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viewer-autosave-fit.py
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viewer-autosave-fit.py
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#! /usr/bin/env python3
from __future__ import division
from __future__ import print_function
import os, sys, inspect
cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe()))[0],"src")))
if cmd_subfolder not in sys.path:
sys.path.insert(0, cmd_subfolder)
from TLeCroy import * # lecroy.py
import matplotlib.pyplot as plt
from scipy.stats import norm
import numpy as np
import scipy.integrate as integrate
import matplotlib.mlab as mlab
from scipy.signal import find_peaks, peak_widths, peak_prominences
# --------------------------------
# constants to be set by the user
# --------------------------------
channelNr = 4
StartTrace = 0
StopTrace =20
#inName = "--testpulse--"
#inName = "--testpulse--autosave--"
inName = "--testpulse--autosave--segments--"
inDir = "/Users/Lennard/Bachelor_Thesis_LF/BA_Lennard_Franz/cs137-longzylinder/"
#inDir = "/Users/Lennard/Bachelor_Thesis_LF/BA_Lennard_Franz/ra226-longzylinder/"
#inDir = "/volumes/lennad/segment/segment-CeBr-cs137/"
#inDir = "/volumes/lennad/Lennard Franz/th-zylinder/"
#inDir = "/volumes/lennad/Lennard Franz/cs137-longzylinder-shaper/"
#inDir = "/Users/Lennard/desktop/osci/lennard/segment-flat-CeBr-CS137/"
#inDir = "/Users/Lennard/desktop/osci/lennard/cs137-longzylinder-shaper/"
#inDir = "/Users/Lennard/destop/osci/lennard/cebr-big-4ch-cs137/"
#inDir = "/Users/Lennard/desktop/osci/lennard/segment-CeBr-ra226/"
#inDir = "/Users/Lennard/desktop/osci/lennard/ra226-filter/"
#inDir = "/Users/Lennard/desktop/autosave-segments/"
#inDir = "/Users/Lennard/desktop/osci/lennard/gag-am241/"
#inDir = "/home/lfranz/work/osci/lennard/segment-longcable-CeBr-CS137/"
#inDir = "/home/lfranz/work/osci/lennard/segment-flat-CeBr-CS137/"
#inDir = "/home/lfranz/work/osci/lennard/segment-flat-plexi-background/"
#inDir = "/home/lfranz/work/osci/lennard/segment-CeBr-am241/"
#inDir = "/home/lfranz/work/osci/lennard/segment-CeBr-cs137/"
#inDir = "/home/lfranz/work/osci/lennard/segment-CeBr-ra226/"
#inDir = "/home/lfranz/work/osci/lennard/segment/"
#inDir = "/home/lfranz/work/autosave-segments/"
#inDir = "/ZIH.fast/projects/d-lab_data/MAPMT/2019_gsi/segment/"
#inDir = "/ZIH.fast/projects/d-lab_data/MAPMT/2019_gsi/segments-new/"
#inDir = "/ZIH.fast/projects/d-lab_data/MAPMT/2019_gsi/segments-new/Autosave-new/"
# inDir = "/ZIH.fast/projects/d-lab_data/MAPMT/2019_gsi/Autosave/"
# --------------------------------
import numpy as np
import scipy.integrate as integrate
#empty array for area of sequnces for every trace
A = np.empty([0])
print(inDir)
for traceNr in range(StartTrace, StopTrace + 1, 1):
print(" INFO: working on trace: ", "C"+ str(channelNr).zfill(1) + inName + str(traceNr).zfill(5) + ".trc")
fileName = inDir + "C" + str(channelNr).zfill(1) + inName + str(traceNr).zfill(5) + ".trc"
TLC = TLeCroy(fileName, debug=True)
#TLC.PrintPrivate()
# TLC.PlotTrace(raw=False)
header = TLC.GetHeader()
x, y = TLC.GetTrace()
y = y - header['VERTICAL_OFFSET']
y = -y
nano = 1e-9
#print('----------------')
#print(' vertical gain:', header['VERTICAL_GAIN'])
#print('----------------')
seqLength = header['WAVE_ARRAY_COUNT'] // header['SUBARRAY_COUNT']
seqFirst = header['FIRST_VALID_PNT']
#print(' LENGTH', seqLength)
#plt.plot(x / nano, y)
#arrays
idxHigh = np.zeros(header['SUBARRAY_COUNT'],dtype=np.int)
