print subset_expr
dictorigin, n = modgiseis.dbgetorigins(catalogpath, subset_expr)
print "- number of events in past week = {}".format(n)
# if > 0, load all time history and bin them
if n > 0:
subset_expr = "time > %f && deg2km(distance(lat, lon, %s, %s))<20.0" % (epoch1989, lat[c], lon[c])
print "'%s'" % subset_expr
dictorigin, n = modgiseis.dbgetorigins(catalogpath, subset_expr)
print "- number of events in all-time = {}".format(n)
time = dictorigin['time']
time_firstevent = time[0] # assuming they are sorted
if verbose:
print 'firstevent: %s' % modgiseis.datenum2datestr(time_firstevent)
print 'lastevent: %s' % modgiseis.datenum2datestr(time[-1])
bin_edges, snum, enum = modgiseis.compute_bins(dictorigin, time_firstevent, datenumnow, 7.0) # function name is a misnomer - we are computing bin_edges
# now we get our array of counts per week
counts = modgiseis.bin_counts(dictorigin['time'], bin_edges)
energy = modgiseis.ml2energy(dictorigin['ml'])
binned_energy = modgiseis.bin_irregular(dictorigin['time'], energy, bin_edges)
binned_ml = modgiseis.energy2ml(binned_energy)
# check again that we really do have events in the last week
if sum(counts[-2:-1]) > 0:
#if sum(counts) > 0:
if verbose:
for index in [0, -2, -1]:
print 'edge %d: %s' % (index, modgiseis.datenum2datestr(bin_edges[index]))
VOLCANO.append(place[c])
BIN_EDGES.append(bin_edges)
# Let matplotlib automatically decide where to put date (x-axis) tick marks, and what style of labels to use locator = mpl.dates.AutoDateLocator() formatter = mpl.dates.AutoDateFormatter(locator) # create the figure canvas fig1 = plt.figure() # add subplot - ml versus time ax1 = fig1.add_subplot(311) modgiseis.plot_time_ml(ax1, dictorigin, locator, formatter, snum, enum) if numevents > 1: # Compute bin_edges based on the first and last event times bin_edges, snum, enum = modgiseis.compute_bins(dictorigin, snum, enum) # add subplot - counts versus time ax2 = fig1.add_subplot(312) modgiseis.plot_counts(ax2, dictorigin, locator, formatter, bin_edges, snum, enum) # add subplot - energy versus time ax3 = fig1.add_subplot(313) modgiseis.plot_energy(ax3, dictorigin, locator, formatter, bin_edges, snum, enum) ####### SAVE FIGURE # save the figure to outfile print "- saving to " + outfile fig1.savefig(outfile)
# if > 0, load all time history and bin them
if n > 0:
subset_expr = "time > %f && deg2km(distance(lat, lon, %s, %s))<20.0" % (
epoch1989, lat[c], lon[c])
print "'%s'" % subset_expr
dictorigin, n = modgiseis.dbgetorigins(catalogpath, subset_expr)
print "- number of events in all-time = {}".format(n)
time = dictorigin['time']
time_firstevent = time[0] # assuming they are sorted
if verbose:
print 'firstevent: %s' % modgiseis.datenum2datestr(
time_firstevent)
print 'lastevent: %s' % modgiseis.datenum2datestr(time[-1])
bin_edges, snum, enum = modgiseis.compute_bins(
dictorigin, time_firstevent, datenumnow, 7.0
) # function name is a misnomer - we are computing bin_edges
# now we get our array of counts per week
counts = modgiseis.bin_counts(dictorigin['time'], bin_edges)
energy = modgiseis.ml2energy(dictorigin['ml'])
binned_energy = modgiseis.bin_irregular(dictorigin['time'], energy,
bin_edges)
binned_ml = modgiseis.energy2ml(binned_energy)
# check again that we really do have events in the last week
if sum(counts[-2:-1]) > 0:
#if sum(counts) > 0:
if verbose:
for index in [0, -2, -1]:
print 'edge %d: %s' % (
# Let matplotlib automatically decide where to put date (x-axis) tick marks, and what style of labels to use
locator = mpl.dates.AutoDateLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
# create the figure canvas
fig1 = plt.figure()
# add subplot - ml versus time
ax1 = fig1.add_subplot(311)
modgiseis.plot_time_ml(ax1, dictorigin, locator, formatter, snum, enum)
if numevents > 1:
# Compute bin_edges based on the first and last event times
bin_edges, snum, enum = modgiseis.compute_bins(
dictorigin, snum, enum)
# add subplot - counts versus time
ax2 = fig1.add_subplot(312)
modgiseis.plot_counts(ax2, dictorigin, locator, formatter,
bin_edges, snum, enum)
# add subplot - energy versus time
ax3 = fig1.add_subplot(313)
modgiseis.plot_energy(ax3, dictorigin, locator, formatter,
bin_edges, snum, enum)
####### SAVE FIGURE
# save the figure to outfile
print "- saving to " + outfile
