def make_map(station=None, label='map', detectors=False): get_locations = (get_detector_locations if detectors else get_station_locations) latitudes, longitudes = get_locations(station) bounds = (min(latitudes), min(longitudes), max(latitudes), max(longitudes)) map = Map(bounds, margin=0, z=18) image = map.to_pil() map_w, map_h = image.size xmin, ymin = map.to_pixels(map.box[:2]) xmax, ymax = map.to_pixels(map.box[2:]) aspect = abs(xmax - xmin) / abs(ymax - ymin) 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) plot.set_axis_equal() plot.set_xlimits(xmin, xmax) plot.set_ylimits(map_h - ymin, map_h - ymax) x, y = map.to_pixels(array(latitudes), array(longitudes)) marks = cycle(['o'] * 4 + ['triangle'] * 4 + ['*'] * 4) colors = cycle(['black', 'red', 'green', 'blue']) if detectors: for xi, yi in zip(x, y): plot.scatter([xi], [map_h - yi], markstyle="%s, thick" % colors.next(), mark=marks.next()) else: plot.scatter(x, map_h - y, markstyle="black!50!green") plot.set_xlabel('Longitude [$^\circ$]') plot.set_xticks([xmin, xmax]) plot.set_xtick_labels(['%.4f' % x for x in (map.box[1], map.box[3])]) plot.set_ylabel('Latitude [$^\circ$]') plot.set_yticks([map_h - ymin, map_h - ymax]) plot.set_ytick_labels(['%.4f' % x for x in (map.box[0], map.box[2])]) # plot.set_title(label) # save plot to file plot.save_as_pdf(label.replace(' ', '-'))
def plot_compared(): popt, perr = fit_curve(senstech_m_ph, senstech_e_ph) fit = FIT % (popt[0], P1, popt[1]) plot = Plot(width=r'.67\linewidth', height=r'.67\linewidth') plot.set_label(fit, location='upper left') plot.scatter(senstech_e_ph, senstech_m_ph, mark='*') plot.scatter(nikhef_e_ph, nikhef_m_ph, mark='o') inputs = linspace(min(senstech_m_ph), max(senstech_m_ph), 500) plot.plot(ice_cube_pmt_p1(inputs, *popt), inputs, mark=None) plot.plot([0, 6], [0, 6], mark=None, linestyle='gray') plot.set_xlimits(0, 6) plot.set_ylimits(0, 6) plot.set_axis_equal() plot.set_xlabel(r'Sum individual LED pulseheights [\si{\volt}]') plot.set_ylabel(r'Multiple-LED pulseheight [\si{\volt}]') plot.save_as_pdf('plots/linearity_compared')
def scatter_n(): r = 420 with tables.open_file(RESULT_PATH, 'r') as data: cluster = data.root.coincidences._v_attrs.cluster coincidences = data.root.coincidences.coincidences graph = Plot() for n in range(0, len(cluster.stations) + 1): c = coincidences.read_where('N == n') if len(c) == 0: continue graph.plot(c['x'], c['y'], mark='*', linestyle=None, markstyle='mark size=.2pt,color=%s' % COLORS[n % len(COLORS)]) graph1 = Plot() graph1.plot(c['x'], c['y'], mark='*', linestyle=None, markstyle='mark size=.2pt') plot_cluster(graph1, cluster) graph1.set_axis_equal() graph1.set_ylimits(-r, r) graph1.set_xlimits(-r, r) graph1.set_ylabel('y [m]') graph1.set_xlabel('x [m]') graph1.set_title('Showers that caused triggers in %d stations' % n) graph1.save_as_pdf('N_%d' % n) graph_azi = PolarPlot(use_radians=True) plot_azimuth(graph_azi, c['azimuth']) graph_azi.set_label('N = %d' % n) graph_azi.save('azi_%d' % n) graph_azi.save_as_pdf('azi_%d' % n) plot_cluster(graph, cluster) graph.set_axis_equal() graph.set_ylimits(-r, r) graph.set_xlimits(-r, r) graph.set_ylabel('y [m]') graph.set_xlabel('x [m]') graph.set_title('Color indicates the number triggered stations by ' 'a shower.') graph.save_as_pdf('N')
