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9e30c64d43
...
dd6064c6c3
4 changed files with 18 additions and 256 deletions
12
BigBGO.py
12
BigBGO.py
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@ -71,12 +71,12 @@ def main():
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data = [[],[],[],[],[],[],[]]
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for line in f:
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l = line.split()
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if not 'EOF' in l:
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if len(l) > 22:
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if l[0] == 'EI':
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if (not args.xray and not args.muon) or(args.xray and isXray(l)) or (args.muon and ismuon(l)):
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if not args.trigger or (args.trigger and CmV(l,ch[3]) >= thr[3]):
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for i in range(0,len(ch)):
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data[i].append(CmV(l,ch[i])*u[i])
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data[i].append(CmV(l,ch[i]))
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h = do_hist(data)
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if args.plot:
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for i in range(0,len(ch)):
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@ -91,7 +91,7 @@ def main():
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data = [[],[]]
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for line in f:
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l = line.split()
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if not 'EOF' in l:
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if len(l) > 22:
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if l[0] == 'EI':
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if not args.trigger or (args.trigger and CmV(l,ch[3]) >= thr[3]):
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if not args.muon or (args.muon and ismuon(l)):
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@ -132,11 +132,11 @@ def Cphase(line, i):
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def do_hist(data):
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bin = int((maxV-minV)/resV)
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hist, bins = np.histogram(data[0], bins=bin, range=(minV,maxV), density=False)
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df = pd.DataFrame({'keV':bins[1:]/2+ bins[:-1]/2, name[0]: hist})
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df = pd.DataFrame({'mV':bins[1:]/2+ bins[:-1]/2, name[0]: hist})
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for i in range(1, len(ch)):
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hist, bins = np.histogram(data[i], bins=bin, range=(minV,maxV), density=False)
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df1 = pd.DataFrame({'keV':bins[1:]/2+ bins[:-1]/2, name[i]: hist})
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df = pd.merge(df, df1 , left_on = 'keV', right_on = 'keV')
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df1 = pd.DataFrame({'mV':bins[1:]/2+ bins[:-1]/2, name[i]: hist})
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df = pd.merge(df, df1 , left_on = 'mV', right_on = 'mV')
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return df
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def do_hist_sum(data):
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24
histplot.py
24
histplot.py
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@ -49,7 +49,7 @@ def main():
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channels = []
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end = '.hist'
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energy = 'mV'
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keV = False
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raw = args.raw
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u = args.norm
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if not args.channels == None:
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@ -62,7 +62,7 @@ def main():
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else:
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if not args.dhist == None:
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if 'keV' in args.dhist:
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energy = 'keV'
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keV = True
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for ch in args.dhist:
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channels.append(str(ch))
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end = '.'+args.filepath.split('.')[-1]
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@ -84,12 +84,9 @@ def main():
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raw = True
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plt.figure(figsize=(17,12))
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if 'keV' in data:
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energy = 'keV'
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print_data(data,Itime,channels, u, keV)
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print_data(data,Itime,channels, u, energy)
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layout(tit,raw,args.limits,energy)
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layout(tit,raw,args.limits,keV)
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if args.export == None:
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@ -125,7 +122,10 @@ def read_data(file, end, raw, dhist):
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return data, Itime
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def print_data(data, Itime, channels, u,energy):
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def print_data(data, Itime, channels, u,keV):
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energy = 'mV'
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if keV:
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energy = 'keV'
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resV = 0.838214/2
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if not u == None:
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if len(u) == len(channels):
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@ -145,11 +145,11 @@ def print_data(data, Itime, channels, u,energy):
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def layout(tit,raw, limits,energy):
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def layout(tit,raw, limits,keV):
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# energy = 'mV'
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# if keV:
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# energy = 'keV'
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energy = 'mV'
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if keV:
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energy = 'keV'
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fs = 15
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lim = [None, None, None, None]
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118
landau_sum.py
118
landau_sum.py
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@ -1,118 +0,0 @@
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'''
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Autor: Ava Pohley
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'''
