Bayesian Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Jupyter notebook from _create_pdf.ipynb\n",
      "Importing Jupyter notebook from _helper_functions.ipynb\n",
      "Importing Jupyter notebook from _steady_state.ipynb\n",
      "Importing Jupyter notebook from _pulse_rwa.ipynb\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy import integrate\n",
    "from scipy.special import gamma\n",
    "from scipy.optimize import fsolve, basinhopping\n",
    "import matplotlib.pyplot as plt\n",
    "import qutip.ipynbtools\n",
    "from ipywidgets import FloatProgress\n",
    "import qutip.parallel\n",
    "from scipy import signal\n",
    "import scipy.io\n",
    "import gc\n",
    "import matplotlib.colors as colors\n",
    "from IPython.display import clear_output, Javascript, Markdown\n",
    "import datetime\n",
    "import math\n",
    "import uuid\n",
    "import os\n",
    "import time\n",
    "import ipyparallel\n",
    "import qutip.ui\n",
    "import random\n",
    "import sys\n",
    "from bayes_opt import BayesianOptimization\n",
    "import nbimporter\n",
    "from _create_pdf import create_spins;\n",
    "from _helper_functions import tsallis, cauchy, find_nearest, odeintz, find_nearest_sorted_left\n",
    "from _steady_state import a_steady\n",
    "from _pulse_rwa import pulse_rwa\n",
    "from _mbes import mbes, mbes_soc\n",
    "from _do_calculation import do_calculation, do_calculation_stimulated, do_calculation_soc, do_calculation_soc_pump_only\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.550732441417814\n"
     ]
    }
   ],
   "source": [
    "gcoll = 10.7*np.pi #HWHM\n",
    "q = 1.39\n",
    "quali = 949\n",
    "fr = 3.4892e3;\n",
    "kappa = np.pi*fr/quali #HWHM\n",
    "delta = 0*2*np.pi\n",
    "deltac = 0*2*np.pi\n",
    "gperp = 2*np.pi*0.09 #HWHM\n",
    "gpar = 2*np.pi*.001#0.001 #HWHM\n",
    "gammaq = 2*np.pi*9.4 #FWHM\n",
    "nbins = 701#701#5000#20\n",
    "#g0 = gcoll/sqrt(nspins)\n",
    "print(kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcccfd4b710>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2qvqAqtaqam1paekMVGeSyY5mPyvnFeJJgBFOz8bjES6yG9TMDIgmEI4ClaMeVzjrxt1GRHxAPtD5AfteCnxSRA4TPgV1pYj80znUb8xZ9Q8FOHiyN6H7DyJWVhZw8GQvA8MBt0sxSSyaQNgKLBaRahFJJ9xJvHnMNpuBW53lm4AXVVWd9Rudq5CqgcXA26p6t6pWqGqV83ovqup/ikF7jDnjnaM9hJSEvEN5rJWVBQRDyu6jp9wuxSSxCQPB6RO4E3iG8BVBm1R1j4jcKyKfdDZ7ECgWkQbgL4G7nH33AJuAvcDTwB2qaoO7mxkRGeH0oiQJBMBmUDPTyhfNRqr6FPDUmHX3jFoeBG4+y773Afd9wGu/DLwcTR3GTEZ9Szfzi7Mpykl3u5QpK87NoLLIRj4108vuVDZJqz7Bb0gba2VloV16aqaVBYJJSsd7TnPy1FBS9B9ErKws4FjPoI18aqaNBYJJSpFv0ivnJc6UmROJHO3Y/QhmulggmKS0o8VPus9DTVme26XEzLK5eaR5beRTM30sEExSqm/2c+HcPNJ9yfMRPzPyqfUjmGmSPL8txjhGgiF2HfWzsjJ5ThdFhEc+9dvIp2ZaWCCYpHPgRC+DI6GEnCFtIisrC+gfDtLQZiOfmtizQDBJZ0dz+OatZA0EsBvUzPSwQDBJZ0ezn9JZGZQXZLldSsxVl+SQn5VmHctmWlggmKRT3+JnVWUB4Sk5kotIeOTTHdaxbKaBBYJJKt39wzR29Cf0hDgTsZFPzXSxQDBJpb41/M15VRJeYRSxqrKAkMI7rT1ul2KSjAWCSSo7mv14BFZU5LtdyrSJtM36EUysWSCYpLKjuZsl5+WRkxHVQL4JqTg3g3lF2RYIJuYsEEzSCIU03KGcxP0HESttSk0zDSwQTNJo7OindzCQVCOcns3KygKO9wxy0kY+NTFkgWCSRjLfkDZW5Coqu/zUxJIFgkkaO1r8zMr0saAk1+1Spl1NmY18amLPAsEkjR3N4RnSPJ7kuyFtrMw0LzVleWeOioyJBQsEkxT6hwIcOHGKVUk0Ic5E1swvYmern5FgyO1STJKwQDBJYWern5CSEh3KEbVVhQyOhNhz7JTbpZgkYYFgksK2w92IwOoUOkKonR9ua93hLpcrMckiqkAQkfUickBEGkTkrnGezxCRx53nt4hI1ajn7nbWHxCRa511mSLytojsFJE9IvLNWDXIpKatR7pZMmcW+dlpbpcyY2bnZVJZlMW2I9aPYGJjwkAQES/wQ+A6oAa4RURqxmx2G9CtqouA7wD3O/vWABuBZcB64EfO6w0BV6rqRcBKYL2IrItNk0yqCYaU7Ue6qa1KnaODiNr5RdQd6UbVZlAzUxfNEcJaoEFVG1V1GHgM2DBmmw3Aw87yE8BVEh57eAPwmKoOqWoT0ACs1bDIlE9pzj/7RJtzsv/EKfqGAtTOL3K7lBm3Zn4h7b1DNHcNuF2KSQLRBEI50DLqcauzbtxtVDUA9ADFH7SviHhFpB5oA55T1S3jvbmI3C4idSJS197eHkW5JtVETpmk5BFCVaQfwU4bmalzrVNZVYOquhKoANaKyIVn2e4BVa1V1drS0tKZLdIkhK2HuynLz0zKGdImcv7sWczK9FFn/QgmBqIJhKNA5ajHFc66cbcRER+QD3RGs6+q+oGXCPcxGDMpqsrWpi5qq4qScoa0iXg8wup5hWw7YlcamamLJhC2AotFpFpE0gl3Em8es81m4FZn+SbgRQ33cm0GNjpXIVUDi4G3RaRURAoARCQLuBrYP/XmmFRz1H+aE6cGz1yCmYpq5xdy8GQfPQMjbpdiEtyEg8arakBE7gSeAbzAQ6q6R0TuBepUdTPwIPCIiDQAXYRDA2e7TcBeIADcoapBESkDHnauOPIAm1T136ajgSa5pXL/QcQap+3bm7v52AWzXa7GJLKoZhFR1aeAp8asu2fU8iBw81n2vQ+4b8y6XcCqyRZrzFhbD3eRm+HjgvPy3C7FNSsrC/B6hK2HuywQzJTYncomodUd7mb1/EK8KTCg3dlkp/tYNjfPrjQyU2aBYBJWz8AIB072pnT/QcTF1UXUt/oZHAm6XYpJYBYIJmFta+5CNbX7DyLWLShmOBCyCXPMlFggmIT15qFO0r2elBrQ7mxqq4rwCLzV2Ol2KSaBWSCYhPVmYyer5hWQmeZ1uxTX5WelsWxuvgWCmRILBJOQek6PsOfYKS5ZWOx2KXFj3YIidrRYP4I5dxYIJiG93RTuP7hkgQVChPUjmKmyQDAJ6c1DnWT4PKyclzozpE3E+hHMVFkgmIT0ZmMna+YXkuGz/oMI60cwU2WBYBJOd/8w+46fstNF47B+BDMVFggm4WxpCo/saR3K7xfpR9jebHctm8mzQDAJ563GTrLSvKyosP6DsT5UXYTXI7x5yE4bmcmzQDAJ581DndRWFZLus4/vWHmZaaysLOCVdzvcLsUkIPuNMgnlRM8gB072ctmiErdLiVuXLSphV6sf/8Cw26WYBGOBYBLKK++G59W+4nybTvVsrji/BFV4vcFOG5nJsUAwCeWVg+3MnpXBBefNcruUuHVRRQGzMny86oSnMdGyQDAJIxhSXmvo4PLFpSk5f3K0fF4PH15UzKvvdhCeydaY6FggmITxztEe/AMjXHG+9R9M5PLFpRz1n6apo9/tUkwCsUAwCeOVg+2IhP/YmQ92hfPf6FW72shMggWCSRivHGxneXk+RTnpbpcS9+YVZzO/ONv6EcykWCCYhHBqcIQdLf4z33zNxC5fXMKbhzoZCtgwFiY6UQWCiKwXkQMi0iAid43zfIaIPO48v0VEqkY9d7ez/oCIXOusqxSRl0Rkr4jsEZGvxqpBJjm90dBBMKR2uekkfGzJbPqHg2xp7HK7FJMgJgwEEfECPwSuA2qAW0SkZsxmtwHdqroI+A5wv7NvDbARWAasB37kvF4A+Jqq1gDrgDvGeU1jznhpfzuzMnyssuGuo3bpohIy0zw8v++k26WYBBHNEcJaoEFVG1V1GHgM2DBmmw3Aw87yE8BVEr4ucAPwmKoOqWoT0ACsVdXjqrodQFV7gX1A+dSbY5JRMKS8sP8kH71gNmleO8sZrcw0L5ctKuWFfW12+amJSjS/XeVAy6jHrbz/j/eZbVQ1APQAxdHs65xeWgVsib5sk0rqW/x09A1zdc0ct0tJOFfXzOao/zT7jve6XYpJAK5+3RKRXODXwF+o6qmzbHO7iNSJSF17u10xkYqe33cSn0f4iPUfTNqVF8xBBDttZKISTSAcBSpHPa5w1o27jYj4gHyg84P2FZE0wmHwqKr+5mxvrqoPqGqtqtaWltofhFT03N6TXLygiPysNLdLSTilszK4qKKAFywQTBSiCYStwGIRqRaRdMKdxJvHbLMZuNVZvgl4UcMnLTcDG52rkKqBxcDbTv/Cg8A+Vf12LBpiklNTRz8NbX1cvdROF52rq2vmsLO1h5OnBt0uxcS5CQPB6RO4E3iGcOfvJlXdIyL3isgnnc0eBIpFpAH4S+AuZ989wCZgL/A0cIeqBoFLgT8GrhSReuff9TFum0kCkW+2V1kgnLOPO//tXtjX5nIlJt75otlIVZ8Cnhqz7p5Ry4PAzWfZ9z7gvjHrXgNsdDIzoad3n2BpWR6VRdlul5Kwzp+Ty7yibJ7ec4LPXTzP7XJMHLNr+EzcOuY/Td2Rbm5cUeZ2KQlNRLh+eRlvNHTQ3W+T5pizs0Awceupd44DcP1yC4SpunFFGYGQ8uzeE26XYuKYBYKJW7975zjL5uZRXZLjdikJb9ncPOYXZ/Nvu467XYqJYxYIJi61dg+wo9nPDXa6KCZEhBuWl/HGoU46+4bcLsfEKQsEE5cip4tuXD7X5UqSxw0rygiGlGf22D0JZnwWCCYuPbnzOMvL85lXbFcXxUpNWR4LSnJ4cucxt0sxccoCwcSdgyd7eedoD3+wysY7jCURYcPKct5q6qS1e8DtckwcskAwcefX21rxeYQNK+10Uaz94epyVOG328eOPmOMBYKJM4FgiN/sOMpHl8ymJDfD7XKSTmVRNusWFPHE9lYbEtu8jwWCiSuvvttBe+8QN62pcLuUpHXTmkqOdA5Qd6Tb7VJMnLFAMHHlie2tFGanceUFs90uJWldd+F5ZKd7+fW2VrdLMXHGAsHEja7+YZ7bc5INK8tJ99lHc7rkZPi4fnkZT+48Rt9QwO1yTByx3zoTNx7f2sJwMGQDsM2Az108j/7hIL/dYZ3L5vcsEExcCIaUf3rrCOsWFHH+nFlul5P0VlUWcGF5Ho+8edg6l80ZFggmLry0v42j/tN8/pIqt0tJCSLC59dVcfBkH1uautwux8QJCwQTF37x1hHm5GVwdY1NhDNTPnHRXPKz0njkzSNul2LihAWCcd2h9j5eOdjO59bOJ81rH8mZkpXu5TO1FTy95wTH/KfdLsfEAfvtM6574D8ayfB5+KN11pk80279cBUAD77W5G4hJi5YIBhXnegZ5Dc7WvnshyrtzmQXVBRms+Giufzz2802m5qxQDDuevC1RkIKX7x8gdulpKw/+8hCBoaD/ML6ElKeBYJxjX9gmF9uaeYTK8qoLLJhrt2y5LxZfHzpbH7+RhMDw3ajWiqzQDCu+dmrTfQPB/mzjyx0u5SU9+WPLqR7YMSOElJcVIEgIutF5ICINIjIXeM8nyEijzvPbxGRqlHP3e2sPyAi145a/5CItInI7lg0xCSW9t4hHnytiRtXlLG0LM/tclLemvlFfHRJKT9++RA9p0fcLse4ZMJAEBEv8EPgOqAGuEVEasZsdhvQraqLgO8A9zv71gAbgWXAeuBHzusB/NxZZ1LQD158l+FgiK9ds8TtUozjv16zhJ7TIzzwyiG3SzEuieYIYS3QoKqNqjoMPAZsGLPNBuBhZ/kJ4CoREWf9Y6o6pKpNQIPzeqjqK4DdIpmCWroG+OXbzXymtpLqkhy3yzGOC8vz+cRFc3notcO09Q66XY5xQTSBUA60jHrc6qwbdxtVDQA9QHGU+5oU83dP78cjwlevWux2KWaMr119PiPBEN9+9qDbpRgXxH2nsojcLiJ1IlLX3t7udjlmil5v6OB3u47z5Y8u5Lz8TLfLMWNUleTwJx+u4vG6Fupb/G6XY2ZYNIFwFKgc9bjCWTfuNiLiA/KBzij3/UCq+oCq1qpqbWlp6WR2NXFmOBDiG5v3UFmUxZfsyqK49dWPL6YkN4N7/nU3wZCNhJpKogmErcBiEakWkXTCncSbx2yzGbjVWb4JeFHDY+puBjY6VyFVA4uBt2NTukk0P3+jiYa2Pr5x4zIy07wT72BcMSszja9fv5RdrT08trXZ7XLMDJowEJw+gTuBZ4B9wCZV3SMi94rIJ53NHgSKRaQB+EvgLmffPcAmYC/wNHCHqgYBROSfgTeBJSLSKiK3xbZpJp4cau/j7589yMeXzuHjNqJp3Nuwci6XLCjmb5/aT2v3gNvlmBkiiTQ5Rm1trdbV1bldhpmkQDDETT95k8Od/Tz7F1cwO8/6DhJBS9cA67/7CivnFfDIFy7G4xG3SzLnQES2qWptNNvGfaeySXw/faWR+hY/92640MIggVQWZfP1G2p4vaGTf9pidzCnAgsEM63qDnfxnecOcsPyMj6xosztcswk3bK2ko8uKeW+3+1j99Eet8sx08wCwUybjr4h7vjldioKs/jbTy8nfK+iSSQiwt/ffBFFOen8+aPbbViLJGeBYKbFSDDEV/55B/6BEX70R2vIy0xzuyRzjopzM/jB51ZzzH+ar22qt0tRk5gFgok5VeXrv32HNw518n8+tZyauTZ4XaJbM7+Qez5Rw/P72rjvd/vcLsdME5/bBZjk8/0XGthU18pXrlzEp9dUuF2OiZHPX1JFU0c/D73eRHlhFrddVu12SSbGLBBMTD38xmG+8/xB/nB1Of/l6vPdLsfE2F/fUMNx/yD/+3d7mZXp4zO1lRPvZBKGnTIyMfPwG4f5xuY9XFMzh7/7wxXWiZyEvB7huxtXctmiEv77r3exqa5l4p1MwrBAMFOmqvz45UNnwuAHn1tNus8+WskqM83L//t87ZlQePiNw26XZGLEfmvNlASCIf76X3Zw2/C7AAALl0lEQVRz/9P7+cRFcy0MUkQkFK66YA7f2LyHe5/ca1cfJQH7zTXnrKNviD/5x608uqWZL31kId/77EoLgxSSmeblp3+8hj+9tIqHXm/izx7ZZvcpJDj77TXnZEtjJ9d/71W2Hu7iW59ewV3XXWBj3aQgr0f4xieW8c1PLuPlA23c8P1X2dHc7XZZ5hxZIJhJGRwJ8rf/vo9b/t9b5GT4+O2fX8pnPmRXmqS6Wz9cxa++dAkAN//kTb7/wrsMB0IuV2UmywLBRO2Nhg7Wf/cVfvofjXymtpLNd15qN52ZM1bNK+R3X7mc65eX8e3nDnLD98NHkCZx2PDXZkINbb383b/v5/l9bcwryuZv/3A5ly4qcbssE8de2t/GX//Lbo76T3PDijL+2zVLqCrJcbuslDSZ4a8tEMxZHTjRy0//4xD/uvMY2WlevvyxhXzh0mqb7cxEZWA4wE9ePsTPXmtiOBDi5tpKvnh5NQtKc90uLaVYIJhzFgwprzd08PAbh3lhfxtZaV5uWTuPOz62kOLcDLfLMwmorXeQf3ihgcfrWhgJhrh66Ry+cFk1F1cX2c2LM8ACwUxaS9cAv9rWyq+3tXLUf5rC7DT+5MPVfP6S+RTmpLtdnkkC7b1D/OLNwzzy1hH8AyPML87m06sr+PSaCsoLstwuL2lZIJgJqSr7T/Ty7J6TPLPnBHuPn0IELltUwmdqK7m6Zo6dGjLTYmA4wNO7T/DEtlbeONQJwEWVBVxTM4era+aweHauHTnEkAWCeR9VpblrgDcPdfLGoU7ebOykvXcIEVg9r5Brl83h+uVlVBRmu12qSSEtXQNs3nmMZ/eeZGeLH4Dygiwuri5i3YJi1i0oprIoywJiCiwQUlwwpLR2D7DveC/vHPWzq7WH3Ud76B4I30VaOiuDSxYU8+GFxVy5dDazZ9k8x8Z9J08N8vy+k7ze0MGWxi46+4cBKMnN4MLyPJaX57Nsbj7L5uZRXpBlN0JGyQIhBQyOBDnRM8gx/2mO9QxyuKOfQ+19HGrv43DHAMPB8E1BXo+wZM4sVlTks7win4uri1lYmmPfuExcU1Ua2vp4q6mL+mY/e4718G5b35nxkjJ8HqpLclhQmsOCklyqS3IoL8xibn4Wc/IzyPDZ6c6ImAeCiKwHvgd4gZ+p6t+NeT4D+AWwBugEPquqh53n7gZuA4LAV1T1mWheczzJHAjBkNI/HKB3MEBX3zCd/UN09Q/T1T9MZ/8w3c7PEz2DHO85TUff8Hv293qE+UXZLCjNZWFpDgtLc1k8J5elZXnWF2CSwunhIPtOnGL/8V4a2/to7Oinsb2Plu7T7xtYryQ3g7kFmZyXl0lxbgbFOekU5qRTlJNGUU4GRdnpFOWmk5+VRnaaN6mPNiYTCBNOkCMiXuCHwNVAK7BVRDar6t5Rm90GdKvqIhHZCNwPfFZEaoCNwDJgLvC8iERmTZnoNV0RDCmBUMj5qQSDykjkcVDPPD8UcP6NhBgMBBkaCTE06udg5HEgxOBI+Gf/UJC+oRH6hgL0DQboHQrQ7yz3DwfPWpPPIxTmpFOck86cvEwuLM9jbn4WZQVZzC3IZG5+FnMLsmxgOZPUstK9rJ5XyOp5he9ZPxwI0dI9wHH/IMd6TnPcH/7SdKxnkMOd/Wxv7qarf5izDcYqAjnpPnIzfORm+sjJ8DEr4/ePs9O9ZKZ5yfB5zvzMGPM4M81LprM+zSukeT34PILP48HnFXxeIS2yfOanxN2RejQzpq0FGlS1EUBEHgM2AKP/eG8A/sZZfgL4gYRbugF4TFWHgCYRaXBejyheM2Zu/IdXGRgKhv/AO3/QA0F9z+NIAMT6DJoIZPq8ZKR53vOhK8hOp6Iom1kZ4Q9gboaPWZnhn0U56RTnpoe/yeSkk5fpi7sPjjHxIt3nYWFpLgs/4Ia3UEg5NThy5qg78u/U4Mh7v5wNhY/S+4cCtPUOnvmyFvmSF2s+j4wJiXCQeD2CxwNeETweoSQng03OWFHTKZpAKAdGT4vUClx8tm1UNSAiPUCxs/6tMfuWO8sTvSYAInI7cDvAvHnzoij3/RaV5hIIKWleD16PnPkPHv6f4XnPY++o9D6zzushLfLYK3hEyHD+yEf+2L/n24PPS2Za+GeaN/6+BRiTajweoSA7nYLsdBaUnttrqCrDwdDvj/rHng1wzhYMB977pTMQDDHi/Bx3nXP2IRAKMRJUgqEQwRCENPyFNajKrIyZme047udUVtUHgAcg3IdwLq/x3Y2rYlqTMSb1SOSLoM9LXmaa2+VMi2hOOh8FRo9vXOGsG3cbEfEB+YQ7l8+2bzSvaYwxZgZFEwhbgcUiUi0i6YQ7iTeP2WYzcKuzfBPwooYvX9oMbBSRDBGpBhYDb0f5msYYY2bQhKeMnD6BO4FnCF8i+pCq7hGRe4E6Vd0MPAg84nQadxH+A4+z3SbCncUB4A5VDQKM95qxb54xxpho2Y1pxhiTxCZzH4JduG6MMQawQDDGGOOwQDDGGANYIBhjjHEkVKeyiLQDRyaxSwnQMU3luCGZ2pNMbQFrTzxLprbA5NszX1Wjuj87oQJhskSkLtre9USQTO1JpraAtSeeJVNbYHrbY6eMjDHGABYIxhhjHMkeCA+4XUCMJVN7kqktYO2JZ8nUFpjG9iR1H4IxxpjoJfsRgjHGmCglZSCIyP8SkV0iUi8iz4rIXGe9iMj3RaTBeX6127VGQ0T+r4jsd2r+rYgUjHrubqc9B0TkWjfrjIaI3Cwie0QkJCK1Y55LqLZAeG5wp94GEbnL7XomS0QeEpE2Edk9al2RiDwnIu86Pws/6DXihYhUishLIrLX+Yx91VmfqO3JFJG3RWSn055vOuurRWSL85l73BkxOjZUNen+AXmjlr8C/MRZvh74d0CAdcAWt2uNsj3XAD5n+X7gfme5BtgJZADVwCHA63a9E7RlKbAEeBmoHbU+EdvidepcAKQ79de4Xdck23AFsBrYPWrdt4C7nOW7Ip+3eP8HlAGrneVZwEHnc5Wo7REg11lOA7Y4f7c2ARud9T8Bvhyr90zKIwRVPTXqYQ4Q6SjZAPxCw94CCkSkbMYLnCRVfVZVA87DtwhPKASj5qxW1SZg9JzVcUlV96nqgXGeSri2MGq+cVUdBiJzgycMVX2F8JD1o20AHnaWHwb+YEaLOkeqelxVtzvLvcA+wlP2Jmp7VFX7nIdpzj8FriQ8dz3EuD1JGQgAInKfiLQAfwTc46web37o8rH7xrkvED7KgeRoT0QitiURa47GHFU97iyfAOa4Wcy5EJEqYBXhb9UJ2x4R8YpIPdAGPEf4iNQ/6gtiTD9zCRsIIvK8iOwe598GAFX9uqpWAo8Cd7pb7cQmao+zzdcJTzT0qHuVTiyatpjEoOHzEgl1KaKI5AK/Bv5izNmChGuPqgZVdSXhswJrgQum8/0mnDEtXqnqx6Pc9FHgKeAbxPFczhO1R0T+BLgRuMr5UEOctmcS/29Gi8u2TCARa47GSREpU9XjzinVNrcLipaIpBEOg0dV9TfO6oRtT4Sq+kXkJeASwqe6fc5RQkw/cwl7hPBBRGTxqIcbgP3O8mbg887VRuuAnlGHknFLRNYDfwV8UlUHRj11tjmrE1EitiVZ5wYfPUf6rcC/ulhL1ERECE/nu09Vvz3qqURtT2nkikIRyQKuJtwv8hLhuesh1u1xuyd9mnrnfw3sBnYBTwLlo3rtf0j4PNw7jLrKJZ7/Ee5gbQHqnX8/GfXc1532HACuc7vWKNryKcLnPYeAk8AzidoWp+brCV/Ncgj4utv1nEP9/wwcB0ac/y+3AcXAC8C7wPNAkdt1RtmWywifDto16nfl+gRuzwpgh9Oe3cA9zvoFhL8sNQC/AjJi9Z52p7IxxhggSU8ZGWOMmTwLBGOMMYAFgjHGGIcFgjHGGMACwRhjjMMCwRhjDGCBYIwxxmGBYIwxBoD/D4acWvSXSiI1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, gs, spins, pdf = create_spins(gcoll, q, gammaq, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "\n",
