Neon19_Data_Analysis_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries Necessary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import scipy as sp\n",
    "import math\n",
    "import random\n",
    "from scipy.optimize import curve_fit,minimize\n",
    "from numpy.linalg import inv\n",
    "from tqdm import tqdm as tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for reading in the data from a run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This creates a dataframe for each channel\n",
    "\n",
    "def create_string(location):\n",
    "    path0 = 'C:/Users/higgi/Downloads/CH0@DT5725_1146_Data_{0:s}.csv'.format(location)\n",
    "    path2 = 'C:/Users/higgi/Downloads/CH2@DT5725_1146_Data_{0:s}.csv'.format(location)\n",
    "    \n",
    "    path = [path0,path2]\n",
    "    return path\n",
    "\n",
    "#def create_string(location):\n",
    "    #path0 = '/home/cenpa/Ne19/DAQ/FinalConfigSettings/DAQ/{0:s}/UNFILTERED/CH0@DT5725_1146_Data_{0:s}.csv'.format(location)\n",
    "    #path2 = '/home/cenpa/Ne19/DAQ/FinalConfigSettings/DAQ/{0:s}/UNFILTERED/CH2@DT5725_1146_Data_{0:s}.csv'.format(location)\n",
    "    \n",
    "    #path = [path0,path2]\n",
    "    #return path\n",
    "\n",
    "\n",
    "\n",
    "#write the folder location where 'BiTest-03' is\n",
    "run_name = 'Ne19rundeltaE-02-22-20_5'\n",
    "\n",
    "\n",
    "#ch0_df = pd.read_csv(path[0],sep=';')\n",
    "#ch1_df = pd.read_csv(path[1],sep=';')\n",
    "\n",
    "def Run_Data(run_folder_name):\n",
    "    return[pd.read_csv(create_string(run_name)[i],sep=';') for i in range(2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for splitting the data into separate cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Split_Data(ch0_dataFrame,ch2_dataFrame,minimum_counts = 0):\n",
    "    \"\"\"This function returns a list of the counts data split by run for channel0 based on the triggering from channel2\n",
    "    Parameters\n",
    "    ----------\n",
    "    ch0_dataFrame : Pandas Dataframe, counts data.\n",
    "    ch2_dataFrame : Pandas Dataframe, run trigger data.\n",
    "    minimum_counts : int, \n",
    "        minimum counts to remove any possible signals that may occur when not \n",
    "        running but still collecting data. Default is 0.\n",
    "    Returns\n",
    "    -------\n",
    "    ch0split_data : Pandas Dataframe,\n",
    "        Returns a list of Pandas Dataframes that correspond to each cycle in the run.\n",
    "    \"\"\"\n",
    "    #initializing an empty list for coincidencedata ch0 \n",
    "    #by the clock from ch2\n",
    "    \n",
    "    ch0split_data = []\n",
    "    \n",
    "    \n",
    "    #iterating through the number of times ch2 recieved a signal from our clock\n",
    "    for i in np.arange(0,len(ch2_dataFrame)-1):\n",
    "        \n",
    "        #creating a mask to separate the full data into their individual runs for ch0\n",
    "        ch0time_mask = np.where((ch0_dataFrame['TIMETAG'] > ch2_dataFrame['TIMETAG'][i]) & \n",
    "                                (ch0_dataFrame['TIMETAG'] < ch2_dataFrame['TIMETAG'][i+1]-(89*10**12)))#pullback time in units of 10**-12 seconds\n",
    "        \n",
    "        \n",
    "        #This number for minimum counts is to remove any possible signals that may occur when we are not running but still collecting data\n",
    "        if len(ch0time_mask[0]) >= minimum_counts:\n",
    "            \n",
    "            #for each run I am creating a new dataframe and storing in a list\n",
    "            split_dataFrame0 = ch0_dataFrame.iloc[[ch0time_mask][0][0]].copy()\n",
    "            #resetting the index\n",
    "            split_dataFrame0 = split_dataFrame0.reset_index(drop=True)\n",
    "            \n",
    "            ch0split_data.extend([split_dataFrame0])\n",
    "\n",
    "    #returns arrays of dataframes for channel0       \n",
    "    return ch0split_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for binning the data for count rate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bin size is in units of seconds\n",
    "def count_rate(single_run_coincidences,cutoff=0, bin_size = 1):\n",
    "    \"\"\"This function returns a list of the numpy arrays, the first array is the times in seconds and the second array is \n",
    "        the number of counts at those times based on the bin size.\n",
    "    Parameters\n",
    "    ----------\n",
    "    single_run_coincidences : Pandas Dataframe, counts data for a single run after the data has been split.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    bin_size : int, size of the bins in unit seconds.\n",
    "    Returns\n",
    "    -------\n",
    "    time_axis : numpy array,\n",
    "        Returns values that correspond to the time binnings.\n",
    "    counts : numpy array,\n",
    "        Returns values that correspond to the number of counts in each of the\n",
    "        time_axis bins.\n",
    "    \"\"\"    \n",
    "    #Converting units to seconds\n",
    "    bin_seconds = bin_size*(10**12)\n",
    "    \n",
    "    #Setting the run timetags to start at 0\n",
    "    singlerun = (single_run_coincidences['TIMETAG']).where((10**-12)*single_run_coincidences['TIMETAG']>=(10**-12)*single_run_coincidences['TIMETAG'].min()+cutoff).dropna()\n",
    "    singlerun = singlerun-singlerun.min()\n",
    "    #Creating an array of the time spacings\n",
    "    time_axis = np.arange(0,singlerun.max()-bin_seconds,bin_seconds)\n",
    "    \n",
    "    def time_binning(i):\n",
    "        #function to create the number of \n",
    "        number_of_counts = np.where((singlerun >= i) & \n",
    "                                    (singlerun < i+bin_seconds))\n",
    "        return len(number_of_counts[0])\n",
    "    #returning an array of the time axis and the counts\n",
    "    time_axis = time_axis*(10**-12)\n",
    "    counts = np.asarray([time_binning(i) for i in time_axis])\n",
    "    \n",
    "    return time_axis, counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for plotting the counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rate(coincidences,cycle_number, bin_size = 1):\n",
    "    \n",
    "    x,y = count_rate(coincidences[cycle_number],bin_size = bin_size)\n",
    "    fig, ax = plt.subplots(1,figsize=(12,5))\n",
    "\n",
    "    #ax.errorbar(x,y,yerr=np.sqrt(y), label= 'Limits')\n",
    "\n",
    "    ax.plot(x,y)\n",
    "    ax.set_title('Run from {0:s} Cycle Number {1:d}'.format(run_name,cycle_number))\n",
    "    ax.set_ylabel('Counts')\n",
    "    ax.set_xlabel('Seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$N=N_0 e^{-\\lambda t} +B$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$N= {\\frac  {N_{m}}{1-N_{m}D}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$M_i = \\int_{T_{i-1}}^{T_i} \\frac{N}{1+ND}dt$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#not a very important function, but I left it in since it can be used to check approximations quickly\n",
    "def Convert_Measured_to_Actual(counts,background = 0, bin_size = 1,deadtime = 0.000016):\n",
    "    background_subtracted_counts = counts - background\n",
    "    return background_subtracted_counts/(1-background_subtracted_counts*deadtime/bin_size)\n",
    "\n",
    "def optimize_single_cycle(counts, binsize=1, deadtime=0.000018, rc=.039, cutoff=100):\n",
    "    \"\"\"This function returns a numpy minimization object for a single cycle.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : Pandas Dataframe, counts data for a single cycle after using the Split_Data function.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    deadtime : float, system deadtime in seconds.\n",
    "    rc : float, ln(2)/halflife.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    Returns\n",
    "    -------\n",
    "    minimized_function : Numpy minimize object,\n",
    "        Returns information on the minimization of the chi-squared best fit for the data.\n",
    "        May return errors, but this can be due to the method numpy uses to create a hessian matrix and in most cases\n",
    "        does not apply.\n",
    "    \"\"\"\n",
    "    t,measured = count_rate(single_run_coincidences=counts, cutoff=cutoff, bin_size = binsize)\n",
    "    def chisquaredfunction(params):\n",
    "        D,l,N0,B = params\n",
    "        \n",
    "        expected = (np.log(np.absolute((D*N0*np.exp(-l*np.asarray(t[0:len(t)-1]))+B*D+1)/(D*N0*np.exp(-l*np.asarray(t[1:len(t)]))+B*D+1))) + B*D*l*(np.asarray(t[1:len(t)])-np.asarray(t[0:len(t)-1])))/(B*D*D*l+D*l)\n",
    "        \n",
    "        measured[0:len(measured)-1][np.where(measured[0:len(measured)-1]==0)] = 1\n",
    "        chisquared = np.sum((np.square(measured[0:len(measured)-1] - expected)) / measured[0:len(measured)-1])\n",
    "        return chisquared#t,measured[0:len(measured)-1],expected,chisquared\n",
    "    initial_guess = [rc, measured[0]/(1-measured[0]*deadtime/binsize),B]\n",
    "    minimized_function = minimize(chisquaredfunction, initial_guess)\n",
    "    return minimized_function\n",
    "        \n",
    "\n",
    "#functions for doing a very quick exponential fit.   \n",
