From 4903c3e4a32a209441c0f12b8bb09b9bbb9e9c10 Mon Sep 17 00:00:00 2001 From: edparris Date: Thu, 7 Sep 2023 12:27:18 -0400 Subject: [PATCH 01/20] feat: add SICD sensor model with planar projection (#10) --- schemas/sicd.xsdata.xml | 52 + .../sicd/SICD_schema_V1.2.1_2018_12_13.xsd | 1119 +++++ .../SICD_schema_V1.3.0_2021_11_30_FINAL.xsd | 1109 +++++ setup.cfg | 1 + src/aws/osml/formats/__init__.py | 0 src/aws/osml/formats/sicd/__init__.py | 1 + src/aws/osml/formats/sicd/models/__init__.py | 273 ++ .../osml/formats/sicd/models/sicd_v1_2_1.py | 3622 ++++++++++++++++ .../osml/formats/sicd/models/sicd_v1_3_0.py | 3654 +++++++++++++++++ src/aws/osml/gdal/gdal_utils.py | 8 +- src/aws/osml/gdal/sensor_model_factory.py | 22 +- .../osml/gdal/sicd_sensor_model_builder.py | 186 + src/aws/osml/photogrammetry/__init__.py | 19 + .../osml/photogrammetry/sicd_sensor_model.py | 813 ++++ .../osml/gdal/test_sensor_model_factory.py | 29 + .../photogrammetry/test_sicd_sensor_model.py | 197 + test/data/sicd/capella-sicd121-chip1.ntf | 3 + test/data/sicd/capella-sicd121-chip2.ntf | 3 + test/data/sicd/example.sicd121.capella.xml | 402 ++ test/data/sicd/example.sicd121.pfa.xml | 1156 ++++++ test/data/sicd/example.sicd121.rma.xml | 1577 +++++++ test/data/sicd/umbra-sicd121-chip1.ntf | 3 + 22 files changed, 14243 insertions(+), 6 deletions(-) create mode 100644 schemas/sicd.xsdata.xml create mode 100644 schemas/sicd/SICD_schema_V1.2.1_2018_12_13.xsd create mode 100644 schemas/sicd/SICD_schema_V1.3.0_2021_11_30_FINAL.xsd create mode 100644 src/aws/osml/formats/__init__.py create mode 100644 src/aws/osml/formats/sicd/__init__.py create mode 100644 src/aws/osml/formats/sicd/models/__init__.py create mode 100644 src/aws/osml/formats/sicd/models/sicd_v1_2_1.py create mode 100644 src/aws/osml/formats/sicd/models/sicd_v1_3_0.py create mode 100644 src/aws/osml/gdal/sicd_sensor_model_builder.py create mode 100644 src/aws/osml/photogrammetry/sicd_sensor_model.py create mode 100644 test/aws/osml/photogrammetry/test_sicd_sensor_model.py create mode 100644 test/data/sicd/capella-sicd121-chip1.ntf create mode 100644 test/data/sicd/capella-sicd121-chip2.ntf create mode 100644 test/data/sicd/example.sicd121.capella.xml create mode 100644 test/data/sicd/example.sicd121.pfa.xml create mode 100644 test/data/sicd/example.sicd121.rma.xml create mode 100644 test/data/sicd/umbra-sicd121-chip1.ntf diff --git a/schemas/sicd.xsdata.xml b/schemas/sicd.xsdata.xml new file mode 100644 index 0000000..ad89471 --- /dev/null +++ b/schemas/sicd.xsdata.xml @@ -0,0 +1,52 @@ + + + + sicd.models + dataclasses + filenames + reStructuredText + allGlobals + true + false + false + false + false + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/schemas/sicd/SICD_schema_V1.2.1_2018_12_13.xsd b/schemas/sicd/SICD_schema_V1.2.1_2018_12_13.xsd new file mode 100644 index 0000000..df36ea4 --- /dev/null +++ b/schemas/sicd/SICD_schema_V1.2.1_2018_12_13.xsd @@ -0,0 +1,1119 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/schemas/sicd/SICD_schema_V1.3.0_2021_11_30_FINAL.xsd b/schemas/sicd/SICD_schema_V1.3.0_2021_11_30_FINAL.xsd new file mode 100644 index 0000000..c1ee10e --- /dev/null +++ b/schemas/sicd/SICD_schema_V1.3.0_2021_11_30_FINAL.xsd @@ -0,0 +1,1109 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/setup.cfg b/setup.cfg index 5390bc6..cb2cca5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -45,6 +45,7 @@ install_requires = geojson>=3.0.0 pyproj>=3.6.0 omegaconf==2.3.0;python_version<'3.10.0' + xsdata>=23.8 defusedxml>=0.7.1 [options.packages.find] diff --git a/src/aws/osml/formats/__init__.py b/src/aws/osml/formats/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/aws/osml/formats/sicd/__init__.py b/src/aws/osml/formats/sicd/__init__.py new file mode 100644 index 0000000..b2a4ba5 --- /dev/null +++ b/src/aws/osml/formats/sicd/__init__.py @@ -0,0 +1 @@ +# nothing here diff --git a/src/aws/osml/formats/sicd/models/__init__.py b/src/aws/osml/formats/sicd/models/__init__.py new file mode 100644 index 0000000..80d9651 --- /dev/null +++ b/src/aws/osml/formats/sicd/models/__init__.py @@ -0,0 +1,273 @@ +"""This file was generated by xsdata, v23.8, on 2023-08-30 17:21:13 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .sicd_v1_2_1 import SICD as Type21SICD +from .sicd_v1_2_1 import AntennaType as Type21AntennaType +from .sicd_v1_2_1 import AntParamType as Type21AntParamType +from .sicd_v1_2_1 import ArrayDouble as Type21ArrayDouble +from .sicd_v1_2_1 import AzAutofocus as Type21AzAutofocus +from .sicd_v1_2_1 import CollectionInfoType as Type21CollectionInfoType +from .sicd_v1_2_1 import CollectType as Type21CollectType +from .sicd_v1_2_1 import ComplexType as Type21ComplexType +from .sicd_v1_2_1 import CornerStringType as Type21CornerStringType +from .sicd_v1_2_1 import DirParamType as Type21DirParamType +from .sicd_v1_2_1 import DirParamTypeSgn as Type21DirParamTypeSgn +from .sicd_v1_2_1 import DualPolarizationType +from .sicd_v1_2_1 import EarthModel as Type21EarthModel +from .sicd_v1_2_1 import ErrorDecorrFuncType as Type21ErrorDecorrFuncType +from .sicd_v1_2_1 import ErrorStatisticsType as Type21ErrorStatisticsType +from .sicd_v1_2_1 import GainPhasePolyType as Type21GainPhasePolyType +from .sicd_v1_2_1 import GeoDataType as Type21GeoDataType +from .sicd_v1_2_1 import GeoInfoType as Type21GeoInfoType +from .sicd_v1_2_1 import GridType as Type21GridType +from .sicd_v1_2_1 import ImageBeamComp as Type21ImageBeamComp +from .sicd_v1_2_1 import ImageCreationType as Type21ImageCreationType +from .sicd_v1_2_1 import ImageDataType as Type21ImageDataType +from .sicd_v1_2_1 import ImageFormAlgo as Type21ImageFormAlgo +from .sicd_v1_2_1 import ImageFormationType as Type21ImageFormationType +from .sicd_v1_2_1 import ImageGridType as Type21ImageGridType +from .sicd_v1_2_1 import ImagePixelType as Type21ImagePixelType +from .sicd_v1_2_1 import ImagePlane as Type21ImagePlane +from .sicd_v1_2_1 import LatLonCornerStringType as Type21LatLonCornerStringType +from .sicd_v1_2_1 import LatLonCornerType as Type21LatLonCornerType +from .sicd_v1_2_1 import LatLonHAECornerRestrictType as Type21LatLonHAECornerRestrictType +from .sicd_v1_2_1 import LatLonHAECornerStringType as Type21LatLonHAECornerStringType +from .sicd_v1_2_1 import LatLonHAERestrictionType as Type21LatLonHAERestrictionType +from .sicd_v1_2_1 import LatLonHAEType as Type21LatLonHAEType +from .sicd_v1_2_1 import LatLonRestrictionType as Type21LatLonRestrictionType +from .sicd_v1_2_1 import LatLonType as Type21LatLonType +from .sicd_v1_2_1 import LineType as Type21LineType +from .sicd_v1_2_1 import MatchInfoType as Type21MatchInfoType +from .sicd_v1_2_1 import NoiseLevelType as Type21NoiseLevelType +from .sicd_v1_2_1 import OrientationType as Type21OrientationType +from .sicd_v1_2_1 import ParameterType as Type21ParameterType +from .sicd_v1_2_1 import PFAType as Type21PFAType +from .sicd_v1_2_1 import Polarization1Type, Polarization2Type +from .sicd_v1_2_1 import Poly1DType as Type21Poly1DType +from .sicd_v1_2_1 import Poly2DType as Type21Poly2DType +from .sicd_v1_2_1 import PolyCoef1DType as Type21PolyCoef1DType +from .sicd_v1_2_1 import PolyCoef2DType as Type21PolyCoef2DType +from .sicd_v1_2_1 import PolygonType as Type21PolygonType +from .sicd_v1_2_1 import PositionType as Type21PositionType +from .sicd_v1_2_1 import PosVelErrFrame as Type21PosVelErrFrame +from .sicd_v1_2_1 import RadarCollectionType as Type21RadarCollectionType +from .sicd_v1_2_1 import RadarModeType as Type21RadarModeType +from .sicd_v1_2_1 import RadiometricType as Type21RadiometricType +from .sicd_v1_2_1 import RgAutofocus as Type21RgAutofocus +from .sicd_v1_2_1 import RgAzCompType as Type21RgAzCompType +from .sicd_v1_2_1 import RMAImageType as Type21RMAImageType +from .sicd_v1_2_1 import RMAlgorithm as Type21RMAlgorithm +from .sicd_v1_2_1 import RMAType as Type21RMAType +from .sicd_v1_2_1 import RowColType as Type21RowColType +from .sicd_v1_2_1 import RowColvertexType as Type21RowColvertexType +from .sicd_v1_2_1 import SCPCOAType as Type21SCPCOAType +from .sicd_v1_2_1 import SideOfTrack as Type21SideOfTrack +from .sicd_v1_2_1 import STBeamComp as Type21STBeamComp +from .sicd_v1_2_1 import TimelineType as Type21TimelineType +from .sicd_v1_2_1 import WFParametersRcvDemodType as Type21WFParametersRcvDemodType +from .sicd_v1_2_1 import XYZPolyAttributeType as Type21XYZPolyAttributeType +from .sicd_v1_2_1 import XYZPolyType as Type21XYZPolyType +from .sicd_v1_2_1 import XYZType as Type21XYZType +from .sicd_v1_3_0 import SICD as Type30SICD +from .sicd_v1_3_0 import AntennaType as Type30AntennaType +from .sicd_v1_3_0 import AntParamType as Type30AntParamType +from .sicd_v1_3_0 import ArrayDouble as Type30ArrayDouble +from .sicd_v1_3_0 import AzAutofocus as Type30AzAutofocus +from .sicd_v1_3_0 import CollectionInfoType as Type30CollectionInfoType +from .sicd_v1_3_0 import CollectType as Type30CollectType +from .sicd_v1_3_0 import ComplexType as Type30ComplexType +from .sicd_v1_3_0 import CornerStringType as Type30CornerStringType +from .sicd_v1_3_0 import DirParamType as Type30DirParamType +from .sicd_v1_3_0 import DirParamTypeSgn as Type30DirParamTypeSgn +from .sicd_v1_3_0 import EarthModel as Type30EarthModel +from .sicd_v1_3_0 import ErrorDecorrFuncType as Type30ErrorDecorrFuncType +from .sicd_v1_3_0 import ErrorStatisticsType as Type30ErrorStatisticsType +from .sicd_v1_3_0 import GainPhasePolyType as Type30GainPhasePolyType +from .sicd_v1_3_0 import GeoDataType as Type30GeoDataType +from .sicd_v1_3_0 import GeoInfoType as Type30GeoInfoType +from .sicd_v1_3_0 import GridType as Type30GridType +from .sicd_v1_3_0 import ImageBeamComp as Type30ImageBeamComp +from .sicd_v1_3_0 import ImageCreationType as Type30ImageCreationType +from .sicd_v1_3_0 import ImageDataType as Type30ImageDataType +from .sicd_v1_3_0 import ImageFormAlgo as Type30ImageFormAlgo +from .sicd_v1_3_0 import ImageFormationType as Type30ImageFormationType +from .sicd_v1_3_0 import ImageGridType as Type30ImageGridType +from .sicd_v1_3_0 import ImagePixelType as Type30ImagePixelType +from .sicd_v1_3_0 import ImagePlane as Type30ImagePlane +from .sicd_v1_3_0 import LatLonCornerStringType as Type30LatLonCornerStringType +from .sicd_v1_3_0 import LatLonCornerType as Type30LatLonCornerType +from .sicd_v1_3_0 import LatLonHAECornerRestrictType as Type30LatLonHAECornerRestrictType +from .sicd_v1_3_0 import LatLonHAECornerStringType as Type30LatLonHAECornerStringType +from .sicd_v1_3_0 import LatLonHAERestrictionType as Type30LatLonHAERestrictionType +from .sicd_v1_3_0 import LatLonHAEType as Type30LatLonHAEType +from .sicd_v1_3_0 import LatLonRestrictionType as Type30LatLonRestrictionType +from .sicd_v1_3_0 import LatLonType as Type30LatLonType +from .sicd_v1_3_0 import LineType as Type30LineType +from .sicd_v1_3_0 import MatchInfoType as Type30MatchInfoType +from .sicd_v1_3_0 import NoiseLevelType as Type30NoiseLevelType +from .sicd_v1_3_0 import OrientationType as Type30OrientationType +from .sicd_v1_3_0 import ParameterType as Type30ParameterType +from .sicd_v1_3_0 import PFAType as Type30PFAType +from .sicd_v1_3_0 import Poly1DType as Type30Poly1DType +from .sicd_v1_3_0 import Poly2DType as Type30Poly2DType +from .sicd_v1_3_0 import PolyCoef1DType as Type30PolyCoef1DType +from .sicd_v1_3_0 import PolyCoef2DType as Type30PolyCoef2DType +from .sicd_v1_3_0 import PolygonType as Type30PolygonType +from .sicd_v1_3_0 import PositionType as Type30PositionType +from .sicd_v1_3_0 import PosVelErrFrame as Type30PosVelErrFrame +from .sicd_v1_3_0 import RadarCollectionType as Type30RadarCollectionType +from .sicd_v1_3_0 import RadarModeType as Type30RadarModeType +from .sicd_v1_3_0 import RadiometricType as Type30RadiometricType +from .sicd_v1_3_0 import RgAutofocus as Type30RgAutofocus +from .sicd_v1_3_0 import RgAzCompType as Type30RgAzCompType +from .sicd_v1_3_0 import RMAImageType as Type30RMAImageType +from .sicd_v1_3_0 import RMAlgorithm as Type30RMAlgorithm +from .sicd_v1_3_0 import RMAType as Type30RMAType +from .sicd_v1_3_0 import RowColType as Type30RowColType +from .sicd_v1_3_0 import RowColvertexType as Type30RowColvertexType +from .sicd_v1_3_0 import SCPCOAType as Type30SCPCOAType +from .sicd_v1_3_0 import SideOfTrack as Type30SideOfTrack +from .sicd_v1_3_0 import STBeamComp as Type30STBeamComp +from .sicd_v1_3_0 import TimelineType as Type30TimelineType +from .sicd_v1_3_0 import WFParametersRcvDemodType as Type30WFParametersRcvDemodType +from .sicd_v1_3_0 import XYZPolyAttributeType as Type30XYZPolyAttributeType +from .sicd_v1_3_0 import XYZPolyType as Type30XYZPolyType +from .sicd_v1_3_0 import XYZType as Type30XYZType + +__all__ = [ + "Type21AntParamType", + "Type21AntennaType", + "Type21ArrayDouble", + "Type21CollectionInfoType", + "Type21CollectType", + "Type21ComplexType", + "Type21CornerStringType", + "Type21DirParamType", + "Type21DirParamTypeSgn", + "DualPolarizationType", + "Type21ErrorDecorrFuncType", + "Type21ErrorStatisticsType", + "Type21GainPhasePolyType", + "Type21GeoDataType", + "Type21EarthModel", + "Type21GeoInfoType", + "Type21GridType", + "Type21ImagePlane", + "Type21ImageGridType", + "Type21ImageCreationType", + "Type21ImageDataType", + "Type21ImagePixelType", + "Type21ImageFormationType", + "Type21AzAutofocus", + "Type21ImageBeamComp", + "Type21ImageFormAlgo", + "Type21RgAutofocus", + "Type21STBeamComp", + "Type21LatLonCornerStringType", + "Type21LatLonCornerType", + "Type21LatLonHAECornerRestrictType", + "Type21LatLonHAECornerStringType", + "Type21LatLonHAERestrictionType", + "Type21LatLonHAEType", + "Type21LatLonRestrictionType", + "Type21LatLonType", + "Type21LineType", + "Type21MatchInfoType", + "Type21NoiseLevelType", + "Type21OrientationType", + "Type21PFAType", + "Type21ParameterType", + "Polarization1Type", + "Polarization2Type", + "Type21Poly1DType", + "Type21Poly2DType", + "Type21PolyCoef1DType", + "Type21PolyCoef2DType", + "Type21PolygonType", + "Type21PosVelErrFrame", + "Type21PositionType", + "Type21RMAType", + "Type21RMAImageType", + "Type21RMAlgorithm", + "Type21RadarCollectionType", + "Type21RadarModeType", + "Type21RadiometricType", + "Type21RgAzCompType", + "Type21RowColType", + "Type21RowColvertexType", + "Type21SCPCOAType", + "Type21SideOfTrack", + "Type21SICD", + "Type21TimelineType", + "Type21WFParametersRcvDemodType", + "Type21XYZPolyAttributeType", + "Type21XYZPolyType", + "Type21XYZType", + "Type30AntParamType", + "Type30AntennaType", + "Type30ArrayDouble", + "Type30CollectionInfoType", + "Type30CollectType", + "Type30ComplexType", + "Type30CornerStringType", + "Type30DirParamType", + "Type30DirParamTypeSgn", + "Type30ErrorDecorrFuncType", + "Type30ErrorStatisticsType", + "Type30GainPhasePolyType", + "Type30GeoDataType", + "Type30EarthModel", + "Type30GeoInfoType", + "Type30GridType", + "Type30ImagePlane", + "Type30ImageGridType", + "Type30ImageCreationType", + "Type30ImageDataType", + "Type30ImagePixelType", + "Type30ImageFormationType", + "Type30AzAutofocus", + "Type30ImageBeamComp", + "Type30ImageFormAlgo", + "Type30RgAutofocus", + "Type30STBeamComp", + "Type30LatLonCornerStringType", + "Type30LatLonCornerType", + "Type30LatLonHAECornerRestrictType", + "Type30LatLonHAECornerStringType", + "Type30LatLonHAERestrictionType", + "Type30LatLonHAEType", + "Type30LatLonRestrictionType", + "Type30LatLonType", + "Type30LineType", + "Type30MatchInfoType", + "Type30NoiseLevelType", + "Type30OrientationType", + "Type30PFAType", + "Type30ParameterType", + "Type30Poly1DType", + "Type30Poly2DType", + "Type30PolyCoef1DType", + "Type30PolyCoef2DType", + "Type30PolygonType", + "Type30PosVelErrFrame", + "Type30PositionType", + "Type30RMAType", + "Type30RMAImageType", + "Type30RMAlgorithm", + "Type30RadarCollectionType", + "Type30RadarModeType", + "Type30RadiometricType", + "Type30RgAzCompType", + "Type30RowColType", + "Type30RowColvertexType", + "Type30SCPCOAType", + "Type30SideOfTrack", + "Type30SICD", + "Type30TimelineType", + "Type30WFParametersRcvDemodType", + "Type30XYZPolyAttributeType", + "Type30XYZPolyType", + "Type30XYZType", +] diff --git a/src/aws/osml/formats/sicd/models/sicd_v1_2_1.py b/src/aws/osml/formats/sicd/models/sicd_v1_2_1.py new file mode 100644 index 0000000..a458140 --- /dev/null +++ b/src/aws/osml/formats/sicd/models/sicd_v1_2_1.py @@ -0,0 +1,3622 @@ +"""This file was generated by xsdata, v23.8, on 2023-08-30 17:21:13 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional + +from xsdata.models.datatype import XmlDateTime + +__NAMESPACE__ = "urn:SICD:1.2.1" + + +@dataclass +class ArrayDouble: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class CollectType(Enum): + MONOSTATIC = "MONOSTATIC" + BISTATIC = "BISTATIC" + + +@dataclass +class ComplexType: + real: Optional[float] = field( + default=None, + metadata={ + "name": "Real", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + imag: Optional[float] = field( + default=None, + metadata={ + "name": "Imag", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +class CornerStringType(Enum): + FRFC_1 = "1:FRFC" + FRLC_2 = "2:FRLC" + LRLC_3 = "3:LRLC" + LRFC_4 = "4:LRFC" + + +class DirParamTypeSgn(Enum): + PLUS_ONE = 1 + MINUS_ONE = -1 + + +class DualPolarizationType(Enum): + V_V = "V:V" + V_H = "V:H" + V_RHC = "V:RHC" + V_LHC = "V:LHC" + H_V = "H:V" + H_H = "H:H" + H_RHC = "H:RHC" + H_LHC = "H:LHC" + RHC_RHC = "RHC:RHC" + RHC_LHC = "RHC:LHC" + RHC_V = "RHC:V" + RHC_H = "RHC:H" + LHC_RHC = "LHC:RHC" + LHC_LHC = "LHC:LHC" + LHC_V = "LHC:V" + LHC_H = "LHC:H" + OTHER = "OTHER" + UNKNOWN = "UNKNOWN" + + +@dataclass +class ErrorDecorrFuncType: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +class EarthModel(Enum): + WGS_84 = "WGS_84" + + +class ImagePlane(Enum): + SLANT = "SLANT" + GROUND = "GROUND" + OTHER = "OTHER" + + +class ImageGridType(Enum): + RGAZIM = "RGAZIM" + RGZERO = "RGZERO" + XRGYCR = "XRGYCR" + XCTYAT = "XCTYAT" + PLANE = "PLANE" + + +@dataclass +class ImageCreationType: + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "DateTime", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + +class ImagePixelType(Enum): + RE32F_IM32F = "RE32F_IM32F" + RE16I_IM16I = "RE16I_IM16I" + AMP8I_PHS8I = "AMP8I_PHS8I" + + +class AzAutofocus(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +class ImageBeamComp(Enum): + NO = "NO" + SV = "SV" + + +class ImageFormAlgo(Enum): + PFA = "PFA" + RMA = "RMA" + RGAZCOMP = "RGAZCOMP" + OTHER = "OTHER" + + +class RgAutofocus(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +class STBeamComp(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +@dataclass +class LatLonHAERestrictionType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class LatLonHAEType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class LatLonRestrictionType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + + +@dataclass +class LatLonType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +class NoiseLevelType(Enum): + ABSOLUTE = "ABSOLUTE" + RELATIVE = "RELATIVE" + + +class OrientationType(Enum): + UP = "UP" + DOWN = "DOWN" + LEFT = "LEFT" + RIGHT = "RIGHT" + ARBITRARY = "ARBITRARY" + + +@dataclass +class ParameterType: + value: str = field( + default="", + metadata={ + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class Polarization1Type(Enum): + V = "V" + H = "H" + RHC = "RHC" + LHC = "LHC" + OTHER = "OTHER" + UNKNOWN = "UNKNOWN" + SEQUENCE = "SEQUENCE" + + +class Polarization2Type(Enum): + V = "V" + H = "H" + RHC = "RHC" + LHC = "LHC" + OTHER = "OTHER" + + +@dataclass +class PolyCoef1DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolyCoef2DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + exponent2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class PosVelErrFrame(Enum): + ECF = "ECF" + RIC_ECF = "RIC_ECF" + RIC_ECI = "RIC_ECI" + + +class RMAImageType(Enum): + RMAT = "RMAT" + RMCR = "RMCR" + INCA = "INCA" + + +class RMAlgorithm(Enum): + OMEGA_K = "OMEGA_K" + CSA = "CSA" + RG_DOP = "RG_DOP" + + +class RadarModeType(Enum): + SPOTLIGHT = "SPOTLIGHT" + STRIPMAP = "STRIPMAP" + DYNAMIC_STRIPMAP = "DYNAMIC STRIPMAP" + + +@dataclass +class RowColType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +class SideOfTrack(Enum): + L = "L" + R = "R" + + +class WFParametersRcvDemodType(Enum): + STRETCH = "STRETCH" + CHIRP = "CHIRP" + + +@dataclass +class XYZType: + x: Optional[float] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + y: Optional[float] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + z: Optional[float] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class CollectionInfoType: + collector_name: Optional[str] = field( + default=None, + metadata={ + "name": "CollectorName", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + illuminator_name: Optional[str] = field( + default=None, + metadata={ + "name": "IlluminatorName", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + collect_type: Optional[CollectType] = field( + default=None, + metadata={ + "name": "CollectType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + radar_mode: Optional["CollectionInfoType.RadarMode"] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + classification: Optional[str] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + country_code: List[str] = field( + default_factory=list, + metadata={ + "name": "CountryCode", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class RadarMode: + mode_type: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "ModeType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + mode_id: Optional[str] = field( + default=None, + metadata={ + "name": "ModeID", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + +@dataclass +class ErrorStatisticsType: + composite_scp: Optional["ErrorStatisticsType.CompositeSCP"] = field( + default=None, + metadata={ + "name": "CompositeSCP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + components: Optional["ErrorStatisticsType.Components"] = field( + default=None, + metadata={ + "name": "Components", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + additional_parms: Optional["ErrorStatisticsType.AdditionalParms"] = field( + default=None, + metadata={ + "name": "AdditionalParms", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class CompositeSCP: + rg: Optional[float] = field( + default=None, + metadata={ + "name": "Rg", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + az: Optional[float] = field( + default=None, + metadata={ + "name": "Az", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + rg_az: Optional[float] = field( + default=None, + metadata={ + "name": "RgAz", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class Components: + pos_vel_err: Optional["ErrorStatisticsType.Components.PosVelErr"] = field( + default=None, + metadata={ + "name": "PosVelErr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + radar_sensor: Optional["ErrorStatisticsType.Components.RadarSensor"] = field( + default=None, + metadata={ + "name": "RadarSensor", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tropo_error: Optional["ErrorStatisticsType.Components.TropoError"] = field( + default=None, + metadata={ + "name": "TropoError", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + iono_error: Optional["ErrorStatisticsType.Components.IonoError"] = field( + default=None, + metadata={ + "name": "IonoError", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class PosVelErr: + frame: Optional[PosVelErrFrame] = field( + default=None, + metadata={ + "name": "Frame", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p1: Optional[float] = field( + default=None, + metadata={ + "name": "P1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p2: Optional[float] = field( + default=None, + metadata={ + "name": "P2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p3: Optional[float] = field( + default=None, + metadata={ + "name": "P3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v1: Optional[float] = field( + default=None, + metadata={ + "name": "V1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v2: Optional[float] = field( + default=None, + metadata={ + "name": "V2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v3: Optional[float] = field( + default=None, + metadata={ + "name": "V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + corr_coefs: Optional["ErrorStatisticsType.Components.PosVelErr.CorrCoefs"] = field( + default=None, + metadata={ + "name": "CorrCoefs", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + position_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "PositionDecorr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class CorrCoefs: + p1_p2: Optional[float] = field( + default=None, + metadata={ + "name": "P1P2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p1_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P1P3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p1_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P1V1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P1V2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P1V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p2_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P2P3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p2_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P2V1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p2_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P2V2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P2V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p3_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P3V1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p3_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P3V2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + p3_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P3V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "V1V2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V1V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + v2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V2V3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class RadarSensor: + range_bias: Optional[float] = field( + default=None, + metadata={ + "name": "RangeBias", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + clock_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "ClockFreqSF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + transmit_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "TransmitFreqSF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + range_bias_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "RangeBiasDecorr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class TropoError: + tropo_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeVertical", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tropo_range_slant: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeSlant", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tropo_range_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "TropoRangeDecorr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class IonoError: + iono_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeVertical", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + iono_range_rate_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeRateVertical", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + iono_rg_rg_rate_cc: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRgRgRateCC", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + iono_range_vert_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "IonoRangeVertDecorr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class AdditionalParms: + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + + +@dataclass +class ImageFormationType: + rcv_chan_proc: Optional["ImageFormationType.RcvChanProc"] = field( + default=None, + metadata={ + "name": "RcvChanProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tx_rcv_polarization_proc: Optional[DualPolarizationType] = field( + default=None, + metadata={ + "name": "TxRcvPolarizationProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tstart_proc: Optional[float] = field( + default=None, + metadata={ + "name": "TStartProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tend_proc: Optional[float] = field( + default=None, + metadata={ + "name": "TEndProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tx_frequency_proc: Optional["ImageFormationType.TxFrequencyProc"] = field( + default=None, + metadata={ + "name": "TxFrequencyProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + segment_identifier: Optional[str] = field( + default=None, + metadata={ + "name": "SegmentIdentifier", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + image_form_algo: Optional[ImageFormAlgo] = field( + default=None, + metadata={ + "name": "ImageFormAlgo", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + stbeam_comp: Optional[STBeamComp] = field( + default=None, + metadata={ + "name": "STBeamComp", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + image_beam_comp: Optional[ImageBeamComp] = field( + default=None, + metadata={ + "name": "ImageBeamComp", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + az_autofocus: Optional[AzAutofocus] = field( + default=None, + metadata={ + "name": "AzAutofocus", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + rg_autofocus: Optional[RgAutofocus] = field( + default=None, + metadata={ + "name": "RgAutofocus", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + processing: List["ImageFormationType.Processing"] = field( + default_factory=list, + metadata={ + "name": "Processing", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + polarization_calibration: Optional["ImageFormationType.PolarizationCalibration"] = field( + default=None, + metadata={ + "name": "PolarizationCalibration", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class RcvChanProc: + num_chan_proc: Optional[int] = field( + default=None, + metadata={ + "name": "NumChanProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + prfscale_factor: Optional[float] = field( + default=None, + metadata={ + "name": "PRFScaleFactor", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + chan_index: List[int] = field( + default_factory=list, + metadata={ + "name": "ChanIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + + @dataclass + class TxFrequencyProc: + min_proc: Optional[float] = field( + default=None, + metadata={ + "name": "MinProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + max_proc: Optional[float] = field( + default=None, + metadata={ + "name": "MaxProc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class Processing: + type_value: Optional[str] = field( + default=None, + metadata={ + "name": "Type", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + applied: Optional[bool] = field( + default=None, + metadata={ + "name": "Applied", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class PolarizationCalibration: + distort_correction_applied: Optional[bool] = field( + default=None, + metadata={ + "name": "DistortCorrectionApplied", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + distortion: Optional["ImageFormationType.PolarizationCalibration.Distortion"] = field( + default=None, + metadata={ + "name": "Distortion", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class Distortion: + calibration_date: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CalibrationDate", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + a: Optional[float] = field( + default=None, + metadata={ + "name": "A", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + f1: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "F1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + q1: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + q2: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + f2: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "F2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + q3: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q3", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + q4: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q4", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + gain_error_a: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorA", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + gain_error_f1: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorF1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + gain_error_f2: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorF2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + phase_error_f1: Optional[float] = field( + default=None, + metadata={ + "name": "PhaseErrorF1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + phase_error_f2: Optional[float] = field( + default=None, + metadata={ + "name": "PhaseErrorF2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + +@dataclass +class LatLonCornerStringType(LatLonType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerType(LatLonType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerRestrictType(LatLonHAERestrictionType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerStringType(LatLonHAEType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LineType: + endpoint: List["LineType.Endpoint"] = field( + default_factory=list, + metadata={ + "name": "Endpoint", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Endpoint(LatLonType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MatchInfoType: + num_match_types: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchTypes", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + match_type: List["MatchInfoType.MatchType"] = field( + default_factory=list, + metadata={ + "name": "MatchType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + + @dataclass + class MatchType: + type_id: Optional[str] = field( + default=None, + metadata={ + "name": "TypeID", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + current_index: Optional[int] = field( + default=None, + metadata={ + "name": "CurrentIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + num_match_collections: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchCollections", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + match_collection: List["MatchInfoType.MatchType.MatchCollection"] = field( + default_factory=list, + metadata={ + "name": "MatchCollection", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class MatchCollection: + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + match_index: Optional[int] = field( + default=None, + metadata={ + "name": "MatchIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly1DType: + coef: List[PolyCoef1DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly2DType: + coef: List[PolyCoef2DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + order2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + vertex: List["PolygonType.Vertex"] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Vertex(LatLonRestrictionType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RowColvertexType(RowColType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class SCPCOAType: + scptime: Optional[float] = field( + default=None, + metadata={ + "name": "SCPTime", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + arppos: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPPos", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + arpvel: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPVel", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + arpacc: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPAcc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + side_of_track: Optional[SideOfTrack] = field( + default=None, + metadata={ + "name": "SideOfTrack", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + slant_range: Optional[float] = field( + default=None, + metadata={ + "name": "SlantRange", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ground_range: Optional[float] = field( + default=None, + metadata={ + "name": "GroundRange", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + doppler_cone_ang: Optional[float] = field( + default=None, + metadata={ + "name": "DopplerConeAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + graze_ang: Optional[float] = field( + default=None, + metadata={ + "name": "GrazeAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + incidence_ang: Optional[float] = field( + default=None, + metadata={ + "name": "IncidenceAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + twist_ang: Optional[float] = field( + default=None, + metadata={ + "name": "TwistAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + slope_ang: Optional[float] = field( + default=None, + metadata={ + "name": "SlopeAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + azim_ang: Optional[float] = field( + default=None, + metadata={ + "name": "AzimAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 360.0, + }, + ) + layover_ang: Optional[float] = field( + default=None, + metadata={ + "name": "LayoverAng", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 360.0, + }, + ) + + +@dataclass +class DirParamType: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ss: Optional[float] = field( + default=None, + metadata={ + "name": "SS", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + imp_resp_wid: Optional[float] = field( + default=None, + metadata={ + "name": "ImpRespWid", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + sgn: Optional[DirParamTypeSgn] = field( + default=None, + metadata={ + "name": "Sgn", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + imp_resp_bw: Optional[float] = field( + default=None, + metadata={ + "name": "ImpRespBW", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + kctr: Optional[float] = field( + default=None, + metadata={ + "name": "KCtr", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + delta_k1: Optional[float] = field( + default=None, + metadata={ + "name": "DeltaK1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + delta_k2: Optional[float] = field( + default=None, + metadata={ + "name": "DeltaK2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + delta_kcoapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DeltaKCOAPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + wgt_type: Optional["DirParamType.WgtType"] = field( + default=None, + metadata={ + "name": "WgtType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + wgt_funct: Optional["DirParamType.WgtFunct"] = field( + default=None, + metadata={ + "name": "WgtFunct", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class WgtType: + window_name: Optional[str] = field( + default=None, + metadata={ + "name": "WindowName", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class WgtFunct: + wgt: List[ArrayDouble] = field( + default_factory=list, + metadata={ + "name": "Wgt", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class GainPhasePolyType: + gain_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GainPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + phase_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "PhasePoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class GeoInfoType: + desc: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Desc", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + point: Optional[LatLonRestrictionType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + polygon: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + geo_info: List["GeoInfoType"] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class ImageDataType: + pixel_type: Optional[ImagePixelType] = field( + default=None, + metadata={ + "name": "PixelType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + amp_table: Optional["ImageDataType.AmpTable"] = field( + default=None, + metadata={ + "name": "AmpTable", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "NumRows", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "NumCols", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + first_row: Optional[int] = field( + default=None, + metadata={ + "name": "FirstRow", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + first_col: Optional[int] = field( + default=None, + metadata={ + "name": "FirstCol", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + full_image: Optional["ImageDataType.FullImage"] = field( + default=None, + metadata={ + "name": "FullImage", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + scppixel: Optional[RowColType] = field( + default=None, + metadata={ + "name": "SCPPixel", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + valid_data: Optional["ImageDataType.ValidData"] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class AmpTable: + amplitude: List[ArrayDouble] = field( + default_factory=list, + metadata={ + "name": "Amplitude", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 256, + "max_occurs": 256, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class FullImage: + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "NumRows", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "NumCols", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class ValidData: + vertex: List[RowColvertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PFAType: + fpn: Optional[XYZType] = field( + default=None, + metadata={ + "name": "FPN", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ipn: Optional[XYZType] = field( + default=None, + metadata={ + "name": "IPN", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + polar_ang_ref_time: Optional[float] = field( + default=None, + metadata={ + "name": "PolarAngRefTime", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + polar_ang_poly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "PolarAngPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + spatial_freq_sfpoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "SpatialFreqSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + krg1: Optional[float] = field( + default=None, + metadata={ + "name": "Krg1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + krg2: Optional[float] = field( + default=None, + metadata={ + "name": "Krg2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + kaz1: Optional[float] = field( + default=None, + metadata={ + "name": "Kaz1", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + kaz2: Optional[float] = field( + default=None, + metadata={ + "name": "Kaz2", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + stdeskew: Optional["PFAType.STDeskew"] = field( + default=None, + metadata={ + "name": "STDeskew", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class STDeskew: + applied: Optional[bool] = field( + default=None, + metadata={ + "name": "Applied", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + stdsphase_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "STDSPhasePoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class RMAType: + rmalgo_type: Optional[RMAlgorithm] = field( + default=None, + metadata={ + "name": "RMAlgoType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + image_type: Optional[RMAImageType] = field( + default=None, + metadata={ + "name": "ImageType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + rmat: Optional["RMAType.RMAT"] = field( + default=None, + metadata={ + "name": "RMAT", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rmcr: Optional["RMAType.RMCR"] = field( + default=None, + metadata={ + "name": "RMCR", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + inca: Optional["RMAType.INCA"] = field( + default=None, + metadata={ + "name": "INCA", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class RMAT: + pos_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "PosRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + vel_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "VelRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + dop_cone_ang_ref: Optional[float] = field( + default=None, + metadata={ + "name": "DopConeAngRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class RMCR: + pos_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "PosRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + vel_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "VelRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + dop_cone_ang_ref: Optional[float] = field( + default=None, + metadata={ + "name": "DopConeAngRef", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class INCA: + time_capoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "TimeCAPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + r_ca_scp: Optional[float] = field( + default=None, + metadata={ + "name": "R_CA_SCP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + freq_zero: Optional[float] = field( + default=None, + metadata={ + "name": "FreqZero", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + drate_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DRateSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + dop_centroid_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DopCentroidPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + dop_centroid_coa: Optional[bool] = field( + default=None, + metadata={ + "name": "DopCentroidCOA", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + +@dataclass +class RadarCollectionType: + tx_frequency: Optional["RadarCollectionType.TxFrequency"] = field( + default=None, + metadata={ + "name": "TxFrequency", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ref_freq_index: Optional[int] = field( + default=None, + metadata={ + "name": "RefFreqIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + waveform: Optional["RadarCollectionType.Waveform"] = field( + default=None, + metadata={ + "name": "Waveform", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_polarization: Optional[Polarization1Type] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tx_sequence: Optional["RadarCollectionType.TxSequence"] = field( + default=None, + metadata={ + "name": "TxSequence", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_channels: Optional["RadarCollectionType.RcvChannels"] = field( + default=None, + metadata={ + "name": "RcvChannels", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + area: Optional["RadarCollectionType.Area"] = field( + default=None, + metadata={ + "name": "Area", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class TxFrequency: + min: Optional[float] = field( + default=None, + metadata={ + "name": "Min", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + max: Optional[float] = field( + default=None, + metadata={ + "name": "Max", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class Waveform: + wfparameters: List["RadarCollectionType.Waveform.WFParameters"] = field( + default_factory=list, + metadata={ + "name": "WFParameters", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class WFParameters: + tx_pulse_length: Optional[float] = field( + default=None, + metadata={ + "name": "TxPulseLength", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_rfbandwidth: Optional[float] = field( + default=None, + metadata={ + "name": "TxRFBandwidth", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_freq_start: Optional[float] = field( + default=None, + metadata={ + "name": "TxFreqStart", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_fmrate: Optional[float] = field( + default=None, + metadata={ + "name": "TxFMRate", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_demod_type: Optional[WFParametersRcvDemodType] = field( + default=None, + metadata={ + "name": "RcvDemodType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_window_length: Optional[float] = field( + default=None, + metadata={ + "name": "RcvWindowLength", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + adcsample_rate: Optional[float] = field( + default=None, + metadata={ + "name": "ADCSampleRate", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_ifbandwidth: Optional[float] = field( + default=None, + metadata={ + "name": "RcvIFBandwidth", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_freq_start: Optional[float] = field( + default=None, + metadata={ + "name": "RcvFreqStart", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_fmrate: Optional[float] = field( + default=None, + metadata={ + "name": "RcvFMRate", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class TxSequence: + tx_step: List["RadarCollectionType.TxSequence.TxStep"] = field( + default_factory=list, + metadata={ + "name": "TxStep", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class TxStep: + wfindex: Optional[int] = field( + default=None, + metadata={ + "name": "WFIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_polarization: Optional[Polarization2Type] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class RcvChannels: + chan_parameters: List["RadarCollectionType.RcvChannels.ChanParameters"] = field( + default_factory=list, + metadata={ + "name": "ChanParameters", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class ChanParameters: + tx_rcv_polarization: Optional[DualPolarizationType] = field( + default=None, + metadata={ + "name": "TxRcvPolarization", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + rcv_apcindex: Optional[int] = field( + default=None, + metadata={ + "name": "RcvAPCIndex", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Area: + corner: Optional["RadarCollectionType.Area.Corner"] = field( + default=None, + metadata={ + "name": "Corner", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + plane: Optional["RadarCollectionType.Area.Plane"] = field( + default=None, + metadata={ + "name": "Plane", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class Corner: + acp: List[LatLonHAECornerRestrictType] = field( + default_factory=list, + metadata={ + "name": "ACP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + @dataclass + class Plane: + ref_pt: Optional["RadarCollectionType.Area.Plane.RefPt"] = field( + default=None, + metadata={ + "name": "RefPt", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + xdir: Optional["RadarCollectionType.Area.Plane.XDir"] = field( + default=None, + metadata={ + "name": "XDir", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ydir: Optional["RadarCollectionType.Area.Plane.YDir"] = field( + default=None, + metadata={ + "name": "YDir", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + segment_list: Optional["RadarCollectionType.Area.Plane.SegmentList"] = field( + default=None, + metadata={ + "name": "SegmentList", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + orientation: Optional[OrientationType] = field( + default=None, + metadata={ + "name": "Orientation", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class RefPt: + ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + line: Optional[float] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + sample: Optional[float] = field( + default=None, + metadata={ + "name": "Sample", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + @dataclass + class XDir: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + line_spacing: Optional[float] = field( + default=None, + metadata={ + "name": "LineSpacing", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + num_lines: Optional[int] = field( + default=None, + metadata={ + "name": "NumLines", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + first_line: Optional[int] = field( + default=None, + metadata={ + "name": "FirstLine", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class YDir: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + sample_spacing: Optional[float] = field( + default=None, + metadata={ + "name": "SampleSpacing", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + num_samples: Optional[int] = field( + default=None, + metadata={ + "name": "NumSamples", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + first_sample: Optional[int] = field( + default=None, + metadata={ + "name": "FirstSample", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class SegmentList: + segment: List["RadarCollectionType.Area.Plane.SegmentList.Segment"] = field( + default_factory=list, + metadata={ + "name": "Segment", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Segment: + start_line: Optional[int] = field( + default=None, + metadata={ + "name": "StartLine", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + start_sample: Optional[int] = field( + default=None, + metadata={ + "name": "StartSample", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + end_line: Optional[int] = field( + default=None, + metadata={ + "name": "EndLine", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + end_sample: Optional[int] = field( + default=None, + metadata={ + "name": "EndSample", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + identifier: Optional[str] = field( + default=None, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RadiometricType: + noise_level: Optional["RadiometricType.NoiseLevel"] = field( + default=None, + metadata={ + "name": "NoiseLevel", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcssfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RCSSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + sigma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "SigmaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + beta_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "BetaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + gamma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GammaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class NoiseLevel: + noise_level_type: Optional[NoiseLevelType] = field( + default=None, + metadata={ + "name": "NoiseLevelType", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + noise_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "NoisePoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class RgAzCompType: + az_sf: Optional[float] = field( + default=None, + metadata={ + "name": "AzSF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + kaz_poly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "KazPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class TimelineType: + collect_start: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CollectStart", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + collect_duration: Optional[float] = field( + default=None, + metadata={ + "name": "CollectDuration", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ipp: Optional["TimelineType.IPP"] = field( + default=None, + metadata={ + "name": "IPP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class IPP: + set: List["TimelineType.IPP.Set"] = field( + default_factory=list, + metadata={ + "name": "Set", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Set: + tstart: Optional[float] = field( + default=None, + metadata={ + "name": "TStart", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + tend: Optional[float] = field( + default=None, + metadata={ + "name": "TEnd", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ippstart: Optional[int] = field( + default=None, + metadata={ + "name": "IPPStart", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ippend: Optional[int] = field( + default=None, + metadata={ + "name": "IPPEnd", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + ipppoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "IPPPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class XYZPolyType: + x: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + y: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + z: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class AntParamType: + xaxis_poly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "XAxisPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + yaxis_poly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "YAxisPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + freq_zero: Optional[float] = field( + default=None, + metadata={ + "name": "FreqZero", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + eb: Optional["AntParamType.EB"] = field( + default=None, + metadata={ + "name": "EB", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + array: Optional[GainPhasePolyType] = field( + default=None, + metadata={ + "name": "Array", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + elem: Optional[GainPhasePolyType] = field( + default=None, + metadata={ + "name": "Elem", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + gain_bspoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "GainBSPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + ebfreq_shift: Optional[bool] = field( + default=None, + metadata={ + "name": "EBFreqShift", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + mlfreq_dilation: Optional[bool] = field( + default=None, + metadata={ + "name": "MLFreqDilation", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class EB: + dcxpoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "DCXPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + dcypoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "DCYPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class GeoDataType: + earth_model: Optional[EarthModel] = field( + default=None, + metadata={ + "name": "EarthModel", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + scp: Optional["GeoDataType.SCP"] = field( + default=None, + metadata={ + "name": "SCP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + image_corners: Optional["GeoDataType.ImageCorners"] = field( + default=None, + metadata={ + "name": "ImageCorners", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + valid_data: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + geo_info: List[GeoInfoType] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class SCP: + ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECF", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + llh: Optional[LatLonHAERestrictionType] = field( + default=None, + metadata={ + "name": "LLH", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + @dataclass + class ImageCorners: + icp: List[LatLonCornerStringType] = field( + default_factory=list, + metadata={ + "name": "ICP", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + +@dataclass +class GridType: + image_plane: Optional[ImagePlane] = field( + default=None, + metadata={ + "name": "ImagePlane", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + type_value: Optional[ImageGridType] = field( + default=None, + metadata={ + "name": "Type", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + time_coapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "TimeCOAPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + row: Optional[DirParamType] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + col: Optional[DirParamType] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + + +@dataclass +class XYZPolyAttributeType(XYZPolyType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class AntennaType: + tx: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "Tx", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "Rcv", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + two_way: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "TwoWay", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + +@dataclass +class PositionType: + arppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "ARPPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "required": True, + }, + ) + grppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "GRPPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + tx_apcpoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "TxAPCPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + rcv_apc: Optional["PositionType.RcvAPC"] = field( + default=None, + metadata={ + "name": "RcvAPC", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + }, + ) + + @dataclass + class RcvAPC: + rcv_apcpoly: List[XYZPolyAttributeType] = field( + default_factory=list, + metadata={ + "name": "RcvAPCPoly", + "type": "Element", + "namespace": "urn:SICD:1.2.1", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class SICD: + class Meta: + namespace = "urn:SICD:1.2.1" + + collection_info: Optional[CollectionInfoType] = field( + default=None, + metadata={ + "name": "CollectionInfo", + "type": "Element", + "required": True, + }, + ) + image_creation: Optional[ImageCreationType] = field( + default=None, + metadata={ + "name": "ImageCreation", + "type": "Element", + }, + ) + image_data: Optional[ImageDataType] = field( + default=None, + metadata={ + "name": "ImageData", + "type": "Element", + "required": True, + }, + ) + geo_data: Optional[GeoDataType] = field( + default=None, + metadata={ + "name": "GeoData", + "type": "Element", + "required": True, + }, + ) + grid: Optional[GridType] = field( + default=None, + metadata={ + "name": "Grid", + "type": "Element", + "required": True, + }, + ) + timeline: Optional[TimelineType] = field( + default=None, + metadata={ + "name": "Timeline", + "type": "Element", + "required": True, + }, + ) + position: Optional[PositionType] = field( + default=None, + metadata={ + "name": "Position", + "type": "Element", + "required": True, + }, + ) + radar_collection: Optional[RadarCollectionType] = field( + default=None, + metadata={ + "name": "RadarCollection", + "type": "Element", + "required": True, + }, + ) + image_formation: Optional[ImageFormationType] = field( + default=None, + metadata={ + "name": "ImageFormation", + "type": "Element", + "required": True, + }, + ) + scpcoa: Optional[SCPCOAType] = field( + default=None, + metadata={ + "name": "SCPCOA", + "type": "Element", + "required": True, + }, + ) + radiometric: Optional[RadiometricType] = field( + default=None, + metadata={ + "name": "Radiometric", + "type": "Element", + }, + ) + antenna: Optional[AntennaType] = field( + default=None, + metadata={ + "name": "Antenna", + "type": "Element", + }, + ) + error_statistics: Optional[ErrorStatisticsType] = field( + default=None, + metadata={ + "name": "ErrorStatistics", + "type": "Element", + }, + ) + match_info: Optional[MatchInfoType] = field( + default=None, + metadata={ + "name": "MatchInfo", + "type": "Element", + }, + ) + rg_az_comp: Optional[RgAzCompType] = field( + default=None, + metadata={ + "name": "RgAzComp", + "type": "Element", + }, + ) + pfa: Optional[PFAType] = field( + default=None, + metadata={ + "name": "PFA", + "type": "Element", + }, + ) + rma: Optional[RMAType] = field( + default=None, + metadata={ + "name": "RMA", + "type": "Element", + }, + ) diff --git a/src/aws/osml/formats/sicd/models/sicd_v1_3_0.py b/src/aws/osml/formats/sicd/models/sicd_v1_3_0.py new file mode 100644 index 0000000..f98f745 --- /dev/null +++ b/src/aws/osml/formats/sicd/models/sicd_v1_3_0.py @@ -0,0 +1,3654 @@ +"""This file was generated by xsdata, v23.8, on 2023-08-30 17:21:13 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional + +from xsdata.models.datatype import XmlDateTime + +__NAMESPACE__ = "urn:SICD:1.3.0" + + +@dataclass +class ArrayDouble: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class CollectType(Enum): + MONOSTATIC = "MONOSTATIC" + BISTATIC = "BISTATIC" + + +@dataclass +class ComplexType: + real: Optional[float] = field( + default=None, + metadata={ + "name": "Real", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + imag: Optional[float] = field( + default=None, + metadata={ + "name": "Imag", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +class CornerStringType(Enum): + FRFC_1 = "1:FRFC" + FRLC_2 = "2:FRLC" + LRLC_3 = "3:LRLC" + LRFC_4 = "4:LRFC" + + +class DirParamTypeSgn(Enum): + PLUS_ONE = 1 + MINUS_ONE = -1 + + +@dataclass +class ErrorDecorrFuncType: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +class EarthModel(Enum): + WGS_84 = "WGS_84" + + +class ImagePlane(Enum): + SLANT = "SLANT" + GROUND = "GROUND" + OTHER = "OTHER" + + +class ImageGridType(Enum): + RGAZIM = "RGAZIM" + RGZERO = "RGZERO" + XRGYCR = "XRGYCR" + XCTYAT = "XCTYAT" + PLANE = "PLANE" + + +@dataclass +class ImageCreationType: + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "DateTime", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + +class ImagePixelType(Enum): + RE32F_IM32F = "RE32F_IM32F" + RE16I_IM16I = "RE16I_IM16I" + AMP8I_PHS8I = "AMP8I_PHS8I" + + +class AzAutofocus(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +class ImageBeamComp(Enum): + NO = "NO" + SV = "SV" + + +class ImageFormAlgo(Enum): + PFA = "PFA" + RMA = "RMA" + RGAZCOMP = "RGAZCOMP" + OTHER = "OTHER" + + +class RgAutofocus(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +class STBeamComp(Enum): + NO = "NO" + GLOBAL = "GLOBAL" + SV = "SV" + + +@dataclass +class LatLonHAERestrictionType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class LatLonHAEType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class LatLonRestrictionType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + + +@dataclass +class LatLonType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +class NoiseLevelType(Enum): + ABSOLUTE = "ABSOLUTE" + RELATIVE = "RELATIVE" + + +class OrientationType(Enum): + UP = "UP" + DOWN = "DOWN" + LEFT = "LEFT" + RIGHT = "RIGHT" + ARBITRARY = "ARBITRARY" + + +@dataclass +class ParameterType: + value: str = field( + default="", + metadata={ + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolyCoef1DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolyCoef2DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + exponent2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class PosVelErrFrame(Enum): + ECF = "ECF" + RIC_ECF = "RIC_ECF" + RIC_ECI = "RIC_ECI" + + +class RMAImageType(Enum): + RMAT = "RMAT" + RMCR = "RMCR" + INCA = "INCA" + + +class RMAlgorithm(Enum): + OMEGA_K = "OMEGA_K" + CSA = "CSA" + RG_DOP = "RG_DOP" + + +class RadarModeType(Enum): + SPOTLIGHT = "SPOTLIGHT" + STRIPMAP = "STRIPMAP" + DYNAMIC_STRIPMAP = "DYNAMIC STRIPMAP" + + +@dataclass +class RowColType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +class SideOfTrack(Enum): + L = "L" + R = "R" + + +class WFParametersRcvDemodType(Enum): + STRETCH = "STRETCH" + CHIRP = "CHIRP" + + +@dataclass +class XYZType: + x: Optional[float] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + y: Optional[float] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + z: Optional[float] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class CollectionInfoType: + collector_name: Optional[str] = field( + default=None, + metadata={ + "name": "CollectorName", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + illuminator_name: Optional[str] = field( + default=None, + metadata={ + "name": "IlluminatorName", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + collect_type: Optional[CollectType] = field( + default=None, + metadata={ + "name": "CollectType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + radar_mode: Optional["CollectionInfoType.RadarMode"] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + classification: Optional[str] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + country_code: List[str] = field( + default_factory=list, + metadata={ + "name": "CountryCode", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class RadarMode: + mode_type: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "ModeType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + mode_id: Optional[str] = field( + default=None, + metadata={ + "name": "ModeID", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + +@dataclass +class ErrorStatisticsType: + composite_scp: Optional["ErrorStatisticsType.CompositeSCP"] = field( + default=None, + metadata={ + "name": "CompositeSCP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + components: Optional["ErrorStatisticsType.Components"] = field( + default=None, + metadata={ + "name": "Components", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + unmodeled: Optional["ErrorStatisticsType.Unmodeled"] = field( + default=None, + metadata={ + "name": "Unmodeled", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + additional_parms: Optional["ErrorStatisticsType.AdditionalParms"] = field( + default=None, + metadata={ + "name": "AdditionalParms", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class CompositeSCP: + rg: Optional[float] = field( + default=None, + metadata={ + "name": "Rg", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + az: Optional[float] = field( + default=None, + metadata={ + "name": "Az", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + rg_az: Optional[float] = field( + default=None, + metadata={ + "name": "RgAz", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class Components: + pos_vel_err: Optional["ErrorStatisticsType.Components.PosVelErr"] = field( + default=None, + metadata={ + "name": "PosVelErr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + radar_sensor: Optional["ErrorStatisticsType.Components.RadarSensor"] = field( + default=None, + metadata={ + "name": "RadarSensor", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + tropo_error: Optional["ErrorStatisticsType.Components.TropoError"] = field( + default=None, + metadata={ + "name": "TropoError", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + iono_error: Optional["ErrorStatisticsType.Components.IonoError"] = field( + default=None, + metadata={ + "name": "IonoError", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class PosVelErr: + frame: Optional[PosVelErrFrame] = field( + default=None, + metadata={ + "name": "Frame", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p1: Optional[float] = field( + default=None, + metadata={ + "name": "P1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p2: Optional[float] = field( + default=None, + metadata={ + "name": "P2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p3: Optional[float] = field( + default=None, + metadata={ + "name": "P3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v1: Optional[float] = field( + default=None, + metadata={ + "name": "V1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v2: Optional[float] = field( + default=None, + metadata={ + "name": "V2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v3: Optional[float] = field( + default=None, + metadata={ + "name": "V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + corr_coefs: Optional["ErrorStatisticsType.Components.PosVelErr.CorrCoefs"] = field( + default=None, + metadata={ + "name": "CorrCoefs", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + position_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "PositionDecorr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class CorrCoefs: + p1_p2: Optional[float] = field( + default=None, + metadata={ + "name": "P1P2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p1_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P1P3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p1_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P1V1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P1V2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P1V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p2_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P2P3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p2_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P2V1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p2_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P2V2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P2V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p3_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P3V1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p3_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P3V2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + p3_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P3V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "V1V2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V1V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + v2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V2V3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class RadarSensor: + range_bias: Optional[float] = field( + default=None, + metadata={ + "name": "RangeBias", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + clock_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "ClockFreqSF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + transmit_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "TransmitFreqSF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + range_bias_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "RangeBiasDecorr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class TropoError: + tropo_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeVertical", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tropo_range_slant: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeSlant", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tropo_range_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "TropoRangeDecorr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class IonoError: + iono_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeVertical", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + iono_range_rate_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeRateVertical", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + iono_rg_rg_rate_cc: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRgRgRateCC", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + iono_range_vert_decorr: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "IonoRangeVertDecorr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class Unmodeled: + xrow: Optional[float] = field( + default=None, + metadata={ + "name": "Xrow", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ycol: Optional[float] = field( + default=None, + metadata={ + "name": "Ycol", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + xrow_ycol: Optional[float] = field( + default=None, + metadata={ + "name": "XrowYcol", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + unmodeled_decorr: Optional["ErrorStatisticsType.Unmodeled.UnmodeledDecorr"] = field( + default=None, + metadata={ + "name": "UnmodeledDecorr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class UnmodeledDecorr: + xrow: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "Xrow", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ycol: Optional[ErrorDecorrFuncType] = field( + default=None, + metadata={ + "name": "Ycol", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class AdditionalParms: + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class ImageFormationType: + rcv_chan_proc: Optional["ImageFormationType.RcvChanProc"] = field( + default=None, + metadata={ + "name": "RcvChanProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + tx_rcv_polarization_proc: Optional[str] = field( + default=None, + metadata={ + "name": "TxRcvPolarizationProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "pattern": r"(([VHXYSE]|RHC|LHC|OTHER[^:]*):([VHXYSE]|RHC|LHC|OTHER[^:]*))|OTHER|UNKNOWN", + }, + ) + tstart_proc: Optional[float] = field( + default=None, + metadata={ + "name": "TStartProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + tend_proc: Optional[float] = field( + default=None, + metadata={ + "name": "TEndProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + tx_frequency_proc: Optional["ImageFormationType.TxFrequencyProc"] = field( + default=None, + metadata={ + "name": "TxFrequencyProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + segment_identifier: Optional[str] = field( + default=None, + metadata={ + "name": "SegmentIdentifier", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + image_form_algo: Optional[ImageFormAlgo] = field( + default=None, + metadata={ + "name": "ImageFormAlgo", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + stbeam_comp: Optional[STBeamComp] = field( + default=None, + metadata={ + "name": "STBeamComp", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + image_beam_comp: Optional[ImageBeamComp] = field( + default=None, + metadata={ + "name": "ImageBeamComp", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + az_autofocus: Optional[AzAutofocus] = field( + default=None, + metadata={ + "name": "AzAutofocus", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + rg_autofocus: Optional[RgAutofocus] = field( + default=None, + metadata={ + "name": "RgAutofocus", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + processing: List["ImageFormationType.Processing"] = field( + default_factory=list, + metadata={ + "name": "Processing", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + polarization_calibration: Optional["ImageFormationType.PolarizationCalibration"] = field( + default=None, + metadata={ + "name": "PolarizationCalibration", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class RcvChanProc: + num_chan_proc: Optional[int] = field( + default=None, + metadata={ + "name": "NumChanProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + prfscale_factor: Optional[float] = field( + default=None, + metadata={ + "name": "PRFScaleFactor", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + chan_index: List[int] = field( + default_factory=list, + metadata={ + "name": "ChanIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + + @dataclass + class TxFrequencyProc: + min_proc: Optional[float] = field( + default=None, + metadata={ + "name": "MinProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + max_proc: Optional[float] = field( + default=None, + metadata={ + "name": "MaxProc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class Processing: + type_value: Optional[str] = field( + default=None, + metadata={ + "name": "Type", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + applied: Optional[bool] = field( + default=None, + metadata={ + "name": "Applied", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class PolarizationCalibration: + distort_correction_applied: Optional[bool] = field( + default=None, + metadata={ + "name": "DistortCorrectionApplied", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + distortion: Optional["ImageFormationType.PolarizationCalibration.Distortion"] = field( + default=None, + metadata={ + "name": "Distortion", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class Distortion: + calibration_date: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CalibrationDate", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + a: Optional[float] = field( + default=None, + metadata={ + "name": "A", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + f1: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "F1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + q1: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + q2: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + f2: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "F2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + q3: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q3", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + q4: Optional[ComplexType] = field( + default=None, + metadata={ + "name": "Q4", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + gain_error_a: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorA", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + gain_error_f1: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorF1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + gain_error_f2: Optional[float] = field( + default=None, + metadata={ + "name": "GainErrorF2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + phase_error_f1: Optional[float] = field( + default=None, + metadata={ + "name": "PhaseErrorF1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + phase_error_f2: Optional[float] = field( + default=None, + metadata={ + "name": "PhaseErrorF2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + +@dataclass +class LatLonCornerStringType(LatLonType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerType(LatLonType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerRestrictType(LatLonHAERestrictionType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerStringType(LatLonHAEType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LineType: + endpoint: List["LineType.Endpoint"] = field( + default_factory=list, + metadata={ + "name": "Endpoint", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Endpoint(LatLonType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MatchInfoType: + num_match_types: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchTypes", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + match_type: List["MatchInfoType.MatchType"] = field( + default_factory=list, + metadata={ + "name": "MatchType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + + @dataclass + class MatchType: + type_id: Optional[str] = field( + default=None, + metadata={ + "name": "TypeID", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + current_index: Optional[int] = field( + default=None, + metadata={ + "name": "CurrentIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + num_match_collections: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchCollections", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + match_collection: List["MatchInfoType.MatchType.MatchCollection"] = field( + default_factory=list, + metadata={ + "name": "MatchCollection", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class MatchCollection: + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + match_index: Optional[int] = field( + default=None, + metadata={ + "name": "MatchIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly1DType: + coef: List[PolyCoef1DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly2DType: + coef: List[PolyCoef2DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + order2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + vertex: List["PolygonType.Vertex"] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Vertex(LatLonRestrictionType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RowColvertexType(RowColType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class SCPCOAType: + scptime: Optional[float] = field( + default=None, + metadata={ + "name": "SCPTime", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + arppos: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPPos", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + arpvel: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPVel", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + arpacc: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ARPAcc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + side_of_track: Optional[SideOfTrack] = field( + default=None, + metadata={ + "name": "SideOfTrack", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + slant_range: Optional[float] = field( + default=None, + metadata={ + "name": "SlantRange", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ground_range: Optional[float] = field( + default=None, + metadata={ + "name": "GroundRange", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + doppler_cone_ang: Optional[float] = field( + default=None, + metadata={ + "name": "DopplerConeAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + graze_ang: Optional[float] = field( + default=None, + metadata={ + "name": "GrazeAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + incidence_ang: Optional[float] = field( + default=None, + metadata={ + "name": "IncidenceAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + twist_ang: Optional[float] = field( + default=None, + metadata={ + "name": "TwistAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + slope_ang: Optional[float] = field( + default=None, + metadata={ + "name": "SlopeAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + azim_ang: Optional[float] = field( + default=None, + metadata={ + "name": "AzimAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 360.0, + }, + ) + layover_ang: Optional[float] = field( + default=None, + metadata={ + "name": "LayoverAng", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "min_inclusive": 0.0, + "max_inclusive": 360.0, + }, + ) + + +@dataclass +class DirParamType: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ss: Optional[float] = field( + default=None, + metadata={ + "name": "SS", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + imp_resp_wid: Optional[float] = field( + default=None, + metadata={ + "name": "ImpRespWid", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + sgn: Optional[DirParamTypeSgn] = field( + default=None, + metadata={ + "name": "Sgn", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + imp_resp_bw: Optional[float] = field( + default=None, + metadata={ + "name": "ImpRespBW", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + kctr: Optional[float] = field( + default=None, + metadata={ + "name": "KCtr", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + delta_k1: Optional[float] = field( + default=None, + metadata={ + "name": "DeltaK1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + delta_k2: Optional[float] = field( + default=None, + metadata={ + "name": "DeltaK2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + delta_kcoapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DeltaKCOAPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + wgt_type: Optional["DirParamType.WgtType"] = field( + default=None, + metadata={ + "name": "WgtType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + wgt_funct: Optional["DirParamType.WgtFunct"] = field( + default=None, + metadata={ + "name": "WgtFunct", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class WgtType: + window_name: Optional[str] = field( + default=None, + metadata={ + "name": "WindowName", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class WgtFunct: + wgt: List[ArrayDouble] = field( + default_factory=list, + metadata={ + "name": "Wgt", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class GainPhasePolyType: + gain_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GainPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + phase_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "PhasePoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class GeoInfoType: + desc: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Desc", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + point: Optional[LatLonRestrictionType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + polygon: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + geo_info: List["GeoInfoType"] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class ImageDataType: + pixel_type: Optional[ImagePixelType] = field( + default=None, + metadata={ + "name": "PixelType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + amp_table: Optional["ImageDataType.AmpTable"] = field( + default=None, + metadata={ + "name": "AmpTable", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "NumRows", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "NumCols", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + first_row: Optional[int] = field( + default=None, + metadata={ + "name": "FirstRow", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + first_col: Optional[int] = field( + default=None, + metadata={ + "name": "FirstCol", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + full_image: Optional["ImageDataType.FullImage"] = field( + default=None, + metadata={ + "name": "FullImage", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + scppixel: Optional[RowColType] = field( + default=None, + metadata={ + "name": "SCPPixel", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + valid_data: Optional["ImageDataType.ValidData"] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class AmpTable: + amplitude: List[ArrayDouble] = field( + default_factory=list, + metadata={ + "name": "Amplitude", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 256, + "max_occurs": 256, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class FullImage: + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "NumRows", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "NumCols", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class ValidData: + vertex: List[RowColvertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PFAType: + fpn: Optional[XYZType] = field( + default=None, + metadata={ + "name": "FPN", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ipn: Optional[XYZType] = field( + default=None, + metadata={ + "name": "IPN", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + polar_ang_ref_time: Optional[float] = field( + default=None, + metadata={ + "name": "PolarAngRefTime", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + polar_ang_poly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "PolarAngPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + spatial_freq_sfpoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "SpatialFreqSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + krg1: Optional[float] = field( + default=None, + metadata={ + "name": "Krg1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + krg2: Optional[float] = field( + default=None, + metadata={ + "name": "Krg2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + kaz1: Optional[float] = field( + default=None, + metadata={ + "name": "Kaz1", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + kaz2: Optional[float] = field( + default=None, + metadata={ + "name": "Kaz2", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + stdeskew: Optional["PFAType.STDeskew"] = field( + default=None, + metadata={ + "name": "STDeskew", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class STDeskew: + applied: Optional[bool] = field( + default=None, + metadata={ + "name": "Applied", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + stdsphase_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "STDSPhasePoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class RMAType: + rmalgo_type: Optional[RMAlgorithm] = field( + default=None, + metadata={ + "name": "RMAlgoType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + image_type: Optional[RMAImageType] = field( + default=None, + metadata={ + "name": "ImageType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + rmat: Optional["RMAType.RMAT"] = field( + default=None, + metadata={ + "name": "RMAT", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rmcr: Optional["RMAType.RMCR"] = field( + default=None, + metadata={ + "name": "RMCR", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + inca: Optional["RMAType.INCA"] = field( + default=None, + metadata={ + "name": "INCA", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class RMAT: + pos_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "PosRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + vel_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "VelRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + dop_cone_ang_ref: Optional[float] = field( + default=None, + metadata={ + "name": "DopConeAngRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class RMCR: + pos_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "PosRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + vel_ref: Optional[XYZType] = field( + default=None, + metadata={ + "name": "VelRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + dop_cone_ang_ref: Optional[float] = field( + default=None, + metadata={ + "name": "DopConeAngRef", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class INCA: + time_capoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "TimeCAPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + r_ca_scp: Optional[float] = field( + default=None, + metadata={ + "name": "R_CA_SCP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + freq_zero: Optional[float] = field( + default=None, + metadata={ + "name": "FreqZero", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + drate_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DRateSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + dop_centroid_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "DopCentroidPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + dop_centroid_coa: Optional[bool] = field( + default=None, + metadata={ + "name": "DopCentroidCOA", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + +@dataclass +class RadarCollectionType: + tx_frequency: Optional["RadarCollectionType.TxFrequency"] = field( + default=None, + metadata={ + "name": "TxFrequency", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ref_freq_index: Optional[int] = field( + default=None, + metadata={ + "name": "RefFreqIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + waveform: Optional["RadarCollectionType.Waveform"] = field( + default=None, + metadata={ + "name": "Waveform", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_polarization: Optional[str] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "pattern": r"[VHXYSE]|RHC|LHC|UNKNOWN|SEQUENCE|OTHER[^:]*", + }, + ) + tx_sequence: Optional["RadarCollectionType.TxSequence"] = field( + default=None, + metadata={ + "name": "TxSequence", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_channels: Optional["RadarCollectionType.RcvChannels"] = field( + default=None, + metadata={ + "name": "RcvChannels", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + area: Optional["RadarCollectionType.Area"] = field( + default=None, + metadata={ + "name": "Area", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class TxFrequency: + min: Optional[float] = field( + default=None, + metadata={ + "name": "Min", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + max: Optional[float] = field( + default=None, + metadata={ + "name": "Max", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class Waveform: + wfparameters: List["RadarCollectionType.Waveform.WFParameters"] = field( + default_factory=list, + metadata={ + "name": "WFParameters", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class WFParameters: + tx_pulse_length: Optional[float] = field( + default=None, + metadata={ + "name": "TxPulseLength", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_rfbandwidth: Optional[float] = field( + default=None, + metadata={ + "name": "TxRFBandwidth", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_freq_start: Optional[float] = field( + default=None, + metadata={ + "name": "TxFreqStart", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_fmrate: Optional[float] = field( + default=None, + metadata={ + "name": "TxFMRate", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_demod_type: Optional[WFParametersRcvDemodType] = field( + default=None, + metadata={ + "name": "RcvDemodType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_window_length: Optional[float] = field( + default=None, + metadata={ + "name": "RcvWindowLength", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + adcsample_rate: Optional[float] = field( + default=None, + metadata={ + "name": "ADCSampleRate", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_ifbandwidth: Optional[float] = field( + default=None, + metadata={ + "name": "RcvIFBandwidth", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_freq_start: Optional[float] = field( + default=None, + metadata={ + "name": "RcvFreqStart", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_fmrate: Optional[float] = field( + default=None, + metadata={ + "name": "RcvFMRate", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class TxSequence: + tx_step: List["RadarCollectionType.TxSequence.TxStep"] = field( + default_factory=list, + metadata={ + "name": "TxStep", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class TxStep: + wfindex: Optional[int] = field( + default=None, + metadata={ + "name": "WFIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_polarization: Optional[str] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "pattern": r"[VHXYSE]|RHC|LHC|UNKNOWN|OTHER[^:]*", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class RcvChannels: + chan_parameters: List["RadarCollectionType.RcvChannels.ChanParameters"] = field( + default_factory=list, + metadata={ + "name": "ChanParameters", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class ChanParameters: + tx_rcv_polarization: Optional[str] = field( + default=None, + metadata={ + "name": "TxRcvPolarization", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + "pattern": r"(([VHXYSE]|RHC|LHC|OTHER[^:]*):([VHXYSE]|RHC|LHC|OTHER[^:]*))|OTHER|UNKNOWN", + }, + ) + rcv_apcindex: Optional[int] = field( + default=None, + metadata={ + "name": "RcvAPCIndex", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Area: + corner: Optional["RadarCollectionType.Area.Corner"] = field( + default=None, + metadata={ + "name": "Corner", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + plane: Optional["RadarCollectionType.Area.Plane"] = field( + default=None, + metadata={ + "name": "Plane", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class Corner: + acp: List[LatLonHAECornerRestrictType] = field( + default_factory=list, + metadata={ + "name": "ACP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + @dataclass + class Plane: + ref_pt: Optional["RadarCollectionType.Area.Plane.RefPt"] = field( + default=None, + metadata={ + "name": "RefPt", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + xdir: Optional["RadarCollectionType.Area.Plane.XDir"] = field( + default=None, + metadata={ + "name": "XDir", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ydir: Optional["RadarCollectionType.Area.Plane.YDir"] = field( + default=None, + metadata={ + "name": "YDir", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + segment_list: Optional["RadarCollectionType.Area.Plane.SegmentList"] = field( + default=None, + metadata={ + "name": "SegmentList", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + orientation: Optional[OrientationType] = field( + default=None, + metadata={ + "name": "Orientation", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class RefPt: + ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + line: Optional[float] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + sample: Optional[float] = field( + default=None, + metadata={ + "name": "Sample", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + @dataclass + class XDir: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + line_spacing: Optional[float] = field( + default=None, + metadata={ + "name": "LineSpacing", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + num_lines: Optional[int] = field( + default=None, + metadata={ + "name": "NumLines", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + first_line: Optional[int] = field( + default=None, + metadata={ + "name": "FirstLine", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class YDir: + uvect_ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "UVectECF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + sample_spacing: Optional[float] = field( + default=None, + metadata={ + "name": "SampleSpacing", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + num_samples: Optional[int] = field( + default=None, + metadata={ + "name": "NumSamples", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + first_sample: Optional[int] = field( + default=None, + metadata={ + "name": "FirstSample", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class SegmentList: + segment: List["RadarCollectionType.Area.Plane.SegmentList.Segment"] = field( + default_factory=list, + metadata={ + "name": "Segment", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Segment: + start_line: Optional[int] = field( + default=None, + metadata={ + "name": "StartLine", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + start_sample: Optional[int] = field( + default=None, + metadata={ + "name": "StartSample", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + end_line: Optional[int] = field( + default=None, + metadata={ + "name": "EndLine", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + end_sample: Optional[int] = field( + default=None, + metadata={ + "name": "EndSample", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + identifier: Optional[str] = field( + default=None, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RadiometricType: + noise_level: Optional["RadiometricType.NoiseLevel"] = field( + default=None, + metadata={ + "name": "NoiseLevel", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcssfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RCSSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + sigma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "SigmaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + beta_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "BetaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + gamma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GammaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class NoiseLevel: + noise_level_type: Optional[NoiseLevelType] = field( + default=None, + metadata={ + "name": "NoiseLevelType", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + noise_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "NoisePoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class RgAzCompType: + az_sf: Optional[float] = field( + default=None, + metadata={ + "name": "AzSF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + kaz_poly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "KazPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class TimelineType: + collect_start: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CollectStart", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + collect_duration: Optional[float] = field( + default=None, + metadata={ + "name": "CollectDuration", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ipp: Optional["TimelineType.IPP"] = field( + default=None, + metadata={ + "name": "IPP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class IPP: + set: List["TimelineType.IPP.Set"] = field( + default_factory=list, + metadata={ + "name": "Set", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Set: + tstart: Optional[float] = field( + default=None, + metadata={ + "name": "TStart", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + tend: Optional[float] = field( + default=None, + metadata={ + "name": "TEnd", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ippstart: Optional[int] = field( + default=None, + metadata={ + "name": "IPPStart", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ippend: Optional[int] = field( + default=None, + metadata={ + "name": "IPPEnd", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + ipppoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "IPPPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class XYZPolyType: + x: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + y: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + z: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class AntParamType: + xaxis_poly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "XAxisPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + yaxis_poly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "YAxisPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + freq_zero: Optional[float] = field( + default=None, + metadata={ + "name": "FreqZero", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + eb: Optional["AntParamType.EB"] = field( + default=None, + metadata={ + "name": "EB", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + array: Optional[GainPhasePolyType] = field( + default=None, + metadata={ + "name": "Array", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + elem: Optional[GainPhasePolyType] = field( + default=None, + metadata={ + "name": "Elem", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + gain_bspoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "GainBSPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + ebfreq_shift: Optional[bool] = field( + default=None, + metadata={ + "name": "EBFreqShift", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + mlfreq_dilation: Optional[bool] = field( + default=None, + metadata={ + "name": "MLFreqDilation", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class EB: + dcxpoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "DCXPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + dcypoly: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "DCYPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class GeoDataType: + earth_model: Optional[EarthModel] = field( + default=None, + metadata={ + "name": "EarthModel", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + scp: Optional["GeoDataType.SCP"] = field( + default=None, + metadata={ + "name": "SCP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + image_corners: Optional["GeoDataType.ImageCorners"] = field( + default=None, + metadata={ + "name": "ImageCorners", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + valid_data: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + geo_info: List[GeoInfoType] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class SCP: + ecf: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECF", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + llh: Optional[LatLonHAERestrictionType] = field( + default=None, + metadata={ + "name": "LLH", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + @dataclass + class ImageCorners: + icp: List[LatLonCornerStringType] = field( + default_factory=list, + metadata={ + "name": "ICP", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + +@dataclass +class GridType: + image_plane: Optional[ImagePlane] = field( + default=None, + metadata={ + "name": "ImagePlane", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + type_value: Optional[ImageGridType] = field( + default=None, + metadata={ + "name": "Type", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + time_coapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "TimeCOAPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + row: Optional[DirParamType] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + col: Optional[DirParamType] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + + +@dataclass +class XYZPolyAttributeType(XYZPolyType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class AntennaType: + tx: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "Tx", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "Rcv", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + two_way: Optional[AntParamType] = field( + default=None, + metadata={ + "name": "TwoWay", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + +@dataclass +class PositionType: + arppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "ARPPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "required": True, + }, + ) + grppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "GRPPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + tx_apcpoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "TxAPCPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + rcv_apc: Optional["PositionType.RcvAPC"] = field( + default=None, + metadata={ + "name": "RcvAPC", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + }, + ) + + @dataclass + class RcvAPC: + rcv_apcpoly: List[XYZPolyAttributeType] = field( + default_factory=list, + metadata={ + "name": "RcvAPCPoly", + "type": "Element", + "namespace": "urn:SICD:1.3.0", + "min_occurs": 1, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class SICD: + class Meta: + namespace = "urn:SICD:1.3.0" + + collection_info: Optional[CollectionInfoType] = field( + default=None, + metadata={ + "name": "CollectionInfo", + "type": "Element", + "required": True, + }, + ) + image_creation: Optional[ImageCreationType] = field( + default=None, + metadata={ + "name": "ImageCreation", + "type": "Element", + }, + ) + image_data: Optional[ImageDataType] = field( + default=None, + metadata={ + "name": "ImageData", + "type": "Element", + "required": True, + }, + ) + geo_data: Optional[GeoDataType] = field( + default=None, + metadata={ + "name": "GeoData", + "type": "Element", + "required": True, + }, + ) + grid: Optional[GridType] = field( + default=None, + metadata={ + "name": "Grid", + "type": "Element", + "required": True, + }, + ) + timeline: Optional[TimelineType] = field( + default=None, + metadata={ + "name": "Timeline", + "type": "Element", + "required": True, + }, + ) + position: Optional[PositionType] = field( + default=None, + metadata={ + "name": "Position", + "type": "Element", + "required": True, + }, + ) + radar_collection: Optional[RadarCollectionType] = field( + default=None, + metadata={ + "name": "RadarCollection", + "type": "Element", + "required": True, + }, + ) + image_formation: Optional[ImageFormationType] = field( + default=None, + metadata={ + "name": "ImageFormation", + "type": "Element", + "required": True, + }, + ) + scpcoa: Optional[SCPCOAType] = field( + default=None, + metadata={ + "name": "SCPCOA", + "type": "Element", + "required": True, + }, + ) + radiometric: Optional[RadiometricType] = field( + default=None, + metadata={ + "name": "Radiometric", + "type": "Element", + }, + ) + antenna: Optional[AntennaType] = field( + default=None, + metadata={ + "name": "Antenna", + "type": "Element", + }, + ) + error_statistics: Optional[ErrorStatisticsType] = field( + default=None, + metadata={ + "name": "ErrorStatistics", + "type": "Element", + }, + ) + match_info: Optional[MatchInfoType] = field( + default=None, + metadata={ + "name": "MatchInfo", + "type": "Element", + }, + ) + rg_az_comp: Optional[RgAzCompType] = field( + default=None, + metadata={ + "name": "RgAzComp", + "type": "Element", + }, + ) + pfa: Optional[PFAType] = field( + default=None, + metadata={ + "name": "PFA", + "type": "Element", + }, + ) + rma: Optional[RMAType] = field( + default=None, + metadata={ + "name": "RMA", + "type": "Element", + }, + ) diff --git a/src/aws/osml/gdal/gdal_utils.py b/src/aws/osml/gdal/gdal_utils.py index 90e2696..8e86f5c 100644 --- a/src/aws/osml/gdal/gdal_utils.py +++ b/src/aws/osml/gdal/gdal_utils.py @@ -40,16 +40,14 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod if xml_tres is not None and len(xml_tres) > 0: parsed_tres = ElementTree.fromstring(xml_tres[0]) - # If this image has SICD Metadata parse it - parsed_dess = None + # If this image has NITF DES segments read them xml_dess = ds.GetMetadata("xml:DES") - if xml_dess is not None and len(xml_dess) > 0: - parsed_dess = ElementTree.fromstring(xml_dess[0]) selected_sensor_model_types = [ SensorModelTypes.AFFINE, SensorModelTypes.PROJECTIVE, SensorModelTypes.RPC, + SensorModelTypes.SICD, # TODO: Enable RSM model once testing complete # SensorModelTypes.RSM, ] @@ -58,7 +56,7 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod ds.RasterXSize, ds.RasterYSize, xml_tres=parsed_tres, - xml_dess=parsed_dess, + xml_dess=xml_dess, geo_transform=geo_transform, ground_control_points=ground_control_points, selected_sensor_model_types=selected_sensor_model_types, diff --git a/src/aws/osml/gdal/sensor_model_factory.py b/src/aws/osml/gdal/sensor_model_factory.py index bca8282..b969aa3 100644 --- a/src/aws/osml/gdal/sensor_model_factory.py +++ b/src/aws/osml/gdal/sensor_model_factory.py @@ -1,3 +1,4 @@ +import base64 import logging from enum import Enum from typing import List, Optional @@ -8,9 +9,11 @@ from aws.osml.photogrammetry import ChippedImageSensorModel, CompositeSensorModel, ImageCoordinate, SensorModel from .gdal_sensor_model_builder import GDALAffineSensorModelBuilder, GDALGCPSensorModelBuilder +from .nitf_des_accessor import NITFDESAccessor from .projective_sensor_model_builder import ProjectiveSensorModelBuilder from .rpc_sensor_model_builder import RPCSensorModelBuilder from .rsm_sensor_model_builder import RSMSensorModelBuilder +from .sicd_sensor_model_builder import SICDSensorModelBuilder from .xmltre_utils import get_tre_field_value @@ -57,6 +60,7 @@ class SensorModelTypes(Enum): PROJECTIVE = "PROJECTIVE" RPC = "RPC" RSM = "RSM" + SICD = "SICD" ALL_SENSOR_MODEL_TYPES = [item for item in SensorModelTypes] @@ -74,7 +78,7 @@ def __init__( actual_image_width: int, actual_image_height: int, xml_tres: Optional[ET.Element] = None, - xml_dess: Optional[ET.Element] = None, + xml_dess: Optional[List[str]] = None, geo_transform: Optional[List[float]] = None, ground_control_points: Optional[List[gdal.GCP]] = None, selected_sensor_model_types: Optional[List[SensorModelTypes]] = None, @@ -172,6 +176,22 @@ def build(self) -> Optional[SensorModel]: # TODO: Maybe create a projective sensor model from corner locations derived from the precision model # TODO: Consider using the rough corners from IGEOLO + if self.xml_dess is not None and len(self.xml_dess) > 0: + des_accessor = NITFDESAccessor(self.xml_dess) + + xml_data_content_segments = des_accessor.get_segments_by_name("XML_DATA_CONTENT") + if xml_data_content_segments is not None: + for xml_data_segment in xml_data_content_segments: + xml_bytes = des_accessor.parse_field_value(xml_data_segment, "DESDATA", base64.b64decode) + xml_str = xml_bytes.decode("utf-8") + if "SIDD" in xml_str: + # This looks like a SIDD file. Skip for now + # SIDD images will contain SICD extensions but the SIDD should come first + break + elif "SICD" in xml_str and SensorModelTypes.SICD in self.selected_sensor_model_types: + precision_sensor_model = SICDSensorModelBuilder(sicd_xml=xml_str).build() + break + # If we have both an approximate and a precision sensor model return them as a composite so applications # can choose which model best meets their needs. If we were only able to construct one or the other then # return what we were able to build. diff --git a/src/aws/osml/gdal/sicd_sensor_model_builder.py b/src/aws/osml/gdal/sicd_sensor_model_builder.py new file mode 100644 index 0000000..37aba27 --- /dev/null +++ b/src/aws/osml/gdal/sicd_sensor_model_builder.py @@ -0,0 +1,186 @@ +import logging +from typing import Optional, Union + +import numpy as np +from xsdata.formats.dataclass.parsers import XmlParser + +import aws.osml.formats.sicd.models.sicd_v1_2_1 as sicd121 +import aws.osml.formats.sicd.models.sicd_v1_3_0 as sicd130 + +from ..photogrammetry import ( + ImageCoordinate, + INCAProjectionSet, + PFAProjectionSet, + PlaneProjectionSet, + Polynomial2D, + PolynomialXYZ, + RGAZCOMPProjectionSet, + SARImageCoordConverter, + SensorModel, + SICDSensorModel, + WorldCoordinate, +) +from .sensor_model_builder import SensorModelBuilder + +logger = logging.getLogger(__name__) + + +def xyztype_to_ndarray(xyztype: Union[sicd121.XYZType, sicd130.XYZType]) -> np.ndarray: + """ + Convert the XYZType to a 1-d NumPy array. + + :param xyztype: the XYZType dataclass + :return: the NumPy array + """ + return np.array([xyztype.x, xyztype.y, xyztype.z]) + + +def poly1d_to_native(poly1d: Union[sicd121.Poly1DType, sicd130.XYZType]) -> np.polynomial.Polynomial: + """ + Convert the Poly1DType to a NumPy Polynomial. + + :param poly1d: the Poly1D dataclass + :return: the NumPy polynomial with matching coefficients + """ + coefficients = [0] * (poly1d.order1 + 1) + for coef in poly1d.coef: + coefficients[coef.exponent1] = coef.value + return np.polynomial.Polynomial(coefficients) + + +def poly2d_to_native(poly2d: Union[sicd121.Poly2DType, sicd130.Poly2DType]) -> Polynomial2D: + """ + Convert the Poly2D dataclass to a Polynomial2D. + + :param poly2d: the Poly2D dataclass + :return: the Polynomial2D with matching coefficients + """ + coefficients = [0] * (poly2d.order1 + 1) + for row in range(0, len(coefficients)): + coefficients[row] = [0] * (poly2d.order2 + 1) + for coef in poly2d.coef: + coefficients[coef.exponent1][coef.exponent2] = coef.value + return Polynomial2D(np.array(coefficients)) + + +def xyzpoly_to_native(xyzpoly: Union[sicd121.XYZPolyType, sicd130.XYZPolyType]) -> PolynomialXYZ: + """ + Convert the XYZPoly dataclass into a PolynomialXYZ. + + :param xyzpoly: the XYZPoly dataclass + :return: the PolynomialXYZ with matching coefficients + """ + return PolynomialXYZ( + x_polynomial=poly1d_to_native(xyzpoly.x), + y_polynomial=poly1d_to_native(xyzpoly.y), + z_polynomial=poly1d_to_native(xyzpoly.z), + ) + + +class SICDSensorModelBuilder(SensorModelBuilder): + """ + This builder is used to create sensor models for images that have SICD metadata. The metadata is provided + as XML that conforms to the SICD specifications. We intend to support multiple SICD versions but the current + software was implemented using the v1.2.1 and v1.3.0 specifications. + """ + + def __init__(self, sicd_xml: str): + """ + Construct the builder given the SICD XML. + + :param sicd_xml: the XML string + """ + super().__init__() + self.sicd_xml = sicd_xml + + def build(self) -> Optional[SensorModel]: + """ + Attempt to build a precise SAR sensor model. This sensor model handles chipped images natively. + + :return: the sensor model; if available + """ + try: + if self.sicd_xml is None or len(self.sicd_xml) == 0: + return None + + parser = XmlParser() + sicd = parser.from_string(self.sicd_xml) + return SICDSensorModelBuilder.from_dataclass(sicd) + except Exception as e: + logging.error("Exception caught attempting to build SICD sensor model.", e) + return None + + @staticmethod + def from_dataclass(sicd: Union[sicd121.SICD, sicd130.SICD]) -> Optional[SensorModel]: + """ + This method constructs a SICD sensor model from the python dataclasses generated when parsing the XML. + + :param sicd: the dataclass object constructed from the XML + :return: the sensor model; if available + """ + + scp_ecf = WorldCoordinate(xyztype_to_ndarray(sicd.geo_data.scp.ecf)) + scp_pixel = ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + time_coa_poly = poly2d_to_native(sicd.grid.time_coapoly) + arp_poly = xyzpoly_to_native(sicd.position.arppoly) + + coord_converter = SARImageCoordConverter( + scp_pixel=scp_pixel, + scp_ecf=scp_ecf, + u_row=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + u_col=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + row_ss=sicd.grid.row.ss, + col_ss=sicd.grid.col.ss, + first_pixel=ImageCoordinate([sicd.image_data.first_col, sicd.image_data.first_row]), + ) + + projection_set = None + ugpn = None + if sicd.grid.type_value == sicd121.ImageGridType.RGAZIM: + if sicd.image_formation.image_form_algo == sicd121.ImageFormAlgo.PFA: + ugpn = xyztype_to_ndarray(sicd.pfa.fpn) + projection_set = PFAProjectionSet( + scp_ecf=scp_ecf, + polar_ang_poly=poly1d_to_native(sicd.pfa.polar_ang_poly), + spatial_freq_sf_poly=poly1d_to_native(sicd.pfa.spatial_freq_sfpoly), + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + elif sicd.image_formation.image_form_algo == sicd121.ImageFormAlgo.RGAZCOMP: + projection_set = RGAZCOMPProjectionSet( + scp_ecf=scp_ecf, az_scale_factor=sicd.rg_az_comp.az_sf, coa_time_poly=time_coa_poly, arp_poly=arp_poly + ) + elif sicd.grid.type_value == sicd121.ImageGridType.RGZERO: + projection_set = INCAProjectionSet( + r_ca_scp=sicd.rma.inca.r_ca_scp, + inca_time_coa_poly=poly1d_to_native(sicd.rma.inca.time_capoly), + drate_sf_poly=poly2d_to_native(sicd.rma.inca.drate_sfpoly), + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + elif sicd.grid.type_value in [ + sicd121.ImageGridType.PLANE, + sicd121.ImageGridType.XCTYAT, + sicd121.ImageGridType.XRGYCR, + ]: + projection_set = PlaneProjectionSet( + scp_ecf=scp_ecf, + image_plane_urow=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + image_plane_ucol=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + else: + logger.warning(f"SICD image with unknown grid type {sicd.grid.type_value}. No sensor model created.") + return None + + sicd_sensor_model = SICDSensorModel( + coord_converter=coord_converter, + coa_projection_set=projection_set, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + u_gpn=ugpn, + ) + + return sicd_sensor_model diff --git a/src/aws/osml/photogrammetry/__init__.py b/src/aws/osml/photogrammetry/__init__.py index 20a746f..f62e01e 100644 --- a/src/aws/osml/photogrammetry/__init__.py +++ b/src/aws/osml/photogrammetry/__init__.py @@ -31,6 +31,17 @@ ) from .rpc_sensor_model import RPCPolynomial, RPCSensorModel from .sensor_model import SensorModel, SensorModelOptions +from .sicd_sensor_model import ( + COAProjectionSet, + INCAProjectionSet, + PFAProjectionSet, + PlaneProjectionSet, + Polynomial2D, + PolynomialXYZ, + RGAZCOMPProjectionSet, + SARImageCoordConverter, + SICDSensorModel, +) from .srtm_dem_tile_set import SRTMTileSet __all__ = [ @@ -47,7 +58,14 @@ "ConstantElevationModel", "ElevationModel", "GDALAffineSensorModel", + "SARImageCoordConverter", + "INCAProjectionSet", + "PlaneProjectionSet", + "PFAProjectionSet", + "PolynomialXYZ", + "Polynomial2D", "ProjectiveSensorModel", + "RGAZCOMPProjectionSet", "RSMContext", "RSMGroundDomain", "RSMGroundDomainForm", @@ -60,5 +78,6 @@ "RPCSensorModel", "SensorModel", "SensorModelOptions", + "SICDSensorModel", "SRTMTileSet", ] diff --git a/src/aws/osml/photogrammetry/sicd_sensor_model.py b/src/aws/osml/photogrammetry/sicd_sensor_model.py new file mode 100644 index 0000000..29f8ef6 --- /dev/null +++ b/src/aws/osml/photogrammetry/sicd_sensor_model.py @@ -0,0 +1,813 @@ +import logging +from abc import abstractmethod +from enum import Enum +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import numpy.typing as npt + +from . import ElevationModel +from .coordinates import ( + GeodeticWorldCoordinate, + ImageCoordinate, + WorldCoordinate, + geocentric_to_geodetic, + geodetic_to_geocentric, +) +from .sensor_model import SensorModel + +logger = logging.getLogger(__name__) + + +class Polynomial2D: + """ + This class contains coefficients for a two-dimensional polynomial. + """ + + def __init__(self, coef: npt.ArrayLike): + """ + Constructor that takes the coefficients of the polynomial. The coefficients should be ordered so that the + coefficient of the term of multi-degree i,j is contained in coef[i,j]. + + :param coef: array-like structure of coefficients + """ + self.coef = np.array(coef) + if len(self.coef.shape) != 2: + raise ValueError( + f"Coefficients for class Polynomial2D must be two-dimensional. " + f"Received numpy.ndarray of shape {self.coef.shape}" + ) + + def __call__(self, x: Union[float, np.ndarray], y: Union[float, np.ndarray]) -> np.ndarray: + """ + Invoke NumPy's polyval2d given the inputs and the coefficients of the polynomial. + + :param x: the first input parameter + :param y: the second input parameter + :return: the values of the 2-d polynomial at points formed with pairs of corresponding values from x and y. + """ + return np.polynomial.polynomial.polyval2d(x, y, self.coef) + + +class PolynomialXYZ: + """ + This class is an aggregation 3 one-dimensional polynomials all with the same input variable. The result of + evaluating this class on the input variable is an [x, y, z] vector. + """ + + def __init__( + self, + x_polynomial: np.polynomial.Polynomial, + y_polynomial: np.polynomial.Polynomial, + z_polynomial: np.polynomial.Polynomial, + ): + """ + Constructor that accepts the 3 NumPy 1-d polynomials one for each component. + + :param x_polynomial: polynomial for the x component + :param y_polynomial: polynomial for the y component + :param z_polynomial: polynomial for the z component + """ + self.x_polynomial = x_polynomial + self.y_polynomial = y_polynomial + self.z_polynomial = z_polynomial + + def __call__(self, t: float) -> np.ndarray: + """ + Evaluate the x, y, and z polynomials at t and return the result as a vector. + + :param t: the value + :return: the polynomial result + """ + x = self.x_polynomial(t) + y = self.y_polynomial(t) + z = self.z_polynomial(t) + + return np.array([x, y, z], dtype=x.dtype) + + def deriv(self, m: int = 1): + """ + Create a new PolynomialXYZ that is the derivative of the current PolynomialXYZ. + + :param m: find the derivative of order m + :return: the new polynomial derivative + """ + x_derivative = self.x_polynomial.deriv(m=m) + y_derivative = self.y_polynomial.deriv(m=m) + z_derivative = self.z_polynomial.deriv(m=m) + + return PolynomialXYZ(x_polynomial=x_derivative, y_polynomial=y_derivative, z_polynomial=z_derivative) + + +class SARImageCoordConverter: + """ + This class contains image grid and image plane coordinate conversions for a provided set of SICD parameters. The + equations are mostly defined in Section 2 of the SICD Standard Volume 3. + """ + + def __init__( + self, + scp_pixel: ImageCoordinate, + scp_ecf: WorldCoordinate, + u_row: np.ndarray, + u_col: np.ndarray, + row_ss: float, + col_ss: float, + first_pixel: ImageCoordinate = ImageCoordinate([0.0, 0.0]), + ): + """ + Construct the coordinate converter given parameters from the metadata. The names of these parameters have been + chosen to align with the names in the specification. + + :param scp_pixel: location of the scene center point (SCP) in the global pixel grid + :param scp_ecf: location of the scene center point (SCP) in earth centered fixed (ECF) coordinates + :param u_row: unit vector in the increasing row direction in ECF coordinates. + :param u_col: unit vector in the increasing column direction in ECF coordinates. + :param row_ss: sample spacing in the row direction + :param col_ss: sample spacing in the column direction + :param first_pixel: location of the first row/column of the pixel array. For a full image array [0, 0] + """ + self.scp_pixel = scp_pixel + self.scp_ecf = scp_ecf + self.row_ss = row_ss + self.col_ss = col_ss + self.u_row = u_row + self.u_col = u_col + self.first_pixel = first_pixel + # Section 2.4 calculation of the image plane unit normal vector + ipn = np.cross(self.u_row, self.u_col) + self.uvect_ipn = ipn / np.linalg.norm(ipn) + + # Section 2.4 calculation of transform from ipp to xrow, ycol + cos_theta = np.dot(self.u_row, self.u_col) + sin_theta = np.sqrt(1 - cos_theta * cos_theta) + ipp_transform = np.array([[1, -cos_theta], [-cos_theta, 1]], dtype="float64") / (sin_theta * sin_theta) + row_col_transform = np.zeros((3, 2), dtype="float64") + row_col_transform[:, 0] = self.u_row + row_col_transform[:, 1] = self.u_col + self.matrix_transform = np.dot(row_col_transform, ipp_transform) + + def rowcol_to_xrowycol(self, row_col: np.ndarray) -> np.ndarray: + """ + This function converts the row and column indexes (row, col) in the global image grid to SCP centered + image coordinates (xrow, ycol) using equations (2) (3) in Section 2.2 of the SICD Specification + Volume 3. + + :param row_col: the [row, col] location as an array + :return: the [xrow, ycol] location as an array + """ + xrow_ycol = np.zeros(2, dtype="float64") + xrow_ycol[0] = (row_col[0] - self.scp_pixel.r) * self.row_ss + xrow_ycol[1] = (row_col[1] - self.scp_pixel.c) * self.col_ss + return xrow_ycol + + def xrowycol_to_rowcol(self, xrow_ycol: np.ndarray) -> np.ndarray: + """ + This function converts the SCP centered image coordinates (xrow, ycol) to row and column indexes (row, col) + in the global image grid using equations (2) (3) in Section 2.2 of the SICD Specification Volume 3. + + :param xrow_ycol: the [xrow, ycol] location as an array + :return: the [row, col] location as an array + """ + row_col = np.zeros(2, dtype="float64") + row_col[0] = xrow_ycol[0] / self.row_ss + self.scp_pixel.r + row_col[1] = xrow_ycol[1] / self.col_ss + self.scp_pixel.c + return row_col + + def xrowycol_to_ipp(self, xrow_ycol: np.ndarray) -> np.ndarray: + """ + This function converts SCP centered image coordinates (xrow, ycol) to a ECF coordinate, image plane point (IPP), + on the image plane using equations in Section 2.4 of the SICD Specification Volume 3. + + :param xrow_ycol: the [xrow, ycol] location as an array + :return: the image plane point [x, y, z] ECF location on the image plane + """ + delta_ipp = xrow_ycol[0] * self.u_row + xrow_ycol[1] * self.u_col + return self.scp_ecf.coordinate + delta_ipp + + def ipp_to_xrowycol(self, ipp: np.ndarray) -> np.ndarray: + """ + This function converts an ECF location on the image plane into SCP centered image coordinates (xrow, ycol) + using equations in Section 2.4 of the SICD Specification volume 3. + + :param ipp: the image plane point [x, y, z] ECF location on the image plane + :return: the [xrow, ycol] location as an array + """ + delta_ipp = ipp - self.scp_ecf.coordinate + xrow_ycol = np.dot(delta_ipp, self.matrix_transform) + return xrow_ycol + + +class COAProjectionSet: + """ + This is an abstract base class for R/Rdot projection contour computations described in Section 4 of the SICD + Standard Volume 3. + """ + + def __init__( + self, + coa_time_poly: Polynomial2D, + arp_poly: PolynomialXYZ, + delta_arp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + delta_varp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + range_bias: float = 0.0, + ): + """ + Constructor with parameters supporting the calculations common to all R/Rdot projections (i.e. the + calculations that do not depend on grid type and image formation algorithm). + + :param coa_time_poly: Center Of Aperture (COA) time polynomial. + :param arp_poly: Aperture Reference Point (ARP) position polynomial coefficients. + :param delta_arp: the ARP position offset + :param delta_varp: the ARP velocity offset + :param range_bias: the range bias offset + """ + self.coa_time_poly = coa_time_poly + self.arp_poly = arp_poly + self.varp_poly = self.arp_poly.deriv(m=1) + + self.delta_arp = delta_arp + self.delta_varp = delta_varp + self.range_bias = float(range_bias) + + def precise_rrdot_computation( + self, xrow_ycol: np.ndarray + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + This executes the precise image pixel grid location to R/Rdot projection. This function invokes + the _grid_specific_projection() function implemented by subclasses which should handle the portions + of the calculation that are dependent on the image grid and image formation algorithm. + + :param xrow_ycol: the [xrow, ycol] location as an array + :return: the COA projection set { Rcoa, Rdotcoa, tcoa, arpcoa, varpcoa } + """ + # These are the common calculations for image COA time (coa_time), COA ARP position and velocity + # (arp_position and arp_velocity) as described in Section 2 of the SICD specification Volume 3. + coa_time = self.coa_time_poly(xrow_ycol[0], xrow_ycol[1]) + arp_position = self.arp_poly(coa_time) + arp_velocity = self.varp_poly(coa_time) + + # These are the image grid and image formation algorithm dependent calculations for the precise + # computation of the R/Rdot contour. Each subclass should implement an approach as described in + # sections 4.1 through 4.6 of the SICD specification Volume 3. + r_tgt_coa, r_dot_tgt_coa = self._grid_specific_projection(xrow_ycol, coa_time, arp_position, arp_velocity) + + # If provided the Adjustable Parameter Offsets are incorporated into the computation from + # image pixel location to COA projection parameters. See Section 8.1 of the SICD specification Volume 3. + # TODO: Check this. This is the same approach as SarPy but I'm not 100% sure it is correct + arp_position += self.delta_arp + arp_velocity += self.delta_varp + r_tgt_coa += self.range_bias + + return r_tgt_coa, r_dot_tgt_coa, coa_time, arp_position, arp_velocity + + @abstractmethod + def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_velocity) -> Tuple[np.ndarray, np.ndarray]: + """ + The precise computation of the R/Rdot contour is dependent upon the image grid type and the image + formation algorithm that produced the image but the computation of the COA time, ARP position, and + velocity is the same for all image products. + + This abstract method should be overriden by subclasses to perform the R/Rdot calculations for a + specific image grid and formation algorithm. + + :param xrow_ycol: the [xrow, ycol] location as an array + :param coa_time: Center Of Aperture (COA) time + :param arp_position: Aperture Reference Point (ARP) position + :param arp_velocity: Aperture Reference Point (ARP) velocity + :return: the tuple containing range and range rate relative to the ARP at COA time + """ + + +class PFAProjectionSet(COAProjectionSet): + """ + This Center Of Aperture (COA) Projection set is to be used with a range azimuth image grid (RGAZIM) and polar + formatted (PFA) phase history data. See section 4.1 of the SICD Specification Volume 3. + """ + + def __init__( + self, + scp_ecf: WorldCoordinate, + polar_ang_poly, + spatial_freq_sf_poly, + coa_time_poly: Polynomial2D, + arp_poly: PolynomialXYZ, + delta_arp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + delta_varp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + range_bias: float = 0.0, + ): + """ + Constructor for this projection set. