Camera Calibration 16-09-06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Camera Calibrations 16-09-06"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is based on the camera calibrations template notebook.\n",
    "\n",
    "This notebook analyzes a series of pictures and from them derives the photons/count rate of the camera for the given mode.\n",
    "\n",
    "It can do either averaging all pixels over a large picture (where we worry about spatial inhomogeneity) or averaging a single location over many pictures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Imports and Defines"
   ]
  },
  {
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   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T02:11:05.098439",
     "start_time": "2016-09-08T02:11:04.697878"
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "from scipy.optimize import curve_fit\n",
    "import winsound\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Set Matplotlib Defaults :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T02:11:06.768348",
     "start_time": "2016-09-08T02:11:06.710306"
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   "source": [
    "# set matplotlib plot defaults :D\n",
    "# These values are nice for a dark theme.\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "mpl.rcParams['text.color'] = '#ffffff'\n",
    "mpl.rcParams['figure.edgecolor'] = '#ffffff'\n",
    "mpl.rcParams['xtick.color'] = '#ffffff'\n",
    "mpl.rcParams['ytick.color'] = '#ffffff'\n",
    "mpl.rcParams['figure.facecolor'] = '#000000'\n",
    "mpl.rcParams['axes.facecolor'] = '#0a0a0a'\n",
    "mpl.rcParams['figure.figsize'] = (18.0, 8.0)\n",
    "mpl.rcParams['axes.labelcolor'] = '#ffffff'\n",
    "mpl.rcParams['grid.color'] = '#aaaaff'\n",
    "mpl.rcParams['axes.edgecolor'] = '#ffffff'\n",
    "mpl.rcParams['legend.facecolor'] = '#0a0a0a'\n",
    "mpl.rcParams['axes.grid'] = True\n",
    "mpl.rcParams['axes.formatter.useoffset'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Function Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T03:35:23.443264",
     "start_time": "2016-09-08T03:35:23.358204"
    },
    "code_folding": [],
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   "source": [
    "def line(x, a, b):\n",
    "    return a * x + b\n",
    "\n",
    "\n",
    "def analyzeSinglePicture(date, firstRun, lastRun, printProgress=True):\n",
    "    \"\"\"\n",
    "    This function is for calculating averages of pictures, finding the average pixel signal and variance\n",
    "    of all pixel signals on the pictures.\n",
    "    \n",
    "    :return: This function returns an array of the averages and an array of the variances of the pictures\n",
    "    taken.\n",
    "    \"\"\"\n",
    "    allPictures = []\n",
    "    allAverages = []\n",
    "    allVariances = []\n",
    "    dataRepositoryPath = \"\\\\\\\\andor\\\\share\\\\Data and documents\\\\Data repository\\\\\"\n",
    "    for runInc in range(firstRun, lastRun + 1, 1):\n",
    "        dataPath = dataRepositoryPath + date + \"\\\\Raw Data\\\\data_\" + str(runInc) + \".fits\"\n",
    "        #for repInc in range(0, 1000):\n",
    "        if printProgress:\n",
    "            print('analyzing Run #' + str(runInc))\n",
    "        allPictures.append(fits.open(dataPath, \"append\")[0].data)\n",
    "        # concatenate the data twice, collapsing all pictures into one 2D array, and then all rows within\n",
    "        # said 2D array into a single array. This is for calculating the mean and std of all of the data.\n",
