{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9fb5db6",
   "metadata": {},
   "source": [
    "This notebook walks throug the reproducibility process step-by-step. \n",
    "\n",
    "We start with general setup, which includes installation of dependencies, loading required libraries and creating a folder to store the figures. The remainder of this notebook follows the ordering from the paper, where each step contains a brief overview of the used data and requirements. \n",
    "\n",
    "General note: Intermediate files are provided in the repository so some cpu-intensive steps can be skipped, but some cells require output from previous ones in order to function, so if an execution fails because of a missing variable please check where it is supposed to be initialized. \n",
    "\n",
    "The first step is to install the dependencies as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec14e9cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ioh==0.3.14 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 1)) (0.3.14)\n",
      "Requirement already satisfied: modcma==1.0.2 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 2)) (1.0.2)\n",
      "Requirement already satisfied: modde==0.0.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 3)) (0.0.1)\n",
      "Requirement already satisfied: nevergrad==0.4.3.post8 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 4)) (0.4.3.post8)\n",
      "Requirement already satisfied: pandas==2.0.3 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 5)) (2.0.3)\n",
      "Requirement already satisfied: pflacco==1.2.2 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 6)) (1.2.2)\n",
      "Requirement already satisfied: scikit-learn==1.2.2 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 7)) (1.2.2)\n",
      "Requirement already satisfied: scipy==1.10.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 8)) (1.10.1)\n",
      "Requirement already satisfied: seaborn==0.13.2 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 9)) (0.13.2)\n",
      "Requirement already satisfied: umap-learn==0.5.6 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from -r requirements.txt (line 10)) (0.5.6)\n",
      "Requirement already satisfied: numpy in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from ioh==0.3.14->-r requirements.txt (line 1)) (1.24.4)\n",
      "Requirement already satisfied: numba in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from modde==0.0.1->-r requirements.txt (line 3)) (0.58.1)\n",
      "Requirement already satisfied: cma>=2.6.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from nevergrad==0.4.3.post8->-r requirements.txt (line 4)) (3.3.0)\n",
      "Requirement already satisfied: bayesian-optimization>=1.2.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from nevergrad==0.4.3.post8->-r requirements.txt (line 4)) (1.4.3)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from nevergrad==0.4.3.post8->-r requirements.txt (line 4)) (4.8.0)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pandas==2.0.3->-r requirements.txt (line 5)) (2023.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pandas==2.0.3->-r requirements.txt (line 5)) (2023.3.post1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pandas==2.0.3->-r requirements.txt (line 5)) (2.8.2)\n",
      "Requirement already satisfied: SALib~=1.4.5 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pflacco==1.2.2->-r requirements.txt (line 6)) (1.4.7)\n",
      "Requirement already satisfied: pyDOE~=0.3.8 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pflacco==1.2.2->-r requirements.txt (line 6)) (0.3.8)\n",
      "Requirement already satisfied: numdifftools~=0.9.40 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from pflacco==1.2.2->-r requirements.txt (line 6)) (0.9.41)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from scikit-learn==1.2.2->-r requirements.txt (line 7)) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from scikit-learn==1.2.2->-r requirements.txt (line 7)) (3.2.0)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from seaborn==0.13.2->-r requirements.txt (line 9)) (3.8.0)\n",
      "Requirement already satisfied: pynndescent>=0.5 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from umap-learn==0.5.6->-r requirements.txt (line 10)) (0.5.10)\n",
      "Requirement already satisfied: tqdm in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from umap-learn==0.5.6->-r requirements.txt (line 10)) (4.66.1)\n",
      "Requirement already satisfied: colorama>=0.4.6 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from bayesian-optimization>=1.2.0->nevergrad==0.4.3.post8->-r requirements.txt (line 4)) (0.4.6)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (0.12.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (3.1.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (10.1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (1.4.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (1.1.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (23.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r requirements.txt (line 9)) (4.43.1)\n",
      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from numba->modde==0.0.1->-r requirements.txt (line 3)) (0.41.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas==2.0.3->-r requirements.txt (line 5)) (1.16.0)\n",
      "Requirement already satisfied: multiprocess in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from SALib~=1.4.5->pflacco==1.2.2->-r requirements.txt (line 6)) (0.70.15)\n",
      "Requirement already satisfied: dill>=0.3.7 in /home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages (from multiprocess->SALib~=1.4.5->pflacco==1.2.2->-r requirements.txt (line 6)) (0.3.7)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2251609-df89-4593-b428-fe8db7fbf214",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from functools import partial\n",
    "import glob\n",
    "import seaborn as sbs\n",
    "import matplotlib.pyplot as plt\n",
    "import ioh\n",
    "from scipy.stats import kendalltau, rankdata\n",
    "from copy import copy, deepcopy\n",
    "import os\n",
    "import umap.umap_ as umap\n",
    "UMAP = umap.UMAP\n",
    "\n",
    "font = {'size'   : 24}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "#Font-requirement for GECCO\n",
    "import matplotlib\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581d482e",
   "metadata": {},
   "source": [
    "To follow along will all figure-generating steps, please make sure to create an empty directory called 'Figures' in the current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5aba289e",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir(\"Figures\")\n",
    "os.mkdir(\"Figures_RF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c0595b5-f5a3-41e9-a0fa-5ff54dff38ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "ela_features = ['ela_meta.lin_simple.adj_r2', 'ela_meta.lin_simple.intercept',\n",
    "       'ela_meta.lin_simple.coef.min', 'ela_meta.lin_simple.coef.max',\n",
    "       'ela_meta.lin_simple.coef.max_by_min', 'ela_meta.lin_w_interact.adj_r2',\n",
    "       'ela_meta.quad_simple.adj_r2', 'ela_meta.quad_simple.cond',\n",
    "       'ela_meta.quad_w_interact.adj_r2', 'ela_distr.skewness',\n",
    "       'ela_distr.kurtosis', 'ela_distr.number_of_peaks',\n",
    "       'ela_level.mmce_lda_10', 'ela_level.mmce_qda_10',\n",
    "       'ela_level.lda_qda_10', 'ela_level.mmce_lda_25',\n",
    "       'ela_level.mmce_qda_25', \n",
    "       'ela_level.mmce_lda_50', 'ela_level.mmce_qda_50',\n",
    "       'ela_level.lda_qda_50',  'nbc.nn_nb.sd_ratio',\n",
    "       'nbc.nn_nb.mean_ratio', 'nbc.nn_nb.cor', 'nbc.dist_ratio.coeff_var',\n",
    "       'nbc.nb_fitness.cor', 'disp.ratio_mean_02', 'disp.ratio_mean_05',\n",
    "       'disp.ratio_mean_10', 'disp.ratio_mean_25', 'disp.ratio_median_02',\n",
    "       'disp.ratio_median_05', 'disp.ratio_median_10', 'disp.ratio_median_25',\n",
    "       'disp.diff_mean_02', 'disp.diff_mean_05', 'disp.diff_mean_10',\n",
    "       'disp.diff_mean_25', 'disp.diff_median_02', 'disp.diff_median_05',\n",
    "       'disp.diff_median_10', 'disp.diff_median_25', 'ic.h_max', 'ic.eps_s',\n",
    "       'ic.m0']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c821b8-21c9-4970-81f6-58ed130d26be",
   "metadata": {},
   "source": [
    "# Part 1: Figures illustrating the MA-BBOB generator (Section 3)\n",
    "\n",
    "These figures showcase the design decisions taken with MA-BBOB, and thus do not require any performance or ELA data yet. We showcase the scale factors used, as well as the impact the design choices have on some 2-dimensional landscapes to motivate our choices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c72042-8174-44fc-a1dc-9f1b1e467eda",
   "metadata": {},
   "source": [
    "## Figure 1: pairwise transition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cff680-6340-4d9b-8fc9-9a31736b5ac9",
   "metadata": {},
   "source": [
    "This figure is taken directly from the GECCO 2023 paper, for which the reproduction steps can be found here: https://zenodo.org/records/7629706"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cade3763-eded-465f-b23a-c0018a743b8f",
   "metadata": {},
   "source": [
    "## Scale factors (Section 3.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b2a01d9-4bff-490a-93f8-5a65a76db6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "max_vals = np.zeros((24,39))\n",
    "mean_vals = np.zeros((24,39))\n",
    "min_vals = np.zeros((24,39))\n",
    "for dim in range(2,41):\n",
    "    x_temp = np.random.uniform(low=-5,high=5, size=(50000,dim))\n",
    "    for fid in range(1,25):\n",
    "        f = ioh.get_problem(fid, instance=1, dimension=dim)\n",
    "        vals = np.array([f(x) for x in x_temp])\n",
    "        max_vals[fid-1, dim-2] = np.max(vals) - f.optimum.y\n",
    "        mean_vals[fid-1, dim-2] = 10**np.mean(np.log10(np.clip(vals - f.optimum.y, 1e-10,1e20)))\n",
    "        min_vals[fid-1, dim-2] = np.min(vals) - f.optimum.y\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04c826a7-0b09-4f10-8488-eeee78e33511",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure taking log doesn't result in NA\n",
    "min_vals = np.clip(min_vals, 1e-12, 1e20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9047fdcf-d4ef-4439-a1f2-4c5ee16f1204",
   "metadata": {},
   "outputs": [],
   "source": [
    "sf_temp = (np.log10(min_vals)+np.log10(mean_vals) / 2)+8\n",
    "dt_sf = pd.DataFrame(sf_temp)\n",
