def test_read_container(temp_h5_file):
r0tel1 = R0CameraContainer()
r0tel2 = R0CameraContainer()
mc = MCEventContainer()
reader = HDF5TableReader(str(temp_h5_file))
# get the generators for each table
mctab = reader.read('/R0/MC', mc)
r0tab1 = reader.read('/R0/tel_001', r0tel1)
r0tab2 = reader.read('/R0/tel_002', r0tel2)
# read all 3 tables in sync
for ii in range(3):
m = next(mctab)
r0_1 = next(r0tab1)
r0_2 = next(r0tab2)
print("MC:", m)
print("t0:", r0_1.image)
print("t1:", r0_2.image)
print("---------------------------")
assert 'test_attribute' in r0_1.meta
assert r0_1.meta['date'] == "2020-10-10"
reader.close()
def test_read_container(temp_h5_file):
r0tel1 = R0CameraContainer()
r0tel2 = R0CameraContainer()
mc = MCEventContainer()
with HDF5TableReader(temp_h5_file) as reader:
# get the generators for each table
mctab = reader.read("/R0/MC", mc)
r0tab1 = reader.read("/R0/tel_001", r0tel1)
r0tab2 = reader.read("/R0/tel_002", r0tel2)
# read all 3 tables in sync
for ii in range(3):
m = next(mctab)
r0_1 = next(r0tab1)
r0_2 = next(r0tab2)
print("MC:", m)
print("t0:", r0_1.waveform)
print("t1:", r0_2.waveform)
print("---------------------------")
assert "test_attribute" in r0_1.meta
assert r0_1.meta["date"] == "2020-10-10"
def test_read_whole_table(temp_h5_file):
mc = MCEventContainer()
with HDF5TableReader(temp_h5_file) as reader:
for cont in reader.read("/R0/MC", mc):
print(cont)
def test_read_whole_table(temp_h5_file):
mc = MCEventContainer()
reader = HDF5TableReader(str(temp_h5_file))
for cont in reader.read('/R0/MC', mc):
print(cont)
def test_write_container(temp_h5_file):
r0tel = R0CameraContainer()
mc = MCEventContainer()
mc.reset()
r0tel.waveform = np.random.uniform(size=(50, 10))
r0tel.image = np.random.uniform(size=50)
r0tel.num_samples = 10
r0tel.meta['test_attribute'] = 3.14159
r0tel.meta['date'] = "2020-10-10"
writer = HDF5TableWriter(str(temp_h5_file),
group_name='R0',
filters=tables.Filters(complevel=7))
writer.exclude("tel_002", ".*samples") # test exclusion of columns
for ii in range(100):
r0tel.waveform[:] = np.random.uniform(size=(50, 10))
r0tel.image[:] = np.random.uniform(size=50)
r0tel.num_samples = 10
mc.energy = 10**np.random.uniform(1, 2) * u.TeV
mc.core_x = np.random.uniform(-1, 1) * u.m
mc.core_y = np.random.uniform(-1, 1) * u.m
writer.write("tel_001", r0tel)
writer.write("tel_002", r0tel) # write a second table too
writer.write("MC", mc)
writer.close()
def test_with_context_reader(temp_h5_file):
mc = MCEventContainer()
with HDF5TableReader(str(temp_h5_file)) as h5_table:
assert h5_table._h5file.isopen == True
for cont in h5_table.read('/R0/MC', mc):
print(cont)
assert h5_table._h5file.isopen == False
def test_with_context_reader(temp_h5_file):
mc = MCEventContainer()
with HDF5TableReader(temp_h5_file) as h5_table:
assert h5_table._h5file.isopen == 1
for cont in h5_table.read("/R0/MC", mc):
print(cont)
assert h5_table._h5file.isopen == 0
def test_write_container(temp_h5_file):
r0tel = R0CameraContainer()
mc = MCEventContainer()
mc.reset()
r0tel.adc_samples = np.random.uniform(size=(50, 10))
r0tel.adc_sums = np.random.uniform(size=50)
r0tel.num_samples = 10
r0tel.meta['test_attribute'] = 3.14159
r0tel.meta['date'] = "2020-10-10"
writer = HDF5TableWriter(str(temp_h5_file), group_name='R0',
