Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading dataframes for each protein into python \n",
    "Renaming columns to be easily accessable, normalizing the excitation and emission spectra, taking sum of column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mEmerald = pd.read_csv('fluoro_spectra/mEmerald_fpbase_spectra.csv')\n",
    "df_mEmerald.fillna(0, inplace = True)\n",
    "df_mTagBFP2 = pd.read_csv('fluoro_spectra/mTagBFP2_fpbase_spectra.csv')\n",
    "df_mTagBFP2.fillna(0, inplace = True)\n",
    "df_mCherry = pd.read_csv('fluoro_spectra/mCherry_fpbase_spectra.csv')\n",
    "df_mCherry.fillna(0, inplace = True)\n",
    "df_mNeptune2p5 = pd.read_csv('fluoro_spectra/mNeptune2p5_fbpase_spectra.csv')\n",
    "df_mNeptune2p5.fillna(0, inplace = True)\n",
    "\n",
    "mEmerald_columns = df_mEmerald.rename(columns ={'mEmerald ex': 'excitation', 'mEmerald em': 'emission'})\n",
    "mTagBFP2_columns = df_mTagBFP2.rename(columns ={'mTagBFP2 ex': 'excitation', 'mTagBFP2 em': 'emission'})\n",
    "mCherry_columns = df_mCherry.rename(columns ={'mCherry ex': 'excitation', 'mCherry em': 'emission'})\n",
    "mNeptune2p5_columns = df_mNeptune2p5.rename(columns ={'mNeptune2.5 ex': 'excitation', 'mNeptune2.5 em': 'emission'})\n",
    "\n",
    "spectra_list = [mEmerald_columns, mTagBFP2_columns, mCherry_columns, mNeptune2p5_columns]\n",
    "for j in range(0,4):\n",
    "    excitation_column = spectra_list[j]['excitation']\n",
    "    area = np.trapz(excitation_column, dx = 1)\n",
    "    normalized = excitation_column/area\n",
    "    excitation_column = normalized\n",
    "    spectra_list[j]['excitation'] = normalized\n",
    "    new_area = np.trapz(excitation_column, dx = 1)\n",
    "    column = spectra_list[j]['emission']\n",
    "    area_emission = np.trapz(column, dx = 1)\n",
    "    normalized_em = column/area_emission\n",
    "    column = normalized_em\n",
    "    spectra_list[j]['emission'] = normalized_em\n",
    "    new_area_em = np.trapz(column, dx = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making sure each spectra dataframe has data for 400-900nm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelength = 701\n",
    "for index in range(401, 601):\n",
    "    mEmerald_columns.loc[len(mEmerald_columns.index)] = [wavelength, 0.0, 0.0]\n",
    "    wavelength = wavelength + 1\n",
    "\n",
    "wavelength = 651\n",
    "for index in range(341, 591):\n",
    "    mTagBFP2_columns.loc[len(mTagBFP2_columns.index)] = [wavelength, 0.0, 0.0]\n",
    "    wavelength = wavelength + 1\n",
    "\n",
    "wavelength = 851\n",
    "for index in range(621, 671):\n",
    "    mNeptune2p5_columns.loc[len(mNeptune2p5_columns.index)] = [wavelength, 0.0, 0.0]\n",
    "    wavelength = wavelength + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to solve emission bin area using $i$ and $j$ \n",
    "Corresponding lambda_min and lambda_max to the min and max wavelengths in channel i in a list of the 200 total bins "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra_list = [mEmerald_columns, mTagBFP2_columns, mCherry_columns, mNeptune2p5_columns]\n",
    "\n",
    "def calc_emission_bin(i, j):\n",
    "    bins_list = [*range(400, 900, 10)]\n",
    "    total_bins = 4*bins_list \n",
    "    df_spectra = spectra_list[j]\n",
    "    lambda_min = total_bins[i]\n",
    "    lambda_max = lambda_min + 10\n",
    "    bin_i = range(lambda_min, lambda_max)\n",
    "    df_sliced_data = df_spectra.loc[df_spectra['wavelength'].isin(bin_i)]\n",
    "    # print(df_sliced_data)\n",
    "    sliced_data_emmision = df_sliced_data['emission']\n",
    "    emission_bin = np.trapz(sliced_data_emmision)\n",
    "    # print('Emission bin sum for i = ', i, 'and j = ', j, 'is', emission_bin)\n",
    "    return emission_bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate the excitation values from the illumination wavelengths\n",
    "Excitation value corresponding to excitation wavelengths for each protein: 405nm, 488nm, 561nm, 637nm. Found these values on csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_excitation(i, j): \n",
    "    illumation_wavelength_list = [405, 488, 561, 637]\n",
    "    if 0 <= i < 50: \n",
    "        illumation_wavelength = illumation_wavelength_list[0]\n",
    "    elif 50 <= i < 100: \n",
    "        illumation_wavelength = illumation_wavelength_list[1]\n",
    "    elif 100 <= i < 150:\n",
    "        illumation_wavelength = illumation_wavelength_list[2]\n",
    "    elif 150 <= i < 200:\n",
    "        illumation_wavelength = illumation_wavelength_list[3]\n",
    "    else:\n",
    "        print('Invalid i value')\n",
    "    df_sliced = spectra_list[j].loc[spectra_list[j]['wavelength'] == illumation_wavelength]\n",
    "    excitation_wavelength = df_sliced['excitation'].iloc[0]\n",
    "    \n",
    "    return excitation_wavelength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to index over brightness values using a given $j$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_brightness(j):\n",
    "    # Found brightness values on slack \n",
    "    brightness_list = [39.1, 32.38, 15.85, 22.8] \n",
    "    brightness = brightness_list[j]\n",
    "    # print('Brightness value for j = ', j, 'is', brightness)\n",
    "    return brightness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imaging model without before $k$ is specified\n",
    "Using the average of matrix A (per 50 channels) from this imaging model to calculate K for the real imaging model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_a_ij_wo_k(i, j) :\n",
    "    k = 1\n",
    "    brightness_value = calc_brightness(j)\n",
    "    excitation_value = calc_excitation(i, j)\n",
    "    emission_bin_area = calc_emission_bin(i, j)\n",
    "    a_ij = k * brightness_value * excitation_value * emission_bin_area\n",
    "    # print('Matrix A for image channel i = ', i, ' and spectrum j = ', j, 'is ', a)\n",
    "    return a_ij\n",
    "\n",
    "def form_matrix_A_wo_k():\n",
    "    A = np.zeros((200, 4))\n",
    "    for j in range(0, 4):\n",
    "        for i in range(0, 200):\n",
    "            A[i, j] = calc_a_ij_wo_k(i, j)\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017846902914808855"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_a_ij_wo_k(10, 0)\n",
    "# B = form_matrix_A_wo_k() \n",
    "# B[10,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for the variable $k_i$\n",
    "We are trying to give each fluorophore a fair chance. Each of our illumination wavelengths is targetted mainly at a specific fluorophore. Experimentally, we can choose the illumination strengths. So, we want to balance out our illumination strengths so that a similar amount of information (photons) is collected from each fluorophore emissions.\n",
    "\n",
    "Depends on the channel--each set of 50 channels will have a different $k_i$. Could make it depend on the brightnesses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_k_strength(i, desired_photons):\n",
    "        A_constant_k = form_matrix_A_wo_k()\n",
    "        illumination_one = A_constant_k[0:50, 0:4]\n",
    "        illumination_two = A_constant_k[50:100, 0:4]\n",
    "        illumination_three = A_constant_k[100:150, 0:4]\n",
    "        illumination_four = A_constant_k[150:200, 0:4]\n",
    "        if 0 <= i < 50: \n",
    "                sliced_average = np.average(illumination_one)\n",
    "                k = 1/(sliced_average) * desired_photons\n",
    "        elif 50 <= i < 100:\n",
    "                sliced_average = np.average(illumination_two)\n",
    "                k = 1/(sliced_average) *desired_photons\n",
    "        elif 100 <= i < 150:\n",
    "                sliced_average = np.average(illumination_three)\n",
    "                k = 1/(sliced_average) * desired_photons\n",
    "        elif 150 <= i < 200:\n",
    "                sliced_average = np.average(illumination_four)\n",
    "                k = 1/(sliced_average) * desired_photons\n",
    "        return k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30740.88858655248"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_k_strength(10, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imaging model with $k$ specified\n",
    "Created a list of k values since there are only four total (reduces run time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def form_k_list(desired_photons):\n",
    "    k_list = []\n",
    "    for i in [0, 50, 100, 150]:\n",
    "        k = calc_k_strength(i, desired_photons)\n",
    "        k_list.append(k)\n",
    "        \n",
    "    return k_list\n",
    "\n",
    "def calc_a_ij(i, j, k_list):\n",
    "    # Replaced line below because it is too computationally intensive\n",
    "    # k = calc_k_strength(i, desired_photons)\n",
    "    if 0 <= i < 50: \n",
    "        k = k_list[0]\n",
    "    elif 50 <= i < 100:\n",
    "        k = k_list[1]\n",
    "    elif 100 <= i < 150:\n",
    "        k = k_list[2]\n",
    "    elif 150 <= i < 200:\n",
    "        k = k_list[3]\n",
    "    brightness_value = calc_brightness(j)\n",
    "    excitation_value = calc_excitation(i, j)\n",
    "    emission_bin_area = calc_emission_bin(i, j)\n",
    "    a_ij = k * brightness_value * excitation_value * emission_bin_area\n",
    "    # print('Matrix A for image channel i = ', i, ' and spectrum j = ', j, 'is ', a)\n",
    "    return a_ij\n",
    "\n",
    "def form_matrix_A(desired_photons):\n",
    "    k_list = form_k_list(desired_photons)\n",
    "    A = np.zeros((200, 4))\n",
    "    for j in range(0, 4):\n",
    "        for i in range(0, 200):\n",
    "            A[i, j] = calc_a_ij(i, j, k_list)\n",
    "    return A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Created a list of matrices so we are now able to easily create multiple \"A\" matrices for varying desired photon values. \n",
    "Can choose a specific one by indexing over the list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_A_for_varying_photons(desired_photons_list):\n",
    "    # Input: list of average detected photons desired\n",
    "    # Output: list of corresponding A matrices\n",
    "    matrix_A_list = []\n",
    "    for desired_photons in desired_photons_list:\n",
    "        A = form_matrix_A(desired_photons)\n",
    "        matrix_A_list.append(A)\n",
    "\n",
    "    return matrix_A_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a vector consisting of some concentration of each fluorophore\n",
    "The concentrations don't have to all be the same. Modeling our imaging system -- using one of each fluorophore in this example. Each index corresponds to a fluorophore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 1, 1, 1] ##might have to switch orientation if error (transpose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate FOM from $A$ and $x$ \n",
    "1. Multiplying Matrix A by Vector x (calculating the measurements of y using imaging model)\n",
    "2. Transposing A using simple command '.T'\n",
    "3. Creating diagonal matrix of 1/y\n",
    "4. Multiply all three matrices and compute the fisher information matrix\n",
    "5. Inversing the fisher information matrix. \n",
    "6. Take out all the diagonal elements in the fisher information matrix (these are the CRLB numbers).\n",
    "7. Square root all the CRLB numbers.\n",
    "8. Divide the fluorophore concentrations by the square-rooted CRLB numbers (would have to change code if don't use 1 for each concentration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you inverse the fisher information matrix and multiply it by the initial fisher information matrix, should get a diagonal matrix of all 1's on the diagonal (and the rest of the elements should be 0. Ours are very small numbers that would round to zero).\n",
    "\n",
    "i.e. (idk = FIM_inverse @ FIM) should result in that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_FOM(A, x):\n",
    "    y = A @ x \n",
    "    y_reshaped = np.reshape(y, (200, 1))\n",
    "    transposed_A = A.T\n",
    "    FIM_y = 1/y\n",
    "    FIM_y[FIM_y == np.inf] = 0\n",
    "    diagonal_matrix = np.diag(FIM_y)\n",
    "    FIM = transposed_A @ diagonal_matrix @ A\n",
    "    FIM_inverse = np.linalg.inv(FIM)\n",
    "    CRLB_0 = FIM_inverse[0,0] \n",
    "    CRLB_1 = FIM_inverse[1,1]\n",
    "    CRLB_2 = FIM_inverse[2,2]\n",
    "    CRLB_3 = FIM_inverse[3,3]\n",
    "    CRLB_list = [CRLB_0, CRLB_1, CRLB_2, CRLB_3]\n",
