Monte Carlo Spring Area.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.ticker as tick\n",
    "\n",
    "import scipy.stats as sp\n",
    "import statsmodels.api as sm\n",
    "from pandas.stats.api import ols\n",
    "\n",
    "from pylab import rcParams\n",
    "import platform\n",
    "rcParams['figure.figsize'] = 15, 10\n",
    "\n",
    "import re\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "\n",
    "from scipy.optimize import minimize\n",
    "from scipy.optimize import differential_evolution\n",
    "\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import spotpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operating System Windows 7\n",
      "Python Version 2.7.10 (default, May 23 2015, 09:40:32) [MSC v.1500 32 bit (Intel)]\n",
      "Pandas Version 0.18.1\n",
      "Numpy Version 1.11.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Operating System \" + platform.system() + \" \" + platform.release())\n",
    "print(\"Python Version \" + str(sys.version))\n",
    "print(\"Pandas Version \" + str(pd.__version__))\n",
    "print(\"Numpy Version \" + str(np.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.25'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotpy.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization with scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discharge_rng = (20000.0,35000.0)\n",
    "thickness_rng = (500.0,8000.0)\n",
    "eff_porosity_rng = (0.01,0.2)\n",
    "age_rng = (7700.0, 16600.0)\n",
    "delT_rng = (3.738,17.738)\n",
    "heatFlux_rng = (39.0,155.0)\n",
    "n = 1000\n",
    "bnds = [discharge_rng, thickness_rng, eff_porosity_rng, age_rng, delT_rng, heatFlux_rng]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "recharge rate=discharge/area <br>\n",
    "Equation 2a <br>\n",
    "area = (discharge*age)/(thickness*effective porosity)<br>\n",
    "Equation 3a <br>\n",
    "area = (discharge*(p*C*delT))/heat flux<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "((Q*age)/(thick*poros)) - ((Q*(999.58*1.001*delT))/heatFlux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def area(x):\n",
    "    arg1 = ((x[0]*x[3])/(x[1]*x[2]))\n",
    "    arg2 = ((x[0]*(999.58*1.001*x[4]))/x[5])\n",
    "    return np.abs(arg1-arg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%timeit\n",
    "Q, thick, poros, age, delT, heatFlux = [],[],[],[],[],[]\n",
    "\n",
    "for i in range(n):\n",
    "    x = differential_evolution(area,bnds,maxiter=5000).x\n",
    "    Q.append(x[0])\n",
    "    thick.append(x[1])\n",
    "    poros.append(x[2])\n",
    "    age.append(x[3])\n",
    "    delT.append(x[4])\n",
    "    heatFlux.append(x[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = {'discharge':Q, 'thickness':thick, 'effective porosity':poros, 'age':age,\n",
    "    'delT':delT, 'heat flux':heatFlux}\n",
    "results = pd.DataFrame(data = d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results['area'] = results[['discharge','age','thickness','effective porosity']].apply(lambda x: (x[0]*x[1])/(x[2]*x[3]),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12eb0210>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rstUPrM+/L8n9d336Fetzn+aaa67JsWPHkiSXXHJJjh8//sl/uc+Uey/k41tuuXnXr3a1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\nJUmSJEmdfgI/TN2D5DyL0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e9f9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['area'].hist(bins=20)\n",
    "plt.xlabel('Area (acres)')\n",
    "plt.ylabel('Number of Occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'describe'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-0b814dd2603c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'describe'"
     ]
    }
   ],
   "source": [
    "results.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12a908b0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AmXIY5JTq6AAW1dEBrLCrMTfzmZ8Zzc+M5mY+2+YwCAAAsEF2BrvYGeynrd/Mfdr62BkE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12768f30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['discharge'].hist(bins=20)\n",
    "plt.xlabel('Annual Discharge')\n",
    "plt.ylabel('Number of Occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12d18330>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t84zd5jECsAan9j6ESd58cRjc+m9Jur0pPXBSpt4BsHBT7wBYNDuEMIZDdwir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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12cfe5b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['effective porosity'].hist(bins=20)\n",
    "plt.xlabel('Effective Porosity (%)')\n",
    "plt.ylabel('Number of Occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12d35350>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oANhhHh0AC+bRAZuxZpj7D621r2ytffDpn69M8kM38kFaa49rrb2+tfbtp8d3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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d57070>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['age'].hist(bins=20)\n",
    "plt.xlabel('Age (years)')\n",
    "plt.ylabel('Number of Occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128.34513785786407"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['heat flux'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.to_excel(u'U:\\GWP\\Groundwater\\Staff\\Inkenbrandt\\montecarloFS.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization with SPOTPY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://fb09-pasig.umwelt.uni-giessen.de/spotpy/Tutorial/5-Hydrological_model/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://fb09-pasig.umwelt.uni-giessen.de/cmf/wiki/CmfDownload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class spotpy_setup(object):\n",
    "    def __init__(self):\n",
    "        self.params = [spotpy.parameter.Uniform('discharge',20000.0,35000.0,1,25000.0),\n",
    "                       spotpy.parameter.Uniform('thickness',500.0,8000.0,1,3000.0),\n",
    "                       spotpy.parameter.Uniform('porosity',0.01,0.2,0.01,0.1),\n",
    "                       spotpy.parameter.Uniform('age',7700.0,16600.0,5.0,9000.0),\n",
    "                       spotpy.parameter.Uniform('delT',3.738,17.738,0.1,10.0),\n",
