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()
