demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89142399",
   "metadata": {},
   "source": [
    "A demo for computing the imaging matrix and its Fisher information matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49ed75e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from data_objects import Fluorophore, get_fluorophore_list\n",
    "from imaging_model import fast_form_A\n",
    "from information_matrix import read_qe, fast_form_q_vec, FIM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046ca4e6",
   "metadata": {},
   "source": [
    "First, we need to get our Fluorophores. The function `get_fluorophore_list` will read all of the fluorophore data we have available into a list. In order for the system to read the fluorophores, it needs a range of wavelengths to read the data from. The default is $400 {\\rm nm}$ to $900 {\\rm nm}$. Let's see what flurophores we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92d07f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<data_objects.Fluorophore at 0x1200a3f3af0>,\n",
       " <data_objects.Fluorophore at 0x1200a3f34f0>,\n",
       " <data_objects.Fluorophore at 0x1200d895790>,\n",
       " <data_objects.Fluorophore at 0x1200da76be0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wavelength_range = (400,900)\n",
    "fluorophore_list = get_fluorophore_list(wavelength_range=wavelength_range)\n",
    "fluorophore_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "455b5f5a",
   "metadata": {},
   "source": [
    "Right now, our fluorophores are all stored as `Fluorophore` objects, which encode the name, brightness, and spectra of each fluorophore. Let's take a closer look at our first fluorophore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54919673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<data_objects.Fluorophore at 0x1200a3f3af0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fluorophore.name\n",
    "names = map(Fluorophore.get_name, fluorophore_list)\n",
    "name_list = list(names)\n",
    "# idx, name = \n",
    "# enumerate(list(names))\n",
    "# print(enumerate([0,1]))\n",
    "dict = {name_list[i]:fluorophore_list[i] for i in range(len(fluorophore_list))}\n",
    "# dict = {name_list[0]: fluorophore_list[0]}\n",
    "# list(names)[0]\n",
    "dict['mCherry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c2d5884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name = mCherry\n",
      "brightness = 15.85\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>excitation</th>\n",
       "      <th>emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>400</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>401</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>402</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>403</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>895</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>896</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>897</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>898</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>899</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wavelength  excitation  emission\n",
       "400         400      0.0313       0.0\n",
       "401         401      0.0290       0.0\n",
       "402         402      0.0267       0.0\n",
       "403         403      0.0245       0.0\n",
       "404         404      0.0216       0.0\n",
       "..          ...         ...       ...\n",
       "895         895      0.0000       0.0\n",
       "896         896      0.0000       0.0\n",
       "897         897      0.0000       0.0\n",
       "898         898      0.0000       0.0\n",
       "899         899      0.0000       0.0\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluorophore = fluorophore_list[0]\n",
    "print(\"name = {}\".format(fluorophore.name))\n",
    "print(\"brightness = {}\".format(fluorophore.brightness))\n",
    "\n",
    "idx = np.arange(*wavelength_range)\n",
    "plt.plot(idx, fluorophore.spectra.emission/fluorophore.spectra.emission.max(), label=\"emission spectrum of {}\".format(fluorophore.name))\n",
    "plt.plot(idx, fluorophore.spectra.excitation, label=\"excitation spectrum of {}\".format(fluorophore.name))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fluorophore.spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb22fb2",