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param polar_ang_poly: Polar Angle polynomial coefficients + :param spatial_freq_sf_poly: Spatial Frequency Scale Factor polynomial coefficients + :param coa_time_poly: Center Of Aperture (COA) time polynomial + :param arp_poly: Aperture Reference Point (ARP) position polynomial coefficients + :param delta_arp: the ARP position offset + :param delta_varp: the ARP velocity offset + :param range_bias: the range bias offset + """ + super().__init__(coa_time_poly, arp_poly, delta_arp, delta_varp, range_bias) + self.scp_ecf = scp_ecf + self.polar_ang_poly = polar_ang_poly + self.spatial_freq_sf_poly = spatial_freq_sf_poly + self.polar_ang_poly_der = polar_ang_poly.deriv(m=1) + self.spatial_freq_sf_poly_der = spatial_freq_sf_poly.deriv(m=1) + + def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_velocity) -> Tuple[np.ndarray, np.ndarray]: + """ + These are the calculations for the precise computation of the R/Rdot contour unique to these grid and + image formation algorithm types. See SICD Volume 3 Section 4.1 + + :param xrow_ycol: the [xrow, ycol] location as an array + :param coa_time: Center Of Aperture (COA) time + :param arp_position: Aperture Reference Point (ARP) position + :param arp_velocity: Aperture Reference Point (ARP) velocity + :return: the tuple containing range and range rate relative to the ARP at COA time + """ + # For the RGAZIM grid, the image coordinates are range and azimuth. The row coordinate is the range + # coordinate, xrow = rg. The column coordinate is the azimuth coordinate, ycol = az. + rg = xrow_ycol[0] + az = xrow_ycol[1] + + # (2) Compute the range and range rate to the SCP at the pixel COA time + arp_minus_scp = arp_position - self.scp_ecf.coordinate + range_to_scp = np.linalg.norm(arp_minus_scp, axis=-1) + rdot_to_scp = np.sum(arp_velocity * arp_minus_scp, axis=-1) / range_to_scp + + # (3) Compute the polar angle (theta) and its derivative with respect to time (d_theta_d_time) + # at the pixel COA time. + theta = self.polar_ang_poly(coa_time) + d_theta_d_time = self.polar_ang_poly_der(coa_time) + + # (4) Compute the polar aperture scale factor (KSF) and its derivative with respect to polar angle + # (d_ksf_d_theta) at the pixel COA time + ksf = self.spatial_freq_sf_poly(theta) + d_ksf_d_theta = self.spatial_freq_sf_poly_der(theta) + + # (5) Compute the spatial frequency domain phase slopes in the radial (ka) and cross radial + # (kc) directions (d_phi_d_ka and d_phi_d_kc) for the radial direction at theta. Note: The sign COA + # parameter (SGN) for the phase may be ignored as it is cancelled in a subsequent computation. + d_phi_d_ka = rg * np.cos(theta) + az * np.sin(theta) + d_phi_d_kc = -rg * np.sin(theta) + az * np.cos(theta) + + # (6) Compute range relative to the SCP (delta_range) at the COA. + delta_range = ksf * d_phi_d_ka + + # (7) Compute the derivative of the range relative to the SCP with respect to polar angle + # (d_delta_range_d_theta) at the COA. Scale by the derivative of the polar angle with respect + # to time to yield the derivative with respect to time (delta_r_dot_tgt_coa). + d_delta_range_d_theta = d_ksf_d_theta * d_phi_d_ka + ksf * d_phi_d_kc + delta_r_dot_tgt_coa = d_delta_range_d_theta * d_theta_d_time + + # (8) Compute the range and range rate relative to the ARP at COA ( r_tgt_coa and rdot_tgt_coa). + # The projection to three-dimensional scene point for grid location (rgTGT, azTGT) is along this + # R/Rdot contour. + r_tgt_coa = range_to_scp + delta_range + rdot_tgt_coa = rdot_to_scp + delta_r_dot_tgt_coa + + return r_tgt_coa, rdot_tgt_coa + + +class RGAZCOMPProjectionSet(COAProjectionSet): + def __init__( + self, + scp_ecf: WorldCoordinate, + az_scale_factor: float, + coa_time_poly: Polynomial2D, + arp_poly: PolynomialXYZ, + delta_arp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + delta_varp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + range_bias: float = 0.0, + ): + """ + Constructor for this projection set. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param az_scale_factor: Scale factor that converts azimuth coordinate to an increment in cosine of the DCA at COA + :param coa_time_poly: Center Of Aperture (COA) time polynomial + :param arp_poly: Aperture Reference Point (ARP) position polynomial coefficients + :param delta_arp: the ARP position offset + :param delta_varp: the ARP velocity offset + :param range_bias: the range bias offset + """ + super().__init__(coa_time_poly, arp_poly, delta_arp, delta_varp, range_bias) + self.scp_ecf = scp_ecf + self.az_scale_factor = az_scale_factor + + def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_velocity) -> Tuple[np.ndarray, np.ndarray]: + """ + These are the calculations for the precise computation of the R/Rdot contour unique to these grid and + image formation algorithm types. See SICD Volume 3 Section 4.2 + + :param xrow_ycol: the [xrow, ycol] location as an array + :param coa_time: Center Of Aperture (COA) time + :param arp_position: Aperture Reference Point (ARP) position + :param arp_velocity: Aperture Reference Point (ARP) velocity + :return: the tuple containing range and range rate relative to the ARP at COA time + """ + # For the RGAZIM grid, the image coordinates are range and azimuth. The row coordinate is the range + # coordinate, xrow = rg. The column coordinate is the azimuth coordinate, ycol = az. + rg = xrow_ycol[0] + az = xrow_ycol[1] + + # (2) Compute the range and range rate to the SCP at COA. + arp_minus_scp = arp_position - self.scp_ecf.coordinate + range_to_scp = np.linalg.norm(arp_minus_scp, axis=-1) + rdot_to_scp = np.sum(arp_velocity * arp_minus_scp, axis=-1) / range_to_scp + + # (3) Compute the increment in cosine of the DCA at COA of the target (delta_cos_dca_tgt_coa) by + # scaling the azimuth coordinate by the azimuth to DCA scale factor. Compute the increment + # in range rate (delta_rdot_tgt_coa) by scaling by the magnitude of the velocity vector at COA. + delta_cos_dca_tgt_coa = self.az_scale_factor * az + delta_r_dot_tgt_coa = -np.linalg.norm(arp_velocity, axis=-1) * delta_cos_dca_tgt_coa + + # (4) Compute the range and range rate to the target at COA as follows. + r_tgt_coa = range_to_scp + rg + rdot_tgt_coa = rdot_to_scp + delta_r_dot_tgt_coa + + return r_tgt_coa, rdot_tgt_coa + + +class INCAProjectionSet(COAProjectionSet): + def __init__( + self, + r_ca_scp: float, + inca_time_coa_poly: np.polynomial.Polynomial, + drate_sf_poly: Polynomial2D, + coa_time_poly: Polynomial2D, + arp_poly: PolynomialXYZ, + delta_arp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + delta_varp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + range_bias: float = 0.0, + ): + """ + Constructor for this projection set. + + :param r_ca_scp: Range at Closest Approach for the SCP (m) + :param inca_time_coa_poly: Time of Closest Approach polynomial coefficients + :param drate_sf_poly: Doppler Rate Scale Factor polynomial coefficients + :param coa_time_poly: Center Of Aperture (COA) time polynomial + :param arp_poly: Aperture Reference Point (ARP) position polynomial coefficients + :param delta_arp: the ARP position offset + :param delta_varp: the ARP velocity offset + :param range_bias: the range bias offset + """ + super().__init__(coa_time_poly, arp_poly, delta_arp, delta_varp, range_bias) + self.r_ca_scp = r_ca_scp + self.inca_time_coa_poly = inca_time_coa_poly + self.drate_sf_poly = drate_sf_poly + + def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_velocity) -> Tuple[np.ndarray, np.ndarray]: + """ + These are the calculations for the precise computation of the R/Rdot contour unique to these grid and + image formation algorithm types. See SICD Volume 3 Section 4.3 + + :param xrow_ycol: the [xrow, ycol] location as an array + :param coa_time: Center Of Aperture (COA) time + :param arp_position: Aperture Reference Point (ARP) position + :param arp_velocity: Aperture Reference Point (ARP) velocity + :return: the tuple containing range and range rate relative to the ARP at COA time + """ + # For the RGZERO grid, the image coordinates are range and azimuth. The row coordinate is the range + # coordinate, xrow = rg. The column coordinates is the azimuth coordinate, ycol = az. + rg = xrow_ycol[0] + az = xrow_ycol[1] + + # (2) Compute the range at closest approach and the time of closest approach for the image + # grid location. The range at closest approach, R TGT , is computed from the range coordinate. + # The time of closest approach, tTGT , is computed from the azimuth coordinate. CA + range_ca_tgt = self.r_ca_scp + rg + time_ca_tgt = self.inca_time_coa_poly(az) + + # (2 repeated in v1.3.0 of the spec) Compute the ARP velocity at the time of closest approach + # and the magnitude of the vector. + arp_velocity_ca_tgt = self.varp_poly(time_ca_tgt) + mag_arp_velocity_ca_tgt = np.sum(arp_velocity_ca_tgt, axis=-1) + + # (3) Compute the Doppler Rate Scale Factor (drsf_tgt) for image grid location (rg, az). + drsf_tgt = self.drate_sf_poly(rg, az) + + # (4) Compute the time difference between the COA time and the CA time (delta_coa_tgt). + delta_coa_tgt = coa_time - time_ca_tgt + + # (5) Compute the range and range rate relative to the ARP at COA ( RTGT and RdotTGT ). + r_tgt_coa = np.sqrt(range_ca_tgt**2 + drsf_tgt * mag_arp_velocity_ca_tgt**2 * delta_coa_tgt**2) + r_dot_tgt_coa = (drsf_tgt / r_tgt_coa) * mag_arp_velocity_ca_tgt**2 * delta_coa_tgt + + return r_tgt_coa, r_dot_tgt_coa + + +class PlaneProjectionSet(COAProjectionSet): + def __init__( + self, + scp_ecf: WorldCoordinate, + image_plane_urow: np.ndarray, + image_plane_ucol: np.ndarray, + coa_time_poly: Polynomial2D, + arp_poly: PolynomialXYZ, + delta_arp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + delta_varp: np.ndarray = np.array([0.0, 0.0, 0.0], dtype="float64"), + range_bias: float = 0.0, + ): + """ + Constructor for this projection set. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param image_plane_urow: Unit vector in the increasing row direction in ECF coordinates (uRow) + :param image_plane_ucol: Unit vector in the increasing column direction in ECF coordinates (uCol) + :param coa_time_poly: Center Of Aperture (COA) time polynomial + :param arp_poly: Aperture Reference Point (ARP) position polynomial coefficients + :param delta_arp: the ARP position offset + :param delta_varp: the ARP velocity offset + :param range_bias: the range bias offset + """ + super().__init__(coa_time_poly, arp_poly, delta_arp, delta_varp, range_bias) + self.scp_ecf = scp_ecf + self.image_plane_urow = image_plane_urow + self.image_plane_ucol = image_plane_ucol + + def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_velocity) -> Tuple[np.ndarray, np.ndarray]: + """ + These are the calculations for the precise computation of the R/Rdot contour unique to these grid and + image formation algorithm types. See SICD Volume 3 Sections 4.4, 4.5, and 4.6. + + Note that the calculations in sections 4.4, 4.5, and 4.6 are the same with the only difference being the + interpretation of the xrow and ycol gird positions. To share this one implementation for all three grid + planes assume: xrow = xrg = xct and ycol = ycr = yat + + :param xrow_ycol: the [xrow, ycol] location as an array + :param coa_time: Center Of Aperture (COA) time + :param arp_position: Aperture Reference Point (ARP) position + :param arp_velocity: Aperture Reference Point (ARP) velocity + :return: the tuple containing range and range rate relative to the ARP at COA time + """ + + # xrow = xrg = xct and ycol = ycr = yat + + # (2) The samples of an XRGYCR, XCTYAT, or PLANE grid are uniformly spaced locations in the image plane + # formed by the SCP, and image plane vectors uRow and uCol. Vectors uRow and uCol are orthogonal. Compute + # the point the image plane point for image grid location (xrgTGT, ycrTGT). + image_plane_point = ( + self.scp_ecf.coordinate + xrow_ycol[0] * self.image_plane_urow + xrow_ycol[1] * self.image_plane_ucol + ) + + # (3) Compute the range and range rate relative to the ARP at COA (r_tgt_coa and rdot_tgt_coa) for image plane + # point (image_plane_point). + arp_minus_ipp = arp_position - image_plane_point + r_tgt_coa = np.linalg.norm(arp_minus_ipp, axis=-1) + rdot_tgt_coa = np.sum(arp_velocity * arp_minus_ipp, axis=-1) / r_tgt_coa + + return r_tgt_coa, rdot_tgt_coa + + +class RRDotSurfaceProjection: + """ + This is the base class for calculations that project the R/RDot contour onto a surface model. The SICD specification + defines a way to do this for planes, a surface of constant height above an ellipsoid, or a digital elevation model. + """ + + @abstractmethod + def rrdot_to_ground(self, r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity) -> np.ndarray: + """ + Subclasses should implement this method to compute the R/RDot Contour Ground Plane intersection with a + specific surface type (e.g. planar, HAE, DEM) + + :param r_tgt_coa: target COA range + :param r_dot_tgt_coa: target COA range rate + :param arp_position: ARP position + :param arp_velocity: ARP velocity + :return: the intersection between the R/Rdot Contour and the ground plane + """ + + +class GroundPlaneRRDotSurfaceProjection(RRDotSurfaceProjection): + """ + This class implements the Precise R/RDot Ground Plane Projection described in Section 5 of the SICD Specification + Volume 3 (v1.3.0). + """ + + class GroundPlaneNormalType(Enum): + SPHERICAL_EARTH = "SPHERICAL_EARTH" + GEODETIC_EARTH = "GEODETIC_EARTH" + + def __init__( + self, + ref_ecf: WorldCoordinate, + gpn: Optional[np.ndarray], + gpn_type: GroundPlaneNormalType = GroundPlaneNormalType.GEODETIC_EARTH, + ): + """ + The ground plane is defined by a reference point in the plane (ref_ect) and the vector normal to the plane + (gpn). The reference point and plane orientation may be based upon specific terrain height and slope + information for the imaged area. When only a reference point is specified, a ground plane normal may be + derived assuming the plane is tangent to a spherical earth model or a surface of constant geodetic height + above the WGS-84 reference ellipsoid passing through (ref_ect). + + :param ref_ecf: reference point in the plane, GREF in the specification + :param gpn: optional vector normal to the ground plane; if missing it will be computed using gpn_type + :param gpn_type: method to derive the ground plan normal + """ + self.ref_ecf = ref_ecf + + if gpn is not None: + self.u_gpn = gpn / np.linalg.norm(gpn) + elif gpn_type == self.GroundPlaneNormalType.SPHERICAL_EARTH: + self.u_gpn = ref_ecf.coordinate / np.linalg.norm(ref_ecf.coordinate) + elif gpn_type == self.GroundPlaneNormalType.GEODETIC_EARTH: + ref_lle = geocentric_to_geodetic(ref_ecf) + self.u_gpn = np.array( + [ + np.cos(ref_lle.latitude) * np.cos(ref_lle.longitude), + np.cos(ref_lle.latitude) * np.sin(ref_lle.longitude), + np.sin(ref_lle.latitude), + ] + ) + else: + raise ValueError(f"Provided gpn_type, {gpn_type}, is invalid.") + + def rrdot_to_ground(self, r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity) -> np.ndarray: + """ + This method implements the R/RDot Contour Ground Plane Intersection described in section 5.2 + + :param r_tgt_coa: target COA range + :param r_dot_tgt_coa: target COA range rate + :param arp_position: ARP position + :param arp_velocity: ARP velocity + :return: the intersection between the R/Rdot Contour and the ground plane + """ + # (1) Compute the unit vector in the +Z direction (normal to the ground plane). + uvect_z = self.u_gpn / np.linalg.norm(self.u_gpn) + + # (2) Compute the ARP distance from the plane (arp_z). Also compute the ARP ground plane nadir (agpn). + arp_z = np.sum((arp_position - self.ref_ecf.coordinate) * uvect_z, axis=-1) + if arp_z > r_tgt_coa: + raise ValueError("No solution exists. Distance between ARP and the plane is greater than range.") + + agpn = arp_position - np.outer(arp_z, uvect_z) + + # (3) Compute the ground plane distance (gp_distance) from the ARP nadir to the circle of constant range. Also + # compute the sine and cosine of the grazing angle (sin_graz and cos_graz). + gp_distance = np.sqrt(r_tgt_coa * r_tgt_coa - arp_z * arp_z) + sin_graz = arp_z / r_tgt_coa + cos_graz = gp_distance / r_tgt_coa + + # (4) Compute velocity components normal to the ground plane (v_z) and parallel to the ground plane (v_x). + v_z = np.dot(arp_velocity, uvect_z) + v_mag = np.linalg.norm(arp_velocity, axis=-1) + v_x = np.sqrt(v_mag * v_mag - v_z * v_z) + + # (5) Orient the +X direction in the ground plane such that the v_x > 0. Compute unit vectors uvect_x + # and uvect_y. + uvect_x = (arp_velocity - (v_z * uvect_z)) / v_x + uvect_y = np.cross(uvect_z, uvect_x) + + # (6) Compute the cosine of the azimuth angle to the ground plane point. + cos_az = (-r_dot_tgt_coa + v_z * sin_graz) / (v_x * cos_graz) + if np.abs(cos_az) > 1: + raise ValueError("No solution exists. cos_az < -1 or cos_az > 1.") + + # (7) Compute the sine of the azimuth angle. Use parameter LOOK to establish the correct sign corresponding + # to the correct Side of Track. + look = np.sign(np.dot(np.cross(arp_position - self.ref_ecf.coordinate, arp_velocity), uvect_z)) + sin_az = look * np.sqrt(1 - cos_az * cos_az) + + # (8) Compute GPPTGT at distance G from the AGPN and at the correct azimuth angle. + return agpn + uvect_x * (gp_distance * cos_az) + uvect_y * (gp_distance * sin_az) + + +class SICDSensorModel(SensorModel): + """ + This is an implementation of the SICD sensor model as described by SICD Volume 3 Image Projections Description + NGA.STND.0024-3_1.3.0 (2021-11-30) + """ + + def __init__( + self, + coord_converter: SARImageCoordConverter, + coa_projection_set: COAProjectionSet, + scp_arp: np.ndarray, + scp_varp: np.ndarray, + side_of_track: str, + u_gpn: Optional[np.ndarray] = None, + ): + """ + Constructs a SICD sensor model from the information derived from the XML metadata. + + :param coord_converter: converts coordinates between image grid and image plane + :param coa_projection_set: projects image locations to the r/rdot contour + :param scp_arp: aperture reference point position + :param scp_varp: aperture reference point velocity + :param side_of_track: side of track imaged + :param u_gpn: optional unit normal for ground plane + """ + super().__init__() + self.coa_projection_set = coa_projection_set + self.image_plane = coord_converter + self.uvect_gpn = u_gpn + self.scp_arp = scp_arp + self.scp_varp = scp_varp + self.side_of_track = side_of_track + + self.uvect_spn = np.cross(scp_varp, coord_converter.scp_ecf.coordinate - scp_arp) + if side_of_track == "R": + self.uvect_spn *= -1.0 + self.uvect_spn /= np.linalg.norm(self.uvect_spn) + + # TODO: Add option for HAE ground assumption, does world_to_image always need a GroundPlaneProjection? + self.default_surface_projection = GroundPlaneRRDotSurfaceProjection(self.image_plane.scp_ecf, self.uvect_gpn) + + def image_to_world( + self, + image_coordinate: ImageCoordinate, + elevation_model: Optional[ElevationModel] = None, + options: Optional[Dict[str, Any]] = None, + ) -> GeodeticWorldCoordinate: + """ + This is an implementation of an Image Grid to Scene point projection that first projects the image + location to the R/RDot contour and then intersects the R/RDot contour with the elevation model. + + :param image_coordinate: the x,y image coordinate + :param elevation_model: the optional elevation model, if none supplied a plane tangent to SCP is assumed + :param options: no additional options are supported at this time + :return: the lon, lat, elev geodetic coordinate of the surface matching the image coordinate + """ + row_col = np.array( + [image_coordinate.r + self.image_plane.first_pixel.r, image_coordinate.c + self.image_plane.first_pixel.c] + ) + xrow_ycol = self.image_plane.rowcol_to_xrowycol(row_col=row_col) + r_tgt_coa, r_dot_tgt_coa, time_coa, arp_coa, varp_coa = self.coa_projection_set.precise_rrdot_computation(xrow_ycol) + + if elevation_model is not None: + raise NotImplementedError("SICD sensor model with DEM not yet implemented") + else: + surface_projection = self.default_surface_projection + + # Note that for a DEM the r/rdot contour may intersect the surface at multiple locations + # resulting in an ambiguous location. Here we are arbitrarily selecting the first result. + # TODO: Is there a better way to handle multiple DEM intersections? + coords_ecf = surface_projection.rrdot_to_ground(r_tgt_coa, r_dot_tgt_coa, arp_coa, varp_coa) + + return geocentric_to_geodetic(WorldCoordinate(coords_ecf[0])) + + def world_to_image(self, world_coordinate: GeodeticWorldCoordinate) -> ImageCoordinate: + """ + This is an implementation of Section 6.1 Scene To Image Grid Projection for a single point. + + :param world_coordinate: lon, lat, elevation coordinate of the scene point + :return: the x,y pixel location in this image + """ + ecf_world_coordinate = geodetic_to_geocentric(world_coordinate) + + # TODO: Consider making these options like we have for image_to_world + tolerance = 1e-2 + max_iterations = 10 + + # (2) Ground plane points are projected along straight lines to the image plane to establish points. + # The GP to IP projection direction is along the SCP COA slant plane normal. Also, compute the image + # plane unit normal, uIPN. Compute projection scale factor SF as shown. + uvect_proj = self.uvect_spn + scale_factor = float(np.dot(uvect_proj, self.image_plane.uvect_ipn)) + + # (3) Set initial ground plane position G1 to the scene point position S. + scene_point = np.array([ecf_world_coordinate.x, ecf_world_coordinate.y, ecf_world_coordinate.z]) + g_n = scene_point.copy() + + xrow_ycol_n = None + cont = True + iteration = 0 + while cont: + iteration += 1 + + # (4) Project ground plane point g_n to image plane point i_n. The projection distance is dist_n. Compute + # image coordinates xrown and ycoln. + dist_n = np.dot(self.image_plane.scp_ecf.coordinate - g_n, self.image_plane.uvect_ipn) / scale_factor + i_n = g_n + dist_n * uvect_proj + xrow_ycol_n = self.image_plane.ipp_to_xrowycol(i_n) + + # (5) Compute the precise projection for image grid location (xrown, ycoln) to the ground plane containing + # the scene point S. The result is point p_n. For image grid location (xrown, ycoln), compute COA + # parameters per Section 2. Compute the precise R/Rdot projection contour per Section 4. Compute the + # R/Rdot intersection with the ground plane per Section 5. + r_tgt_coa, r_dot_tgt_coa, time_coa, arp_coa, varp_coa = self.coa_projection_set.precise_rrdot_computation( + xrow_ycol_n + ) + p_n = self.default_surface_projection.rrdot_to_ground(r_tgt_coa, r_dot_tgt_coa, arp_coa, varp_coa) + + # (6) Compute the displacement between ground plane point Pn and the scene point S. + diff_n = scene_point - p_n[0] + delta_gpn = np.linalg.norm(diff_n) + g_n += diff_n + + # If the displacement is greater than the threshold (GP_MAX), compute point Gn+1 and repeat the + # projections in steps (4) and (5) above. If the displacement is less than the threshold, accept image + # grid location (xrown, ycoln) as the precise image grid location for scene point S. + cont = delta_gpn > tolerance and (iteration < max_iterations) + + row_col = self.image_plane.xrowycol_to_rowcol(xrow_ycol_n) + + # Convert the row_col image grid location to an x,y image coordinate. Note that row_col is in reference + # to the full image, so we subtract off the first_pixel offset to make the image coordinate correct if this + # is a chip. + return ImageCoordinate([row_col[1] - self.image_plane.first_pixel.x, row_col[0] - self.image_plane.first_pixel.y]) diff --git a/test/aws/osml/gdal/test_sensor_model_factory.py b/test/aws/osml/gdal/test_sensor_model_factory.py index c3f3e83..f4d0f67 100644 --- a/test/aws/osml/gdal/test_sensor_model_factory.py +++ b/test/aws/osml/gdal/test_sensor_model_factory.py @@ -166,6 +166,35 @@ def test_sensor_model_builder_gcps(self): np.array([radians(121.7518378), radians(13.89145147), 0.0]), ) + def test_sicd_sensor_models(self): + from aws.osml.gdal.sensor_model_factory import SensorModelFactory + from aws.osml.photogrammetry import ImageCoordinate, SICDSensorModel, geocentric_to_geodetic + + test_examples = [ + "./test/data/sicd/capella-sicd121-chip1.ntf", + "./test/data/sicd/capella-sicd121-chip2.ntf", + "./test/data/sicd/umbra-sicd121-chip1.ntf", + ] + for image_path in test_examples: + ds = gdal.Open(image_path) + xml_dess = ds.GetMetadata("xml:DES") + factory = SensorModelFactory(ds.RasterXSize, ds.RasterYSize, xml_dess=xml_dess) + sm = factory.build() + assert sm is not None + assert isinstance(sm, SICDSensorModel) + + scp_image_coord = ImageCoordinate( + [ + sm.image_plane.scp_pixel.x - sm.image_plane.first_pixel.x, + sm.image_plane.scp_pixel.y - sm.image_plane.first_pixel.y, + ] + ) + scp_world_coord = geocentric_to_geodetic(sm.image_plane.scp_ecf) + + assert np.allclose(scp_image_coord.coordinate, sm.world_to_image(scp_world_coord).coordinate, atol=1.0) + + assert np.allclose(scp_world_coord.coordinate, sm.image_to_world(scp_image_coord).coordinate) + if __name__ == "__main__": unittest.main() diff --git a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py new file mode 100644 index 0000000..357e1b0 --- /dev/null +++ b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py @@ -0,0 +1,197 @@ +import unittest +from math import radians +from pathlib import Path + +import numpy as np +from xsdata.formats.dataclass.parsers import XmlParser + +import aws.osml.formats.sicd.models.sicd_v1_2_1 as sicd121 +from aws.osml.gdal.sicd_sensor_model_builder import poly1d_to_native, poly2d_to_native, xyzpoly_to_native, xyztype_to_ndarray +from aws.osml.photogrammetry import ( + GeodeticWorldCoordinate, + ImageCoordinate, + INCAProjectionSet, + PFAProjectionSet, + PlaneProjectionSet, + SARImageCoordConverter, + SICDSensorModel, + WorldCoordinate, + geodetic_to_geocentric, +) + + +class TestSICDSensorModel(unittest.TestCase): + def setUp(self): + pass + + def tearDown(self): + pass + + def test_xrgycr(self): + sicd: sicd121.SICD = XmlParser().from_path(Path("./test/data/sicd/example.sicd121.rma.xml")) + + scp_ecf = WorldCoordinate(xyztype_to_ndarray(sicd.geo_data.scp.ecf)) + scp_pixel = ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + + time_coa_poly = poly2d_to_native(sicd.grid.time_coapoly) + arp_poly = xyzpoly_to_native(sicd.position.arppoly) + + first_pixel = ImageCoordinate([sicd.image_data.first_col, sicd.image_data.first_row]) + + coord_converter = SARImageCoordConverter( + scp_pixel=scp_pixel, + scp_ecf=scp_ecf, + u_row=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + u_col=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + row_ss=sicd.grid.row.ss, + col_ss=sicd.grid.col.ss, + first_pixel=first_pixel, + ) + + projection_set = PlaneProjectionSet( + scp_ecf=scp_ecf, + image_plane_urow=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + image_plane_ucol=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + + sicd_sensor_model = SICDSensorModel( + coord_converter=coord_converter, + coa_projection_set=projection_set, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ) + + geodetic_world_coordinate = sicd_sensor_model.image_to_world( + ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + ) + ecf_world_coordinate = geodetic_to_geocentric(geodetic_world_coordinate) + + assert np.allclose(ecf_world_coordinate.coordinate, scp_ecf.coordinate) + + geo_scp_world_coordinate = GeodeticWorldCoordinate( + [radians(sicd.geo_data.scp.llh.lon), radians(sicd.geo_data.scp.llh.lat), sicd.geo_data.scp.llh.hae] + ) + + assert np.allclose(geo_scp_world_coordinate.coordinate, geodetic_world_coordinate.coordinate) + + calculated_image_scp = sicd_sensor_model.world_to_image(geo_scp_world_coordinate) + + assert np.allclose(calculated_image_scp.coordinate, scp_pixel.coordinate) + + def test_rgzero_inca(self): + sicd: sicd121.SICD = XmlParser().from_path(Path("./test/data/sicd/example.sicd121.capella.xml")) + + scp_ecf = WorldCoordinate(xyztype_to_ndarray(sicd.geo_data.scp.ecf)) + scp_pixel = ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + + time_coa_poly = poly2d_to_native(sicd.grid.time_coapoly) + arp_poly = xyzpoly_to_native(sicd.position.arppoly) + + first_pixel = ImageCoordinate([sicd.image_data.first_col, sicd.image_data.first_row]) + + image_plane = SARImageCoordConverter( + scp_pixel=scp_pixel, + scp_ecf=scp_ecf, + u_row=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + u_col=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + row_ss=sicd.grid.row.ss, + col_ss=sicd.grid.col.ss, + first_pixel=first_pixel, + ) + + projection_set = INCAProjectionSet( + r_ca_scp=sicd.rma.inca.r_ca_scp, + inca_time_coa_poly=poly1d_to_native(sicd.rma.inca.time_capoly), + drate_sf_poly=poly2d_to_native(sicd.rma.inca.drate_sfpoly), + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + + sicd_sensor_model = SICDSensorModel( + coord_converter=image_plane, + coa_projection_set=projection_set, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ) + + geodetic_world_coordinate = sicd_sensor_model.image_to_world( + ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + ) + ecf_world_coordinate = geodetic_to_geocentric(geodetic_world_coordinate) + + assert np.allclose(ecf_world_coordinate.coordinate, scp_ecf.coordinate) + + geo_scp_world_coordinate = GeodeticWorldCoordinate( + [radians(sicd.geo_data.scp.llh.lon), radians(sicd.geo_data.scp.llh.lat), sicd.geo_data.scp.llh.hae] + ) + + assert np.allclose(geo_scp_world_coordinate.coordinate, geodetic_world_coordinate.coordinate) + + calculated_image_scp = sicd_sensor_model.world_to_image(geo_scp_world_coordinate) + + assert np.allclose(calculated_image_scp.coordinate, scp_pixel.coordinate) + + def test_rgazim_pfa(self): + sicd: sicd121.SICD = XmlParser().from_path(Path("./test/data/sicd/example.sicd121.pfa.xml")) + + scp_ecf = WorldCoordinate(xyztype_to_ndarray(sicd.geo_data.scp.ecf)) + scp_pixel = ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + + polar_ang_poly = poly1d_to_native(sicd.pfa.polar_ang_poly) + spatial_freq_sf_poly = poly1d_to_native(sicd.pfa.spatial_freq_sfpoly) + time_coa_poly = poly2d_to_native(sicd.grid.time_coapoly) + arp_poly = xyzpoly_to_native(sicd.position.arppoly) + + first_pixel = ImageCoordinate([sicd.image_data.first_col, sicd.image_data.first_row]) + + image_plane = SARImageCoordConverter( + scp_pixel=scp_pixel, + scp_ecf=scp_ecf, + u_row=xyztype_to_ndarray(sicd.grid.row.uvect_ecf), + u_col=xyztype_to_ndarray(sicd.grid.col.uvect_ecf), + row_ss=sicd.grid.row.ss, + col_ss=sicd.grid.col.ss, + first_pixel=first_pixel, + ) + + projection_set = PFAProjectionSet( + scp_ecf=scp_ecf, + polar_ang_poly=polar_ang_poly, + spatial_freq_sf_poly=spatial_freq_sf_poly, + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + + # FPN is the default ground plane normal for a PFA projection otherwise we calculate it as a normal + # from WGS84 ellipsoid + ugpn = xyztype_to_ndarray(sicd.pfa.fpn) + + sicd_sensor_model = SICDSensorModel( + coord_converter=image_plane, + coa_projection_set=projection_set, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + u_gpn=ugpn, + ) + + geodetic_world_coordinate = sicd_sensor_model.image_to_world( + ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + ) + ecf_world_coordinate = geodetic_to_geocentric(geodetic_world_coordinate) + + assert np.allclose(ecf_world_coordinate.coordinate, scp_ecf.coordinate) + + geo_scp_world_coordinate = GeodeticWorldCoordinate( + [radians(sicd.geo_data.scp.llh.lon), radians(sicd.geo_data.scp.llh.lat), sicd.geo_data.scp.llh.hae] + ) + + assert np.allclose(geo_scp_world_coordinate.coordinate, geodetic_world_coordinate.coordinate) + + calculated_image_scp = sicd_sensor_model.world_to_image(geo_scp_world_coordinate) + + assert np.allclose(calculated_image_scp.coordinate, scp_pixel.coordinate) diff --git a/test/data/sicd/capella-sicd121-chip1.ntf b/test/data/sicd/capella-sicd121-chip1.ntf new file mode 100644 index 0000000..1516370 --- /dev/null +++ b/test/data/sicd/capella-sicd121-chip1.ntf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f11dbe73eff3a8b55c50a523d634a63eb98966fa0d4cf32a33b7339f725b59f0 +size 1094244 diff --git a/test/data/sicd/capella-sicd121-chip2.ntf b/test/data/sicd/capella-sicd121-chip2.ntf new file mode 100644 index 0000000..478f79a --- /dev/null +++ b/test/data/sicd/capella-sicd121-chip2.ntf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c6950dad897c1ff4c99e1a1256b99c4b18261e20c43fccca03ac75c17e8b6bf +size 1064611 diff --git a/test/data/sicd/example.sicd121.capella.xml b/test/data/sicd/example.sicd121.capella.xml new file mode 100644 index 0000000..fd24b36 --- /dev/null +++ b/test/data/sicd/example.sicd121.capella.xml @@ -0,0 +1,402 @@ + + + capella-2 + 15JAN21capella-2173921 + MONOSTATIC + + STRIPMAP + + UNCLASSIFIED + + + Capella SAR Processor (2.5.3, 37a73cc02e5a357756980ce065e2b53adeae4e9d) + 2021-09-01T22:48:40.000000Z + Unknown + sarpy 1.2.5 + + + RE16I_IM16I + 5388 + 19083 + 0 + 0 + + 5388 + 19083 + + + 2694 + 9541 + + + + 0 + 0 + + + 0 + 19083 + + + 5388 + 19083 + + + 5388 + 0 + + + 0 + 0 + + + + + WGS_84 + + + 5271232.528561848 + -703918.7036014228 + 3509547.755004264 + + + 33.59934615859317 + -7.606259320191953 + 54.63396231038757 + + + + + 33.57557419233318 + -7.715737959893586 + + + 33.66247968391572 + -7.521776845384764 + + + 33.62304102343928 + -7.496736772717166 + + + 33.53576476994573 + -7.690344834684546 + + + + + 33.57557419233318 + -7.715737959893586 + + + 33.66247968391572 + 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b/test/data/sicd/umbra-sicd121-chip1.ntf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:efc1eaa5e44a25a11ba3d963c9344574b35dac0622c3c349cbdbd2a4dadfb984 +size 2136682 From a190b1863eb1b2a40faa11db2c639f07f109cc27 Mon Sep 17 00:00:00 2001 From: edparris Date: Thu, 7 Sep 2023 12:33:16 -0400 Subject: [PATCH 02/20] feat: add optional dynamic pixel range operations to tile factory (#9) The GDAL tile factory can now automatically rescale pixel values to match a desired output type. This is commonly used when converting panchromatic imagery which often has 11-16 bits per pixel into an 8-bits per pixel representation for visualization. These operations look at the histogram of the input image pixel values and then map them to the output range. --- src/aws/osml/gdal/__init__.py | 3 +- src/aws/osml/gdal/dynamic_range_adjustment.py | 86 +++++++++++++ src/aws/osml/gdal/gdal_utils.py | 120 ++++++++++++------ src/aws/osml/gdal/typing.py | 16 +++ .../image_processing/gdal_tile_factory.py | 21 ++- .../gdal/test_dynamic_range_adjustment.py | 18 +++ .../test_gdal_tile_factory.py | 29 ++++- 7 files changed, 248 insertions(+), 45 deletions(-) create mode 100644 src/aws/osml/gdal/dynamic_range_adjustment.py create mode 100644 test/aws/osml/gdal/test_dynamic_range_adjustment.py diff --git a/src/aws/osml/gdal/__init__.py b/src/aws/osml/gdal/__init__.py index 30adabf..a5c7e08 100644 --- a/src/aws/osml/gdal/__init__.py +++ b/src/aws/osml/gdal/__init__.py @@ -10,7 +10,7 @@ from .gdal_utils import get_image_extension, get_type_and_scales, load_gdal_dataset from .nitf_des_accessor import NITFDESAccessor from .sensor_model_factory import ChippedImageInfoFacade, SensorModelFactory, SensorModelTypes -from .typing import GDALCompressionOptions, GDALImageFormats +from .typing import GDALCompressionOptions, GDALImageFormats, RangeAdjustmentType __all__ = [ "set_gdal_default_configuration", @@ -21,6 +21,7 @@ "GDALConfigEnv", "GDALDigitalElevationModelTileFactory", "GDALImageFormats", + "RangeAdjustmentType", "NITFDESAccessor", "ChippedImageInfoFacade", "SensorModelFactory", diff --git a/src/aws/osml/gdal/dynamic_range_adjustment.py b/src/aws/osml/gdal/dynamic_range_adjustment.py new file mode 100644 index 0000000..97b0602 --- /dev/null +++ b/src/aws/osml/gdal/dynamic_range_adjustment.py @@ -0,0 +1,86 @@ +from typing import List + + +class DRAParameters: + """ + This class manages a set of parameters used to perform a Dynamic Range Adjustment that is applied when + converting imagery pixel values (e.g. 11-bit per pixel panchromatic imagery to an 8-bit per pixel grayscale). + """ + + def __init__( + self, suggested_min_value: float, suggested_max_value: float, actual_min_value: float, actual_max_value: float + ): + """ + Constructor for this class. + + :param suggested_min_value: suggested minimum value of the relevant pixel range + :param suggested_max_value: suggested maximum value of the relevant pixel range + :param actual_min_value: actual minimum value of pixels in the image + :param actual_max_value: actual maximum value of pixels in the image + """ + self.suggested_min_value = suggested_min_value + self.suggested_max_value = suggested_max_value + self.actual_min_value = actual_min_value + self.actual_max_value = actual_max_value + + @staticmethod + def from_counts( + counts: List[float], min_percentage: float = 0.02, max_percentage: float = 0.98, a: float = 0.2, b: float = 0.4 + ) -> "DRAParameters": + """ + This static factory method computes a new set of DRA parameters given a histogram of pixel values. + + :param counts: histogram of the pixel values + :param min_percentage: set point for low intensity pixels that may be outliers + :param max_percentage: set point for high intensity pixels that may be outliers + :param a: weighting factor for the low intensity range + :param b: weighting factor for the high intensity range + :return: a set of DRA parameters containing recommended and actual ranges of values + """ + num_histogram_bins = len(counts) + + # Find the first and last non-zero counts + actual_min_value = 0 + while actual_min_value < num_histogram_bins and counts[actual_min_value] == 0: + actual_min_value += 1 + + actual_max_value = num_histogram_bins - 1 + while actual_max_value > 0 and counts[actual_max_value] == 0: + actual_max_value -= 1 + + # Compute the cumulative distribution + cumulative_counts = counts.copy() + for i in range(1, len(cumulative_counts)): + cumulative_counts[i] = cumulative_counts[i] + cumulative_counts[i - 1] + + # Find the values that exclude the lowest and highest percentages of the counts. + # This identifies the range that contains most of the pixels while excluding outliers. + max_counts = cumulative_counts[-1] + low_threshold = min_percentage * max_counts + e_min = 0 + while cumulative_counts[e_min] < low_threshold: + e_min += 1 + + high_threshold = max_percentage * max_counts + e_max = num_histogram_bins - 1 + while cumulative_counts[e_max] > high_threshold: + e_max -= 1 + + min_value = max([actual_min_value, e_min - a * (e_max - e_min)]) + max_value = min([actual_max_value, e_max + b * (e_max - e_min)]) + + return DRAParameters( + suggested_min_value=min_value, + suggested_max_value=max_value, + actual_min_value=actual_min_value, + actual_max_value=actual_max_value, + ) + + def __repr__(self): + return ( + f"DRAParameters(min_value={self.suggested_min_value}, " + f"max_value={self.suggested_max_value}, " + f"e_first={self.actual_min_value}, " + f"e_last={self.actual_max_value}, " + f")" + ) diff --git a/src/aws/osml/gdal/gdal_utils.py b/src/aws/osml/gdal/gdal_utils.py index 8e86f5c..2633691 100644 --- a/src/aws/osml/gdal/gdal_utils.py +++ b/src/aws/osml/gdal/gdal_utils.py @@ -7,7 +7,9 @@ from aws.osml.photogrammetry import SensorModel +from .dynamic_range_adjustment import DRAParameters from .sensor_model_factory import SensorModelFactory, SensorModelTypes +from .typing import RangeAdjustmentType logger = logging.getLogger(__name__) @@ -65,54 +67,100 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod return ds, sensor_model -def get_type_and_scales(raster_dataset: gdal.Dataset) -> Tuple[int, List[List[int]]]: +def get_minmax_for_type(gdal_type: int) -> Tuple[float, float]: + """ + This function computes the minimum and maximum values that can be stored in a given GDAL pixel type. + + :param gdal_type: the pixel type + :return: tuple of min, max values + """ + min_value = 0 + max_value = 255 + if gdal_type == gdalconst.GDT_Byte: + min_value = 0 + max_value = 2**8 - 1 + elif gdal_type == gdalconst.GDT_UInt16: + min_value = 0 + max_value = 2**16 - 1 + elif gdal_type == gdalconst.GDT_Int16: + min_value = -(2**15) + max_value = 2**15 - 1 + elif gdal_type == gdalconst.GDT_UInt32: + min_value = 0 + max_value = 2**32 - 1 + elif gdal_type == gdalconst.GDT_Int32: + min_value = -(2**31) + max_value = 2**31 - 1 + elif gdal_type == gdalconst.GDT_UInt64: + min_value = 0 + max_value = 2**64 - 1 + elif gdal_type == gdalconst.GDT_Int64: + min_value = -(2**63) + max_value = 2**63 - 1 + elif gdal_type == gdalconst.GDT_Float32: + min_value = -3.4028235e38 + max_value = 3.4028235e38 + elif gdal_type == gdalconst.GDT_Float64: + min_value = -1.7976931348623157e308 + max_value = 1.7976931348623157e308 + else: + logger.warning("Image uses unsupported GDAL datatype {}. Defaulting to [0,255] range".format(gdal_type)) + + return min_value, max_value + + +def get_type_and_scales( + raster_dataset: gdal.Dataset, + desired_output_type: Optional[int] = None, + range_adjustment: RangeAdjustmentType = RangeAdjustmentType.NONE, +) -> Tuple[int, List[List[int]]]: """ Get type and scales of a provided raster dataset :param raster_dataset: the raster dataset containing the region + :param desired_output_type: type to be output after dynamic range adjustments + :param range_adjustment: the type of pixel scaling effort to :return: a tuple containing type and scales """ scale_params = [] - num_bands = raster_dataset.RasterCount output_type = gdalconst.GDT_Byte - min = 0 - max = 255 + num_bands = raster_dataset.RasterCount for band_num in range(1, num_bands + 1): band = raster_dataset.GetRasterBand(band_num) - output_type = band.DataType - if output_type == gdalconst.GDT_Byte: - min = 0 - max = 2**8 - 1 - elif output_type == gdalconst.GDT_UInt16: - min = 0 - max = 2**16 - 1 - elif output_type == gdalconst.GDT_Int16: - min = -(2**15) - max = 2**15 - 1 - elif output_type == gdalconst.GDT_UInt32: - min = 0 - max = 2**32 - 1 - elif output_type == gdalconst.GDT_Int32: - min = -(2**31) - max = 2**31 - 1 - # TODO: Add these 64-bit cases in once GDAL is upgraded to a version that supports them - # elif output_type == gdalconst.GDT_UInt64: - # min = 0 - # max = 2**64-1 - # elif output_type == gdalconst.GDT_Int64: - # min = -2**63 - # max = 2**63-1 - elif output_type == gdalconst.GDT_Float32: - min = -3.4028235e38 - max = 3.4028235e38 - elif output_type == gdalconst.GDT_Float64: - min = -1.7976931348623157e308 - max = 1.7976931348623157e308 - else: - logger.warning("Image uses unsupported GDAL datatype {}. Defaulting to [0,255] range".format(output_type)) + band_type = band.DataType + min_value, max_value = get_minmax_for_type(band_type) - scale_params.append([min, max, min, max]) + if desired_output_type is None: + output_type = band_type + output_min = min_value + output_max = max_value + else: + output_type = desired_output_type + output_min, output_max = get_minmax_for_type(desired_output_type) + + # If a range adjustment is requested compute the range of source pixel values that will be mapped to the full + # output range. + selected_min = min_value + selected_max = max_value + if range_adjustment is not RangeAdjustmentType.NONE: + num_buckets = int(max_value - min_value) + if band_type == gdalconst.GDT_Float32 or band_type == gdalconst.GDT_Float64: + num_buckets = 255 + dra = DRAParameters.from_counts( + band.GetHistogram(buckets=num_buckets, max=max_value, min=min_value, include_out_of_range=1, approx_ok=1) + ) + if range_adjustment == RangeAdjustmentType.DRA: + selected_min = dra.suggested_min_value + selected_max = dra.suggested_max_value + elif range_adjustment == RangeAdjustmentType.MINMAX: + selected_min = dra.actual_min_value + selected_max = dra.actual_max_value + else: + logger.warning(f"Unknown range adjustment selected {range_adjustment}. Skipping.") + + band_scale_parameters = [selected_min, selected_max, output_min, output_max] + scale_params.append(band_scale_parameters) return output_type, scale_params diff --git a/src/aws/osml/gdal/typing.py b/src/aws/osml/gdal/typing.py index d37882d..ed68333 100644 --- a/src/aws/osml/gdal/typing.py +++ b/src/aws/osml/gdal/typing.py @@ -22,3 +22,19 @@ class GDALImageFormats(str, Enum): JPEG = "JPEG" PNG = "PNG" GTIFF = "GTiff" + + +class RangeAdjustmentType(str, Enum): + """ + Enumeration defining ways to scale raw image pixels to an output value range. + + - NONE indicates that the full range available to the input type will be used. + - MINMAX chooses the portion of the input range that actually contains values. + - DRA is a dynamic range adjustment that attempts to select the most important portion of the input range. + It differs from MINMAX in that it can exclude outliers to reduce the impact of unusually bright/dark + spots in an image. + """ + + NONE = "NONE" + MINMAX = "MINMAX" + DRA = "DRA" diff --git a/src/aws/osml/image_processing/gdal_tile_factory.py b/src/aws/osml/image_processing/gdal_tile_factory.py index 4818fcf..f80af71 100644 --- a/src/aws/osml/image_processing/gdal_tile_factory.py +++ b/src/aws/osml/image_processing/gdal_tile_factory.py @@ -6,7 +6,7 @@ from defusedxml import ElementTree from osgeo import gdal, gdalconst -from aws.osml.gdal import GDALCompressionOptions, GDALImageFormats, NITFDESAccessor, get_type_and_scales +from aws.osml.gdal import GDALCompressionOptions, GDALImageFormats, NITFDESAccessor, RangeAdjustmentType, get_type_and_scales from aws.osml.photogrammetry import ImageCoordinate, SensorModel from .sicd_updater import SICDUpdater @@ -26,6 +26,8 @@ def __init__( sensor_model: Optional[SensorModel] = None, tile_format: GDALImageFormats = GDALImageFormats.NITF, tile_compression: GDALCompressionOptions = GDALCompressionOptions.NONE, + output_type: Optional[int] = None, + range_adjustment: RangeAdjustmentType = RangeAdjustmentType.NONE, ): """ Constructs a new factory capable of producing tiles from a given GDAL raster dataset. @@ -34,6 +36,8 @@ def __init__( :param sensor_model: the sensor model providing mensuration support for this image :param tile_format: the output tile format :param tile_compression: the output tile compression + :param output_type: the GDAL pixel type in the output tile + :param range_adjustment: the type of scaling used to convert raw pixel values to the output range """ self.tile_format = tile_format self.tile_compression = tile_compression @@ -42,6 +46,8 @@ def __init__( self.des_accessor = None self.sicd_updater = None self.sicd_des_header = None + self.range_adjustment = range_adjustment + self.output_type = output_type if self.raster_dataset.GetDriver().ShortName == "NITF": xml_des = self.raster_dataset.GetMetadata("xml:DES") @@ -58,6 +64,8 @@ def __init__( self.sicd_des_header = self.des_accessor.extract_des_header(sicd_des) self.sicd_updater = SICDUpdater(sicd_metadata) + self.default_gdal_translate_kwargs = self._create_gdal_translate_kwargs() + def create_encoded_tile(self, src_window: List[int]) -> Optional[bytearray]: """ This method cuts a tile from the full image, updates the metadata as needed, and finally compresses/encodes @@ -71,10 +79,10 @@ def create_encoded_tile(self, src_window: List[int]) -> Optional[bytearray]: # Use the request and metadata from the raster dataset to create a set of keyword # arguments for the gdal.Translate() function. This will configure that function to # create image tiles using the format, compression, etc. requested by the client. - gdal_translate_kwargs = self._create_gdal_translate_kwargs() + gdal_translate_kwargs = self.default_gdal_translate_kwargs.copy() # Create a new IGEOLO value based on the corner points of this tile - if self.sensor_model is not None: + if self.sensor_model is not None and self.tile_format == GDALImageFormats.NITF: gdal_translate_kwargs["creationOptions"].append("ICORDS=G") gdal_translate_kwargs["creationOptions"].append("IGEOLO=" + self.create_new_igeolo(src_window)) @@ -148,9 +156,10 @@ def _create_gdal_translate_kwargs(self) -> Dict[str, Any]: :return: Dict[str, any] = the dictionary of translate keyword arguments """ - # Figure out what type of image this is and calculate a scale that does not force any range - # remapping - output_type, scale_params = get_type_and_scales(self.raster_dataset) + # Figure out what type of image this is and calculate a scale to map input pixels to the output type + output_type, scale_params = get_type_and_scales( + self.raster_dataset, desired_output_type=self.output_type, range_adjustment=self.range_adjustment + ) gdal_translate_kwargs = { "scaleParams": scale_params, diff --git a/test/aws/osml/gdal/test_dynamic_range_adjustment.py b/test/aws/osml/gdal/test_dynamic_range_adjustment.py new file mode 100644 index 0000000..eb24fff --- /dev/null +++ b/test/aws/osml/gdal/test_dynamic_range_adjustment.py @@ -0,0 +1,18 @@ +import unittest + + +class TestDRAParameters(unittest.TestCase): + def test_from_counts(self): + from aws.osml.gdal.dynamic_range_adjustment import DRAParameters + + counts = [0] * 1024 + counts[1:99] = [1] * (99 - 1) + counts[100:400] = [200] * (400 - 100) + counts[1022] = 1 + + dra_parameters = DRAParameters.from_counts(counts=counts) + + self.assertEquals(dra_parameters.actual_min_value, 1) + self.assertEquals(dra_parameters.actual_max_value, 1022) + self.assertAlmostEqual(dra_parameters.suggested_min_value, 47, delta=1) + self.assertAlmostEquals(dra_parameters.suggested_max_value, 506, delta=1) diff --git a/test/aws/osml/image_processing/test_gdal_tile_factory.py b/test/aws/osml/image_processing/test_gdal_tile_factory.py index 560996f..f6e3fc7 100644 --- a/test/aws/osml/image_processing/test_gdal_tile_factory.py +++ b/test/aws/osml/image_processing/test_gdal_tile_factory.py @@ -2,9 +2,9 @@ from secrets import token_hex from unittest import TestCase -from osgeo import gdal +from osgeo import gdal, gdalconst -from aws.osml.gdal import GDALCompressionOptions, GDALImageFormats, load_gdal_dataset +from aws.osml.gdal import GDALCompressionOptions, GDALImageFormats, RangeAdjustmentType, load_gdal_dataset from aws.osml.image_processing import GDALTileFactory @@ -35,6 +35,31 @@ def test_create_encoded_sicd_tile_png(self): assert tile_dataset.RasterYSize == 256 assert tile_dataset.GetDriver().ShortName == GDALImageFormats.PNG + def test_create_png_with_dra(self): + full_dataset, sensor_model = load_gdal_dataset("./test/data/small.ntf") + tile_factory = GDALTileFactory( + full_dataset, + sensor_model, + GDALImageFormats.PNG, + GDALCompressionOptions.NONE, + output_type=gdalconst.GDT_Byte, + range_adjustment=RangeAdjustmentType.DRA, + ) + + full_dataset.GetRasterBand(1).ComputeStatistics(approx_ok=0) + assert full_dataset.GetRasterBand(1).GetMinimum() == 0 + assert full_dataset.GetRasterBand(1).GetMaximum() == 255 + encoded_tile_data = tile_factory.create_encoded_tile([10, 10, 128, 256]) + temp_ds_name = "/vsimem/" + token_hex(16) + ".PNG" + gdal.FileFromMemBuffer(temp_ds_name, encoded_tile_data) + tile_dataset = gdal.Open(temp_ds_name) + assert tile_dataset.RasterXSize == 128 + assert tile_dataset.RasterYSize == 256 + assert tile_dataset.GetDriver().ShortName == GDALImageFormats.PNG + tile_dataset.GetRasterBand(1).ComputeStatistics(approx_ok=0) + assert tile_dataset.GetRasterBand(1).GetMinimum() == 0 + assert tile_dataset.GetRasterBand(1).GetMaximum() == 185 + # Test data here could be improved. We're reusing a nitf file for everything and just # testing a single raster scale def test_create_gdal_translate_kwargs(self): From 93f044cc390549ffc8c1767959b22861990cb981 Mon Sep 17 00:00:00 2001 From: edparris Date: Wed, 13 Sep 2023 11:39:29 -0400 Subject: [PATCH 03/20] feat: add HAE and DEM projections to SICD sensor model (#12) --- src/aws/osml/formats/sicd/__init__.py | 1 - src/aws/osml/gdal/gdal_dem_tile_factory.py | 39 ++- src/aws/osml/photogrammetry/__init__.py | 3 +- .../photogrammetry/digital_elevation_model.py | 35 +- .../osml/photogrammetry/elevation_model.py | 37 ++ .../osml/photogrammetry/sicd_sensor_model.py | 320 +++++++++++++++++- .../osml/gdal/test_gdal_dem_tile_factory.py | 8 +- .../osml/gdal/test_sensor_model_factory.py | 6 +- .../test_digital_elevation_model.py | 6 +- .../photogrammetry/test_sicd_sensor_model.py | 30 +- 10 files changed, 446 insertions(+), 39 deletions(-) diff --git a/src/aws/osml/formats/sicd/__init__.py b/src/aws/osml/formats/sicd/__init__.py index b2a4ba5..e69de29 100644 --- a/src/aws/osml/formats/sicd/__init__.py +++ b/src/aws/osml/formats/sicd/__init__.py @@ -1 +0,0 @@ -# nothing here diff --git a/src/aws/osml/gdal/gdal_dem_tile_factory.py b/src/aws/osml/gdal/gdal_dem_tile_factory.py index f89fb6a..6ec3b53 100644 --- a/src/aws/osml/gdal/gdal_dem_tile_factory.py +++ b/src/aws/osml/gdal/gdal_dem_tile_factory.py @@ -1,9 +1,16 @@ import logging from typing import Any, Optional, Tuple +import numpy as np from osgeo import gdal -from aws.osml.photogrammetry import DigitalElevationModelTileFactory, GDALAffineSensorModel +from aws.osml.photogrammetry import ( + DigitalElevationModelTileFactory, + ElevationRegionSummary, + GDALAffineSensorModel, + ImageCoordinate, + geodetic_to_geocentric, +) class GDALDigitalElevationModelTileFactory(DigitalElevationModelTileFactory): @@ -24,7 +31,9 @@ def __init__(self, tile_directory: str) -> None: super().__init__() self.tile_directory = tile_directory - def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[GDALAffineSensorModel]]: + def get_tile( + self, tile_path: str + ) -> Tuple[Optional[Any], Optional[GDALAffineSensorModel], Optional[ElevationRegionSummary]]: """ Retrieve a numpy array of elevation values and a sensor model. @@ -32,7 +41,7 @@ def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[GDALAffineSe :param tile_path: the location of the tile to load - :return: an array of elevation values and a sensor model or (None, None) + :return: an array of elevation values, a sensor model, and a summary or (None, None, None) """ tile_location = f"{self.tile_directory}/{tile_path}" tile_location = tile_location.replace("s3:/", "/vsis3", 1) @@ -43,15 +52,31 @@ def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[GDALAffineSe # information isn't available. if not ds: logging.debug(f"No DEM tile available for {tile_path}. Checked {tile_location}") - return None, None + return None, None, None # If the raster exists but doesn't have a geo transform then it is likely invalid input data. geo_transform = ds.GetGeoTransform(can_return_null=True) if not geo_transform: logging.warning(f"DEM tile does not have geo transform metadata and can't be used: {tile_location}") - return None, None + return None, None, None - band_as_array = ds.GetRasterBand(1).ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize) + raster_band = ds.GetRasterBand(1) + band_as_array = raster_band.ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize) + height, width = band_as_array.shape sensor_model = GDALAffineSensorModel(geo_transform) - return band_as_array, sensor_model + # Compute the distance in meters from the upper left to the lower right corner. Divide that by the distance + # in pixels to get an approximate pixel size in meters + ul_corner_ecf = geodetic_to_geocentric(sensor_model.image_to_world(ImageCoordinate([0, 0]))).coordinate + lr_corner_ecf = geodetic_to_geocentric(sensor_model.image_to_world(ImageCoordinate([width, height]))).coordinate + post_spacing = np.linalg.norm(ul_corner_ecf - lr_corner_ecf) / np.sqrt(width * width + height * height) + + stats = raster_band.GetStatistics(True, True) + summary = ElevationRegionSummary( + min_elevation=stats[0], + max_elevation=stats[1], + no_data_value=raster_band.GetNoDataValue(), + post_spacing=post_spacing, + ) + + return band_as_array, sensor_model, summary diff --git a/src/aws/osml/photogrammetry/__init__.py b/src/aws/osml/photogrammetry/__init__.py index f62e01e..2fc0fac 100644 --- a/src/aws/osml/photogrammetry/__init__.py +++ b/src/aws/osml/photogrammetry/__init__.py @@ -16,7 +16,7 @@ geodetic_to_geocentric, ) from .digital_elevation_model import DigitalElevationModel, DigitalElevationModelTileFactory, DigitalElevationModelTileSet -from .elevation_model import ConstantElevationModel, ElevationModel +from .elevation_model import ConstantElevationModel, ElevationModel, ElevationRegionSummary from .gdal_sensor_model import GDALAffineSensorModel from .projective_sensor_model import ProjectiveSensorModel from .replacement_sensor_model import ( @@ -57,6 +57,7 @@ "DigitalElevationModelTileSet", "ConstantElevationModel", "ElevationModel", + "ElevationRegionSummary", "GDALAffineSensorModel", "SARImageCoordConverter", "INCAProjectionSet", diff --git a/src/aws/osml/photogrammetry/digital_elevation_model.py b/src/aws/osml/photogrammetry/digital_elevation_model.py index b5577c3..a843cd9 100644 --- a/src/aws/osml/photogrammetry/digital_elevation_model.py +++ b/src/aws/osml/photogrammetry/digital_elevation_model.py @@ -9,7 +9,7 @@ from scipy.interpolate import RectBivariateSpline from .coordinates import GeodeticWorldCoordinate -from .elevation_model import ElevationModel +from .elevation_model import ElevationModel, ElevationRegionSummary from .sensor_model import SensorModel @@ -52,7 +52,7 @@ def __init__(self) -> None: pass @abstractmethod - def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[SensorModel]]: + def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[SensorModel], Optional[ElevationRegionSummary]]: """ Retrieve a numpy array of elevation values and a sensor model. @@ -60,7 +60,7 @@ def get_tile(self, tile_path: str) -> Tuple[Optional[Any], Optional[SensorModel] :param tile_path: the location of the tile to load - :return: an array of elevation values and a sensor model + :return: an array of elevation values, a sensor model, and a summary """ @@ -115,14 +115,31 @@ def set_elevation(self, geodetic_world_coordinate: GeodeticWorldCoordinate) -> N if not tile_id: return - interpolation_grid, sensor_model = self.get_interpolation_grid(tile_id) + interpolation_grid, sensor_model, summary = self.get_interpolation_grid(tile_id) if interpolation_grid is not None and sensor_model is not None: image_coordinate = sensor_model.world_to_image(geodetic_world_coordinate) geodetic_world_coordinate.elevation = interpolation_grid(image_coordinate.x, image_coordinate.y)[0][0] + def describe_region(self, geodetic_world_coordinate: GeodeticWorldCoordinate) -> Optional[ElevationRegionSummary]: + """ + Get a summary of the region near the provided world coordinate + + :param geodetic_world_coordinate: the coordinate at the center of the region of interest + :return: a summary of the elevation data in this tile + """ + + tile_id = self.tile_set.find_tile_id(geodetic_world_coordinate) + if not tile_id: + return + + interpolation_grid, sensor_model, summary = self.get_interpolation_grid(tile_id) + return summary + @cachedmethod(operator.attrgetter("raster_cache")) - def get_interpolation_grid(self, tile_path: str) -> Tuple[Optional[RectBivariateSpline], Optional[SensorModel]]: + def get_interpolation_grid( + self, tile_path: str + ) -> Tuple[Optional[RectBivariateSpline], Optional[SensorModel], Optional[ElevationRegionSummary]]: """ This method loads and converts an array of elevation values into a class that can interpolate values that lie between measured elevations. The sensor model is also @@ -135,13 +152,13 @@ def get_interpolation_grid(self, tile_path: str) -> Tuple[Optional[RectBivariate :param tile_path: the location of the tile to load - :return: the cached interpolation object and sensor model + :return: the cached interpolation object, sensor model, and summary """ - elevations_array, sensor_model = self.tile_factory.get_tile(tile_path) + elevations_array, sensor_model, summary = self.tile_factory.get_tile(tile_path) if elevations_array is not None and sensor_model is not None: height, width = elevations_array.shape x = range(0, width) y = range(0, height) - return RectBivariateSpline(x, y, elevations_array.T, kx=1, ky=1), sensor_model + return RectBivariateSpline(x, y, elevations_array.T, kx=1, ky=1), sensor_model, summary else: - return None, None + return None, None, None diff --git a/src/aws/osml/photogrammetry/elevation_model.py b/src/aws/osml/photogrammetry/elevation_model.py index 894dc0c..907cb1b 100644 --- a/src/aws/osml/photogrammetry/elevation_model.py +++ b/src/aws/osml/photogrammetry/elevation_model.py @@ -1,8 +1,22 @@ from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Optional from .coordinates import GeodeticWorldCoordinate +@dataclass +class ElevationRegionSummary: + """ + This class contains a general summary of an elevation tile. + """ + + min_elevation: float + max_elevation: float + no_data_value: int + post_spacing: float + + class ElevationModel(ABC): """ An elevation model associates a height z for a given x, y of a world coordinate. It typically provides information @@ -24,6 +38,15 @@ def set_elevation(self, world_coordinate: GeodeticWorldCoordinate) -> None: :return: None """ + @abstractmethod + def describe_region(self, world_coordinate: GeodeticWorldCoordinate) -> Optional[ElevationRegionSummary]: + """ + Get a summary of the region near the provided world coordinate + + :param world_coordinate: the coordinate at the center of the region of interest + :return: the summary information + """ + class ConstantElevationModel(ElevationModel): """ @@ -50,3 +73,17 @@ def set_elevation(self, world_coordinate: GeodeticWorldCoordinate) -> None: :return: None """ world_coordinate.elevation = self.constant_elevation + + def describe_region(self, world_coordinate: GeodeticWorldCoordinate) -> Optional[ElevationRegionSummary]: + """ + Get a summary of the region near the provided world coordinate + + :param world_coordinate: the coordinate at the center of the region of interest + :return: [min elevation value, max elevation value, no elevation data value, post spacing] + """ + return ElevationRegionSummary( + min_elevation=self.constant_elevation, + max_elevation=self.constant_elevation, + no_data_value=-32767, + post_spacing=30.0, + ) diff --git a/src/aws/osml/photogrammetry/sicd_sensor_model.py b/src/aws/osml/photogrammetry/sicd_sensor_model.py index 29f8ef6..5edb348 100644 --- a/src/aws/osml/photogrammetry/sicd_sensor_model.py +++ b/src/aws/osml/photogrammetry/sicd_sensor_model.py @@ -677,6 +677,294 @@ def rrdot_to_ground(self, r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity) return agpn + uvect_x * (gp_distance * cos_az) + uvect_y * (gp_distance * sin_az) +class HAERRDotSurfaceProjection(RRDotSurfaceProjection): + """ + This class implements the Precise R/RDot Height Above Ellipsioid (HAE) Projection described in Section 9 of the + SICD Specification Volume 3 (v1.3.0). + """ + + def __init__( + self, + scp_ecf: WorldCoordinate, + side_of_track: str, + hae: float, + height_threshold: float = 1.0, + max_number_iterations: int = 3, + ): + """ + Constructor for the projection that takes the image parameters and a height above theWGS-84 ellipsoid. + The parameter defaults are the recommended values for the user selectable parameters in section 9.2. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param side_of_track: side of track imaged + :param hae: the surface height (m) above the WGS-84 reference ellipsoid + :param height_threshold: the height threshold for convergence of iterative projection sequence + :param max_number_iterations: maximum number of iterations allowed + """ + self.scp_ecf = scp_ecf + self.scp_lle = geocentric_to_geodetic(scp_ecf) + self.side_of_track = side_of_track + self.hae = hae + + # Integer based on side of track parameter + self.look = 1.0 + if side_of_track == "R": + self.look = -1.0 + + self.delta_hae_max = height_threshold + self.nlim = max_number_iterations + + def rrdot_to_ground(self, r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity) -> np.ndarray: + """ + This method implements the R/RDot Contour Ground Plane Intersection described in section 9.2. + + The precise projection to a surface of constant height above the WGS-84 reference ellipsoid along an + R/Rdot contour. The R/Rdot contour is relative to an ARP Center Of Aperture position and velocity. The + algorithm computes the R/Rdot projection to one or more ground planes that are tangent to the constant + height surface. Each ground plane projection point computed is slightly above the constant HAE surface. + The final surface position is computed by projecting from the final ground plane projection point down + to the HAE surface. + + :param r_tgt_coa: target COA range + :param r_dot_tgt_coa: target COA range rate + :param arp_position: ARP position + :param arp_velocity: ARP velocity + :return: the intersection between the R/Rdot Contour and the ground plane + """ + # (1) Compute the geodetic ground plane normal at the SCP. Compute the parameters for the initial ground plane. + # The reference point position is gref and the unit normal is u_gpn. + u_gpn = np.array( + [ + np.cos(self.scp_lle.latitude) * np.cos(self.scp_lle.longitude), + np.cos(self.scp_lle.latitude) * np.sin(self.scp_lle.longitude), + np.sin(self.scp_lle.latitude), + ] + ) + gref = self.scp_ecf.coordinate + (self.hae - self.scp_ecf.z) * u_gpn + + cont = True + n = 1 + while cont: + # (2) Compute the precise projection along the R/Rdot contour to Ground Plane. The result is ground plane + # point position gpp_ecf. Convert from ECF coordinates to geodetic coordinates (gpp_lle). + gp_surface_projection = GroundPlaneRRDotSurfaceProjection(ref_ecf=WorldCoordinate(gref), gpn=u_gpn) + gpp_ecf = gp_surface_projection.rrdot_to_ground(r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity)[0] + gpp_lle = geocentric_to_geodetic(WorldCoordinate(coordinate=gpp_ecf)) + + # (3) Compute the unit vector in the increasing height direction at point gpp_lle, (u_up). Also + # compute the height difference at point gpp_lle relative to the desired surface height (delta_hae). + u_up = np.array( + [ + np.cos(gpp_lle.latitude) * np.cos(gpp_lle.longitude), + np.cos(gpp_lle.latitude) * np.sin(gpp_lle.longitude), + np.sin(gpp_lle.latitude), + ] + ) + delta_hae = gpp_lle.elevation - self.hae + + # (4) Test to see if the point is sufficiently close the surface or if the maximum number of iterations + # has been reached. Otherwise, compute a new ground reference point (gref) and unit normal (u_up); repeat + # Steps 2, 3 and 4. + if delta_hae <= self.delta_hae_max or n >= self.nlim: + cont = False + else: + gref = gpp_ecf - delta_hae * u_up + u_gpn = u_up + n += 1 + + # (5) Compute the unit slant plane normal vector, u_spn, that is tangent to the R/Rdot contour at point gpp. + # Unit vector u_spn points away from the center of the earth and in a direction of increasing HAE at gpp. + spn = np.cross(self.look * arp_velocity, gpp_ecf - arp_position) + u_spn = spn / np.linalg.norm(spn) + + # (6) Compute the straight line projection from point gpp_ecf along the slant plane normal to point slp. + # Point slp is very close to the precise R/Rdot contour intersection with the constant height surface. + # Convert the position of point slp from ECF coordinates to geodetic coordinates (slp_lle). + sf = np.dot(u_up, u_spn) + slp = gpp_ecf - (delta_hae / sf) * u_spn + slp_lle = geocentric_to_geodetic(WorldCoordinate(slp)) + + # (7) Assign surface point spp position by adjusting the HAE to be on the desired surface. Convert from + # geodetic coordinates to ECF coordinates. + spp_lle = GeodeticWorldCoordinate([slp_lle.longitude, slp_lle.latitude, self.hae]) + spp_ecf = geodetic_to_geocentric(spp_lle) + + return np.array([spp_ecf.coordinate]) + + +class DEMRRDotSurfaceProjection(RRDotSurfaceProjection): + """ + This class implements the Precise R/RDot Height Above a Digital Elevation Model (DEM) Projection described in + Section 10 of the SICD Specification Volume 3 (v1.3.0). + """ + + def __init__( + self, + scp_ecf: WorldCoordinate, + side_of_track: str, + elevation_model: ElevationModel, + max_adjacent_point_distance: float = 10.0, + height_threshold: float = 0.001, + ): + """ + Constructor for the projection that takes the image parameters and a digital elevation model (DEM). + The parameter defaults are the recommended values for the user selectable parameters in section 10.3. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param side_of_track: side of track imaged + :param elevation_model: the digital elevation model + :param max_adjacent_point_distance: Maximum distance between adjacent points along the R/Rdot contour + :param height_threshold: threshold for determining if a R/Rdot contour point is on the DEM surface (m) + """ + self.scp_ecf = scp_ecf + self.scp_lle = geocentric_to_geodetic(scp_ecf) + self.side_of_track = side_of_track + self.elevation_model = elevation_model + + # Integer based on Side of Track parameter. + self.look = 1.0 + if side_of_track == "R": + self.look = -1.0 + + elevation_summary = elevation_model.describe_region(self.scp_lle) + self.hae_min = elevation_summary.min_elevation + self.hae_max = elevation_summary.max_elevation + self.hae_max_surface_projection = HAERRDotSurfaceProjection( + scp_ecf=scp_ecf, side_of_track=side_of_track, hae=self.hae_max + ) + self.hae_min_surface_projection = HAERRDotSurfaceProjection( + scp_ecf=scp_ecf, side_of_track=side_of_track, hae=self.hae_min + ) + self.delta_dist_dem = 0.5 * elevation_summary.post_spacing + + self.delta_dist_rrc = max_adjacent_point_distance + self.delta_hd_lim = height_threshold + + def rrdot_to_ground(self, r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity) -> np.ndarray: + """ + This method implements the R/RDot Contour Ground Plane Intersection described in section 10.3 + + The R/Rdot contour is relative to an ARP Center Of Aperture position and velocity. The earth surface is + described by a Digital Elevation Model (DEM) that defines a unique surface height as a function of two + horizontal coordinates. The projection computation may yield one or more surface points that lie along + the R/Rdot contour. + + :param r_tgt_coa: target COA range + :param r_dot_tgt_coa: target COA range rate + :param arp_position: ARP position + :param arp_velocity: ARP velocity + :return: the intersection between the R/Rdot Contour and the DEM, if multiple intersections occur they will + be returned in order of increasing height above the WGS-84 ellipsoid. + """ + + # (1) Compute the center point (ctr) and the radius of the R/Rdot projection contour (rrrc). + v_mag = np.linalg.norm(arp_velocity) + u_vel = arp_velocity / v_mag + cos_dca = -1.0 * r_dot_tgt_coa / v_mag + sin_dca = np.sqrt(1 - cos_dca * cos_dca) + ctr = arp_position + r_tgt_coa * cos_dca * u_vel + rrrc = r_tgt_coa * sin_dca + + # (2) Compute the unit vectors u_rrx and u_rry to be used to compute points located on the R/Rdot contour. + dec_arp = np.linalg.norm(arp_position) + u_up = arp_position / dec_arp + rry = np.cross(u_up, u_vel) + u_rry = rry / np.linalg.norm(rry) + u_rrx = np.cross(u_rry, u_vel) + + # (3) Compute the projection along the R/Rdot contour to the surface of constant HAE at height hae_max. + # The projection point at height hae_max is point_a. Also compute the cosine and sine of the contour angle + # to point_a, cos_caa and sin_caa. + point_a = self.hae_max_surface_projection.rrdot_to_ground(r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity)[0] + cos_caa = np.dot(point_a - ctr, u_rrx) / rrrc + # This variable is defined in the specification but it does not appear to be used anywhere + # sin_caa = self.look * np.sqrt(1 - cos_caa * cos_caa) + + # (4) Compute the projection along the R/Rdot contour to the surface of constant HAE at height hae_min. + # The projection point at height hae_min is point_b. Also compute the cosine and sine of the contour angle + # to point_b, cos_cab and sin_cab. + point_b = self.hae_min_surface_projection.rrdot_to_ground(r_tgt_coa, r_dot_tgt_coa, arp_position, arp_velocity)[0] + cos_cab = np.dot(point_b - ctr, u_rrx) / rrrc + sin_cab = self.look * np.sqrt(1 - cos_cab * cos_cab) + + # (5) A set of points along the R/Rdot contour are to be computed. The points will be spaced in equal + # increments of the cosine of the contour angle. Compute the step size, delta_cos_ca. + + # (5.1) Step size delta_cos_rrc is computed such that the distance between adjacent points on the R/Rdot + # contour is approximately equal to delta_dist_rrc. + delta_cos_rrc = self.delta_dist_rrc * np.abs(sin_cab) / rrrc + + # (5.2) Step size delta_cos_dem is computed such that the horizontal distance between adjacent points on + # the R/Rdot contour is approximately equal to delta_dist_dem. + delta_cos_dem = self.delta_dist_dem * (np.abs(sin_cab) / cos_cab) / rrrc + + # (5.3) Set delta_cos_ca (Note the value of delta_cos_ca is < 0) + delta_cos_ca = -1.0 * min(delta_cos_rrc, delta_cos_dem) + + # (6) Determine the number of points along the R/Rdot contour to be computed, npts. + npts = int(np.floor((cos_caa - cos_cab) / delta_cos_ca)) + 2 + + # (7) Compute the set of points along the R/Rdot contour, {Pn} for n =0, 2, ..., npts-1. Initial point P1 is + # located on the hae_min surface. The final point is located above the hae_max surface. Point Pn is computed + # in ECF coordinates. Note that here n ranges from [0, npts-1] while in the specification n is [1, npts]. + # Equations have been modified accordingly. + points_ecf = [] + for n in range(0, npts): + cos_can = cos_cab + n * delta_cos_ca # n-1 is unnecessary since n is zero based here + sin_can = self.look * np.sqrt(1 - cos_can * cos_can) + pn = ctr + rrrc * (cos_can * u_rrx + sin_can * u_rry) + points_ecf.append(pn) + + # (8 - 10) For each of the NPTS points, convert from ECF coordinates to DEM coordinates (lon, lat, ele). Also + # compute the DEM surface height for the point with DEM horizontal coordinates (lon, lat). Compute the + # difference in height (delta_height) between the point on the contour and DEM surface point. Also, + # set an indicator to track if that point is ABOVE, ON, or BELOW the DEM surface. + # + # Contour points that are within delta_hd_lim of the surface point are considered to be “on” the surface and + # will be added to the result set. Also compute a result point when points n and n+1 when both are “off” + # the surface and the R/Rdot contour intersects the surface between them (i.e. indicator n-1 x indicator n = -1) + # + # All height coordinates are in meters. + intersection_points = [] + prev_indicator = None + prev_delta_height = None + for n in range(0, npts): + point_lle = geocentric_to_geodetic(WorldCoordinate(points_ecf[n])) + point_dem = GeodeticWorldCoordinate(point_lle.coordinate.copy()) + self.elevation_model.set_elevation(point_dem) + delta_height = point_lle.elevation - point_dem.elevation + + # Determine if the contour point is ABOVE (indicator = 1), ON (indicator = 0), or BELOW (indicator = -1) + if np.abs(delta_height) < self.delta_hd_lim: + # Contour point is on the DEM surface, add it to the result set + indicator = 0 + intersection_points.append(points_ecf[n]) + elif delta_height > self.delta_hd_lim: + # Contour point is above the DEM surface + indicator = 1 + else: + # Contour point is below the DEM surface + indicator = -1 + + # If in two adjacent points one is ABOVE and the other BELOW then the contour intersected the DEM + # surface between the two points. Interpolate an intersection point and add it to the result. + if prev_indicator and prev_indicator * indicator == -1: + # contour crossed between two points; compute the surface point between + frac = prev_delta_height / (prev_delta_height - delta_height) + cos_cas = cos_cab + (n - 1 + frac) * delta_cos_ca # here the -1 is necessary for previous point + sin_cas = self.look * np.sqrt(1 - cos_cas * cos_cas) + sm = ctr + rrrc * (cos_cas * u_rrx + sin_cas * u_rry) + intersection_points.append(sm) + + # Keep track of the current indicator and delta height for comparison to the next point + prev_indicator = indicator + prev_delta_height = delta_height + + # Return the set of surface points found. Points will be ordered of increasing height above the WGS-84 + # ellipsoid. + return np.array(intersection_points) + + class SICDSensorModel(SensorModel): """ This is an implementation of the SICD sensor model as described by SICD Volume 3 Image Projections Description @@ -704,7 +992,7 @@ def __init__( """ super().__init__() self.coa_projection_set = coa_projection_set - self.image_plane = coord_converter + self.coord_converter = coord_converter self.uvect_gpn = u_gpn self.scp_arp = scp_arp self.scp_varp = scp_varp @@ -715,8 +1003,7 @@ def __init__( self.uvect_spn *= -1.0 self.uvect_spn /= np.linalg.norm(self.uvect_spn) - # TODO: Add option for HAE ground assumption, does world_to_image always need a GroundPlaneProjection? - self.default_surface_projection = GroundPlaneRRDotSurfaceProjection(self.image_plane.scp_ecf, self.uvect_gpn) + self.default_surface_projection = GroundPlaneRRDotSurfaceProjection(self.coord_converter.scp_ecf, self.uvect_gpn) def image_to_world( self, @@ -726,7 +1013,9 @@ def image_to_world( ) -> GeodeticWorldCoordinate: """ This is an implementation of an Image Grid to Scene point projection that first projects the image - location to the R/RDot contour and then intersects the R/RDot contour with the elevation model. + location to the R/RDot contour and then intersects the R/RDot contour with the elevation model. If + an elevation model is provided then this routine intersects the R/Rdot contour with the DEM surface which + may result in multiple solutions. In that case the solution with the lowest HAE is returned. :param image_coordinate: the x,y image coordinate :param elevation_model: the optional elevation model, if none supplied a plane tangent to SCP is assumed @@ -734,13 +1023,18 @@ def image_to_world( :return: the lon, lat, elev geodetic coordinate of the surface matching the image coordinate """ row_col = np.array( - [image_coordinate.r + self.image_plane.first_pixel.r, image_coordinate.c + self.image_plane.first_pixel.c] + [ + image_coordinate.r + self.coord_converter.first_pixel.r, + image_coordinate.c + self.coord_converter.first_pixel.c, + ] ) - xrow_ycol = self.image_plane.rowcol_to_xrowycol(row_col=row_col) + xrow_ycol = self.coord_converter.rowcol_to_xrowycol(row_col=row_col) r_tgt_coa, r_dot_tgt_coa, time_coa, arp_coa, varp_coa = self.coa_projection_set.precise_rrdot_computation(xrow_ycol) if elevation_model is not None: - raise NotImplementedError("SICD sensor model with DEM not yet implemented") + surface_projection = DEMRRDotSurfaceProjection( + self.coord_converter.scp_ecf, self.side_of_track, elevation_model=elevation_model + ) else: surface_projection = self.default_surface_projection @@ -768,7 +1062,7 @@ def world_to_image(self, world_coordinate: GeodeticWorldCoordinate) -> ImageCoor # The GP to IP projection direction is along the SCP COA slant plane normal. Also, compute the image # plane unit normal, uIPN. Compute projection scale factor SF as shown. uvect_proj = self.uvect_spn - scale_factor = float(np.dot(uvect_proj, self.image_plane.uvect_ipn)) + scale_factor = float(np.dot(uvect_proj, self.coord_converter.uvect_ipn)) # (3) Set initial ground plane position G1 to the scene point position S. scene_point = np.array([ecf_world_coordinate.x, ecf_world_coordinate.y, ecf_world_coordinate.z]) @@ -782,9 +1076,9 @@ def world_to_image(self, world_coordinate: GeodeticWorldCoordinate) -> ImageCoor # (4) Project ground plane point g_n to image plane point i_n. The projection distance is dist_n. Compute # image coordinates xrown and ycoln. - dist_n = np.dot(self.image_plane.scp_ecf.coordinate - g_n, self.image_plane.uvect_ipn) / scale_factor + dist_n = np.dot(self.coord_converter.scp_ecf.coordinate - g_n, self.coord_converter.uvect_ipn) / scale_factor i_n = g_n + dist_n * uvect_proj - xrow_ycol_n = self.image_plane.ipp_to_xrowycol(i_n) + xrow_ycol_n = self.coord_converter.ipp_to_xrowycol(i_n) # (5) Compute the precise projection for image grid location (xrown, ycoln) to the ground plane containing # the scene point S. The result is point p_n. For image grid location (xrown, ycoln), compute COA @@ -805,9 +1099,11 @@ def world_to_image(self, world_coordinate: GeodeticWorldCoordinate) -> ImageCoor # grid location (xrown, ycoln) as the precise image grid location for scene point S. cont = delta_gpn > tolerance and (iteration < max_iterations) - row_col = self.image_plane.xrowycol_to_rowcol(xrow_ycol_n) + row_col = self.coord_converter.xrowycol_to_rowcol(xrow_ycol_n) # Convert the row_col image grid location to an x,y image coordinate. Note that row_col is in reference # to the full image, so we subtract off the first_pixel offset to make the image coordinate correct if this # is a chip. - return ImageCoordinate([row_col[1] - self.image_plane.first_pixel.x, row_col[0] - self.image_plane.first_pixel.y]) + return ImageCoordinate( + [row_col[1] - self.coord_converter.first_pixel.x, row_col[0] - self.coord_converter.first_pixel.y] + ) diff --git a/test/aws/osml/gdal/test_gdal_dem_tile_factory.py b/test/aws/osml/gdal/test_gdal_dem_tile_factory.py index 96d94d0..cb38444 100644 --- a/test/aws/osml/gdal/test_gdal_dem_tile_factory.py +++ b/test/aws/osml/gdal/test_gdal_dem_tile_factory.py @@ -21,7 +21,7 @@ def test_load_geotiff_tile(self): from aws.osml.photogrammetry import GeodeticWorldCoordinate tile_factory = GDALDigitalElevationModelTileFactory("./test/data") - elevation_array, sensor_model = tile_factory.get_tile("n47_e034_3arc_v2.tif") + elevation_array, sensor_model, summary = tile_factory.get_tile("n47_e034_3arc_v2.tif") assert elevation_array is not None assert elevation_array.shape == (1201, 1201) @@ -32,6 +32,12 @@ def test_load_geotiff_tile(self): assert center_image.x == pytest.approx(600.5, abs=1.0) assert center_image.y == pytest.approx(600.5, abs=1.0) + assert summary.min_elevation == -1.0 + assert summary.max_elevation == 152.0 + assert summary.no_data_value == -32767 + # The 3-arc second test file used here is somewhere around 90 meter resolution + assert abs(90.0 - summary.post_spacing) < 20.0 + if __name__ == "__main__": unittest.main() diff --git a/test/aws/osml/gdal/test_sensor_model_factory.py b/test/aws/osml/gdal/test_sensor_model_factory.py index f4d0f67..c347ad9 100644 --- a/test/aws/osml/gdal/test_sensor_model_factory.py +++ b/test/aws/osml/gdal/test_sensor_model_factory.py @@ -185,11 +185,11 @@ def test_sicd_sensor_models(self): scp_image_coord = ImageCoordinate( [ - sm.image_plane.scp_pixel.x - sm.image_plane.first_pixel.x, - sm.image_plane.scp_pixel.y - sm.image_plane.first_pixel.y, + sm.coord_converter.scp_pixel.x - sm.coord_converter.first_pixel.x, + sm.coord_converter.scp_pixel.y - sm.coord_converter.first_pixel.y, ] ) - scp_world_coord = geocentric_to_geodetic(sm.image_plane.scp_ecf) + scp_world_coord = geocentric_to_geodetic(sm.coord_converter.scp_ecf) assert np.allclose(scp_image_coord.coordinate, sm.world_to_image(scp_world_coord).coordinate, atol=1.0) diff --git a/test/aws/osml/photogrammetry/test_digital_elevation_model.py b/test/aws/osml/photogrammetry/test_digital_elevation_model.py index 4b4ea65..b54119c 100644 --- a/test/aws/osml/photogrammetry/test_digital_elevation_model.py +++ b/test/aws/osml/photogrammetry/test_digital_elevation_model.py @@ -13,6 +13,7 @@ def test_dem_interpolation(self): DigitalElevationModelTileFactory, DigitalElevationModelTileSet, ) + from aws.osml.photogrammetry.elevation_model import ElevationRegionSummary from aws.osml.photogrammetry.sensor_model import SensorModel mock_tile_set = mock.Mock(DigitalElevationModelTileSet) @@ -20,6 +21,7 @@ def test_dem_interpolation(self): # This is a sample 3x3 grid of elevation data test_elevation_data = np.array([[0.0, 1.0, 4.0], [1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]) + test_elevation_summary = ElevationRegionSummary(0.0, 4.0, -1, 30.0) # These are the points we will test for interpolation test_grid_coordinates = [ @@ -44,7 +46,7 @@ def test_dem_interpolation(self): # This mock tile factory will always return the 3x3 elevation grid and the sensor model mock_tile_factory = mock.Mock(DigitalElevationModelTileFactory) - mock_tile_factory.get_tile.return_value = test_elevation_data, mock_sensor_model + mock_tile_factory.get_tile.return_value = test_elevation_data, mock_sensor_model, test_elevation_summary dem = DigitalElevationModel(mock_tile_set, mock_tile_factory) @@ -96,7 +98,7 @@ def test_missing_tile(self): mock_tile_set = mock.Mock(DigitalElevationModelTileSet) mock_tile_set.find_tile_id.return_value = "MockN00E000V0.tif" mock_tile_factory = mock.Mock(DigitalElevationModelTileFactory) - mock_tile_factory.get_tile.return_value = None, None + mock_tile_factory.get_tile.return_value = None, None, None dem = DigitalElevationModel(mock_tile_set, mock_tile_factory) diff --git a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py index 357e1b0..f43cc2f 100644 --- a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py +++ b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py @@ -1,6 +1,7 @@ import unittest from math import radians from pathlib import Path +from typing import Optional, Tuple import numpy as np from xsdata.formats.dataclass.parsers import XmlParser @@ -8,6 +9,8 @@ import aws.osml.formats.sicd.models.sicd_v1_2_1 as sicd121 from aws.osml.gdal.sicd_sensor_model_builder import poly1d_to_native, poly2d_to_native, xyzpoly_to_native, xyztype_to_ndarray from aws.osml.photogrammetry import ( + ConstantElevationModel, + ElevationRegionSummary, GeodeticWorldCoordinate, ImageCoordinate, INCAProjectionSet, @@ -16,6 +19,7 @@ SARImageCoordConverter, SICDSensorModel, WorldCoordinate, + geocentric_to_geodetic, geodetic_to_geocentric, ) @@ -64,18 +68,38 @@ def test_xrgycr(self): side_of_track=str(sicd.scpcoa.side_of_track.value), ) + # This is a test class that forces a constant elevation model to have a min/max height range of 100 + # meters. It's necessary to force the DEM intersection code with the SICD sensor model to iterate through + # several points searching for an intersection. + class TestElevationModel(ConstantElevationModel): + def __init__(self, hae: float): + super().__init__(constant_elevation=hae) + + def describe_region( + self, world_coordinate: GeodeticWorldCoordinate + ) -> Optional[Tuple[float, float, float, float]]: + summary = super().describe_region(world_coordinate) + return ElevationRegionSummary( + min_elevation=summary.min_elevation - 100, + max_elevation=summary.max_elevation + 100, + no_data_value=summary.no_data_value, + post_spacing=summary.post_spacing, + ) + + scp_lle = geocentric_to_geodetic(scp_ecf) + elevation_model = TestElevationModel(scp_lle.elevation) geodetic_world_coordinate = sicd_sensor_model.image_to_world( - ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]) + ImageCoordinate([sicd.image_data.scppixel.col, sicd.image_data.scppixel.row]), elevation_model=elevation_model ) ecf_world_coordinate = geodetic_to_geocentric(geodetic_world_coordinate) - assert np.allclose(ecf_world_coordinate.coordinate, scp_ecf.coordinate) + assert np.allclose(ecf_world_coordinate.coordinate, scp_ecf.coordinate, atol=0.001) geo_scp_world_coordinate = GeodeticWorldCoordinate( [radians(sicd.geo_data.scp.llh.lon), radians(sicd.geo_data.scp.llh.lat), sicd.geo_data.scp.llh.hae] ) - assert np.allclose(geo_scp_world_coordinate.coordinate, geodetic_world_coordinate.coordinate) + assert np.allclose(geo_scp_world_coordinate.coordinate, geodetic_world_coordinate.coordinate, atol=1.0e-5) calculated_image_scp = sicd_sensor_model.world_to_image(geo_scp_world_coordinate) From c445ce817d01efea64fd6c1ce2335f723d4672fc Mon Sep 17 00:00:00 2001 From: Ranbir Aulakh Date: Thu, 14 Sep 2023 21:46:38 -0400 Subject: [PATCH 04/20] feat: Fix documentation commands for github actions (#13) --- .github/workflows/documentation.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/documentation.yaml b/.github/workflows/documentation.yaml index 7e840d1..8fad896 100644 --- a/.github/workflows/documentation.yaml +++ b/.github/workflows/documentation.yaml @@ -13,14 +13,15 @@ jobs: pip install sphinx python -m pip install sphinx-autoapi python -m pip install sphinx_rtd_theme + python -m pip install tox tox-gh-actions - name: Sphinx build run: | - sphinx-build doc _build + tox -e docs - name: Deploy uses: peaceiris/actions-gh-pages@f09b4d7e2f12f433806a6273c6b0025df1f7859d if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} with: publish_branch: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: _build/ + publish_dir: .tox/docs/tmp/html/ force_orphan: true From 95a395180264237910bf2bf33df73efcbe49e70f Mon Sep 17 00:00:00 2001 From: drduhe Date: Tue, 19 Sep 2023 13:56:55 -0600 Subject: [PATCH 05/20] Updating CONTRIBUTING.md (#11) feat: Updating CONTRIBUTING.md to reflect project requirements. --- CONTRIBUTING.md | 275 ++++++++++++++++++++++++++----- README.md | 43 ++--- images/release-deployment.png | Bin 0 -> 79023 bytes images/release_deployment.sketch | Bin 0 -> 83380 bytes 4 files changed, 249 insertions(+), 69 deletions(-) mode change 100644 => 100755 CONTRIBUTING.md create mode 100644 images/release-deployment.png create mode 100644 images/release_deployment.sketch diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md old mode 100644 new mode 100755 index c4b6a1c..b879f17 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,59 +1,260 @@ -# Contributing Guidelines +# OSML Contributing Guidelines -Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional -documentation, we greatly value feedback and contributions from our community. +Thank you for your interest in contributing to our project! This document will guide you through the process of +contributing to our repository using a release-based GitFlow workflow. Please read the guidelines carefully in order to +submit your pull requests effectively. -Please read through this document before submitting any issues or pull requests to ensure we have all the necessary -information to effectively respond to your bug report or contribution. +## Table of Contents +- [Release-based GitFlow](#release-based-gitflow) +- [Branches](#branches) +- [Linting](#linting) +- [Use Cases](#use-cases) + - [Develop a new feature](#develop-a-new-feature) + - [Develop multiple features in parallel](#develop-multiple-features-in-parallel) + - [Create and deploy a release](#create-and-deploy-a-release) + - [Production hot fix](#production-hot-fix) +- [Code Style](#code-style) +- [Commit Messages](#commit-messages) +- [Issue Tracking](#issue-tracking) -## Reporting Bugs/Feature Requests +## Release-based GitFlow -We welcome you to use the GitHub issue tracker to report bugs or suggest features. +We follow a release-based GitFlow branching model to manage our codebase. It involves different types of branches to +ensure proper version control and collaboration. The primary branches in this workflow are: -When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already -reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: +note- `main`: This branch represents the latest production-ready state of the project code. +- `dev`: This branch is used as the integration branch, combining all feature branches for a specific release. +- `release/vX.Y.Z`: Branches in the format of `release/vX.Y.Z` are used to prepare a specific release. +- `feature/*`: Feature branches are created for the development of new features or significant enhancements. +- `hotfix/*`: Hotfix branches are created for bug fixes against production code. -* A reproducible test case or series of steps -* The version of our code being used -* Any modifications you've made relevant to the bug -* Anything unusual about your environment or deployment +## Branches -## Contributing via Pull Requests -Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: +Here's a brief description of the supported branches in our repository: -1. You are working against the latest source on the *main* branch. -2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. -3. You open an issue to discuss any significant work - we would hate for your time to be wasted. +![Release Deployment workflow](images/release-deployment.png) -To send us a pull request, please: +| Branch | Protected? | Base Branch | Description | +|:-----------------|:-----------|:-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `main` | YES | N/A | What is live in production (**stable**).
A pull request is required to merge code into `main`. | +| `dev` | YES | `main` | The latest state of development (**unstable**).
A pull request is required to merge code into `dev` | +| `feature/*` | NO | `dev` | Cutting-edge features (**unstable**). These branches are used for any maintenance features / active development. | +| `release/vX.Y.Z` | NO | `dev` | A temporary release branch that follows the [semver](http://semver.org/) versioning. This is what is sent to UAT.
A pull request is required to merge code into any `release/vX.Y.Z` branch. | +| `hotfix/*` | NO | `main` | These are bug fixes against production.
This is used because develop might have moved on from the last published state.
Remember to merge this back into develop and any release branches. | -1. Fork the repository. -2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. -3. Ensure local tests pass. -4. Commit to your fork using clear commit messages. -5. Send us a pull request, answering any default questions in the pull request interface. -6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. -GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and -[creating a pull request](https://help.github.com/articles/creating-a-pull-request/). +## Linting +This package uses a number of tools to enforce formatting, linting, and general best practices: +* [eslint](https://github.com/pre-commit/mirrors-eslint) to check pep8 compliance and logical errors in code +* [prettier](https://github.com/pre-commit/mirrors-prettier) to check pep8 compliance and logical errors in code +* [pre-commit](https://github.com/pre-commit/pre-commit-hooks) to install and control linters in githooks -## Finding contributions to work on -Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start. +After pulling the repository, you should enable auto linting git commit hooks by running: +```bash +python3 -m pip install pre-commit +pre-commit install +``` -## Code of Conduct -This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). -For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact -opensource-codeofconduct@amazon.com with any additional questions or comments. +In addition to running the linters against files included in a commit, you can perform linting on all the files +in the package by running: +```bash +pre-commit run --all-files --show-diff-on-failure +``` +or if using tox +```bash +tox -e lint +```` +## Use Cases -## Security issue notifications -If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue. +### Develop a new feature +1. Make sure your `dev` branch is up-to-date -## Licensing +2. Create a feature branch based off of `dev` -See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. + ```bash + $ git checkout dev + $ git checkout -b feature/new-documentation + $ git push --set-upstream feature/new-documentation + ``` + +3. Develop the code for the new feature and commit. Push your changes often. This + allows others to see your changes and suggest improvements/changes as well as + provides a safety net should your hard drive crash. + + ```bash + $ ... make changes + $ git add -A . + $ git commit -m "Add new documentation files" + $ ... make more changes + $ git add -A . + $ git commit -m "Fix some spelling errors" + $ git push + ``` + +4. Navigate to the project on [GitHub](https://github.com) and open a pull request with + the following branch settings: + * Base: `dev` + * Compare: `feature/new-documentation` + +5. When the pull request was reviewed, merge and close it and delete the + `feature/new-documentation` branch. + +### Develop multiple features in parallel + +There's nothing special about that. Each developer follows the above [Develop a new feature](#develop-a-new-feature) process. + +### Create and deploy a release +1. Merge `main` into `dev` to ensure the new release will contain the + latest production code. This reduces the chance of a merge conflict during + the release. + + ```bash + $ git checkout dev + $ git merge main + ``` + +2. Create a new `release/vX.Y.Z` release branch off of `dev`. + + ```bash + $ git checkout -b release/vX.Y.Z + $ git push --set-upstream release/vX.Y.Z + ``` + +3. Stabilize the release by using bugfix branches off of the `release/vX.Y.Z` branch + (the same way you would do a feature branch off of `dev`). + + ```bash + $ git checkout release/vX.Y.Z + $ git checkout -b hotfix/fix-label-alignment + $ git push --set-upstream hotfix/fix-label-alignment + ... do work + $ git commit -m "Adjust label to align with button" + $ git push + ``` + +4. When the code is ready to release, navigate to the project on + [GitHub](https://github.com) and open a pull request with the following branch + settings: + * Base: `main` + * Compare: `release/vX.Y.Z` + Paste the Release Checklist into the PR body. Each project should define a release + checklist. TBD - will link release checklist here. + +5. At some point in the checklist you will merge the release branch into `main`. + You can do this by using the "Merge pull request" button on the release PR. + +6. Now you are ready to create the actual release. Navigate to the project page + on GitHub and draft a new release with the following settings: + * Tag version: `vX.Y.Z` + * Target: `main` + * Release title: `Release vX.Y.Z` + * Description: Includes a high-level list of things changed in this release. + Click `Publish release`. + +7. Merge the `release/vX.Y.Z` into `dev`. + + ```bash + $ git checkout dev + $ git merge release/vX.Y.Z + $ git push + ``` + +8. Finish off the tasks in the release checklist. Once everything is done, close + the release PR. + +9. Delete the release branch on GitHub. + +### Production hot fix + +A production hotfix is very similar to a full-scale release except that you do +your work in a branch taken directly off of `main`. Hotfixes are useful in cases +where you want to patch a bug in a released version, but `dev` has unreleased +code in it already. + +**TBD: Insert diagram** + +1. Create a hot fix branch based off of `main`. + + ```bash + $ git checkout main + $ git checkout -b hotfix/documentation-broken-links + $ git push --set-upstream origin hotfix/documentation-broken-links + ``` + +2. Add a test case to validate the bug, fix the bug, and commit. + + ```bash + ... add test, fix bug, verify + $ git add -A . + $ git commit -m "Fix broken links" + $ git push + ``` + +3. Navigate to the project on [GitHub](https://github.com) and open a pull request + with the following branch settings: + * Base: `main` + * Compare: `hotfix/documentation-broken-links` + Paste your release checklist into the PR and work through the PR to get the + hotfix into production. + +4. At some point in the checklist you will merge the hotfix branch into `main`. + You can do this by using the "Merge pull request" button on the release PR. + +5. Now that the hotfix code is in `main` you are ready to create the actual + release. Navigate to the project page on GitHub and draft a new release with + the following settings: + * Tag version: `vX.Y.Z` + * Target: `main` + * Release title: `Release vX.Y.Z (hotfix)` + * Description: Includes a high-level list of things changed in this release. + +Click `Publish release`. + +*Note: Hotfix releases _are_ actual releases. You should bump at least the +patch part of the version when releasing a hotfix, and so even hotfixes go +through the process of creating a release like this.* + +1. Merge the `hotfix/documentation-broken-links` into `dev`. + + ```bash + $ git checkout dev + $ git merge hotfix/documentation-broken-links + $ git push + ``` + +2. Finish off the tasks in the release checklist. Once everything is done, close + the hotfix PR. + +## Code Style + +We maintain a consistent code style throughout the project, so please ensure your changes align with our existing style. +Take a look at the existing codebase to understand the patterns and conventions we follow. + +## Commit Messages + +The [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/#summary) specification is a +lightweight convention on top of commit messages. It provides an easy set of rules for creating an explicit +commit history, which makes it easier to write automated tools on top of. This convention dovetails with SemVer +by describing the features, fixes, and breaking changes made in commit messages. Please refer to the linked +documentation for examples and additional context. + +<type>[optional scope]: <description> + +[optional body] + +[optional footer(s)] + + +## Issue Tracking + +We utilize the issue tracking functionality of GitHub to manage our project's roadmap and track bugs or feature requests. +If you encounter any problems or have a new idea, please search the issues to ensure it hasn't already been reported. +If necessary, open a new issue providing a clear description, steps to reproduce, and any relevant information. + +We greatly appreciate your effort and contribution to our project! Let's build something awesome together! diff --git a/README.md b/README.md index 986ac2b..37b624f 100644 --- a/README.md +++ b/README.md @@ -7,13 +7,14 @@ Imagery Transmission Format (NITF) standards. ## Installation -The intent is to vend / distribute this software through a Python Package Index. If your environment has a distribution +The intent is to vend / distribute this software through a Python Package Index. +If your environment has a distribution, you should be able to install it using pip: ```shell pip install osml-imagery-toolkit[gdal] ``` -If you are working from a source code you can build and install the package from the root directory of the +If you are working from a source code, you can build and install the package from the root directory of the distribution. ```shell pip install .[gdal] @@ -22,32 +23,6 @@ Note that GDAL is listed as an extra dependency for this package. This is done t don't want to use GDAL or those that have their own custom installation steps for that library. Future versions of this package will include image IO backbones that have fewer dependencies. -## Linting/Formatting - -This package uses a number of tools to enforce formatting, linting, and general best practices: - -- [black](https://github.com/psf/black) -- [isort](https://github.com/PyCQA/isort) for formatting with a max line length of 100 -- [mypy](https://github.com/pre-commit/mirrors-mypy) to enforce static type checking -- [flake8](https://github.com/PyCQA/flake8) to check pep8 compliance and logical errors in code -- [autopep](https://github.com/pre-commit/mirrors-autopep8) to check pep8 compliance and logical errors in code -- [eslint](https://github.com/pre-commit/mirrors-eslint) to check pep8 compliance and logical errors in code -- [prettier](https://github.com/pre-commit/mirrors-prettier) to check pep8 compliance and logical errors in code -- [pre-commit](https://github.com/pre-commit/pre-commit-hooks) to install and control linters in githooks - -```bash -python3 -m pip install pre-commit -pre-commit install -``` - -Additionally, you can perform linting on all the files in the package by running: -```bash -pre-commit run --all-files --show-diff-on-failure -``` -or if using tox -```bash -tox -e lint -``` ## Documentation @@ -74,8 +49,10 @@ from aws.osml.photogrammetry import ImageCoordinate, GeodeticWorldCoordinate, Se ### Tiling with Updated Image Metadata Many applications break large remote sensing images into smaller chips or tiles for distributed processing or -dissemination. GDAL's Translate function provides basic capabilities but it does not correctly update geospatial -metadata to reflect the new image extent. These utilities provide those functions so tile consumers can correctly +dissemination. +GDAL's Translate function provides basic capabilities, but it does not correctly update geospatial +metadata to reflect the new image extent. +These utilities provide those functions so tile consumers can correctly interpret the pixel information they have been provided. ```python @@ -94,8 +71,10 @@ nitf_encoded_tile_bytes = tile_factory.create_encoded_tile([0, 0, 1024, 1024]) ### More Precise Sensor Models OversightML provides implementations of the Replacement Sensor Model (RSM) and Rational Polynomial Camera (RPC) sensor -models to assist in geo positioning. When loading a dataset you will automatically get the most accurate sensor model -from the available image metadata. That sensor model can be used in conjunction with an optional elevation model to +models to assist in geo positioning. +When loading a dataset, you will automatically get the most accurate sensor model +from the available image metadata. +That sensor model can be used in conjunction with an optional elevation model to convert between image and geodetic coordinates. ```python diff --git a/images/release-deployment.png b/images/release-deployment.png new file mode 100644 index 0000000000000000000000000000000000000000..65dcc2b3848359ff516be42dba8edc11ced862e5 GIT binary patch literal 79023 zcmce;b95!qzAhTuwrzH-jykq&+fH}vj&0jcI<{@ww(jbE_C9-`ci$c3{c*>uHAYrd zO;ycVwdU``Oqjf^I6Mpv3=j|yyu=R?MIa!sY#<=uFenJXUnRc0K|lhSnUIVS5KwIl z?7IOt;F!qhhoTG+kS7HYkbfW$&@14S|1l7d3lk8~sXh=8cM1>?rhP`60xuvz-b7u( zR7M7f2CxqW1Oki#1PaG`*EP%lNu@3|Us0pxv<^caYH3#HhsbJYTp#Rzj4*Q!? z%=i!$2ndALLRsBeT}GPQ$j*krz}U{vgu&g${%-~#UUzQ5u8oPa0g=0nwXGAkJ0HnE za&QCo|DI+fA^JxaXDdDubs2dgVLL|?qHhdL3``{aFhoQ|ypG1E+=?P%|11tT;v+G4 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z(j))OG^^!1vwmt#Nui~^I^Cy$e*RbCYME;lU5owICTIm|?a`M@5Bnh=KMH>Ylx^a* zf8ls_z6i2Cpw#|b8xKYF<+|*@qwurv4!}vwv623zQBd$}z#2&Zh6pJ5_%qWhfACzn ze@#&a0DAU!L^fXn?8B`mzhNx-pTr)=-Dvn7V5F&T?7Gte_qzRRB*xiFMVCX%}*aXMxLDHy#;}Oe(US<@q<8nKtiC+ zYwweoe>akaK%n{aAkZIcZ+`u-cenF$b#S|*a0~73_J2WVqz2XPbU~o>LjoZ1uTUVR ztsszx4axxxR=Rxp{N;0}PsyuXIdfM2>=jjI`Sa(M&d95rR#rTHR^^K7<%{S4v+|4( z1^NI`sWcx5wB@(Tcfvs+FSLW_e}Wko`( Date: Wed, 20 Sep 2023 10:19:04 -0400 Subject: [PATCH 06/20] fix: correct calculation of INCA R/Rdot projection (#14) --- .../osml/photogrammetry/sicd_sensor_model.py | 2 +- .../photogrammetry/test_sicd_sensor_model.py | 27 +++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/src/aws/osml/photogrammetry/sicd_sensor_model.py b/src/aws/osml/photogrammetry/sicd_sensor_model.py index 5edb348..12decb7 100644 --- a/src/aws/osml/photogrammetry/sicd_sensor_model.py +++ b/src/aws/osml/photogrammetry/sicd_sensor_model.py @@ -484,7 +484,7 @@ def _grid_specific_projection(self, xrow_ycol, coa_time, arp_position, arp_veloc # (2 repeated in v1.3.0 of the spec) Compute the ARP velocity at the time of closest approach # and the magnitude of the vector. arp_velocity_ca_tgt = self.varp_poly(time_ca_tgt) - mag_arp_velocity_ca_tgt = np.sum(arp_velocity_ca_tgt, axis=-1) + mag_arp_velocity_ca_tgt = np.linalg.norm(arp_velocity_ca_tgt) # (3) Compute the Doppler Rate Scale Factor (drsf_tgt) for image grid location (rg, az). drsf_tgt = self.drate_sf_poly(rg, az) diff --git a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py index f43cc2f..ad7fe65 100644 --- a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py +++ b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py @@ -159,6 +159,33 @@ def test_rgzero_inca(self): assert np.allclose(calculated_image_scp.coordinate, scp_pixel.coordinate) + for icp in sicd.geo_data.image_corners.icp: + geo_point = GeodeticWorldCoordinate([radians(icp.lon), radians(icp.lat), sicd.geo_data.scp.llh.hae]) + + if icp.index == sicd121.CornerStringType.FRFC_1: + image_point = ImageCoordinate([sicd.image_data.first_col, sicd.image_data.first_row]) + elif icp.index == sicd121.CornerStringType.FRLC_2: + image_point = ImageCoordinate( + [sicd.image_data.first_col + sicd.image_data.num_cols, sicd.image_data.first_row] + ) + elif icp.index == sicd121.CornerStringType.LRLC_3: + image_point = ImageCoordinate( + [ + sicd.image_data.first_col + sicd.image_data.num_cols, + sicd.image_data.first_row + sicd.image_data.num_rows, + ] + ) + elif icp.index == sicd121.CornerStringType.LRFC_4: + image_point = ImageCoordinate( + [sicd.image_data.first_col, sicd.image_data.first_row + sicd.image_data.num_rows] + ) + else: + # Unknown image corner + assert False + + new_geo_point = sicd_sensor_model.image_to_world(image_point) + assert np.allclose(new_geo_point.coordinate[0:2], geo_point.coordinate[0:2], atol=0.00001) + def test_rgazim_pfa(self): sicd: sicd121.SICD = XmlParser().from_path(Path("./test/data/sicd/example.sicd121.pfa.xml")) From 4c0b73988e6e593ba543e3deee5382917c86de68 Mon Sep 17 00:00:00 2001 From: edparris Date: Thu, 21 Sep 2023 11:59:54 -0400 Subject: [PATCH 07/20] feat: update DRA to work with SICDs (#15) --- src/aws/osml/gdal/dynamic_range_adjustment.py | 25 ++++++++++++++----- src/aws/osml/gdal/gdal_utils.py | 13 +++++++++- 2 files changed, 31 insertions(+), 7 deletions(-) diff --git a/src/aws/osml/gdal/dynamic_range_adjustment.py b/src/aws/osml/gdal/dynamic_range_adjustment.py index 97b0602..6ac78f7 100644 --- a/src/aws/osml/gdal/dynamic_range_adjustment.py +++ b/src/aws/osml/gdal/dynamic_range_adjustment.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Optional class DRAParameters: @@ -25,12 +25,20 @@ def __init__( @staticmethod def from_counts( - counts: List[float], min_percentage: float = 0.02, max_percentage: float = 0.98, a: float = 0.2, b: float = 0.4 + counts: List[float], + first_bucket_value: Optional[float] = None, + last_bucket_value: Optional[float] = None, + min_percentage: float = 0.02, + max_percentage: float = 0.98, + a: float = 0.2, + b: float = 0.4, ) -> "DRAParameters": """ This static factory method computes a new set of DRA parameters given a histogram of pixel values. :param counts: histogram of the pixel values + :param first_bucket_value: pixel value of the first bucket, defaults to 0 + :param last_bucket_value: pixel value of the last bucket, defaults to bucket index :param min_percentage: set point for low intensity pixels that may be outliers :param max_percentage: set point for high intensity pixels that may be outliers :param a: weighting factor for the low intensity range @@ -38,6 +46,10 @@ def from_counts( :return: a set of DRA parameters containing recommended and actual ranges of values """ num_histogram_bins = len(counts) + if not first_bucket_value: + first_bucket_value = 0 + if not last_bucket_value: + last_bucket_value = num_histogram_bins # Find the first and last non-zero counts actual_min_value = 0 @@ -69,11 +81,12 @@ def from_counts( min_value = max([actual_min_value, e_min - a * (e_max - e_min)]) max_value = min([actual_max_value, e_max + b * (e_max - e_min)]) + value_step = (last_bucket_value - first_bucket_value) / num_histogram_bins return DRAParameters( - suggested_min_value=min_value, - suggested_max_value=max_value, - actual_min_value=actual_min_value, - actual_max_value=actual_max_value, + suggested_min_value=min_value * value_step + first_bucket_value, + suggested_max_value=max_value * value_step + first_bucket_value, + actual_min_value=actual_min_value * value_step + first_bucket_value, + actual_max_value=actual_max_value * value_step + first_bucket_value, ) def __repr__(self): diff --git a/src/aws/osml/gdal/gdal_utils.py b/src/aws/osml/gdal/gdal_utils.py index 2633691..178bf46 100644 --- a/src/aws/osml/gdal/gdal_utils.py +++ b/src/aws/osml/gdal/gdal_utils.py @@ -144,12 +144,23 @@ def get_type_and_scales( selected_min = min_value selected_max = max_value if range_adjustment is not RangeAdjustmentType.NONE: + # GetStatistics(1,1) means it is ok to approximate but force computation is stats not already available + stats = band.GetStatistics(1, 1) + min_value = stats[0] + max_value = stats[1] + num_buckets = int(max_value - min_value) if band_type == gdalconst.GDT_Float32 or band_type == gdalconst.GDT_Float64: num_buckets = 255 + dra = DRAParameters.from_counts( - band.GetHistogram(buckets=num_buckets, max=max_value, min=min_value, include_out_of_range=1, approx_ok=1) + counts=band.GetHistogram( + buckets=num_buckets, max=max_value, min=min_value, include_out_of_range=1, approx_ok=1 + ), + first_bucket_value=min_value, + last_bucket_value=max_value, ) + if range_adjustment == RangeAdjustmentType.DRA: selected_min = dra.suggested_min_value selected_max = dra.suggested_max_value From bf895a1725d3593144d6c2a281ca48c42f109bfb Mon Sep 17 00:00:00 2001 From: edparris Date: Mon, 25 Sep 2023 16:51:43 -0400 Subject: [PATCH 08/20] feat: enable RSM sensor model by default (#16) --- src/aws/osml/gdal/gdal_utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/aws/osml/gdal/gdal_utils.py b/src/aws/osml/gdal/gdal_utils.py index 178bf46..ecddab9 100644 --- a/src/aws/osml/gdal/gdal_utils.py +++ b/src/aws/osml/gdal/gdal_utils.py @@ -50,8 +50,7 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod SensorModelTypes.PROJECTIVE, SensorModelTypes.RPC, SensorModelTypes.SICD, - # TODO: Enable RSM model once testing complete - # SensorModelTypes.RSM, + SensorModelTypes.RSM, ] # Create the best sensor model available sensor_model = SensorModelFactory( From 0303dd2550cd2e2be7ddf09be153802a023a08eb Mon Sep 17 00:00:00 2001 From: edparris Date: Tue, 26 Sep 2023 17:54:53 -0400 Subject: [PATCH 09/20] feat: gdal sensor model now supports non-wgs84 crs (#17) --- .../osml/gdal/gdal_sensor_model_builder.py | 6 ++- src/aws/osml/gdal/gdal_utils.py | 2 + src/aws/osml/gdal/sensor_model_factory.py | 5 ++- .../osml/photogrammetry/gdal_sensor_model.py | 39 ++++++++++++++++--- 4 files changed, 44 insertions(+), 8 deletions(-) diff --git a/src/aws/osml/gdal/gdal_sensor_model_builder.py b/src/aws/osml/gdal/gdal_sensor_model_builder.py index 72ff844..288c98e 100644 --- a/src/aws/osml/gdal/gdal_sensor_model_builder.py +++ b/src/aws/osml/gdal/gdal_sensor_model_builder.py @@ -13,16 +13,18 @@ class GDALAffineSensorModelBuilder(SensorModelBuilder): This builder is used to create sensor models for images that have GDAL geo transforms. """ - def __init__(self, geo_transform: List[float]) -> None: + def __init__(self, geo_transform: List[float], proj_wkt: Optional[str] = None) -> None: """ Constructor for the builder accepting the required GDAL geotransform. :param geo_transform: the geotransform for this image + :param proj_wkt: the well known text string of the CRS used by the image :return: None """ super().