sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Locating project \"calibration-module\" within \"C:\\Users\\narayas2\\Documents\\MATLAB\\projects\".\n",
      "  Found at \"C:\\Users\\narayas2\\Documents\\MATLAB\\projects\\calibration-module\".\n",
      "Updating \"ToolboxRegistry\".\n",
      "Already up-to-date.\n",
      "Updating \"calibration-module\".\n",
      "Host key verification failed. \n",
      "fatal: Could not read from remote repository.\n",
      "\n",
      "Please make sure you have the correct access rights\n",
      "and the repository exists.\n",
      "Updating \"matlab-persistence\".\n",
      "Already up-to-date.\n",
      "Found \"jsonlab_v1.2\" and skipping update.\n",
      "Updating \"logging4matlab\".\n",
      "Already up-to-date.\n",
      "Updating \"MatlabQuery\".\n",
      "Already up-to-date.\n",
      "Updating \"mdepin\".\n",
      "Already up-to-date.\n",
      "Adding \"ToolboxToolbox\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\ToolboxToolbox\".\n",
      "Adding \"calibration-module\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\projects\\calibration-module\".\n",
      "Adding \"matlab-persistence\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\toolboxes\\matlab-persistence\".\n",
      "Adding \"jsonlab_v1.2\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\toolboxes\\jsonlab_v1.2\".\n",
      "Adding \"logging4matlab\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\toolboxes\\logging4matlab\".\n",
      "Adding \"MatlabQuery\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\toolboxes\\MatlabQuery\".\n",
      "Adding \"mdepin\" to path at \"C:\\Users\\narayas2\\Documents\\MATLAB\\toolboxes\\mdepin\".\n",
      "Checking for \"calibration-module\" local hook.\n",
      "Checking for \"matlab-persistence\" local hook.\n",
      "Checking for \"jsonlab_v1.2\" local hook.\n",
      "Checking for \"logging4matlab\" local hook.\n",
      "Checking for \"MatlabQuery\" local hook.\n",
      "Checking for \"mdepin\" local hook.\n",
      "Error: \"calibration-module\" had status 1, message \"Host key verification failed.\n",
      "fatal: Could not read from remote repository.\n",
      "\n",
      "Please make sure you have the correct access rights\n",
      "and the repository exists.\"\n",
      "Something went wrong with resolved toolboxes, please see above.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%% Ignore below lines of code, Go to next section please\n",
    "\n",
    "tbUseProject('calibration-module');\n",
    "javaaddpath(which('mpa-jutil-0.0.1-SNAPSHOT.jar'));\n",
    "javaaddpath(which('java-uuid-generator-3.1.4.jar'));\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "% setup classpath\n",
    "\n",
    "import ala_laurila_lab.*;\n",
    "path = which('test-symphony-persistence.xml');\n",
    "config = struct();\n",
    "config.service.class = 'ala_laurila_lab.service.CalibrationService';\n",
    "config.service.dataPersistence = 'test-rig-data';\n",
    "config.service.logPersistence = 'test-rig-log';\n",
    "config.service.persistenceXml = which('test-symphony-persistence.xml');\n",
    "service = mdepin.createApplication(config, 'service');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ndf A1A + A4B\n",
      "rstar       voltage\n",
      "------------------------\n",
      "1.5104      56.6533\n",
      "2.4766      90.9948\n",
      "3.4319      125.9567\n",
      "4.3886      161.9321\n",
      "5.3437      198.7421\n",
      "10.1191      395.3637\n",