    "    sqrt_CRLB_list = np.sqrt(CRLB_list)\n",
    "    FOM_list = [1/i for i in sqrt_CRLB_list] #would have to change this line of code if don't use 1 for each concentration\n",
    "    \n",
    "    return FOM_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forming our imaging models based on the number of detected photons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the average number of detected photons\n",
    "Checking if the number of photons matches what we would expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_avg_detected_photons(A):\n",
    "    avg_photons_detected_list = []\n",
    "    for illum_count in range(4):\n",
    "        # start = 50 * illum_count\n",
    "        # end = 50 * (illum_count + 1)\n",
    "        # print(f\"Start row: {start}, End row: {end}\")\n",
    "        A_section = A[50 * illum_count : 50 * (illum_count + 1), :]\n",
    "        avg_photons_detected = np.average(A_section)\n",
    "        avg_photons_detected_list.append(avg_photons_detected)\n",
    "\n",
    "    return avg_photons_detected_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to determine average photons detected using a for-loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_multiple_photons_detected(desired_photons_list):\n",
    "    # Output: a list of the photons detected on average for each of the fluorophores \n",
    "    #       (i.e fist list corresponds to the avg photons detected for each desired photon value for mEmerald)\n",
    "    A_for_desired_photons = calc_A_for_varying_photons(desired_photons_list) \n",
    "    photons_detected_list = []\n",
    "    for matrix in A_for_desired_photons:\n",
    "        photons_detected_varying = calc_avg_detected_photons(matrix)\n",
    "        photons_detected_list.append(photons_detected_varying)\n",
    "    # return photons_detected_list\n",
    "    multiple_photons_detected_list = []\n",
    "    for j in range(4):\n",
    "        photon_spectra = [item[j] for item in photons_detected_list]\n",
    "        multiple_photons_detected_list.append(photon_spectra)\n",
    "    \n",
    "    return multiple_photons_detected_list \n",
    "        \n",
    "\n",
    "multiple_photons_detected_list = calc_multiple_photons_detected([100, 200, 300]) \n",
    "print(multiple_photons_detected_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[199.99999999999991, 200.00000000000003, 200.00000000000003, 200.0]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = form_matrix_A(200)\n",
    "calc_avg_detected_photons(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the FOMs against avg # of detected photons for desired_photons = 100, 200, 300\n",
    "Using $x_j$ = [1, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to input desired_photon_list and have a list of multiple FOMs for each fluorophore returned \n",
    "Function will solve for matrix A for each of the desired photon values, then solve for the FOMs for each of the matrices, then return a list of multiple values of FOMs for each fluorophore. Turns the lists of FOMs for each fluorophore into a function that returns a list of lists, with each list containing the FOMs for a specific fluorophore and the inputted desired photon values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_multiple_FOMs(desired_photons_list, x):\n",
    "    # Input: two lists \n",
    "    # Output: returns a list of the FOMs for each of the fluorophoress \n",
    "    #       (i.e fist list corresponds to the FOMs for each desired photon value for mEmerald)\n",
    "    A_for_desired_photons = calc_A_for_varying_photons(desired_photons_list) # this produces a list of matrices, each 200x4\n",
    "    # x = [1, 1, 1, 1] # concentration of each fluorophore\n",
    "    FOM_list_varying_photons = []\n",
    "    for matrix in A_for_desired_photons: # for each matrix in A_for_desired_photons\n",
    "        calculated_FOMs = calc_FOM(matrix, x) # do this. it will loop on its own (that's what the for-loop is for)\n",
    "        FOM_list_varying_photons.append(calculated_FOMs) # append the FOM list you get for each matrix into the empty list you created\n",
    "    FOM_spectra_list = []\n",
    "    for j in range(4):\n",
    "        FOM_spectra = [item[j] for item in FOM_list_varying_photons]\n",
    "        FOM_spectra_list.append(FOM_spectra)\n",
    "    \n",
    "    return FOM_spectra_list \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[225.15167228636608, 168.84383722782076, 102.30197673088698, 303.7025137549262]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_avg_detected_photons_per_fluor(A):\n",
    "    # Input: 200x4 numpy array\n",
    "    # Output: list of average photons detected\n",
    "    avg_photons_detected_list = []\n",
    "    for j in range(4):\n",
    "        A_section = A[:, j]\n",
    "        avg_photons_detected = np.average(A_section)\n",
    "        avg_photons_detected_list.append(avg_photons_detected)\n",
    "\n",
    "    return avg_photons_detected_list\n",
    "\n",
    "calc_avg_detected_photons_per_fluor(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_photons_detected_per_fluor(desired_photons_list):\n",
    "    # Input: list of desired photons \n",
    "    # Output: a list of the photons detected on average for each of the fluorophores \n",
    "    #       (i.e fist list corresponds to the avg photons detected for each desired photon value for mEmerald)\n",
    "    A_for_desired_photons = calc_A_for_varying_photons(desired_photons_list) \n",
    "    photons_detected_list = []\n",
    "    for matrix in A_for_desired_photons:\n",
    "        photons_detected = calc_avg_detected_photons_per_fluor(matrix)\n",
    "        photons_detected_list.append(photons_detected)\n",
    "    multiple_photons_detected_list = []\n",
    "    for j in range(4):\n",
    "        photon_spectra = [item[j] for item in photons_detected_list]\n",
    "        multiple_photons_detected_list.append(photon_spectra)\n",
    "    \n",
    "    return multiple_photons_detected_list \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating a large set of data to later plot subsets of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    }
   ],
   "source": [
    "desired_photons_list = [*range(1000, 100000, 1000)]\n",
    "FOM_spectra_list = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list = calc_photons_detected_per_fluor(desired_photons_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving data as csv\n",
    "fluorophore_names = ['mEmerald', 'mTagBFP2', 'mCherry', 'mNeptune2.5']\n",
    "results_list = [] # will be list of dataframes\n",
    "for j in range(4):\n",
    "    dict = {'desired photons': desired_photons_list, 'FOM': FOM_spectra_list[j], 'avg detected': avg_photons_detected_list[j]}\n",
    "    df = pd.DataFrame(dict)\n",
    "    df.to_csv('FOM/' + fluorophore_names[j] + '.csv', index=False)\n",
    "    results_list.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n"
     ]
    }
   ],
   "source": [
    "print(len(desired_photons_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I like this one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WpUsXA+yoo46y7du35zhnuQWUWYLv1KzVLMJ1N4W3zcNiwBnA1JA+FRgd1s8AHjGzL81sNbASGCipHdDGzOaHgjwQd0xWTZ8OpaVw553R6/TptXPe2BDlTZo0STpEeU2TJ0+mb9++DBo0iPfff5933nmHV199lRNPPJFDDjmE5s2bc+aZZ1Y7JtYM9fHHH/Ovf/2LRx55pGpbfDNUbLh0gJNOOoni4mI2bNjApEmTqtIrKyspLS3l0ksvpXPnzrXzQThXT9TlpEINRVZLLakpsBA4CrjTzF6RdLiZrQUws7WSvhJ2bw/E1+8qQtr2sF4zPetmzIDNm6P1zZuj96NG7ft593SI8hdeeIFZs2Yxf/58WrVqxfDhw9m6dWusBpdW8+bNGTlyJHPmzGHMmDEp9509ezaHHXbYbukTJkyga9euXH755Rld02XOb4OtW43httfakNUObjPbYWbFQCFRLaFXit0T9UNYivTdTyBNkFQmqWzdunV7nN+aRoyAVq2i9Vatovd14YsvvuDggw+mVatWrFixoqrNdODAgbz44ot89tlnVFZW8sQTTyQ83sx4+eWX6dKly15d/2c/+xlffPEFt99++94WwaXgt8FmX2O/7bU25ORuKDP7HHgBGAl8FJqWCK8fh90qgCPjDisEPgzphQnSE13nHjMrMbOSgoKCfc73qFEwbRpMnBi91katYm+MHDmSyspK+vTpw3/8x39U3ZbXvn17rr32Wo477ji+9rWv0aNHDw466KCq42677TaKi4vp1asXlZWVXHzxxXt87YqKCm666SaWLVtG//79KS4u5t577621srnk/DbY2uG3vdaSRB0ZtbEABUDbsN4SeAk4HbiV6h3ct4T1nlTv4H6XXR3crxF1jsc6uL+R7vq11cFd323cuNHMok64008/3f7617/WcY72Xj7+fTLhHdnZk2nn9EsvvWSSbO7cuTnOYf1Dkg7ubPZZtAOmhn6LJsBjZvaMpPnAY5J+ALwHnBmC1lJJjwHLgEpgopnFpm67CJhCFHSeDYsjevhu1qxZbN26lREjRjB69Oi6zpJLIlnfhM91nT2ZzlvtfRLp+eRHrt7I97+PT0CUPWvXrk34RV+fJhVqKHzyI+dcXvLbXnPDg4VzrsHKZCiOvH+yOkc8zDpXy7xvIncy7ZPwvoh958HCuVqW7LkJv/V17yXrk4jd9rpq1Sq/7TXLvBmqnnr22WcpKSnhmGOOoXv37lx11VVANLz4448/Xse5cy53vE+ifvBPth568803ueSSS/jf//1funfvTmVlZbWRZvdW7H7pJk2aJHzvMudDdOSGD8VRf/i3RI5lMkT5Lbfcwk9/+lO6d+8ORL+e4p++njNnDscffzydO3euVsu49dZbOfbYY+nTpw/XXXdd1fWOOeYYLr74Yvr3789LL71U7f1//dd/ccUVV1Sd449//CM/+tGPcvRpNFzJmprAh+moTT4URz2S6Em9fFhq5Qnu7dvNHnjAbOfO6LUWhibOZIjyfv36WXl5ecLjx48fb9/5zndsx44dtnTpUuvSpYuZmT333HN2wQUX2M6dO23Hjh122mmn2YsvvmirV682STZ//vyq68e/37Rpk3Xu3Nm2bdtmZmaDBw+uGrI81xrSE9z+1HVuNLbhwesDcj1EeV6YNg3GjYMmTaLXadNq5bR7M0R5vNGjR9OkSRN69OjBRx99BMCMGTOYMWMG/fr1o3///qxYsYJ33nkHgI4dO1br+It/f8ABB3DyySfzzDPPsGLFCrZv315tyHLn6pL3SdQf/smn8r3vRUEi/n0tSDdEeb9+/Vi4cCF9+/ZNe7yFJ/DNjEmTJvHDH/6w2r5r1qzhgAMOqJZW8/3555/PzTffTPfu3TnvvPP2vmCNiN8GmzveJ1E/eM0ilQcfTP0+S3784x9z88038/bbbwOwc+dOfvOb36Q85tRTT+VPf/oTmzZF80198MEHfPzxxymPiTnuuON4//33efjhhyktLd23zDcSPiJsbnmgqHtes0gl9sX5ve9FgSJHX6R9+vTh9ttvp7S0lM2bNyOJ0047LeUxI0aMYPny5QwePBiAAw88kAcffJCmTZtmdM2zzjqL8vLyPZqn2znXePhAgg6A008/nSuuuIJTTjmlzvLgfx/n6p4PJOgS+vzzz+nWrRstW7as00DhnKvfvBmqkWvbtm1V34hzziXjNQvnnHNpebBwzjmXVtaChaQjJc2WtFzSUkmXhfRHJZWHZY2k8pBeJGlL3La74841QNISSSslTZa0+5RjzjnnsiabfRaVwJVm9rqk1sBCSTPN7OzYDpJ+DXwRd8wqMytOcK67gAnAAuBvwEh8Hm63B6ZPhxkzYMQIGDWqrnPjXMOTtZqFma01s9fD+kZgOdA+tj3UDs4CUo6hIakd0MbM5odxSx4ARmcr3/XBlClTaNKkCYsXL65K69WrV0ZDgSTy1FNPsWzZslrKXXVjx47l6KOPplevXnz/+99n+/btCfdr2rQpxcXFFBcXMyrH39bTp0ePyNx5Z/Q6fXpOL+9cXshJn4WkIqAf8Epc8gnAR2b2TlxaJ0lvSHpR0gkhrT1QEbdPBXFBJ18VFhZy00031cq5sh0sVqxYwZIlS9iyZQv33ntvwv1atmxJeXk55eXlTM/xt/WMGbB5c7S+eXP03jm3Z7IeLCQdCDwBXG5m8WMilFK9VrEW6GBm/YAfAQ9LagMk6p9I+CShpAmSyiSVrVu3rnYKQDRLV23JZIhyiB6SW7p0KW+99dZu55gxYwaDBw+mf//+nHnmmVVDfBQVFXH11VczcOBABg4cyMqVK3n55ZeZPn06P/7xjykuLmbVqlUMHz6c2AOLn3zyCUVFRUBUo/nWt77FyJEj6dq1Kz/5yU/SXvMb3/gGkpDEwIEDqaiooK5Nnw6XXLKrBjFiBLRqFa23ahW9d87toURD0dbWAjQHngN+VCO9GfARUJji2BeAEqAdsCIuvRT4Q7pr18oQ5Wb20ksvmSSbO3fuHh+bSCZDlN9///02ceJEmzp1qo0bN87MzHr27GmrV6+2devW2QknnGCbNm0yM7Nf/vKXdsMNN5iZWceOHe3nP/+5mZlNnTrVTjvtNDOLhjX/y1/+UpWHE0880V577TUzM1u3bp117NjRzMzuv/9+69Spk33++ee2ZcsW69Chg7333nsprxmzbds269evn82ZMydhuZs2bWoDBgyw4447zp588smE+9TGEOVPP23WqpUZRK9PP70rfeLEXe+dc4mRZIjyrHVwhz6J+4DlZlZzFLyvhQBQEbd/AfCpme2Q1BnoCrxrZp9K2ihpEFEz1jjgd9nKd7xMZunaG7EhyoGUQ5R/97vf5aabbmL16tVVaQsWLGDZsmUMGTIEgG3btlWNBwVUDQRYWlpabVKjTJ1yyikcdNBBAPTo0YN//OMffP755ymvCXDxxRczbNgwTjjhhN3OCfDee+9xxBFH8O6773LyySfTu3dvunTpssf5SydRk9OoUbsW59zeyebdUEOAc4AlsdtjgWvN7G/AGHbv2B4G3CipEtgBXGhmn4ZtFwFTgJZEd0Hl5E6oRLN0DR06dJ/Pm26I8phmzZpx5ZVX8qtf/aoqzcz4t3/7N6YlmVsj/q7iZHcYN2vWjJ07dwKwdevWpHlr2rQplZWVaa95ww03sG7dOv7whz8k3A5wxBFHANC5c2eGDx/OG2+8USvBouZdTiNGwP33R4HCm5ycqz3ZvBtqrpnJzPqYWXFY/ha2nWtmd9fY/wkz62lmfc2sv5n9T9y2MjPrZWZdzOySUFXKusGDB9O+fdSXXlhYWG0CoVw599xzmTVrFrE+mEGDBjFv3jxWrlwJwObNm6sN1/Hoo49WvcZ+/bdu3ZqNG3fNvVBUVMTChQsBqk3Lmkyqa957770899xzTJs2Lelc3p999hlffvklEPWRzJs3jx49emT+ISSR6C6nUaOiOaomToxevTbhXO3wJ7hTqA+zdO23335ceumlVXNTFBQUMGXKFEpLS+nTpw+DBg1ixYoVVft/+eWXHHfccfz2t7/ltttuA2DMmDHceuut9OvXj1WrVnHVVVdx1113cfzxx/PJJ5+kzUOqa1544YV89NFHDB48mOLiYm688UYAysrKOP/884FoNNmSkhL69u3LSSedxDXXXFMrwSLZXU6jRsEdd3igcK42+RDlGVi7dm2DmHylqKiIsrIyDjvssLrOyl7J5O8T3+wEUY0i1uTkNQnn9l2yIcp91NkMNIRA0RjEmp02b476JaZNixZ/Mtu57PNgkUf29gnvhiJRs5M3NzmXG42uzyJfm90aukR/F3+4zrn6o1HVLFq0aMH69es59NBDk95W6nLPzFi/fj0tWrSoSkvU5BS708mbnZzLvUYVLAoLC6moqKA2hwJxtaNFixYUFhZWvfeH65yrXxpVsGjevDmdOnWq62y4BKZPh1//2h+uc66+alTBwtVP3uTkXP3nwcLVOW9ycq7+a3R3Q7m653c5OdfweM3C5ZQ3OTnXMHmwcDnlTU7ONUxpm6EkPZ9JmnPJxDc7eZOTcw1T0pqFpBZAK+AwSQeza3rTNsAROcibywM+npNz+SFVM9QPgcuJAsPrcekbgDuzmCeXR3w8J+fyQ9JmKDP7rZl1Aq4ys05xS18zuyOHeXQNiN/p5Fx+StUMdbKZ/R34QNK3am43s7+mOrGkI4EHgK8CO4F7zOy3kq4HLgBiY27EplpF0iTgB0TTql5qZs+F9AHsmlb1b8BluZotz2XO73RyLn+laoY6Efg78M0E2wxIGSyASuBKM3tdUmtgoaSZYdttZvbf8TtL6kE0N3dPoqavWZK6mdkO4C5gArCAKFiMJEfzcLvM+Z1OzuWvpMHCzK6T1AR41swe29MTm9laYG1Y3yhpOdA+xSFnAI+Y2ZfAakkrgYGS1gBtzGw+gKQHgNF4sKh3fDwn5/JXyltnzWwncMm+XkRSEdAPeCUkXSJpsaQ/hTutIAok78cdVhHS2of1mumujtXsn4g1OU2c6FOcOpdvMhnuY6akqyQdKemQ2JLpBSQdCDwBXG5mG4ialLoAxUQ1j1/Hdk1wuKVIT3StCZLKJJX5MOTZFeufuPPO6DU+YPjdTs7ln0yCxfeBicAcYGFYyjI5uaTmRIHioViHuJl9ZGY7Qq3lj8DAsHsFcGTc4YXAhyG9MEH6bszsHjMrMbOSgoKCTLLo9lKi/gnnXP5KGyxq3DYbWzqnO07RVHT3AcvN7Ddx6e3idvt34M2wPh0YI2l/SZ2ArsCroe9jo6