    "        allPictures[-1] = [item for sublist in list(allPictures[-1]) for item in sublist]\n",
    "        allPictures[-1] = [item for sublist in allPictures[-1] for item in sublist]\n",
    "        allAverages.append(np.mean(allPictures[-1]))\n",
    "        allVariances.append(np.std(allPictures[-1])**2)\n",
    "    return allAverages, allVariances\n",
    "    \n",
    "def analyzeSingleLocation(date, firstRun, lastRun, location, printStatus=False, plot=False):\n",
    "    \"\"\"\n",
    "    Analyzes a single location, averaging over many pictures.\n",
    "    The function assumes that there is a data file for all runs in between firstrun and lastrun.\n",
    "    \n",
    "    :return: The function returns the fit object (i.e. an array of [a, b] for the linear fit y = a x + b) \n",
    "    and the covariance of said fit.\n",
    "    \"\"\"\n",
    "    allAverages = []\n",
    "    allVariances = []\n",
    "    dataRepositoryPath = \"\\\\\\\\andor\\\\share\\\\Data and documents\\\\Data repository\\\\\"\n",
    "\n",
    "    # concatenate the data twice.\n",
    "    for runInc in range(firstRun, lastRun + 1, 1):\n",
    "        allPoints = []\n",
    "        if (printStatus):\n",
    "            print(\"Analyzing run # \" + str(runInc))\n",
    "        todaysDataPath = dataRepositoryPath + date + \"\\\\Raw Data\\\\data_\" + str(runInc) + \".fits\"\n",
    "        dataFile = fits.open(todaysDataPath, \"append\")\n",
    "        for repInc in range(0, 1000):\n",
    "            allPoints.append(dataFile[0].data[repInc][location[0]][location[1]])\n",
    "        allAverages.append(np.mean(allPoints))\n",
    "        allVariances.append(np.std(allPoints)**2)\n",
    "        \n",
    "    # average on y, variance on x, slope is average/variance = 1/counts\n",
    "    param, cov = curve_fit(line, allVariances, allAverages, [1, 0])\n",
    "    \n",
    "    return param, cov, param[0], allAverages, allVariances\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Photons per AD Count & per EM Count Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion of Normalization Factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have the data, I need to normalize properly for the EM Gain mode. Consider that the EM Gain setting effectively multiplies each count by some number. So, since mean is defined as\n",
    "$$ \\bar n = \\frac{\\sum_{i=1}^{N}{n_i}}{N}$$\n",
    "Multiplying counts by the em Gain means\n",
    "$$\\bar n_{\\text{measured}} = \\frac{\\sum_{i=1}^{N}{G n_i}}{N} = G \\bar n$$ \n",
    "However, for the variance, since the variance is defined as\n",
    "$$ \\sigma_n^2 = \\frac{\\sum_{i=1}^N{(n_i-\\bar n)^2}}{N}$$\n",
    "In addition, however, For significant EM gains, there is noise due to the EM gain process. For EMCCD cameras this corresponds to a variance of the signal of $\\sqrt{2}$. See the Andor hardware guide (page 27?) for a few more details. The combined effect of this the EM Gain is\n",
    "$$ \\sigma_{n, \\text{observed}}^2 = \\frac{\\sum_{i=1}^N{(\\sqrt{2}(G n_i-G\\times\\bar n))^2}}{N} = 2G^2 \\sigma_n$$\n",
    "\n",
    "\n",
    "Therefore If I observe some slope\n",
    "$$ s_\\text{obs} = \\frac{\\bar n_\\text{obs}}{\\sigma_{n,\\text{obs}}} = \\frac{G\\bar n}{2G^2\\sigma_n} = \\frac{\\bar n}{2G \\sigma_n} = \\frac{1}{2G} s$$ \n",
    "It's off by a factor of $(2G)^{-1}$.\n",
    "\n",
    "This slope gives me photons per AD Counts (modifying for the EM Gain) which is the number quoted by Andor (that's the only reason we care about taking it into account)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Load EM Gain Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C = A*n\n",
