BIN_EDGES = list() COUNTS = list() CUMML = list() if n > 0: print "- number of places = {}".format(n) for c in range(n): # for each volcano in the list print "PROCESSING %s" % place[c] # how many earthquakes have there been in the last week? subset_expr = "time > %f && deg2km(distance(lat, lon, %s, %s))<20.0" % (twoweeksagoepoch, lat[c], lon[c]) 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
verbose = True elif o in ("-h", "--help"): usage() sys.exit() else: assert False, "unhandled option" if verbose: print "dbpath = " + dbpath print "outfile = " + outfile print "subset_expr = " + subset_expr ######## LOAD THE EVENTS # load events from the database dbpath, and apply subset_expr if there is one dictorigin = dict(); dictorigin, numevents = modgiseis.dbgetorigins(dbpath, subset_expr) # if we loaded some events, create plots if numevents > 0: ###### PLOT DATA HERE # 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)
BIN_EDGES = list() COUNTS = list() CUMML = list() if n > 0: print "- number of places = {}".format(n) for c in range(n): # for each volcano in the list print "PROCESSING %s" % place[c] # how many earthquakes have there been in the last week? subset_expr = "time > %f && deg2km(distance(lat, lon, %s, %s))<20.0" % ( twoweeksagoepoch, lat[c], lon[c]) 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])
try: os.makedirs(htmldir) except: sys.exit("Could not make directory %s" % htmldir) else: assert False, "unhandled option" if verbose: print "dbpath = " + dbpath print "description = " + description ######## LOAD THE EVENTS # load events from the database dbpath, and apply subset_expr if there is one dictorigin = dict(); dictorigin, numevents = giseis.dbgetorigins(dbpath, subset_expr) # read the dict of places dictplaces = dict() dictplaces = read_volcanoes() n_week = dict() n_year = dict() RADIUS_IN_KM = 25.0 for index in dictplaces.keys(): record = dictplaces[index] print "\nProcessing " + record['place'] + ":" radius_expr = "deg2km(distance(lat, lon, " + record['lat'] + ", " + record['lon'] + ")) < %f" % RADIUS_IN_KM # create the figure canvas fig1 = plt.figure()