diff --git a/_PeakOTronAP2.py b/_PeakOTronAP2.py
deleted file mode 100644
index dd2c6601caf8df7acb06ca5f8ae4c121b792d8a7..0000000000000000000000000000000000000000
--- a/_PeakOTronAP2.py
+++ /dev/null
@@ -1,2388 +0,0 @@
-## code starts###
-
-import os
-import numpy as np
-import pandas as pd
-from itertools import chain, zip_longest
-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
-
-
-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:]
-
- if prmt is not None: #rename parameters from the front
- for i, p in enumerate(prmt):
- self.co_varnames[i] = p
-
- 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)
- self.log_eps_inv = np.log(self.eps_inv)
- self.loss = []
-
-
- 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)
- logL = self.y*(self.Logify(y_hat) - self.Logify(self.y)) + (self.y - y_hat)
- nlogL = -np.sum(logL)
-
- self.loss.append(nlogL)
- 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=2000,
- n_iterations_minuit = 20
- ):
-
-
- 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-5
- 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,
- "truncate":True,
- "n_bootstrap": 1000,
- "n_bootstrap_kde":100,
- "bw":None,
- "peak_dist_factor":0.8,
- "peak_width_factor":0.5,
- "peak_ppf":0.95,
- "peak_prominence": 0.0,
- "peak_min_n_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
-
-
-
-
-
-
-
- def DRM_basic(self,
- x,
- mu,
- x_0,
- G,
- lbda,
- sigma_0,
- sigma_1,
- 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([
- gpoisson.pmf(_k, mu, lbda)*norm.pdf(x, x_0 + _k*G, self.SigmaK(_k, sigma_0, sigma_1))
- for _k in k
- ])
-
-
- f = np.sum(np.vstack([f0, f1s]), axis=0)
-
- return f
-
-
- def dPApdPH(self,
- x,
- k,
- x_0,
- G,
- tau,
- r_Ap,
- tgate,
- pAp):
-
- PH = (x - x_0)/G - k
- tauAp = r_Ap*tau
-
- PH_max = 1 + np.exp(-tgate/tau) - 2*np.exp(-0.5*tgate/tau)
-
- cond_PH_max = (PH<PH_max)
-
- PH_temp = np.where(cond_PH_max, PH, PH_max)
-
- t_b0 = 0.5*tgate - tau*np.arccosh(0.5*((1 - PH_temp)*np.exp(tgate/(2*tau)) + np.exp(-tgate/(2*tau))))
- t_b1 = tgate-t_b0
-
- dpApdt_b0 = pAp*(1-np.exp(-t_b0/tau))*np.exp(-t_b0/tauAp)/tauAp
- dpApdt_b1 = pAp*(1-np.exp(-t_b1/tau))*np.exp(-t_b1/tauAp)/tauAp
-
- dPHdt_b0 = -2*np.sinh((t_b0-0.5*tgate)/tau)*np.exp(-0.5*tgate/tau)/tau
- dPHdt_b1 = -2*np.sinh((t_b1-0.5*tgate)/tau)*np.exp(-0.5*tgate/tau)/tau
-
- dpApdPH_b0 = dpApdt_b0/((np.where(np.abs(dPHdt_b0)>1e-5, np.abs(dPHdt_b0), 1e-5)))
- dpApdPH_b1 = dpApdt_b1/((np.where(np.abs(dPHdt_b1)>1e-5, np.abs(dPHdt_b1), 1e-5)))
-
-
-
- dpApdPH_b0 = np.where(cond_PH_max, dpApdPH_b0, 0)
- dpApdPH_b1 = np.where(cond_PH_max, dpApdPH_b1, 0)
-
-
- dpApdPH_b0 = np.where(dpApdPH_b0>0, dpApdPH_b0, 0)
- dpApdPH_b1 = np.where(dpApdPH_b0>0, dpApdPH_b1, 0)
-
-
- return PH_max*(dpApdPH_b0)/G
-
-
-
- def ApplyAp(self,
- x,
- y_light,
- mu,
- x_0,
- G,
- lbda,
- DCR,
- tgate,
- t_0,
- tau,
- r_Ap,
- pAp,
- k_low,
- k_hi,
- k_dcr_low,
- k_dcr_hi):
-
- k = np.arange(k_low, k_hi)
- k = sorted(list(k[k!=0]))
-
- mu_dcr = DCR*(tgate+abs(t_0))
-
- k_dcr = np.arange(k_dcr_low, k_dcr_hi)
- k_dcr = sorted(list(k_dcr[k_dcr!=0]))
-
-
- ps_light = [gpoisson.pmf(_k, mu, lbda) for _k in k]
- ps_dark = [gpoisson.pmf(_k, mu_dcr, lbda) for _k in k_dcr]
-
- f0s = np.asarray([_p_l + _p_d for _p_l, _p_d in zip_longest(ps_light, ps_dark, fillvalue=0)]).reshape(-1,1)
-
-
- f1s = np.asarray([
- _k*self.dPApdPH(x, _k, x_0, G, tau, r_Ap, tgate, pAp)
- for _k in k
- ])
-
- f = np.sum(f0s*f1s, axis=0)
-
-
- return y_light+f
-
-
-
-
-
- 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 dpDCRdPH(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,
- lbda,
- DCR,
- tgate,
- t_0,
- tau,
- r_fast,
- 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.dpDCRdPH(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.dpDCRdPH(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(