    "plt.plot(f, pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcccc4e5eb8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initsteady = (0.1,0.1)\n",
    "fstart = 50\n",
    "deltaarr = np.linspace(-fstart*2*np.pi,fstart*2*np.pi, 2000)\n",
    "etasteady = 0.00001\n",
    "losasteady = np.zeros(np.shape(deltaarr))\n",
    "losareal = np.zeros(np.shape(deltaarr))\n",
    "losaimag = np.zeros(np.shape(deltaarr))\n",
    "\n",
    "newinit = initsteady\n",
    "for i, delt in enumerate(deltaarr):\n",
    "    ar, ai = a_steady(etasteady, delt, gs, spins, gpar, gperp, kappa, newinit)\n",
    "    newinit = (ar, ai)\n",
    "    losasteady[i] = ai**2+ar**2\n",
    "    losareal[i] = ar\n",
    "    losaimag[i] = ai\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(deltaarr/2/np.pi, 10*np.log10(losasteady/0.0000000001))\n",
    "#deltaarr[find(losasteady == max(losasteady))]/2/pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_calculation(drive, tlistpump, tlistafter, init):\n",
    "\n",
    "    #this should be very fast as well because scipy uses fortran \n",
    "    asolpump, infodict = odeintz(mbes, init, tlistpump, args=(drive,), full_output=True)\n",
    "    init2 = asolpump[-1,:]\n",
    "    asoldecay = odeintz(mbes, init2, tlistafter, args=(0,))\n",
    "    \n",
    "    #this is necessary because we are clustering the spins\n",
    "    asolpump[:,2::2] = asolpump[:,2::2]*pdf\n",
    "    asolpump[:,1::2] = asolpump[:,1::2]*pdf\n",
    "    asoldecay[:,2::2] = asoldecay[:,2::2]*pdf\n",
    "    asoldecay[:,1::2] = asoldecay[:,1::2]*pdf\n",
    "    return asolpump, asoldecay, infodict\n",
    "    #solution is that the first entry is the cavity, and then the first spin (sigma-, sigmaz) then the second etc...\n",
    "    \n",
    "def do_calculation_stimulated(drivepump, driveprobe, tlistpump, tlistwait, tlistprobe, tlistafter, init):\n",
    "    #this should be very fast as well because scipy uses fortran \n",
    "    asolpump, infodict = odeintz(mbes, init, tlistpump, args=(drivepump,), full_output=True)\n",
    "    init2 = asolpump[-1,:]\n",
    "    asolwait = odeintz(mbes, init2, tlistwait, args=(0,))\n",
    "    init3 = asolwait[-1,:]\n",
    "    asolprobe = odeintz(mbes, init3, tlistprobe, args=(driveprobe,))\n",
    "    init4 = asolprobe[-1,:]\n",
    "    asoldecay = odeintz(mbes, init4, tlistafter, args=(0,))\n",
    "    \n",
    "    #this is necessary because we are clustering the spins\n",
    "    asolpump[:,2::2] = asolpump[:,2::2]*pdf\n",
    "    asolpump[:,1::2] = asolpump[:,1::2]*pdf\n",
    "    asoldecay[:,2::2] = asoldecay[:,2::2]*pdf\n",
    "    asoldecay[:,1::2] = asoldecay[:,1::2]*pdf\n",
    "    asolwait[:, 2::2] = asolwait[:, 2::2]*pdf\n",
    "    asolwait[:, 1::2] = asolwait[:, 1::2]*pdf\n",
    "    asolprobe[:, 1::2] = asolprobe[:, 1::2]*pdf\n",
    "    asolprobe[:, 2::2] = asolprobe[:, 2::2]*pdf\n",
    "    return asolpump, asolwait, asolprobe, asoldecay, infodict\n",
    "    \n",
    "def do_calculation_soc(ilist, qlist, tlistpump, tlistafter, init):\n",
    "    \"\"\"this method is if you want to use shaped drive, which means that the drive-lists as i and q channels\n",
    "    shape should be given as first arguments\n",
    "    \n",
    "    factor is the number by which one has to multiply tlistpump, to get a all integer list as 0,1,2,3...numsteps-1\n",
    "    \"\"\"\n",
    "    #this should be very fast as well because scipy uses fortran \n",
    "    asolpump, infodict = odeintz(mbes_soc, init, tlistpump, args=(ilist, qlist, tlistpump), full_output=True)\n",
    "    init2 = asolpump[-1,:]\n",
    "    asoldecay = odeintz(mbes, init2, tlistafter, args=(0,))\n",
    "\n",
    "    asolpump[:,2::2] = asolpump[:,2::2]*pdf\n",
    "    asolpump[:,1::2] = asolpump[:,1::2]*pdf\n",
    "    asoldecay[:,2::2] = asoldecay[:,2::2]*pdf\n",
    "    asoldecay[:,1::2] = asoldecay[:,1::2]*pdf\n",
    "    \n",
    "    return asolpump, asoldecay, infodict\n",
    "    #solution is that the first entry is the cavity, and then the first spin (sigma-, sigmaz) then the second etc...\n",
    "    \n",
    "def do_calculation_soc_pump_only(ilist, qlist, tlist, init):\n",
    "    \"\"\"this method is if you want to use shaped drive, which means that the drive-lists as i and q channels\n",
    "    shape should be given as first arguments\n",
    "    \n",
    "    it doesn't use a decay afterwards such that it is better for genetic algorithm\n",
    "    \n",
    "    factor is the number by which one has to multiply tlistpump, to get a all integer list as 0,1,2,3...numsteps-1\n",
    "    \"\"\"\n",
    "    #this should be very fast as well because scipy uses fortran\n",
    "    asol, infodict = odeintz(mbes_soc, init, tlist, args=(ilist, qlist, tlist), full_output=True)\n",
    "    \n",
    "    asol[:,2::2] = asol[:,2::2]*pdf\n",
    "    asol[:,1::2] = asol[:,1::2]*pdf\n",
    "    \n",
    "    return asol, infodict\n",
    "    #solution is that the first entry is the cavity, and then the first spin (sigma-, sigmaz) then the second etc..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibrate power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime = 200e-3\n",
    "decaytime = 300e-3\n",
    "dt = 0.5e-3\n",
    "numsteps = int(pumptime/dt)\n",
    "tlist = np.arange(0,pumptime+dt,dt)\n",
    "tlistdecay = np.arange(0, decaytime+dt, dt)\n",
    "\n",
    "# test time it takes to calculate\n",
    "\n",
    "\n",
    "init = np.ones(nbins*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0\n",
    "\n",
    "pulsei = np.ones((len(tlist)))\n",
    "\n",
    "\n",
    "pin = np.arange(-45, 0,1)\n",
    "aref = 2*np.pi*50\n",
    "\n",
    "alos = np.zeros((len(pin), len(tlist)+len(tlistdecay)))\n",
    "szlos = np.zeros(np.shape(alos))\n",
    "\n",
    "for ctr, p in enumerate(pin):\n",
    "    dr = aref*10**(p/20.)\n",
    "    asolpump, asoldecay, infodict = do_calculation(dr, tlist, tlistdecay, init)\n",
    "    alos[ctr, : ] = np.hstack((abs(asolpump[:,0])**2, abs(asoldecay[:,0])**2))\n",
    "    szlos[ctr, :] = np.hstack((np.real(np.sum(asolpump[:, 2::2], axis=1)), np.real(np.sum(asoldecay[:, 2::2], axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f55359a1a90>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tplot = np.linspace(0, 1000*(pumptime+decaytime), len(tlist)+len(tlistdecay))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5))\n",
    "\n",
    "ax1.pcolor(tplot, pin, alos, norm=colors.LogNorm())\n",
    "ax1.set_xlabel('Time (ns)')\n",
    "ax1.set_ylabel('Input power (dB)');\n",
    "\n",
    "ax2.pcolor(tplot, pin, szlos)\n",
    "ax2.set_xlabel('Time (ns)')\n",
    "ax2.set_ylabel('Input power (dB)');\n",
    "\n",
    "f = plt.figure()\n",
    "idx = min(range(len(tplot)), key=lambda i: abs(tplot[i]-pumptime*1000))\n",
    "plt.plot(pin, szlos[:, idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = np.linspace(0,pumptime,numsteps)\n",
    "wgrund = np.pi/tp[-1]\n",
    "\n",
    "\n",
    "def calculate_fitness(**kwargs):\n",
    "    if kwargs:\n",
    "        fcomps = np.array([kwargs[i] for i in kwargs])\n",
    "    fcompsi = fcomps[:int(len(fcomps)/2)]\n",
    "    fcompsq = fcomps[int(len(fcomps)/2):]\n",
    "    pulsei = pulse_rwa(tp, [wgrund, fcompsi])\n",
    "    pulseq = pulse_rwa(tp, [wgrund, fcompsq])\n",
    "    init = np.ones(nbins*2+1)*(-1)\n",
    "    init[0] = 0\n",
    "    init[1::2] = 0\n",
    "    solution, infodict = do_calculation_soc_pump_only(pulsei, pulseq, tp, init)\n",
    "    return np.real(np.sum(solution[:, 2::2], axis=1)[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Bayes:\n",
    "    def __init__(self):\n",
    "        self.number_fcp = 8\n",
    "        self.max_ampl = 2*np.pi*90/self.number_fcp\n",
    "        self.pbounds = {}\n",
    "        for i in range(self.number_fcp*2):\n",
    "            self.pbounds['p'+str(i)] = (-self.max_ampl, self.max_ampl)\n",
    "        self.number_initial = 90\n",
    "        self.n_iter = 800\n",
    "        self\n",
    "            \n",
    "            \n",
    "    def main(self, init=None):\n",
    "        populationPulse = np.zeros((numsteps, 2))\n",
    "        populationFourier = np.zeros((self.number_fcp, 2))\n",
    "        populationFitness = np.zeros((1))\n",
    "        populationSolution = np.zeros((numsteps, 2*nbins+1), dtype=complex)\n",
    "        \n",
    "        frand = np.random.uniform(-self.max_ampl, self.max_ampl, (self.number_fcp, 2))\n",
    "        \n",
    "        \n",
    "        \n",
    "        bo = BayesianOptimization(calculate_fitness, self.pbounds)\n",
    "        \n",
    "        if init:\n",
    "            guess = {}\n",
    "            for i, val in enumerate(init):\n",
    "                guess['p'+str(i)] = [val]\n",
    "            bo.explore(guess)\n",
    "        \n",
    "        bo.maximize(init_points=self.number_initial, n_iter=self.n_iter, kappa=3)\n",
    "        \n",
    "        return bo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInitialization\u001b[0m\n",
      "\u001b[94m-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |        p0 |        p1 |       p10 |       p11 |       p12 |       p13 |       p14 |       p15 |        p2 |        p3 |        p4 |        p5 |        p6 |        p7 |        p8 |        p9 | \n",
      "    1 | 00m04s | \u001b[35m   0.29035\u001b[0m | \u001b[32m  74.5647\u001b[0m | \u001b[32m -74.6541\u001b[0m | \u001b[32m  71.9558\u001b[0m | \u001b[32m  70.3838\u001b[0m | \u001b[32m  70.3080\u001b[0m | \u001b[32m  69.9201\u001b[0m | \u001b[32m  12.8512\u001b[0m | \u001b[32m  64.8409\u001b[0m | \u001b[32m -73.4228\u001b[0m | \u001b[32m -71.2305\u001b[0m | \u001b[32m   8.6770\u001b[0m | \u001b[32m -70.2516\u001b[0m | \u001b[32m -69.1954\u001b[0m | \u001b[32m -20.3361\u001b[0m | \u001b[32m -75.4180\u001b[0m | \u001b[32m  75.3283\u001b[0m | \n",
      "    2 | 00m04s |   -0.67116 |  -16.6123 |  -69.2147 |   -9.9523 |   -2.6044 |  -46.1159 |    8.2022 |  -27.3665 |  -17.2166 |    0.3714 |   56.0317 |   43.0142 |   23.8196 |   26.8173 |  -52.5302 |   17.3582 |  -66.3957 | \n",
      "    3 | 00m04s |   -0.76015 |   44.3273 |    5.3551 |  -41.2740 |  -22.1319 |   36.4350 |   44.5039 |   43.4459 |   33.5204 |    5.5159 |  -46.1743 |  -39.7631 |  -28.8380 |   53.4609 |   67.4434 |  -41.4761 |   43.9149 | \n",
      "    4 | 00m04s |   -0.81589 |  -46.7618 |    7.1732 |  -65.8147 |  -16.9845 |    9.3722 |   12.9806 |   35.3540 |    5.9507 |   65.5661 |    9.4711 |   58.2355 |  -12.2204 |  -66.9954 |   -9.9408 |    5.8983 |   -4.0740 | \n",
      "    5 | 00m04s |   -0.73437 |   64.3839 |  -39.3783 |   -3.5423 |   40.1194 |  -32.1768 |  -10.1859 |  -31.4452 |   20.7686 |   21.3946 |   -4.4195 |  -22.9699 |  -34.9207 |   69.3290 |   47.6616 |   10.2933 |  -28.5437 | \n",
      "    6 | 00m04s |   -0.63975 |   41.4252 |   55.8455 |   30.1107 |   19.2384 |   66.2138 |  -46.1679 |   61.7202 |   14.3050 |   52.6350 |  -25.3168 |  -57.8438 |   -6.0806 |  -65.8484 |  -12.4625 |    5.5085 |   66.4173 | \n",
      "    7 | 00m04s |   -0.52267 |   70.1582 |   -2.2004 |   35.2743 |  -61.2611 |    1.1473 |  -16.8672 |  -48.9634 |  -58.1223 |  -61.8457 |  -57.0283 |  -48.5854 |   68.1193 |   43.9447 |   56.6572 |  -56.1832 |  -44.2714 | \n",