    "std(Counts) = A * std(photons) = A * sqrt(n)\n",
    "std^2 = A^2 n"
   ]
  },
  {
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   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T01:36:10.051417",
     "start_time": "2016-09-08T01:21:44.883631"
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    "collapsed": true,
    "hidden": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Initialize some arrays\n",
    "em200_1x1Fits = []\n",
    "em200_1x1Covs = []\n",
    "em200_1x1PhotonPerCounts = []\n",
    "em200_1x1Averages = []\n",
    "em200_1x1Variances = []\n",
    "\n",
    "em200_3x3Fits = []\n",
    "em200_3x3Covs = []\n",
    "em200_3x3PhotonPerCounts = []\n",
    "em200_3x3Averages = []\n",
    "em200_3x3Variances = []\n",
    "\n",
    "em50_1x1Fits = []\n",
    "em50_1x1Covs = []\n",
    "em50_1x1PhotonPerCounts = []\n",
    "em50_1x1Averages = []\n",
    "em50_1x1Variances = []\n",
    "\n",
    "em50_3x3Fits = []\n",
    "em50_3x3Covs = []\n",
    "em50_3x3PhotonPerCounts = []\n",
    "em50_3x3Averages = []\n",
    "em50_3x3Variances = []\n",
    "\n",
    "# Some of the details about individual data sets here are in the onenote.\n",
    "# Average over all the corners.\n",
    "for location in [[0, 0], [-1, 0], [0, -1], [-1, -1]]:\n",
    "    print(location)\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation('160906', 40, 49, location, True)\n",
    "    em200_1x1Fits = np.append(em200_1x1Fits, fit)\n",
    "    em200_1x1Covs = np.append(em200_1x1Covs, cov)\n",
    "    em200_1x1PhotonPerCounts = np.append(em200_1x1PhotonPerCounts, fit1)\n",
    "    em200_1x1Averages = np.append(em200_1x1Averages, averages)\n",
    "    em200_1x1Variances = np.append(em200_1x1Variances, variances)\n",
    "    print('finished 1')\n",
    "    winsound.Beep(2500,100)\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation('160906', 50, 60, location, True)\n",
    "    em200_3x3Fits = np.append(em200_3x3Fits, fit)\n",
    "    em200_3x3Covs = np.append(em200_3x3Covs, cov)\n",
    "    em200_3x3PhotonPerCounts = np.append(em200_3x3PhotonPerCounts, fit1)\n",
    "    em200_3x3Averages = np.append(em200_3x3Averages, averages)\n",
    "    em200_3x3Variances = np.append(em200_3x3Variances, variances)\n",
    "\n",
    "    print('finished 2')\n",
    "    winsound.Beep(2500, 100)\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation('160906', 22, 31, location, True)\n",
    "    em50_3x3Fits = np.append(em50_3x3Fits, fit)\n",
    "    em50_3x3Covs = np.append(em50_3x3Covs, cov)\n",
    "    em50_3x3PhotonPerCounts = np.append(em50_3x3PhotonPerCounts, fit1)\n",
    "    em50_3x3Averages = np.append(em50_3x3Averages, averages)\n",
    "    em50_3x3Variances = np.append(em50_3x3Variances, variances)\n",
    "\n",
    "    print('finished 3')\n",
    "    winsound.Beep(2500,100)\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation('160906', 32, 39, location, True)\n",
    "    em50_1x1Fits = np.append(em50_1x1Fits, fit)\n",
    "    em50_1x1Covs = np.append(em50_1x1Covs, cov)\n",
    "    em50_1x1PhotonPerCounts = np.append(em50_1x1PhotonPerCounts, fit1)\n",
    "    em50_1x1Averages = np.append(em50_1x1Averages, averages)\n",
    "    em50_1x1Variances = np.append(em50_1x1Variances, variances)\n",
    "    print('finished 4')\n",
    "print('COMPLETE!')\n",
    "winsound.Beep(2500,500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Load Conventional Gain Data"
   ]
  },
  {
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     "end_time": "2016-09-08T02:00:24.264372",