__init__() self.geo_transform = geo_transform + self.proj_wkt = proj_wkt def build(self) -> Optional[GDALAffineSensorModel]: """ @@ -32,7 +34,7 @@ def build(self) -> Optional[GDALAffineSensorModel]: """ if self.geo_transform is None: return None - return GDALAffineSensorModel(self.geo_transform) + return GDALAffineSensorModel(self.geo_transform, self.proj_wkt) class GDALGCPSensorModelBuilder(SensorModelBuilder): diff --git a/src/aws/osml/gdal/gdal_utils.py b/src/aws/osml/gdal/gdal_utils.py index ecddab9..e0da938 100644 --- a/src/aws/osml/gdal/gdal_utils.py +++ b/src/aws/osml/gdal/gdal_utils.py @@ -35,6 +35,7 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod # Get a GDAL Geo Transform and any available GCPs geo_transform = ds.GetGeoTransform(can_return_null=True) ground_control_points = ds.GetGCPs() + proj_wkt = ds.GetProjection() # If this image has NITF TREs defined parse them parsed_tres = None @@ -59,6 +60,7 @@ def load_gdal_dataset(image_path: str) -> Tuple[gdal.Dataset, Optional[SensorMod xml_tres=parsed_tres, xml_dess=xml_dess, geo_transform=geo_transform, + proj_wkt=proj_wkt, ground_control_points=ground_control_points, selected_sensor_model_types=selected_sensor_model_types, ).build() diff --git a/src/aws/osml/gdal/sensor_model_factory.py b/src/aws/osml/gdal/sensor_model_factory.py index b969aa3..99cb28c 100644 --- a/src/aws/osml/gdal/sensor_model_factory.py +++ b/src/aws/osml/gdal/sensor_model_factory.py @@ -80,6 +80,7 @@ def __init__( xml_tres: Optional[ET.Element] = None, xml_dess: Optional[List[str]] = None, geo_transform: Optional[List[float]] = None, + proj_wkt: Optional[str] = None, ground_control_points: Optional[List[gdal.GCP]] = None, selected_sensor_model_types: Optional[List[SensorModelTypes]] = None, ) -> None: @@ -93,6 +94,7 @@ def __init__( :param xml_tres: XML representing metadata in the tagged record extensions(TRE) :param xml_dess: XML representing data contained in the data extension segments (DES) :param geo_transform: a GDAL affine transform + :param proj_wkt: the well known text string of the CRS used by the image :param ground_control_points: a list of GDAL GCPs that identify correspondences in the image :param selected_sensor_model_types: a list of sensor models that should be attempted by this factory @@ -105,6 +107,7 @@ def __init__( self.xml_tres = xml_tres self.xml_dess = xml_dess self.geo_transform = geo_transform + self.proj_wkt = proj_wkt self.ground_control_points = ground_control_points self.selected_sensor_model_types = selected_sensor_model_types @@ -121,7 +124,7 @@ def build(self) -> Optional[SensorModel]: if SensorModelTypes.AFFINE in self.selected_sensor_model_types: if self.geo_transform is not None: - approximate_sensor_model = GDALAffineSensorModelBuilder(self.geo_transform).build() + approximate_sensor_model = GDALAffineSensorModelBuilder(self.geo_transform, self.proj_wkt).build() if SensorModelTypes.PROJECTIVE in self.selected_sensor_model_types: if self.ground_control_points is not None and len(self.ground_control_points) > 3: diff --git a/src/aws/osml/photogrammetry/gdal_sensor_model.py b/src/aws/osml/photogrammetry/gdal_sensor_model.py index 2f7fdef..42f3f29 100644 --- a/src/aws/osml/photogrammetry/gdal_sensor_model.py +++ b/src/aws/osml/photogrammetry/gdal_sensor_model.py @@ -2,8 +2,10 @@ from typing import Any, Dict, List, Optional import numpy as np +import pyproj +from pyproj.enums import TransformDirection -from .coordinates import GeodeticWorldCoordinate, ImageCoordinate +from .coordinates import LLA_PROJ, GeodeticWorldCoordinate, ImageCoordinate from .elevation_model import ElevationModel from .sensor_model import SensorModel @@ -23,11 +25,12 @@ class GDALAffineSensorModel(SensorModel): The necessary transform matrix can be obtained from a dataset using the GetGeoTransform() operation. """ - def __init__(self, geo_transform: List) -> None: + def __init__(self, geo_transform: List, proj_wkt: Optional[str] = None) -> None: """ Construct the sensor model from the affine transform provided by transform :param geo_transform: the 6 coefficients of the affine transform + :param proj_wkt: the well known text string of the CRS used by the image :return: None """ @@ -46,6 +49,11 @@ def __init__(self, geo_transform: List) -> None: ) # Use NumPy to calculate an inverse transform self.inv_transform = np.linalg.inv(self.transform) + + self.image_to_wgs84 = None + if proj_wkt: + self.image_to_wgs84 = pyproj.Transformer.from_crs(pyproj.CRS.from_string(proj_wkt), LLA_PROJ.crs) + except np.linalg.LinAlgError: raise ValueError("GeoTransform can not be inverted. Not a valid matrix for a sensor model.") @@ -67,7 +75,17 @@ def image_to_world( """ # The transform is expecting coordinates [x, y, 1.0] as an input. augmented_image_coord = np.append(image_coordinate.coordinate, [1.0]) - lonlat_coordinate = np.matmul(self.transform, augmented_image_coord) + image_crs_coordinate = np.matmul(self.transform, augmented_image_coord) + if self.image_to_wgs84 is not None: + lonlat_coordinate = self.image_to_wgs84.transform( + image_crs_coordinate[0], + image_crs_coordinate[1], + image_crs_coordinate[2], + radians=False, + direction=TransformDirection.FORWARD, + ) + else: + lonlat_coordinate = image_crs_coordinate world_coordinate = GeodeticWorldCoordinate([radians(lonlat_coordinate[0]), radians(lonlat_coordinate[1]), 0.0]) if elevation_model: elevation_model.set_elevation(world_coordinate) @@ -85,6 +103,17 @@ def world_to_image(self, world_coordinate: GeodeticWorldCoordinate) -> ImageCoor """ # The GDAL geo transform does not support elevation data. The inverse transform was created assuming the input # coordinate is a 2D geo + 1.0 (i.e. [longitude, latitude, 1.0] - lonlat_degrees_coordinate = np.array([degrees(world_coordinate.longitude), degrees(world_coordinate.latitude), 1.0]) - xy_coordinate = np.matmul(self.inv_transform, lonlat_degrees_coordinate) + if self.image_to_wgs84 is not None: + image_crs_coordinate = np.array( + self.image_to_wgs84.transform( + degrees(world_coordinate.longitude), + degrees(world_coordinate.latitude), + 1.0, + radians=False, + direction=TransformDirection.INVERSE, + ) + ) + else: + image_crs_coordinate = np.array((degrees(world_coordinate.longitude), degrees(world_coordinate.latitude), 1.0)) + xy_coordinate = np.matmul(self.inv_transform, image_crs_coordinate) return ImageCoordinate([xy_coordinate[0], xy_coordinate[1]]) From 12f6b4fd50c44d8ba5aee8c1bc31b7daa10cf811 Mon Sep 17 00:00:00 2001 From: edparris Date: Wed, 27 Sep 2023 17:23:07 -0400 Subject: [PATCH 10/20] docs: update readme for SAR features (#18) --- README.md | 65 ++++++++++++++++++++++++++++++++++++------------------- 1 file changed, 43 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 37b624f..59ee09d 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ # OversightML Imagery Toolkit -The OversightML Imagery Core is a Python package that contains image processing and photogrammetry routines commonly +The OversightML Imagery Toolkit is a Python package that contains image processing and photogrammetry routines commonly used during the analysis of imagery collected by satellites and unmanned aerial vehicles (UAVs). It builds upon GDAL -by providing additional support for images compliant with the Sensor Independent Complex Data (SICD) and National -Imagery Transmission Format (NITF) standards. +by providing additional support for images compliant with the National Imagery Transmission Format (NITF) and Sensor +Independent Complex Data (SICD) standards. ## Installation @@ -35,10 +35,11 @@ tox -e docs ## Example Usage -This library contains three core packages under the `aws.osml` namespace. +This library contains four core packages under the `aws.osml` namespace. * photogrammetry: convert locations between the image (x, y) and geodetic (lon, lat, elev) coordinate systems * gdal: help load and manage datasets loaded by GDAL * image_processing: common image manipulation routines +* formats: utilities for handling format specific information; normally not accessed directly ```python from aws.osml.gdal import GDALImageFormats, GDALCompressionOptions, load_gdal_dataset @@ -49,11 +50,10 @@ from aws.osml.photogrammetry import ImageCoordinate, GeodeticWorldCoordinate, Se ### Tiling with Updated Image Metadata Many applications break large remote sensing images into smaller chips or tiles for distributed processing or -dissemination. -GDAL's Translate function provides basic capabilities, but it does not correctly update geospatial -metadata to reflect the new image extent. -These utilities provide those functions so tile consumers can correctly -interpret the pixel information they have been provided. +dissemination. GDAL's Translate function provides basic capabilities, but it does not correctly update geospatial +metadata to reflect the new image extent. These utilities provide those functions so tile consumers can correctly +interpret the pixel information they have been provided. For NITF imagery that includes the addition of a new ICHIPB +TRE. With SICD the XML ImageData elements are adjusted to identify the sub-image bounds. ```python # Load the image and create a sensor model @@ -68,29 +68,50 @@ tile_factory = GDALTileFactory(ds, nitf_encoded_tile_bytes = tile_factory.create_encoded_tile([0, 0, 1024, 1024]) ``` +### Tiling for Display + +Some images, for example 11-bit panchromatic images or SAR imagery with floating point complex data, can not be +displayed directly without remapping the pixels into an 8-bit per pixel grayscale or RGB color model. The TileFactory +supports creation of tiles suitable for human review by setting both the output_type and range_adjustment options. + +```python +viz_tile_factory = GDALTileFactory(ds, + sensor_model, + GDALImageFormats.PNG, + GDALCompressionOptions.NONE, + output_type=gdalconst.GDT_Byte, + range_adjustment=RangeAdjustmentType.DRA) + +viz_tile = viz_tile_factory.create_encoded_tile([0, 0, 1024, 1024]) +``` + ### More Precise Sensor Models -OversightML provides implementations of the Replacement Sensor Model (RSM) and Rational Polynomial Camera (RPC) sensor -models to assist in geo positioning. -When loading a dataset, you will automatically get the most accurate sensor model -from the available image metadata. -That sensor model can be used in conjunction with an optional elevation model to -convert between image and geodetic coordinates. +OversightML provides implementations of the Replacement Sensor Model (RSM), Rational Polynomial +Camera (RPC), and Sensor Independent Complex Data (SICD) sensor models to assist in geo positioning. +When loading a dataset, the toolkit will construct the most accurate sensor model +from the available image metadata. That sensor model can be used in conjunction with an optional +elevation model to convert between image and geodetic coordinates. ```python ds, sensor_model = load_gdal_dataset("./imagery/sample.nitf") -elevation_model = DigitalElevationModel(SRTMTileSet(version="1arc_v3"), - GDALDigitalElevationModelTileFactory("./local-SRTM-tiles") - ) +elevation_model = DigitalElevationModel( + SRTMTileSet(version="1arc_v3"), + GDALDigitalElevationModelTileFactory("./local-SRTM-tiles")) -geodetic_location_of_ul_corner = sensor_model.image_to_world(ImageCoordinate([0, 0]), elevation_model=elevation_model) +# Note the order of ImageCoordinate is (x, y) +geodetic_location_of_ul_corner = sensor_model.image_to_world( + ImageCoordinate([0, 0]), + elevation_model=elevation_model) lon_degrees = -77.404453 lat_degrees = 38.954831 meters_above_ellipsoid = 100.0 -image_location = sensor_model.world_to_image(GeodeticWorldCoordinate([radians(lon_degrees), - radians(lat_degrees), - meters_above_ellipsoid])) + +image_location = sensor_model.world_to_image( + GeodeticWorldCoordinate([radians(lon_degrees), + radians(lat_degrees), + meters_above_ellipsoid])) ``` ## Contributing From 5a3b771ca2c738b30e426ff4047bad1f2bbdfd83 Mon Sep 17 00:00:00 2001 From: edparris Date: Tue, 3 Oct 2023 19:23:43 -0400 Subject: [PATCH 11/20] feat: SAR examples and display processing (#21) --- examples/OSML-ImageToolkit-SAR-Examples.ipynb | 1995 +++++++++++++++++ src/aws/osml/image_processing/__init__.py | 1 + .../image_processing/sar_complex_imageop.py | 157 ++ .../test_sar_complex_imageop.py | 80 + 4 files changed, 2233 insertions(+) create mode 100644 examples/OSML-ImageToolkit-SAR-Examples.ipynb create mode 100644 src/aws/osml/image_processing/sar_complex_imageop.py create mode 100644 test/aws/osml/image_processing/test_sar_complex_imageop.py diff --git a/examples/OSML-ImageToolkit-SAR-Examples.ipynb b/examples/OSML-ImageToolkit-SAR-Examples.ipynb new file mode 100644 index 0000000..d365643 --- /dev/null +++ b/examples/OSML-ImageToolkit-SAR-Examples.ipynb @@ -0,0 +1,1995 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "8debab01-baf8-4145-bea1-142b2f531387", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# !pip install osml-imagery-toolkit" + ] + }, + { + "cell_type": "markdown", + "id": "57d34325-e6fe-47a4-87b1-ad6a24cad853", + "metadata": {}, + "source": [ + "# Examples of SICD Data Using OversightML Imagery Toolkit\n", + "\n", + "The data used in these examples was provided by Capella and Umbra as part of the AWS Open Data program. \n", + "The files can be found in the open data catalog or downloaded directly using the links below.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "be69d6c2-f93c-4fce-9bbb-96a7ae49124e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!wget https://capella-open-data.s3.amazonaws.com/data/2021/9/30/CAPELLA_C03_SP_SICD_HH_20210930100314_20210930100317/CAPELLA_C03_SP_SICD_HH_20210930100314_20210930100317.ntf\n", + "#!wget https://capella-open-data.s3.amazonaws.com/data/2021/2/2/CAPELLA_C02_SM_SICD_HH_20210202043514_20210202043519/CAPELLA_C02_SM_SICD_HH_20210202043514_20210202043519.ntf\n", + "#!wget https://umbra-open-data-catalog.s3.amazonaws.com/sar-data/tasks/Melbourne,%20Australia/b90c0aa0-ff9b-480c-b866-5f3778e8f013/2023-04-01-22-58-40_UMBRA-04/2023-04-01-22-58-40_UMBRA-04_SICD.nitf" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ac807186-b8ba-44d1-b527-87fdcc628308", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from osgeo import gdal, gdalconst\n", + "gdal.UseExceptions()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f6feca7-c436-4309-957c-e8866fbc1f25", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from aws.osml.gdal import load_gdal_dataset, GDALImageFormats, GDALCompressionOptions\n", + "from aws.osml.image_processing import GDALTileFactory" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1c5f0473-48e2-4d6e-bbc8-546b9dbe0f76", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded SICD image with dimensions: (10599, 10626) (rows, cols)\n", + "Using Sensor Model Implementation: SICDSensorModel\n" + ] + } + ], + "source": [ + "#image_file = \"./CAPELLA_C03_SP_SICD_HH_20210930100314_20210930100317.ntf\"\n", + "#image_file = \"./CAPELLA_C02_SM_SICD_HH_20210202043514_20210202043519.ntf\"\n", + "image_file = \"./2023-04-01-22-58-40_UMBRA-04_SICD.nitf\"\n", + "sicd_dataset, sm = load_gdal_dataset(image_file)\n", + "width = sicd_dataset.RasterXSize\n", + "height = sicd_dataset.RasterYSize\n", + "\n", + "print(f\"Loaded SICD image with dimensions: ({height}, {width}) (rows, cols)\") \n", + "print(f\"Using Sensor Model Implementation: {type(sm.precision_sensor_model).__name__}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d25d5c3b-b7c8-42b4-8dba-8d8fa74fe343", + "metadata": {}, + "source": [ + "## Example Create SICD Image Tiles with Updated Metadata\n", + "Create a GDALTileFactory that will produce SICD tiles. Setting the output format to NITF format will cause the GDALTileFactory \n", + "to update the ImageData XML elements to reflect the reduced image dimensions. These updates are necessary to allow consumers\n", + "of the tile to correctly calculate image/world positions for the pixels. The following example shows SICD metadata for a \n", + "512x512 tile with an upper left corner that was at row 5043 and column 5057. Note that the original full image size is preserved\n", + "while NumRows and NumCols have been updated to be the current tile size.\n", + "\n", + " \n", + "\t\t...\n", + "\t\t512\n", + "\t\t512\n", + "\t\t5043\n", + "\t\t5057\n", + "\t\t\n", + "\t\t\t10599\n", + "\t\t\t10626\n", + "\t\t\n", + " ...\n", + " \n", + "\n", + "The image is saved to a file to demonstrate that the data has been correctly updated.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "372dc855-773c-4fac-976a-e558616f12dc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR 6: The 4 GCPs image coordinates must be exactly at the *center* of the 4 corners of the image ( (0.5, 0.5), (511.5 0.5), (511.5 511.5), (511.5 0.5) ).\n" + ] + } + ], + "source": [ + "tile_factory = GDALTileFactory(sicd_dataset, sm, GDALImageFormats.NITF, GDALCompressionOptions.NONE)\n", + "\n", + "center_x = width / 2\n", + "center_y = height / 2\n", + "tile_size = 512\n", + "image_tile = tile_factory.create_encoded_tile([int(center_x - tile_size/2),\n", + " int(center_y - tile_size/2),\n", + " tile_size,\n", + " tile_size])\n", + "\n", + "with open(\"./sample-sicd-tile.ntf\", \"wb\") as output_file:\n", + " output_file.write(image_tile)" + ] + }, + { + "cell_type": "markdown", + "id": "f010c7f6-58f5-4c4c-8870-9790c574be84", + "metadata": { + "tags": [] + }, + "source": [ + "## Example Demonstrate Use of SICD Sensor Models\n", + "This example demonstrates how the SICD sensor model's image_to_world function can be used to convert\n", + "image coordinates (x, y) to world coordinates (longitude, latitude, elevation). The corners of both the\n", + "full image and the previously created SICD tile are used to create polygons that show the extent of each\n", + "image on the ground then those footprints are overlaid on a map for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7627934b-c341-470f-8e39-c308093d559e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from ipyleaflet import *\n", + "from aws.osml.photogrammetry import ImageCoordinate, GeodeticWorldCoordinate\n", + "from math import degrees" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2f0b13b6-317c-42d8-8f14-c85b5e51590a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "35ae06e7b90346a78936952a3d416ea8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[-37.846022478600695, 144.91293953312228], controls=(ZoomControl(options=['position', 'zoom_in_text…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the previously saved SICD tile cut from the center of the image\n", + "sicd_tile, tile_sm = load_gdal_dataset(\"./sample-sicd-tile.ntf\")\n", + "\n", + "# Compute WGS-84 world coordinates for each image corner and create a red polygon \n", + "# to show the full image footprint\n", + "image_corners = [[0, 0], [width, 0], [width, height], [0, height]]\n", + "geo_image_corners = [sm.image_to_world(ImageCoordinate(corner)) for corner in image_corners]\n", + "locations = [(degrees(p.latitude), degrees(p.longitude)) for p in geo_image_corners]\n", + "locations.append(locations[0])\n", + " \n", + "image_footprint = Polygon(\n", + " locations=locations,\n", + " color=\"green\",\n", + " fill_color=\"green\"\n", + ")\n", + "\n", + "# Compute the WGS-84 world coordinates for each tile corner and create a red polygon \n", + "# to show the tile footprint\n", + "tile_corners = [[0, 0], [tile_size, 0], [tile_size, tile_size], [0, tile_size]]\n", + "geo_tile_corners = [tile_sm.image_to_world(ImageCoordinate(corner)) for corner in tile_corners]\n", + "tile_locations = [(degrees(p.latitude), degrees(p.longitude)) for p in geo_tile_corners]\n", + "tile_locations.append(tile_locations[0])\n", + "\n", + "tile_footprint = Polygon(\n", + " locations=tile_locations,\n", + " color=\"red\",\n", + " fill_color=\"red\"\n", + ")\n", + "\n", + "# Compute the WGS-84 center of the image so we can center the map\n", + "center_geo = sm.image_to_world(ImageCoordinate([center_x, center_y]))\n", + "center = (degrees(center_geo.latitude),\n", + " degrees(center_geo.longitude))\n", + "\n", + "# Render the map and overlays using Leaflet\n", + "m = Map(center=center, zoom=12, basemap=basemaps.OpenStreetMap.Mapnik)\n", + "m.add_layer(image_footprint)\n", + "m.add_layer(tile_footprint)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "26a60182-b79e-4a9c-8e73-59f8ca2c0f9a", + "metadata": {}, + "source": [ + "## Example Dump SICD Metadata\n", + "This example shows how to identify the NITF data extension segment containing SICD metadata and prety print it\n", + "for review. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bf4826cd-59fd-4a2c-ab3f-564dec68e159", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import base64\n", + "import xml.dom.minidom\n", + "from aws.osml.gdal import NITFDESAccessor" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a70c805e-dcb5-49ef-a9cc-edd27e8788a3", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\t\n", + "\t\tUmbra-04\n", + "\t\t2023-04-01T22:58:42_Umbra-04\n", + "\t\tMONOSTATIC\n", + "\t\t\n", + "\t\t\tSPOTLIGHT\n", + "\t\t\n", + "\t\tUNCLASSIFIED - https://creativecommons.org/licenses/by/4.0/\n", + "\t\t4675b40d-27a0-4c43-9799-20b971c5543c\n", + "\t\n", + "\t\n", + "\t\tValkyrie Systems Sage | Umbra Image Formation processor 0.3.22.0\n", + "\t\t2023-04-02T03:05:08.045326Z\n", + "\t\n", + "\t\n", + "\t\tRE32F_IM32F\n", + "\t\t512\n", + "\t\t512\n", + 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xml_data_content_segments:\n", + " xml_bytes = des_accessor.parse_field_value(xml_data_segment, \"DESDATA\", base64.b64decode)\n", + " xml_str = xml_bytes.decode(\"utf-8\")\n", + " if \"SICD\" in xml_str:\n", + " temp = xml.dom.minidom.parseString(xml_str)\n", + " new_xml = temp.toprettyxml()\n", + " print(new_xml)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "6341388e-8f4e-49ad-966c-a8eeab8a1201", + "metadata": { + "tags": [] + }, + "source": [ + "## Experimental: Visualization of Complex Data\n", + "There are a variety of different techniques to convert complex SAR data to a simple image suitable for human display.\n", + "The toolkit contains two helper functions that can convert complex image data into an 8-bit grayscle representation\n", + "The equations are described in Sections 3.1 and 3.2 of SAR Image Scaling, Dynamic Range, Radiometric Calibration,\n", + "and Display ([SAND2019-2371](https://www.osti.gov/servlets/purl/1761879))." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "53f67c70-e9c6-4fc6-9317-c9a90c16d45e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from aws.osml.image_processing import histogram_stretch, quarter_power_image" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6a8b47d2-4a9c-4fc3-8be2-0cfaf19c3156", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_image(pixels,show_histo = False):\n", + " min_value = np.min(pixels)\n", + " max_value = np.max(pixels)\n", + " #print(f\"Pixels in range of: {min_value} .. {max_value}\")\n", + " \n", + " if show_histo:\n", + " f, axs = plt.subplots(1, 2, figsize=(15,10), gridspec_kw={'width_ratios': [2, 1]})\n", + " img_axs = axs[0]\n", + " else:\n", + " f, axs = plt.subplots(1, 1, figsize=(10,10))\n", + " img_axs = axs\n", + " \n", + " f.tight_layout()\n", + " img_axs.imshow(pixels, cmap=\"grey\", vmin=min_value, vmax=max_value)\n", + " img_axs.set_axis_off()\n", + " \n", + " if show_histo:\n", + " counts, bins = np.histogram(pixels, bins=256)\n", + " axs[1].set_yscale('log')\n", + " axs[1].stairs(counts, bins)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "25431a21-2a3a-45e9-b3ab-e6f818b6f255", + "metadata": {}, + "outputs": [], + "source": [ + "complex_pixels = sicd_dataset.ReadAsArray()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6fff6873-d9fa-4031-9433-40934234ecba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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"display_name": "Python [conda env:py310gdal] (arn:aws:sagemaker:us-west-2:081189585635:image/sagemaker-geospatial-v1-0)", + "language": "python", + "name": "conda-env-py310gdal-py__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:081189585635:image/sagemaker-geospatial-v1-0" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/aws/osml/image_processing/__init__.py b/src/aws/osml/image_processing/__init__.py index 847f598..fbb8d08 100644 --- a/src/aws/osml/image_processing/__init__.py +++ b/src/aws/osml/image_processing/__init__.py @@ -6,5 +6,6 @@ """ from .gdal_tile_factory import GDALTileFactory +from .sar_complex_imageop import histogram_stretch, quarter_power_image __all__ = ["GDALTileFactory"] diff --git a/src/aws/osml/image_processing/sar_complex_imageop.py b/src/aws/osml/image_processing/sar_complex_imageop.py new file mode 100644 index 0000000..b990197 --- /dev/null +++ b/src/aws/osml/image_processing/sar_complex_imageop.py @@ -0,0 +1,157 @@ +import logging +from typing import Optional, Tuple + +import numpy as np + +TWO_PI = np.pi * 2.0 + +logger = logging.getLogger(__name__) + + +def image_pixels_to_complex( + image_pixels: np.ndarray, pixel_type: Optional[str] = None, amplitude_table: Optional[np.typing.ArrayLike] = None +) -> np.ndarray: + """ + This function converts SAR pixels from SICD imagery into complex values using equations + found in SICD Volume 1 Section 4.2. + + :param image_pixels: the SAR image pixels + :param pixel_type: "AMP8I_PHS8I", "RE32F_IM32F", or "RE16I_IM16I" + :param amplitude_table: optional lookup table of amplitude values for AMP8I_PHS8I image pixels + :return: + """ + + if pixel_type is None or pixel_type in ["RE32F_IM32F", "RE16I_IM16I"]: + # For these pixel types the complex value is already stored in the file + return image_pixels + elif pixel_type == "AMP8I_PHS8I": + # If the data is 8-bit amplitude/phase with an optional amplitude lookup table need to + # convert it to the complex image value + amplitude = image_pixels[0] + phase = image_pixels[1] / 256.0 + if amplitude_table is not None: + amplitude_lut = np.array(amplitude_table) + amplitude = amplitude_lut[amplitude] + return np.array([amplitude * np.cos(TWO_PI * phase), amplitude * np.sin(TWO_PI * phase)]) + else: + raise ValueError(f"Unknown SAR Pixel Type: {pixel_type}") + + +def complex_to_power_value(complex_data: np.ndarray) -> np.ndarray: + """ + This function converts SAR complex data into the pixel power values (sometimes + called pixel intensity) using the equation found in SICD Volume 1 Section 4.10. + + :param complex_data: the SAR complex image signal with real and imaginary components + :return: the power values + """ + return np.sum(np.square(complex_data), axis=0) + + +def power_value_in_decibels(power_values: np.ndarray) -> np.ndarray: + """ + This function converts SAR power values to decibels using the equation found in SICD Volume 1 Section 4.10. + + :param power_values: the SAR power values + :return: the power values in decibels + """ + return 10.0 * np.log10(power_values) + + +def get_value_bounds(magnitude_values: np.ndarray) -> Tuple[float, float]: + """ + This function calculates the minimum and maximum of a set of values. + + :param magnitude_values: SAR magnitude values + :return: (min value, max value) + """ + return np.min(magnitude_values), np.max(magnitude_values) + + +def linear_mapping(magnitude_values: np.ndarray) -> np.ndarray: + """ + This function accepts an array of magnitude values and scales them to be in the range [0:1]. + + :param magnitude_values: SAR magnitude values + :return: the scaled values in range [0:1] + """ + min_value, max_value = get_value_bounds(magnitude_values) + if max_value == min_value: + return np.full(magnitude_values.shape, 0.5) + + return np.clip((magnitude_values - min_value) / (max_value - min_value), 0, 1.0) + + +def histogram_stretch_mag_values(magnitude_values: np.ndarray, scale_factor: float = 8.0): + """ + This function converts image pixel magnitudes to an 8-bit image by scaling the pixels and + cropping to the desired range. This is histogram stretching without any gamma correction. + + :param magnitude_values: SAR magnitude values + :param scale_factor: a scale factor, default = 8.0 + :return: the quantized grayscale image clipped to the range of [0:255] + """ + mean_value = np.mean(magnitude_values[np.isfinite(magnitude_values)]) + u = 1 / (scale_factor * mean_value) + return np.clip(255.0 * u * magnitude_values, 0.0, 255.0) + + +def quarter_power_mag_values(magnitude_values: np.ndarray, scale_factor: float = 3.0): + """ + This function converts image pixel magnitudes to a Quarter-Power Image using equations + found in Section 3.2 of SAR Image Scaling, Dynamic Range, Radiometric Calibration, and Display + (SAND2019-2371). + + :param magnitude_values: SAR magnitude values + :param scale_factor: a brightness factor that is typically between 5 and 3 + :return: the quantized grayscale image clipped to the range of [0:255] + """ + sqrt_magnitude = np.sqrt(np.abs(magnitude_values)) + mean_value = np.mean(sqrt_magnitude[np.isfinite(sqrt_magnitude)]) + b = 1 / (scale_factor * mean_value) + return np.clip(255.0 * b * sqrt_magnitude, 0.0, 255.0) + + +def histogram_stretch( + image_pixels: np.ndarray, + pixel_type: Optional[str] = None, + amplitude_table: Optional[np.typing.ArrayLike] = None, + scale_factor: float = 8.0, +) -> np.ndarray: + """ + This function converts SAR image pixels to an 8-bit grayscale image by scaling the pixels and + cropping to the desired range [0:255]. This is histogram stretching without any gamma correction. + The equations are described in Section 3.1 of SAR Image Scaling, Dynamic Range, Radiometric Calibration, + and Display (SAND2019-2371). + + :param image_pixels: the SAR image pixels + :param pixel_type: "AMP8I_PHS8I", "RE32F_IM32F", or "RE16I_IM16I" + :param amplitude_table: optional lookup table of amplitude values for AMP8I_PHS8I image pixels + :param scale_factor: a scale factor, default = 8.0 + :return: the quantized grayscale image clipped to the range of [0:255] + """ + complex_data = image_pixels_to_complex(image_pixels, pixel_type=pixel_type, amplitude_table=amplitude_table) + power_values = complex_to_power_value(complex_data) + return histogram_stretch_mag_values(power_values, scale_factor=scale_factor) + + +def quarter_power_image( + image_pixels: np.ndarray, + pixel_type: Optional[str] = None, + amplitude_table: Optional[np.typing.ArrayLike] = None, + scale_factor: float = 3.0, +) -> np.ndarray: + """ + This function converts SAR image pixels to an 8-bit grayscale image pixel magnitudes to a Quarter-Power + Image using equations found in Section 3.2 of SAR Image Scaling, Dynamic Range, Radiometric Calibration, + and Display (SAND2019-2371). + + :param image_pixels: the SAR image pixels + :param pixel_type: "AMP8I_PHS8I", "RE32F_IM32F", or "RE16I_IM16I" + :param amplitude_table: optional lookup table of amplitude values for AMP8I_PHS8I image pixels + :param scale_factor: a brightness factor that is typically between 5 and 3 + :return: the quantized grayscale image clipped to the range of [0:255] + """ + complex_data = image_pixels_to_complex(image_pixels, pixel_type=pixel_type, amplitude_table=amplitude_table) + power_values = complex_to_power_value(complex_data) + return quarter_power_mag_values(power_values, scale_factor=scale_factor) diff --git a/test/aws/osml/image_processing/test_sar_complex_imageop.py b/test/aws/osml/image_processing/test_sar_complex_imageop.py new file mode 100644 index 0000000..66a0c1b --- /dev/null +++ b/test/aws/osml/image_processing/test_sar_complex_imageop.py @@ -0,0 +1,80 @@ +from unittest import TestCase + +import numpy as np +from osgeo import gdal + +from aws.osml.gdal import load_gdal_dataset +from aws.osml.image_processing import histogram_stretch, quarter_power_image +from aws.osml.image_processing.sar_complex_imageop import image_pixels_to_complex, linear_mapping + +gdal.UseExceptions() + + +class TestSARImageOPs(TestCase): + def test_histogram_stretch(self): + dataset, sensor_model = load_gdal_dataset("./test/data/sicd/capella-sicd121-chip1.ntf") + pixels = dataset.ReadAsArray() + + # Sanity check to ensure the image needs to be scaled + self.assertLessEqual(np.min(pixels), 0.0) + self.assertGreaterEqual(np.max(pixels), 255.0) + + grayscale = histogram_stretch(pixels) + + min_grayscale_value = np.min(grayscale) + self.assertGreaterEqual(min_grayscale_value, 0) + self.assertLessEqual(min_grayscale_value, 15) + + max_grayscale_value = np.max(grayscale) + self.assertGreaterEqual(max_grayscale_value, 240) + self.assertLessEqual(max_grayscale_value, 255) + + def test_quarter_power_image(self): + dataset, sensor_model = load_gdal_dataset("./test/data/sicd/umbra-sicd121-chip1.ntf") + pixels = dataset.ReadAsArray() + + # Sanity check to ensure the image needs to be scaled + self.assertLessEqual(np.min(pixels), -1.0) + self.assertGreaterEqual(np.max(pixels), 3.0) + + grayscale = quarter_power_image(pixels) + + min_grayscale_value = np.min(grayscale) + self.assertGreaterEqual(min_grayscale_value, 0) + self.assertLessEqual(min_grayscale_value, 15) + + max_grayscale_value = np.max(grayscale) + self.assertGreaterEqual(max_grayscale_value, 240) + self.assertLessEqual(max_grayscale_value, 255) + + def test_linear_mapping(self): + dataset, sensor_model = load_gdal_dataset("./test/data/sicd/capella-sicd121-chip2.ntf") + pixels = dataset.ReadAsArray() + + # Sanity check to ensure the image needs to be scaled + self.assertLessEqual(np.min(pixels), 0.0) + self.assertGreaterEqual(np.max(pixels), 255.0) + + linear = linear_mapping(pixels) + min_value = np.min(linear) + self.assertGreaterEqual(min_value, 0.0) + self.assertLessEqual(min_value, 0.02) + + max_value = np.max(linear) + self.assertGreaterEqual(max_value, 0.98) + self.assertLessEqual(max_value, 1.0) + + def test_linear_mapping_all_same(self): + fake_pixels = np.full((3, 3), 42.0) + linear = linear_mapping(fake_pixels) + self.assertTrue(np.allclose(linear, np.full(fake_pixels.shape, 0.5))) + + def test_pixel_lut(self): + amp = [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + phase = np.full((3, 3), 256) + fake_pixels = np.array([amp, phase]) + lut = [10, 11, 12, 13, 14, 15, 16, 17, 18] + + fake_complex = image_pixels_to_complex(fake_pixels, pixel_type="AMP8I_PHS8I", amplitude_table=lut) + self.assertTrue(np.allclose(fake_complex[1], np.full((3, 3), 0.0))) + self.assertTrue(np.allclose(fake_complex[0], np.array([[10, 11, 12], [13, 14, 15], [16, 17, 18]]))) From 077e839e1b410b40624a834f0c7d681ed1fce362 Mon Sep 17 00:00:00 2001 From: edparris Date: Thu, 12 Oct 2023 09:51:49 -0400 Subject: [PATCH 12/20] feat: add support for sidd imagery (#22) --- .pre-commit-config.yaml | 2 +- schemas/sidd.xsdata.xml | 56 + schemas/sidd/SFA.xsd | 213 + schemas/sidd/SICommonTypes.xsd | 502 +++ schemas/sidd/SICommonTypes_V1.0.xsd | 731 ++++ .../sidd/SIDD_schema_V1.0.0_2011_08_31.xsd | 1042 +++++ .../sidd/SIDD_schema_V2.0.0_2019_05_31.xsd | 1740 +++++++++ schemas/sidd/SIDD_schema_V3.0.0.xsd | 1715 ++++++++ .../Schema/ISM/CVEGenerated/CVEnumISM25X.xsd | 208 + .../CVEnumISMAtomicEnergyMarkings.xsd | 85 + .../ISM/CVEGenerated/CVEnumISMAttributes.xsd | 209 + .../CVEnumISMClassificationAll.xsd | 54 + .../CVEnumISMClassificationUS.xsd | 49 + .../CVEGenerated/CVEnumISMCompliesWith.xsd | 68 + .../ISM/CVEGenerated/CVEnumISMDissem.xsd | 102 + .../ISM/CVEGenerated/CVEnumISMExemptFrom.xsd | 53 + .../ISM/CVEGenerated/CVEnumISMNonIC.xsd | 95 + .../CVEGenerated/CVEnumISMNonUSControls.xsd | 52 + .../ISM/CVEGenerated/CVEnumISMNotice.xsd | 137 + .../ISM/CVEGenerated/CVEnumISMPocType.xsd | 72 + .../Schema/ISM/CVEGenerated/CVEnumISMSAR.xsd | 57 + .../ISM/CVEGenerated/CVEnumISMSCIControls.xsd | 150 + .../external/ISM-v13/Schema/ISM/IC-ISM.xsd | 1548 ++++++++ .../CVEGenerated/CVEnumISMCATFGIOpen.xsd | 1568 ++++++++ .../CVEGenerated/CVEnumISMCATFGIProtected.xsd | 1568 ++++++++ .../CVEnumISMCATOwnerProducer.xsd | 1573 ++++++++ .../ISMCAT/CVEGenerated/CVEnumISMCATRelTo.xsd | 1568 ++++++++ .../Schema/ISMCAT/SchemaGuideSchema.xsd | 88 + .../ISM/Schema/CVEGenerated/CVEnumISM25X.xsd | 76 + .../CVEGenerated/CVEnumISMAttributes.xsd | 166 + .../CVEnumISMClassificationAll.xsd | 51 + .../CVEnumISMClassificationNonUS.xsd | 51 + .../CVEnumISMClassificationUS.xsd | 46 + .../CVEGenerated/CVEnumISMCompliesWith.xsd | 44 + .../Schema/CVEGenerated/CVEnumISMDissem.xsd | 132 + .../Schema/CVEGenerated/CVEnumISMFGIOpen.xsd | 1385 +++++++ .../CVEGenerated/CVEnumISMFGIProtected.xsd | 1385 +++++++ .../Schema/CVEGenerated/CVEnumISMNonIC.xsd | 79 + .../CVEGenerated/CVEnumISMNonUSControls.xsd | 49 + .../Schema/CVEGenerated/CVEnumISMNotice.xsd | 104 + .../CVEGenerated/CVEnumISMOwnerProducer.xsd | 1390 +++++++ .../Schema/CVEGenerated/CVEnumISMRelTo.xsd | 1385 +++++++ .../ISM/Schema/CVEGenerated/CVEnumISMSAR.xsd | 46 + .../CVEGenerated/CVEnumISMSCIControls.xsd | 77 + .../CVEGenerated/CVEnumISMSourceMarked.xsd | 71 + .../external/ISM/Schema/CVEGeneratedTypes.xsd | 149 + schemas/sidd/external/ISM/Schema/IC-ISM.xsd | 891 +++++ src/aws/osml/formats/sidd/__init__.py | 0 src/aws/osml/formats/sidd/models/__init__.py | 690 ++++ .../formats/sidd/models/external/__init__.py | 6 + .../sidd/models/external/ism/__init__.py | 6 + .../models/external/ism/schema/__init__.py | 6 + .../ism/schema/cvegenerated/__init__.py | 32 + .../ism/schema/cvegenerated/cvenum_ism25_x.py | 47 + .../cvenum_ismclassification_all.py | 28 + .../cvegenerated/cvenum_ismcomplies_with.py | 22 + .../schema/cvegenerated/cvenum_ismdissem.py | 46 + .../schema/cvegenerated/cvenum_ismfgiopen.py | 561 +++ .../cvegenerated/cvenum_ismfgiprotected.py | 561 +++ .../schema/cvegenerated/cvenum_ismnon_ic.py | 36 + .../cvegenerated/cvenum_ismnon_uscontrols.py | 24 + .../cvegenerated/cvenum_ismowner_producer.py | 563 +++ .../schema/cvegenerated/cvenum_ismrel_to.py | 561 +++ .../cvegenerated/cvenum_ismscicontrols.py | 24 + .../cvegenerated/cvenum_ismsource_marked.py | 36 + .../sidd/models/external/ism_v13/__init__.py | 6 + .../external/ism_v13/schema/__init__.py | 6 + .../external/ism_v13/schema/ism/__init__.py | 34 + .../schema/ism/cvegenerated/__init__.py | 30 + .../schema/ism/cvegenerated/cvenum_ism25_x.py | 81 + .../cvenum_ismatomic_energy_markings.py | 26 + .../cvenum_ismclassification_all.py | 28 + .../cvegenerated/cvenum_ismcomplies_with.py | 33 + .../ism/cvegenerated/cvenum_ismdissem.py | 44 + .../ism/cvegenerated/cvenum_ismexempt_from.py | 26 + .../ism/cvegenerated/cvenum_ismnon_ic.py | 30 + .../cvegenerated/cvenum_ismnon_uscontrols.py | 24 + .../ism/cvegenerated/cvenum_ismnotice.py | 60 + .../ism/cvegenerated/cvenum_ismpoc_type.py | 33 + .../ism/cvegenerated/cvenum_ismscicontrols.py | 42 + .../external/ism_v13/schema/ism/ic_ism.py | 1427 +++++++ .../ism_v13/schema/ismcat/__init__.py | 6 + .../schema/ismcat/cvegenerated/__init__.py | 16 + .../cvegenerated/cvenum_ismcatfgiopen.py | 620 +++ .../cvegenerated/cvenum_ismcatfgiprotected.py | 620 +++ .../cvenum_ismcatowner_producer.py | 622 +++ .../cvegenerated/cvenum_ismcatrel_to.py | 620 +++ src/aws/osml/formats/sidd/models/sfa.py | 481 +++ .../formats/sidd/models/sicommon_types.py | 1526 ++++++++ .../sidd/models/sicommon_types_v1_0.py | 1823 +++++++++ .../osml/formats/sidd/models/sidd_v1_0_0.py | 2016 ++++++++++ .../osml/formats/sidd/models/sidd_v2_0_0.py | 3467 +++++++++++++++++ .../osml/formats/sidd/models/sidd_v3_0_0.py | 3454 ++++++++++++++++ src/aws/osml/gdal/sensor_model_factory.py | 6 +- .../osml/gdal/sicd_sensor_model_builder.py | 8 +- .../osml/gdal/sidd_sensor_model_builder.py | 136 + .../osml/photogrammetry/sicd_sensor_model.py | 43 +- test/aws/osml/formats/test_sidd_schemas.py | 13 + .../photogrammetry/test_sicd_sensor_model.py | 24 +- .../photogrammetry/test_sidd_sensor_model.py | 70 + test/data/sidd/example.sidd-chip.xml | 544 +++ test/data/sidd/example.sidd.xml | 520 +++ 102 files changed, 44672 insertions(+), 24 deletions(-) create mode 100644 schemas/sidd.xsdata.xml create mode 100644 schemas/sidd/SFA.xsd create mode 100644 schemas/sidd/SICommonTypes.xsd create mode 100644 schemas/sidd/SICommonTypes_V1.0.xsd create mode 100644 schemas/sidd/SIDD_schema_V1.0.0_2011_08_31.xsd create mode 100644 schemas/sidd/SIDD_schema_V2.0.0_2019_05_31.xsd create mode 100644 schemas/sidd/SIDD_schema_V3.0.0.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISM25X.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAtomicEnergyMarkings.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAttributes.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationAll.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationUS.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMCompliesWith.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMDissem.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMExemptFrom.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonIC.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonUSControls.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNotice.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMPocType.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSAR.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSCIControls.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISM/IC-ISM.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIOpen.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIProtected.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATOwnerProducer.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATRelTo.xsd create mode 100644 schemas/sidd/external/ISM-v13/Schema/ISMCAT/SchemaGuideSchema.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISM25X.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMAttributes.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationAll.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationNonUS.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationUS.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMCompliesWith.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMDissem.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIOpen.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIProtected.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonIC.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonUSControls.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNotice.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMOwnerProducer.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMRelTo.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSAR.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSCIControls.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSourceMarked.xsd create mode 100644 schemas/sidd/external/ISM/Schema/CVEGeneratedTypes.xsd create mode 100644 schemas/sidd/external/ISM/Schema/IC-ISM.xsd create mode 100644 src/aws/osml/formats/sidd/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ism25_x.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismclassification_all.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismcomplies_with.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismdissem.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiopen.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiprotected.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_ic.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_uscontrols.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismowner_producer.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismrel_to.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismscicontrols.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismsource_marked.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ism25_x.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismatomic_energy_markings.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismclassification_all.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismcomplies_with.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismdissem.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismexempt_from.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_ic.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_uscontrols.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnotice.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismpoc_type.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismscicontrols.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/ic_ism.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/__init__.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiopen.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiprotected.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatowner_producer.py create mode 100644 src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatrel_to.py create mode 100644 src/aws/osml/formats/sidd/models/sfa.py create mode 100644 src/aws/osml/formats/sidd/models/sicommon_types.py create mode 100644 src/aws/osml/formats/sidd/models/sicommon_types_v1_0.py create mode 100644 src/aws/osml/formats/sidd/models/sidd_v1_0_0.py create mode 100644 src/aws/osml/formats/sidd/models/sidd_v2_0_0.py create mode 100644 src/aws/osml/formats/sidd/models/sidd_v3_0_0.py create mode 100644 src/aws/osml/gdal/sidd_sensor_model_builder.py create mode 100644 test/aws/osml/formats/test_sidd_schemas.py create mode 100644 test/aws/osml/photogrammetry/test_sidd_sensor_model.py create mode 100644 test/data/sidd/example.sidd-chip.xml create mode 100644 test/data/sidd/example.sidd.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ee1e4e9..2da2e33 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -24,7 +24,7 @@ repos: rev: 6.0.0 hooks: - id: flake8 - args: ["--ignore=E203,W503,W605", "--max-line-length=125"] + args: ["--ignore=E203,W503,W605", "--max-line-length=160", "--extend-exclude=src/aws/osml/formats"] types: [file, python] - repo: https://github.com/pre-commit/mirrors-autopep8 diff --git a/schemas/sidd.xsdata.xml b/schemas/sidd.xsdata.xml new file mode 100644 index 0000000..5e774ef --- /dev/null +++ b/schemas/sidd.xsdata.xml @@ -0,0 +1,56 @@ + + + + sidd.models + dataclasses + filenames + reStructuredText + allGlobals + true + false + false + false + false + true + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/SFA.xsd b/schemas/sidd/SFA.xsd new file mode 100644 index 0000000..295ce2d --- /dev/null +++ b/schemas/sidd/SFA.xsd @@ -0,0 +1,213 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/SICommonTypes.xsd b/schemas/sidd/SICommonTypes.xsd new file mode 100644 index 0000000..062bfdd --- /dev/null +++ b/schemas/sidd/SICommonTypes.xsd @@ -0,0 +1,502 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Represents range and azimuth + + + + + Range dimension. + + + + + Azimuth dimension. + + + + + + + The reference point + + + + + The XYZ ECEF (units = m) reference point. + + + + + The row and column (units = pixels) which maps to the ECEF point. + + + + + + Used for implementation specific signifier for the reference point. + + + + diff --git a/schemas/sidd/SICommonTypes_V1.0.xsd b/schemas/sidd/SICommonTypes_V1.0.xsd new file mode 100644 index 0000000..ef0541a --- /dev/null +++ b/schemas/sidd/SICommonTypes_V1.0.xsd @@ -0,0 +1,731 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Represents range and azimuth + + + + + Range dimension. + + + + + Azimuth dimension. + + + + + + + The reference point + + + + + The XYZ ECEF (units = m) reference point. + + + + + The row and column (units = pixels) which maps to the ECEF point. + + + + + + Used for implementation specific signifier for the reference point. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/SIDD_schema_V1.0.0_2011_08_31.xsd b/schemas/sidd/SIDD_schema_V1.0.0_2011_08_31.xsd new file mode 100644 index 0000000..6d9ab6c --- /dev/null +++ b/schemas/sidd/SIDD_schema_V1.0.0_2011_08_31.xsd @@ -0,0 +1,1042 @@ + + + + + + + + + + + + + Any comma int triple. + + + + + + + + + + + + + + Size of LUT + + + + + + + + + + + Size of LUT. + + + + + + + + Object representing that the data requires color display. + + + + + LUT-base remap indicating that the color display is done through index-based color. + + + + + + + This remap works by taking the input space and using the LUT to map it to a log space (for 8-bit only). +From the log space the C0 and Ch fields are applied to get to display-ready density space. +The density should then be rendered by the TTC and monitor comp. +This means that the default DRA should not apply anything besides the clip points. +If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. +Examples: +Remap LUT Clips +============================= +PEDF PEDF->D 0,255 +LLG LLG->A->LogA C0,Ch +Log N/A C0,Ch +NRL N/A 0,255 (Supposed to be display ready) + + + + + + Name of remap applied (for informational purposes only). + + + + + Lookup table for remap to log amplitude for display. Used during the "Product Generation Option" portion of the SIPS display chain. Required for 8-bit data. Not to be used for 16-bit data. + + + + + Textual remap parameter. Filled based upon remap type (for informational purposes only). For example, if the data is linlog encoded a RemapParameter could be used to describe any amplitude scaling that was performed prior to linlog encoding the data. + + + + + + + Default ELT magnification method for this data. + + + + + + + + + + Default ELT decimation method for this data. Also used as default for reduced resolution dataset generation (if applicable). + + + + + + + + + + + Describes monitor compensation that may have been applied to the product during processing. + + + + + Gamma value for monitor compensation pre-applied to the image. + + + + + Xmin value for monitor compensation pre-applied to the image. + + + + + + + + + Information for proper color display of the data. + + + + + Information for proper monochrome display of the data. + + + + + + + + + Suggested override for the lower end-point of the display histogram in the ELT DRA application. Referred to as Pmin in SIPS documentation. + + + + + Suggested override for the upper end-point of the display histogram in the ELT DRA application. Referred to as Pmax in SIPS documentation. + + + + + + + Type for describing proper display of the derived product. + + + + + Defines the pixel type, based on enumeration and definition in Design and Exploitation document. + + + + + Information regarding the encoding of the pixel data. Used for 8-bit pixel types. + + + + + Recommended ELT magnification method for this data. + + + + + Recommended ELT decimation method for this data. Also used as default for reduced resolution dataset generation (if applicable). + + + + + Recommended ELT DRA overrides. + + + + + Describes monitor compensation that may have been applied to the product during processing. + + + + + Extensible parameters used to support profile-specific needs related to product display. + + + + + + + Plane definition for the product. + + + + + Unit vector of the plane defined to be aligned in the increasing row direction of the product. (Defined as Rpgd in Design and Exploitation document) + + + + + Unit vector of the plane defined to be aligned in the increasing column direction of the product. (Defined as Cpgd in Design and Exploitation document) + + + + + + + + + Reference point for the geometrical system. + + + + + + + + + + + Sample spacing in row and column. + + + + + Time (units = seconds) at which center of aperture for a given pixel coordinate in the product occurs. + + + + + + + + + Planar representation of the pixel grid + + + + + + + Plane definition for the product. + + + + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + + + Polynomial that converts Row/Col to Latitude (degrees). + + + + + Polynomial that converts Row/Col to Longitude (degrees). + + + + + Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row location. + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column location + + + + + + + + + Geographic mapping of the pixel grid. + + + + + + + + Cylindrical mapping of the pixel grid. + + + + + + + Along stripmap direction + + + + + Radius of Curvature defined at scene center. If not present, the radius of curvature will be derived based upon the equations provided in the Design and Exploitation Document + + + + + + + + + Geometric SAR information required for measurement/geolocation. + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + Geographic mapping of the pixel grid referred to as GGD in the Design and Exploitation document. + + + + + Planar representation of the pixel grid referred to as PGD in the Design and Exploitation document. + + + + + Cylindrical mapping of the pixel grid referred to as CGD in the Design and Exploitation document. + + + + + + Size of the image. + + + + + Center of aperture polynomial (units = m) based upon time into the collect. + + + + + + + Finest achievable resolution parameters. + + + + + + + + + + + + + + + Classification guidance authority (only if file is classified). + + + + + Classifying authority. + + + + + Date that the authority was provided. Specified in YYYY-MM-DD. + + + + + + + The overall classification of the product. + + + + + Extensible parameters used to support profile-specific needs related to product security. + + + + + + + + + + + + Software application name and version number. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Creation location of product. + + + + + Product-specific profile applied during product processing. + + + + + + + Contains general information about product creation. + + + + + Details regarding processor. + + + + + The overall classification of the product. + + + + + The output product name defined by the processor. + + + + + Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change Detection, etc.). + + + + + Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only needed if there is a suite of associated products. + + + + + Extensible parameters used to support profile-specific needs related to product creation. + + + + + + + + + + + + + + + Target may have one or more identifiers. Examples: names, BE numbers, etc. Use the "name" attribute to describe what this is. + + + + + Target footprint as defined by polygonal shape. + + + + + Generic extension. Could be used to indicate type of target, terrain, etc. + + + + + + + + + Country identifier for this geographic region. + + + + + Specifies classification level or special handling designators for this geographic region + + + + + Implementation specific geographic information. + + + + + + + + + Identifier for the georegion. + + + + + Estimated ground footprint of the product. + + + + + + Used to represent hierarchical decomposition into sub-regions. + + + + + Specifics about the georegion. + + + + + + + + + + Provides geographic coverage information. + + + + + Provides target specific geographic information. + + + + + + + + + + + + Size of the chipped product in pixels. + + + + + Upper-left corner with respect to the original product. + + + + + Upper-right corner with respect to the original product. + + + + + Lower-left corner with respect to the original product. + + + + + Lower-right corner with respect to the original product. + + + + + + + + + + + + Application which applied a modification. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Type of interpolation applied to the data. + + + + + Descriptor for the processing event. + + + + + + + + + + + + Contains information related to downstream chipping of the product. + + + + + Contains information related to downstream processing of the product. + + + + + + + + + + + + Processing module to keep track of the name and any parameters associated with the algorithms used to produce the SIDD. + + + + + + + + + + + + The name of the algorithm used in processing the product. + + + + + + Parameters associated with the algorithm used in processing the product. + + + + + ProcessingModule is a repeatable structure within itself to create an algorithm as a subset of another algorithm. + + + + + + + + Metadata regarding the product. + + + + + Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + + + + + Counter-clockwise angle from increasing row direction to north at the center of the image. + + + + + Exploitation feature extension for the end product + + + + + + + Computed metadata regarding the collect. + + + + + Metadata regarding one of the input collections. + + + + + + + + + + + + Metadata regarding the product. + + + + + + + ROI representing portion of input data used to make this product. + + + + + Number of rows and columns extracted from the input. + + + + + The upper-left pixel extracted from the input. + + + + + + + + + Polarization transmit type + + + + + Receive polarization type + + + + + Optional angle offset for the receive polarization defined at aperture center. + + + + + Optional flag to describe whether this input polarization was used in processing the product. + + + + + + + General collection information. + + + + + The name of the sensor. + + + + + Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + + + + + Collection date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Date and time defined in local time. + + + + + The duration of the collection (units = seconds). + + + + + Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + + + + + ROI representing portion of input data used to make this product. + + + + + Transmit and receive polarization. + + + + + + + Key geometry parameters independent of product processing. + + + + + Angle clockwise from north indicating the ETP line of sight vector. + + + + + Angle between the ETP at scene center and the range vector perpendicular to the direction of motion. + + + + + Angle from the ground track to platform velocity vector at nadir. Left-look is negative, right-look is positive. + + + + + Angle between the ETP and the line of sight vector. + + + + + Angle between the ETP and the cross range vector. Also known as the twist angle. + + + + + Exploitation feature extension related to geometry for a single input image + + + + + + + Phenomenology related to both the geometry and the final product processing. + + + + + The phenomon where vertical objects occlude radar energy. + + + + + The phenomenon where vertical objects appear as ground objects with the same range/range rate. + + + + + This is a range dependent phenomenon which describes the energy from a single scatter returned to the radar via more than one path and results in a nominally constant direction in the ETP. + + + + + Counter-clockwise angle from increasing row direction to ground track at the center of the image. + + + + + Exploitation feature extension related to phenomenology for a single input image + + + + + + + + + General collection information. + + + + + Key geometry parameters independent of product processing. + + + + + Phenomenology related to both the geometry and the final product processing. + + + + + + + + + Annotation Object. + + + + + + + + Geometrical representation of the annotation. + + + + + + + + + + + + + + + + + Single annotation. + + + + + Identifier for the annotation which idicates the type of object represented by this annotation. + + + + + Spatial reference system of the annotation. Assumed to be WGS-84 geographic coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above ellipsoid). + + + + + + The geometrical representation of the annotation. + + + + + + + Root element of the SIDD document. + + + + + + Information related to processor, classification, and product type. + + + + + Contains information on the parameters needed to display the product in an exploitation tool. + + + + + Contains generic and extensible targeting and geographic region information. + + + + + Contains the metadata necessary for performing measurements. + + + + + Computed metadata regarding the input collections and final product. + + + + + Contains metadata related to algorithms used during product generation. + + + + + Contains metadata related to downstream processing of the product. + + + + + See SICD documentation for metadata definitions. + + + + + Radiometric information about the product. + + + + + List of annotations for the imagery. + + + + + + diff --git a/schemas/sidd/SIDD_schema_V2.0.0_2019_05_31.xsd b/schemas/sidd/SIDD_schema_V2.0.0_2019_05_31.xsd new file mode 100644 index 0000000..90f93d4 --- /dev/null +++ b/schemas/sidd/SIDD_schema_V2.0.0_2019_05_31.xsd @@ -0,0 +1,1740 @@ + + + + + + + + + + + + Any comma int triple. + + + + + + + + + + + + + + Size of LUT + + + + + + + + + + + Size of LUT. + + + + + + + + Object representing that the data requires color display. + + + + + LUT-base remap indicating that the color display is done through index-based color. + + + + + + + + This remap works by taking the input space and using the LUT to map it to a log space (for 8-bit only). + From the log space the C0 and Ch fields are applied to get to display-ready density space. + The density should then be rendered by the TTC and monitor comp. + This means that the default DRA should not apply anything besides the clip points. + If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. + Examples: + Remap LUT Clips + ============================= + PEDF PEDF->D 0,255 + LLG LLG->A->LogA C0,Ch + Log N/A C0,Ch + NRL N/A 0,255 (Supposed to be display ready) + + + + + + Name of remap applied (for informational purposes only). + + + + + Textual remap parameter. Filled based upon remap type (for informational purposes only). For example, if the data is linlog encoded a RemapParameter could be used to describe any amplitude scaling that was performed prior to linlog encoding the data. + + + + + + + + + Information for proper color display of the data. + + + + + Information for proper monochrome display of the data. + + + + + + + + + Suggested override for the lower end-point of the display histogram in the ELT DRA application. Referred to as Pmin in SIPS documentation. + + + + + Suggested override for the upper end-point of the display histogram in the ELT DRA application. Referred to as Pmax in SIPS documentation. + + + + + + + Type for describing proper display of the derived product. + + + + + + Defines the pixel type, based on enumeration and definition in Design and Exploitation document. + + + + + + + Number of bands contained in the image. Populate with the number of bands present after remapping. For example an 8-bit RGB image (RGBLU) this should be populated with 3. + + + + + + + Indicates which band to display by default. Valid range = 1 to NumBands. + + + + + + + + + + Optional extensible parameters used to support profile-specific needs related to product display. Predefined filter types. + + + + + + + + + + Performs several key actions on an image to prepare it for necessary additional processing to achieve the desired output product. + + + + + + + Creates a set of sub-sampled versions of an image to provide processing chains with quick access to lower mangification values + for faster computation speeds and performance. + + + + + + + + + + Performs several key actions on an image to prepare it for necessary additional processing to achieve the desired output product. + + + + + + + Band equalization ensures that real-world neutral colors have equal digital count values + (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + + + + + + Filter must be no larger than 15x15. + + + + + + Data remapping refers to the specific need to convert the data of incoming, expanded or uncompressed image band data to non-mapped image data. + + + + + + + + + + + + + + + + + + + + + Algorithm used to perform RRDS downsampling + + + + + Only included if DownSamplingMethod=DECIMET + + + + + Only included if DownSamplingMethod=DECIMET + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Band equalization ensures that real-world neutral colors have equal digital count values + (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + + + + + + Allowed values: 1DLUT + + + + + + + + + + + + + + + + + + + + + + + + + + + + Database name of LUT to use. + + + + + + + + Index specifying the remap family. + + + + + + + Index specifying the member for the remap family. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The geometric transform element is used to perform various geometric distortions to each band of image data. These distortions + include image chipping, scaling, rotation, shearing, etc. + + + + + + + + Specifies the recommended ELT DRA overrides + + + + + + The 1-D LUT element uses one or more 1-D LUTs to stretch or compress tome data in valorous regions within a digital image's dynamic range. + 1-D LUT can be implemented using a Tonal Transfer Curve (TTC). There are 12 families of TTCs: Range = [0,11]. There are 64 members for each family: Range=[0, 63]. + + + + + + + + + + + The geometric transform element is used to perform various geometric distortions to each band of image data. These distortions + include image chipping, scaling, rotation, shearing, etc. + + + + + + + Parameters describing the default orientation of the product + + + + + + + + + + + Anti-Alias Filter used for scaling. + Refer to program-specific documentation for population guidance + + + + + + + Interpolation Filter used for scaling. + Refer to program-specific documentation for population guidance. + + + + + + + + + Parameters describing the default orientation of the product + + + + + + Descirbes the shadow direciton relative to the pixels in the file. + + + + + + + Descirbes the shadow direciton relative to the pixels in the file. + + + + + + + + + + + + + + Note: If defining a custom Filter, it must be no larger than 5x5. + + + + + Note: If defining a custom Filter, it must be no larger than 5x5. + + + + + + + + + Parameters describing the Color Management Module (CMM). + + + + + + + Parameters describing the Color Management Module (CMM). + + + + + + Name of sensor profile in ICC Profile database. + + + + + Name of display profile in ICC Profile database. + + + + + Valid ICC profile signature. + + + + + + + + + + + + + + + Parameter describing DRA. + + + + + Algorithm used for dynamic range adjustment. + + + + + + Indicates which band to use in computing statistics for DRA. Valid range = 1 to NumBands. + + + + + + + + + + + + + DRA clip low point. This is the cumulative histogram percentage value that defines the lower end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + + + + + + + DRA clip high point. This is the cumulative histogram percentage value that defines the upper end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + + + + + + The pixel value corresponding to the Pmin percentage poitn in the image histogram. Range: [0.0 to 1.0]/ + + + + + The pixel value corresponding to the Pmax percentage poitn in the image histogram. Range: [0.0 to 1.0]/ + + + + + + + Algorithm used for dynamic range adjustment. + + + + + + + + + + + + Subtractor value used to reduce haze in the image. Range: [0.0 to 2047.0] + + + + + Multiplier value used to reduce haze in the image. Range: [0.0 to 2047.0] + + + + + + + Plane definition for the product. + + + + + Unit vector of the plane defined to be aligned in the increasing row direction of the product. (Defined as Rpgd in Design and Exploitation document) + + + + + Unit vector of the plane defined to be aligned in the increasing column direction of the product. (Defined as Cpgd in Design and Exploitation document) + + + + + + + + + Reference point for the geometrical system. + + + + + + + + + + + Sample spacing in row and column. + + + + + Time (units = seconds) at which center of aperture for a given pixel coordinate in the product occurs. + + + + + + + + + Planar representation of the pixel grid + + + + + + + Plane definition for the product. + + + + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + + + Polynomial that converts Row/Col to Latitude (degrees). + + + + + Polynomial that converts Row/Col to Longitude (degrees). + + + + + Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row location. + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column location + + + + + + + + + Geographic mapping of the pixel grid. + + + + + + + + Cylindrical mapping of the pixel grid. + + + + + + + Along stripmap direction + + + + + Radius of Curvature defined at scene center. If not present, the radius of curvature will be derived based upon the equations provided in the Design and Exploitation Document + + + + + + + + + Geometric SAR information required for measurement/geolocation. + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + Geographic mapping of the pixel grid referred to as GGD in the Design and Exploitation document. + + + + + Planar representation of the pixel grid referred to as PGD in the Design and Exploitation document. + + + + + Cylindrical mapping of the pixel grid referred to as CGD in the Design and Exploitation document. + + + + + + + Size of the image in pixels. + + + + + + Flag indicating whether ARP polynomial is based on the best available ("collect time" or "predicted") ephemeris. + + + + + + Based on ephemeries at time of collect + + + + + Based on predicted ephemeries (i.e. pre-collect) + + + + + Ephemeris has been refined after data collection + + + + + + + + Center of aperture polynomial (units = m) based upon time into the collect. + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + + Finest achievable resolution parameters. + + + + + + + + + + + + + + + Classification guidance authority (only if file is classified). + + + + + Classifying authority. + + + + + Date that the authority was provided. Specified in YYYY-MM-DD. + + + + + + + The overall classification of the product. + + + + + Extensible parameters used to support profile-specific needs related to product security. + + + + + + + + + + + Software application name and version number. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Creation location of product. + + + + + Product-specific profile applied during product processing. + + + + + + + Contains general information about product creation. + + + + + Details regarding processor. + + + + + The overall classification of the product. + + + + + The output product name defined by the processor. + + + + + Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change Detection, etc.). + + + + + Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only needed if there is a suite of associated products. + + + + + Extensible parameters used to support profile-specific needs related to product creation. + + + + + + + This block describes the geographic coordinates of the region covered by the image. + + + + + Identifies the earth model used for latitude, longitude and height parameters. All height values are Height Above The Ellipsoid (HAE). + + + + + + Parameters apply to image corners of the product projected to the same height as the SCP. + These corners are an approximate geographic location that is not intended for analytical use. + + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + Parameters describing geographic features. Note: the GeoInfo block may be used as a block within itself. + + + + + + + Identifies the earth model used for latitude, longitude and height parameters. All height values are Height Above The Ellipsoid (HAE). + + + + + + + + + Parameters apply to image corners of the product projected to the same height as the SCP. + These corners are an approximate geographic location that is not intended for analytical use. + + + + + + Image Corner Point (ICP) data for the 4 corners in product. ICPs indexed x = 1, 2, 3, 4, clockwise. + + + + + + + + + + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) minimum row index, (2) minimum column index if 2 vertices with minimum row index, + 1st and last vertices are connected to form the polygon. + + + + + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) minimum row index, (2) minimum column index if 2 vertices with minimum row index, + 1st and last vertices are connected to form the polygon. + + + + + + + + + + Contains information related to downstream chipping of the product. There is only one instance, and the instance is updated with respect to the full image parameters. + For example, if an image is chipped out of a smaller chip, the new chip needs to be updated to the original full image corners. + Since this relationship is linear, bi-linear interpolation is sufficient to determine an arbitrary chip coordinate in terms + of the original full image coordinates. Chipping is typically done using an exploitation tool, and should not be done in the initial product creation. + + + + + + Size of the chipped product in pixels. + + + + + Upper-left corner with respect to the original product. + + + + + Upper-right corner with respect to the original product. + + + + + Lower-left corner with respect to the original product. + + + + + Lower-right corner with respect to the original product. + + + + + + + + + + + + Application which applied a modification. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Type of interpolation applied to the data. + + + + + Descriptor for the processing event. + + + + + + + + + + + + Contains information related to downstream chipping of the product. + + + + + Contains information related to downstream processing of the product. + + + + + + + + Computed metadata regarding one or more of the input collections and final product. + + + + + + Processing module to keep track of the name and any parameters associated with the algorithms used to produce the SIDD. + + + + + + + + + + + + The name of the algorithm used in processing the product. + + + + + + Parameters associated with the algorithm used in processing the product. + + + + + ProcessingModule is a repeatable structure within itself to create an algorithm as a subset of another algorithm. + + + + + + + + Metadata regarding the product. + + + + + Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + + + + + Ellipticity of the 2D-IPR at the ORP, measured in the Earth Geodetic Tangent Plane (EGTP). Ellipticity is the ratio of the IPR ellipse's major axis to minor axis. + + + + + Describes the processed transmit and receive polarizations for the product. + + + + + Counter-clockwise angle from increasing row direction to north at the center of the image. + + + + + Exploitation feature extension for the end product + + + + + + + Computed metadata regarding the collect. + + + + + Metadata regarding one of the input collections. + + + + + + + + + + + + Metadata regarding the product. + + + + + + + ROI representing portion of input data used to make this product. + + + + + Number of rows and columns extracted from the input. + + + + + The upper-left pixel extracted from the input. + + + + + + + + + Polarization transmit type + + + + + Receive polarization type + + + + + Optional angle offset for the receive polarization defined at aperture center. + + + + + + + + + Polarization transmit type + + + + + Receive polarization type + + + + + + + General collection information. + + + + + The name of the sensor. + + + + + Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + + + + + Collection date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Date and time defined in local time. + + + + + The duration of the collection (units = seconds). + + + + + Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + + + + + ROI representing portion of input data used to make this product. + + + + + Transmit and receive polarization. + + + + + + + Key geometry parameters independent of product processing. All values computed at the center time of the full collection. + + + + + Angle clockwise from north indicating the ETP line of sight vector. + + + + + Angle between the ETP at scene center and the range vector perpendicular to the direction of motion. + + + + + Angle from the ground track to platform velocity vector at nadir. Left-look is positive, right-look is negative. + + + + + Angle between the ETP and the line of sight vector. + + + + + Angle between the ETP and the cross range vector. Also known as the twist angle. + + + + + The angle between the velocity vector and the radar line-of-sight vector. Also known as the slant plane squint angle. + + + + + Exploitation feature extension related to geometry for a single input image + + + + + + + Phenomenology related to both the geometry and the final product processing. All values computed at the center time of the full collection. + + + + + The phenomon where vertical objects occlude radar energy. + + + + + The phenomenon where vertical objects appear as ground objects with the same range/range rate. + + + + + This is a range dependent phenomenon which describes the energy from a single scatter returned to the radar via more than one path and results in a nominally constant direction in the ETP. + + + + + Counter-clockwise angle from increasing row direction to ground track at the center of the image. + + + + + Exploitation feature extension related to phenomenology for a single input image + + + + + + + + + General collection information. + + + + + Key geometry parameters independent of product processing. + + + + + Phenomenology related to both the geometry and the final product processing. + + + + + + + + Contains information regarding any compression that has occured to the image data. + + + + + + Block describing details of JPEG 2000 compression. + + + + + + + + + + + Conditional fields that exist only for parsed images. + + + + + + + + + + + The default number of wavelet decompositionlevels performed per tile in the original image out of the processors. + + + + + + + The number of spectral bands in the original image out of the processors. + + + + + + + Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + + + + + + + + Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + + + + + + Layer Index Number indicates the number of layers being described. Layers are numbered from 0 to (numLayers - 1). + + + + + + + + + + + + The bit rate target associated with the layer. It may happen that the bit rate was not achieved due to data characteristics. + Note: for JPEG 2000 numerically lossless quality, the bit rate for the final layer is an expected value, based on performance. + + + + + + + + + + This block describes the Digital ElevatioNData when it is included with the SIDD product. + + + + + + + Describes the Local Geographic Coordinate system linking row/column to the absolute geographic coordinate (lat/lon) + + + + + + + Describes the absolute coordinate system to which the data is referenced. + + + + + + + Describes the horizontal and vertical point and regional information for the DED. + + + + + + + + + + Describes the Local Geographic Coordinate system linking row/column to the absolute geographic coordinate (lat/lon) + + + + + + + Pixel ground spacing in E/W direction that is the number of pixels or element intervals in 360 degrees. + + + + + + + Pixel ground spacing in N/S direction that is the number of pixels or element intervals in 360 degrees. + + + + + + + Northwest corner Latitude/Longitude - product NW corner + + + + + + + + + Describes the absolute coordinate system to which the data is referenced. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Z values false origin + + + + + + Gride zone number, required for UTM, not include for GCS. (4 character field) Values: +001 to +060 (northern hemisphere) -001 to -060 (southern hemisphere) + + + + + + + + + Describes the horizontal and vertical point and regional information for the DED. + + + + + + + Number of positional accuracy regions. + + + + + + + + + + + + + + + + + + Annotation Object. + + + + + + + Geometrical representation of the annotation. + + + + + + + + + + + + + + + Single annotation. + + + + + Identifier for the annotation which idicates the type of object represented by this annotation. + + + + + Spatial reference system of the annotation. Assumed to be WGS-84 geographic coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above ellipsoid). + + + + + The geometrical representation of the annotation. + + + + + + + Root element of the SIDD document. + + + + + + Information related to processor, classification, and product type. + + + + + Contains information on the parameters needed to display the product in an exploitation tool. + + + + + Contains generic and extensible targeting and geographic region information. + + + + + Contains the metadata necessary for performing measurements. + + + + + Computed metadata regarding the input collections and final product. + + + + + Contains metadata related to downstream processing of the product. + + + + + See SICD documentation for metadata definitions. + + + + + Radiometric information about the product. + + + + + + Information about other collections that are matched to the current collection. The current collection is the collection from which this SIDD product was generated. + + + + + + + Contains information regarding any compression that has occured to the image data. + + + + + + + This block describes the Digital ElevatioNData when it is included with the SIDD product. + + + + + + Contains metadata related to algorithms used during product generation. + + + + + List of annotations for the imagery. + + + + + + diff --git a/schemas/sidd/SIDD_schema_V3.0.0.xsd b/schemas/sidd/SIDD_schema_V3.0.0.xsd new file mode 100644 index 0000000..b168504 --- /dev/null +++ b/schemas/sidd/SIDD_schema_V3.0.0.xsd @@ -0,0 +1,1715 @@ + + + + + + + + + + + + Any comma int triple. + + + + + + + + + + + + + + Size of LUT + + + + + + + + + + + Size of LUT. + + + + + + + + Object representing that the data requires color display. + + + + + LUT-base remap indicating that the color display is done through index-based color. + + + + + + + + This remap works by taking the input space and using the LUT to map it to a log space (for 8-bit only). + From the log space the C0 and Ch fields are applied to get to display-ready density space. + The density should then be rendered by the TTC and monitor comp. + This means that the default DRA should not apply anything besides the clip points. + If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. + Examples: + Remap LUT Clips + ============================= + PEDF PEDF->D 0,255 + LLG LLG->A->LogA C0,Ch + Log N/A C0,Ch + NRL N/A 0,255 (Supposed to be display ready) + + + + + + Name of remap applied (for informational purposes only). + + + + + Textual remap parameter. Filled based upon remap type (for informational purposes only). For example, if the data is linlog encoded a RemapParameter could be used to describe any amplitude scaling that was performed prior to linlog encoding the data. + + + + + + + + + Information for proper color display of the data. + + + + + Information for proper monochrome display of the data. + + + + + + + + + Suggested override for the lower end-point of the display histogram in the ELT DRA application. Referred to as Pmin in SIPS documentation. + + + + + Suggested override for the upper end-point of the display histogram in the ELT DRA application. Referred to as Pmax in SIPS documentation. + + + + + + + Type for describing proper display of the derived product. + + + + + + Defines the pixel type, based on enumeration and definition in Design and Exploitation document. + + + + + + + Number of bands contained in the image. Populate with the number of bands present after remapping. For example an 8-bit RGB image (RGBLU) this should be populated with 3. + + + + + + + Indicates which band to display by default. Valid range = 1 to NumBands. + + + + + + + + + + Optional extensible parameters used to support profile-specific needs related to product display. Predefined filter types. + + + + + + + + + + Performs several key actions on an image to prepare it for necessary additional processing to achieve the desired output product. + + + + + + + Creates a set of sub-sampled versions of an image to provide processing chains with quick access to lower mangification values + for faster computation speeds and performance. + + + + + + + + + + Performs several key actions on an image to prepare it for necessary additional processing to achieve the desired output product. + + + + + + + Band equalization ensures that real-world neutral colors have equal digital count values + (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + + + + + + Filter must be no larger than 15x15. + + + + + + Data remapping refers to the specific need to convert the data of incoming, expanded or uncompressed image band data to non-mapped image data. + + + + + + + + + + + + + + + + + + + + + Algorithm used to perform RRDS downsampling + + + + + Only included if DownSamplingMethod=DECIMET + + + + + Only included if DownSamplingMethod=DECIMET + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Band equalization ensures that real-world neutral colors have equal digital count values + (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + + + + + + Allowed values: 1DLUT + + + + + + + + + + + + + + + + + + + + + + + + + + + + Database name of LUT to use. + + + + + + + + Index specifying the remap family. + + + + + + + Index specifying the member for the remap family. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The geometric transform element is used to perform various geometric distortions to each band of image data. These distortions + include image chipping, scaling, rotation, shearing, etc. + + + + + + + + Specifies the recommended ELT DRA overrides + + + + + + The 1-D LUT element uses one or more 1-D LUTs to stretch or compress tome data in valorous regions within a digital image's dynamic range. + 1-D LUT can be implemented using a Tonal Transfer Curve (TTC). There are 12 families of TTCs: Range = [0,11]. There are 64 members for each family: Range=[0, 63]. + + + + + + + + + + + The geometric transform element is used to perform various geometric distortions to each band of image data. These distortions + include image chipping, scaling, rotation, shearing, etc. + + + + + + + Parameters describing the default orientation of the product + + + + + + + + + + + Anti-Alias Filter used for scaling. + Refer to program-specific documentation for population guidance + + + + + + + Interpolation Filter used for scaling. + Refer to program-specific documentation for population guidance. + + + + + + + + + Parameters describing the default orientation of the product + + + + + + Descirbes the shadow direciton relative to the pixels in the file. + + + + + + + Descirbes the shadow direciton relative to the pixels in the file. + + + + + + + + + + + + + + Note: If defining a custom Filter, it must be no larger than 5x5. + + + + + Note: If defining a custom Filter, it must be no larger than 5x5. + + + + + + + + + Parameters describing the Color Management Module (CMM). + + + + + + + Parameters describing the Color Management Module (CMM). + + + + + + Name of sensor profile in ICC Profile database. + + + + + Name of display profile in ICC Profile database. + + + + + Valid ICC profile signature. + + + + + + + + + + + + + + + Parameter describing DRA. + + + + + Algorithm used for dynamic range adjustment. + + + + + + Indicates which band to use in computing statistics for DRA. Valid range = 1 to NumBands. + + + + + + + + + + + + + DRA clip low point. This is the cumulative histogram percentage value that defines the lower end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + + + + + + + DRA clip high point. This is the cumulative histogram percentage value that defines the upper end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + + + + + + The pixel value corresponding to the Pmin percentage poitn in the image histogram. Range: [0.0 to 1.0]/ + + + + + The pixel value corresponding to the Pmax percentage poitn in the image histogram. Range: [0.0 to 1.0]/ + + + + + + + Algorithm used for dynamic range adjustment. + + + + + + + + + + + + Subtractor value used to reduce haze in the image. Range: [0.0 to 2047.0] + + + + + Multiplier value used to reduce haze in the image. Range: [0.0 to 2047.0] + + + + + + + Plane definition for the product. + + + + + Unit vector of the plane defined to be aligned in the increasing row direction of the product. (Defined as Rpgd in Design and Exploitation document) + + + + + Unit vector of the plane defined to be aligned in the increasing column direction of the product. (Defined as Cpgd in Design and Exploitation document) + + + + + + + + + Reference point for the geometrical system. + + + + + + + + + + + Sample spacing in row and column. + + + + + Time (units = seconds) at which center of aperture for a given pixel coordinate in the product occurs. + + + + + + + + + Planar representation of the pixel grid + + + + + + + Plane definition for the product. + + + + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + + + Polynomial that converts Row/Col to Latitude (degrees). + + + + + Polynomial that converts Row/Col to Longitude (degrees). + + + + + Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row location. + + + + + Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column location + + + + + + + + + Geographic mapping of the pixel grid. + + + + + + + + Cylindrical mapping of the pixel grid. + + + + + + + Along stripmap direction + + + + + Radius of Curvature defined at scene center. If not present, the radius of curvature will be derived based upon the equations provided in the Design and Exploitation Document + + + + + + + + + Geometric SAR information required for measurement/geolocation. + + + + + + Polynomial pixel to ground. Only used for sensor systems where the radar geometry parameters are not recorded. + + + + + Geographic mapping of the pixel grid referred to as GGD in the Design and Exploitation document. + + + + + Planar representation of the pixel grid referred to as PGD in the Design and Exploitation document. + + + + + Cylindrical mapping of the pixel grid referred to as CGD in the Design and Exploitation document. + + + + + + + Size of the image in pixels. + + + + + + Flag indicating whether ARP polynomial is based on the best available ("collect time" or "predicted") ephemeris. + + + + + + Based on ephemeries at time of collect + + + + + Based on predicted ephemeries (i.e. pre-collect) + + + + + Ephemeris has been refined after data collection + + + + + + + + Center of aperture polynomial (units = m) based upon time into the collect. + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + + Finest achievable resolution parameters. + + + + + + + + + + + + + + + Classification guidance authority (only if file is classified). + + + + + Classifying authority. + + + + + Date that the authority was provided. Specified in YYYY-MM-DD. + + + + + + + The overall classification of the product. + + + + + Extensible parameters used to support profile-specific needs related to product security. + + + + + + + + + + + Software application name and version number. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Creation location of product. + + + + + Product-specific profile applied during product processing. + + + + + + + Contains general information about product creation. + + + + + Details regarding processor. + + + + + The overall classification of the product. + + + + + The output product name defined by the processor. + + + + + Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change Detection, etc.). + + + + + Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only needed if there is a suite of associated products. + + + + + Extensible parameters used to support profile-specific needs related to product creation. + + + + + + + + + + + + + + + Identifies the earth model used for latitude, longitude and height parameters. All height values are Height Above The Ellipsoid (HAE). + + + + + + + + + Parameters apply to image corners of the product projected to the same height as the SCP. + These corners are an approximate geographic location that is not intended for analytical use. + + + + + + Image Corner Point (ICP) data for the 4 corners in product. ICPs indexed x = 1, 2, 3, 4, clockwise. + + + + + + + + + + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) minimum row index, (2) minimum column index if 2 vertices with minimum row index, + 1st and last vertices are connected to form the polygon. + + + + + + + + + + Indicates the full image includes both valid data and some zero filled pixels. + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices in clockwise order. + + + + + + + Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) minimum row index, (2) minimum column index if 2 vertices with minimum row index, + 1st and last vertices are connected to form the polygon. + + + + + + + + + + Contains information related to downstream chipping of the product. There is only one instance, and the instance is updated with respect to the full image parameters. + For example, if an image is chipped out of a smaller chip, the new chip needs to be updated to the original full image corners. + Since this relationship is linear, bi-linear interpolation is sufficient to determine an arbitrary chip coordinate in terms + of the original full image coordinates. Chipping is typically done using an exploitation tool, and should not be done in the initial product creation. + + + + + + Size of the chipped product in pixels. + + + + + Upper-left corner with respect to the original product. + + + + + Upper-right corner with respect to the original product. + + + + + Lower-left corner with respect to the original product. + + + + + Lower-right corner with respect to the original product. + + + + + + + + + + + + Application which applied a modification. + + + + + Date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Type of interpolation applied to the data. + + + + + Descriptor for the processing event. + + + + + + + + + + + + Contains information related to downstream chipping of the product. + + + + + Contains information related to downstream processing of the product. + + + + + + + + Computed metadata regarding one or more of the input collections and final product. + + + + + + Processing module to keep track of the name and any parameters associated with the algorithms used to produce the SIDD. + + + + + + + + + + + + The name of the algorithm used in processing the product. + + + + + + Parameters associated with the algorithm used in processing the product. + + + + + ProcessingModule is a repeatable structure within itself to create an algorithm as a subset of another algorithm. + + + + + + + + Metadata regarding the product. + + + + + Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + + + + + Ellipticity of the 2D-IPR at the ORP, measured in the Earth Geodetic Tangent Plane (EGTP). Ellipticity is the ratio of the IPR ellipse's major axis to minor axis. + + + + + Describes the processed transmit and receive polarizations for the product. + + + + + Counter-clockwise angle from increasing row direction to north at the center of the image. + + + + + Exploitation feature extension for the end product + + + + + + + Computed metadata regarding the collect. + + + + + Metadata regarding one of the input collections. + + + + + + + + + + + + Metadata regarding the product. + + + + + + + ROI representing portion of input data used to make this product. + + + + + Number of rows and columns extracted from the input. + + + + + The upper-left pixel extracted from the input. + + + + + + + + + Polarization transmit type + + + + + Receive polarization type + + + + + Optional angle offset for the receive polarization defined at aperture center. + + + + + + + + + Polarization transmit type + + + + + Receive polarization type + + + + + + + General collection information. + + + + + The name of the sensor. + + + + + Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + + + + + Collection date and time defined in Coordinated Universal Time (UTC). The seconds should be followed by a Z to indicate UTC. + + + + + Date and time defined in local time. + + + + + The duration of the collection (units = seconds). + + + + + Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + + + + + ROI representing portion of input data used to make this product. + + + + + Transmit and receive polarization. + + + + + + + Key geometry parameters independent of product processing. All values computed at the center time of the full collection. + + + + + Angle clockwise from north indicating the ETP line of sight vector. + + + + + Angle between the ETP at scene center and the range vector perpendicular to the direction of motion. + + + + + Angle from the ground track to platform velocity vector at nadir. Left-look is positive, right-look is negative. + + + + + Angle between the ETP and the line of sight vector. + + + + + Angle between the ETP and the cross range vector. Also known as the twist angle. + + + + + The angle between the velocity vector and the radar line-of-sight vector. Also known as the slant plane squint angle. + + + + + Exploitation feature extension related to geometry for a single input image + + + + + + + Phenomenology related to both the geometry and the final product processing. All values computed at the center time of the full collection. + + + + + The phenomon where vertical objects occlude radar energy. + + + + + The phenomenon where vertical objects appear as ground objects with the same range/range rate. + + + + + This is a range dependent phenomenon which describes the energy from a single scatter returned to the radar via more than one path and results in a nominally constant direction in the ETP. + + + + + Counter-clockwise angle from increasing row direction to ground track at the center of the image. + + + + + Exploitation feature extension related to phenomenology for a single input image + + + + + + + + + General collection information. + + + + + Key geometry parameters independent of product processing. + + + + + Phenomenology related to both the geometry and the final product processing. + + + + + + + + Contains information regarding any compression that has occured to the image data. + + + + + + Block describing details of JPEG 2000 compression. + + + + + + + + + + + Conditional fields that exist only for parsed images. + + + + + + + + + + + The default number of wavelet decompositionlevels performed per tile in the original image out of the processors. + + + + + + + The number of spectral bands in the original image out of the processors. + + + + + + + Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + + + + + + + + Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + + + + + + Layer Index Number indicates the number of layers being described. Layers are numbered from 0 to (numLayers - 1). + + + + + + + + + + + + The bit rate target associated with the layer. It may happen that the bit rate was not achieved due to data characteristics. + Note: for JPEG 2000 numerically lossless quality, the bit rate for the final layer is an expected value, based on performance. + + + + + + + + + + This block describes the Digital ElevatioNData when it is included with the SIDD product. + + + + + + + Describes the Local Geographic Coordinate system linking row/column to the absolute geographic coordinate (lat/lon) + + + + + + + Describes the absolute coordinate system to which the data is referenced. + + + + + + + Describes the horizontal and vertical point and regional information for the DED. + + + + + + + + + + Describes the Local Geographic Coordinate system linking row/column to the absolute geographic coordinate (lat/lon) + + + + + + + Pixel ground spacing in E/W direction that is the number of pixels or element intervals in 360 degrees. + + + + + + + Pixel ground spacing in N/S direction that is the number of pixels or element intervals in 360 degrees. + + + + + + + Northwest corner Latitude/Longitude - product NW corner + + + + + + + + + Describes the absolute coordinate system to which the data is referenced. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Z values false origin + + + + + + Gride zone number, required for UTM, not include for GCS. (4 character field) Values: +001 to +060 (northern hemisphere) -001 to -060 (southern hemisphere) + + + + + + + + + Describes the horizontal and vertical point and regional information for the DED. + + + + + + + Number of positional accuracy regions. + + + + + + + + + + + + + + + + + + Annotation Object. + + + + + + + Geometrical representation of the annotation. + + + + + + + + + + + + + + + Single annotation. + + + + + Identifier for the annotation which idicates the type of object represented by this annotation. + + + + + Spatial reference system of the annotation. Assumed to be WGS-84 geographic coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above ellipsoid). + + + + + The geometrical representation of the annotation. + + + + + + + Root element of the SIDD document. + + + + + + Information related to processor, classification, and product type. + + + + + Contains information on the parameters needed to display the product in an exploitation tool. + + + + + Contains geographic data. + + + + + Contains the metadata necessary for performing measurements. + + + + + Computed metadata regarding the input collections and final product. + + + + + Contains metadata related to downstream processing of the product. + + + + + See SICD documentation for metadata definitions. + + + + + Radiometric information about the product. + + + + + + Information about other collections that are matched to the current collection. The current collection is the collection from which this SIDD product was generated. + + + + + + + Contains information regarding any compression that has occured to the image data. + + + + + + + This block describes the Digital ElevatioNData when it is included with the SIDD product. + + + + + + Contains metadata related to algorithms used during product generation. + + + + + List of annotations for the imagery. + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISM25X.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISM25X.xsd new file mode 100644 index 0000000..00889a7 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISM25X.xsd @@ -0,0 +1,208 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISM25X Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISM25X.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently authorized authority block declass date/event exemptions. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISM25X.xml + + + + + + + + When using a source document that contains portions of Restricted Data (RD) + or Formerly Restricted Data (FRD) where the RD/FRD source document(s) + do not have declassification instructions, the derivatively classified + document shall not contain a declassification date or event on the + Declassify On line. The following shall be annotated on the Declassify On + line: "Not Applicable or (N/A) to RD/FRD portions" and + "See source list for NSI portions" separated by a period. + The source list must include the declassification instruction + for each of the source documents classified under E.O. 13526 and + shall not appear in the classification authority block + + + + + + + Since NATO information is not to be declassified or downgraded without the prior consent + of NATO, the “Declassify on” line of documents that commingle information classified by + NATO and U.S. classified NSI, will read “N/A to NATO portions. + See source list for NSI portions.” + The NSI source list will appear beneath the classification authority block + in a manner that clearly identifies it as separate and distinct. + + + + + + + Handles special case of BOTH NATO and AEA as a single exemption. + + + + + + + Reveal the identity of a confidential + human source, a human intelligence source, + a relationship with an intelligence or security + service of a foreign government or + international organization, or a non-human + intelligence source; or impair the + effectiveness of an intelligence method + currently in use, available for use, or under + development. + + + + + + + "25X1, EO 12951" (prescribed by the DNI for use on information described in E.O. 12951, + Release of Imagery Acquired by Space-Based National Intelligence Reconnaissance Systems) + + + + + + + Reveal information that would assist + in the development, production, or use of + weapons of mass destruction. + + + + + + + Reveal information that would + impair U.S. cryptologic systems or activities. + + + + + + + Reveal information that would + impair the application of state-of-the-art + technology within a U.S. weapon system. + + + + + + + Reveal formally named or numbered + U.S. military war plans that remain in effect, + or reveal operational or tactical elements of + prior plans that are contained in such active + plans; + + + + + + Reveal information, including foreign + government information, that would cause + serious harm to relations between the United + States and a foreign government, or to + ongoing diplomatic activities of the United + States + + + + + + + Reveal information that would + impair the current ability of United States + Government officials to protect the President, + Vice President, and other protectees for + whom protection services, in the interest of + the national security, are authorized. + + + + + + + Reveal information that would + seriously impair current national security + emergency preparedness plans or reveal + current vulnerabilities of systems, + installations, or infrastructures relating to the + national security. + + + + + + + Violate a statute, treaty, or + international agreement that does not permit + the automatic or unilateral declassification of + information at 25 years. + + + + + + + When the information clearly and + demonstrably could be expected to + reveal the identity of a confidential + human source or a human intelligence + source. + + + + + + + The ISCAP has authorized use of this code in the FBI’s + classification guidance (which results in a 75-year classification + period) for any agency sourcing/reusing the information. + + + + + + + When the information clearly and + demonstrably could reveal key design + concepts of weapons of mass + destruction. + + + + + + + The ISCAP has authorized use of this code in the FBI’s + classification guidance (which results in a 75-year classification + period) for any agency sourcing/reusing the information. + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAtomicEnergyMarkings.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAtomicEnergyMarkings.xsd new file mode 100644 index 0000000..839d44e --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAtomicEnergyMarkings.xsd @@ -0,0 +1,85 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMatomicEnergyMarkings Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMatomicEnergyMarkings.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid Atomic Energy information markings from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMatomicEnergyMarkings.xml + + + + + + + + + RD-SIGMA-#, # represents the SIGMA number which may be 14, 15, 18, or 20. + + + + + FRD-SIGMA-#, # represents the SIGMA number which may be 14, 15, 18, or 20. + + + + + + + + + RESTRICTED DATA + + + + + RD-CRITICAL NUCLEAR WEAPON DESIGN INFORMATION + + + + + FORMERLY RESTRICTED DATA + + + + + DoD CONTROLLED NUCLEAR INFORMATION + + + + + DoE CONTROLLED NUCLEAR INFORMATION + + + + + TRANSCLASSIFIED FOREIGN NUCLEAR INFORMATION + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAttributes.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAttributes.xsd new file mode 100644 index 0000000..e857fbb --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMAttributes.xsd @@ -0,0 +1,209 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMAttributes Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMAttributes.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently authorized ISM attribute names + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMAttributes.xml + + + + + + + compliesWith attribute + + + + + classification attribute + + + + + ownerProducer attribute + + + + + SCIcontrols attribute + + + + + SARIdentifier attribute + + + + + atomicEnergyMarkings attribute + + + + + disseminationControls attribute + + + + + FGIsourceOpen attribute + + + + + FGIsourceProtected attribute + + + + + releasableTo attribute + + + + + displayOnlyTo attribute + + + + + nonICmarkings attribute + + + + + classifiedBy attribute + + + + + derivativelyClassifiedBy attribute + + + + + classificationReason attribute + + + + + nonUSControls attribute + + + + + derivedFrom attribute + + + + + declassDate attribute + + + + + declassEvent attribute + + + + + declassException attribute + + + + + resourceElement attribute + + + + + excludeFromRollup attribute + + + + + createDate attribute + + + + + compilationReason attribute + + + + + noticeType attribute + + + + + externalNotice attribute + + + + + DESVersion attribute + + + + + ISMCATCESVersion attribute + + + + + notice date attribute + + + + + notice Reason attribute + + + + + exemptFrom attribute + + + + + unregisteredNoticeType attribute + + + + + Specifies a point-of contact for a security-related + requirement. + + + + + Indicator that multiple ownerProducers should be interpreted + as JOINT. + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationAll.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationAll.xsd new file mode 100644 index 0000000..29588fe --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationAll.xsd @@ -0,0 +1,54 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMClassificationAll Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMClassificationAll.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid classification marks + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationAll.xml + + + + + + + RESTRICTED + + + + + CONFIDENTIAL + + + + + SECRET + + + + + TOP SECRET + + + + + UNCLASSIFIED + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationUS.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationUS.xsd new file mode 100644 index 0000000..3a2c1da --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMClassificationUS.xsd @@ -0,0 +1,49 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMClassificationUS Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMClassificationUS.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid US classification marks + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationUS.xml + + + + + + + TOP SECRET + + + + + SECRET + + + + + CONFIDENTIAL + + + + + UNCLASSIFIED + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMCompliesWith.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMCompliesWith.xsd new file mode 100644 index 0000000..5e17a70 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMCompliesWith.xsd @@ -0,0 +1,68 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMCompliesWith Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMCompliesWith.xml CVE it is based on, instead of here. + + + + + + + + (U) ISM rule sets documents may comply + with. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCompliesWith.xml + + + + + + + Document claims compliance with all rules encoded in ISM for + documents produced by the US Federal Government. This is the minimum set of rules + for US documents to adhere to, and all US documents should claim compliance with + USGov. For example, a US Intelligence Community document should claim + ism:compliesWith="USGov USIC". + + + + + Document claims compliance with all rules encoded in ISM for + documents produced by the US Intelligence Community. Documents that claim compliance + with USIC MUST also claim compliance with USGov. + + + + + Document claims compliance with all rules encoded in ISM for + documents produced by the US Department of Defense. Documents that claim compliance + with USDOD MUST also claim compliance with USGov. + + + + + Document claims compliance with an authority other than the + USGov, USIC, or USDOD. This token is not allowed if the ism:ownerProducer contains + USA. + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMDissem.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMDissem.xsd new file mode 100644 index 0000000..ee30fad --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMDissem.xsd @@ -0,0 +1,102 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMDissem Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMDissem.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid Dissemination controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMDissem.xml + + + + + + + RISK SENSITIVE + + + + + FOR OFFICIAL USE ONLY + + + + + ORIGINATOR CONTROLLED + + + + + ORIGINATOR CONTROLLED US GOVERNMENT + + + + + CONTROLLED IMAGERY + + + + + NOT RELEASABLE TO FOREIGN NATIONALS + + + + + CAUTION-PROPRIETARY INFORMATION INVOLVED + + + + + AUTHORIZED FOR RELEASE TO + + + + + RELEASABLE BY INFORMATION DISCLOSURE OFFICIAL + + + + + EYES ONLY + + + + + DEA SENSITIVE + + + + + FOREIGN INTELLIGENCE SURVEILLANCE ACT + + + + + AUTHORIZED FOR DISPLAY BUT NOT RELEASE TO + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMExemptFrom.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMExemptFrom.xsd new file mode 100644 index 0000000..d127cba --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMExemptFrom.xsd @@ -0,0 +1,53 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMExemptFrom Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMExemptFrom.xml CVE it is based on, instead of here. + + + + + + + + (U) Current rule set names that documents may comply + with + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMExemptFrom.xml + + + + + + + Document claims exemption from ICD-710 rules mandating the + use of Foreign Disclosure and Release markings that have been encoded in ISM. + Currently, the requirement for FD&R is only mandatory for Disseminated Analytic + Product; however, it is strongly encouraged otherwise. + + + + + Document claims exemption from the rules in DoD5230.24 + requiring DoD Distribution Statements that have been encoded into + ISM. + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonIC.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonIC.xsd new file mode 100644 index 0000000..1c38f92 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonIC.xsd @@ -0,0 +1,95 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMNonIC Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMNonIC.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid Non-IC markings from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNonIC.xml + + + + + + + + + The name of the ALTERNATE COMPENSATORY CONTROL MEASURE, substituting "_" for a space. + + + + + NAVAL NUCLEAR PROPULSION INFORMATION + + + + + + + + + LIMITED DISTRIBUTION + + + + + EXCLUSIVE DISTRIBUTION + + + + + NO DISTRIBUTION + + + + + SENSITIVE BUT UNCLASSIFIED + + + + + SENSITIVE BUT UNCLASSIFIED NOFORN + + + + + LAW ENFORCEMENT SENSITIVE + + + + + LAW ENFORCEMENT SENSITIVE NOFORN + + + + + SENSITIVE SECURITY INFORMATION + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonUSControls.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonUSControls.xsd new file mode 100644 index 0000000..a193d44 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNonUSControls.xsd @@ -0,0 +1,52 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMNonUSControls Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMNonUSControls.xml CVE it is based on, instead of here. + + + + + + + + (U) NonUS Control markings supported by ISM + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNonUSControls.xml + + + + + + + NATO Atomal mark + + + + + NATO Bohemia mark + + + + + NATO Balk mark + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNotice.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNotice.xsd new file mode 100644 index 0000000..7f002a0 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMNotice.xsd @@ -0,0 +1,137 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMNotice Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMNotice.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently authorized Notice values + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNotice.xml + + + + + + + FISA Warning statement + + + + + IMCON Warning statement + + + + + Controled Nuclear Weapon Design Information Warning statement + + + + + RD Warning statement + + + + + FRD Warning statement + + + + + LIMDIS caveat + + + + + LES Notice + + + + + LES-NF Notice + + + + + DSEN Notice + + + + + DoD Distribution statement A from DoD Directive 5230.24 + + + + + DoD Distribution statement B from DoD Directive 5230.24 + + + + + DoD Distribution statement C from DoD Directive 5230.24 + + + + + DoD Distribution statement D from DoD Directive 5230.24 + + + + + DoD Distribution statement E from DoD Directive 5230.24 + + + + + DoD Distribution statement F from DoD Directive 5230.24 + + + + + DoD Distribution statement X from DoD Directive 5230.24 + + + + + US Person info Notice + + + + + Indicates that an instance document must abide by rules pertaining to ORIGINATOR CONTROLLED data issued prior to Executive Order 13526. + + + + + Indicates that the contents of this notice specify the contact information for a required point-of-contact. + + + + + COMSEC Notice + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMPocType.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMPocType.xsd new file mode 100644 index 0000000..c29049b --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMPocType.xsd @@ -0,0 +1,72 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMPocType Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMPocType.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently authorized types for ISM-related points-of-contact. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMPocType.xml + + + + + + + Point-of-contact for an ICD-710 notice. + + + + + DoD Distribution statement B from DoD Directive 5230.24 + + + + + DoD Distribution statement C from DoD Directive 5230.24 + + + + + DoD Distribution statement D from DoD Directive 5230.24 + + + + + DoD Distribution statement E from DoD Directive 5230.24 + + + + + DoD Distribution statement F from DoD Directive 5230.24 + + + + + DoD Distribution statement X from DoD Directive 5230.24 + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSAR.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSAR.xsd new file mode 100644 index 0000000..ff969b6 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSAR.xsd @@ -0,0 +1,57 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMSAR Version 1 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMSAR.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid SAR controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMSAR.xml + + + + + + + SPECIAL ACCESS REQUIRED-XXX,Within the nickname or name of a SAR all spaces must be replaced with a "_". The XSL will restore the spaces for rendering. + + + + + SPECIAL ACCESS REQUIRED-XXX, the Digraph or Trigraph of the SAR is represented by the XXX + + + + + SPECIAL ACCESS REQUIRED-XXX, the Digraph or Trigraph of the SAR is represented by the XXX + + + + + SPECIAL ACCESS REQUIRED-XXX, the Digraph or Trigraph of the SAR is represented by the XXX + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSCIControls.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSCIControls.xsd new file mode 100644 index 0000000..896794b --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/CVEGenerated/CVEnumISMSCIControls.xsd @@ -0,0 +1,150 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMSCIControls Version 2 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMSCIControls.xml CVE it is based on, instead of here. + + + + + + + + (U) All currently valid SCI controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMSCIControls.xml + + + + + + + + + KDK-BLFH-xxxxxx, xxxxxx represents up to 6 alphanumeric characters indicating a sub BLUEFISH compartment + + + + + KDK-IDIT-xxxxxx, xxxxxx represents up to 6 alphanumeric characters indicating a sub IDITAROD compartment + + + + + KDK-KAND-xxxxxx, xxxxxx represents up to 6 alphanumeric characters indicating a sub KANDIK compartment + + + + + RSV-XXX, XXX represents 3 alpha numeric characters to indicate sub Reserve compartments + + + + + G-AAAA, AAAA represents 4 alpha characters to indicate sub Gamma compartments + + + + + SPECIAL INTELLIGENCE compartment + + + + + SPECIAL INTELLIGENCE sub-compartment + + + + + + + + + ENDSEAL + + + + + ECRU + + + + + NONBOOK + + + + + HCS + + + + + HCS-O + + + + + HCS-P + + + + + KLONDIKE + + + + + KDK BLUEFISH + + + + + KDK IDITAROD + + + + + KDK KANDIK + + + + + RESERVE + + + + + SPECIAL INTELLIGENCE + + + + + SI-GAMMA + + + + + TALENT KEYHOLE + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISM/IC-ISM.xsd b/schemas/sidd/external/ISM-v13/Schema/ISM/IC-ISM.xsd new file mode 100644 index 0000000..8eab1a0 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISM/IC-ISM.xsd @@ -0,0 +1,1548 @@ + + + + + + Intelligence Community + Technical Specification XML Data Encoding Specification for Information Security + Marking Metadata (ISM.XML) + + + Notices + distEditionBlockReplace + + + + Description + W3C XML Schema for the XML Data + Encoding Specification Intelligence Community Metadata Standard for Information + Security Marking (ISM.XML). + + + Introduction + This XML Schema file is one + component of the XML Data Encoding Specification (DES). Please see the document + titled + XML Data Encoding Specification for + Information Security Marking Metadata + for a complete description of the encoding as well as list of all + components. + It is envisioned that this + schema or its components, as well as other parts of the DES may be overridden for + localized implementations. Therefore, permission to use, copy, modify and distribute + this XML Schema and the other parts of the DES for any purpose is hereby granted in + perpetuity. + Please reference the preceding + two paragraphs in all copies or variations. The developers make no representation + about the suitability of the schema or DES for any purpose. It is provided "as is" + without expressed or implied warranty. + If you modify this XML Schema + in any way label your schema as a variant of ISM.XML. + Please direct all questions, + bug reports,or suggestions for changes to the points of contact identified in the + document referenced above. + + + Implementation Notes + + The IC ISM schema is not a + standalone construct; it should be imported into a parent XML schema. + Refer to the + XML Data Encoding Specification for + Information Security Marking Metadata + Data Encoding Specification (ISM.XML DES) for an explanation of the + relationships of the IC ISM attributes and the associated controlled + vocabularies. The CAPCO Register and CAPCO Implementation Manual provide + additional business rules (that may be classified) not provided in this schema + or the associated documentation. + The IC ISM attributes are + intended to support all CAPCO security markings. However, the attribute values + are NOT intended to be verbatim pieces of portionmarks and banners. Instead, the + values should be interpreted by XSLT stylesheets or other formatting speci- + fications to produce the required portionmarks and banners. + The controlled vocabularies + containing the required values for popu- lating the attributes are described in + the ISM.XML DES. + Attribute group + "SecurityAttributesGroup" should be referenced in the attribute definition list + of any element that REQUIRES security metadata. + Attribute group + "SecurityAttributesOptionGroup" may be referenced in the attribute definition + list of any element for which security metadata may be appropriate but is not + required (such as, an individual cell of a table). + Elements declared in this + specification are conveniences to developers of Schema. Their use is not + required but was determined to be helpful for many Schemas that would otherwise + have to declare these simple elements. Schema developers are free to implement + their own versions of these elements. + This file provides an + XML-based schema for specification of metadata for classification and controls + markings. The goal of the IC ISM XML Schema is to provide a common set of XML + attributes for implementing security-based metadata throughout the IC. The IC + ISM XML Schema provides markup for the tokens that are used to format the CAPCO + markings. + The IC ISM XML Schema may + be incorporated into organizational XML-based schemas by (a) declaring the IC + ISM namespace and (b) inserting an "import" statement: + <xsd:schema xmlns="...my namespace name..." targetNamespace="...my + namespace name..." xmlns:xsd="http://www.w3.org/2001/XMLSchema" + xmlns:ism="urn:us:gov:ic:ism"> ... <xsd:import + namespace="urn:us:gov:ic:ism schemaLocation="IC-ISM.xsd" /> + + + + + Creators + Office of the Director of + National Intelligence Intelligence Community Chief Information Officer + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying DoE + markings. It is manifested in portion marks and security banners. The + permissible values for this simple type are defined in the Controlled Value + Enumeration: CVEnumISMAtomicEnergyMarkings.