2.82525e-01              1          0.130         44.177\n",
+      "            27             27    7.15288e+00    2.82525e-01              1          0.138         45.471\n",
+      "            28             28    7.15288e+00    2.82525e-01              1          0.155         43.815\n",
+      "            29             29    7.15288e+00    2.82525e-01              1          0.123         43.459\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            30             30    7.15288e+00    2.82525e-01              1          0.235         45.051\n",
+      "            31             31    7.15288e+00    2.82525e-01              1          0.135         44.822\n",
+      "            32             32    7.15288e+00    2.82525e-01              1          0.138         44.149\n",
+      "            33             33    7.15288e+00    2.82525e-01              1          0.247         45.132\n",
+      "            34             34    7.15288e+00    2.82525e-01              1          0.155         45.245\n",
+      "            35             35    7.15288e+00    2.82525e-01              1          0.159         44.315\n",
+      "            36             36    7.15288e+00    2.82525e-01              1          0.180         44.179\n",
+      "            37             37    7.15288e+00    2.82525e-01              1          0.167         44.302\n",
+      "            38             38    7.15288e+00    2.82525e-01              1          0.176         45.661\n",
+      "            39             39    7.15288e+00    2.82525e-01              1          0.177         44.298\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            40             40    7.15288e+00    2.82525e-01              1          0.186         45.382\n",
+      "            41             41    7.15288e+00    2.82525e-01              1          0.196         44.272\n",
+      "            42             42    7.15288e+00    2.82525e-01              1          0.345         44.370\n",
+      "            43             43    7.15288e+00    2.82525e-01              1          0.197         44.233\n",
+      "            44             44    7.15288e+00    2.82525e-01              1          0.338         44.219\n",
+      "            45             45    7.15288e+00    2.82525e-01              1          0.349         44.778\n",
+      "            46             46    7.15288e+00    2.82525e-01              1          0.220         44.140\n",
+      "            47             47    7.15288e+00    2.82525e-01              1          0.346         44.160\n",
+      "            48             48    7.15288e+00    2.82525e-01              1          0.217         45.088\n",
+      "            49             49    7.15288e+00    2.82525e-01              1          0.255         44.350\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            50             50    7.15288e+00    2.82525e-01              1          0.275         45.018\n",
+      "            51             51    7.15288e+00    2.82525e-01              1          0.224         44.049\n",
+      "            52             52    7.15288e+00    2.82525e-01              1          0.348         44.072\n",
+      "            53             53    7.15288e+00    2.82525e-01              1          0.350         44.101\n",
+      "            54             54    7.15288e+00    2.82525e-01              1          0.242         44.705\n",
+      "            55             55    7.15288e+00    2.82525e-01              1          0.237         44.852\n",
+      "            56             56    7.15288e+00    2.82525e-01              1          0.497         43.908\n",
+      "            57             57    7.15288e+00    2.82525e-01              1          0.238         44.464\n",
+      "            58             58    7.15288e+00    2.82525e-01              1          0.289         45.633\n",
+      "            59             59    7.15288e+00    2.82525e-01              1          0.540         44.168\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            60             60    7.15288e+00    2.82525e-01              1          0.312         44.294\n",
+      "            61             61    7.15288e+00    2.82525e-01              1          0.313         45.061\n",
+      "            62             62    7.15288e+00    2.82525e-01              1          0.292         45.513\n",
+      "            63             63    7.15288e+00    2.82525e-01              1          0.318         44.484\n",
+      "            64             64    7.15288e+00    2.82525e-01              1          0.329         45.032\n",
+      "            65             65    7.15288e+00    2.82525e-01              1          0.319         45.131\n",
+      "            66             66    7.15288e+00    2.82525e-01              1          0.320         44.080\n",
+      "            67             67    7.15288e+00    2.82525e-01              1          0.319         45.151\n",
+      "            68             68    7.15288e+00    2.82525e-01              1          0.504         45.026\n",
+      "            69             69    7.15288e+00    2.82525e-01              1          0.344         44.324\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            70             70    7.15288e+00    2.82525e-01              1          0.553         45.657\n",
+      "            71             71    7.15288e+00    2.82525e-01              1          0.354         44.314\n",
+      "            72             72    7.15288e+00    2.82525e-01              1          0.510         45.227\n",
+      "            73             73    7.15288e+00    2.82525e-01              1          0.382         44.263\n",
+      "            74             74    7.15288e+00    2.82525e-01              1          0.418         44.368\n",
+      "            75             75    7.15288e+00    2.82525e-01              1          0.398         45.369\n",
+      "            76             76    7.15288e+00    2.82525e-01              1          0.406         45.638\n",
+      "            77             77    7.15288e+00    2.82525e-01              1          0.419         45.046\n",
+      "            78             78    7.15288e+00    2.82525e-01              1          0.558         44.374\n",
+      "            79             79    7.15288e+00    2.82525e-01              1          0.539         45.444\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            80             80    7.15288e+00    2.82525e-01              1          0.446         45.339\n",
+      "            81             81    7.15288e+00    2.82525e-01              1          0.545         45.451\n",
+      "            82             82    7.15288e+00    2.82525e-01              1          0.474         44.605\n",
+      "            83             83    7.15288e+00    2.82525e-01              1          0.635         44.607\n",
+      "            84             84    7.15288e+00    2.82525e-01              1          0.664         45.469\n",
+      "            85             85    7.15288e+00    2.82525e-01              1          0.477         44.675\n",
+      "            86             86    7.15288e+00    2.82525e-01              1          0.467         46.015\n",
+      "            87             87    7.15288e+00    2.82525e-01              1          0.664         45.390\n",
+      "            88             88    7.15288e+00    2.82525e-01              1          0.494         45.568\n",
+      "            89             89    7.15288e+00    2.82525e-01              1          0.679         46.160\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            90             90    7.15288e+00    2.82525e-01              1          0.769         44.542\n",
+      "            91             91    7.15288e+00    2.82525e-01              1          0.527         46.391\n",
+      "            92             92    7.15288e+00    2.82525e-01              1          0.657         44.740\n",
+      "            93             93    7.15288e+00    2.82525e-01              1          0.621         44.751\n",
+      "            94             94    7.15288e+00    2.82525e-01              1          0.568         44.966\n",
+      "            95             95    7.15288e+00    2.82525e-01              1          0.768         44.621\n",
+      "            96             96    7.15288e+00    2.82525e-01              1          0.889         44.735\n",
+      "            97             97    7.15288e+00    2.82525e-01              1          0.602         46.176\n",
+      "            98             98    7.15288e+00    2.82525e-01              1          0.617         44.415\n",
+      "            99             99    7.15288e+00    2.82525e-01              1          0.992         44.696\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           100            100    7.15288e+00    2.82525e-01              1          0.895         46.201\n",
+      "           101            101    7.15288e+00    2.82525e-01              1          0.692         46.101\n",
+      "           102            102    7.15288e+00    2.82525e-01              1          0.670         44.620\n",
+      "           103            103    7.15288e+00    2.82525e-01              1          0.747         44.845\n",
+      "           104            104    7.15288e+00    2.82525e-01              1          0.660         44.660\n",
+      "           105            105    7.15288e+00    2.82525e-01              1          0.723         45.078\n",
+      "           106            106    7.15288e+00    2.82525e-01              1          0.995         44.328\n",
+      "           107            107    7.15288e+00    2.82525e-01              1          0.881         44.146\n",
+      "           108            108    7.15288e+00    2.82525e-01              1          0.667         45.417\n",
+      "           109            109    7.15288e+00    2.82525e-01              1          0.799         44.368\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           110            110    7.15288e+00    2.82525e-01              1          0.750         45.976\n",
+      "           111            111    7.15288e+00    2.82525e-01              1          1.022         46.274\n",
+      "           112            112    7.15288e+00    2.82525e-01              1          0.780         46.214\n",
+      "           113            113    7.15288e+00    2.82525e-01              1          1.098         45.816\n",
+      "           114            114    7.15288e+00    2.82525e-01              1          1.110         44.660\n",
+      "           115            115    7.15288e+00    2.82525e-01              1          0.888         45.637\n",
+      "           116            116    7.15288e+00    2.82525e-01              1          0.859         46.363\n",
+      "           117            117    7.15288e+00    2.82525e-01              1          0.877         46.080\n",
+      "           118            118    7.15288e+00    2.82525e-01              1          0.971         44.805\n",
+      "           119            119    7.15288e+00    2.82525e-01              1          0.919         45.651\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           120            120    7.15288e+00    2.82525e-01              1          0.920         44.907\n",
+      "           121            121    7.15288e+00    2.82525e-01              1          0.947         44.933\n",
+      "           122            122    7.15288e+00    2.82525e-01              1          1.195         45.564\n",
+      "           123            123    7.15288e+00    2.82525e-01              1          1.006         44.841\n",
+      "           124            124    7.15288e+00    2.82525e-01              1          1.097         44.359\n",
+      "           125            125    7.15288e+00    2.82525e-01              1          0.972         44.325\n",
+      "           126            126    7.15288e+00    2.82525e-01              1          1.037         44.143\n",
+      "           127            127    7.15288e+00    2.82525e-01              1          1.245         45.582\n",
+      "           128            128    7.15288e+00    2.82525e-01              1          1.006         44.160\n",
+      "           129            129    7.15288e+00    2.82525e-01              1          1.341         44.304\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           130            130    7.15288e+00    2.82525e-01              1          1.453         45.743\n",
+      "           131            131    7.15288e+00    2.82525e-01              1          1.080         44.444\n",
+      "           132            132    7.15288e+00    2.82525e-01              1          1.115         44.897\n",
+      "           133            133    7.15288e+00    2.82525e-01              1          1.150         45.988\n",
+      "           134            134    7.15288e+00    2.82525e-01              1          1.402         44.849\n",
+      "           135            135    7.15288e+00    2.82525e-01              1          1.512         45.229\n",
+      "           136            136    7.15288e+00    2.82525e-01              1          1.334         45.131\n",
+      "           137            137    7.15288e+00    2.82525e-01              1          1.729         46.094\n",
+      "Running naive BiSEGO on benchmark problem ZDT3\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "             0              0    6.23665e+00    4.50364e-01            nan            nan            nan\n",
+      "             1              1    6.23665e+00    4.50364e-01              1          0.068         12.053\n",
+      "             2              2    6.23665e+00    4.50364e-01              1          0.072         12.317\n",
+      "             3              3    6.23665e+00    4.50364e-01              1          0.071         12.012\n",
+      "             4              4    6.23665e+00    4.50364e-01              1          0.063         12.153\n",
+      "             5              5    6.23665e+00    4.50364e-01              1          0.244         12.196\n",
+      "             6              6    6.23665e+00    4.50364e-01              1          0.071         12.530\n",
+      "             7              7    6.23665e+00    4.50364e-01              1          0.069         12.230\n",
+      "             8              8    6.23665e+00    4.50364e-01              1          0.079         12.462\n",
+      "             9              9    6.23665e+00    4.50364e-01              1          0.083         12.845\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            10             10    6.23665e+00    4.50364e-01              1          0.083         13.544\n",
+      "            11             11    6.23665e+00    4.50364e-01              1          0.089         13.480\n",
+      "            12             12    6.23665e+00    4.50364e-01              1          0.138         13.421\n",
+      "            13             13    6.23665e+00    4.50364e-01              1          0.095         13.365\n",
+      "            14             14    6.23665e+00    4.50364e-01              1          0.091         12.746\n",
+      "            15             15    6.23665e+00    4.50364e-01              1          0.109         12.709\n",
+      "            16             16    6.23665e+00    4.50364e-01              1          0.109         12.906\n",
+      "            17             17    6.23665e+00    4.50364e-01              1          0.108         13.087\n",
+      "            18             18    6.23665e+00    4.50364e-01              1          0.102         12.506\n",
+      "            19             19    6.23665e+00    4.50364e-01              1          0.114         13.147\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            20             20    6.23665e+00    4.50364e-01              1          0.115         13.820\n",
+      "            21             21    6.23665e+00    4.50364e-01              1          0.224         14.563\n",
+      "            22             22    6.23665e+00    4.50364e-01              1          0.125         14.279\n",
+      "            23             23    6.23665e+00    4.50364e-01              1          0.124         14.028\n",
+      "            24             24    6.23665e+00    4.50364e-01              1          0.126         13.269\n",
+      "            25             25    6.23665e+00    4.50364e-01              1          0.134         14.626\n",
+      "            26             26    6.23665e+00    4.50364e-01              1          0.128         14.155\n",
+      "            27             27    6.23665e+00    4.50364e-01              1          0.141         15.056\n",
+      "            28             28    6.23665e+00    4.50364e-01              1          0.136         14.362\n",
+      "            29             29    6.23665e+00    4.50364e-01              1          0.151         14.475\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            30             30    6.23665e+00    4.50364e-01              1          0.205         14.359\n",
+      "            31             31    6.23665e+00    4.50364e-01              1          0.145         14.724\n",
+      "            32             32    6.23665e+00    4.50364e-01              1          0.154         13.898\n",
+      "            33             33    6.23665e+00    4.50364e-01              1          0.158         14.791\n",
+      "            34             34    6.23665e+00    4.50364e-01              1          0.173         14.164\n",
+      "            35             35    6.23665e+00    4.50364e-01              1          0.158         14.153\n",
+      "            36             36    6.23665e+00    4.50364e-01              1          0.172         15.099\n",
+      "            37             37    6.23665e+00    4.50364e-01              1          0.186         14.992\n",
+      "            38             38    6.23665e+00    4.50364e-01              1          0.174         15.981\n",
+      "            39             39    6.23665e+00    4.50364e-01              1          0.184         14.617\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            40             40    6.23665e+00    4.50364e-01              1          0.215         15.455\n",
+      "            41             41    6.23665e+00    4.50364e-01              1          0.229         15.827\n",
+      "            42             42    6.23665e+00    4.50364e-01              1          0.366         16.882\n",
+      "            43             43    6.28715e+00    2.31286e-01              1          0.255         16.694\n",
+      "            44             44    6.28715e+00    2.31286e-01              1          0.204         15.837\n",
+      "            45             45    6.28715e+00    2.31286e-01              1          0.220         14.488\n",
+      "            46             46    6.28715e+00    2.31286e-01              1          0.276         15.949\n",
+      "            47             47    6.28715e+00    2.31286e-01              1          0.222         14.993\n",
+      "            48             48    6.28715e+00    2.31286e-01              1          0.216         15.811\n",
+      "            49             49    6.28715e+00    2.31286e-01              1          0.213         15.537\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            50             50    6.28715e+00    2.31286e-01              1          0.218         15.313\n",
+      "            51             51    6.28715e+00    2.31286e-01              1          0.217         16.304\n",
+      "            52             52    6.28715e+00    2.31286e-01              1          0.244         14.236\n",
+      "            53             53    6.28715e+00    2.31286e-01              1          0.233         16.286\n",
+      "            54             54    6.28715e+00    2.31286e-01              1          0.250         16.352\n",
+      "            55             55    6.28715e+00    2.31286e-01              1          0.240         16.033\n",
+      "            56             56    6.28715e+00    2.31286e-01              1          0.256         15.438\n",
+      "            57             57    6.28715e+00    2.31286e-01              1          0.259         14.979\n",
+      "            58             58    6.28715e+00    2.31286e-01              1          0.327         17.017\n",
+      "            59             59    6.28715e+00    2.31286e-01              1          0.400         16.177\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            60             60    6.28715e+00    2.31286e-01              1          0.278         16.826\n",
+      "            61             61    6.28715e+00    2.31286e-01              1          0.275         17.728\n",
+      "            62             62    6.28715e+00    2.31286e-01              1          0.282         15.938\n",
+      "            63             63    6.28715e+00    2.31286e-01              1          0.289         16.975\n",
+      "            64             64    6.28715e+00    2.31286e-01              1          0.374         18.952\n",
+      "            65             65    6.28715e+00    2.31286e-01              1          0.727         17.337\n",
+      "            66             66    6.28715e+00    2.31286e-01              1          0.398         18.531\n",
+      "            67             67    6.28715e+00    2.31286e-01              1          0.346         16.417\n",
+      "            68             68    6.28715e+00    2.31286e-01              1          0.323         16.522\n",
+      "            69             69    6.28715e+00    2.31286e-01              1          0.476         16.714\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            70             70    6.28715e+00    2.31286e-01              1          0.412         15.973\n",
+      "            71             71    6.28715e+00    2.31286e-01              1          0.332         16.525\n",