      "    8 | 00m04s |   -0.75309 |  -55.8315 |  -11.4757 |   46.3373 |   46.7236 |   45.5690 |   32.8338 |  -48.5883 |   13.2574 |  -24.4051 |  -22.8191 |  -18.9530 |  -16.0893 |   46.2879 |   37.1726 |    1.3676 |   29.7032 | \n",
      "    9 | 00m04s |   -0.78033 |    6.7130 |  -61.0105 |   -4.8465 |   64.0657 |   27.2959 |   11.5416 |  -32.0114 |   45.7734 |   68.4822 |  -13.4284 |  -68.0451 |  -23.7777 |  -36.3086 |   17.4969 |   16.8368 |    1.8327 | \n",
      "   10 | 00m04s |   -0.87626 |  -11.2650 |   -1.4002 |    9.5488 |   -5.7846 |  -14.1302 |  -66.7709 |  -57.0547 |   50.1751 |  -11.9850 |   26.9394 |   51.0879 |  -42.7481 |   -1.1965 |  -30.8089 |   50.5921 |   30.7074 | \n",
      "   11 | 00m04s |   -0.51484 |  -26.8683 |   36.0360 |   21.6476 |   50.8143 |    4.8716 |   27.9401 |  -13.6072 |   31.7093 |   58.4232 |  -48.1517 |    8.5487 |   17.3897 |   34.3426 |   19.6502 |  -64.4137 |   65.6590 | \n",
      "   12 | 00m04s |   -0.76672 |   31.7327 |   29.5657 |  -52.4869 |  -44.1000 |  -67.9992 |   39.8717 |   68.5908 |   42.4977 |  -12.8583 |   25.8330 |    1.6750 |  -66.2571 |  -18.7514 |   41.9754 |  -54.1956 |   14.3866 | \n",
      "   13 | 00m04s |   -0.57551 |  -34.3880 |  -55.9374 |  -31.3051 |   14.8576 |   19.0770 |  -29.7014 |  -32.3155 |  -51.4190 |   -9.4529 |   -0.8331 |  -32.9778 |  -40.8279 |   58.1395 |   -7.4318 |  -66.9012 |   50.2049 | \n",
      "   14 | 00m04s |   -0.79701 |    3.8536 |  -30.7654 |   46.2742 |   12.2762 |   68.0524 |  -65.6834 |   29.8763 |  -20.0659 |   16.5437 |    6.3102 |   27.7337 |  -41.4813 |  -19.8173 |  -21.2501 |  -58.6116 |  -14.6817 | \n",
      "   15 | 00m04s |   -0.62958 |   52.8852 |    3.5078 |   38.9292 |   12.3213 |   56.5512 |   55.3899 |   -3.4952 |  -68.4716 |   18.6585 |  -24.1191 |   16.6516 |   28.4703 |  -65.9006 |   51.1124 |   57.4652 |  -52.4928 | \n",
      "   16 | 00m04s |   -0.55251 |  -64.5913 |  -45.6520 |  -14.2433 |   46.8936 |   57.6332 |   24.9721 |   20.2004 |  -33.9809 |   53.6528 |   34.5029 |   69.5825 |   -6.9338 |   -2.9220 |  -34.6820 |   62.1279 |  -16.2283 | \n",
      "   17 | 00m04s |   -0.73477 |  -65.4401 |    8.2886 |  -55.1808 |   28.3790 |   15.4894 |   13.0063 |  -57.4538 |   53.4878 |   30.7337 |  -57.2968 |   49.8758 |  -18.6649 |  -12.2749 |    6.5509 |   28.6991 |   25.6220 | \n",
      "   18 | 00m04s |   -0.63041 |  -36.1308 |  -66.1913 |  -42.7956 |   17.2682 |  -17.4146 |  -39.8908 |  -31.7641 |  -66.9153 |  -16.6937 |  -53.1038 |   64.7394 |  -29.2695 |  -50.0586 |   52.2525 |   56.4062 |  -17.8714 | \n",
      "   19 | 00m04s |   -0.67894 |   50.5431 |   15.7863 |   69.1478 |   -8.0226 |  -33.2369 |  -70.3466 |   41.9623 |  -57.6548 |   21.7206 |  -27.1377 |  -49.2236 |   59.0197 |   22.2596 |   51.2880 |   63.7913 |    7.6874 | \n",
      "   20 | 00m04s |   -0.59477 |  -60.7719 |  -58.5114 |  -68.6519 |  -49.7416 |   46.3137 |   67.5467 |  -52.6262 |  -31.4793 |  -69.7681 |  -12.5527 |   47.3840 |  -28.2260 |   53.2060 |   61.3797 |   36.6821 |   54.4216 | \n",
      "   21 | 00m04s |   -0.62683 |   38.2584 |   44.8864 |   -5.2103 |   68.3029 |   12.6342 |  -13.4706 |  -42.3218 |  -37.8964 |   21.5289 |   27.7319 |  -47.0329 |   50.8447 |  -10.5534 |  -57.8405 |  -57.6958 |  -60.8064 | \n",
      "   22 | 00m04s |   -0.75690 |    6.5007 |  -47.2607 |  -53.3311 |   46.4204 |   48.0729 |  -12.4606 |   55.0848 |   60.1531 |  -64.0128 |   45.9438 |  -54.0476 |   -8.2567 |  -64.1048 |   55.1416 |  -17.9911 |  -60.2211 | \n",
      "   23 | 00m04s |   -0.67420 |   19.9506 |  -25.7264 |   43.6865 |   24.3319 |   43.7658 |    3.3267 |  -42.3902 |    1.0126 |  -51.0455 |   27.7431 |  -14.5486 |  -11.0331 |  -37.7395 |  -38.2023 |  -57.4759 |  -69.2215 | \n",
      "   24 | 00m04s |   -0.68298 |  -55.3824 |   39.4993 |   -8.5157 |  -19.3742 |   17.0158 |  -42.1911 |  -27.5299 |   27.9326 |  -17.2161 |  -16.1717 |   27.5063 |  -27.6105 |   23.8017 |   37.6121 |  -33.8229 |   49.2625 | \n",
      "   25 | 00m04s |   -0.79158 |   40.9356 |    6.4045 |  -58.5653 |   59.5335 |   11.7025 |   46.7580 |   12.7962 |  -39.7787 |   -7.3975 |  -63.3876 |  -39.3360 |   33.4478 |  -46.0526 |   -8.4786 |  -13.6844 |  -58.8555 | \n",
      "   26 | 00m04s |   -0.80767 |    5.4163 |  -14.4890 |   54.6026 |  -69.1290 |   20.5750 |   49.6971 |   66.6230 |   18.6760 |   29.9070 |  -55.4121 |   50.0549 |   35.0807 |   -0.6734 |  -39.0158 |    9.7002 |   59.1902 | \n",
      "   27 | 00m04s |   -0.79094 |   25.6662 |   26.7984 |  -49.9333 |   49.3848 |   32.5283 |  -52.8403 |   14.5610 |   65.9775 |  -27.1936 |   27.9062 |   18.9650 |   23.4478 |  -48.6111 |  -59.6346 |  -54.6578 |  -32.2731 | \n",
      "   28 | 00m04s |   -0.80374 |   39.6416 |   -4.2084 |   21.5479 |   29.2465 |   41.0772 |   64.6185 |   40.9427 |   13.3893 |   30.0416 |  -62.9775 |    1.5369 |  -43.4035 |  -61.5458 |  -11.7143 |  -44.3893 |  -21.3834 | \n",
      "   29 | 00m04s |   -0.66282 |  -51.9453 |  -44.3255 |  -66.1431 |   -9.2425 |   17.3958 |   26.7834 |  -21.4903 |   10.1760 |    3.6008 |  -10.2067 |  -34.5958 |  -15.1069 |   -5.0388 |   27.2345 |    8.7235 |  -61.5172 | \n",
      "   30 | 00m04s |   -0.79197 |   19.1349 |   37.8403 |   44.0709 |  -60.2974 |  -20.7784 |  -57.4371 |   46.8228 |   14.2188 |   45.5501 |  -16.7384 |  -11.0945 |  -62.4616 |  -12.7133 |  -64.8808 |  -12.9008 |   65.3827 | \n",
      "   31 | 00m04s |   -0.90701 |   -2.2472 |   21.5149 |  -18.6655 |  -29.2706 |  -50.4104 |   36.9155 |   -5.3496 |  -67.8686 |   15.8228 |  -48.9432 |  -33.2093 |   67.8660 |  -55.2998 |   23.2552 |   39.4202 |    6.0356 | \n",
      "   32 | 00m04s |   -0.77568 |   55.1645 |    0.3456 |   -1.2018 |  -21.5839 |   20.2609 |  -52.7310 |    5.3038 |   -5.8192 |  -55.6918 |  -57.9699 |  -11.3242 |   47.8268 |  -44.8823 |   16.7865 |  -12.3931 |   38.2622 | \n",
      "   33 | 00m04s |   -0.62318 |   26.7902 |   67.2159 |  -67.2325 |   52.4715 |   12.7321 |  -31.9226 |  -67.8363 |  -64.0598 |   65.7830 |   28.9985 |   -2.6130 |  -51.8282 |   40.9503 |  -38.5074 |  -37.7078 |   33.9670 | \n",
      "   34 | 00m04s |   -0.65538 |  -44.2351 |   30.1248 |   61.4265 |  -17.9126 |  -66.3387 |   21.9962 |  -10.1412 |   56.2546 |   23.7485 |  -66.8403 |  -56.0416 |  -17.1189 |   46.5347 |  -34.5813 |   65.7452 |   18.5451 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   35 | 00m04s |   -0.67852 |   31.4265 |   27.7855 |   28.2524 |  -43.2971 |    8.6856 |  -17.7126 |  -18.2187 |   41.0624 |  -65.0421 |   46.3307 |  -49.2143 |  -61.4126 |  -26.7287 |   51.8043 |  -59.8792 |   46.9142 | \n",
      "   36 | 00m04s |   -0.91284 |    4.8407 |   -2.8073 |  -23.3549 |   21.3579 |  -44.5914 |  -53.3860 |   57.4647 |  -17.1412 |   21.1396 |   27.3660 |  -40.0019 |   25.6800 |  -69.6715 |  -32.9468 |   21.4474 |   43.6101 | \n",
      "   37 | 00m04s |   -0.62099 |  -29.5459 |  -23.2272 |   46.7871 |   -5.6671 |  -35.4929 |   14.2743 |   33.4282 |   19.0298 |  -67.1380 |   37.8893 |   52.1197 |   -2.5872 |  -36.8181 |  -11.7362 |   57.5406 |  -60.5164 | \n",
      "   38 | 00m04s |   -0.75646 |  -19.3684 |   32.0264 |  -10.3983 |  -52.4486 |   22.8109 |  -58.6367 |   -9.6449 |   39.5803 |   60.0367 |   21.4641 |  -44.1400 |  -70.2875 |  -48.3452 |  -60.7984 |  -62.2417 |   -2.7036 | \n",
      "   39 | 00m04s |   -0.65024 |   29.3073 |   11.6931 |   31.3196 |  -23.2363 |  -22.7389 |   11.9412 |   62.1052 |  -23.9365 |   19.3649 |   12.8859 |   19.8788 |   47.5683 |   41.5242 |  -18.9987 |   53.7383 |   70.0986 | \n",
      "   40 | 00m04s |   -0.72522 |   30.7802 |    1.5240 |  -27.6110 |   28.1973 |   62.2740 |   68.7835 |  -26.5645 |   48.0343 |  -50.1414 |   68.0535 |  -53.9555 |  -70.6059 |   37.6801 |  -54.1798 |   22.1502 |   69.0550 | \n",
      "   41 | 00m04s |   -0.84394 |   13.8343 |  -30.5461 |   68.4132 |  -24.7433 |  -32.1784 |   -9.8585 |   70.2209 |   44.3479 |   26.6539 |  -51.6985 |  -10.3282 |  -27.6958 |   69.9505 |   38.2342 |   -5.5733 |  -31.2169 | \n",
      "   42 | 00m04s |   -0.70472 |  -44.9284 |   12.7249 |   10.3377 |   -2.8366 |    2.1812 |  -64.8027 |   66.0495 |   55.6495 |   51.7919 |   49.3618 |   56.1517 |  -36.1084 |   42.1480 |   37.5346 |  -35.1941 |   45.0220 | \n",
      "   43 | 00m04s |   -0.73210 |  -16.2550 |   69.3614 |   57.3971 |  -45.8496 |   48.3185 |   12.5099 |   58.0812 |   60.0399 |  -38.1211 |  -37.4847 |  -31.7785 |  -19.1038 |   61.1326 |  -34.7647 |  -25.2623 |  -17.1213 | \n",
      "   44 | 00m04s |   -0.73423 |   28.8235 |   37.9650 |   41.4825 |  -47.0687 |  -41.0313 |  -66.4176 |  -50.0675 |   -0.2878 |  -63.4960 |  -53.7426 |   51.8225 |  -35.9286 |   38.9877 |   41.2496 |  -51.8360 |   26.0982 | \n",
      "   45 | 00m04s |   -0.69697 |   51.0391 |  -20.5117 |  -57.9236 |  -23.7224 |  -49.4377 |   16.3576 |   62.3955 |   25.7579 |   23.1455 |   45.4490 |   24.0138 |  -48.3556 |  -26.6408 |  -35.0317 |   58.0057 |    7.5018 | \n",
      "   46 | 00m04s |   -0.80626 |   54.9489 |   41.1213 |  -29.9754 |   36.9298 |  -63.0420 |   45.9695 |   35.1252 |  -32.4524 |   29.2693 |   22.6137 |  -64.2226 |    4.5716 |  -32.2884 |   14.1160 |   11.2588 |   14.1970 | \n",
      "   47 | 00m04s |   -0.65890 |   37.4621 |   35.8420 |   32.5143 |   21.5797 |   -1.0859 |   11.4125 |  -36.4307 |  -69.9270 |  -53.2496 |  -66.3858 |  -57.3961 |  -36.5042 |   53.2600 |   28.2987 |   27.1024 |  -67.3048 | \n",
      "   48 | 00m04s |   -0.87307 |    5.8164 |    8.8976 |  -30.0221 |   10.0270 |   49.8610 |   21.0561 |  -66.7593 |  -44.3058 |  -63.2423 |  -64.3057 |   12.7610 |   34.8835 |    4.6844 |  -38.2260 |   28.9134 |   21.7988 | \n",
      "   49 | 00m04s |   -0.76064 |   51.1940 |   30.5566 |   61.1507 |  -55.3801 |   18.8319 |    5.7703 |  -44.1009 |   22.9445 |  -56.9855 |   13.7145 |  -27.2522 |  -19.4127 |  -25.6546 |  -68.9045 |  -22.8042 |   31.7628 | \n",
      "   50 | 00m04s |   -0.81223 |  -12.6240 |   -0.2038 |   39.8539 |   31.5567 |  -49.4808 |   29.1890 |  -56.4165 |  -15.2947 |  -68.2093 |    0.7828 |   52.0100 |   46.1771 |   22.9588 |   16.0374 |  -11.9485 |   63.1165 | \n",
      "   51 | 00m04s |   -0.90484 |    7.6893 |    5.1693 |   49.5523 |  -65.6957 |   11.4201 |  -35.2415 |   -0.8520 |  -35.1500 |   24.0361 |  -34.8228 |   28.1513 |   15.6179 |   49.0439 |  -51.7516 |   27.6086 |   36.1003 | \n",
      "   52 | 00m04s |   -0.75556 |  -31.6390 |  -66.1073 |   -3.9239 |  -38.5301 |   50.3981 |   55.6053 |    8.0983 |  -13.2319 |   15.6859 |  -69.9090 |  -24.2380 |   -7.9492 |   47.8516 |  -69.8516 |   22.9241 |    5.5259 | \n",
      "   53 | 00m04s |   -0.58047 |  -27.3181 |   64.7524 |   39.8042 |   18.5404 |   -5.5283 |   17.2762 |   -4.5460 |   65.0623 |  -19.0271 |  -36.8310 |   68.8723 |   32.8461 |   54.8955 |   -7.8400 |  -31.1695 |   65.0585 | \n",
      "   54 | 00m04s |   -0.80119 |  -15.5715 |   41.0311 |   51.1367 |   -3.8409 |   56.0519 |  -55.6584 |  -51.1169 |   -5.4910 |   54.3205 |  -46.5367 |   27.2028 |  -13.9350 |   32.9477 |   -5.5549 |  -41.7418 |   12.3730 | \n",
      "   55 | 00m04s |   -0.82530 |  -33.3680 |   44.5482 |    7.7599 |   70.5842 |  -14.1036 |   68.4213 |   48.4866 |   61.1997 |   29.9037 |  -34.3541 |   26.1204 |   -5.7645 |  -31.9496 |   58.4930 |   -2.2031 |  -12.4574 | \n",
      "   56 | 00m04s |   -0.89341 |  -22.4872 |   28.6039 |   19.3733 |  -42.2284 |    5.1072 |   68.8949 |   40.6369 |  -36.1394 |  -43.9758 |   66.9435 |  -59.2828 |   65.2277 |   67.2356 |    3.0837 |   -2.4552 |    6.3265 | \n",
      "   57 | 00m04s |   -0.65117 |  -17.9374 |   49.6409 |  -26.5992 |   53.6759 |   27.5402 |   44.6854 |   51.0143 |   -1.3594 |   62.0160 |   69.5297 |  -61.5756 |  -32.6511 |   22.5834 |  -51.6533 |  -49.0566 |   45.7031 | \n",