    "                       spotpy.parameter.Uniform('flux',39.5,155.5,0.5,100.0)\n",
    "                       ]\n",
    "    \n",
    "    def parameters(self):\n",
    "        return spotpy.parameter.generate(self.params)\n",
    "    \n",
    "    def simulation(self,vector):\n",
    "        simulations = [(((vector[0]*vector[3])/(vector[1]*vector[2])) - ((vector[0]*(999.58*1.001*vector[4]))/vector[5]))]\n",
    "        return simulations\n",
    "\n",
    "    def evaluation(self):\n",
    "        observations=[0]\n",
    "        return observations\n",
    "    \n",
    "    def objectivefunction(self,simulation,evaluation):\n",
    "        objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation)      \n",
    "        return objectivefunction\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "spotpy_setup=spotpy_setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of sampling\n",
      "4099 of 5000 (best like=-1126.81)\n",
      "Best parameter set\n",
      "[20869.589854888691, 5951.1101897400113, 0.012204893523304962, 8005.2895611914018, 7.5529524052071597, 68.601897890382929]\n",
      "Duration:1.3 s\n",
      "Creating LatinHyperCube Matrix\n",
      "Start sampling\n",
      "End of sampling\n",
      "40 of 5000 (best like=-1248.39)\n",
      "Best parameter set\n",
      "[22267.999007778584, 5739.2104468010612, 0.024118802722346802, 9041.4678299536536, 6.1337939679499662, 93.88088609749866]\n",
      "Duration:1.04 s\n",
      "burnIn...\n",
      "Beginn Random Walk\n",
      "2901 of 5000 (best like=-29.0667)\n",
      "End of sampling\n",
      "Acceptance rate = 0.0016\n",
      "3028 of 5000 (best like=-20.9412)\n",
      "Best parameter set:\n",
      "[34461.7783882365, 1706.5194312330445, 0.11871092856442772, 8580.12844779708, 4.4285484602948015, 104.62005795741325]\n",
      "Duration:2.44 s\n",
      "burnIn...\n",
      "Beginn of Random Walk\n",
      "End of sampling\n",
      "Acceptance rate = 0.0016\n",
      "4099 of 5000 (best like=-29351.5)\n",
      "Best parameter set\n",
      "[33056.60271641102, 4705.244500213183, 0.08336399523425939, 13504.615370249696, 4.373258944556426, 127.09430179365887]\n",
      "Duration:1.25 s\n",
      "3666 of 5000 (best like=-38.3294)\n",
      "*** OPTIMIZATION SEARCH TERMINATED BECAUSE THE LIMIT\n",
      "ON THE MAXIMUM NUMBER OF TRIALS \n",
      "5000\n",
      "HAS BEEN EXCEEDED.\n",
      "SEARCH WAS STOPPED AT TRIAL NUMBER: 5018\n",
      "NORMALIZED GEOMETRIC RANGE = 0.139698\n",
      "THE BEST POINT HAS IMPROVED IN LAST 100 LOOPS BY 742.681330\n",
      "Best parameter set\n",
      "[  2.68711479e+04   2.73674120e+03   6.18542810e-02   9.06774744e+03\n",
      "   6.61628661e+00   1.23586214e+02]\n",
      "Duration:2.78 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\sceua.py:332: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  BESTX=BESTX.reshape(BESTX.size/self.nopt,self.nopt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 of 5000 (best like=-360.03)\n",
      "Duration:1.1 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\demcz.py:438: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return self._combined_history[(np.ceil(self.relevantHistoryStart) *self._nChains):(self.relevantHistoryEnd * self._nChains),self.group_indicies[name]]\n",
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\demcz.py:426: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return self._sequence_histories[:,self.group_indicies[name], np.ceil(self.relevantHistoryStart):self.relevantHistoryEnd]\n",
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\demcz.py:508: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  covariance1 = np.cov(relevantHistory[0:midpoint, :].transpose())\n",
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\demcz.py:509: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  covariance2 = np.cov(relevantHistory[midpoint:end, :].transpose())\n",
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\demcz.py:455: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return self._combined_history[start:end,self.group_indicies[name]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155 of 166\n",
      "index error\n",
      "index error\n",
      "index error\n",
      "index error\n",
      "Gelman Rubin R=[ 5.72997449  4.39802492  4.36982863  5.3216956   9.60121755  4.04066748]\n",
      "Duration:3.49 s\n",
      "100 input vectors with 6 parameters\n",
      "Generating 1000 parameters:\n",
      "(29, 101)\n",
      "(67, 201)\n",
      "(97, 301)\n",
      "(130, 401)\n",
      "(157, 501)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\paulinkenbrandt\\documents\\github\\env\\lib\\site-packages\\spotpy\\algorithms\\rope.py:252: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  S[i,:]=[X[row-1,j] for j in range(NP)] # S: random sample from X\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 601)\n",
      "(219, 701)\n",
      "(250, 801)\n",
      "(276, 901)\n",
      "(305, 1001)\n",
      "(329, 1101)\n",
      "(364, 1201)\n",
      "(396, 1301)\n",
      "(440, 1401)\n",
      "(471, 1501)\n",
      "(502, 1601)\n",
      "(531, 1701)\n",
      "(560, 1801)\n",
      "(576, 1901)\n",
      "(609, 2001)\n",
      "(625, 2101)\n",
      "(654, 2201)\n",
      "(679, 2301)\n",
      "(711, 2401)\n",
      "(735, 2501)\n",
      "(769, 2601)\n",
      "(800, 2701)\n",
      "(826, 2801)\n",
      "(853, 2901)\n",
      "(878, 3001)\n",
      "(909, 3101)\n",
      "(933, 3201)\n",
      "(958, 3301)\n",
      "(981, 3401)\n",
      "(1022, 3501)\n",
      "100 input vectors with 6 parameters\n",
      "Generating 1000 parameters:\n",
      "(18, 101)\n",
      "(44, 201)\n",
      "(75, 301)\n",
      "(99, 401)\n",
      "(119, 501)\n",
      "(133, 601)\n",
      "(167, 701)\n",
      "(191, 801)\n",
      "(218, 901)\n",
      "(238, 1001)\n",
      "(256, 1101)\n",
      "(278, 1201)\n",
      "(303, 1301)\n",
      "(322, 1401)\n",
      "(361, 1501)\n",
      "(385, 1601)\n",
      "(411, 1701)\n",
      "(444, 1801)\n",
      "(473, 1901)\n",
      "(493, 2001)\n",
      "(516, 2101)\n",
      "(543, 2201)\n",
      "(573, 2301)\n",
      "(587, 2401)\n",
      "(602, 2501)\n",
      "(630, 2601)\n",