idxLow = np.zeros(header['SUBARRAY_COUNT'],dtype=np.int)
mean = np.zeros(header['SUBARRAY_COUNT'])
std = np.zeros(header['SUBARRAY_COUNT'])
area = np.zeros(header['SUBARRAY_COUNT'])
area_all = np.zeros(header['SUBARRAY_COUNT'])
area_all_sum = np.zeros(header['SUBARRAY_COUNT'])
area_bool =np.zeros(header['SUBARRAY_COUNT'])
tot = np.zeros(header['SUBARRAY_COUNT'])
# determine background
for seqNr in range(1, header['SUBARRAY_COUNT'] + 1):
#bondarys of sequences
idxHigh[seqNr-1] = seqFirst +( seqLength * seqNr - 1)
idxLow[seqNr-1] = idxHigh[seqNr-1] - seqLength + 1
#finding basline
#y[...] has to be adjusted depending vertical position of the puls
mean[seqNr-1], std[seqNr-1] = norm.fit(y[ idxLow[seqNr-1] : idxLow[seqNr-1] + int(0.35*seqLength)])
mean_all = sum(mean)/header['SUBARRAY_COUNT']
std_all = sum(std)/header['SUBARRAY_COUNT']
#Histogram of values for baseline
#plt.hist(y[ idxLow[seqNr-1] : idxLow[seqNr-1] + int(0.35*seqLength)])
#determination of area of pulses
for seqNr in range(1, header['SUBARRAY_COUNT'] + 1):
# if mean[seqNr -1] > 0.0285 and mean[seqNr - 1] < 0.0315 and np.min( y[idxLow[seqNr-1] : idxHigh[seqNr-1]]-mean[seqNr-1]) > -0.0035:
#area of the pulse
#area[seqNr-1] = np.trapz((y[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]- mean[seqNr-1]) > 3*std[seqNr-1] ,
#x[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]/nano)
#area_all[seqNr-1] = np.sum(y[idxLow[seqNr - 1] : idxHigh[seqNr - 1]])/(nano/header['HORIZ_INTERVAL']) - ((mean_all)*seqLength*header['HORIZ_INTERVAL']/nano)
#print(area_all_sum)
#print(area_all)
#condition to differentiate values of pulse from background
#boolArr = np.where((y[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]- mean[seqNr - 1]) > (3*std[seqNr -1]))
boolArr = (y[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]- mean[seqNr-1]) > (3*std[seqNr-1])
#sum of all values which meet condition and scaling
#substracting of area belonging to background
#print(y[boolArr[0]] - mean[seqNr-1])
#area_bool[seqNr-1] = np.sum(y[boolArr[0]])*(header['HORIZ_INTERVAL']/nano) - (mean[seqNr-1])*(len(boolArr[0])*header['HORIZ_INTERVAL']/nano)
area_bool[seqNr-1] = np.sum((y[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]*(header['HORIZ_INTERVAL']/nano)),where = boolArr) - mean[seqNr-1]*np.sum(boolArr)*header['HORIZ_INTERVAL']/nano
tot[seqNr-1] = (np.sum(boolArr)-1)*header['HORIZ_INTERVAL']/nano
#print(area_bool[seqNr - 1],tot[seqNr - 1] )
#area_bool[seqNr-1] = np.sum(y[boolArr[0]])
#- (mean[seqNr-1])*(len(boolArr[0]))
#print(len(boolArr[0])*header['HORIZ_INTERVAL']/nano)
#plt.hist(len(boolArr[0])*header['HORIZ_INTERVAL']/nano)
#plt.plot(x[idxLow[seqNr - 1]:idxHigh[seqNr - 1]] / nano, y[idxLow[seqNr - 1]:idxHigh[seqNr - 1]]- mean_all)
# plt.plot(x[idxLow[2]:idxHigh[2]] / nano, y[idxLow[2]:idxHigh[2]]- mean_all)
#print("-----------------------")
#print("mean all:",mean_all,"sigma all:", std_all)
#print("array of mean of segments:",mean,"array of sigma of segments",std)
#print("-----------------------")
#appending area of segments to A for every trace
#length of A is number of sequnces * number of traces
#print(y[boolArr[0]] - mean[seqNr-1])
A = np.append(A,area_bool)
#ohne Shaper
E_real = np.array([569, 662,1173,1332,1063])
E_fit = np.array([6.485, 7.2341,10.7222,11.7675,10.2165])
#E_real = np.array([80,186,242,295,352,32,662,1173,1332,1063,569])
#E_fit = np.array([0.97844,2.4133,3.137,3.7597,4.3779,0.3457,7.2341,10.7222,11.7675,10.2165,6.485])