      "   58 | 00m04s |   -0.63817 |   49.3031 |   18.3980 |   70.2487 |    1.3197 |   28.7447 |    3.5520 |   35.3285 |  -35.2885 |   -1.1171 |   22.5992 |  -16.5977 |  -34.2963 |   66.9776 |   31.8402 |  -48.1459 |   44.9132 | \n",
      "   59 | 00m04s |   -0.62955 |  -30.8489 |  -53.5493 |  -67.2367 |   19.7494 |  -49.3854 |  -33.5631 |   -8.3661 |   63.5516 |   -3.3873 |   22.8429 |  -49.0105 |   20.6417 |  -69.4198 |  -19.6953 |  -32.1050 |   64.9416 | \n",
      "   60 | 00m04s |   -0.66304 |  -45.7149 |   67.4197 |  -31.0171 |   32.9306 |   59.1818 |   30.1785 |   28.6804 |  -45.5376 |  -17.0311 |  -26.4141 |   14.6770 |   22.3292 |  -31.1823 |   42.9524 |   46.5810 |    3.6527 | \n",
      "   61 | 00m04s |   -0.69989 |  -49.7726 |   45.4240 |  -64.8585 |  -50.1050 |  -39.5813 |   42.1870 |   10.8913 |  -28.5470 |  -50.7546 |   27.5660 |  -44.3467 |  -49.6282 |   26.4318 |   67.2005 |   -9.5389 |    4.6883 | \n",
      "   62 | 00m04s |   -0.64387 |  -67.6740 |  -28.3768 |   12.7340 |    9.8530 |   23.3411 |  -68.3905 |  -66.5124 |   49.9393 |   -3.8088 |  -30.6488 |   46.1370 |   18.6242 |   16.4671 |  -59.3560 |  -20.5601 |  -65.9244 | \n",
      "   63 | 00m04s |   -0.85443 |    5.5669 |   39.4506 |   55.3606 |   19.5752 |    0.9414 |  -55.1599 |  -23.8782 |   35.2772 |   33.1709 |   -9.6256 |   69.6454 |   22.0028 |   62.2200 |  -18.1464 |   -6.5765 |   13.5853 | \n",
      "   64 | 00m04s |   -0.71735 |  -56.0667 |   41.3764 |   47.2054 |  -31.5868 |  -23.9450 |  -48.5302 |  -68.5504 |  -54.3274 |  -27.8596 |  -25.2671 |  -56.2077 |   70.4386 |  -54.5031 |   -6.0994 |  -32.9417 |    2.7477 | \n",
      "   65 | 00m04s |   -0.87083 |   30.8439 |   40.4258 |   -5.7738 |   48.2558 |   -5.3964 |   68.8944 |    3.2963 |  -29.1920 |    7.3733 |  -24.3117 |   43.3458 |   20.5103 |   27.8599 |   46.0496 |    2.8440 |   10.8919 | \n",
      "   66 | 00m04s |   -0.65401 |  -38.5936 |  -30.0815 |   52.7456 |  -50.9724 |   52.9912 |  -60.5441 |  -67.7322 |   18.4563 |   43.2255 |   56.2639 |   55.4834 |   52.5920 |   63.6546 |  -63.7715 |   61.0086 |  -26.8522 | \n",
      "   67 | 00m04s |   -0.67663 |  -58.9342 |  -24.2107 |   50.1934 |    8.7454 |  -15.5602 |  -70.5161 |  -42.5913 |   45.9368 |   55.7817 |  -24.2726 |  -56.8183 |   62.9409 |   27.2102 |  -23.1020 |  -38.6462 |  -48.5933 | \n",
      "   68 | 00m04s |   -0.74359 |   41.3598 |   17.0072 |  -33.7797 |   -7.9153 |   -7.2873 |   13.0313 |   50.9871 |  -25.3848 |   27.8151 |    2.9678 |   46.0086 |   45.4743 |   39.9579 |   29.1953 |    1.3980 |  -58.8417 | \n",
      "   69 | 00m04s |   -0.59117 |  -53.7731 |   38.3323 |  -62.4039 |  -12.1778 |   64.0063 |  -25.8241 |  -18.7912 |   54.3249 |   59.4655 |   54.0716 |  -28.8246 |    1.4064 |    3.9387 |    2.5420 |  -12.5266 |  -68.1645 | \n",
      "   70 | 00m04s |   -0.68493 |    5.4291 |   -6.1533 |   -1.3046 |   -6.6098 |  -43.1987 |    0.8450 |   26.4591 |  -52.0448 |   59.8813 |  -13.8459 |   40.8303 |  -27.6448 |   16.0217 |  -32.8019 |   55.2843 |  -62.6412 | \n",
      "   71 | 00m04s |   -0.81573 |  -43.1702 |  -33.5222 |   -8.1806 |   65.7624 |    2.5035 |   34.4929 |   -5.0747 |   56.4411 |   -5.7737 |   14.5933 |  -48.7739 |  -24.4385 |  -19.9365 |   45.9344 |   12.0384 |  -43.4503 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   72 | 00m04s |   -0.59861 |   45.4931 |   31.8862 |   25.5847 |   64.4897 |   42.9363 |  -45.5526 |  -21.3738 |  -30.7059 |   30.9592 |  -20.0464 |   69.8660 |   26.9268 |  -68.4506 |   50.2552 |   55.2344 |  -60.9431 | \n",
      "   73 | 00m04s |   -0.65105 |   -7.9267 |   34.4695 |  -42.6484 |  -40.6189 |  -51.0242 |   -8.3356 |  -68.2082 |   70.1649 |   -0.7740 |   29.3418 |   11.8697 |  -19.1876 |  -55.9885 |  -26.1836 |   67.5104 |  -40.5486 | \n",
      "   74 | 00m04s |   -0.85138 |  -17.1111 |  -20.5203 |  -17.3900 |   -3.8508 |  -59.6517 |  -27.7105 |   21.1509 |  -33.1136 |  -59.0042 |   -9.7718 |   40.6456 |  -17.4658 |    6.2006 |  -53.3283 |  -32.2623 |  -13.1019 | \n",
      "   75 | 00m04s |   -0.64936 |   67.1448 |   31.4134 |  -13.5668 |    9.8356 |   62.6900 |   22.5691 |  -16.8593 |   61.5979 |   68.4949 |   28.2341 |  -67.9206 |  -50.5345 |   11.3760 |  -26.0848 |  -39.4383 |  -43.4434 | \n",
      "   76 | 00m04s |   -0.82322 |  -11.3683 |  -43.3843 |   10.0620 |   20.8693 |  -38.9983 |  -12.3170 |   38.6682 |  -57.1817 |    4.5184 |  -49.9550 |  -15.4622 |  -47.6317 |  -42.1732 |    4.7654 |   41.0166 |   -4.5433 | \n",
      "   77 | 00m04s |   -0.63250 |    2.0727 |  -60.9516 |   26.8639 |   11.2456 |   49.2547 |  -11.7387 |   69.3564 |  -60.9598 |  -60.4243 |  -15.9237 |   47.0318 |  -54.2596 |  -17.4630 |  -26.7734 |    4.3284 |   67.6091 | \n",
      "   78 | 00m04s |   -0.73772 |  -14.4981 |  -69.5522 |  -55.9082 |   52.8379 |   40.2914 |   60.9399 |   -7.9016 |  -38.8055 |  -25.0586 |  -28.9015 |   34.9833 |  -15.3399 |   59.6682 |  -34.4437 |    6.1798 |   42.4598 | \n",
      "   79 | 00m04s |   -0.62876 |  -38.3210 |  -68.3239 |  -44.0455 |  -57.7779 |    1.9145 |  -39.8519 |   10.5225 |   34.8895 |   42.7262 |   68.4931 |   13.9322 |   37.0301 |  -27.7221 |    3.1789 |   -5.8544 |  -63.3597 | \n",
      "   80 | 00m04s |   -0.65511 |  -66.4865 |  -44.4753 |   38.4514 |   44.3113 |   18.0904 |  -53.2787 |   58.2713 |   67.3353 |  -49.1165 |   -0.8880 |    5.4698 |  -44.0797 |   54.0735 |   58.9809 |   27.9931 |   -1.8533 | \n",
      "   81 | 00m04s |   -0.92094 |   22.3665 |    0.0296 |    5.1812 |    7.0649 |  -33.7815 |    9.7801 |  -40.9785 |   38.4279 |   34.6034 |   60.0384 |   -8.9359 |  -31.2349 |  -15.9009 |  -43.9775 |   18.7726 |  -25.2356 | \n",
      "   82 | 00m04s |   -0.50428 |  -56.4114 |  -69.6969 |   66.9108 |   48.9026 |   23.2485 |  -62.7089 |   53.4271 |   57.4114 |   15.2383 |    2.3581 |  -35.9425 |  -23.6568 |   -1.4503 |    5.7530 |  -37.5538 |   57.6497 | \n",
      "   83 | 00m04s |   -0.63900 |  -15.7133 |   55.9695 |   39.6528 |   34.2362 |  -36.1119 |  -49.6343 |   59.4353 |  -48.2076 |   62.2969 |  -21.4458 |  -42.6053 |   67.6909 |   -1.4906 |   66.4399 |  -57.6771 |  -55.5484 | \n",
      "   84 | 00m04s |   -0.81146 |  -55.1344 |   -6.9287 |   36.2750 |    6.4838 |   26.0448 |  -10.0538 |  -41.7684 |    2.6741 |   11.9970 |  -43.3246 |  -36.6329 |  -40.3811 |  -20.2744 |  -31.3524 |   15.2144 |   -4.7458 | \n",
      "   85 | 00m04s |   -0.59988 |   67.9373 |  -59.8333 |    5.6756 |   -7.8704 |   27.9832 |  -49.1022 |   18.5599 |  -45.7609 |  -36.1130 |   -9.4989 |  -25.9108 |  -62.7457 |    5.0768 |  -55.2863 |   20.7120 |    0.8629 | \n",
      "   86 | 00m04s |   -0.82975 |   -0.4422 |  -30.1088 |   20.2718 |   67.6492 |    6.2988 |   37.2483 |   19.3528 |   52.3965 |   41.1073 |   51.8589 |  -39.1860 |   58.1309 |  -54.1840 |   24.6220 |   -1.7440 |   35.1011 | \n",
      "   87 | 00m04s |   -0.91783 |   18.9677 |   16.3270 |   69.3203 |  -33.0481 |  -65.4165 |  -15.3974 |  -37.2398 |   -0.1641 |   11.3666 |   11.7936 |  -23.8507 |   12.3722 |   44.5456 |  -51.6828 |   12.6407 |   -7.9026 | \n",
      "   88 | 00m04s |   -0.78438 |   33.0558 |   25.4442 |   14.4789 |  -19.3032 |   46.4718 |   65.9194 |   45.6961 |   -8.5235 |    0.8985 |   29.1410 |  -28.1998 |  -39.3958 |   24.3354 |   19.7356 |  -24.4275 |   53.9226 | \n",
      "   89 | 00m04s |   -0.68995 |   20.8598 |  -67.4284 |  -42.5672 |   12.4660 |  -49.4356 |   -6.4263 |   25.1365 |   62.7044 |  -27.8397 |  -63.5410 |  -34.3319 |  -62.2530 |   53.2363 |   -3.9181 |  -18.1584 |   30.7566 | \n",
      "   90 | 00m04s |   -0.71799 |   50.1691 |  -16.3425 |  -36.3518 |  -28.7997 |  -58.7154 |   10.3323 |  -13.6742 |   -0.6698 |   51.7204 |  -43.2803 |  -30.4431 |   -0.0534 |  -52.8595 |   49.2724 |   10.7139 |   61.4737 | \n",
      "   91 | 00m04s |   -0.70589 |   55.4976 |   28.0875 |  -20.3863 |  -27.4272 |   57.2535 |  -61.2559 |   12.4072 |   46.3952 |  -34.2118 |   18.9376 |    0.6691 |   36.3658 |  -23.1215 |  -46.6425 |  -44.9846 |  -42.9110 | \n",
      "\u001b[31mBayesian Optimization\u001b[0m\n",
      "\u001b[94m-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |        p0 |        p1 |       p10 |       p11 |       p12 |       p13 |       p14 |       p15 |        p2 |        p3 |        p4 |        p5 |        p6 |        p7 |        p8 |        p9 | \n",
      "   92 | 01m22s |    0.11380 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "   93 | 01m28s |    0.25013 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "   94 | 01m24s |    0.13790 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "   95 | 01m24s |   -0.07667 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "   96 | 01m25s |    0.23140 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "   97 | 01m26s |   -0.23601 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "   98 | 01m30s |   -0.10571 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "   99 | 01m31s |    0.25013 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  100 | 01m36s |    0.25218 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  101 | 01m29s |   -0.33817 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  102 | 01m29s |    0.15138 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  103 | 01m32s |   -0.19466 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  104 | 01m34s |   -0.25086 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  105 | 01m32s |   -0.19532 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  106 | 01m31s |    0.08609 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  107 | 01m34s |   -0.39383 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  108 | 01m37s |    0.24903 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  109 | 01m36s |   -0.42392 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 | \n",
      "  110 | 01m45s |    0.17966 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  111 | 01m37s |    0.16613 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([ -2.41288509e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 5, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  112 | 01m38s |   -0.40949 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  113 | 01m34s |   -0.00921 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  114 | 01m36s |    0.13359 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  115 | 01m40s |   -0.39190 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 | \n",
      "  116 | 01m49s |   -0.21383 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  117 | 01m40s |    0.06081 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  118 | 01m43s |   -0.02045 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  119 | 01m42s |   -0.10238 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  120 | 01m44s |    0.09166 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  121 | 01m40s |   -0.01177 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  122 | 01m45s |    0.21313 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  123 | 01m48s |    0.25218 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  124 | 01m50s |   -0.01728 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  125 | 01m48s |    0.23746 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  126 | 01m53s |    0.04602 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  127 | 01m52s |    0.12245 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  128 | 01m57s |   -0.38829 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 | \n",