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import argparse
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import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from scipy import special
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from scipy import constants
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from scipy.optimize import curve_fit
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import os.path
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file = 'data/2023-07-24-BigBGO-Bi207h_sum'
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keV = True
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tit = "Comparison sumA-sumB of '2023-04-07-BigBGO-Bi207'"
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plt.figure(figsize=(17,12))
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data = pd.read_csv(file+'.hist', sep = ' ', delim_whitespace=False, skiprows=0)
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f = file
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Itime = None
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while len(f) > 0:
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if os.path.isfile(f+'.Itime'):
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Itime = int(pd.read_csv(f+'.Itime', sep = ' ', delim_whitespace=False, skiprows=0, names = ['time'])['time'][0])
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f=''
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else:
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f=f[:-1]
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# FIT FUNCTIONS
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def landau(x):
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return np.sqrt(np.exp(np.subtract(-x,np.exp(-x)))/np.pi/2)
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def mips(x, a,e,s):
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return a * landau(np.subtract(x,e)/s)
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def find_par(data, energy, e1, e2):
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a=0
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go = True
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while go == True:
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a = a+1
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if data[energy][a]>=e1:
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go = False
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go = True
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b = a
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while go == True:
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b = b+1
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if data[energy][b]>=e2:
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go = False
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return [a,b]
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def find_par_new(f1, f2, pa,u, pos):
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val_a = pa[1]-f1*pa[2]
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val_b = pa[1]+f2*pa[2]
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a = pos
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b = pos
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go = True
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while go == True:
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a = a-1
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if u[a]<=val_a:
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go = False
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go = True
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while go == True:
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b = b+1
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if u[b]>=val_b:
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go = False
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return [a,b]
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a = 670
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b = 1000
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startparams = [3000,220,45]
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energy = 'keV'
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# plt.axvline(data[energy][a])
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# plt.axvline(data[energy][b])
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resV = 0.838214/2
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for ch in ['sumA','sumB']:
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sig = np.power(data[ch]+1, 0.5)
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par, cov = curve_fit(mips, data[energy][a:b], data[ch][a:b], startparams, sigma = sig[a:b], absolute_sigma=True)
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print(par)
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perr = np.sqrt(np.diag(cov))
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print(perr)
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if not Itime==None:
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plt.plot(data[energy], np.multiply(data[ch], 3600/resV/Itime), label=ch+' ; $e$ = ('+ str(round(par[1],2)) + '$\pm$' +str(round(perr[1]+0.005,2))+')'+energy)
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plt.plot(data[energy], np.multiply(mips(data[energy], *par), 3600/resV/Itime), 'k--')
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else:
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plt.plot(data[energy], data[ch], label=ch+' ; $e$ = ('+ str(round(par[1],2)) + '$\pm$' +str(round(perr[1]+0.005,2))+')'+energy)
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plt.plot(data[energy], mips(data[energy]*par), 'k--')
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fs = 15
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ylab = 'counts [1/h/keV]'
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xlab = 'A [' + energy +']'
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plt.plot(0, 0,'k--', linewidth=1, label = 'landau_fit')
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plt.legend(fontsize = fs)
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plt.xticks(fontsize=fs)
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plt.yticks(fontsize=fs)
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plt.title(tit, fontsize = fs+10, pad = 15)
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plt.yscale('log')
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plt.ylabel(ylab, fontsize = fs+5)
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plt.xlabel(xlab, fontsize=fs+5)
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plt.ylim(3,100)
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plt.xlim(100,550)
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plt.show()
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#plt.savefig('comparison_sums.pdf')
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#---------------------------------------------------------------------------------------------------------
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@ -1,120 +0,0 @@
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'''
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Autor: Ava Pohley
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'''
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from scipy import special
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from scipy import constants
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from scipy.optimize import curve_fit
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import os.path
