16-prob-inf-using-NUM-LNA-Tan-25.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (16) MLE probabilistic inference using the LNA on molecule number for Tanouchi data - $25^{\\circ}$C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we try a very different inference technique. First we fit the mean predicted by the model and use a least square fitting to determine $\\alpha_0 \\beta_0$ and $\\alpha_1+\\beta_1$, reducing the number of parameters needed to be inferred by 2 - leaving 2 parameters needing to be inferred. Then we use the LNA to approximate the time evolution of each generation and calculate the probability of having the set of experimental measurements in each lineage given at parameter set $\\{\\alpha_0, \\alpha_1, \\beta_0, \\beta_1\\}$. We maximise this probability in order to infer the parameters for the given lineage.\n",
    "\n",
    "In this notebook our sole focus is on the inference of articial simulated data for which we know the parameters.\n",
    "\n",
    "First we import the Tanouchi data from the $25^{\\circ}C$ dataset. We do this to get the required data form for our simulated data later on."
   ]
  },
  {
   "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_25C/MC4100_25C/\"); # 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_25C/MC4100_25C/\", file), ',', Float64, '\\n'))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_25.csv\")[1];\n",
    "exp_grs = readdlm(\"exp_grs_all_25.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": [
    "Now let's plot the first 10 generations of the first lineage of 27$^{\\circ}C$ Tanouchi data. Later we wish to infer parameters of simulated data that at a glance resembles this experimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots, Statistics, StatsBase, Distances, Distributions, LaTeXStrings, LinearAlgebra; pyplot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_gen = 7;\n",
    "num_gens = deepcopy(num_sep_all_lins[e_gen]);\n",
    "fig, ax = PyPlot.subplots(1,1);\n",
    "for i in 1:10#length(conc_gens)\n",
    "    ax.plot(num_gens[i])\n",
    "end\n",
    "PyPlot.xlabel(L\"t/min\"); PyPlot.ylabel(L\"n(t)\"); PyPlot.title(\"First 10 generations from Tanouchi lin. 1\")\n",
    "PyPlot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the model we are attempting to fit the data to is formed of only 1 linear reaction, i.e., $\\varnothing \\xrightarrow{\\alpha_0 V(t)^{\\alpha_1}} m P$, with $m \\sim \\text{Geom}(\\beta_0 V(t)^{\\beta_1})$, the rate equation for this process gives the exact evolution of the mean of this process. Solving the rate equation for this process we find:\n",
    "$$ \\langle n(t)\\rangle = \\frac{\\alpha_0\\beta_0 V_0^{\\alpha_1+\\beta_1}}{(\\alpha_1+\\beta_1)\\theta}\\left(e^{(\\alpha_1+\\beta_1)\\theta t} -1 \\right) + \\langle n(0)\\rangle$$\n",
    "\n",
    "We now fit this each to each generation in the lineage using the method of least squares, and in doing so obtaining values of $\\alpha_0\\beta_0$ and $\\alpha_1+\\beta_1$ for each lineage. Our task is then to find the value best representing $\\alpha_0\\beta_0$ and $\\alpha_1+\\beta_1$ over the entire lineage, and compare to what should be the true values seen in `true_par`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "using LsqFit; # least square fitting package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69×3 Array{Float64,2}:\n",
       " 2.72374   1.0358       550.296\n",
       " 2.07852   1.27828      436.672\n",
       " 5.41176   3.60023e-11  314.036\n",
       " 4.1311    0.498159     355.196\n",
       " 3.5469    0.716281     376.289\n",
       " 0.974361  1.74847      494.127\n",
       " 6.50002   3.0745e-9    364.268\n",
       " 1.85383   1.31291      359.629\n",
       " 0.970209  1.94983      453.45\n",
       " 5.23076   0.469959     469.962\n",
       " 3.03412   0.702627     497.624\n",
       " 0.788658  1.65186      523.996\n",
       " 2.93281   0.690848     436.636\n",
       " ⋮                      \n",
       " 2.72723   0.794256     471.858\n",
       " 2.59385   1.1912       367.451\n",
       " 3.37368   0.898052     366.597\n",
       " 1.43185   1.5494       536.96\n",
       " 3.409     0.534012     448.334\n",
       " 1.74423   1.18987      375.752\n",
       " 1.41106   1.47105      355.82\n",
       " 1.26406   1.65385      419.389\n",
       " 2.14839   1.15681      565.586\n",
       " 3.71982   0.618025     524.83\n",
       " 1.34892   1.36197      448.935\n",
       " 4.23795   0.027595     361.898"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_pars = [];\n",
    "for i in 1:length(v0_n0_th[e_gen])\n",
    "    v0 = v0_n0_th[e_gen][i][1]; n0 = v0_n0_th[e_gen][i][2]; th = v0_n0_th[e_gen][i][3];\n",
    "    m(t,ab) = ((10 .^ab[1]*v0^(10 .^ab[2]))/(10 .^ab[2]*th)).*(exp.(10 .^ab[2]*th.*t) .-1) .+ 10 .^ab[3];\n",
    "    ab0 = [0.1, 0.1, 2.0];\n",
    "    n_data = num_gens[i];\n",
    "    fit = 10 .^curve_fit(m, LinRange(1,length(n_data), length(n_data)), n_data, ab0).param\n",
    "    push!(comb_pars,fit)\n",
    "end\n",
    "comb_pars = convert(Array{Float64,2},transpose(hcat(comb_pars...)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's just check that the individual values of $\\alpha_0\\beta_0$ and $\\alpha_1+\\beta_1$ determined for each trajectory are well fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": [
    "avg_traj = [];\n",
    "for i in 1:length(v0_n0_th[1])\n",
    "    v0 = v0_n0_th[e_gen][i][1]; n0 = v0_n0_th[e_gen][i][2]; th = v0_n0_th[e_gen][i][3];\n",
    "    ab = comb_pars[i,:];\n",
    "    traj = [((ab[1]*v0^(ab[2]))/((ab[2])*th))*(exp.((ab[2])*th*t) .-1) .+ ab[3] for t in 1:length(num_gens[i])];\n",
    "    push!(avg_traj,traj)\n",
    "end\n",
    "\n",
    "#print(avg_traj[1]);\n",
    "colors = [\"r\",\"g\",\"black\",\"r\",\"g\",\"black\",\"r\",\"g\",\"black\"];\n",
    "fig, ax = PyPlot.subplots(1,1);\n",
    "for i in 1:6#length(conc_gens)\n",
    "    ax.plot(num_gens[i+10], color = colors[i])\n",
    "    ax.plot(avg_traj[i+10], color = colors[i])\n",
    "end\n",
    "PyPlot.xlabel(L\"t/min\"); PyPlot.ylabel(L\"n(t)\")\n",
    "PyPlot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the histograms for the determined values of $\\alpha_0\\beta_0$ and $\\alpha_1+\\beta_1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "fig, ax = PyPlot.subplots(1,2, sharey = true)\n",
    "\n",
    "ax[1].hist(comb_pars[:,1], bins = LinRange(0.0, 20, 20), color = \"b\"); ax[1].set_ylabel(\"Frequency\"), ax[1].set_xlabel(L\"\\alpha_0 \\beta_0\");\n",
