2a-FPM_and_EGR-37.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2) Calculation of the fluorescence per molecule and the exponential growth rate - $37^\\circ$C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this worksheet we estimate the exponential growth rate for each generation in each lineage, as well as the proportionality constant between the fluorescence and `molecule #` for each lineage.\n",
    "\n",
    "We first import the data from the $37^\\circ$C dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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(filelist)\n",
    "    push!(raw_data,readdlm(string(\"Analysis_MC4100_37C/MC4100_37C/\", file), ',', Float64, '\\n'))\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the first few rows of data for the first cell lineage. Note the second column is the division indicator and is equal to 1 if the cell has divided most recently at that time point and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34×5 Array{Float64,2}:\n",
       "  1.0  1.0  2.09708  338326.0  5546.33\n",
       "  2.0  0.0  2.10993  344303.0  5553.27\n",
       "  3.0  0.0  2.29029  370118.0  5442.91\n",
       "  4.0  0.0  2.47987  405355.0  5629.93\n",
       "  5.0  0.0  2.39596  381219.0  5689.84\n",
       "  6.0  0.0  2.46906  387991.0  5542.73\n",
       "  7.0  0.0  2.49225  415390.0  5690.27\n",
       "  8.0  0.0  2.49002  414404.0  5676.77\n",
       "  9.0  0.0  2.54599  435754.0  5733.61\n",
       " 10.0  0.0  2.54237  430027.0  5811.18\n",
       " 11.0  0.0  2.65326  438526.0  5695.14\n",
       " 12.0  0.0  2.64758  436573.0  5669.78\n",
       " 13.0  0.0  2.72036  472680.0  5764.39\n",
       "  ⋮                            \n",
       " 23.0  0.0  3.52673  614441.0  5689.27\n",
       " 24.0  0.0  3.65501  616226.0  5653.45\n",
       " 25.0  0.0  3.73755  657481.0  5767.38\n",
       " 26.0  0.0  3.81613  662422.0  5710.53\n",
       " 27.0  0.0  3.96744  661705.0  5961.31\n",
       " 28.0  0.0  4.13382  716700.0  5643.31\n",
       " 29.0  0.0  4.23825  726548.0  5906.89\n",
       " 30.0  0.0  4.30962  733140.0  5960.49\n",
       " 31.0  0.0  4.50653  772114.0  5894.0\n",
       " 32.0  0.0  4.69967  798307.0  5827.06\n",
       " 33.0  0.0  4.73342  796625.0  5944.96\n",
       " 34.0  1.0  2.53289  464989.0  5961.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_few_rows = raw_data[1][1:34,:]; # first 34 timepoints of lineage 1 data.\n",
    "display(first_few_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the first division of the cell occurs at *t=34*. Now for the calculation of the proportionality between molecule number and fluorescence, and the exponential growth rate, we need to separate the fluorescence data for each lineage into cell-cycle generations. Let's do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fluor_all_lins = [raw_data[x][:,4] for x in 1:length(raw_data)]; # the fluor. data for each lineage.\n",
    "vol_all_lins = [raw_data[x][:,3] for x in 1:length(raw_data)]; # the fluor. data for each lineage.\n",
    "times_all_lins = [raw_data[x][:,1] for x in 1:length(raw_data)]; # the fluor. data for each lineage.\n",
    "div_flag_all_lins = [raw_data[x][:,2] for x in 1:length(raw_data)]; # the division flag data for each lineage.\n",
    "# now need to loop over all lineages and find the indices where division occurs.\n",
    "div_times = [findall(y-> y == 1.0, div_flag_all_lins[x]) for x in 1:length(div_flag_all_lins)]\n",
    "\n",
    "# for each lineage i div_times[i] contains the times of division.\n",
    "# Now need to form the 3-d array containing the separated generations for each lineage.\n",
    "\n",
    "fluor_sep_all_lins = []; # 3-d array to contain all generation array for each lineage for fluor.\n",
    "vol_sep_all_lins = []; # 3-d array to contain all generation array for each lineage for vol.\n",
    "time_sep_all_lins = [];\n",
    "\n",
    "for x in 1:length(fluor_all_lins)\n",
    "    gens = [];\n",
    "    gens_vol = []; \n",
    "    gens_time = [];\n",
    "    \n",
    "    fluor_lin = fluor_all_lins[x];\n",
    "    vol_lin = vol_all_lins[x];\n",
    "    times_lin = times_all_lins[x]\n",
    "    \n",
    "    dt_lin = div_times[x];\n",
    "    for y in 1:length(dt_lin)\n",
    "        if y < length(dt_lin)\n",
    "            push!(gens,fluor_lin[dt_lin[y]:dt_lin[y+1]-1]);\n",
    "            push!(gens_vol,vol_lin[dt_lin[y]:dt_lin[y+1]-1]);\n",
    "            push!(gens_time,times_lin[dt_lin[y]:dt_lin[y+1]-1]);\n",
    "        else\n",
    "            push!(gens,fluor_lin[dt_lin[y]:length(fluor_lin)]);\n",
    "            push!(gens_vol,vol_lin[dt_lin[y]:length(fluor_lin)]);\n",
    "            push!(gens_time,times_lin[dt_lin[y]:length(fluor_lin)]);\n",
    "        end\n",
    "    end\n",
    "    push!(fluor_sep_all_lins,gens);\n",
    "    push!(vol_sep_all_lins,gens_vol);\n",
    "    push!(time_sep_all_lins,gens_time);\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `fluor_sep_all_lins[1]` gives the 2-d array for each lineage which contains the 1-d arrays for each generation.\n",
    "- `fluor_sep_all_lins[1][1]` gives the 1-d fluorescence array for generation 1 in lineage 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's form a generic method to extract the generations for data of each type (e.g., fluroescence, volume etc.). We can then save this as a module to use with ease in later notebooks. The inputs will be:\n",
    "\n",
    "- (1) `dat_arr`: the 2-d array of Tanouchi data formated as above.\n",
    "- (2) `col`: the column of data one wishes to extract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"function extract_gens(dat_arr,col::Int)\\n    all_lins_data = [dat_arr[x][:,col] for x in 1:length(dat_arr)]; # extract the column of interest.\\n    div_flag_all_lins = [dat_arr[x][:,2] for x in 1:length(dat_arr)]; # the division flag data for each lineage.\\n    \\n    # now need to loop over all lineages and find the indices where division occurs.\\n    div_times = [findall(y-> y == 1.0, div_flag_all_lins[x]) for x in 1:length(div_flag_all_lins)];\\n    \\n    # init the 3-d generation sep data array.\\n    gen_sep_lins = [];\\n    \\n    # start loop over all lineages.    \\n    for x in 1:length(all_lins_data)\\n        gens = []; # init the 2-d generation measurements array.\\n        lin_data = all_lins_data[x]; # take the data for that lin.\\n        dt_lin = div_times[x]; # find the division times for that lin and then loop over.\\n        for y in 1:length(dt_lin)\\n            if y < length(dt_lin)\\n                push!(gens,lin_data[dt_lin[y]:dt_lin[y+1]-1]);\\n            else\\n                continue\\n            end\\n        end\\n        push!(gen_sep_lins,gens);\\n    end\\n    \\n    return gen_sep_lins\\nend\\n\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "function extract_gens(dat_arr,col::Int)\n",