      "19.6384      848.9841\n",
      "29.0782      1372.9216\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%% Trying to calculate intensity to rstar\n",
    "% From calibration api mode\n",
    "\n",
    "LAMDA_MAX = 497;                        % Toda et al. 1999\n",
    "ROD_PHOTORECEPTOR_AREA = 0.5 * 1e-12;   % um^2, collective area of rod (Murphy & Rieke (2011))\n",
    "\n",
    "% get all the required instances, led names are weird but we can fix that in real calibration\n",
    "\n",
    "intensity = service.getIntensityMeasurement('Blue');\n",
    "spectrum = service.getSpectralMeasurement('blue', 'led');\n",
    "linearity = service.getLinearityByStimulsDuration(5000, 'BlueLed');\n",
    "\n",
    "% calculate rstar per second\n",
    "\n",
    "powerPerUnitArea = intensity(end).getPowerPerUnitArea();\n",
    "powerSpectrumPerArea = spectrum.getNormalizedPowerSpectrum() * powerPerUnitArea;\n",
    "rstarPerSecond = util.photonToIsomerisation(powerSpectrumPerArea, spectrum.wavelength, LAMDA_MAX, ROD_PHOTORECEPTOR_AREA);\n",
    "\n",
    "% get the transmitance value from ndf\n",
    "\n",
    "ndf2 = service.getNDFMeasurement('A4B');\n",
    "ndf3 = service.getNDFMeasurement('A1A');\n",
    "trans = 10^(-(ndf2(end).opticalDensity + ndf3(end).opticalDensity));\n",
    "\n",
    "% sample r-star table for ndf 5 \n",
    "\n",
    "disp('ndf A1A + A4B');\n",
    "disp('rstar       voltage')\n",
    "disp('------------------------')\n",
    "\n",
    "for voltage = [56.6532853243682, 90.9947797625159, 125.95670244329, 161.932053656434, 198.742115631752, 395.363673745042, 848.984144716212, 1372.9216466916]\n",
    "    flux = linearity.getFluxByInput(voltage * 10^-3, 'factorized', true);\n",
    "    rstar = flux * rstarPerSecond * trans;\n",
    "    disp([num2str(rstar) '      ' num2str(voltage)]);\n",
    "end\n",
    "\n",
    "clear service;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: MATLAB has disabled some advanced graphics rendering features by switching to software OpenGL. For more information, click <a href=\"matlab:opengl('problems')\">here</a>.\n",
      "\n"
     ]
    },
    {
     "data": {
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WtVVaWhoaGhobGyuE0Gq1KSkpOp3OukB0dHRhYaH0R+x+fn5ms9mVP/UHAKiBGm9qMBgM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\nSAAAVSCQAACqQCABAFSBQAI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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B9a/VD9D4Quum+h7mSdh40HhOQ/solmbRprX+0hFDfu8QGFyGHpJJDOoY9YRNI079qrDF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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close all;\n",
    "clc;\n",
    "import ala_laurila_lab.*;\n",
    "\n",
    "LAMDA_MAX = 497;                        % Toda et al. 1999\n",
    "ROD_PHOTORECEPTOR_AREA = 0.5 * 1e-12;   % um^2, collective area of rod (Murphy & Rieke (2011))\n",
    "\n",
    "spectrum = ala_laurila_lab.util.loadSpectralFile('src/test/resources/spectrum_aalto_rig', 'projector');\n",
    "figure;\n",
    "\n",
    "[p ,graph] = spectrum.getPowerSpectrum(25, 500, 'um');\n",
    "a = axes(); graph(a)\n",
    "\n",
    "\n",
    "linearity = loadjson('src\\test\\resources\\projector-linearity.json');\n",
    "figure; \n",
    "plot(linearity.ledCurrents, linearity.powerInMilliWattFor);\n",
    "xlabel('LED current');\n",
    "ylabel('Power in milli watt');\n",
    "title('Power vs LED current for spot size of 1000 micron');\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Ledurrents \t | rstarPerSecond | ndf 1 | ndf 3 | ndf 5 |\n",