   "metadata": {},
   "source": [
    "The fluorophores in `fluorophore_list` are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a20045f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mCherry\n",
      "mEmerald\n",
      "mNeptune2.5\n",
      "mTagBFP2\n"
     ]
    }
   ],
   "source": [
    "for fluorophore in fluorophore_list:\n",
    "    print(fluorophore.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5020f6",
   "metadata": {},
   "source": [
    "At this point, if we only want to use some of these fluorophores, we can remove some from the list. Alternatively, if `get_fluorophore_list` imports many fluorophores when you only want to use a few, you also have the option to read the fluorophores from the data yourself by using the `Fluorophore` constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1bc93eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name = mEmerald\n",
      "brightness = 39.1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>excitation</th>\n",
       "      <th>emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>400</td>\n",
       "      <td>0.1683</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>401</td>\n",
       "      <td>0.1682</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>402</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>403</td>\n",
       "      <td>0.1701</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>895</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>896</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>897</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>898</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>899</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wavelength  excitation  emission\n",
       "400         400      0.1683       0.0\n",
       "401         401      0.1682       0.0\n",
       "402         402      0.1700       0.0\n",
       "403         403      0.1701       0.0\n",
       "404         404      0.1710       0.0\n",
       "..          ...         ...       ...\n",
       "895         895      0.0000       0.0\n",
       "896         896      0.0000       0.0\n",
       "897         897      0.0000       0.0\n",
       "898         898      0.0000       0.0\n",
       "899         899      0.0000       0.0\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluorophore = Fluorophore(\"mEmerald\", 39.1, index_range=wavelength_range)\n",
    "\n",
    "print(\"name = {}\".format(fluorophore.name))\n",
    "print(\"brightness = {}\".format(fluorophore.brightness))\n",
    "\n",
    "idx = np.arange(*wavelength_range)\n",
    "plt.plot(idx, fluorophore.spectra.emission/fluorophore.spectra.emission.max(), label=\"emission spectrum of {}\".format(fluorophore.name))\n",
    "plt.plot(idx, fluorophore.spectra.excitation, label=\"excitation spectrum of {}\".format(fluorophore.name))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fluorophore.spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "517bb97e",
   "metadata": {},
   "source": [
    "The `Fluorophore` constructor assumes that fluorophore data is in a standard format, specifically that of data downloaded from fpbase.org. In order for this program to read data that is not in this format, you must either convert it to that format, or use the optional arguments in the `Fluorophore` constructor to tell the program how to read the data. Furthermore, if you want `get_fluorophore_data` to read new fluorophore data, you must alter the function to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc3eefa",
   "metadata": {},
   "source": [
    "Now that we have fluorophores, we must specify our other parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "077e2aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "illumination_wavelengths = np.array([405, 488, 561, 637]) # numpy array with the wavelength of each illumination\n",
    "k = np.array([1, 1, 1, 1])                                # numpy array with (photon flux)*(voxel volume) for each illumination wavelength\n",
    "bin_wavelength_range = wavelength_range                   # length 2 ordered int tuple with first and last wavelengths detected\n",
