{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. _phenom_constr:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phenomenological model constraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "from jetset.test_data_helper import  test_SEDs\n",
    "from jetset.data_loader import ObsData,Data\n",
    "from jetset.plot_sedfit import PlotSED\n",
    "from jetset.test_data_helper import  test_SEDs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/orion/anaconda3/envs/jetset/lib/python3.7/site-packages/jetset-1.1.2-py3.7-macosx-10.9-x86_64.egg/jetset/test_data/SEDs_data/SED_MW_Mrk421_EBL_DEABS.ecsv\n"
     ]
    }
   ],
   "source": [
    "print(test_SEDs[1])\n",
    "data=Data.from_file(test_SEDs[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================================================================================\n",
      "\n",
      "***  binning data  ***\n",
      "---> N bins= 90\n",
      "---> bin_widht= 0.2\n",
      "===================================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from jetset.cosmo_tools import Cosmo\n",
    "c=Cosmo()\n",
    "sed_data=ObsData(data_table=data,cosmo=c)\n",
    "sed_data.group_data(bin_width=0.2)\n",
    "sed_data.add_systematics(0.2,[10.**6,10.**29])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sed_data.save('Mrk_501.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=sed_data.plot_sed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================================================================================\n",
      "\n",
      "*** evaluating spectral indices for data ***\n",
      "===================================================================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jetset.sed_shaper import  SEDShape\n",
    "my_shape=SEDShape(sed_data)\n",
    "my_shape.eval_indices()\n",
    "p=my_shape.plot_indices()\n",
    "p.rescale(y_min=-15,y_max=-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================================================================================\n",
      "\n",
      "*** Log-Polynomial fitting of the synchrotron component ***\n",
      "---> first blind fit run,  fit range: [10, 21]\n",
      "---> class:  HSP\n",
      "\n",
      "---> class:  HSP\n",
      "\n",
      "\n",
      " model name     name      bestfit val     err +     err -   start val   fit range min fit range max frozen\n",
      "----------- ------------ ------------- ------------ ----- ------------- ------------- ------------- ------\n",
      "   LogCubic            b -7.192333e-02 1.382165e-02    -- -5.476187e-02 -1.000000e+01  0.000000e+00  False\n",
      "   LogCubic            c -2.757549e-03 2.075296e-03    --  3.835964e-03 -1.000000e+01  1.000000e+01  False\n",
      "   LogCubic           Ep  1.697013e+01 1.495082e-01    --  1.603696e+01  0.000000e+00  3.000000e+01  False\n",
      "   LogCubic           Sp -1.029110e+01 3.685184e-02    -- -1.021047e+01 -3.000000e+01  0.000000e+00  False\n",
      "host_galaxy nuFnu_p_host -1.007762e+01 7.831960e-02    -- -1.021047e+01 -1.221047e+01 -8.210473e+00  False\n",
      "host_galaxy     nu_scale  3.438156e-02 1.030235e-03    --  0.000000e+00 -5.000000e-01  5.000000e-01  False\n",
      "---> sync       nu_p=+1.697013e+01 (err=+1.495082e-01)  nuFnu_p=-1.029110e+01 (err=+3.685184e-02) curv.=-7.192333e-02 (err=+1.382165e-02)\n",
      "===================================================================================================================\n",
      "\n",
      "-------------------------------------------------------------------------------------------\n",
      "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
      "-------------------------------------------------------------------------------------------\n",
      "| 0 | par_0 |  -0.072   |   0.014   |   -0.014   |    0.014   |   -10   |    0    |       |\n",
      "| 1 | par_1 |  -2.8E-3  |   2.1E-3  |  -2.0E-3   |   2.0E-3   |   -10   |   10    |       |\n",
      "| 2 | par_2 |   16.97   |    0.15   |   -0.15    |    0.15    |    0    |   30    |       |\n",
      "| 3 | par_3 |  -10.29   |    0.04   |   -0.04    |    0.04    |   -30   |    0    |       |\n",
      "| 4 | par_4 |  -10.08   |    0.08   |   -0.08    |    0.08    |-12.2105 |-8.21047 |       |\n",
      "| 5 | par_5 |  0.344E-1 |  0.010E-1 | -0.010E-1  |  0.010E-1  |  -0.5   |   0.5   |       |\n",
      "-------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm,best_fit=my_shape.sync_fit(check_host_gal_template=True,\n",
    "                  Ep_start=None,\n",
    "                  minimizer='minuit',\n",
    "                  silent=True,\n",
    "                  fit_range=[10,21])\n",
    "\n",
    "\n",
    "mm.minimizer.minos_errors()\n",
    "x,y,z,fig,ax=mm.minimizer.draw_contour('Ep','b')\n",
    "x,y,fig,ax=mm.minimizer.draw_profile('Ep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================================================================================\n",
      "\n",
      "*** Log-Polynomial fitting of the IC component ***\n",
      "---> fit range: [21, 29]\n",
      "---> LogCubic fit\n",
      "\n",
      "**************************************************************************************************\n",
      "Fit report\n",
      "\n",
      "Model: IC-shape-fit\n",
      "model name name   par type       units          val      phys. bound. min phys. bound. max  log  frozen\n",
      "---------- ---- ------------ ------------- ------------- ---------------- ---------------- ----- ------\n",
      "  LogCubic    b    curvature               -1.239376e-01    -1.000000e+01     0.000000e+00 False  False\n",
      "  LogCubic    c third-degree               -2.636682e-02    -1.000000e+01     1.000000e+01 False  False\n",
      "  LogCubic   Ep    peak freq            Hz  2.545172e+01     0.000000e+00     3.000000e+01  True  False\n",
      "  LogCubic   Sp    peak flux erg / (cm2 s) -1.057063e+01    -3.000000e+01     0.000000e+00  True  False\n",
      "\n",
      "converged=True\n",
      "calls=33\n",
      "Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000 and the relative error between two consecutive iterates is at \n",
      "  most 0.000000\n",
      "dof=9\n",
      "chisq=1.256424, chisq/red=0.139603 null hypothesis sig=0.998583\n",
      "\n",
      "best fit pars\n",
      "model name name  bestfit val     err +     err -   start val   fit range min fit range max frozen\n",
      "---------- ---- ------------- ------------ ----- ------------- ------------- ------------- ------\n",
      "  LogCubic    b -1.239376e-01 1.501286e-02    -- -1.000000e+00 -1.000000e+01  0.000000e+00  False\n",
      "  LogCubic    c -2.636682e-02 6.064144e-03    -- -1.000000e+00 -1.000000e+01  1.000000e+01  False\n",
      "  LogCubic   Ep  2.545172e+01 7.473138e-02    --  2.546097e+01  0.000000e+00  3.000000e+01  False\n",
      "  LogCubic   Sp -1.057063e+01 2.131005e-02    -- -1.000000e+01 -3.000000e+01  0.000000e+00  False\n",
      "**************************************************************************************************\n",
      "\n",
      "\n",
      "\n",
      "model name name  bestfit val     err +     err -   start val   fit range min fit range max frozen\n",
      "---------- ---- ------------- ------------ ----- ------------- ------------- ------------- ------\n",
      "  LogCubic    b -1.239376e-01 1.501286e-02    -- -1.000000e+00 -1.000000e+01  0.000000e+00  False\n",
