User contributed plug-ins for Pyrocko's seismic waveform browser Snuffler.
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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):
'''
<html>
<body>
<h1>Cumulative Number of Events</h1>
</body>
</html>
'''
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 == []:
self.fail('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:
plt.show()
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()]