untested refactor to isolate noise loading from annotation

Author: LiangJYu

src/s1reader/s1_annotation.py

@@ -201,114 +201,6 @@ def from_et(cls, et_in: ET, path_annotation: str):
         return cls
 
 
-@dataclass
-class NoiseAnnotation(AnnotationBase):
-    '''
-    Reader for Noise Annotation Data Set (NADS) for IW SLC
-    Based on ESA documentation: "Thermal Denoising of Products Generated by the S-1 IPF"
-    '''
-
-    basename_annotation: str
-    rg_list_azimuth_time: np.ndarray
-    rg_list_line: list
-    rg_list_pixel: list
-    rg_list_noise_range_lut: list
-    az_first_azimuth_line: int
-    az_first_range_sample: int
-    az_last_azimuth_line: int
-    az_last_range_sample: int
-    az_line: np.ndarray
-    az_noise_azimuth_lut: np.ndarray
-
-    @classmethod
-    def from_et(cls,et_in: ET, ipf_version: version.Version, path_annotation: str):
-        '''
-        Extracts list of noise information from etree
-
-        Parameter
-        ----------
-        et_in : xml.etree.ElementTree
-            Parsed NADS annotation .xml
-
-        Return
-        -------
-        cls: NoiseAnnotation
-            Parsed NADS from et_in
-        '''
-
-        cls.xml_et = et_in
-        cls.basename_annotation = os.path.basename(path_annotation)
-
-        if ipf_version < min_ipf_version_az_noise_vector:  # legacy SAFE data
-            cls.rg_list_azimuth_time = \
-                cls._parse_vectorlist('noiseVectorList',
-                                      'azimuthTime',
-                                      'datetime')
-            cls.rg_list_line = \
-                cls._parse_vectorlist('noiseVectorList',
-                                      'line',
-                                      'scalar_int')
-            cls.rg_list_pixel = \
-                cls._parse_vectorlist('noiseVectorList',
-                                      'pixel',
-                                      'vector_int')
-            cls.rg_list_noise_range_lut = \
-                cls._parse_vectorlist('noiseVectorList',
-                                      'noiseLut',
-                                      'vector_float')
-
-            cls.az_first_azimuth_line = None
-            cls.az_first_range_sample = None
-            cls.az_last_azimuth_line = None
-            cls.az_last_range_sample = None
-            cls.az_line = None
-            cls.az_noise_azimuth_lut = None
-
-        else:
-            cls.rg_list_azimuth_time = \
-                cls._parse_vectorlist('noiseRangeVectorList',
-                                      'azimuthTime',
-                                      'datetime')
-            cls.rg_list_line = \
-                cls._parse_vectorlist('noiseRangeVectorList',
-                                      'line',
-                                      'scalar_int')
-            cls.rg_list_pixel = \
-                cls._parse_vectorlist('noiseRangeVectorList',
-                                      'pixel',
-                                      'vector_int')
-            cls.rg_list_noise_range_lut = \
-                cls._parse_vectorlist('noiseRangeVectorList',
-                                      'noiseRangeLut',
-                                      'vector_float')
-            cls.az_first_azimuth_line = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'firstAzimuthLine',
-                                      'scalar_int')[0]
-            cls.az_first_range_sample = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'firstRangeSample',
-                                      'scalar_int')[0]
-            cls.az_last_azimuth_line = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'lastAzimuthLine',
-                                      'scalar_int')[0]
-            cls.az_last_range_sample = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'lastRangeSample',
-                                      'scalar_int')[0]
-            cls.az_line = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'line',
-                                      'vector_int')[0]
-            cls.az_noise_azimuth_lut = \
-                cls._parse_vectorlist('noiseAzimuthVectorList',
-                                      'noiseAzimuthLut',
-                                      'vector_float')[0]
-
-        return cls
-
-
 @dataclass
 class ProductAnnotation(AnnotationBase):
     '''
@@ -557,135 +449,6 @@ def closest_block_to_azimuth_time(vector_azimuth_time: np.ndarray,
     return np.argmin(np.abs(vector_azimuth_time-azimuth_time_burst))
 
