def _find_sicd(self):
self._is_sicd = False
self._sicd_meta = None
if self.des_subheader_offsets is None:
return
for i in range(self.des_subheader_offsets.size):
subhead_bytes = self.get_des_subheader_bytes(i)
if subhead_bytes.startswith(b'DEXML_DATA_CONTENT'):
des_header = DataExtensionHeader.from_bytes(subhead_bytes, start=0)
des_bytes = self.get_des_bytes(i)
# noinspection PyBroadException
try:
root_node, xml_ns = parse_xml_from_string(des_bytes.decode('utf-8').strip())
if 'SIDD' in root_node.tag: # namespace makes this ugly
# NOTE that SIDD files are supposed to have the corresponding
# SICD xml as one of the DES AFTER the SIDD xml.
# The same basic format is used for both headers.
# So, abandon if we find a SIDD xml
self._des_index = None
self._des_header = None
self._is_sicd = False
break
elif 'SICD' in root_node.tag: # namespace makes this ugly
self._des_index = i
self._des_header = des_header
self._is_sicd = True
self._sicd_meta = SICDType.from_node(root_node, xml_ns, ns_key='default')
break
except Exception:
continue
elif subhead_bytes.startswith(b'DESIDD_XML'):
# This is an old format SIDD and can't be a SICD
self._des_index = None
self._des_header = None
self._is_sicd = False
break
elif subhead_bytes.startswith(b'DESICD_XML'):
# This is an old format SICD
des_bytes = self.get_des_bytes(i)
try:
root_node, xml_ns = parse_xml_from_string(des_bytes)
if 'SICD' in root_node.tag: # namespace makes this ugly
self._des_index = i
self._des_header = None
self._is_sicd = True
self._sicd_meta = SICDType.from_node(root_node, xml_ns, ns_key='default')
break
except Exception as e:
logging.error('We found an apparent old-style SICD DES header, '
'but failed parsing with error {}'.format(e))
continue
if not self._is_sicd:
return
self._sicd_meta.derive()
def _find_sidd(self):
self._is_sidd = False
if self.des_subheader_offsets is None:
return
self._sidd_meta = []
self._sicd_meta = []
for i in range(self.des_subheader_offsets.size):
subhead_bytes = self.get_des_subheader_bytes(i)
if subhead_bytes.startswith(b'DEXML_DATA_CONTENT'):
des_bytes = self.get_des_bytes(i)
try:
root_node, xml_ns = parse_xml_from_string(des_bytes)
if 'SIDD' in root_node.tag:
self._is_sidd = True
self._sidd_meta.append(SIDDType.from_node(root_node, xml_ns, ns_key='default'))
elif 'SICD' in root_node.tag:
self._sicd_meta.append(SICDType.from_node(root_node, xml_ns, ns_key='default'))
except Exception as e:
logger.error('Failed checking des xml header at index {} with error {}'.format(i, e))
continue
elif subhead_bytes.startswith(b'DESIDD_XML'):
# This is an old format SIDD header
des_bytes = self.get_des_bytes(i)
try:
root_node, xml_ns = parse_xml_from_string(des_bytes)
if 'SIDD' in root_node.tag:
self._is_sidd = True
self._sidd_meta.append(SIDDType.from_node(root_node, xml_ns, ns_key='default'))
except Exception as e:
logger.error(
'We found an apparent old-style SIDD DES header at index {},\n\t'
'but failed parsing with error {}'.format(i, e))
continue
elif subhead_bytes.startswith(b'DESICD_XML'):
# This is an old format SICD header
des_bytes = self.get_des_bytes(i)
try:
root_node, xml_ns = parse_xml_from_string(des_bytes)
if 'SICD' in root_node.tag:
self._sicd_meta.append(SICDType.from_node(root_node, xml_ns, ns_key='default'))
except Exception as e:
logger.error(
'We found an apparent old-style SICD DES header at index {},\n\t'
'but failed parsing with error {}'.format(i, e))
continue
if not self._is_sidd:
return
for sicd in self._sicd_meta:
sicd.derive()
def update_geodata(sicd: 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)
def update_image_data(
sicd: SICDType,
band_name: str) -> (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=pixeltype_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 update_scp_prelim(sicd: SICDType, band_name: 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_id_text.format(self._mission_id))
sicd.GeoData = GeoDataType(
SCP=SCPType(LLH=LLH)) # EarthModel & ECF will be populated
def validate_sicd_for_writing(sicd_meta: SICDType) -> SICDType:
"""
Helper method which ensures the provided SICD structure provides enough
information to support file writing, as well as ensures a few basic items
are populated as appropriate.
Parameters
----------
sicd_meta : SICDType
Returns
-------
SICDType
This returns a deep copy of the provided SICD structure, with any
necessary modifications.
"""
if not isinstance(sicd_meta, SICDType):
raise ValueError('sicd_meta is required to be an instance of SICDType, got {}'.format(type(sicd_meta)))
if sicd_meta.ImageData is None:
raise ValueError('The sicd_meta has un-populated ImageData, and nothing useful can be inferred.')
if sicd_meta.ImageData.NumCols is None or sicd_meta.ImageData.NumRows is None:
raise ValueError('The sicd_meta has ImageData with unpopulated NumRows or NumCols, '
'and nothing useful can be inferred.')
if sicd_meta.ImageData.PixelType is None:
logger.warning('The PixelType for sicd_meta is unset, so defaulting to RE32F_IM32F.')
sicd_meta.ImageData.PixelType = 'RE32F_IM32F'
sicd_meta = sicd_meta.copy()
profile = '{} {}'.format(__title__, __version__)
if sicd_meta.ImageCreation is None:
sicd_meta.ImageCreation = ImageCreationType(
Application=profile,
DateTime=numpy.datetime64(datetime.now()),
Profile=profile)
else:
sicd_meta.ImageCreation.Profile = profile
if sicd_meta.ImageCreation.DateTime is None:
sicd_meta.ImageCreation.DateTime = numpy.datetime64(datetime.now())
return sicd_meta
def get_sicd(self):
"""
Extract the SICD details.
Returns
-------
SICDType
"""
if self._sicd is not None:
return self._sicd
if self._user_data is None:
self._read_user_data()
if self._caspr_data is None:
self._find_caspr_data()
# Check if the user data contains a sicd structure.
sicd_string = None
for nam in ['SICDMETA', 'SICD_META', 'SICD']:
if sicd_string is None:
sicd_string = self._user_data.get(nam, None)
# If so, assume that this SICD is valid and simply present it
if sicd_string is not None:
root_node, xml_ns = parse_xml_from_string(sicd_string)
self._sicd = SICDType.from_node(root_node, xml_ns)
self._sicd.derive()
else:
# otherwise, we populate a really minimal sicd structure
num_rows, num_cols = self.data_size
self._sicd = SICDType(ImageData=ImageDataType(NumRows=num_rows,
NumCols=num_cols,
FirstRow=0,
FirstCol=0,
PixelType=self.pixel_type,
FullImage=FullImageType(NumRows=num_rows,
NumCols=num_cols),
SCPPixel=RowColType(Row=num_rows/2,
Col=num_cols/2)))
return self._sicd
def check_file(file_name):
"""
Check the SICD validity for the given file SICD (i.e. appropriately styled NITF)
or xml file containing the SICD structure alone.