      "  LogCubic    c -2.636682e-02 6.064144e-03    -- -1.000000e+00 -1.000000e+01  1.000000e+01  False\n",
      "  LogCubic   Ep  2.545172e+01 7.473138e-02    --  2.546097e+01  0.000000e+00  3.000000e+01  False\n",
      "  LogCubic   Sp -1.057063e+01 2.131005e-02    -- -1.000000e+01 -3.000000e+01  0.000000e+00  False\n",
      "---> IC         nu_p=+2.545172e+01 (err=+7.473138e-02)  nuFnu_p=-1.057063e+01 (err=+2.131005e-02) curv.=-1.239376e-01 (err=+1.501286e-02)\n",
      "===================================================================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_shape.IC_fit(fit_range=[21,29],minimizer='lsb')\n",
    "p=my_shape.plot_shape_fit()\n",
    "p.rescale(y_min=-15,x_min=7,x_max=29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================================================================================\n",
      "\n",
      "***  constrains parameters from observable ***\n",
      "\n",
      "---> ***  emitting region parameters  ***\n",
      "---> name = beam_obj          type = beaming               units = Lorentz-factor*   val = +1.500000e+01  phys-bounds = [+1.000000e-04,No           ] islog = False  froze= False \n",
      "---> setting par type redshift, corresponding to par z_cosm\n",
      "--->  name = z_cosm            type = redshift              units =                   val = +3.360000e-02  phys-bounds = [+0.000000e+00,No           ] islog = False  froze= False \n",
      "\n",
      "---> setting par type magnetic_field, corresponding to par B\n",
      "--->  name = B                 type = magnetic_field        units = G                 val = +5.500000e-02  phys-bounds = [+0.000000e+00,No           ] islog = False  froze= False \n",
      "\n",
      "---> setting par type region_size, corresponding to par R\n",
      "--->  name = R                 type = region_size           units = cm                val = +3.759008e+16  phys-bounds = [+1.000000e+03,+1.000000e+30] islog = False  froze= False \n",
      "\n",
      "\n",
      "---> *** electron distribution parameters ***\n",
      "---> distribution type:  lppl\n",
      "---> r elec. spec. curvature =3.596166e-01\n",
      "---> setting par type curvature, corresponding to par r\n",
      "--->  name = r                 type = spectral_curvature    units =                   val = +3.596166e-01  phys-bounds = [-1.500000e+01,+1.500000e+01] islog = False  froze= False \n",
      "\n",
      "---> s_radio_mm -0.47152657988709734 1.9430531597741947\n",
      "---> s_X 3.2697987439778373\n",
      "---> s_Fermi 1.742749327549109\n",
      "---> s_UV_X 2.745697034461969\n",
      "---> s_Opt_UV -1.6299328530633286 4.259865706126657\n",
      "---> s from synch log-log fit -1.0\n",
      "---> s from (s_Fermi + s_UV)/2\n",
      "---> power-law index s, class obj=HSP s chosen is 2.244223\n",
      "---> setting par type LE_spectral_slope, corresponding to par s\n",
      "--->  name = s                 type = LE_spectral_slope     units =                   val = +2.244223e+00  phys-bounds = [-1.000000e+01,+1.000000e+01] islog = False  froze= False \n",
      "\n",
      "---> gamma_3p_Sync= 1.777932e+05, assuming B=5.500000e-02\n",
      "---> gamma_max=2.858471e+06 from nu_max_Sync= 2.413075e+19, using B=5.500000e-02\n",
      "---> setting par type high-energy-cut-off, corresponding to par gmax\n",
      "--->  name = gmax              type = high-energy-cut-off   units = lorentz-factor*   val = +6.456134e+00  phys-bounds = [+0.000000e+00,+1.500000e+01] islog = True  froze= False \n",
      "\n",
      "---> setting par type low-energy-cut-off, corresponding to par gmin\n",
      "--->  name = gmin              type = low-energy-cut-off    units = lorentz-factor*   val = +2.114333e+00  phys-bounds = [+0.000000e+00,+9.000000e+00] islog = True  froze= False \n",
      "\n",
      "---> setting par type turn-over energy, corresponding to par gamma0_log_parab\n",
      "---> using gamma_3p_Sync= 177793.17843886977\n",
      "--->  name = gamma0_log_parab  type = turn-over-energy      units = lorentz-factor*   val = +4.199106e+00  phys-bounds = [+0.000000e+00,+9.000000e+00] islog = True  froze= False \n",
      "\n",
      "nu_p_seed_blob 6432719309927042.0\n",
      "COMP FACTOR 9.256256900025962 23213.162343496708\n",
      "---> gamma_3p_SSCc= %e 315936.8202363262\n",
      "---> setting par type turn-over energy, corresponding to par gamma0_log_parab\n",
      "---> using gamma_3p_SSC= 315936.8202363262\n",
      "--->  name = gamma0_log_parab  type = turn-over-energy      units = lorentz-factor*   val = +4.448791e+00  phys-bounds = [+0.000000e+00,+9.000000e+00] islog = True  froze= False \n",
      "\n",
      "\n",
      "---> setting par type emitters_density, corresponding to par N\n",
      "---> name = N                 type = emitters_density      units = 1 / cm3           val = +2.705773e+00  phys-bounds = [+0.000000e+00,No           ] islog = False  froze= False \n",
      "---> B from nu_p_S=1.741776e-02\n",
      "---> get B from best matching of nu_p_IC\n",
      "---> B=1.042631e-01, out of boundaries 1.000000e-02 1.000000e-01, rejected\n",
      "     Best B not found, (temporary set to 1.000000e-01)\n",
      "---> setting par type magnetic_field, corresponding to par B\n",
      "--->  name = B                 type = magnetic_field        units = G                 val = +1.000000e-01  phys-bounds = [+0.000000e+00,No           ] islog = False  froze= False \n",
      "\n",
      "---> constrain failed, B set to:  name = B                 type = magnetic_field        units = G                 val = +1.000000e-01  phys-bounds = [+0.000000e+00,No           ] islog = False  froze= False \n",
      "\n",
      "\n",
      "---> update pars for new B \n",
      "---> setting par type low-energy-cut-off, corresponding to par gmin\n",
      "--->  name = gmin              type = low-energy-cut-off    units = lorentz-factor*   val = +1.984515e+00  phys-bounds = [+0.000000e+00,+9.000000e+00] islog = True  froze= False \n",
      "\n",
      "---> setting par type low-energy-cut-off, corresponding to par gamma0_log_parab\n",
      "---> using gamma_3p_Sync= 131854.9500954275\n",
      "--->  name = gamma0_log_parab  type = turn-over-energy      units = lorentz-factor*   val = +4.069287e+00  phys-bounds = [+0.000000e+00,+9.000000e+00] islog = True  froze= False \n",
      "\n",
      "---> gamma_max=2.119899e+06 from nu_max_Sync= 2.413075e+19, using B=1.000000e-01\n",
      "---> setting par type high-energy-cut-off, corresponding to par gmax\n",
      "--->  name = gmax              type = high-energy-cut-off   units = lorentz-factor*   val = +6.326315e+00  phys-bounds = [+0.000000e+00,+1.500000e+01] islog = True  froze= False \n",
      "\n",
      "---> setting par type emitters_density, corresponding to par N\n",
      "---> get R from Compoton Dominance (CD)\n",
      "     Best R=1.390058e+16\n",
      "---> setting par type region_size, corresponding to par R\n",
      "--->  name = R                 type = region_size           units = cm                val = +1.390058e+16  phys-bounds = [+1.000000e+03,+1.000000e+30] islog = False  froze= False \n",
      "\n",
      "---> setting par type emitters_density, corresponding to par N\n",
      "---> t_var (days) 0.36979382052626786\n",
      "\n",
      "show pars\n",
      "      name             par type           units          val      phys. bound. min phys. bound. max  log  frozen\n",
