2c-FPM_and_EGR-25.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2) Calculation of the fluorescence per molecule and the exponential growth rate - $25^\\circ$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 $25^\\circ$C dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DelimitedFiles;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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(filelist)\n",
    "    push!(raw_data,readdlm(string(\"Analysis_MC4100_25C/MC4100_25C/\", 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": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34×5 Array{Float64,2}:\n",
       "  1.0  1.0  1.5261   216721.0  4611.09\n",
       "  2.0  0.0  1.68142  227982.0  4652.69\n",
       "  3.0  0.0  1.53322  211008.0  4689.07\n",
       "  4.0  0.0  1.49913  206440.0  4691.82\n",
       "  5.0  0.0  1.52529  213933.0  4650.72\n",
       "  6.0  0.0  1.52529  213325.0  4637.5\n",
       "  7.0  0.0  1.53322  215622.0  4791.6\n",
       "  8.0  0.0  1.50543  204034.0  4744.98\n",
       "  9.0  0.0  1.51407  204854.0  4764.05\n",
       " 10.0  0.0  1.52765  221111.0  4704.49\n",
       " 11.0  0.0  1.65323  218376.0  4549.5\n",
       " 12.0  0.0  1.50131  213586.0  4746.36\n",
       " 13.0  0.0  1.63509  234148.0  4682.96\n",
       "  ⋮                            \n",
       " 23.0  0.0  1.87009  269844.0  4734.11\n",
       " 24.0  0.0  1.83434  278036.0  4633.93\n",
       " 25.0  0.0  1.95759  272669.0  4701.19\n",
       " 26.0  0.0  1.98514  284353.0  4586.34\n",
       " 27.0  0.0  1.87564  275061.0  4742.43\n",
       " 28.0  0.0  1.92005  287236.0  4708.79\n",
       " 29.0  0.0  1.92005  290275.0  4758.61\n",
       " 30.0  0.0  2.08473  306341.0  4641.53\n",
       " 31.0  0.0  2.10093  298301.0  4734.94\n",
       " 32.0  0.0  2.1496   304830.0  4689.69\n",
       " 33.0  0.0  2.1496   303229.0  4665.06\n",
       " 34.0  0.0  2.23406  323440.0  4620.57"
      ]
     },
     "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": 6,
   "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": 7,
   "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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"./extract_gens.jl\"); using .extract;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013068623552201997"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyPlot; using LaTeXStrings;"
   ]
  },
  {
   "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 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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       "   0.5374772091360176\n",
       " 296.8865241247223"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       "   0.5374772091360176\n",
       " 296.8865241247223"
      ]
     },
     "execution_count": 20,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593.7730482494446"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Base.Iterators, LaTeXStrings, PyPlot;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "findfirst(x -> x > 150000, prop_consts) # find lineage with oddly high fluor. Plot below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "BoundsError: attempt to access 54-element Array{Array{Float64,1},1} at index [151]",
     "output_type": "error",
     "traceback": [
      "BoundsError: attempt to access 54-element Array{Array{Float64,1},1} at index [151]",
      "",
      "Stacktrace:",
      " [1] getindex(::Array{Array{Float64,1},1}, ::Int64) at ./array.jl:809",
      " [2] top-level scope at In[23]:5",
      " [3] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091",
      " [4] execute_code(::String, ::String) at /home/s1402978/.julia/packages/IJulia/a1SNk/src/execute_request.jl:27",
      " [5] execute_request(::ZMQ.Socket, ::IJulia.Msg) at /home/s1402978/.julia/packages/IJulia/a1SNk/src/execute_request.jl:86",
      " [6] #invokelatest#1 at ./essentials.jl:710 [inlined]",
      " [7] invokelatest at ./essentials.jl:709 [inlined]",
      " [8] eventloop(::ZMQ.Socket) at /home/s1402978/.julia/packages/IJulia/a1SNk/src/eventloop.jl:8",
      " [9] (::IJulia.var\"#15#18\")() at ./task.jl:356"
     ]
    }
   ],
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "writedlm(\"MN_props_25.csv\", fpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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