diff --git a/Bootstrapping.py b/Bootstrapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..74362f63f52e4cfad202d0637bfa504f7c2dd2ff
--- /dev/null
+++ b/Bootstrapping.py
@@ -0,0 +1,154 @@
+from HelperFunctions import FakeFuncCode, SelectRangeNumba, EmpiricalPPF
+from KDEpy import FFTKDE
+import numpy as np
+from scipy.interpolate import interp1d
+from iminuit import Minuit
+from astropy.stats import bootstrap, scott_bin_width
+import scipy.special as sc
+import logging
+
+
+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):
+
+ x_kde, y_kde = FFTKDE(kernel = self.kernel, bw=bw).fit(self.data).evaluate(self.n_kde_samples)
+ loss = -np.mean(np.log(y_kde))
+ return loss
+
+
+
+ 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
+ PPF = EmpiricalPPF(self.data)
+ x_low, x_hi = PPF(0.5-alpha/2), PPF(0.5+alpha/2)
+ self.data = SelectRangeNumba(self.data, x_low, x_hi)
+ 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
+
+
+def Bootstrap(data, statistic, n_bootstrap, alpha=0.95, ):
+ if not (0 < alpha < 1):
+ raise ValueError("confidence level must be in (0, 1)")
+
+
+ if len(data) < 1:
+ raise ValueError("data must contain at least one measurement.")
+
+
+ boot_stat = bootstrap(data, n_bootstrap, 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(data,
+ n_bootstrap,
+ n_call=1000,
+ n_iterations=10,
+ kernel = "gaussian",
+ n_kde_samples=2**14,
+ alpha=0.95,
+ bw_limits=(1e-6, None),
+ limits=None,
+
+ ):
+
+ if not (0 < alpha < 1):
+ raise ValueError("Bootstrap confidence level, alpha, must be in (0, 1).")
+
+ if len(data) <= 3:
+ raise ValueError("Bootstrap data must contain at least three measurements.")
+
+ try:
+ logging.debug("Optimising bandwidth...")
+ BWO= BandWidthOptimiser(data, kernel=kernel)
+ bw_scott = scott_bin_width(data)
+ minuit_kde = Minuit(BWO, bw=bw_scott/2)
+ minuit_kde.limits["bw"]=bw_limits
+ minuit_kde.strategy=2
+ minuit_kde.migrad(ncall=n_call,
+ iterate=n_iterations)
+
+ bw = minuit_kde.values["bw"]
+ logging.debug("Bandwidth optimised to {:3.3E} units.".format(bw))
+ except:
+ logging.warning("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_bootstrap)
+ 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 Bootstrap(data, statistic, n_bootstrap, alpha=0.95):
+ if not (0 < alpha < 1):
+ raise ValueError("confidence level must be in (0, 1)")
+
+
+ if len(data) < 1:
+ raise ValueError("data must contain at least one measurement.")
+
+
+ boot_stat = bootstrap(data, n_bootstrap, 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
\ No newline at end of file
diff --git a/Example.py b/Example.py
new file mode 100644
index 0000000000000000000000000000000000000000..3da5c9f9770157991e72ad278d6f3fe2c1670854
--- /dev/null
+++ b/Example.py
@@ -0,0 +1,96 @@
+import os
+import sys
+import numpy as np
+import pandas as pd
+from PeakOTron import PeakOTron
+from joblib import dump
+
+
+print("--------------------")
+print('EXAMPLE SIPM CALIBRATION RUN')
+print("--------------------")
+
+f_data = PeakOTron(verbose=False)
+f_data.SetMaxNPeaks(15) #SET MAX N PEAKS - THRESHOLDING REQUIRED WHERE LAMBDA INCORRECTLY ESTIMATED, OR FIT WILL GO ON FOREVER.
+f_data.SetMaxNDCRPeaks(6)
+
+out_dict = {}
+files_to_fit = []
+for root, dirs, files in os.walk("./data/hamamatsu_pcb6/Light"):
+
+ for file in files:
+
+ if file.endswith(".txt"):
+ files_to_fit.append([file, os.path.join(root, file)])
+
+
+print("Files to fit:")
+for i, (file, _) in enumerate(files_to_fit):
+ print('File {0}: {1}'.format(i, file))
+
+
+
+SiPM = "PM1125NS_SBO"
+
+for i, (file, path) in enumerate(files_to_fit):
+ items = file.split('_')
+
+ V = float(items[2].replace('V', '').replace('p', '.'))
+
+
+ while (True):
+
+
+
+
+ print("\n Fit {0}: {1} \n".format(i, file))
+
+ data = np.loadtxt(path, skiprows=8)
+
+ f_data.Fit(data,
+ tau=21.953, #SLOW PULSE COMPONENT TIME CONSTANT (ns)
+ tau_err=5.648,
+ t_gate=104, #GATE LENGTH (ns)
+ t_0 = 64, #INTEGRATION TIME BEFORE GATE (ns)
+
+ ) #BINNING RULE "knuth", "freedman", "scott" - use bw= #### to override. it is still required for DCR calculation.
+
+
+ f_data.PlotFit(xlabel="Charge [Vs]", save_directory="./Results/{0}_fit.png".format(os.path.splitext(file)[0]))
+
+ chi2, ndf = f_data.GetChi2()
+ if(V-51.1>3):
+ if(chi2/ndf<4):
+ break
+ else:
+ if(chi2/ndf<10):
+ break
+
+ dump(f_data, "./Results/{0}".format(os.path.splitext(file)[0]))
+
+
+
+ fit_out = {}
+ fit_val, fit_err = f_data.GetFitResults()
+ for key, val in fit_val.items():
+ print("{:s} : {:3.3E}".format(key, val))
+
+ fit_out["SiPM"] = SiPM
+ fit_out["V"] = V
+
+ for key, value in fit_err.items():
+ fit_out["d_{:s}".format(key)] = value
+
+ fit_out.update(fit_val)
+ out_dict.update()
+ if out_dict == {}:
+ for key in fit_out.keys():
+ out_dict[key] = []
+
+ for key in fit_out.keys():
+ out_dict[key].append(fit_out[key])
+
+
+
+df = pd.DataFrame.from_dict(out_dict)
+df.to_csv("./fit_results_{:s}.csv".format(SiPM))
diff --git a/HelperFunctions.py b/HelperFunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a631f7605fd1e1a85aa592fa0d2dfd154130a42
--- /dev/null
+++ b/HelperFunctions.py
@@ -0,0 +1,154 @@
+from itertools import chain
+from iminuit import Minuit
+from numba import njit
+from iminuit.util import describe
+from scipy.interpolate import interp1d
+import numpy as np
+from scipy.stats import skew as sc_skew
+import matplotlib.pyplot as plt
+
+
+#HELPER FUNCTIONS
+
+
+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:]))
+
+
+
+
+def LatexFormat(value, scirange=[0.01,1000]):
+ if(np.abs(value)>scirange[0] and np.abs(value)<scirange[1]):
+ float_str = r"${:3.3f}$".format(value)
+ else:
+ try:
+ float_str = "{:3.3E}".format(value)
+ base, exponent = float_str.split("E")
+ float_str = r"${0} \times 10^{{{1}}}$".format(base, int(exponent))
+ except:
+ float_str=str(value)
+ return float_str
+
+
+@njit
+def SelectRangeNumba(array, low_lim, hi_lim):
+ index = (array >= low_lim) & (array <= hi_lim)
+ return array[index]
+
+
+def EmpiricalPPF(data):
+ x = np.sort(data)
+ n = x.size
+ y = np.arange(1, n+1) / n
+ ppf = interp1d(y, x, fill_value="extrapolate")
+ return ppf
+
+
+def EmpiricalCDF(data):
+ x = np.sort(data)
+ n = x.size
+ y = np.arange(1, n+1) / n
+ ppf = interp1d(x, y, fill_value="extrapolate")
+ return ppf
+
+
+def Logify(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 Linear(x, m, c):
+ return m*x + c
+
+def GetStats(data):
+ return np.mean(data), np.std(data), sc_skew(data)
+
+
+
+def GP_lbda(mu, sigma, gamma):
+ lbda = 0.5*(((mu*gamma)/(sigma))- 1)
+ return lbda
+
+def GP_gain(mu, sigma, gamma):
+ lbda = GP_lbda(mu, sigma, gamma)
+ gain = (sigma**2/mu)*((1 - lbda)**2)
+ return gain
+
+def GP_muGP(mu, sigma, gamma):
+ lbda = GP_lbda(mu, sigma, gamma)
+ mu_gp = (1/(1-lbda))*(mu**2/sigma**2)
+ return mu_gp
+
+
+
+def FormatExponent(ax, axis='y'):
+
+ # Change the ticklabel format to scientific format
+ ax.ticklabel_format(axis=axis, style='sci', scilimits=(-2, 2))
+
+ # Get the appropriate axis
+ if axis == 'y':
+ ax_axis = ax.yaxis
+ x_pos = 0.0
+ y_pos = 1.0
+ horizontalalignment='left'
+ verticalalignment='bottom'
+ else:
+ ax_axis = ax.xaxis
+ x_pos = 1.0
+ y_pos = -0.05
+ horizontalalignment='right'
+ verticalalignment='top'
+
+ # Run plt.tight_layout() because otherwise the offset text doesn't update
+ plt.tight_layout()
+ ##### THIS IS A BUG
+ ##### Well, at least it's sub-optimal because you might not
+ ##### want to use tight_layout(). If anyone has a better way of
+ ##### ensuring the offset text is updated appropriately
+ ##### please comment!
