Update PeakOTron.py

PeakOTron.py

-from joblib import dump, load
+## code starts###
 
+import os
+import numpy as np
+import pandas as pd
+from itertools import chain
+from iminuit import Minuit
+from iminuit.util import describe
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from matplotlib import cm
+from numba import njit
+from difflib import ndiff
+import re
+from KDEpy import FFTKDE
+import scipy.special as sc
+from scipy.integrate import quad
+from scipy.stats import poisson, skew, norm, binom
+from scipy.interpolate import interp1d
+from scipy.signal import find_peaks, peak_widths
+from scipy.signal import fftconvolve as convolve
+from astropy.stats import knuth_bin_width, freedman_bin_width, scott_bin_width, bootstrap
+from sklearn.preprocessing import RobustScaler
+from AdditionalPDFs import gpoisson, borel
 
-f_data = PeakOTron(verbose=True)
-f_data.SetMaxNPeaks(20) #SET MAX N PEAKS - THRESHOLDING REQUIRED WHERE LAMBDA INCORRECTLY ESTIMATED, OR FIT WILL GO ON FOREVER.
-f_data.SetMaxNDCRPeaks(6)
 
-out_dict = {}
-for _, row in scan_df.sort_values(by=["DCR"], ascending=True).iterrows():
-    dcr_orig = row["DCR"]
+class FakeFuncCode:
+        def __init__(self, f, prmt=None, dock=0, append=None):
+            #f can either be tuple or function object
+            self.co_varnames = describe(f)
+            self.co_argcount = len(self.co_varnames)
+            self.co_argcount -= dock
+            self.co_varnames = self.co_varnames[dock:]
 
-    for i in range(20):
-        data = np.random.choice(row["ChargeSpectrum"], size=20000)
+            if prmt is not None:  #rename parameters from the front
+                for i, p in enumerate(prmt):
+                    self.co_varnames[i] = p
 
