new_model_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4825b4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from data_objects import Fluorophore\n",
    "from imaging_model import fast_form_A, fast_form_A_from_photons\n",
    "from information_matrix import read_qe, fast_form_q_vec, FIM, FIM_poisson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc553051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mEmerald', 'mTagBFP2', 'mCherry', 'mNeptune2.5']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluorophore_string_list = [\"mEmerald\", \"mTagBFP2\", \"mCherry\", \"mNeptune2.5\"]\n",
    "brightness_list         = [39.1,       32.38,      15.85,     22.8         ]\n",
    "wavelength_range = (400,900)\n",
    "\n",
    "fluorophore_list = []\n",
    "for fluorophore_name, fluorophore_brightness in zip(fluorophore_string_list, brightness_list):\n",
    "    fluorophore_list.append(Fluorophore(fluorophore_name, fluorophore_brightness, index_range=wavelength_range))\n",
    "\n",
    "list(map(Fluorophore.get_name, fluorophore_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3d5c54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "illumination_wavelengths = np.array([405, 488, 561, 637])\n",
    "bin_width = 10\n",
    "x = [1,1,1,1]\n",
    "\n",
    "params_wo_k = (illumination_wavelengths, wavelength_range, bin_width, fluorophore_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0ca5d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "qe = read_qe(wavelength_range)\n",
    "q_vec = fast_form_q_vec(qe, wavelength_range, bin_width, len(illumination_wavelengths))\n",
    "q_vec = np.ones(q_vec.shape)\n",
    "\n",
    "def calc_FOM(A, x, variance=2, normal_approximation=True):\n",
    "    if normal_approximation:\n",
    "        F = FIM(A, x, variance, q_vec = 1)\n",
    "    else:\n",
    "        F = FIM_poisson(A, x)\n",
    "    FIM_inv = np.linalg.inv(F)\n",
    "    CRLB = np.diagonal(FIM_inv)\n",
    "    FOM_list = np.array(x) / np.sqrt(CRLB)\n",
    "    \n",
    "    return FOM_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ea6d860",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FOM_from_desired_photons(desired_photons, variance=2, normal_approximation=True):\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k, qe = qe)\n",
    "    return calc_FOM(A, x, variance=variance, normal_approximation=normal_approximation)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "161d239d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([9.11864532, 7.63262266, 3.92307683, 8.95905141])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOM_from_desired_photons(100, variance=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f83e02a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n",
      "hi\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([10.98468492,  8.63427548,  4.77689122, 11.46378799])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOM_from_desired_photons(100, variance=1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17ef66dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000., 1000., 1000., 1000.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fast_form_A_from_photons(1000, *params_wo_k).sum(axis=1).reshape((4,50)).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "346e547d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "X = np.linspace(0.01,1000,50)\n",
    "Y = {}\n",
    "for fluorophore in fluorophore_list:\n",
    "    Y[fluorophore] = []\n",
    "for desired_photons in X:\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k)\n",
    "    figures_of_merit = calc_FOM(A,x)\n",
    "    for j in range(len(fluorophore_list)):\n",
    "        Y[fluorophore_list[j]].append(figures_of_merit[j])\n",
    "\n",
    "\n",
    "color = {\n",
    "    'mEmerald'    : 'g',\n",
    "    'mTagBFP2'    : 'b',\n",
    "    'mCherry'     : 'r',\n",
    "    'mNeptune2.5' : 'black'\n",
    "}\n",
    "marker = {\n",
    "    'mEmerald'    : 's',\n",
    "    'mTagBFP2'    : 'o',\n",
    "    'mCherry'     : 'x',\n",
    "    'mNeptune2.5' : 'd'\n",
    "}\n",
    "\n",
    "for fluorophore in fluorophore_list:\n",
    "    ax.scatter(X, Y[fluorophore], s=10, c=color[fluorophore.name], marker=marker[fluorophore.name], label=fluorophore.name)\n",
    "\n",
    "\n",
    "    \n",
    "\"\"\" \n",
    "Y2 = {}\n",
    "for fluorophore in fluorophore_list:\n",
    "    Y2[fluorophore] = []\n",
    "for desired_photons in X:\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k)\n",
    "    figures_of_merit = calc_FOM(A, x, variance=1e-9)\n",
    "    for j in range(len(fluorophore_list)):\n",
    "        Y2[fluorophore_list[j]].append(figures_of_merit[j])\n",
    "\n",
    "\n",
    "color = {\n",
    "    'mEmerald'    : 'g',\n",
    "    'mTagBFP2'    : 'b',\n",
    "    'mCherry'     : 'r',\n",
    "    'mNeptune2.5' : 'black'\n",
    "}\n",
    "marker = {\n",
    "    'mEmerald'    : 's',\n",
    "    'mTagBFP2'    : 'o',\n",
    "    'mCherry'     : 'x',\n",
    "    'mNeptune2.5' : 'd'\n",
    "}\n",
    "\n",
    "for fluorophore in fluorophore_list:\n",
    "    ax.scatter(X, Y2[fluorophore], s=10, c=color[fluorophore.name], marker=marker[fluorophore.name]) #, label=fluorophore.name)\n",
    "    \n",
    "\"\"\"\n",
    "    \n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Total Photons Detected Per Illumination')\n",
