def _get_zero_doppler_data(hf, base_sicd):
"""
Gets zero-doppler parameters.
Parameters
----------
hf : h5py.File
base_sicd : SICDType
Returns
-------
(numpy.ndarray, float, numpy.ndarray, numpy.ndarray)
The azimuth zero-doppler time array, azimuth zero-doppler time spacing,
grid range array, range zero doppler time array.
"""
gp = hf['/science/LSAR/SLC/swaths']
ds = gp['zeroDopplerTime']
ref_time = _get_ref_time(ds.attrs['units'])
zd_time = ds[:] + get_seconds(ref_time, base_sicd.Timeline.CollectStart, precision='ns')
ss_az_s = gp['zeroDopplerTimeSpacing'][()]
if base_sicd.SCPCOA.SideOfTrack == 'L':
zd_time = zd_time[::-1]
ss_az_s *= -1
gp = hf['/science/LSAR/SLC/metadata/processingInformation/parameters']
grid_r = gp['slantRange'][:]
ds = gp['zeroDopplerTime']
ref_time = _get_ref_time(ds.attrs['units'])
grid_zd_time = ds[:] + get_seconds(ref_time, base_sicd.Timeline.CollectStart, precision='ns')
return zd_time, ss_az_s, grid_r, grid_zd_time
def get_rma():
# type: () -> RMAType
img_geometry = img['image_geometry']
near_range = img_geometry['range_to_first_sample']
center_time = parse_timestring(img['center_pixel']['center_time'],
precision='us')
first_time = parse_timestring(img_geometry['first_line_time'],
precision='us')
zd_time_scp = get_seconds(center_time, first_time, 'us')
r_ca_scp = near_range + image_data.SCPPixel.Row * grid.Row.SS
time_ca_poly = numpy.array(
[zd_time_scp, -look * ss_zd_s / grid.Col.SS], dtype='float64')
timecoa_value = get_seconds(center_time, start_time)
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
vm_ca = numpy.linalg.norm(arp_velocity)
inca = INCAType(R_CA_SCP=r_ca_scp,
FreqZero=fc,
TimeCAPoly=time_ca_poly,
DRateSFPoly=[
[1 / (vm_ca * ss_zd_s / grid.Col.SS)],
])
return RMAType(RMAlgoType='RG_DOP', INCA=inca)
def get_position() -> PositionType:
gp = hf['/science/LSAR/SLC/metadata/orbit']
ref_time = _get_ref_time(gp['time'].attrs['units'])
T = gp['time'][:] + get_seconds(ref_time, collect_start, precision='ns')
Pos = gp['position'][:]
Vel = gp['velocity'][:]
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def _get_position(self):
"""
Gets the Position.
Returns
-------
PositionType
"""
start_time = self._get_start_time()
# get radar position state information
state_vectors = self._findall('./sourceAttributes'
'/orbitAndAttitude'
'/orbitInformation'
'/stateVector')
# convert to relevant numpy arrays for polynomial fitting
T = numpy.array([
get_seconds(parse_timestring(state_vec.find('timeStamp').text),
start_time,
precision='us') for state_vec in state_vectors
],
dtype=numpy.float64)
Pos = numpy.hstack((numpy.array([
float(state_vec.find('xPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('yPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('zPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:,
numpy.newaxis]))
Vel = numpy.hstack((numpy.array([
float(state_vec.find('xVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('yVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('zVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:,
numpy.newaxis]))
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def extract_state_vector():
# type: () -> (numpy.ndarray, numpy.ndarray, numpy.ndarray)
vecs = collect['state']['state_vectors']
times = numpy.zeros((len(vecs), ), dtype=numpy.float64)
positions = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
velocities = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
for i, entry in enumerate(vecs):
times[i] = get_seconds(parse_timestring(entry['time'], precision='ns'), start_time, precision='ns')
positions[i, :] = entry['position']
velocities[i, :] = entry['velocity']
return times, positions, velocities
def get_grid():
# type: () -> GridType
img = collect['image']
image_plane = 'OTHER'
grid_type = 'PLANE'
if self._img_desc_tags['product_type'] == 'SLC' and img['algorithm'] != 'backprojection':
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'], precision='ns')
row_imp_rsp_bw = 2*bw/speed_of_light
row = DirParamType(
SS=img['pixel_spacing_column'],
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2*fc/speed_of_light,
DeltaK1=-0.5*row_imp_rsp_bw,
DeltaK2=0.5*row_imp_rsp_bw,
DeltaKCOAPoly=[[0.0, ], ],
WgtType=WgtTypeType(
WindowName=img['range_window']['name'],
Parameters=convert_string_dict(img['range_window']['parameters'])))
# get timecoa value
timecoa_value = get_seconds(coa_time, start_time) # TODO: constant?
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
# find velocity at coatime
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value, der_order=1)
arp_speed = numpy.linalg.norm(arp_velocity)
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
# ss_zd_s = col_ss/arp_speed
col = DirParamType(
SS=col_ss,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw/arp_speed,
KCtr=0,
WgtType=WgtTypeType(
WindowName=img['azimuth_window']['name'],
Parameters=convert_string_dict(img['azimuth_window']['parameters'])))
# TODO: from Wade - account for numeric WgtFunct
return GridType(
ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[[timecoa_value, ], ],
Row=row,
Col=col)
def get_position():
# type: () -> PositionType
# fetch the state information
times_str = hf['state_vector_time_utc'][:, 0]
times = numpy.zeros((times_str.shape[0], ), dtype='float64')
positions = numpy.zeros((times.size, 3), dtype='float64')
velocities = numpy.zeros((times.size, 3), dtype='float64')
for i, entry in enumerate(times_str):
times[i] = get_seconds(_parse_time(entry), start_time, precision='us')
positions[:, 0], positions[:, 1], positions[:, 2] = hf['posX'][:], hf['posY'][:], hf['posZ'][:]
velocities[:, 0], velocities[:, 1], velocities[:, 2] = hf['velX'][:], hf['velY'][:], hf['velZ'][:]
# fir the the position polynomial using cross validation
P_x, P_y, P_z = fit_position_xvalidation(times, positions, velocities, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def calculate_doppler_polys():
# extract doppler centroid coefficients
dc_estimate_coeffs = hf['dc_estimate_coeffs'][:]
dc_time_str = hf['dc_estimate_time_utc'][:, 0]
dc_zd_times = numpy.zeros((dc_time_str.shape[0], ), dtype='float64')
for i, entry in enumerate(dc_time_str):
dc_zd_times[i] = get_seconds(_parse_time(entry), start_time, precision='us')
# create a sampled doppler centroid
samples = 49 # copied from corresponding matlab, we just need enough for appropriate refitting
# create doppler time samples
diff_time_rg = first_pixel_time - zd_ref_time + \
numpy.linspace(0, number_of_range_samples/range_sampling_rate, samples)
# doppler centroid samples definition
dc_sample_array = numpy.zeros((samples, dc_zd_times.size), dtype='float64')
for i, coeffs in enumerate(dc_estimate_coeffs):
dc_sample_array[:, i] = polynomial.polyval(diff_time_rg, coeffs)
# create arrays for range/azimuth from scp in meters
azimuth_scp_m, range_scp_m = numpy.meshgrid(
col_ss*(dc_zd_times - zd_time_scp)/ss_zd_s,
(diff_time_rg + zd_ref_time - rg_time_scp)*speed_of_light/2)
# fit the doppler centroid sample array
x_order = min(3, range_scp_m.shape[0]-1)
y_order = min(3, range_scp_m.shape[1]-1)
t_dop_centroid_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m, azimuth_scp_m, dc_sample_array, x_order=x_order, y_order=y_order,
x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The dop_centroid_poly fit details:\n\troot mean square '
'residuals = {}\n\trank = {}\n\tsingular values = {}'.format(
residuals, rank, sing_values))
# define and fit the time coa array
doppler_rate_sampled = polynomial.polyval(azimuth_scp_m, drate_ca_poly)
time_coa = dc_zd_times + dc_sample_array/doppler_rate_sampled
t_time_coa_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m, azimuth_scp_m, time_coa, x_order=x_order, y_order=y_order,
x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The time_coa_poly fit details:\n\troot mean square '
'residuals = {}\n\trank = {}\n\tsingular values = {}'.format(
residuals, rank, sing_values))
return t_dop_centroid_poly, t_time_coa_poly
def _get_collection_times(hf):
"""
Gets the collection start and end times, and inferred duration.
Parameters
----------
hf : h5py.File
The h5py File object.
Returns
-------
(numpy.datetime64, numpy.datetime64, float)
Start and end times and duration
"""
start = parse_timestring(_stringify(hf['/science/LSAR/identification/zeroDopplerStartTime'][()]), precision='ns')
end = parse_timestring(_stringify(hf['/science/LSAR/identification/zeroDopplerEndTime'][()]), precision='ns')
duration = get_seconds(end, start, precision='ns')
return start, end, duration
def _get_collection_times(hf: h5pyFile) -> Tuple[numpy.datetime64, numpy.datetime64, float]:
"""
Gets the collection start and end times, and inferred duration.
Parameters
----------
hf : h5py.File
The h5py File object.
Returns
-------
start_time : numpy.datetime64
end_time : numpy.datetime64
duration : float
"""
start_time = parse_timestring(_stringify(hf['/science/LSAR/identification/zeroDopplerStartTime'][()]), precision='ns')
end_time = parse_timestring(_stringify(hf['/science/LSAR/identification/zeroDopplerEndTime'][()]), precision='ns')
duration = get_seconds(end_time, start_time, precision='ns')
return start_time, end_time, duration
def is_same_start_time(sicd1, sicd2):
"""
Do the two SICD structures have the same start time with millisecond resolution?
Parameters
----------
sicd1 : sarpy.io.complex.sicd_elements.SICD.SICDType
sicd2 : sarpy.io.complex.sicd_elements.SICD.SICDType
Returns
-------
bool
"""
if sicd1 is sicd2:
return True
try:
return abs(
get_seconds(sicd1.Timeline.CollectStart,
sicd2.Timeline.CollectStart,
precision='ms')) < 2e-3
except AttributeError:
return False
def get_sicd(self):
"""
Get the SICD metadata for the image.