-        f_data.Fit(data, 
-          tau=20,  #SLOW PULSE COMPONENT TIME CONSTANT (ns)
-          tgate=100, #GATE LENGTH (ns)
-          r_fast=0.1, #FRACTION OF FAST PULSE COMPONENT 
-          t_0 = 100, #INTEGRATION TIME BEFORE GATE (ns)
-          pedestal = 0.0,
-          prominence=0.0,
-          peak_dist_factor=0.6,
-          min_n_peak_evts=25,
-          bandwidth_kde="optimise",
-          bw=0.05
+            if isinstance(append, str): append = [append]
+
+            if append is not None:
+                old_count = self.co_argcount
+                self.co_argcount += len(append)
+                self.co_varnames = tuple(
+                    list(self.co_varnames[:old_count]) +
+                    append +
+                    list(self.co_varnames[old_count:]))
+
+
+class BandWidthOptimiser:
+    
+    #Hobbema, Hermans, and Van den Broeck (1971) and by Duin (1976)
+    #leave-one-out cross-validation approach
+    #https://cran.r-project.org/web/packages/kedd/vignettes/kedd.pdf
+    def _Dummy(self, _, bw):
+        return bw
+    
+    
+    def _KDE(self, bw):
+        try:
+            x_kde, y_kde = FFTKDE(kernel = self.kernel, bw=bw).fit(self.data).evaluate(self.n_kde_samples)
+        
+            loss = -np.sum(np.log(y_kde))
+            
+        except:
+            
+            loss = self.eps_inv
+            
+        return loss
+        
+    def _PPF(self, data):
+        """ Compute ECDF """
+        x = np.sort(data)
+        n = x.size
+        y = np.arange(1, n+1) / n
+        _ppf = interp1d(y, x)
+        return _ppf
+
+        
+
+    def __init__(self, data, kernel="gaussian", alpha = 0.95, n_kde_samples=2**13):
+        if(alpha<=0.5):
+            raise ValueError("alpha must be above 0.5")
+        
+        self.data = data
+        self.PPF = self._PPF(self.data)
+        self.data = self.data[(self.data<self.PPF(alpha))]
+        self.N = len(data)
+        self.kernel=kernel
+        self.n_kde_samples=n_kde_samples
+        self.last_arg = None
+        self.func_code = FakeFuncCode(self._Dummy, dock=True)
+        self.eps = np.finfo(np.float64).eps * 10
+        self.eps_inv = 1/self.eps
+        
+
+    def __call__(self, *arg):
+        self.last_arg = arg
+        loss = self._KDE(*arg)
+        return loss
+
+
+               
+                                              
+class BinnedLH:
+    
+
+    def __init__(self, f, x, y):
+        self.f = f
+        self.x = x
+        self.y = y
+        self.last_arg = None
+        self.func_code = FakeFuncCode(f, dock=True)
+        self.ndof = len(self.y) - (self.func_code.co_argcount - 1)
+        self.eps = np.finfo(np.float64).eps * 10
+        self.eps_inv = 1/self.eps
+        self.log_eps = np.log(self.eps)
+        
+        
+    def Logify(self, y):
+     
+        return np.where(y>self.eps, np.log(y), self.log_eps)
+
+        
+
+    def __call__(self, *arg):
+        self.last_arg = arg
+        y_hat = self.f(self.x, *arg)
+        # y_hat = np.nan_to_num(y_hat, nan=self.eps_inv)
+        logL = self.y*(self.Logify(y_hat) - self.Logify(self.y)) + (self.y - y_hat)
+        nlogL = -np.sum(logL)
+
+        return nlogL
+    
+    
+
+    
+    
+class Chi2Regression:
+    
+
+    def __init__(self, f, x, y, y_err, epsilon=1.35):
+        self.f = f
+        self.x = x
+        self.y = y
+        self.y_err = y_err
+        self.eps = np.finfo(np.float64).eps * 10
+        self.y_err[self.y_err<self.eps] = self.eps
+        self.last_arg = None
+        self.func_code = FakeFuncCode(f, dock=True)
+        self.ndof = len(self.y) - (self.func_code.co_argcount - 1)
+      
+
+    def __call__(self, *arg):
+        
+        self.last_arg = arg
+        
+        loss = ((self.f(self.x, *arg) - self.y)/(self.y_err))**2
+        
+        return np.sum(loss)
+    
+    
+    
+    
+class HuberRegression:
+    
+
+    def __init__(self, f, x, y, y_err, delta=1.35):
+        self.f = f
+        self.x = x
+        self.y = y
+        self.y_err = y_err
+        self.delta = delta
+        self.eps = np.finfo(np.float64).eps * 10
+        self.y_err[self.y_err<self.eps] = self.eps
+        self.last_arg = None
+        self.func_code = FakeFuncCode(f, dock=True)
+        self.ndof = len(self.y) - (self.func_code.co_argcount - 1)
+        
+
+    def __call__(self, *arg):
+        
+        self.last_arg = arg
+        
+        a = abs((self.y - self.f(self.x, *arg))/self.y_err)
+        cond_flag = (a>self.delta)
+        
+        loss = np.sum((~cond_flag) * (0.5 * a ** 2) - (cond_flag) * self.delta * (0.5 * self.delta - a), -1)
+        
+        return np.sum(loss)
+        
+
+
+@njit
+def _SelectRangeNumba(array, low_lim, hi_lim):
+    index = (array >=  low_lim) & (array <= hi_lim)
+    return array[index]
+        
+
+class PeakOTron:
+    
+    def __init__(self,
+                 verbose=False,
+                 n_call_minuit=1000,
+                 n_iterations_minuit = 10
+                ):
+        
+        
+        self.Init()
+        self.greek_letters = ["Alpha",
+                      "alpha",
+                      "Beta",
+                      "beta",
+                      "Gamma",
+                      "gamma",
+                      "Delta",
+                      "delta",
+                      "Epsilon",
+                      "epsilon",
+                      "Zeta",
+                      "zeta",
+                      "Eta",
+                      "eta",
+                      "Theta",
+                      "theta",
+                      "Iota",
+                      "iota",
+                      "Kappa",
+                      "kappa",
+                      "Lbda",
+                      "lbda",
+                      "Mu",
+                      "mu",
+                      "Nu",
+                      "nu",
+                      "Xi",
+                      "xi",
+                      "Omicron",
+                      "omicron",
+                      "Pi",
+                      "pi",
+                      "Rho",
+                      "rho",
+                      "Sigma",
+                      "sigma",
+                      "Tau",
+                      "tau",
+                      "Upsilon",
+                      "upsilon",
+                      "Phi",
+                      "phi",
+                      "Chi",
+                      "chi",
+                      "Psi",
+                      "psi",
+                      "Omega",
+                      "omega"]
+        
+        self._eps = np.finfo(np.float64).eps * 10
+        self._eps_kde = 1e-6
+        self._FWHM2Sigma = 1/(2*np.sqrt(2*np.log(2)))
+        
+        self._plot_figsize= (10,10)
+        self._plot_fontsize= 20
+        self._cmap = cm.get_cmap('viridis')
+        
+        self._max_n_peaks = 12
+        self._max_n_dcr_peaks = 6
+        self._len_DCR_pad = int(100)
+        
+        self._verbose=verbose
+        self._n_call_minuit = n_call_minuit
+        self._n_iterations_minuit = n_iterations_minuit
+        self._is_histogram = False
+        
+  
+        self._default_fit_kwargs={
+                "tau":None,
+                "t_0":None,
+                "r_fast":None,
+                "tgate":None,
+                "pedestal":None,
+                "n_bootstrap": 1000,
+                "n_bootstrap_kde":100,
+                "bw":None,
+                "peak_dist_factor":0.8,
+                "peak_width_factor":0.5,
+                "prominence": 0.0,
+                "min_n_peak_evts": 100,
+                "kernel_kde":"gaussian",
+                "bandwidth_kde":"ISJ",
+                "ppf_mainfit":1 - 1e-6,
+                "bin_method":"knuth"
+        }
+        
+
+    
+
+        
+    def LevDist(self, str1, str2):
+        counter = {"+": 0, "-": 0}
+        distance = 0
+        for edit_code, *_ in ndiff(str1, str2):
+            if edit_code == " ":
+                distance += max(counter.values())
+                counter = {"+": 0, "-": 0}
+            else:
+                counter[edit_code] += 1
+        distance += max(counter.values())
+        return distance
+    
+
+    ###HELPER FUNCTIONS
+        
+    def Init(self):
+        
+        self._peak_data = {}
+        self._GD_data = {}
+        self._DCR_data={}
+        
+        
+        self._fit_values= {}
+        self._fit_errors = {}
+        self._fit = None
+        self._failed = False
+        
+        
+        
+    def LatexFormat(self, f, scirange=[0.01,1000]):
+        if(np.abs(f)>scirange[0] and np.abs(f)<scirange[1]):
+            float_str = r"${:3.3f}$".format(f)
+        else:
+            try:
+                float_str = "{:3.3E}".format(f)
+                base, exponent = float_str.split("E")
+                float_str = r"${0} \times 10^{{{1}}}$".format(base, int(exponent))
+            except:
+                float_str=str(f)
+        return float_str
+
+
+        
+
+    def SelectRange(self, array, low_lim, hi_lim):
+        return _SelectRangeNumba(array, low_lim, hi_lim)
+    
+    
+    
+    def Logify(self, y):
+        log_y = np.log(y)
+        min_log_y = np.min(log_y[y>0])
+        
+        return np.where(y>0, log_y, min_log_y)
+
+    
+    def SetMaxNPeaks(self, max_n_peaks):
+        max_n_peaks = int(max_n_peaks)
+        if(max_n_peaks<1):
+            raise Exception("Maximum number of peaks must be greater than 2.")
+        
+
+        self._max_n_peaks =  max_n_peaks
+        print("Set maximum number of peaks to {:d}.".format(self._max_n_peaks))
+        
+
+                
+        
+    def SetMaxNDCRPeaks(self, max_n_dcr_peaks):
+        max_n = int( max_n_dcr_peaks)
+        if(max_n_dcr_peaks<2):
+            raise Exception("Maximum number of peaks must be greater than 2.")
+        
+
+        self._max_n_dcr_peaks =  max_n_dcr_peaks
+        print("Set maximum number of dark peaks to {:d}.".format(self._max_n_dcr_peaks))
+
+
+    def DummyLinear(self, x, m, c):
+        return m*x + c
+
+    def DummyLogGPois(self, k, mu, lbda, A):
+        
+        
+        return A + gpoisson.logpmf(k, mu, lbda)
+
+    def SigmaK(self, k, sigma_0, sigma_1):
+            return np.sqrt(sigma_0**2 + k*sigma_1**2)
+    
+
+    def PoisPPF(self, q, mu):
+        vals = np.ceil(sc.pdtrik(q, mu))
+        vals1 = np.maximum(vals - 1, 0)
+        temp = sc.pdtr(vals1, mu)
+        return np.where(temp >= q, vals1, vals)
+    
+    def GPoisPPF(self, q, mu, lbda, k_max):
+        _sum = 0
+        _count = 0
+        while _count<=k_max:
+            _sum +=  gpoisson.pmf(_count, mu, lbda)
+            if(_sum<q):
+                _count+=1
+            else:
+                break
+        
+        return _count
+    
+
+
+    def EstLbda(self, data_sub_x_0):
+        _skew = skew(data_sub_x_0)
+        _mean = np.mean(data_sub_x_0)
+        _std = np.std(data_sub_x_0)
+
+        _lbda = 0.5*(((_mean*_skew)/(_std))- 1)
+        return _lbda
+
+    def EstGain(self, data_sub_x_0):
+        _skew = skew(data_sub_x_0)
+        _mean = np.mean(data_sub_x_0)
+        _std = np.std(data_sub_x_0)
+
+        _lbda = 0.5*(((_mean*_skew)/(_std))- 1)
+        _gain = (_std**2/_mean)*((1 - _lbda)**2)
+        return _gain
+
+    def EstMu(self, data_sub_x_0):
+        _skew = skew(data_sub_x_0)
+        _mean = np.mean(data_sub_x_0)
+        _std = np.std(data_sub_x_0)
+        _lbda = 0.5*(((_mean*_skew)/(_std))- 1)
+        _mu = (1/(1-_lbda))*(_mean**2/_std**2)
+        return _mu
+    
+  
+
+    def Bootstrap(self, data, statistic, alpha=0.95, n_samples=10000):
+        if not (0 < alpha < 1):
+            raise ValueError("confidence level must be in (0, 1)")
+
+        n = n_samples
+        if n <= 0:
+            raise ValueError("data must contain at least one measurement.")
+
+
+
+        boot_stat = bootstrap(data, n_samples, bootfunc=statistic)
+
+        stat_data = statistic(data)
+        mean_stat = np.mean(boot_stat)
+        est_stat = 2*stat_data - mean_stat
+        std_err = np.std(boot_stat)
+        z_score = np.sqrt(2.0)*sc.erfinv(alpha)
+        conf_interval = est_stat + z_score*np.array((-std_err, std_err))
+
+
+        return est_stat, std_err, conf_interval
+    
+    
+  
+
+    def BootstrapKDE(self, data, kernel = "gaussian", bw="optimise",
+                     n_kde_samples=2**13,
+                     alpha=0.95,
+                     n_samples=1000,
+                     limits=None
+                    ):
+
+        if not (0 < alpha < 1):
+            raise ValueError("Bootstrap confidence level, alpha, must be in (0, 1).")
+
+        n = n_samples
+        if n <= 0:
+            raise ValueError("Bootstrap data must contain at least one measurement.")
+
+        kernels = list(FFTKDE._available_kernels.keys())
+        if kernel not in kernels:
+            raise ValueError("Selected KDE kernel not available. Please choose from these options: {:s}.".format(", ".join(kernels)))
