11.4-filter_app_no_log-PS4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (11.4) Application of filter with new distance - 1st Tanouchi lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we use the new objective function $D_{new} = D_{HD}\\times D_{euc}$ in combination with the averaging filter designed in notebook 10. The filter width will be chosen so that the autocorrelation function of the artificial experimental lineage is $1/e$.\n",
    "\n",
    "Let's start by importing the SSA and the experimental data (only to be used to given initial volumes, molecule numbers and exponential growth rates) and processing it as was done in notebook 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"./SSA_file.jl\"); using .SSAmod;\n",
    "include(\"./extract_gens.jl\"); using .extract;\n",
    "using DelimitedFiles;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filelist = readdir(\"Analysis_MC4100_37C/MC4100_37C/\"); # take the file names - each file is for a different lineage.\n",
    "raw_data = []; # define the raw data array.\n",
    "for (i,file) in enumerate(sort!(filelist))\n",
    "    push!(raw_data,readdlm(string(\"Analysis_MC4100_37C/MC4100_37C/\", file), ',', Float64, '\\n'))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fluor_all_lins = [raw_data[x][:,4] for x in 1:length(raw_data)]; # non-separated fluor data.\n",
    "vol_all_lins = [raw_data[x][:,3] for x in 1:length(raw_data)]; # non-separated vol data.\n",
    "\n",
    "fluor_sep_all_lins = extract_gens(raw_data,4); # the fluor. data for each lineage sep into gens.\n",
    "vol_sep_all_lins = extract_gens(raw_data,3); # the vol. data for each lineage sep into gens.\n",
    "times_sep_all_lins = extract_gens(raw_data,1); # the time pt data for each lineage sep into gens.\n",
    "\n",
    "fpm = readdlm(\"MN_props.csv\")[1];\n",
    "exp_grs = readdlm(\"exp_grs_all.csv\", ',', Float64, '\\n');\n",
    "\n",
    "num_sep_all_lins = []; # sep into gens.\n",
    "conc_sep_all_lins = [];\n",
    "for i in 1:length(fluor_sep_all_lins)\n",
    "    num_i = fluor_sep_all_lins[i]./fpm;\n",
    "    conc_i = [num_i[y] ./ vol_sep_all_lins[i][y] for y in 1:length(vol_sep_all_lins[i])];\n",
    "    push!(num_sep_all_lins,num_i);\n",
    "    push!(conc_sep_all_lins,conc_i);\n",
    "end\n",
    "\n",
    "# un-sep molecule number and concs for each lineage (including 70th gen to be removed) later).\n",
    "num_all_lins = [fluor_all_lins[x]./fpm for x in 1:length(fluor_all_lins)];\n",
    "conc_all_lins = [num_all_lins[x]./vol_all_lins[x] for x in 1:length(fluor_all_lins)];\n",
    "\n",
    "v0_n0_th = []; # 2-d array to storing (1) the initial values of vol (2) molecule #  and (3) theta for each gen in each lin\n",
    "for x in 1:length(fluor_all_lins) # the lin loop\n",
    "    v0_n0_th_x = [];\n",
    "    for y in 1:length(fluor_sep_all_lins[x]) # the gen loop 1 --> 69\n",
    "        num = fluor_sep_all_lins[x][y][1]./fpm;\n",
    "        push!(v0_n0_th_x, [vol_sep_all_lins[x][y][1], num, exp_grs[x,y]])\n",
    "    end\n",
    "    push!(v0_n0_th, v0_n0_th_x)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second attempt at inference from artificial data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the experimental data of which we wish to infer the parameters of. We again introduce the lineage function created in notebook 5. We set $\\alpha_0 = 5.0$, $\\alpha_1 = 0.0$, $\\beta_0 = 1.0$ and $\\beta_1 = 1.0$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Single_lin (generic function with 1 method)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineage_data = conc_sep_all_lins[1]; # take the data for the first lineage, this gives the data form for Single_lin.\n",
    "lin_ic = v0_n0_th[1]; # pre-set params for lineage 1.\n",
    "\n",
    "function Single_lin(lin_data::Array{Array{Float64,1},1}, inf_par::Array{Float64,1}, ics::Array{Any,1}) # arguments are wrt each lineage.\n",
    "    SSA_data_c = [];\n",
    "    for (i,gen) in enumerate(lin_data)\n",
    "        ssa = SSA_deg_zero(1, inf_par, ics[i], length(gen)); # for each gen run the SSA with correct ics.\n",
    "        push!(SSA_data_c, ssa[2][:]);\n",
    "    end\n",
    "    return convert(Array{Array{Float64,1},1},SSA_data_c) # return \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the initial conditions on molecule numbers and volume from lineage 1 of the Tanouchi data, as well as the fluorescence per molecule and exponenital growth rate in each generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "expt_ssa_conc = Single_lin(lineage_data, true_par, v0_n0_th[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plots.PyPlotBackend()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using GpABC, LambertW, Distances, Plots, Distributions, LaTeXStrings, StatsBase, Statistics, LinearAlgebra; pyplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PyPlot.plot(range(1,length(vcat(lineage_data...)[:]),step=1),vcat(lineage_data...)[:]);\n",
    "PyPlot.ylabel(L\"c_{true}(t)\"); PyPlot.xlabel(L\"t\"); PyPlot.xlim([1,length(vcat(expt_ssa_conc...)[:])]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram (appropriately chosen x axis) and store the bin values as from the PDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0.9899999999999999"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = PyPlot.subplots(1,1);\n",
    "mod_bins = LinRange(0.0, maximum(vcat(lineage_data...)[:])*1.5, 100);\n",
    "true_bin_vals, other_stuff = ax.hist(vcat(lineage_data...)[:], bins = mod_bins, density = true);\n",
    "print(\"\\n\",sum(true_bin_vals*(maximum(vcat(lineage_data...)[:])*1.5/100))) # check the sum of bins is 1.\n",
    "\n",
    "true_bin_vals_form = convert(Array{Float64,2},transpose(true_bin_vals));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose $n = 4$ and plot the filtered experimental SSA lineage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 4;\n",
    "\n",
    "lin_expt = vcat(lineage_data...); filter_expt = filter_lin(lineage_data, n)[2]\n",
    "filter_expt_form = hcat(filter_expt);\n",
    "\n",
    "fig, axs = PyPlot.subplots(2, 1, sharex = true, tight_layout = true);\n",
    "axs[2].set_xlabel(L\"t\"); axs[1].set_ylabel(L\"c_{exp}(t)\"); axs[2].set_ylabel(L\"c_{filter}(t)\"); \n",
    "axs[1].plot(range(1,length(lin_expt),step=1),lin_expt); axs[2].plot(range(1,length(filter_expt),step=1),filter_expt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "simulator_function_conc (generic function with 1 method)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function that returns the SSA concentration for lineage specified abvove.\n",
    "function simulator_function_conc(var_params::Array{Float64,1})\n",
    "    lin_conc = filter_lin(Single_lin(lineage_data, var_params, lin_ic)[:], n)[2] # choose up-sep form [2]\n",
    "    return hcat(lin_conc)::Array{Float64,2}\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the new distance function that takes in the sample trajectory vector and experimental trajectory as inputs and outputs the product of the hellinger distance and the euclidean distance between the two trajectories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_distance (generic function with 1 method)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function new_distance(samp_traj::Array{Float64,1}, exp_traj::Array{Float64,1})\n",
    "    euc_dist = euclidean(samp_traj[:], exp_traj[:]); # euclidean distance\n",
    "    samp_bins = normalize(fit(Histogram, samp_traj[:], mod_bins), mode=:probability).weights;\n",
    "    exp_bins = normalize(fit(Histogram, exp_traj[:], mod_bins), mode=:probability).weights;\n",
    "    hell_dist = hellinger(samp_bins, exp_bins); # HD\n",
    "    \n",
    "    return euc_dist*hell_dist::Float64 # return the product\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC rejection simulation. ϵ = 500.0.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:47\n",
      "┌ Info: GpABC rejection simulation. Accepted 52/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 94/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 142/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 184/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 238/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 299/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 353/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 403/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 457/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 504/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 553/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 607/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 654/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 718/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 776/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 828/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 889/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 946/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC SMC simulation ϵ = 250.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 55/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 106/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 155/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 201/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 262/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 317/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 365/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 423/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 459/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 510/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 570/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 613/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 654/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 712/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 762/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 800/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 848/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 894/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 941/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 150.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 30/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 64/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 100/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 138/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 170/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 200/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 225/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 248/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 282/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 317/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 354/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 381/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 407/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 442/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 478/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 510/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 536/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 566/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 594/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 622/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 651/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 678/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 708/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 738/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 771/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 803/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 836/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 873/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 916/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 950/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 983/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 100.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 17/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 41/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 65/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 81/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 103/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 119/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 142/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 160/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 184/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 203/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 222/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 236/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 259/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 281/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 301/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 322/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 340/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 357/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 379/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 391/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 404/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 421/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 438/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 464/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 485/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 495/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 508/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 520/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 540/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 555/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 572/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 591/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 606/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 622/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 639/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 661/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 683/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 698/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 721/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 747/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 771/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 790/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 807/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 821/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 842/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 861/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 883/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 902/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 919/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 937/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 937\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 80.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 11/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 21/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 37/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 55/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 69/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 79/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 90/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 100/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 115/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 135/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 150/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 167/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 177/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 191/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 205/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 223/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 237/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 249/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 266/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 284/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 297/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 313/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 331/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 348/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 359/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 372/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 391/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 405/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 419/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 430/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 441/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 451/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 460/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 476/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 492/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 503/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 515/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 529/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 544/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 552/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 566/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 575/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 586/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 601/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 613/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 634/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 646/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 659/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 674/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 688/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 688\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 70.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 9/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 20/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 24/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 28/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 34/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 37/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 42/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 56/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 60/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 66/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 72/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 84/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 93/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 100/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 111/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 121/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 125/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 127/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 133/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 138/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 146/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 157/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 162/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 171/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 178/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 183/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 190/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 194/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 198/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 204/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 211/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 219/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 224/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 229/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 236/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 238/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 252/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 257/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 262/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 265/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 273/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 277/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 286/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 292/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 295/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 306/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 311/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 317/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 327/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 329/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 329\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 65.