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file = 'data/2023-07-24-BigBGO-Bi207h_muon_trigger'
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keV = True
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tit = "Comparison single dioden when T1 has triggered '2023-04-07-BigBGO-Bi207'"
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plt.figure(figsize=(17,12))
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data = pd.read_csv(file+'.hist', sep = ' ', delim_whitespace=False, skiprows=0)
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ch = pd.read_csv(file+'.hist', sep = ' ', nrows=0)
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channels=['A1','A2','A3','B1','B2']
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f = file
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Itime = None
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while len(f) > 0:
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if os.path.isfile(f+'.Itime'):
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Itime = int(pd.read_csv(f+'.Itime', sep = ' ', delim_whitespace=False, skiprows=0, names = ['time'])['time'][0])
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f=''
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else:
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f=f[:-1]
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# FIT FUNCTIONS
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def landau(x):
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return np.sqrt(np.exp(np.subtract(-x,np.exp(-x)))/np.pi/2)
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def mips(x, a,e,s):
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return a * landau(np.subtract(x,e)/s)
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def find_par(data, energy, e1, e2):
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a=0
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go = True
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while go == True:
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a = a+1
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if data[energy][a]>=e1:
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go = False
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go = True
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b = a
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while go == True:
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b = b+1
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if data[energy][b]>=e2:
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go = False
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return [a,b]
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def find_par_new(f1, f2, pa,u, pos):
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val_a = pa[1]-f1*pa[2]
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val_b = pa[1]+f2*pa[2]
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a = pos
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b = pos
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go = True
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while go == True:
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a = a-1
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if u[a]<=val_a:
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go = False
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go = True
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while go == True:
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b = b+1
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if u[b]>=val_b:
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go = False
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return [a,b]
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a = [207,196,197,248,233]
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b = [270,270,270,320,320]
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energy = 'keV'
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startparams = [[150,85,10],[141,72,11],[141,72,11],[110,125,15],[110,110,16]]
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resV = 0.838214/2
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# plt.plot(data[energy], np.multiply(mips(data[energy], 150, 75, 15), 3600/resV/Itime),'--',label = '1')
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# plt.plot(data[energy], np.multiply(mips(data[energy], 140, 75, 15), 3600/resV/Itime),'--',label = '2')
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# plt.plot(data[energy], np.multiply(mips(data[energy], 130, 75, 15), 3600/resV/Itime),'--',label = '3')
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# plt.axvline(data[energy][a])
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# plt.axvline(data[energy][a1], color = 'r')
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for i in range (0,len(channels)):
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ch = channels[i]
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sig = np.power(data[channels[i]]+1, 0.5)
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par, cov = curve_fit(mips, data[energy][a[i]:b[i]], data[ch][a[i]:b[i]], startparams[i], sigma = sig[a[i]:b[i]], absolute_sigma=True)
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print(par)
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perr = np.sqrt(np.diag(cov))
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print(perr)
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if not Itime==None:
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#plt.plot(data[energy], np.multiply(data[channels[i]], 3600/resV/Itime), label=channels[i])
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plt.plot(data[energy], np.multiply(data[ch], 3600/resV/Itime), label=ch+' ; $e$ = ('+ str(round(par[1],2)) + '$\pm$' +str(round(perr[1]+0.005,2))+')'+energy)
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plt.plot(data[energy], np.multiply(mips(data[energy], *par), 3600/resV/Itime), 'k--')
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# else:
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# plt.plot(data[energy], data[ch], label=ch+' ; $e$ = ('+ str(round(par[1],2)) + '$\pm$' +str(round(perr[1]+0.005,2))+')'+energy)
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# plt.plot(data[energy], mips(data[energy]*par), 'k--')
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fs = 15
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ylab = 'counts [1/h/keV]'
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xlab = 'A [' + energy +']'
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plt.plot(0, 0,'k--', linewidth=1, label = 'landau_fit')
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plt.legend(fontsize = fs)
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plt.xticks(fontsize=fs)
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plt.yticks(fontsize=fs)
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plt.title(tit, fontsize = fs+10, pad = 15)
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plt.yscale('log')
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plt.ylabel(ylab, fontsize = fs+5)
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plt.xlabel(xlab, fontsize=fs+5)
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plt.ylim(0.09,3)
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plt.xlim(0,300)
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plt.show()
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#plt.savefig('comparison_sums.pdf')
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