{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85bb7bce-87dd-4dd6-9171-26313412cff1",
   "metadata": {},
   "source": [
    "# Limit Plotter\n",
    "\n",
    "This notebook is designed to draw the combination of limits from all reported results (this work as well as STEREO+MURMUR, nEDM, and a UCN beam experiment).\n",
    "\n",
    "Start with importing some Julia packages to get things online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f010f762-4edb-4c60-914c-56ea08d5841d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"C:/Users/ffi/Documents/neutron-mirror-neutron-oscillations\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JLD # Reading in data\n",
    "using PyPlot # Matplotlib (but a bit shittier)\n",
    "\n",
    "# Redefine \"includePath\" for your local system\n",
    "const global includePath = (raw\"C:/Users/ffi/Documents/neutron-mirror-neutron-oscillations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417a57fe-b30b-4ebf-a98b-6712467d309b",
   "metadata": {},
   "source": [
    "### CSV file reading\n",
    "\n",
    "I extracted .csv files from a plot, so let's attempt to get these incorporated in here. I want to turn these into arrays in some way. Let's import a package for delimited files, locate the folder we want to use, and then open up the files we want to use.\n",
    "\n",
    "### Scaling the results\n",
    "\n",
    "I pulled the data from Saenz-Arevalo's plot, and so we have to scale from weird UCN units ($\\mu$T $\\mu_n$). I can say that $1$T = $60$neV, and so scaling this I can say $1 \\mu$T = $60 \\times 10^{-6}$ neV. Thus, I need to multiply everything by that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "752023b8-07d8-405f-a67c-f59cd34335b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "using DelimitedFiles\n",
    "const global plotFolder = (includePath*raw\"/results/Extracted_Plotting_Data\")\n",
    "\n",
    "nedmFile = (plotFolder*raw\"/nEDM_Limits.csv\")\n",
    "murPosFile = (plotFolder*raw\"/MURMUR_positive.csv\")\n",
    "murNegFile = (plotFolder*raw\"/MURMUR_negative.csv\")\n",
    "stePosFile = (plotFolder*raw\"/STEREO_positive.csv\")\n",
    "steNegFile = (plotFolder*raw\"/STEREO_negative.csv\")\n",
    "saenzFile = (plotFolder*raw\"/UCNBeam_Limits.csv\")\n",
    "combSMFile = (plotFolder*raw\"/STEREO_MURMUR_comb.csv\")\n",
    "\n",
    "nedm_res = readdlm(nedmFile,',');\n",
    "ucn_res = readdlm(saenzFile,',');\n",
    "murmurP_res = readdlm(murPosFile,',');\n",
    "murmurN_res = readdlm(murNegFile,',');\n",
    "stereoP_res = readdlm(stePosFile,',');\n",
    "stereoN_res = readdlm(steNegFile,',');\n",
    "combN_res = readdlm(combSMFile,',');\n",
    "\n",
    "muT = 60E-6;\n",
    "hbar = 6.582E-16;\n",
    "\n",
    "goldman_res = 3E-13; # Result from Goldman et al. 2019\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c7fc6d-8287-4fae-b5a4-866be94788c0",
   "metadata": {},
   "source": [
    "### Loading the SNS Limits\n",
    "\n",
    "We have some limits that we need to get from ORNL. We have a bunch of runs that we need to get the limits from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d470bfa4-edb8-494d-81b4-613b9f5ce326",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The run path, with all of the runs combined\n",
    "simPath = (raw\"C:/Users/ffi/Documents/neutron-mirror-neutron-oscillations\")\n",
    "\n",
    "# List of simulations for Cadmium\n",
    "cdName_pos  = (raw\"/SNS_Cadmium_Pos.jld\"); \n",
    "cdName_HPos = (raw\"/SNS_Cadmium_Half_Pos.jld\");\n",
    "cdName_neg  = (raw\"/SNS_Cadmium_Neg.jld\");\n",
    "cdName_HNeg = (raw\"/SNS_Cadmium_Half_Neg.jld\");\n",
    "\n",
    "cdNameN_pos  = (raw\"/SNS_Cadmium_negM_Pos.jld\"); \n",
    "cdNameN_HPos = (raw\"/SNS_Cadmium_negM_Half_Pos.jld\");\n",
    "cdNameN_neg  = (raw\"/SNS_Cadmium_negM_Neg.jld\");\n",
    "cdNameN_HNeg = (raw\"/SNS_Cadmium_negM_Half_Neg.jld\");\n",
    "\n",
    "# Now concatenate those files\n",
    "cdFile_pos  = (simPath*raw\"/results\"*cdName_pos);\n",
    "cdFile_neg  = (simPath*raw\"/results\"*cdName_neg);\n",
    "cdFile_HPos  = (simPath*raw\"/results\"*cdName_HPos);\n",
    "cdFile_HNeg  = (simPath*raw\"/results\"*cdName_HNeg);\n",
    "\n",
    "cdFileN_pos  = (simPath*raw\"/results\"*cdNameN_pos);\n",
    "cdFileN_neg  = (simPath*raw\"/results\"*cdNameN_neg);\n",
    "cdFileN_HPos  = (simPath*raw\"/results\"*cdNameN_HPos);\n",
    "cdFileN_HNeg  = (simPath*raw\"/results\"*cdNameN_HNeg);\n",
    "\n",
    "# Also do zero\n",
    "cdName_zero = (raw\"/SNS_Cadmium_Zero.jld\");\n",
    "cdNameN_zero = (raw\"/SNS_Cadmium_negM_Zero.jld\");\n",
    "\n",
    "cdFile_zero = (simPath*raw\"/results\"*cdName_zero);\n",
    "cdFileN_zero = (simPath*raw\"/results\"*cdNameN_zero);\n",
    "\n",
    "# List of simulations for Low Mass Splittings\n",
    "cdNameLow_pos  = (raw\"/LowMRes/SNS_Cadmium_lowM_Pos.jld\"); \n",
    "cdNameLow_HPos = (raw\"/LowMRes/SNS_Cadmium_lowM_Half_Pos.jld\");\n",
    "cdNameLow_neg  = (raw\"/LowMRes/SNS_Cadmium_lowM_Neg.jld\");\n",
    "cdNameLow_HNeg = (raw\"/LowMRes/SNS_Cadmium_lowM_Half_Neg.jld\");\n",
    "#cdNameLow_pos  = (raw\"/sharpB/SNS_Cadmium_sharp_Pos.jld\"); \n",
    "#cdNameLow_HPos = (raw\"/sharpB/SNS_Cadmium_sharp_Pos.jld\");\n",
    "#cdNameLow_neg  = (raw\"/sharpB/SNS_Cadmium_sharp_Neg.jld\");\n",
    "#cdNameLow_HNeg  = (raw\"/sharpB/SNS_Cadmium_sharp_Neg.jld\");\n",
    "\n",
    "cdNameLowN_pos  = (raw\"/LowMRes/SNS_Cadmium_lowNegM_Pos.jld\"); \n",
    "cdNameLowN_HPos = (raw\"/LowMRes/SNS_Cadmium_lowNegM_Half_Pos.jld\");\n",
    "cdNameLowN_neg  = (raw\"/LowMRes/SNS_Cadmium_lowNegM_Neg.jld\");\n",
    "cdNameLowN_HNeg = (raw\"/LowMRes/SNS_Cadmium_lowNegM_Half_Neg.jld\");\n",
    "\n",
    "# Now concatenate those files\n",
    "cdFileLow_pos  = (simPath*raw\"/results\"*cdNameLow_pos);\n",
    "cdFileLow_neg  = (simPath*raw\"/results\"*cdNameLow_neg);\n",
    "cdFileLow_HPos  = (simPath*raw\"/results\"*cdNameLow_HPos);\n",
    "cdFileLow_HNeg  = (simPath*raw\"/results\"*cdNameLow_HNeg);\n",
    "\n",
    "cdFileLowN_pos  = (simPath*raw\"/results\"*cdNameLowN_pos);\n",
    "cdFileLowN_neg  = (simPath*raw\"/results\"*cdNameLowN_neg);\n",
    "cdFileLowN_HPos  = (simPath*raw\"/results\"*cdNameLowN_HPos);\n",
    "cdFileLowN_HNeg  = (simPath*raw\"/results\"*cdNameLowN_HNeg);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ed7fbc-75af-4c85-8a36-3e7dacc48e9a",
   "metadata": {},
   "source": [
    "We want a couple of functions that average together our various results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1010871c-500e-4496-987c-890ff7f135dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "find_upper_lower (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function vel_average_result(res::String)\n",
    "   \n",
    "    # Load information from the .jld file\n",
    "    Δms = load(res, \"deltaMs\")\n",
    "    θ0s = load(res, \"theta0s\")\n",
    "    ρ11Raw = load(res, \"Pnn\")\n",
    "    \n",
    "    # Use the loaded information to get the sizes of arrays\n",
    "    nΔm = size(Δms)[1]\n",
    "    nθ0 = size(Δms)[2]\n",
    "    nV = size(Δms)[3]\n",
    "    \n",
    "    # I have two different codes producing probabilities.\n",
    "    # One was designed for the NIST experiment, where we cared about\n",
    "    # the probability at the beginning/middle/end. We don't \n",
    "    # care about that here, just the end\n",
    "    if size(ρ11Raw)[2] == 3\n",
    "       ρ11Raw_Out = ρ11Raw[:,3] \n",
    "    else\n",
    "        ρ11Raw_Out = ρ11Raw\n",
    "    end\n",
    "    \n",
    "    # Now we need to combine the values for this\n",
    "    # We have to average over velocity values for each Δm and θ0.\n",
    "    avgρ = Matrix{Float64}(undef,nΔm,nθ0) \n",
    "    ρ11 = reshape(ρ11Raw_Out,nΔm,nθ0,nV)\n",
    "    \n",
    "    # And loop!\n",
    "    for i in 1:nΔm\n",
    "        for j in 1:nθ0\n",
    "            # Average over velocity\n",
    "            avg_ρ_tmp = 0.\n",
    "            for k in 1:nV\n",
    "                avg_ρ_tmp += ρ11[i,j,k]\n",
    "            end\n",
    "            # And put into matrix\n",
    "            avgρ[i,j] = avg_ρ_tmp / Float64(nV)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return Δms[:,:,1], θ0s[:,:,1], avgρ\n",
    "end\n",
    "    \n",
    "function average_two_results(res1::String,res2::String)\n",
    "    \n",
    "    # This function will take from two results and average\n",
    "    # them. This is designed to test polarities.\n",
    "    \n",
    "    # Load the two datafiles we care about\n",
    "    Δms_p,θ0s_p,avgρ_pos = vel_average_result(res1)\n",
    "    Δms_n,θ0s_n,avgρ_neg = vel_average_result(res2)\n",
    "    \n",
    "    # Now average over the two spin states!\n",
    "    avgΔm = (Δms_p .+ Δms_n) ./ 2. # These should be the same...\n",
    "    avgθ0 = (θ0s_p .+ θ0s_n) ./ 2. # See above\n",
    "    avgρ = (avgρ_pos .+ avgρ_neg) ./ 2. # These are NOT the same\n",
    "    return avgΔm, avgθ0, avgρ    \n",
    "end\n",
    "\n",
    "function find_upper_lower(Δms::Matrix{Float64},θ0s::Matrix{Float64},ρ11s::Matrix{Float64};\n",
    "    p0::Float64=0.01,dp::Float64=0.002,uedge::Bool=false)\n",
    "   \n",
    "    # Load the variable number of points to save over\n",
    "    nΔm = size(Δms)[1]\n",
    "    nθ0 = size(θ0s)[2]\n",
    "    \n",
    "    # Now we want to create three arrays, which are\n",
    "    # the output of a \"function\" θ0_min/max = f(Δm)\n",
    "    Δm_val = Array{Float64}(undef,nΔm)\n",
    "    θ_min  = Array{Float64}(undef,nΔm)\n",
    "    θ_max  = Array{Float64}(undef,nΔm)\n",
    "    for i in 1:nΔm\n",
    "        \n",
    "        # Loop through and find min/max\n",
    "        tmin = 1.1\n",
    "        tmax = -0.1\n",
    "        for j in 1:nθ0-1\n",
    "            \n",
    "            # Is this the lower limit?\n",
    "            if ((ρ11s[i,j] < p0-dp) \n",
    "                    && (ρ11s[i,j+1] >= p0-dp))\n",
    "                tmin = θ0s[i,j]\n",
    "            end    \n",
    "            # Is this the upper limit?\n",
    "            if ((ρ11s[i,j] <= p0+dp) \n",
    "                    && (ρ11s[i,j+1] > p0+dp))\n",
    "                tmax = θ0s[i,j+1]\n",
    "            end        \n",
    "        end\n",
    "        # Pass the mass\n",
    "        Δm_val[i] = Δms[i,1]\n",
    "        # Now we do dumb limits just in case\n",
    "        if tmin > 1\n",
    "           if tmax > 0\n",
    "                tmin = θ0s[i,1] \n",
    "            else\n",
    "                if uedge\n",
    "                    tmin = θ0s[i,nθ0-1]\n",
    "                    tmax = θ0s[i,nθ0]\n",
    "                else\n",
    "                    tmin =  NaN\n",
    "                    Δm_val[i] = NaN\n",
    "                end\n",
    "            end\n",
    "            \n",
    "        elseif tmax < 0\n",
    "            tmax=θ0s[i,nθ0]\n",
    "        end\n",
    "        θ_min[i] = tmin\n",
    "        θ_max[i] = tmax\n",
    "    end\n",
    "    # We had a NaN error condition (i.e. off the edge), so let's remove those\n",
    "    reals = findall(.~isnan.(Δm_val))\n",
    "    return Δm_val[reals],θ_min[reals],θ_max[reals]\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b203362-4812-4f1f-babb-d7c209330bd2",
   "metadata": {},
   "source": [
    "### Actual extraction of results\n",
    "\n",
    "Now we go ahead and extract out our actual results. We want to do this for +/- and "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5224a59-2147-41b0-be95-94e33bfd76fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we extract the actual results. Start with Positive Masses\n",
    "cd_Δm, cd_θ0, cd_ρ  = average_two_results(cdFile_pos, cdFile_neg);\n",
    "cd_Δm_H, cd_θ0_H, cd_ρ_H  = average_two_results(cdFile_HPos, cdFile_HNeg);\n",
    "cd_Δm_0,cd_θ0_0,cd_ρ_0 = vel_average_result(cdFile_zero);\n",
    "# And low Masses\n",
    "cdL_Δm, cdL_θ0, cdL_ρ  = average_two_results(cdFileLow_pos, cdFileLow_neg);\n",
    "cdL_Δm_H, cdL_θ0_H, cdL_ρ_H  = average_two_results(cdFileLow_HPos, cdFileLow_HNeg);\n",
    "\n",
    "# Next let's do the Negative Masses\n",
    "cdN_Δm, cdN_θ0, cdN_ρ  = average_two_results(cdFileN_pos, cdFileN_neg);\n",
    "cdN_Δm_H, cdN_θ0_H, cdN_ρ_H  = average_two_results(cdFileN_HPos, cdFileN_HNeg);\n",
    "cdN_Δm_0,cdN_θ0_0,cdN_ρ_0 = vel_average_result(cdFileN_zero);\n",
    "# And low masses too\n",
    "cdLN_Δm, cdLN_θ0, cdLN_ρ  = average_two_results(cdFileLowN_pos, cdFileLowN_neg);\n",
    "cdLN_Δm_H, cdLN_θ0_H, cdLN_ρ_H  = average_two_results(cdFileLowN_HPos, cdFileLowN_HNeg);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5222fb7c-9cf6-4a54-8aa1-9814d83c7e9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d3010f57-f4d0-4ef7-ac68-0c65be90cf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull these from table \n",
    "lim2020_5 = 3.046092184368738e-10#3.8476953907815635e-10#4.529058116232465e-10\n",
    "lim2020_H = 4.308617234468938e-10#5.430861723446894e-10#3.27e-10 # \n",
    "lim2020_0 = 1.7034068136272547e-10#2.464929859719439e-10#1.86e-10 # From the table\n",
    "\n",
    "# We also want to take a weighted average of the 5 and 2.5 fields\n",
    "sWts = (1 ./(lim2020_5*lim2020_5).+ 1 ./ (lim2020_H*lim2020_5))\n",
    "\n",
    "ρ_wts = (cd_ρ./(lim2020_5*lim2020_5).+cd_ρ_H./(lim2020_H*lim2020_H))./sWts\n",
    "ρL_wts = (cdL_ρ./(lim2020_5*lim2020_5).+cdL_ρ_H./(lim2020_H*lim2020_H))./sWts\n",
    "\n",
    "ρN_wts = (cdN_ρ./(lim2020_5*lim2020_5).+cdN_ρ_H./(lim2020_H*lim2020_H))./sWts\n",
    "ρLN_wts = (cdLN_ρ./(lim2020_5*lim2020_5).+cdLN_ρ_H./(lim2020_H*lim2020_H))./sWts\n",
    "\n",
    "# Now lets find our associated upper/lower plot contours\n",
    "ΔmP_A, minθP_A, maxθP_A = find_upper_lower(cd_Δm_H,cd_θ0_H,\n",
    "                                ρ_wts,p0=sqrt(1 ./ sWts),dp=0.1E-8);\n",
    "avgθP_A = (maxθP_A .+ minθP_A) ./ 2.;\n",
    "\n",
    "ΔmPL_A, minθPL_A, maxθPL_A = find_upper_lower(cdL_Δm_H,cdL_θ0_H,\n",
    "                                ρL_wts,p0=sqrt(1 ./ sWts),dp=0.1E-8);\n",
    "avgθPL_A = (maxθPL_A .+ minθPL_A) ./ 2.;\n",
    "\n",
    "# And associated negative ones\n",
    "ΔmN_A, minθN_A, maxθN_A = find_upper_lower(cdN_Δm_H,cdN_θ0_H,\n",
    "                                ρN_wts,p0=sqrt(1 ./ sWts),dp=0.1E-8);\n",
    "avgθN_A = (maxθN_A .+ minθN_A) ./ 2.;\n",
    "\n",
    "ΔmLN_A, minθLN_A, maxθLN_A = find_upper_lower(cdLN_Δm_H,cdLN_θ0_H,\n",
    "                                ρLN_wts,p0=sqrt(1 ./ sWts),dp=0.1E-8);\n",
    "avgθLN_A = (maxθLN_A .+ minθLN_A) ./ 2.;\n",
    "\n",
    "# Lets combine low and high masses\n",
    "avgθP_C = vcat(avgθPL_A,avgθP_A[3:end])\n",
    "ΔmP_C = vcat(ΔmPL_A,ΔmP_A[3:end])\n",
    "avgθN_C = vcat(avgθLN_A,avgθN_A[3:end])\n",
    "ΔmN_C = vcat(ΔmLN_A,ΔmN_A[3:end])\n",
    "#avgθN_C = cat(avgθPL_A,avgθP_A[2:end])\n",
    "#ΔmN_C = cat(ΔmPL_A,ΔmP_A[2:end])\n",
    "\n",
    "\n",
    "#ΔmN_A = 0 .- ΔmN_A;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "40675a33-5d6a-480b-99bf-a7b1f26c0d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell is just a debug for later\n",
    "# Flat average with worse limits\n",
    "ΔmP_Flat, minθP_Flat, maxθP_Flat = find_upper_lower(cd_Δm_H,cd_θ0_H,\n",
    "                                (cd_ρ.+cd_ρ_H)./2,p0=lim2020_5,dp=0.1E-8);\n",
    "avgθP_Flat = (maxθP_Flat .+ minθP_Flat) ./ 2.;\n",
    "\n",
    "# All the positive polarities...\n",
    "ΔmP_5, minθP_5, maxθP_5 = find_upper_lower(cd_Δm,cd_θ0,\n",
    "                                cd_ρ,p0=lim2020_5,dp=0.1E-8);\n",
    "avgθP_5 = (maxθP_5 .+ minθP_5) ./ 2.;\n",
    "ΔmP_H, minθP_H, maxθP_H = find_upper_lower(cd_Δm_H,cd_θ0_H,\n",
    "                                cd_ρ_H,p0=lim2020_H,dp=0.1E-8);\n",
    "avgθP_H = (maxθP_H .+ minθP_H) ./ 2.;\n",
    "# With low range\n",
    "ΔmPL_5, minθPL_5, maxθPL_5 = find_upper_lower(cdL_Δm,cdL_θ0,\n",
    "                                cdL_ρ,p0=lim2020_5,dp=0.1E-8);\n",
    "avgθPL_5 = (maxθPL_5 .+ minθPL_5) ./ 2.;\n",
    "ΔmPL_H, minθPL_H, maxθPL_H = find_upper_lower(cdL_Δm_H,cdL_θ0_H,\n",
    "                                cdL_ρ_H,p0=lim2020_H,dp=0.1E-8);\n",
    "avgθPL_H = (maxθPL_H .+ minθPL_H) ./ 2.;\n",
    "\n",
    "# Negative polarities\n",
    "ΔmN_5, minθN_5, maxθN_5 = find_upper_lower(cdN_Δm,cdN_θ0,\n",
    "                                cdN_ρ,p0=lim2020_5,dp=0.1E-8);\n",
    "avgθN_5 = (maxθN_5 .+ minθN_5) ./ 2.;\n",
    "ΔmN_H, minθN_H, maxθN_H = find_upper_lower(cdN_Δm_H,cdN_θ0_H,\n",
    "                                cdN_ρ_H,p0=lim2020_H,dp=0.1E-8);\n",
    "avgθN_H = (maxθN_H .+ minθN_H) ./ 2.;\n",
    "\n",
    "# With low range\n",
    "ΔmLN_5, minθLN_5, maxθLN_5 = find_upper_lower(cdLN_Δm,cdLN_θ0,\n",
    "                                cdLN_ρ,p0=lim2020_5,dp=0.1E-8);\n",
    "avgθLN_5 = (maxθLN_5 .+ minθLN_5) ./ 2.;\n",
    "ΔmLN_H, minθLN_H, maxθLN_H = find_upper_lower(cdLN_Δm_H,cdLN_θ0_H,\n",
    "                                cdLN_ρ_H,p0=lim2020_H,dp=0.1E-8);\n",
    "avgθLN_H = (maxθLN_H .+ minθLN_H) ./ 2.;\n",
    "\n",
    "# And the zeros\n",
    "ΔmN_0, minθN_0, maxθN_0 = find_upper_lower(cdN_Δm_0,cdN_θ0_0,\n",
    "                                cdN_ρ_0,p0=lim2020_0,dp=0.1E-8);\n",
    "avgθN_0 = (maxθN_0 .+ minθN_0) ./ 2.;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3772f235-1e7a-441b-856c-040b82cf3b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.651195120909783e-10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lim2020_5 = 4.529058116232465e-10\n",
    "lim2020_H = 3.27e-10 # \n",
    "lim2020_0 = 1.86e-10 # From the table\n",
    "\n",
    "sWts = 1 ./ (lim2020_5*lim2020_5) + 1 ./ (lim2020_H*lim2020_H)\n",
    "sqrt(1 ./ sWts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff956eb-6d17-4993-b74f-233adb93444e",
   "metadata": {},
   "source": [
    "### Set up plotting information\n",
    "\n",
    "This is where we go through and do actual plotting information on these numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b5e2806c-dbe8-42df-90a0-408d2ceffeb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_SIZE = 10\n",
    "MEDIUM_SIZE = 11\n",
    "BIGGER_SIZE = 12\n",
    "\n",
    "PLOT_HEIGHT = 4.5\n",
    "PLOT_WIDTH =  6.\n",
    "\n",
    "PyPlot.rc(\"font\", size=SMALL_SIZE)          # controls default text sizes\n",
    "PyPlot.rc(\"axes\", titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
    "PyPlot.rc(\"axes\", labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "PyPlot.rc(\"xtick\", labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "PyPlot.rc(\"ytick\", labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "PyPlot.rc(\"legend\", fontsize=SMALL_SIZE)    # legend fontsize\n",
    "PyPlot.rc(\"figure\", titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "PyPlot.rc(\"figure\", figsize=(2*PLOT_WIDTH,PLOT_HEIGHT)) # This is doubled for this plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c831d99-54cf-45b6-a8ac-f3fc63995928",
   "metadata": {},
   "source": [
    "### Actually plot things here\n",
    "\n",
    "This is where we do the general plotting routines for this plotting system. I'm going to do a single plot with the positive and negative limits since that's what we'll end up wanting to actually plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "12264bd0-97d1-4e2a-8993-3d5b2d09e40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 1200x450 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PyPlot.rc(\"figure\", figsize=(2*PLOT_WIDTH,PLOT_HEIGHT)) # This is doubled for this plot.\n",
    "# Generate a 2-column plot.\n",
    "PyPlot.figure()\n",
    "fig, axes = PyPlot.subplots(nrows=1,ncols=2)\n",
    "\n",
    "xMin = 3E-4\n",
    "xMax = 1E3\n",
    "yMin = 1E-6\n",
    "yMax = 2E2\n",
    "# Start with just the negative\n",
    "# And our result\n",
    "axes[1].plot(ΔmN_C.*-1E9,-2 .*hbar./(avgθN_C.*ΔmN_C),color=\"black\",label=\"This Result\")\n",
    "axes[1].fill_between(ΔmN_C.*-1E9,-2 .*hbar./(avgθN_C.*ΔmN_C),color=\"black\",alpha=0.3)\n",
    "#axes[1].plot(ΔmN_C.*-1E9,-1 .*hbar./(avgθN_C.*ΔmN_C),color=\"black\",label=\"This Result\")\n",
    "#axes[1].fill_between(ΔmN_C.*-1E9,-1 .*hbar./(avgθN_C.*ΔmN_C),color=\"black\",alpha=0.3)\n",
    "\n",
    "\n",
    "# These are the lines of our competitors\n",
    "axes[1].plot(nedm_res[:,1].*muT,2 .*nedm_res[:,2],color=\"red\",label=\"UCN Storage\")\n",
    "axes[1].plot(ucn_res[:,1].*muT,2 .*ucn_res[:,2],color=\"orange\",label=\"UCN Beam\")\n",
    "#axes[1].plot(combN_res[:,1].*muT,2 .*combN_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"Reactors\")\n",
    "#axes[1].plot(nedm_res[:,1].*muT,hbar./2. ./nedm_res[:,2],color=\"red\",label=\"UCN Storage\")\n",
    "#axes[1].plot(ucn_res[:,1].*muT,hbar./2. ./ucn_res[:,2],color=\"orange\",label=\"UCN Beam\")\n",
    "#axes[1].plot(combN_res[:,1].*muT,hbar./2. ./combN_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"Reactors\")\n",
    "axes[1].plot(murmurN_res[:,1].*muT,2 .*murmurN_res[:,2],color=\"green\",linestyle=\"solid\",label=\"MURMUR\")\n",
    "axes[1].plot(stereoN_res[:,1].*muT,2 .*stereoN_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"STEREO\")\n",
    "\n",
    "# This is the shaded exclusion limit\n",
    "axes[1].fill_between(nedm_res[:,1].*muT,2 .*nedm_res[:,2],color=\"red\",alpha=0.1)\n",
    "axes[1].fill_between(ucn_res[:,1].*muT,2 .*ucn_res[:,2],color=\"orange\",alpha=0.1)\n",
    "#axes[1].fill_between(combN_res[:,1].*muT,2 .*combN_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "axes[1].fill_between(murmurN_res[:,1].*muT,2 .*murmurN_res[:,2],color=\"green\",linestyle=\"solid\",alpha=0.1)\n",
    "axes[1].fill_between(stereoN_res[:,1].*muT,2 .*stereoN_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "\n",
    "# General plotting info\n",
    "axes[1].set_title(latexstring(L\"$\\Delta m < 0$\"))\n",
    "axes[1].set_xlabel(latexstring(L\"$|\\Delta m|$ (neV)\"))\n",
    "axes[1].set_ylabel(latexstring(L\"$\\tau_{nn'}$ (s)\"))\n",
    "axes[1].grid(true)\n",
    "axes[1].legend(loc=\"upper left\")\n",
    "axes[1].set_xscale(\"log\")\n",
    "axes[1].set_yscale(\"log\")\n",
    "\n",
    "axes[1].set_xlim([xMax,xMin])\n",
    "axes[1].set_ylim([yMin,yMax])\n",
    "#axes[1].plot([xMin,xMax],[hbar./goldman_res,hbar./goldman_res],color=\"gray\")\n",
    "\n",
    "# --------------------------------------------------------\n",
    "# Now we do the other polarity\n",
    "# And our result\n",
    "axes[2].plot(ΔmP_C.*1E9,2 .*hbar./(avgθP_C.*ΔmP_C),color=\"black\",label=\"This Result\")\n",
    "axes[2].fill_between(ΔmP_C.*1E9,2 .*hbar./(avgθP_C.*ΔmP_C),color=\"black\",alpha=0.3)\n",
    "#axes[2].plot(ΔmP_C.*1E9,hbar./(avgθP_C.*ΔmP_C),color=\"black\",label=\"This Result\")\n",
    "#axes[2].fill_between(ΔmP_C.*1E9,hbar./(avgθP_C.*ΔmP_C),color=\"black\",alpha=0.3)\n",
    "\n",
    "\n",
    "# These are the lines of our competitors\n",
    "axes[2].plot(nedm_res[:,1].*muT,2 .*nedm_res[:,2],color=\"red\",label=\"UCN Storage\")\n",
    "axes[2].plot(ucn_res[:,1].*muT,2 .*ucn_res[:,2],color=\"orange\",label=\"UCN Beam\")\n",
    "axes[2].plot(murmurP_res[:,1].*muT,2 .*murmurP_res[:,2],color=\"green\",linestyle=\"solid\",label=\"MURMUR\")\n",
    "axes[2].plot(stereoP_res[:,1].*muT,2 .*stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"STEREO\")\n",
    "#axes[2].plot(stereoP_res[:,1].*muT,2 .*stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"Reactors\")\n",
    "\n",
    "# This is the shaded exclusion limit\n",
    "axes[2].fill_between(nedm_res[:,1].*muT,2 .*nedm_res[:,2],color=\"red\",alpha=0.1)\n",
    "axes[2].fill_between(ucn_res[:,1].*muT,2 .*ucn_res[:,2],color=\"orange\",alpha=0.1)\n",
    "axes[2].fill_between(murmurP_res[:,1].*muT,2 .*murmurP_res[:,2],color=\"green\",linestyle=\"solid\",alpha=0.1)\n",
    "axes[2].fill_between(stereoP_res[:,1].*muT,2 .*stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "#axes[2].fill_between(stereoP_res[:,1].*muT,2 .*stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "\n",
    "# General plotting info\n",
    "axes[2].set_title(latexstring(L\"$\\Delta m > 0$\"))\n",
    "axes[2].set_xlabel(latexstring(L\"$\\Delta m$ (neV)\"))\n",
    "axes[2].set_ylabel(latexstring(L\"$\\tau_{nn'}$ (s)\"))\n",
    "axes[2].grid(true)\n",
    "axes[2].legend(loc=\"upper right\")\n",
    "axes[2].set_xscale(\"log\")\n",
    "axes[2].set_yscale(\"log\")\n",
    "\n",
    "axes[2].set_xlim([xMin,xMax])\n",
    "axes[2].set_ylim([yMin,yMax])\n",
    "#axes[2].plot([xMin,xMax],[hbar./goldman_res,hbar./goldman_res],color=\"gray\")\n",
    "PyPlot.rc(\"figure\", figsize=(PLOT_WIDTH,PLOT_HEIGHT)) # This is doubled for this plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdd34b4-a43f-423e-b1fe-9daa6d227986",
   "metadata": {},
   "source": [
    "### Individual runs for Negative\n",
    "\n",
    "This is just if we want to make an individual plot for reactor positive and negative. First we'll do the negative runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "039f958c-d575-434d-97c7-227678b67767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1.0e-6, 200.0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the lines of our limits\n",
    "PyPlot.plot(nedm_res[:,1].*muT,nedm_res[:,2],color=\"red\",label=\"UCN Storage\")\n",
    "PyPlot.plot(ucn_res[:,1].*muT,ucn_res[:,2],color=\"orange\",label=\"UCN Beam\")\n",
    "#PyPlot.plot(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",label=\"MURMUR\")\n",
    "PyPlot.plot(murmurN_res[:,1].*muT,murmurN_res[:,2],color=\"green\",label=\"MURMUR\")\n",
    "#PyPlot.plot(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"STEREO\")\n",
    "PyPlot.plot(stereoN_res[:,1].*muT,stereoN_res[:,2],color=\"blue\",label=\"STEREO\")\n",
    "\n",
    "# This is the shaded exclusion limit\n",
    "PyPlot.fill_between(nedm_res[:,1].*muT,nedm_res[:,2],color=\"red\",alpha=0.1)\n",
    "PyPlot.fill_between(ucn_res[:,1].*muT,ucn_res[:,2],color=\"orange\",alpha=0.1)\n",
    "#PyPlot.fill_between(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",alpha=0.1)\n",
    "PyPlot.fill_between(murmurN_res[:,1].*muT,murmurN_res[:,2],color=\"green\",linestyle=\"dashed\",alpha=0.1)\n",
    "#PyPlot.fill_between(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "PyPlot.fill_between(stereoN_res[:,1].*muT,stereoN_res[:,2],color=\"blue\",linestyle=\"dashed\",alpha=0.1)\n",
    "\n",
    "# And our result\n",
    "PyPlot.plot(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A)),color=\"cyan\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A)),color=\"cyan\",alpha=0.1)\n",
    "PyPlot.plot(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A))./1.261,color=\"cyan\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A))./1.261,color=\"cyan\",alpha=0.1)\n",
    "\n",
    "PyPlot.plot(ΔmP_Flat.*1E9,2 .*hbar./(avgθP_Flat.*(ΔmP_Flat)),color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_Flat.*1E9,2 .*hbar./(avgθP_Flat.*(ΔmP_Flat)),color=\"black\",alpha=0.1)\n",
    "PyPlot.plot(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A)),color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A)),color=\"black\",alpha=0.1)\n",
    "\n",
    "\n",
    "# General plotting info\n",
    "PyPlot.title(latexstring(L\"$\\Delta E < 0$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta E$ (neV)\"))\n",
    "PyPlot.ylabel(latexstring(L\"$\\tau_{nn'}$ (s)\"))\n",
    "PyPlot.grid(true)\n",
    "PyPlot.legend(loc=\"upper right\")\n",
    "PyPlot.xscale(\"log\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([xMin,xMax])\n",
    "PyPlot.ylim([yMin,yMax])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "431dd3ae-85ac-40a5-bb26-91a384aa88e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1.0e-18, 1.0e-8)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yMin2,yMax2 = 1E-18,1E-8\n",
    "# These are the lines of our limits\n",
    "PyPlot.plot(nedm_res[:,1].*muT,hbar./ nedm_res[:,2]/2,color=\"red\",label=\"UCN Storage\")\n",
    "PyPlot.plot(ucn_res[:,1].*muT,hbar./ ucn_res[:,2]/2,color=\"orange\",label=\"UCN Beam\")\n",
    "#PyPlot.plot(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",label=\"MURMUR\")\n",
    "PyPlot.plot(murmurN_res[:,1].*muT,hbar ./ murmurN_res[:,2]/2,color=\"green\",label=\"MURMUR\")\n",
    "#PyPlot.plot(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"STEREO\")\n",
    "PyPlot.plot(stereoN_res[:,1].*muT,hbar./ stereoN_res[:,2]/2,color=\"blue\",label=\"STEREO\")\n",
    "\n",
    "# This is the shaded exclusion limit\n",
    "PyPlot.fill_between(nedm_res[:,1].*muT,hbar ./ nedm_res[:,2]/2,1,color=\"red\",alpha=0.1)\n",
    "PyPlot.fill_between(ucn_res[:,1].*muT,hbar./ ucn_res[:,2]/2,1,color=\"orange\",alpha=0.1)\n",
    "#PyPlot.fill_between(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",alpha=0.1)\n",
    "PyPlot.fill_between(murmurN_res[:,1].*muT,hbar ./ murmurN_res[:,2]/2,1,color=\"green\",linestyle=\"dashed\",alpha=0.1)\n",
    "#PyPlot.fill_between(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "PyPlot.fill_between(stereoN_res[:,1].*muT,hbar./ stereoN_res[:,2]/2,1,color=\"blue\",linestyle=\"dashed\",alpha=0.1)\n",
    "\n",
    "# And our result\n",
    "PyPlot.plot(ΔmP_C.*1E9,(avgθP_C.*(ΔmP_C))./2,color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_C.*1E9,(avgθP_C.*(ΔmP_C))./2,1,color=\"black\",alpha=0.1)\n",
    "#PyPlot.plot(ΔmN_A.*-1E9,(avgθN_A.*(-ΔmN_A))./2,color=\"black\",label=\"This Result\")\n",
    "#PyPlot.fill_between(ΔmN_A.*-1E9,(avgθN_A.*(-ΔmN_A))./2,1,color=\"black\",alpha=0.1)\n",
    "\n",
    "# General plotting info\n",
