def plot_delay_spectrum():
x1, y1 = np.loadtxt("data/pm1_delay.txt", unpack=True)
x2, y2 = np.loadtxt("data/pm2_delay.txt", unpack=True)
time = np.hstack((-x1, x2))
count = np.hstack((y1, y2))
error_count = np.sqrt(count + 1)
plt.clf()
plt.errorbar(time, count, yerr=error_count, fmt='s', color="black")
func = lambda x, offset, mu, sigma, A: offset + GAUSS(x, mu, sigma, A)
fit = Fit(func)
fit.offset = 0
fit.mu = -45
fit.sigma = 60
fit.fit(time, count, error_count)
fit.plot(box="tr", units={"mu": "ns", "sigma": "ns"}, color="red")
print(fit)
plt.xlabel(r"$\Delta T / \textrm{ns}$")
plt.ylabel("Count")
plt.savefig("out/delay_fit." + SAVETYPE)
plt.clf()
fit.plot_residual(time,
count,
error_count,
fmt="s",
box="br",
color="black")
plt.xlabel(r"$\Delta T / \textrm{ns}$")
plt.ylabel("Count")
plt.savefig("out/delay_residual." + SAVETYPE)
def plot_coincidence_na():
for spalte in (1, 2):
columns = np.loadtxt("data/natrium_winkel.txt", unpack=True)
angle = columns[0]
count = columns[spalte]
error_angle = 0.3
error_count = np.sqrt(count + 1)
plt.clf()
plt.errorbar(angle,
count,
xerr=error_angle,
yerr=error_count,
fmt=',',
color="black")
fit = Fit(GAUSS)
fit.mu = 0
fit.sigma = 10
for i in range(5):
errors = fit.combine_errors(angle, error_angle, error_count)
fit.fit(angle, count, errors)
fit.plot(fit.mu - 5 * fit.sigma,
fit.mu + 5 * fit.sigma,
box="bl",
color="red")
plt.ylim(0, plt.ylim()[1])
plt.xlabel("Winkel / Grad")
plt.ylabel("Count")
plt.savefig("out/coincidence_na_%d_fit." % spalte + SAVETYPE)
plt.clf()
fit.plot_residual(angle,
count,
errors,
fmt="s",
box="br",
color="black")
plt.xlabel("Winkel / Grad")
plt.ylabel("Count")
plt.savefig("out/coincidence_na_%d_residual." % spalte + SAVETYPE)
def local_fit(x, y, errors, mu, sigma):
func = lambda x, mu, sigma, A, a0, a1: GAUSS(x, mu, sigma, A) + a0 + a1 * x
fit = Fit(func)
fit.mu = _simple_peak(y, mu, sigma)
fit.sigma = sigma
fit.A = y[fit.mu]
fit.a0 = 0
fit.a1 = 0
for i in range(5):
lower = max(fit.mu - 5 * fit.sigma, 0)
upper = min(fit.mu + 5 * fit.sigma, len(x))
lx = x[lower:upper]
ly = y[lower:upper]
lerrors = errors[lower:upper]
if i <= 2:
fit.fit(lx, ly, lerrors, solver="curve_fit")
else:
fit.fit(lx, ly, lerrors, solver="minuit", method="migrad")
return fit