+      "            72             72    6.28715e+00    2.31286e-01              1          0.338         15.826\n",
+      "            73             73    6.28715e+00    2.31286e-01              1          0.346         17.818\n",
+      "            74             74    6.28715e+00    2.31286e-01              1          0.397         16.396\n",
+      "            75             75    6.28715e+00    2.31286e-01              1          0.532         18.290\n",
+      "            76             76    6.28715e+00    2.31286e-01              1          0.380         18.126\n",
+      "            77             77    6.28715e+00    2.31286e-01              1          0.378         19.057\n",
+      "            78             78    6.28715e+00    2.31286e-01              1          0.600         17.413\n",
+      "            79             79    6.28715e+00    2.31286e-01              1          0.392         18.444\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            80             80    6.28715e+00    2.31286e-01              1          0.468         17.953\n",
+      "            81             81    6.28715e+00    2.31286e-01              1          0.405         18.027\n",
+      "            82             82    6.28715e+00    2.31286e-01              1          0.426         19.079\n",
+      "            83             83    6.28715e+00    2.31286e-01              1          0.508         18.837\n",
+      "            84             84    6.28715e+00    2.31286e-01              1          0.440         21.337\n",
+      "            85             85    6.28715e+00    2.31286e-01              1          0.437         21.236\n",
+      "            86             86    6.28715e+00    2.31286e-01              1          0.495         20.718\n",
+      "            87             87    6.28715e+00    2.31286e-01              1          0.668         20.085\n",
+      "            88             88    6.28715e+00    2.31286e-01              1          0.618         19.988\n",
+      "            89             89    6.28715e+00    2.31286e-01              1          0.692         21.769\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            90             90    6.28715e+00    2.31286e-01              1          0.495         22.785\n",
+      "            91             91    6.28715e+00    2.31286e-01              1          0.529         18.507\n",
+      "            92             92    6.28715e+00    2.31286e-01              1          0.515         22.239\n",
+      "            93             93    6.28715e+00    2.31286e-01              1          0.750         20.369\n",
+      "            94             94    6.37071e+00    2.40729e-01              1          0.520         21.607\n",
+      "            95             95    6.37071e+00    2.40729e-01              1          0.754         20.674\n",
+      "            96             96    6.37071e+00    2.40729e-01              1          0.575         21.257\n",
+      "            97             97    6.37071e+00    2.40729e-01              1          0.570         19.899\n",
+      "            98             98    6.37071e+00    2.40729e-01              1          0.738         19.697\n",
+      "            99             99    6.37071e+00    2.40729e-01              1          0.735         24.776\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           100            100    6.37071e+00    2.40729e-01              1          0.583         21.879\n",
+      "           101            101    6.37071e+00    2.40729e-01              1          0.890         23.964\n",
+      "           102            102    6.37071e+00    2.40729e-01              1          0.936         21.172\n",
+      "           103            103    6.37071e+00    2.40729e-01              1          0.786         21.499\n",
+      "           104            104    6.37071e+00    2.40729e-01              1          0.630         21.893\n",
+      "           105            105    6.37071e+00    2.40729e-01              1          0.824         24.818\n",
+      "           106            106    6.37071e+00    2.40729e-01              1          0.680         23.920\n",
+      "           107            107    6.37071e+00    2.40729e-01              1          0.682         22.557\n",
+      "           108            108    6.37071e+00    2.40729e-01              1          0.667         24.751\n",
+      "           109            109    6.37071e+00    2.40729e-01              1          0.675         27.058\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           110            110    6.37071e+00    2.40729e-01              1          0.858         26.820\n",
+      "           111            111    6.37071e+00    2.40729e-01              1          0.717         25.228\n",
+      "           112            112    6.37071e+00    2.40729e-01              1          0.977         23.127\n",
+      "           113            113    6.37071e+00    2.40729e-01              1          0.903         26.729\n",
+      "           114            114    6.37071e+00    2.40729e-01              1          0.972         22.922\n",
+      "           115            115    6.37071e+00    2.40729e-01              1          0.802         26.496\n",
+      "           116            116    6.37071e+00    2.40729e-01              1          1.081         27.888\n",
+      "           117            117    6.37071e+00    2.40729e-01              1          0.813         27.600\n",
+      "           118            118    6.37071e+00    2.40729e-01              1          0.747         26.363\n",
+      "           119            119    6.37071e+00    2.40729e-01              1          1.094         27.767\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           120            120    6.37071e+00    2.40729e-01              1          0.875         27.808\n",
+      "           121            121    6.37071e+00    2.40729e-01              1          1.111         26.879\n",
+      "           122            122    6.37667e+00    2.51458e-01              1          1.088         26.781\n",
+      "           123            123    6.37667e+00    2.51458e-01              1          0.926         30.027\n",
+      "           124            124    6.37667e+00    2.51458e-01              1          0.941         33.014\n",
+      "           125            125    6.37667e+00    2.51458e-01              1          1.148         34.519\n",
+      "           126            126    6.37667e+00    2.51458e-01              1          1.168         32.005\n",
+      "           127            127    6.37667e+00    2.51458e-01              1          1.226         32.232\n",
+      "           128            128    6.37667e+00    2.51458e-01              1          1.160         33.852\n",
+      "           129            129    6.37667e+00    2.51458e-01              1          1.038         34.643\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           130            130    6.37667e+00    2.51458e-01              1          1.246         31.440\n",
+      "           131            131    6.37667e+00    2.51458e-01              1          1.214         35.708\n",
+      "           132            132    6.37667e+00    2.51458e-01              1          1.308         32.750\n",
+      "           133            133    6.37667e+00    2.51458e-01              1          1.092         36.248\n",
+      "           134            134    6.37667e+00    2.51458e-01              1          1.541         35.835\n",
+      "           135            135    6.37667e+00    2.51458e-01              1          1.397         35.827\n",
+      "           136            136    6.37667e+00    2.51458e-01              1          1.407         35.722\n",
+      "           137            137    6.37667e+00    2.51458e-01              1          1.272         36.296\n",
+      "Running MO-SEGO on benchmark problem ZDT3\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "             0              0    6.23665e+00    4.50364e-01            nan            nan            nan\n",
+      "             1              1    7.13262e+00    1.01896e+00              1          0.133          0.824\n",
+      "             2              2    7.25105e+00    1.06358e+00              1          0.065          1.815\n",
+      "             3              3    7.25105e+00    1.06358e+00              1          0.077          1.418\n",
+      "             4              4    7.25105e+00    1.06358e+00              1          0.063          1.263\n",
+      "             5              5    7.25105e+00    1.06358e+00              1          0.112          1.127\n",
+      "             6              6    7.25105e+00    1.06358e+00              1          0.198          1.213\n",
+      "             7              7    7.30646e+00    1.01163e+00              1          0.077          1.424\n",
+      "             8              8    7.36951e+00    9.42122e-01              1          0.084          1.270\n",
+      "             9              9    7.36951e+00    9.42122e-01              1          0.079          0.714\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            10             10    7.37489e+00    8.96372e-01              1          0.087          0.582\n",
+      "            11             11    7.37660e+00    8.60686e-01              1          0.114          0.941\n",
+      "            12             12    7.37660e+00    8.60686e-01              1          0.090          0.766\n",
+      "            13             13    7.37660e+00    8.60686e-01              1          0.080          3.598\n",
+      "            14             14    7.37660e+00    8.60686e-01              1          0.164          0.701\n",
+      "            15             15    7.37660e+00    8.60686e-01              1          0.089          1.043\n",
+      "            16             16    7.38261e+00    8.23166e-01              1          0.102          0.582\n",
+      "            17             17    7.38282e+00    8.02987e-01              1          0.099          0.846\n",
+      "            18             18    7.38282e+00    8.02987e-01              1          0.098          0.877\n",
+      "            19             19    7.38282e+00    8.02987e-01              1          0.152          0.708\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            20             20    7.38310e+00    7.82730e-01              1          0.103          0.588\n",
+      "            21             21    7.38310e+00    7.82730e-01              1          0.126          0.669\n",
+      "            22             22    7.38310e+00    7.82730e-01              1          0.110          5.535\n",
+      "            23             23    7.38310e+00    7.82730e-01              1          0.113          0.737\n",
+      "            24             24    7.38310e+00    7.82730e-01              1          0.125          0.612\n",
+      "            25             25    7.38310e+00    7.82730e-01              1          0.123          0.768\n",
+      "            26             26    7.38310e+00    7.82730e-01              1          0.119          0.662\n",
+      "            27             27    7.38474e+00    7.77482e-01              1          0.122          0.715\n",
+      "            28             28    7.38772e+00    7.99426e-01              1          0.136          0.725\n",
+      "            29             29    7.38855e+00    7.75349e-01              1          0.230          0.612\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            30             30    7.38855e+00    7.75349e-01              1          0.134          2.420\n",
+      "            31             31    7.38891e+00    7.54813e-01              1          0.137          0.776\n",
+      "            32             32    7.38891e+00    7.54813e-01              1          0.256          0.764\n",
+      "            33             33    7.38891e+00    7.54813e-01              1          0.149          0.701\n",
+      "            34             34    7.38891e+00    7.54813e-01              1          0.146          6.982\n",
+      "            35             35    7.38891e+00    7.54813e-01              1          0.155          0.574\n",
+      "            36             36    7.38891e+00    1.45856e-01              1          0.153          0.687\n",
+      "            37             37    7.38891e+00    1.45856e-01              1          0.159          0.679\n",
+      "            38             38    7.38891e+00    1.45856e-01              1          0.294          0.585\n",
+      "            39             39    7.38891e+00    1.45856e-01              1          0.166          0.496\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            40             40    7.38891e+00    1.45856e-01              1          0.330          0.656\n",
+      "            41             41    7.38891e+00    1.45856e-01              1          0.174          2.416\n",
+      "            42             42    7.38891e+00    1.45856e-01              1          0.181          2.582\n",
+      "            43             43    7.38891e+00    1.45665e-01              1          0.179          0.571\n",
+      "            44             44    7.38919e+00    1.12725e-01              1          0.186          0.650\n",
+      "            45             45    7.38919e+00    1.12725e-01              1          0.195          1.850\n",
+      "            46             46    7.38919e+00    1.12725e-01              1          0.299          5.252\n",
+      "            47             47    7.38919e+00    1.12725e-01              1          0.210          0.681\n",
+      "            48             48    7.38919e+00    1.12725e-01              1          0.209          0.529\n",
+      "            49             49    7.38919e+00    1.12725e-01              1          0.326          0.739\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            50             50    7.38919e+00    1.12725e-01              1          0.224          8.324\n",
+      "            51             51    7.39640e+00    9.40174e-02              1          0.218          6.859\n",
+      "            52             52    7.39640e+00    9.40174e-02              1          0.230          0.533\n",
+      "            53             53    7.39640e+00    9.40174e-02              1          0.256          0.664\n",
+      "            54             54    7.39640e+00    9.40174e-02              1          0.240          2.414\n",
+      "            55             55    7.39640e+00    9.40174e-02              1          0.238          7.405\n",
+      "            56             56    7.39640e+00    9.40174e-02              1          0.253          6.051\n",
+      "            57             57    7.39640e+00    9.40174e-02              1          0.251          0.730\n",
+      "            58             58    7.39640e+00    9.40174e-02              1          0.435          4.152\n",
+      "            59             59    7.39640e+00    9.40174e-02              1          0.267          0.616\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            60             60    7.40131e+00    9.41110e-02              1          0.363          5.740\n",
+      "            61             61    7.40131e+00    9.41110e-02              1          0.288          0.812\n",
+      "            62             62    7.40138e+00    9.26868e-02              1          0.457          5.686\n",
+      "            63             63    7.40138e+00    9.26868e-02              1          0.284          3.890\n",
+      "            64             64    7.40138e+00    9.26868e-02              1          0.295          7.017\n",
+      "            65             65    7.40138e+00    9.26868e-02              1          0.299          5.930\n",
+      "            66             66    7.40138e+00    9.26868e-02              1          0.338          3.167\n",
+      "            67             67    7.40933e+00    8.60487e-02              1          0.308          4.662\n",
+      "            68             68    7.40933e+00    8.60487e-02              1          0.335          7.924\n",
+      "            69             69    7.40933e+00    8.60487e-02              1          0.336          3.951\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            70             70    7.40933e+00    8.60487e-02              1          0.349          3.522\n",
+      "            71             71    7.40933e+00    8.60487e-02              1          0.350          5.928\n",
+      "            72             72    7.40933e+00    8.60487e-02              1          0.348          6.183\n",
+      "            73             73    7.41236e+00    5.66560e-02              1          0.357          4.935\n",
+      "            74             74    7.41236e+00    5.66560e-02              1          0.394          5.696\n",
+      "            75             75    7.41236e+00    5.66560e-02              1          0.472          7.141\n",
+      "            76             76    7.41519e+00    5.64745e-02              1          0.385          6.775\n",
+      "            77             77    7.41519e+00    5.64745e-02              1          0.390          7.733\n",
+      "            78             78    7.41521e+00    6.02322e-02              1          0.591          4.728\n",
+      "            79             79    7.41660e+00    5.24776e-02              1          0.403          1.830\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            80             80    7.41660e+00    5.24776e-02              1          0.416          7.598\n",
+      "            81             81    7.41660e+00    5.24776e-02              1          0.514          6.215\n",
+      "            82             82    7.41660e+00    5.24776e-02              1          0.443          4.877\n",
+      "            83             83    7.41660e+00    5.24776e-02              1          0.479          5.505\n",
+      "            84             84    7.41660e+00    5.24776e-02              1          0.457          5.670\n",
+      "            85             85    7.41660e+00    5.24776e-02              1          0.501          3.851\n",
+      "            86             86    7.41882e+00    5.24477e-02              1          0.484          5.624\n",
+      "            87             87    7.41882e+00    5.24477e-02              1          0.515          2.486\n",
+      "            88             88    7.41882e+00    5.24477e-02              1          0.784         10.535\n",
+      "            89             89    7.41985e+00    5.00166e-02              1          0.500          3.577\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "            90             90    7.42013e+00    5.34752e-02              1          0.512          2.946\n",
+      "            91             91    7.42075e+00    4.45493e-02              1          0.766          7.263\n",
+      "            92             92    7.42075e+00    4.45493e-02              1          0.534          7.626\n",
+      "            93             93    7.42075e+00    4.45493e-02              1          0.755          8.535\n",
+      "            94             94    7.42075e+00    4.45493e-02              1          0.562          7.349\n",
+      "            95             95    7.42111e+00    4.38397e-02              1          0.813          6.327\n",
+      "            96             96    7.42153e+00    4.34864e-02              1          0.756          4.782\n",
+      "            97             97    7.42338e+00    4.39277e-02              1          0.586          6.455\n",
+      "            98             98    7.42338e+00    4.39277e-02              1          0.580          6.223\n",
+      "            99             99    7.42338e+00    4.39277e-02              1          0.600          4.701\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           100            100    7.42427e+00    4.16137e-02              1          0.890          3.511\n",
+      "           101            101    7.42427e+00    4.16137e-02              1          0.830          6.116\n",
+      "           102            102    7.42437e+00    4.26843e-02              1          0.628          2.274\n",
+      "           103            103    7.42437e+00    4.26843e-02              1          0.613          8.242\n",
+      "           104            104    7.42437e+00    4.26843e-02              1          0.645          6.516\n",
+      "           105            105    7.42462e+00    4.06565e-02              1          0.659          3.529\n",
+      "           106            106    7.42503e+00    3.82902e-02              1          0.667          4.271\n",
+      "           107            107    7.42557e+00    3.79137e-02              1          0.926          7.313\n",
+      "           108            108    7.42586e+00    3.43876e-02              1          0.681          4.942\n",
+      "           109            109    7.42586e+00    3.43876e-02              1          0.695          4.247\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           110            110    7.42586e+00    3.43876e-02              1          0.927          5.975\n",
+      "           111            111    7.42636e+00    3.71890e-02              1          0.749          5.368\n",
+      "           112            112    7.42663e+00    3.63486e-02              1          0.734          3.820\n",
+      "           113            113    7.42678e+00    3.35708e-02              1          0.740          2.818\n",
+      "           114            114    7.42758e+00    3.31147e-02              1          1.059          3.941\n",
+      "           115            115    7.42758e+00    3.31147e-02              1          1.015          5.003\n",
+      "           116            116    7.42758e+00    3.31147e-02              1          1.054          3.854\n",