+
+ # Get the offset value
+ offset = ax_axis.get_offset_text().get_text()
+
+ if len(offset) > 0:
+ # Get that exponent value and change it into latex format
+ minus_sign = u'\u2212'
+ expo = np.float(offset.replace(minus_sign, '-').split('e')[-1])
+ offset_text = r'x$\mathregular{10^{%d}}$' %expo
+
+ # Turn off the offset text that's calculated automatically
+ ax_axis.offsetText.set_visible(False)
+
+ # Add in a text box at the top of the y axis
+ ax.text(x_pos, y_pos, offset_text, transform=ax.transAxes,
+ horizontalalignment=horizontalalignment,
+ verticalalignment=verticalalignment)
+ return ax
+
+
+
+
+
+
diff --git a/LossFunctions.py b/LossFunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb9c4ef6d00c9188a4f41da8d8aa19b35a0122d3
--- /dev/null
+++ b/LossFunctions.py
@@ -0,0 +1,145 @@
+import numpy as np
+from scipy.interpolate import interp1d
+from iminuit.util import describe
+from HelperFunctions import FakeFuncCode
+import matplotlib.pyplot as plt
+
+
+class UnbinnedLH:
+
+
+ def __init__(self, f, data, bw):
+ self.f = f
+ self.x_min = np.min(data)
+ self.x_max = np.max(data)
+ self.n_bins = np.ceil((np.max(data) - np.min(data))/bw).astype(int)
+ self.x = np.linspace(self.x_min, self.x_max, 10*self.n_bins)
+ self.data = data
+ self.last_arg = None
+ self.func_code = FakeFuncCode(f, dock=True)
+ self.ndof = len(self.data) - (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.n_calls = 0
+
+
+ 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)
+
+ log_y_hat = interp1d(self.x,
+ self.Logify(y_hat),
+ fill_value=(self.log_eps,self.log_eps),
+ bounds_error=False)(self.data)
+
+
+
+ nlogL = -np.sum(log_y_hat)
+
+ self.n_calls+=1
+
+# if(self.n_calls%200==0):
+# print(arg)
+# plt.figure()
+# plt.hist(self.data, bins=200, density=True)
+# plt.plot(self.x, y_hat)
+# plt.yscale("log")
+# plt.show()
+
+ return nlogL
+
+
+
+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=1.345):
+ 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)
\ No newline at end of file
diff --git a/Model_AP1.py b/Model_AP1.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e858ccca686419f5ec0428fd2a5100d4dff9810
--- /dev/null
+++ b/Model_AP1.py
@@ -0,0 +1,263 @@
+import numpy as np
+from scipy.signal import fftconvolve as convolve
+from scipy.stats import norm, binom, poisson,expon
+from scipy.interpolate import interp1d
+from AdditionalPDFs import borel, gpoisson
+import matplotlib.pyplot as plt
+
+
+def SigmaK(k, sigma_0, sigma_1):
+ return np.sqrt(sigma_0**2 + k*sigma_1**2)
+
+
+
+def dPApdPH(x, x_0, G, mu, lbda, tauAp, pAp, tau, t_gate, k_low, k_hi):
+
+ x_lin = np.arange(0, len(x))
+ x_pad_lin = np.arange(-100, len(x)+100)
+ f_lin = interp1d(x, x_lin, fill_value='extrapolate')
+ f_lin_inv = interp1d(x_lin, x, fill_value='extrapolate')
+ dx = abs(f_lin_inv(1) - f_lin_inv(0))
+ G_lin = f_lin(x_0+G) - f_lin(x_0)
+ x_0_lin = f_lin(x_0)
+ y_ap = np.zeros_like(x_pad_lin).astype(float)
+
+ 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))
+
+
+ exp_tgate_2tau = np.exp(t_gate/(2*tau))
+ exp_mtgate_2tau = np.exp(-t_gate/(2*tau))
+ PH_norm = (x_pad_lin - x_0_lin)/G_lin
+ PH_max = (1 - exp_mtgate_2tau)**2
+ norm_factor = PH_max/G_lin
+
+
+ for _n_pg in range(1, int(k_hi)+1):
+ _p_pg = gpoisson.pmf(_n_pg, mu, lbda)
+ for _n_ap in range(1, min(5,_n_pg+1)):
+
+ PH = (PH_norm - _n_pg)/_n_ap
+ cond_PH_max = (PH>0.95*PH_max)
+
+
+ PH[cond_PH_max] = 0.95*PH_max
+
+
+ t_b0 = 0.5*t_gate - tau*np.arccosh(0.5*((1 - PH)*exp_tgate_2tau + exp_mtgate_2tau))
+
+ cond_gate = (t_b0<0) | (t_b0>t_gate/2)
+
+ dpApdt_b0 = _p_pg*binom.pmf(_n_ap, _n_pg, pAp*(1-np.exp(-t_b0/tau)))*expon.pdf(t_b0, loc=0, scale=tauAp)
+ dpApdt_b0[cond_gate] = 0
+
+ dPHdt_b0 = abs(-2*np.sinh((t_b0-0.5*t_gate)/tau)*exp_mtgate_2tau/tau)
+
+ cond_dPHdt = (dPHdt_b0<1e-3)
+ dPHdt_b0[cond_dPHdt] = 1e-3
+
+ dpApdPH_b0 = dpApdt_b0/dPHdt_b0
+
+ dpApdPH_b0[cond_PH_max] = 0
+ dpApdPH_b0[cond_gate] = 0
+
+
+
+ y_ap+=dpApdPH_b0*(norm_factor/_n_ap)
+
+ y_ap = y_ap[idx_orig]/dx
+
+ return y_ap
+
+
+
+
+
+
+def DRM_basic(x, x_0, G, mu, lbda, sigma_0, sigma_1, DCR, tau, t_gate, t_0, tauAp, pAp, k_low, k_hi):
+
+
+ n_pg = np.arange(1, k_hi)
+
+ 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, SigmaK(_k, sigma_0, sigma_1))
+ for _k in n_pg
+ ])
+
+
+ f_light = np.sum(np.vstack([f0, f1s]), axis=0)
+ f_ap = dPApdPH(x, x_0, G, mu, lbda, tauAp, pAp, tau, t_gate, k_low, k_hi)
+
+
+ return f_light + f_ap
+
+
+
+
+def DC_PH_range(t, t_0, r_fast, tau, t_gate):
+
+ if((t>t_0) and (t<0)):
+ PH_min = (1-r_fast)*np.exp(t_0/tau)*(1 - np.exp(-t_gate/tau))
+ PH_max = (1-r_fast)*(1 - np.exp(-t_gate/tau))
+ elif((t>0) and (t<t_gate)):
+ PH_min = r_fast
+ PH_max = (1 - (1-r_fast)*np.exp(-t_gate/tau))
+ else:
+ PH_min = 0
+ PH_max = 0
+
+ return PH_min, PH_max
+
+
+def dpDCRdPH(x, x_0, G, tau, t_gate, t_0):
+
+ PH_bfg_low, PH_bfg_hi = DC_PH_range(-abs(t_0)/2, -abs(t_0), 0, tau, t_gate)
+ PH_dg_low, PH_dg_hi = DC_PH_range(t_gate/2, -abs(t_0), 0, tau, t_gate)
+
+ 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(x, y_light, x_0, G, DCR, t_gate, t_0, tau, lbda, k_dcr_low, k_dcr_hi):
+
+ mu_dcr = DCR*(abs(t_0) + t_gate)
+
+ x_lin = np.arange(0, len(x))
+ x_pad_lin = np.arange(-100, len(x)+100)
+ f_lin = interp1d(x, x_lin, fill_value='extrapolate')
+ f_lin_inv = interp1d(x_lin, x, fill_value='extrapolate')
+ dx = abs(f_lin_inv(1) - f_lin_inv(0))
+ 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))
+
+
+ fs = []
+ pfs = []
+ hs = []
+ phs = []
+
+
+
+ for _n_dcr in range(0, max(2, int(k_dcr_hi))):
+
+ 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(_n_dcr==1):
+ f = dpDCRdPH(x_pad_lin, x_0_lin, G_lin, tau, t_gate, t_0)
+ 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)
+
+
+
+ if(_n_dcr==1):
+ h = np.zeros(len(x_pad_lin))
+ ph=0
+ else:
+ h = dpDCRdPH(x_pad_lin, x_0_lin, G_lin*((_n_dcr-1)+1), tau, t_gate, t_0)
+ h = h/ np.trapz(h, dx = 1)
+ ph = poisson.pmf(1, mu_dcr)*borel.pmf((_n_dcr-1), lbda)/((_n_dcr-1)+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_dark = y_dark/np.trapz(y_dark, dx = 1)
+
+
+ y_model = convolve(y_dark,
+ np.pad(y_light,
+ (100, 100),
+ "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(
+ x,
+ mu,
+ x_0,
+ G,
+ sigma_0,
+ sigma_1,
+ tauAp,
+ pAp,
+ lbda,
+ k_low,
+ k_hi,
+ k_dcr_low,
+ k_dcr_hi,
+ DCR,
+ tau,
+ t_gate,
+ t_0
+ ):
+
+
+ y_light = DRM_basic(x, x_0, G, mu, lbda, sigma_0, sigma_1, DCR, tau, t_gate, t_0, tauAp, pAp, k_low, k_hi)
+
+ y_model = ApplyDCR(x, y_light, x_0, G, DCR, t_gate, t_0, tau, lbda, k_dcr_low, k_dcr_hi)
+
+
+
+ return y_model
diff --git a/PeakOTron.py b/PeakOTron.py
index 72fa55249fe5698e7536216aa870b2a7a922556e..09abb78d75ea9e3813a60de0d530634b271fb2da 100644
--- a/PeakOTron.py
+++ b/PeakOTron.py
@@ -1,390 +1,145 @@
-## code starts###
-
-import os
-import numpy as np
-import pandas as pd
-from itertools import chain
+from Bootstrapping import BootstrapKDE, Bootstrap
+from HelperFunctions import GP_gain, GP_lbda, GP_muGP, GetStats
+from HelperFunctions import LatexFormat, Linear, SelectRangeNumba
+from LossFunctions import *
+from Model_AP1 import DRM
from iminuit import Minuit
-from iminuit.util import describe
+from scipy.signal import find_peaks, peak_widths
+from scipy.integrate import cumtrapz, quad
+from scipy.interpolate import interp1d
+from scipy.stats import poisson
+from scipy.stats import skew as sp_skew
+from scipy.stats import kurtosis as sp_kurt
+from astropy.stats import knuth_bin_width, freedman_bin_width, scott_bin_width
+from matplotlib import cm
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 UnbinnedLH:
-
-
- def __init__(self, f, data, x):
- self.f = f
- self.x = x
- self.data = data
- self.last_arg = None
- self.func_code = FakeFuncCode(f, dock=True)
- self.ndof = len(self.data) - (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)
- log_y_hat = interp1d(self.x,
- self.Logify(y_hat),
- fill_value=(self.log_eps,self.log_eps),
- bounds_error=False)(self.data)
-
- nlogL = -np.sum(log_y_hat)
-
-
- return nlogL
-
-
-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)
-
+from matplotlib.ticker import MaxNLocator, ScalarFormatter
+from itertools import chain
- 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)
-
+import matplotlib.pyplot as plt
-@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
+ n_call_minuit=20000,
+ n_iterations_minuit = 50,
+ n_bootstrap=1000
):
+ self._default_hist_data={
+ "count":None,
+ "density":None,
+ "bin_numbers":None,
+ "bin_centers":None,
+ "bw":None,
+ "peaks":{
+ "peak_position":None,
+ "peak_position_lower":None,
+ "peak_position_upper":None,
+ "peak_height":None,
+ "peak_height_lower":None,
+ "peak_height_upper":None,
+ "peak_mean":None,
+ "peak_mean_error":None,
+ "peak_variance":None,
+ "peak_variance_error":None,
+ "peak_std_deviation":None,
+ "peak_std_deviation_error":None,
+ "peak_skewness":None,
+ "peak_skewness_error":None,
+ "peak_kurtosis":None,
+ "peak_kurtosis_error":None,
+ },
+ "bc2bn":None,
+ "bn2bc":None,
+ "bn2kde":None,
+ "bn2kde_err":None,
+ "bn2bg_sub":None
+ }
+
+ self._default_fit_dict={
+ "x_0":None,
+ "G":None,
+ "lbda":None,
+ "mu":None,
+ "sigma_0":None,
+ "sigma_1":None,
+ "DCR":None,
+ "tau":None,
+ "t_gate":None,
+ "tauAp":None,
+ "pAp":None,
+ "t_0":None,
+ "k_low":None,
+ "k_hi":None,
+ "k_dcr_low":None,
+ "k_dcr_hi":None
+
+ }
- 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._plot_fontsize= 25
+ self._plot_legendfontsize= 18
self._cmap = cm.get_cmap('viridis')
-
- self._max_n_peaks = 12
- self._max_n_dcr_peaks = 6
+ #self._eps = np.finfo(np.float64).eps * 10
+ self._eps = 1e-5
+
+ self._n_bootstrap=n_bootstrap
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._failed = False
self._default_fit_kwargs={
"tau":None,
+ "tau_err":None,
"t_0":None,
- "r_fast":None,
- "tgate":None,
- "n_bootstrap": 1000,
- "n_bootstrap_kde":100,
+ "t_0_err":None,
+ "t_gate":None,
+ "t_gate_err":None,
"bw":None,
"peak_dist_factor":0.8,
"peak_width_factor":0.5,
- "peak_ppf":0.99,
- "peak_prominence":1e-3,
- "kernel_kde":"gaussian",
- "bandwidth_kde":"optimise",
"ppf_mainfit":1 - 1e-6,
"bin_method":"knuth",
- "alpha":0.95
+ "alpha_peaks":0.99,
+ "alpha_fit":1-1e-6,
}
-
-
-
+ self.Init()
- 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._fit_kwargs = self._default_fit_kwargs.copy()
+ self._prefit_values = self._default_fit_dict.copy()
+ self._prefit_errors = self._default_fit_dict.copy()
+ self._fit_values= self._default_fit_dict.copy()
+ self._fit_errors = self._default_fit_dict.copy()
- self._peak_data = {}
- self._GD_data = {}
- self._DCR_data={}
-
-
- self._fit_values= {}
- self._fit_errors = {}
+ self._hist_data = self._default_hist_data.copy()
+
+
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):
@@ -394,9 +149,8 @@ class PeakOTron:
self._max_n_peaks = max_n_peaks
- print("Set maximum number of peaks to {:d}.".format(self._max_n_peaks))
-
-
+ if(self._verbose):
+ print("Set maximum number of peaks to {:d}.".format(self._max_n_peaks))
def SetMaxNDCRPeaks(self, max_n_dcr_peaks):
@@ -406,903 +160,433 @@ class PeakOTron:
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
+ if(self._verbose):
+ print("Set maximum number of dark peaks to {:d}.".format(self._max_n_dcr_peaks))
- 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 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 EmpiricalPPF(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 FFTGain(self, x_kde, y_kde):
- n = len(x_kde)
- dt = abs(x_kde[1]-x_kde[0])
- fhat = np.fft.fft(y_kde, n) #computes the fft
- psd = fhat * np.conj(fhat)/n
- freq = (1/(dt*n)) * np.arange(n) #frequency array
- idxs_half = np.arange(1, np.floor(n/2), dtype=np.int32)
-
- y = np.abs(psd[idxs_half])
- x = freq[idxs_half]
-
- peaks, _= find_peaks(y)
- idx_sort = np.argsort(y[peaks])
- y_peaks = y[peaks][idx_sort]
- x_peaks = x[peaks][idx_sort]
-
- x_peak = x_peaks[-1]
-
-# plt.figure(figsize=(10.0, 10.0))
-# plt.title("$G$={:s} Q.U.".format(self.LatexFormat((1/x_peak)*self.scaler.scale_[0])), fontsize=20)
-# plt.plot(x, y, lw=3)
-# plt.ylabel("Energy Spectral Density $|S(f_{PH})|^{2}$", fontsize=25)
-# plt.xlabel("$f_{PH}{$ [Inv. Q.U.]", fontsize=20)
-# plt.axvline(x=x_peak, color="purple", lw=3, linestyle="--", label="Maximum Peak")
-# plt.yscale("log")
-# plt.xscale("log")
-# plt.xticks(fontsize=20)
-# plt.yticks(fontsize=20)
-# plt.grid(which="both")
-# plt.legend(fontsize=15)
-# plt.show()
-
- gain = 1/x_peak
- return gain
-
-
- 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)")
+ def GetBins(self, data, bw, bin_method):
+ if(data.ndim == 1):
+# print("Data is assumed 1D. Assuming list of charges.")