    "ax[2].hist(comb_pars[:,2], bins = LinRange(0.0, 2.5, 20), color = \"r\"); ax[2].set_xlabel(L\"\\alpha_1 + \\beta_1\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the true values of $\\alpha_0 \\beta_0$ and $\\alpha_1+\\beta_1$ to the mean values over all least square fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Lsq values: 2.76855428116673\t1.0869249576365358\n"
     ]
    }
   ],
   "source": [
    "av_ab_0 = sum(comb_pars[:,1])/length(comb_pars[:,1]); av_ab_1 = sum(comb_pars[:,2])/length(comb_pars[:,2]);\n",
    "print(\"Mean Lsq values: \", av_ab_0, \"\\t\", av_ab_1, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the median?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Lsq values: 2.148392998488585\t1.1568146624624451\n"
     ]
    }
   ],
   "source": [
    "av_ab_0 = median(comb_pars[:,1]); av_ab_1 = median(comb_pars[:,2]);\n",
    "print(\"Median Lsq values: \", av_ab_0, \"\\t\",av_ab_1, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsq_a0b0 = av_ab_0; lsq_a1b1 = av_ab_1; # set this for use later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the median values of the histograms are the best estimate of the true parameter values, so we will use the median instead of the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference to determine $\\{\\alpha_0, \\alpha_1, \\beta_0, \\beta_1\\}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's find the probability of having the set of experiment data point in `expt_ssa_conc` given the parameters `[0.1,1E-6,0.5,1.0]` (in order $\\{\\alpha_0, \\alpha_1, \\beta_0, \\beta_1\\}$, a set denoted by $\\mathcal{S}$). We do this using the LNA for each generation. The variance of the process $\\varnothing \\xrightarrow{\\alpha_0 V(t)^{\\alpha_1}} m P$, with $m \\sim \\text{Geom}(\\beta_0 V(t)^{\\beta_1})$ is given by:\n",
    "$$ \\text{Var}(\\phi(t)) = \\sigma_{\\phi}(t)^2 = \\frac{\\alpha_0\\beta_0 V_0^{\\alpha_1+\\beta_1}}{V(t)^2 \\theta}\\left(\\frac{e^{(\\alpha_1+\\beta_1)\\theta t}- 1}{\\alpha_1+\\beta_1} + \\frac{2 \\beta_0 V_0^{\\beta_1}}{\\alpha_1+2\\beta_1}\\left(e^{(\\alpha_1+2\\beta_1)\\theta t}-1 \\right) \\right). $$\n",
    "\n",
    "Note that the initial condition is exactly known hence $\\text{Var}(\\phi(0)) = 0$. Knowing the variance we can now employ the LNA, which is a Gaussian approximation giving (for generation $j$):\n",
    "$$P_j(\\phi_j(t) ; \\mathcal{S}) = \\frac{1}{\\sqrt{2 \\pi}\\sigma_{\\phi_j}(t ; \\mathcal{S})}\\exp\\left(-\\frac{1}{2}\\left(\\frac{\\phi_j(t)-\\langle \\phi_j(t ; \\mathcal{S})\\rangle}{\\sigma_{\\phi_j}(t ; \\mathcal{S})} \\right)^2 \\right).$$\n",
    "\n",
    "Finally, we find that the maximisation of the product of the probabilities, i.e., $\\prod_{j=1}^{N}\\prod_{t}^{M_j} P_j(\\phi_j(t))$ (where $N$ is the number of generations and $M_j$ is the number of time points in generation $j$) - allowing us to determine the optimal parameters - is equivalent to the minimisation of the negative logarithm of $P_j(\\phi_j(t) ; \\mathcal{S})$, giving the new objective function:\n",
    "\n",
    "$$ -\\ln(P_j(\\phi_j(t) ; \\mathcal{S})) \\propto L(\\mathcal{S}) = \\min_{\\mathcal{S}} \\sum_{j=1}^N\\sum_{t=1}^{M_j} \\left\\{ \\frac{1}{2}\\left(\\frac{\\phi_j(t)-\\langle \\phi_j(t ; \\mathcal{S})\\rangle}{\\sigma_{\\phi_j}(t ; \\mathcal{S})} \\right)^2  +\\ln(\\sigma_{\\phi_j}(t ; \\mathcal{S})) \\right\\}. $$\n",
    "\n",
    "We neglect the $2\\pi$ from the likelihood since it is just a constant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the `BlackBoxOptim` Julia package in order to find the minimum of $L(\\mathcal{S})$. We first import the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "using BlackBoxOptim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can write the black-box function to be optimised, which takes the inference parameters as input and outputs the likelihood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "re_sol (generic function with 1 method)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function re_sol(S::Array{Float64,1}, ic_lin::Int, gen_i::Int, t::Int)\n",
    "    a0, a1 = 10^S[1], 10^S[2];\n",
    "    b0, b1 = lsq_a0b0/a0, lsq_a1b1 - a1;\n",
    "    \n",
    "    v0 = v0_n0_th[ic_lin][gen_i][1]; n0 = v0_n0_th[ic_lin][gen_i][2]; \n",
    "    th = v0_n0_th[ic_lin][gen_i][3];\n",
    "    \n",
    "    re_mn = ((a0*b0*v0^(a1+b1))/((a1+b1)*th))*(exp((a1+b1)*th*t)\n",
    "                            -1) + n0; # get the mean conc\n",
    "    \n",
    "    return re_mn\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sd_sol (generic function with 1 method)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function sd_sol(S::Array{Float64,1}, ic_lin::Int, gen_i::Int, t::Int)\n",
    "    a0, a1 = 10^S[1], 10^S[2];\n",
    "    b0, b1 = lsq_a0b0/a0, lsq_a1b1 - a1;\n",
    "    \n",
    "    v0 = v0_n0_th[ic_lin][gen_i][1]; n0 = v0_n0_th[ic_lin][gen_i][2]; \n",
    "    th = v0_n0_th[ic_lin][gen_i][3];\n",
    "    \n",
    "    sd_sol = sqrt( ((a0*b0*v0^(a1+b1))/(th)) * \n",
    "                    ( ((exp((a1+b1)*th*t)-1)/(a1+b1)) + ((2*b0 * v0^b1)/(a1+2*b1)) \n",
    "                        * (exp((a1+2*b1)*th*t)-1) ) ); # get the sd\n",
    "    \n",
    "    return sd_sol\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bb_fn (generic function with 1 method)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function bb_fn(S::Array{Float64,1})\n",
    "    \"\"\" the input ps is an array of form [a0, log10(a1)] \"\"\"\n",
    "    \n",
    "    sum_neg_log_likelihood = 0.0::Float64; # want to minimise this\n",
    "    for (i,gen) in enumerate(num_gens) # loop over each gen.\n",
    "        for t in 0:length(gen)-1            # loop over all times in that gen.\n",
    "            if t>0                          # don't do for the first measurement in each gen.\n",
    "                \n",
    "                re_mn = re_sol(S, e_gen, i, t)\n",
    "                sd = sd_sol(S, e_gen, i, t);   # get the sd\n",
    "                meas = gen[t+1];            # get the measurement\n",
    "                neg_ll = (0.5)*((meas-re_mn)/sd)^2 + log(sd); # calculate the NLL\n",
    "                \n",
    "                #append!(neg_log_likelihoods, neg_ll); # append to the array.\n",
    "                sum_neg_log_likelihood += neg_ll;\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    return sum_neg_log_likelihood\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting optimization with optimizer DiffEvoOpt{FitPopulation{Float64},RadiusLimitedSelector,BlackBoxOptim.AdaptiveDiffEvoRandBin{3},RandomBound{ContinuousRectSearchSpace}}\n",