Parameters
----------
file_name : str|SICDDetails
Returns
-------
bool
"""
sicd_xml, root_node, xml_ns, urn_string = None, None, None, None
if isinstance(file_name, SICDDetails):
sicd_xml, root_node, xml_ns = _get_sicd_xml_from_nitf(file_name)
else:
# check if this is just an xml file
with open(file_name, 'rb') as fi:
initial_bits = fi.read(100)
if initial_bits.startswith(b'<?xml') or initial_bits.startswith(b'<SICD'):
sicd_xml = fi.read().decode('utf-8')
root_node, xml_ns = parse_xml_from_string(sicd_xml)
if sicd_xml is None:
# try to first test whether this is SICD file
try:
sicd_details = SICDDetails(file_name)
if not sicd_details.is_sicd:
logger.error('File {} is a NITF file, but is apparently not a SICD file.')
return False
sicd_xml, root_node, xml_ns = _get_sicd_xml_from_nitf(sicd_details)
except IOError:
pass
if sicd_xml is None:
logger.error('Could not interpret input {}'.format(file_name))
return False
urn_string = xml_ns['default']
valid_xml = evaluate_xml_versus_schema(sicd_xml, urn_string)
if valid_xml is None:
valid_xml = True
the_sicd = SICDType.from_node(root_node, xml_ns=xml_ns)
valid_sicd_contents = the_sicd.is_valid(recursive=True, stack=False)
return valid_xml & valid_sicd_contents
def _evaluate_xml_string_validity(xml_string):
"""
Check the validity of the SICD xml, as defined by the given string.
Parameters
----------
xml_string : str|bytes
Returns
-------
(bool, str, SICDType)
"""
root_node, xml_ns = parse_xml_from_string(xml_string)
if xml_ns is None:
raise ValueError(
'SICD XML invalid, because no apparent namespace defined in the xml,\n\t'
'which starts `{}...`'.format(xml_string[:15]))
if 'default' not in xml_ns:
raise ValueError(
'Could not properly interpret the namespace collection from xml\n{}'
.format(xml_ns))
sicd_urn = xml_ns['default']
# check that our urn is mapped
try:
_ = get_urn_details(sicd_urn)
check_schema = True
except Exception as e:
logger.exception('SICD: The SICD namespace has unrecognized value')
check_schema = False
valid_xml = None
if check_schema:
valid_xml = evaluate_xml_versus_schema(xml_string, sicd_urn)
if valid_xml is None:
valid_xml = True
# perform the various sicd structure checks
the_sicd = SICDType.from_node(root_node, xml_ns=xml_ns)
valid_sicd_contents = the_sicd.is_valid(recursive=True, stack=False)
return valid_xml & valid_sicd_contents, sicd_urn, the_sicd
def get_sicd_collection(self):
"""
Gets the list of sicd objects, one per polarimetric entry.
Returns
-------
Tuple[SICDType]
"""
nitf, collection_info = self._get_collection_info()
image_creation = self._get_image_creation()
image_data, geo_data = self._get_image_and_geo_data()
position = self._get_position()
grid = self._get_grid()
radar_collection = self._get_radar_collection()
timeline = self._get_timeline()
image_formation = self._get_image_formation(timeline, radar_collection)
scpcoa = self._get_scpcoa()
rma = self._get_rma_adjust_grid(scpcoa, grid, image_data, position,
collection_info)
radiometric = self._get_radiometric(image_data, grid)
base_sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
GeoData=geo_data,
ImageData=image_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation,
SCPCOA=scpcoa,
RMA=rma,
Radiometric=radiometric)
if len(nitf) > 0:
base_sicd._NITF = nitf
self._update_geo_data(base_sicd)
base_sicd.derive() # derive all the fields
# now, make one copy per polarimetric entry, as appropriate
tx_pols, tx_rcv_pols = self._get_polarizations()
sicd_list = []
for i, entry in enumerate(tx_rcv_pols):
this_sicd = base_sicd.copy()
this_sicd.ImageFormation.RcvChanProc.ChanIndices = [
i + 1,
]
this_sicd.ImageFormation.TxRcvPolarizationProc = \
this_sicd.RadarCollection.RcvChannels[i].TxRcvPolarization
this_sicd.populate_rniirs(override=False)
sicd_list.append(this_sicd)
return tuple(sicd_list)
def update_radiometric(sicd: SICDType, band_name: 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 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 extract_sicd(img_header, symmetry, nitf_header=None):
"""
Extract the best available SICD structure from relevant nitf header structures.
Parameters
----------
img_header : ImageSegmentHeader|ImageSegmentHeader0
symmetry : tuple
nitf_header : None|NITFHeader|NITFHeader0
Returns
-------
SICDType
"""
def get_collection_info():
# type: () -> CollectionInfoType
isorce = img_header.ISORCE.strip()
collector_name = None if len(isorce) < 1 else isorce
iid2 = img_header.IID2.strip()
core_name = img_header.IID1.strip() if len(iid2) < 1 else iid2
class_str = img_header.Security.CLAS
if class_str == 'T':
classification = 'TOPSECRET'
elif class_str == 'S':
classification = 'SECRET'
elif class_str == 'C':
classification = 'CONFIDENTIAL'
elif class_str == 'U':
classification = 'UNCLASSIFIED'
else:
classification = ''
ctlh = img_header.Security.CTLH.strip()
if len(ctlh) < 1:
classification += '//' + ctlh
code = img_header.Security.CODE.strip()
if len(code) < 1:
classification += '//' + code
return CollectionInfoType(
CollectorName=collector_name,
CoreName=core_name,
Classification=classification)
def get_image_data():
# type: () -> ImageDataType
pvtype = img_header.PVTYPE
if pvtype == 'C':
if img_header.NBPP != 64:
logger.warning(
'This NITF has complex bands that are not 64-bit.\n\t'
'This is not currently supported.')
pixel_type = 'RE32F_IM32F'
elif pvtype == 'R':
if img_header.NBPP == 64:
logger.warning(
'The real/imaginary data in the NITF are stored as 64-bit floating point.\n\t'
'The closest Pixel Type, RE32F_IM32F, will be used,\n\t'
'but there may be overflow issues if converting this file.')
