Fix Hi L2 naming of bg variables (IMAP-Science-Operations-Center#2677)

Author: tmplummer

imap_processing/ena_maps/ena_maps.py

@@ -657,8 +657,8 @@ class HiPointingSet(LoHiBasePointingSet):
     # renamed to match L2 variables
     l1c_to_l2_var_mapping: ClassVar[dict[str, str]] = {
         "exposure_times": "exposure_factor",
-        "background_rates": "bg_rates",
-        "background_rates_uncertainty": "bg_rates_unc",
+        "background_rates": "bg_rate",
+        "background_rates_uncertainty": "bg_rate_sys_err",
     }
 
     def __init__(self, dataset: xr.Dataset | str | Path):

imap_processing/hi/hi_l2.py

@@ -28,13 +28,13 @@
 SC_FRAME_VARS_TO_PROJECT = {
     "counts",
     "exposure_factor",
-    "bg_rates",
-    "bg_rates_unc",
+    "bg_rate",
+    "bg_rate_sys_err",
     "obs_date",
 }
 HELIO_FRAME_VARS_TO_PROJECT = SC_FRAME_VARS_TO_PROJECT | {"energy_sc"}
 # TODO: is an exposure time weighted average for obs_date appropriate?
-FULL_EXPOSURE_TIME_AVERAGE_SET = {"bg_rates", "bg_rates_unc", "obs_date", "energy_sc"}
+FULL_EXPOSURE_TIME_AVERAGE_SET = {"bg_rate", "bg_rate_sys_err", "obs_date", "energy_sc"}
 
 
 # =============================================================================
@@ -297,7 +297,7 @@ def calculate_all_rates_and_intensities(
     Parameters
     ----------
     map_ds : xarray.Dataset
-        Map dataset with projected PSET data (counts, exposure_factor, bg_rates,
+        Map dataset with projected PSET data (counts, exposure_factor, bg_rate,
         energy_delta_minus, energy_delta_plus, etc.) and an `energy` coordinate
         containing the ESA nominal central energies in keV.
     l2_ancillary_path_dict : dict[str, pathlib.Path]
@@ -362,7 +362,7 @@ def calculate_ena_signal_rates(map_ds: xr.Dataset) -> xr.Dataset:
     Parameters
     ----------
     map_ds : xarray.Dataset
-        Map dataset that has counts, exposure_factor, and bg_rates calculated.
+        Map dataset that has counts, exposure_factor, and bg_rate calculated.
 
     Returns
     -------
@@ -373,7 +373,7 @@ def calculate_ena_signal_rates(map_ds: xr.Dataset) -> xr.Dataset:
     with np.errstate(divide="ignore"):
         # Calculate the ENA Signal Rate
         map_ds["ena_signal_rates"] = (
-            map_ds["counts"] / map_ds["exposure_factor"] - map_ds["bg_rates"]
+            map_ds["counts"] / map_ds["exposure_factor"] - map_ds["bg_rate"]
         )
         # Calculate the ENA Signal Rate Uncertainties
         # The minimum count uncertainty is 1 for any pixel that has non-zero
@@ -440,7 +440,7 @@ def calculate_ena_intensity(
         map_ds["ena_signal_rate_stat_unc"] / flux_conversion_divisor
     )
     map_ds["ena_intensity_sys_err"] = (
-        np.sqrt(map_ds["bg_rates"] * map_ds["exposure_factor"])
+        np.sqrt(map_ds["bg_rate"] * map_ds["exposure_factor"])
         / map_ds["exposure_factor"]
         / flux_conversion_divisor
     )
@@ -562,7 +562,7 @@ def _calculate_improved_stat_variance(
 
     logger.debug("Computing geometric factor normalized signal rates")
 
-    # signal_rates = counts / exposure_factor - bg_rates
+    # signal_rates = counts / exposure_factor - bg_rate
     # signal_rates shape is: (n_epoch, n_energy, n_cal_prod, n_spatial_pixels)
     signal_rates = map_ds["ena_signal_rates"]
 
@@ -581,7 +581,7 @@ def _calculate_improved_stat_variance(
     logger.debug("Including background rates in uncertainty calculation")
     # Convert averaged signal rates back to flux uncertainties
     # Total count rates for Poisson uncertainty calculation
-    total_count_rates_for_uncertainty = map_ds["bg_rates"] + averaged_signal_rates
+    total_count_rates_for_uncertainty = map_ds["bg_rate"] + averaged_signal_rates
 
     logger.debug("Computing improved flux uncertainties")
     # Statistical variance:

imap_processing/tests/ena_maps/test_ena_maps.py

@@ -154,7 +154,7 @@ def test_init(self, hi_pset_cdf_path):
         assert hi_pset.num_points == 3600
         np.testing.assert_array_equal(hi_pset.az_el_points.shape, (3600, 2))
 