    "bin_width = 10                                            # int denoting size of each wavelength bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09243e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([405, 488, 561, 637])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(illumination_wavelengths)\n",
    "df\n",
    "np.array(df.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa82f5f",
   "metadata": {},
   "source": [
    "For ease of use, we can package these together into a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f0e8dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = (illumination_wavelengths, k, bin_wavelength_range, bin_width, fluorophore_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89e02df",
   "metadata": {},
   "source": [
    "Now, we can compute our imaging matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2eb72c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.74253694e-03],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.93924341e-02],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.13017111e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.27270664e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.26254302e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.75163880e+00],\n",
       "       [0.00000000e+00, 3.94788300e-03, 0.00000000e+00, 4.42164468e+00],\n",
       "       [0.00000000e+00, 2.57189571e-02, 0.00000000e+00, 3.70660316e+00],\n",
       "       [0.00000000e+00, 1.48942159e-01, 0.00000000e+00, 3.01868148e+00],\n",
       "       [0.00000000e+00, 5.56882373e-01, 0.00000000e+00, 2.22054480e+00],\n",
       "       [0.00000000e+00, 1.19860960e+00, 0.00000000e+00, 1.54824414e+00],\n",
       "       [0.00000000e+00, 1.25893602e+00, 0.00000000e+00, 1.07961343e+00],\n",
       "       [0.00000000e+00, 8.16226734e-01, 0.00000000e+00, 7.49754901e-01],\n",
       "       [0.00000000e+00, 5.91197403e-01, 0.00000000e+00, 5.08225669e-01],\n",
       "       [0.00000000e+00, 4.74307744e-01, 0.00000000e+00, 3.37533440e-01],\n",
       "       [1.09380812e-04, 3.34431094e-01, 0.00000000e+00, 2.26667563e-01],\n",
       "       [8.11241019e-04, 2.11515720e-01, 3.00278106e-04, 1.53281296e-01],\n",
       "       [2.60446384e-03, 1.35921071e-01, 2.01995591e-03, 1.02974006e-01],\n",
       "       [1.03527160e-02, 9.40257982e-02, 3.28566717e-03, 6.91826938e-02],\n",
       "       [2.52398446e-02, 6.68061838e-02, 6.77329443e-03, 4.67274721e-02],\n",
       "       [3.75331807e-02, 4.59816782e-02, 1.46866518e-02, 3.18386838e-02],\n",
       "       [3.82530487e-02, 3.15907597e-02, 2.85388429e-02, 2.20484242e-02],\n",
       "       [3.13996294e-02, 2.21250753e-02, 4.49483670e-02, 1.50515073e-02],\n",
       "       [2.51293522e-02, 1.67534918e-02, 5.75785042e-02, 1.04411357e-02],\n",
       "       [2.10438010e-02, 1.35597853e-02, 6.29941583e-02, 7.51219372e-03],\n",
       "       [1.80589498e-02, 1.10663855e-02, 6.22047747e-02, 3.52557828e-04],\n",
       "       [1.52924157e-02, 9.99668619e-03, 5.71110500e-02, 0.00000000e+00],\n",
       "       [1.27535358e-02, 9.08859614e-03, 5.00012737e-02, 0.00000000e+00],\n",
       "       [1.00350225e-02, 9.16555293e-03, 4.23083332e-02, 0.00000000e+00],\n",
       "       [7.69000462e-03, 8.99624800e-03, 3.33372586e-02, 0.00000000e+00],\n",
       "       [5.95747481e-03, 4.92523415e-04, 2.36705042e-02, 0.00000000e+00],\n",
       "       [4.67358419e-03, 0.00000000e+00, 1.78133065e-02, 0.00000000e+00],\n",
       "       [3.70583079e-03, 0.00000000e+00, 1.40388888e-02, 0.00000000e+00],\n",
       "       [2.97062277e-03, 0.00000000e+00, 1.12693024e-02, 0.00000000e+00],\n",
       "       [2.40571090e-03, 0.00000000e+00, 9.14605937e-03, 0.00000000e+00],\n",
       "       [1.93150506e-03, 0.00000000e+00, 7.28156659e-03, 0.00000000e+00],\n",
       "       [1.52199398e-03, 0.00000000e+00, 5.64884876e-03, 0.00000000e+00],\n",
       "       [1.21274863e-03, 0.00000000e+00, 4.35225784e-03, 0.00000000e+00],\n",
       "       [9.61973113e-04, 0.00000000e+00, 3.33109932e-03, 0.00000000e+00],\n",
       "       [7.50992645e-04, 0.00000000e+00, 2.55058920e-03, 0.00000000e+00],\n",