    "def fit_function(t,N,l):\n",
    "    return (N*np.exp((-t*l)))\n",
    "\n",
    "def final_analysis(time,counts,rc):\n",
    "    \n",
    "    popt, pcov = curve_fit(fit_function, xdata=time, ydata=counts, p0=[counts[0],rc])\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "    return {'N_0' : popt[0],\"Decay_Constant\": popt[1],\"Covariance Matrix\": pcov}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$M_i = \\dfrac{\\ln\\left(\\left|\\frac{DN_0\\mathrm{e}^{-\\lambda T_{i-1}}+BD+1}{DN_0\\mathrm{e}^{-\\lambda T_i}+BD+1}\\right|\\right)+BD\\lambda( T_i-T_{i-1})}{\\left(BD^2+D\\right)\\lambda}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_sharedvalues(counts, binsize=1, deadtime=0.000018, rc=.039, cutoff=0):\n",
    "    \"\"\"This function returns a numpy minimization object for the best fit of multiple\n",
    "        cycles with the same deadtime and rc.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : list of Pandas Dataframes, counts data for multiple cycles or all cycles in a run.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    deadtime : float, system deadtime in seconds.\n",
    "    rc : float, ln(2)/halflife.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    Returns\n",
    "    -------\n",
    "    minimized_function : Numpy minimize object,\n",
    "        Returns information on the minimization of the chi-squared best fit for the data.\n",
    "        May return errors, but this can be due to the method numpy uses to create a hessian matrix and in most cases\n",
    "        does not apply.\n",
    "    \"\"\"\n",
    "    cycle_data = counts\n",
    "    numbercycles = len(counts)\n",
    "    t=[]\n",
    "    measured=[]\n",
    "    for i in np.arange(0,numbercycles):\n",
    "        t_temporary,measured_temporary = count_rate(single_run_coincidences=cycle_data[i], cutoff=cutoff, bin_size = binsize)\n",
    "        np.asarray(measured_temporary)[0:len(measured_temporary)-1][np.where(measured_temporary[0:len(measured_temporary)-1]==0)] = 1\n",
    "        measured_temporary = measured_temporary[0:len(measured_temporary)-1]\n",
    "        t.extend([[t_temporary]])\n",
    "        measured.extend([[measured_temporary]])\n",
    "    def chisquaredfun(params):\n",
    "\n",
    "        \n",
    "            \n",
    "        \n",
    "        D = deadtime#params[0]\n",
    "        l = params[0]\n",
    "        B = params[1:1+numbercycles]\n",
    "        N0 = params[1+numbercycles:1+2*numbercycles]\n",
    "        \n",
    "        expected = []\n",
    "        for i in np.arange(numbercycles):\n",
    "            expected_temp = (np.log(np.absolute((D*N0[i]*np.exp(-l*np.asarray(t[i][0][0:len(t[i][0])-1]))+B[i]*D+1)/(D*N0[i]*np.exp(-l*np.asarray(t[i][0][1:len(t[i][0])]))+B[i]*D+1))) + B[i]*D*l*(np.asarray(t[i][0][1:len(t[i][0])])-np.asarray(t[i][0][0:len(t[i][0])-1])))/(B[i]*D*D*l+D*l)#the expected equation\n",
    "\n",
    "            expected.extend([[expected_temp]])\n",
    "                        \n",
    "        chisquared = ((np.square(np.asarray(measured) - np.asarray(expected))) / np.asarray(measured))\n",
    "        \n",
    "        return np.sum([np.sum(item[0])for item in chisquared])\n",
    "    B_guess = np.ones(numbercycles)\n",
    "    N0_guess = [item[0][0]for item in measured]\n",
    "\n",
    "    \n",
    "    \n",
    "    initial_guess = [ rc, *np.ones(numbercycles), *N0_guess ]\n",
    "    \n",
    "    minimized_function = minimize(chisquaredfun, initial_guess)#,method = 'L-BFGS-B', bounds = [(0.000001,.001),(.01,.05),(.01,30),(.01,30),(.01,30),(.01,30),(.01,30),(1000,None),(1000,None),(1000,None),(1000,None),(1000,None)]) \n",
    "    return minimized_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$N=N_0 e^{-\\lambda t} + B$$\n",
    "\n",
    "$$M_i = \\frac{N_0}{\\lambda}\\left( e^{-\\lambda T_{i-1}}- e^{-\\lambda T_{i}}\\right) + B(T_i - T_{i-1})$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_sharedvalues_simple(counts, binsize=1, rc=.039, cutoff=0):\n",
    "    \"\"\"This function returns a numpy minimization object for the best fit of multiple\n",
    "        cycles with the same rc with no deadtime.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : list of Pandas Dataframes, counts data for multiple cycles or all cycles in a run.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    rc : float, ln(2)/halflife.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "        cutoff is almost never 0 for this function since the data at early times\n",
    "        is not close to a simple exponential.\n",
    "    Returns\n",
    "    -------\n",
    "    minimized_function : Numpy minimize object,\n",
    "        Returns information on the minimization of the chi-squared best fit for the data.\n",
    "        May return errors, but this can be due to the method numpy uses to create a hessian matrix and in most cases\n",
    "        does not apply.\n",
    "    \"\"\"\n",
    "    cycle_data = counts\n",
    "    numbercycles = len(counts)\n",
    "    t=[]\n",
    "    measured=[]\n",
    "    for i in np.arange(0,numbercycles):\n",
    "        t_temporary,measured_temporary = count_rate(single_run_coincidences=cycle_data[i], cutoff=cutoff, bin_size = binsize)\n",
    "        np.asarray(measured_temporary)[0:len(measured_temporary)-1][np.where(measured_temporary[0:len(measured_temporary)-1]==0)] = 1\n",
    "        \n",
    "        measured_temporary = measured_temporary[0:len(measured_temporary)-1]\n",
    "        t.extend([[t_temporary]])\n",
    "        measured.extend([[measured_temporary]])\n",
    "    def chisquaredfun(params):\n",
    "\n",
    "        \n",
    "            \n",
    "        \n",
    "        l = params[0]\n",
    "        B = params[1:1+numbercycles]\n",
    "        N0 = params[1+numbercycles:1+2*numbercycles]\n",
    "        \n",
    "        expected = []\n",
    "        for i in np.arange(numbercycles):\n",
    "            expected_temp = (N0[i]/l)*(np.exp(-l*np.asarray(t[i][0][0:len(t[i][0])-1]))-np.exp(-l*np.asarray(t[i][0][1:len(t[i][0])]))) + B[i]*(t[i][0][1:len(t[i][0])]-t[i][0][0:len(t[i][0])-1])\n",
    "\n",
    "            expected.extend([[expected_temp]])\n",
    "                        \n",
    "        chisquared = ((np.square(np.asarray(measured) - np.asarray(expected))) / np.asarray(measured))\n",
    "        \n",
    "        return np.sum([np.sum(item[0])for item in chisquared])\n",
    "    B_guess = np.ones(numbercycles)\n",
    "    N0_guess = [item[0][0]for item in measured]\n",
    "\n",
    "    \n",
    "    \n",
    "    initial_guess = [ rc, *np.ones(numbercycles), *N0_guess ]\n",
    "    minimized_function = minimize(chisquaredfun, initial_guess)#,method = 'L-BFGS-B', bounds = [(.01,.05),(0,0),(0,0),(0,0),(0,0),(0,0),(1000,None),(1000,None),(1000,None),(1000,None),(1000,None)]) \n",
    "    return minimized_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$M_i = \\dfrac{\\ln(DN_0e^{-\\lambda T_{i-1}}+BD+1)}{D(BD+1)\\lambda}-\\dfrac{\\ln(DN_0e^{-\\lambda T_{i}}+BD+1)}{D(BD+1)\\lambda} + \\dfrac{B(T_i - T_{i-1})}{BD+1}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$x - \\frac{x^2}{2} + \\frac{x^3}{3} - \\frac{x^4}{4} + \\frac{x^5}{5} - \\frac{x^6}{6} + O(x^7)$$\n",
    "(Taylor series)\n",
    "(converges when abs(x)<1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B = 0 (can make data reflect this condition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$M_i = \\frac{N_0e^{-\\lambda T_{i-1}}}{\\lambda} - \\frac{N_0e^{-\\lambda T_{i}}}{\\lambda} - \\frac{D(N_0e^{-\\lambda T_{i-1}})^2}{\\lambda} +\\frac{D(N_0e^{-\\lambda T_{i}})^2}{\\lambda}+...$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_sharedvalues_expanded(counts, binsize=1, deadtime=0.000016, rc=.039, cutoff=0):\n",
    "\"\"\"This function returns a numpy minimization object for the best fit of multiple\n",
    "        cycles with the same deadtime and rc and uses an approximation with no background counts.\n",
    "        Used more as a tool to check rather than a tool for analysis.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : list of Pandas Dataframes, counts data for multiple cycles or all cycles in a run.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    deadtime : float, system deadtime in seconds.\n",
    "    rc : float, ln(2)/halflife.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    Returns\n",
    "    -------\n",
    "    minimized_function : Numpy minimize object,\n",
    "        Returns information on the minimization of the chi-squared best fit for the data.\n",
    "        May return errors, but this can be due to the method numpy uses to create a hessian matrix and in most cases\n",
    "        does not apply.\n",
    "    \"\"\"\n",
    "    cycle_data = counts\n",
    "    numbercycles = len(counts)\n",
    "    t=[]\n",
    "    measured=[]\n",
    "    for i in np.arange(0,numbercycles):\n",
    "        t_temporary,measured_temporary = count_rate(single_run_coincidences=cycle_data[i], cutoff=cutoff, bin_size = binsize)\n",
    "        np.asarray(measured_temporary)[0:len(measured_temporary)-1][np.where(measured_temporary[0:len(measured_temporary)-1]==0)] = 1\n",
