MNT: Separate out l1b dataset logic into individual functions (IMAP-Science-Operations-Center#2601)

The l1b dataset logic was within the if-blocks based on what
dependency data was passed to the routine. This can be determined
based on the descriptor requested and it can also be broken up
into individual helper routines where the main code logic can be
more easily identified and separated.

Author: greglucas

imap_processing/cli.py

@@ -1026,7 +1026,7 @@ def do_processing(
             for file in science_files:
                 dataset = load_cdf(file)
                 data_dict[dataset.attrs["Logical_source"]] = dataset
-            datasets = lo_l1b.lo_l1b(data_dict, ancillary_files)
+            datasets = lo_l1b.lo_l1b(data_dict, ancillary_files, self.descriptor)
 
         elif self.data_level == "l1c":
             data_dict = {}

imap_processing/lo/l1b/lo_l1b.py

@@ -34,7 +34,9 @@
 logger = logging.getLogger(__name__)
 
 
-def lo_l1b(sci_dependencies: dict, anc_dependencies: list) -> list[Path]:
+def lo_l1b(
+    sci_dependencies: dict, anc_dependencies: list, descriptor: str
+) -> list[Path]:
     """
     Will process IMAP-Lo L1A data into L1B CDF data products.
 
@@ -44,6 +46,8 @@ def lo_l1b(sci_dependencies: dict, anc_dependencies: list) -> list[Path]:
         Dictionary of datasets needed for L1B data product creation in xarray Datasets.
     anc_dependencies : list
         List of ancillary file paths needed for L1B data product creation.
+    descriptor : str
+        Determines which datasets are produced.
 
     Returns
     -------
@@ -61,109 +65,159 @@ def lo_l1b(sci_dependencies: dict, anc_dependencies: list) -> list[Path]:
 
     datasets_to_return = []
 
-    badtimes_ds = create_badtimes_dataset()
-    if badtimes_ds.data_vars:
-        # If it was an empty dataset, then we don't want to
+    if descriptor == "badtimes":
+        logger.info("\nProcessing IMAP-Lo L1B Bad Times...")
+        badtimes_ds = create_badtimes_dataset()
         badtimes_ds.attrs = attr_mgr_l1b.get_global_attributes("imap_lo_l1b_badtimes")
-        datasets_to_return.append(badtimes_ds)
+        if len(badtimes_ds["epoch"]) > 0:
+            # Only add the dataset if there are bad times added
+            datasets_to_return.append(badtimes_ds)
 
     # if the dependencies are used to create Annotated Direct Events
-    if "imap_lo_l1a_de" in sci_dependencies and "imap_lo_l1a_spin" in sci_dependencies:
+    if descriptor == "de":
         logger.info("\nProcessing IMAP-Lo L1B Direct Events...")
-        logical_source = "imap_lo_l1b_de"
-        # get the dependency dataset for l1b direct events
-        l1a_de = sci_dependencies["imap_lo_l1a_de"]
-        spin_data = sci_dependencies["imap_lo_l1a_spin"]
-
-        # Initialize the L1B DE dataset
-        l1b_de = initialize_l1b_de(l1a_de, attr_mgr_l1b, logical_source)
-        pointing_start_met, pointing_end_met = get_pointing_times(
-            l1a_de["met"].values[0].item()
-        )
-
-        # Get the average spin durations for each epoch
-        avg_spin_durations_per_cycle = get_avg_spin_durations_per_cycle(spin_data)
-        # set the spin cycle for each direct event
-        l1b_de = set_spin_cycle(pointing_start_met, l1a_de, l1b_de)
-        # get spin start times for each event
-        spin_start_time = get_spin_start_times(l1a_de, l1b_de, spin_data)
-
-        # get the absolute met for each event
-        l1b_de = set_event_met(
-            l1a_de, l1b_de, spin_start_time, avg_spin_durations_per_cycle
-        )
-        # set the epoch for each event
-        l1b_de = set_each_event_epoch(l1b_de)
-        # Set the ESA mode for each direct event
-        l1b_de = set_esa_mode(
-            pointing_start_met, pointing_end_met, anc_dependencies, l1b_de
-        )
-        # Set the average spin duration for each direct event
-        l1b_de = set_avg_spin_durations_per_event(
-            l1a_de, l1b_de, avg_spin_durations_per_cycle
-        )
-        # calculate the TOF1 for golden triples
-        # store in the l1a dataset to use in l1b calculations
-        l1a_de = calculate_tof1_for_golden_triples(l1a_de)
-        # set the coincidence type string for each direct event
-        l1b_de = set_coincidence_type(l1a_de, l1b_de, attr_mgr_l1a)
-        # convert the TOFs to engineering units
-        l1b_de = convert_tofs_to_eu(l1a_de, l1b_de, attr_mgr_l1a, attr_mgr_l1b)
-        # set the species for each direct event
-        l1b_de = identify_species(l1b_de)
-        # set the pointing direction for each direct event
-        l1b_de = set_pointing_direction(l1b_de)
-        # calculate and set the pointing bin based on the spin phase
-        # pointing bin is 3600 x 40 bins
-        l1b_de = set_pointing_bin(l1b_de)
-        # set the badtimes
-        l1b_de = set_bad_times(l1b_de, anc_dependencies)
-        datasets_to_return.append(l1b_de)
+        ds = l1b_de(sci_dependencies, anc_dependencies, attr_mgr_l1b, attr_mgr_l1a)
+        datasets_to_return.append(ds)
 