pixel_type = 'RE32F_IM32F'
elif pvtype == 'SI':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled PVTYPE {}'.format(pvtype))
if symmetry[2]:
rows = img_header.NCOLS
cols = img_header.NROWS
else:
rows = img_header.NROWS
cols = img_header.NCOLS
return ImageDataType(
PixelType=pixel_type,
NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
FullImage=(rows, cols),
SCPPixel=(0.5 * rows, 0.5 * cols))
def append_country_code(cc):
if len(cc) > 0:
if the_sicd.CollectionInfo is None:
the_sicd.CollectionInfo = CollectionInfoType(CountryCodes=[cc, ])
elif the_sicd.CollectionInfo.CountryCodes is None:
the_sicd.CollectionInfo.CountryCodes = [cc, ]
elif cc not in the_sicd.CollectionInfo.CountryCodes:
the_sicd.CollectionInfo.CountryCodes.append(cc)
def set_image_corners(icps, override=False):
if the_sicd.GeoData is None:
the_sicd.GeoData = GeoDataType(ImageCorners=icps)
elif the_sicd.GeoData.ImageCorners is None or override:
the_sicd.GeoData.ImageCorners = icps
def set_arp_position(arp_ecf, override=False):
if the_sicd.SCPCOA is None:
the_sicd.SCPCOA = SCPCOAType(ARPPos=arp_ecf)
elif override:
# prioritize this information first - it should be more reliable than other sources
the_sicd.SCPCOA.ARPPos = arp_ecf
def set_scp(scp_ecf, scp_pixel, override=False):
def set_scppixel():
if the_sicd.ImageData is None:
the_sicd.ImageData = ImageDataType(SCPPixel=scp_pixel)
else:
the_sicd.ImageData.SCPPixel = scp_pixel
if the_sicd.GeoData is None:
the_sicd.GeoData = GeoDataType(SCP=SCPType(ECF=scp_ecf))
set_scppixel()
elif the_sicd.GeoData.SCP is None or override:
the_sicd.GeoData.SCP = SCPType(ECF=scp_ecf)
set_scppixel()
def set_collect_start(collect_start, override=False):
if the_sicd.Timeline is None:
the_sicd.Timeline = TimelineType(CollectStart=collect_start)
elif the_sicd.Timeline.CollectStart is None or override:
the_sicd.Timeline.CollectStart = collect_start
def set_uvects(row_unit, col_unit):
if the_sicd.Grid is None:
the_sicd.Grid = GridType(Row=DirParamType(UVectECF=row_unit),
Col=DirParamType(UVectECF=col_unit))
return
if the_sicd.Grid.Row is None:
the_sicd.Grid.Row = DirParamType(UVectECF=row_unit)
elif the_sicd.Grid.Row.UVectECF is None:
the_sicd.Grid.Row.UVectECF = row_unit
if the_sicd.Grid.Col is None:
the_sicd.Grid.Col = DirParamType(UVectECF=col_unit)
elif the_sicd.Grid.Col.UVectECF is None:
the_sicd.Grid.Col.UVectECF = col_unit
def try_CMETAA():
tre = None if tres is None else tres['CMETAA'] # type: CMETAA
if tre is None:
return
cmetaa = tre.DATA
if the_sicd.GeoData is None:
the_sicd.GeoData = GeoDataType()
if the_sicd.SCPCOA is None:
the_sicd.SCPCOA = SCPCOAType()
if the_sicd.Grid is None:
the_sicd.Grid = GridType()
if the_sicd.Timeline is None:
the_sicd.Timeline = TimelineType()
if the_sicd.RadarCollection is None:
the_sicd.RadarCollection = RadarCollectionType()
if the_sicd.ImageFormation is None:
the_sicd.ImageFormation = ImageFormationType()
the_sicd.SCPCOA.SCPTime = 0.5*float(cmetaa.WF_CDP)
the_sicd.GeoData.SCP = SCPType(ECF=tre.get_scp())
the_sicd.SCPCOA.ARPPos = tre.get_arp()
the_sicd.SCPCOA.SideOfTrack = cmetaa.CG_LD.strip().upper()
the_sicd.SCPCOA.SlantRange = float(cmetaa.CG_SRAC)
the_sicd.SCPCOA.DopplerConeAng = float(cmetaa.CG_CAAC)
the_sicd.SCPCOA.GrazeAng = float(cmetaa.CG_GAAC)
the_sicd.SCPCOA.IncidenceAng = 90 - float(cmetaa.CG_GAAC)
if hasattr(cmetaa, 'CG_TILT'):
the_sicd.SCPCOA.TwistAng = float(cmetaa.CG_TILT)
if hasattr(cmetaa, 'CG_SLOPE'):
the_sicd.SCPCOA.SlopeAng = float(cmetaa.CG_SLOPE)
the_sicd.ImageData.SCPPixel = [int(cmetaa.IF_DC_IS_COL), int(cmetaa.IF_DC_IS_ROW)]
img_corners = tre.get_image_corners()
if img_corners is not None:
the_sicd.GeoData.ImageCorners = img_corners
if cmetaa.CMPLX_SIGNAL_PLANE.upper() == 'S':
the_sicd.Grid.ImagePlane = 'SLANT'
elif cmetaa.CMPLX_SIGNAL_PLANE.upper() == 'G':
the_sicd.Grid.ImagePlane = 'GROUND'
else:
logger.warning(
'Got unexpected CMPLX_SIGNAL_PLANE value {},\n\t'
'setting ImagePlane to SLANT'.format(cmetaa.CMPLX_SIGNAL_PLANE))
the_sicd.Grid.Row = DirParamType(
SS=float(cmetaa.IF_RSS),
ImpRespWid=float(cmetaa.IF_RGRES),
Sgn=1 if cmetaa.IF_RFFTS.strip() == '-' else -1, # opposite sign convention
ImpRespBW=float(cmetaa.IF_RFFT_SAMP)/(float(cmetaa.IF_RSS)*float(cmetaa.IF_RFFT_TOT)))
the_sicd.Grid.Col = DirParamType(
SS=float(cmetaa.IF_AZSS),
ImpRespWid=float(cmetaa.IF_AZRES),
Sgn=1 if cmetaa.IF_AFFTS.strip() == '-' else -1, # opposite sign convention
ImpRespBW=float(cmetaa.IF_AZFFT_SAMP)/(float(cmetaa.IF_AZSS)*float(cmetaa.IF_AZFFT_TOT)))
cmplx_weight = cmetaa.CMPLX_WEIGHT.strip().upper()
if cmplx_weight == 'UWT':
the_sicd.Grid.Row.WgtType = WgtTypeType(WindowName='UNIFORM')
the_sicd.Grid.Col.WgtType = WgtTypeType(WindowName='UNIFORM')
elif cmplx_weight == 'HMW':
the_sicd.Grid.Row.WgtType = WgtTypeType(WindowName='HAMMING')
the_sicd.Grid.Col.WgtType = WgtTypeType(WindowName='HAMMING')
elif cmplx_weight == 'HNW':
the_sicd.Grid.Row.WgtType = WgtTypeType(WindowName='HANNING')
the_sicd.Grid.Col.WgtType = WgtTypeType(WindowName='HANNING')
elif cmplx_weight == 'TAY':
the_sicd.Grid.Row.WgtType = WgtTypeType(
WindowName='TAYLOR',
Parameters={
'SLL': '-{0:d}'.format(int(cmetaa.CMPLX_RNG_SLL)),
'NBAR': '{0:d}'.format(int(cmetaa.CMPLX_RNG_TAY_NBAR))})
the_sicd.Grid.Col.WgtType = WgtTypeType(
WindowName='TAYLOR',
Parameters={
'SLL': '-{0:d}'.format(int(cmetaa.CMPLX_AZ_SLL)),
