Update InSAR to copy statistics from input product

src/nisarqa/input_product_readers/igram_groups.py

@@ -33,9 +33,9 @@ def _get_path_containing_freq_pol(self, freq: str, pol: str) -> str:
     @contextmanager
     def get_wrapped_igram(
         self, freq: str, pol: str
-    ) -> Iterator[nisarqa.RadarRaster | nisarqa.GeoRaster]:
+    ) -> Iterator[nisarqa.RadarRasterWithStats | nisarqa.GeoRasterWithStats]:
         """
-        Get the complex-valued wrapped interferogram image *Raster.
+        Get the complex-valued wrapped interferogram image *RasterWithStats.
 
         Parameters
         ----------
@@ -44,15 +44,23 @@ def get_wrapped_igram(
 
         Yields
         ------
-        raster : RadarRaster or GeoRaster
+        raster : RadarRasterWithStats or GeoRasterWithStats
             Generated *Raster for the requested dataset.
+
+        Notes
+        -----
+        As of Jan 2026 (Product Spec Version 1.4.0), the wrapped interferogram
+        layers do not contain min/max/mean/std statistics as attributes.
+        This means that the `stats` attribute in the yielded `raster` will
+        contain `None` values for each metric. However, should this change
+        in subsequent ISCE3 releases, these values be populated accordingly.
         """
         parent_path = self._wrapped_group_path(freq=freq, pol=pol)
         path = f"{parent_path}/wrappedInterferogram"
 
         with h5py.File(self.filepath) as f:
             yield self._get_raster_from_path(
-                h5_file=f, raster_path=path, parse_stats=False
+                h5_file=f, raster_path=path, parse_stats=True
             )
 
     @contextmanager

src/nisarqa/processing/calc.py

@@ -239,17 +239,20 @@ def hz2mhz(arr: np.ndarray) -> np.ndarray:
     return arr * 1.0e-06
 
 
-def compute_and_save_basic_statistics(
-    raster: nisarqa.Raster,
+def process_and_save_basic_statistics(
+    raster: nisarqa.RadarRasterWithStats | nisarqa.GeoRasterWithStats,
     stats_h5: h5py.File,
     params: nisarqa.ThresholdParamGroup,
 ) -> None:
     """
-    Compute and save min, max, mean, std, % nan, % zero, % fill, % inf to HDF5.
+    Save min, max, mean, std, % nan, % zero, % fill, % inf to HDF5.
+
+    This function copies the min, max, mean, std provided via the input *Raster,
+    and it computes the % nan, % zero, % fill, % inf.
 
     Parameters
     ----------
-    raster : nisarqa.Raster
+    raster : nisarqa.RadarRasterWithStats or nisarqa.GeoRasterWithStats
         Input Raster.
     stats_h5 : h5py.File
         The output file to save QA metrics to.
@@ -266,85 +269,13 @@ def compute_and_save_basic_statistics(
     -----
     If the fill value is set to None in the input *Raster, that field will
     not be computed nor included in the STATS.h5 file.
-    """
-    arr = raster.data
-    units = raster.units
-    grp_path = raster.stats_h5_group_path
-    fill_value = raster.fill_value
-
-    # Step 1: Compute min/max/mean/STD
-    # TODO: refactor aka redesign this code chunk.
-
-    def _compute_min_max_mean_std(
-        arr: np.ndarray, component: str | None
-    ) -> None:
-        """
-        Compute min, max, mean, and samples standard deviation; save to HDF5.
-
-        Parameters
-        ----------
-        arr : numpy.ndarray
-            The input array to have statistics run on. Note: If `arr` has a
-            complex dtype, this function will need to access the `arr.real`
-            and `arr.imag` parts separately. Unfortunately, h5py.Dataset
-            instances do not allow access to these parts using that syntax,
-            so to be safe, please pass in a Numpy array.
-        component : str or None
-            One of "real", "imag", or None.
-            Per ISCE3 convention, for complex-valued data, the statistics
-            should be computed independently for the real component and
-            for the imaginary component of the data.
-            If the source dataset is real-valued, set this to None.
-            If the source dataset is complex-valued, set this to "real" for the
-            real-valued component's name and description, or set to "imag"
-            for the imaginary component's name and description.
-
-        Notes
-        -----
-        TODO: This is a clunky, kludgy function. When these statistics get
-        implemented for RSLC, GSLC, and GCOV after R4, the developer
-        should consider pulling this function out into a standalone function.
-        For expediency, for R4, all InSAR products will use this function,
-        so this information can live here.
-        """
-        if (component is not None) and (component not in ("real", "imag")):
-            raise ValueError(
-                f"`{component=!r}, must be 'real', 'imag', or None."
-            )
 