- self,
- x,
- mu,
- lbda,
- x_0,
- G,
- sigma_0,
- sigma_1,
- pAp,
- k_low,
- k_hi,
- DCR,
- tau,
- r_Ap,
- tgate,
- t_0,
- r_fast,
- dx,
- k_dcr_low,
- k_dcr_hi,
- ):
-
-
-
- y_light = self.DRM_basic(x,
- mu,
- x_0,
- G,
- lbda,
- sigma_0,
- sigma_1,
- k_low,
- k_hi)
-
- y_lightap = self.ApplyAp(x,
- y_light,
- mu,
- x_0,
- G,
- lbda,
- DCR,
- tgate,
- t_0,
- tau,
- r_Ap,
- pAp,
- k_low,
- k_hi,
- k_dcr_low,
- k_dcr_hi)
-
-
-
- y_model = self.ApplyDCR(x,
- y_lightap,
- x_0,
- G,
- lbda,
- DCR,
- tgate,
- t_0,
- tau,
- r_fast,
- 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.grid(which="both")
- 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.grid(which="both")
- 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="--")
-
-
- if(self._GD_data["truncated"]):
- 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")
- ax.grid(which="both")
-
-
- 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))
- ax.grid(which="both")
-
-
-
- def PlotSigmaEst(self, ax):
-
- c = self._peak_data["cond_valid_peak"]
-
- 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"][c],
- self._peak_data["peak_var_s"][c],
- yerr=self._peak_data["peak_var_err_s"][c],
- label="Extracted Variances",
- fmt="o")
-
-
- ax.plot(self._peak_data["n_peak_s"][c],
- self.DummyLinear(self._peak_data["n_peak_s"][c],
- 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))
- ax.grid(which="both")
-
-
-
- def PlotGainEst(self, ax):
-
- c = self._peak_data["cond_valid_peak"]
-
- 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"][c],
- self._peak_data["peak_mean_s"][c],
- yerr=self._peak_data["peak_mean_err_s"][c],
- label="Extracted Peak Means",
- fmt="o")
-
-
- ax.plot(self._peak_data["n_peak_s"][c],
- self.DummyLinear(self._peak_data["n_peak_s"][c],
- 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))
- ax.grid(which="both")
-
-
-
-
-
-
- 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.grid(which="both")
- 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.grid(which="both")
- 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)
- ax1.grid(which="both")
-
- fig.tight_layout()
-
- fig.subplots_adjust(hspace=.0)
- if(save_directory is not None):
- print("Saving figure to {0}...".format(save_directory))
- fig.savefig(save_directory)
- if(display):
- plt.pause(0.01)
- fig.show()
- else:
- plt.close(fig)
-
-
-
- def PlotSummary(self, display=True, 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):
- plt.pause(0.01)
- fig.show()
- else:
- plt.close(fig)
-
-
- 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,
- peak_ppf,
- peak_prominence,
- peak_min_n_evts,
- truncate,
- pedestal,
- bin_method,
- n_bootstrap,
- n_bootstrap_kde,
- bandwidth_kde,
- kernel_kde):
-
-
- 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)
-
-
-
-
- if(nbins<50):
- print("Binning with bw = {:3.3E} produced only {:d} bins, less than limit of 50 bins. Setting fit fail status and continuing...".format(bw, nbins))
- self._failed = True
- return
-
- #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))
-
-
- limit_s = pedestal_s - 0.5*est_gain
- limit = limit_s*self.scaler.scale_[0] + self.scaler.center_[0]
-
- if(truncate):
-
- cond_limit = (x>limit)
- cond_limit_s = (x_s>limit_s)
- cond_limit_kde_s = (x_kde_s>limit_s)
-
- x = x[cond_limit]
- y = y[cond_limit]
- y_err = y_err[cond_limit]
-
- x_s = x_s[cond_limit_s]
- y_s = y_s[cond_limit_s]
- y_err_s = y_err_s[cond_limit_s]
-
- x_kde_s = x_kde_s[cond_limit_kde_s]
- y_kde_s = y_kde_s[cond_limit_kde_s]
- y_kde_err_s = y_kde_err_s[cond_limit_kde_s]
-
- if(self._verbose):
- print("Truncated distributions to only fit PH > pedestal - statistical gain/2 ...")
