This is a bit dry but not very difficult and important to know. It is used everywhere in ObsPy!
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = 12, 8
from obspy import UTCDateTime
print(UTCDateTime("2014-08-24T10:20:44.0")) # mostly time strings defined by ISO standard
print(UTCDateTime("2014-08-24T01:20:44.0-09:00")) # non-UTC timezone input
print(UTCDateTime(2014, 8, 24, 10, 20, 44)) # year, month, day, hour, min, sec, musec
print(UTCDateTime(1408875644.0)) # timestamp
# Current time can be initialized by leaving out any arguments
print(UTCDateTime())
time = UTCDateTime("2014-08-24T10:20:44.0")
print(time.year)
print(time.julday)
print(time.timestamp)
print(time.weekday)
# try time.<Tab>
+
/-
" defined to add seconds to an UTCDateTime
object-
" defined to get time difference of two UTCDateTime
objectstime = UTCDateTime("2014-08-24T10:20:44.0")
print(time)
# one hour later
print(time + 3600)
# Time differences
time2 = UTCDateTime(2015, 1, 1)
print(time2 - time)
Calculate the number of days passed since the 2014 South Napa earthquake (the timestamp used above).
print((UTCDateTime() - UTCDateTime("2014-08-24T11:20:44.000000Z")) / 86400)
Make a list of 10 UTCDateTime objects, starting today at 10:00 with a spacing of 90 minutes.
t = UTCDateTime(2017, 9, 18, 10)
times = []
for i in range(10):
t2 = t + i * 90 * 60
times.append(t2)
times
Below is a list of strings with origin times of magnitude 8+ earthquakes since 2000 (fetched from IRIS). Assemble a list of interevent times in days. Use matplotlib to display a histogram.
times = ["2000-11-16T04:54:56",
"2001-06-23T20:33:09",
"2003-09-25T19:50:07",
"2004-12-23T14:59:00",
"2004-12-26T00:58:52",
"2005-03-28T16:09:35",
"2006-05-03T15:26:39",
"2006-06-01T18:57:02",
"2006-06-05T00:50:31",
"2006-11-15T11:14:14",
"2007-01-13T04:23:23",
"2007-04-01T20:39:56",
"2007-08-15T23:40:58",
"2007-09-12T11:10:26",
"2009-09-29T17:48:11",
"2010-02-27T06:34:13",
"2011-03-11T05:46:23",
"2012-04-11T08:38:37",
"2012-04-11T10:43:10",
"2013-05-24T05:44:49",
"2014-04-01T23:46:47",
"2015-09-16T22:54:32",
"2017-09-08T04:49:21"]
import matplotlib.pyplot as plt
inter_event_times = []
for i in range(1, len(times)):
dt = UTCDateTime(times[i]) - UTCDateTime(times[i-1])
dt = dt / (3600 * 24)
inter_event_times.append(dt)
plt.hist(inter_event_times, bins=range(0, 1000, 100))
plt.xlabel("Magnitude 8+ interevent times since 2000 [days]")
plt.ylabel("count")
plt.show()