+      "           117            117    7.42758e+00    3.31147e-02              1          0.838          4.417\n",
+      "           118            118    7.42758e+00    3.31147e-02              1          1.193          5.834\n",
+      "           119            119    7.42758e+00    3.31147e-02              1          0.962          1.000\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           120            120    7.42758e+00    3.31147e-02              1          1.094          4.878\n",
+      "           121            121    7.42758e+00    3.31147e-02              1          1.102          6.134\n",
+      "           122            122    7.42779e+00    2.76038e-02              1          1.130          4.346\n",
+      "           123            123    7.42836e+00    2.85102e-02              1          1.072          3.660\n",
+      "           124            124    7.42836e+00    2.85102e-02              1          1.236          5.435\n",
+      "           125            125    7.42861e+00    2.67773e-02              1          1.062          3.759\n",
+      "           126            126    7.42894e+00    2.68831e-02              1          0.994          5.617\n",
+      "           127            127    7.42941e+00    2.64870e-02              1          1.271          4.936\n",
+      "           128            128    7.42960e+00    2.67210e-02              1          1.130          4.216\n",
+      "           129            129    7.42973e+00    2.66260e-02              1          1.259          2.675\n",
+      "          iter         budget             HV        spacing       fidelity        gp_time       acq_time\n",
+      "           130            130    7.42987e+00    2.61156e-02              1          1.057          8.140\n",
+      "           131            131    7.42987e+00    2.61156e-02              1          1.310          6.521\n",
+      "           132            132    7.42987e+00    2.61156e-02              1          1.069          7.484\n",
+      "           133            133    7.43002e+00    2.61500e-02              1          1.377          6.840\n",
+      "           134            134    7.43016e+00    2.61914e-02              1          1.195          2.353\n",
+      "           135            135    7.43024e+00    2.61359e-02              1          1.349          2.578\n",
+      "           136            136    7.43049e+00    2.61401e-02              1          1.236          7.338\n",
+      "           137            137    7.43057e+00    2.59407e-02              1          1.191          4.121\n",
+      "==========================================================\n",
+      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
+      "==========================================================\n",
+      "     1 |       63 |      8 |             - |             -\n",
+      "     2 |       64 |      8 |  0.000000E+00 |             f\n",
+      "     3 |       65 |      9 |  0.0004674947 |             f\n",
+      "     4 |       66 |      9 |  0.0004674947 |             f\n",
+      "     5 |       67 |      9 |  0.0004674947 |             f\n",
+      "     6 |       68 |      9 |  0.0004674947 |             f\n",
+      "     7 |       69 |      9 |  0.0004674947 |             f\n",
+      "     8 |       70 |      9 |  0.0004674947 |             f\n",
+      "     9 |       71 |      9 |  0.0004674947 |             f\n",
+      "    10 |       72 |      9 |  0.0004674947 |             f\n",
+      "    11 |       73 |      9 |  0.0004674947 |             f\n",
+      "    12 |       74 |     10 |  0.0201764806 |             f\n",
+      "    13 |       75 |     10 |  0.000000E+00 |             f\n",
+      "    14 |       76 |     10 |  0.000000E+00 |             f\n",
+      "    15 |       77 |     10 |  0.000000E+00 |             f\n",
+      "    16 |       78 |     10 |  0.000000E+00 |             f\n",
+      "    17 |       79 |     10 |  0.0082855776 |             f\n",
+      "    18 |       80 |     10 |  0.000000E+00 |             f\n",
+      "    19 |       81 |     10 |  0.000000E+00 |             f\n",
+      "    20 |       82 |     11 |  0.0000349768 |             f\n",
+      "    21 |       83 |     11 |  0.0000349768 |             f\n",
+      "    22 |       84 |     11 |  0.0000349768 |             f\n",
+      "    23 |       85 |     11 |  0.0029293651 |             f\n",
+      "    24 |       86 |     10 |  0.0486561239 |         nadir\n",
+      "    25 |       87 |      9 |  0.1919016017 |         nadir\n",
+      "    26 |       88 |     10 |  0.0016074611 |             f\n",
+      "    27 |       89 |     10 |  0.0016074611 |             f\n",
+      "    28 |       90 |     11 |  0.0060275282 |             f\n",
+      "    29 |       91 |     11 |  0.000000E+00 |             f\n",
+      "    30 |       92 |     11 |  0.000000E+00 |             f\n",
+      "    31 |       93 |     11 |  0.000000E+00 |             f\n",
+      "    32 |       94 |     10 |  0.1665678821 |         nadir\n",
+      "    33 |       95 |     10 |  0.0001426164 |             f\n",
+      "    34 |       96 |     10 |  0.0001426164 |             f\n",
+      "    35 |       97 |     10 |  0.0001426164 |             f\n",
+      "    36 |       98 |     10 |  0.0001426164 |             f\n",
+      "    37 |       99 |     10 |  0.0001426164 |             f\n",
+      "    38 |      100 |     10 |  0.0001426164 |             f\n",
+      "    39 |      101 |     10 |  0.0001426164 |             f\n",
+      "    40 |      102 |     10 |  0.1448741317 |         nadir\n",
+      "    41 |      103 |     11 |  0.0039074863 |             f\n",
+      "    42 |      104 |     12 |  0.0043897824 |             f\n",
+      "    43 |      105 |     12 |  0.000000E+00 |             f\n",
+      "    44 |      106 |     12 |  0.000000E+00 |             f\n",
+      "    45 |      107 |     13 |  0.0067913776 |             f\n",
+      "    46 |      108 |     13 |  0.000000E+00 |             f\n",
+      "    47 |      109 |     13 |  0.000000E+00 |             f\n",
+      "    48 |      110 |     13 |  0.000000E+00 |             f\n",
+      "    49 |      111 |     13 |  0.000000E+00 |             f\n",
+      "    50 |      112 |     13 |  0.000000E+00 |             f\n",
+      "    51 |      113 |     13 |  0.0018296148 |             f\n",
+      "    52 |      114 |     13 |  0.0018296148 |             f\n",
+      "    53 |      115 |     14 |  0.0136093442 |             f\n",
+      "    54 |      116 |     14 |  0.0026984494 |             f\n",
+      "    55 |      117 |     15 |  0.0036975521 |             f\n",
+      "    56 |      118 |     15 |  0.000000E+00 |             f\n",
+      "    57 |      119 |     15 |  0.000000E+00 |             f\n",
+      "    58 |      120 |     15 |  0.0016432477 |             f\n",
+      "    59 |      121 |     15 |  0.0017252083 |             f\n",
+      "    60 |      122 |     15 |  0.0028318438 |             f\n",
+      "    61 |      123 |     15 |  0.0011076735 |             f\n",
+      "    62 |      124 |     15 |  0.0011076735 |             f\n",
+      "    63 |      125 |     14 |  0.0238595511 |             f\n",
+      "    64 |      126 |     14 |  0.0004632515 |             f\n",
+      "    65 |      127 |     14 |  0.0004632515 |             f\n",
+      "    66 |      128 |     14 |  0.0004632515 |             f\n",
+      "    67 |      129 |     14 |  0.0004632515 |             f\n",
+      "    68 |      130 |     14 |  0.0004632515 |             f\n",
+      "    69 |      131 |     14 |  0.0004632515 |             f\n",
+      "    70 |      132 |     14 |  0.0004632515 |             f\n",
+      "    71 |      133 |     15 |  0.0017384148 |             f\n",
+      "    72 |      134 |     16 |  0.0016389269 |             f\n",
+      "    73 |      135 |     17 |  0.0023003933 |             f\n",
+      "    74 |      136 |     18 |  0.0115658437 |             f\n",
+      "    75 |      137 |     18 |  0.000000E+00 |             f\n",
+      "    76 |      138 |     18 |  0.000000E+00 |             f\n",
+      "    77 |      139 |     18 |  0.000000E+00 |             f\n",
+      "    78 |      140 |     18 |  0.000000E+00 |             f\n",
+      "    79 |      141 |     18 |  0.000000E+00 |             f\n",
+      "    80 |      142 |     18 |  0.000000E+00 |             f\n",
+      "    81 |      143 |     18 |  0.000000E+00 |             f\n",
+      "    82 |      144 |     18 |  0.0003009130 |             f\n",
+      "    83 |      145 |     18 |  0.0003009130 |             f\n",
+      "    84 |      146 |     18 |  0.0005590483 |             f\n",
+      "    85 |      147 |     18 |  0.0005590483 |             f\n",
+      "    86 |      148 |     18 |  0.0005590483 |             f\n",
+      "    87 |      149 |     19 |  0.0005966152 |             f\n",
+      "    88 |      150 |     20 |  0.0015804596 |             f\n",
+      "    89 |      151 |     20 |  0.0015804596 |             f\n",
+      "    90 |      152 |     20 |  0.0015820819 |             f\n",
+      "    91 |      153 |     20 |  0.0015820819 |             f\n",
+      "    92 |      154 |     20 |  0.0015820819 |             f\n",
+      "    93 |      155 |     20 |  0.0016457247 |             f\n",
+      "    94 |      156 |     20 |  0.0016457247 |             f\n",
+      "    95 |      157 |     20 |  0.0025686749 |             f\n",
+      "    96 |      158 |     20 |  0.0001304665 |             f\n",
+      "    97 |      159 |     20 |  0.0001304665 |             f\n",
+      "    98 |      160 |     20 |  0.0068647866 |             f\n",
+      "    99 |      161 |     20 |  0.000000E+00 |             f\n",
+      "   100 |      162 |     20 |  0.000000E+00 |             f\n",
+      "   101 |      163 |     20 |  0.0003747808 |             f\n",
+      "   102 |      164 |     20 |  0.0003747808 |             f\n",
+      "   103 |      165 |     20 |  0.0003747808 |             f\n",
+      "   104 |      166 |     20 |  0.0003747808 |             f\n",
+      "   105 |      167 |     20 |  0.0003747808 |             f\n",
+      "   106 |      168 |     20 |  0.0003747808 |             f\n",
+      "   107 |      169 |     20 |  0.0156043785 |         nadir\n",
+      "   108 |      170 |     20 |  0.0005029517 |             f\n",
+      "   109 |      171 |     20 |  0.0005029517 |             f\n",
+      "   110 |      172 |     20 |  0.0005029517 |             f\n",
+      "   111 |      173 |     20 |  0.0005029517 |             f\n",
+      "   112 |      174 |     20 |  0.0005029517 |             f\n",
+      "   113 |      175 |     20 |  0.0005029517 |             f\n",
+      "   114 |      176 |     20 |  0.0011757560 |             f\n",
+      "   115 |      177 |     20 |  0.0011757560 |             f\n",
+      "   116 |      178 |     20 |  0.0011757560 |             f\n",
+      "   117 |      179 |     20 |  0.0011757560 |             f\n",
+      "   118 |      180 |     20 |  0.0178234601 |         nadir\n",
+      "   119 |      181 |     20 |  0.000000E+00 |             f\n",
+      "   120 |      182 |     20 |  0.000000E+00 |             f\n",
+      "   121 |      183 |     20 |  1.115393E-14 |             f\n",
+      "   122 |      184 |     20 |  0.0000184372 |             f\n",
+      "   123 |      185 |     20 |  0.0000363426 |             f\n",
+      "   124 |      186 |     19 |  0.0011106745 |             f\n",
+      "   125 |      187 |     19 |  0.0011106745 |             f\n",
+      "   126 |      188 |     20 |  0.0014426735 |             f\n",
+      "   127 |      189 |     20 |  0.0014426735 |             f\n",
+      "   128 |      190 |     20 |  0.0014426735 |             f\n",
+      "   129 |      191 |     19 |  0.0029033494 |             f\n",
+      "   130 |      192 |     19 |  0.000000E+00 |             f\n",
+      "   131 |      193 |     20 |  0.0002631430 |             f\n",
+      "   132 |      194 |     20 |  0.0002631430 |             f\n",
+      "   133 |      195 |     20 |  0.0008193602 |             f\n",
+      "   134 |      196 |     20 |  0.0014810875 |             f\n",
+      "   135 |      197 |     20 |  0.0014810875 |             f\n",
+      "   136 |      198 |     20 |  0.0014810875 |             f\n",
+      "   137 |      199 |     20 |  0.0014810875 |             f\n",
+      "   138 |      200 |     20 |  0.0014810875 |             f\n",
+      "   139 |      201 |     20 |  0.0014810875 |             f\n",
+      "   140 |      202 |     20 |  0.0014810875 |             f\n",
+      "   141 |      203 |     19 |  0.0038040143 |             f\n",
+      "   142 |      204 |     20 |  0.0048980119 |             f\n",
+      "   143 |      205 |     20 |  0.0004926295 |             f\n",
+      "   144 |      206 |     20 |  0.0004926295 |             f\n",
+      "   145 |      207 |     20 |  0.0004926295 |             f\n",
+      "   146 |      208 |     19 |  0.0065110636 |             f\n",
+      "   147 |      209 |     19 |  0.000000E+00 |             f\n",
+      "   148 |      210 |     19 |  0.000000E+00 |             f\n",
+      "   149 |      211 |     19 |  1.238402E-09 |             f\n",
+      "   150 |      212 |     19 |  1.238402E-09 |             f\n",
+      "   151 |      213 |     19 |  1.238428E-09 |             f\n",
+      "   152 |      214 |     19 |  1.238438E-09 |             f\n",
+      "   153 |      215 |     19 |  1.238438E-09 |             f\n",
+      "   154 |      216 |     19 |  0.0002060029 |             f\n",
+      "   155 |      217 |     19 |  0.0002060029 |             f\n",
+      "   156 |      218 |     19 |  0.0002060029 |             f\n",
+      "   157 |      219 |     19 |  0.0002060029 |             f\n",
+      "   158 |      220 |     19 |  0.0002060029 |             f\n",
+      "   159 |      221 |     19 |  0.0002060029 |             f\n",
+      "   160 |      222 |     19 |  0.0002060029 |             f\n",
+      "   161 |      223 |     19 |  0.0002060029 |             f\n",
+      "   162 |      224 |     19 |  0.0002060029 |             f\n",
+      "   163 |      225 |     19 |  0.0002060029 |             f\n",
+      "   164 |      226 |     19 |  0.0036528086 |             f\n",
+      "   165 |      227 |     19 |  0.5222015833 |         nadir\n",
+      "   166 |      228 |     19 |  0.0000824730 |             f\n",
+      "   167 |      229 |     19 |  0.0000824730 |             f\n",
+      "   168 |      230 |     18 |  0.0004171706 |             f\n",
+      "   169 |      231 |     18 |  0.0004171706 |             f\n",
+      "   170 |      232 |     18 |  0.0004171706 |             f\n",
+      "   171 |      233 |     19 |  0.0008079675 |             f\n",
+      "   172 |      234 |     19 |  0.0008079675 |             f\n",
+      "   173 |      235 |     20 |  0.0026231272 |             f\n",
+      "   174 |      236 |     20 |  4.930024E-09 |             f\n",
+      "   175 |      237 |     20 |  0.0007076269 |             f\n",
+      "   176 |      238 |     20 |  0.0007076269 |             f\n",
+      "   177 |      239 |     20 |  0.0007076269 |             f\n",
+      "   178 |      240 |     20 |  0.0007076269 |             f\n",
+      "   179 |      241 |     20 |  0.0007076269 |             f\n",
+      "   180 |      242 |     20 |  0.0007076269 |             f\n",
+      "   181 |      243 |     20 |  0.0007076269 |             f\n",
+      "   182 |      244 |     20 |  0.0007076269 |             f\n",
+      "   183 |      245 |     20 |  0.0007076269 |             f\n",
+      "   184 |      246 |     20 |  0.0007076269 |             f\n",
+      "   185 |      247 |     20 |  0.0007076269 |             f\n",
+      "   186 |      248 |     20 |  0.0007076269 |             f\n",
+      "   187 |      249 |     20 |  0.0007225890 |             f\n",
+      "   188 |      250 |     20 |  0.0007225890 |             f\n",
+      "   189 |      251 |     20 |  0.0007225890 |             f\n",
+      "   190 |      252 |     20 |  0.0007225890 |             f\n",
+      "   191 |      253 |     20 |  0.0007225890 |             f\n",
+      "   192 |      254 |     20 |  0.0007225890 |             f\n",
+      "   193 |      255 |     20 |  0.0009543747 |             f\n",
+      "   194 |      256 |     20 |  0.0009543747 |             f\n",
+      "   195 |      257 |     20 |  0.0009543747 |             f\n",
+      "   196 |      258 |     20 |  0.0009543747 |             f\n",
+      "   197 |      259 |     20 |  0.0009543747 |             f\n",
+      "   198 |      260 |     20 |  0.0009543747 |             f\n",
+      "   199 |      261 |     20 |  0.0013483583 |             f\n",
+      "   200 |      262 |     20 |  0.0013483583 |             f\n",
+      "   201 |      263 |     20 |  0.0013483583 |             f\n",
+      "   202 |      264 |     20 |  0.0013483583 |             f\n",
+      "   203 |      265 |     20 |  0.0013483583 |             f\n",
+      "   204 |      266 |     20 |  0.0013483583 |             f\n",
+      "   205 |      267 |     20 |  0.0012338873 |             f\n",
+      "   206 |      268 |     20 |  0.0012338873 |             f\n",
+      "   207 |      269 |     20 |  0.0012338873 |             f\n",
+      "   208 |      270 |     20 |  0.0012422793 |             f\n",
+      "   209 |      271 |     20 |  0.0014511508 |             f\n",
+      "   210 |      272 |     20 |  0.0014511508 |             f\n",
+      "   211 |      273 |     20 |  0.0014511508 |             f\n",
+      "   212 |      274 |     20 |  0.0014511508 |             f\n",
+      "   213 |      275 |     18 |  0.0057479846 |             f\n",
+      "   214 |      276 |     18 |  0.000000E+00 |             f\n",
+      "   215 |      277 |     19 |  0.0003206350 |             f\n",
+      "   216 |      278 |     20 |  0.0011693500 |             f\n",
+      "   217 |      279 |     19 |  0.0025038203 |             f\n",
+      "   218 |      280 |     20 |  0.0000470135 |             f\n",
+      "   219 |      281 |     20 |  0.0000470135 |             f\n",
+      "   220 |      282 |     20 |  0.0000470135 |             f\n",
+      "   221 |      283 |     20 |  0.0001714614 |             f\n",
+      "   222 |      284 |     20 |  0.0007836378 |             f\n",
+      "   223 |      285 |     20 |  0.0007836378 |             f\n",
+      "   224 |      286 |     20 |  0.0007836378 |             f\n",
+      "   225 |      287 |     20 |  0.0007836378 |             f\n",
+      "   226 |      288 |     20 |  0.0007836378 |             f\n",
+      "   227 |      289 |     20 |  0.0007836378 |             f\n",
+      "   228 |      290 |     20 |  0.0011993247 |             f\n",
+      "   229 |      291 |     20 |  0.0047360921 |             f\n",
+      "   230 |      292 |     20 |  0.000000E+00 |             f\n",
+      "   231 |      293 |     20 |  0.0006904547 |             f\n",
+      "   232 |      294 |     20 |  0.0006904547 |             f\n",
+      "   233 |      295 |     20 |  0.0000463114 |             f\n",
+      "   234 |      296 |     20 |  0.0000463114 |             f\n",
+      "   235 |      297 |     20 |  0.0000463114 |             f\n",
+      "   236 |      298 |     20 |  0.0000463114 |             f\n",
+      "   237 |      299 |     20 |  0.0000463114 |             f\n",
+      "   238 |      300 |     20 |  0.0000463114 |             f\n",
+      "   239 |      301 |     20 |  0.0000463114 |             f\n",
+      "   240 |      302 |     20 |  0.0000463114 |             f\n",
+      "   241 |      303 |     20 |  0.0007839029 |             f\n",
+      "   242 |      304 |     20 |  0.0038425096 |             f\n",
+      "   243 |      305 |     20 |  0.000000E+00 |             f\n",
+      "   244 |      306 |     20 |  0.000000E+00 |             f\n",
+      "   245 |      307 |     20 |  0.000000E+00 |             f\n",
+      "   246 |      308 |     20 |  0.0006651071 |             f\n",
+      "   247 |      309 |     20 |  0.0006651071 |             f\n",
+      "   248 |      310 |     20 |  0.0006651071 |             f\n",
+      "   249 |      311 |     20 |  0.0006651071 |             f\n",
+      "   250 |      312 |     20 |  0.0006651071 |             f\n",
+      "   251 |      313 |     20 |  0.0006651071 |             f\n",
+      "   252 |      314 |     20 |  0.0006651071 |             f\n",
+      "   253 |      315 |     20 |  0.0006651071 |             f\n",
+      "   254 |      316 |     20 |  0.0006651071 |             f\n",
+      "   255 |      317 |     19 |  0.0011471794 |             f\n",
+      "   256 |      318 |     19 |  0.0011471794 |             f\n",
+      "   257 |      319 |     20 |  0.0018422587 |             f\n",
+      "   258 |      320 |     20 |  0.0018422587 |             f\n",
+      "   259 |      321 |     20 |  0.0018422590 |             f\n",
+      "   260 |      322 |     20 |  0.0018210922 |             f\n",
+      "   261 |      323 |     20 |  0.0018210922 |             f\n",
+      "   262 |      324 |     20 |  0.0029343832 |             f\n",
+      "   263 |      325 |     20 |  0.0001152982 |             f\n",
+      "   264 |      326 |     20 |  0.0001152982 |             f\n",
+      "   265 |      327 |     20 |  0.0001152982 |             f\n",
+      "   266 |      328 |     20 |  0.0001152982 |             f\n",
+      "   267 |      329 |     20 |  0.0001152982 |             f\n",
+      "   268 |      330 |     20 |  0.0035990933 |             f\n",
+      "   269 |      331 |     20 |  0.000000E+00 |             f\n",
+      "   270 |      332 |     20 |  0.000000E+00 |             f\n",
+      "   271 |      333 |     20 |  0.000000E+00 |             f\n",
+      "   272 |      334 |     20 |  0.000000E+00 |             f\n",
+      "   273 |      335 |     20 |  7.365953E-07 |             f\n",
+      "   274 |      336 |     20 |  7.365953E-07 |             f\n",