    "PyPlot.title(latexstring(L\"$\\Delta E < 0$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta E$ (neV)\"))\n",
    "PyPlot.ylabel(latexstring(L\"$\\epsilon_{nn'}$ (s)\"))\n",
    "PyPlot.grid(true)\n",
    "PyPlot.legend(loc=\"upper left\")\n",
    "PyPlot.xscale(\"log\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([xMin,xMax])\n",
    "PyPlot.ylim([yMin2,yMax2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd3ae1fd-849e-45f0-aeff-589ea2664fc1",
   "metadata": {},
   "source": [
    "### The same thing for positive\n",
    "\n",
    "Here we're just going to do the positive dataset, so that we can separate it out in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9d49dbab-27b4-4883-b1bd-0eb66b24b0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAG3CAYAAACjR8/oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAADSt0lEQVR4nOzdd3hT1f/A8fdN0r3oglIoFOhilrK3gKyCKIjID5AlIKOKbBVUhgMRVBQBB0pBZagsv1imyJAlUIoIWCiUXSgU6G6TJvn9cZu06W7p7nk9T54k996ce3I78skZnyPp9Xo9giAIgiAIFZiirCsgCIIgCILwpERAIwiCIAhChScCGkEQBEEQKjwR0AiCIAiCUOGJgEYQBEEQhApPBDSCIAiCIFR4IqARBEEQBKHCEwGNIAiCIAgVnghoBEEQBEGo8ERAIwiCIAhChScCGkEQyswXX3yBJEk0adIk12NCQkKQJCnX23fffVdi9UtISGDq1Km4u7tjaWlJ8+bN2bhxY4mdTxCEolOVdQUEQai6vv/+eyRJ4vz585w4cYK2bdtmOyY0NBSA7du3U7169Wz7GzVqVGL1e/755zl58iQfffQRPj4+rF+/nqFDh6LT6Rg2bFiJnVcQhMKTxOKUgiCUhVOnTtG6dWtmz57NsmXLGDVqFN988022455//nn++OMPHj9+jCRJBSr74cOH6HQ6XFxcily/kJAQ+vXrZwxiDHr16sX58+e5ceMGSqWyyOULglC8RJeTIAhl4rvvvkOpVDJt2jSeeeYZNm7cSFJSUrbjTp8+jb+/f4GDGYB//vmHmjVrEhgYyLp164iPjy90/bZu3YqtrS2DBw822T5mzBju3LnDiRMnCl2mIAglRwQ0giCUuuTkZDZs2EBgYCBubm6MGTOG+Ph4fvnlF5PjYmJiuHHjBk2bNiUtLS3bLbcG5nbt2rF27VrMzMwYP3481atXZ/DgwWzZsoXU1NQC1fHff/+lYcOGqFSmPfPNmjUz7hcEofwQAY0gCKXu119/JTY2ljFjxgAYA5usA3wN42dWrlyJmZlZttuFCxdyLN/S0pJhw4bx22+/ce/ePVauXElcXBxDhgyhRo0ajBkzhj179qDVanOtY0xMDE5OTtm2G7bFxMQU6b0LglAyxKBgQRBK3XfffYeLiwvPPPMMAEqlkhEjRrBkyRIuX76Mt7c3IHc3AWzZsgUPD49s5RRkQHC1atUYM2YMY8aM4cGDB/z6669s3LiRPn364OrqyrFjx6hfv36Or82rm6swXWCCIJQ8EdAIglCqIiIiOHToEFOmTMHc3Ny4fcyYMSxZsoTvv/+eRYsWAXILjaWlJc8++2yxDMCNi4vj8ePHxMbGotfrqVatWrYuJQNnZ+ccW2EePnwIkGPrjSAIZUd0OQmCUKq+//579Ho9o0ePNtnesGFD2rZty9q1a41dQaGhoTRp0uSJgpmbN2/y6aef0rZtWxo0aMCXX35J9+7dOXnyJOHh4dSpUyfH1zVt2pSLFy+SlpZmsv3cuXMAeebOEQSh9ImARhCEUqPValm7di0BAQE0b9482/4xY8YQFRXFzp07iY2N5erVq/j7+xf6PPHx8SxfvpxOnTpRt25dPvjgA/z9/dm/fz83btzgk08+oVWrVnmWMXDgQBISEti8ebPJ9rVr1+Lu7p5jzhxBEMqO6HISBKHU7Ny5kzt37tC1a1e2bduWbb+lpSUgj7GxsbFBr9djY2PD8ePHsx1bq1atHMfVgDz25s033+TZZ59l27ZtBAYGYmZmVqi6BgYG0rNnTyZNmkRcXBxeXl5s2LCBXbt28eOPP4ocNIJQzojEeoIglJqBAwfmGMhkpVKpeP/993nzzTdzPWb16tWMHTs2x31xcXEolUpsbGyKWlVAXvpg7ty5/Pzzzzx8+BA/Pz/eeust/u///u+JyhUEofiJgEYQBEEQhApPjKERBEEQBKHCEwGNIAiCIAgVnghoBEEQBEGo8ERAIwiCIAhChScCGkEQBEEQKjwR0AiCIAiCUOGJxHqFpNPpuHPnDnZ2dmJxOkEQBEEoBL1eT3x8PO7u7igUxdumIgKaAlqxYgUrVqxArVZz5cqVsq6OIAiCIFRYN2/epHbt2sVapkisV0ixsbFUq1aNmzdvYm9vX9bVAUCj0bBnzx569eqVkd49MRHc3eXHbdrA3r1lV8EyluP1EQBxbfIirk3exPXJnbg2uXv48CH16tXj8ePHODg4FGvZooWmkAzdTPb29uUqoLG2tsbe3j7jj2f9+owDzMygnNS1LOR4fQRAXJu8iGuTN3F9cieuTe40Gg1AiQzZEIOCK6szZzIeh4eXXT0EQRAEoRSIgKaySk3NePzgAcTElF1dBEEQBKGEiYCmslKrTZ/fvVs29RAEQRCEUiDG0FRWWQMaQRCEYqDX60lLSyM1NRWVSkVKSgparbasq1WuaDSaKnttlEolKpWqTNKaVLmA5ubNm4wYMYLo6GhUKhXvvPMOgwcPLutqFb/MXU6CIAjFQK1WExUVRVJSEnq9Hjc3N27evClycmVR1a+NtbU1NWvWxNzcvFTPW+UCGpVKxbJly2jevDnR0dG0aNGCvn37YmNjU9ZVK16ihUYQhGKk0+mIjIxEqVTi7u6OSqUiMTERW1vbYk+QVtHpdDoSEhKq3LXR6/Wo1Wru379PZGQk3t7epfr+q1xAU7NmTWrWrAlA9erVcXJy4uHDh5UvoBEEQShGarUanU6Hh4cH1tbW6HQ6NBoNlpaWVepDuyB0Oh1qtbpKXhsrKyvMzMy4fv268RqUlgp3pQ8dOkT//v1xd3dHkiS2bduW7ZiVK1dSr149LC0tadmyJYcPH86xrFOnThn/QAVBEIT8VbUPaKHwyup3pMK10CQmJuLv78+YMWMYNGhQtv2bNm1i6tSprFy5ko4dO/L1118TGBjIhQsXqFOnjvG4mJgYRo4cyerVq/M8X2pqKqmZxqPExcUB8qAvQ4KgsmaoR+b6KPV6k2hVk5IC5aS+pS2n6yPIxLXJnbg2pjQaDXq9Hp1Oh06nw5Bk3rBNyFDVr43h90Oj0aBUKk32leTfU4Ve+kCSJLZu3cqAAQOM29q2bUuLFi1YtWqVcVvDhg0ZMGAAixYtAuQgpWfPnowfP54RI0bkeY758+ezYMGCbNvXr1+PtbV18byREtBu4UJqhIYanx/66CMe+fmVYY0EQajIVCoVbm5ueHh4lPpgT6FiUavV3Lx5k7t375KWlmayLykpiWHDhhEbG1vs2fYrXAtNXtRqNadPn+bNN9802d6rVy+OHj0KyBHz6NGj6d69e77BDMBbb73F9OnTjc/j4uLw8PCgV69e5Wrpg71799KzZ09jmm3lihUmx3Ro0gR9r15lUb0yl9P1EWTi2uROXBtTKSkp3Lx5E1tbWywtLY2rJtvZ2VXJmTx5qerXJiUlBSsrK7p06ZJtDE1MCSZ5rVQBzYMHD9BqtdSoUcNke40aNbibnljuyJEjbNq0iWbNmhnH3/zwww80bdo0xzItLCywsLAwrrZtyClgZmZW7v7JmdQpOdlknyo5WV7TqQorjz+z8kJcm9yJayPTarVIkoRCoUChUBi7UgzbyquuXbvSvHlzli1bZrJ927ZtDBw4kMydFGq1mmXLlvHTTz9x+fJlrK2t8fX1Zdy4cbz00kuYmZkxevRo1q5dy6JFi0y+PGcuL7drc+bMGd555x3+/vtv4uLicHNzo23btqxYsQIXFxcOHDhAt27dePToEdWqVSvR61KSFAoFkiTl+LdTkn9LlSqgMcgaEev1euO2Tp06FalPMygoiKCgIOLi4op9hdASce+efG9pCSkpEB9ftvURBEEox9RqNb179+bs2bO89957dOzYEXt7e44fP87SpUsJCAigefPmAFhaWrJ48WImTJiAo6NjgcqPjo6mR48e9O/fn927d1OtWjUiIyP57bffSEpKKtb3otfr0Wq1qFSV8iM+V+U3rC4CFxcXlEqlsTXGIDo6OlurTWGtWLGCRo0a0bp16ycqp9QYAhpPT/leBDSCIBQ3vR4SE0v/VgJDP5ctW8ahQ4f4448/CAoKonnz5tSvX59hw4Zx4sQJvL29jcf26NEDNzc347jMgjh69ChxcXGsXr2agIAA6tWrR/fu3Vm2bBl16tTh2rVrdOvWDQBHR0ckSWL06NGAPO5zypQpVK9eHUtLSzp16sTJkyeNZR84cABJkti9ezetWrXCwsKCw4cPc+XKFZ577jlq1KiBra0trVu3Zt++fSb1ioqKol+/flhZWVGvXj3Wr1+Pp6enSYtWbGwsr7zyCtWrV8fe3p7u3btz9uzZIlzlklWpAhpzc3NatmzJ3r17Tbbv3buXDh06PFHZQUFBXLhwweSXqNxKSoL02VgYZnYV8zcAQRAEkpJQ2NuDrW3p3krg/9lPP/1Ejx49CAgIyLbPzMzMJFeZUqnkww8/ZPny5dy6datA5bu5uZGWlsbWrVvJaS6Oh4cHmzdvBiA8PJyoqCg+//xzAGbPns3mzZtZu3YtoaGheHl50bt3bx4+fGhSxuzZs1m0aBEXL16kWbNmJCQk0LdvX/bt28eZM2fo3bs3/fv358aNG8bXjBw5kjt37nDgwAE2b97MN998Q3R0tHG/Xq+nX79+3L17l5CQEE6fPk2LFi14+umns52/rFW4gCYhIYGwsDDCwsIAiIyMJCwszPgDmj59OqtXr+b777/n4sWLTJs2jRs3bjBx4sQyrHUpM7TOWFiAi4v8OMuYGkEQBCHD5cuX8SvETNCBAwfSvHlz5s2bV6Dj27Vrx5w5cxg2bBguLi4EBgayZMkS7qX/v1YqlTg5OQFy0lc3NzccHBxITExk1apVLFmyhMDAQBo1asS3336LlZUV3333nck5Fi5cSM+ePWnQoAHOzs74+/szYcIEmjZtire3N++//z7169fnt99+A+C///5j3759fPvtt8YZwqtXryY50+fFn3/+yblz5/jll19o1aoV3t7eLF26lGrVqvHrr78W+HqVhgrXwXbq1CljsxxgnIE0atQogoODGTJkCDExMSxcuJCoqCiaNGlCSEgIdevWfaLzZh0UXK5FRcn3Li7yGBoQAY0gCMXP2hpdXFzpDwougZQZmcdaFtTixYvp3r07M2bMKNDxH3zwAdOnT2f//v0cP36cr776ig8//JBDhw7lOjHlypUraDQaOnbsaNxmZmZGmzZtuHjxosmxrVq1MnmemJjIggUL2LFjB3fu3CEtLY3k5GRjA0B4eDgqlYoWLVoYX+Pl5WUyLuj06dMkJCTg7OxsUnZycjJXrlwp0PsuLRUuoOnatWuOzXWZTZ48mcmTJxfreSvUoGBDd5O9vdxKAyKgEQSh+EkS2NhAOZ7lZG9vT2xsbLbtjx8/Nkm94ePjky1AyE+XLl3o3bs3c+bMMY53yY+zszODBw9m8ODBLFq0iICAAJYuXcratWtzPN7weZfXZBeDrEv4zJo1i927d7N06VK8vLywsrLihRdeQJ2+1l9un6WZt+t0OmrWrMmBAweyHVfeZmKV39/CcqZCDQo2ZGJUqTKmaotsp4IgVEF+fn6cOnUq2/aTJ0/i6+trfD5s2DDjWJOs0tLSSExMzLH8RYsW8b///c+Y66wwzM3NadCggbFsQ8LCzD0BXl5emJub89dffxm3aTQaTp06RcOGDfMs//Dhw4wePZqBAwfStGlT3NzcuHbtmnG/n58faWlpJu85IiKCx48fG5+3aNGCu3fvolKp8PLyMrm5GIY0lBMioCmgCjUo2BC8mJnJ36AEQRCqqMmTJ3PlyhWCgoI4e/Ysly5dYsWKFXz33XfMmjXLeNzUqVPp2LEjTz/9NCtWrODs2bNcvXqVn3/+mbZt23L58uUcy2/WrBnDhw9n+fLledZjx44dvPTSS+zYsYNLly4RHh7O0qVLCQkJ4bnnngOgbt26SJLEjh07uH//PgkJCdjY2DBp0iRmzZrFrl27uHDhAuPHjycpKYmxY8fmeU4vLy+2bNlCWFgYZ8+eZdiwYSZpS/z8/OjRowevvPIKf//9N2fOnOGVV17BysrK2PrTo0cP2rdvz4ABA9i9ezfXrl3j6NGjvP322zkGimVJBDSVUeYWGkEQhCrM09PTOIW5V69etG7dmuDgYIKDgxk8eLDxOAsLC/bu3cvs2bP5+uuvadeuHa1bt+aLL75gypQpNGnSJNdzvPfee/kOhWjUqBHW1tbMmDGD5s2b065dO37++WdWr15tzFpfq1YtFixYwJtvvkmNGjV49dVXAfjoo48YNGgQI0aMoEWLFkRERLB79+58c+B89tlnODo60qFDB/r370/v3r1NxssArFu3jho1atClSxcGDhzI+PHjsbOzM2b4lSSJkJAQunTpwssvv4yPjw//93//x7Vr1544HUpxq9BrOZUFwxiakliHoqg0Gg0hISH07dtXzsL4448wYgR07AitWsHnn8OECfDVV2Vd1TKR7foIRuLa5E5cG1MpKSlERkZSr149LC0t0el0xMXFYW9vX64zBZeFinxtbt26hYeHB/v27ePpp58uUhlZf1cyi4mJwcXFRazlVJYq1Cyn9AFfooVGEARByMv+/ftJSEigadOmREVFMXv2bDw9PenSpUtZV63QKlboWIYq5BgaEdAIgiAIedBoNMyZM4fGjRszcOBAXF1dOXDgQIVslRSfeJWRYdq2jQ0olfJjMctJEARByKJ379707t27rKtRLEQLTXmRkgL79sHHH0NExJOVZUhH7egIdnbyY7GWkyAIglCJiRaaAirRMTTffQevvioHNQAnTkD6mh5F8uCBfO/oKK97AiKgEQRBECo10UJTQCU6huaHH+RgxtBnmUNWy0IxtNA4OYmARhAEQagSREBTHjx6JN8/80zxlBcTI987OYkuJ0EQBKFKEAFNeWBIM11ca0QZWmiqVRMtNIIgCEKVIMbQlAeGgMaw0Fem1NRFYmjxcXSE9LVBSEh4sjIFQRAEoRwTLTQFVGKLUyYkZEyzdnOT759kirVOlzEoOPMYGhHQCIIgCJWYCGgKqMQGBV+6JN87OYGrq/z4SQKamzflAcYqFbi7g7MzKBSQnAy3bj15fQVBECqQrl27MnXq1Gzbt23bZlyA0UCtVvPxxx/j7++PtbU1Li4udOzYkTVr1qBJ/788evRoJEnio48+yre8rDw9PZEkCUmSUCqVuLu7M3bsWB4ZWtWFJyICmrJ28aJ8X79+xiynJwloDOV5espBjY0NGBZV+/PPopcrCIJQianVanr37s1HH33EK6+8wtGjR/n7778JCgpi+fLlnD9/3nispaUlixcvLlIgsnDhQqKiorhx4wY//fQThw4dYsqUKcX5VqosEdCUNUMA4uWVEdAY1mIqigsXMsozaN9evt+3r+jlCoIgZKXXQ1pi6d9KYE3lZcuWcejQIf744w+CgoJo3rw59evXZ9iwYZw4cQJvb2/jsT169MDNzY1FixYV+jx2dna4ublRq1YtunXrxsiRIwkNDTU55ujRo3Tp0gUrKys8PDyYMmUKiYmJxv0//vgjrVq1MpY1bNgwoqOjjfsPHDiAJEns3r2bgIAArKys6N69O9HR0ezcuZOGDRtib2/P0KFDSUpKKsLVKp/EoOCyFh4u3zdokDGAtzgCmkx/fHTuDF9/DQcPFr1cQRCErLRJKH6tXfrnfTEBVDbFWuRPP/1Ejx49CAgIyLbPzMzMZG0jpVLJhx9+yLBhw5gyZQq1axftGty+fZsdO3bQtm1b47Zz587Ru3dv3nvvPb777jvu37/Pq6++yquvvsqaNWsAuTXpvffew9fXl+joaKZNm8bo0aMJCQkxKX/+/Pl8+eWXWFtb8+KLL/Liiy9iYWHB+vXrSUhIYODAgSxfvpw33nijSPUvb0QLTVkzzHBycclooUlNLXp5hhafzAFN8+by/fXrUImicUEQhOJy+fJl/Pz8Cnz8wIEDad68OfPmzSvUed544w1sbW2xsrKidu3aSJLEp59+aty/ZMkShg0bxtSpU/H29qZDhw588cUXrFu3jpT0bPIvv/wygYGB1K9fn3bt2vHFF1+wc+dOErJM/nj//ffp2LEjAQEBjB07loMHD7Jq1SoCAgLo3LkzL7zwAn9WoqEIooWmrBmaEW1twcJCfvwkAY2hxcfHJ2ObYU2n+HiIjITGjYteviAIgoHSGt0LcSgUpfzdWGld7EXq9fp8B/VmtXjxYrp3786MGTMK/JpZs2YxevRo9Ho9N2/eZM6cOfTr149Dhw6hVCo5ffo0ERER/PTTTyZ10+l0REZG0rBhQ86cOcP8+fMJCwvj4cOH6NJTfdy4cYNGjRoZX9esWTPj4xo1amBtbU39+vVNtv3999+Fes/lmQhoCqjE1nIyRNQ2NmBpKT82rOlUWHfvylmCJUnuwsqsdm259UYENIIgFBdJkrt+SjugKQR7e3tic1hO5vHjx9jb2xuf+/j4cNHQwl1AXbp0oXfv3syZM4fRo0cX6DUuLi54pY9x9Pb2ZtmyZbRv354///yTHj16oNPpmDBhQo4DhevUqUNiYiK9evWiV69e/Pjjj7i6unLjxg169+6NOstwhczdZJIkmTw3bNM9ad6zckQENAUUFBREUFAQcXFxOBRXRl/ICGisrTMCmiK20EjHjskPfHzk8jKrU0cOaK5eLWJFBUEQKh4/Pz927tyZbfvJkyfx9fU1Ph82bBhz5szhzJkz2cbRpKWlkZqaio1N9nE7ixYtIiAgAJ/MreKFoFQqAUhOTgagRYsWnD9/3hj0ZHXu3DkePHjARx99hIeHBwCnTp0q0rkrm/IbVlcVhi6nzC00ycmQllbooqQjR+QHLVtm31mnjnx/5UoRKikIglAxTZ48mStXrhAUFMTZs2e5dOkSK1as4LvvvmPWrFnG46ZOnUrHjh15+umnWbFiBWfPnuXq1av8/PPPtG3blsuXL+dYfrNmzRg+fDjLly8vUH3i4+O5e/cuUVFR/P3338yaNQsXFxc6dOgAyGNsjh07RlBQEGFhYVy+fJnffvuN1157DZBbaczNzVm+fDlXr17lt99+47333nvCq1Q5iICmrGUOaBwdIT1a5969QhdlbKFp0yb7TkNAI1poBEGoQjw9PTl8+DBXrlyhV69etG7dmuDgYIKDgxk8eLDxOAsLC/bu3cvs2bP5+uuvadeuHa1bt+aLL75gypQpNDHk88rBe++9h76AU8nfffddatasibu7O8888ww2Njbs3bsXZ2dnQA6QDh48yOXLl+ncuTMBAQG888471KxZEwBXV1eCg4P55ZdfaNSoER999BFLly59gitUeUj6gv4UBABjl1NsbKxJ/2uRaLVy8juAc+fkbMGtWkFUFBw/Dpmm8uVFo9Gwa+tWnnnpJSSNBo4ehbp1TQ/avx9GjJCT7J0792T1rmA0Gg0hISH07ds3Wx9yVSeuTe7EtTGVkpJCZGQk9erVw9LSEp1OR1xcHPb29qU/KLicq+rXJuvvSmYxMTG4uLgUz2doFlXvSpcnmafYGfpmDes5FXKZgmoREXIw4+qa0RqTmSHAuXKlSN1ZgiAIglCeiYCmLBkWpTQzy5iynd6sWNiAxuXff+UHLVvKMw+yqldPnhqenAyGYwVBEAShkqiSAc3AgQNxdHTkhRdeKNuKGAKazCPnDQHNjRuFKqqmIZdAjx45H6BQgL+//Pjo0UKVLQiCIAjlXZUMaKZMmcK6deuKr0CtFsaPl3O9REQU/HWGLMGZA5pq1Uz3FcTNm1S7cgW9JEHPnrkfZ5j9ZBg8LAiCIAiVRJUMaLp164adnV3xFThlCqxeDbdvwyefFPx1hnWXPD0zthly3OSQCCo3it9+A0DfooW8hEJuWrWS70+cKHgdBUEQBKECqHABzaFDh+jfvz/u7u5IksS2bduyHbNy5Urj6OqWLVty+PDhkqtQcjJ8913G859+Kvh6SYYVVjNPBzQEWoUIaKQtWwDQ9+mT94GGZFGXL8ODBwUuXxAEQRDKuwoX0CQmJuLv78+XX36Z4/5NmzYxdepU5s6dy5kzZ+jcuTOBgYHcKOSYlAI7fFjO7Fu9ujxDKT6+4GNUzp6V7zMHNIZpbIbxNfkJD0dx+DB6SULXt2/exzo5gSH7ZEkGeYIgCIJQyirc0geBgYEEBgbmuv/TTz9l7NixjBs3DoBly5axe/duVq1axaJFiwp9vtTUVFIzLUUQlx5oaDQaNBoNip07UQK6Tp1Ao0Hxv/+hPXgQ3VNP5V2wTofqn3+QAI2fnzwOB5AcHFAB+jt3SNNo8q2f4vPPUQL3WrbE3t3dWE6ux7dpgzIiAu2+feieeSbf8isDTfp11BTgelY14trkTlwbUxqNxrhIok6nMyaSM2wTMlT1a2P4/dBoNMalHQxK8u+pwgU0eVGr1Zw+fZo333zTZHuvXr04WsSZPYsWLWLBggXZtu/Zswdra2ue2r6dakBo3bqYJybSDIj53/84ZhivkgvLBw/onZiITqFgZ1wc+vTuJ5VaTT9AunWLfRs2oM5j3ShlcjK9165FCVzt14/7hhafPLi7u9MaiN+5k4O9euV7fGWyd+/esq5CuSWuTe7EtZGpVCrc3NxISEgwWQQxPj6+DGtVvlXVa6NWq0lOTubQoUOkZcl7llTQIRlFUKkCmgcPHqDVaqlRo4bJ9ho1anD37l3j8969exMaGkpiYiK1a9dm69attG7dOscy33rrLaZPn258HhcXh4eHB7169cI+IQGzyEj0koT/iBHyStfffIPrf//Rt0sXOe9LLgzLFEhubgRmObe+Xj2kyEh6Ojmh79071zIUX3+NMikJXZ063Pf3p6e/P2ZZouFsatWCpUtxuHaNvu3by8stVHIajYa9e/fSs2dPkfE1C3FtcieujamUlBRu3ryJra0tlpaW6PV64uPjsbOzQ8op91UVVtWvTUpKClZWVnTp0iXHTMElpVIFNAZZf4H0er3Jtt27dxe4LAsLCywsLFixYgUrVqxAm96lY2Zmhtnvv8vna94csxo15HE0Hh5IN29itn8/DBqUe8F37sivrVUrexDSrBlERqI6fRpy6xbS6+Grr+SHI0eCQoGZUpl/QOPuDp6eSNeuYbZrF4wcmf9FqCTMzMzEB1MuxLXJnbg2Mq1WiyRJKBQKFAqFsSvFsK08Gz16NGvXrmXChAl8lf5/02Dy5MmsWrWKUaNGERwcTNeuXWnevDnLli0zOW7btm0MHDjQ2J0UHBzMmDFjjPurV69OmzZt+Oijj2jYsCEgX5uXX365wOcGiuX8jRs3LvK1Kg4KhQJJknL82ynJv6Xy/VtYSC4uLiiVSpPWGIDo6OhsrTaFFRQUxIULFzh58mTGxu3b5XtD140kQefO8uPMx+XEkK+mVq3s+5o3l+8NyfJycuQInD8PlpboXnwx3/qbMCzI9vnncmAkCIJQyXl4eLBx40aSk5ON21JSUtiwYQN1cloupgDs7e2Jiorizp07/P777yQmJtKvXz+TLrmSOndhzl9VVKqAxtzcnJYtW2br8967d69xafaiWrFiBY0aNcromtJqMxLUde+ecaCVlXyf30AwQ8CTUyTdooV8f+xY7oN8v/hCvn/uuYxkfAU1YoS81EJoqMgaLAhCken1ehLViaV+K8qayi1atKBOnTpsSU9zAbBlyxY8PDwIMKS0KCRJknBzc6NmzZq0atWKadOmcf36dcLDw0v83IU5f1VR4bqcEhISiMiUjTcyMpKwsDCcnJyoU6cO06dPZ8SIEbRq1Yr27dvzzTffcOPGDSZOnPhE5w0KCiIoKMi42jaXLslTtK2swM8v+wvyC2hOnZLvDdl7M2veXM4e/OgRhIVlP+b0afj1V/nxyy8X9q2AszMMGACbNsmJADt2LHwZgiBUeUlpSdReXLvUz5vwVgI25jb5H5jFmDFjWLNmDcOHDwfg+++/5+WXX+bAgQNPXKfHjx+zfv16IOdulZI8d0HOXxVUuBaaU6dOERAQYIxqp0+fTkBAAO+++y4AQ4YMYdmyZSxcuJDmzZtz6NAhQkJCqGtYbbqIsrXQGLqDmjUDVaa40N1dvr90KffCbt+Wx9AoldC0afb9KhW0ayc/3rPHdJ9eD0FB8v2AAaY5bArjlVfk++3bMzIWC4IgVGIjRozgr7/+4tq1a1y/fp0jR47w0ksvFbm82NhYbG1tsbGxwdHRkY0bN/Lss8/il8OX3OI+d2HPXxVUuBaarl275tvcOHnyZCZPnlys583WQnP+vLwja0Bi6C4ytMDkxBAMeXuDtXXOxzz1FPzxB+zfD2+9lbH9xx/lpQusrOCdd4r2ZkBuVerVSw6YZsyAnTuLXpYgCFWStcqauDfiSn1QsLVZLv838+Hi4kK/fv1Yu3Yter2efv364ZLXcjH5sLOzIzQ0lLS0NA4ePMiSJUuyDfwtqXMX9vxVQYULaMqNyEj5PvM6TCC3mCiVEBUlt8TkNOj3+HH53jD4NyeGfYbACeQlFd54Q3782mtyZuIn8c47csC0a5cc2FSxvDSCIDwZSZKwMbcp97OcMnv55Zd59dVXAbnlPSt7e3tic1h65vHjx9gbMrmnUygUeKVnX/fz8+Pu3bsMGTIk126k/M5dXOc/dOhQjmVXdhXnt7CMZetyMiylkDWgsbbOWF4gt1aagwfl+5zGzxh4e8v3UVEZyyB8/rn8vFYtmDCh0O8hm/r1M6ZtT5+eb5ZhQRCEiq5Pnz6o1WrUajW9c8jz5efnx6kc/nefPHkSX1/fPMueNm0aZ8+eZevWrUU6d0mfv7ITAU0BZZu2bQhocppyZxhHc/9+9n1372Z0OXXrlvsJ7e3B1VV+/N9/8PgxfPyx/HzWLMiSrKjIpk+Xz3X+PKTnQRAEQaislEolFy9e5OLFi9nS8oM8ZOHKlSsEBQVx9uxZLl26xIoVK/juu++YNWtWnmXb29szbtw4FixYkOPQiPzOXVznnzdvXpFmglV0IqApqqQkOe9M7RxG+BumbicmZt+3das8oLdZM6hZM+9zGKLxEydg3jw5qPHygueff6Kqm3B0lAcZA3zzTfGVKwiCUE7Z29tn674x8PT05PDhw1y5coVevXrRunVrgoODCQ4OZrAhh1ceXn/9dS5evMi2bdsKfe7iPP8vv/yS77GVjRhDU0BZMwUD8hgWC4vsBxsG+iYkZN9naArs0yf/k3boAH/9BQsWyMsqgDxAOL9swIX14ouweLHccnT1qtwVJQiCUEkE59P6nDX4aNmyJbt27crzNaNHj2b06NHZttepU4fU1FTjQsaFPXdxnL+qLqgqWmgKKMdMwbl98BuyEl++bLr9zh3480/5cb9++Z+0Sxf53hDMvPlmwQKhwqpeHdq3lx//+GPxly8IgiAIJUwENE/CMHA3K8PA4axZeL/6CtLS5MHAhoHDeWnWTE6CB3Iw89prRa9rfgzrTv30k1gOQRAEQahwREDzJHx8ct7eqpV8f/kyPHggP1arM8aojB1bsPKVSjmb74YNJRvMAPTtK3efXbokZycWBEEQhApEBDRPIrcWGkfHjO6oEyfk+19+gXv35JlLffsW/BwNG2Z0PZUkOzt4+mn58bp1JX8+QRAEQShGIqApoGx5aCD3gAbAsOCYodvJsJjkiBFQXtfZMKzavW4dpKSUbV0EQRAEoRBEQFNA2QYF29tDXmmrDd1Ox4/LCfb+/lteo2nEiJKvbFF17y7n0Hn4EH7+uaxrIwiCIAgFJgKaovL0lPPQ5Cbzmk4LFsiP+/WTZxSVV0olpK8Ey8qVZVsXQRAEQSgEEdAU1bhxee/39ZUDnrg42LEDFAo5K295N2yY3JJ04gScPVvWtREEQRCEAhEBTVF16pT3fjMz0y6pgQMLNlW7rFWvDoY1RqZOhRwWSRMEQRCE8kYENAWU46Dg/BiWNlCpYObMkqlYSQgKkgOyAwfknDn//lvWNRIEQRCEPImApoByzBScH8NK3EOG5LyIZXnl7w/btskDhK9cgbZtRW4aQRAqrOjoaCZMmECdOnWwsLDAzc2N3r17s2jRIiRJyvMWHBzMgQMHct1/9+5dAObPn2/cplKpaNiwIS+99BI3b940qUvXrl1zLGfixIkmx+3YsYOuXbtiZ2eHtbW1cU0nIXdiLaeSNGuWnHxv/PiyrknhNW8Ou3fDyy/DyZPwyivyjC1FMcfAUfvgzAxouxqcC9H6JQiCUECDBg1Co9Gwdu1a6tevz7179/jjjz9o1KgRUVFRxuNef/114uLiWLNmjXGbg4MDJ9LziYWHh2dbWLJ6pokejRs3Zt++faSlpfHPP//w5ptv8uKLL3Ls2DGT14wfP56FCxeabLM2rAEILF++nKlTp/LGG2+wcuVKzM3N2b59OxMnTuTff/9l6dKlT35RKiER0JSk+vVh2rSyrkXROTnB119D585yUPPttzBhQvGe489egB72dYEhycVbtiAIJUqvh8TE4v+ekx9r67wnmWb2+PFj/vrrLw4cOMBTTz0FQN26dWnTpk22Y62srEhNTcXNzS3HsqpXr061atVyPZdKpcLNzQ2dToetrS3jxo0zBkmZAyFra+tcz3Hz5k1mzJjB1KlT+fDDD43bZ8yYgbm5OVOmTGHw4MG0bdu2IG+/ShFdTkLeatTIGP/z1lsZC2XmJ/kuaFMLcGD6ulFakchPECqapCSwt1dga0up3pKSCl5HW1tbbG1t2bZtG6mpBfmfVDzu3bvH1q1bUSqVKJXKAr/u119/RaPRMDOHcZcTJkzA1taWDRs2FGdVKw0R0Aj5e/lleRr6o0fymlL5LV6ZEAlba8Ku9OSCSbdhZ0u48p3pcddy+KPU62DvU7C7HejSCla/tOTsdUpLhhu/gPoxAAp96f0jEwSh/FCpVAQHB7N27VqqVatGx44dmTNnDv/880+hy6pdu7YxQLK1tcXX19dk/7lz54z7/Pz8OHDgAEFBQdjY2Jgct3LlSpNybG1tWbt2LQCXLl3CwcGBmoZJJZmYm5tTv359Ll26VOi6VwWiy0nIn0oFixfD88/LC2XWqQMffZTzsY/+gZOT5Mex/0LEd3BnBzwKhRPjoEGmhTmPDjN97ZXvwa0n3D+U/voL4Ngs77olRMLvTaDOi9A+o9+bK6vh9BTwfR2pem/6JQ1FH74Imswu3HsXBCFX1tYQF6dDUcp9TpmGmxTIoEGD6NevH4cPH+bYsWPs2rWLjz/+mNWrVzN69OgCl3P48GHs7OyMz1Uq049QX19ffvvtN5KTk/n555/ZsWMHH3zwQbZyhg8fzty5c022VS9g0lW9Xo9U0P62KkYENAW0YsUKVqxYgVarLeuqlI3WrWHRInjjDTm4cXaWBz1ntdPf9PnfWRIQnp4KqY+gfXD2154YC122Zzx/eDr3gObYy/D4DHhPAm0S3NoK+u8zOtbj0r/BPDqDMuJbJHTwzxvQeBYk3QDrOnl3wqclgyYWrHLu5xYEQf4TsrEp/TE0RWFpaUnPnj3p2bMn7777LuPGjWPevHmFCmjq1auX5xgac3NzvLy80Ol0zJgxg5s3bzJp0iR++OEHk+McHBzwyiUvmY+PD7Gxsdy5cwd3d3eTfWq1mqtXr9K9e/cC17kqqQC/huVDkaZtVzYvvSQHNACzZ8OPPxa+jPDP4do62Oqe8/67ezMePzyV8zHJdyFyDTwKgyvprTKaWEi4mnFMarR8Hx+BpM3U4X55JWz3hIivci5bp4XH5+DP3rC9LiTdkrfrdXDmDbgqViIXhMqgUaNGJCYmlug53n77bTZs2EBoaGiBXzNo0CBUKhWffPJJtn1fffUViYmJDB06tDirWWmIFhqhcF57TR4YvHo1jB0rdz916SLvy29sTWYpd3PefndfxuOH6f8Eku/CgUCo/zL4vgZ3QjKOiTme6fFJiL8Ml1ZAwpX0194xLd/w2kfncj7/uflw/v2M5w9OQJ3acP8oXPwYVHZQb4QcTP01GJq9B57in4sglFcxMTEMHjyYl19+mWbNmmFnZ8epU6f4+OOPee655wpVVnR0NCkpphMYnJ2dMTMzy/H4+vXr89xzz/Huu++yY8cO4/akpCRj/hoDCwsLHB0dqVOnDh9//DEzZ87E0tKSESNGYGZmxvbt25kzZw4zZswQM5xyIVpoBFN3N0PYENA8ynm/JMG8edCnD6jVMGAAGAao6Yph4G3cfxmPH/8jt5jc2iYHEBfTv7Hc3pHTK+HhSTnouLMD4i5m261X2crlAKQlyPd/DYGQ5hkzsjIHM5nrE5PeMpcWLwdJN36Vg6bI9Kbk5CjQqgvxRvOh1+W+T5cmn08QhHzZ2trStm1bPvvsM7p06UKTJk145513GD9+PF9++WWhyvL19aVmzZomt9OnT+f5mhkzZvD7778bc9kAfPvtt9nKydzqMm3aNLZu3crhw4dp1aoVTZo0Yf369axatUrkoMmDaKERTN0JhrhQeHQYqj+b8zEKBXz5JQwaJC9g2bevnHRPlz2IeCLaJIi/lDEeJuk6JN4wbcXJ7OGpjC6inOi1GS028Zfh5ha48bP8/N4BqNE1+2sMAU3m7q+4cEhM795KvCbXb0dDcO8LXf8nT0FXWoImDlIfgG39vN9n6AyIvQhdtoHSXG5hCpsD3XeDS7vsxx8bCdc3Qt9/oFqTvMsWhCrOwsKCRYsWsWjRonyPzS0Tb9euXdHn0wI9f/585s+fn217hw4dTF574MCBfOsB8Oyzz/Lss7n8DxZyJFpoBFNp6X3KmnwWpbSygrVroVYteXmEaR3l5HjFxdpDvn94Wg5qDC4ulVtJzJ3Bzsf0NQ/D8gxoJG2mxH0xx+HwoIznmscZrTCZxV+W7x9l6gOPuwgJ1+THidch5m9AJwda13+Bn23h8tfwZyD8z0+eiZXZvYNw9u2Maen/fQpROyF0OhzsD6dehbQ4OXDJycNQQA+PxGrogiAIBiKgEUxp0wMabVzGNr0WTj8D58bIz+/+ArGhoD8Oq2dCU2to/QR5ETpuhBZfZDxX2UCNbvLjh6cgPiJjX8TX8n31ruDa0bSctDjQFbHbJ/Em3NuffXv8JTnIi8v0/mL/k1tmQG5Fepje5KxLgavfydcr8ge5S0qvyWhRijklBzenXoPzH8CdnabnurzCtDstLX0wc0q0aeJBdXpyw9T7pq/Pq5tKEAShkquSAc2OHTvw9fXF29ub1atXl3V1yhddeitGWnzGtpTbEH8GYvbIXVH/TYUz/eHCJHg4Dd5MApcnOKelG1jXznhuUw+qNZcfx5yExEwtHIaAxa0HuHTK2J61taawEiMh+kD27epHcHc/kClYePyP6aDm6MMZjx+kr9kS87fc5WTYn3QH9rSDP57OGLBsGM+TG32aPCB6Wx3Y3zN9mw5SH8qPUx/IY4x0aRC+HH51htshuZcnCIJQiVW5MTRpaWlMnz6dP//8E3t7e1q0aMHzzz+Pk5NTWVetfDBMcU6Lg8RwSLwMVp4Z+x+fyHRwIWY1GSgs5ZaMzCxcQGmV8dy2HlRrKj9+cByTYAJAUkL1p+QByhbVwbI6OAaYdk0VVvxleUaTSV3N5QDq+nr5uZmD3BVnCFoMHp/JeGwIYvQa0GrS38MxeSq4XmsanMX+KycPzI1OI3dv6VLlWVZpiemDl9OvR8p92N1Kzplj5SZ3mx0bCf3+FflzBEGocqpcC83ff/9N48aNqVWrFnZ2dvTt25fdu3eXdbXKB73OtIXmwmS4MAFiM02Nzm32U0FY1oD6YzKeu3aBbnvA3FHeZ/h1tGkAdt5yQGH48LapLwcUAI4twNwBzOyhx2E5GZ9jQNHrBRB9MCOYM3BsKd/f+k2+d+8n3+s1psfl19WTEGE6vdwgai/83jj31+nUGa0x6CCkGZwKylTuFbmVJz48fRwPcnfUsZGFm0IvCIJQCVS4gObQoUP0798fd3d3JEli27Zt2Y5ZuXIl9erVw9LSkpYtW3L4cEaXwJ07d6hVq5bxee3atbl9+3ZpVL38yzxoVhsPKekDbOMyDYhNLeJ0Yeu60CcU6r2Usc22Hjikf6ArzOSWFgA7L/m5vV+mY+uDc/rquDV7ZWw3s5VnFFXLJaOwc1v0lu6oSU9XrrDI+ThjV1Yv6LABngqR6wEZgY5bD1DZFuz9ZhX5Q/Zt+QWHaQnw1wsZzxOuyrObDDK3SGX+2d3dC7Hni1ZPQRCECqrCBTSJiYn4+/vnmj9g06ZNTJ06lblz53LmzBk6d+5MYGAgN27cAMhx6p1YFyOdLlMLheYhaNNztSRmyg2TfKVoZVu6yvcWmdYryTqA1z1Q7n5y7SA/d2iasc+2PjR9D5q8Cw2yLKcA6S06lvJjj8FyArzmH0OnX0nrfphUydC60zzverp0gOpdwNEfbLOkJq/WVG4pMjCrZrrf0IKUk4QiXre8JN3Mvk2VvgieJj77PkEQhEqswo2hCQwMJDAwMNf9n376KWPHjmXcOPlDb9myZezevZtVq1axaNEiatWqZdIic+vWrTyzLqampposOR8XJ4+R0Gi1aMrJuk6GejxxfdRxGPJd6pOvYwjz9ElXMx5n2p4XfYqEZCkHj1rnwei9X0CfpgUsjOfQpTxEm5apzo0WQMN5ICkgTYtk39T4C5pmXQ+9hTt4jpN7oXRZ36uE0rkNivuH0HiOhmZL5HK0ejQ6BWmSFehB69QOZYw8Vkbn0Awp9hxSprFAGse2kF4nybq+8fx6cyfSVNVR2tRDESuv0qtzbo/irjxTSa+0Ru/cHsXdXQW4OiVDLynB3BkpLZE0TQp6jSbf12jSj9EU4NiqRlwbUxqNBr1ej06nQ6fTGb8cGrYJGar6tTH8fmg0GpRKpcm+kvx7qnABTV7UajWnT5/mzTffNNneq1cvjh49CkCbNm34999/uX37Nvb29oSEhPDuu+/mWuaiRYtYsGBBtu17Ll/Gupx1Ve09+2R5Sex110ifLI2keWDcLmUaMyJlHT+SgzTMCVe8SGPktZ5SRxzgyPs9SKohd10Zko3H3L/N0T9zX+PEQauia/rjE5fTeHA17/VQVPpXsLYcQFyoBggz2ddI2QR73XUO3mqAYVm3/epJpFrb0z5lIU66cNTYsPN0Ekjyeax1SaTPLSJa68nxA2fwUdvQMH3buYceGJbiTNA5cf2hG4Y0dxqsMSPLmJxM8ttfFCk4oEnWYw8cP36EGGVcvq8x2Lt3b/4HVVHi2shUKhVubm4kJCSgVme0rsbHi9bA3FTVa6NWq0lOTubQoUOkpaWZ7EtKKt7/e5lVqoDmwYMHaLVaatSoYbK9Ro0axnUzDIt+devWDZ1Ox+zZs3F2ds61zLfeeovp06cbn8fFxeHh4UEvb2/ss5ynrGi0WvaePUtPf3/MskTDhSHF6aA4crV5f4yPQ1u0Fx+g+Pok1vfv02PhfNJ+/xnq1YX0VCsuNhr6PtUi93K0jdHvfgd0qbTpFJgxxqaQNGla9h4eRa1uH9HZ3Iq0uzaQlsBTteVBvsrTXhAVjqp6G/q2aZ3xQr0W/U5zJJ0aF8929G3YAunOHQiVZz01ajcI/eHvkfRp2Dh54ufTD44Fy2W6dYW7IeiVNqAwQ9I8NqmTsmZ3iMplCYcisrRzw1KXBgnQrk0r9DWezvc1Go2GvXv30rNnz1zXo6mqxLUxlZKSws2bN7G1tcXS0hK9Xk98fDx2dnYVutv+2rVrNGjQgNOnT9O8efMcjwkODmb69Ok8fPgwx/1Zlcdrs2DBArZv316ohTKLKiUlBSsrK7p06YKlpaXJvpiYmBI7b6UKaAyy/gLp9XqTbYVJKW1hYYGFhQUrVqxgxYoVaNO7dcyUyicKHkrCE9dJXwxrMTX+FpV9epDS7AN49z5cHI907SZmfQfDrl+Nh0oKFWaqPOqrsoZ2waCJw8y25hNXzczcSj5f7T6mO9yehqjfUdQdgsKkPkqwbQBxF1G6tEKpUoKDd0Z59vXAqhYkXUdh447Cubk8pVyvReHeE5IikRwag/qhnLRPZWfM76Nw75M9oFFYPNF6WJJldTkJH6BSAIX4EDYzMxMf2rkQ10am1WqRJAmFQoFCoTB2pRi2lUf5BROjRo0yLldgeF85GTp0KM8880yB32fWa+Pp6cn169cBsLS0pG7duowdO5aZM2eWWsBjOI/hPYwePZrHjx/nOLHmSSkUCiRJyvFvpyT/lipVQOPi4oJSqcy2iml0dHS2VpvCCgoKIigoiLi4OBwc8hj8WZEZsgQ/CaWN6fMarrDxWxg6ASKvQ+9BsOkDePQ9BHyaf3nVO+V/zJOq8yLUehZUVtn3+S+Wp1y7pXc+2fnICf2sasgDcK1ry2tMWdWSAzB7P3mGkaUbdE/PEHxpuRzQODaXBzpLErjkMG6r/Y9w7t0cF9YsEAtXOdkeyDlvBKGKi4rKmJW5adMm3n33XcLDw43brKysePQo/1QUVlZWWFnl8P+hEBYuXMj48eNJSUlh3759TJo0CXt7eyZMmPBE5QoZymdYXUTm5ua0bNkyW5/33r176dChwxOVvWLFCho1akTr1q3zP7iiypqHpSgUOfzRV3eFjd9AA0+IugeDl0Gd78vPwoqSlHMwA+DcEnyC5AHGAAoVdNoELdOXanDvK0/lrv6U/DzgE2j8Nri0zyij3iioOxT8ZkGTudB4DlhmanFq8i60/0F+jSpLQGjgNTH/92FZA6T07yj6tLyPFYRioAcSy+BW0CxLbm5uxpuDgwOSJGXbZnD16lW6deuGtbU1/v7+HDuWkUAzODiYatWqGZ+fPXuWbt26YWdnh729PS1btuTUqVPkxc7ODjc3Nzw9PRk3bhzNmjVjz549xv1qtZrZs2dTq1YtbGxsaNu2rclCltevX6d///44OjpiY2ND48aNCQkJybF+ANu2bcu19Wf+/PmsXbuW7du3I0kSkiQVeNHM8qzCtdAkJCQQEZGxtk9kZCRhYWE4OTlRp04dpk+fzogRI2jVqhXt27fnm2++4caNG0ycWIAPhDxUjRaahCcvQ5lLYODqAhu+heET4PJVuaVm56/g553z8RVF/dFQb2RGwFOtaUaWYwMzewhYarpNkuRA5v4xqDcalOn5cex8TVf2Nqg7VG4dOpM+nktSZQ9aLGvIAReIFhqhVCQBtcuguykByCX0L7K5c+eydOlSvL29mTt3LkOHDiUiIgKVKvvH5PDhwwkICGDVqlUolUrCwsIK3JWi1+s5ePAgFy9exNs74//fmDFjuHbtGhs3bsTd3Z2tW7fSp08fzp07h7e3N0FBQajVag4dOoSNjQ0XLlzA1rZoebFmzpzJxYsXiYuLY82aNQCVIlt+hQtoTp06Rbdu3YzPDQN2R40aRXBwMEOGDCEmJoaFCxcSFRVFkyZNCAkJoW7duk903qxjaCql4mihUVrnvs/FCdZ/Iwc1l65Anxdg5y/Q8AnXYSprUhH/oXtNkG+ZNX5L7jZSKOVgx5B8T2Vjmg3Zpm723DaW1TE2uupEC40gFMbMmTPp10+eKLBgwQIaN25MREQEfn5+2Y69ceMGs2bNMu7LHJjk5o033uDtt99GrVaj0WiwtLRkypQpAFy5coUNGzZw69Yt3N3djfXZtWsXa9as4cMPP+TGjRsMGjSIpk3lL0z169fP9Vz5sbW1xcrKitTUVNzcKs8yKRUuoOnatWuOyfEymzx5MpMnTy7W81aNFpocxtBkbgkwc5IT7uVFYZn3fmdHWP81vDQJ/ruc3lLzCzTO/k+jSjJ3hHbfy4/1ejj0rLymk2UN02Axt4BGIbqchNJjDcTpdKU+KDiPr01F1qxZRrbxmjXlLuHo6OgcA5rp06czbtw4fvjhB3r06MHgwYNp0KBBnuXPmjWL0aNHc//+febOnUv37t2NQyFCQ0PR6/X4+Jh+uUtNTTXOwp0yZQqTJk1iz5499OjRg0GDBpnUWahkY2iEJ5RTC41Vpm8BmRepBLDItEK2bVOo95Y8yyc/TulBTUMfiHkot9Scy2ORxqpKkqDLb9B1p9wKZO4A9V+GusOyLAvhLc+gsvPJaC0SXU5CKZCQu35K+1YS84IydxkZxp7klhRv/vz5nD9/nn79+rF//34aNWrE1q1b8yzfxcUFLy8v2rdvz+bNm/nss8/Yt2+f8TxKpZLTp08TFhZmvF28eJHPP/8cgHHjxnH16lVGjBjBuXPnaNWqFcuXLwfkWUVZv+hXxYSQIqApoKoxKDiHFhob34zHlp4Zj/1/gea/QL03wSUQmqwGtxeyvTxX1RzkoKaxLzx8BIGD4ey/Ra56pSVJ8s2g2XsQsATMM/V3d9sFvU+BebWMQcG6qvfPTBBKk4+PD9OmTWPPnj08//zzxrEoBeHo6Mhrr73GzJkz0ev1BAQEoNVqiY6OxsvLy+SWuUvIw8ODiRMnsmXLFmbMmMG3334LgKurK/Hx8SQmZvwPDwsLy7MO5ubmlW4IhQhoCigoKIgLFy5w8uTJsq5KycmphcYmPS+uwgosMvW1mjnJq2G7DQbv9zM+SAvDwR5++hqaNoRHj6Hfi3DmXJGqXuUYAhqVvbw4p1n64EDRQiMIJSo5OZlXX32VAwcOcP36dY4cOcLJkydp2LBh/i/OJCgoiPDwcDZv3oyPjw/Dhw9n5MiRbNmyhcjISE6ePMnixYuNM5mmTp3K7t27iYyMJDQ0lP379xvP2bZtW6ytrZkzZw4RERGsX7+e4ODgPM/v6enJP//8Q3h4OA8ePKgULToioBEyGFtoMrUI2DaS782qgXmmTL1Z880Ulb0d/LAKmjWCR7HpQc0/xVN2ZWaRHtCYVzPdLlpoBKFEKZVKYmJiGDlyJD4+Prz44osEBgbmuEROXlxdXRkxYgTz589Hp9OxZs0aRo4cyYwZM/D19eXZZ5/lxIkTeHh4AHJiw6CgIBo2bEifPn3w9fVl5cqVgDxD6ccffyQkJISmTZuyYcMGY8LA3IwfPx5fX19atWqFq6srR44cKdL1KE8kfX4jbAXAdJbTpUuXiA0NLVdLH4SEhtK3RYsnyxR89v/g0WFQOULaI1DaQes/4fZ3YO0nz7y5+CpIZtDuePG9AYD4BBgVBGH/ykHOjo3QsnmxFK1J0xLyZyh9u7XIOzNxRaJ+DIcHQa1+4JexNAcnxkNUiJwnx/e1fIvRaDSEhITQt29fkQ03C3FtTKWkpBAZGUm9evWwtLREp9MRFxeHvb19uc0UXFaq+rXJ+ruSWUxMDC4uLsTGxmJvb1+s5616V7qIqlSXk6ElxqyaPH6j9jhw6pQxCNisBPIV2NnCupXQohnExUO/IXCy5NccqbDMq8HTf5gGMyC6nARBqLJEQCNkMAY0rvK9qprpfisP8HoPvD4omfPb2sDaFdCqudxi88z/wYm8s28KWYguJ0EQqigR0AgZDAGNYdyMtVf2Y1z7gkNA9u3FxcYagr+ENi0gIRH6D4UjJ0rufJWNYdq8aKERBKGKEQFNAVWJadu69IDG6Snw/xXqzS6belhbwZovoF1LSEyCAcPh8LH8XyfI45xAZAoWBKHKEQFNAVWpMTQKa7CuJ0/LLitWVvDd59C+NSQlw8ARELI3/9dVeaKFRhCEqkkENIJMrwdtsvw4twUmS5uVFXy3DDq1heRkeHEMfLpCrquQM0MLjVj6QBCEKkYENIJMlwKkp/lWlJOABsDSElZ/DkMGyIHMOx/C+NchNbWsa1Y+SWItJ0EQqiYR0AgyQ+sMlJ8WGgNzM/jwbXh3JigUsGGzvP7TvftlXbPyRxItNIIgVE0ioCmgSj8o2JAlWDIv2AKTpU2SYPRQ+P4LeXr336HQpS/8c76sa1a+GKdtizE0giBULSKgKaBKPyhYk97aoXIo23rkp0t72LYO6taGW3fg6efgt51lXavyw5hYT7TQCEJBXLt2DUmS8lzMMTg4mGrVqpVanUrC/Pnzad68eVlXo0SJgEaQJd+U7y3dy7YeBVHfE7b9kDEDaug4WPy5GCwMoDC00IiARhAkScrzNnr06AKVM2TIEC5dulTkenh6ehrPaWVlhZ+fH0uWLKEsVx4aPXo0AwYMKLPzlwQR0AiylGvyvUXNMq1GgTnYw9ov4aXB8vOFH8OYIHk2VJUmxtAIgkFUVJTxtmzZMuzt7U22ff755wUqx8rKiurVq+d/YB4WLlxIVFQUFy9eZObMmcyZM4dvvvnmicoUTImARpAl35DvLWqVbT0KQ6WChW/KN5USftkOvQZB1L2yrlnZUYg8NELp0QOJZXAraLuGm5ub8ebg4IAkSdm2GVy9epVu3bphbW2Nv78/x45lJPPM2uV09uxZunXrhp2dHfb29rRs2ZJTp/JepsXOzg43Nzc8PT0ZN24czZo1Y8+ePcb9arWa2bNnU6tWLWxsbGjbti0HDhww7r9+/Tr9+/fH0dERGxsbGjduTEhISI71A9i2bRuSJOVYl/nz57N27Vq2b99ubDnKfK6KSlXWFRDKiZT0LifDApQVyUuD5W6oybMg9Cx0DoRf1kJA07KuWekT07aFUpQE1C6D1aQTAJtiLnPu3LksXboUb29v5s6dy9ChQ4mIiEClyv4xOXz4cAICAli1ahVKpZKwsLACr8iu1+s5ePAgFy9exNvb27h9zJgxXLt2jY0bN+Lu7s7WrVvp06cP586dw9vbm6CgINRqNYcOHcLGxoYLFy5ga2tbpPc6c+ZMLl68SFxcHGvWrAHAyakEFh0uZSKgEWQpt+X7ijCGJicdWsvjasa9DlevQ48B8M0yGNS/rGtWusRaToJQJDNnzqRfv34ALFiwgMaNGxMREYGfn1+2Y2/cuMGsWbOM+zIHJrl54403ePvtt1Gr1Wg0GiwtLZkyZQoAV65cYcOGDdy6dQt3d3djfXbt2sWaNWv48MMPuXHjBoMGDaJpU/mLWv369Yv8Xm1tbbGysiI1NRU3N7cil1PeiIBGkAfTqqPkxxYV+Jfb0wO2rIUpb8GhYzByIvwXDrOnlnXNSo8YFCyUImsgTqdDUcqtNNYlUGazZs2Mj2vWlMcSRkdH5xjQTJ8+nXHjxvHDDz/Qo0cPBg8eTIMGDfIsf9asWYwePZr79+8zd+5cunfvTocOHQAIDQ1Fr9fj4+Nj8prU1FScnZ0BmDJlCpMmTWLPnj306NGDQYMGmdRZEGNoCqxS56HRPAJdeuZd8ycb+Fbm7O3kNaDGDJOff/gZylGTUFaZzMJiULBQeiTkrp/SvuU8MuTJZO4yMow90el0OR47f/58zp8/T79+/di/fz+NGjVi69ateZbv4uKCl5cX7du3Z/PmzXz22Wfs27fPeB6lUsnp06cJCwsz3i5evGgcuDxu3DiuXr3KiBEjOHfuHK1atWL58uUAKBSKbDOmNBpN0S5EBSYCmgKq1HloUu/I9yrHsl2QsrgolfDODDm7sEqFYnsInd56C25HlXXNSp4YFCwIpcLHx4dp06axZ88enn/+eeNYlIJwdHTktddeY+bMmej1egICAtBqtURHR+Pl5WVyy9wl5OHhwcSJE9myZQszZszg22+/BcDV1ZX4+HgSExONx+aVVwfA3NwcrbZy/Z8QAY2QEdBU9NaZrP5vIPy4Cn01B6pdvYqq2zNw6kxZ16pkSaLLSRBKUnJyMq+++ioHDhzg+vXrHDlyhJMnT9KwYcNClRMUFER4eDibN2/Gx8eH4cOHM3LkSLZs2UJkZCQnT55k8eLFxplMU6dOZffu3URGRhIaGsr+/fuN52zbti3W1tbMmTOHiIgI1q9fT3BwcJ7n9/T05J9//iE8PJwHDx5UihYdEdAIGQOCK1tAA9CmBWlb1hJXpw7SvfvQ63nYtLmsa1VyxKBgQShRSqWSmJgYRo4ciY+PDy+++CKBgYEsWLCgUOW4uroyYsQI5s+fj06nY82aNYwcOZIZM2bg6+vLs88+y4kTJ/Dw8ABAq9USFBREw4YN6dOnD76+vqxcuRKQZyj9+OOPhISE0LRpUzZs2MD8+fPzPP/48ePx9fWlVatWuLq6cuTIkSJdj/JE0pdlqsIKKC4uDgcHB2JDQ7GvUaOsqwOARqslJDSUvi1aYKYswjpMEfPh1rdQYzDUf7PY61fWNFode05FEPj1lygOpP/RznwV5r0hL3ZZmVz/Gc5MA7ee0H1PvodrNBpCQkLo27dvgaedVhXi2phKSUkhMjKSevXqYWlpiU6nIy4uDnt7+1IfFFzeVfVrk/V3JbOYmBhcXFyIjY3F3t6+WM9b9a60kF1cejeMTaOyrUcJSrOyQvvVJzB+pLxh6ZcwdCwkJOb9wopGjKERBKGKEgFNVadTQ/w/8mP7Sj4FUKmEt16HJQvAzAx27IHuz8KNW2Vds+IjEusJglBFVcmAZuDAgTg6OvLCCy+UdVXKXsJ50KtBaQ+Wdcu6NqVj0DOw4RtwcoTz/0HnvnC8ksxeM6y2LQYFC4JQxVTJgGbKlCmsW7eurKtRPsSmf5DbNoFc1v2olFo0g+0/gJ83PIiBwMHw46ayrtWTM7bQ5Jw/QxAEobKqkgGNYVExAYj9W7639y/bepSFWjXh1zXQ8ylQa2DCdJizECpybgZJJNYTBKFqKncBzaFDh+jfvz/u7u5IksS2bduyHbNy5Urj6OmWLVty+PDh0q9oZaB5BA//lB87tC3bupQVaytYtRSCxsrPP/8aBo+GuPgyrVaRiWnbgiBUUeVuLafExET8/f0ZM2YMgwYNyrZ/06ZNTJ06lZUrV9KxY0e+/vprAgMDuXDhAnXq1AGgZcuWpOaQ6n7Pnj3Ghb8KKjU11aSsuLg4QJ4qrSkn3+QN9ShsfRS316PUpaC38iLNqhFoK2c3hSb9fWnyen9TJyI18ET51ntIu/ej79qftE3fQ72KNa5I0itQAXqthrQCJMoyJNOqDEm1ipu4NqY0Gg16vR6dTodOpzOm2jdsEzJU9Wtj+P3QaDQos6QSKcm/p3Kdh0aSJLZu3cqAAQOM29q2bUuLFi1YtWqVcVvDhg0ZMGAAixYtKnDZBw4c4Msvv+TXX3/N87j58+fnmDBp/fr1WFuXxBJppUSvpUfyZGz09zhjHsQNs55lXaNyodrly7T98EMsHz0i1c6Ok2++SUzjxmVdrQJz0Z6lY8o84qQ6/Gn9RVlXR6hEVCoVbm5ueHh4YG5eCZZIEUqMWq3m5s2b3L17l7Q00+7vpKQkhg0bViJ5aMpdC01e1Go1p0+f5s03TZO/9erVi6NHj5bIOd966y2mT59ufB4XF4eHhwe9vL3LVWK9vWfP0tPfv8CJ9aSYfagu3EOvtKVJ0/+jidIy/xdVUBqtjr3/RdHTryZmynx6WRvXgnaN0E2YjsWFcDrOm4f20/fRjxpWOpV9QlKMGo6BnY0FfQP75nu8RqNh79699OzZUySPy0JcG1MpKSncvHkTW1tbLC0t0ev1xMfHY2dnZ1zMsSK6du0aDRo04PTp0zRv3jzHY4KDg5k+fToPHz4sUJnl8dosWLCA7du3ExoaWuLnSklJwcrKii5duuSYWK+kVKiA5sGDB2i1WmpkCSRq1KjB3bt3C1xO7969CQ0NJTExkdq1a7N169ZcV9G2sLDAwsKCFStWsGLFCuNiXmZKZdGy8pagQtXpzncASK7PYmZegVuaCsFMqcg/oAGo5Qa/fAez5iOF7EM15U347zIsehdU5fxPRiV/8EpoC/UhbGZmJj60cyGujUyr1SJJEgqFAoVCYexKMWwrj/ILJkaNGmVcIsDwvnIydOhQnnnmmQK/z6zXxtPTk+vXrwNgaWlJ3bp1GTt2LDNnziy1gMdwHsN7GD16NI8fP85xnOqTUigUSJKU499OSf4tlfP/zjnL+gug1+sL9Uuxe/fuQp8zKCiIoKAg49IHFVr8v/D4CKCAmv9X1rUpn6ysYPlH4P2tPFB45Xfw3yX48RtwKN5m0mIlBgULglFUVJTx8aZNm3j33XcJDw83brOysuLRo0f5lmNlZYWVldUT1WXhwoWMHz+elJQU9u3bx6RJk7C3t2fChAlPVK6QoXyG1blwcXFBqVRma42Jjo7O1mpT3FasWEGjRo1ybcmpUG58Kd87Pw2Wtcq2LuWZJMHrr8CXH4GlBew/DE/1gyuRZV2z3BkCGp0IaATBzc3NeHNwcECSpGzbDK5evUq3bt2wtrbG39+fY8eOGfcFBwdTrVo14/OzZ88a03/Y29vTsmVLTp06lWdd7OzscHNzw9PTk3HjxtGsWTP27MlYb02tVjN79mxq1aqFjY0Nbdu25cCBA8b9169fp3///jg6OmJjY0Pjxo2NK3FnrR/Atm3bcv2iP3/+fNauXcv27duRJAlJkkzOVVFVqBYac3NzWrZsyd69exk4cKBx+969e3nuuedK9NyVpoUm+Qbcl/8IqDW6TKtSYfTtCXU9YNxUuHwVuvSFH7+Fbp3KumbZiaUPhFKk1+tJTEws9S4na2vrYu+qmTt3LkuXLsXb25u5c+cydOhQIiIiUOXQzTx8+HACAgJYtWoVSqWSsLCwAnel6PV6Dh48yMWLF/H29jZuHzNmDNeuXWPjxo24u7uzdetW+vTpw7lz5/D29iYoKAi1Ws2hQ4ewsbHhwoUL2NraFum9zpw5k4sXLxIXF8eaNWsAecXuiq7cBTQJCQlEREQYn0dGRhIWFoaTkxN16tRh+vTpjBgxglatWtG+fXu++eYbbty4wcSJE8uw1hXIza8BLdi3Bhu/sq5NxdHYT84s/Mp0+OcCPDcMli6EV0aXdc1MKURAI5SepKQkateuXernTUhIwMbGpljLnDlzJv369QPkAbSNGzcmIiICP7/s/ydv3LjBrFmzjPsyBya5eeONN3j77bdRq9VoNBosLS2ZMmUKAFeuXGHDhg3cunXLmFpk5syZ7Nq1izVr1vDhhx9y48YNBg0aRNOmTQGoX79+kd+rra0tVlZWpKam4ubmVuRyyptyF9CcOnWKbt26GZ8bZhiNGjWK4OBghgwZQkxMDAsXLiQqKoomTZoQEhJC3bolmy8k66DgCknzEO6mp/evNaZs61IRVXeFTath9gL4326YNhcuhMOShfJil+WBIr0eOnXZ1kMQKphmzTIW561ZsyYgD2fIKaCZPn0648aN44cffqBHjx4MHjyYBg0a5Fn+rFmzGD16NPfv32fu3Ll0796dDh06ABAaGoper8fHx8fkNampqTg7OwPykj2TJk1iz5499OjRg0GDBpnUWSiHAU3Xrl3JLzXO5MmTmTx5cinVSFYpupyufQ66ZLD2AYc2ZV2bisnCApZ9AL5e8MlK+HYdXIqAn74Fx2plXTuQDAGNSAYnlDxra2vi4uLKpMupuGXuMjJ0Z+WWFG/+/PkMGzaM33//nZ07dzJv3jw2btxoMhQiKxcXF7y8vPDy8mLz5s14eXnRrl07evTogU6nQ6lUcvr06WyJ6AzdSuPGjaN37978/vvv7Nmzh0WLFvHJJ5/w2muvoVAosn1uVsWEkOUuoBFKSFIk3AmWH9d5rWotRFncJAkmvwxe9WDa23DwKHTpB7+ulQOdsqQQAY1QeiRJwsbGptxO2y5JPj4++Pj4MG3aNIYOHcqaNWvyDGgyc3R05LXXXmPmzJmcOXOGgIAAtFot0dHRdO7cOdfXeXh4MHHiRCZOnMhbb73Ft99+y2uvvYarqyvx8fEkJiYau+LCwsLyrIO5uXnF7nHIQdX7LSyiCj/L6ep78rgKh3bg2KGsa1M59OoGvwaDuxtcvQZdn4F9B8q2ToaARp8G5TcJuCBUWMnJybz66qscOHCA69evc+TIEU6ePEnDhg0LVU5QUBDh4eFs3rwZHx8fhg8fzsiRI9myZQuRkZGcPHmSxYsXG2cyTZ06ld27dxMZGUloaCj79+83nrNt27ZYW1szZ84cIiIiWL9+PcHBwXme39PTk3/++Yfw8HAePHhQKVp0REBTQEFBQVy4cIGTJ0+WdVUK7/FxeLAbUIDn9HwPFwqhoTds+wFaNJMXtBw4AlasLrtgQso0lke00ghCsVMqlcTExDBy5Eh8fHx48cUXCQwMzHGJnLy4uroyYsQI5s+fj06nY82aNYwcOZIZM2bg6+vLs88+y4kTJ/Dw8ADkxIZBQUE0bNiQPn364Ovry8qVKwF5htKPP/5ISEgITZs2ZcOGDcaEgbkZP348vr6+tGrVCldXV44cOVKk61GelOu1nMojwxia2NDQcrX0QUhoKH1btMieKTjlDoS9ACnXofpAaPB22VSyDGm0OkLO36Zv41oFyxRcFKlqmPsBbNkhPx81FJZ9CKW95k1aMuxI7/YaHA9meU/r1Gg0hISE0LdvX5ENNwtxbUylpKQQGRlJvXr1sLS0RKfTERcXh729fZXscspLVb82WX9XMouJicHFxaX8rOUUExPDgQMHOHHiBHfv3iU5ORlnZ2d8fX3p3LkzrVq1KtZKlgcVcpZTym0IGwQpN8HcDeoElXWNKi8Lc1gyH3wawOIvYO0GiLgK61eDSynmd1Bk+pMWM50EQahCChU6HjhwgIEDB+Lu7s7gwYP5+eefuXDhAlFRURw+fJh3332XNm3aUL9+fT744APi4uJKqt6lrsJ1OSXfhDMD5WDGoiY0WQ1mjmVdq8pNkuCVkfDtZ2BjDUdOyEn4LoTn/9piq0PmgEZ0OQmCUHUUOKDp1asXzz33HA4ODmzZsoWHDx9y7do1Tp06xV9//cX58+eJi4vj3LlzTJw4kS1btlC/fn3jgCahFCVFQtjzkHobLGpD49VyUCOUju6dYctaqO0O129Ct/6wc1/pnFuSMoIa0UIjCEIVUuCA5qmnnuLGjRsEBwfTr1+/bOtGgDyFr3HjxsyePZvTp0+zZcuWXOfxCyXk3jY43QdS74ClBzT+FiwqTybICsO7PmxbB60DICERBo+Gz1aWzmBh40wn0UIjCELVUeCAZu7cuYVOKNelSxeeeeaZQleqPCrv07aV+hSU4TPgYhBoE8C2aXowU72sq1Z1OTnCD6tgyAA5kHn7A5gwFVJTS/a8ivSByFrRQiMIQtVRbMOvk5KSiIiIyDfLb0VVrsfQJJznqeQZKKJ/ASR5WYMmq8HctaxrJpibwYdvw7szQaGAn36FwMEQ/aDkzim6nARBqIKKFNAsXbrUZM794cOHqVWrFr6+vnh7e3PlypViq6CQB80juPwuqrD+2OlvozdzgYaroM6rpoNDhbIlSTB6KHz/BdjawInT0DkQzl0omfOJLidBEKqgIgU0q1evNllhddq0aTRu3Jjt27fj4uLCnDlziq2CQg50arj5DZzoALe/Q9KnEaVsQ1qTDVCtfHaJCUCX9vK4mrq14dYd6P4s/G9X8Z/HENCILidBEKqQIn2Nv3nzJl5ecvKu27dvExoaysGDB+ncuTNpaWlMmjSpWCsppNPr4X4IXP1ATpQHYFWfNI/X+ft2XfqaVSvT6gkFUN9Tziw8eTYcOwlDx8G7s2DWlOJbX8vQOidaaARBqEKK1EJjZWVlzDHzxx9/YGtra1wGvVq1asTGxhZfDcuJMh0UnBoFN1bBqZ5w4RU5mDFzgvpzwX8j+mpibaYKxcEegpfDS4PlIHXBx/ByEKSkFE/5hkHBYgyNIOTr2rVrSJKU52KOwcHBOc7srUjmz59P8+bNy7oaJapIAU2bNm346KOP+P3331myZAmBgYHGJc+vXLlCrVq1irWS5UGpDwrWxMKd9XDmBTjWGq6+D4kXQWEBtV6GgO1Q43mQlPmXJZQ/Zmaw8E35plTAz9uh1/MQde/JyzZkCxZdTkIVJ0lSnrfRo0cXqJwhQ4Zw6dKlItfD09PTeE4rKyv8/PxYsmRJmU6iGT16NAMGDCiz85eEInU5LV26lP79+9O/f3/q1q3L+++/b9y3adMmY2uNUEjaZIjZB/c2w8MDpl0Gts3AJRBceoqMv5XJS4OhXl0Img2nz8qZhX8OhoCmRS9TEoOCBQEgKirK+HjTpk28++67hIdnZO62srLi0aNH+ZZjZWWFlZXVE9Vl4cKFjB8/npSUFPbt28ekSZOwt7dnwoQJT1SukKFILTSNGjXiypUr3L9/n8jISLy9vY37PvnkE5YuXVpsFaz0dGnw8CBcnAJH/eHCRIjZK38YWdUDj8kQ8D9ougZqviiCmcqoYxt5XE29unDnLvQYAFt3FL08w6Bg0eUkVHFubm7Gm4ODA5IkZdtmcPXqVbp164a1tTX+/v4cO3bMuC9rl9PZs2fp1q0bdnZ22Nvb07JlS06dOpVnXezs7HBzc8PT05Nx48bRrFkz9uzZY9yvVquZPXs2tWrVwsbGhrZt23LgwAHj/uvXr9O/f38cHR2xsbGhcePGxkz8OXWJbdu2DSmXcXnz589n7dq1bN++3dhylPlcFdUTze11dnbOtq1p0yf4ZllV6PUQdxrubYH7O0ATk7HPvAa49AaXvmDjnXsZQuXi6QFb18Jrb8Lh4/DSBJg7Hd6aXvjBwsaARrTQCCVLr9eTmJhY6itKW1tb5/phXVRz585l6dKleHt7M3fuXIYOHUpERAQqVfaPyeHDhxMQEMCqVatQKpWEhYUVeEV2vV7PwYMHuXjxokljwJgxY7h27RobN27E3d2drVu30qdPH86dO4e3tzdBQUGo1WoOHTqEjY0NFy5cwNbWtkjvdebMmVy8eJG4uDjWrFkDgJNTKS6iW0IKHNB8+eWXjB8/HgsLiwIXfvbsWe7fv0+PHj2KVLlKQ5sM6mh5cG/MfojeJq+zZKCyB6ce4NoX7PxBqnrLzQuAvZ2cq+aDzyB4A3zwqbyw5Tefg3UhmruN07ZLOCOxUOUlJSWZpPAoLQkJCdjY2BRrmTNnzqRfv34ALFiwgMaNGxMREYGfn1+2Y2/cuMGsWbOM+zIHJrl54403ePvtt1Gr1Wg0GiwtLZkyZQogjz3dsGEDt27dwt3d3VifXbt2sWbNGj788ENu3LjBoEGDjI0G9evXL/J7tbW1xcrKitTUVNzcKs/SOAUOaNatW8f777/PsGHDGDx4MK1atcoxIr1z5w47d+5kw4YNnDhxguDg4OKsb/mm10NyJDw+Bo+PQMJ5SI0GbQ6rjisswfEpOYhxaJvxISRUbUqlnFXYpwG8+xFs/R2uXodfg8G9gAuMSqLLSRAKq1mzZsbHNWvKf2vR0dE5BjTTp09n3Lhx/PDDD/To0YPBgwfToEGDPMufNWsWo0eP5v79+8ydO5fu3bsbx5uGhoai1+vx8fExeU1qaqqxJ2TKlClMmjSJPXv20KNHDwYNGmRSZ6EQAc3ff//Nli1b+Pzzz1m2bBnm5ub4+Pjg6uqKhYUFjx8/JjIykujoaJydnRk5ciQ//fQTNWrUKMn6l52Es5D0D8SdAc0DudtI8xB0uXwrlszB3AWsveTBvY6dQflkg8yESuz/BkK9OjB5Fpz9FzoFwi/B0LJ5/q8VXU5CKbG2tiYuLq5MupyKW+Yv6IburNwWV54/fz7Dhg3j999/Z+fOncybN4+NGzcycODAXMt3cXHBy8sLLy8vNm/ejJeXF+3ataNHjx7odDqUSiWnT582zhg2MHQrjRs3jt69e/P777+zZ88eFi1axCeffMJrr72GQqHINmNKo6l6f/+FGkPz/PPP8/zzzxMZGcm+ffs4ffo0UVFRpKSkULduXXr16kXHjh3p2rVrgfsTK4oVK1awYsUKtFqtvCF8DOT0NyWZgW0jsG8JdgHyStfmrqC0Lb7EaULV0LalPFh43FS4fBV6Pg+rlsKQ5/N+nRgULJQSSZKwsbEp9YCmPPDx8cHHx4dp06YxdOhQ1qxZk2dAk5mjoyOvvfYaM2fO5MyZMwQEBKDVaomOjqZz5865vs7Dw4OJEycyceJE3nrrLb799ltee+01XF1diY+PJzEx0dgVl1deHQBzc/OMz7NKokiDguvVq8f48eMZP358cden3AoKCiIoKIi4uDh5ZLzSBpzagn0rsKwJKid5BpK5i5wrRhCKg0ct2BwMr8+BP/+Cl1+Di5fg3dnyYpc5EV1OglBikpOTmTVrFi+88AL16tXj1q1bnDx5kkGDBhWqnKCgIBYvXszmzZt54YUXGD58OCNHjuSTTz4hICCABw8esH//fpo2bUrfvn2ZOnUqgYGB+Pj48OjRI/bv30/Dhg0BaNu2LdbW1syZM4fXXnuNv//+O9/hHp6enuzevZvw8HCcnZ1xcHCo8A0RVS+sLi5+68F3CdQcAo5dwK4JWNYSwYxQ/Gxt4JtPYfxI+fmS5TBsHCQm5Xy8aKERhBKjVCqJiYlh5MiR+Pj48OKLLxIYGGiyYHNBuLq6MmLECObPn49Op2PNmjWMHDmSGTNm4Ovry7PPPsuJEyfw8PAAQKvVEhQURMOGDenTpw++vr6sXLkSkGco/fjjj4SEhNC0aVM2bNjA/Pnz8zz/+PHj8fX1pVWrVri6unLkyJEiXY/yRNKXZarCCsjQQhN7bAf2NQo4SLOEabQ6Qs7fpm/jWpgpRYyaVaW6Ppv/B3M+AI0GGvvB5nVyK05mYW/CtR+g8Vzwfz/nctJpNBpCQkLo27dvhf92VtzEtTGVkpJCZGQk9erVw9LSEp1OR1xcHPb29lWyyykvVf3aZP1dySwmJgYXFxdiY2Oxt7cv1vNWvSstCBXZoP6w4RtwcoTz/0HnvnAiS0Iv0UIjCEIVJAIaQahoWjSD7T+ArxfcfwB9XoD1v2TsF7OcBEGogkRAIwgVUa2a8Osa6PkUqDUwfiq8/T5otWK1bUEQqqQnDmh+/fVXBgwYwNGjR4ujPiXu5s2bdO3alUaNGtGsWTN++eWX/F8kCOWRjbU8jXvyy/Lzz1bBi2PA0DCjTS6zqgmCIJS2Jw5oXnjhBb755hvCw8NZvnx5cdSpRKlUKpYtW8aFCxfYt28f06ZNIzExsayrJQhFo1DAzCD47H2wMIddf8BXG+V9mviyrZsgCEIpeqLFKQ2qV6/OmDFjiqOoElezZk1jWuvq1avj5OTEw4cPi31dEEEoVc8FygtcvjIdrt2Xt0VfL9s6CZVSbtlzBcGgrH5HiiWgMdDpdE88Re3QoUMsWbLEmIV469atDBgwwOSYlStXsmTJEqKiomjcuDHLli3LM7tibk6dOoVOpzPO8xeECs2/CWz/EZaOBe7AuVPw8FuoQgkwhZJjbm6OQqHgzp07uLq6olKpUKvVpKSkVMmpyXnR6XRV8tro9XrUajX3799HoVBgbm5equcvtoDm9ddf57vvvsPKyoqmTZvSokULWrRowbBhwwpVTmJiIv7+/owZMybHzIubNm1i6tSprFy5ko4dO/L1118TGBjIhQsXqFOnDgAtW7YkNTX7mkp79uwxrmRqSIy0evXqPOuTmppqUlZcnLzQpEanR6MtH99UDPUoL/Upb6rU9XF1QZo5C1XkNLDUwyuvoD13Dt3ixaDK/uduWO+lKq77kh9xbbLz8PDg3r173L59G71eT0pKCpaWlsa1jwRZVb82VlZWuLu7o9Vqsy2vUJJ/T8WWWK9WrVpcunQJlUrF2bNnCQ0N5cyZM3z99ddFr5wkZWuhadu2LS1atGDVqlXGbQ0bNmTAgAEsWrSoQOWmpqbSs2dPxo8fz4gRI/I8dv78+TlmgFy/fn2JLJAmCE+qmvYST6XMRp1kg/l4eXzYvRYtODVjBmmia1UoBgqFokq1PAgFp9Pp8uxySkpKYtiwYSWSWK/YApq+ffvy+++/F2s0mjWgUavVWFtb88svv5gsAvb6668TFhbGwYMH8y1Tr9czbNgwfH19800NDTm30Hh4ePDgyP+wr+5W6PdUEjRaHXv/i6KnX82Knwm3BFS565N8DbNzg9Er7dE+/Ajl9OlIKSnofXxI27YNvLyMh2o0Gvbu3UvPnj1FNtwsxLXJm7g+uRPXJncxMTHUrFmzRAKaYutyatiwIRMnTuSjjz7C0dGxuIo18eDBA7RaLTVq1DDZXqNGDe7evVugMo4cOcKmTZto1qwZ27ZtA+CHH36gadOmOR5vYWGBhYVFttW2zRRSuftwNFMqyl2dypMqc33M7QCQtAmonn0WGjSAUaOQLl3CrEMH+PVXePppk5eYmZmJf7y5ENcmb+L65E5cm+xK8noUW0CTmJjI2bNn8fT0pG7durRq1YqWLVsSFBRUXKcwytoKpNfrC9wy1KlTpyKNwM622rYglFdKQ7eSDnTJ0KQJ7NwJo0fD2bPQuzd8/jmUwN+mIAhCWSm2r6tfffUVR48e5fHjx/z888/06tWL27dvF1fxALi4uKBUKrO1xkRHR2drtSluK1asoFGjRrRu3bpEzyMIT0xhhfFPOy09F0316rBlCwwYIGcTfvVVmDRJXuRSEAShEii2gGbq1Kk4ODhQvXp1Jk+ezKlTp2jSpElxFQ/I0wZbtmzJ3r17Tbbv3buXDh06FOu5sgoKCuLChQucPHmyRM8jCE9MkkCZPmBdmym5nqUlfPklvPmmfMxXX6Hs2xezeJGATxCEiq/Yupx+/vln7t69azLL6eDBg4Wetp2QkEBERITxeWRkJGFhYTg5OVGnTh2mT5/OiBEjaNWqFe3bt+ebb77hxo0bTJw4sbjeiiBUfEob0CZAWoLpdkmC114DHx949VUUBw/S5dIl8PeHXMaRCYIgVATFFtA0b94ca2trJEmiTZs2tGnTpkjlnDp1im7duhmfT58+HYBRo0YRHBzMkCFDiImJYeHChURFRdGkSRNCQkKoW7dusbyP3GQdFCwI5ZphHI02l9aX3r3ht9/QjxqF7e3b6Dt1go0bITCw9OooCIJQjIqty8kwy+nRo0dPVE7Xrl3R6/XZbsHBwcZjJk+ezLVr10hNTeX06dN06dLlCWufP9HlJFQohoAmawtNZg0bkrZjBw8aNUKKi4NnnoFPP4XiyeQgCIJQqootoMk8y6lZs2a8/PLLrFixoriKL3NiULBQoRjG0KTlMz7G2ZmjCxagGzwYdDqYMQPGjQO1uuTrKAiCUIyKFNBcvnw527avvvqK48ePm8xyunXr1hNXsLwQLTRChaK0le/T4vI9VG9mhnbpUnj3XXn17u+/h+7d4f79Eq6kIAhC8SlSQOPn58fff/+d4z5JkvDz8+P//u//CrwUgSAIxSy/MTRZSRJMmABr14KtLRw5Aq1bw7lzJVdHQRCEYlSkgKaYVksQBKGkGMfQFHJKdvfusGMH1K0L169D+/bw22/FXz9BEIRiVuQxNLll5o2IiGDmzJlFrlB5JcbQCBWKKr3LqaAtNJl5e8Pvv8vBTGKinIzvo4/EYGFBEMq1Igc0EydOZMKECSxfvpw///yT++n97adOneKrr74qtgqWF2IMjVChGMfQFDFpnqMjbNgAL70kBzJvvQUjRkBKSvHVURAEoRgVOQ+Nm5sbYWFh/PTTTyQlJSFJEpaWlqSmptK3b9/irKMgCIVlCGi0eUzbzo+ZGSxeDH5+MG8e/PQTXL4M27eDW/lYaV4QBMGgyAHNvHnzaNOmDXq9nitXrnD+/HmioqKws7PjueeeK846CoJQWKonbKHJbMwY8PKCV16Bv/+GVq3gf/+DgIAnL1sQBKGYPHGmYEmS8PLywsvLqzjqU26JTMFChVLUQcG56dxZHlczciRERkKnTvKMqBdeKJ7yBUEQnlCRxtDMmDEDJyen4q5LuSbG0AgVipmLfJ96p/gG89avLwc1nTtDUhIMHgwLFojBwoIglAtFCmiWLFlS6VtkBKFCs6oDSPIsJ3UxJshzcIAff4SxY+Xn8+fDkCFygCMIglCGim3pA0EQyhGFBVikD9xNvlK8ZatUsHAhfPyx/PiXX+QuqNu3i/c8giAIhSACGkGorCzTV6BPjCiZ8ocPl1fodnSEM2fkwcK5ZBAXBEEoaSKgKSCRWE+ocKw85fuk7GuvFZv27SEkRE7Gd/cuPPWUPL1bEAShlImApoDEoGChwrGqJ98nlVALjUGdOvI07u7d5cR7L70Ec+fKq3cLgiCUEhHQCEJlZWihSb5a8ueys4PgYJg0SX7+4Yfw/POQ8ASJ/QRBEApBBDSCUFkZApqUW6AthSULlEp4+21YtkzOMrx9O3ToIC9yKQiCUMJEQCMIlZWZc3qCPT0kXyu98w4eDL/+Ci4ucO4ctG4NR46U3vkFQaiSREAjCJWVJIFlHflxSY+jyapVK3mwcMOGcP++PL5mzZrSrYMgCFXKEy99IAhCOWZVFxIvln5AA1CrFvz2G7z6KuzeDS+/DOfPywteKpWlXx+hRDx69IilS5eSmJhI06ZNady4MY0bN65y2eSFsicCmgISazkJFVJpzXTKjbU1rF4NS5bAF1/AJ5/AhQty/hp7+7Kpk1Bs7t69S+fOnbl06VK2fdWrV6dhw4Y0atSIxo0b07RpUxo1aoSLi0sZ1FSoCkRAU0BBQUEEBQURFxeHg4MDj+JU6Kzkb5kS8lo2kkS2+4zHuRxDAY6Rsm8ThAIxBjTFnC24MBQKeOMN8PODadNg505o21ZeF6p+/bKrl/BEjh8/zhtvvEFsbCxubm7069ePiIgILl++zJ07d4iOjiY6OpqDBw+avM7V1dUY6DRp0sTYouPq6lpG70SoLERAU0SevdsDZf8NMyMIaplDsJQ9QCrKMdnPlXPQlbW8JzrGeG69fJz8wlyO1Rt25ViOHj3JGh/eNDfLEkBmv88WVOZQbl6Bp+E1WY/Jqe6GXYr0Y3M85gnrI2ndUCb6IklKtNUcc7y+9+Nb8719NSRJyuV3JGu5+nx/bjmXMwqpY38URw8j/ZcMDY9Cd0v0bu55lmNhAVZWYGMjN/gYbjY28vbM2zI/t7ICS0s5nhKK18aNGxk9ejSpqak0btyYdevW4ebmZtyfkJDA5cuXuXTpEuHh4YSHh3P58mVu377N/fv3uX//PocOHTIp09nZ2RjoNG3alB49euDn51fab02owCS9XiyVWxiGFhqIpTwENIIg5M0Q2BiCHEPQk/U+683CQsvVq+do06YJdnaqHIOmzM9VVeDroV6v54MPPuCdd94BoE2bNqxZt46/XF1JliR809LwSkvDOpePlaSkJGOgc/HiRS5dukRERAS3bt0ip4+iLl26MHnyZAYOHIi5uXmJvrfipNFoCAkJoW/fvpiZmZV1dcqVmJgYXFxciI2Nxb6Yu51FQFNIhoAm9Je9ONZww3D1st6bbpNMn2fan/31kskx6HM4FtPy0rQ6zsXcpImTBwpJkeUcUrZyDE9zPXcex5Clfvqc6pdLPfX6TPXI9vrsx+Z3DDkek+VYQKvVcTn2Hl4ObigkKYefVUa52d9D9nPn9LPOrZ651y/j2Jx+1qZ1MD13zr9j2cszbLOL+Ryl7hFxjpNJM29g8hqtTset2BvUsq+DQlJkew951z37fb4/j/RjJI0Gyz92YH7lInokUlu0J6lrX1Aos71HtRqSk+UFvVNT5cfJyXJS4qz3hscaDaXOzCwjYMoaOOUWBGVuZcraAmVtLS9u7ugo3ywsSv89ZaZWqxk/fjzr1q0D4OWXX6bHs8/yXa9e/GFlZTxO0uuppdXinZaGj0aDX1oaPmlpeKelYZNHoHPlyhX+++8/wsPDOXfuHEePHkWXnm26Ro0ajB07lokTJ+Lh4VHyb/YJiYAmdyUZ0FSB7xQlw1ylx9ysoLFgycaMWp2O2/pU3FzTUIr29Wy0Oh2W9x/QwtW2Sl4fp6hjWCYf4XENb5Ic3U32aXVphN6+RotazigVpfzv4JWe2K4+j/1XH0MopFg/zaOvf0FfzfGJi9Zqswc8OT1OSsp4brglJRn26bh//x5mZjVISVFkC5oMjw2f0RqNfIuLe+Lq58jKCqpVy7gZAh1HR3Bykm+Gx5n3OTrKLVRP4tGjRwwcOJCDBw+iUCh477336DlqFP9nY8NVKyvM9HoaqdVcV6l4rFRyS6XilkrFn1lO7J4e3PikpeGn0RgDHVtra5o2bUrTpk2Nx96+fZsff/yRDRs2cO/ePT788EM++ugj+vbty6uvvkrPnj1RVMG/ZyF3IqARhEouzcwTko+gSi3BRSqLQpJIGD+NtAa+VHvnVSz/+gPXZ9oSs3YH2gY+T1S0Uim3eNjYFL0MrVZLaOjftGjRF6Uy5w9OvV5uNcotaMr8PCNQyj/QMhwXHy8HSHp9xv6oqMK/F0tL02Aoc0Dk5AR168pjtn18wNXVdPzclStXCAwM5PLly1hbW/PVV1/h1rs3zzo5cVelwkGr5bOHD/FPbxZ7pFBwVaUiQqXiqpkZV1UqIlUqHimV3FGpuKNScSBL/dzTu6p809Lw1WjwTUujca1avPHGG0yfPp1du3YRHBzM8ePH2bFjBzt27KBevXpMmDCBsWPHiplTAlAFA5r4+Hi6d++ORqNBq9UyZcoUxo8fX9bVEoQSk2YmzyRSqcto6nY+Urr35UGt33CaOhJV5GVcn2nDo69+JvWpXmVdtXxJkhwsGAKGkqDTyYFNbKx8e/w4495wy7w9NlYOgmJj5dfpdHKAdPeufMuPg4O8eLqPD1hZ/cWmTQNISIjBza0mP/ywjjsBAQxwdCRJoaB2fDzL4+Opk+n1jjodLdVqWqrVJuU+Uii4lh7oXEkPdiJVKh5mCnQyDxOuptXyYnIyoxIT6d+/P/379+fy5csEBwezefNmIiMjefPNN5k3bx6DBg1iwoQJdOrUSbTaFML9+/c5cuQIzzzzDKpKMAisyo2h0Wq1pKamYm1tTVJSEk2aNOHkyZM4OzsX6PWGMTT/bt2DY6ZR/WVJq9MRev8aLVw9q2SXSn6q+vUxTz6FS9TLpKk8iPb622Sf3OV0mha1WpZ+l1MWipj7OE0fjfm/oeiVSmLnf0bSmFfLLFeBVqshNDQkvYWmYo6D0Ovl9UGzBjyZg6HHj+HGDbh6Fe7cyTx+aQMwBkgFWgL/w+6t6sS/pwClRO2rGl5cH0bb9rWp5QlWVkX7KImVJGNLjqFVJyK968qgc0oKo5OS6JGSggp5zM22bdsIDg7m/PnzxuPc3d158cUXGTp0KK1bt0Yqo9+dijKGpl69ely7do3ly5fz6quvlso5xRiaYqRUKrG2tgYgJSUFrVab4+h6Qags0szkXDTKtFugSwaFVT6vKBs6Z1cefLuFau/NwDpkM9XemYLZf/8S+8GX8ohbodAkSV4I3c4OatfO//iUFIiM1PPFF+/x22/zALCz649e8RMJ8+2In5p+4Hdwa5IZn2paG1/r7KrFvU4atepoqV1XS23PNPm+ThrO1XW5Tp930OsJUKsJyNSiowX+srDgZxsbTlhYcNjSksOWlrhptQxPTGS4QsGwYcMYOnQoYWFhrFu3jpCQEO7cucOyZctYtmwZnp6eDBkyhGHDhtG0adMyC26Kw4cffsjRo0fZvHkzFsU4OvzatWsA7Nmzp9QCmpJU7r6uHjp0iP79++Pu7o4kSWzbti3bMStXrqRevXpYWlrSsmVLDh8+XKhzPH78GH9/f2rXrs3s2bNF/6tQqemUTugkOyT0qNTXyro6eTO34PHC5cROeRu9JGHz0zc4D+mB9DCmrGtWJSgUalatGmUMZoYPf4UdB1bR/JoZTJWP6fBHMi/8FU/7TknUr/8Yaxt5JlLMfSXnTluwa6s1q5fZMX+qI+MGutCnpRsdvWryfBdXXnvJiY/m2PPjNzacOGROUmLOQYYSeCo1lRUPH7ItOpoR8fE4aLXcVSr5xN6eNjVqMN7RkaMWFjQPCOCzzz7jn3/+4bvvvqN///5YWVlx7do1Fi9ejL+/P35+fsybN4/Tp08bZ05VFOfOnWPu3Ln8/vvv/PXXXwV+3fnz5/npp59y/cKeeXu9evWeuJ7lQblroUlMTMTf358xY8YwaNCgbPs3bdrE1KlTWblyJR07duTrr78mMDCQCxcuUKeO3JPbsmVLUlNTs712z549uLu7U61aNc6ePcu9e/d4/vnneeGFF6hRo0aJvzdBKBOSRJpZXczV/6JSR5Bm2bCsa5Q3SSJxVBBp9bxxnDsZixOHcO3bmofrdpDm06isa1dpPXr0kLFjB3DixGEUCiVvvPE+XYaOZnwDJy5Zm2Om0zPr+iO6OKfA66DXaUm9GYp57RYkJqiIuqXi7m0lt2+qiLql5O4tFffuKLl/T4k6VeL6FTOuXzFtaVMo9Xg31BDQRk1AWzXN26hxqW4acNTWapkaH8+k+Hj2WVnxi40N58zNCbGyIsTKigYaDSOTknhBkujTpw99+vQhKSmJffv2sXXrVg4cOMClS5dYuHAhCxcuxNnZmaeffpo+ffrQq1cvatWqVZqXOUdpaWkMHz6c2rVrs3TpUpPWpDlz5hgfP3jwoMBlBgQEoNFoMDc3Z/Dgwdn23800oMrw2VnRlbuAJjAwkMDAwFz3f/rpp4wdO5Zx48YBsGzZMnbv3s2qVatYtGgRAKdPny7QuWrUqEGzZs04dOhQjj9wgNTUVJPgKC59TqZWr0NbTiJ9Qz3KS33KG3F9QGNWB3P1vyhT/kNrm/H3pdVpTe7Lk6RO3VF/vx3XaaNR3YzE5Zm2xHz5IylP9y2V82u1GpP7yuzatQhGjOjP9etXsLa2YdGiVTg/3ZMR3i48MFfhoNEy78oDGiapM3INGX5n9Fps7cC7YRreOcTKaRq4f0/J3dty0HPnloo7N1VEXDTn/l0V4f+aE/6vORu/l4+vVUeDfys1AW1Tad4mlbr105AkMAMCExIITEjgkpkZv9jassvamitmZsxzcOB9e3u6pKTwXFISPXU6+vXrR79+/YiLi2PPnj3s2LGD48ePExMTw88//8zPP/8MQMOGDenevTvt2rWjVatW1K9f/4m7pzTpM740BUyIdOLECWN92rVrx4ABAwDYuXMnO3bsMB53+/btApdpOO6HH34wlpdZeHh4xmNJKnC5T6okz1OuBwVLksTWrVuNPwy1Wo21tTW//PILAwcONB73+uuvExYWlm3NkJzcu3cPKysr7O3tiYuLo3379mzYsIFmzZrlePz8+fNZsGBBtu3r1683jsURhPKuvuY3mqq/554ygOOW88q6OoViHhdH68WLcTl/Hr0kcX7UKK4895xY2KyYXLhwgUWLFhEfH4+rqytvv/020W3a8EmrVqSoVNSOj+ed48epkZRU7Oe+f9+S//5z5uJFJy5ccOb6dXuTpI4ADg6p+Pk9pGHDGBo1ekj9+o9RqeSPrSSVigO1a7Pb05PrDg7G15inpdHq3j06375Ni3v3sEj/MpOWlkZ4eDhhYWGEhYURERGRrUvGzs6OBg0a4O3tTfv27alfCuuN7dq1i6+++goAFxcX5s+fz6+//sqBAwdMjhv0wguMeOmlApVp+NysVasWK1asyLb/jz/+YPny5QC4L1zIylw+A4tbUlISw4YNq3qZgrMGNHfu3KFWrVocOXKEDh06GI/78MMPWbt2rUnEmZvTp08zduxY9Ho9er2eSZMmMWnSpFyPz6mFxsPDg7NbduFYo/zMcjobcwN/5zpVchZPfsT1AZX6CjXvDEIvmXO7wb/o0wcGa3VazkaF4V+zOUqFMp9SypBGjeNHc7H9bSMACS+M4NGilSWaPler1XD27F78/XtW2FlO+dmy5SdmzZqARqPGz68pn322hr2N6rPMoxo6SaJZXArvRD7AVpv9Y0Kv06K+fRbzWv5IxfS7kxAvcfEfC/4NteB8mDmXLligUZsGOBaWOhr7q2neJpWAtqk0CVBja6cnwsyMPVZW7LG25namQeQ2Oh09k5N5LimJjikpZE719+jRI/766y+OHj3KP//8w3///ZetBWHkyJF88MEHhRqWoNFo2Lt3Lz179izQLKeJEyfy/fffZ9suSRLP/t//sb1OHVi8mMBRo9j+7bf5lqfVarHKlL05NTU1W6vTwIED+f333wFou3Qph6dMybfc4hATE0PNmjXFLCeDrD8YvV5f4CbCli1bEhYWVuBzWVhYYGFhwYoVK1ixYgVardzMqpQU5e7DUakof3UqT6ry9dFbeJGmrIFKew+rlL9JtX3aZL9SoSzzadt5slAR9+6naL0bYv/ZAmx//QGzyAgefb8NnUv1Ej21UmlW6QIavV7PJ5/M57PPFgLQtWtvFr6/gi+9a/BzdVsAesUkMuVmLCoUeU4fkRTKYgto7BygTWcNbTrLQYVaDREXzfg31Jx/z5hz4awFCXEKQk9YEnrCEpaDQqGngZ+G5q3VtGinZljHGO65KdllZcUeS0uiVSq22diwzcYGC72eNqmpdE1NpVtqKj4uLgwYMMCkF+C///7jzJkzHDp0iF27drFu3Tq2bt3KvHnzmDJlSqGmYZuZmRXo+HPnzgHQevBgTv7yi/y4dWvmzZvHpq5dYf16AO7ev1+g8hISEkyenzp1yqQR4OTJk8ZgBsBJry+16eUleZ5y/B8sOxcXF5RKpclgJoDo6OgSH9QbFBREUFBQpsUpBaECkSRSrTqiStiCRcIf2QKaCkGSSBz2ijxY+I0JWJw+hktgKx6u3UFao9JpLq8MUlNTmT79ZbZtkz8kX3ppAuOmvcObDZw56iC3X4y5E8eL0QmUdaeeuTk08tfQyF/Di2MS0eng1jUV/54x59xpcy6cNefeHRWXL5hz+YI5v6yVX+fdSE27zqm80zUWs0469lezYr+VFQ+USuMU8PeA6lotndMDnBZqNXXNzWnWrBnNmjVj1KhRnDp1irlz5/Lvv/8yc+ZMvv76azp16oSTkxMuLi5Ur16dZ555hurVix5Ua7Va/vn3XwBOLlzIvGefpaGZGZ06deKaSsUGa2tI/3x7cO9egcqMjY01eW5Y5PPtt99m8ODB2VY6V1WS8YUVKqAxNzenZcuW7N2712QMzd69e3nuuedK9NxZW2gEoaJJtemMTcIWLBP/pISWGyoVqe27cX9dCM6vj0B16xouz3bg8fIfSAkcmP+Lq7iHD2N4+eXnOHnyCAqFkjff/JBO/zeScQ2cibA2w0ynZ/b1R3SOTSnrquZIoYA69dOoUz+NvoPkMT0PohWcP2POuVBz/g214FqEmTHA+eFrMLfQ06yVmiFdEqnTT0NUYxXHLS04Y25OtFLJZmtrNqePh3TQ6WiqVuOv0dBCo6FD69bs3LmTjRs3smjRIi5fvszly6ZLiNjZ2RWp9cbg8uXLpCYny6uRenuzpX59tj94wPc2NnxsZ0eaJEF6wBQbHV2gMh8/fmx8bPvssyT89hvLly83jpkBUJqbo+3YEf78s8JNZc9NuQtoEhISiIjISNEeGRlJWFgYTk5O1KlTh+nTpzNixAhatWpF+/bt+eabb7hx4wYTJ04s0XqJFhqhoku1aoseJSrNNZTqa2jNPcu6SkWm9fTi/roQnGaPx+LUEZzGPU/c7PdJmDJHDBbOxdWrlxkxIpBr165gY2PL4sXfUK17D0Y2cOKBuRIHjZYFkQ/xS6pYs7pcqut4qncKT/WWg7BHMQpCj1tw+pgFYScsePhAyakjFpw6YgGLoJqTllYdUpn+dCw2gXr+q2XOaXNzIszMiFUo+MvSkr/SF9V00OmYHRfHiGHD6NevHzt27ODevXs8fvyYx48f8++//xIeHs7MmTP59ttvmTNnDkqlkvv373P+/HlatmxJ7XwyGp45c0Z+0KwZKJWcUyppWaMGj9IzJfuq1bjZ23MQSIiOLtAQi0eGFhpfX1K3bWP9xo288847XLlyBZBndlX/8EMO/vYbgAhoSsqpU6fo1q2b8fn06dMBGDVqFMHBwQwZMoSYmBgWLlxIVFQUTZo0ISQkhLp165ZovUQLjVDR6RW2qC38sUgNxSJhP0lOL5d1lZ6I3sGRmC83YP/Ju9j+Eoz9x2+jCj/P40+/f/LlpSuZY8cOMnbsQGJjH+HmVosvvviBWy2aMc7TkRSlAo8UDe9dfYibuuL/f3N01vF0v2Se7peMXg83rqo4fcyC0OMW/BtqzuOHSvbtsGbfDmuYBnXqaWjTOZUxT8dTrYuOq9XM+NfMjFALC+6oVMytVo0fbGz4wNyc4cOHm5xLp9MZW2/Cw8MZNWqUyf5Nmzbl23oTGhoqP2jenKZqNefMzXmkVFJNq2VifDzPJyWxoVo1DgK61FTi4+PzHUx7zxDQODigkSS8unXjjz/+YP369SQmJtIvKIgubm6QPiVcV37nBhVKuQtounbtmu9SBJMnT2by5MmlVCOZaKERKoNU605YpIZimfhHhQ9oADAzI+7NRaQ18MVhyTtYb9+A6tplHq75DV2NmmVdu3Lh11/XMWPGONLSNDRs6M+yz9ey28+Tz2rbo5ckmsel8M61R9joKseHWmaSBHUbpFG3QRrPv5SIRgP//WPO6WMWnDlhwaULZtyIlG+/rrNFodTTuLmadl1SWdD1ERc7mfFtNXv+MzNjkIsL/ZKT6ZyaSjWdDkedDmedjiHprTfLli3j+PHj2NraYm9vT3h4OJGRkcbWmy+++IJevbIvuGqcpNK8OW/ExnLA0hILvZ4hiYnYpH8W1rS0BFtbSEjg3r17+QY0UY8eyQ/SV0x9xtWVuXFxjBg1ivfs7elgKw/8NqxHUVm+qJe7gEYQhJKTYt0Z+0dfYJ50DHQpVJZ/AUmDR5Pm6YXTrHGYnz2Fa2ArHgb/hqZZy7KuWpnR6/UsXTqPZcveA6B790DmLVzOcm83fq1uA0DvB4m8diu2kvwW5M/MDJq2VNO0pZrRr8aTEC9x9qQFp47K3VNRt1ScO23BudMWfPsZ2NjqaNorlaQ5Cs61MOd3Kyt+tzJdC81Po+EDc3PmzcvI76TVajl16hSXL19m8eLFhIeH07t3b/r378+AAQPYv38/4eHhDBkyhNCzZwFQ+PvTQKOhYQ6J55x0OnlgcHpA4+3tnef7zNxCUystjdsqFa87OvK6o6PJcbZ6PQmAXnQ5VS2iy0moDNLMfdAqXVBqH2Ce/DfJVh3yf1EFoW7difs/7MTp9ZGYXY/AeWBnHi8LJqX/i2VdtVKXkpLC9Olj2L5dztszcuRkXn59Dm82cOaYgyWSXs+YO3EMvp9Y5jOZypKtnZ6O3VPo2F0ef3P3jpLQY/L4m7Mn5Snix7dYwRagOVjP0OHsr8W6jp5Ua4m7SqWx9eaZpCRmJiQg6fU8Viq55OzMkFat6N+/P5988gnBwcH873//43//+5/x/KdOnZIfKBTUbdgQ88TEHOvprNXKAc2VK9wrwEyn++mDgq3t7Pg1OpofbG353taWlExpK16Ni+O39IBGjKGpYkSXk1ApSBKpVu2xTvgflgn7KlVAA6D1qMeDtb/j+NYELI8dwGniEOL/+5f4GfPJdbnnSubhwweMHv0sp08fQ6lU8dZbi2j/4kuM9XLmipUZ5jods68/plM5nclUltzctfQdlPT/7d13fFNl28Dx30nSpuletFJK2aNsKBvZAjIFUUGGgoCiqAwFQaa4AQUVUNBXeVRAkEd4HChURaaDjQgyC6XQvdukbdb7R0sFS6HQpkna6+snH5qTk3OuXKbJ1fvcg35D9ZjNcO5vl8L+NyePuaIfrUJfMClPgyZ5jBmVxcVxGn4I0PGtuzvfXjt7fHAwa/LyeFWnY+HChYwaNYo33niD+Ph4OnbsSGBgIMuXLyclJQXCw2l4kxFSfldbaIDof01bciPJBQWN1tsbV2BcVhYD9Xre9/JCr1KxIC0NndXKNwW/E1LQCCGcUo57F9yzvkGb/QtUmWfvcMqc1cublHc+x/udl/FcuwqvZS+jOX2CtHc/xaqr2MuVnD17itGj+xEdfR4PDy8WL/4Q7249eKSuP8kuanyNZl46n0IDg3ONZLIHtRrqNzZSv7GRh8dnYdAr/PqLGz99p+Pw79r8NahmuqJ60Ur4I0ayFirEhGpwt1rxtFhIt1r529W1sPVmXsOGRWYDHj58OPdt2cKZnj1peJM1jjytVlRVqmABzpWgoLk6ysntmj++gywW5v9rfpqro6WkoKlk5JKTqChydR2wosIl7wxq42V7h2MbajUZ0xZgrN0A39dfQLf1v6gvniNlzTdYQm4+jNZZ7d27g/Hj7ycjI4277qrGe+99zsWWzZhW05cctYowQ/5IpmCjfIbdCZ27lR79DPToZyAtRcXO7W78/J07p467cmKNK6wBF62Vtj1z6TskC/+gQ3zboxNbPDz41t2dn9zcmJSVxb05OezTajmj0TDY1RXD9Omg0dDgJitpK4BbUBB6IKYEl5yuTqzn4eV10/1UV1toKsgop8rRBlsGJk2axIkTJ9i/f7+9QxGiVKxqb/K0TQFwy95h52hsyzD4YZI/2IjZxw/Xv45QpW9rXA79bu+wytzGjWsYMaIPGRlpNG7cgk8/28qe9i2ZXjt/WHaLjByWnkmSYqaM+PpbuG+4nnc+S+KTb+IZPTGDajWMGHMVft6qY/qEKkwe2R3zeJjzUxrNc3MxqFQs8fbmnqAg5vn48JmHB0MDA4nR5Lcr1L/FKtReVaoAcKUEBU3W1YLmVmslVbBLTlLQCFEJ5eo6AeCW/bOdI7G9vJbtSfp8G8Y6DVAnxRM4tCu6TZ/aO6wyYbVaeeON2UydOhaTyUjPnv1ZuXoTHzSvwzuhPlgVhb5J2bx6PqVCDst2BFVDzYx8IouPNify3rpEBo/Iwi/ATFaWK19v8OTlXn5cqaXm7v8zEGAw42K10jo3l956PUpBy0iwyYTPLVpJfAsKmr/27GH9+vUYjUZmzJjBI488wvnz58nLy2Pu3LmsWLGCjILWHq8SttDIKCchhNPK9egCaStx0+9D0T1u73BszhxSnaRPvsV39lPodkfiN/lRNKf+InPW607bWTgnJ4cpUx7lm282AjBmzCQefWYWM+oE8HvBSKZxVzIYWslHMpUXRYF64UbqhRsZPyWVQz9Es2t/c/b97E5irIbE8RoYD7XCjbQblEvf+w2MqZnFx15edMm5dQftGu3bc6pOHfTnzjFixAimTp1aOOJp48aNNGnShIMHD173nFsNYKlofWic8zdZCFEqRteGmFV+qKzZ+Fv+tnc45cLq4Unq22vIHPM0AF4rF+E39j6UrEw7R3b7kpMTefDB7nzzzUbUag1z5y7hgWlzGR8exO8+bmjNFuZcSOUBKWbsQq2GZs2SeG5BKl/8FMfsxSm072ZA42Il6qQLK970ZkC7YN4c4EPrZbl0unzrgiYkMBD+/JPmc+bg5uZGfHw8Li4uREREkJubW6SYAfC7xSUnpYJdcpIWmhKSTsGiQlFU+cO3s7cSbD4MjLF3ROVDpSLzmdmYajfA9+Xn0P34LZpBHUj5z7eYq9e0d3Qlcvbs34wa1ZdLly7g6enN4sUf4tmtO4/U8SelYCTTy+dTqCcjmRyCqxY635ND53tyyMpU2POjjp++03H8kCtH9+fPd7Nkng/tuuQy8CE93e7N4UYjuAMsFtDpqPbii3w4aBCffvop99xzD61bt+b7779nzZo1jBw5kq5du9Ln//6PmIwMajVsCLm5xcamqmAtNFLQlJDMQyMqmlz3zrhnbyXIfMipV9++E4b+D2AKq43/tDG4nPqLwL6tSf2/zeS162zv0G5qz56fmTDhfjIy0qlaNZT33ltLVIsmTKnlS65KRY2CkUxB0vnXIXl6Wbl3iJ57h+hJjFex43sdO7a6E3XGhb0/u7H3Zzf8q5gZ8rCeoaOzCQ75p9AIKPhj+piLC/oaNZg1axbfuLnxiUrFiH796NuvH5FaLVEWC1Vef50YV1e8U1JuGs/VFpqK0rtKLjkJUUnlunfEioKP5QIq063ntqhojE1bkfjZ9+TVb4w6NZmAh3qiW/eRvcMq1hdffMzIkX3IyEinSZOWfPrZVna1a86M2n7kqlS0ysjh7TNJUsw4iSrBFh4ak837GxNZtSmBh8Zm4htgJiVRzf+968WAdsFMHePHrzu1WCwQbjSitViI0Wi4p0oVRvj7M9Hfn7m+vnQNCuJ5Hx/GBgQwoEoVDru6AuB5i5YXVQW75CQFjRCVlEXtR55rOAC6rF/sG4ydWO6qRvLHX2Po0Q/FZMRv+gS8508FB7q0bLFYeO21WTz33DhMJhO9eg1kxepNrGxWm3cLRjL1S8zmZRnJ5LRq1DHx2LOZfP59PC++mUKTVrlYLAq7InU8PSKAwXcH8ctyNz46m8TdBgMmRWFnwYry3gVFzhceHkWO63mLkVNKBRvlJAWNEJVYTuHw7Yo9H83NWHXupL75IRkTpgHg+dEy/Ef3Q8lIv8Uzbc9gMDBx4jBWrHgDgLFjn2HWG+8zvXEIm6t4oFitTLiczjOX01HbOVZRehoX6NI7hyX/l8yqTQkMHJaFu4eFyxc1vPOKD481rYLXKCvP/Z5G25wcZqWl8V18PGMyM/G0WBiTmcnahAQicnOpZzRS+xZz28gop0pqxYoVNGrUiDZt2tg7FCHKTI4uv8+Im2E3WIrvPFjhqVRkTZxOyhursGjdcNu5neD77sYjNtZuISUlJfDAA9347rtNaDQa5s9/m/unzWZceBB/eOePZJoXlSLDsiuoGnVMTJqZwbrIeJ6dnUbt+nkY8xS+/8qdt9r7khahQlkNih6eyczkl7g4nsnMpKHJxOrkZL5ITMTtFueQgqaSkpmCRUWUp22MQfFHZclEl/E/e4djdzm9BpH8f1swVwnG5dwpusyYgXZv+bdenTlzkv7923HkyB94eXmzfPk6ag8fxSMNAonSueBnNLPkbDIdMipxEVpJuOms9HtAz4ovklj2aSLd++lxcbVy+oQrr73gS//WQby/2Iu05Nv/Oi/sQ+NAl1hLQwoaISozRU2Uph8AnqmroIKs6VIaxvDmJH62jdzw5rhmZlJldH/c/7Oy3M6/e/ePDBzYnpiYC1SrFsaaNd+Q3qsnE+oHkuqipqbByLunE2VYdiWjKNCwqZEXXk1j7bY4xk1JJzjERHqamo+WedG/bRCvz/Ih5mLJLz7KKCchRIVywaUPFsUNl9wTuOr32Tsch2CpEkzi6i+51LUrismE74uT8Jn1FNyiT0JprVv3IaNG9SUzM4NmzSL4z3++Y0e7FrxQ2588lULr9PyRTFWMFeMSgbgz3r5WHnw0m4//l8CsN1Ko0zCP3BwVmz71YMjdQcx43I+Tx24wmc2/VLSlD6SgEaKSMype6D36A+CZ8oGdo3EcVjcdh6ZMIe3JGQB4fPo+ASP6oKSllvm5LBYLr776AtOnP47JZKJ37/t474ONvNe8Fsur5c/2OiAxi5eiUnCXkUyigFoDXfvksHxdEm+uTqJV+xwsFoWfvtMxqm8VHn8ggH07tMU2vEpBI4SocDJ9HgHALftH1Hnn7ByNA1EUMh97hpQlH2Nxc0e7bwdV+rdFc7bsloswGAw88cRDrFy5CIDx4ycz682VPN+4Gv8LzB/J9ERMOk9fzpCRTOKGFAWat8njtfdTWLkhgW599ajVVg7+quWZUQEMu6cK323SFWlgrGhLH0hBI4TA5FKDHN3dAHgmr7JzNI4np3tfkj75GlNwCJoLZwkc0A7tjh9KfdzExHiGDu3K1q3/RaPRsGDBUgZNnsXYBlXY760tHMk0JCm7DF6FqAxq1zcx87U0PvkmgftGZKHVWTj3twvzJvsxqEMQn6/2QJ+dP7rpagFglU7BlYsM2xYVXZbvGAB0GV+imMv+soqzM9VvTNLn28ht1hpVZgb+j/THY/XSO+5Iffr0Cfr3b8fRo/vx8vJmxYr11Bw2kkcbBHJB54K/0cxbZ5NkJJO4I0FVzTw5PYPPf4jnkacy8PEzkxCrYelLPvRtHcx7r3lhMhW00FSQwQBS0JSQDNsWFV2eWxuMLvVQWXPwSP3U3uE4JIt/IMmrNqEf8BCKxYLPS9PweX485OXd1nF27Ypk4MD2XL58kdDQGvznP9+Sek8PHi8YyVRLnz+Sqa7BZKNXIioLL28rIyZk8dn38TwzO42q1U1kZahYs8KLg/vcATDmVYyCRhanvENfJX6Pu8kxFqm0WKxEZyVzOjcAlUqm2Po3yU/x/p0b19zGeGSewZK5kvRsf7Bzrw1rwX/5f0FaMVvNBVvAarXk/2y1YsGC1Xp1X0uR51mtVsxYCn/+Z38K9rcU/pw/iFVBAZKyE/kx8yfUKjUqVCiKCjUqlBG10DXqgfveHagvfozlhR0Yhj+G4uGNoqhQoaBW1Pk/X72hQqVS8+u3v7Bx6RosZgs1wmvz6OxJrA5344fa/gDUS7jEsGN7uWCxEI0KRVEKz61SVCioUCtq1CoXNCoNGsUFTcHPauXqNtf8+yoX1Ir0vBH5q373fyB/Ycxff3Fj4yeenLbkvzcuX7Jy330waxa0b2/nQEtBsVorSFtTOSlcbXsm3HIaRiGEuMoC/ATsLbjfDBishvs+gFbj87f9/g5smwbWsuukqaAqKHY0+YWP4oK64L6LyrVg+9ViyOWG++Zvz7+v03jg4eKNu9oDt8w0vIMa4+Hqg4eLF+4aL9xdvPDQeKFV6wpnoq1srBYzuZcOoa3eCkXlmAWl1QrPrV3HibemQ5sHYf9GALp2hQULoFs325w3OTmZwMBA0tPT8fb2LtNjSwvNHWrj1QIXT1d7hwHk/z1pNFtwUatkCvQbkPwU70a5cTNdRme+jFnRkqFthb2vTCuKCgUFlaKQ33KiFLRUKCiKQuF/1/xc3OOqgtdS+Pg1++S3ggAFmbBYLWTlZOCm9YBrWn4sVgsWLIX3VVkZeB7ej5JjwKxRk9G8BTlVggpbfixWC6ZcE6c//ovUI0kABPUJIWBQPS4OWIQ+rC1YzAT+8ibeR9dj9Wx4TQuS5ZoWpastUhZMVhNmqwmz5V//Wk0F7Vf/sGLBaMnDSB7You/npRtvVilq3DWe+UXO1X9d8v/NL3488XTxxd8tCH+3IPy0Qfi5VcHXNQC1Sr6abE1RwMcv/+fAKma6PwibN8POndC9O9x9N7z0Uv7PzlKXyrvmDr1Rexa+wUH2DgPI79B1OTODal7eBR/64lqSn+LdKDcqSxaNEsfgakkm3rMOl/1n2DlK+7BYLFxOvUA1v5qF83UUR90wjdpzpuF15ADWjYe4MnUm8Y8/A4pCUmICU54YTepfSWg0Lkx9YR6N7rufOY1ro3d3Q2s28/TpaFq5DYB2A0oft9WMyWLML3osJkxW43UFj8liLPj3348ZMVn++Tf/GMaCY1zd10iOSY/enIXemIk5J4V0jOSY9ehNWeSYszGYsgsKOTNZxnSyjLe3yKeCgperH37aKvhqA/HX5hc7/rogAtyC8dUGEuhWlSq6ENw07qXOV2V29XderbawbBlMnw7vvgsbNsCePdCzJ3TokN9i06uX4xc2UtAIIa5jUXkS7fMcdVNfJChrLWkevcnWtrB3WA7N7OPLmaWrqL7sDar870uqvf06bqf/5uexT/DMpMeIj7uCl7cP8155C3XnzkxuVIsMVxd8c43MOBlFDX1OmcWiUtS4qtXYvP3YYsEj+SLZATXgmoLParWSazZgMGWhN2VhMBf8a8pGb8xEb8pCb8rEYMomy5hOel4y6XnJZOQlk5GXihULGXkpZOSlcDHz1E1D8HTxJdCtKoG6uwjSVaOKrhrB7qEEuVcjUBdCoNtdaFS3njG3sios1At6nlSrBm++CZMnw/LlsH49/Por9OkDbdvmFzb33uu4hU2lLWj0ej3h4eE8+OCDLFmyxN7hCOFQMtw6kOzWi4CcSGokz+XkXV9iVUmnsZvSuHDp+bkYatWl+ruLOPjtZsZv/R8ZFgtVq1Xn1cXLOd2iGW81CMOoUhGWZWD631H451WskUyKouCmccdN444ft9eKbbGayTSmkZ6bTFpeEmk5SaTlJZJWcD89Nyl/e24iOWY9WcY0soxpXMg8eeNYUPDTViFQV5UqumpUdQ8j1LMO1TxrE+JZC39tUKXt5wP5l3Oh6MR6ISHw2mvw7LP5hc26dfDHH9CvH7RuDfPnQ//+jlfYVNqC5tVXX6Vdu3b2DkMIhxXj8wxeeQdxM10kJG0Fl/2fs3dITiFp6MN8EnORlzetw2yxcLfGhVnPz2FDh9Z8WjMEgBYpGTxzOhq3CjJDa1lRKWp8XAPwcQ0gjPo33VdvzCQpJ46UnDiScuJIyrlCck4cKTnxJOfGk5oTj8lqJCU3gZTcBE6nHS1yDDe1O3e51yDEsyahHrWp5llw86iFrzawwhc7t1r64K674JVX4JlnYOVK+PxzOHAABg6Eli3zC5tBgxynsKmUBc2ZM2f4+++/GThwIMePH7d3OEI4JLPKi0ve06iTNoegrM9I9eiFXtvM3mE5NIvFwtIVb7Fu0zoAhut0rDYaecbfrbCY6XMlkVEXYmUSsFJyd/EizMWLMK96N3zcYrWQmZdKck4cyTmxJObEEqe/SFx2NPGGSyTnxJFj1nMh8+QNW3h0ag9qejekvm9zGvi1pL5fc0I8aqFSKs7/OaWEazkFB+d3EL5a2Hz6KRw+DIMHQ7Nm+YXN4MHXXXm0C4craHbt2sXixYs5ePAgsbGxbN68mcGDB1+3z8qVK1m8eDGxsbE0btyYZcuW0blz5xKf4/nnn2fx4sXs2ycrCwtxM+m6TiTn9CQg5ydqJM/l76obsSpae4flkAw5Bma/NJOde3YAMGrYI9x//0P0rRnE3rZtUJnNzFy3gca1Gtn/k78SUCkqfLQB+GgDqO3TuMjjJouRBMNl4vQXic2+mF/s6KOJN0STkhOPwZzNydSDnEw9CFH5z3HXeFHXpykN/FrQwK8F9X1bUEUX4rQtOVc7BVtLOE1AYCDMmweTJsEHH8CaNXDsGAwdCk2a5D82dKj93t4OV9BkZ2fTvHlzxo4dy9ChQ4s8vmHDBqZMmcLKlSvp1KkTq1atom/fvpw4cYKwsDAAIiIiyM0tOl349u3b2b9/P/Xr16d+/fpS0AhRAjE+z+KdexCdKYqqae9zxW+KvUNyOIlJiUx54Wn+Pn0SjUbDtKen07DvAJ7p0JzLXh64Gwxsuv9++v7wA9Fde/Lr3Fcw62SEjj1pVC6EeNQkxKMmVLn+sTxzLvGGaKLST3I2408uZJzgYuZp9KZMjiXv41jyP98dPq4B1PNtRgPfFjQJbEe4XwRalXMU/YUFzW1e+gwIgNmz4cknYdUq+OQTOH4cHnoIwsPzC5sHHwR1OU/B49AT6ymKUqSFpl27drRq1Yr333+/cFt4eDiDBw/m9ddfv+UxZ82axeeff45arSYrKwuj0chzzz3HvHnzbrh/bm7udcVRRkYG1atXJ3LdFw41bDs2K5Oqnl4yLPkGJD/FK2lufA27qZs+HytqTgZ9it616F+8FY3FaiE2LZqqvmE3vcxw9twZJr8wiYTEBLw8vZg3ayHK3Z1Y2K45GVpX/Aw5TP/jT7r/90varFyK2mwipW59dr3xDvrgu8rxFZUxiwWP1Etk+1WvFC1OJouRmOxznEs/zrmMv4jK/IuYrPNY/jW5j1rRUNsrnHBtLZqF9adJQDt8tP52ivrmlu76ju1Tn8S3xz0cW7P1jo+TlgYffaTik09UZGbmf440aGDlxRfNPPSQ9brCJjk5mapVq9pkYj2nKmjy8vJwd3fnyy+/ZMiQIYX7TZ48mSNHjrBz587bOv6aNWs4fvz4TUc5LViwgJdeeqnI9nXr1uHuLn9hicojIuctQs27yVDC2Kl7C4siw2EPHTrE4sWLMRgMhISEMHfuXM60bs17LVtiVKupnZbGnN9/xz8nf1i2/4kTtH3zTbTp6eT4+vLHzJmkNmxo51ch7lSuJZcLhguc0Z/hVPYpTmSfINmYXGS/UG0o4Z7hNPJoRCOPRgS5OsboqhV//UXk7Nl4de7MZ8+VvtN/VpaG776rzddf1yE7O3/igGrVMnnwwdN07nwZtdqKXq9nxIgRMlNwUlISZrOZ4ODg67YHBwcTFxdnk3POmjWLadOmFd6/2kJTBRW+XmX7P+NOSQvEzUl+inc7uUnymEZw4jG8rdFEqH/gis8z5RSlfdyqhebLLRtY8s6bWCwWmjRqytyZC/gmojmfN6oDQMu4JJ4+fAKtWUu2Ov8SRHbTDiS//QFdF76I38UoOs2dyx/T5xDVd1C5vrYyUclaaIoTSn1C6U33gvuJhiv8nXqQqIS9/Gk4xRX9BWJyY4jJjSEyORIAf20wjfwjaBrQnqYB7anp1cAuBY5LdAKQP7qsVat+ZXLMLl1gzhz4+GMzH32k4vJlL5Yti+Cbb1oxa5aZPn2SyuQ8N+JUBc1V//4fb7Va7+jNMGbMmFvuo9Vq0Wq1rFixghUrVmA25zcvqhTF4b4cHTEmRyL5KV5JcmNR+3LJZyq10xZQNXMNGe490WublFOEdlDQrUClqK6bKdhsNrN0+RLWb1oLQM+u9/DUM8+zok0zfg6rCsC95y8x8uT5/JFM/8qrPrgqkUtW0GHJq1T/fS8dXpuPT9R5jj45GWt5dzooCypVpS5o/q2KRyhVdCHcq21GdkANMk0ZnE47wsnUA5xOO0xU5klScuPZE7uVPbH5l3l8XANoHtiJiKCutArqTBVdtXKJVVWwzpTVakGtLrsWV19fmDYNHn8cPv44v5/NuXMK48drCAvTldl5/s2pCprAwEDUanWR1piEhIQirTZlbdKkSUyaNOmfxSmFqITSdF1JMXTDP/cXaiS/yN9Vv6xUo54MBj2zFsxg975dADzy8BgGjHiUee2b81egHyqLldF/naF3dOxNj2PSubN79ss0+/z/aLJxLY3W/wefC+fZ+9IbmDw8y+OliHLi5epLRFA3IoK6AZBrNnAu/TgnUw9yKvUQZ9KPkp6XzK4rX7PrytcAVPOoTYvATrQO7k6zwA54uNjmasCddgouKU/P/Mn5xo3LHxH1wQcQHW27PyqdqqBxdXUlIiKCyMjI6/rQREZGct9999n03P9uoRGisrrkOwWvxKPoTBcISV1eaSbcS0xK4NkZkzh95hQuLi5Me2Y6De7tz+QOLYj1dMfNZOLZgydonpRasgOqVBx7ZALp1WvS7t3FVPt1N70fH83ORe+RXS3Uti9G2I1WraORfxsa+bcB8jsbn0k/xrHkffyV/BvnM05wOfs8l7PP893Fz1Apaur5NCMiqCtdq91HDe+bTzh4O65eSrVVQXOVh0f+UO+xY2HVKjO2mpzf4QqarKwszp49W3g/KiqKI0eO4O/vT1hYGNOmTWP06NG0bt2aDh06sHr1aqKjo5k4caJN45IWGiHymVU+RHs/VzDh3qekefSs8Gs9nT57imenTyIxKQFvL28WvPgy5o4dmdyuGZlaV/wNOUzff5ywzOzbPvbF7r3IrFadLi+/iO+F8/QZP4I9rywhIaKtDV6JcDQalQvhfhGE+0VA3WfINmZwMvUAR5P28VfK78QbojmVdphTaYdZd3oZNb3C6R46mO6hQwhyL92lKeVfaznZmrs7jB1rqTwFzYEDB+jevXvh/asdch999FHWrFnDsGHDSE5OZuHChcTGxtKkSRO2bt1KjRo1bBqXtNAI8Y/8CfeurvU0m5N3bcKqst21cXva89suXlzwAgaDgWohobwy9zVOtG7J0paNMKlV1EzLZPqBP/HNNd7xOVLqN2Tb0lV0eXk2AedO02PqRPZPm8W5wQ+W4SsRzsDDxZvWQT1oHdQDgCRDLH8m/8qBxB38mbyPC5kn+eTkST45+TqN/NvQPXQIXUIG3tHQcFtfcipvDj1s2xFdbaH5af0Gh5qH5nJmBtW8vKXT6w1IfopXmtyoLVmEJ47B1ZJMgufDxPjPslGU9mGxWPhw7Uo++ugjLBYLzZo0Z84LC/hfRDPWhtcGoFVcEk8fPom2jL4Q1Lm5tFv2BjV35882fOr+YRyaPAOrxuH+9ix2tW2BzXKTZUzn9/hI9sZ+x+m0w1jJ//pWKxraBvekf61HaFWlS4mXZ/i/A7/w5YSRuLduw5n//VFmcd5MWloyjRsHyrBtIYTjMKs8uegzg3qpLxCUtZ40XU+ydBXjMonZbOat9xax4b/rAejVvTcTJ03jvbbN+aV6/mR4fc9dYsTf58t0TSazVsu+GfNIr1GL5p9/TIOvNuB9MYo9r7yFsYw//IXz8XTxoWfoA/QMfYDknDj2xX3Pvtjvic46xa9x2/g1bhtBulD6hA3n3hoPE6C7+cSNhYWPpWK0a0hZXUIrVqygUaNGtGnTxt6hCOEwMt3akqjLn7+iRspcVJbb70PiqC7HXgbgkRFjGD9tFnO7tuOX6nehslgZe+w0o8q4mCmkKPw1/BF2vbgQo9aNqgf/oM/4EXhfjLLF2YSTCnC7i4E1x/J6h4283n4TvUKH4a7xIsEQw2enljA6si3zf3uU3+N+xGwx3fAYilxyqtzkkpPzkfwUryxyo7LoCU98DK0lnkSPoVwKmF/GUdpHcmoyu/f/TNjdfZjXsWX+SCajicmHTtCspCOZSsn3/Fm6LnwRj6QE8jw82btwEbHtO5XLuW9JLjkVz065yTPn8Ht8JD/FfMmZ9KOF2/20QfQIvZ97azxMda+6hds/O7yXtY89hFvzFpzberhcYrTlJSd5FwohSsWicueizwwAqmT/F/+sr+0cUdnQ6XS49e7N1G5tifV0J0Cfw4J9h8utmAFIq12XH5atIqFhY1yzs+g6/WkabPis3EalCOfiqnajc8hAFrT9lDc7fEXv6iPwdPEhNTeB/577gAk/d2Xyzv58F/UZ2cYMFFXBHzEV5P0kBU0JySUnIYqX5daKOI+HAKiRMh8f/c92jqj0tt0VwPyOHclydaFWWgYL9x2iepa+3OPI9fXj59eXcq5nH1QWCxHvLqHt6wtQGe98VJWo+EI96/BowxdY0eUnJjd7i+YBd6NCzam0I7x3bCbDf2jBnivfAhXnkpMUNCU0adIkTpw4wf79++0dihAO6YrXEyS79ULBTK2k6XgZfrN3SHfMqMC6GiGYVCoiYhOZ++vRUg3LLi2Liyu/T5nJwXFPYlFU1P1uCz2eHY82NcVuMQnnoFG50