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    "collapsed": true,
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    {
     "name": "stdout",
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     "text": [
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      "COMPLETE!\n"
     ]
    }
   ],
   "source": [
    "# initialize some arrays\n",
    "conv_1x1Fits = []\n",
    "conv_1x1Covs = []\n",
    "conv_1x1PhotonPerCounts = []\n",
    "conv_1x1Averages = []\n",
    "conv_1x1Variances = []\n",
    "\n",
    "conv_3x2Fits = []\n",
    "conv_3x2Covs = []\n",
    "conv_3x2PhotonPerCounts = []\n",
    "conv_3x2Averages = []\n",
    "conv_3x2Variances = []\n",
    "\n",
    "# Some of the details about individual data sets here are in the onenote.\n",
    "# average over the four corners.\n",
    "for location in [[0, 0], [-1, 0], [0, -1], [-1, -1]]:\n",
    "    print(location)\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation(\"160824\", 38, 46, location, True)\n",
    "    conv_1x1Fits = np.append(conv_1x1Fits, fit)\n",
    "    conv_1x1Covs = np.append(conv_1x1Covs, cov)\n",
    "    conv_1x1PhotonPerCounts = np.append(conv_1x1PhotonPerCounts, fit1)\n",
    "    conv_1x1Averages = np.append(conv_1x1Averages, averages)\n",
    "    conv_1x1Variances = np.append(conv_1x1Variances, variances)\n",
    "    print('finished 1')\n",
    "    winsound.Beep(2500,100)\n",
    "    # Note: 55 and 56 exist, but 55 ended badly somehow and the data shouldn't be analyzed.\n",
    "    fit, cov, fit1, averages, variances = analyzeSingleLocation(\"160824\", 47, 54, location, True)\n",
    "    conv_3x2Fits = np.append(conv_3x2Fits, fit)\n",
    "    conv_3x2Covs = np.append(conv_3x2Covs, cov)\n",
    "    conv_3x2PhotonPerCounts = np.append(conv_3x2PhotonPerCounts, fit1)\n",
    "    conv_3x2Averages = np.append(conv_3x2Averages, averages)\n",
    "    conv_3x2Variances = np.append(conv_3x2Variances, variances)\n",
    "\n",
    "    print('finished 2')\n",
    "    winsound.Beep(2500, 100)\n",
    "print('COMPLETE!')\n",
    "winsound.Beep(2500,500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Extract Meaningful data from raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T02:11:24.480449",
     "start_time": "2016-09-08T02:11:24.440414"
    },
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# no scaling required.\n",
    "conv_1x1PhotonPerADCount = np.mean(conv_1x1PhotonPerCounts)\n",
    "conv_3x2PhotonPerADCount = np.mean(conv_3x2PhotonPerCounts)\n",
    "\n",
    "# Scale the photon per count data. See discussion above.\n",
    "em200_1x1PhotonPerEMCount = np.mean(em200_1x1PhotonPerCounts) * 2\n",
    "em50_1x1PhotonPerEMCount = np.mean(em50_1x1PhotonPerCounts) * 2\n",
    "em200_3x3PhotonPerEMCount = np.mean(em200_3x3PhotonPerCounts) * 2\n",
    "em50_3x3PhotonPerEMCount = np.mean(em50_3x3PhotonPerCounts) * 2\n",
    "# Scale the photon per count data. See discussion above.\n",
    "em200_1x1PhotonPerADCount = np.mean(em200_1x1PhotonPerCounts) * 2 * 200\n",
    "em50_1x1PhotonPerADCount = np.mean(em50_1x1PhotonPerCounts) * 2 * 50\n",
    "em200_3x3PhotonPerADCount = np.mean(em200_3x3PhotonPerCounts) * 2 * 200\n",
    "em50_3x3PhotonPerADCount = np.mean(em50_3x3PhotonPerCounts) * 2 * 50\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output All Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "conv_1x1PhotonPerADCount": "0.65090494306349556",
     "conv_3x2PhotonPerADCount": "0.69779820150366423",
     "em200_1x1PhotonPerADCount": "3.7374758950397808",
