#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()
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)