IMAP-Lo requested tweaks

.readthedocs.yaml

@@ -3,7 +3,7 @@ version: 2
 build:
   os: "ubuntu-22.04"
   tools:
-    python: "3.10"
+    python: "3.12"
 
 sphinx:
   configuration: docs/source/conf.py

imap_processing/cdf/config/imap_enamaps_l2-common_variable_attrs.yaml

@@ -239,6 +239,30 @@ bg_rate_sys_err:
   VAR_NOTES: Systematic error in the background count rate from non-ENA (non-heliospheric) sources.
   VAR_TYPE: support_data
 
+bg_rate_sys_err_minus:
+  <<: *default_float32
+  DEPEND_0: epoch
+  DICT_KEY: SPASE>Particle>ParticleType:Atom,ParticleQuantity:NumberFlux,Qualifier:Uncertainty,CoordinateSystemName:HAE,CoordinateRepresentation:Spherical
+  DISPLAY_TYPE: map_image
+  FIELDNAM: Background Rate Sys Error Lower
+  FORMAT: F6.3
+  LABLAXIS: Rate Error Lower
+  UNITS: count s-1
+  VAR_NOTES: Lower bound of the systematic error in the background count rate from non-ENA (non-heliospheric) sources.
+  VAR_TYPE: support_data
+
+bg_rate_sys_err_plus:
+  <<: *default_float32
+  DEPEND_0: epoch
+  DICT_KEY: SPASE>Particle>ParticleType:Atom,ParticleQuantity:NumberFlux,Qualifier:Uncertainty,CoordinateSystemName:HAE,CoordinateRepresentation:Spherical
+  DISPLAY_TYPE: map_image
+  FIELDNAM: Background Rate Sys Error Upper
+  FORMAT: F6.3
+  LABLAXIS: Rate Error Upper
+  UNITS: count s-1
+  VAR_NOTES: Upper bound of the systematic error in the background count rate from non-ENA (non-heliospheric) sources.
+  VAR_TYPE: support_data
+
 bg_intensity:
   <<: *default_float32
   DEPEND_0: epoch
@@ -275,6 +299,30 @@ bg_intensity_sys_err:
   VAR_NOTES: Non-statistical error in the background intensity from non-ENA (non-heliospheric) sources.
   VAR_TYPE: support_data
 
+bg_intensity_sys_err_minus:
+  <<: *default_float32
+  DEPEND_0: epoch
+  DICT_KEY: SPASE>Particle>ParticleType:Atom,ParticleQuantity:NumberFlux,Qualifier:Uncertainty,CoordinateSystemName:HAE,CoordinateRepresentation:Spherical
+  DISPLAY_TYPE: map_image
+  FIELDNAM: Background Intensity Non-statistical Error Lower
+  FORMAT: F8.3
+  LABLAXIS: Intensity Non-statistical Error Lower
+  UNITS: cm -2 s -1 sr -1 keV -1
+  VAR_NOTES: Lower bound of the non-statistical error in the background intensity from non-ENA (non-heliospheric) sources.
+  VAR_TYPE: support_data
+
+bg_intensity_sys_err_plus:
+  <<: *default_float32
+  DEPEND_0: epoch
+  DICT_KEY: SPASE>Particle>ParticleType:Atom,ParticleQuantity:NumberFlux,Qualifier:Uncertainty,CoordinateSystemName:HAE,CoordinateRepresentation:Spherical
+  DISPLAY_TYPE: map_image
+  FIELDNAM: Background Intensity Non-statistical Error Upper
+  FORMAT: F8.3
+  LABLAXIS: Intensity Non-statistical Error Upper
+  UNITS: cm -2 s -1 sr -1 keV -1
+  VAR_NOTES: Upper bound of the non-statistical error in the background intensity from non-ENA (non-heliospheric) sources.
+  VAR_TYPE: support_data
+
 ena_count:
   <<: *default_float32
   DEPEND_0: epoch

imap_processing/ena_maps/utils/naming.py

@@ -203,9 +203,13 @@ def build_map_var_catdesc(self, support_var_name: str) -> str | None:
             "bg_intensity": "Background Inten",
             "bg_intensity_stat_uncert": "Background Inten Stat. Unc.",
             "bg_intensity_sys_err": "Background Inten Sys. Err.",
+            "bg_intensity_sys_err_minus": "Background Inten Sys. Err. Lower",
+            "bg_intensity_sys_err_plus": "Background Inten Sys. Err. Upper",
             "bg_rate": "Background Count Rate",
             "bg_rate_stat_uncert": "Background Count Rate Stat. Unc.",
             "bg_rate_sys_err": "Background Count Rate Sys. Err.",
+            "bg_rate_sys_err_minus": "Background Count Rate Sys. Err. Lower",
+            "bg_rate_sys_err_plus": "Background Count Rate Sys. Err. Upper",
             "ena_count_rate": "ENA Count Rate",
             "ena_count_rate_stat_uncert": "ENA Count Rate Stat. Unc.",
             "obs_date": "Mean Observation Date",

