#ylim(0, 100)
#legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
if __name__ == '__main__':
# invalid values in arcsin will be ignored (nan handles the situation
# quite well)
np.seterr(invalid='ignore', divide='ignore')
try:
data
except NameError:
data = tables.open_file('master-ch4v2.h5', 'r')
if '/reconstructions' not in data:
print "Reconstructing shower direction..."
do_full_reconstruction(data)
else:
print "Skipping reconstruction!"
utils.set_prefix("DIR-")
artist.utils.set_prefix("DIR-")
do_reconstruction_plots(data)
# These currently don't work
# utils.set_prefix("WIP-")
# do_jos_plots(data)
n, bins = histogram(dts / 1e3, bins=arange(-8, -6, .01))
graph.histogram(0, 0, n, bins)
graph.set_title(0, 0, "Jul 2, 2008")
graph.set_xlabel(r"Time difference [\si{\micro\second}]")
graph.set_ylabel("Counts")
graph.set_ylimits(min=0)
graph.show_xticklabels_for_all([(0, 0), (0, 1)])
graph.show_yticklabels_for_all([(0, 0), (0, 1)])
graph.save('plots/MAT-residual-time-differences')
graph.save_as_pdf('preview')
def make_timestamp(year, month, day, hour=0, minutes=0, seconds=0):
dt = datetime.datetime(year, month, day, hour, minutes, seconds)
timetuple = dt.utctimetuple()
timestamp = calendar.timegm(timetuple)
return timestamp
if __name__ == '__main__':
try:
data
except NameError:
data = tables.open_file('kascade.h5', 'r')
utils.set_prefix('MAT-')
do_matching_plots(data)
self.artistplot_alt_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.histogram(y_charged, bins * VNS)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_title(r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$" % (low, high))
graph.set_xlimits(0, 30)
graph.set_ylimits(1e0, 1e4)
artist.utils.save_graph(graph, suffix, dirname='plots')
p_detection = np.vectorize(lambda x: 1 - np.exp(-.5 * x) if x >= 0 else 0.)
def conv_p_detection(self, x, sigma):
x_step = x[-1] - x[-2]
x2 = np.arange(-2 * max(x), 2 * max(x) + x_step / 2, x_step)
g = stats.norm(scale=sigma).pdf
y2 = landau.discrete_convolution(self.p_detection, g, x2)
y = np.interp(x, x2, y2)
return y
if __name__ == '__main__':
np.seterr(invalid='ignore', divide='ignore')
if 'data' not in globals():
data = tables.open_file('kascade.h5', 'r')
utils.set_prefix('EFF-')
artist.utils.set_prefix('EFF-')
efficiency = ReconstructionEfficiency(data)
efficiency.main()
n, bins = histogram(dts / 1e3, bins=arange(-8, -6, .01))
graph.histogram(0, 0, n, bins)
graph.set_title(0, 0, "Jul 2, 2008")
graph.set_xlabel(r"Time difference [\si{\micro\second}]")
graph.set_ylabel("Counts")
graph.set_ylimits(min=0)
graph.show_xticklabels_for_all([(0, 0), (0, 1)])
graph.show_yticklabels_for_all([(0, 0), (0, 1)])
graph.save('plots/MAT-residual-time-differences')
graph.save_as_pdf('preview')
def make_timestamp(year, month, day, hour=0, minutes=0, seconds=0):
dt = datetime.datetime(year, month, day, hour, minutes, seconds)
timetuple = dt.utctimetuple()
timestamp = calendar.timegm(timetuple)
return timestamp
if __name__ == '__main__':
try:
data
except NameError:
data = tables.open_file('kascade.h5', 'r')
utils.set_prefix('MAT-')
do_matching_plots(data)
c = CoreReconstruction(data, '/reconstructions/poisson')
c.reconstruct_core_positions('/ldfsim/poisson')
c = CoreReconstruction(data, '/reconstructions/poisson_gauss_20')
c.reconstruct_core_positions('/ldfsim/poisson_gauss_20')
c = CoreReconstruction(
data,
'/reconstructions/poisson_gauss_20_nonull',
solver=CorePositionSolverWithoutNullMeasurements(ldf.KascadeLdf()))
c.reconstruct_core_positions('/ldfsim/poisson_gauss_20')
#c = CoreReconstruction(data, '/reconstructions/ground_gauss_20')
#c.reconstruct_core_positions('/groundsim/zenith_0/shower_0')
utils.set_prefix("COR-")
utils.set_suffix("-EXACT")
do_reconstruction_plots(data.root.reconstructions.exact)
utils.set_suffix("-GAUSS_10")
do_reconstruction_plots(data.root.reconstructions.gauss_10)