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 11/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 23/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 29/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 40/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 60/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 75/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 88/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 102/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 115/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 126/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 137/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 147/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 157/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 170/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 180/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 188/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 205/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 220/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 227/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 237/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 251/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 267/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 279/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 292/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 302/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 312/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 321/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 330/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 341/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 355/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 367/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 372/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 387/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 393/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 404/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 413/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 430/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 442/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 454/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 459/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 465/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 476/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 482/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 489/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 496/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 506/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 516/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 524/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 534/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 547/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 547\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 60.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 11/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 22/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 27/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 32/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 42/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 55/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 60/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 71/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 76/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 92/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 102/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 110/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 121/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 127/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 135/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 141/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 148/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 159/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 163/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 166/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 171/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 175/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 182/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 192/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 210/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 221/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 230/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 237/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 246/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 256/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 269/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 281/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 286/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 292/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 302/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 307/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 319/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 327/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 333/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 341/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 350/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 354/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 364/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 375/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 384/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 394/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 403/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 409/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 417/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 425/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 425\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_particles = 1000;\n",
    "threshold_schedule = [500.0, 250.0, 150.0, 100.0, 80.0, 70.0, 65.0, 60.0];\n",
    "\n",
    "priors = [Uniform(0, 10.0), Uniform(0.0, 1.8), Uniform(0, 10.0),\n",
    "          Uniform(0.0, 1.8)];\n",
    "\n",
    "sim_abcsmc_res_conc_1 = SimulatedABCSMC(filter_expt_form,\n",
    "    simulator_function_conc,\n",
    "    priors,\n",
    "    threshold_schedule,\n",
    "    n_particles;\n",
    "    write_progress=true,\n",
    "    distance_function = new_distance,\n",
    "    max_iter = convert(Int,5E4))\n",
    "\n",
    "# plot the results.\n",
    "population_colors=[\"#351D17\", \"#502C23\", \"#754134\", \"#935343\", \"#B86854\",\"#DA7E67\",\"#E57053\",\"#E75935\"]#,\"#E84D26\",\"#E84117\",\"#CF3008\",\"#AB2402\",\"#351D17\"]\n",
    "plot(sim_abcsmc_res_conc_1, population_colors=population_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Float64,1}:\n",
       " 8.375330738321674\n",
       " 0.2219518262930758\n",
       " 0.5439981595594789\n",
       " 0.5164750774030711"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars_conc_1 = [median(sim_abcsmc_res_conc_1.population[end][:,i]) for i in 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot an SSA trajectory using the inferred parameters and compare against the experimental SSA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.012653 seconds (23.47 k allocations: 2.215 MiB)\n"
     ]
    }
   ],
   "source": [
    "single = @time Single_lin(num_sep_all_lins[1], pars_conc_1, v0_n0_th[1]); # do for first gen.\n",
    "concs_ssa_1 = vcat(single[1:length(single)]...)[:]; expt_c = vcat(expt_ssa_conc...)[:];\n",
    "\n",
    "fig, axs = PyPlot.subplots(2, 1, sharex = true, tight_layout = true);\n",
    "axs[2].set_xlabel(L\"t\"); axs[1].set_ylabel(L\"c_{exp}(t)\"); axs[2].set_ylabel(L\"c_{SSA}(t)\"); \n",
    "axs[1].plot(range(1,length(expt_c),step=1),expt_c); axs[2].plot(range(1,length(concs_ssa_1),step=1),concs_ssa_1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inability to infer parameters here seems to arise from the indistinguishability of parameter sets with respect to this objective function of the HD. This is also shown above by the similarity of the behaviour in the concentration trajectories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hellinger distance between these is: 0.4237203056695381"
     ]
    }
   ],
   "source": [
    "fig, ax = PyPlot.subplots(1, 2, sharey = true);\n",
    "true_bin_vals_inf, other_stuff = ax[1].hist(concs_ssa_1[:], bins = mod_bins, density = true);\n",
    "ax[1].set_title(\"inferred params\"); \n",
    "true_bin_vals, other_stuff = ax[2].hist(expt_c[:], bins = mod_bins, density = true);\n",
    "ax[2].set_title(\"real params\");\n",
    "print(\"Hellinger distance between these is: \", hellinger(true_bin_vals_inf[:],true_bin_vals[:])) # print the bin values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run them again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC rejection simulation. ϵ = 500.0.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:47\n",
      "┌ Info: GpABC rejection simulation. Accepted 66/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 118/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 167/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 221/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 284/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 343/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 401/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 457/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 510/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 573/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 621/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 681/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 742/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 794/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 852/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 904/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 952/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC SMC simulation ϵ = 250.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 57/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 115/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 169/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 212/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 251/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 294/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 345/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 394/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 445/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 489/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 531/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 583/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 632/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 679/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 726/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 777/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 820/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 866/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 923/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 973/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 150.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 43/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 87/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 131/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 171/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 214/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 252/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 302/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 345/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 382/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 417/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 457/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 515/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 560/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 611/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 665/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 708/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 748/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 799/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 854/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 897/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 936/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 982/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation ϵ = 100.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 31/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 60/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 90/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 122/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 160/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 199/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 228/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 266/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 301/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 343/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 372/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 412/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 448/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 482/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 522/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 555/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 581/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 610/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 639/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 675/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 703/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 734/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 771/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 808/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 838/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 862/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 893/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 934/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 970/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 996/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 80.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 26/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 52/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 83/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 107/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 128/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 160/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 179/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 207/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 238/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 262/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 286/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 310/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 333/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 357/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 386/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 418/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 441/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 463/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 497/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 529/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 555/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 577/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 608/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 629/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 663/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 692/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 716/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 739/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 751/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 779/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 805/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 844/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 866/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 890/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 915/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 946/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 973/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 70.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 7/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 11/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 28/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 47/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 61/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 70/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 79/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 94/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 104/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 117/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 125/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 141/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 153/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 161/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 172/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 184/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 197/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 212/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 222/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 233/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 247/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 257/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 267/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 283/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 297/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 305/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 319/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 331/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 338/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 355/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 370/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 374/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 382/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 389/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 399/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 412/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 426/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 440/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 445/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 452/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 460/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 469/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 475/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 486/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 507/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 518/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 531/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 544/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 555/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 565/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 565\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 65.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 17/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 28/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 41/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 56/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 68/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 92/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 103/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 113/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 130/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 146/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 156/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 175/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 191/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 201/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 220/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 234/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 251/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 272/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 291/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 309/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 325/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 345/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 367/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 377/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 390/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 395/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 414/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 429/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 447/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 465/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 481/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 495/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 510/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 522/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 534/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 556/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 569/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 590/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 605/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 629/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 646/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 659/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 675/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 693/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 711/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 731/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 744/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 763/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 784/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 798/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 798\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n",
