Bayes_PEA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Readme\n",
    "**Bayesian Parameter estimation**   \n",
    "Author: Wenfan Feng  \n",
    "Date: 2020/12/14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "end_time": "2020-12-23T13:05:18.885826Z",
     "start_time": "2020-12-23T13:05:17.154284Z"
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   "outputs": [],
   "source": [
    "# import pymc \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial\n",
    "from scipy import signal\n",
    "from scipy.fftpack import fft,ifft\n",
    "from astropy import constants as const\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from math import sin,cos,sqrt,atan,acos,exp,log\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "\n",
    "**定值用大写字母，参数用小写字母**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.894921Z",
     "start_time": "2020-12-23T13:05:18.887754Z"
    }
   },
   "outputs": [],
   "source": [
    "MsunInS = 4.926860879228572e-06   # solar mass in [s]\n",
    "MpcInS  = 102927125053532.6       # mega parsec in [s]\n",
    "OrbitRadiusInS = 1e8 /const.c.value    # 1e5 km\n",
    "MearthInS      = const.M_earth.value*const.G.value/const.c.value**3\n",
    "OrbitPeriodInS = 2*np.pi*np.sqrt(OrbitRadiusInS**3/MearthInS)\n",
    "YearInS  = 31536000.0   # one year in [s]\n",
    "AUInS   = const.au.value/const.c.value  # AU in [s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.916287Z",
     "start_time": "2020-12-23T13:05:18.898810Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1.325 2.04 1.02 0.658\n"
     ]
    }
   ],
   "source": [
    "Tc     = 0.49*YearInS    # chirp time\n",
    "# Tc     = 0.0    # chirp time\n",
    "Phic   = 0.954    # chirp phase\n",
    "\n",
    "# ThetaS = np.arccos(np.random.uniform(-1,1)) \n",
    "# PhiS   = np.random.uniform(0,2*np.pi) \n",
    "# Iota   = np.arccos(np.random.uniform(-1,1)) \n",
    "# Psi    = np.random.uniform(0,2*np.pi)  \n",
    "# print(ThetaS,PhiS,Iota,Psi)\n",
    "\n",
    "ThetaS = 1.325\n",
    "PhiS   = 2.04\n",
    "Iota   = 1.02\n",
    "Psi    = 0.658\n",
    "print(ThetaS,PhiS,Iota,Psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.931274Z",
     "start_time": "2020-12-23T13:05:18.922272Z"
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   },
   "outputs": [],
   "source": [
    "Z      = 1.0    # cosmological redshift\n",
    "M1sun  = 1e7    # solar mass as unit\n",
    "M2sun  = 1e6    # solar mass as unit\n",
    "Tobs   = 0.5   # year as unit \n",
    "# Chi1   = 0.1    # dimensionless parameter\n",
    "# Chi2   = 0.0    # dimensionless parameter\n",
    "cosmo  = FlatLambdaCDM(H0=67,Om0=0.32)\n",
    "DL     = cosmo.luminosity_distance(Z).value*MpcInS # Mpc in second\n",
    "M1     = (1+Z)*M1sun*MsunInS   # solar mass in second\n",
    "M2     = (1+Z)*M2sun*MsunInS   # solar mass in second\n",
    "M      = M1 + M2               # total mass\n",
    "Qmr    = M1/M2                 # mass ratio\n",
    "Mu     = M1*M2/M               # reduced mass\n",
    "Mc     = Mu**(3./5)*M**(2./5)  # chirp mass\n",
    "Eta    = M1*M2/M**2            # symmetric mass ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T09:47:47.122050Z",
     "start_time": "2020-12-23T09:47:47.104068Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-domain waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.957208Z",
     "start_time": "2020-12-23T13:05:18.941220Z"
    }
   },
   "outputs": [],
   "source": [
    "def ht_respon_TQ(t, tc, phic, mc, eta, dl, thetaS, phiS, iota, psi):\n",
    "    \n",
    "    #\"\"\"For TianQin (one Michelson interferometer): (thetaS,phiS) is location of source,\n",
    "    #(thJ,phJ) is latitude and longitude of J0806 in heliocentric-ecliptic frame\"\"\"\n",
    "    thJ  = 1.65273\n",
    "    phJ  = 2.10213\n",
    "    kap = 2*np.pi/OrbitPeriodInS* t\n",
    "    Dplus_TQ = np.sqrt(3.)/32*(8*np.cos(2*kap) *((3 + np.cos(2*thetaS)) *np.sin(2*(phiS - phJ))*  \n",
    "            np.cos(thJ) + 2*np.sin(thJ) *np.sin(phiS - phJ)*np.sin(2*thetaS))- 2*np.sin(2*kap)* (3 +               \n",
    "            np.cos(2*(phiS - phJ))*(9 + np.cos(2*thetaS)*(3 + np.cos(2*thJ))) -6 *np.cos(2*thJ)*(np.sin(phiS - phJ))**2 -               \n",
    "            6* np.cos(2*thetaS)*(np.sin(thJ))**2 + 4*np.cos(phiS - phJ)*np.sin(2*thJ)*np.sin(2*thetaS))) \n",
    "    \n",
    "    Dcros_TQ = np.sqrt(3.)/4*(-4*np.cos(2*kap)*(np.cos(2*(phiS-phJ))*np.cos(thJ)*np.cos(thetaS)+                 \n",
    "            np.cos(phiS-phJ)*np.sin(thetaS)*np.sin(thJ))+np.sin(2*kap)*(np.cos(thetaS)*(3+np.cos(2*thJ))*np.sin(2*(phJ-phiS))+                \n",
    "            2*np.sin(phJ-phiS)*np.sin(thetaS)*np.sin(2*thJ)))\n",
    "\n",
    "    #\"\"\"antenna pattern function for '+' mode\"\"\"\n",
    "    Fplus_TQ = (np.cos(2*psi)*Dplus_TQ-np.sin(2*psi)*Dcros_TQ)/2.\n",
    "\n",
    "    #\"\"\"antenna pattern function for '×' mode\"\"\"\n",
    "    Fcros_TQ = (np.sin(2*psi)*Dplus_TQ+np.cos(2*psi)*Dcros_TQ)/2.\n",
    "\n",
    "    #\"\"\"1st MI angular response function for TianQin\"\"\"\n",
    "    Q_TQ = np.sqrt((1+(np.cos(iota))**2)**2/4*(Fplus_TQ)**2 \n",
    "             +(np.cos(iota))**2*(Fcros_TQ)**2)\n",
    "\n",
    "    #\"\"\"1st MI polarization phase for TianQin\"\"\"\n",
    "    phip_TQ = -np.arctan(2*np.cos(iota)*Fcros_TQ/((1+(np.cos(iota))**2)*Fplus_TQ))  \n",
    "\n",
    "    #\"\"\"non-precesnp.sing spinning PN correction to the orbital phase\"\"\"\n",
    "    THETA = eta*(tc-t)/(5*mc/eta**(3./5))\n",
    "    PSI_PN = phic-THETA**(5./8)/eta *(1 \\\n",
    "           + (3715./8064 + 55./96*eta)*THETA**(-1./4) \\\n",
    "           - 3*np.pi/4*THETA**(-3./8)  \\\n",
    "           + (9275495./14450688+284875./258048*eta+1855./2048*eta**2)*THETA**(-1./2)) \n",
    "\n",
    "    # \"\"\"the frequency-domain amplitude: 1st 60 degree MI with antenna response (contain plus and cross mode) for TianQin\"\"\"\n",
    "    ht_TQ = -mc*Q_TQ/dl*((tc-t)/(5*mc))**(-1./4)*np.cos(phip_TQ+2*PSI_PN)\n",
    "#     ht_TQ = np.concatenate([ht_TQ, len(t)*[0]])\n",
    "    return ht_TQ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.980116Z",
     "start_time": "2020-12-23T13:05:18.965156Z"
    }
   },
   "outputs": [],
   "source": [
    "def ht_respon_LISA(t, tc, phic, mc, eta, dl, thetaS, phiS, iota, psi):\n",
    "    \n",
    "    \"\"\"For LISA (one Michelson interferometer): (thetaS,phiS) is location of source\"\"\"\n",
    "#     alpha = 2*np.pi/YearInS* t-np.pi/9 # trailing 20°behind the Earth (-np.pi/9)\n",
    "    alpha = 2*np.pi/YearInS* t \n",
    "    lam = 3*np.pi/4\n",
    "    Dplus_LISA = np.sqrt(3.)/64*(-36*(np.sin(thetaS))**2 *np.sin(2*alpha-2*lam) +(3+np.cos(2*thetaS))*(np.cos(2*phiS)*(9*np.sin(2*lam)-np.sin(4*alpha-2*lam)) \\\n",
    "            + np.sin(2*phiS)*(np.cos(4*alpha-2*lam)-9*np.cos(2*lam)))-4*np.sqrt(3.)*np.sin(2*thetaS)*(np.sin(3*alpha-2*lam-phiS)-3*np.sin(alpha-2*lam+phiS)))\n",
    "    \n",
    "    Dcros_LISA = 1/16*(np.sqrt(3.)*np.cos(thetaS)*(9*np.cos(2*lam-2*phiS)-np.cos(4*alpha-2*lam-2*phiS))-6*np.sin(thetaS)*(np.cos(3*alpha-2*lam-phiS)+3*np.cos(alpha-2*lam+phiS)))\n",
    "    \n",
    "    #\"\"\"antenna pattern function for '+' mode\"\"\"\n",
    "    Fplus_LISA = (np.cos(2*psi)*Dplus_LISA-np.sin(2*psi)*Dcros_LISA)/2.\n",
    "\n",
    "    #\"\"\"antenna pattern function for '×' mode\"\"\"\n",
    "    Fcros_LISA = (np.sin(2*psi)*Dplus_LISA+np.cos(2*psi)*Dcros_LISA)/2.\n",
    "\n",
    "    #\"\"\"1st MI angular response function for TianQin\"\"\"\n",
    "    Q_LISA = np.sqrt((1+(np.cos(iota))**2)**2/4*(Fplus_LISA)**2 \n",
    "             +(np.cos(iota))**2*(Fcros_LISA)**2)\n",
    "\n",
    "    #\"\"\"1st MI polarization phase for TianQin\"\"\"\n",
    "    phip_LISA = -np.arctan(2*np.cos(iota)*Fcros_LISA/((1+(np.cos(iota))**2)*Fplus_LISA))  \n",
    "\n",
    "    #\"\"\"non-precesnp.np.sing spinning PN correction to the GW phase\"\"\"\n",
    "    THETA = eta*(tc-t)/(5*mc/eta**(3./5))\n",
    "    PSI_PN = phic-THETA**(5./8)/eta *(1 \\\n",
    "           + (3715./8064 + 55./96*eta)*THETA**(-1./4) \\\n",
    "           - 3*np.pi/4*THETA**(-3./8)  \\\n",
    "           + (9275495./14450688+284875./258048*eta+1855./2048*eta**2)*THETA**(-1./2)) \n",
    "\n",
    "    # \"\"\"the frequency-domain amplitude: 1st 60 degree MI with antenna response (contain plus and cross mode) for TianQin\"\"\"\n",
    "    ht_LISA = -mc*Q_LISA/dl*((tc-t)/(5*mc))**(-1./4)*np.cos(phip_LISA+2*PSI_PN)\n",
    "    return ht_LISA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:18.994108Z",
     "start_time": "2020-12-23T13:05:18.988095Z"
    }
   },
   "outputs": [],
   "source": [
    "# def ht_model(t, tc, phic, mc, eta, dl, thetaS, phiS, iota, psi):\n",
    "#     ht = np.piecewise(t, [t >= tc, t < tc], \n",
    "#                       [0, partial(ht_respon_TQ, tc=tc, phic=phic, mc=mc, eta=eta, dl=dl, thetaS=thetaS, phiS=phiS, iota=iota, psi=psi)])\n",
    "#     return ht\n",
    "\n",
    "def ht_model(t, tc, phic, mc, eta, dl, thetaS, phiS, iota, psi):\n",
    "    ht = np.piecewise(t, [t >= tc, t < tc], \n",
    "                      [0, partial(ht_respon_LISA, tc=tc, phic=phic, mc=mc, eta=eta, dl=dl, thetaS=thetaS, phiS=phiS, iota=iota, psi=psi)])\n",
    "    return ht"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PSD --> n(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考     \n",
    "[Generate a time series from a PSD](https://groups.google.com/g/comp.soft-sys.matlab/c/tmw2H26MDtI)  \n",
    "[Generate a time series with known PSD](https://zhuanlan.zhihu.com/p/270532154)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:19.011033Z",
     "start_time": "2020-12-23T13:05:19.005049Z"
    }
   },
   "outputs": [],
   "source": [
    "def TQPSD(f):\n",
    "    \"\"\"CQG 2018\"\"\"\n",
    "    Sx = 1e-24\n",
    "    Sa = 1e-30\n",
    "    L0 = np.sqrt(3.)*1e5 *1e3\n",
    "    return (Sx/(L0**2)+ 4*Sa/((2*np.pi*f)**4*L0**2)*(1.+ 1e-4/f))\n",
    "\n",
    "def LISAPSD(f):\n",
    "    \"\"\"MCMC of SMBHB, Cornish and Porter, CQG 2006\"\"\"\n",
    "    Spos = 4e-22\n",
    "    Sacc = 9e-30\n",
    "    L = 5e9\n",
    "    return (4*Spos+ 16*Sacc/(2*np.pi*f)**4)/(4*L**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:19.028016Z",
     "start_time": "2020-12-23T13:05:19.021006Z"
    }
   },
   "outputs": [],
   "source": [
    "### ref：https://groups.google.com/g/comp.soft-sys.matlab/c/tmw2H26MDtI\n",
    "def psd2timeseries(PSD, fNyq):\n",
    "    '''\n",
    "        x = time series\n",
    "        t = time vector\n",
    "        PSD = power spectral density (one-sided, from 0 to fNyq)\n",
    "        fNyq = Nyquist frequency\n",
    "    '''\n",
    "    N = len(PSD)\n",
    "\n",
    "    # Sampling of frequency vector\n",
    "    delta_f = fNyq/(N-1)\n",
    "\n",
    "    # Compute amplitude of frequency spectrum\n",
    "    # multiply PSD by fs*N=2*fNyq*N (a factor to match)\n",
    "    SpectrumAmplitude = np.sqrt(PSD*2*fNyq*N)\n",
    "\n",
    "    # Compute phase of frequency spectrum\n",
    "    SpectrumPhase = np.random.rand(len(PSD))*2*np.pi\n",
    "\n",
    "    # Compute complex spectrum\n",
    "    Spectrum = SpectrumAmplitude*np.exp(1j*SpectrumPhase)\n",
    "\n",
    "    # Generate the two-sided PSD:\n",
    "    end = len(Spectrum)\n",
    "    Spectrum_TwoSided = np.concatenate([Spectrum[0:end:1], np.conj(Spectrum[end-2:0:-1])])\n",
    "\n",
    "    # Compute inverse FFT\n",
    "    x = ifft(Spectrum_TwoSided)\n",
    "#     x= fftshift(x)\n",
    "    x = np.real(x)\n",
    "\n",
    "    # Compute time vector\n",
    "    delta_t = 1./(2.*fNyq)\n",
    "    t= np.arange(0,(N-1)*delta_t,delta_t)\n",
    "\n",
    "    return [x,t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:19.926126Z",
     "start_time": "2020-12-23T13:05:19.040984Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sampling frequency is 2.000000e-01 and its Nyquist frequency is 1.000000e-01\n"
     ]
    },
    {
     "output_type": "execute_result",
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145.066155 \nL 242.831183 144.600643 \nL 242.883093 145.588068 \nL 242.98662 157.230115 \nL 243.141185 144.375122 \nL 243.192514 144.835737 \nL 243.39688 148.433631 \nL 243.447736 148.352499 \nL 243.498497 147.147179 \nL 243.549166 148.472435 \nL 243.650223 150.759943 \nL 243.851233 142.012454 \nL 244.001034 150.856441 \nL 244.10045 143.928444 \nL 244.199508 155.942669 \nL 244.248904 150.574449 \nL 244.445606 142.120451 \nL 244.592217 144.566283 \nL 244.883116 156.998064 \nL 244.931302 165.216911 \nL 245.075356 145.808762 \nL 245.170975 149.948592 \nL 245.21866 147.753076 \nL 245.266263 148.400877 \nL 245.313783 156.403056 \nL 245.361222 186.75587 \nL 245.408579 158.952545 \nL 245.691025 145.615967 \nL 245.737819 145.429269 \nL 245.784533 147.740759 \nL 245.831169 156.837073 \nL 245.877726 154.696552 \nL 246.063169 139.662177 \nL 246.247372 141.273577 \nL 246.29323 141.022048 \nL 246.339013 140.819907 \nL 246.566791 147.715011 \nL 246.612122 162.021868 \nL 246.657377 154.30405 \nL 246.882548 143.718645 \nL 246.972103 151.660094 \nL 247.016772 156.157621 \nL 247.061368 154.155411 \nL 247.194726 148.33345 \nL 247.239035 148.194133 \nL 247.32744 169.916174 \nL 247.415563 161.22186 \nL 247.459518 161.318737 \nL 247.590966 148.538517 \nL 247.67825 149.847167 \nL 247.721789 158.51482 \nL 247.765259 156.640225 \nL 247.851993 147.836722 \nL 247.895259 150.64827 \nL 248.024647 155.058987 \nL 248.196226 144.77882 \nL 248.238954 147.008466 \nL 248.409206 158.455085 \nL 248.451605 157.4601 \nL 248.578412 145.236637 \nL 248.620552 145.786654 \nL 248.704638 152.130794 \nL 248.746585 150.71211 \nL 248.913737 141.864397 \nL 248.996934 147.543897 \nL 249.079881 169.876238 \nL 249.12126 157.965334 \nL 249.162578 153.918218 \nL 249.203834 159.268767 \nL 249.245028 157.461749 \nL 249.327232 143.579042 \nL 249.368242 143.847309 \nL 249.450079 147.831489 \nL 249.490907 146.083444 \nL 249.531674 145.078788 \nL 249.613027 155.683953 \nL 249.653614 150.933363 \nL 249.775017 142.615981 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158.375144 \nL 252.611777 154.370211 \nL 252.684533 150.81998 \nL 252.720839 151.705731 \nL 252.865587 169.084301 \nL 253.009578 146.1289 \nL 253.045458 147.826749 \nL 253.081292 148.642286 \nL 253.188514 158.153759 \nL 253.224162 156.443199 \nL 253.401714 142.105001 \nL 253.472416 142.499108 \nL 253.613277 146.3195 \nL 253.64838 145.685371 \nL 253.683438 145.613992 \nL 253.753421 168.635658 \nL 253.788345 152.460858 \nL 253.823226 150.317719 \nL 253.892855 159.886033 \nL 253.962308 145.894835 \nL 253.996969 148.59035 \nL 254.066161 164.992486 \nL 254.135179 153.766621 \nL 254.169623 154.283401 \nL 254.204025 153.754748 \nL 254.272698 148.020314 \nL 254.306971 150.8507 \nL 254.341202 152.169219 \nL 254.375389 148.961249 \nL 254.409535 151.495474 \nL 254.477699 163.920599 \nL 254.511717 151.385774 \nL 254.681184 140.068814 \nL 254.714952 141.448256 \nL 254.782365 150.293055 \nL 254.81601 174.116354 \nL 254.883177 156.315494 \nL 254.916698 163.521163 \nL 254.95018 156.995293 \nL 255.01702 144.664743 \nL 255.083698 146.155475 \nL 255.150216 148.056276 \nL 255.216573 150.85786 \nL 255.249692 150.056207 \nL 255.31581 147.335493 \nL 255.34881 151.178721 \nL 255.38177 148.154108 \nL 255.447572 142.391827 \nL 255.480414 145.722723 \nL 255.545982 151.916252 \nL 255.578707 151.235778 \nL 255.741752 144.475175 \nL 255.774245 143.464401 \nL 255.8067 145.853429 \nL 255.871496 151.336298 \nL 255.903837 147.780162 \nL 256.000632 142.160637 \nL 256.032822 142.369945 \nL 256.064974 143.205237 \nL 256.097088 142.94744 \nL 256.161205 141.405944 \nL 256.193208 142.56116 \nL 256.288993 150.886966 \nL 256.320848 162.879605 \nL 256.384446 145.364214 \nL 256.416191 144.075728 \nL 256.447899 145.049924 \nL 256.47957 148.294994 \nL 256.542803 145.669631 \nL 256.574366 145.937363 \nL 256.605892 145.846511 \nL 256.762985 139.834806 \nL 256.794296 139.998336 \nL 256.888016 151.09596 \nL 256.919186 164.450531 \nL 256.981419 148.079009 \nL 257.074506 145.288827 \nL 257.105465 148.302139 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149.336728 \nL 259.645622 155.294805 \nL 259.730138 144.64625 \nL 259.758253 144.996393 \nL 259.92634 158.616612 \nL 260.01 146.687571 \nL 260.065633 151.034065 \nL 260.093407 158.072015 \nL 260.148872 150.998437 \nL 260.204225 144.991663 \nL 260.259468 150.833226 \nL 260.287047 153.985204 \nL 260.314599 149.901662 \nL 260.451947 145.099832 \nL 260.506695 155.127575 \nL 260.534028 164.938067 \nL 260.588613 152.925318 \nL 260.615866 156.461475 \nL 260.724605 142.18463 \nL 260.751723 144.540951 \nL 260.886913 171.394274 \nL 260.99459 152.000116 \nL 261.021443 154.09559 \nL 261.04827 155.57189 \nL 261.155319 146.613683 \nL 261.182016 148.669511 \nL 261.208688 152.625829 \nL 261.261953 147.938515 \nL 261.288547 147.436768 \nL 261.368176 150.270631 \nL 261.47399 141.060887 \nL 261.526746 143.834974 \nL 261.60569 142.637403 \nL 261.658193 140.747144 \nL 261.710597 142.549069 \nL 261.893229 152.547331 \nL 261.919221 148.757216 \nL 261.945188 146.884265 \nL 261.971131 148.084774 \nL 262.074658 168.889989 \nL 262.100479 157.92296 \nL 262.152049 147.705486 \nL 262.203522 152.835895 \nL 262.229223 152.935226 \nL 262.280552 149.502088 \nL 262.357368 140.850373 \nL 262.382925 142.750367 \nL 262.459456 151.25062 \nL 262.510358 148.416342 \nL 262.535774 148.74269 \nL 262.561167 147.703126 \nL 262.637204 143.552972 \nL 262.687779 145.076032 \nL 262.713032 145.357224 \nL 262.788652 153.688341 \nL 262.813813 151.093408 \nL 262.964295 143.341751 \nL 263.039229 148.547981 \nL 263.064162 145.434076 \nL 263.11396 143.635404 \nL 263.163668 145.752133 \nL 263.188488 145.96961 \nL 263.213286 145.925837 \nL 263.238062 144.784936 \nL 263.262815 145.271501 \nL 263.336942 150.08375 \nL 263.41087 149.925264 \nL 263.484601 149.337135 \nL 263.533645 153.100198 \nL 263.558134 151.06839 \nL 263.582602 157.080467 \nL 263.631472 174.804514 \nL 263.655874 160.283588 \nL 263.753268 142.168896 \nL 263.777563 143.41886 \nL 263.826089 153.606607 \nL 263.898717 148.556765 \nL 263.94703 150.088416 \nL 264.043402 162.92766 \nL 264.091461 156.177303 \nL 264.211245 147.046333 \nL 264.282866 160.730813 \nL 264.354301 150.613486 \nL 264.401822 151.523067 \nL 264.520266 144.467355 \nL 264.567501 152.159506 \nL 264.638203 146.037772 \nL 264.685236 149.443386 \nL 264.755637 155.598687 \nL 264.779063 149.914115 \nL 264.895899 143.037738 \nL 264.919207 143.078093 \nL 264.965764 141.717653 \nL 265.035452 163.050904 \nL 265.081812 148.234119 \nL 265.151207 144.197675 \nL 265.1743 144.714175 \nL 265.243463 161.051981 \nL 265.33541 153.210774 \nL 265.40417 143.659373 \nL 265.427051 147.009455 \nL 265.449914 156.454051 \nL 265.518389 143.615497 \nL 265.563944 149.003594 \nL 265.609424 164.703493 \nL 265.654829 156.111336 \nL 265.722797 141.971292 \nL 265.768016 144.612818 \nL 265.858231 160.244841 \nL 265.903228 155.110759 \nL 265.948152 145.873699 \nL 265.993003 155.831767 \nL 266.0154 161.989343 \nL 266.060142 145.45597 \nL 266.216166 153.968089 \nL 266.282764 144.528157 \nL 266.304927 148.101889 \nL 266.437536 173.112435 \nL 266.503601 147.793086 \nL 266.547557 154.955454 \nL 266.569508 165.928377 \nL 266.613359 147.679243 \nL 266.635258 147.825325 \nL 266.744493 157.492721 \nL 266.766289 167.916289 \nL 266.809827 151.90076 \nL 266.853297 163.339811 \nL 266.875007 166.304584 \nL 266.918374 149.236473 \nL 267.004904 151.061371 \nL 267.026494 149.883292 \nL 267.048067 152.520419 \nL 267.112686 171.192099 \nL 267.155681 161.393963 \nL 267.220047 149.973647 \nL 267.284264 151.290535 \nL 267.305636 152.224515 \nL 267.326992 158.042628 \nL 267.369654 151.417605 \nL 267.41225 154.752058 \nL 267.518452 144.706638 \nL 267.539643 147.959916 \nL 267.560819 158.687657 \nL 267.624246 145.746279 \nL 267.645357 149.785961 \nL 267.70859 147.381596 \nL 267.792676 166.609068 \nL 267.876507 166.978589 \nL 267.918326 150.491523 \nL 267.980937 144.227094 \nL 268.001775 146.91976 \nL 268.147206 166.344291 \nL 268.250616 148.546716 \nL 268.271252 154.875771 \nL 268.291872 166.183695 \nL 268.374199 153.440793 \nL 268.394742 151.969186 \nL 268.476762 172.75722 \nL 268.497229 155.846912 \nL 268.558539 144.773734 \nL 268.599336 151.193468 \nL 268.619712 160.715547 \nL 268.701066 152.883251 \nL 268.721367 151.497063 \nL 268.761924 155.289614 \nL 268.822648 150.286355 \nL 268.863056 166.862598 \nL 268.983926 142.577642 \nL 269.044163 142.483028 \nL 269.26391 154.401853 \nL 269.303677 167.406586 \nL 269.343386 149.422738 \nL 269.363219 149.760044 \nL 269.442409 145.84388 \nL 269.46217 147.324093 \nL 269.560768 163.567638 \nL 269.619757 156.528919 \nL 269.639392 156.351928 \nL 269.75691 140.925431 \nL 269.776447 141.987608 \nL 269.971065 170.196712 \nL 270.048528 142.504705 \nL 270.106483 144.172295 \nL 270.164316 156.629605 \nL 270.222028 146.701493 \nL 270.241238 146.441285 \nL 270.279619 156.794653 \nL 270.33709 145.399194 \nL 270.413532 152.449148 \nL 270.470725 150.854351 \nL 270.508787 145.923833 \nL 270.546797 151.822095 \nL 270.565783 175.207249 \nL 270.62266 150.869478 \nL 270.641593 151.10275 \nL 270.679421 142.710298 \nL 270.75492 149.752679 \nL 270.792592 186.38169 \nL 270.867781 150.848018 \nL 270.886547 149.176062 \nL 270.924039 153.849687 \nL 270.942766 153.228053 \nL 270.980182 166.62221 \nL 271.036211 157.94158 \nL 271.11074 140.281587 \nL 271.166504 149.182752 \nL 271.203617 156.053316 \nL 271.277695 149.338065 \nL 271.388438 142.596167 \nL 271.406852 145.897625 \nL 271.443644 155.843113 \nL 271.535407 153.019174 \nL 271.572027 158.59617 \nL 271.626866 153.209097 \nL 271.681596 161.740839 \nL 271.7544 147.326791 \nL 271.79073 172.246974 \nL 271.827012 143.998891 \nL 271.863247 139.944141 \nL 271.899434 144.508158 \nL 272.061689 175.065489 \nL 272.151419 140.576712 \nL 272.18723 144.177926 \nL 272.240858 150.375807 \nL 272.294383 144.028976 \nL 272.312201 143.0961 \nL 272.347803 145.922403 \nL 272.472049 167.861868 \nL 272.489753 155.711936 \nL 272.507445 149.337224 \nL 272.578101 156.644574 \nL 272.595737 160.238107 \nL 272.630975 152.275893 \nL 272.683747 157.856162 \nL 272.771476 147.594965 \nL 272.82398 147.849045 \nL 272.963503 154.82056 \nL 272.998273 150.031732 \nL 273.033 155.030087 \nL 273.050347 166.831278 \nL 273.119625 145.658528 \nL 273.17147 148.057873 \nL 273.257662 141.400959 \nL 273.377879 155.865634 \nL 273.41213 147.90362 \nL 273.42924 147.601313 \nL 273.446339 147.958224 \nL 273.480506 159.815753 \nL 273.497573 164.799902 \nL 273.531676 148.948191 \nL 273.565737 160.501591 \nL 273.599756 151.241664 \nL 273.684619 155.367457 \nL 273.735412 147.705249 \nL 273.769222 156.86707 \nL 273.786112 170.162601 \nL 273.853566 140.806509 \nL 273.870404 140.272138 \nL 273.887231 141.634764 \nL 274.004737 162.288949 \nL 274.071658 155.685881 \nL 274.121743 149.370599 \nL 274.171737 154.919351 \nL 274.188381 163.10418 \nL 274.22164 151.398162 \nL 274.271453 156.3693 \nL 274.288037 156.703359 \nL 274.33773 159.191589 \nL 274.436848 144.186463 \nL 274.486274 159.786359 \nL 274.568453 147.571499 \nL 274.617643 150.585925 \nL 274.650387 148.320551 \nL 274.699431 142.405876 \nL 274.732079 148.915961 \nL 274.748388 156.929926 \nL 274.780978 145.12747 \nL 274.82979 153.456796 \nL 274.862284 148.324461 \nL 274.910952 162.232965 \nL 274.927156 167.487016 \nL 274.97571 153.562583 \nL 275.024178 146.318018 \nL 275.088671 152.114318 \nL 275.10477 152.092603 \nL 275.136941 165.004084 \nL 275.169074 150.018573 \nL 275.20117 145.875235 \nL 275.233229 159.220461 \nL 275.249244 159.555209 \nL 275.26525 171.480897 \nL 275.329181 148.073589 \nL 275.34514 147.13975 \nL 275.361091 149.426966 \nL 275.392963 166.265093 \nL 275.456599 144.546029 \nL 275.504229 142.538325 \nL 275.520088 144.720523 \nL 275.58343 157.088495 \nL 275.630842 146.746351 \nL 275.646628 145.485133 \nL 275.70968 147.442211 \nL 275.866683 159.167275 \nL 275.882334 157.711718 \nL 275.94485 173.17764 \nL 276.007224 147.921693 \nL 276.147052 163.412877 \nL 276.224428 141.312063 \nL 276.255318 152.521681 \nL 276.316994 164.879453 \nL 276.34778 147.418931 \nL 276.439932 142.772191 \nL 276.470582 144.259666 \nL 276.608082 152.192404 \nL 276.638545 162.904034 \nL 276.699369 146.619244 \nL 276.714554 146.572134 \nL 276.775211 159.349762 \nL 276.790354 166.94326 \nL 276.865947 149.676151 \nL 276.88104 150.110655 \nL 276.911202 157.823898 \nL 276.941332 149.947455 \nL 276.986464 154.539397 \nL 277.031523 145.915714 \nL 277.061521 165.371197 \nL 277.106457 150.68176 \nL 277.136373 161.401948 \nL 277.181187 144.277605 \nL 277.225928 149.528263 \nL 277.300336 145.726179 \nL 277.315193 143.207742 \nL 277.359718 152.749413 \nL 277.374543 153.933538 \nL 277.38936 147.873756 \nL 277.433765 141.44944 \nL 277.478098 150.802408 \nL 277.49286 147.497848 \nL 277.507614 146.059653 \nL 277.551828 148.579042 \nL 277.581265 156.088597 \nL 277.625362 142.061078 \nL 277.640045 143.233808 \nL 277.742608 166.452705 \nL 277.801045 152.243245 \nL 277.844791 141.532465 \nL 277.903011 152.258261 \nL 277.917547 155.227736 \nL 277.961109 146.255843 \nL 277.975614 143.75666 \nL 278.004602 147.592336 \nL 278.033559 165.528305 \nL 278.091382 142.650894 \nL 278.105818 140.382761 \nL 278.149084 146.94695 \nL 278.16349 154.008085 \nL 278.249772 143.13301 \nL 278.278472 142.377308 \nL 278.307143 144.397825 \nL 278.335784 170.249682 \nL 278.421528 146.205446 \nL 278.435793 144.480094 \nL 278.478543 149.484719 \nL 278.606397 168.654276 \nL 278.663031 144.6349 \nL 278.719549 153.848611 \nL 278.775951 158.87804 \nL 278.790033 152.87771 \nL 278.888409 144.233121 \nL 278.846291 154.111034 \nL 278.916452 145.65716 \nL 278.958463 150.376501 \nL 279.014378 146.882027 \nL 279.05624 143.321358 \nL 279.084113 149.570606 \nL 279.181446 149.489431 \nL 279.209192 153.767538 \nL 279.223055 152.711594 \nL 279.23691 155.05064 \nL 279.250759 166.091385 \nL 279.306085 144.643713 \nL 279.333706 150.4403 \nL 279.347506 154.005679 \nL 279.416403 145.868783 \nL 279.526281 140.286664 \nL 279.485128 146.595224 \nL 279.539985 141.367298 \nL 279.690281 146.344983 \nL 279.703904 146.529494 \nL 279.71752 145.890974 \nL 279.731129 144.142774 \nL 279.785499 148.152198 \nL 279.826206 184.285842 \nL 279.893917 148.568873 \nL 279.934464 142.032763 \nL 279.988434 152.907295 \nL 280.069191 142.788916 \nL 280.028842 157.491408 \nL 280.109481 150.322243 \nL 280.149713 156.839201 \nL 280.203263 150.241458 \nL 280.229999 143.844764 \nL 280.270055 166.716057 \nL 280.469463 144.204495 \nL 280.495942 146.472282 \nL 280.614784 158.888435 \nL 280.627957 156.073725 \nL 280.680586 144.528237 \nL 280.719992 164.968664 \nL 280.733115 169.026388 \nL 280.772446 153.840172 \nL 280.811721 143.431287 \nL 280.890103 147.299897 \nL 280.916181 149.475407 \nL 280.955251 143.405083 \nL 281.007259 149.456082 \nL 281.033227 146.095738 \nL 281.046201 147.921889 \nL 281.072132 162.101509 \nL 281.136851 145.198546 \nL 281.162697 142.418555 \nL 281.188518 145.967501 \nL 281.252965 142.820531 \nL 281.381408 153.402065 \nL 281.39422 153.205545 \nL 281.496497 148.429206 \nL 281.458188 162.83548 \nL 281.509255 150.005654 \nL 281.623812 175.018035 \nL 281.636512 162.151649 \nL 281.712584 147.325064 \nL 281.750542 154.228781 \nL 281.763183 155.279512 \nL 281.775818 150.467024 \nL 281.801071 153.098441 \nL 281.889274 144.163201 \nL 281.864101 154.985016 \nL 281.914423 148.208338 \nL 281.964652 172.674659 \nL 282.01479 149.558163 \nL 282.02731 148.770388 \nL 282.039825 149.200306 \nL 282.064837 159.663848 \nL 282.127267 143.243751 \nL 282.139737 142.229747 \nL 282.189557 145.934605 \nL 282.3261 164.530227 \nL 282.251706 142.899923 \nL 282.33848 156.64894 \nL 282.41264 164.537501 \nL 282.474288 142.369419 \nL 282.486601 142.636316 \nL 282.572639 165.89823 \nL 282.63393 152.552911 \nL 282.768293 139.987461 \nL 282.695086 158.849858 \nL 282.780476 141.875654 \nL 282.841307 166.292715 \nL 282.950465 159.793667 \nL 283.047133 145.634753 \nL 283.071247 153.864263 \nL 283.083296 162.374762 \nL 283.167493 149.66111 \nL 283.191501 150.974052 \nL 283.239457 144.564311 \nL 283.275368 153.352 \nL 283.323178 165.043176 \nL 283.370904 161.000825 \nL 283.430446 141.530434 \nL 283.501727 145.72181 \nL 283.572824 144.003893 \nL 283.549146 146.804544 \nL 283.596483 145.698529 \nL 283.631932 169.430765 \nL 283.738007 152.657173 \nL 283.808497 143.830434 \nL 283.773275 160.205232 \nL 283.843675 148.20507 \nL 283.867102 171.323906 \nL 283.937263 142.328811 \nL 283.96061 146.948324 \nL 283.995594 158.194465 \nL 284.065429 147.383575 \nL 284.077051 146.571808 \nL 284.10028 150.134311 \nL 284.111888 152.741417 \nL 284.14668 144.573268 \nL 284.20457 149.778111 \nL 284.216133 149.159177 \nL 284.227692 150.11463 \nL 284.239245 156.137167 \nL 284.273877 143.144019 \nL 284.331501 149.928974 \nL 284.446387 141.884903 \nL 284.469307 145.440045 \nL 284.583621 169.675134 \nL 284.51509 144.343384 \nL 284.606427 160.017181 \nL 284.6861 144.465837 \nL 284.720175 153.901894 \nL 284.731524 168.29951 \nL 284.810835 148.255158 \nL 284.822147 149.704219 \nL 284.833454 149.730495 \nL 284.856054 143.620304 \nL 284.88992 159.269604 \nL 284.957526 144.460931 \nL 284.968777 144.826294 \nL 284.991267 164.125942 \nL 285.081041 146.4883 \nL 285.103439 142.316803 \nL 285.137002 152.870263 \nL 285.159354 167.894106 \nL 285.215156 145.413461 \nL 285.348621 165.572969 \nL 285.270845 145.247873 \nL 285.392966 152.325889 \nL 285.448297 146.12235 \nL 285.481442 153.415514 \nL 285.525574 161.788141 \nL 285.547613 147.492843 \nL 285.591639 158.875052 \nL 285.624613 148.850093 \nL 285.646573 157.406056 \nL 285.657546 181.991039 \nL 285.690441 153.125047 \nL 285.756112 167.743772 \nL 285.78889 143.008044 \nL 285.876105 154.099943 \nL 285.930475 145.203506 \nL 285.984737 154.335435 \nL 286.006412 170.883704 \nL 286.060525 149.424691 \nL 286.092942 158.664236 \nL 286.10374 158.898955 \nL 286.114532 158.054797 \nL 286.168434 148.955801 \nL 286.189965 165.294741 \nL 286.211479 163.432448 \nL 286.275921 177.127254 \nL 286.382991 141.18054 \nL 286.478998 169.581924 \nL 286.542818 166.13673 \nL 286.60649 148.768371 \nL 286.659438 150.824305 \nL 286.733395 157.51646 \nL 286.712285 143.546155 \nL 286.754489 146.592988 \nL 286.76503 144.665814 \nL 286.796629 154.040062 \nL 286.838704 150.056856 \nL 286.849212 157.05576 \nL 286.912181 146.991071 \nL 286.943611 149.907227 \nL 286.975006 142.644212 \nL 287.027251 156.126339 \nL 287.068975 143.700832 \nL 287.152235 162.524291 \nL 287.214515 149.055232 \nL 287.235244 143.843192 \nL 287.297337 157.725078 \nL 287.307672 152.218473 \nL 287.328331 155.287327 \nL 287.35929 151.729094 \nL 287.390215 153.943951 \nL 287.441679 143.895138 \nL 287.472511 154.857556 \nL 287.513567 144.860083 \nL 287.534072 159.780342 \nL 287.575036 140.570659 \nL 287.636368 153.587862 \nL 287.656782 161.004128 \nL 287.677181 152.162559 \nL 287.738286 146.415261 \nL 287.768788 151.534643 \nL 287.870218 192.518943 \nL 287.799256 149.988727 \nL 287.880341 159.780406 \nL 287.900575 146.756143 \nL 287.991443 155.976075 \nL 288.011596 160.484651 \nL 288.071964 145.309614 \nL 288.152251 163.575094 \nL 288.122171 142.350546 \nL 288.242294 155.829702 \nL 288.282219 142.498703 \nL 288.381779 148.536893 \nL 288.401648 146.887057 \nL 288.431424 153.345115 \nL 288.461168 150.53551 \nL 288.48098 154.530078 \nL 288.49088 161.896771 \nL 288.54033 141.111989 \nL 288.579825 146.499483 \nL 288.599552 165.205465 \nL 288.648806 144.325034 \nL 288.688146 146.857971 \nL 288.756857 142.328337 \nL 288.737243 151.171584 \nL 288.766659 144.906976 \nL 288.786252 159.474681 \nL 288.884009 153.220229 \nL 288.952234 144.802795 \nL 289.000862 169.514377 \nL 289.088177 141.977976 \nL 289.126895 149.343902 \nL 289.146234 160.818334 \nL 289.155898 196.184142 \nL 289.184871 144.024944 \nL 289.252354 154.448827 \nL 289.338877 149.47834 \nL 289.377244 164.742222 \nL 289.405985 145.689306 \nL 289.463376 165.284395 \nL 289.511111 152.834343 \nL 289.520648 165.612034 \nL 289.549239 145.06603 \nL 289.615837 152.44329 \nL 289.625338 153.500893 \nL 289.64433 144.142505 \nL 289.653821 144.788697 \nL 289.672793 145.965354 \nL 289.720167 166.165262 \nL 289.729632 187.634756 \nL 289.786353 145.100509 \nL 289.824103 154.941865 \nL 289.88063 147.252058 \nL 289.852381 168.337446 \nL 289.90885 163.3076 \nL 289.91825 165.21431 \nL 289.946432 148.587105 \nL 290.068221 140.205564 \nL 289.993338 160.186902 \nL 290.077567 140.260387 \nL 290.152221 161.63425 \nL 290.198778 143.797565 \nL 290.217379 147.239384 \nL 290.226674 158.060377 \nL 290.319458 145.168853 \nL 290.365733 144.57481 \nL 290.384221 147.327202 \nL 290.402697 158.286899 \nL 290.45805 142.783743 \nL 290.485685 144.839385 \nL 290.494891 144.429635 \nL 290.504093 147.863032 \nL 290.605117 163.571257 \nL 290.586777 147.589465 \nL 290.614283 154.518112 \nL 290.641762 147.40296 \nL 290.669213 155.465175 \nL 290.724034 154.651493 \nL 290.815159 143.90801 \nL 290.842439 148.483606 \nL 290.933174 159.955882 \nL 290.878769 146.030092 \nL 290.969385 156.20366 \nL 291.086742 145.568224 \nL 291.005548 157.789999 \nL 291.095748 146.759615 \nL 291.122751 165.302168 \nL 291.194629 144.162464 \nL 291.212569 140.737405 \nL 291.239458 153.765909 \nL 291.248415 172.876167 \nL 291.302095 141.230078 \nL 291.34675 149.428287 \nL 291.453625 143.92935 \nL 291.382421 151.879641 \nL 291.462513 145.236524 \nL 291.498034 203.929943 \nL 291.568941 144.826354 \nL 291.577791 143.802423 \nL 291.604326 148.503173 \nL 291.622001 165.619979 \nL 291.692589 143.652592 \nL 291.7014 144.401183 \nL 291.754206 148.962645 \nL 291.727815 144.367242 \nL 291.80691 145.457583 \nL 291.824456 142.166083 \nL 291.850754 152.222849 \nL 291.885779 158.522005 \nL 291.946965 147.286299 \nL 291.964422 151.115982 \nL 292.008016 145.139252 \nL 292.086311 177.447661 \nL 292.121038 146.561963 \nL 292.207664 149.16668 \nL 292.311256 162.642096 \nL 292.233598 146.810196 \nL 292.319871 151.139906 \nL 292.328483 147.44995 \nL 292.388695 164.032916 \nL 292.423043 150.441373 \nL 292.4402 161.612776 \nL 292.500168 146.927318 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142.74639 \nL 293.925955 164.84935 \nL 293.958439 152.55129 \nL 294.055662 173.141583 \nL 293.99899 144.152449 \nL 294.063748 156.242261 \nL 294.112217 142.348064 \nL 294.136419 159.486131 \nL 294.192809 147.940862 \nL 294.289208 168.977021 \nL 294.241051 144.537796 \nL 294.297227 154.563332 \nL 294.369285 141.612211 \nL 294.329276 158.156564 \nL 294.40125 151.782601 \nL 294.4252 157.953018 \nL 294.473037 143.778512 \nL 294.481002 142.850001 \nL 294.488964 147.002254 \nL 294.496924 164.604639 \nL 294.584335 143.839236 \nL 294.592267 143.974131 \nL 294.631897 156.333171 \nL 294.663559 141.681387 \nL 294.703085 148.962392 \nL 294.71888 142.975596 \nL 294.750443 164.789803 \nL 294.80559 151.736286 \nL 294.868481 145.598913 \nL 294.884181 153.16726 \nL 294.923392 143.014001 \nL 294.993833 175.64841 \nL 295.048495 141.877728 \nL 295.110834 156.588115 \nL 295.141951 140.955403 \nL 295.211835 162.51547 \nL 295.219589 167.519421 \nL 295.250583 145.8604 \nL 295.297009 145.924305 \nL 295.335637 148.982995 \nL 295.351073 171.891855 \nL 295.405032 142.67818 \nL 295.443509 146.352557 \nL 295.458885 164.733171 \nL 295.481933 143.144744 \nL 295.550961 144.143039 \nL 295.55862 141.956797 \nL 295.581584 156.411075 \nL 295.589235 165.780112 \nL 295.673251 141.680479 \nL 295.688499 140.14753 \nL 295.711356 147.085956 \nL 295.787407 157.395386 \nL 295.825354 151.689068 \nL 295.832938 151.798482 \nL 295.840519 162.911151 \nL 295.878393 149.342259 \nL 295.938883 152.864989 \nL 296.02937 139.829556 \nL 295.991703 153.388777 \nL 296.066986 143.304137 \nL 296.08953 152.336226 \nL 296.10455 172.843003 \nL 296.194495 146.895626 \nL 296.209457 143.954121 \nL 296.246828 157.674978 \nL 296.291605 149.377491 \nL 296.299061 152.118592 \nL 296.33631 143.697216 \nL 296.373509 144.020941 \nL 296.388374 140.543727 \nL 296.44034 156.124894 \nL 296.469991 149.445747 \nL 296.551367 180.898909 \nL 296.514408 142.254881 \nL 296.573518 147.60432 \nL 296.647229 140.004136 \nL 296.617768 155.528489 \nL 296.68401 146.948739 \nL 296.7134 158.670117 \nL 296.750093 144.892048 \nL 296.779412 146.910381 \nL 296.85988 140.846914 \nL 296.808701 159.519919 \nL 296.881785 144.469947 \nL 296.947396 172.439256 \nL 296.925543 141.771495 \nL 297.01285 161.14139 \nL 297.114361 142.661364 \nL 297.128832 150.20366 \nL 297.201072 152.945457 \nL 297.186639 146.490851 \nL 297.208286 151.49131 \nL 297.316265 143.337081 \nL 297.244326 155.662145 \nL 297.323449 145.273268 \nL 297.38085 160.127916 \nL 297.423822 141.559902 \nL 297.452434 142.967533 \nL 297.509567 157.10122 \nL 297.595045 149.748543 \nL 297.602156 147.828856 \nL 297.623479 158.190494 \nL 297.673168 182.336849 \nL 297.722767 150.352884 \nL 297.729845 150.732675 \nL 297.779343 170.112407 \nL 297.75814 144.450228 \nL 297.821698 144.958343 \nL 297.828751 143.359199 \nL 297.871031 156.984774 \nL 297.927303 144.943959 \nL 297.934329 145.079858 \nL 297.976447 167.44849 \nL 298.011497 144.925605 \nL 298.046501 153.621081 \nL 298.095433 147.26963 \nL 298.137306 165.543107 \nL 298.158218 147.50889 \nL 298.248656 157.203768 \nL 298.234762 147.371336 \nL 298.262543 148.399199 \nL 298.373389 142.211454 \nL 298.345719 155.419586 \nL 298.380302 142.323935 \nL 298.421744 163.912861 \nL 298.5182 150.170612 \nL 298.58003 140.905211 \nL 298.600609 160.433379 \nL 298.628024 148.268043 \nL 298.655411 143.112868 \nL 298.669095 150.191839 \nL 298.730587 159.267855 \nL 298.751054 146.100839 \nL 298.77832 154.307091 \nL 298.819167 144.0093 \nL 298.791942 164.180662 \nL 298.887113 149.879676 \nL 298.893898 158.036832 \nL 298.981955 141.271987 \nL 298.988717 141.802037 \nL 299.036005 170.35673 \nL 299.096684 143.873897 \nL 299.11015 142.456746 \nL 299.130337 152.159884 \nL 299.150509 158.142252 \nL 299.177382 145.239344 \nL 299.237751 151.891857 \nL 299.284613 141.703072 \nL 299.331396 154.411346 \nL 299.371432 142.177602 \nL 299.384764 141.292412 \nL 299.404751 147.209965 \nL 299.537626 178.704916 \nL 299.484553 144.933426 \nL 299.550878 159.200241 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161.37813 \nL 301.240238 142.601892 \nL 301.252696 144.830996 \nL 301.358362 154.848588 \nL 301.302472 141.126073 \nL 301.364565 148.679576 \nL 301.407947 143.632815 \nL 301.426518 152.171119 \nL 301.445077 151.868613 \nL 301.457442 165.882475 \nL 301.500678 145.210359 \nL 301.562327 159.62898 \nL 301.57464 152.125787 \nL 301.636123 143.574271 \nL 301.654541 158.293866 \nL 301.69134 147.729208 \nL 301.709721 143.530697 \nL 301.721969 148.903754 \nL 301.801444 168.288454 \nL 301.758678 146.394176 \nL 301.831951 153.098949 \nL 301.838048 151.767697 \nL 301.850238 167.319152 \nL 301.856332 178.414276 \nL 301.941495 142.707655 \nL 301.947568 140.416167 \nL 301.977913 159.175115 \nL 302.050606 142.691876 \nL 302.159285 165.242517 \nL 302.12914 140.788075 \nL 302.165311 151.336864 \nL 302.189398 142.77615 \nL 302.231501 158.419018 \nL 302.27354 152.922764 \nL 302.285539 159.719777 \nL 302.315514 143.902613 \nL 302.363407 148.951512 \nL 302.387322 140.226833 \nL 302.405244 160.127854 \nL 302.482775 146.503522 \nL 302.571963 165.266626 \nL 302.512536 143.367817 \nL 302.595697 149.925945 \nL 302.672694 143.485015 \nL 302.654945 165.263467 \nL 302.702251 149.208401 \nL 302.708159 149.431341 \nL 302.714065 147.76729 \nL 302.743578 165.334935 \nL 302.826045 142.351016 \nL 302.849562 160.803717 \nL 302.943429 146.041158 \nL 302.966846 178.510807 \nL 303.01362 144.085061 \nL 303.048649 145.767216 \nL 303.054482 144.763313 \nL 303.101108 154.166893 \nL 303.106931 156.981797 \nL 303.188319 143.397998 \nL 303.194123 142.544035 \nL 303.228923 153.567699 \nL 303.246306 151.213957 \nL 303.292608 171.136257 \nL 303.309951 144.430274 \nL 303.350377 146.59504 \nL 303.356147 145.933216 \nL 303.379216 155.149378 \nL 303.431049 149.560114 \nL 303.477042 167.816936 \nL 303.517223 141.224739 \nL 303.528693 142.314487 \nL 303.574523 164.567771 \nL 303.660249 147.943057 \nL 303.665955 147.600638 \nL 303.677363 175.124375 \nL 303.774138 146.095518 \nL 303.808213 142.216005 \nL 303.825235 153.314204 \nL 303.836577 166.505749 \nL 303.881898 146.943617 \nL 303.932795 153.04689 \nL 303.944093 142.93888 \nL 304.000512 176.163201 \nL 304.045564 146.732185 \nL 304.06244 162.226139 \nL 304.124229 141.643445 \nL 304.157874 154.413231 \nL 304.197074 142.587583 \nL 304.258562 152.211564 \nL 304.269727 166.495233 \nL 304.358883 145.03778 \nL 304.364446 146.920977 \nL 304.375568 168.924968 \nL 304.436659 143.27941 \nL 304.475465 165.911822 \nL 304.514216 140.794155 \nL 304.586038 148.901967 \nL 304.679678 170.78257 \nL 304.630143 147.837346 \nL 304.701664 155.395543 \nL 304.789435 147.238284 \nL 304.729123 157.090531 \nL 304.800387 153.469697 \nL 304.805861 159.999334 \nL 304.844151 144.197706 \nL 304.909666 151.631777 \nL 305.023944 141.698739 \nL 304.969585 157.122448 \nL 305.029374 142.631574 \nL 305.12152 155.691442 \nL 305.153969 144.569181 \nL 305.170179 148.886839 \nL 305.18638 168.549066 \nL 305.261857 144.634181 \nL 305.272622 145.627605 \nL 305.283383 143.001683 \nL 305.342496 156.112248 \nL 305.369323 149.907245 \nL 305.380047 166.637467 \nL 305.401481 145.214539 \nL 305.487054 163.508918 \nL 305.535071 139.377439 \nL 305.598965 149.923495 \nL 305.673321 163.523903 \nL 305.625544 142.681701 \nL 305.683927 147.690341 \nL 305.694529 143.897263 \nL 305.752766 168.046699 \nL 305.789762 150.577817 \nL 305.900453 175.797259 \nL 305.874138 141.758992 \nL 305.905713 157.286755 \nL 306.021177 142.570971 \nL 306.000219 162.802761 \nL 306.03165 145.235753 \nL 306.078728 165.151072 \nL 306.141374 143.966219 \nL 306.177852 169.209677 \nL 306.255857 146.509409 \nL 306.318102 143.486638 \nL 306.276621 155.584087 \nL 306.328462 148.500764 \nL 306.385376 166.173396 \nL 306.426693 141.740299 \nL 306.437013 148.269848 \nL 306.534858 170.928556 \nL 306.483404 143.721545 \nL 306.545137 150.316875 \nL 306.657957 140.269893 \nL 306.565684 151.630524 \nL 306.66819 141.38776 \nL 306.755022 171.930665 \nL 306.780508 142.64753 \nL 306.836496 139.834979 \nL 306.811061 150.166637 \nL 306.856827 147.482652 \nL 306.877143 163.203096 \nL 306.953194 143.056718 \nL 306.973439 152.161638 \nL 306.978498 152.094021 \nL 306.988613 166.245269 \nL 307.049225 146.379617 \nL 307.084521 149.367751 \nL 307.139894 144.744795 \nL 307.14995 153.900504 \nL 307.225253 169.678238 \nL 307.245299 142.200293 \nL 307.255317 143.265539 \nL 307.265331 145.048894 \nL 307.280345 166.282818 \nL 307.36527 142.04477 \nL 307.38023 153.491972 \nL 307.430036 143.19171 \nL 307.395181 160.900598 \nL 307.48472 149.891279 \nL 307.489686 155.82652 \nL 307.534339 141.213862 \nL 307.588816 142.325981 \nL 307.593763 142.333567 \nL 307.756522 173.824376 \nL 307.667863 141.549116 \nL 307.77127 151.552673 \nL 307.839993 146.373478 \nL 307.800744 154.521486 \nL 307.864496 153.405746 \nL 307.87429 174.001098 \nL 307.947641 146.833476 \nL 307.967168 156.261603 \nL 308.074321 146.75821 \nL 307.996433 173.5335 \nL 308.079182 147.014089 \nL 308.147141 163.482472 \nL 308.127741 140.56941 \nL 308.171372 141.389659 \nL 308.229439 140.288296 \nL 308.210097 144.667168 \nL 308.248767 142.966717 \nL 308.306671 163.596689 \nL 308.364454 148.133005 \nL 308.431714 144.083862 \nL 308.388494 155.298806 \nL 308.436512 146.181918 \nL 308.479657 163.58587 \nL 308.537078 143.839327 \nL 308.546637 148.795249 \nL 308.575292 171.971946 \nL 308.613454 141.446672 \nL 308.627751 145.362101 \nL 308.651562 175.662469 \nL 308.689618 143.16699 \nL 308.741859 152.700656 \nL 308.779791 142.206517 \nL 308.75609 158.876437 \nL 308.860222 147.944979 \nL 308.935708 157.581338 \nL 308.883834 140.315865 \nL 308.959255 143.563444 \nL 308.968668 140.347075 \nL 309.001589 151.360302 \nL 309.043858 146.632376 \nL 309.114162 158.937096 \nL 309.072001 140.088941 \nL 309.151585 144.417496 \nL 309.188956 158.868683 \nL 309.263548 148.782286 \nL 309.272858 144.53621 \nL 309.310065 163.271075 \nL 309.365782 156.202738 \nL 309.458395 172.684592 \nL 309.439897 142.790034 \nL 309.47226 154.310448 \nL 309.54148 142.690047 \nL 309.523038 164.680507 \nL 309.582929 153.073756 \nL 309.587531 166.172736 \nL 309.605929 141.601533 \nL 309.693156 154.116666 \nL 309.697739 158.868297 \nL 309.766396 145.814436 \nL 309.802943 153.423171 \nL 309.807508 153.466461 \nL 309.862228 141.651776 \nL 309.925932 147.153486 \nL 309.962268 160.130339 \nL 309.971345 169.004004 \nL 310.04385 142.334855 \nL 310.057423 147.078932 \nL 310.098103 140.34724 \nL 310.080031 157.128734 \nL 310.170276 145.660474 \nL 310.228777 182.688849 \nL 310.260226 144.321086 \nL 310.282667 153.253593 \nL 310.327496 143.029265 \nL 310.34093 163.906528 \nL 310.394602 144.26008 \nL 310.408004 162.641738 \nL 310.430326 139.639261 \nL 310.510534 145.591396 \nL 310.514983 145.433949 \nL 310.519432 148.681857 \nL 310.608251 160.135585 \nL 310.532773 144.22257 \nL 310.63484 153.832238 \nL 310.6791 143.283991 \nL 310.656979 155.506716 \nL 310.745356 152.185126 \nL 310.785033 178.427925 \nL 310.811453 146.342601 \nL 310.83345 147.623934 \nL 310.84664 142.401552 \nL 310.894949 161.128429 \nL 310.921263 159.719782 \nL 310.943173 161.692122 \nL 310.965065 147.525385 \nL 311.056823 142.971444 \nL 311.008798 160.931079 \nL 311.078625 144.247444 \nL 311.091698 143.509932 \nL 311.113473 152.185658 \nL 311.204738 170.456719 \nL 311.152623 140.856791 \nL 311.217751 145.233089 \nL 311.222088 143.575086 \nL 311.243759 171.007629 \nL 311.282725 161.630772 \nL 311.287052 177.094545 \nL 311.321636 141.934055 \nL 311.390676 153.07326 \nL 311.416522 153.880715 \nL 311.403602 144.605069 \nL 311.420827 151.699602 \nL 311.438041 140.551235 \nL 311.519661 163.675609 \nL 311.52395 159.38166 \nL 311.618142 141.392979 \nL 311.536814 174.37594 \nL 311.648045 156.263939 \nL 311.741814 171.19761 \nL 311.682179 144.507616 \nL 311.754576 146.410763 \nL 311.835264 167.960128 \nL 311.801319 141.938382 \nL 311.860696 143.138252 \nL 311.873403 157.646536 \nL 311.877637 167.737843 \nL 311.957967 142.631705 \nL 311.979067 151.829279 \nL 312.067513 140.648857 \nL 312.050688 166.914361 \nL 312.096932 145.238558 \nL 312.210112 164.284543 \nL 312.117926 144.869921 \nL 312.214295 154.507406 \nL 312.22684 141.761478 \nL 312.310327 167.846638 \nL 312.326994 147.116251 \nL 312.393562 163.718479 \nL 312.364458 143.268292 \nL 312.426785 144.960021 \nL 312.435085 141.543392 \nL 312.509669 160.372893 \nL 312.513807 169.611797 \nL 312.56341 142.40779 \nL 312.612925 147.107027 \nL 312.62941 155.416149 \nL 312.691141 143.010332 \nL 312.715795 143.243697 \nL 312.719901 142.172636 \nL 312.736323 163.511261 \nL 312.793719 150.781099 \nL 312.887755 145.927351 \nL 312.916312 163.544831 \nL 312.965196 140.962337 \nL 312.932616 187.868404 \nL 313.02618 151.839401 \nL 313.123474 165.699581 \nL 313.107282 141.191891 \nL 313.131566 144.513559 \nL 313.220425 170.833289 \nL 313.200255 144.272859 \nL 313.268773 148.352352 \nL 313.313018 166.837949 \nL 313.385265 140.105725 \nL 313.4053 161.466986 \nL 313.505257 146.934777 \nL 313.533181 142.199481 \nL 313.577003 161.573283 \nL 313.612805 149.016609 \nL 313.719935 165.584953 \nL 313.652532 144.213153 \nL 313.723895 151.240695 \nL 313.751597 143.012052 \nL 313.77532 165.962402 \nL 313.806919 154.283975 \nL 313.854249 163.114374 \nL 313.897563 142.377675 \nL 313.913297 156.950025 \nL 313.932952 141.101386 \nL 314.0271 150.312084 \nL 314.077962 146.79588 \nL 314.089687 160.104458 \nL 314.093594 163.557964 \nL 314.167721 144.202393 \nL 314.19498 155.449807 \nL 314.288238 144.171579 \nL 314.268836 155.735285 \nL 314.307627 151.799924 \nL 314.404368 143.696321 \nL 314.388912 163.717158 \nL 314.423676 146.309 \nL 314.427535 146.32423 \nL 314.431395 144.576053 \nL 314.496921 159.767727 \nL 314.50077 185.452183 \nL 314.55845 142.848058 \nL 314.604507 155.41211 \nL 314.669623 143.050956 \nL 314.619842 163.601384 \nL 314.719313 149.787282 \nL 314.730768 172.363982 \nL 314.795586 142.768764 \nL 314.82984 149.532925 \nL 314.943713 141.016804 \nL 314.883039 177.506411 \nL 314.947501 141.079834 \nL 315.008032 162.660098 \nL 315.068432 159.844748 \nL 315.185079 141.973448 \nL 315.151265 160.865146 \nL 315.188834 142.366844 \nL 315.200095 162.541973 \nL 315.301235 144.896148 \nL 315.31245 141.777262 \nL 315.3498 178.075527 \nL 315.398279 149.641549 \nL 315.480128 177.689963 \nL 315.502408 143.856783 \nL 315.506119 142.972298 \nL 315.543208 159.370635 \nL 315.624627 165.964469 \nL 315.580247 142.562971 \nL 315.646791 152.769026 \nL 315.716858 143.894157 \nL 315.657866 159.125804 \nL 315.724223 156.081339 \nL 315.727905 180.939848 \nL 315.764696 140.574576 \nL 315.834464 152.040989 \nL 315.852795 143.728331 \nL 315.907714 167.843955 \nL 315.911371 173.219803 \nL 315.991711 142.855183 \nL 316.002648 146.676114 \nL 316.013581 156.038917 \nL 316.053632 142.362522 \nL 316.108152 149.6642 \nL 316.115413 142.118292 \nL 316.140812 165.294981 \nL 316.209635 156.746814 \nL 316.224103 168.428283 \nL 316.256626 143.837392 \nL 316.31435 147.946614 \nL 316.361163 143.997599 \nL 316.382742 163.391505 \nL 316.411487 154.366691 \nL 316.497544 174.747951 \nL 316.515439 143.595905 \nL 316.522593 140.903218 \nL 316.579764 161.826266 \nL 316.583333 167.630615 \nL 316.6475 143.493513 \nL 316.683083 150.043562 \nL 316.686639 149.751076 \nL 316.690195 154.909809 \nL 316.69375 160.989463 \nL 316.725723 141.2966 \nL 316.796643 153.219776 \nL 316.810805 141.709311 \nL 316.82496 174.135484 \nL 316.90621 153.790057 \nL 316.923841 171.157832 \nL 316.937938 144.539792 \nL 317.018855 159.220661 \nL 317.099535 146.946269 \nL 317.060978 171.176471 \nL 317.127542 154.318772 \nL 317.13454 177.360217 \nL 317.155521 146.401326 \nL 317.232317 150.952781 \nL 317.253224 140.704518 \nL 317.270635 172.98771 \nL 317.343639 145.19649 \nL 317.433758 168.063811 \nL 317.374867 143.100469 \nL 317.454513 148.108842 \nL 317.489069 144.682056 \nL 317.475252 171.604498 \nL 317.564942 147.183559 \nL 317.623427 141.538506 \nL 317.681789 157.730146 \nL 317.740028 142.165541 \nL 317.702358 159.843202 \nL 317.794728 153.366045 \nL 317.886789 160.865833 \nL 317.852729 144.21695 \nL 317.900402 146.87759 \nL 317.975151 172.864111 \nL 317.914008 144.153532 \nL 317.995502 145.669528 \nL 318.036159 142.844219 \nL 318.026 166.561235 \nL 318.093653 147.716921 \nL 318.151028 169.286411 \nL 318.181355 143.129046 \nL 318.211648 158.115906 \nL 318.288913 141.269393 \nL 318.268778 164.78077 \nL 318.325789 149.059317 \nL 318.34923 162.11351 \nL 318.372652 140.031682 \nL 318.429449 145.589303 \nL 318.499448 140.58449 \nL 318.449467 157.977385 \nL 318.516089 150.016349 \nL 318.62235 175.619319 \nL 318.60909 142.586964 \nL 318.628978 157.988985 \nL 318.724901 141.487218 \nL 318.652162 167.71179 \nL 318.764496 149.508888 \nL 318.794155 164.988975 \nL 318.780977 143.907839 \nL 318.879657 157.57907 \nL 318.991068 142.139889 \nL 318.896069 166.36008 \nL 318.994338 142.856543 \nL 319.108544 157.272105 \nL 319.095516 142.687523 \nL 319.115055 149.7973 \nL 319.170343 142.10947 \nL 319.144339 166.148039 \nL 319.209303 161.07964 \nL 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kpaUxfPjwkMtRmIr+zPhJYGABxyYAd3ueAfcAon+mjXPOgcWL3WxdW7bAKafAnXeCz7ABY0zsqF69Ot988w2nnnoqrVu35s4772T06NGccsopYbtGrVq1eP311/n0009p164db731Vs6z1oK8/PLLbNu2jY4dO3LxxRdz3XXXUa9evTxpRo8ezRdffEGTJk3o0KEDACeddBIff/wxn3/+OUcddRTHHHMMY8aMoVmzZoBrMp8yZQp79uzh6KOP5rLLLss3/Kgk+vTpw7x58+jfvz+tW7fmnHPOITk5mZkzZ+ZZxevnn3+mYcOGNG3alJ49e/Luu+8yYsQIZs+eTUpKSsjlKIz4PyuoaESkJ3Ctb81YRA4HnlTVPiXMszqQnp6eTvXq1cNSzmLZuxduugmefdZ97tED3noLPM0+xlQ0e/fuZdWqVbRo0SLkZ5PG+CrsZysjI4PU1FSAVFXNKCyfqK0Zi0h3EflIRNaLiIpI3wBprhGR1SKyV0Tmi0i4ejG3AnZ6rr9YRG4PU75lIzkZxo51z41TUuDrryEtDWbOjHTJjDHGBBC1wRioCizDjd3NR0QGAI8D9wIdPWmni0g9nzRLReSnAFujQHn6qAQcB1wNdAFOEJETQv9KZWzAAFi0CI44AjZvdnNdjxwJIa67aYwxJryiNhir6jRVvVNVpxSQ5EZgnKqOV9XlwFXAbmCITx5pqto2wLa+iMuvAxaq6lpV3Qd8CqQVlFhEkkSkuncDqhXjq5au1q1h3jy4/HI338C998JJJ8GmTZEumTHGGI+oDcaFEZFE4EhghnefqmZ7Pgc3QrxwC4B6IlJTROKA7sCKQtKPANJ9tr/DUIbwqVwZXnzR9a6uUsU1V6elueZrY4wxEReTwRioA8QD/tW7TUCDYDMRkRnAJOBUEflbRLoAqGomcDvwDfAD8JuqFja91SggFbgZWAn8FmwZytR//+sWmTj8cNi4EY4/3i1AEcQMNMYYY0pPrAbjsFDVPqpaV1WrqGpjVZ3rc2yaqrbzNGsXOrpeVfepaoaqjlbVQ4FOpV74kmrTxk2fOXiwC8J33AGnneaGQhljjImIWA3GW3ATcdT3218f2Fj2xXE8vbuXA99HqgxBqVoVxo+HV15xTdiffeaarefMiXTJjDGmQorJYKyq+4FFQG/vPs+z3d7A3ILOK4Nylc1CEeFyySVuTutDDoF169x45EcftWZrY4wpY1EbjEUkRUTSRCTNs6uF53NTz+fHgctFZJCItAGeww2HGh+B4gIxVDP21bate4580UVuyNOtt7oFKLZujXTJjDGmwojaYIx77rrEs4ELvkuA+wBU9R1ch6n7gKW4oUcnq2rExuzEXM3YKyXF9bR+8UVISoJPPnFTagY5QbsxJvqMHDmStLS0Mr/uhAkTqFGjRplfN9ZFbTBW1VmqKgG2wT5pnlHVZqqapKqdVXV+BIscmzVjLxE3FnnePGjZEv76yy08MWaMG59sjIkpN998MzOjdNY9EcnZUlNT6dq1K19++WXO8X/++YehQ4fStGlTkpKSaNCgASeddBJzfPq1NG/ePCePypUr07x5c84777w8+cSSqA3GsShma8a+0tLcrF3nnecWmLjxRrcAxfbtkS6ZMSYIqkpmZiYpKSl5FkGINuPHj2fDhg3MmTOHOnXqcPrpp/Pnn38CcM4557BkyRImTpzIr7/+yocffkjPnj3Z6vf47L777mPDhg2sXLmSV199lRo1atCnT5+wLC5R5lTVtjBvQHVA09PTNWZlZ6uOHauamKgKqi1aqC5YEOlSGVMie/bs0eXLl+uePXtyd2Znq+7cGZktOzvosu/du1eHDRumdevW1aSkJO3atat+//33Oce/+uorBfTTTz/Vjh07akJCgn711Vd6zz33aPv27XPSHThwQIcNG6apqalaq1YtveWWW3TgwIF61lln5aTp0aOHDhs2TIcPH641a9bU+vXr6z333JOnPKNHj9a2bdtqlSpVtHHjxjp06FDdsWNHzvHx48drampqod8J0ClTpuR8XrdunQL6/PPP67Zt2xTQWbNmFZpHs2bNdMyYMfn233333RoXF6e//PJLoeeHS8CfLY/09HQFFKiuRcQNqxmHUUw3U/sTgauvhu++gxYtYNUq6NoVnnnGmq1N+bB7t+svEYlt9+6gi3nLLbfw/vvvM3HiRBYvXkzLli056aST+Pfff/Oku+2223j44YdZsWIFRxxxRL58HnnkEd544w3Gjx/PnDlzyMjIYOrUqfnSTZw4kapVqzJ//nweffRR7rvvPr744ouc43FxcTz11FP8/PPPTJw4kS+//JJbbrkl+PsegHct5P3795OSkkJKSgpTp05l377ir1x7/fXXo6p88MEHIZWpzBUVrW2roDVjX9u2qfbr52rIoNq/v+r27ZEulTFBC1h72bkz92e6rLedO4Mq986dOzUhIUHfeOONnH379+/XRo0a6aOPPqqquTXjqVOn5jnXv2Zcv359feyxx3I+Z2ZmatOmTfPVjLt165Ynn6OOOkpvvfXWAss4adIkrV27ds7n4taMd+3apVdffbXGx8frsmXLVFX1vffe05o1a2pycrIee+yxOmLEiJxjXgXVjL3fdejQoYWWIVzCVTOuFMG/A0ysqFED3n8fnnoKhg+HSZNgyRL3GoHemsaERZUqsHNn5K4dhD/++IMDBw7QtWvXnH0JCQkcffTRrFiRd7r8Tp0KnvgvPT2dTZs2cfTRud1Z4uPjOfLII8n2m1fAv1bdsGFDNm/enPN5xowZjBo1il9++YWMjAwyMzPZu3cvu3fvpkqQ3wvgggsuID4+nj179lC3bl1efvnlnGufc845nHbaacyePZt58+Yxbdo0Hn30UV566SUGDx5cZN6qiogEXZZoYME4jETkGtySjzHR/P/LLy7GfvihazVr1iz/1rQpNGgAcXEC118PXbq4zl2//w7HHANPPglXXOGatY2JJSJuNrpyomqYvktCQkKezyKSE7BXr17N6aefztChQ3nwwQepVasW3377LZdeein79+8vVjAeM2YMffr0ITU1lbp16+Y7npyczAknnMAJJ5zAXXfdxWWXXcY999xTZDDeunUr//zzDy1atAi6LNHAgnEYqepYYKxnGcX0SJfHnyosXQqTJ7sg7PeHNT/9FPi8xERo0sQboI/mkAGLueDzwTRd+hFcdRVZs74hftwL7lmYMSZsDj74YBITE5kzZw7NmjUD4MCBAyxYsIAbbrgh6HxSU1OpX78+CxYsoHv37gBkZWWxePHiYo1FXrRoEdnZ2YwePZq4OFfnePfdd4M+31eDBg1o2bJl0OkPO+ywgM+4/T355JPExcXRt2/fEpUrUiwYl3PZ2W5diPffd0F41arcYwkJ0Lu3G7nUuLEbWrxmTd5t3TrYvx/++MNtTi1u4wNuYjQPcxuV3n6TbV8uouaMSdCuXSS+pjHlUtWqVRk6dCjDhw+nVq1aNG3alEcffZTdu3dz6aWXFiuvYcOGMWrUKFq2bMmhhx7K008/zbZt24rVnNuyZUsOHDjA008/zRlnnMGcOXN4/vnni/u1CrV161b69+/PkCFDOOKII6hWrRoLFy7k0Ucf5ayzzsqTdseOHWzcuJEDBw6watUqXn/9dV566aWc7xlLLBiXQ5mZMHu2C8BTpsD69bnHKleGk0+Gs8+G0093j4MLc+CAO983QLugLXy85mYWr+rCq/sH0HjzSvZ37Eyl554h7tJLrNnamDB5+OGHyc7O5uKLL2bHjh106tSJ6dOnU7NmzWLlc+utt7Jx40YGDhxIfHw8V1xxBSeddBLx8fFB59G+fXsef/xxHnnkEUaMGEH37t0ZNWoUAwcOLO7XKlBKSgqdO3dmzJgxOc/MmzRpwuWXX87tt9+eJ+3dd9/N3XffTWJiIg0aNOCYY45h5syZ9OrVK2zlKSuiasNUwsXvmfEh6enpVK9evUyuvW8fzJzpar8ffJB3RcRq1VzgPeccF4jD+ZhMFZ66ewutH7iYU/gMgMyLBlLphWfL1fM4E9v27t3LqlWraNGiBcnJyZEuTlTIzs6mTZs2nHfeedx///2RLk7MKuxnKyMjg9TUVIBUVc0oLB+rGYdRWT8z3r3brX74/vvw8ceQ4fNPXbs2nHWWqwH36eOmnC4NInD9/XV485BPuHPwI9ybdSeV3niVzIULqTR5Ehx2WOlc2BhTLGvWrOHzzz+nR48e7Nu3j2eeeYZVq1Zx4YUXRrpoBgvGMSc93a3j8P77MG0a7NmTe6xhQ+jXzwXgHj2gUhn+61743zi+aTqCM0/ryrid59No5XKyOx1F3AvPw8UXl11BjDEBxcXFMWHCBG6++WZUlbZt2zJjxgzatGkT6aIZLBjHhC1bXNPz5MkwY4brUOXVrJlrfj7nHDfSKC6Cg6q6d4d6C7pz5olLGbX2Ik7YMwMGDoSvv4ann3YPrI0xEdGkSZM8Cy2Y6GLBuBRNmRL02P6ANm6EqVNdLMvKyt1/yCG5AbhDh+jqK3XoofDJgnqcedpnzF70ICMZSdzLL8OCBW6SkNatI11EY4yJOtaBqxTkPjNOx82MGbq0NBd8zz47Nh7D7toFF14IOz+cyZtcSH02oykpyLhxcP75kS6eqWC8nWyaN2+eMw+yMeGwZ88eVq9ebR24oon/DFxdu4b23DY52XW+6tcPDj44PGUsK1Wrumb1G27oTdozS3mTC+m1cxZccIGr6o8Z476gMWXAO6vU7t27LRibsNrveW5YnCFigVjNuBR4a8ZlObQpWqm6uHvrTZnczb3cwYPEoa59/d13IcYG5pvYtWHDBrZv3069evWoUqVKzM1dbKJPdnY269evJyEhgaZNm+b7mSpOzdiCcSmwYJzf++/Df/8L3fdO573k/1Jt7xaoXh1eecW1vxtTylSVjRs3sn379kgXxZQjcXFxtGjRgsTExHzHLBhHmAXjwGbOhBNOgIa6jh8PO59ay791B4YNg8ceK73B0Mb4yMrK4sCBA5EuhiknEhMTc+bp9mfBuBhEZArQE5ipquf67D8EeMcn6SHABao6NYg8LRgX4OabYfRoOKh+Jr8NuJPKTz3iDhx1FLzzDsTYSivGGFOQ4gTjmFjqr5Q9CeSbWFVVV6pqmqqmAd2AXcAXZVy2cueBB6BNG1i3qRJDNj/spg6rVcsNferY0Q2oNsaYCqbCB2NVnQXsKCLZmbia867SL1H5lpwMEydCfDy8/Ta8u+s0WLLEzViyfTv07Qs33eRWqDDGmAoiqoOxiHQXkY9EZL2IqIj0DZDmGhFZLSJ7RWS+iBxdCkU5j7xN1iYERx0F3sVXrr4aNiY2dcOdbrzR7Xz8cTed119/Ra6QxhhThqI6GANVgWW4sbv5iMgA4HHgXqCjJ+10Eannk2apiPwUYGsUTAE8z3+PBT4N8bsYH3fe6SYy2boVrrwSNCHRPUyeOtWt6zhvnhv+9MknES6pMcaUvqgOxqo6TVXvVNUpBSS5ERinquNVdTlwFbAbGOKTR5qqtg2wrS8gT39nAZ+r6t6CEohIkohU925AtSDzrrASE+HVVyEhAT780L0H3FJTixe76vO//7q1H2+7zS3SbIwx5VRUB+PCiEgicCQww7tPVbM9n7uE8VLBNFGPwM196d3+DuP1y6127eC++9z7666DtWs9B1q0gNmz3ZAngEcegV69YN26iJTTGGNKW8wGY6AOEA9s8tu/CWgQbCYiMgOYBJwqIn+LSBefY6nA0cD0IrIZBaT6bI2DvX5FN3y467uVkQFDhrgZuwA35vipp9ziEtWrw7ffunbtzz+PZHGNMaZUxHIwDgtV7aOqdVW1iqo2VtW5PsfSVbW+qu4vIo99njFkFwPzgJmlXOxyIz7e9a6uXNktD/n8834Jzj0XFi1ygXjLFjj5ZLjrrrzLWBljTIyL5WC8BcgC6vvtrw9sLPvimJJq3dq1RIObFOSPP/wStGwJc+fCVVe5qvMDD7gVNDZsKPOyGmNMaYjZYOyprS4Cenv3iUic5/Pcgs4r5TKNVdXDcE3bphiuucY9Ft6928XZoUPh5Zdh6VLPkOPkZHjuOXjzTUhJgVmzXG/rL7+McMmNMSZ0UT0dpoikAN5lfZbgek9/Bfyrqn95hjZNBK4EvgduwHW4OlRV/Z8ll0V5fZdQPMSmwyyeNWvgyCPdcCdfSUnQvr07dtBBUHPzSs55pz/1N/1INsJX3Udy4JY7OOnUeGwhHmNMtCg3c1OLSE9c8PU3UVUHe9JcCwzHddpaClynqvPLpoSB2dzUJffPP66yu2gRLFzoRjmlp+dPl8wenuI6LuclAL6gD88e+wZ3PFmPTp3KuNDGGBNAuQnGscqCcfhkZ7tnyIsWuW37doiLAxH32vm31zh/1lUkZe5mPQ05n7dpfEF3HnoImjePdOmNMRWZBeMIsWbqCFm+nP19+5P423KyiONOHmBMwq3cc28cI0ZEunDGmIrKgnGEWc04AnbtchNde6by+pRTGMirfDy3DsccE+GyGWMqJFtCMUI8i1Ysx3UmM2WpalWYMAFefhlNTuZUprGEDrx65Rzs701jTLQrUc1YRM4swbW+UNU9JTgv5ljNOMJ++IED/fqT8OevZBLPb0Meps1LN2FdrY0xZanUm6lFJLuYpyjQSlX/LPbFYpAF4yiwYwdLOl9JhxVvAaCnn4FMnAC1akW2XMaYCqOsmqkbqGpcMBtuJaVyz5qpo0i1ajT/9g3+r/Lz7CUJ+fgj6NgR5kd01JsxxgRU0mA8EShOk/PrQKF/FZQHNgNXdKlZS2h075V0YS6rKrV0s4ocdxw8+ST2INkYE02sN3UpsGbq6LFnj5v7OuPvdL4/4jIO+eE9d+Dss918mzVqRLR8xpjyy3pTG+NRubJbMzmDVLr89S67H3kaEhJg8mTXbL1oUaSLaIwxFoxN+TdwIBx+OGzbLjyVfS18952bnmvVKjj2WBg71pqtjTERVexgLCKdReRJEVkgImtF5FcR+UBErhKR1NIoZKywDlzRKT4ehg1z7z/+GOjUyU163bcv7N8P114L558PGeW+W4MxJkoV65mxiHwM/A18BCwG/gGScSsr9QBOB55W1Q/DX9TYYc+Mo89ff0GzZm4+63/+8YxwUnWduYYPh8xMaNUKJk1yS0QZY0yISm2csYjUUNXtoaYp7ywYR6e2beHnn+Htt2HAAJ8D8+a5HX/95dZrfPppuOwymyTEGBOSUuvAparbRaSyiBzkf0xEDvemKU6expSVU05xr59+6nfgmGNgyRI4/XTYtw+uuAIuvhh27izzMhpjKqZiBWMRORf4DfhERH4Qkc4+h18La8mMCTNvMP7sM7c0Yx61asEHH8Ajj7iHzG+8AUcdBT/9VOblNMZUPMXtwHUncKSqpgGXAC+LyIWeYxW+Tc86cEW3bt0gJQU2b3YV4Xzi4uCWW2DWLDjoIPjlFzj6aBg/vqyLaoypYIobjBNUdROAqi4CugNXisjduPmnKzSbgSu6JSZCnz7ufb6mah8bDu7Gm8OXsP/4k9ysIUOGwCWXwO4KMaurMSYCihuMN4vIEd4PqvovcALQBjiiwLOMiRKnnupep03Lf0wVXnoJ2rSBi26oS7OfPuXH8x9A4+Lc8oxHHw0rVpRpeY0xFUNxg/FdwCbfHaq6X1UvwA1tMiaqeZ8bz5sHW7fm7v/zT1drvvxySE+HKlVg4+Y4jnj7Du7qMpOseg1cV+yjjoLXX49M4Y0x5VZxg/HVwAci8qyI9BWRat4DqjonvEUzJvwaN4Z27Vwt+PPP3b5ff4XOneHLL930maNHu7HI99zjZs58cE5Pzmi8lOxevWHXLtfT+oorXBO2McaEQXGHNl0IdAFeAQ4DporIlyJyl2dmrpjqxCUiU0Rkm4i8F+DY/4nIzyKyXESeirXvZgrmO8Rp/Xo48UTYsgXS0uDHH+HGG13NeORIWLDArSUxbXF9rmw+Hb1npBt/PG4cdOniIrkxxoSo2NNhqrNQVR9S1d7AmcAyYCAQa4vFPokrdx4iUhe4FjgSaOd5PaZsi2ZKi/e58WefwUknuZUVW7WC6dPh4IPzpm3f3k0SEhcHL42P5/n697gqdb16sGwZHHkkvPNO2X8JY0y5EvJCEaq60zP95auqGlO9iFV1FrCjgMOVcFN9Jni2zWVULFPKjj0Wqld3teGffoKGDXPjayAnnQSjRrn3110HP9Tr48ZG9ejhJgY5/3y4+mrYu5f9+90qUY8/HmAsszHGFCCcqzZNCmNeiEh3EflIRNaLiIpI3wBprhGR1SKyV0Tmi0hY/hhQ1X+A/wF/AeuBGar6RzjyNpGXkJA7xKlGDVcjbt688HOGD3fnZGZ6hkU1agQzZsDtt7sEzz1HZueuDOz6B/fcAzfdBIMHw4EDpfc9jDHlR6XiJBaRdws6BNQKvTh5VMU1f78CTA5QlgHA48BVuObxG4DpInKIqm72pFlK4O94oqquL+jCIlITt+hFc2APME1EuqvqNwWkTwKSfHZVC5TORI877nCBdcQI16GrKCKuIjxjhpsLBIBKleDBB+G448i+6L9U+mExL9CR+OTxvHPgbF57zS0ENXmya+Y2xpiCFPdXRB9gIjA2wLYrnAVT1WmqeqeqTikgyY3AOFUdr6rLcUF5NzDEJ480VW0bYCswEHv0AX5X1X9VdQ/wCYU/Mx4BpPtsfwf3LU2kdOzoZr88phg9AQ491L3mBGOvk0/mpWuW8i1dSSWDN/aew2+n3UC1pP188AE891zYim2MKaeKG4xnATtU9Wu/bRbwQ9hLVwARScR1qprh3aeq2Z7PXcJwibXAsSKSLCLxQE9gZSHpRwGpPlvjMJTBRBnfYOy/2NnzHzemF1+x+IRbAGjx4ZP81uA4mrGa226DtWvLuLDGmJhS3KFNZxfUVKuqJ4SnSEGpA8TjNwGJ53ODYDMRkRm4Z92nisjfItIFQFXnAZ8CS3B/ZPwBFLhGs6ru8yyPdTEwD5gZ/FcxsaJVK9fcnJ4Om3x+8las8Mx1XSmBpm8+Ah99BDVrUn/N9/wQ34FeOz9k6ND8AdwYY7xCepIlIkEHvmikqn1Uta6qVlHVxqo61+fYHaraRlUPV9XrNIiFn21u6vItKQn+8x/33ndWzDfecK8nnwx16uCWYlyyBDp3pnrWdj7kLHp+cjPvvO56c+3d6xaHOvtsWL26TL+CMSZKhdqt5POwlKL4tgBZQH2//fWBjWVfHMdWbSr//J8bq8Kbb7r3F13kk7BZM/jmG7jhBgBuZjT/GdKDd/63lkMPhdtugylT3Kgo63FtjAk1GEdkVipV3Q8sAnrnFEQkzvN5bkHnlUG5rGZczvkH47lzYdUqtzTjmWf6JU5MhDFjOPDuZDLiUjk6cy69h3egzZppNG4Mqakwf76b6csYU7GFGoxL7SmYiKSISJqIpHl2tfB8bur5/DhwuYgMEpE2wHO44VARW3zWasbln38wfvtt99qvn5tCM5CE/v34c9JiFsmR1GEr0ziVPweM4KXnMwF47DGb5tqYii6aRz92wnWg8i4D/7jn/X0AqvoOcLPn81IgDTjZu95yJFjNuPzz71H9wQfu87nnFn5e2tn/oc4vc9hz2bUAJIx+mHOe603bWus5cMDNBGaMqbgkiH5JBZ8s8oOq2jrGHiJyDXAN7o+cQ9LT06levXqES2XCaetWTyctXBN1ly6QnOz2F1QzzmfSJLj0Utixg20JdTnvwBsMGHcCl11WasU2xkRARkYGqampAKmeETcFCrVmnBXi+eWK1YzLv9q1oW5d9370aPfap08xAjFA//6waBGkpVHzwD9M5yQOevFuyLL/TsZUVCEFY1XtEK6CGBMrvE3V77/vXs84owSZtGoFc+fy6/FXEodyyoL74YQTYGPEBgMYYyIomp8ZxxzrwFUxeIOx9wnP6aeXMKPkZPY/+TwX8gY7qQpffeUWVf7qq3AU0xgTQ4oVjEXkCxG5zLPer+/+OBHpIiLPisjgsJYwhlgzdcXgDcbgljNu1KjkeR1yCLyfeCGdWMj+1m3d1F59+sD99+dbg3HPHlcLP+ggt1jUvn0lv64xJroUt2bcFzd86GMRWSUii0VkBW66yCuBiao6IbxFNCa6tGmT+75ETdQ+EhLg8MNhJYfy2b3zYcgQF4TvvhtOOQX++QdwtfDzz4ePP4b16936ypPzrWVmjIlVxZ2bepeqPqmqnYFDgNOAY1W1haoOVtX5pVLKGGHN1BWDb8041GAMrmUaYNGKKvDyyzBhAlSuDJ9/7g7Ons2vv8KHH7qlHA85xKW31mxjyo8SPzNW1f2qugF4X0Tu8T8uIjVF5MuQShdjrJm6YmjeHC64AC68EDqEoQvjYYe5119/9ewYNAgWLHBV8PXroVcvdt75MEI2Xbu6SUIAvv469GsbY6JDODpw9QSuFZGpIlLVZ38i0CMM+RsTVUTcfNRvvOHeh+rgg93rH3/47Dz8cPj+e7j4YsjK4sj3RvARZ3B8+60cd5y77q+/WudrY8qLcPWm7oNbunCeiDQPU57GVAjeYPz7734HUlJg4kQYN469ksxpfMqtb6dRY/l3tG/vknwTcEFTY0ysCVcw3oCrBf8ILBCRnmHKN6bYM2NTEt5lGbdtc1seIuw8/zKOYT6/0ooqW/+GHj0YkTgaUFauLOvSGmNKQziCsQKo6j5VvRB4EvgMuDoMeccUe2ZsSiIlBep7FgPN01TtsXAhLNMjOOugRa5LdWYm531/M1Ppy/bV/tHbGBOLwhGM8zw1U9UHgIuAm8KQtzEVQsDnxh4//uheWx9ZzT2sfu45siolchYfcutbHdyzZWNMTAtHMG4B/OO7Q1XfBzoDQ8KQvzHlnjcYL1oEzz0Hu3blHvv5Z/d6+OG4nltXXcXXo+byB/+h3p410K0bPPVU7pRgxpiYE3IwVtU1GmDpJ1X9WVUnhpq/MRWBNxg/9hhcfTXcdlvuseXL3evhh+fuq9KtIx1ZzKeVz4EDB+D6690CFOnpZVdoY0zYlDgYi0j1YLZwFtaY8sobjL0mToQdO1xl11sz9o5HBmjYEDJIpV/mJPTJp9xUXu+/Dx07wuLFZVdwY0xYhFIz3g5sK2TzHq8wrDe1KSn/YLxjB7z+upuq+t9/IS4u78xfDRq41/0HhH8vGgbffgvNmsGff7pFlp97zpqtjYkhoQTjXsDxnq03sA+42Gef93iFYb2pTUn5BuPjjnOvTz+d23nrP/9xM2R6JSVBrVru/YYNwNFHw5IlcOaZsH+/a+u+4AIX1Y0xUS+U6TC/9tlmAVnAPL/9NmGfMUGoWxf++1831/WUKVCjBqxYASNHuuO+TdReDRu61w0bPDtq1kSnTOWnS0aTFVcJ3nkHOnWCH34o8vqqOWtSGGMiwNYzNiYKiMBrr7nFIGrXhltucfu/+869HnNM/nPyBWPgpZeFduNvpFv2N2xIaOLmzOzcGV56qdBm69tvh3r1YNiwMH0hY0yxVOhgLCJTRGSbiLwX4NjNIvKziPwkIv+NRPlMxXXddbkTgfTtCzfckD+N97mxbzCeMsW9zqMLbQ8sYWPHU2HvXrj8chg4EHbuDHi99zz/A555xj12NsaUrXAH41jrMfIkMNB/p4i0Ay4EjgSOwi2EUaNsi2YqsqpV3apMb73lAqXv82Ivb7D2bV729rxu0QL+pTYTzvkIHn4Y4uPh9ddZU/8ozm/3M/v3556zfXveebFnzw771zHGFCGUoU2TfTcgGXg+wP6o5XnWHaiHSxtgrqruVdU9wDLg5LIsmzGHHOJmv4yPD3zc24HLO591Rgb89Zd7711n+Y9VcXDrrfDVV+yv24hmu3/hlZ+O4ve7cqcAWLAgb77ffhvGL2GMCUooNeN0v+11YH2A/SUiIt1F5CMRWS8iKiJ9A6S5RkRWi8heEZkvIuHqxfwT0FNEaohITdwykQeFKW9jwqJmTffqDcbeyUEaNnT9tsBnes3jjuO5y5cwnROpwh4Oe3QwDBkCu3fz008uiXc5yCVLyqL0xhhflUp6oqpeEs6CBFAVVyN9BchXwxaRAcDjwFXAfOAGYLqIHKKqmz1plhL4O56oqusLurCqLheRp4AvcX9QzMP1FjcmaniD8b//ulffaTMDzXX90fx6/B/TuJ2HuJd7iB8/HhYsYFubScChDBgAb7/t8snMhEol/u1gjCmuEtWMReQIEQn6XBE5XESK9V9bVaep6p2qOqWAJDcC41R1vKouxwXl3fjMh62qaaraNsBWYCD2OfcFVe2oqr2AA8BvhXy/JL9Zx6oV57saUxL+zdS/eX5C27TJDcZr18K+fbnvlTge5E76Vp3heoD99BO3vd+JC3iT006DKlVcf698aysbY0pVSZuplwC1i5F+LtC0hNfKR0QScZ2rZnj3qWq253OXMF2jnuf1ENwkHtMLST6CvE3zf4ejDMYUxr9mnJHhXmvVcsOUUlLcaKZVq9z+LVtyz/14Vy92fLMEevWiSvYu3uQiTppyJUcetgcgp+naGFM2StoQJcD9IrI7yPSJJbxOQeoA8cAmv/2bgEPzJw9MRGYA7YGqIvI30F9V53oOfyAiqcAu4BJVzSwkq1G4JvPLPVsc0CrYchhTEv41Y+9KT1Wruue/bdq4zlk//ggtW+YGba/VexvQ7P0vGFPrPu7ifupOfpHXas6nD5P44w/78TWmLJW0ZvwNcAjQIchtLrAn1MKGm6r2UdW6qlpFVRv7BGJUtYuqHqaqR6nqoiLy2aeqGao6WlUPBTqVeuFNheetGe/a5WbA9AbjKlXca/v27nXZMti61b0XgSOOcO/XrIG/1sUzkns5N2U61K1Ls23LWMSR1Pj8XfbuhWuvdc+RjTGlq0Q1Y1XtGeZyFNcWXIeq+n776wMby744johcA1xDBZ9MxZSN1FQXXFWhX7/ceayrVnWvaWnudenS3LHItWq5ea5/+MEF4zjPT+qfB58Any5l0/HnU3/lbK78cgBL+3zDuDmjGTs2idNOg2rWE8KYUhOTQUNV9wOLcAtUAODpUNYbVwuPVLlsoQhTZuLjXUAG+PRT10ELcoOxb83Y+7y4Th23uBN4asaecclNmwKNGvHHi1/yECMASJszljl0pQV/MnVqqX8dYyq0qA3GIpIiImkikubZ1cLz2dsR7HHgchEZJCJtgOdww6HGR6C4gC2haMqe97mxL28wbtfOvf79d24nrrp1c2fu2ro1Nxh7A3TLQytxBw9xCp+yhdp0YhGL6YhOLmhQQ66VK+HBB3Oby40xwYvaYIx77rrEs4ELvkuA+wBU9R3gZs/npUAacLKq+nfqKjNWMzZlzfvc2Jc3GFevDomerpO//upe69Z1+yHvjF1NmuQeB/iMU+jAEn6reyw1SGfg1LPdBNm+82j6ueACuPNON6+2MaZ4ojYYq+osVZUA22CfNM+oajNVTVLVzqo6P4JFtpqxKXOBJubwBmMRtxQj5I5BrlMnbzDevNm99y464Z2FC+BvmrBm4iwe42a348kn3WLLa9bku+Zff+XO3PXmm3Dlldbxy5jiiNpgHIusZmzK2sYA3RW9wRhyg/Eiz3iABg3yBmNvL+s6dQLn36J1ArfwGOcmfuiq4d9/Dx06wEcf5Uk32WeOvL174cUXXU3ZW/M2xhQupGAsInEiMkREPvYsNfijiHwoIgNFfP/GNsaUBt/lE70CBePVq91rr16Bg3Ftnyl8xoxxr2PH5nYQe3//GWR+vxiOPtoNbD7zTLfo8oED7vj7gcvnnS/bGFO4UFZtEuBD4CXcIgo/Aj8DzYAJQNE9PsoZa6Y2Ze3CC/PvCxSMwQXWbt3yBmNvL2vfYHz99W5N46FD8w5n2lW3uVtf8frr3Y7HHoOePdG1f/N9AT/x69YV9xsZUzGFUjMeDHQHeqtqB1W9QFXPV9X2QB/geBHJt1ZweWbN1KasjRnjOk35KigY9+gBCQm5wfiff3J7PvsGYxG3HrIIJCW5cwB27MD1CHviCbLefY89idXhu+/QtDR67f8MyK1Je61bBzt3uprznqib9seY6BFKML4AeEhVv/I/oKpfAg8DF4WQvzGmCDVquNZiX0lJue99g2NTz6BAbzD2LiARF5c/iPry1o537IAVK+Dkk2Hwh+fQdv9iFtGRuH+38hmn8GjiHTSql3fW2PXr4amn4Nxz3R8DxpjAQgnGRwCfFXJ8Gm7e5wrDmqlNJPjWhCFvj2jfmvFBnhW5vcHYq3bt3Jm4AklJca9PPw1nnAHTp8Prr8OfHExX5rC4y9UADN//EK+u701DchdFW7cOPvnEvV+woBhfypgKJpRgXIv8CzX42gQEGAVZflkztYmEwgJpoGCcnJx3SFTtItZf89aMn3su7/rIAPtIZkS1sQzgbXbFV6PTrm9YShq9PQuqrVuX948Fb23cGJNXKME4HihsJaMsSr4qlDEmDAIFY5G8teNgg3FBPv8c3mUAN/dYyO9VjqAe//A5J3IPI9m4LivPHwub/P58/+AD+PDDIr+GMeVeKMFSgAkiUtDfukkF7DfGlJFAwRhcMPYuqeiddasgwS4QcaBFa67LnkffWddzBeMYyb0ct3k2Y7a8AbhZRTZuzH12vX499O3r3u/Z42rsxlRUodSMJwKbgfQCts3Aq6EWMJbYM2MTbeLjc9/7B2OvRo0KzyPYYFyvHkiVylzJi/yX19hFFXrzJROWHMFJnu4lvpOU+A6H8q7JbExFVeKasapeEs6ClAeqOhYYKyLVcX+QGBNRWVm5770dsSDvjFsNGxaeR2HBuGHD3IlH6tXLrd2+wX9ZSCfeYQDts3/gM07hf9zE5r8fAtyE2b4TlmzbVnQ5jCnPbDpMY8qxM8+E1q3hiivy7m/ZMvd9UUHQt6nbn28+devmHVa1kkPpzHye5loAbmY0pz54LPz+O5A7+xfA9u2Fl8GY8i6UGbi6iMjpfvsGisgqEdksIi+KiD03NiaCUlLgl1/ghRfy7m/VKvd9UcE40DKNXkcckfu+QYO8wRhcb+vreJqzmMpWatFo/SI3t/Xrr+c8s4bcZursbLjqKrfQhGrh5TKmPAmlZnw3cLj3g4i0A14GZuAm/DgDPKuUG2MiJtAs8cWpGRfW2/rQQ3Pfd+lScCesDzmL9ixjTqXubkquiy/m7A8GkcIOwO0CN9vmCy+4hSb8h1EZU56FEozTgJk+n88H5qvq5ar6OHAdcF4I+RtjgvTYY+51RJB//rZokfs+lJrxoEFwyilulq0qVfLWjH07jwGsozHdM78k8+57IS6Obn++yiKOpCOLcsYf//lnbvqffw7uuxhTHoQSjGuSd9KPHrhZt7wWAE1CyD/mWG9qEyk33eQexT74YHDpW7d2rykpRQ9tKqxmXK0afPopDBvmPvsGY9/e217ZxLPpirth1izWV2pCa35jLl1o9fEYUGV97uRdAVekMqa8CiUYbwJaAIhIItARmOdzvBpwIIT8Y47NwGUiRQQOPjhwk3QglSvD5s1uaUX/Gqy/QMG4atW8axh7+Qbjww7Le8zbK/uff0C7HUfnxKVMoS+JHKDLpBtZ3Oh0Mn7fnJN+82b33Hj48OBr/MbEqlCC8afAwyJyHDAK2A3M9jl+BGBPfYyJUnXrFj37FgQe2rRjB/Trl3+/7zPjNm3yHvOOZ77tNjjySPh7dy3OZjJXM5a9JNFx46fc8no7TucjwAXjpUvhf/+Dhx8uuKa8YQNccEHBayobEwtCCcZ34abD/Bq4HLhCVff7HB8CfB5C/saYKFC/fv59BdXAfccy+z6Xhtzm8OnTYcmSnJx4jqs5igX8SFtqZ27mI87kRS4nY90Ovvsu9/yVKwNfc9w4ePtttzKUMbGqxMFYVbeoanfcs+OaqurfaNUfuDeUwpUmEWkiIrNEZLmI/CAi/f2Ony4iK0XkNxG5LFLlNCbSUlNhzhy4/343c9frrxectl693PeNG+e+T0wsfLzyT7TjKBbwGDeTjXA5L/HwZ+1J/3ROTpo1a1xnsTvvdJOZpHum1fHt9GU9sE2sCmWccZyI3Iprrp4nIg+LSGXvcVX916+mHG0ygRs8z3hPBJ4QkaoAIlIJeBw4HugADBeRIBr0jCmfjj3WBcFt2+CiQlYp9+0M5huMExIKD8bgxiTfwmP04ivW0JRGe1dx66fdeYgRJLCf8ePhs89cJ7Vzz3W9vFescIHeq2VL2LvXNW936ADTphV4OWOiSijN1HcADwE7gXXA9cDYcBSqLKjqBlVd6nm/EdiCWxYSXAesn1V1naruxPUSPzEiBTUmihS2XCNATZ9FU32D8f79robtz3ecstc39OAIfmACg4gnmxE8zPcczb6FP+akmTrVTRDyySeQ6bd23J9/uufZS5fCqacW+ZWMiQqhBOOBwNWqepKq9sVN8nGRiIRlik0R6S4iH4nIehFREekbIM01IrJaRPaKyHwRKVEvZhE5EohX1bWeXY1wf2B4rQMCDNQwxvjybab2DcwHDgSeEOS8AmYiyCCVS5hAPybzD3VIYxnf7OrIvdxNIrkLxR044DqT+dqxw/USNyaWhBI4m+KaqAFQ1RmA4gJZOFQFlgHXBDooIgNwTcn34oZVLQOmi0g9nzRLReSnAFsjnzS1cKtL+c3ea4wprsaN4fbb4e678wbf+Hg47bT8AbmoZROn0o92/MhUziKBTO7mfpbQgS64nl2ZmfnntT7mmNC/hzFlLZRgXAnY67fvAJAQQp45VHWaqt6pqlMKSHIjME5Vx6vqcuAq3PCqIT55pKlq2wDbegDP3NlTgYdV1affJuvJWxM+yLMvIBFJEpHq3g03xtqYCunBB+Fev66b8fHQqxdkZMArr7h9xx+f93mvr3vuyX2/iQb0YwrnMomN1OcwVvAt3XiKYcTt2pHTkaso//yTt7OXrzlzoHlz1/xtTCSIlnA2dhHJxj1L3eez+wzgS2CXd4eqnh1KAT3XUqCfqk71fE7EBd5zvfs8+ycCNVT1rCDyFOBNYKWqjvQ7VglYAfTELYW4CDhWVbcSgIiMBO7x35+enk5134VjjalgvEOgKleG3btz9//2GzRrBs89BzfckP+8desCz+BVk3/5HzczhPEAbEpozLADo5lEfyDweCvvrzhvWTZtch3NBgxwzdmzZrkavXexipL8Sty/v+A/LLy2bHHPzRPCUl2puFatgjfegGuuyfsoBNy/w5YtRa/RXVYyMjJIdZ0lUlU1o7C0odSMJwKbccHKu72Oq0H67isNdYB48k7HiedzgyDz6AoMAPp6mrOXeha7QFUzgZuAr4ClwOiCArHHKCDVZ2tcSFpjKhz/jl+tWrngVVBgalDA/+Jt1OJSXqEPX/AnLah/4G/eZQBfcjxt+THwSeRd17l+fZgyBSZNggULYP783EDspQoLF+YuYFGYN95wM49NmpR7Lf+A/vff7g+Abt2Kzs+bx/33uz8U/vzTdXR79tngzi1KdrabPe3EE92Y77174Ztv8neEy852E6/s2OHuk/c7HTjgVtV6992irzV7tu+Y8vz5N2/u/ki67rqC85g7F773mWC4c2e46y7Xm75qVfj1VzcxzCmnuM8HHQQ//ODSLl8Ojz4Ke/bkz3fmTLjxRnLmRf/2W9fx76+/iv5epUJVo37DPYvu6/O5kWdfF790j+IWq4hUOa8BlgO/AJqenq7GVGTuV7hqSkrg4y++mJvGd1NVrV498DHvVpldejcjdTfJqqCZxOlTXKu12JIn3UsvqW7dWnhe/teeNCn387ZtwX3HKlVUs7NVjz5aNS1N9cCB3DTPPpubLiur6Pv2+uu56S+/PG/ZfP34o2qHDqrXXaf677+F55mVpTp5sur11+f9vv36udfbbsubfsCAvOkmTXL7X3opcHn271edOVP18MNVP/lEde3a3HRXXKH6yy950/uX47vvVN95R/W339zxdetUjz8+9/imTaqvvhr8v6Pvduyx7p4ecohqz555801MVH3rrbzpR41S/f33ov+dipKenq6eWFVdi4ofRSWIhi1AME7EjRPu65duIvBBBMtpwdgYH0UF41deCfzLU1W1efPgftE2ZbVO4pycHduprndyn6aQkZPm99+D/8WtqnrSSbmfTzwxb5l37HCBx/87pqaq7tqV+/nHH3PT+Ab3d95x+3buVJ06NXf/t9/mpr/33sBlmzs38P0F1bPOcvvWrFH9+ef89/q11wr/3klJqhs3qnburDp0aP7j/fq5fB54IHffwoWqe/eq/vOP+2MkmHt72WXB/1tEegvmD6fClPtg7Nk3H3ja53Mc8DdwWxSUt7oFY2Nyf6kVFIwLChCqqgcfXLxfnMczQ5dyRM6OzdTRG3hcK7NL588PLo+DDnLXPuOMvPuXLHG13r//Vk1OdsHa/zu2aKGakZH7+aOPVGvUcO9vvz13/6RJqn/+WfD3vvXWgsvXrVvudX0DuXfzDXSNG7tA6dWtW9Hfv2fPwo8/91zg/R07Bnd///ijeP+mkd7efTe0n/9yEYyBFNyayWmeL/N/nvdNPccH4HpzDwLaAC8A24D6ESyz1YyN8eH9pVatWuDj/s2DvkGpZcvi//IUsvQ83taVtMrZuZk6uvjMe7QOm4PK49tvA+9v1ix/GX2/I6g+8kju+7i4wPlMmaJ6882Bj+3dW3T5rr02b809mGDy8cfFv5elsQVTe46mrU+f0H7+y0sw7un5Ev7bBJ801wJrcD265wOdI11uT7msZmyM5v5SKygYv/de4F+CqqqNGpX8l2g8B3QIL+mfNM/ZuZtkfZ4rtCMLFbJD/kWt6gJrcc+76KL8z0u925o1oZXJf3vjDVfOvn3Dm29F2kJRnGAc8mxZItLUM0zIf7+ISNOS5quqs1RVAmyDfdI8o6rNVDVJVTur6vySXi8cPDOCLQe+LzKxMabQYT6+PaCLK4tKvMKltOI3zuMdVtfpRGX2ciUvsohOLKEDw3iKumwuOrMCZGcHXkayKG+84YbgBNKsWYmLE9CyZa6nsY2fjn4lHmeck4FIFtBQVTf77a8NbFbVIpYuL388E3+k2zhjU9F5/0yvVs1N+OHv00/dzFz+VGHGDDjnHLf4w+LFbv///R+MGVP8cpxysrLzs9kM5Tn6MYVkz/QI2Qjf0o0p9OMDzmIV/wk6z/nz3TAbU76FEiLLapyxl+Cq4f5SyD9DlzGmAipo/WPf8cf+k2b06ePG/15ySe6+hg1Ldv1pnwmz6c6FvEUj1nMNz7CATsShdGc2Y7iRPzmYVTRnPIMZyERa8StCdoF5WiA24VSppCeKyOOetwrcLyI+8+sQD3TGTZhRYYjINbhOXGFZLMOY8qKgYOzbFF2tGmz1m1onLg4q+fyWKmoZxmBsoxbPcg3Pcg1N+Iu+TKUfU+jGtzRnDYOZyGAmArCTqiyjPUvowC8cyh8czB8czGqac4AiptwyphhKHIxx6/yCqxm3A3yfguzHLdzwvxDyjzmqOhYY622mjnR5jIl22T4Vzxo18gdjyBuMAy3DGIrzbmrK6NHX8TTXUZWdHMt39OIrevA1aSwlhV105Tu68l2e87KIYyMN2ET9nG0jDdhKbXZQjQyqk0H1nPc7SWEfSewnMc9rFvEUNI2nqVhKHIxVtZdnjujPgOtVteC56IwxFVpBNWPfYFytgOVVwl0zBqhSBV57DX76KXffLlL4ghP5wrN0eTyZtOZX0lhKGktpxW+eevEfVGU3B7GegwpePyYo2Qj7SGIfSWRSiSziySauwK2g4xogoIeyL9Tzi3OdaDaPY4DRZXKtUGrGqOp+z3zO9mwYa6Y2prh8m6mrVAmcxrcDTbhqxrs8S9n88kvBabKoxAoOYwWH8RYX+paI+mziINbl1IsbsJH6bKIm2zx14gyqsSPnNYWdJLEvp+OYVxxKZfZS2X6FRqUMyq4DbkjB2ON14FLgtjDkFdOsmdqY4vGtGVeuHDiN7zCg4g5OqFQp/wIIBeUdPGETDdgU9Jo0vpRKZJLIfpLYl+c1gQOF1ImziSerwP35S5i/T22w+0I9vzjXiXabqccpZXStcATjSsAQEemDW2pwl+9BVb0xDNcwxpRDvXu71yOOgBEj3Eo6/fvnTdOlS+77QAH76KPzrurjK1Aw9m0y31vmFVIhkwQySWA3Vcv64iaKhSMYtwU8owBp7XcsNv8cMsaEVUHPjGvWdEv0JSe7wLlunVvi0NcRR7hJK6pXh/gAsxbUr++GGc2f754pb9+eeywxMX/A9S1LyWrGxoRfyMFYVXuFoyDlgT0zNqb4UlJy3xe0KPxZZ7nX9QH6S4nAe+/BAw/AsGHQtm3usUAzfPkG4+yChxEbU6bCUTM2HvbM2JjSFahmHBcHjRvD888Hl95XKFNuGhNOIQdjn8k/imTPj42pmApqpi6uuABtTv55DxwIr77q3hcUvL2sZmyiRThqxh08WwKw0rOvNZBF7rNksOfHxpgQ+QbXY46BefPgNr9xHL5DpAIFY99mcQvGJlqEIxh/BOwABqnqNgARqQmMB2aratmMmDbGRK1w1Yx9g+v48W6uav+xx74BNlAwrlkz9701U5toEY6ORjcBI7yBGMDz/k7PMWOMCQvf4CoSeBKQooLxu+8GTmtMJIWjZlwdqBtgf12ggAnuyifrTW1MYKVRMw70/Bjy1nb9g/GPP+btbW3B2ESLcASNKcB4ETlbRBp7tnOAl4HJYcg/ZqjqWFU9DDg60mUxpjzyDcAFBfirrnKvxx+fPxj7D3WyZmoTLcJRM74KtzrTm7hOXACZuGA8PAz5G2MMkDe4+j779XX00bBhA9SpA2lpeY9V8vuNZzVjEy3CMenHbuBqERkOHOzZ/Yeq7irkNGNMBRKuZupKleDzz92sWrVrF5yugWfaaP+asX8wLu2a8SWXuI5mxhQlHOOMKwPiCb4/iEgz4HIRWa6qn4dcQmOM8XHCCcGnjVQw3rPHrQjVtq0FYxOccDwz/gAYCCAiNYD5uF7UH4jI0DDkX2pEpImIzBKR5SLyg4j09zs+RUS2ich7kSqjMabkigrGRTVT33UXPPJI8a+bnOyayP2vZ0xBwhGMOwKzPe/PBTYBzXAB+row5F+aMoEbPJ2uTgSeEBHfpVSexPOHhjGm+E7xrD83bFhkrh9qML7vPhhuPV9MGQhHMK6Cm/QDXECbrKrZwDxcUI5aqrpBVZd63m8EtgC1fI7PIve7GWOK6f33YfZsuP32yFw/1GAMwT/vPvHE4NIZE0g4gvHvQF8RaQKcBHifE9cDMkLJWES6i8hHIrJeRFRE+gZIc42IrBaRvSIyX0RKNKxIRI4E4lV1bShlNsbkqlwZunUresGG0uI7phhK95lxpL6jKR/CEYzvww1tWg3MV9W5nv0nAktCzLsqsAw3kUY+IjIAeBy4F9dcvgyYLiL1fNIsFZGfAmyNfNLUAl4FrihJIUUkSUSqezcq2GQnxkSrRx6BNm1yP/sHY28T9HnnhXad118Htdn3TQhEw/ATJCINgIbAMk8TNZ4aaoaq/hLyBVx+CvRT1ak+++YDC1T1Ws/nOGAt8LSqPhxkvknAF8A4VX0twPGewLWqem4heYwE7vHfn56eTvXq1YMphjGmlMycCX36uPeZmflrsFu2uGFSgWb08v56LKypeskS11nr5JNh+vS85xV1rol+oYTIjIwMUt2cramqWmhLcVimbVTVjaq6xBuIPfu+D1cgDkREEoEjgRk+18z2fO4SZB4CTAC+DBSIi2EUkOqzNQ4hL2NMGPn+Mg0UcOvUKTpg3ndf3s+XXpr73hvcg/mlXa9e/n0vv1z0eab8i+U5lOsA8bje2742AQ2CzKMrMAD3zHupZ2vnPSgiM4BJwKki8reIBAzyqrrP81fPxbiOazOL91WMMaXFt5NWSWupd90FtWrlfr7zztz33gAfTDAOlOakkwo/55VXis7X17HHFi+9iQ4VehScqn5LIX+QqGqfYuY3FhjreW6cHmLxjDFhEK4pLwtqevbWjIO5TkmaPIsbXA8/HL77rvjXMZEVyzXjLUAWUN9vf31gY9kXJ6dn93Lg+0hc3xiTX6wH44MOghUr4NBDg0tfv37emruJDTEbjFV1P7AI6O3d5+nA1RuYW9B5xpiK5fjjoVkzOO200PIp6NlzUc+Mx4wJ7boiLhA3bBj8OddF+3RLJp+obqYWkRSgpc+uFiKSBvyrqn/hhjVNFJGFuNroDbjhUBGZDdaaqY2JPsnJ8McfBa9/XJDmzfN+LqpmXFAwDtRpqyTS0uCrr8KTl4k+UR2MgU6A74/f457XicBgVX1HROrixjo3AJYCJ6uqf6cuY0wFFsyEHE8/nXfazsWL8x4vqCNYcXpThzJM5r773CQqvXvDUUdBYaMmbcxz7InqZmpVnaWqEmAb7JPmGVVtpqpJqtpZVedHqrz2zNiY2HXttdCrV+5n//WS7/HMJDBoUMmDcShSUuDBB12ze7VCphUKRzmOPx4OOyz0fEzwojoYxxpVHetZdKJEU3IaYyKrsKFPN90EP//sxgX7pitqaJPv/rKqsYbjOpUrh56HCZ4F4zCymrExse1//3OBNlBvZBFXW4yPhxo13GQhkDv+OJje1P61bQjchD5vXt7rFuXTT/N+9pYtFDZzWNmK9mfGMcU6cBkT2zp0gH37ICGh8HTJya6WHBeXmzaYmvGUKTB4MDzwAHz2GRw4AA0CTFFU33/AZiHq189dqtJ7veIsWnH88fDll3n3icBFF8HChcHnY0JjNWNjjPFRVCD2qlcvbw20oJqxbzA+4gjXMezUU+Gpp+C550peTq/iNCc/+GD+fZ99Br/9ln//sGHwxRd5A70pPRaMjTEmDML1PPjee4NLN2kSHHwwTJ5ceDkSE3PfN2pEPgkJ0LIlPPpo3v3x8W6BDbfOQX7vvgvdu8ONNwZX3nCpW7dsr1dWLBiHkT0zNqbiCkcwPuggNw92MHmdey78/rtrWi/MXXflvi8sX+9ykhDc8+L+/eHrrwMH+NKUnFy21ysr9sw4jOyZsTEVVziCcdu2+QNhqB2pLrjABe1gapQ1a8K2bUXPVhbJxSjK6xhqC8bGGBMGwTwzDpZv7a+4M4d5r7d2rVur+eCDYcIEt298EXMT/vyzW2TirLMKT9e7d+HHTfFZMDbGmDC49VYYMADOOSfv/pIE44YNYeRI1zkrKal45zZunPvauJgrqzdsmL/8gfj21i7rmqrv9dLTC36mHWvsmXEY2TNjYyqu886D1avhnXeKf24Xz0rpV1yRu++ee+CWW4LP4+OP4frr8+bhz3eGMSh+Dff2211NO9SFKHynHQ1F9erFu0fFVaNG6eXtz4JxGNkMXMZUbM2aFW+Mr9dXX8Hy5XD22SW/9mmnwRNPFD40y3fxiyeecEOXiuPBB93z50CTlxTHQw+5PwQuuaTgNBdfHHj/FVe4Gn/Xru7zbbdBt25u/HZxnXACzJxZ8PGyXBfagrExxpSiYJpxk5KgTZvSL4uv+PjIzbKVkgIzZsA11xScZtCg/Pseftg9Dli1CmbPdvtq1nTvr7qq+OX4/HM36UlBz/vL8t/EgrExxlRApfGsN5jnzaG49Vb3h0ulSkX3Om/WrPC8rr8+8LkladkIBwvGxhhTisrrUJxAAtVmy4r/fS6sVrtyJYwZk3ff/PluGNibb4a/bMGwYGyMMRVQaTRRh+MPj7Q0N7TrmGNg1iwYMsQ9X/afIaww06cX/v1at85//OijXSBu0qQkpQ6dDW0KIxG5BrgG+yPHGFNBhLvmv2iRW0AjKQl69HBbMHyD64knug5qscSCRhhZb2pjjL+K1EwdDnFxxR9bXR5YMDbGGBMRpdlbuaBm6pL0ui4L1kxtjDGlqLzXjIs7y5evKlVg50431ClUwTwDX7Mmcs+Ei1Kha8Yi0kREZonIchH5QUT6B3PMGGOMM2CAG3L04YclO79qVbe2M4T2nNd/IpJAwblp06KDtnXgioxM4AZVXSoiDYBFIvKpqu4q4pgxxhjcM96HH3bvSxqQhw2D//43tJm9WrZ0wbxOnZLnAa6m/8UXZTsVJlTwYKyqG4ANnvcbRWQLUAvYVdixSJXXGBN7Yr2ZuqyCUqhTbELeiTxC0adPePIpjqhuphaR7iLykYisFxEVkb4B0lwjIqtFZK+IzBeREvVkFpEjgXhVXVucY8YYE4uCHWd8//3Qsye8+mqpFifsIjXVZ0lFe824KrAMeAWY7H9QRAYAjwNXAfOBG4DpInKIqm72pFlK4O95oqqu96SpBbwKXB7gGgUeM8aYosR6zbhOHbeQhSldUR2MVXUaMA1AAv+ZcyMwTlXHe9JcBZwGDAEe9uSRVtg1RCQJmAo8rKrfBXssQDrfkXHVCrumMcZEWjiahaOZb8iYPLnouaojLaqDcWFEJBE4Ehjl3aeq2SIyA+gSZB4CTAC+VNXXgj0WwAjgnqALb4ypMIKdQaqsvPSSW+XovPMiXZKy069fpEtQtKh+ZlyEOkA8sMlv/yagQZB5dAUGAH1FZKlnaxfEMX+jgFTgZmAl8Fsxvocxphxr3Rp++QW2bo10SZxLL4UJE9zKR+EWqEm+du3wXycYdetG5rolFbM143BQ1W8p4A+Swo4FSLsP2AeMBkaLSHUgPVzlNMbEtkMOiXQJIue11+DUU3M/JySUzXVHjYK1a+Gyy8rmeqGK5WC8BcgC6vvtrw9sLPvi2EIRxhjjz3do1Pr1UK2MetTUqweff1421wqHmA0aqrofWAT09u4TkTjP57kRKpMtFGGMMT58m64bNgzP1JflUVTXjEUkBWjps6uFiKQB/6rqX7hhTRNFZCHwPW5oU1VgfBkXFbCasTHGmJKJ6mAMdAJ8R7g97nmdCAxW1XdEpC5wH67T1lLgZFX179RVJlR1LDDWnhkbY4wTa5NvREpUB2NVnQUU+k+pqs8Az5RJgYpgNWNjjDElYUEjjOyZsTGmIjvyyPz7Yn0GsrJiwdgYY0xYNG4Mv/4K7QqakcEUyIJxGHkWrViO60xmjDEVTqtWsTfhRjSwYBxG1kxtjDGmJCwYG2OMMRFmwTiMrJnaGGPgnHPca9OmkS1HLLFgHEbWTG2MMXDllTBtGixeHOmSxI6oHmdsjDEm9sTHw8knR7oUscVqxsYYY0qNjTMOjgXjMLJnxsYYY0rCgnEY2TNjY4zJy+amDo4FY2OMMSbCLBgbY4wpNfbMODgWjI0xxpgIs2BsjDHGRJgF4zCy3tTGGGNKwoJxGFlvamOMySstzb0edFBEixH1bAYuY4wxpaZqVdi1CxISIl2S6GbB2BhjTKmqUiXSJYh+FbqZWkSaiMgsEVkuIj+ISH+fYzVEZKGILBWRn0Tk8kiW1RhjTPlV0WvGmcANqrpURBoAi0TkU1XdBewAuqvqbhGpCvwkIpNVdWtES2yMMabcqdDBWFU3ABs87zeKyBagFrBLVbOA3Z6kSYB4NmOMMSasorqZWkS6i8hHIrJeRFRE+gZIc42IrBaRvSIyX0RK1JNZRI4E4lV1rc++GiKyDPgbeExVt5T0uxhjjDEFiepgDFQFlgHXBDooIgOAx4F7gY6etNNFpJ5PGu8zX/+tkU+aWsCrwBW++avqdlVtD7QALhSR+mH+fsYYY0x0N1Or6jRgGoAEXvrjRmCcqo73pLkKOA0YAjzsySOtsGuISBIwFXhYVb8roBybPDXk44D3CsgjyWdXtcKuaYwxxviK9ppxgUQkETgSmOHdp6rZns9dgsxDgAnAl6r6mt+x+iJSzfM+FegOrCwgqxFAus/2d3G+izHGmIotZoMxUAeIBzb57d8ENAgyj67AAKCvpzl7qYi08xxrBsz21IhnA0+r6o8F5DMKSAVuxgXs34L/GsYYYyq6qG6mLm2q+i0F/EGiqt8DaUHmsw/YB4wGRotIdSA9IyMjTCU1xhgTa4oTA2I5GG8BsgD/TlX1gY1lXxzXsxvX2awSQJMmTSJRDGOMMdGlGlBoZI7ZYKyq+0VkEdAb1wELEYnzfH4mQmUaC4z1PItuhJs4xOt78i8g4b/P93M13LPnxn75lJZA5SuNc4NJW1iago7Z/Q0+bWndX/99ZXmPQ7m/xT2/qLR2f0M7v7zd32rA+qISRXUwFpEUoKXPrhYikgb8q6p/4YY1TRSRhbibeANuONT4Mi5qHqqqwDrffSKSraoZhe3z/ezTe3yH/3mlIVD5SuPcYNIWlqagY3Z/g09bWvfXf19Z3uNQ7m9xzy8qrd3f0M4vh/c3qHOjOhgDnYCvfD4/7nmdCAxW1XdEpC5wH67T1lLgZFX179QVDcYGsS9QmrISyrWLc24waQtLU9Axu7/Bpy2t+xvs9UtDqNcN5z22+xva+RXx/iKuEmeijbcTGJBaFjW3isbub+mze1y67P6WrrK+v7E8tKm824ebWWxfpAtSTtn9LX12j0uX3d/SVab312rGxhhjTIRZzdgYY4yJMAvGxhhjTIRZMDbGGGMizIKxMcYYE2EWjI0xxpgIs2BcTojIahH5wbPy1FdFn2GKS0SqiMgaEflfpMtSnohIDRFZ6PnZ/UlELo90mcoTEWkiIrNEZLnnd0T/SJepvBGRKSKyTUTyrXcfdB42tKl8EJHVQFtV3RnpspRXIvIgbnrWtap6c6TLU16ISDyQpKq7RaQq8BPQSVW3Rrho5YKINATqq+pSEWkALAJaq+quCBet3BCRnrg5qAep6rklycNqxsYEQURaAYcC0yJdlvJGVbNUdbfnYxIgns2EgapuUNWlnvcbcSve1YpoocoZVZ1FiIt1WDAuAyLSXUQ+EpH1IqIi0jdAmms8Tc17RWS+iBR3hRQFvhaRBSJyUVgKHiPK6P7+DxgRlgLHmLK4v56m6mW4VXIeU9UtYSp+1Cujn19vPkcC8aq6NtRyx4qyvL+hiPaFIsqLqsAy4BVgsv9BERmAWwTjKmA+bvWp6SJyiKpu9qRZSuB/rxNVdT3QTVXXeZqkZojIj6r6Q2l8mShUqvcXOAr4VVV/FZFjS+MLRLlS//lV1e1AexGpD0wWkfeidMGX0lAWvx8QkVrAq0BFeyZfJvc3ZKpqWxluuBpsX79984FnfD7H4ZZgvK2E13gMt6pVxL9vebi/wChgLbAa18SXDtwd6e9aXu5vgGs8C5wb6e9anu4vrvn/G+DiSH/H8nh/Pef1BN4radmsmTrCRCQROBKY4d2nqtmez12CzKOqiFTzvE8Bjgd+Dn9pY0847q+qjlDVJqraHLgZGKeq95VCcWNOmH5+6/v8/KYC3YGV4S9t7AnT/RVgAvClqr5WCsWMWeG4v+FizdSRVweIB/yb5DbhOgwFoz4wxbMYdjwuWCwIWwljWzjurylYOO5vM+BFT9AQ4GlV/TF8RYxp4bi/XYEBwA8+z0svtnsMhOn3g4jMANoDVUXkb6C/qs4tTkEsGJcDqvon7gfBlDJVnRDpMpQ3qvo9kBbpcpRXqvot1lm3VKlqn1DzsH+gyNsCZOFqt77qAxvLvjjljt3f0mX3t3TZ/S1dUXN/LRhHmKruxw3C7+3dJyJxns/FauYw+dn9LV12f0uX3d/SFU3315qpy4CnU1VLn10tRCQN+FdV/8J1q58oIguB73Fd66sC48u4qDHJ7m/psvtbuuz+lq6Yub+R7mpeETZcl3cNsE3wSXMtsAbYh+tq3znS5Y6Vze6v3d9Y3uz+2v1VVZub2hhjjIk0e2ZsjDHGRJgFY2OMMSbCLBgbY4wxEWbB2BhjjIkwC8bGGGNMhFkwNsYYYyLMgrExxhgTYRaMjTHGmAizYGyMiVkiMkFE1LP1DXPePX3ynhrOvI3xZ8HYmCjiF1x8t5ZFn11hfQY0BKZ5dxQUnD33d2qQ+X7nyffdMJTRmELZQhHGRJ/PgEv89v3jn0hEEtWtOlPR7VPVsC9357m3G0VkD5AU7vyN8WU1Y2Oizz5V3ei3ZYnILBF5RkSeEJEtwHQAEWkrItNEZKeIbBKR10SkjjczEakqIq96jm8QkZs8eT3hkyZfTVJEtovIYJ/PTUTkXc/+f0XkAxFp7nN8gohMFZGbPdfZKiJjRSTBJ02SiDwiImtFZJ+I/C4il4rzu4jc7FeGtNJqGRCR5gW0QswK97WMKYoFY2NiyyBgP9AVuEpEagBfAkuATsDJuIXRfZtWHwN6AGcBJ+JWselYnIt6Aup0YAdwnOf6O4HPRCTRJ2kv4GDP6yBgsGfzehW4ALgOaANcCexUt2LNK+RvEbgE+EZVfy9OeYO0FtcM7d06AFuBb0rhWsYUypqpjYk+p4vITp/P01S1v+f9b6p6i/eAiNwJLFHV2332DQHWikhrYD1wKfBfVZ3pOT4I+LuYZRqA++P9Mk/gREQuAbbjgvvnnnTbgGtVNQv4RUQ+wS3UPs5TnvOAE1R1hif9nz7XmADcJyJHq+r3nj8ALgTy1JaL4S0RyfLblwR8AuAp40bPd0kGpuIWlB9ZwusZU2IWjI2JPl8BQ30+7/J5v8gvbXugl1/w9joYqAwk4tZoBUBV/xWRlcUsU3vcAu07RMR3f7LnOl4/e4Kc1wagned9GpAFfB3oAqq63hO8h+AWeT8DFzwnFbOsXv8HzPDb9wgQHyDtK0A13B8K2SW8njElZsHYmOizq5Bm2V1+n1OAj4BbA6TdgAugwVBA/PYl+LxPwf0hcFGAc307lx0IkK/3cdieIMrxEvCaiPwfron6HVXdHcR5gWz0v48isgOo4bfvTuAk4GhV3VHCaxkTEgvGxsS2xcA5wGpVzfQ/KCJ/4AJkZ+Avz76aQGvy1lD/wT039Z7XCqjid50BwGZVzShhWX/EBeYe5K+xen2K+4NjKO75d/cSXisoInIOcDdwiqr+UZrXMqYw1oHLmNg2FqiFez56lIgcLCInich4EYlX1Z3Ay8BjInK8iLTFPZv1b4r9ErhWRDqISCfgefLWct8AtgAfiMhxItLCMynGUyLSOJiCqupqYCLwioj09cnjPJ80WZ7yjcI9H59b7DsSJM+9eBXXdP2ziDTwbLVK65rGFMSCsTExTFXX43o2x+M6Uf0IPIHrWOUNuMOB2bjm7BnAt+R/9nwTrnfxbOBN4H9ATvOwp6m4O652PRlYgQvyyUBxaspDgfeAZ4FfgHFAVb80L+Oec48vRr4l0QlX+78T16Tv3SaX8nWNyUc8HSONMRWIZyztUlW9IcJFyUdEjgNmAk1UdVMRaScANVS1bymWp9SvYYzVjI0xUcEzIUhj3NCiSUUFYh+neyY0OT3M5TnO00s9UKc1Y8LKOnAZY6LFBbgm6qXAwCDPuQV4wPN+Q5jLsxA3HAvcBCfGlBprpjbGGGMizJqpjTHGmAizYGyMMcZEmAVjY4wxJsIsGBtjjDERZsHYGGOMiTALxsYYY0yEWTA2xhhjIsyCsTHGGBNhFoyNMcaYCPt/QnANg4QFcNkAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "### check the results\n",
    "df = 1e-5\n",
    "fmin = 1e-5\n",
    "fmax = 1e-1\n",
    "f = np.arange(fmin,fmax,df)\n",
    "################\n",
    "# PSD = TQPSD(f)\n",
    "PSD = LISAPSD(f)\n",
    "################\n",
    "fNyq = fmax\n",
    "TimeSeries = psd2timeseries(PSD, fNyq)[0]\n",
    "TimeVector = psd2timeseries(PSD, fNyq)[1]\n",
    "print(f'Sampling frequency is {1/(TimeVector[1] - TimeVector[0]):2e} and its Nyquist frequency is {1/(2*(TimeVector[1] - TimeVector[0])):2e}')\n",
    "\n",
    "freq, Pxx = signal.periodogram(TimeSeries,2*fNyq, 'flattop', scaling='density')\n",
    "# freq, Pxx = signal.periodogram(TimeSeries,2*fNyq, 'hanning', scaling='density')\n",
    "plt.figure(figsize=(5,3.5),dpi=100)\n",
    "plt.loglog(freq, np.sqrt(Pxx),'b',label='simulated PSD')\n",
    "plt.loglog(f,np.sqrt(PSD),'r',label='orignal PSD')\n",
    "plt.title('sqrtPSD')\n",
    "plt.xlabel('Frequency [Hz]')\n",
    "plt.ylabel('sqrt PSD [Hz$^{(-1/2)}$]')\n",
    "plt.legend()\n",
    "\n",
    "# Pxx, freq = plt.psd(TimeSeries, NFFT=len(f), Fs=2*fNyq)\n",
    "# freqs, psd = signal.welch(TimeSeries, 2*fNyq, 'flattop', 1024)\n",
    "# plt.loglog(freqs, np.sqrt(psd),'b')\n",
    "# plt.loglog(f,np.sqrt(PSD,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.087659Z",