     "em200_1x1PhotonPerEMCount": "0.018687379475198905",
     "em200_3x3PhotonPerADCount": "3.7154885565786269",
     "em200_3x3PhotonPerEMCount": "0.018577442782893135",
     "em50_1x1PhotonPerADCount": "3.527683630992537",
     "em50_1x1PhotonPerEMCount": "0.070553672619850738",
     "em50_3x3PhotonPerADCount": "3.6336482684712692",
     "em50_3x3PhotonPerEMCount": "0.072672965369425385"
    }
   },
   "source": [
    "#### Photons Per EM Count (To use directly)\n",
    "|      | **1x1 Bin** | **3x3 Bin** |\n",
    "| :--- | --- | --- |\n",
    "| **X50**  | {{em50_1x1PhotonPerEMCount}} | {{em50_3x3PhotonPerEMCount}}  | \n",
    "| **x200** | {{em200_1x1PhotonPerEMCount}} | {{em200_3x3PhotonPerEMCount}}  | \n",
    "\n",
    "#### Photons Per AD Count (Compare to Andor #s)\n",
    "|      | **1x1 Bin** | **3x3 Bin** |\n",
    "| :--- | --- | --- |\n",
    "| **X50**  | {{em50_1x1PhotonPerADCount}} | {{em50_3x3PhotonPerADCount}}  | \n",
    "| **x200** | {{em200_1x1PhotonPerADCount}} | {{em200_3x3PhotonPerADCount}}  | \n",
    "\n",
    "**Andor's Value (EM gain): ** 3.97\n",
    "\n",
    "| | **1x1 Bin** | **3x2 Bin** |\n",
    "| --- | --- | --- |\n",
    "| Conventional | {{conv_1x1PhotonPerADCount}} | {{conv_3x2PhotonPerADCount}} |\n",
    "\n",
    "**Andor's Value (conventional gain): ** 0.68\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T03:01:26.149464",
     "start_time": "2016-09-08T03:01:10.369261"
    },
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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nzhy8+OKLOHDgAFauXIns7Gz07NkT99xzD7Zt24Z58+bZH6+qKgYNGoT169dj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RkH+5T8uA/RGLgPsWrjgb4qqHnz5nj33Xfxyy+/IC0tDXl5eUhLS8OOHTswd+5chIaGVsj7CCEg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eew4bNmzA0aNH4fF48Ntvv2HevHmIi4sr9n6apuGvf/0rkpOTkZWVhdTUVCxduhTt27cv9j41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XXXcdWVlZTJs2jVgsVu82Bw8ezDnnnMPcuXPJycmpLqeccgr5+fmMGDGiybhF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c8d163cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IoGRz5sxx3SYIABo0aIAhQ4bg7bffxpAhQ9CtWzecPXvWo2N5U8trMRgMHu9bWcaOHYu9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HIFpSBfEBS5glKQLyhFWMdmsiazG/chuxFfuxFfexFbuxFfexFb+5VjTVZxhb4HHnhADzzwQK/n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\nIbsRX7sRX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TAYlJEjR55wHn369JF//vOfsn37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\nzs7LhEgGRNpF9ghCqW8iC3ovQakSGEYxlNoMpcwTPrdSCt6QF2aJCXNj1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B2C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\nIrlQvkih0JjMv/Qc8jfF198UX/9SbP1N8fUvxVaykfPSnX7x6qv5boEUktm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c867eb240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot things. All the fits on 2 big plots.\n",
    "plt.figure(1)\n",
    "axls = []\n",
    "axls.append(plt.subplot(4, 4, 1))\n",
    "axls.append(plt.subplot(4, 4, 5))\n",
    "axls.append(plt.subplot(4, 4, 9))\n",
    "axls.append(plt.subplot(4, 4, 13))\n",
    "axls[3].set_xlabel('Variances')\n",
    "axb1 = plt.subplot(4, 4, 13)\n",
    "axb2 = plt.subplot(4, 4, 14)\n",
    "axb3 = plt.subplot(4, 4, 15)\n",
    "axb4 = plt.subplot(4, 4, 16)\n",
    "axb1.set_ylabel('Means')\n",
    "#size of a single data set... they got concatenated together so I need to separate them.\n",
    "ds1 = int(len(em200_1x1Variances) / 4)\n",
    "ds2 = int(len(em50_1x1Variances) / 4)\n",
    "ds3 = int(len(em50_3x3Variances) / 4)\n",
    "ds4 = int(len(em200_3x3Variances) / 4)\n",
    "# Locations order reminder:\n",