+
+        
+        if(not isinstance(bw, float)):
+            bws = list(FFTKDE._bw_methods.keys()) + ["optimise"]
+            if bw not in bws:
+                raise ValueError("Selected KDE bandwidth estimation method not available. Please choose from these options: {:s}.".format(", ".join(bws)))
+
+        
+        
+        if(bw=="optimise"):
+            
+            try:
+                BWO= BandWidthOptimiser(data, kernel=kernel)
+                bw_scott = scott_bin_width(data)
+                minuit_kde = Minuit(BWO, bw=bw_scott/2)
+                minuit_kde.limits["bw"]=(self._eps_kde, 3*bw_scott)
+                minuit_kde.strategy=2
+                minuit_kde.migrad(ncall= self._n_call_minuit,
+                           iterate =self._n_iterations_minuit)
+
+                bw = minuit_kde.values["bw"]
+                if(self._verbose):
+                    print("KDE Bandwidth optimised to: {0}".format(bw))
+            except:
+                if(self._verbose):
+                    print("KDE Bandwidth optimisation failed. Defaulting to Improved Sheather Jones.")
+                    bw = "ISJ"
+        
+        kde = FFTKDE(kernel = kernel, bw=bw).fit(data)
+        
+        x_kde, y_kde_orig = kde.evaluate(n_kde_samples)
+
+
+        boot_data = bootstrap(data, n_samples)
+        y_kde_bs = np.vstack([FFTKDE(kernel = kernel, bw=bw).fit(_data).evaluate(x_kde)
+                                   for _data in boot_data])
+
+
+        y_kde_mean = np.mean(y_kde_bs, axis=0)
+        y_kde = 2*y_kde_orig - y_kde_mean
+        y_kde_err = np.std(y_kde_bs, axis=0)
+        z_score = np.sqrt(2.0)*sc.erfinv(alpha)
+        y_kde_conf_interval = y_kde + z_score*np.array((-y_kde_err, y_kde_err))
+        
+        if(limits is not None):
+            cond_inrange =(x_kde>np.min(limits)) & (x_kde<np.max(limits))
+            x_kde = x_kde[cond_inrange]
+            y_kde = y_kde[cond_inrange]
+            y_kde_err = y_kde_err[cond_inrange]
+
+        return x_kde, y_kde, y_kde_err, y_kde_conf_interval
+
+
+        
+    ##MODEL DEFINITIONS
+        
+        
+    
+    def DRM_basic(self, x, mu, x_0, G, sigma_0, sigma_1, alpha, r_beta, lbda, k_low, k_hi):
+
+        k = np.arange(k_low, k_hi)
+        k = sorted(list(k[k!=0]))
+        k_is = [k[0:int(_k)] for _k in k]
+
+        f0 = gpoisson.pmf(0, mu, lbda)*norm.pdf(x, x_0, sigma_0)
+        f1s = np.asarray([
+                binom.pmf(0, _k, alpha)*gpoisson.pmf(_k, mu, lbda)*norm.pdf(x, x_0 + _k*G,  self.SigmaK(_k, sigma_0, sigma_1))
+                for _k in k
+        ])
+
+        f2s = np.vstack([
+                 np.asarray([
+                    binom.pmf(_i, _k, alpha)*gpoisson.pmf(_k, mu, lbda)*self.XTM(x, _k, _i, x_0, G, sigma_0, sigma_1, r_beta)
+                    for _i in _is
+                ])
+
+                for _k, _is in zip(k, k_is)  
+            ])
+
+
+        f = np.sum(np.vstack([f0, f1s, f2s]), axis=0)
+
+        return f
+
+
+
+
+    def XTM(self, x, k, i, x_0, G, sigma_0, sigma_1, r_beta):
+
+        x_k = x - (x_0 + k*G)
+        _sigma_k = self.SigmaK(k, sigma_0, sigma_1)
+        x_ct_pad = np.zeros_like(x_k)
+
+        if(i==1):
+            cond = (x_k > -5*_sigma_k)
+            error_f = 0.5*(1.0 + sc.erf(x_k/(_sigma_k*np.sqrt(2.0))))
+            log_f = -x_k/(r_beta*G) - np.log(r_beta*G)
+            x_ct = np.where(cond, np.exp(log_f)*error_f, x_ct_pad)
+
+        elif(i>1):
+            cond = (x_k > 0)
+            log_f = sc.xlogy(i-1, x_k) - sc.gammaln(i) - sc.xlogy(i, r_beta*G) - x_k/(r_beta*G)
+            x_ct = np.where(cond, np.exp(log_f), x_ct_pad)
+
+        else:
+            x_ct = x_k
+
+        return x_ct
+
+
+
+
+    def PH_range(self, t, t_0, r_fast, tau, tgate):
+
+        if((t>t_0) and (t<0)):
+            PH_min = (1-r_fast)*np.exp(t_0/tau)*(1 - np.exp(-tgate/tau))
+            PH_max = (1-r_fast)*(1 - np.exp(-tgate/tau))  
+        elif((t>0) and (t<tgate)):
+            PH_min = r_fast
+            PH_max = (1 - (1-r_fast)*np.exp(-tgate/tau))
+        else:
+            PH_min = 0
+            PH_max = 0
+
+        return PH_min, PH_max
+
+
+    def dpdPH(self, x, x_0, G, tau, tgate, t_0, r_fast):
+
+
+        PH_bfg_low, PH_bfg_hi = self.PH_range(-abs(t_0)/2, -abs(t_0), r_fast, tau, tgate)
+        PH_dg_low, PH_dg_hi = self.PH_range(tgate/2, -abs(t_0), r_fast, tau, tgate)
+
+        x_norm = (x-x_0)/(G)
+
+        PHs = np.zeros_like(x_norm)
+
+        cond_bfg = (x_norm>PH_bfg_low) & (x_norm<=PH_bfg_hi)
+        cond_dg = (x_norm>PH_dg_low) & (x_norm<=PH_dg_hi)
+
+        PHs[cond_bfg] += 1/x_norm[cond_bfg]
+        PHs[cond_dg] += 1/(1 - x_norm[cond_dg])  
+
+
+        return PHs
+
+
+    def ApplyDCR(self, x, y_light, x_0, G, DCR, tgate, t_0, tau, r_fast, lbda, dx, k_dcr_low, k_dcr_hi):
+    
+        mu_dcr = DCR*(abs(t_0) + tgate)
+
+        x_lin = np.arange(0, len(x))
+        x_pad_lin = np.arange(-self._len_DCR_pad, len(x)+self._len_DCR_pad)
+        f_lin = interp1d(x, x_lin, fill_value='extrapolate')
+        G_lin = f_lin(x_0+G) - f_lin(x_0)
+        x_0_lin = f_lin(x_0)
+
+        idx_orig = np.where(np.in1d(x_pad_lin,
+                                        np.intersect1d(
+                                        x_pad_lin,
+                                        x_lin
+                                    )
+                            ), True, False)
+
+        idx_x_0_lin =  np.argmin(abs(x_pad_lin - x_0_lin))
+
+        
+        n_dcr = np.arange(k_dcr_low, max(2, k_dcr_hi) )
+
+        fs = []
+        pfs = []
+        hs = []
+        phs = []
+
+        for _n_dcr in n_dcr:
+
+            if(_n_dcr==0):
+
+                f = np.zeros(len(x_pad_lin))
+
+                f[idx_x_0_lin] = 1
+
+                pf = poisson.pmf(0, mu_dcr)
+
+                h = np.zeros(len(x_pad_lin))
+
+                ph = 0
+
+
+            else:
+                if(len(fs)<=1):
+                    f = self.dpdPH(x_pad_lin, x_0_lin, G_lin, tau, tgate, t_0, r_fast)
+                else:
+                    f = convolve(fs[1], fs[-1])[idx_x_0_lin:len(x_pad_lin)+idx_x_0_lin]
+
+                f = f/ np.trapz(f, dx = 1)
+
+                pf = poisson.pmf(_n_dcr, mu_dcr)*(borel.pmf(0, lbda)**_n_dcr)
+
+                h = self.dpdPH(x_pad_lin, x_0_lin, G_lin*(_n_dcr+1), tau, tgate, t_0, r_fast)
+                h = h/ np.trapz(h, dx = 1)
+                ph = poisson.pmf(1, mu_dcr)*borel.pmf(_n_dcr, lbda)/(_n_dcr+1)
+
+            fs.append(f)
+            pfs.append(pf)
+            hs.append(h)
+            phs.append(ph)
+            
+            
+        
+
+
+        fs = np.asarray(fs)
+        hs = np.asarray(hs)
+        pfs = np.expand_dims(np.asarray(pfs), -1)
+        phs = np.expand_dims(np.asarray(phs), -1)
+        y_dark = np.sum(fs*pfs, axis=0) + np.sum(hs*phs, axis=0)
+        y_dark = y_dark/np.trapz(h, dx = 1)
+
+
+        y_model = convolve(y_dark,
+                              np.pad(y_light,
+                                     (self._len_DCR_pad, self._len_DCR_pad),
+                                     "constant",
+                                     constant_values=(0.0,0.0)),
+                            )[idx_x_0_lin:len(x_pad_lin)+idx_x_0_lin]
+
+        y_model = y_model/np.trapz(y_model, dx = 1)/dx
+
+        y_model  = y_model[idx_orig]
+
+        return y_model
+
+
+ 
+    def DRM_nodcr(  
+            self,
+            x,
+            mu,
+            x_0,
+            G,
+            sigma_0,
+            sigma_1,
+            alpha,
+            r_beta,
+            lbda,
+            k_low,
+            k_hi):
+        
+        y_model = self.DRM_basic(x,  
+                                 mu,
+                                 x_0,
+                                 G,
+                                 sigma_0,
+                                 sigma_1,
+                                 alpha,
+                                 r_beta,
+                                 lbda,
+                                 k_low,
+                                 k_hi,
+                                 dx
+                                )
+        
+        return y_model
+        
+
+
+    def DRM(
+            self,
+            x,
+            mu,
+            x_0,
+            G,
+            sigma_0,
+            sigma_1,
+            alpha,
+            r_beta,
+            lbda,
+            k_low,
+            k_hi,
+            k_dcr_low,
+            k_dcr_hi,
+            DCR,
+            tau,
+            tgate,
+            t_0,
+            r_fast,
+            dx
+           ):
+        
+        
+        
+
+        y_light = self.DRM_basic(x,  
+                                 mu,
+                                 x_0,
+                                 G,
+                                 sigma_0,
+                                 sigma_1,
+                                 alpha,
+                                 r_beta,
+                                 lbda,
+                                 k_low,
+                                 k_hi
+                                )
+
+        y_model = self.ApplyDCR(x,
+                                y_light,
+                                x_0,
+                                G,
+                                DCR,
+                                tgate,
+                                t_0,
+                                tau,
+                                r_fast,
+                                lbda,
+                                dx,
+                                k_dcr_low,
+                                k_dcr_hi)
+        
+
+            
+        return y_model
+    
+    
+
+    ###PLOTTING
+    
+
+    
+    def PlotOriginalHistogram(self, ax):
+        
+        ax.plot(self._GD_data["x_s"],
+                    self._GD_data["y_s"],
+                    lw=5,
+                    label="Data")
+        ax.set_title("Normalised \n Input \n Distribution",fontsize=self._plot_fontsize)
+        ax.set_xlabel("IQR Normalised Charge Units [Q.U.]", fontsize=self._plot_fontsize)
+        ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+        ax.set_yscale("log")
+        ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+        
+    
+    def PlotKDE(self, ax):
+        ax.plot(self._GD_data["x_s"],
+                    self._GD_data["y_s"],
+                    label="Data",
+                    lw=5
+                    )
+        ax.plot(self._GD_data["x_kde_s"],
+                self._GD_data["y_kde_s"],
+                lw=2.5,
+                label="KDE")
+
+        ax.set_title("Kernel Density Estimate", fontsize=self._plot_fontsize)
+        ax.set_xlabel("IQR Normalised Charge Units [Q.U.]", fontsize=self._plot_fontsize)
+        ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+        ax.set_yscale("log")
+        ax.set_ylim([1e-5, None])
+        ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+        
+      
+
+
+        
+        
+    def PlotPeaks(self, ax):
+    
+            ax.plot(self._GD_data["x_s"],
+                     self._GD_data["y_s"],
+                     label="Input Pulse \n Height \n Spectrum",
+                     lw=5
+                    )
+            
+            color_vals = [self._cmap(_v) for _v in np.linspace(0,0.75, len(self._peak_data["x_peak_s"]))]
+            
+            for _i_x, (_x, _x_w, _c_v) in enumerate(zip(
+                                self._peak_data["x_peak_s"],
+                                self._peak_data["x_width_s"],
+                                color_vals
+                            )):
+                _x_peak =_x
+                _x_width_low = _x - _x_w/2
+                _x_width_hi = _x + _x_w/2
+                _y_peak = self._GD_data["y_s"][np.argmin(abs(self._GD_data["x_s"]-_x_peak))]
+                _y_width_low = self._GD_data["y_s"][np.argmin(abs(self._GD_data["x_s"]-_x_width_low))]
+                _y_width_hi = self._GD_data["y_s"][np.argmin(abs(self._GD_data["x_s"]-_x_width_hi))]
+
+
+                if(_i_x == 0):
+                    annotation_text = "Pedestal"
+                    color = "red"
+                else:
+                    annotation_text = "Peak {:d}".format(_i_x)
+                    color = _c_v
+                    
+                ax.vlines(x=[_x],
+                          ymin=0,
+                          ymax=_y_peak,
+                          color=color,
+                          linestyle="-",
+                          lw=2.5,
+                          label = '\n{:s}: \n $x_{{pos}}$ = {:3.3f} Q.U \n $\Delta x_{{pos}}$ {:3.3f} Q.U \n'.format(
+                                                                                     annotation_text,
+                                                                                     _x_peak,