    "        \n",
    "        measured_temporary = measured_temporary[0:len(measured_temporary)-1]\n",
    "        t.extend([[t_temporary]])\n",
    "        measured.extend([[measured_temporary]])\n",
    "    def chisquaredfun(params):\n",
    "\n",
    "        \n",
    "            \n",
    "        D = params[1]\n",
    "        l = params[0]\n",
    "        N0 = params[2:2+numbercycles]\n",
    "        \n",
    "        \n",
    "        expected = []\n",
    "        for i in np.arange(numbercycles):\n",
    "            expected_temp = (N0[i]*np.exp(-l*np.asarray(t[i][0][0:len(t[i][0])-1])))/l - (N0[i]*np.exp(-l*np.asarray(t[i][0][1:len(t[i][0])])))/l - D*((N0[i]*np.exp(-l*np.asarray(t[i][0][0:len(t[i][0])-1])))**2 - (N0[i]*np.exp(-l*np.asarray(t[i][0][1:len(t[i][0])])))**2)/l #+ (D**2)*((N0[i]*np.exp(-l*np.asarray(t[i][0][0:len(t[i][0])-1])))**3 - (N0[i]*np.exp(-l*np.asarray(t[i][0][1:len(t[i][0])])))**3)/l\n",
    "\n",
    "            expected.extend([[expected_temp]])\n",
    "                        \n",
    "        chisquared = ((np.square(np.asarray(measured) - np.asarray(expected))) / np.asarray(measured))\n",
    "        \n",
    "        return np.sum([np.sum(item[0])for item in chisquared])\n",
    "    B_guess = np.ones(numbercycles)\n",
    "    N0_guess = [item[0][0]for item in measured]\n",
    "\n",
    "    \n",
    "    \n",
    "    initial_guess = [rc,deadtime , *N0_guess ]\n",
    "    minimized_function = minimize(chisquaredfun, initial_guess)#,method = 'L-BFGS-B', bounds = [(.01,.05),(0,.00001),(1000,None),(1000,None),(1000,None),(1000,None),(1000,None)]) \n",
    "    return minimized_function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def residuals(counts,cycle_number,values, binsize=1, cutoff=0,):\n",
    "\"\"\"This function returns a dictionary with residuals information for a simple exponential fit.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : Pandas Dataframe, counts data for a single cycle.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    values : minimized_fuction.x, the values from the minimization best fit.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    Returns\n",
    "    -------\n",
    "    'time' : array of time values\n",
    "    'residuals' : array of normalized residuals\n",
    "    'fit' : array of best fit values\n",
    "    'measured' : array of measured values\n",
    "    \"\"\"\n",
    "    numbercycles = len(counts)\n",
    "    t,measured = count_rate(single_run_coincidences=counts[cycle_number], cutoff=cutoff, bin_size = binsize)\n",
    "    def residual(params):\n",
    "        l = params[0]\n",
    "        B = params[1:1+numbercycles]\n",
    "        N0 = params[1+numbercycles:1+2*numbercycles]\n",
    "        \n",
    "        expected = (N0[cycle_number]/l)*(np.exp(-l*np.asarray(t[0:len(t)-1]))-np.exp(-l*np.asarray(t[1:len(t)]))) + B[cycle_number]*(t[1:len(t)]-t[0:len(t)-1])\n",
    "\n",
    "        measured[0:len(measured)-1][np.where(measured[0:len(measured)-1]==0)] = 1\n",
    "        residuals = (expected - measured[0:len(measured)-1] ) / np.sqrt(measured[0:len(measured)-1])\n",
    "        return residuals, expected, measured\n",
    "    res, exp, meas= residual(values)\n",
    "    return dict(time = t[0:len(t)-1],\n",
    "                residuals = res,\n",
    "                fit = exp,\n",
    "                measured = meas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def residuals_deadtime(counts,cycle_number,values, binsize=1, cutoff=0,deadtime=1.8e-5):\n",
    "\"\"\"This function returns a dictionary with residuals information for a deadtime model fit.\n",
    "    Parameters\n",
    "    ----------\n",
    "    counts : Pandas Dataframe, counts data for a single cycle.\n",
    "    cycle_number : int, cycle number based on the list indexing in the Split_Data function. First cycle = 0.\n",
    "    values : minimized_fuction.x, the values from the minimization best fit.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    deadtime : float, system deadtime in seconds.\n",
    "    Returns\n",
    "    -------\n",
    "    'time' : array of time values\n",
    "    'residuals' : array of normalized residuals\n",
    "    'fit' : array of best fit values\n",
    "    'measured' : array of measured values\n",
    "    \"\"\"\n",
    "    numbercycles = len(counts)\n",
    "    t,measured = count_rate(single_run_coincidences=counts[cycle_number], cutoff=cutoff, bin_size = binsize)\n",
    "    def residual(params):\n",
    "        #D = params[0]\n",
    "        D = deadtime\n",
    "        l = params[0]\n",
    "        B = params[1:1+numbercycles]\n",
    "        N0 = params[1+numbercycles:1+2*numbercycles]\n",
    "        \n",
    "        expected = (np.log(np.absolute((D*N0[cycle_number]*np.exp(-l*np.asarray(t[0:len(t)-1]))+B[cycle_number]*D+1)/(D*N0[cycle_number]*np.exp(-l*np.asarray(t[1:len(t)]))+B[cycle_number]*D+1))) + B[cycle_number]*D*l*(np.asarray(t[1:len(t)])-np.asarray(t[0:len(t)-1])))/(B[cycle_number]*D*D*l+D*l)\n",
    "\n",
    "\n",
    "        measured[0:len(measured)-1][np.where(measured[0:len(measured)-1]==0)] = 1\n",
    "        residuals = (expected - measured[0:len(measured)-1] ) / np.sqrt(measured[0:len(measured)-1])\n",
    "        return residuals, expected, measured\n",
    "    res, exp, meas= residual(values)\n",
    "    return dict(time = t[0:len(t)-1],\n",
    "                residuals = res,\n",
    "                fit = exp,\n",
    "                measured = meas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  y(x_i;a) = \\sum_{j=0} \\frac{N_{0,i}}{\\lambda}\\left( e^{-\\lambda T_{i-1}}- e^{-\\lambda T_{i}}\\right) + B_i(T_i - T_{i-1}) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\alpha_{k,l} = \\sum^{N}_{i=1} \\frac{1}{\\sigma_i^2} \\left[ \\frac{\\partial y(x_i; \\bf a)}{\\partial a_k} \\cdot \\frac{\\partial y(x_i; \\bf a)}{\\partial a_l} \\right]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{\\partial y(x_i;\\bf a)}{\\partial \\lambda} = \\sum_{j=0} -\\frac{N_{0,i}}{\\lambda^2}\\left( e^{-\\lambda T_{i-1}}- e^{-\\lambda T_{i}}\\right) + \\frac{N_{0,i}}{\\lambda}\\left( - T_{i-1}e^{-\\lambda T_{i-1}}+  T_{i}e^{-\\lambda T_{i}}\\right)$$\n",
    "\n",
    "$$\\frac{\\partial y(x_i;\\bf a)}{\\partial N_{0,i}} = \\frac{1}{\\lambda}\\left( e^{-\\lambda T_{i-1}}- e^{-\\lambda T_{i}}\\right)$$\n",
    "\n",
    "$$\\frac{\\partial y(x_i;\\bf a)}{\\partial B_i} = (T_i - T_{i-1})$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def covariance_matrix(best_fit_parameters,counts ,cutoff, binsize):\n",
    "\"\"\"This function returns the covariance matrix for a exponential fit.\n",
    "    Parameters\n",
    "    ----------\n",
    "    best_fit_parameters : list, values of the best fit parameters from the minimization function.\n",
    "    counts : list of Pandas Dataframes, counts data for multiple cycles or all cycles in a run.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    Returns\n",
    "    -------\n",
    "    covariance_matrix : matrix in order of rc,background counts_0,... background counts_i, initial counts_0, initial counts_i.\n",
    "    \"\"\"    \n",
    "    cycle_data = counts\n",
    "    numbercycles = len(counts)\n",
    "    t=[]\n",
    "    measured=[]\n",
    "    for i in np.arange(0,numbercycles):\n",
    "        t_temporary,measured_temporary = count_rate(single_run_coincidences=cycle_data[i], cutoff=cutoff, bin_size = binsize)\n",
    "        np.asarray(measured_temporary)[0:len(measured_temporary)-1][np.where(measured_temporary[0:len(measured_temporary)-1]==0)] = 1\n",
    "        t_temporary = t_temporary[0:int(210/binsize)-cutoff]\n",
    "        measured_temporary = measured_temporary[0:int(210/binsize)-cutoff]\n",
    "        t.extend([[t_temporary]])\n",
    "        measured.extend([[measured_temporary]])\n",
    "    \n",
    "    l = best_fit_parameters[0]\n",
    "    B = best_fit_parameters[1:1+numbercycles]\n",
    "    N0 = best_fit_parameters[1+numbercycles:1+2*numbercycles]\n",
    "\n",
    "    \n",
    "    def yB(time,std):\n",
    "        return (time[1:len(time)] - time[0:len(time)-1])/np.sqrt(std[0:len(std)-1])\n",
    "    \n",
    "    def yl(timelist,l,stdlist,Nlist):\n",
    "        y=0\n",
    "        for i in np.arange(len(Nlist)):\n",
    "            y= y+(-(Nlist[i]/(l**2))*(np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1]) - np.exp(-l*timelist[i][0][1:len(timelist[i][0])])) + (Nlist[i]/l)*(-timelist[i][0][0:len(timelist[i][0])-1]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+timelist[i][0][1:len(timelist[i][0])]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])))/np.sqrt(stdlist[i][0][0:len(stdlist[i][0])-1])\n",
    "            \n",
    "        return np.asarray(y)\n",
    "    \n",
    "    def yN0(time,l,std):\n",
    "        return (1/l)*(np.exp(-l*time[0:len(time)-1])-np.exp(-l*time[1:len(time)]))/np.sqrt(std[0:len(std)-1])\n",
    "    \n",
    "    unique_values = [yl(t,l,measured,N0)]\n",
    "    \n",
    "    for i in np.arange(numbercycles):\n",