+      "==========================================================\n",
+      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
+      "==========================================================\n",
+      "     1 |     1000 |     19 |             - |             -\n",
+      "     2 |     1250 |     19 |  0.0041352136 |             f\n",
+      "     3 |     1500 |     16 |  0.0223255737 |             f\n",
+      "     4 |     1750 |     17 |  0.0287933797 |         nadir\n",
+      "     5 |     2000 |     20 |  0.0165856259 |             f\n",
+      "     6 |     2250 |     25 |  0.0063104886 |         nadir\n",
+      "     7 |     2500 |     24 |  0.0074491377 |         nadir\n",
+      "     8 |     2750 |     26 |  0.0149516184 |         ideal\n",
+      "     9 |     3000 |     18 |  0.0124469381 |         ideal\n",
+      "    10 |     3250 |     17 |  0.0067063314 |             f\n",
+      "    11 |     3500 |     18 |  0.0692423314 |         ideal\n",
+      "    12 |     3750 |     17 |  0.0839586803 |         ideal\n",
+      "    13 |     4000 |     20 |  0.0099709922 |             f\n",
+      "    14 |     4250 |     19 |  0.0219617576 |             f\n",
+      "    15 |     4500 |     21 |  0.0239154011 |             f\n",
+      "    16 |     4750 |     23 |  0.0105449810 |             f\n",
+      "    17 |     5000 |     22 |  0.0473809454 |         ideal\n",
+      "    18 |     5250 |     23 |  0.0114368514 |             f\n",
+      "    19 |     5500 |     19 |  0.0099454745 |             f\n",
+      "    20 |     5750 |     18 |  0.0420727206 |         ideal\n",
+      "    21 |     6000 |     18 |  0.0360119855 |         ideal\n",
+      "    22 |     6250 |     19 |  0.0164316287 |             f\n",
+      "    23 |     6500 |     20 |  0.0343926490 |         nadir\n",
+      "    24 |     6750 |     23 |  0.0195862262 |         ideal\n",
+      "    25 |     7000 |     27 |  0.0083625829 |             f\n",
+      "    26 |     7250 |     25 |  0.0129049383 |         nadir\n",
+      "    27 |     7500 |     23 |  0.0370532354 |         ideal\n",
+      "    28 |     7750 |     27 |  0.0089035065 |             f\n",
+      "    29 |     8000 |     30 |  0.0105849625 |             f\n",
+      "    30 |     8250 |     32 |  0.0733469760 |         nadir\n",
+      "    31 |     8500 |     32 |  0.0057003049 |             f\n",
+      "    32 |     8750 |     42 |  0.0167036131 |         ideal\n",
+      "    33 |     9000 |     46 |  0.0043450951 |             f\n",
+      "    34 |     9250 |     42 |  0.0178072725 |         nadir\n",
+      "    35 |     9500 |     39 |  0.0058382427 |             f\n",
+      "    36 |     9750 |     43 |  0.0086868601 |         ideal\n",
+      "    37 |    10000 |     51 |  0.0512232611 |         nadir\n",
+      "    38 |    10250 |     45 |  0.0066376521 |             f\n",
+      "    39 |    10500 |     42 |  0.0253030431 |         ideal\n",
+      "    40 |    10750 |     44 |  0.0026182543 |             f\n",
+      "    41 |    11000 |     49 |  0.0048362569 |             f\n",
+      "    42 |    11250 |     58 |  0.0038354135 |         ideal\n",
+      "    43 |    11500 |     52 |  0.0119588033 |         ideal\n",
+      "    44 |    11750 |     57 |  0.0067245795 |         ideal\n",
+      "    45 |    12000 |     55 |  0.0150051932 |         ideal\n",
+      "    46 |    12250 |     61 |  0.0024210456 |             f\n",
+      "    47 |    12500 |     67 |  0.0389978329 |         nadir\n",
+      "    48 |    12750 |     64 |  0.0069774658 |         ideal\n",
+      "    49 |    13000 |     71 |  0.0018042845 |             f\n",
+      "    50 |    13250 |     69 |  0.0083418052 |         ideal\n",
+      "    51 |    13500 |     68 |  0.0027351775 |             f\n",
+      "    52 |    13750 |     71 |  0.0016169930 |             f\n",
+      "    53 |    14000 |     78 |  0.0035842088 |         ideal\n",
+      "    54 |    14250 |     75 |  0.0018388003 |             f\n",
+      "    55 |    14500 |     67 |  0.0377514900 |         nadir\n",
+      "    56 |    14750 |     71 |  0.0045124948 |         ideal\n",
+      "    57 |    15000 |     81 |  0.0017765544 |             f\n",
+      "    58 |    15250 |     91 |  0.0041919717 |         ideal\n",
+      "    59 |    15500 |     96 |  0.0951523561 |         nadir\n",
+      "    60 |    15750 |    105 |  0.0013454825 |             f\n",
+      "    61 |    16000 |    116 |  0.0024215761 |             f\n",
+      "    62 |    16250 |    109 |  0.0070390281 |         ideal\n",
+      "    63 |    16500 |     99 |  0.0010418338 |             f\n",
+      "    64 |    16750 |    107 |  0.0019569313 |             f\n",
+      "    65 |    17000 |    115 |  0.0030246072 |         ideal\n",
+      "    66 |    17250 |    125 |  0.0009349116 |             f\n",
+      "    67 |    17500 |    125 |  0.0026758897 |         ideal\n",
+      "    68 |    17750 |    123 |  0.0010643933 |             f\n",
+      "    69 |    18000 |    121 |  0.0689883328 |         nadir\n",
+      "    70 |    18250 |    130 |  0.0012334823 |             f\n",
+      "    71 |    18500 |    137 |  0.0019527149 |             f\n",
+      "    72 |    18750 |    147 |  0.0034879962 |         ideal\n",
+      "    73 |    19000 |    157 |  0.0009370825 |             f\n",
+      "    74 |    19250 |    166 |  0.0029382180 |         ideal\n",
+      "    75 |    19500 |    172 |  0.0008556892 |             f\n",
+      "    76 |    19750 |    176 |  0.0525581239 |         nadir\n",
+      "    77 |    20000 |    192 |  0.0030388301 |         ideal\n",
+      "    78 |    20250 |    202 |  0.0003924091 |             f\n",
+      "    79 |    20500 |    204 |  0.0625176599 |         nadir\n",
+      "    80 |    20750 |    201 |  0.0053754228 |         nadir\n",
+      "    81 |    21000 |    201 |  0.0503006470 |         nadir\n",
+      "    82 |    21250 |    203 |  0.0004393926 |             f\n",
+      "    83 |    21500 |    220 |  0.0007111054 |             f\n",
+      "    84 |    21750 |    221 |  0.0011494484 |             f\n",
+      "    85 |    22000 |    227 |  0.0013649800 |             f\n",
+      "    86 |    22250 |    228 |  0.0015976067 |             f\n",
+      "    87 |    22500 |    242 |  0.0018223856 |             f\n",
+      "    88 |    22750 |    241 |  0.0021587741 |             f\n",
+      "    89 |    23000 |    245 |  0.0023549659 |             f\n",
+      "    90 |    23250 |    251 |  0.0025773271 |             f\n",
+      "    91 |    23500 |    250 |  0.0419511074 |         nadir\n",
+      "    92 |    23750 |    263 |  0.0003311427 |             f\n",
+      "    93 |    24000 |    259 |  0.0366340583 |         nadir\n",
+      "    94 |    24250 |    278 |  0.0089508326 |         nadir\n",
+      "    95 |    24500 |    290 |  0.0002575338 |             f\n",
+      "    96 |    24750 |    308 |  0.0005088190 |             f\n",
+      "    97 |    25000 |    321 |  0.0007558057 |             f\n",
+      "    98 |    25250 |    323 |  0.0009420738 |             f\n",
+      "    99 |    25500 |    335 |  0.0010660367 |             f\n",
+      "   100 |    25750 |    347 |  0.0012159210 |             f\n",
+      "   101 |    26000 |    368 |  0.0013868389 |             f\n",
+      "   102 |    26250 |    376 |  0.0014606455 |             f\n",
+      "   103 |    26500 |    384 |  0.0015901559 |             f\n",
+      "   104 |    26750 |    389 |  0.0016792849 |             f\n",
+      "   105 |    27000 |    391 |  0.0017902517 |             f\n",
+      "   106 |    27250 |    381 |  0.0018474461 |             f\n",
+      "   107 |    27500 |    391 |  0.0046757130 |         ideal\n",
+      "   108 |    27750 |    402 |  0.0001260747 |             f\n",
+      "   109 |    28000 |    407 |  0.0002826961 |             f\n",
+      "   110 |    28250 |    428 |  0.0004512713 |             f\n",
+      "   111 |    28500 |    437 |  0.0005725991 |             f\n",
+      "   112 |    28750 |    445 |  0.0007166013 |             f\n",
+      "   113 |    29000 |    450 |  0.0008448099 |             f\n",
+      "   114 |    29250 |    453 |  0.0009572979 |             f\n",
+      "   115 |    29500 |    462 |  0.0010192222 |             f\n",
+      "   116 |    29750 |    471 |  0.0063732999 |         nadir\n",
+      "   117 |    30000 |    486 |  0.0001279401 |             f\n",
+      "   118 |    30250 |    491 |  0.0002302125 |             f\n",
+      "   119 |    30500 |    505 |  0.0003268923 |             f\n",
+      "   120 |    30750 |    521 |  0.0004137166 |             f\n",
+      "   121 |    31000 |    537 |  0.0005268768 |             f\n",
+      "   122 |    31250 |    544 |  0.0005938340 |             f\n",
+      "   123 |    31500 |    550 |  0.0026684205 |         nadir\n",
+      "   124 |    31750 |    564 |  0.0001090971 |             f\n",
+      "   125 |    32000 |    584 |  0.0002120947 |             f\n",
+      "   126 |    32250 |    592 |  0.0002754485 |             f\n",
+      "   127 |    32500 |    611 |  0.0003387953 |             f\n",
+      "   128 |    32750 |    629 |  0.0044951800 |         nadir\n",
+      "   129 |    33000 |    643 |  0.0000784873 |             f\n",
+      "   130 |    33250 |    653 |  0.0001357328 |             f\n",
+      "   131 |    33500 |    667 |  0.0002002754 |             f\n",
+      "   132 |    33750 |    686 |  0.0002648185 |             f\n",
+      "   133 |    34000 |    702 |  0.0003298521 |             f\n",
+      "   134 |    34250 |    708 |  0.0003619569 |             f\n",
+      "   135 |    34500 |    728 |  0.0004093617 |             f\n",
+      "   136 |    34750 |    745 |  0.0004463888 |             f\n",
+      "   137 |    35000 |    771 |  0.0004820678 |             f\n",
+      "   138 |    35250 |    786 |  0.0005071100 |             f\n",
+      "   139 |    35500 |    805 |  0.0005449066 |             f\n",
+      "   140 |    35750 |    822 |  0.0005780813 |             f\n",
+      "   141 |    36000 |    848 |  0.0006063001 |             f\n",
+      "   142 |    36250 |    861 |  0.0006263503 |             f\n",
+      "   143 |    36500 |    875 |  0.0006487565 |             f\n",
+      "   144 |    36750 |    893 |  0.0006703678 |             f\n",
+      "   145 |    37000 |    914 |  0.0006878346 |             f\n",
+      "   146 |    37250 |    924 |  0.0007048209 |             f\n",
+      "   147 |    37500 |    945 |  0.0033293957 |         nadir\n",
+      "   148 |    37750 |    961 |  0.0000403978 |             f\n",
+      "   149 |    38000 |    980 |  0.0000761369 |             f\n",
+      "   150 |    38250 |    997 |  0.0001085238 |             f\n",
+      "   151 |    38500 |   1000 |  0.0001428987 |             f\n",
+      "   152 |    38750 |   1000 |  0.0001684588 |             f\n",
+      "   153 |    39000 |   1000 |  0.0001933005 |             f\n",
+      "   154 |    39250 |   1000 |  0.0002257196 |             f\n",
+      "   155 |    39500 |   1000 |  0.0002567532 |             f\n",
+      "   156 |    39750 |   1000 |  0.0002902104 |             f\n",
+      "   157 |    40000 |   1000 |  0.0003144614 |             f\n",
+      "   158 |    40250 |   1000 |  0.0003342486 |             f\n",
+      "   159 |    40500 |   1000 |  0.0003499348 |             f\n",
+      "   160 |    40750 |   1000 |  0.0003633775 |             f\n",
+      "   161 |    41000 |   1000 |  0.0003864842 |             f\n",
+      "   162 |    41250 |   1000 |  0.0004001536 |             f\n",
+      "   163 |    41500 |   1000 |  0.0004167879 |             f\n",
+      "   164 |    41750 |   1000 |  0.0004303639 |             f\n",
+      "   165 |    42000 |   1000 |  0.0004434882 |             f\n",
+      "   166 |    42250 |   1000 |  0.0004556435 |             f\n",
+      "   167 |    42500 |   1000 |  0.0004766357 |             f\n",
+      "   168 |    42750 |   1000 |  0.0004836620 |             f\n",
+      "   169 |    43000 |   1000 |  0.0004992011 |             f\n",
+      "   170 |    43250 |   1000 |  0.0005059000 |             f\n",
+      "   171 |    43500 |   1000 |  0.0005157399 |             f\n",
+      "   172 |    43750 |   1000 |  0.0005316468 |             f\n",
+      "   173 |    44000 |   1000 |  0.0005448846 |             f\n",
+      "   174 |    44250 |   1000 |  0.0005494428 |             f\n",
+      "   175 |    44500 |   1000 |  0.0005576612 |             f\n",
+      "   176 |    44750 |   1000 |  0.0005695352 |             f\n",
+      "   177 |    45000 |   1000 |  0.0005793233 |             f\n",
+      "   178 |    45250 |   1000 |  0.0005909394 |             f\n",
+      "   179 |    45500 |   1000 |  0.0005943783 |             f\n",
+      "   180 |    45750 |   1000 |  0.0005992493 |             f\n",
+      "   181 |    46000 |   1000 |  0.0006046532 |             f\n",
+      "   182 |    46250 |   1000 |  0.0006081842 |             f\n",
+      "   183 |    46500 |   1000 |  0.0006152867 |             f\n",
+      "   184 |    46750 |   1000 |  0.0006192769 |             f\n",
+      "   185 |    47000 |   1000 |  0.0006235157 |             f\n",
+      "   186 |    47250 |   1000 |  0.0006264852 |             f\n",
+      "   187 |    47500 |   1000 |  0.0006379416 |             f\n",
+      "   188 |    47750 |   1000 |  0.0006406966 |             f\n",
+      "   189 |    48000 |   1000 |  0.0006434543 |             f\n",
+      "   190 |    48250 |   1000 |  0.0006490440 |             f\n",
+      "   191 |    48500 |   1000 |  0.0006537495 |             f\n",
+      "   192 |    48750 |   1000 |  0.0006568396 |             f\n",
+      "   193 |    49000 |   1000 |  0.0006580310 |             f\n",
+      "   194 |    49250 |   1000 |  0.0006634456 |             f\n",
+      "   195 |    49500 |   1000 |  0.0006627714 |             f\n",
+      "   196 |    49750 |   1000 |  0.0006682076 |             f\n",
+      "   197 |    50000 |   1000 |  0.0006762334 |             f\n",
+      "   198 |    50250 |   1000 |  0.0006813362 |             f\n",
+      "   199 |    50500 |   1000 |  0.0006863799 |             f\n",
+      "   200 |    50750 |   1000 |  0.0006919531 |             f\n",
+      "   201 |    51000 |   1000 |  0.0006948115 |             f\n",
+      "   202 |    51250 |   1000 |  0.0006935785 |             f\n",
+      "   203 |    51500 |   1000 |  0.0006936493 |             f\n",
+      "   204 |    51750 |   1000 |  0.0006975593 |             f\n",
+      "   205 |    52000 |   1000 |  0.0006975854 |             f\n",
+      "   206 |    52250 |   1000 |  0.0007011465 |             f\n",
+      "   207 |    52500 |   1000 |  0.0007009790 |             f\n",
+      "   208 |    52750 |   1000 |  0.0007051026 |             f\n",
+      "   209 |    53000 |   1000 |  0.0007079429 |             f\n",
+      "   210 |    53250 |   1000 |  0.0007116930 |             f\n",
+      "   211 |    53500 |   1000 |  0.0007149071 |             f\n",
+      "   212 |    53750 |   1000 |  0.0026998525 |         nadir\n",
+      "   213 |    54000 |   1000 |  0.0000191922 |             f\n",
+      "   214 |    54250 |   1000 |  0.0000317695 |             f\n",
+      "   215 |    54500 |   1000 |  0.0000488117 |             f\n",
+      "   216 |    54750 |   1000 |  0.0000691420 |             f\n",
+      "   217 |    55000 |   1000 |  0.0000862045 |             f\n",
+      "   218 |    55250 |   1000 |  0.0001001465 |             f\n",
+      "   219 |    55500 |   1000 |  0.0001139865 |             f\n",
+      "   220 |    55750 |   1000 |  0.0001252123 |             f\n",
+      "   221 |    56000 |   1000 |  0.0001358737 |             f\n",
+      "   222 |    56250 |   1000 |  0.0001484509 |             f\n",
+      "   223 |    56500 |   1000 |  0.0001565325 |             f\n",
+      "   224 |    56750 |   1000 |  0.0001680514 |             f\n",
+      "   225 |    57000 |   1000 |  0.0001794388 |             f\n",
+      "   226 |    57250 |   1000 |  0.0001877711 |             f\n",
+      "   227 |    57500 |   1000 |  0.0001939827 |             f\n",
+      "   228 |    57750 |   1000 |  0.0002062273 |             f\n",
+      "   229 |    58000 |   1000 |  0.0002103371 |             f\n",
+      "   230 |    58250 |   1000 |  0.0002213234 |             f\n",
+      "   231 |    58500 |   1000 |  0.0002226604 |             f\n",
+      "   232 |    58750 |   1000 |  0.0002285756 |             f\n",
+      "   233 |    59000 |   1000 |  0.0002314715 |             f\n",
+      "   234 |    59250 |   1000 |  0.0002364066 |             f\n",
+      "   235 |    59500 |   1000 |  0.0002426739 |             f\n",
+      "   236 |    59750 |   1000 |  0.0002474040 |             f\n",
+      "   237 |    60000 |   1000 |  0.0002521709 |             f\n",
+      "   238 |    60250 |   1000 |  0.0002563078 |             f\n",
+      "   239 |    60500 |   1000 |  0.0002585314 |             f\n",
+      "   240 |    60750 |   1000 |  0.0002631278 |             f\n",
+      "   241 |    61000 |   1000 |  0.0002675039 |             f\n",
+      "   242 |    61250 |   1000 |  0.0002737669 |             f\n",
+      "   243 |    61500 |   1000 |  0.0002751208 |             f\n",
+      "   244 |    61750 |   1000 |  0.0002770067 |             f\n",
+      "   245 |    62000 |   1000 |  0.0002792813 |             f\n",
+      "   246 |    62250 |   1000 |  0.0002820048 |             f\n",
+      "   247 |    62500 |   1000 |  0.0002856671 |             f\n",
+      "   248 |    62750 |   1000 |  0.0002876894 |             f\n",
+      "   249 |    63000 |   1000 |  0.0002897441 |             f\n",
+      "   250 |    63250 |   1000 |  0.0002948592 |             f\n",
+      "   251 |    63500 |   1000 |  0.0003001199 |             f\n",
+      "   252 |    63750 |   1000 |  0.0003019764 |             f\n",
+      "   253 |    64000 |   1000 |  0.0003054906 |             f\n",
+      "   254 |    64250 |   1000 |  0.0003038684 |             f\n",
+      "   255 |    64500 |   1000 |  0.0003098318 |             f\n",
+      "   256 |    64750 |   1000 |  0.0003134324 |             f\n",
+      "   257 |    65000 |   1000 |  0.0003156151 |             f\n",
+      "   258 |    65250 |   1000 |  0.0003178436 |             f\n",
+      "   259 |    65500 |   1000 |  0.0003174342 |             f\n",
+      "   260 |    65750 |   1000 |  0.0003209767 |             f\n",
+      "   261 |    66000 |   1000 |  0.0003231274 |             f\n",
+      "   262 |    66250 |   1000 |  0.0003255075 |             f\n",
+      "   263 |    66500 |   1000 |  0.0003270821 |             f\n",
+      "   264 |    66750 |   1000 |  0.0003269209 |             f\n",
+      "   265 |    67000 |   1000 |  0.0003260275 |             f\n",
+      "   266 |    67250 |   1000 |  0.0003272704 |             f\n",
+      "   267 |    67500 |   1000 |  0.0003292941 |             f\n",
+      "   268 |    67750 |   1000 |  0.0003298244 |             f\n",
+      "   269 |    68000 |   1000 |  0.0003310993 |             f\n",
+      "   270 |    68250 |   1000 |  0.0003301981 |             f\n",
+      "   271 |    68500 |   1000 |  0.0003323694 |             f\n",
+      "   272 |    68750 |   1000 |  0.0003349371 |             f\n",
+      "   273 |    69000 |   1000 |  0.0003349002 |             f\n",
+      "   274 |    69250 |   1000 |  0.0003363290 |             f\n",
+      "   275 |    69500 |   1000 |  0.0003351830 |             f\n",
+      "   276 |    69750 |   1000 |  0.0003344866 |             f\n",
+      "   277 |    70000 |   1000 |  0.0003372894 |             f\n",
+      "   278 |    70250 |   1000 |  0.0003377090 |             f\n",
+      "   279 |    70500 |   1000 |  0.0003412700 |             f\n",
+      "   280 |    70750 |   1000 |  0.0003441624 |             f\n",
+      "   281 |    71000 |   1000 |  0.0003453002 |             f\n",
+      "   282 |    71250 |   1000 |  0.0003439138 |             f\n",
+      "   283 |    71500 |   1000 |  0.0003448475 |             f\n",
+      "   284 |    71750 |   1000 |  0.0003461856 |             f\n",
+      "   285 |    72000 |   1000 |  0.0003478950 |             f\n",
+      "   286 |    72250 |   1000 |  0.0003461447 |             f\n",
+      "   287 |    72500 |   1000 |  0.0003459679 |             f\n",
+      "   288 |    72750 |   1000 |  0.0003466272 |             f\n",
+      "   289 |    73000 |   1000 |  0.0003453449 |             f\n",
+      "   290 |    73250 |   1000 |  0.0003452345 |             f\n",
+      "   291 |    73500 |   1000 |  0.0003468947 |             f\n",
+      "   292 |    73750 |   1000 |  0.0003459799 |             f\n",
+      "   293 |    74000 |   1000 |  0.0003476386 |             f\n",
+      "   294 |    74250 |   1000 |  0.0003496578 |             f\n",
+      "   295 |    74500 |   1000 |  0.0003497071 |             f\n",
+      "   296 |    74750 |   1000 |  0.0003526117 |             f\n",
+      "   297 |    75000 |   1000 |  0.0003528785 |             f\n",
+      "   298 |    75250 |   1000 |  0.0003529274 |             f\n",
+      "   299 |    75500 |   1000 |  0.0003559932 |             f\n",
+      "   300 |    75750 |   1000 |  0.0003564512 |             f\n",
+      "   301 |    76000 |   1000 |  0.0003582152 |             f\n",
+      "   302 |    76250 |   1000 |  0.0003582694 |             f\n",
+      "   303 |    76500 |   1000 |  0.0003577311 |             f\n",
+      "   304 |    76750 |   1000 |  0.0003570644 |             f\n",
+      "   305 |    77000 |   1000 |  0.0003592560 |             f\n",
+      "   306 |    77250 |   1000 |  0.0003603121 |             f\n",
+      "   307 |    77500 |   1000 |  0.0003593405 |             f\n",
+      "   308 |    77750 |   1000 |  0.0003583385 |             f\n",
+      "   309 |    78000 |   1000 |  0.0003592999 |             f\n",
+      "   310 |    78250 |   1000 |  0.0003604502 |             f\n",
+      "   311 |    78500 |   1000 |  0.0003589561 |             f\n",