      "  129 | 01m58s |    0.16283 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  130 | 01m55s |    0.06544 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  131 | 02m01s |    0.17215 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -37.0346 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  132 | 01m58s |    0.06899 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  133 | 01m55s |    0.05733 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  134 | 02m01s |    0.09578 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  135 | 02m01s |    0.03118 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  136 | 02m10s |    0.02599 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  137 | 02m01s |    0.21313 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  138 | 02m10s |    0.10270 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  139 | 02m10s |    0.23961 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -62.3780 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  140 | 02m13s |    0.18888 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  141 | 02m10s |    0.15877 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -28.1206 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  142 | 02m05s |   -0.20197 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  143 | 02m12s |    0.04894 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  144 | 02m17s |    0.07472 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  145 | 02m12s |   -0.10806 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  146 | 02m20s |    0.06127 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  147 | 02m21s |    0.05634 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  148 | 02m28s |   -0.10503 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  149 | 02m25s |    0.14115 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  150 | 02m23s |    0.13396 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   -4.7244 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  151 | 02m28s |   -0.27823 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 | \n",
      "  152 | 02m33s |   -0.44609 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  153 | 02m23s |   -0.16914 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  154 | 02m27s |   -0.16390 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  155 | 02m29s |    0.22936 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  156 | 02m32s |    0.16088 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   16.4818 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  157 | 02m32s |    0.08816 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  158 | 02m30s |    0.14876 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.5283 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  159 | 02m37s |    0.21232 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   47.4519 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  160 | 02m39s |    0.06544 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  161 | 02m35s |    0.19005 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  162 | 02m36s |   -0.03922 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  163 | 02m35s |    0.06795 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  164 | 02m42s |    0.24707 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   30.5058 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  165 | 02m35s |    0.07142 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  166 | 02m41s |    0.00950 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  167 | 02m41s |    0.04580 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  168 | 02m45s |    0.10108 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   31.4822 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  169 | 02m39s |    0.21940 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6856 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.2402 |   70.6858 |   70.6858 |    7.1320 |   70.6858 |  -70.6858 | \n",
      "  170 | 02m40s |   -0.14210 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   65.2497 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  171 | 02m48s |    0.07543 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  172 | 03m01s |    0.05217 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   48.1602 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  173 | 02m47s |    0.04655 |   70.6858 |  -70.6858 |   70.6857 |   70.6858 |  -70.6858 |   47.3009 |  -60.4152 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6836 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  174 | 02m47s |   -0.24285 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  175 | 02m52s |    0.05577 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -45.8081 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -65.7294 | \n",
      "  176 | 03m00s |    0.15965 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   43.2742 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  177 | 02m41s |    0.05216 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -20.7641 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  178 | 02m58s |    0.09711 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -69.1747 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  179 | 03m04s |   -0.22470 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 | \n",
      "  180 | 03m02s |    0.16221 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -69.3372 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  181 | 02m53s |    0.09069 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -69.7222 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  182 | 02m56s |   -0.18657 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 | \n",
      "  183 | 03m01s |    0.07715 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6544 |  -70.6858 |  -70.6858 |   70.6858 |   47.1132 |  -58.0458 |  -70.6040 |   70.6243 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  184 | 03m08s |   -0.09421 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  185 | 02m42s |    0.12214 |   70.6858 |  -70.6858 |   70.6858 |  -32.5829 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -65.0823 |  -17.3900 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  186 | 02m58s |    0.07742 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.2092 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  187 | 03m11s |    0.09715 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -60.2809 |   70.6858 |  -70.6858 |  -70.6858 |  -64.8034 |  -70.6858 |   69.9165 |  -64.0021 |   70.5934 |  -70.6858 |   70.6858 | \n",
      "  188 | 03m09s |    0.18154 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -68.1099 |  -70.6858 |   70.2951 |  -70.6761 |  -70.6747 |  -70.3560 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  189 | 02m57s |    0.21361 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.2373 |  -70.6858 |  -16.2042 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -10.0941 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  190 | 03m02s |   -0.19726 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  191 | 03m06s |    0.15320 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6822 |  -70.6858 |   70.6855 |   70.6858 |   70.6858 |  -70.6852 |  -70.6858 |   70.4319 |   70.6858 |  -70.6858 | \n",
      "  192 | 03m01s |    0.08065 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  193 | 03m09s |    0.23654 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  194 | 02m56s |    0.11592 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   62.0784 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   53.9139 |  -70.6858 |   70.6858 | \n",
      "  195 | 03m04s |    0.18724 |   70.6858 |  -70.6858 |   70.6858 |   62.5902 |   56.2333 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.2784 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  196 | 03m07s |    0.24903 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  197 | 02m46s |    0.22326 |  -70.6858 |   70.6858 |  -70.6858 |  -62.2686 |  -67.7072 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -56.6309 |   70.6858 |   37.5483 |   68.4681 |   70.6858 |  -70.6858 | \n",
      "  198 | 02m46s |   -0.34360 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 | \n",
      "  199 | 02m44s |   -0.24402 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   46.4386 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([ -4.61358022e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 52, 'nit': 4, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  200 | 02m47s |   -0.01689 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -68.2562 |   70.6858 |  -63.2907 |   70.6044 |   69.5525 |   70.6858 |   70.6858 |  -69.1743 |   70.6858 |  -70.6858 | \n",
      "  201 | 02m44s |    0.07185 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   54.7124 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  202 | 02m44s |    0.16453 |   70.6858 |  -70.6858 |   70.6858 |   38.5298 |   70.6858 |  -14.3039 |   37.6002 |   70.6858 |  -70.6858 |  -28.3277 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  203 | 02m44s |    0.01054 |   70.6858 |  -70.6858 |   70.6858 |  -65.0647 |   70.6858 |  -41.5202 |   70.6858 |   70.6858 |  -70.6858 |  -47.3105 |  -70.6858 |   70.6858 |   31.9338 |   70.6858 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([ -9.74306624e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 4, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  204 | 02m58s |   -0.04135 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -22.4974 |  -70.6858 |   70.6858 |  -70.6858 |  -39.7429 |  -70.5919 |  -53.2625 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  205 | 02m41s |    0.16342 |  -70.6858 |   70.6858 |  -70.6858 |   13.1191 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   -8.7893 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  206 | 02m52s |    0.23571 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -68.7898 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  207 | 02m50s |    0.14800 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   51.5088 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.3765 |  -70.6858 |   70.6858 |   -4.5439 |  -70.6858 |   70.6858 | \n",
      "  208 | 02m51s |    0.00478 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.0384 |  -70.6858 |   70.6858 |  -59.0382 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  209 | 02m41s |    0.17966 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  210 | 02m48s |    0.09393 |  -70.6858 |   70.6858 |  -70.6858 |   43.7049 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  211 | 02m43s |    0.17252 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -53.0335 |  -70.6858 |  -70.6858 |   70.1424 |   70.6858 |  -52.7728 |   62.4024 |   70.6423 |   68.5032 |   64.3630 |   70.6858 |  -70.6858 | \n",
      "  212 | 02m57s |    0.11198 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.5051 |  -70.6858 |   70.6843 |  -70.6858 |   70.6858 |  -70.4710 |   70.6858 |  -70.6858 |   70.6790 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  213 | 02m50s |    0.20072 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -67.0062 |  -70.6858 |   35.6959 |  -70.6858 |   70.6858 |   70.4786 |   33.6156 |  -40.5298 |   47.8032 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  214 | 02m52s |    0.17179 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   23.6831 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  215 | 02m49s |    0.13675 |  -70.6858 |   70.6858 |  -70.6858 |   41.1081 |  -70.6858 |  -70.6858 |  -70.6858 |   44.5753 |   70.6858 |   64.7585 |   70.6858 |   70.6858 |  -70.0624 |    7.9437 |   70.6858 |  -70.6858 | \n",
      "  216 | 02m50s |    0.03378 |  -70.6858 |   70.6858 |  -70.6858 |   68.6179 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -13.5948 |  -69.3455 |   57.9946 |   70.6858 |   67.0624 |   69.8993 |  -70.6858 | \n",