RB4ZzjgKczLqHLCr8l1rnGJe3YUJJaAT8COpjZBEldgaPN7Jk0hw4BzgGWSCoPadcCpZKKiZqS1hA9/IeZLZX0GLCM6E6qieFOKICL2HXr7LN453ad81tinWtclO5xBUmPEjU9jTOzXpJaAvPNrDgH+dtrJSUlVlaWUWuZc865QNJCMyupmZ5Jn0UXM7sF2A5gZltI3OnsnHMuT2USLLaF2oQBSOoCfJnVXDnnnKtXMpnP4jrg/4AjJT1E1BdxbjYz5Zxzrn5JGyzMbKak14FBRM1Pl5nZJ1nPmXPOuXoj1UCC/WskrQ2vHSR1MLPXax7jnHMuP6WqWZQBS9k1Omx8p7YBJ2crU8455+qXVMHiSuDbwBbgEeBJM9uUk1w555yrV1JNfnSbmQ0lGkjwSOB5SY+FB+qcc841IpkM97GaaHiNGUTjOHXLdqacc87VL6k6uDsTTUZ0BtHQ4Y8AN5nZ1hzlzTnnXD2Rqs9iJbCYqFaxAegAXByN5QfxgwM655zLb6mCxY3smjfiwBzkxTnnXD2ValrV63OYD+ecc/VYJmNDOeeca+Q8WDjnnEsrabCQdFl4HZK77DjnnKuPUtUszguvv8tFRpxzztVfqYLFcklrgKMlLY5blkhanO7Eko6UNFvScklL42oqt0paEc71pKS2Ib1I0hZJ5WG5O+5cA8J1V0qarNj9u84553Ii1d1QpZK+CjwH7M0My5XAlWb2uqTWwEJJM4GZwCQzq5T0K2AScHU4ZlWS6VrvAiYAC4C/ASPxebidcy5nUnZwm9k/zawv0fDkrcPyoZn9I92JzWxtbBhzM9sILAfam9kMM6sMuy0AClOdR1I7oI2ZzbdowvAHgNHpru+cc672pL0bStKJwDvAncDvgbclDduTi0gqAvoBr9TY9H2q1xA6SXpD0ouSTghp7YGKuH0qQppzzrkcyWRa1d8AI8zsLQBJ3YBpwIBMLiDpQOAJ4HIz2xCX/lOipqqHQtJaoIOZrZc0AHhKUk+qz6MRYwnSkDSBqLmKDh06ZJI955xzGcjkOYvmsUABYGZvA80zObmk5kSB4iEz+2tc+njgdGBsaFrCzL40s/VhfSGwimiE2wqqN1UVAh8mup6Z3WNmJWZWUlBQkEkWnXPOZSCTYFEm6T5Jw8PyR2BhuoPCHUv3AcvjBx2UNJKoQ3uUmW2OSy+Q1DSsdwa6Au+a2Vpgo6RB4ZzjiAY3dM45lyOZNENdBEwELiVqEppD1HeRzhDgHGCJpPKQdi0wGdgfmBnugF1gZhcCw4AbJVUCO4ALzezTuDxMAVoS9XH4nVDOOZdDCq1AeaekpMTKysrqOhvOOdegSFpoZiU1031sKOecc2l5sHDOOZdWxsFC0gHZzIhzzrn6K5OH8o6XtIzoCWwk9ZWUSQe3c865PJFJzeI24FQg9gzEIqI7l5xzzjUSGTVDmdn7NZJ2ZCEvzjnn6qlMnrN4X9LxgEnaj+h5i+XZzZZzzrn6JJOaxYVED+XFBvQrDu+dc841EilrFmH4jdvNbGyO8uOcc64eSjefxQ6gIDQ/Oeeca6Qy6bNYA8yTNB34VywxfnBA55xz+S2TYPFhWJoQzZTnnHOukUkbLMzshlxkxDnnXP2VNlhImk2CmenM7OSs5Mg551y9k0kz1FVx6y2AbxNNh+qcc66RyKQZquasePMkvZil/DjnnKuHMmmGOiTubRNgAPDVrOXIOedcvZPJE9wLgbLwOh+4EvhBuoMkHSlptqTlkpZKuiykHyJppqR3wuvBccdMkrRS0luSTo1LHyBpSdg2OczF7ZxzLkfSBgsz62RmncNrVzMbYWZzMzh3JXClmR0DDAImSuoBXAM8b2ZdgefDe8K2MUBPYCTw+/AEOcBdwASga1hG7lEpnXPO7ZNMmqG+lSD5C2CJmX2c7DgzWwusDesbJS0nGl/qDGB42G0q8AJwdUh/xMy+BFZLWgkMlLQGaGNm80N+HgBGA8+mL55zzrnakMndUD8ABgOzw/vhwAKgm6QbzezP6U4gqQjoB7wCHB4CCWa2VtJXwm7tw3ljKkLa9rBeM90551yOZBIsdgLHmNlHAJIOJ2oWOg6YA6QMFpIOBJ4ALjezDSm6GxJtsBTpia41gai5ig4dOqTKlnPOuT2QSQd3USxQBB8D3czsU6Jf/UlJak4UKB4ys7+G5I8ktQvb24XzQVRjODLu8EKiYUYqwnrN9N2Y2T1mVmJmJQUFBRkUzTnnXCYyCRYvSXpG0nhJ44GngTmSDgA+T3ZQuGPpPmB5jUEHpwPjw3rsfLH0MZL2l9SJqCP71dBktVHSoHDOcXHHOOecy4FMmqEmEj21PYSoSegB4AkzM+CkFMcNAc4BlkgqD2nXAr8EHpP0A+A94EwAM1sq6TFgGdGdVBPDEOkAFwFTgJZEHdveue2cczmk6Ds//5SUlFhZWVldZ8M55xoUSQvNrKRmetKahaS5ZjZU0kaqdygLMDNrk4V8Ouecq4dSNUONBTAzn8PCOecauVQd3E/GViQ9kYO8OOecq6dSBYv45xs6Zzsjzjnn6q9UwcKSrDvnnGtkUvVZ9JW0gaiG0TKsg3dwO+dco5M0WJhZ02TbnHPONS6ZPMHtnHOukfNg4ZxzLi0PFs4559LyYOGccy4tDxbOOefS8mDhnHMuLQ8Wzjnn0vJg4ZxzLi0PFs4559LyYOGccy6trAULSX+S9LGkN+PSHpVUHpY1selWJRVJ2hK37e64YwZIWiJppaTJYR5u55xzOZTJHNx7awpwB9Gc3QCY2dmxdUm/Br6I23+VmRUnOM9dwARgAfA3YCQ+B7dzzuVU1moWZjYH+DTRtlA7OAuYluocktoBbcxsvkWThT8AjK7lrDrnnEujrvosTgA+MrN34tI6SXpD0ouSTghp7YGKuH0qQppzzrkcymYzVCqlVK9VrAU6mNl6SQOApyT1pPpsfTFJJ2KSNIGoyYoOHTrUYnadc65xy3nNQlIz4FvAo7E0M/vSzNaH9YXAKqAbUU2iMO7wQuDDZOc2s3vMrMTMSgoKCrKRfeeca5Tqohnqa8AKM6tqXpJUIKlpWO8MdAXeNbO1wEZJg0I/xzjg6TrIs3PONWrZvHV2GjAfOFpShaQfhE1j2L1jexiwWNIi4HHgQjOLdY5fBNwLrCSqcfidUM45l2OKbjLKPyUlJVZWVlbX2XDOuQZF0kIzK6mZ7k9wO+ecS8uDhXPOubQ8WDjnnEvLg4Vzzrm0PFg455xLy4OFc865tDxYOOecS8uDhXPOubQ8WDjnnEvLg4Vzzrm0PFg455xLy4OFc865tDxYOOecS8uDhXPOubQ8WDjnnEvLg4Vzzrm0PFg455xLK5vTqv5J0seS3oxLu17SB5LKw/KNuG2TJK2U9JakU+PSB0haErZNDnNxO+ecy6Fs1iymACMTpN9mZsVh+RuApB5Ec3P3DMf8XlLTsP9dwASga1gSndM551wWZS1YmNkc4NMMdz8DeMTMvjSz1cBKYKCkdkAbM5tv0WThDwCjs5Jh55xzSdVFn8UlkhaHZqqDQ1p74P24fSpCWvuwXjPdOedcDuU6WNwFdAGKgbXAr0N6on4IS5GekKQJksokla1bt24fs+qccy4mp8HCzD4ysx1mthP4IzAwbKoAjozbtRD4MKQXJkhPdv57zKzEzEoKCgpqN/POOdeI5TRYhD6ImH8HYndKTQfGSNpfUieijuxXzWwtsFHSoHAX1Djg6Vzm2TnnHDTL1oklTQOGA4dJqgCuA4ZLKiZqSloD/BDAzJZKegxYBlQCE81sRzjVRUR3VrUEng2Lc865HFJ0k1H+KSkpsbKysrrOhnPONSiSFppZSc10f4I71yor4c9/BrPotbKyrnPknHNpZa0ZytVQWQnTpkVBYvx4GDdu17Zzzqm7fDnnXAY8WOTKtGnVA0TM976X+7w459we8maobIpvckrWN/Tgg7nNk3PO7QUPFrUtPkBccklUm2jSJGp6ijd1KjzwAJSW1k0+nXNuD3gzVG2LNTclanKaOjXqn3jwwShINPOP3znXMHjNojZk0twEIEXLOed4oHDONSgeLGpDrDaRqLnphz+EnTu9yck516B5sNhbqWoTU6fuChB33OG1Cedcg+fBYk9k2nntzU3OuTzjwWJPxDc3/eEP1bfF1ya8uck5l2c8WGQiVqMYOzb5Pl6bcM7lMQ8WySRqcmratPo+3nntnGsk/CdwMqmel9ixAx56KAoQsdqEc87lMa9ZxMv0eYmHHvLmJudco+LBIp4/L+Gccwl5sIhXcwRYf17COeeALAYLSX+S9LGkN+PSbpW0QtJiSU9KahvSiyRtkVQelrvjjhkgaYmklZImh7m4s6PmCLB+h5NzzgHZrVlMAUbWSJsJ9DKzPsDbwKS4bavMrDgsF8al3wVMALqGpeY5a09paVSL8OYm55yrJmvBwszmAJ/WSJthZrF5RBcAhanOIakd0MbM5ls0WfgDwOgsZDfSrFlUi/DahHPOVVOXfRbfB56Ne99J0huSXpR0QkhrD1TE7VMR0pxzzuVQnfx0lvRToBJ4KCStBTqY2XpJA4CnJPUEEvVPJL2nVdIEoiYrOnToULuZds65RiznNQtJ44HTgbGhaQkz+9LM1of1hcAqoBtRTSK+qaoQ+DDZuc3sHjMrMbOSgoKCbBXBOecanZwGC0kjgauBUWa2OS69QFLTsN6ZqCP7XTNbC2yUNCjcBTUOeDqXeXbOOZfFZihJ04DhwGGSKoDriO5+2h+YGe6AXRDufBoG3CipEtgBXGhmsc7xi4jurGpJ1McR38/hnHMuB2SphrVowEpKSqysrKyus+Gccw2KpIVmVrJber4GC0nrgH9ksOthwCdZzk5d8bI1XPlcPi9b/dbRzHbr9M3bYJEpSWWJomg+8LI1XPlcPi9bw+RjQznnnEvLg4Vzzrm0PFjAPXWdgSzysjVc+Vw+L1sD1Oj7LJxzzqXnNQvnnHNpebBwzjmXVoMPFpKOlDRb0nJJSyVdFtIPkTRT0jvh9eC4YyaFyZTeknRqXHrCiZYk7S/p0ZD+iqSiHJavhaRXJS0K5bshn8oXrt80jDj8TB6WbU3IV7mksnwqn6S2kh5XNKHZckmD86Fsko7WronYyiVtkHR5PpRtn5hZg16AdkD/sN6aaFKlHsAtwDUh/RrgV2G9B7CIaNiRTkSDFjYN214FBhONdvss8PWQfjFwd1gfAzyaw/IJODCsNwdeAQblS/nCNX8EPAw8E97nU9nWAIfVSMuL8gFTgfPD+n5A23wpW1wZmwL/BDrmW9n2+LOo6wxk4Y/7NPBvwFtAu5DWDngrrE8CJsXt/1z4Y7YDVsSllwJ/iN8nrDcjekJTdVC2VsDrwHH5Uj6ikYSfB05mV7DIi7KFa65h92DR4MsHtAFW17xWPpStRnlGAPPysWx7ujT4Zqh4oSrXj+jX9+EWjVpLeP1K2K098H7cYbEJlVJNtFR1jEUz/X0BHJqVQiQQmmnKgY+BmWaWT+W7HfgJsDMuLV/KBtH8KzMkLVQ03wrkR/k6A+uA+0MT4r2SDiA/yhZvDDAtrOdb2fZI3gQLSQcCTwCXm9mGVLsmSLMU6amOyQkz22FmxUS/wgdK6pVi9wZTPkmnAx9bNIdJRockSKuXZYszxMz6A18HJkoalmLfhlS+ZkB/4C4z6wf8i6hpJpmGVLbo4tJ+wCjgL+l2TZBWr8u2N/IiWEhqThQoHjKzv4bkjxTN4R2by/vjkF4BHBl3eGxCpVQTLVUdI6kZcBA15hfPBTP7HHgBGEl+lG8IMErSGuAR4GRJD5IfZQPAzD4Mrx8DTwIDyY/yVQAVoZYL8DhR8MiHssV8HXjdzD4K7/OpbHuswQeLcHfBfcByM/tN3KbpwPiwPp5dkyZNB8aEuxE6EU209Kqlnmgp/lzfAf5uobEx2xRNDNU2rLcEvgasIA/KZ2aTzKzQzIqIqvt/N7Pv5UPZACQdIKl1bJ2o/ftN8qB8ZvZP4H1JR4ekU4Bl5EHZ4pSyqwmqZn4aetn2XF13muzrAgwlqr4tBsrD8g2i9r/ngXfC6yFxx/yU6I6Ftwh3J4T0EqL/zKuAO9j1hHsLoqroSqK7GzrnsHx9gDdC+d4E/jOk50X54vI2nF0d3HlRNqJ2/UVhWQr8NM/KVwyUhX+bTwEH51HZWgHrgYPi0vKibHu7+HAfzjnn0mrwzVDOOeeyz4OFc865tDxYOOecS8uDhXPOubQ8WDjnnEvLg4XLOUn/Lskkda/rvKSjXaPGLpI0Q9JXQ/qmPTzPaEk9spPLqmvE8rpE0jJJP5e0f5pj2kq6eB+uuVfl2tPPz9U9DxauLpQCc4kexNtnkprWxnlSOMnM+hI9U3DtXp5jNNHopNl2kpn1JnpSvDPpp/lsSzQC6t4aTW7K5eqYBwuXU2EMryHADwjBQtLXJT0Wt89wSf8T1kdImi/pdUl/CcfHfkX/p6S5wJmSLpD0WqgBPCGpVdivi6QFYduN8b9oJf04pC9WmCckjTnAUXHH3xSut0DS4SGto6Tnwzmfl9RB0vFEYwzdqmh+hC6SisNxiyU9qTA3gqQXJP1K0Rwmb0s6IaT3DGnl4ZiuqTJqZpuAC4HRkg5JUd5fAl3CeW9N9blIGhfSFkn6c5JydZH0f4oGTnwpVnuU1Cn8HV+T9F8ZfNauvqnrpwJ9aVwL8D3gvrD+MtF4Qs2A94ADQvpdYb/DiL6gY+lXs+sJ9jXAT+LOe2jc+s+B/xfWnwFKw/qFwKawPoLoV7eIfjQ9AwxLkN81hCHGiZ7Ajc1hYMA3w/otwM/C+v8A48P694GnwvoU4Dtx510MnBjWbwRuD+svAL8O698AZoX13wFjw/p+QMtUeY1LKyca0j5heYEi4M24/ZPt15Po6eTYZ3FIknI9D3QN68cRDWMB0fAW48L6xNjfwZeGs3jNwuVaKdGggYTXUouGaP4/4JuKBlU7jWgMnUFETRzzFA3RPp5oEpqYR+PWe4VfskuAsURfbhDNKxAbNfThuP1HhOUNojlCuhON6ZPI7HD9NsAvQto2oi9SgIVEX7qx68Wu82ei4WiqkXQQ0NbMXgxJU4m+kGNig2HGn3c+cK2kq4GOZrYlSV53u1x4zbS8yfY7GXjczD4BMLPdBr0Ltb7jgb+Ez+sPRHM6QFSbjI2z9OcM8+7qkWZ1nQHXeEg6lOhLp5ckI5qFzCT9hOiLfyLRyJuvmdnGMPjaTDMrTXLKf8WtTwFGm9kiSecSjTWVMjvAL8zsDxlk/aTYl2Sc7RZ+JgM7SP5/aW/G0/my5nnN7GFJrxAF0ucknW9mf091EkWDGBYRzR6ZsLzafTrPZPtdmkFZmgCfWzScfiI+tlAD5jULl0vfAR4ws45mVmRmRxLNtjaUqPmlP3ABu2oMC4Ahko4CkNRKUrck524NrFU0XP3YuPQFwLfDenyH+nPA9+P6QNpL+gr77uW464wl6sgH2BjyiJl9AXwW648AzgFeJAVJnYF3zWwyUZNOnzT7Hwj8nqgZ7DOSl7cqX0Gy/Z4HzgoBn1g/SI1ybQBWSzoz7CNJfcN+82p8Lq6B8WDhcqmUaE6HeE8A3zWzHUTNOl8Pr5jZOuBcYJqkxURf/Mlut/0PohkSZxIN4R5zOfAjSa8SNYl8Ec49g6i5aH5ounqc6l+ae+tS4LyQ33OAy0L6I8CPFc0q14WoSe3WsF8xUb9FKmcDb4bmne7AA0n2my3pTaKRTN8DfgjJy2tm64ma+d6UdGuK/ZYCNwEvSloExKYDqFmuscAPwj5LgTPCfpcRTf70GtHcDa6B8VFnXV5TdFfUFjMzSWOI+kjOSHecc64677Nw+W4AcEfo//ic6A4l59we8pqFc865tLzPwjnnXFoeLJxzzqXlwcI551xaHiycc86l5cHCOedcWv8f/eG28apjswMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_idx = 40\n",
    "end_idx = 50\n",
    "FOM_spectra_list_subset = np.empty([4, (end_idx - start_idx)])\n",
    "avg_photons_detected_list_subset = np.empty([4, (end_idx - start_idx)])\n",
    "for j in range(4):\n",
    "    FOM_spectra_list_subset[j] = FOM_spectra_list[j][start_idx : end_idx]\n",
    "    avg_photons_detected_list_subset[j] = avg_photons_detected_list[j][start_idx : end_idx]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_subset[0], FOM_spectra_list_subset[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_subset[1], FOM_spectra_list_subset[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_subset[2], FOM_spectra_list_subset[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_subset[3], FOM_spectra_list_subset[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('Figure of Merit')\n",
    "plt.xlabel('Average Photons Detected')\n",
    "plt.title('All Four Fluorophores')\n",
    "plt.savefig('Figures/Average Photons Detected vs FOMs for Same 10 Index Range All Fluorophores.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I like this one too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    }
   ],
   "source": [
    "desired_photons_list = [*range(1000, 100000, 1000)]\n",
    "FOM_spectra_list_no_mNeptune = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 0])\n",
    "avg_photons_detected_list_no_mNeptune = calc_photons_detected_per_fluor(desired_photons_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_idx = 40\n",
    "end_idx = 50\n",
    "FOM_spectra_list_subset_no_mNeptune = np.empty([4, (end_idx - start_idx)])\n",
    "avg_photons_detected_list_subset_no_mNeptune = np.empty([4, (end_idx - start_idx)])\n",
    "for j in range(4):\n",
    "    FOM_spectra_list_subset_no_mNeptune[j] = FOM_spectra_list_no_mNeptune[j][start_idx : end_idx]\n",
    "    avg_photons_detected_list_subset_no_mNeptune[j] = avg_photons_detected_list_no_mNeptune[j][start_idx : end_idx]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_subset_no_mNeptune[0], FOM_spectra_list_subset_no_mNeptune[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_subset_no_mNeptune[1], FOM_spectra_list_subset_no_mNeptune[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_subset_no_mNeptune[2], FOM_spectra_list_subset_no_mNeptune[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "# ax1.scatter(avg_photons_detected_list_subset_no_mNeptune[3], FOM_spectra_list_subset_no_mNeptune[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('Figure of Merit')\n",
    "plt.xlabel('Average Photons Detected')\n",
    "plt.title('No mNeptune Fluorophore')\n",
    "plt.savefig('Figures/Average Photons Detected vs FOMs for Same 10 Index Range No mNeptune2p5.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_idx = 9\n",
    "end_idx = 29\n",
    "FOM_spectra_list_subset = np.empty([4, (end_idx - start_idx)])\n",
    "avg_photons_detected_list_subset = np.empty([4, (end_idx - start_idx)])\n",
    "for j in range(4):\n",
    "    FOM_spectra_list_subset[j] = FOM_spectra_list[j][start_idx : end_idx]\n",
    "    avg_photons_detected_list_subset[j] = avg_photons_detected_list[j][start_idx : end_idx]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_subset[0], FOM_spectra_list_subset[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_subset[1], FOM_spectra_list_subset[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_subset[2], FOM_spectra_list_subset[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_subset[3], FOM_spectra_list_subset[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('All Four Fluorophores')\n",
    "plt.savefig('Figures/Avg Photons Detected vs FOMs for All Fluorophores.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extending data included so that Average Photons Detected is the same for each fluorophore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_list = [] # will be list of dataframes\n",
    "for j in range(4):\n",
    "    dict = {'desired photons': desired_photons_list, 'FOM': FOM_spectra_list[j], 'avg detected': avg_photons_detected_list[j]}\n",
    "    df = pd.DataFrame(dict)\n",
    "    results_list.append(df)\n",
    "\n",
    "results_sliced_list = []\n",
    "for j in range(4):\n",
    "    df = results_list[j]\n",
    "    df_sliced = df.loc[(df['avg detected'] >= 20000) & (df['avg detected'] <= 50000)]\n",