    "# [[0, 0], [-1, 0], [0, -1], [-1, -1]]:\n",
    "for posInc in range(0,4):\n",
    "    xData = np.linspace(0, max(em200_1x1Variances), 25000)\n",
    "    fitData = line(xData, em200_1x1Fits[2 * posInc], em200_1x1Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 4, posInc * 4 + 1, sharey=axls[posInc], sharex=axb1)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(em200_1x1Variances[posInc*ds1 : (posInc+1)*ds1], \n",
    "             em200_1x1Averages[posInc*ds1 : (posInc+1)*ds1],\n",
    "             linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "    else:\n",
    "        plt.xlabel('Variance')\n",
    "        plt.ylabel('Mean')\n",
    "    plt.setp(axTemp.get_yticklabels(), fontsize=6)\n",
    "    plt.title('Gain = 200, Bin = 1x1, loc ' + str(posInc + 1))\n",
    "    #\n",
    "    xData = np.linspace(0, max(em50_1x1Variances), 25000)\n",
    "    fitData = line(xData, em50_1x1Fits[2 * posInc], em50_1x1Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 4, posInc * 4 + 2, sharey=axls[posInc], sharex=axb2)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(em50_1x1Variances[posInc*ds2 : (posInc+1)*ds2], \n",
    "             em50_1x1Averages[posInc*ds2 : (posInc+1)*ds2], linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    plt.setp(axTemp.get_yticklabels(), visible=False)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "    plt.title('Gain = 50, Bin = 1x1, loc ' + str(posInc + 1))\n",
    "    #\n",
    "    xData = np.linspace(0, max(em50_3x3Variances), 25000)\n",
    "    fitData = line(xData, em50_3x3Fits[2 * posInc], em50_3x3Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 4, posInc * 4 + 3, sharey=axls[posInc], sharex=axb3)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(em50_3x3Variances[posInc*ds3 : (posInc+1)*ds3], \n",
    "             em50_3x3Averages[posInc*ds3 : (posInc+1)*ds3],linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    plt.setp(axTemp.get_yticklabels(), visible=False)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "    plt.setp(axTemp.get_yticklabels(), fontsize=6)\n",
    "    plt.title('Gain = 50, Bin = 3x3, loc ' + str(posInc + 1))\n",
    "    #\n",
    "    xData = np.linspace(0, max(em200_3x3Variances), 25000)\n",
    "    fitData = line(xData, em200_3x3Fits[2 * posInc], em200_3x3Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 4, posInc * 4 + 4, sharey=axls[posInc], sharex=axb4)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(em200_3x3Variances[posInc*ds4 : (posInc+1)*ds4], \n",
    "             em200_3x3Averages[posInc*ds4 : (posInc+1)*ds4], linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    plt.setp(axTemp.get_yticklabels(), visible=False)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "        \n",
    "    plt.setp(axTemp.get_yticklabels(), fontsize=6)\n",
    "    plt.title('Gain = 200, Bin = 3x3, loc ' + str(posInc + 1))\n",
    "\n",
    "plt.tight_layout() \n",
    "plt.figure(2)\n",
    "axls = []\n",
    "axls.append(plt.subplot(4, 2, 1))\n",
    "axls.append(plt.subplot(4, 2, 3))\n",
    "axls.append(plt.subplot(4, 2, 5))\n",
    "axls.append(plt.subplot(4, 2, 7))\n",
    "axls[3].set_xlabel('Variances')\n",
    "axb1 = plt.subplot(4, 2, 7)\n",
    "axb2 = plt.subplot(4, 2, 8)\n",
    "axb1.set_ylabel('Means')\n",
    "#size of a single data set... they got concatenated together so I need to separate them.\n",
    "ds1 = int(len(conv_1x1Variances) / 4)\n",
    "ds2 = int(len(conv_3x2Variances) / 4)\n",
    "# Locations order reminder:\n",
    "# [[0, 0], [-1, 0], [0, -1], [-1, -1]]:\n",