      "0.00 secs, 0 evals, 0 steps\n",
      "0.53 secs, 10 evals, 5 steps, improv/step: 0.200 (last = 0.2000), fitness=20537.922793722\n",
      "1.09 secs, 28 evals, 14 steps, improv/step: 0.286 (last = 0.3333), fitness=20488.210828280\n",
      "1.62 secs, 48 evals, 24 steps, improv/step: 0.458 (last = 0.7000), fitness=20488.210828280\n",
      "2.15 secs, 68 evals, 34 steps, improv/step: 0.441 (last = 0.4000), fitness=20488.210828280\n",
      "2.70 secs, 89 evals, 46 steps, improv/step: 0.435 (last = 0.4167), fitness=20488.210828280\n",
      "3.21 secs, 109 evals, 56 steps, improv/step: 0.429 (last = 0.4000), fitness=20488.210828280\n",
      "3.75 secs, 128 evals, 68 steps, improv/step: 0.426 (last = 0.4167), fitness=20488.210828280\n",
      "4.27 secs, 147 evals, 81 steps, improv/step: 0.420 (last = 0.3846), fitness=20463.752965770\n",
      "4.78 secs, 166 evals, 93 steps, improv/step: 0.398 (last = 0.2500), fitness=20463.752965770\n",
      "5.29 secs, 184 evals, 103 steps, improv/step: 0.408 (last = 0.5000), fitness=20463.752965770\n",
      "5.81 secs, 203 evals, 114 steps, improv/step: 0.386 (last = 0.1818), fitness=20463.752965770\n",
      "6.31 secs, 221 evals, 124 steps, improv/step: 0.395 (last = 0.5000), fitness=20463.752965770\n",
      "6.85 secs, 241 evals, 135 steps, improv/step: 0.400 (last = 0.4545), fitness=20463.752965770\n",
      "7.38 secs, 260 evals, 146 steps, improv/step: 0.397 (last = 0.3636), fitness=20463.752965770\n",
      "7.89 secs, 279 evals, 158 steps, improv/step: 0.405 (last = 0.5000), fitness=20463.752965770\n",
      "8.42 secs, 298 evals, 169 steps, improv/step: 0.408 (last = 0.4545), fitness=20463.752965770\n",
      "8.93 secs, 318 evals, 182 steps, improv/step: 0.418 (last = 0.5385), fitness=20463.752965770\n",
      "9.43 secs, 337 evals, 194 steps, improv/step: 0.412 (last = 0.3333), fitness=20463.752965770\n",
      "9.96 secs, 357 evals, 208 steps, improv/step: 0.404 (last = 0.2857), fitness=20463.752965770\n",
      "10.49 secs, 377 evals, 222 steps, improv/step: 0.405 (last = 0.4286), fitness=20463.752965770\n",
      "11.02 secs, 397 evals, 237 steps, improv/step: 0.401 (last = 0.3333), fitness=20463.752965770\n",
      "11.53 secs, 416 evals, 250 steps, improv/step: 0.424 (last = 0.8462), fitness=20463.752965770\n",
      "12.04 secs, 435 evals, 266 steps, improv/step: 0.414 (last = 0.2500), fitness=20463.752965770\n",
      "12.55 secs, 454 evals, 281 steps, improv/step: 0.399 (last = 0.1333), fitness=20463.751350150\n",
      "13.08 secs, 474 evals, 296 steps, improv/step: 0.399 (last = 0.4000), fitness=20463.751350150\n",
      "13.60 secs, 493 evals, 313 steps, improv/step: 0.403 (last = 0.4706), fitness=20463.751350150\n",
      "14.11 secs, 512 evals, 328 steps, improv/step: 0.402 (last = 0.4000), fitness=20463.751350150\n",
      "14.62 secs, 531 evals, 343 steps, improv/step: 0.394 (last = 0.2000), fitness=20463.751350150\n",
      "15.16 secs, 551 evals, 360 steps, improv/step: 0.397 (last = 0.4706), fitness=20463.751350150\n",
      "15.68 secs, 570 evals, 377 steps, improv/step: 0.403 (last = 0.5294), fitness=20463.751350150\n",
      "16.19 secs, 590 evals, 395 steps, improv/step: 0.395 (last = 0.2222), fitness=20463.751350150\n",
      "16.71 secs, 609 evals, 412 steps, improv/step: 0.398 (last = 0.4706), fitness=20450.994740883\n",
      "17.23 secs, 629 evals, 428 steps, improv/step: 0.402 (last = 0.5000), fitness=20443.600510571\n",
      "17.74 secs, 649 evals, 444 steps, improv/step: 0.396 (last = 0.2500), fitness=20443.600510571\n",
      "18.25 secs, 669 evals, 464 steps, improv/step: 0.390 (last = 0.2500), fitness=20441.289066618\n",
      "18.75 secs, 688 evals, 481 steps, improv/step: 0.385 (last = 0.2353), fitness=20441.289066618\n",
      "19.26 secs, 708 evals, 500 steps, improv/step: 0.382 (last = 0.3158), fitness=20441.289066618\n",
      "19.78 secs, 728 evals, 519 steps, improv/step: 0.378 (last = 0.2632), fitness=20441.289066618\n",
      "20.31 secs, 748 evals, 536 steps, improv/step: 0.369 (last = 0.1176), fitness=20441.289066618\n",
      "20.82 secs, 768 evals, 554 steps, improv/step: 0.372 (last = 0.4444), fitness=20441.289066618\n",
      "21.34 secs, 789 evals, 574 steps, improv/step: 0.368 (last = 0.2500), fitness=20441.289066618\n",
      "21.86 secs, 807 evals, 592 steps, improv/step: 0.365 (last = 0.2778), fitness=20441.289066618\n",
      "22.37 secs, 824 evals, 607 steps, improv/step: 0.359 (last = 0.1333), fitness=20441.289066618\n",
      "22.89 secs, 842 evals, 624 steps, improv/step: 0.354 (last = 0.1765), fitness=20441.289066618\n",
      "23.45 secs, 862 evals, 644 steps, improv/step: 0.351 (last = 0.2500), fitness=20441.289066618\n",
      "23.95 secs, 882 evals, 664 steps, improv/step: 0.348 (last = 0.2500), fitness=20441.289066618\n",
      "24.45 secs, 901 evals, 683 steps, improv/step: 0.346 (last = 0.2632), fitness=20441.289066618\n",
      "24.95 secs, 920 evals, 701 steps, improv/step: 0.344 (last = 0.2778), fitness=20441.289066618\n",
      "25.46 secs, 939 evals, 720 steps, improv/step: 0.342 (last = 0.2632), fitness=20441.289066618\n",
      "25.97 secs, 958 evals, 738 steps, improv/step: 0.339 (last = 0.2222), fitness=20441.289066618\n",
      "26.47 secs, 977 evals, 755 steps, improv/step: 0.336 (last = 0.2353), fitness=20441.289066618\n",
      "26.98 secs, 996 evals, 774 steps, improv/step: 0.336 (last = 0.3158), fitness=20441.289066618\n",
      "27.49 secs, 1015 evals, 792 steps, improv/step: 0.332 (last = 0.1667), fitness=20441.289066618\n",
      "28.01 secs, 1034 evals, 810 steps, improv/step: 0.328 (last = 0.1667), fitness=20441.289066618\n",
      "28.52 secs, 1053 evals, 829 steps, improv/step: 0.331 (last = 0.4211), fitness=20441.289066618\n",
      "29.03 secs, 1072 evals, 847 steps, improv/step: 0.327 (last = 0.1667), fitness=20441.289066618\n",
      "29.56 secs, 1092 evals, 867 steps, improv/step: 0.330 (last = 0.4500), fitness=20441.289066618\n",
      "30.07 secs, 1112 evals, 887 steps, improv/step: 0.326 (last = 0.1500), fitness=20441.289066618\n",
      "30.59 secs, 1131 evals, 906 steps, improv/step: 0.323 (last = 0.2105), fitness=20441.289066618\n",
      "31.09 secs, 1149 evals, 924 steps, improv/step: 0.324 (last = 0.3333), fitness=20441.289066618\n",
      "31.60 secs, 1168 evals, 943 steps, improv/step: 0.326 (last = 0.4211), fitness=20441.289066618\n",