imap_processing/lo/ancillary_data/imap_lo_hydrogen-geometric-factor_v004.csv

@@ -0,0 +1,16 @@
+esa_mode,incident_E-Step,Observed_E-Step,Cntr_E,Cntr_E_unc,GF_Trpl_H,GF_Trpl_H_unc_minus,GF_Trpl_H_unc_plus
+# [1],[1],[1],[keV],[keV],[cm^2sr keV/keV],[cm^2sr keV/keV],[cm^2sr keV/keV]
+0,1,1,0.01633,0.28,4.45E-05,4.03E-05,4.20E-05
+0,2,2,0.03047,0.43,5.02E-05,4.53E-05,4.72E-05
+0,3,3,0.05576,0.89,6.16E-05,5.58E-05,5.82E-05
+0,4,4,0.10626,1.9,7.12E-05,4.51E-05,4.89E-05
+0,5,5,0.20004,3.4,8.89E-05,5.85E-05,6.31E-05
+0,6,6,0.40496,7.3,1.12E-04,6.58E-05,7.23E-05
+0,7,7,0.78729,22,1.43E-04,8.25E-05,9.09E-05
+1,1,1,0.01719,0.22,1.05E-04,8.82E-05,9.26E-05
+1,2,2,0.03236,0.36,1.22E-04,1.02E-04,1.07E-04
+1,3,3,0.05948,0.77,1.44E-04,1.21E-04,1.27E-04
+1,4,4,0.11441,1.3,1.73E-04,1.17E-04,1.26E-04
+1,5,5,0.2137,3,2.15E-04,1.37E-04,1.49E-04
+1,6,6,0.43736,5.3,2.82E-04,1.65E-04,1.81E-04
+1,7,7,0.83888,11.7,3.61E-04,2.08E-04,2.29E-04

imap_processing/lo/ancillary_data/imap_lo_oxygen-geometric-factor_v004.csv

@@ -0,0 +1,16 @@
+esa_mode,incident_E-Step,Observed_E-Step,Cntr_E,Cntr_E_unc,GF_Trpl_O,GF_Trpl_O_unc_minus,GF_Trpl_O_unc_plus
+# [1],[1],[1],[keV],[keV],[cm^2sr keV/keV],[cm^2sr keV/keV],[cm^2sr keV/keV]
+0,1,1,0.01919,1.372344105,1.98E-05,1.74E-05,1.81E-05
+0,2,2,0.03675,2.537963082,3.18E-05,2.39E-05,2.53E-05
+0,3,3,0.07121,6.591339559,5.34E-05,4.05E-05,4.29E-05
+0,4,4,0.14141,17.43990907,8.64E-05,6.14E-05,6.56E-05
+0,5,5,0.274,35.83900763,1.32E-04,9.07E-05,9.72E-05
+0,6,6,0.58503,98.06681395,2.46E-04,1.53E-04,1.67E-04
+0,7,7,1.13506,238.1076888,3.81E-04,2.23E-04,2.45E-04
+1,1,1,0.02043303552,1.575924599,5.02E-05,4.41E-05,4.61E-05
+1,2,2,0.03972,2.953020557,8.13E-05,6.09E-05,6.47E-05
+1,3,3,0.07648,7.476494389,1.33E-04,1.01E-04,1.07E-04
+1,4,4,0.15353,14.87953071,2.38E-04,1.69E-04,1.80E-04
+1,5,5,0.29846,41.48950349,3.87E-04,2.67E-04,2.86E-04
+1,6,6,0.61524,83.57629594,6.61E-04,4.11E-04,4.47E-04
+1,7,7,1.24282,212.0806857,1.02E-03,6.10E-04,6.67E-04

imap_processing/lo/constants.py

@@ -58,9 +58,9 @@ class LoConstants:
     # The first matching open interval (low < pivot < high) is used; if none matches,
     # THRESHOLD_BG_RATE_RAM_DEFAULT / THRESHOLD_BG_RATE_ANTI_RAM_DEFAULT apply.
     PIVOT_ANGLE_THRESHOLDS: ClassVar[dict[tuple[float, float], tuple[float, float]]] = {
-        (88.0, 92.0): (0.014, 0.007),
-        (73.0, 77.0): (0.0175, 0.00875),
-        (103.0, 107.0): (0.0112, 0.0056),
+        (88.0, 92.0): (0.028, 0.014),
+        (73.0, 77.0): (0.035, 0.0175),
+        (103.0, 107.0): (0.0224, 0.0112),
     }
 