       "       [3.71272267e-05, 0.00000000e+00, 2.01569664e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.60006348e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.33563417e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.15922966e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 9.84244902e-04, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 6.28241427e-05, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.27527529e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.48225537e-01, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 8.58395283e-01, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 3.20946873e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 6.90792207e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 7.25560010e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 4.70414276e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 3.40723584e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.73356807e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [4.20950396e-04, 1.92741985e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [3.12204877e-03, 1.21902420e+00, 7.71682443e-04, 0.00000000e+00],\n",
       "       [1.00232396e-02, 7.83351116e-01, 5.19106949e-03, 0.00000000e+00],\n",
       "       [3.98422705e-02, 5.41896950e-01, 8.44381132e-03, 0.00000000e+00],\n",
       "       [9.71351595e-02, 3.85022706e-01, 1.74066384e-02, 0.00000000e+00],\n",
       "       [1.44445877e-01, 2.65005261e-01, 3.77431160e-02, 0.00000000e+00],\n",
       "       [1.47216278e-01, 1.82066376e-01, 7.33417575e-02, 0.00000000e+00],\n",
       "       [1.20840998e-01, 1.27512992e-01, 1.15512470e-01, 0.00000000e+00],\n",
       "       [9.67099311e-02, 9.65550549e-02, 1.47970565e-01, 0.00000000e+00],\n",
       "       [8.09867494e-02, 7.81488317e-02, 1.61888213e-01, 0.00000000e+00],\n",
       "       [6.94995949e-02, 6.37786720e-02, 1.59859582e-01, 0.00000000e+00],\n",
       "       [5.88526300e-02, 5.76136960e-02, 1.46769258e-01, 0.00000000e+00],\n",
       "       [4.90817895e-02, 5.23801193e-02, 1.28497897e-01, 0.00000000e+00],\n",
       "       [3.86196321e-02, 5.28236428e-02, 1.08727867e-01, 0.00000000e+00],\n",
       "       [2.95948663e-02, 5.18478912e-02, 8.56731700e-02, 0.00000000e+00],\n",
       "       [2.29272516e-02, 2.83855007e-03, 6.08306507e-02, 0.00000000e+00],\n",
       "       [1.79862179e-02, 0.00000000e+00, 4.57782823e-02, 0.00000000e+00],\n",
       "       [1.42618336e-02, 0.00000000e+00, 3.60784346e-02, 0.00000000e+00],\n",
       "       [1.14323967e-02, 0.00000000e+00, 2.89608955e-02, 0.00000000e+00],\n",
       "       [9.25834194e-03, 0.00000000e+00, 2.35043891e-02, 0.00000000e+00],\n",
       "       [7.43336797e-03, 0.00000000e+00, 1.87128432e-02, 0.00000000e+00],\n",
       "       [5.85737076e-03, 0.00000000e+00, 1.45169339e-02, 0.00000000e+00],\n",
       "       [4.66724474e-03, 0.00000000e+00, 1.11848347e-02, 0.00000000e+00],\n",
       "       [3.70213895e-03, 0.00000000e+00, 8.56056706e-03, 0.00000000e+00],\n",
       "       [2.89018382e-03, 0.00000000e+00, 6.55474000e-03, 0.00000000e+00],\n",
       "       [1.42883569e-04, 0.00000000e+00, 5.18012364e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 4.11199108e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 3.43243621e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 2.97909558e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 2.52940356e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.61451291e-04, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 9.80132615e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 6.38519142e-04, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 3.69775568e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.38256016e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.97576286e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 3.12553401e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.02642896e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.46775337e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.17755387e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [3.53222681e-03, 8.30285050e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [2.61973489e-02, 5.25125632e-03, 6.26278959e-03, 0.00000000e+00],\n",
       "       [8.41057665e-02, 3.37448387e-03, 4.21294747e-02, 0.00000000e+00],\n",