      "(653, 2701)\n",
      "(681, 2801)\n",
      "(697, 2901)\n",
      "(721, 3001)\n",
      "(745, 3101)\n",
      "(770, 3201)\n",
      "(790, 3301)\n",
      "(821, 3401)\n",
      "(861, 3501)\n",
      "(881, 3601)\n",
      "(901, 3701)\n",
      "(921, 3801)\n",
      "(947, 3901)\n",
      "(975, 4001)\n",
      "(996, 4101)\n",
      "(1024, 4201)\n",
      "1000 of 5000 (best like=-1036.67)\n",
      "100 input vectors with 6 parameters\n",
      "Generating 1000 parameters:\n",
      "(12, 101)\n",
      "(27, 201)\n",
      "(47, 301)\n",
      "(63, 401)\n",
      "(74, 501)\n",
      "(95, 601)\n",
      "(105, 701)\n",
      "(125, 801)\n",
      "(143, 901)\n",
      "(159, 1001)\n",
      "(172, 1101)\n",
      "(180, 1201)\n",
      "(194, 1301)\n",
      "(206, 1401)\n",
      "(226, 1501)\n",
      "(244, 1601)\n",
      "(262, 1701)\n",
      "(272, 1801)\n",
      "(286, 1901)\n",
      "(306, 2001)\n",
      "(339, 2101)\n",
      "(360, 2201)\n",
      "(380, 2301)\n",
      "(395, 2401)\n",
      "(414, 2501)\n",
      "(426, 2601)\n",
      "(442, 2701)\n",
      "(456, 2801)\n",
      "(474, 2901)\n",
      "(490, 3001)\n",
      "(504, 3101)\n",
      "(520, 3201)\n",
      "(540, 3301)\n",
      "(559, 3401)\n",
      "(569, 3501)\n",
      "(589, 3601)\n",
      "(605, 3701)\n",
      "(625, 3801)\n",
      "(635, 3901)\n",
      "(655, 4001)\n",
      "(669, 4101)\n",
      "(686, 4201)\n",
      "(704, 4301)\n",
      "(719, 4401)\n",
      "(726, 4501)\n",
      "(739, 4601)\n",
      "(758, 4701)\n",
      "(772, 4801)\n",
      "(786, 4901)\n",
      "(813, 5001)\n",
      "(826, 5101)\n",
      "(848, 5201)\n",
      "(864, 5301)\n",
      "(877, 5401)\n",
      "(896, 5501)\n",
      "(911, 5601)\n",
      "(921, 5701)\n",
      "(943, 5801)\n",
      "(954, 5901)\n",
      "(973, 6001)\n",
      "(983, 6101)\n",
      "(995, 6201)\n",
      "(1011, 6301)\n",
      "100 input vectors with 6 parameters\n",
      "Generating 1000 parameters:\n",
      "(17, 101)\n",
      "(35, 201)\n",
      "(51, 301)\n",
      "(78, 401)\n",
      "(88, 501)\n",
      "(103, 601)\n",
      "(115, 701)\n",
      "(128, 801)\n",
      "(146, 901)\n",
      "(165, 1001)\n",
      "(174, 1101)\n",
      "(192, 1201)\n",
      "(202, 1301)\n",
      "(221, 1401)\n",
      "(235, 1501)\n",
      "(248, 1601)\n",
      "(269, 1701)\n",
      "(281, 1801)\n",
      "(297, 1901)\n",
      "(316, 2001)\n",
      "(332, 2101)\n",
      "(346, 2201)\n",
      "(368, 2301)\n",
      "(389, 2401)\n",
      "(401, 2501)\n",
      "(414, 2601)\n",
      "(426, 2701)\n",
      "(437, 2801)\n",
      "(455, 2901)\n",
      "(475, 3001)\n",
      "(492, 3101)\n",
      "(518, 3201)\n",
      "(534, 3301)\n",
      "(554, 3401)\n",
      "(571, 3501)\n",
      "(582, 3601)\n",
      "(597, 3701)\n",
      "(615, 3801)\n",
      "(636, 3901)\n",
      "(646, 4001)\n",
      "(659, 4101)\n",
      "(679, 4201)\n",
      "(689, 4301)\n",
      "(705, 4401)\n",
      "(718, 4501)\n",
      "(729, 4601)\n",
      "(749, 4701)\n",
      "(767, 4801)\n",
      "(786, 4901)\n",
      "(803, 5001)\n",
      "(821, 5101)\n",
      "(828, 5201)\n",
      "(838, 5301)\n",
      "(859, 5401)\n",
      "(877, 5501)\n",
      "(895, 5601)\n",
      "(916, 5701)\n",
      "(932, 5801)\n",
      "(959, 5901)\n",
      "(979, 6001)\n",
      "(998, 6101)\n",
      "(1005, 6201)\n",
      "3000 of 5000 (best like=-489.398)\n",
      "End of sampling\n",
      "3417 of 5000 (best like=-75.0447)\n",
      "Best parameter set:\n",
      "[22633.649538355196, 5360.0638788330407, 0.077846550338075027, 14820.00544959215, 4.4907956614088098, 126.50140921288489]\n",
      "Duration:6.06 s\n"
     ]
    }
   ],
   "source": [
    "results=[]\n",
    "\n",
    "rep=5000\n",
    "\n",
    "sampler=spotpy.algorithms.mc(spotpy_setup,    dbname='aMC',    dbformat='csv')\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.lhs(spotpy_setup,   dbname='aLHS',   dbformat='csv')\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.mle(spotpy_setup,   dbname='aMLE',   dbformat='csv')\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.mcmc(spotpy_setup,  dbname='aMCMC',  dbformat='csv') # Good for this\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.sceua(spotpy_setup, dbname='aSCEUA', dbformat='csv') # Good for this\n",
    "sampler.sample(rep,ngs=2)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.sa(spotpy_setup,    dbname='aSA',    dbformat='csv')\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.demcz(spotpy_setup, dbname='aDEMCz', dbformat='csv') # Good for this\n",
    "sampler.sample(rep,nChains=30)\n",
    "results.append(sampler.getdata())\n",
    "\n",
    "sampler=spotpy.algorithms.rope(spotpy_setup,  dbname='aROPE',  dbformat='csv')\n",
    "sampler.sample(rep)\n",
    "results.append(sampler.getdata())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "algDf= {}\n",
    "algorithms=['MC','LHS','MLE','MCMC','SCEUA','SA','DEMCz','ROPE']\n",
    "#algorithms=['MC','MCMC','SA']\n",
    "pars = ['like','discharge','thickness','porosity','age','delT','flux','simulation','chain']\n",
    "for i in range(len(algorithms)):\n",
    "    algDf[algorithms[i]] = pd.DataFrame(data = results[i])\n",
    "allAlgs = pd.concat(algDf)\n",
    "allAlgs.reset_index(inplace=True)\n",
    "allAlgs.set_index('level_1',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating FAST Matrix\n",
      "Start sampling\n",
      "9076 of 100002 (best like=-528.827)\n",
      "18613 of 100002 (best like=-48.6195)\n",
      "28101 of 100002 (best like=-48.6195)\n",
      "37619 of 100002 (best like=-48.6195)\n",
      "41961 of 100002 (best like=-48.6195)\n",
      "51541 of 100002 (best like=-48.6195)\n",
      "61234 of 100002 (best like=-48.6195)\n",
      "70458 of 100002 (best like=-35.703)\n",
      "76222 of 100002 (best like=-35.703)\n",
      "85506 of 100002 (best like=-35.703)\n",
      "91206 of 100002 (best like=-35.703)\n",
      "65783 of 100000 (best like=-35.703)\n",
      "Duration:24.44 s\n",
      "100002\n",
      "Parameter First Total\n",
      "discharge 0.026070 0.063934\n",
      "thickness 0.022904 0.450899\n",
