def make_simulation_wavelet(n=100,
dt=0.05,
t0=-1.0,
imp=(20.0, -15.0, 4.0, -1.0),
lag=(20, 24, 35, 45),
npoles=3,
corners=[2.0, 6.0]):
dvec = make_impulse_vector(lag, imp, n)
fsampling = int(1.0 / dt)
sos = signal.butter(npoles,
corners,
btype='bandpass',
output='sos',
fs=fsampling)
f = signal.sosfilt(sos, dvec)
wavelet = TimeSeries(n)
wavelet.set_t0(t0)
wavelet.set_dt(dt)
# This isn't necessary at the moment because relative is the default
#wavelet.set_tref(TimeReferenceType.Relative)
wavelet.set_npts(n)
wavelet.set_live()
for i in range(n):
wavelet.data[i] = f[i]
return wavelet
def get_live_timeseries():
ts = TimeSeries()
ts.set_live()
ts.dt = 1 / sampling_rate
ts.npts = ts_size
# ts.put('net', 'IU')
ts.put('npts', ts_size)
ts.put('sampling_rate', sampling_rate)
ts.tref = TimeReferenceType.UTC
ts.t0 = datetime.utcnow().timestamp()
ts['delta'] = 0.1
ts['calib'] = 0.1
ts['site_id'] = bson.objectid.ObjectId()
ts['channel_id'] = bson.objectid.ObjectId()
ts['source_id'] = bson.objectid.ObjectId()
ts.set_as_origin('test', '0', '0', AtomicType.TIMESERIES)
ts.data = DoubleVector(np.random.rand(ts_size))
return ts
def get_live_timeseries():
ts = TimeSeries()
ts.set_live()
ts.dt = 1 / sampling_rate
ts.npts = ts_size
# ts.put('net', 'IU')
ts.put("npts", ts_size)
ts.put("sampling_rate", sampling_rate)
ts.tref = TimeReferenceType.UTC
ts.t0 = datetime.utcnow().timestamp()
ts["delta"] = 0.1
ts["calib"] = 0.1
ts["site_id"] = bson.objectid.ObjectId()
ts["channel_id"] = bson.objectid.ObjectId()
ts["source_id"] = bson.objectid.ObjectId()
ts.set_as_origin("test", "0", "0", AtomicType.TIMESERIES)
ts.data = DoubleVector(np.random.rand(ts_size))
return ts
def Trace2TimeSeries(trace, history=None):
"""
Convert an obspy Trace object to a TimeSeries object.
An obspy Trace object mostly maps directly into the mspass TimeSeries
object with the stats of Trace mapping (almost) directly to the TimeSeries
Metadata object that is a base class to TimeSeries. A deep copy of the
data vector in the original Trace is made to the result. That copy is
done in C++ for speed (we found a 100+ fold speedup using that mechanism
instead of a simple python loop) There is one important type collision
in copying obspy starttime and endtime stats fields. obspy uses their
UTCDateTime object to hold time but TimeSeries only supports an epoch
time (UTCDateTime.timestamp) so the code here has to convert from the
UTCDateTime to epoch time in the TimeSeries. Note in a TimeSeries
starttime is the t0 attribute.
The biggest mismatch in Trace and TimeSeries is that Trace has no concept
of object level history as used in mspass. That history must be maintained
outside obspy. To maintain full history the user must pass the
history maintained externally through the optional history parameter.
The contents of history will be loaded directly into the result with
no sanity checks.
:param trace: obspy trace object to convert
:type trace: :class:`~obspy.core.trace.Trace`
:param history: mspass ProcessingHistory object to post to result.
:return: TimeSeries object derived from obpsy input Trace object
:rtype: :class:`~mspasspy.ccore.TimeSeries`
"""
# The obspy trace object stats attribute only acts like a dictionary
# we can't use it directly but this trick simplifies the copy to
# mesh with py::dict for pybind11 - needed in TimeSeries constructor below
h = dict(trace.stats)
# These tests are excessively paranoid since starttime and endtime
# are required attributes in Trace, but better save in case
# someone creates one outside obspy
if Keywords.starttime in trace.stats:
t = h[Keywords.starttime]
h[Keywords.starttime] = t.timestamp
else:
# We have to set this to something if it isn't set or
# the TimeSeries constructor may abort
h[Keywords.starttime] = 0.0
# we don't require endtime in TimeSeries so ignore if it is not set
if "endtime" in trace.stats:
t = h["endtime"]
h["endtime"] = t.timestamp
#
# these define a map of aliases to apply when we convert to mspass
# metadata from trace - we redefined these names but others could
# surface as obspy evolves independently from mspass
mspass_aliases = dict()
mspass_aliases["station"] = Keywords.sta
mspass_aliases["network"] = Keywords.net
mspass_aliases["location"] = Keywords.loc
mspass_aliases["channel"] = Keywords.chan
for k in mspass_aliases:
if k in h:
x = h.pop(k)
alias_key = mspass_aliases[k]
h[alias_key] = x
dout = TimeSeries(h, trace.data)
if history != None:
dout.load_history(history)
dout.set_live()
# The following dead_mspass attribute is used by our decorator API
# to determine whether an object was dead before the conversion.
try:
if trace.dead_mspass:
dout.live = False
except AttributeError:
pass
return dout