Db4Hma0WsF7XbYzvN4UQtxrYbTkciHzJAC5Rj1v7p/DxYzzdo62dKSgEUKUDUXFRd8XSNV2QoWR2kmTcc89euvnOSAXK7x67DQPnzzJsweOl9mw7FJRFE4NGcbOea+Rp3Mn6NgR+ox7GJ9zZ+wdmXASvtpABtYcy6KOm1nYdi11fAvWYrNYePfIq3TcUIeHvu3Jtgv/w2xxvjnXpKARQpQdRc0Fv3lkuLZCbTVQN+EpdHmn7R3VHQnKzWPY6dMO9yEZ26Y9299+n8y7quIZH0fvJ0ZTrWDeGiFKQlEU6vg0oW3QPQCoUdMuuAsKCntjf+axyMF03FCXlUcXkZrjPK2Ajva76rCkD40QJWNVXDnv9wpZLo3QWDOpm/A4WuNFe4dVoWRUr8G2pauIa9oCF4OBLrOm0ug/H1aYzp2ifKgLRmCpUFjZYwNfD/yd0Q2fxNvVl5isC7z6xwu0XhfKczvHcSL5mJ2jvTUpaEpI+tAIUXIWlY5z/m+i19TBxZJC3YQJuJji7B1WhZLn5c2Ol5dwut99KFYrLVYvp8NLs1Dl5to7NOEk/j0PTYhndaa0nMfW+w4yp80S6vqEk2M28MXpj+n1VXMGf92Zb85/iamYeW3sTQoaIYRNmFWenPVfTI66GlpzHPUSJqAxJ9s7rArFqtFw4Kmp/DFxMhaVilqR33PPU2NxS0q0d2jCCVwtaPhXHzGdxp0hdUfyRd+f+KjnZnqE9kelqNkfv4eJPz1Eu/U1eefQKyQbHOt9JgWNEMJmTGo/zgS8RZ6qCm6mi9RNeAK1JcPeYVU4ZwcM4eeXl5Dr6UXg339x77iH8fv7hL3DEg7u6iWn4oZtK4pCy6D2LO78Ed8N2s9jjSbjq/UnTn+ZRQfn0npddZ7dMZqjiQfKM+xiSUEjhLApozqYMwFvYVR8cTeepk7CJFSW8p/PpaJLaN6KbUs/ID00DPekRHo9NYawH3+wd1jCgRXODV6CtZyC3KsyqflMvr/vEC+1e5dwv2bkWXL579nP6belDf23tOOrM2vJM+fZNOabkYKmhKRTsBB3LldTnbMBizEpHnjmHaV24hQUq/0++CqqrKrV2P7WSi5HtEWTm8vd81+g6erlRS4pCAGgukULzY24qrUMqP0gn9+7jTW9vqNP2GA0igtHEv/gmV9G0XZ9GIsPzCNeH2ursIslBU0JSadgIUrH4FKXc/5vYkaLd+5v1Ex6AayO2bnQmRk9PNk173VODMmfubnpfz7k7tnTUBukVUxcr3C1be5sdFzTwFa81ul9vrvvAE80eZ4AtyASDfEsO/wybdeF8cSPD3Ewvvwm2JSCRghRbrJdG3Pe7xUsaPAz/ESN5PlgldaDsmZVqzky7il+nTwDs0ZD2K4d9Jr4KO5x5f9Xs3BcxXUKvl2BuiAeb/oc3913gNc6vk+zgNaYrCa+jfqSQV93YOg33dhz+WdsvRa2FDRCiHKV6daaKN95WFERoP+GGslzwep8s5I6g6he/fjptaUYfHzxP3uaPuMeJvDPI/YOSzgItapsCpqrXFQu9KkxmE96f8PaPtvpX/NBNIoLv8XtZNjWngz8Xwd2xmwvk3PdiBQ0Qohyl67rTJTPnGuKmnlS1NhIUqOmbFu6itSatdGlpdLzmfHU2rrF3mEJB3D1kpMtFqds6N+UhR3eZcvAfTxYdwwuKlcOJ/7OUztGlPm5rpKCRghhF2nu3YnyvbaomSNFjY3og4KJXLyc6PZ3ozYa6fDqfFq8twTFLPmuzBTKtoXmRqp6hDKzzet8O2g/oxpORKt2s9m5pKARQthNmq47Ub5zC4qa76SosSGTzp09Ly7kz2GjAGj0xWd0mfEMmuwsO0cm7EVVRn1oSiJQF8TUlvPZcO/PNjuHFDRCCLtK03Ur6FOjLihqZktRYysqFX+OHs+eGXMxubhS7be99J4wCs+YS/aOTNiB6mofmnJcA8xH62uzY0tBU0IyD40QtpOm61rQUqMmQL+VmkmzZEi3DUV36cmPi95F7x+A78Uo+owfQdDBP+wdlihntuxDYw9S0JSQzEMjhG2l6bpyvqClxt/wgxQ1NpZSryHblq4iuW59tJkZ9Jg6kbqbN9g7LFGO1OV4yak8SEEjhHAY6bounPedX1DUbJOixsYMAYH8+OZ7XOjSA5XZTNslr9F6yasoJqO9QxPl4OpMwVLQCCGEDaTrOnPedwEWNFLUlAOzVsu+6XM5MnocAPU3b6T71CdxzUi3c2TC1v5Zy0kKGiGEsIl03d1E+c4vLGpqJb0AVmk1sBlF4cSw0eyc/TJGrRt3HdpPn/Ej8L5w3t6RCRuSS05CCFEO8ouaBQXLJERSJ/FZWaXbxi536EzkkhVkVQnC63IMvSeMouqve+wdlrAR5eolp3Ic5WRLUtAIIRxWuq4T5/0WYsEVn5y91It/DI052d5hVWhpteqwbdlqEsIb46rPpuuMZ2iw/j8V5ktP/EOtSB8ap6fRaGjRogUtWrRg/Pjx9g5HCHETGW4dOB2wFJPijYfxBPXjRqM1Rts7rAot18eXn19bxtl7+qKyWIhY/jbtXpuPKi/P3qGJMnR1GhopaJyYr68vR44c4ciRI3z00Uf2DkcIcQt610acClxOruou3Mwx1I8fjXvucXuHVaFZXFz4Y/IMDo5/Couios7W/9Hj2fFoU6WFrKK4Og8NVqvNV8IuD5WyoBFCOJ9cTXVOBa5Ar6mLiyWVegmP4a3fZe+wKjZF4dTgh9i54HXy3D0I+vMo9z42At+zp+0dmSgDilI4zkkKGlvYtWsXAwcOJCQkBEVR2LJlS5F9Vq5cSa1atXBzcyMiIoLdu3ff1jkyMjKIiIjg7rvvZufOnWUUuRDC1kxqf04HvEO6awRqaw51kiYTkPmVvcOq8GIj2rH9rZVk3hWCR0IcvZ54hNBdtluTR5QPNRWroNHYO4B/y87Opnnz5owdO5ahQ4cWeXzDhg1MmTKFlStX0qlTJ1atWkXfvn05ceIEYWFhAERERJCbm1vkudu3byckJIQLFy4QEhLC8ePH6d+/P3/++Sfe3t43jCc3N/e6Y2VkZABgsVqxOMgb4GocjhKPo5H8FM8Zc2NRdJz1e50a6YsIzPmRGqkL0JjjiPV6Aq75i7O0rFbL1R+kQyyQERrGtrffp9ObC6h69DBdZj/HiZEjOTJhqr1DczxX+6Q4eN8UFf+8r83GPBRcbH5Oiw1XeFesDlyWKYrC5s2bGTx4cOG2du3a0apVK95///3CbeHh4QwePJjXX3/9ts/Rt29fXn75ZVq3bn3DxxcsWMBLL71UZPu6detwd3e/7fMJIcqI1Uq4cS31jZsAiNL04U/Xx7EqajsHVrEpZjNNPv6Y2t99B0BM584cfvppLFqtnSMTt+uKycRTDzwAwJdffomLi+0LGr1ez4gRI0hPTy+2IeFOOVwLzc3k5eVx8OBBZs6ced323r17s2/fvhIdIzU1FXd3d7RaLTExMZw4cYLatWsXu/+sWbOYNm1a4f2MjAyqV69OFVT4epXt/4w7ZbFaic3KpKqn1z/LwYtCkp/iOXtuMniKi9lVCctcTi3TNnw1BqL838Si0pX62OlGA1lZ8WQqXv9MES9ADb9NfJ6ksHq0Xv0eobt3o0tIZtcbSzEEBtk7OsdgseCReolsv+rgwO+d3Nycwp+D6zRH51b635tbyUhPs9mxnaqgSUpKwmw2ExwcfN324OBg4uLiSnSMkydP8sQTT6BSqVAUhXfeeQd/f/9i99dqtWi1WlasWMGKFSswFzSXqRTF4b4AHDEmRyL5KZ4z5ybZ835M6kBqpb2KX84uXJImcK7KCsxqv1IdV7k6AkRRyvRSVkVxtt995IUE0ObNRQScOkGf8SPZ9eY7pIQ3sXdojkOlcuiCRlH/05ppVdSo1LYvCVRq27WgOm6mb0L514eL1Wotsq04HTt25M8//+To0aMcOXLkustZNyOrbQvhuNJ1XTjjvwST4oln3nEaxI/G1RRj77AqvOSmTflh6QekhYbhnpzEPU+NpUbkVnuHJUpIfc33psnB+/uUhFMVNIGBgajV6iKtMQkJCUVabcraihUraNSoEW3atLHpeYQQdyZb25TTAcvJVVXBzRRNg7hR6PJO2DusCi/7rhC2v/0+l1u3R5OXR6cFs2i26j2H7xAruK5V1qo4bHfaEnOqgsbV1ZWIiAgiIyOv2x4ZGUnHjh1tem5poRHC8eW41OB04Er0mlq4WFKoHz8WL8Nee4dV4ZncPdg191VO3D8MgCaffkTnF6eh0cvaW47s2r5h0kJjA1lZWYWz+AJERUVx5MgRoqPzpzqfNm0aH330ER9//DEnT55k6tSpREdHM3HiRDtGLYRwFEZ1IKcD3iXDtQVqq4G6iU/jn/W1vcOq8KxqNUcee5Jfp8zErNFQffcOej0xGve4K/YOTRTj2nloLFLQlL0DBw7QsmVLWrZsCeQXMC1btmTevHkADBs2jGXLlrFw4UJatGjBrl272Lp1KzVq1LBpXHLJSQjnYVF5cs5/ESlu3VEwUzNlDsHpH8p8MuUg6p57+en1dzD4+uF3/iz3jhtB4NFD9g5L3IBK9U9BY7Y4/++GQ89D44gyMjLw8fHhp/Ub8A12jCGKFquVy5kZVPPydtqRKrYk+Slehc+N1UK1jA8I1n8JQILHQ8T4z4ISzFWTlmcgMzOWTJW3DNu+EasVD3M62WqfG44Cc0+Ip8vLL+IfdQ6zRsP+6XM4P2CIHQK1A4sFj+SLZAfUcOhRTrnAY52aA7Dltz+p7hdo83NmpKfSvW0jm8xD47iZFkKI0lJUXPZ5ikteT2FFISh7I7WSpqFYcm79XFEq+qBgIhcvJ7pDZ9QmE+1fX0DLdxaj2HCmWHF7rh3lVBFaaKSgKSG55CSE80r0fJAon7lY0OBn2EG9hAmozen2DqvCM7vp2DPrJf4c/ggA4Rs/p+v0p3HJyrRzZCKftbAFyZmWPymOFDQlJKOchHBuae7dOeu/GJPigWfeUerHP4KLKdbeYVV8KhV/jnqM3S/Mx+SqJeT3ffSeMArPmGh7R1bpqawUFjRm6RQshBDOI0vbgtMB75GnCkBniqJB3Eh0eaftHValcKlzdyIXvYfePwCf6Av0GT+C4AO/2zusSk2Ba1popKCpNOSSkxAVQ45LLU4FrsSgroGrJYn68Y/iafjN3mFVCql16/PDstUk1WuINjOT7lOfpN5/v7B3WJXWtQWNWS45VR5yyUmIisOoDuJ04HIyXZqitmZTN/Ep/LJlyv7ykOMfwI9vvkNU13tQWcy0eft1Wi96GcVktHdolVNBx2C55CSEEE7KrPLkbMASUrVdUGGiVvJMgtI/sXdYlYLFVcuvz8/m8KPjsSoK9f+3ie5TJuJqw5WYRTGkhUYIIZyfVXElym8+Ce6DAQhNX0q1lDehAvQncHiKwskHR7FrzisY3XTcdfgAfcaPxPvCeXtHVrnIKKfKR/rQCFFBKSpivJ8lxutxAIKz1lIraTqKNc/OgVUOl9t1YvuSFWQFBeN1JYbeE0ZRdd9ue4dVeUin4MpH+tAIUYEpCgmeDxPl82LBXDWRtEh5Bo01y96RVQrpNWuzbekq4hs1xVWfTdcXnqXhujWyVEV5KBy27fy5loJGCCEKpLr34pzfG5gVHX7GI3Q2vIi7JcHeYVUKuT6+7Hj1bc726ovKYqHViqW0e2UuqjxpKbOpwj400kIjhBAVSqZbBKcD3iVX8cPbGs29+qfwM5+yd1iVgsXFhT+encGBCU9jUVTU+eEbej49DreUZHuHVnEVjHKS1baFEKICMrjU5aDPO2QoYbhbU+iV9SyheTvtHVbloCicvu8BflnwBnnuHlT56xh9xo3A94wUlTYhMwVXPtIpWIjKJUcdzG7dG1xWtUBDLl0M8wnP+Vz6dZSTuIi2bHv7fTJCQvFIiKP3E48Q+stP9g6rwlG8vADISEu1cySlJwVNCUmnYCEqH5Pizi/aOZxS9wWgZe5HtDO8icoqk8CVh8zQMLa9/T6xzVuhyc2hy+xpNP5klRSVZUjVoCEAl846/xIgUtAIIcRNWBU1B92e4IDLeCwo1DH+QLfs53G1ZNg7tErB6OnFLwsXcWrgEACaf7SSjvNmoM7NsXNkFYMqPL+giT79t50jKT0paIQQogROuw5gl3Y2Rty4y3yU3llP4WWOsXdYlYJVreHgE5P5fdI0LGo1NX/ezj1PjkGXGG/v0JyeOjwcgMvnpIVGCCEqjSua1mzXvUE2gXhbY+id9SRBxsP2DqvSONd3ED+/vIQcL28CTp2kz7gRBJz4095hOTXXhvkFTfTpU1id/FKeFDRCCHEb0lU12aZbTJJSFy2ZdNdPp1bu9/YOq9JIaNaSbUtXkRZWE/fkJO556jFqbPvO3mE5rap16oFOR1ZKMmdOnbB3OKUiBY0QQtymHJUfP+le5aK6I2pMdMh5k+aGVbIGVDnJvqsq25esJKZNe9TGPDotfJFmH7wDFWDocXmrblWgZ08Adv603c7RlI4UNEIIcQfMipa92uc5rnkAgMZ567lbvwC1VTqrlgeTuzu757zKX0MfBqDJZx/TedYUNHq9nSNzLqH6HOibP4rv0IFf7RxN6UhBU0IyD40QoghFxTHtKH51fRYzasJMu7gn61ncLDKzbXmwqtUcHfsE+6bOwuziQvU9O+n1xGg8Yi/bOzSnEWrIhWbNAIiOcu6VzqWgKSGZh0YIUZwolx78rF1IDp4EWE7TJ2sivuZz9g6r0rjQsw8/vr4Mg68ffufP0mfcCKocOWTvsJxCWLYBpWH+0O242MsYDM7bwiUFjRBClIFETWO26xaTroTgYU2kV9bThOTts3dYlUZyw8ZsW7qKlNp1cUtPo8fkCdT+5r/2DsvheZgt1HN1g8BAAC5GOW8hLgWNEEKUkSxVVbbrFhGnaoILBroaZtMwZ73MbFtO9FWCiFy0nIsdu6A2mWj/xkJavbMIxWSyd2gOrUVqJhS00qxZvdzO0dw5KWiEEKIMGRVPdrgt4Kz6HhSstMpdRTv9G6isefYOrVIwu7mxd9ZLHBvxKAANN66l6/OTcMmUmZ2LUyfLAMOHAxD5/dec/vsvO0d0Z6SgEUKIMmZVNPyhncQBl3H5yyWYttEjeypaS5q9Q6scFIXjI8aye+YCTK5aQvb/Ru8Jo/C6dNHekTmkqoZcmDQJunYFYM9O51wEVAoaIYSwBUXhtOtAdmrnkYc7Qea/6JP1OD5m5x5J4kwu3d2NyMXLyQ4IxOfSRXpPGEnwH849NNkW/POMaM1mGDoUgF9377BzRHemUhY0UVFRdO/enUaNGtG0aVOys7PtHZIQooKK1bRku24RmcpdeFoT6J01iWp5e+0dVqWRWqce25atIql+ONrMTLo/N4l6m6Rf07UUClpp+vQB4M8jBzE44Xw+lbKgGTNmDAsXLuTEiRPs3LkTrVZr75CEEBVYhiqUbbrFxKka44KBLoY5hOeslS/VcpLjF8CPbyzjfPdeqCxm2ix9gzaLXkYxGe0dmsNolJ4F9epBWBhGo5HtW/9n75BuW6UraP766y9cXFzo3LkzAP7+/mg0GjtHJYSo6PIUL3a4vcQZdW8UrLTM/ZD2+teks3A5sbhq+W3aixwe8zhWRaHe1/+lx+QncE1Ps3doDuHB6HiCc/Kgf38AFr86h6TEBDtHdXscrqDZtWsXAwcOJCQkBEVR2LJlS5F9Vq5cSa1atXBzcyMiIoLdu3eX+PhnzpzB09OTQYMG0apVK1577bUyjF4IIYpnVTTs1z7JAZcJWFBR2xRJz6zJuFlS7B1a5aAonHxgBDvnvIpRpyP4yEH6jBuBz/mz9o7M7lytVjonpMIrr6AJCMSg17Nv18/2Duu2OFzTRHZ2Ns2bN2fs2LEMLeigdK0NGzYwZcoUVq5cSadOnVi1ahV9+/blxIkThIWFARAREUFubm6R527fvh2j0cju3bs5cuQIQUFB3HvvvbRp04ZevXrdMJ7c3NzrjpWeng5AZnY2qqyssnjJpWaxWtHr9WQoKlSKYu9wHI7kp3iSm+Jl5eWhz8vFYLWilHFujtCdWHMAHXLfQctJOmQ/zl73eaSpa5fpeWzKakWx6NGr1OBk752zTZqRuHAJHRe9hFfsZdo/PorfZi4gtl3HsjmBxYKi16N3zQCVw7UbFKv+JSM0q4f1kdGwdCkb1n5Mx85dcdW6ldk5MjPyv0OttrjcanVggHXz5s3XbWvbtq114sSJ121r2LChdebMmSU65r59+6x9+vQpvL9o0SLrokWLit1//vz5VkBucpOb3OQmN7mV0e3cuXMlLwZKyOFaaG4mLy+PgwcPMnPmzOu29+7dm337SjbFeJs2bYiPjyc1NRUfHx927drFE088Uez+s2bNYtq0aYX3LRYLERERHDp06KZ/tbVp06bYdZ+Ke+zf22+037Xbrv6ckZFB9erVuXTpEt7e3sXGdCdu9jrudP+yyM2Ntt0oN4DN8nO7uSnpc243P7ebm2vvV8b3juTm5tsr2+9VWeXm2vsVJTfFPVaa3KSnpxMWFoa/v/9tvY6ScKqCJikpCbPZTHBw8HXbg4ODiYuLK9ExNBoNr732Gl26dMFqtdK7d28GDBhQ7P5arbbIKCitVouPj89Nz6NWq4t9Ixf32L+332i/a7f9+3Fvb+8y/+C92eu40/3LIjc32naz3EDZ5+d2c1PS59xufm43Nze6X5neO5Kbm2+vbL9XZZWbG9139twU91hZ5EZlg0txTlXQXPXvlhHrbV7j7tu3L3379r3j80+aNKlU+xT32L+332i/a7eVJI7Sut1zlFdubrTN0XNT0ufcbn5uNzcljaO0HPW9I7m5+fbK9ntVVrkpaRylIZ85N6dYrY47EYKiKGzevJnBgwcD+Zec3N3d+fLLLxkyZEjhfpMnT+bIkSPs3LnTTpHaV0ZGBj4+PqSnp5f5X5IVgeSneJKb4klubk7yUzzJTfFsmRvn6X4NuLq6EhERQWRk5HXbIyMj6dixjHqnOyGtVsv8+fNlgsBiSH6KJ7kpnuTm5iQ/xZPcFM+WuXG4FpqsrCzOns2fE6Bly5a8/fbbdO/eHX9/f8LCwtiwYQOjR4/mgw8+oEOHDqxevZoPP/yQv/76ixo1atg5eiGEEELYg8MVNL/88gvdu3cvsv3RRx9lzZo1QP7EeosWLSI2NpYmTZqwdOlSunTpUs6RCiGEEMJROFxBI4QQQghxu5yqD40QQgghxI1IQSOEEEIIpycFjRBCCCGcnhQ0lUxmZiZt2rShRYsWNG3alA8//NDeITmMS5cu0a1bNxo1akSzZs348ssv7R2SQxkyZAh+fn488MAD9g7FIXz77bc0aNCAevXq8dFHH9k7HIci75Ubk8+Ymyvt95N0Cq5kzGYzubm5uLu7o9fradKkCfv37ycgIMDeodldbGws8fHxtGjRgoSEBFq1asWpU6fw8PCwd2gOYceOHWRlZfGf//yHTZs22TscuzKZTDRq1IgdO3bg7e1Nq1at+P33322yPo0zkvfKjclnzM2V9vtJWmgqGbVajbu7OwA5OTmYzWbbLOPuhKpWrUqLFi0ACAoKwt/fn5SUFPsG5UC6d++Ol5eXvcNwCH/88QeNGzemWrVqeHl50a9fP7Zt22bvsByGvFduTD5jbq60309S0DiYXbt2MXDgQEJCQlAUhS1bthTZZ+XKldSqVQs3NzciIiLYvXv3bZ0jLS2N5s2bExoayowZMwgMDCyj6G2rPHJz1YEDB7BYLFSvXr2UUZeP8sxNRVDafF25coVq1aoV3g8NDeXy5cvlEbrNyXupeGWZG2f7jCmJsshPab6fpKBxMNnZ2TRv3pzly5ff8PENGzYwZcoUZs+ezeHDh+ncuTN9+/YlOjq6cJ+IiAiaNGlS5HblyhUAfH19OXr0KFFRUaxbt474+PhyeW2lVR65AUhOTuaRRx5h9erVNn9NZaW8clNRlDZfN/qr8XYWyHVkZfFeqqjKKjfO+BlTEmWRn1J9P1mFwwKsmzdvvm5b27ZtrRMnTrxuW8OGDa0zZ868o3NMnDjRunHjxjsN0W5slZucnBxr586drZ9++mlZhGkXtnzf7Nixwzp06NDShuhQ7iRfe/futQ4ePLjwsWeffda6du1am8da3krzXqqI75Vr3WluKsJnTEmUxefQ7X4/SQuNE8nLy+PgwYP07t37uu29e/dm3759JTpGfHw8GRkZQP6qp7t27aJBgwZlHmt5K4vcWK1WxowZQ48ePRg9erQtwrSLsshNZVKSfLVt25bjx49z+fJlMjMz2bp1K3369LFHuOVK3kvFK0luKupnTEmUJD+l/X7SlF24wtaSkpIwm80EBwdftz04OJi4uLgSHSMmJoZx48ZhtVqxWq08/fTTNGvWzBbhlquyyM3evXvZsGEDzZo1K7z2+9lnn9G0adOyDrdclUVuAPr06cOhQ4fIzs4mNDSUzZs306ZNm7IO1+5Kki+NRsNbb71F9+7dsVgszJgxo1KMFCzpe6myvFeuVZLcVNTPmJIoSX5K+/0kBY0T+ve1eqvVWuLr9xERERw5csQGUTmG0uTm7rvvxmKx2CIsh1Ca3ACVbhTPrfI1aNAgBg0aVN5hOYRb5aayvVeudbPcVPTPmJK4WX5K+/0kl5ycSGBgIGq1ushf1QkJCUWq3spGclM8yc3tkXwVT3JTPMnNzZVHfqSgcSKurq5EREQQGRl53fbIyEg6duxop6gcg+SmeJKb2yP5Kp7kpniSm5srj/zIJScHk5WVxdmzZwvvR0VFceTIEfz9/QkLC2PatGmMHj2a1q1b06FDB1avXk10dDQTJ060Y9TlQ3JTPMnN7ZF8FU9yUzzJzc3ZPT8lHg8lysWOHTusQJHbo48+WrjPihUrrDVq1LC6urpaW7VqZd25c6f9Ai5HkpviSW5uj+SreJKb4klubs7e+ZG1nIQQQgjh9KQPjRBCCCGcnhQ0QgghhHB6UtAIIYQQwulJQSOEEEIIpycFjRBCCCGcnhQ0QgghhHB6UtAIIYQQwulJQSOEEEIIpycFjRBCCCGcnhQ0QgghhHB6UtAIIYQQwulJQSOEsKlWrVqhKAq//PLLDR+fM2cOiqLc8DZ79uybHrtNmza8++67NogaBg4cSL169Yp9/P3330dRFE6fPs0rr7xCr169bBKHEKJkZHFKIYTN/P3334SHh6NSqXjsscf48MMPi+wzaNAgLl26xPvvv1/ksdq1axMUFHTDY3/11VdMnDiRixcvotPpyjz2L774gocffpg//viDNm3aFHm8c+fO5OTksH//ftLS0ggLC2PLli306NGjzGMRQtyatNAIIWxm7dq1eHh4MGHCBDZt2kReXl6RfY4dO0br1q1p3759kVtxxQzAsmXLGDFihE2KGcgvtDw9PVm3bl2Rx6Kjo9m7dy8jR44EwNfXlyFDhvDOO+/YJBYhxK1JQSOEsJl169Zx3333MW7cONLS0ti6det1j6enp3Px4kUaN258W8c9f/48u3fv5oEHHrhu+5gxY2jSpAm//PILLVu2xMPDg7Zt23Lw4MEix/j111/p0aMHHh4e+Pj4MGLECBISEgofd3d3Z/DgwWzYsAGLxXLdc9evX4+iKAwbNqxw24MPPsjWrVtJTEy8rdcihCgbUtAIIWzit99+4/z58wwfPpw2bdpQp04d1q5de90+x44dAyA8PByTyXTd7d9FxLV++uknXFxcbngpKC4ujmeffZbp06ezYcMG9Ho9Q4YMwWg0Fu7z66+/0q1bN3x8fNiwYQOrV69m//79DBo06LpjjRw5ktjY2CL9f9atW0ePHj2oWrVq4bZOnTphMpmK7SskhLAtKWiEEDaxdu1afH196dOnDwDDhw/n22+/JSMjo3CfqwXNvffei4uLy3W31atXF3vsAwcOUL9+fbRabZHHUlJSWL9+PSNGjGDAgAEsW7aMS5cu8fvvvxfuM3PmTFq3bs1XX33FgAEDGDZsGFu2bOGPP/64rhXpnnvuISgoiPXr1xduO3nyJMeOHSu83HSVn58fYWFh151HCFF+pKARQpQ5s9nMxo0buf/++3F1dQXg4YcfJicnh6+++qpwv6NHjxIcHMz+/fuL3EaMGFHs8WNjY6lSpcoNHwsJCbnuElajRo0AiImJAUCv17N3714efPBBzGZzYYtQgwYNqFq1Kvv37y98rkaj4aGHHuK///1vYf+ftWvX4ubmxv3331/k3IGBgcTFxZU0TUKIMiQFjRCizEVGRpKQkMDw4cMLtzVu3JgmTZpcd9np2LFjNG3alNatWxe5eXt7F3v8nJycG7bOQH4H3WtdLahycnIASE1NxWw2M3Xq1CKtQleuXOHSpUvXPX/kyJGkpqbyww8/APn9ZwYMGHDD+Nzc3DAYDDfJjBDCVjT2DkAIUfGsXbuWoKCgIkOYH374YebOnUtcXBzBwcEcP36cCRMm3Pbx/f39uXDhwh3F5uvri6IovPjiiwwePLjI44GBgdfdb9++PbVr12b9+vUEBQVx/vx53nrrrRseOzU19bY7OAshyoYUNEKIMqXX69myZQuPPvooarX6useGDx/O7Nmz+eKLL+jfvz/Z2dk0adLkts/RoEEDduzYcUfxeXh40KFDB06ePMkrr7xSoueMGDGCt99+G3d3d3x9fenXr1+RfSwWC9HR0TRo0OCO4hJClI4UNEKIMvX111+TlZWFv78/W7ZsKfJ4aGgoa9eupXr16gCYTCZ+++236/bx8PCgadOmxZ6jU6dOLFy4kJiYGEJDQ287xsWLF9OjRw+GDRvG8OHD8fPzIyYmhsjISMaOHUu3bt2u23/kyJG88sorfPLJJ4wbN67wMta1Tpw4QXZ2Np07d77teIQQpScFjRCiTF3tI/Pyyy8Xu09MTAybNm0CYOLEiUUeHzhwIF9//XWxz+/WrRuBgYF8//33d3TJqmPHjuzZs4f58+czduxY8vLyCA0NpWfPntStW7fI/g0bNqRVq1YcOnSo2M7KW7dupUaNGjccSi6EsD1Z+kAI4ZSee+45Dh8+zM8//2zvUID8NasGDx7MvHnz7B2KEJWSFDRCCKcUFxdHnTp12LNnDy1btrRrLDt37mTIkCGcP3++yCgrIUT5kGHbQgindNddd7FmzRqHWGogIyODTz/9VIoZIexIWmiEEEII4fSkhUYIIYQQTk8KGiGEEEI4PSlohBBCCOH0pKARQgghhNOTgkYIIYQQTk8KGiGEEEI4PSlohBBCCOH0pKARQgghhNOTgkYIIYQQTu//ATfKaSurx6yOAAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1.0e-6, 200.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the lines of our limits\n",
    "PyPlot.plot(nedm_res[:,1].*muT,nedm_res[:,2],color=\"red\",label=\"UCN Storage\")\n",
    "PyPlot.plot(ucn_res[:,1].*muT,ucn_res[:,2],color=\"orange\",label=\"UCN Beam\")\n",
    "PyPlot.plot(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",label=\"MURMUR\")\n",
    "#PyPlot.plot(murmurN_res[:,1].*muT,murmurN_res[:,2],color=\"green\",label=\"MURMUR\")\n",
    "PyPlot.plot(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",label=\"STEREO\")\n",
    "#PyPlot.plot(stereoN_res[:,1].*muT,stereoN_res[:,2],color=\"blue\",label=\"STEREO\")\n",
    "\n",
    "# This is the shaded exclusion limit\n",
    "PyPlot.fill_between(nedm_res[:,1].*muT,nedm_res[:,2],color=\"red\",alpha=0.1)\n",
    "PyPlot.fill_between(ucn_res[:,1].*muT,ucn_res[:,2],color=\"orange\",alpha=0.1)\n",
    "PyPlot.fill_between(murmurP_res[:,1].*muT,murmurP_res[:,2],color=\"green\",linestyle=\"solid\",alpha=0.1)\n",
    "#PyPlot.fill_between(murmurN_res[:,1].*muT,murmurN_res[:,2],color=\"green\",linestyle=\"dashed\",alpha=0.1)\n",
    "PyPlot.fill_between(stereoP_res[:,1].*muT,stereoP_res[:,2],color=\"blue\",linestyle=\"solid\",alpha=0.1)\n",
    "#PyPlot.fill_between(stereoN_res[:,1].*muT,stereoN_res[:,2],color=\"blue\",linestyle=\"dashed\",alpha=0.1)\n",
    "\n",
    "# And our result\n",
    "PyPlot.plot(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A))./1.261,color=\"cyan\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A))./1.261,color=\"cyan\",alpha=0.1)\n",
    "PyPlot.plot(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A))./1.261,color=\"cyan\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A))./1.261,color=\"cyan\",alpha=0.1)\n",
    "\n",
    "\n",
    "PyPlot.plot(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A)),color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_A.*1E9,2 .*hbar./(avgθP_A.*(ΔmP_A)),color=\"black\",alpha=0.1)\n",
    "PyPlot.plot(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A)),color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmN_A.*1E9,2 .*hbar./(avgθN_A.*(ΔmN_A)),color=\"black\",alpha=0.1)\n",
    "\n",
    "# General plotting info\n",
    "PyPlot.title(latexstring(L\"$\\Delta E > 0$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta E$ (neV)\"))\n",
    "PyPlot.ylabel(latexstring(L\"$\\tau_{nn'}$ (s)\"))\n",
    "PyPlot.grid(true)\n",
    "PyPlot.legend(loc=\"upper right\")\n",
    "PyPlot.xscale(\"log\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([xMin,xMax])\n",
    "PyPlot.ylim([yMin,yMax])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951f6d10-0878-4a2d-abe7-894e1ed992b7",
   "metadata": {},
   "source": [
    "Once we've loaded just the packages, there are two types of data files that we care about. \n",
    "\n",
    "One of these is the output from the simulation, and one of these is the output limits. The limits from Michael's code is sorta a black box (for now) and thus it should be a little easier to get running.\n",
    "\n",
    "Let's go ahead and import this dataset. This will be a function for Julia's sake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a76c7704-7814-402a-826f-1df2a4e6e56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_two_results (generic function with 2 methods)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function vel_average_result(res::String)\n",
    "   \n",
    "    # Load information from the .jld file\n",
    "    Δms = load(res, \"deltaMs\")\n",
    "    θ0s = load(res, \"theta0s\")\n",
    "    ρ11Raw = load(res, \"Pnn\")\n",
    "    \n",
    "    # Use the loaded information to get the sizes of arrays\n",
    "    nΔm = size(Δms)[1]\n",
    "    nθ0 = size(Δms)[2]\n",
    "    nV = size(Δms)[3]\n",
    "    \n",
    "    # I have two different codes producing probabilities.\n",
    "    # One was designed for the NIST experiment, where we cared about\n",
    "    # the probability at the beginning/middle/end. We don't \n",
    "    # care about that here, just the end\n",
    "    if size(ρ11Raw)[2] == 3\n",
    "       ρ11Raw_Out = ρ11Raw[:,3] \n",
    "    else\n",
    "        ρ11Raw_Out = ρ11Raw\n",
    "    end\n",
    "    \n",
    "    # Now we need to combine the values for this\n",
    "    # We have to average over velocity values for each Δm and θ0.\n",
    "    avgρ = Matrix{Float64}(undef,nΔm,nθ0) \n",
    "    ρ11 = reshape(ρ11Raw_Out,nΔm,nθ0,nV)\n",
    "    \n",
    "    # And loop!\n",
    "    for i in 1:nΔm\n",
    "        for j in 1:nθ0\n",
    "            # Average over velocity\n",
    "            avg_ρ_tmp = 0.\n",
    "            for k in 1:nV\n",
    "                avg_ρ_tmp += ρ11[i,j,k]\n",
    "            end\n",
    "            # And put into matrix\n",
    "            avgρ[i,j] = avg_ρ_tmp / Float64(nV)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return Δms[:,:,1], θ0s[:,:,1], avgρ\n",
    "end\n",
    "    \n",
    "function average_two_results(res1::String,res2::String)\n",
    "    \n",
    "    # This function will take from two results and average\n",
    "    # them. This is designed to test polarities.\n",
    "    \n",
    "    # Load the two datafiles we care about\n",
    "    Δms_p,θ0s_p,avgρ_pos = vel_average_result(res1)\n",
    "    Δms_n,θ0s_n,avgρ_neg = vel_average_result(res2)\n",
    "    \n",
    "    # Now average over the two spin states!\n",
    "    avgΔm = (Δms_p .+ Δms_n) ./ 2. # These should be the same...\n",
    "    avgθ0 = (θ0s_p .+ θ0s_n) ./ 2. # See above\n",
    "    avgρ = (avgρ_pos .+ avgρ_neg) ./ 2. # These are NOT the same\n",
    "    return avgΔm, avgθ0, avgρ    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255e765a-6f40-4214-9407-db23ef76eaaa",
   "metadata": {},
   "source": [
    "### Fuck that's a lot of runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e3214262-40d2-4f6e-8577-ef203ce902e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The run path, with all of the runs combined\n",
    "simPath = (raw\"C:/Users/ffi/Documents/neutron-mirror-neutron-oscillations\")\n",
    "\n",
    "# List of simulations for Cadmium\n",
    "cdName_pos  = (raw\"/SNS_Cadmium_Pos.jld\"); \n",
    "cdName_HPos = (raw\"/SNS_Cadmium_Half_Pos.jld\");\n",
    "cdName_neg  = (raw\"/SNS_Cadmium_Neg.jld\");\n",
    "cdName_HNeg = (raw\"/SNS_Cadmium_Half_Neg.jld\");\n",
    "cdName_zero = (raw\"/SNS_Cadmium_Zero.jld\");\n",
    "\n",
    "# Now concatenate those files\n",
    "cdFile_pos  = (simPath*raw\"/results\"*cdName_pos);\n",
    "cdFile_neg  = (simPath*raw\"/results\"*cdName_neg);\n",
    "cdFile_HPos  = (simPath*raw\"/results\"*cdName_HPos);\n",
    "cdFile_HNeg  = (simPath*raw\"/results\"*cdName_HNeg);\n",
    "cdFile_zero = (simPath*raw\"/results\"*cdName_zero);\n",
    "\n",
    "# List of simulations for Cadmium\n",
    "cdNameN_pos  = (raw\"/SNS_Cadmium_negM_Pos.jld\"); \n",
    "cdNameN_HPos = (raw\"/SNS_Cadmium_negM_Half_Pos.jld\");\n",
    "cdNameN_neg  = (raw\"/SNS_Cadmium_negM_Neg.jld\");\n",
    "cdNameN_HNeg = (raw\"/SNS_Cadmium_negM_Half_Neg.jld\");\n",
    "cdNameN_zero = (raw\"/SNS_Cadmium_negM_Zero.jld\");\n",
    "\n",
    "# Now concatenate those files\n",
    "cdFileN_pos  = (simPath*raw\"/results\"*cdNameN_pos);\n",
    "cdFileN_neg  = (simPath*raw\"/results\"*cdNameN_neg);\n",
    "cdFileN_HPos  = (simPath*raw\"/results\"*cdNameN_HPos);\n",
    "cdFileN_HNeg  = (simPath*raw\"/results\"*cdNameN_HNeg);\n",
    "cdFileN_zero = (simPath*raw\"/results\"*cdNameN_zero);\n",
    "\n",
    "# List of simulations for B4C\n",
    "bcName_pos = (raw\"/Pnn_4.8T.jld\")\n",
    "bcName_neg = (raw\"/Pnn_-4.8T.jld\")\n",
    "bcName_pos2 = (raw\"/SNS_Result_Pos.jld\") # From 600-800\n",
    "bcName_neg2 = (raw\"/SNS_Result_Neg.jld\")\n",
    "bcName_pos3 = (raw\"/SNS_HighM_Pos.jld\") # From 800+\n",
    "bcName_neg3 = (raw\"/SNS_HighM_Neg.jld\")\n",
    "\n",
    "bcName_0 = (raw\"/Pnn_0.jld\") # From 400-600\n",
    "#bcName_03 = (raw\"/SNS_ZeroB_Vacuum.jld\")\n",
    "#bcName_05 = (raw\"/SNS_ZeroB_MoreV.jld\")\n",
    "\n",
    "\n",
    "\n",
    "# And concatenate these files \n",
    "bcFile_pos = (includePath*raw\"/results\"*bcName_pos)\n",
    "bcFile_neg = (includePath*raw\"/results\"*bcName_neg)\n",
    "bcFile_pos2 = (includePath*raw\"/results\"*bcName_pos2) # from 600-800\n",
    "bcFile_neg2 = (includePath*raw\"/results\"*bcName_neg2)\n",
    "bcFile_pos3 = (includePath*raw\"/results\"*bcName_pos3) # 800+\n",
    "bcFile_neg3 = (includePath*raw\"/results\"*bcName_neg3)\n",
    "\n",
    "bcFile_0 = (includePath*raw\"/results\"*bcName_0) # From 400-600\n",
    "#bcFile_03 = (includePath*raw\"/results\"*bcName_03)\n",
    "#bcFile_05 = (includePath*raw\"/results\"*bcName_05)\n",
    "\n",
    "# These are the two limit files that we want to be able to load\n",
    "\n",
    "\n",
    "# Now let's average these two to get our limits from experiment\n",
    "bc_Δm,bc_θ0,bc_ρ = average_two_results(bcFile_pos,bcFile_neg);\n",
    "\n",
    "bc_Δm_0,bc_θ0_0,bc_ρ_0 = average_two_results(bcFile_0,bcFile_0)\n",
    "#bc_Δm_02,bc_θ0_02,bc_ρ_02 = average_two_results(bcFile_02,bcFile_02)\n",
    "#bc_Δm_03,bc_θ0_03,bc_ρ_03 = average_two_results(bcFile_03,bcFile_03)\n",
    "\n",
    "\n",
    "# These are the two limit files that we want to be able to load\n",
    "\n",
    "\n",
    "# Now let's average these two to get our limits from experiment\n",
    "bc_Δm_2,bc_θ0_2,bc_ρ_2 = average_two_results(bcFile_pos2,bcFile_neg2);\n",
    "\n",
    "\n",
    "bc_Δm_3,bc_θ0_3,bc_ρ_3 = average_two_results(bcFile_pos3,bcFile_neg3);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672c3aa1-abc5-4cd5-ab81-63bef2456a5e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e0161afe-1ff6-4867-ae02-6f634f8966ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "find_upper_lower (generic function with 1 method)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function find_upper_lower(Δms::Matrix{Float64},θ0s::Matrix{Float64},ρ11s::Matrix{Float64};\n",
    "    p0::Float64=0.01,dp::Float64=0.002,uedge::Bool=false)\n",
    "   \n",
    "    # Load the variable number of points to save over\n",
    "    nΔm = size(Δms)[1]\n",
    "    nθ0 = size(θ0s)[2]\n",
    "    \n",
    "    # Now we want to create three arrays, which are\n",
    "    # the output of a \"function\" θ0_min/max = f(Δm)\n",
    "    Δm_val = Array{Float64}(undef,nΔm)\n",
    "    θ_min  = Array{Float64}(undef,nΔm)\n",
    "    θ_max  = Array{Float64}(undef,nΔm)\n",
    "    for i in 1:nΔm\n",
    "        \n",
    "        # Loop through and find min/max\n",
    "        tmin = 1.1\n",
    "        tmax = -0.1\n",
    "        for j in 1:nθ0-1\n",
    "            \n",
    "            # Is this the lower limit?\n",
    "            if ((ρ11s[i,j] < p0-dp) \n",
    "                    && (ρ11s[i,j+1] >= p0-dp))\n",
    "                tmin = θ0s[i,j]\n",
    "            end    \n",
    "            # Is this the upper limit?\n",
    "            if ((ρ11s[i,j] <= p0+dp) \n",
    "                    && (ρ11s[i,j+1] > p0+dp))\n",
    "                tmax = θ0s[i,j+1]\n",
    "            end        \n",
    "        end\n",
    "        # Pass the mass\n",
    "        Δm_val[i] = Δms[i,1]\n",
    "        # Now we do dumb limits just in case\n",
    "        if tmin > 1\n",
    "           if tmax > 0\n",
    "                tmin = θ0s[i,1] \n",
    "            else\n",
    "                if uedge\n",
    "                    tmin = θ0s[i,nθ0-1]\n",
    "                    tmax = θ0s[i,nθ0]\n",
    "                else\n",
    "                    tmin =  NaN\n",
    "                    Δm_val[i] = NaN\n",
    "                end\n",
    "            end\n",
    "            \n",
    "        elseif tmax < 0\n",
    "            tmax=θ0s[i,nθ0]\n",
    "        end\n",
    "        θ_min[i] = tmin\n",
    "        θ_max[i] = tmax\n",
    "    end\n",
    "    # We had a NaN error condition (i.e. off the edge), so let's remove those\n",
    "    reals = findall(.~isnan.(Δm_val))\n",
    "    return Δm_val[reals],θ_min[reals],θ_max[reals]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef38d5de-7fe5-4327-81c7-88f1a064400e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bc940e78-b94b-4332-b4c4-bcf21865391f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25×100 Matrix{Float64}:\n",
       " 3.30181e-18  4.71293e-18  6.75897e-18  …  1.87229e-6  2.1378e-6   2.3887e-6\n",
       " 2.15823e-18  3.08451e-18  4.42281e-18     1.86143e-6  2.14571e-6  2.38425e-6\n",
       " 1.46027e-18  2.09857e-18  3.00217e-18     1.87049e-6  2.143e-6    2.39274e-6\n",
       " 1.0404e-18   1.49944e-18  2.15039e-18     1.85654e-6  2.13799e-6  2.3631e-6\n",
       " 7.53974e-19  1.08878e-18  1.56259e-18     1.85163e-6  2.12487e-6  2.34614e-6\n",
       " 5.73302e-19  8.19585e-19  1.18339e-18  …  1.83282e-6  2.12257e-6  2.3374e-6\n",
       " 4.36744e-19  6.33581e-19  9.06638e-19     1.82061e-6  2.10686e-6  2.33395e-6\n",
       " 3.33101e-19  4.84177e-19  6.95971e-19     1.82509e-6  2.10421e-6  2.31047e-6\n",
       " 2.67716e-19  3.77967e-19  5.54075e-19     1.80132e-6  2.08736e-6  2.27405e-6\n",
       " 2.09835e-19  3.02787e-19  4.39502e-19     1.79967e-6  2.0456e-6   2.31158e-6\n",
       " 1.79638e-19  2.49546e-19  3.65897e-19  …  1.78868e-6  2.08293e-6  2.27649e-6\n",
       " 1.40265e-19  2.0463e-19   3.00377e-19     1.7989e-6   2.07331e-6  2.29301e-6\n",
       " 1.20119e-19  1.71511e-19  2.48225e-19     1.77878e-6  2.05943e-6  2.25094e-6\n",
       " 1.00525e-19  1.51899e-19  2.22653e-19     1.77103e-6  2.03017e-6  2.22202e-6\n",
       " 8.54882e-20  1.27405e-19  1.7813e-19      1.77248e-6  2.02795e-6  2.23754e-6\n",
       " 7.22629e-20  1.01295e-19  1.58425e-19  …  1.75683e-6  2.02884e-6  2.22018e-6\n",
       " 5.82817e-20  8.85608e-20  1.36475e-19     1.77217e-6  2.03817e-6  2.23844e-6\n",
       " 5.89452e-20  7.59126e-20  1.24849e-19     1.74629e-6  2.01333e-6  2.19193e-6\n",
       " 5.15057e-20  7.03237e-20  1.14164e-19     1.74027e-6  1.99997e-6  2.1842e-6\n",
       " 5.04444e-20  6.5221e-20   9.30798e-20     1.74721e-6  1.98643e-6  2.18012e-6\n",
       " 3.78471e-20  5.10014e-20  8.21966e-20  …  1.7425e-6   2.0019e-6   2.17925e-6\n",
       " 3.54431e-20  5.01058e-20  7.8588e-20      1.7361e-6   1.9783e-6   2.16829e-6\n",
       " 3.21863e-20  3.7762e-20   7.26866e-20     1.73095e-6  1.97169e-6  2.14746e-6\n",
       " 2.86401e-20  4.52304e-20  6.69014e-20     1.7272e-6   1.95086e-6  2.14905e-6\n",
       " 2.15096e-20  3.22065e-20  5.52534e-20     1.71181e-6  1.94467e-6  2.14884e-6"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have to cut out a gap in the middle of our run\n",
    "uZ = findall(i -> \n",
    "        ((bc_Δm_0[i] >= 400E-9) && (bc_Δm_0[i] <= 601E-9)),\n",
    "        1:length(bc_Δm_0))\n",
    "resM1 = Int(size(uZ)[1] / size(bc_θ0_0)[1])\n",
    "zeroM = reshape(bc_Δm_0[uZ],resM1,size(bc_θ0_0)[2])\n",
    "zeroT = reshape(bc_θ0_0[uZ],resM1,size(bc_θ0_0)[2])\n",
    "zeroP = reshape(bc_ρ_0[uZ],resM1,size(bc_θ0_0)[2])\n",
    "\n",
    "uM = findall(i -> \n",
    "        (bc_Δm_2[i] >= 600E-9),\n",
    "        1:length(bc_Δm_2))\n",
    "resM2 = Int(size(uM)[1] / size(bc_θ0)[1])\n",
    "probM = reshape(vec(bc_Δm_2)[uM],resM2,size(bc_θ0_2)[2])\n",
    "probT = reshape(vec(bc_θ0_2)[uM],resM2,size(bc_θ0_2)[2])\n",
    "probP = reshape(vec(bc_ρ_2)[uM],resM2,size(bc_θ0_2)[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f13bf78b-fa4e-4055-909f-0f21f0b40def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01170077571617657\n",
      "0.014632720523533734\n"
     ]
    }
   ],
   "source": [
    "# Write out this stuff\n",
    "Δm_1, minθ_1, maxθ_1 = find_upper_lower(prob_Δm,prob_θ0,prob_ρ,p0=2.5E-8,dp=0.1E-8)\n",
    "Δm_2, minθ_2, maxθ_2 = find_upper_lower(zeroM,zeroT,zeroP,p0=5.5E-8,dp=0.1E-8)\n",
    "Δm_3, minθ_3, maxθ_3 = find_upper_lower(probM,probT,probP,p0=2.5E-8,dp=0.1E-8)\n",
    "Δm_4, minθ_4, maxθ_4 = find_upper_lower(prob3_Δm,prob3_θ0,prob3_ρ,p0=2.5E-8,dp=0.1E-8)\n",
    "#println(Δm_1*1E9,minθ_1,maxθ_1)\n",
    "#println(Δm_2*1E9,minθ_2,maxθ_2)\n",
    "#println(Δm_3*1E9,minθ_3,maxθ_3)\n",
    "#println(Δm_4*1E9,minθ_4,maxθ_4)\n",
    "PyPlot.plot(Δm_1*1E9,minθ_1,color=\"blue\")\n",
    "PyPlot.plot(Δm_2*1E9,minθ_2,color=\"blue\")\n",