    "    results_sliced_list.append(df_sliced)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(results_sliced_list[0]['avg detected'], results_sliced_list[0]['FOM'], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(results_sliced_list[1]['avg detected'], results_sliced_list[1]['FOM'], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(results_sliced_list[2]['avg detected'], results_sliced_list[2]['FOM'], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(results_sliced_list[3]['avg detected'], results_sliced_list[3]['FOM'], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('All Four Fluorophores')\n",
    "# plt.savefig('Figures/Avg Photons Detected vs FOMs for All Fluorophores.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot each fluorophore with points/FOMs for desired photons = [*range(100, 3000, 100)] all on same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    }
   ],
   "source": [
    "desired_photons_list = [*range(100, 3000, 100)]\n",
    "FOM_spectra_list = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list = calc_photons_detected_per_fluor(desired_photons_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJbvvvnuhk+Ocy7G8tvIysyuBK5NmbyDcraRa/2rg6qYcc/369Z6ZFClJ7LDDDnhjCueabsWKFfTokWponcJpDaFX6vDMpHj5Z+Nc082aNYuePXvy3HMpA1QXTL77oTjnXC1dru3C2s/X1kx33qozayY0dJTr0lVVVcVpp52GmXHaaaexcOFC2rUrjr/yVnmH0hJMmjSJ7t27U15eXvNYsGBBsx1/+fLl7LvvvimXDR8+3DuMurxJzExSTbv0XnjhBd55JwzCWFlZyYsvvljgFG1RHNlaiTrppJO47bbb8rb/qqqqorlycc5llk29yIEHHkjPnj1ZunQpvXr1YujQoc2Uusz8DiUPli9fTllZGWeddRb77rsvJ598MjNnzuTggw+mb9++zJ5df2vop556ii9/+cuceOKJ7LXXXowfP557772XIUOG0L9/f5YuDQP3rVq1iuOOO47BgwczePDgmrLUiRMncs455zBixAhOPfVUli9fzqGHHsrAgQMZOHAgzz//fJ1jfvbZZ4wZM4YBAwZw0kkn8dlnn+XnxDjn6pVtvUi7du2YNGkSkpg0aVJRXTQWT0oKJF/luUuWLOFPf/oTd9xxB4MHD+a+++5j1qxZVFRUcM011zB69GimTZvGrFmzarZ54YUQtuzll19m4cKFdOvWjT322IOzzjqL2bNn88tf/pJbb72Vm2++mYsvvphLL72UQw45hLfeeoujjjqKhQsXAjB37lxmzZpFx44dWbduHTNmzKBDhw4sXryYsWPH1inOuv322+nUqRPz589n/vz5DBw4sMnv35WuTL+pzlt1rrO81DW0XuSQQw7hnXfeKbpWXiWfoeSrPHf33Xenf//+AOyzzz4cccQRSKJ///4sX74cqL/Ia/DgwTVflD59+jBixAgA+vfvz5NPPgnAzJkza9W5rFmzhrVrQ9pHjhxJx44dgdDR84ILLmDevHm0bduWN954o87xnnnmGS666CIABgwYwIABA3JxClyJyvSb8gr4ulLVixxyyCFptym2zAQ8Q8mbrbfeuuZ1mzZtaqbbtGlDVVX6EYqz2Xbz5s288MILNRlHom222abm9U033cTOO+/Myy+/zObNm+nQoUPKY3pzXufyJ1PdSDHXizSE16G0UCNGjKh1dzNv3ryU63388cf06NGDNm3acM8997BpU93gA8OGDePee+8F4NVXX2X+/Pl5SbNzpSibupFirhdpiJLPUJLLb5uzPHfatGm1mg2nqjCvzy233MKcOXMYMGAA/fr14ze/+U3K9c477zzuvvtuhg4dyhtvvFHr7qXaueeeyyeffMKAAQP4xS9+wZAhQxr9nlxp6HJtF3SVah5dru1Ss6yQv6lik1w3kq50orpe5OCDD27GFOZWix5TftCgQZZcwbxw4UL23nvvAqXIZcM/o5ZPV9UtIrUrW+5/Sb48++yzjBgxgvXr19OhQwdmzJiRsW6kOUiaa2aDcr3fkr9Dcc65xlixYkXGdarrRoAWXTeSLc9QnHOugVpDn5F8aN3vzjnXKN6XpH6tpc9IPniG4pyrw/uS1K+19BnJBy/ycs65yOtFmsYzFOecw+tFcsEzlAK46667avqebLXVVvTv35/y8nLGjx/foP1MnDiRnj17Ul5eTllZGeeeey6bN28G4LTTTmP33XevOc4tt9wCQO/evenfvz/77bcfI0aM4L333mPdunUcc8wxlJWVsc8++zQ4Ha7lSdePBEqvL0lD+otA6+gzkhdm1mIfX/rSlyzZggUL6swrZrvttputWrWqUdteeeWVdv3115uZ2aZNm+zggw+2f/zjH2ZmNm7cOPvTn/6U9ngTJkywCy+80D799NOa7TZs2GCHHHKI/fWvf21UmrLR0j6j1oiJ1HmUsmeeecY6dOhggHXo0MGeffbZQicpr4A5lof/5LzdoUj6oqR5CY81ki6R1E3SDEmL43PXhG0mSFoi6XVJR+UrbfnW2PD1o0eP5ktf+hL77LMPd9xxR8383//+9+y1114MHz6cs88+mwsuuKDOtp9//jnr16+na9eudZbVZ9iwYSxZsoROnTpx2GGHAbDVVlsxcOBAKisrG/iunStemepGvF4kN/KWoZjZ62ZWbmblwJeAdcBDwHjgCTPrCzwRp5HUDxgD7AMcDfxaUtt8pS9RRQVccEF4zpUlS5Zw8cUXM3/+fBYtWlQTvv6GG27gmmuuSbnNH/7wB+bOncucOXO45ZZbWL16Ne+++y4//elPefHFF5kxYwaLFi2qtc1NN91EeXk5PXr0YK+99qK8vLxm2Q9+8IOaIq9XXnmlzvEeffTRmojI1T766COmT5/OEUcc0fST4FwRKKVYWoXWXHUoRwBLzexNYBRwd5x/NzA6vh4FTDWzDWa2DFgC5D2oVEUFjB0Lv/pVeM5VplIdvr5Nmzb1hq9Pdsstt7DffvsxdOhQ3n77bRYvXszs2bP58pe/TLdu3Wjfvj0nnHBCrW0uvfRS5s2bx8qVK/n000+ZOnVqzbLrr7+eefPmMW/evFoZx2GHHUZ5eTlr1qxhwoQJNfOrqqoYO3YsF110EXvssUduToQrCK8jCUotllahNVc2PAaYEl/vbGYrAMxshaSd4vyeQOLgyJVxXi2SzgHOAfjCF77Q5IQ9/jisWxder1sXpkeObPJuGxy+/qmnnmLmzJm88MILdOrUieHDh7N+/Xosy1hr7du35+ijj+aZZ55hzJgxadd98skn2XHHHevMP+ecc+jbty+XXHJJVsd0xcv7kQQN7TNSKv1F8iXvdyiStgJGAn/KtGqKeXX+Tc3sDjMbZGaDunfv3uT0jRgBnTqF1506helC+Pjjj+natSudOnVi0aJFvPhiyFuHDBnC008/zYcffkhVVRUPPvhgyu3NjOeff54+ffo06vhXXHEFH3/8MTfffHNj34Jzzcr7jBSf5ijy+irwbzN7P06/L6kHQHxeGedXArsmbNcLeDffiRs5EqZMgfPPD8+5uDtpjKOPPpqqqioGDBjAj3/845ovfs+ePfnRj37EAQccwFe+8hX69evHdtttV7NddR3KvvvuS1VVFeedd16Dj11ZWcnVV1/NggULGDhwIOXl5dx55505e2/O5Zr3GSlOeQ9fL2kq8JiZ3RWnrwdWm9l1ksYD3czsMkn7APcR6k12IVTY9zWzuiNCRaUSvv6TTz5h2223paqqimOPPZYzzjiDY489ttDJarTW+Bk1t0yxtjItb8mqqqooKytj6dKl7LnnnhljaUHmERNLTb7C1+c1u5bUCTgS+HbC7OuA+yWdCbwFnABgZq9Juh9YAFQB56fLTErJxIkTmTlzJuvXr2fEiBGMHj260ElyBVbKdSQeS6t45TVDMbN1wA5J81YTWn2lWv9q4Op8pqkluuGGGwqdBOeaTamMv94aeegV51zR8D4jLZt/Es4VoVIcj6Qh44yU0hgjLYlnKM4VmYoKWPvQ1dBnBpRNB0qjjsT7jLR8XuTlXBGpjtzAvy6EB6fAom8UOkk54X1GSoNnKAX0t7/9jUGDBrH33ntTVlbG97//fSCEnn/ggQcKnDpXCImRG9i4DSw9sqDpyQXvM1I6PEMpkFdffZULLriAyZMns3DhQl599dWcxM8ys5oxUVJNu+LQ5douaOxINORWNHZkTaytxMgNtP80FHvRcutIfJyR0uIZSh5kE77+F7/4BZdffjllZWVAuDpL7OX+zDPPcNBBB7HHHnvUulu5/vrrGTx4MAMGDODKK6+sOd7ee+/Neeedx8CBA3n22WdrTf/0pz/l0ksvrdnH7373O7773e8209lwqaydPzwUacWirbXzhwO1Izc88sA22JQK7EprsXUmqepFMvG6kRYsH4OsNNcjJwNsbdxo9sc/mm3eHJ43bmzY9iksW7bM2rZta/Pnz7dNmzbZwIED7fTTT7fNmzfbww8/bKNGjbL999/f5s2bl3L7cePG2fHHH2+bNm2y1157zfr06WNmZo899pidffbZtnnzZtu0aZMdc8wx9vTTT9uyZctMkr3wwgs1x0+c/uSTT2yPPfawzz//3MzMDjzwQJs/f36T32dj+QBbZgy+xcC2PAbfUugk5cXGjRutT58+Btiee+5pG3Pw+3JNR0sbYKvFmDIFTj0V2rQJz1OmZN4mC40JX59o9OjRtGnThn79+vH++yEM2uOPP87jjz/O/vvvz8CBA1m0aBGLFy8GYLfddqtViZk4vc0223D44Yfz6KOPsmjRIjZu3FhnHBTXzPrMCEVaUKtoqyXJpqLd60VKi2cop5ySfrqRMoWv32effZg7d25W21uMt2ZmTJgwoWaMkyVLlnDmmWcCIdNIlDx91llnMWnSJO666y5OP/30pr051yCpBnDrPOApOG4sDL4VjhsbpluQbCvawetFSolnKJMnp5/Okx/84Adcc801vPHGGwBs3ryZG2+8Me02Rx11FH/4wx/45JNPAHjnnXdYuXJl2m2qHXDAAbz99tvcd999jB07tmmJdxnVDHA1diSjjv+0zgBuayasCfUjsy/EplS0qDqShla0g9eLlAq//6z+cz3llJCZNNOf7YABA7j55psZO3Ys69atQxLHHHNM2m1GjBjBwoULOfDAAwHYdtttmTx5Mm3bZjdS8oknnsi8efMaNO68a5yajohLjwzNf8ntAG6F1JjgjK405D18fT6VSvj6XPn617/OpZdeWvDx4lvrZ1RRETKMESNg1EtxvLhF3wituTZuQ6dOhR1zJ1uZgjM2Jny8Ky75Cl/vRV4l4KOPPmKvvfaiY8eOBc9MWqvqHu7VRVs1PdzLptfUlbSEzMSDM7qm8G9CCdh+++1r6mpc7tQK4PiXW2DdhUAo2mq//Bg2xjhclE2n84CnGDnywgKlNDsenNE1ld+hONdItQI2JjQD7tQJHpjwbexKq3m0hEr3hnZC9MzEJfMMxblGqKgg3JUkFW2df37LqCdJxYMzuqbyDMW5Bqo3InDZdG67rTgzE++E6JqDZyjONVB9EYGLNYCjd0J0zSWvGYqk7SU9IGmRpIWSDpTUTdIMSYvjc9eE9SdIWiLpdUlH5TNthTZp0iTatGnD/Pnza+btu+++WYVlSeXhhx9mwYIFOUpdbSeffDJf/OIX2XfffTnjjDPYuHFjyvXatm1LeXk55eXljCzGy/QGyiYicKdO8MgVFxZtPYl3QnTNKd93KL8E/m5mZcB+wEJgPPCEmfUFnojTSOoHjAH2AY4Gfi0pux57LVSvXr24+uqrc7KvfGcoixYt4pVXXuGzzz7jzjvvTLlex44da8LCVCTGGWmhsokIXOz1JY2J9utcY+UtQ5HUBRgG/B7AzD43s4+AUcDdcbW7gdHx9ShgqpltMLNlwBJgSL7SlyybMuZsZRO+HkJHw9dee43XX3+9zj4ef/xxDjzwQAYOHMgJJ5xQE26ld+/e/PCHP2TIkCEMGTKEJUuW8Pzzz1NRUcEPfvADysvLWbp0KcOHD6e60+cHH3xA7969gXBn9L//+78cffTR9O3bl8suuyzjMb/2ta8hCUkMGTKEysrKnJ2ropbQyz15sKuRIyna+pJEXtHumlM+71D2AFYBd0l6SdKdkrYBdjazFQDxeae4fk/g7YTtK+O8WiSdI2mOpDmrVq3KSUIbUsacrSVLlnDxxRczf/58Fi1axH333cesWbO44YYbuOaaa4AQKPKyyy6rma72wQcf8LOf/YyZM2fy73//m0GDBtWK89WlSxdmz57NBRdcwCWXXMJBBx3EyJEjuf7665k3bx59+vRJm7Z58+Yxbdo0XnnlFaZNm8bbb7+d8ZgAGzdu5J577uHoo49Oud/169czaNAghg4dysMPP9yIs1ZkijwisFe0u2KTz29XO2AgcKGZ/VPSL4nFW/VQinl14sKY2R3AHRBCrzQ1kQ3pzNUQ1eHrgbTh67/5zW9y9dVXs2zZspp5L774IgsWLKipGP38889r4ncBNcEdx44dW2vgrGwdccQRbLfddgD069ePN998k48++ijtMQHOO+88hg0bxqGHHppyv2+99Ra77LIL//nPfzj88MPp379/xsytWCSGTam+6+g84CnWMjbcmfSZUVQRgWfNmsWwYcN49tlnM1ageydE11zymaFUApVm9s84/QAhQ3lfUg8zWyGpB7AyYf1dE7bvBbybx/QB+Qt0lyl8fbV27drxve99j5///Oc188yMI488kin1jM0iKeXrRO3atasZ+nf9+vX1pq1t27ZUVVVlPOZVV13FqlWr+O1vf5tyOcAuu+wCwB577MHw4cN56aWXijpDqenpnhBv6667ttSL1K5kL55e7o25CPLMxDWHvBV5mdl7wNuSvhhnHQEsACqAcXHeOOCR+LoCGCNpa0m7A32B2flKX7ViKGM+7bTTmDlzJtVFeEOHDuW5555jyZIlAKxbt65W6JRp06bVPFffRXTu3Jm1a7f03O7du3fNeCuJQwjXJ90x77zzTh577DGmTJlCmzapvzIffvghGzZsAEKR3XPPPUe/fv2yPwkFkC4icDHzinZXrPLdyutC4F5J84Fy4BrgOuBISYuBI+M0ZvYacD8h0/k7cL6Zbcpz+oqijHmrrbbioosuqhnbpHv37kyaNImxY8cyYMAAhg4dyqJFi2rW37BhAwcccAC//OUvuemmmwAYM2YM119/Pfvvvz9Lly7l+9//PrfffjsHHXQQH3zwQcY0pDvmd77zHd5//30OPPBAysvL+clPfgLAnDlzOOuss4AQQXjQoEHst99+HHbYYYwfP74oM5RUg10lh00ZMaIwactWMVwEOZeKh6+PMoXsLha9e/dmzpw57LjjjoVOSqMVKnx9dQ/3detCxrFu5MgQMgVCsdfSI3nkigsL3nIrm+9iQ+pQnEvm4evzrCVkJq5pEnu4V0cErlE2nc7HXl7wzCTbFofeo90VI29D2MI0tid9KaoVXh7ouOZEOnWaVnOHMmXCtxk58tsFTGFtDa1s94sgV2xa5R1KSy7Ga+2a67OpqIC1D129JXAj8Fmf+4u6h7tXtruWrtVlKB06dGD16tWeqRQhM2P16tV06NAhr8epNxowxd3D3SvbXUvX6oq8evXqRWVlJbnqRe9yq0OHDvTq1Suvx0gZDbi68r1Asqlor25xOGzYMO/V7lqkVveNbd++Pbvvvnuhk+EKaMQIuOuumKkkhEwpVHh579XuSkWrazbsSkeXa7uECMAJoVGqe7enCqVSCFVVVZSVlbF06VL23HPPnIX2ca4p8tVs2L/ZrsWqCS+/cRuYd0aIuxWNHFkc9ST5Cu3jXDFqdZXyroSkCS9fLLyi3ZUSz1Bcy1UE4eUzhZAvhtA+zjUXz1Bci5AqBlfnAU/BcWNh8K1w3NhmDy/vvdqdq80r5V3RShVevlOn4uiU6JXtriXzWF6u5BRzeHnv1e5cXZ6huOJXhOHlvbLdubo8Q3FFJeV4JWXTa+pKmqO4y8dqd65xvA7FFY3k8UrsuJP4rM/9Ncs7b9U5aVje3GvoOCMtZRwd5xJ5x0bX6iSHl2//99+wcV0IJ79uHZzfZRq3XTmt2dLjY7U71zRe5OUKJjEzAdjY+y906hReF6KuxCvanWuajBmKpIMlbRNfnyLpRkm75T9prjWrqAD+ckut0PKUTS/oeCVe0e5c02Rzh3I7sE7SfsBlwJvAH7PZuaTlkl6RNE/SnDivm6QZkhbH564J60+QtETS65KOasT7cS1AsY5X4hXtzjVNNhlKlYWa+1HAL83sl0BD4oAfZmblCRVA44EnzKwv8EScRlI/YAywD3A08GtJbRtwHNdCpByvhPyGl8+m5RZ4r3bnmiKbDGWtpAnAt4C/xD/59k045ijg7vj6bmB0wvypZrbBzJYBS4AhTTiOK1IjRlCrruSRKy7ErrS8teDKNkRKNa9od65xsslQTgI2AGeY2XtAT+D6LPdvwOOS5ko6J87b2cxWAMTnneL8nsDbCdtWxnm1SDpH0hxJc3xUxuLW5douaOxINORWNHYkXa7tAoTirOaqK0luuVVVVZW/gzlX4jIWEpvZe5IeBPrGWR8AD2W5/4PN7F1JOwEzJC1Ks65SHT5Feu4A7oDQDyXLdLgCKIbxSnw8EueaTzatvM4GHgB+G2f1BB7OZudm9m58XknIhIYA70vqEffdA1gZV68Edk3YvBfwbjbHcUWqCMYr8ZZbzjWfbIq8zgcOBtYAmNlithRT1UvSNpI6V78GRgCvAhXAuLjaOOCR+LoCGCNpa0m7E+6IZmf/VlzRKYLxSrzllnPNJ5tf1wYz+1wKJVKS2pGiKCqFnYGH4nbtgPvM7O+S/gXcL+lM4C3gBAAze03S/cACoAo438w2NfQNucJJHse984CnQjFXwpjvuZZN6JPqllte2e5cfmWM5SXpF8BHwKnAhcB5wAIzuzzvqcvAY3kVj+Q4XM3RMbGhcbecc0Ehx0MZD6wCXgG+DfwVuCLXCXEtU5dru6CrxKif3VrTt6Q5xizx1lvOFZ+MGYqZbTaz35nZCWZ2fHztrasckBCPq5nHLPG4W84Vn2xaeS2T9J/kR3MkzrUgzTxmibfecq74ZFMpn1jO1oFQid4tP8lxLUFi5XstZdOhbDojR16Y9zRUt94aNmyYt95yrkhkU+S1OuHxjpndDBye/6S5YlRd+f6rX4XnjktPrLU8F/G4PO6Wcy1Txss6SQMTJtsQ7ljyF8XPFbXEwI75GASroS23vCmwc8Ujm3KC/5fwugpYDpyYelXX2iSPqthxzYl06jStpnlwLivfGzNionOueGQTy+uw5kiIK07Joyp+1ud+HpkyrVYHxlzxuFvOtWz1ZiiSvptuQzO7MffJccWkZlTFPjNChXuUr8CO1S23li5d6i23nGuB0lXKd87wcK1YulEV88XjbjnXstX7izWzq5ozIa64pBxVsWx6XkdVBI+75VxLlk0rrw7AmYSheTtUzzezM/KYLldgI0bAXXclxOa64sIm9y/JJpAjeMst51qqbGJ53QP8D3AU8DRhnJK1abdwLUZzjarY0GF4nXMtTzbRhl8ys/0lzTezAZLaA4+ZWcE7N3q04abT2JFbRlVs/ykcNxabUpHTY1RVVVFWVsbSpUvZc889vTmwcwVWyGjDG+PzR5L2BbYDeuc6Ia5AmmFURQ/k6FxpyCZDuUNSV+DHhFEVFwA/z2uqXPNphlEVPZCjc6UhXT+UBcC9wFQz+5BQf7JHcyXMNY/mGFXRAzk6VxrqrUORtB8whhBm5QNgCjDNzLKL3NcMvA6lZcm2lZdzLr+avQ7FzF42swlm1ge4GNgN+Kekf0g6O9cJcflXUQEXXBB7wOdItpGBwZsDO9faZVOHgpm9aGaXEsaV7wrclu0BJLWV9JKkR+N0N0kzJC2Oz10T1p0gaYmk1yUd1cD34lKoHqJXY0cy6vhPa8LO5yJT8abAzrlE2YzYOFjSjZLeBK4C7gB6NuAYFwMLE6bHA0+YWV/giTiNpH6EIrZ9gKOBX0tq24DjuBRqgjsmtObKxZjvPqa7cy5ZvRmKpGskLQVuB94FDjazL5vZ7Wb2QTY7l9QLOAa4M2H2KODu+PpuYHTC/KlmtsHMlgFLgCENeTMujRyP+e5NgZ1zydLdoWwAvmpmg8zsBjOrbMT+bwYuAzYnzNu5umI/Pu8U5/cE3k5Yr5IUd0KSzpE0R9KcVatWNSJJJSrHY757U2DnXLJ0lfJXmdkbjd2xpK8DK81sbrabpEpGinTdETO5Qd27d29s8lq1xMr3WsEcy6bT+djLcxJ63iMDO+eS5fNf4GBgpKSvEYJKdpE0GXhfUg8zWyGpB7Ayrl8J7JqwfS9CUZtrgOqw8+vWheCOU6asycvYJeCRgZ1ztWXVyqsxYpPjXmbWm1DZ/g8zO4XQ235cXG0c8Eh8XQGMkbS1pN2BvsDsfKWvtUoe870xle/eFNg51xjpesoPTLehmf27kce8Drhf0pnAW8AJcX+vSbqfENqlCjjfzDY18hglI9djvs+aNYthw4bx7LPPcvDBB+c4tc651ixdT/nNwGtAdc13Yh2HebTh4qCr6lY9PbK/NWrMd48K7FxpyFdP+XT/Ft8DjgM+A6YCD5nZJ7lOgGu8XI/5nqop8CGHHJKj1DrnWrt0rbxuMrNDgAsIleVPSLpfUnlzJc7VLx9jvntTYOdcU2SslI+dDB8BHid0NNwr34lymaUc8x2aNOa7NwV2zjVFujqUPQits0YROhxOBR41s/XNl7z0SrkOJbF5cKdOuRmmt5pHBXaudStEHcoSYD7h7mQN8AXgPClUApvZjblOjMte9Zjvjal8z8QzE+dcY6TLUH7Clp7q2zZDWlwDNbTy3e88nHP5VG+GYmYTmzEdrh5dru3C2vnDa42ouGbCmgbvx/uXOOfyLW2lvKSvSnpG0geSVkl6OoZScc1k7fzhoRVXbM21dv7wBu+j6EPNV1XBPfeAWXgutvQ557KSLnz92cBPgYmEseT7EMZDmSjpnGZJnas1jklia66GKPpQ81OmwKmnQps24XnKlEKnyDnXCOnuUC4FRpjZP8xsTXz8A/hqXOaaQ8I4JrT/NEw3UNH1L0m+IxkzpvbyU04pTLqcc02SLkORmf03eaaZrc5jelySzgOeqhnHhOPGhukGKrr+Jcl3JBdeWHv55MmFSZdzrknS/bOskbSfmb2cOFPSfsDaerZxOVa7Av7CetfLpOCh5quqQkZyyinhziTRrbfCwQeHZZMnxxAAzrmWJlMsrwpJdwFzCU2IBxNCznuZRAtU0CbD1Xclp55ad9nUqfCtb4XX1c/OuRYnXSyvWYRQK22A04Az4uuhcZnLscSRFrPVkLFLmlWmepK774bNm+GPf/Q7EudaiXTjoXzBzN4C/q8Z01Oy6o60mLnTYlH1LUks0po8GTZtgtNP33JH8txztdeXwsPvSJxrNdJVyj9c/ULSg/lPSmnqcm0XdJUY9bNbGzTSYtH1LUmuaG+T9NW69dZwN+J3Jc61WmlbeSW83iPfCSlVNaMtJjQPzmakxaLoW5JYrFVPkNEa1fUk1XclhW5p5pzLuXQZitXz2uVD2fSa5sHZFHcVRd+SxLuSceNqL6u+E/E7EudKRroMZT9JayStBQbE12skrZXU8GBSLrOy6XDMRVkFfCxI35KGVLSfcorfkThXYtIFh2zblB1L6gA8A2wdj/OAmV0pqRswDegNLAdONLMP4zYTgDOBTcBFZvZYU9JQrCoqtoSd77xV5y3FXjRsgKxm71uS3PTXK9qdcwnyedm4ATjczD6R1B6YJelvwP8CT5jZdZLGA+OBH0rqRxjQax9gF2CmpL3MbFMe09js6rbmWtOksUzynpl4h0TnXJYyDgHcWBZ8Eifbx4cRRoC8O86/GxgdX48CpprZhjjs8BJCP5hWJXHo3mxacxVcunoSr2h3ziXIW4YCIKmtpHnASmCGmf0T2NnMVgDE553i6j0JQw1Xq4zzkvd5jqQ5kuasWrUqn8nPixEjQisuyK41V7PzDonOuUbK6yVlLK4ql7Q98JCkfdOsrhTz6rQuM7M7gDsgjCmfi3