      "---------------- ------------------- --------------- ------------ ---------------- ---------------- ----- ------\n",
      "            gmin  low-energy-cut-off lorentz-factor* 1.984515e+00     0.000000e+00     9.000000e+00  True  False\n",
      "            gmax high-energy-cut-off lorentz-factor* 6.326315e+00     0.000000e+00     1.500000e+01  True  False\n",
      "               N    emitters_density         1 / cm3 4.529281e+01     0.000000e+00               -- False  False\n",
      "               s   LE_spectral_slope                 2.244223e+00    -1.000000e+01     1.000000e+01 False  False\n",
      "               r  spectral_curvature                 3.596166e-01    -1.500000e+01     1.500000e+01 False  False\n",
      "gamma0_log_parab    turn-over-energy lorentz-factor* 4.069287e+00     0.000000e+00     9.000000e+00  True  False\n",
      "               R         region_size              cm 1.390058e+16     1.000000e+03     1.000000e+30 False  False\n",
      "             R_H     region_position              cm 1.000000e+17     0.000000e+00               -- False   True\n",
      "               B      magnetic_field               G 1.000000e-01     0.000000e+00               -- False  False\n",
      "        beam_obj             beaming Lorentz-factor* 1.500000e+01     1.000000e-04               -- False  False\n",
      "          z_cosm            redshift                 3.360000e-02     0.000000e+00               -- False  False\n",
      "eval_model\n",
      "\n",
      "===================================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from jetset.obs_constrain import ObsConstrain\n",
    "from jetset.model_manager import  FitModel\n",
    "from jetset.minimizer import fit_SED\n",
    "sed_obspar=ObsConstrain(beaming=15,\n",
    "                        B_range=[0.01,0.1],\n",
    "                        distr_e='lppl',\n",
    "                        t_var_sec=1*86400,\n",
    "                        nu_cut_IR=5E10,\n",
    "                        SEDShape=my_shape)\n",
    "\n",
    "\n",
    "jet=sed_obspar.constrain_SSC_model(electron_distribution_log_values=True,silent=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P1HPSCnpOWuHuMERERESyRQmWiIiIiIiIiyjBEhERERERcRGPSrCMMT2MMZuNMU5jTONL9j1vjPnbGLPNGNPWXTGKSN5LSnay+8Q5th6OZsScTSQlO90dkoiIiEiWONwdwCU2AXcCky7eaIypDfQC6gDlgCXGmBrW2uT8D1FE8lLPSSvYfeIcx2ISAJiyYi8LNx1h5dDWbo5MREREJHMe1YNlrd1qrd2Wzq4uwHRrbYK1djfwN9Akf6MTkfwSn/jP707iEvVdioiIiBQMntaDlZHywO8XPT6Quu0yxpj+QH+A8uXLs2fPnhw/aVRUVI7PlRRqw9wrzG34+JzdAIztUuXCtjfaRjD2V5i96SQAXgZaVQ+58Luc3jmZKcxtmF/UhrmnNsw9tWHuqQ1zT20omcn3BMsYswQom86uodbaORmdls42m96B1toPgA8AGjdubCMjI3MS5gW5PV/Uhq5QWNvQ3/8wcPnrG12xEstHLSMuMZk7GpZneIfaOLy9rnhOZgprG+YntWHuqQ1zT22Ye2rD3FMbypXke4Jlrc3JRIoDQMWLHlcADrkmIhHxNA5vLyJLFQPg5c513RyNiIiISNYVlCGCc4EvjTFvk1Lk4ipglXtDEpG8NGNAU3eHICIiIpJtHlXkwhhzhzHmANAUmG+MWQhgrd0MzAS2AAuAR1VBUKRgy0kpdpVvFxEREU/nUT1Y1trZwOwM9r0GvJa/EYlIXshJKfaMzoksVUy9XSIiIuIxPKoHS0SKjpyUYs/snJ6TVtBz0orcByciIiKSQ0qwRCTfzRjQlK7X/v9KC94G7miY7soLmZ6j3isRERHxJEqwRMQtRnSsTXiwHyH+Dno3rczwDrXz5BwRERGR/ORRc7BEpOjISSl2lW/PO2lDKy/tEUzb/kbbiHyPSUREpCBSD5aIiIiIiIiLKMESkQJlxoCm6c67Ugn3zGVUBMTVbadiIyIiUpRpiKCIFHg5Kfsu/z/8L6O2S0u84hOTGfsrjK5YCYe3vpcTERG5EiVYIuI2rqwAmJOy74VdRvOqLnVp2x0/m3BZ0jp700mWj1qW66Q1qzGJiIgUVEqwRKTAmzGgKSPmbGLKir1A1sq+FyT2fAL29BG8nDHE/v4b5/fvxXnmFCTGUfKmSsSs2UnMhkM4ExOx55MJb5NSkGLY7D0A7P/Ol8D6ZQkyN3H0VADPPPETTzk28kH5MoS2asqwX7z5Yr8fFoO3gVJBfkDOktaLe71GzNnEiI611eslIiJFSo4SLGNMMSDeWquviEXEI4zoWJuFm44Ql5jMHQ3LF7gS7s7TUZzf8CsJm9cQUDIZb45z4KM/OH8qnqRYS4kayZRtE0bM74ak8w68gwJxlAgB/+I4qtQi0C8CL/8AjJ8/3vWuAgxrd/0KgHdyEnOS6vGHsxwEerHFOjkdk8wbh1fDttM8tnknMTHN2OdfhopnjvBmg+9wHi3NsKMt+DakAU7jjReWOxqUveJr0FBNERGRLCZYxhgvoBdwL3AdkAD4GWOOA98BH1hrd+RZlCIimSgoJdyt00ni5pXEr1pG/Kb1hNUzxG3cxIGFyfgW98a3bCg+bRvh0/BGSj3SEJ8a9fCpVh8TkPLawh+9/JoBqf8ulpTs5P2FJYhPTMbfxzul9yk+CQCn8WJlmcaUfGloyjV7wJFJK/AFxva7FmL6k7RrI0OXLydm20aOmmDqBB3l+ZUPs/3l4vhHBONXvTLBLVsS2OoObHBZjDHAlXu9rtS7paGDIiJSWGS1B+sHYAnwPLDJWusEMMaUBFoCo4wxs621n+dNmCIiBZPz9Alil8zk/MZVlKwRy4l56zi9zRv/CiH4V6uMrdWZYu1fpuaoKhhfv3+cW6xxzp7z0p6k6PgkAnz+f5jelYZQGl9/CKuGT1g1wq7rSvSkFQQAA9pG4FfhRar0/oOE1T8Rv3EdSau/ge2j2f2tP46SIYyvU5N3ynfmy8MBlz2PerdERKSoyGqC1dpam3jpRmvtSeBr4GtjjI9LIxMRKYCcZ6JIXP0dfs4dHJn8Hac3nsU/3I/Aq6tg6/ehVLvRlA6rnOdxXNqT5PAyhAf7ZTiEMqOeo7Tte/bswTh88anTDJ86zQhKO8BaKnVbR9zP84hbs4rH1vwHU/FmNsVUoaZvIk8mH8cZWzXdmLJaiES9WyIiUpBkKcFKL7nKyTEiIoWOtXB8G6c+eJuYX9cQd+AcQVcVo/yg7oQ9MYwyV9+MV0jJfA0pvaIfdzaqwF+HYwAXD6E0Bkf1hgRXb0jwAymbjk74ni77ltIpdjunx+0k6o1RzHiuFUPjb+KLw8GQWkwjrXdLhTFERKQwyXaRC2PMEGvtG3kRjIhIbuRXD4dNPE/cwi+ImfcNnN5NeBMnxF5NiV49KN/xfrxLlQPAnd366RX9yK+k5aN/tQfaA1AasEe3wZ6fGLT0A84eb8Ke0AhqlvRm2C3XZTh0MLJUMWYMaKp5WyIiUuBkmmAZY2Ze/BBoACjBEpEixSYlkbx5Kd4HFrP7tXng5SC4SW2C730NbupMidQiD57Ck4p+mPCaEF6T8Ov7E/3+Iu7cN58O0ZvwmfAmL2yvwDPFH+SYX8SF49OGDmreloiIFERZ6cGKttY+lPbAGDMxD+MREfEoCasWceaz9znz+3aCIn2JGNSbyp9Pw7vqte4OLVOe2LPzyaO3AbelPDh/jhrffUi9RQf426cMTi/vlKGD15bn5S516TlpRYbztjSsUEREPFVWEqzXLnk8NC8CERFxh/SGmSUf3UviT5/hF7WQI1+dJKB2LSpNeBe/69sC4O2WSAsh32IEdH2CUZ2c/PHaPHzPHeXGwJ0M+GoER5bU5LMBz/L6zuDLFpDObFihiIiIO2WaYFlrdwMYY0pZa0+kVg4UESlcnE5i//cxp6dNIWbzcYpfX5HwF16j8oDm4KWUKi85vL0oG14aKM3LD/clceW1nP70ffbf35O+lcvxU8MnOZXsd2Eu2b0frcxxRUIREZG8lp0iF5OBznkViIhIXnl8zm78/Q9f1ruRlOzk8JHj+MWeYMTI+Ty84TsCr2lCmTHP4ChfzU3RFk0X/7/xadqd0k27UypqD/HfvsW8A/9ix8ZKRJbpitf5aulWSbyjYXm3zzUTyS8FocBLejEWhLhFXCE7CZZnzeAWEcmFkSPf48D+kxwIvRanVzg7Ysuw8NoWKqDgQUxYJAEPjqfvxK50qfo5ped8wonJUyndvy8jHnzysiqJIiIiniA7CZbNsyhERPJIUrKTA2cSSDx1nhHfbmRI0DqKHZhJ75Xb+VfVp3BeGP5nNMzMQ336SGugNSQlEP/tGBJ/+hDHV2vpc6QSy2u2Vc+VSAGgwjRSlKgHS0QKrbRiCFGxSYDh8+W7OX14K1c1vIHHZn9D4/nb2XLJMDPxYA4//Lu/gH+XJ3H+Non2f03g5uXzOV1qMKF33Yfx0oc1Kdyc1smxo7spffgnAuKi+HnKj0TXjeS8M4GQ5Vvx8vXF28eX5CrloXokARt34bBeOHz9CClXmYiaDfMlzouTqSavLSEyLJA9UbEuXXJBww3Fk2UnwXo+z6IQEckDgYkxBMUf5RglAXB6efNj1Vs4GhjKYw7fdBfjlQLAJwCvFk9Q5YYHiP3sOY69/zrm4CpCB40BH393RyeSK2fPnODAkR0cDIgl7ruFsOEvvA+fwHHmHM/1c9B+vTft1pznbIAfMfsD2RhQBxNYjLqbNkFSEiQls/dkKf6ypWk5fQPFTidgEpP4O8DyZjcvei09T6NdcLZKOP7X1qfiXfdzVdk6eBnXfEFxaZXP6PgkTsWeJ9n5z4FQF48YULIkhU2WEyxr7SZjTA9ggbU2xhgzDGgIjLTWrs2zCEVEsin5+H5OjRnCC4vW8lmP/uw6UwIwKb1UjSpcGFLmSYvxSg74hxA4YAKVu/0Ni4YT88x1RCc0JPz1d3GUKOHu6ESuyOl0sn/barae2c465x4ajpxD8aOx+CU42dAglFX9GnPjuQRKVa1KyK1tKVWtDr/UbULgfYH/SEg6pF2wQzpP0uqfD7tby6a6q3DEnyRp1Y/ErV/P0+WGUO3PY7Td4ou9piZlrr+ZWjd3ISi4ZI5f26VVPgN8vPH38b6QdGnEgBR22enBAhhurf3KGHMT0BZ4C5gIXO/yyEREsiv2JLFTXuTAxMUUu7osFSe9z0sNW/L9a4tIdJp0e6n0jWnBZ0pVh3umUazu/4h99Vl23daC8BeGE9K1G8ZodLt4hvMJsWw78Rcbdy6n+ISvCNtxHKeXYUnzyqyrfT23PTOYcpF1KVOpFg28HdwPlyVIkLu5TMYYgouHExl5PVff0B6ATsCxm3ay48c5nFr9Oyffe58+f42nQvhVtPs7iEptOlPrhtvx9s7aR8aMqnwO71CbZqOWXTZiQHOzpDDKboKV9pVEB2CitXaOMeYl14YkIpI9yYf3EPWfwQT7rsXvunZUnvIRfvVvurC/Qqgf/v7+6qUq5LzqdSJ8SnNCPvo3pz4bQUi9UtiqtyjJErc4e/4sG9cv4eQ3s3Bs3EHpfdF8dW9FQm64kXJtWlP+1Y6Uq1afdz9cSalkaNQy8y978mqR7TIR1Shz91Nwd8rj5klxrF37PadXfc6J54axKv55Dj53D+07P0Exn2KZXi+94dfpjRjI6PXktpqrhhyKu2U3wTpojJkEtAbeMMb4AfqaQUTcwnnyCFH/eYJTi/4kuH4FHEO/wrtmY7QscBEWUJyAxz4noO1CnLMGsm5eKDNvGcAbIx9wd2RSyDmdTnasXcru77/C/P4n77W3XB1YlRZOB6EP9KHqzV34T6nUYXG35Px58mOR7QBHAM2a3AlN7gTgr5ULmB+1gLljW9PrZHWuf+wVwiKqZHh+doZfZ/Z60kuW1Oslni67CdZdQDvgLWvtaWNMBPCM68MSEcmY88wJkpaMw7Htc5KPVyFy6sf41muW4fFju1QhMjIy/wIU96vRFq9//Ujgvo7cP38MJ8KjCXt0kCoNikudPRPFhkVfsKT8KZK/XUCbX2JIaliNUg/0YWa73gQWC3Xp87lrke1a17djJO3YU+0PNr39Ertu78Dvba/lxiFvUaJERJavc2mPUkav50ryqtdLxJWylWBZa2OBby56fBg47OqgRETSY+POcuqdZ4matYzQxuUp8/pCypa6yt1hiacKLsur17xNv1LjCZ77IcE3XoNfo3QmtYhkw4m4E6ya+yFm+v8I33mKmMqhVBvalxuencLzVY9hjBczuuXd0DR3Vj+NrHkdkZPmc2DHOg689xJ3zutB39IdufOGBwguXiZH17zS60mvpwrypxdPJDey24MlIpL/nMmwYQb7nxkJvsWo8PbrBLS4091RSQGQbHz4qNyTzBi9G7uwPyeW9CS07xP4hOfsw6AUTXu2/M5fsz8jeeUaRt3lTdeE2jS5ozPVO/alYcmyF44z5kS2rpuToW6eUP20wlXXUmHcHBpG7+XXkY+zZegUTnVrQfNBr1EsKKWCZ1bnP2U0NwvIsKcqu71eIvlNCZaIeC5rOTtzHKenfEz5LuGUf3cc3nU0DERyoME9EFwORjzM3h4/UnHyFPyqV8/wcE2Sl21bf2Nx/Dr4aDqNV54iqVEVSvd7gCW334+fX2Cur59XBSvyU+WQylR+81u2r15C1FsjWdyjBXHvPEe3q7rh6+2b6+tn1FOV3V4vzc+S/KYES0Q8UsIvszn62isknj5P6QH3QZ9n8db8GcmGyz9o3UypN6bh88rd7L23F5WnfYVf1Ywn6kvRc2jXRjZ8+T6+S3/HkZDE131bU6pWHwa+2weHz5UThpx8sC8sQ91qNG5Njemt2bx/NRM2fYDXiHdoNOwtqte7OUvnpzc3C8iwpyqjXjzNzxJPoQRLRDxK4uYVeP/5Pknr1hDU4jZKPDEK4x/g7rCkgLli78Ars/Ed2x3fkz9hK1XEOPRWWJSdiTrMqvmTmR6+iypz/6RBYllKDxlMgzZ3U+ajP8CXTJOrnPRGuatgRV6qU7Ex75VvyA9/v8DJvgP58dG7afHg8BxfLyfzzQpL0ioFm74OFhGPkHxkD8ce6cjue/oRHxdOsdfXUPK5sUquJMcy/KBVti4BT8/HLh3Fror/CccAACAASURBVPatiNu4yQ3RSX7rOWnFhaGficmJ/DpvEvPvacnft95K4neLuLtaD54Zt5yOH8ynUbv7srywbpqcfLAf0bE24cF+hPg76N20cr4WrMgrXl5etHpsFMETxmA++Yp3Zg0mITkhR9dK66m6OiKElzvXzbRHcMaApnS99v/nY2l+lriLEiwRca/EeJIXjGJn+3YknTtPlTnfEvjQGPDN/RwHKboy+qB1oTchrBpe902nTI2D7O//IAk7drgpUskv1jopc2gZ3zx1J7fObMnPm+fjf0MTqi5eyO3Tf6J19Xb4eftdOD5tyN/Ww9GMmLOJpGRnhtfO9H7LQHYTiIufz9PnaV19Q3saLvyZ/aUsb4/swrH92/LleQtj0ioFT6Zfzxhj2pCy/tX71to/jTH9rbUf5H1oIlKYWaeTmA9eJmnVLEq2qU+VzyfjU+dGd4clhUimw4vKNSD4iYk4xzzKwUH/psq87zDeWqa6sDkVf4qfP3+TgVP+h9MYEtvewPS2b1O+ZGS6x2dWwS4j7iyf7qmCixVnzC1j+H5pP3b06Mbp8e9So3H25kNdaYhlejyhyqJIVvq//wX0A4YZY0oCDfI2JBEp7OIWfsHRUW9gky1lnn4KOj+Ij7uDkkInSx+0rmpD6MNDCVr6NsRG4XQE4xWgYameLrMqj06nk3ULp3Jg1peMuSWarr4N2NXmXo5F3MzMgRkvSn6x7A750wf79BljuP3VT/kx8hVO9R/EitHP07TVfe4OSyRPZSXBOm6tPQ08bYwZBVyXxzGJSCHlPPgXXstHEfP1Sorf0YXQf41UgQFxv0Z98D6zn9Mj7iD6XB0q/ve/7o5Icuhk/El+mvkOYZPm4HQYAjq1ZG6X5wkKKs2No5YRf+RsphX+Mqtg52qePtTPVVo8OIJ11a5myP7xPLQmmbsa9XV3SHKRNWvWlHE4HB8BddEUoqxwApuSkpIeatSo0bFLd2blk838tB+stc8ZYx5zZXQiUvg5Tx0l6pVHOfXjJqq9fi9lpkzQHCvxLC1eIHTvH5z+ehsnxr8Pfo3dHZFkkdPpZN2CqRyePoWxt5yjTfB1VH1lGNe07M7dH67k4+m72H1iY7ZLd2vIn+td26IHH0ffwMo+dzCv4lzajZmeaYVGV9L6dhlzOBwflS1b9urSpUuf8vLysu6Ox9M5nU5z/Pjx2keOHPkI6Hzp/kwTLGvtHABjTClr7Qlr7Xt5EKeIFEbOZOJmjebAG58SWC2cKtOn4V3zWndHJXI5Ly9M90lU2Nec3TOnUaF1CQ5EVHN3VHIFyZzj6+mvUPq9WST7eOHbqQUz7xxCiRIRlx2bkwp/ORnypw/umasYUpGgD75l5YPdWXz3rdz00TcEFy/j7rAE6iq5yjovLy9bunTpM0eOHEn3j0N2ugAnuyimDBljehhjNhtjnMaYxhdtDzPG/GCMOWuMGZ/XcYhI7sV99xkJo5rhe3wp5V9/kfIzf8JHyZXks2xVWwsui6PnWCLbRnOyTBmw+pzhibavXsKtS4cRHfs8W/2jCH11GLcsW8ttg9+9LLlK+/+v0t2epUTpCtz61TKSShXnxc/u51jsZSOscqUgVFn0QF5KrrIntb3SzaWyM/nBuCacK9oE3AlMumR7PDCclHGhmjkq4sESt67i2Ignid11koin+uF3zzMEmvz48yHiArVux6fhUh5e8S6nVvpjH5yB8VEJFndLdibz64qZnHv9HUocO0f0NTUpY/7FsK7dsnR+Tof76UN63vH1C6TjxP9xaMMHfPRKD/oPmkypcnnXa5xWdj8+MTnTeXiSNV3G/1oTYM6/b8qfGvwFSHburDzPaq21W621l/1Pstaes9b+SkqiJSKeKCEGu2gE+/vdh2/lSlRb9htB9z4LSq6koLltJBX89hFiTxP1cZ4P3pArOH3iIAv/8ygDxrfmswPfkNz6Fhr9/Aerr3+GxIByWb5OTtebkrxljGFA/QHUt+VZf193zkQdzpPn6TlpBTeOWsaxmASi45OYsmIvzUYtuzAnSzyDMaZR165dq6Q9TkxMpESJEvVbtmxZPb3jx40bF3b//fdXyuy6TZo0qRkZGVm3Vq1atWvVqlX74MGDDoC4uDjToUOHqpUqVapbr169Wtu2bbswIbB58+ZXBQcHN8jouTPjaT1YuWaM6Q/0Byhfvjx79uzJ8bWioqJcFFXRpTbMPU9vQ5uchM/0d7C/LKH4PY1xTPqEc6HlOHcqGk5Fuzs8wPPbsCAoSm2Y5LT0Tx5BWKP9lP3lIINq/IhP1chcX7cotWFuHTm+k0Mfv0PF33cRX7sMd9/3ANWuvpmoqCj2HT7K30fPkJBkefLzFTx6Y1kcXpl/RImPT/mONjefCwoDT7wPrx4wkj9PP8aKezpS+e2PCShW3KXXj4+PJzYh8R/bziUkEh8fn6P7wRPbsDAICAhwbtu2LeDs2bMmKCjIzp49OyQ8PDwxvWMTE9PdnKEpU6bsuvnmm2Mv3jZ27NhSoaGhSfv27dv0wQcflHjqqacqzJ8/fxfA008/feTcuXNeH374YemcvJbsJFjP5+QJLmWMWQKUTWfX0LSCGrmRugjyBwCNGze2kZGRubpebs8XtaEreGobxi2axpHXXifRGMKff4nAtncT7O6gMuCpbViQFIU27DlpRcoCs/HF2GVq4VXByfqfovn51kiXXL8otGFurF86k+/2LWCxYztPlqhM+dkzuK7KNUDK/5v4+HgOnz1FVGxKkYrZm06yfO+5TCsCAsx5PDIvQy9QPPE+rDxhDl+81ptv97zHuHYT8fV2XXXBOY9HXlZ2v1vjirlar8wT2zA/JSY7OXAq1jc+0ek9eOafFUd1q7ffxwU9w61atTrz1VdfFe/Xr9+padOmlezWrdvJ5cuXBwE89dRT5Q4fPuyzb98+35IlSya1adPmwre406dPDx01alTE999//3dERERSVp5r3rx5xV966aVDAP369Ts1ZMiQSk6nEy8vL7p06RIzb968HH+kyXKCZa3dlNMnueQ62VvCW0Q8TuLODTjWjiXpl+WU7HEnIQNf1npWUmhcXHHOabxISD5PzA8/ENyypRujKrwSExNYMe1dEr74Cv+YBK7pfweP9xpPoE/6SznkpCKgeD5vbwf3DPucbbMe4dvh93HX6zNcen2V3c+9tDlXAAdOxfpGnUv0Bfh67cEyP247VrxCicDzkLs5Wffdd9/JF198MaJnz56nt27dGvjggw9GpSVYABs2bAhcuXLlX0FBQXbcuHFhAFOmTCk+duzY8MWLF+8oXbp0un8QHnrooUgvLy86dep06o033jjs5eXF0aNHfatUqXIewMfHh6CgoOSjR486spqgXUm2Uk1jTGNjzGxjzFpjzAZjzEZjzIbcBiEiBYPz7GlOPNeb3XfeRfy5EIJH/0Hov19TciWFxmUV57Dc5redw8+/QMKuXW6MrPA5eyaKWQvfofO3nTm2YC6Bfe6h6U9r6Nj3lXSTqxkDmjK2SxVVBCzEHF4OhrR+lfBftrJs4jDXXlvz8FwqPtHpfaXHOXX99dfHHThwwO/DDz8s2bp16zOX7m/Xrt3poKCgC3Uhli9fHjxmzJiyV0quZsyYsWv79u1bVqxY8dfy5cuDJkyYEAZg06kUa4xxSc2J7H4q+gJ4BthIygrGLmWMuQN4DygNzDfG/GmtbZu6bw8QAvgaY7oCt1lrt7g6BhFJh7UkLf+cPU++jl+54kR+/gm+16i6lhRO//imu0EEw/e+RHSPthwdOZJKk1X0IrdOHd/PivdGEDZ/JUlNK/PGi6Op16Nels9XT0ThFlKyLOXHjeXkg4+yuf711Lmxk7tDklQX90wNnvlnxa/XHiwD4GWw7eqWjRpzV4P9rniedu3anX7xxRcrLlq0aNuxY8f+kasUK1bsH/lHpUqVEvbt2+e3adMm/5tvvjk2KSmJunXr1k67zrvvvnuoSpUqiQAlSpRw9uzZ8+SqVauKAVFly5Y9v3v3bt9q1aolJiYmcvbsWe8yZcq4pEs8uwnWcWvtXFc8cXqstbOB2Rnsi8yr5xWRjCWsXMD5uW8QVD6B8i8/R0D7Pu4OSSRP/WOB2a71YNtISiwYxumoCpzftw/fSpkWrZJ0HDtziKk7plHh5c8oVqYcYR9P5PoGt2T7OjlZAFgKlurXtuTnp/vx6fIxvN6wOcX9XVP0QmX3XWdUt3r7f9x2rHh8otO7Xd2yUaO61XNJcgXwyCOPnAgNDU1u0qRJXGbzoCpUqHD+nXfe2X/nnXdWnzFjxs7GjRvH//XXXxc6YBITEzlx4oQjIiIiKSEhwXz33Xeht956awxAhw4dTk+ePDmsdevW5z755JMSTZs2jfHyck3PZnYTrBeNMR8BS4GEtI3W2m9cEo2IeIzko/s4MXwAZ1btpvQ9t2EGvE2At4YCShFUoy1m1QdUea4NRslVtu3dspKNY1/Ff8sezr95D80//45yoRXcHZZ4uJvveYY/VsFno/vw6HNf4fBxXdELyT0fby/S5ly5qucqTbVq1RKHDx+e5dWn69evnzBlypRdPXv2rDZ37ty/69SpcyFHiYuL82rduvVViYmJxul0mubNm0c/9dRTxwEef/zxE926datSqVKluqGhockzZszYmXZeo0aNau7atcs/Li7OOzw8vN6ECRP2dOvWLculkbP7aakfUAvw4f+HCFpACZZIYZGcBGs+4egrozFhkVSdNx9Hhbxb/FHE4xkDtw7DzLyfE+ssjnLlKN61q7uj8njbTm5jxTsvUGfeVkzba2kwYw6t83AhWSl8BjV4jKWvzmLh8H50GPWFu8ORPBYbG7vu0m0dO3aM6dixYwzA22+/fejifYMGDYoCogCaNWsWt3Pnzs2Xnh8SEuLcvHnz1vSeLzAw0H7//ffpTq5ds2ZNrhZPzm6CVd9ae01unlBEPFfs/E84/s7blL+jPBGTZmHKZ31ehEihVr4hFK9EYHACB8eOI6RdO7z8/d0dlUf6c+l09n84gf+29+LuVl2p+eT7XFcyvdVZRK7Mx9efRv/9gu13duW36e/QrNeT7g5JLpKbaoGFXXYHGv5ujNFsUpFCJmnneg71vImDI96keLdueD/ynZIrkUs1fZTAk3MJqFuXk1Onujsaj7Pu97ks6HQDZ4e9RsAN1zOrx1x6t3iCECVXkguly1cn+I2XWLtwKnvO7HF3OCJZkt0erJuAPsaY3aTMwTKAtdbqk5hIAeQ8exr747vYlZ/hiGhC1Unf4l28lLvDEvFMNdrBomGUuftWzv6d5aH4hd76pTP5NGoex08f5IH2rWjy4FB8/dJfw0okJ+q16M5fEU7+M+NfjOk5laDQMHeHJHJF2U2w2uVJFCKSr6zTydkpozg6YSolbqxE2Es/U6Z4RXeHJeLZvLzhhn/hu+srSt7/BYlHjuBTtuj2zmz44SsOjX2b4CMxtHmuH20e+hgfLx93hyWFVI8aPfB7+1N++uUu2n+2GFdVexPJC9m6O621e9P7l1fBiYjr2cMbOXhHE45N+oKyzz5O2LsLQcmVSNY0uAf2rSBp+2p2d+lK8pnL1sEs9DYeWsfjcx8kasQrOG69met+WMXtXQfna3I1Y0BTldwuYowxtBnzJYH7o/jxgxfdHY7IFWUrwTLGfGaMKX7R4xLGGK26KFIAJB/bz+kXe2KmdiXs7i5UXbaaoO4D3R2WSMHiWwwa9sHx9wyCWrbk1JdfujuifLPpl2/5vlszVgx5mGY123LjD6tpPegNfAM0HFDyR2BQcSLefAPnl7PZf0rf74vnym7/aj1r7em0B9baU8C1rg1JRFzJJiVyeswT7GrbhthdJ7EDVhDQazgmIMDdoYkUTE36w4aZhPXuwcnPv8AZF+fuiPLU5qjNfNPnVqIHD8W72fX0+eBH7qp5F34OP3eHJkVQrSZtOTjuSYYvH05S4nl3hyMuNmTIkLLVq1evU6NGjdq1atWqvWzZsmLujiknsjsHy8sYUyI1scIYUzIH1xCR/LJ3OdFjHuf0pkQqvDOKgBZau0ck10IioEY7/KJ/o9yoURhvb3dHlCd2bfyV36a+ySeNz/JY9/Zc22oAAYEh7g5LhN71+sKkL1iy7jHaDZvk7nDERZYsWVJs4cKFxTdu3LglICDAHj582JGQkGDcHVdOZLcHawyw3BjzqjHmFWA58KbrwxKR3EjcuZ5DvW7m7DsPEfLgs1RevEbJlYgr1e8FG2dR7KZmnP3tN2xiorsjcpkje7cwb0BHou7vT6mgcOZ1mcsdnZ5RciUew9vLm1ZPjaHkN7/w18oF7g5HXOTgwYM+JUuWTAoICLAAERERSevXr/dv06bNhRXKZ8+eHXLbbbdVAwgMDLz2scceK1+zZs3a9evXr7V//34HwP79+x1t2rSpVrNmzdo1a9asvXjx4nzvBctukYspQDfgKHAcuNNaq8VARDyEjY0h6oX72H1nTxxlyhDw4o+Y+j0K7TfsInkl0yIKkc0h+iDm5C5OfjaFM/Pm519weeTUiYO8s/pt3p5wP6ZYMaovXED7ER8