example.py

import os
import sys
import numpy as np
import pandas as pd
from PeakOTron import PeakOTron
from joblib import dump
from time import time


print("--------------------")
print('EXAMPLE SIPM CALIBRATION RUN')
print("--------------------")


out_dict = {}
files_to_fit = []

## Find all histograms in directory 
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.
print("Files to fit:")
for i, (file, _) in enumerate(files_to_fit):
    print('File {0}: {1}'.format(i, file))
    
    
    
SiPM = "PM1125NS_SBO"


## Loop thorough files
for i, (file, path) in enumerate(files_to_fit):
    items = file.split('_')

    V = float(items[2].replace('V', '').replace('p', '.'))
        

    ## Load files. 
    data = np.loadtxt(path, skiprows=8)

    ## Create a PeakOTron Fit Object. 
    f_data = PeakOTron(verbose=True)

    ## Perform fit. 
    time_start = time.time()
    f_data.Fit(data, 
          tau=21.953,  #SLOW PULSE COMPONENT TIME CONSTANT (ns)
          t_gate=104, #GATE LENGTH (ns)
          t_0 = 64, #INTEGRATION TIME BEFORE GATE (ns)               
          tau_R=21.953,
          truncate_nG=0.333
              
    ) #BINNING RULE "knuth", "freedman", "scott" - use bw= #### to override. it is still required for DCR calculation. 
    time_end = time.time()

    print("Fit took {:3.3f} s".format(time_end - time_start))

    f_data.PlotFit(xlabel="ADC", save_directory="./Results/{0}_fit.png".format(os.path.splitext(file)[0]))
    

    
    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))