'NBAR': '{0:d}'.format(int(cmetaa.CMPLX_AZ_TAY_NBAR))})
else:
logger.warning(
'Got unsupported CMPLX_WEIGHT value {}.\n\tThe resulting SICD will '
'not have valid weight array populated'.format(cmplx_weight))
the_sicd.Grid.Row.define_weight_function()
the_sicd.Grid.Col.define_weight_function()
# noinspection PyBroadException
try:
date_str = cmetaa.T_UTC_YYYYMMMDD
time_str = cmetaa.T_HHMMSSUTC
date_time = _iso_date_format.format(
date_str[:4], date_str[4:6], date_str[6:8],
time_str[:2], time_str[2:4], time_str[4:6])
the_sicd.Timeline.CollectStart = numpy.datetime64(date_time, 'us')
except Exception:
logger.info('Failed extracting start time from CMETAA')
pass
the_sicd.Timeline.CollectDuration = float(cmetaa.WF_CDP)
the_sicd.Timeline.IPP = [
IPPSetType(TStart=0,
TEnd=float(cmetaa.WF_CDP),
IPPStart=0,
IPPEnd=numpy.floor(float(cmetaa.WF_CDP)*float(cmetaa.WF_PRF)),
IPPPoly=[0, float(cmetaa.WF_PRF)])]
the_sicd.RadarCollection.TxFrequency = TxFrequencyType(
Min=float(cmetaa.WF_SRTFR),
Max=float(cmetaa.WF_ENDFR))
the_sicd.RadarCollection.TxPolarization = cmetaa.POL_TR.upper()
the_sicd.RadarCollection.Waveform = [WaveformParametersType(
TxPulseLength=float(cmetaa.WF_WIDTH),
TxRFBandwidth=float(cmetaa.WF_BW),
TxFreqStart=float(cmetaa.WF_SRTFR),
TxFMRate=float(cmetaa.WF_CHRPRT)*1e12)]
tx_rcv_pol = '{}:{}'.format(cmetaa.POL_TR.upper(), cmetaa.POL_RE.upper())
the_sicd.RadarCollection.RcvChannels = [
ChanParametersType(TxRcvPolarization=tx_rcv_pol)]
the_sicd.ImageFormation.TxRcvPolarizationProc = tx_rcv_pol
if_process = cmetaa.IF_PROCESS.strip().upper()
if if_process == 'PF':
the_sicd.ImageFormation.ImageFormAlgo = 'PFA'
scp_ecf = tre.get_scp()
fpn_ned = numpy.array(
[float(cmetaa.CG_FPNUV_X), float(cmetaa.CG_FPNUV_Y), float(cmetaa.CG_FPNUV_Z)], dtype='float64')
ipn_ned = numpy.array(
[float(cmetaa.CG_IDPNUVX), float(cmetaa.CG_IDPNUVY), float(cmetaa.CG_IDPNUVZ)], dtype='float64')
fpn_ecf = ned_to_ecf(fpn_ned, scp_ecf, absolute_coords=False)
ipn_ecf = ned_to_ecf(ipn_ned, scp_ecf, absolute_coords=False)
the_sicd.PFA = PFAType(FPN=fpn_ecf, IPN=ipn_ecf)
elif if_process in ['RM', 'CD']:
the_sicd.ImageFormation.ImageFormAlgo = 'RMA'
# the remainder of this is guesswork to define required fields
the_sicd.ImageFormation.TStartProc = 0 # guess work
the_sicd.ImageFormation.TEndProc = float(cmetaa.WF_CDP)
the_sicd.ImageFormation.TxFrequencyProc = TxFrequencyProcType(
MinProc=float(cmetaa.WF_SRTFR), MaxProc=float(cmetaa.WF_ENDFR))
# all remaining guess work
the_sicd.ImageFormation.STBeamComp = 'NO'
the_sicd.ImageFormation.ImageBeamComp = 'SV' if cmetaa.IF_BEAM_COMP[0] == 'Y' else 'NO'
the_sicd.ImageFormation.AzAutofocus = 'NO' if cmetaa.AF_TYPE[0] == 'N' else 'SV'
the_sicd.ImageFormation.RgAutofocus = 'NO'
def try_AIMIDA():
tre = None if tres is None else tres['AIMIDA']
if tre is None:
return
aimida = tre.DATA
append_country_code(aimida.COUNTRY.strip())
create_time = datetime.strptime(aimida.CREATION_DATE, '%d%b%y')
if the_sicd.ImageCreation is None:
the_sicd.ImageCreation = ImageCreationType(DateTime=create_time)
elif the_sicd.ImageCreation.DateTime is None:
the_sicd.ImageCreation.DateTime = create_time
collect_start = datetime.strptime(aimida.MISSION_DATE+aimida.TIME, '%d%b%y%H%M')
set_collect_start(collect_start, override=False)
def try_AIMIDB():
tre = None if tres is None else tres['AIMIDB']
if tre is None:
return
aimidb = tre.DATA
append_country_code(aimidb.COUNTRY.strip())
if the_sicd.ImageFormation is not None and the_sicd.ImageFormation.SegmentIdentifier is None:
the_sicd.ImageFormation.SegmentIdentifier = aimidb.CURRENT_SEGMENT.strip()
date_str = aimidb.ACQUISITION_DATE
collect_start = numpy.datetime64(_iso_date_format.format(
date_str[:4], date_str[4:6], date_str[6:8],
date_str[8:10], date_str[10:12], date_str[12:14]), 'us')
set_collect_start(collect_start, override=False)
def try_ACFT():
if tres is None:
return
tre = tres['ACFTA']
if tre is None:
tre = tres['ACFTB']
if tre is None:
return
acft = tre.DATA
sensor_id = acft.SENSOR_ID.strip()
if len(sensor_id) > 1:
if the_sicd.CollectionInfo is None:
the_sicd.CollectionInfo = CollectionInfoType(CollectorName=sensor_id)
elif the_sicd.CollectionInfo.CollectorName is None:
the_sicd.CollectionInfo.CollectorName = sensor_id
row_ss = float(acft.ROW_SPACING)
col_ss = float(acft.COL_SPACING)
if hasattr(acft, 'ROW_SPACING_UNITS') and acft.ROW_SPACING_UNITS.strip().lower() == 'f':
row_ss *= foot
if hasattr(acft, 'COL_SPACING_UNITS') and acft.COL_SPACING_UNITS.strip().lower() == 'f':
col_ss *= foot
# NB: these values are actually ground plane values, and should be
# corrected to slant plane if possible
if the_sicd.SCPCOA is not None:
if the_sicd.SCPCOA.GrazeAng is not None:
col_ss *= numpy.cos(numpy.deg2rad(the_sicd.SCPCOA.GrazeAng))
if the_sicd.SCPCOA.TwistAng is not None:
row_ss *= numpy.cos(numpy.deg2rad(the_sicd.SCPCOA.TwistAng))
if the_sicd.Grid is None:
the_sicd.Grid = GridType(Row=DirParamType(SS=row_ss), Col=DirParamType(SS=col_ss))
return
if the_sicd.Grid.Row is None:
the_sicd.Grid.Row = DirParamType(SS=row_ss)
elif the_sicd.Grid.Row.SS is None:
the_sicd.Grid.Row.SS = row_ss
if the_sicd.Grid.Col is None:
the_sicd.Grid.Col = DirParamType(SS=col_ss)
elif the_sicd.Grid.Col.SS is None:
the_sicd.Grid.Col.SS = col_ss
def try_BLOCKA():
tre = None if tres is None else tres['BLOCKA']
if tre is None:
return
