Quickmaps for IMAP-Lo

imap_processing/cdf/config/imap_lo_global_cdf_attrs.yaml

@@ -110,7 +110,7 @@ imap_lo_l1b_bgrates:
 imap_lo_l1b_goodtimes:
   <<: *instrument_base
   Data_type: L1B_goodtimes>Level-1B Goodtimes List
-  Logical_source: imap_lo_l1b_good-times
+  Logical_source: imap_lo_l1b_goodtimes
   Logical_source_description: IMAP Mission IMAP-Lo Instrument Level-1B Data
 
 imap_lo_l1c_goodtimes:
@@ -125,6 +125,12 @@ imap_lo_l1c_pset:
   Logical_source: imap_lo_l1c_pset
   Logical_source_description: IMAP Mission IMAP-Lo Instrument Level-1C Data
 
+imap_lo_l1c_quickmap:
+  <<: *instrument_base
+  Data_type: L1C_quickmap>Level-1C QuickMap (Histogram Sky Map)
+  Logical_source: imap_lo_l1c_quickmap
+  Logical_source_description: IMAP Mission IMAP-Lo Instrument Level-1C Sky Map
+
 # Global attributes for different sensors and sky tiling types and durations
 imap_lo_l2_enamap:
     <<: *instrument_base

imap_processing/cli.py

@@ -1225,15 +1225,43 @@ def do_processing(
             datasets = lo_l1b.lo_l1b(data_dict, ancillary_files, self.descriptor)
 
         elif self.data_level == "l1c":
-            data_dict = {}
-            anc_dependencies: list = dependencies.get_file_paths(
-                source="lo", data_type="ancillary"
-            )
-            science_files = dependencies.get_file_paths(source="lo", descriptor="de")
-            for file in science_files:
-                dataset = load_cdf(file)
-                data_dict[dataset.attrs["Logical_source"]] = dataset
-            datasets = lo_l1c.lo_l1c(data_dict, anc_dependencies)
+            if self.descriptor == "pset":
+                data_dict = {}
+                anc_dependencies: list = dependencies.get_file_paths(
+                    source="lo", data_type="ancillary"
+                )
+                science_files = dependencies.get_file_paths(
+                    source="lo", descriptor="de"
+                )
+                for file in science_files:
+                    dataset = load_cdf(file)
+                    data_dict[dataset.attrs["Logical_source"]] = dataset
+
+                datasets = lo_l1c.lo_l1c(data_dict, anc_dependencies)
+
+            elif self.descriptor == "quickmap":
+                data_dict = {}
+                science_files = (
+                    dependencies.get_file_paths(source="lo", descriptor="de")
+                    + dependencies.get_file_paths(source="lo", descriptor="nhk")
+                    + dependencies.get_file_paths(source="lo", descriptor="histrates")
+                    + dependencies.get_file_paths(source="lo", descriptor="goodtimes")
+                    + dependencies.get_file_paths(source="lo", descriptor="bgrates")
+                )
+
+                for file in science_files:
+                    dataset = load_cdf(file)
+                    data_dict[dataset.attrs["Logical_source"]] = dataset
+
+                quaternion_files = dependencies.get_file_paths(
+                    source="spacecraft", descriptor="quaternions", data_type="l1a"
+                )
+                quaternion_dependencies = [
+                    load_cdf(dep) for dep in list(set(quaternion_files))
+                ]
+                quaternion_dependencies.sort(key=lambda ds: ds["epoch"].values[0])
+
+                datasets = lo_l1c.lo_l1c_quickmap(data_dict, quaternion_dependencies)
 
         elif self.data_level == "l2":
             data_dict = {}

imap_processing/lo/constants.py

@@ -26,6 +26,7 @@ class LoConstants:
     N_CYCLE_AVE: int = 7  # Cycles to average over when estimating background rates
     N_ESA_LEVELS: int = 7  # Total number of ESA levels
     N_SPINS_PER_ESA_LEVEL: int = 4  # Spins per ESA step within one histogram cycle
+    N_SPIN_ANGLE_BINS: int = 60  # Number of angular bins within a spin
 
     # Nominal spin period [s]. True spin duration is NOT 15 seconds.
     NOMINAL_SPIN_PERIOD_SEC: float = 15.0
@@ -69,3 +70,37 @@ 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
+
+    N_COLAT_BINS: int = 30
+
+    # The following are indexed by ESA level (0-indexed, ESA level = index + 1)
+    ESA_ENERGY: ClassVar[list[float]] = [
+        0.016,
+        0.030,
+        0.056,
+        0.106,
+        0.200,
+        0.405,
+        0.787,
+        1.821,
+    ]
+    GEO_FACTOR: ClassVar[list[float]] = [
+        7.0e-5,
+        7.9e-5,
+        9.7e-5,
+        11.2e-5,
+        14.0e-5,
+        17.7e-5,
+        22.5e-5,
+        6.721e-5,
+    ]
+    GEO_FACTOR_ERR: ClassVar[list[float]] = [
+        4.9e-5,
+        5.5e-5,
+        6.8e-5,
+        3.0e-5,
+        4.5e-5,
+        2.0e-5,
+        1.4e-5,
+        6.721e-5,
+    ]

imap_processing/lo/l1b/lo_l1b.py

@@ -2696,7 +2696,7 @@ def l1b_bgrates_and_goodtimes(  # noqa: PLR0912
             for elem in elems:
                 synthetic_floors[elem] += c.BG_RATES[elem] * exposure
                 proxy_floors[elem] += np.sum(
-                    elem_anti_ram_counts["H"][window_sum_start:window_sum_end]
+                    elem_anti_ram_counts[elem][window_sum_start:window_sum_end]
                 )
 
             goodtime_exposure_avg += exposure