     "start_time": "2020-12-23T13:05:19.939057Z"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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88.400132 \nL 139.686273 79.802147 \nL 139.713538 81.125887 \nL 140.013458 99.166756 \nL 140.054356 100.890181 \nL 140.108886 99.341711 \nL 140.17705 91.174972 \nL 140.231581 87.394296 \nL 140.272479 91.267673 \nL 140.449704 117.165055 \nL 140.504235 110.393521 \nL 140.586031 106.064059 \nL 140.626929 106.973444 \nL 140.940481 146.497382 \nL 140.995012 142.568369 \nL 141.049543 135.265845 \nL 141.526687 85.558437 \nL 141.54032 86.749594 \nL 141.812974 117.121244 \nL 141.840239 116.352303 \nL 141.949301 113.067548 \nL 141.990199 114.005568 \nL 142.017464 114.892229 \nL 142.04473 117.049843 \nL 142.085628 111.500879 \nL 142.181057 108.244066 \nL 142.208322 109.658107 \nL 142.426445 136.373657 \nL 142.521874 147.53562 \nL 142.54914 144.89312 \nL 142.726365 117.637688 \nL 142.75363 120.036321 \nL 142.767263 121.800237 \nL 142.835426 118.032014 \nL 142.876324 117.161311 \nL 142.930855 114.607626 \nL 142.971753 116.402765 \nL 143.203509 129.168328 \nL 143.748817 175.969774 \nL 143.789715 170.535088 \nL 144.035104 139.761219 \nL 144.253227 115.407642 \nL 144.26686 115.545954 \nL 144.280492 115.500185 \nL 144.294125 115.627547 \nL 144.307758 116.470956 \nL 144.348656 113.670669 \nL 144.553146 101.1431 \nL 144.566779 101.235641 \nL 144.703106 110.081665 \nL 144.744004 106.682794 \nL 144.921229 93.701881 \nL 144.97576 97.3249 \nL 144.989393 97.222222 \nL 145.003025 97.380975 \nL 145.016658 98.519036 \nL 145.084822 95.960799 \nL 145.152985 91.848464 \nL 145.193883 95.3859 \nL 145.248414 93.85964 \nL 145.275679 95.070988 \nL 145.425639 104.086116 \nL 145.343843 94.131012 \nL 145.466537 101.080744 \nL 145.602864 90.109834 \nL 145.657395 95.440106 \nL 145.752824 105.152429 \nL 145.889151 102.712722 \nL 145.957314 95.567676 \nL 146.011845 100.218529 \nL 146.161805 112.847609 \nL 146.175438 110.892175 \nL 146.311765 96.301409 \nL 146.352663 98.918131 \nL 146.366295 99.290417 \nL 146.407193 98.320089 \nL 146.584418 83.94943 \nL 146.707113 73.907183 \nL 146.734378 74.409152 \nL 146.816174 66.102598 \nL 146.993399 49.410802 \nL 147.075196 61.214827 \nL 147.388748 95.940272 \nL 147.593238 104.392698 \nL 147.443279 95.912075 \nL 147.634136 102.481656 \nL 147.675034 101.413366 \nL 147.784096 95.471691 \nL 147.824994 97.947704 \nL 148.165812 142.503866 \nL 148.20671 139.915275 \nL 148.4112 114.848834 \nL 148.506629 122.409611 \nL 148.520262 122.363031 \nL 148.642956 126.700341 \nL 148.683854 124.277058 \nL 148.71112 123.804855 \nL 148.738385 125.674328 \nL 148.997406 152.427543 \nL 149.079202 157.679367 \nL 149.201897 169.819347 \nL 149.229162 167.113251 \nL 149.474551 147.961034 \nL 149.542714 152.609632 \nL 149.733572 163.325935 \nL 149.747205 163.289483 \nL 149.842634 148.816413 \nL 150.019859 127.569829 \nL 150.033491 127.486132 \nL 150.047124 127.890436 \nL 150.333411 137.619213 \nL 150.347043 137.486347 \nL 150.415207 131.508426 \nL 150.537901 131.544072 \nL 150.592432 134.662212 \nL 150.742392 149.611243 \nL 150.78329 146.799192 \nL 150.865086 144.963777 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102.049903 \nL 158.608459 136.663668 \nL 158.826582 114.019769 \nL 158.922011 120.026782 \nL 158.935644 120.343944 \nL 158.962909 118.549728 \nL 158.990175 116.698816 \nL 159.044705 120.344559 \nL 159.126501 128.571953 \nL 159.181032 125.108585 \nL 159.37189 110.371068 \nL 159.767238 51.34906 \nL 159.917198 58.795408 \nL 160.462506 84.286219 \nL 160.557935 91.811414 \nL 160.598833 88.714987 \nL 160.639731 86.990536 \nL 160.680629 89.816128 \nL 160.762425 92.779507 \nL 160.803323 91.258108 \nL 160.93965 89.847705 \nL 160.966916 90.146175 \nL 160.980549 89.997573 \nL 160.994181 90.730702 \nL 161.08961 96.902343 \nL 161.157774 94.589539 \nL 161.23957 86.820696 \nL 161.334999 90.053124 \nL 161.403162 97.37759 \nL 161.525857 94.467659 \nL 161.59402 92.735481 \nL 161.621285 94.318498 \nL 161.716714 99.730524 \nL 161.757612 98.53824 \nL 161.771245 98.730107 \nL 161.784878 98.276871 \nL 161.880307 92.497462 \nL 161.934837 93.143524 \nL 162.043899 102.614826 \nL 162.275655 128.957673 \nL 162.289288 127.769066 \nL 162.766432 96.170241 \nL 162.80733 96.210885 \nL 162.861861 94.730261 \nL 163.079984 81.838086 \nL 163.16178 86.056693 \nL 163.352638 95.377077 \nL 163.366271 96.144813 \nL 163.420802 93.925004 \nL 163.488965 94.723269 \nL 163.570761 86.307471 \nL 163.66619 77.533571 \nL 163.720721 78.438942 \nL 163.734354 78.502596 \nL 163.788885 81.585261 \nL 163.829783 78.805093 \nL 164.116069 62.957693 \nL 164.129702 63.238045 \nL 164.184233 62.284379 \nL 164.225131 60.91468 \nL 164.279662 63.067043 \nL 164.32056 60.741706 \nL 164.579581 37.370233 \nL 164.647745 42.006192 \nL 165.015827 82.681734 \nL 165.083991 86.197784 \nL 165.124889 89.881104 \nL 165.206685 88.049801 \nL 165.220318 88.120568 \nL 165.274849 90.497772 \nL 165.315747 88.913237 \nL 165.479339 83.265711 \nL 165.847422 65.451033 \nL 166.051913 79.088817 \nL 166.106443 77.012132 \nL 166.256403 55.807117 \nL 166.297301 59.212757 \nL 166.365465 69.379669 \nL 166.515424 82.124813 \nL 166.597221 88.873266 \nL 166.638119 85.007595 \nL 166.801711 66.523767 \nL 166.842609 69.291526 \nL 166.992569 87.224966 \nL 167.074365 95.232121 \nL 167.10163 91.480377 \nL 167.278855 75.161961 \nL 167.510611 46.154082 \nL 167.578775 51.665567 \nL 167.878694 72.264401 \nL 167.90596 70.972146 \nL 167.919592 69.871046 \nL 167.974123 73.645702 \nL 168.124083 76.588542 \nL 168.246777 83.84172 \nL 168.274042 83.255692 \nL 168.424002 77.767768 \nL 168.505798 78.694027 \nL 168.696656 91.93945 \nL 168.723922 91.322721 \nL 168.737554 91.036143 \nL 168.76482 92.223726 \nL 168.914779 101.167011 \nL 169.010208 98.926472 \nL 169.214699 84.560363 \nL 169.282862 88.217927 \nL 169.351026 95.093149 \nL 169.555516 118.373082 \nL 169.569149 118.43277 \nL 169.637312 111.851425 \nL 169.691843 104.189457 \nL 169.773639 106.022266 \nL 169.800905 104.756655 \nL 169.855436 98.510898 \nL 169.923599 101.957352 \nL 169.937232 101.65943 \nL 169.97813 102.758221 \nL 170.005395 103.708993 \nL 170.059926 102.236148 \nL 170.073559 102.273762 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96.806705 \nL 173.481733 106.67316 \nL 173.795286 124.046277 \nL 173.808918 123.084829 \nL 173.863449 118.125788 \nL 173.91798 120.525891 \nL 174.313328 166.781294 \nL 174.381492 161.533292 \nL 174.599615 143.947279 \nL 174.62688 141.414773 \nL 174.681411 144.439692 \nL 174.695044 144.19956 \nL 174.708676 144.225273 \nL 174.722309 144.175019 \nL 174.763207 142.08349 \nL 174.967698 129.610219 \nL 175.049494 126.155261 \nL 175.090392 128.830386 \nL 175.144923 131.726842 \nL 175.308515 140.645932 \nL 175.322148 141.706117 \nL 175.390311 140.74543 \nL 175.690231 119.268949 \nL 175.717496 120.458109 \nL 175.826558 121.802151 \nL 175.758394 119.701203 \nL 175.84019 121.499829 \nL 176.262804 85.232353 \nL 176.385498 50.506905 \nL 176.453662 59.860338 \nL 176.617254 76.044542 \nL 176.630887 74.980198 \nL 176.658152 72.638067 \nL 176.712683 76.165785 \nL 176.753581 73.846299 \nL 176.889908 85.997633 \nL 177.039868 99.891931 \nL 177.094399 95.063268 \nL 177.230726 76.75149 \nL 177.312522 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246.689327 86.593412 \nL 246.798388 91.684341 \nL 247.043777 111.02748 \nL 247.05741 110.996637 \nL 247.139206 98.250491 \nL 247.302798 82.978542 \nL 247.316431 83.337839 \nL 247.752677 112.078764 \nL 247.76631 111.790275 \nL 247.875372 109.283401 \nL 247.889004 109.534878 \nL 247.943535 112.315739 \nL 247.984433 108.338011 \nL 248.134393 100.015622 \nL 248.188924 102.903273 \nL 248.216189 99.577543 \nL 248.243454 98.01525 \nL 248.297985 100.069574 \nL 248.407047 111.007541 \nL 248.461578 104.847703 \nL 248.570639 78.306615 \nL 248.652435 82.823461 \nL 248.965987 120.903542 \nL 249.020518 116.689915 \nL 249.184111 88.507706 \nL 249.320438 91.674741 \nL 249.361336 94.555847 \nL 249.497663 102.04873 \nL 249.524928 101.849594 \nL 249.661255 91.015823 \nL 249.702153 93.918651 \nL 249.83848 107.84868 \nL 249.879378 104.821107 \nL 250.165665 71.667433 \nL 250.233828 77.935333 \nL 250.288359 80.330701 \nL 250.34289 78.248159 \nL 250.397421 76.887649 \nL 250.451952 82.845593 \nL 250.520115 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265.379757 77.553268 \nL 265.420655 76.81394 \nL 265.461553 78.154673 \nL 265.475186 77.989904 \nL 265.652411 84.396075 \nL 265.666044 83.954039 \nL 265.679676 83.87678 \nL 265.74784 72.503752 \nL 265.911432 53.475321 \nL 266.006861 51.696639 \nL 266.020494 51.737921 \nL 266.170454 66.867116 \nL 266.25225 63.98034 \nL 266.361311 69.199107 \nL 266.484006 75.842694 \nL 266.497638 75.441778 \nL 266.524904 74.505264 \nL 266.565802 76.66448 \nL 266.688496 85.481952 \nL 266.75666 83.133997 \nL 266.824823 80.619744 \nL 266.838456 81.983957 \nL 267.056579 100.700938 \nL 267.11111 103.809461 \nL 267.138375 100.501385 \nL 267.179273 96.737971 \nL 267.247437 100.053281 \nL 267.424662 115.076209 \nL 267.46556 117.674802 \nL 267.533723 115.865991 \nL 267.751847 104.559132 \nL 267.792745 103.566786 \nL 267.806377 104.927974 \nL 267.860908 107.093797 \nL 267.888174 103.679622 \nL 267.915439 103.159393 \nL 268.024501 97.487465 \nL 268.065399 101.108277 \nL 268.338053 132.226598 \nL 268.351685 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102.720275 \nL 275.726975 117.810753 \nL 275.767874 120.670863 \nL 275.836037 118.267342 \nL 275.863302 116.355971 \nL 275.945099 117.986533 \nL 276.326814 133.018688 \nL 276.340447 133.139324 \nL 276.463141 130.573238 \nL 276.626734 151.862627 \nL 276.817591 187.424799 \nL 276.831224 186.749477 \nL 277.076613 166.48975 \nL 277.117511 162.992138 \nL 277.185674 165.643443 \nL 277.199307 165.606371 \nL 277.21294 165.783362 \nL 277.253838 167.835015 \nL 277.281103 166.224591 \nL 277.458328 153.500443 \nL 277.703717 124.651682 \nL 278.099065 67.58178 \nL 278.112698 68.018018 \nL 278.208127 71.400257 \nL 278.221759 70.182813 \nL 278.262657 67.961229 \nL 278.317188 70.777032 \nL 278.330821 70.997975 \nL 278.344454 70.60647 \nL 278.42625 62.532102 \nL 278.467148 67.413904 \nL 278.617108 75.188019 \nL 278.753435 65.941261 \nL 278.807965 70.150281 \nL 279.121518 105.278402 \nL 279.176048 105.628412 \nL 279.203314 104.909443 \nL 279.312375 94.787166 \nL 279.366906 97.474681 \nL 279.394171 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "### data used in the real simulation\n",
    "df = 1/YearInS # frequency resolution = 1/T\n",
    "fmin = 1e-5\n",
    "fmax = 6e-4\n",
    "f = np.arange(fmin,fmax,df)\n",
    "################\n",
    "# PSD = TQPSD(f)\n",
    "PSD = LISAPSD(f)\n",
    "################\n",
    "fNyq = fmax\n",
    "TimeSeries = psd2timeseries(PSD, fNyq)[0]\n",
    "TimeVector = psd2timeseries(PSD, fNyq)[1]\n",
    "Noise_t = TimeSeries[1:len(f)]\n",
    "plt.figure(figsize=(5,3.5),dpi=100)\n",
    "plt.plot(TimeVector,Noise_t)\n",
    "plt.title('Noise time series n(t)')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Noise n(t)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T07:47:58.560864Z",
     "start_time": "2020-12-23T07:47:58.126489Z"
    }
   },
   "source": [
    "# Data model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.113623Z",
     "start_time": "2020-12-23T13:05:20.103617Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tc_true    : 15452640.0\nphic_true  : 0.954\nmc_true    : 24.28408575389752\neta_true   : 0.08264462809917357\ndl_true    : 7.023197515868895e+17\nthetas_true: 1.325\nphis_true  : 2.04\niota_true  : 1.02\npsi_true   : 0.658\n"
     ]
    }
   ],
   "source": [
    "# Let's choose some values for our injected signal\n",
    "tc_true     = Tc\n",
    "phic_true   = Phic\n",
    "mc_true     = Mc\n",
    "eta_true    = Eta\n",
    "dl_true     = DL\n",
    "thetas_true = ThetaS\n",
    "phis_true   = PhiS\n",
    "iota_true   = Iota\n",
    "psi_true    = Psi\n",
    "\n",
    "# injected signal parameter\n",
    "print('tc_true    :', Tc)\n",
    "print('phic_true  :', Phic)\n",
    "print('mc_true    :', Mc)\n",
    "print('eta_true   :', Eta)\n",
    "print('dl_true    :', DL)\n",
    "print('thetas_true:', ThetaS)\n",
    "print('phis_true  :', PhiS)\n",
    "print('iota_true  :', Iota)\n",
    "print('psi_true   :', Psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.340982Z",
     "start_time": "2020-12-23T13:05:20.116610Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Observation time: 0.500000 year\nSampling time interval: 833.333333 second\nSampling frequency is 1.200000e-03 and its Nyquist frequency is 6.000000e-04\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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89.433142 130.710984 \nL 89.532289 125.081737 \nL 89.755369 111.437142 \nL 89.792549 112.057942 \nL 89.916483 120.23229 \nL 90.102383 131.05294 \nL 90.139563 130.551813 \nL 90.251104 123.696522 \nL 90.449397 111.503403 \nL 90.486577 111.891596 \nL 90.585724 117.487271 \nL 90.808805 130.989831 \nL 90.845985 130.352882 \nL 90.969919 122.18711 \nL 91.155819 111.551355 \nL 91.192999 112.090253 \nL 91.304539 118.987518 \nL 91.502833 130.93325 \nL 91.540013 130.485597 \nL 91.651554 123.780688 \nL 91.849847 111.622724 \nL 91.887027 111.986071 \nL 91.986174 117.498933 \nL 92.209255 130.874061 \nL 92.246435 130.235591 \nL 92.370369 122.119419 \nL 92.556269 111.666487 \nL 92.593449 112.235286 \nL 92.704989 119.148214 \nL 92.903283 130.822863 \nL 92.940463 130.316844 \nL 93.052004 123.537254 \nL 93.250297 111.724849 \nL 93.287477 112.175041 \nL 93.399018 118.833273 \nL 93.597311 130.759733 \nL 93.634492 130.358374 \nL 93.733638 124.813274 \nL 93.944326 111.790641 \nL 93.993899 112.500771 \nL 94.117833 120.686566 \nL 94.303733 130.702401 \nL 94.32852 130.36834 \nL 94.427667 124.987937 \nL 94.650747 111.838128 \nL 94.687927 112.483551 \nL 94.811861 120.525553 \nL 94.997761 130.654326 \nL 95.022548 130.352807 \nL 95.121695 125.083363 \nL 95.344776 111.888049 \nL 95.381956 112.498525 \nL 95.505889 120.449001 \nL 95.69179 130.603251 \nL 95.72897 129.999933 \nL 95.852903 122.07504 \nL 96.038804 111.939867 \nL 96.075984 112.542884 \nL 96.199918 120.455716 \nL 96.385818 130.550822 \nL 96.422998 129.94128 \nL 96.546932 122.027077 \nL 96.732832 111.993053 \nL 96.757619 112.2943 \nL 96.856766 117.528807 \nL 97.079846 130.496459 \nL 97.117026 129.853532 \nL 97.24096 121.89725 \nL 97.426861 112.04929 \nL 97.451647 112.38248 \nL 97.550794 117.698427 \nL 97.761481 130.441599 \nL 97.811055 129.734307 \nL 97.934988 121.686268 \nL 98.108495 112.094177 \nL 98.145676 112.49553 \nL 98.257216 118.926302 \nL 98.45551 130.403233 \nL 98.49269 129.953994 \nL 98.60423 123.435791 \nL 98.802524 112.135366 \nL 98.839704 112.639078 \nL 98.951244 119.256161 \nL 99.149538 130.357159 \nL 99.186718 129.792481 \nL 99.310652 122.049856 \nL 99.496552 112.188653 \nL 99.521339 112.497275 \nL 99.620486 117.704633 \nL 99.831173 130.300833 \nL 99.880746 129.588474 \nL 100.017073 120.574733 \nL 100.178187 112.229883 \nL 100.215367 112.676916 \nL 100.326907 119.151959 \nL 100.525201 130.267049 \nL 100.562381 129.731965 \nL 100.673922 123.092787 \nL 100.872215 112.278367 \nL 100.909395 112.907582 \nL 101.033329 120.728015 \nL 101.206836 130.216087 \nL 101.244016 129.825256 \nL 101.355557 123.483111 \nL 101.55385 112.314792 \nL 101.59103 112.814031 \nL 101.702571 119.363133 \nL 101.900864 130.176352 \nL 101.938045 129.562851 \nL 102.061978 121.79777 \nL 102.235485 112.36305 \nL 102.272665 112.759062 \nL 102.384206 119.092334 \nL 102.582499 130.14049 \nL 102.61968 129.616163 \nL 102.73122 123.037962 \nL 102.929514 112.407234 \nL 102.9543 112.734664 \nL 103.053447 117.947534 \nL 103.264134 130.099895 \nL 103.301314 129.637847 \nL 103.412855 123.1918 \nL 103.611149 112.440008 \nL 103.648329 113.049624 \nL 103.772262 120.769637 \nL 103.945769 130.059615 \nL 103.982949 129.632791 \nL 104.09449 123.266552 \nL 104.292783 112.475359 \nL 104.329964 113.063269 \nL 104.453897 120.728773 \nL 104.627404 130.022485 \nL 104.664584 129.603809 \nL 104.776125 123.263423 \nL 104.974418 112.511528 \nL 105.011599 113.104544 \nL 105.135532 120.76758 \nL 105.309039 129.989153 \nL 105.346219 129.55162 \nL 105.45776 123.183076 \nL 105.656053 112.548944 \nL 105.693233 113.173752 \nL 105.817167 120.885512 \nL 105.990674 129.958088 \nL 106.027854 129.474853 \nL 106.139395 123.025625 \nL 106.325295 112.587786 \nL 106.362475 112.935206 \nL 106.461622 118.164952 \nL 106.672309 129.925592 \nL 106.709489 129.370077 \nL 106.833423 121.803989 \nL 107.00693 112.608426 \nL 107.04411 113.042188 \nL 107.15565 119.385231 \nL 107.353944 129.885805 \nL 107.391124 129.231865 \nL 107.515058 121.489328 \nL 107.688565 112.635571 \nL 107.725745 113.181958 \nL 107.849679 120.71893 \nL 108.023186 129.866209 \nL 108.060366 129.42135 \nL 108.171906 123.064585 \nL 108.357806 112.675016 \nL 108.394987 113.024592 \nL 108.494133 118.246361 \nL 108.704821 129.83751 \nL 108.742001 129.240141 \nL 108.865934 121.615518 \nL 109.039441 112.688206 \nL 109.076621 113.204506 \nL 109.188162 119.692482 \nL 109.374062 129.814402 \nL 109.411242 129.372847 \nL 109.522783 123.029313 \nL 109.708683 112.721375 \nL 109.745863 113.094635 \nL 109.857403 119.303383 \nL 110.055697 129.783603 \nL 110.092877 129.136299 \nL 110.216811 121.427178 \nL 110.390318 112.738134 \nL 110.427498 113.331338 \nL 110.551432 120.939442 \nL 110.724939 129.771447 \nL 110.762119 129.225879 \nL 110.886052 121.708241 \nL 111.059559 112.754909 \nL 111.09674 113.25938 \nL 111.20828 119.720871 \nL 111.39418 129.751865 \nL 111.43136 129.281897 \nL 111.542901 122.885638 \nL 111.728801 112.775754 \nL 111.765981 113.217863 \nL 111.877521 119.56132 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124.040949 \nL 116.401099 112.855103 \nL 116.438279 113.275656 \nL 116.549819 119.620075 \nL 116.73572 129.664265 \nL 116.7729 129.169877 \nL 116.88444 122.679658 \nL 117.07034 112.855708 \nL 117.10752 113.430567 \nL 117.231454 121.058496 \nL 117.404961 129.651567 \nL 117.442141 128.989726 \nL 117.566075 121.197297 \nL 117.727189 112.860488 \nL 117.764369 113.275923 \nL 117.875909 119.634291 \nL 118.061809 129.661796 \nL 118.098989 129.144822 \nL 118.21053 122.587655 \nL 118.39643 112.860317 \nL 118.43361 113.485294 \nL 118.557544 121.233761 \nL 118.718658 129.655993 \nL 118.755838 129.258545 \nL 118.867378 122.913492 \nL 119.053278 112.852145 \nL 119.090458 113.372233 \nL 119.201999 119.957259 \nL 119.387899 129.658846 \nL 119.425079 129.00981 \nL 119.549013 121.19244 \nL 119.710127 112.850866 \nL 119.747307 113.292277 \nL 119.858847 119.746067 \nL 120.044747 129.670556 \nL 120.081928 129.085503 \nL 120.205861 121.364555 \nL 120.366975 112.850048 \nL 120.404155 113.23965 \nL 120.515695 119.612024 \nL 120.701596 129.679622 \nL 120.738776 129.131705 \nL 120.862709 121.458338 \nL 121.023823 112.845407 \nL 121.061003 113.210566 \nL 121.16015 118.591024 \nL 121.358444 129.689196 \nL 121.395624 129.151065 \nL 121.519558 121.473314 \nL 121.680671 112.834834 \nL 121.717851 113.20332 \nL 121.816998 118.607727 \nL 122.015292 129.700231 \nL 122.052472 129.144147 \nL 122.176406 121.408875 \nL 122.33752 112.818432 \nL 122.3747 113.218358 \nL 122.48624 119.672933 \nL 122.67214 129.711438 \nL 122.70932 129.109382 \nL 122.833254 121.264262 \nL 122.994368 112.798562 \nL 123.031548 113.258317 \nL 123.143088 119.850176 \nL 123.328989 129.719235 \nL 123.366169 129.043039 \nL 123.502496 120.033187 \nL 123.651216 112.779902 \nL 123.688396 113.328041 \nL 123.81233 121.116214 \nL 123.973443 129.739974 \nL 124.010623 129.31448 \nL 124.122164 122.749674 \nL 124.308064 112.769512 \nL 124.345244 113.434558 \nL 124.469178 121.463324 \nL 124.630292 129.764767 \nL 124.667472 129.207641 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113.086597 \nL 128.992755 120.027066 \nL 129.166262 129.946104 \nL 129.203442 129.455737 \nL 129.314983 122.515284 \nL 129.48849 112.554808 \nL 129.52567 113.042576 \nL 129.63721 119.997003 \nL 129.810717 129.979338 \nL 129.847897 129.4867 \nL 129.959438 122.504136 \nL 130.132945 112.519748 \nL 130.170125 113.024802 \nL 130.281665 120.049724 \nL 130.455172 130.015948 \nL 130.492352 129.490863 \nL 130.603892 122.409421 \nL 130.777399 112.482174 \nL 130.81458 113.034966 \nL 130.92612 120.186905 \nL 131.099627 130.053587 \nL 131.136807 129.465359 \nL 131.260741 121.163028 \nL 131.421854 112.445691 \nL 131.459034 113.077127 \nL 131.582968 121.480647 \nL 131.744082 130.087364 \nL 131.781262 129.404926 \nL 131.905195 120.887707 \nL 132.066309 112.416501 \nL 132.103489 113.157744 \nL 132.239816 122.869754 \nL 132.376143 130.120668 \nL 132.425717 129.301915 \nL 132.562044 119.453821 \nL 132.698371 112.367824 \nL 132.735551 112.865787 \nL 132.847091 120.045597 \nL 133.020598 130.173697 \nL 133.057778 129.593004 \nL 133.181712 121.154586 \nL 133.342826 112.324704 \nL 133.380006 112.996157 \nL 133.503939 121.621839 \nL 133.665053 130.203315 \nL 133.702233 129.433168 \nL 133.83856 119.527734 \nL 133.974887 112.283108 \nL 134.012067 112.768943 \nL 134.123607 120.018473 \nL 134.297114 130.261813 \nL 134.334295 129.660342 \nL 134.458228 121.076955 \nL 134.619342 112.238902 \nL 134.656522 112.964076 \nL 134.780456 121.791215 \nL 134.929176 130.299479 \nL 134.966356 129.839163 \nL 135.077896 122.564345 \nL 135.251403 112.185086 \nL 135.288583 112.786526 \nL 135.412517 121.454615 \nL 135.573631 130.347509 \nL 135.610811 129.596886 \nL 135.747138 119.533212 \nL 135.883465 112.140954 \nL 135.920645 112.648057 \nL 136.032185 120.084814 \nL 136.205692 130.404045 \nL 136.242872 129.729896 \nL 136.366806 120.845423 \nL 136.515526 112.098641 \nL 136.5651 112.958144 \nL 136.701427 123.279784 \nL 136.837754 130.456481 \nL 136.874934 129.827849 \nL 136.998868 120.962452 \nL 137.159981 112.051285 \nL 137.197161 112.872884 \nL 137.333488 123.221129 \nL 137.469815 130.508895 \nL 137.506995 129.893687 \nL 137.630929 120.98985 \nL 137.792043 111.997876 \nL 137.829223 112.824309 \nL 137.96555 123.2514 \nL 138.101877 130.562795 \nL 138.139057 129.927936 \nL 138.262991 120.92554 \nL 138.411711 111.940671 \nL 138.461284 112.813296 \nL 138.597611 123.372522 \nL 138.733938 130.616993 \nL 138.771118 129.928569 \nL 138.895052 120.767441 \nL 139.043772 111.876699 \nL 139.080952 112.394124 \nL 139.192493 120.122948 \nL 139.366 130.667475 \nL 139.40318 129.890906 \nL 139.539507 119.357835 \nL 139.675834 111.813532 \nL 139.713014 112.435822 \nL 139.836948 121.582029 \nL 139.985668 130.720988 \nL 140.035241 129.807569 \nL 140.183962 117.880637 \nL 140.307895 111.759519 \nL 140.345075 112.522407 \nL 140.469009 121.979918 \nL 140.617729 130.792291 \nL 140.65491 130.160102 \nL 140.778843 120.892261 \nL 140.927564 111.70299 \nL 140.964744 112.211075 \nL 141.076284 120.102473 \nL 141.249791 130.842363 \nL 141.286971 130.015401 \nL 141.423298 119.165069 \nL 141.559625 111.634072 \nL 141.596805 112.355538 \nL 141.720739 121.885661 \nL 141.869459 130.91485 \nL 141.906639 130.291728 \nL 142.018179 122.09991 \nL 142.179293 111.577126 \nL 142.216473 112.109393 \nL 142.328013 120.170992 \nL 142.501521 130.96041 \nL 142.538701 130.067875 \nL 142.675028 118.949357 \nL 142.811355 111.513732 \nL 142.848535 112.33507 \nL 142.984862 123.385489 \nL 143.121189 131.04168 \nL 143.158369 130.283606 \nL 143.282302 120.504924 \nL 143.431023 111.440256 \nL 143.468203 112.143254 \nL 143.592136 121.866866 \nL 143.740857 131.109858 \nL 143.778037 130.453515 \nL 143.901971 120.771872 \nL 144.050691 111.375204 \nL 144.087871 111.993522 \nL 144.199411 120.407843 \nL 144.360525 131.173624 \nL 144.397705 130.584504 \nL 144.509245 122.18759 \nL 144.670359 111.31123 \nL 144.707539 111.880158 \nL 144.819079 120.275664 \nL 144.980193 131.238944 \nL 145.017373 130.681033 \nL 145.128913 122.27033 \nL 145.290027 111.243788 \nL 145.327207 111.80002 \nL 145.438748 120.242972 \nL 145.599861 131.30891 \nL 145.637041 130.744866 \nL 145.748582 122.25232 \nL 145.909695 111.171281 \nL 145.946875 111.752772 \nL 146.058416 120.312392 \nL 146.219529 131.383581 \nL 146.256709 130.774872 \nL 146.36825 122.130731 \nL 146.529363 111.095228 \nL 146.566544 111.741053 \nL 146.678084 120.486949 \nL 146.839197 131.459817 \nL 146.876378 130.766866 \nL 147.000311 120.613175 \nL 147.149032 111.020438 \nL 147.186212 111.770618 \nL 147.310145 122.064752 \nL 147.458866 131.53109 \nL 147.496046 130.713506 \nL 147.619979 120.263849 \nL 147.7687 110.955202 \nL 147.80588 111.85041 \nL 147.942207 123.755221 \nL 148.06614 131.588662 \nL 148.115714 130.604249 \nL 148.264434 117.275472 \nL 148.375974 110.87787 \nL 148.425548 111.992556 \nL 148.574268 125.506201 \nL 148.685809 131.684202 \nL 148.735382 130.425414 \nL 148.908889 114.502156 \nL 148.995643 110.795652 \nL 149.045216 112.21224 \nL 149.305477 131.748239 \nL 149.39223 127.446041 \nL 149.602917 110.724696 \nL 149.664884 112.527414 \nL 149.912751 131.840439 \nL 149.999505 127.937811 \nL 150.222586 110.644164 \nL 150.272159 112.183418 \nL 150.520026 131.907993 \nL 150.619173 127.264763 \nL 150.82986 110.556792 \nL 150.879434 111.90882 \nL 151.139694 131.989439 \nL 151.226448 127.594527 \nL 151.437135 110.480648 \nL 151.499102 112.407892 \nL 151.746969 132.079894 \nL 151.821329 128.877329 \nL 152.04441 110.406308 \nL 152.11877 113.080661 \nL 152.354244 132.165098 \nL 152.428604 129.02655 \nL 152.651685 110.327539 \nL 152.726045 112.969995 \nL 152.961519 132.249373 \nL 153.035879 129.096384 \nL 153.258959 110.241496 \nL 153.33332 112.933938 \nL 153.568793 132.333547 \nL 153.643154 129.084761 \nL 153.866234 110.14892 \nL 153.940594 112.975769 \nL 154.176068 132.414739 \nL 154.238035 129.957604 \nL 154.473509 110.054363 \nL 154.547869 113.101676 \nL 154.783343 132.48614 \nL 154.84531 129.807447 \nL 155.080784 109.966419 \nL 155.155144 113.32073 \nL 155.378224 132.592528 \nL 155.440191 130.489648 \nL 155.688058 109.897958 \nL 155.762419 113.64471 \nL 155.985499 132.682496 \nL 156.047466 130.206735 \nL 156.28294 109.786925 \nL 156.3573 112.990428 \nL 156.580381 132.765007 \nL 156.654741 129.811293 \nL 156.667134 113.775433 \nL 156.766281 125.008917 \nL 156.890215 132.8038 \nL 156.927395 131.696395 \nL 157.076115 116.403342 \nL 157.187655 109.654231 \nL 157.224835 110.648078 \nL 157.361162 124.435707 \nL 157.485096 132.913408 \nL 157.522276 132.023244 \nL 157.64621 119.820903 \nL 157.782537 109.559325 \nL 157.819717 110.356114 \nL 157.94365 122.461495 \nL 158.079977 132.997788 \nL 158.129551 131.663644 \nL 158.290665 114.55195 \nL 158.377418 109.481705 \nL 158.426992 110.724157 \nL 158.575712 126.513388 \nL 158.687252 133.077493 \nL 158.724432 131.906404 \nL 158.873153 116.113693 \nL 158.984693 109.384953 \nL 159.021873 110.511989 \nL 159.1582 124.872278 \nL 159.282134 133.184192 \nL 159.319314 132.089608 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108.587998 \nL 163.805711 113.428594 \nL 164.016398 133.981689 \nL 164.065972 132.148954 \nL 164.313839 108.500511 \nL 164.400592 114.147558 \nL 164.598886 134.075004 \nL 164.64846 132.567122 \nL 164.896327 108.380911 \nL 164.995474 115.048544 \nL 165.193768 134.170665 \nL 165.230948 132.898969 \nL 165.379668 115.316572 \nL 165.478815 108.284195 \nL 165.528388 109.869413 \nL 165.776256 134.288475 \nL 165.863009 128.804613 \nL 166.061303 108.195132 \nL 166.12327 110.529364 \nL 166.358744 134.395419 \nL 166.433104 130.394038 \nL 166.656184 108.094876 \nL 166.718151 111.431259 \nL 166.941232 134.499871 \nL 167.015592 130.532742 \nL 167.238672 107.98786 \nL 167.300639 111.340424 \nL 167.52372 134.605498 \nL 167.59808 130.564808 \nL 167.808767 107.887344 \nL 167.883127 111.35074 \nL 168.106208 134.71094 \nL 168.168175 131.712708 \nL 168.391255 107.766363 \nL 168.465615 111.46874 \nL 168.688696 134.809501 \nL 168.750663 131.561367 \nL 168.973743 107.646847 \nL 169.048103 111.704923 \nL 169.258791 134.899272 \nL 169.333151 131.292708 \nL 169.556231 107.543751 \nL 169.618198 110.786723 \nL 169.841279 135.031599 \nL 169.915639 130.890058 \nL 170.126326 107.448864 \nL 170.188293 110.030645 \nL 170.423767 135.125215 \nL 170.498127 130.333381 \nL 170.708814 107.323534 \nL 170.770781 110.516049 \nL 170.993861 135.247464 \nL 171.068222 131.040863 \nL 171.278909 107.226588 \nL 171.340875 109.94533 \nL 171.576349 135.325683 \nL 171.65071 130.222561 \nL 171.861397 107.11955 \nL 171.91097 109.491957 \nL 172.146444 135.461903 \nL 172.220804 130.710083 \nL 172.431491 106.995995 \nL 172.493458 110.275148 \nL 172.716539 135.57687 \nL 172.790899 131.070372 \nL 173.001586 106.886166 \nL 173.063553 109.994559 \nL 173.286633 135.684358 \nL 173.360994 131.308429 \nL 173.571681 106.778897 \nL 173.633648 109.818758 \nL 173.856728 135.792833 \nL 173.931088 131.425889 \nL 174.141775 106.668513 \nL 174.203742 109.746965 \nL 174.426823 135.905088 \nL 174.501183 131.420456 \nL 174.71187 106.555192 \nL 174.773837 109.783059 \nL 174.996917 136.017881 \nL 175.071278 131.285567 \nL 175.281965 106.445335 \nL 175.343932 109.935829 \nL 175.567012 136.121557 \nL 175.628979 132.456988 \nL 175.852059 106.351944 \nL 175.914026 110.219001 \nL 176.124713 136.208278 \nL 176.199074 132.10199 \nL 176.409761 106.233544 \nL 176.484121 110.65097 \nL 176.694808 136.349716 \nL 176.756775 133.003074 \nL 176.979856 106.114214 \nL 177.054216 111.254174 \nL 177.264903 136.438362 \nL 177.314476 133.735255 \nL 177.537557 106.015455 \nL 177.62431 112.054 \nL 177.822604 136.572629 \nL 177.872178 134.320838 \nL 178.107652 105.90039 \nL 178.194405 113.077094 \nL 178.380305 136.662346 \nL 178.429879 134.78081 \nL 178.665353 105.777659 \nL 178.752106 112.496232 \nL 178.9504 136.775795 \nL 178.999974 134.03053 \nL 179.223054 105.675178 \nL 179.309808 112.072558 \nL 179.508101 136.900873 \nL 179.557675 134.32319 \nL 179.780755 105.576341 \nL 179.867509 111.801822 \nL 180.065803 137.015474 \nL 180.115376 134.509421 \nL 180.338457 105.470811 \nL 180.42521 111.683049 \nL 180.623504 137.126199 \nL 180.673078 134.591349 \nL 180.896158 105.354921 \nL 180.982912 111.719218 \nL 181.181205 137.232767 \nL 181.230779 134.5653 \nL 181.453859 105.232081 \nL 181.540613 111.917624 \nL 181.738907 137.327596 \nL 181.78848 134.42148 \nL 182.011561 105.113215 \nL 182.085921 110.497064 \nL 182.284215 137.437017 \nL 182.346182 134.143904 \nL 182.569262 105.017206 \nL 182.643622 110.981307 \nL 182.841916 137.568748 \nL 182.903883 133.710639 \nL 183.11457 104.908869 \nL 183.18893 109.891529 \nL 183.399617 137.649059 \nL 183.461584 133.094468 \nL 183.672271 104.789175 \nL 183.734238 109.000736 \nL 183.944925 137.787208 \nL 184.006892 133.893791 \nL 184.217579 104.685398 \nL 184.291939 109.876572 \nL 184.490233 137.865492 \nL 184.5522 134.526041 \nL 184.775281 104.5948 \nL 184.849641 111.012647 \nL 185.047935 137.992031 \nL 185.097508 135.01062 \nL 185.320589 104.462605 \nL 185.394949 110.505018 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103.442227 \nL 190.823242 110.922981 \nL 191.009142 139.159481 \nL 191.058715 136.32998 \nL 191.281796 103.323311 \nL 191.356156 110.334512 \nL 191.542057 139.244697 \nL 191.604023 135.203618 \nL 191.814711 103.208572 \nL 191.889071 109.937504 \nL 192.074971 139.32647 \nL 192.136938 135.480064 \nL 192.347625 103.10619 \nL 192.421985 109.726648 \nL 192.607886 139.418445 \nL 192.669853 135.60738 \nL 192.88054 103.007534 \nL 192.9549 109.70184 \nL 193.1408 139.52517 \nL 193.202767 135.58437 \nL 193.413454 102.913328 \nL 193.487815 109.868881 \nL 193.673715 139.641597 \nL 193.735682 135.402281 \nL 193.946369 102.834169 \nL 194.008336 108.191227 \nL 194.20663 139.752547 \nL 194.268596 135.044682 \nL 194.46689 102.746879 \nL 194.54125 108.693714 \nL 194.739544 139.832173 \nL 194.801511 134.487811 \nL 194.999805 102.621387 \nL 195.061772 107.43219 \nL 195.260065 139.924369 \nL 195.334426 133.701527 \nL 195.532719 102.567013 \nL 195.582293 106.424002 \nL 195.79298 140.027712 \nL 195.86734 132.651016 \nL 196.053241 102.434331 \nL 196.102814 105.635986 \nL 196.313501 140.12082 \nL 196.387861 133.552454 \nL 196.573762 102.36986 \nL 196.635729 106.655453 \nL 196.846416 140.169199 \nL 196.908383 134.236222 \nL 197.106676 102.280827 \nL 197.15625 106.138971 \nL 197.366937 140.294485 \nL 197.441297 132.566383 \nL 197.627198 102.170364 \nL 197.676771 105.791346 \nL 197.887458 140.394965 \nL 197.961818 132.876342 \nL 198.147719 102.076287 \nL 198.197292 105.60134 \nL 198.40798 140.484924 \nL 198.48234 132.976347 \nL 198.66824 101.989494 \nL 198.717814 105.565704 \nL 198.928501 140.568135 \nL 199.002861 132.863178 \nL 199.188761 101.91168 \nL 199.238335 105.689951 \nL 199.449022 140.637301 \nL 199.523382 132.526951 \nL 199.709283 101.855882 \nL 199.758856 105.988699 \nL 199.95715 140.703972 \nL 200.03151 134.276698 \nL 200.21741 101.746906 \nL 200.279377 106.485509 \nL 200.477671 140.826404 \nL 200.539638 135.699233 \nL 200.737932 101.650083 \nL 200.799899 107.212123 \nL 200.998192 140.885285 \nL 201.060159 134.843663 \nL 201.24606 101.604886 \nL 201.308026 106.129825 \nL 201.50632 140.986822 \nL 201.58068 133.699063 \nL 201.766581 101.491069 \nL 201.816154 105.291338 \nL 202.026841 141.019724 \nL 202.088808 134.596433 \nL 202.274709 101.414393 \nL 202.336676 106.507427 \nL 202.534969 141.14167 \nL 202.596936 135.260716 \nL 202.782837 101.371217 \nL 202.844803 105.985182 \nL 203.043097 141.226432 \nL 203.105064 135.710533 \nL 203.303358 101.294314 \nL 203.365325 107.74554 \nL 203.551225 141.29903 \nL 203.613192 135.958341 \nL 203.811486 101.209102 \nL 203.873452 107.595633 \nL 204.059353 141.372925 \nL 204.12132 136.008813 \nL 204.319614 101.142506 \nL 204.38158 107.65962 \nL 204.567481 141.44941 \nL 204.629448 135.857823 \nL 204.815348 101.082632 \nL 204.889708 107.946265 \nL 205.075609 141.517024 \nL 205.137575 135.492042 \nL 205.323476 100.973653 \nL 205.385443 106.200657 \nL 205.583737 141.550968 \nL 205.645703 134.889186 \nL 205.831604 100.892032 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137.38866 \nL 210.801342 100.327706 \nL 210.875702 107.816292 \nL 211.049209 142.25179 \nL 211.111176 136.697177 \nL 211.297077 100.217492 \nL 211.359044 106.269919 \nL 211.544944 142.324874 \nL 211.606911 135.719994 \nL 211.792811 100.198182 \nL 211.842385 105.059718 \nL 212.028285 142.346106 \nL 212.102645 134.409785 \nL 212.276152 100.111619 \nL 212.338119 106.331975 \nL 212.52402 142.414638 \nL 212.585986 135.404219 \nL 212.759494 100.104695 \nL 212.82146 105.534336 \nL 213.007361 142.480821 \nL 213.069328 136.122661 \nL 213.255228 100.046039 \nL 213.304801 104.984392 \nL 213.490702 142.514706 \nL 213.565062 134.033599 \nL 213.738569 99.976736 \nL 213.788143 104.65957 \nL 213.974043 142.546602 \nL 214.048403 134.281622 \nL 214.22191 99.926224 \nL 214.271484 104.547238 \nL 214.457384 142.591944 \nL 214.531744 134.263321 \nL 214.705251 99.88737 \nL 214.754825 104.646276 \nL 214.940725 142.650653 \nL 215.015086 133.972833 \nL 215.188593 99.868886 \nL 215.238166 104.967867 \nL 215.424066 142.706473 \nL 215.486033 136.091714 \nL 215.65954 99.832262 \nL 215.721507 105.535483 \nL 215.907408 142.726285 \nL 215.969374 135.321739 \nL 216.142882 99.750209 \nL 216.204848 106.383931 \nL 216.378355 142.755332 \nL 216.440322 136.85068 \nL 216.626223 99.75731 \nL 216.688189 107.557207 \nL 216.861697 142.817469 \nL 216.91127 138.029008 \nL 217.09717 99.674557 \nL 217.159137 106.360589 \nL 217.332644 142.843784 \nL 217.394611 136.637122 \nL 217.568118 99.687697 \nL 217.630085 105.4716 \nL 217.815985 142.83953 \nL 217.877952 134.808743 \nL 218.051459 99.634179 \nL 218.101033 104.855734 \nL 218.286933 142.91474 \nL 218.3489 135.369299 \nL 218.522407 99.577999 \nL 218.571981 104.486174 \nL 218.757881 142.958752 \nL 218.819848 135.654221 \nL 218.993355 99.541372 \nL 219.055322 106.821069 \nL 219.228829 142.990647 \nL 219.290796 135.670689 \nL 219.464303 99.513727 \nL 219.513876 104.433753 \nL 219.699777 143.012308 \nL 219.761743 135.414721 \nL 219.93525 99.502047 \nL 219.984824 104.757158 \nL 220.170724 143.007712 \nL 220.232691 134.870737 \nL 220.393805 99.521821 \nL 220.455772 105.340464 \nL 220.629279 143.046849 \nL 220.691246 136.776628 \nL 220.864753 99.43111 \nL 220.92672 106.219794 \nL 221.100227 143.104069 \nL 221.162193 135.732404 \nL 221.3357 99.425571 \nL 221.385274 104.809912 \nL 221.558781 143.066809 \nL 221.633141 134.319985 \nL 221.794255 99.384684 \nL 221.843828 103.750111 \nL 222.029729 143.146905 \nL 222.091696 135.475292 \nL 222.265203 99.421545 \nL 222.314776 105.271396 \nL 222.488283 143.165098 \nL 222.55025 136.305643 \nL 222.723757 99.345742 \nL 222.785724 107.283892 \nL 222.946838 143.148955 \nL 223.008804 136.841572 \nL 223.182312 99.313004 \nL 223.244278 106.860334 \nL 223.405392 143.137994 \nL 223.467359 137.104496 \nL 223.640866 99.293618 \nL 223.702833 106.724598 \nL 223.863946 143.15329 \nL 223.925913 137.103889 \nL 224.09942 99.27662 \nL 224.161387 106.87485 \nL 224.322501 143.196806 \nL 224.384468 136.835657 \nL 224.557975 99.270566 \nL 224.619942 107.321991 \nL 224.781055 143.250659 \nL 224.843022 136.281673 \nL 225.016529 99.304265 \nL 225.066103 105.275631 \nL 225.23961 143.276289 \nL 225.301577 135.410588 \nL 225.46269 99.247505 \nL 225.524657 106.236686 \nL 225.698164 143.213735 \nL 225.747738 137.043864 \nL 225.921245 99.236059 \nL 225.983211 107.565854 \nL 226.144325 143.303945 \nL 226.193899 138.281097 \nL 226.367406 99.223008 \nL 226.429373 106.311191 \nL 226.590486 143.261695 \nL 226.652453 136.700588 \nL 226.82596 99.261401 \nL 226.875534 105.403142 \nL 227.049041 143.293055 \nL 227.111007 134.620517 \nL 227.272121 99.201642 \nL 227.321695 104.802515 \nL 227.495202 143.328119 \nL 227.557169 135.149873 \nL 227.718282 99.181764 \nL 227.767856 104.479987 \nL 227.941363 143.339784 \nL 228.00333 135.365346 \nL 228.164443 99.173933 \nL 228.214017 104.419614 \nL 228.387524 143.345515 \nL 228.449491 135.272463 \nL 228.610604 99.171265 \nL 228.660178 104.620977 \nL 228.833685 143.34141 \nL 228.895652 134.863322 \nL 229.056765 99.188649 \nL 229.106339 105.100046 \nL 229.279846 143.3014 \nL 229.341813 134.116583 \nL 229.490533 99.229053 \nL 229.5525 105.888718 \nL 229.713614 143.333292 \nL 229.77558 136.104049 \nL 229.936694 99.158825 \nL 229.998661 107.032751 \nL 230.159775 143.351578 \nL 230.209348 137.631735 \nL 230.382855 99.216508 \nL 230.444822 108.587676 \nL 230.593542 143.330584 \nL 230.655509 136.104466 \nL 230.816623 99.155286 \nL 230.87859 107.354126 \nL 231.039703 143.320147 \nL 231.089277 137.075058 \nL 231.250391 99.186171 \nL 231.312357 106.487412 \nL 231.473471 143.35765 \nL 231.523045 137.717506 \nL 231.684158 99.243097 \nL 231.746125 105.953024 \nL 231.907239 143.357749 \nL 231.969206 135.239758 \nL 232.130319 99.22025 \nL 232.179893 105.727474 \nL 232.341007 143.352723 \nL 232.402973 135.317361 \nL 232.564087 99.227563 \nL 232.613661 105.801748 \nL 232.774774 143.351768 \nL 232.836741 135.068432 \nL 232.985461 99.242214 \nL 233.047428 106.183122 \nL 233.208542 143.340116 \nL 233.258115 137.443894 \nL 233.419229 99.191984 \nL 233.481196 106.895359 \nL 233.64231 143.278171 \nL 233.691883 136.628471 \nL 233.852997 99.181835 \nL 233.914964 107.977102 \nL 234.063684 143.321101 \nL 234.125651 135.440242 \nL 234.274371 99.264603 \nL 234.336338 106.291645 \nL 234.497452 143.296653 \nL 234.547025 136.903034 \nL 234.708139 99.195178 \nL 234.770106 108.02394 \nL 234.918826 143.315452 \nL 234.968399 137.96598 \nL 235.129513 99.22878 \nL 235.19148 106.975345 \nL 235.3402 143.249823 \nL 235.402167 135.926895 \nL 235.563281 99.298717 \nL 235.612854 106.291826 \nL 235.773968 143.240005 \nL 235.823541 136.440194 \nL 235.984655 99.267104 \nL 236.034229 105.942722 \nL 236.195342 143.251334 \nL 236.244916 136.629184 \nL 236.406029 99.271353 \nL 236.455603 105.911883 \nL 236.616717 143.232996 \nL 236.66629 136.501608 \nL 236.827404 99.305573 \nL 236.876977 106.200425 \nL 237.038091 143.177762 \nL 237.087664 136.047265 \nL 237.236385 99.317898 \nL 237.298352 106.827617 \nL 237.447072 143.227456 \nL 237.509039 135.238097 \nL 237.657759 99.272793 \nL 237.719726 107.829755 \nL 237.868446 143.239291 \nL 237.91802 137.135872 \nL 238.079133 99.316652 \nL 238.128707 106.036713 \nL 238.277427 143.147578 \nL 238.339394 135.711664 \nL 238.488114 99.3095 \nL 238.550081 107.696945 \nL 238.698802 143.208342 \nL 238.748375 136.948657 \nL 238.909489 99.386778 \nL 238.959062 106.568095 \nL 239.107783 143.181598 \nL 239.169749 134.799206 \nL 239.31847 99.338645 \nL 239.368043 105.821579 \nL 239.516764 143.1141 \nL 239.57873 135.417201 \nL 239.727451 99.342866 \nL 239.777024 105.419117 \nL 239.938138 143.07395 \nL 239.987711 135.676738 \nL 240.136432 99.359635 \nL 240.198398 108.474228 \nL 240.347119 143.052814 \nL 240.396692 135.588496 \nL 240.545413 99.377113 \nL 240.594986 105.576739 \nL 240.743706 143.071444 \nL 240.805673 135.144701 \nL 240.954394 99.412542 \nL 241.003967 106.152118 \nL 241.152687 143.103822 \nL 241.214654 134.318957 \nL 241.363375 99.513296 \nL 241.412948 107.102711 \nL 241.561668 143.081564 \nL 241.611242 136.369146 \nL 241.759962 99.435408 \nL 241.821929 108.483311 \nL 241.958256 143.022857 \nL 242.020223 134.872378 \nL 242.168943 99.517574 \nL 242.218517 106.924927 \nL 242.367237 143.040346 \nL 242.41681 136.200744 \nL 242.565531 99.47008 \nL 242.627498 109.071865 \nL 242.763825 143.031226 \nL 242.813398 137.114072 \nL 242.962118 99.513876 \nL 243.024085 108.217243 \nL 243.160412 142.971115 \nL 243.222379 134.575937 \nL 243.358706 99.575621 \nL 243.420673 107.761575 \nL 243.557 142.918654 \nL 243.618967 134.840327 \nL 243.755294 99.611707 \nL 243.81726 107.685875 \nL 243.953587 142.90214 \nL 244.015554 134.72753 \nL 244.151881 99.609418 \nL 244.213848 107.989459 \nL 244.350175 142.918422 \nL 244.412142 134.228386 \nL 244.548469 99.588089 \nL 244.598042 105.461934 \nL 244.746763 142.931628 \nL 244.808729 133.314407 \nL 244.945056 99.600485 \nL 244.99463 106.42217 \nL 245.14335 142.871774 \nL 245.192924 135.453097 \nL 245.329251 99.715513 \nL 245.391217 107.825801 \nL 245.527544 142.865795 \nL 245.589511 133.799528 \nL 245.725838 99.640498 \nL 245.775412 106.341592 \nL 245.924132 142.812738 \nL 245.973705 135.15953 \nL 246.110032 99.705293 \nL 246.171999 108.531054 \nL 246.308326 142.842557 \nL 246.3579 136.078241 \nL 246.50662 99.769086 \nL 246.556194 107.752948 \nL 246.69252 142.811826 \nL 246.742094 136.604435 \nL 246.890814 99.749471 \nL 246.940388 107.379672 \nL 247.076715 142.776998 \nL 247.138682 133.444367 \nL 247.275009 99.768632 \nL 247.324582 107.393795 \nL 247.460909 142.760768 \nL 247.522876 133.216152 \nL 247.659203 99.8224 \nL 247.708776 107.799462 \nL 247.845103 142.750662 \nL 247.894677 136.02854 \nL 248.031004 99.847955 \nL 248.09297 108.623288 \nL 248.229297 142.697557 \nL 248.278871 135.06542 \nL 248.415198 99.803504 \nL 248.464771 106.436738 \nL 248.601098 142.646754 \nL 248.663065 133.632394 \nL 248.799392 99.892798 \nL 248.848966 107.997436 \nL 248.985293 142.66905 \nL 249.034866 135.275607 \nL 249.171193 99.850474 \nL 249.220766 106.607782 \nL 249.357093 142.632746 \nL 249.41906 132.998252 \nL 249.542994 99.935725 \nL 249.604961 109.028498 \nL 249.728894 142.52395 \nL 249.790861 133.865186 \nL 249.927188 99.983519 \nL 249.976762 108.384757 \nL 250.113089 142.542265 \nL 250.162662 134.292954 \nL 250.298989 99.97806 \nL 250.348562 108.168169 \nL 250.484889 142.525793 \nL 250.534463 134.300416 \nL 250.67079 100.014773 \nL 250.720363 108.37071 \nL 250.85669 142.465624 \nL 250.906264 133.883986 \nL 251.030197 100.069917 \nL 251.092164 109.007877 \nL 251.216098 142.481048 \nL 251.278065 133.016449 \nL 251.401998 100.010104 \nL 251.451572 106.557685 \nL 251.587899 142.511812 \nL 251.649866 131.649916 \nL 251.773799 100.037249 \nL 251.823373 107.959226 \nL 251.9597 142.392366 \nL 252.009273 133.651297 \nL 252.133207 100.079943 \nL 252.195174 109.924786 \nL 252.319107 142.463933 \nL 252.368681 135.09854 \nL 252.505008 100.123969 \nL 252.554581 108.630255 \nL 252.678515 142.399112 \nL 252.740481 132.488279 \nL 252.864415 100.093539 \nL 252.913989 107.818825 \nL 253.037922 142.304413 \nL 253.099889 133.118373 \nL 253.223823 100.112468 \nL 253.273396 107.449066 \nL 253.409723 142.290293 \nL 253.459297 133.28535 \nL 253.58323 100.136073 \nL 253.632804 107.501578 \nL 253.756737 142.250282 \nL 253.818704 132.993002 \nL 253.942638 100.162329 \nL 253.992211 107.984166 \nL 254.116145 142.297356 \nL 254.178112 132.220726 \nL 254.302045 100.233909 \nL 254.351619 108.932705 \nL 254.475552 142.323544 \nL 254.525126 134.768788 \nL 254.649059 100.256757 \nL 254.711026 110.407153 \nL 254.83496 142.216637 \nL 254.884533 133.135289 \nL 255.008467 100.241074 \nL 255.05804 108.506737 \nL 255.181974 142.276995 \nL 255.231547 134.744459 \nL 255.355481 100.277801 \nL 255.417448 110.944714 \nL 255.528988 142.17981 \nL 255.578562 135.827667 \nL 255.714889 100.4003 \nL 255.764462 109.950977 \nL 255.888396 142.147368 \nL 255.937969 132.87191 \nL 256.061903 100.357852 \nL 256.111476 109.461931 \nL 256.23541 142.157722 \nL 256.284983 133.117528 \nL 256.408917 100.371633 \nL 256.45849 109.457785 \nL 256.582424 142.121789 \nL 256.631997 132.878786 \nL 256.755931 100.433869 \nL 256.805504 109.944297 \nL 256.917045 142.048257 \nL 256.979012 132.136238 \nL 257.090552 100.422872 \nL 257.140125 107.12828 \nL 257.264059 142.129707 \nL 257.326026 130.844905 \nL 257.437566 100.378704 \nL 257.487139 108.481169 \nL 257.611073 142.100138 \nL 257.660646 133.136989 \nL 257.78458 100.52549 \nL 257.834154 110.444281 \nL 257.945694 142.07847 \nL 257.995267 134.79327 \nL 258.119201 100.422887 \nL 258.168774 108.957218 \nL 258.292708 141.984001 \nL 258.342281 132.122531 \nL 258.453822 100.457228 \nL 258.503395 108.016834 \nL 258.627329 142.039945 \nL 258.676902 132.864421 \nL 258.788442 100.517167 \nL 258.838016 107.57061 \nL 258.96195 142.037575 \nL 259.011523 133.088679 \nL 259.123063 100.541877 \nL 259.18503 111.582015 \nL 259.29657 142.009313 \nL 259.346144 132.801423 \nL 259.457684 100.523892 \nL 259.507257 108.089988 \nL 259.631191 141.932725 \nL 259.680765 131.978101 \nL 259.792305 100.512063 \nL 259.841878 109.107342 \nL 259.953419 141.938464 \nL 260.015385 130.565747 \nL 260.126926 100.614844 \nL 260.176499 110.718943 \nL 260.288039 141.971332 \nL 260.337613 132.854954 \nL 260.449153 100.553577 \nL 260.498727 108.770865 \nL 260.610267 141.904325 \nL 260.672234 130.34578 \nL 260.77138 100.700637 \nL 260.833347 111.48325 \nL 260.944888 141.862091 \nL 260.994461 131.571025 \nL 261.106001 100.622792 \nL 261.155575 110.554359 \nL 261.267115 141.906801 \nL 261.316688 132.212896 \nL 261.428229 100.609326 \nL 261.477802 110.192993 \nL 261.589342 141.901374 \nL 261.638916 132.296747 \nL 261.750456 100.628849 \nL 261.80003 110.387646 \nL 261.91157 141.864757 \nL 261.961143 131.818412 \nL 262.072684 100.693583 \nL 262.122257 111.158974 \nL 262.233797 141.752648 \nL 262.283371 130.740995 \nL 262.382518 100.717449 \nL 262.432091 108.279796 \nL 262.543631 141.858052 \nL 262.593205 133.418434 \nL 262.704745 100.647966 \nL 262.754319 110.093501 \nL 262.865859 141.803518 \nL 262.915432 131.237437 \nL 263.014579 100.755924 \nL 263.064153 108.341259 \nL 263.175693 141.850572 \nL 263.225266 132.787459 \nL 263.336807 100.724044 \nL 263.38638 111.356373 \nL 263.485527 141.7803 \nL 263.5351 133.718255 \nL 263.646641 100.688044 \nL 263.696214 110.668957 \nL 263.795361 141.717254 \nL 263.857328 129.68159 \nL 263.956475 100.692449 \nL 264.006048 110.582558 \nL 264.105195 141.720215 \nL 264.154768 133.895043 \nL 264.266309 100.715854 \nL 264.315882 111.103262 \nL 264.415029 141.778113 \nL 264.464603 133.133571 \nL 264.576143 100.809299 \nL 264.625716 112.273387 \nL 264.724863 141.805151 \nL 264.774437 131.729528 \nL 264.873584 100.751467 \nL 264.923157 109.512689 \nL 265.034697 141.635599 \nL 265.084271 129.585096 \nL 265.183418 100.766438 \nL 265.232991 111.890183 \nL 265.332138 141.791933 \nL 265.381711 131.486167 \nL 265.480858 100.730933 \nL 265.530432 110.345578 \nL 265.629579 141.755653 \nL 265.679152 132.690937 \nL 265.778299 100.798493 \nL 265.827872 109.465672 \nL 265.927019 141.688284 \nL 265.988986 128.55026 \nL 266.07574 100.843592 \nL 266.125313 109.204897 \nL 266.22446 141.677838 \nL 266.286427 128.471897 \nL 266.37318 100.817017 \nL 266.422754 109.556589 \nL 266.521901 141.733428 \nL 266.571474 132.544082 \nL 266.670621 100.751207 \nL 266.720195 110.561366 \nL 266.819341 141.779427 \nL 266.868915 131.183518 \nL 266.968062 100.766705 \nL 267.017635 112.30286 \nL 267.116782 141.648798 \nL 267.166356 129.033858 \nL 267.253109 100.81222 \nL 267.302683 109.944872 \nL 267.40183 141.77992 \nL 267.451403 131.129991 \nL 267.55055 100.804989 \nL 267.600123 113.086298 \nL 267.686877 141.700808 \nL 267.73645 132.467878 \nL 267.835597 100.737014 \nL 267.885171 112.041099 \nL 267.971924 141.602305 \nL 268.033891 128.165578 \nL 268.120645 100.72967 \nL 268.170218 111.718306 \nL 268.269365 141.604746 \nL 268.318938 128.110721 \nL 268.405692 100.72549 \nL 268.455265 112.116843 \nL 268.542019 141.6818 \nL 268.591592 132.3973 \nL 268.690739 100.762002 \nL 268.740313 113.278533 \nL 268.827066 141.792801 \nL 268.87664 130.948319 \nL 268.963393 100.85011 \nL 269.012967 110.105514 \nL 269.112114 141.733926 \nL 269.161687 128.635581 \nL 269.248441 100.706534 \nL 269.298014 112.702637 \nL 269.384768 141.806907 \nL 269.434341 130.779907 \nL 269.521095 100.759704 \nL 269.570668 110.975985 \nL 269.657422 141.690533 \nL 269.706995 132.106759 \nL 269.806142 100.887278 \nL 269.855715 115.318323 \nL 269.942469 141.674582 \nL 269.992042 127.400017 \nL 270.078796 100.827122 \nL 270.128369 115.124379 \nL 270.215123 141.677651 \nL 270.264696 127.173722 \nL 270.339056 100.84913 \nL 270.38863 110.36129 \nL 270.475383 141.747086 \nL 270.524957 131.592207 \nL 270.61171 100.689148 \nL 270.661284 111.702752 \nL 270.748037 141.883509 \nL 270.797611 129.806139 \nL 270.884364 100.638568 \nL 270.933938 113.966229 \nL 271.020691 141.768791 \nL 271.070265 127.032605 \nL 271.144625 100.691595 \nL 271.194198 111.569543 \nL 271.280952 141.925505 \nL 271.330525 129.074852 \nL 271.417279 100.707313 \nL 271.466852 115.635789 \nL 271.541213 141.901706 \nL 271.590786 130.222912 \nL 271.67754 100.602732 \nL 271.727113 114.902458 \nL 271.801473 141.886582 \nL 271.851047 130.511294 \nL 271.9378 100.57926 \nL 271.987374 115.078551 \nL 272.061734 141.948361 \nL 272.111307 129.930802 \nL 272.198061 100.641342 \nL 272.247634 116.201097 \nL 272.321994 142.022 \nL 272.371568 128.414813 \nL 272.445928 100.576189 \nL 272.495502 112.289508 \nL 272.582255 141.895066 \nL 272.631829 125.854953 \nL 272.706189 100.474436 \nL 272.755762 115.241767 \nL 272.830122 142.079013 \nL 272.879696 128.439918 \nL 272.954056 100.466877 \nL 273.003629 113.162621 \nL 273.07799 141.994529 \nL 273.127563 130.025594 \nL 273.201923 100.574833 \nL 273.251497 112.057891 \nL 273.325857 141.914714 \nL 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130.508445 \nL 442.812527 119.509418 \nL 442.837314 120.096372 \nL 442.849707 120.103161 \nL 442.862101 120.022069 \nL 442.924068 118.224663 \nL 442.961248 120.187551 \nL 443.171935 130.456237 \nL 443.283475 123.634772 \nL 443.370229 119.702883 \nL 443.419802 121.661532 \nL 443.729636 146.903281 \nL 443.766816 145.481009 \nL 443.903143 131.688189 \nL 444.00229 123.577017 \nL 444.051864 124.560411 \nL 444.089044 124.19324 \nL 444.101437 124.615357 \nL 444.336911 141.006102 \nL 444.386484 139.679914 \nL 444.535205 118.123423 \nL 444.708712 103.319648 \nL 444.745892 103.615554 \nL 444.770679 103.590305 \nL 444.845039 109.407857 \nL 444.981366 118.397868 \nL 445.030939 117.862803 \nL 445.14248 113.473348 \nL 445.2912 108.153442 \nL 445.315987 108.997429 \nL 445.452314 119.839308 \nL 445.625821 128.951367 \nL 445.638214 129.009864 \nL 445.663001 128.629518 \nL 445.824114 116.878589 \nL 445.910868 113.16657 \nL 445.948048 114.356953 \nL 446.208309 129.198485 \nL 446.233095 129.505452 \nL 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122.806208 \nL 507.964435 119.53865 \nL 508.063582 104.471731 \nL 508.187515 93.77267 \nL 508.212302 95.086943 \nL 508.410596 133.967528 \nL 508.509743 127.11081 \nL 508.68325 103.15111 \nL 508.732823 108.42809 \nL 508.943511 148.160988 \nL 509.017871 138.624062 \nL 509.166591 118.35572 \nL 509.203771 120.03609 \nL 509.364885 150.230705 \nL 509.439245 158.819097 \nL 509.488818 155.373921 \nL 509.699506 122.168694 \nL 509.761472 127.589341 \nL 509.947373 154.900784 \nL 509.996946 151.829117 \nL 510.207634 119.638354 \nL 510.281994 127.583653 \nL 510.443107 149.805254 \nL 510.467894 146.97003 \nL 510.715761 115.450974 \nL 510.740548 117.613895 \nL 510.852088 137.531925 \nL 510.963629 150.941694 \nL 511.013202 149.203405 \nL 511.199103 128.119465 \nL 511.273463 132.900036 \nL 511.44697 156.142509 \nL 511.508937 151.328547 \nL 511.70723 117.695583 \nL 511.769197 127.752994 \nL 511.930311 158.857846 \nL 511.979884 154.930912 \nL 512.215358 119.473085 \nL 512.277325 125.40285 \nL 512.426045 151.599286 \nL 512.475619 148.553099 \nL 512.723486 113.499561 \nL 512.797846 122.339397 \nL 512.92178 139.231373 \nL 512.971353 136.790627 \nL 513.194434 108.366465 \nL 513.268794 116.780245 \nL 513.442301 141.228952 \nL 513.479481 138.050368 \nL 513.702562 96.463907 \nL 513.776922 104.146942 \nL 513.950429 140.773205 \nL 514.037183 132.248469 \nL 514.185903 104.405956 \nL 514.260263 110.828629 \nL 514.421377 131.548653 \nL 514.47095 127.802629 \nL 514.681637 100.543492 \nL 514.718818 105.390307 \nL 514.929505 141.614655 \nL 514.979078 139.035314 \nL 515.152585 116.553427 \nL 515.214552 120.389258 \nL 515.412846 149.039136 \nL 515.474813 143.954974 \nL 515.6855 108.851852 \nL 515.747467 113.515407 \nL 515.896187 143.746772 \nL 515.970547 134.972354 \nL 516.156448 102.147481 \nL 516.193628 104.23986 \nL 516.391921 141.120215 \nL 516.466282 134.40417 \nL 516.627395 109.142229 \nL 516.689362 113.314868 \nL 516.924836 152.896491 \nL 516.986803 143.708892 \nL 517.110737 122.723068 \nL 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368.935692 119.47846 \nL 368.972873 120.046334 \nL 369.084413 126.912559 \nL 369.307493 140.154575 \nL 369.33228 139.819382 \nL 369.431427 134.463217 \nL 369.666901 119.544 \nL 369.716474 120.357365 \nL 369.852801 129.77956 \nL 370.038702 140.101737 \nL 370.063488 139.842012 \nL 370.150242 135.661886 \nL 370.410503 119.619251 \nL 370.460076 120.746499 \nL 370.62119 132.700448 \nL 370.76991 140.038414 \nL 370.80709 139.566082 \nL 370.918631 132.95404 \nL 371.141711 119.673014 \nL 371.178891 120.280746 \nL 371.302825 128.210732 \nL 371.501119 139.971233 \nL 371.538299 139.559442 \nL 371.649839 133.096694 \nL 371.872919 119.732531 \nL 371.9101 120.292536 \nL 372.02164 127.04964 \nL 372.232327 139.904756 \nL 372.269507 139.527433 \nL 372.368654 134.150608 \nL 372.604128 119.794222 \nL 372.641308 120.332289 \nL 372.752848 127.022007 \nL 372.963535 139.84151 \nL 373.000715 139.472723 \nL 373.099862 134.131209 \nL 373.335336 119.856527 \nL 373.372516 120.398287 \nL 373.484057 127.072668 \nL 373.694744 139.782009 \nL 373.731924 139.396053 \nL 373.831071 134.038577 \nL 374.066545 119.919884 \nL 374.103725 120.490694 \nL 374.215265 127.200814 \nL 374.425952 139.724789 \nL 374.463132 139.29626 \nL 374.574672 132.888196 \nL 374.797753 119.986699 \nL 374.834933 120.611532 \nL 374.958867 128.438201 \nL 375.157161 139.666429 \nL 375.194341 139.170321 \nL 375.305881 132.645173 \nL 375.516568 120.048215 \nL 375.553748 120.42159 \nL 375.652895 125.708932 \nL 375.888369 139.601577 \nL 375.925549 139.013421 \nL 376.049483 131.302613 \nL 376.247776 120.10082 \nL 376.284957 120.579766 \nL 376.396497 127.036427 \nL 376.607184 139.537544 \nL 376.644364 139.161653 \nL 376.743511 133.896162 \nL 376.978985 120.167351 \nL 377.016165 120.775609 \nL 377.140099 128.486893 \nL 377.338392 139.484783 \nL 377.375572 138.966144 \nL 377.487113 132.462067 \nL 377.6978 120.222701 \nL 377.73498 120.65792 \nL 377.84652 126.980649 \nL 378.057207 139.415722 \nL 378.094387 139.057613 \nL 378.193534 133.862856 \nL 378.429008 120.288064 \nL 378.466188 120.902016 \nL 378.590122 128.570948 \nL 378.788416 139.36412 \nL 378.813203 139.105397 \nL 378.912349 134.200193 \nL 379.147823 120.3397 \nL 379.185003 120.832074 \nL 379.296544 127.227988 \nL 379.507231 139.305479 \nL 379.544411 138.864456 \nL 379.655951 132.584163 \nL 379.866638 120.403207 \nL 379.903818 120.799221 \nL 380.015359 126.975721 \nL 380.226046 139.238986 \nL 380.263226 138.881622 \nL 380.362373 133.740959 \nL 380.597847 120.470696 \nL 380.635027 121.118131 \nL 380.75896 128.773637 \nL 380.957254 139.180031 \nL 380.982041 138.87198 \nL 381.081188 133.864518 \nL 381.316662 120.52251 \nL 381.353842 121.124038 \nL 381.477776 128.661403 \nL 381.676069 139.125928 \nL 381.700856 138.840049 \nL 381.800003 133.915718 \nL 382.035477 120.57827 \nL 382.072657 121.158775 \nL 382.196591 128.62748 \nL 382.394884 139.068894 \nL 382.419671 138.788433 \nL 382.518818 133.896433 \nL 382.754292 120.636441 \nL 382.791472 121.220665 \nL 382.915406 128.670691 \nL 383.113699 139.009479 \nL 383.138486 138.717826 \nL 383.237633 133.807685 \nL 383.473107 120.697452 \nL 383.510287 121.309869 \nL 383.634221 128.790108 \nL 383.832514 138.946279 \nL 383.857301 138.627031 \nL 383.956448 133.649651 \nL 384.179529 120.754162 \nL 384.229102 121.428354 \nL 384.353036 128.98501 \nL 384.538936 138.89791 \nL 384.576116 138.513002 \nL 384.687656 132.497141 \nL 384.898344 120.802968 \nL 384.935524 121.230132 \nL 385.047064 127.322231 \nL 385.257751 138.846232 \nL 385.294931 138.370917 \nL 385.406471 132.191963 \nL 385.617159 120.859314 \nL 385.654339 121.388585 \nL 385.765879 127.664244 \nL 385.976566 138.783191 \nL 386.001353 138.494254 \nL 386.1005 133.654872 \nL 386.32358 120.923089 \nL 386.373154 121.585436 \nL 386.497087 129.043272 \nL 386.682988 138.732707 \nL 386.720168 138.318651 \nL 386.831708 132.305231 \nL 387.042395 120.969229 \nL 387.079575 121.46152 \nL 387.191116 127.621977 \nL 387.401803 138.673436 \nL 387.438983 138.097514 \nL 387.562917 130.831432 \nL 387.748817 121.031412 \nL 387.785997 121.385934 \nL 387.885144 126.342246 \nL 388.108225 138.624335 \nL 388.145405 138.173397 \nL 388.256945 132.126517 \nL 388.467632 121.082509 \nL 388.504812 121.635032 \nL 388.628746 128.81679 \nL 388.814646 138.562501 \nL 388.851826 138.212126 \nL 