      "┌ Info: GpABC SMC simulation ϵ = 60.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 6/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 11/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 22/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 31/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 46/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 55/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 65/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 73/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 79/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 87/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 93/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 100/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 109/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 121/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 123/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 131/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 135/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 141/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 144/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 152/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 159/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 174/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 182/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 193/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 196/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 204/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 214/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 218/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 225/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 232/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 238/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 247/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 249/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 260/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 262/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 270/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 282/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 288/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 297/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 305/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 315/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 323/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 332/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 341/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 350/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 355/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 360/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 367/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 375/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 381/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 381\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n"
     ]
    },
    {
     "data": {
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"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_particles = 1000;\n",
    "threshold_schedule = [500.0, 250.0, 150.0, 100.0, 80.0, 70.0, 65.0, 60.0];\n",
    "\n",
    "priors = [Uniform(0, 10.0), Uniform(0.0, 1.8), Uniform(0, 10.0),\n",
    "          Uniform(0.0, 1.8)];\n",
    "\n",
    "sim_abcsmc_res_conc_2 = SimulatedABCSMC(filter_expt_form,\n",
    "    simulator_function_conc,\n",
    "    priors,\n",
    "    threshold_schedule,\n",
    "    n_particles;\n",
    "    write_progress=true,\n",
    "    distance_function = new_distance,\n",
    "    max_iter = convert(Int,5E4))\n",
    "\n",
    "# plot the results.\n",
    "population_colors=[\"#351D17\", \"#502C23\", \"#754134\", \"#935343\", \"#B86854\",\"#DA7E67\",\"#E57053\",\"#E75935\",\"#E84D26\",\"#E84117\",\"#CF3008\",\"#AB2402\",\"#351D17\"]\n",
    "plot(sim_abcsmc_res_conc_2, population_colors=population_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Float64,1}:\n",
       " 6.1294024924809944\n",
       " 0.8662682823897021\n",
       " 0.5725066657036281\n",
       " 0.16410358637144903"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars_conc_2 = [median(sim_abcsmc_res_conc_2.population[end][:,i]) for i in 1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC rejection simulation. ϵ = 500.0.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:47\n",
      "┌ Info: GpABC rejection simulation. Accepted 47/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 91/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 145/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 190/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 243/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 290/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 329/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 370/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 429/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 484/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 533/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 588/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 634/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 693/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 746/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 800/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 859/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 919/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC rejection simulation. Accepted 977/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/rejection.jl:87\n",
      "┌ Info: GpABC SMC simulation ϵ = 250.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 50/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 102/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 145/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 184/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 230/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 269/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 315/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 380/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 437/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 484/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 533/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 580/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 624/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 672/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 725/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 763/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 806/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 851/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 899/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 955/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 150.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 51/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 89/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 120/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 164/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 211/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 250/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 303/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 358/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 411/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 466/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 510/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 559/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 603/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 656/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 706/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 762/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 811/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 853/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 917/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 969/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation ϵ = 100.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 30/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 62/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 91/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 128/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 155/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 187/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 228/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 253/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 296/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 334/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 361/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 390/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 422/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 456/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 491/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 524/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 569/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 594/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 620/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 652/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 692/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 734/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 757/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 796/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 834/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 862/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 894/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 918/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 954/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 981/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 80.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 47/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 82/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 127/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 166/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 211/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 247/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 276/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 302/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 339/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 385/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 414/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 453/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 503/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 544/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 575/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 614/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 654/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 682/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 709/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 757/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 804/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 851/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 884/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 921/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 970/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 70.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 28/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 59/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 86/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 123/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 157/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 185/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 224/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 254/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 287/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 323/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 361/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 397/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 428/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 463/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 499/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 537/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 577/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 616/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 648/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 681/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 718/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 755/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 783/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 813/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 850/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 889/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 928/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 954/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 983/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 65.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 20/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 41/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 61/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 87/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 113/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 134/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 160/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 183/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 201/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 220/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 239/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 260/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 293/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 317/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 335/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 360/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 386/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 401/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 425/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 443/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 462/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 481/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 512/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 528/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 539/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 557/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 574/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 599/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 620/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 640/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 674/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 700/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 716/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 730/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 750/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 775/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 806/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 823/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 852/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 871/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: GpABC SMC simulation accepted 901/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 920/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 944/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 963/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 988/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation ϵ = 60.0\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:193\n",
      "┌ Info: GpABC SMC simulation accepted 7/1000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 11/2000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 17/3000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 27/4000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 36/5000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 41/6000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 47/7000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 49/8000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 56/9000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 68/10000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 74/11000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 86/12000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 90/13000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 102/14000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 108/15000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 115/16000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 118/17000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 126/18000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 135/19000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 142/20000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 149/21000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 153/22000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 160/23000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 175/24000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 183/25000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 190/26000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 195/27000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 204/28000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 212/29000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 219/30000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 226/31000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 234/32000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 240/33000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 243/34000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 254/35000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 263/36000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 266/37000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 272/38000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 279/39000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 289/40000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 296/41000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 308/42000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 318/43000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 326/44000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 334/45000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 339/46000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 348/47000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 358/48000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 364/49000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Info: GpABC SMC simulation accepted 376/50000 particles.\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:238\n",