utils.set_suffix("-GAUSS_20")
do_reconstruction_plots(data.root.reconstructions.gauss_20)
utils.set_suffix("-POISSON")
do_reconstruction_plots(data.root.reconstructions.poisson)
utils.set_suffix("-POISSON-GAUSS_20")
do_reconstruction_plots(data.root.reconstructions.poisson_gauss_20)
graph1.set_xticks_for_all(None, range(-180, 181, 90))
graph1.set_xlabel(r'Shower azimuthal angle [\si{\degree}]')
graph1.set_ylabel('Count')
artist.utils.save_graph(graph1, suffix='phi', dirname='plots')
graph2.set_ylimits_for_all(None, 0, 2000)
graph2.set_xlimits_for_all(None, 0, 45)
graph2.show_yticklabels(0, 0)
graph2.show_xticklabels_for_all()
graph2.set_xticklabels_position(0, 1, 'right')
graph2.set_xlabel(r'Shower zenith angle [\si{\degree}]')
graph2.set_ylabel('Count')
artist.utils.save_graph(graph2, suffix='theta', dirname='plots')
if __name__ == '__main__':
if 'data' not in globals():
# For single station plots
#data = tables.openFile('month-single.h5')
# For station / cluster plots
#data = tables.openFile('new.h5')
#data = tables.openFile('newlarge.h5')
# For N vs R plot
#data = tables.openFile('master-large.h5')
# No data
data = None
artist.utils.set_prefix("SP-DIR-")
utils.set_prefix("SP-DIR-")
main(data)
c = CoreReconstruction(data, '/reconstructions/gauss_20')
c.reconstruct_core_positions('/ldfsim/gauss_20')
c = CoreReconstruction(data, '/reconstructions/poisson')
c.reconstruct_core_positions('/ldfsim/poisson')
c = CoreReconstruction(data, '/reconstructions/poisson_gauss_20')
c.reconstruct_core_positions('/ldfsim/poisson_gauss_20')
c = CoreReconstruction(data, '/reconstructions/poisson_gauss_20_nonull', solver=CorePositionSolverWithoutNullMeasurements(ldf.KascadeLdf()))
c.reconstruct_core_positions('/ldfsim/poisson_gauss_20')
#c = CoreReconstruction(data, '/reconstructions/ground_gauss_20')
#c.reconstruct_core_positions('/groundsim/zenith_0/shower_0')
utils.set_prefix("COR-")
utils.set_suffix("-EXACT")
do_reconstruction_plots(data.root.reconstructions.exact)
utils.set_suffix("-GAUSS_10")
do_reconstruction_plots(data.root.reconstructions.gauss_10)
utils.set_suffix("-GAUSS_20")
do_reconstruction_plots(data.root.reconstructions.gauss_20)
utils.set_suffix("-POISSON")
do_reconstruction_plots(data.root.reconstructions.poisson)
utils.set_suffix("-POISSON-GAUSS_20")
do_reconstruction_plots(data.root.reconstructions.poisson_gauss_20)
graph.plot(x, rad2deg(f_y2), mark=None)
graph.plot(x, rad2deg(l_y2), mark=None, linestyle='dashed')
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
artist.utils.save_graph(graph, dirname='plots')
if __name__ == '__main__':
# invalid values in arcsin will be ignored (nan handles the situation
# quite well)
np.seterr(invalid='ignore', divide='ignore')
try:
data
except NameError:
data = tables.openFile('kascade.h5', 'r')
artist.utils.set_prefix("KAS-")
utils.set_prefix("KAS-")
do_reconstruction_plots(data, data.root.reconstructions)
do_lint_comparison(data)
artist.utils.set_prefix("KAS-LINT-")
utils.set_prefix("KAS-LINT-")
do_reconstruction_plots(data, data.root.lint_reconstructions)
artist.utils.set_prefix("KAS-OFFSETS-")
utils.set_prefix("KAS-OFFSETS-")
do_reconstruction_plots(data, data.root.reconstructions_offsets)
artist.utils.set_prefix("KAS-LINT-OFFSETS-")
utils.set_prefix("KAS-LINT-OFFSETS-")
do_reconstruction_plots(data, data.root.lint_reconstructions_offsets)
graph1.set_xlabel(r'Shower azimuthal angle [\si{\degree}]')
graph1.set_ylabel('Count')
artist.utils.save_graph(graph1, suffix='phi', dirname='plots')
graph2.set_ylimits_for_all(None, 0, 2000)
graph2.set_xlimits_for_all(None, 0, 45)
graph2.show_yticklabels(0, 0)
graph2.show_xticklabels_for_all()
graph2.set_xticklabels_position(0, 1, 'right')
graph2.set_xlabel(r'Shower zenith angle [\si{\degree}]')
graph2.set_ylabel('Count')