      "porosity 0.036849 0.538543\n",
      "age 0.001550 0.091821\n",
      "delT 0.214717 0.484031\n",
      "flux 0.182417 0.473122\n",
      "The figure as been saved as \"discharge_thickness_porosity_age_delT_flux_trace.png\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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GFfyC7qGHrCBdx46WFlKQhNIBzj3XqsUXJCVLWk2J33+3mhXukHIKC2+8AS+9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C1iOInD9t09PDzcUA1P+1IaE7zF0QmsM8f3Ap4A3gHuAjpito5rC/w1oLkXGAO0B64AegP7Au9F\nQvBvAYYA3wGuAboDwzFr4kPV7iZgFGZ7t+sxifWOBJbRqNgXxu23w+9+B9OmwXrrlcXVI4pdd4Ub\nb4QhQ+DRR2GrrWotkcfKhHhypjK8rq6GgDIU16yw5koh02UqF64GizwT27yKVVpbZ/Erw2OWpy7T\nEnHlCY+Py+kaHm/DM6v/2Bin4iG+WfJF27qMceVquEqCa0I6GwXbdW2xS//KGgeV8k7E5bONCMhj\nqLCRJ4StPC793HX7vSKGgCIKtuvuE2WMk7BcY40eiM/A7FEj9OhR1c3BqoJqeNonAheIyD4SGY0i\nsidwHjDdks+bwIWY9eY/Bn6EyR6vmH3gAfoC7wNfYfaF3xqjZG8oIn0Cmj2BLzFZ6Edi9nafgDEg\nDApo9gYWY7aGOxYYGvBZhca184UweTKcdBJMmgTfqlo+eg+Affc1W8ANHAjvvltraTxWRthOtG0m\nTSFsFW0XnrahrUW8J1k8w18Bl+3uqmmwqPTuldZ+llmXtQiPL2PynVbaKNiuxqkyx07SOxcxCNm2\nWxlKu4ucrhnfbRTFNDmz+molnra7SLjUY1qytmr3ozTkNUy4GLzyjPF4u1TTEJDFa/jwmYCy334K\nKBttZEpQ1ljDlP36mfKoo0x5wgmmPP54U/7sZ03///xzRdUf/sg+Zs6cmd2ZWxiqobSPBj4AHgQW\niMhHIrIQowQvAU60YaIG56rqd1W1k6p2xnjIAboE+7f3A25U1S1VtYOq9gSOw+wPv1tAOwB4QFW3\nUdV6Ve0GDAMWxmhmqOoOqtpRVbuq6mDg7QhNbvzjH3D44XDvvdC7d1FuHjY47DA49VSztv2TT2ot\njUdrR1zxtJ08uSSZtA1ptZkwZvHMmoRW8pJlhcmmeaO0gvfedmJbpvKapFxoQnh8Ft88yl987XGa\nxzZP6Largl1GeHxS/oI4NEOhzPPOIfIof3HaahqEbOrYdtzb7DSTd9xXQlb/d/FCp8mZxtNF8Y7L\nmed7mSaHS7u6/lYU+W64GPvi7WI7dmwiFkLkbddKfcY2IsClvT08WhtK796q+qmIfA/jxd4J6ILx\nhD8H3KOq8wuw/05Qvgash8nu/krs+f8VkQVAj8DTv0ECzVIRmQmEsREbYSIE4ngrQpMLL78Mw4bB\nuHGw885FOHm4YtQo+Pxz43F/7DGToM7DIw/iiqftRLvIZLOIlyeLZ57w+BC23vE83rEiXuE0+fIa\nP+J1EPfgpU1I44aaNKNAVIa0OrRZXhF/Tpacce+qzUS5pfafIjyLPC8tVDzeB1zqOCuSw0Y+2wSC\nedrNNjy+iJJsy7OS4hivxzzf4CzFMHxGpfqy/a2Ib8tmE6GQVW9xw1ySnEX7SiXYtquLUbvMqAwP\nj9aKqnRvVV0K3BccZeJE4GtgPEbRBpiTQDcXs65+LUw0QRLNHBrX3netQJM7mP3tt2HvveHyy43i\n6NH8GDMGPvsMBg82SxQ6dKi1RB4rA1pLeLzt5K6IcSFLGXbxzJRRj7bh7K6Tu6w2TwsRd1GKixgr\nsuR0mXyHMttmIy+iUBZZEhDCNvFVktLu4kEMkdUXXOojRBljIGy3anyfXHnmCbm3rU+XMVUNZbPM\nb7CLYS5E1hh36Su2EQEu9ZgVSZSnHm0TIbrw9PBobShFaReRHwN3qdlibQRQSTX6WlVvy/mMXYCz\nVfUTEdk0uJT0+V4UnG9nQUNAl0WzAsaMGbP87/j2AB99BHvtBWedBSNGpHHwqDZE4MorTUb5gw82\n2eW9JdajKLLCum28rnG4emRskBUmmzYW8mRpzhPyGOeZ5rmshvKaVY+u4fFFFLWs97ZRgLKWL+SZ\n1MYn9GWEx1cjuiKPISBP37JN9OZSHyHyjtVKcma1m4uBLWtHijzh8Vnj3iVXQpqcZYTH5zHw2PYV\nF29zWruWaTgtQ8EOkTXG8xhjs4zafn7nUQZW9n3arwL+DTyOycReie9iwElpF5EdMNnhJwEXBafn\nBmVS0POqwCxLmpBXFs0KiCrtUcyaBQMGwJFHws9+lna3R3Ohrs5sszdkCBx7LPz1r25WbQ+PNGRN\nniopsPGJVJbSlUeBy1I00yahNmGyRTwyWWHiZUxCbcPEbWXM4pc2AS0SHu+iAMW9rFnvXYlnPKw7\na62viyc0RBmevVDOrIiAJF55DFe1qI8yjEJlGEayPLJpRkWX/m877pN4poWjlxFNkIeXbXi8i5HT\nNjzeJp9IlrHDpU+n/ZbZ/v7YIG35T5GIBQ+PNKzs+7Rvi9meDcwa8I0rHD1dGItIL+ABzLr04arL\nPxVhXvDNYvQdgbWBN9Xs5T4rThOgJyZDPcDMFJqNgX+5yDt/PgwaBP37wznnuNzpUU20bQvjx8Nb\nb5kEdTaTKA+PLKRNnmwU7LQ+mDbRcZlgxxW4NGXGZtuvNPnK8AiGcA0NdvHi2SoXWcijGGbJWkaY\ndXPwdFXUKiFNmcnjeYyH+KZ59pLkyzK22KzLz6pjm0RlIVy99zYyZ7Wbi4KTpTCmKdg2xqE0nmnj\nvhLP+DjNY5jIMpy6ILzH9btRCc3ZV2x4Zv2Wxcd4nv4X51kkKsnDo7WiFKVdVd9Q1WXB3x+q6rsV\njg9t+YrIOsBDwAJMQrl7I8+cDbwEHCUiT4rIPBH5NKBXYGpAOg0YKiINIjJXRGaJyDTMOvbJEZpd\nRWSyiHwZ0E0HtsN4962weDEMHw49e8Jll/mPR0tDx47w4IMwdSpceGGtpfFYGZA2ebLxuqZdS/M+\nFQn7S1OQ8siZpbzaTBjTFJYiyYVslSDbENe061ntUw1FrdK2eXHetuHxZazhTnvnIluIFVmTmtXm\nUeSpj7gHMSuU2MZrGiLNI5tH+QyRJmeRb0ma0S7PuA+RNqZsDDjxsRFffpGnP5VhOI3LZWsIKPIb\nkee7ESLtN8ImgiKLZxmGiiyeLsY+D4/Wiqqt/hCRrTCKcfyTuUBV/2HJZgLGI74Es0Xb+iIyAFgK\nPIJR4scA7THh872BfYH3VPW/AY9bgCGYzPPXAN0xW8fNA54KaG4CRmG2d7seqAeOBJbRqNhXxLJl\ncPTR5m8fft1yseaa8NBDsMsusNZa8NOf1loij9YMVwW7UkhhCFcFKQm2YbJ5vBNZoY95JslZHkEb\npdBWCao0YawUypu1Tjgt7NxF0U6bfNt4GONrxvMaK5JQRKmKI6v/5FGM0uSspJjnWYOdti7fdQ12\npe0PiygjtvXRnOO+iMKYZ8lJnGcR73iRHCC236M8UUSuhgCX8Ph48jib70Y1f8tsefoEdB7fBJSu\ntAfh7PcCW4WnglKDv5cAbS341GEU7bpAzrbA1pit2RToBvQF3sco4KOBjzFK9l4i0kdVXwT2BL4E\n3gNGYsLlJwD7Ybalux/YG7PW/hXgWGB+wGc/4Ajgj5VkVYXRo+Hdd41C6C1+LRvdusGUKdCvH3Tp\nAgcdVGuJPForXENFo4iH9sZ5ZilwlWAb+pgnPD6NZxlrCm3Xo7qEx8cjAipNGPOsJ80KEc8Tyl+k\nLtMm9EUmtWnRFUV4uipGScirGEH+ZRJRpCU9y7MsJi1BX56lB3H54oqYi6Eiztv2m2djaIrLaauI\nueQIKGM5SJnfozIiX9LkLDJ20njmiRqJ8yqSrNCWp1faPb4JqIY/eBxmD/VTgb2A3YPjB0G5qw2T\nINx+dVUVVW0DNABPqGobVV0FmA30A25U1S1VtYOq9gSOwxgHdgtYDQAeUNVtVLVeVbsBwzCe+yjN\nDFXdQVU7qmpXVR0MvB2hScWvfw3TpsEDD0B9vc3bedQavXrBxIkwcqRR4L+pGDy41hJk4/jjay1B\nOmy9hTYTnqwkV2UoF3HvSZkKdhlZe9MmoUWWGuWZhFZCmoJtE4pqO1F28eil0cTbvEj/cTUEFEm8\n1lzh8XmSG6bxKNPb1xztVo3w5JbGM6y3MsPj8yylCJH1bUsz4kaRZaRqzjFugzIMUXH48HiPbzKq\nER6/DfAzVb2xKCNVXVDh8nqY7O6vxO75r4gsAHqIiGDWwsdplorITEzSPDB7vk9MeMZbEZpEXHUV\n3HwzTJ8Oa6xRidKjpaFPH7MF3LBhxuCy447FeT7yCPzgB03P3Xyz2UmgJSH+4z9/vlnz35KwaFHj\nD/E115iypeWJSAtvzrMGO0SaUljmhLFM5cI2lDUtLLgSzyIembRw9ryKgC0/l/D4ELYyukRZpEVC\nFBlDWdtp5Qm3zprQF8kRkLa8Iuk5ZXj9bNdguyS5KxIeHyJtrLqseU7L1J+15jnP2ucsY50LzzLC\n49MU7ErIUrCLjPEQWQbESrDtK/F3b27jVtrSqDhPvyTV45uAanTzl0nePq1sdA3KOQnX5gKdgbUw\n75hEMyegCXll0ayAW2+Fiy4yic3WW89WbI+WhF12MdvBDR0Kr76an8+yZebHNq6wAxxxhLm2dGl+\n/mXg3XeNHEmTgvr6xmu9eze/bCEOOaRRjqTJanjthReaXzbInuikTZpcJlFxZSauuOZRLuJhstX0\nisfhIneZnqM05TWv9z4vvzx5DPIorlkKbNEIA8iefNt4C+OI98345NslLDc+oa/Uz8sYA66GK8iu\n/2oY2LKiTYoYLNIMIy68XTO+Vzs8vkgG+jQUGeNpNLZRREUMX2X+/pSx5CNEKFcZxkgPj9aCanja\nRwP3icgzqvr3KvAPEQx/kob0ouC8DU3IK4tmBRx33BiOPNIoffE9/TxaD/bZB/7wBxg4EJ54wmT/\nt8GNN8JRR9k/p66u8QfoV78yyyqaA64/si+9ZMrPP4e11y5fniR89ZWbp79Pn0YFvjkt7FkTiCLh\nwmmemTImjGk8bRTNLF5leF3TeJYZERBXCvMqr3GjSnyiXCQ8Pq4kuCSgq2bobNoWgkX6e4iQRxlL\nIUJkjccoTRryeIjLqI9qKu1l7mWdFn6ep/+n8cwTtRKnLZIXwqYf2SJu2E2L0KlGeHyR5VnVePew\nTcpY9uTXsntUAw0NDTQ0NNRajBVQeDooIq8D8el2Z+BREfk44Zb5qrpF0edivOmQ7NVfFZNwzoYm\n5JVFswIefngMO+1kJatHC8ehh8IXX8BeexnFfd1102mXLi0+6bngAnMsWtT441gm/vlP2HbbYjzW\nWiuf58wWW2wBr79ejEc01PShh4zhpTmQNnlyCWFPm0jFFaQ8oaxpilyRSXEaz6xJaJR3Gg9XpdBF\nvrxKoa2CkceoklbvRbapiiNuCLDpm/E6dDUk5eFta5yy8VanjZ0kHll1nPSMuOxlRLCk8cyjYGcl\nuUszMOX5XqUpTUV4Z40pmzZxgW0/KmOMh8ijbKZ9L9MiAor8/hSJGnMd40V+f4rkLPDwyELcEXv+\n+efXTpgIyvDhPIrZIs0WX5fwTIB3g3Kz6EkR6QisDbypqnNEZFacJkBPzJ7uADNTaDYG3kwTwCvs\nKxdOPNF4lwcOhIYGWH11ePnlxmtXXFH+M/8/e2ceJ0dVLf7vSQgkJCFI2AMJCRAgYXtgWGSLCgSV\nVZEdldUHPlAfoqhAgqL4HoL64KnoT3iCoiirIEGiJGwJIZAFQkIWSMi+L5NtkszM+f1x6tI9la7u\nqu6enplwvp9Pfe7UrXtPnXvvqZo+d6ttt839E/r85+GJJyqTV8nU1zRy338f9t23MllLlsAuu5RO\nl5UhQ6o7+l5oWnPSD9H4j5KkHyP5+ZJ+DMU3Eiu1rrvQD50kfdPOCCikZymZpUaw09RnqXouJDut\nfll/eJdqn3hnRTm6xuOTOmqSdCp2LbRHlunmaeuyJeynlJ1nsZ+kGSX5MrKMSpZrr/H84e9iZUn7\n3Be7T5KtJjnYae6R9M4LspMcy2Iy4vFpnbtCeStZkpQkq1QnWjnPeNr3URZ7L2cH+lL6VfP5KOcZ\nLyW7JWaiOU5bp2JzV9Wv5Z+LyLXAo6q6JJ5WRI4CioxhZrrvahF5C/gC8KO8SxdhU9pHROcvAZ8T\nkc5hYzsROQVbx/5cXpqvisi/A9dgDnxdlObeaujrtA+GDoVly2xX9V/9Cj7zGdZbrToAACAASURB\nVPjzn+H881v+3o8/buGaNbDDDunz/f3vNsW/FvTrV/7oe0t1KMTJ/1F8333Zdp/fuNE+Afi3v5W+\nRz6lfmwW+nFcqv7iP4rjlPNDNvwQy9KpkfbHcNwxKmd2RiUzOpLyxp3XLM5EIeKjXHG55UzpLVXH\nlYwmlrp/mh/K8dGtLNNbS5WtnGnWceI2F3c8CpWxkmnT5V4vlKaU45jleUpKk3ZUMs07JZBUx5U8\n9+U4Ymntuxy9qjkFO20HTzWf8WLvjUretWn1CWHSu7KS58WdduejSEusBr0H20G+EHtjn4RLhYjs\nIiJDROQ0YCdgx+j8lGhn+P8CDhORP4vI2SJyE3AXcJ+qzovE3Ik530+LyOdF5D8iHYar6pg8nQX4\nFbAE+C3WoSHYt+CdjwgiNqLeqxcMHAjf/35tHPZ8unfPjRjvvXdyurD5Xa0c9jhBx9dfT04zc2Yu\nXWvw1a/avRsaktOcfnpOx223tRkWAAMG2BKBXXbJ7XOwxx4W9uljYVjasNNOFnbrZmHPnhYGWWFz\nvzDDYPfdTT6YreXLCEszguyw1v9jHyuc/phjzFbz5Qd9w54EQUb37s3zBv3CRpr772/hrrtaWXr1\ngo9/3OLC1zGCfqFjKXzmMkzRDPoFXYINH3ZYrszh/gdGC6VCvQWZXbrk9Mi/R5jlEWT26mVl7dQJ\nDjiguayQJl6PQVbfvnxI0LlPH6vnPfbItVmQF+o0yAnxYT1p794WhjoNdbnffjn5e+7Z/FrIG9op\nlDvUdbhXqLeQbsCAXJsHWww2t9deFobOnnjbBx2CnkceaeXdaaecHfTrZ2Gw95A32GBcZrj3fvtZ\nGNp3wAA46qjmugeZIU0oc2in0Obx9IccktMv5A11EOor6BfsKOgV2jz/fRriQn2FMg0caO3Su3fu\nfqFtQ12HtOFZCGUI943bQDg/9FA44ojm5QoyQhieo6BfKFOol1Cmf/s3e8Y6d87FhWcg1F24b6if\noF/QP16+oNOAATBoUHMZoe7CeZARbDjYVbhXkHnAAbl3QSjjwQdbGH9fBpmhfYPs8NyH/PvtZ7r2\n6AH9+ze/b0gb8obrQb/eva0e9torp0ew4/BOiD/joX1DHYT0oX379cvpFp71+DMe6iBuK+Fe4V0Y\n6uLgg63uRHJ2F9o1/owHPYNeoV3zn/HddjMbCtdCPYU2jz8zQWa8jUIdhfPDD899eSfEhbqOt2MI\n47YcdDj4YCtjx46WVyTXRqEsQWZc3yDbcbZqVLXiA5se3y86moAv5Z2H41DgYWBxBrlXRvIaY0cD\nsGuU5ipgKjbtfjYwDOgYk3MWMAFYDywAfgFsH0vzAbAcWAcsBX6POfEbgZ4FdFNn62XTJtVRo2p7\nz5EjRyZeW7TI3Mr//d/a6ZOVpqbg+ra2JsW54QbTsa6u8PXVq1WnT1fdsEF1zRrV5ctVV61SXbhQ\nde1au97YqDp+vGpDg6VRVX33XQvnzbO6WLRItb5eddo0S19fr7pkiercuZZn/XrVxYstnD3bbG7V\nKpMxdqzJWL3azqdNs/O5c+2ey5ZZ2lmzVDdvtrxz51r8ihV2/te/jtSGBitLvqzXXrNw1SrTa+ZM\nS79ggem4apXpvmCB6saNdr/33rOyL1lieUIZQ5nr6uz89dctXLnSZM2dm8u3bp0ds2dbnW7YYLKm\nTrUyLFxosubPt3DKFAvXrjU9Jk2ydKtX2/2WLLGyrlxp9blxo8lau9aOIKOpycqiamFTk+o771i4\nbp3Fh7ZYudLqcN060//dd60+1qyx9KEd5s2zfIsW2flbb9n5hg0ma9o0y796tR0rV1ra1atNt4YG\n1QkTcmlUrZ1UVefMsXDJEivv5Mk5+1mxQvWDD3JlXL7cdP3gA7t3oXZQtTZuarJ0TU1WxjVr7J6b\nN1vdLVpk91y50mQtXmx2MW1azs5Hjhyp48aZzBUrTNZ779n1OXNM1qpVqkuX2r02bjQZc+ZYOUOZ\n8tshtHFTk+obb9j58uWWbs4cq8+FC638a9da3KJFFt/QYGXbuNHspqkpZ0eTJ1u4bp2lmzjRwhUr\nTEZTU/NnY80ay7Npk+VZvtzqadkyu294rkKaDRtMfmj7RYtyz39Dg+qMGRauXWv18d57lmfDBktb\nV5d7xpYtMz3Gj29ul6EMCxeaDSxcmHuGgg5z55qcNWty74GNGy1NY6OVQzVXt6Gup0wx/RYtyumw\nYkVOp02brG5XrrSjsdFkhjbPt4FgE+E5DGUNz9Lq1SZ32TJrx4YGu/+GDapPPjlSVc1egq2q2j03\nbza9GxtN9po1Vr91dXZ99Wqzg/fes2sbNuSex8bG3PtqxQpLt2iR2Wf+Mz59usWFenn33cLP+Ntv\n594V69fbu2bjxtwzvmKFPTPBljZvNpsLz6WqPUuq1kaqln7z5tz7qL7eyjV7dvNnPNj9+vUmv6nJ\n/kc0Nm75/gjP+NKllm/6dKvv8IyHOgjP+MaNOVsNer7+eq4NmpqsTfKf8fCe/OADs5PwjIdnP/8Z\nnzrVwvBOzn9/bN5sMuLP+Ny5pueGDblnvL4+JzO8z8P7IzzjEybYebHfUo5TDpHPVxWfuZJDtApD\nYCLyPeB2SN5pPSQFblPV2yq+aRURkb7Ae8AVmvd9eRE5Hps6f5aqPh3Lo9WoO8cJDBs2jGHDhrW2\nGs5WgtuTU23cppxq4zblVBu3KafaiAiq2oKLStJRrdUgjwLzMaf8fuBu7Hvt+WwApqpqPL4t0Bvr\ncJgci58RhX1qq47jOI7jOI7jOI7jVMlpV9XpwHQAEbkA+D9VjTvAbZmwl3VdLD6cd6+hLo7jOI7j\nOI7jOI4DUJ3p8e0dEbkYeBA4KOqACPEdgc3A91T1J7E8XnGO4ziO4ziO4zhbMVvT9Pj2zpoo7BGL\nDx9bWhXP0BYaz3Ecx3Ecx3Ecx9m6aYlPvrVHZmPr8fvH4sNHgabjOI7jOI7jOI7jODXGnXZjMrAC\n+GIs/hJgNTC65ho5juM4juM4juM4H3l8ejygqk0ichdwu4jcC4wAjgJuAG5W1fpWVdBxHMdxHMdx\nHMf5SOIb0UWIiAA3AVcDuwNzgHtV9Z5WVcxxHMdxHMdxHMf5yOLT4yPUuENV+6pqF1U9oJDDLiIX\nicgkEdkgIgtE5Kcisl1r6Oy0HiIyUEQeimygXkTeEJFPx9KcKiJjRGSdiCwVkd+JyI6xNB8XkRdE\nZI2IrBKRR0WkVyzNviLylIisjNI9LyIDYml2ifRZFt1vtIgc33I14LQUInKpiDSIyPJYvNuTkxox\nrhaRd6J31FIR+U0sjduUkwkR+Q8RmRi182wR+aWIfCzvutuUUxQROV1E5ojI8wWutSn7EZGuInKP\niCwU+90/QUTOrmZ9OJWTZFMisreI/Dp6V9WLyBQROb9A/vZhU6rqR8oDOB9oAn4FnAXcin0S7let\nrZsfNbeFscDjwEXAZdjMjHpgQHT9uMg2HgXOBr4J1AHD82TsG8W9AHwB+CqwEHgb6BCl+RgwD5gY\n2d+l2MaIC4AeUZptgEnALOArwHmRfmuAfq1dV35ksquuUdvOAhrz7MDtyY+stvRbYCPw38CZ0Xvq\n3rzrblN+ZLWpb2C/gX4c2dR3gQ3AY25TfqSwnx2A3wPrgeXAS7Hrbc5+gOHAMuAa4PPReQPwidau\nTz9S2dQjka1cBlwY2UkjcEp7tKlWr/AyGuiiqFI2RJX1U2C7IulPB/4BrATWAk8B+xZI93VgGuZ4\nzcK+zR5PMw14KhZ3N/bDqGdr140ftTuAvWLnp2M/Zn4QnT8PTIyluT56WRwWnf8mejF0zktzZiTn\nrOj8e5Gt756X5rAozdej84siuf+Wl2aXyJZ/1tp15Ucmu/px9I/iBzR32t2e/MhiR4OjNr2sSBq3\nKT+y2tVY4LlY3C+BtW5TfqSwnxuBfwL7AyPZ0sFqU/YDfCLKc05ems7AfOCJ1q5PP1LZVPy3+sFR\nmz6YF9dubKpdTY+PpjT8AdvN/QLg15iz/fOE9NsDfwGmAFcCPwJOBZ6TvCntIvIdzPl+FOtBeRLb\nlO47eWn6YkbxZOw2j2Pfc/9E5SV02guqOi8WNSMKd4ps6ySsgyifx7FPC54YnQ8BntfmGx3+HesE\nyk/zuqouyrv3JOD9WJq5qjohL81S4JW8NE4bR0T6YCNZ/4m9+EO825OTlcuwTu3fF7roNuWUSROw\nUyxuR2Ce25STgntU9WRVnRG/0Ebt57RI7rN5aeqxkVG3sbZBok1B8d/qeXHtxqbaldOOjT49rarX\nqOpTqvoD4B7gchHpGU+squuB/qr6TVV9TFXvAP4X6IdNw0FEOmNTvO5V1e9Hcr+JOeffEhGJxPUG\nFPs8XD7BAPpUt6hOO+PQKJyCbWTYiZitRC+PeqBPZFe9CqRpBGaTs6fe8TQRM2Jp3imRxmn7/Dcw\nUlVHxOLdnpysDAJeVNUmABGJfynGbcoph58DR0ZrRLuKyNXAOcC3cJtySqDFv8TUFu1nb+B9Vd1Y\nIM2OItKtSHmcGlDCpgqR/1ud9mZT7cZpL3ekO0Uvy7FA9wS5O2FTKcCmOICte8gnnHcvor6z9XMd\nNnXmUZJtBWxtS3egJ/b8FUpTR86edqlSGqcNIyLHYWv4bihw2e3JycrewM4iMlxE1gObRORdETkn\nuu425WRGVR/B1nJeACwG/gebPvoMblNOZbRF+ymWBtzO2iPXYzOG/hCdtyubajdOO9Ub6Q69LFPz\n5JIgV/Lkhun0Gku3KSHe+YggIlcAxwO3q+oSkm0FzF40ZRqidNVI47Rt7gZ+q6rvFrjm9uRkpQtw\nCDYt7wzgi9gP3z+LyP64TTllEE1hHoxtmvRHrKP6FyIyELcppzLaov0US5Okh9NGEZGTgYuB36nq\nW1F0u7Kp+JS5tkzFI93RFPoLgAmqGqYwpJW7Jgp7xNJ1isJVpe7vbH2IyFHYaMNw4CdRdJKtgNnL\nqpRpgqxqpHHaKCJyITAAuEhEQjt2icIdsQ00we3JSc9G4P9U9UchQkSmYFP3BmMbioHblJONPwMH\nAYeq6gIRGQo8g20EFT6j5DbllENb/N20BtgzIQ24nbUbRKQf1tE4AdsLLdCubKo9jbRXY6T751iF\n5jdYWrmzsZH3/rF0faNweor7O1sRIrIf8DQ2S+M8jbaBBD6Iwv6x9F2BnYFpqlqHPZxxewLYB/tS\nAZjdFUrTl5zNpUnjtF2OA7bHZvesjI5vY++bpcBVUTq3Jyct87E1ovmEpWLb4+8oJyPRj96zgGGq\nugAg2pTpemBXzG7Abcopj7b4TpoN7CMiHQukmV/GemqnFYgGbJ/DHObP5rdbu7OpcrbYb40D236/\nERgUi++MrU/49xL5r4vS3RSLD5+T2CUWf2CU/rTovAP2Xb0no3P1ww8//PDDDz/88MMPP/zwY6s+\nNgC75fmJB2BLhS6Mzr+B+ZOH56XZGesU+G50fjbmW56dl2Y74D3gvlK+cHsaaZ9NmSPd0eY7PwN+\nrao/iV2eHYWF5GqQG+3CexdwhojcC/CjH1k7/u1vFo4fb+G//mXhXXdZ+JWvWHjYYVvawCGHWPil\nL1l4550Wjhhh4ZtvWvjMMxbecYeFX/yihQcemJO17bYWHn20hVddZeH//q+FL75o4aRJFj72mIW3\n3GLhWWdZ2KdPTma3bhaecIKF119v4W9/a+HYsRa+8YaFf/qThd/5joWnnmrhHnvkZO66q4Unn2zh\nt75l4R/+0Fzm669b+LvfWXjddRaedJKFPXrkZO61l4VnnGHh975n4V//auGECRa+/LKFv/61hV/9\nqoVHHWVhly45mf37W/iFLzQvY9Ar1EGok1BH3bvnZPTu3bxub765ed0Hm3npJQWGfthWoe2OOcbC\n7bbLyQxtHmzgxz+28Omnm7dvsMOf/tTCL3/ZwkMP3dIOQ1zcDoOMoOezz1oYbL+QHQZdP/7x5mX5\n5S/zy7qlHQ4d2ryuQt3l12mo42uvtfD//b/mNjNunIUPP2zhjTdaeNppFu62W05m+DvYYUj74IPV\n7XDceect67vUMX585fcdOnRo6rRjxmTXsX//2nbcNjRk1xGUxsZSHcJ+pD+GtgEd/Ni6jqFtQAc/\ntqbjm99M/7/PDz/SHBGNwN9F5AIRuRxbGvQW9mlxgP8DlgB/FZEvi8jF2Aj/auBXUZqnsZ3rfy0i\n/y4iX4zk9AT+ixK0J6d9MrAC21Ann0uwChktRu/8iyJyAraO4a+qem0BuaOxhigkd6aqvp8X9xPg\nZuBzALl2NMJ5PD4N5eRJKyucNzY2D8uRGZfd0JBdZpKsuJ5BdhZKyWxqKl9WS+pVTv2lPc9CNWSV\nei4qscMkKrHDpPNK2LABROxYtix7/iOOyOVvScI9jj02e97p03P5N8Y/XlJFHn7Y7rFNmTuwdOxo\n+R9/vLp6OY7jOI7zkWEItl/M7zAHezQwRO3zcKjqKuBTwCzg3uiYB3wyukaUdgi2SewdkawOUZr3\nKUG72YhOVZtE5C7g9mikewRwFPaJpJtVtV5ErsN2Mj1TVZ8Rkd2Bp7B1oQ+LyJA8kctV9Q1VXSYi\nDwDXikgdMB44Bduw7vy89Kh1t9wB3CEiGndGgkMYHIc0P7jjMuKyQhiXFc4L3SNJZnBs4k5nkuz8\nvEmyN28uLDNJdiEZ8fhK9ExyvKqhZ8gbZCW1QTltEnc2i9lOkqyk+itmO6Uc11KdHZW0b6jHJIq1\nb1x2vH0refYq4dvfhjvvrFxOPqEsy5fDTjsVT5uGpUth110rl5NP584W/vCHcPPN1ZFZ7Q6LL3wh\n93dTU8t3iDiO4ziOs3Wgqq9iexAVSzMVOLVEmvnAueXo0G6c9ogwtf1q4ApgDnCjqt4Txa/C1hyE\n3ff6YhvP9WDL77C/CwyM/r4O26DgSuBj2JT4i1X10WLKhB994cf+pmjrurgDlsUZCDKDjCSZWWTH\nZWYZKS71w7acUdNiHQ61kllJm1QyQhzXs7nMwVukz6JncKxL2UwW2eXYYdq2KMcOk2RX0jal9C2F\nKnSowZylnj0tPOMM+NvfSqcfPHhws/MTT4SXX66+XvnccosdUJ5j/O67cNBB1dcrTmivmTNb/l5b\nF4NbWwFnq2NwayvgbGUcc8zg1lbBcVqE9jQ9HjXuUNW+qtpFVQ/Ic9hR1YdUtauqvhKdj1HVjgnH\nwLx8m1T1W6q6ZyT3MFX9c1q9gvMRpoiWM326lMxqTCWOO2DV0LOSKeNJxEefKxkRizuypUbvC1HJ\nrIK0eiU57Vlkh/qqph3GZVajTeJ2mGXJQhLx2QWhzcrRc+hQOOSQ0umef97k18Jhz+fpp3PT0ou1\n8eDBg9m8OZe2pR32OB06pL9v0LEWDns+++1X2/u1fwa3tgLOVsfg1lbA2co49tjBra2C47QI7cpp\nb6skOdhZRsGTRg7r65vfI5BFdlzPJJnFZCdNsa5mh0XcOQ5hJdOWSzmyxWSHMiY5/mmmV5dq37R6\npZGdti3SlDkuM24zcRnFnOMkOwx6lpomn4akGQFpyhpn1iyYPDnnSK5bt2U+ERgypHD+WtKpk+ny\nwAPN4++5x+K33bZ19MrnxBMLr89fs6Y26/Ydx3Gcjw7+P8XZWmlv0+PbJKUc7EpktpXR+7jzExzZ\n4HBVY0ZA3PGKO2DlvIir0akQlxH0SnIIi+0JEGhr7RtI2miwEj1LyazEZuJLU6o5MyXQrVv1ZLUU\nl19uR1vHf0w5juM4LUk1N5V1nLaEj7RXgSTHppK1sqWcpSwy4xuAVcNRjDud1XCW4vrF9SznRZw0\nqptlSnapssbbIo2eSR0B8Y0Hs7Rz2o6ASmRWw8GOd/RUYodJNlNNp91xHMdxnPaBO+3O1kpNnHYR\nuU9EzhKRNjBZs/rEHZu4Y1jsBZI0xboaI9jxT2xVczSylINdDvEZC9UcvY/LTKNnUmdHOWUtNdpc\nzvT9pF3jK+mgCCTNpChnyn0gboehTSrZPC6pQyVL+zqO4ziOs3VQjX1yHKctUquR9qOBJ4DFInK/\niJwsIu1+lD/Jwa7E2UxyQipxwLKOiqcZiW0Jp70lRk2roWdaxzDLCHZL1l81Oj2S9KvEDqtZ5lId\nKtXYMM9xHMdxnPaFO+3O1kpNHGdVPRw4FPgF9m3154EFInKviBT95l0cEblIRCaJyAYRWSAiPxWR\n7Urk6SAi3xaRtSLyvQLX7xSRptjRKCJHFZMb36CsHGek1GZilTg4SZuJhbDU2uxCsgLxEexSo6ZZ\nRmJDWauxR0CSzCyObCnHMM1GdIFgM9VwsEvZTNLmcVnaIt6+SW2SZiO6Uh0B1Vj+EPSrxiaGjuM4\njuO0Lttttw8gqY999xVE/PCj+LHPPvu0ij1XQs02olPVycBkYJiIHIh9WP5c4BoRmQv8CXhYVd9O\nkiEi5wN/AO4DbgUOA24BugLXJOQ5EHgA6A10AToXSLYz8BZwI/bUAygwsViZkkZgK3E2S8ms5qhp\nGkdRYt+iL6VnOSOxpWYsxPcISEOSfuWMkic5sOWsGc+qVxoqad9y9SxnE8OsHVJp2jup8yPJac9i\nQ47jOI7jtC4bN36Aeg+8U2WkHf4gbK3d4ztiznM3zEluBL4GfFtERqvqCQn5fgA8rarBQX9KRHYE\nviYiN6vq8gJ57gTGA0OAVUV0WqGqI7IUopqj4kkyqzHVOaujmP9uTPr0WJKDnaRnsWej1IyFckZN\ns7ZNMdnVmNYdyp/WGc5S1rTtK7FZHcVoyY6AuE0nySxndkZ8Y79i7RyvB59S5ziO4ziO47RFarau\nXET6icj3ReQtbFT7P4CXgE8D+wF7AFcBqxPy9wX2B56MXXoc6AR8IuHW56nq11S1rvJSNKeaDna5\no5G1lpm0QVklTl3SFPa4A1bOlPb46Hi8zOXs9J6lrKU2i0ua1p2F1hi9z9LpkSSznM0BA6XKnKVt\n4vo5juM4juM4TluiJiPtIvIKcCzQAPwDuB34m6rW5yVbB9wfHYXojU1ZnxyLnxGFfQplUtUNGfTs\nAmxW1VQ/31vCWSo1GlmOY5M0gl3OqHhaZymLnqUcxPiIexqyOnWVlDnLCHEpWfFPv6UhrYPdkjMV\nilGuHVaiXyUyHcdxHMdxHKctUavp8XXAdcAjCVPY07BLnqy4bIDuZcoF60w4Ces4QEQ+AH6jqncU\nzZSwnracEeK0086zOGClOgAqcbCTRrDLcWRLjT63tlNXSedMvDOg1GhzNTs94hsOppl6H59JEdev\nnI3oWsIOS+39UM6+Ev5td8dxHMdxHKctUiun/V7gZVVdE78gIv2B7qr6ZgkZYYf4uMuxKSE+Czdj\nm9V1BHoBXwJ+JCKqqj9JypQ0cljOtN9STmbcAUszEpvVsUmz+3laPSvpCGiJKfeVOIppp9qnWTNe\nTQc2aS+ASjoC0nZ2ZLHDrDMpgh2WszlgJbNIfKTdcRzHcRzHaYvUyml/GvgM9qm3OEcA/wPsWkJG\ncPh7xOI7RWGxTeaKoqqLgcXhXET+AkwCrgcSnfYxY4YB8NprAIPZuHEwsKXzlMbZTOu4VuKAlZoR\nEBywQhvRldpIrSXXJ5fTEZA0Y6Gcte3VmH4erpWaIt4S7VuNpQCVdASU2rMgvp6/mh0BPtLuOI7j\nOI7jpGXUqFGMGjWqtdXYghZ12kWkA3kfT4zO8+kGHIONcJdidiSnPzAuL75vFE6vSNk8VLVJRKZh\nHQ2JDBw4jIkTYcAAmDixMsch7chwFgc2rWOTRs+0m9plGYlNuyQgSb9CzmdIk3b0uRrT49Nsdpak\nV1L7ZunoqUb7BtLOBIiXvZJOj5Zokyx2GF8S4DiO4ziO43w0GTx4MIMHD/7w/Lbbbms9ZfJosd3j\nReRGYDM2fV2BZ6Pz/GMlttb9rylETgZWAF+MxV+C7Tg/WozeVdC9K3AUJb7TXk1nKWnUtBJnsyWc\n9rQOdtLU8Ur0KzYjIFBqVDep8yPNtOlS07qLOYZpncwsbZFVZjVspppLP0qN3hdq30rtMI3NOI7j\nOI7jOE5boiVH2kdh31UX4Fbgj8DMWJoNwFTgmVLCotHvu4DbReReYATmWN8A3Kyq9SJyHfALETlT\nVZ8BEJEDgH0iPQD6icgQ7Lvs40TkIOA+rOPgA2Bn4KvAbsBXiumU5ISUs4FaSzh1aafcpxm9zzpt\nuhxnKW2HRTE9s3YExB3uSvQs1s6l2iIen+bb9GnLmmbvgiSZlay9z2qHaWQmreMvZTtZbMZxHMdx\nHMdpeWbPns3hhx/OSSedxFNPPdXa6rRpWsxpV9VxRNPYReQC4H+iuEoI68uvBq4A5gA3quo9Ufwq\nrCMgf337LcCFQa3o7wuBd4GBkYzJ2Pr1vYH1wIvAVar6VjFl0johwcEo5ixlHTks5iBm7QCIy8wf\ndQ5/Z3UQ0+iZdif6lnTas8hMO/uhkhkAafSK7y+QtEY8qYOi2Ah2NaeyZ7XDeOdHmtkZaW0njZ5B\nhuM4juM4jtPyrFu3jjVr1rB06dJm8WeffTYvv/wyy5eX+9GxrY+abESnqgdWSY4Cd0RHoesPAQ/F\n4i7BptAnyVwHXFuOPmkdsOC0F3LAso5spnHA0k5HTuNgJ03FLiWrktH7UnqnkZm2HotNk8/qyBZz\nsLNOEU/TJqVsJ8vSirSbAcbPy7HDSto3a6dCllkQPtLuOI7jOI5TOwYOHMj8+fPp0aP5PuOrV69m\n1aqy9xjfKmkxp11Efg/cqqofiMivge2LJF+nqte0lC4tRbkj2flkdVzjn34r5OBkdcDSdARUOnpf\niGrOCAik3TU+jcNdaip2koxKHP80Dmy59VZMZrkzFFpCz3KWVJTTNgF32h3HcRzHcVqH3XffvbVV\naBe02EZ0wCeBntHfJwInFDlObEE9WoysjnYWZyntOvRCTnE1HbBKp8dXYyQ2yWnPlxniSi0zyLLv\nQDU2emsJZzjrZnEt0XlUSdnTruuvZn1m6ZByHMdxHMdp6/zlL39h2223Zg6ZbAAAIABJREFUZc6c\nOTz11FMMGjSI7bffnv79+3PfffdtkX706NGcfvrp9OzZky5dujBgwAB+9KMfsSmMTkXceeeddOnS\nhcWLF3P55ZfTpUsXevfuTUPej9K0sjZv3swPf/hDevfuTefOnTnooIO49957m6Xp0aMH55xzDgAP\nPvggHTp04MUXXwSgQ4cOdOjQgSFDhrBq1Sq22247TjnllIL10b9/fw4//PDsFdlOaDGnXVV7q+r4\n6O8Bqtq3yDGwpfRoSarp2KSdzp1lhDPrWvE0jmLaKdjFyh7/xFZWp7MlRp+LtU3aTc8KjRCXq1ex\nTpms6+PL6TxKazvFZJa7jr8aHRbxe4U2TGOHjuM4juM4bZ0NGzbQ2NjIzTffzDnnnEPPnj25+OKL\nWbZsGddccw3333//h2kff/xxTjrpJF5++WVOO+00vvzlL1NfX88tt9zC5z//+WZy169fz6ZNm7j4\n4ot54oknuPTSS+nXrx9N0Q+nLLJuuukmhg4dyj777MNll11Gz549ueGGG5qlWbNmDatXrwbgyCOP\n5Bvf+Aa9evUC4Jvf/Cbf+MY3uOSSS9hxxx059NBDGTlyJPPnz28mY8yYMcycOZPjjz++OpXbBqnJ\nmvYkRGRHVW23CxZq4WxmGTUt93NYaRzstFPE05Q97brzLGvwy93dPq5fvsMdb69SMgt9E7zcGQrx\nEeJ8WmP0vpodPeXYdtzBztp5FF+Dny8/ntZxHMdxHKc9oKo88sgjPPbYYx+OVl955ZWccMIJ3Hrr\nrVx++eWsX7+eq666iu