388.950973 133.288605 \nL 389.174054 121.132925 \nL 389.211234 121.597396 \nL 389.322774 127.640717 \nL 389.533461 138.508569 \nL 389.558248 138.221443 \nL 389.657395 133.463702 \nL 389.880475 121.189042 \nL 389.917655 121.589887 \nL 390.029196 127.485048 \nL 390.239883 138.460069 \nL 390.277063 137.92812 \nL 390.400997 130.849246 \nL 390.586897 121.245849 \nL 390.624077 121.607546 \nL 390.735617 127.402969 \nL 390.946305 138.408499 \nL 390.983485 137.903792 \nL 391.095025 131.831503 \nL 391.293319 121.300316 \nL 391.330499 121.647257 \nL 391.429646 126.507624 \nL 391.652726 138.355946 \nL 391.689906 137.854378 \nL 391.801447 131.808204 \nL 391.99974 121.351368 \nL 392.03692 121.707762 \nL 392.148461 127.453372 \nL 392.359148 138.302558 \nL 392.396328 137.780245 \nL 392.520262 130.792852 \nL 392.706162 121.399854 \nL 392.743342 121.78965 \nL 392.854882 127.584089 \nL 393.06557 138.24657 \nL 393.10275 137.679962 \nL 393.226683 130.628786 \nL 393.412584 121.448523 \nL 393.449764 121.895317 \nL 393.561304 127.784832 \nL 393.759598 138.193361 \nL 393.796778 137.860836 \nL 393.895925 133.082964 \nL 394.119005 121.501998 \nL 394.156186 122.028907 \nL 394.280119 128.966955 \nL 394.46602 138.151073 \nL 394.5032 137.725738 \nL 394.61474 131.907252 \nL 394.813034 121.558237 \nL 394.850214 121.88772 \nL 394.949361 126.637097 \nL 395.172441 138.09751 \nL 395.209621 137.556893 \nL 395.333555 130.626125 \nL 395.519455 121.598937 \nL 395.556635 122.056331 \nL 395.668176 127.910444 \nL 395.86647 138.049485 \nL 395.90365 137.669537 \nL 396.01519 131.978688 \nL 396.213484 121.659512 \nL 396.250664 121.967906 \nL 396.349811 126.646995 \nL 396.572891 137.999248 \nL 396.610071 137.457342 \nL 396.734005 130.568671 \nL 396.919905 121.696656 \nL 396.957085 122.179429 \nL 397.068626 128.046836 \nL 397.26692 137.955794 \nL 397.3041 137.526306 \nL 397.41564 131.772071 \nL 397.613934 121.746192 \nL 397.651114 122.128296 \nL 397.762654 127.779527 \nL 397.960948 137.902212 \nL 397.998128 137.561555 \nL 398.097275 132.855104 \nL 398.307962 121.802033 \nL 398.357535 122.404762 \nL 398.481469 129.352526 \nL 398.66737 137.853551 \nL 398.692156 137.570014 \nL 398.791303 133.003351 \nL 399.014384 121.842338 \nL 399.051564 122.390147 \nL 399.175497 129.215864 \nL 399.361398 137.812747 \nL 399.386185 137.55683 \nL 399.485331 133.084344 \nL 399.708412 121.884709 \nL 399.745592 122.402856 \nL 399.869526 129.15089 \nL 400.055426 137.769397 \nL 400.092606 137.257325 \nL 400.21654 130.531005 \nL 400.40244 121.92869 \nL 400.43962 122.440506 \nL 400.563554 129.15659 \nL 400.749454 137.724897 \nL 400.786635 137.207543 \nL 400.910568 130.490296 \nL 401.096469 121.973832 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137.440788 \nL 405.607653 137.109067 \nL 405.719193 131.726117 \nL 405.917487 122.246911 \nL 405.954667 122.670644 \nL 406.066207 128.22925 \nL 406.264501 137.407063 \nL 406.301681 136.886348 \nL 406.425615 130.295669 \nL 406.599122 122.28787 \nL 406.636302 122.623989 \nL 406.747842 127.999408 \nL 406.946136 137.376624 \nL 406.983316 136.931597 \nL 407.094856 131.348293 \nL 407.29315 122.325372 \nL 407.317937 122.60328 \nL 407.417084 127.027749 \nL 407.627771 137.342169 \nL 407.664951 136.950002 \nL 407.776491 131.478864 \nL 407.974785 122.353189 \nL 408.011965 122.870606 \nL 408.135899 129.423032 \nL 408.309406 137.307981 \nL 408.346586 136.94571 \nL 408.458126 131.54231 \nL 408.65642 122.383193 \nL 408.6936 122.882187 \nL 408.817534 129.388349 \nL 408.991041 137.276467 \nL 409.028221 136.921111 \nL 409.139761 131.539655 \nL 409.338055 122.413892 \nL 409.375235 122.91722 \nL 409.499168 129.421286 \nL 409.672676 137.248176 \nL 409.709856 136.876816 \nL 409.821396 131.47146 \nL 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131.240871 \nL 418.769404 122.684522 \nL 418.806584 123.076047 \nL 418.918124 128.505342 \nL 419.104025 136.988309 \nL 419.141205 136.568938 \nL 419.252745 131.083962 \nL 419.438645 122.69138 \nL 419.475825 123.144316 \nL 419.599759 129.525947 \nL 419.773266 136.979606 \nL 419.810446 136.487413 \nL 419.93438 130.028577 \nL 420.107887 122.703396 \nL 420.145067 123.240499 \nL 420.269001 129.785567 \nL 420.430114 136.965341 \nL 420.467294 136.665348 \nL 420.566441 132.199586 \nL 420.764735 122.705504 \nL 420.801915 123.062452 \nL 420.913456 128.447332 \nL 421.099356 136.972424 \nL 421.136536 136.552808 \nL 421.248076 131.044179 \nL 421.433977 122.706018 \nL 421.471157 123.193934 \nL 421.59509 129.668204 \nL 421.768598 136.961647 \nL 421.805778 136.399903 \nL 421.929711 129.786013 \nL 422.090825 122.710075 \nL 422.128005 123.062679 \nL 422.239545 128.459398 \nL 422.425446 136.970328 \nL 422.462626 136.531542 \nL 422.574166 130.966091 \nL 422.760067 122.70993 \nL 422.797247 123.240385 \nL 422.92118 129.816962 \nL 423.082294 136.965403 \nL 423.119474 136.628066 \nL 423.231014 131.242648 \nL 423.416915 122.702994 \nL 423.454095 123.144423 \nL 423.565635 128.73352 \nL 423.751536 136.967825 \nL 423.788716 136.416949 \nL 423.912649 129.78189 \nL 424.073763 122.701908 \nL 424.110943 123.076559 \nL 424.222483 128.554269 \nL 424.408384 136.977764 \nL 424.445564 136.481195 \nL 424.569497 129.927974 \nL 424.730611 122.701214 \nL 424.767791 123.031892 \nL 424.879332 128.440499 \nL 425.065232 136.985458 \nL 425.102412 136.520409 \nL 425.226346 130.007574 \nL 425.387459 122.697275 \nL 425.42464 123.007206 \nL 425.523786 127.573916 \nL 425.72208 136.993584 \nL 425.75926 136.536841 \nL 425.883194 130.020285 \nL 426.044308 122.688301 \nL 426.081488 123.001056 \nL 426.180635 127.588093 \nL 426.378928 137.00295 \nL 426.416109 136.53097 \nL 426.540042 129.965592 \nL 426.701156 122.67438 \nL 426.738336 123.01382 \nL 426.849876 128.492196 \nL 427.035777 137.012463 \nL 427.072957 136.501462 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122.136822 \nL 439.65222 122.647299 \nL 439.776153 130.004413 \nL 439.937267 137.552333 \nL 439.974447 136.915236 \nL 440.110774 128.373607 \nL 440.247101 122.099364 \nL 440.284281 122.529772 \nL 440.395822 128.841784 \nL 440.569329 137.600319 \nL 440.606509 137.028129 \nL 440.730442 129.487357 \nL 440.879163 122.063451 \nL 440.928736 122.792961 \nL 441.065063 131.553541 \nL 441.20139 137.644824 \nL 441.23857 137.111268 \nL 441.362504 129.586686 \nL 441.523618 122.023257 \nL 441.560798 122.720596 \nL 441.697125 131.503758 \nL 441.833452 137.689311 \nL 441.870632 137.167148 \nL 441.994565 129.60994 \nL 442.155679 121.977925 \nL 442.192859 122.679367 \nL 442.329186 131.52945 \nL 442.465513 137.735059 \nL 442.502693 137.196217 \nL 442.626627 129.555357 \nL 442.775347 121.929372 \nL 442.824921 122.67002 \nL 442.961248 131.632253 \nL 443.097575 137.78106 \nL 443.134755 137.196754 \nL 443.258688 129.421169 \nL 443.407409 121.875075 \nL 443.444589 122.314244 \nL 443.556129 128.87415 \nL 443.729636 137.823907 \nL 443.766816 137.164788 \nL 443.903143 128.224754 \nL 444.03947 121.821462 \nL 444.07665 122.349636 \nL 444.200584 130.112557 \nL 444.349304 137.869327 \nL 444.398878 137.094054 \nL 444.547598 126.970969 \nL 444.671532 121.775618 \nL 444.708712 122.423126 \nL 444.832645 130.450269 \nL 444.981366 137.929845 \nL 445.018546 137.39327 \nL 445.14248 129.527111 \nL 445.2912 121.727638 \nL 445.32838 122.15888 \nL 445.43992 128.856772 \nL 445.613427 137.972345 \nL 445.650607 137.270453 \nL 445.786934 128.061142 \nL 445.923261 121.669144 \nL 445.960441 122.281494 \nL 446.084375 130.370267 \nL 446.233095 138.033868 \nL 446.270276 137.504989 \nL 446.381816 130.552113 \nL 446.542929 121.620811 \nL 446.58011 122.072576 \nL 446.69165 128.914927 \nL 446.865157 138.072538 \nL 446.902337 137.314991 \nL 447.038664 127.878054 \nL 447.174991 121.567004 \nL 447.212171 122.264122 \nL 447.348498 131.643259 \nL 447.484825 138.141517 \nL 447.522005 137.498095 \nL 447.645939 129.198355 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130.522273 \nL 451.822502 138.556908 \nL 451.859682 137.862976 \nL 451.983616 128.993741 \nL 452.132336 121.092947 \nL 452.169516 121.852763 \nL 452.305843 131.957072 \nL 452.429777 138.605773 \nL 452.47935 137.770243 \nL 452.628071 126.45733 \nL 452.739611 121.027311 \nL 452.789184 121.97341 \nL 452.937905 133.443231 \nL 453.049445 138.686863 \nL 453.099018 137.618456 \nL 453.272525 124.103455 \nL 453.359279 120.957528 \nL 453.408852 122.159869 \nL 453.669113 138.741215 \nL 453.755867 135.089687 \nL 453.966554 120.897304 \nL 454.028521 122.427375 \nL 454.276388 138.81947 \nL 454.363141 135.507081 \nL 454.586222 120.828951 \nL 454.635795 122.135406 \nL 454.883663 138.876807 \nL 454.982809 134.935826 \nL 455.193497 120.754793 \nL 455.24307 121.902339 \nL 455.503331 138.945935 \nL 455.590084 135.215716 \nL 455.800771 120.690165 \nL 455.862738 122.32593 \nL 456.110605 139.02271 \nL 456.184966 136.304506 \nL 456.408046 120.627068 \nL 456.482406 122.896949 \nL 456.71788 139.095027 \nL 456.79224 136.431158 \nL 457.015321 120.560213 \nL 457.089681 122.80302 \nL 457.325155 139.166557 \nL 457.399515 136.49043 \nL 457.622596 120.487183 \nL 457.696956 122.772416 \nL 457.93243 139.238 \nL 458.00679 136.480565 \nL 458.22987 120.408609 \nL 458.304231 122.807921 \nL 458.539705 139.306912 \nL 458.601671 137.221398 \nL 458.837145 120.328352 \nL 458.911505 122.914785 \nL 459.146979 139.367515 \nL 459.208946 137.093951 \nL 459.44442 120.253709 \nL 459.51878 123.10071 \nL 459.741861 139.457812 \nL 459.803828 137.672975 \nL 460.051695 120.195602 \nL 460.126055 123.37569 \nL 460.349136 139.534173 \nL 460.411102 137.43285 \nL 460.646576 120.101362 \nL 460.720936 122.820362 \nL 460.944017 139.604205 \nL 461.018377 137.097216 \nL 461.03077 123.486642 \nL 461.129917 133.021158 \nL 461.253851 139.637131 \nL 461.291031 138.697212 \nL 461.439751 125.717103 \nL 461.551292 119.988737 \nL 461.588472 120.832273 \nL 461.724799 132.534641 \nL 461.848732 139.730162 \nL 461.885912 138.974628 \nL 462.009846 128.617786 \nL 462.146173 119.908185 \nL 462.183353 120.584466 \nL 462.307287 130.859012 \nL 462.443614 139.80178 \nL 462.493187 138.669414 \nL 462.654301 124.145718 \nL 462.741055 119.842304 \nL 462.790628 120.896846 \nL 462.939348 134.298091 \nL 463.050889 139.869431 \nL 463.088069 138.875459 \nL 463.236789 125.47126 \nL 463.348329 119.760185 \nL 463.385509 120.716767 \nL 463.521836 132.905184 \nL 463.64577 139.959992 \nL 463.68295 139.030954 \nL 463.819277 126.826276 \nL 463.943211 119.673237 \nL 463.980391 120.584823 \nL 464.116718 132.820003 \nL 464.240651 140.044457 \nL 464.277831 139.140006 \nL 464.414158 126.859346 \nL 464.538092 119.590552 \nL 464.575272 120.498393 \nL 464.711599 132.839986 \nL 464.835533 140.125625 \nL 464.872713 139.203678 \nL 465.00904 126.785491 \nL 465.132973 119.511094 \nL 465.170154 120.458033 \nL 465.306481 132.968407 \nL 465.430414 140.202711 \nL 465.467594 139.219741 \nL 465.603921 126.601947 \nL 465.727855 119.437524 \nL 465.765035 120.467695 \nL 465.913755 134.474942 \nL 466.012902 140.2788 \nL 466.062476 139.182479 \nL 466.211196 125.041491 \nL 466.310343 119.346567 \nL 466.359916 120.53488 \nL 466.533423 137.065815 \nL 466.607784 140.378345 \nL 466.657357 139.082599 \nL 466.905224 119.252018 \nL 467.004371 124.585541 \nL 467.202665 140.464247 \nL 467.252239 138.907245 \nL 467.500106 119.178987 \nL 467.586859 123.830316 \nL 467.785153 140.535886 \nL 467.84712 138.640172 \nL 468.082594 119.083762 \nL 468.169347 123.19226 \nL 468.380035 140.636874 \nL 468.429608 139.081325 \nL 468.677475 119.009507 \nL 468.764229 123.802487 \nL 468.962523 140.716077 \nL 469.012096 139.436249 \nL 469.259963 118.907996 \nL 469.35911 124.567206 \nL 469.557404 140.79727 \nL 469.594584 139.717907 \nL 469.743304 124.794697 \nL 469.842451 118.825907 \nL 469.892025 120.171374 \nL 470.139892 140.897262 \nL 470.226646 136.242787 \nL 470.424939 118.750314 \nL 470.486906 120.731513 \nL 470.72238 140.988031 \nL 470.79674 137.591825 \nL 471.019821 118.665221 \nL 471.081788 121.497004 \nL 471.304868 141.076686 \nL 471.379228 137.709551 \nL 471.602309 118.574391 \nL 471.664276 121.419907 \nL 471.887356 141.166338 \nL 471.961716 137.736768 \nL 472.172403 118.489076 \nL 472.246764 121.428664 \nL 472.469844 141.255833 \nL 472.531811 138.711058 \nL 472.754892 118.386393 \nL 472.829252 121.528817 \nL 473.052332 141.339487 \nL 473.114299 138.582606 \nL 473.33738 118.284953 \nL 473.41174 121.729279 \nL 473.622427 141.415681 \nL 473.696787 138.354579 \nL 473.919868 118.197449 \nL 473.981834 120.949949 \nL 474.204915 141.527995 \nL 474.279275 138.012827 \nL 474.489962 118.116913 \nL 474.551929 120.308222 \nL 474.787403 141.607452 \nL 474.861763 137.540342 \nL 475.07245 118.010538 \nL 475.134417 120.720212 \nL 475.357498 141.711212 \nL 475.431858 138.140823 \nL 475.642545 117.928254 \nL 475.704512 120.235809 \nL 475.939986 141.777601 \nL 476.014346 137.446283 \nL 476.225033 117.837405 \nL 476.274607 119.851006 \nL 476.51008 141.893219 \nL 476.584441 137.860071 \nL 476.795128 117.732536 \nL 476.857095 120.515746 \nL 477.080175 141.990798 \nL 477.154535 138.165869 \nL 477.365222 117.639318 \nL 477.427189 120.277593 \nL 477.65027 142.08203 \nL 477.72463 138.367922 \nL 477.935317 117.548273 \nL 477.997284 120.12838 \nL 478.220364 142.174098 \nL 478.294725 138.467618 \nL 478.505412 117.454583 \nL 478.567379 120.067446 \nL 478.790459 142.269376 \nL 478.864819 138.463006 \nL 479.075506 117.358401 \nL 479.137473 120.09808 \nL 479.360554 142.36511 \nL 479.434914 138.348518 \nL 479.645601 117.265159 \nL 479.707568 120.227746 \nL 479.930649 142.453106 \nL 479.992615 139.342772 \nL 480.215696 117.185893 \nL 480.277663 120.46809 \nL 480.48835 142.526711 \nL 480.56271 139.041464 \nL 480.773397 117.0854 \nL 480.847757 120.834727 \nL 481.058445 142.646758 \nL 481.120411 139.806266 \nL 481.343492 116.984117 \nL 481.417852 121.346702 \nL 481.628539 142.721997 \nL 481.678113 140.427712 \nL 481.901193 116.900294 \nL 481.987947 122.025561 \nL 482.186241 142.835957 \nL 482.235814 140.92473 \nL 482.471288 116.802632 \nL 482.558041 122.893921 \nL 482.743942 142.912105 \nL 482.793515 141.315135 \nL 483.028989 116.698463 \nL 483.115743 122.400909 \nL 483.314037 143.008396 \nL 483.36361 140.678329 \nL 483.586691 116.611482 \nL 483.673444 122.041313 \nL 483.871738 143.114557 \nL 483.921311 140.926726 \nL 484.144392 116.527593 \nL 484.231145 121.811523 \nL 484.429439 143.211826 \nL 484.479013 141.084792 \nL 484.702093 116.438023 \nL 484.788847 121.710713 \nL 484.98714 143.305804 \nL 485.036714 141.154329 \nL 485.259794 116.339661 \nL 485.346548 121.741412 \nL 485.544842 143.396255 \nL 485.594415 141.132219 \nL 485.817496 116.235399 \nL 485.904249 121.909811 \nL 486.102543 143.476741 \nL 486.152117 141.010151 \nL 486.375197 116.134511 \nL 486.449557 120.704099 \nL 486.647851 143.569613 \nL 486.709818 140.774556 \nL 486.932898 116.053022 \nL 487.007259 121.115104 \nL 487.205552 143.681422 \nL 487.267519 140.406818 \nL 487.478206 115.961071 \nL 487.552567 120.190145 \nL 487.763254 143.749586 \nL 487.825221 139.883838 \nL 488.035908 115.859479 \nL 488.097875 119.434077 \nL 488.308562 143.866841 \nL 488.370528 140.562271 \nL 488.581216 115.771397 \nL 488.655576 120.177451 \nL 488.85387 143.933285 \nL 488.915836 141.098898 \nL 489.138917 115.694501 \nL 489.213277 121.141704 \nL 489.411571 144.040687 \nL 489.461144 141.510188 \nL 489.684225 115.5823 \nL 489.758585 120.71085 \nL 489.956879 144.142494 \nL 490.018846 140.577319 \nL 490.229533 115.487093 \nL 490.2915 118.977904 \nL 490.502187 144.234121 \nL 490.576547 139.348784 \nL 490.774841 115.396828 \nL 490.836808 118.825762 \nL 491.047495 144.324417 \nL 491.121855 139.433065 \nL 491.320149 115.305956 \nL 491.382116 118.792904 \nL 491.592803 144.415533 \nL 491.667163 139.37743 \nL 491.865457 115.215828 \nL 491.927424 118.884157 \nL 492.138111 144.502518 \nL 492.200078 140.696637 \nL 492.410765 115.135094 \nL 492.472732 119.109819 \nL 492.683419 144.572921 \nL 492.745386 140.398221 \nL 492.943679 115.070408 \nL 493.018039 119.485541 \nL 493.216333 144.670303 \nL 493.2783 141.397809 \nL 493.488987 114.949705 \nL 493.563347 120.031818 \nL 493.761641 144.769143 \nL 493.823608 140.864093 \nL 494.034295 114.881869 \nL 494.096262 119.149597 \nL 494.294556 144.838422 \nL 494.356523 141.605666 \nL 494.56721 114.774084 \nL 494.64157 119.986487 \nL 494.839864 144.937426 \nL 494.889437 142.187355 \nL 495.100124 114.716245 \nL 495.186878 121.0656 \nL 495.372778 145.031569 \nL 495.422352 142.630006 \nL 495.645432 114.615315 \nL 495.719793 120.566131 \nL 495.905693 145.103898 \nL 495.96766 141.673997 \nL 496.178347 114.517928 \nL 496.252707 120.229167 \nL 496.438608 145.173303 \nL 496.500574 141.908633 \nL 496.711262 114.431032 \nL 496.785622 120.050201 \nL 496.971522 145.251368 \nL 497.033489 142.016694 \nL 497.244176 114.347296 \nL 497.318536 120.029146 \nL 497.504437 145.341951 \nL 497.566404 141.997164 \nL 497.777091 114.267338 \nL 497.851451 120.170923 \nL 498.037351 145.440769 \nL 498.099318 141.842614 \nL 498.310005 114.200151 \nL 498.371972 118.747 \nL 498.570266 145.53494 \nL 498.632233 141.539099 \nL 498.830527 114.126063 \nL 498.904887 119.17349 \nL 499.103181 145.602522 \nL 499.165147 141.06645 \nL 499.363441 114.019551 \nL 499.425408 118.10276 \nL 499.623702 145.680775 \nL 499.698062 140.399085 \nL 499.896356 113.9734 \nL 499.945929 117.247053 \nL 500.156616 145.768488 \nL 500.230977 139.507454 \nL 500.416877 113.860785 \nL 500.46645 116.578217 \nL 500.677138 145.847514 \nL 500.751498 140.272558 \nL 500.937398 113.806065 \nL 500.999365 117.443498 \nL 501.210052 145.888577 \nL 501.272019 140.852912 \nL 501.470313 113.730497 \nL 501.519886 117.00513 \nL 501.730573 145.994914 \nL 501.804934 139.435621 \nL 501.990834 113.636741 \nL 502.040408 116.71008 \nL 502.251095 146.080197 \nL 502.325455 139.698702 \nL 502.511355 113.556892 \nL 502.560929 116.548812 \nL 502.771616 146.156551 \nL 502.845976 139.783582 \nL 503.031877 113.483226 \nL 503.08145 116.518565 \nL 503.292137 146.227177 \nL 503.366497 139.687529 \nL 503.552398 113.417181 \nL 503.601971 116.624021 \nL 503.812658 146.285882 \nL 503.887019 139.402153 \nL 504.072919 113.369822 \nL 504.122492 116.877585 \nL 504.320786 146.342469 \nL 504.395146 140.887266 \nL 504.581047 113.277328 \nL 504.643014 117.299257 \nL 504.841307 146.446385 \nL 504.903274 142.094655 \nL 505.101568 113.195148 \nL 505.163535 117.915977 \nL 505.361829 146.49636 \nL 505.423796 141.368483 \nL 505.609696 113.156787 \nL 505.671663 116.997367 \nL 505.869957 146.582541 \nL 505.944317 140.396993 \nL 506.130217 113.060184 \nL 506.179791 116.285694 \nL 506.390478 146.610467 \nL 506.452445 141.158643 \nL 506.638345 112.995104 \nL 506.700312 117.31786 \nL 506.898606 146.71397 \nL 506.960572 141.72246 \nL 507.146473 112.958458 \nL 507.20844 116.8746 \nL 507.406734 146.785912 \nL 507.4687 142.104246 \nL 507.666994 112.893186 \nL 507.728961 118.36872 \nL 507.914861 146.84753 \nL 507.976828 142.314575 \nL 508.175122 112.820861 \nL 508.237089 118.241484 \nL 508.422989 146.910249 \nL 508.484956 142.357413 \nL 508.68325 112.764338 \nL 508.745217 118.295794 \nL 508.931117 146.975166 \nL 508.993084 142.229259 \nL 509.178984 112.713519 \nL 509.253345 118.539086 \nL 509.439245 147.032555 \nL 509.501212 141.918799 \nL 509.687112 112.621023 \nL 509.749079 117.057487 \nL 509.947373 147.061365 \nL 510.00934 141.407121 \nL 510.19524 112.551745 \nL 510.257207 117.625663 \nL 510.443107 147.144773 \nL 510.505074 142.623716 \nL 510.703368 112.54419 \nL 510.765335 118.424872 \nL 510.951235 147.199976 \nL 511.000809 143.579022 \nL 511.199103 112.435396 \nL 511.273463 119.490321 \nL 511.44697 147.260212 \nL 511.508937 142.62825 \nL 511.694837 112.410995 \nL 511.769197 118.643783 \nL 511.955098 147.279968 \nL 512.004671 143.272848 \nL 512.202965 112.346448 \nL 512.264932 118.019677 \nL 512.450832 147.368888 \nL 512.512799 141.890556 \nL 512.698699 112.274515 \nL 512.760666 117.600617 \nL 512.946567 147.429916 \nL 513.008533 142.211976 \nL 513.194434 112.21968 \nL 513.256401 117.374683 \nL 513.442301 147.481865 \nL 513.504268 142.343645 \nL 513.690168 112.16875 \nL 513.752135 117.337053 \nL 513.938036 147.532169 \nL 514.000002 142.286423 \nL 514.185903 112.120186 \nL 514.24787 117.491002 \nL 514.43377 147.57634 \nL 514.495737 142.032805 \nL 514.681637 112.084634 \nL 514.743604 117.848275 \nL 514.929505 147.597443 \nL 514.979078 143.52857 \nL 515.164979 112.072769 \nL 515.239339 118.428771 \nL 515.412846 147.656194 \nL 515.474813 142.941668 \nL 515.660713 111.979224 \nL 515.72268 117.116273 \nL 515.90858 147.718224 \nL 515.970547 142.112276 \nL 516.156448 111.962834 \nL 516.206021 116.089105 \nL 516.391921 147.736245 \nL 516.466282 141.000224 \nL 516.639789 111.889364 \nL 516.701756 117.168944 \nL 516.887656 147.794412 \nL 516.949623 141.844259 \nL 517.12313 111.883487 \nL 517.185097 116.491941 \nL 517.370997 147.850586 \nL 517.432964 142.454043 \nL 517.618864 111.833702 \nL 517.668438 116.025172 \nL 517.854338 147.879345 \nL 517.928698 140.680933 \nL 518.102206 111.77488 \nL 518.151779 115.749476 \nL 518.337679 147.906418 \nL 518.41204 140.891446 \nL 518.585547 111.732008 \nL 518.63512 115.654133 \nL 518.821021 147.944902 \nL 518.895381 140.875912 \nL 519.068888 111.69903 \nL 519.118461 115.738192 \nL 519.304362 147.994732 \nL 519.378722 140.629358 \nL 519.552229 111.683341 \nL 519.601802 116.011145 \nL 519.787703 148.042109 \nL 519.84967 142.427777 \nL 520.023177 111.652257 \nL 520.085144 116.492914 \nL 520.271044 148.058925 \nL 520.333011 141.774253 \nL 520.506518 111.582614 \nL 520.568485 117.213042 \nL 520.741992 148.083579 \nL 520.803959 143.071955 \nL 520.989859 111.588641 \nL 521.051826 118.20887 \nL 521.225333 148.136319 \nL 521.274906 144.072071 \nL 521.460807 111.518403 \nL 521.522774 117.19323 \nL 521.696281 148.158654 \nL 521.758248 142.890696 \nL 521.931755 111.529556 \nL 521.993721 116.438693 \nL 522.179622 148.155043 \nL 522.241589 141.338844 \nL 522.415096 111.484132 \nL 522.464669 115.915972 \nL 522.65057 148.218878 \nL 522.712536 141.81462 \nL 522.886044 111.436449 \nL 522.935617 115.602305 \nL 523.121517 148.256234 \nL 523.183484 142.05645 \nL 523.356991 111.405362 \nL 523.418958 117.584067 \nL 523.592465 148.283305 \nL 523.654432 142.070428 \nL 523.827939 111.381898 \nL 523.877513 115.557812 \nL 524.063413 148.301689 \nL 524.12538 141.853172 \nL 524.298887 111.371984 \nL 524.34846 115.832305 \nL 524.534361 148.297788 \nL 524.596328 141.391462 \nL 524.757441 111.388768 \nL 524.819408 116.327391 \nL 524.992915 148.331006 \nL 525.054882 143.009102 \nL 525.228389 111.311776 \nL 525.290356 117.073729 \nL 525.463863 148.379573 \nL 525.52583 142.122809 \nL 525.699337 111.307075 \nL 525.74891 115.87708 \nL 525.922417 148.347947 \nL 525.996778 140.924006 \nL 526.157891 111.272371 \nL 526.207465 114.977564 \nL 526.393365 148.41593 \nL 526.455332 141.904583 \nL 526.628839 111.303658 \nL 526.678412 116.268769 \nL 526.85192 148.431372 \nL 526.913886 142.60935 \nL 527.087393 111.239319 \nL 527.14936 117.976892 \nL 527.310474 148.41767 \nL 527.372441 143.064224 \nL 527.545948 111.211532 \nL 527.607915 117.617393 \nL 527.769028 148.408367 \nL 527.830995 143.287383 \nL 528.004502 111.195078 \nL 528.066469 117.502186 \nL 528.227583 148.421349 \nL 528.28955 143.286869 \nL 528.463057 111.180651 \nL 528.525024 117.629714 \nL 528.686137 148.458284 \nL 528.748104 143.059204 \nL 528.921611 111.175513 \nL 528.983578 118.009228 \nL 529.144692 148.503992 \nL 529.206658 142.589006 \nL 529.380166 111.204115 \nL 529.429739 116.272363 \nL 529.603246 148.525746 \nL 529.665213 141.849665 \nL 529.826327 111.15594 \nL 529.888293 117.088066 \nL 530.061801 148.472653 \nL 530.111374 143.235922 \nL 530.284881 111.146225 \nL 530.346848 118.216209 \nL 530.507962 148.549219 \nL 530.557535 144.286033 \nL 530.731042 111.135147 \nL 530.793009 117.151303 \nL 530.954123 148.513359 \nL 531.016089 142.944564 \nL 531.189597 111.167734 \nL 531.23917 116.380589 \nL 531.412677 148.539976 \nL 531.474644 141.179085 \nL 531.635758 111.117013 \nL 531.685331 115.870802 \nL 531.858838 148.569736 \nL 531.920805 141.628381 \nL 532.081919 111.100141 \nL 532.131492 115.597053 \nL 532.304999 148.579637 \nL 532.366966 141.811265 \nL 532.52808 111.093495 \nL 532.577653 115.545811 \nL 532.75116 148.584502 \nL 532.813127 141.73243 \nL 532.974241 111.09123 \nL 533.023814 115.71672 \nL 533.197321 148.581017 \nL 533.259288 141.385168 \nL 533.420402 111.105985 \nL 533.469975 116.123334 \nL 533.643482 148.547059 \nL 533.705449 140.751367 \nL 533.854169 111.140278 \nL 533.916136 116.792725 \nL 534.07725 148.574127 \nL 534.139217 142.438246 \nL 534.300331 111.080672 \nL 534.362297 117.763734 \nL 534.523411 148.589647 \nL 534.572985 143.734882 \nL 534.746492 111.129631 \nL 534.808458 119.083489 \nL 534.957179 148.571829 \nL 535.019146 142.438599 \nL 535.180259 111.077668 \nL 535.242226 118.036503 \nL 535.40334 148.562971 \nL 535.452913 143.262398 \nL 535.614027 111.103882 \nL 535.675994 117.300872 \nL 535.837108 148.594801 \nL 535.886681 143.807681 \nL 536.047795 111.152198 \nL 536.109761 116.847306 \nL 536.270875 148.594886 \nL 536.332842 141.704671 \nL 536.493956 111.132807 \nL 536.543529 116.655869 \nL 536.704643 148.59062 \nL 536.76661 141.770537 \nL 536.927723 111.139014 \nL 536.977297 116.718909 \nL 537.138411 148.589809 \nL 537.200377 141.559257 \nL 537.349098 111.151449 \nL 537.411065 117.042604 \nL 537.572178 148.579919 \nL 537.621752 143.57545 \nL 537.782865 111.108816 \nL 537.844832 117.647121 \nL 538.005946 148.527343 \nL 538.055519 142.883353 \nL 538.216633 111.100202 \nL 538.2786 118.565259 \nL 538.42732 148.56378 \nL 538.489287 141.874834 \nL 538.638007 111.170452 \nL 538.699974 117.134714 \nL 538.861088 148.54303 \nL 538.910661 143.116391 \nL 539.071775 111.111526 \nL 539.133742 118.605014 \nL 539.282462 148.558986 \nL 539.332036 144.018575 \nL 539.493149 111.140047 \nL 539.555116 117.71501 \nL 539.703837 148.503282 \nL 539.765803 142.287885 \nL 539.926917 111.199406 \nL 539.976491 117.134867 \nL 540.137604 148.49495 \nL 540.187178 142.723552 \nL 540.348292 111.172574 \nL 540.397865 116.838562 \nL 540.558979 148.504565 \nL 540.608552 142.883958 \nL 540.769666 111.17618 \nL 540.819239 116.812387 \nL 540.980353 148.489001 \nL 541.029926 142.775677 \nL 541.19104 111.205225 \nL 541.240614 117.05729 \nL 541.401727 148.44212 \nL 541.451301 142.39005 \nL 541.600021 111.215686 \nL 541.661988 117.589624 \nL 541.810708 148.484298 \nL 541.872675 141.703261 \nL 542.021395 111.177403 \nL 542.083362 118.440197 \nL 542.232083 148.494343 \nL 542.281656 143.314014 \nL 542.44277 111.214629 \nL 542.492343 116.918338 \nL 542.641064 148.416501 \nL 542.70303 142.105206 \nL 542.851751 111.208558 \nL 542.913718 118.327474 \nL 543.062438 148.468075 \nL 543.112011 143.155114 \nL 543.273125 111.274149 \nL 543.322699 117.369353 \nL 543.471419 148.445376 \nL 543.533386 141.330749 \nL 543.682106 111.233296 \nL 543.73168 116.735741 \nL 543.8804 148.388086 \nL 543.942367 141.855277 \nL 544.091087 111.236878 \nL 544.14066 116.394148 \nL 544.301774 148.354008 \nL 544.351348 142.075561 \nL 544.500068 111.251111 \nL 544.562035 118.987199 \nL 544.710755 148.336069 \nL 544.760329 142.000666 \nL 544.909049 111.265945 \nL 544.958622 116.527931 \nL 545.107343 148.351881 \nL 545.16931 141.623991 \nL 545.31803 111.296016 \nL 545.367603 117.016289 \nL 545.516324 148.379363 \nL 545.578291 140.923134 \nL 545.727011 111.381532 \nL 545.776584 117.823113 \nL 545.925305 148.360471 \nL 545.974878 142.663249 \nL 546.123599 111.315424 \nL 546.185565 118.994908 \nL 546.321892 148.310643 \nL 546.383859 141.392854 \nL 546.532579 111.385163 \nL 546.582153 117.672217 \nL 546.730873 148.325487 \nL 546.780447 142.520316 \nL 546.929167 111.344852 \nL 546.991134 119.494448 \nL 547.127461 148.317746 \nL 547.177034 143.295511 \nL 547.325755 111.382024 \nL 547.387722 118.769081 \nL 547.524048 148.266727 \nL 547.586015 141.141248 \nL 547.722342 111.434431 \nL 547.784309 118.382329 \nL 547.920636 148.2222 \nL 547.982603 141.365651 \nL 548.11893 111.465059 \nL 548.180897 118.318078 \nL 548.317224 148.208184 \nL 548.379191 141.269913 \nL 548.515518 