def output_stream(dd, state, output_sampling=None, **options): # if output_sampling is not None: # state['aggregate_state'] = state.get('aggregate_state', {}) # dd = misc.aggregate(dd, state['aggregate_state'], # output_sampling/60./60./24. # ) return misc.stream(dd, state, write, output=output)
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 read_stream(dd, state): return misc.stream(dd, state, read)
def stream(dd, state, dirname): if 'd_idx' not in state: d_idx = ds.readdir(dirname, ['time'], merge='time') state['d_idx'] = d_idx return misc.stream(dd, state, couple, d_idx=state['d_idx'])
def stream(dd, state, **options): return misc.stream(dd, state, lidar_ratio, **options)
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)
def stream(dd, state, zres=None, zlim=None, **options): return misc.stream(dd, state, zsample, zres=zres, zlim=zlim)
def stream(dd, state, **options): return misc.stream(dd, state, calibration, **options)
def stream(dd, state, **options): return misc.stream(dd, state, cloud_detection, **options)