     # Default background-rate thresholds [counts/s] when no pivot range matches.
@@ -75,3 +75,17 @@ class LoConstants:
     # Padding [s] added to begin/end of each goodtime interval to ensure complete
     # cycles are covered at interval edges.
     GOODTIME_PADDING: float = 2.0
+
+    # Star-sensor spin-angle binning offset (fractional bin-index shift used when
+    # computing sample centers), keyed by the IFB star-sync housekeeping state
+    # (ifb_ctrl_star_sync). Flight software 4.8 enabled star sync ("EN"),
+    # switching from binning to the bin center (+0.5) to the left edge (+0.0).
+    STAR_BIN_OFFSET_BY_SYNC: ClassVar[dict[str | None, float]] = {
+        "DS": 0.5,  # star sync disabled (pre FSW 4.8)
+        "EN": 0.0,  # star sync enabled (FSW 4.8+)
+    }
+
+    # Number of ending bins to exclude from each star-sensor profile average.
+    STAR_END_BINS_TO_EXCLUDE: int = 2
+    # Minimum COUNT value for a star-sensor record to be considered valid.
+    STAR_MIN_COUNT_THRESHOLD: int = 700

imap_processing/lo/l1b/lo_l1b.py

@@ -1986,7 +1986,7 @@ def split_rate_dataset(
 
 def filter_valid_star_records(
     l1a_star: xr.Dataset,
-    min_count: int = 700,
+    min_count: int = c.STAR_MIN_COUNT_THRESHOLD,
     time_window_offset: float = 0.0,
     time_window_duration: float | None = None,
 ) -> np.ndarray:
@@ -2014,7 +2014,7 @@ def filter_valid_star_records(
     valid_mask : np.ndarray
         Boolean array indicating valid records.
     """
-    # Section 5: Acceptance Criteria - COUNT >= 700
+    # Section 5: Acceptance Criteria - COUNT >= min_count
     count_mask = l1a_star["count"].values >= min_count
 
     # shcoarse is already in MET seconds
@@ -2049,7 +2049,7 @@ def filter_valid_star_records(
 def calculate_star_sensor_profile_for_group(
     data: np.ndarray,
     counts: np.ndarray,
-    end_bins_to_exclude: int = 2,
+    end_bins_to_exclude: int = c.STAR_END_BINS_TO_EXCLUDE,
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Calculate averaged star sensor amplitude profile for a group of records.
@@ -2098,14 +2098,63 @@ def calculate_star_sensor_profile_for_group(
     return avg_amplitude, count_array
 
 
+def get_star_bin_offset(l1b_nhk: xr.Dataset, reference_epoch: int) -> float:
+    """
+    Determine the star-sensor binning offset from the IFB star-sync state.
+
+    Reads ``ifb_ctrl_star_sync`` from the NHK housekeeping at the record nearest
+    on or before ``reference_epoch`` and maps it to a binning offset via
+    ``LoConstants.STAR_BIN_OFFSET_BY_SYNC``.
+
+    Parameters
+    ----------
+    l1b_nhk : xr.Dataset
+        L1B NHK dataset containing ``ifb_ctrl_star_sync`` and an ``epoch``
+        coordinate (TT2000 nanoseconds since J2000).
+    reference_epoch : int
+        Epoch at which to evaluate the sync state, in TT2000 nanoseconds since
+        J2000. The NHK record in effect at or before this time is used.
+
+    Returns
+    -------
+    bin_offset : float
+        Fractional bin-index offset to use when computing sample spin-angle
+        centers.
+
+    Raises
+    ------
+    KeyError
+        If ``ifb_ctrl_star_sync`` is not present in ``l1b_nhk``.
+    """
+    if "ifb_ctrl_star_sync" not in l1b_nhk:
+        raise KeyError(
+            "ifb_ctrl_star_sync field not found in L1B NHK dataset. "
+            "Cannot determine star-sensor binning offset."
+        )
+
+    nhk_epoch = l1b_nhk["epoch"].values
+    sync_state = l1b_nhk["ifb_ctrl_star_sync"].values
+
+    # Use the housekeeping record in effect at the reference epoch (the last
+    # NHK sample at or before it), clamping to the first sample if the reference
+    # epoch falls before NHK coverage.
+    idx = max(int(np.searchsorted(nhk_epoch, reference_epoch, side="right")) - 1, 0)
+    state = str(sync_state[idx])
+
+    offset = c.STAR_BIN_OFFSET_BY_SYNC[state]
+    logger.info(f"Star sync state '{state}' -> bin offset {offset}")
+    return offset
+
+
 def calculate_star_sensor_profiles_by_group(
     l1a_star: xr.Dataset,
     sampling_cadence: float,
     spin_period: float,
     group_size: int = 64,
     start_angle_offset: float = 62.0,
-    end_bins_to_exclude: int = 2,
-    min_count_threshold: int = 700,
+    end_bins_to_exclude: int = c.STAR_END_BINS_TO_EXCLUDE,
+    min_count_threshold: int = c.STAR_MIN_COUNT_THRESHOLD,
+    bin_offset: float = 0.5,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Calculate averaged star sensor amplitude profiles for groups of records.
@@ -2129,6 +2178,10 @@ def calculate_star_sensor_profiles_by_group(
         Number of ending bins to exclude from each average (default: 2).
     min_count_threshold : int
         Minimum COUNT value for valid record (default: 700).
+    bin_offset : float
+        Fractional offset applied to bin indices when computing sample
+        spin-angle centers (default: 0.5). Use 0.5 to bin to the bin center
+        and 0.0 to bin to the left edge.
 