      "32.12 secs, 1187 evals, 962 steps, improv/step: 0.323 (last = 0.2105), fitness=20441.289066618\n",
      "32.64 secs, 1205 evals, 980 steps, improv/step: 0.322 (last = 0.2778), fitness=20441.289066618\n",
      "33.16 secs, 1223 evals, 998 steps, improv/step: 0.320 (last = 0.1667), fitness=20441.289066618\n",
      "\n",
      "Optimization stopped after 1001 steps and 33.23 seconds\n",
      "Termination reason: Max number of steps (1000) reached\n",
      "Steps per second = 30.12\n",
      "Function evals per second = 36.90\n",
      "Improvements/step = 0.32000\n",
      "Total function evaluations = 1226\n",
      "\n",
      "\n",
      "Best candidate found: [-0.523283, 0.0568809]\n",
      "\n",
      "Fitness: 20441.289066618\n",
      "\n"
     ]
    }
   ],
   "source": [
    "SRange = [(-1.0, 1.0),(-3.0, log10(lsq_a1b1))];\n",
    "opts = bbsetup(bb_fn; Method = :adaptive_de_rand_1_bin_radiuslimited, SearchRange = SRange, NumDimensions = 2, MaxSteps = 1e3, PopulationSize = 100)\n",
    "res = bboptimize(opts);\n",
    "opt_par_a = [best_candidate(res)[1], 10^best_candidate(res)[2]];\n",
    "opt_par = [opt_par_a[1], opt_par_a[2], log10(lsq_a0b0/10^opt_par_a[1]), lsq_a1b1 - opt_par_a[2]];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Float64,1}:\n",
       " -0.5232830151756183\n",
       "  1.1399371707235226\n",
       "  0.8553967435361614\n",
       "  0.0168774917389225"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Single_lin (generic function with 1 method)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineage_data = num_sep_all_lins[e_gen]; # take the data for the first lineage, this gives the data form for Single_lin.\n",
    "lin_ic = v0_n0_th[e_gen]; # 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_n = [];\n",
    "    for (i,gen) in enumerate(lin_data)\n",
    "        ssa = SSA_deg_zero_log(1, inf_par, ics[i], length(gen)); # for each gen run the SSA with correct ics.\n",
    "        push!(SSA_data_n, ssa[1][:]);\n",
    "    end\n",
    "    return vcat(SSA_data_n)# return in the same order as the SSA.\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "simulator_function_conc (generic function with 1 method)"
      ]
     },
     "execution_count": 31,
     "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",
    "    pars = [var_params[1], var_params[2], var_params[3], var_params[4]];\n",
    "    lin_num = Single_lin(num_sep_all_lins[e_gen], pars, v0_n0_th[e_gen])\n",
    "    return hcat(vcat(lin_num...))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "fig, ax = PyPlot.subplots(2,1, sharex = true);\n",
    "\n",
    "ax[1].plot(vcat(num_gens...)); ax[1].set_title(\"\\\"Expt.\\\" data\"); ax[1].set_ylabel(L\"n(t)\");\n",
    "ax[2].plot(simulator_function_conc(opt_par)[:], color = \"g\"); ax[2].set_title(\"Inferred traj.\"); ax[2].set_ylabel(L\"n(t)\");\n",
    "ax[2].set_xlabel(L\"t/min\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's automate the procedure for all lineages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11\t12\t13\t14\t15\t16\t17\t18\t19\t20\t21\t22\t23\t24\t25\t26\t27\t28\t29\t30\t31\t32\t33\t34\t35\t36\t37\t38\t39\t40\t41\t42\t43\t44\t45\t46\t47\t48\t49\t50\t51\t52\t53\t54\t55\t56\t57\t58\t59\t60\t61\t62\t63\t64\t65\t"
     ]
    }
   ],
   "source": [
    "lin_opt_pars = [];\n",
    "for (x,lin) in enumerate(num_sep_all_lins)\n",
    "    # step 1: take lineage\n",
    "    e_gen = x;\n",
    "    num_gens = deepcopy(lin);\n",
    "    \n",
    "    # step 2: find the constrained parameters on the mean\n",
    "    comb_pars = [];\n",
    "    for i in 1:length(v0_n0_th[e_gen])\n",
    "        v0 = v0_n0_th[e_gen][i][1]; n0 = v0_n0_th[e_gen][i][2]; th = v0_n0_th[e_gen][i][3];\n",
    "        m(t,ab) = ((10 .^ab[1]*v0^(10 .^ab[2]))/(10 .^ab[2]*th)).*(exp.(10 .^ab[2]*th.*t) .-1) .+ n0;\n",
    "        ab0 = [0.1, 0.1];\n",
    "        n_data = num_gens[i];\n",
    "        fit = 10 .^curve_fit(m, LinRange(1,length(n_data), length(n_data)), n_data, ab0).param\n",
    "        push!(comb_pars,fit)\n",
    "    end\n",
    "    comb_pars = convert(Array{Float64,2},transpose(hcat(comb_pars...)));\n",
    "    \n",
    "    lsq_a0b0 = median(comb_pars[:,1]); lsq_a1b1 = median(comb_pars[:,2]);\n",
    "    \n",
    "    # step 3: do the MLE\n",
    "    SRange = [(-1.0, 1.0),(-5.0, log10(lsq_a1b1))];\n",
    "    opts = bbsetup(bb_fn; Method = :adaptive_de_rand_1_bin_radiuslimited, SearchRange = SRange, NumDimensions = 2, MaxSteps = 1e3, PopulationSize = 100, TraceMode = :silent)\n",
    "    res = bboptimize(opts);\n",
    "    opt_par_a = [best_candidate(res)[1], 10^best_candidate(res)[2]];\n",
    "    opt_par = [opt_par_a[1], opt_par_a[2], log10(lsq_a0b0/10^opt_par_a[1]), lsq_a1b1 - opt_par_a[2]];\n",
    "    \n",
    "    # step 4: store these optimal par values\n",
    "    push!(lin_opt_pars, opt_par)\n",
    "    print(x, \"\\t\")\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65×4 Array{Float64,2}:\n",
       " -0.598423  1.04763     1.00661   0.00685223\n",
       " -0.775439  1.07624     0.993156  0.140634\n",
       " -0.690572  1.08941     0.986519  0.0915509\n",
       " -0.679812  1.18936     0.953508  0.00859324\n",
       " -0.524639  0.53564     0.817415  0.706508\n",
       " -0.718206  1.15304     0.957043  0.053117\n",
       " -0.663908  1.28109     0.897263  0.0232343\n",
       " -0.437731  0.749639    0.69585   0.520051\n",
       " -0.763742  1.24598     1.03442   0.00512293\n",
       " -0.510873  0.822336    0.863924  0.186359\n",
       " -0.445137  0.271288    0.819927  0.72283\n",
       " -0.636251  0.964582    1.0143    0.0896196\n",
       " -0.630165  0.981002    0.927657  0.208391\n",
       "  ⋮                               \n",
       " -0.730604  1.17583     0.990224  0.0808243\n",
       " -0.690412  1.20441     1.01899   0.0282493\n",
       " -0.642394  1.07798     0.957256  0.114372\n",
       " -0.611927  1.04426     0.997763  0.00175918\n",
       " -0.654305  1.09874     1.00327   0.0547006\n",
       " -0.674162  0.973922    0.965421  0.236784\n",
       " -0.407889  0.588516    0.685137  0.732531\n",
       " -0.625879  0.956283    1.0015    0.0867607\n",
       " -0.655201  0.935183    0.958183  0.275295\n",
       " -0.650571  0.956285    0.911415  0.310508\n",
       " -0.38125   1.82995e-5  0.739227  1.00663\n",