S6Bfk7tBELlPhqms5N7AHh557TkMFC4muXbtGHzp0yDcyMrJu7969K82fPz+oU6dOMX///bf/oUOHHACTJ08O69u37wmAuLg4r6ZNm57dtm3blqZNm5597733SgMMHDiwUvPmzWO2bdu2ZfPmzVsaNmwYn9+vJdvD+6y1W4AteRCLiOSUtSStmon38tdIPFicyKkf41uvmbujEim8vB1wdWfYPJtS/R/myGuvE9qlM6YAlo6OPXuan8c+R4mvf8Y83YqnRsyjbLGiW35eCo5bHnqRl+LWc27vAjpV7+zucAqXl0Ibuf6aZ9ZcaXdoaKhz06ZNWxYsWBC8dOnS4D59+lQbMWLEgbvuuivqww8/LPnoo49GrV27Nuibb77ZDeDj42N79ep1BqBRo0bnlixZEgKwfPny4FmzZu0GcDgchIWFJbv8tWRC86dECriENT9wdMQzcD6eSv99j7JPtnR3SCJFQ9074fshBA4cjFdAAOd++YWgW25xd1RZluRM4rtfP6HU02NJrhpG2McTeOLaFu4OSyTLvLy86HTXC8x9fzBth7XG118VLV0mk2QorzgcDjp27BjTsWPHmHr16sVNnTo1bPLkyXs7dOhQ3d/f33bq1OmUj49P2rE2bT00h8NBUlKSx8zXKnhftYlIivhoTg2/m70PPEKxxg2oOHcVVFNyJZJvKjWFcycwJ3ZQceIEijVv7u6IssTpdPLrF28x7D+38e255QS98xodpv9EdSVXUgBdF9GEZhuT+HmS1sYq6NavX++3cePGC+VJ161bF1ChQoXzkZGRieHh4YljxoyJePjhh09kdp1mzZrFjB49ujRAUlISJ0+ezPd8J1s9WMaYp9LZfAZYY6390zUhiciV2KQkoicOI/jsbAIr3UjVOd/iiKzl7rBEih4vb6jTFTbPxtFiCNGLFuEoVZrAhp67esn6H2dx7D+j8D6fTNfBj3J9uwcxxmO+9BXJkfJPPUvMM8OJe3i4irEUYNHR0d6DBg2qFB0d7e3t7W0jIyMTPvvss70AvXr1inr//fcdjRo1ynQ+1cSJE/f17du3co0aNUp5eXkxfvz4va1btz6X96/g/2V3iGDj1H//S33cAfgDGGiM+cpaq4qCInkobslMjrw2EiwEvPUufo3buzskkaKtzh3wv8ehxRCcMWc5MX06lSZPdndUlzlwei9j14/nmglLqXRHJ5o/PBxvb80SkMLhmlvu4LvId1j6+X/o2P8/7g5Hcqh58+ax69at+yu9fb/++mtwWnGLNLGxsevSfu7Xr9+pfv36nQKoWLFi0tKlS3fmbbRXlt0uszCgobV2sLV2MCnJVmngZqCvi2MTkTRnj5H42YMceHYEJbq0I3LJWnyVXIm4X4UmkBADR7cQ2qkjCbv3ELdxk7ujuiAmJopvX+zDjg4dqB4USY8vf6PFwJeVXEmhU/Xd8bwVuoLYxFh3hyIuVqdOnau3bNkSMHDgwCh3x5JV2U2wKgEX18JMBCpba+OABJdFJSIA2PhYvCcO5fhDzfApW5bqP/1K8SfexDj04UjEI3h5pfRibf4G4+tLWL++xCxb6u6oSHImMe/bt1h32y0Ebj9ArclTGdDoUQIcAe4OTSRP1CpXj25HK7Hs7afdHYq42ObNm7euXr16W9r6WAVBdj+lfQn8boyZk/q4EzDNGFMMlW4XcalzX0/kyJjxeAX7EjLyv3BdKzRTQsQD1bkTvnkYWg6lRO/ebi/V/vuvMxm/dyrhziAeenEYV7fr5dZ4RPJLh5b9OdGnP2ceOkxoWIS7w5EiLMvvAiZlFuynwMPAaVKKWwy01r5irT1nrb03b0IUKVqS92+B6fcSN2c8pfvfBxO/xu+6Vu4OS0QyUr4hOJPgyAaMlxexa9ZwdPTofA9jz76NzOzfBq/HX2ZgcAfe6v2lkispUqpecxPH6lVg+dih7g5FirgsJ1jWWgt8a61dY60da61911q7Og9jEylSnNEnOT64Jzs73UlSSC1KfbyGkL7Puf3bcBHJhDFQqwPsWASAX7VqnJn1NYkHD+bL0589f5Z3V7zJwe69KFWsNNcs+oGbOg9UdUApkmoPHsG+neuIOR/j7lCkCMvuJ7ffjTHX5UkkIkWVtcTPn8iuVs1I2HOAKtO/wHH7cPDxd3dkIpJVVW6BXT8B4F28OCV69+boG3lbWNdpnSz8ajT/HdSSE8nRVP92Dre+8yUBYWXy9HlFPFnVa25iz8B2TNs41d2hSBGW3QSrJSlJ1k5jzAZjzEZjzIa8CEykKEhYuZD4MW3x+XsqEcMGU+Hr3/Cp1cjdYYlIdlW+EQ6uhcQ4AMIefgjvEiVwJuRN/ac/1y1kTvemBL/7Oe3a/ouRN40kvFz1PHkukYLm4ci7ufpf7xMddcTdoUg2DRkypGz16tXr1KhRo3atWrVqL1u2rNi0adNCr7766to1a9asXa1atTqjR48ulXb8+PHjw6666qo61atXr1OtWrU6I0aMCHdn/GmyW+RCdaFFXCD52H5ODBvImVU7KftQJ/wf+Q/FVDZZpODyD4Hw2rB/JVRtgZe/PxEvv0Ti0WMQGoKXv2t6pE+cPsQ7m94n4Osl3HJdM5o8+ToOP/V2i1ysasV6bLm6HL+NG0r7lz92dziSRUuWLCm2cOHC4hs3btwSEBBgDx8+7IiJifHq1atXtRUrVmytVq1aYlxcnNm+fbsvwMyZM0MmTJhQZvHixdsjIyMTY2NjzcSJE8Pc/Tog+z1Y+4DmQB9r7V7AAh6RKYoUCM5k7OpP2detDc74BKrOm0fIv0eDkiuRgq/KLbD7539sOjbmLaI+/CjXl05KSmTRhOfZ0aYNFU4annxzGc2fe1vJlUgGrn5qBGH/W0H0SfViFRQHDx70KVmyZFJaOfaIiIik0NDQ5KSkJBMeHp4EEBAQYOvXr58A8Oabb0aMGjXqQGRkZCJAYGCgHTx48ImMnyH/ZDfBmgA0Be5OfRwDvO/SiEQKqbjF0zjUozGs+4LKn35KxJQlOCpoSI9IoVHl5ssSrDJPPsmpL77g/J49Ob7sli0/81P7G/Cas5iS777JI51HUsynWC6DFSncqtVrzoa7GjBrywx3hyJZ1LVr1+hDhw75RkZG1u3du3el+fPnB4WHhye3adPmdKVKlep16tSpysSJE0smJycDsGPHjoBmzZp55MrS2f3a/HprbUNjzDoAa+0pY4xvHsQlUmgk7dnCsaGPcm7rEUr3vQMeeBUvb293hyUirlbxeji2FeKjU4YMAj4REYQ9/DBH33oLns7eAqin9u3gy98n8U3iHzx/Txda3v8C3urtFsmyVo+8xuAvetO9dk9CSpZ1dzgFyjWfXePyCeEb+2xcc6X9oaGhzk2bNm1ZsGBB8NKlS4P79OlTbcSIEQdmzJixd9WqVce+//774HHjxpVdsmRJyNdff73H1fG5Unb/UicaY7xJGRqIMaY04HR5VCKFgI07B398QNzM8XiH1qXq4i/x1sKHIoWXj3/Kmlh7l0PNdhc2l7z/PkJub8+BM2dIPnsO76Ar9z4lnT3L72OH4TdrEaHdG/L14DkU9y+e19GLFDpVQqvwwKpAfjs2lPYvaS5WdmSWDOUVh8NBx44dYzp27BhTr169uKlTp4YNGjQoqkmTJnFNmjSJ69+//8nq1atfA+ypXr163G+//RbYuXNnj6vJn90hguOA2UAZY8xrwK/A6y6PSqSAOzdrPLtuvY6YRd8T/PIiwifMUXIlUhRUuQV2//SPTcbHB5+ICJJ//Y093bsTv3Vruqfa5GR2ndnFkntac2jD7/h+OpbeQz9XciWSC7WeHEbYXM3FKgjWr1/vt3HjRr+0x+vWrQsoXbp04rx584LTtq1cuTKgXLly5wGeffbZIy+88EKFffv2OQDi4uLMyJEjPWKdimz1YFlrvzDGrAFaAQboaq1N/51CpAhyHtnOoUfuJ/7AGcL//QBB9z+dsgipiBQNVW6BeU+ku8vRuhUlwsPZ98CDhD83hNAuXYj58UfO795D7N/bObTmF57pAwNHPsJdde/F4aXhgCK5Vb3+Lcy7pjxR7w2n/YsfujscuYLo6GjvQYMGVYqOjvb29va2kZGRCRMmTNj34IMPVv73v/9d2d/f3xkYGOj8+OOPdwP07NnzzJEjRxytWrWqaa3FGMO9997rEUUusvTX2xhjrLUWwFr7F/DXlY4RKWqc0VHEz/g/9u48PKrq/uP4+5uFrIR9TYAoiIgICtEWFBfEHa1WRa37TwVqrVpra3HBpdaldau1Km4FkSrWfUNcEKWIKMi+iAhB9j0JkIQsc35/JNgQEkgykzkzyef1PHnI3Dv33g+Hm0u+c+49526St75D06NPp+OI+4lJbeY7loiEW8cjIGcV7NgEqW32jTzBggAAIABJREFUWt3szCEkHdaL7ZM/A6BoRTY/LvuWSTu/oeDaI3lt8O20TY6ID2BFGoxD7riP3069iYFFO0htkuo7jlRj4MCB+bNnz96rxvj888+XVbfNDTfcsOWGG27YUr/Jaq+mtwh+Zma/NbPOFReaWRMzG2RmY4HLQx9PJLK5QIC85+5h+aBjyPlsLoyYRrObn1RxJdJYxcZBl/6QPbXatzTJzKTV/11JTmEOD3dbysi+Sxn4h4e4+6zHVVyJ1IOu3Y7kmNQ+fDTmbt9RpJGoaYF1KlAKvGxma81skZmtAL6nbMj2R51zY+opo0hk2rCILSMGsvlfr9Lh9t/T8d+fQ7N036lExLcq5sOqyDnHhys+5Jx3zqFZQjPeOOsNjk4/OowBRRqfod3PJ330+3oWS8KiRrcIOucKKZsD60kziwdaAwXOuZz6DCcSiUo3r2XzHcNp2WYRLS66kVYDrsE02aeI7HbAsfBN1c96rN+5nr989RdWbV/FYyc8Rp82fcIcTqRx6tbnOJb0SmfrE3dw2ig9iyX1q7ajCOKcK3bOrVNxJY2NCwTIe+p2lp88iNId+diwScSe8FsVVyKyp7Y9y+bCyln106KACzBx7USGvjuUQ1odwqtnvqriSiTMOl54Ke6bub5jSCOgIYpEasCtX0Dpazex9Y31pN83iuRTf+U7kohEqpiYsuewVs2A5p3Izs3mrul3sSN/By+c8gLdWnTznVCkUep54nn8evM/OK54Jynx+56PTiQYKrBE9iGwYxtb7ria4u/n0/HOP5J57ZUQE+s7lohEug6HU7z2W8aSw9iFYxnWexgDkgbQtUVX38lEGq3EJsmcuTGDuf99kwEnXOI7jjRgtb5FUKSx2Pn6k6w4cQCF2etp89gbcOTVKq5EpEYWp7Xm4nWT+Hrd17x8xstc2vNSYk3XDxHf+uY0Y9u77/qOIdW45ZZb2nfr1u3Q7t279+zRo0fPyZMnR2VXo3qwRMpdMHo6AP8+J4PYL+6iZPIM2l4/jKYX/85zMhGJFsWBYp6d9ywTlrzM73Jy+MVlT2Mx+ixTJFJ0GHgSW//6sO8YUoVPPvkkZdKkSc3nz5+/KCkpya1bty5u165d5jtXXeiqL1LBaUsmsPzUkyjMS6LZY1+ruBKRGvt+2/dc/P7FzN88n1fPfI2zXRK2bYXvWCJSQc+Bv6DFxgJyN6/1HUUqWbNmTXzLli1LkpKSHECHDh1KMjMzi9PT0w9bt25dHMAXX3yRfNRRRx0McNNNN3X85S9/mXn00UcflJ6eftjYsWObjxgxIqN79+49Bw4ceJDP4kwFlghQtHUDLWdO4q0dXRhz+Z+Ju/xxaJLsO5aIRIHSQCnPz3+eqyZdxYU9LuTJE5+kXUo76NAH1s3xHU9EKkhISmXcbUcyO/8731GkkrPPPjtv7dq1TTIzM3tdcsklnd9///3U/W2zcuXKhMmTJy977bXXlo0YMeKAQYMG5S1dunRRYmJi4NVXX20WjtxVqdMtgmaWAhQ650pDGcbMzgfuAg4BjnLOzSxffhLwANAEKAL+4JybHMpjS+P1p7+PoXTjEia1HEDAYpmzDj55YDIzbhvsO5qIRLjs3Gxum3YbibGJvDLkFTqmdvzfyo6Hw9o50OtcfwFFZC992h3OD1Pe4fjLTvAdJWJtuP+BjlvHju2w+3WXl8YtBlh5yaWH7F7W8vLL17Ub+ae1S/sP6F26bVs8QJOuXfO7vv/e4jU3/b5L3gcftN793q6ffjKvSXp68b6O2axZs8CCBQsWffjhh00//fTTppdffnnXUaNGrd7XNoMHD85NSEhwRx11VEFpaamdd955eQCHHnpowYoVK5rU7W8fvBoVWGYWA1wIXAwcCewCEsxsE/AB8Ixz7vsQ5FkA/BIYXWn5ZuBM59xaM+sFTALSQ3A8aeRyn7yDq16ewP+ddA+BCg+gFxSH9LMDEWlgAi7Avxf/m9HzRvPrPr/mwh4XEmOVbgrpcAR8+Xc/AUWkWkeUZrBlzDi4zHeSyNVu5J/Wthv5p73uozxkyeJZlZd1n/7lvMrL0h95eGX6Iw+vrO1x4+LiGDJkyPYhQ4Zs7927d8G4ceNaxcbGukAgAEBBQcEeF9qEhAQHEBsbS1xcnIspf+Y1JiaGkpKSiL9F8DOgKzASaO+c6+ScawsMBL4CHjCzoMe7dM4tds7t1WfrnJvtnNv9j7wQSDSzhGCPJ42X21XIhl+fxaYxr9Hp4Uc5IavXT+tiDc7pq/pdRKq2evtqrpp0FZOyJ/HS6S/xq0N+tXdxBWU9WOvmgnPhDyki1TrkmCE031zIto0/+o4iFcydOzdh/vz5P/1+P3v27KSMjIyijIyMomnTpiUDvPrqqy38Jay5mt4iONg5t1e3nnNuK/A68LqZxYc0WfXOBWY753ZVtdLMhgHDANLT08nOzq7zgbZs2VLnbaVMJLahFefT4vXfUvjjNuKffI71rdO5rIPj/bmx7CpxnNS9OZf2Sgnq3AmlSGzDaKM2DJ7aEJxzTFo3iXHZ4zi307n8IuMXuK2O7K3Z1W6TEZvE+vlTKUnrrDYMAbVh8NSGZdZlNmP9G8/T89Qra72t2rB+5OXlxV5//fWd8/LyYmNjY11mZuausWPHrpw7d27iiBEjMh988MHifv367fSdsyZqVGBVVVzV5T0AZvYJ0L6KVbc5597ez7aHAg8CJ+8jxzPAMwBZWVkuMzOzJrGqFez2ElltGMjZQs7tl9J00KGk/eEJiP3fj0C3dusBePSS/r7iVSuS2jBaqQ2D15jbcMPODdz55Z3k7MrhxdNfpGvzGk4Y3CmLjJhNkHks0LjbMFTUhsFTG8KXw89mfUIBp9exLdSGoTdw4MD82bNnL6m8vEOHDjuys7MXVF7+yCOP7HELY35+/uzq1oVb2EcRdM4Nds71quJrf8VVBvAmcJlz7ofwpJWGJJC7hVVDB1O4NRbOfHyP4kpEpDqTsicx9L2h9Gnbh3Gnj6t5cQXQoXygCxGJKL36ncrypV/7jiENVFT8hmlmzYH3gZHOuWm+80j0CeRtZdX5g4lv1YwOYz/C4vceWGbC8