    "    all_lins_data = [dat_arr[x][:,col] for x in 1:length(dat_arr)]; # extract the column of interest.\n",
    "    div_flag_all_lins = [dat_arr[x][:,2] for x in 1:length(dat_arr)]; # the division flag data for each lineage.\n",
    "    \n",
    "    # now need to loop over all lineages and find the indices where division occurs.\n",
    "    div_times = [findall(y-> y == 1.0, div_flag_all_lins[x]) for x in 1:length(div_flag_all_lins)];\n",
    "    \n",
    "    # init the 3-d generation sep data array.\n",
    "    gen_sep_lins = [];\n",
    "    \n",
    "    # start loop over all lineages.    \n",
    "    for x in 1:length(all_lins_data)\n",
    "        gens = []; # init the 2-d generation measurements array.\n",
    "        lin_data = all_lins_data[x]; # take the data for that lin.\n",
    "        dt_lin = div_times[x]; # find the division times for that lin and then loop over.\n",
    "        for y in 1:length(dt_lin)\n",
    "            if y < length(dt_lin)\n",
    "                push!(gens,lin_data[dt_lin[y]:dt_lin[y+1]-1]);\n",
    "            else\n",
    "                continue\n",
    "            end\n",
    "        end\n",
    "        push!(gen_sep_lins,gens);\n",
    "    end\n",
    "    \n",
    "    return gen_sep_lins\n",
    "end\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test this function to extract the fluorescence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"./extract_gens.jl\"); using .extract;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = extract_gens(raw_data,4);\n",
    "test[1]; # show for the first gen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Exponential growth rate for each generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have separated each lineage into its respective generations let's estimate the exponential growth rate in each generation. We start by creating a model to fit to the volume for each generation. We will use the LsqFit.jl package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "using LsqFit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of how the fitting program works here. We specifiy the model with the params `p` to be selected by minimising the model to the data. This below finds the initial vol and the exp growth rate as via the least squares fitting procedure for generation 1 in lineage 1. Note that the units of the `p[1]` volume param are $\\mu$m (volume $\\propto$ length) and the units of the `p[2]` volume param for exponential growth rate are min$^{-1}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02760090975872231"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m(t,p) = 10^p[1] * exp.(10^p[2] * t);\n",
    "p0 = [-0.3,-2.0]\n",
    "t_data = time_sep_all_lins[1][10]\n",
    "fit = 10^curve_fit(m,t_data,vol_sep_all_lins[1][10],p0).param[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now extend this fitting procedure for all the generations in the first two lineages. Note that in an additional worksheet (2.5) we do this for all generations of all lineages and save the result. We do only the first two lineages to save computational time in the evaluation of the worksheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_grs = []; # init. 3-d array to store the models for each gen of each lin.\n",
    "for x in 1:length(vol_sep_all_lins[1:2]) # loop over lins - do for first 2. \n",
    "    gens = [];\n",
    "    lin_vols_x = vol_sep_all_lins[x] # 2-d array of gen vols for lin x.\n",
    "    for y in 1:length(lin_vols_x) # loop over gens in lin x.\n",
    "        m(t,p) = 10^p[1] * exp.(10^p[2] * t); # create the model.\n",
    "        p0 = [-0.3,-2.0]; # initial search for params V0 = 2, exp_gr = 0.02.\n",
    "        t_data = time_sep_all_lins[x][y] # take the time over which the vols were obtained.\n",
    "        exp_gr = 10^curve_fit(m,t_data,lin_vols_x[y],p0).param[2] # collect the exp growth param.\n",
    "        push!(gens,exp_gr)\n",
    "    end\n",
    "    push!(exp_grs,gens)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the histograms of the growth rates for the first two lineages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyPlot; using LaTeXStrings;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = PyPlot.subplots(1, 2, sharey = true, tight_layout = true);\n",
    "n_bins = 20;\n",
    "axs[1].set_xlabel(L\"\\beta\");axs[2].set_xlabel(L\"\\beta\"); axs[1].set_ylabel(L\"P(\\beta)\"); axs[1].set_title(\"Lineage 1\"); axs[2].set_title(\"Lineage 2\");axs[1].grid(true);axs[2].grid(true)\n",
    "axs[1].hist(exp_grs[1], bins=n_bins, facecolor= \"g\", alpha=0.75);\n",
    "axs[2].hist(exp_grs[2], bins=n_bins, facecolor= \"r\", alpha=0.75);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a separate worksheet we have performed the calculation for all generations in all lineages and saved the result to \"exp_grs_all.csv\". We did this in Mathematica since its function `NonLinearModelFit` in fitting parameters is much faster than the `LsqFit` package used in Julia above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of proportionality for each lineage and molecule number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to collect the array of fluorescence measurements at the end and start of each generation in each lineage. Once these have been found one can output the proportionality $\\kappa$ (i.e., the fluorescence per molecule) given by: \n",
    "$$\\kappa = 2\\gamma_a - \\gamma_b, $$\n",
    "where $\\gamma_a$ and $\\gamma_b$ are the Fano factors before and after cell division respectively. One would think this must be done for each lineage..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop_consts = []; # array containing the prop. for each lineage.\n",
    "fs_bs = [];\n",
    "fs_as = [];\n",
    "for x in 1:length(fluor_sep_all_lins)\n",
    "    lin_fluor = fluor_sep_all_lins[x];\n",
    "    fs_b = [lin_fluor[y][end] for y in 1:(length(lin_fluor)-1)]; # fluor's from before.\n",
    "    fs_a = [lin_fluor[y+1][1] for y in 1:(length(lin_fluor)-1)]; # flour's from after.\n",
    "    ffs_b = var(fs_b)/mean(fs_b); ffs_a = var(fs_a)/mean(fs_a);\n",
    "    props = 2*ffs_a - ffs_b;\n",