    "        unique_values.extend([yB(t[i][0],measured[i][0])])\n",
    "        \n",
    "    for i in np.arange(numbercycles):\n",
    "        unique_values.extend([yN0(t[i][0],l,measured[i][0])])\n",
    "\n",
    "    \n",
    "    hessian=[]\n",
    "    for i in np.arange(0,1+numbercycles*2):\n",
    "        for j in np.arange(0,1+numbercycles*2):\n",
    "            hessian.extend([np.sum(unique_values[i]*unique_values[j])])\n",
    "    \n",
    "    h = np.asarray(hessian)\n",
    "    \n",
    "    #hessian = np.array([[a11,a12,a13,a14],[a12,a22,a23,a24],[a13,a23,a33,a34],[a14,a24,a34,a44]])\n",
    "    covariance_matrix =  inv(h.reshape((1+numbercycles*2,1+numbercycles*2)))\n",
    "    return covariance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  y(x_i;a) = \\sum_{j=0} \\dfrac{\\ln\\left(\\left|\\frac{DN_{0,i}\\mathrm{e}^{-\\lambda T_{i-1}}+B_iD+1}{DN_{0,i}\\mathrm{e}^{-\\lambda T_i}+B_iD+1}\\right|\\right)+B_iD\\lambda( T_i-T_{i-1})}{\\left(B_iD^2+D\\right)\\lambda} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'sym'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-279-fa9acc09907a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msympy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msym\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msyp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'l D N B ti ti1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0minit_printing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muse_unicode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'sym'"
     ]
    }
   ],
   "source": [
    "from sympy import sym\n",
    "\n",
    "L, d, n, b, ti, ti1 = syp.symbols('l D N B ti ti1', real = True)\n",
    "init_printing(use_unicode=True)\n",
    "\n",
    "expression = (sym.log((d*n*sym.exp(-L*ti1)+b*d+1)/(d*n*sym.exp(-L*ti)+b*d+1)) + b*d*L*(ti-ti1))/(d*L*(b*d+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def covariance_matrixd(best_fit_parameters,counts,deadtime ,cutoff, binsize):\n",
    "\"\"\"This function returns the covariance matrix for a deadtime model fit.\n",
    "    Parameters\n",
    "    ----------\n",
    "    best_fit_parameters : list, values of the best fit parameters from the minimization function.\n",
    "    counts : list of Pandas Dataframes, counts data for multiple cycles or all cycles in a run.\n",
    "    deadtime : float, system deadtime in seconds.\n",
    "    cutoff : int, a time in seconds to indicate where to start making the bins after t=0.\n",
    "    binsize : int, size of the bins in seconds.\n",
    "    Returns\n",
    "    -------\n",
    "    covariance_matrix : matrix in order of deadtime,rc,background counts_0,... background counts_i, initial counts_0, initial counts_i.\n",
    "    \"\"\"      \n",
    "    cycle_data = counts\n",
    "    numbercycles = len(counts)\n",
    "    t=[]\n",
    "    measured=[]\n",
    "    for i in np.arange(0,numbercycles):\n",
    "        t_temporary,measured_temporary = count_rate(single_run_coincidences=cycle_data[i], cutoff=cutoff, bin_size = binsize)\n",
    "        np.asarray(measured_temporary)[0:len(measured_temporary)-1][np.where(measured_temporary[0:len(measured_temporary)-1]==0)] = 1\n",
    "        t_temporary = t_temporary[0:int(210/binsize)-cutoff]\n",
    "        measured_temporary = measured_temporary[0:int(210/binsize)-cutoff]\n",
    "        t.extend([[t_temporary]])\n",
    "        measured.extend([[measured_temporary]])\n",
    "    \n",
    "    D = deadtime\n",
    "    l = best_fit_parameters[0]\n",
    "    B = best_fit_parameters[1:1+numbercycles]\n",
    "    N0 = best_fit_parameters[1+numbercycles:1+2*numbercycles]\n",
    "\n",
    "    \n",
    "    def yB(timelist,l,D,stdlist,N,B,i):\n",
    "        y = (-1*((B*D*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+np.log((B*D+D*N*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)/(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))))/(l*(B*D+1)**2)+(D*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+(((D/(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))-(D*(B*D+D*N*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)/(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)**2 ))*(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)/(1+B*D+D*N*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])))/(D*l*(B*D+1))))/np.sqrt(stdlist[i][0][0:len(stdlist[i][0])-1])\n",
    "        return np.asarray(y)\n",
    "    \n",
    "    def yl(timelist,l,D,stdlist,Nlist,Blist):\n",
    "        y=0\n",
    "        for i in np.arange(len(Nlist)):\n",
    "            y += ((Blist[i]*D*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+((D*Nlist[i]*timelist[i][0][1:len(timelist[i][0])]*(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)**2-(D*Nlist[i]*timelist[i][0][0:len(timelist[i][0])-1]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1]))/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))*(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1))/(D*l*(Blist[i]*D+1)) - (Blist[i]*D*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+np.log((Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)))/(D*(l**2)*(Blist[i]*D+1)))/np.sqrt(stdlist[i][0][0:len(stdlist[i][0])-1])\n",
    "        return np.asarray(y)\n",
    "    \n",
    "    def yN0(timelist,l,D,stdlist,N,B,i):\n",
    "        y = (((D*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])/(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1) - (D*(B*D+D*N*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)*np.exp(-l*timelist[i][0][1:len(timelist[i][0])]))/(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)**2)*(B*D+D*N*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))/(D*l*(B*D+1)*(B*D+D*N*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)))/np.sqrt(stdlist[i][0][0:len(stdlist[i][0])-1])\n",
    "        return np.asarray(y)\n",
    "    \n",
    "    def yD(timelist,l,D,stdlist,Nlist,Blist):\n",
    "        y=0\n",
    "        for i in np.arange(len(Nlist)):\n",
    "            \n",
    "            y += (-(Blist[i]*(Blist[i]*D*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+np.log((Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))))/(D*l*(Blist[i]*D+1)**2)  +  (Blist[i]*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+((((-Blist[i]-Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])]))*(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1))/((Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)**2))+(Blist[i]+Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1]))/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1))*(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1))/(D*l*(Blist[i]*D+1))  -  (Blist[i]*D*l*(timelist[i][0][1:len(timelist[i][0])]-timelist[i][0][0:len(timelist[i][0])-1])+np.log((Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][0:len(timelist[i][0])-1])+1)/(Blist[i]*D+D*Nlist[i]*np.exp(-l*timelist[i][0][1:len(timelist[i][0])])+1)))/((D**2)*l*(Blist[i]*D+1)))/np.sqrt(stdlist[i][0][0:len(stdlist[i][0])-1])\n",
    "        return np.asarray(y)\n",
    "    \n",
    "    unique_values = [yD(t,l,D,measured,N0,B)]\n",
    "    unique_values.extend([yl(t,l,D,measured,N0,B)])\n",
    "    for i in np.arange(numbercycles):\n",
    "        unique_values.extend([yB(t,l,D,measured,N0[i],B[i],i)])\n",
    "    for i in np.arange(numbercycles):\n",
    "        unique_values.extend([yN0(t,l,D,measured,N0[i],B[i],i)])\n",
    "\n",
    "    \n",
    "    hessian=[]\n",
    "    for i in np.arange(0,2+numbercycles*2):\n",
    "        for j in np.arange(0,2+numbercycles*2):\n",
    "            hessian.extend([np.sum(unique_values[i]*unique_values[j])])\n",
    "    \n",
    "    h = np.asarray(hessian)\n",
    "    \n",
    "    #hessian = np.array([[a11,a12,a13,a14],[a12,a22,a23,a24],[a13,a23,a33,a34],[a14,a24,a34,a44]])\n",
    "    covariance_matrix =  inv(h.reshape((2+numbercycles*2,2+numbercycles*2)))\n",
    "    return covariance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Use:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a pandas dataframe of the counts for ch1 and ch2 of the digitizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Run_Data(run_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "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>TIMETAG</th>\n",
       "      <th>ENERGY</th>\n",
       "      <th>ENERGYSHORT</th>\n",
       "      <th>FLAGS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>175704934272</td>\n",
       "      <td>147</td>\n",
       "      <td>140</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>316757152710</td>\n",
       "      <td>144</td>\n",
       "      <td>140</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>491867333157</td>\n",
       "      <td>154</td>\n",
       "      <td>149</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>602186772266</td>\n",
       "      <td>136</td>\n",
       "      <td>132</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>617917005222</td>\n",
       "      <td>154</td>\n",
       "      <td>149</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13765028</th>\n",
       "      <td>1862390168485414</td>\n",