      "┌ Warning: Simulation reached maximum 50000 iterations before finding 1000 particles - will return 376\n",
      "└ @ GpABC /home/s1402978/.julia/packages/GpABC/cR3wm/src/abc/smc.jl:248\n"
     ]
    },
    {
     "data": {
      "image/png": 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k5eWh1WrJzs6+8DyOwfkO/YTAEggEAoHAN/wisEaNGsXZs2eZMWMGS5YsUa4HBgYyd+5cRo8e7Y9pLj5VytBcdji4OXfu3Mljjz1WYz+BQCAQCAS+47c8WE8++SRjx45l165d5OfnEx0dzQ033EBYWJi/prj4OJahuQTSL9SKSpGoiKvL+VkEAoFAILhE8ZvAAggLC2PgwIH+HPKi4uQ+c+QySb/gFrtITH4AflxR47PYn99isaBWq5WvIILgBQKBQCDwhF8E1ltvvcWff/7J3LlzXdqeeeYZmjVrxoQJE/wxVb1z5swZTpw4QW5urtug/SuG6MTq271wh9qD4ENCQvy4MIFAIBAILn/8kgfr7bffplWrVm7bkpKSePvtt/0xTb2TnZ1N+/bt6dat2wVxlfzAxV3UxcLRHXrndNv39vfiMs4JJhAIBAJBQ+AXC9bJkydp3bq127aWLVte3GLMXpKdnc13331ne+EYl1STpedKx9GFaH8vHILgDx06hEajEaVyBAKBQCBwwC8WrLCwMDIyMty2HT9+nKCgIH9MA0B6ejq9e/cmKSmJnj17cvDgwTqPaS8Vo7jDErpAdIs6j3tF4+BC7NnrhmozxQsEAoFAcLXhF4F12223MWPGDDIzM52uZ2Vl8dJLLzF48GB/TAPA2LFjGTNmDEePHuWpp57i4Ycf9nmM7Oxs0tLS2Lt3L2lpaRcsV1erO7A22F2IAyZjEK5CgUAgEAic8IuLcO7cufTq1Ys2bdpw6623KolGv/vuO2JjY5kzZ44/piEnJ4e0tDS+/fZbAIYPH87EiRM5ceKE1/UIqxZvdkKrt309cwjyT9i+L8p2vVZTe12uXW7j6AIRCAQCgUDgjCTLsuyPgc6dO8f8+fP57rvvlDxY/fv3Z8qUKeTn53PttdfWeY59+/bx4IMPOrkFe/bsybx58+jTp49T37S0NLp168YNN9xAeHg4w4YNY/jw4WRlZdGpUyfXwSUVyNYLX2u6Vpt7GnrsBhpHpdbwc9o+mjZtqrydIiZLIBAIBFczfsuDFRUVxaxZs5TXZ8+eZdWqVQwaNIiffvoJi8Xil3kkSXJ67UkflpeXA7bC0e3atQNQgu03btxIQUEBJpNJKVJt/37u3Lk888wzTtfc9aupvaZr9nnqY2xv5/FlnOraX3vtNcxms9NhhhMnTtC2bVu/xt8JBAKBQHC54DcLFkBJSQnr1q1j+fLlfPfdd1itVrp3787o0aMZP358ncfPycmhdevW5Ofno9FokGWZxo0bs3v3bhcX4fLlyy/fkjZXCPv27aNr164XexkCgUAgEDQ4dbZgmc1mvvzyS5YvX87GjRspLy+nadOmWK1WVq9ezfDhw/2xTgDi4uLo0qULy5YtIzU1lbVr15KYmOg2/mrgwIEsW7aMxMREAgNFnNDFoG3bthd7CQKBQCAQXBRqbcHavn07y5cvZ82aNZw/f56YmBjuvfdeRowYQbt27YiKimLbtm0usVF15ciRI6SmppKfn09YWBhLly6lffv2fp1DIBAIBAKBoC7UWmCpVCokSSIlJYUpU6YwYMAApUZdYWEhkZGR9SKwBAKBQCAQCC51au0i7Ny5M/v372fbtm2oVCpycnK46667CA0N9ef6BAKBQCAQCC47ap1oNC0tjUOHDvH0009z7NgxUlNTadSoEffeey8bNmxwOe0nEAgEAoFAcLXgt1OEu3fvZvny5Xz66afk5OQgSRJ/+ctfePzxx4WbUCAQCAQCwVWFX0rlAPTq1Yu33nqL06dP8+WXXzJixAg2bdpEv379aNmypb+mqXdmzJiBJEkcOHCgXsY3GAxMnDiR1q1b0759+3pJJfHNN9/QrVs3unTpQocOHVi6dKnfxp40aRKJiYlu36P6qBMpEAgEAsHliN8SjdpRqVQMGjSIQYMGUV5ezoYNG1ixYoW/p6kX0tLS2L17NwkJCfU2xzPPPINKpeLo0aNIkkR2drZfx5dlmQceeICtW7fSsWNHJeHnsGHD/BIfd/fdd/PUU09x0003ubTZ60SmpqayZs0aHn74YXbt2lXnOQUCgUAguNzwa6LRyxmDwcAtt9zCihUr6NevHxs3bqRDhw5+naO0tJRrrrmGrKwsQkJC/Dq2HVmWiYmJYf369fTp04dff/2VwYMHk5GRgU6n89s8iYmJTu9RTk4OSUlJ5OXl1ZgEViAQCASCKx2/uQgvd/71r38xcuRIWrRoUW9z/PHHH0RHRzNz5ky6d+/OzTffzJYtW/w6hyRJrF69mmHDhtG8eXNuuukmli5d6ldx5Y7MzEyaNGmCRqNR1pGQkMCpU6fqdV6BQCAQCC5FhMACdu3axd69e/1Szqc6TCYTx48f57rrruOnn35i4cKF3HfffeTm5vptDrPZzJw5c9iwYQMnT55ky5YtjB49mnPnzvltDk94WydSIBAIBIIrHSGwgO+//57Dhw/TokULEhMTycrKYuDAgXz11Vd+nad58+aoVCpGjBgBQKdOnWjRogW///673+bYv38/p0+f5sYbbwSgR48eNGnShF9++cVvc7ijWbNmZGVlYTabAZu4yszMrNd4NoFAIBAILlWEwMIWeH769GlOnDjBiRMnaNq0Kd988w2DBw/26zwxMTGkpKTwzTffAHDy5EkyMjJo06aN3+awC50jR44AcOzYMf744w+SkpL8Noc7HOtEAtXWiRQIBAKB4EpHBLm7oWoAtz85fvw4f/vb38jPz0etVvPCCy9w1113+XWOlStXMnv2bFQqFbIs89xzz3Hffff5ZewJEyawYcMGzpw5Q0xMDCEhIRw7dgwQdSIFAoFAILAjBJZAIBAIBAKBnxEuQoFAIBAIBAI/IwSWQCAQCAQCgZ8RAksgEAgEAoHAzwiBJRAIBAKBQOBnhMASCAQCgUAg8DNCYAkEAoFAIBD4matCYE2fPh1JkpR/sbGxpKSksGPHjou9NL+ybds2Zs+e3SBz/fTTTzz00EO0a9cOlUrF0KFDG2RegUAgEAguB64KgQUQGBjIrl272LVrF++88w75+fmkpKTw22+/Xeyl+Y2GFFj/+9//2LFjB127dhXlcAQCgUAgqMJVI7BUKhW9evWiV69e3H333Xz++eeYzWaWLFlS6zFlWcZgMPhxlZcW5eXlHtsee+wxjh07xvLly0U5HIFAIBAIqnDVCKyqJCQkEBMTQ0ZGBgDz58+nR48ehIeHExcXx9ChQzl69KjTPampqXTo0IEvv/ySTp06odfr+fzzzyktLWXixIm0adOGoKAgEhMTGTduHIWFhU73JyYmMnHiRObPn0/Tpk0JCQlh1KhRVFRUsH//fm688UaCg4Pp0aOHi2VNlmXmzZtHUlISer2eli1b8vrrryvt06dPZ8aMGZSWliqu0FtuuUVpP3ToEHfeeSfh4eEEBwdz++2388cffzjNIUkSc+fO5emnnyY+Pp7Y2FiP759KddVuHYFAIBAIakRzsRdwsSgqKuLcuXM0adIEgKysLCZOnEjz5s0pKipi8eLF9O7dm6NHjxIVFaXcd/r0aR5//HGmTZtGs2bNaNasGWVlZVgsFmbNmkVsbCyZmZnMmjWLu+66i++++85p3g0bNtCxY0feffddjh8/zpQpU9Dr9ezevZspU6bQqFEjnn76ae655x4OHjyoCJnHH3+c9957j3/+858kJyezc+dOnn76aQIDAxk3bhx///vfycrKYsWKFcqcYWFhgK3+Ye/evenQoQMfffQRKpWKWbNmkZKSwpEjR9Dr9cr63nzzTXr37s0HH3yA0Wis15+BQCAQCARXLLKfMRqN8smTJ/09bJ144YUX5ODgYNlkMskmk0nOyMiQhw0bJgPy119/7dLfbDbLZWVlckhIiLxkyRLl+ujRo2VA/vHHH6udz2QyyT/88IMMyEeOHFGuN2/eXG7WrJlsMBiUa8OHD5cB+auvvlKuffHFFzIg79+/X5ZlWT527JgsSZLTWmRZlp988kk5Pj5etlgsTs9ZlVGjRsktWrSQy8vLlWs5OTlycHCwvGjRIuUaILdv3162Wq3VPl9V+vbtK99+++0+3SMQCAQCwZWMT36ed955h1atWhEYGEjXrl1ZuXKlS5+0tDRatGjhB+nnX0pLS9FqtWi1Wlq0aMHWrVtZuHAhAwcOBGD37t0MGDCA6OhoNBoNQUFBlJSUuLgJY2Ji6Nmzp8v4n3zyCV26dCEkJAStVstNN90E4HJ/nz590Ol0yuukpCRUKhW33nqr0zWAzMxMADZv3gzA8OHDMZvNyr+UlBTOnDmj9PPEt99+y5133olGo1HujYyMpFOnTuzdu9ep7+DBg5EkqdrxBAKBQCAQVI/XAuvTTz9lwoQJdO7cmX/+85/ExMQwYsQI7r///svClRQYGMjevXv56aefOHHiBHl5eUyYMAGAU6dOcdttt2GxWFiyZAn/+9//2Lt3L3FxcVRUVDiNExcX5zL2+vXrGTVqFD179mT16tXs3r2b9evXA7jcHxER4fRap9MRGBjoJLrs39vvzcvLQ5ZlYmJiFJGo1WoZNGgQQI0CKy8vjzfeeMPpXq1Wy86dO13udfd8AoFAIBAIfMPrGKxXX32VSZMm8cYbbyjXNmzYwEMPPcTAgQP57LPPCA8Pr481+gWVSkX37t3dtn399deUlJSwbt06RQCZzWbOnTvn0teddefTTz+lc+fOTicSv//+e/8sHIiKikKSJH744QcnIWanTZs2Nd5/++23M378eJe20NBQp9fCeiUQCAQCQd3xWmAdOXKEuXPnOl278847+eGHHxg0aBB9+vTh66+/9vsCG4Ly8nIkSUKr1SrXVq9ejdls9vr+qsJn+fLlfltfSkoKAPn5+dxxxx0e++l0OrdpI/r378+BAwfo0qULarXab+sSCAQCgUDgHq8FVnBwMMXFxS7Xr7vuOnbu3Mltt91G7969mTFjhl8X2BDY458eeughxo4dy8GDB5k3b56LO88TAwYMYMKECbz44ov07t2br776ii1btvhtfUlJSUyYMIEHH3yQJ598kuTkZEwmE0ePHmXr1q189tlnALRr1w6z2aycBAwLC6NNmzbMmDGDHj16MHDgQMaMGUOjRo04c+YM33//PTfffDP333+/z2vKzc1VrHS5ubmUlJSwZs0aAIYMGUJQUJDfnl8gEAgEgssNrwVWx44d2bhxI3feeadLW9OmTRVL1iOPPOLXBTYE119/PR9++CEzZsxg6NChdO7cmTVr1nDPPfd4df/YsWM5fvw4CxcuZN68eQwcOJAVK1bQq1cvv61xwYIFtGnThiVLlvDiiy8SHBxMmzZtuPfee5U+d9xxB+PHj2fOnDnk5OTQp08ftm3bxrXXXsuePXuYNm0a48ePp6SkhMaNG9OnTx86duxYq/X8/vvvLu+P/XVGRoZIPioQCASCqxpJlmXZm45Llixh1qxZ/Pzzz0RHR7vtU1payrBhw9i0aRNWq9WvCxUIBAKBQCC4XPBaYAkEAoFAIBAIvEPUOxEIBAKBQCDwM0JgCQQCgUAgEPgZIbAEAoFAIBAI/MxVW+xZcHnw8ccf+9R/1KhR9bQSgUAgEAi8RwS5Cy5pVCpnI6s907zjtnXMPm+xWBpmYQKBQCAQVIPPLsKKigrCwsL4/PPP62M9AoETubm5yr9du3aRkJDAM888w759+8jKymLfvn08/fTTJCQksHPnzou9XIFAIBAIgFq4CAMCAggODnZbE08g8DeOOdfuu+8+xowZw7PPPqtca9KkCV26dCEkJITnnnvOrxn0BQKBQCCoLbUKch89ejTvv/++v9ciEFTLzp07PRbs7t69O7t3727gFQkEAoFA4J5aBblHRkayc+dOOnXqxKBBg4iLi3OKg5EkiSeeeMJvi6wNZWVlHD58mLZt24q6eFcIcXFxrFq1igEDBri0/ec//yE2NvYirErsNYFAIBC4Uqsg96qBxy6DStJFDzZOS0ujW7du7Nu3j1atWhEeHs6781/lg7dedynjo5IgQK+nz60pHPxxJxazGVmWKagwABARoEeSJGRZpthkYcLzLzJspO202mdLP2D5m69iNplQqdScL6/AarUo92i0Wv7+zL8YeO/9fLN6Je/NfRGzyeR0HaDwXD4njx5h5YcfsOWrjcraBt45jJfeetvr53Y3R8+UAaQfOkTrdu2IjI6hsLCQ8PBwt/evW/Yx86dPw2Q0otXpmDp9pvKs3lDd2HXl3//+N2PHjqVv37785S9/IS4ujpycHNavX8/27dtZsmTJRamF6bjXunbtqlz39F5kZ2eTnZ0NQExMDAkJCS5tVa87Uh/vcX393C63cQUCgcBfXLGnCB0/9CLCwnhp8gROHj5IhdmE2UFfBek0TgLK0RJnf2uqXss3mPl89z5UksTf+9+E2WSq9h61RsOM9z9h+t9HOfXV6nT8e9MOdm/+VhFFdmFXZjQr/VZt2U6L1kk1PnPhuXyX9ajUanJKysFiQVarmTJ9JrcMGUpUVJTL/RnpR3lg4K1YzBfm1un1fLF7H5HRMTXOD3Du3Dm3Y/uLjRs3MmvWLPbt24fZbEaj0dC1a1f++c9/cscdd9TbvNXhSWC5ey+ys7Np0qSJ8jogMIgjhw+RkJDg1OZ4vSr18R7X18/tchtXIBAI/MUVn2h0+zdfMn5of86fOEZ4oJ64kGCigwJQSTbLlV1cgbMosr92d002mzl2+DAH9+11EjOe7rGYzTx+/z0ufU1GI3u//45/z5mhtEmSRESAHpXDEP/7zn3g9vn8PPb8sIPz+XkAnDx6xGUOq8VCdICWmJBAYgK0vD3zBQrOnXMZa92yjxlRRVwBGA0Gftm7h19376TwXL7bddjXsuXLjezY/K2ynvpg6NCh7Nq1i4qKCrKzs6moqGD37t0XTVz5it1yxYOL4eGPqCgvIy8vz7ltwGSn6wKBQCC4/Kh1olGTycT777/P3r17yczMZNGiRbRu3ZpVq1bRsWNH2rVr58911gqNSuLTd98mNjjQSUQFaDXEa4IpMZpcxJAjnixYqFSk79/H+vcXe7yv6j1lhgpCtMHKdbUkoddqeOXZJwnWOv8YJElCo1JjrHSzlpeVucyxbtnHvPni8+glMMjw+L9eImXI7Wi0WheLmuOzh2hUHNq/j5bXXgvYhFHaj7uZ/8I/MVcRVwBhQYEsem6q4nLsf8/93DtmvJNFa92yj3nl+WexVq53rlrNky/N8cm16Ct2N7TVaq3RZe2JiooK7rvvPg4ePEhQUBDx8fEsXryYxMREANLT0xk9ejR5eXlERETw0Ucfcd111/k8j6PbTyGhi+cb4tv6PIdAIBAILi1q9cl0/Phx2rRpw5NPPsmRI0fYsmULxcXFAGzfvp1XXnnFr4usLSE6HXq12q2IkiSJEJ2W6jykVZNa2sVKTKCO1e8scLEWOd7neE+J0davxGhCUqkIC9CRGB3ONeEhdGwcQ5jeOeWFLMtYZQt6tRqVBEvfXuBkFTr5Rzor58+ixzWxdL4mjh7XxLJ83iysskz/e+53WW/VtWnVNkG3btnHDE3uyrPj/o7JzbNotVrCA3TKc5pNJr5avpQ7kruybpktw3pG+lFedRBXYEv2OX/6NI/va1345ptv6NWrFwEBASQkJPDrr78CMGbMGJYvX+7zeGPGjOHIkSPs37+foUOHMmbMGKVt7NixjBkzhqNHj/LUU0/x8MMP+zy+3e3XrVs32rRtd8FKJRAIBIIrmloJrEmTJhEbG0tGRgbbtm1zEil9+/Zl+/btfltgXVCroMJs8iii3Lnzquvj+NWb++zzhup1xIUEE6rXIVmtxIYEoaq8XyVJxIYGKT8IlVpNmclMXEgwMSGBxIcGo5EtHDt8GLAFsT95z//RPCLUaYwWEaH8vu8n7h0znrwKE/ml5ZwvrXB5dpVajdFiJiP9qBLM7g6NVssLr853Ek7Ke2Cx8NqM51m2+G1bzJabAw2exq0LK1euZMiQITRv3pwFCxY4HVZo1aoVH374oU/jBQQEMGTIEOVn2atXL44fPw5ATk4OaWlpjBw5EoDhw4eTkZHBiRMnfJqjqtuvoKDApc+hQ4c4deqUT+Pax05LS2Pv3r1OX2szlkAgEAj8S61chNu2bWPlypXExMS4fLjGx8dfMn+lW6xgtkKFyUyAVuNWFLmz8viLqqkrAPRajSKM7Kgq3YVlRhMTX3yZN5+b6iTmIgL0/PrTjyS1a8d7c19Ei+w6hkoiRK8F4IYbbyR93x6HwH0VsmxFpVZTUG7gxSmPo9Fo3LoEwRbYPuWFl2jTqQtqjcYpLkuWZcxWC1aDhUWvzHaJ2bKjrYdEtC+99BKTJ09m/vz5WCwWHn30UaWtffv2vP7663Uaf8GCBUosV2ZmJk2aNEGjsf2KSJJEQkICp06dUlyIVZk4cSLh4eEMGzaM4cOHc/78eQoLC22NlW6/kpKSCzcU26ySI0eORB8YxNIPP3Aar7CwkHNu4uXOnz/PmTNnaN++vdt16AOD2LN7F02bNvX62c+fP+91X19oiHFFsLtAILgUqZXA0mg0Hq1CZ8+eJSQkxOcxJ02axOeff87Jkyf57bff6NChg9t+iYmJBAQEEBAQAMCzzz7LX//6V7d9K8w219a5cgMhFgthDgHtdupLXHnCaLZglZ0FklWWMZgtSJJE4bk8t2tc++93uDY6Ao0sY7S4jiED+/f/wsSHHyImQOsk0NQaNWOen8WMJ6dirLQsuRNXWp2OmQvfoXOPZLZ+9SUPDExBi9XplGVBhQGrjEuslyMqlYqp02fW9a1y4fjx4wwZMsRtW3Bw8AUxUwtmz55Neno6ixdfiKur+nOo6cDtwoULnU4RAi5rcvrdKKl0+w6YjGHTGy4/k/DwcI/iQbGkJT8AP6648NVhLF+FR30JlcttXIFAIPAHtXIR9u3bl/nz5zvF7dg/gN99911SUlJ8HvPuu+/mhx9+oHnz5jX2XbNmDfv372f//v0exRWgxBoBlJncW1p8wR8ZLSyyTG5xGdbKsayyTG7JhdfNr72WIJ0WtcOHe7tG0Uy4sRMV//uaB3u2J6lRlNM9VqvM0ZxzvD3/FSSLxUUYmE0mzuefU8SVIxqtzeql0+uZOn0m/QbdDqC4D8uMZs4Ul3Ku3EhehYkyoxmdXs/4p571aKVSazT0G+xeCNWF+Ph4Dle6Sqvy66+/erV33DFv3jzWrVvHV199pSQKbdasGVlZWYrokWWZzMxMj7mp6oSHoHZPrsMzZ85w6NAh24voROevIkBeIBAILglqZcF6+eWX6d27N+3atePOO+9EkiQWLVrEgQMHSE9PZ8+ePT6P2adPn9ospVpMlguiKkSvq5O1qrauRPt9jl9PFhSRWVBMfFgwRrMFS6VQCgvQsWrGM1wTHqIIL6PZyk2tmqKuPCmnVqm4qWVTPtnzOxl5BRitVs6VVmCqjEcyWS0ua9XqdPTql4J27iyn2CidXs+yrzaTe/YsMXFx5J49y/n8PNIPHXLqZ5Wh3Gjk5SXvExIWzrVt2xIZHUNQcAjzXvin2/QTxw4fpseNN/n8flXHAw88wPTp02nbti233HILYBP2Bw4c4JVXXnFyGXrLa6+9xsqVK9m8eTMRERHK9bi4OLp06cKyZctITU1l7dq1JCYmenQP+pUym9Vr5MiRLvmwsrOzPboGBQKBQHDpUCsLVtu2bdm3bx833ngjK1euRK1Ws3HjRq699lr27NlDq1at/L1OJ0aMGMH111/P3//+d3Jzcz32C9bZrDMqCUIqv68tpQb3wfKyLHu0bMmyTLHB4CSuiiqTiJaZTJQZTYq4UksScSFBSlC5SpKIDQkiLjRIEVd21CoVgXoNuSXl5JaUKeIKbGKooMKgrEmj1XLfY1MIiYxiXKXVSSVBUICe1ImTCIuK5vDvvzFyUAoTH7iHoT27sH/vj2i1zu+XVqcjMaktrdpdhyypOHfuHLcMGcrbq9ahVqtd+rauhzQd06dPp3fv3gwYMID4+HgABg8eTKdOnejevTvPPPOMT+NlZWUxdepUCgoK6NevH507dyY5OVlpX7JkCUuWLCEpKYm5c+c2XP3N8kq3opt8WEp8Y/IDDbMWgUAgENSKWufBatGiBUuXLvXnWrxi+/btJCQkYDKZmDZtGqNHj+bLL79021dX6SLUqdynavCEuzxWxUYjJtnqlJgULrhG3VmqykxmQvXOiUzDAvSUmcyKELKPF+gmPkwlSSDbXIAqh8yjFquMhER0SKDbzO9lRjMVJjMDBg1m+P0juLZzN0IiInlwzKOEaDUse3MeVouFb5a+R3RYGItfmYPJZLIlX7Va+eTtt3j0yWdZPP9ljAaDEvTewo1wjoqK4smX5vDajOcxGgxKeZ2IqGiv329v0el0bNiwga1bt7Jp0yby8vKIioqif//+9O/f3+fxmjZtWq3bt02bNuzatasuS64b1bn77C5BgUAgEFyS1Epg3Xrrrbz99tu0bev6AXD06FHGjRvHd999V+fFucPuKtFqtUyePJmkJM8lZM6Xl9M0IgR88OxV94FrMlsxW60EaDSK5Qlswqmw3IDJYuWmAQOJjoxg3aoVqCU1YXodOo1acQU6JhGtMJkp02pJ6X8bwx8Zx3v/GI+1SqCzVqPicE4+beKiUaskLFaZIzn5FJRVKHNHBOipqBRtdppEhmPOOMKKF59FrdVy58R/0O7Gvvxn4WvoVRJGq4TZZGL5gvlYTEaXkkGl5/P5Yvc+jh0+rLgEPTFs5Cj6DR7CscOHiW4UryQx9TenTp2icePG9OvXj379+jm1mc1mTp8+XT8xUn4kIyOjVm0CgUAguLyolYtw27ZtFBUVuW0rKiqqtzxYpaWlTnmEVq5cSZcunjNiV5itJLZug7kyLqkm1JJEoFaDpopLTpIkEqPCuaFFE66LjyExOpywgAsB3rIsEx6oJyYkkFv6D+Dhp55DUmuJCQkgMTqcphGhyj2yLCNpNAz9v7/QLj6WjvHR5P+exr+nPEqL612fRSVJ7D15mh3HTpGWeYYdf5xif9ZZJcDdvr7bhv6fEnQeFBhA65gILJWnKC0mE58vnM+X771Ns7Bgp/VYzGYC9XqXkkHfrlqGSpK4tm1b0g8dcil/U15YQOavaZQX2n4ekdEx9LjxJiLq8WRXixYt+Pnnn922/fLLL7Ro0aLe5q4zlXFVzz//vG9tAoFAILgsqXUtQk8ut507dxIXF+fzeBMmTKBp06ZkZWXRv39/rnWwggwZMoSffvqJs2fP0q9fPzp27Mj111/P999/z8cff1ztuDcPHIzGCxehPbt6s8gwFwGlliSaVknsGRsSpJz0cxz7vbkvsu6Tj5BkmVYxkeg1aiKDAtBr1MSGBNH55lt4e+Uaft/1PdFBemVM2Wrhj/37nIQT2E4ZVpgspOee59c/c/jp1BlKHatVYxN4bTt1ZeOPaSxauYbXl/wb2ercx2wysv/bjS7PoNVoGXTnMJf3x2qx8MnrL/Pk/w3g2YdGMNQhe/uBbzfy4dj72fDiU3w49n4OfLux2vfWX1Qnkg0GA3q9vkHWUSvscVXuYqeqaxMIBALBZYnXLsI5c+YwZ84cwCYo+vXr51IDzmAwYDabGT9+vM8LWbRoEYsWLXLb5hhj5cmC4YmjB361JYmqBnWl2KgqPkoNtiB0nUbtNjloiF5HoFZDQXkFFWZbcLrZZOLDN18nSKehWUQoSXHRqFRSZSqFfLZs38rBH/9HgErlMiaylbySMmKCg2z3VKZ0kLGld7CYLQTrtHS9JYU9m752yk319qtz6HfbbcSHBiGrw9w+Z1XRpZIktCqIiol1iTtLTmzCrfJ5BvZPxmSx8PGPv/HajOe54aab2P7BIqyV1jGr2cT2DxbRKvkmAsMjavhp+M7hw4c5ePCg8nrbtm1kZWU59amoqGDlypW0bNnS5/Gry7/mS841r6kudqoB46oc6yNe6m5VgUAguBzxWmD17t2bqVOnIssyL774Ivfff79LpmidTke7du2UbNgXG41KYu/270F2TV3giCcBpdOoKTeZ3SYHDdZp6ZYQfyH1wrlC/sgrUDKdy7JaEVdgy7SeFBeNRqVGlmyB6+fKyikzOhdmjgsNxirLnC4sptxoxlkS2cTtjq++pHFEMNkFpZwvL8cqQ+vYUNY9/SiyxYKkUhOi11FicM575S54X6dW8eWyD5T3KzRAj9Vq5S+d2yqnF7VqNam9OvLzqdNsWbVcEVd2rGYTO1Z9zG1jJnnxU/GNVatWMWPGDOXZPZ0UjIiI8LlUDtjyrz311FPcdJP7lBJr1qzxmPT2csVeHxFQ0kDUJjmwQCAQCDzjtcDq27cvffv2BWwfdI888ojyn/SlSqBWi9VqQVVDkHt12dUBzFYrp84XkRARpgimqOBAp5il5lHh/FlQTLnJTIheR1xokNPJP7CJrACdhnKTGZVKIio4UBFYjuJHJUlcEx5KmcnE2eIyrFZZCZTv3rwx/9epDVq1WrEs/e+PTHo2b4xcmeJBtlqIDAqgzGhyidVynEeSJKKDAymuMBIfHqIIwpjgQLepIbo3b8LnKz/hplbNnLPIyzLb162i970PeveD8YHJkyeTmpqKLMu0bNmSdevWucTd6XQ64uPja5WnrD7yr9UX2dnZFxKM1nEcwJYGYtMb5OXlCYElEAgEfqZWpwhfeOEF5fvMzEwyMzPp1KkTwcHBfluYPyg3mZAkCa1KVe2Hr6UyqafdTVg1u7okSeSVlHOmqIS4kCBaREW4LWcTGRRARVEpUUEBhOp1WKxWJ6HiKNrAJqTOlVagV0u0jQknr9yIwSoTFqAnNMCWGLV1nMy5UpulS6dRc2entmjUFyxLD/a8nrPFpQRoNYQG6CmusJ1mtD+3waFWZNV0D/Y1BOm0LtY2d0hI6NQ2oRei12G2WpFlmfNlFRiNRs4cP0ZUon9zoIWHhxMeHg7YTtk1btwYXT3UOfTEiBEjsFqtJCcnM2fOHGJjYz32rbYWYR0oLCzk4MGDPiUY9VTH0N4GKGkgCgsLRS1CgUAg8DO1zoP17rvvMmPGDLKzs5Ekib1799K1a1fuuusubrnlFh5//HF/rrNWmK0yKf83jO8+X1tjJvaiCiOlBpNTSgU79lOCYXodsZWJP2VZRqNSKf3NVisVJjOJUeG0iI5ApZLILSm3BcOrJMxWK3kl5U4WJYtVpn1UEMOTrkGjUmGxyvxSUMaJ0gsuOFWllanCZCYmJAiNBCqzEatKA5Xz92nVjN4tm6JSqbBaZQ6dyeXUuUKMjuJKlskqLKZpRChBOi1RwYEXhJta7SSqzpdVuFj0LFYrUcGBjOh5PRq1CovVyp6MPzmYnYtVltFodTRu1RqDpapT03/YS+Fs3ryZ3bt3k52dTePGjUlOTmbAgAF+n8+XnGvgXS3C2hAeHk5FhS0th1Jz0It7oqKi3MZa2QWrY9/IyEgXoeKvOC1Ri1AgEFyN1EpgvfHGGzz99NNMnjyZW2+9lcGDByttt9xyC6tXr74kBBbA3Q89zL4ftlFaUkxIQPWWD4ssU16lZqFaktBpNZgtVmJDL1i4tCoVzaPDldcn8wtpGhFKbGiwIrzKjCZOnitEq1Zx6EweVhlFfFmsMqfPFzC+Y3MlLYRaJdEpMojT5UUYrc6uvRC9lqSwQIJKziFhi9s3BgRj1AXSq4VNXGG1orGaadcoikPZuU6x/RIQoJLoEBfODa1boFJrsFitbDp0nJzi006CymSxcuh0Dm0bx6JW2cRU+tl8hnS49kLJHuCGZnFknSug0GTh/yZOJTg8AoMHq4k/OHPmDMOHD2fXrl2EhYURFxdHTk4ORUVF9OrVi3Xr1ikZ3v2BLznXGgwfAuHdxVp5K5Tqcq9AIBAIapmm4a233uL555/n5Zdfdsmg3aZNG44cOeLzmJMmTSIxMVGpLeeJ9PR0evfuTVJSEj179nQ6YeaObz9by/n8fIqNxhpzYdnzYNnTL9hTNzSNCKV5VJgiQCICAxRxBTYrU/PocBqHhxIeqHfKfRWs11JhttAqJpKmESHklpRy+GwemecK6NWsEdqqsU6SRLjWufRM04hQ7unajk6RgUrOVAnQVZRSdP4cGgk0xnKCSs4RWFZESGkB7SOdY2o6Rofy0k3tua1JJCGlBWiM5ahVKm5r14rkxCYuuVizCorZduQEe0/8yfdHT1BsMCriyj5XcEUxI1vHE6ORada+Y7XvrT8YN24cGRkZbN68mYKCAo4ePUpBQQGbNm3ixIkTjBs3zm9z+Zpz7VLEKdaqSsmd+rxXIBAIBLW0YP3555/07t3bbZtWq6W0tNTnMWs6zWVn7NixjBkzhtTUVNasWcPDDz/ssZyJRiWxfukHXs0fFqBzisHKLy0jOvhC6gZJkpAAvVZtC2B3c+owQKtxm+7B7uazj32+tJzI4ED0KgkZ2UncyECFFQI0aqKCgzCYzbRrHIfGanIRQRJwjdqKXHJOeW3/2jkqiIP5egICdKisFvo1jVaC/e3izKzRo1Kp6N+uJYfO5JF13jl5rNlq5VxpOQDFFQZbDBcyuopSZS6NSuKvSU15b/woBk74B0k33uLV+10bNm3axOLFi7n11ludrqekpDB37txaFXueMGECGzZs4MyZM/Tv35+QkBCOHTvG2bNnGT58OBaLRQmwrynnWn1R58D26kru1Oe9AoFAcBVTK4HVvHlz9uzZ4/JBB/Djjz/WypXizWmunJwc0tLS+PbbbwEYPnw4EydO5MSJEyQmJrr0D9RqFatVRGCAxxgsd3mwYoKDnPqH6HVEBtnGMFmsbk8d2u91xDZWoNMJQfsJRLVGgzFApwgWu9vvumuCCa+cyx47ZkWDjPuqP+6uaVQS93ZogUUfiMpiRlVe5HKPxlCOVaMGtY628TGcLSrBZLESrFXTKEjPmVIDZZVB+SaLlUPZObSPjXCZT6OS6N4khr1L3yG+5bX1FhsTGRlJZGSkx7aIiAifx/SUf61ly5Y+51zzO5UZ3keOHHlx1yEQCAQCn6mVwHrkkUeYPn06sbGxDBs2DACTycR///tfXn31VWbNmuXXRdrJzMykSZMmaDS2ZUuSREJCAqdOnXIrsMpNtmBxjcpmEfKEuzxYjuJKVXlC0H7N06lDWz1AV+HlaWyzxYpJG4BZrUVjNmLW6ECtIVzrnEpBlmUklQqzRofGbPS6tGKAqRzZVI5RF+gizmRAbyoHU6Ww0weRGBZEuEZFswCVTfCF6cgyQo4ZcopKyMgvwGqx0DNc65ymAUi6Jp7WTSXyPppPRZ/baXvHvV6u0nsmT57M3Llz6du3L6Ghocr14uJiXn755Usm7s9v2DO8P7gYis7AhukXdTkCgUAg8J5aCax//OMfnDp1ijFjxjB27FgAbrzxRgDGjx9fq0zu3lLVClVdXJXZKtOuS1cyf/+t2hOEZovV7SlDW7JRFTq1a6kdx1OHBtOFhKC5xWVOwfB5pZWZ2d3Mr1Gr0JoqFAuWzlCGMSAYsy4QrFZUFiPIElaNFqwWn8SVHQnQGcsVC5njV6c+hjKuD3G2kklAUx3EhIfQJiYclSwjqzUYTBXoK0pRSZIizlRWM1aVhvCgQOS9W8iPDCf6poE+rrZ6Tp48yYkTJ2jWrBn9+vVTgty3bt1KaGgoWVlZTJpkS3YqSRJvvvmmX+e/aCR0gezDF3sVAoFAIPCBWqdpWLBgAZMnT2bTpk3k5+cTFRVFSkoKrVu39uf6nGjWrBlZWVmYzWY0Gg2yLJOZmVnt6aaw8AgMFhOyrHMrsuyxV47uOFmWCQ3QO1mt3GVBtwBlRpPT9cIKA3/knVeSdVoB2YoiuhzHsZjN6CpMLoHryDI6Q5lyXXZorw2Sh6/V9XG8Hlhe4iTOzBrn99K+VruL06wL5NzmDX4XWBs3bkSr1RIZGcn+/fuV63a34RdffHFh3VeSwLpEEOV1BAKBwHtqLbDAFqdit2A1BHFxcXTp0oVly5aRmprK2rVrSUxMdOsetNOtdx9+2fG9W3FVNfZKqrQ6lRmNJESFO93jLrGoO6uXJEnEhFyI39IBISoZo9FIoFpCUmsVwRQkyW4FjaO4sl+72DgKMEdLmoslrDJ4vvZlxD2TkZHh9zGrq0WYnp7O6NGjycvLIyIigo8++ojrrrvO72u4HMjNzaVbt26ASNsgEAgE3lAngfXbb7+RmZl5IQmiA/bYLG/xdJoLYMiQIbz44ot0796dJUuWkJqayuzZswkLC2Pp0qXVjltcVOBRoQRoNS6uu7AAPc2riCtPeOojSRJhATrahAfRVAeS1YJsMiFptcgqOG2S0Ws1RGN2uddTILs3eHIBVtfX1za8aFNZzVg1ATWs4NKgutOrvpxYvdJR0jQ4lNcRAksgEAg8UyuBdeDAAe69916OHDniNgZKkiQsDlnEvcHTaS7AKXt2mzZtfPqQy848hdHivthzVSNL1WD22qJVqYgPCeIaLUhlxUjF51FR6eoLjaRxYCgSZhehUn2WLud+nlZowTvjUXVPaNLoUVlMqGWrz2JPBqySisDm9eMqzsvLY968eezdu5fMzEzWr19P+/btefPNN0lOTqZXr14+jefp9KqvJ1bdcebMGb/UDvQnda5nKNI2CAQCgVfUSmD97W9/Q6PR8Pnnn5OUlNSgteF8JTImGqsMxQYDoXq9k3iqWtRFq66+ZqE3hAXoiAsJIkgNKtmCVHze2Z1WfB5VQBBIrqcajdpA28k+ZYEWMJlAqwXVhf4uK6zsJ2m1qFVqj/d5gwyYdHqCygy1ElcSEFRagDH9Vx/vrpm0tDRSUlIIDQ3l5ptvZtu2bRgMBsCWm+31119n1apVfpnL1xOr4FyL8MYbb/SpdqC/cVei5/jx44qbr2rfqjUDq95fUlLi0u6p1qEjohahQCC4WqmVwDp48CBr1qxh0KBB/l6P/5Ft9pxSo5lQvd6pqWpaBZOb04R2C11N1+BCTJckSWh0OmRDoYs1SQKsBgOoVEgOAkjG5lpTrFOVli+7y08OjYSgUFyo2k8fhOQQdC6HRkJAkFeCyxbArkVrqr24sj+jrqzYxxFq5oknnuCGG25gw4YNSJLEypUrlbbk5GS/iSs7vpxYBedahGlpaZUL8652oL+pWm8QUMRo1TWFh4djMBg4ceIEADExMS73h4Q4Vwaw1zr0BlGLUCAQXI3USmB17tyZnJwcf6+lXjh7OhMArco11YKlMo1CbGVSUassc76sQnETyrJMcYURs8VCZGVyUFmWKSizxZxFVHEn2vNpqSSJqNAQjFoIKMhxyT+lKspzK5w0lsoThVb3li85IMhZILnr5xAgb78PRwEWEgEanVuxZQtgN3kdx1X13upe+4O9e/eybt06tFqtiws6NjbWr3uyNidW3eJD7cAGo8qacnNznf5YCggMYs2nqxt4UQKBQHBlUauzXgsXLmT+/Pls2rQJs9k1UPtSIif7NAAW2eLWAmE0W51EUonByJ8FxRSV20RUWKCeyOBAyowmxboVERTgIq5sY1mwyjKBGhVqiwmzPhhDzDVKbFXVdAsSIBWftwklh+sYK9wLFqPB+aLJffmcqq+d5ispQFWQg5T7J9itTFYLGCqUdbgbx1dcz0fWneDgYIqKity2nTp1iujoaL/N5XhiFfDqxOqljqdTmEoA+4OL4eGPqCgvu/hZ7AUCgeAyp1YWrOuuu47k5GQGDRqESqUiMDDQqV2SJLcxIA2NRiVx9DdbLJBacrVgwQVRVPU0YWjAhXgtSZII0mmdXrvDIsuEymYSVaAqK7K53HQByLHXIJtNYLGiKnIumisBstFgc+PZkb0UJ1qt2wzt3pwAVKxisoxUUlCzKxK8ju2yJx/1NwMHDmTmzJmkpKQoZXEkSaK8vJw333yTIUOG+DxmdadXfT2xeslSWXLn+eefr75fQhcozvOur0AgEAiqpdalclatWsWwYcMu6SD3QK0Wq9UWym63YFUVR8F6rYsgcRfs7k3we1KonhaBGuxdJUBrMSGr1Eg6W/C5OwEkFeYhVwRBQCBotCC79pMBNFV+XBVlTi/tMVg4xGDZ1+EOCaBSXCmv3bkiwfuYsMpxrGrfguu94eWXX+bGG2+kdevW9OvXD0mSmDZtGgcPHkSSJGbOnOnzmNWdXvX1xOoli73kjjfxYCV53vcVCAQCgUdqJbDWrVvHa6+95teSON4mdUxMTCQgIICAAFuepWeffZa//vWvbscsN5mUuCl3FizHoHRH3AW714ROJXFdeADublEuqdQ2YeIQN2VvlwxlYCirtqCzXF4GqgqwmEElIZUWu/bVaJCD4ioFmdYmwhyEUU3WLgls1jZdDbFenoQYF1I1+JtrrrmG/fv38/rrr7Np0yZatWpFfn4+I0aMYMqUKSLouSZ8iQe7FGPHBAKB4DKiVgIrIiKCli1b+nUhviR1XLNmjVPGbU+YrTIpd97F5s/WubVguSvyDLYCzcUVRsIC9S5tngjXuh/LhaBQZJUKqTDfbXN1yT9VZe7jjxzvlUqLkEuLkLV6iIixzRcQhFxRbhNbpooLVqigUJtlqso8mE02cWYXTx5ivVyEmEObSq6aBMM/REREMGPGDGbMmFEv