    "dt_sf['fid'] = range(1,25)\n",
    "dt_plot = dt_sf.melt(id_vars=['fid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dccdb69d-80e1-4364-b1fb-8ae001ab6dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "sbs.lineplot(dt_plot, x='variable', y='value', hue='fid', legend=False, palette=sbs.color_palette(n_colors=24), lw=3)\n",
    "plt.xlabel(\"Dimension\")\n",
    "plt.xticks([0,3,8,13,18,23,28,33,38], np.array([0,3,8,13,18,23,28,33,38])+2)\n",
    "plt.ylabel(\"Log(max)+Log(min))/2\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/Scale_factors.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5d1d5d6-c0e6-4d7a-adad-3f81b2a2e12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factors = (np.log10(min_vals)+np.log10(mean_vals) / 2)+8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13cc4bdb-5f85-44dc-b976-b5d1637fc379",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factors_rounded = np.round(np.median(scale_factors,axis=1), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7583b0fd-3c93-4977-a876-e5531a7aacd6",
   "metadata": {},
   "source": [
    "## Design choice illustration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179df63f-2e29-476a-9ae8-6468f905ed69",
   "metadata": {},
   "source": [
    "To illustrate the design choices, we don't use the final model integrated in ioh, but make a separate class so we can more easily modify the intenal settings of the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4577d6ba-e33d-45f0-ba10-9c22fe7ffeb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factors = [11. , 17.5, 12.3, 12.6, 11.5, 15.3, 12.1, 15.3, 15.2, 17.4, 13.4,\n",
    "       20.4, 12.9, 10.4, 12.3, 10.3,  9.8, 10.6, 10. , 14.7, 10.7, 10.8,\n",
    "        9. , 12.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41915817-af04-4fb4-8fcf-bcf4f7f62a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ManyAffine():\n",
    "    def __init__(self, weights, instances, opt_loc=1, dim = 5, sf_type = 'hc'):\n",
    "        self.weights = weights / np.sum(weights)\n",
    "        self.fcts = [ioh.get_problem(fid, int(iid), dim) for fid, iid in zip(range(1,25), instances)]\n",
    "        self.opts = [f.optimum.y for f in self.fcts]\n",
    "        if sf_type == 'minmax':\n",
    "            self.scale_factors = scale_factors\n",
    "        elif sf_type == 'mean':\n",
    "            self.scale_factors = np.log10(mean_vals[:,dim-2])+8\n",
    "        elif sf_type == 'max':\n",
    "            self.scale_factors = np.log10(max_vals[:,dim-2])+8\n",
    "        elif sf_type == 'min':\n",
    "            self.scale_factors = np.log10(min_vals[:,dim-2])+8\n",
    "        elif sf_type == 'min_max':\n",
    "            self.scale_factors = (np.log10(min_vals[:,dim-2])+np.log10(mean_vals[:,dim-1]) / 2)+8\n",
    "        else:\n",
    "            self.scale_factors = np.ones(24)*10\n",
    "        if type(opt_loc) == int:\n",
    "            self.opt_x = self.fcts[opt_loc].optimum.x\n",
    "        else:\n",
    "            self.opt_x = opt_loc\n",
    "\n",
    "    def __call__(self, x):\n",
    "        raw_vals = np.array([ np.clip(f(x+f.optimum.x - self.opt_x)-o,1e-12,1e20) for f, o in zip(self.fcts, self.opts)])\n",
    "        weighted = (np.log10(raw_vals)+8)/self.scale_factors * self.weights\n",
    "        return 10**(10*np.sum(weighted)-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45533c27-0253-4b38-8430-3c1022baf03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vary_normalization(seed = None):\n",
    "    dim = 2\n",
    "    X, Y = np.meshgrid(np.arange(-5,5,0.1), np.arange(-5,5,0.1))\n",
    "    Z = np.zeros(X.shape)\n",
    "    \n",
    "    fig, axs = plt.subplots(1,5, sharey=True, sharex=True, figsize=(32,9),\n",
    "                        gridspec_kw={'hspace': 0, 'wspace': 0})\n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "    weights = np.clip(np.random.uniform(size=24, low=-1),0,1)\n",
    "    iids = np.random.randint(1,100, size=24)\n",
    "    opt_loc = np.random.uniform(size=(5,2), low=-5, high=5)\n",
    "    sf_methods = ['mean', 'max', 'minmax', 'min', 'equal']\n",
    "    for idx, ax in enumerate(axs):\n",
    "        f_new = ManyAffine(weights, iids, opt_loc[1,:], dim=2, sf_type=sf_methods[idx])\n",
    "        \n",
    "        for idx1 in range(100):\n",
    "            for idx2 in range(100):\n",
    "                Z[idx1, idx2] = np.log10(f_new([X[idx1, idx2], Y[idx1, idx2]]))\n",
    "        im = ax.contourf(X, Y, Z, 40, vmin = -4, vmax = 4);\n",
    "        \n",
    "        ax.scatter(f_new.opt_x[0], f_new.opt_x[1], c='r', s=25, linewidths=25, alpha=0.5)\n",
    "        ax.set_xlabel(sf_methods[idx])\n",
    "\n",
    "\n",
    "        if idx == 4:\n",
    "            divider = make_axes_locatable(ax)\n",
    "            cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "            fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/Example_scalefactors_S{seed}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f250eaf-61d0-4b23-9c47-ad3c66eef8c9",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 3200x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_vary_normalization(41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1e84237-17ff-4bf1-ae99-14b20944920c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vary_instance(seed = None):\n",
    "    dim = 2\n",
    "    X, Y = np.meshgrid(np.arange(-5,5,0.1), np.arange(-5,5,0.1))\n",
    "    Z = np.zeros(X.shape)\n",
    "    \n",
    "    fig, axs = plt.subplots(1,5, sharey=True, sharex=True, figsize=(32,9),\n",
    "                        gridspec_kw={'hspace': 0, 'wspace': 0})\n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "    weights = np.clip(np.random.uniform(size=24, low=-1),0,1)\n",
    "    iids = np.random.randint(1,100, size=24)\n",
    "    opt_loc = np.random.uniform(size=(5,2), low=-5, high=5)\n",
    "    for idx, ax in enumerate(axs):\n",
    "        f_new = ManyAffine(weights, iids, opt_loc[idx,:], dim=2)\n",
    "        \n",
    "        for idx1 in range(100):\n",
    "            for idx2 in range(100):\n",
    "                Z[idx1, idx2] = np.log10(f_new([X[idx1, idx2], Y[idx1, idx2]]))\n",
    "        im = ax.contourf(X, Y, Z, 40, vmin = -4, vmax = 4);\n",
    "        \n",
    "        ax.scatter(f_new.opt_x[0], f_new.opt_x[1], c='r', s=25, linewidths=25, alpha=0.5)\n",
    "\n",
    "        if idx == 4:\n",
    "            divider = make_axes_locatable(ax)\n",
    "            cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "            fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/Example_opt_loc_S{seed}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e30d89c-5584-47cb-9e5d-ecbd96c4a6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3200x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_vary_instance(41)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad41de65-0168-44f2-bb83-9c677454816d",
   "metadata": {},
   "source": [
    "# Part 2: Pairwise function combinations\n",
    "\n",
    "For the next part, we limit ourselves to pairs of functions, and look at both performance and landscape features as we transition between the two used component functions. This allows us to investigate small changes in weight-space and how this impacts the resulting landscape / performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b12d42-042c-41ee-9824-353fb6398458",
   "metadata": {},
   "source": [
    "## Setup data collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a1b635-c165-4176-8b9f-cf6f5f67332e",
   "metadata": {},
   "source": [
    "This is the code used for creating the MA-BBOB settings for the pairwise function combination experiments. Note that this experiment did not use proper seeding, so if using these settings your results will be slightly different from the data in the processed files we provide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d982fedd-5acd-4e3f-9852-d914159fc5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_locs_all = np.random.uniform(size=(51, 5), low=-5, high=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a80810c-1bd4-420c-896f-3fb586302df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = []\n",
    "iids = []\n",
    "opt_locs = []\n",
    "for f1 in range(2,25):\n",
    "    for f1iid in range(1,51):\n",
    "        for alpha in np.arange(0,1.01,0.05):\n",
    "            w = np.zeros(24)\n",
    "            iid = np.ones(24)\n",
    "            w[f1-1] = alpha\n",
    "            w[0] = 1-alpha\n",
    "            iid[f1-1] = f1iid\n",
    "            weights.append(w)\n",
    "            iids.append(iid)\n",
    "            opt_locs.append(opt_locs_all[f1iid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01c1dfdb-fa37-471c-84f8-44a4e430e5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "for f1 in [2, 9, 11, 16, 21]:\n",
    "    for f2 in [2, 9, 11, 16, 21]:\n",
    "        for f1iid in range(1,26):\n",
    "            for alpha in np.arange(0,1.01,0.05):\n",
    "                w = np.zeros(24)\n",
    "                iid = np.ones(24)\n",
    "                w[f1-1] = alpha\n",
    "                w[f2-1] = 1-alpha\n",
    "                iid[f1-1] = f1iid\n",
    "                weights.append(w)\n",
    "                iids.append(iid)\n",
    "                opt_locs.append(opt_locs_all[f1iid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e7d31ae-8310-48e0-b051-217e3ad0cd96",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(weights).to_csv(\"weights_gecco_v2.csv\")\n",
    "pd.DataFrame(iids).to_csv(\"iids_gecco_v2.csv\")\n",
    "pd.DataFrame(opt_locs).to_csv(\"opt_locs_gecco_v2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5369c6-86ec-4a7f-9bca-0fa68cbc1571",
   "metadata": {},
   "source": [
    "With these settings, we then run the scripts 'ela_calculation.py' and 'collect_performance.py'. It is recommended to run these on a cluster, as the experiments can take some time. When running these scripts, make sure to update the 'rootname' variable, as this determines where the data is stored.\n",
    "\n",
    "For the performance data, the file 'auc_calculation.py' should be run aften collecting the raw data (with the same 'rootname' parameter setting as the data collection), since this calculates the used performance measure (area over convergence curve, but this is equivalent to area under ecdf with infinite targets, so we stick to the auc abbreviation to match the previous data from the GECCO and AutoML papers)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569eb1cb-ca07-4a60-9f2f-069c2e77f108",