-        for var_name in ["exposure_factor", "bg_rates", "bg_rates_unc"]:
+        for var_name in ["exposure_factor", "bg_rate", "bg_rate_sys_err"]:
             assert var_name in hi_pset.data
 
     def test_from_cdf(self, hi_pset_cdf_path):

imap_processing/tests/hi/test_hi_l2.py

@@ -128,10 +128,10 @@ def sample_map_dataset():
             "ena_intensity_sys_err": xr.DataArray(
                 np.random.rand(*shape) * 5 + 1, dims=list(coords.keys())
             ),
-            "bg_rates": xr.DataArray(
+            "bg_rate": xr.DataArray(
                 np.random.rand(*shape) * 20 + 5, dims=list(coords.keys())
             ),
-            "bg_rates_unc": xr.DataArray(
+            "bg_rate_sys_err": xr.DataArray(
                 np.random.rand(*shape) * 2 + 1, dims=list(coords.keys())
             ),
             "exposure_factor": xr.DataArray(
@@ -301,10 +301,10 @@ def test_calculate_ena_signal_rates(empty_rectangular_map_dataset):
                 name="exposure_factor",
                 dims=list(exposure_sizes.keys()),
             ),
-            "bg_rates": xr.DataArray(
+            "bg_rate": xr.DataArray(
                 np.arange(np.prod(tuple(map_ds.sizes.values()))).reshape(counts_shape)
                 % 2,
-                name="bg_rates",
+                name="bg_rate",
                 dims=list(map_ds.sizes.keys()),
             ),
         }
@@ -316,7 +316,7 @@ def test_calculate_ena_signal_rates(empty_rectangular_map_dataset):
         assert var_name in result_ds
         assert result_ds[var_name].shape == counts_shape
     # Verify that there are no negative signal rates. The synthetic data combination
-    # where counts = 0, exposure_factor = 1, and bg_rates = 1 would result in
+    # where counts = 0, exposure_factor = 1, and bg_rate = 1 would result in
     # an ena_signal_rate of (0 / 1) - 1 = -1
     assert np.nanmin(result_ds["ena_signal_rates"].values) >= 0
     # Verify that the minimum finite uncertainty is sqrt(1) / exposure_factor.
@@ -345,9 +345,9 @@ def ena_intensity_map_ds(empty_rectangular_map_dataset):
                 name="ena_signal_rate_stat_unc",
                 dims=list(map_ds.sizes.keys()),
             ),
-            "bg_rates_unc": xr.DataArray(
+            "bg_rate_sys_err": xr.DataArray(
                 np.arange(np.prod(tuple(map_ds.sizes.values()))).reshape(var_shape) % 3,
-                name="bg_rates_unc",
+                name="bg_rate_sys_err",
                 dims=list(map_ds.sizes.keys()),
             ),
         }
@@ -359,7 +359,7 @@ def ena_intensity_map_ds(empty_rectangular_map_dataset):
     )
     map_ds.update(
         {
-            "bg_rates": xr.DataArray(
+            "bg_rate": xr.DataArray(
                 np.ones(bg_shape) * 5.0,
                 dims=[d for d in map_ds.sizes.keys() if d != "calibration_prod"],
             ),
@@ -566,7 +566,7 @@ def test_weighted_average_mathematical_correctness():
                 np.array([100.0, 400.0]).reshape(1, 1, 2, 1, 1),
                 dims=list(coords.keys()),
             ),
-            "bg_rates": xr.DataArray(
+            "bg_rate": xr.DataArray(
                 np.array([5.0]).reshape(1, 1, 1, 1),
                 dims=[d for d in coords.keys() if d != "calibration_prod"],
             ),
@@ -624,7 +624,7 @@ def test_statistical_uncertainty_combination_correctness():
                 sys_err_values, dims=list(coords.keys())
             ),
             "ena_signal_rates": xr.DataArray(flux_values, dims=list(coords.keys())),
-            "bg_rates": xr.DataArray(
+            "bg_rate": xr.DataArray(
                 np.array([1.0, 2.0]).reshape(1, 1, 2, 1, 1), dims=list(coords.keys())
             ),
             "exposure_factor": xr.DataArray(
@@ -783,8 +783,8 @@ def test_process_single_pset_renames_variables(
 
     # Check that variables were renamed
     assert "exposure_factor" in result
-    assert "bg_rates" in result
-    assert "bg_rates_unc" in result
+    assert "bg_rate" in result
+    assert "bg_rate_sys_err" in result
     # Original names should not exist
     assert "exposure_times" not in result
     assert "background_rates" not in result
@@ -834,16 +834,16 @@ def test_process_single_pset_exposure_time_weighting(
     descriptor = MapDescriptor.from_string("h90-ena-h-sf-nsp-full-gcs-6deg-3mo")
     energy_kev = xr.DataArray([0.5, 0.75, 1.1], dims=["esa_energy_step"])
 
-    # bg_rates should be multiplied by exposure_factor
+    # bg_rate should be multiplied by exposure_factor
     result = process_single_pset(
         mock_pset_dataset,
         energy_kev,
         descriptor,
-        vars_to_exposure_time_average={"bg_rates"},
+        vars_to_exposure_time_average={"bg_rate"},
     )
 
-    # bg_rates was 5.0, exposure_factor is 100.0, so result should be 500.0
-    assert np.allclose(result["bg_rates"].values, 500.0)
+    # bg_rate was 5.0, exposure_factor is 100.0, so result should be 500.0
+    assert np.allclose(result["bg_rate"].values, 500.0)
 
 
 @mock.patch("imap_processing.hi.hi_l2.calculate_ram_mask")
@@ -973,10 +973,8 @@ def mock_map_dataset_for_rates():
                 np.ones(exposure_shape) * 10.0,
                 dims=["epoch", "esa_energy_step", "longitude", "latitude"],
             ),
-            "bg_rates": xr.DataArray(
-                np.ones(shape) * 2.0, dims=list(coords.keys())[:5]
-            ),
-            "bg_rates_unc": xr.DataArray(
+            "bg_rate": xr.DataArray(np.ones(shape) * 2.0, dims=list(coords.keys())[:5]),
+            "bg_rate_sys_err": xr.DataArray(
                 np.ones(shape) * 0.5, dims=list(coords.keys())[:5]
             ),
             "obs_date": xr.DataArray(