def lifetimes_in_TRange(kre: KrEvent, krnb: KrNBins, krb: KrBins,
krr: KrRanges, TL) -> List[KrFit]:
""" Plots lifetime fitted to a range of T values"""
# Specify the range and number of bins in Z
Znbins = krnb.Z
Zrange = krr.Z
kfs = []
for tlim in TL:
# select data
kre_t = select_in_TRange(kre, *tlim)
z, e = kre_t.Z, kre_t.E
x, y, yu = fitf.profileX(z, e, Znbins, Zrange)
# Fit profile to an exponential
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
kf = KrFit(par=np.array(f.values),
err=np.array(f.errors),
chi2=chi2(f, x, y, yu))
#krf.print_fit(kf)
kfs.append(kf)
return kfs
def fit_lifetime(kB, kf, title="Lifetime Fit"):
x = shift_to_bin_centers(kB.Z)
y = kf.par
yu = kf.err
plt.errorbar(x, y, yu, np.diff(x)[0] / 2, fmt="kp", ms=7, lw=3)
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
plt.plot(x, f.fn(x), "r-", lw=4)
print_fit(f)
print('chi2 = {}'.format(chi2(f, x, y, yu)))
def lifetimes_in_XYRange(kre: KrEvent,
krnb: KrNBins,
krb: KrBins,
krr: KrRanges,
xyr: XYRanges,
XL=[(-125, -75), (-125, -75), (75, 125), (75, 125)],
YL=[(-125, -75), (75, 125), (75, 125), (-125, -75)],
nx=2,
ny=2,
figsize=(8, 8)) -> KrFit:
""" Plots lifetime fitted to a range of XY values"""
# Specify the range and number of bins in Z
Znbins = krnb.Z
Zrange = krr.Z
fig = plt.figure(figsize=figsize)
# XL = [(-125, -75), (-125, -75), (75, 125),(75, 125)]
# YL = [(-125, -75), (75, 125), (75, 125),(-125, -75)]
KF = []
for i, pair in enumerate(zip(XL, YL)):
xlim = pair[0]
ylim = pair[1]
print(f'xlim = {xlim}, ylim ={ylim}')
# select data in region defined by xyr
xyr = XYRanges(X=xlim, Y=ylim)
kre_xy = select_in_XYRange(kre, xyr)
z, e = kre_xy.Z, kre_xy.E
ax = fig.add_subplot(nx, ny, i + 1)
x, y, yu = fitf.profileX(z, e, Znbins, Zrange)
plt.errorbar(x, y, yu, np.diff(x)[0] / 2, fmt="kp", ms=7, lw=3)
# Fit profile to an exponential
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
# plot fitted value
plt.plot(x, f.fn(x), "r-", lw=4)
labels("", "Energy (pes)", "Lifetime fit")
kf = KrFit(par=np.array(f.values),
err=np.array(f.errors),
chi2=chi2(f, x, y, yu))
KF.append(kf)
return KF
def lifetime_in_XYRange(kre: KrEvent, krnb: KrNBins, krb: KrBins,
krr: KrRanges, xyr: XYRanges) -> KrFit:
""" Fits lifetime to a range of XY values"""
# select data in region defined by xyr
kre_xy = select_in_XYRange(kre, xyr)
z, e = kre_xy.Z, kre_xy.E
# Specify the range and number of bins in Z
Znbins = krnb.Z
Zrange = krr.Z
# create a figure and plot 2D histogram and profile
frame_data = plt.gcf().add_axes((.1, .3, .8, .6))
plt.hist2d(z, e, (krb.Z, krb.E))
x, y, yu = fitf.profileX(z, e, Znbins, Zrange)
plt.errorbar(x, y, yu, np.diff(x)[0] / 2, fmt="kp", ms=7, lw=3)
# Fit profile to an exponential
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
# plot fitted value
plt.plot(x, f.fn(x), "r-", lw=4)
# labels and ticks
frame_data.set_xticklabels([])
labels("", "Energy (pes)", "Lifetime fit")
# add a second frame
lims = plt.xlim()
frame_res = plt.gcf().add_axes((.1, .1, .8, .2))
# Plot (y - f(x)) / sigma(y) as a function of x
plt.errorbar(x, (f.fn(x) - y) / yu, 1, np.diff(x)[0] / 2, fmt="p", c="k")
plt.plot(lims, (0, 0), "g--")
plt.xlim(*lims)
plt.ylim(-5, +5)
labels("Drift time (µs)", "Standarized residual")
return KrFit(par=np.array(f.values),
err=np.array(f.errors),
chi2=chi2(f, x, y, yu))
def fit_lifetime_from_profile(kre: KrEvent,
kR: KrRanges,
kNB: KrNBins,
kB: KrBins,
kL: KrRanges,
title="Lifetime Fit") -> KrFit:
sel = in_range(kre.X, *kL.XY) & in_range(kre.Y, *kL.XY)
z, e = kre.Z[sel], kre.E[sel]
frame_data = plt.gcf().add_axes((.1, .35, .8, .6))
plt.hist2d(z, e, (kB.Z, kB.E))
x, y, yu = fitf.profileX(z, e, kNB.Z, kR.Z, kR.E)
plt.errorbar(x, y, yu, np.diff(x)[0] / 2, fmt="kp", ms=7, lw=3)
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
plt.plot(x, f.fn(x), "r-", lw=4)
frame_data.set_xticklabels([])
labels("", "Energy (pes)", title)
lims = plt.xlim()
frame_res = plt.gcf().add_axes((.1, .1, .8, .2))
plt.errorbar(x, (f.fn(x) - y) / yu, 1, np.diff(x)[0] / 2, fmt="p", c="k")
plt.plot(lims, (0, 0), "g--")
plt.xlim(*lims)
plt.ylim(-5, +5)
labels("Drift time (µs)", "Standarized residual")
print_fit(f)
print('chi2 = {}'.format(chi2(f, x, y, yu)))
return KrFit(par=np.array(f.values[1]),
err=np.array(f.errors[1]),
chi2=np.array(chi2(f, x, y, yu)),
valid=np.ones(1))
def fit_lifetime_slices(kre: KrEvent,
krnb: KrNBins,
krb: KrBins,
krr: KrRanges,
fit_var="E",
min_entries=1e2) -> KrLTSlices:
"""
Slice the data in x and y, make the profile in z of E,
fit it to a exponential and return the relevant values.