Returns
-------
SICDType
"""
def convert_string_dict(dict_in):
# type: (dict) -> dict
dict_out = OrderedDict()
for key, val in dict_in.items():
if isinstance(val, string_types):
dict_out[key] = val
elif isinstance(val, int):
dict_out[key] = str(val)
elif isinstance(val, float):
dict_out[key] = '{0:0.16G}'.format(val)
else:
raise TypeError('Got unhandled type {}'.format(type(val)))
return dict_out
def extract_state_vector():
# type: () -> (numpy.ndarray, numpy.ndarray, numpy.ndarray)
vecs = collect['state']['state_vectors']
times = numpy.zeros((len(vecs), ), dtype=numpy.float64)
positions = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
velocities = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
for i, entry in enumerate(vecs):
times[i] = get_seconds(parse_timestring(entry['time'],
precision='ns'),
start_time,
precision='ns')
positions[i, :] = entry['position']
velocities[i, :] = entry['velocity']
return times, positions, velocities
def get_collection_info():
# type: () -> CollectionInfoType
coll_name = collect['platform']
start_dt = start_time.astype('datetime64[us]').astype(datetime)
mode = collect['mode'].strip().lower()
if mode == 'stripmap':
radar_mode = RadarModeType(ModeType='STRIPMAP')
elif mode == 'sliding_spotlight':
radar_mode = RadarModeType(ModeType='DYNAMIC STRIPMAP')
else:
raise ValueError('Got unhandled radar mode {}'.format(mode))
return CollectionInfoType(CollectorName=coll_name,
CoreName='{}{}{}'.format(
start_dt.strftime('%d%b%y').upper(),
coll_name,
start_dt.strftime('%H%M%S')),
RadarMode=radar_mode,
Classification='UNCLASSIFIED',
CollectType='MONOSTATIC')
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application=self._tiff_details.tags['Software'],
DateTime=parse_timestring(
self._img_desc_tags['processing_time'], precision='us'),
Profile='sarpy {}'.format(__version__),
Site='Unknown')
def get_image_data():
# type: () -> ImageDataType
img = collect['image']
rows = int(
img['columns']) # capella uses flipped row/column definition?
cols = int(img['rows'])
if img['data_type'] == 'CInt16':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled data_type {}'.format(
img['data_type']))
scp_pixel = (int(0.5 * rows), int(0.5 * cols))
if collect['radar']['pointing'] == 'left':
scp_pixel = (rows - scp_pixel[0] - 1, cols - scp_pixel[1] - 1)
return ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
PixelType=pixel_type,
FullImage=(rows, cols),
SCPPixel=scp_pixel)
def get_geo_data():
# type: () -> GeoDataType
return GeoDataType(SCP=SCPType(
ECF=collect['image']['center_pixel']['target_position']))
def get_position():
# type: () -> PositionType
px, py, pz = fit_position_xvalidation(state_time,
state_position,
state_velocity,
max_degree=6)
return PositionType(ARPPoly=XYZPolyType(X=px, Y=py, Z=pz))
def get_grid():
# type: () -> GridType
img = collect['image']
image_plane = 'OTHER'
grid_type = 'PLANE'
if self._img_desc_tags['product_type'] == 'SLC' and img[
'algorithm'] != 'backprojection':
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_imp_rsp_bw = 2 * bw / speed_of_light
row = DirParamType(SS=img['pixel_spacing_column'],
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtType=WgtTypeType(
WindowName=img['range_window']['name'],
Parameters=convert_string_dict(
img['range_window']['parameters'])))
# get timecoa value
timecoa_value = get_seconds(coa_time,
start_time) # TODO: constant?
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
# find velocity at coatime
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
arp_speed = numpy.linalg.norm(arp_velocity)
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
# ss_zd_s = col_ss/arp_speed
col = DirParamType(SS=col_ss,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw / arp_speed,
KCtr=0,
WgtType=WgtTypeType(
WindowName=img['azimuth_window']['name'],
Parameters=convert_string_dict(
img['azimuth_window']['parameters'])))
# TODO: from Wade - account for numeric WgtFunct
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
def get_radar_colection():
# type: () -> RadarCollectionType
radar = collect['radar']
freq_min = fc - 0.5 * bw
return RadarCollectionType(
TxPolarization=radar['transmit_polarization'],
TxFrequency=TxFrequencyType(Min=freq_min, Max=freq_min + bw),
Waveform=[
WaveformParametersType(
TxRFBandwidth=bw,
TxPulseLength=radar['pulse_duration'],
RcvDemodType='CHIRP',
ADCSampleRate=radar['sampling_frequency'],
TxFreqStart=freq_min)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']))
])
def get_timeline():
# type: () -> TimelineType
prf = collect['radar']['prf'][0]['prf']
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=duration * prf,
IPPPoly=(0, prf)),
])
def get_image_formation():
# type: () -> ImageFormationType
radar = collect['radar']
algo = collect['image']['algorithm'].upper()
processings = None
if algo == 'BACKPROJECTION':
processings = [
ProcessingType(Type='Backprojected to DEM', Applied=True),
]
if algo not in ('PFA', 'RMA', 'RGAZCOMP'):
logging.warning(
'Image formation algorithm {} not one of the recognized SICD options, '
'being set to "OTHER".'.format(algo))
algo = 'OTHER'
return ImageFormationType(
RcvChanProc=RcvChanProcType(NumChanProc=1, PRFScaleFactor=1),
ImageFormAlgo=algo,
TStartProc=0,
TEndProc=duration,
TxRcvPolarizationProc='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']),
TxFrequencyProc=TxFrequencyProcType(
MinProc=radar_collection.TxFrequency.Min,
MaxProc=radar_collection.TxFrequency.Max),
STBeamComp='NO',
ImageBeamComp='NO',
AzAutofocus='NO',
RgAutofocus='NO',
Processings=processings)
# TODO: From Wade - Radiometric is not suitable?
# extract general use information
collect = self._img_desc_tags['collect']
start_time = parse_timestring(collect['start_timestamp'],
precision='ns')
end_time = parse_timestring(collect['stop_timestamp'], precision='ns')
duration = get_seconds(end_time, start_time, precision='ns')
state_time, state_position, state_velocity = extract_state_vector()
bw = collect['radar']['pulse_bandwidth']
fc = collect['radar']['center_frequency']
# define the sicd elements
collection_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
position = get_position()
grid = get_grid()
radar_collection = get_radar_colection()
timeline = get_timeline()
image_formation = get_image_formation()
sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation)
sicd.derive()
# this would be a rough estimate - waiting for radiometric data
# sicd.populate_rniirs(override=False)
return sicd
def get_sicd(self):
"""
Gets the SICD structure.
Returns
-------
Tuple[SICDType, tuple, tuple]
The sicd structure, the data size argument, and the symmetry argument.
"""
def get_collection_info():
# type: () -> CollectionInfoType
return CollectionInfoType(
CollectorName=_stringify(hf['satellite_name'][()]),
CoreName=_stringify(hf['product_name'][()]),
CollectType='MONOSTATIC',
Classification='UNCLASSIFIED',
RadarMode=RadarModeType(
ModeType=_stringify(hf['acquisition_mode'][()]).upper(),
ModeID=_stringify(hf['product_type'][()])))
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application='ICEYE_P_{}'.format(hf['processor_version'][()]),
DateTime=_parse_time(hf['processing_time'][()]),
Site='Unknown',
Profile='sarpy {}'.format(__version__))
def get_image_data():
# type: () -> ImageDataType
samp_prec = _stringify(hf['sample_precision'][()])
if samp_prec.upper() == 'INT16':
pixel_type = 'RE16I_IM16I'
elif samp_prec.upper() == 'FLOAT32':
pixel_type = 'RE32F_IM32F'
else:
raise ValueError(
'Got unhandled sample precision {}'.format(samp_prec))
num_rows = int_func(number_of_range_samples)
num_cols = int_func(number_of_azimuth_samples)
scp_row = int_func(coord_center[0]) - 1
scp_col = int_func(coord_center[1]) - 1
if 0 < scp_col < num_rows - 1:
if look_side == 'left':
scp_col = num_cols - scp_col - 1
else:
# early ICEYE processing bug led to nonsensical SCP
scp_col = int_func(num_cols / 2.0)
return ImageDataType(PixelType=pixel_type,
NumRows=num_rows,
NumCols=num_cols,
FirstRow=0,
FirstCol=0,
FullImage=(num_rows, num_cols),
SCPPixel=(scp_row, scp_col))
def get_geo_data():