blocka = tre.DATA
icps = []
for fld_name in ['FRFC_LOC', 'FRLC_LOC', 'LRLC_LOC', 'LRFC_LOC']:
value = getattr(blocka, fld_name)
# noinspection PyBroadException
try:
lat_val = float(value[:10])
lon_val = float(value[10:21])
except ValueError:
lat_val = lat_lon_parser(value[:10])
lon_val = lat_lon_parser(value[10:21])
icps.append([lat_val, lon_val])
set_image_corners(icps, override=False)
def try_MPDSRA():
def valid_array(arr):
return numpy.all(numpy.isfinite(arr)) and numpy.any(arr != 0)
tre = None if tres is None else tres['MPDSRA']
if tre is None:
return
mpdsra = tre.DATA
scp_ecf = foot*numpy.array(
[float(mpdsra.ORO_X), float(mpdsra.ORO_Y), float(mpdsra.ORO_Z)], dtype='float64')
if valid_array(scp_ecf):
set_scp(scp_ecf, (int(mpdsra.ORP_COLUMN) - 1, int(mpdsra.ORP_ROW) - 1), override=False)
arp_pos_ned = foot*numpy.array(
[float(mpdsra.ARP_POS_N), float(mpdsra.ARP_POS_E), float(mpdsra.ARP_POS_D)], dtype='float64')
arp_vel_ned = foot*numpy.array(
[float(mpdsra.ARP_VEL_N), float(mpdsra.ARP_VEL_E), float(mpdsra.ARP_VEL_D)], dtype='float64')
arp_acc_ned = foot*numpy.array(
[float(mpdsra.ARP_ACC_N), float(mpdsra.ARP_ACC_E), float(mpdsra.ARP_ACC_D)], dtype='float64')
arp_pos = ned_to_ecf(arp_pos_ned, scp_ecf, absolute_coords=True) if valid_array(arp_pos_ned) else None
set_arp_position(arp_pos, override=False)
arp_vel = ned_to_ecf(arp_vel_ned, scp_ecf, absolute_coords=False) if valid_array(arp_vel_ned) else None
if the_sicd.SCPCOA.ARPVel is None:
the_sicd.SCPCOA.ARPVel = arp_vel
arp_acc = ned_to_ecf(arp_acc_ned, scp_ecf, absolute_coords=False) if valid_array(arp_acc_ned) else None
if the_sicd.SCPCOA.ARPAcc is None:
the_sicd.SCPCOA.ARPAcc = arp_acc
if the_sicd.PFA is not None and the_sicd.PFA.FPN is None:
# TODO: is this already in meters?
fpn_ecf = numpy.array(
[float(mpdsra.FOC_X), float(mpdsra.FOC_Y), float(mpdsra.FOC_Z)], dtype='float64') # *foot
if valid_array(fpn_ecf):
the_sicd.PFA.FPN = fpn_ecf
def try_MENSRB():
tre = None if tres is None else tres['MENSRB']
if tre is None:
return
mensrb = tre.DATA
arp_llh = numpy.array(
[lat_lon_parser(mensrb.ACFT_LOC[:12]),
lat_lon_parser(mensrb.ACFT_LOC[12:25]),
foot*float(mensrb.ACFT_ALT)], dtype='float64')
scp_llh = numpy.array(
[lat_lon_parser(mensrb.RP_LOC[:12]),
lat_lon_parser(mensrb.RP_LOC[12:25]),
foot*float(mensrb.RP_ELV)], dtype='float64')
# TODO: handle the conversion from msl to hae
arp_ecf = geodetic_to_ecf(arp_llh)
scp_ecf = geodetic_to_ecf(scp_llh)
set_arp_position(arp_ecf, override=True)
set_scp(scp_ecf, (int(mensrb.RP_COL)-1, int(mensrb.RP_ROW)-1), override=False)
row_unit_ned = numpy.array(
[float(mensrb.C_R_NC), float(mensrb.C_R_EC), float(mensrb.C_R_DC)], dtype='float64')
col_unit_ned = numpy.array(
[float(mensrb.C_AZ_NC), float(mensrb.C_AZ_EC), float(mensrb.C_AZ_DC)], dtype='float64')
set_uvects(ned_to_ecf(row_unit_ned, scp_ecf, absolute_coords=False),
ned_to_ecf(col_unit_ned, scp_ecf, absolute_coords=False))
def try_MENSRA():
tre = None if tres is None else tres['MENSRA']
if tre is None:
return
mensra = tre.DATA
arp_llh = numpy.array(
[lat_lon_parser(mensra.ACFT_LOC[:10]),
lat_lon_parser(mensra.ACFT_LOC[10:21]),
foot*float(mensra.ACFT_ALT)], dtype='float64')
scp_llh = numpy.array(
[lat_lon_parser(mensra.CP_LOC[:10]),
lat_lon_parser(mensra.CP_LOC[10:21]),
foot*float(mensra.CP_ALT)], dtype='float64')
# TODO: handle the conversion from msl to hae
arp_ecf = geodetic_to_ecf(arp_llh)
scp_ecf = geodetic_to_ecf(scp_llh)
set_arp_position(arp_ecf, override=True)
# TODO: is this already zero based?
set_scp(geodetic_to_ecf(scp_llh), (int(mensra.CCRP_COL), int(mensra.CCRP_ROW)), override=False)
row_unit_ned = numpy.array(
[float(mensra.C_R_NC), float(mensra.C_R_EC), float(mensra.C_R_DC)], dtype='float64')
col_unit_ned = numpy.array(
[float(mensra.C_AZ_NC), float(mensra.C_AZ_EC), float(mensra.C_AZ_DC)], dtype='float64')
set_uvects(ned_to_ecf(row_unit_ned, scp_ecf, absolute_coords=False),
ned_to_ecf(col_unit_ned, scp_ecf, absolute_coords=False))
def extract_corners():
icps = extract_image_corners(img_header)
if icps is None:
return
# TODO: include symmetry transform issue
set_image_corners(icps, override=False)
def extract_start():
# noinspection PyBroadException
try:
date_str = img_header.IDATIM
collect_start = numpy.datetime64(
_iso_date_format.format(
date_str[:4], date_str[4:6], date_str[6:8],
date_str[8:10], date_str[10:12], date_str[12:14]), 'us')
except Exception:
logger.info('failed extracting start time from IDATIM tre')
return
set_collect_start(collect_start, override=False)
# noinspection PyUnresolvedReferences
tres = None if img_header.ExtendedHeader.data is None \
else img_header.ExtendedHeader.data # type: Union[None, TREList]
collection_info = get_collection_info()
image_data = get_image_data()
the_sicd = SICDType(
CollectionInfo=collection_info,
ImageData=image_data)
# apply the various tres and associated logic
# NB: this should generally be in order of preference
try_CMETAA()
try_AIMIDB()
try_AIMIDA()
try_ACFT()
try_BLOCKA()
try_MPDSRA()
try_MENSRA()
try_MENSRB()
extract_corners()
extract_start()
return the_sicd
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_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_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_base_sicd(self, hf):
"""
Defines the base SICD object, to be refined with further details.