+                                                                                     _x_width_hi - _x_width_low)
+                        
+                         )
+                
+                ax.vlines(x=[_x_width_low],
+                          ymin=0,
+                          ymax=_y_width_low,
+                          color=color,
+                          lw=2.5,
+                          linestyle="--")
+                
+                ax.vlines(x=[_x_width_hi],
+                          ymin=0,
+                          ymax=_y_width_hi,
+                          color=color,
+                          lw=2.5,
+                          linestyle="--")
+
+
+            ax.axvline(x=[self._GD_data["limit_s"]],
+                       color = "purple",
+                       lw=5,
+                       label="Pedestal Threshold ($PH_{{0}} - \\frac{G_{est}}{2}$)")
+
+            box = ax.get_position()
+            ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+
+            # Put a legend to the right of the current axis
+            ax.legend(loc='center left',
+                      bbox_to_anchor=(1, 0.5),
+                      fontsize=max(10, self._plot_fontsize//2),
+                      ncol=len(self._peak_data["x_peak_s"])//6 + 1
+                     )
+            ax.set_title("Peak Finding \n # Peaks = {:d}".format(len(self._peak_data["x_peak_s"])) ,
+                         fontsize=self._plot_fontsize)
+
+            ax.set_xlabel("IQR Normalised Charge Units [Q.U.]", fontsize=self._plot_fontsize)
+            ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+            ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+            ax.set_yscale("log")
+            
+            
+    def PlotGenPois(self, ax):
+    
+            ax.scatter(self._peak_data["n_peak_s"],
+                        self.Logify(self._peak_data["y_peak_s"]),
+                        label="Extracted Peak Heights"
+                       )
+
+
+            y_lgp = self.DummyLogGPois(self._peak_data["n_peak_s"],
+                                        self._GD_data["fit_GP_mu"],
+                                        self._GD_data["fit_GP_lbda"],
+                                        self._GD_data["fit_GP_A"]
+                                      )
+            
+            ax.plot(self._peak_data["n_peak_s"],
+                     y_lgp,
+                     linestyle="--",
+                     lw=2.5,
+                     color="red",
+                     label="Fit")
+            
+            ax.set_title(
+                "Generalised Poisson Fit \n $\\mu$ = {0} $\pm$ {1} \n $\\lambda$ = {2} $\pm$ {3} \n $A$ = {4} $\pm$ {5}".format(
+                self.LatexFormat(self._GD_data["fit_GP_mu"]),
+                self.LatexFormat(self._GD_data["fit_GP_mu_err"]),
+                self.LatexFormat(self._GD_data["fit_GP_lbda"]),
+                self.LatexFormat(self._GD_data["fit_GP_lbda_err"]),
+                self.LatexFormat(self._GD_data["fit_GP_A"]),
+                self.LatexFormat(self._GD_data["fit_GP_A_err"])
+                
+            ), fontsize=self._plot_fontsize)
+
+
+            ax.set_xlabel("Peak", fontsize=self._plot_fontsize)
+            ax.set_ylabel("$\\log{\\frac{dP}{dPH}}$", fontsize=self._plot_fontsize)
+            ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+            ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+            ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+            
+            
+            
+    def PlotSigmaEst(self, ax):
+        
+        ax.set_title("Estimated Peak Variance \n $\\sigma^{{2}}_{{0}}$={0} $\pm$ {1} \n $\\sigma^{{2}}_{{1}}$={2} $\pm$ {3}".format(
+                                                                                        self.LatexFormat(self._GD_data["fit_var0"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_var0_err"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_var1"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_var1_err"]),
+                                                                                       ), fontsize=self._plot_fontsize)
+
+
+        ax.errorbar(self._peak_data["n_peak_s"],
+         self._peak_data["peak_var_s"],
+         yerr=self._peak_data["peak_var_err_s"],
+         label="Extracted Variances",
+         fmt="o")
+
+
+        ax.plot(self._peak_data["n_peak_s"],
+                self.DummyLinear(self._peak_data["n_peak_s"],
+                                 self._GD_data["fit_var1"],
+                                 self._GD_data["fit_var0"]
+                                 ),
+                 linestyle="--",
+                 lw=2.5,
+                 color="red",
+                 label="Fit")
+
+        ax.set_xlabel("Peak", fontsize=self._plot_fontsize)
+        ax.set_ylabel("$\sigma^{2}_{peak}$", fontsize=self._plot_fontsize)
+        ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+        ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+        
+        
+    
+    def PlotGainEst(self, ax):
+        
+        ax.set_title("Estimated Gain \n $G$={0} $\pm$ {1} \n $PH_{{0}}$={2} $\pm$ {3}".format(
+                                                                                        self.LatexFormat(self._GD_data["fit_G"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_G_err"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_x_0"]),
+                                                                                        self.LatexFormat(self._GD_data["fit_x_0_err"]),
+                                                                                       ), fontsize=self._plot_fontsize)
+
+
+        ax.errorbar(self._peak_data["n_peak_s"],
+         self._peak_data["peak_mean_s"],
+         yerr=self._peak_data["peak_mean_err_s"],
+         label="Extracted Peak Means",
+         fmt="o")
+
+
+        ax.plot(self._peak_data["n_peak_s"],
+                self.DummyLinear(self._peak_data["n_peak_s"],
+                                 self._GD_data["fit_G"],
+                                 self._GD_data["fit_x_0"]
+                                 ),
+                 linestyle="--",
+                 lw=2.5,
+                 color="red",
+                 label="Fit")
+
+        ax.set_xlabel("Peak", fontsize=self._plot_fontsize)
+        ax.set_ylabel("$<Peak>$", fontsize=self._plot_fontsize)
+        ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+        ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+        
+            
+        
+        
+
+            
+    def PlotDCREst(self, ax):
+        
+        
+        
+        x_dc_kde = self._DCR_data["x_dc_kde"]
+        y_dc_kde = self._DCR_data["y_dc_kde"]
+        y_dc_kde_err = self._DCR_data["y_dc_kde_err"]
+        
+        cond_p_0toLM  = self._DCR_data["cond_p_0toLM"]
+        cond_p_LMto2LM = self._DCR_data["cond_p_LMtoL2M"]
+        cond_interpeak = self._DCR_data["cond_interpeak"]
+        
+        
+        int_p_0toLM = self._DCR_data["int_p_0toLM"]
+        dint_p_0toLM = self._DCR_data["dint_p_0toLM"]
+        int_p_LMto2LM = self._DCR_data["int_p_LMto2LM"]
+        dint_p_LMto2LM = self._DCR_data["dint_p_LMto2LM"]
+        mean_p_interpeak = self._DCR_data["mean_p_interpeak"]
+        dmean_p_interpeak = self._DCR_data["dmean_p_interpeak"]
+        
+  
+        ax.fill_between(
+                     x_dc_kde[cond_p_0toLM],
+                     y_dc_kde[cond_p_0toLM],
+                     color='none',
+                     hatch="///",
+                     edgecolor="b",
+                     label = r"$I_{{0 \rightarrow x_{{min}}}}$ = {:s} $\pm$ {:s}".format(self.LatexFormat(int_p_0toLM),
+                                                                                   self.LatexFormat(dint_p_0toLM)                                                                                  )
+                    )
+        
+        ax.fill_between(
+                     x_dc_kde[cond_p_LMto2LM],
+                     y_dc_kde[cond_p_LMto2LM],
+                     color='none',
+                     hatch="///",
+                     edgecolor="r",
+                     label = r"$I_{{x_{{min}} \rightarrow 2x_{{min}}}}$ = {:s} $\pm$ {:s}".format(self.LatexFormat(int_p_LMto2LM),
+                                                                                   self.LatexFormat(dint_p_LMto2LM)                                                                                  )
+                    )
+
+
+        ax.fill_between(
+                     x_dc_kde[cond_interpeak],
+                     y_dc_kde[cond_interpeak],
+                     color='none',
+                     hatch="xxx",
+                     edgecolor="g",
+                     label = r"$<I_{{x_{{min}} - x_{{lim}} \rightarrow x_{{min}} + x_{{lim}} }}>$ = {:s} $\pm$ {:s}".format(
+                                                                                  self.LatexFormat(mean_p_interpeak),
+                                                                                  self.LatexFormat(dmean_p_interpeak)                                                                                  )
+                    )
+        ax.plot(x_dc_kde,
+                y_dc_kde,
+                linestyle="-",
+                color="purple",
+                label="KDE, 95% Confidence"
+               )
+        
+        ax.fill_between(x_dc_kde,
+                         y1=y_dc_kde - 1.96*y_dc_kde_err,
+                         y2=y_dc_kde + 1.96*y_dc_kde_err,
+                         alpha=0.2,
+                         color="purple",
+                         label="KDE, 95% Confidence"
+                       )
+        
+        
+        ax.set_title("Dark Count Rate \n DCR = {0} $\pm$ {1}".format(
+                      self.LatexFormat(self._DCR_data["fit_DCR"]),
+                      self.LatexFormat(self._DCR_data["fit_DCR_err"])),
+                      fontsize=self._plot_fontsize)
+        
+        ax.set_xlabel("$PH_{norm, est}$", fontsize=self._plot_fontsize)
+        
+        ax.set_ylabel("$\\frac{dp_{DCR}}{PH_{norm, est}}$", fontsize=self._plot_fontsize)
+        
+        ax.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+        ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+        ax.set_yscale("log")
+        
+        
+
+    
+      
+    def PlotFit(self,
+                figsize=(10.5,10.5),
+                labelsize=None,
+                ticksize=None,
+                titlesize=None,
+                legendsize=None,
+                title=None,
+                xlabel = None,
+                scaled=False,
+                save_directory=None,
+                y_limits = [None, None],
+                residual_limits = [-5, 5],
+                linewidth_main = 5,  
+                x_limits = [None, None],
+                display=True
+               ):
+    
+    
+        if(labelsize is None):
+            labelsize=self._plot_fontsize
+        
+        if(ticksize is None):
+            ticksize=self._plot_fontsize
+            
+        if(titlesize is None):
+            titlesize = self._plot_fontsize
+            
+        if(legendsize is None):
+            legendsize = max(10, self._plot_fontsize//1.5)
+            
+
+        if(scaled):
+            x = self._GD_data["x_s"]
+            y = self._GD_data["y_s"]
+            y_err = self._GD_data["y_err_s"]
+            y_hat = self.GetModelIQR(self._GD_data["x_s"])
+            if(xlabel is None):
+                xlabel = "IQR Normalised Charge Units [Q.U.]"
+            
+        else:
+            x = self._GD_data["x"]
+            y = self._GD_data["y"]
+            y_err = self._GD_data["y_err"]
+            y_hat = self.GetModel(self._GD_data["x"])
+            if(xlabel is None):
+                xlabel = "Input Charge Units [Q.U.]"
+            
+        
+    
+        fig = plt.figure(figsize=figsize)
+        gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
+        ax0 = plt.subplot(gs[0])    
+        ax0.plot(x, y, label="Data", lw=5, color="C0")
+        ax0.plot(x, y_hat, linestyle="--", label="Model", lw=5, color="C1")
+        ax0.set_yscale("log")
+        ax0.set_xlabel("$\\frac{dp}{dPH}$", fontsize=labelsize)
+        ax0.tick_params(axis="x", labelsize=ticksize)
+        ax0.tick_params(axis="y", labelsize=ticksize)
+        ax0.set_ylim(y_limits)
+        ax0.set_xlim(x_limits)
+        if(title is not None):
+            ax0.set_title(title, fontsize=titlesize)
+            
+
+        ax0.legend(fontsize=legendsize)
+
+
+        ax1 = plt.subplot(gs[1], sharex = ax0)
+        ax1.scatter(x, (y - y_hat)/(y_err), color="C1")    
+
+
+        ax1.tick_params(axis="x", labelsize=ticksize)
+        ax1.tick_params(axis="y", labelsize=ticksize)
+        ax1.set_xlabel(xlabel, fontsize=labelsize)
+        ax1.set_ylim(residual_limits)
