Addressed review comments

IMAP-Science-Operations-Center · Sep 3, 2024 · 940348a · 940348a
1 parent ba75ec4
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Showing 2 changed files with 50 additions and 58 deletions.
diff --git a/imap_processing/codice/codice_l1a.py b/imap_processing/codice/codice_l1a.py
@@ -22,7 +22,6 @@
 
 from imap_processing import imap_module_directory
 from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
-from imap_processing.cdf.utils import met_to_j2000ns
 from imap_processing.codice import constants
 from imap_processing.codice.decompress import decompress
 from imap_processing.codice.utils import CODICEAPID
@@ -100,16 +99,18 @@ def configure_data_products(self, apid: int) -> None:
         self.dataset_name = config["dataset_name"]
         self.instrument = config["instrument"]
 
-    def create_science_dataset(self, met: np.int64, data_version: str) -> xr.Dataset:
+    def create_science_dataset(
+        self, packet: xr.Dataset, data_version: str
+    ) -> xr.Dataset:
         """
         Create an ``xarray`` dataset for the unpacked science data.
 
         The dataset can then be written to a CDF file.
 
         Parameters
         ----------
-        met : numpy.int64
-            The mission elapsed time of the packet, used to determine epoch data.
+        packet : xarray.Dataset
+            The packet to process.
         data_version : str
             Version of the data product being created.
 
@@ -126,7 +127,7 @@ def create_science_dataset(self, met: np.int64, data_version: str) -> xr.Dataset
 
         # Define coordinates
         epoch = xr.DataArray(
-            [met_to_j2000ns(met)],
+            packet.epoch,
             name="epoch",
             dims=["epoch"],
             attrs=cdf_attrs.get_variable_attributes("epoch"),
@@ -371,9 +372,6 @@ def create_event_dataset(
     elif apid == CODICEAPID.COD_HI_PHA:
         dataset_name = "imap_codice_l1a_hi_pha"
 
-    # Determine the start time of the packet
-    met = packet.acq_start_seconds.data[0]
-
     # Extract the data
     # event_data = packet.event_data.data (Currently turned off, see TODO)
 
@@ -384,7 +382,7 @@ def create_event_dataset(
 
     # Define coordinates
     epoch = xr.DataArray(
-        met_to_j2000ns([met]),
+        packet.epoch,
         name="epoch",
         dims=["epoch"],
         attrs=cdf_attrs.get_variable_attributes("epoch"),
@@ -426,10 +424,7 @@ def create_hskp_dataset(
     cdf_attrs.add_global_attribute("Data_version", data_version)
 
     epoch = xr.DataArray(
-        met_to_j2000ns(
-            packet.shcoarse.data,
-            reference_epoch=np.datetime64("2010-01-01T00:01:06.184", "ns"),
-        ),
+        packet.epoch,
         name="epoch",
         dims=["epoch"],
         attrs=cdf_attrs.get_variable_attributes("epoch"),
@@ -493,10 +488,10 @@ def get_params(packet: xr.Dataset) -> tuple[int, int, int, int]:
     view_id : int
         Provides information about how data was collapsed and/or compressed.
     """
-    table_id = packet.table_id.data[0]
-    plan_id = packet.plan_id.data[0]
-    plan_step = packet.plan_step.data[0]
-    view_id = packet.view_id.data[0]
+    table_id = int(packet.table_id.data)
+    plan_id = int(packet.plan_id.data)
+    plan_step = int(packet.plan_step.data)
+    view_id = int(packet.view_id.data)
 
     return table_id, plan_id, plan_step, view_id
 
@@ -534,9 +529,6 @@ def process_codice_l1a(file_path: Path, data_version: str) -> xr.Dataset:
             dataset = create_event_dataset(apid, packet, data_version)
 
         elif apid in constants.APIDS_FOR_SCIENCE_PROCESSING:
-            # Determine the start time of the packet
-            met = packet.acq_start_seconds.data[0]
-
             # Extract the data
             science_values = packet.data.data[0]
 