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. A single indicator of the highest + level of classification applicable to an information resource or portion within + the domain of classified national security information. The Classification + element is always used in conjunction with the Owner Producer element. Taken + together, the two elements specify the classification category and the type of + classification (US, non-US, or Joint). It is manifested in portion marks and + security banners. PERMISSIBLE VALUES The permissible values for this simple type + are defined in the Controlled Value Enumeration: CVEnumISMClassificationAll.xml + + + + + + + + + This attribute is used + primarily at the resource level. One or more reason indicators or explanatory + text describing the basis for an original classification decision. It is + manifested only in the 'Reason' line of a resource's classification authority + block. + + + + + + + + + + + + + This attribute is used + primarily at the resource level. The identity, by name or personal identifier, + and position title of the original classification authority for a resource. It + is manifested only in the 'Classified By' line of a resource's classification + authority block. + + + + + + + + + + + + + A description of the + reasons that the classification of this element is more restrictive than a + simple roll-up of the sub elements would result in. This acts as an indicator to + rule engines that there is not accidental over classification going on and to + users that special care beyond what the portion marks reveal must be taken when + using this data. Use of this mark does not replace the need for the compilation + reason being defined in the prose in accordance with ISOO Directive 1. For + example this would document why 3 Unclassified bullet items form a Secret List. + Without this reason being noted the above described document would be considered + to be miss-marked and overclassified. + + + + + + + + + + + + + This attribute is used at + the resource level. An indicator of what optional ISM rule sets the documents + complies with. This allows systems to know that the document claims compliance + with these rule sets and they should be enforced. PERMISSIBLE VALUES The + permissible values for this simple type are defined in the Controlled Value + Enumeration: CVEnumISMCompliesWith.xml + + + + + + + + This attribute is used to designate what date the ISM was produced/updated on. This is the date that will be used by various constraint rules to determine if the ISM markings meet all the business rules. It must be on the element where resourceElement is true. + + + + + + + + + + + This attribute is used + primarily at the resource level. A specific year, month, and day upon which the + information shall be automatically declassified if not properly exempted from + automatic declassification. It is manifested in the 'Declassify On' line of a + resource's classification authority block. + + + + + + + + + + + This attribute is used + primarily at the resource level. A description of an event upon which the + information shall be automatically declassified if not properly exempted from + automatic declassification. It is manifested only in the 'Declassify On' line of + a resource's classification authority block. + + + + + + + + + + + + + This attribute is used + primarily at the resource level. A single indicator describing an exemption to + the nominal 25-year point for automatic declassification. This element is used + in conjunction with the Declassification Date or Declassification Event. It is + manifested in the 'Declassify On' line of a resource's classification authority + block. ISOO has stated it should be a SINGLE value giving the longest + protection. + + + PERMISSIBLE VALUE: The + permissible value for this attribute is defined in the Controlled Value + Enumeration: CVEnumISMN25X.xml + + + + + + + + This attribute is used + primarily at the resource level. The identity, by name or personal identifier, + of the derivative classification authority. It is manifested only in the + 'Classified By' line of a resource's classification authority block. + + + + + + + + + + + + + This attribute is used + primarily at the resource level. A citation of the authoritative source or + reference to multiple sources of the classification markings used in a + classified resource. It is manifested only in the 'Derived From' line of a + document's classification authority block. ISOO's guidance is: Source of + derivative classification. (1) The derivative classifier shall concisely + identify the source document or the classification guide on the ‘‘Derived From’’ + line, including the agency and, where available, the office of origin, and the + date of the source or guide. An example might appear as: Derived From: Memo, + ‘‘Funding Problems,’’ October 20, 2008, Office of Administration, Department of + Good Works or Derived From: CG No. 1, Department of Good Works, dated October + 20, 2008 (i) When a document is classified derivatively on the basis of more + than one source document or classification guide, the ‘‘Derived From’’ line + shall appear as: Derived From: Multiple Sources (ii) The derivative classifier + shall include a listing of the source materials on, or attached to, each + derivatively classified document. + + + + + + + + + + + + + The version number of the + DES. Should there be multiple specified in an instance document the first + one found is the one that will apply to the entire document. + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying the + country or countries and/or international organization(s) to which classified + information may be displayed but NOT released based on the determination of an + originator in accordance with established foreign disclosure procedures. This + element is used in conjunction with the DisplayOnly Dissemination Controls + value. It is manifested in portion marks and security banners. PERMISSIBLE + VALUES The permissible values for this attribute are defined in the Controlled + Value Enumeration: CVEnumISMRelTo.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying the + expansion or limitation on the distribution of information. It is manifested in + portion marks and security banners. PERMISSIBLE VALUES The permissible values + for this attribute are defined in the Controlled Value Enumeration: + CVEnumISMDissem.xml + + + + + + + + This attribute is used to + designate that an element's ISM attributes should not be used in a rollup. + Generally this is because the element is defining the security attributes of a + remote object NOT indicating security constraints for data in this document. + This allows an Unclassified document to assert that some document not included + has a Top Secret classification without the TS attribute value causing rollup to + make the document TS. + + + + + + + + + + + This attribute is used to declare + specific exemptions within a rule set - for example exemption from ICD 710 + FD&R requirements. This attribute is used on the resource node of a document + in conjunction with compliesWith. PERMISSIBLE VALUES The permissible values for + this simple type are defined in the Controlled Value Enumeration: + CVEnumISMExemptFrom.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying + information which qualifies as foreign government information for which the + source(s) of the information is not concealed. The attribute can indicate that + the source of information of foreign origin is unknown. It is manifested in + portion marks and security banners. PERMISSIBLE VALUES 1) The value "UNKNOWN" is + permitted under the circumstances described above. 2) The full set of values are + defined in the Controlled Value Enumeration: CVEnumISMFGIOpen.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. This attribute has unique specific + rules concerning its usage. A single indicator that information qualifies as + foreign government information for which the source(s) of the information must + be concealed. Within protected internal organizational spaces this element may + be used to maintain a record of the one or more indicators identifying + information which qualifies as foreign government information for which the + source(s) of the information must be concealed. Measures must be taken prior to + dissemination of the information to conceal the source(s) of the foreign + government information. An indication that information qualifies as foreign + government information according to CAPCO guidelines for which the source(s) of + the information must be concealed when the information is disseminated in shared + spaces This data element has a dual purpose. Within shared spaces, the data + element serves only to indicate the presence of information which is categorized + as foreign government information according to CAPCO guidelines for which the + source(s) of the information is concealed, in which case, this data element's + value will always be "FGI". The data element may also be employed in this manner + within protected internal organizational spaces. However, within protected + internal organizational spaces this data element may alternatively be used to + maintain a formal record of the foreign country or countries and/or registered + international organization(s) that are the non-disclosable owner(s) and/or + producer(s) of information which is categorized as foreign government + information according to CAPCO guidelines for which the source(s) of the + information must be concealed when the resource is disseminated to shared + spaces. If the data element is employed in this manner, then additional measures + must be taken prior to dissemination of the resource to shared spaces so that + any indications of the non-disclosable owner(s) and/or producer(s) of + information within the resource are eliminated. In all cases, the corresponding + portion marking or banner marking should be compliant with CAPCO guidelines for + FGI when the source must be concealed. In other words, even if the data element + is being employed within protected internal organizational spaces to maintain a + formal record of the non-disclosable owner(s) and/or producer(s) within an XML + resource, if the resource is rendered for display within the protected internal + organizational spaces in any format by a stylesheet or as a result of any other + transformation process, then the non-disclosable owner(s) and/or producer(s) + should not be included in the corresponding portion marking or banner marking. + PERMISSIBLE VALUES 1) The value "FGI" is permitted under the circumstances + described above. 2) The full set of values are defined in the Controlled Value + Enumeration: CVEnumISMFGIProtected.xml + + + + + + + + The version number of the + ISM CAT CVE Encoding Sepcification(CES). Should there be multiple specified in an instance document the first + one found is the one that will apply to the entire document. + + + + + + + + The group of Information + Security Marking attributes for use on a notice element without externalNotice + + + + + + + + + + + + The group of Information + Security Marking attributes for use on a notice element adding optional externalNotice + + + + + + + + + + The group of Information + Security Marking attributes for use on a notice element adding required externalNotice=true + + + + + + + + + + + An attribute group to be + used on the element that represents the resource node of an instance + document. + + + + + + + + + + + + An attribute group to be + used on the element that represents the resource node of an instance + document. + + + + + + + + + + + + An attribute group to be + used on the root node of a schema implementing ISM. ISM being entirely + attributes based groups such as this are the only way to specify required use. + + + + + + + + + + + An attribute group to be + used on the root node of a schema implementing ISM. ISM being entirely + attributes based groups such as this are the only way to specify required use. + This group has all the attributes as optional.This group could be used in a + schema where many element may be the root node. When the element is acting as + the root element it should have attributes used similar to + ISMRootNodeAttributeGroup. + + + + + + + + + + A long string, less than + 32000 characters. + + + + + + + + + + + + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators of the + expansion or limitation on the distribution of an information resource or + portion within the domain of information originating from non-intelligence + components. It is manifested in portion marks and security banners. PERMISSIBLE + VALUES The permissible values for this attribute are defined in the Controlled + Value Enumeration: CVEnumISMNonIC.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators of the + expansion or limitation on the distribution of an information resource or + portion within the domain of information originating from non-US components. It + is manifested in portion marks and security banners. PERMISSIBLE VALUES The + permissible values for this attribute are defined in the Controlled Value + Enumeration: CVEnumISMNonUSControls.xml + + + + + + + + Base type for Notices. Does not include any attributes. + + + + + + + + + + + A single Notice that may + consist of 1 or more NoticeText + + + + + + + + + + + + + A single Notice that may + consist of 1 or more NoticeText for use when the notice refers to something external. + + + + + + + + + + + + + A single Notice that may + consist of 1 or more NoticeText + + + + + + + + A single Notice that may + consist of 1 or more NoticeText for use when the notice refers to something external. + + + + + + + + The group of Information + Security Marking attributes for use on a notice element in which the use of + attributes 'classification' and 'ownerProducer' is required. + + + + + + + + + + The group of Information + Security Marking attributes for use on a notice element in which the use of + Security on the notice is optional. + + + + + + + + + + The group of Information + Security Marking attributes for use on a notice element in which the use of + attributes 'classification' and 'ownerProducer' is required and the notice is for something external to the object. + + + + + + + + + + The group of Information + Security Marking attributes for use on a notice element in which the use of + Security on the notice is optional and the notice is for something external to the object. + + + + + + + + + + + A Date associated with a + notice such as the DoD Distribution notice date. + + + + + + + + + + + + + A list of Notices + + + + + + + + + + + + + + A list of Notices + + + + + + + + + + + + + A Reason (less than 2048 + chars) associated with a notice such as the DoD Distribution reason. + + + + + + + + + + + + + The actual text of a + notice. + + + + + + + + + + + + + + + This attribute is an + indicator that the element contains a security-related notice and is used to + categorize which of the required notices is specified in the element. These + categories include those described in the CAPCO Register, as well as additional + well-defined and formally recognized security notice types described in other + directives, such as US-Person and DoD Distribution. The element could contain + any structure that the implementing schema defines, and details of the rendering + would be relegated to the implementing schema. The permissible value for this + attribute are defined in the Controlled Value Enumeration: CVEnumISMNotice.xml + + + + + + + + + This attribute is an + indicator that the element contains a security-related notice NOT in this document. This flag allows + for a notice to exist in a document without the data that would normally require the notice. Example a + FISA notice when there is no FISA data present. + A common use case is source citations where the notice if for the sourced document and should + not impact the requirements for that type of data in this document. + + + + + + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying the + national government or international organization that have purview over the + classification marking of an information resource or portion therein. This + element is always used in conjunction with the Classification element. Taken + together, the two elements specify the classification category and the type of + classification (US, non-US, or Joint). Within protected internal organizational + spaces this element may include one or more indicators identifying information + which qualifies as foreign government information for which the source(s) of the + information must be concealed. Measures must be taken prior to dissemination of + the information to conceal the source(s) of the foreign government information. + Specifically, under these specific circumstances, when data are moved to the + shared spaces, the non-disclosable owner(s) and/or producer(s) listed in this + data element's value should be removed and replaced with "FGI". The attribute + value may be manifested in portion marks or security banners. PERMISSIBLE VALUES + 1) The value "FGI" is permitted under the circumstances described above. 2) The + full set of values are defined in the Controlled Value Enumeration: + CVEnumISMOwnerProducer.xml + + + + + + + + + This attribute, when true, is used to signify that + multiple values in the ownerProducer attribute are + JOINT owners of the data. + + + + + + + + + An attribute group to be + used on the element that represents an entity that can be designated as a + point-of-contact. This node may be a single person or an organization. + + + + + + + + + + Indicates that the element + specifies a point-of-contact (POC) and the methods with which to contact that + individual. As certain POCs are required for different reasons (ICD-710 + compliance, DoD Distribution statements, etc), the values for this attribute + specify the reason(s) why the POC is provided. + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying the + country or countries and/or international organization(s) to which classified + information may be released based on the determination of an originator in + accordance with established foreign disclosure procedures. This element is used + in conjunction with the Dissemination Controls element. It is manifested in + portion marks and security banners. PERMISSIBLE VALUES The permissible values + for this attribute are defined in the Controlled Value Enumeration: + CVEnumISMRelTo.xml + + + + + + + + This attribute is used to + designate which element has the ISM attributes representing the classification + for the entire resource. Every document must have at least one element with this + indicator as true. It should be rare that a document has more than one. Mainly + this would occur in some sort of aggregator schema. In that unusual case the + first one encountered in XML document order is the one used for all constraint + rules. + + + + + + + + + + + An attribute group to be + used on the element that represents the resource node of an instance document. + This node's ISM attributes would be used to generate banner marks and the E.O. + 13526 classification authority block. Implementing Schemas might use this on the + Root node or any other node. + + + + + + + + + + + An attribute group to be + used on the element that represents the resource node of an instance document. + This node's ISM attributes would be used to generate banner marks and the E.O. + 13526 classification authority block. Implementing Schemas might use this on the + Root node or any other node. This group has all the attributes as optional. It + could be used in a schema where many nodes may be the resource element. When the + element is acting as the resource element it should have attributes used similar + to ResourceNodeAttributeGroup. + + + + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying the + defense or intelligence programs for which special access is required. It is + manifested in portion marks and security banners. PERMISSIBLE VALUES The + permissible values for this attribute are defined in the Controlled Value + Enumeration: CVEnumISMSAR.xml + + + + + + + + This attribute is used at + both the resource and the portion levels. One or more indicators identifying + sensitive compartmented information control system(s). It is manifested in + portion marks and security banners. PERMISSIBLE VALUES The permissible values + for this attribute are defined in the Controlled Value Enumeration: + CVEnumISMSCIControls.xml + + + + + + + + The group of Information + Security Marking attributes in which the use of attributes 'classification' and + 'ownerProducer' is required. + + + This group is to be + contrasted with group 'SecurityAttributesOptionGroup' in which use of those + attributes is optional. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The group of Information + Security Marking attributes in which the use of attributes 'classification' and + 'ownerProducer' is optional. This group is to be contrasted with group + 'SecurityAttributesGroup' in which use of these attributes is required. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + A short string, less than + 256 characters. + + + + + + + + + + + + + + + + + + + A notice that is of a + category that is not described in the CAPCO Register and/or is not sufficiently + defined to be represented in the Controlled Value Enumeration + CVEnumISMNotice.xml. This attribute can be used by specifications that import + ISM to represent a wider variety of security-related notices. + + + + + + + + + + + + + + Include all of the generated CVE + types applicable. + + + + + Formal Change List + + Change History + + + Version + Date + By + Description + + + + + 12 + 2013-05-20 + ODNI/OCIO/ME/D&I + + + Decoupled the + specification from the country code CVEs. Created a new ISMCAT specification + that can rev independently of ISM. + + + + + 11 + 2013-02-15 + ODNI/OCIO/ME/D&I + + + Added joint + attribute for signifying that multiple values in the ownerProducer + attribute are both producers of the portion/document. + Added + attribute for joint ownership. [artf13902]. + + + + + 9 + 2012-3-19 + ODNI/OCIO/ME/D&I + + + Changed + DESVersion attribute from xsd:int to ShortStringType. + Changed + declaration of NoticeText to be simple content. + [artf12153]. + + + + + 8 + 2011-12-22 + Sun, ODNI/OCIO/ICEA + + + Added + unique namespaces to generated CVE schema fragments. + Removed + CVEGenerated schema import from and moved schema fragment + imports directly to the base ISM schema. + + + + + 7 + 2011-08-10 + Colbert, ODNI/OCIO/ICEA + + + Added a + complex type for NoticeList + + + + + 7 + 2011-07-14 + + + Colbert, + ODNI/OCIO/ICEA + Gilsenan, + ODNI/OCIO/ICEA + + + + + Renamed + @ism:notice to @ism:noticeType + Removed + @ism:ORCONPOC and @ism:noticePOC. They're replaced with the new + @ism:pocType attribute which indicates that an element specifies + a point-of-contact's name and contact method. + + + + + 7 + 2011-07-07 + + + Colbert, + ODNI/OCIO/ICEA + Gilsenan, + ODNI/OCIO/ICEA + + + + + Added + version information to the header + Removed + ACCM attribute + + + + + 7 + 2011-06-10 + Hansen, ODNI/OCIO/ICEA + + + Removed + @fixed="true" from the resourceElement attribute definition in + the ISMResourceNodeAttributeGroup and + ISMResourceNodeAttributeOptionGroup + + + + + 7 + 2011-05-11 + Colbert, ODNI/OCIO/ICEA + + + Added + ORCONPOC attribute and POCAttributeGroup + + + + + 7 + 2011-04-26 + Colbert, ODNI/OCIO/ICEA + + + Added + String types LongStringType, ShortStringType, + LongStringWithSecurityType, and + ShortStringWithSecurityType + Added + attribute unregisteredNoticeType and included it in + ISMNoticeAttributeGroup + + + + + 7 + 2011-04-22 + Colbert, ODNI/OCIO/ICEA + + + Explicitly + declared minOccurs and maxOccurs where appropriate. + + + + + 7 + 2011-04-19 + Hodges, ODNI/OCIO/ICEA + + + (CR 2010-4) + Add ISMNoticeAttributeGroup to hold the Notice specific + attributes and changed the NoticeAttribute groups to reference + it. ISMResourceAttributeGroup also added and Resource specific + attributes have been removed from the ResourceAttribute groups + and the new group added. + + + + + 7 + 2011-04-15 + Colbert, ODNI/OCIO/ICEA + + + Add + elements NoticeList, Notice, and NoticeText + + + + + 6 + 2011-01-27 + ODNI/OCIO/ICEA + + + Add + ACCM + + + + + 5 + 2010-09-25 + ODNI/OCIO/ICEA + + + Add + atomicEnergyMarkings + Remove + typeOfExemptedSource and dateOfExemptedSource + Add + ResourceNodeAttributeOptionGroup + Add + ISMRootNodeAttributeOptionGroup + + + + + 4 + 2010-06-01 + ODNI OCIO ICIS + + + Add DoD + Distro statements + Add NATO + refactor + Add Use of + Generated CVE schema types + + + + + 3 + 2010-01-22 + ODNI OCIO ICIS + + + (CR + 2010-02) Add notice attribute, NoticeAttributesGroup and + NoticeAttributesOptionGroup + Final + review before signature 2010-06-06 + Remove comment about LES not being in the Register + since it is now in the register. + Correct NoticeAttributesOptionGroup to have + SecurityAttributesOptionGroup so that NoteInline in PUBS + works correctly. + + + + + + + 2 + 2009-12-01 + ODNI OCIO ICIS + + + (CR + 2009-09) Added "compilationReason" to allow capturing + information about the reason that the document or portion bears + a more restrictive classification than the data would appear to + support. + (CR + 2009-07) Point to CVE files for enumeration values. + (CR + 2009-22) Change declassException and typeOfExemptedSource to + NMTOKEN. + (CR + 2009-16) Add ability to specify DES Version. + (CR + 2009-05) Add createDate, excludeFromRollup, resourceElement to + allow ISM rules to be independent of implementing + schema. + (CR + 2009-05) Add ISMRootNodeAttributeGroup and + ResourceNodeAttributeGroup. + + + + + 2.1 + 2008-08-19 + ODNI OCIO ICIS + Updated to support IC Standard for Information Security Marking + Metadata (2007-500-2) + Added + "DerivativelyClassifiedBy" to allow capturing information about + a derivative classifier separate from an original + classifier + + + + + 2.0 + 2004-04-30 + IC MWG + Updated to support changes to the CAPCO Register and + Implementation Manual. + Added + "ownerProducer" as a required attribute for entity + "SecurityAttributes" and as an optional attribute for entity + "SecurityAttributesOption." Purpose is to provide a single + method for specification of US, non-US, and joint + classifications. + Changed the + enumerated list that is the declared value of attribute + "classification" in order to accommodate non-US + classifications. + Added + optional attribute "SARIdentifier" as a separate container for + DoD/DoE special-access-required nicknames, codewords, or + trigraph/ digraph to support elevation of SAR to the same level + as SCI controls. + Added + optional attributes "classifiedBy" and "classificationReason" to + support generation of EO 12958 classification/declassification + blocks. + Changed the + declared value of "derivedFrom" to CDATA to allow the titles and + dates of source documents or classification guides to be + specified. + Replaced + the single attribute "declassification" with distinct attri- + butes for date-determined and event-determined declassification + and for the 25X declassification exceptions. + Added + attributes "typeOfExemptedSource" and "dateOfExemptedSource" for + use in specifying that one or more sources was marked OADR, X1 + through X8, or X-Foreign Relations. + Added + attribute "declassManualReview" for use in forcing "MR" to + appear in header and footer banners (regardless of whether any + caveats in the portions would necessitate manual + review). + + + + + 1.0 + 2002-07-05 + IC MWG + Released as a registered, production XML entity set + + + + + + + + Formal CVE Change + List + + Change History + + + Version + Date + By + Description + + + + + 7 + 2011-07-18 + Colbert, ODNI/OCIO/ICEA + + + Added + pocType + + + + + 7 + 2011-01-27 + ODNI/OCIO/ICEA + + + Remove ACCM + as attribute and move its values to nonICmarkings + + + + + 6 + 2011-01-27 + ODNI/OCIO/ICEA + + + Add + ACCM + + + + + 5 + 2010-09-25 + ODNI/OCIO/ICEA + + + Add + atomicEnergyMarkings + Remove + typeOfExemptedSource + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIOpen.xsd b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIOpen.xsd new file mode 100644 index 0000000..4173967 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIOpen.xsd @@ -0,0 +1,1568 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMCATFGIOpen Version 2 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMCATFGIOpen.xml CVE it is based on, instead of here. + + + + + + + + (U) + All currently valid GENC trigraphs except USA in alphabetical order by trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. UNKNOWN removed since GENC has it as AX1 + + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCATFGIOpen.xml + + + + + + + + + North Atlantic Treaty Organization Special Words + + + + + + + + + Aruba + + + + + Islamic Republic of Afghanistan + + + + + Republic of Angola + + + + + Anguilla + + + + + Republic of Albania + + + + + Principality of Andorra + + + + + United Arab Emirates + + + + + Argentine Republic + + + + + Republic of Armenia + + + + + Territory of American Samoa + + + + + Antarctica + + + + + French Southern and Antarctic Lands + + + + + Antigua and Barbuda + + + + + Commonwealth of Australia + + + + + Republic of Austria + + + + + Unknown + + + + + Guantanamo Bay Naval Base + + + + + Republic of Azerbaijan + + + + + Republic of Burundi + + + + + Kingdom of Belgium + + + + + Republic of Benin + + + + + Bonaire, Sint Eustatius, and Saba + + + + + Burkina Faso + + + + + People's Republic of Bangladesh + + + + + Republic of Bulgaria + + + + + Kingdom of Bahrain + + + + + Commonwealth of The Bahamas + + + + + Bosnia and Herzegovina + + + + + Saint Barthelemy + + + + + Republic of Belarus + + + + + Belize + + + + + Bermuda + + + + + Plurinational State of Bolivia + + + + + Federative Republic of Brazil + + + + + Barbados + + + + + Brunei Darussalam + + + + + Kingdom of Bhutan + + + + + Bouvet Island + + + + + Republic of Botswana + + + + + Central African Republic + + + + + Canada + + + + + Territory of Cocos (Keeling) Islands + + + + + Swiss Confederation + + + + + Republic of Chile + + + + + People's Republic of China + + + + + Republic of Côte d'Ivoire + + + + + Republic of Cameroon + + + + + Democratic Republic of the Congo + + + + + Republic of the Congo + + + + + Cook Islands + + + + + Republic of Colombia + + + + + Union of the Comoros + + + + + Clipperton Island + + + + + Republic of Cape Verde + + + + + Republic of Costa Rica + + + + + Republic of Cuba + + + + + Curaçao + + + + + Territory of Christmas Island + + + + + Cayman Islands + + + + + Republic of Cyprus + + + + + Czech Republic + + + + + Federal Republic of Germany + + + + + Diego Garcia + + + + + Republic of Djibouti + + + + + Commonwealth of Dominica + + + + + Kingdom of Denmark + + + + + Dominican Republic + + + + + People's Democratic Republic of Algeria + + + + + Republic of Ecuador + + + + + Arab Republic of Egypt + + + + + State of Eritrea + + + + + Western Sahara + + + + + Kingdom of Spain + + + + + Republic of Estonia + + + + + Federal Democratic Republic of Ethiopia + + + + + Republic of Finland + + + + + Republic of Fiji + + + + + Falkland Islands (Islas Malvinas) + + + + + French Republic + + + + + Faroe Islands + + + + + Federated States of Micronesia + + + + + Gabonese Republic + + + + + United Kingdom of Great Britain and Northern Ireland + + + + + Georgia + + + + + Bailiwick of Guernsey + + + + + Republic of Ghana + + + + + Gibraltar + + + + + Republic of Guinea + + + + + Department of Guadeloupe + + + + + Republic of The Gambia + + + + + Republic of Guinea-Bissau + + + + + Republic of Equatorial Guinea + + + + + Hellenic Republic + + + + + Grenada + + + + + Greenland + + + + + Republic of Guatemala + + + + + Department of Guiana + + + + + Territory of Guam + + + + + Co-operative Republic of Guyana + + + + + Hong Kong Special Administrative Region + + + + + Territory of Heard Island and McDonald Islands + + + + + Republic of Honduras + + + + + Republic of Croatia + + + + + Republic of Haiti + + + + + Hungary + + + + + Republic of Indonesia + + + + + Isle of Man + + + + + Republic of India + + + + + British Indian Ocean Territory + + + + + Ireland + + + + + Islamic Republic of Iran + + + + + Republic of Iraq + + + + + Republic of Iceland + + + + + State of Israel + + + + + Italian Republic + + + + + Jamaica + + + + + Bailiwick of Jersey + + + + + Hashemite Kingdom of Jordan + + + + + Japan + + + + + Republic of Kazakhstan + + + + + Republic of Kenya + + + + + Kyrgyz Republic + + + + + Kingdom of Cambodia + + + + + Republic of Kiribati + + + + + Federation of Saint Kitts and Nevis + + + + + Republic of Korea + + + + + State of Kuwait + + + + + Lao People's Democratic Republic + + + + + Lebanese Republic + + + + + Republic of Liberia + + + + + Libya + + + + + Saint Lucia + + + + + Principality of Liechtenstein + + + + + Democratic Socialist Republic of Sri Lanka + + + + + Kingdom of Lesotho + + + + + Republic of Lithuania + + + + + Grand Duchy of Luxembourg + + + + + Republic of Latvia + + + + + Macau Special Administrative Region + + + + + Saint Martin + + + + + Kingdom of Morocco + + + + + Principality of Monaco + + + + + Republic of Moldova + + + + + Republic of Madagascar + + + + + Republic of Maldives + + + + + United Mexican States + + + + + Republic of the Marshall Islands + + + + + Republic of Macedonia + + + + + Republic of Mali + + + + + Republic of Malta + + + + + Union of Burma + + + + + Montenegro + + + + + Mongolia + + + + + Commonwealth of the Northern Mariana Islands + + + + + Republic of Mozambique + + + + + Islamic Republic of Mauritania + + + + + Montserrat + + + + + Department of Martinique + + + + + Republic of Mauritius + + + + + Republic of Malawi + + + + + Malaysia + + + + + Department of Mayotte + + + + + Republic of Namibia + + + + + New Caledonia + + + + + Republic of the Niger + + + + + Territory of Norfolk Island + + + + + Federal Republic of Nigeria + + + + + Republic of Nicaragua + + + + + Niue + + + + + Kingdom of the Netherlands + + + + + Kingdom of Norway + + + + + Federal Democratic Republic of Nepal + + + + + Republic of Nauru + + + + + New Zealand + + + + + Sultanate of Oman + + + + + Islamic Republic of Pakistan + + + + + Republic of Panama + + + + + Pitcairn, Henderson, Ducie, and Oeno Islands + + + + + Republic of Peru + + + + + Republic of the Philippines + + + + + Republic of Palau + + + + + Independent State of Papua New Guinea + + + + + Republic of Poland + + + + + Commonwealth of Puerto Rico + + + + + Democratic People's Republic of Korea + + + + + Portuguese Republic + + + + + Republic of Paraguay + + + + + Palestinian Territory + + + + + French Polynesia + + + + + State of Qatar + + + + + Department of Reunion + + + + + Romania + + + + + Russian Federation + + + + + Republic of Rwanda + + + + + Kingdom of Saudi Arabia + + + + + Republic of the Sudan + + + + + Republic of Senegal + + + + + Republic of Singapore + + + + + South Georgia and South Sandwich Islands + + + + + Saint Helena, Ascension, and Tristan da Cunha + + + + + Solomon Islands + + + + + Republic of Sierra Leone + + + + + Republic of El Salvador + + + + + Republic of San Marino + + + + + Somalia, Federal Republic of + + + + + Territorial Collectivity of Saint Pierre and Miquelon + + + + + Republic of Serbia + + + + + Republic of South Sudan + + + + + Democratic Republic of Sao Tome and Principe + + + + + Republic of Suriname + + + + + Slovak Republic + + + + + Republic of Slovenia + + + + + Kingdom of Sweden + + + + + Kingdom of Swaziland + + + + + Sint Maarten + + + + + Republic of Seychelles + + + + + Syrian Arab Republic + + + + + Turks and Caicos Islands + + + + + Republic of Chad + + + + + Togolese Republic + + + + + Kingdom of Thailand + + + + + Republic of Tajikistan + + + + + Tokelau + + + + + Turkmenistan + + + + + Democratic Republic of Timor-Leste + + + + + Kingdom of Tonga + + + + + Republic of Trinidad and Tobago + + + + + Tunisian Republic + + + + + Republic of Turkey + + + + + Tuvalu + + + + + Taiwan + + + + + United Republic of Tanzania + + + + + Republic of Uganda + + + + + Ukraine + + + + + Oriental Republic of Uruguay + + + + + Republic of Uzbekistan + + + + + State of the Vatican City + + + + + Saint Vincent and the Grenadines + + + + + Bolivarian Republic of Venezuela + + + + + Virgin Islands, British + + + + + United States Virgin Islands + + + + + Socialist Republic of Vietnam + + + + + Republic of Vanuatu + + + + + Wallis and Futuna + + + + + Independent State of Samoa + + + + + Territory of Ashmore and Cartier Islands + + + + + Entity 1 + + + + + Bassas da India + + + + + Baker Island + + + + + Entity 2 + + + + + Coral Sea Islands Territory + + + + + Entity 3 + + + + + Europa Island + + + + + Glorioso Islands + + + + + Gaza Strip + + + + + Howland Island + + + + + Johnston Atoll + + + + + Jan Mayen + + + + + Juan de Nova Island + + + + + Jarvis Island + + + + + Entity 4 + + + + + Entity 5 + + + + + Kingman Reef + + + + + Republic of Kosovo + + + + + Midway Islands + + + + + Navassa Island + + + + + Palmyra Atoll + + + + + Paracel Islands + + + + + Etorofu, Habomai, Kunashiri, and Shikotan Islands + + + + + Akrotiri + + + + + Spratly Islands + + + + + Svalbard + + + + + Tromelin Island + + + + + West Bank + + + + + Wake Island + + + + + Dhekelia + + + + + No Man's Land + + + + + Republic of Yemen + + + + + Republic of South Africa + + + + + Republic of Zambia + + + + + Republic of Zimbabwe + + + + + FOUR EYES + + + + + Suppressed + + + + + Biological Weapons Convention States + + + + + ROK/US Combined Forces Command, Korea + + + + + Combined Maritime Forces Central + + + + + Cooperative Maritime Forces Pacific + + + + + Civilian Protection Monitoring Team for Sudan + + + + + Countering Transnational Organized Crime + + + + + Chemical Weapons Convention States + + + + + FIVE EYES + + + + + Global Counter-Terrorism Forces + + + + + Global Maritime Interception Forces + + + + + International Security Assistance Force for Afghanistan + + + + + Stabilization Forces in Kosovo + + + + + Multi-Lateral Enduring Contingency + + + + + North African Counter-Terrorism Forces + + + + + North Atlantic Treaty Organization + + + + + NATO Convention Armed Forces in Europe + + + + + Open Skies Treaty + + + + + Suppressed + + + + + THREE EYES + + + + + United Nations Command, Korea + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIProtected.xsd b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIProtected.xsd new file mode 100644 index 0000000..7b10f85 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATFGIProtected.xsd @@ -0,0 +1,1568 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMCATFGIProtected Version 2 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMCATFGIProtected.xml CVE it is based on, instead of here. + + + + + + + + (U) + FGI, followed by all currently valid GENC trigraphs except USA in alphabetical order by trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCATFGIProtected.xml + + + + + + + + + North Atlantic Treaty Organization Special Words + + + + + + + + + Foreign Government Information + + + + + Aruba + + + + + Islamic Republic of Afghanistan + + + + + Republic of Angola + + + + + Anguilla + + + + + Republic of Albania + + + + + Principality of Andorra + + + + + United Arab Emirates + + + + + Argentine Republic + + + + + Republic of Armenia + + + + + Territory of American Samoa + + + + + Antarctica + + + + + French Southern and Antarctic Lands + + + + + Antigua and Barbuda + + + + + Commonwealth of Australia + + + + + Republic of Austria + + + + + Guantanamo Bay Naval Base + + + + + Republic of Azerbaijan + + + + + Republic of Burundi + + + + + Kingdom of Belgium + + + + + Republic of Benin + + + + + Bonaire, Sint Eustatius, and Saba + + + + + Burkina Faso + + + + + People's Republic of Bangladesh + + + + + Republic of Bulgaria + + + + + Kingdom of Bahrain + + + + + Commonwealth of The Bahamas + + + + + Bosnia and Herzegovina + + + + + Saint Barthelemy + + + + + Republic of Belarus + + + + + Belize + + + + + Bermuda + + + + + Plurinational State of Bolivia + + + + + Federative Republic of Brazil + + + + + Barbados + + + + + Brunei Darussalam + + + + + Kingdom of Bhutan + + + + + Bouvet Island + + + + + Republic of Botswana + + + + + Central African Republic + + + + + Canada + + + + + Territory of Cocos (Keeling) Islands + + + + + Swiss Confederation + + + + + Republic of Chile + + + + + People's Republic of China + + + + + Republic of Côte d'Ivoire + + + + + Republic of Cameroon + + + + + Democratic Republic of the Congo + + + + + Republic of the Congo + + + + + Cook Islands + + + + + Republic of Colombia + + + + + Union of the Comoros + + + + + Clipperton Island + + + + + Republic of Cape Verde + + + + + Republic of Costa Rica + + + + + Republic of Cuba + + + + + Curaçao + + + + + Territory of Christmas Island + + + + + Cayman Islands + + + + + Republic of Cyprus + + + + + Czech Republic + + + + + Federal Republic of Germany + + + + + Diego Garcia + + + + + Republic of Djibouti + + + + + Commonwealth of Dominica + + + + + Kingdom of Denmark + + + + + Dominican Republic + + + + + People's Democratic Republic of Algeria + + + + + Republic of Ecuador + + + + + Arab Republic of Egypt + + + + + State of Eritrea + + + + + Western Sahara + + + + + Kingdom of Spain + + + + + Republic of Estonia + + + + + Federal Democratic Republic of Ethiopia + + + + + Republic of Finland + + + + + Republic of Fiji + + + + + Falkland Islands (Islas Malvinas) + + + + + French Republic + + + + + Faroe Islands + + + + + Federated States of Micronesia + + + + + Gabonese Republic + + + + + United Kingdom of Great Britain and Northern Ireland + + + + + Georgia + + + + + Bailiwick of Guernsey + + + + + Republic of Ghana + + + + + Gibraltar + + + + + Republic of Guinea + + + + + Department of Guadeloupe + + + + + Republic of The Gambia + + + + + Republic of Guinea-Bissau + + + + + Republic of Equatorial Guinea + + + + + Hellenic Republic + + + + + Grenada + + + + + Greenland + + + + + Republic of Guatemala + + + + + Department of Guiana + + + + + Territory of Guam + + + + + Co-operative Republic of Guyana + + + + + Hong Kong Special Administrative Region + + + + + Territory of Heard Island and McDonald Islands + + + + + Republic of Honduras + + + + + Republic of Croatia + + + + + Republic of Haiti + + + + + Hungary + + + + + Republic of Indonesia + + + + + Isle of Man + + + + + Republic of India + + + + + British Indian Ocean Territory + + + + + Ireland + + + + + Islamic Republic of Iran + + + + + Republic of Iraq + + + + + Republic of Iceland + + + + + State of Israel + + + + + Italian Republic + + + + + Jamaica + + + + + Bailiwick of Jersey + + + + + Hashemite Kingdom of Jordan + + + + + Japan + + + + + Republic of Kazakhstan + + + + + Republic of Kenya + + + + + Kyrgyz Republic + + + + + Kingdom of Cambodia + + + + + Republic of Kiribati + + + + + Federation of Saint Kitts and Nevis + + + + + Republic of Korea + + + + + State of Kuwait + + + + + Lao People's Democratic Republic + + + + + Lebanese Republic + + + + + Republic of Liberia + + + + + Libya + + + + + Saint Lucia + + + + + Principality of Liechtenstein + + + + + Democratic Socialist Republic of Sri Lanka + + + + + Kingdom of Lesotho + + + + + Republic of Lithuania + + + + + Grand Duchy of Luxembourg + + + + + Republic of Latvia + + + + + Macau Special Administrative Region + + + + + Saint Martin + + + + + Kingdom of Morocco + + + + + Principality of Monaco + + + + + Republic of Moldova + + + + + Republic of Madagascar + + + + + Republic of Maldives + + + + + United Mexican States + + + + + Republic of the Marshall Islands + + + + + Republic of Macedonia + + + + + Republic of Mali + + + + + Republic of Malta + + + + + Union of Burma + + + + + Montenegro + + + + + Mongolia + + + + + Commonwealth of the Northern Mariana Islands + + + + + Republic of Mozambique + + + + + Islamic Republic of Mauritania + + + + + Montserrat + + + + + Department of Martinique + + + + + Republic of Mauritius + + + + + Republic of Malawi + + + + + Malaysia + + + + + Department of Mayotte + + + + + Republic of Namibia + + + + + New Caledonia + + + + + Republic of the Niger + + + + + Territory of Norfolk Island + + + + + Federal Republic of Nigeria + + + + + Republic of Nicaragua + + + + + Niue + + + + + Kingdom of the Netherlands + + + + + Kingdom of Norway + + + + + Federal Democratic Republic of Nepal + + + + + Republic of Nauru + + + + + New Zealand + + + + + Sultanate of Oman + + + + + Islamic Republic of Pakistan + + + + + Republic of Panama + + + + + Pitcairn, Henderson, Ducie, and Oeno Islands + + + + + Republic of Peru + + + + + Republic of the Philippines + + + + + Republic of Palau + + + + + Independent State of Papua New Guinea + + + + + Republic of Poland + + + + + Commonwealth of Puerto Rico + + + + + Democratic People's Republic of Korea + + + + + Portuguese Republic + + + + + Republic of Paraguay + + + + + Palestinian Territory + + + + + French Polynesia + + + + + State of Qatar + + + + + Department of Reunion + + + + + Romania + + + + + Russian Federation + + + + + Republic of Rwanda + + + + + Kingdom of Saudi Arabia + + + + + Republic of the Sudan + + + + + Republic of Senegal + + + + + Republic of Singapore + + + + + South Georgia and South Sandwich Islands + + + + + Saint Helena, Ascension, and Tristan da Cunha + + + + + Solomon Islands + + + + + Republic of Sierra Leone + + + + + Republic of El Salvador + + + + + Republic of San Marino + + + + + Somalia, Federal Republic of + + + + + Territorial Collectivity of Saint Pierre and Miquelon + + + + + Republic of Serbia + + + + + Republic of South Sudan + + + + + Democratic Republic of Sao Tome and Principe + + + + + Republic of Suriname + + + + + Slovak Republic + + + + + Republic of Slovenia + + + + + Kingdom of Sweden + + + + + Kingdom of Swaziland + + + + + Sint Maarten + + + + + Republic of Seychelles + + + + + Syrian Arab Republic + + + + + Turks and Caicos Islands + + + + + Republic of Chad + + + + + Togolese Republic + + + + + Kingdom of Thailand + + + + + Republic of Tajikistan + + + + + Tokelau + + + + + Turkmenistan + + + + + Democratic Republic of Timor-Leste + + + + + Kingdom of Tonga + + + + + Republic of Trinidad and Tobago + + + + + Tunisian Republic + + + + + Republic of Turkey + + + + + Tuvalu + + + + + Taiwan + + + + + United Republic of Tanzania + + + + + Republic of Uganda + + + + + Ukraine + + + + + Oriental 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Kunashiri, and Shikotan Islands + + + + + Akrotiri + + + + + Spratly Islands + + + + + Svalbard + + + + + Tromelin Island + + + + + West Bank + + + + + Wake Island + + + + + Dhekelia + + + + + No Man's Land + + + + + Republic of Yemen + + + + + Republic of South Africa + + + + + Republic of Zambia + + + + + Republic of Zimbabwe + + + + + FOUR EYES + + + + + Suppressed + + + + + Biological Weapons Convention States + + + + + ROK/US Combined Forces Command, Korea + + + + + Combined Maritime Forces Central + + + + + Cooperative Maritime Forces Pacific + + + + + Civilian Protection Monitoring Team for Sudan + + + + + Countering Transnational Organized Crime + + + + + Chemical Weapons Convention States + + + + + FIVE EYES + + + + + Global Counter-Terrorism Forces + + + + + Global Maritime Interception Forces + + + + + International Security Assistance Force for Afghanistan + + + + + Stabilization Forces in Kosovo + + + + + Multi-Lateral Enduring Contingency + + + + + North African Counter-Terrorism Forces + + + + + North Atlantic Treaty Organization + + + + + NATO Convention Armed Forces in Europe + + + + + Open Skies Treaty + + + + + Suppressed + + + + + THREE EYES + + + + + United Nations Command, Korea + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATOwnerProducer.xsd b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATOwnerProducer.xsd new file mode 100644 index 0000000..ef6385a --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATOwnerProducer.xsd @@ -0,0 +1,1573 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMCATOwnerProducer Version 2 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMCATOwnerProducer.xml CVE it is based on, instead of here. + + + + + + + + (U) + FGI, followed by all currently valid GENC trigraphs in alphabetical order by trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCATOwnerProducer.xml + + + + + + + + + North Atlantic Treaty Organization Special Words + + + + + + + + + Foreign Government Information + + + + + Aruba + + + + + Islamic Republic of Afghanistan + + + + + Republic of Angola + + + + + Anguilla + + + + + Republic of Albania + + + + + Principality of Andorra + + + + + United Arab Emirates + + + + + Argentine Republic + + + + + Republic of Armenia + + + + + Territory of American Samoa + + + + + Antarctica + + + + + French Southern and Antarctic Lands + + + + + Antigua and Barbuda + + + + + Commonwealth of Australia + + + + + Republic of Austria + + + + + Guantanamo Bay Naval Base + + + + + Republic of Azerbaijan + + + + + Republic of Burundi + + + + + Kingdom of Belgium + + + + + Republic of Benin + + + + + Bonaire, Sint Eustatius, and Saba + + + + + Burkina Faso + + + + + People's Republic of Bangladesh + + + + + Republic of Bulgaria + + + + + Kingdom of Bahrain + + + + + Commonwealth of The Bahamas + + + + + Bosnia and Herzegovina + + + + + Saint Barthelemy + + + + + Republic of Belarus + + + + + Belize + + + + + Bermuda + + + + + Plurinational State of Bolivia + + + + + Federative Republic of Brazil + + + + + Barbados + + + + + Brunei Darussalam + + + + + Kingdom of Bhutan + + + + + Bouvet Island + + + + + Republic of Botswana + + + + + Central African Republic + + + + + Canada + + + + + Territory of Cocos (Keeling) Islands + + + + + Swiss Confederation + + + + + Republic of Chile + + + + + People's Republic of China + + + + + Republic of Côte d'Ivoire + + + + + Republic of Cameroon + + + + + Democratic Republic of the Congo + + + + + Republic of the Congo + + + + + Cook Islands + + + + + Republic of Colombia + + + + + Union of the Comoros + + + + + Clipperton Island + + + + + Republic of Cape Verde + + + + + Republic of Costa Rica + + + + + Republic of Cuba + + + + + Curaçao + + + + + Territory of Christmas Island + + + + + Cayman Islands + + + + + Republic of Cyprus + + + + + Czech Republic + + + + + Federal Republic of Germany + + + + + Diego Garcia + + + + + Republic of Djibouti + + + + + Commonwealth of Dominica + + + + + Kingdom of Denmark + + + + + Dominican Republic + + + + + People's Democratic Republic of Algeria + + + + + Republic of Ecuador + + + + + Arab Republic of Egypt + + + + + State of Eritrea + + + + + Western Sahara + + + + + Kingdom of Spain + + + + + Republic of Estonia + + + + + Federal Democratic Republic of Ethiopia + + + + + Republic of Finland + + + + + Republic of Fiji + + + + + Falkland Islands (Islas Malvinas) + + + + + French Republic + + + + + Faroe Islands + + + + + Federated States of Micronesia + + + + + Gabonese Republic + + + + + United Kingdom of Great Britain and Northern Ireland + + + + + Georgia + + + + + Bailiwick of Guernsey + + + + + Republic of Ghana + + + + + Gibraltar + + + + + Republic of Guinea + + + + + Department of Guadeloupe + + + + + Republic of The Gambia + + + + + Republic of Guinea-Bissau + + + + + Republic of Equatorial Guinea + + + + + Hellenic Republic + + + + + Grenada + + + + + Greenland + + + + + Republic of Guatemala + + + + + Department of Guiana + + + + + Territory of Guam + + + + + Co-operative Republic of Guyana + + + + + Hong Kong Special Administrative Region + + + + + Territory of Heard Island and McDonald Islands + + + + + Republic of Honduras + + + + + Republic of Croatia + + + + + Republic of Haiti + + + + + Hungary + + + + + Republic of Indonesia + + + + + Isle of Man + + + + + Republic of India + + + + + British Indian Ocean Territory + + + + + Ireland + + + + + Islamic Republic of Iran + + + + + Republic of Iraq + + + + + Republic of Iceland + + + + + State of Israel + + + + + Italian Republic + + + + + Jamaica + + + + + Bailiwick of Jersey + + + + + Hashemite Kingdom of Jordan + + + + + Japan + + + + + Republic of Kazakhstan + + + + + Republic of Kenya + + + + + Kyrgyz Republic + + + + + Kingdom of Cambodia + + + + + Republic of Kiribati + + + + + Federation of Saint Kitts and Nevis + + + + + Republic of Korea + + + + + State of Kuwait + + + + + Lao People's Democratic Republic + + + + + Lebanese Republic + + + + + Republic of Liberia + + + + + Libya + + + + + Saint Lucia + + + + + Principality of Liechtenstein + + + + + Democratic Socialist Republic of Sri Lanka + + + + + Kingdom of Lesotho + + + + + Republic of Lithuania + + + + + Grand Duchy of Luxembourg + + + + + Republic of Latvia + + + + + Macau Special Administrative Region + + + + + Saint Martin + + + + + Kingdom of Morocco + + + + + Principality of Monaco + + + + + Republic of Moldova + + + + + Republic of Madagascar + + + + + Republic of Maldives + + + + + United Mexican States + + + + + Republic of the Marshall Islands + + + + + Republic of Macedonia + + + + + Republic of Mali + + + + + Republic of Malta + + + + + Union of Burma + + + + + Montenegro + + + + + Mongolia + + + + + Commonwealth of the Northern Mariana Islands + + + + + Republic of Mozambique + + + + + Islamic Republic of Mauritania + + + + + Montserrat + + + + + Department of Martinique + + + + + Republic of Mauritius + + + + + Republic of Malawi + + + + + Malaysia + + + + + Department of Mayotte + + + + + Republic of Namibia + + + + + New Caledonia + + + + + Republic of the Niger + + + + + Territory of Norfolk Island + + + + + Federal Republic of Nigeria + + + + + Republic of Nicaragua + + + + + Niue + + + + + Kingdom of the Netherlands + + + + + Kingdom of Norway + + + + + Federal Democratic Republic of Nepal + + + + + Republic of Nauru + + + + + New Zealand + + + + + Sultanate of Oman + + + + + Islamic Republic of Pakistan + + + + + Republic of Panama + + + + + Pitcairn, Henderson, Ducie, and Oeno Islands + + + + + Republic of Peru + + + + + Republic of the Philippines + + + + + Republic of Palau + + + + + Independent State of Papua New Guinea + + + + + Republic of Poland + + + + + Commonwealth of Puerto Rico + + + + + Democratic People's Republic of Korea + + + + + Portuguese Republic + + + + + Republic of Paraguay + + + + + Palestinian Territory + + + + + French Polynesia + + + + + State of Qatar + + + + + Department of Reunion + + + + + Romania + + + + + Russian Federation + + + + + Republic of Rwanda + + + + + Kingdom of Saudi Arabia + + + + + Republic of the Sudan + + + + + Republic of Senegal + + + + + Republic of Singapore + + + + + South Georgia and South Sandwich Islands + + + + + Saint Helena, Ascension, and Tristan da Cunha + + + + + Solomon Islands + + + + + Republic of Sierra Leone + + + + + Republic of El Salvador + + + + + Republic of San Marino + + + + + Somalia, Federal Republic of + + + + + Territorial Collectivity of Saint Pierre and Miquelon + + + + + Republic of Serbia + + + + + Republic of South Sudan + + + + + Democratic Republic of Sao Tome and Principe + + + + + Republic of Suriname + + + + + Slovak Republic + + + + + Republic of Slovenia + + + + + Kingdom of Sweden + + + + + Kingdom of Swaziland + + + + + Sint Maarten + + + + + Republic of Seychelles + + + + + Syrian Arab Republic + + + + + Turks and Caicos Islands + + + + + Republic of Chad + + + + + Togolese Republic + + + + + Kingdom of Thailand + + + + + Republic of Tajikistan + + + + + Tokelau + + + + + Turkmenistan + + + + + Democratic Republic of Timor-Leste + + + + + Kingdom of Tonga + + + + + Republic of Trinidad and Tobago + + + + + Tunisian Republic + + + + + Republic of Turkey + + + + + Tuvalu + + + + + Taiwan + + + + + United Republic of Tanzania + + + + + Republic of Uganda + + + + + Ukraine + + + + + Oriental Republic of Uruguay + + + + + United States of America + + + + + Republic of Uzbekistan + + + + + State of the Vatican City + + + + + Saint Vincent and the Grenadines + + + + + Bolivarian Republic of Venezuela + + + + + Virgin Islands, British + + + + + United States Virgin Islands + + + + + Socialist Republic of Vietnam + + + + + Republic of Vanuatu + + + + + Wallis and Futuna + + + + + Independent State of Samoa + + + + + Territory of Ashmore and Cartier Islands + + + + + Entity 1 + + + + + Bassas da India + + + + + Baker Island + + + + + Entity 2 + + + + + Coral Sea Islands Territory + + + + + Entity 3 + + + + + Europa Island + + + + + Glorioso Islands + + + + + Gaza Strip + + + + + Howland Island + + + + + Johnston Atoll + + + + + Jan Mayen + + + + + Juan de Nova Island + + + + + Jarvis Island + + + + + Entity 4 + + + + + Entity 5 + + + + + Kingman Reef + + + + + Republic of Kosovo + + + + + Midway Islands + + + + + Navassa Island + + + + + Palmyra Atoll + + + + + Paracel Islands + + + + + Etorofu, Habomai, Kunashiri, and Shikotan Islands + + + + + Akrotiri + + + + + Spratly Islands + + + + + Svalbard + + + + + Tromelin Island + + + + + West Bank + + + + + Wake Island + + + + + Dhekelia + + + + + No Man's Land + + + + + Republic of Yemen + + + + + Republic of South Africa + + + + + Republic of Zambia + + + + + Republic of Zimbabwe + + + + + FOUR EYES + + + + + Suppressed + + + + + Biological Weapons Convention States + + + + + ROK/US Combined Forces Command, Korea + + + + + Combined Maritime Forces Central + + + + + Cooperative Maritime Forces Pacific + + + + + Civilian Protection Monitoring Team for Sudan + + + + + Countering Transnational Organized Crime + + + + + Chemical Weapons Convention States + + + + + FIVE EYES + + + + + Global Counter-Terrorism Forces + + + + + Global Maritime Interception Forces + + + + + International Security Assistance Force for Afghanistan + + + + + Stabilization Forces in Kosovo + + + + + Multi-Lateral Enduring Contingency + + + + + North African Counter-Terrorism Forces + + + + + North Atlantic Treaty Organization + + + + + NATO Convention Armed Forces in Europe + + + + + Open Skies Treaty + + + + + Suppressed + + + + + THREE EYES + + + + + United Nations Command, Korea + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATRelTo.xsd b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATRelTo.xsd new file mode 100644 index 0000000..3cd4cb3 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/CVEGenerated/CVEnumISMCATRelTo.xsd @@ -0,0 +1,1568 @@ + + + + + The W3C XML Schema fragment encoding types for CVEnumISMCATRelTo Version 2 controlled vocabulary enumerations. This file is generated, so edits should be made to the CVEnumISMCATRelTo.xml CVE it is based on, instead of here. + + + + + + + + (U) + USA, followed by all currently valid GENC trigraphs except USA in alphabetical order by trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCATRelTo.xml + + + + + + + + + North Atlantic Treaty Organization Special Words + + + + + + + + + United States + + + + + Aruba + + + + + Islamic Republic of Afghanistan + + + + + Republic of Angola + + + + + Anguilla + + + + + Republic of Albania + + + + + Principality of Andorra + + + + + United Arab Emirates + + + + + Argentine Republic + + + + + Republic of Armenia + + + + + Territory of American Samoa + + + + + Antarctica + + + + + French Southern and Antarctic Lands + + + + + Antigua and Barbuda + + + + + Commonwealth of Australia + + + + + Republic of Austria + + + + + Guantanamo Bay Naval Base + + + + + Republic of Azerbaijan + + + + + Republic of Burundi + + + + + Kingdom of Belgium + + + + + Republic of Benin + + + + + Bonaire, Sint Eustatius, and Saba + + + + + Burkina Faso + + + + + People's Republic of Bangladesh + + + + + Republic of Bulgaria + + + + + Kingdom of Bahrain + + + + + Commonwealth of The Bahamas + + + + + Bosnia and Herzegovina + + + + + Saint Barthelemy + + + + + Republic of Belarus + + + + + Belize + + + + + Bermuda + + + + + Plurinational State of Bolivia + + + + + Federative Republic of Brazil + + + + + Barbados + + + + + Brunei Darussalam + + + + + Kingdom of Bhutan + + + + + Bouvet Island + + + + + Republic of Botswana + + + + + Central African Republic + + + + + Canada + + + + + Territory of Cocos (Keeling) Islands + + + + + Swiss Confederation + + + + + Republic of Chile + + + + + People's Republic of China + + + + + Republic of Côte d'Ivoire + + + + + Republic of Cameroon + + + + + Democratic Republic of the Congo + + + + + Republic of the Congo + + + + + Cook Islands + + + + + Republic of Colombia + + + + + Union of the Comoros + + + + + Clipperton Island + + + + + Republic of Cape Verde + + + + + Republic of Costa Rica + + + + + Republic of Cuba + + + + + Curaçao + + + + + Territory of Christmas Island + + + + + Cayman Islands + + + + + Republic of Cyprus + + + + + Czech Republic + + + + + Federal Republic of Germany + + + + + Diego Garcia + + + + + Republic of Djibouti + + + + + Commonwealth of Dominica + + + + + Kingdom of Denmark + + + + + Dominican Republic + + + + + People's Democratic Republic of Algeria + + + + + Republic of Ecuador + + + + + Arab Republic of Egypt + + + + + State of Eritrea + + + + + Western Sahara + + + + + Kingdom of Spain + + + + + Republic of Estonia + + + + + Federal Democratic Republic of Ethiopia + + + + + Republic of Finland + + + + + Republic of Fiji + + + + + Falkland Islands (Islas Malvinas) + + + + + French Republic + + + + + Faroe Islands + + + + + Federated States of Micronesia + + + + + Gabonese Republic + + + + + United Kingdom of Great Britain and Northern Ireland + + + + + Georgia + + + + + Bailiwick of Guernsey + + + + + Republic of Ghana + + + + + Gibraltar + + + + + Republic of Guinea + + + + + Department of Guadeloupe + + + + + Republic of The Gambia + + + + + Republic of Guinea-Bissau + + + + + Republic of Equatorial Guinea + + + + + Hellenic Republic + + + + + Grenada + + + + + Greenland + + + + + Republic of Guatemala + + + + + Department of Guiana + + + + + Territory of Guam + + + + + Co-operative Republic of Guyana + + + + + Hong Kong Special Administrative Region + + + + + Territory of Heard Island and McDonald Islands + + + + + Republic of Honduras + + + + + Republic of Croatia + + + + + Republic of Haiti + + + + + Hungary + + + + + Republic of Indonesia + + + + + Isle of Man + + + + + Republic of India + + + + + British Indian Ocean Territory + + + + + Ireland + + + + + Islamic Republic of Iran + + + + + Republic of Iraq + + + + + Republic of Iceland + + + + + State of Israel + + + + + Italian Republic + + + + + Jamaica + + + + + Bailiwick of Jersey + + + + + Hashemite Kingdom of Jordan + + + + + Japan + + + + + Republic of Kazakhstan + + + + + Republic of Kenya + + + + + Kyrgyz Republic + + + + + Kingdom of Cambodia + + + + + Republic of Kiribati + + + + + Federation of Saint Kitts and Nevis + + + + + Republic of Korea + + + + + State of Kuwait + + + + + Lao People's Democratic Republic + + + + + Lebanese Republic + + + + + Republic of Liberia + + + + + Libya + + + + + Saint Lucia + + + + + Principality of Liechtenstein + + + + + Democratic Socialist Republic of Sri Lanka + + + + + Kingdom of Lesotho + + + + + Republic of Lithuania + + + + + Grand Duchy of Luxembourg + + + + + Republic of Latvia + + + + + Macau Special Administrative Region + + + + + Saint Martin + + + + + Kingdom of Morocco + + + + + Principality of Monaco + + + + + Republic of Moldova + + + + + Republic of Madagascar + + + + + Republic of Maldives + + + + + United Mexican States + + + + + Republic of the Marshall Islands + + + + + Republic of Macedonia + + + + + Republic of Mali + + + + + Republic of Malta + + + + + Union of Burma + + + + + Montenegro + + + + + Mongolia + + + + + Commonwealth of the Northern Mariana Islands + + + + + Republic of Mozambique + + + + + Islamic Republic of Mauritania + + + + + Montserrat + + + + + Department of Martinique + + + + + Republic of Mauritius + + + + + Republic of Malawi + + + + + Malaysia + + + + + Department of Mayotte + + + + + Republic of Namibia + + + + + New Caledonia + + + + + Republic of the Niger + + + + + Territory of Norfolk Island + + + + + Federal Republic of Nigeria + + + + + Republic of Nicaragua + + + + + Niue + + + + + Kingdom of the Netherlands + + + + + Kingdom of Norway + + + + + Federal Democratic Republic of Nepal + + + + + Republic of Nauru + + + + + New Zealand + + + + + Sultanate of Oman + + + + + Islamic Republic of Pakistan + + + + + Republic of Panama + + + + + Pitcairn, Henderson, Ducie, and Oeno Islands + + + + + Republic of Peru + + + + + Republic of the Philippines + + + + + Republic of Palau + + + + + Independent State of Papua New Guinea + + + + + Republic of Poland + + + + + Commonwealth of Puerto Rico + + + + + Democratic People's Republic of Korea + + + + + Portuguese Republic + + + + + Republic of Paraguay + + + + + Palestinian Territory + + + + + French Polynesia + + + + + State of Qatar + + + + + Department of Reunion + + + + + Romania + + + + + Russian Federation + + + + + Republic of Rwanda + + + + + Kingdom of Saudi Arabia + + + + + Republic of the Sudan + + + + + Republic of Senegal + + + + + Republic of Singapore + + + + + South Georgia and South Sandwich Islands + + + + + Saint Helena, Ascension, and Tristan da Cunha + + + + + Solomon Islands + + + + + Republic of Sierra Leone + + + + + Republic of El Salvador + + + + + Republic of San Marino + + + + + Somalia, Federal Republic of + + + + + Territorial Collectivity of Saint Pierre and Miquelon + + + + + Republic of Serbia + + + + + Republic of South Sudan + + + + + Democratic Republic of Sao Tome and Principe + + + + + Republic of Suriname + + + + + Slovak Republic + + + + + Republic of Slovenia + + + + + Kingdom of Sweden + + + + + Kingdom of Swaziland + + + + + Sint Maarten + + + + + Republic of Seychelles + + + + + Syrian Arab Republic + + + + + Turks and Caicos Islands + + + + + Republic of Chad + + + + + Togolese Republic + + + + + Kingdom of Thailand + + + + + Republic of Tajikistan + + + + + Tokelau + + + + + Turkmenistan + + + + + Democratic Republic of Timor-Leste + + + + + Kingdom of Tonga + + + + + Republic of Trinidad and Tobago + + + + + Tunisian Republic + + + + + Republic of Turkey + + + + + Tuvalu + + + + + Taiwan + + + + + United Republic of Tanzania + + + + + Republic of Uganda + + + + + Ukraine + + + + + Oriental Republic of Uruguay + + + + + Republic of Uzbekistan + + + + + State of the Vatican City + + + + + Saint Vincent and the Grenadines + + + + + Bolivarian Republic of Venezuela + + + + + Virgin Islands, British + + + + + United States Virgin Islands + + + + + Socialist Republic of Vietnam + + + + + Republic of Vanuatu + + + + + Wallis and Futuna + + + + + Independent State of Samoa + + + + + Territory of Ashmore and Cartier Islands + + + + + Entity 1 + + + + + Bassas da India + + + + + Baker Island + + + + + Entity 2 + + + + + Coral Sea Islands Territory + + + + + Entity 3 + + + + + Europa Island + + + + + Glorioso Islands + + + + + Gaza Strip + + + + + Howland Island + + + + + Johnston Atoll + + + + + Jan Mayen + + + + + Juan de Nova Island + + + + + Jarvis Island + + + + + Entity 4 + + + + + Entity 5 + + + + + Kingman Reef + + + + + Republic of Kosovo + + + + + Midway Islands + + + + + Navassa Island + + + + + Palmyra Atoll + + + + + Paracel Islands + + + + + Etorofu, Habomai, Kunashiri, and Shikotan Islands + + + + + Akrotiri + + + + + Spratly Islands + + + + + Svalbard + + + + + Tromelin Island + + + + + West Bank + + + + + Wake Island + + + + + Dhekelia + + + + + No Man's Land + + + + + Republic of Yemen + + + + + Republic of South Africa + + + + + Republic of Zambia + + + + + Republic of Zimbabwe + + + + + FOUR EYES + + + + + Suppressed + + + + + Biological Weapons Convention States + + + + + ROK/US Combined Forces Command, Korea + + + + + Combined Maritime Forces Central + + + + + Cooperative Maritime Forces Pacific + + + + + Civilian Protection Monitoring Team for Sudan + + + + + Countering Transnational Organized Crime + + + + + Chemical Weapons Convention States + + + + + FIVE EYES + + + + + Global Counter-Terrorism Forces + + + + + Global Maritime Interception Forces + + + + + International Security Assistance Force for Afghanistan + + + + + Stabilization Forces in Kosovo + + + + + Multi-Lateral Enduring Contingency + + + + + North African Counter-Terrorism Forces + + + + + North Atlantic Treaty Organization + + + + + NATO Convention Armed Forces in Europe + + + + + Open Skies Treaty + + + + + Suppressed + + + + + THREE EYES + + + + + United Nations Command, Korea + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM-v13/Schema/ISMCAT/SchemaGuideSchema.xsd b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/SchemaGuideSchema.xsd new file mode 100644 index 0000000..7cb3a14 --- /dev/null +++ b/schemas/sidd/external/ISM-v13/Schema/ISMCAT/SchemaGuideSchema.xsd @@ -0,0 +1,88 @@ + + + + + Intelligence Community + Technical Specification XML CVE Encoding Specification for ISM Country Codes and Tetragraphs (ISMCAT.XML) + SchemaGuide + + + + Notices + distEditionBlockReplace + + + + Description + W3C XML Schema used to + facilitate generation of the SchemaGuide for the XML Data Encoding Specification for + CVE Encoding Specification for ISM Country Codes and Tetragraphs (ISMCAT.XML). + + + Introduction + This XML Schema file is only + used to produce the schemaGuide for the XML Data Encoding Specification (DES). + Please see the document titled + XML Data Encoding Specification for + CVE Encoding Specification for ISM Country Codes and Tetragraphs + for a complete description of the encoding as well as list of all + components. + It is envisioned that this + schema or its components, as well as other parts of the DES may be overridden for + localized implementations. Therefore, permission to use, copy, modify and distribute + this XML Schema and the other parts of the DES for any purpose is hereby granted in + perpetuity. + Please reference the preceding + two paragraphs in all copies or variations. The developers make no representation + about the suitability of the schema or DES for any purpose. It is provided "as is" + without expressed or implied warranty. + If you modify this XML Schema + in any way label your schema as a variant of ISMCAT.XML. + Please direct all questions, + bug reports,or suggestions for changes to the points of contact identified in the + document referenced above. + + + Implementation Notes + ISMCAT.XML is a collection of 4 CVEs + + CVEnumISMCATFGIOpen + + CVEnumISMCATFGIProtected + + CVEnumISMCATOwnerProducer + + + CVEnumISMCATRelTo + + + + + Creators + Office of the Director of + National Intelligence Intelligence Community Chief Information Officer + + + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISM25X.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISM25X.xsd new file mode 100644 index 0000000..ec1eeb5 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISM25X.xsd @@ -0,0 +1,76 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISM25X. This file is generated so edits should be made to the CVEnumISM25X the CVE it is based on instead of here. + + + + + + + + (U) All currently authorized 25X values. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISM25X.xml + + + + + + + Reveal information about the application of an intelligence source or method. + + + + + Reveal the identity of a confidential human source or human intelligence source. + + + + + Reveal information that would assist in the development or use of weapons of mass destruction. + + + + + Reveal information that would impair U.S. cryptologic systems or activities. + + + + + Reveal information that would impair the application of state-of-the-art technology within a U.S. weapon system. + + + + + Reveal actual U.S. military war plans that remain in effect. + + + + + Reveal information, including foreign government information, that would seriously and demonstrably impair relations between the United States and a foreign government or seriously and demonstrably undermine ongoing diplomatic activities of the United States. + + + + + Reveal information that would clearly and demonstrably impair the current ability of United States Government officials to protect the President, Vice President, or other protectees for whom protection services, in the interest of national security, are authorized. + + + + + Reveal information that would seriously and demonstrably impair current national security emergency preparedness plans or reveal current vulnerabilities of systems, installations, infrastructures, or projects relating to the national security. + + + + + Violate a statue, treaty, or international agreement. + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMAttributes.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMAttributes.xsd new file mode 100644 index 0000000..3021caf --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMAttributes.xsd @@ -0,0 +1,166 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMAttributes. This file is generated so edits should be made to the CVEnumISMAttributes the CVE it is based on instead of here. + + + + + + + + (U) All currently authorized ISM attribute names + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMAttributes.xml + + + + + + + classification attribute + + + + + ownerProducer attribute + + + + + SCIcontrols attribute + + + + + SARIdentifier attribute + + + + + disseminationControls attribute + + + + + FGIsourceOpen attribute + + + + + FGIsourceProtected attribute + + + + + releasableTo attribute + + + + + nonICmarkings attribute + + + + + classifiedBy attribute + + + + + derivativelyClassifiedBy attribute + + + + + classificationReason attribute + + + + + derivedFrom attribute + + + + + declassDate attribute + + + + + declassEvent attribute + + + + + declassException attribute + + + + + typeOfExemptedSource attribute + + + + + dateOfExemptedSource attribute + + + + + resourceElement attribute + + + + + excludeFromRollup attribute + + + + + createDate attribute + + + + + compilationReason attribute + + + + + notice attribute + + + + + DESVersion attribute + + + + + notice date attribute + + + + + notice POC attribute + + + + + notice Reason attribute + + + + + compliesWith attribute + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationAll.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationAll.xsd new file mode 100644 index 0000000..dba0877 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationAll.xsd @@ -0,0 +1,51 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMClassificationAll. This file is generated so edits should be made to the CVEnumISMClassificationAll the CVE it is based on instead of here. + + + + + + + + (U) All currently valid classification marks + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationAll.xml + + + + + + + RESTRICTED + + + + + CONFIDENTIAL + + + + + SECRET + + + + + TOP SECRET + + + + + UNCLASSIFIED + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationNonUS.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationNonUS.xsd new file mode 100644 index 0000000..0b4b836 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationNonUS.xsd @@ -0,0 +1,51 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMClassificationNonUS. This file is generated so edits should be made to the CVEnumISMClassificationNonUS the CVE it is based on instead of here. + + + + + + + + (U) All currently valid Non-US classification marks excluding NATO + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationNonUS.xml + + + + + + + TOP SECRET + + + + + SECRET + + + + + CONFIDENTIAL + + + + + RESTRICTED + + + + + UNCLASSIFIED + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationUS.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationUS.xsd new file mode 100644 index 0000000..cac5e6a --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMClassificationUS.xsd @@ -0,0 +1,46 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMClassificationUS. This file is generated so edits should be made to the CVEnumISMClassificationUS the CVE it is based on instead of here. + + + + + + + + (U) All currently valid US classification marks + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationUS.xml + + + + + + + TOP SECRET + + + + + SECRET + + + + + CONFIDENTIAL + + + + + UNCLASSIFIED + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMCompliesWith.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMCompliesWith.xsd new file mode 100644 index 0000000..a133a28 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMCompliesWith.xsd @@ -0,0 +1,44 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMCompliesWith. This file is generated so edits should be made to the CVEnumISMCompliesWith the CVE it is based on instead of here. + + + + + + + + (U) Current rule set names that documents may comply with + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMCompliesWith.xml + + + + + + + Document claims compliance with the rules in ICD-710 that have been encoded into ISM + + + + + Document claims compliance with the rules in DoD5230.24 that have been encoded into ISM + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMDissem.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMDissem.xsd new file mode 100644 index 0000000..2659984 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMDissem.xsd @@ -0,0 +1,132 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMDissem. This file is generated so edits should be made to the CVEnumISMDissem the CVE it is based on instead of here. + + + + + + + + (U) All currently valid Dissemination controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMDissem.xml + + + + + + + + + RD-SIGMA-#, # represents the SIGMA number which may be between 1 and 99. + + + + + FRD-SIGMA-#, # represents the SIGMA number which may be between 1 and 99. + + + + + + + + + FOR OFFICIAL USE ONLY + + + + + ORIGINATOR CONTROLLED + + + + + CONTROLLED IMAGERY + + + + + SOURCES AND METHODS INFORMATION + + + + + NOT RELEASABLE TO FOREIGN NATIONALS + + + + + CAUTION-PROPRIETARY INFORMATION INVOLVED + + + + + AUTHORIZED FOR RELEASE TO + + + + + RELEASABLE BY INFORMATION DISCLOSURE OFFICIAL + + + + + RESTRICTED DATA + + + + + RD-CRITICAL NUCLEAR WEAPON DESIGN INFORMATION + + + + + FORMERLY RESTRICTED DATA + + + + + DoD CONTROLLED NUCLEAR INFORMATION + + + + + DoE CONTROLLED NUCLEAR INFORMATION + + + + + EYES ONLY + + + + + DEA SENSITIVE + + + + + FOREIGN INTELLIGENCE SURVEILLANCE ACT + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIOpen.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIOpen.xsd new file mode 100644 index 0000000..675b9fc --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIOpen.xsd @@ -0,0 +1,1385 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMFGIOpen. This file is generated so edits should be made to the CVEnumISMFGIOpen the CVE it is based on instead of here. + + + + + + + + (U) UNKNOWN followed by all currently valid ISO Trigraphs except USA in alphabetical order by Trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMFGIOpen.xml + + + + + + + Unknown + + + + + Trigraph for Aruba + + + + + Trigraph for Afghanistan + + + + + Trigraph for Angola + + + + + Trigraph for Anguilla + + + + + Trigraph for Åland Islands + + + + + Trigraph for Albania + + + + + Trigraph for Andorra + + + + + Trigraph for Netherlands Antilles + + + + + Trigraph for United Arab Emirates + + + + + Trigraph for Argentina + + + + + Trigraph for Armenia + + + + + Trigraph for American Samoa + + + + + Trigraph for Antarctica + + + + + Trigraph for French Southern Territories + + + + + Trigraph for Antigua and Barbuda + + + + + Trigraph for Australia + + + + + Trigraph for Austria + + + + + Trigraph for Azerbaijan + + + + + Trigraph for Burundi + + + + + Trigraph for Belgium + + + + + Trigraph for Benin + + + + + Trigraph for Burkina Faso + + + + + Trigraph for Bangladesh + + + + + Trigraph for Bulgaria + + + + + Trigraph for Bahrain + + + + + Trigraph for Bahamas + + + + + Trigraph for Bosnia and Herzegovina + + + + + Trigraph for Saint Barthélemy + + + + + Trigraph for Belarus + + + + + Trigraph for Belize + + + + + Trigraph for Bermuda + + + + + Trigraph for Bolivia + + + + + Trigraph for Brazil + + + + + Trigraph for Barbados + + + + + Trigraph for Brunei Darussalam + + + + + Trigraph for Bhutan + + + + + Trigraph for Bouvet Island + + + + + Trigraph for Botswana + + + + + Trigraph for Central African Republic + + + + + Trigraph for Canada + + + + + Trigraph for Cocos (Keeling) Islands + + + + + Trigraph for Switzerland + + + + + Trigraph for Chile + + + + + Trigraph for China + + + + + Trigraph for Côte d'Ivoire + + + + + Trigraph for Cameroon + + + + + Trigraph for Congo, The Democratic Republic of the + + + + + Trigraph for Congo + + + + + Trigraph for Cook Islands + + + + + Trigraph for Colombia + + + + + Trigraph for Comoros + + + + + Trigraph for Cape Verde + + + + + Trigraph for Costa Rica + + + + + Trigraph for Cuba + + + + + Trigraph for Christmas Island + + + + + Trigraph for Cayman Islands + + + + + Trigraph for Cyprus + + + + + Trigraph for Czech Republic + + + + + Trigraph for Germany + + + + + Trigraph for Djibouti + + + + + Trigraph for Dominica + + + + + Trigraph for Denmark + + + + + Trigraph for Dominican Republic + + + + + Trigraph for Algeria + + + + + Trigraph for Eucador + + + + + Trigraph for Egypt + + + + + Trigraph for Eritrea + + + + + Trigraph for Western Sahara + + + + + Trigraph for Spain + + + + + Trigraph for Estonia + + + + + Trigraph for Ethiopia + + + + + Trigraph for Finland + + + + + Trigraph for Fiji + + + + + Trigraph for Falkland Islands (Malvinas) + + + + + Trigraph for France + + + + + Trigraph for Faroe Islands + + + + + Trigraph for Micronesia, Federated States of + + + + + Trigraph for Gabon + + + + + Trigraph for United Kingdom + + + + + Trigraph for Georgia + + + + + Trigraph for Guernsey + + + + + Trigraph for Ghana + + + + + Trigraph for Gibraltar + + + + + Trigraph for Guinea + + + + + Trigraph for Guadeloupe + + + + + Trigraph for Gambia + + + + + Trigraph for Guinea-Bissau + + + + + Trigraph for Equatorial Guinea + + + + + Trigraph for Greece + + + + + Trigraph for Grenada + + + + + Trigraph for Greenland + + + + + Trigraph for Guatemala + + + + + Trigraph for French Guiana + + + + + Trigraph for Guam + + + + + Trigraph for Guyana + + + + + Trigraph for Hong Kong + + + + + Trigraph for Heard Island and McDonald Islands + + + + + Trigraph for Honduras + + + + + Trigraph for Croatia + + + + + Trigraph for Haiti + + + + + Trigraph for Hungary + + + + + Trigraph for Indonesia + + + + + Trigraph for Isle of Man + + + + + Trigraph for India + + + + + Trigraph for British Indian Ocean Territory + + + + + Trigraph for Ireland + + + + + Trigraph for Iran, Islamic Republic of + + + + + Trigraph for Iraq + + + + + Trigraph for Iceland + + + + + Trigraph for Israel + + + + + Trigraph for Italy + + + + + Trigraph for Jamaica + + + + + Trigraph for Jersey + + + + + Trigraph for Jordan + + + + + Trigraph for Japan + + + + + Trigraph for Kazakhstan + + + + + Trigraph for Kenya + + + + + Trigraph for Kyrgyzstan + + + + + Trigraph for Cambodia + + + + + Trigraph for Kiribati + + + + + Trigraph for Saint Kitts and Nevis + + + + + Trigraph for Korea, Republic of + + + + + Trigraph for Kuwait + + + + + Trigraph for Lao People's Democratic Republic + + + + + Trigraph for Lebanon + + + + + Trigraph for Liberia + + + + + Trigraph for Libyan Arab Jamahiriya + + + + + Trigraph for Saint Lucia + + + + + Trigraph for Liechtenstein + + + + + Trigraph for Sri Lanka + + + + + Trigraph for Lesotho + + + + + Trigraph for Lithuania + + + + + Trigraph for Luxembourg + + + + + Trigraph for Latvia + + + + + Trigraph for Macao + + + + + Trigraph for Saint Martin (French part) + + + + + Trigraph for Morocco + + + + + Trigraph for Monaco + + + + + Trigraph for Moldova (the Republic of) + + + + + Trigraph for Madagascar + + + + + Trigraph for Maldives + + + + + Trigraph for Mexico + + + + + Trigraph for Marshall Islands + + + + + Trigraph for Macedonia, The former Yugoslav Republic of + + + + + Trigraph for Mali + + + + + Trigraph for Malta + + + + + Trigraph for Myanmar + + + + + Trigraph for Montenegro + + + + + Trigraph for Mongolia + + + + + Trigraph for Northern Mariana Islands + + + + + Trigraph for Mozambique + + + + + Trigraph for Mauritania + + + + + Trigraph for Montserrat + + + + + Trigraph for Martinique + + + + + Trigraph for Mauritius + + + + + Trigraph for Malawi + + + + + Trigraph for Malaysia + + + + + Trigraph for Mayotte + + + + + Trigraph for Namibia + + + + + Trigraph for New Caledonia + + + + + Trigraph for Niger + + + + + Trigraph for Norfolk Island + + + + + Trigraph for Nigeria + + + + + Trigraph for Nicaragua + + + + + Trigraph for Niue + + + + + Trigraph for Netherlands + + + + + Trigraph for Norway + + + + + Trigraph for Nepal + + + + + Trigraph for Nauru + + + + + Trigraph for New Zealand + + + + + Trigraph for Oman + + + + + Trigraph for Pakistan + + + + + Trigraph for Panama + + + + + Trigraph for Pitcairn + + + + + Trigraph for Peru + + + + + Trigraph for Philippines + + + + + Trigraph for Palau + + + + + Trigraph for Papua New Guinea + + + + + Trigraph for Poland + + + + + Trigraph for Puerto Rico + + + + + Trigraph for Korea, Democratic People's Republic of + + + + + Trigraph for Portugal + + + + + Trigraph for Paraguay + + + + + Trigraph for Palestinian Territory, Occupied + + + + + Trigraph for French Polynesia + + + + + Trigraph for Qatar + + + + + Trigraph for Réunion + + + + + Trigraph for Romania + + + + + Trigraph for Russian Federation + + + + + Trigraph for Rwanda + + + + + Trigraph for Saudi Arabia + + + + + Trigraph for Sudan + + + + + Trigraph for Senegal + + + + + Trigraph for Singapore + + + + + Trigraph for South Georgia and the South Sandwich Islands + + + + + Trigraph for Saint Helena + + + + + Trigraph for Svalbard and Jan Mayen + + + + + Trigraph for Solomon Islands + + + + + Trigraph for Sierra Leone + + + + + Trigraph for El Salvador + + + + + Trigraph for San Marino + + + + + Trigraph for Somalia + + + + + Trigraph for Saint Pierre and Miquelon + + + + + Trigraph for Serbia + + + + + Trigraph for Sao Tome and Principe + + + + + Trigraph for Suriname + + + + + Trigraph for Slovakia + + + + + Trigraph for Slovenia + + + + + Trigraph for Sweden + + + + + Trigraph for Swaziland + + + + + Trigraph for Seychelles + + + + + Trigraph for Syrian Arab Republic + + + + + Trigraph for Turks and Caicos Islands + + + + + Trigraph for Chad + + + + + Trigraph for Togo + + + + + Trigraph for Thailand + + + + + Trigraph for Tajikistan + + + + + Trigraph for Tokelau + + + + + Trigraph for Turkmenistan + + + + + Trigraph for Timor-Leste + + + + + Trigraph for Tonga + + + + + Trigraph for Trinidad and Tobago + + + + + Trigraph for Tunisia + + + + + Trigraph for Turkey + + + + + Trigraph for Tuvalu + + + + + Trigraph for Taiwan, Province of China + + + + + Trigraph for Tanzania, United Republic of + + + + + Trigraph for Uganda + + + + + Trigraph for Ukraine + + + + + Trigraph for United States Minor Outlying Islands + + + + + Trigraph for Uruguay + + + + + Trigraph for Uzbekistan + + + + + Trigraph for Holy See (Vatican City State) + + + + + Trigraph for Saint Vincent and the Grenadines + + + + + Trigraph for Venezuela + + + + + Trigraph for Virgin Islands, British + + + + + Trigraph for Virgin Islands, U.S. + + + + + Trigraph for Viet Nam + + + + + Trigraph for Vanuatu + + + + + Trigraph for Wallis and Futuna + + + + + Trigraph for Samoa + + + + + Trigraph for Yemen + + + + + Trigraph for South Africa + + + + + Trigraph for Zambia + + + + + Trigraph for Zimbabwe + + + + + Tetragraph for FOUR EYES + + + + + Suppressed + + + + + Tetragraph for Biological Weapons Convention States + + + + + Tetragraph for ROK/US Combined Forces Command, Korea + + + + + Tetragraph for Combined Maritime Forces + + + + + Tetragraph for Cooperative Maritime Forces Pacific + + + + + Tetragraph for Civilian Protection Monitoring Team for Sudan + + + + + Tetragraph for Chemical Weapons Convention States + + + + + Tetragraph for European Union Stabilization Forces in Bosnia + + + + + Tetragraph for European Union DARFUR + + + + + Tetragraph for FIVE EYES + + + + + Tetragraph for Global Counter-Terrorism Forces + + + + + Tetragraph for Global Maritime Interception Forces + + + + + Tetragraph for International Events Security Coalition + + + + + Tetragraph for International Security Assistance Force for Afghanistan + + + + + Tetragraph for Stabilization Forces in Kosovo + + + + + Tetragraph for Multinational Coalition Forces - Iraq + + + + + Tetragraph for Multinational Interim Force Haiti + + + + + Tetragraph for Multi-Lateral Enduring Contingency + + + + + Tetragraph for North African Counter-Terrorism Forces + + + + + Tetragraph for North Atlantic Treaty Organization + + + + + Suppressed + + + + + Tetragraph for THREE EYES + + + + + Tetragraph for United Nations Command, Korea + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIProtected.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIProtected.xsd new file mode 100644 index 0000000..684dcd1 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMFGIProtected.xsd @@ -0,0 +1,1385 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMFGIProtected. This file is generated so edits should be made to the CVEnumISMFGIProtected the CVE it is based on instead of here. + + + + + + + + (U) FGI followed by all currently valid ISO Trigraphs except USA in alphabetical order by Trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMFGIProtected.xml + + + + + + + Foreign Government Information + + + + + Trigraph for Aruba + + + + + Trigraph for Afghanistan + + + + + Trigraph for Angola + + + + + Trigraph for Anguilla + + + + + Trigraph for Åland Islands + + + + + Trigraph for Albania + + + + + Trigraph for Andorra + + + + + Trigraph for Netherlands Antilles + + + + + Trigraph for United Arab Emirates + + + + + Trigraph for Argentina + + + + + Trigraph for Armenia + + + + + Trigraph for American Samoa + + + + + Trigraph for Antarctica + + + + + Trigraph for French Southern Territories + + + + + Trigraph for Antigua and Barbuda + + + + + Trigraph for Australia + + + + + Trigraph for Austria + + + + + Trigraph for Azerbaijan + + + + + Trigraph for Burundi + + + + + Trigraph for Belgium + + + + + Trigraph for Benin + + + + + Trigraph for Burkina Faso + + + + + Trigraph for Bangladesh + + + + + Trigraph for Bulgaria + + + + + Trigraph for Bahrain + + + + + Trigraph for Bahamas + + + + + Trigraph for Bosnia and Herzegovina + + + + + Trigraph for Saint Barthélemy + + + + + Trigraph for Belarus + + + + + Trigraph for Belize + + + + + Trigraph for Bermuda + + + + + Trigraph for Bolivia + + + + + Trigraph for Brazil + + + + + Trigraph for Barbados + + + + + Trigraph for Brunei Darussalam + + + + + Trigraph for Bhutan + + + + + Trigraph for Bouvet Island + + + + + Trigraph for Botswana + + + + + Trigraph for Central African Republic + + + + + Trigraph for Canada + + + + + Trigraph for Cocos (Keeling) Islands + + + + + Trigraph for Switzerland + + + + + Trigraph for Chile + + + + + Trigraph for China + + + + + Trigraph for Côte d'Ivoire + + + + + Trigraph for Cameroon + + + + + Trigraph for Congo, The Democratic Republic of the + + + + + Trigraph for Congo + + + + + Trigraph for Cook Islands + + + + + Trigraph for Colombia + + + + + Trigraph for Comoros + + + + + Trigraph for Cape Verde + + + + + Trigraph for Costa Rica + + + + + Trigraph for Cuba + + + + + Trigraph for Christmas Island + + + + + Trigraph for Cayman Islands + + + + + Trigraph for Cyprus + + + + + Trigraph for Czech Republic + + + + + Trigraph for Germany + + + + + Trigraph for Djibouti + + + + + Trigraph for Dominica + + + + + Trigraph for Denmark + + + + + Trigraph for Dominican Republic + + + + + Trigraph for Algeria + + + + + Trigraph for Eucador + + + + + Trigraph for Egypt + + + + + Trigraph for Eritrea + + + + + Trigraph for Western Sahara + + + + + Trigraph for Spain + + + + + Trigraph for Estonia + + + + + Trigraph for Ethiopia + + + + + Trigraph for Finland + + + + + Trigraph for Fiji + + + + + Trigraph for Falkland Islands (Malvinas) + + + + + Trigraph for France + + + + + Trigraph for Faroe Islands + + + + + Trigraph for Micronesia, Federated States of + + + + + Trigraph for Gabon + + + + + Trigraph for United Kingdom + + + + + Trigraph for Georgia + + + + + Trigraph for Guernsey + + + + + Trigraph for Ghana + + + + + Trigraph for Gibraltar + + + + + Trigraph for Guinea + + + + + Trigraph for Guadeloupe + + + + + Trigraph for Gambia + + + + + Trigraph for Guinea-Bissau + + + + + Trigraph for Equatorial Guinea + + + + + Trigraph for Greece + + + + + Trigraph for Grenada + + + + + Trigraph for Greenland + + + + + Trigraph for Guatemala + + + + + Trigraph for French Guiana + + + + + Trigraph for Guam + + + + + Trigraph for Guyana + + + + + Trigraph for Hong Kong + + + + + Trigraph for Heard Island and McDonald Islands + + + + + Trigraph for Honduras + + + + + Trigraph for Croatia + + + + + Trigraph for Haiti + + + + + Trigraph for Hungary + + + + + Trigraph for Indonesia + + + + + Trigraph for Isle of Man + + + + + Trigraph for India + + + + + Trigraph for British Indian Ocean Territory + + + + + Trigraph for Ireland + + + + + Trigraph for Iran, Islamic Republic of + + + + + Trigraph for Iraq + + + + + Trigraph for Iceland + + + + + Trigraph for Israel + + + + + Trigraph for Italy + + + + + Trigraph for Jamaica + + + + + Trigraph for Jersey + + + + + Trigraph for Jordan + + + + + Trigraph for Japan + + + + + Trigraph for Kazakhstan + + + + + Trigraph for Kenya + + + + + Trigraph for Kyrgyzstan + + + + + Trigraph for Cambodia + + + + + Trigraph for Kiribati + + + + + Trigraph for Saint Kitts and Nevis + + + + + Trigraph for Korea, Republic of + + + + + Trigraph for Kuwait + + + + + Trigraph for Lao People's Democratic Republic + + + + + Trigraph for Lebanon + + + + + Trigraph for Liberia + + + + + Trigraph for Libyan Arab Jamahiriya + + + + + Trigraph for Saint Lucia + + + + + Trigraph for Liechtenstein + + + + + Trigraph for Sri Lanka + + + + + Trigraph for Lesotho + + + + + Trigraph for Lithuania + + + + + Trigraph for Luxembourg + + + + + Trigraph for Latvia + + + + + Trigraph for Macao + + + + + Trigraph for Saint Martin (French part) + + + + + Trigraph for Morocco + + + + + Trigraph for Monaco + + + + + Trigraph for Moldova (the Republic of) + + + + + Trigraph for Madagascar + + + + + Trigraph for Maldives + + + + + Trigraph for Mexico + + + + + Trigraph for Marshall Islands + + + + + Trigraph for Macedonia, The former Yugoslav Republic of + + + + + Trigraph for Mali + + + + + Trigraph for Malta + + + + + Trigraph for Myanmar + + + + + Trigraph for Montenegro + + + + + Trigraph for Mongolia + + + + + Trigraph for Northern Mariana Islands + + + + + Trigraph for Mozambique + + + + + Trigraph for Mauritania + + + + + Trigraph for Montserrat + + + + + Trigraph for Martinique + + + + + Trigraph for Mauritius + + + + + Trigraph for Malawi + + + + + Trigraph for Malaysia + + + + + Trigraph for Mayotte + + + + + Trigraph for Namibia + + + + + Trigraph for New Caledonia + + + + + Trigraph for Niger + + + + + Trigraph for Norfolk Island + + + + + Trigraph for Nigeria + + + + + Trigraph for Nicaragua + + + + + Trigraph for Niue + + + + + Trigraph for Netherlands + + + + + Trigraph for Norway + + + + + Trigraph for Nepal + + + + + Trigraph for Nauru + + + + + Trigraph for New Zealand + + + + + Trigraph for Oman + + + + + Trigraph for Pakistan + + + + + Trigraph for Panama + + + + + Trigraph for Pitcairn + + + + + Trigraph for Peru + + + + + Trigraph for Philippines + + + + + Trigraph for Palau + + + + + Trigraph for Papua New Guinea + + + + + Trigraph for Poland + + + + + Trigraph for Puerto Rico + + + + + Trigraph for Korea, Democratic People's Republic of + + + + + Trigraph for Portugal + + + + + Trigraph for Paraguay + + + + + Trigraph for Palestinian Territory, Occupied + + + + + Trigraph for French Polynesia + + + + + Trigraph for Qatar + + + + + Trigraph for Réunion + + + + + Trigraph for Romania + + + + + Trigraph for Russian Federation + + + + + Trigraph for Rwanda + + + + + Trigraph for Saudi Arabia + + + + + Trigraph for Sudan + + + + + Trigraph for Senegal + + + + + Trigraph for Singapore + + + + + Trigraph for South Georgia and the South Sandwich Islands + + + + + Trigraph for Saint Helena + + + + + Trigraph for Svalbard and Jan Mayen + + + + + Trigraph for Solomon Islands + + + + + Trigraph for Sierra Leone + + + + + Trigraph for El Salvador + + + + + Trigraph for San Marino + + + + + Trigraph for Somalia + + + + + Trigraph for Saint Pierre and Miquelon + + + + + Trigraph for Serbia + + + + + Trigraph for Sao Tome and Principe + + + + + Trigraph for Suriname + + + + + Trigraph for Slovakia + + + + + Trigraph for Slovenia + + + + + Trigraph for Sweden + + + + + Trigraph for Swaziland + + + + + Trigraph for Seychelles + + + + + Trigraph for Syrian Arab Republic + + + + + Trigraph for Turks and Caicos Islands + + + + + Trigraph for Chad + + + + + Trigraph for Togo + + + + + Trigraph for Thailand + + + + + Trigraph for Tajikistan + + + + + Trigraph for Tokelau + + + + + Trigraph for Turkmenistan + + + + + Trigraph for Timor-Leste + + + + + Trigraph for Tonga + + + + + Trigraph for Trinidad and Tobago + + + + + Trigraph for Tunisia + + + + + Trigraph for Turkey + + + + + Trigraph for Tuvalu + + + + + Trigraph for Taiwan, Province of China + + + + + Trigraph for Tanzania, United Republic of + + + + + Trigraph for Uganda + + + + + Trigraph for Ukraine + + + + + Trigraph for United States Minor Outlying Islands + + + + + Trigraph for Uruguay + + + + + Trigraph for Uzbekistan + + + + + Trigraph for Holy See (Vatican City State) + + + + + Trigraph for Saint Vincent and the Grenadines + + + + + Trigraph for Venezuela + + + + + Trigraph for Virgin Islands, British + + + + + Trigraph for Virgin Islands, U.S. + + + + + Trigraph for Viet Nam + + + + + Trigraph for Vanuatu + + + + + Trigraph for Wallis and Futuna + + + + + Trigraph for Samoa + + + + + Trigraph for Yemen + + + + + Trigraph for South Africa + + + + + Trigraph for Zambia + + + + + Trigraph for Zimbabwe + + + + + Tetragraph for FOUR EYES + + + + + Suppressed + + + + + Tetragraph for Biological Weapons Convention States + + + + + Tetragraph for ROK/US Combined Forces Command, Korea + + + + + Tetragraph for Combined Maritime Forces + + + + + Tetragraph for Cooperative Maritime Forces Pacific + + + + + Tetragraph for Civilian Protection Monitoring Team for Sudan + + + + + Tetragraph for Chemical Weapons Convention States + + + + + Tetragraph for European Union Stabilization Forces in Bosnia + + + + + Tetragraph for European Union DARFUR + + + + + Tetragraph for FIVE EYES + + + + + Tetragraph for Global Counter-Terrorism Forces + + + + + Tetragraph for Global Maritime Interception Forces + + + + + Tetragraph for International Events Security Coalition + + + + + Tetragraph for International Security Assistance Force for Afghanistan + + + + + Tetragraph for Stabilization Forces in Kosovo + + + + + Tetragraph for Multinational Coalition Forces - Iraq + + + + + Tetragraph for Multinational Interim Force Haiti + + + + + Tetragraph for Multi-Lateral Enduring Contingency + + + + + Tetragraph for North African Counter-Terrorism Forces + + + + + Tetragraph for North Atlantic Treaty Organization + + + + + Suppressed + + + + + Tetragraph for THREE EYES + + + + + Tetragraph for United Nations Command, Korea + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonIC.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonIC.xsd new file mode 100644 index 0000000..1b42f50 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonIC.xsd @@ -0,0 +1,79 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMNonIC. This file is generated so edits should be made to the CVEnumISMNonIC the CVE it is based on instead of here. + + + + + + + + (U) All currently valid Non-IC markings from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNonIC.xml + + + + + + + SPECIAL CATEGORY + + + + + SENSITIVE INFORMATION + + + + + LIMITED DISTRIBUTION + + + + + EXCLUSIVE DISTRIBUTION + + + + + NO DISTRIBUTION + + + + + SENSITIVE BUT UNCLASSIFIED + + + + + SENSITIVE BUT UNCLASSIFIED NOFORN + + + + + LAW ENFORCEMENT SENSITIVE + + + + + LAW ENFORCEMENT SENSITIVE NOFORN + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonUSControls.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonUSControls.xsd new file mode 100644 index 0000000..4b5beb0 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNonUSControls.xsd @@ -0,0 +1,49 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMNonUSControls. This file is generated so edits should be made to the CVEnumISMNonUSControls the CVE it is based on instead of here. + + + + + + + + (U) NonUS Control markings supported by ISM + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNonUSControls.xml + + + + + + + NATO Atomal mark + + + + + NATO Bohemia mark + + + + + NATO Balk mark + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNotice.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNotice.xsd new file mode 100644 index 0000000..a56be09 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMNotice.xsd @@ -0,0 +1,104 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMNotice. This file is generated so edits should be made to the CVEnumISMNotice the CVE it is based on instead of here. + + + + + + + + (U) All currently authorized Notice values + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMNotice.xml + + + + + + + FISA Warning statement + + + + + IMCON Warning statement + + + + + RD Warning statement + + + + + FRD Warning statement + + + + + LIMDIS caveat + + + + + LES Notice + + + + + LES Notice + + + + + DoD Distribution statment A from DoD Directive 5230.24 + + + + + DoD Distribution statment B from DoD Directive 5230.24 + + + + + DoD Distribution statment C from DoD Directive 5230.24 + + + + + DoD Distribution statment D from DoD Directive 5230.24 + + + + + DoD Distribution statment E from DoD Directive 5230.24 + + + + + DoD Distribution statment F from DoD Directive 5230.24 + + + + + DoD Distribution statment X from DoD Directive 5230.24 + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMOwnerProducer.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMOwnerProducer.xsd new file mode 100644 index 0000000..0d24d44 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMOwnerProducer.xsd @@ -0,0 +1,1390 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMOwnerProducer. This file is generated so edits should be made to the CVEnumISMOwnerProducer the CVE it is based on instead of here. + + + + + + + + (U) FGI followed by all currently valid ISO Trigraphs in alphabetical order by Trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMOwnerProducer.xml + + + + + + + Foreign Government Information + + + + + Trigraph for Aruba + + + + + Trigraph for Afghanistan + + + + + Trigraph for Angola + + + + + Trigraph for Anguilla + + + + + Trigraph for Åland Islands + + + + + Trigraph for Albania + + + + + Trigraph for Andorra + + + + + Trigraph for Netherlands Antilles + + + + + Trigraph for United Arab Emirates + + + + + Trigraph for Argentina + + + + + Trigraph for Armenia + + + + + Trigraph for American Samoa + + + + + Trigraph for Antarctica + + + + + Trigraph for French Southern Territories + + + + + Trigraph for Antigua and Barbuda + + + + + Trigraph for Australia + + + + + Trigraph for Austria + + + + + Trigraph for Azerbaijan + + + + + Trigraph for Burundi + + + + + Trigraph for Belgium + + + + + Trigraph for Benin + + + + + Trigraph for Burkina Faso + + + + + Trigraph for Bangladesh + + + + + Trigraph for Bulgaria + + + + + Trigraph for Bahrain + + + + + Trigraph for Bahamas + + + + + Trigraph for Bosnia and Herzegovina + + + + + Trigraph for Saint Barthélemy + + + + + Trigraph for Belarus + + + + + Trigraph for Belize + + + + + Trigraph for Bermuda + + + + + Trigraph for Bolivia + + + + + Trigraph for Brazil + + + + + Trigraph for Barbados + + + + + Trigraph for Brunei Darussalam + + + + + Trigraph for Bhutan + + + + + Trigraph for Bouvet Island + + + + + Trigraph for Botswana + + + + + Trigraph for Central African Republic + + + + + Trigraph for Canada + + + + + Trigraph for Cocos (Keeling) Islands + + + + + Trigraph for Switzerland + + + + + Trigraph for Chile + + + + + Trigraph for China + + + + + Trigraph for Côte d'Ivoire + + + + + Trigraph for Cameroon + + + + + Trigraph for Congo, The Democratic Republic of the + + + + + Trigraph for Congo + + + + + Trigraph for Cook Islands + + + + + Trigraph for Colombia + + + + + Trigraph for Comoros + + + + + Trigraph for Cape Verde + + + + + Trigraph for Costa Rica + + + + + Trigraph for Cuba + + + + + Trigraph for Christmas Island + + + + + Trigraph for Cayman Islands + + + + + Trigraph for Cyprus + + + + + Trigraph for Czech Republic + + + + + Trigraph for Germany + + + + + Trigraph for Djibouti + + + + + Trigraph for Dominica + + + + + Trigraph for Denmark + + + + + Trigraph for Dominican Republic + + + + + Trigraph for Algeria + + + + + Trigraph for Eucador + + + + + Trigraph for Egypt + + + + + Trigraph for Eritrea + + + + + Trigraph for Western Sahara + + + + + Trigraph for Spain + + + + + Trigraph for Estonia + + + + + Trigraph for Ethiopia + + + + + Trigraph for Finland + + + + + Trigraph for Fiji + + + + + Trigraph for Falkland Islands (Malvinas) + + + + + Trigraph for France + + + + + Trigraph for Faroe Islands + + + + + Trigraph for Micronesia, Federated States of + + + + + Trigraph for Gabon + + + + + Trigraph for United Kingdom + + + + + Trigraph for Georgia + + + + + Trigraph for Guernsey + + + + + Trigraph for Ghana + + + + + Trigraph for Gibraltar + + + + + Trigraph for Guinea + + + + + Trigraph for Guadeloupe + + + + + Trigraph for Gambia + + + + + Trigraph for Guinea-Bissau + + + + + Trigraph for Equatorial Guinea + + + + + Trigraph for Greece + + + + + Trigraph for Grenada + + + + + Trigraph for Greenland + + + + + Trigraph for Guatemala + + + + + Trigraph for French Guiana + + + + + Trigraph for Guam + + + + + Trigraph for Guyana + + + + + Trigraph for Hong Kong + + + + + Trigraph for Heard Island and McDonald Islands + + + + + Trigraph for Honduras + + + + + Trigraph for Croatia + + + + + Trigraph for Haiti + + + + + Trigraph for Hungary + + + + + Trigraph for Indonesia + + + + + Trigraph for Isle of Man + + + + + Trigraph for India + + + + + Trigraph for British Indian Ocean Territory + + + + + Trigraph for Ireland + + + + + Trigraph for Iran, Islamic Republic of + + + + + Trigraph for Iraq + + + + + Trigraph for Iceland + + + + + Trigraph for Israel + + + + + Trigraph for Italy + + + + + Trigraph for Jamaica + + + + + Trigraph for Jersey + + + + + Trigraph for Jordan + + + + + Trigraph for Japan + + + + + Trigraph for Kazakhstan + + + + + Trigraph for Kenya + + + + + Trigraph for Kyrgyzstan + + + + + Trigraph for Cambodia + + + + + Trigraph for Kiribati + + + + + Trigraph for Saint Kitts and Nevis + + + + + Trigraph for Korea, Republic of + + + + + Trigraph for Kuwait + + + + + Trigraph for Lao People's Democratic Republic + + + + + Trigraph for Lebanon + + + + + Trigraph for Liberia + + + + + Trigraph for Libyan Arab Jamahiriya + + + + + Trigraph for Saint Lucia + + + + + Trigraph for Liechtenstein + + + + + Trigraph for Sri Lanka + + + + + Trigraph for Lesotho + + + + + Trigraph for Lithuania + + + + + Trigraph for Luxembourg + + + + + Trigraph for Latvia + + + + + Trigraph for Macao + + + + + Trigraph for Saint Martin (French part) + + + + + Trigraph for Morocco + + + + + Trigraph for Monaco + + + + + Trigraph for Moldova (the Republic of) + + + + + Trigraph for Madagascar + + + + + Trigraph for Maldives + + + + + Trigraph for Mexico + + + + + Trigraph for Marshall Islands + + + + + Trigraph for Macedonia, The former Yugoslav Republic of + + + + + Trigraph for Mali + + + + + Trigraph for Malta + + + + + Trigraph for Myanmar + + + + + Trigraph for Montenegro + + + + + Trigraph for Mongolia + + + + + Trigraph for Northern Mariana Islands + + + + + Trigraph for Mozambique + + + + + Trigraph for Mauritania + + + + + Trigraph for Montserrat + + + + + Trigraph for Martinique + + + + + Trigraph for Mauritius + + + + + Trigraph for Malawi + + + + + Trigraph for Malaysia + + + + + Trigraph for Mayotte + + + + + Trigraph for Namibia + + + + + Trigraph for New Caledonia + + + + + Trigraph for Niger + + + + + Trigraph for Norfolk Island + + + + + Trigraph for Nigeria + + + + + Trigraph for Nicaragua + + + + + Trigraph for Niue + + + + + Trigraph for Netherlands + + + + + Trigraph for Norway + + + + + Trigraph for Nepal + + + + + Trigraph for Nauru + + + + + Trigraph for New Zealand + + + + + Trigraph for Oman + + + + + Trigraph for Pakistan + + + + + Trigraph for Panama + + + + + Trigraph for Pitcairn + + + + + Trigraph for Peru + + + + + Trigraph for Philippines + + + + + Trigraph for Palau + + + + + Trigraph for Papua New Guinea + + + + + Trigraph for Poland + + + + + Trigraph for Puerto Rico + + + + + Trigraph for Korea, Democratic People's Republic of + + + + + Trigraph for Portugal + + + + + Trigraph for Paraguay + + + + + Trigraph for Palestinian Territory, Occupied + + + + + Trigraph for French Polynesia + + + + + Trigraph for Qatar + + + + + Trigraph for Réunion + + + + + Trigraph for Romania + + + + + Trigraph for Russian Federation + + + + + Trigraph for Rwanda + + + + + Trigraph for Saudi Arabia + + + + + Trigraph for Sudan + + + + + Trigraph for Senegal + + + + + Trigraph for Singapore + + + + + Trigraph for South Georgia and the South Sandwich Islands + + + + + Trigraph for Saint Helena + + + + + Trigraph for Svalbard and Jan Mayen + + + + + Trigraph for Solomon Islands + + + + + Trigraph for Sierra Leone + + + + + Trigraph for El Salvador + + + + + Trigraph for San Marino + + + + + Trigraph for Somalia + + + + + Trigraph for Saint Pierre and Miquelon + + + + + Trigraph for Serbia + + + + + Trigraph for Sao Tome and Principe + + + + + Trigraph for Suriname + + + + + Trigraph for Slovakia + + + + + Trigraph for Slovenia + + + + + Trigraph for Sweden + + + + + Trigraph for Swaziland + + + + + Trigraph for Seychelles + + + + + Trigraph for Syrian Arab Republic + + + + + Trigraph for Turks and Caicos Islands + + + + + Trigraph for Chad + + + + + Trigraph for Togo + + + + + Trigraph for Thailand + + + + + Trigraph for Tajikistan + + + + + Trigraph for Tokelau + + + + + Trigraph for Turkmenistan + + + + + Trigraph for Timor-Leste + + + + + Trigraph for Tonga + + + + + Trigraph for Trinidad and Tobago + + + + + Trigraph for Tunisia + + + + + Trigraph for Turkey + + + + + Trigraph for Tuvalu + + + + + Trigraph for Taiwan, Province of China + + + + + Trigraph for Tanzania, United Republic of + + + + + Trigraph for Uganda + + + + + Trigraph for Ukraine + + + + + Trigraph for United States Minor Outlying Islands + + + + + Trigraph for Uruguay + + + + + Trigraph for United States + + + + + Trigraph for Uzbekistan + + + + + Trigraph for Holy See (Vatican City State) + + + + + Trigraph for Saint Vincent and the Grenadines + + + + + Trigraph for Venezuela + + + + + Trigraph for Virgin Islands, British + + + + + Trigraph for Virgin Islands, U.S. + + + + + Trigraph for Viet Nam + + + + + Trigraph for Vanuatu + + + + + Trigraph for Wallis and Futuna + + + + + Trigraph for Samoa + + + + + Trigraph for Yemen + + + + + Trigraph for South Africa + + + + + Trigraph for Zambia + + + + + Trigraph for Zimbabwe + + + + + Tetragraph for FOUR EYES + + + + + Suppressed + + + + + Tetragraph for Biological Weapons Convention States + + + + + Tetragraph for ROK/US Combined Forces Command, Korea + + + + + Tetragraph for Combined Maritime Forces + + + + + Tetragraph for Cooperative Maritime Forces Pacific + + + + + Tetragraph for Civilian Protection Monitoring Team for Sudan + + + + + Tetragraph for Chemical Weapons Convention States + + + + + Tetragraph for European Union Stabilization Forces in Bosnia + + + + + Tetragraph for European Union DARFUR + + + + + Tetragraph for FIVE EYES + + + + + Tetragraph for Global Counter-Terrorism Forces + + + + + Tetragraph for Global Maritime Interception Forces + + + + + Tetragraph for International Events Security Coalition + + + + + Tetragraph for International Security Assistance Force for Afghanistan + + + + + Tetragraph for Stabilization Forces in Kosovo + + + + + Tetragraph for Multinational Coalition Forces - Iraq + + + + + Tetragraph for Multinational Interim Force Haiti + + + + + Tetragraph for Multi-Lateral Enduring Contingency + + + + + Tetragraph for North African Counter-Terrorism Forces + + + + + Tetragraph for North Atlantic Treaty Organization + + + + + Suppressed + + + + + Tetragraph for THREE EYES + + + + + Tetragraph for United Nations Command, Korea + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMRelTo.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMRelTo.xsd new file mode 100644 index 0000000..029e65b --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMRelTo.xsd @@ -0,0 +1,1385 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMRelTo. This file is generated so edits should be made to the CVEnumISMRelTo the CVE it is based on instead of here. + + + + + + + + (U) USA followed by all currently valid ISO Trigraphs except USA in alphabetical order by Trigraph, + followed by all currently valid CAPCO Coalition tetragraphs in alphabetical order by tetragraph. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMRelTo.xml + + + + + + + Trigraph for United States + + + + + Trigraph for Aruba + + + + + Trigraph for Afghanistan + + + + + Trigraph for Angola + + + + + Trigraph for Anguilla + + + + + Trigraph for Åland Islands + + + + + Trigraph for Albania + + + + + Trigraph for Andorra + + + + + Trigraph for Netherlands Antilles + + + + + Trigraph for United Arab Emirates + + + + + Trigraph for Argentina + + + + + Trigraph for Armenia + + + + + Trigraph for American Samoa + + + + + Trigraph for Antarctica + + + + + Trigraph for French Southern Territories + + + + + Trigraph for Antigua and Barbuda + + + + + Trigraph for Australia + + + + + Trigraph for Austria + + + + + Trigraph for Azerbaijan + + + + + Trigraph for Burundi + + + + + Trigraph for Belgium + + + + + Trigraph for Benin + + + + + Trigraph for Burkina Faso + + + + + Trigraph for Bangladesh + + + + + Trigraph for Bulgaria + + + + + Trigraph for Bahrain + + + + + Trigraph for Bahamas + + + + + Trigraph for Bosnia and Herzegovina + + + + + Trigraph for Saint Barthélemy + + + + + Trigraph for Belarus + + + + + Trigraph for Belize + + + + + Trigraph for Bermuda + + + + + Trigraph for Bolivia + + + + + Trigraph for Brazil + + + + + Trigraph for Barbados + + + + + Trigraph for Brunei Darussalam + + + + + Trigraph for Bhutan + + + + + Trigraph for Bouvet Island + + + + + Trigraph for Botswana + + + + + Trigraph for Central African Republic + + + + + Trigraph for Canada + + + + + Trigraph for Cocos (Keeling) Islands + + + + + Trigraph for Switzerland + + + + + Trigraph for Chile + + + + + Trigraph for China + + + + + Trigraph for Côte d'Ivoire + + + + + Trigraph for Cameroon + + + + + Trigraph for Congo, The Democratic Republic of the + + + + + Trigraph for Congo + + + + + Trigraph for Cook Islands + + + + + Trigraph for Colombia + + + + + Trigraph for Comoros + + + + + Trigraph for Cape Verde + + + + + Trigraph for Costa Rica + + + + + Trigraph for Cuba + + + + + Trigraph for Christmas Island + + + + + Trigraph for Cayman Islands + + + + + Trigraph for Cyprus + + + + + Trigraph for Czech Republic + + + + + Trigraph for Germany + + + + + Trigraph for Djibouti + + + + + Trigraph for Dominica + + + + + Trigraph for Denmark + + + + + Trigraph for Dominican Republic + + + + + Trigraph for Algeria + + + + + Trigraph for Eucador + + + + + Trigraph for Egypt + + + + + Trigraph for Eritrea + + + + + Trigraph for Western Sahara + + + + + Trigraph for Spain + + + + + Trigraph for Estonia + + + + + Trigraph for Ethiopia + + + + + Trigraph for Finland + + + + + Trigraph for Fiji + + + + + Trigraph for Falkland Islands (Malvinas) + + + + + Trigraph for France + + + + + Trigraph for Faroe Islands + + + + + Trigraph for Micronesia, Federated States of + + + + + Trigraph for Gabon + + + + + Trigraph for United Kingdom + + + + + Trigraph for Georgia + + + + + Trigraph for Guernsey + + + + + Trigraph for Ghana + + + + + Trigraph for Gibraltar + + + + + Trigraph for Guinea + + + + + Trigraph for Guadeloupe + + + + + Trigraph for Gambia + + + + + Trigraph for Guinea-Bissau + + + + + Trigraph for Equatorial Guinea + + + + + Trigraph for Greece + + + + + Trigraph for Grenada + + + + + Trigraph for Greenland + + + + + Trigraph for Guatemala + + + + + Trigraph for French Guiana + + + + + Trigraph for Guam + + + + + Trigraph for Guyana + + + + + Trigraph for Hong Kong + + + + + Trigraph for Heard Island and McDonald Islands + + + + + Trigraph for Honduras + + + + + Trigraph for Croatia + + + + + Trigraph for Haiti + + + + + Trigraph for Hungary + + + + + Trigraph for Indonesia + + + + + Trigraph for Isle of Man + + + + + Trigraph for India + + + + + Trigraph for British Indian Ocean Territory + + + + + Trigraph for Ireland + + + + + Trigraph for Iran, Islamic Republic of + + + + + Trigraph for Iraq + + + + + Trigraph for Iceland + + + + + Trigraph for Israel + + + + + Trigraph for Italy + + + + + Trigraph for Jamaica + + + + + Trigraph for Jersey + + + + + Trigraph for Jordan + + + + + Trigraph for Japan + + + + + Trigraph for Kazakhstan + + + + + Trigraph for Kenya + + + + + Trigraph for Kyrgyzstan + + + + + Trigraph for Cambodia + + + + + Trigraph for Kiribati + + + + + Trigraph for Saint Kitts and Nevis + + + + + Trigraph for Korea, Republic of + + + + + Trigraph for Kuwait + + + + + Trigraph for Lao People's Democratic Republic + + + + + Trigraph for Lebanon + + + + + Trigraph for Liberia + + + + + Trigraph for Libyan Arab Jamahiriya + + + + + Trigraph for Saint Lucia + + + + + Trigraph for Liechtenstein + + + + + Trigraph for Sri Lanka + + + + + Trigraph for Lesotho + + + + + Trigraph for Lithuania + + + + + Trigraph for Luxembourg + + + + + Trigraph for Latvia + + + + + Trigraph for Macao + + + + + Trigraph for Saint Martin (French part) + + + + + Trigraph for Morocco + + + + + Trigraph for Monaco + + + + + Trigraph for Moldova (the Republic of) + + + + + Trigraph for Madagascar + + + + + Trigraph for Maldives + + + + + Trigraph for Mexico + + + + + Trigraph for Marshall Islands + + + + + Trigraph for Macedonia, The former Yugoslav Republic of + + + + + Trigraph for Mali + + + + + Trigraph for Malta + + + + + Trigraph for Myanmar + + + + + Trigraph for Montenegro + + + + + Trigraph for Mongolia + + + + + Trigraph for Northern Mariana Islands + + + + + Trigraph for Mozambique + + + + + Trigraph for Mauritania + + + + + Trigraph for Montserrat + + + + + Trigraph for Martinique + + + + + Trigraph for Mauritius + + + + + Trigraph for Malawi + + + + + Trigraph for Malaysia + + + + + Trigraph for Mayotte + + + + + Trigraph for Namibia + + + + + Trigraph for New Caledonia + + + + + Trigraph for Niger + + + + + Trigraph for Norfolk Island + + + + + Trigraph for Nigeria + + + + + Trigraph for Nicaragua + + + + + Trigraph for Niue + + + + + Trigraph for Netherlands + + + + + Trigraph for Norway + + + + + Trigraph for Nepal + + + + + Trigraph for Nauru + + + + + Trigraph for New Zealand + + + + + Trigraph for Oman + + + + + Trigraph for Pakistan + + + + + Trigraph for Panama + + + + + Trigraph for Pitcairn + + + + + Trigraph for Peru + + + + + Trigraph for Philippines + + + + + Trigraph for Palau + + + + + Trigraph for Papua New Guinea + + + + + Trigraph for Poland + + + + + Trigraph for Puerto Rico + + + + + Trigraph for Korea, Democratic People's Republic of + + + + + Trigraph for Portugal + + + + + Trigraph for Paraguay + + + + + Trigraph for Palestinian Territory, Occupied + + + + + Trigraph for French Polynesia + + + + + Trigraph for Qatar + + + + + Trigraph for Réunion + + + + + Trigraph for Romania + + + + + Trigraph for Russian Federation + + + + + Trigraph for Rwanda + + + + + Trigraph for Saudi Arabia + + + + + Trigraph for Sudan + + + + + Trigraph for Senegal + + + + + Trigraph for Singapore + + + + + Trigraph for South Georgia and the South Sandwich Islands + + + + + Trigraph for Saint Helena + + + + + Trigraph for Svalbard and Jan Mayen + + + + + Trigraph for Solomon Islands + + + + + Trigraph for Sierra Leone + + + + + Trigraph for El Salvador + + + + + Trigraph for San Marino + + + + + Trigraph for Somalia + + + + + Trigraph for Saint Pierre and Miquelon + + + + + Trigraph for Serbia + + + + + Trigraph for Sao Tome and Principe + + + + + Trigraph for Suriname + + + + + Trigraph for Slovakia + + + + + Trigraph for Slovenia + + + + + Trigraph for Sweden + + + + + Trigraph for Swaziland + + + + + Trigraph for Seychelles + + + + + Trigraph for Syrian Arab Republic + + + + + Trigraph for Turks and Caicos Islands + + + + + Trigraph for Chad + + + + + Trigraph for Togo + + + + + Trigraph for Thailand + + + + + Trigraph for Tajikistan + + + + + Trigraph for Tokelau + + + + + Trigraph for Turkmenistan + + + + + Trigraph for Timor-Leste + + + + + Trigraph for Tonga + + + + + Trigraph for Trinidad and Tobago + + + + + Trigraph for Tunisia + + + + + Trigraph for Turkey + + + + + Trigraph for Tuvalu + + + + + Trigraph for Taiwan, Province of China + + + + + Trigraph for Tanzania, United Republic of + + + + + Trigraph for Uganda + + + + + Trigraph for Ukraine + + + + + Trigraph for United States Minor Outlying Islands + + + + + Trigraph for Uruguay + + + + + Trigraph for Uzbekistan + + + + + Trigraph for Holy See (Vatican City State) + + + + + Trigraph for Saint Vincent and the Grenadines + + + + + Trigraph for Venezuela + + + + + Trigraph for Virgin Islands, British + + + + + Trigraph for Virgin Islands, U.S. + + + + + Trigraph for Viet Nam + + + + + Trigraph for Vanuatu + + + + + Trigraph for Wallis and Futuna + + + + + Trigraph for Samoa + + + + + Trigraph for Yemen + + + + + Trigraph for South Africa + + + + + Trigraph for Zambia + + + + + Trigraph for Zimbabwe + + + + + Tetragraph for FOUR EYES + + + + + Suppressed + + + + + Tetragraph for Biological Weapons Convention States + + + + + Tetragraph for ROK/US Combined Forces Command, Korea + + + + + Tetragraph for Combined Maritime Forces + + + + + Tetragraph for Cooperative Maritime Forces Pacific + + + + + Tetragraph for Civilian Protection Monitoring Team for Sudan + + + + + Tetragraph for Chemical Weapons Convention States + + + + + Tetragraph for European Union Stabilization Forces in Bosnia + + + + + Tetragraph for European Union DARFUR + + + + + Tetragraph for FIVE EYES + + + + + Tetragraph for Global Counter-Terrorism Forces + + + + + Tetragraph for Global Maritime Interception Forces + + + + + Tetragraph for International Events Security Coalition + + + + + Tetragraph for International Security Assistance Force for Afghanistan + + + + + Tetragraph for Stabilization Forces in Kosovo + + + + + Tetragraph for Multinational Coalition Forces - Iraq + + + + + Tetragraph for Multinational Interim Force Haiti + + + + + Tetragraph for Multi-Lateral Enduring Contingency + + + + + Tetragraph for North African Counter-Terrorism Forces + + + + + Tetragraph for North Atlantic Treaty Organization + + + + + Suppressed + + + + + Tetragraph for THREE EYES + + + + + Tetragraph for United Nations Command, Korea + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSAR.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSAR.xsd new file mode 100644 index 0000000..b151130 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSAR.xsd @@ -0,0 +1,46 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMSAR. This file is generated so edits should be made to the CVEnumISMSAR the CVE it is based on instead of here. + + + + + + + + (U) All currently valid SAR controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMSAR.xml + + + + + + + + + SPECIAL ACCESS REQUIRED-XXX, XXX represents the Digraph or Trigraph of the SAR + + + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSCIControls.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSCIControls.xsd new file mode 100644 index 0000000..3a33797 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSCIControls.xsd @@ -0,0 +1,77 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMSCIControls. This file is generated so edits should be made to the CVEnumISMSCIControls the CVE it is based on instead of here. + + + + + + + + (U) All currently valid SCI controls from the published register + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMSCIControls.xml + + + + + + + + + G-AAAA, AAAA represents 4 alpha characters to indicate sub Gamma compartments + + + + + ECI-AAA, AAA represents 3 alpha characters to indicate ECI compartments + + + + + + + + + HCS + + + + + Klondike + + + + + COMINT + + + + + SI-GAMMA + + + + + TALENT KEYHOLE + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSourceMarked.xsd b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSourceMarked.xsd new file mode 100644 index 0000000..fb8d53e --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGenerated/CVEnumISMSourceMarked.xsd @@ -0,0 +1,71 @@ + + + + + W3C XML Schema fragment encoding types for Controlled vocabulary encodings CVEnumISMSourceMarked. This file is generated so edits should be made to the CVEnumISMSourceMarked the CVE it is based on instead of here. + + + + + + + + (U) All currently authorized Source Marked values + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMSourceMarked.xml + + + + + + + Source Marked OADR (Originating Agency's Determination Required) + + + + + Source Marked X1 + + + + + Source Marked X2 + + + + + Source Marked X3 + + + + + Source Marked X4 + + + + + Source Marked X5 + + + + + Source Marked X6 + + + + + Source Marked X7 + + + + + Source Marked X8 + + + + + diff --git a/schemas/sidd/external/ISM/Schema/CVEGeneratedTypes.xsd b/schemas/sidd/external/ISM/Schema/CVEGeneratedTypes.xsd new file mode 100644 index 0000000..d7ac637 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/CVEGeneratedTypes.xsd @@ -0,0 +1,149 @@ + + + + + + + + + + + + + + + + + + Include for all the generated CVE types applicable. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/schemas/sidd/external/ISM/Schema/IC-ISM.xsd b/schemas/sidd/external/ISM/Schema/IC-ISM.xsd new file mode 100644 index 0000000..945ebb1 --- /dev/null +++ b/schemas/sidd/external/ISM/Schema/IC-ISM.xsd @@ -0,0 +1,891 @@ + + + + + + + + + + + + + + + + + + W3C XML Schema for the Intelligence Community Metadata Standard for Information Security Marking (IC-ISM), which is part of the XML DATA ENCODING SPECIFICATION FOR INFORMATION SECURITY MARKING METADATA. + + + + + + + + + + + + + + + + + + The group of Information Security Marking attributes in which the use of attributes 'classification' and 'ownerProducer' is required. + + This group is to be contrasted with group 'SecurityAttributesOptionGroup' in which use of those attributes is optional. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The group of Information Security Marking attributes in which the use of attributes 'classification' and 'ownerProducer' is optional. + + This group is to be contrasted with group 'SecurityAttributesGroup' in which use of these attributes is required. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The group of Information Security Marking attributes for use on a notice element in which the use of attributes 'classification' and 'ownerProducer' is required. + + + + + + + + + + + + + + + + The group of Information Security Marking attributes for use on a notice element in which the use of Security on the notice is optional. + + + + + + + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + A single indicator of the highest level of classification applicable to an information resource or portion within the domain of classified national security information. The Classification element is always used in conjunction with the Owner Producer element. Taken together, the two elements specify the classification category and the type of classification (US, non-US, or Joint). + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMClassificationAll.xml + + + + + + + + + + + + This attribute is used at the resource level. + + An indicator of what optional ISM rule sets the documents complies with. This allows sytems to know that the document claims compliance with these rule sets and they should be enforced. + PERMISSIBLE VALUES + + The permissible values for this simple type are defined in the Controlled Value Enumeration: + + CVEnumISMcompliesWith.xml + + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying the national government or international organization that have purview over the classification marking of an information resource or portion therein. This element is always used in conjunction with the Classification element. Taken together, the two elements specify the classification category and the type of classification (US, non-US, or Joint). + + Within protected internal organizational spaces this element may include one or more indicators identifying information which qualifies as foreign government information for which the source(s) of the information must be concealed. Measures must be taken prior to dissemination of the information to conceal the source(s) of the foreign government information. + + Specifically, under these specific circumstances, when data are moved to the shared spaces, the non-disclosable owner(s) and/or producer(s) listed in this data element's value should be removed and replaced with "FGI". + + The attribute value may be manifested in portion marks or security banners. + + PERMISSIBLE VALUES + + 1) The value "FGI" is permited under the circumstances described above. + + 2) The full set of values are defined in the Controlled Value Enumeration: + + CVEnumISMOwnerProducer.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying sensitive compartmented information control system(s). + + It is manifested in portion marks and security banners. + PERMISSIBLE VALUES + + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMSCIControls.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying the defense or intelligence programs for which special access is required. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMSAR.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying the expansion or limitation on the distribution of information. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMDissem.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying information which qualifies as foreign government information for which the source(s) of the information is not concealed. + + The attribute can indicate that the source of information of foreign origin is unknown. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + 1) The value "UNKNOWN" is permited under the circumstances described above. + + 2) The full set of values are defined in the Controlled Value Enumeration: + + CVEnumISMFGIOpen.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + This attribute has unique specific rules concerning its usage. + + A single indicator that information qualifies as foreign government information for which the source(s) of the information must be concealed. + + Within protected internal organizational spaces this element may be used to maintain a record of the one or more indicators identifying information which qualifies as foreign government information for which the source(s) of the information must be concealed. Measures must be taken prior to dissemination of the information to conceal the source(s) of the foreign government information. + + An indication that information qualifies as foreign government information according to CAPCO guidelines for which the source(s) of the information must be concealed when the information is disseminated in shared spaces + + This data element has a dual purpose. Within shared spaces, the data element serves only to indicate the presence of information which is categorized as foreign government information according to CAPCO guidelines for which the source(s) of the information is concealed, in which case, this data element's value will always be "FGI". The data element may also be employed in this manner within protected internal organizational spaces. However, within protected internal organizational spaces this data element may alternatively be used to maintain a formal record of the foreign country or countries and/or registered international organization(s) that are the non-disclosable owner(s) and/or producer(s) of information which is categorized as foreign government information according to CAPCO guidelines for which the source(s) of the information must be concealed when the resource is disseminated to shared spaces. If the data element is employed in this manner, then additional measures must be taken prior to dissemination of the resource to shared spaces so that any indications of the non-disclosable owner(s) and/or producer(s) of information within the resource are eliminated. + + In all cases, the corresponding portion marking or banner marking should be compliant with CAPCO guidelines for FGI when the source must be concealed. In other words, even if the data element is being employed within protected internal organizational spaces to maintain a formal record of the non-disclosable owner(s) and/or producer(s) within an XML resource, if the resource is rendered for display within the protected internal organizational spaces in any format by a stylesheet or as a result of any other transformation process, then the non-disclosable owner(s) and/or producer(s) should not be included in the corresponding portion marking or banner marking. + + PERMISSIBLE VALUES + + 1) The value "FGI" is permited under the circumstances described above. + + 2) The full set of values are defined in the Controlled Value Enumeration: + + CVEnumISMFGIProtected.xml + + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators identifying the country or countries and/or international organization(s) to which classified information may be released based on the determination of an originator in accordance with established foreign disclosure procedures. This element is used in conjunction with the Dissemination Controls element. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMRelTo.xml + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators of the expansion or limitation on the distribution of an information resource or portion within the domain of information originating from non-intelligence components. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMNonIC.xml + + + + + + + + + + + This attribute is used at both the resource and the portion levels. + + One or more indicators of the expansion or limitation on the distribution of an information resource or portion within the domain of information originating from non-US components. + + It is manifested in portion marks and security banners. + + PERMISSIBLE VALUES + The permissible values for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMNonUSControls.xml + + + + + + + + + + + This attribute is used primarily at the resource level. + + The identity, by name or personal identifier, and position title of the original classification authority for a resource. + + It is manifested only in the 'Classified By' line of a resource's classification authority block. + + + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + + The identity, by name or personal identifier, of the derivative classification authority. + + It is manifested only in the 'Classified By' line of a resource's classification authority block. + + + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + One or more reason indicators or explanatory text describing the basis for an original classification decision. + + It is manifested only in the 'Reason' line of a resource's classification authority block. + + + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + A citation of the authoritative source or reference to multiple sources of the classification markings used in a classified resource. + + It is manifested only in the 'Derived From' line of a document's classification authority block. + + + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + A specific year, month, and day upon which the information shall be automatically declassified if not properly exempted from automatic declassification. + + It is manifested in the 'Declassify On' line of a resource's classification authority block. + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + A description of an event upon which the information shall be automatically declassified if not properly exempted from automatic declassification. + + It is manifested only in the 'Declassify On' line of a resource's classification authority block. + + + + + + + + + + + + + + + This attribute is used primarily at the resource level. + + A single indicator describing an exemption to the nominal 25-year point for automatic declassification. This element is used in conjunction with the Declassification Date or Declassification Event. + + It is manifested in the 'Declassify On' line of a resource's classification authority block. + + ISOO has stated it should be a SINGLE value giving the longest protection. + + PERMISSIBLE VALUE + + The permissible value for this attribute is defined in the Controlled Value Enumeration: + + CVEnumISMN25X.xml + + + + + + + + + + + This attribute is used primarily at the resource level. + + A declassification marking of a source document that causes the current, derivative document to be exempted from automatic declassification. This element is always used in conjunction with the Date Of Exempted Source element. + + It is manifested only in the 'Declassify On' line of a document's classification authority block. + + ISOO has stated it should be a SINGLE value giving the longest protection. + + PERMISSIBLE VALUE + + The permissible value for this attribute is defined in the Controlled Value Enumeration: + + CVEnumISMSourceMarked.xml + + + + + + + + + + + This attribute is used primarily at the resource level. + + A specific year, month, and day of publication or release of a source document, or the most recent source document, that was itself marked with a declassification constraint. This element is always used in conjunction with the Type Of Exempted Source element. + + It is manifested only in the 'Declassify On' line of a resource's classification authority block. + + + + + + + + + + + + + + This attribute is used to designate which element has the ISM attributes representing the classification for the entire resource. + Every document must have at least one element with this indicator as true. It should be rare that a document has more than one. Mainly + this would occur in some sort of aggregator schema. In that unusual case the first one encountered in XML document order is the one used for + all constraint rules. + + + + + + + + + + + + + + This attribute is used to designate that an element's ISM attributes should not be used in a rollup. Generally + this is because the element is defining the security attributes of a remote object NOT indicating security constraints for + data in this document. This allows an Unclassified document to assert that some document not included has a Top Secret classification without + the TS attribute value causing rollup to make the document TS. + + + + + + + + + + + + + + This attribute is used to designate what date the document was produced on. This is the date that will be used by + various constraint rules to determine if the document meets all the business rules. It must be on the element where + resourceElement is true. + + + + + + + + + + + + + + A description of the reasons that the classification of this element is more restrictive than a simple roll-up of the + sub elements would result in. This acts as an indicator to rule engines that there is not accidental over classification + going on and to users that special care beyond what the portion marks reveal must be taken when using this data. Use of this + mark does not replace the need for the compilation reason being defined in the prose in accordance with ISOO Directive 1. + For example this would document why 3 Unclassified bullet items form a Secret List. + Without this reason being noted the above described document would be considered to be miss-marked and overclassified. + + + + + + + + + + + + + + + + + A categorization defining which of the required Notices, described in the CAPCO Register, is included in the element. + This attribute is an indicator that the element contains a Notice. The element could contain any structure the implementing + schema defined and details of the rendering would be up to the schema in question. + The permissible value for this attribute are defined in the Controlled Value Enumeration: + + CVEnumISMNotice.xml + + + + + + + + + + + + A Date associated with a notice such as the DoD Distribution notice date. + + + + + + + + + + + + + + + A Reason (less than 2048 chars) associated with a notice such as the DoD Distribution reason. + + + + + + + + + + + + + + + + + A Point of Contact POC (less than 2048 chars) associated with a notice such as the DoD Distribution POC. + + + + + + + + + + + + + + + An attribute group to be used on the root node of a schema implementing ISM. + ISM being entirely attributes based groups such as this are the only way to specify required use. + + + + + The version number of the DES. Should there be multiple specified in an instance document + the one at the root node is the one that will apply to the entire document. + + + + + + + + + An attribute group to be used on the element that represents the resource + node of an instance document. This node's ISM attributes would be used to + generate banner marks and the E.O. 12958 classification authority block. + Implementing Schemas might use this on the Root node or any other node. + + + + + + + + + + + + + + diff --git a/src/aws/osml/formats/sidd/__init__.py b/src/aws/osml/formats/sidd/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/aws/osml/formats/sidd/models/__init__.py b/src/aws/osml/formats/sidd/models/__init__.py new file mode 100644 index 0000000..2a05b62 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/__init__.py @@ -0,0 +1,690 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .sfa import ( + AbstractReferenceSystemType, + CurveType, + DatumType, + GeocentricCoordinateSystemType, + GeographicCoordinateSystemType, + GeometryCollectionType, + GeometryType, + LinearRingType, + LineStringType, +) +from .sfa import LineType as SfaLineType +from .sfa import MultiCurveType, MultiLineStringType, MultiPointType, MultiPolygonType, MultiSurfaceType +from .sfa import ParameterType as SfaParameterType +from .sfa import PointType +from .sfa import PolygonType as SfaPolygonType +from .sfa import ( + PolyhedralSurfaceType, + PrimeMeridianType, + ProjectedCoordinateSystemType, + ProjectionType, + ReferenceSystemType, + SpheriodType, + SurfaceType, + TriangleType, + TriangulatedIrregularNetworkType, + UNITType, +) +from .sicommon_types import AngleMagnitudeType as AngleMagnitudeType +from .sicommon_types import ArrayDoubleType as ArrayDoubleType +from .sicommon_types import CollectIdentifierType as CollectIdentifierType +from .sicommon_types import CollectionInfoType as CollectionInfoType +from .sicommon_types import ComplexType as ComplexType +from .sicommon_types import CornerStringType as CornerStringType +from .sicommon_types import ErrorFrameType as ErrorFrameType +from .sicommon_types import ErrorStatisticsType as ErrorStatisticsType +from .sicommon_types import GammaZeroSFIncidenceMapType +from .sicommon_types import ImageCreationType as ImageCreationType +from .sicommon_types import LatLonCornerStringType as LatLonCornerStringType +from .sicommon_types import LatLonCornerType as LatLonCornerType +from .sicommon_types import LatLonHAECornerType as LatLonHAECornerType +from .sicommon_types import LatLonType as LatLonType +from .sicommon_types import LatLonVertexType as LatLonVertexType +from .sicommon_types import LineType as SicommontypesLineType +from .sicommon_types import LLHCornerStringType as LLHCornerStringType +from .sicommon_types import LLHType as LLHType +from .sicommon_types import ModeIdentifierType as ModeIdentifierType +from .sicommon_types import ParameterType as SicommontypesParameterType +from .sicommon_types import PolarizationType +from .sicommon_types import Poly1DType as Poly1DType +from .sicommon_types import Poly2DType as Poly2DType +from .sicommon_types import PolyCoef1DType as PolyCoef1DType +from .sicommon_types import PolyCoef2DType as PolyCoef2DType +from .sicommon_types import PolygonType as SicommontypesPolygonType +from .sicommon_types import RadarModeType as RadarModeType +from .sicommon_types import RadiometricType as RadiometricType +from .sicommon_types import RangeAzimuthType as RangeAzimuthType +from .sicommon_types import ReferencePointType as ReferencePointType +from .sicommon_types import RowColDoubleType as RowColDoubleType +from .sicommon_types import RowColIntType as RowColIntType +from .sicommon_types import RowColVertexType as RowColVertexType +from .sicommon_types import SigmaZeroSFIncidenceMapType +from .sicommon_types import ValidDataType as ValidDataType +from .sicommon_types import XYZAttributeType as XYZAttributeType +from .sicommon_types import XYZPolyAttributeType as XYZPolyAttributeType +from .sicommon_types import XYZPolyType as XYZPolyType +from .sicommon_types import XYZType as XYZType +from .sicommon_types_v1_0 import AngleMagnitudeType as V10AngleMagnitudeType +from .sicommon_types_v1_0 import AngleZeroToExclusive360MagnitudeType +from .sicommon_types_v1_0 import ArrayDoubleType as V10ArrayDoubleType +from .sicommon_types_v1_0 import CollectIdentifierType as V10CollectIdentifierType +from .sicommon_types_v1_0 import CollectionInfoType as V10CollectionInfoType +from .sicommon_types_v1_0 import ComplexType as V10ComplexType +from .sicommon_types_v1_0 import CornerStringType as V10CornerStringType +from .sicommon_types_v1_0 import ErrorFrameType as V10ErrorFrameType +from .sicommon_types_v1_0 import ErrorStatisticsType as V10ErrorStatisticsType +from .sicommon_types_v1_0 import GeoInfo, GeoInfoType +from .sicommon_types_v1_0 import ImageCreationType as V10ImageCreationType +from .sicommon_types_v1_0 import LatLonCornerStringType as V10LatLonCornerStringType +from .sicommon_types_v1_0 import LatLonCornerType as V10LatLonCornerType +from .sicommon_types_v1_0 import LatLonHAECornerType as V10LatLonHAECornerType +from .sicommon_types_v1_0 import LatLonRestrictionType +from .sicommon_types_v1_0 import LatLonType as V10LatLonType +from .sicommon_types_v1_0 import LatLonVertexType as V10LatLonVertexType +from .sicommon_types_v1_0 import LineType as V10LineType +from .sicommon_types_v1_0 import LLHCornerStringType as V10LLHCornerStringType +from .sicommon_types_v1_0 import LLHType as V10LLHType +from .sicommon_types_v1_0 import MatchInfoType +from .sicommon_types_v1_0 import ModeIdentifierType as V10ModeIdentifierType +from .sicommon_types_v1_0 import NoiseLevelNoiseLevelType +from .sicommon_types_v1_0 import ParameterType as V10ParameterType +from .sicommon_types_v1_0 import Polarization1Typevalue +from .sicommon_types_v1_0 import Poly1DType as V10Poly1DType +from .sicommon_types_v1_0 import Poly2DType as V10Poly2DType +from .sicommon_types_v1_0 import PolyCoef1DType as V10PolyCoef1DType +from .sicommon_types_v1_0 import PolyCoef2DType as V10PolyCoef2DType +from .sicommon_types_v1_0 import PolygonType as V10PolygonType +from .sicommon_types_v1_0 import RadarModeType as V10RadarModeType +from .sicommon_types_v1_0 import RadiometricType as V10RadiometricType +from .sicommon_types_v1_0 import RadiometricTypeSigmaZeroSFIncidenceMap +from .sicommon_types_v1_0 import RangeAzimuthType as V10RangeAzimuthType +from .sicommon_types_v1_0 import ReferencePointType as V10ReferencePointType +from .sicommon_types_v1_0 import RowColDoubleType as V10RowColDoubleType +from .sicommon_types_v1_0 import RowColIntType as V10RowColIntType +from .sicommon_types_v1_0 import RowColVertexType as V10RowColVertexType +from .sicommon_types_v1_0 import ValidDataType as V10ValidDataType +from .sicommon_types_v1_0 import XYZAttributeType as V10XYZAttributeType +from .sicommon_types_v1_0 import XYZPolyAttributeType as V10XYZPolyAttributeType +from .sicommon_types_v1_0 import XYZPolyType as V10XYZPolyType +from .sicommon_types_v1_0 import XYZType as V10XYZType +from .sidd_v1_0_0 import SIDD as V1SIDD +from .sidd_v1_0_0 import AcheivedResolutionType as V1AcheivedResolutionType +from .sidd_v1_0_0 import AnnotationObjectType as V1AnnotationObjectType +from .sidd_v1_0_0 import AnnotationsType as V1AnnotationsType +from .sidd_v1_0_0 import AnnotationType as V1AnnotationType +from .sidd_v1_0_0 import BaseProjectionType as V1BaseProjectionType +from .sidd_v1_0_0 import ClassificationGuidanceType as V1ClassificationGuidanceType +from .sidd_v1_0_0 import ColorDisplayRemapType as V1ColorDisplayRemapType +from .sidd_v1_0_0 import CylindricalProjectionType as V1CylindricalProjectionType +from .sidd_v1_0_0 import DecimationMethodType +from .sidd_v1_0_0 import DownstreamReprocessingType as V1DownstreamReprocessingType +from .sidd_v1_0_0 import DRAHistogramOverridesType as V1DRAHistogramOverridesType +from .sidd_v1_0_0 import ExploitationFeaturesCollectionGeometryType as V1ExploitationFeaturesCollectionGeometryType +from .sidd_v1_0_0 import ExploitationFeaturesCollectionInformationType as V1ExploitationFeaturesCollectionInformationType +from .sidd_v1_0_0 import ExploitationFeaturesCollectionPhenomenologyType as V1ExploitationFeaturesCollectionPhenomenologyType +from .sidd_v1_0_0 import ExploitationFeaturesCollectionType as V1ExploitationFeaturesCollectionType +from .sidd_v1_0_0 import ExploitationFeaturesProductType as V1ExploitationFeaturesProductType +from .sidd_v1_0_0 import ExploitationFeaturesType as V1ExploitationFeaturesType +from .sidd_v1_0_0 import FootprintType, GeographicAndTargetType, GeographicCoverageType, GeographicInformationType +from .sidd_v1_0_0 import GeographicProjectionType as V1GeographicProjectionType +from .sidd_v1_0_0 import GeometricChipType as V1GeometricChipType +from .sidd_v1_0_0 import InputROIType as V1InputROIType +from .sidd_v1_0_0 import Lookup3TableType as V1Lookup3TableType +from .sidd_v1_0_0 import LookupTableType as V1LookupTableType +from .sidd_v1_0_0 import MagnificationMethodType +from .sidd_v1_0_0 import MeasurableProjectionType as V1MeasurableProjectionType +from .sidd_v1_0_0 import MeasurementType as V1MeasurementType +from .sidd_v1_0_0 import MonitorCompensationAppliedType +from .sidd_v1_0_0 import MonochromeDisplayRemapType as V1MonochromeDisplayRemapType +from .sidd_v1_0_0 import PixelType as V1PixelType +from .sidd_v1_0_0 import PlaneProjectionType as V1PlaneProjectionType +from .sidd_v1_0_0 import PolynomialProjectionType as V1PolynomialProjectionType +from .sidd_v1_0_0 import ProcessingEventType as V1ProcessingEventType +from .sidd_v1_0_0 import ProcessingModuleType as V1ProcessingModuleType +from .sidd_v1_0_0 import ProcessorInformationType as V1ProcessorInformationType +from .sidd_v1_0_0 import ProductClassificationType as V1ProductClassificationType +from .sidd_v1_0_0 import ProductCreationType as V1ProductCreationType +from .sidd_v1_0_0 import ProductDisplayType as V1ProductDisplayType +from .sidd_v1_0_0 import ProductPlaneType as V1ProductPlaneType +from .sidd_v1_0_0 import ProductProcessingType as V1ProductProcessingType +from .sidd_v1_0_0 import RemapChoiceType as V1RemapChoiceType +from .sidd_v1_0_0 import TargetInformationType +from .sidd_v1_0_0 import TxRcvPolarizationType as V1TxRcvPolarizationType +from .sidd_v2_0_0 import SIDD as V2SIDD +from .sidd_v2_0_0 import AccuracyType as V2AccuracyType +from .sidd_v2_0_0 import AcheivedResolutionType as V2AcheivedResolutionType +from .sidd_v2_0_0 import AnnotationObjectType as V2AnnotationObjectType +from .sidd_v2_0_0 import AnnotationsType as V2AnnotationsType +from .sidd_v2_0_0 import AnnotationType as V2AnnotationType +from .sidd_v2_0_0 import BandEqualizationType as V2BandEqualizationType +from .sidd_v2_0_0 import BankCustomType as V2BankCustomType +from .sidd_v2_0_0 import BaseProjectionType as V2BaseProjectionType +from .sidd_v2_0_0 import ClassificationGuidanceType as V2ClassificationGuidanceType +from .sidd_v2_0_0 import ColorDisplayRemapType as V2ColorDisplayRemapType +from .sidd_v2_0_0 import ColorManagementModuleType as V2ColorManagementModuleType +from .sidd_v2_0_0 import ColorSpaceTransformType as V2ColorSpaceTransformType +from .sidd_v2_0_0 import CompressionType as V2CompressionType +from .sidd_v2_0_0 import CustomLookupType as V2CustomLookupType +from .sidd_v2_0_0 import CylindricalProjectionType as V2CylindricalProjectionType +from .sidd_v2_0_0 import DigitalElevationDataType as V2DigitalElevationDataType +from .sidd_v2_0_0 import DownsamplingMethodType as V2DownsamplingMethodType +from .sidd_v2_0_0 import DownstreamReprocessingType as V2DownstreamReprocessingType +from .sidd_v2_0_0 import DRAHistogramOverridesType as V2DRAHistogramOverridesType +from .sidd_v2_0_0 import DRAOverrides as V2DRAOverrides +from .sidd_v2_0_0 import DRAParameters as V2DRAParameters +from .sidd_v2_0_0 import DynamicRangeAdjustmentType as V2DynamicRangeAdjustmentType +from .sidd_v2_0_0 import EarthModelType as V2EarthModelType +from .sidd_v2_0_0 import EqualizationAlgorithmType as V2EqualizationAlgorithmType +from .sidd_v2_0_0 import ExploitationFeaturesCollectionGeometryType as V2ExploitationFeaturesCollectionGeometryType +from .sidd_v2_0_0 import ExploitationFeaturesCollectionInformationType as V2ExploitationFeaturesCollectionInformationType +from .sidd_v2_0_0 import ExploitationFeaturesCollectionPhenomenologyType as V2ExploitationFeaturesCollectionPhenomenologyType +from .sidd_v2_0_0 import ExploitationFeaturesCollectionType as V2ExploitationFeaturesCollectionType +from .sidd_v2_0_0 import ExploitationFeaturesProductType as V2ExploitationFeaturesProductType +from .sidd_v2_0_0 import ExploitationFeaturesType as V2ExploitationFeaturesType +from .sidd_v2_0_0 import FilterBankCoefType as V2FilterBankCoefType +from .sidd_v2_0_0 import FilterBankType as V2FilterBankType +from .sidd_v2_0_0 import FilterDatabaseNameType as V2FilterDatabaseNameType +from .sidd_v2_0_0 import FilterKernelCoefType as V2FilterKernelCoefType +from .sidd_v2_0_0 import FilterKernelType as V2FilterKernelType +from .sidd_v2_0_0 import FilterOperationType as V2FilterOperationType +from .sidd_v2_0_0 import FilterType as V2FilterType +from .sidd_v2_0_0 import GeoDataType as V2GeoDataType +from .sidd_v2_0_0 import GeographicCoordinatesType as V2GeographicCoordinatesType +from .sidd_v2_0_0 import GeographicProjectionType as V2GeographicProjectionType +from .sidd_v2_0_0 import GeometricChipType as V2GeometricChipType +from .sidd_v2_0_0 import GeometricTransformType as V2GeometricTransformType +from .sidd_v2_0_0 import GeopositioningType as V2GeopositioningType +from .sidd_v2_0_0 import GeopositioningTypeCoordinateSystemType as V2GeopositioningTypeCoordinateSystemType +from .sidd_v2_0_0 import GeopositioningTypeGeodeticDatum as V2GeopositioningTypeGeodeticDatum +from .sidd_v2_0_0 import GeopositioningTypeReferenceEllipsoid as V2GeopositioningTypeReferenceEllipsoid +from .sidd_v2_0_0 import GeopositioningTypeSoundingDatum as V2GeopositioningTypeSoundingDatum +from .sidd_v2_0_0 import GeopositioningTypeVerticalDatum as V2GeopositioningTypeVerticalDatum +from .sidd_v2_0_0 import ImageCornersType as V2ImageCornersType +from .sidd_v2_0_0 import InputROIType as V2InputROIType +from .sidd_v2_0_0 import InteractiveProcessingType as V2InteractiveProcessingType +from .sidd_v2_0_0 import J2KSubtype as V2J2KSubtype +from .sidd_v2_0_0 import J2KType as V2J2KType +from .sidd_v2_0_0 import KernelCustomType as V2KernelCustomType +from .sidd_v2_0_0 import LayerInfoType as V2LayerInfoType +from .sidd_v2_0_0 import LayerType as V2LayerType +from .sidd_v2_0_0 import Lookup3TableType as V2Lookup3TableType +from .sidd_v2_0_0 import LookupTableType as V2LookupTableType +from .sidd_v2_0_0 import LUTInfoType as V2LUTInfoType +from .sidd_v2_0_0 import MeasurableProjectionType as V2MeasurableProjectionType +from .sidd_v2_0_0 import MeasurementType as V2MeasurementType +from .sidd_v2_0_0 import MeasurementTypeARPFlag as V2MeasurementTypeARPFlag +from .sidd_v2_0_0 import MonochromeDisplayRemapType as V2MonochromeDisplayRemapType +from .sidd_v2_0_0 import NewLookupTableType as V2NewLookupTableType +from .sidd_v2_0_0 import NonInteractiveProcessingType as V2NonInteractiveProcessingType +from .sidd_v2_0_0 import Orientation as V2Orientation +from .sidd_v2_0_0 import PixelType as V2PixelType +from .sidd_v2_0_0 import PlaneProjectionType as V2PlaneProjectionType +from .sidd_v2_0_0 import PolygonType as V2PolygonType +from .sidd_v2_0_0 import PolynomialProjectionType as V2PolynomialProjectionType +from .sidd_v2_0_0 import PositionalAccuracyType as V2PositionalAccuracyType +from .sidd_v2_0_0 import PredefinedFilterType as V2PredefinedFilterType +from .sidd_v2_0_0 import PredefinedLookupType as V2PredefinedLookupType +from .sidd_v2_0_0 import ProcessingEventType as V2ProcessingEventType +from .sidd_v2_0_0 import ProcessingModuleType as V2ProcessingModuleType +from .sidd_v2_0_0 import ProcessorInformationType as V2ProcessorInformationType +from .sidd_v2_0_0 import ProcTxRcvPolarizationType as V2ProcTxRcvPolarizationType +from .sidd_v2_0_0 import ProductClassificationType as V2ProductClassificationType +from .sidd_v2_0_0 import ProductCreationType as V2ProductCreationType +from .sidd_v2_0_0 import ProductDisplayType as V2ProductDisplayType +from .sidd_v2_0_0 import ProductGenerationOptionsType as V2ProductGenerationOptionsType +from .sidd_v2_0_0 import ProductPlaneType as V2ProductPlaneType +from .sidd_v2_0_0 import ProductProcessingType as V2ProductProcessingType +from .sidd_v2_0_0 import RangeAdjustmentAlgorithmType as V2RangeAdjustmentAlgorithmType +from .sidd_v2_0_0 import RemapChoiceType as V2RemapChoiceType +from .sidd_v2_0_0 import RenderingIntentType as V2RenderingIntentType +from .sidd_v2_0_0 import RRDSType as V2RRDSType +from .sidd_v2_0_0 import ScalingType as V2ScalingType +from .sidd_v2_0_0 import ShadowDirectionType as V2ShadowDirectionType +from .sidd_v2_0_0 import SharpnessEnhancementType as V2SharpnessEnhancementType +from .sidd_v2_0_0 import TxRcvPolarizationType as V2TxRcvPolarizationType +from .sidd_v2_0_0 import ValidDataType as V2ValidDataType +from .sidd_v3_0_0 import SIDD as V3SIDD +from .sidd_v3_0_0 import AccuracyType as V3AccuracyType +from .sidd_v3_0_0 import AcheivedResolutionType as V3AcheivedResolutionType +from .sidd_v3_0_0 import AnnotationObjectType as V3AnnotationObjectType +from .sidd_v3_0_0 import AnnotationsType as V3AnnotationsType +from .sidd_v3_0_0 import AnnotationType as V3AnnotationType +from .sidd_v3_0_0 import BandEqualizationType as V3BandEqualizationType +from .sidd_v3_0_0 import BankCustomType as V3BankCustomType +from .sidd_v3_0_0 import BaseProjectionType as V3BaseProjectionType +from .sidd_v3_0_0 import ClassificationGuidanceType as V3ClassificationGuidanceType +from .sidd_v3_0_0 import ColorDisplayRemapType as V3ColorDisplayRemapType +from .sidd_v3_0_0 import ColorManagementModuleType as V3ColorManagementModuleType +from .sidd_v3_0_0 import ColorSpaceTransformType as V3ColorSpaceTransformType +from .sidd_v3_0_0 import CompressionType as V3CompressionType +from .sidd_v3_0_0 import CustomLookupType as V3CustomLookupType +from .sidd_v3_0_0 import CylindricalProjectionType as V3CylindricalProjectionType +from .sidd_v3_0_0 import DigitalElevationDataType as V3DigitalElevationDataType +from .sidd_v3_0_0 import DownsamplingMethodType as V3DownsamplingMethodType +from .sidd_v3_0_0 import DownstreamReprocessingType as V3DownstreamReprocessingType +from .sidd_v3_0_0 import DRAHistogramOverridesType as V3DRAHistogramOverridesType +from .sidd_v3_0_0 import DRAOverrides as V3DRAOverrides +from .sidd_v3_0_0 import DRAParameters as V3DRAParameters +from .sidd_v3_0_0 import DynamicRangeAdjustmentType as V3DynamicRangeAdjustmentType +from .sidd_v3_0_0 import EarthModelType as V3EarthModelType +from .sidd_v3_0_0 import EqualizationAlgorithmType as V3EqualizationAlgorithmType +from .sidd_v3_0_0 import ExploitationFeaturesCollectionGeometryType as V3ExploitationFeaturesCollectionGeometryType +from .sidd_v3_0_0 import ExploitationFeaturesCollectionInformationType as V3ExploitationFeaturesCollectionInformationType +from .sidd_v3_0_0 import ExploitationFeaturesCollectionPhenomenologyType as V3ExploitationFeaturesCollectionPhenomenologyType +from .sidd_v3_0_0 import ExploitationFeaturesCollectionType as V3ExploitationFeaturesCollectionType +from .sidd_v3_0_0 import ExploitationFeaturesProductType as V3ExploitationFeaturesProductType +from .sidd_v3_0_0 import ExploitationFeaturesType as V3ExploitationFeaturesType +from .sidd_v3_0_0 import FilterBankCoefType as V3FilterBankCoefType +from .sidd_v3_0_0 import FilterBankType as V3FilterBankType +from .sidd_v3_0_0 import FilterDatabaseNameType as V3FilterDatabaseNameType +from .sidd_v3_0_0 import FilterKernelCoefType as V3FilterKernelCoefType +from .sidd_v3_0_0 import FilterKernelType as V3FilterKernelType +from .sidd_v3_0_0 import FilterOperationType as V3FilterOperationType +from .sidd_v3_0_0 import FilterType as V3FilterType +from .sidd_v3_0_0 import GeoDataType as V3GeoDataType +from .sidd_v3_0_0 import GeographicCoordinatesType as V3GeographicCoordinatesType +from .sidd_v3_0_0 import GeographicProjectionType as V3GeographicProjectionType +from .sidd_v3_0_0 import GeometricChipType as V3GeometricChipType +from .sidd_v3_0_0 import GeometricTransformType as V3GeometricTransformType +from .sidd_v3_0_0 import GeopositioningType as V3GeopositioningType +from .sidd_v3_0_0 import GeopositioningTypeCoordinateSystemType as V3GeopositioningTypeCoordinateSystemType +from .sidd_v3_0_0 import GeopositioningTypeGeodeticDatum as V3GeopositioningTypeGeodeticDatum +from .sidd_v3_0_0 import GeopositioningTypeReferenceEllipsoid as V3GeopositioningTypeReferenceEllipsoid +from .sidd_v3_0_0 import GeopositioningTypeSoundingDatum as V3GeopositioningTypeSoundingDatum +from .sidd_v3_0_0 import GeopositioningTypeVerticalDatum as V3GeopositioningTypeVerticalDatum +from .sidd_v3_0_0 import ImageCornersType as V3ImageCornersType +from .sidd_v3_0_0 import InputROIType as V3InputROIType +from .sidd_v3_0_0 import InteractiveProcessingType as V3InteractiveProcessingType +from .sidd_v3_0_0 import J2KSubtype as V3J2KSubtype +from .sidd_v3_0_0 import J2KType as V3J2KType +from .sidd_v3_0_0 import KernelCustomType as V3KernelCustomType +from .sidd_v3_0_0 import LayerInfoType as V3LayerInfoType +from .sidd_v3_0_0 import LayerType as V3LayerType +from .sidd_v3_0_0 import Lookup3TableType as V3Lookup3TableType +from .sidd_v3_0_0 import LookupTableType as V3LookupTableType +from .sidd_v3_0_0 import LUTInfoType as V3LUTInfoType +from .sidd_v3_0_0 import MeasurableProjectionType as V3MeasurableProjectionType +from .sidd_v3_0_0 import MeasurementType as V3MeasurementType +from .sidd_v3_0_0 import MeasurementTypeARPFlag as V3MeasurementTypeARPFlag +from .sidd_v3_0_0 import MonochromeDisplayRemapType as V3MonochromeDisplayRemapType +from .sidd_v3_0_0 import NewLookupTableType as V3NewLookupTableType +from .sidd_v3_0_0 import NonInteractiveProcessingType as V3NonInteractiveProcessingType +from .sidd_v3_0_0 import Orientation as V3Orientation +from .sidd_v3_0_0 import PixelType as V3PixelType +from .sidd_v3_0_0 import PlaneProjectionType as V3PlaneProjectionType +from .sidd_v3_0_0 import PolygonType as V3PolygonType +from .sidd_v3_0_0 import PolynomialProjectionType as V3PolynomialProjectionType +from .sidd_v3_0_0 import PositionalAccuracyType as V3PositionalAccuracyType +from .sidd_v3_0_0 import PredefinedFilterType as V3PredefinedFilterType +from .sidd_v3_0_0 import PredefinedLookupType as V3PredefinedLookupType +from .sidd_v3_0_0 import ProcessingEventType as V3ProcessingEventType +from .sidd_v3_0_0 import ProcessingModuleType as V3ProcessingModuleType +from .sidd_v3_0_0 import ProcessorInformationType as V3ProcessorInformationType +from .sidd_v3_0_0 import ProcTxRcvPolarizationType as V3ProcTxRcvPolarizationType +from .sidd_v3_0_0 import ProductClassificationType as V3ProductClassificationType +from .sidd_v3_0_0 import ProductCreationType as V3ProductCreationType +from .sidd_v3_0_0 import ProductDisplayType as V3ProductDisplayType +from .sidd_v3_0_0 import ProductGenerationOptionsType as V3ProductGenerationOptionsType +from .sidd_v3_0_0 import ProductPlaneType as V3ProductPlaneType +from .sidd_v3_0_0 import ProductProcessingType as V3ProductProcessingType +from .sidd_v3_0_0 import RangeAdjustmentAlgorithmType as V3RangeAdjustmentAlgorithmType +from .sidd_v3_0_0 import RemapChoiceType as V3RemapChoiceType +from .sidd_v3_0_0 import RenderingIntentType as V3RenderingIntentType +from .sidd_v3_0_0 import RRDSType as V3RRDSType +from .sidd_v3_0_0 import ScalingType as V3ScalingType +from .sidd_v3_0_0 import ShadowDirectionType as V3ShadowDirectionType +from .sidd_v3_0_0 import SharpnessEnhancementType as V3SharpnessEnhancementType +from .sidd_v3_0_0 import TxRcvPolarizationType as V3TxRcvPolarizationType +from .sidd_v3_0_0 import ValidDataType as V3ValidDataType + +__all__ = [ + "AbstractReferenceSystemType", + "CurveType", + "DatumType", + "GeocentricCoordinateSystemType", + "GeographicCoordinateSystemType", + "GeometryCollectionType", + "GeometryType", + "LineStringType", + "SfaLineType", + "LinearRingType", + "MultiCurveType", + "MultiLineStringType", + "MultiPointType", + "MultiPolygonType", + "MultiSurfaceType", + "SfaParameterType", + "PointType", + "SfaPolygonType", + "PolyhedralSurfaceType", + "PrimeMeridianType", + "ProjectedCoordinateSystemType", + "ProjectionType", + "ReferenceSystemType", + "SpheriodType", + "SurfaceType", + "TriangleType", + "TriangulatedIrregularNetworkType", + "UNITType", + "AngleMagnitudeType", + "ArrayDoubleType", + "CollectIdentifierType", + "CollectionInfoType", + "ComplexType", + "CornerStringType", + "ErrorFrameType", + "ErrorStatisticsType", + "GammaZeroSFIncidenceMapType", + "ImageCreationType", + "LLHCornerStringType", + "LLHType", + "LatLonCornerStringType", + "LatLonCornerType", + "LatLonHAECornerType", + "LatLonType", + "LatLonVertexType", + "SicommontypesLineType", + "ModeIdentifierType", + "SicommontypesParameterType", + "PolarizationType", + "Poly1DType", + "Poly2DType", + "PolyCoef1DType", + "PolyCoef2DType", + "SicommontypesPolygonType", + "RadarModeType", + "RadiometricType", + "RangeAzimuthType", + "ReferencePointType", + "RowColDoubleType", + "RowColIntType", + "RowColVertexType", + "SigmaZeroSFIncidenceMapType", + "ValidDataType", + "XYZAttributeType", + "XYZPolyAttributeType", + "XYZPolyType", + "XYZType", + "V10AngleMagnitudeType", + "AngleZeroToExclusive360MagnitudeType", + "V10ArrayDoubleType", + "V10CollectIdentifierType", + "V10CollectionInfoType", + "V10ComplexType", + "V10CornerStringType", + "V10ErrorFrameType", + "V10ErrorStatisticsType", + "GeoInfo", + "GeoInfoType", + "V10ImageCreationType", + "V10LLHCornerStringType", + "V10LLHType", + "V10LatLonCornerStringType", + "V10LatLonCornerType", + "V10LatLonHAECornerType", + "LatLonRestrictionType", + "V10LatLonType", + "V10LatLonVertexType", + "V10LineType", + "MatchInfoType", + "V10ModeIdentifierType", + "NoiseLevelNoiseLevelType", + "V10ParameterType", + "Polarization1Typevalue", + "V10Poly1DType", + "V10Poly2DType", + "V10PolyCoef1DType", + "V10PolyCoef2DType", + "V10PolygonType", + "V10RadarModeType", + "V10RadiometricType", + "RadiometricTypeSigmaZeroSFIncidenceMap", + "V10RangeAzimuthType", + "V10ReferencePointType", + "V10RowColDoubleType", + "V10RowColIntType", + "V10RowColVertexType", + "V10ValidDataType", + "V10XYZAttributeType", + "V10XYZPolyAttributeType", + "V10XYZPolyType", + "V10XYZType", + "V1AcheivedResolutionType", + "V1AnnotationObjectType", + "V1AnnotationType", + "V1AnnotationsType", + "V1BaseProjectionType", + "V1ClassificationGuidanceType", + "V1ColorDisplayRemapType", + "V1CylindricalProjectionType", + "V1DRAHistogramOverridesType", + "DecimationMethodType", + "V1DownstreamReprocessingType", + "V1ExploitationFeaturesCollectionGeometryType", + "V1ExploitationFeaturesCollectionInformationType", + "V1ExploitationFeaturesCollectionPhenomenologyType", + "V1ExploitationFeaturesCollectionType", + "V1ExploitationFeaturesProductType", + "V1ExploitationFeaturesType", + "FootprintType", + "GeographicAndTargetType", + "GeographicCoverageType", + "GeographicInformationType", + "V1GeographicProjectionType", + "V1GeometricChipType", + "V1InputROIType", + "V1Lookup3TableType", + "V1LookupTableType", + "MagnificationMethodType", + "V1MeasurableProjectionType", + "V1MeasurementType", + "MonitorCompensationAppliedType", + "V1MonochromeDisplayRemapType", + "V1PixelType", + "V1PlaneProjectionType", + "V1PolynomialProjectionType", + "V1ProcessingEventType", + "V1ProcessingModuleType", + "V1ProcessorInformationType", + "V1ProductClassificationType", + "V1ProductCreationType", + "V1ProductDisplayType", + "V1ProductPlaneType", + "V1ProductProcessingType", + "V1RemapChoiceType", + "V1SIDD", + "TargetInformationType", + "V1TxRcvPolarizationType", + "V2AccuracyType", + "V2AcheivedResolutionType", + "V2AnnotationObjectType", + "V2AnnotationType", + "V2AnnotationsType", + "V2BandEqualizationType", + "V2BankCustomType", + "V2BaseProjectionType", + "V2ClassificationGuidanceType", + "V2ColorDisplayRemapType", + "V2ColorManagementModuleType", + "V2ColorSpaceTransformType", + "V2CompressionType", + "V2CustomLookupType", + "V2CylindricalProjectionType", + "V2DRAHistogramOverridesType", + "V2DRAOverrides", + "V2DRAParameters", + "V2DigitalElevationDataType", + "V2DownsamplingMethodType", + "V2DownstreamReprocessingType", + "V2DynamicRangeAdjustmentType", + "V2EarthModelType", + "V2EqualizationAlgorithmType", + "V2ExploitationFeaturesCollectionGeometryType", + "V2ExploitationFeaturesCollectionInformationType", + "V2ExploitationFeaturesCollectionPhenomenologyType", + "V2ExploitationFeaturesCollectionType", + "V2ExploitationFeaturesProductType", + "V2ExploitationFeaturesType", + "V2FilterBankCoefType", + "V2FilterBankType", + "V2FilterDatabaseNameType", + "V2FilterKernelCoefType", + "V2FilterKernelType", + "V2FilterOperationType", + "V2FilterType", + "V2GeoDataType", + "V2GeographicCoordinatesType", + "V2GeographicProjectionType", + "V2GeometricChipType", + "V2GeometricTransformType", + "V2GeopositioningType", + "V2GeopositioningTypeCoordinateSystemType", + "V2GeopositioningTypeGeodeticDatum", + "V2GeopositioningTypeReferenceEllipsoid", + "V2GeopositioningTypeSoundingDatum", + "V2GeopositioningTypeVerticalDatum", + "V2ImageCornersType", + "V2InputROIType", + "V2InteractiveProcessingType", + "V2J2KSubtype", + "V2J2KType", + "V2KernelCustomType", + "V2LUTInfoType", + "V2LayerInfoType", + "V2LayerType", + "V2Lookup3TableType", + "V2LookupTableType", + "V2MeasurableProjectionType", + "V2MeasurementType", + "V2MeasurementTypeARPFlag", + "V2MonochromeDisplayRemapType", + "V2NewLookupTableType", + "V2NonInteractiveProcessingType", + "V2Orientation", + "V2PixelType", + "V2PlaneProjectionType", + "V2PolygonType", + "V2PolynomialProjectionType", + "V2PositionalAccuracyType", + "V2PredefinedFilterType", + "V2PredefinedLookupType", + "V2ProcTxRcvPolarizationType", + "V2ProcessingEventType", + "V2ProcessingModuleType", + "V2ProcessorInformationType", + "V2ProductClassificationType", + "V2ProductCreationType", + "V2ProductDisplayType", + "V2ProductGenerationOptionsType", + "V2ProductPlaneType", + "V2ProductProcessingType", + "V2RRDSType", + "V2RangeAdjustmentAlgorithmType", + "V2RemapChoiceType", + "V2RenderingIntentType", + "V2SIDD", + "V2ScalingType", + "V2ShadowDirectionType", + "V2SharpnessEnhancementType", + "V2TxRcvPolarizationType", + "V2ValidDataType", + "V3AccuracyType", + "V3AcheivedResolutionType", + "V3AnnotationObjectType", + "V3AnnotationType", + "V3AnnotationsType", + "V3BandEqualizationType", + "V3BankCustomType", + "V3BaseProjectionType", + "V3ClassificationGuidanceType", + "V3ColorDisplayRemapType", + "V3ColorManagementModuleType", + "V3ColorSpaceTransformType", + "V3CompressionType", + "V3CustomLookupType", + "V3CylindricalProjectionType", + "V3DRAHistogramOverridesType", + "V3DRAOverrides", + "V3DRAParameters", + "V3DigitalElevationDataType", + "V3DownsamplingMethodType", + "V3DownstreamReprocessingType", + "V3DynamicRangeAdjustmentType", + "V3EarthModelType", + "V3EqualizationAlgorithmType", + "V3ExploitationFeaturesCollectionGeometryType", + "V3ExploitationFeaturesCollectionInformationType", + "V3ExploitationFeaturesCollectionPhenomenologyType", + "V3ExploitationFeaturesCollectionType", + "V3ExploitationFeaturesProductType", + "V3ExploitationFeaturesType", + "V3FilterBankCoefType", + "V3FilterBankType", + "V3FilterDatabaseNameType", + "V3FilterKernelCoefType", + "V3FilterKernelType", + "V3FilterOperationType", + "V3FilterType", + "V3GeoDataType", + "V3GeographicCoordinatesType", + "V3GeographicProjectionType", + "V3GeometricChipType", + "V3GeometricTransformType", + "V3GeopositioningType", + "V3GeopositioningTypeCoordinateSystemType", + "V3GeopositioningTypeGeodeticDatum", + "V3GeopositioningTypeReferenceEllipsoid", + "V3GeopositioningTypeSoundingDatum", + "V3GeopositioningTypeVerticalDatum", + "V3ImageCornersType", + "V3InputROIType", + "V3InteractiveProcessingType", + "V3J2KSubtype", + "V3J2KType", + "V3KernelCustomType", + "V3LUTInfoType", + "V3LayerInfoType", + "V3LayerType", + "V3Lookup3TableType", + "V3LookupTableType", + "V3MeasurableProjectionType", + "V3MeasurementType", + "V3MeasurementTypeARPFlag", + "V3MonochromeDisplayRemapType", + "V3NewLookupTableType", + "V3NonInteractiveProcessingType", + "V3Orientation", + "V3PixelType", + "V3PlaneProjectionType", + "V3PolygonType", + "V3PolynomialProjectionType", + "V3PositionalAccuracyType", + "V3PredefinedFilterType", + "V3PredefinedLookupType", + "V3ProcTxRcvPolarizationType", + "V3ProcessingEventType", + "V3ProcessingModuleType", + "V3ProcessorInformationType", + "V3ProductClassificationType", + "V3ProductCreationType", + "V3ProductDisplayType", + "V3ProductGenerationOptionsType", + "V3ProductPlaneType", + "V3ProductProcessingType", + "V3RRDSType", + "V3RangeAdjustmentAlgorithmType", + "V3RemapChoiceType", + "V3RenderingIntentType", + "V3SIDD", + "V3ScalingType", + "V3ShadowDirectionType", + "V3SharpnessEnhancementType", + "V3TxRcvPolarizationType", + "V3ValidDataType", +] diff --git a/src/aws/osml/formats/sidd/models/external/__init__.py b/src/aws/osml/formats/sidd/models/external/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism/__init__.py b/src/aws/osml/formats/sidd/models/external/ism/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/__init__.py b/src/aws/osml/formats/sidd/models/external/ism/schema/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/__init__.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/__init__.py new file mode 100644 index 0000000..8fc761a --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/__init__.py @@ -0,0 +1,32 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .cvenum_ism25_x import CVEnumISM25X +from .cvenum_ismclassification_all import CVEnumISMClassificationAll +from .cvenum_ismcomplies_with import CVEnumISMCompliesWithValues +from .cvenum_ismdissem import CVEnumISMDissemValuesvalue +from .cvenum_ismfgiopen import CVEnumISMFGIOpenValues +from .cvenum_ismfgiprotected import CVEnumISMFGIProtectedValues +from .cvenum_ismnon_ic import CVEnumISMNonICValues +from .cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .cvenum_ismowner_producer import CVEnumISMOwnerProducerValues +from .cvenum_ismrel_to import CVEnumISMRelToValues +from .cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue +from .cvenum_ismsource_marked import CVEnumISMSourceMarked + +__all__ = [ + "CVEnumISM25X", + "CVEnumISMClassificationAll", + "CVEnumISMCompliesWithValues", + "CVEnumISMDissemValuesvalue", + "CVEnumISMFGIOpenValues", + "CVEnumISMFGIProtectedValues", + "CVEnumISMNonICValues", + "CVEnumISMNonUSControlsValues", + "CVEnumISMOwnerProducerValues", + "CVEnumISMRelToValues", + "CVEnumISMSCIControlsValuesvalue", + "CVEnumISMSourceMarked", +] diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ism25_x.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ism25_x.py new file mode 100644 index 0000000..0e0b244 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ism25_x.py @@ -0,0 +1,47 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISM25X(Enum): + """(U) All currently authorized 25X values. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISM25X.xml + + :cvar VALUE_25_X1: Reveal information about the application of an intelligence source or method. + :cvar VALUE_25_X1_HUMAN: Reveal the identity of a confidential human source or human intelligence source. + :cvar VALUE_25_X2: Reveal information that would assist in the development or use of weapons of mass + destruction. + :cvar VALUE_25_X3: Reveal information that would impair U.S. cryptologic systems or activities. + :cvar VALUE_25_X4: Reveal information that would impair the application of state-of-the-art technology within + a U.S. weapon system. + :cvar VALUE_25_X5: Reveal actual U.S. military war plans that remain in effect. + :cvar VALUE_25_X6: Reveal information, including foreign government information, that would seriously and + demonstrably impair relations between the United States and a foreign government or seriously and + demonstrably undermine ongoing diplomatic activities of the United States. + :cvar VALUE_25_X7: Reveal information that would clearly and demonstrably impair the current ability of + United States Government officials to protect the President, Vice President, or other protectees for whom + protection services, in the interest of national security, are authorized. + :cvar VALUE_25_X8: Reveal information that would seriously and demonstrably impair current national security + emergency preparedness plans or reveal current vulnerabilities of systems, installations, + infrastructures, or projects relating to the national security. + :cvar VALUE_25_X9: Violate a statue, treaty, or international agreement. + """ + + VALUE_25_X1 = "25X1" + VALUE_25_X1_HUMAN = "25X1-human" + VALUE_25_X2 = "25X2" + VALUE_25_X3 = "25X3" + VALUE_25_X4 = "25X4" + VALUE_25_X5 = "25X5" + VALUE_25_X6 = "25X6" + VALUE_25_X7 = "25X7" + VALUE_25_X8 = "25X8" + VALUE_25_X9 = "25X9" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismclassification_all.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismclassification_all.py new file mode 100644 index 0000000..707fa26 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismclassification_all.py @@ -0,0 +1,28 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMClassificationAll(Enum): + """(U) All currently valid classification marks + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMClassificationAll.xml + + :cvar R: RESTRICTED + :cvar C: CONFIDENTIAL + :cvar S: SECRET + :cvar TS: TOP SECRET + :cvar U: UNCLASSIFIED + """ + + R = "R" + C = "C" + S = "S" + TS = "TS" + U = "U" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismcomplies_with.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismcomplies_with.py new file mode 100644 index 0000000..04796c3 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismcomplies_with.py @@ -0,0 +1,22 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMCompliesWithValues(Enum): + """(U) Current rule set names that documents may comply with + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMCompliesWith.xml + + :cvar ICD_710: Document claims compliance with the rules in ICD-710 that have been encoded into ISM + :cvar DO_D5230_24: Document claims compliance with the rules in DoD5230.24 that have been encoded into ISM + """ + + ICD_710 = "ICD-710" + DO_D5230_24 = "DoD5230.24" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismdissem.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismdissem.py new file mode 100644 index 0000000..f21dcb3 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismdissem.py @@ -0,0 +1,46 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMDissemValuesvalue(Enum): + """ + :cvar FOUO: FOR OFFICIAL USE ONLY + :cvar OC: ORIGINATOR CONTROLLED + :cvar IMC: CONTROLLED IMAGERY + :cvar SAMI: SOURCES AND METHODS INFORMATION + :cvar NF: NOT RELEASABLE TO FOREIGN NATIONALS + :cvar PR: CAUTION-PROPRIETARY INFORMATION INVOLVED + :cvar REL: AUTHORIZED FOR RELEASE TO + :cvar RELIDO: RELEASABLE BY INFORMATION DISCLOSURE OFFICIAL + :cvar RD: RESTRICTED DATA + :cvar RD_CNWDI: RD-CRITICAL NUCLEAR WEAPON DESIGN INFORMATION + :cvar FRD: FORMERLY RESTRICTED DATA + :cvar DCNI: DoD CONTROLLED NUCLEAR INFORMATION + :cvar UCNI: DoE CONTROLLED NUCLEAR INFORMATION + :cvar EYES: EYES ONLY + :cvar DSEN: DEA SENSITIVE + :cvar FISA: FOREIGN INTELLIGENCE SURVEILLANCE ACT + """ + + FOUO = "FOUO" + OC = "OC" + IMC = "IMC" + SAMI = "SAMI" + NF = "NF" + PR = "PR" + REL = "REL" + RELIDO = "RELIDO" + RD = "RD" + RD_CNWDI = "RD-CNWDI" + FRD = "FRD" + DCNI = "DCNI" + UCNI = "UCNI" + EYES = "EYES" + DSEN = "DSEN" + FISA = "FISA" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiopen.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiopen.py new file mode 100644 index 0000000..5dc7a10 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiopen.py @@ -0,0 +1,561 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMFGIOpenValues(Enum): + """(U) UNKNOWN followed by all currently valid ISO Trigraphs except USA in + alphabetical order by Trigraph, followed by all currently valid CAPCO Coalition + tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMFGIOpen.xml + + :cvar UNKNOWN: Unknown + :cvar ABW: Trigraph for Aruba + :cvar AFG: Trigraph for Afghanistan + :cvar AGO: Trigraph for Angola + :cvar AIA: Trigraph for Anguilla + :cvar ALA: Trigraph for Åland Islands + :cvar ALB: Trigraph for Albania + :cvar AND: Trigraph for Andorra + :cvar ANT: Trigraph for Netherlands Antilles + :cvar ARE: Trigraph for United Arab Emirates + :cvar ARG: Trigraph for Argentina + :cvar ARM: Trigraph for Armenia + :cvar ASM: Trigraph for American Samoa + :cvar ATA: Trigraph for Antarctica + :cvar ATF: Trigraph for French Southern Territories + :cvar ATG: Trigraph for Antigua and Barbuda + :cvar AUS: Trigraph for Australia + :cvar AUT: Trigraph for Austria + :cvar AZE: Trigraph for Azerbaijan + :cvar BDI: Trigraph for Burundi + :cvar BEL: Trigraph for Belgium + :cvar BEN: Trigraph for Benin + :cvar BFA: Trigraph for Burkina Faso + :cvar BGD: Trigraph for Bangladesh + :cvar BGR: Trigraph for Bulgaria + :cvar BHR: Trigraph for Bahrain + :cvar BHS: Trigraph for Bahamas + :cvar BIH: Trigraph for Bosnia and Herzegovina + :cvar BLM: Trigraph for Saint Barthélemy + :cvar BLR: Trigraph for Belarus + :cvar BLZ: Trigraph for Belize + :cvar BMU: Trigraph for Bermuda + :cvar BOL: Trigraph for Bolivia + :cvar BRA: Trigraph for Brazil + :cvar BRB: Trigraph for Barbados + :cvar BRN: Trigraph for Brunei Darussalam + :cvar BTN: Trigraph for Bhutan + :cvar BVT: Trigraph for Bouvet Island + :cvar BWA: Trigraph for Botswana + :cvar CAF: Trigraph for Central African Republic + :cvar CAN: Trigraph for Canada + :cvar CCK: Trigraph for Cocos (Keeling) Islands + :cvar CHE: Trigraph for Switzerland + :cvar CHL: Trigraph for Chile + :cvar CHN: Trigraph for China + :cvar CIV: Trigraph for Côte d'Ivoire + :cvar CMR: Trigraph for Cameroon + :cvar COD: Trigraph for Congo, The Democratic Republic of the + :cvar COG: Trigraph for Congo + :cvar COK: Trigraph for Cook Islands + :cvar COL: Trigraph for Colombia + :cvar COM: Trigraph for Comoros + :cvar CPV: Trigraph for Cape Verde + :cvar CRI: Trigraph for Costa Rica + :cvar CUB: Trigraph for Cuba + :cvar CXR: Trigraph for Christmas Island + :cvar CYM: Trigraph for Cayman Islands + :cvar CYP: Trigraph for Cyprus + :cvar CZE: Trigraph for Czech Republic + :cvar DEU: Trigraph for Germany + :cvar DJI: Trigraph for Djibouti + :cvar DMA: Trigraph for Dominica + :cvar DNK: Trigraph for Denmark + :cvar DOM: Trigraph for Dominican Republic + :cvar DZA: Trigraph for Algeria + :cvar ECU: Trigraph for Eucador + :cvar EGY: Trigraph for Egypt + :cvar ERI: Trigraph for Eritrea + :cvar ESH: Trigraph for Western Sahara + :cvar ESP: Trigraph for Spain + :cvar EST: Trigraph for Estonia + :cvar ETH: Trigraph for Ethiopia + :cvar FIN: Trigraph for Finland + :cvar FJI: Trigraph for Fiji + :cvar FLK: Trigraph for Falkland Islands (Malvinas) + :cvar FRA: Trigraph for France + :cvar FRO: Trigraph for Faroe Islands + :cvar FSM: Trigraph for Micronesia, Federated States of + :cvar GAB: Trigraph for Gabon + :cvar GBR: Trigraph for United Kingdom + :cvar GEO: Trigraph for Georgia + :cvar GGY: Trigraph for Guernsey + :cvar GHA: Trigraph for Ghana + :cvar GIB: Trigraph for Gibraltar + :cvar GIN: Trigraph for Guinea + :cvar GLP: Trigraph for Guadeloupe + :cvar GMB: Trigraph for Gambia + :cvar GNB: Trigraph for Guinea-Bissau + :cvar GNQ: Trigraph for Equatorial Guinea + :cvar GRC: Trigraph for Greece + :cvar GRD: Trigraph for Grenada + :cvar GRL: Trigraph for Greenland + :cvar GTM: Trigraph for Guatemala + :cvar GUF: Trigraph for French Guiana + :cvar GUM: Trigraph for Guam + :cvar GUY: Trigraph for Guyana + :cvar HKG: Trigraph for Hong Kong + :cvar HMD: Trigraph for Heard Island and McDonald Islands + :cvar HND: Trigraph for Honduras + :cvar HRV: Trigraph for Croatia + :cvar HTI: Trigraph for Haiti + :cvar HUN: Trigraph for Hungary + :cvar IDN: Trigraph for Indonesia + :cvar IMN: Trigraph for Isle of Man + :cvar IND: Trigraph for India + :cvar IOT: Trigraph for British Indian Ocean Territory + :cvar IRL: Trigraph for Ireland + :cvar IRN: Trigraph for Iran, Islamic Republic of + :cvar IRQ: Trigraph for Iraq + :cvar ISL: Trigraph for Iceland + :cvar ISR: Trigraph for Israel + :cvar ITA: Trigraph for Italy + :cvar JAM: Trigraph for Jamaica + :cvar JEY: Trigraph for Jersey + :cvar JOR: Trigraph for Jordan + :cvar JPN: Trigraph for Japan + :cvar KAZ: Trigraph for Kazakhstan + :cvar KEN: Trigraph for Kenya + :cvar KGZ: Trigraph for Kyrgyzstan + :cvar KHM: Trigraph for Cambodia + :cvar KIR: Trigraph for Kiribati + :cvar KNA: Trigraph for Saint Kitts and Nevis + :cvar KOR: Trigraph for Korea, Republic of + :cvar KWT: Trigraph for Kuwait + :cvar LAO: Trigraph for Lao People's Democratic Republic + :cvar LBN: Trigraph for Lebanon + :cvar LBR: Trigraph for Liberia + :cvar LBY: Trigraph for Libyan Arab Jamahiriya + :cvar LCA: Trigraph for Saint Lucia + :cvar LIE: Trigraph for Liechtenstein + :cvar LKA: Trigraph for Sri Lanka + :cvar LSO: Trigraph for Lesotho + :cvar LTU: Trigraph for Lithuania + :cvar LUX: Trigraph for Luxembourg + :cvar LVA: Trigraph for Latvia + :cvar MAC: Trigraph for Macao + :cvar MAF: Trigraph for Saint Martin (French part) + :cvar MAR: Trigraph for Morocco + :cvar MCO: Trigraph for Monaco + :cvar MDA: Trigraph for Moldova (the Republic of) + :cvar MDG: Trigraph for Madagascar + :cvar MDV: Trigraph for Maldives + :cvar MEX: Trigraph for Mexico + :cvar MHL: Trigraph for Marshall Islands + :cvar MKD: Trigraph for Macedonia, The former Yugoslav Republic of + :cvar MLI: Trigraph for Mali + :cvar MLT: Trigraph for Malta + :cvar MMR: Trigraph for Myanmar + :cvar MNE: Trigraph for Montenegro + :cvar MNG: Trigraph for Mongolia + :cvar MNP: Trigraph for Northern Mariana Islands + :cvar MOZ: Trigraph for Mozambique + :cvar MRT: Trigraph for Mauritania + :cvar MSR: Trigraph for Montserrat + :cvar MTQ: Trigraph for Martinique + :cvar MUS: Trigraph for Mauritius + :cvar MWI: Trigraph for Malawi + :cvar MYS: Trigraph for Malaysia + :cvar MYT: Trigraph for Mayotte + :cvar NAM: Trigraph for Namibia + :cvar NCL: Trigraph for New Caledonia + :cvar NER: Trigraph for Niger + :cvar NFK: Trigraph for Norfolk Island + :cvar NGA: Trigraph for Nigeria + :cvar NIC: Trigraph for Nicaragua + :cvar NIU: Trigraph for Niue + :cvar NLD: Trigraph for Netherlands + :cvar NOR: Trigraph for Norway + :cvar NPL: Trigraph for Nepal + :cvar NRU: Trigraph for Nauru + :cvar NZL: Trigraph for New Zealand + :cvar OMN: Trigraph for Oman + :cvar PAK: Trigraph for Pakistan + :cvar PAN: Trigraph for Panama + :cvar PCN: Trigraph for Pitcairn + :cvar PER: Trigraph for Peru + :cvar PHL: Trigraph for Philippines + :cvar PLW: Trigraph for Palau + :cvar PNG: Trigraph for Papua New Guinea + :cvar POL: Trigraph for Poland + :cvar PRI: Trigraph for Puerto Rico + :cvar PRK: Trigraph for Korea, Democratic People's Republic of + :cvar PRT: Trigraph for Portugal + :cvar PRY: Trigraph for Paraguay + :cvar PSE: Trigraph for Palestinian Territory, Occupied + :cvar PYF: Trigraph for French Polynesia + :cvar QAT: Trigraph for Qatar + :cvar REU: Trigraph for Réunion + :cvar ROU: Trigraph for Romania + :cvar RUS: Trigraph for Russian Federation + :cvar RWA: Trigraph for Rwanda + :cvar SAU: Trigraph for Saudi Arabia + :cvar SDN: Trigraph for Sudan + :cvar SEN: Trigraph for Senegal + :cvar SGP: Trigraph for Singapore + :cvar SGS: Trigraph for South Georgia and the South Sandwich Islands + :cvar SHN: Trigraph for Saint Helena + :cvar SJM: Trigraph for Svalbard and Jan Mayen + :cvar SLB: Trigraph for Solomon Islands + :cvar SLE: Trigraph for Sierra Leone + :cvar SLV: Trigraph for El Salvador + :cvar SMR: Trigraph for San Marino + :cvar SOM: Trigraph for Somalia + :cvar SPM: Trigraph for Saint Pierre and Miquelon + :cvar SRB: Trigraph for Serbia + :cvar STP: Trigraph for Sao Tome and Principe + :cvar SUR: Trigraph for Suriname + :cvar SVK: Trigraph for Slovakia + :cvar SVN: Trigraph for Slovenia + :cvar SWE: Trigraph for Sweden + :cvar SWZ: Trigraph for Swaziland + :cvar SYC: Trigraph for Seychelles + :cvar SYR: Trigraph for Syrian Arab Republic + :cvar TCA: Trigraph for Turks and Caicos Islands + :cvar TCD: Trigraph for Chad + :cvar TGO: Trigraph for Togo + :cvar THA: Trigraph for Thailand + :cvar TJK: Trigraph for Tajikistan + :cvar TKL: Trigraph for Tokelau + :cvar TKM: Trigraph for Turkmenistan + :cvar TLS: Trigraph for Timor-Leste + :cvar TON: Trigraph for Tonga + :cvar TTO: Trigraph for Trinidad and Tobago + :cvar TUN: Trigraph for Tunisia + :cvar TUR: Trigraph for Turkey + :cvar TUV: Trigraph for Tuvalu + :cvar TWN: Trigraph for Taiwan, Province of China + :cvar TZA: Trigraph for Tanzania, United Republic of + :cvar UGA: Trigraph for Uganda + :cvar UKR: Trigraph for Ukraine + :cvar UMI: Trigraph for United States Minor Outlying Islands + :cvar URY: Trigraph for Uruguay + :cvar UZB: Trigraph for Uzbekistan + :cvar VAT: Trigraph for Holy See (Vatican City State) + :cvar VCT: Trigraph for Saint Vincent and the Grenadines + :cvar VEN: Trigraph for Venezuela + :cvar VGB: Trigraph for Virgin Islands, British + :cvar VIR: Trigraph for Virgin Islands, U.S. + :cvar VNM: Trigraph for Viet Nam + :cvar VUT: Trigraph for Vanuatu + :cvar WLF: Trigraph for Wallis and Futuna + :cvar WSM: Trigraph for Samoa + :cvar YEM: Trigraph for Yemen + :cvar ZAF: Trigraph for South Africa + :cvar ZMB: Trigraph for Zambia + :cvar ZWE: Trigraph for Zimbabwe + :cvar ACGU: Tetragraph for FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Tetragraph for Biological Weapons Convention States + :cvar CFCK: Tetragraph for ROK/US Combined Forces Command, Korea + :cvar CMFC: Tetragraph for Combined Maritime Forces + :cvar CMFP: Tetragraph for Cooperative Maritime Forces Pacific + :cvar CPMT: Tetragraph for Civilian Protection Monitoring Team for Sudan + :cvar CWCS: Tetragraph for Chemical Weapons Convention States + :cvar EFOR: Tetragraph for European Union Stabilization Forces in Bosnia + :cvar EUDA: Tetragraph for European Union DARFUR + :cvar FVEY: Tetragraph for FIVE EYES + :cvar GCTF: Tetragraph for Global Counter-Terrorism Forces + :cvar GMIF: Tetragraph for Global Maritime Interception Forces + :cvar IESC: Tetragraph for International Events Security Coalition + :cvar ISAF: Tetragraph for International Security Assistance Force for Afghanistan + :cvar KFOR: Tetragraph for Stabilization Forces in Kosovo + :cvar MCFI: Tetragraph for Multinational Coalition Forces - Iraq + :cvar MIFH: Tetragraph for Multinational Interim Force Haiti + :cvar MLEC: Tetragraph for Multi-Lateral Enduring Contingency + :cvar NACT: Tetragraph for North African Counter-Terrorism Forces + :cvar NATO: Tetragraph for North Atlantic Treaty Organization + :cvar SPAA: Suppressed + :cvar TEYE: Tetragraph for THREE EYES + :cvar UNCK: Tetragraph for United Nations Command, Korea + """ + + UNKNOWN = "UNKNOWN" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALA = "ALA" + ALB = "ALB" + AND = "AND" + ANT = "ANT" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SJM = "SJM" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + UMI = "UMI" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CWCS = "CWCS" + EFOR = "EFOR" + EUDA = "EUDA" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + IESC = "IESC" + ISAF = "ISAF" + KFOR = "KFOR" + MCFI = "MCFI" + MIFH = "MIFH" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiprotected.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiprotected.py new file mode 100644 index 0000000..aab5a19 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismfgiprotected.py @@ -0,0 +1,561 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMFGIProtectedValues(Enum): + """(U) FGI followed by all currently valid ISO Trigraphs except USA in + alphabetical order by Trigraph, followed by all currently valid CAPCO Coalition + tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMFGIProtected.xml + + :cvar FGI: Foreign Government Information + :cvar ABW: Trigraph for Aruba + :cvar AFG: Trigraph for Afghanistan + :cvar AGO: Trigraph for Angola + :cvar AIA: Trigraph for Anguilla + :cvar ALA: Trigraph for Åland Islands + :cvar ALB: Trigraph for Albania + :cvar AND: Trigraph for Andorra + :cvar ANT: Trigraph for Netherlands Antilles + :cvar ARE: Trigraph for United Arab Emirates + :cvar ARG: Trigraph for Argentina + :cvar ARM: Trigraph for Armenia + :cvar ASM: Trigraph for American Samoa + :cvar ATA: Trigraph for Antarctica + :cvar ATF: Trigraph for French Southern Territories + :cvar ATG: Trigraph for Antigua and Barbuda + :cvar AUS: Trigraph for Australia + :cvar AUT: Trigraph for Austria + :cvar AZE: Trigraph for Azerbaijan + :cvar BDI: Trigraph for Burundi + :cvar BEL: Trigraph for Belgium + :cvar BEN: Trigraph for Benin + :cvar BFA: Trigraph for Burkina Faso + :cvar BGD: Trigraph for Bangladesh + :cvar BGR: Trigraph for Bulgaria + :cvar BHR: Trigraph for Bahrain + :cvar BHS: Trigraph for Bahamas + :cvar BIH: Trigraph for Bosnia and Herzegovina + :cvar BLM: Trigraph for Saint Barthélemy + :cvar BLR: Trigraph for Belarus + :cvar BLZ: Trigraph for Belize + :cvar BMU: Trigraph for Bermuda + :cvar BOL: Trigraph for Bolivia + :cvar BRA: Trigraph for Brazil + :cvar BRB: Trigraph for Barbados + :cvar BRN: Trigraph for Brunei Darussalam + :cvar BTN: Trigraph for Bhutan + :cvar BVT: Trigraph for Bouvet Island + :cvar BWA: Trigraph for Botswana + :cvar CAF: Trigraph for Central African Republic + :cvar CAN: Trigraph for Canada + :cvar CCK: Trigraph for Cocos (Keeling) Islands + :cvar CHE: Trigraph for Switzerland + :cvar CHL: Trigraph for Chile + :cvar CHN: Trigraph for China + :cvar CIV: Trigraph for Côte d'Ivoire + :cvar CMR: Trigraph for Cameroon + :cvar COD: Trigraph for Congo, The Democratic Republic of the + :cvar COG: Trigraph for Congo + :cvar COK: Trigraph for Cook Islands + :cvar COL: Trigraph for Colombia + :cvar COM: Trigraph for Comoros + :cvar CPV: Trigraph for Cape Verde + :cvar CRI: Trigraph for Costa Rica + :cvar CUB: Trigraph for Cuba + :cvar CXR: Trigraph for Christmas Island + :cvar CYM: Trigraph for Cayman Islands + :cvar CYP: Trigraph for Cyprus + :cvar CZE: Trigraph for Czech Republic + :cvar DEU: Trigraph for Germany + :cvar DJI: Trigraph for Djibouti + :cvar DMA: Trigraph for Dominica + :cvar DNK: Trigraph for Denmark + :cvar DOM: Trigraph for Dominican Republic + :cvar DZA: Trigraph for Algeria + :cvar ECU: Trigraph for Eucador + :cvar EGY: Trigraph for Egypt + :cvar ERI: Trigraph for Eritrea + :cvar ESH: Trigraph for Western Sahara + :cvar ESP: Trigraph for Spain + :cvar EST: Trigraph for Estonia + :cvar ETH: Trigraph for Ethiopia + :cvar FIN: Trigraph for Finland + :cvar FJI: Trigraph for Fiji + :cvar FLK: Trigraph for Falkland Islands (Malvinas) + :cvar FRA: Trigraph for France + :cvar FRO: Trigraph for Faroe Islands + :cvar FSM: Trigraph for Micronesia, Federated States of + :cvar GAB: Trigraph for Gabon + :cvar GBR: Trigraph for United Kingdom + :cvar GEO: Trigraph for Georgia + :cvar GGY: Trigraph for Guernsey + :cvar GHA: Trigraph for Ghana + :cvar GIB: Trigraph for Gibraltar + :cvar GIN: Trigraph for Guinea + :cvar GLP: Trigraph for Guadeloupe + :cvar GMB: Trigraph for Gambia + :cvar GNB: Trigraph for Guinea-Bissau + :cvar GNQ: Trigraph for Equatorial Guinea + :cvar GRC: Trigraph for Greece + :cvar GRD: Trigraph for Grenada + :cvar GRL: Trigraph for Greenland + :cvar GTM: Trigraph for Guatemala + :cvar GUF: Trigraph for French Guiana + :cvar GUM: Trigraph for Guam + :cvar GUY: Trigraph for Guyana + :cvar HKG: Trigraph for Hong Kong + :cvar HMD: Trigraph for Heard Island and McDonald Islands + :cvar HND: Trigraph for Honduras + :cvar HRV: Trigraph for Croatia + :cvar HTI: Trigraph for Haiti + :cvar HUN: Trigraph for Hungary + :cvar IDN: Trigraph for Indonesia + :cvar IMN: Trigraph for Isle of Man + :cvar IND: Trigraph for India + :cvar IOT: Trigraph for British Indian Ocean Territory + :cvar IRL: Trigraph for Ireland + :cvar IRN: Trigraph for Iran, Islamic Republic of + :cvar IRQ: Trigraph for Iraq + :cvar ISL: Trigraph for Iceland + :cvar ISR: Trigraph for Israel + :cvar ITA: Trigraph for Italy + :cvar JAM: Trigraph for Jamaica + :cvar JEY: Trigraph for Jersey + :cvar JOR: Trigraph for Jordan + :cvar JPN: Trigraph for Japan + :cvar KAZ: Trigraph for Kazakhstan + :cvar KEN: Trigraph for Kenya + :cvar KGZ: Trigraph for Kyrgyzstan + :cvar KHM: Trigraph for Cambodia + :cvar KIR: Trigraph for Kiribati + :cvar KNA: Trigraph for Saint Kitts and Nevis + :cvar KOR: Trigraph for Korea, Republic of + :cvar KWT: Trigraph for Kuwait + :cvar LAO: Trigraph for Lao People's Democratic Republic + :cvar LBN: Trigraph for Lebanon + :cvar LBR: Trigraph for Liberia + :cvar LBY: Trigraph for Libyan Arab Jamahiriya + :cvar LCA: Trigraph for Saint Lucia + :cvar LIE: Trigraph for Liechtenstein + :cvar LKA: Trigraph for Sri Lanka + :cvar LSO: Trigraph for Lesotho + :cvar LTU: Trigraph for Lithuania + :cvar LUX: Trigraph for Luxembourg + :cvar LVA: Trigraph for Latvia + :cvar MAC: Trigraph for Macao + :cvar MAF: Trigraph for Saint Martin (French part) + :cvar MAR: Trigraph for Morocco + :cvar MCO: Trigraph for Monaco + :cvar MDA: Trigraph for Moldova (the Republic of) + :cvar MDG: Trigraph for Madagascar + :cvar MDV: Trigraph for Maldives + :cvar MEX: Trigraph for Mexico + :cvar MHL: Trigraph for Marshall Islands + :cvar MKD: Trigraph for Macedonia, The former Yugoslav Republic of + :cvar MLI: Trigraph for Mali + :cvar MLT: Trigraph for Malta + :cvar MMR: Trigraph for Myanmar + :cvar MNE: Trigraph for Montenegro + :cvar MNG: Trigraph for Mongolia + :cvar MNP: Trigraph for Northern Mariana Islands + :cvar MOZ: Trigraph for Mozambique + :cvar MRT: Trigraph for Mauritania + :cvar MSR: Trigraph for Montserrat + :cvar MTQ: Trigraph for Martinique + :cvar MUS: Trigraph for Mauritius + :cvar MWI: Trigraph for Malawi + :cvar MYS: Trigraph for Malaysia + :cvar MYT: Trigraph for Mayotte + :cvar NAM: Trigraph for Namibia + :cvar NCL: Trigraph for New Caledonia + :cvar NER: Trigraph for Niger + :cvar NFK: Trigraph for Norfolk Island + :cvar NGA: Trigraph for Nigeria + :cvar NIC: Trigraph for Nicaragua + :cvar NIU: Trigraph for Niue + :cvar NLD: Trigraph for Netherlands + :cvar NOR: Trigraph for Norway + :cvar NPL: Trigraph for Nepal + :cvar NRU: Trigraph for Nauru + :cvar NZL: Trigraph for New Zealand + :cvar OMN: Trigraph for Oman + :cvar PAK: Trigraph for Pakistan + :cvar PAN: Trigraph for Panama + :cvar PCN: Trigraph for Pitcairn + :cvar PER: Trigraph for Peru + :cvar PHL: Trigraph for Philippines + :cvar PLW: Trigraph for Palau + :cvar PNG: Trigraph for Papua New Guinea + :cvar POL: Trigraph for Poland + :cvar PRI: Trigraph for Puerto Rico + :cvar PRK: Trigraph for Korea, Democratic People's Republic of + :cvar PRT: Trigraph for Portugal + :cvar PRY: Trigraph for Paraguay + :cvar PSE: Trigraph for Palestinian Territory, Occupied + :cvar PYF: Trigraph for French Polynesia + :cvar QAT: Trigraph for Qatar + :cvar REU: Trigraph for Réunion + :cvar ROU: Trigraph for Romania + :cvar RUS: Trigraph for Russian Federation + :cvar RWA: Trigraph for Rwanda + :cvar SAU: Trigraph for Saudi Arabia + :cvar SDN: Trigraph for Sudan + :cvar SEN: Trigraph for Senegal + :cvar SGP: Trigraph for Singapore + :cvar SGS: Trigraph for South Georgia and the South Sandwich Islands + :cvar SHN: Trigraph for Saint Helena + :cvar SJM: Trigraph for Svalbard and Jan Mayen + :cvar SLB: Trigraph for Solomon Islands + :cvar SLE: Trigraph for Sierra Leone + :cvar SLV: Trigraph for El Salvador + :cvar SMR: Trigraph for San Marino + :cvar SOM: Trigraph for Somalia + :cvar SPM: Trigraph for Saint Pierre and Miquelon + :cvar SRB: Trigraph for Serbia + :cvar STP: Trigraph for Sao Tome and Principe + :cvar SUR: Trigraph for Suriname + :cvar SVK: Trigraph for Slovakia + :cvar SVN: Trigraph for Slovenia + :cvar SWE: Trigraph for Sweden + :cvar SWZ: Trigraph for Swaziland + :cvar SYC: Trigraph for Seychelles + :cvar SYR: Trigraph for Syrian Arab Republic + :cvar TCA: Trigraph for Turks and Caicos Islands + :cvar TCD: Trigraph for Chad + :cvar TGO: Trigraph for Togo + :cvar THA: Trigraph for Thailand + :cvar TJK: Trigraph for Tajikistan + :cvar TKL: Trigraph for Tokelau + :cvar TKM: Trigraph for Turkmenistan + :cvar TLS: Trigraph for Timor-Leste + :cvar TON: Trigraph for Tonga + :cvar TTO: Trigraph for Trinidad and Tobago + :cvar TUN: Trigraph for Tunisia + :cvar TUR: Trigraph for Turkey + :cvar TUV: Trigraph for Tuvalu + :cvar TWN: Trigraph for Taiwan, Province of China + :cvar TZA: Trigraph for Tanzania, United Republic of + :cvar UGA: Trigraph for Uganda + :cvar UKR: Trigraph for Ukraine + :cvar UMI: Trigraph for United States Minor Outlying Islands + :cvar URY: Trigraph for Uruguay + :cvar UZB: Trigraph for Uzbekistan + :cvar VAT: Trigraph for Holy See (Vatican City State) + :cvar VCT: Trigraph for Saint Vincent and the Grenadines + :cvar VEN: Trigraph for Venezuela + :cvar VGB: Trigraph for Virgin Islands, British + :cvar VIR: Trigraph for Virgin Islands, U.S. + :cvar VNM: Trigraph for Viet Nam + :cvar VUT: Trigraph for Vanuatu + :cvar WLF: Trigraph for Wallis and Futuna + :cvar WSM: Trigraph for Samoa + :cvar YEM: Trigraph for Yemen + :cvar ZAF: Trigraph for South Africa + :cvar ZMB: Trigraph for Zambia + :cvar ZWE: Trigraph for Zimbabwe + :cvar ACGU: Tetragraph for FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Tetragraph for Biological Weapons Convention States + :cvar CFCK: Tetragraph for ROK/US Combined Forces Command, Korea + :cvar CMFC: Tetragraph for Combined Maritime Forces + :cvar CMFP: Tetragraph for Cooperative Maritime Forces Pacific + :cvar CPMT: Tetragraph for Civilian Protection Monitoring Team for Sudan + :cvar CWCS: Tetragraph for Chemical Weapons Convention States + :cvar EFOR: Tetragraph for European Union Stabilization Forces in Bosnia + :cvar EUDA: Tetragraph for European Union DARFUR + :cvar FVEY: Tetragraph for FIVE EYES + :cvar GCTF: Tetragraph for Global Counter-Terrorism Forces + :cvar GMIF: Tetragraph for Global Maritime Interception Forces + :cvar IESC: Tetragraph for International Events Security Coalition + :cvar ISAF: Tetragraph for International Security Assistance Force for Afghanistan + :cvar KFOR: Tetragraph for Stabilization Forces in Kosovo + :cvar MCFI: Tetragraph for Multinational Coalition Forces - Iraq + :cvar MIFH: Tetragraph for Multinational Interim Force Haiti + :cvar MLEC: Tetragraph for Multi-Lateral Enduring Contingency + :cvar NACT: Tetragraph for North African Counter-Terrorism Forces + :cvar NATO: Tetragraph for North Atlantic Treaty Organization + :cvar SPAA: Suppressed + :cvar TEYE: Tetragraph for THREE EYES + :cvar UNCK: Tetragraph for United Nations Command, Korea + """ + + FGI = "FGI" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALA = "ALA" + ALB = "ALB" + AND = "AND" + ANT = "ANT" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SJM = "SJM" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + UMI = "UMI" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CWCS = "CWCS" + EFOR = "EFOR" + EUDA = "EUDA" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + IESC = "IESC" + ISAF = "ISAF" + KFOR = "KFOR" + MCFI = "MCFI" + MIFH = "MIFH" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_ic.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_ic.py new file mode 100644 index 0000000..5210b5a --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_ic.py @@ -0,0 +1,36 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMNonICValues(Enum): + """(U) All currently valid Non-IC markings from the published register + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMNonIC.xml + + :cvar SC: SPECIAL CATEGORY + :cvar SINFO: SENSITIVE INFORMATION + :cvar DS: LIMITED DISTRIBUTION + :cvar XD: EXCLUSIVE DISTRIBUTION + :cvar ND: NO DISTRIBUTION + :cvar SBU: SENSITIVE BUT UNCLASSIFIED + :cvar SBU_NF: SENSITIVE BUT UNCLASSIFIED NOFORN + :cvar LES: LAW ENFORCEMENT SENSITIVE + :cvar LES_NF: LAW ENFORCEMENT SENSITIVE NOFORN + """ + + SC = "SC" + SINFO = "SINFO" + DS = "DS" + XD = "XD" + ND = "ND" + SBU = "SBU" + SBU_NF = "SBU-NF" + LES = "LES" + LES_NF = "LES-NF" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_uscontrols.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_uscontrols.py new file mode 100644 index 0000000..40b4f14 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismnon_uscontrols.py @@ -0,0 +1,24 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMNonUSControlsValues(Enum): + """(U) NonUS Control markings supported by ISM + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMNonUSControls.xml + + :cvar ATOMAL: NATO Atomal mark + :cvar BOHEMIA: NATO Bohemia mark + :cvar BALK: NATO Balk mark + """ + + ATOMAL = "ATOMAL" + BOHEMIA = "BOHEMIA" + BALK = "BALK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismowner_producer.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismowner_producer.py new file mode 100644 index 0000000..d1021d0 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismowner_producer.py @@ -0,0 +1,563 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMOwnerProducerValues(Enum): + """(U) FGI followed by all currently valid ISO Trigraphs in alphabetical order + by Trigraph, followed by all currently valid CAPCO Coalition tetragraphs in + alphabetical order by tetragraph. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMOwnerProducer.xml + + :cvar FGI: Foreign Government Information + :cvar ABW: Trigraph for Aruba + :cvar AFG: Trigraph for Afghanistan + :cvar AGO: Trigraph for Angola + :cvar AIA: Trigraph for Anguilla + :cvar ALA: Trigraph for Åland Islands + :cvar ALB: Trigraph for Albania + :cvar AND: Trigraph for Andorra + :cvar ANT: Trigraph for Netherlands Antilles + :cvar ARE: Trigraph for United Arab Emirates + :cvar ARG: Trigraph for Argentina + :cvar ARM: Trigraph for Armenia + :cvar ASM: Trigraph for American Samoa + :cvar ATA: Trigraph for Antarctica + :cvar ATF: Trigraph for French Southern Territories + :cvar ATG: Trigraph for Antigua and Barbuda + :cvar AUS: Trigraph for Australia + :cvar AUT: Trigraph for Austria + :cvar AZE: Trigraph for Azerbaijan + :cvar BDI: Trigraph for Burundi + :cvar BEL: Trigraph for Belgium + :cvar BEN: Trigraph for Benin + :cvar BFA: Trigraph for Burkina Faso + :cvar BGD: Trigraph for Bangladesh + :cvar BGR: Trigraph for Bulgaria + :cvar BHR: Trigraph for Bahrain + :cvar BHS: Trigraph for Bahamas + :cvar BIH: Trigraph for Bosnia and Herzegovina + :cvar BLM: Trigraph for Saint Barthélemy + :cvar BLR: Trigraph for Belarus + :cvar BLZ: Trigraph for Belize + :cvar BMU: Trigraph for Bermuda + :cvar BOL: Trigraph for Bolivia + :cvar BRA: Trigraph for Brazil + :cvar BRB: Trigraph for Barbados + :cvar BRN: Trigraph for Brunei Darussalam + :cvar BTN: Trigraph for Bhutan + :cvar BVT: Trigraph for Bouvet Island + :cvar BWA: Trigraph for Botswana + :cvar CAF: Trigraph for Central African Republic + :cvar CAN: Trigraph for Canada + :cvar CCK: Trigraph for Cocos (Keeling) Islands + :cvar CHE: Trigraph for Switzerland + :cvar CHL: Trigraph for Chile + :cvar CHN: Trigraph for China + :cvar CIV: Trigraph for Côte d'Ivoire + :cvar CMR: Trigraph for Cameroon + :cvar COD: Trigraph for Congo, The Democratic Republic of the + :cvar COG: Trigraph for Congo + :cvar COK: Trigraph for Cook Islands + :cvar COL: Trigraph for Colombia + :cvar COM: Trigraph for Comoros + :cvar CPV: Trigraph for Cape Verde + :cvar CRI: Trigraph for Costa Rica + :cvar CUB: Trigraph for Cuba + :cvar CXR: Trigraph for Christmas Island + :cvar CYM: Trigraph for Cayman Islands + :cvar CYP: Trigraph for Cyprus + :cvar CZE: Trigraph for Czech Republic + :cvar DEU: Trigraph for Germany + :cvar DJI: Trigraph for Djibouti + :cvar DMA: Trigraph for Dominica + :cvar DNK: Trigraph for Denmark + :cvar DOM: Trigraph for Dominican Republic + :cvar DZA: Trigraph for Algeria + :cvar ECU: Trigraph for Eucador + :cvar EGY: Trigraph for Egypt + :cvar ERI: Trigraph for Eritrea + :cvar ESH: Trigraph for Western Sahara + :cvar ESP: Trigraph for Spain + :cvar EST: Trigraph for Estonia + :cvar ETH: Trigraph for Ethiopia + :cvar FIN: Trigraph for Finland + :cvar FJI: Trigraph for Fiji + :cvar FLK: Trigraph for Falkland Islands (Malvinas) + :cvar FRA: Trigraph for France + :cvar FRO: Trigraph for Faroe Islands + :cvar FSM: Trigraph for Micronesia, Federated States of + :cvar GAB: Trigraph for Gabon + :cvar GBR: Trigraph for United Kingdom + :cvar GEO: Trigraph for Georgia + :cvar GGY: Trigraph for Guernsey + :cvar GHA: Trigraph for Ghana + :cvar GIB: Trigraph for Gibraltar + :cvar GIN: Trigraph for Guinea + :cvar GLP: Trigraph for Guadeloupe + :cvar GMB: Trigraph for Gambia + :cvar GNB: Trigraph for Guinea-Bissau + :cvar GNQ: Trigraph for Equatorial Guinea + :cvar GRC: Trigraph for Greece + :cvar GRD: Trigraph for Grenada + :cvar GRL: Trigraph for Greenland + :cvar GTM: Trigraph for Guatemala + :cvar GUF: Trigraph for French Guiana + :cvar GUM: Trigraph for Guam + :cvar GUY: Trigraph for Guyana + :cvar HKG: Trigraph for Hong Kong + :cvar HMD: Trigraph for Heard Island and McDonald Islands + :cvar HND: Trigraph for Honduras + :cvar HRV: Trigraph for Croatia + :cvar HTI: Trigraph for Haiti + :cvar HUN: Trigraph for Hungary + :cvar IDN: Trigraph for Indonesia + :cvar IMN: Trigraph for Isle of Man + :cvar IND: Trigraph for India + :cvar IOT: Trigraph for British Indian Ocean Territory + :cvar IRL: Trigraph for Ireland + :cvar IRN: Trigraph for Iran, Islamic Republic of + :cvar IRQ: Trigraph for Iraq + :cvar ISL: Trigraph for Iceland + :cvar ISR: Trigraph for Israel + :cvar ITA: Trigraph for Italy + :cvar JAM: Trigraph for Jamaica + :cvar JEY: Trigraph for Jersey + :cvar JOR: Trigraph for Jordan + :cvar JPN: Trigraph for Japan + :cvar KAZ: Trigraph for Kazakhstan + :cvar KEN: Trigraph for Kenya + :cvar KGZ: Trigraph for Kyrgyzstan + :cvar KHM: Trigraph for Cambodia + :cvar KIR: Trigraph for Kiribati + :cvar KNA: Trigraph for Saint Kitts and Nevis + :cvar KOR: Trigraph for Korea, Republic of + :cvar KWT: Trigraph for Kuwait + :cvar LAO: Trigraph for Lao People's Democratic Republic + :cvar LBN: Trigraph for Lebanon + :cvar LBR: Trigraph for Liberia + :cvar LBY: Trigraph for Libyan Arab Jamahiriya + :cvar LCA: Trigraph for Saint Lucia + :cvar LIE: Trigraph for Liechtenstein + :cvar LKA: Trigraph for Sri Lanka + :cvar LSO: Trigraph for Lesotho + :cvar LTU: Trigraph for Lithuania + :cvar LUX: Trigraph for Luxembourg + :cvar LVA: Trigraph for Latvia + :cvar MAC: Trigraph for Macao + :cvar MAF: Trigraph for Saint Martin (French part) + :cvar MAR: Trigraph for Morocco + :cvar MCO: Trigraph for Monaco + :cvar MDA: Trigraph for Moldova (the Republic of) + :cvar MDG: Trigraph for Madagascar + :cvar MDV: Trigraph for Maldives + :cvar MEX: Trigraph for Mexico + :cvar MHL: Trigraph for Marshall Islands + :cvar MKD: Trigraph for Macedonia, The former Yugoslav Republic of + :cvar MLI: Trigraph for Mali + :cvar MLT: Trigraph for Malta + :cvar MMR: Trigraph for Myanmar + :cvar MNE: Trigraph for Montenegro + :cvar MNG: Trigraph for Mongolia + :cvar MNP: Trigraph for Northern Mariana Islands + :cvar MOZ: Trigraph for Mozambique + :cvar MRT: Trigraph for Mauritania + :cvar MSR: Trigraph for Montserrat + :cvar MTQ: Trigraph for Martinique + :cvar MUS: Trigraph for Mauritius + :cvar MWI: Trigraph for Malawi + :cvar MYS: Trigraph for Malaysia + :cvar MYT: Trigraph for Mayotte + :cvar NAM: Trigraph for Namibia + :cvar NCL: Trigraph for New Caledonia + :cvar NER: Trigraph for Niger + :cvar NFK: Trigraph for Norfolk Island + :cvar NGA: Trigraph for Nigeria + :cvar NIC: Trigraph for Nicaragua + :cvar NIU: Trigraph for Niue + :cvar NLD: Trigraph for Netherlands + :cvar NOR: Trigraph for Norway + :cvar NPL: Trigraph for Nepal + :cvar NRU: Trigraph for Nauru + :cvar NZL: Trigraph for New Zealand + :cvar OMN: Trigraph for Oman + :cvar PAK: Trigraph for Pakistan + :cvar PAN: Trigraph for Panama + :cvar PCN: Trigraph for Pitcairn + :cvar PER: Trigraph for Peru + :cvar PHL: Trigraph for Philippines + :cvar PLW: Trigraph for Palau + :cvar PNG: Trigraph for Papua New Guinea + :cvar POL: Trigraph for Poland + :cvar PRI: Trigraph for Puerto Rico + :cvar PRK: Trigraph for Korea, Democratic People's Republic of + :cvar PRT: Trigraph for Portugal + :cvar PRY: Trigraph for Paraguay + :cvar PSE: Trigraph for Palestinian Territory, Occupied + :cvar PYF: Trigraph for French Polynesia + :cvar QAT: Trigraph for Qatar + :cvar REU: Trigraph for Réunion + :cvar ROU: Trigraph for Romania + :cvar RUS: Trigraph for Russian Federation + :cvar RWA: Trigraph for Rwanda + :cvar SAU: Trigraph for Saudi Arabia + :cvar SDN: Trigraph for Sudan + :cvar SEN: Trigraph for Senegal + :cvar SGP: Trigraph for Singapore + :cvar SGS: Trigraph for South Georgia and the South Sandwich Islands + :cvar SHN: Trigraph for Saint Helena + :cvar SJM: Trigraph for Svalbard and Jan Mayen + :cvar SLB: Trigraph for Solomon Islands + :cvar SLE: Trigraph for Sierra Leone + :cvar SLV: Trigraph for El Salvador + :cvar SMR: Trigraph for San Marino + :cvar SOM: Trigraph for Somalia + :cvar SPM: Trigraph for Saint Pierre and Miquelon + :cvar SRB: Trigraph for Serbia + :cvar STP: Trigraph for Sao Tome and Principe + :cvar SUR: Trigraph for Suriname + :cvar SVK: Trigraph for Slovakia + :cvar SVN: Trigraph for Slovenia + :cvar SWE: Trigraph for Sweden + :cvar SWZ: Trigraph for Swaziland + :cvar SYC: Trigraph for Seychelles + :cvar SYR: Trigraph for Syrian Arab Republic + :cvar TCA: Trigraph for Turks and Caicos Islands + :cvar TCD: Trigraph for Chad + :cvar TGO: Trigraph for Togo + :cvar THA: Trigraph for Thailand + :cvar TJK: Trigraph for Tajikistan + :cvar TKL: Trigraph for Tokelau + :cvar TKM: Trigraph for Turkmenistan + :cvar TLS: Trigraph for Timor-Leste + :cvar TON: Trigraph for Tonga + :cvar TTO: Trigraph for Trinidad and Tobago + :cvar TUN: Trigraph for Tunisia + :cvar TUR: Trigraph for Turkey + :cvar TUV: Trigraph for Tuvalu + :cvar TWN: Trigraph for Taiwan, Province of China + :cvar TZA: Trigraph for Tanzania, United Republic of + :cvar UGA: Trigraph for Uganda + :cvar UKR: Trigraph for Ukraine + :cvar UMI: Trigraph for United States Minor Outlying Islands + :cvar URY: Trigraph for Uruguay + :cvar USA: Trigraph for United States + :cvar UZB: Trigraph for Uzbekistan + :cvar VAT: Trigraph for Holy See (Vatican City State) + :cvar VCT: Trigraph for Saint Vincent and the Grenadines + :cvar VEN: Trigraph for Venezuela + :cvar VGB: Trigraph for Virgin Islands, British + :cvar VIR: Trigraph for Virgin Islands, U.S. + :cvar VNM: Trigraph for Viet Nam + :cvar VUT: Trigraph for Vanuatu + :cvar WLF: Trigraph for Wallis and Futuna + :cvar WSM: Trigraph for Samoa + :cvar YEM: Trigraph for Yemen + :cvar ZAF: Trigraph for South Africa + :cvar ZMB: Trigraph for Zambia + :cvar ZWE: Trigraph for Zimbabwe + :cvar ACGU: Tetragraph for FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Tetragraph for Biological Weapons Convention States + :cvar CFCK: Tetragraph for ROK/US Combined Forces Command, Korea + :cvar CMFC: Tetragraph for Combined Maritime Forces + :cvar CMFP: Tetragraph for Cooperative Maritime Forces Pacific + :cvar CPMT: Tetragraph for Civilian Protection Monitoring Team for Sudan + :cvar CWCS: Tetragraph for Chemical Weapons Convention States + :cvar EFOR: Tetragraph for European Union Stabilization Forces in Bosnia + :cvar EUDA: Tetragraph for European Union DARFUR + :cvar FVEY: Tetragraph for FIVE EYES + :cvar GCTF: Tetragraph for Global Counter-Terrorism Forces + :cvar GMIF: Tetragraph for Global Maritime Interception Forces + :cvar IESC: Tetragraph for International Events Security Coalition + :cvar ISAF: Tetragraph for International Security Assistance Force for Afghanistan + :cvar KFOR: Tetragraph for Stabilization Forces in Kosovo + :cvar MCFI: Tetragraph for Multinational Coalition Forces - Iraq + :cvar MIFH: Tetragraph for Multinational Interim Force Haiti + :cvar MLEC: Tetragraph for Multi-Lateral Enduring Contingency + :cvar NACT: Tetragraph for North African Counter-Terrorism Forces + :cvar NATO: Tetragraph for North Atlantic Treaty Organization + :cvar SPAA: Suppressed + :cvar TEYE: Tetragraph for THREE EYES + :cvar UNCK: Tetragraph for United Nations Command, Korea + """ + + FGI = "FGI" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALA = "ALA" + ALB = "ALB" + AND = "AND" + ANT = "ANT" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SJM = "SJM" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + UMI = "UMI" + URY = "URY" + USA = "USA" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CWCS = "CWCS" + EFOR = "EFOR" + EUDA = "EUDA" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + IESC = "IESC" + ISAF = "ISAF" + KFOR = "KFOR" + MCFI = "MCFI" + MIFH = "MIFH" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismrel_to.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismrel_to.py new file mode 100644 index 0000000..b1a6d55 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismrel_to.py @@ -0,0 +1,561 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMRelToValues(Enum): + """(U) USA followed by all currently valid ISO Trigraphs except USA in + alphabetical order by Trigraph, followed by all currently valid CAPCO Coalition + tetragraphs in alphabetical order by tetragraph. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMRelTo.xml + + :cvar USA: Trigraph for United States + :cvar ABW: Trigraph for Aruba + :cvar AFG: Trigraph for Afghanistan + :cvar AGO: Trigraph for Angola + :cvar AIA: Trigraph for Anguilla + :cvar ALA: Trigraph for Åland Islands + :cvar ALB: Trigraph for Albania + :cvar AND: Trigraph for Andorra + :cvar ANT: Trigraph for Netherlands Antilles + :cvar ARE: Trigraph for United Arab Emirates + :cvar ARG: Trigraph for Argentina + :cvar ARM: Trigraph for Armenia + :cvar ASM: Trigraph for American Samoa + :cvar ATA: Trigraph for Antarctica + :cvar ATF: Trigraph for French Southern Territories + :cvar ATG: Trigraph for Antigua and Barbuda + :cvar AUS: Trigraph for Australia + :cvar AUT: Trigraph for Austria + :cvar AZE: Trigraph for Azerbaijan + :cvar BDI: Trigraph for Burundi + :cvar BEL: Trigraph for Belgium + :cvar BEN: Trigraph for Benin + :cvar BFA: Trigraph for Burkina Faso + :cvar BGD: Trigraph for Bangladesh + :cvar BGR: Trigraph for Bulgaria + :cvar BHR: Trigraph for Bahrain + :cvar BHS: Trigraph for Bahamas + :cvar BIH: Trigraph for Bosnia and Herzegovina + :cvar BLM: Trigraph for Saint Barthélemy + :cvar BLR: Trigraph for Belarus + :cvar BLZ: Trigraph for Belize + :cvar BMU: Trigraph for Bermuda + :cvar BOL: Trigraph for Bolivia + :cvar BRA: Trigraph for Brazil + :cvar BRB: Trigraph for Barbados + :cvar BRN: Trigraph for Brunei Darussalam + :cvar BTN: Trigraph for Bhutan + :cvar BVT: Trigraph for Bouvet Island + :cvar BWA: Trigraph for Botswana + :cvar CAF: Trigraph for Central African Republic + :cvar CAN: Trigraph for Canada + :cvar CCK: Trigraph for Cocos (Keeling) Islands + :cvar CHE: Trigraph for Switzerland + :cvar CHL: Trigraph for Chile + :cvar CHN: Trigraph for China + :cvar CIV: Trigraph for Côte d'Ivoire + :cvar CMR: Trigraph for Cameroon + :cvar COD: Trigraph for Congo, The Democratic Republic of the + :cvar COG: Trigraph for Congo + :cvar COK: Trigraph for Cook Islands + :cvar COL: Trigraph for Colombia + :cvar COM: Trigraph for Comoros + :cvar CPV: Trigraph for Cape Verde + :cvar CRI: Trigraph for Costa Rica + :cvar CUB: Trigraph for Cuba + :cvar CXR: Trigraph for Christmas Island + :cvar CYM: Trigraph for Cayman Islands + :cvar CYP: Trigraph for Cyprus + :cvar CZE: Trigraph for Czech Republic + :cvar DEU: Trigraph for Germany + :cvar DJI: Trigraph for Djibouti + :cvar DMA: Trigraph for Dominica + :cvar DNK: Trigraph for Denmark + :cvar DOM: Trigraph for Dominican Republic + :cvar DZA: Trigraph for Algeria + :cvar ECU: Trigraph for Eucador + :cvar EGY: Trigraph for Egypt + :cvar ERI: Trigraph for Eritrea + :cvar ESH: Trigraph for Western Sahara + :cvar ESP: Trigraph for Spain + :cvar EST: Trigraph for Estonia + :cvar ETH: Trigraph for Ethiopia + :cvar FIN: Trigraph for Finland + :cvar FJI: Trigraph for Fiji + :cvar FLK: Trigraph for Falkland Islands (Malvinas) + :cvar FRA: Trigraph for France + :cvar FRO: Trigraph for Faroe Islands + :cvar FSM: Trigraph for Micronesia, Federated States of + :cvar GAB: Trigraph for Gabon + :cvar GBR: Trigraph for United Kingdom + :cvar GEO: Trigraph for Georgia + :cvar GGY: Trigraph for Guernsey + :cvar GHA: Trigraph for Ghana + :cvar GIB: Trigraph for Gibraltar + :cvar GIN: Trigraph for Guinea + :cvar GLP: Trigraph for Guadeloupe + :cvar GMB: Trigraph for Gambia + :cvar GNB: Trigraph for Guinea-Bissau + :cvar GNQ: Trigraph for Equatorial Guinea + :cvar GRC: Trigraph for Greece + :cvar GRD: Trigraph for Grenada + :cvar GRL: Trigraph for Greenland + :cvar GTM: Trigraph for Guatemala + :cvar GUF: Trigraph for French Guiana + :cvar GUM: Trigraph for Guam + :cvar GUY: Trigraph for Guyana + :cvar HKG: Trigraph for Hong Kong + :cvar HMD: Trigraph for Heard Island and McDonald Islands + :cvar HND: Trigraph for Honduras + :cvar HRV: Trigraph for Croatia + :cvar HTI: Trigraph for Haiti + :cvar HUN: Trigraph for Hungary + :cvar IDN: Trigraph for Indonesia + :cvar IMN: Trigraph for Isle of Man + :cvar IND: Trigraph for India + :cvar IOT: Trigraph for British Indian Ocean Territory + :cvar IRL: Trigraph for Ireland + :cvar IRN: Trigraph for Iran, Islamic Republic of + :cvar IRQ: Trigraph for Iraq + :cvar ISL: Trigraph for Iceland + :cvar ISR: Trigraph for Israel + :cvar ITA: Trigraph for Italy + :cvar JAM: Trigraph for Jamaica + :cvar JEY: Trigraph for Jersey + :cvar JOR: Trigraph for Jordan + :cvar JPN: Trigraph for Japan + :cvar KAZ: Trigraph for Kazakhstan + :cvar KEN: Trigraph for Kenya + :cvar KGZ: Trigraph for Kyrgyzstan + :cvar KHM: Trigraph for Cambodia + :cvar KIR: Trigraph for Kiribati + :cvar KNA: Trigraph for Saint Kitts and Nevis + :cvar KOR: Trigraph for Korea, Republic of + :cvar KWT: Trigraph for Kuwait + :cvar LAO: Trigraph for Lao People's Democratic Republic + :cvar LBN: Trigraph for Lebanon + :cvar LBR: Trigraph for Liberia + :cvar LBY: Trigraph for Libyan Arab Jamahiriya + :cvar LCA: Trigraph for Saint Lucia + :cvar LIE: Trigraph for Liechtenstein + :cvar LKA: Trigraph for Sri Lanka + :cvar LSO: Trigraph for Lesotho + :cvar LTU: Trigraph for Lithuania + :cvar LUX: Trigraph for Luxembourg + :cvar LVA: Trigraph for Latvia + :cvar MAC: Trigraph for Macao + :cvar MAF: Trigraph for Saint Martin (French part) + :cvar MAR: Trigraph for Morocco + :cvar MCO: Trigraph for Monaco + :cvar MDA: Trigraph for Moldova (the Republic of) + :cvar MDG: Trigraph for Madagascar + :cvar MDV: Trigraph for Maldives + :cvar MEX: Trigraph for Mexico + :cvar MHL: Trigraph for Marshall Islands + :cvar MKD: Trigraph for Macedonia, The former Yugoslav Republic of + :cvar MLI: Trigraph for Mali + :cvar MLT: Trigraph for Malta + :cvar MMR: Trigraph for Myanmar + :cvar MNE: Trigraph for Montenegro + :cvar MNG: Trigraph for Mongolia + :cvar MNP: Trigraph for Northern Mariana Islands + :cvar MOZ: Trigraph for Mozambique + :cvar MRT: Trigraph for Mauritania + :cvar MSR: Trigraph for Montserrat + :cvar MTQ: Trigraph for Martinique + :cvar MUS: Trigraph for Mauritius + :cvar MWI: Trigraph for Malawi + :cvar MYS: Trigraph for Malaysia + :cvar MYT: Trigraph for Mayotte + :cvar NAM: Trigraph for Namibia + :cvar NCL: Trigraph for New Caledonia + :cvar NER: Trigraph for Niger + :cvar NFK: Trigraph for Norfolk Island + :cvar NGA: Trigraph for Nigeria + :cvar NIC: Trigraph for Nicaragua + :cvar NIU: Trigraph for Niue + :cvar NLD: Trigraph for Netherlands + :cvar NOR: Trigraph for Norway + :cvar NPL: Trigraph for Nepal + :cvar NRU: Trigraph for Nauru + :cvar NZL: Trigraph for New Zealand + :cvar OMN: Trigraph for Oman + :cvar PAK: Trigraph for Pakistan + :cvar PAN: Trigraph for Panama + :cvar PCN: Trigraph for Pitcairn + :cvar PER: Trigraph for Peru + :cvar PHL: Trigraph for Philippines + :cvar PLW: Trigraph for Palau + :cvar PNG: Trigraph for Papua New Guinea + :cvar POL: Trigraph for Poland + :cvar PRI: Trigraph for Puerto Rico + :cvar PRK: Trigraph for Korea, Democratic People's Republic of + :cvar PRT: Trigraph for Portugal + :cvar PRY: Trigraph for Paraguay + :cvar PSE: Trigraph for Palestinian Territory, Occupied + :cvar PYF: Trigraph for French Polynesia + :cvar QAT: Trigraph for Qatar + :cvar REU: Trigraph for Réunion + :cvar ROU: Trigraph for Romania + :cvar RUS: Trigraph for Russian Federation + :cvar RWA: Trigraph for Rwanda + :cvar SAU: Trigraph for Saudi Arabia + :cvar SDN: Trigraph for Sudan + :cvar SEN: Trigraph for Senegal + :cvar SGP: Trigraph for Singapore + :cvar SGS: Trigraph for South Georgia and the South Sandwich Islands + :cvar SHN: Trigraph for Saint Helena + :cvar SJM: Trigraph for Svalbard and Jan Mayen + :cvar SLB: Trigraph for Solomon Islands + :cvar SLE: Trigraph for Sierra Leone + :cvar SLV: Trigraph for El Salvador + :cvar SMR: Trigraph for San Marino + :cvar SOM: Trigraph for Somalia + :cvar SPM: Trigraph for Saint Pierre and Miquelon + :cvar SRB: Trigraph for Serbia + :cvar STP: Trigraph for Sao Tome and Principe + :cvar SUR: Trigraph for Suriname + :cvar SVK: Trigraph for Slovakia + :cvar SVN: Trigraph for Slovenia + :cvar SWE: Trigraph for Sweden + :cvar SWZ: Trigraph for Swaziland + :cvar SYC: Trigraph for Seychelles + :cvar SYR: Trigraph for Syrian Arab Republic + :cvar TCA: Trigraph for Turks and Caicos Islands + :cvar TCD: Trigraph for Chad + :cvar TGO: Trigraph for Togo + :cvar THA: Trigraph for Thailand + :cvar TJK: Trigraph for Tajikistan + :cvar TKL: Trigraph for Tokelau + :cvar TKM: Trigraph for Turkmenistan + :cvar TLS: Trigraph for Timor-Leste + :cvar TON: Trigraph for Tonga + :cvar TTO: Trigraph for Trinidad and Tobago + :cvar TUN: Trigraph for Tunisia + :cvar TUR: Trigraph for Turkey + :cvar TUV: Trigraph for Tuvalu + :cvar TWN: Trigraph for Taiwan, Province of China + :cvar TZA: Trigraph for Tanzania, United Republic of + :cvar UGA: Trigraph for Uganda + :cvar UKR: Trigraph for Ukraine + :cvar UMI: Trigraph for United States Minor Outlying Islands + :cvar URY: Trigraph for Uruguay + :cvar UZB: Trigraph for Uzbekistan + :cvar VAT: Trigraph for Holy See (Vatican City State) + :cvar VCT: Trigraph for Saint Vincent and the Grenadines + :cvar VEN: Trigraph for Venezuela + :cvar VGB: Trigraph for Virgin Islands, British + :cvar VIR: Trigraph for Virgin Islands, U.S. + :cvar VNM: Trigraph for Viet Nam + :cvar VUT: Trigraph for Vanuatu + :cvar WLF: Trigraph for Wallis and Futuna + :cvar WSM: Trigraph for Samoa + :cvar YEM: Trigraph for Yemen + :cvar ZAF: Trigraph for South Africa + :cvar ZMB: Trigraph for Zambia + :cvar ZWE: Trigraph for Zimbabwe + :cvar ACGU: Tetragraph for FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Tetragraph for Biological Weapons Convention States + :cvar CFCK: Tetragraph for ROK/US Combined Forces Command, Korea + :cvar CMFC: Tetragraph for Combined Maritime Forces + :cvar CMFP: Tetragraph for Cooperative Maritime Forces Pacific + :cvar CPMT: Tetragraph for Civilian Protection Monitoring Team for Sudan + :cvar CWCS: Tetragraph for Chemical Weapons Convention States + :cvar EFOR: Tetragraph for European Union Stabilization Forces in Bosnia + :cvar EUDA: Tetragraph for European Union DARFUR + :cvar FVEY: Tetragraph for FIVE EYES + :cvar GCTF: Tetragraph for Global Counter-Terrorism Forces + :cvar GMIF: Tetragraph for Global Maritime Interception Forces + :cvar IESC: Tetragraph for International Events Security Coalition + :cvar ISAF: Tetragraph for International Security Assistance Force for Afghanistan + :cvar KFOR: Tetragraph for Stabilization Forces in Kosovo + :cvar MCFI: Tetragraph for Multinational Coalition Forces - Iraq + :cvar MIFH: Tetragraph for Multinational Interim Force Haiti + :cvar MLEC: Tetragraph for Multi-Lateral Enduring Contingency + :cvar NACT: Tetragraph for North African Counter-Terrorism Forces + :cvar NATO: Tetragraph for North Atlantic Treaty Organization + :cvar SPAA: Suppressed + :cvar TEYE: Tetragraph for THREE EYES + :cvar UNCK: Tetragraph for United Nations Command, Korea + """ + + USA = "USA" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALA = "ALA" + ALB = "ALB" + AND = "AND" + ANT = "ANT" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SJM = "SJM" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + UMI = "UMI" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CWCS = "CWCS" + EFOR = "EFOR" + EUDA = "EUDA" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + IESC = "IESC" + ISAF = "ISAF" + KFOR = "KFOR" + MCFI = "MCFI" + MIFH = "MIFH" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismscicontrols.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismscicontrols.py new file mode 100644 index 0000000..0a462a0 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismscicontrols.py @@ -0,0 +1,24 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMSCIControlsValuesvalue(Enum): + """ + :cvar HCS: HCS + :cvar KDK: Klondike + :cvar SI: COMINT + :cvar SI_G: SI-GAMMA + :cvar TK: TALENT KEYHOLE + """ + + HCS = "HCS" + KDK = "KDK" + SI = "SI" + SI_G = "SI-G" + TK = "TK" diff --git a/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismsource_marked.py b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismsource_marked.py new file mode 100644 index 0000000..13daaf7 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism/schema/cvegenerated/cvenum_ismsource_marked.py @@ -0,0 +1,36 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:ism-cvenum" + + +class CVEnumISMSourceMarked(Enum): + """(U) All currently authorized Source Marked values + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMSourceMarked.xml + + :cvar OADR: Source Marked OADR (Originating Agency's Determination Required) + :cvar X1: Source Marked X1 + :cvar X2: Source Marked X2 + :cvar X3: Source Marked X3 + :cvar X4: Source Marked X4 + :cvar X5: Source Marked X5 + :cvar X6: Source Marked X6 + :cvar X7: Source Marked X7 + :cvar X8: Source Marked X8 + """ + + OADR = "OADR" + X1 = "X1" + X2 = "X2" + X3 = "X3" + X4 = "X4" + X5 = "X5" + X6 = "X6" + X7 = "X7" + X8 = "X8" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/__init__.py new file mode 100644 index 0000000..496c865 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/__init__.py @@ -0,0 +1,34 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .ic_ism import ( + LongStringWithSecurityType, + Notice, + NoticeBaseType, + NoticeExternal, + NoticeExternalList, + NoticeExternalListType, + NoticeExternalType, + NoticeList, + NoticeListType, + NoticeText, + NoticeType, + ShortStringWithSecurityType, +) + +__all__ = [ + "LongStringWithSecurityType", + "Notice", + "NoticeBaseType", + "NoticeExternal", + "NoticeExternalList", + "NoticeExternalListType", + "NoticeExternalType", + "NoticeList", + "NoticeListType", + "NoticeText", + "NoticeType", + "ShortStringWithSecurityType", +] diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/__init__.py new file mode 100644 index 0000000..52c9a92 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/__init__.py @@ -0,0 +1,30 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .cvenum_ism25_x import CVEnumISM25X +from .cvenum_ismatomic_energy_markings import CVEnumISMatomicEnergyMarkingsValuesvalue +from .cvenum_ismclassification_all import CVEnumISMClassificationAll +from .cvenum_ismcomplies_with import CVEnumISMCompliesWithValues +from .cvenum_ismdissem import CVEnumISMDissemValues +from .cvenum_ismexempt_from import CVEnumISMExemptFromValues +from .cvenum_ismnon_ic import CVEnumISMNonICValuesvalue +from .cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .cvenum_ismnotice import CVEnumISMNoticeValues +from .cvenum_ismpoc_type import CVEnumISMPocTypeValues +from .cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue + +__all__ = [ + "CVEnumISM25X", + "CVEnumISMatomicEnergyMarkingsValuesvalue", + "CVEnumISMClassificationAll", + "CVEnumISMCompliesWithValues", + "CVEnumISMDissemValues", + "CVEnumISMExemptFromValues", + "CVEnumISMNonICValuesvalue", + "CVEnumISMNonUSControlsValues", + "CVEnumISMNoticeValues", + "CVEnumISMPocTypeValues", + "CVEnumISMSCIControlsValuesvalue", +] diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ism25_x.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ism25_x.py new file mode 100644 index 0000000..e77f876 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ism25_x.py @@ -0,0 +1,81 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:25x" + + +class CVEnumISM25X(Enum): + """(U) All currently authorized authority block declass date/event exemptions. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISM25X.xml + + :cvar AEA: When using a source document that contains portions of Restricted Data (RD) or Formerly Restricted + Data (FRD) where the RD/FRD source document(s) do not have declassification instructions, the + derivatively classified document shall not contain a declassification date or event on the Declassify On + line. The following shall be annotated on the Declassify On line: "Not Applicable or (N/A) to RD/FRD + portions" and "See source list for NSI portions" separated by a period. The source list must include the + declassification instruction for each of the source documents classified under E.O. 13526 and shall not + appear in the classification authority block + :cvar NATO: Since NATO information is not to be declassified or downgraded without the prior consent of NATO, + the “Declassify on” line of documents that commingle information classified by NATO and U.S. classified + NSI, will read “N/A to NATO portions. See source list for NSI portions.” The NSI source list will appear + beneath the classification authority block in a manner that clearly identifies it as separate and + distinct. + :cvar NATO_AEA: Handles special case of BOTH NATO and AEA as a single exemption. + :cvar VALUE_25_X1: Reveal the identity of a confidential human source, a human intelligence source, a + relationship with an intelligence or security service of a foreign government or international + organization, or a non-human intelligence source; or impair the effectiveness of an intelligence method + currently in use, available for use, or under development. + :cvar VALUE_25_X1_EO_12951: "25X1, EO 12951" (prescribed by the DNI for use on information described in E.O. + 12951, Release of Imagery Acquired by Space-Based National Intelligence Reconnaissance Systems) + :cvar VALUE_25_X2: Reveal information that would assist in the development, production, or use of weapons of + mass destruction. + :cvar VALUE_25_X3: Reveal information that would impair U.S. cryptologic systems or activities. + :cvar VALUE_25_X4: Reveal information that would impair the application of state-of-the-art technology within + a U.S. weapon system. + :cvar VALUE_25_X5: Reveal formally named or numbered U.S. military war plans that remain in effect, or reveal + operational or tactical elements of prior plans that are contained in such active plans; + :cvar VALUE_25_X6: Reveal information, including foreign government information, that would cause serious + harm to relations between the United States and a foreign government, or to ongoing diplomatic activities + of the United States + :cvar VALUE_25_X7: Reveal information that would impair the current ability of United States Government + officials to protect the President, Vice President, and other protectees for whom protection services, in + the interest of the national security, are authorized. + :cvar VALUE_25_X8: Reveal information that would seriously impair current national security emergency + preparedness plans or reveal current vulnerabilities of systems, installations, or infrastructures + relating to the national security. + :cvar VALUE_25_X9: Violate a statute, treaty, or international agreement that does not permit the automatic + or unilateral declassification of information at 25 years. + :cvar VALUE_50_X1_HUM: When the information clearly and demonstrably could be expected to reveal the identity + of a confidential human source or a human intelligence source. + :cvar VALUE_50_X1: The ISCAP has authorized use of this code in the FBI’s classification guidance (which + results in a 75-year classification period) for any agency sourcing/reusing the information. + :cvar VALUE_50_X2_WMD: When the information clearly and demonstrably could reveal key design concepts of + weapons of mass destruction. + :cvar VALUE_50_X6: The ISCAP has authorized use of this code in the FBI’s classification guidance (which + results in a 75-year classification period) for any agency sourcing/reusing the information. + """ + + AEA = "AEA" + NATO = "NATO" + NATO_AEA = "NATO-AEA" + VALUE_25_X1 = "25X1" + VALUE_25_X1_EO_12951 = "25X1-EO-12951" + VALUE_25_X2 = "25X2" + VALUE_25_X3 = "25X3" + VALUE_25_X4 = "25X4" + VALUE_25_X5 = "25X5" + VALUE_25_X6 = "25X6" + VALUE_25_X7 = "25X7" + VALUE_25_X8 = "25X8" + VALUE_25_X9 = "25X9" + VALUE_50_X1_HUM = "50X1-HUM" + VALUE_50_X1 = "50X1" + VALUE_50_X2_WMD = "50X2-WMD" + VALUE_50_X6 = "50X6" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismatomic_energy_markings.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismatomic_energy_markings.py new file mode 100644 index 0000000..f08f2e1 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismatomic_energy_markings.py @@ -0,0 +1,26 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:atomicEnergyMarkings" + + +class CVEnumISMatomicEnergyMarkingsValuesvalue(Enum): + """ + :cvar RD: RESTRICTED DATA + :cvar RD_CNWDI: RD-CRITICAL NUCLEAR WEAPON DESIGN INFORMATION + :cvar FRD: FORMERLY RESTRICTED DATA + :cvar DCNI: DoD CONTROLLED NUCLEAR INFORMATION + :cvar UCNI: DoE CONTROLLED NUCLEAR INFORMATION + :cvar TFNI: TRANSCLASSIFIED FOREIGN NUCLEAR INFORMATION + """ + + RD = "RD" + RD_CNWDI = "RD-CNWDI" + FRD = "FRD" + DCNI = "DCNI" + UCNI = "UCNI" + TFNI = "TFNI" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismclassification_all.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismclassification_all.py new file mode 100644 index 0000000..9322dc1 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismclassification_all.py @@ -0,0 +1,28 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:classification:all" + + +class CVEnumISMClassificationAll(Enum): + """(U) All currently valid classification marks + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMClassificationAll.xml + + :cvar R: RESTRICTED + :cvar C: CONFIDENTIAL + :cvar S: SECRET + :cvar TS: TOP SECRET + :cvar U: UNCLASSIFIED + """ + + R = "R" + C = "C" + S = "S" + TS = "TS" + U = "U" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismcomplies_with.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismcomplies_with.py new file mode 100644 index 0000000..69edb69 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismcomplies_with.py @@ -0,0 +1,33 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:complieswith" + + +class CVEnumISMCompliesWithValues(Enum): + """(U) ISM rule sets documents may comply with. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMCompliesWith.xml + + :cvar USGOV: Document claims compliance with all rules encoded in ISM for documents produced by the US + Federal Government. This is the minimum set of rules for US documents to adhere to, and all US documents + should claim compliance with USGov. For example, a US Intelligence Community document should claim + ism:compliesWith="USGov USIC". + :cvar USIC: Document claims compliance with all rules encoded in ISM for documents produced by the US + Intelligence Community. Documents that claim compliance with USIC MUST also claim compliance with USGov. + :cvar USDOD: Document claims compliance with all rules encoded in ISM for documents produced by the US + Department of Defense. Documents that claim compliance with USDOD MUST also claim compliance with USGov. + :cvar OTHER_AUTHORITY: Document claims compliance with an authority other than the USGov, USIC, or USDOD. + This token is not allowed if the ism:ownerProducer contains USA. + """ + + USGOV = "USGov" + USIC = "USIC" + USDOD = "USDOD" + OTHER_AUTHORITY = "OtherAuthority" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismdissem.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismdissem.py new file mode 100644 index 0000000..c536b95 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismdissem.py @@ -0,0 +1,44 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:dissem" + + +class CVEnumISMDissemValues(Enum): + """(U) All currently valid Dissemination controls from the published register + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMDissem.xml + + :cvar RS: RISK SENSITIVE + :cvar FOUO: FOR OFFICIAL USE ONLY + :cvar OC: ORIGINATOR CONTROLLED + :cvar OC_USGOV: ORIGINATOR CONTROLLED US GOVERNMENT + :cvar IMC: CONTROLLED IMAGERY + :cvar NF: NOT RELEASABLE TO FOREIGN NATIONALS + :cvar PR: CAUTION-PROPRIETARY INFORMATION INVOLVED + :cvar REL: AUTHORIZED FOR RELEASE TO + :cvar RELIDO: RELEASABLE BY INFORMATION DISCLOSURE OFFICIAL + :cvar EYES: EYES ONLY + :cvar DSEN: DEA SENSITIVE + :cvar FISA: FOREIGN INTELLIGENCE SURVEILLANCE ACT + :cvar DISPLAYONLY: AUTHORIZED FOR DISPLAY BUT NOT RELEASE TO + """ + + RS = "RS" + FOUO = "FOUO" + OC = "OC" + OC_USGOV = "OC-USGOV" + IMC = "IMC" + NF = "NF" + PR = "PR" + REL = "REL" + RELIDO = "RELIDO" + EYES = "EYES" + DSEN = "DSEN" + FISA = "FISA" + DISPLAYONLY = "DISPLAYONLY" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismexempt_from.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismexempt_from.py new file mode 100644 index 0000000..a22a214 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismexempt_from.py @@ -0,0 +1,26 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:exemptfrom" + + +class CVEnumISMExemptFromValues(Enum): + """(U) Current rule set names that documents may comply + with + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMExemptFrom.xml + + :cvar IC_710_MANDATORY_FDR: Document claims exemption from ICD-710 rules mandating the use of Foreign + Disclosure and Release markings that have been encoded in ISM. Currently, the requirement for FD&R is + only mandatory for Disseminated Analytic Product; however, it is strongly encouraged otherwise. + :cvar DOD_DISTRO_STATEMENT: Document claims exemption from the rules in DoD5230.24 requiring DoD Distribution + Statements that have been encoded into ISM. + """ + + IC_710_MANDATORY_FDR = "IC_710_MANDATORY_FDR" + DOD_DISTRO_STATEMENT = "DOD_DISTRO_STATEMENT" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_ic.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_ic.py new file mode 100644 index 0000000..d39a935 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_ic.py @@ -0,0 +1,30 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:nonic" + + +class CVEnumISMNonICValuesvalue(Enum): + """ + :cvar DS: LIMITED DISTRIBUTION + :cvar XD: EXCLUSIVE DISTRIBUTION + :cvar ND: NO DISTRIBUTION + :cvar SBU: SENSITIVE BUT UNCLASSIFIED + :cvar SBU_NF: SENSITIVE BUT UNCLASSIFIED NOFORN + :cvar LES: LAW ENFORCEMENT SENSITIVE + :cvar LES_NF: LAW ENFORCEMENT SENSITIVE NOFORN + :cvar SSI: SENSITIVE SECURITY INFORMATION + """ + + DS = "DS" + XD = "XD" + ND = "ND" + SBU = "SBU" + SBU_NF = "SBU-NF" + LES = "LES" + LES_NF = "LES-NF" + SSI = "SSI" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_uscontrols.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_uscontrols.py new file mode 100644 index 0000000..faff25f --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnon_uscontrols.py @@ -0,0 +1,24 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:nonuscontrols" + + +class CVEnumISMNonUSControlsValues(Enum): + """(U) NonUS Control markings supported by ISM + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMNonUSControls.xml + + :cvar ATOMAL: NATO Atomal mark + :cvar BOHEMIA: NATO Bohemia mark + :cvar BALK: NATO Balk mark + """ + + ATOMAL = "ATOMAL" + BOHEMIA = "BOHEMIA" + BALK = "BALK" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnotice.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnotice.py new file mode 100644 index 0000000..d11f322 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismnotice.py @@ -0,0 +1,60 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:notice" + + +class CVEnumISMNoticeValues(Enum): + """(U) All currently authorized Notice values + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMNotice.xml + + :cvar FISA: FISA Warning statement + :cvar IMC: IMCON Warning statement + :cvar CNWDI: Controled Nuclear Weapon Design Information Warning statement + :cvar RD: RD Warning statement + :cvar FRD: FRD Warning statement + :cvar DS: LIMDIS caveat + :cvar LES: LES Notice + :cvar LES_NF: LES-NF Notice + :cvar DSEN: DSEN Notice + :cvar DO_D_DIST_A: DoD Distribution statement A from DoD Directive 5230.24 + :cvar DO_D_DIST_B: DoD Distribution statement B from DoD Directive 5230.24 + :cvar DO_D_DIST_C: DoD Distribution statement C from DoD Directive 5230.24 + :cvar DO_D_DIST_D: DoD Distribution statement D from DoD Directive 5230.24 + :cvar DO_D_DIST_E: DoD Distribution statement E from DoD Directive 5230.24 + :cvar DO_D_DIST_F: DoD Distribution statement F from DoD Directive 5230.24 + :cvar DO_D_DIST_X: DoD Distribution statement X from DoD Directive 5230.24 + :cvar US_PERSON: US Person info Notice + :cvar PRE13526_ORCON: Indicates that an instance document must abide by rules pertaining to ORIGINATOR + CONTROLLED data issued prior to Executive Order 13526. + :cvar POC: Indicates that the contents of this notice specify the contact information for a required point- + of-contact. + :cvar COMSEC: COMSEC Notice + """ + + FISA = "FISA" + IMC = "IMC" + CNWDI = "CNWDI" + RD = "RD" + FRD = "FRD" + DS = "DS" + LES = "LES" + LES_NF = "LES-NF" + DSEN = "DSEN" + DO_D_DIST_A = "DoD-Dist-A" + DO_D_DIST_B = "DoD-Dist-B" + DO_D_DIST_C = "DoD-Dist-C" + DO_D_DIST_D = "DoD-Dist-D" + DO_D_DIST_E = "DoD-Dist-E" + DO_D_DIST_F = "DoD-Dist-F" + DO_D_DIST_X = "DoD-Dist-X" + US_PERSON = "US-Person" + PRE13526_ORCON = "pre13526ORCON" + POC = "POC" + COMSEC = "COMSEC" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismpoc_type.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismpoc_type.py new file mode 100644 index 0000000..eb2c48a --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismpoc_type.py @@ -0,0 +1,33 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:pocType" + + +class CVEnumISMPocTypeValues(Enum): + """(U) All currently authorized types for ISM-related points-of-contact. + + PERMISSIBLE VALUES + The permissible values for this simple type are defined in the Controlled Value Enumeration: + CVEnumISMPocType.xml + + :cvar ICD_710: Point-of-contact for an ICD-710 notice. + :cvar DO_D_DIST_B: DoD Distribution statement B from DoD Directive 5230.24 + :cvar DO_D_DIST_C: DoD Distribution statement C from DoD Directive 5230.24 + :cvar DO_D_DIST_D: DoD Distribution statement D from DoD Directive 5230.24 + :cvar DO_D_DIST_E: DoD Distribution statement E from DoD Directive 5230.24 + :cvar DO_D_DIST_F: DoD Distribution statement F from DoD Directive 5230.24 + :cvar DO_D_DIST_X: DoD Distribution statement X from DoD Directive 5230.24 + """ + + ICD_710 = "ICD-710" + DO_D_DIST_B = "DoD-Dist-B" + DO_D_DIST_C = "DoD-Dist-C" + DO_D_DIST_D = "DoD-Dist-D" + DO_D_DIST_E = "DoD-Dist-E" + DO_D_DIST_F = "DoD-Dist-F" + DO_D_DIST_X = "DoD-Dist-X" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismscicontrols.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismscicontrols.py new file mode 100644 index 0000000..c65c35f --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/cvegenerated/cvenum_ismscicontrols.py @@ -0,0 +1,42 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ism:scicontrols" + + +class CVEnumISMSCIControlsValuesvalue(Enum): + """ + :cvar EL: ENDSEAL + :cvar EL_EU: ECRU + :cvar EL_NK: NONBOOK + :cvar HCS: HCS + :cvar HCS_O: HCS-O + :cvar HCS_P: HCS-P + :cvar KDK: KLONDIKE + :cvar KDK_BLFH: KDK BLUEFISH + :cvar KDK_IDIT: KDK IDITAROD + :cvar KDK_KAND: KDK KANDIK + :cvar RSV: RESERVE + :cvar SI: SPECIAL INTELLIGENCE + :cvar SI_G: SI-GAMMA + :cvar TK: TALENT KEYHOLE + """ + + EL = "EL" + EL_EU = "EL-EU" + EL_NK = "EL-NK" + HCS = "HCS" + HCS_O = "HCS-O" + HCS_P = "HCS-P" + KDK = "KDK" + KDK_BLFH = "KDK-BLFH" + KDK_IDIT = "KDK-IDIT" + KDK_KAND = "KDK-KAND" + RSV = "RSV" + SI = "SI" + SI_G = "SI-G" + TK = "TK" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/ic_ism.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/ic_ism.py new file mode 100644 index 0000000..ccf10bc --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ism/ic_ism.py @@ -0,0 +1,1427 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from typing import List, Optional, Union + +from xsdata.models.datatype import XmlDate + +from ..ismcat.cvegenerated.cvenum_ismcatfgiopen import CVEnumISMCATFGIOpenValuesvalue +from ..ismcat.cvegenerated.cvenum_ismcatfgiprotected import CVEnumISMCATFGIProtectedValuesvalue +from ..ismcat.cvegenerated.cvenum_ismcatowner_producer import CVEnumISMCATOwnerProducerValuesvalue +from ..ismcat.cvegenerated.cvenum_ismcatrel_to import CVEnumISMCATRelToValuesvalue +from .cvegenerated.cvenum_ism25_x import CVEnumISM25X +from .cvegenerated.cvenum_ismatomic_energy_markings import CVEnumISMatomicEnergyMarkingsValuesvalue +from .cvegenerated.cvenum_ismclassification_all import CVEnumISMClassificationAll +from .cvegenerated.cvenum_ismdissem import CVEnumISMDissemValues +from .cvegenerated.cvenum_ismnon_ic import CVEnumISMNonICValuesvalue +from .cvegenerated.cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .cvegenerated.cvenum_ismnotice import CVEnumISMNoticeValues +from .cvegenerated.cvenum_ismpoc_type import CVEnumISMPocTypeValues +from .cvegenerated.cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue + +__NAMESPACE__ = "urn:us:gov:ic:ism:13" + + +@dataclass +class LongStringWithSecurityType: + value: str = field( + default="", + metadata={ + "required": True, + "max_length": 32000, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class ShortStringWithSecurityType: + value: str = field( + default="", + metadata={ + "required": True, + "max_length": 256, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class NoticeText(LongStringWithSecurityType): + """ +