+        ax1.axhline(y=-1.0, color="purple", lw=2.5, linestyle="--")
+        ax1.axhline(y=1.0, color="purple", lw=2.5, linestyle="--")
+        ax1.tick_params(axis="x", labelsize=self._plot_fontsize)
+        ax1.tick_params(axis="y", labelsize=self._plot_fontsize)
+
+
+        fig.tight_layout()
+
+        fig.subplots_adjust(hspace=.0)
+        plt.pause(0.01)
+        if(save_directory is not None):
+            print("Saving figure to {0}...".format(save_directory))
+            fig.savefig(save_directory)
+        if(display):
+            fig.show()
+        
+    
+  
+    def PlotSummary(self, save_directory=None):
+    
+            fig = plt.figure(figsize=(20,40))
+            gs = gridspec.GridSpec(4, 2)
+            gs.update(wspace=0.5, hspace=0.5)
+              
+            ax0 = fig.add_subplot(gs[0,0])
+            self.PlotOriginalHistogram(ax0)
+            
+            ax1 = fig.add_subplot(gs[0,1])
+            self.PlotKDE(ax1)
+            
+            ax2 = fig.add_subplot(gs[1,:])
+            self.PlotPeaks(ax2)
+                        
+            ax3 = fig.add_subplot(gs[2,0])
+            self.PlotGenPois(ax3)
+            
+            ax4 = fig.add_subplot(gs[2,1])
+            self.PlotSigmaEst(ax4)
+            
+            ax5 = fig.add_subplot(gs[3,0])
+            self.PlotGainEst(ax5)
+          
+            ax6 = fig.add_subplot(gs[3,1])
+            self.PlotDCREst(ax6)
+
+            if(save_directory is not None):
+                print("Saving figure to {0}...".format(save_directory))
+                fig.savefig(save_directory)
+            if(display):
+                fig.show()
+ 
+            
+    def GetModel(self, x):
+        return self.DRM(x, **self._fit_values)
+        
+    def GetModelIQR(self, x):
+        return self.DRM(x, **self._fit_values_s)
+        
+        
+    def GetOriginalDistribution(self):
+        return _self._GD_data["x"], self._GD_data["y"]
+  
+    def GetFitResults(self):
+        return self._fit_values, self._fit_errors
+    
+    def Failed(self):
+        return self._failed
+    
+    
+    ## PREFIT
+    
+            
+    
+    def GetHistAndPeaks(self,
+                        data,
+                        bw,
+                        peak_dist_factor,
+                        peak_width_factor,
+                        pedestal,
+                        prominence,
+                        bin_method,
+                        n_bootstrap,
+                        n_bootstrap_kde,
+                        bandwidth_kde,
+                        kernel_kde,
+                        min_n_peak_evts):
+        
+ 
+        self.scaler = RobustScaler()
+        data_s = np.squeeze(self.scaler.fit_transform(data.reshape(-1,1)))
+        pedestal_s = (pedestal - self.scaler.center_[0])/self.scaler.scale_[0]
+        
+        if(bw is None):
+            if(bin_method == "knuth"):
+                bw_s, bins_s = knuth_bin_width(data_s, return_bins=True)
+            elif(bin_method=="freedman"):
+                bw_s, bins_s = freedman_bin_width(data_s, return_bins=True)
+            elif(bin_method=="scott"):
+                bw_s, bins_s = scott_bin_width(data_s, return_bins=True)
+            else:
+                raise Exception ("Please specify a bin width by setting 'bw' or choose a method from 'knuth', 'freedman' or 'scott'")
+    
+            nbins_s = len(bins_s)
+        
+            bw = bw_s*self.scaler.scale_[0]
+            nbins = np.ceil((data.max() - data.min()) / bw)
+            nbins = max(1, nbins)
+            bins = data.min() + bw * np.arange(nbins + 1)
+        
+
+        else:
+            
+            bw_s = bw/self.scaler.scale_[0]
+            
+            nbins = np.ceil((data.max() - data.min()) / bw)
+            nbins = max(1, nbins)
+            bins = data.min() + bw * np.arange(nbins + 1)
+                        
+            nbins_s = np.ceil((data_s.max() - data_s.min()) / bw_s)
+            nbins_s = max(1, nbins_s)
+            bins_s = data_s.min() + bw_s * np.arange(nbins_s + 1)
+            
+    
+            
+        #Calculate histogram information
+            
+        y_N, x = np.histogram(data, bins=bins, density=False)
+        
+        N = np.sum(y_N)
+        
+        y = y_N/N/bw
+        
+        y_err = y*np.sqrt(1/y_N + 1/N)
+        
+        x = (x[:-1] + x[1:])/2.
+        
+
+        
+        y_N_s, x_s = np.histogram(data_s, bins=bins_s, density=False)
+        
+        N_s = np.sum(y_N_s)
+        
+        y_s = y_N_s/N_s/bw_s
+        
+        y_err_s = y_s*np.sqrt(1/y_N_s + 1/N_s)
+        
+        x_s = (x_s[:-1] + x_s[1:])/2.
+        
+        
+        
+        try:
+            
+            x_kde_s, y_kde_s, y_kde_err_s, _, = self.BootstrapKDE(data_s,
+                                                       kernel=kernel_kde,
+                                                       bw=bandwidth_kde,
+                                                       n_samples=n_bootstrap_kde)
+
+            f_lin_kde_s = interp1d(x_kde_s, np.arange(len(x_kde_s)), fill_value='extrapolate')
+
+
+            
+        except:
+            print("Initial KDE failed. Setting fit fail status and continuing...")
+            self._failed = True
+            return
+          
+          
+
+        
+        est_mu, std_err_mu, _ = self.Bootstrap(data_s-pedestal_s,
+                                       self.EstMu,
+                                       n_samples=n_bootstrap)
+        
+        est_lbda, std_err_lbda, _ = self.Bootstrap(data_s - pedestal_s,
+                                   self.EstLbda,
+                                   n_samples=n_bootstrap)
+        
+        
+        est_gain, std_err_gain, _ = self.Bootstrap(data_s - pedestal_s,
+                                           self.EstGain,
+                                           n_samples=n_bootstrap)
+        
+        
+
+        
+        est_gain_lin = abs(f_lin_kde_s(est_gain) - f_lin_kde_s(0))
+        
+        
+        peaks, properties = find_peaks(y_kde_s,
+                                       distance=peak_dist_factor*est_gain_lin,
+                                       prominence=prominence
+                                      )
+        
+
+        
+        
+        if(len(peaks)<3):
+            if(self._verbose):
+                print("Not enough peaks found with distance criterion to fit. Removing distance criterion and finding all peaks...")
+            
+            peaks, properties = find_peaks(y_kde_s,
+                                       prominence=prominence
+                                      )
+            
+            
+        limit_s = pedestal_s - 0.5*est_gain
+        limit = pedestal - 0.5*est_gain*self.scaler.scale_[0]
+        cond_limit_s = (x_kde_s[peaks]>limit_s)
+        if(sum(cond_limit_s)<3):
+            if(self._verbose):
+                print("No peaks observed above pedestal - gain/2. Continuing without thresholding...")
+        else:
+            peaks = peaks[cond_limit_s]
+                
+                
+  
+        
+        x_peaks_s = x_kde_s[peaks]
+        y_peaks_s = y_kde_s[peaks]
+        y_peaks_err_s = y_kde_err_s[peaks]
+        
+            
+        temp_widths = np.asarray(peak_widths(y_kde_s, peaks, rel_height=peak_width_factor))
+        temp_widths[2:] = x_kde_s[temp_widths[2:].astype(int)]
+        x_widths_s = temp_widths[3] - temp_widths[2]
+        
+
+        self._peak_data["x_peak_s"] = []
+        self._peak_data["y_peak_s"] = []
+        self._peak_data["y_peak_err_s"] = []
+        self._peak_data["n_peak_s"] = []
+        self._peak_data["x_width_s"] = []
+
+        n_valid_peaks = 0
+        for x_i, (y_peak, y_peak_err, x_peak, x_width) in enumerate(zip(y_peaks_s, y_peaks_err_s, x_peaks_s, x_widths_s)):
+            
+            N_evt_peak = len(self.SelectRange(data_s, x_peak-x_width/2, x_peak+x_width/2))
+      
+            if(N_evt_peak<min_n_peak_evts):
+                if(self._verbose):
+                    print("Peak {:d} has {:d} events. Less than event threshold {:d}. Ignoring...".format(x_i,
+                                                                                                N_evt_peak,
+                                                                                                min_n_peak_evts))
+            else:
+                if(self._verbose):
+                    print("Peak {:d} has {:d} events. Adding...".format(x_i,
+                                                                              N_evt_peak,
+                                                                              min_n_peak_evts))
+                
+                self._peak_data["x_peak_s"].append(x_peak)
+                self._peak_data["y_peak_s"].append(y_peak)
+                self._peak_data["y_peak_err_s"].append(y_peak_err)
+                self._peak_data["n_peak_s"].append(x_i)
+                self._peak_data["x_width_s"].append(x_width)
+                n_valid_peaks+=1
+          
+        
+                    
+        self._peak_data["x_peak_s"] = np.asarray(self._peak_data["x_peak_s"])
+        self._peak_data["y_peak_s"] = np.asarray(self._peak_data["y_peak_s"])
+        self._peak_data["y_peak_err_s"] = np.asarray(self._peak_data["y_peak_err_s"])
+        self._peak_data["n_peak_s"] = np.asarray(self._peak_data["n_peak_s"])
+        self._peak_data["x_width_s"] = np.asarray(self._peak_data["x_width_s"])
+        
+        if(n_valid_peaks<3):
+            print("Minimum 3 peaks covering a threshold {0} events required. Too few events in peaks to proceed. Setting fit fail status and continuing...".format(min_n_peak_evts))
+            self._failed = True
+            return
+        
+        elif(self._peak_data["n_peak_s"][0]>0):
+            if(self._verbose):
+                print("Estimated Pedestal had fewer than threshold {0} events. Shifting the pedestal forward...")
+            self._peak_data["n_peak_s"] = self._peak_data["n_peak_s"] - self._peak_data["n_peak_s"][0]
+                        
+                
+        self._GD_data["x"] = x
+        self._GD_data["y"] = y
+        self._GD_data["y_err"] = y_err
+        self._GD_data["bw"] = bw
+        self._GD_data["N"] = N
+        self._GD_data["nbins"] = nbins
+
+                
+
+        self._GD_data["x_s"] = x_s
+        self._GD_data["y_s"] = y_s
+        self._GD_data["y_err_s"] = y_err_s
+        self._GD_data["x_kde_s"] = x_kde_s
+        self._GD_data["y_kde_s"] = y_kde_s
+        self._GD_data["y_kde_err_s"] = y_kde_err_s
+        self._GD_data["bw_s"] = bw_s
+        self._GD_data["N_s"] = N_s
+        self._GD_data["nbins_s"] = nbins_s
+        
+        self._GD_data["fit_GP_mu"] = max(est_mu, self._eps)
+        self._GD_data["fit_GP_mu_err"] = std_err_mu
+        self._GD_data["fit_GP_lbda"] = max(est_lbda, self._eps)
+        self._GD_data["fit_GP_lbda_err"] = std_err_lbda
+        self._GD_data["fit_G"] = est_gain
+        self._GD_data["fit_G_err"] = std_err_gain
+        self._GD_data["fit_x_0"] = self._peak_data["x_peak_s"][0]
+        self._GD_data["fit_x_0_err"] = self._peak_data["x_width_s"][0]
+        self._GD_data["scaler"] = self.scaler
+        self._GD_data["limit_s"] = limit_s
+        
+        
+        
+        
+    
+        
+
+    
+    def PreFitGenPoisToPeaks(self, ppf_mainfit, n_bootstrap):
+        
+
+        
+        ########
+        #STEP 1
+        ########
+
+        
+        fit_lbda = self._GD_data["fit_GP_lbda"]
+        fit_dlbda = self._GD_data["fit_GP_lbda_err"]
+        fit_x_0 = self._GD_data["fit_x_0"]
+        
+        fit_mu = self._GD_data["fit_GP_mu"]
+        fit_dmu = self._GD_data["fit_GP_mu_err"]
+        
+        x_peak = self._peak_data["x_peak_s"]
+        n_peak = self._peak_data["n_peak_s"]
+        y_peak = self._peak_data["y_peak_s"]
+        y_peak_err = self._peak_data["y_peak_err_s"]
+
+        
+        
+        log_y_peak = self.Logify(y_peak)
+        dlog_y_peak = y_peak_err/y_peak
+        
+        fit_A_estspec = gpoisson.logpmf(n_peak, fit_mu, fit_lbda)
+        fit_A = np.nanmean(log_y_peak - fit_A_estspec)
+        fit_dA = np.nanmean(2*dlog_y_peak)
+        
+        
+        
+        
+        fit_dict_pois = {
+                "A": fit_A,
+                "mu":fit_mu,
+                "lbda":fit_lbda,
+        }
+
+        fit_pois = Chi2Regression(
+                            self.DummyLogGPois,
+                            n_peak,
+                            log_y_peak,
+                            dlog_y_peak
+                           )
+
+
+        minuit_pois = Minuit(fit_pois, **fit_dict_pois)
+        minuit_pois.errordef=Minuit.LIKELIHOOD
+        minuit_pois.strategy=2
+
+        
+        
+        minuit_pois.errors["mu"] = fit_dmu
+        minuit_pois.errors["lbda"] = fit_dlbda
+        
+        minuit_pois.limits["lbda"] = (self._eps, 1-self._eps)
