RunFit.py

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")