artist.utils.save_graph(graph2, suffix='theta', dirname='plots')
if __name__ == '__main__':
if 'data' not in globals():
# For single station plots
#data = tables.open_file('month-single.h5')
# For station / cluster plots
#data = tables.open_file('new.h5')
#data = tables.open_file('newlarge.h5')
data = tables.open_file('master.h5')
# For N vs R plot
#data = tables.open_file('master-large.h5')
# No data
#data = None
artist.utils.set_prefix("SP-DIR-")
utils.set_prefix("SP-DIR-")
main(data)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_title(
r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$" % (low, high)
)
graph.set_xlimits(0, 30)
graph.set_ylimits(1e0, 1e4)
artist.utils.save_graph(graph, suffix, dirname="plots")
p_detection = np.vectorize(lambda x: 1 - np.exp(-0.5 * x) if x >= 0 else 0.0)
def conv_p_detection(self, x, sigma):
x_step = x[-1] - x[-2]
x2 = np.arange(-2 * max(x), 2 * max(x) + x_step / 2, x_step)
g = stats.norm(scale=sigma).pdf
y2 = landau.discrete_convolution(self.p_detection, g, x2)
y = np.interp(x, x2, y2)
return y
if __name__ == "__main__":
np.seterr(invalid="ignore", divide="ignore")
if "data" not in globals():
data = tables.openFile("kascade.h5", "r")
utils.set_prefix("EFF-")
artist.utils.set_prefix("EFF-")
efficiency = ReconstructionEfficiency(data)
efficiency.main()
self.artistplot_alt_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.histogram(y_charged, bins * VNS)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_title(r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$" % (low, high))
graph.set_xlimits(0, 30)
graph.set_ylimits(1e0, 1e4)
artist.utils.save_graph(graph, suffix, dirname='plots')
p_detection = np.vectorize(lambda x: 1 - np.exp(-.5 * x) if x >= 0 else 0.)
def conv_p_detection(self, x, sigma):
x_step = x[-1] - x[-2]
x2 = np.arange(-2 * max(x), 2 * max(x) + x_step / 2, x_step)
g = stats.norm(scale=sigma).pdf
y2 = landau.discrete_convolution(self.p_detection, g, x2)
y = np.interp(x, x2, y2)
return y
if __name__ == '__main__':
np.seterr(invalid='ignore', divide='ignore')
if 'data' not in globals():
data = tables.openFile('kascade.h5', 'r')
utils.set_prefix('EFF-')
artist.utils.set_prefix('EFF-')
efficiency = ReconstructionEfficiency(data)
efficiency.main()
#ylim(0, 100)
#legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
if __name__ == '__main__':
# invalid values in arcsin will be ignored (nan handles the situation
# quite well)
np.seterr(invalid='ignore', divide='ignore')
try:
data
except NameError:
data = tables.openFile('master-ch4v2.h5', 'r')
if '/reconstructions' not in data:
print "Reconstructing shower direction..."
do_full_reconstruction(data)
else:
print "Skipping reconstruction!"
utils.set_prefix("DIR-")
artist.utils.set_prefix("DIR-")
do_reconstruction_plots(data)
# These currently don't work
# utils.set_prefix("WIP-")
# do_jos_plots(data)
graph.plot(x, rad2deg(f_y2), mark=None)
graph.plot(x, rad2deg(l_y2), mark=None, linestyle='dashed')
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
artist.utils.save_graph(graph, dirname='plots')
if __name__ == '__main__':
# invalid values in arcsin will be ignored (nan handles the situation
# quite well)
np.seterr(invalid='ignore', divide='ignore')
try:
data
except NameError:
data = tables.openFile('kascade.h5', 'r')
artist.utils.set_prefix("KAS-")
utils.set_prefix("KAS-")
do_reconstruction_plots(data, data.root.reconstructions)
do_lint_comparison(data)
artist.utils.set_prefix("KAS-LINT-")
utils.set_prefix("KAS-LINT-")
do_reconstruction_plots(data, data.root.lint_reconstructions)
artist.utils.set_prefix("KAS-OFFSETS-")
utils.set_prefix("KAS-OFFSETS-")
do_reconstruction_plots(data, data.root.reconstructions_offsets)
artist.utils.set_prefix("KAS-LINT-OFFSETS-")
utils.set_prefix("KAS-LINT-OFFSETS-")
do_reconstruction_plots(data, data.root.lint_reconstructions_offsets)
