def initialise_algorithm(self, input_data: TestDataLoader) -> None:
self.cnf = ConfigurationFile(flag_cal1_corrections_cnf=True)
self.cst = ConstantsFile()
self.chd = CharacterisationFile(
self.cst,
N_ku_pulses_burst_chd=input_data['N_ku_pulses_burst_chd'],
N_samples_sar_chd=input_data['N_samples_sar_chd'])
self.cal1_algorithm = CAL1Algorithm(self.chd, self.cst, self.cnf)
def initialise_algotithm(self, input_data):
self.cnf = ConfigurationFile(
zp_fact_range_cnf=input_data["zp_fact_range_cnf"],
N_looks_stack_cnf=input_data["n_looks_stack_cnf"])
self.cst = ConstantsFile()
self.chd = CharacterisationFile(
self.cst, N_samples_sar_chd=input_data['n_samples_sar_chd'])
self.range_compression_algorithm = RangeCompressionAlgorithm(
self.chd, self.cst, self.cnf)
def setUp(self):
self.chd = CharacterisationFile(self.cst_file, self.chd_file)
self.cnf = ConfigurationFile(self.cnf_file)
self.cst = ConstantsFile(self.cst_file)
self.writer = L1BWriter(self.chd, self.cnf, self.cst,
self._output_name)
self.writer.create_all_dimensions()
self.writer.create_all_variables()
def initialise_algorithm(self, input_data):
self.cnf = ConfigurationFile(
N_looks_stack_cnf=input_data["n_looks_stack_cnf"])
self.cst = ConstantsFile(pi_cst=input_data['pi_cst'])
self.chd = CharacterisationFile(
self.cst,
N_samples_sar_chd=input_data['n_samples_sar_chd'],
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'])
self.stacking_algorithm = StackGatheringAlgorithm(
self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data):
self.cnf = ConfigurationFile()
self.cst = ConstantsFile(c_cst=input_data['c_cst'],
pi_cst=input_data['pi_cst'])
self.chd = CharacterisationFile(
self.cst,
freq_ku_chd=input_data['freq_ku_chd'],
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'],
prf_sar_chd=1. / input_data['pri_sar_pre_dat'])
self.beam_angles_algorithm = BeamAnglesAlgorithm(
self.chd, self.cst, self.cnf)
def initilise_algorithm(self, input_data):
self.cnf = ConfigurationFile(
zp_fact_range_cnf=input_data["zp_fact_range_cnf"],
N_looks_stack_cnf=input_data["n_looks_stack_cnf"],
flag_remove_doppler_ambiguities_cnf=input_data["flag_remove_doppler_ambiguities_cnf"],
flag_stack_masking_cnf=input_data["flag_stack_masking_cnf"]
)
self.cst = ConstantsFile()
self.chd = CharacterisationFile(
self.cst,
N_samples_sar_chd=input_data['n_samples_sar_chd'],
i_sample_start_chd=input_data['i_sample_start_chd']
)
self.stack_masking_algorithm = StackMaskingAlgorithm(self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data):
self.cnf = ConfigurationFile(
zp_fact_range_cnf=input_data['zp_fact_range_cnf'],
flag_avoid_zeros_in_multilooking=input_data[
'flag_avoid_zeros_in_multilooking'],
N_looks_stack_cnf=input_data['n_looks_stack_cnf'],
flag_antenna_weighting_cnf=
False # TODO: add tests for this ( will need to add field to input file )
)
self.cst = ConstantsFile(pi_cst=input_data['pi_cst'])
self.chd = CharacterisationFile(
self.cst, N_samples_sar_chd=input_data['n_samples_sar_chd'])
self.multilooking_algorithm =\
MultilookingAlgorithm(self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data):
self.cnf = ConfigurationFile()
self.cst = ConstantsFile(
c_cst=input_data['c_cst'],
pi_cst=input_data['pi_cst'],
semi_major_axis_cst=input_data['semi_major_axis_cst'],
flat_coeff_cst=input_data['flat_coeff_cst'],
semi_minor_axis_cst=input_data['semi_minor_axis_cst'],
sec_in_day_cst=60*60*24
)
self.chd = CharacterisationFile(
self.cst,
freq_ku_chd=input_data['freq_ku_chd'],
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'],
pri_sar_pre_dat=input_data['pri_sar_pre_dat']
)
self.surface_location_algorithm = SurfaceLocationAlgorithm(self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data):
self.cnf = ConfigurationFile(
zp_fact_range_cnf=input_data["zp_fact_range_cnf"])
self.cst = ConstantsFile(
pi_cst=input_data['pi_cst'],
earth_radius_cst=input_data['earth_radius_cst'],
c_cst=input_data['c_cst'])
self.chd = CharacterisationFile(
self.cst,
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'],
N_samples_sar_chd=input_data['n_samples_sar_chd'],
wv_length_ku_chd=input_data['wv_length_ku'],
chirp_slope_ku_chd=input_data['chirp_slope_ku'],
pulse_length_chd=input_data['pulse_length_chd'],
power_tx_ant_ku_chd=input_data['power_tx_ant_ku_chd'],