+      "   312 |    78750 |   1000 |  0.0003590017 |             f\n",
+      "   313 |    79000 |   1000 |  0.0003588605 |             f\n",
+      "   314 |    79250 |   1000 |  0.0003580298 |             f\n",
+      "   315 |    79500 |   1000 |  0.0003563892 |             f\n",
+      "   316 |    79750 |   1000 |  0.0003569706 |             f\n",
+      "   317 |    80000 |   1000 |  0.0003575403 |             f\n",
+      "   318 |    80250 |   1000 |  0.0003579511 |             f\n",
+      "   319 |    80500 |   1000 |  0.0003577502 |             f\n",
+      "   320 |    80750 |   1000 |  0.0003613796 |             f\n",
+      "   321 |    81000 |   1000 |  0.0003606144 |             f\n",
+      "   322 |    81250 |   1000 |  0.0003627417 |             f\n",
+      "   323 |    81500 |   1000 |  0.0003650119 |             f\n",
+      "   324 |    81750 |   1000 |  0.0003644585 |             f\n",
+      "   325 |    82000 |   1000 |  0.0003642132 |             f\n",
+      "   326 |    82250 |   1000 |  0.0003651060 |             f\n",
+      "   327 |    82500 |   1000 |  0.0003631749 |             f\n",
+      "   328 |    82750 |   1000 |  0.0003640799 |             f\n",
+      "   329 |    83000 |   1000 |  0.0003654718 |             f\n",
+      "   330 |    83250 |   1000 |  0.0003659935 |             f\n",
+      "   331 |    83500 |   1000 |  0.0003654362 |             f\n",
+      "   332 |    83750 |   1000 |  0.0003649378 |             f\n",
+      "   333 |    84000 |   1000 |  0.0003650226 |             f\n",
+      "   334 |    84250 |   1000 |  0.0003651015 |             f\n",
+      "   335 |    84500 |   1000 |  0.0003646594 |             f\n",
+      "   336 |    84750 |   1000 |  0.0003645458 |             f\n",
+      "   337 |    85000 |   1000 |  0.0003647232 |             f\n",
+      "   338 |    85250 |   1000 |  0.0003653742 |             f\n",
+      "   339 |    85500 |   1000 |  0.0003647724 |             f\n",
+      "   340 |    85750 |   1000 |  0.0003646271 |             f\n",
+      "   341 |    86000 |   1000 |  0.0003694928 |             f\n",
+      "   342 |    86250 |   1000 |  0.0003688499 |             f\n",
+      "   343 |    86500 |   1000 |  0.0003687357 |             f\n",
+      "   344 |    86750 |   1000 |  0.0003669596 |             f\n",
+      "   345 |    87000 |   1000 |  0.0003676149 |             f\n",
+      "   346 |    87250 |   1000 |  0.0003703671 |             f\n",
+      "   347 |    87500 |   1000 |  0.0003689669 |             f\n",
+      "   348 |    87750 |   1000 |  0.0003685884 |             f\n",
+      "   349 |    88000 |   1000 |  0.0003685946 |             f\n",
+      "   350 |    88250 |   1000 |  0.0003671544 |             f\n",
+      "   351 |    88500 |   1000 |  0.0003661158 |             f\n",
+      "   352 |    88750 |   1000 |  0.0003671593 |             f\n",
+      "   353 |    89000 |   1000 |  0.0003694977 |             f\n",
+      "   354 |    89250 |   1000 |  0.0003713087 |             f\n",
+      "   355 |    89500 |   1000 |  0.0003714561 |             f\n",
+      "   356 |    89750 |   1000 |  0.0003717448 |             f\n",
+      "   357 |    90000 |   1000 |  0.0003690912 |             f\n",
+      "   358 |    90250 |   1000 |  0.0003678464 |             f\n",
+      "   359 |    90500 |   1000 |  0.0003665872 |             f\n",
+      "   360 |    90750 |   1000 |  0.0003673198 |             f\n",
+      "   361 |    91000 |   1000 |  0.0003683005 |             f\n",
+      "   362 |    91250 |   1000 |  0.0003702847 |             f\n",
+      "   363 |    91500 |   1000 |  0.0003692584 |             f\n",
+      "   364 |    91750 |   1000 |  0.0003702100 |             f\n",
+      "   365 |    92000 |   1000 |  0.0003709686 |             f\n",
+      "   366 |    92250 |   1000 |  0.0003703074 |             f\n",
+      "   367 |    92500 |   1000 |  0.0003726784 |             f\n",
+      "   368 |    92750 |   1000 |  0.0003721122 |             f\n",
+      "   369 |    93000 |   1000 |  0.0003727997 |             f\n",
+      "   370 |    93250 |   1000 |  0.0003731614 |             f\n",
+      "   371 |    93500 |   1000 |  0.0003717236 |             f\n",
+      "   372 |    93750 |   1000 |  0.0003710262 |             f\n",
+      "   373 |    94000 |   1000 |  0.0003701916 |             f\n",
+      "   374 |    94250 |   1000 |  0.0003712467 |             f\n",
+      "   375 |    94500 |   1000 |  0.0003708408 |             f\n",
+      "   376 |    94750 |   1000 |  0.0003705364 |             f\n",
+      "   377 |    95000 |   1000 |  0.0003727733 |             f\n",
+      "   378 |    95250 |   1000 |  0.0003703055 |             f\n",
+      "   379 |    95500 |   1000 |  0.0003713116 |             f\n",
+      "   380 |    95750 |   1000 |  0.0003718142 |             f\n",
+      "   381 |    96000 |   1000 |  0.0003712918 |             f\n",
+      "   382 |    96250 |   1000 |  0.0003740238 |             f\n",
+      "   383 |    96500 |   1000 |  0.0003735747 |             f\n",
+      "   384 |    96750 |   1000 |  0.0003726515 |             f\n",
+      "   385 |    97000 |   1000 |  0.0003751985 |             f\n",
+      "   386 |    97250 |   1000 |  0.0003736109 |             f\n",
+      "   387 |    97500 |   1000 |  0.0003758754 |             f\n",
+      "   388 |    97750 |   1000 |  0.0003766555 |             f\n",
+      "   389 |    98000 |   1000 |  0.0003754864 |             f\n",
+      "   390 |    98250 |   1000 |  0.0003763178 |             f\n",
+      "   391 |    98500 |   1000 |  0.0003780698 |             f\n",
+      "   392 |    98750 |   1000 |  0.0003791107 |             f\n",
+      "   393 |    99000 |   1000 |  0.0003781959 |             f\n",
+      "   394 |    99250 |   1000 |  0.0003793862 |             f\n",
+      "   395 |    99500 |   1000 |  0.0003802321 |             f\n",
+      "   396 |    99750 |   1000 |  0.0003780833 |             f\n",
+      "   397 |   100000 |   1000 |  0.0003749138 |             f\n",
+      "   398 |   100250 |   1000 |  0.0003733391 |             f\n",
+      "   399 |   100500 |   1000 |  0.0003729156 |             f\n",
+      "   400 |   100750 |   1000 |  0.0003738253 |             f\n",
+      "   401 |   101000 |   1000 |  0.0003725618 |             f\n",
+      "   402 |   101250 |   1000 |  0.0003713330 |             f\n",
+      "   403 |   101500 |   1000 |  0.0003710275 |             f\n",
+      "   404 |   101750 |   1000 |  0.0003733831 |             f\n",
+      "   405 |   102000 |   1000 |  0.0003719823 |             f\n",
+      "   406 |   102250 |   1000 |  0.0003731886 |             f\n",
+      "   407 |   102500 |   1000 |  0.0003721752 |             f\n",
+      "   408 |   102750 |   1000 |  0.0003738218 |             f\n",
+      "   409 |   103000 |   1000 |  0.0003745000 |             f\n",
+      "   410 |   103250 |   1000 |  0.0003722830 |             f\n",
+      "   411 |   103500 |   1000 |  0.0003719117 |             f\n",
+      "   412 |   103750 |   1000 |  0.0003713229 |             f\n",
+      "   413 |   104000 |   1000 |  0.0003720141 |             f\n",
+      "   414 |   104250 |   1000 |  0.0003730101 |             f\n",
+      "   415 |   104500 |   1000 |  0.0003730001 |             f\n",
+      "   416 |   104750 |   1000 |  0.0003736865 |             f\n",
+      "   417 |   105000 |   1000 |  0.0003733737 |             f\n",
+      "   418 |   105250 |   1000 |  0.0003737236 |             f\n",
+      "   419 |   105500 |   1000 |  0.0003750129 |             f\n",
+      "   420 |   105750 |   1000 |  0.0003774644 |             f\n",
+      "   421 |   106000 |   1000 |  0.0003787598 |             f\n",
+      "   422 |   106250 |   1000 |  0.0003787468 |             f\n",
+      "   423 |   106500 |   1000 |  0.0003783749 |             f\n",
+      "   424 |   106750 |   1000 |  0.0003773627 |             f\n",
+      "   425 |   107000 |   1000 |  0.0003786734 |             f\n",
+      "   426 |   107250 |   1000 |  0.0003785655 |             f\n",
+      "   427 |   107500 |   1000 |  0.0003808684 |             f\n",
+      "   428 |   107750 |   1000 |  0.0003810972 |             f\n",
+      "   429 |   108000 |   1000 |  0.0003818056 |             f\n",
+      "   430 |   108250 |   1000 |  0.0003821758 |             f\n",
+      "   431 |   108500 |   1000 |  0.0003821464 |             f\n",
+      "   432 |   108750 |   1000 |  0.0003798571 |             f\n",
+      "   433 |   109000 |   1000 |  0.0003810726 |             f\n",
+      "   434 |   109250 |   1000 |  0.0003816290 |             f\n",
+      "   435 |   109500 |   1000 |  0.0003817834 |             f\n",
+      "   436 |   109750 |   1000 |  0.0003823369 |             f\n",
+      "   437 |   110000 |   1000 |  0.0003820677 |             f\n",
+      "   438 |   110250 |   1000 |  0.0003815950 |             f\n",
+      "   439 |   110500 |   1000 |  0.0109629191 |         nadir\n",
+      "   440 |   110750 |   1000 |  8.939953E-06 |             f\n",
+      "   441 |   111000 |   1000 |  0.0000180086 |             f\n",
+      "   442 |   111250 |   1000 |  0.0000298408 |             f\n",
+      "   443 |   111500 |   1000 |  0.0000431991 |             f\n",
+      "   444 |   111750 |   1000 |  0.0000511054 |             f\n",
+      "   445 |   112000 |   1000 |  0.0000605212 |             f\n",
+      "   446 |   112250 |   1000 |  0.0000669326 |             f\n",
+      "   447 |   112500 |   1000 |  0.0115314245 |         nadir\n",
+      "   448 |   112750 |   1000 |  0.0000106336 |             f\n",
+      "   449 |   113000 |   1000 |  0.0000249323 |             f\n",
+      "   450 |   113250 |   1000 |  0.0000366775 |             f\n",
+      "   451 |   113500 |   1000 |  0.0000476299 |             f\n",
+      "   452 |   113750 |   1000 |  0.0000564474 |             f\n",
+      "   453 |   114000 |   1000 |  0.0000626312 |             f\n",
+      "   454 |   114250 |   1000 |  0.0000702846 |             f\n",
+      "   455 |   114500 |   1000 |  0.0000789848 |             f\n",
+      "   456 |   114750 |   1000 |  0.0000843717 |             f\n",
+      "   457 |   115000 |   1000 |  0.0000935906 |             f\n",
+      "   458 |   115250 |   1000 |  0.0001029036 |             f\n",
+      "   459 |   115500 |   1000 |  0.0001071097 |             f\n",
+      "   460 |   115750 |   1000 |  0.0001124869 |             f\n",
+      "   461 |   116000 |   1000 |  0.0001187114 |             f\n",
+      "   462 |   116250 |   1000 |  0.0001238084 |             f\n",
+      "   463 |   116500 |   1000 |  0.0001280841 |             f\n",
+      "   464 |   116750 |   1000 |  0.0001334089 |             f\n",
+      "   465 |   117000 |   1000 |  0.0001372897 |             f\n",
+      "   466 |   117250 |   1000 |  0.0001435754 |             f\n",
+      "   467 |   117500 |   1000 |  0.0001454052 |             f\n",
+      "   468 |   117750 |   1000 |  0.0001507737 |             f\n",
+      "   469 |   118000 |   1000 |  0.0001558748 |             f\n",
+      "   470 |   118250 |   1000 |  0.0001576854 |             f\n",
+      "   471 |   118500 |   1000 |  0.0001624846 |             f\n",
+      "   472 |   118750 |   1000 |  0.0001657791 |             f\n",
+      "   473 |   119000 |   1000 |  0.0001675401 |             f\n",
+      "   474 |   119250 |   1000 |  0.0001699470 |             f\n",
+      "   475 |   119500 |   1000 |  0.0001719355 |             f\n",
+      "   476 |   119750 |   1000 |  0.0001769162 |             f\n",
+      "   477 |   120000 |   1000 |  0.0001835723 |             f\n",
+      "   478 |   120250 |   1000 |  0.0001909860 |             f\n",
+      "   479 |   120500 |   1000 |  0.0001938355 |             f\n",
+      "   480 |   120750 |   1000 |  0.0001978146 |             f\n",
+      "   481 |   121000 |   1000 |  0.0002003106 |             f\n",
+      "   482 |   121250 |   1000 |  0.0002065064 |             f\n",
+      "   483 |   121500 |   1000 |  0.0002089805 |             f\n",
+      "   484 |   121750 |   1000 |  0.0002092467 |             f\n",
+      "   485 |   122000 |   1000 |  0.0002102294 |             f\n",
+      "   486 |   122250 |   1000 |  0.0002140886 |             f\n",
+      "   487 |   122500 |   1000 |  0.0002140424 |             f\n",
+      "   488 |   122750 |   1000 |  0.0002162492 |             f\n",
+      "   489 |   123000 |   1000 |  0.0002200504 |             f\n",
+      "   490 |   123250 |   1000 |  0.0002224481 |             f\n",
+      "   491 |   123500 |   1000 |  0.0002234314 |             f\n",
+      "   492 |   123750 |   1000 |  0.0002243900 |             f\n",
+      "   493 |   124000 |   1000 |  0.0002250778 |             f\n",
+      "   494 |   124250 |   1000 |  0.0002281201 |             f\n",
+      "   495 |   124500 |   1000 |  0.0002275870 |             f\n",
+      "   496 |   124750 |   1000 |  0.0002299989 |             f\n",
+      "   497 |   125000 |   1000 |  0.0002330028 |             f\n",
+      "   498 |   125250 |   1000 |  0.0002364704 |             f\n",
+      "   499 |   125500 |   1000 |  0.0002389281 |             f\n",
+      "   500 |   125750 |   1000 |  0.0002406591 |             f\n",
+      "   501 |   126000 |   1000 |  0.0002415980 |             f\n",
+      "   502 |   126250 |   1000 |  0.0002409376 |             f\n",
+      "   503 |   126500 |   1000 |  0.0002425629 |             f\n",
+      "   504 |   126750 |   1000 |  0.0002450598 |             f\n",
+      "   505 |   127000 |   1000 |  0.0002464319 |             f\n",
+      "   506 |   127250 |   1000 |  0.0002460056 |             f\n",
+      "   507 |   127500 |   1000 |  0.0002480742 |             f\n",
+      "   508 |   127750 |   1000 |  0.0002474496 |             f\n",
+      "   509 |   128000 |   1000 |  0.0002507397 |             f\n",
+      "   510 |   128250 |   1000 |  0.0002509316 |             f\n",
+      "   511 |   128500 |   1000 |  0.0002516450 |             f\n",
+      "   512 |   128750 |   1000 |  0.0002500231 |             f\n",
+      "   513 |   129000 |   1000 |  0.0002495690 |             f\n",
+      "   514 |   129250 |   1000 |  0.0002497335 |             f\n",
+      "   515 |   129500 |   1000 |  0.0002494375 |             f\n",
+      "   516 |   129750 |   1000 |  0.0002527760 |             f\n",
+      "   517 |   130000 |   1000 |  0.0002538673 |             f\n",
+      "   518 |   130250 |   1000 |  0.0002555950 |             f\n",
+      "   519 |   130500 |   1000 |  0.0002582215 |             f\n",
+      "   520 |   130750 |   1000 |  0.0002605504 |             f\n",
+      "   521 |   131000 |   1000 |  0.0002590066 |             f\n",
+      "   522 |   131250 |   1000 |  0.0002596138 |             f\n",
+      "   523 |   131500 |   1000 |  0.0002634593 |             f\n",
+      "   524 |   131750 |   1000 |  0.0002675374 |             f\n",
+      "   525 |   132000 |   1000 |  0.0002660258 |             f\n",
+      "   526 |   132250 |   1000 |  0.0002690954 |             f\n",
+      "   527 |   132500 |   1000 |  0.0002680898 |             f\n",
+      "   528 |   132750 |   1000 |  0.0002694892 |             f\n",
+      "   529 |   133000 |   1000 |  0.0002716570 |             f\n",
+      "   530 |   133250 |   1000 |  0.0002714885 |             f\n",
+      "   531 |   133500 |   1000 |  0.0002719518 |             f\n",
+      "   532 |   133750 |   1000 |  0.0002713560 |             f\n",
+      "   533 |   134000 |   1000 |  0.0002711034 |             f\n",
+      "   534 |   134250 |   1000 |  0.0002723513 |             f\n",
+      "   535 |   134500 |   1000 |  0.0002749594 |             f\n",
+      "   536 |   134750 |   1000 |  0.0002760572 |             f\n",
+      "   537 |   135000 |   1000 |  0.0002800506 |             f\n",
+      "   538 |   135250 |   1000 |  0.0002791157 |             f\n",
+      "   539 |   135500 |   1000 |  0.0002793756 |             f\n",
+      "   540 |   135750 |   1000 |  0.0002792084 |             f\n",
+      "   541 |   136000 |   1000 |  0.0002779750 |             f\n",
+      "   542 |   136250 |   1000 |  0.0002778976 |             f\n",
+      "   543 |   136500 |   1000 |  0.0002798535 |             f\n",
+      "   544 |   136750 |   1000 |  0.0002804506 |             f\n",
+      "   545 |   137000 |   1000 |  0.0002806774 |             f\n",
+      "   546 |   137250 |   1000 |  0.0002798258 |             f\n",
+      "   547 |   137500 |   1000 |  0.0002796730 |             f\n",
+      "   548 |   137750 |   1000 |  0.0002816343 |             f\n",
+      "   549 |   138000 |   1000 |  0.0002852889 |             f\n",
+      "   550 |   138250 |   1000 |  0.0002839073 |             f\n",
+      "   551 |   138500 |   1000 |  0.0002851611 |             f\n",
+      "   552 |   138750 |   1000 |  0.0002862550 |             f\n",
+      "   553 |   139000 |   1000 |  0.0002879030 |             f\n",
+      "   554 |   139250 |   1000 |  0.0002837211 |             f\n",
+      "   555 |   139500 |   1000 |  0.0002818515 |             f\n",
+      "   556 |   139750 |   1000 |  0.0002837136 |             f\n",
+      "   557 |   140000 |   1000 |  0.0002841911 |             f\n",
+      "   558 |   140250 |   1000 |  0.0002844267 |             f\n",
+      "   559 |   140500 |   1000 |  0.0002831739 |             f\n",
+      "   560 |   140750 |   1000 |  0.0002829683 |             f\n",
+      "   561 |   141000 |   1000 |  0.0002873160 |             f\n",
+      "   562 |   141250 |   1000 |  0.0002884959 |             f\n",
+      "   563 |   141500 |   1000 |  0.0002885341 |             f\n",
+      "   564 |   141750 |   1000 |  0.0002894499 |             f\n",
+      "   565 |   142000 |   1000 |  0.0002894441 |             f\n",
+      "   566 |   142250 |   1000 |  0.0002906659 |             f\n",
+      "   567 |   142500 |   1000 |  0.0002891934 |             f\n",
+      "   568 |   142750 |   1000 |  0.0002876433 |             f\n",
+      "   569 |   143000 |   1000 |  0.0002862835 |             f\n",
+      "   570 |   143250 |   1000 |  0.0002891160 |             f\n",
+      "   571 |   143500 |   1000 |  0.0002880772 |             f\n",
+      "   572 |   143750 |   1000 |  0.0002889127 |             f\n",
+      "   573 |   144000 |   1000 |  0.0002869644 |             f\n",
+      "   574 |   144250 |   1000 |  0.0002856928 |             f\n",
+      "   575 |   144500 |   1000 |  0.0002848761 |             f\n",
+      "   576 |   144750 |   1000 |  0.0002852995 |             f\n",
+      "   577 |   145000 |   1000 |  0.0002869201 |             f\n",
+      "   578 |   145250 |   1000 |  0.0002898973 |             f\n",
+      "   579 |   145500 |   1000 |  0.0002913346 |             f\n",
+      "   580 |   145750 |   1000 |  0.0002934408 |             f\n",
+      "   581 |   146000 |   1000 |  0.0002937141 |             f\n",
+      "   582 |   146250 |   1000 |  0.0002924622 |             f\n",
+      "   583 |   146500 |   1000 |  0.0002946980 |             f\n",
+      "   584 |   146750 |   1000 |  0.0002957817 |             f\n",
+      "   585 |   147000 |   1000 |  0.0002942436 |             f\n",
+      "   586 |   147250 |   1000 |  0.0002933719 |             f\n",
+      "   587 |   147500 |   1000 |  0.0002935758 |             f\n",
+      "   588 |   147750 |   1000 |  0.0002945065 |             f\n",
+      "   589 |   148000 |   1000 |  0.0002946585 |             f\n",
+      "   590 |   148250 |   1000 |  0.0002961489 |             f\n",
+      "   591 |   148500 |   1000 |  0.0002992556 |             f\n",
+      "   592 |   148750 |   1000 |  0.0003007101 |             f\n",
+      "   593 |   149000 |   1000 |  0.0002995740 |             f\n",
+      "   594 |   149250 |   1000 |  0.0002991335 |             f\n",
+      "   595 |   149500 |   1000 |  0.0002960660 |             f\n",
+      "   596 |   149750 |   1000 |  0.0002972286 |             f\n",
+      "   597 |   150000 |   1000 |  0.0002968808 |             f\n",
+      "   598 |   150250 |   1000 |  0.0002954499 |             f\n",
+      "   599 |   150500 |   1000 |  0.0002983534 |             f\n",
+      "   600 |   150750 |   1000 |  0.0002990451 |             f\n",
+      "   601 |   151000 |   1000 |  0.0002994701 |             f\n",
+      "   602 |   151250 |   1000 |  0.0002997212 |             f\n",
+      "   603 |   151500 |   1000 |  0.0003020300 |             f\n",
+      "   604 |   151750 |   1000 |  0.0003036915 |             f\n",
+      "   605 |   152000 |   1000 |  0.0003013467 |             f\n",
+      "   606 |   152250 |   1000 |  0.0003034705 |             f\n",
+      "   607 |   152500 |   1000 |  0.0003044652 |             f\n",
+      "   608 |   152750 |   1000 |  0.0003022780 |             f\n",
+      "   609 |   153000 |   1000 |  0.0003038996 |             f\n",
+      "   610 |   153250 |   1000 |  0.0003089886 |             f\n",
+      "   611 |   153500 |   1000 |  0.0003077325 |             f\n",
+      "   612 |   153750 |   1000 |  0.0003070747 |             f\n",
+      "   613 |   154000 |   1000 |  0.0003046803 |             f\n",
+      "   614 |   154250 |   1000 |  0.0003051488 |             f\n",
+      "   615 |   154500 |   1000 |  0.0003045486 |             f\n",
+      "   616 |   154750 |   1000 |  0.0003054495 |             f\n",
+      "   617 |   155000 |   1000 |  0.0003069516 |             f\n",
+      "   618 |   155250 |   1000 |  0.0003049549 |             f\n",
+      "   619 |   155500 |   1000 |  0.0003050028 |             f\n",
+      "   620 |   155750 |   1000 |  0.0003066808 |             f\n",
+      "   621 |   156000 |   1000 |  0.0003072951 |             f\n",
+      "   622 |   156250 |   1000 |  0.0003072197 |             f\n",
+      "   623 |   156500 |   1000 |  0.0003069960 |             f\n",
+      "   624 |   156750 |   1000 |  0.0003105032 |             f\n",