#mit shaper
#E_real = np.array([662,1173,1332,569,1063])
#E_fit = np.array([0.7535521180502402,1.1657715187599953,1.273437535904615,0.701803960984309,1.1086378999695787])
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(E_fit, E_real)
#shaper wolfram alpha
#slope = 1298.14
#intercept = -339.482
#shaper python
#slope = 1298.93696929096
#intercept= -340.41065484554497
#no shaper wolfram
#slope = 143.049
#intercept = -363.694
print(intercept)
print(slope)
E = intercept+slope*A
print(E)
#histogram of all sequences of all traces
n,bins,patches = plt.hist(E,bins=500,histtype='step', color='k')
#print(n,bins,patches)
plt.grid(True)
bin_width= (bins[1]-bins[0])
#plt.xlim(0.,15.0)
peaks,properties = find_peaks(n,width=3, distance=1,height=300, rel_height=0.7)
print("bins of peaks:", peaks)
print("X Values of peaks:", bins[peaks] )
print(properties)
#print(bins[peaks])
#print(bins[np.int(peaks[6] - 1.5* properties["widths"][6]) :np.int( peaks[6]+ 1.5* (properties["widths"][6])) ])
#print(peaks[2] - 1.5* properties["widths"][2])
#print(np.int(peaks[2] - 1.5* properties["widths"][2]))
#print(n,bins)
#peakNr = 6
#print (bins[np.int(peaks[peakNr] - 1.5 * properties["widths"][peakNr]) :np.int( peaks[peakNr]+ 1.5 * (properties["widths"][peakNr])) ])
#print (n[np.int(peaks[peakNr] - 1.5* properties["widths"][peakNr]) :np.int( peaks[peakNr]+ 1.5* (properties["widths"][peakNr])) ] )
from scipy.optimize import curve_fit
def gauss (x,a,sigma,mu):
#return (1/np.sqrt(2*np.pi*sigma**2))* np.exp(- (x-mu)**2 / 2*sigma**2)
return a*np.exp(-(x-mu)**2/(2*sigma**2))
bins = np.delete(bins,[len(bins)-1])
for peakNr in range(1, len(peaks) ):
fit_width = 0.7
x_values = bins[np.int(peaks[peakNr] - fit_width * properties["widths"][peakNr]) :np.int( peaks[peakNr]+ fit_width * (properties["widths"][peakNr])) ]
y_values = n[np.int(peaks[peakNr] - fit_width * properties["widths"][peakNr]) :np.int( peaks[peakNr]+ fit_width * (properties["widths"][peakNr])) ]
#print(x_values)
#print(y_values)
a_bound = [0, 1.1*np.max(y_values)]
sigma_bound = [0, 50]
mu_bound = [np.min(x_values), np.max(x_values)]
#print(a_bound)
#print(sigma_bound)
#print(mu_bound)
#popt, pcov = curve_fit(gauss, bins[np.int(peaks[peakNr] - 1.5 * properties["widths"][peakNr]) :np.int( peaks[peakNr]+ 1.5 * (properties["widths"][peakNr])) ] , n[np.int(peaks[peakNr] - 1.5* properties["widths"][peakNr]) :np.int( peaks[peakNr]+ 1.5* (properties["widths"][peakNr])) ] )
popt, pcov = curve_fit(gauss, x_values , y_values, bounds= ([a_bound[0],sigma_bound[0],mu_bound[0]], [a_bound[1],sigma_bound[1],mu_bound[1]] ) )
plt.plot(bins, gauss(bins, *popt),color='g')
plt.plot((popt[2], popt[2]), (0, 1600), 'r-')
#statistic error of curve fit
perr = np.sqrt(np.diag(pcov))
#FWHM
FWHM = 2* np.sqrt(2*np.log(2)) * popt[1]
delta_FWHM = 2* np.sqrt(2*np.log(2)) * perr[1]
print("Peak Nr:" , peakNr)
print("Energy:" , popt[2], "statistic error of energy:" , perr[2], "FWHM:", FWHM, "statistic error of FWHM", delta_FWHM )
plt.title("Histogramm der deponierten Ladung im Detektor")
plt.xlabel("deponierte Ladung (nVs)")
plt.ylabel("Anzahl N ")
plt.xlim(-500,1000)
plt.ylim(0,1300)
#determination of mean and sigma of data in histogram
#MEAN, STD = norm.fit(A)
#print("--------------------------")
#print("Entries:" , len(A))
#print("--------------------------")
#print("Mean of pulse energy :", MEAN ,"Sigma of pulse energy:", STD)
#print("--------------------------")
plt.show()