    "for posInc in range(0,4):\n",
    "    xData = np.linspace(0, max(conv_1x1Variances), 25000)\n",
    "    fitData = line(xData, conv_1x1Fits[2 * posInc], conv_1x1Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 2, posInc * 2 + 1, sharey=axls[posInc], sharex=axb1)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(conv_1x1Variances[posInc*ds1 : (posInc+1)*ds1], \n",
    "             conv_1x1Averages[posInc*ds1 : (posInc+1)*ds1],\n",
    "             linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "    else:\n",
    "        plt.xlabel('Variance')\n",
    "        plt.ylabel('Mean')\n",
    "    plt.setp(axTemp.get_yticklabels(), fontsize=6)\n",
    "    plt.title('Conventional Gain, Bin = 1x1, loc ' + str(posInc + 1))\n",
    "    #\n",
    "    xData = np.linspace(0, max(conv_3x2Variances), 25000)\n",
    "    fitData = line(xData, conv_3x2Fits[2 * posInc], conv_3x2Fits[2 * posInc + 1])\n",
    "    axTemp = plt.subplot(4, 2, posInc * 2 + 2, sharey=axls[posInc], sharex=axb2)\n",
    "    plt.plot(xData, fitData)\n",
    "    plt.plot(conv_3x2Variances[posInc*ds2 : (posInc+1)*ds2], \n",
    "             conv_3x2Averages[posInc*ds2 : (posInc+1)*ds2], linestyle='none', marker='o')\n",
    "    plt.setp(axTemp.get_xticklabels(), fontsize=6)\n",
    "    plt.setp(axTemp.get_yticklabels(), visible=False)\n",
    "    if not posInc == 3:\n",
    "        plt.setp(axTemp.get_xticklabels(), visible=False)\n",
    "    plt.title('Conventional Gain, Bin = 3x2, loc ' + str(posInc + 1))\n",
    "\n",
    "\n",
    "plt.tight_layout() \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things look pretty normal. The points match up to the Andor values pretty nicely although a bit low, and the fits all look quite linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dark Counts Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Analyze the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T03:52:00.367876",
     "start_time": "2016-09-08T03:47:59.822486"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analyzing Run #60\n",
      "analyzing Run #61\n",
      "analyzing Run #62\n",
      "analyzing Run #63\n",
      "analyzing Run #64\n",
      "analyzing Run #65\n",
      "analyzing Run #66\n",
      "analyzing Run #67\n",
      "analyzing Run #68\n",
      "analyzing Run #69\n",
      "analyzing Run #70\n",
      "analyzing Run #71\n",
      "analyzing Run #72\n",
      "analyzing Run #73\n",
      "analyzing Run #74\n",
      "analyzing Run #75\n",
      "analyzing Run #76\n",
      "analyzing Run #77\n",
      "Finished conventional...\n",
      "analyzing Run #1\n",
      "analyzing Run #2\n",
      "analyzing Run #3\n",
      "analyzing Run #4\n",
      "analyzing Run #5\n",
      "analyzing Run #6\n",
      "analyzing Run #7\n",
      "analyzing Run #8\n",
      "analyzing Run #9\n",
      "analyzing Run #10\n",
      "analyzing Run #11\n",
      "analyzing Run #12\n",
      "analyzing Run #13\n",
      "analyzing Run #14\n",
      "analyzing Run #15\n",
      "analyzing Run #16\n",
      "analyzing Run #17\n",
      "Finished all!\n"
     ]
    }
   ],
   "source": [
    "# This data analysis is much simpler than the data analysis above because my function averages the pictures \n",
    "# and therefore don't have to loop through different locations to get a good feel for things.\n",
    "\n",
    "em200Avgs, em200Vars = analyzeSinglePicture('160906', 60, 77)\n",
    "print('Finished conventional...')\n",
    "convAvgs, convVars = analyzeSinglePicture('160907', 1, 17)\n",
    "print('Finished all!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T03:52:05.272809",
