Use the Reduction Velocity to shift traces dependent on epicentral
distance to the activated event.
If Auto-Run is activated the figure is updated automatically when
modifying a value on the panel.
When saving a figure accepted file endings are 'eps', 'png', 'jpeg',
'pdf', and 'svg'.
'''
def setup(self):
self.set_name("Plot Waveforms")
self.add_parameter(
Switch('Include Selected Markers', 'add_markers', False))
self.add_parameter(Switch('Fill positive', 'fill_between', False))
self.add_parameter(
Param(
'Reduction Velocity [km/s]',
't_red', 20., 1., 20., high_is_none=True))
self.add_parameter(Param(
'Amplitude Gain', 'yscale', 1., 0.1, 100.))
self.add_parameter(Choice(
'Pre-scale Amplitudes', 'ampl_scaler', 'trace min/max',
['total min/max', 'trace min/max', 'standard deviation']))
self.add_trigger('Save Last Figure', self.save)
self.set_live_update(False)
self.fig = None
def call(self):
self.cleanup()
viewer = self.get_viewer()
vtmin, vtmax = viewer.get_time_range()
pile = self.get_pile()
traces = [
tr for tr in pile.chopper(
tmin=vtmin, tmax=vtmax, trace_selector=viewer.trace_selector)]
event, stations = self.get_active_event_and_stations()
traces = [tr for trs in traces for tr in trs]
trace_nsls = {station_key(tr) for tr in traces}
stations = [s for s in self.get_stations() if
s.nsl() in trace_nsls]
distances = [
ortho.distance_accurate50m(event, s)/1000. for s in stations]
distances = dict(zip([s.nsl() for s in stations], distances))
matching_traces = [x for x in traces if util.match_nslc(
self.get_station_patterns(stations), x.nslc_id)]
if self.add_markers:
markers = self.get_markers()
markers = [
m for m in markers if m.tmax <= vtmax and
m.tmin >= vtmin and m.selected]
markers = dict(zip([tuple(m.nslc_ids) for m in markers], markers))
if self.fig is None or self.fframe.closed or not self._live_update:
self.fframe = self.pylab(get='figure_frame')
self.fig = self.fframe.gcf()
if self._live_update:
self.fig.clf()
maxd = max(distances.values())
mind = min(distances.values())
ymin = mind-0.06*(maxd-mind)
ymax = maxd+0.06*(maxd-mind)
ax = self.fig.add_subplot(111)
xmin = 9E9
xmax = -xmin
texts = []
manual_scale = 0.1 * (maxd-mind)*self.yscale
if self.ampl_scaler == 'total min/max':
max_trace = max(
matching_traces, key=lambda x: max(abs(x.get_ydata())))
tr_maxy = max(abs(max_trace.get_ydata()))
ampl_scale = float(tr_maxy)
for tr in matching_traces:
if viewer.highpass:
tr.highpass(4, viewer.highpass)
if viewer.lowpass:
tr.lowpass(4, viewer.lowpass)
if tr.nslc_id[:3] not in distances.keys():
continue
if self.t_red:
red = distances[tr.nslc_id[:3]]/self.t_red
else:
red = 0.
y_pos = distances[tr.nslc_id[:3]]
xdata = tr.get_xdata()-red-event.time
xmin = min(xmin, min(xdata))
xmax = max(xmax, max(xdata))
tr_ydata = tr.get_ydata()
if self.ampl_scaler == 'trace min/max':
ampl_scale = float(max(abs(tr_ydata)))
elif self.ampl_scaler == 'standard deviation':
ampl_scale = float(num.std(tr_ydata))
ydata = (tr_ydata/ampl_scale * manual_scale) + y_pos
ax.plot(xdata, ydata, c='black', linewidth=0.2)
if self.fill_between:
ax.fill_between(
xdata, y_pos, ydata, where=ydata > y_pos, color='black',
alpha=0.5)
texts.append(
ax.text(
xmax, y_pos, '%s.%s.%s.%s' % tr.nslc_id,
horizontalalignment='right', fontsize=6.))
if self.add_markers:
for ids, m in markers.items():
if m.match_nslc(tr.nslc_id) or ids == ():
c = m.select_color(m.color_b)
c = [ci/255. for ci in c]
t = m.tmin
x = [t-red-event.time, t-red-event.time]
y = [y_pos-(maxd-mind)*0.025, y_pos+(maxd-mind)*0.025]
ax.plot(x, y, linewidth=1, color=c)
label = m.get_label()
if not label:
label = ''
ax.text(x[1]-x[1]*0.005, y[1], label, color=c,
fontsize=6,
verticalalignment='top',
horizontalalignment='right')
for txt in texts:
txt.set_x(xmax)
vred_str = '= '+str(round(self.t_red, 2)) + 'km/s' if self.t_red \
else 'off'
ax.text(0.5, 0.01, 'time window: %s - %s | Reduction velocity %s' %
(util.tts(vtmin), util.tts(vtmax), vred_str),
verticalalignment='bottom', horizontalalignment='center',
transform=self.fig.transFigure)
ax.set_ylim([ymin, ymax])
ax.set_xlim([xmin, xmax])
ax.set_ylabel('Distance [km]')
ax.set_xlabel('(red.) Time [s]')
self.fig.canvas.draw()
def save(self):
fn = self.output_filename('Select Filename', 'snuffled_traces.png')
self.fig.savefig(fn, pad_inches=0.1, bbox_inches='tight', dpi=320)
def set_center_latlon(self):
self.lat_c, self.lon_c, self.z_c = self.center_lat_lon(
self.get_stations())
self.set_parameter('lat_c', self.lat_c)
self.set_parameter('lon_c', self.lon_c)
def get_station_patterns(self, stations):
return ['%s.%s.%s.*' % s.nsl() for s in stations]
def __snufflings__():
return [TracePlotter()]