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 test_TimeSeries():
ts = TimeSeries()
ts.npts = 100
ts.t0 = 0.0
ts.dt = 0.001
ts.live = 1
ts.tref = TimeReferenceType.Relative
ts.data.append(1.0)
ts.data.append(2.0)
ts.data.append(3.0)
ts.data.append(4.0)
ts.sync_npts()
assert ts.npts == 104
assert ts.npts == ts["npts"]
ts += ts
for i in range(4):
ts.data[i] = i * 0.5
ts_copy = pickle.loads(pickle.dumps(ts))
assert ts.data == ts_copy.data
assert ts.data[3] == 1.5
assert ts.data[103] == 8
assert ts.time(100) == 0.1
assert ts.sample_number(0.0998) == 100