- n = n_samples
- if n <= 0:
- raise ValueError("data must contain at least one measurement.")
-
-
-
- boot_stat = bootstrap(data, n_samples, bootfunc=statistic)
+ if(bw is None):
+ print("Bin width not set. Binning with {:s}".format(bin_method))
+ if(bin_method == "knuth"):
+ bw = knuth_bin_width(data)
+ elif(bin_method == "freedman"):
+ bw = freedman_bin_width(data)
+ elif(bin_method == "scott"):
+ bw = scott_bin_width(data)
+ else:
+ raise Exception("Binning method not recognised. Please select bin_method from 'knuth', 'freedman' or 'scott'")
- 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))
+ x_min, x_max = data.min(), data.max()
+ elif(data.ndim == 2):
+# print("Data is assumed 2D. Assuming input is a histogram, with columns [bin_centre, counts].")
+ _x, _y = data[:,0], data[:,1]
+ data = np.array(list(chain.from_iterable([[__x]*int(__y) for __x,__y in zip(_x,_y)])))
+ f_bin = interp1d(np.arange(0, len(_x)), _x)
+ bw = abs(f_bin(1) - f_bin(0))
+ idx_nonzero = np.squeeze(np.argwhere((_y>0)))
+ x_min, x_max = _x[np.min(idx_nonzero)]-bw/2, _x[np.max(idx_nonzero)]+bw/2
- return est_stat, std_err, conf_interval
+
+ nbins = np.ceil((x_max - x_min) / bw)
+ nbins = max(1, nbins)
+ bins = x_min + bw * np.arange(nbins + 1)
+ return data, bw, nbins, bins
-
-
- 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)))
-
+
+
+ def GetEstPedestal(self, data, gain_fft):
+ mu, sigma, gamma = GetStats(data)
- 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)))
-
+ f_pedestal = lambda x_0: (GP_gain(mu-x_0, sigma, gamma) - gain_fft)**2
+ m_gain = Minuit(f_pedestal, x_0 = mu)
+ m_gain.migrad()
+ m_gain.hesse()
- 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"
+ return m_gain.values["x_0"]
+
+
+
+ def PlotOriginalHistogram(self,
+ ax
+ ):
- 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))
+ counts = self._hist_data["counts"]
+ bin_numbers = self._hist_data["bin_numbers"]
- 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
-
-
+ ax.plot(bin_numbers, counts, label="Histogram", color="C0", lw=5)
+ ax.grid(which="both")
+ ax.set_ylabel("$f_{Q}(Q)$ [$Q^{-1}$]", fontsize=self._plot_fontsize)
+ ax.set_xlabel("Bin Number", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
+ ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+ ax.legend(fontsize=self._plot_legendfontsize)
- ##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, 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')
- f_lin_inv = interp1d(x_lin, x, fill_value='extrapolate')
- dx = abs(f_lin_inv(1) - f_lin_inv(0))
- 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))
-
+ def GetChi2(self, prefit=False):
- 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)
-
+ if(any(_ is None for _ in [self._hist_data["bin_centres"],
+ self._hist_data["density_orig"],
+ self._hist_data["density_orig_error"],
+ self._fit_values
+ ])):
+ return np.nan, np.nan
+ else:
+ x = self._hist_data["bin_centres"]
+ y = self._hist_data["density_orig"]
+ min_error = 1/self._hist_data["bw"]/np.sum(self._hist_data["counts"])
+ y_err = np.where(self._hist_data["density_orig_error"]<min_error,
+ min_error,
+ self._hist_data["density_orig_error"])
-
-
-
- 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_dark = y_dark/np.trapz(y_dark, 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_hat = self.GetModel(x, prefit)
- 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
- ):
-
-
-
-
- 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,
- k_dcr_low,
- k_dcr_hi)
+ chi2 = np.nansum(((y_hat - y)/y_err)**2)
+ ndof = len(x) - 9
+ return chi2, ndof
-
-
- return y_model
-
-
- ###PLOTTING
+ def PlotDensity(self, ax):
+
+ density = self._hist_data["density"]
+ density_error = self._hist_data["density_error"]
+ bin_numbers = self._hist_data["bin_numbers"]
+
+ ax.plot(bin_numbers, density, label="KDE", color="purple", lw=5)
+ plt.fill_between(bin_numbers,
+ density - 1.96*density_error,
+ density + 1.96*density_error,
+ label="KDE, 95% Confidence",
+ alpha=0.3,
+ color="purple", lw=0)
+ ax.grid(which="both")
+ ax.set_ylabel("$f_{Q}(Q)$ [$Q^{-1}$]", fontsize=self._plot_fontsize)
+ ax.set_xlabel("Bin Number", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
+ ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+ ax.legend(fontsize=self._plot_legendfontsize)
+
+ def PlotFFT(self, ax):
+ fft_freq = self._hist_data["fft_freq"]
+ fft_amplitude = self._hist_data["fft_amplitude"]
+ G_fft = self._hist_data["G_fft"]
+ inv_G_fft = 1/G_fft
- 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.plot(fft_freq,
+ fft_amplitude,
+ color="purple",
+ label="Absolute Square of FFT\n of KDE",
+ lw=5)
+
+ ax.fill_between(fft_freq[fft_freq<inv_G_fft],
+ fft_amplitude[fft_freq<inv_G_fft], 0,
+ edgecolor="red",
+ facecolor = 'none',
+ lw=2,
+ hatch = "//",
+ label="High Pass Filter Range")
+
+
+ ax.axvline(x=inv_G_fft, color="green", lw=2, label="$G_{{FFT}}$ = {:3.3f} bins".format(G_fft))
+
ax.set_yscale("log")
+ ax.set_xscale("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.set_xlabel("Inverse Bin Number", fontsize=25)
+ ax.set_ylabel("$|\\mathcal{F}(f_{Q}(Q))|^{2}$", fontsize=25)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
ax.tick_params(axis="y", labelsize=self._plot_fontsize)
- ax.set_yscale("log")
+ ax.legend(fontsize=self._plot_legendfontsize)
+
+ def PlotBGSub(self, ax):
+ density = self._hist_data["density"]
+ density_bgsub = self._hist_data["density_bgsub"]
+ bin_numbers = self._hist_data["bin_numbers"]
+
+ ax.plot(bin_numbers,
+ density,
+ label="KDE",
+ color="purple",
+ lw=5)
+ ax.plot(bin_numbers,
+ density_bgsub,
+ label="KDE,\n Filtered",
+ color="red",
+ linestyle="--",
+ lw=5)
ax.grid(which="both")
- ax.set_ylim([1e-5, None])
- ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+ ax.set_xlabel("Bin Number", fontsize=25)
+ ax.set_ylabel("$f_{Q}(Q)$ [$Q^{-1}$]", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
+ ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+ ax.legend(fontsize=self._plot_legendfontsize)
+
+
+ def PlotPeaks(self, ax):
+
+ density = self._hist_data["density"]
+ bin_numbers = self._hist_data["bin_numbers"]
+ x_0_est = self._hist_data["x_0_est"]
+ peak_position = self._hist_data["peaks"]["peak_position"]
+ peak_position_lower = self._hist_data["peaks"]["peak_position_lower"]
+ peak_position_upper = self._hist_data["peaks"]["peak_position_upper"]
+ peak_height = self._hist_data["peaks"]["peak_height"]
+ peak_height_lower = self._hist_data["peaks"]["peak_height_lower"]
+ peak_height_upper = self._hist_data["peaks"]["peak_height_upper"]
+
+ ax.plot(bin_numbers, density, label="KDE", color="purple", lw=5)
+ color_vals = [self._cmap(_v) for _v in np.linspace(0,0.75, len(peak_position))]
+
+
+ for peak_i, (pp, ppl, ppu, ph, phl, phu) in enumerate(zip(
+ peak_position,
+ peak_position_lower,
+ peak_position_upper,
+ peak_height,
+ peak_height_lower,
+ peak_height_upper
+ )):
+ if(peak_i == 0):
+ annotation_text = "$Q_{{Ped}}$"
+ color = "red"
+ else:
+ annotation_text = "Peak {:d}".format(peak_i)
+ color = color_vals[peak_i]
+
+ ax.vlines(x=[pp],
+ ymin=0,
+ ymax=ph,
+ color=color,
+ linestyle="-",
+ lw=2.5,
+ label = '{:s}'.format(annotation_text)
+ )
+ ax.vlines(x=[ppl],
+ ymin=0,
+ ymax=phl,
+ color=color,
+ lw=2.5,
+ linestyle="--")
+
+ ax.vlines(x=[ppu],
+ ymin=0,
+ ymax=phu,
+ color=color,
+ lw=2.5,
+ linestyle="--")
+
+
+ ax.axvline(x=x_0_est,
+ color="green",
+ lw=2,
+ label= "$Q_{{Ped}}$ Estimate")
+
+ ax.set_xlabel("Bin Number", fontsize=self._plot_fontsize)
+ ax.set_ylabel("$f_{Q}(Q)$ [$Q^{-1}$]", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
+ ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+ ax.grid(which="both")
+ ax.legend(fontsize=self._plot_legendfontsize)
+
+ def PlotVarianceFit(self, ax):
+ density = self._hist_data["density"]
+ bin_numbers = self._hist_data["bin_numbers"]
- 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
+ peak_position_norm = self._hist_data["peaks"]["peak_position_norm"]
+ peak_variance = self._hist_data["peaks"]["peak_variance"]
+ peak_variance_error = self._hist_data["peaks"]["peak_variance_error"]
+
+
+ _offset = self._hist_data["bc_min"]
+ _scale = self._hist_data["bw"]
+
+ sigma_0 = self._prefit_values["sigma_0"]/_scale
+ dsigma_0 = self._prefit_errors["sigma_0"]/_scale
+ sigma_1 = self._prefit_values["sigma_1"]/_scale
+ dsigma_1 = self._prefit_errors["sigma_1"]/_scale
+
+ ax.errorbar(peak_position_norm,
+ peak_variance,
+ yerr=peak_variance_error,
+ fmt="o",
+ lw=3,
+ markersize=10,
+ color="C0",
+ label="Peak Variances"
)
-
- 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="--")
-
-
-
- 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"])
+
+ ax.plot(peak_position_norm,
+ Linear(peak_position_norm,
+ sigma_1**2,
+ sigma_0**2
+ ),
+ color="C0",
+ linestyle="--",
+ lw=3,
+ label = "Linear Fit: \n $\sigma^{{2}}_{{0}}$ = {:s} $\pm$ {:s} bins \n $\sigma^{{2}}_{{1}}$ = {:s} $\pm$ {:s} bins".format(
+ LatexFormat(sigma_0**2),
+ LatexFormat(2*sigma_0*dsigma_0),
+ LatexFormat(sigma_1**1),
+ LatexFormat(2*sigma_0*dsigma_1),
- ), 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.set_xlabel("$k$ [n.p.e]", fontsize=self._plot_fontsize)
+ ax.set_ylabel("$\sigma^{2}_{k}$ [bins]", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
ax.tick_params(axis="y", labelsize=self._plot_fontsize)
- ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
- ax.grid(which="both")
+ plt.legend(fontsize=self._plot_legendfontsize)
+ plt.show()
-
-# 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 PlotMeanFit(self, ax):
+ density = self._hist_data["density"]
+ bin_numbers = self._hist_data["bin_numbers"]
-
-
- def PlotDCREst(self, ax):