The actual text of a notice.

+ """ + + class Meta: + namespace = "urn:us:gov:ic:ism:13" + + poc_type: List[CVEnumISMPocTypeValues] = field( + default_factory=list, + metadata={ + "name": "pocType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + + +@dataclass +class NoticeBaseType: + """

Base type for Notices. + + Does not include any attributes.

+ """ + + notice_text: List[NoticeText] = field( + default_factory=list, + metadata={ + "name": "NoticeText", + "type": "Element", + "namespace": "urn:us:gov:ic:ism:13", + "min_occurs": 1, + }, + ) + + +@dataclass +class NoticeExternalType(NoticeBaseType): + """ +

A single Notice that may consist of 1 or more NoticeText + for use when the notice refers to something external.

+ """ + + notice_type: List[CVEnumISMNoticeValues] = field( + default_factory=list, + metadata={ + "name": "noticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + notice_reason: Optional[str] = field( + default=None, + metadata={ + "name": "noticeReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + notice_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "noticeDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + unregistered_notice_type: Optional[str] = field( + default=None, + metadata={ + "name": "unregisteredNoticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + external_notice: bool = field( + init=False, + default=True, + metadata={ + "name": "externalNotice", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class NoticeType(NoticeBaseType): + """ +

A single Notice that may consist of 1 or more + NoticeText

+ """ + + notice_type: List[CVEnumISMNoticeValues] = field( + default_factory=list, + metadata={ + "name": "noticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + notice_reason: Optional[str] = field( + default=None, + metadata={ + "name": "noticeReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + notice_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "noticeDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + unregistered_notice_type: Optional[str] = field( + default=None, + metadata={ + "name": "unregisteredNoticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + external_notice: Optional[bool] = field( + default=None, + metadata={ + "name": "externalNotice", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class Notice(NoticeType): + """ +

A single Notice that may consist of 1 or more + NoticeText

+ """ + + class Meta: + namespace = "urn:us:gov:ic:ism:13" + + +@dataclass +class NoticeExternal(NoticeExternalType): + """ +

A single Notice that may consist of 1 or more NoticeText + for use when the notice refers to something external.

+ """ + + class Meta: + namespace = "urn:us:gov:ic:ism:13" + + +@dataclass +class NoticeExternalListType: + """ +

A list of Notices

+ """ + + notice_external: List[NoticeExternal] = field( + default_factory=list, + metadata={ + "name": "NoticeExternal", + "type": "Element", + "namespace": "urn:us:gov:ic:ism:13", + "min_occurs": 1, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class NoticeListType: + """ +

