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sumlab_auto.py 4.32 KiB
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from PeakOTron import PeakOTron
import pandas as pd
import numpy as np
import argparse
def float_or_none(value):
return None if value.lower() == 'none' else float(value)
parser = argparse.ArgumentParser(description='Fit SiPM data')
parser.add_argument('-V_bd_hmt', type=float,
default=26.1, help='V_bd_hmt value')
parser.add_argument('-V_0_hmt', type=float, default=1.4, help='V_0_hmt value')
parser.add_argument('-tau', type=float, default=20.0,
help='SLOW COMPONENT OF SIPM PULSE')
parser.add_argument('-t_0', type=float, default=100.0,
help='PRE-INTEGRATION TIME')
parser.add_argument('-t_gate', type=float, default=100.0, help='GATE LENGTH')
parser.add_argument('-bin_0', type=float_or_none, default=-100.0,
help='SELECT FIRST BIN OF SPECTRUM')
parser.add_argument('-truncate_nsigma0_up', type=float_or_none,
default=2.0, help='SCAN SPECTRUM FROM Q < Q_0 - 4 sigma_0')
parser.add_argument('-truncate_nsigma0_do', type=float_or_none, default=2.0,
help='EVALUATE SPECTRUM CHI2 IN Q_0 - x*sigma_0 < Q < Q_0 + 2*sigma_0')
parser.add_argument('-prefit_only', action='store_true',
help='FIT THE WHOLE SPECTRUM')
parser.add_argument('-folder', type=str, default='data/sumlab',
help='Directory containing the data files')
args = parser.parse_args()
def C_tau(V, V_bd, V_0): return (V - V_bd)/V_0
def f_tau(V, V_bd, V_0): return -1/np.log((1-np.exp(C_tau(V, V_bd, V_0)
* np.exp(-1)))/(1 - np.exp(C_tau(V, V_bd, V_0))))
V_bd_hmt = args.V_bd_hmt
V_0_hmt = args.V_0_hmt
tau = args.tau # SLOW COMPONENT OF SIPM PULSE
t_0 = args.t_0 # PRE-INTEGRATION TIME
t_gate = args.t_gate # GATE LENGTH
bin_0 = args.bin_0 # SELECT FIRST BIN OF SPECTRUM (CAN BE AUTOMATIC)
# SCAN SPECTRUM FROM Q < Q_0 - 4 sigma_0
truncate_nsigma0_up = args.truncate_nsigma0_up
# EVALUATE SPECTRUM CHI2 IN Q_0 - x*sigma_0 < Q < Q_0 + 2*sigma_0
truncate_nsigma0_do = args.truncate_nsigma0_do
prefit_only = args.prefit_only # FIT THE WHOLE SPECTRUM
out_dict = {}
files_to_fit = []
# Find all histograms in directory
folder = args.folder
for root, dirs, files in os.walk(folder):
for file in files:
if file.endswith(".txt"):
files_to_fit.append([file, os.path.join(root, file)])
print("\033[95m\n=======================================")
print(" PeakOTron")
print("=======================================\033[0m")
# Loop thorough files
for i, (file, path) in enumerate(files_to_fit):
print("\033[95mFitting: {:s}\033[0m".format(file))
V = float(file.split('deg')[1].split('V')[0].replace('_', '.'))
f_tau_hmt = f_tau(V, V_bd_hmt, V_0_hmt)
# Load files.
data = np.loadtxt(path, skiprows=0)
# Create a PeakOTron Fit Object.
f_data = PeakOTron(verbose=False)
# Perform fit.
f_data.Fit(data,
tau=tau, # SLOW PULSE COMPONENT TIME CONSTANT (ns)
t_gate=t_gate, # GATE LENGTH (ns)
t_0=t_0, # INTEGRATION TIME BEFORE GATE (ns)
tau_R=f_tau_hmt*tau,
bin_0=bin_0,
truncate_nsigma0_up=truncate_nsigma0_up,
truncate_nsigma0_do=truncate_nsigma0_do
)
f_data.PlotFit(plot_in_bins=True, display=False,
save_directory=f"{folder}/{file[:-4]}_fit.png")
fit_out = {}
prefit_val, prefit_err = f_data.GetPrefitResults(bin_units=False)
print("\033[95m"+rf"Prefit: G = {prefit_val.get('G')} d_G = {prefit_err.get('G')}"+"\033[0m")
for key, value in prefit_val.items():
fit_out["prefit_{:s}".format(key)] = value
for key, value in prefit_err.items():
fit_out["prefit_d_{:s}".format(key)] = value
#if not prefit_only:
fit_val, fit_err = f_data.GetFitResults(bin_units=False)
print("\033[95m"+rf"Fit: G = {fit_val.get('G')} d_G = {fit_err.get('G')}"+"\033[0m")
for key, value in fit_val.items():
fit_out["{:s}".format(key)] = value
for key, value in fit_err.items():
fit_out["d_{:s}".format(key)] = value
df = pd.DataFrame.from_dict([fit_out])
df.to_csv("{}/fit_results_{:s}.csv".format(folder, file[:-4]))
print("\033[95m=======================================\033[0m")