+        minuit_pois.limits["mu"] = (self._eps, None)
+        
+        minuit_pois.migrad(ncall= self._n_call_minuit,
+                           iterate =self._n_iterations_minuit)
+        minuit_pois.hesse()
+        
+        
+    
+        
+        
+        
+        if(not minuit_pois.valid):
+            if(self._verbose):
+                print('Minuit returned invalid for Generalised Poisson fit to peaks. Using basic estimated parameters instead...')
+            self._GD_data["fit_GP_mu"] = fit_mu
+            self._GD_data["fit_GP_mu_err"] = fit_dmu
+            self._GD_data["fit_GP_lbda"] = fit_lbda
+            self._GD_data["fit_GP_lbda_err"] = fit_dlbda
+            self._GD_data["fit_GP_A"] = fit_A
+            self._GD_data["fit_GP_A_err"] = fit_dA
+            
+  
+            
+        else:
+            self._GD_data["fit_GP_mu"] = minuit_pois.values["mu"]
+            self._GD_data["fit_GP_mu_err"] = minuit_pois.errors["mu"]
+            self._GD_data["fit_GP_lbda"] = minuit_pois.values["lbda"]
+            self._GD_data["fit_GP_lbda_err"] = minuit_pois.errors["lbda"]
+            self._GD_data["fit_GP_A"] =  minuit_pois.values["A"]
+            self._GD_data["fit_GP_A_err"] =  minuit_pois.errors["A"]
+            
+            
+            
+        k_low = 0
+        k_hi = self.GPoisPPF(ppf_mainfit,
+                             self._GD_data["fit_GP_mu"],
+                             self._GD_data["fit_GP_lbda"],
+                             self._max_n_peaks
+                            )
+
+        self._GD_data["fit_GP_k_low"] = k_low
+        self._GD_data["fit_GP_k_hi"] = k_hi
+
+            
+    
+    
+        if(self._GD_data["fit_GP_k_hi"]<2):
+            if(self._verbose):
+                print("Too few primary Geiger peaks to fit. Artificially increasing max number of peaks from {:d} to 2...".format(self._GD_data["fit_GP_k_hi"]))
+            self._GD_data["fit_GP_k_hi"] = max(self._GD_data["fit_GP_k_hi"], 2)
+            
+        elif(self._GD_data["fit_GP_k_hi"]>self._max_n_peaks):
+            if(self._verbose):
+                print("Too many primary Geiger peaks to fit. Artificially decreasing max number of peaks from {:d} to {:d}...".format(self._GD_data["fit_GP_k_hi"],
+                                                                                                                   self._max_n_peaks))
+            self._GD_data["fit_GP_k_hi"] = min(self._GD_data["fit_GP_k_hi"], self._max_n_peaks)
+
+        
+    
+    
+    
+    def PreFitSigmaAndGain(self, data, n_bootstrap):
+    
+        
+        peak_val = self._peak_data["n_peak_s"]
+        
+        data_s = np.squeeze(self.scaler.transform(data.reshape(-1,1)))
+        
+        self._peak_data["peak_mean_s"]=[]
+        self._peak_data["peak_mean_err_s"]=[]
+        self._peak_data["peak_var_s"]=[]
+        self._peak_data["peak_var_err_s"]=[]
+            
+
+        for x_i, y_peak, x_peak, x_width in zip(self._peak_data["n_peak_s"],
+                                                           self._peak_data["y_peak_s"],
+                                                           self._peak_data["x_peak_s"],
+                                                           self._peak_data["x_width_s"]):
+        
+            data_range = self.SelectRange(data_s, x_peak - x_width/2, x_peak + x_width/2)
+
+
+            est_mean, std_err_mean, _ = self.Bootstrap(data_range,
+                                                           np.mean,
+                                                           n_samples=n_bootstrap)
+
+            est_var, std_err_var, _ = self.Bootstrap(data_range,
+                                                         np.var,
+                                                         n_samples=n_bootstrap)
+
+            self._peak_data["peak_mean_s"].append(est_mean)
+            self._peak_data["peak_mean_err_s"].append(std_err_mean)
+            self._peak_data["peak_var_s"].append(est_var)
+            self._peak_data["peak_var_err_s"].append(std_err_var)
+            
+            
+        self._peak_data["peak_mean_s"] = np.asarray(self._peak_data["peak_mean_s"])
+        self._peak_data["peak_mean_err_s"] = np.asarray(self._peak_data["peak_mean_err_s"])
+        self._peak_data["peak_var_s"] = np.asarray(self._peak_data["peak_var_s"])
+        self._peak_data["peak_var_err_s"] = np.asarray(self._peak_data["peak_var_err_s"])
+        
+        n_peaks_select = self._peak_data["n_peak_s"]
+        peaks_var_select = self._peak_data["peak_var_s"]
+        peaks_var_err_select = self._peak_data["peak_var_err_s"]
+        peaks_mean_select = self._peak_data["peak_mean_s"]
+        peaks_mean_err_select = self._peak_data["peak_mean_err_s"]
+
+        fit_lin_var = HuberRegression(self.DummyLinear,
+                                 n_peaks_select,
+                                 peaks_var_select,
+                                 peaks_var_err_select
+                       )
+                
+        
+        m_var_temp = np.median(np.gradient(peaks_var_select, n_peaks_select))
+        c_var_temp = peaks_var_select[np.argmin(peaks_var_select)]
+        
+        fit_dict_lin_var = {
+                    "m": m_var_temp,
+                    "c": c_var_temp,
+                   }
+        
+       
+        minuit_lin_var = Minuit(fit_lin_var, **fit_dict_lin_var)
+        minuit_lin_var.limits["m"] = (self._eps, None)
+        
+        
+        
+        minuit_lin_var.strategy=2
+        
+        minuit_lin_var.migrad(ncall= self._n_call_minuit,
+                              iterate =self._n_iterations_minuit)
+        minuit_lin_var.hesse()
+        
+        
+        
+        fit_lin_G = HuberRegression(self.DummyLinear,
+                                n_peaks_select,
+                                peaks_mean_select,
+                                peaks_mean_err_select
+                               )
+
+        
+        m_G_temp = self._GD_data["fit_G"]
+        c_G_temp = self._GD_data["fit_x_0"]
+        
+
+        
+        fit_dict_lin_G = {
+                    "m": m_G_temp,
+                    "c": c_G_temp,
+                   }
+
+        
+        
+        minuit_lin_G = Minuit(fit_lin_G, **fit_dict_lin_G)
+        
+        minuit_lin_G.limits["m"] = (self._eps, None)
+        minuit_lin_G.strategy=2
+        
+        minuit_lin_G.migrad(ncall= self._n_call_minuit,
+                            iterate =self._n_iterations_minuit)
+        minuit_lin_G.hesse()
+        
+        
+        
+
+            
+        self._GD_data["fit_var0"] = minuit_lin_var.values["c"]
+        self._GD_data["fit_var0_err"] = minuit_lin_var.errors["c"]
+        self._GD_data["fit_var1"] =  minuit_lin_var.values["m"]
+        self._GD_data["fit_var1_err"] =  minuit_lin_var.errors["m"]
+
+
+        self._GD_data["fit_sigma0"] = np.sqrt(self._GD_data["fit_var0"])
+        self._GD_data["fit_sigma0_err"] = (0.5/self._GD_data["fit_sigma0"])*self._GD_data["fit_var0_err"]
+        self._GD_data["fit_sigma1"] =  np.sqrt(self._GD_data["fit_var1"])
+        self._GD_data["fit_sigma1_err"] = (0.5/self._GD_data["fit_sigma1"])*self._GD_data["fit_var1_err"]
+
+
+        self._GD_data["fit_G"] = minuit_lin_G.values["m"]
+        self._GD_data["fit_G_err"] = minuit_lin_G.errors["m"]
+        self._GD_data["fit_x_0"] = minuit_lin_G.values["c"]
+        self._GD_data["fit_x_0_err"] = minuit_lin_G.errors["c"]
+       
+    
+            
+    
+    
+    def PreFitDCR(self,
+                  data,
+                  tau,
+                  tgate,
+                  r_fast,
+                  t_0,
+                  ppf_mainfit,
+                  kernel_kde,
+                  bandwidth_kde,
+                  n_bootstrap_kde,
+                  dcr_lim=0.05):
+        
+
+        G = self._GD_data["fit_G"]
+        mu = self._GD_data["fit_GP_mu"]
+        lbda = self._GD_data["fit_GP_lbda"]
+        x_0 = self._GD_data["fit_x_0"]
+
+        
+        data_norm = (np.squeeze(self.scaler.transform(data.reshape(-1,1))) - x_0)/G
+        
+
+        try:
+            x_dc_kde, y_dc_kde, y_dc_kde_err, _ = self.BootstrapKDE(
+                                               data_norm,
+                                               kernel=kernel_kde,
+                                               bw=bandwidth_kde,
+                                               n_samples=n_bootstrap_kde                      
+                                            )
+        except:
+            print("DCR KDE failed. Setting fit fail status and continuing...")
+            self._failed = True
+            return
+    
+        
+        cond_interpeak = (x_dc_kde>0) & (x_dc_kde<1)
+        x_dc_kde = x_dc_kde[cond_interpeak]
+        y_dc_kde = y_dc_kde[cond_interpeak]
+        y_dc_kde_err = y_dc_kde_err[cond_interpeak]    
+        
+            
+
+            
+        f_lin_kde_dc = interp1d(x_dc_kde, np.arange(0, len(x_dc_kde)), fill_value='extrapolate')
+        f_lin_kde_dc_inv = interp1d(np.arange(0, len(x_dc_kde)), x_dc_kde, fill_value='extrapolate')
+        
+        f_log_kde_dc = interp1d(x_dc_kde, self.Logify(y_dc_kde), fill_value='extrapolate')
+        f_log_kde_upper_dc = interp1d(x_dc_kde, self.Logify(y_dc_kde + y_dc_kde_err), fill_value='extrapolate')
+        f_log_kde_lower_dc = interp1d(x_dc_kde, self.Logify(y_dc_kde - y_dc_kde_err), fill_value='extrapolate')
+      
+            
+        cond_inrange = (x_dc_kde>0) & (x_dc_kde<1)
+        x_dc_kde = x_dc_kde[cond_inrange]
+        y_dc_kde = y_dc_kde[cond_inrange]
+        y_dc_kde_err = y_dc_kde_err[cond_inrange]
+            
+            
+        x_dc_min = x_dc_kde[np.argmin(y_dc_kde)]
+        x_dc_lim_min_low = x_dc_min - dcr_lim
+        x_dc_lim_min_hi =  x_dc_min + dcr_lim
+        x_dc_lim_low = 0
+        x_dc_lim_mid = x_dc_min
+        x_dc_lim_hi = f_lin_kde_dc_inv(f_lin_kde_dc(0) + 2*abs(f_lin_kde_dc(x_dc_min) - f_lin_kde_dc(0)))
+        
+        
+        cond_interpeak = (x_dc_kde> x_dc_lim_min_low) & (x_dc_kde<=x_dc_lim_min_hi)
+        cond_p_0toLM = (x_dc_kde> x_dc_lim_low) & (x_dc_kde<=x_dc_lim_mid)
+        cond_p_LMto2LM = (x_dc_kde> x_dc_lim_mid) & (x_dc_kde<=x_dc_lim_hi)
+        
+        
+        
+        
+        mean_p_interpeak = (quad(lambda x: np.exp(f_log_kde_dc(x)),
+                              x_dc_lim_min_low,
+                              x_dc_lim_min_hi)[0])/(2*dcr_lim)
+        
+        
+        mean_p_interpeak_upper = (quad(lambda x: np.exp(f_log_kde_upper_dc(x)),
+                              x_dc_lim_min_low,
+                              x_dc_lim_min_hi)[0])/(2*dcr_lim)
+        
+        mean_p_interpeak_lower = (quad(lambda x: np.exp(f_log_kde_lower_dc(x)),
+                              x_dc_lim_min_low,
+                              x_dc_lim_min_hi)[0])/(2*dcr_lim)
+        
+        
+        dmean_p_interpeak =  abs(mean_p_interpeak_upper -  mean_p_interpeak_lower)
+        
+        
+        
+        int_p_0toLM = quad(lambda x: np.exp(f_log_kde_dc(x)),
+                              x_dc_lim_low,
+                              x_dc_lim_mid)[0]
+        
+        int_p_0toLM_upper = quad(lambda x: np.exp(f_log_kde_upper_dc(x)),
+                              x_dc_lim_low,
+                              x_dc_lim_mid)[0]
+        int_p_0toLM_lower = quad(lambda x: np.exp(f_log_kde_lower_dc(x)),
+                              x_dc_lim_low,
+                              x_dc_lim_mid)[0]
+        
+        dint_p_0toLM = abs(int_p_0toLM_upper -  int_p_0toLM_lower)
+        
+        
+        
+        int_p_LMto2LM = quad(lambda x: np.exp(f_log_kde_dc(x)),
+                              x_dc_lim_mid,
+                              x_dc_lim_hi)[0]
+        
+        int_p_LMto2LM_upper = quad(lambda x: np.exp(f_log_kde_upper_dc(x)),
+                              x_dc_lim_mid,
+                              x_dc_lim_hi)[0]
+        int_p_LMto2LM_lower = quad(lambda x: np.exp(f_log_kde_lower_dc(x)),
+                              x_dc_lim_mid,
+                              x_dc_lim_hi)[0]
+        
+        dint_p_LMto2LM = abs(int_p_LMto2LM_upper -  int_p_LMto2LM_lower)
+        
+            
+      
+        int_p = int_p_0toLM + 0.5*int_p_LMto2LM
+        dint_p = np.sqrt(dint_p_0toLM**2 + 0.5*dint_p_LMto2LM**2)
+
+        
+              
+        DCR = mean_p_interpeak/(max(int_p, self._eps)*4*tau)
+        dDCR = DCR*np.sqrt((dmean_p_interpeak/max(mean_p_interpeak, self._eps))**2  + (dint_p/max(int_p, self._eps))**2)
+        
+        
+        mu_dcr=  DCR*(abs(t_0) + tgate)
+        k_dcr_low = 0
+        k_dcr_hi = self.GPoisPPF(ppf_mainfit,
+                                 mu_dcr,
+                                 self._GD_data["fit_GP_lbda"],
+                                 self._max_n_dcr_peaks
                                 )
         