+      "   625 |   157000 |   1000 |  0.0003107243 |             f\n",
+      "   626 |   157250 |   1000 |  0.0003114987 |             f\n",
+      "   627 |   157500 |   1000 |  0.0003092215 |             f\n",
+      "   628 |   157750 |   1000 |  0.0003098257 |             f\n",
+      "   629 |   158000 |   1000 |  0.0003109567 |             f\n",
+      "   630 |   158250 |   1000 |  0.0003086993 |             f\n",
+      "   631 |   158500 |   1000 |  0.0003074353 |             f\n",
+      "   632 |   158750 |   1000 |  0.0003064372 |             f\n",
+      "   633 |   159000 |   1000 |  0.0003049703 |             f\n",
+      "   634 |   159250 |   1000 |  0.0003054093 |             f\n",
+      "   635 |   159500 |   1000 |  0.0003049830 |             f\n",
+      "   636 |   159750 |   1000 |  0.0003026131 |             f\n",
+      "   637 |   160000 |   1000 |  0.0003040338 |             f\n",
+      "   638 |   160250 |   1000 |  0.0003019112 |             f\n",
+      "   639 |   160500 |   1000 |  0.0003037559 |             f\n",
+      "   640 |   160750 |   1000 |  0.0003046946 |             f\n",
+      "   641 |   161000 |   1000 |  0.0003051675 |             f\n",
+      "   642 |   161250 |   1000 |  0.0003060524 |             f\n",
+      "   643 |   161500 |   1000 |  0.0003049636 |             f\n",
+      "   644 |   161750 |   1000 |  0.0003055752 |             f\n",
+      "   645 |   162000 |   1000 |  0.0003070972 |             f\n",
+      "   646 |   162250 |   1000 |  0.0003071669 |             f\n",
+      "   647 |   162500 |   1000 |  0.0003085834 |             f\n",
+      "   648 |   162750 |   1000 |  0.0003084057 |             f\n",
+      "   649 |   163000 |   1000 |  0.0003046499 |             f\n",
+      "   650 |   163250 |   1000 |  0.0003050574 |             f\n",
+      "   651 |   163500 |   1000 |  0.0003044973 |             f\n",
+      "   652 |   163750 |   1000 |  0.0003050844 |             f\n",
+      "   653 |   164000 |   1000 |  0.0003063666 |             f\n",
+      "   654 |   164250 |   1000 |  0.0003074917 |             f\n",
+      "   655 |   164500 |   1000 |  0.0003075874 |             f\n",
+      "   656 |   164750 |   1000 |  0.0003073637 |             f\n",
+      "   657 |   165000 |   1000 |  0.0003075094 |             f\n",
+      "   658 |   165250 |   1000 |  0.0003087421 |             f\n",
+      "   659 |   165500 |   1000 |  0.0003086990 |             f\n",
+      "   660 |   165750 |   1000 |  0.0003099264 |             f\n",
+      "   661 |   166000 |   1000 |  0.0003068350 |             f\n",
+      "   662 |   166250 |   1000 |  0.0003053024 |             f\n",
+      "   663 |   166500 |   1000 |  0.0003046318 |             f\n",
+      "   664 |   166750 |   1000 |  0.0003048293 |             f\n",
+      "   665 |   167000 |   1000 |  0.0003059077 |             f\n",
+      "   666 |   167250 |   1000 |  0.0003061350 |             f\n",
+      "   667 |   167500 |   1000 |  0.0003061806 |             f\n",
+      "   668 |   167750 |   1000 |  0.0003079914 |             f\n",
+      "   669 |   168000 |   1000 |  0.0003090338 |             f\n",
+      "   670 |   168250 |   1000 |  0.0003122281 |             f\n",
+      "   671 |   168500 |   1000 |  0.0003120440 |             f\n",
+      "   672 |   168750 |   1000 |  0.0003121398 |             f\n",
+      "   673 |   169000 |   1000 |  0.0003098933 |             f\n",
+      "   674 |   169250 |   1000 |  0.0003101027 |             f\n",
+      "   675 |   169500 |   1000 |  0.0003103184 |             f\n",
+      "   676 |   169750 |   1000 |  0.0003127071 |             f\n",
+      "   677 |   170000 |   1000 |  0.0003100115 |             f\n",
+      "   678 |   170250 |   1000 |  0.0003131543 |             f\n",
+      "   679 |   170500 |   1000 |  0.0003129843 |             f\n",
+      "   680 |   170750 |   1000 |  0.0003128080 |             f\n",
+      "   681 |   171000 |   1000 |  0.0003118902 |             f\n",
+      "   682 |   171250 |   1000 |  0.0003139357 |             f\n",
+      "   683 |   171500 |   1000 |  0.0003135089 |             f\n",
+      "   684 |   171750 |   1000 |  0.0003159947 |             f\n",
+      "   685 |   172000 |   1000 |  0.0003148146 |             f\n",
+      "   686 |   172250 |   1000 |  0.0003142883 |             f\n",
+      "   687 |   172500 |   1000 |  0.0003138000 |             f\n",
+      "   688 |   172750 |   1000 |  0.0003150949 |             f\n",
+      "   689 |   173000 |   1000 |  0.0003150995 |             f\n",
+      "   690 |   173250 |   1000 |  0.0003142147 |             f\n",
+      "   691 |   173500 |   1000 |  0.0003143809 |             f\n",
+      "   692 |   173750 |   1000 |  0.0003132983 |             f\n",
+      "   693 |   174000 |   1000 |  0.0003152530 |             f\n",
+      "   694 |   174250 |   1000 |  0.0003130235 |             f\n",
+      "   695 |   174500 |   1000 |  0.0003122879 |             f\n",
+      "   696 |   174750 |   1000 |  0.0003133832 |             f\n",
+      "   697 |   175000 |   1000 |  0.0003141272 |             f\n",
+      "   698 |   175250 |   1000 |  0.0003129125 |             f\n",
+      "   699 |   175500 |   1000 |  0.0003128477 |             f\n",
+      "   700 |   175750 |   1000 |  0.0003098014 |             f\n",
+      "   701 |   176000 |   1000 |  0.0003096196 |             f\n",
+      "   702 |   176250 |   1000 |  0.0003102710 |             f\n",
+      "   703 |   176500 |   1000 |  0.0003099285 |             f\n",
+      "   704 |   176750 |   1000 |  0.0003111081 |             f\n",
+      "   705 |   177000 |   1000 |  0.0003107276 |             f\n",
+      "   706 |   177250 |   1000 |  0.0003114025 |             f\n",
+      "   707 |   177500 |   1000 |  0.0003096673 |             f\n",
+      "   708 |   177750 |   1000 |  0.0003101203 |             f\n",
+      "   709 |   178000 |   1000 |  0.0003097343 |             f\n",
+      "   710 |   178250 |   1000 |  0.0003112047 |             f\n",
+      "   711 |   178500 |   1000 |  0.0003115460 |             f\n",
+      "   712 |   178750 |   1000 |  0.0003101388 |             f\n",
+      "   713 |   179000 |   1000 |  0.0003068920 |             f\n",
+      "   714 |   179250 |   1000 |  0.0003075168 |             f\n",
+      "   715 |   179500 |   1000 |  0.0003084323 |             f\n",
+      "   716 |   179750 |   1000 |  0.0003076112 |             f\n",
+      "   717 |   180000 |   1000 |  0.0003067689 |             f\n",
+      "   718 |   180250 |   1000 |  0.0003044790 |             f\n",
+      "   719 |   180500 |   1000 |  0.0003028415 |             f\n",
+      "   720 |   180750 |   1000 |  0.0003046637 |             f\n",
+      "   721 |   181000 |   1000 |  0.0003082877 |             f\n",
+      "   722 |   181250 |   1000 |  0.0003092385 |             f\n",
+      "   723 |   181500 |   1000 |  0.0003089676 |             f\n",
+      "   724 |   181750 |   1000 |  0.0003102697 |             f\n",
+      "   725 |   182000 |   1000 |  0.0003105193 |             f\n",
+      "   726 |   182250 |   1000 |  0.0003103762 |             f\n",
+      "   727 |   182500 |   1000 |  0.0003090631 |             f\n",
+      "   728 |   182750 |   1000 |  0.0003097899 |             f\n",
+      "   729 |   183000 |   1000 |  0.0003097767 |             f\n",
+      "   730 |   183250 |   1000 |  0.0003097820 |             f\n",
+      "   731 |   183500 |   1000 |  0.0003114209 |             f\n",
+      "   732 |   183750 |   1000 |  0.0003096552 |             f\n",
+      "   733 |   184000 |   1000 |  0.0003104180 |             f\n",
+      "   734 |   184250 |   1000 |  0.0003105330 |             f\n",
+      "   735 |   184500 |   1000 |  0.0003112212 |             f\n",
+      "   736 |   184750 |   1000 |  0.0003123791 |             f\n",
+      "   737 |   185000 |   1000 |  0.0003126255 |             f\n",
+      "   738 |   185250 |   1000 |  0.0003126370 |             f\n",
+      "   739 |   185500 |   1000 |  0.0003146789 |             f\n",
+      "   740 |   185750 |   1000 |  0.0003128144 |             f\n",
+      "   741 |   186000 |   1000 |  0.0003119508 |             f\n",
+      "   742 |   186250 |   1000 |  0.0003125069 |             f\n",
+      "   743 |   186500 |   1000 |  0.0003126896 |             f\n",
+      "   744 |   186750 |   1000 |  0.0003128544 |             f\n",
+      "   745 |   187000 |   1000 |  0.0003148826 |             f\n",
+      "   746 |   187250 |   1000 |  0.0003148730 |             f\n",
+      "   747 |   187500 |   1000 |  0.0003166672 |             f\n",
+      "   748 |   187750 |   1000 |  0.0003166679 |             f\n",
+      "   749 |   188000 |   1000 |  0.0003163055 |             f\n",
+      "   750 |   188250 |   1000 |  0.0036205526 |         nadir\n",
+      "   751 |   188500 |   1000 |  0.0000132834 |             f\n",
+      "   752 |   188750 |   1000 |  0.0000244776 |             f\n",
+      "   753 |   189000 |   1000 |  0.0000323269 |             f\n",
+      "   754 |   189250 |   1000 |  0.0000452193 |             f\n",
+      "   755 |   189500 |   1000 |  0.0000551176 |             f\n",
+      "   756 |   189750 |   1000 |  0.0000668777 |             f\n",
+      "   757 |   190000 |   1000 |  0.0000719649 |             f\n",
+      "   758 |   190250 |   1000 |  0.0000787113 |             f\n",
+      "   759 |   190500 |   1000 |  0.0000869484 |             f\n",
+      "   760 |   190750 |   1000 |  0.0000970883 |             f\n",
+      "   761 |   191000 |   1000 |  0.0001039066 |             f\n",
+      "   762 |   191250 |   1000 |  0.0001122510 |             f\n",
+      "   763 |   191500 |   1000 |  0.0001149152 |             f\n",
+      "   764 |   191750 |   1000 |  0.0001210316 |             f\n",
+      "   765 |   192000 |   1000 |  0.0001234490 |             f\n",
+      "   766 |   192250 |   1000 |  0.0001276752 |             f\n",
+      "   767 |   192500 |   1000 |  0.0001335614 |             f\n",
+      "   768 |   192750 |   1000 |  0.0001372233 |             f\n",
+      "   769 |   193000 |   1000 |  0.0001400168 |             f\n",
+      "   770 |   193250 |   1000 |  0.0001444378 |             f\n",
+      "   771 |   193500 |   1000 |  0.0001458835 |             f\n",
+      "   772 |   193750 |   1000 |  0.0001481068 |             f\n",
+      "   773 |   194000 |   1000 |  0.0001538016 |             f\n",
+      "   774 |   194250 |   1000 |  0.0001557112 |             f\n",
+      "   775 |   194500 |   1000 |  0.0048031731 |         nadir\n",
+      "   776 |   194750 |   1000 |  0.0047282098 |         nadir\n",
+      "   777 |   195000 |   1000 |  0.0000111108 |             f\n",
+      "   778 |   195250 |   1000 |  0.0000246779 |             f\n",
+      "   779 |   195500 |   1000 |  0.0000348462 |             f\n",
+      "   780 |   195750 |   1000 |  0.0000470942 |             f\n",
+      "   781 |   196000 |   1000 |  0.0000545255 |             f\n",
+      "   782 |   196250 |   1000 |  0.0000669820 |             f\n",
+      "   783 |   196500 |   1000 |  0.0000735654 |             f\n",
+      "   784 |   196750 |   1000 |  0.0000779670 |             f\n",
+      "   785 |   197000 |   1000 |  0.0000834108 |             f\n",
+      "   786 |   197250 |   1000 |  0.0000903057 |             f\n",
+      "   787 |   197500 |   1000 |  0.0000967380 |             f\n",
+      "   788 |   197750 |   1000 |  0.0001052086 |             f\n",
+      "   789 |   198000 |   1000 |  0.0001126790 |             f\n",
+      "   790 |   198250 |   1000 |  0.0001218616 |             f\n",
+      "   791 |   198500 |   1000 |  0.0001272672 |             f\n",
+      "   792 |   198750 |   1000 |  0.0001328106 |             f\n",
+      "   793 |   199000 |   1000 |  0.0001362973 |             f\n",
+      "   794 |   199250 |   1000 |  0.0001405796 |             f\n",
+      "   795 |   199500 |   1000 |  0.0001443721 |             f\n",
+      "   796 |   199750 |   1000 |  0.0001485324 |             f\n",
+      "   797 |   200000 |   1000 |  0.0001522023 |             f\n",
+      "   798 |   200250 |   1000 |  0.0001549486 |             f\n",
+      "   799 |   200500 |   1000 |  0.0001593580 |             f\n",
+      "   800 |   200750 |   1000 |  0.0001618155 |             f\n",
+      "   801 |   201000 |   1000 |  0.0001637585 |             f\n",
+      "   802 |   201250 |   1000 |  0.0001702431 |             f\n",
+      "   803 |   201500 |   1000 |  0.0001752149 |             f\n",
+      "   804 |   201750 |   1000 |  0.0001803519 |             f\n",
+      "   805 |   202000 |   1000 |  0.0001831096 |             f\n",
+      "   806 |   202250 |   1000 |  0.0001873879 |             f\n",
+      "   807 |   202500 |   1000 |  0.0001894125 |             f\n",
+      "   808 |   202750 |   1000 |  0.0001957151 |             f\n",
+      "   809 |   203000 |   1000 |  0.0001998814 |             f\n",
+      "   810 |   203250 |   1000 |  0.0002037605 |             f\n",
+      "   811 |   203500 |   1000 |  0.0002073650 |             f\n",
+      "   812 |   203750 |   1000 |  0.0002076517 |             f\n",
+      "   813 |   204000 |   1000 |  0.0002102599 |             f\n",
+      "   814 |   204250 |   1000 |  0.0002145395 |             f\n",
+      "   815 |   204500 |   1000 |  0.0002159970 |             f\n",
+      "   816 |   204750 |   1000 |  0.0002167601 |             f\n",
+      "   817 |   205000 |   1000 |  0.0002174935 |             f\n",
+      "   818 |   205250 |   1000 |  0.0002224316 |             f\n",
+      "   819 |   205500 |   1000 |  0.0002236798 |             f\n",
+      "   820 |   205750 |   1000 |  0.0002273165 |             f\n",
+      "   821 |   206000 |   1000 |  0.0002280346 |             f\n",
+      "   822 |   206250 |   1000 |  0.0002317947 |             f\n",
+      "   823 |   206500 |   1000 |  0.0002348925 |             f\n",
+      "   824 |   206750 |   1000 |  0.0810438077 |         nadir\n",
+      "   825 |   207000 |   1000 |  0.0000129921 |             f\n",
+      "   826 |   207250 |   1000 |  0.0000239571 |             f\n",
+      "   827 |   207500 |   1000 |  0.0479212035 |         nadir\n",
+      "   828 |   207750 |   1000 |  0.0000109895 |             f\n",
+      "   829 |   208000 |   1000 |  0.0000205167 |             f\n",
+      "   830 |   208250 |   1000 |  0.0000282230 |             f\n",
+      "   831 |   208500 |   1000 |  0.0000372046 |             f\n",
+      "   832 |   208750 |   1000 |  0.0000471463 |             f\n",
+      "   833 |   209000 |   1000 |  0.0000533120 |             f\n",
+      "   834 |   209250 |   1000 |  0.0000594516 |             f\n",
+      "   835 |   209500 |   1000 |  0.0000667556 |             f\n",
+      "   836 |   209750 |   1000 |  0.0000725521 |             f\n",
+      "   837 |   210000 |   1000 |  0.0000757892 |             f\n",
+      "   838 |   210250 |   1000 |  0.0000850352 |             f\n",
+      "   839 |   210500 |   1000 |  0.0000912046 |             f\n",
+      "   840 |   210750 |   1000 |  0.0000973941 |             f\n",
+      "   841 |   211000 |   1000 |  0.0001024503 |             f\n",
+      "   842 |   211250 |   1000 |  0.0001064160 |             f\n",
+      "   843 |   211500 |   1000 |  0.0001149521 |             f\n",
+      "   844 |   211750 |   1000 |  0.0034801841 |         nadir\n",
+      "   845 |   212000 |   1000 |  0.0000117407 |             f\n",
+      "   846 |   212250 |   1000 |  0.0000263730 |             f\n",
+      "   847 |   212500 |   1000 |  0.0000387479 |             f\n",
+      "   848 |   212750 |   1000 |  0.0000557725 |             f\n",
+      "   849 |   213000 |   1000 |  0.0000616105 |             f\n",
+      "   850 |   213250 |   1000 |  0.0000711225 |             f\n",
+      "   851 |   213500 |   1000 |  0.0000802145 |             f\n",
+      "   852 |   213750 |   1000 |  0.0000879063 |             f\n",
+      "   853 |   214000 |   1000 |  0.0000954161 |             f\n",
+      "   854 |   214250 |   1000 |  0.0001009336 |             f\n",
+      "   855 |   214500 |   1000 |  0.0001095519 |             f\n",
+      "   856 |   214750 |   1000 |  0.0001153844 |             f\n",
+      "   857 |   215000 |   1000 |  0.0001204844 |             f\n",
+      "   858 |   215250 |   1000 |  0.0001242432 |             f\n",
+      "   859 |   215500 |   1000 |  0.0001311226 |             f\n",
+      "   860 |   215750 |   1000 |  0.0001363584 |             f\n",
+      "   861 |   216000 |   1000 |  0.0001400540 |             f\n",
+      "   862 |   216250 |   1000 |  0.0001461789 |             f\n",
+      "   863 |   216500 |   1000 |  0.0368194410 |         nadir\n",
+      "   864 |   216750 |   1000 |  0.0000101841 |             f\n",
+      "   865 |   217000 |   1000 |  0.0000220398 |             f\n",
+      "   866 |   217250 |   1000 |  0.0000329290 |             f\n",
+      "   867 |   217500 |   1000 |  0.0000425591 |             f\n",
+      "   868 |   217750 |   1000 |  0.0000495231 |             f\n",
+      "   869 |   218000 |   1000 |  0.0000582861 |             f\n",
+      "   870 |   218250 |   1000 |  0.0000628767 |             f\n",
+      "   871 |   218500 |   1000 |  0.0000716004 |             f\n",
+      "   872 |   218750 |   1000 |  0.0000767715 |             f\n",
+      "   873 |   219000 |   1000 |  0.0000820391 |             f\n",
+      "   874 |   219250 |   1000 |  0.0000885654 |             f\n",
+      "   875 |   219500 |   1000 |  0.0000971513 |             f\n",
+      "   876 |   219750 |   1000 |  0.0001056875 |             f\n",
+      "   877 |   220000 |   1000 |  0.0001117059 |             f\n",
+      "   878 |   220250 |   1000 |  0.0001173131 |             f\n",
+      "   879 |   220500 |   1000 |  0.0001262818 |             f\n",
+      "   880 |   220750 |   1000 |  0.0001298098 |             f\n",
+      "   881 |   221000 |   1000 |  0.0001357617 |             f\n",
+      "   882 |   221250 |   1000 |  0.0001392773 |             f\n",
+      "   883 |   221500 |   1000 |  0.0001476203 |             f\n",
+      "   884 |   221750 |   1000 |  0.0001516821 |             f\n",
+      "   885 |   222000 |   1000 |  0.0001558312 |             f\n",
+      "   886 |   222250 |   1000 |  0.0001581820 |             f\n",
+      "   887 |   222500 |   1000 |  0.0001602797 |             f\n",
+      "   888 |   222750 |   1000 |  0.0001620256 |             f\n",
+      "   889 |   223000 |   1000 |  0.0001684582 |             f\n",
+      "   890 |   223250 |   1000 |  0.0001728623 |             f\n",
+      "   891 |   223500 |   1000 |  0.0001785805 |             f\n",
+      "   892 |   223750 |   1000 |  0.0001793326 |             f\n",
+      "   893 |   224000 |   1000 |  0.0001842405 |             f\n",
+      "   894 |   224250 |   1000 |  0.0001903565 |             f\n",
+      "   895 |   224500 |   1000 |  0.0001925857 |             f\n",
+      "   896 |   224750 |   1000 |  0.0001968152 |             f\n",
+      "   897 |   225000 |   1000 |  0.0002008531 |             f\n",
+      "   898 |   225250 |   1000 |  0.0002028504 |             f\n",
+      "   899 |   225500 |   1000 |  0.0002055845 |             f\n",
+      "   900 |   225750 |   1000 |  0.0002085424 |             f\n",
+      "   901 |   226000 |   1000 |  0.0002107619 |             f\n",
+      "   902 |   226250 |   1000 |  0.0002119419 |             f\n",
+      "   903 |   226500 |   1000 |  0.0002134906 |             f\n",
+      "   904 |   226750 |   1000 |  0.0002151741 |             f\n",
+      "   905 |   227000 |   1000 |  0.0002171685 |             f\n",
+      "   906 |   227250 |   1000 |  0.0002206730 |             f\n",
+      "   907 |   227500 |   1000 |  0.0002218060 |             f\n",
+      "   908 |   227750 |   1000 |  0.0002257005 |             f\n",
+      "   909 |   228000 |   1000 |  0.0002273301 |             f\n",
+      "   910 |   228250 |   1000 |  0.0002287108 |             f\n",
+      "   911 |   228500 |   1000 |  0.0002306032 |             f\n",
+      "   912 |   228750 |   1000 |  0.0002327068 |             f\n",
+      "   913 |   229000 |   1000 |  0.0002355620 |             f\n",
+      "   914 |   229250 |   1000 |  0.0002355831 |             f\n",
+      "   915 |   229500 |   1000 |  0.0002371394 |             f\n",
+      "   916 |   229750 |   1000 |  0.0002400346 |             f\n",
+      "   917 |   230000 |   1000 |  0.0002414901 |             f\n",
+      "   918 |   230250 |   1000 |  0.0002448986 |             f\n",
+      "   919 |   230500 |   1000 |  0.0002470423 |             f\n",
+      "   920 |   230750 |   1000 |  0.0002509405 |             f\n",
+      "   921 |   231000 |   1000 |  0.0002512371 |             f\n",
+      "   922 |   231250 |   1000 |  0.0002530651 |             f\n",
+      "   923 |   231500 |   1000 |  0.0002544840 |             f\n",
+      "   924 |   231750 |   1000 |  0.0002567113 |             f\n",
+      "   925 |   232000 |   1000 |  0.0002619686 |             f\n",
+      "   926 |   232250 |   1000 |  0.0002637381 |             f\n",
+      "   927 |   232500 |   1000 |  0.0002638563 |             f\n",
+      "   928 |   232750 |   1000 |  0.0002674394 |             f\n",
+      "   929 |   233000 |   1000 |  0.0002672814 |             f\n",
+      "   930 |   233250 |   1000 |  0.0002652672 |             f\n",
+      "   931 |   233500 |   1000 |  0.0002650311 |             f\n",
+      "   932 |   233750 |   1000 |  0.0002648670 |             f\n",
+      "   933 |   234000 |   1000 |  0.0002660613 |             f\n",
+      "   934 |   234250 |   1000 |  0.0002658610 |             f\n",
+      "   935 |   234500 |   1000 |  0.0002671561 |             f\n",
+      "   936 |   234750 |   1000 |  0.0002685134 |             f\n",
+      "   937 |   235000 |   1000 |  0.0002687147 |             f\n",
+      "   938 |   235250 |   1000 |  0.0002686611 |             f\n",