Returns
-------
SICDType
"""
def get_collection_info():
# type: () -> CollectionInfoType
gp = hf['/science/LSAR/identification']
return CollectionInfoType(
CollectorName=_stringify(hf.attrs['mission_name']),
CoreName='{0:07d}_{1:s}'.format(gp['absoluteOrbitNumber'][()],
_stringify(gp['trackNumber'][()])),
CollectType='MONOSTATIC',
Classification='UNCLASSIFIED',
RadarMode=RadarModeType(ModeType='STRIPMAP'))
def get_image_creation():
# type: () -> ImageCreationType
application = 'ISCE'
# noinspection PyBroadException
try:
application = '{} {}'.format(
application,
_stringify(hf['/science/LSAR/SLC/metadata/processingInformation/algorithms/ISCEVersion'][()]))
except:
pass
from sarpy.__about__ import __version__
# TODO: DateTime?
return ImageCreationType(
Application=application,
Site='Unknown',
Profile='sarpy {}'.format(__version__))
def get_geo_data():
# type: () -> GeoDataType
# seeds a rough SCP for projection usage
poly_str = _stringify(hf['/science/LSAR/identification/boundingPolygon'][()])
beg_str = 'POLYGON (('
if not poly_str.startswith(beg_str):
raise ValueError('Unexpected polygon string {}'.format(poly_str))
parts = poly_str[len(beg_str):-2].strip().split(',')
if len(parts) != 5:
raise ValueError('Unexpected polygon string parts {}'.format(parts))
lats_lons = numpy.zeros((4, 2), dtype=numpy.float64)
for i, part in enumerate(parts[:-1]):
spart = part.strip().split()
if len(spart) != 2:
raise ValueError('Unexpected polygon string parts {}'.format(parts))
lats_lons[i, :] = float(spart[1]), float(spart[0])
llh = numpy.zeros((3, ), dtype=numpy.float64)
llh[0:2] = numpy.mean(lats_lons, axis=0)
llh[2] = numpy.mean(hf['/science/LSAR/SLC/metadata/processingInformation/parameters/referenceTerrainHeight'][:])
return GeoDataType(SCP=SCPType(LLH=llh))
def get_grid():
# type: () -> GridType
# TODO: Future Change Required - JPL states that uniform weighting in data simulated
# from UAVSAR is a placeholder, not an accurate description of the data.
# At this point, it is not clear what the final weighting description for NISAR
# will be.
gp = hf['/science/LSAR/SLC/metadata/processingInformation/parameters']
row_wgt = gp['rangeChirpWeighting'][:]
win_name = 'UNIFORM' if numpy.all(row_wgt == row_wgt[0]) else 'UNKNOWN'
row = DirParamType(
Sgn=-1,
DeltaKCOAPoly=[[0,]],
WgtFunct=numpy.cast[numpy.float64](row_wgt),
WgtType=WgtTypeType(WindowName=win_name))
col_wgt = gp['azimuthChirpWeighting'][:]
win_name = 'UNIFORM' if numpy.all(col_wgt == col_wgt[0]) else 'UNKNOWN'
col = DirParamType(
Sgn=-1,
KCtr=0,
WgtFunct=numpy.cast[numpy.float64](col_wgt),
WgtType=WgtTypeType(WindowName=win_name))
return GridType(ImagePlane='SLANT', Type='RGZERO', 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=duration, IPPStart=0, IPPEnd=0), ])
def get_position():
# type: () -> 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_scpcoa():
# type: () -> SCPCOAType
# remaining fields set later
sot = _stringify(hf['/science/LSAR/identification/lookDirection'][()])[0].upper()
return SCPCOAType(SideOfTrack=sot)
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
return RMAType(RMAlgoType='OMEGA_K', INCA=INCAType(DopCentroidCOA=True))
collect_start, collect_end, duration = self._get_collection_times(hf)
collection_info = get_collection_info()
image_creation = get_image_creation()
geo_data = get_geo_data()
grid = get_grid()
timeline = get_timeline()
position = get_position()
scpcoa = get_scpcoa()
image_formation = get_image_formation()
rma = get_rma()
return SICDType(
CollectionInfo=collection_info,
ImageCreation=image_creation,
GeoData=geo_data,
Grid=grid,
Timeline=timeline,
Position=position,
SCPCOA=scpcoa,
ImageFormation=image_formation,
RMA=rma)
def _get_freq_specific_sicd(
gp: h5pyGroup,
base_sicd: SICDType) -> Tuple[SICDType, List[str], List[str], float]:
"""
Gets the frequency specific sicd.
Parameters
----------
gp : h5py.Group
base_sicd : SICDType
Returns
-------
sicd: SICDType
pol_names : numpy.ndarray
pols : List[str]
center_frequency : float
"""
def update_grid() -> None:
row_imp_resp_bw = 2*gp['processedRangeBandwidth'][()]/speed_of_light
t_sicd.Grid.Row.SS = gp['slantRangeSpacing'][()]
t_sicd.Grid.Row.ImpRespBW = row_imp_resp_bw
t_sicd.Grid.Row.DeltaK1 = -0.5*row_imp_resp_bw
t_sicd.Grid.Row.DeltaK2 = -t_sicd.Grid.Row.DeltaK1
def update_timeline() -> None:
prf = gp['nominalAcquisitionPRF'][()]
t_sicd.Timeline.IPP[0].IPPEnd = prf*t_sicd.Timeline.CollectDuration
t_sicd.Timeline.IPP[0].IPPPoly = [0, prf]
def define_radar_collection() -> List[str]:
tx_rcv_pol_t = []
tx_pol = []
for entry in pols:
tx_rcv_pol_t.append('{}:{}'.format(entry[0], entry[1]))
if entry[0] not in tx_pol:
tx_pol.append(entry[0])
center_freq_t = gp['acquiredCenterFrequency'][()]
bw = gp['acquiredRangeBandwidth'][()]
tx_freq = (center_freq_t - 0.5*bw, center_freq_t + 0.5*bw)
rcv_chans = [ChanParametersType(TxRcvPolarization=pol) for pol in tx_rcv_pol_t]
if len(tx_pol) == 1:
tx_sequence = None
tx_pol = tx_pol[0]
else:
tx_sequence = [TxStepType(WFIndex=j+1, TxPolarization=pol) for j, pol in enumerate(tx_pol)]
tx_pol = 'SEQUENCE'
t_sicd.RadarCollection = RadarCollectionType(
TxFrequency=tx_freq,
RcvChannels=rcv_chans,
TxPolarization=tx_pol,
TxSequence=tx_sequence)
return tx_rcv_pol_t
def update_image_formation() -> float:
center_freq_t = gp['processedCenterFrequency'][()]
bw = gp['processedRangeBandwidth'][()]
t_sicd.ImageFormation.TxFrequencyProc = (center_freq_t - 0.5*bw, center_freq_t + 0.5*bw)
return center_freq_t
pols = _get_string_list(gp['listOfPolarizations'][:])
t_sicd = base_sicd.copy()
update_grid()
update_timeline()
tx_rcv_pol = define_radar_collection()
center_freq = update_image_formation()
return t_sicd, pols, tx_rcv_pol, center_freq
def _get_pol_specific_sicd(
hf: h5pyFile,
ds: h5pyDataset,
base_sicd: SICDType,
pol_name: str,
freq_name: str,
j: int,
pol: str,
r_ca_sampled: numpy.ndarray,
zd_time: numpy.ndarray,
grid_zd_time: numpy.ndarray,
grid_r: numpy.ndarray,
doprate_sampled: numpy.ndarray,
dopcentroid_sampled: numpy.ndarray,
center_freq: float,
ss_az_s: float,
dop_bw: float,
beta0,
gamma0,
sigma0) -> Tuple[SICDType, Tuple[int, ...], numpy.dtype]:
"""
Gets the frequency/polarization specific sicd.
