Merge pull request #68 from gshiroma/changes_after_calval_point_relea…

…se_4

Changes after calval point release 4

src/rtc/core.py

@@ -461,7 +461,7 @@ def check_ancillary_inputs(check_ancillary_inputs_coverage,
             if flag_1_ok and flag_2_ok:
                 # print messages to the user
                 logger.info(f'    {coverage_logger_str}:'
-                            ' Full (with antimeridian crossing')
+                            ' Full (with antimeridian crossing)')
 
                 # update RTC-S1 product metadata
                 metadata_dict[coverage_metadata_str] = \

src/rtc/defaults/rtc_s1.yaml

@@ -77,6 +77,9 @@ runconfig:
             # Location from where the output product can be retrieved (URL or DOI)
             product_data_access:
 
+            # Location of the static layers product associated with this product (URL or DOI
+            static_layers_data_access:
+
             # Save RTC-S1 bursts
             save_bursts: True
 

src/rtc/defaults/rtc_s1_static.yaml

@@ -78,6 +78,9 @@ runconfig:
             # Location from where the output product can be retrieved (URL or DOI)
             product_data_access:
 
+            # Location of the static layers product associated with this product (URL or DOI
+            static_layers_data_access:
+
             # Save RTC-S1 bursts
             save_bursts: True
 

src/rtc/h5_prep.py

@@ -258,14 +258,20 @@ def create_hdf5_file(product_id, output_hdf5_file, orbit, burst, cfg,
 
     hdf5_obj = h5py.File(output_hdf5_file, 'w')
     hdf5_obj.attrs['Conventions'] = np.string_("CF-1.8")
-    hdf5_obj.attrs["contact"] = np.string_("operaops@jpl.nasa.gov")
+    hdf5_obj.attrs["contact"] = np.string_("operasds@jpl.nasa.gov")
     hdf5_obj.attrs["institution"] = np.string_("NASA JPL")
     hdf5_obj.attrs["project"] = np.string_("OPERA")
     hdf5_obj.attrs["reference_document"] = np.string_(
         "Product Specification Document for the OPERA Radiometric"
         " Terrain-Corrected SAR Backscatter from Sentinel-1,"
-        " JPL D-108758, Rev. Working Version 1, May 31, 2023")
-    hdf5_obj.attrs["title"] = np.string_("OPERA RTC-S1 Product")
+        " JPL D-108758, Rev. Working Version 1, Aug 31, 2023")
+
+    # product type
+    product_type = cfg.groups.primary_executable.product_type
+    if product_type == STATIC_LAYERS_PRODUCT_TYPE:
+        hdf5_obj.attrs["title"] = np.string_("OPERA RTC-S1-STATIC Product")
+    else:
+        hdf5_obj.attrs["title"] = np.string_("OPERA RTC-S1 Product")
 
     populate_metadata_group(product_id, hdf5_obj, burst, cfg,
                             processing_datetime, is_mosaic)
@@ -383,6 +389,12 @@ def get_metadata_dict(product_id: str,
     if not product_data_access:
         product_data_access = '(NOT PROVIDED)'
 
+    # static layers data access (URL or DOI)
+    static_layers_data_access = \
+        cfg_in.groups.product_group.static_layers_data_access
+    if not static_layers_data_access:
+        static_layers_data_access = '(NOT PROVIDED)'
+
     # platform ID
     if burst_in.platform_id == 'S1A':
         platform_id = 'Sentinel-1A'
@@ -488,6 +500,11 @@ def get_metadata_dict(product_id: str,
              ALL_PRODUCTS,
              'NASA JPL',
              'Institution that created this product'],
+        'identification/contactInformation':
+            ['contact_information',
+             ALL_PRODUCTS,
+             '[email protected]',
+             'Contact information for producer of this product'],
         'identification/productVersion':
             ['product_version',
              ALL_PRODUCTS,
@@ -583,6 +600,12 @@ def get_metadata_dict(product_id: str,
              product_data_access,
              'Location from where this product can be retrieved'
              ' (URL or DOI)'],
+        'identification/staticLayersDataAccess':
+            ['static_layers_data_access',
+             STANDARD_RTC_S1_ONLY,
+             static_layers_data_access,
+             'Location of the static layers product associated with this'
+             ' product (URL or DOI)'],
         'metadata/sourceData/numberOfAcquisitions':  # 1.6.4
             ['source_data_number_of_acquisitions',
              ALL_PRODUCTS,

src/rtc/rtc_s1_single_job.py

@@ -52,6 +52,8 @@
 BROWSE_IMAGE_MIN_PERCENTILE = 3
 BROWSE_IMAGE_MAX_PERCENTILE = 97
 
+FLAG_BROWSE_DUAL_POL_REPEAT_COPOL = True
+
 
 def populate_product_id(product_id, burst_in, processing_datetime,
                         product_version, pixel_spacing, product_type,
@@ -263,6 +265,37 @@ def compute_correction_lut(burst_in, dem_raster, scratch_path,
     return rg_lut, az_lut
 