    "plt.title('Photons Detected Per Illumination vs FOMs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513d9e69",
   "metadata": {},
   "source": [
    "## Same graph with no electronic read noise (variance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4ae807b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_0 = plt.figure()\n",
    "ax = fig_0.add_subplot(111)\n",
    "\n",
    "X = np.linspace(0.01,100,50)\n",
    "Y = {}\n",
    "for fluorophore in fluorophore_list:\n",
    "    Y[fluorophore] = []\n",
    "for desired_photons in X:\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k)\n",
    "    figures_of_merit = calc_FOM(A, x, variance=1e-9)\n",
    "    for j in range(len(fluorophore_list)):\n",
    "        Y[fluorophore_list[j]].append(figures_of_merit[j])\n",
    "\n",
    "\n",
    "color = {\n",
    "    'mEmerald'    : 'g',\n",
    "    'mTagBFP2'    : 'b',\n",
    "    'mCherry'     : 'r',\n",
    "    'mNeptune2.5' : 'black'\n",
    "}\n",
    "marker = {\n",
    "    'mEmerald'    : 's',\n",
    "    'mTagBFP2'    : 'o',\n",
    "    'mCherry'     : 'x',\n",
    "    'mNeptune2.5' : 'd'\n",
    "}\n",
    "\n",
    "for fluorophore in fluorophore_list:\n",
    "    ax.scatter(X, Y[fluorophore], s=10, c=color[fluorophore.name], marker=marker[fluorophore.name], label=fluorophore.name)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Total Photons Detected Per Illumination')\n",
    "plt.title('Photons Detected Per Illumination vs FOMs; No Read Noise')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac106245",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig.savefig('photons vs FOM with read noise.png')\n",
    "#fig_0.savefig('photons vs FOM without read noise.png'#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1dc729",
   "metadata": {},
   "source": [
    "## Effect of read noise on FOM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d8bcef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "photons = 100\n",
    "fluorophore_index = 0\n",
    "X = np.linspace(0.01, 5, 100)\n",
    "\n",
    "Y = []\n",
    "for variance in X:\n",
    "    figures_of_merit = FOM_from_desired_photons(photons, variance=variance)\n",
    "    Y.append(figures_of_merit[fluorophore_index])\n",
    "\n",
    "fig_1 = plt.figure()\n",
    "ax = fig_1.add_subplot(111)\n",
    "ax.scatter(X, Y, s=10)\n",
    "\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Per Pixel Electronic Read Noise (Variance)')\n",
    "plt.title('FOM vs Read Noise for ' + fluorophore_string_list[fluorophore_index] + \" (\" + str(photons) + \" photons per illumination)\" )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d0baa4",
   "metadata": {},
   "source": [
    "## Poisson with no read noise vs Normal Approx. with no read noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf09f112",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n",
      "C:\\Users\\buffs\\Documents\\GitHub\\hyperspectral\\information_matrix.py:59: RuntimeWarning: divide by zero encountered in divide\n",
      "  y_inv = 1 / y\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "ax = fig1.add_subplot(111)\n",
    "\n",
    "# poisson approx\n",
    "X = np.linspace(0.01,1000,50)\n",
    "Y = {}\n",
    "for fluorophore in fluorophore_list:\n",
    "    Y[fluorophore] = []\n",
    "for desired_photons in X:\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k)\n",
    "    figures_of_merit = calc_FOM(A, x, normal_approximation=False)\n",
    "    for j in range(len(fluorophore_list)):\n",
    "        Y[fluorophore_list[j]].append(figures_of_merit[j])\n",
    "\n",
    "\n",
    "color = {\n",
    "    'mEmerald'    : 'g',\n",
    "    'mTagBFP2'    : 'b',\n",
    "    'mCherry'     : 'r',\n",
    "    'mNeptune2.5' : 'black'\n",
    "}\n",
    "marker = {\n",
    "    'mEmerald'    : 's',\n",
    "    'mTagBFP2'    : 'o',\n",
    "    'mCherry'     : 'x',\n",
    "    'mNeptune2.5' : 'd'\n",
    "}\n",
    "\n",
    "for fluorophore in fluorophore_list:\n",
    "    ax.scatter(X, Y[fluorophore], s=15, c=color[fluorophore.name], marker=marker[fluorophore.name], label=fluorophore.name)\n",
    "\n",
    "\n",
    "    \n",
    "# normal approx\n",
    "Y2 = {}\n",
    "for fluorophore in fluorophore_list:\n",
    "    Y2[fluorophore] = []\n",
    "for desired_photons in X:\n",
    "    A = fast_form_A_from_photons(desired_photons, *params_wo_k)\n",
    "    figures_of_merit = calc_FOM(A, x, variance=1e-15)\n",
    "    for j in range(len(fluorophore_list)):\n",
    "        Y2[fluorophore_list[j]].append(figures_of_merit[j])\n",
    "\n",
    "\n",
    "color = {\n",
    "    'mEmerald'    : 'g',\n",
    "    'mTagBFP2'    : 'b',\n",
    "    'mCherry'     : 'r',\n",
    "    'mNeptune2.5' : 'black'\n",
    "}\n",
    "marker = {\n",
    "    'mEmerald'    : 's',\n",
    "    'mTagBFP2'    : 'o',\n",
    "    'mCherry'     : 'x',\n",
    "    'mNeptune2.5' : 'd'\n",
    "}\n",
    "\n",
    "for fluorophore in fluorophore_list:\n",
    "    ax.scatter(X, Y2[fluorophore], s=10, c=color[fluorophore.name], marker=marker[fluorophore.name]) #, label=fluorophore.name)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('FOM Values')\n",
    "plt.xlabel('Total Photons Detected Per Illumination')\n",
    "plt.title('Photons Detected Per Illumination vs FOMs')\n",
    "plt.show()\n",
    "\n",
    "#fig1.savefig('poisson vs normal approx.png', dpi=300)"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}