# type: () -> GeoDataType
# NB: the remainder will be derived.
return GeoDataType(SCP=SCPType(
LLH=[coord_center[2], coord_center[3], avg_scene_height]))
def get_timeline():
# type: () -> TimelineType
acq_prf = hf['acquisition_prf'][()]
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(index=0,
TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=int_func(
round(acq_prf * duration)),
IPPPoly=[0, acq_prf]),
])
def get_position():
# type: () -> PositionType
# fetch the state information
times_str = hf['state_vector_time_utc'][:, 0]
times = numpy.zeros((times_str.shape[0], ), dtype='float64')
positions = numpy.zeros((times.size, 3), dtype='float64')
velocities = numpy.zeros((times.size, 3), dtype='float64')
for i, entry in enumerate(times_str):
times[i] = get_seconds(_parse_time(entry),
start_time,
precision='us')
positions[:, 0], positions[:, 1], positions[:, 2] = hf[
'posX'][:], hf['posY'][:], hf['posZ'][:]
velocities[:, 0], velocities[:, 1], velocities[:, 2] = hf[
'velX'][:], hf['velY'][:], hf['velZ'][:]
# fir the the position polynomial using cross validation
P_x, P_y, P_z = fit_position_xvalidation(times,
positions,
velocities,
max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_radar_collection():
# type : () -> RadarCollection
return RadarCollectionType(
TxPolarization=tx_pol,
TxFrequency=TxFrequencyType(Min=min_freq, Max=max_freq),
Waveform=[
WaveformParametersType(
TxFreqStart=min_freq,
TxRFBandwidth=tx_bandwidth,
TxPulseLength=hf['chirp_duration'][()],
ADCSampleRate=hf['range_sampling_rate'][()],
RcvDemodType='CHIRP',
RcvFMRate=0,
index=1)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization=polarization, index=1)
])
def get_image_formation():
# type: () -> ImageFormationType
return ImageFormationType(
TxRcvPolarizationProc=polarization,
ImageFormAlgo='RMA',
TStartProc=0,
TEndProc=duration,
TxFrequencyProc=TxFrequencyProcType(MinProc=min_freq,
MaxProc=max_freq),
STBeamComp='NO',
ImageBeamComp='SV',
AzAutofocus='NO',
RgAutofocus='NO',
RcvChanProc=RcvChanProcType(NumChanProc=1,
PRFScaleFactor=1,
ChanIndices=[
1,
]),
)
def get_radiometric():
# type: () -> RadiometricType
return RadiometricType(BetaZeroSFPoly=[
[
float(hf['calibration_factor'][()]),
],
])
def calculate_drate_sf_poly():
r_ca_coeffs = numpy.array([r_ca_scp, 1], dtype='float64')
dop_rate_coeffs = hf['doppler_rate_coeffs'][:]
# Prior to ICEYE 1.14 processor, absolute value of Doppler rate was
# provided, not true Doppler rate. Doppler rate should always be negative
if dop_rate_coeffs[0] > 0:
dop_rate_coeffs *= -1
dop_rate_poly = Poly1DType(Coefs=dop_rate_coeffs)
# now adjust to create
t_drate_ca_poly = dop_rate_poly.shift(t_0=zd_ref_time -
rg_time_scp,
alpha=2 / speed_of_light,
return_poly=False)
return t_drate_ca_poly, -polynomial.polymul(
t_drate_ca_poly, r_ca_coeffs) * speed_of_light / (
2 * center_freq * vm_ca_sq)
def calculate_doppler_polys():
# extract doppler centroid coefficients
dc_estimate_coeffs = hf['dc_estimate_coeffs'][:]
dc_time_str = hf['dc_estimate_time_utc'][:, 0]
dc_zd_times = numpy.zeros((dc_time_str.shape[0], ),
dtype='float64')
for i, entry in enumerate(dc_time_str):
dc_zd_times[i] = get_seconds(_parse_time(entry),
start_time,
precision='us')
# create a sampled doppler centroid
samples = 49 # copied from corresponding matlab, we just need enough for appropriate refitting
# create doppler time samples
diff_time_rg = first_pixel_time - zd_ref_time + \
numpy.linspace(0, number_of_range_samples/range_sampling_rate, samples)
# doppler centroid samples definition
dc_sample_array = numpy.zeros((samples, dc_zd_times.size),
dtype='float64')
for i, coeffs in enumerate(dc_estimate_coeffs):
dc_sample_array[:,
i] = polynomial.polyval(diff_time_rg, coeffs)
# create arrays for range/azimuth from scp in meters
azimuth_scp_m, range_scp_m = numpy.meshgrid(
col_ss * (dc_zd_times - zd_time_scp) / ss_zd_s,
(diff_time_rg + zd_ref_time - rg_time_scp) * speed_of_light /
2)
# fit the doppler centroid sample array
x_order = min(3, range_scp_m.shape[0] - 1)
y_order = min(3, range_scp_m.shape[1] - 1)
t_dop_centroid_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m,
azimuth_scp_m,
dc_sample_array,
x_order=x_order,
y_order=y_order,
x_scale=1e-3,
y_scale=1e-3,
rcond=1e-40)
logging.info(
'The dop_centroid_poly fit details:\nroot mean square '
'residuals = {}\nrank = {}\nsingular values = {}'.format(
residuals, rank, sing_values))
# define and fit the time coa array
doppler_rate_sampled = polynomial.polyval(azimuth_scp_m,
drate_ca_poly)
time_coa = dc_zd_times + dc_sample_array / doppler_rate_sampled
t_time_coa_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m,
azimuth_scp_m,
time_coa,
x_order=x_order,
y_order=y_order,
x_scale=1e-3,
y_scale=1e-3,
rcond=1e-40)
logging.info(
'The time_coa_poly fit details:\nroot mean square '
'residuals = {}\nrank = {}\nsingular values = {}'.format(
residuals, rank, sing_values))
return t_dop_centroid_poly, t_time_coa_poly
def get_rma():
# type: () -> RMAType
dop_centroid_poly = Poly2DType(Coefs=dop_centroid_poly_coeffs)
dop_centroid_coa = True
if collect_info.RadarMode.ModeType == 'SPOTLIGHT':
dop_centroid_poly = None
dop_centroid_coa = None
# NB: DRateSFPoly is defined as a function of only range - reshape appropriately
inca = INCAType(
R_CA_SCP=r_ca_scp,
FreqZero=center_freq,
DRateSFPoly=Poly2DType(
Coefs=numpy.reshape(drate_sf_poly_coefs, (-1, 1))),
DopCentroidPoly=dop_centroid_poly,
DopCentroidCOA=dop_centroid_coa,
TimeCAPoly=Poly1DType(Coefs=time_ca_poly_coeffs))
return RMAType(RMAlgoType='OMEGA_K', INCA=inca)
def get_grid():
# type: () -> GridType
time_coa_poly = Poly2DType(Coefs=time_coa_poly_coeffs)
if collect_info.RadarMode.ModeType == 'SPOTLIGHT':
time_coa_poly = Poly2DType(Coefs=[
[
float(time_coa_poly_coeffs[0, 0]),
],
])
row_win = _stringify(hf['window_function_range'][()])
if row_win == 'NONE':
row_win = 'UNIFORM'
row = DirParamType(SS=row_ss,
Sgn=-1,
KCtr=2 * center_freq / speed_of_light,
ImpRespBW=2 * tx_bandwidth / speed_of_light,
DeltaKCOAPoly=Poly2DType(Coefs=[[
0,
]]),
WgtType=WgtTypeType(WindowName=row_win))
col_win = _stringify(hf['window_function_azimuth'][()])
if col_win == 'NONE':
col_win = 'UNIFORM'
col = DirParamType(
SS=col_ss,
Sgn=-1,
KCtr=0,
ImpRespBW=col_imp_res_bw,
WgtType=WgtTypeType(WindowName=col_win),
DeltaKCOAPoly=Poly2DType(Coefs=dop_centroid_poly_coeffs *
ss_zd_s / col_ss))
return GridType(Type='RGZERO',
ImagePlane='SLANT',
TimeCOAPoly=time_coa_poly,
Row=row,
Col=col)
def correct_scp():
scp_pixel = sicd.ImageData.SCPPixel.get_array()
scp_ecf = sicd.project_image_to_ground(scp_pixel,
projection_type='HAE')
sicd.update_scp(scp_ecf, coord_system='ECF')
with h5py.File(self._file_name, 'r') as hf:
# some common use variables
look_side = _stringify(hf['look_side'][()])
coord_center = hf['coord_center'][:]
avg_scene_height = float(hf['avg_scene_height'][()])
start_time = _parse_time(hf['acquisition_start_utc'][()])
end_time = _parse_time(hf['acquisition_end_utc'][()])
duration = get_seconds(end_time, start_time, precision='us')
center_freq = float(hf['carrier_frequency'][()])
tx_bandwidth = float(hf['chirp_bandwidth'][()])
min_freq = center_freq - 0.5 * tx_bandwidth
max_freq = center_freq + 0.5 * tx_bandwidth
pol_temp = _stringify(hf['polarization'][()])
tx_pol = pol_temp[0]
rcv_pol = pol_temp[1]
polarization = tx_pol + ':' + rcv_pol
first_pixel_time = float(hf['first_pixel_time'][()])
near_range = first_pixel_time * speed_of_light / 2
number_of_range_samples = float(hf['number_of_range_samples'][()])
number_of_azimuth_samples = float(
hf['number_of_azimuth_samples'][()])
range_sampling_rate = float(hf['range_sampling_rate'][()])
row_ss = speed_of_light / (2 * range_sampling_rate)
# define the sicd elements
collect_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
timeline = get_timeline()
position = get_position()
radar_collection = get_radar_collection()
image_formation = get_image_formation()
radiometric = get_radiometric()
# calculate some zero doppler parameters
ss_zd_s = float(hf['azimuth_time_interval'][()])
if look_side == 'left':
ss_zd_s *= -1
zero_doppler_left = _parse_time(hf['zerodoppler_end_utc'][()])
else:
zero_doppler_left = _parse_time(
hf['zerodoppler_start_utc'][()])
dop_bw = hf['total_processed_bandwidth_azimuth'][()]
zd_time_scp = get_seconds(zero_doppler_left, start_time, precision='us') + \
image_data.SCPPixel.Col*ss_zd_s
zd_ref_time = first_pixel_time + number_of_range_samples / (
2 * range_sampling_rate)
vel_scp = position.ARPPoly.derivative_eval(zd_time_scp,
der_order=1)
vm_ca_sq = numpy.sum(vel_scp * vel_scp)
rg_time_scp = first_pixel_time + image_data.SCPPixel.Row / range_sampling_rate
r_ca_scp = rg_time_scp * speed_of_light / 2
# calculate the doppler rate sf polynomial
drate_ca_poly, drate_sf_poly_coefs = calculate_drate_sf_poly()
# calculate some doppler dependent grid parameters
col_ss = float(
numpy.sqrt(vm_ca_sq) * abs(ss_zd_s) * drate_sf_poly_coefs[0])
col_imp_res_bw = dop_bw * abs(ss_zd_s) / col_ss
time_ca_poly_coeffs = [zd_time_scp, ss_zd_s / col_ss]
# calculate the doppler polynomials
dop_centroid_poly_coeffs, time_coa_poly_coeffs = calculate_doppler_polys(
)
# finish definition of sicd elements
rma = get_rma()
grid = get_grid()
sicd = SICDType(CollectionInfo=collect_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Timeline=timeline,
Position=position,
RadarCollection=radar_collection,
ImageFormation=image_formation,
Radiometric=radiometric,
RMA=rma,
Grid=grid)
# adjust the scp location
correct_scp()
# derive sicd fields
sicd.derive()
# TODO: RNIIRS?
data_size = (image_data.NumCols, image_data.NumRows)
symmetry = (True, False,
True) if look_side == 'left' else (False, False, True)
return sicd, data_size, symmetry
def _get_rma_adjust_grid(self, scpcoa, grid, image_data, position,
collection_info):
"""
Gets the RMA metadata, and adjust the Grid.Col metadata.
Parameters
----------
scpcoa : SCPCOAType
grid : GridType
image_data : ImageDataType
position : PositionType
collection_info : CollectionInfoType
Returns
-------
RMAType
"""
look = scpcoa.look
start_time = self._get_start_time()
center_freq = self._get_center_frequency()
doppler_bandwidth = float(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/totalProcessedAzimuthBandwidth').text)
zero_dop_last_line = parse_timestring(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/zeroDopplerTimeLastLine').text)
zero_dop_first_line = parse_timestring(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/zeroDopplerTimeFirstLine').text)
if look > 1: # SideOfTrack == 'L'
# we explicitly want negative time order
if zero_dop_first_line < zero_dop_last_line:
zero_dop_first_line, zero_dop_last_line = zero_dop_last_line, zero_dop_first_line
else:
# we explicitly want positive time order
if zero_dop_first_line > zero_dop_last_line:
zero_dop_first_line, zero_dop_last_line = zero_dop_last_line, zero_dop_first_line
col_spacing_zd = get_seconds(zero_dop_last_line,
zero_dop_first_line,
precision='us') / (image_data.NumCols - 1)
# zero doppler time of SCP relative to collect start
time_scp_zd = get_seconds(zero_dop_first_line, start_time, precision='us') + \
image_data.SCPPixel.Col*col_spacing_zd
if self.generation == 'RS2':
near_range = float(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/slantRangeNearEdge').text)
elif self.generation == 'RCM':
near_range = float(
self._find('./sceneAttributes'
'/imageAttributes'
'/slantRangeNearEdge').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
inca = INCAType(R_CA_SCP=near_range +
(image_data.SCPPixel.Row * grid.Row.SS),
FreqZero=center_freq)
# doppler rate calculations
velocity = position.ARPPoly.derivative_eval(time_scp_zd, 1)
vel_ca_squared = numpy.sum(velocity * velocity)
# polynomial representing range as a function of range distance from SCP
r_ca = numpy.array([inca.R_CA_SCP, 1], dtype=numpy.float64)
# doppler rate coefficients
if self.generation == 'RS2':
doppler_rate_coeffs = numpy.array([
float(entry) for entry in self._find(
'./imageGenerationParameters'
'/dopplerRateValues'
'/dopplerRateValuesCoefficients').text.split()
],
dtype=numpy.float64)
doppler_rate_ref_time = float(
self._find('./imageGenerationParameters'
'/dopplerRateValues'
'/dopplerRateReferenceTime').text)
elif self.generation == 'RCM':
doppler_rate_coeffs = numpy.array([
float(entry) for entry in self._find(
'./dopplerRate'
'/dopplerRateEstimate'
'/dopplerRateCoefficients').text.split()
],
dtype=numpy.float64)
doppler_rate_ref_time = float(
self._find('./dopplerRate'
'/dopplerRateEstimate'
'/dopplerRateReferenceTime').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