A list of Notices

+ """ + + notice: List[Notice] = field( + default_factory=list, + metadata={ + "name": "Notice", + "type": "Element", + "namespace": "urn:us:gov:ic:ism:13", + "min_occurs": 1, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class NoticeExternalList(NoticeExternalListType): + class Meta: + namespace = "urn:us:gov:ic:ism:13" + + +@dataclass +class NoticeList(NoticeListType): + class Meta: + namespace = "urn:us:gov:ic:ism:13" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/__init__.py new file mode 100644 index 0000000..63a499c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/__init__.py @@ -0,0 +1,6 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:58:35 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +# nothing here diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/__init__.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/__init__.py new file mode 100644 index 0000000..493cab8 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/__init__.py @@ -0,0 +1,16 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from .cvenum_ismcatfgiopen import CVEnumISMCATFGIOpenValuesvalue +from .cvenum_ismcatfgiprotected import CVEnumISMCATFGIProtectedValuesvalue +from .cvenum_ismcatowner_producer import CVEnumISMCATOwnerProducerValuesvalue +from .cvenum_ismcatrel_to import CVEnumISMCATRelToValuesvalue + +__all__ = [ + "CVEnumISMCATFGIOpenValuesvalue", + "CVEnumISMCATFGIProtectedValuesvalue", + "CVEnumISMCATOwnerProducerValuesvalue", + "CVEnumISMCATRelToValuesvalue", +] diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiopen.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiopen.py new file mode 100644 index 0000000..6a51a93 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiopen.py @@ -0,0 +1,620 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ismcat:fgiopen" + + +class CVEnumISMCATFGIOpenValuesvalue(Enum): + """ + :cvar ABW: Aruba + :cvar AFG: Islamic Republic of Afghanistan + :cvar AGO: Republic of Angola + :cvar AIA: Anguilla + :cvar ALB: Republic of Albania + :cvar AND: Principality of Andorra + :cvar ARE: United Arab Emirates + :cvar ARG: Argentine Republic + :cvar ARM: Republic of Armenia + :cvar ASM: Territory of American Samoa + :cvar ATA: Antarctica + :cvar ATF: French Southern and Antarctic Lands + :cvar ATG: Antigua and Barbuda + :cvar AUS: Commonwealth of Australia + :cvar AUT: Republic of Austria + :cvar AX1: Unknown + :cvar AX2: Guantanamo Bay Naval Base + :cvar AZE: Republic of Azerbaijan + :cvar BDI: Republic of Burundi + :cvar BEL: Kingdom of Belgium + :cvar BEN: Republic of Benin + :cvar BES: Bonaire, Sint Eustatius, and Saba + :cvar BFA: Burkina Faso + :cvar BGD: People's Republic of Bangladesh + :cvar BGR: Republic of Bulgaria + :cvar BHR: Kingdom of Bahrain + :cvar BHS: Commonwealth of The Bahamas + :cvar BIH: Bosnia and Herzegovina + :cvar BLM: Saint Barthelemy + :cvar BLR: Republic of Belarus + :cvar BLZ: Belize + :cvar BMU: Bermuda + :cvar BOL: Plurinational State of Bolivia + :cvar BRA: Federative Republic of Brazil + :cvar BRB: Barbados + :cvar BRN: Brunei Darussalam + :cvar BTN: Kingdom of Bhutan + :cvar BVT: Bouvet Island + :cvar BWA: Republic of Botswana + :cvar CAF: Central African Republic + :cvar CAN: Canada + :cvar CCK: Territory of Cocos (Keeling) Islands + :cvar CHE: Swiss Confederation + :cvar CHL: Republic of Chile + :cvar CHN: People's Republic of China + :cvar CIV: Republic of Côte d'Ivoire + :cvar CMR: Republic of Cameroon + :cvar COD: Democratic Republic of the Congo + :cvar COG: Republic of the Congo + :cvar COK: Cook Islands + :cvar COL: Republic of Colombia + :cvar COM: Union of the Comoros + :cvar CPT: Clipperton Island + :cvar CPV: Republic of Cape Verde + :cvar CRI: Republic of Costa Rica + :cvar CUB: Republic of Cuba + :cvar CUW: Curaçao + :cvar CXR: Territory of Christmas Island + :cvar CYM: Cayman Islands + :cvar CYP: Republic of Cyprus + :cvar CZE: Czech Republic + :cvar DEU: Federal Republic of Germany + :cvar DGA: Diego Garcia + :cvar DJI: Republic of Djibouti + :cvar DMA: Commonwealth of Dominica + :cvar DNK: Kingdom of Denmark + :cvar DOM: Dominican Republic + :cvar DZA: People's Democratic Republic of Algeria + :cvar ECU: Republic of Ecuador + :cvar EGY: Arab Republic of Egypt + :cvar ERI: State of Eritrea + :cvar ESH: Western Sahara + :cvar ESP: Kingdom of Spain + :cvar EST: Republic of Estonia + :cvar ETH: Federal Democratic Republic of Ethiopia + :cvar FIN: Republic of Finland + :cvar FJI: Republic of Fiji + :cvar FLK: Falkland Islands (Islas Malvinas) + :cvar FRA: French Republic + :cvar FRO: Faroe Islands + :cvar FSM: Federated States of Micronesia + :cvar GAB: Gabonese Republic + :cvar GBR: United Kingdom of Great Britain and Northern Ireland + :cvar GEO: Georgia + :cvar GGY: Bailiwick of Guernsey + :cvar GHA: Republic of Ghana + :cvar GIB: Gibraltar + :cvar GIN: Republic of Guinea + :cvar GLP: Department of Guadeloupe + :cvar GMB: Republic of The Gambia + :cvar GNB: Republic of Guinea-Bissau + :cvar GNQ: Republic of Equatorial Guinea + :cvar GRC: Hellenic Republic + :cvar GRD: Grenada + :cvar GRL: Greenland + :cvar GTM: Republic of Guatemala + :cvar GUF: Department of Guiana + :cvar GUM: Territory of Guam + :cvar GUY: Co-operative Republic of Guyana + :cvar HKG: Hong Kong Special Administrative Region + :cvar HMD: Territory of Heard Island and McDonald Islands + :cvar HND: Republic of Honduras + :cvar HRV: Republic of Croatia + :cvar HTI: Republic of Haiti + :cvar HUN: Hungary + :cvar IDN: Republic of Indonesia + :cvar IMN: Isle of Man + :cvar IND: Republic of India + :cvar IOT: British Indian Ocean Territory + :cvar IRL: Ireland + :cvar IRN: Islamic Republic of Iran + :cvar IRQ: Republic of Iraq + :cvar ISL: Republic of Iceland + :cvar ISR: State of Israel + :cvar ITA: Italian Republic + :cvar JAM: Jamaica + :cvar JEY: Bailiwick of Jersey + :cvar JOR: Hashemite Kingdom of Jordan + :cvar JPN: Japan + :cvar KAZ: Republic of Kazakhstan + :cvar KEN: Republic of Kenya + :cvar KGZ: Kyrgyz Republic + :cvar KHM: Kingdom of Cambodia + :cvar KIR: Republic of Kiribati + :cvar KNA: Federation of Saint Kitts and Nevis + :cvar KOR: Republic of Korea + :cvar KWT: State of Kuwait + :cvar LAO: Lao People's Democratic Republic + :cvar LBN: Lebanese Republic + :cvar LBR: Republic of Liberia + :cvar LBY: Libya + :cvar LCA: Saint Lucia + :cvar LIE: Principality of Liechtenstein + :cvar LKA: Democratic Socialist Republic of Sri Lanka + :cvar LSO: Kingdom of Lesotho + :cvar LTU: Republic of Lithuania + :cvar LUX: Grand Duchy of Luxembourg + :cvar LVA: Republic of Latvia + :cvar MAC: Macau Special Administrative Region + :cvar MAF: Saint Martin + :cvar MAR: Kingdom of Morocco + :cvar MCO: Principality of Monaco + :cvar MDA: Republic of Moldova + :cvar MDG: Republic of Madagascar + :cvar MDV: Republic of Maldives + :cvar MEX: United Mexican States + :cvar MHL: Republic of the Marshall Islands + :cvar MKD: Republic of Macedonia + :cvar MLI: Republic of Mali + :cvar MLT: Republic of Malta + :cvar MMR: Union of Burma + :cvar MNE: Montenegro + :cvar MNG: Mongolia + :cvar MNP: Commonwealth of the Northern Mariana Islands + :cvar MOZ: Republic of Mozambique + :cvar MRT: Islamic Republic of Mauritania + :cvar MSR: Montserrat + :cvar MTQ: Department of Martinique + :cvar MUS: Republic of Mauritius + :cvar MWI: Republic of Malawi + :cvar MYS: Malaysia + :cvar MYT: Department of Mayotte + :cvar NAM: Republic of Namibia + :cvar NCL: New Caledonia + :cvar NER: Republic of the Niger + :cvar NFK: Territory of Norfolk Island + :cvar NGA: Federal Republic of Nigeria + :cvar NIC: Republic of Nicaragua + :cvar NIU: Niue + :cvar NLD: Kingdom of the Netherlands + :cvar NOR: Kingdom of Norway + :cvar NPL: Federal Democratic Republic of Nepal + :cvar NRU: Republic of Nauru + :cvar NZL: New Zealand + :cvar OMN: Sultanate of Oman + :cvar PAK: Islamic Republic of Pakistan + :cvar PAN: Republic of Panama + :cvar PCN: Pitcairn, Henderson, Ducie, and Oeno Islands + :cvar PER: Republic of Peru + :cvar PHL: Republic of the Philippines + :cvar PLW: Republic of Palau + :cvar PNG: Independent State of Papua New Guinea + :cvar POL: Republic of Poland + :cvar PRI: Commonwealth of Puerto Rico + :cvar PRK: Democratic People's Republic of Korea + :cvar PRT: Portuguese Republic + :cvar PRY: Republic of Paraguay + :cvar PSE: Palestinian Territory + :cvar PYF: French Polynesia + :cvar QAT: State of Qatar + :cvar REU: Department of Reunion + :cvar ROU: Romania + :cvar RUS: Russian Federation + :cvar RWA: Republic of Rwanda + :cvar SAU: Kingdom of Saudi Arabia + :cvar SDN: Republic of the Sudan + :cvar SEN: Republic of Senegal + :cvar SGP: Republic of Singapore + :cvar SGS: South Georgia and South Sandwich Islands + :cvar SHN: Saint Helena, Ascension, and Tristan da Cunha + :cvar SLB: Solomon Islands + :cvar SLE: Republic of Sierra Leone + :cvar SLV: Republic of El Salvador + :cvar SMR: Republic of San Marino + :cvar SOM: Somalia, Federal Republic of + :cvar SPM: Territorial Collectivity of Saint Pierre and Miquelon + :cvar SRB: Republic of Serbia + :cvar SSD: Republic of South Sudan + :cvar STP: Democratic Republic of Sao Tome and Principe + :cvar SUR: Republic of Suriname + :cvar SVK: Slovak Republic + :cvar SVN: Republic of Slovenia + :cvar SWE: Kingdom of Sweden + :cvar SWZ: Kingdom of Swaziland + :cvar SXM: Sint Maarten + :cvar SYC: Republic of Seychelles + :cvar SYR: Syrian Arab Republic + :cvar TCA: Turks and Caicos Islands + :cvar TCD: Republic of Chad + :cvar TGO: Togolese Republic + :cvar THA: Kingdom of Thailand + :cvar TJK: Republic of Tajikistan + :cvar TKL: Tokelau + :cvar TKM: Turkmenistan + :cvar TLS: Democratic Republic of Timor-Leste + :cvar TON: Kingdom of Tonga + :cvar TTO: Republic of Trinidad and Tobago + :cvar TUN: Tunisian Republic + :cvar TUR: Republic of Turkey + :cvar TUV: Tuvalu + :cvar TWN: Taiwan + :cvar TZA: United Republic of Tanzania + :cvar UGA: Republic of Uganda + :cvar UKR: Ukraine + :cvar URY: Oriental Republic of Uruguay + :cvar UZB: Republic of Uzbekistan + :cvar VAT: State of the Vatican City + :cvar VCT: Saint Vincent and the Grenadines + :cvar VEN: Bolivarian Republic of Venezuela + :cvar VGB: Virgin Islands, British + :cvar VIR: United States Virgin Islands + :cvar VNM: Socialist Republic of Vietnam + :cvar VUT: Republic of Vanuatu + :cvar WLF: Wallis and Futuna + :cvar WSM: Independent State of Samoa + :cvar XAC: Territory of Ashmore and Cartier Islands + :cvar XAZ: Entity 1 + :cvar XBI: Bassas da India + :cvar XBK: Baker Island + :cvar XCR: Entity 2 + :cvar XCS: Coral Sea Islands Territory + :cvar XCY: Entity 3 + :cvar XEU: Europa Island + :cvar XGL: Glorioso Islands + :cvar XGZ: Gaza Strip + :cvar XHO: Howland Island + :cvar XJA: Johnston Atoll + :cvar XJM: Jan Mayen + :cvar XJN: Juan de Nova Island + :cvar XJV: Jarvis Island + :cvar XKM: Entity 4 + :cvar XKN: Entity 5 + :cvar XKR: Kingman Reef + :cvar XKS: Republic of Kosovo + :cvar XMW: Midway Islands + :cvar XNV: Navassa Island + :cvar XPL: Palmyra Atoll + :cvar XPR: Paracel Islands + :cvar XQP: Etorofu, Habomai, Kunashiri, and Shikotan Islands + :cvar XQZ: Akrotiri + :cvar XSP: Spratly Islands + :cvar XSV: Svalbard + :cvar XTR: Tromelin Island + :cvar XWB: West Bank + :cvar XWK: Wake Island + :cvar XXD: Dhekelia + :cvar XXX: No Man's Land + :cvar YEM: Republic of Yemen + :cvar ZAF: Republic of South Africa + :cvar ZMB: Republic of Zambia + :cvar ZWE: Republic of Zimbabwe + :cvar ACGU: FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Biological Weapons Convention States + :cvar CFCK: ROK/US Combined Forces Command, Korea + :cvar CMFC: Combined Maritime Forces Central + :cvar CMFP: Cooperative Maritime Forces Pacific + :cvar CPMT: Civilian Protection Monitoring Team for Sudan + :cvar CTOC: Countering Transnational Organized Crime + :cvar CWCS: Chemical Weapons Convention States + :cvar FVEY: FIVE EYES + :cvar GCTF: Global Counter-Terrorism Forces + :cvar GMIF: Global Maritime Interception Forces + :cvar ISAF: International Security Assistance Force for Afghanistan + :cvar KFOR: Stabilization Forces in Kosovo + :cvar MLEC: Multi-Lateral Enduring Contingency + :cvar NACT: North African Counter-Terrorism Forces + :cvar NATO: North Atlantic Treaty Organization + :cvar NCFE: NATO Convention Armed Forces in Europe + :cvar OSTY: Open Skies Treaty + :cvar SPAA: Suppressed + :cvar TEYE: THREE EYES + :cvar UNCK: United Nations Command, Korea + """ + + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALB = "ALB" + AND = "AND" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AX1 = "AX1" + AX2 = "AX2" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BES = "BES" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPT = "CPT" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CUW = "CUW" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DGA = "DGA" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + SSD = "SSD" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SXM = "SXM" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + XAC = "XAC" + XAZ = "XAZ" + XBI = "XBI" + XBK = "XBK" + XCR = "XCR" + XCS = "XCS" + XCY = "XCY" + XEU = "XEU" + XGL = "XGL" + XGZ = "XGZ" + XHO = "XHO" + XJA = "XJA" + XJM = "XJM" + XJN = "XJN" + XJV = "XJV" + XKM = "XKM" + XKN = "XKN" + XKR = "XKR" + XKS = "XKS" + XMW = "XMW" + XNV = "XNV" + XPL = "XPL" + XPR = "XPR" + XQP = "XQP" + XQZ = "XQZ" + XSP = "XSP" + XSV = "XSV" + XTR = "XTR" + XWB = "XWB" + XWK = "XWK" + XXD = "XXD" + XXX = "XXX" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CTOC = "CTOC" + CWCS = "CWCS" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + ISAF = "ISAF" + KFOR = "KFOR" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + NCFE = "NCFE" + OSTY = "OSTY" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiprotected.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiprotected.py new file mode 100644 index 0000000..8bd2e65 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatfgiprotected.py @@ -0,0 +1,620 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ismcat:fgiprotected" + + +class CVEnumISMCATFGIProtectedValuesvalue(Enum): + """ + :cvar FGI: Foreign Government Information + :cvar ABW: Aruba + :cvar AFG: Islamic Republic of Afghanistan + :cvar AGO: Republic of Angola + :cvar AIA: Anguilla + :cvar ALB: Republic of Albania + :cvar AND: Principality of Andorra + :cvar ARE: United Arab Emirates + :cvar ARG: Argentine Republic + :cvar ARM: Republic of Armenia + :cvar ASM: Territory of American Samoa + :cvar ATA: Antarctica + :cvar ATF: French Southern and Antarctic Lands + :cvar ATG: Antigua and Barbuda + :cvar AUS: Commonwealth of Australia + :cvar AUT: Republic of Austria + :cvar AX2: Guantanamo Bay Naval Base + :cvar AZE: Republic of Azerbaijan + :cvar BDI: Republic of Burundi + :cvar BEL: Kingdom of Belgium + :cvar BEN: Republic of Benin + :cvar BES: Bonaire, Sint Eustatius, and Saba + :cvar BFA: Burkina Faso + :cvar BGD: People's Republic of Bangladesh + :cvar BGR: Republic of Bulgaria + :cvar BHR: Kingdom of Bahrain + :cvar BHS: Commonwealth of The Bahamas + :cvar BIH: Bosnia and Herzegovina + :cvar BLM: Saint Barthelemy + :cvar BLR: Republic of Belarus + :cvar BLZ: Belize + :cvar BMU: Bermuda + :cvar BOL: Plurinational State of Bolivia + :cvar BRA: Federative Republic of Brazil + :cvar BRB: Barbados + :cvar BRN: Brunei Darussalam + :cvar BTN: Kingdom of Bhutan + :cvar BVT: Bouvet Island + :cvar BWA: Republic of Botswana + :cvar CAF: Central African Republic + :cvar CAN: Canada + :cvar CCK: Territory of Cocos (Keeling) Islands + :cvar CHE: Swiss Confederation + :cvar CHL: Republic of Chile + :cvar CHN: People's Republic of China + :cvar CIV: Republic of Côte d'Ivoire + :cvar CMR: Republic of Cameroon + :cvar COD: Democratic Republic of the Congo + :cvar COG: Republic of the Congo + :cvar COK: Cook Islands + :cvar COL: Republic of Colombia + :cvar COM: Union of the Comoros + :cvar CPT: Clipperton Island + :cvar CPV: Republic of Cape Verde + :cvar CRI: Republic of Costa Rica + :cvar CUB: Republic of Cuba + :cvar CUW: Curaçao + :cvar CXR: Territory of Christmas Island + :cvar CYM: Cayman Islands + :cvar CYP: Republic of Cyprus + :cvar CZE: Czech Republic + :cvar DEU: Federal Republic of Germany + :cvar DGA: Diego Garcia + :cvar DJI: Republic of Djibouti + :cvar DMA: Commonwealth of Dominica + :cvar DNK: Kingdom of Denmark + :cvar DOM: Dominican Republic + :cvar DZA: People's Democratic Republic of Algeria + :cvar ECU: Republic of Ecuador + :cvar EGY: Arab Republic of Egypt + :cvar ERI: State of Eritrea + :cvar ESH: Western Sahara + :cvar ESP: Kingdom of Spain + :cvar EST: Republic of Estonia + :cvar ETH: Federal Democratic Republic of Ethiopia + :cvar FIN: Republic of Finland + :cvar FJI: Republic of Fiji + :cvar FLK: Falkland Islands (Islas Malvinas) + :cvar FRA: French Republic + :cvar FRO: Faroe Islands + :cvar FSM: Federated States of Micronesia + :cvar GAB: Gabonese Republic + :cvar GBR: United Kingdom of Great Britain and Northern Ireland + :cvar GEO: Georgia + :cvar GGY: Bailiwick of Guernsey + :cvar GHA: Republic of Ghana + :cvar GIB: Gibraltar + :cvar GIN: Republic of Guinea + :cvar GLP: Department of Guadeloupe + :cvar GMB: Republic of The Gambia + :cvar GNB: Republic of Guinea-Bissau + :cvar GNQ: Republic of Equatorial Guinea + :cvar GRC: Hellenic Republic + :cvar GRD: Grenada + :cvar GRL: Greenland + :cvar GTM: Republic of Guatemala + :cvar GUF: Department of Guiana + :cvar GUM: Territory of Guam + :cvar GUY: Co-operative Republic of Guyana + :cvar HKG: Hong Kong Special Administrative Region + :cvar HMD: Territory of Heard Island and McDonald Islands + :cvar HND: Republic of Honduras + :cvar HRV: Republic of Croatia + :cvar HTI: Republic of Haiti + :cvar HUN: Hungary + :cvar IDN: Republic of Indonesia + :cvar IMN: Isle of Man + :cvar IND: Republic of India + :cvar IOT: British Indian Ocean Territory + :cvar IRL: Ireland + :cvar IRN: Islamic Republic of Iran + :cvar IRQ: Republic of Iraq + :cvar ISL: Republic of Iceland + :cvar ISR: State of Israel + :cvar ITA: Italian Republic + :cvar JAM: Jamaica + :cvar JEY: Bailiwick of Jersey + :cvar JOR: Hashemite Kingdom of Jordan + :cvar JPN: Japan + :cvar KAZ: Republic of Kazakhstan + :cvar KEN: Republic of Kenya + :cvar KGZ: Kyrgyz Republic + :cvar KHM: Kingdom of Cambodia + :cvar KIR: Republic of Kiribati + :cvar KNA: Federation of Saint Kitts and Nevis + :cvar KOR: Republic of Korea + :cvar KWT: State of Kuwait + :cvar LAO: Lao People's Democratic Republic + :cvar LBN: Lebanese Republic + :cvar LBR: Republic of Liberia + :cvar LBY: Libya + :cvar LCA: Saint Lucia + :cvar LIE: Principality of Liechtenstein + :cvar LKA: Democratic Socialist Republic of Sri Lanka + :cvar LSO: Kingdom of Lesotho + :cvar LTU: Republic of Lithuania + :cvar LUX: Grand Duchy of Luxembourg + :cvar LVA: Republic of Latvia + :cvar MAC: Macau Special Administrative Region + :cvar MAF: Saint Martin + :cvar MAR: Kingdom of Morocco + :cvar MCO: Principality of Monaco + :cvar MDA: Republic of Moldova + :cvar MDG: Republic of Madagascar + :cvar MDV: Republic of Maldives + :cvar MEX: United Mexican States + :cvar MHL: Republic of the Marshall Islands + :cvar MKD: Republic of Macedonia + :cvar MLI: Republic of Mali + :cvar MLT: Republic of Malta + :cvar MMR: Union of Burma + :cvar MNE: Montenegro + :cvar MNG: Mongolia + :cvar MNP: Commonwealth of the Northern Mariana Islands + :cvar MOZ: Republic of Mozambique + :cvar MRT: Islamic Republic of Mauritania + :cvar MSR: Montserrat + :cvar MTQ: Department of Martinique + :cvar MUS: Republic of Mauritius + :cvar MWI: Republic of Malawi + :cvar MYS: Malaysia + :cvar MYT: Department of Mayotte + :cvar NAM: Republic of Namibia + :cvar NCL: New Caledonia + :cvar NER: Republic of the Niger + :cvar NFK: Territory of Norfolk Island + :cvar NGA: Federal Republic of Nigeria + :cvar NIC: Republic of Nicaragua + :cvar NIU: Niue + :cvar NLD: Kingdom of the Netherlands + :cvar NOR: Kingdom of Norway + :cvar NPL: Federal Democratic Republic of Nepal + :cvar NRU: Republic of Nauru + :cvar NZL: New Zealand + :cvar OMN: Sultanate of Oman + :cvar PAK: Islamic Republic of Pakistan + :cvar PAN: Republic of Panama + :cvar PCN: Pitcairn, Henderson, Ducie, and Oeno Islands + :cvar PER: Republic of Peru + :cvar PHL: Republic of the Philippines + :cvar PLW: Republic of Palau + :cvar PNG: Independent State of Papua New Guinea + :cvar POL: Republic of Poland + :cvar PRI: Commonwealth of Puerto Rico + :cvar PRK: Democratic People's Republic of Korea + :cvar PRT: Portuguese Republic + :cvar PRY: Republic of Paraguay + :cvar PSE: Palestinian Territory + :cvar PYF: French Polynesia + :cvar QAT: State of Qatar + :cvar REU: Department of Reunion + :cvar ROU: Romania + :cvar RUS: Russian Federation + :cvar RWA: Republic of Rwanda + :cvar SAU: Kingdom of Saudi Arabia + :cvar SDN: Republic of the Sudan + :cvar SEN: Republic of Senegal + :cvar SGP: Republic of Singapore + :cvar SGS: South Georgia and South Sandwich Islands + :cvar SHN: Saint Helena, Ascension, and Tristan da Cunha + :cvar SLB: Solomon Islands + :cvar SLE: Republic of Sierra Leone + :cvar SLV: Republic of El Salvador + :cvar SMR: Republic of San Marino + :cvar SOM: Somalia, Federal Republic of + :cvar SPM: Territorial Collectivity of Saint Pierre and Miquelon + :cvar SRB: Republic of Serbia + :cvar SSD: Republic of South Sudan + :cvar STP: Democratic Republic of Sao Tome and Principe + :cvar SUR: Republic of Suriname + :cvar SVK: Slovak Republic + :cvar SVN: Republic of Slovenia + :cvar SWE: Kingdom of Sweden + :cvar SWZ: Kingdom of Swaziland + :cvar SXM: Sint Maarten + :cvar SYC: Republic of Seychelles + :cvar SYR: Syrian Arab Republic + :cvar TCA: Turks and Caicos Islands + :cvar TCD: Republic of Chad + :cvar TGO: Togolese Republic + :cvar THA: Kingdom of Thailand + :cvar TJK: Republic of Tajikistan + :cvar TKL: Tokelau + :cvar TKM: Turkmenistan + :cvar TLS: Democratic Republic of Timor-Leste + :cvar TON: Kingdom of Tonga + :cvar TTO: Republic of Trinidad and Tobago + :cvar TUN: Tunisian Republic + :cvar TUR: Republic of Turkey + :cvar TUV: Tuvalu + :cvar TWN: Taiwan + :cvar TZA: United Republic of Tanzania + :cvar UGA: Republic of Uganda + :cvar UKR: Ukraine + :cvar URY: Oriental Republic of Uruguay + :cvar UZB: Republic of Uzbekistan + :cvar VAT: State of the Vatican City + :cvar VCT: Saint Vincent and the Grenadines + :cvar VEN: Bolivarian Republic of Venezuela + :cvar VGB: Virgin Islands, British + :cvar VIR: United States Virgin Islands + :cvar VNM: Socialist Republic of Vietnam + :cvar VUT: Republic of Vanuatu + :cvar WLF: Wallis and Futuna + :cvar WSM: Independent State of Samoa + :cvar XAC: Territory of Ashmore and Cartier Islands + :cvar XAZ: Entity 1 + :cvar XBI: Bassas da India + :cvar XBK: Baker Island + :cvar XCR: Entity 2 + :cvar XCS: Coral Sea Islands Territory + :cvar XCY: Entity 3 + :cvar XEU: Europa Island + :cvar XGL: Glorioso Islands + :cvar XGZ: Gaza Strip + :cvar XHO: Howland Island + :cvar XJA: Johnston Atoll + :cvar XJM: Jan Mayen + :cvar XJN: Juan de Nova Island + :cvar XJV: Jarvis Island + :cvar XKM: Entity 4 + :cvar XKN: Entity 5 + :cvar XKR: Kingman Reef + :cvar XKS: Republic of Kosovo + :cvar XMW: Midway Islands + :cvar XNV: Navassa Island + :cvar XPL: Palmyra Atoll + :cvar XPR: Paracel Islands + :cvar XQP: Etorofu, Habomai, Kunashiri, and Shikotan Islands + :cvar XQZ: Akrotiri + :cvar XSP: Spratly Islands + :cvar XSV: Svalbard + :cvar XTR: Tromelin Island + :cvar XWB: West Bank + :cvar XWK: Wake Island + :cvar XXD: Dhekelia + :cvar XXX: No Man's Land + :cvar YEM: Republic of Yemen + :cvar ZAF: Republic of South Africa + :cvar ZMB: Republic of Zambia + :cvar ZWE: Republic of Zimbabwe + :cvar ACGU: FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Biological Weapons Convention States + :cvar CFCK: ROK/US Combined Forces Command, Korea + :cvar CMFC: Combined Maritime Forces Central + :cvar CMFP: Cooperative Maritime Forces Pacific + :cvar CPMT: Civilian Protection Monitoring Team for Sudan + :cvar CTOC: Countering Transnational Organized Crime + :cvar CWCS: Chemical Weapons Convention States + :cvar FVEY: FIVE EYES + :cvar GCTF: Global Counter-Terrorism Forces + :cvar GMIF: Global Maritime Interception Forces + :cvar ISAF: International Security Assistance Force for Afghanistan + :cvar KFOR: Stabilization Forces in Kosovo + :cvar MLEC: Multi-Lateral Enduring Contingency + :cvar NACT: North African Counter-Terrorism Forces + :cvar NATO: North Atlantic Treaty Organization + :cvar NCFE: NATO Convention Armed Forces in Europe + :cvar OSTY: Open Skies Treaty + :cvar SPAA: Suppressed + :cvar TEYE: THREE EYES + :cvar UNCK: United Nations Command, Korea + """ + + FGI = "FGI" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALB = "ALB" + AND = "AND" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AX2 = "AX2" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BES = "BES" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPT = "CPT" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CUW = "CUW" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DGA = "DGA" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + SSD = "SSD" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SXM = "SXM" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + XAC = "XAC" + XAZ = "XAZ" + XBI = "XBI" + XBK = "XBK" + XCR = "XCR" + XCS = "XCS" + XCY = "XCY" + XEU = "XEU" + XGL = "XGL" + XGZ = "XGZ" + XHO = "XHO" + XJA = "XJA" + XJM = "XJM" + XJN = "XJN" + XJV = "XJV" + XKM = "XKM" + XKN = "XKN" + XKR = "XKR" + XKS = "XKS" + XMW = "XMW" + XNV = "XNV" + XPL = "XPL" + XPR = "XPR" + XQP = "XQP" + XQZ = "XQZ" + XSP = "XSP" + XSV = "XSV" + XTR = "XTR" + XWB = "XWB" + XWK = "XWK" + XXD = "XXD" + XXX = "XXX" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CTOC = "CTOC" + CWCS = "CWCS" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + ISAF = "ISAF" + KFOR = "KFOR" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + NCFE = "NCFE" + OSTY = "OSTY" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatowner_producer.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatowner_producer.py new file mode 100644 index 0000000..304220c --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatowner_producer.py @@ -0,0 +1,622 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ismcat:ownerproducer" + + +class CVEnumISMCATOwnerProducerValuesvalue(Enum): + """ + :cvar FGI: Foreign Government Information + :cvar ABW: Aruba + :cvar AFG: Islamic Republic of Afghanistan + :cvar AGO: Republic of Angola + :cvar AIA: Anguilla + :cvar ALB: Republic of Albania + :cvar AND: Principality of Andorra + :cvar ARE: United Arab Emirates + :cvar ARG: Argentine Republic + :cvar ARM: Republic of Armenia + :cvar ASM: Territory of American Samoa + :cvar ATA: Antarctica + :cvar ATF: French Southern and Antarctic Lands + :cvar ATG: Antigua and Barbuda + :cvar AUS: Commonwealth of Australia + :cvar AUT: Republic of Austria + :cvar AX2: Guantanamo Bay Naval Base + :cvar AZE: Republic of Azerbaijan + :cvar BDI: Republic of Burundi + :cvar BEL: Kingdom of Belgium + :cvar BEN: Republic of Benin + :cvar BES: Bonaire, Sint Eustatius, and Saba + :cvar BFA: Burkina Faso + :cvar BGD: People's Republic of Bangladesh + :cvar BGR: Republic of Bulgaria + :cvar BHR: Kingdom of Bahrain + :cvar BHS: Commonwealth of The Bahamas + :cvar BIH: Bosnia and Herzegovina + :cvar BLM: Saint Barthelemy + :cvar BLR: Republic of Belarus + :cvar BLZ: Belize + :cvar BMU: Bermuda + :cvar BOL: Plurinational State of Bolivia + :cvar BRA: Federative Republic of Brazil + :cvar BRB: Barbados + :cvar BRN: Brunei Darussalam + :cvar BTN: Kingdom of Bhutan + :cvar BVT: Bouvet Island + :cvar BWA: Republic of Botswana + :cvar CAF: Central African Republic + :cvar CAN: Canada + :cvar CCK: Territory of Cocos (Keeling) Islands + :cvar CHE: Swiss Confederation + :cvar CHL: Republic of Chile + :cvar CHN: People's Republic of China + :cvar CIV: Republic of Côte d'Ivoire + :cvar CMR: Republic of Cameroon + :cvar COD: Democratic Republic of the Congo + :cvar COG: Republic of the Congo + :cvar COK: Cook Islands + :cvar COL: Republic of Colombia + :cvar COM: Union of the Comoros + :cvar CPT: Clipperton Island + :cvar CPV: Republic of Cape Verde + :cvar CRI: Republic of Costa Rica + :cvar CUB: Republic of Cuba + :cvar CUW: Curaçao + :cvar CXR: Territory of Christmas Island + :cvar CYM: Cayman Islands + :cvar CYP: Republic of Cyprus + :cvar CZE: Czech Republic + :cvar DEU: Federal Republic of Germany + :cvar DGA: Diego Garcia + :cvar DJI: Republic of Djibouti + :cvar DMA: Commonwealth of Dominica + :cvar DNK: Kingdom of Denmark + :cvar DOM: Dominican Republic + :cvar DZA: People's Democratic Republic of Algeria + :cvar ECU: Republic of Ecuador + :cvar EGY: Arab Republic of Egypt + :cvar ERI: State of Eritrea + :cvar ESH: Western Sahara + :cvar ESP: Kingdom of Spain + :cvar EST: Republic of Estonia + :cvar ETH: Federal Democratic Republic of Ethiopia + :cvar FIN: Republic of Finland + :cvar FJI: Republic of Fiji + :cvar FLK: Falkland Islands (Islas Malvinas) + :cvar FRA: French Republic + :cvar FRO: Faroe Islands + :cvar FSM: Federated States of Micronesia + :cvar GAB: Gabonese Republic + :cvar GBR: United Kingdom of Great Britain and Northern Ireland + :cvar GEO: Georgia + :cvar GGY: Bailiwick of Guernsey + :cvar GHA: Republic of Ghana + :cvar GIB: Gibraltar + :cvar GIN: Republic of Guinea + :cvar GLP: Department of Guadeloupe + :cvar GMB: Republic of The Gambia + :cvar GNB: Republic of Guinea-Bissau + :cvar GNQ: Republic of Equatorial Guinea + :cvar GRC: Hellenic Republic + :cvar GRD: Grenada + :cvar GRL: Greenland + :cvar GTM: Republic of Guatemala + :cvar GUF: Department of Guiana + :cvar GUM: Territory of Guam + :cvar GUY: Co-operative Republic of Guyana + :cvar HKG: Hong Kong Special Administrative Region + :cvar HMD: Territory of Heard Island and McDonald Islands + :cvar HND: Republic of Honduras + :cvar HRV: Republic of Croatia + :cvar HTI: Republic of Haiti + :cvar HUN: Hungary + :cvar IDN: Republic of Indonesia + :cvar IMN: Isle of Man + :cvar IND: Republic of India + :cvar IOT: British Indian Ocean Territory + :cvar IRL: Ireland + :cvar IRN: Islamic Republic of Iran + :cvar IRQ: Republic of Iraq + :cvar ISL: Republic of Iceland + :cvar ISR: State of Israel + :cvar ITA: Italian Republic + :cvar JAM: Jamaica + :cvar JEY: Bailiwick of Jersey + :cvar JOR: Hashemite Kingdom of Jordan + :cvar JPN: Japan + :cvar KAZ: Republic of Kazakhstan + :cvar KEN: Republic of Kenya + :cvar KGZ: Kyrgyz Republic + :cvar KHM: Kingdom of Cambodia + :cvar KIR: Republic of Kiribati + :cvar KNA: Federation of Saint Kitts and Nevis + :cvar KOR: Republic of Korea + :cvar KWT: State of Kuwait + :cvar LAO: Lao People's Democratic Republic + :cvar LBN: Lebanese Republic + :cvar LBR: Republic of Liberia + :cvar LBY: Libya + :cvar LCA: Saint Lucia + :cvar LIE: Principality of Liechtenstein + :cvar LKA: Democratic Socialist Republic of Sri Lanka + :cvar LSO: Kingdom of Lesotho + :cvar LTU: Republic of Lithuania + :cvar LUX: Grand Duchy of Luxembourg + :cvar LVA: Republic of Latvia + :cvar MAC: Macau Special Administrative Region + :cvar MAF: Saint Martin + :cvar MAR: Kingdom of Morocco + :cvar MCO: Principality of Monaco + :cvar MDA: Republic of Moldova + :cvar MDG: Republic of Madagascar + :cvar MDV: Republic of Maldives + :cvar MEX: United Mexican States + :cvar MHL: Republic of the Marshall Islands + :cvar MKD: Republic of Macedonia + :cvar MLI: Republic of Mali + :cvar MLT: Republic of Malta + :cvar MMR: Union of Burma + :cvar MNE: Montenegro + :cvar MNG: Mongolia + :cvar MNP: Commonwealth of the Northern Mariana Islands + :cvar MOZ: Republic of Mozambique + :cvar MRT: Islamic Republic of Mauritania + :cvar MSR: Montserrat + :cvar MTQ: Department of Martinique + :cvar MUS: Republic of Mauritius + :cvar MWI: Republic of Malawi + :cvar MYS: Malaysia + :cvar MYT: Department of Mayotte + :cvar NAM: Republic of Namibia + :cvar NCL: New Caledonia + :cvar NER: Republic of the Niger + :cvar NFK: Territory of Norfolk Island + :cvar NGA: Federal Republic of Nigeria + :cvar NIC: Republic of Nicaragua + :cvar NIU: Niue + :cvar NLD: Kingdom of the Netherlands + :cvar NOR: Kingdom of Norway + :cvar NPL: Federal Democratic Republic of Nepal + :cvar NRU: Republic of Nauru + :cvar NZL: New Zealand + :cvar OMN: Sultanate of Oman + :cvar PAK: Islamic Republic of Pakistan + :cvar PAN: Republic of Panama + :cvar PCN: Pitcairn, Henderson, Ducie, and Oeno Islands + :cvar PER: Republic of Peru + :cvar PHL: Republic of the Philippines + :cvar PLW: Republic of Palau + :cvar PNG: Independent State of Papua New Guinea + :cvar POL: Republic of Poland + :cvar PRI: Commonwealth of Puerto Rico + :cvar PRK: Democratic People's Republic of Korea + :cvar PRT: Portuguese Republic + :cvar PRY: Republic of Paraguay + :cvar PSE: Palestinian Territory + :cvar PYF: French Polynesia + :cvar QAT: State of Qatar + :cvar REU: Department of Reunion + :cvar ROU: Romania + :cvar RUS: Russian Federation + :cvar RWA: Republic of Rwanda + :cvar SAU: Kingdom of Saudi Arabia + :cvar SDN: Republic of the Sudan + :cvar SEN: Republic of Senegal + :cvar SGP: Republic of Singapore + :cvar SGS: South Georgia and South Sandwich Islands + :cvar SHN: Saint Helena, Ascension, and Tristan da Cunha + :cvar SLB: Solomon Islands + :cvar SLE: Republic of Sierra Leone + :cvar SLV: Republic of El Salvador + :cvar SMR: Republic of San Marino + :cvar SOM: Somalia, Federal Republic of + :cvar SPM: Territorial Collectivity of Saint Pierre and Miquelon + :cvar SRB: Republic of Serbia + :cvar SSD: Republic of South Sudan + :cvar STP: Democratic Republic of Sao Tome and Principe + :cvar SUR: Republic of Suriname + :cvar SVK: Slovak Republic + :cvar SVN: Republic of Slovenia + :cvar SWE: Kingdom of Sweden + :cvar SWZ: Kingdom of Swaziland + :cvar SXM: Sint Maarten + :cvar SYC: Republic of Seychelles + :cvar SYR: Syrian Arab Republic + :cvar TCA: Turks and Caicos Islands + :cvar TCD: Republic of Chad + :cvar TGO: Togolese Republic + :cvar THA: Kingdom of Thailand + :cvar TJK: Republic of Tajikistan + :cvar TKL: Tokelau + :cvar TKM: Turkmenistan + :cvar TLS: Democratic Republic of Timor-Leste + :cvar TON: Kingdom of Tonga + :cvar TTO: Republic of Trinidad and Tobago + :cvar TUN: Tunisian Republic + :cvar TUR: Republic of Turkey + :cvar TUV: Tuvalu + :cvar TWN: Taiwan + :cvar TZA: United Republic of Tanzania + :cvar UGA: Republic of Uganda + :cvar UKR: Ukraine + :cvar URY: Oriental Republic of Uruguay + :cvar USA: United States of America + :cvar UZB: Republic of Uzbekistan + :cvar VAT: State of the Vatican City + :cvar VCT: Saint Vincent and the Grenadines + :cvar VEN: Bolivarian Republic of Venezuela + :cvar VGB: Virgin Islands, British + :cvar VIR: United States Virgin Islands + :cvar VNM: Socialist Republic of Vietnam + :cvar VUT: Republic of Vanuatu + :cvar WLF: Wallis and Futuna + :cvar WSM: Independent State of Samoa + :cvar XAC: Territory of Ashmore and Cartier Islands + :cvar XAZ: Entity 1 + :cvar XBI: Bassas da India + :cvar XBK: Baker Island + :cvar XCR: Entity 2 + :cvar XCS: Coral Sea Islands Territory + :cvar XCY: Entity 3 + :cvar XEU: Europa Island + :cvar XGL: Glorioso Islands + :cvar XGZ: Gaza Strip + :cvar XHO: Howland Island + :cvar XJA: Johnston Atoll + :cvar XJM: Jan Mayen + :cvar XJN: Juan de Nova Island + :cvar XJV: Jarvis Island + :cvar XKM: Entity 4 + :cvar XKN: Entity 5 + :cvar XKR: Kingman Reef + :cvar XKS: Republic of Kosovo + :cvar XMW: Midway Islands + :cvar XNV: Navassa Island + :cvar XPL: Palmyra Atoll + :cvar XPR: Paracel Islands + :cvar XQP: Etorofu, Habomai, Kunashiri, and Shikotan Islands + :cvar XQZ: Akrotiri + :cvar XSP: Spratly Islands + :cvar XSV: Svalbard + :cvar XTR: Tromelin Island + :cvar XWB: West Bank + :cvar XWK: Wake Island + :cvar XXD: Dhekelia + :cvar XXX: No Man's Land + :cvar YEM: Republic of Yemen + :cvar ZAF: Republic of South Africa + :cvar ZMB: Republic of Zambia + :cvar ZWE: Republic of Zimbabwe + :cvar ACGU: FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Biological Weapons Convention States + :cvar CFCK: ROK/US Combined Forces Command, Korea + :cvar CMFC: Combined Maritime Forces Central + :cvar CMFP: Cooperative Maritime Forces Pacific + :cvar CPMT: Civilian Protection Monitoring Team for Sudan + :cvar CTOC: Countering Transnational Organized Crime + :cvar CWCS: Chemical Weapons Convention States + :cvar FVEY: FIVE EYES + :cvar GCTF: Global Counter-Terrorism Forces + :cvar GMIF: Global Maritime Interception Forces + :cvar ISAF: International Security Assistance Force for Afghanistan + :cvar KFOR: Stabilization Forces in Kosovo + :cvar MLEC: Multi-Lateral Enduring Contingency + :cvar NACT: North African Counter-Terrorism Forces + :cvar NATO: North Atlantic Treaty Organization + :cvar NCFE: NATO Convention Armed Forces in Europe + :cvar OSTY: Open Skies Treaty + :cvar SPAA: Suppressed + :cvar TEYE: THREE EYES + :cvar UNCK: United Nations Command, Korea + """ + + FGI = "FGI" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALB = "ALB" + AND = "AND" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AX2 = "AX2" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BES = "BES" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPT = "CPT" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CUW = "CUW" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DGA = "DGA" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + SSD = "SSD" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SXM = "SXM" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + URY = "URY" + USA = "USA" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + XAC = "XAC" + XAZ = "XAZ" + XBI = "XBI" + XBK = "XBK" + XCR = "XCR" + XCS = "XCS" + XCY = "XCY" + XEU = "XEU" + XGL = "XGL" + XGZ = "XGZ" + XHO = "XHO" + XJA = "XJA" + XJM = "XJM" + XJN = "XJN" + XJV = "XJV" + XKM = "XKM" + XKN = "XKN" + XKR = "XKR" + XKS = "XKS" + XMW = "XMW" + XNV = "XNV" + XPL = "XPL" + XPR = "XPR" + XQP = "XQP" + XQZ = "XQZ" + XSP = "XSP" + XSV = "XSV" + XTR = "XTR" + XWB = "XWB" + XWK = "XWK" + XXD = "XXD" + XXX = "XXX" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CTOC = "CTOC" + CWCS = "CWCS" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + ISAF = "ISAF" + KFOR = "KFOR" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + NCFE = "NCFE" + OSTY = "OSTY" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatrel_to.py b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatrel_to.py new file mode 100644 index 0000000..7c7ec48 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/external/ism_v13/schema/ismcat/cvegenerated/cvenum_ismcatrel_to.py @@ -0,0 +1,620 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from enum import Enum + +__NAMESPACE__ = "urn:us:gov:ic:cvenum:ismcat:relto" + + +class CVEnumISMCATRelToValuesvalue(Enum): + """ + :cvar USA: United States + :cvar ABW: Aruba + :cvar AFG: Islamic Republic of Afghanistan + :cvar AGO: Republic of Angola + :cvar AIA: Anguilla + :cvar ALB: Republic of Albania + :cvar AND: Principality of Andorra + :cvar ARE: United Arab Emirates + :cvar ARG: Argentine Republic + :cvar ARM: Republic of Armenia + :cvar ASM: Territory of American Samoa + :cvar ATA: Antarctica + :cvar ATF: French Southern and Antarctic Lands + :cvar ATG: Antigua and Barbuda + :cvar AUS: Commonwealth of Australia + :cvar AUT: Republic of Austria + :cvar AX2: Guantanamo Bay Naval Base + :cvar AZE: Republic of Azerbaijan + :cvar BDI: Republic of Burundi + :cvar BEL: Kingdom of Belgium + :cvar BEN: Republic of Benin + :cvar BES: Bonaire, Sint Eustatius, and Saba + :cvar BFA: Burkina Faso + :cvar BGD: People's Republic of Bangladesh + :cvar BGR: Republic of Bulgaria + :cvar BHR: Kingdom of Bahrain + :cvar BHS: Commonwealth of The Bahamas + :cvar BIH: Bosnia and Herzegovina + :cvar BLM: Saint Barthelemy + :cvar BLR: Republic of Belarus + :cvar BLZ: Belize + :cvar BMU: Bermuda + :cvar BOL: Plurinational State of Bolivia + :cvar BRA: Federative Republic of Brazil + :cvar BRB: Barbados + :cvar BRN: Brunei Darussalam + :cvar BTN: Kingdom of Bhutan + :cvar BVT: Bouvet Island + :cvar BWA: Republic of Botswana + :cvar CAF: Central African Republic + :cvar CAN: Canada + :cvar CCK: Territory of Cocos (Keeling) Islands + :cvar CHE: Swiss Confederation + :cvar CHL: Republic of Chile + :cvar CHN: People's Republic of China + :cvar CIV: Republic of Côte d'Ivoire + :cvar CMR: Republic of Cameroon + :cvar COD: Democratic Republic of the Congo + :cvar COG: Republic of the Congo + :cvar COK: Cook Islands + :cvar COL: Republic of Colombia + :cvar COM: Union of the Comoros + :cvar CPT: Clipperton Island + :cvar CPV: Republic of Cape Verde + :cvar CRI: Republic of Costa Rica + :cvar CUB: Republic of Cuba + :cvar CUW: Curaçao + :cvar CXR: Territory of Christmas Island + :cvar CYM: Cayman Islands + :cvar CYP: Republic of Cyprus + :cvar CZE: Czech Republic + :cvar DEU: Federal Republic of Germany + :cvar DGA: Diego Garcia + :cvar DJI: Republic of Djibouti + :cvar DMA: Commonwealth of Dominica + :cvar DNK: Kingdom of Denmark + :cvar DOM: Dominican Republic + :cvar DZA: People's Democratic Republic of Algeria + :cvar ECU: Republic of Ecuador + :cvar EGY: Arab Republic of Egypt + :cvar ERI: State of Eritrea + :cvar ESH: Western Sahara + :cvar ESP: Kingdom of Spain + :cvar EST: Republic of Estonia + :cvar ETH: Federal Democratic Republic of Ethiopia + :cvar FIN: Republic of Finland + :cvar FJI: Republic of Fiji + :cvar FLK: Falkland Islands (Islas Malvinas) + :cvar FRA: French Republic + :cvar FRO: Faroe Islands + :cvar FSM: Federated States of Micronesia + :cvar GAB: Gabonese Republic + :cvar GBR: United Kingdom of Great Britain and Northern Ireland + :cvar GEO: Georgia + :cvar GGY: Bailiwick of Guernsey + :cvar GHA: Republic of Ghana + :cvar GIB: Gibraltar + :cvar GIN: Republic of Guinea + :cvar GLP: Department of Guadeloupe + :cvar GMB: Republic of The Gambia + :cvar GNB: Republic of Guinea-Bissau + :cvar GNQ: Republic of Equatorial Guinea + :cvar GRC: Hellenic Republic + :cvar GRD: Grenada + :cvar GRL: Greenland + :cvar GTM: Republic of Guatemala + :cvar GUF: Department of Guiana + :cvar GUM: Territory of Guam + :cvar GUY: Co-operative Republic of Guyana + :cvar HKG: Hong Kong Special Administrative Region + :cvar HMD: Territory of Heard Island and McDonald Islands + :cvar HND: Republic of Honduras + :cvar HRV: Republic of Croatia + :cvar HTI: Republic of Haiti + :cvar HUN: Hungary + :cvar IDN: Republic of Indonesia + :cvar IMN: Isle of Man + :cvar IND: Republic of India + :cvar IOT: British Indian Ocean Territory + :cvar IRL: Ireland + :cvar IRN: Islamic Republic of Iran + :cvar IRQ: Republic of Iraq + :cvar ISL: Republic of Iceland + :cvar ISR: State of Israel + :cvar ITA: Italian Republic + :cvar JAM: Jamaica + :cvar JEY: Bailiwick of Jersey + :cvar JOR: Hashemite Kingdom of Jordan + :cvar JPN: Japan + :cvar KAZ: Republic of Kazakhstan + :cvar KEN: Republic of Kenya + :cvar KGZ: Kyrgyz Republic + :cvar KHM: Kingdom of Cambodia + :cvar KIR: Republic of Kiribati + :cvar KNA: Federation of Saint Kitts and Nevis + :cvar KOR: Republic of Korea + :cvar KWT: State of Kuwait + :cvar LAO: Lao People's Democratic Republic + :cvar LBN: Lebanese Republic + :cvar LBR: Republic of Liberia + :cvar LBY: Libya + :cvar LCA: Saint Lucia + :cvar LIE: Principality of Liechtenstein + :cvar LKA: Democratic Socialist Republic of Sri Lanka + :cvar LSO: Kingdom of Lesotho + :cvar LTU: Republic of Lithuania + :cvar LUX: Grand Duchy of Luxembourg + :cvar LVA: Republic of Latvia + :cvar MAC: Macau Special Administrative Region + :cvar MAF: Saint Martin + :cvar MAR: Kingdom of Morocco + :cvar MCO: Principality of Monaco + :cvar MDA: Republic of Moldova + :cvar MDG: Republic of Madagascar + :cvar MDV: Republic of Maldives + :cvar MEX: United Mexican States + :cvar MHL: Republic of the Marshall Islands + :cvar MKD: Republic of Macedonia + :cvar MLI: Republic of Mali + :cvar MLT: Republic of Malta + :cvar MMR: Union of Burma + :cvar MNE: Montenegro + :cvar MNG: Mongolia + :cvar MNP: Commonwealth of the Northern Mariana Islands + :cvar MOZ: Republic of Mozambique + :cvar MRT: Islamic Republic of Mauritania + :cvar MSR: Montserrat + :cvar MTQ: Department of Martinique + :cvar MUS: Republic of Mauritius + :cvar MWI: Republic of Malawi + :cvar MYS: Malaysia + :cvar MYT: Department of Mayotte + :cvar NAM: Republic of Namibia + :cvar NCL: New Caledonia + :cvar NER: Republic of the Niger + :cvar NFK: Territory of Norfolk Island + :cvar NGA: Federal Republic of Nigeria + :cvar NIC: Republic of Nicaragua + :cvar NIU: Niue + :cvar NLD: Kingdom of the Netherlands + :cvar NOR: Kingdom of Norway + :cvar NPL: Federal Democratic Republic of Nepal + :cvar NRU: Republic of Nauru + :cvar NZL: New Zealand + :cvar OMN: Sultanate of Oman + :cvar PAK: Islamic Republic of Pakistan + :cvar PAN: Republic of Panama + :cvar PCN: Pitcairn, Henderson, Ducie, and Oeno Islands + :cvar PER: Republic of Peru + :cvar PHL: Republic of the Philippines + :cvar PLW: Republic of Palau + :cvar PNG: Independent State of Papua New Guinea + :cvar POL: Republic of Poland + :cvar PRI: Commonwealth of Puerto Rico + :cvar PRK: Democratic People's Republic of Korea + :cvar PRT: Portuguese Republic + :cvar PRY: Republic of Paraguay + :cvar PSE: Palestinian Territory + :cvar PYF: French Polynesia + :cvar QAT: State of Qatar + :cvar REU: Department of Reunion + :cvar ROU: Romania + :cvar RUS: Russian Federation + :cvar RWA: Republic of Rwanda + :cvar SAU: Kingdom of Saudi Arabia + :cvar SDN: Republic of the Sudan + :cvar SEN: Republic of Senegal + :cvar SGP: Republic of Singapore + :cvar SGS: South Georgia and South Sandwich Islands + :cvar SHN: Saint Helena, Ascension, and Tristan da Cunha + :cvar SLB: Solomon Islands + :cvar SLE: Republic of Sierra Leone + :cvar SLV: Republic of El Salvador + :cvar SMR: Republic of San Marino + :cvar SOM: Somalia, Federal Republic of + :cvar SPM: Territorial Collectivity of Saint Pierre and Miquelon + :cvar SRB: Republic of Serbia + :cvar SSD: Republic of South Sudan + :cvar STP: Democratic Republic of Sao Tome and Principe + :cvar SUR: Republic of Suriname + :cvar SVK: Slovak Republic + :cvar SVN: Republic of Slovenia + :cvar SWE: Kingdom of Sweden + :cvar SWZ: Kingdom of Swaziland + :cvar SXM: Sint Maarten + :cvar SYC: Republic of Seychelles + :cvar SYR: Syrian Arab Republic + :cvar TCA: Turks and Caicos Islands + :cvar TCD: Republic of Chad + :cvar TGO: Togolese Republic + :cvar THA: Kingdom of Thailand + :cvar TJK: Republic of Tajikistan + :cvar TKL: Tokelau + :cvar TKM: Turkmenistan + :cvar TLS: Democratic Republic of Timor-Leste + :cvar TON: Kingdom of Tonga + :cvar TTO: Republic of Trinidad and Tobago + :cvar TUN: Tunisian Republic + :cvar TUR: Republic of Turkey + :cvar TUV: Tuvalu + :cvar TWN: Taiwan + :cvar TZA: United Republic of Tanzania + :cvar UGA: Republic of Uganda + :cvar UKR: Ukraine + :cvar URY: Oriental Republic of Uruguay + :cvar UZB: Republic of Uzbekistan + :cvar VAT: State of the Vatican City + :cvar VCT: Saint Vincent and the Grenadines + :cvar VEN: Bolivarian Republic of Venezuela + :cvar VGB: Virgin Islands, British + :cvar VIR: United States Virgin Islands + :cvar VNM: Socialist Republic of Vietnam + :cvar VUT: Republic of Vanuatu + :cvar WLF: Wallis and Futuna + :cvar WSM: Independent State of Samoa + :cvar XAC: Territory of Ashmore and Cartier Islands + :cvar XAZ: Entity 1 + :cvar XBI: Bassas da India + :cvar XBK: Baker Island + :cvar XCR: Entity 2 + :cvar XCS: Coral Sea Islands Territory + :cvar XCY: Entity 3 + :cvar XEU: Europa Island + :cvar XGL: Glorioso Islands + :cvar XGZ: Gaza Strip + :cvar XHO: Howland Island + :cvar XJA: Johnston Atoll + :cvar XJM: Jan Mayen + :cvar XJN: Juan de Nova Island + :cvar XJV: Jarvis Island + :cvar XKM: Entity 4 + :cvar XKN: Entity 5 + :cvar XKR: Kingman Reef + :cvar XKS: Republic of Kosovo + :cvar XMW: Midway Islands + :cvar XNV: Navassa Island + :cvar XPL: Palmyra Atoll + :cvar XPR: Paracel Islands + :cvar XQP: Etorofu, Habomai, Kunashiri, and Shikotan Islands + :cvar XQZ: Akrotiri + :cvar XSP: Spratly Islands + :cvar XSV: Svalbard + :cvar XTR: Tromelin Island + :cvar XWB: West Bank + :cvar XWK: Wake Island + :cvar XXD: Dhekelia + :cvar XXX: No Man's Land + :cvar YEM: Republic of Yemen + :cvar ZAF: Republic of South Africa + :cvar ZMB: Republic of Zambia + :cvar ZWE: Republic of Zimbabwe + :cvar ACGU: FOUR EYES + :cvar APFS: Suppressed + :cvar BWCS: Biological Weapons Convention States + :cvar CFCK: ROK/US Combined Forces Command, Korea + :cvar CMFC: Combined Maritime Forces Central + :cvar CMFP: Cooperative Maritime Forces Pacific + :cvar CPMT: Civilian Protection Monitoring Team for Sudan + :cvar CTOC: Countering Transnational Organized Crime + :cvar CWCS: Chemical Weapons Convention States + :cvar FVEY: FIVE EYES + :cvar GCTF: Global Counter-Terrorism Forces + :cvar GMIF: Global Maritime Interception Forces + :cvar ISAF: International Security Assistance Force for Afghanistan + :cvar KFOR: Stabilization Forces in Kosovo + :cvar MLEC: Multi-Lateral Enduring Contingency + :cvar NACT: North African Counter-Terrorism Forces + :cvar NATO: North Atlantic Treaty Organization + :cvar NCFE: NATO Convention Armed Forces in Europe + :cvar OSTY: Open Skies Treaty + :cvar SPAA: Suppressed + :cvar TEYE: THREE EYES + :cvar UNCK: United Nations Command, Korea + """ + + USA = "USA" + ABW = "ABW" + AFG = "AFG" + AGO = "AGO" + AIA = "AIA" + ALB = "ALB" + AND = "AND" + ARE = "ARE" + ARG = "ARG" + ARM = "ARM" + ASM = "ASM" + ATA = "ATA" + ATF = "ATF" + ATG = "ATG" + AUS = "AUS" + AUT = "AUT" + AX2 = "AX2" + AZE = "AZE" + BDI = "BDI" + BEL = "BEL" + BEN = "BEN" + BES = "BES" + BFA = "BFA" + BGD = "BGD" + BGR = "BGR" + BHR = "BHR" + BHS = "BHS" + BIH = "BIH" + BLM = "BLM" + BLR = "BLR" + BLZ = "BLZ" + BMU = "BMU" + BOL = "BOL" + BRA = "BRA" + BRB = "BRB" + BRN = "BRN" + BTN = "BTN" + BVT = "BVT" + BWA = "BWA" + CAF = "CAF" + CAN = "CAN" + CCK = "CCK" + CHE = "CHE" + CHL = "CHL" + CHN = "CHN" + CIV = "CIV" + CMR = "CMR" + COD = "COD" + COG = "COG" + COK = "COK" + COL = "COL" + COM = "COM" + CPT = "CPT" + CPV = "CPV" + CRI = "CRI" + CUB = "CUB" + CUW = "CUW" + CXR = "CXR" + CYM = "CYM" + CYP = "CYP" + CZE = "CZE" + DEU = "DEU" + DGA = "DGA" + DJI = "DJI" + DMA = "DMA" + DNK = "DNK" + DOM = "DOM" + DZA = "DZA" + ECU = "ECU" + EGY = "EGY" + ERI = "ERI" + ESH = "ESH" + ESP = "ESP" + EST = "EST" + ETH = "ETH" + FIN = "FIN" + FJI = "FJI" + FLK = "FLK" + FRA = "FRA" + FRO = "FRO" + FSM = "FSM" + GAB = "GAB" + GBR = "GBR" + GEO = "GEO" + GGY = "GGY" + GHA = "GHA" + GIB = "GIB" + GIN = "GIN" + GLP = "GLP" + GMB = "GMB" + GNB = "GNB" + GNQ = "GNQ" + GRC = "GRC" + GRD = "GRD" + GRL = "GRL" + GTM = "GTM" + GUF = "GUF" + GUM = "GUM" + GUY = "GUY" + HKG = "HKG" + HMD = "HMD" + HND = "HND" + HRV = "HRV" + HTI = "HTI" + HUN = "HUN" + IDN = "IDN" + IMN = "IMN" + IND = "IND" + IOT = "IOT" + IRL = "IRL" + IRN = "IRN" + IRQ = "IRQ" + ISL = "ISL" + ISR = "ISR" + ITA = "ITA" + JAM = "JAM" + JEY = "JEY" + JOR = "JOR" + JPN = "JPN" + KAZ = "KAZ" + KEN = "KEN" + KGZ = "KGZ" + KHM = "KHM" + KIR = "KIR" + KNA = "KNA" + KOR = "KOR" + KWT = "KWT" + LAO = "LAO" + LBN = "LBN" + LBR = "LBR" + LBY = "LBY" + LCA = "LCA" + LIE = "LIE" + LKA = "LKA" + LSO = "LSO" + LTU = "LTU" + LUX = "LUX" + LVA = "LVA" + MAC = "MAC" + MAF = "MAF" + MAR = "MAR" + MCO = "MCO" + MDA = "MDA" + MDG = "MDG" + MDV = "MDV" + MEX = "MEX" + MHL = "MHL" + MKD = "MKD" + MLI = "MLI" + MLT = "MLT" + MMR = "MMR" + MNE = "MNE" + MNG = "MNG" + MNP = "MNP" + MOZ = "MOZ" + MRT = "MRT" + MSR = "MSR" + MTQ = "MTQ" + MUS = "MUS" + MWI = "MWI" + MYS = "MYS" + MYT = "MYT" + NAM = "NAM" + NCL = "NCL" + NER = "NER" + NFK = "NFK" + NGA = "NGA" + NIC = "NIC" + NIU = "NIU" + NLD = "NLD" + NOR = "NOR" + NPL = "NPL" + NRU = "NRU" + NZL = "NZL" + OMN = "OMN" + PAK = "PAK" + PAN = "PAN" + PCN = "PCN" + PER = "PER" + PHL = "PHL" + PLW = "PLW" + PNG = "PNG" + POL = "POL" + PRI = "PRI" + PRK = "PRK" + PRT = "PRT" + PRY = "PRY" + PSE = "PSE" + PYF = "PYF" + QAT = "QAT" + REU = "REU" + ROU = "ROU" + RUS = "RUS" + RWA = "RWA" + SAU = "SAU" + SDN = "SDN" + SEN = "SEN" + SGP = "SGP" + SGS = "SGS" + SHN = "SHN" + SLB = "SLB" + SLE = "SLE" + SLV = "SLV" + SMR = "SMR" + SOM = "SOM" + SPM = "SPM" + SRB = "SRB" + SSD = "SSD" + STP = "STP" + SUR = "SUR" + SVK = "SVK" + SVN = "SVN" + SWE = "SWE" + SWZ = "SWZ" + SXM = "SXM" + SYC = "SYC" + SYR = "SYR" + TCA = "TCA" + TCD = "TCD" + TGO = "TGO" + THA = "THA" + TJK = "TJK" + TKL = "TKL" + TKM = "TKM" + TLS = "TLS" + TON = "TON" + TTO = "TTO" + TUN = "TUN" + TUR = "TUR" + TUV = "TUV" + TWN = "TWN" + TZA = "TZA" + UGA = "UGA" + UKR = "UKR" + URY = "URY" + UZB = "UZB" + VAT = "VAT" + VCT = "VCT" + VEN = "VEN" + VGB = "VGB" + VIR = "VIR" + VNM = "VNM" + VUT = "VUT" + WLF = "WLF" + WSM = "WSM" + XAC = "XAC" + XAZ = "XAZ" + XBI = "XBI" + XBK = "XBK" + XCR = "XCR" + XCS = "XCS" + XCY = "XCY" + XEU = "XEU" + XGL = "XGL" + XGZ = "XGZ" + XHO = "XHO" + XJA = "XJA" + XJM = "XJM" + XJN = "XJN" + XJV = "XJV" + XKM = "XKM" + XKN = "XKN" + XKR = "XKR" + XKS = "XKS" + XMW = "XMW" + XNV = "XNV" + XPL = "XPL" + XPR = "XPR" + XQP = "XQP" + XQZ = "XQZ" + XSP = "XSP" + XSV = "XSV" + XTR = "XTR" + XWB = "XWB" + XWK = "XWK" + XXD = "XXD" + XXX = "XXX" + YEM = "YEM" + ZAF = "ZAF" + ZMB = "ZMB" + ZWE = "ZWE" + ACGU = "ACGU" + APFS = "APFS" + BWCS = "BWCS" + CFCK = "CFCK" + CMFC = "CMFC" + CMFP = "CMFP" + CPMT = "CPMT" + CTOC = "CTOC" + CWCS = "CWCS" + FVEY = "FVEY" + GCTF = "GCTF" + GMIF = "GMIF" + ISAF = "ISAF" + KFOR = "KFOR" + MLEC = "MLEC" + NACT = "NACT" + NATO = "NATO" + NCFE = "NCFE" + OSTY = "OSTY" + SPAA = "SPAA" + TEYE = "TEYE" + UNCK = "UNCK" diff --git a/src/aws/osml/formats/sidd/models/sfa.py b/src/aws/osml/formats/sidd/models/sfa.py new file mode 100644 index 0000000..99d7838 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sfa.py @@ -0,0 +1,481 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from typing import List, Optional + +__NAMESPACE__ = "urn:SFA:1.2.0" + + +@dataclass +class GeometryType: + pass + + +@dataclass +class ParameterType: + parameter_name: Optional[str] = field( + default=None, + metadata={ + "name": "ParameterName", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + value: Optional[float] = field( + default=None, + metadata={ + "name": "Value", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class PrimeMeridianType: + name: Optional[str] = field( + default=None, + metadata={ + "name": "Name", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + longitude: Optional[float] = field( + default=None, + metadata={ + "name": "Longitude", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class ProjectionType: + projection_name: Optional[str] = field( + default=None, + metadata={ + "name": "ProjectionName", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class SpheriodType: + spheriod_name: Optional[str] = field( + default=None, + metadata={ + "name": "SpheriodName", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + semi_major_axis: Optional[float] = field( + default=None, + metadata={ + "name": "SemiMajorAxis", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + inverse_flattening: Optional[float] = field( + default=None, + metadata={ + "name": "InverseFlattening", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class UNITType: + unit_name: Optional[str] = field( + default=None, + metadata={ + "name": "UnitName", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + conversion_factor: Optional[float] = field( + default=None, + metadata={ + "name": "ConversionFactor", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class CurveType(GeometryType): + pass + + +@dataclass +class DatumType: + spheroid: Optional[SpheriodType] = field( + default=None, + metadata={ + "name": "Spheroid", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class GeometryCollectionType(GeometryType): + pass + + +@dataclass +class PointType(GeometryType): + x: Optional[float] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + y: Optional[float] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + z: Optional[float] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + m: Optional[float] = field( + default=None, + metadata={ + "name": "M", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + + +@dataclass +class SurfaceType(GeometryType): + pass + + +@dataclass +class GeocentricCoordinateSystemType: + csname: Optional[str] = field( + default=None, + metadata={ + "name": "Csname", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + datum: Optional[DatumType] = field( + default=None, + metadata={ + "name": "Datum", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + prime_meridian: Optional[PrimeMeridianType] = field( + default=None, + metadata={ + "name": "PrimeMeridian", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + linear_unit: Optional[str] = field( + default=None, + metadata={ + "name": "LinearUnit", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class GeographicCoordinateSystemType: + csname: Optional[str] = field( + default=None, + metadata={ + "name": "Csname", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + datum: Optional[DatumType] = field( + default=None, + metadata={ + "name": "Datum", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + prime_meridian: Optional[PrimeMeridianType] = field( + default=None, + metadata={ + "name": "PrimeMeridian", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + angular_unit: Optional[str] = field( + default=None, + metadata={ + "name": "AngularUnit", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + linear_unit: Optional[str] = field( + default=None, + metadata={ + "name": "LinearUnit", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + + +@dataclass +class LineStringType(CurveType): + vertex: List[PointType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 2, + }, + ) + + +@dataclass +class MultiCurveType(GeometryCollectionType): + pass + + +@dataclass +class MultiPointType(GeometryCollectionType): + vertex: List[PointType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 2, + }, + ) + + +@dataclass +class MultiSurfaceType(GeometryCollectionType): + pass + + +@dataclass +class LineType(LineStringType): + pass + + +@dataclass +class LinearRingType(LineStringType): + pass + + +@dataclass +class ProjectedCoordinateSystemType: + csname: Optional[str] = field( + default=None, + metadata={ + "name": "Csname", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + geographic_coordinate_system: Optional[GeographicCoordinateSystemType] = field( + default=None, + metadata={ + "name": "GeographicCoordinateSystem", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + projection: Optional[ProjectionType] = field( + default=None, + metadata={ + "name": "Projection", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + parameter: Optional[ParameterType] = field( + default=None, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + linear_unit: Optional[str] = field( + default=None, + metadata={ + "name": "LinearUnit", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "required": True, + }, + ) + + +@dataclass +class AbstractReferenceSystemType: + projected_coordinate_system: Optional[ProjectedCoordinateSystemType] = field( + default=None, + metadata={ + "name": "ProjectedCoordinateSystem", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + geographic_coordinate_system: Optional[GeographicCoordinateSystemType] = field( + default=None, + metadata={ + "name": "GeographicCoordinateSystem", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + geocentric_coordinate_system: Optional[GeocentricCoordinateSystemType] = field( + default=None, + metadata={ + "name": "GeocentricCoordinateSystem", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + + +@dataclass +class MultiLineStringType(MultiCurveType): + element: List[LineType] = field( + default_factory=list, + metadata={ + "name": "Element", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + + +@dataclass +class PolygonType(SurfaceType): + ring: List[LinearRingType] = field( + default_factory=list, + metadata={ + "name": "Ring", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class MultiPolygonType(MultiSurfaceType): + element: List[PolygonType] = field( + default_factory=list, + metadata={ + "name": "Element", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + }, + ) + + +@dataclass +class PolyhedralSurfaceType(SurfaceType): + patch: List[PolygonType] = field( + default_factory=list, + metadata={ + "name": "Patch", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class ReferenceSystemType(AbstractReferenceSystemType): + axis_name: List[str] = field( + default_factory=list, + metadata={ + "name": "AxisName", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class TriangleType(PolygonType): + pass + + +@dataclass +class TriangulatedIrregularNetworkType(PolyhedralSurfaceType): + triangular_patch: List[TriangleType] = field( + default_factory=list, + metadata={ + "name": "TriangularPatch", + "type": "Element", + "namespace": "urn:SFA:1.2.0", + "min_occurs": 1, + }, + ) diff --git a/src/aws/osml/formats/sidd/models/sicommon_types.py b/src/aws/osml/formats/sidd/models/sicommon_types.py new file mode 100644 index 0000000..362a357 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sicommon_types.py @@ -0,0 +1,1526 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional + +from xsdata.models.datatype import XmlDateTime + +__NAMESPACE__ = "urn:SICommon:0.1" + + +@dataclass +class AngleMagnitudeType: + angle: Optional[float] = field( + default=None, + metadata={ + "name": "Angle", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + magnitude: Optional[float] = field( + default=None, + metadata={ + "name": "Magnitude", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class ArrayDoubleType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class CollectIdentifierType(Enum): + MONOSTATIC = "MONOSTATIC" + BISTATIC = "BISTATIC" + + +@dataclass +class ComplexType: + real: Optional[float] = field( + default=None, + metadata={ + "name": "Real", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + imag: Optional[float] = field( + default=None, + metadata={ + "name": "Imag", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +class CornerStringType(Enum): + VALUE_1_FRFC = "1:FRFC" + VALUE_2_FRLC = "2:FRLC" + VALUE_3_LRLC = "3:LRLC" + VALUE_4_LRFC = "4:LRFC" + + +class ErrorFrameType(Enum): + ECF = "ECF" + RIC_ECF = "RIC_ECF" + RIC_ECI = "RIC_ECI" + + +class GammaZeroSFIncidenceMapType(Enum): + APPILED = "APPILED" + NOT_APPLIED = "NOT_APPLIED" + + +@dataclass +class ImageCreationType: + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "DateTime", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + +@dataclass +class LLHType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class LatLonVertexType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LineType: + endpoint: List["LineType.Endpoint"] = field( + default_factory=list, + metadata={ + "name": "Endpoint", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Endpoint: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class ModeIdentifierType(Enum): + SPOTLIGHT = "SPOTLIGHT" + STRIPMAP = "STRIPMAP" + DYNAMIC_STRIPMAP = "DYNAMIC STRIPMAP" + SCANSAR = "SCANSAR" + + +@dataclass +class ParameterType: + value: str = field( + default="", + metadata={ + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class PolarizationType(Enum): + V = "V" + H = "H" + RHC = "RHC" + LHC = "LHC" + OTHER = "OTHER" + + +@dataclass +class PolyCoef1DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolyCoef2DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + exponent2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + vertex: List["PolygonType.Vertex"] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Vertex: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RangeAzimuthType: + """ + Represents range and azimuth. + + :ivar range: Range dimension. + :ivar azimuth: Azimuth dimension. + """ + + range: Optional[float] = field( + default=None, + metadata={ + "name": "Range", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + azimuth: Optional[float] = field( + default=None, + metadata={ + "name": "Azimuth", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class RowColDoubleType: + row: Optional[float] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + col: Optional[float] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class RowColIntType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class RowColVertexType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class SigmaZeroSFIncidenceMapType(Enum): + APPLIED = "APPLIED" + NOT_APPLIED = "NOT_APPLIED" + + +@dataclass +class XYZType: + x: Optional[float] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + y: Optional[float] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + z: Optional[float] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class ErrorStatisticsType: + composite_scp: Optional["ErrorStatisticsType.CompositeSCP"] = field( + default=None, + metadata={ + "name": "CompositeSCP", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + components: Optional["ErrorStatisticsType.Components"] = field( + default=None, + metadata={ + "name": "Components", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + additional_parms: Optional["ErrorStatisticsType.AdditionalParms"] = field( + default=None, + metadata={ + "name": "AdditionalParms", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class CompositeSCP: + rg_az_err: Optional["ErrorStatisticsType.CompositeSCP.RgAzErr"] = field( + default=None, + metadata={ + "name": "RgAzErr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + row_col_err: Optional["ErrorStatisticsType.CompositeSCP.RowColErr"] = field( + default=None, + metadata={ + "name": "RowColErr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class RgAzErr: + rg: Optional[float] = field( + default=None, + metadata={ + "name": "Rg", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + az: Optional[float] = field( + default=None, + metadata={ + "name": "Az", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + rg_az: Optional[float] = field( + default=None, + metadata={ + "name": "RgAz", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class RowColErr: + row: Optional[float] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + col: Optional[float] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + row_col: Optional[float] = field( + default=None, + metadata={ + "name": "RowCol", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class Components: + pos_vel_err: Optional["ErrorStatisticsType.Components.PosVelErr"] = field( + default=None, + metadata={ + "name": "PosVelErr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + radar_sensor: Optional["ErrorStatisticsType.Components.RadarSensor"] = field( + default=None, + metadata={ + "name": "RadarSensor", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + tropo_error: Optional["ErrorStatisticsType.Components.TropoError"] = field( + default=None, + metadata={ + "name": "TropoError", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + iono_error: Optional["ErrorStatisticsType.Components.IonoError"] = field( + default=None, + metadata={ + "name": "IonoError", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class PosVelErr: + frame: Optional[ErrorFrameType] = field( + default=None, + metadata={ + "name": "Frame", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p1: Optional[float] = field( + default=None, + metadata={ + "name": "P1", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p2: Optional[float] = field( + default=None, + metadata={ + "name": "P2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p3: Optional[float] = field( + default=None, + metadata={ + "name": "P3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v1: Optional[float] = field( + default=None, + metadata={ + "name": "V1", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v2: Optional[float] = field( + default=None, + metadata={ + "name": "V2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v3: Optional[float] = field( + default=None, + metadata={ + "name": "V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + corr_coefs: Optional["ErrorStatisticsType.Components.PosVelErr.CorrCoefs"] = field( + default=None, + metadata={ + "name": "CorrCoefs", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + position_decorr: Optional["ErrorStatisticsType.Components.PosVelErr.PositionDecorr"] = field( + default=None, + metadata={ + "name": "PositionDecorr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class CorrCoefs: + p1_p2: Optional[float] = field( + default=None, + metadata={ + "name": "P1P2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p1_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P1P3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p1_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P1V1", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P1V2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P1V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p2_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P2P3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p2_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P2V1", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p2_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P2V2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P2V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p3_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P3V1", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p3_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P3V2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + p3_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P3V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "V1V2", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V1V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + v2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V2V3", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class PositionDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class RadarSensor: + range_bias: Optional[float] = field( + default=None, + metadata={ + "name": "RangeBias", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + clock_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "ClockFreqSF", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + transmit_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "TransmitFreqSF", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + range_bias_decorr: Optional["ErrorStatisticsType.Components.RadarSensor.RangeBiasDecorr"] = field( + default=None, + metadata={ + "name": "RangeBiasDecorr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class RangeBiasDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class TropoError: + tropo_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeVertical", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + tropo_range_slant: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeSlant", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + tropo_range_decorr: Optional["ErrorStatisticsType.Components.TropoError.TropoRangeDecorr"] = field( + default=None, + metadata={ + "name": "TropoRangeDecorr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class TropoRangeDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class IonoError: + iono_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeVertical", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + iono_range_rate_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeRateVertical", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + iono_rg_rg_rate_cc: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRgRgRateCC", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + iono_range_vert_decorr: Optional["ErrorStatisticsType.Components.IonoError.IonoRangeVertDecorr"] = field( + default=None, + metadata={ + "name": "IonoRangeVertDecorr", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + @dataclass + class IonoRangeVertDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + @dataclass + class AdditionalParms: + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 1, + }, + ) + + +@dataclass +class LLHCornerStringType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerStringType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly1DType: + coef: List[PolyCoef1DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly2DType: + coef: List[PolyCoef2DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + order2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RadarModeType: + mode_type: Optional[ModeIdentifierType] = field( + default=None, + metadata={ + "name": "ModeType", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + mode_id: Optional[str] = field( + default=None, + metadata={ + "name": "ModeID", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + +@dataclass +class ReferencePointType: + """ + The reference point. + + :ivar ecef: The XYZ ECEF (units = m) reference point. + :ivar point: The row and column (units = pixels) which maps to the ECEF point. + :ivar name: Used for implementation specific signifier for the reference point. + """ + + ecef: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECEF", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + point: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + +@dataclass +class ValidDataType: + vertex: List[RowColVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class XYZAttributeType(XYZType): + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + +@dataclass +class CollectionInfoType: + collector_name: Optional[str] = field( + default=None, + metadata={ + "name": "CollectorName", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + illuminator_name: Optional[str] = field( + default=None, + metadata={ + "name": "IlluminatorName", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + collect_type: Optional[CollectIdentifierType] = field( + default=None, + metadata={ + "name": "CollectType", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + radar_mode: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + classification: Optional[str] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + country_code: List[str] = field( + default_factory=list, + metadata={ + "name": "CountryCode", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + +@dataclass +class RadiometricType: + noise_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "NoisePoly", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + rcssfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RCSSFPoly", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + beta_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "BetaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + sigma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "SigmaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + sigma_zero_sfincidence_map: Optional[SigmaZeroSFIncidenceMapType] = field( + default=None, + metadata={ + "name": "SigmaZeroSFIncidenceMap", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + gamma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GammaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + gamma_zero_sfincidence_map: Optional[GammaZeroSFIncidenceMapType] = field( + default=None, + metadata={ + "name": "GammaZeroSFIncidenceMap", + "type": "Element", + "namespace": "urn:SICommon:0.1", + }, + ) + + +@dataclass +class XYZPolyType: + x: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + y: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + z: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICommon:0.1", + "required": True, + }, + ) + + +@dataclass +class XYZPolyAttributeType(XYZPolyType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) diff --git a/src/aws/osml/formats/sidd/models/sicommon_types_v1_0.py b/src/aws/osml/formats/sidd/models/sicommon_types_v1_0.py new file mode 100644 index 0000000..4f41cfe --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sicommon_types_v1_0.py @@ -0,0 +1,1823 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional + +from xsdata.models.datatype import XmlDateTime + +__NAMESPACE__ = "urn:SICommon:1.0" + + +@dataclass +class AngleMagnitudeType: + angle: Optional[float] = field( + default=None, + metadata={ + "name": "Angle", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + magnitude: Optional[float] = field( + default=None, + metadata={ + "name": "Magnitude", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class AngleZeroToExclusive360MagnitudeType: + angle: Optional[float] = field( + default=None, + metadata={ + "name": "Angle", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + "min_inclusive": 0.0, + "max_exclusive": 360.0, + }, + ) + magnitude: Optional[float] = field( + default=None, + metadata={ + "name": "Magnitude", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class ArrayDoubleType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class CollectIdentifierType(Enum): + MONOSTATIC = "MONOSTATIC" + BISTATIC = "BISTATIC" + + +@dataclass +class ComplexType: + real: Optional[float] = field( + default=None, + metadata={ + "name": "Real", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + imag: Optional[float] = field( + default=None, + metadata={ + "name": "Imag", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +class CornerStringType(Enum): + VALUE_1_FRFC = "1:FRFC" + VALUE_2_FRLC = "2:FRLC" + VALUE_3_LRLC = "3:LRLC" + VALUE_4_LRFC = "4:LRFC" + + +class ErrorFrameType(Enum): + ECF = "ECF" + RIC_ECF = "RIC_ECF" + RIC_ECI = "RIC_ECI" + + +@dataclass +class ImageCreationType: + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "DateTime", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + +@dataclass +class LLHType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonHAECornerType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + "min_inclusive": 1, + "max_inclusive": 4, + }, + ) + + +@dataclass +class LatLonRestrictionType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + "min_inclusive": -90.0, + "max_inclusive": 90.0, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + + +@dataclass +class LatLonType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class LatLonVertexType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LineType: + endpoint: List["LineType.Endpoint"] = field( + default_factory=list, + metadata={ + "name": "Endpoint", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 2, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Endpoint: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class ModeIdentifierType(Enum): + SPOTLIGHT = "SPOTLIGHT" + STRIPMAP = "STRIPMAP" + DYNAMIC_STRIPMAP = "DYNAMIC STRIPMAP" + SCANSAR = "SCANSAR" + + +class NoiseLevelNoiseLevelType(Enum): + ABSOLUTE = "ABSOLUTE" + RELATIVE = "RELATIVE" + + +@dataclass +class ParameterType: + value: str = field( + default="", + metadata={ + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class Polarization1Typevalue(Enum): + V = "V" + H = "H" + X = "X" + Y = "Y" + S = "S" + E = "E" + RHC = "RHC" + LHC = "LHC" + OTHER = "OTHER" + UNKNOWN = "UNKNOWN" + SEQUENCE = "SEQUENCE" + + +@dataclass +class PolyCoef1DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolyCoef2DType: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + exponent1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + exponent2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + vertex: List["PolygonType.Vertex"] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Vertex: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class RadiometricTypeSigmaZeroSFIncidenceMap(Enum): + APPLIED = "APPLIED" + NOT_APPLIED = "NOT_APPLIED" + + +@dataclass +class RangeAzimuthType: + """ + Represents range and azimuth. + + :ivar range: Range dimension. + :ivar azimuth: Azimuth dimension. + """ + + range: Optional[float] = field( + default=None, + metadata={ + "name": "Range", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + azimuth: Optional[float] = field( + default=None, + metadata={ + "name": "Azimuth", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class RowColDoubleType: + row: Optional[float] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + col: Optional[float] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class RowColIntType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class RowColVertexType: + row: Optional[int] = field( + default=None, + metadata={ + "name": "Row", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "name": "Col", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class XYZType: + x: Optional[float] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + y: Optional[float] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + z: Optional[float] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class ErrorStatisticsType: + composite_scp: Optional["ErrorStatisticsType.CompositeSCP"] = field( + default=None, + metadata={ + "name": "CompositeSCP", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + components: Optional["ErrorStatisticsType.Components"] = field( + default=None, + metadata={ + "name": "Components", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + unmodeled: Optional["ErrorStatisticsType.Unmodeled"] = field( + default=None, + metadata={ + "name": "Unmodeled", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + additional_parms: Optional["ErrorStatisticsType.AdditionalParms"] = field( + default=None, + metadata={ + "name": "AdditionalParms", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class CompositeSCP: + rg: Optional[float] = field( + default=None, + metadata={ + "name": "Rg", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + az: Optional[float] = field( + default=None, + metadata={ + "name": "Az", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + rg_az: Optional[float] = field( + default=None, + metadata={ + "name": "RgAz", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class Components: + pos_vel_err: Optional["ErrorStatisticsType.Components.PosVelErr"] = field( + default=None, + metadata={ + "name": "PosVelErr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + radar_sensor: Optional["ErrorStatisticsType.Components.RadarSensor"] = field( + default=None, + metadata={ + "name": "RadarSensor", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + tropo_error: Optional["ErrorStatisticsType.Components.TropoError"] = field( + default=None, + metadata={ + "name": "TropoError", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + iono_error: Optional["ErrorStatisticsType.Components.IonoError"] = field( + default=None, + metadata={ + "name": "IonoError", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class PosVelErr: + frame: Optional[ErrorFrameType] = field( + default=None, + metadata={ + "name": "Frame", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p1: Optional[float] = field( + default=None, + metadata={ + "name": "P1", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p2: Optional[float] = field( + default=None, + metadata={ + "name": "P2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p3: Optional[float] = field( + default=None, + metadata={ + "name": "P3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v1: Optional[float] = field( + default=None, + metadata={ + "name": "V1", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v2: Optional[float] = field( + default=None, + metadata={ + "name": "V2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v3: Optional[float] = field( + default=None, + metadata={ + "name": "V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + corr_coefs: Optional["ErrorStatisticsType.Components.PosVelErr.CorrCoefs"] = field( + default=None, + metadata={ + "name": "CorrCoefs", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + position_decorr: Optional["ErrorStatisticsType.Components.PosVelErr.PositionDecorr"] = field( + default=None, + metadata={ + "name": "PositionDecorr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class CorrCoefs: + p1_p2: Optional[float] = field( + default=None, + metadata={ + "name": "P1P2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p1_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P1P3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p1_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P1V1", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P1V2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P1V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p2_p3: Optional[float] = field( + default=None, + metadata={ + "name": "P2P3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p2_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P2V1", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p2_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P2V2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P2V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p3_v1: Optional[float] = field( + default=None, + metadata={ + "name": "P3V1", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p3_v2: Optional[float] = field( + default=None, + metadata={ + "name": "P3V2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + p3_v3: Optional[float] = field( + default=None, + metadata={ + "name": "P3V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v1_v2: Optional[float] = field( + default=None, + metadata={ + "name": "V1V2", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v1_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V1V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + v2_v3: Optional[float] = field( + default=None, + metadata={ + "name": "V2V3", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class PositionDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class RadarSensor: + range_bias: Optional[float] = field( + default=None, + metadata={ + "name": "RangeBias", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + clock_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "ClockFreqSF", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + transmit_freq_sf: Optional[float] = field( + default=None, + metadata={ + "name": "TransmitFreqSF", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + range_bias_decorr: Optional["ErrorStatisticsType.Components.RadarSensor.RangeBiasDecorr"] = field( + default=None, + metadata={ + "name": "RangeBiasDecorr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class RangeBiasDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class TropoError: + tropo_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeVertical", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + tropo_range_slant: Optional[float] = field( + default=None, + metadata={ + "name": "TropoRangeSlant", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + tropo_range_decorr: Optional["ErrorStatisticsType.Components.TropoError.TropoRangeDecorr"] = field( + default=None, + metadata={ + "name": "TropoRangeDecorr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class TropoRangeDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class IonoError: + iono_range_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeVertical", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + iono_range_rate_vertical: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRangeRateVertical", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + iono_rg_rg_rate_cc: Optional[float] = field( + default=None, + metadata={ + "name": "IonoRgRgRateCC", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + iono_range_vert_decorr: Optional["ErrorStatisticsType.Components.IonoError.IonoRangeVertDecorr"] = field( + default=None, + metadata={ + "name": "IonoRangeVertDecorr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class IonoRangeVertDecorr: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class Unmodeled: + xrow: Optional[float] = field( + default=None, + metadata={ + "name": "Xrow", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + ycol: Optional[float] = field( + default=None, + metadata={ + "name": "Ycol", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + xrow_ycol: Optional[float] = field( + default=None, + metadata={ + "name": "XrowYcol", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + unmodeled_decorr: Optional["ErrorStatisticsType.Unmodeled.UnmodeledDecorr"] = field( + default=None, + metadata={ + "name": "UnmodeledDecorr", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class UnmodeledDecorr: + xrow: Optional["ErrorStatisticsType.Unmodeled.UnmodeledDecorr.Xrow"] = field( + default=None, + metadata={ + "name": "Xrow", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + ycol: Optional["ErrorStatisticsType.Unmodeled.UnmodeledDecorr.Ycol"] = field( + default=None, + metadata={ + "name": "Ycol", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class Xrow: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class Ycol: + corr_coef_zero: Optional[float] = field( + default=None, + metadata={ + "name": "CorrCoefZero", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + decorr_rate: Optional[float] = field( + default=None, + metadata={ + "name": "DecorrRate", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + @dataclass + class AdditionalParms: + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class GeoInfoType: + desc: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Desc", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + point: Optional[LatLonRestrictionType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + polygon: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + geo_info: List["GeoInfoType"] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LLHCornerStringType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + hae: Optional[float] = field( + default=None, + metadata={ + "name": "HAE", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LatLonCornerStringType: + lat: Optional[float] = field( + default=None, + metadata={ + "name": "Lat", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + lon: Optional[float] = field( + default=None, + metadata={ + "name": "Lon", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MatchInfoType: + num_match_types: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchTypes", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + match_type: List["MatchInfoType.MatchType"] = field( + default_factory=list, + metadata={ + "name": "MatchType", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 1, + }, + ) + + @dataclass + class MatchType: + type_id: Optional[str] = field( + default=None, + metadata={ + "name": "TypeID", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + current_index: Optional[int] = field( + default=None, + metadata={ + "name": "CurrentIndex", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + num_match_collections: Optional[int] = field( + default=None, + metadata={ + "name": "NumMatchCollections", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + match_collection: List["MatchInfoType.MatchType.MatchCollection"] = field( + default_factory=list, + metadata={ + "name": "MatchCollection", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class MatchCollection: + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + match_index: Optional[int] = field( + default=None, + metadata={ + "name": "MatchIndex", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly1DType: + coef: List[PolyCoef1DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Poly2DType: + coef: List[PolyCoef2DType] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 1, + }, + ) + order1: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + order2: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class RadarModeType: + mode_type: Optional[ModeIdentifierType] = field( + default=None, + metadata={ + "name": "ModeType", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + mode_id: Optional[str] = field( + default=None, + metadata={ + "name": "ModeID", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + +@dataclass +class ReferencePointType: + """ + The reference point. + + :ivar ecef: The XYZ ECEF (units = m) reference point. + :ivar point: The row and column (units = pixels) which maps to the ECEF point. + :ivar name: Used for implementation specific signifier for the reference point. + """ + + ecef: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ECEF", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + point: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + +@dataclass +class ValidDataType: + vertex: List[RowColVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class XYZAttributeType(XYZType): + name: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + }, + ) + + +@dataclass +class CollectionInfoType: + collector_name: Optional[str] = field( + default=None, + metadata={ + "name": "CollectorName", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + illuminator_name: Optional[str] = field( + default=None, + metadata={ + "name": "IlluminatorName", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + core_name: Optional[str] = field( + default=None, + metadata={ + "name": "CoreName", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + collect_type: Optional[CollectIdentifierType] = field( + default=None, + metadata={ + "name": "CollectType", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + radar_mode: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + classification: Optional[str] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + country_code: List[str] = field( + default_factory=list, + metadata={ + "name": "CountryCode", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Parameter", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + +@dataclass +class GeoInfo(GeoInfoType): + class Meta: + namespace = "urn:SICommon:1.0" + + +@dataclass +class RadiometricType: + noise_level: Optional["RadiometricType.NoiseLevel"] = field( + default=None, + metadata={ + "name": "NoiseLevel", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + noise_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "NoisePoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + rcssfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RCSSFPoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + sigma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "SigmaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + beta_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "BetaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + sigma_zero_sfincidence_map: Optional[RadiometricTypeSigmaZeroSFIncidenceMap] = field( + default=None, + metadata={ + "name": "SigmaZeroSFIncidenceMap", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + gamma_zero_sfpoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "GammaZeroSFPoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + @dataclass + class NoiseLevel: + noise_level_type: Optional[NoiseLevelNoiseLevelType] = field( + default=None, + metadata={ + "name": "NoiseLevelType", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + noise_poly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "NoisePoly", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class XYZPolyType: + x: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "X", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + y: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Y", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + z: Optional[Poly1DType] = field( + default=None, + metadata={ + "name": "Z", + "type": "Element", + "namespace": "urn:SICommon:1.0", + "required": True, + }, + ) + + +@dataclass +class XYZPolyAttributeType(XYZPolyType): + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) diff --git a/src/aws/osml/formats/sidd/models/sidd_v1_0_0.py b/src/aws/osml/formats/sidd/models/sidd_v1_0_0.py new file mode 100644 index 0000000..da2f8ba --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sidd_v1_0_0.py @@ -0,0 +1,2016 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional, Union + +from xsdata.models.datatype import XmlDate, XmlDateTime + +from .external.ism.schema.cvegenerated.cvenum_ism25_x import CVEnumISM25X +from .external.ism.schema.cvegenerated.cvenum_ismclassification_all import CVEnumISMClassificationAll +from .external.ism.schema.cvegenerated.cvenum_ismcomplies_with import CVEnumISMCompliesWithValues +from .external.ism.schema.cvegenerated.cvenum_ismdissem import CVEnumISMDissemValuesvalue +from .external.ism.schema.cvegenerated.cvenum_ismfgiopen import CVEnumISMFGIOpenValues +from .external.ism.schema.cvegenerated.cvenum_ismfgiprotected import CVEnumISMFGIProtectedValues +from .external.ism.schema.cvegenerated.cvenum_ismnon_ic import CVEnumISMNonICValues +from .external.ism.schema.cvegenerated.cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .external.ism.schema.cvegenerated.cvenum_ismowner_producer import CVEnumISMOwnerProducerValues +from .external.ism.schema.cvegenerated.cvenum_ismrel_to import CVEnumISMRelToValues +from .external.ism.schema.cvegenerated.cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue +from .external.ism.schema.cvegenerated.cvenum_ismsource_marked import CVEnumISMSourceMarked +from .sfa import ( + LinearRingType, + LineType, + MultiLineStringType, + MultiPointType, + MultiPolygonType, + PointType, + PolygonType, + PolyhedralSurfaceType, + ReferenceSystemType, +) +from .sicommon_types import ( + AngleMagnitudeType, + ErrorStatisticsType, + LatLonVertexType, + ParameterType, + PolarizationType, + Poly2DType, + RadarModeType, + RadiometricType, + RangeAzimuthType, + ReferencePointType, + RowColDoubleType, + RowColIntType, + XYZPolyType, + XYZType, +) + +__NAMESPACE__ = "urn:SIDD:1.0.0" + + +@dataclass +class AcheivedResolutionType: + """ + Finest achievable resolution parameters. + """ + + +@dataclass +class ClassificationGuidanceType: + """ + Classification guidance authority (only if file is classified). + + :ivar authority: Classifying authority. + :ivar date: Date that the authority was provided. Specified in YYYY-MM-DD. + """ + + authority: Optional[str] = field( + default=None, + metadata={ + "name": "Authority", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "Date", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAHistogramOverridesType: + """ + :ivar clip_min: Suggested override for the lower end-point of the display histogram in the ELT DRA + application. Referred to as Pmin in SIPS documentation. + :ivar clip_max: Suggested override for the upper end-point of the display histogram in the ELT DRA + application. Referred to as Pmax in SIPS documentation. + """ + + clip_min: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMin", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + clip_max: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMax", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +class DecimationMethodType(Enum): + """Default ELT decimation method for this data. + + Also used as default for reduced resolution dataset generation (if applicable). + """ + + NEAREST_NEIGHBOR = "NEAREST_NEIGHBOR" + BILINEAR = "BILINEAR" + BRIGHTEST_PIXEL = "BRIGHTEST_PIXEL" + LAGRANGE = "LAGRANGE" + + +@dataclass +class Lookup3TableType: + """ + :ivar value: + :ivar size: Size of LUT + """ + + value: List[str] = field( + default_factory=list, + metadata={ + "pattern": r"([0-9]+),([0-9]+),([0-9]+)", + "tokens": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LookupTableType: + """ + :ivar value: + :ivar size: Size of LUT. + """ + + value: List[int] = field( + default_factory=list, + metadata={ + "tokens": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class MagnificationMethodType(Enum): + """ + Default ELT magnification method for this data. + """ + + NEAREST_NEIGHBOR = "NEAREST_NEIGHBOR" + BILINEAR = "BILINEAR" + LAGRANGE = "LAGRANGE" + + +@dataclass +class MonitorCompensationAppliedType: + """ + Describes monitor compensation that may have been applied to the product during + processing. + + :ivar gamma: Gamma value for monitor compensation pre-applied to the image. + :ivar xmin: Xmin value for monitor compensation pre-applied to the image. + """ + + gamma: Optional[float] = field( + default=None, + metadata={ + "name": "Gamma", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + xmin: Optional[float] = field( + default=None, + metadata={ + "name": "XMin", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +class PixelType(Enum): + MONO8_I = "MONO8I" + MONO8_LU = "MONO8LU" + MONO16_I = "MONO16I" + RGB8_LU = "RGB8LU" + RGB24_I = "RGB24I" + + +@dataclass +class ProcessorInformationType: + """ + :ivar application: Software application name and version number. + :ivar processing_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar site: Creation location of product. + :ivar profile: Product-specific profile applied during product processing. + """ + + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + processing_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "ProcessingDateTime", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class AnnotationObjectType: + """ + Geometrical representation of the annotation. + """ + + point: Optional[PointType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + linear_ring: Optional[LinearRingType] = field( + default=None, + metadata={ + "name": "LinearRing", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + polygon: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + polyhedral_surface: Optional[PolyhedralSurfaceType] = field( + default=None, + metadata={ + "name": "PolyhedralSurface", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + multi_polygon: Optional[MultiPolygonType] = field( + default=None, + metadata={ + "name": "MultiPolygon", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + multi_line_string: Optional[MultiLineStringType] = field( + default=None, + metadata={ + "name": "MultiLineString", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + multi_point: Optional[MultiPointType] = field( + default=None, + metadata={ + "name": "MultiPoint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class BaseProjectionType: + """ + :ivar reference_point: Reference point for the geometrical system. + """ + + reference_point: Optional[ReferencePointType] = field( + default=None, + metadata={ + "name": "ReferencePoint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class ColorDisplayRemapType: + """ + Object representing that the data requires color display. + + :ivar remap_lut: LUT-base remap indicating that the color display is done through index-based color. + """ + + remap_lut: Optional[Lookup3TableType] = field( + default=None, + metadata={ + "name": "RemapLUT", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionGeometryType: + """ + Key geometry parameters independent of product processing. + + :ivar azimuth: Angle clockwise from north indicating the ETP line of sight vector. + :ivar slope: Angle between the ETP at scene center and the range vector perpendicular to the direction of + motion. + :ivar squint: Angle from the ground track to platform velocity vector at nadir. Left-look is negative, right- + look is positive. + :ivar graze: Angle between the ETP and the line of sight vector. + :ivar tilt: Angle between the ETP and the cross range vector. Also known as the twist angle. + :ivar extension: Exploitation feature extension related to geometry for a single input image + """ + + azimuth: Optional[float] = field( + default=None, + metadata={ + "name": "Azimuth", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + slope: Optional[float] = field( + default=None, + metadata={ + "name": "Slope", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + squint: Optional[float] = field( + default=None, + metadata={ + "name": "Squint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + graze: Optional[float] = field( + default=None, + metadata={ + "name": "Graze", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + tilt: Optional[float] = field( + default=None, + metadata={ + "name": "Tilt", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionPhenomenologyType: + """ + Phenomenology related to both the geometry and the final product processing. + + :ivar shadow: The phenomon where vertical objects occlude radar energy. + :ivar layover: The phenomenon where vertical objects appear as ground objects with the same range/range rate. + :ivar multi_path: This is a range dependent phenomenon which describes the energy from a single scatter + returned to the radar via more than one path and results in a nominally constant direction in the ETP. + :ivar ground_track: Counter-clockwise angle from increasing row direction to ground track at the center of + the image. + :ivar extension: Exploitation feature extension related to phenomenology for a single input image + """ + + shadow: Optional[AngleMagnitudeType] = field( + default=None, + metadata={ + "name": "Shadow", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + layover: Optional[AngleMagnitudeType] = field( + default=None, + metadata={ + "name": "Layover", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + multi_path: Optional[float] = field( + default=None, + metadata={ + "name": "MultiPath", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + ground_track: Optional[float] = field( + default=None, + metadata={ + "name": "GroundTrack", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesProductType: + """ + Metadata regarding the product. + + :ivar resolution: Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + :ivar north: Counter-clockwise angle from increasing row direction to north at the center of the image. + :ivar extension: Exploitation feature extension for the end product + """ + + resolution: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + north: Optional[float] = field( + default=None, + metadata={ + "name": "North", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class FootprintType: + vertex: List[LatLonVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class GeographicInformationType: + """ + :ivar country_code: Country identifier for this geographic region. + :ivar security_info: Specifies classification level or special handling designators for this geographic + region + :ivar geographic_info_extension: Implementation specific geographic information. + """ + + country_code: List[str] = field( + default_factory=list, + metadata={ + "name": "CountryCode", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + security_info: Optional[str] = field( + default=None, + metadata={ + "name": "SecurityInfo", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + geographic_info_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "GeographicInfoExtension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class GeometricChipType: + """ + :ivar chip_size: Size of the chipped product in pixels. + :ivar original_upper_left_coordinate: Upper-left corner with respect to the original product. + :ivar original_upper_right_coordinate: Upper-right corner with respect to the original product. + :ivar original_lower_left_coordinate: Lower-left corner with respect to the original product. + :ivar original_lower_right_coordinate: Lower-right corner with respect to the original product. + """ + + chip_size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "ChipSize", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + original_upper_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + original_upper_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + original_lower_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + original_lower_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class InputROIType: + """ + ROI representing portion of input data used to make this product. + + :ivar size: Number of rows and columns extracted from the input. + :ivar upper_left: The upper-left pixel extracted from the input. + """ + + size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "Size", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + upper_left: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "UpperLeft", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class MonochromeDisplayRemapType: + """This remap works by taking the input space and using the LUT to map it to a + log space (for 8-bit only). + + From the log space the C0 and Ch fields are applied to get to display-ready density space. + The density should then be rendered by the TTC and monitor comp. + This means that the default DRA should not apply anything besides the clip points. + If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. + Examples: + Remap LUT Clips + ============================= + PEDF PEDF->D 0,255 + LLG LLG->A->LogA C0,Ch + Log N/A C0,Ch + NRL N/A 0,255 (Supposed to be display ready) + + :ivar remap_type: Name of remap applied (for informational purposes only). + :ivar remap_lut: Lookup table for remap to log amplitude for display. Used during the "Product Generation + Option" portion of the SIPS display chain. Required for 8-bit data. Not to be used for 16-bit data. + :ivar remap_parameter: Textual remap parameter. Filled based upon remap type (for informational purposes + only). For example, if the data is linlog encoded a RemapParameter could be used to describe any + amplitude scaling that was performed prior to linlog encoding the data. + """ + + remap_type: Optional[str] = field( + default=None, + metadata={ + "name": "RemapType", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + remap_lut: Optional[LookupTableType] = field( + default=None, + metadata={ + "name": "RemapLUT", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + remap_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "RemapParameter", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ProcessingEventType: + """ + :ivar application_name: Application which applied a modification. + :ivar applied_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar interpolation_method: Type of interpolation applied to the data. + :ivar descriptor: Descriptor for the processing event. + """ + + application_name: Optional[str] = field( + default=None, + metadata={ + "name": "ApplicationName", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + applied_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "AppliedDateTime", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + interpolation_method: Optional[str] = field( + default=None, + metadata={ + "name": "InterpolationMethod", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + descriptor: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Descriptor", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ProcessingModuleType: + """ + :ivar module_name: The name of the algorithm used in processing the product. + :ivar module_parameter: Parameters associated with the algorithm used in processing the product. + :ivar processing_module: ProcessingModule is a repeatable structure within itself to create an algorithm as a + subset of another algorithm. + """ + + module_name: Optional[ParameterType] = field( + default=None, + metadata={ + "name": "ModuleName", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + module_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ModuleParameter", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + processing_module: List["ProcessingModuleType"] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ProductClassificationType: + """ + The overall classification of the product. + + :ivar security_extension: Extensible parameters used to support profile-specific needs related to product + security. + :ivar desversion: The version number of the DES. Should there be multiple specified in an instance document + the one at the root node is the one that will apply to the entire document. + :ivar resource_element: + :ivar create_date: + :ivar complies_with: + :ivar classification: + :ivar owner_producer: + :ivar scicontrols: + :ivar saridentifier: + :ivar dissemination_controls: + :ivar fgisource_open: + :ivar fgisource_protected: + :ivar releasable_to: + :ivar non_icmarkings: + :ivar classified_by: + :ivar compilation_reason: + :ivar derivatively_classified_by: + :ivar classification_reason: + :ivar non_uscontrols: + :ivar derived_from: + :ivar declass_date: + :ivar declass_event: + :ivar declass_exception: + :ivar type_of_exempted_source: + :ivar date_of_exempted_source: + """ + + security_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "SecurityExtension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + desversion: int = field( + init=False, + default=4, + metadata={ + "name": "DESVersion", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "required": True, + }, + ) + resource_element: bool = field( + init=False, + default=True, + metadata={ + "name": "resourceElement", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "required": True, + }, + ) + create_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "createDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "required": True, + }, + ) + complies_with: List[CVEnumISMCompliesWithValues] = field( + default_factory=list, + metadata={ + "name": "compliesWith", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "required": True, + }, + ) + owner_producer: List[CVEnumISMOwnerProducerValues] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "required": True, + "tokens": True, + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "pattern": r"SI-G-[A-Z][A-Z][A-Z][A-Z]|SI-ECI-[A-Z][A-Z][A-Z]", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "pattern": r"SAR-[A-Z][A-Z][A-Z]?", + "tokens": True, + }, + ) + dissemination_controls: List[Union[str, CVEnumISMDissemValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "pattern": r"RD-SG-[1-9][0-9]?|FRD-SG-[1-9][0-9]?", + "tokens": True, + }, + ) + fgisource_open: List[CVEnumISMFGIOpenValues] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + fgisource_protected: List[CVEnumISMFGIProtectedValues] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + releasable_to: List[CVEnumISMRelToValues] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + non_icmarkings: List[CVEnumISMNonICValues] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + }, + ) + type_of_exempted_source: Optional[CVEnumISMSourceMarked] = field( + default=None, + metadata={ + "name": "typeOfExemptedSource", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + }, + ) + date_of_exempted_source: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "dateOfExemptedSource", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism", + }, + ) + + +@dataclass +class ProductPlaneType: + """ + Plane definition for the product. + + :ivar row_unit_vector: Unit vector of the plane defined to be aligned in the increasing row direction of the + product. (Defined as Rpgd in Design and Exploitation document) + :ivar col_unit_vector: Unit vector of the plane defined to be aligned in the increasing column direction of + the product. (Defined as Cpgd in Design and Exploitation document) + """ + + row_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "RowUnitVector", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + col_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ColUnitVector", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class TxRcvPolarizationType: + """ + :ivar tx_polarization: Polarization transmit type + :ivar rcv_polarization: Receive polarization type + :ivar rcv_polarization_offset: Optional angle offset for the receive polarization defined at aperture center. + :ivar processed: Optional flag to describe whether this input polarization was used in processing the + product. + """ + + tx_polarization: Optional[PolarizationType] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + rcv_polarization: Optional[PolarizationType] = field( + default=None, + metadata={ + "name": "RcvPolarization", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + rcv_polarization_offset: Optional[float] = field( + default=None, + metadata={ + "name": "RcvPolarizationOffset", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + processed: Optional[bool] = field( + default=None, + metadata={ + "name": "Processed", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class AnnotationType: + """ + Single annotation. + + :ivar identifier: Identifier for the annotation which idicates the type of object represented by this + annotation. + :ivar spatial_reference_system: Spatial reference system of the annotation. Assumed to be WGS-84 geographic + coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above + ellipsoid). + :ivar object_value: The geometrical representation of the annotation. + """ + + identifier: Optional[str] = field( + default=None, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + spatial_reference_system: Optional[ReferenceSystemType] = field( + default=None, + metadata={ + "name": "SpatialReferenceSystem", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + object_value: List[AnnotationObjectType] = field( + default_factory=list, + metadata={ + "name": "Object", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class DownstreamReprocessingType: + """ + :ivar geometric_chip: Contains information related to downstream chipping of the product. + :ivar processing_event: Contains information related to downstream processing of the product. + """ + + geometric_chip: Optional[GeometricChipType] = field( + default=None, + metadata={ + "name": "GeometricChip", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + processing_event: List[ProcessingEventType] = field( + default_factory=list, + metadata={ + "name": "ProcessingEvent", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionInformationType: + """ + General collection information. + + :ivar sensor_name: The name of the sensor. + :ivar radar_mode: Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, + DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + :ivar collection_date_time: Collection date and time defined in Coordinated Universal Time (UTC). The seconds + should be followed by a Z to indicate UTC. + :ivar local_date_time: Date and time defined in local time. + :ivar collection_duration: The duration of the collection (units = seconds). + :ivar resolution: Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + :ivar input_roi: ROI representing portion of input data used to make this product. + :ivar polarization: Transmit and receive polarization. + """ + + sensor_name: Optional[str] = field( + default=None, + metadata={ + "name": "SensorName", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + radar_mode: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + collection_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CollectionDateTime", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + local_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "LocalDateTime", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + collection_duration: Optional[float] = field( + default=None, + metadata={ + "name": "CollectionDuration", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + resolution: Optional[RangeAzimuthType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + input_roi: Optional[InputROIType] = field( + default=None, + metadata={ + "name": "InputROI", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + polarization: List[TxRcvPolarizationType] = field( + default_factory=list, + metadata={ + "name": "Polarization", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class GeographicCoverageType: + """ + :ivar georegion_identifier: Identifier for the georegion. + :ivar footprint: Estimated ground footprint of the product. + :ivar sub_region: Used to represent hierarchical decomposition into sub-regions. + :ivar geographic_info: Specifics about the georegion. + """ + + georegion_identifier: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "GeoregionIdentifier", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + footprint: Optional[FootprintType] = field( + default=None, + metadata={ + "name": "Footprint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + sub_region: List["GeographicCoverageType"] = field( + default_factory=list, + metadata={ + "name": "SubRegion", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + geographic_info: Optional[GeographicInformationType] = field( + default=None, + metadata={ + "name": "GeographicInfo", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class MeasurableProjectionType(BaseProjectionType): + """ + :ivar sample_spacing: Sample spacing in row and column. + :ivar time_coapoly: Time (units = seconds) at which center of aperture for a given pixel coordinate in the + product occurs. + """ + + sample_spacing: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "SampleSpacing", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + time_coapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "TimeCOAPoly", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class PolynomialProjectionType(BaseProjectionType): + """Polynomial pixel to ground. + + Only used for sensor systems where the radar geometry parameters are not recorded. + + :ivar row_col_to_lat: Polynomial that converts Row/Col to Latitude (degrees). + :ivar row_col_to_lon: Polynomial that converts Row/Col to Longitude (degrees). + :ivar row_col_to_alt: Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + :ivar lat_lon_to_row: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row + location. + :ivar lat_lon_to_col: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column + location + """ + + row_col_to_lat: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLat", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + row_col_to_lon: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLon", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + row_col_to_alt: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToAlt", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + lat_lon_to_row: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToRow", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + lat_lon_to_col: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToCol", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductCreationType: + """ + Contains general information about product creation. + + :ivar processor_information: Details regarding processor. + :ivar classification: The overall classification of the product. + :ivar product_name: The output product name defined by the processor. + :ivar product_class: Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change + Detection, etc.). + :ivar product_type: Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only + needed if there is a suite of associated products. + :ivar product_creation_extension: Extensible parameters used to support profile-specific needs related to + product creation. + """ + + processor_information: Optional[ProcessorInformationType] = field( + default=None, + metadata={ + "name": "ProcessorInformation", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + classification: Optional[ProductClassificationType] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + product_name: Optional[str] = field( + default=None, + metadata={ + "name": "ProductName", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + product_class: Optional[str] = field( + default=None, + metadata={ + "name": "ProductClass", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + product_type: Optional[str] = field( + default=None, + metadata={ + "name": "ProductType", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + product_creation_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ProductCreationExtension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ProductProcessingType: + """ + :ivar processing_module: Processing module to keep track of the name and any parameters associated with the + algorithms used to produce the SIDD. + """ + + processing_module: List[ProcessingModuleType] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class RemapChoiceType: + """ + :ivar color_display_remap: Information for proper color display of the data. + :ivar monochrome_display_remap: Information for proper monochrome display of the data. + """ + + color_display_remap: Optional[ColorDisplayRemapType] = field( + default=None, + metadata={ + "name": "ColorDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + monochrome_display_remap: Optional[MonochromeDisplayRemapType] = field( + default=None, + metadata={ + "name": "MonochromeDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class TargetInformationType: + """ + :ivar identifier: Target may have one or more identifiers. Examples: names, BE numbers, etc. Use the "name" + attribute to describe what this is. + :ivar footprint: Target footprint as defined by polygonal shape. + :ivar target_information_extension: Generic extension. Could be used to indicate type of target, terrain, + etc. + """ + + identifier: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 1, + }, + ) + footprint: Optional[FootprintType] = field( + default=None, + metadata={ + "name": "Footprint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + target_information_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "TargetInformationExtension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class AnnotationsType: + """ + :ivar annotation: Annotation Object. + """ + + annotation: List[AnnotationType] = field( + default_factory=list, + metadata={ + "name": "Annotation", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class CylindricalProjectionType(MeasurableProjectionType): + """ + Cylindrical mapping of the pixel grid. + + :ivar stripmap_direction: Along stripmap direction + :ivar curvature_radius: Radius of Curvature defined at scene center. If not present, the radius of curvature + will be derived based upon the equations provided in the Design and Exploitation Document + """ + + stripmap_direction: Optional[XYZType] = field( + default=None, + metadata={ + "name": "StripmapDirection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + curvature_radius: Optional[float] = field( + default=None, + metadata={ + "name": "CurvatureRadius", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionType: + """ + :ivar information: General collection information. + :ivar geometry: Key geometry parameters independent of product processing. + :ivar phenomenology: Phenomenology related to both the geometry and the final product processing. + """ + + information: Optional[ExploitationFeaturesCollectionInformationType] = field( + default=None, + metadata={ + "name": "Information", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + geometry: Optional[ExploitationFeaturesCollectionGeometryType] = field( + default=None, + metadata={ + "name": "Geometry", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + phenomenology: Optional[ExploitationFeaturesCollectionPhenomenologyType] = field( + default=None, + metadata={ + "name": "Phenomenology", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class GeographicAndTargetType: + """ + :ivar geographic_coverage: Provides geographic coverage information. + :ivar target_information: Provides target specific geographic information. + """ + + geographic_coverage: Optional[GeographicCoverageType] = field( + default=None, + metadata={ + "name": "GeographicCoverage", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + target_information: List[TargetInformationType] = field( + default_factory=list, + metadata={ + "name": "TargetInformation", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class GeographicProjectionType(MeasurableProjectionType): + """ + Geographic mapping of the pixel grid. + """ + + +@dataclass +class PlaneProjectionType(MeasurableProjectionType): + """ + Planar representation of the pixel grid. + + :ivar product_plane: Plane definition for the product. + """ + + product_plane: Optional[ProductPlaneType] = field( + default=None, + metadata={ + "name": "ProductPlane", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductDisplayType: + """ + Type for describing proper display of the derived product. + + :ivar pixel_type: Defines the pixel type, based on enumeration and definition in Design and Exploitation + document. + :ivar remap_information: Information regarding the encoding of the pixel data. Used for 8-bit pixel types. + :ivar magnification_method: Recommended ELT magnification method for this data. + :ivar decimation_method: Recommended ELT decimation method for this data. Also used as default for reduced + resolution dataset generation (if applicable). + :ivar drahistogram_overrides: Recommended ELT DRA overrides. + :ivar monitor_compensation_applied: Describes monitor compensation that may have been applied to the product + during processing. + :ivar display_extension: Extensible parameters used to support profile-specific needs related to product + display. + """ + + pixel_type: Optional[PixelType] = field( + default=None, + metadata={ + "name": "PixelType", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + remap_information: Optional[RemapChoiceType] = field( + default=None, + metadata={ + "name": "RemapInformation", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + magnification_method: Optional[MagnificationMethodType] = field( + default=None, + metadata={ + "name": "MagnificationMethod", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + decimation_method: Optional[DecimationMethodType] = field( + default=None, + metadata={ + "name": "DecimationMethod", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + drahistogram_overrides: Optional[DRAHistogramOverridesType] = field( + default=None, + metadata={ + "name": "DRAHistogramOverrides", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + monitor_compensation_applied: Optional[MonitorCompensationAppliedType] = field( + default=None, + metadata={ + "name": "MonitorCompensationApplied", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + display_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "DisplayExtension", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesType: + """ + Computed metadata regarding the collect. + + :ivar collection: Metadata regarding one of the input collections. + :ivar product: Metadata regarding the product. + """ + + collection: List["ExploitationFeaturesType.Collection"] = field( + default_factory=list, + metadata={ + "name": "Collection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "min_occurs": 1, + }, + ) + product: Optional[ExploitationFeaturesProductType] = field( + default=None, + metadata={ + "name": "Product", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + @dataclass + class Collection(ExploitationFeaturesCollectionType): + identifier: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MeasurementType: + """ + Geometric SAR information required for measurement/geolocation. + + :ivar polynomial_projection: Polynomial pixel to ground. Only used for sensor systems where the radar + geometry parameters are not recorded. + :ivar geographic_projection: Geographic mapping of the pixel grid referred to as GGD in the Design and + Exploitation document. + :ivar plane_projection: Planar representation of the pixel grid referred to as PGD in the Design and + Exploitation document. + :ivar cylindrical_projection: Cylindrical mapping of the pixel grid referred to as CGD in the Design and + Exploitation document. + :ivar pixel_footprint: Size of the image. + :ivar arppoly: Center of aperture polynomial (units = m) based upon time into the collect. + """ + + polynomial_projection: Optional[PolynomialProjectionType] = field( + default=None, + metadata={ + "name": "PolynomialProjection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + geographic_projection: Optional[GeographicProjectionType] = field( + default=None, + metadata={ + "name": "GeographicProjection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + plane_projection: Optional[PlaneProjectionType] = field( + default=None, + metadata={ + "name": "PlaneProjection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + cylindrical_projection: Optional[CylindricalProjectionType] = field( + default=None, + metadata={ + "name": "CylindricalProjection", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + }, + ) + pixel_footprint: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "PixelFootprint", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + arppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "ARPPoly", + "type": "Element", + "namespace": "urn:SIDD:1.0.0", + "required": True, + }, + ) + + +@dataclass +class SIDD: + """ + Root element of the SIDD document. + + :ivar product_creation: Information related to processor, classification, and product type. + :ivar display: Contains information on the parameters needed to display the product in an exploitation tool. + :ivar geographic_and_target: Contains generic and extensible targeting and geographic region information. + :ivar measurement: Contains the metadata necessary for performing measurements. + :ivar exploitation_features: Computed metadata regarding the input collections and final product. + :ivar product_processing: Contains metadata related to algorithms used during product generation. + :ivar downstream_reprocessing: Contains metadata related to downstream processing of the product. + :ivar error_statistics: See SICD documentation for metadata definitions. + :ivar radiometric: Radiometric information about the product. + :ivar annotations: List of annotations for the imagery. + """ + + class Meta: + namespace = "urn:SIDD:1.0.0" + + product_creation: Optional[ProductCreationType] = field( + default=None, + metadata={ + "name": "ProductCreation", + "type": "Element", + "required": True, + }, + ) + display: Optional[ProductDisplayType] = field( + default=None, + metadata={ + "name": "Display", + "type": "Element", + "required": True, + }, + ) + geographic_and_target: Optional[GeographicAndTargetType] = field( + default=None, + metadata={ + "name": "GeographicAndTarget", + "type": "Element", + "required": True, + }, + ) + measurement: Optional[MeasurementType] = field( + default=None, + metadata={ + "name": "Measurement", + "type": "Element", + "required": True, + }, + ) + exploitation_features: Optional[ExploitationFeaturesType] = field( + default=None, + metadata={ + "name": "ExploitationFeatures", + "type": "Element", + "required": True, + }, + ) + product_processing: Optional[ProductProcessingType] = field( + default=None, + metadata={ + "name": "ProductProcessing", + "type": "Element", + }, + ) + downstream_reprocessing: Optional[DownstreamReprocessingType] = field( + default=None, + metadata={ + "name": "DownstreamReprocessing", + "type": "Element", + }, + ) + error_statistics: Optional[ErrorStatisticsType] = field( + default=None, + metadata={ + "name": "ErrorStatistics", + "type": "Element", + }, + ) + radiometric: Optional[RadiometricType] = field( + default=None, + metadata={ + "name": "Radiometric", + "type": "Element", + }, + ) + annotations: Optional[AnnotationsType] = field( + default=None, + metadata={ + "name": "Annotations", + "type": "Element", + }, + ) diff --git a/src/aws/osml/formats/sidd/models/sidd_v2_0_0.py b/src/aws/osml/formats/sidd/models/sidd_v2_0_0.py new file mode 100644 index 0000000..20426c7 --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sidd_v2_0_0.py @@ -0,0 +1,3467 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional, Union + +from xsdata.models.datatype import XmlDate, XmlDateTime + +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ism25_x import CVEnumISM25X +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismatomic_energy_markings import ( + CVEnumISMatomicEnergyMarkingsValuesvalue, +) +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismclassification_all import CVEnumISMClassificationAll +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismcomplies_with import CVEnumISMCompliesWithValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismdissem import CVEnumISMDissemValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismexempt_from import CVEnumISMExemptFromValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnon_ic import CVEnumISMNonICValuesvalue +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnotice import CVEnumISMNoticeValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatfgiopen import CVEnumISMCATFGIOpenValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatfgiprotected import CVEnumISMCATFGIProtectedValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatowner_producer import CVEnumISMCATOwnerProducerValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatrel_to import CVEnumISMCATRelToValuesvalue +from .sfa import LinearRingType, LineType, MultiLineStringType, MultiPointType, MultiPolygonType, PointType +from .sfa import PolygonType as SfaPolygonType +from .sfa import PolyhedralSurfaceType, ReferenceSystemType +from .sicommon_types_v1_0 import ( + AngleMagnitudeType, + CornerStringType, + ErrorStatisticsType, + GeoInfo, + LatLonType, + LatLonVertexType, + MatchInfoType, + ParameterType, + Polarization1Typevalue, + Poly2DType, + RadarModeType, + RadiometricType, + RangeAzimuthType, + ReferencePointType, + RowColDoubleType, + RowColIntType, + RowColVertexType, + XYZPolyType, + XYZType, +) + +__NAMESPACE__ = "urn:SIDD:2.0.0" + + +@dataclass +class AccuracyType: + horizontal: List[float] = field( + default_factory=list, + metadata={ + "name": "Horizontal", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + vertical: List[float] = field( + default_factory=list, + metadata={ + "name": "Vertical", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class AcheivedResolutionType: + """ + Finest achievable resolution parameters. + """ + + +@dataclass +class ClassificationGuidanceType: + """ + Classification guidance authority (only if file is classified). + + :ivar authority: Classifying authority. + :ivar date: Date that the authority was provided. Specified in YYYY-MM-DD. + """ + + authority: Optional[str] = field( + default=None, + metadata={ + "name": "Authority", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "Date", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAHistogramOverridesType: + """ + :ivar clip_min: Suggested override for the lower end-point of the display histogram in the ELT DRA + application. Referred to as Pmin in SIPS documentation. + :ivar clip_max: Suggested override for the upper end-point of the display histogram in the ELT DRA + application. Referred to as Pmax in SIPS documentation. + """ + + clip_min: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMin", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + clip_max: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMax", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAOverrides: + """ + :ivar subtractor: Subtractor value used to reduce haze in the image. Range: [0.0 to 2047.0] + :ivar multiplier: Multiplier value used to reduce haze in the image. Range: [0.0 to 2047.0] + """ + + subtractor: Optional[float] = field( + default=None, + metadata={ + "name": "Subtractor", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + multiplier: Optional[float] = field( + default=None, + metadata={ + "name": "Multiplier", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAParameters: + """ + :ivar pmin: DRA clip low point. This is the cumulative histogram percentage value that defines the lower end- + point of the dynamic range to be displayed. Range: [0.0 to 1.0] + :ivar pmax: DRA clip high point. This is the cumulative histogram percentage value that defines the upper + end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + :ivar emin_modifier: The pixel value corresponding to the Pmin percentage poitn in the image histogram. + Range: [0.0 to 1.0]/ + :ivar emax_modifier: The pixel value corresponding to the Pmax percentage poitn in the image histogram. + Range: [0.0 to 1.0]/ + """ + + pmin: Optional[float] = field( + default=None, + metadata={ + "name": "Pmin", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + pmax: Optional[float] = field( + default=None, + metadata={ + "name": "Pmax", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + emin_modifier: Optional[float] = field( + default=None, + metadata={ + "name": "EminModifier", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + emax_modifier: Optional[float] = field( + default=None, + metadata={ + "name": "EmaxModifier", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +class DownsamplingMethodType(Enum): + DECIMATE = "DECIMATE" + MAX_PIXEL = "MAX PIXEL" + AVERAGE = "AVERAGE" + NEAREST_NEIGHBOR = "NEAREST NEIGHBOR" + BILINEAR = "BILINEAR" + LAGRANGE = "LAGRANGE" + + +class EarthModelType(Enum): + """Identifies the earth model used for latitude, longitude and height + parameters. + + All height values are Height Above The Ellipsoid (HAE). + """ + + WGS_84 = "WGS_84" + + +class EqualizationAlgorithmType(Enum): + VALUE_1_DLUT = "1DLUT" + + +@dataclass +class FilterBankCoefType: + coef: List["FilterBankCoefType.Coef"] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + num_phasings: Optional[int] = field( + default=None, + metadata={ + "name": "numPhasings", + "type": "Attribute", + "required": True, + }, + ) + num_points: Optional[int] = field( + default=None, + metadata={ + "name": "numPoints", + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Coef: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + phasing: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + point: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class FilterDatabaseNameType(Enum): + BILINEAR = "BILINEAR" + CUBIC = "CUBIC" + LAGRANGE = "LAGRANGE" + NEAREST_NEIGHBOR = "NEAREST NEIGHBOR" + + +@dataclass +class FilterKernelCoefType: + coef: List["FilterKernelCoefType.Coef"] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "numRows", + "type": "Attribute", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "numCols", + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Coef: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + row: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class FilterOperationType(Enum): + CONVOLUTION = "CONVOLUTION" + CORRELATION = "CORRELATION" + + +class GeopositioningTypeCoordinateSystemType(Enum): + GCS = "GCS" + UTM = "UTM" + + +class GeopositioningTypeGeodeticDatum(Enum): + WORLD_GEODETIC_SYSTEM_1984 = "World Geodetic System 1984" + + +class GeopositioningTypeReferenceEllipsoid(Enum): + WORLD_GEODETIC_SYSTEM_1984 = "World Geodetic System 1984" + + +class GeopositioningTypeSoundingDatum(Enum): + MEAN_SEA_LEVEL = "Mean Sea Level" + + +class GeopositioningTypeVerticalDatum(Enum): + MEAN_SEA_LEVEL = "Mean Sea Level" + + +@dataclass +class LayerType: + """ + :ivar bitrate: The bit rate target associated with the layer. It may happen that the bit rate was not + achieved due to data characteristics. Note: for JPEG 2000 numerically lossless quality, the bit rate for + the final layer is an expected value, based on performance. + :ivar index: + """ + + bitrate: Optional[float] = field( + default=None, + metadata={ + "name": "Bitrate", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Lookup3TableType: + """ + :ivar value: + :ivar size: Size of LUT + """ + + value: List[str] = field( + default_factory=list, + metadata={ + "pattern": r"([0-9]+),([0-9]+),([0-9]+)", + "tokens": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LookupTableType: + """ + :ivar value: + :ivar lut: Size of LUT. + """ + + value: List[int] = field( + default_factory=list, + metadata={ + "tokens": True, + }, + ) + lut: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class MeasurementTypeARPFlag(Enum): + """ + :cvar REALTIME: Based on ephemeries at time of collect + :cvar PREDICTED: Based on predicted ephemeries (i.e. pre-collect) + :cvar POST_PROCESSED: Ephemeris has been refined after data collection + """ + + REALTIME = "REALTIME" + PREDICTED = "PREDICTED" + POST_PROCESSED = "POST PROCESSED" + + +class PixelType(Enum): + MONO8_I = "MONO8I" + MONO8_LU = "MONO8LU" + MONO16_I = "MONO16I" + RGB8_LU = "RGB8LU" + RGB24_I = "RGB24I" + + +@dataclass +class PredefinedLookupType: + """ + :ivar database_name: Database name of LUT to use. + :ivar remap_family: Index specifying the remap family. + :ivar remap_member: Index specifying the member for the remap family. + """ + + database_name: Optional[str] = field( + default=None, + metadata={ + "name": "DatabaseName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + remap_family: Optional[int] = field( + default=None, + metadata={ + "name": "RemapFamily", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + remap_member: Optional[int] = field( + default=None, + metadata={ + "name": "RemapMember", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ProcessorInformationType: + """ + :ivar application: Software application name and version number. + :ivar processing_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar site: Creation location of product. + :ivar profile: Product-specific profile applied during product processing. + """ + + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + processing_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "ProcessingDateTime", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +class RangeAdjustmentAlgorithmType(Enum): + """ + Algorithm used for dynamic range adjustment. + """ + + AUTO = "AUTO" + MANUAL = "MANUAL" + NONE = "NONE" + + +class RenderingIntentType(Enum): + PERCEPTUAL = "PERCEPTUAL" + SATURATION = "SATURATION" + RELATIVE = "RELATIVE" + ABSOLUTE = "ABSOLUTE" + + +class ShadowDirectionType(Enum): + """ + Descirbes the shadow direciton relative to the pixels in the file. + """ + + UP = "UP" + DOWN = "DOWN" + LEFT = "LEFT" + RIGHT = "RIGHT" + ARBITRARY = "ARBITRARY" + + +@dataclass +class AnnotationObjectType: + """ + Geometrical representation of the annotation. + """ + + point: Optional[PointType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + linear_ring: Optional[LinearRingType] = field( + default=None, + metadata={ + "name": "LinearRing", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + polygon: Optional[SfaPolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + polyhedral_surface: Optional[PolyhedralSurfaceType] = field( + default=None, + metadata={ + "name": "PolyhedralSurface", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + multi_polygon: Optional[MultiPolygonType] = field( + default=None, + metadata={ + "name": "MultiPolygon", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + multi_line_string: Optional[MultiLineStringType] = field( + default=None, + metadata={ + "name": "MultiLineString", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + multi_point: Optional[MultiPointType] = field( + default=None, + metadata={ + "name": "MultiPoint", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class BankCustomType: + filter_coefficients: Optional[FilterBankCoefType] = field( + default=None, + metadata={ + "name": "FilterCoefficients", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class BaseProjectionType: + """ + :ivar reference_point: Reference point for the geometrical system. + """ + + reference_point: Optional[ReferencePointType] = field( + default=None, + metadata={ + "name": "ReferencePoint", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class ColorDisplayRemapType: + """ + Object representing that the data requires color display. + + :ivar remap_lut: LUT-base remap indicating that the color display is done through index-based color. + """ + + remap_lut: Optional[Lookup3TableType] = field( + default=None, + metadata={ + "name": "RemapLUT", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class ColorManagementModuleType: + """ + Parameters describing the Color Management Module (CMM). + + :ivar rendering_intent: + :ivar source_profile: Name of sensor profile in ICC Profile database. + :ivar display_profile: Name of display profile in ICC Profile database. + :ivar iccprofile_signature: Valid ICC profile signature. + """ + + rendering_intent: Optional[RenderingIntentType] = field( + default=None, + metadata={ + "name": "RenderingIntent", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + source_profile: Optional[str] = field( + default=None, + metadata={ + "name": "SourceProfile", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + display_profile: Optional[str] = field( + default=None, + metadata={ + "name": "DisplayProfile", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + iccprofile_signature: Optional[str] = field( + default=None, + metadata={ + "name": "ICCProfileSignature", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class DynamicRangeAdjustmentType: + """ + Parameter describing DRA. + + :ivar algorithm_type: Algorithm used for dynamic range adjustment. + :ivar band_stats_source: Indicates which band to use in computing statistics for DRA. Valid range = 1 to + NumBands. + :ivar draparameters: + :ivar draoverrides: + """ + + algorithm_type: Optional[RangeAdjustmentAlgorithmType] = field( + default=None, + metadata={ + "name": "AlgorithmType", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + band_stats_source: Optional[int] = field( + default=None, + metadata={ + "name": "BandStatsSource", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + draparameters: Optional[DRAParameters] = field( + default=None, + metadata={ + "name": "DRAParameters", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + draoverrides: Optional[DRAOverrides] = field( + default=None, + metadata={ + "name": "DRAOverrides", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionGeometryType: + """Key geometry parameters independent of product processing. + + All values computed at the center time of the full collection. + + :ivar azimuth: Angle clockwise from north indicating the ETP line of sight vector. + :ivar slope: Angle between the ETP at scene center and the range vector perpendicular to the direction of + motion. + :ivar squint: Angle from the ground track to platform velocity vector at nadir. Left-look is positive, right- + look is negative. + :ivar graze: Angle between the ETP and the line of sight vector. + :ivar tilt: Angle between the ETP and the cross range vector. Also known as the twist angle. + :ivar doppler_cone_angle: The angle between the velocity vector and the radar line-of-sight vector. Also + known as the slant plane squint angle. + :ivar extension: Exploitation feature extension related to geometry for a single input image + """ + + azimuth: Optional[float] = field( + default=None, + metadata={ + "name": "Azimuth", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + slope: Optional[float] = field( + default=None, + metadata={ + "name": "Slope", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + squint: Optional[float] = field( + default=None, + metadata={ + "name": "Squint", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + graze: Optional[float] = field( + default=None, + metadata={ + "name": "Graze", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + tilt: Optional[float] = field( + default=None, + metadata={ + "name": "Tilt", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + doppler_cone_angle: Optional[float] = field( + default=None, + metadata={ + "name": "DopplerConeAngle", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": 0.0, + "max_exclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionPhenomenologyType: + """Phenomenology related to both the geometry and the final product processing. + + All values computed at the center time of the full collection. + + :ivar shadow: The phenomon where vertical objects occlude radar energy. + :ivar layover: The phenomenon where vertical objects appear as ground objects with the same range/range rate. + :ivar multi_path: This is a range dependent phenomenon which describes the energy from a single scatter + returned to the radar via more than one path and results in a nominally constant direction in the ETP. + :ivar ground_track: Counter-clockwise angle from increasing row direction to ground track at the center of + the image. + :ivar extension: Exploitation feature extension related to phenomenology for a single input image + """ + + shadow: Optional[AngleMagnitudeType] = field( + default=None, + metadata={ + "name": "Shadow", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + layover: Optional[AngleMagnitudeType] = field( + default=None, + metadata={ + "name": "Layover", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + multi_path: Optional[float] = field( + default=None, + metadata={ + "name": "MultiPath", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + ground_track: Optional[float] = field( + default=None, + metadata={ + "name": "GroundTrack", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class GeographicCoordinatesType: + """ + Describes the Local Geographic Coordinate system linking row/column to the + absolute geographic coordinate (lat/lon) + + :ivar longitude_density: Pixel ground spacing in E/W direction that is the number of pixels or element + intervals in 360 degrees. + :ivar latitude_density: Pixel ground spacing in N/S direction that is the number of pixels or element + intervals in 360 degrees. + :ivar reference_origin: Northwest corner Latitude/Longitude - product NW corner + """ + + longitude_density: Optional[float] = field( + default=None, + metadata={ + "name": "LongitudeDensity", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + latitude_density: Optional[float] = field( + default=None, + metadata={ + "name": "LatitudeDensity", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + reference_origin: Optional[LatLonType] = field( + default=None, + metadata={ + "name": "ReferenceOrigin", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class GeometricChipType: + """Contains information related to downstream chipping of the product. + + There is only one instance, and the instance is updated with respect to the full image parameters. For + example, if an image is chipped out of a smaller chip, the new chip needs to be updated to the original full + image corners. Since this relationship is linear, bi-linear interpolation is sufficient to determine an + arbitrary chip coordinate in terms of the original full image coordinates. Chipping is typically done using + an exploitation tool, and should not be done in the initial product creation. + + :ivar chip_size: Size of the chipped product in pixels. + :ivar original_upper_left_coordinate: Upper-left corner with respect to the original product. + :ivar original_upper_right_coordinate: Upper-right corner with respect to the original product. + :ivar original_lower_left_coordinate: Lower-left corner with respect to the original product. + :ivar original_lower_right_coordinate: Lower-right corner with respect to the original product. + """ + + chip_size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "ChipSize", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + original_upper_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + original_upper_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + original_lower_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + original_lower_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class GeopositioningType: + """ + Describes the absolute coordinate system to which the data is referenced. + + :ivar coordinate_system_type: + :ivar geodetic_datum: + :ivar reference_ellipsoid: + :ivar vertical_datum: + :ivar sounding_datum: + :ivar false_origin: Z values false origin + :ivar utmgrid_zone_number: Gride zone number, required for UTM, not include for GCS. (4 character field) + Values: +001 to +060 (northern hemisphere) -001 to -060 (southern hemisphere) + """ + + coordinate_system_type: Optional[GeopositioningTypeCoordinateSystemType] = field( + default=None, + metadata={ + "name": "CoordinateSystemType", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + geodetic_datum: Optional[GeopositioningTypeGeodeticDatum] = field( + default=None, + metadata={ + "name": "GeodeticDatum", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + reference_ellipsoid: Optional[GeopositioningTypeReferenceEllipsoid] = field( + default=None, + metadata={ + "name": "ReferenceEllipsoid", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + vertical_datum: Optional[GeopositioningTypeVerticalDatum] = field( + default=None, + metadata={ + "name": "VerticalDatum", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + sounding_datum: Optional[GeopositioningTypeSoundingDatum] = field( + default=None, + metadata={ + "name": "SoundingDatum", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + false_origin: Optional[int] = field( + default=None, + metadata={ + "name": "FalseOrigin", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + utmgrid_zone_number: Optional[int] = field( + default=None, + metadata={ + "name": "UTMGridZoneNumber", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ImageCornersType: + """Parameters apply to image corners of the product projected to the same + height as the SCP. + + These corners are an approximate geographic location that is not intended for analytical use. + + :ivar icp: Image Corner Point (ICP) data for the 4 corners in product. ICPs indexed x = 1, 2, 3, 4, + clockwise. + """ + + icp: List["ImageCornersType.ICP"] = field( + default_factory=list, + metadata={ + "name": "ICP", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + @dataclass + class ICP(LatLonType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class InputROIType: + """ + ROI representing portion of input data used to make this product. + + :ivar size: Number of rows and columns extracted from the input. + :ivar upper_left: The upper-left pixel extracted from the input. + """ + + size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "Size", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + upper_left: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "UpperLeft", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class KernelCustomType: + filter_coefficients: Optional[FilterKernelCoefType] = field( + default=None, + metadata={ + "name": "FilterCoefficients", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class LUTInfoType: + lutvalues: List[LookupTableType] = field( + default_factory=list, + metadata={ + "name": "LUTValues", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + num_luts: Optional[int] = field( + default=None, + metadata={ + "name": "numLuts", + "type": "Attribute", + "required": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LayerInfoType: + """Original Layer Information. + + The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + :ivar layer: Layer Index Number indicates the number of layers being described. Layers are numbered from 0 to + (numLayers - 1). + :ivar num_layers: + """ + + layer: List[LayerType] = field( + default_factory=list, + metadata={ + "name": "Layer", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + num_layers: Optional[int] = field( + default=None, + metadata={ + "name": "numLayers", + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MonochromeDisplayRemapType: + """This remap works by taking the input space and using the LUT to map it to a + log space (for 8-bit only). + + From the log space the C0 and Ch fields are applied to get to display-ready density space. + The density should then be rendered by the TTC and monitor comp. + This means that the default DRA should not apply anything besides the clip points. + If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. + Examples: + Remap LUT Clips + ============================= + PEDF PEDF->D 0,255 + LLG LLG->A->LogA C0,Ch + Log N/A C0,Ch + NRL N/A 0,255 (Supposed to be display ready) + + :ivar remap_type: Name of remap applied (for informational purposes only). + :ivar remap_parameter: Textual remap parameter. Filled based upon remap type (for informational purposes + only). For example, if the data is linlog encoded a RemapParameter could be used to describe any + amplitude scaling that was performed prior to linlog encoding the data. + """ + + remap_type: Optional[str] = field( + default=None, + metadata={ + "name": "RemapType", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + remap_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "RemapParameter", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class Orientation: + """ + Parameters describing the default orientation of the product. + + :ivar shadow_direction: Descirbes the shadow direciton relative to the pixels in the file. + """ + + shadow_direction: Optional[ShadowDirectionType] = field( + default=None, + metadata={ + "name": "ShadowDirection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + """Indicates the full image includes both valid data and some zero filled + pixels. + + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). + Vertices in clockwise order. + + :ivar vertex: Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) + minimum row index, (2) minimum column index if 2 vertices with minimum row index, 1st and last vertices + are connected to form the polygon. + :ivar size: + """ + + vertex: List[LatLonVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PositionalAccuracyType: + """ + Describes the horizontal and vertical point and regional information for the + DED. + + :ivar num_regions: Number of positional accuracy regions. + :ivar absolute_accuracy: + :ivar point_to_point_accuracy: + """ + + num_regions: Optional[int] = field( + default=None, + metadata={ + "name": "NumRegions", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + absolute_accuracy: Optional[AccuracyType] = field( + default=None, + metadata={ + "name": "AbsoluteAccuracy", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + point_to_point_accuracy: Optional[AccuracyType] = field( + default=None, + metadata={ + "name": "PointToPointAccuracy", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class PredefinedFilterType: + database_name: Optional[FilterDatabaseNameType] = field( + default=None, + metadata={ + "name": "DatabaseName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + filter_family: Optional[int] = field( + default=None, + metadata={ + "name": "FilterFamily", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + filter_member: Optional[int] = field( + default=None, + metadata={ + "name": "FilterMember", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ProcTxRcvPolarizationType: + """ + :ivar tx_polarization_proc: Polarization transmit type + :ivar rcv_polarization_proc: Receive polarization type + """ + + tx_polarization_proc: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "TxPolarizationProc", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization_proc: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "RcvPolarizationProc", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + + +@dataclass +class ProcessingEventType: + """ + :ivar application_name: Application which applied a modification. + :ivar applied_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar interpolation_method: Type of interpolation applied to the data. + :ivar descriptor: Descriptor for the processing event. + """ + + application_name: Optional[str] = field( + default=None, + metadata={ + "name": "ApplicationName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + applied_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "AppliedDateTime", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + interpolation_method: Optional[str] = field( + default=None, + metadata={ + "name": "InterpolationMethod", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + descriptor: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Descriptor", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ProcessingModuleType: + """ + :ivar module_name: The name of the algorithm used in processing the product. + :ivar module_parameter: Parameters associated with the algorithm used in processing the product. + :ivar processing_module: ProcessingModule is a repeatable structure within itself to create an algorithm as a + subset of another algorithm. + """ + + module_name: Optional[ParameterType] = field( + default=None, + metadata={ + "name": "ModuleName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + module_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ModuleParameter", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + processing_module: List["ProcessingModuleType"] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ProductClassificationType: + """ + The overall classification of the product. + + :ivar security_extension: Extensible parameters used to support profile-specific needs related to product + security. + :ivar desversion: + :ivar ismcatcesversion: + :ivar resource_element: + :ivar complies_with: + :ivar create_date: + :ivar exempt_from: + :ivar classification: + :ivar owner_producer: + :ivar joint: + :ivar scicontrols: + :ivar saridentifier: + :ivar atomic_energy_markings: + :ivar dissemination_controls: + :ivar display_only_to: + :ivar fgisource_open: + :ivar fgisource_protected: + :ivar releasable_to: + :ivar non_icmarkings: + :ivar classified_by: + :ivar compilation_reason: + :ivar derivatively_classified_by: + :ivar classification_reason: + :ivar non_uscontrols: + :ivar derived_from: + :ivar declass_date: + :ivar declass_event: + :ivar declass_exception: + :ivar notice_type: + :ivar notice_reason: + :ivar notice_date: + :ivar unregistered_notice_type: + :ivar external_notice: + """ + + security_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "SecurityExtension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + desversion: Optional[str] = field( + default=None, + metadata={ + "name": "DESVersion", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "max_length": 256, + }, + ) + ismcatcesversion: Optional[str] = field( + default=None, + metadata={ + "name": "ISMCATCESVersion", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "max_length": 256, + }, + ) + resource_element: Optional[bool] = field( + default=None, + metadata={ + "name": "resourceElement", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + complies_with: List[CVEnumISMCompliesWithValues] = field( + default_factory=list, + metadata={ + "name": "compliesWith", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "tokens": True, + }, + ) + create_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "createDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + exempt_from: List[CVEnumISMExemptFromValues] = field( + default_factory=list, + metadata={ + "name": "exemptFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + notice_type: List[CVEnumISMNoticeValues] = field( + default_factory=list, + metadata={ + "name": "noticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + notice_reason: Optional[str] = field( + default=None, + metadata={ + "name": "noticeReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + notice_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "noticeDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + unregistered_notice_type: Optional[str] = field( + default=None, + metadata={ + "name": "unregisteredNoticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + external_notice: Optional[bool] = field( + default=None, + metadata={ + "name": "externalNotice", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class ProductPlaneType: + """ + Plane definition for the product. + + :ivar row_unit_vector: Unit vector of the plane defined to be aligned in the increasing row direction of the + product. (Defined as Rpgd in Design and Exploitation document) + :ivar col_unit_vector: Unit vector of the plane defined to be aligned in the increasing column direction of + the product. (Defined as Cpgd in Design and Exploitation document) + """ + + row_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "RowUnitVector", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + col_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ColUnitVector", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class TxRcvPolarizationType: + """ + :ivar tx_polarization: Polarization transmit type + :ivar rcv_polarization: Receive polarization type + :ivar rcv_polarization_offset: Optional angle offset for the receive polarization defined at aperture center. + """ + + tx_polarization: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "RcvPolarization", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization_offset: Optional[float] = field( + default=None, + metadata={ + "name": "RcvPolarizationOffset", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + + +@dataclass +class ValidDataType: + """Indicates the full image includes both valid data and some zero filled + pixels. + + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). + Vertices in clockwise order. + + :ivar vertex: Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) + minimum row index, (2) minimum column index if 2 vertices with minimum row index, 1st and last vertices + are connected to form the polygon. + :ivar size: + """ + + vertex: List[RowColVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class AnnotationType: + """ + Single annotation. + + :ivar identifier: Identifier for the annotation which idicates the type of object represented by this + annotation. + :ivar spatial_reference_system: Spatial reference system of the annotation. Assumed to be WGS-84 geographic + coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above + ellipsoid). + :ivar object_value: The geometrical representation of the annotation. + """ + + identifier: Optional[str] = field( + default=None, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + spatial_reference_system: Optional[ReferenceSystemType] = field( + default=None, + metadata={ + "name": "SpatialReferenceSystem", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + object_value: List[AnnotationObjectType] = field( + default_factory=list, + metadata={ + "name": "Object", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class ColorSpaceTransformType: + """ + :ivar color_management_module: Parameters describing the Color Management Module (CMM). + """ + + color_management_module: Optional[ColorManagementModuleType] = field( + default=None, + metadata={ + "name": "ColorManagementModule", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class CustomLookupType: + lutinfo: Optional[LUTInfoType] = field( + default=None, + metadata={ + "name": "LUTInfo", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class DigitalElevationDataType: + """ + This block describes the Digital ElevatioNData when it is included with the + SIDD product. + + :ivar geographic_coordinates: Describes the Local Geographic Coordinate system linking row/column to the + absolute geographic coordinate (lat/lon) + :ivar geopositioning: Describes the absolute coordinate system to which the data is referenced. + :ivar positional_accuracy: Describes the horizontal and vertical point and regional information for the DED. + :ivar null_value: + """ + + geographic_coordinates: Optional[GeographicCoordinatesType] = field( + default=None, + metadata={ + "name": "GeographicCoordinates", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + geopositioning: Optional[GeopositioningType] = field( + default=None, + metadata={ + "name": "Geopositioning", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + positional_accuracy: Optional[PositionalAccuracyType] = field( + default=None, + metadata={ + "name": "PositionalAccuracy", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + null_value: Optional[int] = field( + default=None, + metadata={ + "name": "NullValue", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class DownstreamReprocessingType: + """ + :ivar geometric_chip: Contains information related to downstream chipping of the product. + :ivar processing_event: Contains information related to downstream processing of the product. + """ + + geometric_chip: Optional[GeometricChipType] = field( + default=None, + metadata={ + "name": "GeometricChip", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + processing_event: List[ProcessingEventType] = field( + default_factory=list, + metadata={ + "name": "ProcessingEvent", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionInformationType: + """ + General collection information. + + :ivar sensor_name: The name of the sensor. + :ivar radar_mode: Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, + DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + :ivar collection_date_time: Collection date and time defined in Coordinated Universal Time (UTC). The seconds + should be followed by a Z to indicate UTC. + :ivar local_date_time: Date and time defined in local time. + :ivar collection_duration: The duration of the collection (units = seconds). + :ivar resolution: Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + :ivar input_roi: ROI representing portion of input data used to make this product. + :ivar polarization: Transmit and receive polarization. + """ + + sensor_name: Optional[str] = field( + default=None, + metadata={ + "name": "SensorName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + radar_mode: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + collection_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CollectionDateTime", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + local_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "LocalDateTime", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + collection_duration: Optional[float] = field( + default=None, + metadata={ + "name": "CollectionDuration", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + resolution: Optional[RangeAzimuthType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + input_roi: Optional[InputROIType] = field( + default=None, + metadata={ + "name": "InputROI", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + polarization: List[TxRcvPolarizationType] = field( + default_factory=list, + metadata={ + "name": "Polarization", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesProductType: + """ + Metadata regarding the product. + + :ivar resolution: Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + :ivar ellipticity: Ellipticity of the 2D-IPR at the ORP, measured in the Earth Geodetic Tangent Plane (EGTP). + Ellipticity is the ratio of the IPR ellipse's major axis to minor axis. + :ivar polarization: Describes the processed transmit and receive polarizations for the product. + :ivar north: Counter-clockwise angle from increasing row direction to north at the center of the image. + :ivar extension: Exploitation feature extension for the end product + """ + + resolution: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + ellipticity: Optional[float] = field( + default=None, + metadata={ + "name": "Ellipticity", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + polarization: List[ProcTxRcvPolarizationType] = field( + default_factory=list, + metadata={ + "name": "Polarization", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + north: Optional[float] = field( + default=None, + metadata={ + "name": "North", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class FilterBankType: + predefined: Optional[PredefinedFilterType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + custom: Optional[BankCustomType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class FilterKernelType: + predefined: Optional[PredefinedFilterType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + custom: Optional[KernelCustomType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class GeoDataType: + """ + This block describes the geographic coordinates of the region covered by the + image. + + :ivar earth_model: Identifies the earth model used for latitude, longitude and height parameters. All height + values are Height Above The Ellipsoid (HAE). + :ivar image_corners: Parameters apply to image corners of the product projected to the same height as the + SCP. These corners are an approximate geographic location that is not intended for analytical use. + :ivar valid_data: Indicates the full image includes both valid data and some zero filled pixels. Simple + convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices + in clockwise order. + :ivar geo_info: Parameters describing geographic features. Note: the GeoInfo block may be used as a block + within itself. + """ + + earth_model: Optional[EarthModelType] = field( + default=None, + metadata={ + "name": "EarthModel", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + image_corners: Optional[ImageCornersType] = field( + default=None, + metadata={ + "name": "ImageCorners", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + valid_data: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + geo_info: List[GeoInfo] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + +@dataclass +class J2KSubtype: + """ + :ivar num_wavelet_levels: The default number of wavelet decompositionlevels performed per tile in the + original image out of the processors. + :ivar num_bands: The number of spectral bands in the original image out of the processors. + :ivar layer_info: Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., + numLayers - 1). The default number of layers per tile in original image out of the original processor. + """ + + num_wavelet_levels: Optional[int] = field( + default=None, + metadata={ + "name": "NumWaveletLevels", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + num_bands: Optional[int] = field( + default=None, + metadata={ + "name": "NumBands", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + layer_info: Optional[LayerInfoType] = field( + default=None, + metadata={ + "name": "LayerInfo", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class MeasurableProjectionType(BaseProjectionType): + """ + :ivar sample_spacing: Sample spacing in row and column. + :ivar time_coapoly: Time (units = seconds) at which center of aperture for a given pixel coordinate in the + product occurs. + """ + + sample_spacing: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "SampleSpacing", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + time_coapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "TimeCOAPoly", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class PolynomialProjectionType(BaseProjectionType): + """Polynomial pixel to ground. + + Only used for sensor systems where the radar geometry parameters are not recorded. + + :ivar row_col_to_lat: Polynomial that converts Row/Col to Latitude (degrees). + :ivar row_col_to_lon: Polynomial that converts Row/Col to Longitude (degrees). + :ivar row_col_to_alt: Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + :ivar lat_lon_to_row: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row + location. + :ivar lat_lon_to_col: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column + location + """ + + row_col_to_lat: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLat", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + row_col_to_lon: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLon", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + row_col_to_alt: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToAlt", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + lat_lon_to_row: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToRow", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + lat_lon_to_col: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToCol", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductCreationType: + """ + Contains general information about product creation. + + :ivar processor_information: Details regarding processor. + :ivar classification: The overall classification of the product. + :ivar product_name: The output product name defined by the processor. + :ivar product_class: Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change + Detection, etc.). + :ivar product_type: Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only + needed if there is a suite of associated products. + :ivar product_creation_extension: Extensible parameters used to support profile-specific needs related to + product creation. + """ + + processor_information: Optional[ProcessorInformationType] = field( + default=None, + metadata={ + "name": "ProcessorInformation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + classification: Optional[ProductClassificationType] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + product_name: Optional[str] = field( + default=None, + metadata={ + "name": "ProductName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + product_class: Optional[str] = field( + default=None, + metadata={ + "name": "ProductClass", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + product_type: Optional[str] = field( + default=None, + metadata={ + "name": "ProductType", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + product_creation_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ProductCreationExtension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ProductProcessingType: + """ + Computed metadata regarding one or more of the input collections and final + product. + + :ivar processing_module: Processing module to keep track of the name and any parameters associated with the + algorithms used to produce the SIDD. + """ + + processing_module: List[ProcessingModuleType] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class RemapChoiceType: + """ + :ivar color_display_remap: Information for proper color display of the data. + :ivar monochrome_display_remap: Information for proper monochrome display of the data. + """ + + color_display_remap: Optional[ColorDisplayRemapType] = field( + default=None, + metadata={ + "name": "ColorDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + monochrome_display_remap: Optional[MonochromeDisplayRemapType] = field( + default=None, + metadata={ + "name": "MonochromeDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class AnnotationsType: + """ + :ivar annotation: Annotation Object. + """ + + annotation: List[AnnotationType] = field( + default_factory=list, + metadata={ + "name": "Annotation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class CylindricalProjectionType(MeasurableProjectionType): + """ + Cylindrical mapping of the pixel grid. + + :ivar stripmap_direction: Along stripmap direction + :ivar curvature_radius: Radius of Curvature defined at scene center. If not present, the radius of curvature + will be derived based upon the equations provided in the Design and Exploitation Document + """ + + stripmap_direction: Optional[XYZType] = field( + default=None, + metadata={ + "name": "StripmapDirection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + curvature_radius: Optional[float] = field( + default=None, + metadata={ + "name": "CurvatureRadius", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionType: + """ + :ivar information: General collection information. + :ivar geometry: Key geometry parameters independent of product processing. + :ivar phenomenology: Phenomenology related to both the geometry and the final product processing. + """ + + information: Optional[ExploitationFeaturesCollectionInformationType] = field( + default=None, + metadata={ + "name": "Information", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + geometry: Optional[ExploitationFeaturesCollectionGeometryType] = field( + default=None, + metadata={ + "name": "Geometry", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + phenomenology: Optional[ExploitationFeaturesCollectionPhenomenologyType] = field( + default=None, + metadata={ + "name": "Phenomenology", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class FilterType: + filter_name: Optional[str] = field( + default=None, + metadata={ + "name": "FilterName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + filter_kernel: Optional[FilterKernelType] = field( + default=None, + metadata={ + "name": "FilterKernel", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + filter_bank: Optional[FilterBankType] = field( + default=None, + metadata={ + "name": "FilterBank", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + operation: Optional[FilterOperationType] = field( + default=None, + metadata={ + "name": "Operation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class GeographicProjectionType(MeasurableProjectionType): + """ + Geographic mapping of the pixel grid. + """ + + +@dataclass +class J2KType: + """ + :ivar original: + :ivar parsed: Conditional fields that exist only for parsed images. + """ + + original: Optional[J2KSubtype] = field( + default=None, + metadata={ + "name": "Original", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + parsed: Optional[J2KSubtype] = field( + default=None, + metadata={ + "name": "Parsed", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class NewLookupTableType: + lutname: Optional[str] = field( + default=None, + metadata={ + "name": "LUTName", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + predefined: Optional[PredefinedLookupType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + custom: Optional[CustomLookupType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class PlaneProjectionType(MeasurableProjectionType): + """ + Planar representation of the pixel grid. + + :ivar product_plane: Plane definition for the product. + """ + + product_plane: Optional[ProductPlaneType] = field( + default=None, + metadata={ + "name": "ProductPlane", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class BandEqualizationType: + """ + Band equalization ensures that real-world neutral colors have equal digital + count values (i.e. are represented as neutral colors) across the dynamic range + of the imaged scene. + + :ivar algorithm: Allowed values: 1DLUT + :ivar band_lut: + """ + + algorithm: Optional[EqualizationAlgorithmType] = field( + default=None, + metadata={ + "name": "Algorithm", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + band_lut: List["BandEqualizationType.BandLUT"] = field( + default_factory=list, + metadata={ + "name": "BandLUT", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + @dataclass + class BandLUT(NewLookupTableType): + k: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class CompressionType: + """ + Contains information regarding any compression that has occured to the image + data. + + :ivar j2_k: Block describing details of JPEG 2000 compression. + """ + + j2_k: Optional[J2KType] = field( + default=None, + metadata={ + "name": "J2K", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class ExploitationFeaturesType: + """ + Computed metadata regarding the collect. + + :ivar collection: Metadata regarding one of the input collections. + :ivar product: Metadata regarding the product. + """ + + collection: List["ExploitationFeaturesType.Collection"] = field( + default_factory=list, + metadata={ + "name": "Collection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + product: List[ExploitationFeaturesProductType] = field( + default_factory=list, + metadata={ + "name": "Product", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + + @dataclass + class Collection(ExploitationFeaturesCollectionType): + identifier: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MeasurementType: + """ + Geometric SAR information required for measurement/geolocation. + + :ivar polynomial_projection: Polynomial pixel to ground. Only used for sensor systems where the radar + geometry parameters are not recorded. + :ivar geographic_projection: Geographic mapping of the pixel grid referred to as GGD in the Design and + Exploitation document. + :ivar plane_projection: Planar representation of the pixel grid referred to as PGD in the Design and + Exploitation document. + :ivar cylindrical_projection: Cylindrical mapping of the pixel grid referred to as CGD in the Design and + Exploitation document. + :ivar pixel_footprint: Size of the image in pixels. + :ivar arpflag: Flag indicating whether ARP polynomial is based on the best available ("collect time" or + "predicted") ephemeris. + :ivar arppoly: Center of aperture polynomial (units = m) based upon time into the collect. + :ivar valid_data: Indicates the full image includes both valid data and some zero filled pixels. Simple + convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices + in clockwise order. + """ + + polynomial_projection: Optional[PolynomialProjectionType] = field( + default=None, + metadata={ + "name": "PolynomialProjection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + geographic_projection: Optional[GeographicProjectionType] = field( + default=None, + metadata={ + "name": "GeographicProjection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + plane_projection: Optional[PlaneProjectionType] = field( + default=None, + metadata={ + "name": "PlaneProjection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + cylindrical_projection: Optional[CylindricalProjectionType] = field( + default=None, + metadata={ + "name": "CylindricalProjection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + pixel_footprint: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "PixelFootprint", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + arpflag: Optional[MeasurementTypeARPFlag] = field( + default=None, + metadata={ + "name": "ARPFlag", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + arppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "ARPPoly", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + valid_data: Optional[ValidDataType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class RRDSType: + """ + :ivar downsampling_method: Algorithm used to perform RRDS downsampling + :ivar anti_alias: Only included if DownSamplingMethod=DECIMET + :ivar interpolation: Only included if DownSamplingMethod=DECIMET + """ + + downsampling_method: Optional[DownsamplingMethodType] = field( + default=None, + metadata={ + "name": "DownsamplingMethod", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + anti_alias: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AntiAlias", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + interpolation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "Interpolation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class ScalingType: + """ + :ivar anti_alias: Anti-Alias Filter used for scaling. Refer to program-specific documentation for population + guidance + :ivar interpolation: Interpolation Filter used for scaling. Refer to program-specific documentation for + population guidance. + """ + + anti_alias: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AntiAlias", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + interpolation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "Interpolation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class SharpnessEnhancementType: + """ + :ivar modular_transfer_function_compensation: Note: If defining a custom Filter, it must be no larger than + 5x5. + :ivar modular_transfer_function_enhancement: Note: If defining a custom Filter, it must be no larger than + 5x5. + """ + + modular_transfer_function_compensation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionCompensation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + modular_transfer_function_enhancement: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionEnhancement", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class GeometricTransformType: + """ + :ivar scaling: + :ivar orientation: Parameters describing the default orientation of the product + """ + + scaling: Optional[ScalingType] = field( + default=None, + metadata={ + "name": "Scaling", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + orientation: Optional[Orientation] = field( + default=None, + metadata={ + "name": "Orientation", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductGenerationOptionsType: + """ + Performs several key actions on an image to prepare it for necessary additional + processing to achieve the desired output product. + + :ivar band_equalization: Band equalization ensures that real-world neutral colors have equal digital count + values (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + :ivar modular_transfer_function_restoration: Filter must be no larger than 15x15. + :ivar data_remapping: Data remapping refers to the specific need to convert the data of incoming, expanded or + uncompressed image band data to non-mapped image data. + :ivar asymmetric_pixel_correction: + """ + + band_equalization: Optional[BandEqualizationType] = field( + default=None, + metadata={ + "name": "BandEqualization", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + modular_transfer_function_restoration: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionRestoration", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + data_remapping: Optional[NewLookupTableType] = field( + default=None, + metadata={ + "name": "DataRemapping", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + asymmetric_pixel_correction: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AsymmetricPixelCorrection", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class InteractiveProcessingType: + """ + :ivar geometric_transform: The geometric transform element is used to perform various geometric distortions + to each band of image data. These distortions include image chipping, scaling, rotation, shearing, etc. + :ivar sharpness_enhancement: + :ivar color_space_transform: + :ivar dynamic_range_adjustment: Specifies the recommended ELT DRA overrides + :ivar tonal_transfer_curve: The 1-D LUT element uses one or more 1-D LUTs to stretch or compress tome data in + valorous regions within a digital image's dynamic range. 