111.463116 \nL 548.577484 118.575747 \nL 548.713811 148.222003 \nL 548.775778 140.84626 \nL 548.912105 111.445013 \nL 548.961679 116.430489 \nL 549.110399 148.233212 \nL 549.172366 140.070514 \nL 549.308693 111.455535 \nL 549.358266 117.245498 \nL 549.506987 148.18241 \nL 549.55656 141.885745 \nL 549.692887 111.553165 \nL 549.754854 118.436842 \nL 549.891181 148.177335 \nL 549.953148 140.482264 \nL 550.089475 111.489496 \nL 550.139048 117.177106 \nL 550.287768 148.132303 \nL 550.337342 141.636577 \nL 550.473669 111.544491 \nL 550.535636 119.035431 \nL 550.671963 148.157612 \nL 550.721536 142.416341 \nL 550.870256 111.598635 \nL 550.91983 118.375007 \nL 551.056157 148.131529 \nL 551.10573 142.862952 \nL 551.254451 111.581988 \nL 551.304024 118.058186 \nL 551.440351 148.101968 \nL 551.502318 140.180818 \nL 551.638645 111.598251 \nL 551.688218 118.070173 \nL 551.824545 148.088193 \nL 551.886512 139.987118 \nL 552.022839 111.643887 \nL 552.072413 118.414486 \nL 552.20874 148.079615 \nL 552.258313 142.374157 \nL 552.39464 111.665577 \nL 552.456607 119.113715 \nL 552.592934 148.034542 \nL 552.642507 141.5567 \nL 552.778834 111.627849 \nL 552.828408 117.257862 \nL 552.964735 147.991423 \nL 553.026702 140.340407 \nL 553.163029 111.703637 \nL 553.212602 118.582518 \nL 553.348929 148.010346 \nL 553.398502 141.735098 \nL 553.534829 111.667714 \nL 553.584403 117.403037 \nL 553.72073 147.979533 \nL 553.782697 139.802174 \nL 553.90663 111.740072 \nL 553.968597 119.45764 \nL 554.092531 147.887191 \nL 554.154498 140.537991 \nL 554.290825 111.780638 \nL 554.340398 118.91126 \nL 554.476725 147.902737 \nL 554.526298 140.901063 \nL 554.662625 111.776005 \nL 554.712199 118.727429 \nL 554.848526 147.888755 \nL 554.898099 140.907397 \nL 555.034426 111.807165 \nL 555.084 118.899338 \nL 555.220327 147.837686 \nL 555.2699 140.553948 \nL 555.393834 111.853969 \nL 555.455801 119.440138 \nL 555.579734 147.850778 \nL 555.641701 139.817619 \nL 555.765635 111.803202 \nL 555.815208 117.360517 \nL 555.951535 147.876889 \nL 556.013502 138.657762 \nL 556.137436 111.826242 \nL 556.187009 118.550087 \nL 556.323336 147.775508 \nL 556.372909 140.356452 \nL 556.496843 111.862478 \nL 556.55881 120.218373 \nL 556.682744 147.836251 \nL 556.732317 141.584812 \nL 556.868644 111.899846 \nL 556.918217 119.119628 \nL 557.042151 147.781234 \nL 557.104118 139.36933 \nL 557.228051 111.874018 \nL 557.277625 118.43092 \nL 557.401559 147.700857 \nL 557.463525 139.904127 \nL 557.587459 111.890084 \nL 557.637032 118.117085 \nL 557.773359 147.688873 \nL 557.822933 140.045851 \nL 557.946867 111.910119 \nL 557.99644 118.161654 \nL 558.120374 147.654913 \nL 558.18234 139.797718 \nL 558.306274 111.932404 \nL 558.355847 118.571255 \nL 558.479781 147.694868 \nL 558.541748 139.142242 \nL 558.665682 111.993158 \nL 558.715255 119.376335 \nL 558.839189 147.717095 \nL 558.888762 141.304932 \nL 559.012696 112.01255 \nL 559.074663 120.627786 \nL 559.198596 147.626357 \nL 559.24817 139.918486 \nL 559.372103 111.99924 \nL 559.421677 119.014791 \nL 559.54561 147.677586 \nL 559.595184 141.284282 \nL 559.719117 112.030412 \nL 559.781084 121.084045 \nL 559.892624 147.5951 \nL 559.942198 142.203664 \nL 560.078525 112.134384 \nL 560.128098 120.240603 \nL 560.252032 147.567564 \nL 560.301605 139.69494 \nL 560.425539 112.098356 \nL 560.475112 119.825521 \nL 560.599046 147.576352 \nL 560.64862 139.90341 \nL 560.772553 112.110053 \nL 560.822127 119.822002 \nL 560.94606 147.545854 \nL 560.995634 139.700776 \nL 561.119567 112.162876 \nL 561.169141 120.234933 \nL 561.280681 147.483443 \nL 561.342648 139.070533 \nL 561.454188 112.153542 \nL 561.503762 117.844814 \nL 561.627695 147.552574 \nL 561.689662 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REREREZH+raCGFTezc4FjgOOSTF8OlAetGpSNfImI9Fsut80eRERE+pqCqWEys3HAz4APOeeak9ztq0BD0LIpS9kTEen/zPKdAxEREdSHKbZjgRHAS2bWbmbtwGnAZwPvo82meA1QF7SMzVluRURERESk4BVSk7xHgDlh624GVgLXOuc6wndwzrUALV3vTU9HRUREREQkBQUTMDnnDgBLg9eZ2UFgt3NuafS9RERERERE0ldITfJERERERERyqmBqmKJxzs3Pdx5ERPo1jZInIiJ9jMtx2aQaJhERERERkRgUMImISGwaLEdERAY4BUwiIiIiIiIxKGASEREREZECoj5MIiIiIiIifYICJhERERERkRgUMImISGwaVlxERAY4BUwiIiIiIlJA1IdJRERERESkT1DAJCIiIiIiEoMCJhERERERkRgUMImIiIiISAFRHyYREREREZE+QQGTiIjEpmHFRURkgFPAJCIiyVHwJCIifYLl9GwKmEREJDbLbaEkIiLS1yhgEhERERGRwtHZyZAXobQhN6cryc1pREREREREem/IPZsZ9y1oGQq8I/vnUw2TiIiIiIgUjNpHdwBQvjs35yuYgMnMLjWzxWa2P7AsMLOz850vEZF+TQM9pMTMrjQzZ2bX5TsvIiKSGQUTMAGbgCuBY4F5wKPAP83siLzmSkREBDCz44BPAIvznRcRkf5NE9dG5Zy7xzn3H+fcGufcaufc14FG4IR8501ERAY2M6sBbgcuAfbmOTsiIpJBBRMwBTOzYjM7F6gGFuQ7PyIiMuBdD/zbOfdwvjMiIiKZVVCj5JnZHHyAVIGvXXqXc255nPTlQHnQqkHZzaGIiAw0gQd4xwDHJZleZZOISG/kuHttodUwrQKOAl4H/Bq41cxmxUn/VaAhaNmU7QyKiMjAYWbjgJ8BH3LONSe5m8omEZECUlABk3Ou1Tm31jn3onPuq8DLwOfi7HINUBe0jM1BNkVEZOA4FhgBvGRm7WbWDpwGfDbwvjjKPiqbREQKSEE1yYuiiNBmDSGccy1AS9d7M8tFnkRE+g8NK57II8CcsHU3AyuBa51zHeE7qGwSESksBRMwmdk1wH3ABnx77w8C84E35zFbIiIygDnnDgBLg9eZ2UFgt3NuafS9RESkkBRMwIRv8nAbcBi+zfdi4M3OuYfymisRkYFCtU0iItIX5Lg8KpiAyTl3cb7zICIy4Ki5WMqcc/PznQcREcmcghr0QUREREREBrgcP8tTwCQiIiIiIhKDAiYREREREZEYFDCJiEhsGuhBREQGOAVMIiIiIiJSOHL8LE8Bk4iIiIiISAwKmERERERERGJQwCQiIiIiIhKDAiYREREREZEYFDCJiEhsGiVPREQGOAVMIiIiIiJSOHL8ME8Bk4iIxLZ6db5zICIiklcKmEREJLY778x3DkREREKZ5fR0CphERCQ56s8kIiIDkAImERGJLcdP8URERPoaBUwiIiIiIlI4NOiDiIj0GWqGJyIiA5wCJhERERERkRgUMImIiIiIiMRQMAGTmX3VzJ43swNmtsPM7jaz6fnOl4iIiIiI5E572+6cnq9gAibgNOB64ATgTKAUeNDMqvOaKxERERER6bdK8p2BZDnnzgp+b2YXADuAY4En8pEnEZF+b8+efOdAREQkVI7HIyqkGqZwdYGfKs1FRLLlwQfznQMREZG8KpgapmBmVgRcBzzlnFsaJ105UB60alCWsyYiIiIiIv1IodYwXQ/MBs5NkO6rQEPQsinL+RIRERERkWyy3J6uVwFToAYnp8zsl8DbgNOdc4kCoGvwTfe6lrFZzp6IiIiIiPQjKTXJM7Oz8bU6rwfGAUVmdhBYCDwI3Oyc25LxXPpzG/AL4F3AfOfcq4n2cc61AC1Bx8hG1kREREREpJ9KqobJzN5lZquBm4B24Frg3cCbgY8BjwNvBNaZ2W/MbHgW8no98GHgg8ABMxsVWCqzcC4REQnncjwskYiISDQ5Lo6SrWH6CvAF4D7nXGeU7X8BMLMxwGX4wOanGclhj0sDPx8LW38hcEuGzyUiIiIiIpJcwOScOzHJdJuBK3uVo9jHVns6ERERERHJqZQHfTCzb5lZVZT1lWb2rcxkS0REJDlmNtPMrjazR83sFTPbamaLzexWM/tgPgYoEhGR/iOdUfK+DdREWV8V2CYiIpJ1ZnaMmT2MH3joFOBZ/Bx93wT+iB949rvAFjO7QoGTiIikI52Ja43oXa2OBPb0LjsiIiJJ+xvwQ+C9zrl9sRKZ2YnA54AvAt/LTdZERKS/SDpgMrO9+EDJAavNLDhoKsbXOv0ms9kTERGJaZpzri1RIufcAmCBmZXmIE8iMoA1Na1ly5bfMG7cFykvPyzf2em3iptze75Uapg+j69dugnf9K4haFsrsD5QKImIiGRdcLBkZucDdwbm3yNofRlwrnPutmSCKxGR3li48BTa2rZz4MCzHH30kxk//iuvfJlDh17hiCP+ilk6PWv6h8Ev5/Z8SQdMzrlbAczsVeAp51x71nIlIiKSmpuB+4EdYesHBbbdlvMciciA09a2HYCGhv9l5fgbN/4IgP37n6Gu7qSsnEMiJTtxbXXXa+fc44mCpeD0IiJSoJpz3Oahd2L1rx1LaIsIEZGC19nZkjhRP9LZ2UZz82t5O3+ydXlrzexKM4vZGNO8M83sPuCzmcmeiIjkzYMP5jsHCZnZQjN7CR8sPWJmLwUtLwNPAg/nN5ciIpkW7flQ//Xyy2fwzDMT2bv3kbycP9kmefPxIwtdFSiAXgC2AM3AEGAWcCLQDlwD/DbjORURkdz64x/znYNk3B34eRTwANAYtK0VWI8fTU9ERApUQ4PvD7Zly+8YMuSMnJ8/qYDJObcKeI+ZjQfeB7weOAmoBHbh58C4BLjPOdeRpbyKiEgu3XVXvnOQkHPuagAzW48f9KGg2hGKiKRnYNUwdTlw4HkOHlxJrvv+pDQPk3NuA/DjwCIiIpI3ZmbOOQc9AxPlIQ9fBd4NzAAOAU8DVwQeNIqIZEV7+8Dsmtnc/CrPPz+T+Tk+78Adj1BERFLj+twTzWVmdm5g6PCYzOxwM/u1mV2ZhTycBlwPnACcCZQCD2rwIxHJpmXL3kNj45J8ZyNEW9tuOjqaMn7cjo78NxxIqYYJwMyKgQuAM4ARhAVdzrk3ZCRnIiIi8V0GXAv8ysweInr/2lOAI4BfAr/OdAacc2cFvzezC/BDmx8LPJHp84mIdNmy5VdMm5bxr7W0tLXt46mnhlFUVMGppx7K6LG3bbspo8dLRzo1TD8LLMXAUuDlsEVERArdmjX5zkFCzrlHnHPzgHPwQcqH8IHR7cBVwOH4+ZfGOueucM7log1LXeDnnhycS0TiaG7exJo1n6epaW2+s5LQwYMraWvbm9I+W7b8Jku5SV1j40IAOjubaWnZnNFjd3QcyOjx0pFyDRNwLvB+59x/Mp0ZERHpI6ZNy3cOkuac+x+QnVkiU2BmRcB1+Mndl8ZJVw6UB60alOWsiQxIy5e/n/37F7Bjxx2cfPK2iO2HDq1jx447GDPmM5SU1OYhh97Bg8t4/vnZmJVy2mmtectH71j3qzVrPsPs2f/IY14yL52AqRXo+6G6iIhIbl0PzMY3A4znq8C3s58dkYFt//4FALS1bY+6feHC19PauoWDB5cza1b+plHYu9dPFedcW97y0FtmPQFTa2v033chSydg+jHwOTP7TNfoRCIi0o9si3wS2xeZWdKTpDvnfp7lvPwSeBtwqnNuU4Lk1wA/CXo/CEi0j4j0wsGDK6munhGyrrV1CwD79j2WhxwFs8RJgEzcdnd0HGTRojOorp7J9Ok3hQQ6vRN8nNj57OhopqioPMXzZiqP6UsqYDKzv4etegNwtpktA0LCYefcuzOUNxERyYfDDst3DpL1hbD3w4EqYF/g/WCgCd+/KSsBk/lS/xfAu4D5zrlXE+3jnGsBWoKOkY2siUiQbdtuYsqUH0Tdlv//wWTPn3rA1NFxiH37HmPw4PkUF1eyZctvOXDgWQ4ceJYhQ97MyJHnpnzMaNav/0736/37n4maprl5E888M46hQ9/BnDl3Z+S8uZLsoA8NYcs/gMfxk9aGbxMRkUK1ObOddbPJOTepawG+DiwCZjrn6p1z9cBM4CXgm1nMxvXAh4EPAgfMbFRgqcziOWWA6+xso7lZlZKZEz1gWbfu66xceVFGanbSOX+k1POxatXHWLLkLaxe/UmAkGG/t279bcrHi2XfvkcSptm27WYAdu/+Z8bOmytJ1TA55y7MdkaSYWanAl/GD9d6GPAu59zdec2UiEh/ceAAjB2b71yk6/8B7w2eMNY5t8rMvgD8FT9yXjZcGvj5WNj6C4FbsnROGeAWLTqd/fuf4uijn6au7sR8Z6cfiB6wbNjwPQDGjv0CNTVzMn7W9vYGDhx4kWQDoXQCtx07/gTA9u23MXPmrSG1ac51pHSstrZ9LF9+LiNHfohRoz6Scl76QtO6dKXThymfqvFDl98EhDcTFBGRdO3eDcOG5TsXvXEY0cu0YmBktk7qnCvcO4A+YvXqT9HR0cSMGTf3gaZRhWH//qcA2Lr1BgVMSYsXbMT/u+vsbIm7PV3PPTeT1tatVFXNSnKPTNR0BQdMnSntuWHD99i79wH27n0gzYCpcKUzD1PeOOfuc859wznXv8YqFJHs6noq197ul9ZWaGuDjg44dMhvdw46O/26ztQKkYL3wx8WerAE8AjwWzM7pmuFmR2Ln6z24bzlSuLq6Ghmy5Zfs337rbS0bMh3djKutXUXDQ0Lsnb8RDUE7e0NOWhOVpj273+h+3XiQL13ZYJzjgMHXqSjo2dC14aGp2lt3QpAU9PyZI+U0nlbWrZEWZv+Q4m2tt5NL5f+A5H8/w0XWg2TiAxEra1+WbUK1q6FZctg5Up48EFo6CNdJ0eMgKoqOP54mDMHZs2CqVOhpgbq66GuDvrS0/ONG30eGxvznZNMuQi4FXjBzLoGIyoBHgA+lrdc5cjBg8tYvfpSJk68miFDTs93dlLQcyOaraf4XTZuvI6ionLGjLk0ceIMefbZqXR0NDB37gPU178pC2eIfSN/4MAiXnzxaEaMOJdZs/6chXMXro6OJtavDx7ZP/K7OTTQ7N0N+7ZtN7Fq1ceorT2ZY47xU8Zt2nRdGkdKLR8LF54cZW3PtaYSwDjnupv3pbJPrHM0Ni6mpmZuSsfrllpLwozo1wGTJgcUKQBNTbBmDTzzTM+ycmW+c5W6HTv8z/Xr4S9/Se8Yp54K48f7QGv4cJg3zwdco0dDZSWUlvqgK9lCrqvGbM8eePhhePRRuOmm9PLWxznndgJvMbPD8YM9AKx0zq3OY7ZyZunS93Do0CpefvkNzJ+f/6exyQpuEuRce9bO09q6k1de8YMqjhp1IcXFFVk7V7CODv9AZ/fue7MSMMVrUrVpkx+5fseOOwo2YIp3w53Osbo899wMWlo2Bm2Ndo6e321b265enXvLlt8BPU0pvdQbeaVaW9jcvD7aUVI+L8CePQ/Q2dlTQ9bUtIaqqsNjfkZNTatZuPBUxo//CuPGXR6xff/+BWkHTGV5eE7arwMmNDmgSN/Q3OxrhR5/3N+wL1uW7xz1TU88ke8cFDzn3BpgTb7zkWutrYUxd1ak3Dwq7ug42P3aj+qem4CpR3aC2PhN8jLX66KtbTcHDrzAkCFnYpab3hzbtv2RtWs/x+zZdzN48OszeuzQYAkS1TAtWfI2Zs68nZEjP5jW+cyKk1qXWOTfUWPjUmpqZqdxrNQCsMbGhSHvn3tuGqec0sD//lcXNf3atZfT1radV175YlDAFNx/Kvlzh6etjz5qeVYlOw9Tn5kcMEWaHFAkVzo7YetWePppuP12+GfhDRsqCagvRJ+Vq5vYTAu94fd/Xy0tmykpGUxxcXUGzxTc9K81g8dNTvb6EcXrW5O5c77wwrG0tLzGtGm/Y/ToSzJ23HhWrvSDCixZ8lZe//r9WT1X9P+f0N/f2rWfSzlg6qp9iR4wpfM/G/mZrlhxHscdtySNY0FnZ3MKqSODyljBUuDoCY6XSrD2Usj7w3+R9K4Zk2wNU/jkgLE4sjQ5YDo0OaBIlhw4AE8+CX/4A9xxR75zIyJBtm+/g0GDjqWq6vB8ZyWh8BqSQ4fW8+yzkygtHcbJJ+/M2HmCg6RNm37C5MnXZOzYyYl/c7hy5cU0Nr7MMcc8TVFRWfJHjTvKWfRtra3b2b3734wYcR7FxclNF9bS8hoAO3f+JWcBU5dMBbitrZvZtOkXjBr10Shb4zfJA98s79lnZzBjxk3U1Z0U9RyHDoXOW71370N0dBykpSXas/rkA6YDBxbR1radurpTIrZ1dKTaD7Xnb7Gx8cWk90r9Hjpa+uB1yQdMO3feFfK+OJU4L0OSnYdpUrYzkgwzqwGmBq2aZGZHAXucc/1veB2RfOvshC1b4M9/hh//GLZvz3eORCSqnhuRFSvOAyiQvkzBfZg62bfvUSB6n5HOzlZ27LiTwYNPp6IitfnCgp+kb9jw/TwETPFt2+b7Fj7xRDmjR3+KadOuB6CtbS/NzesYNOjYGHv6gHP37vtYs+ZTzJhxC4MHnwbErtVavPgsGhsXceDA80yb9uuU8tnZ2ZY4UcZlZtTSHTvuCCyR/bkOHYrs6hjt9+f7CZ7BqaceitgGsH9/aFuxVas+3h1shktlkJMXXzwagGOPfSFiW6pDg6cvE7XYhVtxUWh1+POAhYEFfHO7hcB38pYjkf6ivR2eew4uv9wPOGAGxcUwbhx85SsKlkT6tMK8EQmuYfKvY1/Hxo0/ZuXK83nhhSPTOE92R+BLLPmb2i1bftX9evHis3jxxXns3ftY1LRdN8tLlryF5ub1LFr0huCt3a+2bbut+3Vj4yLABxCpy0cQnt45Y/Xv2r8/+jDvBw+GDzYU/TNLpRlbrGAJYOfOO5M+TpemplVR1ubqM0ntOya45qutbR9AyKARhSatQR/MbCxwDjAeCKk7ds5FDoWRIc65xyjUUkGkL2lpgZdegr/9zdccifQTZlZF9LJpcX5ylBu5aHLe3t5Aa+tOqqqmJk6cpNCAKf4oebt33xvIR+pzwaTWV6PvOHDgOQB27PgzQ4bMj9i+e/e/2L799qA1wTf5PTfSK1d+lFGjzg/ZN73aotwHTOnWoGzfntoQ2C0tm6iunhF85pT2P3hwOStWpDcoRDzbtv2h+3W030U2a5hWrfo4ZqVMm3Z9yt8xDQ09gxgdOPAs9fVv5rXXgus3CqEGvEfKAZOZnQH8C1gHzACWAhPxgcxLsfcUkbxob4fFi+HWW+HnfaaLoUhGmdlw4Gbg7BhJ0hmSqoBkP2BasGAsHR2NHH/8KqqqpmXkmAcOPB/0LtGIeenfGGZijqfm5tcoLR1OcXFVr4+Vutif74oVH466PtGNdHrDuBdODVNz87qU0ocHBKkO1LFoUXLznznXmdKADytXBge6kZ9pa+vmpI/V1rY76vqOjoMRg6y0tGxj69YbAAJNWNP/jsnkoCelvZs7N23pNMm7BviRc24O0Ay8BxgHPA7cFW9HEcmylhY/dPenPtUzX09pKRx7rIIl6e+uAwYDrwMOAWcBH8UPMX5O3nLVj3Q1sdm795GQ9du338HKlRenXGPR0rKVZcve0/3e38DHvrHqzTxNva1hOnhwBc88M5Enn8zkyH2pSCdYjH+T6lz0zyvezW0mb3w7Og4mGcime87e9jpJ7Xfe1rYjqXTbt/8hcaKA8GaFK1dGG7Ai0fDyPVpathIe+Kxb91WefLImrKYy9P8tUXPZxCI/w3if/Z49D7N27eVR0xyXp2nI0/lrmgl0NYZtByqdc43At4ArMpUxEUmgsRHuuQfe976e4KiiAubPh1+n1pFXpB94A3C5c+4F/J3Oa865PwJfwc/J18/1vobJuc6Q+Ypipwu9OVux4jy2bbup+2l0ssJrABLd9B040NPhPdkb9w0bfsgrr3yl1yOtbd58fa/2jye5a0knaEh9WOfGxsU89dRwNm36WZrHTE5HRzNPPlnH00+PpqHhKVau/FjM2o9UtbRsZeXKizhwIPkR4Lzw/6Hs1Kbt3v3vhGna2vbS0XGIhobo/a3C7diRbH1F5DVt2PB9ILKmMrjGrfcBU+Tf+SuvfDFm2sWLz2TTpp+yefMvI7aV7e1VNtKWTsB0kJ624VuBKUHbhvU6RyISaetWP+HrGWf0BEeDBsE558Bf/5rv3In0BdVA1yPevcDwwOslwDF5yVFOxZ94Mxkvv3wmTz89hra2RG1eot80Rx86OZ7QVpKp1CA99dRQtm69OW4a5zpZt+4rbNz4Q5qaejdZdm3t8d2vM9G8L1TiICSdfiqJP3/HoUOhQeuaNZ+lvX03a9d+PuXzpcIHyx20t+9h4cJT2LbtRtaujd0FfseOO2luTm4w5FWrPsa2bTeze3fv5gLM1txZiR4MtLXt5amn6nn66VFJH3PFivNoaooc6a93er5Tnn12Mq+8kuwMQ5GWLHkrO3f+LWL9ypUXxt0v+CEJQGUex8NOJ2B6BugaCP4/wI/N7OvATYFt/VNTE7z+9f5GddIkeMc74Otfhz/9CV54wY8g1p5+cwERwP+dPfYYXHEFzJjRExyNHg0XXwyPPprvHIr0VauA6YHXLwOfMLMxwCfxD/cGnLVrk7/B6exsZ9++R+noaKCh4X8JUke/dYg2OWc84f04/I2kC3rf87q5eWNI2vb2vaxadVHc47e3N3S/7m2TvOLi2u7X4UNHJyN+M7fIG+jw9Nu2xQ8Og/UEdIlv+JcvDx+kIFHTrswEEUVF5RHrmprCR6nrsXz5uTzzzMSkjh3vOPGFPnTo7GxK8zjxJXow0BVEdHTsT6mvU3Pz+pD34XNCBc6e9PGCfx8dHQdS2C8ax/Ll74tYu23bLXFrFsNHcnxd9BaJdHZm//47nVHyLgdqAq+/HXj9AXw78ayNkJd3t94K/wsUIuvX++Vf/4qdfvhwmDcPJk+Go47yPydNghEjoKrK3wTLwHXoECxfDg89BHfeCYsW5TtHIoXuZ8BhgddXA/cDHwJagQvylKccirzR3bz5Zxx++HVJ7b1w4cndrxMFPrFu4l577XtMnPidmKNp7dp1D42Ni5gw4RuBNOEBU/hNTyddtVAbNlwbN0/RONca9DpRfx5HR8cBSkpqY6To+f0uWjQ/o3NcRQ+Y2jErDV5DS8tmysvHsGNH/FYFr776LaZMuZZkbo5THRghU7UuodfWdezMBGupBBlhe4a8e/nlN8VMuWXL72lqWsGUKT9KY4TK+LWF6daMrV17Occe+wLFxRUALFp0akSa9vb9cY8ROiBFbu5T29r2UFo6NGG6sX+Jvc1PG5DWwN9JS/nozrl1Qa8P4p/e9X+HHZY4TbCdO+G++xKnGzUK5s6FI46AmTN9YDVhgl9fXa3AqpC1t8PevbBiBSxbBt/5Dmzblu9cifRLgf5KXa9fNLMJ+JFcNzjnImdB7Ue2b78j6kSvXTo721i16iLq6l7P6NEfj5qma/hqALNEtwaxbkg72bPnAYYOPSvq1qVL/dgbgwYdx9ChZ0WpYWoLe9+Rcq1V+P7dOQurYXLOdd/odna289JLx9HYuIhjjnme2tp5cY+Vjng31dEDplYgNKhob2+gvHxM1Cf1wXbsuDPpgCnyvImb8WVGevc2zjkaGp6gunoOpaX1MVJlZorReM04V6++BID6+rOpr39jDiePja2paRmbNl3HhAlX0tnZFrWJ7NKl5zBu3FdiHqOjo4mSkpqY27Nh3bqvMHv2P+KmGX03TI3TNXvr1psYO/ayzGYsTNrhmJmVASMI+8t0zuWxhWEWzZyZneNu2+aXBx+Mn27wYJ+H6dN9U63p02HiRF+TNXw4lJXF31+yo7MTNmzwNURPPeWb070QORO3iGSXmX0LP4JrE0Dg50tmVmlm33LO9dsJzlesOC/mtsceM2pqjqaxcSHbt/8xZsAUKjJICb6RjhfEtLYmbv3YcyMXemO7dOk7mDz5h0Hn7Akk0plnKn7A1NEdGK5Z85nuyVw3bPges2f/Pcqxemq/xo1LfXyr+IFIZMDU2dlGccSv2SL6HMU4W+CcydzEpxoAZSpgSu84O3bcwYoVH6SsbAwnnRSrz1x6wVg6f2Ndf++7d9+T0n69GaQhnkOH1tDQsICFC0+Kur29PdGICY7OzhYaGv5HRcWklM6drsbGJfETDBnCtH3xk6xd+9m+FzCZ2TTgRiD80zD8J9s/57oojaw+zql9+2DBAr8kMngwHH44zJnjl8mTYdw4GDkS6uqgshKKMvMEpl9zzvcp2rzZ1xC98IL//f/3v/nOmYhE+jbwGyC840FVYFu/DZgSaWxcmFL6aDVMoc3lUqstiaWjI7J50LZtN0Ycy7mOiL4MXTZv/g1jxkRv6BIvYPJBir/OrVt/mzCvoRPspjPha3LHjn8OY+nSdyc8XkvLBlpatpBeUJKbGqZowVx7+14WLz6bjo6DzJoVvf3Vzp2+OWK8uYdyMYlzuNbWnT1vOsHaofiQ/08p3wbFzVC2G6o3QMnB/9DWdi8zmqBsn09bvgtK90JrPVgnlByA9looO+yTHLMfiloBB50VcGg0uBLoqARXDK2Dob3Gb6+a8iobxl9MeWtg2xB6/l2T+rU4Vq/+dMj/YfZF+ZvqhFn/D0Y8BrAvh3mJLZ0appvxw4m/Dd+RtrCm6k1XSXbbRmbUvn3w/PN+ScaECXD00T6gmjDBB1fjx/uBBoYP9wFWf6nBcg46OmD/fj9Qx/r1sGSJXxYv9ouIRJelUaMypOuhXbgjgTxNddg37dhxF21tuxgz5tLAjWvor83M2LDhWhobFzFz5u2YFYXcwMfrI7J69SWMGnU+RUWhZcbKlcGTpzg6O9tYtOi0iP1Db6R9ILFly+9iNjlcs+bSOAFTT5C3b98jYdtSa2IXOidNvgKmDpqalid1zPXrryYbt2eZGzkuMmBqbl7X3adqwYJY3SCSuetPN2CKsp8LBD9NULUZatZCzWqo2gCDlwCcD5zPaGB00ueJXfNX2tjzuqwB2LiU8F51tSviHfu/jE14/q8yIcYWN3wKYwbvYtgI2HeUD84OzOgJzjpL8L+mDMakzc3r/L3YDTfA974HwPzMHT5j0okCjgKOdc6lOwxJYcp3DVM2vfaaX1I1ZIjvazVypP85apTv6zV6NJSX+5qu6mo//HVFhV+qqnzwVVzsa7nM6G534Jx/H/6F3Nnpg5zWVmhu9j+bmqChwf/ctcsHPzt2+OaNmzf3vN+4MSLbItJ/mNle/J2hA1abWfAXSDF+YKLf5CNvfdXy5e8HYMiQM1m+/NyIEbCc62DduisBGDnyowwdelZYDVP8FgpbtvyGsWM/2/2+o+NQyBNrs6I4TYPC536JnCg3eT2BSHh/jtgBU/SAoC/UMKUyMXBn56GkgpvUA6BM9dVJL/CKVnsU3B8tnWyUHIDqdVBx7+/hiS91P2yen94RC5rt3MWgnTBoDQx7Kpdnzk3zv95IJ2BazkCcb6mQaphyZe/enkENRETy4/P4u+yb8E3vGoK2tQLrnXPJzf5YgFLtbB58g9zSsonGxsjJPYNv4LuCqVg1TFu33hKxf0tLaHOpyIDAkppzqWu/oqL0HljGq0VKtYYpuElfKoFLcqL1YYqcaNc3S0w20Oh6hpCq4L+PbRQVlaY0PH3SZ0l7kITQwGjXrn+xatUlzJx5O/X1b4y6R1EzVL8Ktctg2NMwJGYL1T+kmScZCNKJAq4AfmBmX8NPCBjyzeGciz9mYaFSwCQi0uc4524FMLNXgaddph//93FbtvwupfTBgUqsp/LBQ3h3BRaxgoRVq6JNPBl6MxwZHFnUgCCQOuLc0YagTkb8oCj6tvCJMsEHgGvX9nQoDx6uPObRO5qTmM+q63jJ1TBt3HhtEiMYBgv9HNra9lJaOiROPhz79/c8W1i16iLKyg5j+/bgQCIzTfK6+iKlLvRvdunSdwCw5MUzOW3KK7Q+eBdTb1xBvcZekgxLJwp4OPAzvI68fw/6EDlcjYiI9BHOuce7XptZBVAWtr1fPszbuPGHiRMFSSaeDO3v0zXwQnAfnvi1M+HNvKIFTLECltBjdwVM6T2wTKeGqaVlEw0NT1FX1zMv1ebNvwzb1/8Od+78B3v3Psjhh/8qIvhcvfoTbN9+W/BeKeUl1ueUfBO6ooi069Z9henTbwg/YverQ4dWh2w5eHBJ0CS4qZ4/vldf/Vpa+9mhdgatgKHPAp8+gvkhXbqmUAbEGmxc+q+pU3+R9XOk8y10esZzUQgUMImI9FlmVgX8AHg/EG0WxH75JR5v/qVoQm/Ek+9AH7pfouZU8WuY/CASsZrkBfcV6nqdev+UgweXsWnTT2Nu3779j4wbd3nUbbt3/zskYCoqqgrZ3lXbtmyZH7Fu0KDjOOywi8KOfxvJihYwdXa2xQhOkmtK2NDwJM3Nr4asa2qK3/U8shaxKKJ2L1cVuEWtMGgVDPsfjLofSrsfdwQP+Z7cABjS/40d+5msnyOdiWsfT5yqH1LAJCLSl/0Q/0DvUnxnhE8DY4BPAFfmMV9ZNXXqT1m16uKk04c2hUumtqArYEq+hin8uNFqmGIdI3RwhfQDppdeOoGOjsaY21955YsxA6bIAK84bHto0BAetLa2bo921Di5jfxdtLVt79VkueHBEkT7HEJF1uRZxLU3NS2nrW0fpaWD085byBlaoeZVGPqUD4wqdibeRyQf0pmHaW6MTQ5oxs+q3hIjTeFSwCQi0pe9HTjfOfeYmd0MPOmcW2tmrwEfAm7Pb/ayo6ioMqX0nZ2Hul8nM/BCtBom5zppadmadLOx8PM0Ni5k377HYuwbGTClMwJavGApkcjrKoq7vbh4UMj7V1/9dsQxt269gUmTvkdZWeSYWdECox077mDIkDOTzHGyIkc3bG/fw/btt/Paa99l0qTvhWzzv/fI3/3OnXcyevQnUjt1ezu8/DL85z/w5z8zX2NFSYFJp0neIuI/KmkzszuBTzjnwmeKK1wKmERE+rJ6YF3g9X56ujL8D/h1XnKUA6n27wkOmJIZ7a0rWAkNmNpZsCDerDPxm+TFayoXum/yNUwbNlzLrl33MHfu/ZSU1CRMD7Br1z8ZNuwdEevDR3ALn3fKudDmcuHb9+2LPrn55s2/ZNKkq6KcLzJgGjTodb0YSS6W6L/HFSs+DBClpjJ6+rj5am6GRYvg3nvhH/+A5Wo2J/1D/MkUonsXsAb4OH5OpqMCr1cBHwQuBt4A/F9GcthXFKXzqxIRkRxZR89kHivxfZnA1zzty0eGcsGsmLI9fnLNZAQ3yUuuP4q/aQ6uEQoeYjs6H0xs3XoLy5d/mM7OpuQyB7S37wvKX/IB07p1V7J//1MRAzTEs3TpO6OuD2+GFn6r5PsXtcXcnqpoAdP69d9OepS9TAn+3Xsx5oJ2gcnfH30ULr/cT3Bv5pfKSjjxRPjudxUsSb+STg3T14HPOeceCFq3xMw2Af/POXe8mR0Efgx8KROZDGZmnwa+DIwCXgYuc849l+nziIhIQbkZOBJ4HPg+cI+ZfQYoBaJ3VukHKu58gpMui1zfUg8HJ0PjFGgdCs0j4dBocJN3UdQKnaXJDY/dFQysXfv57jWJAibn2mlt3d495Hi8oazDBR87nT5Mr776VQ477GNJp48mOGDavfs+du++N2S7c60ho8f5QSyCJ0+Nnt/IQKxLZMDU3r6HxYsz2yQvcdPG0O3Fh6DytX2MfRzqn4P67im7LgssIgNHOgHTHOC1KOtfC2wD32zvsDTzFJOZfQD4CfBJ4Fn8hIUPmNl059yOTJ9PREQKg3Pup0GvHzazGcCxwFrn3OL85Sy7Bl32s6jry/f4JXI+mjdwavfr9zIfaKuFpnE+oGo+LPBzFLQMh6Lxh6C+3Vc0BO6ng5v1RbNly2/YsuU33e9bWramfF2QfB+mfftCa2JeffXraZ2vhw9s2tsPsGTJW6LkK7SGadWqj7Fjx53MnftA3LzGCpiamlZHXZ95YXlzfvS56nUwaDUMfqmDoSGPn9fR08pVZGBLJ2BaCVxpZh93gcdT5sedvDKwDfzIRNGGiemty4EbnHM3B877SeCtwEX4J4oiIiI4514j+sO9fqXpig9Tde0fe3WM0v1Qt8wvkc4Hzmd+0JqO2h8zbDQcnAjNo6F5OLSM9AFWWy10VIALmgUr3XGgevo+xQ+YwgOk7dtj/T5iNDELTxUIbDo6ok/d5Vx7RDO6vXsfor19L6Wl9TGDplgB04oVH0yYp3RYq/9sqzZCzRoY+spKeKaO+f1yRjKR7EonYPo08C9gk5l1PbWbg38k87bA+8nAr3qfvR5mVoZ/WnhN1zrnXKeZPQycmMlziYhIFL/9LVx6qe+n0IeY73V/AfBuYCL+rvhV4K/AH1ymZtvsg9yw3E/TWby/hdr9UBt/Wp8g93JoFLSM8LVXh8YGarCGQlt9IMiq8s0EQ2OjZIfVDh2EIHafqeT+DMIHcQjX2LiI9va9Eeu3bPkdEyZcSewAL0ODR3VAySEo3wGVm6DmFR8Q1a6EsshsBdGY3SLpSmcepqfNbBJ+mNZpgdV3AX9yzh0IpPlD5rLYbRj+2ya85mo7MCPaDmZWDpQHrRoULZ2IiCThi1+E88/vUwGT+cf5/wLegu/XugR/xzoTuAUfRL0zT9nLvtLSxGn6gMptfhmcQuNIV3oKTJ7C2EH7qRsMLcN8LVbLMGgdAu2DoL0WKG/HOsAVkc6UTVH4wCbePEgvvXRSxLqmpqhVdN0iRjR0Djo7KT7oa4JK90PpPqjYAlWboWoDVG70vzcRya90apgIBEa/SZgw/74KRE6IICIiqXvXu6CsLHG63LoAOBU4wzkXMp6zmb0BuNvMznfO3ZaPzGVdeZ/7PDLG2tph1SqqgKq4KZ9JeKzmkb4Gq6MK2quho9L/5I8fY+o+34yws8ynqRv5LIz6FUXtezhsY1B+OoBOMAfWsRvrAGvzgyMUtcKQ6g3Qch7T123ADvjgp2wvFHePY/GFwBLq9Un9NkQkn5IKmMzsHOA+51xb4HVMzrl/ZSRnkXbh6+dHhq0fCcR6/nINfpCILoOATZnPmojIAPD3v+c7B9GcB3wvPFgCcM49ambfx7eI6JcBk5WWJ04kVMTsVX0jYyPW3Q/cTxkwPaWzPAFAXUr7iEghSLaG6W78MN47Aq9jcWSskW7YgZ1rNbMXgTO68hBot34GEHXiBed7mnb3Nk1ntnAREenT5gJfibP9PuCzOcpL7hWn1VBERERSkNQ3rXOuKNrrPPgJcKuZvQA8hx9WvBo//4aIiAw89cQflXU7kPxEQAXGSvtvkzwRkb6ioB5NOefuNLPhwHfwNV6LgLOcc9kYwlxERPq+YqA9zvYOCqysS4UrKYxBH0REClnShYiZnQgMdc7dG7TufOBqfC3P3cBlLt0JF5LknPslMZrgiYjIgGPALWYWq+zp1518rFgBk4hItqXy1O1bwGPAvQBmNge4ET9s6wrgy8AW4KpMZlBERCSOW5NI0y8HfAA1yRMRyYVUAqajgG8GvT8XeNY5dwmAmW3E1zZdlanMiYiIxOOcuzDfeQAws0/jHxyOws8HdZlz7rmsn7hEAZOISLalMoDDEEI71p6GH32oy/PAuExkSkREpFCY2QfwgxJdDRyDD5geMLMRWT93gUxcKyJSyFIJmLYDkwDMrAxfKATPFjcIaMtc1kRERArC5cANzrmbnXPLgU8CTcBFWT9zsWqYRESyLZWA6T/A983s9fgJYZuAJ4O2zwVeyWDeRERE+rTAA8RjgYe71jnnOgPvT8x6BtQkT0QGuhtuyPopUgmYvokfuvVx4BLgEudca9D2i4AHM5g3ERGRvm4Yfmjz8OkttuP7M0Uws3Izq+1a8C000qJBH0RkwPvVr7J+iqQHfXDO7QJONbM6oNE51xGW5H1AYyYzJwWoqAjGjoX6ehgxAqqrYcgQqKqCQYP8UlMD5eVQUuJ/lpf7/czAOf+zowNaWqC11S/NzdDWBgcPQmMj7N4NO3dCQwPs2AFbtsCBA/m+ehGRZHwV+HYmDqSASUQGvPe8J+unSHkyP+dcQ4z1e3qfHcm70lKYPBmmToWJE2H8eJgxA4YP9wFQV9BTVgbFxfnObXI6OnyQtXMn7NoFa9bAqlWwciWsWAHLl+c7hyJSuHbhJ8cdGbZ+JLAtxj7X4AeJ6DII2JTW2dWHSUQGum98I+un6Lezn0tARYUPeGbMgOnTYcoUHwSNGeNrgsrLfY1Of1ZcDHV1fpk6FU44IX761lZfc7V0qV+WLIEnn/QBlohIEOdcq5m9CJyBn8AdMysKvI86yXpggvfuiXatF9/BqmESEck+BUyFqKYGjjwS5szxtUGTJ8OECT4IGjrU1xL19yAom8rKfI3a6af7JZrWVti0CV56CZ56Cv77X3j55dzmU0T6ip8At5rZC8BzwOeBauDmrJ9Zgz6IiGSdAqa+ZMwYmDcPDj+8p0Zo3Dh/815ZqSCoLykr6wlW3/ve0G0dHbB3LyxbBg88ANddB4cO5SWbIpJ9zrk7zWw48B38QA+LgLOcc+EDQWSclWgeJhGRbDPnXL7zkDOB0YgaGhoaqK2tTecA6Z147Fg46qiepnFz5/p1w4b52iAZONrbYcMGeOQR+Ne/4N57850jkeT1orzYv38/dXV1AHXOuf0Zy1M/0KuyacMG38JARGSgykHZpBqmVHzta/C97/W8LynxgdCcOTB7tq8Z6uojVFOTt2xKH1ZS0lMzdcklPevb22HjRrjvPh9E3Xdf/vIoIoWjRMW4iEi2qYYpVU1NfohskVxobobnn4d//ANuu80Ppy6SL6phyopelU07dsDI8AH6REQGkByUTalMXCugYElyq6ICXv96+MlP/JDozvU06/vZz+CII/KdQxHJp0KZ3kFEpIApYBIpNMXFfjCQz37WD3vuHHR2+lH7brsN3vjGfOdQRHJFTfJERLJO37Qi/YGZH2XxIx/xS5f9++Gee+D662HBgvzlT0SyQwFTaoqLobKSjsEVHCrdRWcFdFRARyWUVI2ipXMbrgRcCXSW4B8rO7+U10ygpfE1itrB2qGoBYraoKgVipuh+BCUHPBLcXsR1taZ54sVkUzRN61If1ZbCx/6kF+67NkDDz3ka6P+85/85U1Eem8ABEwd5dA2qpKKGaf6QZUmTqRtWAUr9nyR9hpoq4WOajjixIfZ37KQV9Z/GQKD2s6fH71vw95d/2Tp0neGrBs69Dh2774nZj7q649gz57Xksrz+PFfYfLkawBYuHA+DQ2P92x0YJ1Q1AwlTT7AKtsDR9b+lrY1L7PnpV9RsR2qXoPSxqROJyJZ1v+/aUUkVH09fOADfumydy/8+99w001+El4RKQwF1IepY1AJTaPaaR4FzSNh3CnX9Uy6PmIETy6Z6Gt6yutpb98Tsu/gwa/jqKPu737f2bKNPQu+GHqC6mqKXGV3sBRfZI8Es/i/S7NUejHEOZaBK/ZBXkc1tAyHg5OB+R+no3kjK575VcQu3YFfWxvs3AlbtsDatbBkCSxaBC++CNuzPu2XyIBVMAGTmX0deCtwFNDqnBuc1wyJ9CdDhsCHP+yXLk1N8NxzvjnfX/+av7yJSGz5DJhmzYJp02DiRL9MmMCS/Z9lf81GOqqhs4yQ4KW+/kz27OmZMmHc/M+FHG7w/reze/c9jBp1IZs2/Thkm1lJ2PvI6zYrxqwsqawnCo6iSz5gSi24Ct4vQb5KS2H0aL/MmwfnnhuyefnyD7Njx+1YB5Tsh6E7pjHjwCU03PN9qhbupvRAWtkSGfAKJmACyoC7gAXAxXnOi0j/V1UF8+f7pUtnJyxbBv/8J/z61/4pp4jkT7oTqofpLIGiI470k6tPmMBrpX9j75BXaK2HtsG+2dvR8xawadOP2bnTP0CZP39ZxHF2P/aumOdwrj1uHqZPv4G9ex9m+PAPRARM4cFKrIBp+PB3s3r1x+OeJ9b+iaZZSSUISi8gS3+/LpMmXc2OHbfjiqFtCDSOGwTzvsSrb/w3+/Y91p1uwthvsGXp/1G5CWpXQN0SqH8Oilt6dXqRfqtgAibn3LcBzOyCPGdFZOAqKvITNc+ZA9/4Rs/6xkZ4+mm44w4/Z9S+fXnLosiAU1TkH2aEmzTJT5I9bhxMm8bStq/RPAraa6F1SGQN0Pz5i7pf737pCfbvfyXkcHV1J7BpU/ygYe7cB1m8+E1RtxUXx59jqqxsJCNHfijG1tDAMHpgUURp6VCGD38fO3feFfdc0WqLJk++ht27/xl3r4kTr2L9+qsSHDv68ZMTeV1jx34+6b0rK6dw2mmdPP541/l9EBgRDBabD4QHw/7ZsOl9oZutFSq3Qe1SGLIQhj/uB7gQGagKJmASkT6spgbe9Ca/3HRTz/rWVli8GP72Nx9MrV+ftyyK9Fu7d/uHFkOHQnm5D6Ci2PXY15I+pHOxRniLXwtTX38mJ5ywgYULT6KlZVPItqlTf8quXX9LOg/BIgOk6DVM4IOG1I8H1dUzGTXqYrZtuzHGXkWMGHFeUgFTJpvkTZ78gxSP0ftaR1cGTeP9su0tsOLrUFk5nUOHVlF0CGrWQf3zUP8s1K7s9elE+rx+HTCZWTlQHrRqUL7yIjIglZX5dvbz5sE114Rua2qCdev8IBN33OFrqCTS2LF+guJx4+Coo2DYML9u/HhobvajpA0dCg0NMHgwdHT4iY2nToW//x1uvRUefjjfVyHZNHiwXzIqVmAUP2ACqKgYhy9+o60vw7nWlHMTHoBECyyKivw5x4//Kq2t2xg+/AMRaeLt79fHCzaMzs5k895z/FQCmGiBVu+a6bmwn12KOPLIR3j55TOSPtJhh13IunVX0lkJ+4/wy/oLgvLZDmW7fRA1/Amof7EX2RbpY/IaMJnZ94ErEiSb6ZxL9/nFV4Fvp7mviGRTVRXMnu2Xyy6L3N7R4UeD2rDB95t69FFYuRJeeCH3ee2NadN8wHjOOT6Iqa/3w71XV0NFRebOUxvU3Knr5jl8II8uTU1w3HGwfHnmzi/9SuwapuTEvslPt/YjcZO8rgEfSkpqmTHj5gTHi1UDFDt/ZkVUVU1LcNyetF0S9Y0KFe331psao+gBU2lpPcXF1SkeK37g5kqgZSRsPccv3TqhfDcMftEHUsM0JaAUoHzXMP0YuCVBmnW9OP41wE+C3g8CNsVIKyJ9SXExjBrll+OPhwsvjJ/eOR9ktbX5jvDO+SaABw74AKGpCTZu9EFYTY3f5+BBXwu2a5d/P3o01NX5/SdO9Onq66GlxdfidHTAyJE9x89Qh/ucq6ryQSjApk2+9kokRG8DpugBiZmRTPwwfvxX2bAhuFY6+Rqm5PIXuv+QIWcmsxdFRWVMmPBNXnvt/6V0/OTzFXlblk4Tu5EjP8L27X9gwoRvRt0+YsR5NDcnN6dUTz7S7JdV5IdO336WX7o5qNjia6KGP+b7Son0VXkNmJxzO4GdWTx+C9A95ksm2vWKSB9l5punBU/kOXNmds/XH4wd64O/xx8PHRFRBrThw99HY2PkHWzytSWp1+AEmzDhGyEBU+TNeuTxi4qSG1LcHy80oJky5YcJ81dRMSFwnsokztCTv1TuPVIJ+uKZMeMWJk/+PuXloyO2FRfXUVY2PKKPWSK9HcEv8oDQPAa2jIEtQTVS1g5lu2Dos3DYvTBobWZPK5KOdIdxyTkzG29mRwHjgWIzOyqw1OQ5ayIihe+00+CJJ/KdC+kjxo37UowtyQVMsWsjkgseImtawpvkRR4nWr+p2MJrrErjph416oLu2prQwCFWX6jkmuSFjxxoZhxzzPMcdtglcfOTiFlR1GDJn7MqkCbVACizt4zHH78q6npXAke9Yy1b3gEv3gAHG1ewe9e/eexheOZ2WHU57Dsyo1kRSSjfTfJS8R3go0Hvux59nQ48lvPciIj0N69/PVx0UehIhzIgFRWVUll5OIcOrQnbklzA1NkZa0Kf9AKmZJqD9aaGKVpTuGDBfaKC9zUrwrkOAKZM+RGvvBIZaPrGLrHyEXldtbXzaG5+la1bb4ibp9RYxOtUA6a0m+RFMXLkR2L2B6uomEJ5+fju9+XlY2luXgfF0Dwato6GrW8P2sFBSSMMXggjHoOhC6C4OWNZFQEKKGByzl0AXJDnbIiI9G833qiASWKqqJiYVLrYzb3SraFKHGglqiUKTRseMHXtm0xAFxwwFeOcn6Bo1KiLowZMFRWT2b//mVg5ibq2tvb4JPKRip7z9Fxr9IApeqAcO32mve51qzAr5oQTXsO5NkpKEjQkMmgfBLtO9UvIpg44bcRiVt88l8EvwYjHs5dv6d8KJmASEZEc+c9/4C1vyXcuJIuKiirp7DzU/b6kpD5Kqp6b7PJy339nwoSv09S0gpEjPxolfY8pU37EmjWfjrIllRHjgkXWbpSUDKG9fW/3+9T6KafWJC80bXDgENxXKXoNzNSpP8WslO3bb412tKj7VFRM4PjjV1NaGu1z6Z3p03/nzxyzhslRX382e/bcF7I2US1caqL/HZSWjuzOV0XF+ITpE56lGJgzhy3n+H5Sy4HjjlvOihUfovHAQsp2w6DVULfU11BpTimJRQGTiIiEOvvsfOdAsqi6eg5tbbtobfUB0/jxVzJq1MVRUvbczB977LMAlJYOZe7c+6KkDTV69KXU1Z3KkiVnpzy4QDTRgqEjj3yIF1+cl+bxojfJi3aeysqpcfZ1Mdb3KCsbwcyZt8QImGKrqjo8pfTJqq9/ExA7v7H6XBUXJzPYRfqGDHkjU6b8KOq23g5zH8nAoHUYVB19BetOujbKSWFY7dnMHvorFt09iepXYPBiqH4FqjZnODt9SGcp7DkecNA8EvbPhkOjfS1eRwUceepLNDQ9w+p1nwKDE07YSFnZSJqallNdPZfNm3/B2rWfi3n88eO/zoYN3wV8DWBRCwxeBLO/CZbpjzmDFDCJiEik738frrwy37mQrDCCg6HJk6+Jka7nxrmsbGRqZzCjpmZ299xI0Y6ZmmgTuqZ/CxMeLBQV+RqmaMHCnDn3hu/d/aqqaiaNjS/FzGNfEX2QjNg1TNEkN/R6+ubM+U/35xCuq59YIkce+TDLl5/L2LGfZ9u225gw4WvRjkbwNU6Z8n02bowSMBm4sjKYOJGKsy5m87Yb2fyeOCd3QCfMn7cPtm6FxkY6t29hzWPvoHwnjLX3sa3hLip2QOVmPxKgdUDJoTjHjGXECBg6lOam9bRWHqJlODTMBlcK1ubnw2qvhoMT4cjXLaCxaRErNl3q/0QDfwrHH7+K556bnvQphwx5MyNHfojq4UfTsOX57uNUVIwFoKYmuZE4Jk/+v+6AyRVDRxXsPgkef6QnTfU6OC7aM5w8UsAkIiKRzjlHAVO/lWzTtXSDm9jnSm0S19jH8XrTp6YnuBk0aB6lpUMD70LzV1xcS1VV7JvKGTNuZcOG7zN27GVpDbvd1f8p+1L5/UV+RiefvJfS0sHd7ysrp1JZOZ2hQ9/KmjWfykwO4wwqUVyc3IDIQ4acwUkn7cDMmDDh6zFSuaRrrLryNH36bxk37nKef/6IOInxv9K6Or8AdLayNTA/8Ijj/4+1z90VsVtwjUsyTjllHyUl/vhlne0880SC5qT1g3GllbA1WoaTV1//JkaN+kjgXbz/495/bxycDI/9F+iEEz8A5bt6fche67uPQ0REJH+yOYeV9AGJb5aSvUmNe5aImo30bqai3UxnqoZp8uTvB21JJn8911RRMY5Zs/5Ibe3rSOeWqqbmqJT3yZTYTfI6Cf89BAdLALW1JzB37r2MGXNpumePsi7272/IkDckf+QotWm1tSeErUk2YCru/lldPSvpPPTouabwIeS7DBp0TIrH7Lm+oqJk/gdcdx/EWMdJ9bzxlJVFH84+LUWw4C7YeUrmDtmLrIiIiMhA4YOPxIHBjBm3UVk5nVmz7ujN2Xqxb499+x6LPHIvJlKNPZdS+O8lUf5DhxiPp6xsDABjx36xe9348V9j4sSrmTfv5QTn6a3eN8kDOOqoJxk16iKmTr0uI7k6/PBfBuUn9u/arIhBg9LrrwYwd+79Qe+Kkq5hKisblfY5wQc0M2f+ienTf095efRjpVrLmM7f/eDBp0U5Tvr/m/FqiocPj9duMT3L/h/szvTAkSlSwCQiIjKgGMk8Ya+pmc3rXreSESM+0Itzhd9mpFfDNGTIGRHrehMwhY5u1/OUfvDg0PMkuqkMDpISBUwnnbSJk07azpQpPf1liosrmTjxW9TUzE0q1+nLTMA0ePApzJhxY1ATxnT5c4wYcV4vj5Oc0FEQjYkTvwX4+aDimTjx6pjbhg59e8xtwUaOPI/DDovdISd8UJF4eZk8+fsUF1cnlb6Hi/F3nFrAVF4+LuSYsZgVUVfXUyUU/MCltHR4IE3yc6Z1WRKlm1kuKWASEZHoPtCbG2Xpy9LvS5SaTDXJmzLlJ1GOnZkapuDXI0Z8gPLysQn2DZ7TKLU8lJWNIPTWKzM1cOmJnveSkiFZP3PX319paT0nnriFU07Zl/Q+6QkObI0RI97PCSdsZMaMW+LuFT6se23tyb3IQ3SDBh3LrFl3cswxz8fJx0gmTvwW48dfkcYZYv3ekvvbq6iYxIQJ32D48Hence7QJnp1da8H4NhjX2DUqAtSP9hTT6WVh0xQwCQiItH96lf5zoFkgX/anqvxe3t/m1FXdxqlpZE38cE1Q2PGxB7GOLroNUxmxtChbws+S9LHSVZvmkKlK5VR8mbN+lPWA+rg2rjy8sO6BzGIrydPydbK9Jwv+Pr964qKsQlrBcMdccRfU0qfrBEj3k9tbewmh3Pn/jsLZ03u77C+/i1MmvT/Qn5XQ4e+FfCTMqd2bL++pmYOM2bcTFVViv3CTjoptfQZpIBJRESiq8/8pJmSf0VF5WRmBLzESkuHhbzP7I14zw3/mDGpjdQWWsMUeitUWTkt0d4x901VOk2TMiVWwFRdHWckuCyfO77gv53eBJ3pf2ax+iGlK3TAkdh6NwBL9N91bwL3iorxnHTSTo4/fkUSqWOf5/DDf576yadMSX2fDFDAJCIiMgB09bkYN+6LOWuSN3367xk0aB5HHPF3IBM3yj184OclakYXLl4+xoz5dHDKaHsH5yKl83YZO/aLDBv2Lmprc9WTPVoNU3Zmlpkx4zaGDj0nQTO7dH5v6QdMZmXU1p5IdfVsKisnpXHu5Myd+1Cv9q+vf2vK+4wde3nc7bFr73pX01lWNoyiougBf3n5+OhnDAvSiosHpX7iZ59NfZ8MUMAkIiIyABxxxN953eteZdiwcxg69Gwg9o1NplRVHc6xxz7P8OHvAvxoZSUlQ5k16y+9PnZp6RBmzryd2bP/SXFxVYp7x+5HFOsmsEtnZ3PPnjGe0icKSKdO/RGzZ/+91zVUyYpWQxEcNHbVqg0ePL9X55k27XeMGvUR5sz5Z9xmdukEztXVPROjJjuRbc/5jKOPfop5816Oe+7UJucN/YwnTrya+vo3ppSvcLNn/z3GltjBzZQpP+LEEyMmWWLMmMs4/PBfUV5+GAAzZ/4p6WP21tSpP46xJQPnzFPLB01cKyIiMgAUFZVQWTkR8MM519QczfDh78tpHoYMOZ2TT96ZYnOg2MHHyJEfTCsfoTfNqd3E7djx57TOmU9Tp/6U1tbtjBsXXBvRE6xNmfJDOjtbqa/vChhSr4GcOfOPjBz5oSRTpx4oTp36U0pKBjNq1PksW9YzdPWxx77IihUfYtKka+Lu7//m4n/Ww4a9m717k6slyszDhshgvaiois7OppD18ZrkmVnUpoLhzd0i56KKbfDg+d1D+acT3HaNhteVv9jSqOnOQx9AUMAkIiIy4JSU1IXdPOdOPgY9iMxD+gHTqFEXsX//ggTH71u3VxUVEzjmmNARxoI/h6KiSoYNOyetY8+Y8Qf271/AiBHnJr1PXV3qo82Vlg7h8MOvA0JrmAYNOibJvjSJFRdXUVo6nLa2nTHTHHHE32lp2UBJyRC2bAkeGCdTzVwjB2QpL8/EZLDx/87nzLmXJUveFjhfz0S36dWCxh/0oRCpSZ6IiIj0YdnobxU6zHQqkul31NcCpkSKiirC1iT/Ox816sNMm3Z9SjURyddERTdhQtc8Suf36jiRLKRvXDTDh7+LsWM/R+qTHEcTeRs+evSnQ95XVGSmz1W0v/P6+rd0v+4a+S5Sb0eCjPU6/UFgGt6e+4EfCus/WkRERAaUioqJGT9msjf30foz1dTMZdasu6ioiN0kq1ACphEjzqO5+VVqa