     # If dependencies are used to create Histogram Rates
-    if (
-        "imap_lo_l1a_histogram" in sci_dependencies
-        and "imap_lo_l1a_spin" in sci_dependencies
-    ):
+    if descriptor == "histrates":
         logger.info("\nProcessing IMAP-Lo L1B Histogram Rates...")
-        logical_source = "imap_lo_l1b_histrates"
-        # get the dependency dataset for l1b histogram rates
-        l1a_hist = sci_dependencies["imap_lo_l1a_histogram"]
-        spin_data = sci_dependencies["imap_lo_l1a_spin"]
-        # initialize the L1B Histogram Rates dataset from the L1A Histogram Rates
-        # This carries over the epoch and count fields from L1A
-        l1b_histrates = initialize_l1b_histrates(l1a_hist, attr_mgr_l1b, logical_source)
-        # set spin cycle and remove invalid spin ASCs
-        l1b_histrates = set_spin_cycle_from_spin_data(
-            l1a_hist, l1b_histrates, spin_data
-        )
-
-        pointing_start_met, pointing_end_met = get_pointing_times(
-            ttj2000ns_to_met(l1a_hist["epoch"].values[0].item())
-        )
-        l1b_histrates = set_esa_mode(
-            pointing_start_met, pointing_end_met, anc_dependencies, l1b_histrates
-        )
-        # resweep the histogram data
-        l1b_histrates, exposure_factor = resweep_histogram_data(
-            l1b_histrates, anc_dependencies
-        )
-        # Get the start and end times for each spin epoch
-        acq_start, acq_end = convert_start_end_acq_times(spin_data)
-        # Get the average spin durations for each epoch
-        avg_spin_durations_per_cycle = get_avg_spin_durations_per_cycle(spin_data)
-        l1b_histrates = calculate_histogram_rates(
-            l1b_histrates,
-            acq_start,
-            acq_end,
-            avg_spin_durations_per_cycle,
-            exposure_factor,
-        )
-        datasets_to_return.append(l1b_histrates)
+        ds = l1b_histrates(sci_dependencies, anc_dependencies, attr_mgr_l1b)
+        datasets_to_return.append(ds)
 