       "       [3.34319525e-01, 2.33435873e-03, 6.85279471e-02, 0.00000000e+00],\n",
       "       [8.15068517e-01, 1.65858309e-03, 1.41268101e-01, 0.00000000e+00],\n",
       "       [1.21205635e+00, 1.14157746e-03, 3.06314075e-01, 0.00000000e+00],\n",
       "       [1.23530300e+00, 7.84297151e-04, 5.95224110e-01, 0.00000000e+00],\n",
       "       [1.01398601e+00, 5.49294594e-04, 9.37471496e-01, 0.00000000e+00],\n",
       "       [8.11500393e-01, 4.15935420e-04, 1.20089361e+00, 0.00000000e+00],\n",
       "       [6.79565978e-01, 3.36645939e-04, 1.31384590e+00, 0.00000000e+00],\n",
       "       [5.83176390e-01, 2.74742827e-04, 1.29738202e+00, 0.00000000e+00],\n",
       "       [4.93836898e-01, 2.48185627e-04, 1.19114409e+00, 0.00000000e+00],\n",
       "       [4.11849031e-01, 2.25640666e-04, 1.04285811e+00, 0.00000000e+00],\n",
       "       [3.24060272e-01, 2.27551256e-04, 8.82409287e-01, 0.00000000e+00],\n",
       "       [2.48332775e-01, 2.23347958e-04, 6.95302896e-01, 0.00000000e+00],\n",
       "       [1.92384313e-01, 1.22277753e-05, 4.93686968e-01, 0.00000000e+00],\n",
       "       [1.50923724e-01, 0.00000000e+00, 3.71525557e-01, 0.00000000e+00],\n",
       "       [1.19672132e-01, 0.00000000e+00, 2.92803921e-01, 0.00000000e+00],\n",
       "       [9.59301112e-02, 0.00000000e+00, 2.35039680e-01, 0.00000000e+00],\n",
       "       [7.76874519e-02, 0.00000000e+00, 1.90755984e-01, 0.00000000e+00],\n",
       "       [6.23739564e-02, 0.00000000e+00, 1.51868946e-01, 0.00000000e+00],\n",
       "       [4.91496438e-02, 0.00000000e+00, 1.17815953e-01, 0.00000000e+00],\n",
       "       [3.91632058e-02, 0.00000000e+00, 9.07734350e-02, 0.00000000e+00],\n",
       "       [3.10649297e-02, 0.00000000e+00, 6.94755087e-02, 0.00000000e+00],\n",
       "       [2.42517524e-02, 0.00000000e+00, 5.31966974e-02, 0.00000000e+00],\n",
       "       [1.19894691e-03, 0.00000000e+00, 4.20406408e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 3.33719332e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 2.78568289e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 2.41776251e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 2.05280326e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.31029995e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 6.15432107e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 4.00930624e-04, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.32184659e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 8.68119173e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.86850226e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.96254461e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.27240888e-02, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 9.21612580e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 7.39394290e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [3.70126989e-05, 5.21341775e-03, 0.00000000e+00, 0.00000000e+00],\n",
       "       [2.74510850e-04, 3.29730048e-03, 1.63484746e-03, 0.00000000e+00],\n",
       "       [8.81308470e-04, 2.11886196e-03, 1.09975377e-02, 0.00000000e+00],\n",
       "       [3.50319176e-03, 1.46576013e-03, 1.78886324e-02, 0.00000000e+00],\n",
       "       [8.54075549e-03, 1.04143589e-03, 3.68768252e-02, 0.00000000e+00],\n",
       "       [1.27006217e-02, 7.16804452e-04, 7.99606600e-02, 0.00000000e+00],\n",
       "       [1.29442135e-02, 4.92465653e-04, 1.55378145e-01, 0.00000000e+00],\n",
       "       [1.06251271e-02, 3.44905908e-04, 2.44718887e-01, 0.00000000e+00],\n",
       "       [8.50336665e-03, 2.61168752e-04, 3.13482967e-01, 0.00000000e+00],\n",
       "       [7.12088216e-03, 2.11382334e-04, 3.42968195e-01, 0.00000000e+00],\n",
       "       [6.11085677e-03, 1.72512938e-04, 3.38670440e-01, 0.00000000e+00],\n",
       "       [5.17470631e-03, 1.55837487e-04, 3.10937939e-01, 0.00000000e+00],\n",
       "       [4.31559041e-03, 1.41681349e-04, 2.72229157e-01, 0.00000000e+00],\n",
       "       [3.39568943e-03, 1.42881021e-04, 2.30345370e-01, 0.00000000e+00],\n",
       "       [2.60217328e-03, 1.40241741e-04, 1.81502852e-01, 0.00000000e+00],\n",
       "       [2.01591319e-03, 7.67790543e-06, 1.28872745e-01, 0.00000000e+00],\n",
       "       [1.58146536e-03, 0.00000000e+00, 9.69835575e-02, 0.00000000e+00],\n",