Q2p2yG7i3oyIpeT+Kca6RmKaMws48kPQUcDbwvqYeZrZDUg3D3AuGOZNeEzXoB7zZH+vKpy7Vd6lS6r5lQf71JQXq/ez2Jcy4H8lbkJal7vDNBUkfgK8AioIIQYJL4/Eh8XQGMkbS1pN2BvsDsfKWvuSRmJqmmExWs97vXkzjnciCfdSg9gCclzQf+RahDeRS4DjhS0mLgyDiNmb0G3A8sAP4OnN/aWnhl0my9372exDmXB3m73DSz+cD+KeavBo6oZ5urgavzlabmVlEB/OWWEFalbHrG9at7vy9dujS/vd+9nsQ5lwd5beVVyqr7m/CvC+HBKbDoG0D6jot56/2e6Y7EAzc653LAC8TzJLG/CRu34fwdKrjtyszb5aX3e6Y7Eh/gyjmXA36HkidN6W+Sk8wkXSRgvyNxzuWBLFPY8SI2aNAgmzNnTqGTUa/EmF1NCa/SKPfck3q4XQiZiN+JOFeyJM01s0G53q/foeTRyJFw223NlJl4yy3nXIF5HUozymsPeG+55ZwrML9DaSazZs2iZ8+ePJf8R98UXk/inCsiXoeSA12u7cLa+cNh6ZHQZwadBzzFmglbxiCrqqqirKyMpUuXsueee7Jw4cLcNAn2ehLnXCN4HUoRWzt/eOhrEvucrJ0/vNbyvPWAP+WU2tNeT+KcKyDPUHJh6ZGwcZvweuM2YTpBzsZ/T654v/vu2ssT60k85pZzrpl5hpILfWZA+0/D6/afhukEOesBnxjEsbqoy+tJnHNFwutQciBTHUq1BrfySgwrP3lyaAq81VZblm/eHO5InHOuAfJVh+LlIjlQO/O4sN71GtxkOFNT4MmTveLdOVc0vMirmCVXuntTYOdcEfMMpdgkVryfe27tZT7YlXOuiHmGUmwSK95/+1v49rf9jsQ51yJ4htJAFRVwwQVx8KxcyBSD6/bb/Y7EOdcieIbSANWDZv3qV+E5J5lKclPgC5Mq9SdPzsFBnHMu/zxDaYDEQbPWrQvTK1asaNpOveLdOddK5C1DkbSrpCclLZT0mqSL4/xukmZIWhyfuyZsM0HSEkmvSzoqX2lrqC7XdkFXiV+tHlnTgbFTJ+jVq5EBH73i3TnXCuXzDqUK+J6Z7Q0MBc6X1A8YDzxhZn2BJ+I0cdkYYB/gaODXktrmMX1ZW/v52vCibDocNxYG38rkyVXceedpmBmnnXYaVVVV2e/QK96dc61Q3jIUM1thZv+Or9cCC4GewCigOgjV3cDo+HoUMNXMNpjZMmAJMCRf6Wu0sulwzEXsuGMTAj4mF3N5xbtzrhVoljoUSb2B/YF/Ajub2QoImQ6wU1ytJ/B2wmaVcV7yvs6RNEfSnFWrVuU13ek0KOBjpqCOXvHunGsF8p6hSNoWeBC4xMzqBrhKWDXFvDqBxszsDjMbZGaDunfvnqtkptV5q851phsU8NGDOjrnSkBeg0NKag88CjxmZjfGea8Dw81shaQewFNm9kVJEwDM7Nq43mPARDN7ob79F0NwyKwCPpqFzKSaB3V0zhVQixtgS5KA3wMLqzOTqAIYF1+PAx5JmD9G0taSdgf6ArPzlb5cqTczSdeSy4u4nHOtUD6LvA4GvgUcLmlefHwNuA44UtJi4Mg4jZm9BtwPLAD+DpxvZpvymL788pZczrkS4+Oh1KOiInRcHDECRo5sxA68mMs5V6RaXJFXS9aoECvekss5V+I8Q0khVYiVjLwll3OuxHmGksKIESG0CoTnESOy2Ci5s+K4cR5CxTlXUjxDSWHkyHDDcf754TmrOpTkIi0v4nLOlRi/bK7HyJEZMpKqqpDbnHJKyDxOOCHMr572Ii7nXInxDKWxqutMqutLIBRtJT4751wJ8SKvxkquM0meds65EuMZSj0yDpzldSbOOVeLZyhsGUCr+tHp251SD5yV2Ndk0ya46y5vFuycc5FnKCQMoAWwCT7702epB85K7Gty+unQtq03C3bOucgzlGSVQMxf6gyc5fUmzjlXL89QkvUC4vAndQbO8noT55yrl2coJA2g1RY6ntAxDJx15520mzJlS3yuE07wcCrOOVePki/4r6iAU99ZUyeq8IqJK+gxc6b3NXHOuSyVdPj66qjC69aFmF11wqx4CHrnXCvk4evzIGNUYa8zcc65rJV0hpIxqvDYsV5n4pxzWSrpDCVlVOHEzotTpoRMxPuaOOdcRiX/D1knqnC6oI/OOefqlbc7FEl/kLRS0qsJ87pJmiFpcXzumrBsgqQlkl6XdFS+0pWRd150zrlGyWeR1yTg6KR544EnzKwv8EScRlI/YAywT9zm15La5jFt9fOKeOeca5S8ZShm9gzw36TZo4C74+u7gdEJ86ea2QYzWwYsAYbkK21peUW8c841SnNXyu9sZisA4vNOcX5P4O2E9SrjvDoknSNpjqQ5q1atanqKEivh77knzPOx4J1zrsGKpZVXqt6CKXtcmtkdZjbIzAZ179696UdOjCB86qlh2jnnXIM1d4byvqQeAPF5ZZxfCeyasF4v4N1mSZFXwjvnXE40d4ZSAYyLr8cBjyTMHyNpa0m7A32B2c2SIq+Ed865nMhbBYGkKcBwYEdJlcCVwHXA/ZLOBN4CTgAws9ck3Q8sAKqA881sU77SVkt1pfspp4TMxCvhnXOuUUo6OKRzzpUiDw7pnHOuqHmG4pxzLidKM0NJ7ntSVVXoFDnnXItXmhmK9z1xzrmcK80MxfueOOdczpVmhuJ9T5xzLudKM1CV9z1xzrmcK80MpV27LYNm+eBZzjmXE6VZ5OWccy7nPENxzjmXE56hOOecywnPUJxzzuWEZyjOOedywjMU55xzOdGiw9dLWgW82YBNdgQ+yFNyWjI/L6n5eUnNz0tqLem87GZmORhDvbYWnaE0lKQ5+RgDoKXz85Kan5fU/Lyk5ufFi7ycc87liGcozjnncqLUMpQ7Cp2AIuXnJTU/L6n5eUmt5M9LSdWhOOecy59Su0NxzjmXJ56hOOecy4mSyFAkHS3pdUlLJI0vdHryTdIfJK2U9GrCvG6SZkhaHJ+7JiybEM/N65KOSpj/JUmvxGW3SFJzv5dckrSrpCclLZT0mqSL4/ySPjeSOkiaLenleF6uivNL+rwASGor6SVJj8bpkj8naZlZq34AbYGlwB7AVsDLQL9CpyvP73kYMBB4NWHeL4Dx8fV44Ofxdb94TrYGdo/nqm1cNhs4EBDwN+CrhX5vTTwvPYCB8XVn4I34/kv63MT3sG183R74JzC01M9LfD/fBe4DHo3TJX9O0j1K4Q5lCLDEzP5jZp8DU4FRBU5TXpnZM8B/k2aPAu6Or+8GRifMn2pmG8xsGbAEGCKpB9DFzF6w8Kv4Y8I2LZKZrTCzf8fXa4GFQE9K/NxY8EmcbB8fRomfF0m9gGOAOxNml/Q5yaQUMpSewNsJ05VxXqnZ2cxWQPhjBXaK8+s7Pz3j6+T5rYKk3sD+hKvxkj83sWhnHrASmGFmfl7gZuAyYHPCvFI/J2mVQoaSqrzS20pvUd/5abXnTdK2wIPAJWa2Jt2qKea1ynNjZpvMrBzoRbiy3jfN6q3+vEj6OrDSzOZmu0mKea3qnGSjFDKUSmDXhOlewLsFSkshvR9vv4nPK+P8+s5PZXydPL9Fk9SekJnca2Z/jrP93ERm9hHwFHA0pX1eDgZGSlpOKCY/XNJkSvucZFQKGcq/gL6Sdpe0FTAGqChwmgqhAhgXX48DHkmYP0bS1pJ2B/oCs+Pt/FpJQ2OrlFMTtmmR4vv4PbDQzG5MWFTS50ZSd0nbx9cdga8Aiyjh82JmE8ysl5n1Jvxn/MPMTqGEz0lWCt0qoDkewNcILXqWApcXOj3N8H6nACuAjYQrpDOBHYAngMXxuVvC+pfHc/M6CS1QgEHAq3HZbcTICi31ARxCKG6YD8yLj6+V+rkBBgAvxfPyKvB/cX5Jn5eE9zScLa28/JykeXjoFeecczlRCkVezjnnmoFnKM4553LCMxTnnHM54RmKc865nPAMxTnnXE54huKKgqRjJZmkshzt7zRJqyTNk7RA0tlx/kRJ32/AfraXdF4u0pTmGNVpfSlGsX1M0kFZbDdaUr9GHrNR76uh58+VFs9QXLEYC8widCLLlWkWwokMB66RtHMj9rE9kNcMJZpmZvubWV/gOuDPkvbOsM1oQpTbxtie5nlfroR4huIKLsbWOpjQAXNMnPdVSfcnrDNc0vT4+kxJb0h6StLvJN2Wbv9mtpLQqWy3OKtf3PY/ki5KOMZ3Jb0aH5fE2dcBfeKdzvUKro/rvCLppIT0PSXpAUmLJN1bPe6FpOviXdJ8STdkOh9m9iRhfPJz4vZ9JP1d0lxJz0oqi3cwI4HrY9r6pFovbr+zpIcUxjt5OW5b633F9X4g6V8xnVclnJfLFcb4mAl8MVP6XQkrdM9Kf/gDOAX4fXz9PGEsl3bAW8A2cf7tcb1dgOVAN0KY9WeB21Ls87Tq+YSxcFbGbSbGY2wN7Aisjvv5EvAKsA2wLfAaIRpxb2qPK3McMIMwzs7OMY09CHdBHxNiNbUBXiD0zO9G6Dld3Yl4+3RpTZg3GvhbfP0E0De+PoAQBgRgEnB8wjb1rTeNEAiTmO7tUryvEYRMTDH9jxLG1ak+L52ALoSw7N8v9HfGH8X5aJc6m3GuWY0lhAqHEIhvrJn9W9LfgW9IeoAwLsVlwBHA02b2XwBJfwL2qme/J0k6BNgAfNvM/htvGv5iZhuADZJWEjKGQ4CHzOzTuN8/A4dSN+7bIcAUM9tECBT4NDAYWEOI3VQZt59H+NN+EVgP3CnpL4Q/6mxU391sCxwE/ElbBvrbus7K6dc7nBBDipjuj5Uw0mA0Ij5eitPbEuJRdSacl3XxOKUYB89lyTMUV1CSdiD84e0ryQhX0CbpMsKV9fmEwcL+ZWZrpQYNnzrNzC5IMX9DwutNhN9BtvtNt16d/ZpZlaQhhIxwDHAB4f1msj9hALA2wEcW6oLSyXa9+gi41sx+W2tmKPrz+EwuK16H4grteOCPZrabmfU2s12BZYQ7gacIxV9nEzIXCMOpfllSV0ntCEVQufAMMFpSJ0nbAMcSitPWEq7SE9c7SWFAqu6EYqHZ9e003jlsZ2Z/BS4ByjMlRNKXCfUnv7MwXssySSfEZZK0X1y1Jm0Z1nsCODfObyupS4r39RhwRkwvknpK2im+32MldZTUGfhGpvS70uUZiiu0scBDSfMeBL4Zi2ceBb4anzGzd4BrCCMtzgQWEOoumsTC0MCTCJnDP4E7zewlM1sNPBcr4a+PaZ1PGD/8H8BlZvZeml13Bh6VNB94Gri0nvVOihXkbwA/Ao4zs4Vx2cnAmZJeJtTtVA9hPRX4gUJz4z5p1rsYOEzSK8BcYJ/k92VmjxPGTn8hrvcA0Dmel2mEyMwPEjJZ51LyaMOuxZG0rZl9Eu9QHgL+YGbJmZJzrpn5HYpriSbGSu9XCcVjDxc0Nc45wO9QnHPO5YjfoTjnnMsJz1Ccc87lhGcozjnncsIzFOeccznhGYpzzrmc+P9CkITo8JEI9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list[0], FOM_spectra_list[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list[1], FOM_spectra_list[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list[2], FOM_spectra_list[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list[3], FOM_spectra_list[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Avg Photons Detected vs FOMs.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Old method) Plotting desired photon list vs FOMs for all fluorophores and desired photons = [*range(100, 3000, 100)] all on same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(desired_photons_list, FOM_spectra_list[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(desired_photons_list, FOM_spectra_list[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(desired_photons_list, FOM_spectra_list[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(desired_photons_list, FOM_spectra_list[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for desired_photons = [*range(1000, 10000, 1000)] & x = [1, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(1000, 10000, 1000)]\n",
    "FOM_spectra_list_large_range = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list_large_range = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_large_range[0], FOM_spectra_list_large_range[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_large_range[1], FOM_spectra_list_large_range[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_large_range[2], FOM_spectra_list_large_range[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_large_range[3], FOM_spectra_list_large_range[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for Large Range.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for desired_photons = [*range(1000, 100000, 1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(1000, 100000, 1000)]\n",
    "FOM_spectra_list_huge_range = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list_huge_range = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_huge_range[0], FOM_spectra_list_huge_range[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_huge_range[1], FOM_spectra_list_huge_range[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_huge_range[2], FOM_spectra_list_huge_range[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_huge_range[3], FOM_spectra_list_huge_range[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for Ranging to 100000.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for desired_photons = [*range(100000, 200000, 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(100000, 200000, 10000)]\n",
    "FOM_spectra_list_range_100000_to_200000 = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list_range_100000_to_200000 = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_range_100000_to_200000[0], FOM_spectra_list_range_100000_to_200000[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_range_100000_to_200000[1], FOM_spectra_list_range_100000_to_200000[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_range_100000_to_200000[2], FOM_spectra_list_range_100000_to_200000[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_range_100000_to_200000[3], FOM_spectra_list_range_100000_to_200000[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for Ranging 100000 to 200000.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for desired_photons = [*range(500000, 1000000, 100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(500000, 1000000, 100000)]\n",
    "FOM_spectra_list_range_500000_to_1000000 = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 1])\n",
    "avg_photons_detected_list_range_500000_to_1000000 = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_list_range_500000_to_1000000[0], FOM_spectra_list_range_500000_to_1000000[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_list_range_500000_to_1000000[1], FOM_spectra_list_range_500000_to_1000000[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_list_range_500000_to_1000000[2], FOM_spectra_list_range_500000_to_1000000[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_list_range_500000_to_1000000[3], FOM_spectra_list_range_500000_to_1000000[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for Ranging 500000 to 1000000.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for x = [0, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2382.0181284562573, 3368.682343081416, 4125.776423036365, 4764.036256912515, 5326.3544588650175, 5834.728972777182, 6302.227586342764, 6737.364686162832, 7146.0543853687695], [129.73862788726467, 183.47812712184597, 224.71389520501486, 259.47725577452934, 290.10439126347376, 317.79343825261844, 343.2561448284516, 366.95625424369194, 389.2158836617941], [64.92683121898445, 91.82040527179667, 112.45657044573016, 129.8536624379689, 145.18080816930475, 159.037607102317, 171.78024882089744, 183.64081054359335, 194.78049365695327], [143.35082317802897, 202.72867831571597, 248.2909090511684, 286.70164635605795, 320.54218525662526, 351.13637099410704, 379.2706283654584, 405.45735663143194, 430.0524695340869]]\n"
     ]
    }
   ],
   "source": [
    "desired_photons_list = [*range(100, 1000, 100)]\n",
    "concentration_0_1_1_1 = calc_multiple_FOMs(desired_photons_list, [0, 1, 1, 1])\n",
    "concentration_0_1_1_1_photons = calc_photons_detected_per_fluor(desired_photons_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "# ax1.scatter(concentration_0_1_1_1_photons[0], concentration_0_1_1_1[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(concentration_0_1_1_1_photons[1], concentration_0_1_1_1[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(concentration_0_1_1_1_photons[2], concentration_0_1_1_1[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(concentration_0_1_1_1_photons[3], concentration_0_1_1_1[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Fluorophores Except mEmerald')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for [0, 1, 1, 1].pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for x = [1, 0, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    }
   ],
   "source": [
    "desired_photons_list = [*range(100, 1000, 100)]\n",
    "concentration_1_0_1_1 = calc_multiple_FOMs(desired_photons_list, [1, 0, 1, 1])\n",