 
-@dataclass
-class BurstNoise:
-    '''Noise correction information for Sentinel-1 burst'''
-    basename_nads: str
-    range_azimith_time: datetime.datetime
-    range_line: float
-    range_pixel: np.ndarray
-    range_lut: np.ndarray
-
-    azimuth_first_azimuth_line: int
-    azimuth_first_range_sample: int
-    azimuth_last_azimuth_line: int
-    azimuth_last_range_sample: int
-    azimuth_line: np.ndarray
-    azimuth_lut: np.ndarray
-
-    line_from: int
-    line_to: int
-
-    @classmethod
-    def from_noise_annotation(cls, noise_annotation: NoiseAnnotation,
-                              azimuth_time: datetime.datetime,
-                              line_from: int,
-                              line_to: int,
-                              ipf_version: version.Version):
-        '''
-        Extracts the noise correction information for individual burst from NoiseAnnotation
-
-        Parameters
-        ----------
-        noise_annotation: NoiseAnnotation
-            Subswath-wide noise annotation information
-        azimuth_time : datetime.datetime
-            Azimuth time of the burst
-        line_from: int
-            First line of the burst in the subswath
-        line_to: int
-            Last line of the burst in the subswath
-        ipf_version: float
-            IPF version of the SAFE data
-
-        Returns
-        -------
-        cls: BurstNoise
-            Instance of BurstNoise initialized by the input parameters
-
-        '''
-
-        basename_nads = noise_annotation.basename_annotation
-        id_closest = closest_block_to_azimuth_time(noise_annotation.rg_list_azimuth_time,
-                                                   azimuth_time)
-
-        range_azimith_time = noise_annotation.rg_list_azimuth_time[id_closest]
-        range_line = noise_annotation.rg_list_line[id_closest]
-        range_pixel = noise_annotation.rg_list_pixel[id_closest]
-        range_lut = noise_annotation.rg_list_noise_range_lut[id_closest]
-
-        azimuth_first_azimuth_line = noise_annotation.az_first_azimuth_line
-        azimuth_first_range_sample = noise_annotation.az_first_range_sample
-        azimuth_last_azimuth_line = noise_annotation.az_last_azimuth_line
-        azimuth_last_range_sample = noise_annotation.az_last_range_sample
-
-        if ipf_version >= min_ipf_version_az_noise_vector:
-            # Azimuth noise LUT exists - crop to the extent of the burst
-            id_top = np.argmin(np.abs(noise_annotation.az_line-line_from))
-            id_bottom = np.argmin(np.abs(noise_annotation.az_line-line_to))
-
-            # put some margin when possible
-            if id_top > 0:
-                id_top -= 1
-            if id_bottom < len(noise_annotation.az_line)-1:
-                id_bottom += 1
-            azimuth_line = noise_annotation.az_line[id_top:id_bottom + 1]
-            azimuth_lut = noise_annotation.az_noise_azimuth_lut[id_top:id_bottom + 1]
-
-        else:
-            azimuth_line = None
-            azimuth_lut = None
-
-        return cls(basename_nads, range_azimith_time, range_line, range_pixel, range_lut,
-                   azimuth_first_azimuth_line, azimuth_first_range_sample,
-                   azimuth_last_azimuth_line, azimuth_last_range_sample,
-                   azimuth_line, azimuth_lut,
-                   line_from, line_to)
-
-
-    def compute_thermal_noise_lut(self, shape_lut):
-        '''
-        Calculate thermal noise LUT whose shape is `shape_lut`
-
-        Parameter:
-        ----------
-        shape_lut: tuple or list
-            Shape of the output LUT
-
-        Returns
-        -------
-        arr_lut_total: np.ndarray
-            2d array containing thermal noise correction look up table values
-        '''
-
-        nrows, ncols = shape_lut
-
-        # Interpolate the range noise vector
-        rg_lut_interp_obj = InterpolatedUnivariateSpline(self.range_pixel,
-                                                         self.range_lut,
-                                                         k=1)
-        if self.azimuth_last_range_sample is not None:
-            vec_rg = np.arange(self.azimuth_last_range_sample + 1)
-        else:
-            vec_rg = np.arange(ncols)
-        rg_lut_interpolated = rg_lut_interp_obj(vec_rg)
-
-        # Interpolate the azimuth noise vector
-        if (self.azimuth_line is None) or (self.azimuth_lut is None):
-            az_lut_interpolated = np.ones(nrows)
-        else:  # IPF >= 2.90
-            az_lut_interp_obj = InterpolatedUnivariateSpline(self.azimuth_line,
-                                                             self.azimuth_lut,
-                                                             k=1)
-            vec_az = np.arange(self.line_from, self.line_to + 1)
-            az_lut_interpolated = az_lut_interp_obj(vec_az)
-
-        arr_lut_total = np.matmul(az_lut_interpolated[..., np.newaxis],
-                                  rg_lut_interpolated[np.newaxis, ...])
-
-        return arr_lut_total
-
-
 @dataclass
 class BurstCalibration:
     '''Calibration information for Sentinel-1 IW SLC burst