# the doppler_rate_coeffs represents a polynomial in time, relative to
# doppler_rate_ref_time.
# to construct the doppler centroid polynomial, we need to change scales
# to a polynomial in space, relative to SCP.
doppler_rate_poly = Poly1DType(Coefs=doppler_rate_coeffs)
alpha = 2.0 / speed_of_light
t_0 = doppler_rate_ref_time - alpha * inca.R_CA_SCP
dop_rate_scaled_coeffs = doppler_rate_poly.shift(t_0,
alpha,
return_poly=False)
# DRateSFPoly is then a scaled multiple of this scaled poly and r_ca above
coeffs = -numpy.convolve(dop_rate_scaled_coeffs, r_ca) / (
alpha * center_freq * vel_ca_squared)
inca.DRateSFPoly = Poly2DType(
Coefs=numpy.reshape(coeffs, (coeffs.size, 1)))
# modify a few of the other fields
ss_scale = numpy.sqrt(vel_ca_squared) * inca.DRateSFPoly[0, 0]
grid.Col.SS = col_spacing_zd * ss_scale
grid.Col.ImpRespBW = -look * doppler_bandwidth / ss_scale
inca.TimeCAPoly = Poly1DType(Coefs=[time_scp_zd, 1. / ss_scale])
# doppler centroid
if self.generation == 'RS2':
doppler_cent_coeffs = numpy.array([
float(entry) for entry in self._find(
'./imageGenerationParameters'
'/dopplerCentroid'
'/dopplerCentroidCoefficients').text.split()
],
dtype=numpy.float64)
doppler_cent_ref_time = float(
self._find('./imageGenerationParameters'
'/dopplerCentroid'
'/dopplerCentroidReferenceTime').text)
doppler_cent_time_est = parse_timestring(
self._find('./imageGenerationParameters'
'/dopplerCentroid'
'/timeOfDopplerCentroidEstimate').text)
elif self.generation == 'RCM':
doppler_cent_coeffs = numpy.array([
float(entry) for entry in self._find(
'./dopplerCentroid'
'/dopplerCentroidEstimate'
'/dopplerCentroidCoefficients').text.split()
],
dtype=numpy.float64)
doppler_cent_ref_time = float(
self._find('./dopplerCentroid'
'/dopplerCentroidEstimate'
'/dopplerCentroidReferenceTime').text)
doppler_cent_time_est = parse_timestring(
self._find('./dopplerCentroid'
'/dopplerCentroidEstimate'
'/timeOfDopplerCentroidEstimate').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
doppler_cent_poly = Poly1DType(Coefs=doppler_cent_coeffs)
alpha = 2.0 / speed_of_light
t_0 = doppler_cent_ref_time - alpha * inca.R_CA_SCP
scaled_coeffs = doppler_cent_poly.shift(t_0, alpha, return_poly=False)
inca.DopCentroidPoly = Poly2DType(
Coefs=numpy.reshape(scaled_coeffs, (scaled_coeffs.size, 1)))
# adjust doppler centroid for spotlight, we need to add a second
# dimension to DopCentroidPoly
if collection_info.RadarMode.ModeType == 'SPOTLIGHT':
doppler_cent_est = get_seconds(doppler_cent_time_est,
start_time,
precision='us')
doppler_cent_col = (doppler_cent_est -
time_scp_zd) / col_spacing_zd
dop_poly = numpy.zeros((scaled_coeffs.shape[0], 2),
dtype=numpy.float64)
dop_poly[:, 0] = scaled_coeffs
dop_poly[0, 1] = -look * center_freq * alpha * numpy.sqrt(
vel_ca_squared) / inca.R_CA_SCP
# dopplerCentroid in native metadata was defined at specific column,
# which might not be our SCP column. Adjust so that SCP column is correct.
dop_poly[0, 0] = dop_poly[0, 0] - (dop_poly[0, 1] *
doppler_cent_col * grid.Col.SS)
inca.DopCentroidPoly = Poly2DType(Coefs=dop_poly)
grid.Col.DeltaKCOAPoly = Poly2DType(
Coefs=inca.DopCentroidPoly.get_array() * col_spacing_zd /
grid.Col.SS)
# compute grid.Col.DeltaK1/K2 from DeltaKCOAPoly
coeffs = grid.Col.DeltaKCOAPoly.get_array()[:, 0]
# get roots
roots = polynomial.polyroots(coeffs)
# construct range bounds (in meters)
range_bounds = (
numpy.array([0, image_data.NumRows - 1], dtype=numpy.float64) -
image_data.SCPPixel.Row) * grid.Row.SS
possible_ranges = numpy.copy(range_bounds)
useful_roots = ((roots > numpy.min(range_bounds)) &
(roots < numpy.max(range_bounds)))
if numpy.any(useful_roots):
possible_ranges = numpy.concatenate(
(possible_ranges, roots[useful_roots]), axis=0)
azimuth_bounds = (
numpy.array([0, (image_data.NumCols - 1)], dtype=numpy.float64) -
image_data.SCPPixel.Col) * grid.Col.SS
coords_az_2d, coords_rg_2d = numpy.meshgrid(azimuth_bounds,
possible_ranges)
possible_bounds_deltak = grid.Col.DeltaKCOAPoly(
coords_rg_2d, coords_az_2d)
grid.Col.DeltaK1 = numpy.min(
possible_bounds_deltak) - 0.5 * grid.Col.ImpRespBW
grid.Col.DeltaK2 = numpy.max(
possible_bounds_deltak) + 0.5 * grid.Col.ImpRespBW
# Wrapped spectrum
if (grid.Col.DeltaK1 < -0.5 / grid.Col.SS) or (grid.Col.DeltaK2 >
0.5 / grid.Col.SS):
grid.Col.DeltaK1 = -0.5 / abs(grid.Col.SS)
grid.Col.DeltaK2 = -grid.Col.DeltaK1
time_coa_poly = fit_time_coa_polynomial(inca,
image_data,
grid,
dop_rate_scaled_coeffs,
poly_order=2)
if collection_info.RadarMode.ModeType == 'SPOTLIGHT':
# using above was convenience, but not really sensible in spotlight mode
grid.TimeCOAPoly = Poly2DType(Coefs=[
[
time_coa_poly.Coefs[0, 0],
],
])
inca.DopCentroidPoly = None
elif collection_info.RadarMode.ModeType == 'STRIPMAP':
# fit TimeCOAPoly for grid
grid.TimeCOAPoly = time_coa_poly
inca.DopCentroidCOA = True
else:
raise ValueError('unhandled ModeType {}'.format(
collection_info.RadarMode.ModeType))
return RMAType(RMAlgoType='OMEGA_K', INCA=inca)
def _get_band_specific_sicds(self, base_sicd, h5_dict, band_dict, shape_dict):
# type: (SICDType, dict, dict, dict) -> Dict[str, SICDType]
az_ref_time, rg_ref_time, dop_poly_az, dop_poly_rg, dop_rate_poly_rg = self._get_dop_poly_details(h5_dict)
center_frequency = h5_dict['Radar Frequency']
# relative times in csk are wrt some reference time - for sicd they should be relative to start time
collect_start = parse_timestring(h5_dict['Scene Sensing Start UTC'], precision='ns')
ref_time = parse_timestring(h5_dict['Reference UTC'], precision='ns')
ref_time_offset = get_seconds(ref_time, collect_start, precision='ns')
def update_scp_prelim(sicd, band_name):
# type: (SICDType, str) -> None
LLH = band_dict[band_name]['Centre Geodetic Coordinates']
sicd.GeoData = GeoDataType(SCP=SCPType(LLH=LLH)) # EarthModel & ECF will be populated
def update_image_data(sicd, band_name):
# type: (SICDType, str) -> Tuple[float, float, float, float]
cols, rows = shape_dict[band_name]
t_az_first_time = band_dict[band_name]['Zero Doppler Azimuth First Time']
# zero doppler time of first column
t_ss_az_s = band_dict[band_name]['Line Time Interval']
if base_sicd.SCPCOA.SideOfTrack == 'L':
# we need to reverse time order
t_ss_az_s *= -1
t_az_first_time = band_dict[band_name]['Zero Doppler Azimuth Last Time']
# zero doppler time of first row
t_rg_first_time = band_dict[band_name]['Zero Doppler Range First Time']
# row spacing in range time (seconds)
t_ss_rg_s = band_dict[band_name]['Column Time Interval']
sicd.ImageData = ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
PixelType='RE16I_IM16I',
SCPPixel=RowColType(Row=int(rows/2),
Col=int(cols/2)))
return t_rg_first_time, t_ss_rg_s, t_az_first_time, t_ss_az_s
def update_timeline(sicd, band_name):
# type: (SICDType, str) -> None
prf = band_dict[band_name]['PRF']
duration = sicd.Timeline.CollectDuration
ipp_el = sicd.Timeline.IPP[0]
ipp_el.IPPEnd = duration
ipp_el.TEnd = duration
ipp_el.IPPPoly = Poly1DType(Coefs=(0, prf))
def update_radar_collection(sicd, band_name, ind):
# type: (SICDType, str, int) -> None
chirp_length = band_dict[band_name]['Range Chirp Length']
chirp_rate = abs(band_dict[band_name]['Range Chirp Rate'])
sample_rate = band_dict[band_name]['Sampling Rate']
ref_dechirp_time = band_dict[band_name]['Reference Dechirping Time']
win_length = band_dict[band_name]['Echo Sampling Window Length']
rcv_fm_rate = 0 if numpy.isnan(ref_dechirp_time) else ref_dechirp_time # TODO: is this the correct value?