4wsEAoFA4A9qZWZ44oknWLRokd8C3O1JHUeOHAnYkjpmZGQox8brwt2pf0ej0bi1YNnjr9xRVGGo8Vi+I4UmCxZv+2sulMvxFl9P9KlMhguB7EXnkYrPoTLZAvftliuprBjZYS3266ri885B8FrX9cpgi8myuiaUlbElPPU3mZmZF9IfVCEtLY2srCy/zpeYmEjbtm3p3LkznTt39nsaCIFAIBBcudTKgpWRkcEvv/zCtddeS58+fZSAYzu+Ftr1NanjiBEjsFqtJCcnM2fOHGJjYz2OPX7MGKxmM1bJtWCzRZbJLSlzqkdox13KBk8uQ7UkoVar+a2ggusjAlBLErJcaXVyd4vF9wzptUFJ0wDO7j3H780mz23F55FVKtAFuI3tUhXmu43HsrkzHRKm+olHH32U1q1bK7mmHFmxYgXp6els2LDBr3N6ay0VCAQCgcCRWgmsjRs3oq4MYt6xY4dLu68Cy36PI56sR9u3bychIQGTycS0adMYPXq0Uymdqvxl8G2sW/oBVhlKDSZCApwD8osqjMiyTHxYiMu9JQYjZUYTQTotUcGBLu1gy9xuF2hWWWZvoQGNbMVolYgIDiBIBQlaB6FltYDVWqeag75Q0xw1nTiUKkWUY9/qhJjdrajRamux2ur58ccfPRYX79evHx9//LHf5xQIBAKBoDbU2oLlT3xJ6mi/ptVqmTx5MklJSdWOfX33ZNYt/QAAg8WCq4wCazX+OnsKB3cWLHuQvN36pZIkgiuD7wNkmQAJDGYL58wmovVaW4Z1x5N4NIzIqiveiDSpqjXLxxI93lBSUoLWg3BTqVQUF/s/e7wv1lLHUjnXXnut39fiC76kSalaBsebPqJUjkAgEFRPrQSWv3FM6piamuoxqWNpaSkmk0lxSa5cuZIuXbpUO3bjZs1o1KQJZ0+fxmx1n6qhwmT26AIM1esID9S7bfMUJB+llmmsAVX5hdQG1mLXpJ9XGnZrljUgCF2F/8VOu3btWL9+vdsSTZ999hlt2rTx63y+Wkvdlsq5SLgrleOJqmVwvOkjSuUIBAJB9dRJYB07doyjR49SUVHh0jZs2DCfxqouqeOQIUN48cUXiYqKYvjw4VgsNqHUsmVLr9xCT816hakPjfSYbNRWMqecmMpyOHZUlVnbPcVeuUtSqqZSXFUp9Oz/pAWXJhIgmU2o3JwurCuTJ08mNTUVtVrN3/72N5o0acLp06f58MMPee+99/jggw/8Op+v1tJLiUOHDnnd1xuLtLs+2dnZZGdnA7b6hT6XERIIBIIrmFoJrKKiIoYNG8bWrVsB98WPq9aKq4nqkjo6Wg1qU8LjmuaJAJg8WLAANCrX5KLuEo464i5IPkBVGW9ldE1tUJWaEoFejtjzb9XHM40aNYqzZ88yY8YMlixZolwPDAxk7ty5jB492m9z1cZaeklQmbXdfiLXm77VZm330Cc3N5du3boprwMCgzhy+JAQWQKBQFBJrQTW008/TXZ2Njt27OCmm25i/fr1REZGsmzZMr777jtWrlzp73XWiYz0IwAeA93VkkSEm5xX3iQcLaowUm400zwqDEmSqLDa5lG5KWNTFccTeVcKcmX8VX0915NPPsnYsWPZtWsX+fn5REdHc8MNNxAWFubXec6ePVsra+lFx561/cHFUHQGNkyvuW91Wds99HGqX6gLoOL9VPLy8tBqtWRnZwuLlkAguOqplcD6+uuvmTVrFsnJyQA0adKEHj160KdPH/7xj38wf/58/vOf//h1oXUhOjZO+b7YaLSVx6mScNSdiKouVYOj1U7jYOmyIJFtlmmsUaNyyNruzlp1uQS5e4sMEBhsy+Rej08WFhbGwIED6218gJYtW17eBY8TukD2Ye/6epO13VOfhAtWPUerlt2i5U18l0AgEFyJ1Epg5eTk0KxZM9RqNcHBweTnX8hKPnjwYIYPH+63BfqDg7/+onxvlaGwwkC4QzFno9mz69CeqkGrVqFXqwl3iMmqMJkJ1GldYrHOWSSKLDKJ0degCotGV1aEpFIh5Z9xGvtyFFfViUIJIP8MhEZijmzk97nfeust/vzzT+bOnevS9swzz9CsWTMmTJjg93kFrriLyVKsWgMmU7HpDfLy8igpKeHEiRNuLVr2GC6LxYJarVa+CuuXQCC4EqhV7HWzZs2U/0xbt27N559/rrTt3LlTqRN4qXDq+B9Or0uNZgzmmmPE7FYqqyxjslidxJUkSQRobSkl7LFYVof+Rhlyyw3ozEYkXUCDJRetb7xK2VB8HnM9PO3bb79Nq1at3LYlJSXx9ttv+31OQRW8iduKbwvYLFrt27enW7dutGnbjlOnTildsrOzadKkCd26daNnz55OX6v2FQgEgsuRWgmsAQMGsHnzZsBWNmfx4sV069aNG264genTpzNq1Ci/LrKuJLR0/lBWSaDXXDjlFhnsPg2D4zV3Ae+SJCkpHooqjJzILySroJgzRaWoJAmz0XBBZrgpN3OlIgFqi3/KKDly8uRJWrdu7batZcuWfimtJKgBx5isGnCyaJWXXXgNyulDZRz7Vzd9BQKB4HKkVgLr5ZdfVortPvjgg6xdu5a2bdvSuHFjFi5c6NaFUxPp6en07t2bpKQkevbsycGDB+vUz5HrOnZyeq1VXYi5UksS4V5Y3OwB71UJ0F7wslpkmXKTmQqTGassKwHvgC3wOzTyqhBZMmDV+D+Te1hYmMeUAsePHycoKMiv89Vmr101eBO3ZafSonXo0CFXy5R9HPvXyr6OZGdnk5aWRlpamrBsCQSCywafBZbRaOSrr75yyhR91113sXz5ctatW8ejjz6KSuW7bhs7dixjxozh6NGjPPXUUzz88MN16ufIIYcYLACLbFHEkqdkoVWxyjLFFUaX65IkuVi27C5DkwzZ5gsiyxoYSkFgONYa5nInwi4XYWbP5q6y1vSUvnPbbbcxY8YMMjMzna5nZWXx0ksvMXjwYL/OV5u9JnCDQ+oIX91/jq5E4T4UCASXEz4rIZ1Ox4gRI1w+5OpCTk4OaWlpSu6e4cOHk5GR4eLy8bZfVbZt2ez0WuNgwbIHqHtDUYXBY41E1742l+GRwnIOG+C4EdLLLYSXF1b7pnsKIr/U47dkwBoUihx7DXJQKOeMJr/PMXfuXKxWK23atGHo0KGMGTOGoUOHkpSUhNVqZc6cOX6bq7Z7zYWibPdfzxyC/Izq+9Sm78W454wtqak9Lx7nTjq/Plt5mrHrMCrKy/j3v//N+vXr3Y9Xee/69etZvnz5hbi6AZPh9mcV9+HatWsRCASCSxlJ9lYxONC5c2emTp3Kgw8+6JdF7Nu3jwcffNDJBdOzZ0/mzZtHnz59fO4HtlIl9riwjPR0mgVeiLkKUKuJDrlQvLlqwWYJ1+LTdkL0Oqe0DVTTF2wuyBbR4UiSRIi5nFalOS597KJKliQMEY3Qnz/jks4BGk5k+TqfDMjR8aC5kF+ssONNtHtosp9XBufOnWP+/Pl89913Sh6s/v37M2XKFPLz8/1WA7C2e81eizA5OZkuXbthtZhBUoFsdf0Kntvq0vdi3GOn6jVv+lbXp8qY+sAg9uzexRNPPMGnn34KiJI5AoHg0qRWaRrmzJnD5MmTue6665yyOdeFqiLFk+7ztl95eTkAEyZMYNGiRXRoGs/+3TsBMFbJ6F5UYaTUYEKnUWM0WwjWa50EV7nJRKBWi0qSKK4wKGkbTBYrkUEBBOm0bgWXLMtYQCnDU67SYcXZbChLEqUJ7VCZTVh1AVjUGsqLi4gwl10oCq0PAq0OSgr8JrLcWcpkQA4IhtAIqChTcni5uxcuJEqVQyOdxJVFhoo23SkrK/N7XFRUVBSzZs1SXp89e5ZVq1YxaNAgfvrpJ58rCFRHbfZau3btADCbzXTu1JEpU6ZgNBrR6XQuXwGPbdX1nTt3Ls8884xP99T01T6mL/c4zpOXl0deXh4xMTHExMQorz/99FOef/55pa/9OqD0rTqevY9er8dgMCj9ACIiIjCbzZSXlyvWxBMnTtC2bVu/7zWBQCCoC7WyYF1//fWcPn2agoICYmJiiIuLc/owkiSJX375pZoRnMnJyaF169bk5+ej0dhSHzRu3Jjdu3c7FXz2th/A8uXLXcqFNAkLoklYCJIkEaTTEFGZC0uWZcxWq1IuR5ZlKowmgvW2Dw+7KNJrNYQH6AjR65xEVInBQGRQILJsExw6tQokWzC9vZZheICOmJAgYowlXFNxDlXluH8GRhPapBlqlQqL1crx3HOcyCugsV5DG72VsKAAVFqdTegVFRJSUaQIG3NQKOagUJDUqMqK0ZUWKG1Vk5s6CioZKFXpCLYaHURcIIRFIavUyjWLDJric0jlJU73ysHhEBQCZhNotKC6YB20WGWe2/ozy37LYN++fUrxY39SUlLCunXrWL58Od999x1Wq5Xu3bszevRoxo8f75c56rrXBA1Lfe01gUAgqC21smB169aN7t27+20RcXFxdOnShWXLlpGamsratWtJTEx0+SDzth/AwIEDWbZsGYmJiQQGBrq0X0wc042qgTKH1/GV/wAqKv85UuzHdRT5cSxHnhgNTwBt27qeCKstZrOZL7/8kuXLl7Nx40bKy8tp2rQpVquV1atX+z257ZWy164W/LnXBAKBwB/UyoJVHxw5coTU1FTy8/MJCwtj6dKltG/fHoAhQ4bw4osv0r1792r7Ca48tm/fzvLly1mzZg3nz58nJiaGe++9lxEjRtCuXTuioqLYtm2bS1yUPxB7TSAQCAS15ZIRWAKBO1SVbtuUlBSmTJnCgAEDUKttLsnCwkIiIyPrTWAJBAKBQFBbauUiBCgoKGDNmjUcPXqUioqqjixYsGBBnRYmEIDtxOr+/fvZtm0bKpWKnJwc7rrrLkJDQy/20gQCgUAg8EitLFj2DNcGg4HS0lJiY2M5d+4cZrOZyMhIwsPDOX78eH2sV3AVcuTIEZYtW8bKlSs5fvw4AQEBDB06lKFDh/LQQw+xdetWYcESCAQCwSVFrQTWHXfcgSzLfPrppwQHB/PTTz/RqVMnVq1axXPPPcfatWv9lr5BIHBk9+7dLF++nE8//ZScnBwkSeIvf/kLjz/+uBBZAoFAILhkqFUtwj179jBu3Dj0ej1gy5ujVqt54IEHmDp1KpMmTfLrImuivuoYVlRU8Je//IWkpCQ6d+7MoEGDPGbyTkxMpG3btnTu3JnOnTuzatWqGtft7T2+rrugoEAZs3PnziQlJaHRaDh37lyt1jBp0iQSExORJIkDBw7Uam3+quvXq1cv3nrrLU6fPs2XX37JiBEj2LRpE/369aNly5a1GrMu1Nfz19Tfl71Z2zXMmDHD7c+8tuMaDAYmTpxI69atad++fY2pLbwd95tvvqFbt2506dKFDh06sHTp0mrHFQgEggZBrgXh4eHy999/L8uyLEdHR8tr165V2rZs2SIHBQXVZtha069fP/nDDz+UZVmWP/30U7lXr1516menvLxc/u9//ytbrVZZlmX5rbfekgcMGOC2b/PmzeXffvvNp3V7e4+v667Kq6++Kg8dOrTWa/j+++/lzMxMt33r6733hbKyMnnlypXyHXfc4bcxvaW+nr+m/r7szdqsYd++ffKgQYPkhISEGveHt+NOnjxZfuyxx5Q1nz59us7jWq1WOSoqSv7ll19kWZbljIwMWa/Xy0VFRdWOLRAIBPVNrQRWjx495I8//liWZVnu37+/nJKSIhcVFcllZWXyvffeK1977bV+XWR1nD17Vg4PD5dNJpMsy7b/cBs1aiRnZGTUql917N27V27VqpXbtvoSWP5Y93XXXSevX7++1mvw1Lch3/tLkfp6/tq8X9XtTV/HrKiokHv16iUfP368xv3h7bglJSVyeHi4XFxc7HGs2oxrF1j2P/h++eUXuUmTJrLBYPBqHoFAIKgvauUi/Otf/8r+/fsBeOmll9i7dy+RkZGEhYWxdu1aXnjhBX8a2aolMzOTJk2aoNHYDkRKkkRCQgKnTp2qVb/qWLBgAXfccYfH9hEjRnD99dfz97//ndzcXK/GrOmeuq57165d5OfnM3ToUL+u25e1+eO9vxSpr+evzftV0970Zcx//etfjBw5khYtWlTz9L6N+8cffxAdHc3MmTPp3r07N998M1u2bKnzuJIksXr1aoYNG0bz5s256aabWLp0qVLCRyAQCC4WtRJYU6dOZf78+YAtJubAgQO88847zJ8/n/379zd42RB/1zF0x+zZs0lPT3eqg+fI9u3b+eWXX0hLSyM6OprRo0fXOKa399Rl3R988AGjRo1SPqj8se7arK0uz3ApU1/P70v/mvamL2Pu2rWLvXv3+lRyyJtxTSYTx48f57rrruOnn35i4cKF3HfffdUKem/GNZvNzJkzhw0bNnDy5Em2bNnC6NGj3cYbCgQCQYPii7nrwIED8mOPPSYPHTpUHjt2rLxp0ya/mtNqw9mzZ+WwsDCv3DTe9HPHq6++Knfr1k0+f/68V2s6ffq0HBIS4stjeLynLusuKSmRQ0ND5UOHDvll3e5chPX93l/K1Nfz+9Lf273p7Zhz5syRGzduLDdv3lxu3ry5rFar5SZNmshffvllncbNzc2VVSqVbDablWs9evSQt27dWqdx9+7dK7dr187pWvfu3eXvvvuuurdDIBAI6h2vBdaOHTtknU4nq1QqOS4uTtZoNLJKpZLfeeed+lyfV/Tt29cpGDY5OblO/RyZP3++3LVrV/ncuXMe+5SUlDh9wM2fP1+++eabqx3Xl3tqs25ZluUPP/xQvvHGG/22bnfxOPX53l8O1Nfze9Pfm71ZlzXIsncxet6OO2DAAPm///2vLMuyfOLECTkmJqbaQHdvxj1z5owcGhoqHz58WJZlWU5PT5cjIyPlrKysmh5NIBAI6hWvBVZKSorcuXNn+dSpU7Isy3JhYaE8bNgwOTo6ut4W5y2HDx+We/XqJbdu3Vru1q2bfODAAaVt8ODB8t69e2vs547MzEwZkFu2bCl36tRJ7tSpk9yzZ0+Xsf/44w+5c+fO8vXXXy936NBB/r//+78arTM13VOXddu56aab5A8++MDluq/rHj9+vHzNNdfIarVabtSokVMwdX2995cL9fX8NY27Y8eOavdmXdbqiDcCy9tx//jjD7lv375yhw4d5E6dOsnr1q3zy7grVqyQO3ToIHfs2FG+/vrr5ZUrV1Y7rkAgEDQEXicajYuLY/HixQwbNky5duLECVq2bMnJkydp1qxZPTkxBQKBQCAQCC4vvA5yz8vLo2nTpk7X7KIqLy/Pv6sSCAQCgUAguIzx6RRh1VM9AoFAIBAIBAJXvHYRqlQqgoKCUKmcNVlJSYnLdUmSKCws9O9KBQKBQCAQCC4T3CdHckNDJg8VCAQCgUAguJzx2oIlEAgEAoFAIPCOWmVyFwgEAoFAIBB45qoQWNOnT0eSJOVfbGwsKSkp7Nix42Ivza9s27aN2bNnN8hcP/30Ew899BDt2rVDpVJVW+vwakHsM/+zZMkSBg4cSOPGjQkLC6NHjx6sXr26QeYWCASCunBVCCyAwMBAdu3axa5du3jnnXfIz88nJSWF33777WIvzW805Aff//73P3bs2EHXrl1JSEhokDkvB8Q+8y8zZ86kadOmvP3226xbt44bbriBv/71ryxatKhB5hcIBILa4nWQ++WOSqWiV69eyuuePXuSmJjIkiVLWLhwYa3GlGUZo9GIXq/31zIvKcrLywkMDHTb9thjj/H4448DcMsttzTgqi5txD7zner2WVpaGrGxscrr/v37k5WVxbx585gwYUJDLVEgEAh85qqxYFUlISGBmJgYMjIyAJg/fz49evQgPDycuLg4hg4dytGjR53uSU1NpUOHDnz55Zd06tQJvV7P559/TmlpKRMnTqRNmzYEBQWRmJjIuHHjXFJVJCYmMnHiRObPn0/Tpk0JCQlh1KhRVFRUsH//fm688UaCg4Pp0aOHi8VDlmXmzZtHUlISer2eli1b8vrrryvt06dPZ8aMGZSWliouKkfhc+jQIe68807Cw8MJDg7m9ttv548//nCaQ5Ik5s6dy9NPP018fLzTB1tVqqbrELhH7LO67TN3bV26dOH06dPVv/ECgUBwkblqLFhVKSoq4ty5czRp0gSArKwsJk6cSPPmzSkqKmLx4sX07t2bo0ePEhUVpdx3+vRpHn/8caZNm0azZs1o1qwZZWVlWCwWZs2aRWxsLJmZmcyaNYu77rqL7777zmneDRs20LFjR959912OHz/OlClT0Ov17N69mylTptCoUSOefvpp7rnnHg4ePKgImccff5z33nuPf/7znyQnJ7Nz506efvppAgMDGTduHH//+9/JyspixYoVypxhYWEAHD9+nN69e9OhQwc++ugjVCoVs2bNIiUlhSNHjjhZRt5880169+7NBx98gNForNefwdWA2Gf+32c7duygXbt2vv8wBAKBoCFxV6DQarXKZrPZ6dq+ffvkVatWuS0Ie6nzwgsvyMHBwbLJZJJNJpOckZEhDxs2TAbkr7/+2qW/2WyWy8rK5JCQEHnJkiXK9dGjR8uA/OOPP1Y7n8lkkn/44QcZkI8cOaJcb968udysWTPZYDAo14YPHy4D8ldffaVc++KLL2RA3r9/vyzLsnzs2DFZkiSntciyLD/55JNyfHy8bLFYnJ6zKqNGjZJbtGghl5eXK9dycnLk4OBgedGiRco1QG7fvr1stVqrfb6q9O3bV7799tt9ukeWxT4T+8y3fSbLsvzZZ5/JgPzJJ5/4fK9AIBA0JE5+HqPRyLhx4wgNDSUoKIgpU6YANpdF9+7due+++0hOTmbo0KGYTKaG0oB+obS0FK1Wi1arpUWLFmzdupWFCxcycOBAAHbv3s2AAQOIjo5Go9EQFBRESUmJi/smJiaGnj17uoz/ySef0KVLF0JCQtBqtdx0000ALvf36dMHnU6nvE5KSkKlUnHrrbc6XQPIzMwEYPPmzQAMHz4cs9ms/EtJSeHMmTNKP098++233HnnnWg0GuXeyMhIOnXqxN69e536Dh48uN5LIol9JvZZbfbZwYMHSU1N5Z577mHkyJE+3SsQCAQNjZOL8JVXXuHjjz9mypQpREVF8eabb1JQUMDGjRtZs2YN3bp1Y8eOHUyYMIF33nmHSZMmXax1+0xgYCDbt29HkiRiYmJo1qyZ4hY5deoUt912G927d2fJkiU0adIEnU7H7bffTkVFhdM4cXFxLmOvX7+eUaNGMWbMGGbNmkV0dDTZ2dncddddLvdHREQ4vdbpdAQGBjp9GNq/t9+bl5eHLMvExMS4fbbMzEyaN2/u8dnz8vJ44403eOONN9y+LzU9n78R+0zsM1/3WVZWFoMGDeL666/n448/9ulegUAguBg4Caxly5Yxffp0nnrqKQB69OhB3759eeONNxg2bBgAzZs35/jx4yxduvSy+uBTqVR0797dbdvXX39NSUkJ69atUz6YzGYz586dc+nr7q/uTz/9lM6dO7NkyRLl2vfff++fhQNRUVFIksQPP/zg9AFpp02bNjXef/vttzN+/HiXttDQUKfXDVHQW+wzsc982Wf5+fncdttthIWFsWHDBgICAry+VyAQCC4WTgLr5MmTJCcnK6/tHxRVPzBuuOEGt3+lXq6Ul5cjSRJarVa5tnr1asxms9f3V/1AWr58ud/Wl5KSAtg+aO644w6P/XQ6HQaDweV6//79OXDgAF26dEGtVvttXbVF7DOxz7ylpKSEwYMHU1JSws6dO4mMjPTLuAKBQFDfOAksezyIHft/5lX/YrRarZddbEx12ONSHnroIcaOHcvBgweZN2+ei5vFEwMGDGDChAm8+OKL9O7dm6+++ootW7b4bX1JSUlMmDCBBx98kCeffJLk5GRMJhNHjx5l69atfPbZZwC0a9cOs9msnNAKCwujTZs2zJgxgx49ejBw4EDGjBlDo0aNOHPmDN9//z0333wz999/v89rys3NVawnubm5lJSUsGbNGgCGDBlCUFCQx3vFPhP7zFuGDx/Ozz//zL///W+ysrLIyspS2rp06XLF5gYTCASXP04CKykpiZ9//pnbb78dALVajclkcvlr9NChQ9XGYlxuXH/99Xz44YfMmDGDoUOH0rlzZ9asWcM999zj1f1jx47l+PHjLFy4kHnz5jFw4EBWrFjhlHCyrixYsIA2bdqwZMkSXnzxRYKDg2nTpg333nuv0ueOO+5g/PjxzJkzh5ycHPr06cO2bdu49tpr2bNnD9OmTWP8+PGUlJTQuHFj+vTpQ8eOHWu1nt9//93l/bG/zsjIIDEx0eO9Yp+JfeYt3377LWATpVWpaZ8JBALBxUSSZVm2v1i+fDnnzp3jscceq/amvn370qNHD+bNm1fvCxRceYh9JhAIBIIrHSeBJRAIBAKBQCCoO6LeiUAgEAgEAoGfEQJLIBAIBAKBwM8IgSUQCAQCgUDgZ67aYs8CgeDqwtcM8KNGjaqnlQgEgqsBEeQuEAiuCuwli+zYs8k7/hfomGHeYrE0zMIEAsEViVsXYUVFBWFhYXz++ecNvR7BVYTYZ4KGJDc3V/m3a9cuEhISeOaZZ9i3bx9ZWVns27ePp59+moSEBHbu3HmxlysQCC5z3LoIAwICCA4OdluPTCDwF2KfCRqS6Oho5fv77ruPMWPG8OyzzyrXmjRpQpcuXQgJCeG5557za5Z8gUBw9eExyH306NG8//77fpto0qRJJCYmIkkSBw4ccGpLT0+nd+/eJCUl0bNnTw4ePOh2DG/7CS4f/L3PQOw1Qc3s3LnTY1Hu7t27s3v37gZekUAguNLwGOQeGRnJzp076dSpE4MGDSIuLs4pPkGSJJ544gmvJ7r77rt56qmnuOmmm1zaxo4dy5gxY0hNTWXNmjU8/PDD7Nq1q9b9AMrKyjh8+DBt27atti6e4OLi730GYq8JaiYuLo5Vq1YxYMAAl7b//Oc/xMbGXoRVib0kEFxRyB6QJKnafyqVytOt1dK8eXP5t99+U16fPXtWDg8Pl00mkyzLsmy1WuVGjRrJGRkZTvd528/Ovn37ZEDet2+ffGuHJLlHs0bKv5Tr28jn8nLlLf/9Qr6hZVOnthuvTZDP5eV6/TwFBQW+vQEXedxLbez62meyfHH2mv35jx89IvdqcU2d9pYnanqP136yVO7dqpnco1kjuXerZvLaT5bW2Fb1+rJ3F9d6LH89R33y7rvvypIkybfccov8xhtvyCtWrJDfeOMNuW/fvrIkSfK77757UdbluJc84e/3rTbjnT59Wt63b5988uRJv4zniUvhWQWC2uLRgmW1WhtE4GVmZtKkSRM0GttSJEkiISGBU6dOORVy9bZfVcaljoLCQqdrRQUFDOp6vdPpITtGg4Gf9+6hc0/vCuieP3++Xk4b1de4DTV2VFSUV/0bap9B/e+1iRMnEhgYSKfWrdj1zX9d3mOjwcCO7zYTHBJKi9ZtiKjhPSo4d46M9CMufav7+Z06/gevPv+s0m4yGpn/wjQ69+oNwPwX/onJZLrQNn0aLdu2c7n+9txZtO/S1W3/6sbq3Kt3jc/lzXP4grd7zZFHHnmExo0bM2vWLJ588knMZjMajYauXbuyYcMG7rjjDp/Gq6io4L777uPgwYMEBQURHx/P4sWLlf2Snp7O6NGjycvLIyIigo8++ojrrrvO53WD/083+jpednY2TZo0ASAgMIgjhw+RkJBQL+u72M8qENSFSyIPlqNLCHArfHzp50hibDQnCvKU1yoJdGo1yGC0WrBWGUKn19O1Z7LXHxJQu//gL+a4l/PYdaU+99rChQuJCAtjxIC+bv8jV2s0vPzMk5hMJrQ6HVOnz2TYSPe5ltYt+5j506dhMhrd9nV8jwvP5XPy6BEO/PoLC+fOcpnbZDLy2fKPuSllACaTCZUEWpUak9WCyWjkt317FaHkeI/b60Yj+WfPIMuy27b/bfmWoXffS2R0TI3vV9XnaGiGDh3K0KFDsVqt5ObmEhsb65LKwRfGjBnD4MGDkSSJhQsXMmbMGL799lvAN5fzpU52drbtmwGTqdj0Bnl5eU4CSyAQ2Kj2fxOTycTixYt5+OGHue2220hPTwdg1apVHDp0yC8LaNasGVlZWZjNZsD2QZaZmenyC+ttv6r0G/J/yvchOg3xocFEBwcSHRJIfGgwQboLGlOn1zPlhZeIiIp2N5SgnmiIfQb1v9cAfkv7ya24klQqZKvVyeLz2oznOZ+f59L3fH6eIq7sfee98E9+2buHPT/soODcOaXvN6tX8vf+N/HCI6NYvWAeOg+/0Ws/+YjYRo0IDQwgPjSYmMr9HxYUSJfuPQgK0KNy0JRanY6bUwagrXLCU6fX07pdO1q3a+fSBvDWrBcZmtyVdcuqT+p5Pj+Pn3/c5fb5GxpJkrBYLHWypgYEBDBkyBBFmPfq1Yvjx48DkJOTQ1paGiNHjgRg+PDhZGRkcOLEiTqvvaHIzs7m1KlTzhfj27r0SUtLIysrqwFXJhBcuni0YB0/fpz+/fuTm5tLp06d2LVrF8XFxQBs376dr7/+mg8//LDOC4iLi6NLly4sW7aM1NRU1q5dS2Jioosrxtt+VSkstH0YBes0hAXoXQKoIwL0GM1mtBotoyY85tGiIKgfGmqfQf3vNTuOFiK7hVS2WqlqAzMaDBw7fJgeNzoH46cfOqSIKztmk4lHhtv+WFCp1fzfXx9gxCNjeG/ui5grRZt9P1eYzC6WWZPRyMk/jhERqMdaKQAlSSJMr2X63x8kUq9F1mkoqDBgltSMffIZEq9tzaP/eIa3X52D2WRy+QNk6vSZvDbjeYwGg8tcr814nn6Dh7i1ZNVknWsovvnmG1544QV+/vlnLBYLe/bsoWvXrowZM4a+ffsyYsSIWo+9YMECxc1YG5fzxIkTCQ8PZ9iwYQwfPtyp7fz587VelztqGu/MmTO0b98egF9++YXCKiEXhYWFHDx4UOmjDwhkz4+7adq0ab2vzd/jXcrWd8Hlh0eBNWnSJGJjY9mzZw8RERFOuYr69u3rlD/GGyZMmMCGDRs4c+YM/fv3JyQkhGPHjgGwZMkSUlNTmT17NmFhYSxdulS5b8iQIbz44ot079692n5uH04l8dnHH6KSILyKuLIjSRJxIcFIksQX771DVHg4dz30iE/PJqg9/t5ncHH2mp2S3LPEh9r2kyzLFFQYKDOa3fZVqdW0btfO5brdOlRVZNmxWix8tuITvv50JdFBAU5tkiShUakxVrGi6fR6dGqVIq7syFYrlkrLjSRJxISGMH/NF4RGRrNu2ce8M28uZpMJjUbDuH887SSEho0cRb/BQ9i4ZjVvzXrRaVxP4tGdda46MVZfrFy5kpEjR3L33Xfz0EMP8eijjyptrVq14sMPP6y1wJo9ezbp6eksXrxYueary3nhwoV07drVY7u/hYDjeNnZ2WRnZxMTE0NCQoKTpc1sNhMeHu50b3h4OJmZmbYXAyZj2PQGZrPZb2usz2cVCOoTjwJr27ZtrFy5kpiYGBeXR3x8/AU/vJcsWrSIRYsWuW1r06aNx3iEL7/80qt+7gjUarFarejVarfiCmz/0dnbJEli+YL53HrnMMKFm7BB8Pc+g4uz1wBKi4pY++4ip/3kyaIENqG08dNVjBw73ul6ZHQMU6fPZN4L/1SsU+4wms1O+xds+9lstaCutJZYzGbF8tS+Ww80Wm21Y1rMZgrz8rDIkpMQMpvNLJ73MrdXia+KjI5h6N33svjVuU6C0O5KrIo765xdjF3bti3phw7Rul27ehdbL730EpMnT2b+/PlYLBYngdW+fXtef/31Wo07b9481q1bx+bNm5U0C44uZ41G45PLuaFxF8BeE7m5uQwaNMj2oorbUCC4mvEYg2X/j8AdZ8+eJSQkpN4W5S/KTSZUKhUmq8Xts1T9cALbB8zJ9KMNtcSrnithn9n58j/LkKvE8UiSRJBW6xTf5MjCOTOd4pDO5+ex54cd9Bs8hOVfb1GEkjusMhRUGJQ+Gq2OfsPuZc7i9/ly736+3LufV979gJcXLSZlyO2ER0Xz92f+pcROaXQ6l/2vUqtJTGpDRvoRj0KoKnZBqNPrgepjGd3Fbun0eo78/htDk7sy8YF7vIrhqivHjx9nyJAhbtuCg4Nd3GDe8Nprr7Fy5Uo2bdpERESEct3R5Qz47HJuSJwC2MvLyMurOUbOmz4CwdWIR4HVt29f5s+f73RSyO72ePfdd0lJSWmQBdYFs1Xm1v/7i/JB5PhB7ulDXavTkZjUpqGWeNVzJewzsLmjv//mK5d9Jcsy4YF6lwMVdqxWKz/v+RGwxSY5ioyff9zNhKeeq3ZeEypeW7uR/vc9SHZRCcs/+pBpjz3K1q++ZM+WTSx6birzpj7G3/vfxDerVzLw3vv596YdzHjvE+at3kChwaSsWZZlCsoryM/LIzo2TokZsuPJKgU2d+EXu/exaOUavti9z2NMlTsxNm7q07zzyhwXt2F9BsDHx8dz2I1YBPj1119p3ry5T+NlZWUxdepUCgoK6NevH507dyY5OVlpX7JkCUuWLCEpKYm5c+f6vXqB3xGWKIGgznj88/jll1+md+/etGvXjjvvvBNJkli0aBEHDhwgPT2dPXv2NOQ6a01YeAQAFSYzBOiV6+5chmqNhoeffp6wSOGjbyiulH0WqNVitlgoqDAQURnvV9X97MldKElw8o90Frz4PBaTs8h4cuZcj3Oq1GqmTp9JaHgEKz5830mgvD7jeeJDgxR3oNlk4v2XX6JX/9sIj4qmY/IN7PlhB8XlFZRWgEalxlwZlH//gFsA5xxlNZ2wPZ+f57V7zx679fPePXTp0bNat2HVGC5/8cADDzB9+nTatm3LLbfcAqCUVnrllVecXIbe0LRp02rjqmrjchYIBJc3Hi1Ybdu2Zd++fdx4442sXLkStVrNxo0bufbaa9mzZw+tWrVqyHXWmvhrbHEOWpXnOCw7ki6Agffe3xDLElRypeyz8spA8DKjmTPFpRSWG1z2mz0AvSo7/vsFU+++g0i91snSZTQYmPWk5zJBc95+1xZk/ulq14B4s9kl1spkNDq5v1u3a4dGq8Uqg9Fy4cSj1Wp1ElcarZZPvtxUbc4uX917kdExdO7Zi8joGI9uQ0/WMn8wffp0evfuzYABA4iPjwdg8ODBdOrUie7du/PMM8/U29yXE7m5uU6pUg4dOsTvv/9+EVckEFw+VJtotEWLFl6fnqoLBQUFyl+RYKvHdfz4cXJyclxOfCQmJhIQEEBAgO301LPPPstf//pXj2Of/OMIgBKHVZ3IMpUWs3PLZnqn9K/D0wh85UrYZ2arTIceyezf9T+sMlSYTYTJzjFOKpUKs+wco6WSIG3rJq8D4+3o9Hr+PHWKaY896iKuVBJotBpUarXTqcGq7u+tX33pEjPmLsWE2WQiLyeHFq2TXNbhj1OBdrehPeVDQ+Sj0+l0bNiwga1bt7Jp0yby8vKIioqif//+9O8vfv/tKMHrldhzeQkEgprxKLBuvfVW3n77bdq2dfXFHz16lHHjxvHdd9/5ZRERERHs379feT1v3jy+//57j8dp16xZQ4cOHbwaOyg4DIAArfOjuhNbkiTx9sx/0a5z5wY9Mn41c6XsM41K4rc9uwEI0mlc3ISyLJNfWoa1impyZ1m1W7rMVgtalRqLbEEt2UQPgF6r49ahd/LOK7Ndsqk7zm2pTJQqSRIqtZo7HxqjuL/twsjx5GbVddtTTFRnTfKXe8/uNrSfJqzv379Tp07RuHFj+vXrR79+/ZzazGYzp0+fviRP+V0Ukh+AH1e4fi8QCKrFo4tw27ZtFBUVuW0rKipi+/bt9baoDz/8kIcfftgvY0XGRKOSUD447HiyZJXknuX27p3q/RSTwMaVss8CtVosFovLXrOLlZySUrf5sNydcJVlGa1aUjKux4VcyLweHxpMVKCOnzf9F70ko1erlROKKgmigoOc5gbILy3nz/OFvPnKXJ5/bDzn8/Nc3Iru1h0RoEdfgzXJn+69yOgYetx4kyKu7Ccq6yPYvUWLFvz8889u23755RdatGjh85iTJk0iMTFRieVyJDExkbZt29K5c2c6d+7MqlWrarXui0J0ovvvq+H3338nLS3NNfu7QHAVUa2L0JMI2blzJ3FxcfWyoF27dpGfn8/QoUM99hkxYgRWq5Xk5GTmzJlDbGysx77/fuftavNgVcV2rF7NvBf+6VXhWn9nGq7vcRtqbF+S+V0J+0zC5mrzZJFSSWrAtYSO/YSro+WoqMLglBi36lf792FVrE0ykts0EVYZxd33zYZ1bP7v54p1y47Oze+IJElM/dcMOvbqzZav/uu+QLWkYvSESXz0li25pFanY+yTz2BF4pxDSR/HwtUAGelHiIpr5PH9/O+aVSx+2Wah02q1jHv6OW6/272LtjaJI6sLSDcYDOj1eo/tnrj77rt56qmnuOkm95Y7Xyyily1ltvQWo0bZ4vXcFYMWCK4WnATWnDlzmDNnDmD7z7Vfv34uxU8NBgNms5nx452TI/qLDz74gFGjRrkcEbezfft2EhISMJlMTJs2jdGjRzsliHREo5JQlRX7vIYQnZaS4lLyz56h5bXX1thfFHv2bewrbZ8BRAQGAmCRXWP9ZFnGIruKKztlRjMVJrOTW9CbPwiqWptySstQa4KdxJM98agdlQQaWUaWcIrx0mi1bufISD/CqzP+5bGszbplH7N00QLMZjNqjYZH//EMI8c4n8BzLI2jUquRAIvFglarZeqMWS7B8+fz81j8ypwLdRtNJpa8Opehw++pk+vw8OHDHDx4UHm9bds2l7p5FRUVrFy5kpYtW/o8fp8+fWq9tiuGcof8YaIYtOAqx+nTpXfv3kydOhVZlnnxxRe5//77XepJ6XQ62rVrp9TZ8ielpaWsWrWq2qP59l9UrVbL5MmTSUpyDby1Y3fbyJL7mCtPSJKEVqMhtpHnv7AFtedK22cA+WVlNAoNQi25twSpJTWocVsIGlBO8oF3BzKqIkkSKlQ0a9eBUwd/w2qxKJYtu5DyFGN1Y0p/7hmZyvwp451OD8qyzOpPPlbWXDWAvWqAu8Vs5p15c7n9nnud3HyOfRyD7k0mk9uA+PpK27Bq1SpmzJihvF+eTgpGRET4rf6lI75YRC9WLcLaJFgtKSlx31CZS6uwsNDJmlnbtfkDUYtQ0JA4Cay+ffvSt29fwPYf0COPPKKUTWgIPv30Uzp27Og24BlsH4wmk0nJkrxy5Uq6dOnicTx7Jner1UphFbeLI+4sDiazmRGDUi5aIdormSttnwGUGi00bdmK0xl/uN1PVjf7zt2JPXDvNnRXy67qHDq1xI+7d7Ny01b+PP4H27d+p8QSeoqxqjCZ+d+Wzfxvy2a3AqyqIHQUOnYh5PgcJqORDxcuYMoLttqE7sSSp/HsxDZqhEajwVxpiVNJEBgQQKPG8dX+DGpi8uTJpKamIssyLVu2ZN26dS4/V51OR3x8vE/i1ht8tYherFqEVesMekNN1RbCw8PrtF5Ri1BwueIxyP2FF15QPvQyMzPZuXMnpaWl9bqY999/323Q8ZAhQ/jpp584e/Ys/fr1o2PHjlx//fV8//33fPyx52B0s1XmmsQWBOk0HsUVXAhEdiQsQN8gGaWvdq6EfWZn2mtv0bPXDU7XZFmm2GSzJjm67oJ0GiWI3V2Wd3s+rfMGEwP/OkJxocqyTLHBSFGVygT2mCyrxUzOmbOERkbz1er/KAHw1Z1WrDpnbkk5Z4rdB+U7BrDv37Pb7XN8uvQD5XfGXRC8p/HA5k4cObi/Iq7s40foNEy9+w6+Wb3S41g1ER4eTvPmzUlMTCQjI4MhQ4bQvHlzp3+NGzf2u7gCV4vojh07/D6HQCC4tKg2yP3dd99lxowZZGdnI0kSe/fupWvXrtx1113ccsstPP74435djKf/dBz/0vN08scdGpVE9skTxAUH+OxuCdJqKDEY6j2jtODy32d2Nm9Yx6nff3Xaaxqtlin/msWMf0y+cE3l3ppktcrOCT9lKKswUFBWTuf+g9n2xXrKjUbMVgjwEJSu12pZsfANMn77mahAHXKAloIKAxUmz4WhHXF0Vbqj36DbiYiK5nx+HksXvklskOtz5JSU8vIzUxk07F469Ux2ynHlWIRaq9M5nVA8n5/H/Bf+qcReVbW6Vc1GXxfspXA2b97M7t27yc7OpnHjxiQnJzNgwIA6jV2V2lhEBQLB5Y9HgfXGG2/w9NNPM3nyZG699VYGDx6stN1yyy2sXr3a7x98/iZQq0WN51Nqjrj9sNJoUUmqes0ofbVzJewzsIn5L5d/TGSgs7XGYjYTGRmBVqfDZDQ6ueEckSSJ6OBAp9goO998upKIAD1heh2hOi1lJjNBWtdfXZtr20TGbz+7iJ4zJrOL29ExPsvu5nPMueUu0enWr/+rlMVRecglFxcSzJHd/+Pwrh8oM1tJnfIUX+zep+S4Ajh2+DDRjeKdDpF8+NabTnm93Fnd7NnoOyY7Wwp95cyZMwwfPpxdu3YRFhZGXFwcOTk5FBUV0atXL9atW6dkePeWCRMmsGHDBs6cOUP//v0JCQnh2LFjnD17luHDh9viQSvdk95YRGtDdnY2JpNJBJULBJcAHgXWW2+9xfPPP8+0adNc4jDatGnDkSNH6n1xdaXcZMKC7HPAMFT+dS/DlOn1m1H6audK2GdgE/MVJiNygNY5nYJKxbmCAh598hkWvzqXiCrtVamayd1d7FSQVuNxDI2HIHu9VkdyygBGPjKOs5knmPXUVFt9TjwnRq0q9OBCzFTrdu2Q1Wq3VjHHtQZr1axeMI9ArYa7HnpE6dfjxpucAp/P5+ex5pOPnOZyF+yvUqv9Uox93LhxZGRksHnzZm699Vbl+pYtW3jwwQcZN24cn332mU9jLlq0iEWLFrlcb9myZa0sor6SnZ2tuNtPnjwpRJZAcJHxGIP1559/0rt3b7dtWq223uNk/IHZKhMQEkapwVRz5ypIksS/Xn1NBLjXM1fCPoPKAxVqjS0fVWVslCzLnCstY+Y/JrPk5Vm0adnSa2tqeIBesSq5E0ye7gvWa90mLl20eh0vL3mf08eP8c6MaUQE6IgPDSakikWtquVLVWUqrU6nFHSeMn0mJWar0/O6W5skSSxfMJ/Cc/kenzn90CGX2olWGQoNJqfxCyuMWKrk+qoNmzZt4uWXX3YSVwApKSnMnTuXTZs21XmOhiY7O1v5Pi9PxI0KBBcbjwKrefPmHo+x//jjjzUeW78UCNCoKCkswFBNTEl1lBb5fmRZ4BtXwj4Dm5i/95FxmGQVOSW2Ys85JTZxGB8aTExwIPmnM70eL0hnK/ys07gewKguSaZeo6a0iijpNWgoHbp2p/BcPu/NfVFJleCYrNQd7opTD38wVbHoDhs5ik//t5fJ897i5r/czTmD2ePaLGazU6HpqrRu1w5tlVxcao2GUoPRKfC+qKycY4cPexzHWyIjI4mMjPTYZo+XEvif7Oxs9u7dKzK9C654PAqsRx55hJkzZ/L+++8rpUxMJhP//e9/efXVVxk7dqxfF+JtKYn09HR69+5NUlISPXv2dEocWJXwykK9RotrOZLqUEsSgVoNy954ld/T9vLr7p3V/vUtqD1Xwj6z02fgEG6+pR9xIcGEB+qJCwkmIkCPRqUiUKtBo3L/6+Zpb9oEUAAVVaxE1SFJEiEBtjiwJq3b8OaGrxn77PNs+XIjK999x8VKVJ1FrWoQvEajYdiIB53K10RGx3DLoCFMeWkuG3bvY/CIVCQ3z+no2is8l8+vu3dSXHAhJ9HWr750skypNRomPP0cWp1OCby3yrUvw1OVyZMnM3fuXIqLnRMRFxcX8/LLL18WcX+XI3Y3Zs+ePenWrRtt2rYTIktwxeIxBusf//gHp06dYsyYMcqH3I033gjA+PHj6yXDtjelJMaOHcuYMWNITU1lzZo1PPzww+zatctt38KKCiDU6/nVkkRkUAARgfrKEiMyr4wZTZHBiEar5e/P/IuB997vyyMJauBK2Gd23p41g7wjB5zcbGEBOmJDglDZ45rKDZwvq8DiIJiKKwyEVmNJCtRplBI4viTLzT2Zwf+2bGbBnJlYK+skxocG1yiq3AXBA9w8YCAjBqV4zOoeGR3D2GefZ9C99/HI7QMI0qiVseyuvW9Wr+S9uS9iNpmU36meKQOYP32aUxJSlUrF7XffS1BwiHICUVdDXURfOHnyJCdOnKBZs2b069dPCXLfunUroaGhZGVlMWnSJOW9fPPNN+s856WMPTi+IeZREJneBVc41aZpWLBgAZMnT2bTpk3k5+cTFRVFSkoKrVu3bqj1OZGTk0NaWhrffvstAMOHD2fixImcOHGCxMREl/4VZitNW7QkL/NkjR9Mjh+EdlSSRGxoEKVGk1+PiAucudz3GUDj0CBO/PYzofoLpwjVkuS0p6RKAR8eqCe3pExxIwbpdNXvz1oc0gDbibslr85RhItVhhKjyWmNVbGJPzC7OUW4/duvPWZ1d+Rs9hkKyiookkCjUmOtPJm4a+sWPqwUV3Ah7YI+NMwlGanJaOTY4cMMGzmKfoOHKCcQ61Iqx5GNGzei1WqJjIxk//79ynW72/CLL75QrnkrsCZNmsTnn3/OyZMn+e2335xEfHp6OqNHjyYvL4+IiAg++ugjrrvuOr88S105c+YM7du3B2zvC6DUFKxX4t0n+hUIrhSqFVhgOwHjbzeNJ2oqJZGZmUmTJk2U+nGSJJGQkMCpU6c8fvD9WVyOvgbvYNUPQkdUkoROo6bcZMZkNLJ721Z63GILjC04d47f0vZxfdduNRaF9pWrqdgzXP77rGlEKCE65xginUbtcU/FhgRRajChVatd7vMXao0Gg7Gkck5bwHyZ0UiIzv1JRlmWMVjcp2cAkK0WgrRq1JKKCosJyWxm59Yt3HDrAKdizjHxjdFqtZhMJjRqiYgAm9Xs/Zn/crJSgU1IGcrLlf6Oa9cFBNhOGkoq4ho35ve0n2jWqjWhEc6xU7XJzJ2RkeHzPTVRXbHn2lhEG4qzZ88q3xcUFNi+KRfxpwJBXalRYP32229kZmZSUVHh0jZs2DC/LcTbUhLuSoZUx+OPPcZ7s/5VbaoGTx+EAFZZxmC2KHO99NQUpkyfCaDUWHPnLvEHV2qxZ3dc7vtM7ybxp9FswSrL1Qr3QI3nlAve4Glfy7JMfKskTuXvcUnDYLJY0apVLukVSoyeXURV83eFyjar20ezp/Pb7p188dl6p9+FqTNm8fqM54nQX3i+quIKbLFZN/S7lakzZimuQLAFxY//6zCmTp9JsE7r4la8FF31noo918YiWl/UR56smsRqbm4uaWlpxMT4x/ooEFwueBRYBw4c4N577+XIkSNuP1wkSfJYuLY2eFNct1mzZmRlZWE2m9FoNMiyTGZmZrX/Wej0+mpPSoHnD0KrLJNbUoZVlivjSAwYjWbmT59mCwCu/Iu7OneJoHqulH1msLjmbLJU7h931lG7cA/QanzO06auFGdGswULUG40EeCQG8suon7dt48grdolDUNVcWUnVK8jRKd1yX9VNfO8fRywufl2f70Ri8nm4rP/Lnyxex+N4mKZN/Uxl3kc47zssVnDRo6iS3IvRgy8VSmTYzIaeWPG88QFB2KxmJX5/OWqz8vLY968eezdu5fMzEzWr19P+/btefPNN0lOTqZXr151Gt9ObSyitSn27Fio2V2BZUdX4C+//KIUWLcfLoFqCje7o9KN+Pzzz1fbbdCgQQDoA4NY+uEHbtftqRi0KPYsuJzxKLD+9re/odFo+Pzzz0lKSkJXTT2xuuJtKYm4uDi6dOnCsmXLSE1NZe3atSQmJlb7V2Bm+uEaP7zsH4RxIUEX/tqWZfJKyyiqMNo+0LQaiipshW1VFgsmq+ciuALvuVL22anzRQS7qXlZVGGk1GAiIkhPRGAAqsrDE3bh7q4OZnU4xgpaZZnc4jIXgWYXUdEhgW7H8JSryv41IkCP0WzGbMXtM7m7V6NSKyV27L8LTVtd6zYR6dmSUtSSWonzssdX/bBlsyKuoNJqptco4sqOP7K5p6WlkZKSQmhoKDfffDPbtm3DUGk9+/PPP3n99dc9njCtDb5aRGtT7NmxULO7AssnTpxQvjebzUp7WFiYcr2mws1O2N2IyQ/Ajyuq7ztgMoZNbzj9fKtbqyOi2LPgcsWjwDp48CBr1qxR/vqoT2oqJTFkyBBefPFFunfvzpIlS0hNTWX27NmEhYWxdOnSasdu1rqtVxaCUoMJOQTsvVSSRExwEBK2EiYqSaJJeAi5xWUUGYwuma79dXz8auNK2WdlJgtVPzLt+85SaVFyh128G80W5WShJ6rGCtoPYZSbzGjUKqcx6uJ2tJe7KTeZCawma7wddzUNYxs1sgW7uynPY7GCzfZm+705cuA3Jo9+wCnQvWoGe0e0Ol2ds7k/8cQT3HDDDWzYsAFJkli58kIR6eTkZL+Kq9pYRC8rohNr7iMC2gVXIR4FVufOncnJyWmQRdRUSsIxRqZNmzY+BYfu/G4TpQaTkhvIE5FBAS5uHJvIClT+k7d/oFlkmQqTWSlpotH57/j41caVss/C9FpFEDi68MxWKzq12sk6qnIQSnbxbrdqFVU4n6aTZRmNSkWAVoNOrXK7R5tHhSlpRdyNURPu/gCRKnPBeRJX9nsklYrzpWUugfG5Z88S26gRRiucKS5Fo1K7nEyUVCrG/eNp3n5ltkt+LncZ7AE0Gi0PP/08YZF1s0Ls3buXdevWodVqXVzQsbGxft2TtbGI+gNv4q2ys7M5etRzAlh/Ux+HCwSCSxWPAmvhwoWMHj2axo0b069fPyV+4HJCo5L44duvUUkQrPdcA04tSUQE6t22Vb3Hbsmyf5gldOxCh+7JdEn2T7zG1caVsM8AWkSFu+S9ssoyJQYjIXrXNAxuxXvlyUKVZCt5U240ExqgU/KygasYcnztOEZN1jBvqE5clVYYMCNhMJswVzHO6fR6dm79jpXvLcZamb/L6CaOTpKtpP1vB1aza3C9uzqEao2G19dtpGmLVnV4KhvBwcFOsUeOnDp1iuho3/9Y8lTsGfDZIlpXcnNz6datG+C5LqFj7cJ6x8t4LYHgSsLjp9l1111HcnIygwYNQqVSERjoHM8hSZJTUOWlSLjeJpqsMtUmcwzW+36Sy/5h9utPe/h59y6+/GAxrTp15V9vLRZ5snzgSthnYHMtO7rwVJKEXqNGLUkurkPA7aEKlSTRKCyIIK3WqfCy0zwO1z2NYU8rYrekGUxmrFyI+/G2tqHHZ5UkghW3n84lKL5J02Ysf/ftasewn0rM+Hkv8aHBLmNYZZzcixqtjr8/87xfxBXAwIEDmTlzJikpKUpMniRJlJeX8+abbzJkyBCfx/RU7Bl8t4jWFcdahJ4SeSpJP72JoaorvsRrCQRXCB4F1iOPPMKqVasYNmxYvQcf1xc67YWSHeVmM6G4t1KFVZN4sTpUkkS3hHjiw0JQq1RYy88x7a7bSLplIC2TkjCXldKj/yCa+OlD4UrkSthnABUWC9pKF16IXkdkUIAihs6XVVBiuOC2kysPUMQGB7lYo+ziCqov6pxbUkapwURCVJiTyLKfTnQJhq9MbOpJuPlK1aB4k8WMCjUmq4UTfxxz6W/Pw2U/HFL1ZKPd3e7oQiwzmm0ueJUa1EZKq0kj4Ssvv/wyN954I61bt6Zfv35IksS0adM4ePAgkiQxc+ZMv811sTl06FD1KRIcYqjq3YXnTbyWQHCF4FFgrVu3jtdee61eSpU0FEUVBgi3lcoJ0Lh3EaoliQBt7RI92tyFoRdcNCqJa6Mj2P/V55zcbovp+mHFh7TqdTPj5rzO+fw80g8donW7diKdQyVXwj4D214rrhRRdnEFF7K3lxlNSrqPzPPFBOpcf/Vk8JiPzRGrLFNUYUQCyk0mJ4tXicF23SUYPiSIcqMZncZWvNlgMqO1p3rwwp1YnSiTJInY4GCnQHZHa1TVPFwGs8WtFU2jUmPBlhvLYjIqgsxosYDFwvzp0/yWCuWaa65h//79vP7662zatIlWrVqRn5/PiBEjmDJlyhV10mzkyJEEBAax5tPVNfYVLjyBwH94FFgRERG0bNmyQRZRUVHBfffdx8GDBwkKCiI+Pp7Fixe7DQJNTEwkICCAgMpCzs8++yx//etf3Y6rktQX5jCbCJNdY2GuiQip9V/z7vIJqVW2GC1DZcyJJEkc272D8Xfdzv5ffsFsNtdbYtLLkSthnwGEBejRq9WYrVa34kGrVinCollkqHLdEW/ElazEdWld8mtJldazMqPJrevQ0drl6Ga0n4ytOo9j3FeZyUxQNUHvVa1RVouM0YO1KkDrRlzKMpJGw5MvvIRkMrDsjXlYK+Ow7ILNZDTyzqsv89zcV2t8n7whIiKCGTNmMGPGDL+Md8lSWfOv6gGP3Nxc174X0YWXnZ1NVlYWarWamJiYK+eUpeCqxaPAeuKJJ1i0aBH9+/dvkMDjMWPGMHjwYCRJYuHChYwZM0bJfFwVb4r1Apgc8ucEuHkGvVqNTq12ue4tJovVbdCxyeoc9auSJIpOHScuSE+J0eZGWvDSv0RiUq6MfQbQLCKUFtHhaFQq93vCIU2Dr4LecTy7iAp1EzgPlXFcocFux6gqxuz9Y0ODKDEYcdy1GpXqwklIWaaowkCp0eji1nSHJElEhwQq2eG9eV5Jkpjy3DRuHXI7f+9/E1brhT9QHN2Hn634BAkY9+TTdfrdyczMJDc3122uqbS0NOLi4pREnP7AV8HuV8IaA87WqdzcXPepUS6SC69qwH1AYBBHDh/yLS+XQHCJ4fETLSMjg19++YVrr72WPn36KIGgdiTJfxXmAwICnIJKe/XqxRtvvFHncbVq2+OpJNxmcw8LrKHIrhtUkoReq6qs62bmfFmFS7yN1Y3LpXFYMCUGHcF6rRIXs+LVWYx8atpVHRR/JewzgBbR4crPteqeKK6wJbBUVVqyTBar2z3iCXeWLk9juXPl1RRzZdvTGspNtj9I3MVvGcwWSgzeiSXHdYfotF7HfK1c+DrxTZu5pGyomsh0/YpP+OLT//CPGbNqbQV+9NFHad26tVuBtWLFCtLT09mwYUOtxvaEL4Ldr7gJMHcMgr8UUALuQbG45eXlCYEluKzxKLA2btyIutK6s2PHDpd2f37wVWXBggXccccdHttrKtZrp7zyP2pPOXWKyo3VZqnWqFQEaTWYK/NeBem0TvE14YEyZUYTfxYUKx92AIFaDTIyRvOFDz+b5UHrZDnI+HEHj952E3dPnMot/+dcb+9qKfZ8JewzcHbvlRlNmK0WAjVaQgJ0hAUGEBpgO2DhKfDdF/FVNYi+uMJIUYUBlWTLXVVuMmN2sKKeLSolLizYrdvQ7rq019vUqlRu47dKKowYzWaPaSI8iShJkig3mgj0oqC1yWikpLQEjVbrJLLcJTI1m0zMnz6Nzr160/Laa2scuyo//vijx+Li/fr1c0pAe8VwuQSYi6SkgiuEai1YF4PZs2eTnp7O4sWL3bZ7W6wXwGyVufWOO9m2cYPbDwCDxUKF2UyAm4K70cGBBOkuCCJ3R9wlSSJIp8VktlBkMBKq1xHhIMBkWaagrAKjxYLFKqNWSU4foCpJ4prQYL57byFRkZF0ufkW1CoV2cfTCYyMueQKMtfH2FfCPoMLaReqih93JwLtge8WqxWD2eIk3N2JL0dUlfc6jhsWqCe0MpGuJElEyDbhn19ajlWWKTWZnWoiqiSJ0ACdYtW1VvYvNhidEqI6zqnXagiV3Z/CrU5AybJMudnslcACaNepCw88NoVlb86zFYeWJArKK1wSmcL/s3fe4VJU5+P/zMz23dsLvUuzASJFbEFARPHrT4xpFjQmahRLNEaTmIgdo9gbxmiiGKwYu4kFFSMqihiJKCAgIPX2tm1mzu+P3Zm7ZXbv7t69wMX9PM997u7MmTNn5rx75p33vOd9IwpZ7Y7tOSlYLS0t2FMsbpFlmebm5qzr7IhsFPbO5iLsKKdgVjkHu5hUYVgaGxsLuQgLdGv2qqiOt912G4sXL+bNN9/E4/FYlskkWW8sRx57PG+/9ALNwSBFzmRrVVMghMsXfxscihKnXEH6JfMlHhdOuy0u4a6xrzThYWsXGs5wiDohURsIo+mCMo+LV++ax+LbbqDU40bVNOx2Gz/42c/Zb9QYwkh89Pab2BSJw2eeXAj70Em6Qs52NrXSv7yY3iU+wpqOlmZazIFOqazjLHITFPEKmKF8BRKsUAapEjVbKf6N/iBhTaPK52ZHcxshVWN4jwoqYgKcQkSBGlZdwZYG68CbRuiHVOdNlbDacI4PqslBQ1Pxwj8WsvixRymyR6zOEYVQTln+6/99kVP+z5EjR/L8889b+iH985//ZPjwzqXiSSRbhb2zuQg7mlqzdHDfQ7S1tdGrV6+k7aFQiE2bNmG32+nfv39SZPpt27axbdu2rB3iC0pUgd1FhwrWunXrWLNmDYFAIGnfrFmzLI7Ijdtvv51Fixbx5ptvJvnhGGSarDeWxrpaAJqDKnZFibNWGYEhYwf+WAtEpthkmXKvG1XT43LCKZJEqdNGhSKo02Wq9QAjaEV2gg5sqyylXpep8wcpQ6VGdtCgRR6g5V43O996gTUvP0VI1WhoC9AcDPG/fz7FwfuPYPqV1+PtO9Bsg95UT3jTWuz9h9Kmw7b1a+k1eCi+0rKMr2NP0t3lrF95MYcP6RdRCDQVrbUJdI2AJmiVFModNmpV6COrDNNbUCTQBKyTvGgOFzvViCWoTBG0oNCrf0+2NTTjD6tomsCmSIQ0nd6lPhRJQhMQCKspQywYio+q6xQ5HZS5XfQqLUq5UlGWI4FRjalCA10IGv0BYo+KTQUUq0habffYbWi6TlMgaOkHmdjmp/76F8o97eV0XafM6yYkIg/cRB68dR4nnHJq1g7vl156KWeddRaKovDzn/+c3r17s3XrVh599FEefvhhHnnkkazq64hsFfauZreEY2jrIEBwdP/MmTN5+eWXk3Ybyq/L7eG9d99h/PjxQCQyvd1uN53iDYf4wqrDAnsbKRWspqYmZs2axZIlSwDrKbLEHF65smXLFi6//HIGDx7M5MmTAXA6nXz00UdAexLe8vLytMl6regzcBCKoqBpGq1BFXfMtIDDpsQ9cBKnXzIh1iE4dul7IBRipF2lpLkWSRVmNG+jZhnoHWygDyBske2CIEHFhuzxojl0EALZpbBVc7LOaaeX3sb+cgC59msaf/sjdqLQVNYTd1sDRcFWJEADPvUrfN6q0csh0fegMZQdeiReh51eahvFEyeDsyhOIZOLy/ZYjK59Rc4mDe6LLEn0DDZQHWiM9LME2CLxrSQNRKSTMS5NkWAYrUihVkYSKRstQljW0Xp6qLG5qQkJZC1MpQKypuJRA9QqLnSc7Ajp7PQnp8axyTJlHheqrqNqOr1K0ocjiV3pGOsPJgE9i32UedzsaG4FSHKAbwqELB3jmwKhyBSmy2k60CeeM9GfK1a5MtA1jT8/8BDfrF/PPTdeF7cvFAyy7quvsrZinXnmmezYsYNrr72WBQsWmNvdbjfz5s1j9uzZWdWXjlwU9i5nd0Zvz2B/Q0ODdZmow3tsvsQ4B/0Yh/iCglVgbyOlgnXllVeybds2li5dyhFHHMHzzz9PWVkZCxcu5O23347LPt9Z+vbtaz5YrYg1padL1mtFUUkJJ/7kZ/zzicfRRPxURShh6iLV9Esq7LIc57MiSRIKgl52qHAqyLu2mwqVVa2J+yTAJVRobYz8RRkIDAAkOV5Bc6Phrv8urk4FGOfWGOcGCQHfrkB8uwKAMFDzzP1oNjtBNWw+zLd5KtjW0MSHO5pRXE4OmX4ix513CTu/WsX2V56kV5/euA4/ni83b8XntDF09KF5s4ztK3Jmk2WGBetwBZqT+jobGTA+O9qaoK2JvkBvJGSE2V8SUEzkPEOAgM9H2O1D03TWtIao8nnp5VLQPMWoio2vt9dQ09KGEIISlwNZU6kNqoRiHJskSaItpFLhdVEenUI07pVNlrErMiUuJ/X+QJwDfLXPQzCsWTrGx+ZEdCfEvkrnFJ+IEIKvv/wfw0buj91uJxzjAO9wOhk6cqTFne2YK664gvPOO49ly5ZRW1tLRUUFhx12GMXFxTnVl4odO3ZkrbB3OfuKw3vBIb7AXkxKBev111/nxhtvZMKECQD07t2bcePGcdRRR/Gb3/yG+fPn8+STT+62hnaGn5xzHi/8YyE2KV6B0oSgptVvJt21imuVCuONPbZsuSLobYtaKIJhywdqLkhYP5zTlU/1XQJsMcl1JaBXWy29HXBIPxcCEKveYtecf6OEQ/QFWAOhJc8zyOag0VnMxoUCh8+Hw+Gk2e7G53Ehubz8V3fTY/gBjJk8DUdRCZmwr8iZSwtbKledRQKUmGyGVkq5O9iCK9iCBIwHaGpAaoooY2pxJQeVlNHqsVEkVOySQJKd6ALWt6lsbAvRGgyj6jqlbgflXndcDCyfy06lN6I89SktYu3OOrY0tDuAS5JE37LkqUc5OkWZKr1Npi8yhh/XC3+5H0mSqHI7aJUlmkMhJMXGZddcT2knwpwUFxczffr0nI/PhMGDB2etsBcoUKD7k1LB2rlzJ/369UNRFLxeL7W1tea+GTNmJK1s2duRJMlSS2nwB5GIrBrUISmGkXlsDLFJfc1tCHoZyhWA3W5aG/Z2Yh/WEkDIb1nGpoYoV2silpS2WiTACQibA7zFjJVk1C0r+c8bz7Hfj35OvyOndXjufUXOBgdq9mhfp7KE2ZpqUNQg7rZm0wImisqQPUUM8droU1FKfSBMk7+N7U47VV4XXq8HRVFo9gfRhcBptxGK+mYNrS6nwR+gLaSaKyed0fQ7HoedlmDInGrsVexlZ3SqsDPERpCXJAmfKxJPrjmsMXlG9kmZAe655x6+++475s2bl7Tvqquuol+/flx44YWdandXE+vkbbfbWb16tblvT63OzZWO2tvdrqdAAYjMNFnSr18/c6576NChvPjii+a+Dz74wIxI3B3YsPZrdF0nFDXRJ1LvD1LT2mamIfmuoZkdTS1819BMQ1sg6ZhE3y0AlxwJaGoiKwhvCaknpLonVhYUWQ0hNdagNOzEses7hoXqaH6947xnsO/ImU3tnBLRVUiA1NYcr0Q314OuRaxfgRZ6EWSEW+HoHj4O8NkYIIWotksMKvEwpNRL72Ivg0t9lLscVDgUhlaW0qe0iAqvmz6lRQytLmfS4L4c0q8nRwzuR5+SyAo2KTqFqFhYq1w2G31KfJYZFuLaL0kppxKL7AoL5v85p/ty//33M2SI9WrcYcOGcf/99+dU7+7CiHw+duxYhg0fQe/evTn99NPN/d0tp2Bce2Od46Ofu9v1FCgAaRSsadOm8eabbwKRdCYPPvggY8eO5bDDDmPu3LmceWb3yaM3aOhw7A4HuoCGQNBUmGIVpwZ/iJpWP0IIc1m6LgTNwRDbGlviyoai+2Jp00iO1WN3dgsLVj6IU7ia6yluySxS9L4iZ3tzP1tOGSdMEyf+dwTbcAdacAda8LQ24PY30ZsgfRWNoyo9HNqzlBK3i14lPgZWlOJQFFx2GzZFZlh1hWnVkiSJXsVe7HJkqLHLMmP79eCM8Qdw4kFDOWP8AYzsGT/FZ5dlSt1O85iU1yVJvPb0Iuprs49K/u233zJ06FDLfYMHD2bjxo1Z17k7MSOfT7uUYCBqcZ7ws/YCsZ+7A7HtjXWOj41CX6BANyPlCHbLLbeYSVDPOOMMnnvuOUaMGEGvXr249957LU3rnWHt2rVMmjSJYcOGMX78eL788stOlYultLycy+fegM1upy2ksr25lV0tfpoTQgI0+IN8W9eUpDypuk59jCVLi66SMsrpQrC9pY21rSHiDo1OE8YiUnzel5AASbX2vUlkX5Kz7oIAsKUP/Jk41Zj4v79D4phhAxjVrxe9SovpV1FK77ISBlSWUeJxMaC8BJ8zEvzU7bAzoLyYAeXF7FdVyth+vVCiypMiyxw+uK9pyepR5GFAeTFVvsj/HkXWccoMnLLEyo8/yur6IeJ7lWraaf369Snjo+VKZ+Vp27ZtrFixguXLl/PZZ5+1TwfGOnnHOq53Fyd2g47aG7N/9erV/O9//4vbvWvXLlasWMGmTZs6PJVxLzMpW6BAZ7C0z4dCIV577TVGjx5NZWVkyf7JJ5/MySef3GUNOe+88zj33HM566yzePbZZznnnHNYtmxZzuUSmXX6mYyZMJGfHTsZTdMIaRqqDkWueKf2sK7HRb3WhcAfjqQIaQuFcdhkqnxemgIR5+DYuD//C4Ro1CSqXXZ8CpQrCnJRGTTXt/u/+EoRkgy6Cg4XQlXB34KkhuJWiXUX/y0rBCB18ACHfUvO9vb+ipUrUVQGcu5JzonWpQgNnXZLlfG/osiL2LaLMo+LtlAY3QiyK8sUuZzIcvydisSRc7GruS0uibUkSRQ5HdS1BpISqBt4HHbacohKfuyxx3LttdcydepU+vXrZ27fsmUL119/PTNmzMi6znTkOm5BciLk7zuxU6EGsTGz0sXE2r59OwcccEBGZQsU6CyWFiyHw8Fpp53G5s2bd0sjdu7cyYoVK8wfzimnnMKGDRuSzPSZlkvkueeeA2DQ0GFccf3NKNG3ZaspQyEETYEQG2sb2dLQzMbaRrY2trChtpGtjc1sbWihLjqVqEWVL1XXzTq2NLXy312NfNkQYHUA1tuKWFPcl7qSarTKPuAtRrh96N5ScLjA44OKnoiqPuhl1ZE/XynC7jQtXAIsP2Px2cpilqnVLB8WNQER37MMHuD7kpw1FFVlff866qtsj09br8ONXlaNqOoDnqIszpL6HLps7T8lSxLeqKJkV9qHGEmSaAmG0BPm0lVdp67Vj9dpT/K3kiQJrzO1si5JEmF/W9btnzdvHrquM3z4cGbOnMm5557LzJkzGTZsGLquc/PNN2ddZypyHbcMzOnAEZPbNxqf675t39a0Lf3njvbvbWVTHRd7H2Kv/5BZBPxt/OUvf+GJJ57giSee4OGHH477/Lvf/S6u7N6W9LrAvkVKD9MRI0bstgff5s2b6d27N7ao4iNJEv3792fTpk0MHDgw63KxzJkzh7Vr1wKRge0Hx89k9MRJrPrsE1Z88AGv//M5tje3YpOVSIJeu40SlxONSNJeYzWhBjQHw0CYsK7TGgrjsisIAVsbW0BAv7JiWgJBdKFTXeSjNRymd2kRsqSwSbhoaw6i6CGCAko9bnwKlMhQqoAkKwiHQsjuQtJ17FoI1BAiGEA4XSArkWk3mx10Db21GUlo4PIhhAaaBi4PBNqgtandWuEpiih1rc0QDoLDjdA1CLUhaVp7OZsTikoQug5aGDQ9YuVQJAiHIdCGJPT28nYnCB1JbQ9HYVrovMWIDJfh7yty9sDqbVx6yBCcwdZIv4TDkfutKKDrSOF2C6WQFbDbI30XDkX6TAJ8peBvgVCgvawkRYKTxsTBEi4vON2gqdDSkLzdZotYRjUVhB6RC5sj4/uUGBQ3drtxrpDLCyl8pDRdpzn64mKsKIT2YKZrdtYytLoCRZZQdZ3/rN9CQI3k66z0JgcfbQmmnm4WQlBbV5fxtRn07t2blStXMn/+fN5++23WrFlDRUUFl19+OZdddlncatbOkuu4ZeQinDBhAk63h+BXSwApslT5qyWRwHivRBVBSYb3H23fn/i5o/17W9l0x8Xeh9jrX7EYgBtuuCF9h0TLOt0ebDYbdTHyU0ijUyCfpFSwbr75Zi699FL2339/xo4d2+UNscpj1plyfn/E8fPCCy/kvvvuY+zYsXFvjH2HDKPvkGFM/r+T+e7bDZSUVdBYX0tJWQX/Xvw0K5a+g6ZryJKCLjQUScFlV/A67DQFQrQEQjjtNoJhFeMRUtvmN9u3qaEFh03BLsv0LPFS0xx5Q/c57fgcdhxKM26HnZCm4rEp9C/2ERZgd0Kj349HsVHlcuByOAgpdpySgsuhIaHjskmEfGXImo4S9oMOqgw6Nuw+J3hKUdQgwuZAkyVsgFRUikz7AxLKIopaVGmT5Ei0pdiJGJ2oidMNFJcj61q7khe1UAldQ4SCUSuJE01WCLq8NMlOytvaOvRl6e5yBhFZu2fpZwwZPoIDZBf9vE6QZDQ9jBMdDbDrGooaJmSzo0TvnY6M7JaRijTsUQUKlwddDSFFFWthc0T6S9eQY/oKokqV22v2iZAVdCL9ptsc5o/b6HfzWoifhhaAGrlohJAJuT1Iih1nWyOK3q6Eq4od1eFGV2zogBQTM86IH6fpOl9tryGkatS3BUw/RSEEYV3HLst819jCjuZWZFlm3a56M1dhWNdpjiZNN15smoMhy5yMRp0NgSC9h+xHWwaylkh5eTk33nij+X3Hjh089dRTHHfccXzyySd5yyAAuY1bI6MBVFVV5blnnqahoYFQKMTtt9/OVVddRSgUwuFwmFaYyspKM52Qw+FI+my1DeCOO+7g17/+dUZlM6l33rx5XHbZZVm1oaO2pSsbe/01NTXU1NTgdEYSkweDwbjPTz75JHPmzKGyspLS0lJUVY17LmzcuJERI0bk3QevwPcTSaT4pR900EFs3bqVhoYGKisrqa6uTkoo+/nnn+elETt37mTo0KHU1tZis0USx/bq1YsPP/ww7g0v03IATzzxhOVcfabYZAmfw44iRywIsizhUBQkBF6ng7o2P267naCq0qvYh12WsdsknIodTejoevvDSxcaHocdoQvaVBVVB7ddptLjIRAO4bI7UFUNn8eBTZIpcjoodjujeROhJRBiZ3MrTpuN/hUluGwKQU1jV3Mr5V43lV4PLYEw1cUemgIBJGR8LgcNrW34XG68TgWPw4G/zU9vjwPJGbHQuZDQbAq6plPmdtIUUlFsdiShoenQ6vdTXlxEWFPRdIGmqhTZZGQEWkilyAGqAEVxEFJsCJuDxkCAO97+iGc/X8enn36aNmEtdH85g3hZO7hXFVOH9qO62AvRaeSGNj9DKiLO322hMGFVoy0cxm23oYtI2I9yt5NqtwNV09nc2EyZ24kqKbgcDhRFxh8KUex2UmRTcKESVHUCcmTqrNSuoGoqms2BqgvCqo6OwGGzIQF2WULRVWqbmgmLiAVNEgK7zYZLkdnZFoj0s6qxqa4Bp92OLEnUtQXQwkF6ux1saQ2CYuOLbbtw2mz4w2HKvR6KnQ6EEAQ1FXRYV1NPqcdFsdMJUmQBSEsgRF1bgKCmEdYiVuJ2vyodt92BPxxCQcZht+GwSXjtDlpCIRyKLS5ZuhCCQFjFH1YJqBq7Wv1sqGvKSNasaGlpYfHixTzxxBO8/fbb6LrOoYceyuzZs7ngggtykLJkdue4VaDz5CpLBQokktKCNXbsWA499NDd0ojq6mrGjBnDwoULOeuss3juuecYOHBg0uCTaTmA6dOns3DhQgYOHIjb7d4t19EdMbxbgkSChhoogIOIgmijXVC06B9AzGJqExfwu9Phd0Sm/zqiu8sZ5F/WDuhgfzD634gQFrsWVon+xWJYtVIlgOkb8/mgFGUGRP8f3kHb9hSZyJqBqqq8+uqrPPHEE7z88sv4/X769u2Lrus8/fTTeQ9uWxi3uhfZyFKBAmkRewlfffWVmDhxohg6dKgYO3asWLVqlblvxowZYvny5R2WK1CgIwpy9v3l3XffFeeee64oLy8XkiSJqqoqceGFF4oPPvhA1NfXC0mSxLvvvtsl5y7IU4EC3z9SThEWKFCgwL6ELEdykU6ZMoXLLruMadOmoSgRe19jYyNlZWW88847HHXUUXu4pQUKFNgXSJunoqGhgWeffZY1a9YQSAjKCXD33Xd3WcMKfH8oyFmB3cHo0aNZuXIl77zzDrIss3PnTk4++WSKijoftqJAgQIFEklpwTIiDweDQVpbW6mqqqKurg5VVSkrK6OkpIT169fv7vYW2McoyFmB3cnXX3/NwoULWbRoEevXr8flcjFz5kxmzpzJ2WefzZIlSwoWrAIFCuSFlKlyLrvsMiZMmMCOHTsQQvDqq6/i9/tZuHAhRUVFPPPMM7uznQX2UQpyVmB3Mnz4cK6//nrWrVvHBx98wDnnnMN7773HWWedBcBdd93Fe++9t2cbWaBAgX2DVM5Z1dXV4qWXXhKapglJksSyZcvMfXfffbeYNGlSF7uHZc6aNWvEYYcdJoYOHSrGjRsn/ve//3WqnIHf7xcnnXSSGDp0qBg1apSYPn262LBhg2XZAQMGiOHDh4tRo0aJUaNGiSeffLLDdmd6TLbtrq+vN+scNWqUGDp0qFAURdTW1ubUhosuukgMGDBAAOKLL77IqW2pynUnOTPoKnnL5dhc5M4gXb/m6zoyPUdnrqOzaJomXnvtNXHGGWeIoqIiIcuyGDRo0G47vxD56e/EOl599dW0dXY0TsSeq7KyUlRWVnZKVmL3VVdXi969e6esL5Ox12hfZWWlsNvtnZKxzsh4gQKpSKlglZSUmCtqKioqxHPPPWfue+utt4TH4+n61mXI5MmTxaOPPiqEEOKZZ54REydO7FQ5A7/fL1555RWh67oQQoh77rlHTJs2zbLsgAED0v64O3NMtu1O5NZbbxUzZ87MuQ3vvvuu2Lx5s2XZzt777iRnBl0lb7kcm4vcGaTr11za0plzdOY68klbW5tYtGiROPHEE3frefPR34l1FBcXZ9VvieNE7LnyISux+6699loxZsyYlPVlMvYax+ZDxjo7xhYoYEVKBWvcuHHiscceE0IIMXXqVDFlyhTR1NQk2traxI9+9COx33777bZGpmPHjh2ipKREhMNhIYQQuq6LHj16JL3tZFouHcuXLxdDhgyx3NdVClY+2r3//vuL559/Puc2pCqbj3vfXeTMYHfIWzbH5kMxSVdHPuQvk3buLQrWniAf/Z1Yx/bt24UkSWLt2rUd1mmQOE5YnStXWUm1r0+fPhn1u9XYm9iWXGUsXzJeoEAiKX2wfvzjH7Ny5UoArr/+epYvX05ZWRnFxcU899xzXHPNNV0/f5kB6fJ85VIuHXfffTcnnnhiyv2nnXYaBx10EL/4xS/YtWtXRnV2dExn271s2TJqa2uZOXNmXtudTdvSlesucmawO+Qt22Nz7b9MyMfvJlO68jr2ZvLR34l1bNmyBafTydatWzOqM9U4kU2fpLuOVPsyTUeUauyNbZ+eIqVSR9ezO2W8wPeLlArW5Zdfzvz58wGYOHEiq1at4oEHHmD+/PmsXLlyr0rn0BX55RK56aabWLt2bVzusljee+89Pv/8c1asWEFFRQWzZ8/usM5Mj+lMux955BHOPPNMc/DIR7tzaVuqct1Jzgx2h7xlemxn+y+fbekMu+M69mby0d+JdSSSrt+sxolc+iTddeQqR6nG3sT2GTkJU5HNvesKGS/wPSTRpLVq1Spx0UUXiZkzZ4rzzjtPvPHGG7vHlpYjO3bsEMXFxRlN2WRSzopbb71VjB07VtTX12fUpq1btwqfz5fNZaQ8pjPtbmlpEUVFRWL16tV5abfVFGGu976iokLMnj2728iZwe6Qt1yPzUXuhOh42ifX68j0HInkeh3dlXz0d2Id2UwRZjJOGOfKVVZS7etoijDTsXfr1q1CkqScZCxfMl6gQCJxFqz333+fQw45hPvuu4+PP/6Yv/71r0yfPp0HH3xwT+l/HRKb5wvIKL9cunKJ3H777SxatIg33niD0tJSyzKtra00NDSY3xctWsSYMWPS1pvpMbm2G+CZZ57h4IMPTplbK5d259K2xHI33HADdXV1PP74491Gzgy6Wt6yObaz/ZcJnbmOTNkd17E3k4/+Tqxj6dKl+Hw+3n///bR1gvU4kUufpLuOVPtSWdYh/dhr1T6Hw5GyrmzuXVfIeIHvKbHa1pQpU8To0aPFpk2bhBBCNDY2ilmzZomKioo9ov1lSlfll9u8ebMAxODBg82lvePHj0+q+5tvvhGjR48WBx10kDjwwAPF//3f/3X49tPRMfnIi3fEEUeIRx55JGl7tu2+4IILRJ8+fYSiKKJHjx5xzqa53PuioiIxYsSIbidnBrsjn2Em58hF7mJJ16/5uo5MztHZ69gXyEd/J9bx0ksvZSSnVuNE4rkGDhwoevbs2SlZid1XVVUlevTokbK+pUuXph17Y9tXXl4uXC5Xp2SskCuyQFcQF8m9urqaBx98kFmzZpkK2MaNGxk8eDDffvst/fr12wMqYIF9jYKcFShQoECBfZ24KcKamhr69u0bV8B42HXkQFigQKYU5KxAgQIFCuzrJK0i7GglSoEC+aAgZwUKFChQYF8mbopQlmU8Hg+yHK93tbS0JG2XJInGxsbd19IC+wwFOStQoECBAvs6cUs49ragjgX2TQpyVqBAgQIF9nXiLFgFChQoUKBAgQIFOk/KSO4FChQoUKBAgQIFcuN7oWDNnTsXSZLMv6qqKqZMmcLSpUv3dNPyyjvvvMNNN920W871ySefcPbZZzNy5EhkWU6b6/D7QkHO8s+CBQuYPn06vXr1ori4mHHjxvH000/vlnPvSQqylH8WLlzIxIkTKS8vx+VyMXz4cK6//nqCweBuOX+B7x/fCwULwO12s2zZMpYtW8YDDzxAbW0tU6ZM4YsvvtjTTcsbu3Ow+s9//sPSpUs55JBD6N+//245Z3egIGf55YYbbqBv377cf//9LF68mMMOO4wf//jH3Hfffbvl/HuSgizll7q6Oo4//nj+/ve/89prr3H22Wdz0003cdFFF+2W8xf4/pE6T8E+hizLTJw40fw+fvx4Bg4cyIIFC7j33ntzqlMIQSgUwul05quZexV+vx+3222576KLLuKSSy4B4Ac/+MFubNXeTUHOsiednK1YsYKqqirz+9SpU9myZQu33XYbF1544e5q4h6hIEvZk06WLr744rjvkydPprm5mTvuuIMHHngARVF2RxMLfI/43liwEunfvz+VlZVs2LABgPnz5zNu3DhKSkqorq5m5syZrFmzJu6Ys846iwMPPJBXX32VUaNG4XQ6efHFF2ltbWXOnDkMHz4cj8fDwIEDOf/885PCCwwcOJA5c+Ywf/58+vbti8/n48wzzyQQCLBy5UoOP/xwvF4v48aNS3pLFUJw2223MWzYMJxOJ4MHD+aOO+4w98+dO5drr72W1tZWc1ohVvFZvXo1J510EiUlJXi9Xk444QS++eabuHNIksS8efO48sor6dmzZ9yDLZHEEAsFrCnIWefkzGrfmDFj2Lp1a/obvw9SkKXOyZIVFRUVhMNhdF3P6rgCBTLhe2PBSqSpqYm6ujp69+4NwJYtW5gzZw4DBgygqamJBx98kEmTJrFmzRrKy8vN47Zu3coll1zC1VdfTb9+/ejXrx9tbW1omsaNN95IVVUVmzdv5sYbb+Tkk0/m7bffjjvvCy+8wMEHH8xDDz3E+vXrueyyy3A6nXz44Ydcdtll9OjRgyuvvJJTTz2VL7/80lRkLrnkEh5++GH+8Ic/MGHCBD744AOuvPJK3G43559/Pr/4xS/YsmUL//jHP8xzFhcXA7B+/XomTZrEgQceyN/+9jdkWebGG29kypQpfP3113Fvs3fddReTJk3ikUceIRQKdWkffB8oyFn+5Wzp0qWMHDky+87o5hRkKT+ypKoqoVCITz/9lDvvvJMLLrgAu93euc4pUMCKfCQ0bGtrEz/96U/F6tWr81Fd3rnmmmuE1+sV4XBYhMNhsWHDBjFr1iwBiNdffz2pvKqqoq2tTfh8PrFgwQJz++zZswUgPvroo7TnC4fD4v333xeA+Prrr83tAwYMEP369RPBYNDcdsoppwhAvPbaa+a2l156SQBi5cqVQggh1q1bJyRJimuLEEJcccUVomfPnkLTtLjrTOTMM88UgwYNEn6/39y2c+dO4fV6xX333WduA8QBBxwgdF1Pe32JHH300eKEE07I6phcKMhZPN83ORNCiH/+858CEI8//njWxyayN8tTQZa6RpbC4bAAzL/Zs2ebbSlQIN9kPM9TV1eX8m/79u08+eSTrFu3zty2t9Ha2ordbsdutzNo0CCWLFnCvffey/Tp0wH48MMPmTZtGhUVFdhsNjweDy0tLUkm98rKSsaPH59U/+OPP86YMWPw+XzY7XaOOOIIgKTjjzrqKBwOh/l92LBhyLLMMcccE7cNYPPmzQC8+eabAJxyyimoqmr+TZkyhe3bt5vlUvHvf/+bk046CZvNZh5bVlbGqFGjWL58eVzZGTNm7NE0NgU5i1CQs2S+/PJLzjrrLE499VROP/30jI7pzvJUkKX8y5LNZmP58uUsXbqUO+64g5deeomzzz47o2MLFMiWjKcIM5nbPumkk8zPmqbl1qIuwu1289577yFJEpWVlfTr1880ZW/atIljjz2WQw89lAULFtC7d28cDgcnnHACgUAgrp7q6uqkup9//nnOPPNMzj33XG688UYqKirYtm0bJ598ctLxpaWlcd8dDgdutztuADM+G8fW1NQghKCystLy2jZv3syAAQNSXntNTQ133nknd955p+V96ej6dicFOYtQkLN4tmzZwnHHHcdBBx3EY489lvFx3VmeCrLUNbJ06KGHAnDEEUfQr18/fvjDH3LRRReZ2wsUyBcZK1hut5vi4mKuuOKKuPl9wHSY/O1vf8uIESPy3sh8IMtyyh/Q66+/TktLC4sXLzYHE1VVLd9ord6UnnnmGUaPHs2CBQvMbe+++25+Gg6Ul5cjSRLvv/9+3KBmMHz48A6PP+GEE7jggguS9hUVFcV939NJmAtyFqEgZ+3U1tZy7LHHUlxczAsvvIDL5cr42O4sTwVZ6voxa+zYsQCsW7euoGAVyDsZK1hff/01l112GTfddBPXXXcd559/vinYjY2NzJkzhxkzZnDUUUd1WWO7Cr/fjyRJcY6OTz/9NKqqZnx84iDyxBNP5K19U6ZMASIPmhNPPDFlOYfDYRk0b+rUqaxatYoxY8bs9UuRC3KW/vjvm5y1tLQwY8YMWlpa+OCDDygrK8vq+H1VngqylB/ef/99AAYPHtxl5yjw/SVjBatPnz489dRTvP3221x00UU8+OCD3HXXXftEDCTDl+Dss8/mvPPO48svv+S2225LMo2nYtq0aVx44YVcd911TJo0iddee4233norb+0bNmwYF154IWeccQZXXHEFEyZMIBwOs2bNGpYsWcI///lPAEaOHImqquaqmuLiYoYPH861117LuHHjmD59Oueeey49evRg+/btvPvuuxx55JH89Kc/zbpNu3btMt94d+3aRUtLC88++ywAxx9/PB6PJ6drLchZar6PcnbKKafw2Wef8Ze//IUtW7awZcsWc9+YMWM6jOe0r8pTQZayl6WjjjqKk08+2cw+8eGHH3Lbbbdx3HHHWfqoFSjQaXLxjA+Hw+LWW28VxcXF4tRTTxUrV64UkiSJd999N68e+Pki1UqVWP7+97+LwYMHC5fLJSZOnCg+/vhjMWDAAHHhhReaZWbPni0OOOCApGNVVRWXX365qKqqEkVFReKHP/yh+PDDDwUgnnnmGbNcYn2p2rZhw4akY3VdF/fcc4848MADhcPhEGVlZWLixIni9ttvN8uEw2FxwQUXiB49eghJksTRRx9t7luzZo340Y9+JCoqKoTT6RQDBw4UZ555pli1apVZBhC33npr2vtksGTJkrjVOLF/GzZsyKiOjijIWTzfRzlLJWO5yFl3kqeCLOVfln7961+LkSNHCo/HI0pKSsTo0aPF7bffLgKBQEbHFyiQLZIQQuSqnG3dupXf/OY3PPfcc6iqypIlS7qdqb3A3k9Bzgrkk4I8FShQYHfQKQXLYNmyZaxZs4YZM2bs8VVoBfZdCnJWIJ8U5KlAgQJdSV4UrAIFChQoUKBAgQLtFBLKFShQoECBAgUK5JmCglWgQIECBQoUKJBnvrfJngsUKFCgQIFMySaDAMCZZ57ZRS0p0F0o+GAVKFCgQIECHWCkKTIwAtbGPkJjo8rvTWmXCuwZsp4iDAQCFBcX8+KLL3ZFewoUAApyViC/FOSpQGfZtWuX+bds2TL69+/PVVddxaeffsqWLVv49NNPufLKK+nfvz8ffPDBnm5ugb2ArKcIXS4XXq/XMr9UgQL5oiBnBfJJQZ4KdJaKigrz809+8hPOPfdcfve735nbevfuzZgxY/D5fPz+97/Pa2T8At2TnJzcZ8+ezV//+td8t6VAgTgKclYgnxTkqUC++OCDD1Imhz700EP58MMPd3OLCuyN5OTkXlZWxgcffMCoUaM47rjjqK6ujpt7liSJX//613lrZC60tbXx1VdfMWLEiJzz4hXYs3QHOYOCrHUXuoM8FWSpe1BdXc1TTz3FtGnTkvY9+eSTVFVV7YFWFeRnbyMnJ/dEZ7+kSiVpjzv4rVixgrFjx/Lpp5+y+tPl3H/LjYRDIewOB7MvuIh1X63mvTf+ha5pKDYbY8ZPZOXHH1pmo//FpZczevxEho4cSVlFpbn9X08v4uF516GGw9jsdqae+lN+dO4FcWUAGutq+XbN1wwYNpyS8orE6pNYvPAx5l/zB8LhMHaHg8vn3sCs03NbkWJ17if+siDufhj119fWsHb16qTrzOp8jY2UlJTkdGwi3UHOIF7WKsrKWLf6f7Q2N/Ppsg/49wuL0TQNh8PBT8/6OadfcBFvvfoK8+deTTgUQrHZOPigg9jxzRokSUIIwcTjZnLVbXcBUF9bw8wJhxAOhZAlsMsKQlF46aMVlFVUxvXZktdeNetNlJvFCx8z99lsNi648vec8MMfpezvTPoxts5c5DSfspIJ3UGeDFm646brWfTgfUgIHLKC067gddhNGWkIBGkLxY9ViqIgAF3TzP5oa2nhvj/fhKaqcX307YYN7PhuS6d+66noqn7tSnnJtu6//OUvnHfeeRx99NH8v//3/6iurmbnzp08//zzvPfeeyxYsIBf/vKXXdLWdMSORYcccoi5PfH6tm3bxrZt26isrKR///45nSvf/dEV/bun69xnVxEagvbO22/z+1+cSTgc7nSdsQNUY10tv5h6BGpMvUIIagJhLot50CQqYb+46k9M/1F7JvhEBSj2gWrgcDp56cNPsx4Irc49fso0Zo4fE3c/HE4n5//mSh64dR7hUChOGchEIYylrq6O8vLyrI7p7sQ+FJ9ccD8IHbusENY1dAEeh41SlxNJklBsNurbAjT7AwDIEvQs8sZZUoQQ3PXC6wwYMpSP31/KnJ+dGleHEIIZp51FVb8B7UqT3Y7Q9TgFwZAbIEmmACRZRui6pXLUUT/mQ06/j7LSEStWrGDCuEM5pG8P3HbF7PNEhBBsb25FTzN6y4qCnqAwmr/1P9+clxc4K7qqX7tSXnKp++WXX+bGG2/k008/RVVVbDYbhxxyCH/4wx848cQTu6SdHZFKwYq9vm3bttG7d28AXG4PX3+1OiclK9/90RX9u6fr3OcDjX63cUOnlStZAqeioIVDzJ97NRvWruFfi5+NU64gukRX07j92j9SX1tDY12tqeAAqOEwf73lehrraoGIAvSLqUdwzS/P5BdTj+BfTy9i7erVcQ8tWQJJVfl8+cdZtTn23IokYUfw6J+v5/OPP0q6H6FgkPtvuYlwKITHYaPSZefNpxaabSrQMTZZ4qmHHsBlk+lZ5KXS56ZnkRdfrHIlSTgkKHXYkKPPTLusJD1AJUninVdfprGulvod23DYbHEPWkmSePPpf0SsnFFZUcPhJOtLKBhk3VdfJcmUgdB1AMKhkCmzmWJVp3G+Ap3D53AgISh1ObHJMm67DcVCRmyykraeROUKYn7r0TEgHIqMaan6vr62ho/fX5qVbHxfmDlzJsuWLSMQCLBt2zYCgQAffvjhHlOuMmXbtm2RD9MuJeBvo6am0LddRc4KVjgc5sEHH+Scc87h2GOPZe3atQA89dRTrF69Om8N7Cx9Bg7CbrfnfLzHYYt7YNrR+cm0H3D79deQaPwTQqDqmvmg+XbN10lKWDgU4l+Ln+Xbb9by0E1z45Svh26ayxeffAREFKsSp8M8932/v7xDZSd2MDTOXexyMLCihL6lRfQt8vDdFyuS7ofNbkdVVWSJuAe5Gg7z8Lx2hXBP0F3kzG23I3QtSREqjn6P7YdBlaX0KSkCQBOapRw9fs+dnPWDiTw49/dUepKtGKoaxq0kWzZisTscNDc2svLjeIdb44VBjjk8W+Vo6MiR2BNW5DmcToaOHJlxHXuCbiFPUmRasMTtNGVmYEUJxa72+22MNVlXLctJbhDhUIhH7707qezihY8xc8IhzPnZqcyccAiLF2YXaPP7gjG1rEdfWLoNPUfs6Rbs8+SkYK1fv57hw4dzxRVX8PXXX/PWW2/R3NwMwHvvvcef//znvDayMxSVlDB7ziU5HZuocEiSFPkudHQBDYGg+XA0/CJ00f6gGTBsOLYEZUYIwe3XX8Ps46YkvWHqmsbj991tKnU+lyNO2Ym1fsUqU/W1Ndx+7Z+YOX6MORiu+u/nOBx2qnwe5GgdsiSx6q3X+MUlv8bhdALgcbs448zZuF1OS2uKGg6x8IF7c7p/naU7yZk/HMZptydZHCRJQoakfhhUXoJdllEkawuWz2GDqGwZ04KJ+Bx2bHK7siQrCnZ75CGsKAqapnHV+efw8J3zzWMSXxg8jsg6l2yVo7KKSi6fe4MpRw6nk8uuuZ7SmCnlvc360V3kqSUYxmlXkmSmyudJsmSlwmpaEdqtlok89/jf4vqpvrbGnH6G3Kyc+zr/+te/mDhxIi6Xi/79+/Pf//4XgHPPPZcnnnhiD7euwN5ATgrWxRdfTFVVFRs2bOCdd96JG/yPPvpo3nvvvbw1MB8MGZH+wWH1Rg+pp28M03xbSGVnSysDDxpNXVClLaRGfBwuv5I1X36JLgSTps0wjxVC0BZW0QUEQiFLy0UoHErpd6GHw3z42ks89ZcF5pvl8YeOYsbYg3nyrw/Fmf3vv3UeM075sTlAG2jhMKNGjealDz/lsssu47ABvdjw7r+Y0K8H1SU+yzY9+bdH9sjA2p3kTNUFxxxzTJLFQQhBWNeT+kGWJbxOO2Hd2oIV2/+KJOFx2C2niap97crSfoMHAwKnoiB0LUmBT/XC4LRQjjJh1uln8tKHn3Lfomd56cNP4/x4rKwfe1rh6i7ypOqC3r17J8uMJOGwRcaejqYIpagcJI5pqQiHQnEWzMIUcHoWLVrE8ccfz4ABA7j77rvjrFdDhgzh0Ucf3YOtK7C3kFOYhnfeeYdFixZRWVmZ5PfRs2fP9jnevYRBQyOWpMTpOiDJeTh2dY4xfZPogGyY5o1jN36xkh5FHqb+/Bd4yyt48NZ5SJoGikK1z20eK0kSHruNpkAQgJZQGF/MyqDmsIbdZrdUropdDqp9Ht76633oQlDpsrMtFEq56ikUDLLfoRP5+p1/o6nt122zO6geMIi2xgZWvrK4/SEsBIPKimjxVbB9w7q4+xEMqbz87NPM/OGP8r7iKB3dSc7cdhttG75OsjhsbWxB1QS6EHEPTF0IRNQS2hgIUpKg+BhyV+xymJYMXQh2tbTRFIg8+GJlU5IkWnZspcrtSLnSLNULw8k/+ampHBmrEit79srIkbOsopJxhx8Rt83K+nHrn37Pbdf8ATXGsfoHx8/M6h53lm4lT74iRGt90tgT1nTzsyxFlOZER/d0Y1oqbHY7VT16mP1f1aMHdocjaRHD3j4FvLu4/vrrufTSS5k/fz6apvGrX/3K3HfAAQdwxx137MHWFdhbyMmCZbPZLKcsAHbs2IHP58u6zosvvpiBAwciSRKrVq1KWW7gwIGMGDGC0aNHM3r0aJ566qmM6u/Zq0/StlRv9LIUGaSqfd646ZnYaUArf6U3n/kHj86/hUqXnUqfmwqXPcmKEJn+ifhWFTkj0znNgRC1QZVJxx4fqdftxK60d40SfVhLMQ/voZVl2GUZp02hZ7EXpy3+bdbhdHLgoeM46aLfYItOG8k2G9POPo8l/36dX554nIWFQ2LT+nXs8ofY1eJne3OrOTA/dMuNnDPlcJ7560MZ3e980J3kbGhlKSLhftpkmSGVpfQu8RIItz/gDEXJ63Dgc9hQJJlyrzuuz1NNLRrTRImKv3GMlSwbpLKWPf/kIupra1i44H5OGDeaOT87lTOmT87Y5ybRMmVl/dBU1XzBMaabGurqMqo/X3QneQq3tURkQJLwOGwUuRzYFSXud17hjZ/mhfRjWjrUcJifTvsBMw4dxZyfncrpM6ZyxDHTTBcHu8PBrNNnp7x/3zfWr1/P8ccfb7nP6/XS2NiYVX2BQID/9//+H8OGDWP06NEcd9xxbNy40dy/du1aJk2axLBhwxg/fjxffvllZ5pfYDeRk4J19NFHM3/+/LjVaIYi8tBDDzFlypSs6/zhD3/I+++/z4ABAzos++yzz7Jy5UpWrlzJj3/847Rl3/vXq/xs6lFs2bQxaV+qN3q7rCQNUkIIdra0KxzW/kphihy2pONiEULgc7b76thkmVKfh/sXPcu3Hy/l/x00lLH9e3HE4H70KYkM+A6bYjnFtH+vCn44ejjHjhjED0cPZ2hVWaRtDoc55TPxxFkc9rOfs63Fz7rtNdw770b+Pn8eja1t6Alt03VBY6ufUeMmgM1mvhkPrSrjh6OHc8x+/dj6ypN8/PyTALQ01LN2xce0NNSn7YNc6U5y5nHY4u6nz+mgT2kRB/auZuKgPozoWUnf0mIEsKW+mbCmY5NlRvas5MSD9uOQfj3j+jxirdAsp4kS+y0VidNIqfwGg8Egd15/LXffeJ3pAB0OhzPyuUmcCly44H62f7elw7aFgkE2rluT0XXki+4iTzZZYvu27aYMVfq8lHnc9CrxsV9Vqdlu43+sAtWRW0M6NK19WjkcCrHk9VdQw2FkWUZTVZ7860MFZ/coPXv25KsU06X//e9/M5KHRM4991y+/vprVq5cycyZMzn33HPNfeeddx7nnnsua9as4be//S3nnHNOzm0vsPvIaYrwlltuYdKkSYwcOZKTTjoJSZK47777WLVqFWvXruXjj7MLKQBw1FFH5dKUtNhkiaf+8mDK1R3GG32iGV6Rk51EI2+TChAZgHSsj7Uc3Gw27BKoSDS0+elV5Ema9lnx7luM7VOFEg2GKMsSQ6sr2NnSRjCsmlNMsiRhV2TCms6BvdrLK7LM+AG9KBk0jBsX/BW7orB2xcfY7A5e/cu9uBWJoBwJFTC8qoyNtQ2s3VXP0MoyZFlC1wVra+oJ6zqff/IxC197k/fffpOHbrmR8QN6xZ1n+ZOPElQ1Xn34PrRwGMVu56Q5v2HiibM612EJdBc5A9B0wTc19QypLMOhKJR5XNhkGYdNIaRqaEIgyxL9y4qRAUNF6ltW3G6hivb5juZWwprAYbNZTi1CsnwKi+3GdLZNBpfNTkAN0xZSCYRVbLKCGo3TBfDa4meSrsnwuTGmAK1itiVOBd5943UZ3S+H08mgocMzKpsvuos8ue12QppKmcfVHt4jKkcVXg+9S7zsaGpDi+lzm6wQ0rSUbg2dCZ8aO34a1sfJM47fre4Cexs/+9nPmDt3LiNGjOAHP/gBgGnF/POf/xw3ZZgJLpcrziI2ceJE7rzzTgB27tzJihUr+Pe//w3AKaecwpw5c9i4cSMDBw7s9LWsXr3aMuBoPoKRft/JScEaMWIEn376KXPnzmXRokUoisLLL7/M1KlTeeKJJxgyZEi+2xnHaaedhq7rTJgwgZtvvjllWgK33Y5mEZndwHijj/VXkCSJUrcrre9VqduJx26LO8ZwYDe2G5S4nVR53WYZWej0KIpM6Rn+FFU+D+8/9ySjescPWIos4XM62FjbiKOhmeE9KqiI1mWFIsv4N63j8ZuuYesXK/AoMmUeFwf0jDgvFzkdVPncKLKMpuu8+r9vWLZxK16nndZgmHBMXKQN36zj8CnH8vz9d5nKlXkvNI03H3kALWoJ0MJhXrj3NvoeOJpg9Imdj+Bu3UXOgKgiJFPsdFDmdUf63cJ3Soo+LIOqhtPCMqnIEgMrSlm3qx4telxcPa1+UtmvYuXPsE4Vu9pltVg4aAurNPiDhDTNXNwRjlG0YrHZbFT27EVdXR3vvLiYRXfPN4PW/vTiyynp0csyvpYViqJEQgREfbDOu+IqdCJB+6xoqKtjw9qvGTR0OKUdyFKmstZd5CmsqVRHXQKsfPD6lhTjdTjY0dxKUyBkjk2JvlexcvCD40/grZdezMt1hIJBPlv+MaPHT7TcX18fb9HOpi/TkVhvPjHqzlSW5s6dy//+9z+mTZtmJoGeMWMGu3btYubMmVx11VWdas/dd99txtPavHkzvXv3xmaLPK4lSaJ///5s2rQppYI1Z84cSkpKmDVrFqecckrcvTOnL9si/08//XScbg8ff7iMvn37ArB9+3YOOOAAgKR9Bvnuj67o391dZ6L85KRgAQwaNIi///3vuR6eM++99x79+/cnHA5z9dVXM3v2bF599VXLsv6oeTtdfJK2kEpIVU1/K0i9LB7ArhCnRBllG/0hStyOOOVHBipjFCJJkhgRoyQJIahvC9ASDCGjoesRK4eBFv1+aP+euOxKknKVqARquiAQUtm84iMUWaasyGNaUVRNp7qo3Z9HkWVm7D+Er2ubaPIH4u6RzW5n8H5DGbjffvz04svZ+sqTcUqWpCgIXY2brtLCYQL1tZQPHJLXyLndQc4AdjS38X8H7mdarqx8pzRd4A+FCWuRVYWKLFtaPfuVFfNtXSNhTacpEKI1GDYtGEKSkCQZIeJl2lhAsbOlFVmKWKes/HE8dhstwSAum82M0ZXKEfrsiy5l17atyOimcgWRqfAn77mdW59+IckR2gojhMPkGcez7quvqKyuZteOHUhCWMrKwgfvt0zvkg+6gzzZFRsBVU3pg9caDONzOfA47KzaugtVCJxp3BpUHXr17mt5rlxwOJ0cMn5CWmXJ6NfOplNKVW9XkE3dDoeDF154gSVLlvDGG29QU1NDeXk5U6dOZerUqZ1qx0033cTatWt58MEHzW2pLNapuPfee+MiuUP79ZlpXvxRRWvapQTfuBNVVc0ypv+XxT6rOjuDYSmz2Wxd8pLTFTLTpZHcjznmmJTzz2vWrOGYY47JpdqMMEyVdrudSy+9lKVLl2Z1vFVIhlSxiBK/l7qcVHm91vssgkE67bY4C4VNluOUJEmSKPO4kCWJIqeTtbvq0KKmBE0XbNhVR9+SIvqVFVNd5LM8r6Hg6Lpg7c5aJDni6Oy0xQcqHFBenGQtsSkys08/jd/eMM+MZ+S0KVS6Hfz8hGNZvPAxTj3nXA4741xE9FhdF2hhleoiH31Ki/BFHfVtdge9hgzt4O5nR3eSM6/Tbk7pWPrMSRK9S3wMqixlYHlJ1LfGk9SnZtliH3ZFxq7IFLudqLqOJgS6rjN41GjL4yL9bjen/lwWK1IlSaLY6TSVK2NboiP0oKFDeeKBe7j4tFM5a8ZUy4C5jTU1/OqKq5JivRnYHQ5uWfCIGcKhrKKSzRvWc/qMqSkd6RcuuJ+7b7rOtDyHQyFuu+YPbFjbeX+t7iJPYU2lNRBGkeWUoRokSaLC68YTXYVc7ku2bLe7NUTua+R465A0sUyecUJc3kZZllFs7fHSMg3psWHtGm790+9TxtLa02E7OsOmTZsIh8NMnjyZm266iYceeoh58+YxdepUVFVl06ZNOdV72223sXjxYl577TUzWXO/fv3YsmWL6R8phGDz5s35m7ZLF3C0i4ORGml7xo4dy/iJh+V83/ZWclKw3nnnHZqamiz3NTU1dVk8mdbWVhoaGszvixYtYsyYMSnLu+32OMuMVZBFWQJF7viNAMDtsA6hYGCPTskZq8EM3ymIOD33KrFWkuxKZCCtbfWzvqYeXQgUWWJwdXnaKUGJiDUtNj5SSNXwOOz0KPLGvf1aWeU0Xef1xc8ghYM8/uobDO9ZaTrNn3TAIF6451bW//czWluaeX31Bpas+RYkTCubqSDKMkf95Ey8JaUd3sNs6C5yBuC2tVs1Q6qW0hFdliQGVJRgS5N4WAjBftXlHDG4H0cM6RfnAC+E4H+fLDf7UpEkM7CpEIJSt5OeRV5KXI6UP25XwjQ2xDtCexw2gju2Uua0R+pyJBu6hRD86+UXeeDWeZF0TDYbk487IS7w6OVzb2D0+PGsXb3aDIgb57OV4EhfX1vD/bfclHQuNRzmZ9OP6bRzdXeRJ7tiQ5Yi1k2rRTKGa0Fk7EjtvJ4Y7T1VkNm4czscLH3z33HjpmKz8Y9/vc0tC/7KtXfdx+QZ1qvnDBrq6rj92j/xs+nHJLloGH593T1K/KBBg/jss88s933++ecMGjQo6zpvv/12Fi1axBtvvEFpaam5vbq6mjFjxrBw4UIAnnvuOQYOHJgX/6tEtm3bxooVK7LKamAck6gcpdqeWAaIWMr2wbQ9OU8Rpnrof/DBB1RXV2dd34UXXsgLL7zA9u3bmTp1Kj6fj3Xr1gFw/PHHc91111FeXs4pp5yCpkUcOQcPHsxjj6X+YfrDYTOidarly6QI6pktfUp8DKuuMB3G1+ys5bvGFna1tNGjyGtaNxIRQqDpkThJDkVhSGVZ3JRAKqx8M4ZVV9AcCFHudSdZz4xrNpymdSFoCgQ5bGBfPnziYdxOJ+P69jCVJ4eicOSg3rx+3RXowHHDB7CrpdWyTqci89Zjf+G7bzdy8sVXdPJOxtMd5Axge3Nb3BRvoz9Aidtl2YeGFcIfTvYPjJ0yjJ0ulmWJYdFFDwapYmRJkmRaFjNZiGFsTzWtaFW2IRDkmb+3B1PUVJX/LHmTx199g5qdO6msrub5fyw0E4vbHQ5OOeOslMErxx1+BGtXr05K4xJb//y5V1NaXs6YCRNzdrDuDvIU1lSqiz3YbdaW9VK3k5pWP0IIip126tr8lvXo0VhZVmFljPEvEA18bDDq0PF88sH78e0JhXj+Hwt57vG/dTjVt3jhY9z2p9+n7Ee7w0FVjx5cOvtnSZat7uQ4n+6FPBgM4oy+aGTKli1buPzyyxk8eDCTJ08GwOl08tFHkdRpCxYs4KyzzuKmm26iuLi4S6a5d+3axdixY7M6JlXi6KwTSu+jaXsyVrBuvvlmbr75ZiDy45w8eXKcGRkigqWqKhdccEHWDbnvvvu47777LPfF+iqkemuwQtUFpRWV1O7ckXL5cjpiV+9oFj8o42FlV2SG9ahIWg22M/rAs8sK/ctLLM8RsRxIhDToV1YU91BN1QZ7Ch+f1mA4sipQkkwrSuIKtE11TRS7HJR5XJR53JS4XRS5Haxb/Lh57lQP7uqiFHGCJIHLbmPNe2+y8+QfdWrOuzvKGUBNaxsb6hoY07eHee9SDcJCCIKq9bqudDIpyxIH96riu8YWmgLBlDIQK6uJDs+pFCZ/9EHrVJJ/J4nUtgYIpkgkXLNzJ5s3rOeSM38aN60YDoV47vG/WQb8/fp/XzDu8CPM/IapfLrCoRBXnf8LbDYbF1z5e04/r+P+747yZFdslHtcEZ87iz4rcTupbwugAYMqy/iuqYWwpieNFYosU+3z0hZW8YdUa+t51GppLHRYufwjFJstzvJkdzh49rFHk+KYJSpEhoUylXIFEWvk4oWPpVW091a++uqruPhT77zzDlu2xIckCQQCLFq0iMGDB2dVd9++fdMqbcOHD2fZsmXZNThLTOvRGQ9C03Z4YW6Hx8Qljn7jTmpqakwFy2r7942MFaxJkyZx+eWXI4Tguuuu46c//WnSqgKHw8HIkSP3mmziLptM7c4dgHVIhnSki6AN7aZ6uyIzsLzEcjVYlc+NP6zhD4eTHNhjqSryUt8WoC0UjmujVRsAc4VRLIZVpMTrMpdlJ61Aix4fa00zHsz+UBN2RTGDmnb04FYkCZc9ki9P9vnQo/fkk+eeYMSo9NNp6eiOcgaR+9TD60malk1FsctBgz9ouS+dYl/uddOrtIhgWKWm1U9LsF0mU1nGJEmi0R8koIbjFnPE7nfbbTQGgh3+TiKpf5JT8NhlBclmo37HVuZHI7YnEg6FOH7WqbyaEBLiwVvnccIpp5r5DW+/9o+Egtb3BkBVVe6+8TraWlo59/L0FtPuKE9hTUWWkv2vDGL7WZYlBpaXsKO5NaU102O30RYKWlozK7yuhIUOyf02atx4PvlPvFXLUIj2GzHCjPz+/ltvdrjgQQjBs4//DbvdHhePrDtEiX/qqae49tprgchvJtVKwdLS0u6dKqf/GNgW76uYKpSDSdQCtXr1ajRNY82aNXHbv69krGAdffTRHH300UBEuH75y1+aJsC9lRKXy/ysC8zVNx2RiZJhk2U8joiPV9/SYst6RvSojEzL6YLWUIgSlxOn3Zb04DR8mQJhlYa2AKVpVqJJWD+49ahVxNgnhEhageZ12ulfXmypnPW3cIKP3e+y22iNDr6xip9xLsNa1/jVF/gbG3Dn6I/VHeUMYP+elVTGrNJMhyRJVHrdNAdCSQpUOsU+cZ/P6WBdTR0hVUcXwpSBWAVNjob6aA6EUPXUvwHDB8vnTE5ObuyXZJmGVn/clFJsaACAB675PVVuBw2SSFqVKCsKr7+wOOncoWCQzz7+iGNmnMCs0880Vxt+9uEyHr5rflJ5g4fvmo/H501ryeqO8uSy25Bl6FmcrAxDpE/sikRIldCEoG9Z/PiTOF5F5M2bFMYDkgOWJk4ZAny+/OMky6PD6eTrVV/ETfVlihoO85NzzmXxwr8TCgazcpzfk1x66aWcddZZ5jTv4sWLk3zpHA4HPXv2zIvbyV5BTCiHtFN9MeUKtJOTk/s111xjDlKbN2/mgw8+oLW1Na8NyweNgUDc92aLBMtWpFoFZiRaLXY5GFxZypDKUoZVl6e0TMX60vQpKWJQZWlcIuBYbLLM0KoyKqOpUFK1IdWAW9vqj3OsNspp0ekfiF/ynXh8R4pBz2IvxS5HkvJpnMtM4aJrfLL4H2nrypTuImcAh/atRtOTnZJTEVkF5oykRTKCuFoo9tU+D85UlsUiD72Ki+hTWmROG/kcdnPl6IG9q0wn+RMP2o+hVWW0hq1/A0IIXLaIJStRxhpaA9S2+tna0ERVv/YI1Yl+PbHXZpWeRdeSE1Ab/OGCc1n4YGSlm5HfcPQE6zhLsdz/55szXoXWXeRJkSRqWtqS0l8ZSJJEjyKfOY7IkoTbEV82drwyjvHYbTQH2i1Z6RY6xBIOhfjhmWfHLWA4/zdX8sCtN2etXBns2PpdykTheyslJSUMGDCAgQMHsmHDBjPZc+xfr169clau0qVdyjVFXKeJCeUQSOeEbpSb8LP4/2nYtm1bVs703ZGcFCyAhx56iD59+jBgwACOPPJIvv76awBOPvlk7rrrrrw1sDMEVJ3yGMfVxFQhqbBaBRZrHRhUUUq/smJ6FPuoKvJ2WJ8SfRha5ZSDiMI2qKKE3qVFDK4sZVBlKU6bklVKlAqvO0lpi8VKYYs9viOMNrssnOeN/caAvupfL+FvbMio7R3RHeQMIKhq5vL5TCl2uajyeRhQXkyPIo9lH0mSRL+yInoUey0VbmNFYInbxei+PRhSVWamYKrwuuPino0f0IseRV7LFYwRx/hkx1xJkijzuSn3uHDZbWz8Zp25z8qvMfa4TNKzGGiaxt03XWeGEwBMn6x0qOEw61KEXrCiO8iTpsPIqPU7HbHjSKXXay5sgPbxKhZJkihKs6gncdVhLLNOOyNOIRp2wEE5K1cAS157hYa6OsYdfkSHju1GOIfdnbsyHQMGDMDhcPDmm29yww03cOGFF3LDDTfwxhtv5FxnR2mXskndlXcyneqrGBj/PwWGE/y+bvHKScG68847ueiiizj99NN57bXX4hSMH/zgBzz99NN5a2BnsMkS9bviNe62kBrnS2WFEUHbUHBiLUQehz0pllVHA6GVUmIoJHZZTvKpkqMKU61FvsBUJCptiaQLHZApxjVY1RM7oGtqmNpNGzp1Lug+cgbW96QjYmWoyOlAT2EBM6wPVufoFbUsQsQK2rvER++SIsuQIIosc2CvqjgLamxoEatQHrFtSLRKqaS22Akh0ISG22HvMNFwLIkWqZk//LEZwTrROR2y893pLvIU0lTawuGMrKGxcbGMmHp6wvhl0JFvXVM0kX3yOeD1557m848/Yr8RIyirqGToyJEp45/JstLeZ2nCSLzx8osdxsGKDedgxE3bG+Jnbd++ncMPP5xjjz2W2267jTfeeIPbbruN6dOnM2nSJLZv3551nUcddVSSf2C+2L59e4chEzJh9erVLF++nBUrViQ5+GeD6QSfgaWrO5NTmIZ77rmHP/7xj1x99dVoCSb/4cOHm2+F2XDxxRfz4osv8u233/LFF19w4IEHWpZbu3Yts2fPpqamhtLSUv72t7+x//77W5Z12+1JEa8BJEmkHGyM6Tl/SKXJH6A4utS+wutGFwKXLXkKJR2G30wiRl3lJdbhG2RJotLrSbv6y+oYn9OGQ7HjtCvUtLQRMJQeIWgJhihyOnI2YQshcChy3H0xtte1Bih2OWgNhglpGq7SzkfP7S5yBrB0/XdMGtQnq4UUsUiSRI9iD4JIfDOr/S3BIF6Hw8xJafhZGVOHlTEZAnYmLMqA9pAgbruNHkVeil1OhlaVJ4UWSddGn9Nh1qtpOk0hlWKHddqoHlGHeuFy0BIK0xIMJT3ADQd5YxWbYZHavGE9t/7xd2a/S7LM0cfO4N1/v2bGaFJstqx8d/ItT10lSx6Hg0CovX2xfZ3Ksg7GqkCZ9TUN6CLSz6lWQCciSRJhLbmc4WP3r4WP8roQNIc1LvxjJN+ksMiQoShKxBIWbVOqKWGAR++5M220fqu4abf+6ffMv+YPZuiPfEb5z4bzzz+fDRs28Oabb8YFqH3rrbc444wzOP/88/nnP/+Z13Nmk7orNlXO4YcfHpf25u+PPmJ5TEtL6t++lY+V0+Xm73/LzJm/sbExLi2Wma4nwdKVWK6zdMtUOd999x2TJk2y3Ge323Pya/jhD3/Ib3/7W444Iv0yXSOr+FlnncWzzz7LOeeck3L5alhLXi4sS1DktDaTxzoRJz4oDQtRkz95NU4qrPyVoN3nyVjBY0XsOTJZam8wpKqCyiKvWXZTbQNrd0YE1pdGudKFoDUQxJdmCsHwtTId9XWdRn8QRW6/lkqvoDkQYufG9VT2szZ1Z0p3kTOAuraAaRVK90BMh9OW/ufodTjQdJ0Sryeuj2uaW+MWP0hR363ElZ9BVTUtW0IIBO1WydjQIkYgSyt8DrupKDmcTsZNnsq7r7+CTVbQhYYsRf4npp4qcjrwOexmSh5ZisijLxq811jFpkpKJE7SmT+NU4KErrPk9Vfi2iLLcodBL2PJtzx1lSw1BYL0KPaYU3qxfV3b0kpTdPWpEIK2YDjO0h7WdUrcTkrcTssVhemo8LriUiZZxc4qsivcPvdqdCGSlFTAclsqYqP1W4V9WLt6ddI0pKaqZuLqTOJn1dfWsHb1aoaOHJnXGFtvvPEGDz74YFL0/ylTpjBv3ryskz13RLapu2JT5axYsSKyMSbtjRU+X4owPBDvY/XRPzqsK5GSkhKCwaCZPNpM12NRLt+pbbpdqpwBAwakzDz/0UcfMWzYsKzrzMQ8amQVN7ToU045hQ0bNrTnTUrAriQ/sIoc1kpGojKUqFw5bQo2WSagqtRFg/wBltGWDVL5PUkW54jFSonKRLmSJckcjI1j+lVEnPGrfW7Lthjna2rz09PjxJZmDC5yORlYVU7vkiIGl/oojQ7ksYpb5KHgQLQ2pq4oQ7qLnAGM79+DSp+HYreLAZVl9C4rYUB5CVUuGwoCBYFXjvxPxPCl6mihQUQObUl9XFHkTUrILUkS5d72VbRCCJy2+ByaVqFFYv14rDB8q+wOB+f/5kqWvvnvyHYiPo4hTcNrt/6NGdOMvmhE8aIEuSl1Ofnx2eew9M034pbwpyIcCmXlf5VveeoqWbLJEoocuadmX2sq9pCfCreTYnekXyVJwuu0m1H8m/xB7LJMuafdupzOdSBx3EqcBk4ZO1DTksJwZJKCJx1G2AeD+toatn/X8RRU4nGxdGW0+LKyMsrKylLui43Eng86myIOyE/IBMPilGVdu3btMlPiDB8xsn2KcB8nJwvWL3/5S+bOnUtVVRWzZs0CIubbV155hVtvvZUbb7wxr400yDareKIFS5YiOeOsSPWA8zkdZtwoIQSBsEq9PxKzymFTCGs6JS4nRe5kB2GrYJ+ZkM6ClM464rApSEIga2F02QbRXGZOu82cejKsT7HHVipwYLnLjPq8TRXUafFtsMkylUVe7OEAjkDEYtIPsNslahJeYiRJwh7O3QHWoLvIGcAhfXuiRO+RJEnYgm04gm34gB5R0ZAs7m+ihaKmuZXmQBAFgUeW0BUFfzQMA2DpSG/IRCAh/lWJy0lda8Bcqt8Rmi7i4mpBckyu9ojvEkteewWPIuFzt7e/ORhM+Rsz7mVxCiupJEk8+8jD+DN0nrY7HFT27JVRWdgz8pSLLFV6PZR4ItkYJEnC0daETQ0hAQ7AaXPQErVORXyvnNS0Bih2OynxuOKSyENymBUDqz6wyTK9in3UtLQhYZ0JQEgykizMKcLYUB2pEofHnSOawixx+vCzjz9iyMj9eeXZp3jwlpsyUrINGUicVmqoqzOnEiGijM+fezWjJ06yTFJtTPtkapm49NJLmTdvHkcffTRFRUXm9ubmZm655RYuueSSjOrJhNbWVsLhsKm0ZZK6a3exYUNmvrbLly+PfIgGHo1NHbUvk5OC9Zvf/IZNmzZx7rnnct555wFw+OGHA3DBBRfkFBE5UxIHhXSOoIkWrFSrnlL5ScmSFBeUU5IkhlSV8V1Dc1yZVLG1DGf5VOERMiFWofI6HXEP47rmFtRQkIAOGhI+oeJpqYsMjEDI5SVsd6FqesoHeYUi6GWLCSkhQS8bBGWFoCawKTIOm40KnwdJCFO5gojC1tMmCNucNCYEYXX1GZjT9cbSXeQMIBBWTeXHUK7M+xRTlXF/m6N9ZmWNcoSD9LKDhECgEvR42R7UIkvsNQ05HEJXIgo0RKZ3QxaR4SUpdUqeRIQQbK5tRJEkdCkSXykpJldzG9uaWyOJpGWoXfcVRTEWr8hUYMepp9JZboMpFHPFZmPqCf/HktdfiYudNGjIkA6vzWBPyVO2slTv91PT3EbPIh9CDZvKFUR+czY1RJXHzY7WSBiaUrcLVRdm4FrD4b0t1D59aDVVkag8xfb3kMpSdrW00WgRDFfXVYRuxAPsOAWPkSbp5J+dTs3Onew3YgTP/P1RHr4zPsbZY/ffzbSZJ/Lgn2/OWLm6fO4NljKw7sv/JdURDoWo3bGdwfvtZ1lfNlNJ3377LRs3bqRfv35MnjyZ6upqdu7cyZIlSygqKmLLli1cfPHFQOSeZLJCNVXapR07dmSduqvLifpk/fGPf8yu3Pcs8GjOuQjvvvtuLr30Ut544w1qa2spLy9nypQpDB06NJ/tiyM2q7jNZuswq7g/HI5bGaWJ5CjVqfykAHNlVSySJFHicpi+Slb1xb7xB8OapdNyJiQqRYBpoZI1lX4EkRwRq8h2VdBTIW4gdgRaUW1OHDYlUk+MdauiyAtCpxchEi9dlqBXkQdNscddo6yFk65FAipddvRQiDZdoBFZVblm1X/pf8j4HK+8ne4gZxCZGgurGkINxylXVsgSDCgtoj6kJcmXrGv0som4fnQGW6n0llGERjlhJH+TqUCH7C5qm1tNS2msddPINtBRyieIyPWAyhKKPU6aAkFqWtuo9CbH3WoJhmgJBi3jXxn1ZJr/MBYhBI2BYJzDu1HfpMlTuPgPf2LQ0GHU19aYEcRz8anZ3fKUiyyBTFMgGLEEhwKWv7kSSSfgcdLYFoz6PsYHrpWkSAovwwHeyqsutq8yzeAgSRI2SUZDSwoyG1dGVghpGr/49W8YMnwEh0TzRw4aOozFCx/j0XuTFY5QMMj7b3ccDd7g+nse4JgZJ1jus0q7lM9o8S+//DJ2u52ysjJWrlxpbjemDV966SVzW6YKVqq0S4MHD846dVcuZGqNApJ9sjpbbh8lZwULIh1vvAnuDmKzip911lkdZhVXdcGUk07mzX9Gokdb5VlL5/sS1nTLh4Mv4Y3NKJP4xh9UVctVh4qu4dZD+GUHqiSjyHLStJ+xKkyJKkQSYA+2YQ8HTAuVUassEbV4xCMBkqaiSBFlK/bYkNNDD68LqS15MBMQOWfMNQLokpy0yk0A7kALAx3t01+7wgJ7ibV/Qi7s7XIG0KPYQy+XDW9LHVI4DHY7pIgDFbm/MqWO6EosWQZdb+/fhI6UAEUNRZSrmG2OQCubmwMIWabY7YpYGRMtlF4XVUVewqqGqutJKZ/izhNj+YhVrgyM6WaXzZ5WYZIkCZnIdHQwrNIUCqf07Yr9fZVYTDMJIfjP22/y8dJ3ufzaG5l1+pmdzle3O+UpF1kKayotwRBOm4KsW1sfZV2l3FuKQwgCzU20yXZKPU5qo1atyIpfhWB0TOnIipkuuHLisRVeF02BYMqpXmMaWZIkHr37DoSu4XI6Ofc3VzHjlFO57Zo/xOU6NLDZ7Rw89tC0+SjN9jqdHJImEG1i2qV8R4vPShnZ28nUGmVFB/Gusi63j9EpBeuLL75g8+bNBBIipgOmj0OmZJKV/tBDD806q/iBYw7lzX8uxuOwUeLKLsO5HvVliJ0mhGSTf6o3QCvlqjzYTJ9AHTKRt8q64moclX2SHoyVdvC21psKEcQrNlbKlJXy4/e3MgANKRxf1hFsI+RwWx4TcriRdRWd9mkoW8hvTg/GHhN7rDH91aBK9KTzPlgG3UHOenjclLXWIjW395koKgNPkWXoBU9rg1lOtTnipoGscAVbLfu8T5Eb3eZA1tSIci5J5lSjJMGgqvI42QKSrBJxdUYtH4YFLFZ+jZAAaooXD4PEF41vauotrVrG+WL/G58Tp5nC4TC3X/tHxkyYyIa1axBgWkWyJV/y1FWy5LLbCOsRa7UtReBPm64hGnbSq3YrkhAIJDa7SmmITu9KkkSpx0VrNL+pz2m3VKxt0Zc7VdMtk8NbJSVP50dnKMeRfhM4FYlSb8QK//Tdt/HBv1+3zFMJkRAdF/z4FI45/kTefu3llEpWpspSbNolw+LZVasKuxNJyuH33MrUleSkYK1atYof/ehHfP311ykDI2azXBcyz0qfbVbxkvKKlCk9IDINmO5h0RIMoek6VUXetOdJFYU7FkXXTOUKIn4RFc27aK3ogYhaBSqKvASCQaok3VKJMdE1iLGUGIqRI+RvLy+gHC3JImLU6Qj5CTk95pRW5GFvj9QR8pvTUKrNmeR7lQpZArcCW/79Avuf9NM0JTumO8nZ6OpipOZt8feouR7h8iAlWLKkmL6TZKVD5cqszwIlHMIVaInzu1MdkRWjFb5k/67WYCitX5YQAlkGp01he1MrZR4XTlvEErKrpQ1N1wkLjdZgGK+zfQrZOIfVi8aQijL+u3UnHofdUqGyvN6YaSaDUDDIT6f9AISOXVbQgN9cf3PGcZDyLU9dJUsIifU1DXhjxgCTqOxIioyzdnuMvAn6BupZay+jPhQTFyuqLFtN9yUqwv5wGLfdnjZYqUGqqeAdLa0YUT6swjxs+WqVuZjGinA4zJuvvMg//vU2z/9jIc89/jfCoRAOp5MzL7iIMeMnZqUsGWmXILKq0IirlY8YWjU1Ndx2220sX76czZs38/zzz3PAAQdw1113MWHCBCZO7DjVUyzp4qplG0vNko4sVd9TK1NXkpOC9fOf/xybzcaLL77IsGHDcHSQzmJPsm3zppTO7YokxUVlT4VNTlbCEr9brRhMLOPWQ0nOppIQyKEAmhydDpBkKmwSsiSSlCiTtuYkS4nkKcIR8kf2G4NwmmkqdA0pHEZOaJFNTZ6G0l1yxn5kAggJCSGyU3ys6E5y5pJSWBX9reB0RfKf2O0QaEvuO5fHup8zwK6FkvpLtTkRUnKGAVmSsEenjawwlI6qaDyc6iKvGStLF4K2UJhGf5AePi8Om4LXYaemxU9tWxtehwOf02E91SRHzpvJSsbYtlilbXHbZEpc7QFV77v+T2njIMXSXeSpJRTCJks4FYU476mE333i3ZQBHxpGGETDBw8ifeh12pAlmdZo7KxERdjrcJgrEOvbAmkDlBo+c7HTuq3BMLGHpArzkKg4J6KpKhvXreWya67j7DkXmxYoIcmUl5dTX1vD7XP/yLOP/w01w4CjSUFLM4ihlY4VK1YwZcoUioqKOPLII3nnnXcIBiMLAr777jvuuOOOrPMFpourlm0sNUsKlqrdTk4K1pdffsmzzz7Lcccdl+/25B1/WythPdm5HdLn5zOQo6Z2q4EilsQVg7oQhDUNR8yDxS870Ilf0SMkCVnXcTbtQgqHEXY7XkWxVKLwFEUUo+h2sLCUpDoulpgyjpgyVndCAhB6ygjjVvT1OtngzywAXTq6k5w1S3bL6Va5pQHR0j6FS0wZo+/oqL9i6rNS4hK/2wOthC3q0IVga2NzWqtE4ndjiyxJDO8RCURa7fMwrLrCjAD/9c5ac2Wt1YuGHg3fUK50/DKTDp/DFjc1JUmRwJefL/+YHxzXccDR7iJPqi7oWV2FiFWuLH73ifKgA7vC8VuNsQgwg79WegVt4XDKGH1G4vBUiyOMcByqJtjZ0orHEQkY63M58Drbg8lajbuJinOsBTShJUC8Baquro6FD97PfbfcGGdpjFWWGurqeP+tNzliylQGDW2Pa2YVtNSIoZWLT9+vf/1rDjvsMF544QUkSWLRokXmvgkTJuSUjPmoo46y3G7EUvv3vyMx50455RTmzJnDxo0b0/rypWQvsFTtUz5sachJwRo9ejQ7d+7Md1u6hIA/sqw8EFZxO+Lj82QSp8pqJWEqmgIhWoNhyj0uStxOnNFVQwoClwQBWWabq4xegXpkQCAhfKU4WurilSJfKVJLQ7ISJcsgJMuHqlDDIKdWvkzLSBoFzcp6IgBFU9MqV4k+WV41gOLrfPTc7iRnNrstohxZWBjSTa1KiZ9j+sJSIYPUls0odjVIWPea/nMG9c0t2HUVhywR0kXKB2iq7ZIk0bPIw35VEeUKItap4dUV1LX6CUTLJ75o7GppQyDREAjGxUsy6rQislrXSWMgiKTY+OFpZ7L0+acslUAjyXhHdCd5KnYoSLI90tcAfr/17978L7Fe8REOyxiqvCRF4l+1BENJmSE8dntK14jIS6WTUnd8bkPDh8sMx+FK7sdE/7nEPm9IyHdopVzJisKYCROStj/+wL08EZMMPJZQMMgf5pzPJ/95H4B7brqO6SfN4vp7IuWrevSwPC7V9o5Yvnw5ixcvxm63J00rV1VV5VXOcomlFpsqZ78UYSn2CB1MUxZS5RAJwz979mx69erF5MmTzY7fG9mxdSsAzaEgLnu807nxMEhMthxLqpWEqShyOShxt79lV9gijt+yBEKAcBZBkQdNDYPNHlnpt+u7+IdojHJFzHapsTaigBH/YBYA4VCcUhZ7nFDD4Ig+hMLWoRZiyyQqB3Y1mNaCZVWfVrsrRenM6U5yhqZGHNqFsOy/TJEAEQqCJFtP8WZgoZQAWQ2jO9oXddhCfvoRpH+VD10I1rSGCSqOpAdoUuyrhFWHQ6srkqceZYkhlWVsaWyi0R8yXzQMJQ1FIaxpBNXIi45NVlB1DZc9PkBlYr0ehx233caQg0ax7OXFKX6DEgeOHZfRve1O8mQDWjWNUmPqOdhmWc6Ug4oeDFQcOMLwVWOkz0RMfDQrxVQIYfmCqQthKldgHbIhnS+dJEm47Xb84TBtITWuz3UBp513AU/+9SHLlYQAh046nPraWtZ8+SVVPXqwfu0alv77dV5d/Gzq+2W3m8qVwb9eWMzPL76UQUOHsWvHDsvjdu3YwcD9sg/R4fV6aWpqsty3adMmKirys1rRIPE+dxRLzTJVzt5AB9OUW7dupaSkhMrKyg5CmWTOnkyVk9MIs//++zNhwgSOO+44ZFnG7XbH7ZckqT2Z4x5m59bvAJDJzgfEINVKQisUqT3hbiQSd7tyBSAZg6WkIBkKT9A6zk0qhSZxmwCEp8hSuTL2Y4ux3Nmtp7IIBcDhsjxHtnct/U8/c7qTnO1q8TOo1IHcgXJltSI0cb/UWGM5NUwokLn1MTahua7HLVKQJYlhXgdfBUGj/QHqD6kdxkJK5dys6jpVXg8SEg3+IFpMaIBTf/kr7r3tVnRVNdPpJJLOkvXNF5+nXLHWElbRLJIOW9Fd5Mllk1m/fReOkf0ifaZrSOHkFY8GEiDaWpCLy+lth0ajz4gaMVO4OxlTwLFWKF0IGv1ByjyuuLKpQjZYIYSIpNFyOczpQqPPL/79nxh24EEpLVEAHy99jx9PsZ4uS8WYCRNZ/n5y+pj/vP0Wg4YOy3tcrOnTp3PDDTcwZcoUM8K6JEn4/X7uuusujj8+8xyZHZFbLLW9nMRpyoRk0i63h6+/Wt29r5FOpMp56qmnmDVr1l7tLGqTJdau+gLA0h/AWPFk6Qwsy4T1yNLllmAIl03B7Ugf/8ewkJVHo6Nb5uVKnN5JofBkqtREHsQipXIlnMkPX+HyRJytY+qgtSlSi6/E8jzZtmlwZWmGpVPTXeQMoKfPjRRsTtkPcf3rdCMcbqTmurTKrKlA6RpSa1NKhTvOQhk9H0JCEgKEwBZKnl6SJahw2dgZ0KLfIy8HVrGQyjwualr97eeUIr5XsiyZTtFG2pbEgJcAB44dz1lzLomL3J0YATwdqZSrpkCQlpCasR9Nd5GnEpcLj8OOT5YijlUWwUYTkfwtCF8JsqzgVjBT47htdkKqjl2RCWu6pf+doVhta2jGH1ZNPyyrkA2ppnatktEnThfesuCvjB4/gTuvm5vxvZAlkoLPWvHpB/+x3H7k1GnmasNfXXEVD952S17iYt1yyy0cfvjhDB06lMmTJyNJEldffTVffvklkiRxww035FSvFbnEUut2GJatMx4Eh4vAX8+ipqbm+6lgLV68mNtvvz2vqSUyXYY6cOBAXC4XLlfkDet3v/sdP/7xjy3rdMfMj7vsyZdq5eSemHuwvi1g+m+lexgYUysKaZSrFNM7qXx3MkEAUluL5T5jakHoWkTJijm/ZdnWRoTHl2wNUUNIgQC4XGBLeChZ+AMlWc1ypLvIGUBZuBWppeP5fgmQgn5E0N9hWaM8KZQrSL7XxjPIHWju0FpW7HSi6UGawhphkTpPp+H0bChNmi745NutDCgvQY1aj5zRvJw6mFODDptCMKyy6vPPGT2+fcl6qgjgmRIbDsDucGRshegu8tQWDqHpgpAu4QHQOrYJS4BoaUQUlROOMej5XA58LkfceJaYbxKizvBE3