      "  217 | 02m54s |    0.02681 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6208 |  -70.6858 |   70.6858 |  -70.6858 |  -66.4144 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  218 | 02m48s |    0.20219 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -67.9640 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  219 | 02m57s |    0.06502 |  -70.6858 |   70.6858 |  -70.6858 |   51.7896 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -69.8237 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  220 | 02m58s |    0.15108 |  -70.6858 |   70.6858 |  -70.6858 |   59.5348 |  -63.0017 |  -58.6918 |  -61.9387 |   70.6858 |   70.6858 |   33.4717 |   70.6858 |   70.6858 |   62.2842 |   58.3336 |   70.6858 |  -70.6858 | \n",
      "  221 | 02m50s |    0.17728 |   70.6858 |  -70.6858 |   70.6566 |  -70.6858 |   70.6858 |   61.8622 |  -50.6206 |   70.6858 |  -70.6858 |  -70.1450 |  -70.6858 |  -67.2941 |  -59.3802 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  222 | 02m44s |   -0.01513 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6852 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -58.2213 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  223 | 02m44s |    0.19523 |   70.6858 |  -70.6858 |   70.6858 |   70.4329 |   70.6858 |   19.0897 |   25.5696 |  -43.7193 |  -70.6858 |  -70.6858 |  -70.6858 |   45.0960 |  -70.6858 |  -70.0759 |  -70.6858 |   70.6858 | \n",
      "  224 | 02m38s |    0.06863 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   69.3587 |   70.1989 |  -70.6858 |  -70.6858 |   70.6858 |  -54.5840 |  -67.6376 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  225 | 02m34s |    0.01740 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  226 | 02m34s |    0.11038 |  -70.6858 |   70.6858 |  -70.6858 |  -51.6133 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   49.5663 |  -70.6858 |   69.7261 |   70.6858 |  -70.6858 | \n",
      "  227 | 02m36s |    0.11706 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   57.6080 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -28.5128 |  -70.6858 |   70.6858 | \n",
      "  228 | 02m37s |    0.12279 |  -70.6850 |   70.6858 |  -70.6830 |    1.5736 |  -70.5497 |  -17.9699 |   16.2865 |  -70.6744 |   46.7343 |   70.6689 |   70.6858 |   67.5784 |   70.6685 |  -27.6764 |   70.6858 |  -70.6858 | \n",
      "  229 | 02m23s |    0.01414 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -50.8210 |   70.6858 |   67.9777 |   70.6858 |   70.6858 |   70.6858 |   63.7279 |  -70.6858 |   70.6858 | \n",
      "  230 | 02m35s |    0.13750 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   29.9684 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  231 | 02m30s |    0.16140 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   59.3905 |  -70.6858 |   70.6858 |  -70.6858 |  -25.5843 |  -70.6858 |  -70.6858 |   17.7339 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  232 | 02m30s |    0.19547 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  233 | 02m29s |    0.07111 |   70.6858 |  -70.6858 |   66.0389 |  -68.5364 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -67.9074 |  -70.6858 |  -70.6858 |   26.2211 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  234 | 02m39s |    0.13753 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   24.5200 |  -16.7078 |  -42.7860 |   19.5339 |   70.6858 |   43.5448 |    9.9969 |   54.7259 |   67.6283 |   66.5761 |   70.6858 |  -70.6858 | \n",
      "  235 | 02m33s |    0.01345 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -15.7725 |   24.0618 |  -70.6858 |  -68.0589 |   70.6351 |   65.9045 |   50.5733 |  -70.6858 |  -70.6858 |   70.4089 |   70.6858 |  -70.6858 | \n",
      "  236 | 02m31s |   -0.29444 |   70.6858 |   70.6509 |  -59.0444 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   66.0237 |   70.6858 |  -70.6858 |   70.6858 |   -5.9939 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  237 | 02m45s |    0.09129 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -64.3055 |  -68.3539 |   70.6858 |  -70.6858 |  -70.2445 |  -69.2189 |  -69.4801 |  -69.4887 |   30.0837 |  -70.6858 |   70.6858 | \n",
      "  238 | 02m31s |    0.14526 |   70.6858 |  -70.6858 |   70.6858 |  -44.6678 |   70.6858 |  -44.5571 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  239 | 02m38s |    0.03327 |   70.6858 |  -70.6858 |   69.0444 |  -11.0413 |  -65.1611 |   70.6858 |   57.4138 |  -64.5815 |  -67.3364 |  -46.8566 |  -68.3848 |   42.9012 |  -20.8144 |  -65.9237 |  -70.6858 |   70.6858 | \n",
      "  240 | 02m41s |    0.12780 |  -70.6858 |   70.6858 |  -70.6858 |   70.4384 |  -70.6858 |  -70.6858 |  -70.6858 |   58.6489 |   70.6858 |   66.4702 |   70.6858 |  -52.9719 |   70.5173 |   67.6507 |   70.6858 |  -70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  241 | 02m34s |    0.08065 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  242 | 02m49s |   -0.05964 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  243 | 02m47s |    0.03798 |   70.6858 |  -70.6858 |   49.6571 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -68.5620 |  -70.6858 |   70.6858 | \n",
      "  244 | 02m43s |    0.22591 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |    5.0658 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   50.8904 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  245 | 02m02s |   -0.10485 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6088 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  246 | 02m04s |   -0.24615 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  247 | 01m53s |   -0.03434 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -61.3464 |   -9.2916 |   70.6858 |   70.5844 |   70.6858 |   70.6858 |   70.6858 |  -59.7239 |   70.6858 |   46.7154 |   70.6858 |  -70.6858 | \n",
      "  248 | 02m15s |   -0.09902 |   59.2279 |  -70.6858 |   70.6858 |   70.1656 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -69.6628 |  -28.0440 |  -12.9265 |   69.9341 |   67.9670 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  249 | 01m54s |   -0.03653 |  -70.6858 |   70.6858 |   -4.3800 |  -70.6858 |  -68.9060 |  -70.6779 |   70.6858 |  -70.6858 |   70.6858 |   66.8884 |  -70.6858 |   25.9225 |   41.8795 |   31.8803 |   70.6858 |  -70.6858 | \n",
      "  250 | 02m01s |   -0.44098 |   49.6883 |   70.6858 |   66.2746 |  -21.0931 |   70.6858 |  -70.1643 |   70.0422 |   70.6858 |  -70.0344 |  -70.6858 |  -13.8855 |   70.6858 |  -70.6858 |   69.5730 |  -70.6858 |   70.6858 | \n",
      "  251 | 02m07s |   -0.24842 |  -65.3574 |   70.6858 |   10.0359 |  -70.6858 |  -70.6858 |   70.6858 |   16.8218 |   70.6858 |  -66.7135 |   45.6367 |   70.6858 |   40.2251 |   33.8388 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  252 | 02m07s |   -0.16420 |  -70.6858 |   70.6858 |   70.4938 |   64.0620 |   60.5629 |   70.4817 |  -70.6858 |   -6.8824 |  -18.8673 |  -70.6858 |   70.6858 |  -70.6723 |   70.6858 |   67.0356 |   70.6858 |  -70.6858 | \n",
      "  253 | 01m59s |   -0.02401 |   61.4877 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6432 |  -70.6858 |  -42.0025 |  -70.6847 |  -10.9671 |   70.6858 |  -70.6858 |  -70.6831 |   70.6858 | \n",
      "  254 | 02m03s |   -0.26304 |  -60.7783 |   64.7370 |   -7.3288 |   62.2738 |   55.2719 |   61.3970 |  -13.9467 |   14.1832 |   70.6125 |  -42.0504 |  -15.4009 |   43.1341 |   69.8538 |  -54.2783 |   66.5550 |  -67.8459 | \n",
      "  255 | 02m03s |   -0.02583 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -32.9316 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  256 | 02m14s |   -0.27049 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  257 | 02m35s |   -0.01367 |   70.6858 |  -70.6858 |   12.7051 |   11.2343 |   23.2880 |   50.8597 |   16.7133 |   69.5561 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   69.9640 |   69.1486 |  -70.6858 |   70.6858 | \n",
      "  258 | 02m16s |   -0.21090 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   55.6766 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   21.1325 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  259 | 02m14s |   -0.24506 |   70.6858 |  -70.6858 |   60.2197 |  -70.6858 |   70.6858 |   64.1515 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  260 | 02m13s |    0.05536 |  -70.6858 |   70.6858 |   24.2272 |   70.6858 |   70.6813 |   70.6858 |  -46.3450 |  -11.8223 |   70.0727 |  -43.7603 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  261 | 02m07s |    0.07263 |   70.6858 |  -70.6858 |   70.6858 |  -54.4224 |   70.6858 |  -70.6858 |   70.6858 |   -8.8481 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  262 | 02m19s |   -0.10540 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  263 | 02m21s |   -0.01134 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  264 | 02m25s |    0.12449 |   62.4367 |  -70.6858 |   66.5198 |   65.2903 |   70.6858 |  -53.2343 |  -53.1006 |   35.7302 |  -70.6858 |  -67.8699 |  -70.6858 |  -70.6858 |   -3.0563 |  -63.4544 |  -70.6858 |   70.6141 | \n",
      "  265 | 02m15s |   -0.09426 |   70.6858 |  -57.8629 |   70.6673 |   70.6206 |   70.6814 |  -70.5164 |    6.4319 |   70.5419 |   43.0918 |   70.2847 |  -70.4566 |   40.5061 |   43.9040 |   70.6146 |  -70.6858 |   70.6858 | \n",
      "  266 | 02m21s |   -0.24745 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   37.3106 |  -17.3493 |   67.9392 |  -70.6858 |   33.6515 |  -70.6858 |   70.6858 |   70.3514 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  267 | 02m14s |   -0.37488 |  -70.6858 |  -60.4436 |  -51.0362 |  -70.6858 |   69.2749 |  -31.7327 |  -66.8846 |  -70.6858 |   66.3669 |  -60.8715 |  -58.6397 |   70.6858 |   69.4361 |   70.6858 |   70.6858 |  -70.1396 | \n",
      "  268 | 02m25s |   -0.02683 |  -70.6858 |   69.1126 |  -70.6858 |  -70.6858 |  -70.6858 |    8.7112 |  -13.7272 |   -3.3378 |   42.5359 |  -70.6858 |    0.0828 |  -70.6858 |  -12.4483 |    3.3477 |   70.6858 |  -70.6858 | \n",
      "  269 | 02m19s |   -0.43368 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   60.1957 |  -68.0112 |   70.6858 |   70.6858 |  -70.6858 |   70.6837 |   70.6858 |   70.6858 |  -70.6858 |  -51.1522 |  -70.6858 |  -70.6858 | \n",
      "  270 | 02m18s |   -0.45830 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 | \n",
      "  271 | 02m15s |   -0.32791 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   63.2278 |  -70.6858 |  -48.7299 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 | \n",
      "  272 | 02m19s |   -0.37910 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 | \n",
      "  273 | 02m13s |    0.09555 |   70.4923 |  -70.6858 |   70.6858 |   67.4414 |   63.5447 |   70.6858 |   70.6858 |   67.5636 |  -61.6354 |   47.8022 |  -40.4989 |   -8.5231 |   -5.0613 |   70.6858 |  -66.5597 |   70.4308 | \n",
      "  274 | 02m32s |   -0.20087 |   70.6858 |  -70.6858 |  -70.6858 |    8.4693 |  -10.9782 |  -15.9888 |  -20.3809 |  -70.6858 |   43.8304 |   53.8994 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  275 | 02m26s |   -0.26428 |  -69.8676 |   70.6858 |  -70.6858 |  -61.2567 |  -63.1581 |   70.6414 |  -59.0410 |   21.6842 |  -40.5175 |  -70.6858 |  -49.3178 |   69.4163 |   70.6858 |  -46.8836 |  -69.8157 |  -65.2125 | \n",
      "  276 | 02m26s |   -0.20355 |   59.2958 |  -70.6858 |   70.6858 |   62.3847 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   53.9176 |   68.6912 |   35.7789 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  277 | 02m33s |   -0.08579 |   67.2898 |  -70.6858 |   57.0662 |  -66.8231 |  -58.2138 |   10.7643 |   70.6858 |  -62.9816 |  -70.6858 |  -53.7317 |   58.5911 |  -67.1856 |   60.2570 |  -52.1100 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  278 | 00m11s |   -0.38242 |  -66.4335 |  -36.9563 |   62.6985 |   49.6016 |   50.8069 |    8.0885 |   60.1345 |  -25.1298 |   44.1419 |  -47.8174 |  -35.8342 |   12.7287 |   51.8733 |  -32.6147 |  -69.5603 |   57.8703 | \n",
      "  279 | 02m22s |    0.00239 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   32.6297 |  -33.2157 |   63.4803 |  -70.6858 |   70.6858 |   30.6951 |  -70.6858 |   63.7242 |  -34.1885 |  -40.0584 |   70.6858 |  -70.6858 | \n",