1-D LUT can be implemented using a Tonal + Transfer Curve (TTC). There are 12 families of TTCs: Range = [0,11]. There are 64 members for each + family: Range=[0, 63]. + :ivar band: + """ + + geometric_transform: Optional[GeometricTransformType] = field( + default=None, + metadata={ + "name": "GeometricTransform", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + sharpness_enhancement: Optional[SharpnessEnhancementType] = field( + default=None, + metadata={ + "name": "SharpnessEnhancement", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + color_space_transform: Optional[ColorSpaceTransformType] = field( + default=None, + metadata={ + "name": "ColorSpaceTransform", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + dynamic_range_adjustment: Optional[DynamicRangeAdjustmentType] = field( + default=None, + metadata={ + "name": "DynamicRangeAdjustment", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + tonal_transfer_curve: Optional[NewLookupTableType] = field( + default=None, + metadata={ + "name": "TonalTransferCurve", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + band: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class NonInteractiveProcessingType: + """ + :ivar product_generation_options: Performs several key actions on an image to prepare it for necessary + additional processing to achieve the desired output product. + :ivar rrds: Creates a set of sub-sampled versions of an image to provide processing chains with quick access + to lower mangification values for faster computation speeds and performance. + :ivar band: + """ + + product_generation_options: Optional[ProductGenerationOptionsType] = field( + default=None, + metadata={ + "name": "ProductGenerationOptions", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + rrds: Optional[RRDSType] = field( + default=None, + metadata={ + "name": "RRDS", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + band: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class ProductDisplayType: + """ + Type for describing proper display of the derived product. + + :ivar pixel_type: Defines the pixel type, based on enumeration and definition in Design and Exploitation + document. + :ivar num_bands: Number of bands contained in the image. Populate with the number of bands present after + remapping. For example an 8-bit RGB image (RGBLU) this should be populated with 3. + :ivar default_band_display: Indicates which band to display by default. Valid range = 1 to NumBands. + :ivar non_interactive_processing: + :ivar interactive_processing: + :ivar display_extension: Optional extensible parameters used to support profile-specific needs related to + product display. Predefined filter types. + """ + + pixel_type: Optional[PixelType] = field( + default=None, + metadata={ + "name": "PixelType", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + num_bands: Optional[int] = field( + default=None, + metadata={ + "name": "NumBands", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "required": True, + }, + ) + default_band_display: Optional[int] = field( + default=None, + metadata={ + "name": "DefaultBandDisplay", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + non_interactive_processing: List[NonInteractiveProcessingType] = field( + default_factory=list, + metadata={ + "name": "NonInteractiveProcessing", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + interactive_processing: List[InteractiveProcessingType] = field( + default_factory=list, + metadata={ + "name": "InteractiveProcessing", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + "min_occurs": 1, + }, + ) + display_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "DisplayExtension", + "type": "Element", + "namespace": "urn:SIDD:2.0.0", + }, + ) + + +@dataclass +class SIDD: + """ + Root element of the SIDD document. + + :ivar product_creation: Information related to processor, classification, and product type. + :ivar display: Contains information on the parameters needed to display the product in an exploitation tool. + :ivar geo_data: Contains generic and extensible targeting and geographic region information. + :ivar measurement: Contains the metadata necessary for performing measurements. + :ivar exploitation_features: Computed metadata regarding the input collections and final product. + :ivar downstream_reprocessing: Contains metadata related to downstream processing of the product. + :ivar error_statistics: See SICD documentation for metadata definitions. + :ivar radiometric: Radiometric information about the product. + :ivar match_info: Information about other collections that are matched to the current collection. The current + collection is the collection from which this SIDD product was generated. + :ivar compression: Contains information regarding any compression that has occured to the image data. + :ivar digital_elevation_data: This block describes the Digital ElevatioNData when it is included with the + SIDD product. + :ivar product_processing: Contains metadata related to algorithms used during product generation. + :ivar annotations: List of annotations for the imagery. + """ + + class Meta: + namespace = "urn:SIDD:2.0.0" + + product_creation: Optional[ProductCreationType] = field( + default=None, + metadata={ + "name": "ProductCreation", + "type": "Element", + "required": True, + }, + ) + display: Optional[ProductDisplayType] = field( + default=None, + metadata={ + "name": "Display", + "type": "Element", + "required": True, + }, + ) + geo_data: Optional[GeoDataType] = field( + default=None, + metadata={ + "name": "GeoData", + "type": "Element", + "required": True, + }, + ) + measurement: Optional[MeasurementType] = field( + default=None, + metadata={ + "name": "Measurement", + "type": "Element", + "required": True, + }, + ) + exploitation_features: Optional[ExploitationFeaturesType] = field( + default=None, + metadata={ + "name": "ExploitationFeatures", + "type": "Element", + "required": True, + }, + ) + downstream_reprocessing: Optional[DownstreamReprocessingType] = field( + default=None, + metadata={ + "name": "DownstreamReprocessing", + "type": "Element", + }, + ) + error_statistics: Optional[ErrorStatisticsType] = field( + default=None, + metadata={ + "name": "ErrorStatistics", + "type": "Element", + }, + ) + radiometric: Optional[RadiometricType] = field( + default=None, + metadata={ + "name": "Radiometric", + "type": "Element", + }, + ) + match_info: Optional[MatchInfoType] = field( + default=None, + metadata={ + "name": "MatchInfo", + "type": "Element", + }, + ) + compression: Optional[CompressionType] = field( + default=None, + metadata={ + "name": "Compression", + "type": "Element", + }, + ) + digital_elevation_data: Optional[DigitalElevationDataType] = field( + default=None, + metadata={ + "name": "DigitalElevationData", + "type": "Element", + }, + ) + product_processing: Optional[ProductProcessingType] = field( + default=None, + metadata={ + "name": "ProductProcessing", + "type": "Element", + }, + ) + annotations: Optional[AnnotationsType] = field( + default=None, + metadata={ + "name": "Annotations", + "type": "Element", + }, + ) diff --git a/src/aws/osml/formats/sidd/models/sidd_v3_0_0.py b/src/aws/osml/formats/sidd/models/sidd_v3_0_0.py new file mode 100644 index 0000000..002367f --- /dev/null +++ b/src/aws/osml/formats/sidd/models/sidd_v3_0_0.py @@ -0,0 +1,3454 @@ +"""This file was generated by xsdata, v23.8, on 2023-10-05 09:59:45 + +Generator: DataclassGenerator +See: https://xsdata.readthedocs.io/ +""" +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Optional, Union + +from xsdata.models.datatype import XmlDate, XmlDateTime + +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ism25_x import CVEnumISM25X +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismatomic_energy_markings import ( + CVEnumISMatomicEnergyMarkingsValuesvalue, +) +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismclassification_all import CVEnumISMClassificationAll +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismcomplies_with import CVEnumISMCompliesWithValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismdissem import CVEnumISMDissemValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismexempt_from import CVEnumISMExemptFromValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnon_ic import CVEnumISMNonICValuesvalue +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnon_uscontrols import CVEnumISMNonUSControlsValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismnotice import CVEnumISMNoticeValues +from .external.ism_v13.schema.ism.cvegenerated.cvenum_ismscicontrols import CVEnumISMSCIControlsValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatfgiopen import CVEnumISMCATFGIOpenValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatfgiprotected import CVEnumISMCATFGIProtectedValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatowner_producer import CVEnumISMCATOwnerProducerValuesvalue +from .external.ism_v13.schema.ismcat.cvegenerated.cvenum_ismcatrel_to import CVEnumISMCATRelToValuesvalue +from .sfa import LinearRingType, LineType, MultiLineStringType, MultiPointType, MultiPolygonType, PointType +from .sfa import PolygonType as SfaPolygonType +from .sfa import PolyhedralSurfaceType, ReferenceSystemType +from .sicommon_types_v1_0 import ( + AngleZeroToExclusive360MagnitudeType, + CornerStringType, + ErrorStatisticsType, + GeoInfo, + LatLonType, + LatLonVertexType, + MatchInfoType, + ParameterType, + Polarization1Typevalue, + Poly2DType, + RadarModeType, + RadiometricType, + RangeAzimuthType, + ReferencePointType, + RowColDoubleType, + RowColIntType, + RowColVertexType, + XYZPolyType, + XYZType, +) + +__NAMESPACE__ = "urn:SIDD:3.0.0" + + +@dataclass +class AccuracyType: + horizontal: List[float] = field( + default_factory=list, + metadata={ + "name": "Horizontal", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + vertical: List[float] = field( + default_factory=list, + metadata={ + "name": "Vertical", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class AcheivedResolutionType: + """ + Finest achievable resolution parameters. + """ + + +@dataclass +class ClassificationGuidanceType: + """ + Classification guidance authority (only if file is classified). + + :ivar authority: Classifying authority. + :ivar date: Date that the authority was provided. Specified in YYYY-MM-DD. + """ + + authority: Optional[str] = field( + default=None, + metadata={ + "name": "Authority", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "Date", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAHistogramOverridesType: + """ + :ivar clip_min: Suggested override for the lower end-point of the display histogram in the ELT DRA + application. Referred to as Pmin in SIPS documentation. + :ivar clip_max: Suggested override for the upper end-point of the display histogram in the ELT DRA + application. Referred to as Pmax in SIPS documentation. + """ + + clip_min: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMin", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + clip_max: Optional[int] = field( + default=None, + metadata={ + "name": "ClipMax", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAOverrides: + """ + :ivar subtractor: Subtractor value used to reduce haze in the image. Range: [0.0 to 2047.0] + :ivar multiplier: Multiplier value used to reduce haze in the image. Range: [0.0 to 2047.0] + """ + + subtractor: Optional[float] = field( + default=None, + metadata={ + "name": "Subtractor", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + multiplier: Optional[float] = field( + default=None, + metadata={ + "name": "Multiplier", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class DRAParameters: + """ + :ivar pmin: DRA clip low point. This is the cumulative histogram percentage value that defines the lower end- + point of the dynamic range to be displayed. Range: [0.0 to 1.0] + :ivar pmax: DRA clip high point. This is the cumulative histogram percentage value that defines the upper + end-point of the dynamic range to be displayed. Range: [0.0 to 1.0] + :ivar emin_modifier: The pixel value corresponding to the Pmin percentage poitn in the image histogram. + Range: [0.0 to 1.0]/ + :ivar emax_modifier: The pixel value corresponding to the Pmax percentage poitn in the image histogram. + Range: [0.0 to 1.0]/ + """ + + pmin: Optional[float] = field( + default=None, + metadata={ + "name": "Pmin", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + pmax: Optional[float] = field( + default=None, + metadata={ + "name": "Pmax", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + emin_modifier: Optional[float] = field( + default=None, + metadata={ + "name": "EminModifier", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + emax_modifier: Optional[float] = field( + default=None, + metadata={ + "name": "EmaxModifier", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +class DownsamplingMethodType(Enum): + DECIMATE = "DECIMATE" + MAX_PIXEL = "MAX PIXEL" + AVERAGE = "AVERAGE" + NEAREST_NEIGHBOR = "NEAREST NEIGHBOR" + BILINEAR = "BILINEAR" + LAGRANGE = "LAGRANGE" + + +class EarthModelType(Enum): + """Identifies the earth model used for latitude, longitude and height + parameters. + + All height values are Height Above The Ellipsoid (HAE). + """ + + WGS_84 = "WGS_84" + + +class EqualizationAlgorithmType(Enum): + VALUE_1_DLUT = "1DLUT" + + +@dataclass +class FilterBankCoefType: + coef: List["FilterBankCoefType.Coef"] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + num_phasings: Optional[int] = field( + default=None, + metadata={ + "name": "numPhasings", + "type": "Attribute", + "required": True, + }, + ) + num_points: Optional[int] = field( + default=None, + metadata={ + "name": "numPoints", + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Coef: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + phasing: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + point: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class FilterDatabaseNameType(Enum): + BILINEAR = "BILINEAR" + CUBIC = "CUBIC" + LAGRANGE = "LAGRANGE" + NEAREST_NEIGHBOR = "NEAREST NEIGHBOR" + + +@dataclass +class FilterKernelCoefType: + coef: List["FilterKernelCoefType.Coef"] = field( + default_factory=list, + metadata={ + "name": "Coef", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + num_rows: Optional[int] = field( + default=None, + metadata={ + "name": "numRows", + "type": "Attribute", + "required": True, + }, + ) + num_cols: Optional[int] = field( + default=None, + metadata={ + "name": "numCols", + "type": "Attribute", + "required": True, + }, + ) + + @dataclass + class Coef: + value: Optional[float] = field( + default=None, + metadata={ + "required": True, + }, + ) + row: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + col: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class FilterOperationType(Enum): + CONVOLUTION = "CONVOLUTION" + CORRELATION = "CORRELATION" + + +class GeopositioningTypeCoordinateSystemType(Enum): + GCS = "GCS" + UTM = "UTM" + + +class GeopositioningTypeGeodeticDatum(Enum): + WORLD_GEODETIC_SYSTEM_1984 = "World Geodetic System 1984" + + +class GeopositioningTypeReferenceEllipsoid(Enum): + WORLD_GEODETIC_SYSTEM_1984 = "World Geodetic System 1984" + + +class GeopositioningTypeSoundingDatum(Enum): + MEAN_SEA_LEVEL = "Mean Sea Level" + + +class GeopositioningTypeVerticalDatum(Enum): + MEAN_SEA_LEVEL = "Mean Sea Level" + + +@dataclass +class LayerType: + """ + :ivar bitrate: The bit rate target associated with the layer. It may happen that the bit rate was not + achieved due to data characteristics. Note: for JPEG 2000 numerically lossless quality, the bit rate for + the final layer is an expected value, based on performance. + :ivar index: + """ + + bitrate: Optional[float] = field( + default=None, + metadata={ + "name": "Bitrate", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + index: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class Lookup3TableType: + """ + :ivar value: + :ivar size: Size of LUT + """ + + value: List[str] = field( + default_factory=list, + metadata={ + "pattern": r"([0-9]+),([0-9]+),([0-9]+)", + "tokens": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LookupTableType: + """ + :ivar value: + :ivar lut: Size of LUT. + """ + + value: List[int] = field( + default_factory=list, + metadata={ + "tokens": True, + }, + ) + lut: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +class MeasurementTypeARPFlag(Enum): + """ + :cvar REALTIME: Based on ephemeries at time of collect + :cvar PREDICTED: Based on predicted ephemeries (i.e. pre-collect) + :cvar POST_PROCESSED: Ephemeris has been refined after data collection + """ + + REALTIME = "REALTIME" + PREDICTED = "PREDICTED" + POST_PROCESSED = "POST PROCESSED" + + +class PixelType(Enum): + MONO8_I = "MONO8I" + MONO8_LU = "MONO8LU" + MONO16_I = "MONO16I" + RGB8_LU = "RGB8LU" + RGB24_I = "RGB24I" + + +@dataclass +class PredefinedLookupType: + """ + :ivar database_name: Database name of LUT to use. + :ivar remap_family: Index specifying the remap family. + :ivar remap_member: Index specifying the member for the remap family. + """ + + database_name: Optional[str] = field( + default=None, + metadata={ + "name": "DatabaseName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + remap_family: Optional[int] = field( + default=None, + metadata={ + "name": "RemapFamily", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + remap_member: Optional[int] = field( + default=None, + metadata={ + "name": "RemapMember", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ProcessorInformationType: + """ + :ivar application: Software application name and version number. + :ivar processing_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar site: Creation location of product. + :ivar profile: Product-specific profile applied during product processing. + """ + + application: Optional[str] = field( + default=None, + metadata={ + "name": "Application", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + processing_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "ProcessingDateTime", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + site: Optional[str] = field( + default=None, + metadata={ + "name": "Site", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + profile: Optional[str] = field( + default=None, + metadata={ + "name": "Profile", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +class RangeAdjustmentAlgorithmType(Enum): + """ + Algorithm used for dynamic range adjustment. + """ + + AUTO = "AUTO" + MANUAL = "MANUAL" + NONE = "NONE" + + +class RenderingIntentType(Enum): + PERCEPTUAL = "PERCEPTUAL" + SATURATION = "SATURATION" + RELATIVE = "RELATIVE" + ABSOLUTE = "ABSOLUTE" + + +class ShadowDirectionType(Enum): + """ + Descirbes the shadow direciton relative to the pixels in the file. + """ + + UP = "UP" + DOWN = "DOWN" + LEFT = "LEFT" + RIGHT = "RIGHT" + ARBITRARY = "ARBITRARY" + + +@dataclass +class AnnotationObjectType: + """ + Geometrical representation of the annotation. + """ + + point: Optional[PointType] = field( + default=None, + metadata={ + "name": "Point", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + line: Optional[LineType] = field( + default=None, + metadata={ + "name": "Line", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + linear_ring: Optional[LinearRingType] = field( + default=None, + metadata={ + "name": "LinearRing", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + polygon: Optional[SfaPolygonType] = field( + default=None, + metadata={ + "name": "Polygon", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + polyhedral_surface: Optional[PolyhedralSurfaceType] = field( + default=None, + metadata={ + "name": "PolyhedralSurface", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + multi_polygon: Optional[MultiPolygonType] = field( + default=None, + metadata={ + "name": "MultiPolygon", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + multi_line_string: Optional[MultiLineStringType] = field( + default=None, + metadata={ + "name": "MultiLineString", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + multi_point: Optional[MultiPointType] = field( + default=None, + metadata={ + "name": "MultiPoint", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class BankCustomType: + filter_coefficients: Optional[FilterBankCoefType] = field( + default=None, + metadata={ + "name": "FilterCoefficients", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class BaseProjectionType: + """ + :ivar reference_point: Reference point for the geometrical system. + """ + + reference_point: Optional[ReferencePointType] = field( + default=None, + metadata={ + "name": "ReferencePoint", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class ColorDisplayRemapType: + """ + Object representing that the data requires color display. + + :ivar remap_lut: LUT-base remap indicating that the color display is done through index-based color. + """ + + remap_lut: Optional[Lookup3TableType] = field( + default=None, + metadata={ + "name": "RemapLUT", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class ColorManagementModuleType: + """ + Parameters describing the Color Management Module (CMM). + + :ivar rendering_intent: + :ivar source_profile: Name of sensor profile in ICC Profile database. + :ivar display_profile: Name of display profile in ICC Profile database. + :ivar iccprofile_signature: Valid ICC profile signature. + """ + + rendering_intent: Optional[RenderingIntentType] = field( + default=None, + metadata={ + "name": "RenderingIntent", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + source_profile: Optional[str] = field( + default=None, + metadata={ + "name": "SourceProfile", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + display_profile: Optional[str] = field( + default=None, + metadata={ + "name": "DisplayProfile", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + iccprofile_signature: Optional[str] = field( + default=None, + metadata={ + "name": "ICCProfileSignature", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class DynamicRangeAdjustmentType: + """ + Parameter describing DRA. + + :ivar algorithm_type: Algorithm used for dynamic range adjustment. + :ivar band_stats_source: Indicates which band to use in computing statistics for DRA. Valid range = 1 to + NumBands. + :ivar draparameters: + :ivar draoverrides: + """ + + algorithm_type: Optional[RangeAdjustmentAlgorithmType] = field( + default=None, + metadata={ + "name": "AlgorithmType", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + band_stats_source: Optional[int] = field( + default=None, + metadata={ + "name": "BandStatsSource", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + draparameters: Optional[DRAParameters] = field( + default=None, + metadata={ + "name": "DRAParameters", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + draoverrides: Optional[DRAOverrides] = field( + default=None, + metadata={ + "name": "DRAOverrides", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionGeometryType: + """Key geometry parameters independent of product processing. + + All values computed at the center time of the full collection. + + :ivar azimuth: Angle clockwise from north indicating the ETP line of sight vector. + :ivar slope: Angle between the ETP at scene center and the range vector perpendicular to the direction of + motion. + :ivar squint: Angle from the ground track to platform velocity vector at nadir. Left-look is positive, right- + look is negative. + :ivar graze: Angle between the ETP and the line of sight vector. + :ivar tilt: Angle between the ETP and the cross range vector. Also known as the twist angle. + :ivar doppler_cone_angle: The angle between the velocity vector and the radar line-of-sight vector. Also + known as the slant plane squint angle. + :ivar extension: Exploitation feature extension related to geometry for a single input image + """ + + azimuth: Optional[float] = field( + default=None, + metadata={ + "name": "Azimuth", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_exclusive": 360.0, + }, + ) + slope: Optional[float] = field( + default=None, + metadata={ + "name": "Slope", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + squint: Optional[float] = field( + default=None, + metadata={ + "name": "Squint", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + graze: Optional[float] = field( + default=None, + metadata={ + "name": "Graze", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_inclusive": 90.0, + }, + ) + tilt: Optional[float] = field( + default=None, + metadata={ + "name": "Tilt", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + doppler_cone_angle: Optional[float] = field( + default=None, + metadata={ + "name": "DopplerConeAngle", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_exclusive": 180.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionPhenomenologyType: + """Phenomenology related to both the geometry and the final product processing. + + All values computed at the center time of the full collection. + + :ivar shadow: The phenomon where vertical objects occlude radar energy. + :ivar layover: The phenomenon where vertical objects appear as ground objects with the same range/range rate. + :ivar multi_path: This is a range dependent phenomenon which describes the energy from a single scatter + returned to the radar via more than one path and results in a nominally constant direction in the ETP. + :ivar ground_track: Counter-clockwise angle from increasing row direction to ground track at the center of + the image. + :ivar extension: Exploitation feature extension related to phenomenology for a single input image + """ + + shadow: Optional[AngleZeroToExclusive360MagnitudeType] = field( + default=None, + metadata={ + "name": "Shadow", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + layover: Optional[AngleZeroToExclusive360MagnitudeType] = field( + default=None, + metadata={ + "name": "Layover", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + multi_path: Optional[float] = field( + default=None, + metadata={ + "name": "MultiPath", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_exclusive": 360.0, + }, + ) + ground_track: Optional[float] = field( + default=None, + metadata={ + "name": "GroundTrack", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_exclusive": 360.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class GeographicCoordinatesType: + """ + Describes the Local Geographic Coordinate system linking row/column to the + absolute geographic coordinate (lat/lon) + + :ivar longitude_density: Pixel ground spacing in E/W direction that is the number of pixels or element + intervals in 360 degrees. + :ivar latitude_density: Pixel ground spacing in N/S direction that is the number of pixels or element + intervals in 360 degrees. + :ivar reference_origin: Northwest corner Latitude/Longitude - product NW corner + """ + + longitude_density: Optional[float] = field( + default=None, + metadata={ + "name": "LongitudeDensity", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + latitude_density: Optional[float] = field( + default=None, + metadata={ + "name": "LatitudeDensity", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + reference_origin: Optional[LatLonType] = field( + default=None, + metadata={ + "name": "ReferenceOrigin", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class GeometricChipType: + """Contains information related to downstream chipping of the product. + + There is only one instance, and the instance is updated with respect to the full image parameters. For + example, if an image is chipped out of a smaller chip, the new chip needs to be updated to the original full + image corners. Since this relationship is linear, bi-linear interpolation is sufficient to determine an + arbitrary chip coordinate in terms of the original full image coordinates. Chipping is typically done using + an exploitation tool, and should not be done in the initial product creation. + + :ivar chip_size: Size of the chipped product in pixels. + :ivar original_upper_left_coordinate: Upper-left corner with respect to the original product. + :ivar original_upper_right_coordinate: Upper-right corner with respect to the original product. + :ivar original_lower_left_coordinate: Lower-left corner with respect to the original product. + :ivar original_lower_right_coordinate: Lower-right corner with respect to the original product. + """ + + chip_size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "ChipSize", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + original_upper_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + original_upper_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalUpperRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + original_lower_left_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerLeftCoordinate", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + original_lower_right_coordinate: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "OriginalLowerRightCoordinate", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class GeopositioningType: + """ + Describes the absolute coordinate system to which the data is referenced. + + :ivar coordinate_system_type: + :ivar geodetic_datum: + :ivar reference_ellipsoid: + :ivar vertical_datum: + :ivar sounding_datum: + :ivar false_origin: Z values false origin + :ivar utmgrid_zone_number: Gride zone number, required for UTM, not include for GCS. (4 character field) + Values: +001 to +060 (northern hemisphere) -001 to -060 (southern hemisphere) + """ + + coordinate_system_type: Optional[GeopositioningTypeCoordinateSystemType] = field( + default=None, + metadata={ + "name": "CoordinateSystemType", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + geodetic_datum: Optional[GeopositioningTypeGeodeticDatum] = field( + default=None, + metadata={ + "name": "GeodeticDatum", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + reference_ellipsoid: Optional[GeopositioningTypeReferenceEllipsoid] = field( + default=None, + metadata={ + "name": "ReferenceEllipsoid", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + vertical_datum: Optional[GeopositioningTypeVerticalDatum] = field( + default=None, + metadata={ + "name": "VerticalDatum", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + sounding_datum: Optional[GeopositioningTypeSoundingDatum] = field( + default=None, + metadata={ + "name": "SoundingDatum", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + false_origin: Optional[int] = field( + default=None, + metadata={ + "name": "FalseOrigin", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + utmgrid_zone_number: Optional[int] = field( + default=None, + metadata={ + "name": "UTMGridZoneNumber", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ImageCornersType: + """Parameters apply to image corners of the product projected to the same + height as the SCP. + + These corners are an approximate geographic location that is not intended for analytical use. + + :ivar icp: Image Corner Point (ICP) data for the 4 corners in product. ICPs indexed x = 1, 2, 3, 4, + clockwise. + """ + + icp: List["ImageCornersType.ICP"] = field( + default_factory=list, + metadata={ + "name": "ICP", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 4, + "max_occurs": 4, + }, + ) + + @dataclass + class ICP(LatLonType): + index: Optional[CornerStringType] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class InputROIType: + """ + ROI representing portion of input data used to make this product. + + :ivar size: Number of rows and columns extracted from the input. + :ivar upper_left: The upper-left pixel extracted from the input. + """ + + size: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "Size", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + upper_left: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "UpperLeft", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class KernelCustomType: + filter_coefficients: Optional[FilterKernelCoefType] = field( + default=None, + metadata={ + "name": "FilterCoefficients", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class LUTInfoType: + lutvalues: List[LookupTableType] = field( + default_factory=list, + metadata={ + "name": "LUTValues", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + num_luts: Optional[int] = field( + default=None, + metadata={ + "name": "numLuts", + "type": "Attribute", + "required": True, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class LayerInfoType: + """Original Layer Information. + + The following fileds repeat for all layers in (0, 1, ..., numLayers - 1). + The default number of layers per tile in original image out of the original processor. + + :ivar layer: Layer Index Number indicates the number of layers being described. Layers are numbered from 0 to + (numLayers - 1). + :ivar num_layers: + """ + + layer: List[LayerType] = field( + default_factory=list, + metadata={ + "name": "Layer", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + num_layers: Optional[int] = field( + default=None, + metadata={ + "name": "numLayers", + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MonochromeDisplayRemapType: + """This remap works by taking the input space and using the LUT to map it to a + log space (for 8-bit only). + + From the log space the C0 and Ch fields are applied to get to display-ready density space. + The density should then be rendered by the TTC and monitor comp. + This means that the default DRA should not apply anything besides the clip points. + If a different contrast/brightness is applied it should be done through modification of the clip points via DRA. + Examples: + Remap LUT Clips + ============================= + PEDF PEDF->D 0,255 + LLG LLG->A->LogA C0,Ch + Log N/A C0,Ch + NRL N/A 0,255 (Supposed to be display ready) + + :ivar remap_type: Name of remap applied (for informational purposes only). + :ivar remap_parameter: Textual remap parameter. Filled based upon remap type (for informational purposes + only). For example, if the data is linlog encoded a RemapParameter could be used to describe any + amplitude scaling that was performed prior to linlog encoding the data. + """ + + remap_type: Optional[str] = field( + default=None, + metadata={ + "name": "RemapType", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + remap_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "RemapParameter", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class Orientation: + """ + Parameters describing the default orientation of the product. + + :ivar shadow_direction: Descirbes the shadow direciton relative to the pixels in the file. + """ + + shadow_direction: Optional[ShadowDirectionType] = field( + default=None, + metadata={ + "name": "ShadowDirection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class PolygonType: + """Indicates the full image includes both valid data and some zero filled + pixels. + + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). + Vertices in clockwise order. + + :ivar vertex: Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) + minimum row index, (2) minimum column index if 2 vertices with minimum row index, 1st and last vertices + are connected to form the polygon. + :ivar size: + """ + + vertex: List[LatLonVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class PositionalAccuracyType: + """ + Describes the horizontal and vertical point and regional information for the + DED. + + :ivar num_regions: Number of positional accuracy regions. + :ivar absolute_accuracy: + :ivar point_to_point_accuracy: + """ + + num_regions: Optional[int] = field( + default=None, + metadata={ + "name": "NumRegions", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + absolute_accuracy: Optional[AccuracyType] = field( + default=None, + metadata={ + "name": "AbsoluteAccuracy", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + point_to_point_accuracy: Optional[AccuracyType] = field( + default=None, + metadata={ + "name": "PointToPointAccuracy", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class PredefinedFilterType: + database_name: Optional[FilterDatabaseNameType] = field( + default=None, + metadata={ + "name": "DatabaseName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + filter_family: Optional[int] = field( + default=None, + metadata={ + "name": "FilterFamily", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + filter_member: Optional[int] = field( + default=None, + metadata={ + "name": "FilterMember", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ProcTxRcvPolarizationType: + """ + :ivar tx_polarization_proc: Polarization transmit type + :ivar rcv_polarization_proc: Receive polarization type + """ + + tx_polarization_proc: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "TxPolarizationProc", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization_proc: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "RcvPolarizationProc", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + + +@dataclass +class ProcessingEventType: + """ + :ivar application_name: Application which applied a modification. + :ivar applied_date_time: Date and time defined in Coordinated Universal Time (UTC). The seconds should be + followed by a Z to indicate UTC. + :ivar interpolation_method: Type of interpolation applied to the data. + :ivar descriptor: Descriptor for the processing event. + """ + + application_name: Optional[str] = field( + default=None, + metadata={ + "name": "ApplicationName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + applied_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "AppliedDateTime", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + interpolation_method: Optional[str] = field( + default=None, + metadata={ + "name": "InterpolationMethod", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + descriptor: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Descriptor", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ProcessingModuleType: + """ + :ivar module_name: The name of the algorithm used in processing the product. + :ivar module_parameter: Parameters associated with the algorithm used in processing the product. + :ivar processing_module: ProcessingModule is a repeatable structure within itself to create an algorithm as a + subset of another algorithm. + """ + + module_name: Optional[ParameterType] = field( + default=None, + metadata={ + "name": "ModuleName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + module_parameter: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ModuleParameter", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + processing_module: List["ProcessingModuleType"] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ProductClassificationType: + """ + The overall classification of the product. + + :ivar security_extension: Extensible parameters used to support profile-specific needs related to product + security. + :ivar desversion: + :ivar ismcatcesversion: + :ivar resource_element: + :ivar complies_with: + :ivar create_date: + :ivar exempt_from: + :ivar classification: + :ivar owner_producer: + :ivar joint: + :ivar scicontrols: + :ivar saridentifier: + :ivar atomic_energy_markings: + :ivar dissemination_controls: + :ivar display_only_to: + :ivar fgisource_open: + :ivar fgisource_protected: + :ivar releasable_to: + :ivar non_icmarkings: + :ivar classified_by: + :ivar compilation_reason: + :ivar derivatively_classified_by: + :ivar classification_reason: + :ivar non_uscontrols: + :ivar derived_from: + :ivar declass_date: + :ivar declass_event: + :ivar declass_exception: + :ivar notice_type: + :ivar notice_reason: + :ivar notice_date: + :ivar unregistered_notice_type: + :ivar external_notice: + """ + + security_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "SecurityExtension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + desversion: Optional[str] = field( + default=None, + metadata={ + "name": "DESVersion", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "max_length": 256, + }, + ) + ismcatcesversion: Optional[str] = field( + default=None, + metadata={ + "name": "ISMCATCESVersion", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "max_length": 256, + }, + ) + resource_element: Optional[bool] = field( + default=None, + metadata={ + "name": "resourceElement", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + complies_with: List[CVEnumISMCompliesWithValues] = field( + default_factory=list, + metadata={ + "name": "compliesWith", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "tokens": True, + }, + ) + create_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "createDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + exempt_from: List[CVEnumISMExemptFromValues] = field( + default_factory=list, + metadata={ + "name": "exemptFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + classification: Optional[CVEnumISMClassificationAll] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + }, + ) + owner_producer: List[Union[str, CVEnumISMCATOwnerProducerValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "ownerProducer", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "required": True, + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + joint: Optional[bool] = field( + default=None, + metadata={ + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + scicontrols: List[Union[str, CVEnumISMSCIControlsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "SCIcontrols", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"KDK-BLFH-[A-Z0-9]{1,6}|KDK-IDIT-[A-Z0-9]{1,6}|KDK-KAND-[A-Z0-9]{1,6}|RSV-[A-Z0-9]{3}|SI-G-[A-Z]{4}|SI-[A-Z]{3}|SI-[A-Z]{3}-[A-Z]{4}", + "tokens": True, + }, + ) + saridentifier: List[str] = field( + default_factory=list, + metadata={ + "name": "SARIdentifier", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"[A-Z_0-9\-]{1,100}|[A-Z]{2,}|[A-Z]{2,}-[A-Z][A-Z0-9]+|[A-Z]{2,}-[A-Z][A-Z0-9]+-[A-Z0-9]{2,}", + "tokens": True, + }, + ) + atomic_energy_markings: List[Union[str, CVEnumISMatomicEnergyMarkingsValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "atomicEnergyMarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"RD-SG-((14)|(15)|(18)|(20))|FRD-SG-((14)|(15)|(18)|(20))", + "tokens": True, + }, + ) + dissemination_controls: List[CVEnumISMDissemValues] = field( + default_factory=list, + metadata={ + "name": "disseminationControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + display_only_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "displayOnlyTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_open: List[Union[str, CVEnumISMCATFGIOpenValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceOpen", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + fgisource_protected: List[Union[str, CVEnumISMCATFGIProtectedValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "FGIsourceProtected", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + releasable_to: List[Union[str, CVEnumISMCATRelToValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "releasableTo", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"NATO/[a-zA-Z\-_]", + "tokens": True, + }, + ) + non_icmarkings: List[Union[str, CVEnumISMNonICValuesvalue]] = field( + default_factory=list, + metadata={ + "name": "nonICmarkings", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "pattern": r"ACCM-[A-Z0-9\-_]{1,61}|NNPI", + "tokens": True, + }, + ) + classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "classifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + compilation_reason: Optional[str] = field( + default=None, + metadata={ + "name": "compilationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + derivatively_classified_by: Optional[str] = field( + default=None, + metadata={ + "name": "derivativelyClassifiedBy", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + classification_reason: Optional[str] = field( + default=None, + metadata={ + "name": "classificationReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 4096, + }, + ) + non_uscontrols: List[CVEnumISMNonUSControlsValues] = field( + default_factory=list, + metadata={ + "name": "nonUSControls", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + derived_from: Optional[str] = field( + default=None, + metadata={ + "name": "derivedFrom", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "declassDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + declass_event: Optional[str] = field( + default=None, + metadata={ + "name": "declassEvent", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 1024, + }, + ) + declass_exception: Optional[CVEnumISM25X] = field( + default=None, + metadata={ + "name": "declassException", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + notice_type: List[CVEnumISMNoticeValues] = field( + default_factory=list, + metadata={ + "name": "noticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "tokens": True, + }, + ) + notice_reason: Optional[str] = field( + default=None, + metadata={ + "name": "noticeReason", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + notice_date: Optional[XmlDate] = field( + default=None, + metadata={ + "name": "noticeDate", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + unregistered_notice_type: Optional[str] = field( + default=None, + metadata={ + "name": "unregisteredNoticeType", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + "max_length": 2048, + }, + ) + external_notice: Optional[bool] = field( + default=None, + metadata={ + "name": "externalNotice", + "type": "Attribute", + "namespace": "urn:us:gov:ic:ism:13", + }, + ) + + +@dataclass +class ProductPlaneType: + """ + Plane definition for the product. + + :ivar row_unit_vector: Unit vector of the plane defined to be aligned in the increasing row direction of the + product. (Defined as Rpgd in Design and Exploitation document) + :ivar col_unit_vector: Unit vector of the plane defined to be aligned in the increasing column direction of + the product. (Defined as Cpgd in Design and Exploitation document) + """ + + row_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "RowUnitVector", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + col_unit_vector: Optional[XYZType] = field( + default=None, + metadata={ + "name": "ColUnitVector", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class TxRcvPolarizationType: + """ + :ivar tx_polarization: Polarization transmit type + :ivar rcv_polarization: Receive polarization type + :ivar rcv_polarization_offset: Optional angle offset for the receive polarization defined at aperture center. + """ + + tx_polarization: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "TxPolarization", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization: Optional[Union[str, Polarization1Typevalue]] = field( + default=None, + metadata={ + "name": "RcvPolarization", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + "pattern": r"OTHER.*", + }, + ) + rcv_polarization_offset: Optional[float] = field( + default=None, + metadata={ + "name": "RcvPolarizationOffset", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": -180.0, + "max_inclusive": 180.0, + }, + ) + + +@dataclass +class ValidDataType: + """Indicates the full image includes both valid data and some zero filled + pixels. + + Simple convex polygon enclosed the valid data (may include some zero filled pixels for simplification). + Vertices in clockwise order. + + :ivar vertex: Vertices indexed n = 1, 2, ..., NumVertices. NumVertices >= 3. Vertex 1 is determined by (1) + minimum row index, (2) minimum column index if 2 vertices with minimum row index, 1st and last vertices + are connected to form the polygon. + :ivar size: + """ + + vertex: List[RowColVertexType] = field( + default_factory=list, + metadata={ + "name": "Vertex", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 3, + }, + ) + size: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class AnnotationType: + """ + Single annotation. + + :ivar identifier: Identifier for the annotation which idicates the type of object represented by this + annotation. + :ivar spatial_reference_system: Spatial reference system of the annotation. Assumed to be WGS-84 geographic + coordinate system if not specified with (lat, lon, h) units in (arc-sec, arc-sec, meters above + ellipsoid). + :ivar object_value: The geometrical representation of the annotation. + """ + + identifier: Optional[str] = field( + default=None, + metadata={ + "name": "Identifier", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + spatial_reference_system: Optional[ReferenceSystemType] = field( + default=None, + metadata={ + "name": "SpatialReferenceSystem", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + object_value: List[AnnotationObjectType] = field( + default_factory=list, + metadata={ + "name": "Object", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class ColorSpaceTransformType: + """ + :ivar color_management_module: Parameters describing the Color Management Module (CMM). + """ + + color_management_module: Optional[ColorManagementModuleType] = field( + default=None, + metadata={ + "name": "ColorManagementModule", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class CustomLookupType: + lutinfo: Optional[LUTInfoType] = field( + default=None, + metadata={ + "name": "LUTInfo", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class DigitalElevationDataType: + """ + This block describes the Digital ElevatioNData when it is included with the + SIDD product. + + :ivar geographic_coordinates: Describes the Local Geographic Coordinate system linking row/column to the + absolute geographic coordinate (lat/lon) + :ivar geopositioning: Describes the absolute coordinate system to which the data is referenced. + :ivar positional_accuracy: Describes the horizontal and vertical point and regional information for the DED. + :ivar null_value: + """ + + geographic_coordinates: Optional[GeographicCoordinatesType] = field( + default=None, + metadata={ + "name": "GeographicCoordinates", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + geopositioning: Optional[GeopositioningType] = field( + default=None, + metadata={ + "name": "Geopositioning", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + positional_accuracy: Optional[PositionalAccuracyType] = field( + default=None, + metadata={ + "name": "PositionalAccuracy", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + null_value: Optional[int] = field( + default=None, + metadata={ + "name": "NullValue", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class DownstreamReprocessingType: + """ + :ivar geometric_chip: Contains information related to downstream chipping of the product. + :ivar processing_event: Contains information related to downstream processing of the product. + """ + + geometric_chip: Optional[GeometricChipType] = field( + default=None, + metadata={ + "name": "GeometricChip", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + processing_event: List[ProcessingEventType] = field( + default_factory=list, + metadata={ + "name": "ProcessingEvent", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionInformationType: + """ + General collection information. + + :ivar sensor_name: The name of the sensor. + :ivar radar_mode: Radar collection mode. The ModeType refers to the collection type [SPOTLIGHT, STRIPMAP, + DYNAMIC STRIPMAP]. The optional ModeID is used to represent system-specific mode identifiers. + :ivar collection_date_time: Collection date and time defined in Coordinated Universal Time (UTC). The seconds + should be followed by a Z to indicate UTC. + :ivar local_date_time: Date and time defined in local time. + :ivar collection_duration: The duration of the collection (units = seconds). + :ivar resolution: Uniformly-weighted resolution (range and azimuth) processed in the slant plane. + :ivar input_roi: ROI representing portion of input data used to make this product. + :ivar polarization: Transmit and receive polarization. + """ + + sensor_name: Optional[str] = field( + default=None, + metadata={ + "name": "SensorName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + radar_mode: Optional[RadarModeType] = field( + default=None, + metadata={ + "name": "RadarMode", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + collection_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "CollectionDateTime", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + local_date_time: Optional[XmlDateTime] = field( + default=None, + metadata={ + "name": "LocalDateTime", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + collection_duration: Optional[float] = field( + default=None, + metadata={ + "name": "CollectionDuration", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + resolution: Optional[RangeAzimuthType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + input_roi: Optional[InputROIType] = field( + default=None, + metadata={ + "name": "InputROI", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + polarization: List[TxRcvPolarizationType] = field( + default_factory=list, + metadata={ + "name": "Polarization", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesProductType: + """ + Metadata regarding the product. + + :ivar resolution: Uniformly-weighted resolution projected into the Earth Tangent Plane (ETP). + :ivar ellipticity: Ellipticity of the 2D-IPR at the ORP, measured in the Earth Geodetic Tangent Plane (EGTP). + Ellipticity is the ratio of the IPR ellipse's major axis to minor axis. + :ivar polarization: Describes the processed transmit and receive polarizations for the product. + :ivar north: Counter-clockwise angle from increasing row direction to north at the center of the image. + :ivar extension: Exploitation feature extension for the end product + """ + + resolution: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "Resolution", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + ellipticity: Optional[float] = field( + default=None, + metadata={ + "name": "Ellipticity", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + polarization: List[ProcTxRcvPolarizationType] = field( + default_factory=list, + metadata={ + "name": "Polarization", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + north: Optional[float] = field( + default=None, + metadata={ + "name": "North", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_inclusive": 0.0, + "max_exclusive": 360.0, + }, + ) + extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "Extension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class FilterBankType: + predefined: Optional[PredefinedFilterType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + custom: Optional[BankCustomType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class FilterKernelType: + predefined: Optional[PredefinedFilterType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + custom: Optional[KernelCustomType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class GeoDataType: + earth_model: Optional[EarthModelType] = field( + default=None, + metadata={ + "name": "EarthModel", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + image_corners: Optional[ImageCornersType] = field( + default=None, + metadata={ + "name": "ImageCorners", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + valid_data: Optional[PolygonType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + geo_info: List[GeoInfo] = field( + default_factory=list, + metadata={ + "name": "GeoInfo", + "type": "Element", + "namespace": "urn:SICommon:1.0", + }, + ) + + +@dataclass +class J2KSubtype: + """ + :ivar num_wavelet_levels: The default number of wavelet decompositionlevels performed per tile in the + original image out of the processors. + :ivar num_bands: The number of spectral bands in the original image out of the processors. + :ivar layer_info: Original Layer Information. The following fileds repeat for all layers in (0, 1, ..., + numLayers - 1). The default number of layers per tile in original image out of the original processor. + """ + + num_wavelet_levels: Optional[int] = field( + default=None, + metadata={ + "name": "NumWaveletLevels", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + num_bands: Optional[int] = field( + default=None, + metadata={ + "name": "NumBands", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + layer_info: Optional[LayerInfoType] = field( + default=None, + metadata={ + "name": "LayerInfo", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class MeasurableProjectionType(BaseProjectionType): + """ + :ivar sample_spacing: Sample spacing in row and column. + :ivar time_coapoly: Time (units = seconds) at which center of aperture for a given pixel coordinate in the + product occurs. + """ + + sample_spacing: Optional[RowColDoubleType] = field( + default=None, + metadata={ + "name": "SampleSpacing", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + time_coapoly: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "TimeCOAPoly", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class PolynomialProjectionType(BaseProjectionType): + """Polynomial pixel to ground. + + Only used for sensor systems where the radar geometry parameters are not recorded. + + :ivar row_col_to_lat: Polynomial that converts Row/Col to Latitude (degrees). + :ivar row_col_to_lon: Polynomial that converts Row/Col to Longitude (degrees). + :ivar row_col_to_alt: Polynomial that converts Row/Col to Altitude (meters above WGS-84 ellipsoid). + :ivar lat_lon_to_row: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel row + location. + :ivar lat_lon_to_col: Polynomial that converts Latitude (degrees) and Longitude (degrees) to pixel column + location + """ + + row_col_to_lat: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLat", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + row_col_to_lon: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToLon", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + row_col_to_alt: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "RowColToAlt", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + lat_lon_to_row: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToRow", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + lat_lon_to_col: Optional[Poly2DType] = field( + default=None, + metadata={ + "name": "LatLonToCol", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductCreationType: + """ + Contains general information about product creation. + + :ivar processor_information: Details regarding processor. + :ivar classification: The overall classification of the product. + :ivar product_name: The output product name defined by the processor. + :ivar product_class: Class of product. (examples: Dynamic Image, Amplitude Change Detection, Coherent Change + Detection, etc.). + :ivar product_type: Type of sub-product. (examples: Frame #, Reference, Match, etc.). This field is only + needed if there is a suite of associated products. + :ivar product_creation_extension: Extensible parameters used to support profile-specific needs related to + product creation. + """ + + processor_information: Optional[ProcessorInformationType] = field( + default=None, + metadata={ + "name": "ProcessorInformation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + classification: Optional[ProductClassificationType] = field( + default=None, + metadata={ + "name": "Classification", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + product_name: Optional[str] = field( + default=None, + metadata={ + "name": "ProductName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + product_class: Optional[str] = field( + default=None, + metadata={ + "name": "ProductClass", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + product_type: Optional[str] = field( + default=None, + metadata={ + "name": "ProductType", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + product_creation_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "ProductCreationExtension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ProductProcessingType: + """ + Computed metadata regarding one or more of the input collections and final + product. + + :ivar processing_module: Processing module to keep track of the name and any parameters associated with the + algorithms used to produce the SIDD. + """ + + processing_module: List[ProcessingModuleType] = field( + default_factory=list, + metadata={ + "name": "ProcessingModule", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class RemapChoiceType: + """ + :ivar color_display_remap: Information for proper color display of the data. + :ivar monochrome_display_remap: Information for proper monochrome display of the data. + """ + + color_display_remap: Optional[ColorDisplayRemapType] = field( + default=None, + metadata={ + "name": "ColorDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + monochrome_display_remap: Optional[MonochromeDisplayRemapType] = field( + default=None, + metadata={ + "name": "MonochromeDisplayRemap", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class AnnotationsType: + """ + :ivar annotation: Annotation Object. + """ + + annotation: List[AnnotationType] = field( + default_factory=list, + metadata={ + "name": "Annotation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + +@dataclass +class CylindricalProjectionType(MeasurableProjectionType): + """ + Cylindrical mapping of the pixel grid. + + :ivar stripmap_direction: Along stripmap direction + :ivar curvature_radius: Radius of Curvature defined at scene center. If not present, the radius of curvature + will be derived based upon the equations provided in the Design and Exploitation Document + """ + + stripmap_direction: Optional[XYZType] = field( + default=None, + metadata={ + "name": "StripmapDirection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + curvature_radius: Optional[float] = field( + default=None, + metadata={ + "name": "CurvatureRadius", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ExploitationFeaturesCollectionType: + """ + :ivar information: General collection information. + :ivar geometry: Key geometry parameters independent of product processing. + :ivar phenomenology: Phenomenology related to both the geometry and the final product processing. + """ + + information: Optional[ExploitationFeaturesCollectionInformationType] = field( + default=None, + metadata={ + "name": "Information", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + geometry: Optional[ExploitationFeaturesCollectionGeometryType] = field( + default=None, + metadata={ + "name": "Geometry", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + phenomenology: Optional[ExploitationFeaturesCollectionPhenomenologyType] = field( + default=None, + metadata={ + "name": "Phenomenology", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class FilterType: + filter_name: Optional[str] = field( + default=None, + metadata={ + "name": "FilterName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + filter_kernel: Optional[FilterKernelType] = field( + default=None, + metadata={ + "name": "FilterKernel", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + filter_bank: Optional[FilterBankType] = field( + default=None, + metadata={ + "name": "FilterBank", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + operation: Optional[FilterOperationType] = field( + default=None, + metadata={ + "name": "Operation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class GeographicProjectionType(MeasurableProjectionType): + """ + Geographic mapping of the pixel grid. + """ + + +@dataclass +class J2KType: + """ + :ivar original: + :ivar parsed: Conditional fields that exist only for parsed images. + """ + + original: Optional[J2KSubtype] = field( + default=None, + metadata={ + "name": "Original", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + parsed: Optional[J2KSubtype] = field( + default=None, + metadata={ + "name": "Parsed", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class NewLookupTableType: + lutname: Optional[str] = field( + default=None, + metadata={ + "name": "LUTName", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + predefined: Optional[PredefinedLookupType] = field( + default=None, + metadata={ + "name": "Predefined", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + custom: Optional[CustomLookupType] = field( + default=None, + metadata={ + "name": "Custom", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class PlaneProjectionType(MeasurableProjectionType): + """ + Planar representation of the pixel grid. + + :ivar product_plane: Plane definition for the product. + """ + + product_plane: Optional[ProductPlaneType] = field( + default=None, + metadata={ + "name": "ProductPlane", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class BandEqualizationType: + """ + Band equalization ensures that real-world neutral colors have equal digital + count values (i.e. are represented as neutral colors) across the dynamic range + of the imaged scene. + + :ivar algorithm: Allowed values: 1DLUT + :ivar band_lut: + """ + + algorithm: Optional[EqualizationAlgorithmType] = field( + default=None, + metadata={ + "name": "Algorithm", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + band_lut: List["BandEqualizationType.BandLUT"] = field( + default_factory=list, + metadata={ + "name": "BandLUT", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + @dataclass + class BandLUT(NewLookupTableType): + k: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class CompressionType: + """ + Contains information regarding any compression that has occured to the image + data. + + :ivar j2_k: Block describing details of JPEG 2000 compression. + """ + + j2_k: Optional[J2KType] = field( + default=None, + metadata={ + "name": "J2K", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class ExploitationFeaturesType: + """ + Computed metadata regarding the collect. + + :ivar collection: Metadata regarding one of the input collections. + :ivar product: Metadata regarding the product. + """ + + collection: List["ExploitationFeaturesType.Collection"] = field( + default_factory=list, + metadata={ + "name": "Collection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + product: List[ExploitationFeaturesProductType] = field( + default_factory=list, + metadata={ + "name": "Product", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + + @dataclass + class Collection(ExploitationFeaturesCollectionType): + identifier: Optional[str] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class MeasurementType: + """ + Geometric SAR information required for measurement/geolocation. + + :ivar polynomial_projection: Polynomial pixel to ground. Only used for sensor systems where the radar + geometry parameters are not recorded. + :ivar geographic_projection: Geographic mapping of the pixel grid referred to as GGD in the Design and + Exploitation document. + :ivar plane_projection: Planar representation of the pixel grid referred to as PGD in the Design and + Exploitation document. + :ivar cylindrical_projection: Cylindrical mapping of the pixel grid referred to as CGD in the Design and + Exploitation document. + :ivar pixel_footprint: Size of the image in pixels. + :ivar arpflag: Flag indicating whether ARP polynomial is based on the best available ("collect time" or + "predicted") ephemeris. + :ivar arppoly: Center of aperture polynomial (units = m) based upon time into the collect. + :ivar valid_data: Indicates the full image includes both valid data and some zero filled pixels. Simple + convex polygon enclosed the valid data (may include some zero filled pixels for simplification). Vertices + in clockwise order. + """ + + polynomial_projection: Optional[PolynomialProjectionType] = field( + default=None, + metadata={ + "name": "PolynomialProjection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + geographic_projection: Optional[GeographicProjectionType] = field( + default=None, + metadata={ + "name": "GeographicProjection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + plane_projection: Optional[PlaneProjectionType] = field( + default=None, + metadata={ + "name": "PlaneProjection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + cylindrical_projection: Optional[CylindricalProjectionType] = field( + default=None, + metadata={ + "name": "CylindricalProjection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + pixel_footprint: Optional[RowColIntType] = field( + default=None, + metadata={ + "name": "PixelFootprint", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + arpflag: Optional[MeasurementTypeARPFlag] = field( + default=None, + metadata={ + "name": "ARPFlag", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + arppoly: Optional[XYZPolyType] = field( + default=None, + metadata={ + "name": "ARPPoly", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + valid_data: Optional[ValidDataType] = field( + default=None, + metadata={ + "name": "ValidData", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class RRDSType: + """ + :ivar downsampling_method: Algorithm used to perform RRDS downsampling + :ivar anti_alias: Only included if DownSamplingMethod=DECIMET + :ivar interpolation: Only included if DownSamplingMethod=DECIMET + """ + + downsampling_method: Optional[DownsamplingMethodType] = field( + default=None, + metadata={ + "name": "DownsamplingMethod", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + anti_alias: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AntiAlias", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + interpolation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "Interpolation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class ScalingType: + """ + :ivar anti_alias: Anti-Alias Filter used for scaling. Refer to program-specific documentation for population + guidance + :ivar interpolation: Interpolation Filter used for scaling. Refer to program-specific documentation for + population guidance. + """ + + anti_alias: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AntiAlias", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + interpolation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "Interpolation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class SharpnessEnhancementType: + """ + :ivar modular_transfer_function_compensation: Note: If defining a custom Filter, it must be no larger than + 5x5. + :ivar modular_transfer_function_enhancement: Note: If defining a custom Filter, it must be no larger than + 5x5. + """ + + modular_transfer_function_compensation: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionCompensation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + modular_transfer_function_enhancement: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionEnhancement", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class GeometricTransformType: + """ + :ivar scaling: + :ivar orientation: Parameters describing the default orientation of the product + """ + + scaling: Optional[ScalingType] = field( + default=None, + metadata={ + "name": "Scaling", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + orientation: Optional[Orientation] = field( + default=None, + metadata={ + "name": "Orientation", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + + +@dataclass +class ProductGenerationOptionsType: + """ + Performs several key actions on an image to prepare it for necessary additional + processing to achieve the desired output product. + + :ivar band_equalization: Band equalization ensures that real-world neutral colors have equal digital count + values (i.e. are represented as neutral colors) across the dynamic range of the imaged scene. + :ivar modular_transfer_function_restoration: Filter must be no larger than 15x15. + :ivar data_remapping: Data remapping refers to the specific need to convert the data of incoming, expanded or + uncompressed image band data to non-mapped image data. + :ivar asymmetric_pixel_correction: + """ + + band_equalization: Optional[BandEqualizationType] = field( + default=None, + metadata={ + "name": "BandEqualization", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + modular_transfer_function_restoration: Optional[FilterType] = field( + default=None, + metadata={ + "name": "ModularTransferFunctionRestoration", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + data_remapping: Optional[NewLookupTableType] = field( + default=None, + metadata={ + "name": "DataRemapping", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + asymmetric_pixel_correction: Optional[FilterType] = field( + default=None, + metadata={ + "name": "AsymmetricPixelCorrection", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class InteractiveProcessingType: + """ + :ivar geometric_transform: The geometric transform element is used to perform various geometric distortions + to each band of image data. These distortions include image chipping, scaling, rotation, shearing, etc. + :ivar sharpness_enhancement: + :ivar color_space_transform: + :ivar dynamic_range_adjustment: Specifies the recommended ELT DRA overrides + :ivar tonal_transfer_curve: The 1-D LUT element uses one or more 1-D LUTs to stretch or compress tome data in + valorous regions within a digital image's dynamic range. 1-D LUT can be implemented using a Tonal + Transfer Curve (TTC). There are 12 families of TTCs: Range = [0,11]. There are 64 members for each + family: Range=[0, 63]. + :ivar band: + """ + + geometric_transform: Optional[GeometricTransformType] = field( + default=None, + metadata={ + "name": "GeometricTransform", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + sharpness_enhancement: Optional[SharpnessEnhancementType] = field( + default=None, + metadata={ + "name": "SharpnessEnhancement", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + color_space_transform: Optional[ColorSpaceTransformType] = field( + default=None, + metadata={ + "name": "ColorSpaceTransform", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + dynamic_range_adjustment: Optional[DynamicRangeAdjustmentType] = field( + default=None, + metadata={ + "name": "DynamicRangeAdjustment", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + tonal_transfer_curve: Optional[NewLookupTableType] = field( + default=None, + metadata={ + "name": "TonalTransferCurve", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + band: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class NonInteractiveProcessingType: + """ + :ivar product_generation_options: Performs several key actions on an image to prepare it for necessary + additional processing to achieve the desired output product. + :ivar rrds: Creates a set of sub-sampled versions of an image to provide processing chains with quick access + to lower mangification values for faster computation speeds and performance. + :ivar band: + """ + + product_generation_options: Optional[ProductGenerationOptionsType] = field( + default=None, + metadata={ + "name": "ProductGenerationOptions", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + rrds: Optional[RRDSType] = field( + default=None, + metadata={ + "name": "RRDS", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + band: Optional[int] = field( + default=None, + metadata={ + "type": "Attribute", + "required": True, + }, + ) + + +@dataclass +class ProductDisplayType: + """ + Type for describing proper display of the derived product. + + :ivar pixel_type: Defines the pixel type, based on enumeration and definition in Design and Exploitation + document. + :ivar num_bands: Number of bands contained in the image. Populate with the number of bands present after + remapping. For example an 8-bit RGB image (RGBLU) this should be populated with 3. + :ivar default_band_display: Indicates which band to display by default. Valid range = 1 to NumBands. + :ivar non_interactive_processing: + :ivar interactive_processing: + :ivar display_extension: Optional extensible parameters used to support profile-specific needs related to + product display. Predefined filter types. + """ + + pixel_type: Optional[PixelType] = field( + default=None, + metadata={ + "name": "PixelType", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + num_bands: Optional[int] = field( + default=None, + metadata={ + "name": "NumBands", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "required": True, + }, + ) + default_band_display: Optional[int] = field( + default=None, + metadata={ + "name": "DefaultBandDisplay", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + non_interactive_processing: List[NonInteractiveProcessingType] = field( + default_factory=list, + metadata={ + "name": "NonInteractiveProcessing", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + interactive_processing: List[InteractiveProcessingType] = field( + default_factory=list, + metadata={ + "name": "InteractiveProcessing", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + "min_occurs": 1, + }, + ) + display_extension: List[ParameterType] = field( + default_factory=list, + metadata={ + "name": "DisplayExtension", + "type": "Element", + "namespace": "urn:SIDD:3.0.0", + }, + ) + + +@dataclass +class SIDD: + """ + Root element of the SIDD document. + + :ivar product_creation: Information related to processor, classification, and product type. + :ivar display: Contains information on the parameters needed to display the product in an exploitation tool. + :ivar geo_data: Contains geographic data. + :ivar measurement: Contains the metadata necessary for performing measurements. + :ivar exploitation_features: Computed metadata regarding the input collections and final product. + :ivar downstream_reprocessing: Contains metadata related to downstream processing of the product. + :ivar error_statistics: See SICD documentation for metadata definitions. + :ivar radiometric: Radiometric information about the product. + :ivar match_info: Information about other collections that are matched to the current collection. The current + collection is the collection from which this SIDD product was generated. + :ivar compression: Contains information regarding any compression that has occured to the image data. + :ivar digital_elevation_data: This block describes the Digital ElevatioNData when it is included with the + SIDD product. + :ivar product_processing: Contains metadata related to algorithms used during product generation. + :ivar annotations: List of annotations for the imagery. + """ + + class Meta: + namespace = "urn:SIDD:3.0.0" + + product_creation: Optional[ProductCreationType] = field( + default=None, + metadata={ + "name": "ProductCreation", + "type": "Element", + "required": True, + }, + ) + display: Optional[ProductDisplayType] = field( + default=None, + metadata={ + "name": "Display", + "type": "Element", + "required": True, + }, + ) + geo_data: Optional[GeoDataType] = field( + default=None, + metadata={ + "name": "GeoData", + "type": "Element", + "required": True, + }, + ) + measurement: Optional[MeasurementType] = field( + default=None, + metadata={ + "name": "Measurement", + "type": "Element", + "required": True, + }, + ) + exploitation_features: Optional[ExploitationFeaturesType] = field( + default=None, + metadata={ + "name": "ExploitationFeatures", + "type": "Element", + "required": True, + }, + ) + downstream_reprocessing: Optional[DownstreamReprocessingType] = field( + default=None, + metadata={ + "name": "DownstreamReprocessing", + "type": "Element", + }, + ) + error_statistics: Optional[ErrorStatisticsType] = field( + default=None, + metadata={ + "name": "ErrorStatistics", + "type": "Element", + }, + ) + radiometric: Optional[RadiometricType] = field( + default=None, + metadata={ + "name": "Radiometric", + "type": "Element", + }, + ) + match_info: Optional[MatchInfoType] = field( + default=None, + metadata={ + "name": "MatchInfo", + "type": "Element", + }, + ) + compression: Optional[CompressionType] = field( + default=None, + metadata={ + "name": "Compression", + "type": "Element", + }, + ) + digital_elevation_data: Optional[DigitalElevationDataType] = field( + default=None, + metadata={ + "name": "DigitalElevationData", + "type": "Element", + }, + ) + product_processing: Optional[ProductProcessingType] = field( + default=None, + metadata={ + "name": "ProductProcessing", + "type": "Element", + }, + ) + annotations: Optional[AnnotationsType] = field( + default=None, + metadata={ + "name": "Annotations", + "type": "Element", + }, + ) diff --git a/src/aws/osml/gdal/sensor_model_factory.py b/src/aws/osml/gdal/sensor_model_factory.py index 99cb28c..d92a3d5 100644 --- a/src/aws/osml/gdal/sensor_model_factory.py +++ b/src/aws/osml/gdal/sensor_model_factory.py @@ -14,6 +14,7 @@ from .rpc_sensor_model_builder import RPCSensorModelBuilder from .rsm_sensor_model_builder import RSMSensorModelBuilder from .sicd_sensor_model_builder import SICDSensorModelBuilder +from .sidd_sensor_model_builder import SIDDSensorModelBuilder from .xmltre_utils import get_tre_field_value @@ -188,8 +189,9 @@ def build(self) -> Optional[SensorModel]: xml_bytes = des_accessor.parse_field_value(xml_data_segment, "DESDATA", base64.b64decode) xml_str = xml_bytes.decode("utf-8") if "SIDD" in xml_str: - # This looks like a SIDD file. Skip for now - # SIDD images will contain SICD extensions but the SIDD should come first + # SIDD images will often contain SICD XML metadata as well but the SIDD should come first + # so we can stop processing other XML data segments + precision_sensor_model = SIDDSensorModelBuilder(sidd_xml=xml_str).build() break elif "SICD" in xml_str and SensorModelTypes.SICD in self.selected_sensor_model_types: precision_sensor_model = SICDSensorModelBuilder(sicd_xml=xml_str).build() diff --git a/src/aws/osml/gdal/sicd_sensor_model_builder.py b/src/aws/osml/gdal/sicd_sensor_model_builder.py index 37aba27..9d9ea5b 100644 --- a/src/aws/osml/gdal/sicd_sensor_model_builder.py +++ b/src/aws/osml/gdal/sicd_sensor_model_builder.py @@ -177,8 +177,12 @@ def from_dataclass(sicd: Union[sicd121.SICD, sicd130.SICD]) -> Optional[SensorMo sicd_sensor_model = SICDSensorModel( coord_converter=coord_converter, coa_projection_set=projection_set, - scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), - scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + u_spn=SICDSensorModel.compute_u_spn( + scp_ecf=scp_ecf, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ), side_of_track=str(sicd.scpcoa.side_of_track.value), u_gpn=ugpn, ) diff --git a/src/aws/osml/gdal/sidd_sensor_model_builder.py b/src/aws/osml/gdal/sidd_sensor_model_builder.py new file mode 100644 index 0000000..3151099 --- /dev/null +++ b/src/aws/osml/gdal/sidd_sensor_model_builder.py @@ -0,0 +1,136 @@ +import logging +from typing import Optional, Union + +from xsdata.formats.dataclass.parsers import XmlParser + +import aws.osml.formats.sidd.models.sidd_v1_0_0 as sidd100 +import aws.osml.formats.sidd.models.sidd_v2_0_0 as sidd200 +import aws.osml.formats.sidd.models.sidd_v3_0_0 as sidd300 + +from ..photogrammetry import ( + ChippedImageSensorModel, + ImageCoordinate, + PlaneProjectionSet, + SARImageCoordConverter, + SensorModel, + SICDSensorModel, + WorldCoordinate, +) +from .sensor_model_builder import SensorModelBuilder +from .sicd_sensor_model_builder import poly2d_to_native, xyzpoly_to_native, xyztype_to_ndarray + +logger = logging.getLogger(__name__) + + +class SIDDSensorModelBuilder(SensorModelBuilder): + """ + This builder is used to create sensor models for images that have SIDD metadata. The metadata is provided + as XML that conforms to the SIDD specifications. We intend to support multiple SIDD versions but the current + software was implemented using the v2.0.0 and v3.0.0 specifications. + + Note that the SIDD sensor models rely heavily on the SICD projections so the class of the returned model + will be a SICDSensorModel. Future versions may rename this to SISensorModel or SARSensorModel. + """ + + def __init__(self, sidd_xml: str): + """ + Construct the builder given the SIDD XML. + + :param sidd_xml: the XML string + """ + super().__init__() + self.sidd_xml = sidd_xml + + def build(self) -> Optional[SensorModel]: + """ + Attempt to build a precise SAR sensor model. This sensor model handles chipped images natively. + + :return: the sensor model; if available + """ + try: + if self.sidd_xml is None or len(self.sidd_xml) == 0: + return None + + parser = XmlParser() + sicd = parser.from_string(self.sidd_xml) + return SIDDSensorModelBuilder.from_dataclass(sicd) + except Exception as e: + logging.error("Exception caught attempting to build SIDD sensor model.", e) + return None + + @staticmethod + def from_dataclass(sidd: Union[sidd100.SIDD, sidd200.SIDD, sidd300.SIDD]) -> Optional[SensorModel]: + """ + This method constructs a SIDD sensor model from the python dataclasses generated when parsing the XML. If + the metadata shows that this is a chip then a ChippedImageSensorModel will be constructed to wrap the + SICDSensorModel used for the full image. + + :param sidd: the dataclass object constructed from the XML + :return: the sensor model; if available + """ + + plane_projection = sidd.measurement.plane_projection + scp_ecf = WorldCoordinate(xyztype_to_ndarray(plane_projection.reference_point.ecef)) + scp_pixel = ImageCoordinate([plane_projection.reference_point.point.col, plane_projection.reference_point.point.row]) + time_coa_poly = poly2d_to_native(plane_projection.time_coapoly) + arp_poly = xyzpoly_to_native(sidd.measurement.arppoly) + + u_row = xyztype_to_ndarray(plane_projection.product_plane.row_unit_vector) + u_col = xyztype_to_ndarray(plane_projection.product_plane.col_unit_vector) + coord_converter = SARImageCoordConverter( + scp_pixel=scp_pixel, + scp_ecf=scp_ecf, + u_row=u_row, + u_col=u_col, + row_ss=plane_projection.sample_spacing.row, + col_ss=plane_projection.sample_spacing.col, + first_pixel=ImageCoordinate([0, 0]), + ) + + projection_set = PlaneProjectionSet( + scp_ecf=scp_ecf, + image_plane_urow=u_row, + image_plane_ucol=u_col, + coa_time_poly=time_coa_poly, + arp_poly=arp_poly, + ) + + u_gpn = SICDSensorModel.compute_u_gpn(scp_ecf=scp_ecf, u_row=u_row, u_col=u_col) + + sidd_sensor_model = SICDSensorModel( + coord_converter=coord_converter, + coa_projection_set=projection_set, + u_spn=u_gpn, + u_gpn=u_gpn, + ) + + if sidd.downstream_reprocessing is None or sidd.downstream_reprocessing.geometric_chip is None: + return sidd_sensor_model + else: + # Since this SIDD image is a chip of a full image wrap the regular sensor model in a sensor model that + # will handle the conversions between the chipped image coordinates and the full image coordinates. + # This 4 corner transformation handles images that are cropped, rotated, and scaled, from the full + # SIDD image grid. + geo_chip = sidd.downstream_reprocessing.geometric_chip + chip_num_rows = geo_chip.chip_size.row + chip_num_cols = geo_chip.chip_size.col + + chipped_image_coords = [ + ImageCoordinate(coord) + for coord in [[0, 0], [chip_num_cols, 0], [chip_num_cols, chip_num_rows], [0, chip_num_rows]] + ] + full_image_coords = [ + ImageCoordinate([x.col, x.row]) + for x in [ + geo_chip.original_upper_left_coordinate, + geo_chip.original_upper_right_coordinate, + geo_chip.original_lower_right_coordinate, + geo_chip.original_lower_left_coordinate, + ] + ] + + return ChippedImageSensorModel( + full_image_coords, + chipped_image_coords, + sidd_sensor_model, + ) diff --git a/src/aws/osml/photogrammetry/sicd_sensor_model.py b/src/aws/osml/photogrammetry/sicd_sensor_model.py index 12decb7..7d2695c 100644 --- a/src/aws/osml/photogrammetry/sicd_sensor_model.py +++ b/src/aws/osml/photogrammetry/sicd_sensor_model.py @@ -975,9 +975,8 @@ def __init__( self, coord_converter: SARImageCoordConverter, coa_projection_set: COAProjectionSet, - scp_arp: np.ndarray, - scp_varp: np.ndarray, - side_of_track: str, + u_spn: np.ndarray, + side_of_track: str = "L", u_gpn: Optional[np.ndarray] = None, ): """ @@ -985,26 +984,44 @@ def __init__( :param coord_converter: converts coordinates between image grid and image plane :param coa_projection_set: projects image locations to the r/rdot contour - :param scp_arp: aperture reference point position - :param scp_varp: aperture reference point velocity - :param side_of_track: side of track imaged + :param u_spn: slant plane normal + :param side_of_track: side of track imaged, "L" or "R", default "L" :param u_gpn: optional unit normal for ground plane """ super().__init__() self.coa_projection_set = coa_projection_set self.coord_converter = coord_converter self.uvect_gpn = u_gpn - self.scp_arp = scp_arp - self.scp_varp = scp_varp + self.uvect_spn = u_spn self.side_of_track = side_of_track - self.uvect_spn = np.cross(scp_varp, coord_converter.scp_ecf.coordinate - scp_arp) - if side_of_track == "R": - self.uvect_spn *= -1.0 - self.uvect_spn /= np.linalg.norm(self.uvect_spn) - self.default_surface_projection = GroundPlaneRRDotSurfaceProjection(self.coord_converter.scp_ecf, self.uvect_gpn) + @staticmethod + def compute_u_spn(scp_ecf: WorldCoordinate, scp_arp: np.ndarray, scp_varp: np.ndarray, side_of_track: str) -> np.ndarray: + """ + This helper function computes the slant plane normal. + + :param scp_ecf: Scene Center Point position in ECF coordinates + :param scp_arp: aperture reference point position + :param scp_varp: aperture reference point velocity + :param side_of_track: side of track imaged + :return: unit vector for the slant plane normal + """ + u_spn = np.cross(scp_varp, scp_ecf.coordinate - scp_arp) + if side_of_track == "R": + u_spn *= -1.0 + u_spn /= np.linalg.norm(u_spn) + return u_spn + + @staticmethod + def compute_u_gpn(scp_ecf: WorldCoordinate, u_row: np.ndarray, u_col: np.ndarray) -> np.ndarray: + u_gpn = np.cross(u_row, u_col) + u_gpn /= np.linalg.norm(u_gpn) + if np.dot(u_gpn, scp_ecf.coordinate) < 0: + u_gpn *= -1 + return u_gpn + def image_to_world( self, image_coordinate: ImageCoordinate, diff --git a/test/aws/osml/formats/test_sidd_schemas.py b/test/aws/osml/formats/test_sidd_schemas.py new file mode 100644 index 0000000..5717613 --- /dev/null +++ b/test/aws/osml/formats/test_sidd_schemas.py @@ -0,0 +1,13 @@ +import unittest +from pathlib import Path + +from xsdata.formats.dataclass.parsers import XmlParser + +import aws.osml.formats.sidd.models.sidd_v2_0_0 as sidd2 + + +class TestSIDDFormats(unittest.TestCase): + def test_sidd_20(self): + sidd = XmlParser().from_path(Path("./test/data/sidd/example.sidd.xml")) + + assert isinstance(sidd, sidd2.SIDD) diff --git a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py index ad7fe65..b16ecd7 100644 --- a/test/aws/osml/photogrammetry/test_sicd_sensor_model.py +++ b/test/aws/osml/photogrammetry/test_sicd_sensor_model.py @@ -63,8 +63,12 @@ def test_xrgycr(self): sicd_sensor_model = SICDSensorModel( coord_converter=coord_converter, coa_projection_set=projection_set, - scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), - scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + u_spn=SICDSensorModel.compute_u_spn( + scp_ecf=scp_ecf, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ), side_of_track=str(sicd.scpcoa.side_of_track.value), ) @@ -137,8 +141,12 @@ def test_rgzero_inca(self): sicd_sensor_model = SICDSensorModel( coord_converter=image_plane, coa_projection_set=projection_set, - scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), - scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + u_spn=SICDSensorModel.compute_u_spn( + scp_ecf=scp_ecf, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ), side_of_track=str(sicd.scpcoa.side_of_track.value), ) @@ -224,8 +232,12 @@ def test_rgazim_pfa(self): sicd_sensor_model = SICDSensorModel( coord_converter=image_plane, coa_projection_set=projection_set, - scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), - scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + u_spn=SICDSensorModel.compute_u_spn( + scp_ecf=scp_ecf, + scp_arp=xyztype_to_ndarray(sicd.scpcoa.arppos), + scp_varp=xyztype_to_ndarray(sicd.scpcoa.arpvel), + side_of_track=str(sicd.scpcoa.side_of_track.value), + ), side_of_track=str(sicd.scpcoa.side_of_track.value), u_gpn=ugpn, ) diff --git a/test/aws/osml/photogrammetry/test_sidd_sensor_model.py b/test/aws/osml/photogrammetry/test_sidd_sensor_model.py new file mode 100644 index 0000000..302bdf4 --- /dev/null +++ b/test/aws/osml/photogrammetry/test_sidd_sensor_model.py @@ -0,0 +1,70 @@ +import unittest +from math import radians +from pathlib import Path + +import numpy as np +from xsdata.formats.dataclass.parsers import XmlParser + +import aws.osml.formats.sidd.models.sidd_v2_0_0 as sidd200 +from aws.osml.gdal.sidd_sensor_model_builder import SIDDSensorModelBuilder +from aws.osml.photogrammetry import ( + ChippedImageSensorModel, + GeodeticWorldCoordinate, + ImageCoordinate, + SICDSensorModel, + geocentric_to_geodetic, +) + + +class TestSIDDSensorModel(unittest.TestCase): + def test_planar_projection(self): + sidd: sidd200.SIDD = XmlParser().from_path(Path("./test/data/sidd/example.sidd.xml")) + + sm = SIDDSensorModelBuilder.from_dataclass(sidd) + + assert sm is not None + assert isinstance(sm, SICDSensorModel) + + scp_image_coord = ImageCoordinate( + [ + sm.coord_converter.scp_pixel.x - sm.coord_converter.first_pixel.x, + sm.coord_converter.scp_pixel.y - sm.coord_converter.first_pixel.y, + ] + ) + scp_world_coord = geocentric_to_geodetic(sm.coord_converter.scp_ecf) + + assert np.allclose(scp_image_coord.coordinate, sm.world_to_image(scp_world_coord).coordinate, atol=1.0) + assert np.allclose(scp_world_coord.coordinate, sm.image_to_world(scp_image_coord).coordinate) + + num_cols = sidd.measurement.pixel_footprint.col + num_rows = sidd.measurement.pixel_footprint.row + for icp in sidd.geo_data.image_corners.icp: + world_location = GeodeticWorldCoordinate([radians(icp.lon), radians(icp.lat), scp_world_coord.elevation]) + if icp.index.value == "1:FRFC": + image_location = ImageCoordinate([0, 0]) + elif icp.index.value == "2:FRLC": + image_location = ImageCoordinate([num_cols, 0]) + elif icp.index.value == "3:LRLC": + image_location = ImageCoordinate([num_cols, num_rows]) + elif icp.index.value == "4:LRFC": + image_location = ImageCoordinate([0, num_rows]) + else: + raise ValueError(f"Unexpected ICP in test data {icp.index.value}") + + computed_world_location = sm.image_to_world(image_location) + computed_image_location = sm.world_to_image(world_location) + + assert np.allclose(computed_world_location.coordinate[0:2], world_location.coordinate[0:2], atol=0.000001) + assert np.allclose(computed_image_location.coordinate, image_location.coordinate, atol=0.5) + + def test_chipped_sidd(self): + sidd: sidd200.SIDD = XmlParser().from_path(Path("./test/data/sidd/example.sidd-chip.xml")) + + sm = SIDDSensorModelBuilder.from_dataclass(sidd) + assert sm is not None + assert isinstance(sm, ChippedImageSensorModel) + + coord_calculated_from_chip = sm.image_to_world(ImageCoordinate([0, 0])) + coord_calculated_from_full = sm.full_image_sensor_model.image_to_world(ImageCoordinate([512, 512])) + + assert np.allclose(coord_calculated_from_chip.coordinate, coord_calculated_from_full.coordinate) diff --git a/test/data/sidd/example.sidd-chip.xml b/test/data/sidd/example.sidd-chip.xml new file mode 100644 index 0000000..9dc79d1 --- /dev/null +++ b/test/data/sidd/example.sidd-chip.xml @@ -0,0 +1,544 @@ + + + + + Valkyrie Systems Sage | Umbra Image Formation processor 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-60.159807541752805 + 62.80213750317631 + -27.12051599161032 + 77.84707473804855 + + + + 1.8456022013212559 + 0.5138835041367932 + + + 151.91025936378222 + 2.245553205890211 + + 157.35408103384742 + 62.329913319642756 + + + + + 0.4509416741723185 + 0.450941239806248 + + 1.0000014537021478 + + UNKNOWN + UNKNOWN + + -107.00944236281063 + ada7854e-1acb-4b93-8a8c-925260db5abe + + + + + + RIC_ECI + 5.0 + 5.0 + 5.0 + 0.015 + 0.015 + 0.015 + + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + 149.896229 + + + 1.0 + + + + From b37d047647ae23df5131c9891c70c153a0586627 Mon Sep 17 00:00:00 2001 From: Ranbir Aulakh Date: Wed, 18 Oct 2023 15:57:08 -0400 Subject: [PATCH 13/20] feat: Update GitHub actions (#23) * Update Github Actions --- ...entation.yaml => documentation-publish.yml} | 12 ++++++++---- .../workflows/osml-imagery-toolkit-build.yml | 10 ++++++++++ .../workflows/osml-imagery-toolkit-publish.yml | 18 ++++++++++++++++++ .github/workflows/python-publish.yml | 10 +++++----- .../{python-package.yml => python-tox.yml} | 14 ++++---------- 5 files changed, 45 insertions(+), 19 deletions(-) rename .github/workflows/{documentation.yaml => documentation-publish.yml} (76%) create mode 100644 .github/workflows/osml-imagery-toolkit-build.yml create mode 100644 .github/workflows/osml-imagery-toolkit-publish.yml rename .github/workflows/{python-package.yml => python-tox.yml} (76%) diff --git a/.github/workflows/documentation.yaml b/.github/workflows/documentation-publish.yml similarity index 76% rename from .github/workflows/documentation.yaml rename to .github/workflows/documentation-publish.yml index a9bc5ca..18bb5a1 100644 --- a/.github/workflows/documentation.yaml +++ b/.github/workflows/documentation-publish.yml @@ -1,12 +1,17 @@ -name: Docs -on: [push, pull_request, workflow_dispatch] +name: Generate and Publish Documentation for OSML Imagery Toolkit + +on: + workflow_call: + permissions: contents: write + jobs: docs: + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v3 - name: Install dependencies run: | @@ -19,7 +24,6 @@ jobs: tox -e docs - name: Deploy uses: peaceiris/actions-gh-pages@v3.9.3 - if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} with: publish_branch: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/osml-imagery-toolkit-build.yml b/.github/workflows/osml-imagery-toolkit-build.yml new file mode 100644 index 0000000..e5015a7 --- /dev/null +++ b/.github/workflows/osml-imagery-toolkit-build.yml @@ -0,0 +1,10 @@ +name: "OSML Imagery Toolkit Build Workflow" + +on: + pull_request: + branches: ["main", "dev"] + +jobs: + Build_Validate_Tox: + uses: ./.github/workflows/python-tox.yml + secrets: inherit diff --git a/.github/workflows/osml-imagery-toolkit-publish.yml b/.github/workflows/osml-imagery-toolkit-publish.yml new file mode 100644 index 0000000..7dbc10c --- /dev/null +++ b/.github/workflows/osml-imagery-toolkit-publish.yml @@ -0,0 +1,18 @@ +name: "OSML Imagery Toolkit Build and Publish Workflow" + +on: + push: + branches: ["main"] + +jobs: + Build_Validate_Tox: + uses: ./.github/workflows/python-tox.yml + secrets: inherit + Publish_Python: + needs: [Build_Validate_Tox] + uses: ./.github/workflows/python-publish.yml + secrets: inherit + Publish_Documentation: + needs: [Publish_Python] + uses: ./.github/workflows/documentation-publish.yml + secrets: inherit diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index bdaab28..942a1c4 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -6,9 +6,10 @@ # separate terms of service, privacy policy, and support # documentation. -name: Upload Python Package +name: Publish Python Package on: + workflow_call: release: types: [published] @@ -17,11 +18,10 @@ permissions: jobs: deploy: - + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v3 with: @@ -33,7 +33,7 @@ jobs: - name: Build package run: python -m build - name: Publish package - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + uses: pypa/gh-action-pypi-publish@v1.8.10 with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-tox.yml similarity index 76% rename from .github/workflows/python-package.yml rename to .github/workflows/python-tox.yml index 225d42c..456c8f9 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-tox.yml @@ -1,27 +1,21 @@ # This workflow will install Python dependencies, run tests and lint with a single version of Python # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python -name: Tox Build/Validation +name: Build/Validation with Tox on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] + workflow_call: permissions: contents: read jobs: build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: - lfs: 'true' - ssh-key: ${{ secrets.git_ssh_key }} + lfs: 'true' - name: Set up Python 3.10 uses: actions/setup-python@v3 with: From ba5942a478c79547f01c484ebd57fb84714a30f7 Mon Sep 17 00:00:00 2001 From: edparris Date: Tue, 24 Oct 2023 21:54:09 -0400 Subject: [PATCH 14/20] fix: errors constructing RSMSectionedPolynomialSensorModel from TREs --- setup.cfg | 2 +- src/aws/osml/gdal/rsm_sensor_model_builder.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/setup.cfg b/setup.cfg index c58d35f..7509979 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = osml-imagery-toolkit -version = 1.1.1 +version = 1.1.2 description = Toolkit to work with imagery collected by satellites and UAVs long_description = file: README.md long_description_content_type = text/markdown diff --git a/src/aws/osml/gdal/rsm_sensor_model_builder.py b/src/aws/osml/gdal/rsm_sensor_model_builder.py index 167268a..a1c4e33 100644 --- a/src/aws/osml/gdal/rsm_sensor_model_builder.py +++ b/src/aws/osml/gdal/rsm_sensor_model_builder.py @@ -280,7 +280,7 @@ def _build_loworder_rsm_polynomial(rsmpi_tre: ET.Element, polynomial_prefix: str coefficients = [] for coeff_suffix in ["0", "X", "Y", "Z", "XX", "XY", "XZ", "YY", "YZ", "ZZ"]: - coefficients.append(get_tre_field_value(rsmpi_tre, f"{polynomial_prefix}{coeff_suffix}", int)) + coefficients.append(get_tre_field_value(rsmpi_tre, f"{polynomial_prefix}{coeff_suffix}", float)) return RSMLowOrderPolynomial(coefficients) @staticmethod @@ -306,9 +306,9 @@ def _build_rsm_sectioned_polynomial_sensor_model( sensor_model_grid_map[(sensor_model.section_row, sensor_model.section_col)] = sensor_model section_sensor_model_grid = [] - for row in range(0, num_section_rows): + for row in range(1, num_section_rows + 1): row_of_sensor_models = [] - for col in range(0, num_section_cols): + for col in range(1, num_section_cols + 1): row_of_sensor_models.append(sensor_model_grid_map[(row, col)]) section_sensor_model_grid.append(row_of_sensor_models) From e82189949de00a0ab50ec10bedaaa970e0062e9d Mon Sep 17 00:00:00 2001 From: edparris Date: Tue, 14 Nov 2023 17:54:30 -0500 Subject: [PATCH 15/20] feat: adds features module with 2D spatial index and basic property utils --- doc/index.rst | 1 + setup.cfg | 1 + src/aws/osml/features/__init__.py | 8 + src/aws/osml/features/feature_index.py | 67 ++++++++ .../imaged_feature_property_accessor.py | 136 ++++++++++++++++ test/aws/osml/features/test_feature_index.py | 29 ++++ .../test_imaged_feature_property_accessor.py | 145 ++++++++++++++++++ 7 files changed, 387 insertions(+) create mode 100644 src/aws/osml/features/__init__.py create mode 100644 src/aws/osml/features/feature_index.py create mode 100644 src/aws/osml/features/imaged_feature_property_accessor.py create mode 100644 test/aws/osml/features/test_feature_index.py create mode 100644 test/aws/osml/features/test_imaged_feature_property_accessor.py diff --git a/doc/index.rst b/doc/index.rst index f50b85e..b6c5157 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -13,6 +13,7 @@ Imagery Transmission Format (NITF) standards. _apidoc/aws.osml.photogrammetry _apidoc/aws.osml.gdal _apidoc/aws.osml.image_processing + _apidoc/aws.osml.features Indices and tables diff --git a/setup.cfg b/setup.cfg index 7509979..c399047 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,6 +43,7 @@ install_requires = scikit-optimize>=0.9.0 cachetools>=5.3.0 geojson>=3.0.0 + shapely>=2.0.2 pyproj>=3.6.0 omegaconf==2.3.0;python_version<'3.10.0' xsdata>=23.8 diff --git a/src/aws/osml/features/__init__.py b/src/aws/osml/features/__init__.py new file mode 100644 index 0000000..5167245 --- /dev/null +++ b/src/aws/osml/features/__init__.py @@ -0,0 +1,8 @@ +from .feature_index import Feature2DSpatialIndex, STRFeature2DSpatialIndex +from .imaged_feature_property_accessor import ImagedFeaturePropertyAccessor + +__all__ = [ + "Feature2DSpatialIndex", + "ImagedFeaturePropertyAccessor", + "STRFeature2DSpatialIndex", +] diff --git a/src/aws/osml/features/feature_index.py b/src/aws/osml/features/feature_index.py new file mode 100644 index 0000000..f69509e --- /dev/null +++ b/src/aws/osml/features/feature_index.py @@ -0,0 +1,67 @@ +from abc import ABC, abstractmethod +from typing import Iterable, Optional + +import geojson +import shapely + +from .imaged_feature_property_accessor import ImagedFeaturePropertyAccessor + + +class Feature2DSpatialIndex(ABC): + """ + A query-only spatial index allowing clients to lookup features using 2D geometries + """ + + @abstractmethod + def find_intersects(self, geometry: shapely.Geometry) -> Iterable[geojson.Feature]: + """ + Return the features intersecting the input geometry. + + :param geometry: geometry to query the index + :return: the features + """ + + @abstractmethod + def find_nearest(self, geometry: shapely.Geometry, max_distance: Optional[float] = None) -> Iterable[geojson.Feature]: + """ + Return the nearest feature for the input geometry based on distance within two-dimensional Cartesian space. + + :param geometry: geometry to query the index + :param max_distance: maximum distance + :return: the nearest features + """ + + +class STRFeature2DSpatialIndex(Feature2DSpatialIndex): + """ + Implementation of the 2D spatial index for GeoJSON features using Shapely's Sort-Tile-Recursive (STR) + tree datastructure. + """ + + def __init__( + self, + feature_collection: geojson.FeatureCollection, + use_image_geometries: bool = True, + property_accessor: ImagedFeaturePropertyAccessor = ImagedFeaturePropertyAccessor(), + ) -> None: + self.use_image_geometries = use_image_geometries + self.features = feature_collection.features + if use_image_geometries and property_accessor is not None: + geometries = [property_accessor.find_image_geometry(feature) for feature in self.features] + else: + geometries = [(shapely.shape(feature.geometry), feature) for feature in self.features] + + self.index = shapely.STRtree(geometries) + + def find_intersects(self, geometry: shapely.Geometry) -> Iterable[geojson.Feature]: + result_indexes = self.index.query(geometry, predicate="intersects") + return [self.features[i] for i in result_indexes] + + def find_nearest(self, geometry: shapely.Geometry, max_distance: Optional[float] = None) -> Iterable[geojson.Feature]: + if max_distance is None: + if self.use_image_geometries: + max_distance = 50 + else: + max_distance = 1.0 + result_indexes = self.index.query_nearest(geometry, max_distance=max_distance) + return [self.features[i] for i in result_indexes] diff --git a/src/aws/osml/features/imaged_feature_property_accessor.py b/src/aws/osml/features/imaged_feature_property_accessor.py new file mode 100644 index 0000000..a4b3321 --- /dev/null +++ b/src/aws/osml/features/imaged_feature_property_accessor.py @@ -0,0 +1,136 @@ +import json +from typing import Optional + +import geojson +import shapely + + +class ImagedFeaturePropertyAccessor: + """ + This class contains utility functions that ensure the property names / values for features derived from imagery + are consistently implemented. These specifications are still evolving so the intent is to encapsulate all of the + names in this one class so that changes do not ripple through the rest of the software baseline. + """ + + IMAGE_GEOMETRY = "imageGeometry" + IMAGE_BBOX = "imageBBox" + + BOUNDS_IMCORDS = "bounds_imcoords" + GEOM_IMCOORDS = "geom_imcoords" + DETECTION = "detection" + TYPE = "type" + COORDINATES = "coordinates" + PIXEL_COORDINATES = "pixelCoordinates" + + def __init__(self, allow_deprecated: bool = True): + """ + Construct an instance of the property accessor with configuration options. + + :param allow_deprecated: if true the accessor will work with deprecated property names. + """ + self.allow_deprecated = allow_deprecated + pass + + def find_image_geometry(self, feature: geojson.Feature) -> Optional[shapely.Geometry]: + """ + This function searches through the properties of a GeoJSON feature that are known to contain the geometry + of the feature in image coordinates. If found an appropriate 2D shape is constructed and returned. Note that + this search is conducted in priority order giving preference to the current preferred "imageGeometry" and + "bboxGeometry" properties. If neither of those is available and the accessor has been configured to search + deprecated properties then the "geom_imcoords", "detection", and "bounds_imcoords" properties are searched + in that order. + + :param feature: a GeoJSON feature that might contain an image geometry property + :return: a 2D shape representing the image geometry or None + """ + # The "imageGeometry" property is the current preferred encoding of image geometries for these + # features. The format follows the same type and coordinates structure used by shapely so we can + # construct the geometry directly from these values. + if self.IMAGE_GEOMETRY in feature.properties: + return shapely.geometry.shape(feature.properties[self.IMAGE_GEOMETRY]) + + # If a full image geometry is not provided we might be able to construct a Polygon boundary from the + # "imageBBox" property. The property contains a [minx, miny, maxx, maxy] bounding box. If available we + # can construct a Polygon boundary from those 4 corners. + if self.IMAGE_BBOX in feature.properties: + bbox = feature.properties[self.IMAGE_BBOX] + return shapely.geometry.box(minx=bbox[0], miny=bbox[1], maxx=bbox[2], maxy=bbox[3]) + + # !!!!! ALL PROPERTIES BELOW THIS LINE ARE DEPRECATED !!!!! + if self.allow_deprecated: + # The current convention for the "geom_imcoords" allows a single external ring for a Polygon boundary to be + # captured as a list of coordinates. + if self.GEOM_IMCOORDS in feature.properties: + return shapely.geometry.Polygon(shell=feature.properties[self.GEOM_IMCOORDS]) + + # Some inputs may have a "detection" property with child "type" and "pixelCoordinates" properties. If these + # are found we can construct the appropriate shape. + if self.DETECTION in feature.properties and self.PIXEL_COORDINATES in feature.properties[self.DETECTION]: + temp_geom = { + self.TYPE: feature.properties[self.DETECTION][self.TYPE], + self.COORDINATES: feature.properties[self.DETECTION][self.PIXEL_COORDINATES], + } + return shapely.geometry.shape(temp_geom) + + # The current convention for "bounds_imcoords" is a [minx, miny, maxx, maxy] bounding box. If available we + # can construct a Polygon boundary from those 4 corners. + if self.BOUNDS_IMCORDS in feature.properties: + bbox = feature.properties[self.BOUNDS_IMCORDS] + return shapely.geometry.box(minx=bbox[0], miny=bbox[1], maxx=bbox[2], maxy=bbox[3]) + + # All properties that might contain the image geometry are missing. This feature does not have image + # coordinates. + return None + + def update_existing_image_geometries(self, feature: geojson.Feature, geometry: shapely.Geometry) -> None: + """ + This function searches through the properties of a GeoJSON feature that are known to contain the geometry + of the feature in image coordinates. If found each property is overwritten with information from the + geometry provided. Note that for bounding box properties the bounds of the input geometry are used. + + :param feature: a GeoJSON feature that might contain an image geometry property + :param geometry: the geometry to set property values for. + """ + if self.IMAGE_GEOMETRY in feature.properties: + ImagedFeaturePropertyAccessor.set_image_geometry(feature, geometry) + + if self.IMAGE_BBOX in feature.properties: + ImagedFeaturePropertyAccessor.set_image_bbox(feature, geometry) + + # !!!!! ALL PROPERTIES BELOW THIS LINE ARE DEPRECATED !!!!! + if self.allow_deprecated: + if self.GEOM_IMCOORDS in feature.properties: + coordinates = shapely.geometry.mapping(geometry)[self.COORDINATES] + if isinstance(geometry, shapely.geometry.Polygon): + feature.properties[self.GEOM_IMCOORDS] = coordinates[0] + else: + feature.properties[self.GEOM_IMCOORDS] = coordinates + + if self.DETECTION in feature.properties and self.PIXEL_COORDINATES in feature.properties[self.DETECTION]: + geometry_mapping = shapely.geometry.mapping(geometry) + feature.properties[self.DETECTION][self.TYPE] = geometry_mapping[self.TYPE] + feature.properties[self.DETECTION][self.PIXEL_COORDINATES] = geometry_mapping[self.COORDINATES] + + if self.BOUNDS_IMCORDS in feature.properties: + feature.properties[self.BOUNDS_IMCORDS] = list(geometry.bounds) + + @classmethod + def set_image_geometry(cls, feature: geojson.Feature, geometry: shapely.Geometry) -> None: + """ + Add or set the "imageGeometry" property for a feature. This is a 2D geometry that supports a variety of + types (points, lines, polygons, etc.) + + :param feature: a GeoJSON feature that will contain the property + :param geometry: the geometry value + """ + feature.properties[cls.IMAGE_GEOMETRY] = json.loads(shapely.to_geojson(geometry)) + + @classmethod + def set_image_bbox(cls, feature: geojson.Feature, geometry: shapely.Geometry) -> None: + """ + Add or set the "imageBBox" property for a feature. this is a [minx, miny, maxx, maxy] bounds for this object. + + :param feature: a GeoJSON feature that will contain the property + :param geometry: the geometry value + """ + feature.properties[cls.IMAGE_BBOX] = list(geometry.bounds) diff --git a/test/aws/osml/features/test_feature_index.py b/test/aws/osml/features/test_feature_index.py new file mode 100644 index 0000000..301b35e --- /dev/null +++ b/test/aws/osml/features/test_feature_index.py @@ -0,0 +1,29 @@ +import unittest + +import geojson +import shapely + + +class TestFeatureIndex(unittest.TestCase): + def setUp(self): + from aws.osml.features import STRFeature2DSpatialIndex + + test_features = [] + for r in range(0, 30, 10): + for c in range(0, 30, 10): + test_features.append(geojson.Feature(geometry=None, properties={"imageBBox": [c, r, c + 5, r + 5]})) + test_fc = geojson.FeatureCollection(features=test_features) + + self.index = STRFeature2DSpatialIndex(test_fc, use_image_geometries=True) + + def test_find_partial_intersects(self): + results = self.index.find_intersects(shapely.box(-1, -1, 11, 11)) + assert len(list(results)) == 4 + + def test_find_contains(self): + results = self.index.find_intersects(shapely.box(-1, -1, 31, 31)) + assert len(list(results)) == 9 + + def test_find_nearest(self): + results = self.index.find_nearest(shapely.Point(1, 1), max_distance=5) + assert len(list(results)) == 1 diff --git a/test/aws/osml/features/test_imaged_feature_property_accessor.py b/test/aws/osml/features/test_imaged_feature_property_accessor.py new file mode 100644 index 0000000..46d9a47 --- /dev/null +++ b/test/aws/osml/features/test_imaged_feature_property_accessor.py @@ -0,0 +1,145 @@ +import unittest + +import geojson +import shapely + + +class TestImagedFeaturePropertiesAccessor(unittest.TestCase): + def test_find_imagegeometry_point(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + point_feature = geojson.Feature( + geometry=geojson.Point((-1.0, 2.0)), + properties={ + ImagedFeaturePropertyAccessor.IMAGE_GEOMETRY: { + ImagedFeaturePropertyAccessor.TYPE: "Point", + ImagedFeaturePropertyAccessor.COORDINATES: [5.1, 10.2], + } + }, + ) + + image_geometry = accessor.find_image_geometry(point_feature) + + assert image_geometry == shapely.Point(5.1, 10.2) + + def test_find_imagegeometry_polygon(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + polygon_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), + properties={ + ImagedFeaturePropertyAccessor.IMAGE_GEOMETRY: { + ImagedFeaturePropertyAccessor.TYPE: "Polygon", + ImagedFeaturePropertyAccessor.COORDINATES: [ + [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]] + ], + } + }, + ) + + image_geometry = accessor.find_image_geometry(polygon_feature) + + assert image_geometry == shapely.Polygon(shell=[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]) + + def test_find_imagebbox(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + bbox_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), properties={ImagedFeaturePropertyAccessor.IMAGE_BBOX: [0.0, 0.0, 1.0, 1.0]} + ) + + image_geometry = accessor.find_image_geometry(bbox_feature) + + assert image_geometry == shapely.Polygon(shell=[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]) + + def test_find_bounds_imcoords(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + bbox_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), + properties={ImagedFeaturePropertyAccessor.BOUNDS_IMCORDS: [0.0, 0.0, 1.0, 1.0]}, + ) + + image_geometry = accessor.find_image_geometry(bbox_feature) + + assert image_geometry == shapely.Polygon(shell=[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]) + + def test_find_geom_imcoords(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + polygon_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), + properties={ + ImagedFeaturePropertyAccessor.GEOM_IMCOORDS: [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]] + }, + ) + + image_geometry = accessor.find_image_geometry(polygon_feature) + + assert image_geometry == shapely.Polygon(shell=[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]) + + def test_find_detection(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + detection_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), + properties={ + ImagedFeaturePropertyAccessor.DETECTION: { + ImagedFeaturePropertyAccessor.TYPE: "Polygon", + ImagedFeaturePropertyAccessor.PIXEL_COORDINATES: [ + [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]] + ], + } + }, + ) + + image_geometry = accessor.find_image_geometry(detection_feature) + + assert image_geometry == shapely.Polygon(shell=[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]) + + def test_update_all(self): + from aws.osml.features import ImagedFeaturePropertyAccessor + + accessor = ImagedFeaturePropertyAccessor() + + image_feature = geojson.Feature( + geometry=geojson.Point((0.0, 0.0)), + properties={ + ImagedFeaturePropertyAccessor.DETECTION: { + ImagedFeaturePropertyAccessor.TYPE: "Polygon", + ImagedFeaturePropertyAccessor.PIXEL_COORDINATES: [ + [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]] + ], + }, + ImagedFeaturePropertyAccessor.GEOM_IMCOORDS: [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]], + ImagedFeaturePropertyAccessor.BOUNDS_IMCORDS: [0.0, 0.0, 1.0, 1.0], + ImagedFeaturePropertyAccessor.IMAGE_BBOX: [0.0, 0.0, 1.0, 1.0], + ImagedFeaturePropertyAccessor.IMAGE_GEOMETRY: { + ImagedFeaturePropertyAccessor.TYPE: "Polygon", + ImagedFeaturePropertyAccessor.COORDINATES: [ + [[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]] + ], + }, + }, + ) + + accessor.update_existing_image_geometries(image_feature, shapely.box(3.0, 4.0, 5.0, 6.0)) + + assert image_feature.properties[ImagedFeaturePropertyAccessor.DETECTION][ + ImagedFeaturePropertyAccessor.PIXEL_COORDINATES + ] != [[[1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0], [1.0, 0.0]]] + + assert image_feature.properties[ImagedFeaturePropertyAccessor.IMAGE_BBOX] == [3.0, 4.0, 5.0, 6.0] + assert image_feature.properties[ImagedFeaturePropertyAccessor.BOUNDS_IMCORDS] == [3.0, 4.0, 5.0, 6.0] From 500542d0ae90104910831f1f9c855d2ffd0ec2a4 Mon Sep 17 00:00:00 2001 From: edparris Date: Fri, 24 Nov 2023 14:01:37 -0500 Subject: [PATCH 16/20] feat: adds generic DEM tile set and geodetic coordinate formatting --- src/aws/osml/photogrammetry/__init__.py | 2 + src/aws/osml/photogrammetry/coordinates.py | 148 ++++++++++++++---- .../photogrammetry/generic_dem_tile_set.py | 53 +++++++ .../osml/photogrammetry/test_coordinates.py | 29 ++++ .../test_generic_dem_tile_set.py | 16 ++ 5 files changed, 218 insertions(+), 30 deletions(-) create mode 100644 src/aws/osml/photogrammetry/generic_dem_tile_set.py create mode 100644 test/aws/osml/photogrammetry/test_generic_dem_tile_set.py diff --git a/src/aws/osml/photogrammetry/__init__.py b/src/aws/osml/photogrammetry/__init__.py index 2fc0fac..247347b 100644 --- a/src/aws/osml/photogrammetry/__init__.py +++ b/src/aws/osml/photogrammetry/__init__.py @@ -18,6 +18,7 @@ from .digital_elevation_model import DigitalElevationModel, DigitalElevationModelTileFactory, DigitalElevationModelTileSet from .elevation_model import ConstantElevationModel, ElevationModel, ElevationRegionSummary from .gdal_sensor_model import GDALAffineSensorModel +from .generic_dem_tile_set import GenericDEMTileSet from .projective_sensor_model import ProjectiveSensorModel from .replacement_sensor_model import ( RSMContext, @@ -59,6 +60,7 @@ "ElevationModel", "ElevationRegionSummary", "GDALAffineSensorModel", + "GenericDEMTileSet", "SARImageCoordConverter", "INCAProjectionSet", "PlaneProjectionSet", diff --git a/src/aws/osml/photogrammetry/coordinates.py b/src/aws/osml/photogrammetry/coordinates.py index 059c25b..30fd187 100644 --- a/src/aws/osml/photogrammetry/coordinates.py +++ b/src/aws/osml/photogrammetry/coordinates.py @@ -6,14 +6,9 @@ class WorldCoordinate: """ - A world coordinate is a vector representing a position in 3D space. The ground coordinate system specified is - either Geodetic (latitude, longitude, and height above the WGS 84 reference ellipsoid), or Rectangular (cartesian - coordinates in reference to a local tangent plane). Regardless whether the coordinate system is specified as - Geodetic or Rectangular, associated ground point locations are represented as a triple – x, y, and z. - - A Rectangular system should be specified when the image footprint is near the earth’s North or South Pole. Either - a Rectangular or Geodetic system can be specified when the footprint is near 180 degrees East longitude. However, - if Geodetic, the range for longitude is then specified as (0,2pi) radians instead of the usual (-pi, +pi) radians. + A world coordinate is a vector representing a position in 3D space. Note that this type is a simple value with + 3 components that does not distinguish between geodetic or other cartesian coordinate systems (e.g. geocentric + Earth-Centered Earth-Fixed or coordinates based on a local tangent plane). """ def __init__(self, coordinate: npt.ArrayLike = None) -> None: @@ -57,11 +52,50 @@ def z(self) -> float: def z(self, value: float) -> None: self.coordinate[2] = value + def __repr__(self): + return f"WorldCoordinate(coordinate={np.array_repr(self.coordinate)})" + class GeodeticWorldCoordinate(WorldCoordinate): """ A GeodeticWorldCoordinate is an WorldCoordinate where the x,y,z components can be interpreted as longitude, - latitude, and elevation. + latitude, and elevation. It is important to note that longitude, and latitude are in radians while elevation + is meters above the ellipsoid. + + This class uses a custom format specification for a geodetic coordinate uses % directives similar to datetime. + These custom directives can be combined as needed with literal values to produce a wide + range of output formats. For example, '%ld%lm%ls%lH%od%om%os%oh' will produce a ddmmssXdddmmssY formatted + coordinate. The first half, ddmmssX, represents degrees, minutes, and seconds of latitude with X representing + North or South (N for North, S for South). The second half, dddmmssY, represents degrees, minutes, and seconds + of longitude with Y representing East or West (E for East, W for West), respectively. + + + ========= ================================================ ===== + Directive Meaning Notes + ========= ================================================ ===== + %L latitude in decimal radians 1 + %l latitude in decimal degrees 1 + %ld latitute degrees 2 + %lm latitude minutes + %ls latitude seconds + %lh latitude hemisphere (n or s) + %lH latitude hemisphere uppercase (N or S) + %O longitude in decimal radians 1 + %o longitude in decimal degrees 1 + %od longitude degrees 2 + %om longitude minutes + %os longitude seconds + %oh longitude hemisphere (e or w) + %oH longitude hemisphere uppercase (E or W) + %E elevation in meters + %% used to represent a literal % in the output + ========= ================================================ ===== + + Notes: + + #. Formatting in decimal degrees or radians will be signed values + #. Formatting for the degrees, minutes, seconds will always be unsigned assuming hemisphere will be included + #. Any unknown directives will be ignored """ def __init__(self, coordinate: npt.ArrayLike = None) -> None: @@ -109,34 +143,85 @@ def to_dms_string(self) -> str: :return: the formatted coordinate string """ - result_parts = [] + return f"{self:%ld%lm%ls%lH%od%om%os%oH}" + + def __repr__(self): + return f"GeodeticWorldCoordinate(coordinate={np.array_repr(self.coordinate)})" + + def __format__(self, format_spec: str) -> str: + if format_spec is None or format_spec == "": + format_spec = "%ld%lm%ls%lH %od%om%os%oH %E" + lat_degrees = np.degrees(self.latitude) - direction = "N" + lh = "N" if lat_degrees < 0: lat_degrees *= -1.0 - direction = "S" - d = int(lat_degrees) - m = int(round(lat_degrees - d, 6) * 60) - s = int(round(lat_degrees - d - m / 60, 6) * 3600) - result_parts.append(format(d, "02d")) - result_parts.append(format(m, "02d")) - result_parts.append(format(s, "02d")) - result_parts.append(direction) + lh = "S" + ld = int(lat_degrees) + lm = int(round(lat_degrees - ld, 6) * 60) + ls = int(round(lat_degrees - ld - lm / 60, 6) * 3600) lon_degrees = np.degrees(self.longitude) - direction = "E" + oh = "E" if lon_degrees < 0: lon_degrees *= -1.0 - direction = "W" - d = int(lon_degrees) - m = int(round(lon_degrees - d, 6) * 60) - s = int(round(lon_degrees - d - m / 60, 6) * 3600) - result_parts.append(format(d, "03d")) - result_parts.append(format(m, "02d")) - result_parts.append(format(s, "02d")) - result_parts.append(direction) - - return "".join(result_parts) + oh = "W" + od = int(lon_degrees) + om = int(round(lon_degrees - od, 6) * 60) + os = int(round(lon_degrees - od - om / 60, 6) * 3600) + + result = [] + i = 0 + while i < len(format_spec): + if format_spec[i] == "%" and (i + 1) < len(format_spec): + i += 1 + directive = format_spec[i] + if directive == "L": + result.append(str(self.latitude)) + elif directive == "O": + result.append(str(self.longitude)) + elif directive == "l": + if (i + 1) < len(format_spec) and format_spec[i + 1] in ["d", "m", "s", "h", "H"]: + i += 1 + part = format_spec[i] + if part == "d": + result.append(format(ld, "02d")) + elif part == "m": + result.append(format(lm, "02d")) + elif part == "s": + result.append(format(ls, "02d")) + elif part == "h": + result.append(lh.lower()) + else: + # part must equal 'H' + result.append(lh) + else: + result.append(str(lat_degrees)) + elif directive == "o": + if (i + 1) < len(format_spec) and format_spec[i + 1] in ["d", "m", "s", "h", "H"]: + i += 1 + part = format_spec[i] + if part == "d": + result.append(format(od, "03d")) + elif part == "m": + result.append(format(om, "02d")) + elif part == "s": + result.append(format(os, "02d")) + elif part == "h": + result.append(oh.lower()) + else: + # part must equal 'H' + result.append(oh) + else: + result.append(str(lon_degrees)) + elif directive == "E": + result.append(str(self.elevation)) + elif directive == "%": + result.append("%") + else: + result.append(format_spec[i]) + i += 1 + return "".join(result) # These are common definitions of projections used by Pyproj. They are used when converting between an Earth Centered @@ -248,3 +333,6 @@ def y(self) -> float: @y.setter def y(self, value: float) -> None: self.r = value + + def __repr__(self): + return f"ImageCoordinate(coordinate={np.array_repr(self.coordinate)})" diff --git a/src/aws/osml/photogrammetry/generic_dem_tile_set.py b/src/aws/osml/photogrammetry/generic_dem_tile_set.py new file mode 100644 index 0000000..8ad78c9 --- /dev/null +++ b/src/aws/osml/photogrammetry/generic_dem_tile_set.py @@ -0,0 +1,53 @@ +from math import degrees, floor +from typing import Optional + +from .coordinates import GeodeticWorldCoordinate +from .digital_elevation_model import DigitalElevationModelTileSet + + +class GenericDEMTileSet(DigitalElevationModelTileSet): + """ + A generalizable tile set with a naming convention that can be described as a format string. + """ + + def __init__(self, format_spec: str = "%od%oh/%ld%lh.dt2", + min_latitude_degrees: float = -90.0, + max_latitude_degrees: float = 90.0, + min_longitude_degrees: float = -180.0, + max_longitude_degrees: float = 180.0) -> None: + """ + Construct a tile set from a limited collection of configurable parameters. This implementation uses the + custom formatting directives supplied with GeodeticWorldCoordinate to allow users to create tile IDs + that match a variety of situations. For example the default format_spec of '%od%oh/%ld%lh.dt2' will + generate tile ids like: '115e/45s.dt2' which would match some common 1-degree cell based DEM file + hierarchies. + + :param format_spec: the format specification for the GeodeteticWorldCoordinate + + + :return: None + """ + super().__init__() + self.format_string = format_spec + self.min_latitude_degrees = min_latitude_degrees + self.max_latitude_degrees = max_latitude_degrees + self.min_longitude_degrees = min_longitude_degrees + self.max_longitude_degrees = max_longitude_degrees + + def find_tile_id(self, geodetic_world_coordinate: GeodeticWorldCoordinate) -> Optional[str]: + """ + This method creates tile IDs that based on the format string provided. + + :param geodetic_world_coordinate: the world coordinate of interest + + :return: the tile path or None if the DEM does not have coverage for this location + """ + longitude_degrees = floor(degrees(geodetic_world_coordinate.longitude)) + latitude_degrees = floor(degrees(geodetic_world_coordinate.latitude)) + + # The SRTM mission only covers latitudes N59 through S56 so if the requested location is outside those + # ranges we know there is no file available for it. + if latitude_degrees > self.max_latitude_degrees or latitude_degrees < self.min_latitude_degrees or longitude_degrees > self.max_longitude_degrees or longitude_degrees < self.min_longitude_degrees: + return None + + return f"{geodetic_world_coordinate:{self.format_string}}" diff --git a/test/aws/osml/photogrammetry/test_coordinates.py b/test/aws/osml/photogrammetry/test_coordinates.py index 68ce93a..9ac358d 100644 --- a/test/aws/osml/photogrammetry/test_coordinates.py +++ b/test/aws/osml/photogrammetry/test_coordinates.py @@ -14,6 +14,12 @@ def test_worldcoordinate_list_constructor(self): assert world_coordinate.z == 3.0 assert world_coordinate.coordinate.shape == (3,) # 1D numpy array + def test_worldcoordinate_repr(self): + from aws.osml.photogrammetry.coordinates import WorldCoordinate + + world_coordinate = WorldCoordinate([1.0, 2.0, 3.0]) + assert f"{world_coordinate!r}" == "WorldCoordinate(coordinate=array([1., 2., 3.]))" + def test_imagecoordinate_list_constructor(self): from aws.osml.photogrammetry.coordinates import ImageCoordinate @@ -38,6 +44,12 @@ def test_imagecoordinate_bad_values(self): image_coordinate = ImageCoordinate([1.0, 2.0, 3.0]) # noqa: F841 assert "must have 2 components" in str(value_error.value) + def test_imagecoordinate_repr(self): + from aws.osml.photogrammetry.coordinates import ImageCoordinate + + image_coordinate = ImageCoordinate([-10.2, 5.0]) + assert f"{image_coordinate!r}" == "ImageCoordinate(coordinate=array([-10.2, 5. ]))" + def test_geodeticworldcoordinate_list_constructor(self): from aws.osml.photogrammetry.coordinates import GeodeticWorldCoordinate @@ -115,6 +127,23 @@ def test_geodeticworldcoordinate_to_dms_string(self): geodetic_coordinate = GeodeticWorldCoordinate([radians(1.5125), radians(1.5125), 10.0]) assert geodetic_coordinate.to_dms_string() == "013045N0013045E" + def test_geodeticworldcoordinate_format(self): + from aws.osml.photogrammetry.coordinates import GeodeticWorldCoordinate + + geodetic_coordinate = GeodeticWorldCoordinate([radians(115.25), radians(-45.5), 3.0]) + assert f"{geodetic_coordinate:%ld%lm%ls%lH %od%om%os%oH %E}" == "453000S 1151500E 3.0" + assert f"{geodetic_coordinate:%ld %lm %ls %lh %od %om %os %oh %E}" == "45 30 00 s 115 15 00 e 3.0" + assert f"{geodetic_coordinate:%l %o %E}" == "45.5 115.25 3.0" + assert f"{geodetic_coordinate:%L %O %E}" == "-0.7941248096574199 2.011491962923465 3.0" + assert f"{geodetic_coordinate}" == "453000S 1151500E 3.0" + assert f"{geodetic_coordinate:100%% unexpected usage: %X}" == "100% unexpected usage: " + + def test_geodeticworldcoordinate_repr(self): + from aws.osml.photogrammetry.coordinates import GeodeticWorldCoordinate + + geodetic_coordinate = GeodeticWorldCoordinate([1.1, 2.2, 3.3]) + assert f"{geodetic_coordinate!r}" == "GeodeticWorldCoordinate(coordinate=array([1.1, 2.2, 3.3]))" + def test_ecef_to_geodetic(self): from aws.osml.photogrammetry.coordinates import WorldCoordinate, geocentric_to_geodetic diff --git a/test/aws/osml/photogrammetry/test_generic_dem_tile_set.py b/test/aws/osml/photogrammetry/test_generic_dem_tile_set.py new file mode 100644 index 0000000..a6e4ed7 --- /dev/null +++ b/test/aws/osml/photogrammetry/test_generic_dem_tile_set.py @@ -0,0 +1,16 @@ +import unittest +from math import radians + + +class TestGenericDEMTileSet(unittest.TestCase): + def test_default_format_spec(self): + from aws.osml.photogrammetry.coordinates import GeodeticWorldCoordinate + from aws.osml.photogrammetry.generic_dem_tile_set import GenericDEMTileSet + + tile_set = GenericDEMTileSet() + tile_path = tile_set.find_tile_id(GeodeticWorldCoordinate([radians(142), radians(3), 0.0])) + assert "142e/03n.dt2" == tile_path + + +if __name__ == "__main__": + unittest.main() From db3e9dcbc59198e3f91c2dd0ee29aa75abb1d0ed Mon Sep 17 00:00:00 2001 From: edparris Date: Fri, 24 Nov 2023 14:24:59 -0500 Subject: [PATCH 17/20] fix: lint error in generic_dem_tile_set.py --- .../photogrammetry/generic_dem_tile_set.py | 20 +++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/src/aws/osml/photogrammetry/generic_dem_tile_set.py b/src/aws/osml/photogrammetry/generic_dem_tile_set.py index 8ad78c9..f8eafc7 100644 --- a/src/aws/osml/photogrammetry/generic_dem_tile_set.py +++ b/src/aws/osml/photogrammetry/generic_dem_tile_set.py @@ -10,11 +10,14 @@ class GenericDEMTileSet(DigitalElevationModelTileSet): A generalizable tile set with a naming convention that can be described as a format string. """ - def __init__(self, format_spec: str = "%od%oh/%ld%lh.dt2", - min_latitude_degrees: float = -90.0, - max_latitude_degrees: float = 90.0, - min_longitude_degrees: float = -180.0, - max_longitude_degrees: float = 180.0) -> None: + def __init__( + self, + format_spec: str = "%od%oh/%ld%lh.dt2", + min_latitude_degrees: float = -90.0, + max_latitude_degrees: float = 90.0, + min_longitude_degrees: float = -180.0, + max_longitude_degrees: float = 180.0, + ) -> None: """ Construct a tile set from a limited collection of configurable parameters. This implementation uses the custom formatting directives supplied with GeodeticWorldCoordinate to allow users to create tile IDs @@ -47,7 +50,12 @@ def find_tile_id(self, geodetic_world_coordinate: GeodeticWorldCoordinate) -> Op # The SRTM mission only covers latitudes N59 through S56 so if the requested location is outside those # ranges we know there is no file available for it. - if latitude_degrees > self.max_latitude_degrees or latitude_degrees < self.min_latitude_degrees or longitude_degrees > self.max_longitude_degrees or longitude_degrees < self.min_longitude_degrees: + if ( + latitude_degrees > self.max_latitude_degrees + or latitude_degrees < self.min_latitude_degrees + or longitude_degrees > self.max_longitude_degrees + or longitude_degrees < self.min_longitude_degrees + ): return None return f"{geodetic_world_coordinate:{self.format_string}}" From 38d9be80d145501d28a01604176dac55d7b372c4 Mon Sep 17 00:00:00 2001 From: edparris Date: Thu, 7 Dec 2023 10:25:50 -0500 Subject: [PATCH 18/20] feat: add ability to create scaled tiles and tiles from SIDD --- .../image_processing/gdal_tile_factory.py | 43 ++-- src/aws/osml/image_processing/sicd_updater.py | 86 +++----- src/aws/osml/image_processing/sidd_updater.py | 191 ++++++++++++++++++ .../test_gdal_tile_factory.py | 45 +++++ test/data/sidd/umbra-sidd200-chip1.ntf | 3 + 5 files changed, 289 insertions(+), 79 deletions(-) create mode 100644 src/aws/osml/image_processing/sidd_updater.py create mode 100644 test/data/sidd/umbra-sidd200-chip1.ntf diff --git a/src/aws/osml/image_processing/gdal_tile_factory.py b/src/aws/osml/image_processing/gdal_tile_factory.py index bd1beab..a95902e 100644 --- a/src/aws/osml/image_processing/gdal_tile_factory.py +++ b/src/aws/osml/image_processing/gdal_tile_factory.py @@ -1,15 +1,15 @@ import base64 import logging from secrets import token_hex -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Tuple -from defusedxml import ElementTree from osgeo import gdal, gdalconst from aws.osml.gdal import GDALCompressionOptions, GDALImageFormats, NITFDESAccessor, RangeAdjustmentType, get_type_and_scales from aws.osml.photogrammetry import ImageCoordinate, SensorModel from .sicd_updater import SICDUpdater +from .sidd_updater import SIDDUpdater logger = logging.getLogger(__name__) @@ -44,8 +44,8 @@ def __init__( self.raster_dataset = raster_dataset self.sensor_model = sensor_model self.des_accessor = None - self.sicd_updater = None - self.sicd_des_header = None + self.sar_updater = None + self.sar_des_header = None self.range_adjustment = range_adjustment self.output_type = output_type @@ -56,22 +56,27 @@ def __init__( xml_data_content_segments = self.des_accessor.get_segments_by_name("XML_DATA_CONTENT") if xml_data_content_segments is not None and len(xml_data_content_segments) > 0: # This appears to be SICD or SIDD data - # TODO: Check to make sure this is the right XML_DATA_CONTENT segment containing SICD - sicd_des = xml_data_content_segments[0] - sicd_bytes = self.des_accessor.parse_field_value(sicd_des, "DESDATA", base64.b64decode) - sicd_xml = sicd_bytes.decode("utf-8") - sicd_metadata = ElementTree.fromstring(sicd_xml) - self.sicd_des_header = self.des_accessor.extract_des_header(sicd_des) - self.sicd_updater = SICDUpdater(sicd_metadata) + xml_data_segment = xml_data_content_segments[0] + xml_bytes = self.des_accessor.parse_field_value(xml_data_segment, "DESDATA", base64.b64decode) + xml_str = xml_bytes.decode("utf-8") + if "SIDD" in xml_str: + self.sar_des_header = self.des_accessor.extract_des_header(xml_data_segment) + self.sar_updater = SIDDUpdater(xml_str) + elif "SICD" in xml_str: + self.sar_des_header = self.des_accessor.extract_des_header(xml_data_segment) + self.sar_updater = SICDUpdater(xml_str) self.default_gdal_translate_kwargs = self._create_gdal_translate_kwargs() - def create_encoded_tile(self, src_window: List[int]) -> Optional[bytearray]: + def create_encoded_tile( + self, src_window: List[int], output_size: Optional[Tuple[int, int]] = None + ) -> Optional[bytearray]: """ This method cuts a tile from the full image, updates the metadata as needed, and finally compresses/encodes the result in the output format requested. :param src_window: the [left_x, top_y, width, height] bounds of this tile + :param output_size: an optional size of the output tile (width, height) :return: the encoded image tile or None if one could not be produced """ temp_ds_name = f"/vsimem/{token_hex(16)}.{self.tile_format}" @@ -81,23 +86,27 @@ def create_encoded_tile(self, src_window: List[int]) -> Optional[bytearray]: # create image tiles using the format, compression, etc. requested by the client. gdal_translate_kwargs = self.default_gdal_translate_kwargs.copy() + if output_size is not None: + gdal_translate_kwargs["width"] = output_size[0] + gdal_translate_kwargs["height"] = output_size[1] + # Create a new IGEOLO value based on the corner points of this tile if self.sensor_model is not None and self.tile_format == GDALImageFormats.NITF: gdal_translate_kwargs["creationOptions"].append("ICORDS=G") gdal_translate_kwargs["creationOptions"].append("IGEOLO=" + self.create_new_igeolo(src_window)) - if self.sicd_updater is not None and self.tile_format == GDALImageFormats.NITF: - # If we're outputting a SICD tile we need to update the XML metadata to include the new chip + if self.sar_updater is not None and self.tile_format == GDALImageFormats.NITF: + # If we're outputting a SICD or SIDD tile we need to update the XML metadata to include the new chip # origin and size. This will allow applications using the tile to correctly interpret the remaining # image metadata. - self.sicd_updater.update_image_data_for_chip(src_window) - updated_sicd_des = self.sicd_des_header + self.sicd_updater.encode_current_xml() + self.sar_updater.update_image_data_for_chip(src_window, output_size) + updated_sar_des = self.sar_des_header + self.sar_updater.encode_current_xml() gdal_translate_kwargs["creationOptions"].append("ICAT=SAR") gdal_translate_kwargs["creationOptions"].append("IREP=NODISPLY") gdal_translate_kwargs["creationOptions"].append("IREPBAND= , ") gdal_translate_kwargs["creationOptions"].append("ISUBCAT=I,Q") - gdal_translate_kwargs["creationOptions"].append("DES=XML_DATA_CONTENT=" + updated_sicd_des) + gdal_translate_kwargs["creationOptions"].append("DES=XML_DATA_CONTENT=" + updated_sar_des) # Use GDAL to create an encoded tile of the image region # From GDAL documentation: diff --git a/src/aws/osml/image_processing/sicd_updater.py b/src/aws/osml/image_processing/sicd_updater.py index e91d1ec..bc8eeb8 100644 --- a/src/aws/osml/image_processing/sicd_updater.py +++ b/src/aws/osml/image_processing/sicd_updater.py @@ -1,45 +1,36 @@ import logging -import re -from typing import Callable, List, TypeVar -from xml.etree import ElementTree as ET +from math import floor +from typing import List, Optional, Tuple -from defusedxml import ElementTree +from xsdata.formats.dataclass.parsers import XmlParser +from xsdata.formats.dataclass.serializers import XmlSerializer +from xsdata.formats.dataclass.serializers.config import SerializerConfig logger = logging.getLogger(__name__) -# This is a type placeholder needed by the parse_element_text() type hints -T = TypeVar("T") - class SICDUpdater: """ This class provides a means to perform common updates to a SICD XML metadata document. """ - def __init__(self, sicd_element: ET.Element): + def __init__(self, xml_str: str): """ Construct a new instance of this class to manage a given set of SICD metadata. - :param sicd_element: the SICD XML metadata to update + :param xml_str: the SICD XML metadata to update """ - self.sicd_element = sicd_element - - # Extract the XML namespace from the root SICD element and store it for later use in element queries - namespace_match = re.match(r"{.*}", self.sicd_element.tag) - self.namespace = namespace_match.group(0) if namespace_match else "" - - # We don't currently have many examples of SICD data. An attempt has been made to make this code - # work so long as the portions of the XML schema we depend upon don't change. This warning is just - # an attempt to provide diagnostic information incase future datasets don't work. - if self.namespace != "{urn:SICD:1.2.1}": - logger.warning(f"Attempting to process SICD metadata with an untested namespace {self.namespace}") + self.xml_str = xml_str + if self.xml_str is not None and len(self.xml_str) > 0: + parser = XmlParser() + self.sicd = parser.from_string(self.xml_str) # Here we're storing off the original first row/col to support the case where multiple chips are # created from a SICD image that has already been chipped. - self.original_first_row = self.parse_element_text(".//{0}FirstRow".format(self.namespace), int) - self.original_first_col = self.parse_element_text(".//{0}FirstCol".format(self.namespace), int) + self.original_first_row = self.sicd.image_data.first_row + self.original_first_col = self.sicd.image_data.first_col - def update_image_data_for_chip(self, chip_bounds: List[int]) -> None: + def update_image_data_for_chip(self, chip_bounds: List[int], output_size: Optional[Tuple[int, int]]) -> None: """ This updates the SICD ImageData structure so that the FirstRow, FirstCol and NumRows, NumCols elements match the new chip boundary.A sample of this XML structure is shown below:: @@ -57,30 +48,16 @@ def update_image_data_for_chip(self, chip_bounds: List[int]) -> None: :param chip_bounds: the [col, row, width, height] of the chip boundary + :param output_size: the [width, height] of the output chip """ - first_col_element = self.sicd_element.find(".//{0}FirstCol".format(self.namespace)) - first_row_element = self.sicd_element.find(".//{0}FirstRow".format(self.namespace)) - num_cols_element = self.sicd_element.find(".//{0}NumCols".format(self.namespace)) - num_rows_element = self.sicd_element.find(".//{0}NumRows".format(self.namespace)) - if first_row_element is None or first_col_element is None or num_cols_element is None or num_rows_element is None: - logger.warning("SICD ImageData structures were not found. No updates applied.") - return - first_col_element.text = str(self.original_first_col + chip_bounds[0]) - first_row_element.text = str(self.original_first_row + chip_bounds[1]) - num_cols_element.text = str(chip_bounds[2]) - num_rows_element.text = str(chip_bounds[3]) + if output_size is not None and (output_size[0] != chip_bounds[2] or output_size[1] != chip_bounds[3]): + raise ValueError("SICD chipping does not support scaling operations.") - if logger.isEnabledFor(logging.DEBUG): - image_data_element = self.sicd_element.find(".//{0}ImageData".format(self.namespace)) - if image_data_element is not None: - logger.debug("Updated SICD ImageData element for chip:") - logger.debug( - ElementTree.tostring( - image_data_element, - encoding="unicode", - ) - ) + self.sicd.image_data.first_row = floor(float(self.original_first_row)) + int(chip_bounds[1]) + self.sicd.image_data.first_col = floor(float(self.original_first_col)) + int(chip_bounds[0]) + self.sicd.image_data.num_rows = int(chip_bounds[3]) + self.sicd.image_data.num_cols = int(chip_bounds[2]) def encode_current_xml(self) -> str: """ @@ -88,21 +65,6 @@ def encode_current_xml(self) -> str: :return: xml encoded SICD metadata """ - return ElementTree.tostring(self.sicd_element, encoding="unicode") - - def parse_element_text(self, element_xpath: str, type_conversion: Callable[[str], T]) -> T: - """ - This function finds the first element matching the provided xPath and then runs the text of that element - through the provided conversion function. - - :param element_xpath: the xPath of the element - :param type_conversion: the desired type of the output, must support construction from a string - :return: the element text converted to the requested type - """ - field_element = self.sicd_element.find(element_xpath) - if field_element is None: - raise ValueError(f"Unable to find element {element_xpath}") - str_value = field_element.text - if str_value is None: - raise ValueError(f"Element {element_xpath} does not have text.") - return type_conversion(str_value) + serializer = XmlSerializer(config=SerializerConfig(pretty_print=False)) + updated_xml = serializer.render(self.sicd) + return updated_xml diff --git a/src/aws/osml/image_processing/sidd_updater.py b/src/aws/osml/image_processing/sidd_updater.py new file mode 100644 index 0000000..c6ac56d --- /dev/null +++ b/src/aws/osml/image_processing/sidd_updater.py @@ -0,0 +1,191 @@ +import logging +from typing import List, Optional, Tuple + +from xsdata.formats.dataclass.parsers import XmlParser +from xsdata.formats.dataclass.serializers import XmlSerializer +from xsdata.formats.dataclass.serializers.config import SerializerConfig + +import aws.osml.formats.sidd.models.sidd_v1_0_0 as sidd100 +import aws.osml.formats.sidd.models.sidd_v2_0_0 as sidd200 +import aws.osml.formats.sidd.models.sidd_v3_0_0 as sidd300 + +logger = logging.getLogger(__name__) + + +class SIDDUpdater: + def __init__(self, xml_str: str): + """ + Construct a new instance of this class to manage a given set of SIDD metadata. + + :param xml_str: the SIDD XML metadata to update + """ + self.xml_str = xml_str + if self.xml_str is not None and len(self.xml_str) > 0: + parser = XmlParser() + self.sidd = parser.from_string(self.xml_str) + + def update_image_data_for_chip(self, chip_bounds: List[int], output_size: Optional[Tuple[int, int]]) -> None: + """ + This adds or updates the SIDD GeometricChip structure so that the ChipSize and original corner coordinates + are recorded. A sample of this XML structure is shown below: + + + + 512 + 512 + + + 7408 + 7407 + + + 7408 + 7919 + + + 7920 + 7407 + + + 7920 + 7919 + + + + + :param chip_bounds: the [col, row, width, height] of the chip boundary + :param output_size: the [width, height] of the output chip if different from the chip boundary + """ + if not output_size: + output_size = chip_bounds[2], chip_bounds[3] + + # The xsdata code generators produced different types for each version of the SIDD specification. + # in this case the types are all equivalent so the logic isn't different but this piece of code + # ensures we're constructing the correct type from the right version of SIDD constructs. + if isinstance(self.sidd, sidd100.SIDD): + sidd_namespace = sidd100 + elif isinstance(self.sidd, sidd200.SIDD): + sidd_namespace = sidd200 + elif isinstance(self.sidd, sidd300.SIDD): + sidd_namespace = sidd300 + else: + logger.warning("sidd_updater.py has not been updated to support a new SIDD version. Defaulting to 3.0") + sidd_namespace = sidd300 + + # The DownstreamReprocessing element is optional so if it is not set create it first. + if not self.sidd.downstream_reprocessing: + self.sidd.downstream_reprocessing = sidd_namespace.DownstreamReprocessingType() + + # Identify the location of the UL, UR, LR, LL corners of this chip in the full image. If the image is already + # a chip of a full image these coordinates need to be updated, so they are still the positions of the new chip + # in the original full image. + full_image_chip_corners = [ + (chip_bounds[0], chip_bounds[1]), + (chip_bounds[0] + chip_bounds[2] - 1, chip_bounds[1]), + (chip_bounds[0] + chip_bounds[2] - 1, chip_bounds[1] + chip_bounds[3] - 1), + (chip_bounds[0], chip_bounds[1] + chip_bounds[3] - 1), + ] + if self.sidd.downstream_reprocessing.geometric_chip: + original_chip_size = ( + self.sidd.downstream_reprocessing.geometric_chip.chip_size.col, + self.sidd.downstream_reprocessing.geometric_chip.chip_size.row, + ) + original_corners = [ + ( + self.sidd.downstream_reprocessing.geometric_chip.original_upper_left_coordinate.col, + self.sidd.downstream_reprocessing.geometric_chip.original_upper_left_coordinate.row, + ), + ( + self.sidd.downstream_reprocessing.geometric_chip.original_upper_right_coordinate.col, + self.sidd.downstream_reprocessing.geometric_chip.original_upper_right_coordinate.row, + ), + ( + self.sidd.downstream_reprocessing.geometric_chip.original_lower_right_coordinate.col, + self.sidd.downstream_reprocessing.geometric_chip.original_lower_right_coordinate.row, + ), + ( + self.sidd.downstream_reprocessing.geometric_chip.original_lower_left_coordinate.col, + self.sidd.downstream_reprocessing.geometric_chip.original_lower_left_coordinate.row, + ), + ] + + full_image_chip_corners = [ + SIDDUpdater.chipped_coordinate_to_full(corner, original_chip_size, original_corners) + for corner in full_image_chip_corners + ] + + # Create the new DownstreamReprocessing.GeometricChip element that contains the information needed to + # relate this chip to the original full image. + self.sidd.downstream_reprocessing.geometric_chip = sidd_namespace.GeometricChipType( + chip_size=sidd_namespace.RowColIntType(row=output_size[1], col=output_size[0]), + original_upper_left_coordinate=sidd_namespace.RowColDoubleType( + row=full_image_chip_corners[0][1], col=full_image_chip_corners[0][0] + ), + original_upper_right_coordinate=sidd_namespace.RowColDoubleType( + row=full_image_chip_corners[1][1], col=full_image_chip_corners[1][0] + ), + original_lower_left_coordinate=sidd_namespace.RowColDoubleType( + row=full_image_chip_corners[3][1], col=full_image_chip_corners[3][0] + ), + original_lower_right_coordinate=sidd_namespace.RowColDoubleType( + row=full_image_chip_corners[2][1], col=full_image_chip_corners[2][0] + ), + ) + + def encode_current_xml(self) -> str: + """ + Returns a copy of the current SIDD metadata encoded in XML. + + :return: xml encoded SIDD metadata + """ + serializer = XmlSerializer(config=SerializerConfig(pretty_print=False)) + updated_xml = serializer.render(self.sidd) + return updated_xml + + @staticmethod + def chipped_coordinate_to_full( + chip_coordinate: Tuple[float, float], + chip_size: Tuple[int, int], + original_corner_coordinates: List[Tuple[float, float]], + ) -> Tuple[float, float]: + """ + This function converts pixel locations in a chip to the pixel locations in a full image using a bi-linear + interpolation method described in section 5.1.1 of the Sensor Independent Derived Data (SIDD) specification + v3.0 Volume 1. + + :param chip_coordinate: the [x, y] coordinate of the pixel in the chip + :param chip_size: the size of the chip [width, height] + :param original_corner_coordinates: the [x, y] location of the UL, UR, LR, LL corners in the original image + :return: the [x, y] coordinate of the pixel in the original image + """ + # Step 1: Normalize the chip coordinates + u = chip_coordinate[1] / (chip_size[1] - 1) + v = chip_coordinate[0] / (chip_size[0] - 1) + + # Step 2: Compute original full image row coordinate bi-linear coefficients + a_r = original_corner_coordinates[0][1] + b_r = original_corner_coordinates[3][1] - original_corner_coordinates[0][1] + d_r = original_corner_coordinates[1][1] - original_corner_coordinates[0][1] + f_r = ( + original_corner_coordinates[0][1] + + original_corner_coordinates[2][1] + - original_corner_coordinates[1][1] + - original_corner_coordinates[3][1] + ) + + # Step 3: Compute original full image column coordinate bi-linear coefficients + a_c = original_corner_coordinates[0][0] + b_c = original_corner_coordinates[3][0] - original_corner_coordinates[0][0] + d_c = original_corner_coordinates[1][0] - original_corner_coordinates[0][0] + f_c = ( + original_corner_coordinates[0][0] + + original_corner_coordinates[2][0] + - original_corner_coordinates[1][0] + - original_corner_coordinates[3][0] + ) + + # Step 4: Compute the full image row and column coordinate + r = a_r + u * b_r + v * d_r + u * v * f_r + c = a_c + u * b_c + v * d_c + u * v * f_c + + return c, r diff --git a/test/aws/osml/image_processing/test_gdal_tile_factory.py b/test/aws/osml/image_processing/test_gdal_tile_factory.py index f6e3fc7..c5c0aed 100644 --- a/test/aws/osml/image_processing/test_gdal_tile_factory.py +++ b/test/aws/osml/image_processing/test_gdal_tile_factory.py @@ -35,6 +35,32 @@ def test_create_encoded_sicd_tile_png(self): assert tile_dataset.RasterYSize == 256 assert tile_dataset.GetDriver().ShortName == GDALImageFormats.PNG + def test_create_sicd_chip_from_chip(self): + full_dataset, sensor_model = load_gdal_dataset("./test/data/sicd/capella-sicd121-chip1.ntf") + + tile_factory = GDALTileFactory(full_dataset, sensor_model, GDALImageFormats.NITF, GDALCompressionOptions.NONE) + encoded_tile_data = tile_factory.create_encoded_tile([10, 10, 128, 256]) + + temp_ds_name = "/vsimem/" + token_hex(16) + ".NITF" + gdal.FileFromMemBuffer(temp_ds_name, encoded_tile_data) + tile_dataset = gdal.Open(temp_ds_name) + assert tile_dataset.RasterXSize == 128 + assert tile_dataset.RasterYSize == 256 + assert tile_dataset.GetDriver().ShortName == GDALImageFormats.NITF + + def test_create_sidd_chip_from_chip(self): + full_dataset, sensor_model = load_gdal_dataset("./test/data/sidd/umbra-sidd200-chip1.ntf") + + tile_factory = GDALTileFactory(full_dataset, sensor_model, GDALImageFormats.NITF, GDALCompressionOptions.NONE) + encoded_tile_data = tile_factory.create_encoded_tile([10, 10, 128, 256]) + + temp_ds_name = "/vsimem/" + token_hex(16) + ".NITF" + gdal.FileFromMemBuffer(temp_ds_name, encoded_tile_data) + tile_dataset = gdal.Open(temp_ds_name) + assert tile_dataset.RasterXSize == 128 + assert tile_dataset.RasterYSize == 256 + assert tile_dataset.GetDriver().ShortName == GDALImageFormats.NITF + def test_create_png_with_dra(self): full_dataset, sensor_model = load_gdal_dataset("./test/data/small.ntf") tile_factory = GDALTileFactory( @@ -60,6 +86,25 @@ def test_create_png_with_dra(self): assert tile_dataset.GetRasterBand(1).GetMinimum() == 0 assert tile_dataset.GetRasterBand(1).GetMaximum() == 185 + def test_create_png_with_output_size(self): + full_dataset, sensor_model = load_gdal_dataset("./test/data/small.ntf") + tile_factory = GDALTileFactory( + full_dataset, + sensor_model, + GDALImageFormats.PNG, + GDALCompressionOptions.NONE, + output_type=gdalconst.GDT_Byte, + range_adjustment=RangeAdjustmentType.DRA, + ) + + encoded_tile_data = tile_factory.create_encoded_tile([0, 0, 256, 512], output_size=(128, 256)) + temp_ds_name = "/vsimem/" + token_hex(16) + ".PNG" + gdal.FileFromMemBuffer(temp_ds_name, encoded_tile_data) + tile_dataset = gdal.Open(temp_ds_name) + assert tile_dataset.RasterXSize == 128 + assert tile_dataset.RasterYSize == 256 + assert tile_dataset.GetDriver().ShortName == GDALImageFormats.PNG + # Test data here could be improved. We're reusing a nitf file for everything and just # testing a single raster scale def test_create_gdal_translate_kwargs(self): diff --git a/test/data/sidd/umbra-sidd200-chip1.ntf b/test/data/sidd/umbra-sidd200-chip1.ntf new file mode 100644 index 0000000..44d9564 --- /dev/null +++ b/test/data/sidd/umbra-sidd200-chip1.ntf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00d6e8ed7ea4348778ae68d2768b37c11a4f528a1ceb8deb188c7129fa3f5531 +size 283610 From 37c6d4a584c607ae49f0a4ddd99f71686d84c201 Mon Sep 17 00:00:00 2001 From: edparris Date: Mon, 11 Dec 2023 11:29:47 -0500 Subject: [PATCH 19/20] docs: add examples to package level documentation --- README.md | 111 +++--------------- doc/conf.py | 2 +- doc/images/Photogrammetry-OODiagram.png | Bin 0 -> 249316 bytes doc/images/SAR-HistogramStretchImage.png | Bin 0 -> 563121 bytes doc/images/SAR-QuarterPowerImage.png | Bin 0 -> 834110 bytes doc/index.rst | 8 +- src/aws/osml/features/__init__.py | 4 + src/aws/osml/gdal/__init__.py | 54 +++++++++ src/aws/osml/image_processing/__init__.py | 87 +++++++++++++- src/aws/osml/photogrammetry/__init__.py | 127 ++++++++++++++++++--- src/aws/osml/photogrammetry/coordinates.py | 2 +- 11 files changed, 277 insertions(+), 118 deletions(-) create mode 100644 doc/images/Photogrammetry-OODiagram.png create mode 100644 doc/images/SAR-HistogramStretchImage.png create mode 100644 doc/images/SAR-QuarterPowerImage.png diff --git a/README.md b/README.md index 5f159aa..f46c9bf 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,24 @@ The OversightML Imagery Toolkit is a Python package that contains image processing and photogrammetry routines commonly used during the analysis of imagery collected by satellites and unmanned aerial vehicles (UAVs). It builds upon GDAL -by providing additional support for images compliant with the National Imagery Transmission Format (NITF) and Sensor -Independent Complex Data (SICD) standards. +by providing additional support for images compliant with the National Imagery Transmission Format (NITF), Sensor +Independent Complex Data (SICD), and Sensor Independent Derived Data (SIDD) standards. +This library contains four core packages under the `aws.osml` namespace: +* **photogrammetry**: convert locations between the image (x, y) and geodetic (lon, lat, elev) coordinate systems +* **gdal**: utilities to work with datasets loaded by GDAL +* **image_processing**: common image manipulation routines +* **features**: common geospatial feature manipulation routines + +## Documentation + +* **APIs**: You can find API documentation for the OSML Imagery Toolkit hosted on our [GitHub project page](https://aws-solutions-library-samples.github.io/osml-imagery-toolkit/). +If you are working from the source code running `tox -e docs` will trigger the Sphinx documentation build. +* **Example Notebooks**: Example notebooks for some operations are in the `examples` directory ## Installation -The intent is to vend / distribute this software through a Python Package Index. -If your environment has a distribution, -you should be able to install it using pip: +This software is available through a Python Package Index. +If your environment has a distribution, you should be able to install it using pip: ```shell pip install osml-imagery-toolkit[gdal] ``` @@ -23,97 +33,6 @@ Note that GDAL is listed as an extra dependency for this package. This is done t don't want to use GDAL or those that have their own custom installation steps for that library. Future versions of this package will include image IO backbones that have fewer dependencies. - -## Documentation - -You can find documentation for this library in the `./doc` directory. Sphinx is used to construct a searchable HTML -version of the API documents. - -```shell -tox -e docs -``` - -## Example Usage - -This library contains four core packages under the `aws.osml` namespace. -* photogrammetry: convert locations between the image (x, y) and geodetic (lon, lat, elev) coordinate systems -* gdal: help load and manage datasets loaded by GDAL -* image_processing: common image manipulation routines -* formats: utilities for handling format specific information; normally not accessed directly - -```python -from aws.osml.gdal import GDALImageFormats, GDALCompressionOptions, load_gdal_dataset -from aws.osml.image_processing import GDALTileFactory -from aws.osml.photogrammetry import ImageCoordinate, GeodeticWorldCoordinate, SensorModel -``` - -### Tiling with Updated Image Metadata - -Many applications break large remote sensing images into smaller chips or tiles for distributed processing or -dissemination. GDAL's Translate function provides basic capabilities, but it does not correctly update geospatial -metadata to reflect the new image extent. These utilities provide those functions so tile consumers can correctly -interpret the pixel information they have been provided. For NITF imagery that includes the addition of a new ICHIPB -TRE. With SICD the XML ImageData elements are adjusted to identify the sub-image bounds. - -```python -# Load the image and create a sensor model -ds, sensor_model = load_gdal_dataset("./imagery/sample.nitf") -tile_factory = GDALTileFactory(ds, - sensor_model, - GDALImageFormats.NITF, - GDALCompressionOptions.NONE - ) - -# Bounds are [left_x, top_y, width, height] -nitf_encoded_tile_bytes = tile_factory.create_encoded_tile([0, 0, 1024, 1024]) -``` - -### Tiling for Display - -Some images, for example 11-bit panchromatic images or SAR imagery with floating point complex data, can not be -displayed directly without remapping the pixels into an 8-bit per pixel grayscale or RGB color model. The TileFactory -supports creation of tiles suitable for human review by setting both the output_type and range_adjustment options. - -```python -viz_tile_factory = GDALTileFactory(ds, - sensor_model, - GDALImageFormats.PNG, - GDALCompressionOptions.NONE, - output_type=gdalconst.GDT_Byte, - range_adjustment=RangeAdjustmentType.DRA) - -viz_tile = viz_tile_factory.create_encoded_tile([0, 0, 1024, 1024]) -``` - -### More Precise Sensor Models - -OversightML provides implementations of the Replacement Sensor Model (RSM), Rational Polynomial -Camera (RPC), and Sensor Independent Complex Data (SICD) sensor models to assist in geo positioning. -When loading a dataset, the toolkit will construct the most accurate sensor model -from the available image metadata. That sensor model can be used in conjunction with an optional -elevation model to convert between image and geodetic coordinates. - -```python -ds, sensor_model = load_gdal_dataset("./imagery/sample.nitf") -elevation_model = DigitalElevationModel( - SRTMTileSet(version="1arc_v3"), - GDALDigitalElevationModelTileFactory("./local-SRTM-tiles")) - -# Note the order of ImageCoordinate is (x, y) -geodetic_location_of_ul_corner = sensor_model.image_to_world( - ImageCoordinate([0, 0]), - elevation_model=elevation_model) - -lon_degrees = -77.404453 -lat_degrees = 38.954831 -meters_above_ellipsoid = 100.0 - -image_location = sensor_model.world_to_image( - GeodeticWorldCoordinate([radians(lon_degrees), - radians(lat_degrees), - meters_above_ellipsoid])) -``` - ## Contributing This project welcomes contributions and suggestions. If you would like to submit a pull request, see our diff --git a/doc/conf.py b/doc/conf.py index 2377b78..5544ef4 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -46,7 +46,7 @@ def setup(app): # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = "OversightML Imagery Core" +project = "OversightML Imagery Toolkit" copyright = "{}, Amazon.com".format(datetime.datetime.now().year) author = "Amazon Web Services" diff --git a/doc/images/Photogrammetry-OODiagram.png b/doc/images/Photogrammetry-OODiagram.png new file mode 100644 index 0000000000000000000000000000000000000000..27ed237fc2b1c8d2982f88cd5cd5dafdbe3fa5f8 GIT binary patch literal 249316 zcmeFZbyQT_`#-KIA|PPUNT?TShLCQH4ngS@>27IIhDRE!%%$bW$ zXUoaGh&QkoiRy<4peGJB#GyW!L&VL`H4t%07rO^NT@p&%s?9Uk!F-}KcbV@vT zdW}VazE4M%WCQrXww6${J9CDk4*l;e{WJB^GiOB4NZuEF{QN9@94k`m!SI<$Nsy?- z-K!WMd?vD_uJ}B7{X#0@HZ8s4$0&I*k2a}CcaEcK6;Rd 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--git a/doc/index.rst b/doc/index.rst index b6c5157..371ea9c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,11 +1,11 @@ -aws-osml-imagery-core +osml-imagery-toolkit ===================== -The OversightML Imagery Core is a Python library that contains image processing and photogrammetry routines commonly +The OversightML Imagery Toolkit is a Python package that contains image processing and photogrammetry routines commonly used during the analysis of imagery collected by satellites and unmanned aerial vehicles (UAVs). It builds upon GDAL -by providing additional support for images compliant with the Sensor Independent Complex Data (SICD) and National -Imagery Transmission Format (NITF) standards. +by providing additional support for images compliant with the National Imagery Transmission Format (NITF), Sensor +Independent Complex Data (SICD), and Sensor Independent Derived Data (SIDD) standards. .. toctree:: :maxdepth: 1 diff --git a/src/aws/osml/features/__init__.py b/src/aws/osml/features/__init__.py index 5167245..7e04349 100644 --- a/src/aws/osml/features/__init__.py +++ b/src/aws/osml/features/__init__.py @@ -1,6 +1,10 @@ from .feature_index import Feature2DSpatialIndex, STRFeature2DSpatialIndex from .imaged_feature_property_accessor import ImagedFeaturePropertyAccessor +""" +The features package contains classes that assist with working with geospatial features derived from imagery. +""" + __all__ = [ "Feature2DSpatialIndex", "ImagedFeaturePropertyAccessor", diff --git a/src/aws/osml/gdal/__init__.py b/src/aws/osml/gdal/__init__.py index a5c7e08..19b5b64 100644 --- a/src/aws/osml/gdal/__init__.py +++ b/src/aws/osml/gdal/__init__.py @@ -3,6 +3,60 @@ # flake8: noqa """ The gdal package contains utilities that assist with loading imagery and metadata using the OSGeo GDAL library. + +Loading Imagery and Sensor Models with OSML +******************************************* + +OSML provides utilities to load a dataset and automatically construct an appropriate sensor model from metadata +available in the image. Metadata handled by GDAL (e.g. GeoTIFF tags or NITF segment metadata and TREs) is available +through the dataset accessors. + +.. code-block:: python + :caption: Example of loading a dataset and sensor model using OSML + + from aws.osml.gdal import load_gdal_dataset + + # Load the image and create a sensor model + dataset, sensor_model = load_gdal_dataset("./imagery/sample.nitf") + width = dataset.RasterXSize + height = dataset.RasterYSize + + print(f"Loaded image with dimensions: ({height}, {width}) (rows, cols)") + print(f"Using Sensor Model Implementation: {type(sensor_model).__name__}") + print(dataset.GetMetadata()) + + +Access to NITF Data Extension Segments +************************************** + +SICD and SIDD imagery contains additional metadata in a XML Data Extension Segment that is not currently processed +by GDAL. This information can be accessed with the help of the NITFDESAccessor. + +.. code-block:: python + :caption: Example of loading a dataset and sensor model using OSML + + import base64 + import xml.dom.minidom + from aws.osml.gdal import load_gdal_dataset, NITFDESAccessor + + dataset, sensor_model = load_gdal_dataset("./sample-sicd.nitf") + + des_accessor = NITFDESAccessor(dataset.GetMetadata("xml:DES")) + xml_data_content_segments = des_accessor.get_segments_by_name("XML_DATA_CONTENT") + if xml_data_content_segments is not None: + for xml_data_segment in xml_data_content_segments: + xml_bytes = des_accessor.parse_field_value(xml_data_segment, "DESDATA", base64.b64decode) + xml_str = xml_bytes.decode("utf-8") + if "SICD" in xml_str: + temp = xml.dom.minidom.parseString(xml_str) + new_xml = temp.toprettyxml() + print(new_xml) + break + +------------------------- + +APIs +**** """ from .gdal_config import GDALConfigEnv, set_gdal_default_configuration diff --git a/src/aws/osml/image_processing/__init__.py b/src/aws/osml/image_processing/__init__.py index fbb8d08..fc4be9d 100644 --- a/src/aws/osml/image_processing/__init__.py +++ b/src/aws/osml/image_processing/__init__.py @@ -3,9 +3,94 @@ # flake8: noqa """ The image_processing package contains various utilities for manipulating overhead imagery. + +Image Tiling: Tiling With Updated Image Metadata +************************************************ + +Many applications break large remote sensing images into smaller chips or tiles for distributed processing or +dissemination. GDAL's Translate function provides basic capabilities, but it does not correctly update geospatial +metadata to reflect the new image extent. These utilities provide those functions so tile consumers can correctly +interpret the pixel information they have been provided. + +.. code-block:: python + :caption: Example showing creation of a NITF tile from the upper left corner of an image + + # Load the image and create a sensor model + ds, sensor_model = load_gdal_dataset("./imagery/sample.nitf") + tile_factory = GDALTileFactory(ds, + sensor_model, + GDALImageFormats.NITF, + GDALCompressionOptions.NONE + ) + + # Bounds are [left_x, top_y, width, height] + nitf_encoded_tile_bytes = tile_factory.create_encoded_tile([0, 0, 1024, 1024]) + + +Image Tiling: Tiles for Display +******************************* + +Some images, for example 11-bit panchromatic images or SAR imagery with floating point complex data, can not be +displayed directly without remapping the pixels into an 8-bit per pixel grayscale or RGB color model. The TileFactory +supports creation of tiles suitable for human review by setting both the output_type and range_adjustment options. +Note that the output_size parameter can be used to generate lower resolution tiles. This operation will make use of +GDAL generated overviews if they are available to the dataset. + +.. code-block:: python + :caption: Example showing creation of a PNG tile scaled down from the full resolution image + + viz_tile_factory = GDALTileFactory(ds, + sensor_model, + GDALImageFormats.PNG, + GDALCompressionOptions.NONE, + output_type=gdalconst.GDT_Byte, + range_adjustment=RangeAdjustmentType.DRA) + + viz_tile = viz_tile_factory.create_encoded_tile([0, 0, 1024, 1024], output_size=(512, 512)) + + +Complex SAR Data Display +************************ + +There are a variety of different techniques to convert complex SAR data to a simple image suitable for human display. +The toolkit contains two helper functions that can convert complex image data into an 8-bit grayscle representation +The equations implemented are described in Sections 3.1 and 3.2 of SAR Image Scaling, Dynamic Range, Radiometric +Calibration, and Display (SAND2019-2371). + +.. code-block:: python + :caption: Example converting complex SAR data into a 8-bit per pixel image for display + + import numpy as np + from aws.osml.image_processing import histogram_stretch, quarter_power_image + + sicd_dataset, sensor_model = load_gdal_dataset("./sample-sicd.nitf") + complex_pixels = sicd_dataset.ReadAsArray() + + histo_stretch_pixels = histogram_stretch(complex_pixels) + quarter_power_pixels = quarter_power_image(complex_pixels) + + +.. figure:: ../images/SAR-HistogramStretchImage.png + :width: 400 + :alt: Histogram Stretch Applied to Sample SICD Image + + Example of applying histogram_stretch to a sample SICD image. + + +.. figure:: ../images/SAR-QuarterPowerImage.png + :width: 400 + :alt: Quarter Power Image Applied to Sample SICD Image + + Example of applying quarter_power_image to a sample SICD image. + + +------------------------- + +APIs +**** """ from .gdal_tile_factory import GDALTileFactory from .sar_complex_imageop import histogram_stretch, quarter_power_image -__all__ = ["GDALTileFactory"] +__all__ = ["GDALTileFactory", "histogram_stretch", "quarter_power_image"] diff --git a/src/aws/osml/photogrammetry/__init__.py b/src/aws/osml/photogrammetry/__init__.py index 247347b..b3191bd 100644 --- a/src/aws/osml/photogrammetry/__init__.py +++ b/src/aws/osml/photogrammetry/__init__.py @@ -2,8 +2,105 @@ # __init__.py file. # flake8: noqa """ -The photogrammetry package contains implementations of various sensor and elevation models used to convert between -the image (x, y) and geodetic (lon, lat, elev) coordinate systems. +Many users need to estimate the geographic position of an object found in a georeferenced image. The osml-imagery-toolkit +provides open source implementations of the image-to-world and world-to-image equations for some common replacement +sensor models. These sensor models work with many georeferenced imagery types and do not require orthorectification of +the image. In the current implementation support is provided for: + +* **Rational Polynomials**: Models based on rational polynomials specified using RSM and RPC metadata found in NITF TREs +* **SAR Sensor Independent Models**: Models as defined by the SICD and SIDD standards with metadata found in the NITF XML data segment. +* **Perspective and Affine Projections**: Simple matrix based projections that can be computed from geolocations of the 4 image corners or `tags found in GeoTIFF images `_. + +*Note that the current implementation does not support the RSM Grid based sensor models or the adjustable parameter +options. These features will be added in a future release.* + +.. figure:: ../images/Photogrammetry-OODiagram.png + :width: 400 + :alt: Photogrammetry Class Diagram + + Class diagram of the aws.osml.photogrammetry package showing public and private classes. + +Geolocating Image Pixels: Basic Examples +**************************************** + +Applications do not typically interact with a specific sensor model implementation directly. Instead, they let the +SensorModel abstraction encapsulate the details and rely on the image IO utilities to construct the appropriate +type given the available metadata. + +.. code-block:: python + :caption: Example showing calculation of an image location for a geodetic location + + dataset, sensor_model = load_gdal_dataset("./imagery/sample.nitf") + + lon_degrees = -77.404453 + lat_degrees = 38.954831 + meters_above_ellipsoid = 100.0 + + # Note the GeodeticWorldCoordinate is (longitude, latitude, elevation) with longitude and latitude in **radians** + # and elevation in meters above the WGS84 ellipsoid. The resulting ImageCoordinate is returned in (x, y) pixels. + image_location = sensor_model.world_to_image( + GeodeticWorldCoordinate([radians(lon_degrees), + radians(lat_degrees), + meters_above_ellipsoid])) + +.. code-block:: python + :caption: Example showing use of a SensorModel to geolocate 4 image corners + + dataset, sensor_model = load_gdal_dataset("./imagery/sample.nitf") + width = dataset.RasterXSize + height = dataset.RasterYSize + + image_corners = [[0, 0], [width, 0], [width, height], [0, height]] + geo_image_corners = [sensor_model.image_to_world(ImageCoordinate(corner)) + for corner in image_corners] + + # GeodeticWorldCoordinates have custom formatting defined that supports a variety of common output formats. + # The example shown below will produce a ddmmssXdddmmssY formatted coordinate (e.g. 295737N0314003E) + for geodetic_corner in geo_image_corners: + print(f"{geodetic_corner:%ld%lm%ls%lH%od%om%os%oH}") + +Geolocating Image Pixels: Addition of an External Elevation Model +***************************************************************** + +The image-to-world calculation can optionally use an external digital elevation model (DEM) when geolocating points +on an image. How the elevation model will be used varies by sensor model but examples include: + +* Using DEM elevations as a constraint during iterations of a rational polynomial camera's image-to-world calculation. +* Computing the intersection of a R/Rdot contour with a DEM for sensor independent SAR models. + +All of these approaches make the fundamental assumption that the pixel lies on the terrain surface. If a DEM is not +available we assume a constant surface with elevation provided in the image metadata. + +.. code-block:: python + :caption: Example showing use of an external SRTM DEM to provide elevation data for image center + + ds, sensor_model = load_gdal_dataset("./imagery/sample.nitf") + elevation_model = DigitalElevationModel( + SRTMTileSet(version="1arc_v3"), + GDALDigitalElevationModelTileFactory("./local-SRTM-tiles")) + + # Note the order of ImageCoordinate is (x, y) and the resulting geodetic coordinate is + # (longitude, latitude, elevation) with longitude and latitude in **radians** and elevation in meters. + geodetic_location_of_image_center = sensor_model.image_to_world( + ImageCoordinate([ds.RasterXSize/2, ds.RasterYSize/2]), + elevation_model=elevation_model) + + +External References +******************* + +* Manual of Photogrammetry: https://www.amazon.com/Manual-Photogrammetry-PhD-Chris-McGlone/dp/1570830991 +* NITF Compendium of Controlled Support Data Extensions: https://nsgreg.nga.mil/doc/view?i=5417 +* The Replacement Sensor Model (RSM): Overview, Status, and Performance Summary: https://citeseerx.ist.psu.edu/doc_view/pid/c25de8176fe790c28cf6e1aff98ecdea8c726c96 +* RPC Whitepaper: https://users.cecs.anu.edu.au/~hartley/Papers/cubic/cubic.pdf +* SICD Volume 3, Image Projections Description Document: https://nsgreg.nga.mil/doc/view?i=5383 +* WGS84 Standard: https://nsgreg.nga.mil/doc/view?i=4085 + +------------------------- + +APIs +**** + """ from .chipped_image_sensor_model import ChippedImageSensorModel @@ -48,27 +145,25 @@ __all__ = [ "ChippedImageSensorModel", "CompositeSensorModel", - "GeodeticWorldCoordinate", - "ImageCoordinate", - "WorldCoordinate", - "geocentric_to_geodetic", - "geodetic_to_geocentric", + "ConstantElevationModel", "DigitalElevationModel", "DigitalElevationModelTileFactory", "DigitalElevationModelTileSet", - "ConstantElevationModel", "ElevationModel", "ElevationRegionSummary", "GDALAffineSensorModel", "GenericDEMTileSet", - "SARImageCoordConverter", + "GeodeticWorldCoordinate", "INCAProjectionSet", - "PlaneProjectionSet", + "ImageCoordinate", "PFAProjectionSet", - "PolynomialXYZ", + "PlaneProjectionSet", "Polynomial2D", + "PolynomialXYZ", "ProjectiveSensorModel", "RGAZCOMPProjectionSet", + "RPCPolynomial", + "RPCSensorModel", "RSMContext", "RSMGroundDomain", "RSMGroundDomainForm", @@ -77,10 +172,12 @@ "RSMPolynomial", "RSMPolynomialSensorModel", "RSMSectionedPolynomialSensorModel", - "RPCPolynomial", - "RPCSensorModel", - "SensorModel", - "SensorModelOptions", + "SARImageCoordConverter", "SICDSensorModel", "SRTMTileSet", + "SensorModel", + "SensorModelOptions", + "WorldCoordinate", + "geocentric_to_geodetic", + "geodetic_to_geocentric", ] diff --git a/src/aws/osml/photogrammetry/coordinates.py b/src/aws/osml/photogrammetry/coordinates.py index 30fd187..d2718df 100644 --- a/src/aws/osml/photogrammetry/coordinates.py +++ b/src/aws/osml/photogrammetry/coordinates.py @@ -64,7 +64,7 @@ class GeodeticWorldCoordinate(WorldCoordinate): This class uses a custom format specification for a geodetic coordinate uses % directives similar to datetime. These custom directives can be combined as needed with literal values to produce a wide - range of output formats. For example, '%ld%lm%ls%lH%od%om%os%oh' will produce a ddmmssXdddmmssY formatted + range of output formats. For example, '%ld%lm%ls%lH%od%om%os%oH' will produce a ddmmssXdddmmssY formatted coordinate. The first half, ddmmssX, represents degrees, minutes, and seconds of latitude with X representing North or South (N for North, S for South). The second half, dddmmssY, represents degrees, minutes, and seconds of longitude with Y representing East or West (E for East, W for West), respectively. From 69a63b0969a2fe3cb22819db30a6855110eeba35 Mon Sep 17 00:00:00 2001 From: edparris Date: Mon, 11 Dec 2023 13:23:09 -0500 Subject: [PATCH 20/20] build: update library version to v1.2.0 --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index c399047..b5b1647 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = osml-imagery-toolkit -version = 1.1.2 +version = 1.2.0 description = Toolkit to work with imagery collected by satellites and UAVs long_description = file: README.md long_description_content_type = text/markdown

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