def main(): """Event display for an event of station 503 Date Time Timestamp Nanoseconds 2012-03-29 10:51:36 1333018296 870008589 Number of MIPs 35.0 51.9 35.8 78.9 Arrival time 15.0 17.5 20.0 27.5 """ # Detector positions in ENU relative to the station GPS x = [-6.34, -2.23, -3.6, 3.46] y = [6.34, 2.23, -3.6, 3.46] # Scale mips to fit the graph n = [35.0, 51.9, 35.8, 78.9] # Make times relative to first detection t = [15., 17.5, 20., 27.5] dt = [ti - min(t) for ti in t] plot = Plot() plot.scatter([0], [0], mark='triangle') plot.add_pin_at_xy(0, 0, 'Station 503', use_arrow=False, location='below') plot.scatter_table(x, y, dt, n) plot.set_scalebar(location="lower right") plot.set_colorbar('$\Delta$t [ns]') plot.set_axis_equal() plot.set_mlimits(max=16.) plot.set_slimits(min=10., max=100.) plot.set_xlabel('x [m]') plot.set_ylabel('y [m]') plot.save('event_display') # Add event by Station 508 # Detector positions in ENU relative to the station GPS x508 = [6.12, 0.00, -3.54, 3.54] y508 = [-6.12, -13.23, -3.54, 3.54] # Event GPS timestamp: 1371498167.016412100 # MIPS n508 = [5.6, 16.7, 36.6, 9.0] # Arrival Times t508 = [15., 22.5, 22.5, 30.] dt508 = [ti - min(t508) for ti in t508] plot = MultiPlot(1, 2, width=r'.33\linewidth') plot.set_xlimits_for_all(min=-10, max=15) plot.set_ylimits_for_all(min=-15, max=10) plot.set_mlimits_for_all(min=0., max=16.) plot.set_colorbar('$\Delta$t [ns]', False) plot.set_colormap('blackwhite') plot.set_scalebar_for_all(location="upper right") p = plot.get_subplot_at(0, 0) p.scatter([0], [0], mark='triangle') p.add_pin_at_xy(0, 0, 'Station 503', use_arrow=False, location='below') p.scatter_table(x, y, dt, n) p.set_axis_equal() p = plot.get_subplot_at(0, 1) p.scatter([0], [0], mark='triangle') p.add_pin_at_xy(0, 0, 'Station 508', use_arrow=False, location='below') p.scatter_table(x508, y508, dt508, n508) p.set_axis_equal() plot.show_yticklabels_for_all([(0, 0)]) plot.show_xticklabels_for_all([(0, 0), (0, 1)]) plot.set_xlabel('x [m]') plot.set_ylabel('y [m]') plot.save('multi_event_display')
def display_coincidences(cluster, coincidence_events, coincidence, reconstruction, map): offsets = { s.number: [d.offset + s.gps_offset for d in s.detectors] for s in cluster.stations } ts0 = coincidence_events[0][1]['ext_timestamp'] latitudes = [] longitudes = [] t = [] p = [] for station_number, event in coincidence_events: station = cluster.get_station(station_number) for detector in station.detectors: latitude, longitude, _ = detector.get_lla_coordinates() latitudes.append(latitude) longitudes.append(longitude) t.extend( event_utils.relative_detector_arrival_times( event, ts0, DETECTOR_IDS, offsets=offsets[station_number])) p.extend(event_utils.detector_densities(event, DETECTOR_IDS)) image = map.to_pil() map_w, map_h = image.size aspect = float(map_w) / float(map_h) width = 0.67 height = width / aspect plot = Plot(width=r'%.2f\linewidth' % width, height=r'%.2f\linewidth' % height) plot.draw_image(image, 0, 0, map_w, map_h) x, y = map.to_pixels(np.array(latitudes), np.array(longitudes)) mint = np.nanmin(t) xx = [] yy = [] tt = [] pp = [] for xv, yv, tv, pv in zip(x, y, t, p): if np.isnan(tv) or np.isnan(pv): plot.scatter([xv], [map_h - yv], mark='diamond') else: xx.append(xv) yy.append(map_h - yv) tt.append(tv - mint) pp.append(pv) plot.scatter_table(xx, yy, tt, pp) transform = geographic.FromWGS84ToENUTransformation(cluster.lla) # Plot reconstructed core dx = np.cos(reconstruction['azimuth']) dy = np.sin(reconstruction['azimuth']) direction_length = reconstruction['zenith'] * 300 core_x = reconstruction['x'] core_y = reconstruction['y'] core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0)) core_x, core_y = map.to_pixels(core_lat, core_lon) plot.scatter([core_x], [image.size[1] - core_y], mark='10-pointed star', markstyle='red') plot.plot([core_x, core_x + direction_length * dx], [ image.size[1] - core_y, image.size[1] - (core_y - direction_length * dy) ], mark=None) # Plot simulated core dx = np.cos(reconstruction['reference_azimuth']) dy = np.sin(reconstruction['reference_azimuth']) direction_length = reconstruction['reference_zenith'] * 300 core_x = reconstruction['reference_x'] core_y = reconstruction['reference_y'] core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0)) core_x, core_y = map.to_pixels(core_lat, core_lon) plot.scatter([core_x], [image.size[1] - core_y], mark='asterisk', markstyle='orange') plot.plot([core_x, core_x + direction_length * dx], [ image.size[1] - core_y, image.size[1] - (core_y - direction_length * dy) ], mark=None) plot.set_scalebar(location="lower left") plot.set_slimits(min=1, max=30) plot.set_colorbar('$\Delta$t [\si{n\second}]') plot.set_axis_equal() plot.set_colormap('viridis') nw = num2deg(map.xmin, map.ymin, map.z) se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE, map.z) x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0)) x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0)) plot.set_xlabel('x [\si{\meter}]') plot.set_xticks([0, map_w]) plot.set_xtick_labels([int(x0), int(x1)]) plot.set_ylabel('y [\si{\meter}]') plot.set_yticks([0, map_h]) plot.set_ytick_labels([int(y1), int(y0)]) plot.save_as_pdf('map/event_display_%d' % coincidence['id'])
def display_coincidences(coincidence_events, c_id, map): cluster = CLUSTER ts0 = coincidence_events[0][1]['ext_timestamp'] latitudes = [] longitudes = [] t = [] p = [] for station_number, event in coincidence_events: station = cluster.get_station(station_number) for detector in station.detectors: latitude, longitude, _ = detector.get_lla_coordinates() latitudes.append(latitude) longitudes.append(longitude) t.extend( event_utils.relative_detector_arrival_times( event, ts0, DETECTOR_IDS)) p.extend(event_utils.detector_densities(event, DETECTOR_IDS)) image = map.to_pil() map_w, map_h = image.size aspect = float(map_w) / float(map_h) width = 0.67 height = width / aspect plot = Plot(width=r'%.2f\linewidth' % width, height=r'%.2f\linewidth' % height) plot.draw_image(image, 0, 0, map_w, map_h) x, y = map.to_pixels(array(latitudes), array(longitudes)) mint = nanmin(t) xx = [] yy = [] tt = [] pp = [] for xv, yv, tv, pv in zip(x, y, t, p): if isnan(tv) or isnan(pv): plot.scatter([xv], [map_h - yv], mark='diamond') else: xx.append(xv) yy.append(map_h - yv) tt.append(tv - mint) pp.append(pv) plot.scatter_table(xx, yy, tt, pp) transform = geographic.FromWGS84ToENUTransformation(cluster.lla) plot.set_scalebar(location="lower left") plot.set_slimits(min=1, max=60) plot.set_colorbar('$\Delta$t [\si{n\second}]') plot.set_axis_equal() nw = num2deg(map.xmin, map.ymin, map.z) se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE, map.z) x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0)) x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0)) plot.set_xlabel('x [\si{\meter}]') plot.set_xticks([0, map_w]) plot.set_xtick_labels([int(x0), int(x1)]) plot.set_ylabel('y [\si{\meter}]') plot.set_yticks([0, map_h]) plot.set_ytick_labels([int(y1), int(y0)]) # plot.set_xlimits(min=-250, max=350) # plot.set_ylimits(min=-250, max=250) # plot.set_xlabel('x [\si{\meter}]') # plot.set_ylabel('y [\si{\meter}]') plot.save_as_pdf('coincidences/event_display_%d_%d' % (c_id, ts0))