   "metadata": {},
   "source": [
    "## Loading the performance data\n",
    "After loading the auc-data, this section adds the function-information and alligns this with the performance so it can be used in future cells to create figures. Additionally, some processing of the ela-features is done to create the normalized version which will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d65aac87-6f2d-4e8f-bd12-3fcb52eb7ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set to path where the 'auc' table is stored, after the 'auc_calculation' script was used.\n",
    "dt = pd.read_csv(\"auc.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f7a39ce9-9b47-413b-be0f-b2f4a58a583f",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>/datanaco/vermettendl/MABBOB/Ext/AffineExt/232...</td>\n",
       "      <td>0.629688</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>/datanaco/vermettendl/MABBOB/Ext/AffineExt/232...</td>\n",
       "      <td>0.618625</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>/datanaco/vermettendl/MABBOB/Ext/AffineExt/232...</td>\n",
       "      <td>0.618625</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>/datanaco/vermettendl/MABBOB/Ext/AffineExt/232...</td>\n",
       "      <td>0.625829</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>/datanaco/vermettendl/MABBOB/Ext/AffineExt/232...</td>\n",
       "      <td>0.625829</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9318750 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                fname       auc  run\n",
       "0   /datanaco/vermettendl/MABBOB/Ext/AffineExt/355...  0.502114    1\n",
       "1   /datanaco/vermettendl/MABBOB/Ext/AffineExt/355...  0.374391    2\n",
       "2   /datanaco/vermettendl/MABBOB/Ext/AffineExt/355...  0.606142    3\n",
       "3   /datanaco/vermettendl/MABBOB/Ext/AffineExt/355...  0.760112    4\n",
       "4   /datanaco/vermettendl/MABBOB/Ext/AffineExt/355...  0.609565    5\n",
       "..                                                ...       ...  ...\n",
       "45  /datanaco/vermettendl/MABBOB/Ext/AffineExt/232...  0.629688   46\n",
       "46  /datanaco/vermettendl/MABBOB/Ext/AffineExt/232...  0.618625   47\n",
       "47  /datanaco/vermettendl/MABBOB/Ext/AffineExt/232...  0.618625   48\n",
       "48  /datanaco/vermettendl/MABBOB/Ext/AffineExt/232...  0.625829   49\n",
       "49  /datanaco/vermettendl/MABBOB/Ext/AffineExt/232...  0.625829   50\n",
       "\n",
       "[9318750 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20a11252-8771-4d34-989f-cbf0e3947cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This processes the used weighs / instances above into the format used for the pairwise analysis, and then adds it to the performance table\n",
    "f1s = []\n",
    "f2s = []\n",
    "f1iids = []\n",
    "f2iids = []\n",
    "alphas = []\n",
    "\n",
    "\n",
    "for f1 in range(2,25):\n",
    "    for f1iid in range(1,51):\n",
    "        for alpha in np.arange(0,1.01,0.05):\n",
    "            w = np.zeros(24)\n",
    "            iid = np.ones(24)\n",
    "            w[f1-1] = alpha\n",
    "            w[0] = 1-alpha\n",
    "            iid[f1-1] = f1iid\n",
    "            f1s.append(f1)\n",
    "            f2s.append(1)\n",
    "            f1iids.append(f1iid)\n",
    "            f2iids.append(1)\n",
    "            alphas.append(alpha)\n",
    "            \n",
    "            \n",
    "for f1 in [2, 9, 11, 16, 21]:\n",
    "    for f2 in [2, 9, 11, 16, 21]:\n",
    "        for f1iid in range(1,26):\n",
    "            for alpha in np.arange(0,1.01,0.05):\n",
    "                w = np.zeros(24)\n",
    "                iid = np.ones(24)\n",
    "                w[f1-1] = alpha\n",
    "                w[f2-1] = 1-alpha\n",
    "                iid[f1-1] = f1iid\n",
    "                f1s.append(f1)\n",
    "                f2s.append(f2)\n",
    "                f1iids.append(f1iid)\n",
    "                f2iids.append(1)\n",
    "                alphas.append(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b0c8693-22f7-4d52-a063-1b815e74a452",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_info = pd.DataFrame(np.transpose([f1s, f2s, f1iids, f2iids, alphas]))\n",
    "dt_info.columns = ['f1', 'f2', 'f1_iid', 'f2_iid', 'alpha']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "236d4482-9cfb-4f4f-afe5-c39dd079a2ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['funcId'] = [int(fname.split('/')[-3].split('_')[0]) for fname in dt['fname']]\n",
    "dt['funcId'] = dt['funcId'].astype(int)\n",
    "dt['AlgId'] = [fname.split('/')[-3].split('_')[1] for fname in dt['fname']]\n",
    "\n",
    "dt_w_info = dt.merge(dt_info, left_on='funcId', right_index=True)\n",
    "dt_w_info['ID'] = dt_w_info['AlgId']\n",
    "dt_w_info.to_csv(\"auc_w_info.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "baa9dc60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f1_iid</th>\n",
       "      <th>f2_iid</th>\n",
       "      <th>alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37270</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37271</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37272</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37273</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37274</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37275 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1    f2  f1_iid  f2_iid  alpha\n",
       "0       2.0   1.0     1.0     1.0   0.00\n",
       "1       2.0   1.0     1.0     1.0   0.05\n",
       "2       2.0   1.0     1.0     1.0   0.10\n",
       "3       2.0   1.0     1.0     1.0   0.15\n",
       "4       2.0   1.0     1.0     1.0   0.20\n",
       "...     ...   ...     ...     ...    ...\n",
       "37270  21.0  21.0    25.0     1.0   0.80\n",
       "37271  21.0  21.0    25.0     1.0   0.85\n",
       "37272  21.0  21.0    25.0     1.0   0.90\n",
       "37273  21.0  21.0    25.0     1.0   0.95\n",
       "37274  21.0  21.0    25.0     1.0   1.00\n",
       "\n",
       "[37275 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ed9974aa-824b-4775-aef0-483caa111cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_w_info = pd.read_csv(\"auc_w_info.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce113042",
   "metadata": {},
   "source": [
    "This next cell uses the data form the 'ELA.zip' folder, so make sure to unzip it before excuting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7d27bca-2659-4848-b0a3-cca351397163",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m     dt_temp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mID\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(f\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m      5\u001b[0m     ela_dts\u001b[38;5;241m.\u001b[39mappend(dt_temp)\n\u001b[0;32m----> 6\u001b[0m ela_dt \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mela_dts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m ela_dt\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdt_ela.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/venvs/venv_MABBOB/lib/python3.10/site-packages/pandas/core/reshape/concat.py:372\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m    370\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 372\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m    \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
      "File \u001b[0;32m~/venvs/venv_MABBOB/lib/python3.10/site-packages/pandas/core/reshape/concat.py:429\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    426\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    428\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 429\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    432\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "ela_dts = []\n",
    "for f in glob.glob(\"ELA/*.csv\"):\n",
    "    dt_temp = pd.read_csv(f, index_col=0)\n",
    "    dt_temp['ID'] = int(f.split('/')[-1].split('_')[0])\n",
    "    ela_dts.append(dt_temp)\n",
    "ela_dt = pd.concat(ela_dts)\n",
    "ela_dt.to_csv(\"dt_ela.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03f733c2-4a46-4573-adea-19676e57622b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ela_dt = pd.read_csv(\"dt_ela.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d7266aa-c5e3-44cf-92f5-9329a98e7611",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_ela_info_nonnorm = ela_dt.merge(dt_info, left_on='ID', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f20a7186-7a71-41a2-81ee-8e9b52749915",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_ela_info = copy(dt_ela_info_nonnorm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "326fe78f-69b3-4452-b7cc-fe7a9a4b4b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ela_features = ['ela_meta.lin_simple.adj_r2', 'ela_meta.lin_simple.intercept',\n",
    "       'ela_meta.lin_simple.coef.min', 'ela_meta.lin_simple.coef.max',\n",
    "       'ela_meta.lin_simple.coef.max_by_min', 'ela_meta.lin_w_interact.adj_r2',\n",
    "       'ela_meta.quad_simple.adj_r2', 'ela_meta.quad_simple.cond',\n",
    "       'ela_meta.quad_w_interact.adj_r2', 'ela_distr.skewness',\n",
    "       'ela_distr.kurtosis', 'ela_distr.number_of_peaks',\n",
    "       'ela_level.mmce_lda_10', 'ela_level.mmce_qda_10',\n",
    "       'ela_level.lda_qda_10', 'ela_level.mmce_lda_25',\n",
    "       'ela_level.mmce_qda_25', \n",
    "       'ela_level.mmce_lda_50', 'ela_level.mmce_qda_50',\n",
    "       'ela_level.lda_qda_50',  'nbc.nn_nb.sd_ratio',\n",
    "       'nbc.nn_nb.mean_ratio', 'nbc.nn_nb.cor', 'nbc.dist_ratio.coeff_var',\n",
    "       'nbc.nb_fitness.cor', 'disp.ratio_mean_02', 'disp.ratio_mean_05',\n",
    "       'disp.ratio_mean_10', 'disp.ratio_mean_25', 'disp.ratio_median_02',\n",
    "       'disp.ratio_median_05', 'disp.ratio_median_10', 'disp.ratio_median_25',\n",
    "       'disp.diff_mean_02', 'disp.diff_mean_05', 'disp.diff_mean_10',\n",
    "       'disp.diff_mean_25', 'disp.diff_median_02', 'disp.diff_median_05',\n",
    "       'disp.diff_median_10', 'disp.diff_median_25', 'ic.h_max', 'ic.eps_s',\n",