      "  280 | 02m38s |   -0.21681 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -64.6133 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  281 | 02m21s |   -0.03419 |   70.6858 |  -70.6858 |   70.6858 |   47.4915 |    3.7465 |  -42.7779 |   29.8354 |   43.5736 |  -70.6858 |   15.2317 |  -70.6858 |  -24.2236 |   16.9264 |   -7.7537 |  -56.9207 |   70.6858 | \n",
      "  282 | 02m29s |   -0.39792 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -38.3800 |  -45.1822 |   17.2714 |  -70.6858 |  -70.6858 |  -70.6858 |  -58.8250 |   70.6858 |    0.9523 |  -70.6858 |  -70.6858 | \n",
      "  283 | 02m26s |   -0.08986 |   70.6858 |  -61.4248 |   70.6858 |   62.1561 |   54.0501 |    6.1885 |   -8.7704 |  -48.2258 |  -35.0251 |  -17.2320 |   15.4154 |   -4.3708 |  -57.7666 |   41.3625 |  -70.6858 |   68.0953 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([ 0.0001182]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 52, 'nit': 4, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  284 | 02m38s |    0.15257 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -16.8497 |  -70.6858 |  -70.6858 |  -54.8704 |   46.9491 |   67.5618 |    0.7305 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  285 | 02m30s |   -0.25422 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   68.5952 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -66.9493 | \n",
      "  286 | 02m24s |   -0.22505 |  -68.2041 |   39.9529 |  -57.2935 |  -67.8521 |    3.1242 |  -20.5820 |   10.0324 |  -47.2502 |  -59.2118 |  -59.2795 |    4.9559 |  -56.3598 |   52.1669 |   59.4033 |   68.4540 |  -70.5902 | \n",
      "  287 | 02m27s |    0.03666 |  -70.6858 |   70.6858 |  -70.6858 |   70.6180 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  288 | 02m27s |   -0.22570 |   70.6858 |  -70.6858 |   70.6858 |   18.2075 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  289 | 02m33s |   -0.26379 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  290 | 02m33s |   -0.45817 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -51.5469 |   65.8954 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 | \n",
      "  291 | 02m33s |   -0.10721 |  -68.7207 |   67.8026 |  -66.5484 |  -52.1368 |  -32.1498 |  -66.1890 |    3.4528 |  -41.6569 |    3.5208 |  -50.9818 |   11.4020 |   43.0407 |  -23.9894 |   23.9873 |   67.4984 |  -67.5515 | \n",
      "  292 | 02m28s |   -0.25556 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   52.2784 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   60.5039 |   70.6858 |  -70.6858 | \n",
      "  293 | 02m26s |   -0.24883 |   70.6858 |  -70.6858 |  -70.6858 |   70.5235 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -26.4572 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.00018967]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 57, 'nit': 5, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  294 | 02m25s |   -0.08900 |  -66.0054 |   65.2644 |  -57.4508 |    7.5856 |  -58.3380 |  -54.4583 |  -62.0693 |  -65.1588 |   39.5362 |   60.4862 |   35.3340 |  -39.4413 |  -20.1742 |    1.9846 |   69.8491 |  -64.7553 | \n",
      "  295 | 02m37s |   -0.18075 |   56.1495 |  -69.0535 |   65.6100 |    8.6781 |  -27.4767 |   60.5073 |  -37.0760 |   39.3864 |  -56.6832 |  -61.9973 |  -31.0334 |  -20.3618 |  -12.3519 |   68.4278 |  -70.4883 |   55.2939 | \n",
      "  296 | 02m37s |   -0.25666 |  -57.1443 |   67.9958 |  -30.0351 |   35.1750 |  -53.7398 |   36.4824 |   63.4787 |  -45.7648 |   51.3160 |   12.7342 |   58.4430 |   -9.6122 |   67.6351 |   65.8054 |   69.0771 |  -49.6376 | \n",
      "  297 | 02m26s |   -0.16315 |  -66.5497 |   67.3342 |  -30.3198 |  -27.8587 |  -33.9571 |  -50.5460 |  -15.0137 |  -12.9645 |   64.6749 |   13.2125 |   59.8727 |   19.6302 |   68.5605 |   48.5300 |   59.4371 |  -48.0335 | \n",
      "  298 | 02m33s |   -0.32730 |  -69.6170 |   61.8180 |  -56.5095 |  -54.8821 |   36.5055 |   -8.7461 |   28.6790 |  -68.4916 |  -27.9102 |  -25.1396 |   49.0316 |   69.2060 |   32.6940 |   70.0801 |   47.5419 |  -65.2655 | \n",
      "  299 | 02m30s |   -0.43648 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   11.8546 |  -70.6262 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   69.8587 |   70.6858 |   47.5793 |  -70.6858 |  -70.6858 | \n",
      "  300 | 02m27s |   -0.31193 |   70.6858 |  -53.6464 |    1.9559 |   12.8978 |   69.6480 |  -68.7377 |  -69.9025 |   64.7446 |   12.0175 |   70.6858 |   70.6858 |  -63.7364 |  -20.1941 |  -61.4505 |  -70.6858 |   70.6858 | \n",
      "  301 | 02m31s |   -0.27803 |   70.6858 |   70.6858 |  -70.6858 |   53.5158 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -10.6818 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  302 | 02m43s |   -0.24646 |  -70.6858 |  -70.6858 |   24.2946 |  -11.7052 |   70.1246 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |    3.1752 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   69.9827 | \n",
      "  303 | 02m38s |   -0.22448 |   68.4980 |  -66.1544 |   56.4593 |   15.4618 |  -58.3531 |  -18.6150 |   69.2630 |  -10.1944 |  -19.6822 |  -49.9288 |  -62.9707 |   19.0477 |  -68.9155 |   10.5441 |  -57.1566 |   70.0112 | \n",
      "  304 | 02m46s |   -0.19510 |  -56.0441 |   45.7940 |   48.1745 |   43.6852 |   26.9046 |   47.5034 |   58.6575 |   28.8957 |   63.8451 |   60.9343 |   57.4324 |   51.4383 |   50.6931 |   46.4184 |   69.2004 |  -70.6146 | \n",
      "  305 | 02m44s |   -0.11004 |   70.6858 |  -70.6858 |   10.8755 |  -70.6858 |   70.6858 |  -70.6858 |   70.6763 |   70.6858 |   70.6858 |   70.6838 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  306 | 02m41s |   -0.20214 |  -69.2904 |   41.1724 |  -53.8397 |  -27.1526 |  -21.3169 |  -28.9020 |   25.0910 |  -65.8210 |   66.8952 |   15.9877 |  -41.7945 |   36.3161 |   50.9570 |  -47.6009 |   69.4078 |  -64.7422 | \n",
      "  307 | 02m43s |   -0.53895 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 | \n",
      "  308 | 02m52s |   -0.26280 |   48.4353 |  -70.2361 |  -38.3156 |   40.5448 |   59.6325 |   70.6645 |  -51.6650 |   57.5895 |  -70.6858 |  -70.6721 |   64.7757 |  -66.9799 |   63.4457 |   52.6259 |  -70.6858 |   70.6858 | \n",
      "  309 | 02m52s |   -0.20246 |  -68.4687 |   70.0118 |  -69.2206 |  -68.5527 |  -55.9417 |  -66.3349 |  -50.1218 |   43.5240 |   27.0559 |    6.9687 |  -69.0844 |  -67.3182 |   56.2734 |   39.0586 |   45.0710 |  -59.9335 | \n",
      "  310 | 02m50s |   -0.13168 |  -70.6858 |   70.6858 |   70.6858 |   23.0646 |   70.6858 |  -14.1146 |   70.6858 |  -36.3179 |   48.9925 |   70.6858 |   70.6858 |   55.9807 |  -70.6858 |   58.5517 |   70.6858 |  -33.6219 | \n",
      "  311 | 02m41s |   -0.49622 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 | \n",
      "  312 | 02m52s |   -0.36888 |   70.6858 |   69.9701 |   60.9510 |   48.2400 |  -17.8023 |   47.1574 |  -70.6858 |   -0.3052 |  -70.5344 |  -70.6858 |  -67.8324 |  -70.5220 |  -27.4702 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  313 | 02m45s |   -0.12974 |   70.6858 |  -70.6858 |  -69.8893 |    5.1992 |  -68.4675 |  -70.2887 |   34.9781 |    9.8331 |  -65.9156 |  -27.9417 |  -57.3731 |    6.2547 |   -9.2439 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  314 | 02m52s |   -0.72077 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 | \n",
      "  315 | 03m00s |   -0.19595 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   40.9880 |  -70.6858 |   61.0735 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  316 | 03m06s |   -0.22032 |  -65.7956 |   56.1192 |  -64.8979 |   15.0602 |   60.4707 |   52.7685 |  -62.9978 |    3.7424 |   68.6272 |  -67.8130 |    6.7460 |   65.5250 |   58.2760 |   65.2956 |   63.1280 |  -63.1183 | \n",
      "  317 | 02m51s |    0.14724 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   50.7264 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  318 | 03m07s |   -0.31250 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.4314 |  -70.6858 |  -70.6858 |    9.8547 | \n",
      "  319 | 02m53s |   -0.23458 |   66.1394 |  -70.1053 |  -49.6839 |   44.8907 |   65.7589 |  -26.6197 |  -49.0786 |  -33.0402 |  -67.8825 |  -23.2081 |  -43.9955 |   -2.1754 |   -6.9099 |   43.9621 |  -66.8344 |   70.1585 | \n",
      "  320 | 03m04s |   -0.27555 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  321 | 03m17s |   -0.07765 |   70.6858 |  -70.6858 |    6.5059 |  -70.6858 |   70.6858 |  -70.6858 |   37.7435 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |  -64.6213 |  -47.8049 |  -70.6858 |   70.6858 | \n",
      "  322 | 02m55s |   -0.26847 |  -62.7689 |   58.8694 |  -54.2961 |  -59.5018 |   42.0989 |  -15.4434 |  -45.1982 |  -69.8663 |   68.5243 |   60.8436 |  -68.1353 |  -43.7172 |   -8.1806 |  -28.0981 |   61.9475 |  -55.1316 | \n",
      "  323 | 03m00s |   -0.02873 |  -70.6858 |   65.5560 |  -70.6858 |   65.3617 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -55.1309 |   70.6858 |   45.4625 |   20.4892 |  -33.0124 |   61.8454 |  -70.6858 | \n",
      "  324 | 03m00s |   -0.29693 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   47.0066 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.3408 |  -70.6858 |   70.6858 |   70.6858 |   12.1972 |   70.6858 |   70.6858 | \n",
      "  325 | 02m54s |   -0.14488 |  -67.0591 |   52.5683 |  -58.4783 |  -55.5179 |   30.5261 |  -54.4267 |  -23.9182 |   -9.8656 |   67.5509 |   53.8451 |   55.9463 |   36.7616 |  -14.5758 |   68.3893 |   47.2840 |  -67.8081 | \n",
      "  326 | 03m46s |   -0.34425 |  -70.6858 |   40.6789 |   68.9098 |   70.6858 |  -70.6858 |  -70.6858 |   69.9265 |  -70.4149 |   69.2343 |  -70.6038 |  -70.6577 |   43.3524 |   69.9833 |  -39.7929 |   70.6858 |  -70.6858 | \n",
      "  327 | 03m42s |    0.09116 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   -6.4523 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  328 | 03m44s |   -0.03955 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  329 | 03m29s |   -0.23217 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  330 | 03m49s |   -0.31487 |  -69.9933 |  -37.6193 |   70.6858 |   70.6858 |   70.6858 |   28.8425 |   70.6858 |  -67.3825 |   53.4501 |  -68.8586 |    1.3743 |   70.6858 |   22.5595 |   70.5645 |  -70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  331 | 03m51s |    0.15807 |   70.6858 |  -70.6858 |   70.6858 |   26.1789 |   70.6858 |   20.3997 |   70.6858 |   61.4792 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   63.6080 |  -36.6689 |  -61.6540 |   70.6858 | \n",
      "  332 | 03m52s |   -0.18920 |   70.6858 |  -70.6858 |  -70.6858 |   69.9645 |   70.6858 |   70.6858 |   65.7114 |  -70.6858 |   17.9788 |   70.6858 |   70.2290 |  -70.6858 |   29.5287 |  -57.6351 |   70.6858 |  -70.6858 | \n",
      "  333 | 03m32s |   -0.15978 |  -70.6858 |   70.6858 |  -56.8358 |  -70.6858 |  -70.6858 |  -20.1348 |   70.6858 |   52.4430 |  -23.4657 |   26.1643 |  -27.1630 |   54.7725 |  -48.4099 |   48.1688 |   70.6858 |  -70.6858 | \n",
      "  334 | 03m54s |   -0.18047 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -24.8853 |   -9.0861 |   31.0504 |  -42.5966 |   70.6858 |   10.2236 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  335 | 03m47s |   -0.30167 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -15.9174 |   63.7787 |   70.2639 |  -24.5509 |   27.4911 |   70.6858 |  -68.9916 |   70.6858 |   70.0506 |   70.6858 | \n",
      "  336 | 03m49s |   -0.15595 |  -70.6858 |   55.1043 |   14.0150 |  -10.0683 |   70.6858 |   57.1600 |   70.6858 |  -34.4116 |   60.9500 |   70.6858 |   54.3548 |   70.6858 |   47.3705 |  -60.3961 |   70.6858 |  -70.6858 | \n",
      "  337 | 03m54s |    0.05605 |   70.6858 |  -70.6858 |   64.4146 |  -70.6858 |  -67.1650 |   24.3643 |   70.1553 |   18.9243 |  -70.0616 |  -70.6858 |  -70.6858 |  -70.6858 |   65.3960 |  -26.7307 |  -70.6858 |   70.6858 | \n",