antenna_gain_ku_chd=input_data['antenna_gain_ku_chd'])
self.sigma0_algorithm =\
Sigma0ScalingFactorAlgorithm(self.chd, self.cst, self.cnf)
def initialize_algorithm(self, input_data):
self.cnf = ConfigurationFile(
N_looks_stack_cnf=input_data["n_looks_stack_cnf"],
flag_doppler_range_correction_cnf=input_data["flag_doppler_range_correction_cnf"],
flag_slant_range_correction_cnf=input_data["flag_slant_range_correction_cnf"]
)
self.cst = ConstantsFile(
c_cst=input_data['c_cst'],
pi_cst=input_data['pi_cst']
)
self.chd = CharacterisationFile(
self.cst,
N_samples_sar_chd=input_data['n_samples_sar_chd'],
pulse_length_chd=input_data['pulse_length_chd'],
bw_ku_chd=input_data['bw_ku_chd'],
wv_length_ku_chd=input_data['wv_length_ku']
)
self.geometry_corrections_algorithm =\
GeometryCorrectionsAlgorithm(self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data):
"""
:param input_data: the input data
create cst and chd objects from input_data, then initialise
an instance of the azimuth processing algorithm
"""
self.cnf = ConfigurationFile(
flag_azimuth_windowing_method_cnf=AzimuthWindowingMethod.disabled,
azimuth_window_width_cnf=64
)
self.cst = ConstantsFile(
pi_cst=input_data['pi_cst']
)
self.chd = CharacterisationFile(
self.cst,
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'],
N_samples_sar_chd=input_data['n_samples_sar_chd']
)
self.azimuth_processing_algorithm = AzimuthProcessingAlgorithm(self.chd, self.cst, self.cnf)
def initialise_algorithm(self, input_data: TestDataLoader):
self.cnf = ConfigurationFile(
flag_surface_focusing_cnf=input_data["flag_surface_focusing_cnf"],
surface_focusing_lat_cnf=input_data.get("surface_focusing_lat_cnf"),
surface_focusing_lon_cnf=input_data.get("surface_focusing_lon_cnf"),
surface_focusing_alt_cnf=input_data.get("surface_focusing_alt_cnf")
)
self.cst = ConstantsFile(
c_cst=input_data['c_cst'],
pi_cst=input_data['pi_cst'],
semi_major_axis_cst=input_data['semi_major_axis_cst'],
flat_coeff_cst=input_data['flat_coeff_cst'],
semi_minor_axis_cst=input_data['semi_minor_axis_cst'],
sec_in_day_cst=60*60*24
)
self.chd = CharacterisationFile(
self.cst,
freq_ku_chd=input_data['freq_ku_chd'],
N_ku_pulses_burst_chd=input_data['n_ku_pulses_burst_chd'],
pri_sar_pre_dat=input_data['pri_sar_pre_dat']
)
self.surface_location_algorithm = SurfaceLocationAlgorithm(self.chd, self.cst, self.cnf)
def setUp(self):
self.expected = TestDataLoader(self._expected_file)
self.actual = ConfigurationFile(self._input_file)
def __init__(self,
name: str,
cnf_file: str,
cst_file: str,
chd_file: str,
out_path: str,
skip_l1bs: bool = True):
"""
initialise the processor
"""
if not name:
raise ValueError('name must be given')
if not cnf_file:
raise ValueError('cnf_file must be given')
if not cst_file:
raise ValueError('cst_file must be given')
if not chd_file:
raise ValueError('chd_file must be given')
if out_path is None:
raise ValueError('out_path must be given')
# store conf objects
self.cst = ConstantsFile(cst_file)
self.chd = CharacterisationFile(self.cst, chd_file)
self.cnf = ConfigurationFile(cnf_file)
self.skip_l1bs = skip_l1bs
self.out_path = out_path
self.name = name
self.l1a_file = None
# init. surface & packets arrays
self._surfaces = []
self._packets = []
self.surfaces_count = 0
self.min_surfs = 64 + 16 # 16 elem. margin
# set defaults for beam angles
self.beam_angles_list_size_prev = -1
self.beam_angles_trend_prev = -1
# initialise the algorithm classes
self.surface_locations_algorithm = \
SurfaceLocationAlgorithm(self.chd, self.cst, self.cnf)
self.beam_angles_algorithm = \
BeamAnglesAlgorithm(self.chd, self.cst, self.cnf)
self.azimuth_processing_algorithm = \
AzimuthProcessingAlgorithm(self.chd, self.cst, self.cnf)
self.stack_gathering_algorithm = \
StackGatheringAlgorithm(self.chd, self.cst, self.cnf)
self.geometry_corrections_algorithm = \
GeometryCorrectionsAlgorithm(self.chd, self.cst, self.cnf)
self.range_compression_algorithm = \
RangeCompressionAlgorithm(self.chd, self.cst, self.cnf)
self.stack_masking_algorithm = \
StackMaskingAlgorithm(self.chd, self.cst, self.cnf)
self.multilooking_algorithm = \
MultilookingAlgorithm(self.chd, self.cst, self.cnf)
self.sigma_zero_algorithm = \
Sigma0ScalingFactorAlgorithm(self.chd, self.cst, self.cnf)
# set threshold for gaps
self.gap_threshold = self.chd.bri_sar * 1.5