-        # Fill all invalid pixels in array with NaN, to easily compute metrics
-        arr_copy = np.where(
-            (np.isfinite(arr) & (arr != fill_value)), arr, np.nan
-        )
-
-        # Compute min/max/mean/std of valid pixels
-        for key, func in [
-            ("min", np.nanmin),
-            ("max", np.nanmax),
-            ("mean", np.nanmean),
-            ("std", lambda x: np.nanstd(x, ddof=1)),
-        ]:
-
-            name, descr = nisarqa.get_stats_name_descr(
-                stat=key, component=component
-            )
-
-            nisarqa.create_dataset_in_h5group(
-                h5_file=stats_h5,
-                grp_path=grp_path,
-                ds_name=name,
-                ds_data=func(arr_copy),
-                ds_units=units,
-                ds_description=descr,
-            )
+    If the input raster's stats attribute contains metrics with values
+    of `None`, those metrics will not appear in the output stats HDF5.
+    """
 
-    if raster.is_complex:
-        # HDF5 Datasets cannot access .real nor .imag, so we need
-        # to read the array into a numpy array in memory first.
-        _compute_min_max_mean_std(arr[()].real, "real")
-        _compute_min_max_mean_std(arr[()].imag, "imag")
-    else:
-        _compute_min_max_mean_std(arr[()], None)
+    # Step 1: Copy min/max/mean/STD
+    nisarqa.copy_imagery_metrics_to_stats_h5(raster=raster, stats_h5=stats_h5)
 
     # Step 2: Compute % NaN, % Inf, % Fill, % near-zero, % invalid
     compute_percentage_metrics(

src/nisarqa/processing/insar/az_and_slant_rng_offsets.py

@@ -252,11 +252,11 @@ def process_range_and_az_offsets(
     """
     # Compute Statistics first, in case of malformed layers
     # (which could cause plotting to fail)
-    nisarqa.compute_and_save_basic_statistics(
+    nisarqa.process_and_save_basic_statistics(
         raster=az_offset, stats_h5=stats_h5, params=params
     )
 
-    nisarqa.compute_and_save_basic_statistics(
+    nisarqa.process_and_save_basic_statistics(
         raster=rg_offset, stats_h5=stats_h5, params=params
     )
 

src/nisarqa/processing/insar/az_and_slant_rng_variances.py

@@ -62,12 +62,12 @@ def process_az_and_slant_rg_variances_from_offset_product(
                 ):
                     # Compute Statistics first, in case of malformed layers
                     # (which could cause plotting to fail)
-                    nisarqa.compute_and_save_basic_statistics(
+                    nisarqa.process_and_save_basic_statistics(
                         raster=az_off_var,
                         params=params,
                         stats_h5=stats_h5,
                     )
-                    nisarqa.compute_and_save_basic_statistics(
+                    nisarqa.process_and_save_basic_statistics(
                         raster=rg_off_var,
                         params=params,
                         stats_h5=stats_h5,

src/nisarqa/processing/insar/corr_surface_peak_and_cross_variance.py

@@ -58,7 +58,7 @@ def process_surface_peak(
                 ) as surface_peak,
             ):
                 # Compute Statistics first, in case of malformed layers
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=surface_peak,
                     params=params_surface_peak,
                     stats_h5=stats_h5,
@@ -249,12 +249,12 @@ def process_cross_variance_and_surface_peak(
                     ) as surface_peak,
                 ):
                     # Compute Statistics first, in case of malformed layers
-                    nisarqa.compute_and_save_basic_statistics(
+                    nisarqa.process_and_save_basic_statistics(
                         raster=cross_off_var,
                         params=params_cross_offset,
                         stats_h5=stats_h5,
                     )
-                    nisarqa.compute_and_save_basic_statistics(
+                    nisarqa.process_and_save_basic_statistics(
                         raster=surface_peak,
                         params=params_surface_peak,
                         stats_h5=stats_h5,

src/nisarqa/processing/insar/ionosphere_phase_screen.py

@@ -57,12 +57,12 @@ def process_ionosphere_phase_screen(
             ):
                 # Compute Statistics first, in case of malformed layers
                 # (which could cause plotting to fail)
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=iono_phs,
                     stats_h5=stats_h5,
                     params=params_iono_phs_screen,
                 )
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=iono_uncertainty,
                     stats_h5=stats_h5,
                     params=params_iono_phs_uncert,

src/nisarqa/processing/insar/unwrapped_coh_mag.py

@@ -42,7 +42,7 @@ def process_unw_coh_mag(
             with product.get_unwrapped_coh_mag(freq=freq, pol=pol) as coh_mag:
                 # Compute Statistics first, in case of malformed layers
                 # (which could cause plotting to fail)
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=coh_mag,
                     params=params,
                     stats_h5=stats_h5,

src/nisarqa/processing/insar/unwrapped_phase_image.py

@@ -47,7 +47,7 @@ def process_phase_image_unwrapped(
             with product.get_unwrapped_phase(freq=freq, pol=pol) as img:
                 # Compute Statistics first, in case of malformed layers
                 # (which could cause plotting to fail)
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=img, stats_h5=stats_h5, params=params
                 )
 