       " -0.698972  1.16599     0.999111  0.100891"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_opt_pars_df = convert(Array{Float64,2}, transpose(hcat(lin_opt_pars...)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plots.PyPlotBackend()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots, LaTeXStrings, Statistics; pyplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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><th>a0b0</th><th>a1_plus_b1</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th></tr></thead><tbody><p>65 rows × 6 columns</p><tr><th>1</th><td>0.252102</td><td>1.04763</td><td>10.1534</td><td>0.00685223</td><td>2.55969</td><td>1.05448</td></tr><tr><th>2</th><td>0.167711</td><td>1.07624</td><td>9.84364</td><td>0.140634</td><td>1.65088</td><td>1.21688</td></tr><tr><th>3</th><td>0.203905</td><td>1.08941</td><td>9.69435</td><td>0.0915509</td><td>1.97673</td><td>1.18096</td></tr><tr><th>4</th><td>0.20902</td><td>1.18936</td><td>8.98479</td><td>0.00859324</td><td>1.878</td><td>1.19795</td></tr><tr><th>5</th><td>0.298786</td><td>0.53564</td><td>6.56772</td><td>0.706508</td><td>1.96234</td><td>1.24215</td></tr><tr><th>6</th><td>0.191335</td><td>1.15304</td><td>9.05822</td><td>0.053117</td><td>1.73315</td><td>1.20616</td></tr><tr><th>7</th><td>0.216816</td><td>1.28109</td><td>7.89338</td><td>0.0232343</td><td>1.71141</td><td>1.30432</td></tr><tr><th>8</th><td>0.36498</td><td>0.749639</td><td>4.96421</td><td>0.520051</td><td>1.81184</td><td>1.26969</td></tr><tr><th>9</th><td>0.172289</td><td>1.24598</td><td>10.8249</td><td>0.00512293</td><td>1.86502</td><td>1.25111</td></tr><tr><th>10</th><td>0.308409</td><td>0.822336</td><td>7.31011</td><td>0.186359</td><td>2.2545</td><td>1.00869</td></tr><tr><th>11</th><td>0.358809</td><td>0.271288</td><td>6.60582</td><td>0.72283</td><td>2.37022</td><td>0.994118</td></tr><tr><th>12</th><td>0.231073</td><td>0.964582</td><td>10.3346</td><td>0.0896196</td><td>2.38806</td><td>1.0542</td></tr><tr><th>13</th><td>0.234334</td><td>0.981002</td><td>8.46559</td><td>0.208391</td><td>1.98377</td><td>1.18939</td></tr><tr><th>14</th><td>0.411083</td><td>0.564376</td><td>6.11513</td><td>0.415185</td><td>2.51383</td><td>0.979562</td></tr><tr><th>15</th><td>0.232892</td><td>1.05265</td><td>10.8411</td><td>0.0179753</td><td>2.5248</td><td>1.07063</td></tr><tr><th>16</th><td>0.274557</td><td>0.910167</td><td>8.8944</td><td>0.131052</td><td>2.44202</td><td>1.04122</td></tr><tr><th>17</th><td>0.299444</td><td>0.994625</td><td>7.2799</td><td>0.0973464</td><td>2.17992</td><td>1.09197</td></tr><tr><th>18</th><td>0.199423</td><td>0.973302</td><td>8.97397</td><td>0.203765</td><td>1.78962</td><td>1.17707</td></tr><tr><th>19</th><td>0.175404</td><td>1.29343</td><td>9.4865</td><td>0.0581933</td><td>1.66397</td><td>1.35163</td></tr><tr><th>20</th><td>0.282963</td><td>1.13168</td><td>6.92933</td><td>0.0214545</td><td>1.96075</td><td>1.15313</td></tr><tr><th>21</th><td>0.282037</td><td>0.842577</td><td>8.23236</td><td>0.335142</td><td>2.32183</td><td>1.17772</td></tr><tr><th>22</th><td>0.303884</td><td>0.948693</td><td>7.09029</td><td>0.17856</td><td>2.15463</td><td>1.12725</td></tr><tr><th>23</th><td>0.323927</td><td>1.18528</td><td>6.60876</td><td>0.000258994</td><td>2.14076</td><td>1.18554</td></tr><tr><th>24</th><td>0.211779</td><td>1.21755</td><td>9.05276</td><td>0.0920782</td><td>1.91718</td><td>1.30963</td></tr><tr><th>25</th><td>0.306887</td><td>1.00475</td><td>8.03479</td><td>0.0691407</td><td>2.46577</td><td>1.07389</td></tr><tr><th>26</th><td>0.135783</td><td>1.3319</td><td>10.5303</td><td>0.0421377</td><td>1.42984</td><td>1.37404</td></tr><tr><th>27</th><td>0.283531</td><td>0.854052</td><td>6.00994</td><td>0.403666</td><td>1.704</td><td>1.25772</td></tr><tr><th>28</th><td>0.35186</td><td>0.551237</td><td>4.98926</td><td>0.696704</td><td>1.75552</td><td>1.24794</td></tr><tr><th>29</th><td>0.290719</td><td>0.865552</td><td>6.46604</td><td>0.347655</td><td>1.8798</td><td>1.21321</td></tr><tr><th>30</th><td>0.165327</td><td>1.21945</td><td>12.1775</td><td>0.00501148</td><td>2.01327</td><td>1.22446</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccc}\n",
       "\t& alpha\\_0 & alpha\\_1 & beta\\_0 & beta\\_1 & a0b0 & a1\\_plus\\_b1\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.252102 & 1.04763 & 10.1534 & 0.00685223 & 2.55969 & 1.05448 \\\\\n",
       "\t2 & 0.167711 & 1.07624 & 9.84364 & 0.140634 & 1.65088 & 1.21688 \\\\\n",
       "\t3 & 0.203905 & 1.08941 & 9.69435 & 0.0915509 & 1.97673 & 1.18096 \\\\\n",
       "\t4 & 0.20902 & 1.18936 & 8.98479 & 0.00859324 & 1.878 & 1.19795 \\\\\n",
       "\t5 & 0.298786 & 0.53564 & 6.56772 & 0.706508 & 1.96234 & 1.24215 \\\\\n",
       "\t6 & 0.191335 & 1.15304 & 9.05822 & 0.053117 & 1.73315 & 1.20616 \\\\\n",
       "\t7 & 0.216816 & 1.28109 & 7.89338 & 0.0232343 & 1.71141 & 1.30432 \\\\\n",
       "\t8 & 0.36498 & 0.749639 & 4.96421 & 0.520051 & 1.81184 & 1.26969 \\\\\n",
       "\t9 & 0.172289 & 1.24598 & 10.8249 & 0.00512293 & 1.86502 & 1.25111 \\\\\n",
       "\t10 & 0.308409 & 0.822336 & 7.31011 & 0.186359 & 2.2545 & 1.00869 \\\\\n",
       "\t11 & 0.358809 & 0.271288 & 6.60582 & 0.72283 & 2.37022 & 0.994118 \\\\\n",
       "\t12 & 0.231073 & 0.964582 & 10.3346 & 0.0896196 & 2.38806 & 1.0542 \\\\\n",
       "\t13 & 0.234334 & 0.981002 & 8.46559 & 0.208391 & 1.98377 & 1.18939 \\\\\n",
       "\t14 & 0.411083 & 0.564376 & 6.11513 & 0.415185 & 2.51383 & 0.979562 \\\\\n",
       "\t15 & 0.232892 & 1.05265 & 10.8411 & 0.0179753 & 2.5248 & 1.07063 \\\\\n",
       "\t16 & 0.274557 & 0.910167 & 8.8944 & 0.131052 & 2.44202 & 1.04122 \\\\\n",
       "\t17 & 0.299444 & 0.994625 & 7.2799 & 0.0973464 & 2.17992 & 1.09197 \\\\\n",
       "\t18 & 0.199423 & 0.973302 & 8.97397 & 0.203765 & 1.78962 & 1.17707 \\\\\n",
       "\t19 & 0.175404 & 1.29343 & 9.4865 & 0.0581933 & 1.66397 & 1.35163 \\\\\n",
       "\t20 & 0.282963 & 1.13168 & 6.92933 & 0.0214545 & 1.96075 & 1.15313 \\\\\n",
       "\t21 & 0.282037 & 0.842577 & 8.23236 & 0.335142 & 2.32183 & 1.17772 \\\\\n",
       "\t22 & 0.303884 & 0.948693 & 7.09029 & 0.17856 & 2.15463 & 1.12725 \\\\\n",
       "\t23 & 0.323927 & 1.18528 & 6.60876 & 0.000258994 & 2.14076 & 1.18554 \\\\\n",
       "\t24 & 0.211779 & 1.21755 & 9.05276 & 0.0920782 & 1.91718 & 1.30963 \\\\\n",
       "\t25 & 0.306887 & 1.00475 & 8.03479 & 0.0691407 & 2.46577 & 1.07389 \\\\\n",
       "\t26 & 0.135783 & 1.3319 & 10.5303 & 0.0421377 & 1.42984 & 1.37404 \\\\\n",
       "\t27 & 0.283531 & 0.854052 & 6.00994 & 0.403666 & 1.704 & 1.25772 \\\\\n",
       "\t28 & 0.35186 & 0.551237 & 4.98926 & 0.696704 & 1.75552 & 1.24794 \\\\\n",
       "\t29 & 0.290719 & 0.865552 & 6.46604 & 0.347655 & 1.8798 & 1.21321 \\\\\n",