def plot_ranges(): k = arange(26) p_low = [percentile_low_density_for_n(ki) for ki in k] p_median = [median_density_for_n(ki) for ki in k] p_mean = [mean_density_for_n(ki) for ki in k] # p_mpv = [most_probable_density_for_n(ki) for ki in k] p_high = [percentile_high_density_for_n(ki) for ki in k] plot = Plot(height=r'\defaultwidth') plot.plot([0, 1.5 * max(k)], [0, 1.5 * max(k)], mark=None, linestyle='dashed') plot.scatter(k, p_median) # plot.plot(k, p_mean, mark=None, linestyle='green') # plot.plot(k, p_mpv, mark=None, linestyle='red') plot.shade_region(k, p_low, p_high) plot.set_xlimits(min(k) - 0.05 * max(k), 1.05 * max(k)) plot.set_ylimits(min(k) - 0.05 * max(k), 1.05 * max(k)) plot.set_axis_equal() plot.set_xlabel('Detected number of particles') plot.set_ylabel('Expected actual number of particles') plot.save_as_pdf('plots/poisson_ranges')
def plot_detectors(cluster): station = cluster.stations[0] detectors = station.detectors timestamps = set(station.timestamps).union(detectors[0].timestamps) plot = Plot() for timestamp in sorted(timestamps): cluster.set_timestamp(timestamp) for i in range(4): x, y = detectors[i].get_xy_coordinates() plot.scatter([x], [y], mark='*', markstyle=COLORS[i]) x, y = station.get_xy_coordinates() plot.scatter([x], [y], markstyle='purple') # print timestamp, gps_to_datetime(timestamp), x, y plot.set_xlabel(r'Easting [\si{\meter}]') plot.set_ylabel(r'Northing [\si{\meter}]') plot.set_axis_equal() plot.save_as_pdf('locations_%d' % station.number)
def main(): locations = np.genfromtxt( 'data/SP-DIR-plot_sciencepark_cluster-detectors.txt', names=['x', 'y']) stations = np.genfromtxt( 'data/SP-DIR-plot_sciencepark_cluster-stations.txt', names=['id', 'x', 'y']) graph = Plot() graph.scatter(locations['x'], locations['y']) graph.set_axis_equal() locations = ['right'] * len(stations) locations[0] = 'left' locations[5] = 'above right' locations = iter(locations) for num, x, y in stations: graph.add_pin_at_xy(x, y, int(num), location=next(locations), use_arrow=False, style='gray,label distance=1ex') x = [stations['x'][u] for u in [0, 2, 5]] y = [stations['y'][u] for u in [0, 2, 5]] x.append(x[0]) y.append(y[0]) graph.plot(x, y, mark=None, linestyle='dashed') graph.add_pin_at_xy([x[0], x[1]], [y[0], y[1]], r'\SI{128}{\meter}', relative_position=.4, location='below right', use_arrow=False) graph.add_pin_at_xy([x[0], x[2]], [y[0], y[2]], r'\SI{151}{\meter}', relative_position=.5, location='left', use_arrow=False) graph.add_pin_at_xy([x[2], x[1]], [y[2], y[1]], r'\SI{122}{\meter}', relative_position=.5, location='above right', use_arrow=False) graph.set_xlabel(r"Distance [\si{\meter}]") graph.set_ylabel(r"Distance [\si{\meter}]") graph.save('sciencepark')
def plot_fit_pulseheight(ph_in, ph_out): popt, perr = fit_curve(ph_out, ph_in) fit = FIT % (popt[0], P1, popt[1]) outputs = linspace(0, max(ph_out) + 0.2, 500) plot = Plot(width=r'.67\linewidth', height=r'.67\linewidth') plot.scatter(ph_in, ph_out, mark='o') plot.plot(ice_cube_pmt_p1(outputs, *popt), outputs, mark=None) plot.plot([0, 6], [0, 6], mark=None, linestyle='gray') plot.set_xlimits(0, 6) plot.set_ylimits(0, 6) plot.set_axis_equal() plot.set_xlabel(r'Sum individual LED pulseheights [\si{\volt}]') plot.set_ylabel(r'Multiple-LED pulseheight [\si{\volt}]') return plot