    "       'ic.m0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "48ccaf8b-e48b-4afa-ab51-9d4330b5335b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_ela_info[ela_features] = (dt_ela_info[ela_features] - dt_ela_info[ela_features].min()) / ( dt_ela_info[ela_features].max() -  dt_ela_info[ela_features].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f7a22220-139e-43f4-a8cf-ccdd6fdf602a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_ela_info.to_csv(\"ela_normalized.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "50de1c01-d7ca-4665-b651-02a55079c551",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_ela_info = pd.read_csv(\"ela_normalized.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9705ce97-80b8-4583-bdf6-27802dd005c2",
   "metadata": {},
   "source": [
    "## Adding global structure (Section 4.2)\n",
    "This section looks at the pairwise combinations where one of the components is the sphere function. It creates several heatmaps showing the transition from sphere to the 23 other functions, as well as some distibution plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231a2499-3030-4035-b547-c1e8d5d16796",
   "metadata": {},
   "source": [
    "### Per-algorithm heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1ad82f99-641e-46aa-8685-73b100ab7dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_plot1 = dt_w_info.query('f2 == 1').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6fa9067b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if dt_plot1['f1'].nunique() < 23:\n",
    "    print(\"Loading data failed, please verify whether the 'dt_w_info' table has been successfully loaded from 'auc_w_info.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bfadbb89-cfb1-4ce0-bc1e-dc0f0b9e220e",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hdFVbsGCBvv76a0lXk7T1ZTdVZXbt2qXvv/9eWVlZys3N1c8//6wtW7bo2Wef1Y033qi///3vstlsrg6zXCVLE3Ts2FFxcXHq2rWrFixYoOTkZGVnZysjI0PJyclavHixunXrpujoaOPf/+qrZcuWGR+THj166NZbb3VxROVr0aKF8XRl6eoOmCeeeEJ79+5Venq6rly5ouTkZP3973/XHXfcoYyMDHl6euqtt97STTfd5MLIy9ekSROjffLkySr7l+zz/fff10pMte3w4cPGU82DgoKqtWOs5BPNDx48WGuxofrOnDmjXbt2Ga/r4/1DTk6Ojh07pn//+9+67bbbjH+zDwkJ0aRJk1wbXBUuXryoqVOnSrqa2Cxu11eZmZn69NNPde7cOeXn5+vSpUs6fPiw3n77bQ0ZMkTh4eHGDtq6qLCwUElJScbrZs2aafDgwRozZoz+85//6Ny5c8rLy9O5c+f0xRdfaNq0aQoNDb2uST9nu3z5cqn/sKnLJTQk6emnn1ZAQICkq/d64eHhevfdd/XTTz8Z96r//e9/NWTIEL3++uuSpNtvv12vvPKKK8MuV03uHaTre/9Q1e+Z9e2eoL7/3nwtZ67H1fcFNV1LXbsnsGc9ZrsngGuY//+xcF0dPHhQb7/9tvG6vnxj+vOf/1xq18G1fHx89O9//1vjxo27jlHZLzk5WfPmzZMkTZo0SQMHDnRtQE7QvHlz9enTR506dVLDhg2VmZmpAwcOaNOmTbJarcrJydGcOXN07NixUjti6oqSv8wmJSVp8eLFKigoUMuWLXXfffepXbt2SktL02effabk5GTZbDYtWrRIWVlZevPNN10YueMKCwu1YsUK43Vd3dVcbNGiRSosLNS//vUv5ebm6vXXXzd+MSzJ3d1dQ4YM0fPPP19nS9P07NlTGzdulCStX7++0u8BFy9e1LZt24zXaWlpKigoqHf/qn3u3DmjXd1dFyXrppe8Hq7zzDPPGAmCyMhIderUycURVS0nJ6fKXYD9+/fX+++/Lz8/v+sUlWNmzJihs2fPytvbW2+99ZYsFourQ3KYm5ubwsPDddttt6lVq1Zyc3PTmTNntHXrViUnJ0u6+vM4PDxcO3bsUJcuXVwccVlnzpwx/qtOkiZMmKDt27dLkgYOHKg777xTHh4eOnDggD7++GPl5ubq+PHj6tevn77++muFhIS4KnSHvfvuu8aab7jhBt17770ujqhyN954ozZv3qxRo0bpzJkz2r9/vx555JFy+7Zr106TJk3SnDlz5OnpeZ0jrVrJshh79uzR2bNn1apVqwr7f/TRR6VeX6+fo9X5PbM+3RPU19+bK+Ls9bjyvsCRtdTlewJ712OmewK4kEsfTwhTycrKsnXr1s14OumoUaNcHVK1zZ4924j72mP06NG2X375xdUhVqmgoMB2++23/3/t3XlUFFfaBvCn2WwREEEURUVwEE3cN4yOJBKjTtSoaEAnrhn1JIrjkoxL8ItmRhMlcR1IxmjcDQomLiEoKoo7KCpRYhRNALfIJigosnTX94eHOt2yNXR1V7c8v3M4p6r7VvG+dNPcerl1rwBAcHV1FXJzc8u10cwrNTXV6DHWxMGDB4Vjx44JKpWqwuczMzOFv//971o5rV+/3shRVm/EiBHl3lMBAQFCQUGBVjuVSiUsXbpUq11UVJRMUesnKipKzMHW1lZ49OiR3CHp5MyZM8LgwYMr/Szw9PQUli1bVuHvlqnYsWOHGK+NjY1w+vTpCtupVCph7Nix5XLUdUVrQ3J3dxfj0WXF8sjISLG9j4+PTt8jLCxMPGbYsGF6Rly5muZSFWOtOl8VKfPRpPm+tbKyEhITEyU7d1X0zaewsLDSz4smTZoIkZGR0gddhdrm89NPP4nHffHFF+Wel2Pl+drmsnz5ciEjI6PS5/fv3y+4uLiI5/bw8BAKCwsliLhqNc0nKSmp3HvKwcFBOHr0aLm2t27dEtq2bSu269GjhwEy0GaIz4IePXqI5wwODpbknLrSJ5/c3Fxh1apVQsOGDSv8LLCwsBBGjx4t6WdmdWqaj1qtFry8vMRjhg8fLpSUlFTY9uLFi4Ktra1WjjNnzpQ4g/J0vc405T6BJimum02hX1BG6jqAXP0CQah9LqbWJyhT03xMsU9A5sm8hi+RyVKr1Rg/fjySkpIAPP9P8oYNG+QNqgaGDBki3mZVUlKCzMxMnDt3DgkJCdi9ezcSEhKwcuXKKldnltvKlSuRkJAAAAgLCxMXuzBXgwcPrvJ5FxcX7Ny5E5aWluLq0YsXL8bEiRNRr149Y4Sok/z8fK39Dh06YMeOHeXm67OwsEBwcDBSUlKwbds2AMAXX3yBIUOGGC1WqWiOMB89ejQcHBxkjEY38fHxCA4OxokTJ9CgQQMMGTIEr7zyCqysrJCWlobo6Gj88ccfCA4ORlhYGH788Uf4+PjIHXY5Y8aMwerVq3Hx4kUUFxfjrbfewvz58zF27Fi0bt0ahYWFOH/+PD7//HPExcVBqVSKK9ADwNOnT3Ve1dpUaM41qeuIMc3FKjVHD5LxUpyWIAAAIQ1JREFUnTt3Tut2+aVLl+q0uKUpsLa2xpdffinu5+fnIyUlBTExMcjMzMSYMWPw3nvvYdWqVVq3qZuSvLw8cYRT165d8fHHH8sckX7mz59f5fPvvPMOjh49ij59+uDJkydITU3F+vXrMWvWLCNFqJsX+w4A8L///a/Cu1XatGmD/fv3o3PnziguLkZiYiKOHj2KAQMGGCNUSVy5ckWcNkShUJj8tDNlnj17htWrVyM0NBSPHj1Cp06d4OfnhyZNmiA/Px8XLlxAbGws9uzZgz179mDq1Kn45ptvTG5KPoVCgZCQEIwcORLA8wX2fH19sWjRIvTp0wcNGjTA7du3ERkZiWXLluHp06da/QdD/x2tyXWmOfQJzP26+UVS5yNnv0CfXEyxT1DTfF62PgHJTO5qN5k/tVotTJkyRWvkhTH/+2hICQkJQps2bcTcQkND5Q6pQtevXxeUSqUAQPD396+0HTT+w2rqI5t1lZOTI9jZ2Yl5HT58WO6QtAwdOlTr575169Yq21+/fl1sq1AohIcPHxopUmk8ePBAsLa2FnM4efKk3CFVa9u2bYKlpaUAQHjrrbeErKyscm2Ki4uFf/3rX2Jejo6Owu3bt2WItnp37tzRGuVW2Zetra2wceNGrcdMYRQ6RzZXzBRGMEk9mvGXX34RnJycxHOOGzdOUKvV+geqI0ON1C4sLBTmzZsnntvb29sodw3UJp9JkyaJI8cuXbpUYRtzGtmsq08++UQ8f58+fSQ//4tqmk9iYqLWZ7Onp2e1vxtjxowR28+aNUuawCsh9eszc+ZM8Xx+fn76B1hDtcknLy9P6NmzpwBAUCqVwq5duypsd/nyZaF169bi+efOnSth5BXT584AhUJRbf9h1KhRwqhRo8T9OXPmGCyXml5nmnKfQBCkvW42hX6B1HUAOfsFhqppyNUnqE0+ptonIPPEBQJJb0FBQeLqqvb29jh06JDZjEqqTq9evRAbGyvOqzRnzhzcunVL5qi0qdVqTJ48Gc+ePYOjoyPCwsLkDsmonJyctEbvnDt3TsZoyitbPKaMn59fle29vb3RvHlzAIAgCEZfFVtf27ZtQ0lJCYDnC2CY+mInN27cwNSpU6FSqeDh4YF9+/ZVuJiMtbU1QkJC8O677wJ4/p//Tz/91Njh6qRFixZITEzERx99VO79V+b111/HxYsXteYqtbS0rLS9KdOM+cmTJzodozlyyRxzfhn8+uuvGDBgAB4+fAgACAwMxJYtW16KeQGVSiVWrFiBoKAgAM8/Z0xxdNDBgwfF+fU//vhjdO3aVd6AjCggIEDcPn/+vMktyvvi51L//v2r/d3Q7F9cunTJIHEZQlFREXbu3Cnum/rCgGVmzZolLsi9atUqBAYGVtiuS5cuOHDggDiaec2aNUhNTTVanDUxf/58HD58uNI7txo1aoQVK1YgIiJC6++tIe+mrOl1pqn3CV6262Yp85G7X2Co10auPkFN86nLfQIyDBabSS8zZ87E119/DQCws7PDwYMH8dprr8kclbTc3d0xbtw4AM+n2NBc+MwUbN68WSywrly5Eq6urjJHZHyaq0hnZGTIGEl5mgusKBQKsZBcFc0FTbKzsw0Sl6FoLj5hDheM//3vf1FUVATg+WIZtra2VbafO3euuL1v3z6TK1CUsbe3x1dffYWsrCycOHEC3333HdauXYvvv/8eN2/eRFxcHNq1a4fff/9dPKZNmzZmWejT/My7e/euTsdotmvatKnkMVHVrl27Bj8/P2RlZQF4Pt3Ojh07TO7Wcn0tXLhQ/J3auXOnyU3ZMm3aNACAl5cXFi9eLHM0xqXZbygtLUVOTo6M0ZT3Yl9Ol4XOzLXvsHfvXrG41KhRI5Oesq5MRkYGduzYAeD539vqFkLu2LGjODBCrVZj3759hg6x1gYMGID4+Hikp6cjIiICoaGhWL9+PWJiYvDnn39i3rx5sLCw0Oo/eHl5GSSW2lxnmnKf4GW7bpYyH7n7BcZ4bYzZJ6hNPnW5T0CGwTmbqdZmzpyJ0NBQAECDBg0QHR2Nvn37yhyVYXTq1Enc/uWXX2SMpLw7d+6I2//4xz90LvB5eHiI24sXL8aSJUukDs1oTLlA9uqrr4rbCoVCp1hNOZ+qnD59GtevXwcAWFlZYeLEiTJHVD3NkfAdO3astr3mZ0FeXh6ys7PRpEkTg8QmhXr16sHX1xe+vr4VPl82hxsAs73g8fb2hoWFBdRqtfiaVDQ6XZPmHSqav6NkeNeuXUP//v2RmZkJAPD390d4eDisrF6+Lmnz5s3RuHFjZGVlobi4GL/99ptJjWArK7DcvHkT9evX1+mYrVu3YuvWreK+IAgGic3QTP3vrIODA1q2bCn28V7mvkPZyDsAGDdunNb8uabq/PnzUKlUAIB27dqVW4ejIp06dUJMTAyA579zpq5Vq1Zo1apVhc8VFBRoFZsN0X+o7XWmqfYJXrbrZinzkbtfYKzXxlh9gtrmU5f7BGQYHNlMtRIUFCR+iNna2uLnn382+dvl9VFaWipul3UuyXRodhJNbZRijx49xG21Wo379+9Xe4zmCAtTLmS+SHNhwKFDh5rca1ERzUWYanOhbmFh3n9G9+/fL25XtyinqVIqlXjllVfE/TNnzlTZXq1W4+zZs+I+bxM0nl9//VXrgnLEiBHYtWvXS1loLsP+g2nS7DdYWlqa5AKOmv0HXUZommPfITU1FceOHRP3zeGOKIB9hwMHDoh3dnl7e6N169aSnl+f60xT7BO8bNfNUuYjd7/A2K+NofsEL9t7jczby9u7J4OZMWOGeFtG2YfY66+/LnNUhlU2Jxug262MxtSlSxedR5Bq/udx1KhRsLOzE89hrnJzc3H06FFxv7J55uTSqVMntGnTRhwBEhsbiwkTJlTaPiUlBffu3QPw/AK4W7duRolTX/n5+YiMjBT3q7ul1FQ0btxYHGF05coV/O1vf6uy/ZUrV8TtevXqwcnJyaDxGVJ0dDRSUlIAPP8njTnculyZ4cOHIzk5GQAQGRmJ4cOHV9o2NjYWubm5AJ5Pk2Quv2PmruyCsuwW2eHDhyMiIkKnEYHm6vfffxffa4Dp9R907TskJSWJd3W1adMGf/3rXw0ZllFo/r3q2bOnSRb//P39sXfvXgDAsWPHIAhClYVNzaJtr169DB6fFDZv3iyOhOvRowc6d+4sc0S60Rwpe/36dZSUlFT7WabZf9CcYs3cCIKAdevWifvTp0+X9PxSXGeaUp/gZbtuljIfufsFxn5tDN0n0DefutwnIAORc3VCMj/Tp08XVx+1tbUVjh07JndIBpecnCzY2NiIeUdGRsodUq2V5QBASE1NlTscSUyePFnMycXFRSgsLJQ7pHKWLl0qxtixY0ehuLi40rYTJkwQ2w4aNMiIUern22+/FeN2c3MTSktL5Q5JJ7Nnzxbj9vT0FJ4+fVpl+9GjR4vt33zzTSNFKb0HDx4IHh4eYi7r1q2TOySRu7u7GNfx48d1OubatWuChYWFAECwsbERrl27VmE7lUol9OvXTzz/okWLJIy8vNrkUhlTWHW+tvkkJycLLi4u4rHvvPNOlZ+DxiLl61OR9957Tzz/q6++Kvn5X2SofORYed6Qr01ycrJgZ2cnnn/lypWSnr8itcnn8ePHQqNGjcTjwsPDK217/fp1rb7quXPnJIq8YlK8PiqVSmjZsqV4nm+++UbaIGugpvnk5OQIlpaW4jHr16+vsv0vv/wi/o0CIJw6dUqiyCtmyN+fkJAQ8dzu7u5Cfn6+ZOeW6jrTVPoEhr5uNna/QMp85O4XyFHTMGSfwJj5yNEnIPPEYjPp7GUqNP/73/8WPvvsM+HOnTtVtouKihJcXV3FvNu3by8UFRUZKUrpmUuxOSQkRJgzZ45w48aNSttkZ2drFWYBCGFhYUaMUndPnjwR3NzcxDgDAwOFgoICrTYqlUpYtmyZ2MbCwkI4c+aMTBHXXK9evcTYg4OD5Q5HZ0lJSVoXgIMHDxays7PLtSsqKhI++ugjrffbDz/8IEPE1YuKihLi4uIEtVpd4fPx8fFCu3btxDz69+9faVs51PYiedKkSVqf1WlpaVrPl5aWCkFBQWKbxo0bC3l5eRJHr43FZvkvKKtS03zy8vKEN998Uzh48KBQUlJSabusrCyt9yMAYfv27RJGXrG6Xmzu3r27EBERUeU/naOiooQmTZqI527VqlW1/2SUQm1fmzVr1ojHOTg4CLGxseXa3Lp1S/D29hbbGeMf1VK816Kjo7WuKx49eiRtkDVQm3z8/f3FY+rXry/s2bOnwnaXLl3SOn/nzp0FlUolYfTl1SYflUolrFixQsjKyqrw+adPnwoLFy7U6qdW9H6sLamvM+XuExjjutmY/QIWmsszlT6BsWs0LDaTrhSCwFm8qXqff/45goODxf1hw4ZVuuDUiwIDA9GyZUtDhVYrs2fPxtq1a6FQKNC5c2d06dIFLVq0gJ2dHQoLC5GWloZTp07hjz/+EI9p3Lgxjh49aja3+FVE8/bL1NRUyedYk8qSJUvw2WefAXi+8EqPHj3g7u4Oe3t7FBQU4OrVqzhy5IjWKr6TJk3C5s2b5Qq5WqdOncLAgQPx7NkzAM9Xyx45ciRatmyJnJwcREdH47fffhPb/+c//8GiRYvkCrdGkpOTxcX1FAoFbt26BU9PT5mj0t2CBQuwYsUKcd/Ozg5Dhw5F+/btYWVlhbS0NPz8889a820HBgZi165dcoRbrUWLFmHZsmVo3rw5fH194eXlhfr16yMzMxNnzpxBYmKieNuyj48PDh06BEdHR1liHT16dLnHDh48KP5u+/r6wsXFRev5gIAABAQElDsuJycHffv2xY0bNwA8v4XQ398f3t7eyM3Nxf79+8XpbKysrLBv3z4MGTLEJHOJiIhARESE1mPp6elITEwE8PzvUUW3Ru7Zs6fW8b9Iinzy8vLg7e0tzsVob2+PBQsWwMbGptrv37JlSwQGBuqTghap8mnUqBEAwMnJCT4+Pmjfvj2cnJxgYWGBnJwcXL16FXFxcSguLhaPmzZtGtavXy9ZLlLloyvNv8kTJ07Eli1bah5wFaTKpayPY2dnh969e6NDhw5wdnaGhYUF7t+/j+PHj+PatWti+4YNG+LEiROS9+ukfG1UKhX8/f1x4MAB8bEBAwagT58+sLKyQnJyMg4cOCD2Ldzc3JCYmAhXV1eTzOfF8/7www8ADPO+qur7vqg2+aSlpaF3797IyMgQH+vSpQv8/Pzg4uKC/Px8nD9/HrGxseLf3AYNGiAuLk5rPm5Tyae0tBTW1tawsrLCa6+9hm7duqFJkyZ49uwZbt26hUOHDolTAFhaWmLLli0YN26cJDkY4jpTzj6BIfKRs18gZT5y9wukzkXuPoEcNRpD9wnoJSJvrZvMxcSJE7X+G1eTL0PcmqqvOXPm1CiHAQMGCDdv3pQ7bL1p5mTKI5s1/2Na3Zetra3w5ZdfmtTIzMrExsYKLVq0qDIfpVIprFmzRu5Qa0RzKgo/Pz+5w6mVZcuWad2CXNmXQqEQ/vnPf5r0HQ7BwcHV5mFpaSlMnz5d0ttfa6M2f1MWL15c6fnS09OF1157rcrjGzVqVOkINFPJpSafgZpfppZPampqrfsOUo/SkiKfvLy8Gh3fsGFDg01RI/XvTlUMPYpJqlxqcnyfPn0M1q+T+rUpLCzUmjKssq9evXoJt2/fNvl8BEEQMjMzBWtra7H9yZMnJY+7MlLm89tvvwndu3fX6Ryenp4Gmd5EqnxKSkp0OrZt27bCkSNHJM3BUNeZcvUJDJGPnP0CKfORu18gZS6m0CeQo0bDkc2kKy4QSHVSSEgIRowYgbi4OFy4cAEpKSn4888/8eTJEyiVSjg6OsLb2xu9evXCu+++i+7du8sdcp0ya9Ys9OjRA/Hx8bhw4QLu3r2L7Oxs5ObmQqlUwtnZGZ07d4afnx/Gjx8v/lfZ1Pn5+eHatWvYsmUL9u7dixs3biArKwv29vbw9PTEwIEDMX36dLi5uckdqs6Ki4uxY8cOcd9cFgZ80SeffIIJEyZg69at4ui33NxcqFQqODo6wsvLC/369cP777+Ptm3byh1ulT788EO0atUKx48fR3JyMjIyMpCXlwdnZ2e0atUKgwYNwtixY9G+fXu5Q5Vcq1atcPr0aezevRvh4eFISkpCRkYG7O3t4eHhgXfeeQdTp06VdNQf1R0NGzbEvXv3cPjwYZw9exZXr15FamoqcnNzoVarYW9vj2bNmqFz584YOHAgRo0aBXt7e7nDrjMuX76M+Ph4xMfH49q1a8jKykJ2djaKiorg6OgId3d39O7dGwEBAejXr5/c4epMqVRi06ZNmDp1KrZu3Yq4uDjcv38fJSUlaNKkCXx8fBAYGAh/f/8qFxA0Jdu3b0dJSQkAwNvb26xeD03t2rVDQkICDh06hMjISCQmJuLevXvIz89H/fr10bRpU3