src/nisarqa/processing/insar/wrapped_phase_image_and_coh_mag.py

@@ -57,12 +57,12 @@ def process_phase_image_wrapped(
             ):
                 # Compute Statistics first, in case of malformed layers
                 # (which could cause plotting to fail)
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=complex_img,
                     params=params_wrapped_igram,
                     stats_h5=stats_h5,
                 )
-                nisarqa.compute_and_save_basic_statistics(
+                nisarqa.process_and_save_basic_statistics(
                     raster=coh_img,
                     params=params_coh_mag,
                     stats_h5=stats_h5,

src/nisarqa/processing/stats_h5_writer/metrics_writer.py

@@ -592,11 +592,65 @@ def get_stats_name_descr(stat: str, component: str | None) -> tuple[str, str]:
         )
 
 
+def copy_imagery_metrics_to_stats_h5(
+    raster: nisarqa.RadarRasterWithStats | nisarqa.GeoRasterWithStats,
+    stats_h5: h5py.File,
+) -> None:
+    """
+    Copy min, max, mean, and std from input raster to HDF5.
+
+    Parameters
+    ----------
+    raster : nisarqa.RadarRasterWithStats or nisarqa.GeoRasterWithStats
+        Input Raster.
+    stats_h5 : h5py.File
+        The output file to save metrics to.
+
+    Notes
+    -----
+    If the input raster's stats attribute contains metrics with values
+    of `None`, those metrics will not appear in the output stats HDF5.
+    """
+    for m in ("min", "max", "mean", "std"):
+        metrics = []
+        if raster.is_complex:
+            for component in ("real", "imag"):
+                # get tuple of (val, name, descr)
+                val_name_descr = raster.get_stat_val_name_descr(
+                    stat=m, component=component
+                )
+                metrics.append(val_name_descr)
+        else:
+            # get tuple of (val, name, descr)
+            val_name_descr = raster.get_stat_val_name_descr(stat=m)
+            metrics.append(val_name_descr)
+
+        for val, name, descr in metrics:
+            if val is not None:
+                # XXX: This will create a dataset with a snake_case name,
+                # which differs from the usual camelCase naming convention,
+                # but matches the name of the attribute in the input
+                # product.
+                nisarqa.create_dataset_in_h5group(
+                    h5_file=stats_h5,
+                    grp_path=raster.stats_h5_group_path,
+                    ds_name=name,
+                    ds_data=val,
+                    ds_description=descr,
+                    ds_units=raster.units,
+                )
+            else:
+                nisarqa.get_logger().error(
+                    f"Attribute `{name}` is missing or has no"
+                    f" value. Dataset: {raster.name}"
+                )
+
+
 def copy_non_insar_imagery_metrics(
     product: nisarqa.NonInsarProduct, stats_h5: h5py.File
 ) -> None:
     """
-    Copy min/max/mean/std metrics of freq+pol imagery layers to QA HDF5.
+    Copy min/max/mean/std metrics of all freq+pol imagery layers to QA HDF5.
 
     This function accommodates both real and complex datasets.
 
@@ -607,49 +661,12 @@ def copy_non_insar_imagery_metrics(
     stats_h5 : h5py.File
         Handle to an HDF5 file where the metrics should be saved.
     """
-    log = nisarqa.get_logger()
 
     for freq in product.freqs:
         for pol in product.get_pols(freq=freq):
             with product.get_raster(freq=freq, pol=pol) as img:
-                dest_path = (
-                    f"{nisarqa.STATS_H5_QA_FREQ_GROUP % (product.band, freq)}"
-                    f"/{pol}"
-                )
 
-                for m in ("min", "max", "mean", "std"):
-                    metrics = []
-                    if img.is_complex:
-                        for component in ("real", "imag"):
-                            # get tuple of (val, name, descr)
-                            val_name_descr = img.get_stat_val_name_descr(
-                                stat=m, component=component
-                            )
-                            metrics.append(val_name_descr)
-                    else:
-                        # get tuple of (val, name, descr)
-                        val_name_descr = img.get_stat_val_name_descr(stat=m)
-                        metrics.append(val_name_descr)
-
-                    for val, name, descr in metrics:
-                        if val is not None:
-                            # XXX: This will create a dataset with a snake_case name,
-                            # which differs from the usual camelCase naming convention,
-                            # but matches the name of the attribute in the input
-                            # product.
-                            nisarqa.create_dataset_in_h5group(
-                                h5_file=stats_h5,
-                                grp_path=dest_path,
-                                ds_name=name,
-                                ds_data=val,
-                                ds_description=descr,
-                                ds_units=img.units,
-                            )
-                        else:
-                            log.error(
-                                f"Attribute `{name}` is missing or has no"
-                                f" value. Dataset: {img.name}"
-                            )
+                copy_imagery_metrics_to_stats_h5(raster=img, stats_h5=stats_h5)
 
 
 __all__ = nisarqa.get_all(__name__, objects_to_skip)