band_width = chirp_length*chirp_rate
fr_min = center_frequency - 0.5*band_width
fr_max = center_frequency + 0.5*band_width
sicd.RadarCollection.TxFrequency = TxFrequencyType(Min=fr_min,
Max=fr_max)
sicd.RadarCollection.Waveform = [
WaveformParametersType(index=0,
TxPulseLength=chirp_length,
TxRFBandwidth=band_width,
TxFreqStart=fr_min,
TxFMRate=chirp_rate,
ADCSampleRate=sample_rate,
RcvFMRate=rcv_fm_rate,
RcvWindowLength=win_length/sample_rate), ]
sicd.ImageFormation.RcvChanProc.ChanIndices = [ind+1, ]
sicd.ImageFormation.TxFrequencyProc = TxFrequencyProcType(MinProc=fr_min,
MaxProc=fr_max)
sicd.ImageFormation.TxRcvPolarizationProc = sicd.RadarCollection.RcvChannels[ind].TxRcvPolarization
def update_rma_and_grid(sicd, band_name):
# type: (SICDType, str) -> None
rg_scp_time = rg_first_time + (ss_rg_s*sicd.ImageData.SCPPixel.Row)
az_scp_time = az_first_time + (ss_az_s*sicd.ImageData.SCPPixel.Col)
r_ca_scp = rg_scp_time*speed_of_light/2
sicd.RMA.INCA.R_CA_SCP = r_ca_scp
# compute DRateSFPoly
scp_ca_time = az_scp_time + ref_time_offset
vel_poly = sicd.Position.ARPPoly.derivative(der_order=1, return_poly=True)
vel_ca_vec = vel_poly(scp_ca_time)
vel_ca_sq = numpy.sum(vel_ca_vec*vel_ca_vec)
vel_ca = numpy.sqrt(vel_ca_sq)
r_ca = numpy.array([r_ca_scp, 1.], dtype=numpy.float64)
dop_rate_poly_rg_shifted = dop_rate_poly_rg.shift(
rg_ref_time-rg_scp_time, alpha=ss_rg_s/row_ss, return_poly=False)
drate_sf_poly = -(polynomial.polymul(dop_rate_poly_rg_shifted, r_ca) *
speed_of_light/(2*center_frequency*vel_ca_sq))
# update grid.row
sicd.Grid.Row.SS = row_ss
sicd.Grid.Row.ImpRespBW = row_bw
sicd.Grid.Row.DeltaK1 = -0.5 * row_bw
sicd.Grid.Row.DeltaK2 = 0.5 * row_bw
# update grid.col
col_ss = vel_ca*ss_az_s*drate_sf_poly[0]
sicd.Grid.Col.SS = col_ss
col_bw = min(band_dict[band_name]['Azimuth Focusing Bandwidth'] * abs(ss_az_s), 1) / col_ss
sicd.Grid.Col.ImpRespBW = col_bw
# update inca
sicd.RMA.INCA.DRateSFPoly = Poly2DType(Coefs=numpy.reshape(drate_sf_poly, (-1, 1)))
sicd.RMA.INCA.TimeCAPoly = Poly1DType(Coefs=[scp_ca_time, ss_az_s/col_ss])
# compute DopCentroidPoly & DeltaKCOAPoly
dop_centroid_poly = numpy.zeros((dop_poly_rg.order1+1, dop_poly_az.order1+1), dtype=numpy.float64)
dop_centroid_poly[0, 0] = dop_poly_rg(rg_scp_time-rg_ref_time) + \
dop_poly_az(az_scp_time-az_ref_time) - \
0.5*(dop_poly_rg[0] + dop_poly_az[0])
dop_poly_rg_shifted = dop_poly_rg.shift(rg_ref_time-rg_scp_time, alpha=ss_rg_s/row_ss)
dop_poly_az_shifted = dop_poly_az.shift(az_ref_time-az_scp_time, alpha=ss_az_s/col_ss)
dop_centroid_poly[1:, 0] = dop_poly_rg_shifted[1:]
dop_centroid_poly[0, 1:] = dop_poly_az_shifted[1:]
sicd.RMA.INCA.DopCentroidPoly = Poly2DType(Coefs=dop_centroid_poly)
sicd.RMA.INCA.DopCentroidCOA = True
sicd.Grid.Col.DeltaKCOAPoly = Poly2DType(Coefs=dop_centroid_poly*ss_az_s/col_ss)
# fit TimeCOAPoly
sicd.Grid.TimeCOAPoly = fit_time_coa_polynomial(
sicd.RMA.INCA, sicd.ImageData, sicd.Grid, dop_rate_poly_rg_shifted, poly_order=2)
def update_radiometric(sicd, band_name):
# type: (SICDType, str) -> None
if h5_dict['Range Spreading Loss Compensation Geometry'] != 'NONE':
slant_range = h5_dict['Reference Slant Range']
exp = h5_dict['Reference Slant Range Exponent']
sf = slant_range**(2*exp)
if h5_dict['Calibration Constant Compensation Flag'] == 0:
rsf = h5_dict['Rescaling Factor']
cal = band_dict[band_name]['Calibration Constant']
sf /= cal*(rsf**2)
sicd.Radiometric = RadiometricType(BetaZeroSFPoly=Poly2DType(Coefs=[[sf, ], ]))
def update_geodata(sicd): # type: (SICDType) -> None
ecf = point_projection.image_to_ground([sicd.ImageData.SCPPixel.Row, sicd.ImageData.SCPPixel.Col], sicd)
sicd.GeoData.SCP = SCPType(ECF=ecf) # LLH will be populated
out = {}
for i, bd_name in enumerate(band_dict):
t_sicd = base_sicd.copy()
update_scp_prelim(t_sicd, bd_name) # set preliminary value for SCP (required for projection)
row_bw = band_dict[bd_name]['Range Focusing Bandwidth']*2/speed_of_light
row_ss = band_dict[bd_name]['Column Spacing']
rg_first_time, ss_rg_s, az_first_time, ss_az_s = update_image_data(t_sicd, bd_name)
update_timeline(t_sicd, bd_name)
update_radar_collection(t_sicd, bd_name, i)
update_rma_and_grid(t_sicd, bd_name)
update_radiometric(t_sicd, bd_name)
update_geodata(t_sicd)
t_sicd.derive()
t_sicd.populate_rniirs(override=False)
out[bd_name] = t_sicd
return out
def get_grid():
# type: () -> GridType
def get_weight(window_dict):
window_name = window_dict['name']
if window_name.lower() == 'rectangular':
return WgtTypeType(WindowName='UNIFORM')
else:
# TODO: what is the proper interpretation for the avci-nacaroglu window?
return WgtTypeType(WindowName=window_name,
Parameters=convert_string_dict(
window_dict['parameters']))
img = collect['image']
img_geometry = img['image_geometry']
if img_geometry.get('type', None) == 'slant_plane':
image_plane = 'SLANT'
else:
image_plane = 'OTHER'
grid_type = 'PLANE'
if self._img_desc_tags['product_type'] == 'SLC' and img[
'algorithm'] != 'backprojection':
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_imp_rsp_bw = 2 * bw / speed_of_light
row = DirParamType(SS=img['pixel_spacing_column'],
Sgn=-1,
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtType=get_weight(img['range_window']))
# get timecoa value
timecoa_value = get_seconds(
coa_time, start_time) # TODO: this is not generally correct
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
# find velocity at coatime
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
arp_speed = numpy.linalg.norm(arp_velocity)
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
# ss_zd_s = col_ss/arp_speed
col = DirParamType(SS=col_ss,
Sgn=-1,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw / arp_speed,
KCtr=0,
WgtType=get_weight(img['azimuth_window']))