+      "   939 |   235500 |   1000 |  0.0002696740 |             f\n",
+      "   940 |   235750 |   1000 |  0.0002714029 |             f\n",
+      "   941 |   236000 |   1000 |  0.0002703791 |             f\n",
+      "   942 |   236250 |   1000 |  0.0002717670 |             f\n",
+      "   943 |   236500 |   1000 |  0.0002718345 |             f\n",
+      "   944 |   236750 |   1000 |  0.0002678050 |             f\n",
+      "   945 |   237000 |   1000 |  0.0002694450 |             f\n",
+      "   946 |   237250 |   1000 |  0.0002703272 |             f\n",
+      "   947 |   237500 |   1000 |  0.0002739905 |             f\n",
+      "   948 |   237750 |   1000 |  0.0002751220 |             f\n",
+      "   949 |   238000 |   1000 |  0.0002752502 |             f\n",
+      "   950 |   238250 |   1000 |  0.0002755489 |             f\n",
+      "   951 |   238500 |   1000 |  0.0002746919 |             f\n",
+      "   952 |   238750 |   1000 |  0.0002742944 |             f\n",
+      "   953 |   239000 |   1000 |  0.0002770105 |             f\n",
+      "   954 |   239250 |   1000 |  0.0002754627 |             f\n",
+      "   955 |   239500 |   1000 |  0.0002762743 |             f\n",
+      "   956 |   239750 |   1000 |  0.0002773687 |             f\n",
+      "   957 |   240000 |   1000 |  0.0002769793 |             f\n",
+      "   958 |   240250 |   1000 |  0.0002771285 |             f\n",
+      "   959 |   240500 |   1000 |  0.0002780429 |             f\n",
+      "   960 |   240750 |   1000 |  0.0002795600 |             f\n",
+      "   961 |   241000 |   1000 |  0.0002798996 |             f\n",
+      "   962 |   241250 |   1000 |  0.0002793225 |             f\n",
+      "   963 |   241500 |   1000 |  0.0002776058 |             f\n",
+      "   964 |   241750 |   1000 |  0.0002782974 |             f\n",
+      "   965 |   242000 |   1000 |  0.0002805562 |             f\n",
+      "   966 |   242250 |   1000 |  0.0002840880 |             f\n",
+      "   967 |   242500 |   1000 |  0.0002824757 |             f\n",
+      "   968 |   242750 |   1000 |  0.0002861367 |             f\n",
+      "   969 |   243000 |   1000 |  0.0002860096 |             f\n",
+      "   970 |   243250 |   1000 |  0.0002886408 |             f\n",
+      "   971 |   243500 |   1000 |  0.0002883780 |             f\n",
+      "   972 |   243750 |   1000 |  0.0002881909 |             f\n",
+      "   973 |   244000 |   1000 |  0.0002898665 |             f\n",
+      "   974 |   244250 |   1000 |  0.0002910230 |             f\n",
+      "   975 |   244500 |   1000 |  0.0002925388 |             f\n",
+      "   976 |   244750 |   1000 |  0.0002945100 |             f\n",
+      "   977 |   245000 |   1000 |  0.0002915934 |             f\n",
+      "   978 |   245250 |   1000 |  0.0002930296 |             f\n",
+      "   979 |   245500 |   1000 |  0.0002959319 |             f\n",
+      "   980 |   245750 |   1000 |  0.0002948772 |             f\n",
+      "   981 |   246000 |   1000 |  0.0002946452 |             f\n",
+      "   982 |   246250 |   1000 |  0.0002950842 |             f\n",
+      "   983 |   246500 |   1000 |  0.0002940039 |             f\n",
+      "   984 |   246750 |   1000 |  0.0002934333 |             f\n",
+      "   985 |   247000 |   1000 |  0.0002948297 |             f\n",
+      "   986 |   247250 |   1000 |  0.0002944497 |             f\n",
+      "   987 |   247500 |   1000 |  0.0002963053 |             f\n",
+      "   988 |   247750 |   1000 |  0.0002984346 |             f\n",
+      "   989 |   248000 |   1000 |  0.0002987894 |             f\n",
+      "   990 |   248250 |   1000 |  0.0002988148 |             f\n",
+      "   991 |   248500 |   1000 |  0.0002981089 |             f\n",
+      "   992 |   248750 |   1000 |  0.0002985058 |             f\n",
+      "   993 |   249000 |   1000 |  0.0002954942 |             f\n",
+      "   994 |   249250 |   1000 |  0.0002958267 |             f\n",
+      "   995 |   249500 |   1000 |  0.0002937295 |             f\n",
+      "   996 |   249750 |   1000 |  0.0002921999 |             f\n",
+      "   997 |   250000 |   1000 |  0.0002920810 |             f\n",
+      "   998 |   250250 |   1000 |  0.0002940134 |             f\n",
+      "   999 |   250500 |   1000 |  0.0002929695 |             f\n",
+      "  1000 |   250750 |   1000 |  0.0002976228 |             f\n",
+      "Results successfully saved to benchmark_run_ZDT3_10_2026-07-23_16-22-45.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "filenames=[]\n",
+    "\n",
+    "for bproblem in L_problems[2:]:\n",
+    "    for dim in [10]:\n",
+    "        bproblem.set_dim(dim)\n",
+    "        state_composite,state_naive,state_mosego,X,F,X2,F2=run_all_benchmarks(bproblem)\n",
+    "        D_composite,Y_composite=get_DoE(state_composite)\n",
+    "        D_naive,Y_naive=get_DoE(state_naive)\n",
+    "        D_mosego,Y_mosego=get_DoE(state_mosego)\n",
+    "        data_to_save = {\n",
+    "            \"name\":bproblem.name,\n",
+    "            \"num_dim\":bproblem.num_dim,\n",
+    "            \"D_composite\":D_composite,\n",
+    "            \"Y_composite\":Y_composite,\n",
+    "            \"D_naive\":D_naive,\n",
+    "            \"Y_naive\":Y_naive,\n",
+    "            \"D_mosego\":D_mosego,\n",
+    "            \"Y_mosego\":Y_mosego,\n",
+    "            \"X\":X,\n",
+    "            \"F\":F,\n",
+    "            \"X2\":X2,\n",
+    "            \"F2\":F2,\n",
+    "            \"parameters\":parameters,\n",
+    "        }\n",
+    "\n",
+    "        current_time = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
+    "\n",
+    "        filename = f\"benchmark_run_{bproblem.name}_{dim}_{current_time}.pkl\"\n",
+    "        filenames.append(filename)\n",
+    "\n",
+    "        with open(filename, \"wb\") as file:\n",
+    "            pickle.dump(data_to_save, file)\n",
+    "\n",
+    "        print(f\"Results successfully saved to {filename}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7b6486",
+   "metadata": {},
+   "source": [
+    "Open all files to get data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0a9e9529",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully loaded: benchmark_run_ZDT1_10_2026-07-21_18-40-25.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT1_2_2026-07-21_15-49-58.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT1_5_2026-07-21_16-09-43.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT2_10_2026-07-21_21-23-05.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT2_2_2026-07-21_18-42-40.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT2_5_2026-07-21_19-00-14.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT3_10_2026-07-23_16-22-45.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT3_2_2026-07-21_21-25-31.pkl\n",
+      "Successfully loaded: benchmark_run_ZDT3_5_2026-07-21_21-44-44.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "saved_files = glob.glob(\"benchmark_run_*.pkl\")\n",
+    "saved_files.sort()\n",
+    "\n",
+    "data = []\n",
+    "\n",
+    "for filename in saved_files:\n",
+    "    with open(filename, \"rb\") as file:\n",
+    "        results = pickle.load(file)\n",
+    "        data.append(results)\n",
+    "        print(f\"Successfully loaded: {filename}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fc90b334",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1400 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1400 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1400 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "assert(len(data)==9)\n",
+    "\n",
+    "fig,axs=plt.subplots(3,3)\n",
+    "fig.set_size_inches(20,14)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    for j in range(3):\n",
+    "        index = i*3+j\n",
+    "        num_dim=data[index][\"num_dim\"]\n",
+    "        ax=axs[i][j]\n",
+    "\n",
+    "        initial_budget=(init_factor + 2*min_factor) * num_dim + 3\n",
+    "        total_budget=budget_factor * num_dim\n",
+    "\n",
+    "        D_composite,Y_composite=data[index][\"D_composite\"],data[index][\"Y_composite\"]\n",
+    "        D_naive,Y_naive=data[index][\"D_naive\"],data[index][\"Y_naive\"]\n",
+    "        D_mosego,Y_mosego=data[index][\"D_mosego\"],data[index][\"Y_mosego\"]\n",
+    "        X,F=data[index][\"X\"],data[index][\"F\"]\n",
+    "        pareto_points_pymoo = [(X[i],F[i]) for i in get_Pareto_front(X,F)]\n",
+    "        X2,F2 = data[index][\"X2\"],data[index][\"F2\"]\n",
+    "\n",
+    "        T=[i for i in range(initial_budget-1,total_budget)]\n",
+    "        IGD_ac=[]\n",
+    "        IGD_naive=[]\n",
+    "        IGD_mosego=[]\n",
+    "        IGD_pymoo_budget=[]\n",
+    "        for t in T:\n",
+    "            pareto_points_composite = [(D_composite[i],Y_composite[i]) for i in get_Pareto_front(D_composite[:t+1],Y_composite[:t+1])]\n",
+    "            pareto_points_pymoo_budget = [(X2[i],F2[i]) for i in get_Pareto_front(X2[:t+1],F2[:t+1])]\n",
+    "            pareto_points_naive = [(D_naive[i],Y_naive[i]) for i in get_Pareto_front(D_naive[:t+1],Y_naive[:t+1])]\n",
+    "            pareto_points_mosego =[(D_mosego[i],Y_mosego[i]) for i in get_Pareto_front(D_mosego[:t+1],Y_mosego[:t+1])]\n",
+    "            IGD_ac.append(IGDPlus(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo])).do(np.array([pareto_point[1] for pareto_point in pareto_points_composite])))\n",
+    "            IGD_pymoo_budget.append(IGDPlus(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo])).do(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo_budget])))\n",
+    "            IGD_naive.append(IGDPlus(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo])).do(np.array([pareto_point[1] for pareto_point in pareto_points_naive])))\n",
+    "            IGD_mosego.append(IGDPlus(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo])).do(np.array([pareto_point[1] for pareto_point in pareto_points_mosego])))\n",
+    "        ax.semilogy(T,IGD_ac,label=\"composite Bi-SEGO\")\n",
+    "        ax.semilogy(T,IGD_naive,label=\"naive Bi-SEGO\")\n",
+    "        ax.semilogy(T,IGD_mosego,label=\"MO-SEGO\")\n",
+    "        ax.semilogy(T,IGD_pymoo_budget,label=\"pymoo\")\n",
+    "        ax.title.set_text(f\"{data[index][\"name\"]}\")\n",
+    "        ax.legend(loc=\"best\")\n",
+    "        ax.set_xlabel(\"budget\")\n",
+    "        ax.set_ylabel(\"IGD+\")\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "fig,axs=plt.subplots(3,3)\n",
+    "fig.set_size_inches(20,14)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    for j in range(3):\n",
+    "        index = i*3+j\n",
+    "        num_dim=data[index][\"num_dim\"]\n",
+    "        ax=axs[i][j]\n",
+    "\n",
+    "        initial_budget=(init_factor + 2*min_factor) * num_dim + 3\n",
+    "        total_budget=budget_factor * num_dim\n",
+    "\n",
+    "        D_composite,Y_composite=data[index][\"D_composite\"],data[index][\"Y_composite\"]\n",
+    "        D_naive,Y_naive=data[index][\"D_naive\"],data[index][\"Y_naive\"]\n",
+    "        D_mosego,Y_mosego=data[index][\"D_mosego\"],data[index][\"Y_mosego\"]\n",
+    "        X,F=data[index][\"X\"],data[index][\"F\"]\n",
+    "        pareto_points_pymoo = [(X[i],F[i]) for i in get_Pareto_front(X,F)]\n",
+    "        X2,F2 = data[index][\"X2\"],data[index][\"F2\"]\n",
+    "\n",
+    "        # Find nadir point\n",
+    "        all_pareto_points_composite = [(D_composite[i],Y_composite[i]) for i in get_Pareto_front(D_composite,Y_composite)]\n",
+    "        all_pareto_points_pymoo_budget = [(X2[i],F2[i]) for i in get_Pareto_front(X2[:len(D_composite)],F2[:len(D_composite)])]\n",
+    "        all_pareto_points_naive = [(D_naive[i],Y_naive[i]) for i in get_Pareto_front(D_naive[:len(D_composite)+1],Y_naive[:len(D_composite)+1])]\n",
+    "        ref_point=np.array([max([y[1][0] for y in all_pareto_points_composite]+[y[1][0] for y in all_pareto_points_naive]+[y[1][0] for y in all_pareto_points_pymoo_budget]),max([y[1][1] for y in all_pareto_points_composite]+[y[1][1] for y in all_pareto_points_naive]+[y[1][1] for y in all_pareto_points_pymoo_budget])])\n",
+    "\n",
+    "        T=[i for i in range(initial_budget-1,total_budget)]\n",
+    "        HV_ac=[]\n",
+    "        HV_naive=[]\n",
+    "        HV_mosego=[]\n",
+    "        HV_pymoo_budget=[]\n",
+    "        for t in T:\n",
+    "            pareto_points_composite = [(D_composite[i],Y_composite[i]) for i in get_Pareto_front(D_composite[:t+1],Y_composite[:t+1])]\n",
+    "            pareto_points_pymoo_budget = [(X2[i],F2[i]) for i in get_Pareto_front(X2[:t+1],F2[:t+1])]\n",
+    "            pareto_points_naive = [(D_naive[i],Y_naive[i]) for i in get_Pareto_front(D_naive[:t+1],Y_naive[:t+1])]\n",
+    "            pareto_points_mosego =[(D_mosego[i],Y_mosego[i]) for i in get_Pareto_front(D_mosego[:t+1],Y_mosego[:t+1])]\n",
+    "            HV_ac.append(HV(ref_point=ref_point).do(np.array([pareto_point[1] for pareto_point in pareto_points_composite])))\n",
+    "            HV_pymoo_budget.append(HV(ref_point=ref_point).do(np.array([pareto_point[1] for pareto_point in pareto_points_pymoo_budget])))\n",
+    "            HV_naive.append(HV(ref_point=ref_point).do(np.array([pareto_point[1] for pareto_point in pareto_points_naive])))\n",
+    "            HV_mosego.append(HV(ref_point=ref_point).do(np.array([pareto_point[1] for pareto_point in pareto_points_mosego])))\n",
+    "\n",
+    "        ax.plot(T,HV_ac,label=\"composite Bi-SEGO\")\n",
+    "        ax.plot(T,HV_naive,label=\"naive Bi-SEGO\")\n",
+    "        ax.plot(T,HV_mosego,label=\"MO-SEGO\")\n",
+    "        ax.plot(T,HV_pymoo_budget,label=\"pymoo\")\n",
+    "        ax.title.set_text(f\"{data[index][\"name\"]}\")\n",
+    "        ax.legend(loc=\"best\")\n",
+    "        ax.set_xlabel(\"budget\")\n",
+    "        ax.set_ylabel(\"Hypervolume\")\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "fig,axs=plt.subplots(3,3)\n",
+    "fig.set_size_inches(20,14)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    for j in range(3):\n",
+    "        index = i*3+j\n",
+    "        num_dim=data[index][\"num_dim\"]\n",
+    "        ax=axs[i][j]\n",
+    "\n",
+    "        D_composite,Y_composite=data[index][\"D_composite\"],data[index][\"Y_composite\"]\n",
+    "        D_naive,Y_naive=data[index][\"D_naive\"],data[index][\"Y_naive\"]\n",
+    "        D_mosego,Y_mosego=data[index][\"D_mosego\"],data[index][\"Y_mosego\"]\n",
+    "        X,F=data[index][\"X\"],data[index][\"F\"]\n",
+    "        pareto_points_pymoo = [(X[i],F[i]) for i in get_Pareto_front(X,F)]\n",
+    "        X2,F2 = data[index][\"X2\"],data[index][\"F2\"]\n",
+    "\n",
+    "        ax.scatter([p[1][0] for p in pareto_points_pymoo],[p[1][1] for p in pareto_points_pymoo],color=\"red\",marker=\".\",s=5,label=\"True Pareto front\")\n",
+    "\n",
+    "        pareto_points_composite = [(D_composite[i],Y_composite[i]) for i in get_Pareto_front(D_composite,Y_composite)]\n",
+    "        ax.scatter([p[1][0] for p in pareto_points_composite],[p[1][1] for p in pareto_points_composite],color=\"blue\",label=\"Composite Bi-SEGO\")\n",
+    "\n",
+    "        pareto_points_naive=[(D_naive[i],Y_naive[i]) for i in get_Pareto_front(D_naive,Y_naive)]\n",
+    "        ax.scatter([p[1][0] for p in pareto_points_naive],[p[1][1] for p in pareto_points_naive],color=\"green\",label=\"Naive Bi-SEGO\",marker=\"+\",s=60)\n",
+    "        \n",
+    "        pareto_points_mosego=[(D_mosego[i],Y_mosego[i]) for i in get_Pareto_front(D_mosego,Y_mosego)]\n",
+    "        ax.scatter([p[1][0] for p in pareto_points_mosego],[p[1][1] for p in pareto_points_mosego],color=\"orange\",label=\"MO-SEGO\",marker=\"1\",s=60)\n",
+    "\n",
+    "        pareto_points_pymoo_budget = [(X2[i],F2[i]) for i in get_Pareto_front(X2[:len(D_composite)],F2[:len(D_composite)])]\n",
+    "        ax.scatter([p[1][0] for p in pareto_points_pymoo_budget],[p[1][1] for p in pareto_points_pymoo_budget],marker=\"x\",color=\"purple\",label=\"Pymoo with same budget\")\n",
+    "\n",
+    "        ax.title.set_text(f\"\"\"{data[index][\"name\"]}\"\"\")\n",
+    "        ax.legend(loc=\"best\")\n",
+    "        ax.set_xlabel(\"f1\")\n",
+    "        ax.set_ylabel(\"f2\")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "smt-optim (3.14.2)",
+   "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": "3.14.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/smt_optim/acquisition_functions/composite_expected_improvement.py b/src/smt_optim/acquisition_functions/composite_expected_improvement.py
new file mode 100644
index 0000000..688b08e
--- /dev/null
+++ b/src/smt_optim/acquisition_functions/composite_expected_improvement.py
@@ -0,0 +1,53 @@
+import numpy as np
+
+def PositivePart(x):
+    return max(x,0)
+
+def init_bi_obj_composite_ei(state,kwargs):
+
+    phi=kwargs["phi"]
+    n_expectancy=kwargs.get("n_accuracy",1000)
+
+    def composite_expected_improvement(mu: float, s2: float, f_min: float, n_expectancy=n_expectancy) -> float:
+        """
+        Expected Improvement composite acquisition function.
+
+        Parameters
+        ----------
+        mu: np.array
+            Mean prediction.
+        s2: np.array
+            Variance prediction.
+        f_min: float
+            Best minimum objective value in training data.
+        phi: np.array -> float
+
+        Returns
+        -------
+        float
+            Expected Improvement value.
+        """
+
+        S=np.atleast_1d(0.0)
+        for i in range(n_expectancy):
+            sampleZ = np.random.multivariate_normal(np.array([0,0]),np.array([[1,0],[0,1]]))
+            S+=PositivePart(f_min-phi(mu+s2*sampleZ))
+        ei=S/n_expectancy
+
+        return ei[0]
+    
+    models=state.obj_models
+    f_min=min([phi(y) for y in state.scaled_dataset.export_data([0,1],0)])
+
+    def composite_ei(x_pred):
+        s = np.array([
+            np.sqrt(models[0].predict_variances(x_pred)).item(),
+            np.sqrt(models[1].predict_variances(x_pred)).item()
+        ])
+
+        y = np.array([
+            models[0].predict_values(x_pred).item(),
+            models[1].predict_values(x_pred).item(),
+        ])
+        return composite_expected_improvement(y,s,f_min)
+    return composite_ei
\ No newline at end of file
diff --git a/src/smt_optim/acquisition_strategies/__init__.py b/src/smt_optim/acquisition_strategies/__init__.py
index 77e3d7d..1af5da5 100644
--- a/src/smt_optim/acquisition_strategies/__init__.py
+++ b/src/smt_optim/acquisition_strategies/__init__.py
@@ -2,10 +2,12 @@
 from .mfsego import MFSEGO
 from .vfpi import VFPI
 from .mosego import MOSEGO
+from .bisego import BiSEGO
 
 __all__ = [
     "AcquisitionStrategy",
     "MFSEGO",
     "VFPI",
     "MOSEGO",
+    "BiSEGO",
 ]
diff --git a/src/smt_optim/acquisition_strategies/bisego.py b/src/smt_optim/acquisition_strategies/bisego.py
new file mode 100644
index 0000000..2e78818
--- /dev/null
+++ b/src/smt_optim/acquisition_strategies/bisego.py
@@ -0,0 +1,313 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import optimize as so, stats as stats
+
+from smt_optim.acquisition_functions import log_ei
+from smt_optim.acquisition_strategies import AcquisitionStrategy
+
+from smt_optim.core.state import State
+
+from smt_optim.utils.get_fmin import get_fmin
+
+from smt_optim.subsolvers import multistart_minimize
+
+from smt_optim.acquisition_functions.composite_expected_improvement import init_bi_obj_composite_ei
+
+from scipy.stats import norm
+from smt.surrogate_models import KRG
+
+def Dominates(p,q):
+    #Returns True if point p strictly dominates point q, else returns False
+    return (p[0]<=q[0] and p[1]<q[1]) or (p[0]<q[0] and p[1]<=q[1])
+
+def ParetoFront(D,Y):
+    #Given a DoE (D,Y), returns the list of indices of non-dominated points, sorted by ascending value of f1
+    t=len(D)
+    front=[]
+    for i in range(t):
+        if all([not Dominates(q,Y[i]) for q in Y]):
+            front.append((Y[i][0],i))
+    front.sort()
+    return [p[1] for p in front]
+
+def PositivePart(x):
+    return max(x,0)
+
+def SingleObjectiveNormalized(y,r,s=None):
+    #Returns a single-objective normalized formulation of the problem
+    if s==None:
+        s=[1]*len(y)
+    return max((y[i]-r[i])/s[i] for i in range(len(y)))
+
+def SingleObjectiveProduct(y,r):
+    #Returns a single objective product formulation of the problem
+    return -np.prod([PositivePart(r[i]-y[i])**2 for i in range(len(y))])
+
+def Norm(p):
+    #Returns the 2-norm of a point
+    return np.sqrt(sum(x**2 for x in p))
+
+def Dist(p,q):
+    #Returns the distance between two points
+    return Norm([p[i]-q[i] for i in range(len(p))])
+
+def DistToNeighbors(p1,p2,p3,w):
+    #Returns the sum of squared distances from p2 to its neighbors p1 and p3 on the Pareto front, coefficiented by the Weight.
+    return (Dist(p1,p2)**2+Dist(p2,p3)**2)/(w+1)
+
+class BiSEGO(AcquisitionStrategy):
+    """
+    Bi-objective Scalarized Efficient Global Optimization
+
+    This acquisition strategy can perform bi-objective optimization on unconstrained optimization problems.
+
+    Parameters
+    ----------
+    state : State
+        Optimization state containing the current problem definition,
+        surrogate models, and dataset.
+
+    acq_func : acquisition function, optional
+        Acquisition function used for the Pareto front initialization.
+        Default is 'log_ei'.
+
+    acq_func_bi : acquisition function, optional
+        Acquisition function used for the composite case.
+        Default is 'init_bi_obj_composite_ei'.
+
+    acq_func_naive : acquisition function, optional
+        Acquisition function used for the naive case.
+        Default is 'log_ei'.
+
+    n_multi_start : int, optional
+        Number of starting points used for local acquisition optimization.
+        Default is 10.
+
+    n_accuracy : int, optional
+        Number of Monte-Carlo calls for the composite acquisition function
+        Default is 100
+
+    sp_method : str, optional
+        Local optimization method used for acquisition refinement.
