6.2.10.3. eqcorrscan.utils.plotting.detection_multiplot

eqcorrscan.utils.plotting.detection_multiplot(stream, template, times, streamcolour='k', templatecolour='r', **kwargs)[source]

Plot a stream of data with a template on top of it at detection times.

Parameters:
  • stream (obspy.core.stream.Stream) – Stream of data to be plotted as the background.
  • template (obspy.core.stream.Stream) – Template to be plotted on top of the base stream.
  • times (list) – list of detection times, one for each event
  • streamcolour (str) – String of matplotlib colour types for the stream
  • templatecolour (str) – Colour to plot the template in.
  • title (str) – Title of figure
  • show (bool) – Whether to show the figure or not (defaults to True)
  • save (bool) – Whether to save the figure or not (defaults to False)
  • savefile (str) – Filename to save figure to, if save==True (defaults to “EQcorrscan_figure.png”)
  • return_figure (bool) – Whether to return the figure or not (defaults to True), if False then the figure will be cleared and closed.
  • size (tuple of float) – Figure size as (width, height) in inches. Defaults to (10.5, 7.5)
Returns:

matplotlib.figure.Figure

Example

>>> from obspy import read, read_events
>>> import os
>>> from eqcorrscan.core import template_gen
>>> from eqcorrscan.utils.plotting import detection_multiplot
>>> # Get the path to the test data
>>> import eqcorrscan
>>> import os
>>> TEST_PATH = os.path.dirname(eqcorrscan.__file__) + '/tests/test_data'
>>>
>>> test_file = os.path.join(TEST_PATH, 'REA',
...                          'TEST_', '01-0411-15L.S201309')
>>> test_wavefile = os.path.join(
...     TEST_PATH, 'WAV', 'TEST_', '2013-09-01-0410-35.DFDPC_024_00')
>>> event = read_events(test_file)[0]
>>> st = read(test_wavefile)
>>> st = st.filter('bandpass', freqmin=2.0, freqmax=15.0)
>>> for tr in st:
...     tr = tr.trim(tr.stats.starttime + 30, tr.stats.endtime - 30)
...     # Hack around seisan 2-letter channel naming
...     tr.stats.channel = tr.stats.channel[0] + tr.stats.channel[-1]
>>> template = template_gen._template_gen(event.picks, st, 2)
>>> times = [min([pk.time -0.05 for pk in event.picks])]
>>> detection_multiplot(stream=st, template=template,
...                     times=times) # doctest: +SKIP

(Source code)