def gausanpassung(x1, y1, x1_error, y1_error):
model = odr.Model(gauss)
width = 0.7
height = 1.
for i in range(0, int(eckdaten[0, 1])):
# print(eckdaten[3+i*2,:])
x = x1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
y = y1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
x_error = x1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
y_error = y1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
# Create a RealData object
data = odr.RealData(x, y, sx=x_error, sy=y_error)
# Set up ODR with the model and data.
presets = [ecal.E(eckdaten[3 + i * 2, 1]), 10, 20, 0., 25.]
# presets=[eckdaten[3+i*2,1], width, y1[int(eckdaten[3+i*2,1])]*height, 0. ,1.]
out = odr.ODR(data, model, beta0=presets).run()
popt = out.beta
perr = out.sd_beta
# maxima[0,i]=popt[0]#a
# maxima[1,i]=perr[0]#a_err
# maxima[2,i]=popt[2]#d
# maxima[3,i]=perr[2]#d_err
# print(popt,min(x), max(x))
maxima[i] = np.vstack((popt, perr))
b = ufloat(popt[1], perr[1])
x_fit = np.linspace(min(x), max(x), 10000)
y_fit = gauss(maxima[i, 0], x_fit)
print(popt[0], perr[0])
# print(np.hstack(np.stack((popt,perr),axis=1)))
plt.plot(x_fit,
y_fit,
label='137-Cs Peak (' + str(round(popt[0], 2)) +
str(round(perr[0], 2)) + '+/-' + str(round(perr[0], 2)) +
') keV')
plt.errorbar(x=popt[0],
y=popt[2] + popt[3] * popt[0] + popt[4],
yerr=perr[2],
xerr=perr[0],
marker='x',
linestyle='None',
color='red',
zorder=3)
# plt.plot(x_fit, gauss(presets, x_fit), color='grey', zorder=-3)
return maxima
def gausanpassung(x1, y1, x1_error, y1_error):
model = odr.Model(gauss)
width = 3.
height = 1.
if detektortyp == 'H':
width = 0.7
height = 1.
for i in range(0, int(eckdaten[0, 1])):
x = x1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
y = y1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
x_error = x1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
y_error = y1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
# Create a RealData object
data = odr.RealData(x, y, sx=x_error, sy=y_error)
# Set up ODR with the model and data.
presets = [
ecal.E(eckdaten[3 + i * 2, 1]), width,
y1[int(eckdaten[3 + i * 2, 1])] * height, 0., 1.
]
out = odr.ODR(data, model, beta0=presets).run()
popt = out.beta
perr = out.sd_beta
b = ufloat(popt[1], perr[1])
maxima[0, i] = popt[0] #a
maxima[1, i] = perr[0] #a_err
print(perr[0])
maxima[2, i] = popt[2] #d
maxima[3, i] = perr[2] #d_err
relint[i] = eckdaten[3 + i * 2, 4]
FWHM = 2 * np.sqrt(2 * np.log(2)) * b
# print(str(i+1)+'. Peak;'+str(round(popt[0],2))+'plusundminus'+str(round(perr[0],2))+';'+str(round(popt[2],2))+'plusundminus'+str(round(perr[2],2)))
# print(datensatz+';'+str(i+1)+'. Peak;'+str(b)+';'+str(FWHM))
x_fit = np.linspace(min(x), max(x), 10000)
y_fit = gauss(popt, x_fit)
plt.plot(x_fit,
y_fit,
label=str(i + 1) + '. Peak (E=(' + str(round(popt[0], 2)) +
'$\pm$' + str(round(perr[0], 2)) + ')keV)')
def gausanpassung(x1, y1, x1_error, y1_error):
model = odr.Model(gauss)
width = 0.7
height = 1.
for i in range(0, int(eckdaten[0, 1])):
# print(eckdaten[3+i*2,:])
x = x1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
y = y1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
x_error = x1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
y_error = y1_error[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2,
2])]
# Create a RealData object
data = odr.RealData(x, y, sx=x_error, sy=y_error)
# Set up ODR with the model and data.
presets = [
ecal.E(eckdaten[3 + i * 2, 1]), width,
y1[int(eckdaten[3 + i * 2, 1])] * height, 0., 1.