-
-
-
-
- peaks, properties = find_peaks(y_kde_s,
- distance=peak_dist_factor*est_gain_lin,
- prominence=peak_prominence
- )
-
-
-
-
- if(len(peaks)<3):
- print("Not enough peaks found with current distance criterion to fit. Setting fit fail status and continuing..")
- self._failed=True
- return
-
-
-
-
- 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"] = []
-
-
- if(peak_ppf is not None):
- peak_threshold = self.GPoisPPF(peak_ppf, est_mu, est_lbda, self._max_n_peaks)
- if(self._verbose):
- print("For {:3.3}% of distribution, accepting all peaks below {:d}".format(peak_ppf*100.0, peak_threshold))
- else:
- peak_threshold = 0
- print("Accepting all peaks.")
-
- 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(x_i>peak_threshold and N_evt_peak<peak_min_n_evts):
- if(self._verbose):
- print("Peak {:d} has {:d} events. Less than event threshold {:d}. Ignoring...".format(x_i,
- N_evt_peak,
- peak_min_n_evts))
- else:
- if(self._verbose):
- print("Peak {:d} has {:d} events. Adding...".format(x_i,
- N_evt_peak,
- peak_min_n_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(peak_min_n_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
- self._GD_data["truncated"] = truncate
-
-
-
-
-
-
-
-
- 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"]=[]
-
- cond_peak = np.ones(len(peak_val), bool)
- 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)
-
- try:
- 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)
- cond_peak[x_i]=True
- except:
- cond_peak[x_i]=False
-
-
- self._peak_data["peak_mean_s"].append(None)
- self._peak_data["peak_mean_err_s"].append(None)
- self._peak_data["peak_var_s"].append(None)
- self._peak_data["peak_var_err_s"].append(None)
-
- if(sum(cond_peak)<3):
- print("Fewer than 3 peaks had adequate statistics to determine peak width. Setting fit fail status...")
- self._failed=True
- return
-
- self._peak_data["cond_valid_peak"] = cond_peak
- 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"][cond_peak]
- peaks_var_select = self._peak_data["peak_var_s"][cond_peak]
- peaks_var_err_select = self._peak_data["peak_var_err_s"][cond_peak]
- peaks_mean_select = self._peak_data["peak_mean_s"][cond_peak]
- peaks_mean_err_select = self._peak_data["peak_mean_err_s"][cond_peak]
-
- 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(np.abs(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(np.abs(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 = min(1-self._eps, 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
- )
-
-
-
-
-
- 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,
- peak_ppf,
- peak_prominence,
- peak_min_n_evts,
- truncate,
- 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,
- peak_ppf,
- peak_prominence,
- peak_min_n_evts,
- truncate,
- pedestal,
- bin_method,
- n_bootstrap,
- n_bootstrap_kde,
- bandwidth_kde,
- kernel_kde)
-
- 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,
- "r_Ap": 0.25,
- "pAp": 0.25,
- "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["pAp"] = 0.05
- minuit.errors["r_Ap"] = 0.05
- 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)+0.01
- if(not np.isnan(dsigma_1) and dsigma_1 is not None):
- minuit.errors["sigma_1"] = abs(dsigma_0)+0.01
- 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"] = (G-5*dG, G+5*dG)
- minuit.limits["x_0"] = (x_0-G/2, x_0+G/2)
- minuit.limits["r_Ap"] = (self._eps, 1-self._eps)
- minuit.limits["pAp"] = (self._eps, 1-self._eps)
- minuit.limits["lbda"] = (self._eps, 1-self._eps)
- minuit.limits["sigma_0"] = (self._eps, G-self._eps)
- minuit.limits["sigma_1"] = (self._eps, G-self._eps)
- minuit.limits["DCR"] = (self._eps, 1)
-
-
-
- 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.simplex()
- minuit.tol=1
- minuit.migrad(ncall= self._n_call_minuit,
- iterate =self._n_iterations_minuit)
-
-
- minuit.hesse()
-
- print("tauAp = {:3.3f} ns".format(minuit.values["tau"]*minuit.values["r_Ap"]))
-
-
- 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
-
-
-
- data = []
- prms = []
- for par in self._fit_minuit.parameters:
- if(self._fit_minuit.fixed[par]):
- continue
-
- prms.append(par)
- data.append([self._fit_values[par], self._fit_errors[par]])
-
- data = np.asarray(data)
- prms = np.asarray(prms)
-
- _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}'
- })
-
-