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