Tv3h3Dhw/H6NGjdRq9KRcrKyvExMQgLi4Op0+fxp07d5CRkQFLS0u4urqiW7duGDFiBEaNGmXSeWhin4AMiX0CoqpxGg0iIiIiIiIiIiIi0puF3AEQERERERERERERkfljsZmIiIiIiIiIiIiI9MZiMxERERERERERERHpjcVmIiIiIiIiIiIiItIbi81EREREREREREREpDcWm4mIiIiIiIiIiIhIbyw2ExEREREREREREZHeWGwmIiIiIiIiIiIiIr2x2ExEREREREREREREemOxmYiIiIiIiIiIiIj0xmIzEREREREREREREemNxWYiIiIiIiIiIiIi0huLzUREREREVWjdujUUCgUUCgXS0tLkDoeIiIiIyGSx2ExERERkht544w2xAFqTr7i4OLlDJzMQFxen9b6pzJIlSyp9r1lZWcHZ2Rmenp7o3r07Jk2ahNDQUFy+fNmImRARERGRMbHYTERERER1BkcpG49KpcLDhw+RmpqKS5cuYevWrZg5cya6deuGrl27Yv369VCpVHKHSUREREQSspI7ACIiIiLST8+ePdGrVy+d2rq5uRk4GqqrmjdvjpEjR2o9lp+fj7y8PKSlpSE5ORlqtRoAkJSUhA8++ABbtmzB9u3b8Ze//EWOkImIiIhIYiw2ExEREZm5t99+G0uWLJE7jJcWR0DrxsvLC6GhoZU+n5+fj6ioKKxatQqJiYkAgPj4ePTq1Qvx8fFo27atsUIlIiIiIgPhNBpERERERGRw9vb2GDt2LM6fP4/PP/8clpaWAIDc3FwMHToUjx49kjlCIiIiItIXi81ERERERGQ0CoUCCxcuxBdffCE+dvPmTaxdu1bGqIiIiIhICiw2ExEREdVRb7zxhrhYXlxcXLXtlyxZIravbNqOitqUlpZi27ZtGDBgANzc3FCvXj00a9YMI0aMQFRUVI3jvnLlChYsWAAfHx+4urrCxsYGdnZ28Pb2RmBgIL777jutUbJpaWliTOnp6eLjHh4e4uOaXy/+LGq6qGB6ejo+/fRT9O7dG02bNoWNjQ2aNm2K3r17Y/Hixbhz506154iLixO/5xtvvCE+fuzYMYwZMwaenp5QKpVwdnaGr68vQkNDUVJSUu15TcnHH3+Mvn37ivtr165FQUGBjBERERERkb44ZzMRERERGcy9e/cQEBCAs2fPaj3+4MED7N+/H/v378fkyZOxceNGWFhUPQ4iLy8PH3zwASIiIiAIgtZzJSUlSElJQUpKCiIiIhAcHIwHDx5Ink91li1bhqVLl+LZs2daj2dmZiIzMxMJCQkICQnBkiVLMH/+fJ3PW1xcjKCgIGzYsEHr8aKiIpw6dQqnTp3C5s2bERMTg8aNG0uSi6EpFArMnTsXZ86cAQA8fPgQR44cKbfIIBERERGZDxabiYiIiMggCgoKMHjwYCQnJ8PW1hb9+vVDy5YtkZ+fj+PHjyMzMxMAsHnzZnh7e1dZfL1//z78/Pxw48YN8TFHR0f07dsXzZo1Q0lJCW7fvo2LFy/i8ePHWsVeBwcHzJgxAwCwbds25OfnAwAmTJgAe3v7ct/Lzc2tVvkGBQUhLCxM3Lezs0P//v3h6uqKBw8e4Pjx4ygoKMCzZ8+wYMECPHjwAKtXr9bp3NOmTcPWrVthYWEBHx8ftGvXDmq1GvHx8eLP5NKlS5gwYQKio6NrFb8chg0bBqVSKb5ep06dYrGZiIiIyIyx2ExEREREBhEaGoqioiJMnDgRq1atgpOTk/jc06dPMWXKFISHhwMAli5diqCgIDRo0KDceUpLSxEQECAWVevXr48vv/wS06ZNg7W1tVbb4uJixMTEYNOmTeJjTk5OCA0NBQBERUWJxebPPvsMrVu3liTXiIgIrULzpEmTsHbtWjg4OIiPPX78GDNmzMCOHTsAAGvWrEG/fv3g7+9f5bnj4+Nx4sQJ9OzZE9u2bUO7du3E5wRBwLp16zB79mwAwMGDB3Hy5En4+vpKkpehWVtbo2vXrjh37hyA57kSERERkflisZmIiIjIzEVHRyM7O7vadgsXLqz1qN3aKCoqwtixY7Fly5Zyz9na2mLTpk04ffo07ty5g4KCAkRFRSEwMLBc2y1btohTLVhbWyMmJgb9+vWr8Hva2Nhg2LBhGDZsmKS5VEWtVmPBggXi/rvvvotNmzZBoVBotXNwcBBHVu/fvx8AMG/ePIwYMaLKKUSKiorg5eWFY8eOwc7OTus5hUKBWbNm4fTp09izZw8AIDw83GyKzQDg7e0tFpszMjJkjoaIiIiI9MFiMxEREZGZu3DhAi5cuFBtuylTphi12GxjY4NVq1ZV+rxSqcTYsWMREhICADh//nyFxeaVK1eK23Pnzq200CyXw4cPIzU1FcDznNetW1eu0FxGoVAgLCwM0dHRKCkpwe+//44jR45g0KBBVX6P5cuXlys0a3r//ffFYvP58+drmYk8GjZsKG7n5ubKGAkRERER6avqVViIiIiIiGrpr3/9K1xdXats07VrV3E7LS2t3PPp6em4fv26uB8UFCRZfFI5duyYuP32229Xm7ObmxsGDx4s7h8/frzK9kqlstqR2tX9HE2ZZhG9bIoTIiIiIjJPLDYTERERmbnFixdDEIRqv7p06WLUuDp27FhtG2dnZ3H78ePH5Z7XnMPXy8sLLVq0kCY4CV2+fFnc7tOnj07H9O3bV9y+dOlSlW29vb3LzU39oup+jqZMs8CsOcc1EREREZkfFpuJiIiIyCA0p0eojGYRtaSkpNzzmnP4enp6ShOYxLKyssRtd3d3nY7RXJiwuvm2a/pzLC0t1SkGU/Ho0SNxW3MRSSIiIiIyPyw2ExEREZFBVDZvcU1ojnqtas5iORUUFIjbDRo00OkYzXbVTR0hxc/RlGlOk1LdFCREREREZNpYbCYiIiIinajVaqN/T3t7e3Fbs6hrSjSL4E+ePNHpGM12mjnWNcXFxUhKShL3e/fuLV8wRERERKQ3FpuJiIiI6qiaTr2gOd2BsTRt2lTcTk1NNfr314WLi4u4ffv2bZ2O0VzEr3HjxlKHZDYOHDiAoqIicd/X11fGaIiIiIhIXyw2ExEREdVRmoux5eTkVNv+6tWrhgynQpojXVNSUnD37l29zmeIKSm6du0qbp89e1anYzTbdevWTfKYzIEgCFi9erW47+LigjfffFPGiIiIiIhIXyw2ExEREdVRmovUaU5lUJH79+/j9OnThg2oAu7u7mjfvr24HxYWptf5lEqluF3RgoS14efnJ25HR0cjMzOzyvb379/HwYMHKzy+Lvnqq6+0iu5z5syBra2tjBERERERkb5YbCYiIiKqo3x8fMTt8PBwrekMXjRnzhzJirM1NXfuXHF75cqVOHXqVK3P5ezsLG7fu3dPr7jKDBw4EB4eHgCAoqIizJ49u9K2giBg5syZ4s+yTZs2GDBggCRxmAtBELB8+XIsXLhQfKx9+/aYOXOmjFERERERkRRYbCYiIiKqo4YOHSouTpeeno4pU6agsLBQq83Dhw8xYcIEREREoF69enKEiUmTJqFPnz4Ano9GHjx4ML7++usKi9/FxcX46aefMHLkyArP1aFDB3E7MjJSkvgsLCywfPlycT88PBxTp04tt6Bhfn4+Jk+ejB9//FF8LCQkBBYWdaNLXlBQgF27dsHHxwcLFy6ESqUC8HzO6qioKK2FFomIiIjIPFnJHQARERERycPW1hb/93//h3nz5gEAduzYgcOHD6N///5wcHDAnTt3cPLkSTx9+hQdOnTAoEGDsHLlSqPHaWVlhd27d8PPzw83b97E06dPMWPGDAQHB6Nv375o1qwZSktLkZ6ejosXL+Lx48do2LBhhecaNWoU1q9fDwD4+uuvcfHiRXTr1k1r+oYPP/wQbdq0qVGMAQEBOHnypDjNx8aNG7F79270798fTZs2RWZmJmJjY7UK0LNnz4a/v39Nfxwm6+bNmwgKCtJ6rKCgAHl5eUhLS0NycrJYYC7Tt29fbN++XRwZTkRERETmjcVmIiIiojrso48+QkpKCjZu3AgAyMzMxO7du7Xa9O7dG3v27MGGDRvkCBEA0KJFC8THx2PKlCnYu3cvACAvLw8///xzhe0rGyX71ltvYezYsQgPDwcAJCQkICEhQavN0KFDa1xsBoDQ0FC4urpi6dKlKCoqQn5+Pg4cOFCunVKpxKeffqo1jcTL4P79+zrPqd2tWzd8+OGHeP/99+vMyG4iIiKiuoDFZiIiIqI6zMLCAhs2bMDIkSPx7bffIiEhATk5OXB2dkb79u0xbtw4jB8/HtbW1nKHCicnJ/z444+4cOECvv/+e8TFxeHu3bvIzc1F/fr10aJFC3Tp0gWDBw/G6NGjKz3Pzp07MXToUISHhyMpKQnZ2dl49uyZJDEuWrQI48ePx8aNGxETE4PU1FTk5eXB0dERnp6eGDRoEKZMmYJWrVpJ8v1MmaWlJRwcHODg4ABnZ2d07NgR3bt3h6+vLzp37ix3eERERERkAApBEAS5gyAiIiIiIiIiIiIi88Z71oiIiIiIiIiIiIhIbyw2ExEREREREREREZHeWGwmIiIiIiIiIiIiIr2x2ExEREREREREREREemOxmYiIiIiIiIiIiIj0xmIzEREREREREREREemNxWYiIiIiIiIiIiIi0huLzURERERERERERESkNxabiYiIiIiIiIiIiEhvLDYTERERERERERERkd5YbCYiIiIiIiIiIiIivbHYTERERERERERERER6Y7GZiIiIiIiIiIiIiPTGYjMRERERERERERER6Y3FZiIiIiIiIiIiIiLS2/8D1aKoU+j8ca8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_aucs = np.zeros((23, 21))\n",
    "budget = 10000\n",
    "for algid in np.unique(dt_plot1['ID']):\n",
    "    for fid in range(2, 25):\n",
    "        # aucs = np.array(dt_plot1[(dt_plot1['f1'] == fid) & (dt_plot1['ID'] == algid)].groupby(['alpha', 'x']).agg(np.mean).groupby('alpha').agg(np.max)['auc'])\n",
    "        dt_part = dt_plot1[(dt_plot1['f1'] == fid) & (dt_plot1['ID'] == algid)].groupby(['f1_iid', 'alpha']).agg(max).reset_index()\n",
    "        # dt_part['auc_fixed'] = (dt_part['auc'] * dt_part['x'] + 10000 - dt_part['x']) / dt_part['x']\n",
    "        aucs = dt_part[['alpha', 'auc']].groupby('alpha').agg(np.mean)['auc']\n",
    "        heatmap_aucs[fid-2,:] = aucs\n",
    "        \n",
    "    fig, ax = plt.subplots(figsize=(16,9))\n",
    "    # divider = make_axes_locatable(ax)\n",
    "    # cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    # plt.scatter(x=xs[:,0], y=xs[:,1],c=np.log(ys[0,:]), s=25, alpha=0.9)\n",
    "    # im = ax.imshow(heatmap_aucs.transpose(), vmin=0, vmax = 1)\n",
    "    im = sbs.heatmap(heatmap_aucs.transpose(), ax=ax, vmin=0, vmax = 1, cbar_kws = dict(pad=0.01), cmap='viridis')\n",
    "    # ax.colorbar()\n",
    "    # fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "\n",
    "    # fig = plt.figure(figsize=(16,9))\n",
    "    # ax1 = plt.imshow(heatmap_aucs.transpose())\n",
    "    # ax1_divider = make_axes_locatable(ax1)\n",
    "    # cax1 = ax1_divider.append_axes(\"right\", size=\"7%\", pad=\"2%\")\n",
    "    # cb1 = fig.colorbar(im1, cax=cax1)\n",
    "    \n",
    "    ax.set_yticks([x+0.5 for x in range(21)], [f\"{x:.2f}\" for x in np.arange(0,1.01,0.05)], fontsize=20, rotation=0)\n",
    "    ax.set_xticks([0.5+x for x in range(23)], range(2,25), fontsize=23)\n",
    "    ax.set_xlabel(\"Function ID\")\n",
    "    ax.set_ylabel(\"Alpha\")\n",
    "    # plt.colorbar()\n",
    "    # plt.title(algid)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/{algid}_heatmap_alphas_v2.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d5a087-fbc4-4a74-a0f0-c87774af86b4",
   "metadata": {},
   "source": [
    "### Ranking heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bf9e6c12-f43d-43ef-aeb4-d28b79213c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "algs_shortname = ['dCMA', 'DE', 'Cobyla', 'modcma', 'modde']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5e6a3af2-1fc8-41bc-9566-53155a932656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "algs = np.unique(dt_plot1['ID'])\n",
    "heatmap_aucs = np.zeros((23, 21))\n",
    "for fid in range(2, 25):\n",
    "    aucs_fid = np.zeros((5,21))\n",
    "    for aidx, alg in enumerate(algs):\n",
    "        dt_part = dt_plot1[(dt_plot1['f1'] == fid) & (dt_plot1['ID'] == alg)].groupby(['f1_iid', 'alpha']).agg(max).reset_index()\n",
    "        aucs = dt_part[['auc','alpha']].groupby('alpha').agg(np.mean)['auc']\n",
    "        aucs_fid[aidx,:] = aucs\n",
    "    heatmap_aucs[fid-2,:] = np.argmax(aucs_fid, axis=0)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "\n",
    "cmap = sbs.color_palette(\"deep\", len(algs))\n",
    "\n",
    "im = sbs.heatmap(heatmap_aucs.transpose(), ax=ax, cmap=cmap, vmin=0, vmax=4)#, norm=norm, cbar_kws = dict(pad=0.01))\n",
    "\n",
    "\n",
    "ax.set_yticks([0.5+x for x in range(21)], [f\"{x:.2f}\" for x in np.arange(0,1.01,0.05)], fontsize=20, rotation=0)\n",
    "ax.set_xticks([0.5+x for x in range(23)], range(2,25), fontsize=23)\n",
    "ax.set_xlabel(\"Function ID\")\n",
    "ax.set_ylabel(\"Alpha\")\n",
    "\n",
    "\n",
    "colorbar = ax.collections[0].colorbar \n",
    "r = colorbar.vmax - colorbar.vmin\n",
    "n = len(algs)\n",
    "colorbar.set_ticks([colorbar.vmin + 0.5 * r / (n) + r * i / (n) for i in range(n)])\n",
    "colorbar.set_ticklabels(algs_shortname, fontsize=34)\n",
    "    \n",
    "for label in colorbar.ax.get_yticklabels():\n",
    "    label.set_verticalalignment('center')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"Figures/Rank_heatmap_alphas.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5b72f8-3b01-4ad2-a647-91d9d9eba7b9",
   "metadata": {},
   "source": [
    "### Per-function AUC distribution change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2445ac5d-7224-4a35-b59e-88da1ea73ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for fid in [10]: #range(2,25):\n",
    "    dt_plot3 = dt_w_info.query(f\"f1 == {fid} & f2 == 1\")\n",
    "    plt.figure(figsize=(16,6))\n",
    "    sbs.boxenplot(data=dt_plot3, x='alpha',y='auc',hue='ID')\n",
    "    lgnd = plt.legend(title=\"\", fontsize=22, bbox_to_anchor=(0.500, 1.3), ncol=3, loc='upper center', fancybox=True)\n",
    "    # for lghandle in lgnd.legend_handles :\n",
    "    #     lghandle.set_markersize(15)\n",
    "    # plt.legend(loc='best', fontsize=18)\n",
    "    plt.xticks(range(21), [f\"{x:.2f}\" for x in np.arange(0,1.01,0.05)], fontsize=18)\n",
    "    plt.xlabel('Alpha')\n",
    "    plt.ylabel(\"AOCC\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/AUC_distr_F{fid}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f901d9-0d1b-42a4-87da-35eb7b308ce5",
   "metadata": {},
   "source": [
    "### ELA analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb95c0c",
   "metadata": {},
   "source": [
    "For the next part of the analysis, we look at the ELA features of the functions where the sphere model is added (both normal distribution and distribution of standard deviations to check variability). These figures are saved in a separate folder and only 1 of them is included in the paper itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "20a5f070",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[Errno 17] File exists: 'Figures_ELA_sphered'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mFigures_ELA_sphered\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'Figures_ELA_sphered'"