    "PyPlot.plot(Δm_3*1E9,minθ_3,color=\"blue\")\n",
    "PyPlot.plot(Δm_4*1E9,minθ_4,color=\"blue\")\n",
    "PyPlot.plot(Δm_1*1E9,maxθ_1,color=\"red\")\n",
    "PyPlot.plot(Δm_2*1E9,maxθ_2,color=\"red\")\n",
    "PyPlot.plot(Δm_3*1E9,maxθ_3,color=\"red\")\n",
    "PyPlot.plot(Δm_4*1E9,maxθ_4,color=\"red\")\n",
    "\n",
    "bc_Δm_tot = pushfirst!(vcat(vcat(Δm_1,Δm_2),vcat(Δm_3,Δm_4)),9E-9)\n",
    "bc_minθ_tot = pushfirst!(vcat(vcat(minθ_1,minθ_2),vcat(minθ_3,minθ_4)),1.)\n",
    "bc_maxθ_tot = pushfirst!(vcat(vcat(maxθ_1,maxθ_2),vcat(maxθ_3,maxθ_4)),1.)\n",
    "\n",
    "\n",
    "\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.ylim([1E-5,7E-2])\n",
    "PyPlot.xlim([0,1600])\n",
    "PyPlot.grid(\"true\")\n",
    "println(last(minθ_4))\n",
    "println(last(maxθ_4))\n",
    "#println(bc_Δm_tot)\n",
    "#saveFile = open(\"SNS_limits.csv\",\"w\")\n",
    "#for i in 1:length(Δm_1)\n",
    "#     write(saveFile,\"$(Δm_1[i]),$(minθ_1[i])\\n\")\n",
    "#end\n",
    "#for i in 1:length(Δm_2)\n",
    "#     write(saveFile,\"$(Δm_2[i]),$(minθ_2[i])\\n\")\n",
    "#end\n",
    "#for i in 1:length(Δm_3)\n",
    "#     write(saveFile,\"$(Δm_3[i]),$(minθ_3[i])\\n\")\n",
    "#end\n",
    "#for i in 1:length(Δm_4)\n",
    "#     write(saveFile,\"$(Δm_4[i]),$(minθ_4[i])\\n\")\n",
    "#end\n",
    "#close(saveFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25516c2-c005-40dc-8cf5-2310594c0bb9",
   "metadata": {},
   "source": [
    "### Actual plotting \n",
    "\n",
    "Here we actually plot things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bddea260-9538-476b-a0e0-11af4c751c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\r\n"
     ]
    }
   ],
   "source": [
    "function plot_sim_limits(plotM::Matrix{Float64},plotT::Matrix{Float64},plotP::Matrix{Float64};\n",
    "            pLim::Float64=2.5E-8,\n",
    "            xMin::Float64=0.,xMax::Float64=1200.,\n",
    "            yMin::Float64=1E-7,yMax::Float64=1E-1,color=\"Greys\")\n",
    "\n",
    "    # Begin by opening the first figure and setting up limits\n",
    "    PyPlot.figure()\n",
    "\n",
    "    # These are the (contour) plots for our probabilities.\n",
    "    PyPlot.contourf(plotM.*1E9,plotT,plotP,levels=[pLim,2],cmap=color,alpha=0.5)\n",
    "    PyPlot.contour( plotM.*1E9,plotT,plotP,levels=[pLim],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,plotT,plotP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,plotT,plotP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,plotT,plotP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,plotT,plotP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    \n",
    "    # And the info for the legend\n",
    "    PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"Limit ($95\\% $ CL)\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=latexstring(L\"$P=10^{-11}$\"))\n",
    "    PyPlot.grid(true)\n",
    "    PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "    PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "    #PyPlot.xlim([xMin,xMax])\n",
    "    PyPlot.ylim([yMin,yMax])\n",
    "    #PyPlot.xscale(\"log\")\n",
    "    PyPlot.yscale(\"log\")\n",
    "    PyPlot.legend(loc=\"lower center\")\n",
    "    #PyPlot.legend()\n",
    "    PyPlot.show()\n",
    "    \n",
    "end\n",
    "\n",
    "lim2020 = 4.529058116232465e-10\n",
    "plot_sim_limits(cd_Δm_H, cd_θ0_H, (cd_ρ.+cd_ρ_H)./2, pLim=lim2020)\n",
    "plot_sim_limits(cdN_Δm_H, cdN_θ0_H, (cdN_ρ.+cdN_ρ_H)./2, pLim=lim2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f17960a8-965b-400e-9d14-9e3ccd6b0ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function plot_sim_limits_stereo(plotM::Matrix{Float64},plotT::Matrix{Float64},plotP::Matrix{Float64};\n",
    "            pLim::Float64=2.5E-8,\n",
    "            xMin::Float64=0.,xMax::Float64=1200.,\n",
    "            yMin::Float64=1E-7,yMax::Float64=1E-1,color=\"Greys\",\n",
    "            title=\"\")\n",
    "\n",
    "    # Begin by opening the first figure and setting up limits\n",
    "    PyPlot.figure()\n",
    "    PyPlot.title(title)\n",
    "    # These are the (contour) plots for our probabilities.\n",
    "    PyPlot.contourf(plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[pLim,2],cmap=color,alpha=0.5)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[pLim],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotM)./2,plotP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    \n",
    "    # And the info for the legend\n",
    "    PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"Limit ($95\\% $ CL)\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=latexstring(L\"$P=10^{-11}$\"))\n",
    "    PyPlot.grid(true)\n",
    "    PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "    PyPlot.ylabel(latexstring(L\"$2 (\\epsilon / \\Delta m)^2$\"))\n",
    "    #PyPlot.xlim([xMin,xMax])\n",
    "    #PyPlot.ylim([yMin,yMax])\n",
    "    PyPlot.xscale(\"log\")\n",
    "    PyPlot.yscale(\"log\")\n",
    "    PyPlot.legend(loc=\"lower center\")\n",
    "    #PyPlot.legend()\n",
    "    PyPlot.show()\n",
    "    \n",
    "end\n",
    "\n",
    "lim2020 = 4.529058116232465e-10\n",
    "plot_sim_limits_stereo(cd_Δm_H, cd_θ0_H, (cd_ρ.+cd_ρ_H)./2, pLim=lim2020,title=\"Positive Mass\")\n",
    "plot_sim_limits_stereo(-cdN_Δm_H, cdN_θ0_H, (cdN_ρ.+cdN_ρ_H)./2, pLim=lim2020,title=\"Negative Mass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "385cd111-f25f-491a-815f-ad4058f487aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function plot_sim_limits_pm(\n",
    "            plotMP::Matrix{Float64},plotTP::Matrix{Float64},plotPP::Matrix{Float64},\n",
    "            plotMN::Matrix{Float64},plotTN::Matrix{Float64},plotPN::Matrix{Float64};\n",
    "            pLim::Float64=2.5E-8,\n",
    "            xMin::Float64=0.,xMax::Float64=1200.,\n",
    "            yMin::Float64=1E-7,yMax::Float64=1E-1,color=\"Greys\",\n",
    "            title=\"\")\n",
    "\n",
    "    # Begin by opening the first figure and setting up limits\n",
    "    PyPlot.figure()\n",
    "    PyPlot.title(title)\n",
    "    # These are the (contour) plots for our probabilities.\n",
    "    PyPlot.contourf(plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[pLim,2],cmap=color,alpha=0.5)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[pLim],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    PyPlot.contour( plotM.*1E9,(plotT.*plotT)./2,plotP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    \n",
    "    # And the info for the legend\n",
    "    PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"Limit ($95\\% $ CL)\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "    PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=latexstring(L\"$P=10^{-11}$\"))\n",
    "    PyPlot.grid(true)\n",
    "    PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "    PyPlot.ylabel(latexstring(L\"$2 (\\epsilon / \\Delta m)^2$\"))\n",
    "    #PyPlot.xlim([xMin,xMax])\n",
    "    #PyPlot.ylim([yMin,yMax])\n",
    "    PyPlot.xscale(\"log\")\n",
    "    PyPlot.yscale(\"log\")\n",
    "    PyPlot.legend(loc=\"lower center\")\n",
    "    #PyPlot.legend()\n",
    "    PyPlot.show()\n",
    "    \n",
    "end\n",
    "\n",
    "lim2020 = 4.529058116232465e-10\n",
    "plot_sim_limits_stereo(cd_Δm_H, cd_θ0_H, (cd_ρ.+cd_ρ_H)./2, pLim=lim2020,title=\"Positive Mass\")\n",
    "plot_sim_limits_stereo(-cdN_Δm_H, cdN_θ0_H, (cdN_ρ.+cdN_ρ_H)./2, pLim=lim2020,title=\"Negative Mass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82685019-dbc7-412d-bb2f-da227bf22ac5",
   "metadata": {},
   "source": [
    "I'm going to extrapolate with the various fields to get an estimate as $\\Delta m \\rightarrow 0$. First, we don't want to combine things together and instead go towards a constant value. Let's extract all of this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "177134e2-048a-4726-8c68-acfce5bd2169",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's extract single polarity results for Δm → 0 \n",
    "cd_Δm_P5, cd_θ0_P5, cd_ρ_P5  = vel_average_result(cdFile_pos);\n",
    "cd_Δm_N5, cd_θ0_N5, cd_ρ_N5  = vel_average_result(cdFile_neg);\n",
    "cd_Δm_P2, cd_θ0_P2, cd_ρ_P2  = vel_average_result(cdFile_HPos);\n",
    "cd_Δm_N2, cd_θ0_N2, cd_ρ_N2  = vel_average_result(cdFile_HNeg);\n",
    "\n",
    "cdN_Δm_P5, cdN_θ0_P5, cdN_ρ_P5  = vel_average_result(cdFileN_pos);\n",
    "cdN_Δm_N5, cdN_θ0_N5, cdN_ρ_N5  = vel_average_result(cdFileN_neg);\n",
    "cdN_Δm_P2, cdN_θ0_P2, cdN_ρ_P2  = vel_average_result(cdFileN_HPos);\n",
    "cdN_Δm_N2, cdN_θ0_N2, cdN_ρ_N2  = vel_average_result(cdFileN_HNeg);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb460554-6c00-4542-b154-ca9a948e23ee",
   "metadata": {},
   "source": [
    "Now we have a single-polarity value. Let's go and use the \n",
    "*find_upper_lower* function to find the point at which our theta crosses for each one of these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1302d216-8261-47e5-a4e9-1259dd2328ee",
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "UndefVarError: cd_θ0_2 not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: cd_θ0_2 not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope",
      "   @ In[66]:2",
      " [2] eval",
      "   @ .\\boot.jl:360 [inlined]",
      " [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
      "   @ Base .\\loading.jl:1116"
     ]
    }
   ],
   "source": [
    "# Remove this cell later\n",
    "cd_θ0_0 = cd_θ0_2\n",
    "cdN_θ0_0 = cdN_θ0_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a068d13-8797-4950-9250-00d52dc007ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the positive polarities...\n",
    "Δm_P5, minθ_P5, maxθ_P5 = find_upper_lower(cd_Δm_P5,cd_θ0_P5,\n",
    "                                cd_ρ_P5,p0=lim2020,dp=0.1E-8);\n",
    "Δm_N5, minθ_N5, maxθ_N5 = find_upper_lower(cd_Δm_N5,cd_θ0_N5,\n",
    "                                cd_ρ_N5,p0=lim2020,dp=0.1E-8);\n",
    "Δm_P2, minθ_P2, maxθ_P2 = find_upper_lower(cd_Δm_P2,cd_θ0_P2,\n",
    "                                cd_ρ_P2,p0=lim2020,dp=0.1E-8);\n",
    "Δm_N2, minθ_N2, maxθ_N2 = find_upper_lower(cd_Δm_N2,cd_θ0_N2,\n",
    "                                cd_ρ_N2,p0=lim2020,dp=0.1E-8);\n",
    "Δm_0, minθ_0, maxθ_0 = find_upper_lower(cd_Δm_0,cd_θ0_0,\n",
    "                                cd_ρ_0,p0=lim2020,dp=0.1E-8);\n",
    "\n",
    "# And associated negative ones\n",
    "ΔmN_P5, minθN_P5, maxθN_P5 = find_upper_lower(cdN_Δm_P5,cdN_θ0_P5,\n",
    "                                cdN_ρ_P5,p0=lim2020,dp=0.1E-8);\n",
    "ΔmN_N5, minθN_N5, maxθN_N5 = find_upper_lower(cdN_Δm_N5,cdN_θ0_N5,\n",
    "                                cdN_ρ_N5,p0=lim2020,dp=0.1E-8);\n",
    "ΔmN_P2, minθN_P2, maxθN_P2 = find_upper_lower(cdN_Δm_P2,cdN_θ0_P2,\n",
    "                                cdN_ρ_P2,p0=lim2020,dp=0.1E-8);\n",
    "ΔmN_N2, minθN_N2, maxθN_N2 = find_upper_lower(cdN_Δm_N2,cdN_θ0_N2,\n",
    "                                cdN_ρ_N2,p0=lim2020,dp=0.1E-8);\n",
    "ΔmN_0, minθN_0, maxθN_0 = find_upper_lower(cdN_Δm_0,cdN_θ0_0,\n",
    "                                cdN_ρ_0,p0=lim2020,dp=0.1E-8);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1130581-2ba8-4eca-87a2-4008a7028a49",
   "metadata": {},
   "source": [
    "Next let's do a debug plot to see what we're working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "324bbd10-e59e-4d30-a3eb-e0055c413cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\r\n"
     ]
    }
   ],
   "source": [
    "# We need to average each of these\n",
    "PyPlot.figure()\n",
    "PyPlot.plot(log10.(Δm_P5.*1E9),((minθ_P5.+maxθ_P5)./2 .*Δm_P5),color=\"red\",marker=\"x\",label=\"+5\")\n",
    "PyPlot.plot(log10.(Δm_N5.*1E9),((minθ_N5.+maxθ_N5)./2 .*Δm_N5),color=\"blue\",marker=\"x\",label=\"-5\")\n",
    "PyPlot.plot(log10.(Δm_P2.*1E9),((minθ_P2.+maxθ_P2)./2 .*Δm_P2),color=\"orange\",marker=\"+\",label=\"+2.5\")\n",
    "PyPlot.plot(log10.(Δm_N2.*1E9),((minθ_N2.+maxθ_N2)./2 .*Δm_N2),color=\"cyan\",marker=\"+\",label=\"-2.5\")\n",
    "PyPlot.plot(log10.(Δm_0.*1E9), ((minθ_0.+maxθ_0)./2 .*Δm_0),color=\"green\",marker=\"o\",label=\"0\")\n",
    "\n",
    "PyPlot.plot(0 .-log10.(0 .-ΔmN_P5.*1E9),((minθN_P5.+maxθN_P5)./2 .*ΔmN_P5),color=\"red\",marker=\"x\")\n",
    "PyPlot.plot(0 .-log10.(0 .-ΔmN_N5.*1E9),((minθN_N5.+maxθN_N5)./2 .*ΔmN_N5),color=\"blue\",marker=\"x\")\n",
    "PyPlot.plot(0 .-log10.(0 .-ΔmN_P2.*1E9),((minθN_P2.+maxθN_P2)./2 .*ΔmN_P2),color=\"orange\",marker=\"+\")\n",
    "PyPlot.plot(0 .-log10.(0 .-ΔmN_N2.*1E9),((minθN_N2.+maxθN_N2)./2 .*ΔmN_N2),color=\"cyan\",marker=\"+\")\n",
    "PyPlot.plot(0 .-log10.(0 .-ΔmN_0.*1E9), ((minθN_0.+maxθN_0)./2 .*ΔmN_0),color=\"green\",marker=\"o\")\n",
    "PyPlot.legend()\n",
    "PyPlot.grid()\n",
    "PyPlot.xlim([-2.5,2.5])\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bcebeaf0-a7c2-4147-8b10-385da4b5c8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_SIZE = 10\n",
    "MEDIUM_SIZE = 11\n",
    "BIGGER_SIZE = 12\n",
    "PyPlot.rc(\"font\", size=SMALL_SIZE)          # controls default text sizes\n",
    "PyPlot.rc(\"axes\", titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
    "PyPlot.rc(\"axes\", labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "PyPlot.rc(\"xtick\", labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "PyPlot.rc(\"ytick\", labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "PyPlot.rc(\"legend\", fontsize=SMALL_SIZE)    # legend fontsize\n",
    "PyPlot.rc(\"figure\", titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "PyPlot.rc(\"figure\", figsize=(6.,4.5))\n",
    "\n",
    "#PyPlot.rcParams[\"figure.figsize\"] = (6.,4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a8d14751-e36a-4321-9cc2-422cfaec62b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\r\n"
     ]
    }
   ],
   "source": [
    "PyPlot.figure()\n",
    "\n",
    "# Start with plotting titles/axes\n",
    "PyPlot.grid(true)\n",
    "#PyPlot.title(\"Transition Probability Exclusion Plot\")\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "#PyPlot.xlim([0,1200])\n",
    "PyPlot.xlim([1E-1,1200])\n",
    "PyPlot.xscale(\"log\")\n",
    "PyPlot.ylim([1E-6,1E-1])\n",
    "#PyPlot.xlim([0,60])\n",
    "#PyPlot.ylim([1E-2,1E-1])\n",
    "\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.plot(bc_Δm_tot*1E9,(bc_minθ_tot.+bc_maxθ_tot)./2,color=\"blue\",label=\"PRL 128.212503 (2022)\")\n",
    "PyPlot.fill_between(bc_Δm_tot.*1E9,(bc_minθ_tot.+bc_maxθ_tot)./2,0.1,color=\"blue\",alpha=0.1)\n",
    "\n",
    "PyPlot.plot(ΔmP_C*1E9,avgθP_C,color=\"black\",label=\"This Result\")\n",
    "PyPlot.fill_between(ΔmP_C*1E9,avgθP_C,0.1,color=\"gray\",alpha=0.1)\n",
    "\n",
    "#avgθP_C = vcat(avgθPL_A,avgθP_A[3:end])\n",
    "#ΔmP_C = vcat(ΔmPL_A,ΔmP_A[3:end])\n",
    "#PyPlot.contourf(cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[lim2020,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour( cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[lim2020],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour( cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour( cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#PyPlot.contour( cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour( cd_Δm_H.*1E9,cd_θ0_H,cd_ρ_tot,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    \n",
    "# And the info for the legend\n",
    "#PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=(\"Improved SNS Limits\"))\n",
    "\n",
    "# These are the (contour) plots for our probabilities.\n",
    "#PyPlot.contourf(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#PyPlot.contour(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(bc_Δm.*1E9,bc_θ0,bc_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "# These are the (contour) plots for our probabilities.\n",
    "# These connect on the other side\n",
    "#PyPlot.contour(bc_Δm_3.*1E9,bc_θ0_3,bc_ρ_3,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour(bc_Δm_3.*1E9,bc_θ0_3,bc_ρ_3,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#PyPlot.contour(prob2_Δm.*1E9,prob2_θ0,prob2_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "\n",
    "#PyPlot.contourf(prob3_Δm.*1E9,bc_θ0,prob3_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(prob3_Δm.*1E9,bc_θ0,prob3_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(prob3_Δm.*1E9,bc_θ0,bc_ρ_3,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(prob3_Δm.*1E9,bc_θ0,bc_ρ_3,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "#nw = findall(i -> \n",
    "#        (prob4_Δm[i] >= 50E-9) ,\n",
    "#        1:length(prob4_Δm))\n",
    "#resE  = Int(size(nw)[1] / size(prob4_Δm)[2])\n",
    "#extM = reshape(prob4_Δm[nw],resE,size(prob4_θ0)[2])\n",
    "#extT = reshape(prob4_θ0[nw],resE,size(prob4_θ0)[2])\n",
    "#extP = reshape(prob4_ρ[nw],resE,size(prob4_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(extM.*1E9,extT,extP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(extM.*1E9,extT,extP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#\n",
    "\n",
    "#PyPlot.contourf(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "#uZ = findall(i -> \n",
    "#        ((zero_Δm[i] >= 400E-9) && (zero_Δm[i] <= 601E-9)),\n",
    "        #((zero_Δm[i] >= 400E-9)),\n",
    "#        1:length(zero_Δm))\n",
    "#resM1 = Int(size(uZ)[1] / size(zero_θ0)[1])\n",
    "#zeroM = reshape(zero_Δm[uZ],resM1,size(zero_θ0)[2])\n",
    "#zeroT = reshape(zero_θ0[uZ],resM1,size(zero_θ0)[2])\n",
    "#zeroP = reshape(zero_ρ[uZ],resM1,size(zero_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "#uZ2 = findall(i -> \n",
    "        #((zero2_Δm[i] >= 400E-9) && (zero2_Δm[i] <= 601E-9)),\n",
    "#        ((zero2_θ0[i] < 5E-2)),\n",
    "#        1:length(zero2_θ0))\n",
    "#resM12 = Int(size(uZ2)[2] / size(zero2_θ0)[2])\n",
    "#zeroM2 = reshape(zero2_Δm[uZ2],size(zero2_Δm)[1],resM12)#,size(zero2_θ0)[2])\n",
    "#zeroT2 = reshape(zero2_θ0[uZ2],size(zero2_Δm)[1],resM12)#size(zero2_θ0)[2])\n",
    "#zeroP2 = reshape(zero2_ρ[uZ2],size(zero2_Δm)[1],resM12)#size(zero2_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contourf(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "#PyPlot.contourf(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "#uM = findall(i -> \n",
    "#        (prob2_Δm[i] >= 600E-9),\n",
    "        #(prob2_Δm[i] >= 400E-9),\n",
    "#        1:length(prob2_Δm))\n",
    "#resM2 = Int(size(uM)[1] / size(prob_θ0)[1])\n",
    "#probM = reshape(vec(prob2_Δm)[uM],resM2,size(prob2_θ0)[2])\n",
    "#probT = reshape(vec(prob2_θ0)[uM],resM2,size(prob2_θ0)[2])\n",
    "#probP = reshape(vec(prob2_ρ)[uM],resM2,size(prob2_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(probM.*1E9,probT,probP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contourf(probM.*1E9,probT,probP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "#PyPlot.fill_between(400:601,5.1E-2,7E-2,linewidth=0,color=\"gray\",alpha=0.4)\n",
    "\n",
    "#PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"$P=2.5\\times10^{-8}$ (95% CL)\"))\n",
    "#PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"$95\\% $ CL\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=latexstring(L\"$P=10^{-11}$\"))\n",
    "\n",
    "# These are the (contour) plots for our probabilities.\n",
    "cd_ρ_tot = (cd_ρ.+cd_ρ_H)./2\n",
    "\n",
    "\n",
    "#PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "#PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=(\"Possible at HFIR\"))\n",
    "#plotP = (prob_ρ.+prob_ρ2)./2\n",
    "#PyPlot.contourf(prob_Δm2.*1E9,plotT,plotP,levels=[pLim,2],cmap=color,alpha=0.5)\n",
    "#PyPlot.contour( prob_Δm2.*1E9,plotT,plotP,levels=[pLim],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour( prob_Δm2.*1E9,plotT,plotP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour( prob_Δm2.*1E9,plotT,plotP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#PyPlot.contour( prob_Δm2.*1E9,plotT,plotP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour( prob_Δm2.*1E9,prob_θ02,plotP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "    \n",
    "# And the info for the legend\n",
    "#PyPlot.xscale(\"log\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.legend(loc=\"lower left\")\n",
    "PyPlot.show()\n",
    "    \n",
    "lim2020 = 4.529058116232465e-10\n",
    "#plot_sim_limits(prob_Δm2, prob_θ02, (prob_ρ.+prob_ρ2)./2, pLim=lim2020)\n",
    "\n",
    "# These are the minumum and maximum we just loaded from a file.\n",
    "#PyPlot.fill_between(comb_Δm.*1E9,comb_min,comb_max,color=\"firebrick\")\n",
    "#PyPlot.fill_between(comb2_Δm.*1E9,comb2_min,comb2_max,color=\"firebrick\")\n",
    "#PyPlot.fill_between(top_Δm*1E9,top_min,top_max,color=\"firebrick\")\n",
    "\n",
    "#xline = LinRange(0,60,60)\n",
    "#lowLim = 1E-3.*ones(length(xline))\n",
    "#upLim  = 1E-1.*ones(length(xline))\n",
    "#PyPlot.fill_between(xline,lowLim,upLim,color=\"orange\",alpha=0.3)\n",
    "#PyPlot.plot(xline,lowLim,color=\"orange\")\n",
    "#PyPlot.axvline(60,ymin=0.5,color=\"orange\") # This is dumb.\n",
    "#PyPlot.plot([],[],color=\"orange\",label=\"UCN Traps\")\n",
    "\n",
    "#PyPlot.plot([],[],color=\"firebrick\")\n",
    "#PyPlot.legend(loc=\"lower right\")#upper center\")\n",
    "PyPlot.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c74d28ca-af02-4356-b2b5-320213fb86f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_two_results (generic function with 2 methods)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function average_two_results(res1,res2)#;\n",
    "        #nΔm::Int64=100,nθ0::Int64=100,nV::Int64=2000)\n",
    "    \n",
    "    # Load the two datafiles we care about\n",
    "    Δms_p = load(res1, \"deltaMs\")\n",
    "    θ0s_p = load(res1, \"theta0s\")\n",
    "    ρ11Raw_p = load(res1, \"Pnn\")\n",
    "    \n",
    "    Δms_n = load(res2, \"deltaMs\")\n",
    "    θ0s_n = load(res2, \"theta0s\")\n",
    "    ρ11Raw_n = load(res2, \"Pnn\")\n",
    "    \n",
    "    nΔm = size(Δms_n)[1]\n",
    "    nθ0 = size(Δms_n)[2]\n",
    "    nV = size(Δms_n)[3]\n",
    "    \n",
    "    if size(ρ11Raw_p)[2] == 3\n",
    "       ρ11Raw_pOut = ρ11Raw_p[:,3] \n",
    "    else\n",
    "        ρ11Raw_pOut = ρ11Raw_p\n",
    "    end\n",
    "    if size(ρ11Raw_n)[2] == 3\n",
    "       ρ11Raw_nOut = ρ11Raw_n[:,3] \n",
    "    else\n",
    "        ρ11Raw_nOut = ρ11Raw_n\n",
    "    end\n",
    "\n",
    "    # Now we need to combine the values for this\n",
    "    # We have to average over velocity values for each Δm and θ0.\n",
    "    avgρ_pos = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "    avgρ_neg = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "        \n",
    "    ρ11_p = reshape(ρ11Raw_pOut,nΔm,nθ0,nV)\n",
    "    ρ11_n = reshape(ρ11Raw_nOut,nΔm,nθ0,nV)\n",
    "    \n",
    "    # Loop\n",
    "    for i in 1:nΔm\n",
    "        for j in 1:nθ0\n",
    "            # Average over velocity\n",
    "            avg_p = 0.\n",
    "            avg_n = 0.\n",
    "            for k in 1:nV\n",
    "                avg_p += ρ11_p[i,j,k]\n",
    "                avg_n += ρ11_n[i,j,k]\n",
    "            end\n",
    "            # And put into matrix\n",
    "            avgρ_pos[i,j] = avg_p / Float64(nV)\n",
    "            avgρ_neg[i,j] = avg_n / Float64(nV)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    # Now average over the two spin states!\n",
    "    avgΔm = (Δms_p .+ Δms_n) ./ 2. # These should be the same...\n",
    "    avgθ0 = (θ0s_p .+ θ0s_n) ./ 2. # See above\n",
    "    avgρ = (avgρ_pos .+ avgρ_neg) ./ 2. # These are NOT the same\n",
    "        \n",
    "    return avgΔm[:,:,1],avgθ0[:,:,1],avgρ\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65513cde-3f3b-4479-b348-27c61c37a7f6",
   "metadata": {},
   "source": [
    "The slightly more tricky part is now to determine the contour that corresponds to 1%. This is \"tricky\" because we have to actually combine a couple of runs together.\n",
    "\n",
    "We want to zoom in on the region where the magnetic field is highest (and we're thus more sensitive to the spikes we'd previously seen). Furthermore, we've gone through and added in multiple magnetic fields in this region. We want to take the most conservative bounds. \n",
    "\n",
    "Let's first write a simple function that loads a given dataset. Then, we can write a function that looks along the X-axis ($\\Delta m$) for the y-axis \"upper limit\" and \"lower limits\" ($\\theta_0$) for each $\\Delta m$ and turn this into a function -- possibly a spline fit here. Finally, we can write a function that compares two datasets and fill between these.\n",
    "\n",
    "This loads the simulation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b1b965d4-c969-4df5-a2e9-8b08478be194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "load_sim_result (generic function with 1 method)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function load_sim_result(resultFile)\n",
    "   \n",
    "    plot_Δms = load(resultFile,\"deltaMs\")\n",
    "    plot_θ0s = load(resultFile, \"theta0s\")\n",
    "    ρ11s = load(resultFile, \"Pnn\")\n",
    "\n",
    "    # From the loaded data, we need to load the sizes. \n",
    "    # I think the plotting matrices are (Δm x θ0 x v):\n",
    "    nΔm = size(plot_Δms)[1] # Remember Julia is 1-indexed\n",
    "    nθ0 = size(plot_Δms)[2] # all the plotting things should be sized the same \n",
    "    nVels = size(plot_Δms)[3]\n",
    "    nTraj = size(ρ11s)[1]\n",
    "\n",
    "    # These should have already averaged the probabilities inside our ROI.\n",
    "    # This means that we just have to average over velocities\n",
    "    plot_Prob = reshape(ρ11s[:,2],nΔm,nθ0,nVels)\n",
    "    plot_Endρ = reshape(ρ11s[:,3],nΔm,nθ0,nVels)\n",
    "    plot_vAvgProb = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "    plot_vAvgEnd  = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "\n",
    "    # Now we can loop over the Δm and θ0 values for these and average the v\n",
    "    for m in 1:nΔm\n",
    "        for θ in 1:nθ0\n",
    "            avgρ = 0.\n",
    "            finρ = 0.\n",
    "            for v in 1:nVels\n",
    "                avgρ += plot_Prob[m,θ,v]\n",
    "                finρ += plot_Endρ[m,θ,v]\n",
    "            end\n",
    "            plot_vAvgProb[m,θ] = avgρ / Float64(nVels)\n",
    "            plot_vAvgEnd[m,θ] = finρ / Float64(nVels)\n",
    "        end\n",
    "    end\n",
    "    return plot_Δms[:,:,1],plot_θ0s[:,:,1],plot_vAvgProb,plot_vAvgEnd\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1799d97f-0af8-49be-b4c4-2828947048fd",
   "metadata": {},
   "source": [
    "Our goal is to find a band of points that we can use to put a lower limit for the lifetime shift. We can just loop through each mass and find a minimum and maximum to account for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7372ef25-89c9-4274-962d-44c2d1631bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_minmax_from_file (generic function with 1 method)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function find_upper_lower(Δms::Matrix{Float64},θ0s::Matrix{Float64},ρ11s::Matrix{Float64};\n",
    "    p0::Float64=0.01,dp::Float64=0.002,uedge::Bool=false)\n",
    "   \n",
    "    # Load the variable number of points to save over\n",
    "    nΔm = size(Δms)[1]\n",
    "    nθ0 = size(θ0s)[2]\n",
    "    \n",
    "    # Now we want to create three arrays, which are\n",
    "    # the output of a \"function\" θ0_min/max = f(Δm)\n",
    "    Δm_val = Array{Float64}(undef,nΔm)\n",
    "    θ_min  = Array{Float64}(undef,nΔm)\n",
    "    θ_max  = Array{Float64}(undef,nΔm)\n",
    "    for i in 1:nΔm\n",
    "        \n",
    "        # Loop through and find min/max\n",
    "        tmin = 1.1\n",
    "        tmax = -0.1\n",
    "        for j in 1:nθ0-1\n",
    "            \n",
    "            # Is this the lower limit?\n",
    "            if ((ρ11s[i,j] < p0-dp) \n",
    "                    && (ρ11s[i,j+1] >= p0-dp))\n",
    "                tmin = θ0s[i,j]\n",
    "            end    \n",
    "            # Is this the upper limit?\n",
    "            if ((ρ11s[i,j] <= p0+dp) \n",
    "                    && (ρ11s[i,j+1] > p0+dp))\n",
    "                tmax = θ0s[i,j+1]\n",
    "            end        \n",
    "        end\n",
    "        # Pass the mass\n",
    "        Δm_val[i] = Δms[i,1]\n",
    "        # Now we do dumb limits just in case\n",
    "        if tmin > 1\n",
    "           if tmax > 0\n",
    "                tmin = θ0s[i,1] \n",
    "            else\n",
    "                if uedge\n",
    "                    tmin = θ0s[i,nθ0-1]\n",
    "                    tmax = θ0s[i,nθ0]\n",
    "                else\n",
    "                    tmin =  NaN\n",
    "                    Δm_val[i] = NaN\n",
    "                end\n",
    "            end\n",
    "            \n",
    "        elseif tmax < 0\n",
    "            tmax=θ0s[i,nθ0]\n",
    "        end\n",
    "        θ_min[i] = tmin\n",
    "        θ_max[i] = tmax\n",
    "    end\n",
    "    # We had a NaN error condition (i.e. off the edge), so let's remove those\n",
    "    reals = findall(.~isnan.(Δm_val))\n",
    "    return Δm_val[reals],θ_min[reals],θ_max[reals]\n",
    "end\n",
    "\n",
    "function get_minmax_from_file(filename;uedge=false)\n",
    "   \n",
    "    plot_Δm,plot_θ0,ρMid,ρEnd = load_sim_result(filename)\n",
    "    Δm, minθ, maxθ = find_upper_lower(plot_Δm,plot_θ0,(1. .- ρMid ./ ρEnd),uedge=uedge)\n",
    "        \n",
    "    return Δm, minθ, maxθ\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60e3e3f-e671-472e-9be2-e54847495b72",