       "      <td>156</td>\n",
       "      <td>149</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13765029</th>\n",
       "      <td>1863958730877177</td>\n",
       "      <td>144</td>\n",
       "      <td>140</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13765030</th>\n",
       "      <td>1864129248381229</td>\n",
       "      <td>154</td>\n",
       "      <td>149</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13765031</th>\n",
       "      <td>1864410007964693</td>\n",
       "      <td>144</td>\n",
       "      <td>140</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13765032</th>\n",
       "      <td>1864756965044332</td>\n",
       "      <td>137</td>\n",
       "      <td>133</td>\n",
       "      <td>0x4400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13765033 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       "0             175704934272     147          140  0x4400\n",
       "1             316757152710     144          140  0x4400\n",
       "2             491867333157     154          149  0x4400\n",
       "3             602186772266     136          132  0x4400\n",
       "4             617917005222     154          149  0x4400\n",
       "...                    ...     ...          ...     ...\n",
       "13765028  1862390168485414     156          149  0x4400\n",
       "13765029  1863958730877177     144          140  0x4400\n",
       "13765030  1864129248381229     154          149  0x4400\n",
       "13765031  1864410007964693     144          140  0x4400\n",
       "13765032  1864756965044332     137          133  0x4400\n",
       "\n",
       "[13765033 rows x 4 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data for channel 0\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data into the individual cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = Split_Data(data[0],data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       " 0       140236947896901     159          154  0x4400\n",
       " 1       140237041556435     141          137  0x4400\n",
       " 2       140237049948941     165          158  0x4400\n",
       " 3       140237071872949     149          145  0x4400\n",
       " 4       140237073773033     153          148  0x4400\n",
       " ...                 ...     ...          ...     ...\n",
       " 720175  350604249566979     150          137  0x4400\n",
       " 720176  350690904100759     156          151  0x4400\n",
       " 720177  351090341124902     143          139  0x4400\n",
       " 720178  351143782810951     148          137  0x4400\n",
       " 720179  351277579336957     165          158  0x4400\n",
       " \n",
       " [720180 rows x 4 columns],\n",
       "                 TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       " 0       440281016497726     153          145  0x4400\n",
       " 1       440281103562381     150          140  0x4400\n",
       " 2       440281127116951     150          146  0x4400\n",
       " 3       440281132829048     155          150  0x4400\n",
       " 4       440281179205796     153          144  0x4400\n",
       " ...                 ...     ...          ...     ...\n",
       " 730481  651014928928646     139          136  0x4400\n",
       " 730482  651069564229878     160          143  0x4400\n",
       " 730483  651074132785345     158          150  0x4400\n",
       " 730484  651133061276777     153          148  0x4400\n",
       " 730485  651270265688151     139          135  0x4400\n",
       " \n",
       " [730486 rows x 4 columns],\n",
       "                 TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       " 0       740325130641335     156          150  0x4400\n",
       " 1       740325149365573     154          147  0x4400\n",
       " 2       740325203508796     141          137  0x4400\n",
       " 3       740325206525195     161          153  0x4400\n",
       " 4       740325208320950     149          145  0x4400\n",
       " ...                 ...     ...          ...     ...\n",
       " 725353  950671654265055     154          149  0x4400\n",
       " 725354  950896822728456     147          142  0x4400\n",
       " 725355  950905040349451     160          148  0x4400\n",
       " 725356  951282324538249     149          141  0x4400\n",
       " 725357  951346537289743     155          145  0x4400\n",
       " \n",
       " [725358 rows x 4 columns],\n",
       "                  TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       " 0       1040370390902325     149          140  0x4400\n",
       " 1       1040370408312000     137          133  0x4400\n",
       " 2       1040370419300927     144          140  0x4400\n",
       " 3       1040370452125389     155          149  0x4400\n",
       " 4       1040370512496202     142          138  0x4400\n",
       " ...                  ...     ...          ...     ...\n",
       " 795559  1250923721785330     156          150  0x4400\n",
       " 795560  1251141219140944     151          147  0x4400\n",
       " 795561  1251163307236848     153          148  0x4400\n",
       " 795562  1251216771709789     159          144  0x4400\n",
       " 795563  1251375277989332     160          150  0x4400\n",
       " \n",
       " [795564 rows x 4 columns],\n",
       "                  TIMETAG  ENERGY  ENERGYSHORT   FLAGS\n",
       " 0       1340413352545180     161          153  0x4400\n",
       " 1       1340413381309538     155          147  0x4400\n",
       " 2       1340413469844950     165          158  0x4400\n",
       " 3       1340413495372452     146          142  0x4400\n",
       " 4       1340413511092852     145          141  0x4400\n",
       " ...                  ...     ...          ...     ...\n",
       " 765812  1551196673134743     157          138  0x4400\n",
       " 765813  1551198387073198     161          153  0x4400\n",
       " 765814  1551294201948935     156          151  0x4400\n",
       " 765815  1551337361241337     161          150  0x4400\n",
       " 765816  1551447326870484     149          139  0x4400\n",
       " \n",
       " [765817 rows x 4 columns]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data for channel 0 split by the triggering in channel 1\n",
    "cycles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example of how to find the best fit deadtime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/40 [00:00<?, ?it/s]\n",
      "  2%|▎         | 1/40 [00:27<17:40, 27.19s/it]\n",
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      " 95%|█████████▌| 38/40 [12:01<00:37, 18.93s/it]\n",
      " 98%|█████████▊| 39/40 [12:19<00:18, 18.63s/it]\n",
      "100%|██████████| 40/40 [12:39<00:00, 19.21s/it]\n"
     ]
    }
   ],
   "source": [
    "#this takes a while to run, deadtime = 1.853e-05 for cutoff = 0 for data right now\n",
    "deadtimelist = np.arange(1e-8,2e-5,5e-7)\n",
    "fitlist = []\n",
    "for i in tqdm(deadtimelist):\n",
    "    op2 = optimize_sharedvalues(cycles,cutoff=30,deadtime=i,binsize=1)\n",
    "    fitlist[0:0] = [op2]\n",
    "fitlist.reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Deadtime minimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.251e-05]), array([876.06622777]))"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(11,8))\n",
    "\n",
    "chilist = np.asarray([fitlist[i].fun for i in np.arange(0,len(fitlist)) ])\n",
    "plt.scatter(deadtimelist,chilist)\n",
    "plt.xlim(1.3e-8,2e-5)\n",
    "plt.ylim(0,2000)\n",
    "\n",
    "#Finding the deadtime that corresponds to the minimum chi-squared value\n",
    "np.asarray(deadtimelist)[np.where(chilist==chilist.min())],chilist[np.where(chilist==chilist.min())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the best fit for shared deadtime and halflife using a simple exponential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py:1013: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  rhok = 1.0 / (numpy.dot(yk, sk))\n"
     ]
    }
   ],
   "source": [
    "op = optimize_sharedvalues_simple(cycles,cutoff=70,binsize=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 700.8710513653325\n",
       " hess_inv: array([[1.76797988e-08, 2.47789045e-05, 3.31142169e-05, 2.93744645e-05,\n",
       "        4.02065455e-05, 5.27255230e-05, 1.55921070e-03, 1.19075226e-03,\n",
       "        1.44287503e-03, 1.09732746e-03, 4.68584050e-04],\n",
       "       [2.47789045e-05, 2.20855553e-01, 4.69194683e-02, 5.34568045e-02,\n",
       "        6.10257835e-02, 8.09493643e-02, 9.52806246e-01, 1.47069152e+00,\n",
       "        1.52223947e+00, 1.32802504e+00, 5.32301566e-01],\n",
       "       [3.31142169e-05, 4.69194683e-02, 2.54443996e-01, 7.51061572e-02,\n",
       "        8.17970240e-02, 9.69507940e-02, 2.65067992e+00, 1.63389763e+00,\n",
       "        2.11449691e+00, 1.45422726e+00, 9.52354709e-01],\n",
       "       [2.93744645e-05, 5.34568045e-02, 7.51061572e-02, 2.13924135e-01,\n",
       "        7.35873285e-02, 8.36945173e-02, 2.11931529e+00, 1.49531250e+00,\n",
       "        1.80681968e+00, 1.33721231e+00, 9.07973182e-01],\n",
       "       [4.02065455e-05, 6.10257835e-02, 8.17970240e-02, 7.35873285e-02,\n",