       "\t30 & 0.165327 & 1.21945 & 12.1775 & 0.00501148 & 2.01327 & 1.22446 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "65×6 DataFrame\n",
       "│ Row │ alpha_0  │ alpha_1    │ beta_0  │ beta_1     │ a0b0    │ a1_plus_b1 │\n",
       "│     │ \u001b[90mFloat64\u001b[39m  │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m    │\n",
       "├─────┼──────────┼────────────┼─────────┼────────────┼─────────┼────────────┤\n",
       "│ 1   │ 0.252102 │ 1.04763    │ 10.1534 │ 0.00685223 │ 2.55969 │ 1.05448    │\n",
       "│ 2   │ 0.167711 │ 1.07624    │ 9.84364 │ 0.140634   │ 1.65088 │ 1.21688    │\n",
       "│ 3   │ 0.203905 │ 1.08941    │ 9.69435 │ 0.0915509  │ 1.97673 │ 1.18096    │\n",
       "│ 4   │ 0.20902  │ 1.18936    │ 8.98479 │ 0.00859324 │ 1.878   │ 1.19795    │\n",
       "│ 5   │ 0.298786 │ 0.53564    │ 6.56772 │ 0.706508   │ 1.96234 │ 1.24215    │\n",
       "│ 6   │ 0.191335 │ 1.15304    │ 9.05822 │ 0.053117   │ 1.73315 │ 1.20616    │\n",
       "│ 7   │ 0.216816 │ 1.28109    │ 7.89338 │ 0.0232343  │ 1.71141 │ 1.30432    │\n",
       "│ 8   │ 0.36498  │ 0.749639   │ 4.96421 │ 0.520051   │ 1.81184 │ 1.26969    │\n",
       "│ 9   │ 0.172289 │ 1.24598    │ 10.8249 │ 0.00512293 │ 1.86502 │ 1.25111    │\n",
       "│ 10  │ 0.308409 │ 0.822336   │ 7.31011 │ 0.186359   │ 2.2545  │ 1.00869    │\n",
       "⋮\n",
       "│ 55  │ 0.20398  │ 1.20441    │ 10.447  │ 0.0282493  │ 2.13099 │ 1.23265    │\n",
       "│ 56  │ 0.227828 │ 1.07798    │ 9.06266 │ 0.114372   │ 2.06472 │ 1.19236    │\n",
       "│ 57  │ 0.244384 │ 1.04426    │ 9.94862 │ 0.00175918 │ 2.43129 │ 1.04602    │\n",
       "│ 58  │ 0.221664 │ 1.09874    │ 10.0755 │ 0.0547006  │ 2.23338 │ 1.15344    │\n",
       "│ 59  │ 0.211757 │ 0.973922   │ 9.23465 │ 0.236784   │ 1.9555  │ 1.21071    │\n",
       "│ 60  │ 0.39094  │ 0.588516   │ 4.84325 │ 0.732531   │ 1.89342 │ 1.32105    │\n",
       "│ 61  │ 0.236658 │ 0.956283   │ 10.0345 │ 0.0867607  │ 2.37475 │ 1.04304    │\n",
       "│ 62  │ 0.221207 │ 0.935183   │ 9.08203 │ 0.275295   │ 2.00901 │ 1.21048    │\n",
       "│ 63  │ 0.223578 │ 0.956285   │ 8.15483 │ 0.310508   │ 1.82324 │ 1.26679    │\n",
       "│ 64  │ 0.415671 │ 1.82995e-5 │ 5.48564 │ 1.00663    │ 2.28022 │ 1.00665    │\n",
       "│ 65  │ 0.199999 │ 1.16599    │ 9.97955 │ 0.100891   │ 1.9959  │ 1.26688    │"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_pars = DataFrame(alpha_0 = 10 .^lin_opt_pars_df[:,1], alpha_1 = lin_opt_pars_df[:,2], beta_0 = 10 .^lin_opt_pars_df[:,3], beta_1 = lin_opt_pars_df[:,4], a0b0 = 10 .^(lin_opt_pars_df[:,1]+lin_opt_pars_df[:,3]), a1_plus_b1 = lin_opt_pars_df[:,2]+lin_opt_pars_df[:,4])\n",
    "#using CSV; CSV.write(\"Tan_MLE_inf/df_27_tan_expt.csv\", df_pars)\n",
    "using CSV; df_pars = CSV.read(\"Tan_MLE_inf/df_27_tan_expt.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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.hist(log10.(df_pars[:,2]./df_pars[:,4]), alpha = 0.5, bins = LinRange(-2.0, 5, 20));\n",
    "PyPlot.xlabel(L\"log_{10}(\\alpha_1/\\beta_1)\"); PyPlot.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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.hist((df_pars[:,1]+df_pars[:,3]), alpha = 0.5, color =\"r\");\n",
    "PyPlot.xlabel(L\"\\alpha_0\\beta_0\"); PyPlot.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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.hist(df_pars[:,2]+df_pars[:,4], alpha = 0.5, color =\"g\");\n",
    "PyPlot.xlabel(L\"\\alpha_1+\\beta_1\"); PyPlot.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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.hist(df_pars[:,2], alpha = 0.5, color =\"purple\", bins = LinRange(0.0, 2, 30), label=L\"\\alpha_1\", density = true);\n",
    "PyPlot.hist(df_pars[:,4], alpha = 0.5, color =\"g\", bins = LinRange(0.0, 2, 30), label=L\"\\beta_1\", density = true);\n",
    "PyPlot.plot([median(df_pars[:,2]),median(df_pars[:,2])],[0,80], color = \"purple\", label = L\"\\alpha_1\\;\\; median\")\n",
    "PyPlot.plot([median(df_pars[:,4]),median(df_pars[:,4])],[0,80], color = \"g\", label = L\"\\beta_1\\;\\; median\")\n",
    "\n",
    "PyPlot.xlabel(L\"\\alpha_1, \\beta_1\"); PyPlot.ylabel(\"Frequency\"); PyPlot.legend(); PyPlot.ylim([0,11.0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zrV27VjfccIMk6f7779fSpUuv6fu6ipEbAADczGq1Kjo6Wt27d9eXX36phQsXauvWrZo5c6bKysqUlJSk7du3KyMjQy+//HKlS0hX2++wYcMcwUaScnNzNWLECKe5MLm5uYqIiJAk5eTk6LPPPtO8efN03XXXOZZHH31U3t7ejj7bt29XUlKS0/F8fHwcgcZqtTqFm5ycHE2YMEHdu3dX8+bNdd111+n9999X27ZtHX1sNpvuvfdeR7CRpB9++EGdOnW61lPpEkZuAAANQoBPgM5Nr/8JuQE+ATXexmq1auTIkQoKClJcXJzi4uJ06NAh7dmzR3v37lVUVJTCw8MlSYmJifrkk080YsSIavdrs9mUnJxc6VgTJkxwrBcXF+vIkSOO+T02m00tWrSocp6Lv7+/Yx/e3t7q0aOH0+cZGRmOW79tNpuGDRsmSTp16pTi4uKUkJCg1NRUhYeHq6KiQrGxsU7zimw2m6ZOneq0z8zMTPXv37/a71obhBsAQINgsVhqfHnICGVlZTp48KAiIyOd2m02m+655x6dOHHCEWwkqW3btsrLy6t2v0VFRfrxxx8VHR3taMvJydHp06ed2g4ePKjy8nLHKIuPj4/Onj2r0NDQK97p1KRJE1VUVKi0tNQxmrN582YdPHhQvXv3dhz78j43b96ssrIyvfvuu7JYLJKkxYsXq7S01BFuCgsLlZOT41SbdClIPf3009V+39rgshQAAG506NAhFRcX64UXXlBmZqYOHz6sSZMmKTs7W0888USVE5QvB4SrsdlsatKkiXr27OlouzzJ98Ybb3Tq17FjRwUFBUmSbr31VgUHB2vUqFGyWq06evSotmzZookTJzq2iYmJkY+Pj6ZMmaIffvhBGzdu1BNPPCFJ6tWrl2w2m7y8vBQVFSXp0i3hRUVF2rhxo7777julpqZq1qxZCg8Pd1yCurzNLy9lnTlzptZ3jVWHcAMAgBtZrVbHKEm/fv3Uv39/HTt2TDt27FBoaKjCw8OdRmqOHz+u0NDQavdrs9kUGRnpuJQkXbrE8+/h4XK/fw8PLVq00ObNm3XmzBkNGDBAffr00YwZM5wCUVhYmFasWKEPP/xQsbGxWrRokcaMGaOWLVsqPDzccezLD/AbOnSoxo4dq1GjRqlv377Ky8vTQw89VOmSVFX1/jKM1QWL3ZV73BqwoqIiNWvWTIWFhQoODq6TY3w24zPtTtktSeo7va+8fL00cNbAOjkWAEjS+dLzjjuJXLm7x9MUFxcrOztbERERatq0qdHl1MiUKVP0/fffa8OGDVV+XlZWpq5duyotLU3BwcHq06eP9uzZo5CQkHqu1PNc7e9ek3+/mXMDAIAbWa1W3XHHHVf83NvbWwsWLFBCQoIqKiqUnJxMsHEzwg0AAG5ks9n05JNPXrXPsGHDHHcewf0INwAAuNFPP/1kdAmNHhOKAQCAqRBuAACAqRBuAACAqRBuAACAqRgablJSUnTLLbcoKChIrVq10v3336/Dhw9fdZu0tDRZLJZKy7+/hRQAADRehoabnTt3avz48dqzZ4+2bdumsrIyDR48WOfPn69228OHDys/P9+xdO7cuR4qBgAAns7QW8G3bNnitL5q1Sq1atVK+/fvr/aNoa1atdL1119fh9UBAICGyKPm3BQWFkq69B6M6kRHRys0NFSDBg3Sjh07rtivpKRERUVFTgsAADAvjwk3drtdSUlJ6tu3r7p3737FfqGhoVq+fLnWrVun9evXq0uXLho0aJB27dpVZf+UlBQ1a9bMsbRr166uvgIAAPAAHvOE4gkTJujAgQPavXv3Vft16dJFXbp0cazHx8fr2LFjmj9/fpWXsqZPn66kpCTHelFREQEHABqwtFlp9XYsXnrcMHnEyM0f//hHbdy4UTt27FDbtm1rvP1tt92m7777rsrP/Pz8FBwc7LQAAADzMjTc2O12TZgwQevXr9f27dsVERHh0n4yMzMVGhrq5uoAAHDd3r17NXDgQPn7+ysyMlJfffWVli9fzgsz64Ghl6XGjx+vd955R//4xz8UFBSkgoICSVKzZs3k7+8v6dJlpby8PL399tuSpIULF+rGG29UVFSUSktLtWbNGq1bt07r1q0z7HsAAPDv9uzZo4SEBM2cOVNvvPGGpk6dqlmzZunIkSN6//33jS7P9AwduVm6dKkKCws1cOBAhYaGOpa1a9c6+uTn5ys3N9exXlpaqilTpqhnz57q16+fdu/erY8++kgPPvigEV8BAIBKkpKSNHz4cE2bNk2dO3fWww8/rC1btqhHjx6Kjo6WJG3atEldunRR586dtWLFCoMrNhdDR27sdnu1fVavXu20npycrOTk5DqqCACA2jl+/LjS09M1b948R5uvr6/sdrtmz54tSSorK1NSUpJ27Nih4OBg9enTRw8++OA1PQoF1fOICcUAAJhFVlaWJCk2NtbRdvjwYcXFxalHjx6SLs3HiYqKUnh4uIKCgpSYmKhPPvnEkHrNiHADAIAbFRYWysvLy7F++vRpzZ07V35+fo62EydOKDw83LHetm1b5eXl1WudZka4AQDAjXr37q3y8nLNnTtXhw4d0ogRI9ShQwdlZWUpJydHUtXTMiwWS32XalqEGwAA3KhTp06aM2eOFi1a5HhV0NatW9WuXTvdeeedkqTw8HCnkZrjx4/zSBM38pgnFAMAcC0awlOD//znP+vPf/6zU9v+/fsd/x0XF6dvvvlGeXl5Cg4O1ubNm/X888/Xd5mmRbgBAKCeeXt7a8GCBUpISFBFRYWSk5MVEhJidFmmQbgBAMAAw4YN42nFdYQ5NwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAA1JG33npL3bp1U0BAgCIjI7Vp0yajS2oUeHEmAKBBmZU2q/6ONdD1Y23YsEHjx4/X8uXLddttt+nVV1/VuHHjdPz4cfcViCoxcgMAQB1YsGCBkpKSNHLkSHXs2FGJiYk6d+6c0WU1CoQbAADc7OzZs0pPT9fQoUMdbVu2bFHv3r2NK6oR4bIUAABuZrPZZLFY1LNnT124cEH/8z//o1dffVXr1q0zurRGgZEbAADczGq1KjIyUlarVYGBgfr973+vX//6146RnE2bNqlLly7q3LmzVqxYYXC15kO4AQDAzaxWq6Kjo9W9e3d9+eWXWrhwobZu3aqZM2eqrKxMSUlJ2r59uzIyMvTyyy/r9OnTRpdsKoQbAADc7HK4CQoKUlxcnCZOnKhRo0Zpz5492rt3r6KiohQeHq6goCAlJibqk08+MbpkUyHcAADgRmVlZTp48KAiIyOd2m02m/r166cTJ04oPDzc0d62bVvl5eXVd5mmxoRiAADc6NChQyouLtYLL7yg0NBQBQQEaOnSpcrOztYTTzyh3bt3V9rGYrEYUKl5EW4AAA1KbR6sVx+sVqtCQ0MVGBiofv36KTAwUH379tWOHTsUGhqq8PBwp5Ga48eP69ZbbzWwYvMh3AAA4EZWq1W33nqrNmzYUOXncXFx+uabb5SXl6fg4GBt3rxZzz//fD1XaW6EGwAA3MhqteqOO+644ufe3t5asGCBEhISVFFRoeTkZIWEhNRjheZHuAEAwI1sNpuefPLJq/YZNmyYhg0bVk8VNT6EGwAA3Oinn34yuoRGj1vBAQCAqRBuAACAqRBuAACAqRBuAAAey263G10C6pG7/t6EGwCAx/Hx8ZEkXbhwweBKUJ9KS0slSV5eXrXaD3dLAQA8jpeXl66//nqdPHlSkhQQEMArCkyuoqJCP/30kwICAuTtXbt4QrgBAHikNm3aSJIj4MD8mjRpovbt29c6yBJuAAAeyWKxKDQ0VK1atdLFixeNLgf1wNfXV02a1H7GDOEGAODRvLy8aj0HA40LE4oBAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG7qUdqsNKNLAADA9AwNNykpKbrlllsUFBSkVq1a6f7779fhw4er3W7nzp2KiYlR06ZN1bFjRy1btqweqgUAAA2BoeFm586dGj9+vPbs2aNt27aprKxMgwcP1vnz56+4TXZ2thITE9WvXz9lZmZqxowZevrpp7Vu3bp6rBwAAHgqQ1+/sGXLFqf1VatWqVWrVtq/f7/69+9f5TbLli1T+/bttXDhQklS165dtW/fPs2fP1/Dhw+v65IBAICH86g5N4WFhZKkFi1aXLFPenq6Bg8e7NR29913a9++fVW+WK2kpERFRUVOCwAAMC+PCTd2u11JSUnq27evunfvfsV+BQUFat26tVNb69atVVZWplOnTlXqn5KSombNmjmWdu3aub12AADgOTwm3EyYMEEHDhzQu+++W21fi8XitG6326tsl6Tp06ersLDQsRw7dsw9BQMAAI9k6Jyby/74xz9q48aN2rVrl9q2bXvVvm3atFFBQYFT28mTJ+Xt7a2QkJBK/f38/OTn5+fWegEAgOcydOTGbrdrwoQJWr9+vbZv366IiIhqt4mPj9e2bduc2rZu3arY2Fj5+PjUVakAAKCBMDTcjB8/XmvWrNE777yjoKAgFRQUqKCgQP/3f//n6DN9+nSNHj3asT5u3Djl5OQoKSlJWVlZevPNN7Vy5UpNmTLFiK8AAAA8jKHhZunSpSosLNTAgQMVGhrqWNauXevok5+fr9zcXMd6RESENm/erLS0NPXu3Vt/+ctf9Morr3AbOAAAkGTwnJvLE4GvZvXq1ZXaBgwYoIyMjDqoCAAANHQec7cUAACAOxBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqbgUbrKzs91dBwAAgFu4FG46deqkhIQErVmzRsXFxe6uCQAAwGUuhRubzabo6GhNnjxZbdq00R/+8Aft3bvX3bUBAADUmEvhpnv37kpNTVVeXp5WrVqlgoIC9e3bV1FRUUpNTdVPP/3k7joBAACuSa0mFHt7e+uBBx7Q+++/r5dfflnff/+9pkyZorZt22r06NHKz893V50AAADXpFbhZt++fXrqqacUGhqq1NRUTZkyRd9//722b9+uvLw83Xfffe6qEwAA4Jp4u7JRamqqVq1apcOHDysxMVFvv/22EhMT1aTJpawUERGh119/XZGRkW4tFgAAoDouhZulS5fqscce0+9+9zu1adOmyj7t27fXypUra1UcAABATbkUbr777rtq+/j6+mrMmDGu7B4AAMBlLs25WbVqlf72t79Vav/b3/6mt956q9ZFAQAAuMqlcPPSSy+pZcuWldpbtWqlF198sdZFAQAAuMqlcJOTk6OIiIhK7R06dFBubm6tiwIAAHCVS+GmVatWOnDgQKV2m82mkJCQWhcFAADgKpfCzcMPP6ynn35aO3bsUHl5ucrLy7V9+3ZNnDhRDz/8sLtrBAAAuGYu3S31wgsvKCcnR4MGDZK396VdVFRUaPTo0cy5AQAAhnIp3Pj6+mrt2rX6y1/+IpvNJn9/f/Xo0UMdOnRwd30AAAA14lK4uezmm2/WzTff7K5aAAAAas2lcFNeXq7Vq1frs88+08mTJ1VRUeH0+fbt291SHAAAQE25FG4mTpyo1atXa+jQoerevbssFou76wIAAHCJS+Hmvffe0/vvv6/ExER31wMAAFArLt0K7uvrq06dOrm7FgAAgFpzKdxMnjxZixYtkt1ud3c9AAAAteLSZandu3drx44d+vjjjxUVFSUfHx+nz9evX++W4gAAAGrKpXBz/fXX64EHHnB3LQAAALXmUrhZtWqVu+sAAABwC5fm3EhSWVmZPv30U73++us6e/asJOnEiRM6d+6c24oDAACoKZdGbnJycnTPPfcoNzdXJSUluuuuuxQUFKS5c+equLhYy5Ytc3edAAAA18SlkZuJEycqNjZWZ86ckb+/v6P9gQce0Geffea24gAAAGrK5bul/vnPf8rX19epvUOHDsrLy3NLYQAAAK5waeSmoqJC5eXlldqPHz+uoKCga97Prl27dO+99yosLEwWi0V///vfr9o/LS1NFoul0nLo0KGafoU6kzYrzS19AACAa1wKN3fddZcWLlzoWLdYLDp37pxmzpxZo1cynD9/Xr169dJrr71Wo+MfPnxY+fn5jqVz58412h4AAJiXS5el/uu//ksJCQnq1q2biouLNXLkSH333Xdq2bKl3n333Wvez5AhQzRkyJAaH79Vq1a6/vrra7wdAAAwP5fCTVhYmKxWq959911lZGSooqJCY8eO1SOPPOI0wbiuREdHq7i4WN26ddNzzz2nhISEK/YtKSlRSUmJY72oqKjO6wMAAMZxKdxIkr+/vx577DE99thj7qznqkJDQ7V8+XLFxMSopKRE//3f/61BgwYpLS1N/fv3r3KblJQUzZ49u95qBAAAxnIp3Lz99ttX/Xz06NEuFVOdLl26qEuXLo71+Ph4HTt2TPPnz79iuJk+fbqSkpIc60VFRWrXrl2d1AcAAIznUriZOHGi0/rFixd14cIF+fr6KiAgoM7CTVVuu+02rVmz5oqf+/n5yc/Pr97qAQAAxnLpbqkzZ844LefOndPhw4fVt2/fGk0odofMzEyFhobW6zEBAIDncnnOzS917txZL730kh599NFrfu7MuXPndPToUcd6dna2rFarWrRoofbt22v69OnKy8tzXAZbuHChbrzxRkVFRam0tFRr1qzRunXrtG7dOnd9DQAA0MC5LdxIkpeXl06cOHHN/fft2+d0p9PluTFjxozR6tWrlZ+fr9zcXMfnpaWlmjJlivLy8uTv76+oqCh99NFHNXq2DgAAMDeXws3GjRud1u12u/Lz8/Xaa6/pjjvuuOb9DBw4UHa7/Yqfr1692mk9OTlZycnJNaoVAAA0Li6Fm/vvv99p3WKx6IYbbtCvfvUrLViwwB11AQAAuMSlcFNRUeHuOgAAANzCpbulAAAAPJVLIzf//lC86qSmprpyCAAAAJe4FG4yMzOVkZGhsrIyxxODjxw5Ii8vL/Xp08fRz2KxuKdKAACAa+RSuLn33nsVFBSkt956S82bN5d06cF+v/vd79SvXz9NnjzZrUUCAABcK5fm3CxYsEApKSmOYCNJzZs31wsvvMDdUgAAwFAuhZuioiL97//+b6X2kydP6uzZs7UuCgAAwFUuhZsHHnhAv/vd7/TBBx/o+PHjOn78uD744AONHTtWDz74oLtrBAAAuGYuzblZtmyZpkyZokcffVQXL168tCNvb40dO1bz5s1za4EAAAA14VK4CQgI0JIlSzRv3jx9//33stvt6tSpkwIDA91dHwAAQI3U6iF++fn5ys/P180336zAwMCrvicKAACgPrgUbn7++WcNGjRIN998sxITE5Wfny9Jevzxx7kNHAAAGMqlcPPMM8/Ix8dHubm5CggIcLT/9re/1ZYtW9xWHAAAQE25NOdm69at+uSTT9S2bVun9s6dOysnJ8cthQEAALjCpZGb8+fPO43YXHbq1Cn5+fnVuigAAABXuRRu+vfvr7ffftuxbrFYVFFRoXnz5ikhIcFtxQEAANSUS5el5s2bp4EDB2rfvn0qLS1VcnKyDh48qNOnT+uf//ynu2sEAAC4Zi6N3HTr1k0HDhxQXFyc7rrrLp0/f14PPvigMjMzddNNN7m7RgAAgGtW45GbixcvavDgwXr99dc1e/bsuqgJAADAZTUeufHx8dE333wji8VSF/UAAADUikuXpUaPHq2VK1e6uxYAAIBac2lCcWlpqVasWKFt27YpNja20julUlNT3VIcAABATdUo3Pzwww+68cYb9c0336hPnz6SpCNHjjj14XIVAAAwUo3CTefOnZWfn68dO3ZIuvS6hVdeeUWtW7euk+IAAABqqkZzbn751u+PP/5Y58+fd2tBAAAAteHShOLLfhl2AAAAjFajcGOxWCrNqWGODQAA8CQ1mnNjt9v1n//5n46XYxYXF2vcuHGV7pZav369+yoEAACogRqFmzFjxjitP/roo24tBgAAoLZqFG5WrVpVV3UAAAC4Ra0mFAMAAHgawg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVQ8PNrl27dO+99yosLEwWi0V///vfq91m586diomJUdOmTdWxY0ctW7as7gsFAAANhqHh5vz58+rVq5dee+21a+qfnZ2txMRE9evXT5mZmZoxY4aefvpprVu3ro4rBQAADYW3kQcfMmSIhgwZcs39ly1bpvbt22vhwoWSpK5du2rfvn2aP3++hg8fXkdVukfarDSjSwAAoFFoUHNu0tPTNXjwYKe2u+++W/v27dPFixcNqgoAAHgSQ0duaqqgoECtW7d2amvdurXKysp06tQphYaGVtqmpKREJSUljvWioqI6rxMAABinQY3cSJLFYnFat9vtVbZflpKSombNmjmWdu3a1XmNAADAOA0q3LRp00YFBQVObSdPnpS3t7dCQkKq3Gb69OkqLCx0LMeOHauPUgEAgEEa1GWp+Ph4ffjhh05tW7duVWxsrHx8fKrcxs/PT35+fvVRHgAA8ACGjtycO3dOVqtVVqtV0qVbva1Wq3JzcyVdGnUZPXq0o/+4ceOUk5OjpKQkZWVl6c0339TKlSs1ZcoUI8oHAAAeyNCRm3379ikhIcGxnpSUJEkaM2aMVq9erfz8fEfQkaSIiAht3rxZzzzzjBYvXqywsDC98sorHn8bOAAAqD+GhpuBAwc6JgRXZfXq1ZXaBgwYoIyMjDqsCgAANGQNakIxAABAdQg