def main(): stations = np.genfromtxt("data/cluster-utrecht-stations.txt", names=["x", "y"]) image = Image.open("data/cluster-utrecht-background.png") graph = Plot(width=r".75\linewidth", height=r".5\linewidth") graph.scatter(stations["x"], stations["y"]) graph.draw_image(image) graph.set_axis_equal() nw = ["%.4f" % i for i in (52.10650519075632, 5.053710938)] se = ["%.4f" % i for i in (52.05249047600099, 5.185546875)] graph.set_xlabel("Longitude [$^\circ$]") graph.set_xticks([0, image.size[0]]) graph.set_xtick_labels([nw[1], se[1]]) graph.set_ylabel("Latitude [$^\circ$]") graph.set_yticks([0, image.size[1]]) graph.set_ytick_labels([se[0], nw[0]]) graph.save("utrecht")
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 make_map(country=None, cluster=None, subcluster=None, station=None, stations=None, label='map', detectors=False, weather=False, knmi=False): get_locations = (get_detector_locations if detectors else get_station_locations) if (country is None and cluster is None and subcluster is None and station is None and stations is None): latitudes, longitudes = ([], []) else: latitudes, longitudes = get_locations(country, cluster, subcluster, station, stations) if weather: weather_latitudes, weather_longitudes = get_weather_locations() else: weather_latitudes, weather_longitudes = ([], []) if knmi: knmi_latitudes, knmi_longitudes = get_knmi_locations() else: knmi_latitudes, knmi_longitudes = ([], []) bounds = (min(latitudes + weather_latitudes + knmi_latitudes), min(longitudes + weather_longitudes + knmi_longitudes), max(latitudes + weather_latitudes + knmi_latitudes), max(longitudes + weather_longitudes + knmi_longitudes)) map = Map(bounds, margin=.1) # map.save_png('map-tiles-background.png') image = map.to_pil() map_w, map_h = image.size xmin, ymin = map.to_pixels(map.box[:2]) xmax, ymax = map.to_pixels(map.box[2:]) aspect = abs(xmax - xmin) / abs(ymax - ymin) 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) plot.set_axis_equal() plot.set_xlimits(xmin, xmax) plot.set_ylimits(map_h - ymin, map_h - ymax) if knmi: x, y = map.to_pixels(array(knmi_latitudes), array(knmi_longitudes)) plot.scatter( x, map_h - y, mark='square', markstyle="mark size=0.5pt, black!50!blue, thick, opacity=0.6") x, y = map.to_pixels(array(latitudes), array(longitudes)) if detectors: mark_size = 1.5 else: mark_size = 3 plot.scatter(x, map_h - y, markstyle="mark size=%fpt, black!50!green, " "thick, opacity=0.9" % mark_size) if weather: x, y = map.to_pixels(array(weather_latitudes), array(weather_longitudes)) plot.scatter( x, map_h - y, markstyle="mark size=1.5pt, black!30!red, thick, opacity=0.9") plot.set_xlabel('Longitude [$^\circ$]') plot.set_xticks([xmin, xmax]) plot.set_xtick_labels(['%.4f' % x for x in (map.box[1], map.box[3])]) plot.set_ylabel('Latitude [$^\circ$]') plot.set_yticks([map_h - ymin, map_h - ymax]) plot.set_ytick_labels(['%.4f' % x for x in (map.box[0], map.box[2])]) # plot.set_title(label) # save plot to file plot.save_as_pdf(label.replace(' ', '-'))
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(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))