-        if(not f_data.Failed()):
 
-            sd_init = "/beegfs/desy/user/rolphjac/SIPM/Validation/DCRScan/DCR{:3.3E}_{:d}_init.png".format(dcr_orig, i)
-            sd_fit = "/beegfs/desy/user/rolphjac/SIPM/Validation/DCRScan/DCR{:3.3E}_{:d}_fit.png".format(dcr_orig, i)
-            sd_dump = "/beegfs/desy/user/rolphjac/SIPM/Validation/DCRScan/DCR{:3.3E}_{:d}_dump.joblib".format(dcr_orig, i)
-            f_data.PlotSummary(save_directory=sd_init)
-            f_data.PlotFit(scaled=False, save_directory=sd_fit, title="$DCR$={:3.3E} GHz".format(row["DCR"]), xlabel="Input Charge Units [Q.U.]")
-                    #save_directory="./SiPMSpectra2/woTile/{0}_fit.png".format(os.path.splitext(file)[0]))
         
-            dump(f_data, sd_dump)
         
         
+        self._DCR_data["x_dc_kde"] = x_dc_kde
+        self._DCR_data["y_dc_kde"] = y_dc_kde
+        self._DCR_data["y_dc_kde_err"] = y_dc_kde_err
+        
+        
+        self._DCR_data["cond_p_0toLM"] = cond_p_0toLM
+        self._DCR_data["cond_p_LMtoL2M"] = cond_p_LMto2LM
+        self._DCR_data["cond_interpeak"] = cond_interpeak
+
+        self._DCR_data["int_p_0toLM"] = int_p_0toLM
+        self._DCR_data["dint_p_0toLM"] = dint_p_0toLM
+        self._DCR_data["int_p_LMto2LM"] = int_p_LMto2LM
+        self._DCR_data["dint_p_LMto2LM"] = dint_p_LMto2LM
+        self._DCR_data["mean_p_interpeak"] = mean_p_interpeak
+        self._DCR_data["dmean_p_interpeak"] = dmean_p_interpeak
+        
+        
+        self._DCR_data["fit_tau"] = tau
+        self._DCR_data["fit_tgate"] = tgate
+        self._DCR_data["fit_t_0"] = t_0
+        self._DCR_data["fit_DCR"] = DCR
+        self._DCR_data["fit_DCR_err"] = dDCR
+        self._DCR_data["fit_dcr_k_low"] = k_dcr_low
+        self._DCR_data["fit_dcr_k_hi"] = k_dcr_hi
+        if(r_fast is None):
+            self._DCR_data["fit_r_fast"] = 0
+        else:
+            self._DCR_data["fit_r_fast"] = r_fast
+
+        if(self._DCR_data["fit_dcr_k_hi"]<2):
+            print("Too few dark peaks to fit. Artificially increasing max number of peaks from {:d} to 2...".format(self._DCR_data["fit_dcr_k_hi"]))
+            self._DCR_data["fit_dcr_k_hi"] = max(self._DCR_data["fit_dcr_k_hi"], 2)
+            
+        elif(self._DCR_data["fit_dcr_k_hi"]>self._max_n_peaks):
+            print("Too many dark peaks to fit. Artificially decreasing max number of peaks from {:d} to {:d}...".format(self._DCR_data["fit_dcr_k_hi"],
+                                                                                                                   self._max_n_peaks))
+            self._DCR_data["fit_dcr_k_hi"] = min(self._DCR_data["fit_dcr_k_hi"], self._max_n_dcr_peaks)
+
+
+        
+            
+    
+    def InitFit(self,
+                data,
+                tau,
+                t_0,
+                r_fast,
+                tgate,
+                pedestal,
+                n_bootstrap,
+                n_bootstrap_kde,
+                bw,
+                peak_dist_factor,
+                peak_width_factor,
+                prominence,
+                min_n_peak_evts,
+                kernel_kde,
+                bandwidth_kde,
+                ppf_mainfit,
+                bin_method
+               ):
+        
+ 
+            
+
+        
+        if(self._is_histogram):
+            data = np.array(list(chain.from_iterable([[x]*int(y) for x,y in zip(data[:,0], data[:,1])])))
+            
+        
+        if(not self._failed):
+            if(self._verbose):
+                print("Getting histogram and peak values...")
+                
+            self.GetHistAndPeaks(
+                            data,
+                            bw,
+                            peak_dist_factor,
+                            peak_width_factor,
+                            pedestal,
+                            prominence,
+                            bin_method,
+                            n_bootstrap,
+                            n_bootstrap_kde,
+                            bandwidth_kde,
+                            kernel_kde,
+                            min_n_peak_evts)
+
+        if(not self._failed):
+            if(self._verbose):
+                print("Fitting Generalised Poisson distribution...")  
+            self.PreFitGenPoisToPeaks(ppf_mainfit, n_bootstrap)
+            
+        if(not self._failed):
+            if(self._verbose):
+                print("Fitting peak widths via bootstrap resampling...")  
+            self.PreFitSigmaAndGain(data, n_bootstrap)
+            
+        if(not self._failed):
+            if(self._verbose):
+                print("Estimating DCR from interpeak region...")
+            
+            self.PreFitDCR(data,
+                           tau,
+                           tgate,
+                           r_fast,
+                           t_0,
+                           ppf_mainfit,
+                           kernel_kde,
+                           bandwidth_kde,
+                           n_bootstrap_kde)
+        if(not self._failed):
+            if(self._verbose):
+                print("Prefitting complete.")
+            
+          
+
+
+
+    # FIT
+        
+        
+    def Fit(self,
+            data,
+            **kwargs):
+        
+        
+        
+        kwargs_fit =  self._default_fit_kwargs.copy()
+        kwargs_fit.update(kwargs)
+        
+        if(not isinstance(data,np.ndarray)):    
+            raise Exception("Data is in {0} format. Please provide data in the format of a NumPy array.".format(type(data)))
+    
+        
+        else:
+            
+            if(data.ndim==1):
+                if(self._verbose):
+                    print("Data is 1D. Assuming list of charges as input...")
+                self._is_histogram = False
+                
+            elif(data.ndim==2):
+                if(self._verbose):
+                    print("Data is 2D. Assuming histogram as input...")
+                self._is_histogram = True
+            else:
+                raise Exception("Please provide data in the format of either a list of charges (1D) or a histogram (2D)")
+                
+                
+        if(kwargs_fit["t_0"] is None):
+            raise Exception("t_0 is not set. Please set t_0 as a keyword argument to be the integration period before gate in nanoseconds.")
+            
+        if(kwargs_fit["tgate"] is None):
+            raise Exception("tgate is not set. Please set tgate as a keyword argument to be the integration gate length in nanoseconds.")
+            
+        if(kwargs_fit["tau"] is None):
+            raise Exception("tau is not set. Please set tau as a keyword argument to be the slow component of the SiPM pulse in nanoseconds.")
+            
+        if(kwargs_fit["r_fast"] is None):
+            raise Exception("r_fast is not set. Please set r_fast as a keyword argument to be the fraction of the fast component of the SiPM pulse in nanoseconds.")
+      
+        if(kwargs_fit["pedestal"] is None):
+            raise Exception("pedestal is not set. Please set pedestal to be the pedestal position in units of the input charge spectrum being fitted.")
+      
+        
+        print("Performing fit initialisation...")
+        self.Init()
+        self.InitFit(data, **kwargs_fit)
+
+        if(not self._failed):
+                        
+            print("Fitting...")
+            
+            self.N = len(data)
+
+
+            G = self._GD_data["fit_G"]
+            dG = self._GD_data["fit_G_err"]
+            mu = self._GD_data["fit_GP_mu"]
+            dmu = self._GD_data["fit_GP_mu_err"]
+            lbda = self._GD_data["fit_GP_lbda"]
+            dlbda = self._GD_data["fit_GP_lbda_err"]
+            k_low = self._GD_data["fit_GP_k_low"]
+            k_hi = self._GD_data["fit_GP_k_hi"]
+            x_0 = self._GD_data["fit_x_0"]
+            dx_0 = self._GD_data["fit_x_0_err"]
+            sigma_0 = self._GD_data["fit_sigma0"]
+            sigma_1 = self._GD_data["fit_sigma1"]
+            dsigma_0 = self._GD_data["fit_sigma0_err"]
+            dsigma_1 = self._GD_data["fit_sigma1_err"]
+            dx = self._GD_data["bw_s"]
+
+            tau = self._DCR_data["fit_tau"]
+            tgate = self._DCR_data["fit_tgate"]
+            t_0 = self._DCR_data["fit_t_0"]
+            DCR = self._DCR_data["fit_DCR"]
+            dDCR = self._DCR_data["fit_DCR_err"]
+            r_fast = self._DCR_data["fit_r_fast"]
+            k_dcr_low = self._DCR_data["fit_dcr_k_low"]
+            k_dcr_hi =self._DCR_data["fit_dcr_k_hi"]
+
+
+
+            # beta is set to G/2 good guess
+            self.fit_dict = {
+                "mu":mu,
+                "G":G,
+                "x_0": x_0,
+                "sigma_0": sigma_0,
+                "sigma_1": sigma_1,
+                "alpha": 0.2,
+                "r_beta": 0.2,
+                "lbda":lbda,
+                "tau": tau,
+                "tgate":tgate,
+                "t_0": t_0,
+                "DCR":DCR,
+                "r_fast":r_fast,
+                "k_low":k_low,
+                "k_hi":k_hi,
+                "k_dcr_low":k_dcr_low,
+                "k_dcr_hi":k_dcr_hi,
+                "dx": dx,
+            }
+            
+
+            bl = BinnedLH(self.DRM, self._GD_data["x_s"], self._GD_data["y_s"])
+            minuit = Minuit(bl, **self.fit_dict)
+
+            minuit.errors["alpha"] = 0.01
+            minuit.errors["r_beta"] = 0.01
+            if(not np.isnan(dmu) and dmu is not None):
+                minuit.errors["mu"] = abs(dmu)
+            if(not np.isnan(dG) and dG is not None):  
+                minuit.errors["G"] = abs(dG)
+            if(not np.isnan(dx_0) and dx_0 is not None):  
+                minuit.errors["x_0"] = abs(dx_0)                
+            if(not np.isnan(dlbda) and dlbda is not None):
+                minuit.errors["lbda"] = abs(dlbda)
+            if(not np.isnan(dsigma_0) and dsigma_0 is not None):
+                minuit.errors["sigma_0"] = abs(dsigma_0)
+            if(not np.isnan(dsigma_1) and dsigma_1 is not None):
+                minuit.errors["sigma_1"] = abs(dsigma_1)
+            if(not np.isnan(dDCR) and dDCR is not None):
+                minuit.errors["DCR"] = abs(dDCR)
+            
+            
+            minuit.limits["mu"] = (self._eps, max(1, k_hi))
+            minuit.limits["G"] = (self._eps, None)
+            minuit.limits["x_0"] = (x_0-G/2, x_0+G/2)
+            minuit.limits["alpha"] = (self._eps, 1-self._eps)
+            minuit.limits["r_beta"] = (self._eps, 1-self._eps)
+            minuit.limits["lbda"] = (self._eps, 1-self._eps)    
+            minuit.limits["sigma_0"] = (self._eps, G)
+            minuit.limits["sigma_1"] = (self._eps, G)
+            minuit.limits["DCR"] = (self._eps, None)
+            
+  
+
+            minuit.fixed["k_low"] = True
+            minuit.fixed["k_hi"] = True
+            minuit.fixed["tgate"] = True
+            minuit.fixed["tau"] = True
+            minuit.fixed["t_0"] = True
+            minuit.fixed["r_fast"] = True
+            minuit.fixed["dx"] = True
+            minuit.fixed["k_dcr_low"] = True
+            minuit.fixed["k_dcr_hi"] = True
+            minuit.fixed["tgate"] = True
+
+
+            minuit.strategy=2
+            minuit.errordef=minuit.LIKELIHOOD
+            minuit.migrad(ncall= self._n_call_minuit,
+                          iterate =self._n_iterations_minuit)
+            minuit.hesse()
+            
+            if(self._verbose):
+                print(minuit)
+            
+            self._fit_minuit = minuit
+            self._fit_values_s = minuit.values.to_dict()
+            self._fit_errors_s = minuit.errors.to_dict()
+            
+            self._fit_values = {}
+            self._fit_errors = {}
+            
+    
+            
+            for key, value in self._fit_values_s.items():
+                if(key=="x_0"):
+                    self._fit_values[key] = value*self.scaler.scale_[0] + self.scaler.center_[0]
+                elif(key=="G"):
+                    self._fit_values[key] = value*self.scaler.scale_[0]
+                elif(key=="sigma_0"):
+                    self._fit_values[key] = value*self.scaler.scale_[0]
+                elif(key=="sigma_1"):
+                    self._fit_values[key] = value*self.scaler.scale_[0]
+                elif(key=="dx"):
+                    self._fit_values[key] = self._GD_data["bw"]
+                else:
+                    self._fit_values[key] = value
+                    
+                    
+        
+ 
+            for key, value in self._fit_errors_s.items():
+                if(key=="x_0"):
+                    self._fit_errors[key] = value*self.scaler.scale_[0] + self.scaler.center_[0]
+                elif(key=="G"):
+                    self._fit_errors[key] = value*self.scaler.scale_[0]
+                elif(key=="sigma_0"):
+                    self._fit_errors[key] = value*self.scaler.scale_[0]
+                elif(key=="sigma_1"):
+                    self._fit_errors[key] = value*self.scaler.scale_[0]
+                elif(key=="dx"):
+                    self._fit_errors[key] = 0.1*self._GD_data["bw"]
+                else:
+                    self._fit_errors[key] = value
             