+ peak_position_norm = self._hist_data["peaks"]["peak_position_norm"]
+ peak_mean = self._hist_data["peaks"]["peak_mean"]
+ peak_mean_error = self._hist_data["peaks"]["peak_mean_error"]
+ _offset = self._hist_data["bc_min"]
+ _scale = self._hist_data["bw"]
+ x_0 = (self._prefit_values["x_0"] - _offset)/_scale
+ dx_0 = self._prefit_errors["x_0"]/_scale
+ G = self._prefit_values["G"]/_scale
+ dG = self._prefit_errors["G"]/_scale
- 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"]
+ ax.errorbar(peak_position_norm,
+ peak_mean,
+ yerr=peak_mean_error,
+ fmt="o",
+ markersize=10,
+ lw=3,
+ color="C0",
+ label="Peak Means"
+ )
- 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"]
+ ax.plot(peak_position_norm,
+ Linear(peak_position_norm,
+ G,
+ x_0,
+ ),
+ color="C0",
+ linestyle="--",
+ lw=3,
+ markersize=10,
+ label = "Linear Fit: \n $Q_{{Ped}}$ = {:s} $\pm$ {:s} bins \n $G$ = {:s} $\pm$ {:s} bins".format(
+ LatexFormat(x_0),
+ LatexFormat(dx_0),
+ LatexFormat(G),
+ LatexFormat(dG),
+
+ ),
+ )
- 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.set_xlabel("$k$ [n.p.e]", fontsize=self._plot_fontsize)
+ ax.set_ylabel("$\\langle Q_{k} \\rangle$ [bins]", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
+ ax.tick_params(axis="y", labelsize=self._plot_fontsize)
+ ax.legend(fontsize=self._plot_legendfontsize)
+
+
+
+
+ def PlotDCR(self, ax):
+
+ x_0 = self._prefit_values["x_0"]
+ G = self._prefit_values["G"]
+ DCR = self._prefit_values["DCR"]
+ dDCR = self._prefit_errors["DCR"]
+
+ bin_numbers_norm = np.linspace(0,1,1000)
+ density = self._hist_data["k2PDF"](bin_numbers_norm)
+ density_errors = self._hist_data["k2PDF_err"](bin_numbers_norm)
+ k_DC_min = self._hist_data["k_DC_min"]
+
+ cond_02Min = (bin_numbers_norm>=0) & (bin_numbers_norm<k_DC_min)
+ cond_Min22Min = (bin_numbers_norm>=k_DC_min) & (bin_numbers_norm<min(2*k_DC_min, 1))
+
+
ax.fill_between(
- x_dc_kde[cond_p_0toLM],
- y_dc_kde[cond_p_0toLM],
+ bin_numbers_norm[cond_02Min],
+ density[cond_02Min],
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) )
- )
+ label = "$P_{0}$")
- 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],
+ bin_numbers_norm[cond_Min22Min],
+ density[cond_Min22Min],
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"
- )
+ hatch="///",
+ edgecolor="r",
+ label = "$P_{1}$")
- 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,
+ ax.axvline(x=k_DC_min, color="green", lw=2, label="$k_{min}$")
+
+
+ ax.plot(bin_numbers_norm,
+ density,
+ label="KDE",
+ color="purple",
+ lw=5)
+
+ ax.fill_between(bin_numbers_norm,
+ y1=density - 1.96*density_errors,
+ y2=density + 1.96*density_errors,
+ alpha=0.3,
color="purple",
- label="KDE, 95% Confidence"
- )
+ 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_xlabel("$k$ [n.p.e]", 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.set_ylabel("$f_{k}(k)$ [$k^{-1}$]", fontsize=self._plot_fontsize)
+ ax.tick_params(axis="x", labelsize=self._plot_fontsize, rotation=45)
ax.tick_params(axis="y", labelsize=self._plot_fontsize)
- ax.legend(fontsize=max(10, self._plot_fontsize//1.5))
+ ax.legend(title="$DCR$ = {:s} \n $\pm$ {:s} MHz".format(LatexFormat(DCR*1e3),
+ LatexFormat(dDCR*1e3)),
+ fontsize=self._plot_legendfontsize,
+ title_fontsize=self._plot_legendfontsize
+ )
+ ax.set_xlim([0,1])
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))
@@ -1313,14 +597,14 @@ class PeakOTron:
self.PlotOriginalHistogram(ax0)
ax1 = fig.add_subplot(gs[0,1])
- self.PlotKDE(ax1)
+ self.PlotDensity(ax1)
- ax2 = fig.add_subplot(gs[1,:])
- self.PlotPeaks(ax2)
-
- ax3 = fig.add_subplot(gs[2,0])
+ ax3 = fig.add_subplot(gs[1,0])
self.PlotGenPois(ax3)
+ ax2 = fig.add_subplot(gs[1,:])
+ self.PlotPeaks(ax2)
+
ax4 = fig.add_subplot(gs[2,1])
self.PlotSigmaEst(ax4)
@@ -1335,948 +619,685 @@ class PeakOTron:
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 GetPrefitResults(self):
+ return self._prefit_values, self._prefit_errors
-
- def GetHistAndPeaks(self,
- data,
- bw,
- peak_dist_factor,
- peak_width_factor,
- peak_ppf,
- peak_prominence,
- bin_method,
- n_bootstrap,
- n_bootstrap_kde,
- bandwidth_kde,
- kernel_kde,
- alpha
-
- ):
-
-
- self.scaler = RobustScaler()
- data_s = np.squeeze(self.scaler.fit_transform(data.reshape(-1,1)))
-
- if(bw is None):
-
- ppf = self.EmpiricalPPF(data_s)
-
- if(bin_method == "knuth"):
- bw_s = knuth_bin_width(data_s[data_s<ppf(alpha)])
- elif(bin_method=="freedman"):
- bw_s = freedman_bin_width(data_s[data_s<ppf(alpha)])
- elif(bin_method=="scott"):
- bw_s = scott_bin_width(data_s[data_s<ppf(alpha)])
- else:
- raise Exception ("Please specify a bin width by setting 'bw' or choose a method from 'knuth', 'freedman' or 'scott'")
+
+ def PlotFit(self,
+ figsize=(10.0,10.0),
+ labelsize=None,
+ ticksize=None,
+ titlesize=None,
+ legendsize=None,
+ title=None,
+ xlabel = None,
+ scaled=False,
+ save_directory=None,
+ data_label=None,
+ y_limits = [None, None],
+ residual_limits = [-5, 5],
+ residualticksize=None,
+ linewidth_main = 5,
+ x_limits = [None, None],
+ display=True,
+ prefit=False
+ ):
- 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)
-
-
- 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)
+ if(labelsize is None):
+ labelsize=self._plot_fontsize
-
- else:
+ if(ticksize is None):
+ ticksize=self._plot_fontsize
+
+ if(titlesize is None):
+ titlesize = self._plot_fontsize
- bw_s = bw/self.scaler.scale_[0]
+ if(legendsize is None):
+ legendsize = self._plot_legendfontsize
- 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
+ if(residualticksize is None):
+ residualticksize = int(0.8*self._plot_fontsize)
+
+ x = self._hist_data["bin_centres"]
+ y = self._hist_data["density_orig"]
+ y_err = self._hist_data["density_orig_error"]
+ y_hat = self.GetModel(x, prefit)
+ chi2, ndf = self.GetChi2(prefit)
- #Calculate histogram information
+ if(xlabel is None):
+ xlabel = "Q"
- 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.
+ fig = plt.figure(figsize=figsize)
+ gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
+ ax0 = plt.subplot(gs[0])
+ if(data_label is None):
+ data_label = "Histogram"
+ ax0.plot(x, y, label=data_label, lw=5, color="C0")
+ ax0.plot(x, y_hat, linestyle="--", label="Model", lw=5, color="C1")
+ ax0.plot([], [], ' ', label="$\\frac{{\\chi^{{2}}}}{{NDF}}$ = {:s}".format(LatexFormat(chi2/ndf)))
-
- 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)
+ ax0.set_ylabel("$f_{Q}(Q)$ [$Q^{-1}$]", fontsize=labelsize)
+ ax0.tick_params(axis="y", labelsize=ticksize)
- x_s = (x_s[:-1] + x_s[1:])/2.
-
-
- try:
+ if(y_limits[0] is None):
+ y_limits[0] = np.min(y[y>0])
+ ax0.set_ylim(y_limits)
+ ax0.set_xlim(x_limits)
+ if(title is not None):
+ ax0.set_title(title, fontsize=titlesize)
- 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,
- alpha=alpha
- )
+ ax0.grid(which="both")
+
+ ax0.set_yscale("log")
+ ax0.legend(fontsize=legendsize)
+
- f_lin_kde_s = interp1d(x_kde_s, np.arange(len(x_kde_s)), fill_value='extrapolate')
+ ax1 = plt.subplot(gs[1], sharex = ax0)
+ ax1.scatter(x, (y - y_hat)/(y_err), color="C1")
-
- except:
- print("Initial KDE failed. Setting fit fail status and continuing...")
- self._failed = True
- return
-
-
+ ax1.tick_params(axis="x", labelsize=ticksize, rotation=45)
+ ax1.tick_params(axis="y", labelsize=ticksize)
+ ax1.set_ylabel("$Residuals$", fontsize=labelsize)
+ ax1.set_xlabel(xlabel, fontsize=labelsize)
+ ax1.set_ylim(residual_limits)
+ ax1.set_yticks(np.arange(int(np.floor(np.min(residual_limits))),
+ int(np.ceil(np.max(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=ticksize)
+ ax1.tick_params(axis="y", labelsize=residualticksize)
+ ax1.grid(which="both")
+
-
- est_gain_s = self.FFTGain(x_kde_s, y_kde_s)
+ fig.tight_layout()
- est_gain_lin = abs(f_lin_kde_s(est_gain_s) - f_lin_kde_s(0))
-
-
- 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
-
-
- 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_peaks_s = x_kde_s[peaks]
- y_peaks_s = y_kde_s[peaks]
- y_peaks_err_s = y_kde_err_s[peaks]
- x_widths_s = temp_widths[3] - temp_widths[2]
+ mf = ScalarFormatter(useMathText=True)
+ mf.set_powerlimits((-2,2))
+ plt.gca().xaxis.set_major_formatter(mf)
+ offset = ax1.xaxis.get_offset_text()
+ offset.set_size(int(0.8*ticksize))
- pedestal_s = x_kde_s[peaks][0]
+
+ 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)
-
- est_mu_s, std_err_mu_s, _ = self.Bootstrap(data_s-pedestal_s,
- self.EstMu,
- n_samples=n_bootstrap)
+ def Fit(self, data, **kwargs):
- est_lbda_s, std_err_lbda_s, _ = self.Bootstrap(data_s - pedestal_s,
- self.EstLbda,
- n_samples=n_bootstrap)
+ self.Init()
-
-
- _, std_err_gain_s, _ = self.Bootstrap(data_s - pedestal_s,
- self.EstGain,
- n_samples=n_bootstrap)
-
-
- 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 = max(3, self.GPoisPPF(peak_ppf, est_mu_s, est_lbda_s, 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)):
+ if not set(kwargs.keys()).issubset(self._fit_kwargs.keys()):
+ wrong_keys = list(set(kwargs.keys()).difference(set(self._fit_kwargs.keys())))
- data_peak_s = self.SelectRange(data_s, x_peak-x_width/2, x_peak+x_width/2)
+ raise Exception ("Arguments {:s} not recognised.".format(",".join(wrong_keys)))
+
+ if not "tau" in kwargs.keys():
+ raise Exception ("Please provide tau (slow component of SiPM pulse) in nanoseconds.")
- N_evt_peak = len(data_peak_s)
+ if not "t_0" in kwargs.keys():
+ raise Exception ("Please provide t_0 (integration region before start of SiPM pulse integration gate) in nanoseconds.")
- if(x_i==0):
- est_x_0_s, std_err_x_0_s, _ = self.Bootstrap(data_peak_s,
- np.mean,
- n_samples=n_bootstrap)
+ if not "t_gate" in kwargs.keys():
+ raise Exception ("Please provide t_gate (length of SiPM pulse integration gate) in nanoseconds.")