filters=tables.Filters(
complevel=7))
writer.exclude("tel_002",".*samples") # test exclusion of columns
for ii in range(100):
r0tel.adc_samples[:] = np.random.uniform(size=(50, 10))
r0tel.adc_sums[:] = np.random.uniform(size=50)
r0tel.num_samples = 10
mc.energy = 10**np.random.uniform(1,2) * u.TeV
mc.core_x = np.random.uniform(-1, 1) * u.m
mc.core_y = np.random.uniform(-1, 1) * u.m
writer.write("tel_001", r0tel)
writer.write("tel_002", r0tel) # write a second table too
writer.write("MC", mc)
class ArrayEventContainer(Container):
container_prefix = ''
run_id = Field(-1, 'run id')
array_event_id = Field(-1, 'array event id')
reco = Field(ReconstructedShowerContainer(),
'reconstructed shower container')
mc = Field(MCEventContainer(), 'array wide MC information')
total_intensity = Field(np.nan, 'sum of all intensities')
num_triggered_telescopes = Field(np.nan, 'Number of triggered Telescopes')
num_triggered_lst = Field(np.nan, 'Number of triggered LSTs')
num_triggered_mst = Field(np.nan, 'Number of triggered MSTs')
num_triggered_sst = Field(np.nan, 'Number of triggered SSTs')
class CalibrationContainer(Container):
"""
This Container() is used for the camera calibration pipeline.
It is meant to save each step of the calibration pipeline
"""
config = Field(list, 'List of the input parameters'
' of the calibration analysis') # Should use dict?
pixel_id = Field(ndarray, 'pixel ids')
data = CalibrationEventContainer()
event_id = Field(int, 'event_id')
event_type = Field(CameraEventType, 'Event type')
hillas = Field(HillasParametersContainer, 'Hillas parameters')
info = CalibrationContainerMeta()
slow_data = Field(None, "Slow Data Information")
mc = Field(MCEventContainer(), "Monte-Carlo data")
def test_read_container(temp_h5_file):
r0tel1 = R0CameraContainer()
r0tel2 = R0CameraContainer()
mc = MCEventContainer()
reader = SimpleHDF5TableReader(str(temp_h5_file))
# get the generators for each table
mctab = reader.read('/R0/MC', mc)
r0tab1 = reader.read('/R0/tel_001', r0tel1)
r0tab2 = reader.read('/R0/tel_002', r0tel2)
# read all 3 tables in sync
for ii in range(3):
print("MC:", next(mctab))
print("t0:", next(r0tab1).adc_sums)
print("t1:", next(r0tab2).adc_sums)
print("---------------------------")
class DataContainer(Container):
""" Top-level container for all event information.
Each field is representing a specific data processing level from (R0 to
DL2) Please keep in mind that the data level definition and the associated
fields might change rapidly as there is not a final data format. The data
levels R0, R1, DL1, contains sub-containers for each telescope.
After DL2 the data is not processed at the telescope level.
"""
r0 = Field(R0Container(), "Raw Data")
r1 = Field(R1Container(), "Raw Common Data")
dl0 = Field(DL0Container(), "DL0 Data Volume Reduced Data")
dl1 = Field(DL1Container(), "DL1 Calibrated image")
dl2 = Field(ReconstructedContainer(), "Reconstructed Shower Information")
mc = Field(MCEventContainer(), "Monte-Carlo data")
mcheader = Field(MCHeaderContainer(), "Monte-Carlo run header data")
inst = Field(InstrumentContainer(), "Instrumental information")
slow_data = Field(None, "Slow Data Information")
trig = Field(CentralTriggerContainer(), "central trigger information")
count = Field(0, "number of events processed")