CYksPSj02OUqKTzSxItwVBSQFlJkvA5HASR+Ow/S/nDBb9E1TKzOGaT41C3sGJOP2kWny77IC40w69+cxXDDzqY/UZEUrZ8/P7SiPxIVsmEUtOnTx9WrlzJHXfcwRtvvMGQIUOora3ltNNO47LLLstpWipdXLVsY6l1W/qP2dMtyCs5KVilpaUMHjw4rw3JZhnqs88+GxcjJBX+cBjFZkNoquWAHlLjrVpWuQfLPC4aA4G0DwO7LJt+XD1sIqXlynJ6x+EExY6o6GkmT6V+p+VUYCZThlb7RVNdZJVgCuUqrmyCNYSGGiQjTlZr1CJWGl3WnMYfyJZmSiNTuoucAZS0JFujDLLpt2ysmeY9j1VsY8p3NFXpCrbSW4ZeTmiQHGwNqJYWjkh4BxlN1dB0wdqdtbSEwmxpbGJQeSnl3vawCbWtfhw2md4lPvPh/My9tzP3rwuxyeC22SmKPvDTkcrv0XjQanokP+Hlc2/I2ArRXeTJabPTEgrTKkmUCJCUzBRRyd8CvhJskoJLUWglHHcPjfEsNkehgRACh03GH46UT/SjS1zsYDVN3BYO4bV4EfU57ZQoCktfeJZqr5uWUJiWYCitRcoqhlZi8Nl0XH3bnRw5ZSozJxwSF5rhwfm38NKHn7LktVfjFK/zf/s7zjj3Vx1XHENpaSnXXnst1157bVbHpSJdXLWsY6ntA6xevTqvvlh7guzU9ii//vWvue+++1BTOClmi7EM1Zh/PeWUU9iwYQMbN27sVL2qLjj0iKNSxsGCeH+hVLkH5TQ/6GKXg/7lxZFAjoqgPMWCJpHCuVyq3Y7csBOpdjs0NyBFlavEU1ptyxQ56DfrTUeS5UkNmcqV2d5gG/ibI/sSFEapuT6iSBKJFN9ZuoucAUh58jyL7edUypUA9JJKRFWfOAd3Kba8rkEw0L4KLcW5jP+lIsSAsiKKXJGpodgUUroQ7Gpq5b9bdvDppm1sb4pEhG8LqZTFxJGTJIlyr5tqnzch0GgxC679PdU+L8Xu3KcFDWRZ4erb7uTV5SuzChTZXeSpLRRC1XVkxRYJ1GCzZyRdEiCCQYICyrwuehQlJ7CXJAmnxarLyPSuByVhEZA/rFrGw0q0ZEmSRJXXS8jCkhSZUtbNz0VOBz2LvHgcqd/v08XQkuX0jy2H08lRU6elDM3w2ccfJcXEevDPN1NfW5O23lg2b96c0nl8xYoVbNmyJeO6CiTQHOmH008/neEjRrJp06Y93KDcycmCtWHDBj7//HP2228/jjrqKNPJz0CSMktuaZDtMtTTTjsNXdeZMGECN998M1VVVSnr/ujTFcgZxsFKtWKwyO2y3B4btdqYGkx6LsREXk5nnZAASW0fDHKxVqUjE+VK+ErjpwcD1g74UlN9yphMhgWsM2016E5ylu3UbjpiZcJKZoTNAQ5n6qCkGa40TPzubWvE5vSiFpWZD9Ca5la+rWvA53YysKKUlmAIWZLY1tRiBiGNRY4+wGODmcqShKe1gRK30zJdS6JfT7rk1JIkUeywocgSQpKpq6vLeDqmu8iTIiu0BsOR4KwqoFlEdE+B3FRDpauUHc4SipwO6loDhBOUHpFCXTMU69ZQOKME4UCSz5XDIiizFR1ZpIyk8bFKnBACFIVbH3qU3/5ituUqRLvDwZkXXMS7b75Ba0sLNrs9LjWP3eGgpbklSfEKh0J8tvxjjskgnhrAr371K4YOHWqu1IvlH//4B2vXruWFF17IqK5MyNZloVvTElV0p11K4I07u7UvVk4K1ssvv4yiRAb3pUuTV25kO1AZx8SSahnqe++9R//+/QmHw1x99dXMnj07Li1FItfcdAvXXnQeAVXDneCHlRgHK13uQSGSc7PZFdk81iVbKFeJDzqnB2LS0uTrgZwrSW1IvAChZTU1GWsBy4c9pzvJWVeRKCuGIi52fWcdlDTLOGeJ53IGW9HsTpAk02l9e20dTkVGcjvN6b0exV5qW9ssp4pCFpHirVYkmueNOb6jMBFGeadNydrPpbvIkz8cJixAiIg12GohTMr2AD0CDdhsNr5TvJHAn/5gXPtCamofKJ/DhttuozRqaUzVB2BtZTS2ZapkpYrqrmoqDf4gxc74KcfJx0zlyGOmcMV1N3Hrn34fp2RJsswRx0zj0bvvMH1vJUlCsdnQVNV0bD9yypSkFYWKzcYh45Njb6Xio48+SpkwfPLkyTz22GMZ15Up2bgs7BP0HLGnW9BpcrZg5ZNslqEa2+x2O5deeinDhg1LKhOLr7gYWQKXhVk8MSgipM49aOXcGY5JkBrQI/m1TP8rqwddzLTZnlauEkl6EIcCSG3Wq+KsEIBwW0cgz5XuJGdd2Z8pFVwrxSmDOGcdnUvWQuiyy0zwPbHCgyYE69pU1jT6CWt6ZErc60lSrtpC4YgsWDxgO1rqb5XHMJVS9uC1V6OFwkz/Ueb5LruLPKm6oLpnD0Q4mgZJVhCKHUmzTpKciARUtNSwo8hFSzCzYwyKEnxV0/VB2jZkMA1sRHVPtVKwLRSiOGbhhSRJrFr2Hv/821+YfOLJ3HbNSNp56AAAKYxJREFUH+Lr03WWvP5K0jmEEPzxtjs5YspUMy3O5XNv4JarrzL9XjVVZf41fzQDk3ZES0sLdrv1ohBZlmlubs6ongKZsW3bNrZt24amaSiKYvpmGdv3Vl+tvSLSXqbLUFtbWwmHw6Zpf9GiRYwZMyZt3R+/+05aH6ymQAi3zUaxuz0oY1C1nlJM/B6roCFJbA0LetujQUXD4eS8g2lbuntJOcUXCkZCOMT4XmWK7G9G+JvN3Ih7G10pZ4kpkHYHZn+5YnxtUsU5y2JVp8vfQkjXzSTgEFF+hnnt9Kmu5tv6JtbX1Jt57sz2SBIehz0StoFkJctY6p/yvDF+XwbGtFVLMBRXl6aqPHTTXCZOPZaSHJfad5aukiebLBGor0UXwxFEFWaXF1obMm6bBJTrATwOW5z1SYouWEjVD1bjpFWojs4ScYpXk4LNxq4UdKQYtxfeNZ/SHr3jpv7SoWsaN1/1G35z3U1MnnE8a1evZsjwEe2LiqLEBibtiJEjR/L8889bpl365z//yfDhwzNqWzZk47Kw10Zyz4H169czduzYuG1Ot4dXXnqRqVOnmt8//nAZffv2jSvXLSO5G6xbt441a9YQCCSvGJs1a1ZWdaVbhnr88cdz3XXXUV5ezimnnIKmRRSgwYMHd2iK3bR+LZqwVpggMiXhc8UvLTYS23o6CM0A8attNumCxrIi3ApouoNhSHlzfs43qa5KaqxJu9+KxCksmuvRXckOtrnSHeSs1eaiSO38yslskRprEHrMVKGsJIX9EEVlkX3BQEYJpSWIU65itytCY2BlGf3LS1mzs4bmQIjmQNBUtowHuD+sUtvqpyIaW8uYapIAt93WoW9PUpssfoe6pvG/T5czaVp2uQX3dnly2+0EVQ1ZaO2z9jZb1m4FnkALVb7qOOuTEAItk2V4CSSG6rAiU78tiCrjdhuemOwaSX5ZKS5WU1WWvfVGVu1XVZU///F3/PmPv0PXtJSO8kZg0o649NJLOeuss1AUhZ///Of07t2brVu38uijj/Lwww/zyCOPZNW+jsjWZWGvjeSeA1999VXkgxH9fdqlBN+4s31VZfS7qqqWbgPdLpJ7U1MTs2bNYsmSJUCykyqQlJ+pI9ItQ40VpM8++yyreo889ng+X/qu5QCdOCVhIEffxNMpV7EKW2zU6oZgCM3poNjtIeDsi2vXZnOc2Bv8rjoil/ZZOsKr2U1NWNGd5Ay3D5r3gIIFyVOFniKEyxOxbglAV810TKmc3q3qFSLeLS8SwFQHXUeWZYb3qDSDjq7evost9U2mD1ajPxBVvkLmQ9frtDOwoiSlf1UgrFr6dKWaUhRCsHTJ2xkrWN1FnvzhML1LiwhpavsIncV0vUGRFsAu9LhpWUmSUGQJKzesdH5TsQ7wVmTiO5dIKh8uwy8rpFm/GAshePHZJ5OPleUkq1QsekzfWsXNgkhg0kw488wz2bFjB9deey0LFiwwt7vdbubNm8fs2bMzqidTsnVZ2CdIzFtoRH8v7hW/fS/21cppVuPKK69k27ZtLF26FCEEzz//PO+88w7nnHMOgwYN4sMPP8x3O3Nm/ZrVKbUGq0CjkFmC50AonOSTpUgSPqcDRZapLPKi+UqJPfnerlzli8hKt84HGu1OcuZxOveYrVICCAXjNwbakBprkJtqkhKHG+E00iGABskWFzICwO1vwdNSF4kOb/hKyRIjelZik2UzqnuZx83AihK8Trv5cLfyr0oMC7AzGtASIpbk2O9x7YtOJ7349JMZL6/vLvKk6oKB+x9Mkxqz3i8cTHeIJTLg1EJx04FCiKSpXYNUQUQNUoVVSOU7p3QwhhrtSfyuxshmazB+nDUUrh6+5DAPl/7puiTLlCyBU1GsYxMmsP+o0QwYkvl02hVXXMHWrVt59dVXefzxx3n11VfZunUrV1xxRcZ1ZEJraysNDQ3m90xcFvYJYvMWZrJ9LyQnBev111/nD3/4AxOiGc979+7NUUcdxUMPPcT/+3//j/nz53dQw+7BJkv887FHUXXNcuAwVhEmYoRrSIUQArezPTqyUdZYVeiwRXwH7K2NaacI987Jw84hAOEt6XAaKhO6i5wBSIqCyDIadF7P31gDbVHHWqsFFrFlAdJYGAWg2hzYi0po85Xjd3nj6pEAR6AVYqwAiizjczpQdT0ucK/xoLV6mTF+K7E0BUJsrG1kS0MzG2sbTSuI8RsTQhAIq2xvbqUtpBIOhVhnTCF0QHeSpy/Xr283H0Zjy2WLDmzxtwePFULQEFWA09HoD1iOf8Uup6XSlGnfWpEcrDQyPehx2OhZ5E1y30icTpSlqBJlU7jn+mviLFNGHZU+d4dxtwC+WvVFVrGwAIqLi5k+fTo/+9nPmD59OsXFxVkdnwk7duxg8uTJHHzwwRx00EG8++67XbJKca8lMW9hR9v3InJ6IuzcuZN+/fqhKAper5fa2lpz34wZM3j99dfz1sDO4Lbb0XXdVHgSMZzUEwccI1xDUgwWks3oxueGNj+b65vQ9cgUidA0bNEwB7F0FESyuyMBOJx5ubbuImcAQlORRGYpQLoC0zLlb+swd11HTu8SYFNDSLqGPdiGKyZRdGwZWW+fuhNCMGFwX342/iBG9Kw0txsPWtXipUUIkZQ2RQhhGeDSeJnZ1dpKbVvAXG2m2GwZp8rpLvJkkyW2btpMc3NL5L5brAzNhC2Si9pgpI+EEDS2BWi2SJNjYEwDBlWdxkCyxSyV0mT1otrRggYrDL+sxDyVqWYTjDQ8PYu8VHrdVHtd+KJKVKpI8OksWbqm8cCtt2TU1nvuuYerrrrKct9VV12VMiJ7LgwePJjPPvuM//73v3zxxRe88MIL+1Yewn2YnBSsfv36UVMT0fSHDh3Kiy++aO774IMPzGBoe5qwFhlc7GmsKU2BEJvqmpIG/5ZgiO8amtnV0pIUTM9AkSTcdhs2WabU46bS5+G7xmZUXaeltQUp6nAcq1QJXym6y9Otlat077/Gwzsf1rnuImcAqLk9BPOJRCTQpNRYm1axj02vk64uT2sDjrC1sqYLgSa112EGCZVlDhvSz8z9KYTAFmPVjTuHJGFT5KRtKdskSdgkJX7KJ8aC3BHdRZ7cdjthXWNXcyRivrEyNBsEsMNVan6XJIkSj4sip3WCa5/TQZ/SInoU+zikf0+cNlvGSlPii6ouBDWt1lO7HSFJEi5bx4uLICJbvpg4WZIkUexy4nXY0kaCT8dLTy/KqJ33338/Q4YMsdw3bNgw7r8/s3APBfLH6tWrWb58OStWrDD/GxH1t23bxooVK5Kiwqfani9ycnKfNm0ab775JieffDK//vWvmT17Nh999BEOh4OPP/6Yyy+/PN/tzAm7Erk8RU7OnRVLKguXLgS6bj3op3Lq9IdVNtY2UmNXqK72IhsOx2o4YjUItCEFOp9GZk+S1jri9iFkhZCz86sIu4ucAUh2x15jlUxcVGFM2wqHMyKDGUzfprsWTQhWNQTYr8S6HpssU+F1811DM5Ik0bPEhxAiLqgv5GbliE3EbizpX/fVV4w7/IgOj+0u8uQPhyPTZF5PpB9kBSHbkPTMU/yodicNwfh7K0kSpZ6IEhlryUrMwSpLEkOryvli604z4KiBz2m3dF5vCkQi/FdGV41Wej0IQYeO7olEVjlaZ9Qw9sf6ilkpUSUuJztbWi0XTOgivbxZRYe34ttvv2Xo0KGW+wYPHpyX9FsFMiTqDG+krIrF6faw9N13GD9+PAAut4evv1ptxtDq3bt30vZ8kpMF65ZbbjETXJ5xxhk899xzjBgxgl69enHvvfcyb968rOtcu3YtkyZNYtiwYYwfP54vv/yyU+UgMlDZFRmXzZbRG5EVVv5Y6Zw6JUlCE4KWsMZ3YSkylSErCHtkYOso4XJ3RgD4SgAIK513cu8ucgaZD8y7kzg5k2VwuDL2jUslowLwe0tRHU60FCuxdCHw2G34YqwlkiQhRfdB5KVHliQcSna+eolTPg6HI+MpwnzLU1fJkqoLRo4+hHKvO7JB17JSriASNsRqOtBQsmIVXatFPbIs0SMh2LIsSVT7PNgtQhwokkRFTF7KbBzdDYxx1rhuK8ukJEk0RiPTp5s6VKRkuZIkiWoL5/hYHE5nyn2xFBcXpwxcu379ejye/IWpgexl6HtFotO78X/apQT9bfzrX/8yvwf8bdTU1LBt2zbefvvtuO1Lly6Ns2alsm5t3749Y6tX1gpWKBTitddeo7Gx0dx28skn88QTT7B48WJ+9atfdZiM0wojK/2aNWv47W9/yznnnNOpchAZqPY/6OCOVwRGl4dbYeWPlYlTp8Om0KBLfBWE9SFYE4RtfutppO7s7G419SQBUuKqtizpTnIGsLVu71WcM105mAlhuwvJZmdoj0o++3abqWSJmOmh2uZWZFmmLOFBLkkS25ta8TjsHDmkH1OGD+SM8QcwsmdugUIlSeKUn55GaQaBRrtCnrpKlgBEOIRDkJMPlgACqorPaf2SY8QqM7B6iYys5tMtLUT9y4spTnA+74yje2zdHfldpWpXYhlJSh0GIpUvlpFKJxOOPfZYrr32WjZv3hy3fcuWLVx//fXMmDEjo3oyJRcZ+t5hOL13EM5h165d9O7du93iFS13+umnM3bsWIaPGMny5cvp3bu3+T1W6TrggAOStqci6yeUw+HgtNNOSxKszpBpVvpcstcf/X+zOvTR0IRgVzSvmhWmP1ZzqxnnJ5V/grGq0CijIdGqS4SQqJccJL7z7y3TSrliTkFV9DRjKwmgVe/cVXU3OXtmQ91erSh3tHIwEwQQjk79KrLMzpZWnlq+ii11DWyubWBrfSObaupp9AcIqlrSg1zXBa2hEOMH9EKJKjOKLHP44L64bLnFPB6VEOE5FfmWp66UJYDBfXqxuaUtIlNZ+mBJQHW4lV4e61V/iaEaEl8iRfR7Kh8qK+tUMKzmxdE97jos2t4SDJvxsawwpo7TlZEkyfTLtTscXPyHP3H86Wfz0oefMuv0MzNq27x589B1neHDhzNz5kzOPfdcZs6cybBhw9B1nZtvvjnDq+yYXGUoiaZtmf3fvhpqN2R3TL7+5/PcO6Kri0dMjvyv+xaAZ555Jn67UW7apXDC7wj429pjmx0yi4C/jb/85S888cQT7b510e2GT2cqcpoiHDFiRF4ffOmy0udSLpZ773+AtjQWKoOQmv6tSI+ubGpoC6DqepJT566WNjRdp74twHcNzdS3+dnVHO/oqckK29wViOh5hCQRLs7+7V2wd1m9JIhbsq9LEvVZ5kCzojvJ2bNfb6FF6vy0aFfR0crBtMdF/4dc3shUI6DpOjUtbbSGwry7dhMBVSUQVuN+G7EPWF0I1tbUU+p2mcqVgU2WKffm5mCeTQCDfMpTV8oSwH9WrKQ5qOI3XAw8vqx+8zLgEeE4BRdSh2owXiJ3NLXwXUMzLdHpRT3FIoJE65QO1LQGksbETB3dhcV5rL63hELoAhoCQXO/rCjMnP0L6gMhM3xHYplEFJvC/zvtTB5//W2OP/UnLP3o46zCrPTu3ZuVK1fy61//mtraWt555x1qa2u5/PLLWblyJX5//tIK5SJDc+bMYcaMGfzlL3/BZrMhKzZ4/1GQ5I7///UseGFuZmXz/b8rzv3Vksj/VyJK71//+tf47Ua5N+5MLrNiMQA33HADp59+OjfccIO53en2YLPZqKurM/8Syem18eabb+bSSy9l//33T8oRlCuZZqXPtJwh4Oecdz5/ufEaWkNBqrzelEqUYXGKNXNbOUluqG1kWNRvpDUYGcAa/AEa/UEz+TNAgz9IW6iOtlCYgRUlZj01Dh8hXwlVdhmcEV8De1NtyqlDKeG78BaDy4PQdeT6nWmnHBOdnTMhXdlU+2If3gJotrkJ+f20tbV1yhehO8gZRGTtu6YWHt7i59I+tj1ikYx1aCfms2lhTLNy0KpfBRBwedFkGwqRFYNCkpABVddZ9s1mgtHfzH+/28nXO+oYVl1m5uWUFIXqcUcRbmiiV6/euMurqFr9P/r26oW2ckmckqXqOnUxee6sfncNgQClLlfcdllWcJdVsGLFCkaMGNGhrOVbnrpKlgBOOvPnrH7275Q4FKb2KkYqKkcEA6CpGcmXDrRKdhr8zTijPqiGchXrm2Xca+MF0hWz4EcXgl3NbexsaeWAXlVpFygodjs/+9PNbPjf57zz1ELCMelyZEXhjKvmUrdjKy/99cFIioCY89dHLZ6JeQnbwqqZSsewTBnhOdpCKiFNcPbFv2bEmEPxFhfTHFZ5+uEF6NEchW0hlZCqUu2LH/clWeaq2++luk9fGpqaaGhqwu/3s3HjRjZu3JiRLEEkXcqNN95oft+xYwdPPfUUxx13HJ988knWWQHSke1z78ILL2Rk1DdRVVVGjzqYyy67jFAohMPh6PA/0GGZefPmcdVVV2VcZ6b1ZXLubOs0vtfU1FBTU0NlZSWVlZWW151YxvjudDoJBoM888wz/PGPf6S0tBRVVeMsiYnyI4lM1zjHcNBBB7F161YaGhqorKykuro6KTbU559/nnF9O3fuZOjQodTW1ppZ6Xv16sWHH34YF+8j03IATzzxhGlSrfK6GVBWhNdpj/sBx/6H5JWBLYEQPpfD/F7T0ka9P0ip20mvYh8ehx1/KExY15Et6oxEtHbRr7SYqqL2ej/asJVit4ND+/dCkiS8rfX0bKtFJv6hqDvcyCF/UooTAeiygtLSYDrNxz1MPUXgLUaEgtGpoRBSa1OSsma52szhQorGUUp8WOu+UlBsSEE/UjQ2UmK7Qkh81aZz79LPuH7hs2Y+rFzoDnIG7bJW5XXz+0MH86NeniQfDwEEZTtOPdmfJvb+W/ZJNKdl7D6I72/hdCOFQwinK7I9GAC7I3JMdOVgKiVMlW3YtHB76idd8FldK5/sbEQhsgpwW2MLG+sbGVBWgiZ084VkS0Mz39Y1Issymq6jyDJhTWf1jjpaUqRVOX/SKGYeOASbLKPqOm98tYENtY3mbyjxwbq+tomN9Y1Ued0c0LMCJXquL7bVsrG+CYBPP/20Q1nLpzx1tSwBHNizghHVZVx86DAGF7ujEfsDEH2xSqVo6cB3rjI+adb4clsNbocdp00hqGr4w2GcNpt5r2tbA/jDYcKajqrr2GQZl02hNRSmzh9kQ20jDYEg4/v1ZHTfanMMW7uzHoGw7IuBZcUc1Mu6n2L36bpg1fYamoNh+pcVIUsSshSxaG6sa6IpGELTdXoX+yiLrn6MHVs31TezKyEBtU2OhM7x2G30Ky2KxNZytCtuie2xIhNZMmhpaWHx4sU88cQTvP322+i6zqGHHsrs2bO54IILMqqjI3J97hXYM8TKT04WrLFjx3LooYfmrUGZZqXPtBzA9OnTWbhwIQMHDsTtduetrfng/yy2bd/treg6egDXnxeZkukM3UHOIFnW9oW+7Bv92x1k5vWSnkxkLZ/ytLtkyWBHlu1TgAnRvwLZ0ZEsqarKq6++yhNPPMHLL7+M3++nb9++6LrO008/zSmnnJLX9uwrz73vC3HyI/YSvvrqKzFx4kQxdOhQMXbsWLFq1Spz34wZM8Ty5cs7LFegQEcU5KxAvijI0veLd999V5x77rmivLxcSJIkqqqqxIUXXig++OADUV9fLyRJEu+++26XnLsgQ92TnKYICxQoUKBAge8TshyJFzZlyhQuu+wypk2bhhL1x21sbKSsrIx33nmHo446ag+3tMDeQm5ro4GGhgaeffZZ1qxZQyAQSNp/9913d6phBQpAQc4K5JeCPBXIldGjR7Ny5UreeecdZFlm586dnHzyyRQVFe3pphXYS8nJgmVElQ0Gg7S2tlJVVUVdXR2qqlJWVkZJSQnr16/vivYW+B5RkLMC+aQgTwU6y9dff83ChQtZtGgR69evx+VyMXPmTGbOnMnZZ5/NkiVLChasAiY5xcG67LLLmDBhAjt27EAIwauvvorf72fhwoUUFRW1B/IqUKATFOSsQD4pyFOBzjJ8+HCuv/561q1bxwcffMA555zDe++9x1lnnQXAXXfdxXvvvbdnG1lg7yEXx63q6mrx0ksvCU3ThCRJYtmyZea+u+++W0yaNKnz3mFZsGbNGnHYYYeJoUOHinHjxon//e9/nSpn4Pf7xUknnSSGDh0qRo0aJaZPny42bNhgWXbAgAFi+PDhYtSoUWLUqFHiySef7LDdmR6Tbbvr6+vNOkeNGiWGDh0qFEURtbW1ObXhoosuEgMGDBCA+OKLL3JqW7bXIMTeJ2cGXXnN2R6bi9wZpOvXfF1HpufozHVkyt4oT91x7CqMW/FomiZee+01ccYZZ4iioiIhy7IYNGhQ2mvtCrpCljoqm42cZXtuIYSYO3du3sanQCAgLrzwQrHffvuJ/fffX5x22mmdqu/1118XhxxyiBg9erQ44IADxN/+9rekMjkpWCUlJeZqiYqKCvHcc8+Z+9566y3h8XhyqTZnJk+eLB599FEhhBDPPPOMmDhxYqfKGfj9fvHKK68IXdeFEELcc889Ytq0aZZlBwwYkFYIOnNMtu1O5NZbbxUzZ87MuQ3vvvuu2Lx5s2XZrrr3Qux9cmbQldec7bG5yJ1Bun7NpS2dOUdnriNT9kZ56o5jV2HcSk1bW5tYtGiROPHEE9OW6wq64po6KpuNnGV77k8//VQcd9xxon///nkZny699FJx0UUXmW3dunVrzvXpui7Ky8vF559/LoQQYsOGDcLpdIqmpqa4cjkpWOPGjROPPfaYEEKIqVOniilTpoimpibR1tYmfvSjH4n99tsvl2pzYseOHaKkpESEw2EhROTCe/TokaRFZ1ouHcuXLxdDhgyx3NdVClY+2r3//vuL559/Puc2pCrb1fd+b5Izg90hb9kcmw/FJF0d+ZC/TNq5OxSsvU2euuvYVRi3Mr+G3UVXXFMu159OzrKpLxAIiIkTJ4r169fnZXxqaWkRJSUlorm5OWXbs6nPULCMF7bPP/9c9O7dWwSDwbhyOflg/fjHP2blypUAXH/99SxfvpyysjKKi4t57rnnuOaaa/I0gdkxXZ0TLJa7776bE088MeX+0047jYMOOohf/OIX7Nq1K6M6Ozqms+1etmwZtbW1zJw5M6/tzqZtuV7D3iRnBrtD3rI9Ntf+y4R8/G4ypSuvA/Y+eerOY1dh3Nq76IpryuX608lZNvX96U9/4vTTT2fQoEF5ue5vvvmGiooKbrjhBg499FCOPPJI3nrrrZzrkySJp59+mlmzZjFgwACOOOII/v73v5vpdgxyUrAuv/xy5s+fD8DEiRNZtWoVDzzwAPPnz2flypW7PVR/V+QES+Smm25i7dq1cbmnYnnvvff4/PPPWbFiBRUVFcyePbvDOjM9pjPtfuSRRzjzzDNNgclHu3NpWy7XsLfJmcHukLdMj+1s/+WzLZ1hd1zH3ihP3XHsKoxbeyddcU3ZlO1IzjKtb9myZSxfvjzjVEOZ1BkOh1m/fj37778/n3zyCffeey8/+clPLBXzTOpTVZWbb76ZF154gW+//Za33nqL2bNnJyd8TmsvS2DVqlXioosuEjNnzhTnnXeeeOONN7I5vEvYsWOHKC4uzsg0mkk5K2699VYxduxYUV9fn1Gbtm7dKnw+XzaXkfKYzrS7paVFFBUVidWrV+el3Vam9q6493ujnBnsDnnL9dhc5E6IjqcIc72OTM+RSK7XkYq9VZ72hbGrMG7tHXTFNWVTNhM5y7S+m2++WfTq1UsMGDBADBgwQCiKInr37i1effXVnOvctWuXkGVZqKpqbhs3bpxYsmRJTvUtX75cjBw5Mm7boYceKt5+++24bRkrWEuXLhUOh0PIsiyqq6uFzWYTsiyLBx54INMquoyjjz46ziltwoQJnSoXy/z588Uhhxwi6urqUpZpaWmJE6z58+eLI488Mm292RyTS7uFEOLRRx8Vhx9+eN7abfWQzPe935vlzKAr5S2bY3OROys6Un46cx2ZnCNf12HF3i5P3W3sKoxb2cv+7qIrrimTspnIWS7nNsjX+DRt2jTxyiuvCCGE2Lhxo6isrLR0dM+kvu3bt4uioiLx1VdfCSGEWLt2rSgrKxNbtmyJK5exgjVlyhQxevRosWnTJiGEEI2NjWLWrFmioqIi0yq6jK7KCbZ582YBiMGDB5vLgcePH59U9zfffCNGjx4tDjroIHHggQeK//u//+vwLaejY/KRy+yII44QjzzySNL2bNt9wQUXiD59+ghFUUSPHj3inBjzfe/3Zjkz2B056DI5Ry5yF0u6fs3XdWRyjs5eRzr2dnnqbmNXYdzae3MAdsU1dVTn0qVL08pZrm2MpSMFK9M6v/nmG3H00UeLAw88UIwaNUosXry4U/X94x//EAceeKA4+OCDxUEHHSQWLVqUVFfGkdyrq6t58MEHmTVrlrlt48aNDB48mG+//ZZ+/fplUk2BAmkpyFmBfFKQpwIFCuwpMnZyr6mpoW/fvnHbjMGppqYmv60q8L2lIGcF8klBngoUKLCnyGoVYaJ3fYECXUFBzgrkk4I8FShQYE+Q8RShLMt4PB5kOV4na2lpSdouSRKNjY35bWmB7wUFOSuQTwryVKBAgT2FdZARC/ZEUMcC3z8KclYgnxTkqUCBAnuKjC1YBQoUKFCgQIECBTIjp0juBQoUKFCgQIECBVLzvVCw5s6diyRJ5l9VVRVTpkxh6dKle7ppeeWdd97hpptu2i3n+uSTTzj77LMZOXIksiynzRn2faEgZ/lnwYIFTJ8+nV69elFcXMy4ceN4+umnd8u59yQFWco/CxcuZOLEiZSXl+NyuRg+fDjXX389wWBwt5x/d1KQn65ly5Yt+Hw+JElKuxr5e6FgAbjdbpYtW8ayZct44IEHqK2tZcqUKXzxxRd7uml5Y3cK23/+8x+WLl3KIYccQv/+/XfLObsDBTnLLzfccAN9+/bl/vvvZ/HixRx22GH8+Mc/5r777tst59+TFGQpv9TV1XH88cfz97//nddee42zzz6bm266iYsuumi3nH93U5CfruPyyy/H5/N1WC5jJ/fujizLTJw40fw+fvx4Bg4cyIIFC7j33ntzqlMIQSgUwul05quZexV+vx+3222576KLLuKSSy4B4Ac/+MFubNXeTUHOsiednK1YsYKqqirz+9SpU9myZQu33XYbF1544e5q4h6hIEvZk06WLr744rjvkydPprm5mTvuuIMHHngARVF2RxN3GwX5yZ508mPw9ttv8+abb/L73/+e3/zmN2nLfm8sWIn079+fyspKNmzYAMD8+fMZN24cJSUlVFdXM3PmTNasWRN3zFlnncWBBx7Iq6++yqhRo3A6nbz44ou0trYyZ84chg8fjsfjYeDAgZx//vlJS74HDhzInDlzmD9/Pn379sXn83HmmWcSCARYuXIlhx9+OF6vl3HjxiW9ZQghuO222xg2bBhOp5PBgwdzxx13mPvnzp3LtddeS2trq2kWjlV8Vq9ezUknnURJSQler5cTTjiBb775Ju4ckiQxb948rrzySnr27Bn3YEskcdl7AWsKctY5ObPaN2bMGLZu3Zr+xu+DFGSpc7JkRUVFBeFwGF3XszquO1KQn87LTzgcZs6cOVx77bVUVFR0eM+/NxasRJqamqirq6N3795AZE51zpw5DBgwgKamJh588EEmTZrEmjVrKC8vN4/bunUrl1xyCVdffTX9+vWjX79+tLW1oWkaN954I1VVVWzevJkbb7yRk08+mbfffjvuvC+88AIHH3wwDz30EOvXr+eyyy7D6XTy4Ycfctlll9GjRw+uvPJKTj31VL788ktTkbnkkkt4+OGH+cMf/sCECRP44IMPuPLKK3G73Zx//vn84he/YMuWLfzjH/8wz1lcXAzA+vXrmTRpEgceeCB/+9vfkGWZG2+8kSlTpvD111/HvY3cddddTJo0iUceeYRQKNSlffB9oCBn+ZezpUuXMnLkyOw7o5tTkKX8yJKqqoRCIT799FPuvPNOLrjgAux2e+c6pxtQkJ/Oy89dd92Foij86le/4vHHH+/4pqfMoLgPcc011wiv1yvC4bAIh8Niw4YNYtasWQIQr7/+elJ5VVVFW1ub8Pl8YsGCBeb22bNnC0B89NFH/7+9O3ppqo3jAP49sp10NWtt5QjG0kgRi1xZSZlCNp2NCJkVQc28MXB0EeUfUNBFJCZUF9JFlASRUgsvlDCGNWrlRV2IoAhGLpKm0cUpjXPy9168r4d3zulGZy/v5u8DBzzPeX7nPGd8lcdzdrZljyfLMgWDQQJAo6OjarvdbiebzUa/fv1S2zweDwGgvr4+ta23t5cA0IcPH4iIaHx8nARBiBoLEVFraytZrVb6/ft31Hku5vV6KT8/n2ZnZ9W2r1+/0tq1a+nOnTtqGwAqKSmh+fn5Zc9vsaqqKnK73UnVZCLOWWpzRkTk9/sJAHV1dSVdm044S6nJkizLBEBdGhsb1bFkEs6P9vn5/PkzGY1GGhgYICKie/fuEQCKRCJxa1bNfZ4fP35Ar9dDr9cjPz8fgUAAt2/fRm1tLQAgFArB6XTCbDZDp9PBYDBAkqSYS6YWiwX79u2L2X9XVxccDgfWrVsHvV6PiooKAIipr6yshCiK6nphYSGysrJw+PDhqDYAmJycBAAMDAwAADweDxRFUZfq6mpMTU2p/eJ5/vw5jh8/Dp1Op9aaTCbs2rULQ0NDUX3r6ur4q0X+AOcsdTkbGRnBuXPncOLECZw5cyap2nTEWdI+SzqdDkNDQ3j16hVu3ryJ3t5eNDU1JVSbbjg/2ubn8uXLcDqdqK6uXrHvglVzizAnJwcvX76EIAiwWCyw2WzqpchPnz6hpqYGZWVl6OzsxJYtWyCKItxuN+bm5qL2s3nz5ph9P336FF6vF83Nzbh27RrMZjO+fPmC+vr6mPoNGzZErYuiiJycnKgALvy8UDs9PQ0igsViWfLcJicnYbfb45779PQ0Ojo60NHRseTrstL5scRxzlKTs3A4DJfLhZ07d+LBgwdJ1aYrzlJqslRWVgYAqKiogM1mQ0NDAy5cuKC2ZwrOj3b5efPmDXp6evD27Vt8//4dAPDz508Af996NRgMMBgMMXWrZoKVlZUV9xeov78fkiThyZMnahgURcG3b99i+i410+3u7kZpaSk6OzvVtsHBQW0GDmDjxo0QBAHBYDAqlAuKiopWrHe73WhpaYnZZjQao9b56tWf4Zxpn7OZmRnU1NQgNzcXz549Q3Z2dsK16YyzlPq/WXv27AEAjI+PZ9wEi/OjXX5GR0chyzJ2794ds23btm04deoUHj16FLNt1UywljM7OwtBEKLe6Pj48WMoipJw/eIQPHz4ULPxLVySnJmZwbFjx+L2E0VxyQ/NO3LkCIaHh+FwODLuUeR0wjlLniRJqKurgyRJeP36NUwmkyb7TXecJW0Eg0EAQEFBQcqO8X/E+UmOy+VCIBCIauvv78f169fh9/uxffv2Jet4ggWo94Kbmppw/vx5jIyMoK2tLebSZjxOpxM+nw9Xr17FgQMH0NfXhxcvXmg2vsLCQvh8Ppw9exatra3Yv38/ZFnG2NgYAoEA/H4/AKC4uBiKoqhPReTm5qKoqAhXrlzB3r17UVtbi+bmZuTl5WFqagqDg4M4dOgQTp8+nfSYIpGI+h9LJBKBJEno6ekBABw9enTJy6WrHecs+Zx5PB68f/8ed+/eRTgcRjgcVrc5HI6M/TyelXCWks9SZWUl6uvr1W+fCIVCaGtrg8vlWvI9RpmM85NcfqxWK6xWa1Tbx48fAQAHDx6MeytzVT1FuJz79+9TQUEBZWdnU3l5Ob17947sdjv5fD61T2NjI5WUlMTUKopCly5dok2bNpHRaKSGhgYKhUIEgLq7u9V+i/cXb2wTExMxtfPz83Tr1i3asWMHiaJIJpOJysvLqb29Xe0jyzK1tLRQXl4eCYJAVVVV6raxsTE6efIkmc1mWrNmDW3dupW8Xi8NDw+rfQDQjRs3ln2dFgQCgaincf69TExMJLSPTMM50z5n8TKW6TnjLGmfpYsXL1JxcTEZDAZav349lZaWUnt7O83NzSVUn044P9rnZ7FEniIU/jkIY4wxxhjTyKr5mAbGGGOMsf8KT7AYY4wxxjTGEyzGGGOMMY3xBIsxxhhjTGM8wWKMMcYY0xhPsBhjjDHGNPYXfE4MRGpm1YcAAAAASUVORK5CYII="
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_particles = 1000;\n",
    "threshold_schedule = [500.0, 250.0, 150.0, 100.0, 80.0, 70.0, 65.0, 60.0];\n",
    "\n",
    "priors = [Uniform(0, 10.0), Uniform(0.0, 1.8), Uniform(0, 10.0),\n",
    "          Uniform(0.0, 1.8)];\n",
    "\n",
    "sim_abcsmc_res_conc_3 = SimulatedABCSMC(filter_expt_form,\n",
    "    simulator_function_conc,\n",
    "    priors,\n",
    "    threshold_schedule,\n",
    "    n_particles;\n",
    "    write_progress=true,\n",
    "    distance_function = new_distance,\n",
    "    max_iter = convert(Int,5E4))\n",
    "\n",
    "# plot the results.\n",
    "population_colors=[\"#351D17\", \"#502C23\", \"#754134\", \"#935343\", \"#B86854\",\"#DA7E67\",\"#E57053\",\"#E75935\",\"#E84D26\",\"#E84117\",\"#CF3008\",\"#AB2402\",\"#351D17\"]\n",
    "plot(sim_abcsmc_res_conc_3, population_colors=population_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Float64,1}:\n",
       " 6.569938974281577\n",
       " 0.21218751872705788\n",
       " 0.9152670273313928\n",
       " 0.22833506598150718"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars_conc_3 = [median(sim_abcsmc_res_conc_3.population[end][:,i]) for i in 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model that we are attempting to infer the parameter for is:\n",
    "$$\\varnothing \\xrightarrow{a_1(t)}m\\, P\\xrightarrow{d}\\varnothing,$$\n",
    "where $a_1(t) = \\alpha_0 V(t)^{\\alpha_1}$ and $m$ is a random number drawn from a geometric distribution with time-dependent mean burst size $b(t) = \\beta_0 V(t)^{\\beta_1}$ and degradation rate $d = 0$. The volume of the system grows according to exponential growth law: $V(t) = V_0 e^{\\theta t}$. Later on in this sheet we also go onto assess the minimum threshold for the SMC-ABC data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the inferred medians of the posterior distributions in some tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "par_conc = convert(Array{Float64,2},transpose([true_par pars_conc_1 pars_conc_2 pars_conc_3]));\n",
    "df_conc = DataFrame(alpha_0 = par_conc[:,1], alpha_1 = par_conc[:,2], beta_0 = par_conc[:,3], beta_1 = par_conc[:,4]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>alpha_0</th><th>alpha_1</th><th>beta_0</th><th>beta_1</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>5.0</td><td>1.0</td><td>1.0</td><td>1.0e-6</td></tr><tr><th>2</th><td>8.37533</td><td>0.221952</td><td>0.543998</td><td>0.516475</td></tr><tr><th>3</th><td>6.1294</td><td>0.866268</td><td>0.572507</td><td>0.164104</td></tr><tr><th>4</th><td>6.56994</td><td>0.212188</td><td>0.915267</td><td>0.228335</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& alpha\\_0 & alpha\\_1 & beta\\_0 & beta\\_1\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 5.0 & 1.0 & 1.0 & 1.0e-6 \\\\\n",
       "\t2 & 8.37533 & 0.221952 & 0.543998 & 0.516475 \\\\\n",
       "\t3 & 6.1294 & 0.866268 & 0.572507 & 0.164104 \\\\\n",
       "\t4 & 6.56994 & 0.212188 & 0.915267 & 0.228335 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "4×4 DataFrame\n",
       "│ Row │ alpha_0 │ alpha_1  │ beta_0   │ beta_1   │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m  │ \u001b[90mFloat64\u001b[39m  │ \u001b[90mFloat64\u001b[39m  │\n",
       "├─────┼─────────┼──────────┼──────────┼──────────┤\n",
       "│ 1   │ 5.0     │ 1.0      │ 1.0      │ 1.0e-6   │\n",
       "│ 2   │ 8.37533 │ 0.221952 │ 0.543998 │ 0.516475 │\n",
       "│ 3   │ 6.1294  │ 0.866268 │ 0.572507 │ 0.164104 │\n",
       "│ 4   │ 6.56994 │ 0.212188 │ 0.915267 │ 0.228335 │"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.5.2"
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