Parameters
----------
hf : h5py.File
ds : h5py.Dataset
base_sicd : SICDType
pol_name : str
freq_name : str
j : int
pol : str
r_ca_sampled : numpy.ndarray
zd_time : numpy.ndarray
grid_zd_time : numpy.ndarray
grid_r : numpy.ndarray
doprate_sampled : numpy.ndarray
dopcentroid_sampled : numpy.ndarray
center_freq : float
ss_az_s : float
dop_bw : float
Returns
-------
sicd: SICDType
shape : Tuple[int, ...]
numpy.dtype
"""
def define_image_data() -> None:
if dtype.name in ('float32', 'complex64'):
pixel_type = 'RE32F_IM32F'
elif dtype.name == 'int16':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled dtype {}'.format(dtype))
t_sicd.ImageData = ImageDataType(
PixelType=pixel_type,
NumRows=shape[1],
NumCols=shape[0],
FirstRow=0,
FirstCol=0,
SCPPixel=[0.5*shape[0], 0.5*shape[1]],
FullImage=[shape[1], shape[0]])
def update_image_formation() -> None:
t_sicd.ImageFormation.RcvChanProc.ChanIndices = [j, ]
t_sicd.ImageFormation.TxRcvPolarizationProc = pol
def update_inca_and_grid() -> Tuple[numpy.ndarray, numpy.ndarray]:
t_sicd.RMA.INCA.R_CA_SCP = r_ca_sampled[t_sicd.ImageData.SCPPixel.Row]
scp_ca_time = zd_time[t_sicd.ImageData.SCPPixel.Col]
# compute DRateSFPoly
# velocity at scp ca time
vel_ca = t_sicd.Position.ARPPoly.derivative_eval(scp_ca_time, der_order=1)
# squared magnitude
vm_ca_sq = numpy.sum(vel_ca*vel_ca)
# polynomial coefficient for function representing range as a function of range distance from SCP
r_ca_poly = numpy.array([t_sicd.RMA.INCA.R_CA_SCP, 1], dtype=numpy.float64)
# closest Doppler rate polynomial to SCP
min_ind = numpy.argmin(numpy.absolute(grid_zd_time - scp_ca_time))
# define range coordinate grid
coords_rg_m = grid_r - t_sicd.RMA.INCA.R_CA_SCP
# determine dop_rate_poly coordinates
dop_rate_poly = polynomial.polyfit(coords_rg_m, -doprate_sampled[min_ind, :], 4) # why fourth order?
t_sicd.RMA.INCA.FreqZero = center_freq
t_sicd.RMA.INCA.DRateSFPoly = Poly2DType(Coefs=numpy.reshape(
-numpy.convolve(dop_rate_poly, r_ca_poly)*speed_of_light/(2*center_freq*vm_ca_sq), (-1, 1)))
# update Grid.Col parameters
t_sicd.Grid.Col.SS = numpy.sqrt(vm_ca_sq)*abs(ss_az_s)*t_sicd.RMA.INCA.DRateSFPoly.Coefs[0, 0]
t_sicd.Grid.Col.ImpRespBW = min(abs(dop_bw*ss_az_s), 1)/t_sicd.Grid.Col.SS
t_sicd.RMA.INCA.TimeCAPoly = [scp_ca_time, ss_az_s/t_sicd.Grid.Col.SS]
#TimeCOAPoly/DopCentroidPoly/DeltaKCOAPoly
coords_az_m = (grid_zd_time - scp_ca_time)*t_sicd.Grid.Col.SS/ss_az_s
# cerate the 2d grids
coords_rg_2d_t, coords_az_2d_t = numpy.meshgrid(coords_rg_m, coords_az_m, indexing='xy')
coefs, residuals, rank, sing_values = two_dim_poly_fit(
coords_rg_2d_t, coords_az_2d_t, dopcentroid_sampled,
x_order=3, y_order=3, x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The dop_centroid_poly fit details:\n\t'
'root mean square residuals = {}\n\t'
'rank = {}\n\t'
'singular values = {}'.format(residuals, rank, sing_values))
t_sicd.RMA.INCA.DopCentroidPoly = Poly2DType(Coefs=coefs)
t_sicd.Grid.Col.DeltaKCOAPoly = Poly2DType(Coefs=coefs*ss_az_s/t_sicd.Grid.Col.SS)
timeca_sampled = numpy.outer(grid_zd_time, numpy.ones((grid_r.size, )))
time_coa_sampled = timeca_sampled + (dopcentroid_sampled/doprate_sampled)
coefs, residuals, rank, sing_values = two_dim_poly_fit(
coords_rg_2d_t, coords_az_2d_t, time_coa_sampled,
x_order=3, y_order=3, x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The time_coa_poly fit details:\n\t'
'root mean square residuals = {}\n\t'
'rank = {}\n\t'
'singular values = {}'.format(residuals, rank, sing_values))
t_sicd.Grid.TimeCOAPoly = Poly2DType(Coefs=coefs)
return coords_rg_2d_t, coords_az_2d_t
def define_radiometric() -> None:
def get_poly(ds: h5pyDataset, name: str) -> Optional[Poly2DType]:
array = ds[:]
fill = ds.attrs['_FillValue']
boolc = (array != fill)
if numpy.any(boolc):
array = array[boolc]
if numpy.any(array != array[0]):
coefs, residuals, rank, sing_values = two_dim_poly_fit(
coords_rg_2d[boolc], coords_az_2d[boolc], array,
x_order=3, y_order=3, x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The {} fit details:\n\t'
'root mean square residuals = {}\n\t'
'rank = {}\n\t'
'singular values = {}'.format(name, residuals, rank, sing_values))
else:
# it's constant, so just use a constant polynomial
coefs = [[array[0], ], ]
logger.info('The {} values are constant'.format(name))
return Poly2DType(Coefs=coefs)
else:
logger.warning('No non-trivial values for {} provided.'.format(name))
return None
beta0_poly = get_poly(beta0, 'beta0')
gamma0_poly = get_poly(gamma0, 'gamma0')
sigma0_poly = get_poly(sigma0, 'sigma0')
nesz = hf['/science/LSAR/SLC/metadata/calibrationInformation/frequency{}/{}/nes0'.format(freq_name,
pol_name)][:]
noise_samples = nesz - (10 * numpy.log10(sigma0_poly.Coefs[0, 0]))
coefs, residuals, rank, sing_values = two_dim_poly_fit(
coords_rg_2d, coords_az_2d, noise_samples,
x_order=3, y_order=3, x_scale=1e-3, y_scale=1e-3, rcond=1e-40)
logger.info(
'The noise_poly fit details:\n\t'
'root mean square residuals = {}\n\t'
'rank = {}\n\t'
'singular values = {}'.format(
residuals, rank, sing_values))
t_sicd.Radiometric = RadiometricType(
BetaZeroSFPoly=beta0_poly,
GammaZeroSFPoly=gamma0_poly,
SigmaZeroSFPoly=sigma0_poly,
NoiseLevel=NoiseLevelType_(
NoiseLevelType='ABSOLUTE', NoisePoly=Poly2DType(Coefs=coefs)))
def update_geodata() -> None:
ecf = point_projection.image_to_ground(
[t_sicd.ImageData.SCPPixel.Row, t_sicd.ImageData.SCPPixel.Col], t_sicd)
t_sicd.GeoData.SCP = SCPType(ECF=ecf) # LLH will be populated
t_sicd = base_sicd.copy()
shape = ds.shape
dtype = ds.dtype
define_image_data()
update_image_formation()
coords_rg_2d, coords_az_2d = update_inca_and_grid()
define_radiometric()
update_geodata()
t_sicd.derive()
t_sicd.populate_rniirs(override=False)
return t_sicd, shape, dtype
def __init__(self,
file_object: Union[str, BinaryIO],
sicd_meta: SICDType,
user_data: Optional[Dict[str, str]] = None,
check_older_version: bool = False,
check_existence: bool = True):
"""
Parameters
----------
file_object : str|BinaryIO
sicd_meta : SICDType
user_data : None|Dict[str, str]
check_older_version : bool
Try to use an older version (1.1) of the SICD standard, for possible
application compliance issues?