             
-            fit_out = {}
-            fit_val, fit_err = f_data.GetFitResults()
-            print("DCR_ORIG={:3.3E} \t DCR_FIT = {:3.3E}".format(dcr_orig, fit_val["DCR"]))
             
-            fit_out["Index"] = i
-            fit_out["dcr_orig"] = dcr_orig
-            for key, value in fit_err.items():
-                fit_out["d_{:s}".format(key)] = value
+            data = []
+            prms = []
+            for par in self._fit_minuit.parameters:
+                if(self._fit_minuit.fixed[par]):
+                    continue
                 
-            fit_out.update(fit_val)
+                prms.append(par)
+                data.append([self._fit_values[par], self._fit_errors[par]])
             
-            if out_dict == {}:
-                out_dict["dcr_orig"]=[]
-                out_dict["Index"]=[]
-                for key in fit_out.keys():
-                    out_dict[key] = []
+            data = np.asarray(data)
+            prms = np.asarray(prms)
             
-            for key in fit_out.keys():
-                out_dict[key].append(fit_out[key])
+            _print = pd.DataFrame(columns = prms,
+                                    index=["Values", "Errors"], data = data.T).T
 
+            _print.style.format({
+              'Values': lambda val: f'{val:,.2E}',
+              'Errors': lambda val: f'{val:,.2E}'
+            })