def test_stream():
inventory = get_inventory()
channels = ["HN1", "HN2", "HNZ"]
data = np.random.rand(1000)
traces = []
network = inventory.networks[0]
station = network.stations[0]
chlist = station.channels
channelcodes = [ch.code for ch in chlist]
for channel in channels:
chidx = channelcodes.index(channel)
channeldata = chlist[chidx]
header = {
"sampling_rate": channeldata.sample_rate,
"npts": len(data),
"network": network.code,
"location": channeldata.location_code,
"station": station.code,
"channel": channel,
"starttime": UTCDateTime(2010, 1, 1, 0, 0, 0),
}
trace = Trace(data=data, header=header)
traces.append(trace)
invstream = StationStream(traces=traces, inventory=inventory)
inventory2 = invstream.getInventory()
inv2_channel1 = inventory2.networks[0].stations[0].channels[0]
inv_channel1 = inventory2.networks[0].stations[0].channels[0]
assert inv_channel1.code == inv2_channel1.code
# test the streamparam functionality
statsdict = {"name": "Fred", "age": 34}
invstream.setStreamParam("stats", statsdict)
assert invstream.getStreamParamKeys() == ["stats"]
cmpdict = invstream.getStreamParam("stats")
assert statsdict == cmpdict
def test_stream():
inventory = get_inventory()
channels = ['HN1', 'HN2', 'HNZ']
data = np.random.rand(1000)
traces = []
network = inventory.networks[0]
station = network.stations[0]
chlist = station.channels
channelcodes = [ch.code for ch in chlist]
for channel in channels:
chidx = channelcodes.index(channel)
channeldata = chlist[chidx]
header = {
'sampling_rate': channeldata.sample_rate,
'npts': len(data),
'network': network.code,
'location': channeldata.location_code,
'station': station.code,
'channel': channel,
'starttime': UTCDateTime(2010, 1, 1, 0, 0, 0)
}
trace = Trace(data=data, header=header)
traces.append(trace)
invstream = StationStream(traces=traces, inventory=inventory)
inventory2 = invstream.getInventory()
inv2_channel1 = inventory2.networks[0].stations[0].channels[0]
inv_channel1 = inventory2.networks[0].stations[0].channels[0]
assert inv_channel1.code == inv2_channel1.code
# test the streamparam functionality
statsdict = {'name': 'Fred', 'age': 34}
invstream.setStreamParam('stats', statsdict)
assert invstream.getStreamParamKeys() == ['stats']
cmpdict = invstream.getStreamParam('stats')
assert statsdict == cmpdict
def __init__(
self,
streams=None,
config=None,
):
"""Initialize StreamCollection.
Args:
streams (list):
List of StationStream objects.
config (dict):
Configuration options.
"""
self.config = config
if not isinstance(streams, list):
raise TypeError("streams must be a list of StationStream objects.")
newstreams = []
for st in streams:
if not isinstance(st, StationStream):
raise TypeError(
"streams must be a list of StationStream objects.")
else:
if hasattr(st, "tag"):
stream_tag = st.tag
else:
stream_tag = None
stream_params_keys = st.getStreamParamKeys()
for tr in st:
new_st = StationStream(traces=[tr])
new_st.id = ".".join([
tr.stats.network,
tr.stats.station,
tr.stats.location,
tr.stats.channel,
], )
if stream_tag is not None:
new_st.tag = stream_tag
if len(stream_params_keys):
for k in stream_params_keys:
new_st.setStreamParam(k, st.getStreamParam(k))
new_st.use_array = True
newstreams.append(new_st)
self.streams = newstreams
def getStreams(self, eventid, stations=None, labels=None, config=None):
"""Get Stream from ASDF file given event id and input tags.
Args:
eventid (str):
Event ID corresponding to an Event in the workspace.
stations (list):
List of stations to search for.
labels (list):
List of processing labels to search for.
config (dict):
Configuration options.
Returns:
StreamCollection: Object containing list of organized
StationStreams.
"""
trace_auxholder = []
stream_auxholder = []
auxdata = self.dataset.auxiliary_data
if 'TraceProcessingParameters' in auxdata:
trace_auxholder = auxdata.TraceProcessingParameters
if 'StreamProcessingParameters' in auxdata:
stream_auxholder = auxdata.StreamProcessingParameters
streams = []
if stations is None:
stations = self.getStations(eventid)
if labels is None:
labels = self.getLabels()
for waveform in self.dataset.ifilter(
self.dataset.q.station == stations,
self.dataset.q.tag == ['%s_%s' % (eventid, label)
for label in labels]):
tags = waveform.get_waveform_tags()
for tag in tags:
tstream = waveform[tag]
inventory = waveform['StationXML']
for ttrace in tstream:
if isinstance(ttrace.data[0], np.floating):
if ttrace.data[0].nbytes == 4:
ttrace.data = ttrace.data.astype('float32')
else:
ttrace.data = ttrace.data.astype('float64')
else:
if ttrace.data[0].nbytes == 2:
ttrace.data = ttrace.data.astype('int16')
elif ttrace.data[0].nbytes == 4:
ttrace.data = ttrace.data.astype('int32')
else:
ttrace.data = ttrace.data.astype('int64')
trace = StationTrace(data=ttrace.data,
header=ttrace.stats,
inventory=inventory,
config=config)
# get the provenance information
provname = format_nslct(trace.stats, tag)
if provname in self.dataset.provenance.list():
provdoc = self.dataset.provenance[provname]
trace.setProvenanceDocument(provdoc)
# get the trace processing parameters
top = format_netsta(trace.stats)
trace_path = format_nslct(trace.stats, tag)
if top in trace_auxholder:
root_auxholder = trace_auxholder[top]
if trace_path in root_auxholder:
bytelist = root_auxholder[
trace_path].data[:].tolist()
jsonstr = ''.join([chr(b) for b in bytelist])
jdict = json.loads(jsonstr)
for key, value in jdict.items():
trace.setParameter(key, value)
# get the trace spectra arrays from auxiliary,
# repack into stationtrace object
spectra = {}
if 'Cache' in auxdata:
for aux in auxdata['Cache'].list():
auxarray = auxdata['Cache'][aux]
if top not in auxarray.list():
continue
auxarray_top = auxarray[top]
if trace_path in auxarray_top:
specparts = camel_case_split(aux)
array_name = specparts[-1].lower()
specname = '_'.join(specparts[:-1]).lower()
specarray = auxarray_top[trace_path].data[()]
if specname in spectra:
spectra[specname][array_name] = specarray
else:
spectra[specname] = {array_name: specarray}
for key, value in spectra.items():
trace.setCached(key, value)
stream = StationStream(traces=[trace])
stream.tag = tag # testing this out
# get the stream processing parameters
stream_path = format_nslit(
trace.stats, stream.get_inst(), tag)
if top in stream_auxholder:
top_auxholder = stream_auxholder[top]
if stream_path in top_auxholder:
auxarray = top_auxholder[stream_path]
bytelist = auxarray.data[:].tolist()
jsonstr = ''.join([chr(b) for b in bytelist])
jdict = json.loads(jsonstr)
for key, value in jdict.items():
stream.setStreamParam(key, value)
streams.append(stream)
# No need to handle duplicates when retrieving stations from the
# workspace file because it must have been handled before putting them
# into the workspace file.
streams = StreamCollection(
streams, handle_duplicates=False, config=config)
return streams