# TODO:
# column deltakcoa poly - it's in there at ["image"]["frequency_doppler_centroid_polynomial"]
# weight functions?
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
def _get_base_sicd(self, h5_dict, band_dict):
# type: (dict, dict) -> SICDType
def get_collection_info(): # type: () -> CollectionInfoType
mode_type = 'STRIPMAP' if h5_dict['Acquisition Mode'] in \
['HIMAGE', 'PINGPONG', 'WIDEREGION', 'HUGEREGION'] else 'DYNAMIC STRIPMAP'
return CollectionInfoType(Classification='UNCLASSIFIED',
CollectorName=h5_dict['Satellite ID'],
CoreName=str(h5_dict['Programmed Image ID']),
CollectType='MONOSTATIC',
RadarMode=RadarModeType(ModeID=h5_dict['Multi-Beam ID'],
ModeType=mode_type))
def get_image_creation(): # type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
DateTime=parse_timestring(h5_dict['Product Generation UTC'], precision='ns'),
Profile='sarpy {}'.format(__version__))
def get_grid(): # type: () -> GridType
if h5_dict['Projection ID'] == 'SLANT RANGE/AZIMUTH':
image_plane = 'SLANT'
gr_type = 'RGZERO'
else:
image_plane = 'GROUND'
gr_type = None
# Row
row_window_name = h5_dict['Range Focusing Weighting Function'].rstrip().upper()
row_params = None
if row_window_name == 'HAMMING':
row_params = {'COEFFICIENT': '{0:15f}'.format(h5_dict['Range Focusing Weighting Coefficient'])}
row = DirParamType(Sgn=-1,
KCtr=2*center_frequency/speed_of_light,
DeltaKCOAPoly=Poly2DType(Coefs=[[0, ], ]),
WgtType=WgtTypeType(WindowName=row_window_name, Parameters=row_params))
# Col
col_window_name = h5_dict['Azimuth Focusing Weighting Function'].rstrip().upper()
col_params = None
if col_window_name == 'HAMMING':
col_params = {'COEFFICIENT': '{0:15f}'.format(h5_dict['Azimuth Focusing Weighting Coefficient'])}
col = DirParamType(Sgn=-1,
KCtr=0,
WgtType=WgtTypeType(WindowName=col_window_name, Parameters=col_params))
return GridType(ImagePlane=image_plane, Type=gr_type, Row=row, Col=col)
def get_timeline(): # type: () -> TimelineType
return TimelineType(CollectStart=collect_start,
CollectDuration=duration,
IPP=[IPPSetType(index=0, TStart=0, TEnd=0, IPPStart=0, IPPEnd=0), ]) # NB: IPPEnd must be set, but will be replaced
def get_position(): # type: () -> PositionType
T = h5_dict['State Vectors Times'] # in seconds relative to ref time
T += ref_time_offset
Pos = h5_dict['ECEF Satellite Position']
Vel = h5_dict['ECEF Satellite Velocity']
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_radar_collection():
# type: () -> RadarCollectionType
tx_pols = []
chan_params = []
for i, bdname in enumerate(band_dict):
pol = band_dict[bdname]['Polarisation']
tx_pols.append(pol[0])
chan_params.append(ChanParametersType(TxRcvPolarization=self._parse_pol(pol), index=i))
if len(tx_pols) == 1:
return RadarCollectionType(RcvChannels=chan_params, TxPolarization=tx_pols[0])
else:
return RadarCollectionType(RcvChannels=chan_params,
TxPolarization='SEQUENCE',
TxSequence=[TxStepType(TxPolarization=pol,
index=i+1) for i, pol in enumerate(tx_pols)])
def get_image_formation():
# type: () -> ImageFormationType
return ImageFormationType(ImageFormAlgo='RMA',
TStartProc=0,
TEndProc=duration,
STBeamComp='SV',
ImageBeamComp='SV',
AzAutofocus='NO',
RgAutofocus='NO',
RcvChanProc=RcvChanProcType(NumChanProc=1,
PRFScaleFactor=1))
def get_rma():
# type: () -> RMAType
inca = INCAType(FreqZero=center_frequency)
return RMAType(RMAlgoType='OMEGA_K',
INCA=inca)
def get_scpcoa():
# type: () -> SCPCOAType
return SCPCOAType(SideOfTrack=h5_dict['Look Side'][0:1].upper())
# some common use parameters
center_frequency = h5_dict['Radar Frequency']
# relative times in csk are wrt some reference time - for sicd they should be relative to start time
collect_start = parse_timestring(h5_dict['Scene Sensing Start UTC'], precision='ns')
collect_end = parse_timestring(h5_dict['Scene Sensing Stop UTC'], precision='ns')
duration = get_seconds(collect_end, collect_start, precision='ns')
ref_time = parse_timestring(h5_dict['Reference UTC'], precision='ns')
ref_time_offset = get_seconds(ref_time, collect_start, precision='ns')
# assemble our pieces
collection_info = get_collection_info()
image_creation = get_image_creation()
grid = get_grid()
timeline = get_timeline()
position = get_position()
radar_collection = get_radar_collection()
image_formation = get_image_formation()
rma = get_rma()
scpcoa = get_scpcoa()
return SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
Grid=grid,
Timeline=timeline,
Position=position,
RadarCollection=radar_collection,
ImageFormation=image_formation,
RMA=rma,
SCPCOA=scpcoa)
def _get_band_specific_sicds(self, base_sicd, h5_dict, band_dict,
shape_dict, dtype_dict):
# type: (SICDType, dict, dict, dict, dict) -> Dict[str, SICDType]
def update_scp_prelim(sicd, band_name):
# type: (SICDType, str) -> None
if self._mission_id in ['CSK', 'KMPS']:
LLH = band_dict[band_name]['Centre Geodetic Coordinates']
elif self._mission_id == 'CSG':
LLH = h5_dict['Scene Centre Geodetic Coordinates']
else:
raise ValueError('Unhandled mission id {}'.format(
self._mission_id))
sicd.GeoData = GeoDataType(
SCP=SCPType(LLH=LLH)) # EarthModel & ECF will be populated
def update_image_data(sicd, band_name):
# type: (SICDType, str) -> Tuple[float, float, float, float, int]
cols, rows = shape_dict[band_name]
# zero doppler time of first/last columns
t_az_first_time = band_dict[band_name][
'Zero Doppler Azimuth First Time']
t_az_last_time = band_dict[band_name][
'Zero Doppler Azimuth Last Time']
t_ss_az_s = band_dict[band_name]['Line Time Interval']
t_use_sign2 = 1
if h5_dict['Look Side'].upper() == 'LEFT':
t_use_sign2 = -1
t_az_first_time, t_az_last_time = t_az_last_time, t_az_first_time
# zero doppler time of first row
t_rg_first_time = band_dict[band_name][
'Zero Doppler Range First Time']
# row spacing in range time (seconds)
t_ss_rg_s = band_dict[band_name]['Column Time Interval']
sicd.ImageData = ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
FullImage=(rows, cols),
PixelType=dtype_dict[band_name],
SCPPixel=RowColType(
Row=int(rows / 2),
Col=int(cols / 2)))
return t_rg_first_time, t_ss_rg_s, t_az_first_time, t_ss_az_s, t_use_sign2
def check_switch_state():
# type: () -> Tuple[int, Poly1DType]
use_sign = 1 if t_dop_rate_poly_rg[0] < 0 else -1
return use_sign, Poly1DType(Coefs=use_sign * t_dop_rate_poly_rg)
def update_timeline(sicd, band_name):
# type: (SICDType, str) -> None
prf = band_dict[band_name]['PRF']
duration = sicd.Timeline.CollectDuration
ipp_el = sicd.Timeline.IPP[0]
ipp_el.IPPEnd = duration * prf
ipp_el.TEnd = duration
ipp_el.IPPPoly = Poly1DType(Coefs=(0, prf))
def update_radar_collection(sicd, band_name, ind):
# type: (SICDType, str, int) -> None
chirp_length = band_dict[band_name]['Range Chirp Length']
chirp_rate = abs(band_dict[band_name]['Range Chirp Rate'])
sample_rate = band_dict[band_name]['Sampling Rate']
ref_dechirp_time = band_dict[band_name].get(
'Reference Dechirping Time', 0) # TODO: is this right?
win_length = band_dict[band_name]['Echo Sampling Window Length']
rcv_fm_rate = 0 if numpy.isnan(ref_dechirp_time) else chirp_rate
band_width = chirp_length * chirp_rate
fr_min = center_frequency - 0.5 * band_width
fr_max = center_frequency + 0.5 * band_width
sicd.RadarCollection.TxFrequency = TxFrequencyType(Min=fr_min,
Max=fr_max)
sicd.RadarCollection.Waveform = [
WaveformParametersType(index=0,
TxPulseLength=chirp_length,
TxRFBandwidth=band_width,
TxFreqStart=fr_min,
TxFMRate=chirp_rate,
ADCSampleRate=sample_rate,
RcvFMRate=rcv_fm_rate,
RcvWindowLength=win_length /
sample_rate),
]
sicd.ImageFormation.RcvChanProc.ChanIndices = [
ind + 1,
]
sicd.ImageFormation.TxFrequencyProc = TxFrequencyProcType(
MinProc=fr_min, MaxProc=fr_max)
sicd.ImageFormation.TxRcvPolarizationProc = sicd.RadarCollection.RcvChannels[
ind].TxRcvPolarization
def update_rma_and_grid(sicd, band_name):
# type: (SICDType, str) -> None
rg_scp_time = rg_first_time + (ss_rg_s *
sicd.ImageData.SCPPixel.Row)
az_scp_time = az_first_time + (use_sign2 * ss_az_s *
sicd.ImageData.SCPPixel.Col)
r_ca_scp = rg_scp_time * speed_of_light / 2
sicd.RMA.INCA.R_CA_SCP = r_ca_scp
# compute DRateSFPoly
scp_ca_time = az_scp_time + ref_time_offset
vel_poly = sicd.Position.ARPPoly.derivative(der_order=1,
return_poly=True)
vel_ca_vec = vel_poly(scp_ca_time)
vel_ca_sq = numpy.sum(vel_ca_vec * vel_ca_vec)
vel_ca = numpy.sqrt(vel_ca_sq)
r_ca = numpy.array([r_ca_scp, 1.], dtype=numpy.float64)
dop_rate_poly_rg_shifted = dop_rate_poly_rg.shift(
rg_ref_time - rg_scp_time,
alpha=ss_rg_s / row_ss,
return_poly=False)
drate_sf_poly = -(polynomial.polymul(dop_rate_poly_rg_shifted,
r_ca) * speed_of_light /
(2 * center_frequency * vel_ca_sq))
# update grid.row
sicd.Grid.Row.SS = row_ss
sicd.Grid.Row.ImpRespBW = row_bw
sicd.Grid.Row.DeltaK1 = -0.5 * row_bw
sicd.Grid.Row.DeltaK2 = 0.5 * row_bw
# update grid.col
col_ss = abs(vel_ca * ss_az_s * drate_sf_poly[0])
sicd.Grid.Col.SS = col_ss
if self.mission_id == 'CSK':
col_bw = min(
band_dict[band_name]
['Azimuth Focusing Transition Bandwidth'] * ss_az_s,
1) / col_ss
elif self.mission_id in ['CSG', 'KMPS']:
col_bw = min(
band_dict[band_name]['Azimuth Focusing Bandwidth'] *
ss_az_s, 1) / col_ss
else:
raise ValueError('Got unhandled mission_id {}'.format(
self.mission_id))
sicd.Grid.Col.ImpRespBW = col_bw
# update inca
sicd.RMA.INCA.DRateSFPoly = Poly2DType(
Coefs=numpy.reshape(drate_sf_poly, (-1, 1)))
sicd.RMA.INCA.TimeCAPoly = Poly1DType(
Coefs=[scp_ca_time, use_sign2 * ss_az_s / col_ss])
# compute DopCentroidPoly & DeltaKCOAPoly
dop_centroid_poly = numpy.zeros(
(dop_poly_rg.order1 + 1, dop_poly_az.order1 + 1),
dtype=numpy.float64)
dop_centroid_poly[0, 0] = dop_poly_rg(rg_scp_time-rg_ref_time) + \
dop_poly_az(az_scp_time-az_ref_time) - \
0.5*(dop_poly_rg[0] + dop_poly_az[0])
dop_poly_rg_shifted = dop_poly_rg.shift(rg_ref_time - rg_scp_time,
alpha=ss_rg_s / row_ss)
dop_poly_az_shifted = dop_poly_az.shift(az_ref_time - az_scp_time,
alpha=ss_az_s / col_ss)
dop_centroid_poly[1:, 0] = dop_poly_rg_shifted[1:]
dop_centroid_poly[0, 1:] = dop_poly_az_shifted[1:]
sicd.RMA.INCA.DopCentroidPoly = Poly2DType(Coefs=dop_centroid_poly)
sicd.RMA.INCA.DopCentroidCOA = True
sicd.Grid.Col.DeltaKCOAPoly = Poly2DType(
Coefs=use_sign * dop_centroid_poly * ss_az_s / col_ss)
# fit TimeCOAPoly
sicd.Grid.TimeCOAPoly = fit_time_coa_polynomial(
sicd.RMA.INCA,
sicd.ImageData,
sicd.Grid,
dop_rate_poly_rg_shifted,
poly_order=2)
if csk_addin is not None:
csk_addin.check_sicd(sicd, self.mission_id, h5_dict)
def update_radiometric(sicd, band_name):
# type: (SICDType, str) -> None
if self.mission_id in ['KMPS', 'CSG']:
# TODO: skipping for now - strange results for flag == 77. Awaiting gidance - see Wade.
return
if h5_dict['Range Spreading Loss Compensation Geometry'] != 'NONE':
slant_range = h5_dict['Reference Slant Range']
exp = h5_dict['Reference Slant Range Exponent']
sf = slant_range**(2 * exp)
rsf = h5_dict['Rescaling Factor']
sf /= rsf * rsf
if h5_dict.get('Calibration Constant Compensation Flag',
None) == 0:
cal = band_dict[band_name]['Calibration Constant']
sf /= cal
sicd.Radiometric = RadiometricType(BetaZeroSFPoly=Poly2DType(
Coefs=[
[
sf,
],
]))
def update_geodata(sicd): # type: (SICDType) -> None
scp_pixel = [
sicd.ImageData.SCPPixel.Row, sicd.ImageData.SCPPixel.Col
]
ecf = sicd.project_image_to_ground(scp_pixel,
projection_type='HAE')
sicd.update_scp(ecf, coord_system='ECF')
SCP = sicd.GeoData.SCP.ECF.get_array(dtype='float64')
scp_time = sicd.RMA.INCA.TimeCAPoly[0]
ca_pos = sicd.Position.ARPPoly(scp_time)
RG = SCP - ca_pos
sicd.RMA.INCA.R_CA_SCP = numpy.linalg.norm(RG)
out = {}
center_frequency = h5_dict['Radar Frequency']
# relative times in csk are wrt some reference time - for sicd they should be relative to start time
collect_start = parse_timestring(h5_dict['Scene Sensing Start UTC'],
precision='ns')
ref_time = parse_timestring(h5_dict['Reference UTC'], precision='ns')
ref_time_offset = get_seconds(ref_time, collect_start, precision='ns')
for i, bd_name in enumerate(band_dict):
az_ref_time, rg_ref_time, t_dop_poly_az, t_dop_poly_rg, t_dop_rate_poly_rg = \
self._get_dop_poly_details(h5_dict, band_dict, bd_name)
dop_poly_az = Poly1DType(Coefs=t_dop_poly_az)
dop_poly_rg = Poly1DType(Coefs=t_dop_poly_rg)
t_sicd = base_sicd.copy()
update_scp_prelim(
t_sicd, bd_name
) # set preliminary value for SCP (required for projection)
row_bw = band_dict[bd_name][
'Range Focusing Bandwidth'] * 2 / speed_of_light
row_ss = band_dict[bd_name]['Column Spacing']
rg_first_time, ss_rg_s, az_first_time, ss_az_s, use_sign2 = update_image_data(
t_sicd, bd_name)
use_sign, dop_rate_poly_rg = check_switch_state()
update_timeline(t_sicd, bd_name)
update_radar_collection(t_sicd, bd_name, i)
update_rma_and_grid(t_sicd, bd_name)
update_radiometric(t_sicd, bd_name)
update_geodata(t_sicd)
t_sicd.derive()
# t_sicd.populate_rniirs(override=False)
out[bd_name] = t_sicd
return out
def _get_base_sicd(self, h5_dict, band_dict):
# type: (dict, dict) -> SICDType
def get_collection_info(): # type: () -> (dict, CollectionInfoType)
acq_mode = h5_dict['Acquisition Mode'].upper()
if self.mission_id == 'CSK':
if acq_mode in ['HIMAGE', 'PINGPONG']:
mode_type = 'STRIPMAP'
elif acq_mode in ['WIDEREGION', 'HUGEREGION']:
# scansar, processed as stripmap
mode_type = 'STRIPMAP'
elif acq_mode in ['ENHANCED SPOTLIGHT', 'SMART']:
mode_type = 'DYNAMIC STRIPMAP'
else:
logging.warning(
'Got unexpected acquisition mode {}'.format(acq_mode))
mode_type = 'DYNAMIC STRIPMAP'
elif self.mission_id == 'KMPS':
if acq_mode in ['STANDARD', 'ENHANCED STANDARD']:
mode_type = 'STRIPMAP'
elif acq_mode in ['WIDE SWATH', 'ENHANCED WIDE SWATH']:
# scansar, processed as stripmap
mode_type = 'STRIPMAP'
elif acq_mode in [
'HIGH RESOLUTION', 'ENHANCED HIGH RESOLUTION',
'ULTRA HIGH RESOLUTION'
]:
# "spotlight"
mode_type = 'DYNAMIC STRIPMAP'
else:
logging.warning(
'Got unexpected acquisition mode {}'.format(acq_mode))
mode_type = 'DYNAMIC STRIPMAP'
elif self.mission_id == 'CSG':
if acq_mode.startswith('SPOTLIGHT'):
mode_type = 'DYNAMIC STRIPMAP'
elif acq_mode in ['STRIPMAP', 'QUADPOL']:
mode_type = "STRIPMAP"
else:
logging.warning(
'Got unhandled acquisition mode {}, setting to DYNAMIC STRIPMAP'
.format(acq_mode))
mode_type = 'DYNAMIC STRIPMAP'
else:
raise ValueError('Unhandled mission id {}'.format(
self._mission_id))
start_time_dt = collect_start.astype('datetime64[s]').astype(
datetime)
date_str = start_time_dt.strftime('%d%b%y').upper()
time_str = start_time_dt.strftime('%H%M%S') + 'Z'
core_name = '{}_{}_{}'.format(date_str, self.mission_id, time_str)
collect_info = CollectionInfoType(
Classification='UNCLASSIFIED',
CollectorName=h5_dict['Satellite ID'],
CoreName=core_name,
CollectType='MONOSTATIC',
RadarMode=RadarModeType(ModeID=h5_dict['Multi-Beam ID'],
ModeType=mode_type))
return collect_info
def get_image_creation(): # type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
DateTime=parse_timestring(h5_dict['Product Generation UTC'],
precision='ns'),
Site=h5_dict['Processing Centre'],
Application='L0: `{}`, L1: `{}`'.format(
h5_dict.get('L0 Software Version', 'NONE'),
h5_dict.get('L1A Software Version', 'NONE')),
Profile='sarpy {}'.format(__version__))
def get_grid(): # type: () -> GridType
def get_wgt_type(weight_name, coefficient, direction):
if weight_name == 'GENERAL_COSINE':
# probably only for kompsat?
weight_name = 'HAMMING'
coefficient = 1 - coefficient
if coefficient is None:
params = None
else:
params = {'COEFFICIENT': '{0:0.16G}'.format(coefficient)}
out = WgtTypeType(WindowName=weight_name, Parameters=params)
if weight_name != 'HAMMING':
logging.warning(
'Got unexpected weight scheme {} for {}. The weighting will '
'not be properly populated.'.format(
weight_name, direction))
return out
if h5_dict['Projection ID'] == 'SLANT RANGE/AZIMUTH':
image_plane = 'SLANT'
gr_type = 'RGZERO'
else:
image_plane = 'GROUND'
gr_type = None
# Row
row_window_name = h5_dict[
'Range Focusing Weighting Function'].rstrip().upper()
row_coefficient = h5_dict.get(
'Range Focusing Weighting Coefficient', None)
row_weight = get_wgt_type(row_window_name, row_coefficient, 'Row')
row = DirParamType(Sgn=-1,
KCtr=2 * center_frequency / speed_of_light,
DeltaKCOAPoly=Poly2DType(Coefs=[
[
0,
],
]),
WgtType=row_weight)
# Col
col_window_name = h5_dict[
'Azimuth Focusing Weighting Function'].rstrip().upper()
col_coefficient = h5_dict.get(
'Azimuth Focusing Weighting Coefficient', None)
col_weight = get_wgt_type(col_window_name, col_coefficient, 'Col')
col = DirParamType(Sgn=-1, KCtr=0, WgtType=col_weight)
return GridType(ImagePlane=image_plane,
Type=gr_type,
Row=row,
Col=col)
def get_timeline(): # type: () -> TimelineType
# NB: IPPEnd must be set, but will be replaced
return TimelineType(CollectStart=collect_start,
CollectDuration=duration,
IPP=[
IPPSetType(index=0,
TStart=0,
TEnd=0,
IPPStart=0,
IPPEnd=0),
])
def get_position(): # type: () -> PositionType
T = h5_dict[
'State Vectors Times'] # in seconds relative to ref time
T += ref_time_offset
Pos = h5_dict['ECEF Satellite Position']
Vel = h5_dict['ECEF Satellite Velocity']
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_radar_collection():
# type: () -> RadarCollectionType
tx_pols = []
chan_params = []
for i, bdname in enumerate(band_dict):
if 'Polarisation' in band_dict[bdname]:
pol = band_dict[bdname]['Polarisation']
elif 'Polarization' in h5_dict:
pol = h5_dict['Polarization']
else:
raise ValueError(
'Failed finding polarization for file {}\nmission id {}'
.format(self._file_name, self._mission_id))
tx_pols.append(pol[0])
chan_params.append(
ChanParametersType(TxRcvPolarization=self._parse_pol(pol),
index=i))
if len(tx_pols) == 1:
return RadarCollectionType(RcvChannels=chan_params,
TxPolarization=tx_pols[0])
else:
return RadarCollectionType(RcvChannels=chan_params,
TxPolarization='SEQUENCE',
TxSequence=[
TxStepType(TxPolarization=pol,
index=i + 1)
for i, pol in enumerate(tx_pols)
])
def get_image_formation():
# type: () -> ImageFormationType
return ImageFormationType(ImageFormAlgo='RMA',
TStartProc=0,
TEndProc=duration,
STBeamComp='NO',
ImageBeamComp='SV',
AzAutofocus='NO',
RgAutofocus='NO',
RcvChanProc=RcvChanProcType(
NumChanProc=1, PRFScaleFactor=1))
def get_rma():
# type: () -> RMAType
inca = INCAType(FreqZero=center_frequency)
return RMAType(RMAlgoType='OMEGA_K', INCA=inca)
def get_scpcoa():
# type: () -> SCPCOAType
return SCPCOAType(SideOfTrack=h5_dict['Look Side'][0:1].upper())
# some common use parameters
center_frequency = h5_dict['Radar Frequency']
# relative times in csk are wrt some reference time - for sicd they should be relative to start time
collect_start = parse_timestring(h5_dict['Scene Sensing Start UTC'],
precision='ns')
collect_end = parse_timestring(h5_dict['Scene Sensing Stop UTC'],
precision='ns')
duration = get_seconds(collect_end, collect_start, precision='ns')
ref_time = parse_timestring(h5_dict['Reference UTC'], precision='ns')
ref_time_offset = get_seconds(ref_time, collect_start, precision='ns')
# assemble our pieces
collection_info = get_collection_info()
image_creation = get_image_creation()
grid = get_grid()
timeline = get_timeline()
position = get_position()
radar_collection = get_radar_collection()
image_formation = get_image_formation()
rma = get_rma()
scpcoa = get_scpcoa()
sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
Grid=grid,
Timeline=timeline,
Position=position,
RadarCollection=radar_collection,
ImageFormation=image_formation,
RMA=rma,
SCPCOA=scpcoa)
return sicd
def get_sicd(self):
"""
Get the SICD metadata for the image.