MjruRIRf3YU7eD+r+9n7qa5PDHoCQ5rc1jtd9LxcPjy8dCHE5GgHNKyB1eOzmbz2T/QumMtPjQRqYGgerDM7CEze8rMeocijJmdY2argf7A+2Y2qXzVdUA34A4zm1P+1TYUx5RGoLQEe/Mq0vp1psOLVRdXIiIVzdk4h/PfPZ/4mHheHfJq3YorKOvB0kAXIhEnvkkiG7u1ZPGnr/uOEikCgUDA26h70ai8vQJVrQu2B+suoAS428zaOec+DmZnzrk3KbsNsPLye4F7g9m3NF5bbv0VKW0KaPGX9yEmdv8biEijVRIoYfS80fznu/9wR/87OLHzicHtMLUNNEmFbSvwcFe+iOyD9T2M3BlfwqW+k0SEBZs2berZpk2b3JiYGH0itB+BQMA2bdrUjLIppvYSbIF1BtAJSAKeA7oEuT+RkMp79h62fTqfZm+9p+JKRPbpx7wfGTl1JE2bNOU/Z/6HNsltQrPj3c9hpfYNzf5EJCTan34Or0x+jCG+g0SAkpKSq9evX//c+vXre6FPg2oiACwoKSm5uqqVwRZYxcBUYA3wuyD3JRJSu2ZMYv0//02nx+4nLkP3V4tI1ZxzvLnsTR6b9RjD+wznoh4XVT2vVV11PBzWzYGDVGCJRJJDeh/Pf+fczs78XFKSm/mO41W/fv02Amf5ztFQ1Oh/EDMbW9Vy59wbzrkZzrnVzrnS0EYTCUJhLu79W2g/4jySjj/HdxoRiVA5hTncNOUmxi8ez/OnPM/Fh1wc2uIKNJKgSIRKiE1g1Kuw+LM3fEeRBqam/4v8NIiFmX1UT1lEQibnnl/R5IjjSRuhR/dEpGpfrv2Sc989l46pHXn5jJc5qMVB9XOgjhroQiRSFfc8kI0zvvAdQxqYmt4iWPF/hRDdlC5SP7aPf4TNn6wgbeR431FEJAIVlxbz+OzHmbhiIvcefS/9O9bzFA2pbSE+mbgda4AD6vdYIlIrzbN+Rt5/XvMdQxqYmvZgtTezK8zsCEBDOErECuRuYcNjz9L+j78hpmlz33FEJML8mPcjl068lOzcbP5z5n/qv7jarW0P4nNXhOdYIlJj3Y89i7ltCgi4KkfbFqmTmhZYdwFZwGNAhpnNN7NXzOwOMzu33tKJ1NLO5/5I0oFtSD3vN76jiEiEeW/5e1zywSWc2fVMHh/0OC0SW4Tv4K0OIj43O3zHE5EaaZPejamndGRZzjLfUaQBqdEtgs65ZwDMLI2y57FigFTgMOBsQLO0iXdu7Tyaln5G6jPTfEcRkQiSX5zPX2b8hXmb5vHMyc/Qo2WP8IdofRDxy6aH/7gisl+XT4tn5Zrn6P7bv/qOIg1EbYdpnwzMAQ4BNgH/55zbGvJUIrXkSkr48bJLaXvVlSQ16+A7johEiEVbFvHHL/5I37Z9mTBkAsnxyX6CtOpG/KxX/BxbRPapVWYPCmZ96zuGNCC1KrCcc1m7vzezC4CPzexM59zakCcTqYXcv/8RV+pIPH+k7ygiEgGcc7y0+CWenfcsfzrqT5x+4Ol+A7XuTlxett8MIlKlzkefzPqX3/cdQxqQWk80bGaxQHtgOTAJ+Aw4OMS5RGrMFexk078nkvHg3VhcsHNni0i0y92Vy+3Tbmdz/mbGnzGeTk07+Y4EaR2JKd4JhXmQmOY7jYhUcGCfY/m2FWzatoY2LdJ9x5EGoFazKZrZOmArZUXV3ygbb/bdesglUmPu2/G0Ob4DSYOH+o4iIp7N2TiHoe8OJSM1gxdPezEyiisAM4rTusCW730nEZFKYmPjmHLDMczNW+w7ijQQtf24v4dzLrdekojUgdtVSNGH/6D59c/4jiIiHgVcgLELxzJm4Rju7H8ngzoP8h1pL8XNMknY/D2k9/MdRUQqGbS6GblfjoNRg31HkQagRgWWmZkrU21xtfs9oYsmsn95o+8mZ0YcXf4cprlsRCTi5BTmcNu028gpzOHlM16mY2pH35GqVJKWCZvVgyUSibq268mWlz/xHUMaiJreIviZmf3WzDpXXGhmTcxskJmNBS4PfTyR6rniIjb/+21ajxjuO4qIeDJ742zOf+98Dkg7gDGnjonY4grKerB0i6BIZOo+4AzarN1J/o4c31GkAajpLYKnAv8HvGxmBwA5QCIQC3wEPOqcm1M/EUWqtv1f9xObFE/y2cN8RxGRMAu4AC8seIFxi8Zxz4B7OK7Tcb4j7Vdxs0xYMs53DBGpQmqzVqztnIKbN4UjBpztO45EuZpONFwIPAk8aWbxQGugwDmnMl/C4oLRZRN0ThhefitgIEBqwUQS77oFi6nVWC0iEuW2Fm7l1v/eys6inUwYMoH2Ke19R6qR4rQusHU5BEohJtZ3HBGpZN6oc1mXvIUjfAeRqFfr30ydc8XOuXUqrsSn/Df/ya5tMTQ59iLfUUQkjGZtmMXQd4fSvUV3Xjj1hagprgBcfDIkt4TcVb6jiEgVslxnAhPe8R1DGoBajSJoZjdVsTgXmKVbBCWcNj8/lmZDTiPJzHcUEQmDgAvw/PznGb94PPccfQ/HZhzrO1LdtD4INi+DFpm+k4hIJT3b9Sbtg78QuCdAjO6OkSDU9uzJAkYA6eVfw4DjgWfN7I+hjSZSteIlsyhcs4Oml1ZV74tIQ5O7K5ffTv4tX6z+gleGvBK9xRVAq4M00IVIhOp4wGEUNYlhxcJpvqNIlKttgdUK6Ouc+71z7veUFVxtgGOBK0KcTQSAktIAKzbvZPG6PEa9vYCtYx4jrV8XYpq28B1NROrZwi0LueC9C+iS1iXqbgmsUuuDNFS7SATL6d6e7Gkf+Y4hUa62Ew13BooqvC4GujjnCsxsV+hiiZS5YPR0VmzeycbtZafXi9NXUhDbkQf+9GvPyUSkPjnneO371/jHt//gtp/fximZp/iOFBqtusGS93ynEJFq7Lj+V3xTmM2JvoNIVKttgfVv4CszexswYAhlQ7enAItCHU4an71GCwQKi0v3eM+ywg7EHqSJhUUaqoKSAu796l4WbVnE2NPGckCzA3xHCh31YIlEtD4d+7Hk6XFwjO8kEs1qdYugc+7PwDWUzYOVA4xwzt3jnNvpnLu4PgJK4zZheH/OPiL9p9cxrpTuSbGgwS1EGqTs3Gwu/uBiAi7A+NPHN6ziCiAtAwpzYdd230lEpArdWh/MWW+tZ9vGH31HkShWlyFSSoBA+Z/FoY0jjVnlZ61KSgMAjBrSk3ZNE2gZH+DM7GncPeIMz0lFpD58vPJjLpt4GRcefCH3HXMfyfHJviOFXkwMtOwKW5b5TiIiVYhvksjGzGYs+ULDtUvd1XaY9hso68F6nbJbBF8ys2ecc/+oj3DSOOy+LbDys1aTFqxnxm2DiYuNIbN1ChfNfZGs0h9IzHjQZ1wRCbHiQDGPzXqMT1Z+wpODn6RX616+I9Wv1t3KhmrvqOlMRSJR4LDubP36SzjvOt9RJErV9hmsq4CfOed2ApjZg8B0QAWWBK3ys1YFlV736riQjtf+OZyRRKSebczfyB8+/wPJ8clMGDKB5onNfUeqfxqqXSSiNT9/KK8sHI/ul5G6qu0tggZU/K23tHyZSJ1NGN5/r2etYg3O6fu/1y/2yaFVdj4xPRvISGIiwjfrv+HC9y5kQMcB/PPEfzaO4grKB7pY6juFiFTjsJ7HsWHtUooK831HkShV2x6sfwEzzOxNygqrs4EXQp5KGrSqRgqEsmetJi1YT0FxKef0TeeOM3r+tC735ecgsQfExIY1q4iEnnOOcYvG8cKCF7hv4H0M6DjAd6Twan0QTNMzWCKRqmmTpgz/oJTvjprEYced4zuORKFaFVjOuUfMbApwNGUF1uXOuTn1EUwan93PWgHcfdaez2Ds+GYJHe683UcsEQmhgpIC7vryLpbnLmf8GeNJT03f/0YNTatusPUHCATKBr0QkYiT36MTa6d/qgJL6qRGBZaZbQdcxUUV1jnnXFqog0nDtHukwMLiUka9vYBRQ3oSF/u/XzAq92oBFH/3NaWFpSSeeH44o4pIiK3avoobP7uR7i268+JpL5IUl+Q7kh8JTcu+dqyHtI6+04hIFVL6ZpH/2RTfMSRK1eijM+dcU+dcWoWvphW+VFxJjVwwejoDHpjMxu27yCss4cXpKzn6gcn73S5+6zd0vXUQFlfbO1pFJFJMWzONSz64hF8e9EvuO+a+xltc7dYsA3JX+04hItU4cPDZfHzwLpxz+3+zSCW6N0HCan8jBVZl679fIdDlhPqKJCL1yDnHc/Of445pd/DwcQ9z8SEXY5oovLzAWuU7hYhUo3NmbxZ2a8Kardm+o0gUUoElYbO/kQKrUroum01TNhFz8In1nE5EQm1n8U5+//nvmfzjZP59xr/Jap/lO1LkaNZJPVgiEczMuPGtUlZ+9KbvKBKFVGBJWI0a0pN2TRNIS4zjkv5d9hgpsCo7Xn+G5AObE5PWMkwJRSQUsnOzufj9i0lrksaYU8fQPqW970iRRQWWSOSLi6W0uNh3ColCeqhF6s0Fo6dTWFjI2zdk/rRsXyMFVmXHlM9petwx9RVRROrBlFVTuPPLO7nuiOs4v7sGp6lSswxY8bnvFCKyL2ZAwHcKiUIqsCRyleyiQ+9VcNkrvpOISA0EXICn5z7NG9+/weODHqdPmz6+I0UuPYMlEvHWH9SSpNYtfMeQKKQCS8KuqqHYq1I4+WVKA5mktO5Uz4lEJFj5xfmMnDqSbbu28cqQV2id1Np3pMjWrBPkqMASiWSLBx1Ax85dfMeQKKRnsKRe7J7v6octhYx6ewElpbXvYt/28ssUFh9YD+lEJJRWb1/NJRMvoXlic54/+XkVVzWR3BJKi2DXdt9JRKQafd9dSsKsJb5jSBRSD5aE1AWjpwOwYvNONm7fBcCL01cyacF6Ztw2uMb7cYEAO+b9SOff3FIvOUUkNL5Z/w1/+PwPXNP7Gn7V41cagr2mzMpvE1wDbXv4TiMiVWi2YScxuTt8x5AopAJL6kVd5rvaY/sv3iQm3kjIGhTKWCISQq9+9yr/nPNPHhj4AP071uzWX6lg92TDKrBEIpMZzmmQC6m9iLpF0MzON7OFZhYws6wKy48ysznlX3PN7ByfOaV6E4b3r9N8V5Ulkk3n61RciUSi4kAx9351Ly8tfokXT3tRxVVdaaALkYhWkhAHsRH1q7JEiUg7axYAvwS+qGJ5lnPucOBUYLSZqfctgu2e7yq1SUyN5ruqbOfnHxHT87h6SicidZVTmMOIj0ewdsdaxp8+ni5pegC8zjQXlkhE++qyI9h+rEZDldqLqALLObfYOfddFcvznXMl5S8TARfeZFJbu+e76toqkbvP6kVcLT4BcoX5rHltJa591v7fLCJhs2zbMi56/yIObX0o/xj0D5o2aeo7UnRTD5ZIRMuYt4H4Fet8x5AoFDW9QGb2M+AFoAtwaYWCq/L7hgHDANLT08nOzq7zMbds2VLnbQUKCwspKSmu9b9B7PT3iEuLZXWRQRD/fg2FzsPgqQ2D98mKTxizbgxXdb2KE1qdwKofVRjUVuXzMLEgnuYblrFe17ka089y8NSGNZf+VTbF+Wlkd8veY7naUPYn7AWWmX0CtK9i1W3Ouber2845NwM41MwOAcaa2UTnXGEV73sGeAYgKyvLZWZmBpU32O0bs7dvyCQ7O7vWbbj5ma+JPzST9mr7n+g8DJ7asG6cczy/4HleWv8ST570JL3b9PYdKartcR6mOZixSedmLam9gqc2rJkF8fEkp6ZW2V5qQ9mXsBdYzrmaj9Vd9faLzWwn0AuYGZpUEknS2m2Fwef7jiHS6O0q3cWoaaNYmbeSh/s+rOIq1NI6wvb1ECiFmFjfaURkLwZOT6VI7UXUM1jVMbMDdg9qYWZdgIOBbK+hBCib92r33Feh4ArzcWvm0eTnZ4VsnyJSe1sLt3L1pKspdaWMOXUMrRJa+Y7U8MQlQFJL2LHBdxIRqcKiM3qwPau77xgShSKqwDKzc8xsNdAfeN/MJpWvOgaYa2ZzgDeBa51zm33llPpT8PlbrJmeBsktfUcRabSW5y7nkg8u4cj2R/LXY/9KYlyi70gNV7MMyNHzbCKRqDQhnkCcepel9iKqwHLOvemcy3DOJTjn2jnnTilfPs45d6hz7nDnXF/n3Fu+swqUlAZYsXkni9flMertBZSUBj8ZX/6UiaT0zAw+nIjUydfrvubKD6/kmsOu4fq+1xNjEfXfRMOjkQRFItZhby8kbdoC3zEkCkXNKIISWS4YPZ0Vm3eycfsuAF6cvpJJC9Yz47agHrEjf84iWlw4NBQRRaSW3lr2Fo/OepS/Hfs3jupwlO84jUNzzYUlErHMcHoGS+pABZbUWWFx6R6vCyq9rrWSItI6biH5tIuC24+I1ErABXhi9hNMXDGRf536Lw5sdqDvSI1Hs06w+XvfKUSkOi74u3Ok8dG9H1InE4b35+wj0n96HWtwTt/0fWyxf4HlX9GsXwaxbTsHG09EaqiwpJBbvriFr9d/zfgzxqu4CrdmGerBEolQ6w/rQMEBVc0sJLJvKrCkzkYN6Um7pgmkJcZxSf8u3HFGz6D2t/X50WxcoMEtRMJla+FWrv7oagCeP+V5Wibq5y/sVGCJRKx1WV3Y2T24D4+lcVKBJXUWFxtDZusUDumQxt1n9SIuNrjTKX/uEpL7HxuidCKyL8tzlnPx+xdzVPujePDYB0mITfAdqXFq1glyf/SdQkSqcOib82g+Za7vGBKFVGBJRHAFOylYvZPkUy7wHUWkwZuxbgZXTrqS4X2Ga6RA35JaQGkxFOb5TiIilTTZsYuYnYW+Y0gU0iAXEpQJw/uHZD+ly6bTMiuF2LadQrI/Eana7pECHzruIY5sf6TvOGJW1ouVtwYS03ynEZGKzECjCEodqMCSiBCXt5A2Q0/0HUOkwXLO8dz853ht6WsaKTDS7H4Oq+0hvpOISAWFzZOJTdFE61J7ui9EIsKaRyewc1sr3zFEGqTSQCn3f30/E7MnMu70cSquIo0mGxaJSN+f1Zstx/XyHUOikHqw5CcXjJ4OhO62v5pygQA7l22l7ZEnhfW4Io1BUWkRI6eOZGvhVsacOoa0JroNLeI002TDIpGo46wfSWlTCsENkiyNkHqwxLvief/FYo347n19RxFpULYXbWfEJyNwOJ4+6WkVV5GqWQbkqAdLJNK0WrKB5B/W+44hUUgFlnhX+v0MmvXt4DuGSIOyMX8jV3x4BV2bdeVvx/5Nw7BHsubqwRKJSBrkQupIBZYAUFIaYMXmnSxel8eotxdQUhoI27GTElfT9qqhYTueSEO3IncFl028jFMzT+XWn91KbEys70iyL5psWCQymQEqsKT2VGAJF4yezoAHJrNx+y7yCkt4cfpKjn5gctiOv+a5zyhOOihsxxNpyOZtmseVH17J8N7Duab3NZiZ70iyP007wvZ1EAjfB1sisn/LzurDxkG9fceQKKQCSwAoLC7d43VBpdf1pXTjKnb8