    "    append!(prop_consts, props); append!(fs_bs, ffs_b); append!(fs_as, ffs_a);\n",
    "end\n",
    "\n",
    "n_bins = floor(Int64,length(prop_consts));\n",
    "PyPlot.xlabel(L\"\\kappa\");\n",
    "PyPlot.ylabel(L\"P(\\kappa)\");\n",
    "PyPlot.grid(true)\n",
    "PyPlot.title(\"Distribution of proportionality\")\n",
    "PyPlot.xlim(-25000,25000)\n",
    "PyPlot.hist(prop_consts, bins=n_bins, facecolor=\"g\", alpha=0.75, density = true);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, what we see is that on each lineage there is not enough information to get reasonable estimates for the fluorescence per molecule on each lineage - hence we will find the fluorescence per molecule for the whole 37$^{\\circ}$C dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       "    0.20184423806935956\n",
       " 6185.1225536609745"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_bs = convert(Array{Float64,1},fs_bs); fs_as = convert(Array{Float64,1},fs_as);\n",
    "m(t, p) = p[1].*t .+ p[2]; p0 = [0.5, 0.9];\n",
    "fit = curve_fit(m, fs_bs, fs_as, p0).param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PyPlot.scatter(fs_bs, fs_as); PyPlot.xlabel(L\"\\gamma_b\"); PyPlot.ylabel(L\"\\gamma_a\");\n",
    "PyPlot.plot(fs_bs, fit[1].*fs_bs .+ fit[2], \"r--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In principle the y-intercept of this best fit graph should be proportional to the fluorescence per molecule since the above plots the equation $\\gamma_a = \\frac{\\gamma_b}{2}+\\frac{\\beta}{2}$. However, we see that the outliers skew the slope of the best fit line to 0.201 instead of the required 0.5. Hence we must remove these outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_fs_bs = []; new_fs_as = [];\n",
    "for (i,f) in enumerate(fs_bs)\n",
    "    if fs_bs[i] > 40000 && fs_as[i] < 15000\n",
    "        continue\n",
    "    else\n",
    "        append!(new_fs_bs, fs_bs[i]); append!(new_fs_as, fs_as[i]);\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       "   0.5100193412581755\n",
       " 554.3275406939185"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_bs = convert(Array{Float64,1},fs_bs); fs_as = convert(Array{Float64,1},fs_as);\n",
    "m(t, p) = p[1].*t .+ p[2]; p0 = [0.5, 0.9];\n",
    "fit = curve_fit(m, new_fs_bs, new_fs_as, p0).param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EJsCki4iIiMgEmHQRERERmQCTLiIiIiITYNJFREREZAJMuoiIiIhMgEkXERERkQkw6SIiIiIyASZdRERERCZgY+4OEBERUXWacoGjmXnIKSqBp7M9BnR2g7WVzNzdoiZg0kVERGRhYtOysGxnOrJUJVKbt8IeSyL8ERbgbcaeUVNwepGIiMiCxKZlYebm43oJFwBkq0owc/NxxKZlmaln1FRMuoiIiCyEplxg2c50iBqO6dqW7UyHprymCLJ0TLqIiIgsxNHMvGojXJUJAFmqEhzNzDNdp8homHQRERFZiJyi2hOuxsSRZWHSRUREZCE8ne2NGkeWxexJ1/r16xEYGAgXFxe4uLggJCQEe/bskY5PmTIFMplM7yc4OFjvPdRqNebOnQsPDw84OTlhzJgxuHLlil5Mfn4+IiMjoVAooFAoEBkZiYKCAr2YS5cuISIiAk5OTvDw8MC8efNQWlrabNdORERU2YDObvBW2KO2whAyaJ9iHNDZzZTdsgx5eUBICPCPfwCiZa5pM3vS1aFDB6xatQq//vorfv31VwwdOhRjx47FqVOnpJiwsDBkZWVJP7t379Z7j6ioKOzYsQMxMTGIj4/HrVu3EB4eDo1GI8VMmDABKSkpiI2NRWxsLFJSUhAZGSkd12g0GD16NIqLixEfH4+YmBhs374dCxYsaP6bQEREBMDaSoYlEf4AUC3x0r1eEuHf9up17d0LdO0KJCUBK1cCRUXm7lGjyISwvHTRzc0Nq1evxtSpUzFlyhQUFBTgu+++qzFWpVKhffv2+Oqrr/Dkk08CAK5duwZfX1/s3r0bI0eOxOnTp+Hv74+kpCQMHDgQAJCUlISQkBCcOXMG3bt3x549exAeHo7Lly/Dx8cHABATE4MpU6YgJycHLi4uNX6+Wq2GWq2WXhcWFsLX1xcqlarWc4iIiOrCOl1/Sk8HXn8d2Latoi0qCli71ugfVVhYCIVC0azf3xZVHFWj0eCbb75BcXExQkJCpPaDBw/C09MT7dq1w+DBg7FixQp4enoCAJKTk1FWVobQ0FAp3sfHBwEBAUhISMDIkSORmJgIhUIhJVwAEBwcDIVCgYSEBHTv3h2JiYkICAiQEi4AGDlyJNRqNZKTkzFkyJAa+xwdHY1ly5YZ+1YQEVEbFhbgjRH+ylZVkb5BFfbv3gX69QN++0372soKmDdPm4A5O5uu00ZmEUlXamoqQkJCUFJSgnvuuQc7duyAv792eHXUqFH429/+hk6dOiEzMxOvvfYahg4diuTkZMjlcmRnZ8POzg6urq567+nl5YXs7GwAQHZ2tpSkVebp6akX4+XlpXfc1dUVdnZ2UkxNFi1ahPnz50uvdSNdRERETWFtJUNIF3dzd8MoGjRyd/Ik8PzzFQkXABw7Bjz4oIl623wsIunq3r07UlJSUFBQgO3bt2Py5Mk4dOgQ/P39pSlDAAgICEC/fv3QqVMn7Nq1C48//nit7ymEgExWkUFX/nNTYqqSy+WQy+X1XiMREVFbpKuwX3Utk67C/vpJD2oTrxs3gOXLgQ8/BHRrsrt1A9LSADs7k/e7OZh9IT0A2NnZoWvXrujXrx+io6PRu3dvvPfeezXGent7o1OnTjh79iwAQKlUorS0FPn5+XpxOTk50siVUqnE9evXq73XjRs39GKqjmjl5+ejrKys2ggYERER1c/QCvvl8+cDnp7A++9rE64nngCuXAF+/73VJFyAhSRdVQkh9BanV5abm4vLly/D21s7HBkUFARbW1vExcVJMVlZWUhLS8OgQYMAACEhIVCpVDh69KgUc+TIEahUKr2YtLQ0ZGVV7Gm1b98+yOVyBAUFGf0aiYiIWrv6Kux7Ft3Ea5v+BavKC+O3bwf++1/g3ntN0EPTMvv04uLFizFq1Cj4+vqiqKgIMTExOHjwIGJjY3Hr1i0sXboUTzzxBLy9vXHhwgUsXrwYHh4eeOyxxwAACoUCU6dOxYIFC+Du7g43NzcsXLgQvXr1wvDhwwEAPXr0QFhYGKZNm4YNGzYAAKZPn47w8HB0794dABAaGgp/f39ERkZi9erVyMvLw8KFCzFt2jQ+hUhERNQItVXOt9WUYcXeDzEq4xc4l95BubU1rHx8gF9/1Y54tVJmT7quX7+OyMhIZGVlQaFQIDAwELGxsRgxYgTu3LmD1NRUfPnllygoKIC3tzeGDBmCbdu2wbnS0wtr166FjY0Nxo8fjzt37mDYsGHYtGkTrK2tpZgtW7Zg3rx50lOOY8aMwbp166Tj1tbW2LVrF2bNmoWHHnoIDg4OmDBhAt5++23T3QwiIqJWpKbK+WNP/YT3fnxHen3cpzusP/kEvUc/YsqumYVF1ulqyUxR54OIiKgl0JQLPPzmAWSrSuCovo0FP2/Gc8k/SMf3dgvGssmv4+dFw81eDqPN1ekiIiKi1sPaSoYl4T1wdsZ8/DV1P7xv5QIAiuwc8OTEN3Ha836sHxtg9oTLVJh0ERERUfO4eBFhgfchTPeynRL/DJ2Fnzs/CG+FPda3sQr7TLqIiIjIuO7eBd57D/jXv6Qm4eaG7MPH8NcyGWa1ggr7jcGki4iIiIzn/feB+fMrCpwGBQGLFkH2xBMYWPeZrR6TLiIiImo6lQpo167itUIBrFkDTJmi3TuRLLM4KhEREbUQQgDbtgEPPKDfnpICPPccE65KeCeIiIiocf73P8DREXjqKSA7G/DzA1av1iZi991n7t5ZHE4vEhERUcOUlgITJ2q369FZuhR49VVALjdbtywdky4iIiIyXHw88MILwKlTFW3/+x8wdKj5+tRCMOkiIiKi+p07B3TrVvHawwP45z+BuXO5bstAvEtERERUOyGAd9/VT7iefx7IyABefJEJVwNwpIuIiIhq9vvvwMyZwIEDFW3R0dq1W9RgTLqIiIhIX2Eh4O6urSwPAPb2QFSUdjrRycmsXWvJOCZIREREFQ4e1BY21SVcI0cCaWnaES4mXE3CpIuIiIiAGzeAyZOBIUMq2saNA/bsAbp0MVu3WhNOLxIREbVl5eXA4MHaUhAAIJNpS0IsXw64uZm3b60Mky4iIqK2Kj0d6NlTvy0hAQgONk9/WjlOLxIREbU1d+5oF8X36VPRplQCt28z4WpGTLqIiIjakpde0u6XuGIFUFYGREQAf/wBZGUBDg7m7l2rxulFIiKitiA7W1tz67vvKtq+/Va7WF4mM1ev2hQmXURERK3Z3bvAp59qC5qqVBXtV68CPj7m61cbxOlFIiKi1mrLFsDWVjvCpVIBQUHAr79qt/ZhwmVyTLqIiIham+Ji4OWXgUmTKtrWrgWOHNEmXmQWnF4kIiJqTXbuBObOBS5erGg7cgQYMMB8fSIATLqIiIhaBE25wNHMPOQUlcDT2R4DOrvB2qrSAvhjx/QTq44dgQ8/BMLDTd9ZqhGTLiIiIgsXm5aFZTvTkaUqkdq8FfZYEuGPsB6ewEcfAfPmVZwwebI24eJeiRaFSRcREZEFi03LwszNxyGqtGerSrB5+b8RkvYNFKd+qzjwxRfAM8+YtI9kGCZdREREFkpTLrBsZ3q1hKv9rTwc+7AisRIKBWSrVgHTpwNWfEbOUjHpIiIislBHM/P0phQBIPT3RHyyY4VeW3JsAvoF+5uya9QITIeJiIgsVE5RRcLlf/0PbNz+hl7C9Un/x3DfKz/iqr3CHN2jBuJIFxERkYXydLaHdbkGhzZMQ4fCHABAqZUNNgx8AutCxkNtK5fiyPIx6SIiIrJQA3LOYs/m+VLCBQD/9+z7OOfREQAgA6BUaMtHkOVj0kVERGRpLl0Cpk+H9b598BMCt23lOOHTHZHj30C5lTUAbcIFAEsi/PXrdZHFMvuarvXr1yMwMBAuLi5wcXFBSEgI9uzZIx0XQmDp0qXw8fGBg4MDHn30UZw6dUrvPdRqNebOnQsPDw84OTlhzJgxuHLlil5Mfn4+IiMjoVAooFAoEBkZiYKCAr2YS5cuISIiAk5OTvDw8MC8efNQWlrabNdORESkRwggMhLo1AnYu1f7+plnkLj/VyycsUZKuADtCNf6SQ8iLMDbjB2mhjD7SFeHDh2watUqdO3aFQDwxRdfYOzYsThx4gR69uyJt956C2vWrMGmTZvg5+eH5cuXY8SIEcjIyICzszMAICoqCjt37kRMTAzc3d2xYMEChIeHIzk5GdbW2l/QCRMm4MqVK4iNjQUATJ8+HZGRkdi5cycAQKPRYPTo0Wjfvj3i4+ORm5uLyZMnQwiBDz74wAx3hoiI2pQ//gBmzwb+/J4CAHz+OTBlCoYBePThnnVXpCfLJyyQq6ur+PTTT0V5eblQKpVi1apV0rGSkhKhUCjExx9/LIQQoqCgQNja2oqYmBgp5urVq8LKykrExsYKIYRIT08XAERSUpIUk5iYKACIM2fOCCGE2L17t7CyshJXr16VYrZu3SrkcrlQqVQG912lUgkADTqHiKix7mrKRcK5m+K7E1dEwrmb4q6m3NxdooYqLhZi4kQh7O2FAISwsxOiSxchCgrM3bM2xRTf32afXqxMo9EgJiYGxcXFCAkJQWZmJrKzsxEaGirFyOVyDB48GAkJCQCA5ORklJWV6cX4+PggICBAiklMTIRCocDAgQOlmODgYCgUCr2YgIAA+Pj4SDEjR46EWq1GcnJyrX1Wq9UoLCzU+yEiMoXYtCw8/OYBPL0xCS/GpODpjUl4+M0DiE3LMnfXyFD//a92q54tW4CSEmDIEODkSeDcOUDBMhCtjUUkXampqbjnnnsgl8vxwgsvYMeOHfD390d2djYAwMvLSy/ey8tLOpadnQ07Ozu4urrWGePp6Vntcz09PfViqn6Oq6sr7OzspJiaREdHS+vEFAoFfH19G3j1REQNp9sapmrhzGxVCWZuPs7Ey9Ll5Wmrx//tbxVtL7wA/O9/QPfu5usXNSuLSLq6d++OlJQUJCUlYebMmZg8eTLS09Ol4zKZ/py1EKJaW1VVY2qKb0xMVYsWLYJKpZJ+Ll++XGe/iIiaqratYQBIbct2pkNTXlMEmZUQQEQE4OsLbNyobRs6FPj9d2D9eqCe7zZq2Swi6bKzs0PXrl3Rr18/REdHo3fv3njvvfegVCoBoNpIU05OjjQqpVQqUVpaivz8/Dpjrl+/Xu1zb9y4oRdT9XPy8/NRVlZWbQSsMrlcLj15qfshImpONW0NU5kAkKUqwdHMPNN1iuqXnq7dF/HHH4HbtwF/f+DwYe3oVrdu5u4dmYBFJF1VCSGgVqvRuXNnKJVKxMXFScdKS0tx6NAhDBo0CAAQFBQEW1tbvZisrCykpaVJMSEhIVCpVDh69KgUc+TIEahUKr2YtLQ0ZGVVDMnv27cPcrkcQUFBzXq9REQNUXlrGGPEUTNTq4Fly4C+fSva2rUDTpwA/vIXs3WLTM/sJSMWL16MUaNGwdfXF0VFRYiJicHBgwcRGxsLmUyGqKgorFy5Et26dUO3bt2wcuVKODo6YsKECQAAhUKBqVOnYsGCBXB3d4ebmxsWLlyIXr16Yfjw4QCAHj16ICwsDNOmTcOGDRsAaEtGhIeHo/ufc+ehoaHw9/dHZGQkVq9ejby8PCxcuBDTpk3j6BURWRRDt3zh1jAWYOlSIDoa0NV87N8fWLECGDHCrN0i8zB70nX9+nVERkYiKysLCoUCgYGBiI2NxYg/fyFffvll3LlzB7NmzUJ+fj4GDhyIffv2STW6AGDt2rWwsbHB+PHjcefOHQwbNgybNm2SanQBwJYtWzBv3jzpKccxY8Zg3bp10nFra2vs2rULs2bNwkMPPQQHBwdMmDABb7/9tonuBBGRYQZ0doO3wh7ZqpIa13VxaxgLcPMm0L59xWsvL+Ddd4Enn+S6rTZMJoTgSksjKiwshEKhgEql4ggZETUb3dOLAPQSL93XOSuVm4kQwKZNwMKF2icUda5fB2p4ip4shym+vy1yTRcREdUtLMAb6yc9CKVCfwqRW8OY0bffahfKP/ecNuEKDNQumheCCRcBsIDpRSIiapywAG+M8Fdyaxhzu3NHu0brl18q2lavBl58EbC1NV+/yOIw6SIiasGsrWQI6eJu7m60XXFxwMyZwPnzFW2HDgGPPGK+PpHF4vQiERFRQ508qV0QHxqqTbjuvRf45BOgvJwJF9WKSRcREZGhysuBjz8GeveuaJs3T1v4dNo0PplIdeL0IhERkSFSU4EZM4DExIq2DRu0eygSGYBJFxERUV1u3NB/+vCee7QFTl94AbCzM1+/qMXh9CIREVFtdu/WT7iGDwdOn9ZOKTLhogZi0kVERFTVtWvA3/4GjB5d0TZjhvZpxQ4dzNcvatE4vUhERKRTWgr06gX8/rv2tbU18Pe/A6+9BnCXEWoiJl1EREQAcOKEdkNqjUb72tkZ+Pln/ScViZqA04tERNS2qVTa0ax+/SoSrl69gNxcJlxkVEy6iIio7Xr2WaBdO+Ddd7U1uJ58Urue6+RJbuFDRsfpRSIiansuXQImTgTi4yvaYmOBkSPN1ydq9Zh0ERFR21FaCnzwAbBkCVBcXNF+8ybgzj0sqXlxepGIiNqGdesAuRxYuFCbcD38MJCWBgjBhItMgkkXERG1bioVMGcOMHduRdunnwKHDgE9e5qvX9TmcHqRiIhaJyG0eyO+/jqQlVXRnpbGZIvMgkkXERG1PgcOAMOGVbzu1g34+GNg6FDz9YnaPE4vEhFR61FWBqxapZ9wDR+uLQHBhIvMjCNdRETUOvz4I7BokXb6UOf774ExY8zXJ6JKmHQREVHLdu0acO+9Fa89PIB33gEiIwGZzHz9IqqCSRcREbVMQgBbtgDz51e0yWTAmTMsAUEWiWu6iIio5dmzBwgO1o5m3bgBWFkBy5drt/JhwkUWiiNdRETUcqjVgEKh/V8AsLcHXntNW/DUzs68fSOqB5MuIiJqGQ4eBF54oSLhAoDffgP8/MzWJaKG4PQiERFZtjNntCUghgwBMjK0o1uPP66dSmTCRS0Iky4iIrJMQgCTJwM9emiLnQLaka6sLGD7dj6ZSC0OpxeJiMjynD6tTbAOH65o27kTCA83X5+ImohJFxERWY78fKB/f+DSJW11eUdH4LHHgI0bAQcHc/eOqEk4vUhERJZhwwbAzQ04f16bcI0eDaSnA5s3M+GiVoFJFxERmdf168DEidrpRJ2pU7XTiZ06ma9fREbG6UUiIjKPu3e1m1H/+itQXKwtcDpiBPDpp0CHDubuHZHRMekiIiLTS00FAgMrXj/4oHZ6sV8/8/WJqJmZfXoxOjoa/fv3h7OzMzw9PTFu3DhkZGToxUyZMgUymUzvJzg4WC9GrVZj7ty58PDwgJOTE8aMGYMrV67oxeTn5yMyMhIKhQIKhQKRkZEoKCjQi7l06RIiIiLg5OQEDw8PzJs3D6Wlpc1y7UREbU5xMfDKK9okS+eBB4AjR5hwUatn9qTr0KFDmD17NpKSkhAXF4e7d+8iNDQUxcXFenFhYWHIysqSfnbv3q13PCoqCjt27EBMTAzi4+Nx69YthIeHQ6PRSDETJkxASkoKYmNjERsbi5SUFERGRkrHNRoNRo8ejeLiYsTHxyMmJgbbt2/HggULmvcmEBG1BXPmAPfcA7z1lnZq8bHHgJQUbXkIG068UBsgLExOTo4AIA4dOiS1TZ48WYwdO7bWcwoKCoStra2IiYmR2q5evSqsrKxEbGysEEKI9PR0AUAkJSVJMYmJiQKAOHPmjBBCiN27dwsrKytx9epVKWbr1q1CLpcLlUplUP9VKpUAYHA8EVGrd/WqENpSp9ofuVyIH34wd6+I9Jji+9vsI11VqVQqAICbm5te+8GDB+Hp6Qk/Pz9MmzYNOTk50rHk5GSUlZUhNDRUavPx8UFAQAASEhIAAImJiVAoFBg4cKAUExwcDIVCoRcTEBAAHx8fKWbkyJFQq9VITk6usb9qtRqFhYV6P0REBECjAdat004fVnbxIhARYZ4+EZmRRSVdQgjMnz8fDz/8MAICAqT2UaNGYcuWLThw4ADeeecdHDt2DEOHDoX6z01Ps7OzYWdnB1dXV7338/LyQnZ2thTj6elZ7TM9PT31Yry8vPSOu7q6ws7OToqpKjo6WlojplAo4Ovr2/gbQETUWvz739opw7lzgaIiYOBAYP9+7VhXlb9nidoKi5pEnzNnDk6ePIn4+Hi99ieffFL6c0BAAPr164dOnTph165dePzxx2t9PyEEZJX25pLVsE9XY2IqW7RoEebPny+9LiwsZOJFRG3XrVvaGlv/+U9F24cfAjNmANbW5usXkQWwmKRr7ty5+OGHH3D48GF0qKc+i7e3Nzp16oSzZ88CAJRKJUpLS5Gfn6832pWTk4NBgwZJMdevX6/2Xjdu3JBGt5RKJY4cOaJ3PD8/H2VlZdVGwHTkcjnkcrnhF0pE1Fp9/712sXzlJ8ePHwf69jVfn4gsiNmnF4UQmDNnDr799lscOHAAnTt3rvec3NxcXL58Gd7e3gCAoKAg2NraIi4uTorJyspCWlqalHSFhIRApVLh6NGjUsyRI0egUqn0YtLS0pCVlSXF7Nu3D3K5HEFBQUa5XiKiVichAZDJgHHjtAlX587Ajz9qpxKZcBFJZEIIYc4OzJo1C19//TW+//57dO/eXWpXKBRwcHDArVu3sHTpUjzxxBPw9vbGhQsXsHjxYly6dAmnT5+Gs7MzAGDmzJn48ccfsWnTJri5uWHhwoXIzc1FcnIyrP8c0h41ahSuXbuGDRs2AACmT5+OTp06YefOnQC0JSP69OkDLy8vrF69Gnl5eZgyZQrGjRuHDz74wKDrKSwshEKhgEqlgouLizFvFRGRZbl7F1izRlt3S2fRIuCf/9RuVE3Ugpjk+7vZnos0EIAafz7//HMhhBC3b98WoaGhon379sLW1lZ07NhRTJ48WVy6dEnvfe7cuSPmzJkj3NzchIODgwgPD68Wk5ubKyZOnCicnZ2Fs7OzmDhxosjPz9eLuXjxohg9erRwcHAQbm5uYs6cOaKkpMTg62HJCCJqE44cEaJPH71SEJqvt4qEczfFdyeuiIRzN8VdTbm5e0lkMFN8f5t9pKu14UgXEbVqly8DHTtWvHZ1Bd58E7EDRmHZ7gxkqUqkQ94KeyyJ8EdYgLcZOkrUMKb4/jb7mi4iMj5NuUDi+Vx8n3IViedzoSnnv62oiYQAvvlGP+F68kngzBnEhoRj5tbf9BIuAMhWlWDm5uOITctCY/F3mVoTi3l6kYiMIzYtC8t2pnPEgYzn/Hltva09eyraFi8GVqyAplxg2WcHUFMqJADIACzbmY4R/kpYW9Vceqc2/F2m1oYjXUStSGxaFmZuPt4sIw7UBt2+rZ0+7NpVm3DZ2QFLlgB37gArVgAAjmbmVft9q0wAyFKV4GhmXoM+mr/L1Box6SJqJTTlAst2ptc64gBoRxw4PUMGSUgAnJyAgoKKtpMngaVLAXt7qSmnqPaEqzJD4wD+LlPrxaSLqJVorhEHamNyc7XV4x96qKJt2DDtPoqVyvroeDrbV2uriaFxAH+XqfVi0kXUSjTHiAO1IUIAo0cDHh7AJ59o2557Drh5U7tnolXNXxcDOrvBW2GP2lZryaBdhzWgs5vBXeHvMrVWTLqIWonmGHGgNuLcOSA0FNi9u6Lt0CHgs88Ad/c6T7W2kmFJhD8AVEu8dK+XRPg3aBE9f5eptWLSRdRKNMeIA7VyRUXAq68CAQHa0SxAW0m+qAh45BGD3yYswBvrJz0IpUI/CVIq7LF+0oMNftKQv8vUWrFkBFEroRtxmLn5OGSA3iLkxo44UCv2xhvAv/5V8To0FPjoI6BLl0a9XViAN0b4K3E0Mw85RSXwdNYmRY35fePvMrVWTapIf/fuXfz888+wt7eHv78/FAqFMfvWIrEiPZkbaxtRnW7eBF56Cdi0qaLt44+B6dO1m1ZbEP4ukymZ4vu7SUnXuHHj4O7uju+++w4uLi4oLy9Hr1698OOPPxqzjy0Kky6yBJpyYZQRB2pFysuB997T1tfKza1oz8wE7rvPbN2qD3+XyVRM8f3dpOnFzMxMfPfdd0hOTkZKSgree+895OfnG6tvRNRI1lYyhHSpewE0tSF79wJhYRWvAwOBDRuA4GDz9clA/F2m1qRJC+kdHBwAAHZ2digtLcWLL76IQ4cOGaVjRETURCUl2nVblROuyZOBX39tEQkXUWvTpJGuOXPmIC8vD48//jhmz56NQYMG4cKFC0bqGhERNdrq1dp6W+fOaV/b2QH79gGDB5u3X0RtWINGul599VUUFxdLrydNmgQ3Nze8+uqreOihh5Ceno7vv//e6J0kIiIDnT2rXRD/8svahMvHB/jvf7WjXky4iMyqQQvpg4ODcfHiRSxfvhzPPfccZBb2pIsl4EJ6opbF0IXaFr+gu7wc+PRTYMEC4NatinaVCuDfRUT1ssinFzdv3ozFixfD3d0d7777LgbzX056mHQRtRyGliSw+NIF//mPdjrx118r2tavB154wXx9ImphTPH93eCF9JMmTUJGRgbGjh2L0aNH4/HHH8cff/zRHH0jImo2sWlZmLn5eLWNlbNVJZi5+Thi07IaFGcWt28DDg7Ak09qE6577gHefRcoK2PCRWSBGvX0ooODA5YuXYqMjAw4OTkhICAAr7zyCtLS0qDRaIzdRyIio9KUCyzbmY6ahvl1bct2pqP0brlBcZryRpc7bLzdu4GePbVrtXTS04EXXwRsuNkItT6acoHE87n4PuUqEs/nmue/uyZq0H+ZarUav/zyC86cOYOMjAxkZGTgzJkzUKvVePvtt7F69WrI5XL4+/sjOTm5ufpMRNQkRzPzqo1cVSYAZKlK8FXiBYPijmbmma6W1K+/AkOHavdHBABfX2DKFOD1103z+URmYPFT/AZqUNI1ZMgQpKSkIDAwEH5+fvjLX/6CqVOnws/PD35+figpKUFKSgpOnjzZXP0lImqynKLaE6nKLubdNur7NYlGA4werS10qrNgAbB0qXZakaiV0k3xVx3X0k3xN2ZTdXNpUNKVm5uLhIQE9OnTp8bjDg4OGDJkCIYMGWKMvhERNQtPZ3uD4jq5ORr1/RrtxAlgxgzg2LGKtm3bgPHjm/dzicysvqUAMmin+Ef4Ky3raeJaNGhNV0ZGRq0JFxFRSzGgsxu8Ffao7a9oGbRTF5Eh9xkUN6CzW/N0NDsb6NQJ6NdPm3C5uABTpwKlpUy4qE0wdCnA0cw803WqCZq0DRARUUtkbSXDkgh/AKiWUOleL4nwh52NlUFxzfIv7G3bAG9v4NIlbQ2u8eOBM2e0tbhsbY3/eUQWyNCpe5NM8RsBky4iapPCAryxftKDUCr0pwaVCnu9NSKGxhnN5cvAuHHAU09VtC1aVJGEEbUhhk7dN/sUv5HwuWIiarPCArwxwl9Zb6V5Q+OaRK0G/Py0I1uAtuzDrFnaDavdTfRkJJGF0S0FyFaV1LiuSwbtP4CabYrfyJh0EVGbZm0lM6jcg6FxjXLsGDBgQMXrkBDtZtUBAc3zeUQthG4pwMzNxyED9BKvZp/ibwacXiQiMheVCpgzBxg4sKJtwADg55+ZcBH9yeRT/M2II11ERKYmBPD009p1WjoTJwL/+AfQo4f5+kVkoUwyxW8CTLqIiEzpwgWgc+eK115ewObNwPDhZusSUUvQrFP8JsLpRSIiUygrA958E/D3128/c4YJF1EbwaSLiKi5vf02YGcHvPoqcOcO8OijwKlT2mnGdu3M3TsiMhFOLxIRNZf8fG3NrcOHK9q++AKIjARkLWstChE1HZMuIiJjEwLYuhX4+9+BnJyK9owMbS0uImqTzD69GB0djf79+8PZ2Rmenp4YN24cMjIy9GKEEFi6dCl8fHzg4OCARx99FKdOndKLUavVmDt3Ljw8PODk5IQxY8bgypUrejH5+fmIjIyEQqGAQqFAZGQkCgoK9GIuXbqEiIgIODk5wcPDA/PmzUNpaWmzXDsRtUKxsYCVlfZpxJwc4IEHgAMHtIkYEy6iNs3sSdehQ4cwe/ZsJCUlIS4uDnfv3kVoaCiKi4ulmLfeegtr1qzBunXrcOzYMSiVSowYMQJFRUVSTFRUFHbs2IGYmBjEx8fj1q1bCA8Ph0ajkWImTJiAlJQUxMbGIjY2FikpKYiMjJSOazQajB49GsXFxYiPj0dMTAy2b9+OBQsWmOZmEFHLpVYDy5cDo0ZVtL3xBpCSAgwZ0uC305QLJJ7PxfcpV5F4Phea8prqcRNRiyIsTE5OjgAgDh06JIQQory8XCiVSrFq1SoppqSkRCgUCvHxxx8LIYQoKCgQtra2IiYmRoq5evWqsLKyErGxsUIIIdLT0wUAkZSUJMUkJiYKAOLMmTNCCCF2794trKysxNWrV6WYrVu3CrlcLlQqVY39LSkpESqVSvq5fPmyAFBrPBG1Qj/9JMQDDwihHc/S/uzZ0+i325N6TQSv3C86vfKj9BO8cr/Yk3rNeH0mIj0qlarZv7/NPtJVlUqlAgC4uWn3UcrMzER2djZCQ0OlGLlcjsGDByMhIQEAkJycjLKyMr0YHx8fBAQESDGJiYlQKBQYWKnyc3BwMBQKhV5MQEAAfHx8pJiRI0dCrVYjOTm5xv5GR0dL05UKhQK+vr7GuA1E1BJkZGgXxA8Zoi394OkJfP01UF4OhIU16i1j07Iwc/NxZKlK9NqzVSWYufk4YtOyjNFzIjIDi0q6hBCYP38+Hn74YQT8uQVGdnY2AMDLy0sv1svLSzqWnZ0NOzs7uLq61hnj6elZ7TM9PT31Yqp+jqurK+zs7KSYqhYtWgSVSiX9XL58uaGXTUQtjRDApk3a9Vo6I0ZoE6+nn270k4macoFlO9Nr3NhX17ZsZzqnGolaKIt6enHOnDk4efIk4uPjqx2TVflLTAhRra2qqjE1xTcmpjK5XA65XF5nP4ioFUlPB2bNAg4dqmh7+23ACGs/j2bmVRvhqkwAyFKV4GhmXouvzE3UFlnMSNfcuXPxww8/4KeffkKHDh2kdqVSCQDVRppycnKkUSmlUonS0lLk5+fXGXP9+vVqn3vjxg29mKqfk5+fj7KysmojYETUxhQUaEewevbUJlwODtoK86WlRkm4ACCnqPaEqzFxRGRZzJ50CSEwZ84cfPvttzhw4AA6V96TDEDnzp2hVCoRFxcntZWWluLQoUMYNGgQACAoKAi2trZ6MVlZWUhLS5NiQkJCoFKpcPToUSnmyJEjUKlUejFpaWnIyqpYM7Fv3z7I5XIEBQUZ/+KJqGXYvx/o0kW/LT0dePllwNbWaB/j6Wxv1Dgisixmn16cPXs2vv76a3z//fdwdnaWRpoUCgUcHBwgk8kQFRWFlStXolu3bujWrRtWrlwJR0dHTJgwQYqdOnUqFixYAHd3d7i5uWHhwoXo1asXhv+5p1mPHj0QFhaGadOmYcOGDQCA6dOnIzw8HN27dwcAhIaGwt/fH5GRkVi9ejXy8vKwcOFCTJs2DS4uLma4O0RkVmfOaMtAbNlS0fbUU9rF8s1QUX5AZzd4K+yRrSqpcV2XDIBSYY8Bnd2M/tlE1PxkQgizrsisba3U559/jilTpgDQjoYtW7YMGzZsQH5+PgYOHIgPP/xQWmwPACUlJXjppZfw9ddf486dOxg2bBg++ugjvacJ8/LyMG/ePPzwww8AgDFjxmDdunVoV2nvs0uXLmHWrFk4cOAAHBwcMGHCBLz99tsGr9sqLCyEQqGASqViokbUUmk0QHAw8Ouv2tcyGTBnjjYBa+b/rnVPLwLQS7x0f1Oun/QgwgK8m7UPRG2RKb6/zZ50tTZMuohauLQ04PnngSNHKtqOHgX69zdZF2LTsrBsZ7reonpvhT2WRPgz4SJqJqb4/jb79CIRUUNoygWOZuYhp6gEns7aqTZrKyNM9eXmakey1q0D7t7VtnXqpK3FZeInlMMCvDHCX9k810lEZsOki4hajGYbAXr1Ve2TiDrjxgHvvw8YWOy4ORJBaysZy0IQtTJMuoiommYbTWoC3VqnqushdJXaG7XW6do1ICoK+Oabirb//Af4298a1C9OBRKRIZh0EZEeS0wi6qvULoO2UvsIf6VhyWFZGTB7NrBtG1BYCFhbA+3bAydOAH/WBjREsySCRNRqmb1OFxFZDkvd968hldrrFRMD2NkBGzdqE64BA7RPKWZlNSjh4pY9RNRQTLqICIBlJxFGqdR+65a2cvzTT1e0hYcDCQlAnz4N7pNRE0EiahOYdBERAMtOIppcqX3ZMsDfH1izRvvawUE7urVzp3ZqsRG4ZQ8RNRTXdBERAMtOIhpdqf3SJW3ZB53OnYGPPgLCwprcJ27ZQ0QNxZEuIgJg2UmEtZUMSyL8AVRUZtfRvV4S4V+xiP7uXWDtWu3ols4992gLnxoh4QIqEsHalu3LoH0AgVv2EJEOky4iAmD5SURYgDfWT3oQSoV+0qdU2Os/Jbh+PeDkBMyfDxQXA337ahfPFxUBjo5G60+DE0EiavO4DZCRcRsgaslawr5/tdYQKywEFIqKQBcX4O23galTAavm+/elJZbYIKKG496LLRCTLmrpWlwSIQSwfTvw4ovaYqc6588D999vki5YYjFZImoYJl0tEJMuag1aTBJx8CAQEaEtBwEAXbsCzz0HLFpk1m4RUcvDDa+JyCwsft+/sjLgmWe0a7V0/vUvbbJlz6cFicgyMekiopYlMRGYMQNITa1o27cPGDHCfH0iIjIAky4iahkyM/XXaLm7a0e2/v73Zl0oT0RkLPybiogsmxDAhx/qJ1zPPgucOaPd1ocJFxG1EPzbiogs17lzwMiRwJw5FW2vvw78+9+Ah4f5+kVE1AicXiQiy1NUBCiVwO3b2tdyOTBvHvDaa4Czs3n7RkTUSEy6iBqoxZRTaKkOHwYGD654PXy4tsp8167m6xMRkREw6SJqgBZXOLQlyc0FXnoJ+PzzirbRo4GdOwEZk1oiavm4povIQLotcionXACQrSrBzM3HEZuWZaaetXDl5cCwYdo1WrqEa8YM4MYNaH7YicQ/8vB9ylUkns+Fppy1nImo5eJIF5EBNOUCy3amo6avfAHt3oTLdqZjhL+SU40NceYM0KOHftsvvwCDBmlHFTce4KgiEbUaHOkiMsDRzLxqI1yVCQBZqhIczcwzXadaspISbQX5wMCKNjc3oLhYSrg4qkhErQ2TLiID5BTVnnA1Jq5NW7wYcHAA3nhDu53P//0fcPasdk2Xo2O9o4qAdlSRU41E1NJwepHIAJ7Ohu3nZ2hcVW3iicicHGDWLGD79oq2b74BnnhCb6F8Q0YVLXp/SCKiKph0ERlgQGc3eCvska0qqXEERgZAqdAmSw3Vkp+INChZ1Gi0xUxfeQXIz69ov3QJ8PWt9p4cVSSi1opJF5EBrK1kWBLhj5mbj0MG6CVeuhRjSYR/g0endGuXqiZyurVL6yc9aLGJl0HJ4rZtwFNPVZzUty+wYQPQv3+t79vco4pERObCNV1EBgoL8Mb6SQ9CqdD/slcq7BuVHLXktUv1LXTfd+y8djPqygnX6tXA0aN1JlxAxahibemrDNrkrjGjikRE5sSRLqIGCAvwxgh/pVHWX7XUtUv1JYtDzh9Dz5HPA/nZFQf+LANR+T1qu4fNNapIRGRuTLqIGsjaSmaUJKilrl2qLVnsmX0Ou76Ikl6rve+FfP2HwNixenGGTEvqRhWrxilbyFo3IqKaMOkiMhND1yRduFncpM8x9pORVZNAq3INJqTEYnnceqnt255DYPfJxwgf5KcX25A1bMYcVSQisgRMuojMpL4nInXW7j+L7krnRo3uNMeTkZWTxeBLJ/HqwU3ok/W71PbSqBfxTeAIbPXSHw1sTFV/Y40qEhFZArMvpD98+DAiIiLg4+MDmUyG7777Tu/4lClTIJPJ9H6Cg4P1YtRqNebOnQsPDw84OTlhzJgxuHLlil5Mfn4+IiMjoVAooFAoEBkZiYKCAr2YS5cuISIiAk5OTvDw8MC8efNQWlraHJdNJK1dqo8uGWnogvrmquo+oLMb/K3u4MKb4YjZuhh9sn5HoZ0jXhvxAu5/6Xv8N3BEjQvdWdWfiNo6syddxcXF6N27N9atW1drTFhYGLKysqSf3bt36x2PiorCjh07EBMTg/j4eNy6dQvh4eHQaDRSzIQJE5CSkoLY2FjExsYiJSUFkZGR0nGNRoPRo0ejuLgY8fHxiImJwfbt27FgwQLjXzTRn8ICvBE13K/OmMYkI835ZKT1jzuxa9V4vbbhz6/HVw+GQ1hZA6h5obuha9Pi0rPrDyIiaoHMPr04atQojBo1qs4YuVwOpVJZ4zGVSoXPPvsMX331FYYPHw4A2Lx5M3x9fbF//36MHDkSp0+fRmxsLJKSkjBw4EAAwMaNGxESEoKMjAx0794d+/btQ3p6Oi5fvgwfHx8AwDvvvIMpU6ZgxYoVcHFxMeJVE1W4z8PRoLiGLKhvlicjU1O1+yV+9530FOF/Qsbh5Ueel0LqWuhu6Bq2f/9yAQM6u3GxPBG1OmZPugxx8OBBeHp6ol27dhg8eDBWrFgBT09PAEBycjLKysoQGhoqxfv4+CAgIAAJCQkYOXIkEhMToVAopIQLAIKDg6FQKJCQkIDu3bsjMTERAQEBUsIFACNHjoRarUZycjKGDBlSY9/UajXUarX0urCw0NiXT61ccxQDNeqTkXfvAj16AOfOaV/b2AALFwKvvYYn7B3ga+BCd90atrqSQZ1F36bqre0iImoNzD69WJ9Ro0Zhy5YtOHDgAN555x0cO3YMQ4cOlRKd7Oxs2NnZwdXVVe88Ly8vZGdnSzG6JK0yT09PvRgvLy+9466urrCzs5NiahIdHS2tE1MoFPCtYVsToroYoxioplwg8Xwuvk+5isTzufBwkhv02fUmcr/+CgwYUJFwAcCJE0B0NODoKC10H9vnXoR0ca8zSTJ0DRsA5N8uQ9IfuQbFEhG1FBY/0vXkk09Kfw4ICEC/fv3QqVMn7Nq1C48//nit5wkhIKu0iW7lPzclpqpFixZh/vz50uvCwkImXtQgTS0GWtMTikoXe7RztIXqdlnj9oq8cgWYMQOIjQXKywF7e20l+Z9+AqytG3GVWmEB3hgVoMSetPrXbSWez8VDXT0a/VlERJbG4ke6qvL29kanTp1w9uxZAIBSqURpaSnyK2+kCyAnJ0cauVIqlbh+/Xq197px44ZeTNURrfz8fJSVlVUbAatMLpfDxcVF74eooRq7xVBtTyheLyxBwZ8JV9VUTff6tdE9cDQzTxod05QLQAjguee0G1Hv3q1NuCZOBC5cAA4fblLCpdOlvZOBkZa3/RERUVNY/EhXVbm5ubh8+TK8vbVfQkFBQbC1tUVcXBzGj9c+UZWVlYW0tDS89dZbAICQkBCoVCocPXoUAwYMAAAcOXIEKpUKg/7cmiQkJAQrVqxAVlaW9N779u2DXC5HUFCQqS+T2qCGFgM1pO5VO0dbyG2skF1Yse5QqbDHmN7eeGPXab1krW95ATYc2QTPw/sr3mjjRuD5ioXyxhByvwfW/XTeoDgiotbE7EnXrVu3cK7SepHMzEykpKTAzc0Nbm5uWLp0KZ544gl4e3vjwoULWLx4MTw8PPDYY48BABQKBaZOnYoFCxbA3d0dbm5uWLhwIXr16iU9zdijRw+EhYVh2rRp2LBhAwBg+vTpCA8PR/fu3QEAoaGh8Pf3R2RkJFavXo28vDwsXLgQ06ZN4+gVmUxDioEa8oRi/u0ybHl+IKxkMimRyy8uxeyvK6rCy8vUWLHvI/xfRjwcy9Qot7GFlY83kJICVFkraQzBXdzRztEWBbfLao1p52iLYBZFJaJWxuzTi7/++iv69u2Lvn37AgDmz5+Pvn374l//+hesra2RmpqKsWPHws/PD5MnT4afnx8SExPh7OwsvcfatWsxbtw4jB8/Hg899BAcHR2xc+dOWFeaCtmyZQt69eqF0NBQhIaGIjAwEF999ZV03NraGrt27YK9vT0eeughjB8/HuPGjcPbb79tuptB1ADZqjsGxd28pZYWuw/o7IY3dlWMjo38PQEZa57AX9P+B8cyNZJ8AzBhzgZoMi80S8IFaBPLVY/3qjNm1eO9+OQiEbU6MiEEF04YUWFhIRQKBVQqFUfIqNnEpmVh4TcncUt9t97YrdOCpdGzxPO5eHpjElxKbuGVQ5swMSVWiosJDMWrYXMBmUzvnOYSm5aFpT+c0p/6dJFj6ZierNFFRCZniu9vs08vElHDxKZl4YXNx+uNq+kJxZzCO1i/YyUeyTwOpzLt1GSSbwAWj5yDP9w7VMTVU7/LGJtoc0NrImprmHQRtSCacoGlP6QbHK9XauLMGYx9sId07JxbB/xj5Gwc6Vh9qq+u+l3G3ESbG1oTUVti9jVdRJaoarHRxuxR2ByOZuYhu9CwavNRw/20SVBpKbBiBdCnj3Ss2NYe//fsB9USrvoKsTbXJtpERG0BR7qIqjDmSI6xNWT/xfs8HLXJ1vLlQMmf5wUF4depf8ffLrarFl9fIVZDSlQs25nO7XuIiGrBpIuoEt1ITtXEQjeSU1eh0oYydF1U5bibReoa3qm6dncKMbZvxRoteHoCa9cCTz+NfjIZ1tdUxb6exLJZNtEmImpDmHQR/cmUIzmGjqbVFGclA2qd7RQCT6QdwD8P/lu//eRJoNLOCo1ZxG7UTbSJiNogJl1EfzLVSI6ho2m1xdWWcN2fewUr9n2IkEup2oaAAO3U4tixNcY3dBF7vZtjNzCOiKitYdJF9CdTjOQYOpo29AGvWuN0ZDLtVokAEHzpJL74z78g19yFxt4e1kuXAvPnA7a2je5rVQM6u8FbYY9sVUnjNtGuwhhlJ4iIWhImXWTRTPnFbIqRHENH075KvFBnHKBNuP7xfz2gulMG69IOKE/6DKK7H6w/+hDo3LnRfayNtZUMSyL8MXPzccigvx11fYvwq7LkhxWIiJoLky6yWKb+Yjb2SE5NDB0lu5h3u87j7sUFmJK8E15/fRPTHrlf2xicCHh4aIfAmklYgDfWT3qwwYvwKzPlwwpERJaESRdZJHN8MRtzJKc2ho6SdXJzrLFdJsox/mQcFh38HO1KbiFzRwASXWdWjAS6A9bNPEPXlEryLDtBRG0Zky6yOOb8YjbGSA5Q+7SooaNpkSH34dP4TL24bjcuYuXeD9H/qrYi/Wmv+7H8pgt+2ZgknW+qKbrKi/AbMgXMshNE1JYx6SKLY+4v5qbuCVjftOiY3t7YcDizxnMFgKBOrtiTloWn+vti7f6zcCgrwdyEGEw7ugO25RoU29pjzV8mYVNQBDRW1nrnm3qKrqFTwCw7QURtGZMusjiW8MVsSDmFmkZ44tKz65wWnf5IZ3xSS8Kl8+PJLPx4UrudTjtHW0T/922MSj0IANjbLRhLh89Alkv7Gs811kigIaNXjZkCZtkJImrLmHSRxWkJX8w1jfAoXeQouVte57Toxp8z6ywDUZXqdhnW9v8bQm6ex9LBz+G7Tv3rPaepI4GGjF41dgrYFA8rEBFZKm54TRZH98Vc2xhNfZsyN7daN30uVKPgdlmt5wnUUUn+T1blGkw6vgtR8Vukc86274QRMz4xKOGqrDEjgYZuaN2QKeDKdA8rAKj2/6+xHlYgIrJUTLrI4ljyF3NdIzxN5X/9D3y7+SUsj1uPOQnb0O3GRQDaBObGHU2D36+hI4H1jV4B2tErTblo0hSw7mEFpUK/f0qFPctFEFGrxulFskjGeorQ2Oob4WkMx9I7iIr/Gs/9+j1sRDmK7Byw+pFncN69Q/0n10LpIm/wSGBDRq+aOgXc1IcViIhaIiZdZLEs8Ys5Lj270efKoK1bWnmKcdi5I1gW9zE6FN4AAPzY/WG8Pmwacpyrr8Vyc7JFXnHt05eVPT2gY4PvU0NGr8IDfZq8Nquhez8SEbV0nF4ki6b7Yh7b516EdHE3a8IVm5aFf/9yodHnCwCjKo3QKe4UYe3Od9Ch8AYuK7ww5a9LMGfcq9USLt0atuVjAwz+rPs8nBrcv4aMXlnyFDARkaVi0kVkAN16p6baffKq9GeVgzNWDXkW6wf+FSOmfoiDXaovlK+cwPxfoA/+PtzPoM9pzJOdDX2AgWuziIgahtOLRAYwxlquXllnsXLvOrz9yDM4dH8QAODrPqPw1wfvRXS39riUW4ytRy8hu1AtnVN1DducoV2x9ehFvZjKmlJyoTHbIFniFDARkaVi0kVkgKYUYr1HfRsLfv4KzxzfBWtRjgU/f4VDnR8EZDLIAPxyPhdv/rU3rK1kmDO0W50JjLWVDEvH9MTMzccBGH9/yMY8wMC1WUREhmHSRa1SQ/YDNESjCrEKgbDfE7B0/wYob2nrVe3wfxQrhk7VrqhH9UKmhiQwzf1kJ0eviIiaB5MuanUauh+gIeqrpF5VB9V1LIv7GMPOHwMAZLp647URsxDfuW+N8Q0dSWvuxIijV0RExseki1qV2vYDzFKV4IXNx/H34X6YM7Rrg5OTyuudDOF//Q8MO38MpVY2+HjgE/gwZDzUtvJa4xszksbEiIioZWHSRa2GIdXi1+7/HVuPXsTSMT0bNeqlcLStdasfl5JbKLS/BwCwzy8E7w16Gj/0eATnPXxrfT/uNUhE1HawZARZPE25QOL5XHyfchWJ53OhqWUDQ0OfMMwuVOvtI2gI3QhaTQmXS8ktrNi7Dj99Mh1ut1VS+9q/TKw34QKA10b742hmXr3XR0RELRtHusiiNWR9VkPXRS3bmY4R/sp6pxprHUETAmNOH8ZrBzaifXEBAGDE2SRs6z3SoM9XKuwxprc33thl3PVnRERkmZh0kcWqbX1WtqoEMzcfr1aAsyHroqo+NViXmkbQOuZnYfm+j/DIhRMAgPNuHbBq7IuI8+whxXgr7PHa6B5wdZIjp6gEHvfIAQHcLFbD09ke+cVqzP76hMHXZ+wnMomIyLSYdJFFqmt9loB2aq7qSFVDnzAEDBsdq7rf4sykb/DiL1thf7cUamtbrAsZjw0D/4o3J/TDcwoHg5IiTbnAw28eMPj6muOJTCIiMi2u6SKLVN/6rMojVTqV9wM0VH2jY7tPXqu236J34U3Y3y1FfKfeGPncOnzw0NMotbGFUuFg8D6RSX/kGnx9uhG/qvG6EbGGrE0jIiLz4UgXWSRD12dVjtOUCygc7PDsQ/fhu5RryCsurfU8GQAvFznSrqqwJy0LndwcERlyH+xsKv4dsvtkFuZsPYF2dwrhUKZGlkt7AMDqwc/gWAd/7OzxiFTk1LsBTyDGpmXh1e2pBsVmq+7grb0ZDRrxIyIiy8SkiyySoeuzdHE1Tb85ya1RrNZUO0e3r+D1IjVW7D4tta/YfRrT/tIZL4f1wLoD57A2LgOPnzqAfxz4DGc878PEJ1cAMhmK5E7Y6T9Y7z2f6u+LH09eq3dasbZ1arXJKy41eESMNbuIiCyb2acXDx8+jIiICPj4+EAmk+G7777TOy6EwNKlS+Hj4wMHBwc8+uijOHXqlF6MWq3G3Llz4eHhAScnJ4wZMwZXrlzRi8nPz0dkZCQUCgUUCgUiIyNRUFCgF3Pp0iVERETAyckJHh4emDdvHkpLax8toeajW59V29iNDBWjS7VNv+kSLkc7a7123WtRJfMpF8CGw5kIWBKL77cdwNcx/8CaXWvhfqcQ7sUquN0prLEvchsrrN1/Fi/GpODpjUl4+M0DNU75GVJHrOr1ud1Te0HVypqyNyQREZmG2ZOu4uJi9O7dG+vWravx+FtvvYU1a9Zg3bp1OHbsGJRKJUaMGIGioiIpJioqCjt27EBMTAzi4+Nx69YthIeHQ6OpGOWYMGECUlJSEBsbi9jYWKSkpCAyMlI6rtFoMHr0aBQXFyM+Ph4xMTHYvn07FixY0HwXT7WqvD6rauJVeWNnAPUmMrdLtb8H7RxsMW9oV9wpqz76pSO/W4oZP32FPZ/PwaBLJ3HHRo5Vg6cgfMp7yHNU1HiO+m653uva1loZWkdMZ0mEP5QuDRvxIyIiyyUTouq/981HJpNhx44dGDduHADtKJePjw+ioqLwyiuvANCOanl5eeHNN9/EjBkzoFKp0L59e3z11Vd48sknAQDXrl2Dr68vdu/ejZEjR+L06dPw9/dHUlISBg4cCABISkpCSEgIzpw5g+7du2PPnj0IDw/H5cuX4ePjAwCIiYnBlClTkJOTAxcXlxr7rFaroVarpdeFhYXw9fWFSqWq9RwyXH1P7SWez8XTG5MMei/dtGJtOuZnYdM3S3B//jUAwE/3B+G1ETNxpZ2ywf3WVZqPf2WoNNX4fcpVvBiTUu+57RxtserxXggL8JaecqzticyaPoeIiBqusLAQCoWiWb+/zT7SVZfMzExkZ2cjNDRUapPL5Rg8eDASEhIAAMnJySgrK9OL8fHxQUBAgBSTmJgIhUIhJVwAEBwcDIVCoRcTEBAgJVwAMHLkSKjVaiQnJ9fax+joaGnKUqFQwNe39grk1HBhAd6If2Uotk4LxntP9cHWacGIf2UoRvgrkXg+F3sa8OReff+6yHLxgMbKGjlOrpg19lU8+9eljUq4dJ9V9elKQ0ejPny6oj6XoSN+TLiIiCyfRS+kz87W1kfy8vLSa/fy8sLFixelGDs7O7i6ulaL0Z2fnZ0NT0/Pau/v6empF1P1c1xdXWFnZyfF1GTRokWYP3++9Fo30kXGU3Vj55pGvxpDJsox+kw8Yv0G4a61DcqsbfHCY4uRc48biuROTe02AP21VvXVEdONWgVXWRAfFuCN9ZMerHbNStbpIiJqUSw66dKRyfT/FS+EqNZWVdWYmuIbE1OVXC6HXG7YYmdquoY+/VebbjcuYsW+DzHgSjpWDZ6Cj4P/CgA4727chLny6JZu1Grm5uPVpjrrG7UKC/DGCH8lK9ITEbVgFj29qFRqp3aqjjTl5ORIo1JKpRKlpaXIz8+vM+b69evV3v/GjRt6MVU/Jz8/H2VlZdVGwMg8GvL0X23sy0rw0qEvsHvTPAy4ko5iW3sU2xl/EXrlpysr041aKRX6n6lU2Ffb9qcq3YifIcVXiYjI8lj0SFfnzp2hVCoRFxeHvn37AgBKS0tx6NAhvPnmmwCAoKAg2NraIi4uDuPHjwcAZGVlIS0tDW+99RYAICQkBCqVCkePHsWAAQMAAEeOHIFKpcKgQYOkmBUrViArKwve3tovvn379kEulyMoKMik191WNHQvwYY+/VfV4D+S8ca+j9BRpU3A93ULxtLh03HNpfrUszFw1IqIiCoze9J169YtnDt3TnqdmZmJlJQUuLm5oWPHjoiKisLKlSvRrVs3dOvWDStXroSjoyMmTJgAAFAoFJg6dSoWLFgAd3d3uLm5YeHChejVqxeGDx8OAOjRowfCwsIwbdo0bNiwAQAwffp0hIeHo3v37gCA0NBQ+Pv7IzIyEqtXr0ZeXh4WLlyIadOm8SnEZrD7ZBb++X2aXtX4+vYSNLQW1WA/Dxz6/abeFN6sxP/g5cNfAgCuOXtg6fAZ2OcX0pRLAAC4OtpCfbdcKkuho3C0rfO8quvUiIio9TN7yYiDBw9iyJAh1donT56MTZs2QQiBZcuWYcOGDcjPz8fAgQPx4YcfIiAgQIotKSnBSy+9hK+//hp37tzBsGHD8NFHH+ktaM/Ly8O8efPwww8/AADGjBmDdevWoV27dlLMpUuXMGvWLBw4cAAODg6YMGEC3n777Qat2TLFI6ctXfTudGw4nFnjMRlQbZpNNyL2y7mbWPfTuRrPq2zL8wNRVFKGV79NRcHtMgBAl9zL+HFTFLb0CcPahyeiWO7Y6P7/9cF78Re/9vB0tkd+sRqzvj5R43WghmshIiLLZIrvb7MnXa0Nk6667T55rcYkpTLvSnWnGvOkotJFjtXdBI598T3eDxgltbvdVtVa4FSnnaMtVLfLal03VrlvuhpatfWNNbSIiFoOU3x/m316kdoOTbnAP79PqzdOV99Kdae0wU8qOpbewdTvPkPIr9/jISFw0L0LTnr7AUCdCZe7kx1WPKYdPTX06cL61phxX0QiIqqMSReZzNHMPOQVlxkU+/PZHHx7/FqDEq5h545gWdzH6FB4AwCwq/tDyL6n/mTHzckWiYuGwc5G+zCvoTWxDF1jxn0RiYgIYNJFJtSQ5OOjg38YHKssvIkl//sEo37X7i5wWeGF10a8gINd+ht0/vKxAVLCBRj+dKGhFea5LyIREQFMusiEmiP5sNHcxX+3vIQOhTdQZmWNT/s/hvceegoltoZ/lqtT9QclDHm60NAK81VrdRERUdtk0cVRyfw05QKJ53PxfcpVJJ7Phaa88c9d6JIUY7prbYMPQ55Ess8DCJ/yHt58dEqDEi6g8dN/3BeRiIgagk8vGllrenqxpicH66ulZch7NmUbHyf1bSz4eTN+ua83/tdVu4G5TJQDAISscf+G2DotuEkL3ZvjPhERkWmxZEQL1FqSrtqSI2PUn4pNy8LiHakGL6oHAAiBkb8nYun+DfC+lYsrLu0xdNonKLWpuwhpXYxZ0qGh1fWJiMiysGQEmUVdexwKaJOVZTvTMcJf2ajEIizAG0f+yMPnCRcMir9XlYNlcesx/PwxAECmqzdeGzGrSQmXjrGm/1hhnoiI6sOki6oxtP5U0h+5sJLJGjy6oykX+P63q/XG2Wju4rlfv0fUL1/DsUyNUisbfDzwCXwYMh5qW8N3CaiJlQxY9zSrxRMRkekw6aJqDF1YPnvLcRTcqZgiNHQdk6H1ugZcTsPig58DAI74BmBx6Gyc9/Ct5yzDlAvA1cnOKO9FRERkCCZdVI2hpR0qJ1wAkK0qwczNx+td71VXUicT5dKC+IT7+uDr3mE44dMd3/QaDsiMu0aKRUuJiMiUWDKCqtGVdmhoiiP+/Hn121T8cvZmtfISuvITZ6/fquFkgYj0Q4j7dBba38qXmheHzcE3gSOMnnABLa9oqTHLdxARkelxpIuq0dWfqmkPQkMU3C7DxM+OSNONI/yVWHfgHD7/JbPa6BgAdMzPwvJ9H+GRC9qNsGcc+S+WD5vW9Aupwz1ymxZVtJRlKYiIWj6WjDCy1lIyAqj5i76doy0KbhtW6kGXsDnaWeN2qabacVtNGaYd3YF5CTGwv1sKtbUtPgwZj48H/tUoTybWpZ2DDZJfC6134b8llIJozvIdRESkxZIRZFY17UFYXi4w8bMjBp2vSxJqSrj6X07Dir0fwS/3EgAgvlNv/DN0Fi643dukPhs6Mldw5y6OZubVWebBEkaXmrt8BxERmQ7XdFGddPWnxva5VzsdJwPaOTR9FGpURgL8ci/hhmM7vBi+AKsXvN/khAvQFjt97qH7DIqtayG9bnSpaukM3cMCsWlZTemmwQwt33E0M88k/SEiosbjSBcZpKZRnwYRAi7qYhTa3wMAeOcvk1BmbYOPgv8GlYMz5DUtrm+AUQFeeCakMwZ0dsPRzDz8+5cL9Z5T20J6SxpdMvQJSz6JSURk+Zh0Ub12n8zCrK+PN/r8+3OvYPm+j2BdrsFTE6IhZFYoljsieshzUoz6btOWFu5Ju44HlArkFJXAw0kOpYs9rheW1Jg46bb/qW0hfUNGl5q7Cr2hT1i2tCcxiYjaIiZdJKlp0fjetCzM2XqiUe8nv1uKmUnfYGbSN5Br7qLExg7db1zEGc/ORu651tr9v0t/budoK41KVU68dONSdW3/Y0mjS7ryHdmqxiWQRERkOZh0EYCmP6lYVcjF37B830fokqfd7udg5yC8FjoTl9spjdLf+qj+7LeiyjUoDVgIb0mjS3WV7zAkgSQiIsvBpKsNqK3sga59f3o2PqthDVRjEq571LexLG49njj1EwAgx8kVy4ZNx64HHm6WAqe10Y1y2dtYYcvzA3Hzltrgkg+WNroUFuCN9ZMerJYUG5JAEhGR5WDS1crVVvZgTG9v/PBbVuMXxteixMYOD926AiGTYfuACCwLmYgiuZNRP8NQAkB2oRpWMhnG9jH8yUhLHF2qqXyHOWqGERFR4zHpasVqK6qZpSrBhsOZRvucLrmXcVmhRKmNLe5a2yB77XrcKNfgtN29KDLgKcLm1pi1V5Y4uqQr30FERC0Tk65Wqq6yB8YiL1NjbuI2TD/yLT4Y9CQ+eOhptHO0xQungezCMgAXmvHTDdfYtVccXSIiImNi0tVK1Vf2oKke+SMZb8StR6eCbABA9xsXASH+XAfWuMX3hgjp7IbM3NvILqz/2oyx9oqjS0REZCxMulqp5ipn0P5WHv71v42IOPMzACDrHncsHTEDe7uFNPtCeaWLHJunBQNAnQ8A6AjwyT4iIrIcTLpaKWOXM5CJcsxN2Ibnj30HF3UxNDIrbAqKwJqHJ6JY7miUz7C3tUJJWXmti9eXjukpJVADOrth/n9S6ny/do62GOFvmhIVRERE9WHS1UrVV/agIR7IycTKvevw4LUMAMBvym5YPHI2Tim7Nr2jlZSUlePvw7sh5tjlehevGzJ9WnC7rNaq8bWV0SAiImouTLpasLoSh7rKHhjKvbgA325eiHtVObAR5Siyc8DnQWPw7sMTUG5lbdRr0bnPwwnxrwytNyFqStX42sposOYVERE1JyZdLZQhicMIfyWihnfD579cQMGdisXtbk626OvbDicuq5BXXFrj+6/Yuw4TU2Kl17v9BmHZ8Om47uzRTFek5elsb9Di9cZWja+tjEa2qgQzNx/H+kkPMvEiIqJmwaSrBTIkcQBQLSlztLOGlUyGvOIy/O/MDQCAs70NikruSjHKwptIWj9F7303DHhcb3Pq5tDQJw0bUzW+rjIaugr2y3amY4S/klONRERkdEy6WhhDEodXv02F6nZZtZjbpZpq59z6M+GyKtfgmeO7sPDnr/SOPzh3C/IcFUbpe20aU+W9MVXj61sHJqAtHFvbOjAiIqKmYNLVwhiSODRkz0QBoGf2OXzxzRJ43FYBAE4qu+LT/o/hB//BTeytYRpb5b2hVeObsg6MiIioqazM3YH6LF26FDKZTO9HqawoAyCEwNKlS+Hj4wMHBwc8+uijOHXqlN57qNVqzJ07Fx4eHnBycsKYMWNw5coVvZj8/HxERkZCoVBAoVAgMjISBQUFprjEBjFmQuCkvo1/7f8EP3w5Hx63VbhtK8fikbMx9pk1zZ5wuTnZYu2TfbB1WjDiXxna6HVUYQHeiH9lKLZOC8Z7T9X9fo1dB0ZERGQMLWKkq2fPnti/f7/02tq64sm5t956C2vWrMGmTZvg5+eH5cuXY8SIEcjIyICzszMAICoqCjt37kRMTAzc3d2xYMEChIeHIzk5WXqvCRMm4MqVK4iN1S4enz59OiIjI7Fz504TXmn9jJIQCIE1u9bg8VM/SU0/9HgE7/xlEi66+jTprds52Oot2q9KN9m38rFeRluwbmjV+MasAyMiIjKWFpF02djY6I1u6Qgh8O677+If//gHHn/8cQDAF198AS8vL3z99deYMWMGVCoVPvvsM3z11VcYPnw4AGDz5s3w9fXF/v37MXLkSJw+fRqxsbFISkrCwIEDAQAbN25ESEgIMjIy0L17d9NdbD2aWn+r/+U0fPP1q9Lrm44K/D18AX7u/KBR+vfhhAdhZSVDTlEJLtwsxtajl5BdqJaOm3vD6IauAyMiIjKWFpF0nT17Fj4+PpDL5Rg4cCBWrlyJ+++/H5mZmcjOzkZoaKgUK5fLMXjwYCQkJGDGjBlITk5GWVmZXoyPjw8CAgKQkJCAkSNHIjExEQqFQkq4ACA4OBgKhQIJCQl1Jl1qtRpqdUVSUVhYaOSr11df4iCgrcRedSG9jeYuVuz9EE+mxum939BpG1Bof49R+qZ0kSO4i7te0jJnaDeLKkLa0HVgRERExmLxSdfAgQPx5Zdfws/PD9evX8fy5csxaNAgnDp1CtnZ2s2Wvby89M7x8vLCxYsXAQDZ2dmws7ODq6trtRjd+dnZ2fD09Kz22Z6enlJMbaKjo7Fs2bJGX19j1Jc4AMDMzcel9r5Xz2Dl3nXoceOC1DZj3GLs7T7IqP16ekDHagmVJW4YHRbgjRH+SotKBomIqPWz+KRr1KhR0p979eqFkJAQdOnSBV988QWCg7WbH8uqbLQshKjWVlXVmJriDXmfRYsWYf78+dLrwsJC+Pr61nlOQ9VUeb6+xGH6I52xdV8q3t79LkLPJgEA8u2dsS5kPD7vN6ZZKsrf5+Fk9PdsLpaYDBIRUetm8UlXVU5OTujVqxfOnj2LcePGAdCOVHl7V0wL5eTkSKNfSqUSpaWlyM/P1xvtysnJwaBBg6SY69evV/usGzduVBtFq0oul0Mulzf1smpVX+X5GvcV1JSj+KuvsX/nR/AszgcA/DdgGFYOea5Za27xqT8iIqLaWXzJiKrUajVOnz4Nb29vdO7cGUqlEnFxFeuUSktLcejQISmhCgoKgq2trV5MVlYW0tLSpJiQkBCoVCocPXpUijly5AhUKpUUYw66yvNV63LpKs/HpmVVP2n/fljbWGN5zAp4FufjvNu9mPjkciwc/fdmS7hk0CaCfOqPiIiodhY/0rVw4UJERESgY8eOyMnJwfLly1FYWIjJkydDJpMhKioKK1euRLdu3dCtWzesXLkSjo6OmDBhAgBAoVBg6tSpWLBgAdzd3eHm5oaFCxeiV69e0tOMPXr0QFhYGKZNm4YNGzYA0JaMCA8PN9uTiw3esqa4GPD2BoqKpLg1D0/ExwP/ilIb22btqwCf+iMiIqqPxSddV65cwdNPP42bN2+iffv2CA4ORlJSEjp16gQAePnll3Hnzh3MmjUL+fn5GDhwIPbt2yfV6AKAtWvXwsbGBuPHj8edO3cwbNgwbNq0Sa/e15YtWzBv3jzpKccxY8Zg3bp1pr3YShq0ZU3WaeAvf9E7/uxfl+CnLv0b/fnuTnZ4Y2xPKBztMHvL8Tprb7VztMUI/+olPYiIiKiCTAjRmHJPVIvCwkIoFAqoVCq4uLg0+n2+T7mKF2NS6oxR3CnC95d/wH07tlY0hoVh96rPMGvriUZ/tpuTLZIWDYedjRUSz+fi6Y1J9Z6zdVowF6YTEVGLZazv77q0uDVdbUWdi9KFwONp/8Nv7z9dkXBNmwbcuAHNrt14Y/dpgz6j6mSg7M+flY/1gp2N9leD+xUSEREZh8VPL7ZVtVWe75J7Ga/HrcdDF08CAIS1NWQHDwIPPwwAOHo+t85pSZ2/D++GmGOX6y0Qyv0KiYiIjINJl4WqWnneSX0bJ999ElZ/pmAlNna4NHsB/N5aCtjZSecZOuJ0n4cT4l8ZWm+BUO5XSEREZBycXrRgusrzSoU9Pt3+upRwAcCRHw7B792VegkX0LCRKV2B0LF97kVIle17dHTJH1DzdCTAJxeJiIgMwaTLwoUFeCP+laHoEKF9qjJ3ZAQ0dzUYPCq4xnjdyFRtKVBjampVTv4qUyrssX7Sg9yvkIiIyAB8etHITPH0Q310RVWB6htiA2h0olTTdkQc4SIiotbAFN/fTLqMzBKSLqD+7YOIiIiogim+v7mQvpWqb0NsIiIiMi0mXa2YbqE8ERERmR8X0hMRERGZAJMuIiIiIhNg0kVERERkAky6iIiIiEyASRcRERGRCTDpIiIiIjIBJl1EREREJsCki4iIiMgEmHQRERERmQCTLiIiIiITYNJFREREZALce9HIhBAAtLuVExERUcug+97WfY83ByZdRlZUVAQA8PX1NXNPiIiIqKGKioqgUCia5b1lojlTujaovLwc165dg7OzM2Qymbm70yiFhYXw9fXF5cuX4eLiYu7uWCTeo/rxHhmG96l+vEf14z2qX333SAiBoqIi+Pj4wMqqeVZfcaTLyKysrNChQwdzd8MoXFxc+B9vPXiP6sd7ZBjep/rxHtWP96h+dd2j5hrh0uFCeiIiIiITYNJFREREZAJMuqgauVyOJUuWQC6Xm7srFov3qH68R4bhfaof71H9eI/qZwn3iAvpiYiIiEyAI11EREREJsCki4iIiMgEmHQRERERmQCTLiIiIiITYNLVShw+fBgRERHw8fGBTCbDd999p3dcCIGlS5fCx8cHDg4OePTRR3Hq1Cm9GLVajblz58LDwwNOTk4YM2YMrly5oheTn5+PyMhIKBQKKBQKREZGoqCgQC/m0qVLiIiIgJOTEzw8PDBv3jyUlpY2x2UbLDo6Gv3794ezszM8PT0xbtw4ZGRk6MW09Xu0fv16BAYGSoUDQ0JCsGfPHul4W78/NYmOjoZMJkNUVJTUxvsELF26FDKZTO9HqVRKx3mPtK5evYpJkybB3d0djo6O6NOnD5KTk6Xjbf0+3XfffdV+j2QyGWbPng2ghd4fQa3C7t27xT/+8Q+xfft2AUDs2LFD7/iqVauEs7Oz2L59u0hNTRVPPvmk8Pb2FoWFhVLMCy+8IO69914RFxcnjh8/LoYMGSJ69+4t7t69K8WEhYWJgIAAkZCQIBISEkRAQIAIDw+Xjt+9e1cEBASIIUOGiOPHj4u4uDjh4+Mj5syZ0+z3oC4jR44Un3/+uUhLSxMpKSli9OjRomPHjuLWrVtSTFu/Rz/88IPYtWuXyMjIEBkZGWLx4sXC1tZWpKWlCSF4f6o6evSouO+++0RgYKB48cUXpXbeJyGWLFkievbsKbKysqSfnJwc6TjvkRB5eXmiU6dOYsqUKeLIkSMiMzNT7N+/X5w7d06Kaev3KScnR+93KC4uTgAQP/30kxCiZd4fJl2tUNWkq7y8XCiVSrFq1SqpraSkRCgUCvHxxx8LIYQoKCgQtra2IiYmRoq5evWqsLKyErGxsUIIIdLT0wUAkZSUJMUkJiYKAOLMmTNCCG3yZ2VlJa5evSrFbN26VcjlcqFSqZrlehsjJydHABCHDh0SQvAe1cbV1VV8+umnvD9VFBUViW7duom4uDgxePBgKenifdJasmSJ6N27d43HeI+0XnnlFfHwww/Xepz3qboXX3xRdOnSRZSXl7fY+8PpxTYgMzMT2dnZCA0NldrkcjkGDx6MhIQEAEBycjLKysr0Ynx8fBAQECDFJCYmQqFQYODAgVJMcHAwFAqFXkxAQAB8fHykmJEjR0KtVusNm5ubSqUCALi5uQHgPapKo9EgJiYGxcXFCAkJ4f2pYvbs2Rg9ejSGDx+u1877VOHs2bPw8fFB586d8dRTT+GPP/4AwHuk88MPP6Bfv37429/+Bk9PT/Tt2xcbN26UjvM+6SstLcXmzZvx3HPPQSaTtdj7w6SrDcjOzgYAeHl56bV7eXlJx7Kzs2FnZwdXV9c6Yzw9Pau9v6enp15M1c9xdXWFnZ2dFGNuQgjMnz8fDz/8MAICAgDwHumkpqbinnvugVwuxwsvvIAdO3bA39+f96eSmJgYHD9+HNHR0dWO8T5pDRw4EF9++SX27t2LjRs3Ijs7G4MGDUJubi7v0Z/++OMPrF+/Ht26dcPevXvxwgsvYN68efjyyy8B8Hepqu+++w4FBQWYMmUKgJZ7f2waFE0tmkwm03sthKjWVlXVmJriGxNjTnPmzMHJkycRHx9f7Vhbv0fdu3dHSkoKCgoKsH37dkyePBmHDh2Sjrf1+3P58mW8+OKL2LdvH+zt7WuNa+v3adSoUdKfe/XqhZCQEHTp0gVffPEFgoODAfAelZeXo1+/fli5ciUAoG/fvjh16hTWr1+PZ555Ropr6/dJ57PPPsOoUaP0RpuAlnd/ONLVBuieGqqakefk5EjZu1KpRGlpKfLz8+uMuX79erX3v3Hjhl5M1c/Jz89HWVlZtX8pmMPcuXPxww8/4KeffkKHDh2kdt4jLTs7O3Tt2hX9+vVDdHQ0evfujffee4/350/JycnIyclBUFAQbGxsYGNjg0OHDuH999+HjY2N1L+2fp+qcnJyQq9evXD27Fn+Lv3J29sb/v7+em09evTApUuXAPDvpMouXryI/fv34/nnn5faWuz9adAKMGoRUMtC+jfffFNqU6vVNS443LZtmxRz7dq1GhccHjlyRIpJSkqqccHhtWvXpJiYmBizL8gsLy8Xs2fPFj4+PuL333+v8Xhbv0c1GTp0qJg8eTLvz58KCwtFamqq3k+/fv3EpEmTRGpqKu9TLUpKSsS9994rli1bxnv0p6effrraQvqoqCgREhIihODfSZUtWbJEKJVKUVZWJrW11PvDpKuVKCoqEidOnBAnTpwQAMSaNWvEiRMnxMWLF4UQ2kdrFQqF+Pbbb0Vqaqp4+umna3y0tkOHDmL//v3i+PHjYujQoTU+WhsYGCgSExNFYmKi6NWrV42P1g4bNkwcP35c7N+/X3To0MHsjx7PnDlTKBQKcfDgQb1HkG/fvi3FtPV7tGjRInH48GGRmZkpTp48KRYvXiysrKzEvn37hBC8P7Wp/PSiELxPQgixYMECcfDgQfHHH3+IpKQkER4eLpydncWFCxeEELxHQmhLjtjY2IgVK1aIs2fPii1btghHR0exefNmKYb3SQiNRiM6duwoXnnllWrHWuL9YdLVSvz0008CQLWfyZMnCyG0/yrQ/WtBLpeLRx55RKSmpuq9x507d8ScOXOEm5ubcHBwEOHh4eLSpUt6Mbm5uWLixInC2dlZODs7i4kTJ4r8/Hy9mIsXL4rRo0cLBwcH4ebmJubMmSNKSkqa8/LrVdO9ASA+//xzKaat36PnnntOdOrUSdjZ2Yn27duLYcOGSQmXELw/tamadPE+Calekq2trfDx8RGPP/64OHXqlHSc90hr586dIiAgQMjlcvHAAw+ITz75RO8475MQe/fuFQBERkZGtWMt8f7IhBCiYROSRERERNRQXEhPREREZAJMuoiIiIhMgEkXERERkQkw6SIiIiIyASZdRERERCbApIuIiIjIBJh0EREREZkAky4iIiIiE2DSRURERGQCTLqIiIiITIBJFxFRDfz8/BASEoI7d+5IbUIIBAcH4+WXXzZjz4iopWLSRURUg23btuHEiRP45ZdfpLYtW7YgMzMT//znP83YMyJqqZh0ERHVoG/fvujduzfOnDkDALh9+zYWLVqEN954Ay4uLvjxxx8xd+5cM/eSiFoSJl1ERLXw8/NDRkYGAOCtt96Cm5sbpk6dCgA4efIk+vbta87uEVELw6SLiKgW3bt3R0ZGBq5cuYLVq1dj7dq1sLa2BqBNuk6fPo2goCD07NkTv//+u5l7S0SWjkkXEVEtdCNdr776KkaMGIGhQ4dKx06ePImOHTsiOTkZc+fOxZo1a8zYUyJqCWzM3QEiIkvl5+eHy5cv47///S/S0tKkdrVajdLSUsyePRsAEBgYiP3795urm0TUQnCki4ioFn5+fgCAOXPmoGvXrlL7qVOn4O/vDysr7V+hJ06cQGBgoFn6SEQtB0e6iIhqUVJSAiEEnnnmGb32kydP4vz58ygrK0N+fj42bdqEPXv2mKmXRNRSMOkiIqrFb7/9Bjs7O/To0UOvPTU1FU888QT69++P8vJyrF27Fh4eHmbqJRG1FDIhhDB3J4iILNG7776LL774AidOnDB3V4ioFWDSRURERGQCXEhPREREZAJMuoiIiIhMgEkXERERkQkw6SIiIiIyASZdRERERCbApIuIiIjIBJh0EREREZkAky4iIiIiE2DSRURERGQCTLqIiIiITOD/AX3ziEkS8fHZAAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(25000, 40000, '$\\\\gamma_a = 0.51\\\\gamma_b + 5.543$')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PyPlot.scatter(new_fs_bs, new_fs_as); PyPlot.xlabel(L\"\\gamma_b\"); PyPlot.ylabel(L\"\\gamma_a\");\n",
    "PyPlot.plot(new_fs_bs, fit[1].*new_fs_bs .+ fit[2], \"r--\"); PyPlot.text(25000,40000, L\"\\gamma_a = 0.51\\gamma_b + 5.543\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence we find the fluorescence per molecule as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1108.655081387837"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpm = 2*fit[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate the molecule number arrays for each generation in each lineage using these proportionality constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Base.Iterators, LaTeXStrings, PyPlot;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 4 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_sep_all_lins = fluor_sep_all_lins./fpm;\n",
    "\n",
    "fig, axs = PyPlot.subplots(2, 2, tight_layout = true);\n",
    "n_bins = 30;\n",
    "\n",
    "axs[1].set_xlabel(L\"f\");axs[2].set_xlabel(L\"n\"); axs[1].set_ylabel(L\"P(f)\"); axs[2].set_ylabel(L\"P(n)\"); axs[1].set_title(\"Lineage 1 fluor.\"); axs[2].set_title(\"Lineage 1 molecule #s\");axs[1].grid(true);axs[2].grid(true)\n",
    "axs[3].set_xlabel(L\"f\");axs[4].set_xlabel(L\"n\"); axs[3].set_ylabel(L\"P(f)\"); axs[4].set_ylabel(L\"P(n)\"); axs[3].set_title(\"Lineage 2 fluor.\"); axs[4].set_title(\"Lineage 2 molecule #s\");axs[3].grid(true);axs[4].grid(true)\n",
    "\n",
    "axs[1].hist(reduce(vcat,fluor_all_lins[1]), bins=n_bins, facecolor= \"g\", alpha=0.75, density = true);\n",
    "axs[2].hist(reduce(vcat,num_sep_all_lins[1]), bins=n_bins, facecolor= \"r\", alpha=0.75, density = true);\n",
    "axs[3].hist(reduce(vcat,fluor_all_lins[2]), bins=n_bins, facecolor= \"g\", alpha=0.75, density = true);\n",
    "axs[4].hist(reduce(vcat,num_sep_all_lins[2]), bins=n_bins, facecolor= \"r\", alpha=0.75, density = true);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "findfirst(x -> x > 150000, prop_consts) # find lineage with oddly high fluor. Plot below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = PyPlot.subplots(1, 2, tight_layout = true);\n",
    "\n",
    "axs[1].set_xlabel(L\"f\");axs[2].set_xlabel(L\"n\"); axs[1].set_ylabel(L\"P(f)\"); axs[2].set_ylabel(L\"P(n)\"); axs[1].set_title(\"Lineage 151 fluor.\"); axs[2].set_title(\"Lineage 151 molecule #s\");axs[1].grid(true);axs[2].grid(true);\n",
    "\n",
    "axs[1].hist(reduce(vcat,fluor_all_lins[151]), bins=n_bins, facecolor= \"g\", alpha=0.75, density = true);\n",
    "axs[2].hist(reduce(vcat,num_sep_all_lins[151]), bins=n_bins, facecolor= \"r\", alpha=0.75, density = true);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this lineage in particular we see that the large value for the proportionality constant is due to a few very high finds in the fluorescence across the lineage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's the fluorescence per molecule proportionality in a file so that we can use it later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "writedlm(\"MN_props.csv\", fpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.5.2",
   "language": "julia",
   "name": "julia-1.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}