      "  338 | 03m53s |   -0.16692 |  -70.6858 |  -70.6858 |  -52.0289 |  -70.6858 |  -70.6858 |  -68.4402 |   45.5905 |  -70.6858 |  -70.6858 |  -36.5633 |  -56.8337 |  -58.2755 |    2.5862 |  -59.3517 |  -70.6858 |   70.6858 | \n",
      "  339 | 03m54s |   -0.00332 |   70.6858 |  -70.6858 |   60.9307 |   70.6858 |   70.6858 |   29.3674 |   70.6858 |   57.0550 |   35.8381 |   70.6858 |  -70.6858 |  -41.6297 |   70.6858 |   -8.2982 |  -70.6858 |   70.6858 | \n",
      "  340 | 04m13s |    0.00382 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |    0.9466 |  -54.7897 |  -50.7976 |  -63.3612 |  -57.9530 |  -70.6858 |   68.3201 |   65.1923 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  341 | 03m34s |   -0.14256 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   57.1988 |    4.2327 |  -16.7112 |   -3.4823 |   44.7489 |   40.7526 |   70.1462 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 | \n",
      "  342 | 03m51s |   -0.23641 |   68.7442 |  -65.1510 |    3.5219 |  -24.6803 |  -58.3863 |   -0.7505 |   48.3660 |   57.7393 |  -32.7102 |    4.4802 |  -44.5076 |  -65.9817 |  -18.4942 |  -38.9961 |  -67.1155 |   67.9423 | \n",
      "  343 | 04m04s |   -0.12024 |   53.1348 |  -66.6877 |   64.5257 |   59.6055 |   39.6403 |   69.3982 |   56.5779 |   34.8915 |  -70.6017 |   43.4190 |   69.3029 |  -10.0808 |  -21.0342 |   41.1970 |  -61.2980 |   70.2387 | \n",
      "  344 | 03m56s |   -0.20095 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  345 | 03m40s |   -0.10318 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  346 | 03m58s |   -0.17057 |  -70.6858 |   70.6858 |  -70.6858 |   70.5378 |  -68.9964 |   14.9821 |  -51.1208 |  -70.6858 |  -70.6858 |  -70.6858 |   38.3087 |  -46.4955 |   70.0457 |  -64.6744 |   70.6858 |  -70.6858 | \n",
      "  347 | 03m56s |   -0.05872 |  -70.6400 |   70.6854 |  -68.4924 |    1.5827 |  -70.6482 |   32.7152 |   70.6851 |   37.5947 |   70.6858 |   16.7325 |   69.6481 |   61.9646 |    8.3631 |    3.6154 |   70.6850 |  -45.5570 | \n",
      "  348 | 04m16s |   -0.04222 |   70.6858 |  -70.6858 |  -70.6858 |  -70.6858 |  -37.8187 |  -70.6858 |  -70.6858 |   67.4919 |   17.0738 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 | \n",
      "  349 | 03m44s |   -0.24008 |  -70.6858 |  -70.6811 |   70.6858 |   70.6858 |   70.6858 |  -70.2491 |   70.6858 |   70.6858 |  -70.6828 |  -40.3408 |   70.6858 |  -70.6765 |   70.6858 |  -70.6839 |  -70.6858 |   70.6858 | \n",
      "  350 | 04m12s |   -0.20246 |  -70.6858 |   70.6858 |   36.8955 |   68.8511 |   40.0545 |    6.9900 |   -8.1853 |  -70.6858 |   70.6858 |  -70.6858 |   42.8243 |  -70.6858 |  -65.8450 |   -5.4547 |   70.6858 |  -69.3154 | \n",
      "  351 | 03m50s |   -0.13471 |   64.2684 |  -68.7672 |   63.1664 |   40.7828 |   14.0288 |  -61.1527 |   31.9314 |  -43.6977 |  -65.8142 |   11.1182 |  -68.4443 |  -53.1741 |   40.7111 |  -54.7976 |  -64.9607 |   59.8022 | \n",
      "  352 | 04m11s |   -0.25686 |   70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   28.7341 |  -70.6858 |  -70.6858 |   59.9369 |   70.6858 |   70.6858 |   70.6858 | \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andreas/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([ 0.00012212]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 52, 'nit': 4, 'warnflag': 2}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  353 | 03m44s |   -0.23650 |  -70.6846 |   70.6858 |  -69.9055 |   32.0844 |   50.4269 |   70.6858 |   30.2603 |  -70.5507 |   70.6858 |  -38.0118 |   70.6858 |   63.4917 |  -51.9570 |  -70.6858 |  -70.6858 |   61.0880 | \n",
      "  354 | 04m16s |   -0.31348 |  -70.6858 |  -70.6858 |  -70.6858 |  -51.0488 |  -45.4019 |  -35.1424 |   53.9555 |   59.4773 |  -25.4742 |  -57.4650 |   26.5931 |  -13.2891 |   49.8625 |  -70.6858 |   70.6858 |  -70.6858 | \n",
      "  355 | 03m59s |   -0.20706 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -58.9680 |  -70.6858 |   70.6858 |  -70.6858 |  -70.6858 |   70.6858 |   70.6858 |   70.6858 |  -70.6858 |   70.6858 | \n",
      "  356 | 04m15s |   -0.54929 |   70.0208 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   67.2805 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   70.6858 |   67.6650 |   70.6858 |   70.3209 | \n",
      "  357 | 04m14s |   -0.27304 |   62.8374 |  -45.8333 |   62.9484 |   70.3944 |  -69.2458 |   56.8059 |   40.8019 |  -64.3200 |   63.3064 |   55.0461 |   -7.1668 |   63.0512 |  -54.0913 |   38.4758 |  -66.3311 |   68.3388 | \n",
      "  358 | 04m46s |    0.09297 |  -70.6858 |   70.6858 |  -57.7888 |  -65.2657 |  -70.6858 |  -45.8320 |  -70.6858 |  -15.2061 |   70.2312 |   70.6858 |    6.4478 |   70.6858 |  -45.8261 |  -27.5884 |   60.0251 |  -70.6858 | \n",
      "  359 | 04m53s |   -0.26658 |   67.6581 |  -70.6244 |  -41.8626 |  -41.1075 |   63.8237 |  -28.3226 |  -45.0665 |   70.2551 |  -55.2441 |   53.9963 |  -69.5379 |    4.2442 |    5.6953 |  -69.9921 |  -62.3860 |   64.1734 | \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-ab27754f6fc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m            \u001b[0;36m8.67695396\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m70.25159013\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m69.19537669\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20.33609285\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m          \u001b[0;34m-\u001b[0m\u001b[0;36m75.41802364\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m75.32826822\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m71.95575526\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m70.38381166\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m           70.30803821,  69.92008994,  12.85117779,  64.84087746])\n\u001b[0m",
      "\u001b[0;32m<ipython-input-22-e3f048b66ed1>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(self, init)\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0mbo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mguess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mbo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_initial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, xi, **gp_params)\u001b[0m\n\u001b[1;32m    299\u001b[0m                             \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m                             \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m                             **self._acqkw)\n\u001b[0m\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0;31m# Keep track of total number of iterations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/bayes_opt/helpers.py\u001b[0m in \u001b[0;36macq_max\u001b[0;34m(ac, gp, y_max, bounds, random_state, n_warmup, n_iter)\u001b[0m\n\u001b[1;32m     58\u001b[0m                        \u001b[0mx_try\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                        \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m                        method=\"L-BFGS-B\")\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;31m# Store it if better than previous minimum(maximum).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    601\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'l-bfgs-b'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         return _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[0;32m--> 603\u001b[0;31m                                 callback=callback, **options)\n\u001b[0m\u001b[1;32m    604\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tnc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m         return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;31m# Overwrite f and g:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfunc_and_grad\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m             \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_approx_fprime_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_approx_fprime_helper\u001b[0;34m(xk, f, epsilon, args, f0)\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0mei\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mepsilon\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mei\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m         \u001b[0mgrad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxk\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mf0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m         \u001b[0mei\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/bayes_opt/helpers.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mx_try\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx_seeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;31m# Find the minimum of minus the acquisition function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         res = minimize(lambda x: -ac(x.reshape(1, -1), gp=gp, y_max=y_max),\n\u001b[0m\u001b[1;32m     58\u001b[0m                        \u001b[0mx_try\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                        \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/bayes_opt/helpers.py\u001b[0m in \u001b[0;36mutility\u001b[0;34m(self, x, gp, y_max)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mutility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ucb'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ucb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkappa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ei'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ei\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/bayes_opt/helpers.py\u001b[0m in \u001b[0;36m_ucb\u001b[0;34m(x, gp, kappa)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_ucb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmean\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mkappa\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X, return_std, return_cov)\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0;31m# compute inverse K_inv of K based on its Cholesky\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m                 \u001b[0;31m# decomposition L and its inverse L_inv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m                 \u001b[0mL_inv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_triangular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mL_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mL_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m                 \u001b[0mK_inv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mL_inv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL_inv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m                 \u001b[0;31m# Compute variance of predictive distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Diverses/anaconda3/lib/python3.6/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36msolve_triangular\u001b[0;34m(a, b, trans, lower, unit_diagonal, overwrite_b, debug, check_finite)\u001b[0m\n\u001b[1;32m    345\u001b[0m     \u001b[0mtrtrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_lapack_funcs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'trtrs'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     x, info = trtrs(a1, b1, overwrite_b=overwrite_b, lower=lower,\n\u001b[0;32m--> 347\u001b[0;31m                     trans=trans, unitdiag=unit_diagonal)\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "bayes = Bayes()\n",
    "ret2 = bayes.main(init=[ 74.56466659, -74.65413536, -73.42280921, -71.23052582,\n",
    "           8.67695396, -70.25159013, -69.19537669, -20.33609285,\n",
    "         -75.41802364,  75.32826822,  71.95575526,  70.38381166,\n",
    "          70.30803821,  69.92008994,  12.85117779,  64.84087746])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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