     ]
    }
   ],
   "source": [
    "os.mkdir(\"Figures_ELA_sphered\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "468085d2-9504-4c27-bfb8-113cf3774d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_sphere = copy(dt_ela_info)\n",
    "dt_sphere_norm = copy(dt_sphere)\n",
    "dt_sphere_norm = dt_sphere_norm.query(\"f2 == 1\")\n",
    "dt_sphere_ext = dt_sphere_norm.query(\"alpha == 1 or alpha == 0\")\n",
    "dt_sphere_norm[ela_features] = (dt_sphere_norm[ela_features] - dt_sphere_ext[ela_features].min()) / ( dt_sphere_ext[ela_features].max() -  dt_sphere_ext[ela_features].min())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e0a90ca4-49d6-47ab-b778-8333d4ebe8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_heatmap_ela_mean(fid):\n",
    "    dt_part = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"mean\")\n",
    "    plt.figure(figsize=(45,11))\n",
    "    sbs.heatmap(dt_part[ela_features], cmap='viridis', \n",
    "               cbar_kws = dict(pad=0.01), vmin=0, vmax=1)\n",
    "    plt.yticks([i+0.5 for i in range(0,21)], [f\"{x:.2f}\" for x in np.arange(0,1.01,0.05)])\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures_ELA_sphered/F{fid}_ELA_mean.pdf\")\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "80583a00-99a2-4efc-a4a6-cf76e00f65cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_heatmap_ela_std(fid):\n",
    "    dt_part = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"std\")\n",
    "    plt.figure(figsize=(45,11))\n",
    "    sbs.heatmap(dt_part[ela_features], cmap='viridis', \n",
    "               cbar_kws = dict(pad=0.01), vmin=0, vmax=0.5)\n",
    "    plt.yticks([i+0.5 for i in range(0,21)], [f\"{x:.2f}\" for x in np.arange(0,1.01,0.05)], fontsize=14)\n",
    "    plt.xticks(fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures_ELA_sphered/F{fid}_ELA_std.pdf\")\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "75fc6ba4-d2b3-414c-81bd-743fb7195528",
   "metadata": {},
   "outputs": [],
   "source": [
    "for fid in range(2,25):\n",
    "    create_heatmap_ela_mean(fid)\n",
    "    create_heatmap_ela_std(fid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bedea8e9-8853-471a-8cda-8aeccf7d0cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4afd4ab",
   "metadata": {},
   "source": [
    "The next set of figures combines the mean and standard deviation information into a single figure for each function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c41800c3-8e1f-4dee-bee7-ae3d4f3fce11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mixed(fid):\n",
    "    dt_std = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"std\")\n",
    "    dt_mean = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"mean\")\n",
    "    \n",
    "    dt_std_molt = dt_std[ela_features].reset_index().melt(id_vars=['alpha'])\n",
    "    dt_mean_molt = dt_mean[ela_features].reset_index().melt(id_vars=['alpha'])\n",
    "    dt_meanstd = dt_std_molt.merge(dt_mean_molt, left_on=['alpha', 'variable'], right_on=['alpha', 'variable'])\n",
    "    plt.figure(figsize=(32,9))\n",
    "    ax = sbs.scatterplot(dt_meanstd, x='variable', y='alpha', hue='value_x', size='value_y', palette='viridis', sizes=(100,750), marker='s')\n",
    "    # plt.grid(which='both')\n",
    "    plt.colorbar(mpl.cm.ScalarMappable(norm=mpl.colors.Normalize(0, 1), cmap='viridis'), ax=ax, pad=0.01)\n",
    "    plt.xticks(rotation=90, fontsize=18)\n",
    "    plt.xlim(-0.5,len(ela_features)-0.5)\n",
    "    plt.ylim(1.05,-0.05)\n",
    "    ax.get_legend().remove()\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel(\"Alpha\")\n",
    "    for i in np.arange(-0.05,1.01,0.05):\n",
    "        plt.axhline(i+(1/42), c='k', ls=':', alpha=0.5)\n",
    "    for i in range(len(ela_features)+1):\n",
    "        plt.axvline(i-0.5, c='k', ls=':', alpha=0.5)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures_ELA_sphered/F{fid}_mixed.pdf\")\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0e3012c6-11ad-4bb9-92b7-c449d9114ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for fid in range(2,25):\n",
    "    plot_mixed(fid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "879d5aca-e8fc-4577-a00c-b9f987969df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_mixed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421b4155",
   "metadata": {},
   "source": [
    "For the final figure of this section, we aggregate the above information to a single heatmap showiing how much each feature changes as the impact of sphere is modified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f5ce9ac-49c8-4379-9db7-5ef062647360",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_means = np.zeros((23,len(ela_features)))\n",
    "arr_means_tot = np.zeros((23,len(ela_features)))\n",
    "arr_stds = np.zeros((23,len(ela_features)))\n",
    "for fid in range(2,25):\n",
    "    dt_std = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"std\")\n",
    "    dt_mean = dt_sphere_norm.query(f\"f1 == {fid}\").groupby(\"alpha\").agg(\"mean\")\n",
    "    arr_means[fid-2] = np.sum(np.abs(np.array(dt_mean[ela_features])[1:,:] - np.array(dt_mean[ela_features])[:-1,:]), axis=0)\n",
    "    arr_stds[fid-2] = np.sum(np.abs(np.array(dt_std[ela_features])[1:,:] - np.array(dt_std[ela_features])[:-1,:]), axis=0)\n",
    "    arr_means_tot[fid-2] = np.abs(np.array(dt_mean[ela_features])[0] - np.array(dt_mean[ela_features])[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "77a2b1e8-b403-4fec-8d58-ba62db247584",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4500x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(45,14))\n",
    "sbs.heatmap(arr_means, cmap='viridis', cbar_kws={'pad' : 0.01}, vmin=0, vmax=1)\n",
    "plt.xticks([i+0.5 for i in range(len(ela_features))], ela_features, rotation=90, fontsize=24)\n",
    "plt.yticks([i+0.5 for i in range(23)], range(2,25), rotation=0, fontsize=22)\n",
    "plt.ylabel(\"Function ID\", fontsize=35)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/Overview_mean_diffs_ELA_sphered.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421eecc8-6915-42bd-a4ab-fe6b49259a35",
   "metadata": {},
   "source": [
    "## Impact of Optimum Location (Section 4.4)\n",
    "This section uses a subset of the data above to isolate the effect of moving the location of the optimum. This is done by looking at the BBOB-functions themselves and their original vs. moved distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4035de90-1928-49f0-bc26-92d3041dc617",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_moved = dt_ela_info_nonnorm.query(\"((f2 == 1) & (alpha == 1)) or ((f2 == 1) & (alpha == 0))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2b2c9514-5b96-4b7f-ab0c-4e618374ca3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_moved_norm = copy(dt_moved)\n",
    "dt_moved_norm[ela_features] = (dt_moved[ela_features] - dt_moved[ela_features].min()) / ( dt_moved[ela_features].max() -  dt_moved[ela_features].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94be2c1",
   "metadata": {},
   "source": [
    "The following cell uses the unzipped data from 'ELA_BBOB.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6497f33e-ddf8-44e4-86c3-38d0404597ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "dts_bbob_orig = []\n",
    "for f in glob.glob(\"ELA_BBOB/*.csv\"):\n",
    "    dt = pd.read_csv(f, index_col=0)\n",
    "    dt['fid'] = int(os.path.basename(f).split(\"_\")[0][1:])\n",
    "    dt['iid'] = int(os.path.basename(f).split(\"_\")[1][1:])\n",
    "    dts_bbob_orig.append(dt)\n",
    "dt_orig = pd.concat(dts_bbob_orig)\n",
    "dt_orig_norm = copy(dt_orig)\n",
    "dt_orig_norm[ela_features] = (dt_orig[ela_features] - dt_moved[ela_features].min()) / ( dt_moved[ela_features].max() -  dt_moved[ela_features].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9153d941-76b9-4016-b656-a7bccce0e1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_orig_norm.to_csv('dt_orig_norm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e7bed9a1-b8f9-4d8f-b729-761bb9b1c173",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_orig_norm = pd.read_csv('dt_orig_norm.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "58d20440-ca8c-448a-99d7-772ee64410c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distr_diff(fid):\n",
    "    if fid == 1:\n",
    "        dt_moved_part = dt_moved_norm.query(f'alpha==0 & f1 == 2')[ela_features].melt()\n",
    "    else:\n",
    "        dt_moved_part = dt_moved_norm.query(f'alpha==1 & f1 == {fid}')[ela_features].melt()\n",
    "    dt_moved_part['type'] = 'Moved'\n",
    "    dt_orig_part = dt_orig_norm.query(f'fid == {fid}')[ela_features].melt()\n",
    "    dt_orig_part['type'] = 'Original'\n",
    "    \n",
    "    dt_both = pd.concat([dt_moved_part, dt_orig_part])\n",
    "    plt.figure(figsize=(32,9))\n",
    "    sbs.boxenplot(dt_both, x='variable', y='value', hue='type')\n",
    "    plt.ylim(0,1)\n",
    "    plt.ylabel(\"Normalized value\", fontsize=24)\n",
    "    plt.xlabel('')\n",
    "    for i in range(len(ela_features)):\n",
    "        plt.axvline(i+0.5, c='k', ls=':')\n",
    "    plt.xticks(fontsize=17, rotation=90)\n",
    "    plt.legend(title='')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/BBOB_Distr_F{fid}.pdf\")\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b4a6635f-6c9b-4f4b-94d3-7a2f9293451d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for fid in range(1,25):\n",
    "    plot_distr_diff(fid)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f920b3c3",
   "metadata": {},
   "source": [
    "In addition to comparing distributions, we also use UMAP to create 2D projections of the space and look at the overall impact of the transformation in this lower dimensional representation of the ELA vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b14f6191-cab3-42c7-a06b-fa9902dcc305",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = UMAP()\n",
    "mapping.fit(dt_orig_norm[ela_features])\n",
    "res_moved = mapping.transform(dt_moved_norm[ela_features])\n",
    "res_orig = mapping.transform(dt_orig_norm[ela_features])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "872a92fb-0641-449e-bfe8-33e829f9d952",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_umap_moved_vs_orig(fid):\n",