   "metadata": {},
   "source": [
    "We did a number of runs that were zoomed in for some reason or another. In general, we'll want to deal with the run with more granularity rather than the less granular one.\n",
    "\n",
    "This next function allows us to zoom in and put a run with more granularity inside another run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a627021e-5abb-4aa9-8ea9-85c9cc7c712f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zoom_into_runs (generic function with 1 method)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function zoom_into_runs(m_out::Array{Float64},m_in::Array{Float64},\n",
    "                        θ_out::Array{Float64},θ_in::Array{Float64})\n",
    "    \n",
    "    # We're going to first find the ROI to zoom on\n",
    "    mMax = maximum(m_in)\n",
    "    mMin = minimum(m_in)\n",
    "    \n",
    "    # Find all the indices we want to use\n",
    "    indOut = findall(i -> \n",
    "        !((m_out[i] < mMax) && (m_out[i] > mMin)),\n",
    "        1:length(m_out))\n",
    "    \n",
    "    # Now we concatenate the arrays \n",
    "    Δm_comp = cat(m_out[indOut],m_in,dims=1)\n",
    "    θ_comp  = cat(θ_out[indOut],θ_in,dims=1)\n",
    "    \n",
    "    #Finally, we sort and return\n",
    "    indS =  sortperm(Δm_comp)\n",
    "    return Δm_comp[indS],θ_comp[indS]\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d4104c-f901-4e16-99cf-695af8f75878",
   "metadata": {},
   "source": [
    "Now we also did some runs that we'd want to use to find bands of limits. We want to compare the maximum and minimum of these to put the widest band on the 1% value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5bf3aff2-cde6-46a6-80ab-a3cf0905be8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "compare_two_runs_max (generic function with 1 method)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function compare_two_runs_min(Δm1::Array{Float64},min1::Array{Float64},Δm2::Array{Float64},min2::Array{Float64})\n",
    "    \n",
    "    s1 = length(Δm1)\n",
    "    s2 = length(Δm2)\n",
    "    # First let's see if the two are the same.\n",
    "    # This is the easy case.\n",
    "    if ((s1 == s2) && (Δm1[1] == Δm2[1]) && (Δm1[s1] == Δm2[s2]))\n",
    "        \n",
    "        # Just loop through the matrix elements\n",
    "        minOut = Array{Float64}(undef,s1)\n",
    "        for i in 1:s1\n",
    "            if min1[i] < min2[i]\n",
    "               minOut[i] = min1[i]\n",
    "            else\n",
    "                minOut[i] = min2[i]\n",
    "            end\n",
    "        end\n",
    "        # We have our minumum. We can return now.\n",
    "        return Δm1, minOut\n",
    "    end\n",
    "    \n",
    "    # Harder case: We have a mismatch in points! \n",
    "    # Now we want to combine the two.\n",
    "    j = 1 # The index of the second element\n",
    "    for i in 1:s1\n",
    "        \n",
    "    end\n",
    "end\n",
    "\n",
    "function compare_two_runs_max(Δm1::Array{Float64},max1::Array{Float64},Δms2::Array{Float64},max2::Array{Float64})\n",
    "    \n",
    "    s1 = length(Δm1)\n",
    "    s2 = length(Δm2)\n",
    "    # First let's see if the two are the same.\n",
    "    # This is the easy case.\n",
    "    if ((s1 == s2) && (Δm1[1] == Δm2[1]) && (Δm1[s1] == Δm2[s2]))\n",
    "        \n",
    "        # Just loop through the matrix elements\n",
    "        maxOut = Array{Float64}(undef,s1)\n",
    "        for i in 1:s1\n",
    "            if max1[i] > max2[i]\n",
    "               maxOut[i] = max1[i]\n",
    "            else\n",
    "                maxOut[i] = max2[i]\n",
    "            end\n",
    "        end\n",
    "        # We have our minumum. We can return now.\n",
    "        return Δm1, maxOut\n",
    "    end\n",
    "    \n",
    "    # Harder case: We have a mismatch in points! \n",
    "    # Now we want to combine the two.\n",
    "    j = 1 # The index of the second element\n",
    "    for i in 1:s1\n",
    "        \n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a93339-78f2-4663-889c-e6f3c2059b51",
   "metadata": {},
   "source": [
    "### *Loading Simulated Data*\n",
    "\n",
    "In order to produce a limit plot, we need to run a simulation of the neutron beam passing through our absorber. Then, we can use the limit provided by the above algorithm into an overall simulation, and make a contour of that.\n",
    "\n",
    "We need to load the average limits of this result. First, we have to average the probabilities of many different neutron trajectories, each of which has many different velocities. Since we care about the probability *en masse*, we can do a naive average over the velocities.\n",
    "\n",
    "Then, we need to average the positive and negative polarities of these two results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b7664640-53d8-472b-af3a-3203779137ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×6 Matrix{Float64}:\n",
       " 1.0e-8      1.0e-8      1.0e-8      1.0e-8      1.0e-8      1.0e-8\n",
       " 2.63333e-8  2.63333e-8  2.63333e-8  2.63333e-8  2.63333e-8  2.63333e-8\n",
       " 4.26667e-8  4.26667e-8  4.26667e-8  4.26667e-8  4.26667e-8  4.26667e-8\n",
       " 5.9e-8      5.9e-8      5.9e-8      5.9e-8      5.9e-8      5.9e-8"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the two limit files that we want to be able to load\n",
    "dataFile_pos = (raw\"/Pnn_4.8T.jld\")\n",
    "resultFile_pos = (includePath*raw\"/results\"*dataFile_pos)\n",
    "\n",
    "dataFile_neg = (raw\"/Pnn_-4.8T.jld\")\n",
    "resultFile_neg = (includePath*raw\"/results\"*dataFile_neg)\n",
    "\n",
    "# Now let's average these two to get our limits from experiment\n",
    "prob_Δm,prob_θ0,prob_ρ = average_two_results(resultFile_pos,resultFile_neg);\n",
    "\n",
    "# Let's also do the zero-field file\n",
    "dataFile_0 = (raw\"/Pnn_0.jld\")\n",
    "resultFile_0 = (includePath*raw\"/results\"*dataFile_0)\n",
    "zero_Δm,zero_θ0,zero_ρ = average_two_results(resultFile_0,resultFile_0)\n",
    "\n",
    "dataFile_02 = (raw\"/SNS_ZeroB.jld\")\n",
    "resultFile_02 = (includePath*raw\"/results\"*dataFile_02)\n",
    "zero2_Δm,zero2_θ0,zero2_ρ = average_two_results(resultFile_02,resultFile_02)\n",
    "\n",
    "dataFile_03 = (raw\"/SNS_ZeroB_Vacuum.jld\")\n",
    "resultFile_03 = (includePath*raw\"/results\"*dataFile_03)\n",
    "zero3_Δm,zero3_θ0,zero3_ρ = average_two_results(resultFile_03,resultFile_03)\n",
    "\n",
    "dataFile_05 = (raw\"/SNS_ZeroB_MoreV.jld\")\n",
    "resultFile_05 = (includePath*raw\"/results\"*dataFile_05)\n",
    "zero5_Δm,zero5_θ0,zero5_ρ = average_two_results(resultFile_05,resultFile_05)\n",
    "\n",
    "\n",
    "# These are the two limit files that we want to be able to load\n",
    "dataFile_pos2 = (raw\"/SNS_Result_Pos.jld\")\n",
    "resultFile_pos2 = (includePath*raw\"/results\"*dataFile_pos2)\n",
    "\n",
    "dataFile_neg2 = (raw\"/SNS_Result_Neg.jld\")\n",
    "resultFile_neg2 = (includePath*raw\"/results\"*dataFile_neg2)\n",
    "\n",
    "\n",
    "# Now let's average these two to get our limits from experiment\n",
    "prob2_Δm,prob2_θ0,prob2_ρ = average_two_results(resultFile_pos2,resultFile_neg2);\n",
    "\n",
    "dataFile_pos3 = (raw\"/SNS_HighM_Pos.jld\")\n",
    "resultFile_pos3 = (includePath*raw\"/results\"*dataFile_pos3)\n",
    "\n",
    "dataFile_neg3 = (raw\"/SNS_HighM_Neg.jld\")\n",
    "resultFile_neg3 = (includePath*raw\"/results\"*dataFile_neg3)\n",
    "\n",
    "prob3_Δm,prob3_θ0,prob3_ρ = average_two_results(resultFile_pos3,resultFile_neg3);\n",
    "\n",
    "dataFile_pos4 = (raw\"/SNS_HighT_Pos.jld\")\n",
    "resultFile_pos4 = (includePath*raw\"/results\"*dataFile_pos4)\n",
    "\n",
    "dataFile_neg4 = (raw\"/SNS_HighT_Neg.jld\")\n",
    "resultFile_neg4 = (includePath*raw\"/results\"*dataFile_neg4)\n",
    "prob4_Δm,prob4_θ0,prob4_ρ = average_two_results(resultFile_pos4,resultFile_neg4);\n",
    "\n",
    "dataFile_pos5 = (raw\"/SNS_HighT_Pos_2.jld\")\n",
    "resultFile_pos5 = (includePath*raw\"/results\"*dataFile_pos5)\n",
    "\n",
    "dataFile_neg5 = (raw\"/SNS_HighT_Neg_2.jld\")\n",
    "resultFile_neg5 = (includePath*raw\"/results\"*dataFile_neg5)\n",
    "prob5_Δm,prob5_θ0,prob5_ρ = average_two_results(resultFile_pos5,resultFile_neg5);\n",
    "prob5_Δm .+= 9E-9 # To get it to not be aliased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "99a66c7c-d121-45c2-a351-336fd5a790eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([5.0e-8, 5.434782608695652e-8, 5.869565217391304e-8, 6.304347826086957e-8, 6.739130434782609e-8, 7.173913043478261e-8, 7.608695652173913e-8, 8.043478260869565e-8, 8.478260869565216e-8, 8.91304347826087e-8  …  2.5e-7, 2.543478260869565e-7, 2.5869565217391303e-7, 2.6304347826086955e-7, 2.6739130434782607e-7, 2.717391304347826e-7, 2.760869565217391e-7, 2.804347826086956e-7, 2.8478260869565214e-7, 2.8913043478260866e-7], [0.002589737339615606, 0.002589737339615606, 0.002357433181728697, 0.002589737339615606, 0.002589737339615606, 0.002589737339615606, 0.002357433181728697, 0.002357433181728697, 0.002145967130064389, 0.002357433181728697  …  0.00039530216054538503, 0.0003275642784297754, 0.0002981811658297321, 0.00035984283650057603, 0.00039530216054538503, 0.00035984283650057603, 0.00022492163344847217, 0.0011114982412630977, 0.002357433181728697, 0.003771560031677307], [0.002844933014509149, 0.002844933014509149, 0.002589737339615606, 0.002844933014509149, 0.002844933014509149, 0.002844933014509149, 0.002589737339615606, 0.002589737339615606, 0.002589737339615606, 0.002589737339615606  …  0.0004342556868756764, 0.00035984283650057603, 0.0003275642784297754, 0.00039530216054538503, 0.0004342556868756764, 0.00039530216054538503, 0.00024708566806619164, 0.0014735258512759046, 0.0034332442250214986, 0.004999999999999999])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There's a handful of runs we want to load and use now.\n",
    "up_Δm_tmp2, up_max_tmp2, up_min_tmp2 = get_minmax_from_file(includePath*raw\"/results/NISTResultReduced.jld\")\n",
    "up_Δm_tmp, up_max_tmp, up_min_tmp = get_minmax_from_file(includePath*raw\"/results/NISTHighMassSplits.jld\")\n",
    "\n",
    "keep = findall(i -> (up_Δm_tmp[i] > 50E-9), 1:length(up_Δm_tmp))\n",
    "up_Δm  = up_Δm_tmp[keep]\n",
    "up_max = up_max_tmp[keep]\n",
    "up_min = up_min_tmp[keep]\n",
    "\n",
    "bot_Δm_tmp, bot_max_tmp, bot_min_tmp = get_minmax_from_file(includePath*raw\"/results/NISTLowMassSplits_FIX.jld\")\n",
    "keep2 = findall(i -> (bot_Δm_tmp[i] < 60E-9), 1:length(bot_Δm_tmp))\n",
    "bot_Δm  = bot_Δm_tmp[keep2] .- 10E-9\n",
    "bot_max = bot_max_tmp[keep2]\n",
    "bot_min = bot_min_tmp[keep2]\n",
    "\n",
    "top_Δm, top_max, top_min = get_minmax_from_file(includePath*raw\"/results/NISTHighTheta_FIX.jld\",uedge=false)\n",
    "low_Δm, low_max, low_min = get_minmax_from_file(includePath*raw\"/results/NewNISTMagField.jld\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2151b2d9-ee32-4112-be81-db87288a486a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([2.25e-7, 2.2653061224489795e-7, 2.2806122448979591e-7, 2.2959183673469388e-7, 2.3112244897959184e-7, 2.3265306122448977e-7, 2.3418367346938773e-7, 2.357142857142857e-7, 2.3724489795918366e-7, 2.387755102040816e-7  …  2.785714285714286e-7, 2.8010204081632654e-7, 2.816326530612245e-7, 2.8316326530612246e-7, 2.846938775510204e-7, 2.8622448979591833e-7, 2.877551020408163e-7, 2.8928571428571425e-7, 2.908163265306122e-7, 2.923469387755102e-7], [0.0006571638668316372, 0.0006571638668316372, 0.0006571638668316372, 0.0005788854792796428, 0.0005788854792796428, 0.0005788854792796428, 0.000509931259210079, 0.000509931259210079, 0.000509931259210079, 0.000509931259210079  …  0.00044919055396441224, 0.0009614279375592684, 0.0014065649768237806, 0.0018126829105384616, 0.0023360593988185473, 0.00265194737582006, 0.0030105505398003714, 0.0034176449485124583, 0.003879787713136101, 0.004404422614336694])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mid_Δm,mid_max,mid_min = get_minmax_from_file(includePath*raw\"/results/NISTZoomedField_moreV_FIX.jld\")\n",
    "pos_Δm,pos_max,pos_min = get_minmax_from_file(includePath*raw\"/results/NISTPositiveROI_FIX.jld\")\n",
    "neg_Δm,neg_max,neg_min = get_minmax_from_file(includePath*raw\"/results/NISTNegativeROI_FIX.jld\")\n",
    "#neg_Δm,neg_max,neg_min = get_minmax_from_file(includePath*raw\"/results/NISTNegativeField_FIX.jld\")\n",
    "\n",
    "#_x,tmp_min = compare_two_runs_min(pos_Δm,pos_min,mid_Δm,mid_min)\n",
    "mid_Δm,mag_min = compare_two_runs_min(neg_Δm,neg_min,pos_Δm,pos_min)\n",
    "\n",
    "#_x,tmp_max = compare_two_runs_min(pos_Δm,pos_max,mid_Δm,_max)\n",
    "mid_Δm,mag_max = compare_two_runs_min(neg_Δm,neg_max,pos_Δm,pos_max)\n",
    "#mag_min = pos_min\n",
    "#mag_max = pos_max\n",
    "#mid_Δm  = pos_Δm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8d95d40e-555c-4b8e-b8a8-731bbbd87ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([-8.775510204081641e-10, 3.1836734693877552e-9, 7.244897959183673e-9, 1.1306122448979592e-8, 1.5367346938775508e-8, 1.9428571428571428e-8, 2.3489795918367344e-8, 2.7551020408163263e-8, 3.161224489795918e-8, 3.56734693877551e-8  …  7.081632653061225e-7, 7.183673469387755e-7, 7.285714285714286e-7, 7.387755102040816e-7, 7.489795918367347e-7, 7.591836734693878e-7, 7.693877551020408e-7, 7.795918367346939e-7, 7.897959183673469e-7, 8.0e-7], [0.003361678768249667, 0.0028680762552243406, 0.0028680762552243406, 0.0028680762552243406, 0.003361678768249667, 0.0028680762552243406, 0.0028680762552243406, 0.0028680762552243406, 0.0028680762552243406, 0.0028680762552243406  …  0.08951155154097333, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_Δm, tmp_max = zoom_into_runs(up_Δm,low_Δm,up_max,low_max)\n",
    "tmp_Δm, tmp_min = zoom_into_runs(up_Δm,low_Δm,up_min,low_min)\n",
    "\n",
    "comb_Δm, comb_max = zoom_into_runs(tmp_Δm,bot_Δm,tmp_max,bot_max)\n",
    "comb_Δm, comb_min = zoom_into_runs(tmp_Δm,bot_Δm,tmp_min,bot_min)\n",
    "\n",
    "comb2_Δm, comb2_max = zoom_into_runs(comb_Δm,mid_Δm,comb_max,mag_max)\n",
    "comb2_Δm, comb2_min = zoom_into_runs(comb_Δm,mid_Δm,comb_min,mag_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4d4c8258-aace-417d-b643-e016c7bb057b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.049999999999999996\n"
     ]
    }
   ],
   "source": [
    "println(maximum(zero_θ0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c692e7a-4ca9-4954-ad17-eb38f2f828ff",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2d7e15cd-1f7b-4d10-8d49-eeec7979dbf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: UserWarning: No contour levels were found within the data range.\r\n",
      "sys:1: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\r\n"
     ]
    }
   ],
   "source": [
    "PyPlot.figure()\n",
    "\n",
    "# Start with plotting titles/axes\n",
    "PyPlot.grid(true)\n",
    "#PyPlot.title(\"Transition Probability Exclusion Plot\")\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "PyPlot.xlim([0,1200])\n",
    "PyPlot.ylim([1E-5,7E-2])\n",
    "#PyPlot.xlim([0,60])\n",
    "#PyPlot.ylim([1E-2,1E-1])\n",
    "PyPlot.yscale(\"log\")\n",
    "\n",
    "# These are the (contour) plots for our probabilities.\n",
    "PyPlot.contourf(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "# These are the (contour) plots for our probabilities.\n",
    "# These connect on the other side\n",
    "PyPlot.contour(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "PyPlot.contour(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#PyPlot.contour(prob2_Δm.*1E9,prob2_θ0,prob2_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "\n",
    "PyPlot.contourf(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(prob3_Δm.*1E9,prob3_θ0,prob3_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "nw = findall(i -> \n",
    "        (prob4_Δm[i] >= 50E-9) ,\n",
    "        1:length(prob4_Δm))\n",
    "resE  = Int(size(nw)[1] / size(prob4_Δm)[2])\n",
    "extM = reshape(prob4_Δm[nw],resE,size(prob4_θ0)[2])\n",
    "extT = reshape(prob4_θ0[nw],resE,size(prob4_θ0)[2])\n",
    "extP = reshape(prob4_ρ[nw],resE,size(prob4_θ0)[2])\n",
    "\n",
    "PyPlot.contourf(extM.*1E9,extT,extP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(extM.*1E9,extT,extP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "PyPlot.contour(extM.*1E9,extT,extP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "#\n",
    "\n",
    "PyPlot.contourf(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-2],colors=\"blue\",linewidths=1.,linestyles=\"dotted\",alpha=1.)\n",
    "#PyPlot.contour(prob5_Δm.*1E9,prob5_θ0,prob5_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "uZ = findall(i -> \n",
    "        ((zero_Δm[i] >= 400E-9) && (zero_Δm[i] <= 601E-9)),\n",
    "        #((zero_Δm[i] >= 400E-9)),\n",
    "        1:length(zero_Δm))\n",
    "resM1 = Int(size(uZ)[1] / size(zero_θ0)[1])\n",
    "zeroM = reshape(zero_Δm[uZ],resM1,size(zero_θ0)[2])\n",
    "zeroT = reshape(zero_θ0[uZ],resM1,size(zero_θ0)[2])\n",
    "zeroP = reshape(zero_ρ[uZ],resM1,size(zero_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "#uZ2 = findall(i -> \n",
    "        #((zero2_Δm[i] >= 400E-9) && (zero2_Δm[i] <= 601E-9)),\n",
    "#        ((zero2_θ0[i] < 5E-2)),\n",
    "#        1:length(zero2_θ0))\n",
    "#resM12 = Int(size(uZ2)[2] / size(zero2_θ0)[2])\n",
    "#zeroM2 = reshape(zero2_Δm[uZ2],size(zero2_Δm)[1],resM12)#,size(zero2_θ0)[2])\n",
    "#zeroT2 = reshape(zero2_θ0[uZ2],size(zero2_Δm)[1],resM12)#size(zero2_θ0)[2])\n",
    "#zeroP2 = reshape(zero2_ρ[uZ2],size(zero2_Δm)[1],resM12)#size(zero2_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(zeroM.*1E9,zeroT,zeroP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contourf(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "PyPlot.contourf(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[5.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(zero5_Δm.*1E9,zero5_θ0,zero5_ρ,levels=[1E-5],colors=\"green\",linewidths=1.,linestyles=\"dashdot\",alpha=1.)\n",
    "\n",
    "\n",
    "uM = findall(i -> \n",
    "        (prob2_Δm[i] >= 600E-9),\n",
    "        #(prob2_Δm[i] >= 400E-9),\n",
    "        1:length(prob2_Δm))\n",
    "resM2 = Int(size(uM)[1] / size(prob_θ0)[1])\n",
    "probM = reshape(vec(prob2_Δm)[uM],resM2,size(prob2_θ0)[2])\n",
    "probT = reshape(vec(prob2_θ0)[uM],resM2,size(prob2_θ0)[2])\n",
    "probP = reshape(vec(prob2_ρ)[uM],resM2,size(prob2_θ0)[2])\n",
    "\n",
    "#PyPlot.contourf(probM.*1E9,probT,probP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contourf(probM.*1E9,probT,probP,levels=[2.5E-8,2],cmap=\"Greys\",alpha=0.5)\n",
    "#PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(probM.*1E9,probT,probP,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.contour(probM.*1E9,probT,probP,levels=[1E-11],colors=\"red\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "PyPlot.contour(probM.*1E9,probT,probP,levels=[1E-8],colors=\"black\",linewidths=1.,linestyles=\"dashed\",alpha=1.)\n",
    "\n",
    "PyPlot.fill_between(400:601,5.1E-2,7E-2,linewidth=0,color=\"gray\",alpha=0.4)\n",
    "\n",
    "#PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"$P=2.5\\times10^{-8}$ (95% CL)\"))\n",
    "PyPlot.plot([],[],linestyle=\"solid\",color=\"black\",label=latexstring(L\"$95\\% $ CL\"))\n",
    "PyPlot.plot([],[],linestyle=\"dotted\",color=\"blue\",label=latexstring(L\"$P=10^{-2}$\"))\n",
    "PyPlot.plot([],[],linestyle=\"dashdot\",color=\"green\",label=latexstring(L\"$P=10^{-5}$\"))\n",
    "PyPlot.plot([],[],linestyle=\"dashed\",color=\"black\",label=latexstring(L\"$P=10^{-8}$\"))\n",
    "PyPlot.plot([],[],linestyle=\"dashed\",color=\"red\",label=latexstring(L\"$P=10^{-11}$\"))\n",
    "\n",
    "# These are the minumum and maximum we just loaded from a file.\n",
    "PyPlot.fill_between(comb_Δm.*1E9,comb_min,comb_max,color=\"firebrick\")\n",
    "PyPlot.fill_between(comb2_Δm.*1E9,comb2_min,comb2_max,color=\"firebrick\")\n",
    "PyPlot.fill_between(top_Δm*1E9,top_min,top_max,color=\"firebrick\")\n",
    "\n",
    "xline = LinRange(0,60,60)\n",
    "lowLim = 1E-3.*ones(length(xline))\n",
    "upLim  = 1E-1.*ones(length(xline))\n",
    "PyPlot.fill_between(xline,lowLim,upLim,color=\"orange\",alpha=0.3)\n",
    "PyPlot.plot(xline,lowLim,color=\"orange\")\n",
    "PyPlot.axvline(60,ymin=0.5,color=\"orange\") # This is dumb.\n",
    "PyPlot.plot([],[],color=\"orange\",label=\"UCN Traps\")\n",
    "\n",
    "PyPlot.plot([],[],color=\"firebrick\")\n",
    "PyPlot.legend(loc=\"lower right\")#upper center\")\n",
    "PyPlot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "37806d47-4efc-4b9d-8124-d2c1f1e4f06f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\r\n"
     ]
    }
   ],
   "source": [
    "minC = minimum(log10.(prob_ρ))\n",
    "maxC = maximum(log10.(prob_ρ))\n",
    "\n",
    "if minimum(log10.(prob2_ρ)) < minimum(log10.(prob_ρ))\n",
    "    minC = minimum(log10.(prob2_ρ))\n",
    "end\n",
    "\n",
    "if maximum(log10.(prob2_ρ)) > maximum(log10.(prob_ρ))\n",
    "    maxC = maximum(log10.(prob2_ρ))\n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "#PyPlot.contour(prob_Δm.*1E9,prob_θ0,prob_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(prob_Δm.*1E9,prob_θ0,log10.(prob_ρ),cmap=\"plasma\")\n",
    "PyPlot.clim(-10,0)\n",
    "#PyPlot.contour(prob2_Δm.*1E9,prob2_θ0,prob2_ρ,levels=[2.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(prob2_Δm.*1E9,prob2_θ0,log10.(prob2_ρ),cmap=\"plasma\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.pcolormesh(prob4_Δm.*1E9,prob4_θ0,log10.(prob4_ρ),cmap=\"plasma\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.xlim([0,800])\n",
    "PyPlot.ylim([1E-5,1E-1])\n",
    "PyPlot.title(latexstring(L\"$\\log_{10}(ρ_{11})$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.colorbar()\n",
    "PyPlot.grid(\"true\")\n",
    "#PyPlot.fill_between(comb_Δm.*1E9,comb_min,comb_max,color=\"firebrick\")\n",
    "#PyPlot.fill_between(comb2_Δm.*1E9,comb2_min,comb2_max,color=\"firebrick\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e03ecd70-5692-4cdf-905d-3b0ea08ec474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAGaCAYAAACrPfEaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvpUlEQVR4nO3dfXRV1YH//8+5j0mApAUqkEEodbSCQZTAMPiAwLS0VBR8YMSqYKV+Fx0c5Ye26rg6SDsOLmzV5RhQtEg7tpXVmWI71bbSWSJa6kihtAgdCyMKrRGKlYSH5D7u3x/3IffmJpDAvTs3m/fLdc09+zzcszk52Z97ztnneMYYIwAAgBLz9fQKAACA0wOhAwAAWEHoAAAAVhA6AACAFYQOAABgBaEDAABYQegAAABWEDoAAIAVhA4AAGAFoQM4jVx11VWqrKzUoUOHOp3mhhtuUDAY1P79++2tGIDTAqEDOI3Mnz9fra2t+t73vtfh+KamJq1bt04zZszQoEGDLK8dANcROoDTyPTp01VbW6vVq1d3OP773/++WlpaNH/+fMtrBuB0QOgATiN+v1/z5s3Tli1btH379oLxzzzzjIYMGaLx48frH/7hHzRq1Cj17dtXZ5xxhqZOnapXX321YJ5IJKKvfe1rGjlypCoqKjRgwABNmTJFmzZtkiS988478jxPa9asKZjX8zzdf//92eGbb75ZH//4xwumu//+++V5XsG8t912m5555hl98pOfVGVlpcaNG6fXX39dxhg99NBDGjFihPr27aupU6dq9+7d3fvHAlB0hA7gNHPLLbfI87yCox07d+7UG2+8oXnz5mWv+ViyZIleeOEFPfPMM/rEJz6hyZMna8OGDdl54vG4pk+frq9//euaMWOG1q1bpzVr1uiiiy7S3r17S16Xn/zkJ3r66af14IMP6vvf/74OHz6syy+/XHfeead++ctf6vHHH9eqVau0c+dOXXPNNeKh2kDPCvT0CgCw66//+q81adIkPfvss1q+fLmCwaAkZUPILbfcorPPPlsrVqzIzpNIJPSZz3xG77zzjh577DFNnjxZUup0zMsvv6ynnnpKX/ziF7PTX3HFFVbqEolE9NJLL6lPnz6SUkc/Zs2apZdffllbt27NHh3585//rEWLFunNN9/U6NGjrawbgEIc6QBOQ/Pnz9fBgwf14x//WFLqiMWzzz6rSy+9VGeffbYk6YknntDYsWNVUVGhQCCgYDCo//7v/9bvf//77HJ++tOfqqKiQrfcckuP1GPKlCnZwCFJI0eOlJS6diX3dEym/N1337W7ggDyEDqA09C1116rmpoaPfPMM5KkF198Ufv3789eQPrwww/rS1/6kiZMmKD//M//1Ouvv67Nmzfrs5/9rFpaWrLL+fOf/6za2lr5fD3zp6R///55w6FQ6Ljlra2tdlYMQIc4vQKchiorK3X99dfrqaeeUmNjo1avXq1+/fpp9uzZkqRnn31WkydP1sqVK/PmO3z4cN7wxz72Mb322mtKJpOdBo+KigpJqVMhuT744IMOp20/nSQdPHiw65UDULY40gGcpubPn69EIqGHHnpIL774oubMmaOqqipJqWsjwuFw3vS/+93v9Ktf/SqvbPr06Wptbe2wZ0rGoEGDVFFRod/97nd55T/60Y8Kpv34xz+uAwcO5N2YLBqN6uc//3l3qwegDHGkAzhNjRs3Tueff74effRRGWPy7s0xY8YMff3rX9eSJUt02WWX6a233tLXvvY1jRgxQvF4PDvd9ddfr2eeeUYLFizQW2+9pSlTpiiZTOp//ud/NHLkSM2ZM0ee5+nGG2/U6tWrddZZZ2nMmDF64403OrxB2XXXXad//ud/1pw5c/TlL39Zra2teuyxx5RIJKz8mwAoLY50AKex+fPnyxijUaNGacKECdny++67T3feeae+9a1v6fLLL9fTTz+tJ554Qpdcckne/IFAQC+++KLuvfderVu3TjNnztTcuXP12muvafjw4dnpvvnNb+rGG2/U8uXLNXPmTP3qV7/ST37yk4L1GTFihH70ox/p0KFDuvbaa/XlL39Zs2fP1ty5c0v3jwDAGs/QcR0AAFjAkQ4AAGBFWYaOq666Sh/96Ed17bXX9vSqAACAIinL0HH77bfrO9/5Tk+vBgAAKKKyDB1TpkxRv379eno1AABAEXU7dGzcuFFXXHGFamtr5Xmenn/++YJpVqxYoREjRqiiokL19fUdPpkSAACcXrodOo4ePaoxY8bo8ccf73D82rVrtWjRIt133336zW9+o0svvVTTp0/Pe+JkfX296urqCl7vvffeydcEAACUtVPqMut5ntatW6dZs2ZlyyZMmKCxY8fm3T555MiRmjVrlpYtW9blZW/YsEGPP/64/uM//uO400UikbzbJieTSf3lL3/RgAED8h74BAAAjs8Yo8OHD5fsmUpFvSNpNBrVli1bdM899+SVT5s2TZs2bSrmR2UtW7ZMS5cuLcmyAQA4He3bt09Dhw4t+nKLGjoOHjyoRCKhQYMG5ZUPGjRI77//fpeX85nPfEZbt27V0aNHNXToUK1bt07jx4/vcNp7771Xixcvzg43NTVp2LBhmq1/U1CVJ1eRMlJVntf6nrSqnl6BIqtwaPMEfW7dJzDod6s+foe2T3fqYoyUVFIJxRVXXAkTV0KJvOG44koqrnh6XFwxJUzONNnpEtn3CbUNx016fsWVMOn5lch+Vv5wTG1rEFNCcSWVLNm/lSdPfvnlkz/90ye/AvLJk8/LLffL72XGp//v+eXPnS89PnecLzPWSy03YRLaEPtZyTpzlOTZK+1PaxhjunWqozsPdwqHwwUPppKkoCoV8np/ExcyDrVqksKOnfFyqT4hz51GTZKCjtXHb6k+SZNp4GOpZtjE0u9jipt42/uCcemyduMSiitmoukmPqaYYkqkG/G4ibU1/un5MyEgNzAYnVrdU81pIPV/L/1TAfm9QFt5etjvBRRSOH+67LRBBeTPvk8tz6+gL5DTuOc2/gH5PV82GPjzGvt0ePB8ygsVXl5MsH6ZQItp0YbYz0r2uUUNHQMHDpTf7y84qnHgwIGCox8AgDZJk8xp0GPphjqmmFI/k+mGuyshoKOGPm5yGv30fJkAkLuchE7u4XqevHSjHJBfQQW9YLZhznuvoEJehQJeTgBo18AHchp1v+fPaeBzQkC74cwyUu/T4SA97PNK++Ut4NhRtVIqaugIhUKqr6/X+vXrddVVV2XL169fr5kzZxbzowCgqIwx7Rr9mOLpBj8/BGQa/PbDsbyQ0H64cHk54UHRk2rsu9fQhxVQHwUVkt8XSJcGcxrp9Df57PtQ6md6Ofnv2w8HuvWt3KVTReieboeOI0eOaPfu3dnhPXv2aNu2berfv7+GDRumxYsX66abbtK4ceM0ceJErVq1Snv37tWCBQuKuuLtNTQ0qKGhgUdgAw6Lm7giOqaIWhRRqyKmpW3YtCiiFrUq9TOWbFHcRNtCQU5wiCvartFPvbrLl23SgzkNdqjdcFAVqlRfryYdBELZhjuYbbRD7YbTy8kZDvlSn3SyDT1QDrrdZXbDhg2aMmVKQfm8efO0Zs0aSambgy1fvlyNjY2qq6vTI488okmTJhVlhU+kublZNTU1+ryeduKajirHruno49jfR5cuJA118O0z9/x+InO+XbG2nyaRPj+fKU/kTBvLuSivbXzb9PG88/jtPyeRc8g/kg4SxwsGnjyFVaWwKhVWpSq8CoW8UF4jH/AyASHUbriwkU+FgFDmOEFeiEgdIQjK7/lLuUnyuHR0wKW6SG6dXmkxLbq7+f+pqalJ1dXVRV++c4+2z4SOr/hWK+xA6Ag7tnOGg6W7yrsnlOKPTdu5/dR5+dT5+cLz+LHs+2jeOflY7gV9mffp8/uxnMP+8fRyE7nLyfyXvpDvVK/KD2T+8/LPyedexNd2/t7fdmg/5xx/0AuqwqtseynzviKvPKRQ3rf+QMCt3zWfQ38L/D63to1LB5taki1auP+2koWOkvReAXozY0z6+3hEMUUVNdHUexNTTBFFFVUsXZZIZBrqnIbfZM7zx/Ma+GzD39mFgCYVCpIneSFfWyMdand+vu38e+rwfEAVqkyV+4LZeUKeP3sBXqbxzwsKXjoYpJeRFxxyxgfTAcPPoX8A7RA60GskTVIxZQJANNX4pwNANF0W60pZNjREFTWRvPExpabpahe91IV8hQ18tuHPKQt5FapSUEFf+4v2Op8n93B+qqEPKeAFcq4ZaLvK/1Sv0A/63fr2CaD8EDpQdMYYRdWqFh1NvcxRtabfx2NHFVVrTgPfdtSgsCw/YCQU7/I6ZM7DB72QQkqd1w95IQUVVlAhVXiV6qePKOgLKqiwQulpM+Mz86XKQul+++kyhbPjw36+zQNAVzkTOui9UlxJk1RELWpROjCYo8d936Kjas2WH+v0FEEwEVZYYQW9cLYbX1ChdEMfVoWqFFJYQV8op/EP570P5M6XHR9U0AtnA0ap++VneI7dgAoASokLScvcqVxImjTJ7BGG7oWGVHDo7BRDhapUoSpVqo8qvD6qVF9Vqo8qvT7tytPj0u/DqlKfkEPdPeTWVeuunV7hQtLyxYWk5YsLSR3VdiOi9CmEzLUGuT9NVFJr9qZC2ZsMKZq+z0DbcKYsYlqzQSOilg4/25OnCvXJCwpV6qP+vkHpoNAnGxTaQkTf7PCpHUVw648NAKDrnA0dH62Kq8Lr+jUAGameC/HUlQUmknOxYUwxE+mgPNW7IZq99iCnl0NeeTQ7f+Z6hS5drJhQukdA5hqD1MWHIYVS1yOkrzmo9CoUVD+FvQpV+fqoyqtSldcn9coOp8rCXsUpBIfu/5vmcu0bTsChowOu3TvB59C2kdz6Nu3a3wGXTrOaZLSky3c2dLwaXS8jk9P45zb6bY1/28+20NDVngs++bMXG4YUzl6oGEoHgdTFijUK+fKvX2j7mb4eoeBnMDvcN+S3egMiAABKxdnQ8YvojxX2KvJ6IrQ19hXqo2qFfG0hIdPYB9uFh1D6Asfcn5lQ4fdK/8/n97gwFgDgBmdDx9K+/6YKBy4kBQDAFc50JWhoaNCoUaM0fvz4nl4VAADQAWdCx8KFC7Vz505t3ry5p1cFAAB0wJnQAQAAypuz13T0q4qq0tf7e3241CVTknwO3UxLcqvrn0vd/iT36uNzqD6ubRtLN0C2Iljiu3o79E8FAADKGaEDAABYQegAAABWOBM66DILAEB5cyZ00GUWAIDy5kzoAAAA5Y3QAQAArHD2Ph3hcFwVvlN7DHs58Dl0HwjJvf753DuhfLl07wTXOPe75lB9fKa0bQ67JQAAsILQAQAArCB0AAAAKwgdAADACkIHAACwwpnQwR1JAQAob54xxp2+PpKam5tVU1OjNSO+oSpfZU+vzilzqSuWRDdG2OPavoPy5dLv2rFEi27cdbeamppUXV1d9OXTBAAAACsIHQAAwApCBwAAsILQAQAArCB0AAAAKwgdAADACmefMut5xoluTHQxBU6OMV5PrwJOEy79riWTpW10aNIAAIAVhA4AAGCFM6GD26ADAFDenAkdCxcu1M6dO7V58+aeXhUAANABZ0IHAAAob4QOAABghbNdZl1hkj29BjhduNTtT3LryZ+Se9sH5SmZLO3vGUc6AACAFYQOAABgBaEDAABYQegAAABWEDoAAIAVhA4AAGCFs11mjfGc6GKWdKAOLnPhdwy9A79rsCGe9Jd0+RzpAAAAVhA6AACAFc6EDp4yCwBAeXMmdPCUWQAAypszoQMAAJQ3QgcAALDC2S6z8YRfcVParj820E2uvLnUpdm49VBW55gSP/0TkOgyCwAAHEHoAAAAVhA6AACAFYQOAABgBaEDAABYQegAAABWONtlNpHwKW56f6ZyrZuca12A6TJbvlz7XUP5cunvQCRZ2ljQ+1tlAADQKxA6AACAFYQOAABgBaEDAABYQegAAABWEDoAAIAVznaZjcX8Cvh4ymy5SdAFuGy51O3PRSbZ02uAzrj0dyBa4qezc6QDAABYQegAAABWOBM6GhoaNGrUKI0fP76nVwUAAHTAmdCxcOFC7dy5U5s3b+7pVQEAAB1wJnQAAIDyRugAAABWONtlNhILyOfr/dVzrZtcIulWznXpyax0mS1vLv2uucatLrOl/RvtVgsAAADKFqEDAABYQegAAABWEDoAAIAVhA4AAGAFoQMAAFjR+/uUdiIa88nnufCU2Z5eg+Jy7ymzPb0GxeNal1mXto3kXn1c4tK+00qXWQAA4AJCBwAAsILQAQAArHD2mg4AwOknaZIySqb/SyiphBLZ921lSSWVNInslHnl7aZN5E1bOE/CJGXa/acTDHdlmrxhcwrzHqesfXnCxEu6fQgdAFBixph045VpxOLZpjCpuBKmo3HpsvS4hBJtDUS2AUqmS5LHbVTa/ks9zCkzfaf/mc6n6/gz8tejS9OZpNo38AllAkN+AEiY44WC/J+pzy49n3zyyS+ffPLSQ5KX91/74c7KOirPG/a6MM1xlt32n++E8yaV0D7tKtm/G6EDQK+UachTTU08p+FKNU5xk1DCxJVQXHHF0415THGTeR9XXLF0Mx9TwrRNlypPD5v0fMqZz+TMl7P8VHlmukTe+th2/IbIlzONLz2Vr1vTKd0YZko7a+A6a3j92Ubbr6AC8nn+dNPd1phnxvu8dmWeP2f+nOkKynw5y82ftsP5239Ou+m83PdeW48Vt3qvHNP2+K9KtnxnQ0cs7snv9f5fhHii99chl0s7pyRL36mKwxiTbiajiiuWfkVTwyamaE5jnH+4OVHQoGe+obd9E02cYPr231LbvsUXLKuzz2hXXsxvtH4F0q+g/Aoo0G7YL3/OuKACqlTYaxvOzu8Fsu996bl86flTw+lyz9/W0OWWF0yfP137b6uF4cCX/WZczqzuN935sE6mNVIPxMaeES3x32hnQwdQjlINf0JxRdOvmGKZAGCiOUEg1jbe5IaEWDo0pIJC+wCRWk5ueTRnntgpr39bA+nLawxTQx2Xd9TIBhVQuIPytmXlDHvtlpW37ECn5V56nQqDRG5ZMNWYl3kjDbiC0IHTljEmrxGPKpJq8E1bWSxzJEDRdOOfKYtly3IDQl5gMLG8Bj8zvrvf0FPNfCj9DTvzM6igl/NeIYVVrYBCCvraTZd572XKQwrmLstLTeOZoPzpRrjDEOH1rs5uyd50GAo4TRA6UDaSJlnYqCuquMkJBDkhIdXgtwWB3MAQy06fXpZpFyBO4pt/WwMeUjD9Cnj5DXil+qk608B7wbywkJq+XRDIjgsqkA4R7cfZauwT4ts+gNIidEBS22H/RLpZT2QP+cfT3+LjOYfr49lv8fmvuOImmr5uIJa+yC6mmGlbXjY0mHYBQqn5usqTl/eNPT8EZF4VqlK/9Lf/kIIKK5gbHLx2ASLz3stdZlhBhVLf+jto/PkyDQBdR+goMwmTUEStipgWRdSqY8nW7LfyTAOebeDV1sAnOgoBedMWludeVJg4yQvzPHnpRjnVnKfOk4cU8AI539ZTr0qvr/qa1Df6oNfW+IcUToeAYCchoO2UQKY8oCDn4QGglyF0FEncxBVRi1pNi1p1LPszkjfckh5uUas5lv0ZUYsiplURtXb5kH/mm35A7Rp3L7ehTwWACq8qHQgy1wKk33tt0+QtxwsWBIaOpvUrKH83H6rnWu8VAEDXORs6jsX8SnahQUyaZKrRV6rxzwSFvLJMMFDbz1aTP3y8sOCTXxWqUliVCmd+elXqp4EamB4O+SpSP9X2M+RVKGgqFVI4Gxgyr5I+QbejAx7typLpV3f7QyQcOx/hUnVcu/DSseqkb+uFcuTS71qkxJVxNnRsTv5cccVPGCiiau10GZ68tpCQDQuV6qMa9fcGp8q8yg4CRX7ZqZwK4A8NAMAVzoaOjeb5dMOfavwrvCpVqEo13sB0GEiV5YaF1HBbWUhhrhsAAKBInA0dd/pXqsKr6unVAAAAaWV3t599+/Zp8uTJGjVqlM4//3z94Ac/6OlVAgAARVB2RzoCgYAeffRRXXDBBTpw4IDGjh2rz33uc+rTp09PrxoAADgFZRc6hgwZoiFDhkiSzjjjDPXv319/+ctfCB0AAPRy3Q4dGzdu1EMPPaQtW7aosbFR69at06xZs/KmWbFihR566CE1NjbqvPPO06OPPqpLL7202yv361//WslkUmeeeWa3540kJBfu6uzakw1dq49xqK+caz2lHNo0ktzaPi7VxTXREi+/26Hj6NGjGjNmjL7whS/ommuuKRi/du1aLVq0SCtWrNDFF1+sJ598UtOnT9fOnTs1bNgwSVJ9fb0ikUjBvC+99JJqa2slSR988IHmzp2rp59++rjrE4lE8pbV3Nzc3SoBAAALPGNO/rua53kFRzomTJigsWPHauXKldmykSNHatasWVq2bFmXlhuJRPTpT39at956q2666abjTnv//fdr6dKlBeX/n1Yr7EDvFdeODLhWH450lC+HNo0kt7aPS3VxTVTH9LRuUVNTk6qrq4u+/KL2XolGo9qyZYumTZuWVz5t2jRt2rSpS8swxujmm2/W1KlTTxg4JOnee+9VU1NT9rVv376TWncAAFBaRb2Q9ODBg0okEho0aFBe+aBBg/T+++93aRm//OUvtXbtWp1//vl6/vnnJUn//u//rtGjR3c4fTgcVjgcPqX1BgAApVeS3ivt7+JpjOnynT0vueQSJZMcfAMAwDVFPb0ycOBA+f3+gqMaBw4cKDj6AQAATi9FPdIRCoVUX1+v9evX66qrrsqWr1+/XjNnzizmR51Qi9x4aqZrF166Vh+XjskZxy69dGnbSG5dGOvatnFJtMS/ad0OHUeOHNHu3buzw3v27NG2bdvUv39/DRs2TIsXL9ZNN92kcePGaeLEiVq1apX27t2rBQsWFHXF22toaFBDQ4MSCdeaNQAA3NDtLrMbNmzQlClTCsrnzZunNWvWSErdHGz58uVqbGxUXV2dHnnkEU2aNKkoK3wizc3Nqqmp0QKtVlh0mS03rtXHpW9sHOkoby5tHde2jUuiOqbvan7Jusye0n06yhGho7y5Vh+X/ngSOsqbS1vHtW3jklKHjrJ7yiwAAHAToQMAAFjhTOhoaGjQqFGjNH78+J5eFQAA0AFnQsfChQu1c+dObd68uadXBQAAdKAkdyQtB60ySjpw6ZVrF14mvN6/TXK5dEGcS3WRuDAW9rj0uxYzpW11nDnSAQAAyhuhAwAAWEHoAAAAVhA6AACAFc6EDrrMAgBQ3pwJHXSZBQCgvDnbZfaYjOIOdM9MONQVS3KvPsbr6TUoHhe6mOdyrYupS90yXds2LonJlPRBP84c6QAAAOWN0AEAAKwgdAAAACsIHQAAwApCBwAAsMKZ0MF9OgAAKG+eMcadfliSmpubVVNToyu9VQp6lT29OqfMtS6mccfqQ5fZ8uVSF1PJrW6m7v2uuSNuWvRKcqGamppUXV1d9OU7c6QDAACUN0IHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALDC3afMegkFvURPr8Ypc63LrGv1STrwJOMM17oxuva75hLXftdc6s4cV7yky3fmSAc3BwMAoLw5e3OwT/lWcnOwMuRafTjSUb5c+11ziWu/a04d6TAt+nXiDm4OBgAAejdCBwAAsILQAQAArCB0AAAAK5ztMtvixRXzStv1xwbXLoZz7SmzCYcuIXPpoljJrYv7XOPahaQuPdE4oVhJl8+RDgAAYAWhAwAAWEHoAAAAVhA6AACAFc6EDm6DDgBAeXP2NugX+/9NAW6DXnbovVK+6L0CW+i9Ur4SpkU74neV7Dbo7naZVVz+Ej8tz4ao59afTpd2TkmKO9S0udYQOFcfr6fXoHhc2zYuSfKUWQAA4AJCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwwtkus4e9qPyev6dX45TFnbt3glv1idFltmy5Vp+EQ93nXbv/kEuSipR0+RzpAAAAVhA6AACAFYQOAABgBaEDAABYQegAAABWOBM6eLQ9AADlzdlH2w8JPiCfV9HTq3PKYl6ip1ehqFzrKufaU4ABnN6MadWRyNKSPdremSMdAACgvBE6AACAFYQOAABgBaEDAABYQegAAABWEDoAAIAV7j5l1heV55GpAAAoF7TKAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArHAmdDQ0NGjUqFEaP358T68KAADogGeMMT29EsXU3Nysmpoa9Q0vkedV9PTqAADQaxjTqiORpWpqalJ1dXXRl+/MkQ4AAFDeCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCi7ELH4cOHNX78eF1wwQUaPXq0nnrqqZ5eJQAAUASBnl6B9qqqqvTKK6+oqqpKx44dU11dna6++moNGDCgp1cNAACcgrI70uH3+1VVVSVJam1tVSKRkDGmh9cKAACcqm6Hjo0bN+qKK65QbW2tPM/T888/XzDNihUrNGLECFVUVKi+vl6vvvpqtz7j0KFDGjNmjIYOHaqvfOUrGjhwYHdXEwAAlJluh46jR49qzJgxevzxxzscv3btWi1atEj33XeffvOb3+jSSy/V9OnTtXfv3uw09fX1qqurK3i99957kqSPfOQj+u1vf6s9e/boe9/7nvbv33+S1QMAAOXCM6dw7sLzPK1bt06zZs3Klk2YMEFjx47VypUrs2UjR47UrFmztGzZsm5/xpe+9CVNnTpVs2fP7nB8JBJRJBLJDjc3N+vMM89U3/ASeV5Ftz8PAIDTlTGtOhJZqqamJlVXVxd9+UW9piMajWrLli2aNm1aXvm0adO0adOmLi1j//79am5ulpQKEBs3btQnP/nJTqdftmyZampqsq8zzzzz5CsAAABKpqih4+DBg0okEho0aFBe+aBBg/T+++93aRl//OMfNWnSJI0ZM0aXXHKJbrvtNp1//vmdTn/vvfeqqakp+9q3b98p1QEAAJRGSbrMep6XN2yMKSjrTH19vbZt29blzwqHwwqHw91ZPQAA0AOKeqRj4MCB8vv9BUc1Dhw4UHD0AwAAnF6KGjpCoZDq6+u1fv36vPL169froosuKuZHAQCAXqbbp1eOHDmi3bt3Z4f37Nmjbdu2qX///ho2bJgWL16sm266SePGjdPEiRO1atUq7d27VwsWLCjqirfX0NCghoYGJRKJkn4OAAA4Od3uMrthwwZNmTKloHzevHlas2aNpNTNwZYvX67GxkbV1dXpkUce0aRJk4qywifS3NysmpoauswCANBNpe4ye0r36ShHhA4AAE5Or7pPBwAAQGcIHQAAwApCBwAAsMKZ0NHQ0KBRo0Zp/PjxPb0qAACgA1xICgAAJHEhKQAAcAShAwAAWEHoAAAAVhA6AACAFYQOAABghTOhgy6zAACUN7rMAgAASXSZBQAAjiB0AAAAKwgdAADACkIHAACwgtABAACscCZ00GUWAIDyRpdZAAAgiS6zAADAEYQOAABgBaEDAABYQegAAABWEDoAAIAVhA4AAGAFoQMAAFhB6AAAAFY4Ezq4IykAAOWNO5ICAABJ3JEUAAA4gtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsMKZ0MFt0AEAKG/cBh0AAEjiNugAAMARhA4AAGAFoQMAAFhB6AAAAFYQOgAAgBWEDgAAYAWhAwAAWEHoAAAAVhA6AACAFYQOAABgBaEDAABYQegAAABWOBM6eMosAADljafMAgAASTxlFgAAOILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArnAkdDQ0NGjVqlMaPH9/TqwIAADrgGWNMT69EMTU3N6umpkZ9w0vkeRU9vToAAPQaxrTqSGSpmpqaVF1dXfTlO3OkAwAAlDdCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArCB0AAAAKwgdAADACkIHAACwgtABAACsIHQAAAArCB0AAMAKQgcAALCC0AEAAKwgdAAAACsIHQAAwApCBwAAsILQAQAArCjb0HHs2DENHz5cd911V0+vCgAAKIKyDR0PPPCAJkyY0NOrAQAAiqQsQ8euXbv0v//7v/rc5z7X06sCAACKpNuhY+PGjbriiitUW1srz/P0/PPPF0yzYsUKjRgxQhUVFaqvr9err77arc+46667tGzZsu6uGgAAKGOB7s5w9OhRjRkzRl/4whd0zTXXFIxfu3atFi1apBUrVujiiy/Wk08+qenTp2vnzp0aNmyYJKm+vl6RSKRg3pdeekmbN2/WOeeco3POOUebNm064fpEIpG8ZTU1NUmSjClcPgAA6Fym7TTGlOoDTp4ks27duryyv/mbvzELFizIKzv33HPNPffc06Vl3nPPPWbo0KFm+PDhZsCAAaa6utosXbq00+mXLFliJPHixYsXL168ivT6v//7v25ngq7wjDn5OON5ntatW6dZs2ZJkqLRqKqqqvSDH/xAV111VXa6O+64Q9u2bdMrr7zSreWvWbNGb775pr7xjW90Ok37Ix2HDh3S8OHDtXfvXtXU1HSvQr1Ic3OzzjzzTO3bt0/V1dU9vTolc7rUUzp96ko93UI93dLU1KRhw4bpww8/1Ec+8pGiL7/bp1eO5+DBg0okEho0aFBe+aBBg/T+++8X86OywuGwwuFwQXlNTY3TvxgZ1dXV1NMxp0tdqadbqKdbfL7S9DMpaujI8Dwvb9gYU1DWFTfffHOR1ggAAPS0okaZgQMHyu/3FxzVOHDgQMHRDwAAcHopaugIhUKqr6/X+vXr88rXr1+viy66qJgf1alwOKwlS5Z0eMrFJdTTPadLXamnW6inW0pdz25fSHrkyBHt3r1bknThhRfq4Ycf1pQpU9S/f38NGzZMa9eu1U033aQnnnhCEydO1KpVq/TUU09px44dGj58eEkqAQAAyl+3Q8eGDRs0ZcqUgvJ58+ZpzZo1klI3B1u+fLkaGxtVV1enRx55RJMmTSrKCgMAgN7plLrMAgAAdFVZPnsFAAC4h9ABAACsIHQAAAAremXoWLZsmTzP06JFi7Jlxhjdf//9qq2tVWVlpSZPnqwdO3bkzReJRPSP//iPGjhwoPr06aMrr7xSf/zjHy2vfde1r2csFtPdd9+t0aNHq0+fPqqtrdXcuXP13nvv5c03efJkeZ6X95ozZ04P1KBrOtqeN998c0Ed/vZv/zZvvt6+PSUV1DHzeuihh7LTlPv2vP/++wvWb/DgwdnxLu2bx6urS/vnibapK/vnierpwv6Z8ac//Uk33nijBgwYoKqqKl1wwQXasmVLdryt/bTXhY7Nmzdr1apVOv/88/PKly9frocffliPP/64Nm/erMGDB+vTn/60Dh8+nJ1m0aJFWrdunZ577jm99tprOnLkiGbMmKFEImG7GifUUT2PHTumrVu36qtf/aq2bt2qH/7wh/rDH/6gK6+8smD+W2+9VY2NjdnXk08+aXP1u6yz7SlJn/3sZ/Pq8OKLL+aN7+3bU1Je/RobG7V69Wp5nlfwBOdy357nnXde3vpt3749O861fbOzurq2fx5vm0ru7J/Hq6cr++eHH36oiy++WMFgUD/96U+1c+dOffOb38x7toq1/bQkj5ErkcOHD5uzzz7brF+/3lx22WXmjjvuMMYYk0wmzeDBg82DDz6Ynba1tdXU1NSYJ554whhjzKFDh0wwGDTPPfdcdpo//elPxufzmZ/97GdW63EindWzI2+88YaRZN59991s2YnmKRfHq+e8efPMzJkzO53X1e05c+ZMM3Xq1Lyyct+eS5YsMWPGjOlwnGv75vHq2pHeun+eqJ6u7J/d3Z69cf80xpi7777bXHLJJZ2Ot7mf9qojHQsXLtTll1+uT33qU3nle/bs0fvvv69p06Zly8LhsC677DJt2rRJkrRlyxbFYrG8aWpra1VXV5edplx0Vs+ONDU1yfO8gqcBfve739XAgQN13nnn6a677spLq+XiRPXcsGGDzjjjDJ1zzjm69dZbdeDAgew4F7fn/v379cILL2j+/PkF48p9e+7atUu1tbUaMWKE5syZo7fffluSe/um1HldO9Kb988T1dOV/bOr27M3758//vGPNW7cOM2ePVtnnHGGLrzwQj311FPZ8Tb305I88K0UnnvuOW3ZskW//vWvC8ZlnvXS0dNt33333ew0oVBIH/3oRwumKdUTcE/G8erZXmtrq+655x59/vOfz3vq4Q033KARI0Zo8ODBevPNN3Xvvffqt7/9bcHt6XvSieo5ffp0zZ49W8OHD9eePXv01a9+VVOnTtWWLVsUDoed3J7f/va31a9fP1199dV55eW+PSdMmKDvfOc7Ouecc7R//379y7/8iy666CLt2LHDqX1TOn5dBwwYkDdtb94/T1RPV/bP7mzP3rp/StLbb7+tlStXavHixfqnf/onvfHGG7r99tsVDoc1d+5cq/tprwgd+/bt0x133KGXXnpJFRUVnU53Mk+37co0tnS1nlLqorU5c+YomUxqxYoVeeNuvfXW7Pu6ujqdffbZGjdunLZu3aqxY8eWZN27oyv1vO6667Lv6+rqNG7cOA0fPlwvvPBCwU6fq7duT0lavXq1brjhhoJpy317Tp8+Pft+9OjRmjhxos466yx9+9vfzl5c2Nv3zYzj1XXx4sXZcb15/5ROXE8X9k+p69tT6r37pyQlk0mNGzdO//qv/yop9QiTHTt2aOXKlZo7d252Ohv7aa84vbJlyxYdOHBA9fX1CgQCCgQCeuWVV/TYY48pEAhk09nxnm47ePBgRaNRffjhh51O09NOVM/MxTqxWEx///d/rz179mj9+vV536I6MnbsWAWDQe3atctGNU6oq/XMNWTIEA0fPjxbB5e2pyS9+uqreuutt/TFL37xhMstt+3ZXp8+fTR69Gjt2rUr2xOgt++bncmta0Zv3z870lE9c/XG/bMjndWzt++fQ4YM0ahRo/LKRo4cqb1790qS1f20V4SOv/u7v9P27du1bdu27GvcuHG64YYbtG3bNn3iE5/Q4MGD8w5nRaNRvfLKK9mn29bX1ysYDOZN09jYqDfffNPaE3BP5ET19Pv92T9ou3bt0i9+8YuCQ4Ad2bFjh2KxmIYMGWKhFifWlXq298EHH2jfvn3ZOriyPTO+9a1vqb6+XmPGjDnhcstte7YXiUT0+9//XkOGDMkedu7t+2ZncusqyYn9syPt69leb9w/O9JZPXv7/nnxxRfrrbfeyiv7wx/+kH0Iq9X9tBsXwJaV9lcMP/jgg6ampsb88Ic/NNu3bzfXX3+9GTJkiGlubs5Os2DBAjN06FDzi1/8wmzdutVMnTrVjBkzxsTj8R6oQdfk1jMWi5krr7zSDB061Gzbts00NjZmX5FIxBhjzO7du83SpUvN5s2bzZ49e8wLL7xgzj33XHPhhRf2mnoePnzY3HnnnWbTpk1mz5495uWXXzYTJ040f/VXf+XU9sxoamoyVVVVZuXKlQXT94bteeedd5oNGzaYt99+27z++utmxowZpl+/fuadd94xxri1bx6vri7tn8erp0v754l+d43p/funMaleVIFAwDzwwANm165d5rvf/a6pqqoyzz77bHYaW/upM6EjmUyaJUuWmMGDB5twOGwmTZpktm/fnjdPS0uLue2220z//v1NZWWlmTFjhtm7d6/lNe+e3Hru2bPHSOrw9fLLLxtjjNm7d6+ZNGmS6d+/vwmFQuass84yt99+u/nggw96rhJdkFvPY8eOmWnTppmPfexjJhgMmmHDhpl58+YVbKvevj0znnzySVNZWWkOHTpUMH1v2J7XXXedGTJkiAkGg6a2ttZcffXVZseOHdnxLu2bx6urS/vn8erp0v55ot9dY3r//pnxX//1X6aurs6Ew2Fz7rnnmlWrVuWNt7Wf8pRZAABgRa+4pgMAAPR+hA4AAGAFoQMAAFhB6AAAAFYQOgAAgBWEDgAAYAWhAwAAWEHoAAAAVhA6AACAFYQOAABgBaEDAABY8f8DXikBsqdPs6wAAAAASUVORK5CYII=",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0e-7\n"
     ]
    }
   ],
   "source": [
    "PyPlot.figure()\n",
    "#PyPlot.contour(zero_Δm.*1E9,zero_θ0,zero_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(zero_Δm.*1E9,zero_θ0,log10.(zero_ρ),cmap=\"plasma\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.colorbar()\n",
    "PyPlot.plot([],color=\"black\",label=latexstring(L\"$P=5.5\\times10^{-8}$\"))\n",
    "#PyPlot.legend()\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "#PyPlot.xlim([400,600])\n",
    "#PyPlot.ylim([1E-3,5E-2])\n",
    "#PyPlot.title(\"Michael's Simulation\")\n",
    "PyPlot.show()\n",
    "\n",
    "PyPlot.figure()\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero2_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(zero2_Δm.*1E9,zero2_θ0,log10.(zero2_ρ),cmap=\"plasma\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.xlim([400,600])\n",
    "#PyPlot.ylim([1E-3,1E-1])\n",
    "PyPlot.title(\"Air\")\n",
    "PyPlot.show()\n",
    "\n",
    "PyPlot.figure()\n",
    "PyPlot.contour(zero3_Δm.*1E9,zero3_θ0,zero3_ρ,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(zero3_Δm.*1E9,zero3_θ0,log10.(zero3_ρ),cmap=\"plasma\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.xlim([400,600])\n",
    "#PyPlot.ylim([1E-3,1E-1])\n",
    "PyPlot.title(\"Vacuum\")\n",
    "PyPlot.show()\n",
    "\n",
    "println(maximum(zero2_Δm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6d6a8011-ff4f-43b3-b28f-d3ce89402f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2600,)\n",
      "26\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.colorbar.Colorbar object at 0x00000000072D4460>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "println(size(uZ))\n",
    "println(Int(size(uZ)[1] / size(zero_θ0)[1]))\n",
    "\n",
    "uZ = findall(i -> \n",
    "        ((zero_Δm[i] >= 400E-9) && (zero_Δm[i] <= 600E-9)),\n",
    "        1:length(zero_Δm))\n",
    "resM1 = Int(size(uZ)[1] / size(zero_θ0)[1])\n",
    "zeroM = reshape(zero_Δm[uZ],resM1,size(zero_θ0)[2])\n",
    "zeroT = reshape(zero_θ0[uZ],resM1,size(zero_θ0)[2])\n",
    "zeroP = reshape(zero_ρ[uZ],resM1,size(zero_θ0)[2])\n",
    "\n",
    "PyPlot.figure()\n",
    "PyPlot.grid(true)\n",
    "PyPlot.pcolormesh(zeroM.*1E9,zeroT,zeroP)\n",
    "\n",
    "PyPlot.title(latexstring(L\"Fractional change in $\\dot{N}_n$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.colorbar()\n",
    "#PyPlot.xlim([50,300])\n",
    "#PyPlot.colorbar(true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6320bd8-7670-4b96-8e0b-20d7a2e9c375",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f47950c0-1af9-490f-8ab8-b01d86aa0459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fileTmp =  includePath*raw\"/results/NISTHighTheta_FIX.jld\"\n",
    "\n",
    "tmp_Δm,tmp_θ0,tmp_pMid,tmp_pEnd = load_sim_result(fileTmp)\n",
    "PyPlot.figure()\n",
    "PyPlot.grid(true)\n",
    "PyPlot.pcolormesh(tmp_Δm .* 1E9,tmp_θ0,(1. .- tmp_pMid ./ tmp_pEnd),vmin=0.008,vmax=0.012,cmap=\"plasma\")\n",
    "PyPlot.title(latexstring(L\"Fractional Shift in $\\tau_{meas}$\"))\n",
    "PyPlot.xlabel(latexstring(L\"$\\Delta m$\")*\" (neV)\")\n",
    "PyPlot.ylabel(latexstring(L\"$\\theta_0$\"))\n",
    "PyPlot.colorbar()\n",
    "PyPlot.yscale(\"log\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7a988083-a038-4e3a-9a5e-002e890c4f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0e-7\n",
      "6.0e-7\n"
     ]
    }
   ],
   "source": [
    "println(minimum(zero2_Δm))\n",
    "println(maximum(zero2_Δm))\n",
    "\n",
    "zVMin2 = findall(i->(i == minimum(zero2_Δm)),zero2_Δm)\n",
    "zVMax2 = findall(i->(i == maximum(zero2_Δm)),zero2_Δm)\n",
    "\n",
    "PyPlot.plot(zero2_θ0[zVMin2],(zero2_ρ[zVMin2]).^(0.25),\"r.\",label=(\"Δm = 400 neV\"))\n",
    "PyPlot.plot(zero2_θ0[zVMax2],(zero2_ρ[zVMax2]).^(0.25),\"bx\",label=(\"Δm = 600 neV\"))\n",
    "PyPlot.xlabel(\"θ0\")\n",
    "PyPlot.ylabel(\"ρ11^(0.25)\")\n",
    "#PyPlot.xscale(\"log\")\n",
    "#PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([0,0.011])\n",
    "PyPlot.ylim([0,0.03])\n",
    "PyPlot.title(\"Probability in 0T After B4C/Air\")\n",
    "PyPlot.legend()\n",
    "PyPlot.grid(true)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f8adfb45-dee2-49f9-af11-fab5af983198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGvCAYAAACaZ5V7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAABt3klEQVR4nO3de1zTZf8/8NcYG4hHFOUQiuAR0+4UPGARHiG1stI7M1NTNAnLlJ8VaCAnpcz8eptnG5pl5l3aSSlFRbJEU8O05DYszFDQMBVNZWO7fn+sTeYGDtjYwdfz8dhj7vpcn8/n2uVgb66jRAghQERERGSnXGxdACIiIqKaMFghIiIiu8ZghYiIiOwagxUiIiKyawxWiIiIyK4xWCEiIiK7xmCFiIiI7BqDFSIiIrJrDFaIiIjIrjFYIbu2fv16SCQS/cPV1RX+/v6YNGkSzp49a9F7SSQSvPjiixa73unTpyGRSLBo0aI75tW9z9OnT+vTnnvuObRv394gX/v27fHcc8/pX587dw7Jyck4evSoZQpdQ3ksYdeuXQgLC4OHhwe8vLzw3HPP4cKFC/rj7du3N/j/ru6xfv16s+7Xq1evGv8PTp8+jREjRqBly5aQSCSYOXMm9u/fj+TkZFy+fNkC79g8t3/OJRIJWrdujQEDBmDbtm01nnv+/Hm0atUKEokEn3zyick8x44dw6RJkxAYGAh3d3c0adIEvXr1wsKFC/HXX38Z5f/iiy/g6uqKlJQUSCQSfP3119Xef+3atZBIJNi6dWvt3jRRLbjaugBE5li3bh26du2KGzdu4JtvvkFGRgZyc3Nx/PhxNG7c2NbFq7cRI0YgLy8Pvr6+Neb79NNP0axZM/3rc+fOISUlBe3bt8f999/f4OWpjdzcXAwbNgwjRozA559/jgsXLuC1117D4MGDcfjwYbi5ueHTTz9FRUWF/px3330XCoUCX3/9NZo3b65P79Chwx3vd/ToUeTn5wMAFAoFZs+ebZRn1qxZOHjwIDIzM+Hj4wNfX198/PHHSElJwXPPPYcWLVrU/43Xgu5zLoRAaWkpli1bhkcffRRffPEFHn30UZPnTJ8+He7u7tVec+3atYiNjUWXLl3wyiuvoFu3blCpVDh8+DBWrVqFvLw8fPrppwbnbNmyBQ899BBefPFFZGRkIDMzEw8//HC1ZW7dunW15SOyCEFkx9atWycAiEOHDhmkJyYmCgDigw8+qPbcv//+u1b3AiCmT59ep3KaUlRUJACIt956q07nT5w4UQQEBNSY59ChQwKAWLduXZ3u0ZB69+4tunXrJlQqlT7tu+++EwDEihUrTJ4zb948AUD8+eeftb7f9OnTBQAxYsQIAUB89913Rnk6duwohg0bZpD21ltvCQCiqKio1vesSU2fx+o+59evXxdubm5i7NixJs/75JNPRJMmTcR7770nAIiPP/7Y4Pj+/fuFVCoVDz/8sLh586bR+RUVFeLzzz83SFMqlaJFixZi2bJlQgghnnrqKSGXy0VZWZnR+QUFBQKA+H//7/9V+96ILIHdQOSQ+vXrBwD4/fffAWi7TJo0aYLjx48jMjISTZs2xeDBgwEAf/31F2JjY3HPPfdALpcjKCgIc+fONfgLvqrVq1ejc+fOcHNzQ7du3fDRRx8ZHP/zzz8RGxuLbt26oUmTJmjTpg0GDRqEffv2mbyeRqPB/Pnz0a5dO7i7uyM0NBS7d+82yGNut0vVbqC9e/eid+/eAIBJkybpuw+Sk5Px/vvvQyKRIC8vz+gaqampkMlkOHfuXLX3MVWeAQMGoHv37jh06BDCw8Ph4eGBoKAgvPHGG9BoNDWW++zZszh06BDGjx8PV9dbDbr9+/dH586djf6yr6+bN2/iww8/REhICP7v//4PAJCZmak/vnfvXkgkEpw6dQpfffWVvu6ee+45vPLKKwCAwMBAffrevXv1527evBlhYWFo3LgxmjRpgqioKH0Ljk5Nn8facHd3h1wuh0wmMzr2119/Yfr06frPlikLFiyARCLBmjVr4ObmZnRcLpfjscceM0jbvXs3rly5gieeeAIAEB0dDaVSiQ8//NDo/HXr1gEAJk+eDABISUlB37590bJlSzRr1gy9evWCQqGAMLFf7ocffoiwsDA0adIETZo0wf333w+FQqE/fnuXp86AAQMwYMAA/evqfnZ0/8dV/+90n+G8vDz0798fjRo1Qvv27fXvY/v27ejVqxc8PDzQo0ePGru/qGExWCGHdOrUKQBA69at9WlKpRKPPfYYBg0ahM8//xwpKSm4efMmBg4ciA0bNiAuLg7bt2/Hs88+i4ULF+LJJ580uu4XX3yBpUuXIjU1FZ988gkCAgIwduxYg7EAuj7+efPmYfv27Vi3bh2CgoIwYMAAg1+MOsuWLcPXX3+NJUuW4IMPPoCLiwuGDRtmMpCojV69eul/yb7++uvIy8tDXl4epkyZgjFjxsDHxwfLly83OKeyshKrV6/GE088AT8/v1rfs7S0FOPGjcOzzz6LL774AsOGDUNCQgI++OCDGs/76aefAAD33Xef0bH77rtPf9xStm7dikuXLmHy5Mno1KkTHnzwQWzevBnXrl0DoK27vLw8+Pj44IEHHtDXXUpKCl566SX9NXTpvXr1AqD98h87diy6deuG//73v3j//fdx9epVhIeH48SJEwZlMPV5vBO1Wo3KykqoVCoUFxdj5syZ+Pvvv/HMM88Y5Z0xYwYCAwOrHWelVquxZ88ehISEoG3btmbX3ZYtWxAWFqb/fAwZMgQBAQEGwZ7u+u+//z769euHbt26AdCOAZo2bRr++9//YuvWrXjyySfx0ksvIS0tzeDcpKQkjBs3Dn5+fli/fj0+/fRTTJw4Uf/HhzWVlpZi0qRJmDJlCj7//HP06NEDkydPRmpqKhISEvDqq69iy5YtaNKkCR5//PEag3pqQLZu2iGqia55/MCBA0KlUomrV6+Kbdu2idatW4umTZuK0tJSIYS2ywSAyMzMNDh/1apVAoD473//a5D+5ptvCgBi586d+jQAolGjRvprCiFEZWWl6Nq1q+jYsWO1ZaysrBQqlUoMHjxYPPHEE/p0XTeQn5+fuHHjhj69vLxctGzZUgwZMsTofVbtejDVDRQQECAmTpyof11TN9C8efOEXC4X58+f16dt3rxZABC5ubnVvp/qyhMRESEAiIMHDxrk7datm4iKiqrxehs3bhQARF5entGx559/XsjlcpPn1bUbaNCgQcLd3V1cunTJ4P0oFAqDfAEBAWLEiBEGadV1A505c0a4urqKl156ySD96tWrwsfHRzz11FP6tOo+j9XRle/2h5ubm8kusm3btgmZTCaOHz8uhBAiJyfHqBuotLRUABBPP/20WWUQQvtZ9vLyEm+//bZBuu7/4YcfftCnffnllwKAWLt2rclrqdVqoVKpRGpqqmjVqpXQaDRCCCF+++03IZVKxbhx42osy+2fdZ2IiAgRERGhf23qsyrErTrJyckxOBeAOHz4sD7t4sWLQiqVikaNGomzZ8/q048ePSoAiKVLl9ZYTmoYbFkhh9CvXz/IZDI0bdoUjzzyCHx8fPDVV1/B29vbIN+oUaMMXu/ZsweNGzfG6NGjDdJ1zcu3d8cMHjzY4JpSqRRjxozBqVOnUFxcrE9ftWoVevXqBXd3d7i6ukImk2H37t0oKCgwKvuTTz5pMACyadOmePTRR/HNN99ArVbXriJq4YUXXgCgHWCps2zZMvTo0QMPPfRQna7p4+ODPn36GKTdd999Zv9FLJFIapVeF0VFRcjJycGTTz6pHyD773//G02bNjVqHaiNHTt2oLKyEhMmTEBlZaX+4e7ujoiICJOtard/Hu9kw4YNOHToEA4dOoSvvvoKEydOxPTp07Fs2TJ9nitXrmDatGl47bXX0L179zq/H1Nyc3NRVlZm1Oo4adIkuLi4GNTfunXr0LhxY4wZM0aftmfPHgwZMgTNmzeHVCqFTCZDUlISLl68qJ/1lZ2dDbVajenTp1u07Oby9fVFSEiI/nXLli3Rpk0b3H///QatjcHBwQDQIK09dGcMVsgh6H6J5+fn49y5czh27BgeeOABgzweHh4GM2UA4OLFi/Dx8TH6MmzTpg1cXV1x8eJFg3QfHx+je+vSdHkXL16MF154AX379sWWLVtw4MABHDp0CA8//DBu3LhR7fm3pymVSn23hDV4e3tjzJgxWL16NdRqNY4dO4Z9+/bVa3p2q1atjNLc3NxMvm9T591e34C2W61ly5Z1LtPtMjMzIYTA6NGjcfnyZVy+fBkqlQqPPfYYvvvuO/zvf/+r03XPnz8PAOjduzdkMpnBY/PmzSgrKzPIb+rzeCfBwcEIDQ1FaGgoHn74YaxevRqRkZF49dVX9VOp586dC5lMhhdffFH//nSfo+vXr+Py5csQQsDLywseHh4oKioy+/6ffPIJQkJCjKbMBwQEYPDgwfjwww9RUVGBsrIybNu2TR8EAsD333+PyMhIANoA+bvvvsOhQ4cwd+5cANB/Rv78808AgL+/f63qxlJMfdbkcrlRulwuB6Ad/0S2x6nL5BB0v8RrYuqv81atWuHgwYMQQhgcv3DhAiorK+Hl5WWQv7S01OgaujTdF+4HH3yAAQMGYOXKlQb5rl69arJc1V1TLpejSZMmNb6n+nr55Zfx/vvv4/PPP8fXX3+NFi1aYNy4cVa9pym6FoDjx49j+PDhBseOHz9usRYCjUajX4PF1JgkQBvMLFy4sNbX1n1WdGOZ7sRSrUX33XcfduzYgV9++QV9+vTBTz/9hNOnT5sMgidOnAgAuHTpElq0aIHBgwfjq6++QnFx8R2DA41Gg08//RQzZswweTw6OhrZ2dn4/PPPce7cOSiVSkRHR+uPf/TRR5DJZNi2bZtBS+Jnn31mcB3dOLPi4uIax9K4u7ubHARfVlZm8HOru9fteW8PHsmxsWWFnNrgwYNx7do1o1+YGzZs0B+vavfu3fq/oAHtIMLNmzejQ4cO+l/2EonEaGbFsWPHqh0wu3XrVoO/zq5evYovv/wS4eHhkEqldX5vAPTlqK5lIyQkBP3798ebb76JjRs34rnnnrPJujT33HMP+vTpgw8++MCg6+vAgQM4efJktYFFbe3YsQPFxcWYPn06cnJyjB733nsvNmzYgMrKymqvUV2dRkVFwdXVFb/++qu+9eP2hzXoFvzTfckvWbLE6H3pZjwlJycjJydHHwQnJCRACIGpU6dCqVQaXVulUuHLL78EAOzfvx+lpaXVdl09/vjjaNWqFTIzM7Fu3Tp07twZDz74oP64btHGqp/pGzdu4P333ze4TmRkJKRSqVGwf7v27dvj2LFjBmm//PILTp48aZQPgFHeL774osbrk2Nhywo5tQkTJmD58uWYOHEiTp8+jR49euDbb7/FggULMHz4cAwZMsQgv5eXFwYNGoTExEQ0btwYK1aswP/+9z+D6cuPPPII0tLSMG/ePERERODkyZNITU1FYGCgyS9BqVSKoUOHIi4uDhqNBm+++SbKy8vNmh1yJx06dECjRo2wceNGBAcHo0mTJvDz8zPoe3/55ZcxZswYSCQSxMbG1vuedfXmm29i6NCh+Pe//43Y2FhcuHAB8fHx6N69OyZNmmSReygUCri6umLOnDkmZztNmzYNM2bMwPbt2zFy5EiT1+jRowcA4D//+Q8mTpwImUyGLl26oH379khNTcXcuXPx22+/4eGHH4anpyfOnz+P77//Ho0bN673/+lPP/2k/wxdvHgRW7duRXZ2Np544gkEBgYCQI2L/917770G03rDwsKwcuVKxMbGIiQkBC+88ALuvfdeqFQq5OfnY82aNejevTseffRRfPLJJ+jevTs6d+5s8tpubm4YN24c3nnnHQgh8MYbbxgcHzFiBBYvXoxnnnkGzz//PC5evIhFixYZBfbt27fHnDlzkJaWhhs3bmDs2LFo3rw5Tpw4gbKyMn0djh8/Hs8++yxiY2MxatQo/P7771i4cKHBDEBA2y3XpUsXzJ49G5WVlfD09MSnn36Kb7/91qw6Jwdh0+G9RHdQ3WJZt5s4caJo3LixyWMXL14UMTExwtfXV7i6uoqAgACRkJBgtEgW/lkUbsWKFaJDhw5CJpOJrl27io0bNxrkq6ioELNnzxb33HOPcHd3F7169RKfffaZ0ewd3WygN998U6SkpAh/f38hl8tFz549xY4dO0y+z9rOBhJCiE2bNomuXbsKmUwmAIh58+YZldfNzU08/PDD1VfgbaqbDXTvvfca5TVn8TqdnTt3in79+gl3d3fRsmVLMWHCBIPZSrerzWygP//8U8jlcvH4449Xm+fSpUuiUaNG4tFHHxVCmJ4NJIQQCQkJws/PT7i4uBjNKPnss8/EwIEDRbNmzYSbm5sICAgQo0ePFrt27dLnqenzaIqp2UDNmzcX999/v1i8eLHJBd2qMjUbqKqjR4+KiRMninbt2gm5XC4aN24sevbsKZKSksSFCxeEEEK0bdvW6LNzux9//FEAEFKpVJw7d87oeGZmpujSpYtwc3MTQUFBIiMjQygUCpOzdTZs2CB69+4t3N3dRZMmTUTPnj0NZrVpNBqxcOFCERQUJNzd3UVoaKjYs2eP0WwgIYT45ZdfRGRkpGjWrJlo3bq1eOmll8T27dtNzgYy9Rmu7nOg+51AticRwsRqPUTkNL788ks89thj2L59u9F4ESJAOzi2b9++OHbsmL5licieMFghclInTpzA77//jpdffhmNGzfGDz/8YNEpwkREDYUDbImcVGxsLB577DF4enpi06ZNDFSIyGGxZYWIiIjsml20rKxYsQKBgYFwd3dHSEhItRvC6eTm5iIkJATu7u4ICgrCqlWrDI5v3boVoaGhaNGiBRo3boz777/faPpccnKyfpMy3cPUugVERERkWzYPVjZv3oyZM2di7ty5yM/PR3h4OIYNG4YzZ86YzF9UVIThw4cjPDwc+fn5mDNnDmbMmIEtW7bo87Rs2RJz585FXl4ejh07hkmTJmHSpEnYsWOHwbXuvfdelJSU6B/Hjx+36nslIiKi2rN5N1Dfvn3Rq1cvgwWCgoOD8fjjjyMjI8Mo/2uvvYYvvvjCYA+WmJgY/PjjjzXuYturVy+MGDFCv/tncnIyPvvsM/2CS0RERGSfbLoonFKpxJEjRxAfH2+QHhkZif3795s8Jy8vT7//hE5UVBQUCgVUKhVkMpnBMSEE9uzZg5MnT+LNN980OFZYWAg/Pz+4ubmhb9++WLBgAYKCgqotb0VFhcGSzhqNBn/99RdatWrFwYtERES1IITA1atX4efnBxeXmjt6bBqslJWVQa1WG+2c6+3tbXI/FUC7p4qp/JWVlSgrK4Ovry8A7c6k99xzDyoqKiCVSrFixQoMHTpUf07fvn2xYcMGdO7cGefPn0d6ejr69++Pn3/+2eRmbQCQkZFhkVVHiYiISOuPP/64495VdrHc/u2tEuK2TefMyX97etOmTXH06FFcu3YNu3fvRlxcHIKCgvRLUQ8bNkyft0ePHggLC0OHDh3w3nvvIS4uzuR9ExISDI5duXIF7dq1wy+//GLRXWPvZiqVCjk5ORg4cKBRKxnVHuvT8linlsX6tDxHqdOrV68iMDBQv3N3TWwarHh5eUEqlRq1oly4cMGo9UTHx8fHZH5XV1eDFhEXFxd07NgRgHYvjYKCAmRkZBjsm1FV48aN0aNHDxQWFlZbXjc3N6N9LgDtgN7qWmOodlQqFTw8PNCqVSu7/iFzFKxPy2OdWhbr0/IcpU51ZTNnGIVNZwPJ5XKEhIQgOzvbID07Oxv9+/c3eU5YWJhR/p07dyI0NLTG/xQhhMntxnUqKipQUFCg70YiIiIi+2DzbqC4uDiMHz8eoaGhCAsLw5o1a3DmzBnExMQA0Ha9nD17Fhs2bACgnfmzbNkyxMXFYerUqcjLy4NCocCmTZv018zIyEBoaCg6dOgApVKJrKwsbNiwwWDG0ezZs/Hoo4+iXbt2uHDhAtLT01FeXo6JEyc2bAUQERFRjWwerIwZMwYXL15EamoqSkpK0L17d2RlZSEgIAAAUFJSYrDmSmBgILKysjBr1iwsX74cfn5+WLp0KUaNGqXP8/fffyM2NhbFxcVo1KgRunbtig8++ABjxozR5ykuLsbYsWNRVlaG1q1bo1+/fjhw4ID+vkRERGQfbB6sANo9TGJjY00eW79+vVFaREQEfvjhh2qvl56ejvT09Brv+dFHH9WqjEREdwO1Wg2VStVg91OpVHB1dcXNmzehVqsb7L7OzF7qVCaTQSqVWuRadhGsEBGRbQkhUFpaisuXLzf4fX18fPDHH39wvSoLsac6bdGiBXx8fOpdDgYrRESkD1TatGkDDw+PBvuS02g0uHbtGpo0aXLHhcHIPPZQp0IIXL9+HRcuXACAek9eYbBCRHSXU6vV+kCloZdh0Gg0UCqVcHd3Z7BiIfZSp40aNQKgXV6kTZs29eoS4ieDiOgupxuj4uHhYeOSkLPRfabqOw6KwQoREQEwb3Euotqw1GeKwQoRERHZNQYrREREZNcYrBARkUPbv38/JBIJHn74YVsXxSwXL16Ev78/JBKJ0VTx48ePIyIiAo0aNcI999yD1NRU/Wa9Orm5uQgJCYG7uzuCgoKwatUqq5ZXqVTCy8ur2vXLMjIy4OXlBaVSabUyMFghIiKHlpmZibFjxyInJ8dgxXN7FR0djfvuu88ovby8HEOHDoWfnx8OHTqEd955B4sWLcLixYv1eYqKijB8+HCEh4cjPz8fc+bMwYwZM7BlyxarlVcul+PZZ5/F+vXrjQInAFi3bh3Gjx8PuVxutTIwWCEiIsspLgZycrTPDeDvv//G5s2bMXPmTAwaNMho1fO9e/dCIpFgx44d6NmzJxo1aoRBgwbhwoUL+OqrrxAcHIxmzZph7NixuH79utXLu3LlSly+fBmzZ882OrZx40bcvHkT69evR/fu3fHkk09izpw5WLx4sT5IWLVqFdq1a4clS5YgODgYU6ZMweTJk7Fo0aJq76mrg927dyM0NBQeHh7o378/Tp48aZDvyy+/NGixSUlJQWVlJQBtgPXrr7/im2++MThn3759KCwsRHR0dH2rpkYMVoiIyDIUCiAgABg0SPusUFj9lps3b4aPjw/69OmDcePGYd26dSb/+k9OTsayZcuwf/9+/PHHH3jqqaewZMkSfPjhh9i+fTuys7PxzjvvVHufM2fOoEmTJjU+dBvwVufEiRNITU3Fhg0bTK5/kpeXh4iICLi5uenToqKicO7cOZw+fVqfJzIy0uC8qKgoHD58+I7Tg+fOnYu3334bhw8fhqurKyZPnqw/tmPHDjz77LOYMWMGTpw4gdWrV2P9+vWYP38+AKBHjx7o3bs31q1bZ3DNzMxM9OnTB927d6/x3vXFYIWIiOqvuBh4/nlAo9G+1miAadOs3sKiUCgwbtw4AMDjjz+OCxcuYPfu3Ub50tPT8cADD6Bnz56Ijo5Gbm4uVq5ciZ49eyI8PByjR49GTk5Otffx8/PD0aNHa3ykpqZWe35FRQXGjh2Lt956C+3atTOZp7S0FN7e3gZputelpaU15qmsrERZWVm19weA+fPnIyIiAt26dUN8fDz279+Pmzdv6o/Fx8dj4sSJCAoKwtChQ5GWlobVq1frz588eTI++eQTXLt2DQBw7do1fPzxx1ZvVQEYrBARkSUUFt4KVHTUauDUKavd8uTJk9i/fz+eeeYZAECTJk0wcuRIZGZmGuWtOkbE29sbHh4eCAoKMkjTLQ1viqurKzp27Fjjo02bNtWen5CQgODgYDz77LM1vqfb1yXRtRJVTTcnjylV60C3/L3uPR85cgSpqakGLUVTp05FSUmJvnts7Nix0Gg02Lx5MwBtq5YQAk8//XSN97UEBitERFR/nToBt3dtSKVAx45Wu6VCoUDv3r3RuXNnfdq4ceOwdetWXLp0ySCvTCbT/1sikRi81qVpbg+2qqhvN9CePXvw8ccfw9XVFa6urhg8eDAAwMvLC/PmzQMA+Pj46FtQdHTBhK41pbo8rq6ud9wq4fY6AKB/zxqNBikpKQYtRcePH0dhYSHc3d0BAM2bN8fo0aP1XUHr1q3D6NGj0axZsxrvawncG4iIiOrP3x9Ys0bb9aNWawOV1au16VZQWVmJDRs2ID4+3iA9KioKTZs2xcaNG/Hiiy9a7H66bqCa1PSlvWXLFty4cUP/+tChQ5g8eTL27duHDh06AADCwsIwZ84cKJVK/cyanTt3ws/PD+3bt9fn+fLLLw2uvXPnToSGhhoFYLXRq1cvnDx5Eh3vEFxGR0djwIAB2LZtG7777jssWLCgzvesDQYrRERkGdHRQFSUtuunY0erBSoAsG3bNpw/fx7du3fHTz/9ZHAsPDwcCoXCosGKrhuornQBiY5ufElwcDBatGgBAHjmmWeQkpKC5557DnPmzEFhYSEWLFiApKQkfUtITEwMli1bhri4OEydOhV5eXlQKBTYtGlTncsGAElJSXjkkUfQtm1b/Pvf/4aLiwuOHTuG48ePG6yvEhERgY4dO2LChAno2LEjHnrooXrd11wMVoiIyHL8/a0apOgo/plpNHTo0Grz/PDDD1YvhyU1b94c2dnZmD59OkJDQ+Hp6Ym4uDjExcXp8wQGBiIrKwuzZs3C8uXL4efnh6VLl2LUqFH1undUVBS2bduG1NRULFy4EDKZDF27dsWUKVOM8k6ePBlz5szBK6+8Uq971oZEmJrjRWYpLy9H8+bNUVZW1uDbqjsrlUqFrKwsDB8+vF5NmqTF+rQ8Z6zTmzdvoqioCIGBgfrxCQ1Fo9GgvLwczZo1Mzmdl2rPnuq0ps+W7jv0ypUrdxz3wk8GERER2TUGK0RERGTXGKwQERGRXWOwQkRERHaNwQoRERHZNQYrREREZNcYrBAREZFdY7BCREREdo3BChEREdk1BitERERk1xisEBGRQ9u/fz8kEgkefvhhWxfljtavX4/77rsP7u7u8PHxMdps8fjx44iIiECjRo1wzz33IDU1FbfvipObm4uQkBC4u7sjKCgIq1atsmqZlUolvLy8DDY0rCojIwNeXl5QKpVWKwODFSIiqpfkZCAtzfSxtDTtcWvKzMzE2LFjkZOTgzNnzlj3ZvWwePFizJ07F/Hx8fj555+xe/duREVF6Y+Xl5dj6NCh8PPzw6FDh/DOO+9g0aJFWLx4sT5PUVERhg8fjvDwcOTn52POnDmYMWMGtmzZYrVyy+VyPPvss1i/fr1R4AQA69atw/jx4yGXy61WBgiqsytXrggAoqyszNZFcRpKpVJ89tlnQqlU2rooToH1aXnOWKc3btwQJ06cEDdu3KjT+ampQgDaZ3PSq1Kr1eLSpUtCrVbX6d7Xrl0TTZo0EQcPHhQPP/ywSElJMTiek5MjAIivv/5a3H///cLd3V0MHDhQnD9/XmRlZYmuXbuKpk2biqefflr8/fffdSqDOf766y/RqFEjsWvXrmrzrFixQjRv3lzcvHlTn5aRkSH8/PyERqMRQgjx6quviq5duxqcN23aNNGvXz/969vrVFcHu3btEiEhIaJRo0YiLCxM/O9//zO4zhdffCF69eol3NzcRGBgoEhOThYqlUoIIcSxY8cEALF3716Dc7755hsBQBw/ftzke6rps6X7Dr1y5Uq1daLDlhUiIqqXxEQgNRVISrrVwpKWpn2dmqo9bi2bN2+Gj48P+vTpg3HjxmHdunUm//pPTk7GsmXLsH//fvzxxx946qmnsGTJEnz44YfYvn07srOz8c4771R7nzNnzqBJkyY1PmJiYqo9Pzs7GxqNBmfPnkVwcDD8/f3x1FNP4Y8//tDnycvLQ0REBNzc3PRpUVFROHfuHE6fPq3PExkZaXDtqKgoHD58GCqVqsa6mjt3Lt5++20cPnwYrq6umDx5sv7Yjh078Oyzz2LGjBk4ceIEVq9ejfXr12P+/PkAgB49eqB3795Yt26dwTUzMzPRp08fdO/evcZ719sdwxmqFltWLM8Z/2q1Jdan5Tljnda3ZUVH15Iil9+5RUWnvi0r/fv3F/PmzRNCCHH16lXh4eEhsrOz9certiroZGRkCADi119/1adNmzZNREVFVXsflUolCgsLa3ycP3++2vMzMjKETCYTXbp0EV9//bXIy8sTgwcPFl26dBEVFRVCCCGGDh0qpk6danDe2bNnBQCxf/9+IYQQnTp1EvPnzzfI89133wkA4ty5c0KImltWdLZv3y4A6P/Pw8PDxYIFCwyu+/777wtfX1/965UrV4rGjRuLq1evCiG09d24cWOxevXqat83W1aIiMiuJCYCcjmgVGqfrdmiAgAnT57E/v378cwzzwAAmjRpgpEjRyIzM9Mo73333af/t7e3Nzw8PBAUFGSQduHChWrv5erqio4dO9b4aNOmTbXnazQaqFQqLF26FFFRUejXrx82bdqEwsJC5OTk6PNJJBKD88Q/rURV083JY0rVOvD19QUA/Xs+cuQIUlNTDVqKpk6dipKSEly/fh0AMHbsWGg0GmzevBmAtlVLCIGnn366xvtaAoMVIiKyiLS0W4GKUln9oFtLUSgU6N27Nzp37qxPGzduHLZu3YpLly4Z5JXJZPp/SyQSg9e6NI1GU+296tsNpAsOunXrpk9r3bo1vLy89IOCfXx8UFpaanCeLpjw9vauMY+rqytatWpV7f0B4zoAoH/PGo0GKSkpOHr0qP5x/PhxFBYWwt3dHQDQvHlzjB49Wt8VtG7dOowePRrNmjWr8b6W4Gr1OxARkdO7fYyK7jVgnRaWyspKbNiwAfHx8QbpUVFRaNq0KTZu3Gg0Lbg+/Pz8cPTo0Rrz1PSl/cADDwDQtgb5+/sDAP766y+UlZUhICAAABAWFoY5c+ZAqVTqZ9bs3LkTfn5+aN++vT7Pl19+aXDtnTt3IjQ01CgAq41evXrh5MmT6NixY435oqOjMWDAAGzbtg3fffcdFixYUOd71gaDFSJqUMnJgFRq+gssLQ1Qq60/1ZUsy9RgWt2ztQKWbdu24fz58+jevTt++ukng2Ph4eFQKBQWDVZ03UB11blzZ4wcORIvv/wy1qxZg2bNmiEhIQFdu3bFwIEDAQDPPPMMUlJS8Nxzz2HOnDkoLCzEggULkJSUpG8JiYmJwbJlyxAXF4epU6ciLy8PCoUCmzZtqtf7S0pKwiOPPIK2bdvi3//+N1xcXHDs2DEcP37cYH2ViIgIdOzYERMmTEDHjh3x0EMP1eu+5mKwQkQNSio1/QVW9QuPHItabXrWj+61Wm35eyoUCgDA0KFDq83zww8/WP7G9bBhwwbMmjULI0aMgIuLCyIiIvD111/rW0SaN2+O7OxsTJ8+HaGhofD09ERcXBzi4uL01wgMDERWVhZmzZqF5cuXw8/PD0uXLsWoUaPqVbaoqChs27YNqampWLhwIWQyGbp27YopU6YY5Z08eTLmzJmDV155pV73rJU7DsFtAMuXLxft27cXbm5uolevXuKbb76pMf/evXsN5oKvXLnS4PiWLVtESEiIaN68ufDw8BD/+te/xIYNG+p939txNpDlOeNMC1uy1/q8ff0Nc9bjsBf2Wqf1YanZQHVR39lAZMye6tRpZgNt3rwZM2fOxNy5c5Gfn4/w8HAMGzas2lUIzVm9r2XLlpg7dy7y8vJw7NgxTJo0CZMmTcKOHTvqfF8iZ9bQK5BWXZfDza1h1uMgIgdmjUiqNvr06SNiYmIM0rp27Sri4+NN5jdn9T5TevbsKV5//fU639cUtqxYnjP+1WpL5tZnfVYgrQ/dehxyuXWubw3O+Blly4pzsac6dYqWFaVSiSNHjhitxhcZGYn9+/ebPKe2q/cJIbB7926cPHlSPxCoLvclcma2WIG0oae5EpHjsukA27KyMqjVav38cR1vb2+jeeQ6paWlJvNXVlairKxMP5f9ypUruOeee1BRUQGpVIoVK1boB2LV5b4AUFFRgYqKCv3r8vJyAIBKpbrjMsdkHl09sj4tozb1GR8PqNUuSEqSIj1dQKmUYN48NeLjNbD0f8f8+S5ISZFi3jw15s7VYP587X3Vau1re+aMn1GVSgUhBDQaTY1rjViD+GdBM939qf7sqU41Gg2EEFCpVJBKpQbHavMzZBezgUytxlfTSnzmrN7XtGlTHD16FNeuXcPu3bsRFxeHoKAgDBgwoM73zcjIQEpKilF6Tk4OPDw8qj2Pai87O9vWRXAq5tZnz56Aq+sjUCqlcHVVo2fPbcjKsmxZNm/ujE2bgjF2bAF69vwFWVna+44d2xkpKcH45ZdfMGbML5a9qRU402fU1dUVPj4+uHr1KpRKpU3KcPXqVZvc15nZQ51WVFTgxo0b+Oabb1BZWWlwTLcyrjlsGqx4eXlBKpWaXI3v9lYPHXNX73NxcdHPib///vtRUFCAjIwMDBgwoE73BYCEhASDKWTl5eVo27YtBg4ceMeVA8k8KpUK2dnZGDp0aL0WOCKt2tbn/PkuqKyUQi4XUCqlyM9/xOItHYcPu/zTotIRwK11K4YPBzp3VkOt7ozhw+u+noW1OeNnVK1W47fffoOLi0uDrEZalRACV69eRdOmTe+4XDyZx57q9OLFi2jUqBEGDx5s1LKi650wh02DFblcjpCQEGRnZ+OJJ57Qp2dnZ2PkyJEmz6nr6n1CCH0XTl3uCwBubm4Gu2HqyGQyp/mlZS9Yp5ZlTn2mpQEpKboxKpJ/xqxIIZVKLTpm5dbYFKnRsVuzjoyP2Rtn+ozKZDJ4enqirKwMLi4u8PDwaLAvOY1GA6VSiYqKCri42HyCqlOwhzoVQuD69esoKyuDp6enfsn+qmrz82PzbqC4uDiMHz8eoaGhCAsLw5o1a3DmzBn9HgsJCQk4e/YsNmzYAMC81fsyMjIQGhqKDh06QKlUIisrCxs2bMDKlSvNvi/R3cQWK5DaClfQNc3HxwcAatzMzxqEELhx4wYaNWpk81YAZ2FPddqiRQv9Z6s+bB6sjBkzBhcvXkRqaipKSkrQvXt3ZGVl6fdKKCkpMVj7xJzV+/7++2/ExsaiuLgYjRo1QteuXfHBBx9gzJgxZt+XyJ5Y+wvWFiuQ2gpX0DVNIpHA19cXbdq0adDBwyqVCt988w0eeughp2mpsjV7qVOZTGbU9VNnlphHfbfiOiuW54xrWFhCXddBYX2aVp8VdFmnlsX6tDxHqdParLNi85YVIrozU10y1l4HxZlVrc/0dO06L6xHIvvFYIXIQfAL1rISE2/Vo1zOeiSyZxx6TeRAEhNvrfjKL9j64Qq6RI6DwQqRA+EXrGVU7UKrqDDeaoCI7AuDFSJrKi4GcnK0z/XEL1jLqG6aNuuTyH5xzAqRtSgUwPPPAxoN4OICrFkDREfX6VIGX7CTioGcQiRO6gTA3+nWQbG2u2maNpGzYLBCZA3FxbcCFUD7PG0aEBUF+PvX+nL6L1g/BRBwKwBKXLMGSI3mF2wt1LQeDQM+IvvEYIXIGgoLbwUqOmo1cOpUnYKV5GRoA6AA4wAo8XTdAiAiIkfBMStE1tCpk7brpyqpFOhYjw36agqAiIicGIMVImvw99eOUdEtNS2VAqtX168FxBoBEBGRA2CwQmQt0dHA6dPa2UCnT9d5cK2eNQIgIiIHwDErRNbk72/ZYCI6WjtI99QpbYsKAxUiugswWCFyNJYOgIiI7By7gYhMseBibkREVD8MVohup1AAAQHAoEHaZ4XC1iUiIrqrMVghqqq6xdxq0cKSnFz9ku1paTUvSkZERMYYrBBVZYG1TKRS03vM6JbM103mISIi83CALVFVurVMqgYstVzLRLdke9U9e0xtnme3iou1QVunThzIS0R2gS0rRFVZaC2Tqrv4urk5UKDC8TpEZIcYrBDdzkKLuSUmAnI5oFRqn+0+ULHAeB0iImtgsEJkir8/MGBAvbpB0tJuBSpKZfWDbu0G9x6yCQ7IJrozBitEVlB1jEpFxa0uIbsOWLj3kE1wQDbRnXGALZGFmRpMa2rQrd3RjdeZNk3bosK9hxqEww/IJmoADFaILEytNv0lo3utVjd8mczGvYdsomrAkp6u7TZkoEJ0C4MVIguraYyBQ3z5cO8hm0hMvBWoOMSAbKIGxDErRER2wOEGZBM1IAYrREQ25pADsokaELuBiIhsyGEHZBM1IAYrREQ25NADsokaCIMVck7c34YchMMPyCZqAByzQs6H+9sQETkVBivkXLi/DRGR02GwQs6F+9sQETkdBivkXLi/DRGR02GwQs5Ft7+Nbve3Wuxvw91viYjsE4MVcj7R0cDp00BOjvY5Otqs07j7LRGRfeLUZXJOddjfhrvfEhHZJwYrRFVw99sGxvVwiMgM7AYiuk1i4q3N5Lj7rRVxPRwiMpNdBCsrVqxAYGAg3N3dERISgn379tWYPzc3FyEhIXB3d0dQUBBWrVplcHzt2rUIDw+Hp6cnPD09MWTIEHz//fcGeZKTkyGRSAwePj4+Fn9v5Hi4+20D4Ho4RFQLNg9WNm/ejJkzZ2Lu3LnIz89HeHg4hg0bhjNnzpjMX1RUhOHDhyM8PBz5+fmYM2cOZsyYgS1btujz7N27F2PHjkVOTg7y8vLQrl07REZG4uzZswbXuvfee1FSUqJ/HD9+3Krvlewfd79tIFwPh4hqweZjVhYvXozo6GhMmTIFALBkyRLs2LEDK1euREZGhlH+VatWoV27dliyZAkAIDg4GIcPH8aiRYswatQoAMDGjRsNzlm