def stream(dd, state, tres=None, tlim=None, output_sampling=None, **options):
if tres is not None:
state['aggregate_state'] = state.get('aggregate_state', {})
dd = misc.aggregate(dd, state['aggregate_state'], output_sampling)
return misc.stream(dd, state, output_sample,
tres=tres,
output_sampling=output_sampling
)
return dd
def process(dd, state, **options):
state['preprocess'] = state.get('preprocess', {})
state['noise_removal'] = state.get('noise_removal', {})
state['calibration'] = state.get('calibration', {})
state['tsample'] = state.get('tsample', {})
state['zsample'] = state.get('zsample', {})
state['output_sample'] = state.get('output_sample', {})
state['cloud_detection'] = state.get('cloud_detection', {})
state['cloud_base_detection'] = state.get('cloud_base_detection', {})
state['lidar_ratio'] = state.get('lidar_ratio', {})
state['output'] = state.get('output', {})
state['couple'] = state.get('couple', {})
dd = misc.stream(dd, state['preprocess'], preprocess, tshift=tshift)
if couple is not None:
dd = couple_mod.stream(dd, state['couple'], couple)
if noise_removal_mod is not None:
dd = noise_removal_mod.stream(dd, state['noise_removal'],
**options)
if calibration_mod is not None:
dd = calibration_mod.stream(dd, state['calibration'], **options)
if zres is not None or zlim is not None:
dd = zsample.stream(dd, state['zsample'], zres=zres, zlim=zlim)
if tres is not None or tlim is not None:
dd = tsample.stream(dd,
state['tsample'],
tres=tres / 86400.,
tlim=tlim)
if output_sampling is not None:
dd = output_sample.stream(
dd,
state['output_sample'],
tres=tres / 86400.,
output_sampling=output_sampling / 86400.,
)
dd = misc.aggregate(dd, state['output_sample'],
output_sampling / 86400.)
if cloud_detection_mod is not None:
dd = cloud_detection_mod.stream(dd, state['cloud_detection'],
**options)
if cloud_base_detection_mod is not None:
dd = cloud_base_detection_mod.stream(dd,
state['cloud_base_detection'],
**options)
dd = lidar_ratio.stream(dd, state['lidar_ratio'])
dd = output_stream(
dd, state['output']) #, output_sampling=output_sampling)
return dd
def stream(dd, state, noise_removal_sampling=300, **options):
state['aggregate_state'] = state.get('aggregate_state', {})
dd = misc.aggregate(dd, state['aggregate_state'],
noise_removal_sampling / 60. / 60. / 24.)
return misc.stream(dd, state, noise_removal, **options)