@@ -547,7 +539,7 @@ def process_codice_l1a(file_path: Path, data_version: str) -> xr.Dataset:
             pipeline = CoDICEL1aPipeline(table_id, plan_id, plan_step, view_id)
             pipeline.configure_data_products(apid)
             pipeline.unpack_science_data(science_values)
-            dataset = pipeline.create_science_dataset(met, data_version)
+            dataset = pipeline.create_science_dataset(packet, data_version)
 
     logger.info(f"\nFinal data product:\n{dataset}\n")
 

diff --git a/imap_processing/tests/codice/test_codice_l1a.py b/imap_processing/tests/codice/test_codice_l1a.py
@@ -34,23 +34,6 @@
     (1, 1, 1, 128),  # lo-nsw-species
     (1, 128),  # lo-pha
 ]
-EXPECTED_ARRAY_SIZES = [
-    129,  # hskp
-    1,  # hi-counters-aggregated
-    3,  # hi-counters-singles
-    8,  # hi-omni
-    4,  # hi-sectored
-    0,  # hi-pha
-    3,  # lo-counters-aggregated
-    3,  # lo-counters-singles
-    6,  # lo-sw-angular
-    3,  # lo-nsw-angular
-    7,  # lo-sw-priority
-    4,  # lo-nsw-priority
-    18,  # lo-sw-species
-    10,  # lo-nsw-species
-    0,  # lo-pha
-]
 EXPECTED_LOGICAL_SOURCE = [
     "imap_codice_l1a_hskp",
     "imap_codice_l1a_hi-counters-aggregated",
@@ -68,6 +51,23 @@
     "imap_codice_l1a_lo-nsw-species",
     "imap_codice_l1a_lo-pha",
 ]
+EXPECTED_NUM_VARIABLES = [
+    129,  # hskp
+    1,  # hi-counters-aggregated
+    3,  # hi-counters-singles
+    8,  # hi-omni
+    4,  # hi-sectored
+    0,  # hi-pha
+    3,  # lo-counters-aggregated
+    3,  # lo-counters-singles
+    6,  # lo-sw-angular
+    3,  # lo-nsw-angular
+    7,  # lo-sw-priority
+    4,  # lo-nsw-priority
+    18,  # lo-sw-species
+    10,  # lo-nsw-species
+    0,  # lo-pha
+]
 
 
 @pytest.fixture(params=TEST_PACKETS)
@@ -134,26 +134,6 @@ def test_l1a_data_array_shape(test_l1a_data: xr.Dataset, expected_shape: tuple):
             assert dataset[variable].data.shape == expected_shape
 
 
-@pytest.mark.parametrize(
-    "test_l1a_data, expected_size",
-    list(zip(TEST_PACKETS, EXPECTED_ARRAY_SIZES)),
-    indirect=["test_l1a_data"],
-)
-def test_l1a_data_array_size(test_l1a_data: xr.Dataset, expected_size: int):
-    """Tests that the data arrays in the generated CDFs have the expected size.
-
-    Parameters
-    ----------
-    test_l1a_data : xarray.Dataset
-        A ``xarray`` dataset containing the test data
-    expected_size : int
-        The expected size of the data array
-    """
-
-    dataset = test_l1a_data
-    assert len(dataset) == expected_size
-
-
 @pytest.mark.skip("Awaiting validation data")
 @pytest.mark.parametrize(
     "test_l1a_data, validation_data",
@@ -185,3 +165,23 @@ def test_l1a_data_array_values(test_l1a_data: xr.Dataset, validation_data: Path)
             np.testing.assert_array_equal(
                 validation_data[variable].data, generated_dataset[variable].data[0]
             )
+
+
+@pytest.mark.parametrize(
+    "test_l1a_data, expected_num_variables",
+    list(zip(TEST_PACKETS, EXPECTED_NUM_VARIABLES)),
+    indirect=["test_l1a_data"],
+)
+def test_l1a_num_variables(test_l1a_data: xr.Dataset, expected_num_variables: int):
+    """Tests that the data arrays in the generated CDFs have the expected size.
+
+    Parameters
+    ----------
+    test_l1a_data : xarray.Dataset
+        A ``xarray`` dataset containing the test data
+    expected_num_variables : int
+        The expected number of data variables in the CDF
+    """
+
+    dataset = test_l1a_data
+    assert len(dataset) == expected_num_variables