-
- if(x_i<peak_threshold):
- if(self._verbose):
- print("Peak {:d} has {:d} events. Adding...".format(x_i, N_evt_peak))
-
- 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"])
-
-
- self._GD_data["x"] = x
- self._GD_data["y"] = y
- self._GD_data["y_err"] = y_err
- self._GD_data["data"] = data
-
-
- 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["data_s"] = data_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._fit_kwargs.update(kwargs)
- self._GD_data["fit_GP_mu"] = max(est_mu_s, self._eps)
- self._GD_data["fit_GP_mu_err"] = std_err_mu_s
- self._GD_data["fit_GP_lbda"] = min(1, max(est_lbda_s, self._eps))
- self._GD_data["fit_GP_lbda_err"] = std_err_lbda_s
- self._GD_data["fit_G"] = est_gain_s
- self._GD_data["fit_G_err"] = std_err_gain_s
- self._GD_data["fit_x_0"] = est_x_0_s
- self._GD_data["fit_x_0_err"] = std_err_x_0_s
- self._GD_data["scaler"] = self.scaler
-
-
-
- def PreFitGenPoisToPeaks(self, ppf_mainfit, n_bootstrap):
+ self.GetPreFit(data, **self._fit_kwargs)
-
- ########
- #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"]
+ data_bn = self._hist_data["bc2bn"](self._hist_data["data"])
+ f_DRM = UnbinnedLH(DRM, data_bn, 1)
+ m_DRM = Minuit(f_DRM,
+ x_0 = self._prefit_values["x_0"],
+ G = self._prefit_values["G"],
+ mu = self._prefit_values["mu"],
+ lbda = self._prefit_values["lbda"],
+ sigma_0 = self._prefit_values["sigma_0"],
+ sigma_1 = self._prefit_values["sigma_1"],
+ tauAp = self._prefit_values["tauAp"],
+ pAp = self._prefit_values["pAp"],
+ DCR = self._prefit_values["DCR"],
+ tau = self._prefit_values["tau"],
+ t_gate = self._prefit_values["t_gate"],
+ t_0 = self._prefit_values["t_0"],
+ k_low = self._prefit_values["k_low"],
+ k_hi = self._prefit_values["k_hi"],
+ k_dcr_low = self._prefit_values["k_dcr_low"],
+ k_dcr_hi = self._prefit_values["k_dcr_hi"],
+ )
- 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"]
+ m_DRM.errors["x_0"] = self._prefit_errors["x_0"]
+ m_DRM.errors["G"] = self._prefit_errors["G"]
+ m_DRM.errors["mu"] = self._prefit_errors["mu"]
+ m_DRM.errors["lbda"] = self._prefit_errors["lbda"]
+ m_DRM.errors["sigma_0"] = self._prefit_errors["sigma_0"]
+ m_DRM.errors["sigma_1"] = self._prefit_errors["sigma_1"]
+ m_DRM.errors["tauAp"] = self._prefit_errors["tauAp"]
+ m_DRM.errors["pAp"] = self._prefit_errors["pAp"]
+ m_DRM.errors["DCR"] = self._prefit_errors["DCR"]
- 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
-
+ m_DRM.limits["x_0"] = (self._eps, float(self._hist_data["nbins"]))
+ m_DRM.limits["G"] = (self._eps, float(self._hist_data["nbins"]))
- minuit_pois.errors["mu"] = fit_dmu
- minuit_pois.errors["lbda"] = fit_dlbda
+ m_DRM.limits["mu"] = (self._eps, self._prefit_values["k_hi"])
- minuit_pois.limits["lbda"] = (self._eps, 1-self._eps)
- minuit_pois.limits["mu"] = (self._eps, None)
+ m_DRM.limits["lbda"] = (self._eps, 1-self._eps)
- minuit_pois.migrad(ncall= self._n_call_minuit,
- iterate =self._n_iterations_minuit)
- minuit_pois.hesse()
+ m_DRM.limits["sigma_0"] = (self._eps, float(self._hist_data["nbins"]))
+ m_DRM.limits["sigma_1"] = (self._eps, float(self._hist_data["nbins"]))
-
+ m_DRM.limits["tauAp"] = (self._eps,
+ self._prefit_values["tau"]-self._eps)
+ m_DRM.limits["pAp"] = (self._eps, 1-self._eps)
+ m_DRM.limits["DCR"] = (self._eps, None)
- 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._fit_kwargs["tau_err"] is None):
+ m_DRM.fixed["tau"]=True
+ else:
+ m_DRM.fixed["tau"]=False
+ m_DRM.errors["tau"] = abs(self._prefit_errors["tau"])
+ m_DRM.limits["tau"] = (max(self._eps, self._prefit_values["tau"]-self._prefit_errors["tau"]),
+ max(self._eps, self._prefit_values["tau"]+self._prefit_errors["tau"])
+ )
+
+ if(self._fit_kwargs["t_gate_err"] is None):
+ m_DRM.fixed["t_gate"]=True
+ else:
-
-
- 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)
+ m_DRM.fixed["t_gate"]=False
+ m_DRM.errors["t_gate"] = abs(self._prefit_errors["t_gate"])
+ m_DRM.limits["t_gate"] = (max(self._eps, self._prefit_values["t_gate"]-self._prefit_errors["t_gate"]),
+ max(self._eps, self._prefit_values["t_gate"]+self._prefit_errors["t_gate"])
+ )
- 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)
+
+ if(self._fit_kwargs["t_0_err"] is None):
+ m_DRM.fixed["t_0"]=True
+ else:
+ m_DRM.fixed["t_0"]=False
+ m_DRM.errors["t_0"] = abs(self._prefit_errors["t_0"])
+ m_DRM.limits["t_0"] = (max(self._eps, self._prefit_values["t_0"]-self._prefit_errors["t_0"]),
+ max(self._eps, self._prefit_values["t_0"]+self._prefit_errors["t_0"])
+ )
+
+
+ m_DRM.fixed["k_low"]=True
+ m_DRM.fixed["k_hi"]=True
+ m_DRM.fixed["k_dcr_low"]=True
+ m_DRM.fixed["k_dcr_hi"]=True
-
-
-
- def PreFitSigma(self, data, n_bootstrap):
-
+ m_DRM.strategy=2
+ m_DRM.errordef=m_DRM.LIKELIHOOD
+ m_DRM.simplex()
+ m_DRM.migrad(ncall = self._n_call_minuit,
+ iterate= self._n_iterations_minuit)
+ m_DRM.hesse()
- peak_val = self._peak_data["n_peak_s"]
- data_s = np.squeeze(self.scaler.transform(data.reshape(-1,1)))
+ self._fit_minuit = m_DRM
- 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"]):
+ _offset = self._hist_data["bc_min"]
+ _scale = self._hist_data["bw"]
- data_range = self.SelectRange(data_s, x_peak - x_width/2, x_peak + x_width/2)
-
- try:
-
-
- est_var, std_err_var, _ = self.Bootstrap(data_range,
- np.var,
- n_samples=n_bootstrap)
+ for key, value in m_DRM.values.to_dict().items():
+ if(key=="x_0"):
+ self._fit_values[key] = _offset + value*_scale
+ elif(key=="G"):
+ self._fit_values[key] = value*_scale
+ elif(key=="sigma_0"):
+ self._fit_values[key] = value*_scale
+ elif(key=="sigma_1"):
+ self._fit_values[key] = value*_scale
+ else:
+ self._fit_values[key] = value
+
- 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_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_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]
-
- fit_lin_var = HuberRegression(self.DummyLinear,
- n_peaks_select,
- peaks_var_select,
- peaks_var_err_select
- )
+ for key, value in m_DRM.errors.to_dict().items():
+ if(key=="x_0"):
+ self._fit_errors[key] = value*_scale
+ elif(key=="G"):
+ self._fit_errors[key] = value*_scale
+ elif(key=="sigma_0"):
+ self._fit_errors[key] = value*_scale
+ elif(key=="sigma_1"):
+ self._fit_errors[key] = value*_scale
+ else:
+ self._fit_errors[key] = value
+
+
+
+ _prefit_values_temp = self._prefit_values.copy()
+ _prefit_errors_temp = self._prefit_errors.copy()
+
+ for key, value in _prefit_values_temp.items():
+ if(key=="x_0"):
+ _prefit_values_temp[key] = _offset + value*_scale
+ elif(key=="G"):
+ _prefit_values_temp[key] = value*_scale
+ elif(key=="sigma_0"):
+ _prefit_values_temp[key] = value*_scale
+ elif(key=="sigma_1"):
+ _prefit_values_temp[key] = value*_scale
+ else:
+ _prefit_values_temp[key] = value
+
+ for key, value in _prefit_errors_temp.items():
+ if(key=="x_0"):
+ _prefit_errors_temp[key] = value*_scale
+ elif(key=="G"):
+ _prefit_errors_temp[key] = value*_scale
+ elif(key=="sigma_0"):
+ _prefit_errors_temp[key] = value*_scale
+ elif(key=="sigma_1"):
+ _prefit_errors_temp[key] = value*_scale
+ else:
+ _prefit_errors_temp[key] = value
+ self._prefit_values.update(_prefit_values_temp)
+ self._prefit_errors.update(_prefit_errors_temp)
- m_var_temp = np.median(np.gradient(peaks_var_select, n_peaks_select))
- c_var_temp = peaks_var_select[np.argmin(peaks_var_select)]
+# plt.figure()
+# plt.plot(bin_centres, density_orig)
+# plt.plot(bin_centres, DRM(bin_centres, **self._fit_values))
+# plt.show()
- 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)
+ def GetModel(self, x, prefit=False):
+ if(prefit):
+ return DRM(x, **self._prefit_values)
+ else:
+ return DRM(x, **self._fit_values)
- minuit_lin_var.strategy=2
- minuit_lin_var.migrad(ncall= self._n_call_minuit,
- iterate =self._n_iterations_minuit)
- minuit_lin_var.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"]
-
-
-
-
-
- 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
-
+ def GetPreFit(self, data, **kwargs):
- 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]
-
+
+ ###Process Bins
+ data, bw, nbins, bins = self.GetBins(data, kwargs["bw"], kwargs["bin_method"])
+
+# data = np.random.choice(data, size=10000)
+
+ counts, _ = np.histogram(data, bins=bins)
+ counts_error = np.sqrt(counts)
+ density_orig = counts/np.sum(counts)/bw
+ density_orig_error = density_orig*np.sqrt(1/counts + 1/np.sum(counts))
+
+ min_error = 1/np.sum(counts)/bw
+ bin_centres = (bins[:-1] + bins[1:])/2.