    "concentration_1_0_1_1_photons = calc_photons_detected_per_fluor(desired_photons_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wY8I+/DLh7knS/xC+s1fisDsJ69mQbOM/DfQnbN9JwInx2M4W54vAOEKhp9WE/fPLZvZJA+O/DhxH+O5WEY7liwjnzg6E420l4Zj7HGEfbUiqxHPytTNwPuEW+w/j7dczCRcaB2U5rhs8f0RvEB4drCQk3W/E8wqknRMa32pbx8wWEp5rziIk58GEci0pNxLOL/MJhWz+QbiDkvzBkfonwweEY+IHMbc0x08JBaX+nfgefp9totRD5LxTKJ79AuFBapP/T+accxD+rkEotDe60LEUK4W/t9xuZhVZRi0akr4E/N7M0h+TFYW8Vncn6YR4C6AH4XnN3zxROueci49mjoq3l1PPbO/NNl2h5Ltu2K8Tbl0sJVxafzPPy3POOVcaRHjk8y7hNuwi8le2pdla7Tasc845V6pKotUR55xzrpCKosLiQthpp52sd+/ehQ7DOedKyty5c1ebWWOVp7RJ7TZZ9u7dm+rq6kKH4ZxzJUVSgzV6tWV+G9Y555zLwpOlc845l0VRJ8tYvdSzku6P3ZcrtA84L76OSow7UdIShXYD06t8cs4557ZasT+zvIDw35vuiX7XmVl6Y8MDgTGENtd2BR6WtGesbi1n69evp6amho8/blItaa4VdOnShYqKCrbZpqmNvDjnXPMVbbKMFXgfTaj38TtZRj+O0Bj0OmCZpCWEhqJnNT7Z5mpqaujWrRu9e/dGmzVo4ArJzHj77bepqamhT58+hQ7HOdcOFfNt2OsJlZxvTOs/QdJ8STfHavQgND+UbAuthgztWUo6V1K1pOpVq1alD+bjjz9mxx139ERZZCSx4447+hW/c65gijJZKjRn9VZsKSHpBkLbbkMJLUL8IjVJhtlsUTWRmU02s0ozq+zZM/PfhDxRFif/Xtq37ld1R1eo/tX9qu7ZJ3KuBRXrbdhRwLGxAE8XoLuk281sXGoESTeyqQ3KGjZvOLSC3BqCdc6VgLWfrG2027l8K8orSzObaGYVZtabUHDnUTMbJ6lXYrQTCE1+QWjjcYykzpL6ENq5m9OqQbeiqqoqevbsydChQ+tfCxcubLXlL1++nEGDBmUcdsghh3hlD865NqdYrywbco2koYRbrMsJrZpgZgskTQMWEhoP/VZTS8KWmlNOOYXf/va3eZt/XV0dZWWltns451x+FOWVZZKZPW5mx8TPXzWzwWY2xMyONbPaxHiTzKyfme3VAi1pF8zy5csZMGAA55xzDoMGDeK0007j4YcfZtSoUfTv3585cxq+YH788cf53Oc+x8knn8yee+7JpZdeyh133MHw4cMZPHgwS5cuBWDVqlV85StfYf/992f//fdn5szQePnll1/Oueeey+GHH87pp5/O8uXLOeiggxg2bBjDhg3jX//61xbL/OijjxgzZgxDhgzhlFNO4aOPPsrPhnHtWrdO3Rrtdi7f/NKhGbpf1X2zZyfdOnVjzcQ1zZ7vkiVL+POf/8zkyZPZf//9ufPOO5kxYwbTp0/nyiuv5Pjjj2fq1KnMmDGjfppZs8K/ZJ577jkWLVrEDjvsQN++fTnnnHOYM2cOv/rVr/jNb37D9ddfzwUXXMCFF17I6NGjee211zjiiCNYtGgRAHPnzmXGjBl07dqVDz/8kIceeoguXbrw8ssvM3bs2C1usd5www186lOfYv78+cyfP59hw4Y1e/2dS9cSx5VzzeHJshnyVeigT58+DB48GIB99tmHww47DEkMHjyY5cuXAw3fht1///3p1Ss82u3Xrx+HH344AIMHD+axxx4D4OGHH97sGeeaNWtYuzbEfuyxx9K1a1cgVNIwYcIE5s2bR8eOHXnppZe2WN6TTz7J+eefD8CQIUMYMmRIS2wC55wrKp4si1Dnzp3rP3fo0KG+u0OHDtTV1TV72o0bNzJr1qz6pJi07bbb1n++7rrr2GWXXXjuuefYuHEjXbp0ybhM/1uHc6Wltra2/ke1y03RP7N0Le/www/f7Kp03rx5Gcd7//336dWrFx06dOC2225jw4Yty0wdfPDB3HHHHQC88MILzJ8/Py8xO+daxowZMygvL68vq+By48myGQpZ6GDq1Kmb/XUkU+Gbhvz617+murqaIUOGMHDgQH7/+99nHO+8887jlltuYcSIEbz00kubXXWmfPOb3+SDDz5gyJAhXHPNNQwfPnyr18k5l191dXWMHz8eM2P8+PFZ71S5TWS2RUU37UJlZaWlF1ZZtGgRe++9d4Eictn49+Nc8zz11FMcfvjhfPzxx3Tp0oWHHnqI0aNHN2kekuaaWWWeQixafmXpnHPtxMiRIykvD9VmV1RUMGLEiAJHVDo8WTrnXDtRVlZGVVUVkqiqqvKKR5rAt5Rzbqvl67/GLn9Gjx7NihUrvDRsE/mVpXNuq3kF56XJE2XTebJ0zjnnsvBk6ZxzzmXhybJE/fOf/6SyspK9996bAQMG8L3vfQ+A8ePHc/fddxc4OtdeeAXnrr3wAj4l6IUXXmDChAn8/e9/Z8CAAdTV1TF58uRmz9fMMDM6dOiQsdu5dF6Yx7UXfhYsMrk00XXNNddw2WWXMWDAACAUBz/vvPPq5/Hkk09y4IEH0rdv382uMq+99lr2339/hgwZwo9+9KP65e29996cd955DBs2jKeeemqz7p/85CdceOGF9fO48cYb+c53vtNKW8M554pE6uqhvb0++9nPWrqFCxdu0a9R69eb3Xqr2caN4X39+qZNn8GyZcusY8eONn/+fNuwYYMNGzbMzjzzTNu4caPdd999dtxxx9l+++1n8+bNyzj9GWecYSeeeKJt2LDBFixYYP369TMzswceeMC+9rWv2caNG23Dhg129NFH2xNPPGHLli0zSTZr1qz65Se7P/jgA+vbt6998sknZmY2cuRImz9/frPXc2s0+ftxroisXLmy0CG0CKDaiuAc3tovv7JsjilT4PTToUOH8D5lSovMNtVEV4cOHRpsoqsxxx9/PB06dGDgwIG8+eabADz44IM8+OCD7LfffgwbNozFixfz8ssvA7DHHntsVpNHsnvbbbfl0EMP5f7772fx4sWsX7++vvkw51xuvPLy0lfUyVJSR0nPSro/du8g6SFJL8f3HolxJ0paIulFSUe0SoDjxjXevZWyNbO1zz77MHfu3JymDz8Ew/vEiROZN28e8+bNY8mSJZx99tkAW1SQnt59zjnnUFVVxZ/+9CfOPPPM5q2cc+2MV17eNhR1sgQuABYlui8FHjGz/sAjsRtJA4ExwD7AkcDvJHXMe3S33954d55cdNFFXHnllfWNMW/cuJFf/vKXjU5zxBFHcPPNN/PBBx8AsGLFCt56662clnfAAQfw+uuvc+eddzJ27NjmBe9cOzNr1ixWrFgBQE1NDbNnzy5wRG5rFG2ylFQBHA3clOh9HHBL/HwLcHyi/11mts7MlgFLgPy3FTV2LNx6K2zcGN5bKZEMGTKE66+/nrFjx7L33nszaNAgamtrG53m8MMP59RTT2XkyJEMHjyYE088kbVrc69t5eSTT2bUqFH06NEj+8jOuXpeeXnbULRNdEm6G7gK6AZ8z8yOkfSemW2fGOddM+sh6bfAbDO7Pfb/I/BPM7s7bZ7nAucC7L777p999dVXN1umNwHVsGOOOYYLL7yQww47rGAx+PfjStWMGTM4+OCDeeqppxg1alShw2kWb6KriEg6BnjLzBp+MJc2SYZ+W/wKMLPJZlZpZpU9e/ZsVoztxXvvvceee+5J165dC5oonStlqcrLSz1RtmfFWinBKOBYSUcBXYDukm4H3pTUy8xqJfUCUg/daoDdEtNXACtbNeI2avvtt69/Nuqc23peeXlpK8orSzObaGYVZtabUHDnUTMbB0wHzoijnQH8NX6eDoyR1FlSH6A/MKeVw3auaHS/qju6QvWv7ld1L3RIzpW0Yr2ybMjVwDRJZwOvAScBmNkCSdOAhUAd8C0z21C4MJ0rLG86y7mWVfTJ0sweBx6Pn98GMj44M7NJwKRWC8w551y7UZS3YZ1zzrli4smyBFVVVdGhQwfmz59f32/QoEE5VYWXyX333cfChQtbKLrNnXbaaey1114MGjSIs846i/Xr12ccr2PHjgwdOpShQ4dy7LHH5iWW9sSbznKuZXmyLFEVFRVMmtQyd53znSwXL17M888/z0cffcRNN92UcbyuXbvWV8U3ffr0vMTSnqyZuAb7kdW/vCkt55rHk2ULyFZ7TlPk0kQXhEoCFixYwIsvvrjFPB588EFGjhzJsGHDOOmkk+qruOvduzeXXHIJw4cPZ/jw4SxZsoR//etfTJ8+nYsuuoihQ4eydOlSDjnkEKqrqwFYvXo1vXv3BsIV7X//939z5JFH0r9/fy6++OKsyzzqqKOQhCSGDx9OTU1Ni20r57JpyWPTtW+eLJspH60JLFmyhAsuuID58+ezePFi7rzzTmbMmMHPf/5zrrzySiBUqn7xxRfXd6esXr2an/70pzz88MM888wzVFZWblZvbPfu3ZkzZw4TJkzg29/+NgceeCDHHnss1157LfPmzaNfv36NxjZv3jymTp3K888/z9SpU3n99dezLhNg/fr13HbbbRx55JEZ5/vxxx9TWVnJiBEjuO+++7Ziqzm3OW/pw7Wkoi8NW8zSWxNYtGgRZWXN36SpJrqARpvoOvXUU5k0aRLLli2r7zd79mwWLlxYX1PIJ598wsiRI+uHpypCHzt27GaNOufqsMMO49Of/jQAAwcO5NVXX+W9995rdJkA5513HgcffDAHHXRQxvm+9tpr7LrrrrzyyisceuihDB48OGvidq4h+To2Xfvle08zZGpNYPTo0c2eb7YmulLKysr47ne/y89+9rP6fmbGF7/4RaY00LampIyfk8rKyti4cSMQrvgaiq1jx47U1dVlXeYVV1zBqlWr+MMf/pBxOMCuu+4KQN++fTnkkEN49tlnPVm6rZavY9O1X34bthmKoTWB8ePH8/DDD7Nq1SoARowYwcyZM1myZAkAH3744WbV1U2dOrX+PXX1161bt81aIOndu3d9e5l3371ZXfQZNbbMm266iQceeIApU6bQoUPm3e3dd99l3bp1QLiNPHPmTAYOHJj7RnAuTTEcm65t8WTZDGVlZVRVVSGJqqqqgtzm6dSpE+eff35925Q9e/akqqqKsWPHMmTIEEaMGMHixYvrx1+3bh0HHHAAv/rVr7juuusAGDNmDNdeey377bcfS5cu5Xvf+x433HADBx54IKtXr84aQ2PL/MY3vsGbb77JyJEjGTp0KD/+8Y8BqK6u5pxzzgFCayKVlZXsu+++fP7zn+fSSy/1ZOmapRiOTde2FG0TXflWWVlpqRKfKVvbBFRtbW1JVJLcu3dvqqur2WmnnQodylbxJrpcU5XKsVlKvIkut9X8YHSuOPmx6VqK35toR7a2hh/nnGvvPFmmMbMGS4m6wmkrjwu6X9V9sxZAunXq5rXrOFcC/DZsQpcuXXj77bfbzIm5rTAz3n77bbp06VLoUJrNm85yrjT5lWVCRUUFNTU19X/DcMWjS5cuVFRUFDoM51w75ckyYZtttqFPnz6FDsM551yR8duwzrUibzrLudJUlFeWkroATwKdCTHebWY/knQ58DUgdZ/0+2b2jzjNROBsYANwvpk90OqBO5dFWy/M4/9rdG1VsV5ZrgMONbN9gaHAkZJS9VVdZ2ZD4yuVKAcCY4B9gCOB30nqWIC4nWu3vJUP15YVZbK04IPYuU18NVZE9TjgLjNbZ2bLgCXA8DyH6ZyL0lv5SFb471xbUJTJEkBSR0nzgLeAh8zs6ThogqT5km6W1CP2KwdeT0xeE/ulz/NcSdWSqr3Eq3MtJ1MrH861JUWbLM1sg5kNBSqA4ZIGATcA/Qi3ZmuBX8TRM9UisMWVqJlNNrNKM6vs2bNnXuJ2rj3yVj5cW1e0yTLFzN4DHgeONLM3YxLdCNzIplutNcBuickqgJWtGadz7Zm38uHauqJMlpJ6Sto+fu4KfAFYLClZzO4E4IX4eTowRlJnSX2A/sCcVgzZuXZv9OjRrFixglGjRhU6FOdaXLH+/OsF3BJLtHYAppnZ/ZJukzSUcIt1OfB1ADNbIGkasBCoA75lZhsKErlz7Zj/bcS1Vd6epXPOuZx5e5bOOeecy8iTpXPOOZdFsT6zdK5VeTuTzrnG+JWlc3g7k865xnmydM4557LwZOmcc85l4cnSObydSedc47yAj3OUdjuT3oakc/nnV5bOlTBvQ9K51uHJ0rkS5W1IOtd6PFk6V6K8DUnnWo8nS+dKlLch6Vzr8WTpXInyNiSdaz1+dDlXwlJtSHppWOfyy68snStxniidyz9Pls4551wWniydc865LPKaLCWNkrRt/DxO0i8l7ZHDdF0kzZH0nKQFkq6I/XeQ9JCkl+N7j8Q0EyUtkfSipCPyt1bOOefam3xfWd4AfChpX+Bi4FXg1hymWwccamb7AkOBIyWNAC4FHjGz/sAjsRtJA4ExwD7AkcDvJHVs4XVxzjnXTuU7WdaZmQHHAb8ys18BWWuotuCD2LlNfKXmc0vsfwtwfPx8HHCXma0zs2XAEmB4i62Fc865di3ffx1ZK2ki8FXgoHi1t00uE8Zx5wKfAf6fmT0taRczqwUws1pJO8fRy4Fk9SU1sV/6PM8FzgXYfffdt3KVXGvqflX3zRpi7tapW0lXeu6cK035vrI8hXBL9Swze4OQwK7NZUIz22BmQ4EKYLikQY2MrkyzyDDPyWZWaWaVPXv2zCUMV2DJRJmp2znnWkNek2VMkPcAnWOv1cC9TZzHe8DjhGeRb0rqBRDf34qj1QC7JSarAFZubdzOOedcUr5Lw34NuBv4Q+xVDtyXw3Q9JW0fP3cFvgAsBqYDZ8TRzgD+Gj9PB8ZI6iypD9AfmNMya+Gcc669y/czy28RCto8DWBmLyeeMzamF3BLfG7ZAZhmZvdLmgVMk3Q28BpwUpzvAknTgIVAHfAtM9vQ8qvjWlu3Tt22eGbpnHOtLd/Jcp2ZfSKFR4qSysjwLDGdmc0H9svQ/23gsAammQRMala0rugUa2Ge2tpar2bOuXYk3wV8npD0faCrpC8Cfwb+ludlOpdXM2bMoLy8nJkzZxY6FOdcK8l3srwUWAU8D3wd+Afwgzwv07m8qaurY/z48ZgZ48ePp66urtAhOedaQV5vw5rZRuDG+HKu5M2aNYsVK1YAUFNTw+zZsxk9enSBo3LO5Vtek6WkZWT+v2PffC7XuXwZOXIk5eXlLF26lIqKCkaMGFHokJxzrSDfBXwqE5+7EEqv7pDnZTqXN2VlZVRVVXHwwQdTVVVFWZm3n+5ce5Dv27Bvp/W6XtIM4H/zuVzn8mn06NGsWLHCS8M6147k+zbssERnB8KVpv9RzpU8T5TOtS/5vof0i8TnOmA5cHKel+mcc861qHzfhv18PufvnHPOtYa8JEtJ32lsuJn9Mh/Ldc455/IhX1eW/lzSOedcm5GXZGlmV+Rjvs4551wh5Ls0bBfgbGAfwv8sATCzs/K5XOecc64l5btu2NuA/wKOAJ4gNMrsTd0755wrKflOlp8xsx8C/zGzW4CjgcF5XqZzzjnXovL9P8v18f09SYOAN4DeeV6mawXdr+q+RaPMxdr2pHPONVe+k+VkST2AHwLTge3iZ1fikokyU7dzzrUlebkNK2mhpMuAx8zsXTN7wsz6mtnOZvaHHKbfTdJjkhZJWiDpgtj/ckkrJM2Lr6MS00yUtETSi5KOyMd6Oeeca5/ydWU5FhgDPChpNTAFmGpmtTlOXwd818yekdQNmCvpoTjsOjP7eXJkSQPj8vYBdgUelrSnmW1oiZVxzjnXvuXlytLMnjOziWbWD7gA2AN4WtKjkr6Ww/S1ZvZM/LwWWASUNzLJccBdZrbOzJYBS4DhzV4R16Bunbo12p0PtbW5/tZyzrmWle/SsJjZbDO7EDgd6AH8tinTS+oN7Ac8HXtNkDRf0s3xeSiERPp6YrIaMiRXSedKqpZUvWrVqiauiUtaM3EN9iOrf+W7cM+MGTMoLy9n5syZeV2Oc85lktdkKWl/Sb+U9CpwBTCZxq8Q06ffDrgH+LaZrQFuAPoBQ4FaNrVqogyT2xY9zCabWaWZVfbs2bNJ6+IKp66ujvHjx2NmjB8/nrq6ukKH5JxrZ/JVwOdKSUsJyW0lMMrMPmdmN5jZ6hznsQ0hUd5hZn8BMLM3zWyDmW0EbmTTrdYaYLfE5BVxua4NmDVrFitWrACgpqaG2bNnFzgi51x7k68ry3XAl+JV3M/NrKYpE0sS8EdgUbKFEknJFndPAF6In6cDYyR1ltQH6A/MadYauKIxcuRIysvDDYmKigpGjBhR4Iicc+1NsVakPgr4KvC8pHmx3/eBsZKGEm6xLge+Hpe3QNI0YCGhJO23vCRs21FWVkZVVRUHH3wwVVVVlJXl++/Bzjm3OZlt8WivXaisrLTq6upCh+GaoLa2ll69emUf0TmXN5LmmllloeNobXkvDetcS/FE6ZwrlLzcz5I0rLHhqf9QOuecc6UgXw9/qoEFQOrPjMm/dhhwaJ6W65xzzrW4fCXL7wJfAT4C7gLuNbMP8rQs55xzLq/yVd3ddWY2GphA+P/jI5KmxZKszjnnXEnJawGfWE/rX4EHCRUI7JnP5TnnnHP5kK8CPn0JrYAcR6iz9S5gkpl9nI/lOeecc/mUr2eWS4D5hKvKNcDuwHmhYh5I1srjnHPOFbt8Jcsfs6ki8+3ytAznnHOuVeSrurvL8zFf55xzrhDyVsBH0pckPSlptaRVkp6QdFS+luecc87lS74K+HyNUMn5xYQKCgAqgaslVZjZ5Hws1znnnMuHfD2zvBAYbWbvJPo9KulLwAxCI9DOOedcSchXslRaogTAzN5OlYh1+dH9qu6s/WRtfXe3Tt1YM3FNASNyzrnSl69nlmsk7ZveM/Zbm2F810KSiTJTt3POuabLZ92w0yX9CZhL+BvJ/sAZwLg8LdM555zLi3zVDTuDUL1dB2A8cFb8PCIOcyWitra20CE451zB5SVZStrdzN40s/81s6+Y2X+b2Q/N7I0cp99N0mOSFklaIOmC2H8HSQ9Jejm+90hMM1HSEkkvSjoiH+tVCrp16tZod1PMmDGD8vJyZs6c2dywnHOupMnMso/V1JlKz5jZsPj5HjP7ShOn7wX0MrNnJHUj3Mo9nnCV+o6ZXS3pUqCHmV0iaSAwhXA1uyvwMLCnmW1oaBmVlZVWXV3d0OB2r66ujgEDBrB06VI+85nPsGjRIsrK8nXX3jlXKiTNNbPKQsfR2vJVwCdZ5LVvUyc2s1ozeyZ+XgssAsoJFbPfEke7hZBAif3vMrN1saWTJYTE6bbSrFmzWLFiBQA1NTXMnj27wBE551zh5CtZWgOfm0xSb2A/4GlgFzOrhZBQgZ3jaOWE1k1SamK/9HmdK6laUvWqVauaE1abN3LkSMrLwyasqKhgxIgRBY7IOecKJ1/Jcl9JayStBYbEz2skrZWU85/+JG0H3AN828wamy7Tnze3SNJmNtnMKs2ssmfPnrmG0S6VlZVRVVWFJKqqqvwWrHOuXctXadiOZtbdzLqZWVn8nOrunss8JG1DSJR3mNlfYu834/PM1HPNt2L/GmC3xOQVwMqWWZv2a/To0axYsYJRo0YVOhSoq4PbbgOz8F5XV+iInHPtSN4qUm8OhWp+/ggsSmv7cjrhv5rE978m+o+R1FlSH6A/MKe14m3LevXqVegQgilT4PTToUOH8D5lSqEjcs61I3kpDdtckkYDTwHPAxtj7+8TnltOIzQm/RpwUqpaPUmXEf7PWUe4bfvPxpbhpWFLjFlIlCkbN4JXnehcq2uvpWGL8kFUrLigoTPhYQ1MMwmYlLegXGHdfvuW3V/9amFicc61O0WZLJ3bwtix4X3cuJAoU93OOdcKPFm60lBWtulK0q8onXOtrCgL+Lh2wEu3OudKiCdLVxheutU5V0I8WbrCGDeu8W7nnCsinixdYWQq3eqcc0XKC/i4wvDSrc65EuLJ0hWGl251zpUQvw3rWoaXbnXOtWGeLF3L8NKtzrk2zJOlaxleutU514Z5snQtw0u3OufaMC/g41qGl251zrVhnixdy/DSrc65NsxvwzrnnHNZeLJ0DfO/gzjnHODJ0jXG/w7inHNAkSZLSTdLekvSC4l+l0taIWlefB2VGDZR0hJJL0o6ojBRt0H+dxDnnAOKNFkCVcCRGfpfZ2ZD4+sfAJIGAmOAfeI0v5PUsdUibcv87yDOOQcUaWlYM3tSUu8cRz8OuMvM1gHLJC0BhgOz8hXf1uh+VXfWfrK2vrtbp26smbimgBHlwP8O4pxzQPFeWTZkgqT58TZtj9ivHHg9MU5N7LcFSedKqpZUvWrVqnzHuplkoszUXZRSfweRwntZUf62cs65vCulZHkD0A8YCtQCv4j9lWFcyzQDM5tsZpVmVtmzZ8+8BNkSamtrCx2Cc865hJJJlmb2ppltMLONwI2EW60QriR3S4xaAaxs7fhayowZMygvL2fmzJktP3P/K4hzzm2VkkmWknolOk8AUiVlpwNjJHWW1AfoD8xp7fiy6dapW6PdAHV1dYwfPx4zY/z48dS1dDLzv4I459xWKcqHUJKmAIcAO0mqAX4EHCJpKOEW63Lg6wBmtkDSNGAhUAd8y8w2FCDsRuVSmGfWrFmsWLECgJqaGmbPns3o0aNbLohx40KSTHY755zLqiiTpZllKnb5x0bGnwRMyl9ErWPkyJGUl5ezdOlSKioqGDFiRMsuINNfQbweV+ecy6pkbsO2B2VlZVRVVSGJqqoqylq69OnYsXDrrbBxY3j3v4I451xOZJax4GibV1lZadXV1YUOI6Pa2lp69eqVfUTnnGtlkuaaWWWh42htfmVZhDxROudccfFk6ZxzzmXhybKU+f8mnXOuVXiyLGX+v0nnnGsVnixLmTeh5ZxzrcKTZSnzJrScc65VFGWlBC5H3oSWc861Ck+WpSzVhBZ4TTzOOZdHfhvWOeecy8KTpXPOOZeFJ0vnnHMuC0+WxcQrGXDOuaLkybKYeCUDzjlXlDxZFhOvZMA554qSJ8ti4pUMOOdcUSrKZCnpZklvSXoh0W8HSQ9Jejm+90gMmyhpiaQXJR1RmKhbgDfO7JxzRakokyVQBRyZ1u9S4BEz6w88EruRNBAYA+wTp/mdpI6tF2oLSlUyIIX3Mq8zwjnnikFRJkszexJ4J633ccAt8fMtwPGJ/neZ2TozWwYsAYa3RpzOOefah6JMlg3YxcxqAeL7zrF/OfB6Yrya2G8Lks6VVC2petWqVXkN1jnnXNtRSsmyIcrQzzKNaGaTzazSzCp79uyZ57Ccc861FaWULN+U1Asgvr8V+9cAuyXGqwBWtnJszjnn2rBSSpbTgTPi5zOAvyb6j5HUWVIfoD8wpwDxbclr5HHOuTahKItbSpoCHALsJKkG+BFwNTBN0tnAa8BJAGa2QNI0YCFQB3zLzDYUJPB0qRp5Tj99Uz9vSss550qOzDI+3mvzKisrrbq6Or8LMQtV16Vs3Bj+FuKccyVK0lwzqyx0HK2tlG7Dlh6vkcc559qEorwN22akauAZNy4kSq+RxznnSpIny3xK1cgD/qzSOedKmN+Gdc4557LwZOmcc85l4cnSOeecy8KTpXPOOZeFJ0vnnHMuC0+WzjnnXBaeLJ1zzrksPFk655xzWXiybApvRcQ559olT5ZNkWpFpEOH8D5lSqEjcs451wo8WTbFuHGNdzvnnGuTPFk2hbci4pxz7ZJXpN4U3oqIc861S54sm8JbEXHOuXap5JKlpOXAWmADUGdmlZJ2AKYCvYHlwMlm9m6hYnTOOde2lOozy8+b2VAzq4zdlwKPmFl/4JHY7ZxzzrWIUk2W6Y4DbomfbwGOL1wozjnn2ppSTJYGPChprqRzY79dzKwWIL7vnGlCSedKqpZUvWrVqlYK1znnXKkruWeWwCgzWylpZ+AhSYtzndDMJgOTASorKy1fATrnnGtbSu7K0sxWxve3gHuB4cCbknoBxPe3Chehc865tkZmpXOBJWlboIOZrY2fHwJ+DBwGvG1mV0u6FNjBzC7OMq9VwKuJXjsBq/MUej553K2rVOOG0o3d4259jcW+h5n1bM1gikGpJcu+hKtJCLeQ7zSzSZJ2BKYBuwOvASeZ2TtNnHd1onRtyfC4W1epxg2lG7vH3fpKOfZ8Kalnlmb2CrBvhv5vE64unXPOuRZXcs8snXPOudbmyXKTyYUOYCt53K2rVOOG0o3d4259pRx7XpTUM0vnnHOuEPzK0jnnnMvCk6VzzjmXRbtPlpKOlPSipCXxP5pFQ9Jukh6TtEjSAkkXxP47SHpI0svxvUdimolxXV6UdEThogdJHSU9K+n+2F0qcW8v6W5Ji+O2H1kKsUu6MO4nL0iaIqlLMcYt6WZJb0l6IdGvyXFK+qyk5+OwX0tSgWK/Nu4r8yXdK2n7Yos9U9yJYd+TZJJ2Kra4i4qZtdsX0BFYCvQFOgHPAQMLHVcivl7AsPi5G/ASMBC4Brg09r8U+Fn8PDCuQ2egT1y3jgWM/zvAncD9sbtU4r4FOCd+7gRsX+yxA+XAMqBr7J4GjC/GuIGDgWHAC4l+TY4TmAOMBAT8E/hSgWI/HCiLn39WjLFnijv23w14gFBBy07FFncxvdr7leVwYImZvWJmnwB3EVowKQpmVmtmz8TPa4FFhJNiQ62sHAfcZWbrzGwZsISwjq1OUgVwNHBToncpxN2dcGL5I4CZfWJm71ECsRP+N91VUhnwKWAlRRi3mT0JpFca0qQ4Faq17G5msyycxW+lFVobyhS7mT1oZnWxczZQUWyxN7DNAa4DLiY0UJFSNHEXk/aeLMuB1xPdNbFf0ZHUG9gPeJqGW1kppvW5nnAQbkz0K4W4+wKrgD/FW8g3KVStWNSxm9kK4OeEGqxqgffN7EGKPO6EpsZZHj+n9y+0swhXXFDksUs6FlhhZs+lDSrquAulvSfLTPfbi+6/NJK2A+4Bvm1maxobNUO/Vl8fSccAb5nZ3FwnydCvUN9DGeF21Q1mth/wHxpvTLwoYo/P+I4j3DbbFdhW