from pyrocko.gui.snuffling import Snuffling, Param, Choice from pyrocko.gui.util import EventMarker from pyrocko import util from pyrocko import moment_tensor from pyrocko.orthodrome import distance_accurate50m as distance import matplotlib.gridspec as gridspec import matplotlib.dates as md import datetime import numpy as num class CumEvent(Snuffling): '''

Cumulative Number of Events

''' def setup(self): '''Customization of the snuffling.''' self.set_name('Catalog Statistics') self.add_parameter( Param('Latitude:', 'lat', 90, -90., 90., high_is_none=True)) self.add_parameter( Param('Longitude:', 'lon', 180., -180, 180., high_is_none=True)) self.add_parameter( Param('Maximum Distance [km]:', 'maxd', 20000., 0., 20000., high_is_none=True)) self.add_parameter(Choice('Event distribution', 'variation', 'daily', ['daily', 'annual'])) self.add_trigger('Save Figure', self.save_as) self.set_live_update(False) self.fig = None self.cli_mode = False def call(self): '''Main work routine of the snuffling.''' self.cleanup() viewer = self.get_viewer() tmin, tmax = self.get_selected_time_range(fallback=True) event_markers = [x for x in viewer.markers if x.tmin >= tmin and x.tmax <= tmax] event_markers = [ x for x in event_markers if isinstance(x, EventMarker)] if self.maxd: event_markers = [x for x in event_markers if distance(self, x._event) <= self.maxd*1000.] if event_markers == []:'No events in selected area found') events = [m.get_event() for m in event_markers] self.make_time_line(events) def make_time_line(self, events): if self.cli_mode: import matplotlib.pyplot as plt self.fig = plt.figure() else: fframe = self.figure_frame() self.fig = fframe.gcf() gs = gridspec.GridSpec( 2, 1, figure=self.fig, hspace=0.005, top=0.95) gs1 = gridspec.GridSpec( 2, 2, figure=self.fig) ax = self.fig.add_subplot(gs[0]) ax1 = ax.twinx() ax2 = self.fig.add_subplot(gs1[-1]) ax3 = self.fig.add_subplot(gs1[-2]) events.sort(key=lambda x: x.time) magnitudes = [] i_has_magnitude = [] for i, e in enumerate(events): if e.moment_tensor is not None: magnitude = e.moment_tensor.magnitude else: magnitude = e.magnitude if magnitude is not None: magnitudes.append(magnitude) i_has_magnitude.append(i) cum_events = num.cumsum(num.ones(len(i_has_magnitude))) moments = moment_tensor.magnitude_to_moment(num.array(magnitudes)) cum_events_magnitude = num.cumsum(moments) times = num.array([events[i].time for i in i_has_magnitude]) timeslabels = [datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=t) for t in times] ax.plot(timeslabels, cum_events) ax.set_ylabel('Cumulative number of events') ax.axes.get_xaxis().set_ticklabels([]) ax1.plot(timeslabels, cum_events_magnitude, '-b') ax1.set_ylabel('Cumulative moment [Nm]') xfmt = md.DateFormatter('%Y-%m-%d') ax1.xaxis.set_major_formatter(xfmt) ax.yaxis.label.set_color('r') ax1.yaxis.label.set_color('b') ax.grid(True) ax1.grid(True) t0 = min(times) if self.variation == 'daily': normalization = 60 * 60 nbins = 24 t0_day = util.day_start(t0) ax2.set_xlabel('hour of day') elif self.variation == 'annual': normalization = 60 * 60 * 24 nbins = 365 t0_day = util.year_start(t0) ax2.set_xlabel('day of year') binned = (times - t0_day) % (nbins * normalization) ax2.hist(binned/normalization, bins=nbins, color='grey') ax2.set_ylabel('Number of events') ax2.set_xlim((0, nbins)) ax3.hist(magnitudes, bins=21, histtype='stepfilled') ax3.set_xlabel('Magnitude') ax3.set_ylabel('Number of events') if self.cli_mode: else: self.fig.canvas.draw() def save_as(self): if self.fig: fn = self.output_filename() self.fig.savefig(fn, pad_inches=0.05, bbox_inches='tight') def configure_cli_parser(self, parser): parser.add_option( '--events', dest='events_filename', default=None, metavar='FILENAME', help='Read events from FILENAME') def __snufflings__(): '''Returns a list of snufflings to be exported by this module.''' return [CumEvent()]