7duzNu3Dj23XdfANauXcvJJ5/M8OHDeeKJJz7MH+SOHTuWV199lUMPPfTD\n+Kyy/vjHP3LCCSd8OHIOMG3atMTyDBw4kLvvvpsJEyawYMEC7rrrrmbXL7zwQsaPH89jjz3G9ddf\n/2H8448/johw3nnnlVuVbZ6WnB7/ISJyiYj8T955fxF5H1guIpNFZP9a6FFtynUM86nmCHEppz3J\n0Sm2jrrcDoBiDna5m+8VG90tV2axssdHucsZdc66gV+S016s/rJ2BJQz4yNLB0qww7QdPWk2May0\nU6aYHcZlOI7jOI7T/hFpvaOW3Hrrrc0c7qOPPpoTTjiBhQsXMnXqVP72t7+xcuVKrrrqqg+dbIBu\n3bpx2223oao89thjzWSKCEOHDm3msAOZZXXo0IGNGzeieaNiBxxwQNllPf/88wH461//2iz+ySef\nZLfdduPEE9vliutU1MRpB4YBs/LOfwXMBT4P1AM/SytIRC4SkUkiskFEFojIT0VkuxJ5OojIt0Vk\nrYh8LyHN10VkmojUi8ispHT5ZHUYYss8gPROcZKDnT9yWKkzV2hKdqWdCoWc4XLX8ZfjgGWVXUhm\noJp6ZdUzjQNbCzvM0r7VnJ1R6UyALJ1HjuM4juO0f1Rb76gVIsLee++9RfxRRx0FwNy5cxk/fjwi\nUnDqeIibMmVKoox8ssq64oorGDduHJ/+9KcZOXJkylIl06tXL0444QTGjBnDwoULAXj77bd57733\nOPfccyuW35apldO+B/A+gIgcApwEXKaqTwH/DRyXRoiInA/8ARgNXAD8Gvg68PMieQ4EXo3SdQE6\nF0jzHeBu4FHgfOBJ4PYoPpFyHe180o5+J8ku5IBV08FOO8KZZWlAuZ/tqobMUvWb/6KNT02vZv1l\nLXs+paaIp+2gKKdsaabxhzosV1Y5nTJp75GmbRzHcRzHcdoz4RNqy5cvZ+XKlQDssssuW6Tr2rUr\nnTp1Yt26dankZpX1wx/+kJ///OdMnTqVT3/60xx++OGMGTMmc3nyufDCC2lqavpwRP+xxx5DRNxp\nrxKTgUtF5FDgF8BwVX0/utYJ6JhSzg+Ap1X1GlV9SlV/ANwDXC4iPRPy3AmMBw4CtpiwIiKdge8C\n96rq9yO538Qc92+JJE9yKdcZKSSj3I3oKnHAksJC66jTlrWSqc5Z66KcteNZnON43abtACjk+Jer\nT5r2LXcpQDGZrWGHSZvHZZFZquMizTIDx3Ecx3Gc9szy5csB2GGHHejatSuQc7jzqaurY/Pmzey0\n006p5JYj67rrrmPu3Lncf//9zJ07l1NPPZVZs2ZtkT8t5557Lttssw2PPvooYOvZd9999616ajzU\nzmn/DnAKMAHYH/hG3rULgJdKCRCRvlHeJ2OXHscc/08kZD1PVb+mqnUJ148FuifI3Qk4OEmn4JCF\nH/8hDI5BPL7YZl3xPEmyk9IXkpkkK75OuhyZacNCn2eLf7s8KU8pPfOJ58naJsVkBkrJLLZWvFSb\nxGVkaYtSZaukfbPWZyUy0zwvWZ+LuF3kd/TE7bBQZ5XjOI7jOE5bRVVZvHjxFvGvvvoqAAMGDODA\nAw9EVRk9evQW6V5++WUABg0alOp+5craZptt+MpXvsLPf/5z1q9fz7PPPlv0Ph072nhuU/4Pt4ie\nPXty8skn8+qrr/Lqq68yefLkrX6UHWrktKvqKKAPMAjor6rv5V1+FrguhZjegGKj9vnMiMI+Cffe\nkEIuCXIlSa7jOI7jOI7jOE5rMnToUP7xj398eP78888zduxYDjnkEPr27ctZZ51Fp06d+O1vf8v0\n6dM/TLdixQpuvfVWRIQvfelLqe6VRdby5csZMWJEs/zLly9HVdlmm+IfMAvT72fMmFHw+oUXXkhj\nYyNXXnklIsIXv/jFVPq3Z2r2ybfo027jC8T/KqWIsHgiPmIezruXqVpLyXUcx3Ecx3Ecx2kx9txz\nTz73uc9x+umn06NHDx577DE6duzI3XffDcAee+zBbbfdxve//32OPvpozjjjDDp37sxzzz3H/Pnz\n+c53vsPHP/7xVPfKImv27NkMGTKEww47jEGDBrFixQqefvppevXqVXJkfPDgwTzyyCOcffbZnHji\niagqv/nNbz68fvbZZ9O5c2emTZtGr169turvswdqNT0eEfmUiAwXkbkisllEFonIH6ON4tIQdoiP\n78m4KSE+LS0l13Ecx3Ecx3Ecp0UQEW655Rbuvvtu3nrrLR599FEOOeQQnn32WT71qU99mO6mm27i\nwQcf5IADDuCJJ57gz3/+M3vvvTcPP/wwP/7xj5vJ7Nq1Kx07dmT77bcveM+0so444gjuvPNOGhoa\neOihhxgzZgznnHMO//rXv+jZM7cVWbdu3ejevfkY6WWXXca1117LypUrefjhh9ltt92aXe/evTvn\nnnvuR2IDuoBoDb5LICKfAZ4GlmDT4ZcAuwKfA7oBJ6rqhBIyzgSeAI5R1XF58Z2B9cC1qvrrEjKa\ngNtV9da8uOuxT87trqpL8+IPBKYAn1XV5wrIUhhKhw62TnabbQbT0DCYbbe1DbQ6dbI1suE8hCG9\nybB1tdttZ5tgxfMmyYjH5xNPW0qfEMZ1KCazlH5JsmulZ1xGyJNWdjX0zCfp/kl6Jt0rn1L3L1UH\nHTvm1nsHm0xbtjT1lrZspcJCMtM+F1lkFkvjOI7jOE5rIdTCV2mP/P73v+fyyy/ngQceSD29fWvi\nyiuv5IEHHuCVV17h2GOPzZRXJNmuRo0axahRoz48j74/n7gxea2o1Uj7UOwzbf1U9UpV/Z6qXgn0\nwxzjO1LImI2tMe8fi+8bhdMpj9lRWEiuFpc7jO23HwYMo0uXwYA5J8XC/CUc4e94ms6di8uIp+uQ\n14pJMrOG+Wy7beH7ptWzkMy0ZStVJ1lklgo7dcrJCt8MyKpnS+sV2jpr3Rezw2ivj7LLGsKWtsNy\n9YvHp5HpOI7jOI7jtE3q6+t59NFH2WuvvTI77KUYPHgww4YN+/BoK9TKaT8U+IWqNvuwVrRJ3C9J\n3vk9n8nACiC+08AlwGpgtBi9t8hZnNFAY4LcmXmfpitIknOZ5EB0zPu4Xfg7rbORJDtfZnCW0jq9\nSfdMIzOrw51PpR0BIX8+WZ23Ym1TqkMlSVYhBztrR0Q8vlhHT7m2U8gOs9pKsY6Acp32YrYd6rZc\n23an3XEcx3GcrYmP6iyEJ554grq6Os4///zWVqVm1Gojunog6TvqDRT4fnocVW0SkbuA20XkXmAE\ncBRwA3CzqtaLyHXAL0TkTFV9BkBEDgD2ybtHPxEZAqxQ1XGqukxEHgCuFZE6bLO8U7BP0ZW0hNZw\nbAo5imH6dFoHrJTjkz99Ou1IbJqR4kDcac8axp3OxsbyR4iT2mbz5uwy4no1NZXf2VGso6dSm6mk\noyfeAVCso6dSPbfZpnp2WKijJ6kTxHEcx3Ecpy0T1p5369attVWpOcOHD6d79+5ceumlra1KzaiV\n0/4v4FsiMlxV54ZIEdkN+B7wRko5P4nCq4ErgDnAjap6TxS/CtgQhYFbgAujvzX6+0LgXWBgFH8d\nsAa4EvgYNiX+YlV9tJRCWUcQy5mWXI4DVu7IZnCw86c6h7hKHK84lZY9v8xhKnvaUegk57MabZMv\nI+hVzfqrls20ROdRIT3TtkGSg51vh+V2SBWymSSZIXQcx3Ecx2nLnHvuuR+ZTdjiPPjgg62tQs2p\nldP+bWwa+kwRGQ0sxjaiOxZzpK9II0RtDsgdJKyBV9WHgIdicZdgU92Lyd0EfCs6MpHVKS7mYGd1\nQgo5NpU6YOU42FmmncdlluvUFdIzbdnTOHVJHQGlZGZxDMuRGdo63hGQdYp4lo6AUrILdfSUcrDT\n2kz+rK9KO6Sy2IzjOI7jOI7jtBVqsqZdVWcBhwA/BbYF/g3rMHgAOEJVX6uFHi1BJY5NpQ5YrRzs\ncp3hYo5sS3QuVFNmcBazOtxp2iSrA5ulnUvJLtTRE/4utWShGh0o5azjLyUzbZil88hxHMdxHMdx\n2go1GWkXkS7ASlX9fi3uV0vSbhYXHHMpsHo/rYNTbA1xIMkBy9oRkK9n3LHJOjpeaE17WqeznBHt\nrOv4CzmygazOZ7EOiqxrxkO9VbOshZzh0NZpZz+kKXNSh1RWPfPtsNSGiOV0fvhIu+M4juM4jtPW\nqdXu8R8AX63RvWpKJSOwgXJHEAs5NmlHiEuNmuZPS650RkAhp66UrDQb5cWpZj2mXScfd/yrqVeh\nzo5KZRbSL7R1uTMqitl2uUsDCukZ2iRrh1SxDgvfiM5xHMdxHMdp69TKaQfYWMN71YxKnJBApY5N\noa89lPvt7WLOUjUdxbRTsmtZj5XIbIlOmWJ6VdpBUQ2ZxWY9tISeae0waUlAFjt0HMdxHMdxnLZC\nrZz2/wT+S0ROr9H9akbWEex84iOc1VjrHMj6bfVKHNhSZS60JKCUzFKjuoVkBllZR2LT1GOpzoVi\nMpJGiKsxgp20ZCHL9P24zLiMatRfWpupZkdKfJlBMTuM6+k4juM4juM4bYVaOe0XA03AUyIyV0Tm\nxI6paQWJyEUiMklENojIAhH5qYgk/tQWkW1E5DYRmS0i9SLyrohcFUtzp4g0xY5GETmqlD7VcEKy\nfqqsJRzschybckbtA1mnZKeRGdezkg39AmnXZhfTK75mPG2bpGnfUh0UWToCWnNGQLG17HGyrr0v\nVua4THfaHcdxHMdxnLZGrT75NhtYVOT6hjRCROR84A/AfcCtwGHYd9i7AtckZLsH+DIwFJgBfAG4\nT0TqVPWRKM3OwFvAjUBwFxSYWEqnSnbDDpRyDOMjnsUc7HK/WR53vAqtk0/rDKeZNh3IqmcaB6zU\n6G6aDf0CoR5CG5XTQZGkVyVlLfcb8NVcXlCOg11Jp0KgGpsDJunpTrvjOI7jtB22264PUuyHhuOU\nQZ8+fVpbhczUxGlX1SSHOis/AJ7Ok/eUiOwIfE1EblbV5fmJRWQv4Erg26r6syj6SREZANwEPJKX\nfIWqjsiqUCnHJoyEpnnfpB2VTDOCnTQSm9ZZKrROPutIbBqnM2nUNMvu5y2hZyh/CIPTXg29qjGT\nIpDW0S70ycGsMuOOdzWWP1SjU6aaHT2+EZ3jOI7jtB02bpydKX2h36+OszVQy43oABCRTiKyl4hs\nnzFfX2B/4MnYpceBTsAnCmQ7BStjPM8TwKEiskMWHQqR1fHKf5nEnZ20Dk4aZy7tSGyWUfF4R0A1\nnKWsTl1LOLJpOlRCmrbiYJeacl9OWwTSdnoU+tJAkp5pZwRUox6rMQvCcRzHcRzHcdoKNXPaReQr\n0dr1Ddgn4FaLyKsi8smUInpjU9Ynx+JnRGGheQ69gQ2qOishT++Yjl1EJNPsg2qMigfSflIt7oBV\nsg69Gk5dJXqmXScfd2SLOYrVKHPQNWl6fNzBLscxrMQZDtfC/dNOO0/TA522fdOUOVDNzoXQFmlt\np5zRe8dxHMdxHMdpK9TEaReRLwH3A+uBYdj689uwtej/EJFPpRCzSxTWxeLDefeEPGsKxMfzNAAn\nAeuATSIyS0S+m0KnzE5INaYSFxu9L1dmGgc2UMpZCiPx8ZHifD3D32lHYrN0KmStx0pG2rM4w9Wc\nSdEWZKZpk5aYEZB2E8NyZlKEPI7jOI7jOI7TVqjVRnQ3AH8DzlHNuTci8iNgOPBD4IUSMsIYWNw9\n2pQQH/IUio/nuRl4AOgI9AK+BPxIRFRVf1JMqaQRzizTzrN+Bz2LY1FqHXWaNfdxh7SUA5bFwS5X\nZjF94/WZtAlaFj2TNqLL4gzHNxysZH18oNTeBeV0UKRde5+m7EkOdrxNkjp6ilHKZrK0TVym4ziO\n4ziO47QVauW0HwAMzXfYAVRVReT3wP9LISOMmPeIxQcXdlVCnkLr1pvlUdXFwOJwUUT+AkwCrgeK\nOO3DeOkl+2vBgsHA4KqMEJdylipxbMrZTCxOqd27s4zAlpIZr8cso6ZpZRYjPiOglONfzoaD1ehM\naImOgKT2jTvYxb5iEKhGW8Qp1blQiW07juM4juM4Hz1GjRrFqFGjWluNLajVT9TVwF4J17oAm1PI\nmI19jq0/MC4vvm8