+        Default is "SLSQP".
+
+    sp_tol : float, optional
+        Tolerance for the acquisition function optimizer. Default is
+        `sqrt(np.finfo(float).eps)`.
+
+    so_formulation : str, optional
+        Choice of single-objective formulation. "Product" or "Normalized". The normalized
+        scalarisation is not compatible with the naive approach.
+        Default is "Product".
+    
+    single_obj_max_calls : int, optional
+        Maximum number of calls using the same single-objective formulation
+        Default is 'self.n_multi_start'.
+
+    init_calls : int, optional
+        Number of calls for the initial optimization process of f1 and f2 independently.
+        Default is 'self.n_multi_start'.
+
+    naive : bool, optional
+        Choice of the version of the algorithm. If False, the composite expected improvement function is used. If True, log_ei is used.
+        Default is False.
+
+    verbose : bool, optional
+        If True, prints the different steps of the algorithm as it runs.
+        Default is False
+
+    """
+    def __init__(self, state: State, **kwargs):
+        super().__init__()
+
+
+        self.acq_func1 = kwargs.get("acq_func", log_ei) #Acquisition function for min(f1) (to be modified to take only f1 as a parameter)
+        self.acq_func2 = kwargs.get("acq_func", log_ei) #Acquisition function for min(f2) (same for f2)
+        self.acq_func_gen3 = kwargs.get("acq_func_bi", init_bi_obj_composite_ei) #Composite acquisition function for min(f1,f2)
+        self.n_multi_start = kwargs.pop("n_multi_start", 10)
+        self.n_accuracy = kwargs.pop("n_accuracy",100)
+        self.sp_method = kwargs.pop("sp_method", "Cobyla")
+        self.sp_tol = kwargs.pop("sp_tol", np.sqrt(np.finfo(float).eps))
+        self.soformulation=kwargs.pop("so_formulation","Product")
+        self.current_calls = 0
+        self.current_subcalls = 0
+        self.single_obj_max_calls = kwargs.pop("single_obj_max_calls",self.n_multi_start)
+        self.init_calls = kwargs.pop("init_calls",self.n_multi_start)
+        self.acq_func_gen1 = lambda state,kwargs : lambda x : self.acq_func1(state.obj_models[0].predict_values(x),state.obj_models[0].predict_variances(x),min(state.scaled_dataset.export_data([0],0)))[0][0]
+        self.acq_func_gen2 = lambda state,kwargs : lambda x : self.acq_func2(state.obj_models[1].predict_values(x),state.obj_models[1].predict_variances(x),min(state.scaled_dataset.export_data([1],0)))[0][0]
+        self.naive = kwargs.pop("naive",False)
+        self.verbose = kwargs.pop("verbose",False)
+
+        self.r = None
+        self.state = state
+        self.X = None
+        self.W = None
+
+
+    def validate_config(self, state):
+        pass
+
+    def get_scaled_DoE(self):
+        Y=self.state.scaled_dataset.export_as_dict()["obj"]
+        D=self.state.scaled_dataset.export_as_dict()["x"]
+        return (D,Y)
+
+    def get_DoE(self):
+        Y=self.state.dataset.export_as_dict()["obj"]
+        D=self.state.dataset.export_as_dict()["x"]
+        return (D,Y)
+    
+    def get_pareto_front(self):
+        D,Y=self.get_scaled_DoE()
+        self.X = ParetoFront(D,Y)
+
+    def select_reference_point(self):
+        self.get_pareto_front()
+        J=len(self.X)
+        D,Y=self.get_scaled_DoE()
+        X=self.X
+        W=self.W
+        if J>2:
+            #Select a point of the Pareto front relatively far from its neighbors
+            j=max((DistToNeighbors(Y[X[k-1]],Y[X[k]],Y[X[k+1]],W[X[k]]),k) for k in range(1,J-1))[1]
+            r=(Y[X[j+1]][0],Y[X[j-1]][1])
+        elif J==2:
+            j=1
+            r=(Y[X[1]][0],Y[X[0]][1])
+        elif J==1:
+            return None
+        else:
+            raise ValueError("The Pareto Front is empty")
+        self.W[X[j]]+=1
+        return r
+
+    def get_infill(self, state):
+        old_pareto_front=self.X
+        self.get_pareto_front()
+
+        if self.current_calls == 0:
+            if self.current_calls < self.init_calls and self.verbose:
+                print("Min(f1) phase")
+            self.W = [0 for x in range(len(self.state.dataset.export_as_dict()["x"]))]
+
+        # Pre-treatment phase : find min(f1) and min(f2)
+        if self.current_calls < self.init_calls:
+            self.current_calls+=1
+            self.W.append(0)
+            return self.get_infill_custom(state,self.acq_func_gen1)
+        elif self.current_calls < 2*self.init_calls:
+            if self.current_calls==self.init_calls and self.verbose:
+                print("Min(f2) phase")
+            self.current_calls+=1
+            self.W.append(0)
+            return self.get_infill_custom(state,self.acq_func_gen2)
+
+        # Main loop
+        else:
+            if self.current_calls == 2*self.init_calls or self.current_subcalls == 0 or self.current_subcalls == self.single_obj_max_calls or old_pareto_front!=self.X :
+                if self.verbose:
+                    print("The Pareto front is of length", len(self.X))
+                self.current_subcalls = 0
+                r=self.select_reference_point()
+                if r==None: # edge case where there is only one point in the Pareto Front
+                    self.current_subcalls+=1
+                    self.current_calls+=1
+                    self.W.append(0)
+                    if self.current_calls%2:
+                        return self.get_infill_custom(state,self.acq_func_gen1)
+                    else:
+                        return self.get_infill_custom(state,self.acq_func_gen2)
+                if self.verbose:
+                    print("Bi-objective phase with r =",r)
+                if self.soformulation=="Normalized":
+                    self.phi = lambda y: SingleObjectiveNormalized(y,r)
+                elif self.soformulation=="Product":
+                    self.phi = lambda y: SingleObjectiveProduct(y,r)
+                else:
+                    raise ValueError("Unknown single-objective formulation")
+            self.current_subcalls+=1
+            self.current_calls+=1
+            self.W.append(0)
+
+            if not self.naive:
+                return self.get_infill_custom(state,self.acq_func_gen3,phi=self.phi,n_accuracy=self.n_accuracy)
+            else:
+                return self.get_infill_naive(state,self.phi)
+
+    def get_infill_custom(self,state,acq_func_gen,**kwargs):
+        # Gets the infill point using the custom acquisition function provided
+        self.seed = state.iter
+
+        sampler = stats.qmc.LatinHypercube(d=state.problem.num_dim, rng=state.iter)
+        multi_x0 = sampler.random(self.n_multi_start)
+
+        ac_func = acq_func_gen(state,kwargs)
+
+        def sp_wrapper(x):
+            x = x.reshape(1, -1)
+            return -ac_func(x)
+
+        res = multistart_minimize(sp_wrapper,
+                                    bounds=np.array([[0, 1]] * state.problem.num_dim),
+                                    constraints=[],
+                                    n_start=self.n_multi_start,
+                                    multi_x0=multi_x0,
+                                    seed=self.seed,
+                                    tol=self.sp_tol,
+                                    method=self.sp_method, )
+
+        next_x = res.x
+        infill = [next_x.reshape(1, -1)]
+
+        return infill
+
+    def get_infill_naive(self,state,phi):
+        #TODO: refactor to use already defined functions
+
+        D,Y=self.get_scaled_DoE()
+        Phi_values=[phi(y) for y in Y]
+
+        # 1. Train model on DoE
+        # Initialize the model
+        sm = KRG(print_global=False)
+        sm.set_training_values(np.atleast_1d(D), np.atleast_1d(Phi_values))
+        sm.train()
+
+        # Find the current minimum value in the observation history
+        current_best = np.min(Y)
+
+        # Define the negative log Expected Improvement to minimize
+        def log_ei(x):
+            x_reshaped = np.atleast_2d(x)
+            mu = sm.predict_values(x_reshaped)[0, 0]
+            sigma2 = sm.predict_variances(x_reshaped)[0, 0]
+            sigma = np.sqrt(max(sigma2, 1e-12))
+            improvement = current_best - mu
+            z = improvement / sigma
+            ei = improvement * norm.cdf(z) + sigma * norm.pdf(z)
+            return np.log(max(ei, 1e-12))
+
+        # 2. Find maximum of log_EI acquisition function
+        self.seed = state.iter
+
+        sampler = stats.qmc.LatinHypercube(d=state.problem.num_dim, rng=state.iter)
+        multi_x0 = sampler.random(self.n_multi_start)
+
+        def sp_wrapper(x):
+            x = x.reshape(1, -1)
+            return -log_ei(x)
+
+        res = multistart_minimize(sp_wrapper,
+                                    bounds=np.array([[0, 1]] * state.problem.num_dim),
+                                    constraints=[],
+                                    n_start=self.n_multi_start,
+                                    multi_x0=multi_x0,
+                                    seed=self.seed,
+                                    tol=self.sp_tol,
+                                    method=self.sp_method, )
+
+        next_x = res.x
+        infill = [next_x.reshape(1, -1)]
+
+        return infill
+    
diff --git a/src/smt_optim/benchmarks/registry.py b/src/smt_optim/benchmarks/registry.py
index 85c13f7..5164f26 100644
--- a/src/smt_optim/benchmarks/registry.py
+++ b/src/smt_optim/benchmarks/registry.py
@@ -52,6 +52,7 @@ def _register_from_module(module):
 _register_from_module(weldedbeam_variants)
 
 
+
 # def list_problems(**criteria):
 #
 #     results = []
diff --git a/src/smt_optim/utils/inject_doe.py b/src/smt_optim/utils/inject_doe.py
new file mode 100644
index 0000000..a348c82
--- /dev/null
+++ b/src/smt_optim/utils/inject_doe.py
@@ -0,0 +1,36 @@
+import numpy as np
+from smt_optim.core import Sample
+from smt_optim.utils.constraints import compute_rscv
+
+def InjectData(state, xt, yt, ct=None):
+    """
+    Injects matrix-form data (X, Y, and optionally Constraints) into the State.
+    """
+    # 1. Ensure inputs are 2D arrays
+    xt = np.atleast_2d(xt)
+    yt = np.atleast_2d(yt)
+    
+    # Handle constraints
+    num_samples = xt.shape[0]
+    if ct is None:
+        ct = np.empty((num_samples, 0))
+    else:
+        ct = np.atleast_2d(ct)
+
+    # 2. Add to dataset
+    for i in range(num_samples):
+        # We compute RSCV because export_as_dict will crash without it
+        rscv_val = compute_rscv(ct[i:i+1, :], state.problem.cstr_configs).item()
+        
+        sample = Sample(
+            x=xt[i],
+            fidelity=0,
+            obj=yt[i],
+            cstr=ct[i] if ct.size > 0 else np.array([]),
+            eval_time=np.zeros(yt.shape[1] + ct.shape[1]),
+            metadata={"rscv": rscv_val, "iter": 0, "budget": 0.0}
+        )
+        state.dataset.add(sample)
+
+    # Update state metadata if necessary
+    state.iter = 0
diff --git a/src/tests/test_integration/test_bisego.py b/src/tests/test_integration/test_bisego.py
new file mode 100644
index 0000000..0274713
--- /dev/null
+++ b/src/tests/test_integration/test_bisego.py
@@ -0,0 +1,253 @@
+import unittest
+
+import numpy as np
+
+from smt_optim.core import Problem
+from smt_optim.core import ObjectiveConfig, DriverConfig, Driver
+
+from smt_optim.surrogate_models.smt import SmtAutoModel
+
+from smt_optim.acquisition_strategies import BiSEGO
+
+
+from smt_optim.core import Problem
+from smt_optim.surrogate_models.smt import SmtAutoModel
+from smt_optim.core import ObjectiveConfig, DriverConfig
+from smt_optim.core import Driver
+
+from smt_optim.benchmarks.registry import get_problem
+from pymoo.indicators.hv import HV
+
+
+def dominates(p,q):
+    # Returns True if point p strictly dominates point q, else returns False
+    return (p[0]<q[0] and p[1]<=q[1]) or (p[0]<=q[0] and p[1]<q[1])
+
+def get_Pareto_front(Y):
+    # Given a list Y of points in the objective space, returns the list of non-dominated points.
+    t=len(Y)
+    front=[]
+    for i in range(t):
+        if all([not dominates(q,Y[i]) for q in Y]):
+            front.append(Y[i])
+    front.sort()
+    return front
+
+
+class TestOptimization(unittest.TestCase):
+    def test_bisego_composite(self):
+        bproblem = get_problem("ZDT1")
+        bproblem.set_dim(2)
+
+        num_obj=bproblem.num_obj
+        num_cstr=bproblem.num_cstr
+        bounds=bproblem.bounds
+        objective=bproblem.objective
+
+        assert(num_obj==2)
+        assert(num_cstr==0)
+
+        f1=objective[0]
+        f2=objective[1]
+
+
+        obj_config1 = ObjectiveConfig(
+            objective=[f1],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        obj_config2 = ObjectiveConfig(
+            objective=[f2],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        problem = Problem(
+            obj_configs=[obj_config1,obj_config2],
+            design_space=bounds,
+            costs=[1,1]
+        )
+
+        opt_config = DriverConfig(
+            max_iter=25,
+            seed=43,
+            nt_init=10,
+        )
+
+        strategy_kwargs = {
+            "n_multi_start":10,
+            "n_accuracy":100,
+            "so_formulation":"Product",
+            "single_objective_max_calls":5,
+            "init_calls":5,
+            "naive":False,
+            "verbose":False,
+        }
+
+        optimizer = Driver(
+            problem=problem,
+            config=opt_config,
+            strategy=BiSEGO,
+            strategy_kwargs=strategy_kwargs,
+        )
+
+        state = optimizer.optimize()
+
+        y_data = []
+        for i, sample in enumerate(state.dataset.samples):
+            y_data.append((sample.obj[0],sample.obj[1]))
+        
+        ref_point=np.array([1,1])
+        pareto_points_composite=get_Pareto_front(y_data)
+
+        HV_max=HV(ref_point=ref_point).do(np.array(pareto_points_composite))
+
+        self.assertGreaterEqual(HV_max,0.4)
+        # Theoretical maximum is 0.66
+
+    def test_bisego_composite_normalized(self):
+        bproblem = get_problem("ZDT1")
+        bproblem.set_dim(2)
+
+        num_obj=bproblem.num_obj
+        num_cstr=bproblem.num_cstr
+        bounds=bproblem.bounds
+        objective=bproblem.objective
+
+        assert(num_obj==2)
+        assert(num_cstr==0)
+
+        f1=objective[0]
+        f2=objective[1]
+
+
+        obj_config1 = ObjectiveConfig(
+            objective=[f1],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        obj_config2 = ObjectiveConfig(
+            objective=[f2],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        problem = Problem(
+            obj_configs=[obj_config1,obj_config2],
+            design_space=bounds,
+            costs=[1,1]
+        )
+
+        opt_config = DriverConfig(
+            max_iter=35,
+            seed=43,
+            nt_init=10,
+        )
+
+        strategy_kwargs = {
+            "n_multi_start":10,
+            "so_formulation":"Normalized",
+            "single_objective_max_calls":5,
+            "init_calls":5,
+            "naive":False,
+            "verbose":False,
+        }
+
+        optimizer = Driver(
+            problem=problem,
+            config=opt_config,
+            strategy=BiSEGO,
+            strategy_kwargs=strategy_kwargs,
+        )
+
+        state = optimizer.optimize()
+
+        y_data = []
+        for i, sample in enumerate(state.dataset.samples):
+            y_data.append((sample.obj[0],sample.obj[1]))
+        
+        ref_point=np.array([1,1])
+        pareto_points_composite=get_Pareto_front(y_data)
+
+        HV_max=HV(ref_point=ref_point).do(np.array(pareto_points_composite))
+
+        self.assertGreaterEqual(HV_max,0.4)
+        # Theoretical maximum is 0.66
+
+    def test_bisego_naive(self):
+        bproblem = get_problem("ZDT1")
+        bproblem.set_dim(2)
+
+        num_obj=bproblem.num_obj
+        num_cstr=bproblem.num_cstr
+        bounds=bproblem.bounds
+        objective=bproblem.objective
+
+        assert(num_obj==2)
+        assert(num_cstr==0)
+
+        f1=objective[0]
+        f2=objective[1]
+
+
+        obj_config1 = ObjectiveConfig(
+            objective=[f1],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        obj_config2 = ObjectiveConfig(
+            objective=[f2],
+            type="minimize",
+            surrogate=SmtAutoModel,
+        )
+
+        problem = Problem(
+            obj_configs=[obj_config1,obj_config2],
+            design_space=bounds,
+            costs=[1,1]
+        )
+
+        opt_config = DriverConfig(
+            max_iter=35,
+            seed=43,
+            nt_init=10,
+        )
+
+        strategy_kwargs = {
+            "n_multi_start":10,
+            "n_accuracy":100,
+            "so_formulation":"Normalized",
+            "single_objective_max_calls":5,
+            "init_calls":5,
+            "naive":True,
+            "verbose":False,
+        }
+
+        optimizer = Driver(
+            problem=problem,
+            config=opt_config,
+            strategy=BiSEGO,
+            strategy_kwargs=strategy_kwargs,
+        )
+
+        state = optimizer.optimize()
+
+        y_data = []
+        for i, sample in enumerate(state.dataset.samples):
+            y_data.append((sample.obj[0],sample.obj[1]))
+        
+        ref_point=np.array([1,1])
+        pareto_points_composite=get_Pareto_front(y_data)
+
+        HV_max=HV(ref_point=ref_point).do(np.array(pareto_points_composite))
+
+        self.assertGreaterEqual(HV_max,0.4)
+        # Theoretical maximum is 0.66
+
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/src/tests/test_unit/test_composite_ei.py b/src/tests/test_unit/test_composite_ei.py
new file mode 100644
index 0000000..b618866
--- /dev/null
+++ b/src/tests/test_unit/test_composite_ei.py
@@ -0,0 +1,96 @@
+import unittest
+import numpy as np
+
+from smt_optim.acquisition_functions.composite_expected_improvement import init_bi_obj_composite_ei
+
+
+class DummyModel:
+    def __init__(self, value, variance):
+        self.val = value
+        self.var = variance
+
+    def predict_values(self, x_pred):
+        return np.array([self.val])
+
+    def predict_variances(self, x_pred):
+        return np.array([self.var])
+
+
+class DummyDataset:
+    def __init__(self, data):
+        self.data = np.array(data)
+
+    def export_data(self, indices, column):
+        return self.data
+
+
+class DummyState:
+    def __init__(self, model_0, model_1, dataset):
+        self.obj_models = [model_0, model_1]
+        self.scaled_dataset = dataset
+
+
+class TestCompositeExpectedImprovementNoMocks(unittest.TestCase):
+    def setUp(self):
+        # Create a dummy dataset with two training points
+        # If phi(y) = y0 + y1:
+        # Point 1: [1.0, 1.0] -> phi = 2.0
+        # Point 2: [2.0, 2.0] -> phi = 4.0
+        # f_min will be min(2.0, 4.0) = 2.0
+        self.dataset = DummyDataset([[1.0, 1.0], [2.0, 2.0]])
+        self.phi = lambda y: np.sum(y)
+
+    def test_composite_ei_zero_variance_no_improvement(self):
+        """Test that if variance is 0 and mean does not improve, EI is 0."""
+        # Predictions: mean = [1.5, 1.5] -> phi = 3.0 (worse than f_min of 2.0)
+        # Variance: 0.0
+        model_0 = DummyModel(value=1.5, variance=0.0)
+        model_1 = DummyModel(value=1.5, variance=0.0)
+        state = DummyState(model_0, model_1, self.dataset)
+
+        kwargs = {"phi": self.phi, "n_accuracy": 100}
+        composite_ei = init_bi_obj_composite_ei(state, kwargs)
+
+        # Act & Assert
+        ei_val = composite_ei(np.array([0.0]))
+        self.assertEqual(ei_val, 0.0)
+
+    def test_composite_ei_zero_variance_with_improvement(self):
+        """Test deterministic improvement when variance is 0."""
+        # Predictions: mean = [0.5, 0.5] -> phi = 1.0 (better than f_min of 2.0)
+        # Variance: 0.0
+        # Expected Improvement: f_min - phi = 2.0 - 1.0 = 1.0
+        model_0 = DummyModel(value=0.5, variance=0.0)
+        model_1 = DummyModel(value=0.5, variance=0.0)
+        state = DummyState(model_0, model_1, self.dataset)
+
+        kwargs = {"phi": self.phi, "n_accuracy": 10}
+        composite_ei = init_bi_obj_composite_ei(state, kwargs)
+
+        # Act & Assert
+        ei_val = composite_ei(np.array([0.0]))
+        self.assertAlmostEqual(ei_val, 1.0, places=5)
+
+    def test_composite_ei_with_variance(self):
+        """Test Monte Carlo integration with variance (using a random seed)."""
+        model_0 = DummyModel(value=1.0, variance=1.0)
+        model_1 = DummyModel(value=1.0, variance=1.0)
+        state = DummyState(model_0, model_1, self.dataset)
+
+        kwargs = {"phi": self.phi, "n_accuracy": 1000}
+        composite_ei = init_bi_obj_composite_ei(state, kwargs)
+
+        # Set seed for reproducibility
+        np.random.seed(42)
+        ei_val_1 = composite_ei(np.array([0.0]))
+
+        # Re-seed to verify determinism
+        np.random.seed(42)
+        ei_val_2 = composite_ei(np.array([0.0]))
+
+        self.assertEqual(ei_val_1, ei_val_2)
+        self.assertGreater(ei_val_1, 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file