def stream_config(cls):
return [
CtdpfCklWfpRecoveredDataParticle.type(),
CtdpfCklWfpRecoveredMetadataParticle.type(),
CtdpfCklWfpSioMuleMetadataParticle.type(),
CtdpfCklWfpSioMuleDataParticle.type()
]
def stream_config(cls):
return [
CtdpfCklWfpRecoveredDataParticle.type(),
CtdpfCklWfpRecoveredMetadataParticle.type(),
CtdpfCklWfpTelemeteredDataParticle.type(),
CtdpfCklWfpTelemeteredMetadataParticle.type()
]
def stream_config(cls):
return [CtdpfCklWfpRecoveredDataParticle.type(),
CtdpfCklWfpRecoveredMetadataParticle.type(),
CtdpfCklWfpSioMuleMetadataParticle.type(),
CtdpfCklWfpSioMuleDataParticle.type()]
def setUp(self):
ParserUnitTestCase.setUp(self)
self.config = {
DataTypeKey.CTDPF_CKL_WFP_RECOVERED: {
DataSetDriverConfigKeys.PARTICLE_MODULE:
'mi.dataset.parser.ctdpf_ckl_wfp',
DataSetDriverConfigKeys.PARTICLE_CLASS: None,
DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: {
'instrument_data_particle_class':
CtdpfCklWfpRecoveredDataParticle,
'metadata_particle_class':
CtdpfCklWfpRecoveredMetadataParticle
},
},
DataTypeKey.CTDPF_CKL_WFP_TELEMETERED: {
DataSetDriverConfigKeys.PARTICLE_MODULE:
'mi.dataset.parser.ctdpf_ckl_wfp',
DataSetDriverConfigKeys.PARTICLE_CLASS: None,
DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: {
'instrument_data_particle_class':
CtdpfCklWfpTelemeteredDataParticle,
'metadata_particle_class':
CtdpfCklWfpTelemeteredMetadataParticle
}
}
}
self.recovered_start_state = {
StateKey.POSITION: 0,
StateKey.RECORDS_READ: 0,
StateKey.METADATA_SENT: False
}
self.telemetered_start_state = {
StateKey.POSITION: 0,
StateKey.RECORDS_READ: 0,
StateKey.METADATA_SENT: False
}
# Initialize this for later use.
self.parser = None
# Define test data particles and their associated timestamps which will be
# compared with returned results
timefields = struct.unpack('>II', '\x52\x4e\x75\x82\x52\x4e\x76\x9a')
start_time = int(timefields[0])
end_time = int(timefields[1])
# even though there are only 3 samples in TEST_DATA, there are 270 samples in the original file,
# so this needs to be used to determine the time increment for each time sample
time_increment_3 = float(end_time - start_time) / 3.0
time_increment_270 = float(end_time - start_time) / 270.0
start_timestamp = self.calc_timestamp(start_time, time_increment_3, 0)
start_timestamp_long = self.calc_timestamp(start_time,
time_increment_270, 0)
timestamp_2 = self.calc_timestamp(start_time, time_increment_3, 1)
timestamp_2_long = self.calc_timestamp(start_time, time_increment_270,
1)
timestamp_3 = self.calc_timestamp(start_time, time_increment_3, 2)
timestamp_last = self.calc_timestamp(start_time, time_increment_270,
269)
self.particle_meta = CtdpfCklWfpRecoveredMetadataParticle(
(b"\x52\x4e\x75\x82\x52\x4e\x76\x9a", 3.0),
internal_timestamp=start_timestamp)
self.particle_meta_long = CtdpfCklWfpRecoveredMetadataParticle(
(b"\x52\x4e\x75\x82\x52\x4e\x76\x9a", 270.0),
internal_timestamp=start_timestamp_long)
self.particle_a = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x88\x03\xe3\x3b\x00\x03\xeb\x0a\xc8",
internal_timestamp=start_timestamp)
self.particle_a_long = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x88\x03\xe3\x3b\x00\x03\xeb\x0a\xc8",
internal_timestamp=start_timestamp_long)
self.particle_b = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x8c\x03\xe2\xc0\x00\x03\xeb\x0a\x81",
internal_timestamp=timestamp_2)
self.particle_b_long = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x8c\x03\xe2\xc0\x00\x03\xeb\x0a\x81",
internal_timestamp=timestamp_2_long)
self.particle_c = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x90\x03\xe1\x5b\x00\x03\xeb\x0a\x65",
internal_timestamp=timestamp_3)
self.particle_last = CtdpfCklWfpRecoveredDataParticle(
b"\x00\x1a\x8f\x03\xe5\x91\x00\x03\xeb\x0bS",
internal_timestamp=timestamp_last)
self.file_ingested_value = None
self.state_callback_value = None
self.publish_callback_value = None
def stream_config(cls):
return [CtdpfCklWfpRecoveredDataParticle.type(),
CtdpfCklWfpRecoveredMetadataParticle.type(),
CtdpfCklWfpTelemeteredDataParticle.type(),
CtdpfCklWfpTelemeteredMetadataParticle.type()]