0rjGJsnQr+j2fRqOs+jil3QZUAfckeqVYbSiiF3Sp4DLgP/NNDhDv6KIu5Dae7KsIdyzT6kg3LoqGpK2ISTKO8zsL7F3Q62sFMv6jAKOlbSccGv7UEm3U/xxp2KpMbOnY/fdhORZ7LF/AVhmZqvMbD3wF+BAij/ulKbGWcOm253J/gUh6QzgGOC0eIsSijv2foQfVs/F47QCeEbSf1HccRdMe0+W/wb6S+ojqRMwBphe4JjqxZJmfwQWmdkvE4OmA2fEz2cAf030HyOps6Q+QH/CA/lWZWYTzazCzHoTtumjZjaOIo8bwMzeAF6XtFfsdRiwkOKP/TVghKRPxf3mMMIz7mKPO6VJccZbtWsljYjre3pimlYl6UjgEuBYM/swMahoYzez581sZzPrHY/TGkJhwjeKOe6CKnQJo0K/gKMIpUyXApcVOp602EYTbnPMB+bF11HAjsAjwMvxfYfENJfFdXmRIiipBhzCptKwJRE3MBSojtv9PqBHKcQOXAEsBl4AbiOUZiy6uIEphOeq6wkn6bO3Jk6gMq7rUuC3xBrJChD7EsIzvtQx+vtiiz1T3GnDlxNLwxZT3MX08urunHPOuSza+21Y55xzLitPls4551wWniydc865LDxZOuecc1l4snTOOeey8GTpSpqkE2KLCQNaaH7jJa2SNE/SQklfi/0vl/S9Jsxne0nntURMjSwjFeuzCq11PCDpwBymO17SwK1c5latV1O3n3PFxpOlK3VjgRmEyg9aylQzG0r4j+iVknbZinlsD+Q1WUZTzWw/M+sPXA38RdLeWaY5ntCyxNbYntZZL+eKiidLV7JinbmjCH8MHxP7fUnStMQ4h0j6W/x8tqSXJD0u6UZJv21s/mb2FuHP13vEXgPjtK9IOj+xjO8otCH5gqRvx95XA/3iFeq1Cq6N4zwv6ZREfI9rU/uZd6TaCJR0dby6nS/p59m2h5k9BkwGzo3T95P0f5LmSnpK0oB45XkscG2MrV+m8eL0uyi0z/hcfB2Yvl5xvIsk/TvGeUViu1ym0B7iw8BeOFfCygodgHPNcDzwf2b2kqR3JA0DHgL+IGlbM/sPcAowVdKuwA8J9byuBR4ltNnXIEl9Ca2QLIm9BgCfJ7Qt+qKkG4AhwJnAAYSKpp+W9ASh8vVB8QoVSV8h1Ay0L7AT8G9JT8b57gfsQ6hncyYwStJC4ARggJmZEg0KZ/EM8PX4eTLwDTN7WdIBwO/M7FBJ0wm1Kt0dY3skfTzgUODXwBNmdoKkjsB2GdbrcEJ1aMPj+k+XdDChAvoxcd3KYly5VqzvXNHxZOlK2VhCU2AQKmwfa2bPSPo/4MuS7ia0qXkxoa7UJ8zsHQBJfwb2bGC+p0gaDawDvm5m78SLvb+b2TpgnaS3gF0IVRLeGxMzkv4CHMSWdQyPBqaY2QZCpeFPAPsDawj1btbE6ecBvQntIn4M3CTp78D9OW6T1FXpdoSK1P+sTY3Zd95i5MbHO5RQ/ycx7vcVWjdJOjy+no3d2xGSZzfCdvkwLqdo6lx2bmt4snQlSdKOhJP5IEkGdARM0sXAVOBbhMZu/21ma5XIBDmYamYTMvRfl/i8gXD85DrfxsbbYr5mVidpOCHJjwEmENY3m/0IFah3AN5LXQE2ItfxGiLgKjP7w2Y9w+1or0vTtRn+zNKVqhOBW81sDwstJ+wGLCNcwT1OuN36NULihNCixuck9ZBUBnylheJ4EjheobWPbQm3Tp8i3OrtljbeKZI6SuoJHEwjrXzEK75Pm9k/gG8TbuE2StLnCM8rb7TQ7ukySSfFYZK0bxy1PrYs4z0CfDP27yipe4b1egA4K8aLpHJJO8f1PUFSV0ndgC9ni9+5YubJ0pWqscC9af3uAU6NtwzvB74U3zGzFcCVwNPAw4Rmt95vbhBm9gxQRUh8TwM3mdmzZvY2MDMW6Lk2xjqf8Jz0UeBiC80hNaQbcL+k+cATwIUNjHdKLGzzEvB94CtmtigOOw04W9JzwAJC49AQbllfpPCXk36NjHcB8HlJzxOeN+6Tvl5m9iBwJzArjnc30C1ul6mEVjjuIfyAcK5keasjrt2QtJ2ZfRCvLO8Fbjaz9ITrnHNb8CtL155cHgvQvEC4ZXtfQaNxzpUMv7J0zjnnsvArS+eccy4LT5bOOedcFp4snXPOuSw8WTrnnHNZeLJ0zjnnsvj/74tWAd2bjzkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(concentration_1_0_1_1_photons[0], concentration_1_0_1_1[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "# ax1.scatter(concentration_1_0_1_1_photons[1], concentration_1_0_1_1[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(concentration_1_0_1_1_photons[2], concentration_1_0_1_1[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(concentration_1_0_1_1_photons[3], concentration_1_0_1_1[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Fluorophores Except mTagBFP2')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for [1, 0, 1, 1].pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for x = [1, 1, 0, 1]. Correct way to format plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(100, 1000, 100)]\n",
    "concentration_1_1_0_1 = calc_multiple_FOMs(desired_photons_list, [1, 1, 0, 1])\n",
    "concentration_1_1_0_1_photons = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(concentration_1_1_0_1_photons[0], concentration_1_1_0_1[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(concentration_1_1_0_1_photons[1], concentration_1_1_0_1[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "# ax1.scatter(concentration_1_0_1_1_photons[2], concentration_1_0_1_1[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(concentration_1_1_0_1_photons[3], concentration_1_1_0_1[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('Figure of Merit')\n",
    "plt.xlabel('Average Photons Detected')\n",
    "plt.title('All Fluorophores Except mCherry')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for [1, 1, 0, 1].pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Avg Photons Detected vs FOMs for x = [1, 1, 1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jt/_z0pnbx50rs5xql_tz09rgq00000gn/T/ipykernel_36975/4025655968.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  FIM_y = 1/y\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desired_photons_list = [*range(100, 1000, 100)]\n",
    "concentration_1_1_1_0 = calc_multiple_FOMs(desired_photons_list, [1, 1, 1, 0])\n",
    "concentration_1_1_1_0_photons = calc_photons_detected_per_fluor(desired_photons_list)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(concentration_1_1_1_0_photons[0], concentration_1_1_1_0[0], s=10, c='g', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(concentration_1_1_1_0_photons[1], concentration_1_1_1_0[1], s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(concentration_1_1_1_0_photons[2], concentration_1_1_1_0[2], s=10, c='r', marker=\"x\", label='mCherry')\n",
    "# ax1.scatter(concentration_1_1_1_0_photons[3], concentration_1_1_1_0[3], s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Avg Photons Detected versus FOMs for All Fluorophores Except mNeptune2.5')\n",
    "plt.savefig('Figures/Total Avg Photons Detected vs FOMs for [1, 1, 1, 0].pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For mEmerald (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting Avg Photons Detected versus FOMs for mEmerald')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [Avg_photons_100[0], Avg_photons_200[0], Avg_photons_300[0]], y = [FOM_for_100[0], FOM_for_200[0], FOM_for_300[0]])\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg photons detected')\n",
    "plt.title('Plotting Avg Photons Detected versus FOMs for mEmerald')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For mTagBFP2 (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting Avg Photons Detected versus FOMs for mTagBFP2')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D+3eqfENzwjAz6yFOHz2C/v36rFPWv18fTh89oluW74veZmY9ROXCtu+SMjOzDo3Zd0i3JYhq7pIyM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUhqWMCQNk3SrpEckPSTp07l8G0k3SXo0/9+6MM0ESXMkzZY0ulGxmZlZ5zWyhbEa+FxE/BtwIHCKpJHAF4GbI2J34Ob8mjxuLLAncATwQ0l9GhifmZl1QsMSRkQ8GRH35uFlwCPAEOBo4JJc7RJgTB4+GpgSESsjYi4wBzigUfGZmVnndMs1DEnDgX2Bu4HtIuJJSEkF2DZXGwLMK0w2P5dVz+skSTMkzVi0aFFD4zYzs7UanjAkDQCuBT4TEUvrVa1RFi8riLggIkZFxKjBgwdvqDDNzKwDDU0YkvqRksXlETE1Fz8taYc8fgfgmVw+HxhWmHwosLCR8ZmZWXmNvEtKwIXAIxFxXmHUjcD4PDweuKFQPlbSZpJ2BnYHpjcqPjMz65y+DZz3QcCHgFmS7s9lXwK+DVwt6QTgCeBYgIh4SNLVwMOkO6xOiYg1DYzPzMw6oWEJIyLupPZ1CYC3tjPNJGBSo2IyM7Ou8ze9zcysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSmlYwpB0kaRnJD1YKNtH0h8l3S9phqQDCuMmSJojabak0Y2Ky8zMuqaRLYyLgSOqys4Fzo6IfYCz8mskjQTGAnvmaX4oqU8DYzMzs07qMGFIOkjSFnn4g5LOk7RTR9NFxB3Ac9XFwFZ5+JXAwjx8NDAlIlZGxFxgDnAAZmbWMsq0MH4E/FPS3sAZwOPApV1c3meAyZLmAf8JTMjlQ4B5hXrzc9nLSDopd2fNWLRoURfDMDOzziqTMFZHRJBaAd+LiO8BW3ZxeR8HTouIYcBpwIW5XDXqRq0ZRMQFETEqIkYNHjy4i2GYmVlnlUkYyyRNAD4E/CpfW+jXxeWNB6bm4WtY2+00HxhWqDeUtd1VZmbWAsokjPcDK4HjI+IpUlfR5C4ubyFwSB5+C/BoHr4RGCtpM0k7A7sD07u4DDMza4C+HVWIiKckXUs6iAMsBq7raDpJVwKHAoMkzQe+CpwIfE9SX+AF4KS8jIckXQ08DKwGTomINZ1/O2Zm1igdJgxJJ5IO7NsAu5JaGOcDb603XUSMa2fU69qpPwmY1FE8ZmbWHGW6pE4BDgKWAkTEo8C2jQzKzMxaT5mEsTIi/lV5kbuTat7BZGZmG68yCeN2SV8C+ks6jHR30y8aG5aZmbWaMgnji8AiYBbwMeB/gTMbGZSZmbWeMndJvQj8JP+ZWQeuv28Bk6fNZuGSFbQN7M/po0cwZt+aDy4w61HK3CU1lxrXLCJil4ZEZNaDXX/fAiZMncWKVemu8AVLVjBh6iwAJw3r8TpMGMCowvDmwLGkW2zNrMrkabNfShYVK1atYfK02U4Y1uN1eA0jIp4t/C2IiP8ifUvbzKosXLKiU+VmPUmZLqn9Ci83IbU4uvrwQbONWtvA/iyokRzaBvZvQjRmG1aZLqnvFIZXA48B72tINGY93OmjR6xzDQOgf78+nD56RBOjMtswytwl9ebuCMRsY1C5TuG7pGxj1G7CkPTZehNGxHkbPhyznm/MvkOcIGyjVK+F4esUZmb2knYTRkSc3Z2BmJlZaytzl9TmwAnAnqTvYQAQEcc3MC4zM2sxZZ4ldRmwPTAauJ3086nLGhmUmZm1njIJY7eI+ArwfERcArwdeE1jwzIzs1ZTJmGsyv+XSNoLeCUwvGERmZlZSyrzxb0LJG0NfAW4ERiQh83MrBep9z2Mh4HLgSkR8XfS9Qs/odbMrJeq1yU1jtSa+K2kuyV9RtIO3RSXmZm1mHYTRkTMjIgJEbEr8GlgJ+BuSbdIOrHbIjQzs5ZQ5qI3EfHHiDgN+DCwNfCDhkZlZmYtp8wX9/YndU+9h/Sk2guAaxoblpmZtZp6F72/Cbwf+DswBTgoIuZ3V2BmZtZa6rUwVgJHRsRfuisYMzNrXX74oJmZlVLqoreZmZkThpmZlVLvovd+9SaMiHs3fDhmZtaq6l30ngE8BCzKr1UYF8BbGhWUmZm1nnoJ43Ok716sIN1We11ELO+WqMzMrOXUezTIdyPiYOBUYBhws6SrJe3TXcGZmVnr6PCid0TMBW4AfgscAOzR6KDMzKz11LvovQswFjgamEfqlpoUES90U2xmZtZC6rUw5gDvA34D3AXsCHxC0mclfbajGUu6SNIzkh6sKv+kpNmSHpJ0bqF8gqQ5edzorr0dMzNrlHoXvb9GuhsK0u9idNbFpKfaXlopkPRmUovltRGxUtK2uXwkqTWzJ9AG/E7SHhGxpgvLNTOzBqj3aJCJ6zPjiLhD0vCq4o8D346IlbnOM7n8aNIv+60E5kqaQ7pectf6xGBmZhtO3Yveko6UdIekxZIWSbpd0lHrsbw9gDfmX/C7PT86HWAI6TpJxfxcViumkyTNkDRj0aJFtaqYmVkD1LvofSLwMeAM0pf4AEYB35Y0NCIu6OLytgYOBPYHrs4X11WjbtQoIy/3AoBRo0bVrGNmZhtevWsYpwEHR8RzhbJbJB0J3Ek+aHfSfGBqRAQwXdKLwKBcPqxQbyiwsAvzNzOzBqnXJaWqZAFARDy7Hsu7nvxIEUl7AJsCi4EbgbGSNpO0M7A7MH09lmNmZhtYvRbGUkl7R8TMYqGkvYFlHc1Y0pXAocAgSfOBrwIXARflW23/BYzPrY2HJF0NPAysBk7xHVJmZq2lo2dJ3Sjpp8A9pGsK+wPjgQ92NOOIGNfOqJrTRsQkYFJH8zUzs+ao9yypO0m3tm4CHAccn4cPzOPMzKwXqXeX1I4R8QRwVjfGY2ZmLareRe/rKwOSrm18KGZm1srq3iVVGN6l0YGYmVlrq5cwop1hMzPrherdJbW3pKWklkb/PEx+HRGxVcOjMzOzllHv4YN9ujMQMzNrbR3+4p6ZmRk4YZiZWUlOGGZmVooThpmZleKEYWZmpThhmJlZKU4YZmZWihOGmZmV4oRhZmalOGGYmVkpThhmZlaKE4aZmZXihGFmZqU4YZiZWSlOGGZmVooThpmZleKEYWZmpThhmJlZKU4YZmZWihOGmZmV4oRhZmalOGGYmVkpThhmZlaKE4aZmZXihGFmZqU4YZiZWSlOGGZmVkrDEoakiyQ9I+nBGuM+LykkDSqUTZA0R9JsSaMbFZeZmXVNI1sYFwNHVBdKGgYcBjxRKBsJjAX2zNP8UFKfBsZmZmad1LCEERF3AM/VGPVd4AwgCmVHA1MiYmVEzAXmAAc0KjYzM+u8br2GIeldwIKImFk1aggwr/B6fi4zM7MW0be7FiTpFcCXgcNrja5RFjXKkHQScBLAjjvuuMHiMzOz+rqzhbErsDMwU9JjwFDgXknbk1oUwwp1hwILa80kIi6IiFERMWrw4MENDtnMzCq6LWFExKyI2DYihkfEcFKS2C8ingJuBMZK2kzSzsDuwPTuis3MzDrWyNtqrwTuAkZImi/phPbqRsRDwNXAw8BvgFMiYk2jYjMzs85r2DWMiBjXwfjhVa8nAZMaFY+Zma0ff9PbzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzEpxwjAzs1KcMMzMrBQnDDMzK8UJw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxKccIwM7NSnDDMzKwUJwwzMyvFCcPMzErp2+wAmuH6+xYwedpsFi5ZQdvA/pw+egRj9h3S7LDMzFpar0sY19+3gAlTZ7Fi1RoAFixZwYSpswCcNMzM6uh1XVKTp81+KVlUrFi1hsnTZjcpIjOznqHXJYyFS1Z0qtzMzJJelzDaBvbvVLmZmSW9LmGcPnoE/fv1Waesf78+nD56RJMiMjPrGRqWMCRdJOkZSQ8WyiZL+rOkByRdJ2lgYdwESXMkzZY0ulFxjdl3CN9692sYMrA/AoYM7M+33v0aX/A2M+uAIqIxM5beBCwHLo2IvXLZ4cAtEbFa0jkAEfEFSSOBK4EDgDbgd8AeEbGm9tyTUaNGxYwZMxoSv5nZxkrSPRExqrPTNayFERF3AM9Vlf02Ilbnl38Ehubho4EpEbEyIuYCc0jJw8zMWkQzr2EcD/w6Dw8B5hXGzc9lZmbWIpqSMCR9GVgNXF4pqlGtZl+ZpJMkzZA0Y9GiRY0K0czMqnR7wpA0HngH8B+x9gLKfGBYodpQYGGt6SPigogYFRGjBg8e3NhgzczsJd2aMCQdAXwBeFdE/LMw6kZgrKTNJO0M7A5M787YzMysvkbeJXUlcCgwCHga+CowAdgMeDZX+2NEnJzrf5l0XWM18JmI+HX1PGssYxHw+HqEOQhYvB7TN4rj6hzH1TmOq3M2xrh2iohOd9E0LGH0BJJmdOXWskZzXJ3juDrHcXWO41qr133T28zMusYJw8zMSuntCeOCZgfQDsfVOY6rcxxX5ziurFdfwzAzs/J6ewvDzMxKcsIwM7NSNtqE0c7j1beRdJOkR/P/rQvjuuXx6p157Luk4ZJWSLo//53fzXFNlLSgsPyjCuOaub6uKsT0mKT7c3l3rq9hkm6V9IikhyR9Opc3dRurE1dTt7E6cTV1G6sTV1O3MUmbS5ouaWaO6+xc3txjWERslH/Am4D9gAcLZecCX8zDXwTOycMjgZmkLxXuDPwV6NONcR0O9M3D5xTiGl6s14T1NRH4fI26TV1fVeO/A5zVhPW1A7BfHt4S+EteL03dxurE1dRtrE5cTd3G2our2dsY6fl6A/JwP+Bu4MBmb18bbQsjajxenfQY9Uvy8CXAmEJ5tzxevVZc0f5j37tNO+urPU1dXxWSBLyP9Fsq3SoinoyIe/PwMuAR0hOWm7qNtRdXs7exOuurPU1dX5XxzdrGIlmeX/bLf0GTt6+NNmG0Y7uIeBLShgJsm8tb6fHqxce+A+ws6T5Jt0t6YxPiOTV3Y1xUaP62yvp6I/B0RDxaKOv29SVpOLAv6SywZbaxqriKmrqN1YirJbaxdtZX07YxSX1yV9gzwE0R0fTtq7cljPaUfrx6Q4N4+WPfnwR2jIh9gc8CV0jaqhtD+hGwK7BPjuU7lVBr1G3G/dnjWPfMr9vXl6QBwLWk558trVe1RlnD1ll7cTV7G6sRV0tsY3U+x6ZtYxGxJiL2IbUGD5C0V53q3bK+elvCeFrSDgD5/zO5vPTj1RtFNR77npuXz+bhe0j9knt0V0wR8XTeaF8EfsLaJm4rrK++wLuBqypl3b2+JPUjHWQuj4ipubjp21g7cTV9G6sVVytsY3XWV9O3sbycJcBtwBE0efvqbQnjRmB8Hh4P3FAob9rj1dXOY98lDZbUJw/vkuP6WzfGtUPh5TFA5U6lVngc/duAP0fE/EpBd66v3Ld9IfBIRJxXGNXUbay9uJq9jdWJq6nbWJ3PEZq4jeXlDMzD/Sux0Oxj2Ia+it4qf6Rm5JPAKlL2PQF4FXAz8Gj+v02h/pdJZwuzgSO7Oa45pP7H+/Pf+bnue4CHSHc/3Au8s5vjugyYBTyQN8gdWmF95fKLgZOr6nbn+jqY1OR/oPC5HdXsbaxOXE3dxurE1dRtrL24mr2NAa8F7stxPcjau7Saun350SBmZlZKb+uSMjOzLnLCMDOzUpwwzMysFCcMMzMrxQnDzMxKccKwliDpGEkh6dUNXs6hkn7ZyWk+I+kVjYqpxvIulvTeDuocJ6mti/MfLukDjYjLNm5OGNYqxgF3AmObHUgNnwG6LWGUdBzQpYRBeuJqpxOGmROGNV1+js9BpC8Ljs1lR0q6ulDnUEm/yMMnSPqLpNsk/UTSD2rMc6KkyyTdovTbAScWRg+Q9HOl34e4PH/bF0lvzQ+Vm5UfhLeZpE+RDsy3Sro11xuX6zwo6ZzCMpdLmqT0GwZ/lLRdLj82150p6Y4asUrSDyQ9LOlXrH2gHJJep/SQu3skTZO0Qz7LHwVcrvSbDP1r1cvT7ybpd3nZ90raFfg28MY87WlKD7mbLOlPSg8B/FhHcVkv1YhvA/rPf535Az4IXJiH/0D6/Yu+wBPAFrn8R7leG/AYsA3pkc//B/ygxjwnkr6N2x8YRPqWcxtwKPAP0rN2NgHuIn3bd/NcZ488/aWkB9GRlzcoD7fluAbnGG8BxuRxQf7mL+l3C87Mw7NIjxgHGFgj1ncDNwF98vyXAO/N7+8PwOBc7/3ARXn4NmBUHq5X727gmDy8OamldCjwy8LyTyrEuhkwg/SbCjXjavb24r/m/bmFYa1gHDAlD08BxkX67YbfAO9Uegjc20nPzTkAuD0inouIVcA1deZ7Q0SsiIjFwK2sfbDd9IiYH+mBd/eTumhGAHMj4i+5ziWkH2+qtj9wW0QsyjFeXqj3L6ByfeSePF+A3wMX51ZOnxrzfBNwZaSH8C0kJSFyTHsBNyk95vpMav+ORc16krYkJarrACLihSg8R6rgcODDedq7SY+f2L1OXNZL9W12ANa7SXoV8BZgL0lBOqCGpDNITwk9hfQDSn+KiGWV7qOSqp97U3m9slC2hrQflJ1vvXqrIqKyjMp8iYiTJf07KendL2mfyE88rRNrZVkPRcTrS8T0snoq/9htAZ+MiGlV0x/VTlzWS7mFYc32XuDSiNgpIoZHxDBgLqmb6DZS99SJrH3E9HTgEElb55bHe+rM+2il30Z+Fakb5k916v4ZGC5pt/z6Q8DteXgZ6ec7IZ2BHyJpkNJTS8cV6tUkadeIuDsizgIWs+5jqAHuID1ptE++9vDmXD4bGCzp9Xk+/STtWSOmmvUi/a7DfEljcvlm+W6v4rQA04CPKz3mG0l7SNqiTlzWSzlhWLONA66rKrsW+EBErCF18RyZ/xMRC4Bvkg7cvwMeJl2TqGU68CvST5J+PXer1BQRLwAfAa6RNAt4ETg/j74A+LWkWyP9ytkEUhfXTODeiLih1jwLJlcukpMOwjOrxl9HevroLNK1mttzTP8iJdRzJM0kdZ+9IU9zMXB+7kbqU6feh4BPSXqAdJ1je9ITUFfnC+GnAf9DWo/35hh/TGod1YzLei8/rdZ6HEkDImJ5bmFcR7rAe11VnYnA8oj4z2bEaLYxcgvDeqKJ+cz6QVL31fVNjcasl3ALw8zMSnELw8zMSnHCMDOzUpwwzMysFCcMMzMrxQnDzMxK+f87N1VgT+X9JAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [Avg_photons_100[1], Avg_photons_200[1], Avg_photons_300[1]], y = [FOM_for_100[1], FOM_for_200[1], FOM_for_300[1]])\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg photons detected')\n",