UETcweEdWEOkIZq6eirXfXoddw+4m3MOOsd3HKmpuCbQJAUKtvlOIiIVNNlZRKwmGpY6UIElTBjen7OPSP/pdazBOX3T97FF6BRMfpPEjklYYnJYjifSUL297G1un3Y7jw96nOM6Hec7jtRWalvYudF3ChGpoPOU72g5bbHvGBKFVGAJAKOG9KRd0wTSEuO4pH8X7jgjPGOSFi2eSXKvbmE5lkhD5Jzj+fnP8+ScJ/nXKf/i8LaH+44kdZHSFnaowBKJJGaGc7p1V2pP82AJAHGxMWS2TgHg7rPCN6leywNzcD+/NmzHE2lInHM8+u2jTF09lRdPe5F2Ke18R5K6Sm0DOzf5TiEiFekZVqkjFVjyk7BPMLyrkI1vL6SCfCzsAAAgAElEQVTtuVlhPa5IQ+Cc48FvHuTbDd/yr1P+RfPE5r4jSTDUgyUScTYd0YWExBTfMSQKqcASbwq/+pAd6xNpl9radxSRqBJwAe796l6+2/odz53ynCYQbghS2+gZLJEIk9ujI0lxSb5jSBTSM1jiTcHUj0ju1t53DJGoUhooZdS0UfyQ8wPPnPyMiquGIqUt7NAtgiKRpOtrM8l482vfMSQKqcASbwrmzSfpiL6+Y4hEjZJACSP/O5L1O9fz1OCnSInXrSsNhkYRFIk4FghAaXimrZGGRbcIijcdjtwG51/uO4ZIVCguLeaWqbeQX5LPEyc+QWJcou9IEkopbTXIhUikMQPnfKeQKKQCS7wo+XEJ+SsKSEs/1HcUkYi3q3QXv5/ye8yMx094nCaxTXxHklBLbaNbBEUiTGG7ZlhMrO8YEoVUYIkX+Z+8Qe6qVqRpCFSRfSooKeDGz24kNT6VB459gPiYeN+RpD7s7sFyTkNDi0SI9cf3xNDPo9SeCizxovDbr0nsoQmGRfYlvzif307+LW2S23Dv0fcSF6NLdoMVnwhxCVCYC0kacl8kErT9alnZhx6av11qSYNciBcF360k6cijfccQiVg7inYw4pMRpKem85ej/6LiqjFI0WTDIpEkZfU2UlZt8R1DopAKLAm/0mLaHraJ5MG/9J1EJCLl7spl2MfD6N6iO3cNuItYPQPQOKRqsmGRiGJlk7qL1JY+EpWwK/1+BrGtOxLTsoPvKCIRJ3dXLtd8dA392vXjj0f+EdPzOI1HiiYbFokoGkVQ6kgFloRd3juvUrAghY6+g4hEmO1F2xnx8QiObH8kN2fdrOKqsUnVZMMikWTN2UdRUFLgO4ZEId0iKGFXOH8+iYf18h1DJKLkF+dz7SfX0qt1LxVXjVWKJhsWiSTJa3NI+VHPYEntqcCSsCtYvoGk/oN9xxCJGAUlBVw3+Tq6Nu/KyJ+NVHHVWKW20TNYIhGk5bfLaT/jB98xJAqpwJLwyt9Ki24FJP78FN9JRCJCUWkRN352I+2T2zOq/yhiTJflRmv3XFgiEjn0DJbUgf4nl7AKrPiK5icciiUm+o4i4l1poJRb/3sryXHJ3HP0PSquGjuNIigSUcwMVF9JHWiQCwmrbS+NpyQvnnZX+E4i4pdzjvu/vp9thdt4cvCTmudKNIqgSITZetRB7NixzXcMiUL6uFTCqmDRUhL79PMdQ8S7p+c+zbxN8/j7CX8nITbBdxyJBLtHEdQtSSIRoahtc3a0S/MdQ6KQPjKV8AkEKFiVR9tjh/hOIuLVK0te4b3l7zH2tLGkNkn1HUciRZMUsBgo2gEJTX2nEWn02k+cTZuNG+EY30kk2qgHS8ImsG4RqZlxxB/cx3cUEW8+XPEhz85/ltEnjaZ1UmvfcSTSaCRBkYhhmmhY6kgFloRNzMY5dLi4v4aglkbry7Vfcv/X9/PU4KfIaJrhO45EopQ2GklQJEI4K3teVqS2dIughM3mMROI63wgzX0HEfFg/qb5jJw6kkePf5TuLbr7jiORKkUjCYpEisKM1uxMKPYdQ6KQerAkbHYuzCbuQN0eKI3P8tzlXP/Z9dwz4B76tuvrO45EslSNJCgSKbb37caqow/0HUOiUEQVWGZ2vpktNLOAmWVVsb6zme0ws5t95JO6c/m5FG4oJunYM31HEQmrzQWbufaTa7mx740c1+k433Ek0qWUjyQoIt61+HIJB3640HcMiUIRVWABC4BfAl9Us/5RYGL44kiolCz+gqSMRGJbtfEdRSRs8ovzue7T6zi729n8otsvfMeRaJDaVj1YIhGiybYdJG/c7juGRKGIegbLObcYqHIQBDM7G1gO7AxzLAmB+IJldL7+VN8xRMKmNFDKLVNvoVvzbgzvPdx3HIkWKRpFUCRiaBRBqaNI68GqkpmlALcAd/vOInWz5T/vk7+zre8YImHzt5l/o6CkgDv736mRM6XmUttqFEGRCOGaxFMaH+s7hkShsPdgmdknQPsqVt3mnHu7ms3uBh51zu3Y3y8qZjYMGAaQnp5OdnZ2nbNu2bKlzttKmd1tWPr1anYc1wYXxL9HY6XzMHjhbsN317zL1LVTefCIB1mzak1Yj11fdB4GryZtGJdbRLuctazRtbJKOg+DpzasuWX9MlnZw+hT6edRbSj7E/YCyzk3uA6b/Qw4z8z+CjQHAmZW6Jx7oor9PwM8A5CVleUyMzODiUuw2wt0Tipl6XY44IyhxCQk+I4TlXQeBi9cbfj1uq95ffXrjD9jPOmp6WE5ZrjoPAzeftuwsAW8v01tvQ9qm+CpDWsmY1Excau3k3ly5l7r1IayLxH1DFZ1nHMDd39vZncBO6oqriQy7frqQxLbJ6u4kgZv3Y513DL1Fu4feH+DK64kTBLSoLQYivKhSbLvNCKNWsrSNaQvXuU7hkShiHoGy8zOMbPVQH/gfTOb5DuTBC8peQNdRp7vO4ZIvdpVuovfTfkdl/W8jP4d+/uOI9HKTCMJikQKQ4NcSJ1EVIHlnHvTOZfhnEtwzrVzzp1SxXvucs495COf1E3Oh1MoSerqO4ZIvXHO8efpfyajaQZXHHqF7zgS7VLaaC4skUhgEfVrskQRnTlSvwKlbPpsA67VIb6TiNSb/yz9Dwu3LOSeAfdoxEAJnnqwRCLCjp/1YPFpB/uOIVEoKp7BkugVu+JbigIxxHfv5TuKSL1YuGUhT8x+ghdPe5HkeD0zIyGgubBEIoKLj6dEw7RLHagHS+pVzOwvSOzcQp/qS4OUV5THzVNu5taf30pms0zfcaSh0FxYIhEh7atFHPbmfN8xJAqpwJJ6ldo6hw4jzvUdQyTknHOMmjaKgRkDOTXzVN9xpCFJaaseLJEIYBYDGuNC6kAFltSvhfOJ7a4R1aTheWnxS6zfuZ6bs272HUUamtQ2egZLJFJoFEGpAxVYUm9cfg7rPywk0Lyb7ygiITV301yem/8cDx33EE1im/iOIw1NSluNIigSAYoO6MDaw9r7jiFRSINcSL0p+vpDYpJjiWvd1ncUkZDJKczhj5//kTv730lG0wzfcaQh0iiCIhGh6KBOrEhZ7TuGRCH1YEm9KZj+GfFd2viOIRIyARfg1v/eykldTmJQ50G+40hDldQSCrb5TiHS6KVOX0i/CRrkQmpPBZbUm6Smm0k44wTfMURC5l8L/kVeUR439LvBdxRpyJKaQ2Gunv0Q8cwKi0jMK/QdQ6KQCiypNzFbF1JyhD7ll4Zh1oZZjFs0joeOe4j4mHjfcaQhi42H2AQo2uE7iUijZhaD6YMOqQMVWFIvAuuX8cOrUJLQzncUkaBtLdzKLV/cwr3H3Ev7FD3wLGGQ1BwKcnynEGnUAimJ5DdL9B1DopAKLKkXhVPfJaFtMpagEdYk+t034z5OO+A0jkk/xncUaSwSm0OhCiwRnwqPOpSvz+/pO4ZEIRVYUi8KZn5F0sGZvmOIBO3THz9lydYl/Obw3/iOIo2JerBEvEv4YQ3dvvzRdwyJQiqwpF40id9I2qmn+I4hEpS8ojzum3Efd/W/i8Q43SYiYaQeLBHv4tdsJmO+pkyQ2lOBJaEXKCU1aSnJJ53vO4lIUB6Z+QgndDqBrPZZvqNIY6MeLBH/9Fuy1JFOHQm54iXTWf5uM0hu6TuKSJ19te4rvlz7JTf2vdF3FGmM1IMlEgFiNF2C1Emc7wDS8BROnUh82+a+Y4jUWX5xPnd9eRe3//x2Upuk+o4jjZF6sES823VkT6alreQM30Ek6qgHS0KuYM63JPXs7juGSJ09O/9ZerfpzbEZx/qOIo2VerBEvIspKiZxuyYaltpTgSUhF7NrAynHnuQ7hkidrMhdwWtLX+PmrJt9R5HGTD1YIt4lLswm691lvmNIFNItghJaRTtpfeBaOPk830lEas05x30z7uOaw66hbXJb33GkMVMPloh/BqBnsKT21IMlIbVrxkTWze0IcQm+o4jU2kcrP2JL4RZ+dcivfEeRxk49WCL+mam+kjpRgSUhVTDtUwLxGj1Qok9+cT5/++Zv3Paz24iLUee+eKYeLBHvSg/IYNGAjr5jSBRSgSUhVbBgIUm9e/uOIVJrT897mqPaH0W/dv18RxFRD5ZIBAh0bMOK3q19x5AopAJLQqpk0yaSBgz2HUOkVn7I+YG3vn+Lm7Ju8h1FpExicyjM1Rw8Ih4lfLOI00fP8x1DopAKLAmd7evpNKiApIGn+U4iUmO7B7YY3mc4rZP0SaVEiLgmEBsPRTt9JxFptMw5YkoCvmNIFFKBJSFTOO09tm04sOyhUJEo8WH2h+TuyuWCgy/wHUVkT3oOS8QvM/UiS52owJKQ2TnlU3blp/mOIVJjO4t38tDMh7jt5xrYQiKQnsMS8cqlNWVzeqrvGBKFVGBJyBQsWUbSEUf6jiFSY0/NeYr+HfpzRNsjfEcR2Zt6sES8Kj3kAP77iwN8x5AopAJLQiMQoGBVHknHnuE7iUiNfL/te9754R1+1+93vqOIVE09WCJexf2wmiM/XOk7hkQh3RMjobHlew68IIGYHn18JxHZr90DW/z68F/TKqmV7zgiVVMPlohXMdvyyFiW6zuGRCH1YElI7PpqIrviemAa4EKiwJRVU8jZlcPQ7kN9RxGpnnqwRPzSIBdSR+rBkpDInfgxltqKZN9BRPajNFDK47Mf54a+NxAbE+s7jkj11IMl4pXFxIDqK6kD9WBJSBR8v4qkowb6jiGyXxOzJ5ISn8JxGcf5jiKyb+rBEvEqcHhPXh/Rw3cMiUIqsCRornAHheuLSDx2iO8oIvtUXFrMP2f/kxv63qDbWSXyqQdLxKuY3O1kfK9nsKT2VGBJ8NbNp9PZLYhr28F3EpF9enPZm3RO68yR7TWdgEQB9WCJeBX743p+/vEa3zEkCqnAkqCVLPycJr36+Y4hsk8FJQWMnjua64+43ncUkZpRD5aIV84M0zNYUgcqsCRomydMJO8HDRYgke3lJS/Tp20fDm19qO8oIjWjHiwRr0yjCEodqcCSoBUs30jSgMG+Y4hUq6C0gLELx3Ld4df5jiJSc+rBEvHKZaYz9bR03zEkCqnAkqAENq+mKMeR8DMVWBK5Plz7IUe2P5IDmx/oO4pIze3uwdIn6CJeWGoqazM0AY3UngosCYpbNZP2p7YnJinJdxSRKhWVFvHW6re4qtdVvqOI1E5cAsTEQXG+7yQijVLMkuVcOHqp7xgShVRgSVBs4zyan36i7xgi1Xrnh3fITMnkkFaH+I4iUnt6DkvEGz2DJXWlAkuCsvbp98hbodNIIlNJoIQXFrzA0M5DfUcRqRs9hyXijxmaMVHqIqJ+Mzaz881soZkFzCyrwvJMMyswsznlX0/7zCnlnKPgx1wSfn6K7yQiVfp45ce0TmrNoc01cqBEKfVgifjTIo0fDk7znUKiUEQVWMAC4JfAF1Ws+8E5d3j514gw55IqlCydSaAkhiaHag4siTzOOZ6f/zxXH3a17ygidaceLBFvLL0DU05p7zuGRKGIKrCcc4udc9/5ziE1E8j+hpYD0svuURaJMFPXTMXhGJg+0HcUkbpTD5aINzE/ruPMV3/0HUOikLkIfHjPzKYANzvnZpa/zgQWAkuBPOB259zUarYdBgwrf3kwEEzB1hrYHMT2ojYMBbVh8NSGwVMbBk9tGDy1YfDUhsGL9jbs4pxr4ztEQxb2AsvMPgGq6m+9zTn3dvl7prBngZUApDrntphZP+At4FDnXF49Z53pnMva/zulOmrD4KkNg6c2DJ7aMHhqw+CpDYOnNgye2lD2Jy7cB3TO1XpGWufcLmBX+fezzOwHoDswM8TxRERERERE6iyinsGqjpm1MbPY8u8PBA4ClvtNJSIiIiIisqeIKrDM7BwzWw30B943s0nlq44F5pnZXOA1YIRzbmsYIj0ThmM0dGrD4KkNg6c2DJ7aMHhqw+CpDYOnNgye2lD2KSIHuRAREREREYlGEdWDJSIiIiIiEs1UYImIiIiIiIRIoy+wzOxgM5tT4SvPzG6s9J7jzSy3wntG+cobKczsBTPbaGYLKixraWYfm9n35X+2qGbbU83sOzNbZmZ/Cl/qyFJNG/7NzJaY2Twze9PMmlezbbaZzS8/HxvtaJrVtOFdZramws/r6dVsq/OQattwQoX2yzazOdVsq/MQMLNOZvaZmS02s4VmdkP5cl0Ta2Af7afrYQ3tow11PayhfbShrodSa3oGq4LykQrXAD9zzq2ssPx4yublGuIrW6Qxs2OBHcCLzrle5cv+Cmx1zj1QfoFu4Zy7pdJ2sZRNGH0SsBr4BrjIObcorH+BCFBNG54MTHbOlZjZgwCV27D8fdlAlnMumic6DFo1bXgXsMM599A+ttN5WK6qNqy0/mEg1zl3TxXrstF5iJl1ADo45741s6bALOBs4Ap0TdyvfbRfBroe1sg+2nAouh7WSHVtWLEddD2Ummr0PViVnAj8ULG4kqo5574AKo/k+AtgbPn3Yym7uFd2FLDMObfcOVcEvFK+XaNTVRs65z5yzpWUv/yKsl8wpBrVnIc1ofOw3L7a0MyMsl/QXg5rqCjjnFvnnPu2/PvtwGIgHV0Ta6S69tP1sOb2cQ7WRKM/B2H/bajrodSGCqw9XUj1Pzj9zWyumU00s0PDGSqKtHPOrYOyCxXQtor3pAOrKrxeTc3/E2hs/g+YWM06B3xkZrPMbFgYM0WL68pvK3qhmtuydB7WzEBgg3Pu+2rW6zysxMwygSOAGeiaWGuV2q8iXQ9rqIo21PWwlqo5D3U9lBpTgVXOzJoAZwH/qWL1t0AX51wf4B/AW+HM1sBYFct0n2olZnYbUAKMr+YtRzvn+gKnAb8pv81LyjwFdAUOB9YBD1fxHp2HNXMR+/60VudhBWaWCrwO3Oicy6vpZlUsa5TnYnXtp+thzVXRhroe1tI+fo51PZQaU4H1P6cB3zrnNlRe4ZzLc87tKP/+AyDezFqHO2AU2FB+D/Pue5k3VvGe1UCnCq8zgLVhyBY1zOxyYAhwsavmIUnn3NryPzcCb1J2i4cAzrkNzrlS51wAeJaq20bn4X6YWRzwS2BCde/Refg/ZhZP2S9