     return datasets_to_return
 
 
+def l1b_de(
+    sci_dependencies: dict,
+    anc_dependencies: list,
+    attr_mgr_l1b: ImapCdfAttributes,
+    attr_mgr_l1a: ImapCdfAttributes,
+) -> xr.Dataset:
+    """
+    Create the IMAP-Lo L1B Direct Events dataset.
+
+    Parameters
+    ----------
+    sci_dependencies : dict
+        Dictionary of datasets needed for L1B data product creation in xarray Datasets.
+    anc_dependencies : list
+        List of ancillary file paths needed for L1B data product creation.
+    attr_mgr_l1b : ImapCdfAttributes
+        Attribute manager used to get the global attributes.
+    attr_mgr_l1a : ImapCdfAttributes
+        Attribute manager used to get the variable attributes.
+
+    Returns
+    -------
+    l1b_de : xr.Dataset
+        The IMAP-Lo L1B Direct Events dataset.
+    """
+    logical_source = "imap_lo_l1b_de"
+    # get the dependency dataset for l1b direct events
+    l1a_de = sci_dependencies["imap_lo_l1a_de"]
+    spin_data = sci_dependencies["imap_lo_l1a_spin"]
+
+    # Initialize the L1B DE dataset
+    l1b_de = initialize_l1b_de(l1a_de, attr_mgr_l1b, logical_source)
+    pointing_start_met, pointing_end_met = get_pointing_times(
+        l1a_de["met"].values[0].item()
+    )
+
+    # Get the average spin durations for each epoch
+    avg_spin_durations_per_cycle = get_avg_spin_durations_per_cycle(spin_data)
+    # set the spin cycle for each direct event
+    l1b_de = set_spin_cycle(pointing_start_met, l1a_de, l1b_de)
+    # get spin start times for each event
+    spin_start_time = get_spin_start_times(l1a_de, l1b_de, spin_data)
+
+    # get the absolute met for each event
+    l1b_de = set_event_met(
+        l1a_de, l1b_de, spin_start_time, avg_spin_durations_per_cycle
+    )
+    # set the epoch for each event
+    l1b_de = set_each_event_epoch(l1b_de)
+    # Set the ESA mode for each direct event
+    l1b_de = set_esa_mode(
+        pointing_start_met, pointing_end_met, anc_dependencies, l1b_de
+    )
+    # Set the average spin duration for each direct event
+    l1b_de = set_avg_spin_durations_per_event(
+        l1a_de, l1b_de, avg_spin_durations_per_cycle
+    )
+    # calculate the TOF1 for golden triples
+    # store in the l1a dataset to use in l1b calculations
+    l1a_de = calculate_tof1_for_golden_triples(l1a_de)
+    # set the coincidence type string for each direct event
+    l1b_de = set_coincidence_type(l1a_de, l1b_de, attr_mgr_l1a)
+    # convert the TOFs to engineering units
+    l1b_de = convert_tofs_to_eu(l1a_de, l1b_de, attr_mgr_l1a, attr_mgr_l1b)
+    # set the species for each direct event
+    l1b_de = identify_species(l1b_de)
+    # set the pointing direction for each direct event
+    l1b_de = set_pointing_direction(l1b_de)
+    # calculate and set the pointing bin based on the spin phase
+    # pointing bin is 3600 x 40 bins
+    l1b_de = set_pointing_bin(l1b_de)
+    # set the badtimes
+    l1b_de = set_bad_times(l1b_de, anc_dependencies)
+    return l1b_de
+
+
+def l1b_histrates(
+    sci_dependencies: dict, anc_dependencies: list, attr_mgr_l1b: ImapCdfAttributes
+) -> xr.Dataset:
+    """
+    Create the IMAP-Lo L1B Histogram Rates dataset.
+
+    Parameters
+    ----------
+    sci_dependencies : dict
+        Dictionary of datasets needed for L1B data product creation in xarray Datasets.
+    anc_dependencies : list
+        List of ancillary file paths needed for L1B data product creation.
+    attr_mgr_l1b : ImapCdfAttributes
+        Attribute manager used to get the global attributes.
+
+    Returns
+    -------
+    l1b_histrates : xr.Dataset
+        The IMAP-Lo L1B Histogram Rates dataset.
+    """
+    logical_source = "imap_lo_l1b_histrates"
+    # get the dependency dataset for l1b histogram rates
+    l1a_hist = sci_dependencies["imap_lo_l1a_histogram"]
+    spin_data = sci_dependencies["imap_lo_l1a_spin"]
+    # initialize the L1B Histogram Rates dataset from the L1A Histogram Rates
+    # This carries over the epoch and count fields from L1A
+    l1b_histrates = initialize_l1b_histrates(l1a_hist, attr_mgr_l1b, logical_source)
+    # set spin cycle and remove invalid spin ASCs
+    l1b_histrates = set_spin_cycle_from_spin_data(l1a_hist, l1b_histrates, spin_data)
+
+    pointing_start_met, pointing_end_met = get_pointing_times(
+        ttj2000ns_to_met(l1a_hist["epoch"].values[0].item())
+    )
+    l1b_histrates = set_esa_mode(
+        pointing_start_met, pointing_end_met, anc_dependencies, l1b_histrates
+    )
+    # resweep the histogram data
+    l1b_histrates, exposure_factor = resweep_histogram_data(
+        l1b_histrates, anc_dependencies
+    )
+    # Get the start and end times for each spin epoch
+    acq_start, acq_end = convert_start_end_acq_times(spin_data)
+    # Get the average spin durations for each epoch
+    avg_spin_durations_per_cycle = get_avg_spin_durations_per_cycle(spin_data)
+    l1b_histrates = calculate_histogram_rates(
+        l1b_histrates,
+        acq_start,
+        acq_end,
+        avg_spin_durations_per_cycle,
+        exposure_factor,
+    )
+    return l1b_histrates
+
+
 def initialize_l1b_de(
     l1a_de: xr.Dataset, attr_mgr_l1b: ImapCdfAttributes, logical_source: str
 ) -> xr.Dataset:

imap_processing/tests/lo/test_lo_l1b.py

@@ -174,7 +174,7 @@ def test_lo_l1b_de(
     expected_logical_source_de = "imap_lo_l1b_de"
 
     # Act
-    output_files = lo_l1b(data, anc_dependencies)
+    output_files = lo_l1b(data, anc_dependencies, descriptor="de")
 
     # Assert
     assert expected_logical_source_de == output_files[-1].attrs["Logical_source"]
@@ -210,7 +210,7 @@ def test_lo_l1b_histogram_rates(
     }
 
     # Act
-    l1b_datasets = lo_l1b(sci_dependencies, anc_dependencies)
+    l1b_datasets = lo_l1b(sci_dependencies, anc_dependencies, descriptor="histrates")
 
     # Assert
     assert "h_rates" in l1b_datasets[-1].data_vars
@@ -823,6 +823,15 @@ def test_badtimes_with_spin(spice_test_data_path, use_test_spin_data_csv):
     )
     np.testing.assert_array_equal(badtimes_ds["badtime_flag"], 1)
 
+    # There should be a dataset returned from the main code in this case
+    datasets = lo_l1b({}, [], descriptor="badtimes")
+    assert len(datasets) == 1
+
+
+def test_l1b_badtimes_skipped_if_empty():
+    datasets = lo_l1b({}, [], descriptor="badtimes")
+    assert len(datasets) == 0
+
 
 def test_resweep_histogram_success(anc_dependencies):
     # Arrange