    "plt.title('Plotting Avg Photons Detected versus FOMs for mTagBFP2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For mCherry (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting Avg Photons Detected versus FOMs for mCherry')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [Avg_photons_100[2], Avg_photons_200[2], Avg_photons_300[2]], y = [FOM_for_100[2], FOM_for_200[2], FOM_for_300[2]])\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg photons detected')\n",
    "plt.title('Plotting Avg Photons Detected versus FOMs for mCherry')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For mNeptune2.5 (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting Avg Photons Detected versus FOMs for mNeptune2.5')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mk/dmZl2icoLeV4WZmVlpjhwzrM8lkmo+FGZmZqVyYjEzs1I5sZiZWamcWMzMrFROLGZmVionFjMzK1XLEoukEZJulTRb0kxJp1aNP0NSSBpcKJsgaa6kOZLGtSo2MzNrnVb+juVF4PMRca+kLYB7JN0UEbMkjQAOAR6vVJa0GzAe2B0YCtwsadeIWF1r5mZm1je1rMcSEYsi4t48vAyYDVR+1fMt4CwgCpMcAUyKiJURMQ+YC+zbqvjMzKw12nKORdIoYAxwt6T3Agsi4oGqasOAJwrv57MmERXndZKk6ZKmL168uFUhm5nZemp5YpG0OXAdcBrp8NjZwLm1qtYoi3UKIi6JiLERMXbIkCFlhmpmZiVoaWKRNICUVK6KiCnATsAOwAOSHgWGA/dK2pbUQxlRmHw4sLCV8ZmZWflaeVWYgEuB2RFxEUBEzIiI10fEqIgYRUome0fEk8CNwHhJG0vaAdgFmNaq+MzMrDVaeVXY/sBHgBmS7s9lX4qIX9aqHBEzJU0GZpEOmZ3iK8LMzLpPyxJLRNxJ7fMmxTqjqt6fD5zfqpjMzKz1/Mt7MzMrlROLmZmVyonFzMxK5cRiZmalcmIxM7NSObGYmVmpnFjMzKxUTixmZlYqJxYzMyuVE4uZmZXKicXMzErlxGJmZqVyYjEzs1I5sZiZWamcWMzMrFROLGZmVionFjMzK5UTi5mZlcqJxczMSuXEYmZmpXJiMTOzUjmxmJlZqZxYzMysVE4sZmZWKicWMzMrlROLmZmVyonFzMxK5cRiZmalcmIxM7NStSyxSBoh6VZJsyXNlHRqLp8o6Y+SHpR0vaRBhWkmSJoraY6kca2KzczMWqeVPZYXgc9HxBuB/YBTJO0G3ATsERFvAh4GJgDkceOB3YFDge9I6tfC+MzMrAVallgiYlFE3JuHlwGzgWER8ZuIeDFXuwsYnoePACZFxMqImAfMBfZtVXxmZtYabTnHImkUMAa4u2rUCcCv8vAw4InCuPm5rHpeJ0maLmn64sWLWxCtmZm9Ei1PLJI2B64DTouIpYXys0mHy66qFNWYPNYpiLgkIsZGxNghQ4a0ImQzM3sF+rdy5pIGkJLKVRExpVB+HPBu4B8jopI85gMjCpMPBxa2Mj4zMytfK68KE3ApMDsiLiqUHwp8AXhvRPy1MMmNwHhJG0vaAdgFmNaq+MzMrDVa2WPZH/gIMEPS/bnsS8B/ABsDN6Xcw10RcXJEzJQ0GZhFOkR2SkSsbmF8ZmbWAi1LLBFxJ7XPm/yywTTnA+e3KiYzM2s9//LezMxK5cRiZmalcmIxM7NSObGYmVmpnFjMzKxUTixmZlYqJxYzMyuVE4uZmZXKicXMzErlxGJmZqVyYjEzs1I5sZiZWamcWMzMrFROLGZmVqoeE4uk/SVtloc/LOkiSdu3PjQzM+tGzfRY/gv4q6Q9gbOAx4ArWhqVmZl1rWYSy4v5ufRHAN+OiG8DW7Q2LDMz61bNPEFymaQJpMcMHyipHzCgtWGZmVm3aqbHcgywEjghIp4EhgETWxqVmZl1rR4TS04m1wEb56JngOtbGZSZmXWvZq4KOxH4CfC9XDQMuKGFMZmZWRdr5lDYKcD+wFKAiHgEeH0rgzIzs+7VTGJZGRF/q7yR1B+I1oVkZmbdrJnEcrukLwEDJR0CXAv8rLVhmZlZt2omsXwRWAzMAD4B/BI4p5VBmZlZ9+rxdywR8RLw/fwysx7ccN8CJk6dw8IlKxg6aCBnjhvNkWOGdToss7bpMbFImkeNcyoRsWNLIjLrYjfct4AJU2awYtVqABYsWcGEKTMAnFxsg9HML+/HFoY3AY4Gtm5NOGbdbeLUOS8nlYoVq1YzceocJxbbYDTzA8lnC68FEfF/gINbH5pZ91m4ZEWvys1ejZr5geTehddYSSfTxE0oJY2QdKuk2ZJmSjo1l28t6SZJj+S/WxWmmSBprqQ5ksa9ov/MrAOGDhrYq3KzV6Nmrgq7sPD6OvBm4INNTPci8PmIeCOwH3CKpN1IV5ndEhG7ALfk9+Rx44HdgUOB7+QbXpp1jTPHjWbggLWb7cAB/Thz3OgORWTWfs1cFfb29ZlxRCwCFuXhZZJmk24HcwRwUK72Q+A24Au5fFJErATmSZoL7Av8fn2Wb9YJlfMovirMNmR1E4ukzzWaMCIuanYhkkYBY4C7gW1y0iEiFkmq3B5mGHBXYbL5uax6XicBJwGMHDmy2RDM2ubIMcOcSGyD1qjHUsrDvCRtTro78mkRsVRS3ao1ympd5nwJcAnA2LFjfWsZM7M+pm5iiYgvv9KZSxpASipXRcSUXPyUpO1yb2U74OlcPh8YUZh8OLDwlcZgZmbt1cwPJDcBPk46qb5JpTwiTuhhOgGXArOrDpvdCBwHfCP//Wmh/GpJFwFDgV2AaU3/J2Zm1ic0c1XYlcC2wDjgdlJPYlkT0+1PepzxwZLuz6/DSQnlEEmPAIfk90TETGAyMAv4NXBKRKyuPWszM+urFNH4NIWk+yJijKQHI+JN+fDW1Ijo+I8kx44dG9OnT+90GGZmXUXSPRExtuea66eZHsuq/HeJpD2ALYFRrQrIzMy6WzP3Crsk/zr+X0jnQTbPw2ZmZuto9DuWWcBVpB8t/oV0fsV3NDYzs4YaHQo7ltQ7+Y2kuyWdli8PNjMzq6tuYomIByJiQkTsBJwKbA/cLem3kk5sW4RmZtZVmjl5T0TcFRGnAx8FtgIubmlUZmbWtZr5geQ+pMNi7wceJd1O5drWhmVmZt2q0cn7rwHHAH8BJgH7R8T8dgVmZmbdqVGPZSVwWEQ83K5gzMys+7X0JpRmZrbhaerkvZmZWbOcWMzMrFSNTt7v3WjCiLi3/HDMzKzbNTp5Px2YCSzO74tPeAyg43c3NjOzvqdRYvk86bcrK0iXG18fEcvbEpWZmXWtRrd0+VZEHAB8mvTI4FskTZa0V7uCMzOz7tPjyfuImEd6fPBvgH2BXVsdlJmZda9GJ+93BMYDRwBPkA6HnR8RL7QpNjMz60KNzrHMBR4k9VaWAiOBT0npHH5EXNTy6MzMrOs0SixfIV39Bem5LGZmZj1qdEuX89oYh5mZvUo0PHkv6TBJd0h6RtJiSbdLOrxdwZmZWfdpdPL+ROATwFmkH0sCjAW+IWl4RFzShvjMzKzLNDrHcjpwQEQ8Vyj7raTDgDtJD/wyMzNbS6NDYapKKgBExLMtjMfMzLpco8SyVNKe1YW5bFnrQjIzs27W073CbpT038A9pEuP9wGOAz7chtjMzKwLNbpX2J2kW7hsBBwPnJCH98vjzMzM1tHoqrCREfE4cG4b4zEzsy7X6BzLDZUBSdf1dsaSLpP0tKSHCmV7SbpL0v2SpkvatzBugqS5kuZIGtfb5ZmZWd/Q8KqwwvCO6zHvy4FDq8ouAL4cEXuRekIXAEjajXTDy93zNN+R1G89lmlmZh3WKLFEneGmRMQdQPXlygG8Ng9vCSzMw0cAkyJiZb5N/1zS+R0zM+syja4K21PSUlLPZWAeJr+PiHht/UnrOg2YKunfSUntLbl8GHBXod78XLYOSScBJwGMHDlyPUIwM7NWanRVWL+IeG1EbBER/fNw5f36JBWATwKnR8QI0i/7L83lqlG3Zi8pIi6JiLERMXbIkCHrGYaZmbVKj0+QLNlxwJQ8fC1rDnfNJz3+uGI4aw6TmZlZF2l3YlkIvC0PHww8kodvBMZL2ljSDsAuwLQ2x2ZmZiVodI7lFZF0DXAQMFjSfOBfgROBb0vqD7xAPlcSETMlTQZmAS8Cp0TE6lbFZmZmrdOyxBIRx9YZ9eY69c8Hzm9VPGZm1h7tPhRmZmavck4sZmZWKicWMzMrlROLmZmVyonFzMxK5cRiZmalcmIxM7NSObGYmVmpnFjMzKxUTixmZlYqJxYzMyuVE4uZmZXKicXMzErlxGJmZqVyYjEzs1I5sZiZWamcWMzMrFROLGZmVionFjMzK5UTi5mZlcqJxczMSuXEYmZmpXJiMTOzUjmxmJlZqZxYzMysVE4sZmZWKicWMzMrlROLmZmVqmWJRdJlkp6W9FBV+WckzZE0U9IFhfIJkubmceNaFZeZmbVW/xbO+3LgYuCKSoGktwNHAG+KiJWSXp/LdwPGA7sDQ4GbJe0aEatbGJ+ZmbVAy3osEXEH8FxV8SeBb0TEylzn6Vx+BDApIlZGxDxgLrBvq2IzM7PWafc5ll2BAyXdLel2Sfvk8mHAE4V683OZmZl1mVYeCqu3vK2A/YB9gMmSdgRUo27UmoGkk4CTAEaOHNmiMM3MbH21u8cyH5gSyTTgJWBwLh9RqDccWFhrBhFxSUSMjYixQ4YMaXnAZmbWO+1OLDcABwNI2hV4DfAMcCMwXtLGknYAdgGmtTk2MzMrQcsOhUm6BjgIGCxpPvCvwGXAZfkS5L8Bx0VEADMlTQZmAS8Cp/iKMDOz7qS0Xe9OY8eOjenTp3c6DDOzriLpnogY26r5+5f3ZmZWKicWMzMrlROLmZmVyonFzMxK5cRiZmalcmIxM7NSObGYmVmpnFjMzKxUTixmZlYqJxYzMyuVE4uZmZXKicXMzErlxGJmZqVyYjEzs1I5sZiZWamcWMzMrFROLGZmVionFjMzK5UTi5mZlcqJxczMSuXEYmZmpXJiMTOzUjmxmJlZqZxYzMysVP07HUAn3HDfAiZOncPCJSsYOmggZ44bzZFjhnU6LDOzV4UNLrHccN8CJkyZwYpVqwFYsGQFE6bMAHByMTMrwQZ3KGzi1DkvJ5WKFatWM3HqnA5FZGb26rLBJZaFS1b0qtzMzHpng0ssQwcN7FW5mZn1TssSi6TLJD0t6aEa486QFJIGF8omSJoraY6kca2K68xxoxk4oN9aZQMH9OPMcaNbtUgzsw1KK3sslwOHVhdKGgEcAjxeKNsNGA/snqf5jqR+1dOW4cgxw/j6UX/HsEEDETBs0EC+ftTf+cS9mVlJWnZVWETcIWlUjVHfAs4CflooOwKYFBErgXmS5gL7Ar9vRWxHjhnmRGJm1iJtPcci6b3Agoh4oGrUMOCJwvv5uazWPE6SNF3S9MWLF7coUjMzW19tSyySNgXOBs6tNbpGWdSaT0RcEhFjI2LskCFDygzRzMxK0M4fSO4E7AA8IAlgOHCvpH1JPZQRhbrDgYVtjM3MzErSth5LRMyIiNdHxKiIGEVKJntHxJPAjcB4SRtL2gHYBZjWrtjMzKw8rbzc+BrSyffRkuZL+ni9uhExE5gMzAJ+DZwSEavr1Tczs75LETVPZXQFSYuBx17BLAYDz5QUTpkcV+84rt5xXL3zaoxr+4ho2Unqrk4sr5Sk6RExttNxVHNcveO4esdx9Y7j6r0N7pYuZmbWWk4sZmZWqg09sVzS6QDqcFy947h6x3H1juPqpQ36HIuZmZVvQ++xmJlZyZxYzMysVK/axFLreTCStpZ0k6RH8t+tCuPa8jyYOnFNlPRHSQ9Kul7SoFw+StIKSffn13fbHNd5khYUln94YVwn19ePCzE9Kun+XN7O9TVC0q2SZkuaKenUXN7RNtYgro62sQZxdbSNNYiro21M0iaSpkl6IMf15Vze8W1YUyLiVfkC3grsDTxUKLsA+GIe/iLwzTy8G/AAsDHpfmZ/Avq1Ma53Av3z8DcLcY0q1uvA+joPOKNG3Y6ur6rxFwLndmB9bUe6JRHAFsDDeb10tI01iKujbaxBXB1tY/Xi6nQbI92Yd/M8PAC4G9iv0+2r2dertscSEXcAz1UVHwH8MA//EDiyUD4pIlZGxDyg8jyYtsQVEb+JiBfz27tIN+Fsqzrrq56Orq8KSQI+CFzTimU3EhGLIuLePLwMmE161ENH21i9uDrdxhqsr3o6ur4q4zvVxiJZnt8OyK+gD2zDmvGqTSx1bBMRiyA1KOD1ubzp58G0wQnArwrvd5B0n6TbJR3YgXg+nQ+fXFbodveV9XUg8FREPFIoa/v6Unqg3RjSXmWfaWNVcRV1tI3ViKtPtLE666tjbUxSv3wI7mngpojoU+2rkQ0tsdTT9PNgWhqEdDbwInBVLloEjIyIMcDngKslvbaNIf0X6XEHe+VYLqyEWqNuJ65bP5a19yTbvr4kbQ5cB5wWEUsbVa1R1rJ1Vi+uTrexGnH1iTbW4HPsWBuLiNURsRepd7mvpD0aVO8r30lgw0ssT0naDiD/fTqXd/x5MJKOA94N/FPkg6a5W/tsHr6HdNx013bFFBFP5cb9EvB91nSt+8L66g8cBfy4Utbu9SVpAGljdFVETMnFHW9jdeLqeBurFVdfaGMN1lfH21hezhLgNuBQ+kD7asaGllhuBI7Lw8cBPy2Ud+x5MJIOBb4AvDci/looHyKpXx7eMcf15zbGtV3h7fuAypVZfeH5Oe8A/hgR8ysF7Vxf+dj7pcDsiLioMKqjbaxeXJ1uYw3i6mgba/A5QgfbWF7OoDw8sBILfXQbto5OXTXQ6hep+7oIWEXK5h8HXgfcAjyS/25dqH82ae9jDnBYm+OaSzo+en9+fTfXfT8wk3S1x73Ae9oc15XADOBBUsPdri+sr1x+OXByVd12rq8DSIcaHix8bod3uo01iKujbaxBXB1tY/Xi6nQbA94E3Jfjeog1V6V1fBvWzMu3dDEzs1JtaIfCzMysxZxYzMysVE4sZmZWKicWMzMrlROLmZmVyonF+gRJ75MUkt7Q4uUcJOnnvZzmNEmbtiqmGsu7XNIHeqhzvKSh6zn/UZI+1Iq4zMCJxfqOY4E7gfGdDqSG04C2JZYmHQ+sV2Ih3aG314nFrFlOLNZx+T5N+5N+lDk+lx0maXKhzkGSfpaHPy7pYUm3Sfq+pItrzPM8SVdK+q3SsytOLIzeXNJPlJ5PclX+9TWS/jHfXHBGviHixpI+S9qA3yrp1lzv2FznIUnfLCxzuaTzlZ6hcZekbXL50bnuA5LuqBGrJF0saZakX7DmxoJIerPSzQ7vkTRV0na51zAWuErpmSADa9XL0+8s6ea87Hsl7QR8AzgwT3u60s0OJ0r6g9LNID/RU1xmDXXy15l++RURAB8GLs3D/0N6/kp/4HFgs1z+X7neUOBRYGvSrcT/H3BxjXmeR/p19EBgMOlX50OBg4D/Jd1LaSPg96RfX2+S6+yap7+CdENC8vIG5+GhOa4hOcbfAkfmcUH+JTbpuRnn5OEZpFvXAwyqEetRwE1Avzz/JcAH8v/3P8CQXO8Y4LI8fBswNg83qnc38L48vAmp53UQ8PPC8k8qxLoxMJ30TI+acXW6vfjV91/usVhfcCwwKQ9PAo6N9OyQXwPvUboZ4LtI90XaF7g9Ip6LiFXAtQ3m+9OIWBERzwC3suYGh9MiYn6kGx/eTzo0NBqYFxEP5zo/JD1krNo+wG0RsTjHeFWh3t+Ayvmbe/J8AX4HXJ57Tf1qzPOtwDWRbsa4kJSsyDHtAdykdPv0c6j9HJWa9SRtQUpo1wNExAtRuE9YwTuBj+Zp7ybdNmSXBnGZNdS/0wHYhk3S64CDgT0kBWnDG5LOIt1V9hTSg77+EBHLKoetmlR9v6LK+5WFstWk70Gz821Ub1VEVJZRmS8RcbKkvyclx/sl7RX5DrkNYq0sa2ZE/EMTMa1TT83fzl3AZyJiatX0h9eJy6wh91is0z4AXBER20fEqIgYAcwjHZ66jXRY7ETW3Lp8GvA2SVvlnsz7G8z7CKVnh7+OdPjnDw3q/hEYJWnn/P4jwO15eBnpsbWQ9ujfJmmw0l1ujy3Uq0nSThFxd0ScCzzD2rc3B7iDdGfafvncyNtz+RxgiKR/yPMZIGn3GjHVrBfpuSLzJR2ZyzfOV7cVpwWYCnxS6fbxSNpV0mYN4jJryInFOu1Y4PqqsuuAD0XEatKhpcPyXyJiAfA10gb+ZmAW6ZxJLdOAX5Aexftv+XBOTRHxAvAx4FpJM4CXgO/m0ZcAv5J0a6Sn9k0gHVp7ALg3In5aa54FEysn+0kb6weqxl9PulvtDNK5pNtzTH8jJd5vSnqAdNjuLXmay4Hv5sNX/RrU+wjwWUkPks7DbEu6Y+6L+YT+6cAPSOvx3hzj90i9rZpxmfXEdze2riNp84hYnnss15NOVF9fVec8YHlE/HsnYjTbkLnHYt3ovLyn/hDpsNkNHY3GzNbiHouZmZXKPRYzMyuVE4uZmZXKicXMzErlxGJmZqVyYjEzs1L9f2K9yvhus0QvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [Avg_photons_100[3], Avg_photons_200[3], Avg_photons_300[3]], y = [FOM_for_100[3], FOM_for_200[3], FOM_for_300[3]])\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg photons detected')\n",
    "plt.title('Plotting Avg Photons Detected versus FOMs for mNeptune2.5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work on combining all four plots into single plot and should have about 20 desired photon values for each fluorophore (looking for a platoe on the graph which shows the optimal illumination strength). Can't know for sure because would need to take into account other factors in the imaging model (i.e. bleaching). Looking to create plot to give complete picture of how well imaging system works. Maybe make each fluorophore a certain color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting FOMs and Avg Photons Detected for desired_photons = 100, 200, 300 for all four fluorophores on one graph (Old way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_photons_detected_mEmerald = [Avg_photons_100[0], Avg_photons_200[0], Avg_photons_300[0]]\n",
    "FOMs_mEmerald = [FOM_for_100[0], FOM_for_200[0], FOM_for_300[0]]\n",
    "avg_photons_detected_mTag = [Avg_photons_100[1], Avg_photons_200[1], Avg_photons_300[1]]\n",
    "FOMs_mTag = [FOM_for_100[1], FOM_for_200[1], FOM_for_300[1]]\n",
    "avg_photons_detected_mCherry = [Avg_photons_100[2], Avg_photons_200[2], Avg_photons_300[2]]\n",
    "FOMs_mCherry = [FOM_for_100[2], FOM_for_200[2], FOM_for_300[2]]\n",
    "avg_photons_detected_mNeptune = [Avg_photons_100[3], Avg_photons_200[3], Avg_photons_300[3]]\n",
    "FOMs_mNeptune = [FOM_for_100[3], FOM_for_200[3], FOM_for_300[3]]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.scatter(avg_photons_detected_mEmerald, FOMs_mEmerald, s=10, c='r', marker=\"s\", label='mEmerald')\n",
    "ax1.scatter(avg_photons_detected_mTag, FOMs_mTag, s=10, c='b', marker=\"o\", label='mTagBFP2')\n",
    "ax1.scatter(avg_photons_detected_mCherry, FOMs_mCherry, s=10, c='g', marker=\"x\", label='mCherry')\n",
    "ax1.scatter(avg_photons_detected_mNeptune, FOMs_mNeptune, s=10, c='black', marker=\"d\", label='mNeptune2.5')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Avg Photons Detected')\n",
    "plt.title('Plotting Avg Photons Detected versus FOMs for All Four Fluorophores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can look into plotting the emission spectra of the fluorophores -- might display overlap/crosstalk, which lowers FOMs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "vscode": {
   "interpreter": {
    "hash": "d801b28d0c2f9341eaf08410a8d5dd8d794b8daecaeee8ba55dd2443d2219971"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}