       "        2.87937991e-01, 1.28682501e-01, 3.33561255e+00, 2.20728929e+00,\n",
       "        2.81271246e+00, 1.78954606e+00, 1.09985195e+00],\n",
       "       [5.27255230e-05, 8.09493643e-02, 9.69507940e-02, 8.36945173e-02,\n",
       "        1.28682501e-01, 3.33403228e-01, 4.48239404e+00, 3.69827881e+00,\n",
       "        4.47742423e+00, 3.23472384e+00, 5.59047556e-01],\n",
       "       [1.55921070e-03, 9.52806246e-01, 2.65067992e+00, 2.11931529e+00,\n",
       "        3.33561255e+00, 4.48239404e+00, 1.94424287e+02, 1.14345935e+02,\n",
       "        1.50532055e+02, 1.06484507e+02, 4.86433179e+01],\n",
       "       [1.19075226e-03, 1.47069152e+00, 1.63389763e+00, 1.49531250e+00,\n",
       "        2.20728929e+00, 3.69827881e+00, 1.14345935e+02, 1.05065077e+02,\n",
       "        1.21198817e+02, 9.91263054e+01, 2.68344689e+01],\n",
       "       [1.44287503e-03, 1.52223947e+00, 2.11449691e+00, 1.80681968e+00,\n",
       "        2.81271246e+00, 4.47742423e+00, 1.50532055e+02, 1.21198817e+02,\n",
       "        1.47172966e+02, 1.13868452e+02, 3.25530323e+01],\n",
       "       [1.09732746e-03, 1.32802504e+00, 1.45422726e+00, 1.33721231e+00,\n",
       "        1.78954606e+00, 3.23472384e+00, 1.06484507e+02, 9.91263054e+01,\n",
       "        1.13868452e+02, 9.40715802e+01, 2.51625173e+01],\n",
       "       [4.68584050e-04, 5.32301566e-01, 9.52354709e-01, 9.07973182e-01,\n",
       "        1.09985195e+00, 5.59047556e-01, 4.86433179e+01, 2.68344689e+01,\n",
       "        3.25530323e+01, 2.51625173e+01, 5.14449377e+01]])\n",
       "      jac: array([ 3.12042236e-03,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00, -7.62939453e-06,  0.00000000e+00, -1.52587891e-05,\n",
       "        1.52587891e-05,  0.00000000e+00,  7.62939453e-06])\n",
       "  message: 'Desired error not necessarily achieved due to precision loss.'\n",
       "     nfev: 742\n",
       "      nit: 43\n",
       "     njev: 57\n",
       "   status: 2\n",
       "  success: False\n",
       "        x: array([ 3.96412971e-02, -3.68076515e-01, -1.77910888e-01, -6.26017479e-01,\n",
       "       -6.57542450e-01, -1.66666139e+00,  2.13341813e+03,  2.16846480e+03,\n",
       "        2.13573774e+03,  2.40982746e+03,  2.29015061e+03])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = residuals(counts=cycles,cycle_number=4,values=op.x,binsize=1,cutoff=70)\n",
    "len(res['time'])*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(res['time'],res['residuals'])\n",
    "plt.title('Simple Exponential Fit Residuals: Cycle 4');\n",
    "plt.savefig('simpcycle4res.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(res['time'],res['fit'],s=10,label=\"Simple Exponenial Fit\")\n",
    "plt.scatter(res['time'],res['measured'][0:len(res['time'])],s=10,label=\"Our Measured Data\")\n",
    "\n",
    "\n",
    "plt.title('Cycle 4');\n",
    "plt.ylabel('Log Counts')\n",
    "plt.xlabel('Time (s)')\n",
    "#plt.yscale('log')\n",
    "plt.legend();\n",
    "#plt.savefig('cutoff70simpcycle4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the best fit for shared deadtime and halflife using the deadtime model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "opd = optimize_sharedvalues(cycles,cutoff=0,deadtime=1.853e-05,binsize=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 1373.4632245392424\n",
       " hess_inv: array([[ 1.14810871e-10,  8.38792161e-07,  6.42223782e-07,\n",
       "         8.29812694e-07,  9.97377757e-07,  3.25137117e-07,\n",
       "        -6.18165166e-07,  9.42891925e-05,  6.14076206e-05,\n",
       "        -1.36701804e-05,  1.13966773e-04],\n",
       "       [ 8.38792161e-07,  1.30177463e-01,  5.06319427e-03,\n",
       "        -5.80208022e-03,  1.20081145e-03, -2.24997936e-03,\n",
       "         1.61107957e-02, -2.90214321e-01,  1.74014574e-01,\n",
       "         2.12963811e-01, -1.37232869e-01],\n",
       "       [ 6.42223782e-07,  5.06319427e-03,  1.42944771e-01,\n",
       "         5.52796306e-03, -6.03077139e-03,  5.08178391e-03,\n",
       "        -1.15857635e-01,  1.04698650e+00,  3.93172040e-01,\n",
       "        -4.47649832e-01,  1.15344450e+00],\n",
       "       [ 8.29812694e-07, -5.80208022e-03,  5.52796306e-03,\n",
       "         1.28199211e-01,  1.05775487e-03,  8.20833873e-03,\n",
       "         4.86824065e-02,  1.59298689e-01,  4.09388031e-02,\n",
       "        -1.32014800e-02,  1.61607878e-01],\n",
       "       [ 9.97377757e-07,  1.20081145e-03, -6.03077139e-03,\n",
       "         1.05775487e-03,  1.31937100e-01,  6.01280258e-03,\n",
       "        -8.89103164e-02,  1.28561850e+00,  7.37776963e-01,\n",
       "        -5.19061418e-01,  1.46369926e+00],\n",
       "       [ 3.25137117e-07, -2.24997936e-03,  5.08178391e-03,\n",
       "         8.20833873e-03,  6.01280258e-03,  1.46331548e-01,\n",
       "         3.83202508e-02, -5.95074532e-01, -1.22919774e-01,\n",
       "         2.88455506e-01, -5.79471334e-01],\n",
       "       [-6.18165166e-07,  1.61107957e-02, -1.15857635e-01,\n",
       "         4.86824065e-02, -8.89103164e-02,  3.83202508e-02,\n",
       "         1.74958831e+01, -2.19331478e+01,  9.44743071e+00,\n",
       "         3.64906094e+01, -1.97976036e+01],\n",
       "       [ 9.42891925e-05, -2.90214321e-01,  1.04698650e+00,\n",
       "         1.59298689e-01,  1.28561850e+00, -5.95074532e-01,\n",
       "        -2.19331478e+01,  4.08249257e+02,  2.23839739e+02,\n",
       "        -1.06890087e+02,  4.71166723e+02],\n",
       "       [ 6.14076206e-05,  1.74014574e-01,  3.93172040e-01,\n",
       "         4.09388031e-02,  7.37776963e-01, -1.22919774e-01,\n",
       "         9.44743071e+00,  2.23839739e+02,  1.55080517e+02,\n",
       "        -1.52976390e+01,  2.67330285e+02],\n",
       "       [-1.36701804e-05,  2.12963811e-01, -4.47649832e-01,\n",
       "        -1.32014800e-02, -5.19061418e-01,  2.88455506e-01,\n",
       "         3.64906094e+01, -1.06890087e+02, -1.52976390e+01,\n",
       "         9.00841230e+01, -1.12376710e+02],\n",
       "       [ 1.13966773e-04, -1.37232869e-01,  1.15344450e+00,\n",
       "         1.61607878e-01,  1.46369926e+00, -5.79471334e-01,\n",
       "        -1.97976036e+01,  4.71166723e+02,  2.67330285e+02,\n",
       "        -1.12376710e+02,  5.48202138e+02]])\n",
       "      jac: array([ 1.94388454e+03, -1.18777466e+00,  1.27439880e+00,  9.12322998e-02,\n",
       "        2.37411499e-01, -5.06835938e-01, -8.90197754e-02,  2.39562988e-03,\n",
       "       -4.46929932e-02,  5.72052002e-02,  5.47332764e-02])\n",
       "  message: 'Desired error not necessarily achieved due to precision loss.'\n",
       "     nfev: 2209\n",
       "      nit: 46\n",
       "     njev: 169\n",
       "   status: 2\n",
       "  success: False\n",
       "        x: array([4.10083571e-02, 2.07520483e+00, 2.73345696e+00, 1.83637882e+00,\n",
       "       2.03133280e+00, 5.66265503e-01, 3.92240877e+04, 3.99625414e+04,\n",
       "       3.95017600e+04, 4.46488116e+04, 4.25491104e+04])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resd = residuals_deadtime(counts=cycles,cycle_number=4,values=opd.x,binsize=1,cutoff=0)\n",
    "len(resd['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(resd['time'],resd['residuals'])\n",
    "plt.title('Deadtime Fit Residuals: Cycle 4');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(resd['time'],resd['fit'],s=10,label=\"Deadtime Fit\")\n",
    "plt.scatter(resd['time'],resd['measured'][0:len(resd['time'])],s=10,label=\"Our Measured Data\")\n",
    "plt.yscale('log')\n",
    "plt.title('Cycle 4');\n",
    "plt.ylabel('Log Counts')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#covariance matrix for the deadtime model\n",
    "cm = covariance_matrixd(opd.x,cycles,1.853e-05,0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.87189008e-06, 4.14279243e-05, 6.70857165e+04, 6.21420855e+04,\n",
       "       6.58789405e+04, 3.99864723e+04, 8.07539585e+04, 1.01481684e+05,\n",
       "       9.67765152e+04, 9.99761498e+04, 6.87016080e+04, 1.31028389e+05])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.diagonal(cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16.90258350930515, 0.017092785915096442)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit halflife\n",
    "np.log(2)/(opd.x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.18149402e-03, -3.47575415e+03, -3.09546696e+03, -3.33556818e+03,\n",
       "       -1.99094764e+03, -3.84814690e+03,  4.01248008e+00,  3.77316725e+00,\n",
       "        3.92620125e+00,  2.46299699e+00,  4.87707148e+00])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#relative error, formatted in order: decay constant,Background counts, N0 counts \n",
    "np.sqrt(np.diagonal(cm)[1:])/op.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other plots code that may be useful in the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.tight_layout()\n",
    "plt.title('Run 5 from Feburary 22 (One Second Bins)')\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Time (Seconds)')\n",
    "plt.xlim(60,1865)\n",
    "plt.yscale('log')\n",
    "plt.hist(data[0]['TIMETAG']*10**-12,bins=1865);\n",