Returns
-------
SICDType
"""
def convert_string_dict(dict_in):
# type: (dict) -> dict
dict_out = OrderedDict()
for key, val in dict_in.items():
if isinstance(val, str):
dict_out[key] = val
elif isinstance(val, int):
dict_out[key] = str(val)
elif isinstance(val, float):
dict_out[key] = '{0:0.17G}'.format(val)
else:
raise TypeError('Got unhandled type {}'.format(type(val)))
return dict_out
def extract_state_vector():
# type: () -> (numpy.ndarray, numpy.ndarray, numpy.ndarray)
vecs = collect['state']['state_vectors']
times = numpy.zeros((len(vecs), ), dtype=numpy.float64)
positions = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
velocities = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
for i, entry in enumerate(vecs):
times[i] = get_seconds(parse_timestring(entry['time'],
precision='ns'),
start_time,
precision='ns')
positions[i, :] = entry['position']
velocities[i, :] = entry['velocity']
return times, positions, velocities
def get_radar_parameter(name):
if name in radar:
return radar[name]
if len(radar_time_varying) > 0:
element = radar_time_varying[0]
if name in element:
return element[name]
raise ValueError(
'Unable to determine radar parameter `{}`'.format(name))
def get_collection_info():
# type: () -> CollectionInfoType
coll_name = collect['platform']
mode = collect['mode'].strip().lower()
if mode == 'stripmap':
radar_mode = RadarModeType(ModeType='STRIPMAP', ModeID=mode)
elif mode == 'spotlight':
radar_mode = RadarModeType(ModeType='SPOTLIGHT', ModeID=mode)
elif mode == 'sliding_spotlight':
radar_mode = RadarModeType(ModeType='DYNAMIC STRIPMAP',
ModeID=mode)
else:
raise ValueError('Got unhandled radar mode {}'.format(mode))
return CollectionInfoType(CollectorName=coll_name,
CoreName=collect['collect_id'],
RadarMode=radar_mode,
Classification='UNCLASSIFIED',
CollectType='MONOSTATIC')
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application=self._tiff_details.tags['Software'],
DateTime=parse_timestring(
self._img_desc_tags['processing_time'], precision='us'),
Profile='sarpy {}'.format(__version__),
Site='Unknown')
def get_image_data():
# type: () -> ImageDataType
rows = int(
img['columns']) # capella uses flipped row/column definition?
cols = int(img['rows'])
if img['data_type'] == 'CInt16':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled data_type {}'.format(
img['data_type']))
scp_pixel = (int(0.5 * rows), int(0.5 * cols))
if radar['pointing'] == 'left':
scp_pixel = (rows - scp_pixel[0] - 1, cols - scp_pixel[1] - 1)
return ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
PixelType=pixel_type,
FullImage=(rows, cols),
SCPPixel=scp_pixel)
def get_geo_data():
# type: () -> GeoDataType
return GeoDataType(SCP=SCPType(
ECF=img['center_pixel']['target_position']))
def get_position():
# type: () -> PositionType
px, py, pz = fit_position_xvalidation(state_time,
state_position,
state_velocity,
max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=px, Y=py, Z=pz))
def get_grid():
# type: () -> GridType
def get_weight(window_dict):
window_name = window_dict['name']
if window_name.lower() == 'rectangular':
return WgtTypeType(WindowName='UNIFORM'), None
elif window_name.lower() == 'avci-nacaroglu':
return WgtTypeType(
WindowName=window_name.upper(),
Parameters=convert_string_dict(window_dict['parameters'])), \
avci_nacaroglu_window(64, alpha=window_dict['parameters']['alpha'])
else:
return WgtTypeType(WindowName=window_name,
Parameters=convert_string_dict(
window_dict['parameters'])), None
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_bw = img.get('processed_range_bandwidth', bw)
row_imp_rsp_bw = 2 * row_bw / speed_of_light
row_wgt, row_wgt_funct = get_weight(img['range_window'])
row = DirParamType(SS=img['image_geometry']['delta_range_sample'],
Sgn=-1,
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtFunct=row_wgt_funct,
WgtType=row_wgt)
# get timecoa value
timecoa_value = get_seconds(coa_time, start_time)
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
col_wgt, col_wgt_funct = get_weight(img['azimuth_window'])
col = DirParamType(SS=col_ss,
Sgn=-1,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw * abs(ss_zd_s) / col_ss,
KCtr=0,
WgtFunct=col_wgt_funct,
WgtType=col_wgt)
# TODO:
# column deltakcoa poly - it's in there at ["image"]["frequency_doppler_centroid_polynomial"]
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
def get_radar_collection():
# type: () -> RadarCollectionType
freq_min = fc - 0.5 * bw
return RadarCollectionType(
TxPolarization=radar['transmit_polarization'],
TxFrequency=(freq_min, freq_min + bw),
Waveform=[
WaveformParametersType(
TxRFBandwidth=bw,
TxPulseLength=get_radar_parameter('pulse_duration'),
RcvDemodType='CHIRP',
ADCSampleRate=radar['sampling_frequency'],
TxFreqStart=freq_min)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']))
])
def get_timeline():
# type: () -> TimelineType
prf = radar['prf'][0]['prf']
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=duration * prf,
IPPPoly=(0, prf)),
])
def get_image_formation():
# type: () -> ImageFormationType
algo = img['algorithm'].upper()
processings = None
if algo == 'BACKPROJECTION':
processings = [
ProcessingType(Type='Backprojected to DEM', Applied=True),
]
else:
logger.warning('Got unexpected algorithm, the results for the '
'sicd struture might be unexpected')
if algo not in ('PFA', 'RMA', 'RGAZCOMP'):
logger.warning(
'Image formation algorithm {} not one of the recognized SICD options, '
'being set to "OTHER".'.format(algo))
algo = 'OTHER'
return ImageFormationType(
RcvChanProc=RcvChanProcType(NumChanProc=1, PRFScaleFactor=1),
ImageFormAlgo=algo,
TStartProc=0,
TEndProc=duration,
TxRcvPolarizationProc='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']),
TxFrequencyProc=(radar_collection.TxFrequency.Min,
radar_collection.TxFrequency.Max),
STBeamComp='NO',
ImageBeamComp='NO',
AzAutofocus='NO',
RgAutofocus='NO',
Processings=processings)
def get_rma():
# type: () -> RMAType
img_geometry = img['image_geometry']
near_range = img_geometry['range_to_first_sample']
center_time = parse_timestring(img['center_pixel']['center_time'],
precision='us')
first_time = parse_timestring(img_geometry['first_line_time'],
precision='us')
zd_time_scp = get_seconds(center_time, first_time, 'us')
r_ca_scp = near_range + image_data.SCPPixel.Row * grid.Row.SS
time_ca_poly = numpy.array(
[zd_time_scp, -look * ss_zd_s / grid.Col.SS], dtype='float64')
timecoa_value = get_seconds(center_time, start_time)
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
vm_ca = numpy.linalg.norm(arp_velocity)
inca = INCAType(R_CA_SCP=r_ca_scp,
FreqZero=fc,
TimeCAPoly=time_ca_poly,
DRateSFPoly=[
[1 / (vm_ca * ss_zd_s / grid.Col.SS)],
])
return RMAType(RMAlgoType='RG_DOP', INCA=inca)
def get_radiometric():
# type: () -> Union[None, RadiometricType]
if img['radiometry'].lower() != 'beta_nought':
logger.warning('Got unrecognized Capella radiometry {},\n\t'
'skipping the radiometric metadata'.format(
img['radiometry']))
return None
return RadiometricType(BetaZeroSFPoly=[
[
img['scale_factor']**2,
],
])
def add_noise():
if sicd.Radiometric is None:
return
nesz_raw = numpy.array(img['nesz_polynomial']['coefficients'],
dtype='float64')
test_value = polynomial.polyval(rma.INCA.R_CA_SCP, nesz_raw)
if abs(test_value - img['nesz_peak']) > 100:
# this polynomial reversed in early versions, so reverse if evaluated results are nonsense
nesz_raw = nesz_raw[::-1]
nesz_poly_raw = Poly2DType(Coefs=numpy.reshape(nesz_raw, (-1, 1)))
noise_coeffs = nesz_poly_raw.shift(-rma.INCA.R_CA_SCP,
1,
0,
1,
return_poly=False)
# this is in nesz units, so shift to absolute units
noise_coeffs[0] -= 10 * numpy.log10(
sicd.Radiometric.SigmaZeroSFPoly[0, 0])
sicd.Radiometric.NoiseLevel = NoiseLevelType_(
NoiseLevelType='ABSOLUTE', NoisePoly=noise_coeffs)
# extract general use information
collect = self._img_desc_tags['collect']
img = collect['image']
radar = collect['radar']
radar_time_varying = radar.get('time_varying_parameters', [])
start_time = parse_timestring(collect['start_timestamp'],
precision='ns')
end_time = parse_timestring(collect['stop_timestamp'], precision='ns')
duration = get_seconds(end_time, start_time, precision='ns')
state_time, state_position, state_velocity = extract_state_vector()
bw = get_radar_parameter('pulse_bandwidth')
fc = get_radar_parameter('center_frequency')
ss_zd_s = img['image_geometry']['delta_line_time']
look = -1 if radar['pointing'] == 'right' else 1
# define the sicd elements
collection_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
position = get_position()
grid = get_grid()
radar_collection = get_radar_collection()
timeline = get_timeline()
image_formation = get_image_formation()
rma = get_rma()
radiometric = get_radiometric()
sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation,
RMA=rma,
Radiometric=radiometric)
sicd.derive()
add_noise()
sicd.populate_rniirs(override=False)
return sicd
def get_grid():
# type: () -> GridType
def get_weight(window_dict):
window_name = window_dict['name']
if window_name.lower() == 'rectangular':
return WgtTypeType(WindowName='UNIFORM'), None
elif window_name.lower() == 'avci-nacaroglu':
return WgtTypeType(
WindowName=window_name.upper(),
Parameters=convert_string_dict(window_dict['parameters'])), \
avci_nacaroglu_window(64, alpha=window_dict['parameters']['alpha'])
else:
return WgtTypeType(WindowName=window_name,
Parameters=convert_string_dict(
window_dict['parameters'])), None
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_bw = img.get('processed_range_bandwidth', bw)
row_imp_rsp_bw = 2 * row_bw / speed_of_light
row_wgt, row_wgt_funct = get_weight(img['range_window'])
row = DirParamType(SS=img['image_geometry']['delta_range_sample'],
Sgn=-1,
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtFunct=row_wgt_funct,
WgtType=row_wgt)
# get timecoa value
timecoa_value = get_seconds(coa_time, start_time)
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
col_wgt, col_wgt_funct = get_weight(img['azimuth_window'])
col = DirParamType(SS=col_ss,
Sgn=-1,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw * abs(ss_zd_s) / col_ss,
KCtr=0,
WgtFunct=col_wgt_funct,
WgtType=col_wgt)
# TODO:
# column deltakcoa poly - it's in there at ["image"]["frequency_doppler_centroid_polynomial"]
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