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAwPN0uWLFFERISaNm2qmJgYff7551fsm5aWJovFUmk5dOhQPVYMAAA8maHhZu3atZo0aZKeffZZZWZmql+/fhoyZIhyc3Ovut3hw4eVn5/vWDp37lxPFQMAAE9naLhJTU3V2LFj9fjjj6tr165auHCh2rVrp6VLl151u1atWqlNmzaOxcvLq54qBgAAns6wcFNaWqr9+/dr8ODBTu2DBw/WF198cdVto6OjFRoaqkGDBmnHjh1X7VtSUqKioiKnxUhps9IMPT4AAGZnWLg5deqUysvL1bp1a6f21q1bq6CgoMptQkNDtXz5cq1bt07r169Xly5dNGjQIO3ateuKx0lJSVGzZs0cS7t27dz6PQAAgGfxNroAi8XitG632yu1XdalSxd16dLFsR4fH69jx45p/vz56t+/f5XbTJ8+XUlJSY71oqIiAg4AACZm2MhNy5Yt5eXlVWmU5uTJk5VGc67mtttu03fffXfFz/38/BQcHOy0AAAA8zIs3Pj6+iomJkbbtm1zat+2bZtuv/32a95PZmamQkND3V0eAABooAy9LJWUlKRRo0YpNjZW8fHxWr58uXJzczVu3DhJly4p5eXl6e2335YkLVy4UDfeeKOioqJUWlqqNWvWaN26dVq3bp2RXwMAAHgQQ8PNb3/7W/3888+aM2eO8vPz1b17d23evFkdOnSQJOXn5zs986a0tFRTpkxRXl6e/P39FRUVpY8++kiJiYlGfQUAAOBhDJ9Q/NRTT+mpp56q8rPVq1c7rScnJys5ObkeqgIAAA2V4a9fAAAAcCfCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBVvowsAAHiOWWmz6v+YA+v/mDA3Rm4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpcLcUgAbFiLt5jNJY7iLiDi24GyM3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVLgVHAA8VE1ukS4tL3X8918//6t8vXzroCKgYWDkBgAAmAojN4BJ8CA0ALiEkRsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqTCgG4LLG9IZuAA0HIzcAAMBUGLkBADQ6Ro068viE+sHIDQAAMBXCDQAAMBXDw82SJUsUERGhpk2bKiYmRp9//vlV++/cuVMxMTFq2rSpOnbsqGXLltVTpQAAoCEwdM7N2rVrNWnSJC1ZskR33HGHXn/9dQ0ZMkTffvut2rdvX6l/dna2EhMT9cQTT2jNmjX65z//qaeeeko33HCDhg8fbsA3AADg2jWWOwyNnltk6MhNamqqxo4dq8cff1xdu3bVwoUL1a5dOy1durTK/suWLVP79u21cOFCde3aVY8//rgee+wxzZ8/v54rBwAAnsqwkZvS0lLt379f06ZNc2ofPHiwvvjiiyq3SU9P1+DBg53a7r77bq1cuVIXL16Uj49PpW1KSkpUUlLiWC8sLJQkFRUV1fYrVOl8yXmVl5arWMWOdS+7V6V+dXV8NF4l50uq7wTTKi0v1f/72VHJ+RLZvezGFoRGrS7+jbu8T7u9+v9tGxZuTp06pfLycrVu3dqpvXXr1iooKKhym4KCgir7l5WV6dSpUwoNDa20TUpKimbPnl2pvV27drWovgZSr9D+Uv0cHkDjk3rFHx6gfrxUh//InT17Vs2aNbtqH8Ofc2OxWJzW7XZ7pbbq+lfVftn06dOVlJTkWK+oqNDp06cVEhJy1eNUp6ioSO3atdOxY8cUHBzs8n7MhHPijPNRGeekMs6JM85HZZyTS+x2u86ePauwsLBq+xoWblq2bCkvL69KozQnT56sNDpzWZs2bars7+3trZCQkCq38fPzk5+fn1Pb9ddf73rhvxAcHNyo/8dWFc6JM85HZZyTyjgnzjgflXFOVO2IzWWGTSj29fVVTEyMtm3b5tS+bds23X777VVuEx8fX6n/1q1bFRsbW+V8GwAA0PgYerdUUlKSVqxYoTfffFNZWVl65plnlJubq3Hjxkm6dElp9OjRjv7jxo1TTk6OkpKSlJWVpTfffFMrV67UlClTjPoKAADAwxg65+a3v/2tfv75Z82ZM0f5+fnq3r27Nm/erA4dOkiS8vPzlZub6+gfERGhzZs365lnntHixYsVFhamV155xZBn3Pj5+WnmzJmVLnk1ZpwTZ5yPyjgnlXFOnHE+KuOc1JzFfi33VAEAADQQhr9+AQAAwJ0INwAAwFQINwAAwFQINwAAwFQINy5asmSJIiIi1LRpU8XExOjzzz83uiRDpKSk6JZbblFQUJBatWql+++/X4cPHza6LI+SkpIii8WiSZMmGV2KofLy8vToo48qJCREAQEB6t27t/bv3290WYYoKyvTc889p4iICPn7+6tjx46aM2eOKioqjC6t3uzatUv33nuvwsLCZLFY9Pe//93pc7vdrlmzZiksLEz+/v4aOHCgDh48aEyx9eRq5+TixYuaOnWqevToocDAQIWFhWn06NE6ceKEcQV7MMKNC9auXatJkybp2WefVWZmpvr166chQ4Y43bbeWOzcuVPjx4/Xnj17tG3bNpWVlWnw4ME6f/680aV5hK+++krLly9Xz549jS7FUGfOnNEdd9whHx8fffzxx/r222+1YMECtz4tvCF5+eWXtWzZMr322mvKysrS3LlzNW/ePL366qtGl1Zvzp8/r169eum1116r8vO5c+cqNTVVr732mr766iu1adNGd911l86ePVvPldafq52TCxcuKCMjQ3/+85+VkZGh9evX68iRIxo2bJgBlTYAdtRYXFycfdy4cU5tkZGR9mnTphlUkec4efKkXZJ9586dRpdiuLNnz9o7d+5s37Ztm33AgAH2iRMnGl2SYaZOnWrv27ev0WV4jKFDh9ofe+wxp7YHH3zQ/uijjxpUkbEk2Tds2OBYr6iosLdp08b+0ksvOdqKi4vtzZo1sy9btsyACuvfL89JVfbu3WuXZM/JyamfohoQRm5qqLS0VPv379fgwYOd2gcPHqwvvvjCoKo8R2FhoSSpRYsWBldivPHjx2vo0KG68847jS7FcBs3blRsbKx+85vfqFWrVoqOjtYbb7xhdFmG6du3rz777DMdOXJEkmSz2bR7924lJiYaXJlnyM7OVkFBgdPvrJ+fnwYMGMDv7L8pLCyUxWJptCOgV2P4W8EbmlOnTqm8vLzSyz1bt25d6aWejY3dbldSUpL69u2r7t27G12Ood577z1lZGToq6++MroUj/DDDz9o6dKlSkpK0owZM7R37149/fTT8vPzc3rFSmMxdepUFRYWKjIyUl5eXiovL9df//pXjRgxwujSPMLl39KqfmdzcnKMKMnjFBcXa9q0aRo5cmSjf5lmVQg3LrJYLE7rdru9UltjM2HCBB04cEC7d+82uhRDHTt2TBMnTtTWrVvVtGlTo8vxCBUVFYqNjdWLL74oSYqOjtbBgwe1dOnSRhlu1q5dqzVr1uidd95RVFSUrFarJk2apLCwMI0ZM8bo8jwGv7NVu3jxoh5++GFVVFRoyZIlRpfjkQg3NdSyZUt5eXlVGqU5efJkpf+X0Zj88Y9/1MaNG7Vr1y61bdvW6HIMtX//fp08eVIxMTGOtvLycu3atUuvvfaaSkpK5OXlZWCF9S80NFTdunVzauvatavWrVtnUEXG+tOf/qRp06bp4YcfliT16NFDOTk5SklJIdxIatOmjaRLIzihoaGO9sb+OytdCjYPPfSQsrOztX37dkZtroA5NzXk6+urmJgYbdu2zal927Ztuv322w2qyjh2u10TJkzQ+vXrtX37dkVERBhdkuEGDRqkr7/+Wlar1bHExsbqkUcekdVqbXTBRpLuuOOOSo8IOHLkiOMluY3NhQsX1KSJ88+vl5dXo7oV/GoiIiLUpk0bp9/Z0tJS7dy5s1H+zl52Odh89913+vTTTxUSEmJ0SR6LkRsXJCUladSoUYqNjVV8fLyWL1+u3NxcjRs3zujS6t348eP1zjvv6B//+IeCgoIcI1rNmjWTv7+/wdUZIygoqNKco8DAQIWEhDTauUjPPPOMbr/9dr344ot66KGHtHfvXi1fvlzLly83ujRD3HvvvfrrX/+q9u3bKyoqSpmZmUpNTdVjjz1mdGn15ty5czp69KhjPTs7W1arVS1atFD79u01adIkvfjii+rcubM6d+6sF198UQEBARo5cqSBVdetq52TsLAw/cd//IcyMjK0adMmlZeXO35vW7RoIV9fX6PK9kzG3qzVcC1evNjeoUMHu6+vr71Pnz6N9tZnSVUuq1atMro0j9LYbwW32+32Dz/80N69e3e7n5+fPTIy0r58+XKjSzJMUVGRfeLEifb27dvbmzZtau/YsaP92WeftZeUlBhdWr3ZsWNHlb8dY8aMsdvtl24Hnzlzpr1NmzZ2Pz8/e//+/e1ff/21sUXXsaudk+zs7Cv+3u7YscPo0j2OxW632+szTAEAANQl5twAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAaDTeeustdevWTQEBAYqMjNSmTZuMLglAHSDcAGgUNmzYoPHjx+u5557TN998oyFDhjTKl90CjQHvlgLQKPTt21e/+tWvNGfOHEnStm3b9Jvf/Eb/+te/jC0MgNsxcgPAo+zdu1cDBw6Uv7+/IiMj9dVXX2n58uUaNmyYy/s8e/as0tPTNXToUEfbli1b1Lt3bzdUDMDTEG4AeIw9e/ZowIABuueee3TgwAF169ZNs2bN0rx58zR79myX92uz2WSxWNSzZ09duHBBb7zxhl599VVNnjzZjdUD8BSEGwAeIykpScOHD9e0adPUuXNnPfzww9qyZYt69Oih6OhoSdKmTZvUpUsXde7cWStWrLim/VqtVkVGRspqtSowMFC///3v9etf/9ppJMeV/QLwTIQbAB7h+PHjSk9P15NPPulo8/X1ld1ud4zalJWVKSkpSdu3b1dGRoZefvllnT59utp9W61WRUdHq3v37vryyy+1cOFCbd26VTNnzqzVfgF4JsINAI+QlZUlSYqNjXW0HT58WHFxcerRo4ekS/NxoqKiFB4erqCgICUmJuqTTz6pdt+Xw01QUJDi4uI0ceJEjRo1Snv27KnVfgF4JsINAI9QWFgoLy8vx/rp06c1d+5c+fn5OdpOnDih8PBwx3rbtm2Vl5d31f2WlZXp4MGDioyMdGq32Wzq16+fy/sF4LkINwA8Qu/evVVeXq65c+fq0KFDGjFihDp06KCsrCzl5ORIkqp6coXFYrnqfg8dOqTi4mK98MILyszM1OHDhzVp0iRlZ2friSeecHm/ADwX4QaAR+jUqZPmzJmjRYsWKTo6WqGhodq6davatWunO++8U5IUHh7uNKJy/PhxhYaGSpJWr15dZSCxWq0KDQ1VYGCg+vXrp/79++vYsWPasWOHY9ur7RdAw8ND/AA0GGVlZeratavS0tIUHBysPn36aM+ePQoJCdGsWbOUlpamtLQ0p22mTJmi77//Xhs2bHBpvwAaHm+jCwCAa+Xt7a0FCxYoISFBFRUVSk5OdgSQTz75RIsWLaq0jdVq1R133OHyfgE0PIzcADC1G264QcuWLdPw4cONLgVAPSHcAAAAU2FCMQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMJX/D14sBFcoWiGYAAAAAElFTkSuQmCC",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PyPlot.hist(df_pars[:,1], alpha = 0.5, color =\"purple\", label=L\"\\alpha_0\", density = true);\n",
    "PyPlot.hist(df_pars[:,3], alpha = 0.5, color =\"g\", label=L\"\\beta_0\", density = true);\n",
    "PyPlot.plot([median(df_pars[:,1]),median(df_pars[:,1])],[0,80], color = \"purple\", label = L\"\\alpha_0\\;\\; median\")\n",
    "PyPlot.plot([median(df_pars[:,3]),median(df_pars[:,3])],[0,80], color = \"g\", label = L\"\\beta_0\\;\\; median\")\n",
    "\n",
    "PyPlot.xlabel(L\"\\alpha_0, \\beta_0\"); PyPlot.ylabel(\"Frequency\"); PyPlot.legend(); PyPlot.ylim([0,3.0]);"
   ]
  },
  {
   "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
}