    "plt.savefig('Run 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "plt.title('Run 5 from Feburary 22 (One Second Bins)')\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Time (Seconds)')\n",
    "plt.xlim(0,1865)\n",
    "xcoords = data[1]['TIMETAG']*10**-12\n",
    "for xc in xcoords:\n",
    "    plt.axvline(x=xc,color='orange')\n",
    "plt.hist(data[0]['TIMETAG']*10**-12,bins=1865);\n",
    "plt.xlim(1250,1600)\n",
    "plt.yscale('log')\n",
    "plt.savefig('Splits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,3,figsize=(14,8),sharey=True)\n",
    "fig.suptitle('Individual Cycles')\n",
    "l=0\n",
    "\n",
    "for i in np.arange(0,2):\n",
    "    for j in np.arange(0,3):\n",
    "        if l<=4:\n",
    "            ax[i][j].hist((cycles[l]['TIMETAG']-np.min(cycles[l]['TIMETAG']))*10**-12,bins = 200,label='Cycle Number {0:d}'.format(l+1));\n",
    "            ax[i][j].legend()\n",
    "            ax[i][j].set_ylabel('Counts')\n",
    "            l+=1\n",
    "plt.savefig('Cycles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,210)\n",
    "y = 2.29015061e+03*np.exp(-3.96412971e-02*np.arange(-70,210-70))+1.66666139e+00\n",
    "plt.scatter(x,y,label = 'Backwards Extrapolated Fit')\n",
    "plt.scatter(x,resd['measured'][0:210],label = 'Measured Data')\n",
    "plt.xlim(0,210)\n",
    "plt.title('Measured Data and Extrapolated Data')\n",
    "plt.ylabel('Counts/second')\n",
    "plt.xlabel('Time')\n",
    "plt.legend();\n",
    "#plt.savefig('Extrapolatedexponentialfit.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = (y/resd['measured'][0:210])\n",
    "\n",
    "def dfit(m,D):\n",
    "    return (1/(1-m*D))\n",
    "\n",
    "def analysis(m,ratio):\n",
    "    \n",
    "    popt, pcov = curve_fit(dfit, xdata=m, ydata=ratio, p0=[1.853e-05])\n",
    "    return(popt,pcov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.39764085e-05]), array([[8.81583843e-13]]))"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis(resd['measured'][0:210],sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resd['measured'][0:210],sub)\n",
    "plt.title('Fractional Deadtime Plot')\n",
    "plt.xlim(resd['measured'][0:210].min(),resd['measured'][0:210].max())\n",
    "plt.plot(resd['measured'][0:210],1/(1 - 1.853e-05*resd['measured'][0:210]))\n",
    "plt.ylabel('Fit / Measured')\n",
    "plt.xlim(0,10000)\n",
    "plt.xlabel('Measured Counts/second');\n",
    "#plt.savefig('fractionaldeadtime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10000)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resd['measured'][0:210],sub)\n",
    "plt.title('Fractional Deadtime Plot')\n",
    "plt.xlim(resd['measured'][0:210].min(),resd['measured'][0:210].max())\n",
    "plt.plot(resd['measured'][0:210],1/(1 - 1.853e-05*resd['measured'][0:210]))\n",
    "plt.ylabel('Fit / Measured')\n",
    "plt.xlabel('Measured Counts/second');\n",
    "plt.xlim(0,10000)\n",
    "#plt.savefig('fractionaldeadtime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10000)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resd['measured'][0:210],sub)\n",
    "plt.title('Fractional Deadtime Plot')\n",
    "plt.xlim(resd['measured'][0:210].min(),resd['measured'][0:210].max())\n",
    "plt.plot(resd['measured'][0:210],1/(1 - 1.853e-05*resd['measured'][0:210]))\n",
    "plt.ylabel('Fit / Measured')\n",
    "plt.xlabel('Measured Counts/second');\n",
    "plt.xlim(0,10000)\n",
    "#plt.savefig('fractionaldeadtime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10000)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BoK4jsZf+xZxzzrmUAsz6MGQAxfFmxznnXLpIpauY7wxERpxzzqWXPgOMpMfp4bkXM3tHLDlyzjmXFlKpIvtSwnge8H6gNZ7sOOecSxepVJHN65b0jKQnYsqPc865NJFKFdmIhMkM4DhgbArrzQYuADaZ2eE9zP8ycFlCPg4BRoWXja0CaoA2oDWudxU455yLTypVZPOI2mBEVDW2Erg6hfVuAX4O/KGnmWb2I+BHAJIuBL7Y7a2VZ5rZ5hT245xzbghKpYpsyp5s2MyelFSR4uKXEr0x0znnXJpI5X0wH5BUHMa/LuleScf2VwYkFQDnAvckJBvwqKR5kq7pY/1rJM2VNLe/8uScc27vpdLZ5TfMrEbSqcA5wO+BX/VjHi4EnulWPXaKmR0LnAd8WtLpPa8KZjbLzKZ7O41zzg0tqQSYtvD5buBXZvYXoD/7JruEbtVjZrY+fG4C7gNO6Mf9OeecGwCpBJh1km4GPgg8KCk3xfX6JKkEOAP4S0JaYUKVXCFwNrCoP/bnnHNu4KRyF9kHidpIfmxm2ySVA1/uayVJdwIzgTJJa4FvAdkAZnZTWOx9wKNmVpew6hjgPkkd+bvDzB5O7XCcc84NFTJL7e3HkkYTPckPgJmtjitTeyq3vNKaNiwd7Gw459w+Q9K8uNqwU7mL7D2SlhI9//JE+Hwojsw455xLH6m0pXwPmAG8GZ6JOQt4JtZcOeec2+elEmBazGwLkCEpw8weB46OOV/OOef2cak08m+TVAQ8BdwuaRPem7Jzzrk+pFKCuQioB74APAwsJ3o40jnnnEsqlb7I6iRNBirN7Peha5fM+LPmnHNuX5bKXWQfB+4Gbg5J44H748yUc865fV8qVWSfBk4BdgCY2VJgdJyZcs45t+9LJcA0mVlzx4SkLKLejoekVB8cdc45F69UAswTkq4H8iW9C/gz8Nd4s7XnPL4459zQkEqAuQ6oAhYCnwAeBL4eZ6b2hscX55wbGlK5i6wd+HUYhryoikyDnQ3nnNvvJQ0wkl7tbUUzO7L/s7P3vATjnHNDQ28lmHai6/UdRG0uDQOSo73kbTDOOTc0JG2DMbOjgUuBIqIg8wPgMGCdmb01MNnbfeZlGOecGxJ6beQ3s9fN7FtmdixRKeYPwBdT2bCk2ZI2SerxbZSSZkraLmlBGL6ZMO9cSW9IWibput04Hi/BOOfcENFrI7+k8cAlRG+e3EoUXO5Lcdu3AD8nCkrJPGVmF3TbZybwC+BdwFrgJUkPmNniFPfrnHNuCOitkf8JoBj4E/ARoDrMypE0wsyqk60LYGZPSqrYgzydACwzsxUhH3cRdbiZUoDxEoxzzg0NvZVgJhM18n8CuCYhXSF9aj/s/yRJrwDrgS+Z2WtEfZ2tSVhmLXBiqhts9wjjnHNDQtIAY2YVMe97PjDZzGolnU/UgWYlPT/EkjRqSLqGEABzxh7gAcY554aIVJ7kj4WZ7TCz2jD+IJAtqYyoxDIxYdEJRCWcZNuZZWbTzWw6QLvHF+ecGxIGLcBIGitJYfyEkJctwEtApaQpknKIbjJ4INXtemeXzjk3NKTyyuQ9IulOYCZQJmkt8C0gG8DMbgIuBj4pqZXoIc5LLIoOrZI+AzxC9GKz2aFtJiVegnHOuaFByX7xS5oLPAM8BMwxs8aBzNieyC2vtHVLF1FWlDvYWXHOuX2CpHkdTQz9rbcqshlEz7zMJOqy/0FJn5d0YBwZ6S/eyO+cc0NDb3eRtQJzwoCkcuA84PuSDgCeN7NPDUAed4vHF+ecGxpSboMxsw3AbGC2pAzgpNhytRe8BOOcc0PDHjXyh3fEPNPPeekX3sjvnHNDw6DdphyXdo8wzjk3JPQZYCSdkkraUOE1ZM45NzSkUoL53xTThgRvg3HOuaGht96UTwJOBkZJ+veEWcOIHoAckjzAOOfc0NBbI38O0dsss4i67e+wg+gp/CHJw4tzzg0NvT0H8wTRA5a3DOVXJHfnjfzOOTc0JG2DkfTTMPpzSQ90HwYof7vttuf3mVjonHNprbcqslvD548HIiP9ZcGabYOdBeecc/QeYKqgs6psn9HU2j7YWXDOOUfvtynf3zEi6Z4ByEu/8ADjnHNDQ28BJvHVxVPjzkh/aWppG+wsOOeco/cAY0nGh7TmNi/BOOfcUNBbgDlK0g5JNcCRYXyHpBpJO/rasKTZkjZJWpRk/mWSXg3Ds5KOSpi3StJCSQvCi89Strm2ma/c/Qobtw/596M551xa6+05mL19Wv8W4OfAH5LMXwmcYWZbJZ0HzAJOTJh/pplt3pMd/2nuWqpqmvjdVSfsyerOOef6wR51158KM3tSUkUv859NmHwemNCf+9/e0NKfm3POObebhkp3/VcDDyVMG/CopHmSrultRUnXSJrbvSptR2NrDNl0zjmXqthKMKmSdCZRgDk1IfkUM1svaTTwmKTXzezJntY3s1lE1Wvklld23ozgJRjn3H7PDBq3Q/0WqNsM9ZsTPrdE6TEa1AAj6UjgN8B5ZtZ5pGa2PnxuknQfcALQY4BJxgOMcy7ttDRGQaGhOvqs7/7ZEUC27PxsT3ItzC6EwpGxZnfQAoykScC9wBVm9mZCeiGQYWY1Yfxs4Lu7u/1mf+DSOTeUNdfvGigatobpxLTqMF4NLXXJt5dbEgWMgjIonQTjjoHCsmi683Pkzuns/Gi9Lyr5NvdSbAFG0p3ATKBM0lrgW0A2gJndBHwTGAn8UhJAq5lNB8YA94W0LOAOM3s4rnw659xeMYOW+hQDxRaoD/NaG5JvM68E8kdAwUgoGgujD43G84dHnwVhXscy+cMhK2fgjjlFcd5Fdmkf8z8GfKyH9BXAUbuu4ZxzMTOD5tquVU+7lDK6BYr6LdDWlHybeaUhKIyEYeNh7JG7BoruwSJz0JvH+0V6HIVzznXX3g5N20NpYmsvgaK6a1pbc5INqmtgKJ0I5UclBIlupYqCEVFwSZNgsSf23yN3zu0b2tuhcVsUKBo6PqvDZxjqu003VEd3T1mStlhlhEAQgsHwChh/bPJAUTAyqrbKGLJvix+SPMA45wZGe1t00e8SCHoJEJ3j2+i1O8TcEsgvDaWLETB8cjSePzwKEh3jiSWN3BLIGCqPAaYvDzDOud3TU6DoNUCE+Y3b6TVQ5JUkBIbhMHxK1+mCEV2n84fv91VQQ53/ZZzbX7U0RKWDxm09fPZSwkg5UIzoGih2CRAJ03klHijSUFr/RbfVN1NaMPRu3XOu3/QaJHr4bNi6c7y3O5/QriWKkdOSB4iOAOLtFC5BWgeY55Zv4bwjygc7G84lZxYFiVQDRPfPXoMEkDssap/IK40+Rx3UdbrHz+EeKFy/SOsAs6PRu4txA2CvgsTWXm6LDXJLIL/Eg4Tb56R3gGnwHpVdilqboraFzmFbwviObvMSlukIFP0dJDoasD1IuH1YegcYL8HsP1oaew4AHeNNyYJEGFr7eANqRlZ0sU8cisf2HBS6BwwPEm6Iao35FfNpHWCqavqon3ZDg1l0gU9WSkhagkgY+mqLyMjeNUAMG79rWrIhuwAUX6eAbt/V3m6s29ZAbnYGhTlZFORkokH+rjS3trNxeyObahrZVNNEVU1TNL6jiU01TSGtkS11fZS891JaB5h123rpTM71n47+mxp3hJLCjq4Boq/SQ+P2vquYMnN2veiXTuwhGJTuHM8dlhAg8j1AuFj8+qkV/OdDr3dOS1CQnUlBbhZFuVHAKczJojA3SivMyaQwN4vCnCzKinIoL81nXEk+5aV5jCzM2avgZGY88Mp6vve3JWyu7fqjKzNDlBXlMLo4j3EleRw9sYRRxXlc+8M93l2f0jrArN26M8Cs29bAuJK8Qf9lMeS0t0FTzc7AkPjZU1pPn801ybvk6JCV1zUQ5A+H0slJSgylPZQg8gbmfLi9YmY0trRT29RKbVMrdU2tNLa0MWZYHuNK88nM2PP/vx2NLTy7bDOlBTmUl+QxtiSP3KzBr3p8cOEGDhxTxBUnVVAfjrmuuY365lZqm9qitOZWNtc2U1ddT31TG3XN0XLt3R4nysnKoLwkLwz50WdpPuPC9LjSPErys3u8jq3eUs/X/7KIJ9+s4qgJJXzlnIMYPSyX0cV5jB6Wy4iCHDJ6OP/XxnViSPMAs3JzHXVNrWzY3shZNzzB1847mE+cMW2ws9V/2lp3lg6adkSBoksA2J4kMNR0DQ59yciGvGFRiSC3OLrgD6/YmdbTZ/eShAeItLRxeyOfu/Nl1myt7wwo3S+aHXKyMqgYWcCUskKmlBUxtayQKaMKmVJW2Osv96bWNm57fjU//+dSttZ3bVcdWZjD2ISL8dhuF+exJXnkZfcchNrbjYaWNuqb22hobqOhpY12M4pCyaMwN4ucrN67k3l7RyOvrN3Ol885iCtmTO77hCUwM7bUNbNhWyPrtzewYVsDG7Y3sn57Ixu2NfDiymo27mikrdsJzc/OpLw0Lyr1hADU2tbO7GdWkinx7QsP5YqTKvYqmPeXtA4wAI8u3sjh40oAuOPF1UMnwLQ2hYCwvY8SwvbkJYyW+r73k5m7awAoHhPd1dQlaCQuU9J1OisvrauXzIxt9S2s2VrPuq0NjB6Wy1ETSsnKjKevqo79lRb0/Et0X/Lfj7zOgrXbeM9R47pcmItyMynKi6qBcrMz2bi9gRWb61hZVceKqjoef72K5oQG5uK8rCjghOAzZVQhU8sKWbqphh8/8ibrtjVwWmUZn5w5jfZ22LC9gY3hYrxxewNrt9bz0qrqHt9kO6Iwh1FFubS2t9PY0k59cyv1zW00pfBSwpysDIo7jykMeTuPs6Od96xDxuz2uZNEWVEuZUW5HDGhpMdl2tqNqpqmEIAa2bC9gfUdn9sbefPNKqpqmzCDcw4bw7ffcxjlJfm7nZe4xBpgJM0GLgA2mdnhPcwX8DPgfKAe+IiZzQ/zrgS+Hhb9vpn9fk/ysKKqjhGFuQC8taWeN9+u4cAxxXuyqUiXwFDTbdjROV61ZTMLl69heGYjU4rbKc1s7JxnjTtQX43SEDUsdw8AJeN3ti0kKz3kJgaH3D0/1jRiZiyvquWtLfWsqa5nzdaGzs+11fXUNHW9pb04N4uTDxjJaZWjOOPAUUwcUZB020+8WcVLK6s5fsoITqgYQX5O79U2Ty7dzJWzX6SsKIfjJg/n+IoRHDd5OIeNK+nzF/OeaG5tZ9WWOpZvqmV5VS1b61vIzswgJ1PkZGWQnRmGrJ1pRbnZlORnU1oQfZbkZ+9SEnht/Xbue3kd15w2la+df8hu5amt3Vi3tYEVm2tZubmuc3hp1Vb+8sp6LOFH++Hjh/HD9x/BaZWj+txufXMrG7c3sqFj2NbAhh1R43ZOlsjPziI/J4OCnCzyszMpyImGvOxMCnKyyBBdqvdqwmdtY2tn+qaaRlZubqOmMZp39MRSDhxTtFvHn6rMDDE2lMSY1PMyza3t1DS2MLJo6P2vy6yXPoX2duPS6UAt8IckAeZ84LNEAeZE4GdmdqKkEcBcYDpRp0fzgOPMbGtv+8str7TyK3/awxwjlxaKaaBY9Tz2qWPIaqlNGhySj9f03RgNkJFFY2YhVU05NGQUsMMKOHzqePIKSyG3mDmrGlhZm8lZx1QycezYLsHBcodx92s7OLZyEtPGDu