    "    plt.figure(figsize=(16,9))\n",
    "    \n",
    "    filter_moved = (dt_moved['f1'] == fid) & (dt_moved['alpha'] == 1)\n",
    "    plt.scatter(res_moved[filter_moved,0], res_moved[filter_moved,1], label='Moved')\n",
    "    \n",
    "    filter_orig = dt_orig['fid'] == fid\n",
    "    plt.scatter(res_orig[filter_orig,0], res_orig[filter_orig,1], alpha=0.6, label='Original')\n",
    "    lgnd = plt.legend(fontsize=32)\n",
    "    # for lghandle in lgnd.legend_handles:\n",
    "    #     lghandle.set_markersize(20)\n",
    "    # plt.title(f\"F{fid}\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/BBOB_Dim_red_F{fid}.pdf\")\n",
    "    plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "59a65614-6e1e-4cf4-8151-34438add3f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for fid in range(2,25):\n",
    "    plot_umap_moved_vs_orig(fid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8cdffb16-fc22-4358-a58a-00bb32e98a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "colors = sbs.palettes.color_palette(n_colors=23)\n",
    "for fid in range(2,25):\n",
    "    filter_moved = (dt_moved['f1'] == fid) & (dt_moved['alpha'] == 1)\n",
    "    filter_orig = dt_orig['fid'] == fid\n",
    "    for iid in range(1,51):\n",
    "        idx1 = np.where(filter_moved & (dt_moved['f1_iid'] == iid))[0]\n",
    "        idx2 = np.where(filter_orig & (dt_orig['iid'] == iid))[0]\n",
    "\n",
    "        plt.plot([res_moved[idx1,0], res_orig[idx2,0]], [res_moved[idx1,1], res_orig[idx2,1]], \n",
    "                 color=colors[fid-2], ls = '-', alpha=0.2)\n",
    "        plt.scatter(res_moved[idx1,0], res_moved[idx1,1], \n",
    "                 color=colors[fid-2], marker='x', alpha=1, s=150)\n",
    "        plt.scatter(res_orig[idx2,0], res_orig[idx2,1], \n",
    "                 color=colors[fid-2], marker='o', alpha=1, s=150, facecolors='none')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/Projection_moved_bbob_all.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d834e257",
   "metadata": {},
   "source": [
    "Finally, we can calculate the deviation caused by moving the optium in a more aggregated manner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9b0a3db9-b543-4ef2-b801-ea571ad7d5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = um.true_divide(\n"
     ]
    }
   ],
   "source": [
    "records = []\n",
    "for alg in np.unique(dt_w_info['ID']):\n",
    "    for f2 in range(2,25):\n",
    "        for alpha in np.arange(0,1.01, 0.05):\n",
    "            dt_plot = dt_w_info.query(f\"f1 == {f2} & f2 == 1 & ID == '{alg}' & alpha == {alpha}\")\n",
    "            x = np.array(dt_plot['auc'])\n",
    "            np.random.shuffle(x)\n",
    "            shuffled = np.var(np.mean(np.split(x, 10), axis=1))\n",
    "            shuffled_2 = np.mean(np.var(np.split(x, 10), axis=1))\n",
    "            record = [np.mean(dt_plot.groupby('funcId')['auc'].agg(np.var)), \n",
    "                     np.var(dt_plot.groupby('funcId')['auc'].agg(np.mean)),\n",
    "                     np.var(dt_plot['auc']), shuffled, shuffled_2, alg, f2, alpha]\n",
    "            records.append(record)\n",
    "dt_dev = pd.DataFrame.from_records(records, columns=['seed_dev', 'instance_dev', 'total_dev', 'shuffled_instance_dev', 'shuffled_seed_dev', 'alg', 'f2', 'alpha'])\n",
    "\n",
    "dt_dev['seed_dev_rel'] = dt_dev['seed_dev'] / dt_dev['total_dev']\n",
    "dt_dev['instance_dev_rel'] = dt_dev['instance_dev'] / dt_dev['total_dev']\n",
    "dt_dev['shuffled_seed_dev_rel'] = dt_dev['shuffled_seed_dev'] / dt_dev['total_dev']\n",
    "dt_dev['shuffled_instance_dev_rel'] = dt_dev['shuffled_instance_dev'] / dt_dev['total_dev']\n",
    "\n",
    "dt_dev['temp'] = dt_dev['f2'] + (dt_dev['alpha']*0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "330e6e87-f162-4b95-98d6-f74bbb610279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sbs.scatterplot(data=dt_dev, x='temp', y='instance_dev_rel', hue='alg')\n",
    "plt.yscale('log')\n",
    "lgnd = plt.legend(title=\"\", fontsize=22, bbox_to_anchor=(0.500, 1.3), ncol=3, loc='upper center', fancybox=True)\n",
    "for lghandle in lgnd.legend_handles :\n",
    "    lghandle.set_markersize(20)\n",
    "plt.xlabel(\"Function ID (+Alpha)\")\n",
    "plt.ylabel(\"Relative Deviation from Xopt\", fontsize=18)\n",
    "for i in range(2,25):\n",
    "    plt.axvline(i+0.75, c='k', ls = ':')\n",
    "plt.xticks([i+0.25 for i in range(2,25)], range(2,25))\n",
    "plt.xlim(1.75,24.75)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/Instance_dev_rel_v2.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ce2d55-7483-4e9f-8717-29813dc74dc2",
   "metadata": {},
   "source": [
    "## Pairwise combinations (Section 4.5)\n",
    "For the final set of pairwise combinations, we create a grid showing pairwise transitions between functions from different BBOB function groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b61c1bbe-a2cd-42b7-96b0-70de77570dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_plot2 = dt_w_info.query('f2 != 1').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5cd42cf9-1181-440b-b3af-9d16bec9e204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fids = np.unique(dt_plot2['f2'])\n",
    "for algid in np.unique(dt_plot2['ID']):\n",
    "    fig, axs = plt.subplots(nrows=5, ncols=5, figsize=(16,12), sharex=True, sharey=True,\n",
    "                        gridspec_kw={'hspace': 0, 'wspace': 0})\n",
    "\n",
    "    for idx1, f1 in enumerate(fids):\n",
    "        for idx2, f2 in enumerate(fids):\n",
    "            if f1 == f2:\n",
    "                next\n",
    "            else:\n",
    "                aucs = np.array(dt_plot2[(dt_plot2['f1'] == int(f1)) & (dt_plot2['f2'] == int(f2)) & (dt_plot2['ID'] == algid)][['alpha', 'auc']].groupby(['alpha']).agg(np.mean)['auc'])\n",
    "                axs[idx1, idx2].bar(np.arange(0,1.01,0.05), aucs, width=0.04)\n",
    "                axs[idx1, idx2].set_ylim(0,1)\n",
    "            axs[idx1, idx2].set(xlabel=f'F{f2}', ylabel=f'F{f1}', yticks=[0.1,0.5,0.9], xticks=[0,0.5,1], xticklabels=['0','0.5', '1'])\n",
    "    for ax in axs.flat:\n",
    "        ax.label_outer()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures/Grid_combinations_5D_{algid}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82f7ef6-3993-4788-a8ed-a4ce5d1e74c9",
   "metadata": {},
   "source": [
    "# Part 3: Testing Algorithm Selection (Section 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f7772b-b356-4316-9df3-8a8d1fa054ce",
   "metadata": {},
   "source": [
    "For this part, we make use of the performance and ELA data calculated in the AutoML paper, which is publically available on this Zenodo page: https://zenodo.org/records/7826036\n",
    "\n",
    "In particular, we use the following files (which have also been added to this current repository for convenience):\n",
    "- All_2d_info2.csv\n",
    "- All_5d_info2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "998cf4b9-fd71-4056-9a1d-466b2b406afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "import sklearn\n",
    "from sklearn.metrics import mean_absolute_error, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b68bdd68-d7ca-4f73-aa79-c92e0decde95",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_all_2d = pd.read_csv(\"All_2d_info2.csv\", index_col=0)\n",
    "dt_all_5d = pd.read_csv(\"All_5d_info2.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "855c17ac-e38d-4c5f-8db0-1e0f0be3462a",
   "metadata": {},
   "outputs": [],
   "source": [
    "algs = ['DiagonalCMA', 'DifferentialEvolution', 'modcma',\n",
    "       'modde', 'RCobyla']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "845d583e-eed3-4944-a337-1f8936cb738a",
   "metadata": {},
   "outputs": [],
   "source": [
    "algs2 = ['DiagonalCMA', 'DifferentialEvolution',\n",
    "       'modde', 'RCobyla']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "40cbbbc8-d885-44d9-b90f-00b98c0f7e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_algs = [algs[x] for x in np.array(dt_all_2d[algs]).argmax(axis=1)]\n",
    "dt_all_2d['best'] = best_algs\n",
    "best_algs = [algs[x] for x in np.array(dt_all_5d[algs]).argmax(axis=1)]\n",
    "dt_all_5d['best'] = best_algs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8fe4e3a0-f8be-4181-a030-242465cd79d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_algs = [algs2[x] for x in np.array(dt_all_2d[algs2]).argmax(axis=1)]\n",
    "dt_all_2d['best_nocma'] = best_algs\n",
    "best_algs = [algs2[x] for x in np.array(dt_all_5d[algs2]).argmax(axis=1)]\n",
    "dt_all_5d['best_nocma'] = best_algs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7d762a19-5861-461e-98b2-df3fda779ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rf_losses(dim, target, version = 'generalize', input_vars = 'ELA', model='AS'):\n",
    "    if dim == 2:\n",
    "        dt_dim = copy(dt_all_2d)\n",
    "    else:\n",
    "        dt_dim = copy(dt_all_5d)\n",
    "\n",
    "    \n",
    "    if input_vars == 'ELA':\n",
    "        X = np.array(dt_dim[ela_features])\n",
    "    else:\n",
    "        X = np.array(dt_dim[[f'W{x}' for x in range(24)]])\n",
    "\n",
    "    Y = np.array(dt_dim[target])\n",
    "    if target == 'best' or target == 'best_nocma':\n",
    "        rf_model = RandomForestClassifier()\n",
    "        scoring=partial(f1_score, average='weighted')\n",
    "    else:\n",
    "        rf_model = RandomForestRegressor()\n",
    "        scoring=mean_absolute_error\n",
    "    if model == 'AS':\n",
    "        if version == 'generalize':\n",
    "            rf_model.fit(X[dt_dim['kind'] == 'BBOB'], Y[dt_dim['kind'] == 'BBOB'])\n",
    "            preds = rf_model.predict(X[dt_dim['kind'] == 'Affine'])\n",
    "            dt_part = dt_dim[dt_dim['kind'] == 'Affine']\n",
    "        else:\n",
    "            preds = cross_val_predict(rf_model, X, Y, cv=10)\n",
    "            dt_part = dt_dim\n",
    "    else:\n",
    "        if version == 'generalize':\n",
    "            dt_part = dt_dim[dt_dim['kind'] == 'Affine']\n",
    "        else:\n",
    "            dt_part = dt_dim\n",
    "        preds = [model] * len(dt_part)\n",
    "    # print(preds)\n",
    "    pred_vals = np.array([dt_part.iloc[x][y] for x,y in enumerate(preds)])\n",
    "    if target == 'best':\n",
    "        vbs_val = np.max(dt_part[algs], axis=1)\n",
    "    else:\n",
    "        vbs_val = np.max(dt_part[algs2], axis=1)\n",
    "\n",