       "       [1.25399325e-03, 0.00000000e+00, 7.64339500e-02, 0.00000000e+00],\n",
       "       [1.00521074e-03, 0.00000000e+00, 6.13550910e-02, 0.00000000e+00],\n",
       "       [8.14053688e-04, 0.00000000e+00, 4.97952121e-02, 0.00000000e+00],\n",
       "       [6.53590097e-04, 0.00000000e+00, 3.96440848e-02, 0.00000000e+00],\n",
       "       [5.15018164e-04, 0.00000000e+00, 3.07548432e-02, 0.00000000e+00],\n",
       "       [4.10374537e-04, 0.00000000e+00, 2.36956260e-02, 0.00000000e+00],\n",
       "       [3.25516154e-04, 0.00000000e+00, 1.81359852e-02, 0.00000000e+00],\n",
       "       [2.54123774e-04, 0.00000000e+00, 1.38865412e-02, 0.00000000e+00],\n",
       "       [1.25632535e-05, 0.00000000e+00, 1.09743484e-02, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 8.71145670e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 7.27178606e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 6.31136149e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 5.35866669e-03, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 3.42042554e-04, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = fast_form_A(*params)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3141f64f",
   "metadata": {},
   "source": [
    "Before we compute our Fisher information matrix, we need additional parameters. We need the quantum efficiency of our camera at the middle wavelength of each detection bin. We will first read the quantum efficiency data from a file with `read_qe`, and then compute a vector with `fast_form_q_vec` where each component corresponds to the quantum efficiency of the camera for spectral bin $i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1804786a",
   "metadata": {},
   "outputs": [],
   "source": [
    "qe = read_qe(bin_wavelength_range)\n",
    "q_vec = fast_form_q_vec(qe, bin_wavelength_range, bin_width, len(illumination_wavelengths))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecce1c83",
   "metadata": {},
   "source": [
    "Now, we just need to specify the concentration of each of the fluorophores in the voxel we are imaging via `x_vec` and specify the variance caused by the electronic read noise via `variance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a46196f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vec = [1,1,1,1]\n",
    "variance = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59713623",
   "metadata": {},
   "source": [
    "Finally, we can compute the Fisher information matrix and the Cramer-Rao lower bounds for these parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00aa025e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.01795257, 0.04421248, 0.64056542, 0.07372166])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F = FIM(A, x_vec, q_vec, variance)\n",
    "F_inv = np.linalg.inv(F)\n",
    "CRLB = np.diagonal(F_inv)\n",
    "CRLB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97036df0",
   "metadata": {},
   "source": [
    "In summary, our results are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8caba6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fluorophore</th>\n",
       "      <th>CRLB</th>\n",
       "      <th>FOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>1.017953</td>\n",
       "      <td>0.991143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mEmerald</td>\n",
       "      <td>0.044212</td>\n",
       "      <td>4.755843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mNeptune2.5</td>\n",
       "      <td>0.640565</td>\n",
       "      <td>1.249448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mTagBFP2</td>\n",
       "      <td>0.073722</td>\n",
       "      <td>3.683006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fluorophore      CRLB       FOM\n",
       "0      mCherry  1.017953  0.991143\n",
       "1     mEmerald  0.044212  4.755843\n",
       "2  mNeptune2.5  0.640565  1.249448\n",
       "3     mTagBFP2  0.073722  3.683006"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    \"Fluorophore\" : list(map(Fluorophore.get_name, fluorophore_list)),\n",
    "    \"CRLB\"        : CRLB,\n",
    "    \"FOM\"         : x_vec / np.sqrt(CRLB)\n",
    "})"
   ]
  }
 ],
 "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
}