97X65XW+uauWf+Wu54cTUrqna+bjYvO4OJwwuYOKKAicPzmTiigAnDC5gwPJ/V1fU8tbSKJ9/c3HmjSMXIAk6rHMVplWWcNG0kxXnZNLa08YO/L+HW59/q3G5OZgbHTR7OqZVlnHpAGYePL9mlquLW59/iG/cv4vwjxvLa+h28tSUqieZmZXD0xFKmVwxn+uQRHDtpOCUF2bt9zJtrm7j/5XU8v2ILy6vqWF1d36WapTAnk5Z22+3XiudmZXQGnNL8HDbsaKCmsZUnvnwmJfm7n89kGlvaeGtLPSs315KTlcHMA0f32Hbg+oekeeFtwv2/7TgDDICkCuBvSQLMzcAcM7szTL9B9JrlmcBMM/tET8slc8C4Ept19dEUqYFiGiiigSJFn9lq6zOv7Zm51JFPVv4w8otKd5YeEofEaqUuQ5RWr3zmLK9h5ZZ6fvTIGzz4udO4+KZnOb46OI2RAAAWxklEQVRiBLdcdTyS+OIfF3Dfy+vIEHz8tKl8/qxKCnKiwuQLK7bwr7OeZ0RhDrd/7EQOKR+W+slOoq3dyBApVce0txu/e3YVk0YUcMaBo2L5RT1QfvH4Mn72f0tpbm3n2EmlXHzcRA4uL2bi8ALKivq+W8fMWLm5jqeWbubJN6t4bsUW6pvbyMwQx04qpbqumeVVdXz8tCl85sxKXl6zlWeWbebpZVtYsmEHACX52Zw8bWRnwJk8spCbnljODx96ncXfPYeCnCw21TQyb9VWXlq1lXlvVfPa+h20thsSHDi6mOMqhnPspOGMLMwJv7izKMjN3DkeSkyPv76JP89by+Ovb6K13Zg2qpADxxRzwOgipo0q4oDRRUwpK6QwN6vz+FrbjZa2dlpajaa2NlrajKaWNmqbWtne0MK2+ha2N+wcttU3d6bXNLZy9alTeP9xE+L9Q7pYxRlgBrsNZjywJmF6bUhLlr4LSdcA1wAcXp7LSO2g1vJZzWhqyaemPZ9a8qm16LPG8rn8jMM5/qDJbGrO4f2/W0RLViHPffMibnlhA9/922Kog4cvP42Dxya/uDe2tPHEm1W8c9LoLnX1T722kU/d8TLZmdHFa0pZIV8992C+9cBr3DN/HRcfN4FpowoBuPCocdz85AruX7COL519EO88ZAy/fmolxblZ5GZl8KFfP89tHzuRw8b1XD+bisaWNs6/8SkwuOqUCt5/3AQKcrJobzd+OWcZq6vrOfvQsZxaWUZediZ3vrSa7/1tMQDDC7K54MhxvPeY8Rw7qXSXC/Lm2iY+cNNzCDhk3DAOLR/GIeXFHD6uhNHD9qxRv6axhZ/+Yyn1za2MKsqlrDi383PC8HzGDkv9TsC/vrKeqWWF/PSSo3v9WyYjiamjipg6qogrT66gubWd+au38tTSKp5auhkzuPXqEzqrbmYeNJqZB40Gomewnl2+maeXbubpZZt5aNFGACaOyCc3K5MMRY21AKOL8zjviPLOfvPqm1tZsGYb81ZtZe5bW/nrgvXc8cLqXvOaIWg3GFWc23nR76sqWBLZmSI7MwNyAPqvFOIcDH6A6elKYb2k75poNguYBVEV2UXN3+9zpxUZlWyrL2FLbRNrrApaYPnWFuqbd9bDf+GuBYwtyeOKGZN5Zw8NeN//+2Jue341177rQD77zsrO9NJQVWAGBTmZZGeKK2ZM5u+vbuC7f32N0yrLmDYqqq/9+GlTuXzGZL7/9yV8+e5XO7fxyZnTuOT4iVw663ku+80L3Hb1iRw+PrUg84/FbzP7mZUcM6mUk6aW8ezyzayoquPgscV84y+v8aNH3uDSEyaxblsDf3t1AwU5mfxp7lqKcrN4x8GjefyNTcyYOoJrTp/KfS+v509z13Dr828xeWQBpx5QxnGTo1/Tk0cW8I37F7FuawNnHDSKV9du4++vbujMx3GTh3PR0eM4/4hyylKsG15TXc/Hfj+XZVW1DC/IZktdM90L2EW5WUwbXcQBo4qoHLPzc8Lwgl2qouqb2zh2UukeBZee5GRlMGPqSGZMHcmXz+l92VHFuVx09HguOnp8Z0no6WVRwHlu+RYqRhYmDZQFOVmcPK2Mk6eVAVEJdNWWOnY0tFDf3BaGVuqa2jobrBta2pg+eThnHDgqtpsTnNtdaVVFlrwNpqvzDh/b+YuywxHjS1i4bjsA/3bGNG56YnnnvJX/eX6Xi0FjSxuHfPNhcjIzaGs37vvUKZ13gby1pY4zfjSHb114KGccOIqpIZis3FzHuT99ktMqR/GVcw/i7J88yU//9Wjee8x42tuNBxdtYGVVHScfUMYxE0vJyBCrt9Rz6a+fp6axhds+diJHTijt9bheWLGFK2a/SHFuFtsaWjrr3S84spz/vfQY5q/eym+fXsnDizbSbvDVcw/m6lOn8OzyzTy8aCOPLn6b+uZW/v650zqDYE1jC4+89jZ/fWU9897aSm1oDC8tyGZbfQtfPfdgPjkzujNvR2MLb2ys4cWV1fz1lfW8vrGGzAxx6gFlXHT0OM4+bCxFuVmdd1G9XdPIqs31LK+KGp/nvFFFS1s7v7rsOE6tLKO1rZ3q+mY21zSzqaaRNdX1LNtUy9JNtSzbVMumhJ4acrIyOquBKkdHn9fft5DzjyjnP953RJ/fiYHU2tZOu7FPVz+69JHObTDvBj7Dzkb+G83shNDIPw84Niw6n6iRv7q3faUaYKaNKmRNdUOX2yQT3Xb1iVwx+4XOX893fOxETj6gjObWdv7ttnkcM7GUX8xZxgVHjuPppZspzM3krmtOYlRxLo0tbRz8jYf58jkH8ekzD+iy3V8/uYIfPLiEH3/gKL5y9yt8+swDuPbsg3rN65rqKMhsq2/hipMm86ETJu1yR9Nji9/md8+s5LkVW5haVsjd/3Yy2VkZvLSymkXrtnPpiZO6lCLWbq1n4/ZGpleM6LKd1rboAblk79BpazeWbqph3ltbmf/WNjIz4D//5cik99u/sbGGB15Zx18WrGft1gZyszIoK8qlqrZplwbmscPyOLi8mK+/+1AOGJ3aHTnbG1pYtqmW5ZtqWbqphmWballWVcua6p0P2H5y5jS+eu7BKW3Puf3RPhtgJN1JVBopA94GvkWo6DWzm8Jtyj8HziW6TfkqM5sb1v0ocH3Y1A/M7Hd97S+VADNpRAGrq3t/fuS/Lz6SYycN59dPruDh1zZyzKRS/uXYCdz2/Fu8uDKKccPysjhx6kiuPKmCD89+AQMuOmoc/33xURz/g39w0dHj+O5FXWNqW7vx/l89yytrt2EGV8yYzPfeu0vc3cW6bQ1854HX+MeStzHgnQeP5vIZkzm9chRvVdfzzv+Zw4ThBbzvmPFccdLklKukBoqZMX/1Vv76yga2N7TsfLq4OJeKkdHDdkW5/Vdb29Dc1nlL8oypI4bk7ZvODRX7bIAZaKkEmD989AQ+PPtFIHoAq7qHzt4SSx83PPYmN/7fUgDGl+ZzwVHl/O6ZVTS3tnPw2GIe/sLpLH27hj/PW8usJ1fwjoNHM3dVNWcdOoYbPnj0LtteUVXLjx99g1MPGMVFR4/rvKMnFeu3NXDHC6u566XVbK5tZvLIAsqKclm4bjtPf/VMRhf70/LOud2TzneRDbgTpuysFqqua+Zvnz2VHQ0tfOg3LwBw58dncGTCU7VRA/16Ljl+ElefOoWMDNHWZvzm6ZWsrq7HzKgcU8z15x/CpBEFfP3+RZ3r9WTqqCJ+edlxe5T3caX5fOmcg/jcOyt5+LWN3PbcW7y4qprLZ0zy4OKcG3L2uwCTnZnR2UB9y1XHc/j4EtoTHkKbMXVElwb9UcW5/N+1M7ts45Mzp3Hni6upa25jS11zZ5XU5TMmU1qQzda6Zo6ZFN9DkjlZGbznqHG856hxrKmuZ/QwrwJyzg09+12AycwQz173DtqNznr/xKeEm1rbk3aO12FkUfSswY3/XMaGbY1d2jwuOHJcPBlPorcuTJxzbjCl7X2SS7577i5pd358BhA9Z9C9Ufk9R0WBoaklte4z/m3mNL55waFUxtQHkXPO7evStgTT2LJr1zAnTRuZdPmf/OvRfOYdB6Tc91NBThYfPXXKHufPOefSXdqWYOqaW/teKEFmhvaul2XnnHNdpG2AqW/uu3NL55xz8UnbAFPaj92HO+ec231p2wYzelge177rQH45Zzn3fPLkzt6NnXPODYy0DDDD8qLD+uw7K7v0dOycc27gpGUVWeNuvqnPOedc/0vLALO7r4J1zjnX/9IywDjnnBt8HmCcc87FwgOMc865WMQaYCSdK+kNScskXdfD/J9IWhCGNyVtS5jXljDvgd3Z7x+vmdEf2XfOObcXYrtNWVIm8AvgXcBa4CVJD5jZ4o5lzOyLCct/FjgmYRMNZrbrG7v68OGTJnPi1OR9jjnnnBsYcZZgTgCWmdkKM2sG7gIu6mX5S4E793anre3p84ZO55zbl8UZYMYDaxKm14a0XUiaDEwB/pmQnCdprqTnJb032U4kXROWmwt0eXmYc865wRPnk/w99c2S7Op/CXC3mSX2UDnJzNZLmgr8U9JCM1u+ywbNZgGzAHLLK81LMM45NzTEWYJZC0xMmJ4ArE+y7CV0qx4zs/XhcwUwh67tM0m1tvlDls45NxTEGWBeAiolTZGUQxREdrkbTNJBwHDguYS04ZJyw3gZcAqwuPu6PWnxEoxzzg0JsVWRmVmrpM8AjwCZwGwze03Sd4G5ZtYRbC4F7jKzxMhwCHCzpHaiIPjDxLvPeuPdxDjn3NAQa2/KZvYg8GC3tG92m/52D+s9CxyxJ/v0AOOcc0ND2j3J7wHGOeeGhrQLMC3eyO+cc0NC2gWYYf6qZOecGxLSLsB884JDBzsLzjnnSMMAk5edOdhZcM45RxoGmHbz52Ccc24oSKsAM2ZYHuUleYOdDeecc6RZgBldnIvUUxdozjnnBlpaBRjnnHNDhwcY55xzsfAA45xzLhYeYJxzzsXCA4xzzrlYeIBxzjkXCw8wzjnnYuEBxjnnXCxkadS1iqQa4I3BzscQUQZsHuxMDAF+Hnbyc7GTn4udDjKz4jg2HOsbLQfBG2Y2fbAzMRRImuvnws9DIj8XO/m52EnS3Li27VVkzjnnYuEBxjnnXCzSLcDMGuwMDCF+LiJ+Hnbyc7GTn4udYjsXadXI75xzbuhItxKMc865IcIDjHPOuVikRYCRdK6kNyQtk3TdYOcnDpImSnpc0hJJr0n6fEgfIekxSUvD5/CQLkk3hnPyqqRjE7Z1ZVh+qaQrB+uY9oakTEkvS/pbmJ4i6YVwTH+UlBPSc8P0sjC/ImEbXwvpb0g6Z3COZO9JKpV0t6TXw/fjpP34e/HF8P+xSNKdkvL2l++GpNmSNklalJDWb98DScdJWhjWuVGpvN3RzPbpAcgElgNTgRzgFeDQwc5XDMdZDhwbxouBN4FDgf8Grgvp1wH/FcbPBx4CBMwAXgjpI4AV4XN4GB8+2Me3B+fj34E7gL+F6T8Bl4Txm4BPhvFPATeF8UuAP4bxQ8N3JReYEr5DmYN9XHt4Ln4PfCyM5wCl++P3AhgPrATyE74TH9lfvhvA6cCxwKKEtH77HgAvAieFdR4CzuszT4N9UvrhpJ4EPJIw/TXga4OdrwE47r8A7yLquaA8pJUTPWwKcDNwacLyb4T5lwI3J6R3WW5fGIAJwP8B7wD+Fr7wm4Gs7t8J4BHgpDCeFZZT9+9J4nL70gAMCxdVdUvfH78X44E14eKYFb4b5+xP3w2goluA6ZfvQZj3ekJ6l+WSDelQRdbxpeqwNqSlrVCUPwZ4ARhjZhsAwufosFiy85IO5+unwFeA9jA9EthmZq1hOvGYOo83zN8elk+H8wBRyb0K+F2oMvyNpEL2w++Fma0DfgysBjYQ/a3nsf9+N6D/vgfjw3j39F6lQ4DpqR4wbe+9llQE3AN8wcx29LZoD2nWS/o+QdIFwCYzm5eY3MOi1se8ffo8JMgiqhb5lZkdA9QRVYUkk7bnI7QvXERUrTUOKATO62HR/eW70ZvdPfY9OifpEGDWAhMTpicA6wcpL7GSlE0UXG43s3tD8tuSysP8cmBTSE92Xvb183UK8B5Jq4C7iKrJfgqUSuroWy/xmDqPN8wvAarZ989Dh7XAWjN7IUzfTRRw9rfvBcBZwEozqzKzFuBe4GT23+8G9N/3YG0Y757eq3QIMC8BleFOkRyixroHBjlP/S7csfFbYImZ3ZAw6wGg406PK4naZjrSPxzuFpkBbA9F5EeAsyUND7/4zg5p+wQz+5qZTTCzCqK/9T/N7DLgceDisFj389Bxfi4Oy1tIvyTcSTQFqCRqxNynmNlGYI2kg0LSO4HF7Gffi2A1MENSQfh/6TgX++V3I+iX70GYVyNpRji3H07YVnKD3SjVTw1b5xPdVbUc+H+DnZ+YjvFUoiLpq8CCMJxPVGf8f8DS8DkiLC/gF+GcLASmJ2zro8CyMFw12Me2F+dkJjvvIptKdBFYBvwZyA3peWF6WZg/NWH9/xfOzxukcEfMUB2Ao4G54btxP9HdP/vl9wL4DvA6sAi4lehOsP3iuwHcSdT21EJU4ri6P78HwPRwXpcDP6fbjSU9Dd5VjHPOuVikQxWZc865IcgDjHPOuVh4gHHOORcLDzDOOedi4QHGOedcLDzAuAElySTdmjCdJalKoVfkoUrSHEnTe0jPlvTD0PPsIkkvSurp6fG92XeFpA+luOzNkk7pz/3vKUmrJJUNdj7c4PEA4wZaHXC4pPww/S5g3WBkJOHp7r3xPaKOAA83s8OBC4l6u+5PFUBKAQY4EXi+n/fv3B7xAOMGw0PAu8P4pUQPiAEgqTC81+Kl0HnjRSG9QtJTkuaH4eSQXi7pSUkLQgnitJBem7DNiyXdEsZvkXSDpMeB/+plf/mS7grvyvgj0BEQSdhuAfBx4LNm1gRgZm+b2Z/C/EvD+zMWSfqvhPV6y9uNkp6VtEJSx9PnPwROC8f4RUmHhZLSgpC/yrD+IcCbZtYm6XOSFof5d/VxbjMl/Tjk9VVJnw3p7wzLLQzr5Yb0VZK+E/4OCyUdHNJHSno0rHMzPfdf5fYng/30qQ/71wDUAkcS9ZmVR9QjwUx2PpH/H8DlYbyUqIeGQqAAyAvplcDcMH4tofcGoncDFXfsJ2GfFwO3hPFbiLpxz+xjf/8OzA7pRwKtJDztnJD+cpLjHEfUdckoog4p/wm8N4W8/Znoh9+hwLKQ3nl+wvT/ApeF8Rx2vv/k34GPhvH17HxivbSPY/0kUR93HV3ajwh/mzXAgSHtD0QdrAKsIgqqEL1T5Tdh/Ebgm2H83UQ9T5QN9nfOh8EbvATjBpyZvUpU7XMp8GC32WcD10laAMwhutBNArKBX0taSHQRPjQs/xJwlaRvA0eYWU0KWfizmbX1sb/TgdsS8vvqbh7m8cAcizpebAVuD9vsy/1m1m5mi4ExSZZ5Drhe0leByWbWENLPAR4O468Ct0u6nCg4QvJjPYvoxVutAGZWDRxE1HHkm2Hd33fLf0dnq/OI/pbQ9Zz9HdiawvG6NNYfddDO7YkHiN7dMZOov6QOAt5vZm8kLhwCyNvAUUS/8BsBzOxJSacT/WK+VdKPzOwPdO1KPK/bvutS2B/03R35MmCSpOIeAltv1UO95a2pr22Y2R2SXiA65kckfYyo3aXUzDp6uH030QX/PcA3JB1G8mMVux5rX9VbHflso+t1xPuecp28BOMGy2zgu2a2sFv6I8Bnw0UPSceE9BJgg5m1A1cQVYchaTLR+2F+TdTbdMe7xd+WdIikDOB9veQj2f6eBC4LaYcTVYd1YWb1YZ83aud73stDqeEF4AxJZZIyiUprT+xm3jrUkHDjgKSpwAozu5EoUB8JnEnUazBhuxPN7HGiF7OVAkW9HOujwL913PQgaQRRh5EVkg4Iy1yRkP9kEs/ZeUSdbrr9mAcYNyjMbK2Z/ayHWd8jqg57VdKiMA3wS+BKSc8DB7KzFDITWCDpZeD9QMc2ryNqa/knUQ+zySTb36+AIkmvEl2kk3XX/nWiN0ouDuvfD1RZ1L3514gu+q8A882so3vzVPPW4VWgVdIrkr4I/CuwKFR1HUzUPnIeO6vHMoHbQnXiy8BPzGxbL8f6G6L2olclvQJ8yMwagauAP4fttBO9z7433wFOlzSfqDpudQrH5tKY96bsXBoIF/UTLXrRlnNDggcY55xzsfAqMuecc7HwAOOccy4WHmCcc87FwgOMc865WHiAcc45FwsPMM4552Lx/wHFXtS2jBEBEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resd['measured'][0:210],sub)\n",
    "plt.title('Fractional Deadtime Plot')\n",
    "plt.xlim(resd['measured'][0:210].min(),resd['measured'][0:210].max())\n",
    "plt.plot(resd['measured'][0:210],1/(1 - 1.853e-05*resd['measured'][0:210]))\n",
    "plt.ylabel('Fit / Measured')\n",
    "plt.xlabel('Measured Counts/second');\n",
    "plt.xlim(0,10000)\n",
    "#plt.savefig('fractionaldeadtime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10000)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(resd['measured'][0:210],sub)\n",
    "plt.title('Fractional Deadtime Plot')\n",
    "plt.xlim(resd['measured'][0:210].min(),resd['measured'][0:210].max())\n",
    "plt.plot(resd['measured'][0:210],1/(1 - 1.853e-05*resd['measured'][0:210]))\n",
    "plt.ylabel('Fit / Measured')\n",
    "plt.xlabel('Measured Counts/second');\n",
    "plt.xlim(0,10000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}