7di0++eQT7N69GxMmTNCnu7q6sjWF9Lj7bQPSrYdTNWDhejhEVA2bBitKpRJHjhxBfHy8QXpkZCT2799v8py8vDxERkYapEVFRUGhUEClUkEmkxmdc/36dahUKrRs2dIgvbCwEH5+fnBzc0Pfvn2xYMECBAUFVVveiooKVFRU6F+Xl5cDAFQqFVQqVc1vlsyiq0db1KdS6YJ584D4eA2q3j4+HlCrXaBUAiqVpvoL2CFb1meNvL0hWbkS0thYSNRqCKkU6hUrILy9AXsr623stk4dFOvT8hylTmtTPpsGK2VlZVCr1fD29jZI9/b2RmlpqclzSktLTeavrKxEWVkZfH19jc6Jj4/HPffcgyFDhujT+vbtiw0bNqBz5844f/480tPT0b9/f/z8889o1aqVyXtnZGQgJSXFKD0nJwceHh53fL9kvuzs7Aa/Z2io9jkry/hYz57VH3MEtqjPO/L2hvvq1WhcUoK/fX1x08vLoSrYLuvUgbE+Lc/e6/T69etm57V5NxAASCQSg9dCCKO0O+U3lQ4ACxcuxKZNm7B37164u7vr04cNG6b/d48ePRAWFoYOHTrgvffeQ1xcnMn7JiQkGBwrLy9H27ZtMXDgwGoDHKodlUqF7OxsDB061GQrGdUO69PyWKeWxfq0PEepU13vhDlsGqx4eXlBKpUataJcuHDBqPVEx8fHx2R+V1dXo4Bh0aJFWLBgAXbt2oX77ruvxrI0btwYPXr0QGFhYbV53Nzc4ObmZpQuk8ns+gPhiFinlsX6tDzWqWWxPi3P3uu0NmWz6WwguVyOkJAQo6aq7Oxs9O/f3+Q5YWFhRvl37tyJ0NBQgzf+1ltvIS0tDV9//TVCde37NaioqEBBQYHJbiQiIiKyHZtPXY6Li8O7776LzMxMFBQUYNasWThz5gxiYmIAaLteqs7giYmJwe+//464uDgUFBQgMzMTCoUCs2fP1udZuHAhXn/9dWRmZqJ9+/YoLS1FaWkprl27ps8ze/Zs5ObmoqioCAcPHsTo0aNRXl6OiRMnNtybJyIiojuy+ZiVMWPG4OLFi0hNTUVJSQm6d++OrKwsBAQEAABKSkoM1lwJDAxEVlYWZs2aheXLl8PPzw9Lly7VT1sGtIvMKZVKjB492uBe8+bNQ/I/W+cWFxdj7NixKCsrQ+vWrdGvXz8cOHBAf18iIiKyDzYPVgAgNjYWsbGxJo+tX7/eKC0iIgI//PBDtdc7ffr0He/50UcfmVs8IiIisiGbdwMR3VFxMZCTw6XYiYjuUgxWyL5xszsiorsegxWyX9zsjoiIwGCF7Bk3uyMiIjBYIXum2+yuKm52R2QTycnV7z6elqY9TmQtDFbIfvn7A2vWaAMUQPu8erU2nYgalFSq3X389oBFt1u57seUyBrsYuoyUbWio4GoKG3XT8eONQYqycnaX5iJicbH0tK0PUj864+obnQ/V0lJt17rApXUVNM/d0SWwmCF7J+/v1mtKbq//ADDX5xVf6ESUd1VDVjS0wGlkoEKNQwGK+Q0+JcfkfUlJt4KVORy/lxRw2CwQk6Ff/kRWVda2q1ARanUvubPF1kbB9iS00lMvPWLlH/5EVlO1ZbKigrts6lBt0SWxpYVcjr8y4/I8kx1qZrqeiWyBgYr5FRu/4Wqew3wFylRfajVprtUda/V6oYvE909GKyQ0+BffkTWU9O0f/5ckbUxWCGnwb/8iIicE4MVchr8y4+IyDlxNhARERHZNQYrREREZNcYrBAREZFdY7BCDa+4GMjJ0T4TERHdAYMValgKBRAQAAwapH1WKGxdIrqbMXAmcggMVqjhFBcDzz8PaDTa1xoNMG0avyjINhg4EzkMBivUcAoLbwUqOmo1cOqUbcpDdy8GzkQOhcEKNZxOnQCX2z5yUinQsaNtykN3LwbORA6FwQo1HH9/YM0abYACaJ9Xr9amEzUkBs5EDoXBCjWs6Gjg9GntoMbTp7WviRoaA2cih8Ll9qnh+fvzS4FsLzoaiIrSdv107MjPJJEdY7BCRHcvBs5EDqFOwcrFixexd+9eHDx4EKWlpbhx4wZatWqFLl26IDw8HKGhoZYuJxEREd2lahWs7N27F//5z3+QlZUFlUqFdu3awcvLC+7u7jhx4gQ2btyIv//+G+3bt0d0dDReeuklNGvWzFplJyeWmuoCudz0bslpadqJGzXtskxERM7D7AG2kZGRGDlyJJo3b46tW7fir7/+wunTp3H48GF8++23+Pnnn1FeXo7jx48jJiYGW7duRVBQELKysqxZfnJSUimQlKQNTKpKS9Om68ZFEhGR8zO7ZSUiIgIff/wxmjdvXm0eiUSCe++9F/feey9effVVfPPNNygvL7dIQenuMneuBlKpFElJ2teJibcCldRU0y0uRETknMwOVubOnVvriz/00EO1PodIRxeQJCUB6emAUslAhYjobsR1VsiuJSYCcrk2UKluDAsRETm3ek1dVqvV+PTTT3HkyBEAQJ8+fTBy5Ei43L4yJFEdpaXdClSUSu1rBixERHcXs4OV/v37Q6FQIDg4GABw6dIlDBkyBPn5+WjSpAmEEPj777/Rp08f7Nq1C02aNLFaoenucPsYFd1rgAELEdHdxOwmkAMHDuDq1av616+99hp+/fVXbNu2DeXl5bh69So+//xzFBQUIJlzSqme5s93MRpMm5iofW1qlhARETmvOvfXfP7553j99dcxfPhwfdqjjz6K+Ph4bN261SKFo7uXWm16MK0uYFGrbVMuIiJqeHUOVi5evIi+ffsapfft2xdnz56t1bVWrFiBwMBAuLu7IyQkBPv27asxf25uLkJCQuDu7o6goCCsWrXK4PjatWsRHh4OT09PeHp6YsiQIfj+++/rfV9qOElJmmq7ehITuSAcEdHdpFbByt69e7F161Zs3boVrVq1wuXLl43yXL58GR4eHmZfc/PmzZg5cybmzp2L/Px8hIeHY9iwYThz5ozJ/EVFRRg+fDjCw8ORn5+POXPmYMaMGdiyZYtBOceOHYucnBzk5eWhXbt2iIyMNAiiantfIiIishFhJolEYvSYPn26Ub5XX31VhIaGmntZ0adPHxETE2OQ1rVrVxEfH28y/6uvviq6du1qkDZt2jTRr1+/au9RWVkpmjZtKt57770639eUK1euCACirKzM7HOoZkqlUnz22WdCqVTauihOgfVpeaxTy2J9Wp6j1KnuO/TKlSt3zGv2bKCioiKjNDc3N6M0jUaDF154waxrKpVKHDlyBPHx8QbpkZGR2L9/v8lz8vLyEBkZaZAWFRUFhUIBlUoFmUxmdM7169ehUqnQsmXLOt+XzFBcDBQWAp06cSdbIiKyGLODlYCAALPyvfXWW2bfvKysDGq1Gt7e3gbp3t7eKC0tNXlOaWmpyfyVlZUoKyuDr6+v0Tnx8fG45557MGTIkDrfFwAqKipQUVGhf63bSkClUkGlUtXwTp2fZN06SF94ARKNBsLFBeqVKyEmTar1dXT1eLfXp6WwPi2PdWpZrE/Lc5Q6rU356rUonKVIJBKD10IIo7Q75TeVDgALFy7Epk2bsHfvXri7u9frvhkZGUhJSTFKz8nJqdU4HWfjXlaGyJgYSHT/DxoNXF54AdlSKW56edXpmtnZ2ZYs4l2P9Wl5rFPLYn1anr3X6fXr183OW6tg5dy5c1AoFDh37hy6deuGCRMmGG1sWFBQgOnTp2PPnj13vJ6XlxekUqlRa8aFCxeMWj10fHx8TOZ3dXVFq1atDNIXLVqEBQsWYNeuXbjvvvvqdV8ASEhIQFxcnP51eXk52rZti4EDBxrd+24i2btXH6jouGg0GBwQABERUatrqVQqZGdnY+jQoSa79Kh2WJ+Wxzq1LNan5TlKndZmo2Ozg5Vff/0Vffr0wd9//w1/f3+8++67SE9Px7p16wzWWikvL0dubq5Z15TL5QgJCUF2djaeeOIJfXp2djZGjhxp8pywsDB8+eWXBmk7d+5EaGiowX/KW2+9hfT0dOzYsQOhoaH1vi+gHaNjapyOTCaz6w+E1QUHAy4ugEZzK00qhWvXrkAd6+Wur1MLY31aHuvUsliflmfvdVqbspk9dTkhIQGBgYEoLi7GqVOn8Ntvv6F///4YOXIkVq9eXaeCAkBcXBzeffddZGZmoqCgALNmzcKZM2cQExOjv++ECRP0+WNiYvD7778jLi4OBQUFyMzMhEKhwOzZs/V5Fi5ciNdffx2ZmZlo3749SktLUVpaimvXrpl9X6oFf39gzRpAKtW+lkqB1as5yJaI7EZycvUrX6elce0mu2fuFCM/Pz+xdetWo/QFCxYIFxcXkZiYKIQQ4sCBA8LFxcXcywohhFi+fLkICAgQcrlc9OrVS+Tm5uqPTZw4UURERBjk37t3r+jZs6eQy+Wiffv2YuXKlQbHAwICBACjx7x588y+rzk4dfk2f/whRE6O9rmOHGXKnaNgfVoe69SyGqo+U1OFALTP5qQ7Mkf5jFpl6vLVq1fh6elplJ6QkAA/Pz9MnToVpaWleO6552odMMXGxiI2NtbksfXr1xulRURE4Icffqj2eqdPn673fakO/P3ZmkJEdkm3InbVzVBv3yyV7JfZwUqHDh1w4MABDBgwwOjYxIkT4enpiaeffprrlBARkV2qGrCkpwNKJQMVR2H2mJWhQ4dCoVBAU3UQZRWPPfYYvvrqKxQXF1uscOQ82F9MRPYgMRGQy7WBilzOQMVRmB2sxMXFYcmSJQaDVG8XERGBgwcPIjMz0yKFI+chlWr/mrk9YNE1w+rG5hIRWVNa2q1ARams/o8osi9mdwP5+PhgxIgRd8zXpUsXdOnSpV6FIufD/mIisrXbf+foXgP8HWTv7GIFW7o7sL+YiGzF1B9Hpv6IIvtk8WClU6dO0Gg0+PXXXy19aXICiYm3AhX2FxNRQ1GrTf9xpHutVjd8mch8Fg9W2rVrV+0gXCJT/cUMWIjI2moaxM/fQfbP7AG25tq9ezdycnIsfVlyAlWbYSsqtM+mBt0SERFVxTEr1CDYX0xERHVV62ClvLwcu3fvRkFBAf766y9IJBJ4enoiODgYgwcPRrNmzaxRTnJw7C8mIqK6MjtYEUJg3rx5ePvtt3Hjxg14eHjA09MTQghcvnwZ169fR6NGjTB79mwkJydDIpFYs9zkYNhfTEREdWV2sJKWloa3334br7/+OsaNG4d27doZHD9z5gw+/PBDpKenw9XVFYn8BiIiIiILMDtYWbt2Ld56661qN/5r164d4uPj0axZM2RkZDBYISIiIoswezbQxYsXERwcfMd8Xbt2xcWLF+tVKLJDxcVATo72mYisgz9nRCaZHaz06tULS5cuhUqlqjZPZWUl3nnnHfTq1csihSM7oVAAAQHAoEHaZ4XC1iUicj78OSOqltndQIsXL8bQoUPRqVMnPPHEEwgODkaLFi0gkUhw6dIlFBQU4LPPPsOlS5eQnZ1tzTJTQyouBp5/HtAt9KfRANOmAVFRgL+/bctG5Cz4c0ZUI7ODlT59+uDo0aNYuHAhPv30U/znP/8xON6uXTsMHz4cr7zyCgIDAy1eULKRwsJbv0B11Grg1Cn+EiWyFP6cEdWoVuusBAYGYuXKlQCA69ev4/LlywAAT09PNGrUyOKFIzvQqRPg4mL4i1QqBTp2tF2ZiJwNf86IalTn5fY9PDzg5+cHPz8/BirOzN8fWLNG+4sT0D6vXs2/9ogsiT9nRDUyO1hZtmwZKioqanXxH3/8Ebt27ap1ocjOREcDp09rZymcPq19TUSWxZ8zomqZHaxs2LABAQEBiIuLQ15eXrWzgs6dOweFQoEhQ4bgwQcfxJUrVyxWWLIhf39gwAD+pUdkTfw5IzLJ7DEr33//PbZu3Yr//Oc/WLJkCeRyOTp37ozWrVvDzc0Nly9fRlFRES5cuIBWrVphwoQJ2LhxI7y9va1ZfiIiInJytRpg++STT+LJJ59EUVERdu3ahSNHjqCkpAQ3b95EQEAAIiMj8cADD2DAgAGQyWTWKjPZkeRkbfe6qQWL09K0Expq2heIiIjoTmq96zKgnRU0depUTJ061dLlIQcjlQJJSdp/Vw1Y0tK06amptikXERE5jzoFK0Q6ugClasBSNVDhFlFERFRfdQ5WSkpK4Ovra8mykIOqGrCkpwNKJQMVIiKynDqts3Ly5En079/f0mUhB5aYCMjl2kBFLmegQkREllPrYOX7779HeHg4HnvsMWuUhxxUWtqtQEWp1L4mIiKyhFoFK1999RUGDx6MJ554wmhvILp7VR2jUlGhfU5KYsBCRESWYfaYle3bt+PJJ5/EM888g9WrV1uzTORATA2mNTXoloiIqK7MDlYOHz4MmUyG9PR0a5aHHIxabXowre61Wt3wZSIiIudidrAyduxYrFu3DpGRkcjNzYWXl5c1y0UNrbhYu019p061Wuq7pgXf2KJCRESWYPaYlc6dOyMvLw+urq4YMmQILl26ZM1yUUNSKICAAGDQIO2zQmHrEhEREenVaoCtr68v9u3bB09PTwwdOtRaZaKGVFwMPP88oNFoX2s0wLRp2nQiIiI7UOupy82aNcOOHTvQoUMHa5SHGlph4a1ARUetBk6dsk15iIiIblOnReHkcjk++ugjS5eFbKFTJ8Dlto+BVAp07Gib8hAREd2mTsEKAEgkEkuWg2zF3x9Ys0YboADa59WrazXIloiIyJq4kSEB0dFAVJS266djRwYqRERkV+rcslKdb775BoMGDbL0Zcna/P2BAQMYqBARkd2xeLDy559/Ijc3t1bnrFixAoGBgXB3d0dISAj27dtXY/7c3FyEhITA3d0dQUFBWLVqlcHxn3/+GaNGjUL79u0hkUiwZMkSo2skJydDIpEYPHx8fGpVbiIiIrI+s7uBtm7dala+AwcO1KoAmzdvxsyZM7FixQo88MADWL16NYYNG4YTJ06gXbt2RvmLioowfPhwTJ06FR988AG+++47xMbGonXr1hg1ahQA4Pr16wgKCsK///1vzJo1q9p733vvvdi1a5f+tVQ3boOIiIjshtnByujRoyGRSCCEuGPe2gy+Xbx4MaKjozFlyhQAwJIlS7Bjxw6sXLkSGRkZRvlXrVqFdu3a6VtLgoODcfjwYSxatEgfrPTu3Ru9e/cGAMTHx1d7b1dXV7amEBER2TmzgxUvLy+MGDECyTWtrw4gKysLL774olnXVCqVOHLkiFFAERkZif3795s8Jy8vD5GRkQZpUVFRUCgUUKlUkMlkZt0bAAoLC+Hn5wc3Nzf07dsXCxYsQFBQULX5KyoqUFFRoX9dXl4OAFCpVFCpVGbf11GkprpAKgXmztUYHZs/3wVqNZCUZHysPnT16Iz1aQusT8tjnVoW69PyHKVOa1M+s4OVvn374uTJkwgICKgxX5s2bcy+eVlZGdRqNby9vQ3Svb29UVpaavKc0tJSk/krKytRVlYGX19fs+7dt29fbNiwAZ07d8b58+eRnp6O/v374+eff0arVq1MnpORkYGUlBSj9JycHHh4eJh1X0fy66+dsWlTMH755ReMGfOLPn3zZm362LEFyMr6pYYr1F12drZVrnu3Yn1aHuvUsliflmfvdXr9+nWz85odrAwbNgzvv//+HfO1b98eEyZMMLsAgHG3kRCixq4kU/lNpddk2LBh+n/36NEDYWFh6NChA9577z3ExcWZPCchIcHgWHl5Odq2bYuBAwdWG+A4suHDgc6d1UhJCUbnzp0xd64G8+e7YNMmKebNU2Pu3I4ALLt4nEqlQnZ2NoYOHVqrVjIyjfVpeaxTy2J9Wp6j1Kmud8IcZgcrsbGxiI2NvWO+kJAQrFu3zqxrenl5QSqVGrWiXLhwwaj1RMfHx8dkfldX13oFDI0bN0aPHj1QWFhYbR43Nze4ubkZpctkMrv+QNRHcrJ2nbikJCkyMqRQKoHUVCAxUQrAegOSnblObYH1aXmsU8tifVqevddpbcpm8anLtSGXyxESEmLUVJWdnY3+/fubPCcsLMwo/86dOxEaGlqv/5SKigoUFBSY3Y10N0lMBORyQKnUPicm2rpERER0N7FpsAIAcXFxePfdd5GZmYmCggLMmjULZ86cQUxMDABt10vVbqWYmBj8/vvviIuLQ0FBATIzM6FQKDB79mx9HqVSiaNHj+Lo0aNQKpU4e/Ysjh49ilNVNuebPXs2cnNzUVRUhIMHD2L06NEoLy/HxIkTG+7NO4i0tFuBilKpfU1ERPYjObn6381padrjjsziwcqWLVtqtV7JmDFjsGTJEqSmpuL+++/HN998g6ysLP1A3pKSEpw5c0afPzAwEFlZWdi7dy/uv/9+pKWlYenSpfppywBw7tw59OzZEz179kRJSQkWLVqEnj176qdHA0BxcTHGjh2LLl264Mknn4RcLseBAwfuOID4bpOWBiQlabt+Kiq0z0lJDFiIiOyJtrve+Hez7ne4oy8jZhd7A9U0Hmb9+vVGaREREfjhhx+qvV779u3vuB4Md42+s6qBiq7rR/eclGT4moiIbKfq72a12gU9e2qXmEhJMfwd7qjMDlZmzJhhVr5ff/21zoUh+6JWm/6Q616r1Q1fJiIiMu1WwCKFq+sjqKyUOkWgAtQiWFm2bBk8PT3RrFmzGvPVZt40NbDiYqCwEOjUyawNC2vq43SGDz8RkbNJTATS0wWUSinkcoHERPOX9LBnZo9ZCQoKwhNPPIGioqIaH8uXL7dmeamuFAogIAAYNEj7rFDYukRERGRh2gkREri6qqFUSpxmfGGtVrA1Z5NCc/cPogZUXAw8/zyg+WdpfI0GmDYNiIoyq4WFiIjsn26c4bx5avTsuQ35+Y8gKUk7stbRW8PNDlYmTZqEr7766o75evfubfaicNRACgtvBSo6ajVw6hSDFSIiJ1B1QkR8vAZZWdp93aRSqVNMiDA7WBkyZAiGDBlyx3zt2rXjWiX2plMnwMXFMGCRSoGOll0qn4iIbKPqhIiq+wM6y4SIOk9dLisrw5IlS3DgwAGUlJTA19cX/fr1w8svv4zWrVtbsoxUX/7+wJo12q4ftVobqKxezVYVIiIn4ewTIuq0KNz333+PTp06YenSpWjSpAkeeOABNGnSBEuXLkXHjh1x8OBBS5eT6is6Gjh9GsjJ0T5HR9u6RETkqIqLtb9LiottXRK6S9SpZeXFF1/Evffei+3bt6N58+b69CtXrmDYsGF48cUXcejQIYsVkizE35+tKURUPwrFrQH7Li7aVlv+8UNWVqeWlWPHjiE+Pt4gUAGA5s2bIz4+Hj/99JNFCkfW5ex7SRCRhVU3s5AtLGRldQpWGjdujMuXL5s8duXKFQQFBdWnTNRAnH0vCSKysJpmFhJZUZ26gUaNGoXXXnsNbdu2RUREhD597969SE5OxrJlyyxWQLIeU/v8mNoPiIgIAGcWks3UKVj5v//7P/z6668YOHAgWrRogdatW+PPP//ElStX4Onpiddeew2vvfYaAO0icT/++KNFC02WUzVgSU8HlEoGKkRUDc4sJBupU7DSuHFj7N69G7t370Zubi5KS0uhqjqxmxyKdi8JbaAilzNQIaIaREdrV78+dUrbosJAhRpAnddZAYDBgwdj8ODBlioL2Yh2LwltoKJUal8zYCGianFmITWwOg2wJedRdYxKRYX22dSgWyIiIlupV8sKOTZTg2lNDbolIiKyJQYrzqq4WDvNsFOnaptrq+4lUZWz7CVBRETOgcGKMzJzhUln30uCiIicA8esOBuuMElERE6GwYqz4QqTRETkZBisOBvdCpNVcYVJIiJyYAxWnI1uhUndxj5cYZKIiBwcB9g6I64wSUREToTBirPiCpNEROQk2A3k5JKTq1+NNi2t5unLRERE9oDBipOTSk0vn69bvVY3tIWIiMhesRvIyZlaPt/UMvtERET2isHKXaBqwJKert1ZmYEKERE5CnYD3SUSEwG5XBuoyOUMVIiIyHEwWLlLpKXdClSUyuoH3RIREdkbBit3gapjVCoqtM+mBt0SERHZI45ZcQbFxdo9gTp1MlpbxdRgWlODbomIiOwVgxVHp1Dc2mXZxUW71H50tP6wWm16MK3utVrdgGUlIiKqAwYrjqy4+FagAmifp03TLrX/TwtLTYu+sUWFiIgcAcesOLLCwluBio5ard0TiIiIyEkwWHFknTppu36qkkq1mxcSERE5CQYrjszfXztGRbdmvlQKrF7NDQyJiMip2EWwsmLFCgQGBsLd3R0hISHYt29fjflzc3MREhICd3d3BAUFYdWqVQbHf/75Z4waNQrt27eHRCLBkiVLLHJfuxQdDZw+DeTkaJ+rDK4lIiJyBjYPVjZv3oyZM2di7ty5yM/PR3h4OIYNG4YzZ86YzF9UVIThw4cjPDwc+fn5mDNnDmbMmIEtW7bo81y/fh1BQUF444034OPjY5H72jV/f2DAACS/688dlomIyOnYPFhZvHgxoqOjMWXKFAQHB2PJkiVo27YtVq5caTL/qlWr0K5dOyxZsgTBwcGYMmUKJk+ejEWLFunz9O7dG2+99RaefvppuLm5WeS+joA7LBMRkTOyabCiVCpx5MgRREZGGqRHRkZi//79Js/Jy8szyh8VFYXDhw9DpVJZ7b6OIDHReHVa7rBMRESOzqbrrJSVlUGtVsPb29sg3dvbG6WlpSbPKS0tNZm/srISZWVl8PX1tcp9AaCiogIVFRX61+Xl5QAAlUpldqBkbfHxgFrtgqQkKdLTBZRKCebNUyM+XgM7KWKNdPVoL/Xp6Fiflsc6tSzWp+U5Sp3Wpnx2sSicRCIxeC2EMEq7U35T6Za+b0ZGBlJSUozSc3Jy4OHhUat7W1PPnoCr6yNQKqVwdVWjZ89tyMqydalqJzs729ZFcCqsT8tjnVoW69Py7L1Or1+/bnZemwYrXl5ekEqlRq0ZFy5cMGr10PHx8TGZ39XVFa1atbLafQEgISEBcXFx+tfl5eVo27YtBg4caPa9G8L8+S6orJRCLhdQKqXIz38Ec+dq7nyiHVCpVMjOzsbQoUMhk8lsXRyHx/q0PNapZbE+Lc9R6lTXO2EOmwYrcrkcISEhyM7OxhNPPKFPz87OxsiRI02eExYWhi+//NIgbefOnQgNDTX7P6Uu9wUANzc3kwN2ZTJZw30gati0ENCOUUlJ0Y1RkfwzZkUKqVTqUGNWGrRO7wKsT8tjnVoW69Py7L1Oa1M2m3cDxcXFYfz48QgNDUVYWBjWrFmDM2fOICYmBoC2NePs2bPYsGEDACAmJgbLli1DXFwcpk6diry8PCgUCmzatEl/TaVSiRMnTuj/ffbsWRw9ehRNmjRBx39Wd73Tfe3SHTYt5A7LRETkjGwerIwZMwYXL15EamoqSkpK0L17d2RlZSEgIAAAUFJSYrD2SWBgILKysjBr1iwsX74cfn5+WLp0KUaNGqXPc+7cOfTs2VP/etGiRVi0aBEiIiKwd+9es+5rd8zYtJA7LBMRkTOyebACALGxsYiNjTV5bP369UZpERER+OGHH6q9Xvv27fWDbut6X7tT06aF3GGZiIicmM0XhSMzcdNCIiK6SzFYcRTctJCIiO5SdtENRGaKjtaOUTl1StuiwkCFiIjuAgxWHI2/P+Dvj+RkbeOKqbEoaWna4SzcuJCIiJwBu4EcFDctJCKqo+JiICdH+0wOgS0rDsrU+inctJCI6A7usF4V2ScGKw6sasCSng4olQxUiIiqZcZ6VWSf2A3k4BITAblcG6jI5QxUiIiqVdN6VWTXGKzYuzv0raal3QpUlErjMSxERPQPrlflsBis2DOFAggIAAYN0j4rFAaHq45RqajQPpsadEtEROB6VQ6MY1bs1R36VrlpIRFRHXC9KofEYMVe3WEvIG5aSERUR/+sV0WOg8GKvdL1rVYNWKr0rXLTQiIiultwzIq9Yt8qERERALas2Ld/+laTX6+E1MsTidHNjbJwaX0iInJ2bFmxd/7+kHZoj6S3m3NpfSIiuiuxZcUBcGl9IiK6mzFYcRBcWp+IiO5W7AZyIFxan4iI7kYMVuxRNUvsc2l9IiK6GzFYsTfVLLHPpfWJiOhuxTEr9qSaJfbTCkYj6e3mXFqfiIjuSgxW7Ek1S+yryy4hNbU5l9YnIqK7EoMVe/LPEvvJmkRIoUYi0gGpFMnprsA/C9fevggcW1SIiMjZccyKPflniX2pRCAJaUiTJBkssc9F4IiI6G7ElhV7Ex2NxKhiIKkISetSgHNAIrgIHBER3b0YrNgjf38kZgII5CJwRERE7AayY1wEjoiIiMGK/TCxEBwXgSMiImKwYh9MLATHReCIiIi0OGbF1kwsBJc29QySBLgIHBERERis2J6JheDUQoLUSUVITAw0SOcicEREdDdisGJrJhaCS5amA6lT9FmqLgTHFhUiIrrbcMyKrXEhOCIiohqxZcUecCE4IiKiajFYsbXiYu24lU6dkJgZyIXgiIiIbsNuIFsyMWWZC8EREREZYrBiKyamLGPaNKTNvsKF4IiIiKpgsGIrJqYsp6kTkPR2cy4ER0REVIVdBCsrVqxAYGAg3N3dERISgn379tWYPzc3FyEhIXB3d0dQUBBWrVpllGfLli3o1q0b3Nzc0K1bN3z66acGx5OTkyGRSAwePj4+Fn1fNerUCclIRhpeBwCk4XUkIQ2p/+8KEhNvTVdmwEJERHc7mwcrmzdvxsyZMzF37lzk5+cjPDwcw4YNw5kzZ0zmLyoqwvDhwxEeHo78/HzMmTMHM2bMwJYtW/R58vLyMGbMGIwfPx4//vgjxo8fj6eeegoHDx40uNa9996LkpIS/eP48eNWfa8G/P0hHfmIdroyXodaIkPqyCNIXNTcYLpyYqI2YOFCcEREZJbiYngdP26w15zDEzbWp08fERMTY5DWtWtXER8fbzL/q6++Krp27WqQNm3aNNGvXz/966eeeko8/PDDBnmioqLE008/rX89b9488a9//ateZb9y5YoAIMrKyup8jdT/d1kA2mchhEhNFdrXqfUqmsNSKpXis88+E0ql0tZFcQqsT8tjnVoW69PC3n1XaFxchAC0z+++a+sSVUv3HXrlypU75rXp1GWlUokjR44gPj7eID0yMhL79+83eU5eXh4iIyMN0qKioqBQKKBSqSCTyZCXl4dZs2YZ5VmyZIlBWmFhIfz8/ODm5oa+fftiwYIFCAoKqra8FRUVqKio0L8uLy8HAKhUKqhUqju+X73iYkhOnYLo2BHxGf5QN1EjKaU50t8RUColmDdPjfh4DWpzSWehq8da1SdVi/VpeaxTy2J9WlBxMVyffx6Sf8ZDSjQaiGnTUDlokH6hUXtSm/9zmwYrZWVlUKvV8Pb2Nkj39vZGaWmpyXNKS0tN5q+srERZWRl8fX2rzVP1mn379sWGDRvQuXNnnD9/Hunp6ejfvz9+/vlntGrVyuS9MzIykJKSYpSek5MDDw8Ps95zu+xs3L9iBSRCQEgkOBobi55Dh8LV9REolVK4uqrRs+c2ZGWZdTmnlZ2dbesiOBXWp+WxTi2L9Vl/XseP44HbJm5I1Goc3LgRF3v0sFGpqnf9+nWz89rFonASicTgtRDCKO1O+W9Pv9M1hw0bpv93jx49EBYWhg4dOuC9995DXFycyfsmJCQYHCsvL0fbtm0xcODAagMcA8XFcH3ySUh05RUC969ahc/laaislEIuF1AqpcjPfwRz52rucDHnpFKpkJ2djaFDh0Imk9m6OA6P9Wl5rFPLYn1a0H33Qcybp29ZAQAhlaLvuHF22bKi650wh02DFS8vL0ilUqNWlAsXLhi1jOj4+PiYzO/q6qoPGKrLU901AaBx48bo0aMHCgsLq83j5uYGNzc3o3SZTGbeD9np00bTldPVCUj5P89/VqqV/DO4VgqpVHpXLwhndp2SWViflsc6tSzWpwUEBgJr1kBMmwaJWg0hlUKyejVkgYG2LplJtfn/tulsILlcjpCQEKPmv+zsbPTv39/kOWFhYUb5d+7cidDQUP0bry5PddcEtONRCgoK4OvrW5e3Yh7dDsuYhzS8bjRdWWfAAE5XJiKiOoiORmVhIb5NS0NlYSEQHW3rElmEzacux8XF4d1330VmZiYKCgowa9YsnDlzBjExMQC0XS8TJkzQ54+JicHvv/+OuLg4FBQUIDMzEwqFArNnz9bnefnll7Fz5068+eab+N///oc333wTu3btwsyZM/V5Zs+ejdzcXBQVFeHgwYMYPXo0ysvLMXHiROu92dt2WN6DQfrpysCtjQsHDeJ0ZSIiqiN/f+0YFTvs+qkrm49ZGTNmDC5evIjU1FSUlJSge/fuyMrKQkBAAACgpKTEYM2VwMBAZGVlYdasWVi+fDn8/PywdOlSjBo1Sp+nf//++Oijj/D6668jMTERHTp0wObNm9G3b199nuLiYowdOxZlZWVo3bo1+vXrhwMHDujvazUGOywPxKAQbTJ3WCYiIjLN5sEKAMTGxiI2NtbksfXr1xulRURE4IcffqjxmqNHj8bo0aOrPf7RRx/VqowW888uy4mpnbjDMhERkRls3g10V7ltl+VEPwV3WCYiIroDBisNJDmuHGlTfzfYZTlt6hkoldpl9bnDMhERkWkMVhqI9HIZkkSq4caFIgWDel6CWq1tbOEMICIiImMMVhpIYqocqZIkJCENg7ELSUjDIOzBnnztGiu7d3OHZSIiIlPsYoDt3SLx/90A3p6HJJECKSq1U5erDKrVPXPKMhER0S0MVhqCQgE8/zyg0SBRIkG6NBFKtavJQbUcZEtERGSI3UDWVlyM5CnFSNPMAQCkibn/BCoCSiUweDCQnGzbIhIREdkztqxYW2EhpKhEEtKwFwOwB4ORikQk7hiMwWkDsGePrQtIRERk39iyYm2dOiHRZQEGYTf2YDAGYTcSpRlI29YTe/ZoZwHt2cNBtURERNVhy4q1+fsDb76J8Fe+BQDswWC4SSqgfFuqH1yblsZBtURERNVhsGJtCgXw2mtIhgaQSODmooSy0nBwLQfVEhERVY/dQFZ0+6q1tw+uHTjQxgUkIiJyAGxZsSLdqrWAAAAkIQ2pSATGTUbSukDs3avtAmLLChERUfUYrFhR4gsXgfXrkSS0o2dTkQhIXJC0LhCpqdo8SUn/5GXAQkREZBKDFStJfuwHSLd9iUSRhj0YgL0YhHS8DqVww6BB2gG1uvVVOLiWiIioegxWrKG4GNJtn+m7gHIwGG64CSXcIJVqpyoPGKDNyhYVIiKimjFYsYbCQiSKNABCvxicEm6Qugio1RIMGsQghYiIyFycDWRhyclA2hsyAEAi0vWLwUlRCbVGgvbtuQgcERFRbbBlxcKk164gaeeDAF4HgFuByj9VPXmyNh8H1hIREZmHwYqFJY74AXh7D5KQhgHYo29ZMcjzT4DCgbVERER3xmDFgpKTAWmeDIlIB6BdV0VnUP+b0Mjd2aJCRERUSwxWLOj2LiB9ukSDPfvdkZqq3biQAQsREZH5GKxYUNUuoPb4DQC041WEq9EMIHYBERERmYfBioVU7QLaiwEGA2sD26qwZ49Mv7Q+W1SIiIjMx2DFQnRdQHuxyyBQkUo0KPpDxu4fIiKiOuI6Kxbw5psuwF8XkYpE7MFgBOJXbaCCSqiFC9q3B3bvBlJT2f1DRERUW2xZsQCpFEhaF4RUwHARuH+q9/Rp7q5MRERUVwxWLECtBga1K0TSGe1U5aqByqCel6Bp7skuICIiojpiN5AF5O1VYs+ZTvoZQOoqMeCefE8MGsQuICIiorpiy4oFfHuokdFKtRJoIKrEgmxRISIiqhu2rFjAg/dfvm1JfQEBF7RoqsKAAdpZQNy4kIiIqG4YrFjAt0dboAX++ueVACABIHD5qgwuLuwCIiIiqg8GKxYQgN9wGS0BaKALVAAJ3N002LNHmyc52WbFIyIicmgMVizgdwTBHdehrU5dywpws8IFgwaxVYWIiKg+GKxYyE14oGqgAmg3LdyzR7sOCxEREdUNgxWLuhWo6LqA2LJCRERUPwxWrETXBRQezvEqRERE9cFgxUrYBURERGQZdhGsrFixAoGBgXB3d0dISAj27dtXY/7c3FyEhITA3d0dQUFBWLVqlVGeLVu2oFu3bnBzc0O3bt3w6aef1vu+tcEuICIiIsuwebCyefNmzJw5E3PnzkV+fj7Cw8MxbNgwnDlzxmT+oqIiDB8+HOHh4cjPz8ecOXMwY8YMbNmyRZ8nLy8PY8aMwfjx4/Hjjz9i/PjxeOqpp3Dw4ME637c2AtuqAAAaDbuAiIiI6svmwcrixYsRHR2NKVOmIDg4GEuWLEHbtm2xcuVKk/lXrVqFdu3aYcmSJQgODsaUKVMwefJkLFq0SJ9nyZIlGDp0KBISEtC1a1ckJCRg8ODBWLJkSZ3vWxtFf8gwaBAQEVHvSxEREd31bBqsKJVKHDlyBJGRkQbpkZGR2L9/v8lz8vLyjPJHRUXh8OHDUKlUNebRXbMu962t335jqwoREZEl2HQjw7KyMqjVanh7exuke3t7o7S01OQ5paWlJvNXVlairKwMvr6+1ebRXbMu9wWAiooKVFRU6F9fuXLln3+VG+VVqwUuXqys9lpkmkqlwvXr13Hx4kXIZDJbF8fhsT4tj3VqWaxPy3OUOr169SoAQAhxx7x2seuyRCIxeC2EMEq7U/7b0825Zm3vm5GRgZSUFBNH2hql/PEH4OVV7aWIiIgI2qClefPmNeaxabDi5eUFqVRq1Jpx4cIFo1YPHR8fH5P5XV1d0apVqxrz6K5Zl/sCQEJCAuLi4vSvL1++jICAAJw5c+aOFU3mKS8vR9u2bfHHH3+gWbNmti6Ow2N9Wh7r1LJYn5bnKHUqhMDVq1fh5+d3x7w2DVbkcjlCQkKQnZ2NJ554Qp+enZ2NkSNHmjwnLCwMX375pUHazp07ERoaqm/uCgsLQ3Z2NmbNmmWQp3///nW+LwC4ubnBzc3NKL158+Z2/YFwRM2aNWOdWhDr0/JYp5bF+rQ8R6hTc//Qt3k3UFxcHMaPH4/Q0FCEhYVhzZo1OHPmDGJiYgBoWzPOnj2LDRs2AABiYmKwbNkyxMXFYerUqcjLy4NCocCmTZv013z55Zfx0EMP4c0338TIkSPx+eefY9euXfj222/Nvi8RERHZB5sHK2PGjMHFixeRmpqKkpISdO/eHVlZWQgICAAAlJSUGKx9EhgYiKysLMyaNQvLly+Hn58fli5dilGjRunz9O/fHx999BFef/11JCYmokOHDti8eTP69u1r9n2JiIjIPkiEOcNwyaSKigpkZGQgISHBZPcQ1R7r1LJYn5bHOrUs1qflOWOdMlghIiIiu2bzFWyJiIiIasJghYiIiOwagxUiIiKyawxWiIiIyK4xWLnNihUrEBgYCHd3d4SEhGDfvn015s/NzUVISAjc3d0RFBSEVatWGeXZsmULunXrBjc3N3Tr1g2ffvqptYpvdyxdn2vXrkV4eDg8PT3h6emJIUOG4Pvvv7fmW7A71viM6nz00UeQSCR4/PHHLVxq+2WN+rx8+TKmT58OX19fuLu7Izg4GFlZWdZ6C3bHGnW6ZMkSdOnSBY0aNULbtm0xa9Ys3Lx501pvwa7Upj5LSkrwzDPPoEuXLnBxccHMmTNN5nO47yVBeh999JGQyWRi7dq14sSJE+Lll18WjRs3Fr///rvJ/L/99pvw8PAQL7/8sjhx4oRYu3atkMlk4pNPPtHn2b9/v5BKpWLBggWioKBALFiwQLi6uooDBw401NuyGWvU5zPPPCOWL18u8vPzRUFBgZg0aZJo3ry5KC4ubqi3ZVPWqFOd06dPi3vuuUeEh4eLkSNHWvmd2Adr1GdFRYUIDQ0Vw4cPF99++604ffq02Ldvnzh69GhDvS2bskadfvDBB8LNzU1s3LhRFBUViR07dghfX18xc+bMhnpbNlPb+iwqKhIzZswQ7733nrj//vvFyy+/bJTHEb+XGKxU0adPHxETE2OQ1rVrVxEfH28y/6uvviq6du1qkDZt2jTRr18//eunnnpKPPzwwwZ5oqKixNNPP22hUtsva9Tn7SorK0XTpk3Fe++9V/8COwBr1WllZaV44IEHxLvvvismTpx41wQr1qjPlStXiqCgIKFUKi1fYAdgjTqdPn26GDRokEGeuLg48eCDD1qo1PartvVZVUREhMlgxRG/l9gN9A+lUokjR44gMjLSID0yMhL79+83eU5eXp5R/qioKBw+fBgqlarGPNVd01lYqz5vd/36dahUKrRs2dIyBbdj1qzT1NRUtG7dGtHR0ZYvuJ2yVn1+8cUXCAsLw/Tp0+Ht7Y3u3btjwYIFUKvV1nkjdsRadfrggw/iyJEj+i7f3377DVlZWRgxYoQV3oX9qEt9msMRv5dsvty+vSgrK4NarTbaddnb29tod2ad0tJSk/krKytRVlYGX1/favNUd01nYa36vF18fDzuueceDBkyxHKFt1PWqtPvvvsOCoUCR48etVbR7ZK16vO3337Dnj17MG7cOGRlZaGwsBDTp09HZWUlkpKSrPZ+7IG16vTpp5/Gn3/+iQcffBBCCFRWVuKFF15AfHy81d6LPahLfZrDEb+XGKzcRiKRGLwWQhil3Sn/7em1vaYzsUZ96ixcuBCbNm3C3r174e7uboHSOgZL1unVq1fx7LPPYu3atfDy8rJ8YR2ApT+jGo0Gbdq0wZo1ayCVShESEoJz587hrbfecvpgRcfSdbp3717Mnz8fK1asQN++fXHq1Cm8/PLL8PX1RWJiooVLb3+s8R3iaN9LDFb+4eXlBalUahRZXrhwwSgC1fHx8TGZ39XVFa1ataoxT3XXdBbWqk+dRYsWYcGCBdi1axfuu+8+yxbeTlmjTn/++WecPn0ajz76qP64RqMBALi6uuLkyZPo0KGDhd+JfbDWZ9TX1xcymQxSqVSfJzg4GKWlpVAqlZDL5RZ+J/bDWnWamJiI8ePHY8qUKQCAHj164O+//8bzzz+PuXPnwsXFOUc01KU+zeGI30vO+T9cB3K5HCEhIcjOzjZIz87ORv/+/U2eExYWZpR/586dCA0NhUwmqzFPddd0FtaqTwB46623kJaWhq+//hqhoaGWL7ydskaddu3aFcePH8fRo0f1j8ceewwDBw7E0aNH0bZtW6u9H1uz1mf0gQcewKlTp/RBHwD88ssv8PX1depABbBenV6/ft0oIJFKpRDaSSIWfAf2pS71aQ6H/F5q+DG99ks3RUyhUIgTJ06ImTNnisaNG4vTp08LIYSIj48X48eP1+fXTbmbNWuWOHHihFAoFEZT7r777jshlUrFG2+8IQoKCsQbb7xh91PELMUa9fnmm28KuVwuPvnkE1FSUqJ/XL16tcHfny1Yo05vdzfNBrJGfZ45c0Y0adJEvPjii+LkyZNi27Ztok2bNiI9Pb3B358tWKNO582bJ5o2bSo2bdokfvvtN7Fz507RoUMH8dRTTzX4+2tota1PIYTIz88X+fn5IiQkRDzzzDMiPz9f/Pzzz/rjjvi9xGDlNsuXLxcBAQFCLpeLXr16idzcXP2xiRMnioiICIP8e/fuFT179hRyuVy0b99erFy50uiaH3/8sejSpYuQyWSia9euYsuWLdZ+G3bD0vUZEBAgABg95s2b1wDvxj5Y4zNa1d0UrAhhnfrcv3+/6Nu3r3BzcxNBQUFi/vz5orKy0tpvxW5Yuk5VKpVITk4WHTp0EO7u7qJt27YiNjZWXLp0qQHeje3Vtj5N/Y4MCAgwyONo30sSIZy4DY2IiIgcHsesEBERkV1jsEJERER2jcEKERER2TUGK0RERGTXGKwQERGRXWOwQkRERHaNwQoRERHZNQYrROTwjh8/joiICHh4eCAoKAgrV640OK5SqZCQkABfX194eHhg4MCBOHbsmI1KS0S1xWCFiBxaaWkpBg8eDB8fH2zbtg0vvfQSZs6ciczMTH2eWbNmYfny5UhNTcXnn38OV1dXDB482GgzNyKyT1zBlogc2iuvvILPP/8cBQUF+p2O09LSsHLlSvzxxx8oLS1FQEAAli5ditjYWADA1atXERgYiClTpuCNN96wZfGJyAxsWSEih/bZZ59h9OjR+kAFAEaPHo2SkhIcPHgQO3fuhFqtxtNPP60/3rRpUzz66KPYvn27LYpMRLXEYIWIHNaNGzfw66+/Ijg4GJWVlfpHhw4dIJPJ8NNPP6GgoADe3t5o2bKlwbndunXDyZMnodFobFR6IjIXgxUicliXLl2CEAITJkyATCbTP9zc3KBSqXDx4kVcunQJLVq0MDrX09MTKpUK165da/iCE1GtuNq6AERE9ZWRkYEhQ4YYpD3wwAP6f0skEqNzdMP1TB0jIvvCYIWIHFbLli0hkUjQpk0bhIaG6tOVSiVUKhVatWqFS5cu4dKlS0bnXr58GTKZDI0bN27IIhNRHTBYISKH5e7ujo4dO+LUqVMG6b/88guEEOjRowdkMhkuXLiAv/76y2DcyokTJ9ClSxe4uLA3nMje8aeUiBza448/jq1bt6KyslKf9uGHH8LPzw99+vRBZGQkXFxc8N///ld//Nq1a/jyyy8xYsQIWxSZiGqJ66wQkUM7f/487rvvPjzwwAN48cUXkZ+fj4SEBKxduxYTJ04EALz44ot4//338fbbbyMgIACLFi3CkSNH8NNPP8HHx8fG74CI7oTBChE5vOPHj+PFF1/E999/Dx8fH7z66qt44YUX9MeVSiWSkpKwfv16XLlyBX379sV//vMf/Otf/7JhqYnIXAxWiIiIyK5xzAoRERHZNQYrREREZNcYrBAREZFdY7BCREREdo3BChEREdk1BitERERk1xisEBERkV1jsEJERER2jcEKERER2TUGK0RERGTXGKwQERGRXWOwQkRERHbt/wP9w2MFWcBvIAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0e-7\n",
      "6.0e-7\n"
     ]
    }
   ],
   "source": [
    "println(minimum(zero3_Δm))\n",
    "println(maximum(zero3_Δm))\n",
    "\n",
    "zVMin = findall(i->(i == minimum(zero3_Δm)),zero3_Δm)\n",
    "zVMax = findall(i->(i == maximum(zero3_Δm)),zero3_Δm)\n",
    "\n",
    "PyPlot.plot(zero3_θ0[zVMin],(zero3_ρ[zVMin]).^(0.25),\"r.\",label=(\"Δm = 400 neV\"))\n",
    "PyPlot.plot(zero3_θ0[zVMax],(zero3_ρ[zVMax]).^(0.25),\"bx\",label=(\"Δm = 600 neV\"))\n",
    "PyPlot.xlabel(\"θ0\")\n",
    "PyPlot.ylabel(\"ρ11^(0.25)\")\n",
    "#PyPlot.xscale(\"log\")\n",
    "#PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([0,0.11])\n",
    "PyPlot.ylim([0,0.035])\n",
    "PyPlot.title(\"Probability in 0T After B4C/Vacuum\")\n",
    "PyPlot.legend()\n",
    "PyPlot.grid(true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "47c1b519-71f3-42a4-bd9d-2d3ab8ea09d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0e-7\n",
      "6.0e-7\n"
     ]
    }
   ],
   "source": [
    "println(minimum(zero3_Δm))\n",
    "println(maximum(zero3_Δm))\n",
    "\n",
    "zVMin3 = findall(i->(i == minimum(zero3_Δm)),zero3_Δm)\n",
    "zVMax3 = findall(i->(i == maximum(zero3_Δm)),zero3_Δm)\n",
    "\n",
    "PyPlot.plot(zero3_θ0[zVMin3],((zero3_ρ[zVMin3] .- zero2_ρ[zVMin2]))./zero2_ρ[zVMin2],\"r.\",label=(\"Δm = 400 neV\"))\n",
    "PyPlot.plot(zero3_θ0[zVMax3],((zero3_ρ[zVMax3] .- zero2_ρ[zVMax2]))./zero2_ρ[zVMin2],\"bx\",label=(\"Δm = 600 neV\"))\n",
    "PyPlot.xlabel(\"θ0\")\n",
    "PyPlot.ylabel(latexstring(L\"$(ρ_{Vac} - ρ_{Air}) / ρ_{Air}$\"))\n",
    "#PyPlot.xscale(\"log\")\n",
    "#PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([0,0.011])\n",
    "PyPlot.ylim([-0.1,0.1])\n",
    "PyPlot.title(\"Difference between Probability in 0T Air vs. Vacuum\")\n",
    "PyPlot.legend()\n",
    "PyPlot.grid(true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dd03d898-a97f-4c94-a245-2bc2e1253b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([4.0e-7 4.0e-7 … 4.0e-7 4.0e-7; 4.105263157894737e-7 4.105263157894737e-7 … 4.105263157894737e-7 4.105263157894737e-7; … ; 5.894736842105263e-7 5.894736842105263e-7 … 5.894736842105263e-7 5.894736842105263e-7; 6.0e-7 6.0e-7 … 6.0e-7 6.0e-7], [0.0001 0.00011513953993264469 … 0.08685113737513525 0.1; 0.0001 0.00011513953993264469 … 0.08685113737513525 0.1; … ; 0.0001 0.00011513953993264469 … 0.08685113737513525 0.1; 0.0001 0.00011513953993264469 … 0.08685113737513525 0.1], [3.8347659060560703e-16 6.73947584560145e-16 … 1.1504815140849253e-8 1.3112834582340672e-9; 3.5626400451818596e-16 6.261259369895575e-16 … 1.5906831142896503e-8 1.8383676737003955e-9; … ; 1.5894554593508363e-16 2.7935733147440283e-16 … 2.332084992373519e-7 7.213985153022912e-8; 1.578486091461981e-16 2.774240203730413e-16 … 2.4742869411716675e-7 7.871584431018403e-8], [3.8785557666078474e-16 6.816511156404343e-16 … 1.5349195777255029e-10 5.322688409633088e-12; 3.55873192126976e-16 6.25476605893453e-16 … 2.505414060134388e-10 9.757903804763464e-12; … ; 1.608617278825419e-16 2.8271922412474253e-16 … 1.4589505839489025e-8 2.032449999772097e-9; 1.555521311103368e-16 2.7338649409552826e-16 … 1.5813028964291675e-8 2.512162166216818e-9])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function average_two_results_HiLo(res1,res2)#;\n",
    "        #nΔm::Int64=100,nθ0::Int64=100,nV::Int64=2000)\n",
    "    \n",
    "    # Load the two datafiles we care about\n",
    "    Δms_p = load(res1, \"deltaMs\")\n",
    "    θ0s_p = load(res1, \"theta0s\")\n",
    "    ρ11Raw_p = load(res1, \"Pnn\")\n",
    "    \n",
    "    Δms_n = load(res2, \"deltaMs\")\n",
    "    θ0s_n = load(res2, \"theta0s\")\n",
    "    ρ11Raw_n = load(res2, \"Pnn\")\n",
    "    \n",
    "    nΔm = size(Δms_n)[1]\n",
    "    nθ0 = size(Δms_n)[2]\n",
    "    nV = size(Δms_n)[3]\n",
    "    \n",
    "    if size(ρ11Raw_p)[2] == 3\n",
    "       ρ11Raw_pOut = ρ11Raw_p[:,3] \n",
    "    else\n",
    "        ρ11Raw_pOut = ρ11Raw_p\n",
    "    end\n",
    "    if size(ρ11Raw_n)[2] == 3\n",
    "       ρ11Raw_nOut = ρ11Raw_n[:,3] \n",
    "    else\n",
    "        ρ11Raw_nOut = ρ11Raw_n\n",
    "    end\n",
    "\n",
    "    # Now we need to combine the values for this\n",
    "    # We have to average over velocity values for each Δm and θ0.\n",
    "    avgρ_posLo = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "    avgρ_negLo = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "        \n",
    "    ρ11_p = reshape(ρ11Raw_pOut,nΔm,nθ0,nV)\n",
    "    ρ11_n = reshape(ρ11Raw_nOut,nΔm,nθ0,nV)\n",
    "    \n",
    "    avgρ_posHi = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "    avgρ_negHi = Matrix{Float64}(undef,nΔm,nθ0)\n",
    "    \n",
    "    # Loop\n",
    "    for i in 1:nΔm\n",
    "        for j in 1:nθ0\n",
    "            # Average over velocity\n",
    "            avg_pLo = 0.\n",
    "            avg_nLo = 0.\n",
    "            for k in 1:Int(floor(nV/2))\n",
    "                avg_pLo += ρ11_p[i,j,k]\n",
    "                avg_nLo += ρ11_n[i,j,k]\n",
    "            end\n",
    "            # And put into matrix\n",
    "            avgρ_posLo[i,j] = avg_pLo / Float64(nV/2)\n",
    "            avgρ_negLo[i,j] = avg_nLo / Float64(nV/2)\n",
    "            \n",
    "            avg_pHi = 0.\n",
    "            avg_nHi = 0.\n",
    "            for k in Int(floor(nV/2)):nV\n",
    "                avg_pHi += ρ11_p[i,j,k]\n",
    "                avg_nHi += ρ11_n[i,j,k]\n",
    "            end\n",
    "            avgρ_posHi[i,j] = avg_pHi / Float64(nV/2)\n",
    "            avgρ_negHi[i,j] = avg_nHi / Float64(nV/2)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    # Now average over the two spin states!\n",
    "    avgΔm = (Δms_p .+ Δms_n) ./ 2. # These should be the same...\n",
    "    avgθ0 = (θ0s_p .+ θ0s_n) ./ 2. # See above\n",
    "    avgρHi = (avgρ_posHi .+ avgρ_negHi) ./ 2. # These are NOT the same\n",
    "    avgρLo = (avgρ_posLo .+ avgρ_negLo) ./ 2. # These are NOT the same\n",
    "        \n",
    "    return avgΔm[:,:,1],avgθ0[:,:,1],avgρHi,avgρLo\n",
    "    \n",
    "end\n",
    "dataFile_02 = (raw\"/SNS_ZeroB.jld\")\n",
    "resultFile_02 = (includePath*raw\"/results\"*dataFile_02)\n",
    "zero2_Δm,zero2_θ0,zero4_ρHi,zero4_ρLo = average_two_results_HiLo(resultFile_02,resultFile_02)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ab1aede8-9586-4f41-95aa-8e95de235287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\r\n",
      "sys:1: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\r\n"
     ]
    }
   ],
   "source": [
    "PyPlot.figure()\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero4_ρLo,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(zero2_Δm.*1E9,zero2_θ0,log10.(zero4_ρLo./2.),cmap=\"plasma\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.xlim([400,600])\n",
    "PyPlot.ylim([1E-3,1E-1])\n",
    "PyPlot.title(\"High V\")\n",
    "PyPlot.show()\n",
    "\n",
    "PyPlot.figure()\n",
    "PyPlot.contour(zero2_Δm.*1E9,zero2_θ0,zero4_ρHi,levels=[5.5E-8],colors=\"black\",linewidths=1.,linestyles=\"solid\",alpha=1.)\n",
    "PyPlot.pcolormesh(zero2_Δm.*1E9,zero2_θ0,log10.(zero4_ρHi./2),cmap=\"plasma\")\n",
    "PyPlot.yscale(\"log\")\n",
    "PyPlot.clim(-10,0)\n",
    "PyPlot.xlim([400,600])\n",
    "PyPlot.ylim([1E-3,1E-1])\n",
    "PyPlot.title(\"Low V\")\n",
    "PyPlot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4606f64c-b6f0-4795-b019-67108fad8c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zVMin3 = findall(i->(i == minimum(zero3_Δm)),zero3_Δm)\n",
    "zVMax3 = findall(i->(i == maximum(zero3_Δm)),zero3_Δm)\n",
    "zVMin5 = findall(i->(i == minimum(zero5_Δm)),zero5_Δm)\n",
    "zVMax5 = findall(i->(i == maximum(zero5_Δm)),zero5_Δm)\n",
    "\n",
    "\n",
    "\n",
    "PyPlot.plot(zero3_θ0[zVMin3],((zero5_ρ[zVMin5] .- zero3_ρ[zVMin3]))./zero3_ρ[zVMin3],\"r.\",label=(\"Δm = 400 neV\"))\n",
    "PyPlot.plot(zero3_θ0[zVMax3],((zero5_ρ[zVMax5] .- zero3_ρ[zVMax3]))./zero3_ρ[zVMin3],\"bx\",label=(\"Δm = 600 neV\"))\n",
    "PyPlot.xlabel(\"θ0\")\n",
    "PyPlot.ylabel(latexstring(L\"$(ρ_{Lots of V} - ρ_{Not as many V}) / ρ_{Not as many V}$\"))\n",
    "#PyPlot.xscale(\"log\")\n",
    "#PyPlot.yscale(\"log\")\n",
    "PyPlot.xlim([0,0.011])\n",
    "PyPlot.ylim([-0.25,0.25])\n",
    "PyPlot.title(\"Difference between Probability in 0T More V Samples vs. Fewer V Samples\")\n",
    "PyPlot.legend()\n",
    "PyPlot.grid(true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49cc0ba6-8824-4286-9e3c-a218f99b9938",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f18c5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.6.2",
   "language": "julia",
   "name": "julia-1.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