     "start_time": "2016-09-08T03:52:05.255777"
    },
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# there was a bad fits file in the first data set I took.\n",
    "del em200Avgs[1]\n",
    "\n",
    "# Define the array of all exposure times. This was consistent and in-order for both the\n",
    "# conventional en EM Gain mode measurements.\n",
    "\n",
    "# In miliseconds...\n",
    "#print(convAvgs)\n",
    "exposures = [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5, 10, 20, 40, 80, 160, 320, 640]\n",
    "\n",
    "# Fit the data.\n",
    "convFit, convCov = curve_fit(line, exposures, convAvgs, p0=[0, 0])\n",
    "em200Fit, em200Cov = curve_fit(line, exposures, em200Avgs, p0=[0, 0])\n",
    "xData = np.linspace(0, 640, 10000)\n",
    "convFitData = line(xData, *convFit)\n",
    "em200FitData = line(xData, *em200Fit)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot & Display Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-08T04:15:56.870839",
     "start_time": "2016-09-08T04:15:55.714020"
    },
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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teHh44MCBAxg1ahQCAgJM9lcUBV27djVqr1evntHrfeLECQAw+f4AgO7duwMAoqKiTD5+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\nXFx/t/k6PFvPD2gGyOMCTdMsfh415fX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c8d2d4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conventional Gain Slope = -0.000455042297441 +- 9.26507925333e-15\n",
      "EM Gain Slope = -0.00866639933096 +- 5.21550507725e-07\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "fig = plt.figure(1)\n",
    "splt = fig.add_subplot(211)\n",
    "splt.plot(xData, convFitData, label='Linear Fit')\n",
    "splt.plot(exposures, convAvgs, linestyle='', marker='o', label='Data')\n",
    "splt.set_xscale(\"log\", nonposx='clip')\n",
    "splt.set_title('Conventional Gain')\n",
    "splt.legend()\n",
    "\n",
    "splt = plt.subplot(212, sharex=splt)\n",
    "splt.plot(xData, em200FitData)\n",
    "splt.plot(exposures, em200Avgs, linestyle='', marker='o')\n",
    "splt.set_xscale(\"log\", nonposx='clip')\n",
    "splt.set_title('EM Gain = X200')\n",
    "splt.set_xlim([8*10**-3, 1000])\n",
    "\n",
    "ax = fig.add_subplot(111, frameon=False)\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['bottom'].set_color('none')\n",
    "ax.spines['left'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')\n",
    "ax.grid(False)\n",
    "plt.yticks([], [])\n",
    "plt.xticks([], [])\n",
    "ax.set_ylabel('Average Count', labelpad=35)\n",
    "ax.set_xlabel('Exposure Time (ms)', labelpad=25)\n",
    "ax.set_title('Dark Counts Vs. Exposure',y=1.04)\n",
    "plt.show()\n",
    "print('Conventional Gain Slope = ' + str(convFit[0]) + \" +- \" + str(convCov[0][0]**2))\n",
    "print('EM Gain Slope = ' + str(em200Fit[0]) + \" +- \" + str(em200Cov[0][0]**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks very strange! There appears to be very negligible actual dark photon count, but there appears to be something happening at low exposure times (more so for conventional gain!) that causes the background count to rise. Thankfully I don't think this should affect our measurements... the effect appears to pretty clearly be gone by 2ms or so for conventional gain. I have no idea what could cause this. The only thing that I can think of is that there is some extra electronic noise involved in very short exposure times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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