+ bin_numbers = np.arange(0, nbins)
-
- 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)))
+ f_bc2bn = interp1d(bin_centres,bin_numbers, fill_value="extrapolate")
+
+ f_bn2bc = interp1d(bin_numbers,bin_centres,fill_value="extrapolate")
+ data_bn = f_bc2bn(data)
- 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)
+ ###Perform KDE smoothing
+ bin_numbers_kde, density_kde, density_kde_err, _ = BootstrapKDE(data_bn,
+ n_bootstrap=self._n_bootstrap,
+ bw_limits=(0.01,None)
+ )
+ cumint = cumtrapz(density_kde/bw, bin_numbers_kde, initial=0)
+ cumint = cumint/np.max(cumint)
+
+
+ f_bn2CDF = interp1d( bin_numbers_kde, cumint, fill_value = (0,1), bounds_error=False )
+ f_CDF2bn = interp1d( cumint, bin_numbers_kde, fill_value = (bin_numbers_kde[0], bin_numbers_kde[-1]), bounds_error=False)
+ f_bn2PDF = interp1d(bin_numbers_kde, density_kde/bw, fill_value=(0, 0), bounds_error=False)
+ f_bn2PDF_err = interp1d(bin_numbers_kde, np.where(density_kde_err/bw>min_error, density_kde_err/bw, min_error),fill_value=(min_error, min_error),bounds_error=False)
- 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)
+ density = f_bn2PDF(bin_numbers)
+ density_error = f_bn2PDF_err(bin_numbers)
- 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)
+
+ ###Perform FFT, Get Gain Estimate and Background Subtraction
- 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)
+ fhat = np.fft.fft(density) #computes the fft
+ psd = fhat * np.conj(fhat)/nbins
+
- dmean_p_interpeak = abs(mean_p_interpeak_upper - mean_p_interpeak_lower)
+ fft_freq_orig = (1/nbins) * np.arange(nbins) #frequency array
+ idxs_half = np.arange(1, np.floor(nbins/2), dtype=np.int32)
+
+
+ fft_amplitude = np.abs(psd[idxs_half])
+ fft_freq = fft_freq_orig[idxs_half]
+ idxs_fft_peaks, _= find_peaks(fft_amplitude)
+ fft_freq_peaks = fft_freq[idxs_fft_peaks]
+ fft_amplitude_peaks = fft_amplitude[idxs_fft_peaks]
+ _idx_min = np.argmax(fft_amplitude_peaks)
+ inv_G_fft = fft_freq_peaks[_idx_min]
+ G_fft = 1/inv_G_fft
+
+
+ density_bgsub = np.fft.ifft(np.where(fft_freq_orig<inv_G_fft, 0, fhat))
+
+
+ ###Find peaks
+
+ idxs_peaks, _ = find_peaks(density_bgsub,
+ distance=kwargs["peak_dist_factor"]*G_fft)
+ idxs_peak_widths = np.vstack(peak_widths(density_bgsub,
+ idxs_peaks,
+ rel_height=kwargs["peak_width_factor"])[2:]).T.astype(int)
- 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]
+ x_0_est = self.GetEstPedestal(data_bn, G_fft)
- dint_p_0toLM = abs(int_p_0toLM_upper - int_p_0toLM_lower)
+ Q_min = bin_numbers[idxs_peaks][np.argmin(abs(bin_numbers[idxs_peaks] - x_0_est))]
+ if(kwargs["alpha_peaks"] is not None):
+ Q_max = f_CDF2bn(kwargs["alpha_peaks"])
+ else:
+ Q_max = np.max(bin_numbers)
+ _idxs_sel = (bin_numbers[idxs_peaks]>=Q_min) & (bin_numbers[idxs_peaks]<=Q_max)
+
+ idxs_peaks = idxs_peaks[_idxs_sel]
+ idxs_peak_widths = idxs_peak_widths[_idxs_sel]
+ _idxs_sort = np.argsort(idxs_peaks)
- int_p_LMto2LM = quad(lambda x: np.exp(f_log_kde_dc(x)),
- x_dc_lim_mid,
- x_dc_lim_hi)[0]
+ idxs_peaks = idxs_peaks[_idxs_sort]
+ idxs_peak_widths = idxs_peak_widths[_idxs_sort]
- 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)
+ peak_position = bin_numbers[idxs_peaks]
+ peak_position_norm = (peak_position - peak_position[0])/G_fft
+ peak_position_lower = bin_numbers[idxs_peak_widths[:,0]]
+ peak_position_upper = bin_numbers[idxs_peak_widths[:,1]]
+ peak_height = density[idxs_peaks]
+ peak_height_lower = density[idxs_peak_widths[:,0]]
+ peak_height_upper = density[idxs_peak_widths[:,1]]
+ peak_mean = np.empty(len(peak_position))
+ peak_mean_error = np.empty(len(peak_position))
+ peak_variance = np.empty(len(peak_position))
+ peak_variance_error = np.empty(len(peak_position))
+ peak_std_deviation = np.empty(len(peak_position))
+ peak_std_deviation_error = np.empty(len(peak_position))
+ peak_skewness = np.empty(len(peak_position))
+ peak_skewness_error = np.empty(len(peak_position))
+ peak_kurtosis = np.empty(len(peak_position))
+ peak_kurtosis_error = np.empty(len(peak_position))
+
+ for peak_i, (pp, ppl, ppu) in enumerate(zip(
+ peak_position,
+ peak_position_lower,
+ peak_position_upper
+ )):
-
- int_p = int_p_0toLM + 0.5*int_p_LMto2LM
- dint_p = np.sqrt(dint_p_0toLM**2 + 0.5*dint_p_LMto2LM**2)
+
+
+ data_peak = SelectRangeNumba(data_bn, ppl, ppu)
+ try:
+ mean, dmean, _ = Bootstrap(data_peak, np.mean, self._n_bootstrap)
+ except:
+ mean, dmean = np.nan, np.nan
+
+ try:
+ var, dvar, _ = Bootstrap(data_peak, np.var, self._n_bootstrap)
+ std, dstd = np.sqrt(var), (0.5/np.sqrt(var))*var
+ except:
+ var, dvar = np.nan, np.nan
+ std, dstd = np.nan, np.nan
+
+ try:
+ skw, dskw, _ = Bootstrap(data_peak, sp_skew, self._n_bootstrap)
+ except:
+ skw, dskw = np.nan, np.nan
+
+ try:
+ krt, dkrt, _ = Bootstrap(data_peak, sp_kurt, self._n_bootstrap)
+ except:
+ krt, dkrt = np.nan, np.nan
+
+
+ peak_mean[peak_i] = mean
+ peak_mean_error[peak_i] = dmean
+ peak_variance[peak_i] = var
+ peak_variance_error[peak_i] = dvar
+ peak_std_deviation[peak_i] = std
+ peak_std_deviation_error[peak_i] = dstd
+ peak_skewness[peak_i]=skw
+ peak_skewness_error[peak_i] = dskw
+ peak_kurtosis[peak_i] = krt
+ peak_kurtosis_error[peak_i] = dkrt
+
+
+ cond_sel_peak_var = (~np.isnan(peak_variance) & ~np.isnan(peak_variance_error))
+ cond_sel_peak_mean = (~np.isnan(peak_mean) & ~np.isnan(peak_mean_error))
+
+ f_var = HuberRegression(
+ Linear,
+ peak_position_norm[cond_sel_peak_var],
+ peak_variance[cond_sel_peak_var],
+ peak_variance_error[cond_sel_peak_var],
+ )
+
+ f_mean = HuberRegression(
+ Linear,
+ peak_position_norm[cond_sel_peak_mean],
+ peak_mean[cond_sel_peak_mean],
+ peak_mean_error[cond_sel_peak_mean],
+ )
+
+
+ m_var = Minuit(f_var,
+ m=np.mean(np.gradient(peak_variance[cond_sel_peak_var])),
+ c=peak_variance[cond_sel_peak_var][0])
+ m_var.migrad()
+ m_var.hesse()
-
- 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
- )
+
+ m_mean = Minuit(f_mean,
+ m=np.mean(np.gradient(peak_mean[cond_sel_peak_mean])),
+ c=peak_mean[cond_sel_peak_mean][0])
+ m_mean.migrad()
+ m_mean.hesse()
+ x_0 = m_mean.values["c"]
+ G = m_mean.values["m"]
+ dx_0 = m_mean.errors["c"]
+ dG = m_mean.errors["m"]
+ sigma2_0 = m_var.values["c"]
+ sigma2_1 = m_var.values["m"]
+ dsigma2_0 = m_var.errors["c"]
+ dsigma2_1 = m_var.errors["m"]
- 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
+ sigma_0 = np.sqrt(sigma2_0)
+ sigma_1 = np.sqrt(sigma2_1)
+ dsigma_0 = (0.5/sigma_0)*dsigma2_0
+ dsigma_1 = (0.5/sigma_1)*dsigma2_1
- 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
+
+
+ mu, dmu, _ = Bootstrap((data_bn - x_0)/G,
+ lambda _data: GP_muGP(*GetStats(_data)),
+ self._n_bootstrap)
+ lbda, dlbda, _ = Bootstrap((data_bn - x_0)/G,
+ lambda _data: GP_lbda(*GetStats(_data)),
+ self._n_bootstrap)
- 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)
+ k_low = 0
+ k_hi = poisson.ppf(kwargs["alpha_fit"], mu)
+
+ f_k2PDF = interp1d((bin_numbers_kde-x_0)/G, density_kde*G, fill_value=(0,0), bounds_error=False)
+ f_k2PDF_err = interp1d((bin_numbers_kde-x_0)/G, density_kde_err*G, fill_value=(0,0), bounds_error=False)
+
+ f_k2PDF = interp1d((bin_numbers_kde-x_0)/G, density_kde*G, fill_value=(0,0), bounds_error=False)
+ f_k2PDF_err = interp1d((bin_numbers_kde-x_0)/G, density_kde_err*G, fill_value=(0,0), bounds_error=False)
-
- def InitFit(self,
- data,
- bw,
- bin_method,
- tau,
- t_0,
- r_fast,
- tgate,
- n_bootstrap,
- n_bootstrap_kde,
- kernel_kde,
- bandwidth_kde,
- peak_dist_factor,
- peak_width_factor,
- peak_ppf,
- peak_prominence,
- ppf_mainfit,
- alpha
- ):
+ m_k2PDF = Minuit(lambda k: f_k2PDF(k), k=0.5)
+ m_k2PDF.limits["k"] = (0,1)
+ m_k2PDF.migrad()
+ m_k2PDF.hesse()
-
+ k_DC_min = m_k2PDF.values["k"]
- if(self._is_histogram):
- data = np.array(list(chain.from_iterable([[x]*int(y) for x,y in zip(data[:,0], data[:,1])])))
-
+ fk_DC_min = f_k2PDF(k_DC_min)
+ dfk_DC_min = f_k2PDF_err(k_DC_min)
- 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,
- bin_method,
- n_bootstrap,
- n_bootstrap_kde,
- bandwidth_kde,
- kernel_kde,
- alpha)
-
- 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.PreFitSigma(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
+ P_int_02Min = quad(lambda k: f_k2PDF(k), 0, k_DC_min)[0]
+ dP_int_02Min = quad(lambda k: 2*f_k2PDF_err(k), 0, k_DC_min)[0]
- def Fit(self,
- data,
- **kwargs):
+ P_int_Min22Min = quad(lambda k: f_k2PDF(k), k_DC_min, min(2*k_DC_min, 1))[0]
+ dP_int_Min22Min = quad(lambda k: 2*f_k2PDF_err(k), k_DC_min, min(2*k_DC_min, 1))[0]
+ P_sum = P_int_02Min + 0.5*P_int_Min22Min
+ dP_sum = np.sqrt(P_int_02Min**2 + 0.5*P_int_Min22Min**2)
+
+
+ DCR = fk_DC_min/(max(P_sum, self._eps)*4*kwargs["tau"])
+ dDCR = DCR*np.sqrt((dfk_DC_min/max(fk_DC_min, self._eps))**2 + (dP_sum/max(P_sum, self._eps))**2)
+ mu_DCR = DCR*(kwargs["t_0"] + kwargs["t_gate"])
- kwargs_fit = self._default_fit_kwargs.copy()
- kwargs_fit.update(kwargs)
+
+ k_dcr_low = 0
+ k_dcr_hi = poisson.ppf(kwargs["alpha_fit"], mu_DCR)
+
+ self._hist_data["data"] = data
+ self._hist_data["bw"] = bw
+ self._hist_data["bc_min"] = f_bn2bc(0)
+ self._hist_data["bins"]=bins
+ self._hist_data["nbins"]=nbins
+ self._hist_data["counts"] = counts
+ self._hist_data["counts_error"] = counts_error
+ self._hist_data["bin_centres"] = bin_centres
+ self._hist_data["bin_numbers"] = bin_numbers
+ self._hist_data["density_orig"] = density_orig
+ self._hist_data["density_orig_error"] = density_orig_error
+ self._hist_data["density"] = density
+ self._hist_data["density_error"] = density_error
+ self._hist_data["density_bgsub"] = density_bgsub
+ self._hist_data["fft_amplitude"] = fft_amplitude
+ self._hist_data["fft_freq"] = fft_freq
+ self._hist_data["G_fft"] = G_fft
+ self._hist_data["k_DC_min"] = k_DC_min
+ self._hist_data["x_0_est"] = x_0_est
+ self._hist_data["bc2bn"] = f_bc2bn
+ self._hist_data["bn2bc"] = f_bn2bc
+ self._hist_data["bn2PDF"] = f_bn2PDF
+ self._hist_data["bn2PDF_err"] = f_bn2PDF_err
+ self._hist_data["k2PDF"] = f_k2PDF
+ self._hist_data["k2PDF_err"] = f_k2PDF_err
+
+
+ self._hist_data["peaks"]["peak_position"] = peak_position
+ self._hist_data["peaks"]["peak_position_norm"] = peak_position_norm
+ self._hist_data["peaks"]["peak_position_lower"] = peak_position_lower
+ self._hist_data["peaks"]["peak_position_upper"] = peak_position_upper
+ self._hist_data["peaks"]["peak_height"] = peak_height
+ self._hist_data["peaks"]["peak_height_lower"] = peak_height_lower
+ self._hist_data["peaks"]["peak_height_upper"] = peak_height_upper
+ self._hist_data["peaks"]["peak_mean"] = peak_mean
+ self._hist_data["peaks"]["peak_mean_error"] = peak_mean_error
+ self._hist_data["peaks"]["peak_variance"] = peak_variance
+ self._hist_data["peaks"]["peak_variance_error"] = peak_variance_error
+ self._hist_data["peaks"]["peak_std_deviation"] = peak_std_deviation
+ self._hist_data["peaks"]["peak_std_deviation_error"] = peak_std_deviation_error
+ self._hist_data["peaks"]["peak_skewness"] = peak_skewness
+ self._hist_data["peaks"]["peak_skewness_error"] = peak_skewness_error
+ self._hist_data["peaks"]["peak_kurtosis"] = peak_kurtosis
+ self._hist_data["peaks"]["peak_kurtosis_error"] = peak_kurtosis_error
+
+
+ self._prefit_values["x_0"] = x_0
+ self._prefit_values["G"] = G
+ self._prefit_values["mu"] = mu
+ self._prefit_values["lbda"] = lbda
+ self._prefit_values["sigma_0"] = sigma_0
+ self._prefit_values["sigma_1"] = sigma_1
+ self._prefit_values["DCR"] = DCR
+ self._prefit_values["pAp"] = 0.1
+ self._prefit_values["tauAp"] = 0.5*kwargs["tau"]
+ self._prefit_values["tau"] = kwargs["tau"]
+ self._prefit_values["t_0"] = kwargs["t_0"]
+ self._prefit_values["t_gate"] = kwargs["t_gate"]
+ self._prefit_values["k_low"]=k_low
+ self._prefit_values["k_hi"]=max(2,k_hi)
+ self._prefit_values["k_dcr_low"]=k_dcr_low
+ self._prefit_values["k_dcr_hi"]=max(2, k_dcr_hi)
+
+
+
+ self._prefit_errors["x_0"] = dx_0
+ self._prefit_errors["G"] = dG
+ self._prefit_errors["mu"] = dmu
+ self._prefit_errors["lbda"] = dlbda
+ self._prefit_errors["sigma_0"] = dsigma_0
+ self._prefit_errors["sigma_1"] = dsigma_1
+ self._prefit_errors["pAp"] = 0.1
+ self._prefit_errors["tauAp"] = 1
+ self._prefit_errors["tau"] = kwargs["tau_err"]
+ self._prefit_errors["t_0"] = kwargs["t_0_err"]
+ self._prefit_errors["t_gate"] = kwargs["t_gate_err"]
+ self._prefit_errors["DCR"] = dDCR
+
- 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.")