]
# presets=[eckdaten[3+i*2,1], width, y1[int(eckdaten[3+i*2,1])]*height, 0. ,1.]
out = odr.ODR(data, model, beta0=presets).run()
popt = out.beta
perr = out.sd_beta
maxima[0, i] = popt[0] #a
maxima[1, i] = perr[0] #a_err
maxima[2, i] = popt[2] #d
maxima[3, i] = perr[2] #d_err
# print(perr[2])
b = ufloat(popt[1], perr[1])
x_fit = np.linspace(min(x), max(x), 10000)
y_fit = gauss(popt, x_fit)
plt.plot(x_fit,
y_fit,
label=str(i + 1) + '. Peak (' + str(round(popt[0], 2)) +
'+/-' + str(round(perr[0], 2)) + ') keV')
# plt.plot(x_fit, gauss(presets, x_fit), color='grey', zorder=-3)
return maxima
eckdaten = genfromtxt('Cs_eckdaten.txt', delimiter=':')
data = genfromtxt(datensatz + '.txt', delimiter=';')
hintergrund_data = genfromtxt(datensatz + '_Hintergrund.txt', delimiter=';')
x0 = data[:, 0]
y0 = data[:, 1]
hintergrund = hintergrund_data[:, 1]
#plt.plot(x0, hintergrund)
#plt.plot(x0, y0)
hintergrund = 75702 / 72878 * hintergrund
# y0 = y0-hintergrund
y0[y0 < 0] = 0
y0_error = np.sqrt(y0)
x0_error = np.ones(len(x0)) * 0.5
# rechne Kanal in Energie um
x0 = ecal.E(x0)
x0_error = ecal.deltaE(x0, x0_error)
# plt.errorbar(x0, y0, yerr=y0_error, xerr=x0_error, zorder=0, label='data', fmt='none')
# Plotte an alle i Peaks mit den grenzen aus den eckdaten eine Gaussfunktion(gauss) mit hilfe von ODR
def gausanpassung(x1, y1, x1_error, y1_error):
model = odr.Model(gauss)
width = 0.7
height = 1.
for i in range(0, int(eckdaten[0, 1])):
# print(eckdaten[3+i*2,:])
x = x1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
y = y1[int(eckdaten[3 + i * 2, 0]):int(eckdaten[3 + i * 2, 2])]
data = genfromtxt('./Messung_'+vermesser+datensatz+'.txt',delimiter=';')
hintergrund_data = genfromtxt('./Messung_'+vermesser+'Hintergrund_'+detektortyp+'.txt',delimiter=';')
x0 = data[:, 0]
y0 = data[:, 1]
hintergrund = hintergrund_data[:, 1]
#plt.plot(x0, hintergrund)
#plt.plot(x0, y0)
y0 = y0 - hintergrund
y0_error = np.sqrt(y0)
x0_error = np.ones(len(x0))*5
if detektortyp == 'H':
y0_error = np.sqrt(y0)
x0_error = np.ones(len(x0))*0.5
# rechne Kanal in Energie um
x0=ecal.E(x0)
x0_error=ecal.deltaE(x0,x0_error)
plt.errorbar(x0, y0, yerr=y0_error, xerr=x0_error, zorder=0, label='data', fmt='none')
# plt.plot(x0, y0)
model = odr.Model(gauss)
width=3.
height=1.
if detektortyp == 'H':
width=0.7
height=1.
# Plotte an alle i Peaks mit den grenzen aus den eckdaten eine Gaussfunktion(gauss) mit hilfe von ODR
for i in range(0,int(eckdaten[0,1])):
x_fit = np.linspace(min(x), max(x), 10000)
y_fit = gauss(popt, x_fit)
plt.plot(x_fit,
y_fit,
label=str(i + 1) + '. Peak (' + str(round(popt[0], 2)) +
'+/-' + str(round(perr[0], 2)) + ') keV')
# plt.plot(x_fit, gauss(presets, x_fit), color='grey', zorder=-3)
return maxima
hintergrund = 75702 / 72878 * hintergrund
y0_error = np.sqrt(y0)
x0_error = np.ones(len(x0)) * 0.5
y0_uf = unp.uarray(y0, y0_error)
x0_uf = unp.uarray(ecal.E(x0), ecal.deltaE(x0, x0_error))
hintergrund_uf = unp.uarray(hintergrund, np.sqrt(hintergrund))
y0_uf = y0_uf - hintergrund_uf
y0_uf[y0_uf < 0] = 0
# y0_uf=unp.uarray(y0,y0_error)
plt.figure(dpi=400, figsize=(10, 5))
ykorr, ykorr_error = korrektur(y0_uf)
# plt.plot(x0,y0)
plt.errorbar(ecal.E(x0), ykorr, xerr=x0_error, yerr=ykorr_error)
pltdata = genfromtxt('data.txt', delimiter=';')
# for i in range(0,int(eckdaten[0,1])):
# x_fit = np.linspace(pltdata[i,4],pltdata[i,5], 10000)
# y_fit = gauss([pltdata[i,0],pltdata[i,1],pltdata[i,2],0], x_fit)
#
# plt.plot(x_fit, y_fit, label=str(i+1)+'. Peak ('+str(round(pltdata[i,0],2))+') keV')