UTk/I00VEeqnq/Lz4ftE942vdAVDVJhGZBnymuErDGDIEXngBDjrIYuIj2Fk2\nokv69Fs5jleglLNUzshiKWepJWWmmYZeavp0fJQ8jcykNe1ZlioEqtFBkXYTwyy7xsdlJS1ZiOtX\nqMzxuJa0maTODx9pdxzHcRzHcbIwePBgBg8e/OH5bbfd1nrK5FGrjeieAW4WkWPyI0XkcGxt+8sp\nZEwGVgBfjMVfgnUKjBYjf3O5lzBHP57nIuBFVd1Y6EYi0hU4ihTfaU9ywLKM4sZHc5McnCSZadbJ\nJ02frsZU9kpGikvJLGcaepLMLGWPt0mpNe1ZZgAk6RUfwU6zEV2p6edpHOwkkpz2ctqi1N4FWWwm\n7Tr5ctan+0i74ziO4ziO09ao1U/Um4BjgFdFZA42FX1XbMf35cC3SgmIRr/vAm4XkXuBEZhjfQNw\ns6rWi8h1wC9E5ExVfUZVJ4rICOCHItIRG1k/DxiEbTyHiBwE3Af8FdvVfmfgq8BuwFdK6ZXktFcy\nYpfkjCTJrGR0t5JR3Uo6ApJGYpMc2WqMxFbSqRAfaY/rVY0ZAJU4jC0hM8kOy9lfoJp6hnpMssNy\nZlLE9XMcx3Ecx3GctkJNfqKq6lIROQK4FPgk5hhPB34N3K+qy1KKCuvLrwauAOYAN6rqPVH8KuyT\ncvnr288D7sY6DrYH3gY+p6qvRdfnYKP41wN7YzvcvwhcpapvlVKo1LTkNKR1bLJsJhanlMw0o7pZ\nZcYRSZaVtDQgyw7gcbJ2fsR1LXReTWe4GtO5S63fL2fNfVY9s9hO0oyKSjq5qtlxlmUZgeM4juM4\njuPUglqOK3XAdm2fj31abSEwDRtpT0W0kd0d0VHo+kPAQ7G41ZiDnyRzHXBtWh3iJE1LLua4JutS\nWGZbcxQDcT1LOdjF1vMnyYx3VFRjGnoWRzboHJy5UvsLpNEvrV5ZqEX7VrLRYCBe9kpmUgRK2WE5\ntuM4juM4juM4bYVafaf9aGxE+wHga8BngRuBx4GpIrJfLfRoCSpxwNI6ri0hM2mDsnJGYqvhICaN\nGJez0VtcZnxn9XKc47Qj7Vk2tYvPKogvVajGkoWWGMGupMMivl9DORvllZJZyTKSYnzta5XlrwVv\nv93aGpTmu99tbQ0cx3Ecx3HaF7WaDPpLYCVwItBNVfcCugGnA92BX9VIj6qT1tksRNyJSBohjo8c\nJuUrdC0Q168SpzOrzDQkzTKoxhrjcjoX4hvRxde0VzKVPRB3NsN5fJO5YvoFkj79Fq+/LO2bZIdJ\nDnY5eytU0oESqGbnUZw//cnKpQr33mvh2rXVk18tgo4HH5z7u62xfr3p9eMf53T83e9aWyvHcRzH\ncZy2T62c9oOA76vqq+Fb7arapKrDge8Ax9VIj6oTdzazTBFPuhYfIS61WVyaH+jxkdi4Y1ONdfJZ\nnKUknePTvOOObDmyKxm9j0+Pr8QxLLW+uxJnM8kOKxnBjncEJO1/UMh2SrVv3GmvZvtW0lER0j73\nnP19wQVbpunaNed0hnu2BmPHFnfQw7UXX6ytXvnssUdOjy5dtrx++eV2bXOaj346juM4juN8RKmV\n0z4R2Cfh2i7Y+vZ2TXBGylnLnkRwPspxbJLuH3fAAuXIjjvY1RjhbMmR9njZs0w/LzU9vpKN8uKf\neiulSxqZ1RxtDmWtxrTzeEdAS8ykyPIZviTSdnbU11v5Z8wo/15ZaWqyex51VLr0J55o6ZuaWlav\nfGbPtnsuWJAu/Tbb5Jz7z32uRVX7kAsusPvV1dXmfo7jOI7jOOVSK6f9P4HviMgJ+ZHRWvebo+vt\nmuA4ZJl2XmoadCUOWJLsUnpmkZ00I6ASgrNUypHNQrzzI5Bl+nkoa5Je5bRvKWe4kOy4zKRR52q0\nb3z0vhrtG/9cW5YvIiTVY3yJSjmfequU/fZr2Wnpd92Vk19uZ0T4eoMq3HZbdfULBPl9+pQv45ln\nTMby1FuUZmPVKpP/pz/ZuW8+6DiO4zhOW6dWTvu3gXrgnyIyJxzAKGxt+z158e8WEyQiF4nIJBHZ\nICILROSnIpI4SVVEthGR20RktojUi8i7InJVgXRfF5FpUZpZIvK9NAWLOwzxUb5iP+JLbTiW5CwV\nk520Y3aSnkn5s8gup3OhlKxKfkgnrR3PMmOh1O7n1WzfpKUKWdogLjNL+6a1mVLtm69L0kyFuMzQ\n4ZOlTUrZTiUdPdVw4ILj+uijlcvauNFk/WeVuzVvvdXkbthQuaxnn22ZDouddqqu3CCrR4/qyHMc\nx3Ecx6kVtfrk22JgRMq09UkXROR84A/AfcCtwGHALUBX4JqEbPcAXwaGAjOALwD3iUidqj4Syf0O\n8GPsO/CvA4OB20WkUVX/q5iycYehGlPZA0lT2YtRajQyODTVnD6d5KClyRMf1S0lq5zPdqV1xCoZ\n7S1HfjkdFKXSVjKTIqkN4k57NT6dFuww6esFaTpDstpOGj2rOer6hS/kpqVned4GDYLXX6+eHsXo\n3DlXf/vvDzNnps/b2Fi779oHHcePhyOPTJ/v7bdtc75i+Ei74ziO4zhtnZr85FLVa1T1spRHkvMN\n8APg6UjeU6r6A8wpv1xEesYTi8hewJXYJnh3quqTqnopMAG4KUrTGfgucK+qfj+S+03gSeBbIsV/\n0n3sYxZuv72FO+xgYXB0evWycMcdc+fbbWfn++xjcV27WhimlIYp2N26WRg2cNp55+bX+/WzcNdd\ncw7+HntYuOeeFoZ1vrvs0jxv0DfI7t3bwrA+/cADc/LDfXv2bH4e7hlkBX1DWbt3z8nq0MGuh/v1\n7Vs4bQhDuqBvqOfddmueb+BAOPTQ5nlCHQR94rufh3KEet9111wdiJiu4X4HHNC8XkJZQ/uGeg7t\nHtp7zz1z7RvKFNo36BHuH/TbaafmZQ71H/Tt0QP22qv5fYI+u+/ePG+8feP2cOCBuXIH+cFGQhj0\nTGqjEO63X/N77rCDOYD5ecPoZgjjbRLSBdsK54ccAvvu21x+qIO4rYQ2Cm0X4kM5Q5317Gnyu3TJ\ntW/QJ9w/yKoGHTrkRnkvuyw53aJFlqZWDnucGTPs/vPmJaf5+tdzZamVw57PEUekW58f1v2Xctih\ndcrhOI7jOI6TCVVtFwfQF2gCLovFHx/Fn1Egz2VAI9A3Fn9zFL8D8Kno70/G0lwSxR+SoI/Onava\n1KQ6ZYqqquqkSRYuXKi6ebPqiy+qNjSoLlumunq16syZqitXqi5erLp8ucVNnaq6YoX93dio+tpr\nlveDD0zWu+/aPSZMsHDxYks7dqxqfb3JW7JEdf58u2+Qv2GD6ptvqq5fb3GqqmPGmIxZs+x8+nS7\n16RJpufy5aqLFqlOnqy6Zo1qXZ3q3LkWP3u26rp1dp+GBtVXXzV9V6wwWWPHWvj++xb//vsmY+pU\n07Ouzu47a5blWb9e9b33TOa775oe8+aZjBdftHDZMtP3jTfsnrNmWbnmzbMyv/++5V+/3u6zeLEd\nmzbZeUOD6ttvm4wFCywcOdJkL11qer39tuWfN890XLrU6n7BAtN/0yar+zVrLK2qXWtoMD2bmqx+\n6uqsHKtXmw4rVli9z5hh5Vi1yvK89prqxo1Wr6rNbWfkyJG6cKGVacwYu/fKlTmdliwxuYsXW5pJ\nk1TXrrU0TU3WJk1NzW2nsVH1rbfs3kuW2DFxouWvq8vV5dy5Jmv+fGuL116zPMF2Ro+2cPZskzl9\nutXblClWnpUr7b4zZlhZ162z9q2rs7hNm/TD5+Xlly1cscLCsWNN5uzZ1iYffGDXgn2sXWtlWbDA\n6mLjRjvftEn1nXdMRtx2li+36+PHm8x586wNFy2yMobz+nqrj1WrLL6xMSezJamrC65vy91jZDD2\nCgg6rl1buT4txb33mo7331++jClT7Hmqr7fzlSvt/fnBB2YL9fWq06aZraxebWmWL1d95RWz08ZG\ni/vgA3sOw3PT2GjvookT7XlparL3wcSJZoONjfZOmzo19y5StTQvvaQ6Z05Ox3nzLG75cjtvaDC9\n33rL8jU22rth0iT7X9DYaPGTJ5tNb96c0/vll3PvoCB71Ch7vlRl3/bhAAAgAElEQVRNp5AvyJ4/\nX/W3vx2pS5bk9J40yXRoaLB8q1bZu2L2bDsP/29eecXKpGrP3aRJuf8NqlbON9803Zqa7Bl/+227\n/6ZNlqauTvX11+29r2rlmTlTddy43P+hVavs/8X06bl8s2ZZvlWr7Hz1aquzmTMtzaZNJvOtt+xa\nU5OFr79uacK7YMkS1X/9y+o2tO306c1lr1plZZs1y+Ru3mzvv/CuDuUfN85kB5YutTaZP9/ON260\nen3zTSt3SDN+vNVVQ4MdwSY3bLA069dbmmnTTL+mJtP3tdfs/RnSTJpk/9fWrbO4RYvsPTx/vuUJ\nZRs/PpcmvCunTrVyNTSY3bz5ptmdqpVxwgRrt9C28+db2UIaVXtmXn/d6mLkyJEf2tK0abl6mzPH\n7hfsva7O2nbatJy9LVqk+sILzWW//76VN78tQ/usWGHlnDkzJ2PWrFz6YFdTp+beB+F//Pjxuf9h\n4Zl96y0rg2rumZ09O2czhd4H4V0TnqvwP3nBgtwz+8479vyFOly2zP63hf9zqvb3iy/mnquNG3P5\nguyFC+1eixfnZL/1lukQ3lkrVtjzmf8+mDPHyhLeB+vW2fM4daqVvbHR7j9hQu53WmjD8NtL1ep0\nzJjcb87GRmv70aNz7bp6tcmZNs3KoGp1OG5crt7C+2Dy5NxzvXq1pQmyN20ym502rTr/+xwnH3OX\nW98XFq32QsQWQkROAl4AjlHVcXnxu2G7z1+vqvfG8gwFblTVbrH484GHsen1RwL3A7ur6tK8NEcD\nY4AzVfWZAvpoe6k7p30wbNgwhg0b1tpqOFsJbk9OtXGbcqqN25RTbdymnGojIqhqqy+ma08TA6NJ\nu8Q/0BPOuyfkWVMgPj9POXIdx3Ecx3Ecx3Ecp8VpT0572CE+Pry9KSE+5CkUn5+nHLmO4ziO4ziO\n4ziO0+K0p+nxZwJPsOX0+M7AeuBaVf11LM/dwNUFpsefBvwdGAicCvyMLafHHwhMAT6rqs8V0Kd9\nVJzjOI7jOI7jOI5TFm1henytPvlWDWYDAvQHxuXFR/uQMz0hTxcR6aWq8/Pi+wGbgVlRGiK5S/PS\n9MVG2QvJbRON5ziO4ziO4ziO42zdtKfp8ZOBFcAXY/GXAKuB0WL0zrv2Eubox/NcBLykqhuB0dgu\n8YXkzlTV96ukv+M4juM4juM4juNkot2MtKtqk4jcBdwuIvcCI4CjgBuAm1W1XkSuA34hImeq6jOq\nOlFERgA/FJGO2Mj6ecAg4KRI7jIReQC4VkTqgPHAKcAFwPm1L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ddx2LCml/vCC7lvb5ri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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148cc2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampler=spotpy.algorithms.fast(spotpy_setup,  dbname='RosenFAST',  dbformat='csv')\n",
    "sampler.sample(100000)\n",
    "#spotpy.analyser.plot_fast_sensitivity(results\n",
    "results = sampler.getdata()\n",
    "spotpy.analyser.plot_parametertrace(results)  \n",
    "#spotpy.analyser.plot_fast_sensitivity(results,number_of_sensitiv_pars=6)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  },
  "widgets": {
   "state": {},
   "version": "1.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}