-
- 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"]
-
- 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
- }
-
- bl = UnbinnedLH(self.DRM, self._GD_data["data_s"], self._GD_data["x_kde_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"] = (None, None)
- 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["k_dcr_low"] = True
- minuit.fixed["k_dcr_hi"] = True
- minuit.fixed["tgate"] = True
-
-
- minuit.strategy=2
- minuit.errordef=minuit.LIKELIHOOD
- minuit.simplex()
- 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]
- 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]
- 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}'
- })
-
+
diff --git a/README.txt b/README.txt
deleted file mode 100644
index 53c0607e71671cc85d0cfa3f9d85f564456f1ad4..0000000000000000000000000000000000000000
--- a/README.txt
+++ /dev/null
@@ -1,5 +0,0 @@
- - Run "pip install -r requirements.txt" in the terminal to install the necessary packages.
- - Run python "RunFit.py" to test the fitting class on experimental data.
- - The output fits will be stored in the Results folder.
- - The output fitted values will be stored in csv format in the main directory, as "fit_results.csv"
- - Please look at the "RunFit.py" example program for ideas on how to input and output from your data.
diff --git a/RunFit.py b/RunFit.py
deleted file mode 100644
index 4e77e6bd5243653f49a5af922b260980cd061d2e..0000000000000000000000000000000000000000
--- a/RunFit.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-import sys
-import numpy as np
-import pandas as pd
-from PeakOTron import PeakOTron
-
-
-print("--------------------")
-print('EXAMPLE SIPM CALIBRATION RUN')
-print("--------------------")
-
-f_data = PeakOTron(verbose=False)
-f_data.SetMaxNPeaks(15) #SET MAX N PEAKS - THRESHOLDING REQUIRED WHERE LAMBDA INCORRECTLY ESTIMATED, OR FIT WILL GO ON FOREVER.
-f_data.SetMaxNDCRPeaks(6)
-
-out_dict = {}
-files_to_fit = []
-for root, dirs, files in os.walk("./SiPMSpectra/"):
-
- for file in files:
-
- if file.endswith(".dat") and "light" in file:
- files_to_fit.append([file, os.path.join(root, file)])
-
-
-print("Files to fit:")
-for i, (file, _) in enumerate(files_to_fit):
- print('File {0}: {1}'.format(i, file))
-
-
-
-for i, (file, path) in enumerate(files_to_fit):
-
- items = file.split('_')
-
- V = float(items[1].replace('U', '').replace('p', '.'))
- SiPM = items[3]
-
- print("\n Fit {0}: {1} \n".format(i, file))
- data = np.loadtxt(path)
- bw = 1.1*(data[:,0][1] - data[:,0][0])
-
-
- 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,
- prominence=1e-3,
- min_n_peak_evts=200,
- bandwidth_kde="optimise",
- bw=bw
-
- ) #BINNING RULE "knuth", "freedman", "scott" - use bw= #### to override. it is still required for DCR calculation.
-
- if(not f_data.Failed()):
- f_data.PlotSummary(save_directory="./Results/{0}_init.png".format(os.path.splitext(file)[0]))
- f_data.PlotFit(scaled=False, title=file, xlabel="Charge [Vs]", y_limits=[1e7, 1e11], save_directory="./Results/{0}_fit.png".format(os.path.splitext(file)[0]))
-
- fit_out = {}
- fit_val, fit_err = f_data.GetFitResults()
- fit_out["SiPM"] = SiPM
- fit_out["V"] = V
-
- for key, value in fit_err.items():
- fit_out["d_{:s}".format(key)] = value
-
- fit_out.update(fit_val)
- out_dict.update()
- if out_dict == {}:
- for key in fit_out.keys():
- out_dict[key] = []
-
- for key in fit_out.keys():
- out_dict[key].append(fit_out[key])
-
-
-
- else:
- print("Fit failed. \n")
-
-
-df = pd.DataFrame.from_dict(out_dict)
-df.to_csv("./Results/fit_results.csv")
diff --git a/SiPMSpectra/03/F1_U28p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U28p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index f02665b7d6ca6bb93b5fa686ccf2f83d7b601d1d..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U28p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
--3.66e-010 0
--3.65e-010 0
--3.64e-010 0
--3.63e-010 0
--3.62e-010 0
--3.61e-010 0
--3.6e-010 0
--3.59e-010 0
--3.58e-010 0
--3.57e-010 0
--3.56e-010 0
--3.55e-010 0
--3.54e-010 0
--3.53e-010 0
--3.52e-010 0
--3.51e-010 0
--3.5e-010 0
--3.49e-010 0
--3.48e-010 0
--3.47e-010 0
--3.46e-010 0
--3.45e-010 0
--3.44e-010 0
--3.43e-010 0
--3.42e-010 0
--3.41e-010 0
--3.4e-010 0
--3.39e-010 0
--3.38e-010 0
--3.37e-010 0
--3.36e-010 0
--3.35e-010 0
--3.34e-010 0
--3.33e-010 0
--3.32e-010 0
--3.31e-010 0
--3.3e-010 0
--3.29e-010 0
--3.28e-010 0
--3.27e-010 0
--3.26e-010 0
--3.25e-010 0
--3.24e-010 0
--3.23e-010 0
--3.22e-010 0
--3.21e-010 0
--3.2e-010 0
--3.19e-010 0
--3.18e-010 0
--3.17e-010 0
--3.16e-010 0
--3.15e-010 0
--3.14e-010 0
--3.13e-010 0
--3.12e-010 0
--3.11e-010 0
--3.1e-010 0
--3.09e-010 0
--3.08e-010 0
--3.07e-010 0
--3.06e-010 0
--3.05e-010 0
--3.04e-010 0
--3.03e-010 0
--3.02e-010 0
--3.01e-010 0
--3e-010 0
--2.99e-010 0
--2.98e-010 0
--2.97e-010 0
--2.96e-010 0
--2.95e-010 0
--2.94e-010 0
--2.93e-010 0
--2.92e-010 0
--2.91e-010 0
--2.9e-010 0
--2.89e-010 0
--2.88e-010 0
--2.87e-010 0
--2.86e-010 0
--2.85e-010 0
--2.84e-010 0
--2.83e-010 0
--2.82e-010 0
--2.81e-010 0
--2.8e-010 0
--2.79e-010 0
--2.78e-010 0
--2.77e-010 0
--2.76e-010 0
--2.75e-010 0
--2.74e-010 0
--2.73e-010 0
--2.72e-010 0
--2.71e-010 0
--2.7e-010 0
--2.69e-010 0
--2.68e-010 0
--2.67e-010 0
--2.66e-010 0
--2.65e-010 0
--2.64e-010 0
--2.63e-010 0
--2.62e-010 0
--2.61e-010 0
--2.6e-010 0
--2.59e-010 0
--2.58e-010 0
--2.57e-010 0
--2.56e-010 0
--2.55e-010 0
--2.54e-010 0
--2.53e-010 0
--2.52e-010 0
--2.51e-010 0
--2.5e-010 0
--2.49e-010 0
--2.48e-010 0
--2.47e-010 0
--2.46e-010 0
--2.45e-010 0
--2.44e-010 0
--2.43e-010 0
--2.42e-010 0
--2.41e-010 0
--2.4e-010 0
--2.39e-010 0
--2.38e-010 0
--2.37e-010 0
--2.36e-010 0
--2.35e-010 0
--2.34e-010 0
--2.33e-010 0
--2.32e-010 0
--2.31e-010 0
--2.3e-010 0
--2.29e-010 0
--2.28e-010 0
--2.27e-010 0
--2.26e-010 0
--2.25e-010 0
--2.24e-010 0
--2.23e-010 0
--2.22e-010 0
--2.21e-010 0
--2.2e-010 0
--2.19e-010 0
--2.18e-010 0
--2.17e-010 0
--2.16e-010 0
--2.15e-010 0
--2.14e-010 0
--2.13e-010 0
--2.12e-010 0
--2.11e-010 0
--2.1e-010 0
--2.09e-010 0
--2.08e-010 0
--2.07e-010 0
--2.06e-010 0
--2.05e-010 0
--2.04e-010 0
--2.03e-010 0
--2.02e-010 0
--2.01e-010 0
--2e-010 0
--1.99e-010 0
--1.98e-010 0
--1.97e-010 0
--1.96e-010 0
--1.95e-010 0
--1.94e-010 0
--1.93e-010 0
--1.92e-010 0
--1.91e-010 0
--1.9e-010 0
--1.89e-010 0
--1.88e-010 0
--1.87e-010 0
--1.86e-010 0
--1.85e-010 0
--1.84e-010 0
--1.83e-010 0
--1.82e-010 0
--1.81e-010 0
--1.8e-010 0
--1.79e-010 0
--1.78e-010 0
--1.77e-010 0
--1.76e-010 0
--1.75e-010 0
--1.74e-010 0
--1.73e-010 0
--1.72e-010 0
--1.71e-010 0
--1.7e-010 0
--1.69e-010 0
--1.68e-010 0
--1.67e-010 0
--1.66e-010 0
--1.65e-010 0
--1.64e-010 0
--1.63e-010 0
--1.62e-010 0
--1.61e-010 0
--1.6e-010 0
--1.59e-010 0
--1.58e-010 0
--1.57e-010 0
--1.56e-010 0
--1.55e-010 0
--1.54e-010 0
--1.53e-010 0
--1.52e-010 0
--1.51e-010 0
--1.5e-010 0
--1.49e-010 0
--1.48e-010 0
--1.47e-010 0
--1.46e-010 0
--1.45e-010 0
--1.44e-010 0
--1.43e-010 0
--1.42e-010 0
--1.41e-010 0
--1.4e-010 0
--1.39e-010 0
--1.38e-010 0
--1.37e-010 0
--1.36e-010 0
--1.35e-010 0
--1.34e-010 0
--1.33e-010 0
--1.32e-010 0
--1.31e-010 0
--1.3e-010 0
--1.29e-010 0
--1.28e-010 0
--1.27e-010 0
--1.26e-010 0
--1.25e-010 0
--1.24e-010 0
--1.23e-010 0
--1.22e-010 0
--1.21e-010 0
--1.2e-010 0
--1.19e-010 0
--1.18e-010 0
--1.17e-010 0
--1.16e-010 0
--1.15e-010 0
--1.14e-010 0
--1.13e-010 0
--1.12e-010 0
--1.11e-010 0
--1.1e-010 0
--1.09e-010 0
--1.08e-010 0
--1.07e-010 0
--1.06e-010 0
--1.05e-010 0
--1.04e-010 0
--1.03e-010 0
--1.02e-010 0
--1.01e-010 0
--1e-010 0
--9.9e-011 0
--9.8e-011 0
--9.7e-011 0
--9.6e-011 0
--9.5e-011 0
--9.4e-011 0
--9.3e-011 0
--9.2e-011 0
--9.1e-011 0
--9e-011 0
--8.9e-011 0
--8.8e-011 0
--8.7e-011 0
--8.6e-011 0
--8.5e-011 0
--8.4e-011 0
--8.3e-011 0
--8.2e-011 0
--8.1e-011 0
--8e-011 0
--7.9e-011 1
--7.8e-011 1
--7.7e-011 0
--7.6e-011 0
--7.5e-011 0
--7.4e-011 1
--7.3e-011 0
--7.2e-011 2
--7.1e-011 0
--7e-011 0
--6.9e-011 0
--6.8e-011 2
--6.7e-011 1
--6.6e-011 1
--6.5e-011 3
--6.4e-011 6
--6.3e-011 3
--6.2e-011 8
--6.1e-011 5
--6e-011 10
--5.9e-011 3
--5.8e-011 11
--5.7e-011 7
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diff --git a/SiPMSpectra/03/F1_U28p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U28p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index 9015fd5a8c5fc4c8914233f81bbbc33f144eaef5..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U28p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U29p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U29p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index bf8d481a42c9ec88f12f8b42ae63ff9c005d8f00..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U29p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U29p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U29p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index 2cf03b136be127323e8715955f181388494f8cf0..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U29p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U30p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U30p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index dcfb326385fde2c6ada25cfff387d92c0537726d..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U30p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U30p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U30p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index 43c06cd079f007aa4d8505df9efce9330db3897c..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U30p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U31p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U31p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index bec07a7ffc9b309b794e01438ab4d68bdd38a4fe..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U31p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U31p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U31p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index fbd76d334d4ea5036580d587f451aa77015898b6..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U31p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U32p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U32p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index 8ea5c100cf9c1117a74d585a1113003be922a73d..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U32p5_dark_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/03/F1_U32p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat b/SiPMSpectra/03/F1_U32p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
deleted file mode 100644
index 994d80feb8a481f7630f3aa843d19930cc50c688..0000000000000000000000000000000000000000
--- a/SiPMSpectra/03/F1_U32p5_light_PM3325WBA0-03_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/04_ZnS/F1_U28p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U28p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index c599fa21df4147591730e188e7d868e1c3fbafb6..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U28p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/04_ZnS/F1_U28p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U28p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 59abd9912eaa8cb4c3a6093c4ea91bee282a7bb8..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U28p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/04_ZnS/F1_U29p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U29p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index e24854f195cc8dd7838a6f4fe513477dcfb4b7b2..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U29p1_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/04_ZnS/F1_U29p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U29p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index fe7b8e8e24b40911beaf2d47a9e54b830b50e048..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U29p1_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/04_ZnS/F1_U30p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U30p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 0f692c0a3e66bb6c133f8e882de44f9801a7906e..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U30p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/04_ZnS/F1_U30p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U30p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 95ed7d51c9387778be476191da96056cc44691a7..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U30p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/04_ZnS/F1_U31p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U31p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index a5cc74569877a03f85497bdc8fee1f0c916a83d3..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U31p6_dark_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/04_ZnS/F1_U31p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/04_ZnS/F1_U31p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 5ef44a95086586c48e29dc5bd778b980b5b1ed64..0000000000000000000000000000000000000000
--- a/SiPMSpectra/04_ZnS/F1_U31p6_light_PM3325WBA0-04_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/06_ZnS/F1_U28p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U28p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index ad74d2ac87ff7c5eaf2e47da5a595629548b52cb..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U28p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U28p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U28p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 9da15c5fdc9b8f2992016a4ef9bd64c14d991323..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U28p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U29p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U29p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index acd80898cc69c7addbd46ca7ba548f122ad999ec..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U29p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U29p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U29p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 021edf68191f7b63b250173873799e6b0b98d0ce..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U29p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/06_ZnS/F1_U30p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U30p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 865c693664c7cbc1ac881db91e04c3ee881a2828..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U30p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U30p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U30p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 8e7e7cab36852e94d936b83bad8f3ca7e9fe1d58..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U30p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U31p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U31p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index c794cb1e152bb046dec830c735cc43c54ce8f388..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U31p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U31p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U31p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index cdb1eda6e1edb5304753533b118d4c587facb5ae..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U31p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/SiPMSpectra/06_ZnS/F1_U32p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U32p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 612c5a5b415727621e1997c0dba9672c0ac9dac5..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U32p4_dark_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/06_ZnS/F1_U32p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat b/SiPMSpectra/06_ZnS/F1_U32p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