     Returns
     -------
@@ -2152,7 +2205,7 @@ def calculate_star_sensor_profiles_by_group(
     # Calculate spin angles (same for all groups)
     deg_per_bin = 360.0 * (sampling_cadence / 1000.0) / spin_period
     bin_indices = np.arange(720)
-    sample_centers = (bin_indices + 0.5) * deg_per_bin
+    sample_centers = (bin_indices + bin_offset) * deg_per_bin
     spin_angle = (start_angle_offset + sample_centers) % 360.0
 
     if n_valid == 0:
@@ -2282,13 +2335,20 @@ def l1b_star(
     logger.info(f"Using spin duration from spin data: {spin_duration:.6f} s")
 
     # TODO: Read from ancillary config file when available
-    lo_angle_offset = 2.0
-    sc_to_inst_angle_offset = (
-        360 * get_spacecraft_to_instrument_spin_phase_offset(SpiceFrame.IMAP_LO)
-        + lo_angle_offset
+    sc_to_inst_angle_offset = 360 * get_spacecraft_to_instrument_spin_phase_offset(
+        SpiceFrame.IMAP_LO
     )
-    end_bins_to_exclude = 2
-    min_count_threshold = 700
+    end_bins_to_exclude = c.STAR_END_BINS_TO_EXCLUDE
+    min_count_threshold = c.STAR_MIN_COUNT_THRESHOLD
+
+    # Global epoch times from L1A data (used for start_doy/end_doy below).
+    global_start_epoch = l1a_star["epoch"].values[0]
+    global_end_epoch = l1a_star["epoch"].values[-1]
+
+    # Select the star-sensor binning convention from the IFB star-sync state in
+    # housekeeping. Evaluate at the earliest star record's epoch so a pointing that
+    # spans the `EN` event uses the value corresponding to the state at its start.
+    bin_offset = get_star_bin_offset(l1b_nhk, int(global_start_epoch))
 
     # Calculate profiles for each 64-spin group
     (
@@ -2304,12 +2364,9 @@ def l1b_star(
         start_angle_offset=sc_to_inst_angle_offset,
         end_bins_to_exclude=end_bins_to_exclude,
         min_count_threshold=min_count_threshold,
+        bin_offset=bin_offset,
     )
 
-    # Get global epoch times from L1A data for start_doy and end_doy
-    global_start_epoch = l1a_star["epoch"].values[0]
-    global_end_epoch = l1a_star["epoch"].values[-1]
-
     # Create dataset with spin_angle as coordinate and multiple epochs
     group_epochs = met_to_ttj2000ns(group_mets)
     l1b_star_ds = xr.Dataset(
@@ -2375,7 +2432,6 @@ def l1b_star(
     l1b_star_ds.attrs["pointing_mid_met"] = pointing_mid_met
     l1b_star_ds.attrs["sampling_cadence_ms"] = sampling_cadence
     l1b_star_ds.attrs["spin_duration_sec"] = spin_duration
-    l1b_star_ds.attrs["lo_angle_offset_deg"] = lo_angle_offset
     l1b_star_ds.attrs["end_bins_excluded"] = end_bins_to_exclude
     l1b_star_ds.attrs["min_count_threshold"] = min_count_threshold
     l1b_star_ds.attrs["group_size"] = group_size