Hotfix for GLOWS L2 ancillary files

imap_processing/cli.py

@@ -739,7 +739,7 @@ def do_processing(
                 datasets = [glows_l1b_de(input_dataset, conversion_table_dict)]
 
         if self.data_level == "l2":
-            science_files = dependencies.get_file_paths(source="glows")
+            science_files = dependencies.get_file_paths(source="glows", data_type="l1b")
             if len(science_files) != 1:
                 raise ValueError(
                     f"GLOWS L2 requires exactly one input science file, "

imap_processing/glows/l1b/glows_l1b_data.py

@@ -99,22 +99,65 @@ def __post_init__(self, pipeline_dataset: xr.Dataset) -> None:
             Dataset containing pipeline settings data variables.
         """
         # Extract active bad-angle flags (default to all True if not present)
+        _angle_flag_names = [
+            "is_close_to_uv_source",
+            "is_inside_excluded_region",
+            "is_excluded_by_instr_team",
+            "is_suspected_transient",
+        ]
         if "active_bad_angle_flags" in pipeline_dataset.data_vars:
             self.active_bad_angle_flags = list(
                 pipeline_dataset["active_bad_angle_flags"].values
             )
+        elif any(
+            f"active_bad_angle_flags_{n}" in pipeline_dataset.data_vars
+            for n in _angle_flag_names
+        ):
+            # Flattened format from convert_json_to_dataset
+            self.active_bad_angle_flags = [
+                bool(pipeline_dataset[f"active_bad_angle_flags_{name}"].values)
+                for name in _angle_flag_names
+            ]
         else:
             # Default: all 4 bad-angle flags are active
             self.active_bad_angle_flags = [True, True, True, True]
 
         # Extract active bad-time flags (default to all True if not present)
+        _time_flag_names = [
+            "is_pps_missing",
+            "is_time_status_missing",
+            "is_phase_missing",
+            "is_spin_period_missing",
+            "is_overexposed",
+            "is_direct_event_non_monotonic",
+            "is_night",
+            "is_hv_test_in_progress",
+            "is_test_pulse_in_progress",
+            "is_memory_error_detected",
+            "is_generated_on_ground",
+            "is_beyond_daily_statistical_error",
+            "is_temperature_std_dev_beyond_threshold",
+            "is_hv_voltage_std_dev_beyond_threshold",
+            "is_spin_period_std_dev_beyond_threshold",
+            "is_pulse_length_std_dev_beyond_threshold",
+            "is_spin_period_difference_beyond_threshold",
+        ]
         if "active_bad_time_flags" in pipeline_dataset.data_vars:
             self.active_bad_time_flags = list(
                 pipeline_dataset["active_bad_time_flags"].values
             )
+        elif any(
+            f"active_bad_time_flags_{n}" in pipeline_dataset.data_vars
+            for n in _time_flag_names
+        ):
+            # Flattened format from convert_json_to_dataset
+            self.active_bad_time_flags = [
+                bool(pipeline_dataset[f"active_bad_time_flags_{name}"].values)
+                for name in _time_flag_names
+            ]
         else:
             # Default: assume all bad-time flags are active
-            self.active_bad_time_flags = [True] * 16  # Typical number of bad-time flags
+            self.active_bad_time_flags = [True] * FLAG_LENGTH
 
         # Extract sunrise/sunset offsets (default to 0.0 if not present)
         self.sunrise_offset = float(pipeline_dataset.get("sunrise_offset", 0.0))

imap_processing/tests/glows/test_glows_l1b_data.py

@@ -4,12 +4,14 @@
 
 import numpy as np
 import pytest
+import xarray as xr
 
 from imap_processing.glows.l1b.glows_l1b import glows_l1b, glows_l1b_de
 from imap_processing.glows.l1b.glows_l1b_data import (
     AncillaryParameters,
     DirectEventL1B,
     HistogramL1B,
+    PipelineSettings,
 )
 from imap_processing.spice.time import met_to_ttj2000ns
 from imap_processing.tests.glows.conftest import mock_update_spice_parameters
@@ -221,3 +223,61 @@ def test_validation_data_de(
 def test_deserialize_flags(flags, expected):
     output = HistogramL1B.deserialize_flags(flags)
     assert np.array_equal(output, expected)
+
+
+def test_pipeline_settings_from_flattened_json():
+    """PipelineSettings correctly reads flags from flattened JSON format.
+
+    convert_json_to_dataset flattens nested dicts, so
+    active_bad_time_flags.is_night -> active_bad_time_flags_is_night.
+    PipelineSettings must reconstruct the ordered flag lists from these keys.
+    """
+    data_vars = {
+        "active_bad_time_flags_is_pps_missing": ([], True),
+        "active_bad_time_flags_is_time_status_missing": ([], True),
+        "active_bad_time_flags_is_phase_missing": ([], True),
+        "active_bad_time_flags_is_spin_period_missing": ([], True),
+        "active_bad_time_flags_is_overexposed": ([], True),
+        "active_bad_time_flags_is_direct_event_non_monotonic": ([], True),
+        "active_bad_time_flags_is_night": ([], False),
+        "active_bad_time_flags_is_hv_test_in_progress": ([], True),
+        "active_bad_time_flags_is_test_pulse_in_progress": ([], True),
+        "active_bad_time_flags_is_memory_error_detected": ([], True),
+        "active_bad_time_flags_is_generated_on_ground": ([], True),
+        "active_bad_time_flags_is_beyond_daily_statistical_error": (
+            [],
+            True,
+        ),
+        "active_bad_time_flags_is_temperature_std_dev_beyond_threshold": (
+            [],
+            True,
+        ),
+        "active_bad_time_flags_is_hv_voltage_std_dev_beyond_threshold": (
+            [],
+            True,
+        ),
+        "active_bad_time_flags_is_spin_period_std_dev_beyond_threshold": (
+            [],
+            True,
+        ),
+        "active_bad_time_flags_is_pulse_length_std_dev_beyond_threshold": (
+            [],
+            True,
+        ),
+        "active_bad_time_flags_is_spin_period_difference_beyond_threshold": (
+            [],
+            False,
+        ),
+        "active_bad_angle_flags_is_close_to_uv_source": ([], True),
+        "active_bad_angle_flags_is_inside_excluded_region": ([], True),
+        "active_bad_angle_flags_is_excluded_by_instr_team": ([], True),
+        "active_bad_angle_flags_is_suspected_transient": ([], False),
+    }
+    settings = PipelineSettings(xr.Dataset(data_vars))
+
+    assert len(settings.active_bad_time_flags) == 17
+    assert settings.active_bad_time_flags[6] is False  # is_night
+    assert settings.active_bad_time_flags[16] is False  # is_spin_period_diff
+
+    assert len(settings.active_bad_angle_flags) == 4
+    assert settings.active_bad_angle_flags[3] is False  # is_suspected_transient