 
+def _normalize_browse_image_band(band_image):
+    """Helper function to normalize a browse image band
+
+       Parameters
+       ----------
+       band_image : np.ndarray
+           Input image to be normalized
+
+       Returns
+       -------
+       band_image : np.ndarray
+           Normalized image
+
+    """
+    vmin = np.nanpercentile(band_image, BROWSE_IMAGE_MIN_PERCENTILE)
+    vmax = np.nanpercentile(band_image, BROWSE_IMAGE_MAX_PERCENTILE)
+    logger.info(f'        min ({BROWSE_IMAGE_MIN_PERCENTILE}% percentile):'
+                f' {vmin}')
+    logger.info(f'        max ({BROWSE_IMAGE_MAX_PERCENTILE}% percentile):'
+                f' {vmax}')
+
+    # gamma correction: 0.5
+    is_not_negative = band_image - vmin >= 0
+    is_negative = band_image - vmin < 0
+    band_image[is_not_negative] = \
+        np.sqrt((band_image[is_not_negative] - vmin) / (vmax - vmin))
+    band_image[is_negative] = 0
+    band_image = np.clip(band_image, 0, 1)
+    return band_image
+
+
 def save_browse_imagery(imagery_list, browse_image_filename,
                         pol_list, browse_image_height, browse_image_width,
                         temp_files_list, scratch_dir, logger):
@@ -353,37 +386,44 @@ def save_browse_imagery(imagery_list, browse_image_filename,
 
         gdal_band = gdal_ds.GetRasterBand(1)
         band_image = np.asarray(gdal_band.ReadAsArray(), dtype=np.float32)
+
+        band_list_index = expected_pol_order.index(pol)
+
+        if (n_images == 2 and not FLAG_BROWSE_DUAL_POL_REPEAT_COPOL and
+                band_list_index == 0):
+            co_pol_image = band_image.copy()
+        if (n_images == 2 and not FLAG_BROWSE_DUAL_POL_REPEAT_COPOL and
+                band_list_index == 1):
+            cross_pol_image = band_image.copy()
+
         is_valid = np.isfinite(band_image)
         if alpha_channel is None:
             alpha_channel = np.asarray(is_valid,
                                        dtype=np.float32)
-        vmin = np.nanpercentile(band_image, BROWSE_IMAGE_MIN_PERCENTILE)
-        vmax = np.nanpercentile(band_image, BROWSE_IMAGE_MAX_PERCENTILE)
-        logger.info(f'        min ({BROWSE_IMAGE_MIN_PERCENTILE}% percentile):'
-                    f' {vmin}')
-        logger.info(f'        max ({BROWSE_IMAGE_MAX_PERCENTILE}% percentile):'
-                    f' {vmax}')
-
-        # gamma correction: 0.5
-        is_not_negative = np.logical_and(is_valid, band_image - vmin >= 0)
-        is_negative = np.logical_and(is_valid, band_image - vmin < 0)
-        band_image[is_not_negative] = \
-            np.sqrt((band_image[is_not_negative] - vmin) / (vmax - vmin))
-        band_image[is_negative] = 0
-        band_image = np.clip(band_image, 0, 1)
-        band_list_index = expected_pol_order.index(pol)
-        band_list[band_list_index] = band_image
+
+        band_list[band_list_index] = \
+            _normalize_browse_image_band(band_image)
 
     if n_images == 1:
         image = np.dstack((band_list[0],
                            band_list[0],
                            band_list[0],
                            alpha_channel))
-    elif n_images == 2:
+    elif n_images == 2 and FLAG_BROWSE_DUAL_POL_REPEAT_COPOL:
         image = np.dstack((band_list[0],
                            band_list[1],
                            band_list[0],
                            alpha_channel))
+    elif n_images == 2:
+
+        logger.info(f'    pol ratio: {expected_pol_order[0]}/'
+                    f'{expected_pol_order[1]}')
+        blue_channel =  \
+            _normalize_browse_image_band(co_pol_image / cross_pol_image)
+        image = np.dstack((band_list[0],
+                           band_list[1],
+                           blue_channel,
+                           alpha_channel))
     else:
         image = np.dstack((band_list[0],
                            band_list[1],

src/rtc/schemas/rtc_s1.yaml

@@ -85,6 +85,9 @@ runconfig:
             # Location from where the output product can be retrieved (URL or DOI)
             product_data_access: str(required=False)
 
+            # Location of the static layers product associated with this product (URL or DOI
+            static_layers_data_access: str(required=False)
+
             # Save RTC-S1 products
             save_bursts: bool(required=False)