0/M6Xl723frsMMupq7uZCorD89Qjrzi4hpKSgbT0rIpYdphw95JcXEdHR0NaZ8vWhAzefI11Ne/mcWL39SVKu3jh50t6ZQlJT2DMQwadEzGzhv5f5FewORKc1/foxomERER6XNmzbqT2tqTmTTp/zJ+7ERN8mbMuIWSkqHdo/uFGzHivXFrmoJvOPuyWbP+xDHHLKCoKDM3srliZlRXz4yS7/RMnvx9hg17N0OHvp1Zs+6kpuZYZs++J+4+JSV1nHxycNO9dAKbyH2KikoDfcn8LXo6zReTO5cxadJ3ARg37suAH7SjtvYkJkz4NvPmvcy0ab9jxIjzen3myZN/QHn5eCZP/l6vjwVpjq7XS4XxCEREREQGlBEj3s+IEe/P0tGNwYNPp61tT9TJM0eN+igjR56fcnO9SZO+S0PD0wwb9u5MZVRyYPz4K7pfV1fPYt68F5Lar6ioNOhdZprkdTn++OVs3/4nxo79QhrHjSY0f2bGoEFHceqpLd01qaNHX8Lo0ZcAftjwmpq5GTnv+PFfZvz4L0fZll5g3viZMxl010u9y1aKFDCJiIjIgGJmHHnkI4CL2TwsncEpJkz4Wi9z1jfkap6uga6uLnZTyKqq6UyadHXSxxo69B3s3v3POCmi97lKNIx+diX/dzZ37gPdr0tnnZSNzMRVEE3yzGyimd1oZq+a2SEze8XMrrZ8TpEtIiIiBcvMcjYPkvR/gwbNSzrt2LGf58gjH6W29nUZO39obVc04aPv5WbEungPHiorpyZ9nPr6N3W/7pqEO5cKpYZpBj64+wSwFpgN3ABUA1/KY75ERERE+pnIoa0luuOOW8bBg0upr39zwrRz5vybPXvuY/LkazNes5OoVjB8e65qloKHKA9XVjaSefMWsWHDD9ixI3xi3djyMTVBQQRMzrn7gfuDVq0zs+nApShgEhEREcmYzs6W7tdjxnwujznp+6qrZ1FdHdkPLpqhQ9/C0KFvSZwwK0IDptLSoVk923HHraCj40DUSXWD1dQcyYwZN6UUMOVDIddF1wF78p0JEREZmNRcXPorP8+QH41sypQfZvTYhTLkev+T235p1dUzqK09Lqm0iea+6gsK8q/WzKYCl5GgdsnMyoHgT6EwxvkUEZFCoObi0i+NGPF+amqOpKJichJ9Y1Jz1FGPZfR4kiwN5NEbea1hMrPvm5lLsMwI22cMvnneXc65GxKc4qtAQ9CSeCYyERGRJDjn7nfOXeice9A5t8459y/gR4DGlJaCV1U1PePBEmRyXiFJhUY+7J181zD9GLglQZp1XS/MbDTwX+Bp4ONJHP8a4CdB7wehoElERLJHzcVFYpg798F8Z6EfSzRQhwKm3shrwOSc2wnsTJiQ7pql/wIvAhc65xIO4eKcawG6ey7mY1QNEZGCVlYGra35zkVBUHNxkfiqqmYkTiRp6gmIjjnmmYitlZWTc5mZfqcgBn0IBEuPARvwBdFwMxtlZvGH3hARkd75/OfznYOcU3NxkezQvFfZE9zkLtr8TmbFucxOrwwZcma+sxAh303yknUmMDWwhBcsqjYSEcmW6dPznYN8UHNxkaxQwJQ9/afJ3dChb2Pv3ofynY0QBREwOeduIXHhJSIimTYAOwqrubhIdqiGKXsqK6cknba0dHgWc9J7o0dfytq1fWv+L/3lioiIpEHNxUVSpdvObJk48SpGj/5k3GHbjzzyEWpqjmXu3Ptzl7EkFRVVBL0upbr6yLjpXY7/lAqihklERPJEtR/xqLm4SAoKqR9NoSkpqWXatF/HTTNkyBuYN++FHOUoNRUVE2lqWtn9fs6cf/LMMxMBqKycFpG+vQpKG3OVO4X6IiIiaXHO3eKcs2hLvvMm0hccffSCsDW67ZTkVFRMyHcWQugvV0REREQyrq7uhJD36sMksaQ6sW5xSW5nY9BfroiIiIhkxfjxVwa9022npCMymCqy3PYq0l+uiIjENgBHyRORzBk58iPdr1XDJIVKf7kiIiIikhXOtXW/NivLY06kb4t8OGeBWqS6ulOiJM/twzyNkiciIiIiWVFWNrr7dVGRbjsllsgA6LjjlrNjx52MHfvZPOQnlP5yRURERCQrysqGc/TRCygursl3VqRPiwyYqqoOZ+LEb+QhL5EUMImIiIhI1oSPlidSaNSHSURERERE8qasbFS+sxCXAiYREREREcmbGTNuZfDgNzB37v35zkpUCphERCS2OXPynQMREennKisnc9RRj1Bf/+bkdnj/+/3PuXOzl6kg6sMkIiKxHX88/POfMGlSvnMiIiLi/fSncMopcPbZOTmdAiYREYnvnHPynQMREZEeVVXwkY8kTpchapInIiIiIiISgwImERERERGRGBQwiYiIiIiIxKCASUREREREJAYFTCIiIiIiIjEoYBIREREREYlBAZOIiIiIiEgMA3Iepv379+c7CyIiA4q+dxPT70hEJLeS/d4151yWs9J3mNkYYFO+8yEiMoCNdc5tzncm+hKVTSIieRe3bBpoAZMBo4EDaR5iEL5QG9uLY/QV/elaQNfTl/WnawFdT2/PtcUNpIInCSqbQvSna4H+dT396VpA19OX5fpaEpZNA6pJXuAXkfaTTV+mAXDAOVfQbSf607WArqcv60/XArqeXir431c2qGzq0Z+uBfrX9fSnawFdT1+Wh2tJeA4N+iAiIiIiIhKDAiYREREREZEYFDClpgW4OvCz0PWnawFdT1/Wn64FdD3S9/Snz7A/XQv0r+vpT9cCup6+rM9dy4Aa9EFERERERCQVqmESERERERGJQQGTiIiIiIhIDAqYREREREREYlDAJCIiIiIiEsOADpjM7NNmtt7Mms3sWTM7PkH695nZykD6JWb2lrDtZmbfMbOtZnbIzB42s8OzexUh50/6eszsEjN70sz2BpaHw9Ob2S1m5sKW+7N/Jd3nT+V6LoiS1+awNHn7fFK8lseiXIszs38HpcnbZ2Nmp5rZPWa2JXDedyaxz3wze8nMWsxsrZldECVNSv+PmZDqtZjZu83sITPbaWb7zWyBmb05LM1VUT6blVm9kJ5zp3o982P8rY0KS5fzz2YgU9nUd8um/lQuBc7fL8qm/lQuBc6rsqmPlU0DNmAysw8AP8EPW3gM8DLwgJmNiJH+JODPwI3A0cDdwN1mNjso2VeAzwKfBF4HHAwcsyJLlxGcv5SuB5iPv57TgROBjcCDZjYmLN39wGFBy3kZz3wUaVwP+Jmag/M6IWx7Xj6fNK7l3YRex2ygA7grLF1ePhugGn8Nn04msZlNAv4N/Bc4CrgO+H3wl3man3cmpHQtwKnAQ8BbgGPx13SPmR0dlm4ZoZ/NKRnJbWKpXk+X6YTmd0fXhjx+NgOSyqa+Wzb1p3IJ+l3Z1J/KJVDZ1KXvlE3OuQG5AM8Cvwx6XwRsBq6Mkf5O4N6wdc8Avwm8NmAr8KWg7XVAM3BuX7ueKPsX47/Yzw9adwtwd4F8PhcA++IcL2+fTwY+m88HPpvqvvDZhOXNAe9MkOZaYGnYujuA+zP1O8rVtcTYbxnwraD3VwGLCuSzmR9INzhOmrx/NgNpUdkUsX+fKZv6U7mUoc+mT5ZN/alcSvZ6YuynsimDy4CsYTKzMnwE/nDXOudcZ+D9iTF2OzE4fcADQeknAaPCjtmA/0BjHTMj0ryecFVAKbAnbP18M9thZqvM7NdmNjQTeY6nF9dTY2avmdlGM/unmR0RtC0vn0+GPpuLgTuccwfD1uf8s0lT3P+dDP2O8sLMioBBRP7fHB5oerDOzG43s/F5yF4qFplvEvSQmZ3ctbKQP5tCpLIpqj5RNvWncglUNtGPyyVQ2ZSNjAzIgAkYhn9qtT1s/Xb8l1c0oxKkHxW0LtljZko61xPuWmALoV8g9wPnA2cAVwCnAfeZWXGvcptYOtezCrgIeAfwYfzf9tNmNjawPV+fT68+m0B73NnA78M25euzSUes/51aM6skM3+/+fIloAb4S9C6Z/FPls8CLsXfFD1pZoNynrvEtuKbAr0nsGwEHjOzYwLbC/mzKUQqmyL1lbKpP5VLoLKpP5dLoLIp40qycVApLGZ2JXAuMN85190h1Tl3R1CyJWa2GHgFX1X6SE4zmYBzbgGwoOu9mT0NrAA+AXwzX/nKgIuBJc6554JXFtJn01+Z2QeBbwPvcM51t6t2zt0XlGyxmT0LvAa8H9/PpM9wzq3C39R1edrMpgBfAD6Sn1yJeIVeNvXjcglUNvVZKpuyY6DWMO3Cd1QcGbZ+JLAtxj7bEqTfFrQu2WNmSjrXA4CZfQm4EniTc25xvLTOuXWBc01NP6tJSft6ujjn2oCF9OQ1X59Pbz6bavzNQsIvshx+NumI9b+z3zl3iAx83rlmZufin6y+3zkX3qwjhHNuH7CavvnZRPMcPXktuM+mwKlsCuiDZVN/KpdAZVO/K5dAZRNZ/HwGZMDknGsFXsRXGQPd7T3PIOhpUJgFwekDzgxK/yr+Qwo+Zi1+1JtYx8yINK8HM/sK/inXWc65FxKdJ9CMYCi+qjRr0r2eYIHq/zn05DUvn08vr+V9QDnwx0TnydVnk6a4/zuZ+LxzyczOA24GznPO/TuJ9DXAFPrmZxPNUQTyWmifTaFT2dSdps+VTf2pXAKVTfSzcglUNmX988nVqBh9bQE+gB+J5qPATOC3wF5gZGD7bcA1QelPAtqALwIz8KONtAKzg9JcETjGOfgvxbuBdUBFH7yeK4AWfNvQUUFLTWB7DfBD4ARgIv6P8EX804jyPng93wLeBEzGDy/5Z+AQMCvfn0+q1xK035P4DrXh6/P92dTgv7iOwo9i84XA6/GB7dcAtwWln4QfKvcHgf+dTwHtwJuT/R31oWv5IP574FNh/zd1QWl+hG+3PxH/vfEQsBMY3gc/m8/j+1dMxfdHuA7/1O6MfH82A3VJ9fsClU05+/5L41r6bLmUzvUE7dfnyib6UbmU5vWobMry55PVX1BfX4DP4NtvtuA7w70uaNtjwC1h6d+Hb1PZAiwF3hK23YDv4J8YNeM7qU7ri9cDrA/80YYvVwW2V+JHjNmBL3zXA7/LxRdFmtfz06C02/DzKxzdVz6fNP7Wpgc+jzOjHCuvnw09w32GL7cEtt8CPBZln4WB638FuCCV31FfuZbAZxUzfSDNHfhO6i3ApsD7KX3xs8HPAbMWfxO3Gz93x+l94bMZyEsa3xcqm3L3/ddvyqU0/9b6ZNmU6ndf0D59rlxK53pQ2ZT1z8cCJxQREREREZEwA7IPk4iIiIiISDIUMImIiIiIiMSggElERERERCQGBUwiIiIiIiIxKGASERERERGJQQGTiIiIiIhIDAqYREREREREYlDAJJJlZnaLmd2dh/NeYGYusFyX5D63BO3zzuzmUPoSMzvVzO4xsy3pfP5mdlXQ307wcjBLWRaRNKlckkLRV8qmkpRyLSIhzCzRzM9XA5/Dz+aeD/vxM7Mn+8XwOeBKYGvWciR9VTXwMnAT8Pc09v8R8JuwdY8Az/cyXyKSApVL0s/0ibJJAZNI7xwW9PoDwHfwBUGXRudcY26zFMI557alkLgBaDDLVzkq+eKcuw+4DyDa529m5cB3gfOAwcBS4Arn3GOB/RuBxqD0RwKzgE9mN+ciEkblkvQbfaVsUpM8kV5wzm3rWoAGAgVB0NIY3vTBzB4zs1+Y2XVmttfMtpvZJWZWbWY3m9kBM1trZmcHn8vMZpvZfWbWGNjnD2Y2LNU8m9mnzGyNmTUHjvPXXv8iZCD4JXAicC4wF7gLuN/MDo+R/mPAaufckznKn4igckkGnJyUTQqYRPLjo8Au4HjgF8Cv8f/kTwPHAA8CfzCzKgAzGww8CiwE5gFnASOBv6RyUjObB/wc+Bb+ieNZwBO9vhrp18xsPHAh8D7n3JPOuVeccz8C/hdYH56+AvgQcGNucyoivaBySQpKLssmNckTyY+XnXP/B2Bm1+DbZ+9yzt0QWPcd4FL805JngM8AC51zX+s6gJldBGw0s2nOudVJnnc8vt34vc65A8Br+MJOJJ45QDGwOqxJRDmwO0r6dwGDgFuznzURyRCVS1JoclY2KWASyY/FXS+ccx1mthtYErR9e+DniMDPI4HTzSxau/MpQLIF00P4wmidmd0P3A/8wznXlErmZcCpATqAYwM/g0X7m/wY/uZne5RtItI3qVySQpOzskkBk0h+tIW9d8HrnHMu8LSkq9lsDXAPcEWUYyU9cpBz7oCZHQPMB96E7wx8lZkd55zbl+xxZMBZiH+KNyJRu28zmwScDpyTi4yJSMaoXJJCk7OySQGTSGF4CXgPsN45196bAwX2fxh42MyuBvYBbyC94TqlnzCzGmBq0KpJZnYUsMc5t9rMbgduM7Mv4gup4cAZwGLn3L+D9rsIf7N0X25yLiJ5onJJsq6vlE0a9EGkMFwP1AN/NrPjzGyKmb05MHpRcbIHMbO3mdlnzewoM5sAnI//HliVpXxL4ZiHL2y6+g78JPD6O4H3FwK3AT/G/73cDRwHbOg6gJkVARcAtzjnwptHiEj/onJJcqFPlE2qYRIpAM65LWZ2MnAtfqSicnyb7/uBzhQOtQ94N3AVUAGsAc5zzi3LZH6l8ATmrIg50Ylzrg34dmCJlaYTGJfxzIlIn6NySXKhr5RN5lyiCaFFpBCZ2QXAdc65wWns64B3OefuznC2RERkgFK5JIVKTfJE+re6wISC1yaT2Mx+E2PEIxERkUxQuSQFRzVMIv2UmQ3CTyIIsM85tyuJfUYAtYG3W51zB7OVPxERGVhULkmhUsAkIiIiIiISg5rkiYiIiIiIxKCASUREREREJAYFTCIiIiIiIjEoYBIREREREYlBAZOIiIiIiEgMCphERERERERiUMAkIiIiIiISgwImERERERGRGBQwiYiIiIiIxPD/AcWCkQ5XTn4JAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print('Observation time: %f year'%Tobs)\n",
    "print('Sampling time interval: %f second'%(TimeVector[2]-TimeVector[1]))\n",
    "print(f'Sampling frequency is {1/(TimeVector[1] - TimeVector[0]):2e} and its Nyquist frequency is {1/(2*(TimeVector[1] - TimeVector[0])):2e}')\n",
    "h_t = ht_model(TimeVector, tc_true, phic_true, mc_true, eta_true, dl_true, thetas_true, phis_true, iota_true, psi_true)\n",
    "Data = h_t + Noise_t\n",
    "\n",
    "plt.figure(figsize=(10,3),dpi=100)\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(TimeVector,h_t, color='r')\n",
    "plt.title('Signal model')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Signal h(t)')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(TimeVector,Data,color='y')\n",
    "plt.plot(TimeVector,h_t, color='r')\n",
    "plt.title('Data model')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Data d(t)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Year in seconds 3.153600e+07\nTc in seconds 1.545264e+07\n"
     ]
    }
   ],
   "source": [
    "print(f'Year in seconds {YearInS:2e}')\n",
    "print(f'Tc in seconds {Tc:2e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.364916Z",
     "start_time": "2020-12-23T13:05:20.347962Z"
    }
   },
   "outputs": [],
   "source": [
    "mu = np.mean(Noise_t)\n",
    "sigma = np.std(Noise_t)\n",
    "print('mu: ',np.mean(Noise_t))\n",
    "print('sigma: ',np.std(Noise_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.556405Z",
     "start_time": "2020-12-23T13:05:20.370901Z"
    }
   },
   "outputs": [],
   "source": [
    "# import seaborn as sns\n",
    "# sns.distplot(Noise_t,color=\"g\")\n",
    "from scipy.stats import norm\n",
    "x = Noise_t\n",
    "mu =np.mean(x) \n",
    "sigma =np.std(x) \n",
    "num_bins = 60 \n",
    "n, bins, patches = plt.hist(x, num_bins,density=1, alpha=0.75) \n",
    "y = norm.pdf(bins, mu, sigma)\n",
    "plt.grid(True)\n",
    "plt.plot(bins, y, 'r--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Parameter Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用pymc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.564412Z",
     "start_time": "2020-12-23T13:05:20.561391Z"
    }
   },
   "outputs": [],
   "source": [
    "# help(pymc.MCMC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.575354Z",
     "start_time": "2020-12-23T13:05:20.571365Z"
    }
   },
   "outputs": [],
   "source": [
    "t = TimeVector\n",
    "y_true = h_t\n",
    "y_obs = Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.599290Z",
     "start_time": "2020-12-23T13:05:20.580341Z"
    }
   },
   "outputs": [],
   "source": [
    "# Helper data to keep track of everything\n",
    "parameters = ['tc','phic','mc','eta','dl','theta','phi','iota','psi']\n",
    "## lntc,lnmc,lndl,costheta,cosiota\n",
    "# bounds = {'tc':(0,Tobs*YearInS), 'phic':(0,2*np.pi), 'mc':(0,10*Mc), 'eta':(0,0.25), 'dl':(0,10*DL), \n",
    "#           'theta':(-np.pi/2,np.pi/2), 'phi':(0,2*np.pi), 'iota':(-np.pi/2,np.pi/2), 'psi':(0,np.pi)}\n",
    "\n",
    "bounds = {'tc':(Tc-Tc*2e-5,Tc+Tc*2e-5), 'phic':(Phic-0.4,Phic+0.4), 'mc':(Mc-Mc*3e-3,Mc+Mc*3e-3), 'eta':(0,0.25), 'dl':(0.8*DL,1.2*DL), \n",
    "          'theta':(ThetaS-0.2,ThetaS+0.2), 'phi':(PhiS-0.2,PhiS+0.2), 'iota':(Iota-0.6,Iota+0.6), 'psi':(Psi-0.2,Psi+0.2)}\n",
    "\n",
    "# bounds = {'tc':(0,10), 'phic':(0,2*np.pi), 'mc':(0,10), 'eta':(0,0.25), 'dl':(0,20), \n",
    "#           'theta':(-1,1), 'phi':(0,2*np.pi), 'iota':(-np.pi/2,np.pi/2), 'psi':(0,np.pi)}\n",
    "\n",
    "# Defining our parameters using pymc.Uniform()\n",
    "tc = pymc.Uniform('tc', bounds['tc'][0], bounds['tc'][1], \\\n",
    "         value = bounds['tc'][0] + (bounds['tc'][1]-bounds['tc'][0])*np.random.random())\n",
    "\n",
    "phic = pymc.Uniform('phic', bounds['phic'][0], bounds['phic'][1], \\\n",
    "     value = bounds['phic'][0] + (bounds['phic'][1]-bounds['phic'][0])*np.random.random())\n",
    "\n",
    "mc = pymc.Uniform('mc', bounds['mc'][0], bounds['mc'][1], \\\n",
    "    value = bounds['mc'][0] + (bounds['mc'][1]-bounds['mc'][0])*np.random.random())\n",
    "\n",
    "eta = pymc.Uniform('eta', bounds['eta'][0], bounds['eta'][1], \\\n",
    "     value = bounds['eta'][0] + (bounds['eta'][1]-bounds['eta'][0])*np.random.random())\n",
    "\n",
    "dl = pymc.Uniform('dl', bounds['dl'][0], bounds['dl'][1], \\\n",
    "    value = bounds['dl'][0] +  (bounds['dl'][1]-bounds['dl'][0])*np.random.random())\n",
    "\n",
    "theta = pymc.Uniform('theta', bounds['theta'][0], bounds['theta'][1], \\\n",
    "     value = bounds['theta'][0] +  (bounds['theta'][1]-bounds['theta'][0])*np.random.random())\n",
    "\n",
    "phi = pymc.Uniform('phi', bounds['phi'][0], bounds['phi'][1], \\\n",
    "     value = bounds['phi'][0] +  (bounds['phi'][1]-bounds['phi'][0])*np.random.random())\n",
    "\n",
    "iota = pymc.Uniform('iota', bounds['iota'][0], bounds['iota'][1], \\\n",
    "     value = bounds['iota'][0] +  (bounds['iota'][1]-bounds['iota'][0])*np.random.random())\n",
    "\n",
    "psi = pymc.Uniform('psi', bounds['psi'][0], bounds['psi'][1], \\\n",
    "     value = bounds['psi'][0] +  (bounds['psi'][1]-bounds['psi'][0])*np.random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-20T13:36:46.941230Z",
     "start_time": "2020-12-20T13:36:46.923278Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T13:05:20.623260Z",
     "start_time": "2020-12-23T13:05:20.607269Z"
    }
   },
   "outputs": [],
   "source": [
    "@pymc.deterministic\n",
    "def y_model(t=t, tc=tc, phic=phic, mc=mc, eta=eta, dl=dl, theta=theta, phi=phi, iota=iota, psi=psi):\n",
    "    return ht_model(t, tc, phic, mc, eta, dl, theta, phi, iota, psi)\n",
    "\n",
    "# @pymc.deterministic\n",
    "# def y_model(t=t, tc=tc, phic=phic, mc=mc, eta=eta, dl=dl, theta=theta, phi=phi):\n",
    "#     return ht_model(t, tc, phic, mc, eta, dl, theta, phi, Iota, Psi)\n",
    "\n",
    "# @pymc.deterministic\n",
    "# def y_model(t=t, tc=tc, phic=phic, mc=mc, eta=eta, dl=dl, theta=theta, phi=phi):\n",
    "#     return ht_model(t, 10**tc, phic, 10**mc, eta, 10**dl, acos(theta), phi, Iota, Psi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:21:34.268373Z",
     "start_time": "2020-12-23T13:05:20.625249Z"
    }
   },
   "outputs": [],
   "source": [
    "y = pymc.Normal('y', mu=y_model, tau=sigma**-2, observed=True, value=y_obs)\n",
    "M = pymc.MCMC([y, tc, phic, mc, eta, dl, theta, phi, iota, psi, y_model])\n",
    "# M = pymc.MCMC([y, tc, phic, mc, eta, dl, theta, phi, y_model])\n",
    "# M.sample(iter=2e5, burn=1e4, thin=1)\n",
    "M.sample(iter=1e5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:21:45.185476Z",
     "start_time": "2020-12-23T14:21:34.275444Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pymc.Matplot.plot(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:21:50.459326Z",
     "start_time": "2020-12-23T14:21:45.198442Z"
    }
   },
   "outputs": [],
   "source": [
    "# Convert the results of our MCMC sampling\n",
    "def mcmc_dataframe(M, skip_vars):\n",
    "    names = []\n",
    "    data = []\n",
    "    for var in M.variables:\n",
    "        try:\n",
    "            name = var.trace.name\n",
    "            if name not in skip_vars:\n",
    "                names.append(var.trace.name)\n",
    "                data.append(var.trace.gettrace().tolist())\n",
    "        except AttributeError:\n",
    "            pass\n",
    "    df = pd.DataFrame(data).T\n",
    "    df.columns = names\n",
    "    return df\n",
    "\n",
    "# Save as csv\n",
    "mcmc_dataframe(M, ['y_model']).to_csv('Bayes_PE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:21:50.643832Z",
     "start_time": "2020-12-23T14:21:50.461321Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load up the samples. Using pregenerated; change\n",
    "# the filename if you want to load your own.\n",
    "samples = pd.read_csv('Bayes_PE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:21:50.690707Z",
     "start_time": "2020-12-23T14:21:50.644830Z"
    }
   },
   "outputs": [],
   "source": [
    "var_order = ['tc','phic','mc','eta','dl','theta','phi','iota','psi']\n",
    "# var_order = ['tc','phic','mc','eta','dl','theta','phi']\n",
    "for i in range(len(var_order)):\n",
    "    print(var_order[i],':',samples[var_order[i]].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.183448Z",
     "start_time": "2020-12-23T14:21:50.692702Z"
    }
   },
   "outputs": [],
   "source": [
    "# Some settings for Seaborn\n",
    "sns.set(context='notebook', style=\"ticks\", color_codes=True)\n",
    "\n",
    "# Wrapper function for the hexbin plotting style\n",
    "def hexbin(x, y, color, **kwargs):\n",
    "    cmap = sns.light_palette(color, as_cmap=True)\n",
    "    plt.hexbin(x, y, gridsize=15, cmap = cmap, mincnt = 1)\n",
    "    return\n",
    "\n",
    "# Helper arrays\n",
    "truth = [tc_true, phic_true, mc_true, eta_true, dl_true, thetas_true, phis_true, iota_true, psi_true]\n",
    "est = samples[var_order].max().values\n",
    "\n",
    "# Plot a pairwise grid of scatter plots and histograms\n",
    "with sns.axes_style(\"white\"):\n",
    "    g = sns.pairplot(\n",
    "        samples[var_order],\n",
    "        # We don't actually want the scatter plots, so we set the marker size to 0\n",
    "        plot_kws={\"s\": 0}, \n",
    "        diag_kws={'color': 'green', 'lw': 0})\n",
    "    \n",
    "    # Replace the scatter plots with hexbin plots\n",
    "    g.map_offdiag(hexbin, color='green');\n",
    "\n",
    "# Plot the injected values in the histograms\n",
    "for i in range(len(var_order)):\n",
    "    for j in range(len(var_order)):\n",
    "        if i != j:\n",
    "            ax = g.axes[i,j]\n",
    "            ax.scatter([truth[j]], [truth[i]], color = \"r\", marker = '*', s=100, linewidths=5)\n",
    "# plt.savefig('paras.png', dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T03:37:11.224635Z",
     "start_time": "2020-12-15T03:37:11.220681Z"
    }
   },
   "source": [
    "## pymc3-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.224309Z",
     "start_time": "2020-12-23T14:22:12.186410Z"
    }
   },
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "# import matplotlib.pyplot as plt\n",
    "# import matplotlib as mpl\n",
    "# import pymc3 as pm\n",
    "# from pymc3 import Model, Normal, Slice\n",
    "# from pymc3 import sample\n",
    "# from pymc3 import traceplot\n",
    "# from pymc3.distributions import Interpolated\n",
    "# from theano import as_op\n",
    "# import theano.tensor as tt\n",
    "# import numpy as np\n",
    "# from scipy import stats\n",
    "\n",
    "# plt.style.use('seaborn-darkgrid')\n",
    "# print('Running on PyMC3 v{}'.format(pm.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.317092Z",
     "start_time": "2020-12-23T14:22:12.244255Z"
    }
   },
   "outputs": [],
   "source": [
    "# import pymc3 as pm\n",
    "\n",
    "# t = np.arange(-Tobs,-1000,3000)\n",
    "\n",
    "# # Noise strength\n",
    "# sigma = 10**(-18)\n",
    "\n",
    "# # Injecting our signal into the noise\n",
    "# h_ture = ht_respon_TQ(t, Tc, Phic, Mc, Eta, DL, ThetaS, PhiS, Iota, Psi)\n",
    "# D_obs = np.random.normal(h_ture, sigma)\n",
    "\n",
    "# basic_model = pm.Model()\n",
    "# with basic_model:\n",
    "#     tc    = pm.Uniform('tc',-1e8,0)\n",
    "#     phic  = pm.Uniform('phic',0,2*np.pi)\n",
    "#     mc    = pm.Uniform('mc',0,1e2)\n",
    "#     eta   = pm.Uniform('eta',0,0.25)\n",
    "#     dL    = pm.Uniform('dL',0,1e20)\n",
    "# #     theta = acos(pm.Uniform('theta',-1,1)) \n",
    "#     theta = pm.Uniform('theta',-np.pi,np.pi)\n",
    "#     phi   = pm.Uniform('phi',0,2*np.pi) \n",
    "# #     iota  = acos(pm.Uniform('iota',-1,1)) \n",
    "#     iota  = pm.Uniform('iota',-np.pi,np.pi)\n",
    "#     psi   = pm.Uniform('psi',0,2*np.pi) \n",
    "    \n",
    "#     mu = ht_respon_TQ(t, tc, phic, mc, eta, dL, theta, phi, iota, psi)\n",
    "    \n",
    "#     D = pm.Normal('D',mu=mu,sd=sigma,observed=D_obs)\n",
    "\n",
    "#     trace = pm.sample(5000)\n",
    "#     pm.traceplot(trace);\n",
    "\n",
    "# pm.summary(trace)\n",
    "\n",
    "# # map_estimate = pm.find_MAP(model=basic_model)\n",
    "# # from scipy import optimize\n",
    "# # map_estimate2 = pm.find_MAP(model=basic_model,fmin=optimize.fmin_powell)\n",
    "# # print(map_estimate)\n",
    "# # print(map_estimate2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.366962Z",
     "start_time": "2020-12-23T14:22:12.358948Z"
    }
   },
   "outputs": [],
   "source": [
    "# Frequency-domain waveform\n",
    "\n",
    "# def hf_respon_amp_LISA(f, tc, phic, mc, eta, dl, thetaS, phiS, iota, psi):\n",
    "#     \"\"\"the frequency-domain amplitude: 1st 60 degree MI with antenna response (contain plus and cross mode) for LISA\"\"\"\n",
    "\n",
    "#     v = (np.pi*mc*f)**(2./3)*eta**(-2./5)\n",
    "    \n",
    "#     #\"\"\"function t(f) for non-spinning 3PN correction\"\"\"  \n",
    "#     SO, SS = 0, 0 \n",
    "#     rE = 0.577216\n",
    "#     t = tc-5./256*(mc)**(-5./3)*(np.pi*f)**(-8./3)*(1                    \n",
    "#              + 4./3*(743./336 + 11*eta/4.)*v                 \n",
    "#              - 8./5*(4*np.pi - SO)*v**(3./2)                  \n",
    "#              + (3058673./508032 + 5429./504*eta + 617./72*eta**2 - 2*SS)*v**2 )                  \n",
    "\n",
    "#     #\"\"\"For LISA (one Michelson interferometer): (thetaS,phiS) is location of source\"\"\"\n",
    "#     alpha = 2*np.pi/YearInS* t-np.pi/9 # trailing 20°behind the Earth (-np.pi/9)\n",
    "#     lam = 3*np.pi/4-np.pi/9\n",
    "#     Dplus_LISA = np.sqrt(3.)/64*(-36*(np.sin(thetaS))**2 *np.sin(2*alpha-2*lam) +(3+np.cos(2*thetaS))*(np.cos(2*phiS)*(9*np.sin(2*lam)-np.sin(4*alpha-2*lam)) \\\n",
    "#             + np.sin(2*phiS)*(np.cos(4*alpha-2*lam)-9*np.cos(2*lam)))-4*np.sqrt(3.)*np.sin(2*thetaS)*(np.sin(3*alpha-2*lam-phiS)-3*np.sin(alpha-2*lam+phiS)))\n",
    "\n",
    "#     Dcros_LISA = 1/16*(np.sqrt(3.)*np.cos(thetaS)*(9*np.cos(2*lam-2*phiS)-np.cos(4*alpha-2*lam-2*phiS))-6*np.sin(thetaS)*(np.cos(3*alpha-2*lam-phiS)+3*np.cos(alpha-2*lam+phiS)))\n",
    "\n",
    "#     #\"\"\"antenna pattern function for '+' mode\"\"\"\n",
    "#     Fplus_LISA = (np.cos(2*psi)*Dplus_LISA-np.sin(2*psi)*Dcros_LISA)/2.\n",
    "\n",
    "#     #\"\"\"antenna pattern function for '×' mode\"\"\"\n",
    "#     Fcros_LISA = (np.sin(2*psi)*Dplus_LISA+np.cos(2*psi)*Dcros_LISA)/2.\n",
    "\n",
    "#     #\"\"\"1st MI angular response function for LISA\"\"\"\n",
    "#     Q_LISA = np.sqrt((1+(np.cos(iota))**2)**2*(Fplus_LISA)**2 \n",
    "#              +(2*np.cos(iota))**2*(Fcros_LISA)**2)\n",
    "\n",
    "#     #\"\"\"inspiral amplitude including mass and distance terms\"\"\"\n",
    "#     A = -np.sqrt(5./96)*(mc)**(5./6)/np.pi**(2./3)/(dl)\n",
    "\n",
    "#     #\"\"\"1st MI polarization phase for LISA\"\"\"\n",
    "#     phip_LISA = np.arctan(2*np.cos(iota)*Fcros_LISA/((1+(np.cos(iota))**2)*Fplus_LISA))  \n",
    "\n",
    "#     #\"\"\"Doppler phase for LISA\"\"\"\n",
    "#     phi0 = -np.pi/9 # trailing 20°behind the Earth\n",
    "#     phiD_LISA = 2*np.pi*f*AUInS*np.sin(thetaS)*np.cos(2*np.pi*t/YearInS+phi0-phiS)\n",
    "\n",
    "#     #\"\"\"non-precesnp.sing spinning PN correction to the GW phase\"\"\"\n",
    "#     beta  = 0.0\n",
    "#     sigma = 0.0\n",
    "# #         chi   = chi1\n",
    "# #         beta  = 113./12*chi-19./6*eta*(chi1+chi1)\n",
    "# #         sigma = 81./16*chi**2-1./4*eta*chi1*chi1\n",
    "# #         gamma = (732985./2268+140./9*eta)*chi-(12760./81*eta+170./9*eta**2)*(chi1+chi1)\n",
    "# #         xi    = 75./2*np.pi*chi-37./3*np.pi*eta*(chi1+chi1)\n",
    "#     PSI_PN = 2*np.pi*f*tc-phic-np.pi/4 +3./128*(mc*np.pi*f)**(-5./3)* (1    \\\n",
    "#            + (3715./756 + 55./9*eta)*v \\\n",
    "#            + (4*beta-16*np.pi)*v**(3./2)  \\\n",
    "#            + (15293365./508032+27145./504*eta+3085./72*eta**2-10*sigma)*v**2  ) \n",
    "\n",
    "#     #\"\"\"1st MI total GW phase for LISA\"\"\"\n",
    "#     PSI_LISA = PSI_PN + phip_LISA - phiD_LISA\n",
    "\n",
    "#     return Q_LISA*A*f**(-7./6)*np.exp(1j*PSI_LISA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.376900Z",
     "start_time": "2020-12-23T14:22:12.373909Z"
    }
   },
   "outputs": [],
   "source": [
    "## FIM_RMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.395850Z",
     "start_time": "2020-12-23T14:22:12.378895Z"
    }
   },
   "outputs": [],
   "source": [
    "# FIMparams = ['tc','phic','mc','eta','dL','thetaS','phiS','chi1'] # Input parameter space \n",
    "# form_rms = \"[{:.3f}, {:.3f}, {:.3e}, {:.3e}, {:.3e}, {:.4f}, {:.4f}, {:.3e}]\"\n",
    "# # FIMparams = ['tc','phic','mc','eta','dL','thetaS','phiS'] # Input parameter space \n",
    "# # form_rms = \"[{:.3f}, {:.3f}, {:.3e}, {:.3e}, {:.3e}, {:.4f}, {:.4f}]\"\n",
    "\n",
    "# ## FIM\n",
    "# GammaTQ = Gamma_TQ(FIMparams,THETA,Q_TQ,TQfreq_int,TQsqrtPSD_int,HfResponAmp_TQ)\n",
    "# Gamma2TQ = Gamma2_TQ(FIMparams,THETA,Q2_TQ,TQfreq_int,TQsqrtPSD_int,HfResponAmp2_TQ)\n",
    "# GammaLISA = Gamma_LISA(FIMparams,THETA,Q_LISA,LISAfreq_int,LISAsqrtPSD_int,HfResponAmp_LISA)\n",
    "# Gamma2LISA = Gamma2_LISA(FIMparams,THETA,Q2_LISA,LISAfreq_int,LISAsqrtPSD_int,HfResponAmp2_LISA)\n",
    "\n",
    "# ## using linalg.inv to invert FIM\n",
    "# ## 测试显示改方法比SVD计算偏差更小！\n",
    "# GammaT = GammaTQ + Gamma2TQ\n",
    "# GammaL = GammaLISA + Gamma2LISA\n",
    "# GammaTL = GammaT + GammaL\n",
    "# Sigma_TQ = np.linalg.inv(GammaT)\n",
    "# Sigma_LISA = np.linalg.inv(GammaL)\n",
    "# Sigma_TQLISA = np.linalg.inv(GammaTL)\n",
    "\n",
    "# ## RMS errors\n",
    "# RMS_TQ = np.sqrt(np.diag(Sigma_TQ))\n",
    "# RMS_LISA = np.sqrt(np.diag(Sigma_LISA))\n",
    "# RMS_TQLISA = np.sqrt(np.diag(Sigma_TQLISA))\n",
    "\n",
    "# ## sky localization\n",
    "# D_Omega_TQ = 2.*np.pi*abs(sin(ThetaS))*np.sqrt(Sigma_TQ[5,5]*Sigma_TQ[6,6]-Sigma_TQ[5,6]*Sigma_TQ[6,5])\n",
    "# D_Omega_LISA = 2.*np.pi*abs(sin(ThetaS))*np.sqrt(Sigma_LISA[5,5]*Sigma_LISA[6,6]-Sigma_LISA[5,6]*Sigma_LISA[6,5])\n",
    "# D_Omega_TQLISA = 2.*np.pi*abs(sin(ThetaS))*np.sqrt(Sigma_TQLISA[5,5]*Sigma_TQLISA[6,6]-Sigma_TQLISA[5,6]*Sigma_TQLISA[6,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.400837Z",
     "start_time": "2020-12-23T14:22:12.396847Z"
    }
   },
   "outputs": [],
   "source": [
    "## SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.413801Z",
     "start_time": "2020-12-23T14:22:12.406820Z"
    }
   },
   "outputs": [],
   "source": [
    "# form_f = \"[{:.3e}, {:.3e}]\"\n",
    "# print(\"frequency band:\", form_f.format(*FreqLowHigh(THETA)))\n",
    "# TQ_SNR = np.sqrt(GammaT[4,4])\n",
    "# print(\"SNR for TianQin with antenna function: %.1f\" % TQ_SNR)\n",
    "# LISA_SNR = np.sqrt(GammaL[4,4])\n",
    "# print(\"SNR for LISA with antenna function: %.1f\" % LISA_SNR)\n",
    "# TQLISA_SNR = np.sqrt(GammaTL[4,4])\n",
    "# print(\"SNR for TQ+LISA with antenna function: %.1f\" % TQLISA_SNR)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.423775Z",
     "start_time": "2020-12-23T14:22:12.417792Z"
    }
   },
   "outputs": [],
   "source": [
    "## RMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.435742Z",
     "start_time": "2020-12-23T14:22:12.428761Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ### 求导表达式去掉ATomc, QTomc, QToeta\n",
    "# print(FIMparams)\n",
    "# print(\"RMS_TQ:\", form_rms.format(*RMS_TQ))\n",
    "# print(\"D_Omega_TQ: %.3e\" % D_Omega_TQ)\n",
    "# print(\"RMS_LISA:\", form_rms.format(*RMS_LISA)) \n",
    "# print(\"D_Omega_LISA: %.3e\" % D_Omega_LISA)\n",
    "# print(\"RMS_TQLISA:\", form_rms.format(*RMS_TQLISA))\n",
    "# print(\"D_Omega_TQLISA: %.3e\" % D_Omega_TQLISA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.445716Z",
     "start_time": "2020-12-23T14:22:12.441755Z"
    }
   },
   "outputs": [],
   "source": [
    "## Condition number & Max Difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-23T14:22:12.460711Z",
     "start_time": "2020-12-23T14:22:12.455722Z"
    }
   },
   "outputs": [],
   "source": [
    "# ### condition number\n",
    "# print(\"condition number for TQ: %.3e\" % np.linalg.cond(GammaT))\n",
    "# print(\"condition number for LISA: %.3e\" % np.linalg.cond(GammaL))\n",
    "# print(\"condition number for TQ&LISA: %.3e\" % np.linalg.cond(GammaTL))\n",
    "# ### Absolute Difference from the Identity Matrix\n",
    "# AbsDiff_TQ = (np.dot(GammaT, Sigma_TQ)-np.eye(len(FIMparams))).max()\n",
    "# AbsDiff_LISA = (np.dot(GammaL, Sigma_LISA)-np.eye(len(FIMparams))).max()\n",
    "# AbsDiff_TQLISA = (np.dot(GammaTL, Sigma_TQLISA)-np.eye(len(FIMparams))).max()\n",
    "# form = \"[{:.3e}, {:.3e}, {:.3e}]\"\n",
    "# print(\"maximum absolute difference from the identity matrix:\", form.format(AbsDiff_TQ, AbsDiff_LISA, AbsDiff_TQLISA))"
   ]
  }
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