l451zb5Qv1jWxhqppP10Pa6GqNtT1sHb2cR7qeii1ogLrf6r9ZMLM2pffe4uZHUVZu20JY7Zo8Q5wefn3lwNvV/Geb4CDzOyA8l7DC8u3E8pGcgJuAc5yzuVX856U8gdwMbMU4GRgQVXvbYx2/0Jb7hyqbhudh/s3GFjinFtd1Uqdh/9T/v/D88Bi59wjFVbpmlgD1bWfroc1t4821PWwhvbxcwy6HkptOeca/ReQTFnB1KzCshHAiPLvrwMWAnMpe9B2gO/Mvr8oK0bXAcWUffp1FdAK+BT4vvzPluXv7Qh8UGHb0ykbsegH4Dbff5cIa8NllN0LP6f86+nKbQgcWH4uzi0/L9WGe7bhOGA+MI+yXxI6VG7D8tc6D6tpw/LlY3ZfAyu8V+dh1W14DGW3VM2r8LN7uq6JQbefrofBt6Guh0G2Yfk6XQ/1VasvDdMuIiIiIiISIrpFUEREREREJERUYImIiIiIiISICiwREREREZEQUYElIiIiIiISIiqwREREREREQkQFloiIiIiISIiowBIREREREQkRFVgiIo2Yme0IYtskM/vczGKrWd/EzL4ws7ja7qtyLjO7wsye2Mf2NT6WiIhIfVKBJSIidfV/wBvOudKqVjrnioBPgQuC3df+1PJYIiIi9UYFloiIYGY3mdmC8q8bKyy/w8yWmNnHZvaymd1cYbOLgbfL39fczNZX2G6WmTUD3ip/3/78tK8aZB1hZnPKv1aY2Wflq2p6LBERkXqjWylERBo5M+sHXAn8DDBghpl9DsQC5wJHUPb/xbfArPJtmgAHOueyAZxzOWaWYmbxzrliYC7QG/gSOHI/x99jX+WSzGxOhdctgXfKj/U08LSZxQOTgUfK37Ngf8cSERGpbyqwRETkGOBN59xOADN7AxhI2V0ObzvnCsqXv1thm9ZATqX9bADaA6uAHsAG51ypmRWZWVPn3PZqjl/Vvgqcc4fvfmFmVwBZld7zd2Cyc+5dgBoeS0REpF6pwBIREavlcoACILHSsrVARzMbAGx2zi0tX54AFNZyX/tUXnB1Aa6rtGp/xxIREalXegZLRES+AM42s2QzSwHOAaYC/wXONLNEM0sFzti9gXNuGxBrZhULo7XA2cCfKBu0AjNrBWwqv22wStXsq1rltzTeDFzinAtUWL7fY4mIiNQ39WCJiDRyzrlvzWwM8HX5ouecc7MBzOwdyp6nWgnMBHIrbPoRZbcXflL+eg1wHjDIObe5fNkJwAc1iFF5X/tyHWXPZH1mZgAznXNX1+JYIiIi9cacc74ziIhIhDKzVOfcDjNLpqyna5hz7tvydUcANznnLt3H9m8AI51z3+3nOPvdVw2y1uhYIiIi9Uk9WCIisi/PmFlPyp6RGru7uAJwzs02s8/MLLaq+avKRwd8qyYFz/72tT+1OZaIiEh9Ug+WiIiIiIhIiGiQCxERERERkRBRgSUiIiIiIhIiKrBERERERERCRAWWiIiIiIhIiKjAEhERERERCREVWCIiIiIiIiGiAktERERERCREVGCJiIiIiIiEiAosERERERGREFGBJSIiIiIiEiIqsEREREREREIkagosM+tkZp+Z2WIzW2hmN/jOJCIiIiIiUpE553xnqBEz6wB0cM59a2ZNgVnA2c65RZ6jiYiIiIiIAFHUg+WcW+ec+7b8++3AYiDdbyoREREREZH/iZoCqyIzywSOAGb4TSIiIiIiIvI/cb4D1JaZpQKvAzc65/KqWD8MGAaQkpLSr0ePHmFOKCIiIiISmWbNmrXZOdfGd46GLGqewQIws3jgPWCSc+6R/b0/KyvLzZw5s/6DiYiIiIhEATOb5ZzL8p2jIYuaWwTNzIDngcU1Ka5ERERERETCLWoKLOBo4FJgkJnNKf863XcoERERERGR3aLmGSzn3H8B851DRERERESkOtHUgyUiIiIiIhLRVGCJiIiIiIiEiAosERERERGREFGBJSIiIiIiEiIqsEREREREREJEBZaIiIiIiEiIqMASEREREREJERVYIiIiIiIiIaICS0REREREJERUYImIiIiIiISICiwREREREZEQUYElIiIiIiISIiqwREREREREQkQFloiIiIiISIhEVYFlZi+Y2UYzW+A7i4iIiIiISGVRVWABY4BTfYcQERERERGpSlQVWM65L4CtvnOIiIiIiIhUJaoKrJows2FmNtPMZm7atMl3HBERERERaUQaXIHlnHvGOZflnMtq06aN7zgiIiIiItKINLgCS0RERERExBcVWCIiIiIiIiESVQWWmb0MTAcONrPVZnaV70wiIiIiIiK7xfkOUBvOuYt8ZxAREREREalOVPVgiYiIiIiIRDIVWCIiIiIiIiGiAktERERERCREVGCJiIiIiIiEiAosERERERGREFGBJSIiIiIiEiIqsEREREREREJEBZaIiIiIiEiIqMASEREREREJERVYIiIiIiIiIaICS0REREREJETqpcAys7+aWZqZxZvZp2a22cwuqY9jiYiIiIiIRIr66sE62TmXBwwBVgPdgT/U07FEREREREQiQn0VWPHlf54OvOyc2xqKnZrZqWb2nZktM7M/hWKfIiIiIiIioVJfBda7ZrYEyAI+NbM2QGEwOzSzWOCfwGlAT+AiM+sZdFIREREREZEQiauPnTrn/mRmDwJ5zrlSM8sHfhHkbo8CljnnlgOY2Svl+1xU3Qbfffcdxx9//B7LRo8ezcEHH8y7777Lww8/vNc248aNo1OnTkyYMIGnnnpqr/WvvfYarVu3ZsyYMYwZM2av9R988AHJyck8+eSTvPrqq3utnzJlCgAPPfQQ77333h7rkpKSmDhxIgB//vOf+fTTT/dY36pVK15//XUARo4cyfTp0/dYn5GRwUsvvQTAjTfeyJw5c/ZY3717d5555hkAhg0bxtKlS/dYf/jhh/PYY48BcMkll7B69eo91vfv35/7778fgHPPPZctW7bssf7EE0/kjjvuAOC0006joKBgj/VDhgzh5ptvBtjr3wVg6NChXHvtteTn53P66afvtf6KK67giiuuYPPmzZx33nl7rf/1r3/NBRdcwKpVq7j00kv3Wv/73/+eM888k++++47hw4fvtf72229n8ODBzJkzhxtvvHGv9ffddx8DBgzgyy+/5NZbb91r/WOPPcbhhx/OJ598wr333rvXep17Ovd07uncq0znns490Lmncy+8557Uv/oa5CIZ+A2w+4ztSFlvVjDSgVUVXq8uX1b52MPMbKaZzSwuLg7ykCIiIiIiIjVnzrnQ79RsAjALuMw518vMkoDpzrnDg9jn+cApzrmry19fChzlnPttddtkZWW5mTNn1vWQIiIiIiINipnNcs4F2/Eh+1Bfz2B1dc79FSgGcM4VABbkPlcDnSq8zgDWBrlPERERERGRkKmvAquovNfKAZhZV2BXkPv8BjjIzA4wsybAhcA7Qe5TREREREQkZEI+yIWZGfA08CHQyczGA0cDVwSzX+dciZldB0wCYoEXnHMLg4wrIiIiIiISMiEvsJxzzsxuAE4Gfk7ZrYE3OOc2h2DfHwAfBLsfERERERGR+lAvw7QDXwEHOufer6f9i4iIiIiIRJz6KrBOAIab2UpgJ2W9WM4517uejiciIiIiIuJdfRVYp9XTfkVERERERCJWvRRYzrmV9bFfERERERGRSFZfw7SLiIiIiIg0OiqwREREREREQkQFloiIiIiISIiowBIREREREQkRFVgiIiLy/+3de6hlZRnH8e+PUSsvZCalZdiFKCoqayq63yzKrNTuVGgWg5CQhGAxFNFf3SEKEruQRZREjmNkpKZkEV3GabQxr9VEo0vcY5gAAAnaSURBVDZqF23KMPXpj70mt7u99lm7vc7ZZ8/5fmBz9n7X+673nYd3v2c/Z71rjySpJyZYkiRJktQTEyxJkiRJ6okJliRJkiT1xARLkiRJknqyEAlWkjcluSrJvUnWz3s8kiRJkjTOQiRYwHbgBOCyeQ9EkiRJktrsM+8BdFFVVwMkmfdQJEmSJKnVolzB6izJhiRbkmy59dZb5z0cSZIkSWvIqrmCleRi4LAxhzZW1eau56mqs4CzANavX189DU+SJEmSlrRqEqyqOnreY5AkSZKkWex1WwQlSZIkaV4WIsFKcnySncBzge8l+cG8xyRJkiRJo1bNFsFJqmoTsGne45AkSZKkSRbiCpYkSZIkLQITLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9McGSJEmSpJ6YYEmSJElST0ywJEmSJKknJliSJEmS1BMTLEmSJEnqiQmWJEmSJPXEBEuSJEmSemKCJUmSJEk9WYgEK8knk1yT5Mokm5IcPO8xSZIkSdKohUiwgIuAp1TVU4HrgA/OeTySJEmS9D8WIsGqqgur6u7m5c+AI+Y5HkmSJEkaZ595D+D/cDJwTtvBJBuADc3L3UmunaGvQ4HbZmgvY9gHYzg7Yzg7Yzg7Yzg7Yzg7Yzi7RY/hkfMewN4uVTXvMQCQ5GLgsDGHNlbV5qbORmA9cEKtwMCTbKmq9cvdz97MGM7OGM7OGM7OGM7OGM7OGM7OGM7OGGopq+YKVlUdPel4khOBY4GXr0RyJUmSJEnTWjUJ1iRJXgWcAby4qv457/FIkiRJ0jgL8SUXwOeBg4CLkmxLcuYK9XvWCvWzNzOGszOGszOGszOGszOGszOGszOGszOGmmjV3IMlSZIkSYtuUa5gSZIkSdKqZ4IlSZIkST1Z8wlWkic093XtedyR5LSROi9JcvtQnQ/Pa7yrRZKvJLklyfahskOSXJTk+ubnQ1ravirJtUluSPKBlRv16tISw08muSbJlUk2JTm4pe2OJL9u5uOWlRv16tISw48kuXHo/XpMS1vnIa0xPGcofjuSbGtp6zwEkjwqyaVJrk5yVZL3NeWuiR1MiJ/rYUcTYuh62NGEGLoeamregzUkyTrgRuA5VfWHofKXAKdX1bHzGttqk+RFwG7ga1X1lKbsE8BfqupjzQL9kKo6Y6TdOuA64BXATuCXwNuq6jcr+g9YBVpi+Ergkqq6O8nHAUZj2NTbAayvqkX+jw5n1hLDjwC7q+pTE9o5DxvjYjhy/NPA7VX10THHduA8JMnhwOFVtTXJQcDlwHHASbgmLmlC/I7A9bCTCTF8M66HnbTFcDgOrofqas1fwRrxcuC3w8mVxquqy4C/jBS/Hji7eX42g8V91LOBG6rqd1V1F/Ctpt2aMy6GVXVhVd3dvPwZgw8YatEyD7twHjYmxTBJGHxA++aKDmrBVNXNVbW1ef534GrgkbgmdtIWP9fD7ibMwS7W/ByEpWPoeqhpmGDd31tpf+M8N8kVSb6f5MkrOagF8vCquhkGCxXwsDF1Hgn8cej1Trr/ElhrTga+33KsgAuTXJ5kwwqOaVGc2mwr+krLtiznYTcvBHZV1fUtx52HI5I8GjgK+DmuiVMbid8w18OOxsTQ9XBKLfPQ9VCdmWA1kuwHvA749pjDW4Ejq+ppwOeA81ZybHuZjClzn+qIJBuBu4FvtFR5flU9A3g18N5mm5cGvgA8Dng6cDPw6TF1nIfdvI3Jf611Hg5JciDwHeC0qrqja7MxZWtyLrbFz/WwuzExdD2c0oT3seuhOjPBus+rga1VtWv0QFXdUVW7m+cXAPsmOXSlB7gAdjV7mPfsZb5lTJ2dwKOGXh8B3LQCY1sYSU4EjgXeXi03SVbVTc3PW4BNDLZ4CKiqXVV1T1XdC3yR8bFxHi4hyT7ACcA5bXWch/dJsi+DD2XfqKpzm2LXxI5a4ud6OIVxMXQ9nM6Eeeh6qKmYYN2n9S8TSQ5r9t6S5NkM4vbnFRzbojgfOLF5fiKweUydXwKPT/KY5qrhW5t2YvBNTsAZwOuq6p8tdQ5obsAlyQHAK4Ht4+quRXs+0DaOZ3xsnIdLOxq4pqp2jjvoPLxP8/vhy8DVVfWZoUOuiR20xc/1sLsJMXQ97GjC+xhcDzWtqlrzD2B/BgnTg4fKTgFOaZ6fClwFXMHgRtvnzXvM834wSEZvBv7N4K9f7wYeCvwQuL75eUhT9xHABUNtj2HwjUW/BTbO+9+yymJ4A4O98Nuax5mjMQQe28zFK5p5aQzvH8OvA78GrmTwIeHw0Rg2r52HLTFsyr+6Zw0cqus8HB/DFzDYUnXl0Hv3GNfEmePnejh7DF0PZ4xhc8z10MdUD7+mXZIkSZJ64hZBSZIkSeqJCZYkSZIk9cQES5IkSZJ6YoIlSZIkST0xwZIkSZKknphgSZIkSVJPTLAkSZIkqScmWJK0hiXZPUPbByX5UZJ1Lcf3S3JZkn2mPdfouJKclOTzE9p37kuSpOVkgiVJ+n+dDJxbVfeMO1hVdwE/BN4y67mWMmVfkiQtGxMsSRJJ3p9ke/M4baj8Q0muSXJRkm8mOX2o2duBzU29g5P8aajd5UkeDJzX1FvKf8/VYaynJNnWPH6f5NLmUNe+JElaNm6lkKQ1LskzgXcBzwEC/DzJj4B1wBuAoxj8vtgKXN602Q94bFXtAKiqvyU5IMm+VfVv4ArgqcBPgWct0f/9ztV4UJJtQ68PAc5v+joTODPJvsAlwGeaOtuX6kuSpOVmgiVJegGwqar+AZDkXOCFDHY5bK6qO5vy7w61ORT428h5dgGHAX8Engjsqqp7ktyV5KCq+ntL/+POdWdVPX3PiyQnAetH6nwWuKSqvgvQsS9JkpaVCZYkKVOWA9wJPHCk7CbgEUmeB9xWVdc15Q8A/jXluSZqEq4jgVNHDi3VlyRJy8p7sCRJlwHHJdk/yQHA8cCPgZ8Ar03ywCQHAq/Z06Cq/gqsSzKcGN0EHAd8gMGXVpDkocCtzbbBsVrO1arZ0ng68I6quneofMm+JElabl7BkqQ1rqq2Jvkq8Ium6EtV9SuAJOczuJ/qD8AW4Pahphcy2F54cfP6RuCNwMuq6ram7KXABR2GMXquSU5lcE/WpUkAtlTVe6boS5KkZZOqmvcYJEmrVJIDq2p3kv0ZXOnaUFVbm2NHAe+vqndOaH8u8MGqunaJfpY8V4exdupLkqTl5BUsSdIkZyV5EoN7pM7ek1wBVNWvklyaZN24/7+q+XbA87okPEudaynT9CVJ0nLyCpYkSZIk9cQvuZAkSZKknphgSZIkSVJPTLAkSZIkqScmWJIkSZLUExMsSZIkSeqJCZYkSZIk9cQES5IkSZJ68h+8+pm91igjigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl=jet.plot_model(sed_data=sed_data)\n",
    "pl.rescale(y_min=-15,x_min=7,x_max=29)\n",
    "jet.save_model('constrained_jet.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}