deleted file mode 100644
index 8364aa73ece488f737a834e5f3f8b27a23f0a998..0000000000000000000000000000000000000000
--- a/SiPMSpectra/06_ZnS/F1_U32p4_light_PM3315WBA0-06_ZnS_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U28p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U28p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index cfb1a7225b28ab71dcdfd0dace329b3f4460970c..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U28p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U28p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U28p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 24546a55c64dae69b32180281fe91c6f2183fe1a..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U28p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U30p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U30p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 76ab51df839fa07ca04d60c8d6aaa66b2236aba8..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U30p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
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diff --git a/SiPMSpectra/07/F1_U30p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U30p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index b0cd2641a5a07741f761598e6502a32462102790..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U30p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U31p6_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U31p6_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 0311cbaee2d2d24c60ae04d20c634974cd9ecc56..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U31p6_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U31p6_light_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U31p6_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 440563956b76a3c73f73c64333b2f1719f6086fb..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U31p6_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U33p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U33p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 3e374b7e1fa9e843cb4a455e439fbc19a7f343f2..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U33p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U33p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U33p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 3812c41246359f6103609684e5948dc49b058296..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U33p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U35p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U35p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index a5dd7e70fd97de985d0ca79f828e4803a0796618..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U35p1_dark_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/07/F1_U35p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat b/SiPMSpectra/07/F1_U35p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
deleted file mode 100644
index 045b39410e1acdff50257e7af0c9777dc2446c6c..0000000000000000000000000000000000000000
--- a/SiPMSpectra/07/F1_U35p1_light_PM3315WBA0-07_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U28p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U28p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index fed4cc3a0b114b778d9a703dc499c63ac532992a..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U28p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U28p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U28p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index 486a5f672bf76ee0e3cf68631c43f63da87c297f..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U28p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U29p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U29p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index 3d46d79adebea9a06425fc65be731235d5d947d3..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U29p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U29p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U29p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index d5aa0f94432df7914a11b7e22171e67f926c56cc..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U29p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U31p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U31p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index d58e2b78567c42cb3535989b90b141159fd2ef35..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U31p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U31p1_light_PM33125WBA0-08_AmpG101_660nm_00001.dat b/SiPMSpectra/08/F1_U31p1_light_PM33125WBA0-08_AmpG101_660nm_00001.dat
deleted file mode 100644
index e9c4410b88e914e973c4881eeebf7b74f9eb7d9a..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U31p1_light_PM33125WBA0-08_AmpG101_660nm_00001.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U32p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U32p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index ca1ca5cb4026eda1f12f9afe7f35752a8b705164..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U32p6_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U32p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U32p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index 08f8f3dd6293db44c8f36e252436bf4ba4f5fd32..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U32p6_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U34p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U34p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index e5dd891c1e548d3a36440c13ab3af8006988f089..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U34p1_dark_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/08/F1_U34p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat b/SiPMSpectra/08/F1_U34p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
deleted file mode 100644
index 468eb3d55a7855e451a3f1cf056655e9a00fc628..0000000000000000000000000000000000000000
--- a/SiPMSpectra/08/F1_U34p1_light_PM33125WBA0-08_AmpG101_660nm_00000.dat
+++ /dev/null
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diff --git a/SiPMSpectra/KETEK_spectra.opju b/SiPMSpectra/KETEK_spectra.opju
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index 2dde048287fd3b692f20707641b640ad6c3bbb7f..0000000000000000000000000000000000000000
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_53.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_53.5V_histo.txt
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index 0000000000000000000000000000000000000000..e3dc34872c8ba55fe3ab05ebfe1c362e5e6ff2c4
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_53V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_53V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d253b21e0781a54bc1020c2ef3e36ee54fef8f57
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_53V_histo.txt
@@ -0,0 +1,3008 @@
+Digitizer
+ch.0: TRUE ch.1: FALSE DC offset 0: 0 DC offset 1: 0 act ch: 0 trig mode: FALSE rise time: 8 trig mean: 8 trig thr 0: 30 trig thr 1: 10 gate mode: FALSE gate width: 104 pre gate: 64 holdoff: 504 bsln mean: 16 bsln thr: 8 noflattime: 512 GPO: 2 coinc: FALSE coinc time: 0
+PSAU
+ch.0: TRUE ch.1: FALSE Vset 0: 53.000000 Vset 1: 71.000000 Gain0: 32 Gain1: 32 comp0: FALSE comp1: FALSE thr0: -14.000000 thr1: -15.000000 width0: 110 width1: 110 DOUT0: TRUE DOUT1: FALSE outputlevel: TTL coinc: FALSE coinc time: 20
+
+Vmon 0: 0.000000 Vmon 1: 0.000000 Temp 0: 0.000000 Temp 1: 0.000000 Temp board: 0.000000
+day&time: 16Feb17085101
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_54.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_54.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ead983e2aaa67184b76c645820f567204b90177b
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_54.5V_histo.txt
@@ -0,0 +1,3008 @@
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_54V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_54V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f987c263c1f906e16a4c635962ff03ac024a87a
--- /dev/null
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_55.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_55.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb219e726f6d6508c94f22e203c037126944d16d
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_55.5V_histo.txt
@@ -0,0 +1,3008 @@
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_55V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_55V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..df8f133e7b6c109ac822bcd4ce7eb755c74229e3
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_56.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_56.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c6202a30320fe79c2f642c63ccd82350e07a3cf2
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_56.5V_histo.txt
@@ -0,0 +1,3008 @@
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+ch.0: TRUE ch.1: FALSE DC offset 0: 0 DC offset 1: 0 act ch: 0 trig mode: FALSE rise time: 8 trig mean: 8 trig thr 0: 30 trig thr 1: 10 gate mode: FALSE gate width: 104 pre gate: 64 holdoff: 504 bsln mean: 16 bsln thr: 8 noflattime: 512 GPO: 2 coinc: FALSE coinc time: 0
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+ch.0: TRUE ch.1: FALSE Vset 0: 56.500000 Vset 1: 71.000000 Gain0: 32 Gain1: 32 comp0: FALSE comp1: FALSE thr0: -14.000000 thr1: -15.000000 width0: 110 width1: 110 DOUT0: TRUE DOUT1: FALSE outputlevel: TTL coinc: FALSE coinc time: 20
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_56V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_56V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a47d1ab13a8cde23eee651df6895c4145013e74
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_57.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_57.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dbdd5e300aeaaf4b5dd4aaef499c4bb8eccaaff1
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_57.5V_histo.txt
@@ -0,0 +1,3008 @@
+Digitizer
+ch.0: TRUE ch.1: FALSE DC offset 0: 0 DC offset 1: 0 act ch: 0 trig mode: FALSE rise time: 8 trig mean: 8 trig thr 0: 30 trig thr 1: 10 gate mode: FALSE gate width: 104 pre gate: 64 holdoff: 504 bsln mean: 16 bsln thr: 8 noflattime: 512 GPO: 2 coinc: FALSE coinc time: 0
+PSAU
+ch.0: TRUE ch.1: FALSE Vset 0: 57.500000 Vset 1: 71.000000 Gain0: 32 Gain1: 32 comp0: FALSE comp1: FALSE thr0: -14.000000 thr1: -15.000000 width0: 110 width1: 110 DOUT0: TRUE DOUT1: FALSE outputlevel: TTL coinc: FALSE coinc time: 20
+
+Vmon 0: 57.330002 Vmon 1: 0.000000 Temp 0: 23.299999 Temp 1: 20.799999 Temp board: 25.100000
+day&time: 16Feb17095226
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_57V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_57V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c86a2519b41fc1d10d6478cab90dbaa52463cf7f
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_57V_histo.txt
@@ -0,0 +1,3008 @@
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_58.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_58.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ed40c04b6198b5d24925a03bd87b1b4d6cf60b9
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_58.5V_histo.txt
@@ -0,0 +1,3008 @@
+Digitizer
+ch.0: TRUE ch.1: FALSE DC offset 0: 0 DC offset 1: 0 act ch: 0 trig mode: FALSE rise time: 8 trig mean: 8 trig thr 0: 30 trig thr 1: 10 gate mode: FALSE gate width: 104 pre gate: 64 holdoff: 504 bsln mean: 16 bsln thr: 8 noflattime: 512 GPO: 2 coinc: FALSE coinc time: 0
+PSAU
+ch.0: TRUE ch.1: FALSE Vset 0: 58.500000 Vset 1: 71.000000 Gain0: 32 Gain1: 32 comp0: FALSE comp1: FALSE thr0: -14.000000 thr1: -15.000000 width0: 110 width1: 110 DOUT0: TRUE DOUT1: FALSE outputlevel: TTL coinc: FALSE coinc time: 20
+
+Vmon 0: 58.360001 Vmon 1: 0.000000 Temp 0: 23.500000 Temp 1: 20.799999 Temp board: 25.100000
+day&time: 16Feb17100521
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_58V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_58V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0493a2b671dbc89deddecda3fbb44045b8971eb3
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_58V_histo.txt
@@ -0,0 +1,3008 @@
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_59.5V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_59.5V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f862b69d21279f558d798fe516cd7b7d00b8383b
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_59V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_59V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e1f10799cd1c58216569b6f40373ac24f17a235
--- /dev/null
+++ b/data/hamamatsu_pcb6/Light/PCB6_MPPC_59V_histo.txt
@@ -0,0 +1,3008 @@
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diff --git a/data/hamamatsu_pcb6/Light/PCB6_MPPC_60V_histo.txt b/data/hamamatsu_pcb6/Light/PCB6_MPPC_60V_histo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..221f08739af4c80f0448a2833738fb6a55a93844
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diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 9863e31955a0d76b22814ceecb1f6ce3332255fa..0000000000000000000000000000000000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-astropy==4.0.2
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-scikit_learn==1.0.1
-scipy==1.5.2