"""
xybins = krb.XY
nbins_xy = np.size(xybins) - 1
nbins_z = krnb.Z
nbins = nbins_xy, nbins_xy
const = np.zeros(nbins)
slope = np.zeros(nbins)
constu = np.zeros(nbins)
slopeu = np.zeros(nbins)
chi2 = np.zeros(nbins)
valid = np.zeros(nbins, dtype=bool)
zrange = krr.Z
for i in range(nbins_xy):
sel_x = in_range(kre.X, *xybins[i:i + 2])
for j in range(nbins_xy):
#print(f' bin =({i},{j}); index = {index}')
sel_y = in_range(kre.Y, *xybins[j:j + 2])
sel = sel_x & sel_y
entries = np.count_nonzero(sel)
if entries < min_entries:
#print(f'entries ={entries} not enough to fit bin (i,j) =({i},{j})')
valid[i, j] = False
continue
try:
z = kre.Z[sel]
t = kre.E[sel]
if fit_var == "Q":
t = kre.Q[sel]
x, y, yu = fitf.profileX(z, t, nbins_z, zrange)
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
re = np.abs(f.errors[1] / f.values[1])
#print(f' E +- Eu = {f.values[0]} +- {f.errors[0]}')
#print(f' LT +- LTu = {-f.values[1]} +- {f.errors[1]}')
#print(f' LTu/LT = {re} chi2 = {f.chi2}')
const[i, j] = f.values[0]
constu[i, j] = f.errors[0]
slope[i, j] = -f.values[1]
slopeu[i, j] = f.errors[1]
chi2[i, j] = f.chi2
valid[i, j] = True
if re > 0.5:
# print(f'Relative error to large, re ={re} for bin (i,j) =({i},{j})')
# print(f' LT +- LTu = {-f.values[1]} +- {f.errors[1]}')
# print(f' LTu/LT = {re} chi2 = {f.chi2}')
valid[i, j] = False
except:
print(f'fit failed for bin (i,j) =({i},{j})')
pass
return KrLTSlices(Es=Measurement(const, constu),
LT=Measurement(slope, slopeu),
chi2=chi2,
valid=valid)
def fit_and_plot_slices_2d_expo(
kre: KrEvent,
krnb: KrNBins,
krb: KrBins,
krr: KrRanges,
fit_var="E",
min_entries=1e2,
figsize=(12, 12)) -> KrLTSlices:
"""
Slice the data in x and y, make the profile in z of E,
fit it to a exponential and return the relevant values.
"""
xybins = krb.XY
nbins_xy = np.size(xybins) - 1
nbins_z = krnb.Z
nbins = nbins_xy, nbins_xy
const = np.zeros(nbins)
slope = np.zeros(nbins)
constu = np.zeros(nbins)
slopeu = np.zeros(nbins)
chi2 = np.zeros(nbins)
valid = np.zeros(nbins, dtype=bool)
zrange = krr.Z
fig = plt.figure(figsize=figsize) # Creates a new figure
k = 0
index = 0
for i in range(nbins_xy):
sel_x = in_range(kre.X, *xybins[i:i + 2])
for j in range(nbins_xy):
index += 1
#print(f' bin =({i},{j}); index = {index}')
if k % 25 == 0:
k = 0
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(5, 5, k + 1)
k += 1
sel_y = in_range(kre.Y, *xybins[j:j + 2])
sel = sel_x & sel_y
entries = np.count_nonzero(sel)
if entries < min_entries:
print(
f'entries ={entries} not enough to fit bin (i,j) =({i},{j})'
)
valid[i, j] = False
continue
try:
z = kre.Z[sel]
t = kre.E[sel]
if fit_var == "Q":
t = kre.Q[sel]
x, y, yu = fitf.profileX(z, t, nbins_z, zrange)
ax.errorbar(x, y, yu, np.diff(x)[0] / 2, fmt="kp", ms=7, lw=3)
seed = expo_seed(x, y)
f = fitf.fit(fitf.expo, x, y, seed, sigma=yu)
plt.plot(x, f.fn(x), "r-", lw=4)
plt.grid(True)
re = np.abs(f.errors[1] / f.values[1])
#print(f' E +- Eu = {f.values[0]} +- {f.errors[0]}')
#print(f' LT +- LTu = {-f.values[1]} +- {f.errors[1]}')
#print(f' LTu/LT = {re} chi2 = {f.chi2}')
if re > 0.5:
print(
f'Relative error to large, re ={re} for bin (i,j) =({i},{j})'
)
print(f' LT +- LTu = {-f.values[1]} +- {f.errors[1]}')
print(f' LTu/LT = {re} chi2 = {f.chi2}')
valid[i, j] = False
const[i, j] = f.values[0]
constu[i, j] = f.errors[0]
slope[i, j] = -f.values[1]
slopeu[i, j] = f.errors[1]
chi2[i, j] = f.chi2
valid[i, j] = True
except:
print(f'fit failed for bin (i,j) =({i},{j})')
pass
plt.tight_layout()
return KrLTSlices(Ez0=Measurement(const, constu),
LT=Measurement(slope, slopeu),
chi2=chi2,
valid=valid)