    "    return  vbs_val - pred_vals\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b5313c9b-1eae-405a-b598-91861f12090a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 5 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 5 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "items = []\n",
    "for target_type in ['best', 'best_nocma']:\n",
    "    for dim in [2,5]:\n",
    "        # for target in ['best'] + algs:\n",
    "        for input_var in ['ELA', 'Weights']:\n",
    "            if target_type == 'best':\n",
    "                algs_sel = algs\n",
    "            else:\n",
    "                algs_sel = algs2\n",
    "            for alg in algs_sel:\n",
    "                res = get_rf_losses(dim, target_type, 'cv', input_var, alg)\n",
    "                for r in res:\n",
    "                    items.append([r, dim, target_type, alg, 'Single'])\n",
    "            \n",
    "            for version in ['generalize', 'cv']:\n",
    "                res = get_rf_losses(dim, target_type, version, input_var, 'AS')\n",
    "                for r in res:\n",
    "                    items.append([r, dim, target_type, input_var, version])\n",
    "                \n",
    "dt_rf_loss = pd.DataFrame.from_records(items, columns=['loss', 'dim', 'target', 'input_var', 'version'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "06cb36e4-8de6-4a68-ba86-36898b7ed222",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_plot = dt_rf_loss.query('version != \"generalize\" and dim == 2') \n",
    "plt.figure(figsize=(16,9))\n",
    "g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.8)#, ls='dim')\n",
    "\n",
    "g.legend_.set_title('Method')\n",
    "plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "plt.ylabel('Proportion', fontsize=32)\n",
    "plt.xlabel('Loss', fontsize=32)\n",
    "plt.ylim(0,1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures_RF/AS_Model_losses_cumulative_cv_2D.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ddbd6ae3-839c-462f-8add-23c0aa7e89de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LAAAAAAAAAADwciQLAAAAAAAAAADwciQLAAAAAAAAAADwciQLAAAAAAAAAADwcv8/ns24KhZlArcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_plot = dt_rf_loss.query('version != \"generalize\" and dim == 5') \n",
    "plt.figure(figsize=(16,9))\n",
    "g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.8)#, ls='dim')\n",
    "g.legend_.set_title('Method')\n",
    "plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "plt.ylabel('Proportion', fontsize=32)\n",
    "plt.xlabel('Loss', fontsize=32)\n",
    "plt.ylim(0,1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures_RF/AS_Model_losses_cumulative_cv_5D.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "364cefa0-0f30-4c06-8bb3-95e08e559cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LAAAAAAAAAADwciQLAAAAAAAAAADwciQLAAAAAAAAAADwciQLAAAAAAAAAADwcv8/ns24KhZlArcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_plot = dt_rf_loss.query('version != \"generalize\" and dim == 5') \n",
    "plt.figure(figsize=(16,9))\n",
    "g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.8)#, ls='dim')\n",
    "g.legend_.set_title('Method')\n",
    "plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "plt.ylabel('Proportion', fontsize=32)\n",
    "plt.xlabel('Loss', fontsize=32)\n",
    "plt.ylim(0,1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures_RF/AS_Model_losses_cumulative_cv_5D_nomodcma.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ee7447ff-a9d6-40b7-bc0d-c33018be0bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_plot = dt_rf_loss.query('(version == \"generalize\" or version == \"Single\") and dim == 5') \n",
    "plt.figure(figsize=(16,9))\n",
    "g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.8)#, ls='dim')\n",
    "g.legend_.set_title('Method')\n",
    "plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "plt.ylabel('Proportion', fontsize=32)\n",
    "plt.xlabel('Loss', fontsize=32)\n",
    "plt.ylim(0,1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/AS_Model_losses_cumulative_generalize_5D_nomodcma.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6e83ba26-c81c-4dbd-a92a-cc7662ed7373",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rf_losses_shuffled(dim, target, version = 'generalize', input_vars = 'ELA', model='AS'):\n",
    "    if dim == 2:\n",
    "        dt_dim = copy(dt_all_2d)\n",
    "    else:\n",
    "        dt_dim = copy(dt_all_5d)\n",
    "\n",
    "    \n",
    "    if input_vars == 'ELA':\n",
    "        X = np.array(dt_dim[ela_features])\n",
    "    else:\n",
    "        X = np.array(dt_dim[[f'W{x}' for x in range(24)]])\n",
    "\n",
    "    Y = np.array(dt_dim[target])\n",
    "    np.random.shuffle(Y)\n",
    "    if target == 'best' or target == 'best_nocma':\n",
    "        rf_model = RandomForestClassifier()\n",
    "        scoring=partial(f1_score, average='weighted')\n",
    "    else:\n",
    "        rf_model = RandomForestRegressor()\n",
    "        scoring=mean_absolute_error\n",
    "    if model == 'AS':\n",
    "        if version == 'generalize':\n",
    "            rf_model.fit(X[dt_dim['kind'] == 'BBOB'], Y[dt_dim['kind'] == 'BBOB'])\n",
    "            preds = rf_model.predict(X[dt_dim['kind'] == 'Affine'])\n",
    "            dt_part = dt_dim[dt_dim['kind'] == 'Affine']\n",
    "        else:\n",
    "            preds = cross_val_predict(rf_model, X, Y, cv=10)\n",
    "            dt_part = dt_dim\n",
    "    else:\n",
    "        if version == 'generalize':\n",
    "            dt_part = dt_dim[dt_dim['kind'] == 'Affine']\n",
    "        else:\n",
    "            dt_part = dt_dim\n",
    "        preds = [model] * len(dt_part)\n",
    "    # print(preds)\n",
    "    pred_vals = np.array([dt_part.iloc[x][y] for x,y in enumerate(preds)])\n",
    "    if target == 'best':\n",
    "        vbs_val = np.max(dt_part[algs], axis=1)\n",
    "    else:\n",
    "        vbs_val = np.max(dt_part[algs2], axis=1)\n",
    "\n",
    "    return  vbs_val - pred_vals\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "42e3e23d-3ed6-4c30-b10e-9c8d28363c5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 5 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n",
      "/home/diedie/venvs/venv_MABBOB/lib/python3.10/site-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 5 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "items = []\n",
    "for target_type in ['best', 'best_nocma']:\n",
    "    for dim in [2,5]:\n",
    "        # for target in ['best'] + algs:\n",
    "        for input_var in ['ELA', 'Weights']:\n",
    "            for alg in algs:\n",
    "                res = get_rf_losses_shuffled(dim, target_type, 'cv', input_var, alg)\n",
    "                for r in res:\n",
    "                    items.append([r, dim, target_type, alg, 'Single'])\n",
    "            \n",
    "            for version in ['generalize', 'cv']:\n",
    "                res = get_rf_losses_shuffled(dim, target_type, version, input_var, 'AS')\n",
    "                for r in res:\n",
    "                    items.append([r, dim, target_type, input_var, version])\n",
    "                \n",
    "dt_rf_loss_shuffled = pd.DataFrame.from_records(items, columns=['loss', 'dim', 'target', 'input_var', 'version'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c4161e1a-ae62-4377-9247-f7065d2210fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ecdf_figure_cv(dim, target):\n",
    "    \n",
    "    plt.figure(figsize=(16,9))\n",
    "    dt_plot1 = dt_rf_loss_shuffled.query(f'target == \"{target}\" and version != \"generalize\" and dim == {dim} and input_var == \"ELA\"') \n",
    "    dt_plot1['input_var'] = 'Shuffled'\n",
    "    dt_plot2 = dt_rf_loss.query(f'target == \"{target}\" and version != \"generalize\" and dim == {dim}') \n",
    "    dt_plot = pd.concat([dt_plot1, dt_plot2])\n",
    "    g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.9)\n",
    "    g.legend_.set_title('Method')\n",
    "    plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "    plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "    plt.ylabel('Proportion', fontsize=32)\n",
    "    plt.xlabel('Loss', fontsize=32)\n",
    "    plt.ylim(0,1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures_RF/ECDF_RF_loss_CV_{target}_{dim}D.pdf\")\n",
    "    \n",
    "def plot_ecdf_figure_generalize(dim, target):\n",
    "    plt.figure(figsize=(16,9))\n",
    "    # dt_plot1 = dt_rf_loss_shuffled.query(f'target == \"{target}\" and version == \"generalize\" and dim == {dim} and input_var == \"ELA\"') \n",
    "    # dt_plot1['input_var'] = 'Shuffled'\n",
    "    dt_plot1 = dt_rf_loss.query(f'target == \"{target}\" and version == \"Single\" and dim == {dim}') \n",
    "\n",
    "    dt_plot2 = dt_rf_loss.query(f'target == \"{target}\" and version == \"generalize\" and dim == {dim}') \n",
    "    dt_plot = pd.concat([dt_plot1, dt_plot2])\n",
    "    g = sbs.ecdfplot(dt_plot, x='loss', hue='input_var', lw=5, alpha=0.9)\n",
    "    plt.setp(g.get_legend().get_texts(), fontsize='30')\n",
    "    plt.setp(g.get_legend().get_title(), fontsize='34') \n",
    "    plt.ylabel('Proportion', fontsize=32)\n",
    "    plt.xlabel('Loss', fontsize=32)\n",
    "    g.legend_.set_title('Method')\n",
    "    plt.ylim(0,1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"Figures_RF/ECDF_RF_loss_generalize_{target}_{dim}D.pdf\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee97e7c8",
   "metadata": {},
   "source": [
    "The final versions of these figures we use are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e48c232d-9136-49c2-ab99-b0baafc015a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13501/2570620108.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dt_plot1['input_var'] = 'Shuffled'\n",
      "/tmp/ipykernel_13501/2570620108.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dt_plot1['input_var'] = 'Shuffled'\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ecdf_figure_cv(2, 'best')\n",
    "plot_ecdf_figure_cv(5, 'best')\n",
    "plot_ecdf_figure_generalize(5, 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6df095f",
   "metadata": {},
   "source": [
    "Note: the final figure (fig. 15 in the paper, showing rank distributions) is taken directly from https://zenodo.org/doi/10.5281/zenodo.7826035"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_MABBOB",
   "language": "python",
   "name": "env_mabbob"
  },
  "language_info": {
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