How to use the landau.Scintillator function in Landau
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HiSPARC / sapphire / reconstruction_efficiency / conv_landau_fit.py View on Github 
from __future__ import division
import tables
from scipy import optimize
from landau import Scintillator
try:
data
except NameError:
data = tables.openFile('kascade.h5', 'r')
events = data.root.efficiency.events.read()
dens = events['k_cosdens_charged'][:,1]
ph0 = events[:]['pulseheights'][:,1]
s = Scintillator()
figure()
# Fit of convoluted Landau
n, bins, patches = hist(ph0, bins=linspace(0, 2000, 101), histtype='step',
label="Data")
nx = bins[:-1] + .5 * (bins[1] - bins[0])
x = linspace(-2000, 2000, 200)
y = interp(x, nx, n)
p = optimize.fmin(s.residuals, (10 ** 4, 3.38 / 380., 1), (x, y, 350, 500))
plot(x, s.conv_landau(x, *p), label='Charged particles')
# Fit of gamma spectrum
f = lambda x, N, a: N * x ** -a
x2 = x.compress((0 <= x) & (x < 100))
y2 = y.compress((0 <= x) & (x < 100))
popt, pcov = optimize.curve_fit(f, x2, y2, sigma=y2)def plot_charged_particles_poisson(use_known=False):
events = data.getNode(GROUP, 'events')
s = Scintillator()
if use_known:
s.mev_scale = 0.0086306040898338834
s.gauss_scale = 0.84289265239940525
s.mev_scale *= 0.9
else:
ph = events[:]['pulseheights'][:,1]
analyze_charged_particle_spectrum(s, ph, constrained=False)
bins = linspace(0, 5, 41)
x = bins[:-1] + .5 * (bins[1] - bins[0])
y, yerr = [], []
events = events.read()
dens = events['k_cosdens_charged'][:,1] # center detector
for num, (u, v) in enumerate(zip(bins[:-1], bins[1:])):