check_existence : bool
Should we check if the given file already exists, and raises an exception if so?
"""
self._data_written = True
if isinstance(file_object, str):
if check_existence and os.path.exists(file_object):
raise SarpyIOError(
'Given file {} already exists, and a new SIO file cannot be created here.'
.format(file_object))
file_object = open(file_object, 'wb')
if not is_file_like(file_object):
raise ValueError(
'file_object requires a file path or BinaryIO object')
self._file_object = file_object
if is_real_file(file_object):
self._file_name = file_object.name
self._in_memory = False
else:
self._file_name = None
self._in_memory = True
# choose magic number (with user data) and corresponding endian-ness
magic_number = 0xFD7F02FF
endian = SIODetails.ENDIAN[magic_number]
# define basic image details
raw_shape = (sicd_meta.ImageData.NumRows, sicd_meta.ImageData.NumCols,
2)
pixel_type = sicd_meta.ImageData.PixelType
if pixel_type == 'RE32F_IM32F':
raw_dtype = numpy.dtype('{}f4'.format(endian))
element_type = 13
element_size = 8
format_function = ComplexFormatFunction(raw_dtype,
order='MP',
band_dimension=2)
elif pixel_type == 'RE16I_IM16I':
raw_dtype = numpy.dtype('{}i2'.format(endian))
element_type = 12
element_size = 4
format_function = ComplexFormatFunction(raw_dtype,
order='MP',
band_dimension=2)
else:
raw_dtype = numpy.dtype('{}u1'.format(endian))
element_type = 11
element_size = 2
format_function = ComplexFormatFunction(
raw_dtype,
order='MP',
band_dimension=2,
magnitude_lookup_table=sicd_meta.ImageData.AmpTable)
# construct the sio header
header = numpy.array([
magic_number, raw_shape[0], raw_shape[1], element_type,
element_size
],
dtype='>u4')
# construct the user data - must be {str : str}
if user_data is None:
user_data = {}
uh_args = sicd_meta.get_des_details(check_older_version)
user_data['SICDMETA'] = sicd_meta.to_xml_string(tag='SICD',
urn=uh_args['DESSHTN'])
# write the initial things to the buffer
self._file_object.seek(0, os.SEEK_SET)
self._file_object.write(struct.pack('{}5I'.format(endian), *header))
# write the user data - name size, name, value size, value
for name in user_data:
name_bytes = name.encode('utf-8')
self._file_object.write(
struct.pack('{}I'.format(endian), len(name_bytes)))
self._file_object.write(
struct.pack('{}{}s'.format(endian, len(name_bytes)),
name_bytes))
val_bytes = user_data[name].encode('utf-8')
self._file_object.write(
struct.pack('{}I'.format(endian), len(val_bytes)))
self._file_object.write(
struct.pack('{}{}s'.format(endian, len(val_bytes)), val_bytes))
self._data_offset = self._file_object.tell()
# initialize the single data segment
if self._in_memory:
underlying_array = numpy.full(raw_shape,
fill_value=0,
dtype=raw_dtype)
data_segment = NumpyArraySegment(underlying_array,
'complex64',
raw_shape[:2],
format_function=format_function,
mode='w')
self._data_written = False
else:
data_segment = NumpyMemmapSegment(self._file_object,
self._data_offset,
raw_dtype,
raw_shape,
'complex64',
raw_shape[:2],
format_function=format_function,
mode='w',
close_file=False)
self._data_written = True
BaseWriter.__init__(self, data_segment)
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_band_specific_sicds(self, base_sicd: SICDType, h5_dict: dict,
band_dict: dict, shape_dict: dict,
pixeltype_dict: dict) -> Dict[str, SICDType]:
def update_scp_prelim(sicd: SICDType, band_name: 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_id_text.format(self._mission_id))
sicd.GeoData = GeoDataType(
SCP=SCPType(LLH=LLH)) # EarthModel & ECF will be populated
def update_image_data(
sicd: SICDType,
band_name: str) -> (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=pixeltype_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() -> (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: SICDType, band_name: 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: SICDType, band_name: str) -> None:
ind = None
for the_chan_index, chan in enumerate(
sicd.RadarCollection.RcvChannels):
if chan.TxRcvPolarization == polarization:
ind = the_chan_index
break
if ind is None:
raise ValueError(
'Failed to find receive channel for polarization {}'.
format(polarization))
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 = (fr_min, 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 = (fr_min, fr_max)
def update_rma_and_grid(sicd: SICDType, band_name: 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: SICDType, band_name: 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: 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 bd_name in band_dict:
polarization = self._parse_pol(
self._get_polarization(h5_dict, band_dict, bd_name))
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()
t_sicd.ImageFormation.TxRcvPolarizationProc = polarization
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)
update_rma_and_grid(t_sicd, bd_name)
update_radiometric(t_sicd, bd_name)
update_scp_prelim(
t_sicd, bd_name
) # set preliminary value for SCP (required for projection)
update_geodata(t_sicd)
t_sicd.derive()
# t_sicd.populate_rniirs(override=False)
out[bd_name] = t_sicd
return out