      "| --------- | --------- | --------- | --------- | --------- |\n",
      "| 6 \t | 137030.47 | 13703.047 \t | 137.0305 \t | 1.3703 \t |\n",
      "| 7 \t | 1636321.41 | 163632.141 \t | 1636.3214 \t | 16.3632 \t |\n",
      "| 8 \t | 3420181.9 | 342018.19 \t | 3420.1819 \t | 34.2018 \t |\n",
      "| 9 \t | 5623178 | 562317.8 \t | 5623.178 \t | 56.2318 \t |\n",
      "| 10 \t | 7408572.94 | 740857.294 \t | 7408.5729 \t | 74.0857 \t |\n",
      "| 11 \t | 9169695.77 | 916969.577 \t | 9169.6958 \t | 91.697 \t |\n",
      "| 12 \t | 10971411.61 | 1097141.161 \t | 10971.4116 \t | 109.7141 \t |\n",
      "| 13 \t | 12818835.27 | 1281883.527 \t | 12818.8353 \t | 128.1884 \t |\n",
      "| 14 \t | 14495377.7 | 1449537.77 \t | 14495.3777 \t | 144.9538 \t |\n",
      "| 15 \t | 16147694.53 | 1614769.453 \t | 16147.6945 \t | 161.4769 \t |\n",
      "| 20 \t | 24654557.14 | 2465455.714 \t | 24654.5571 \t | 246.5456 \t |\n",
      "| 30 \t | 40715578.84 | 4071557.884 \t | 40715.5788 \t | 407.1558 \t |\n",
      "| 40 \t | 55189474.32 | 5518947.432 \t | 55189.4743 \t | 551.8947 \t |\n",
      "| 50 \t | 68913026.87 | 6891302.687 \t | 68913.0269 \t | 689.1303 \t |\n",
      "| 60 \t | 81440968.81 | 8144096.881 \t | 81440.9688 \t | 814.4097 \t |\n",
      "| 70 \t | 93826904.94 | 9382690.494 \t | 93826.9049 \t | 938.269 \t |\n",
      "| 80 \t | 105597622.19 | 10559762.219 \t | 105597.6222 \t | 1055.9762 \t |\n",
      "| 90 \t | 117166164.88 | 11716616.488 \t | 117166.1649 \t | 1171.6616 \t |\n",
      "| 100 \t | 128127579.41 | 12812757.941 \t | 128127.5794 \t | 1281.2758 \t |\n",
      "| 120 \t | 149042418.35 | 14904241.835 \t | 149042.4183 \t | 1490.4242 \t |\n",
      "| 140 \t | 168501302.74 | 16850130.274 \t | 168501.3027 \t | 1685.013 \t |\n",
      "| 160 \t | 186718496.78 | 18671849.678 \t | 186718.4968 \t | 1867.185 \t |\n",
      "| 180 \t | 203286210.38 | 20328621.038 \t | 203286.2104 \t | 2032.8621 \t |\n",
      "| 200 \t | 218499289.23 | 21849928.923 \t | 218499.2892 \t | 2184.9929 \t |\n",
      "| 220 \t | 232104503.59 | 23210450.359 \t | 232104.5036 \t | 2321.045 \t |\n",
      "| 240 \t | 244228468.43 | 24422846.843 \t | 244228.4684 \t | 2442.2847 \t |\n",
      "| 255 \t | 252012096.58 | 25201209.658 \t | 252012.0966 \t | 2520.121 \t |\n",
      "\n"
     ]
    }
   ],
   "source": [
    "radius = 1000 * 10^-6/2;\n",
    "area = pi*(radius)^2; % of 1000 um diamater\n",
    "\n",
    "powerPerArea = @(power) power * 10^-3 / area;\n",
    "powerSpectrumPerArea = @(powerPerUnitArea) spectrum.getNormalizedPowerSpectrum() * powerPerUnitArea;\n",
    "rstarPerSecond = @(powerPerUnitArea) util.photonToIsomerisation(powerSpectrumPerArea(powerPerUnitArea), spectrum.wavelength, LAMDA_MAX, ROD_PHOTORECEPTOR_AREA);\n",
    "\n",
    "\n",
    "\n",
    "fprintf('| Ledurrents \\t | rstarPerSecond | ndf 1 | ndf 3 | ndf 5 |\\n');\n",
    "fprintf('| --------- | --------- | --------- | --------- | --------- |\\n')\n",
    "for i = 7 : numel(linearity.ledCurrents)\n",
    "     rstar = rstarPerSecond(powerPerArea(linearity.powerInMilliWattFor(i)));  % in milli watts for 1000 micron and no ndf\n",
    "     rstar = round(rstar, 2);\n",
    "     fprintf('| %s \\t | %s | %s \\t | %s \\t | %s \\t |\\n', num2str(linearity.ledCurrents(i)), num2str(rstar) , num2str(rstar * 10^(-1)), num2str(rstar * 10^(-3)), num2str(rstar * 10^(-5)));\n",
    "end\n"
   ]
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