Update pointing table to include subsecond fields (IMAP-Science-Operations-Center#1500)

* Update pointing table to include subsecond fields

* remove floating point met columns from fake pointing df creator

* Improve docstrings

Author: tmplummer

imap_processing/spice/repoint.py

@@ -16,6 +16,11 @@ def get_repoint_data() -> pd.DataFrame:
     """
     Read repointing file using environment variable and return as dataframe.
 
+    Pointing and repointing nomenclature can be confusing. In this case,
+    repoint is taken to mean a repoint maneuver. Thus, repoint_start and repoint_end
+    are the times that bound when the spacecraft is performing a repointing maneuver.
+    This is different from a pointing which is the time between repointing maneuvers.
+
     REPOINT_DATA_FILEPATH environment variable should point to a local
     file where the repointing csv file is located.
 
@@ -24,8 +29,14 @@ def get_repoint_data() -> pd.DataFrame:
     repoint_df : pd.DataFrame
         The repointing csv loaded into a pandas dataframe. The dataframe will
         contain the following columns:
-            - `repoint_start_time`: Starting MET time of each repoint maneuver.
-            - `repoint_end_time`: Ending MET time of each repoint maneuver.
+            - `repoint_start_sec`: Starting MET seconds of repoint maneuver.
+            - `repoint_start_subsec`: Starting MET milliseconds of repoint maneuver.
+            - `repoint_start_met`: Floating point MET of repoint maneuver start time.
+            Derived by combining `repoint_start_sec` and `repoint_start_subsec`.
+            - `repoint_end_sec`: Ending MET seconds of repoint maneuver.
+            - `repoint_end_subsec`: Ending MET milliseconds of repoint maneuver.
+            - `repoint_end_met`: Floating point MET of repoint maneuver end time.
+            Derived by combining `repoint_end_sec` and `repoint_end_subsec`.
             - `repoint_id`: Unique ID number of each repoint maneuver.
     """
     repoint_data_filepath = os.getenv("REPOINT_DATA_FILEPATH")
@@ -38,6 +49,14 @@ def get_repoint_data() -> pd.DataFrame:
     logger.info(f"Reading repointing data from {path_to_spin_file}")
     repoint_df = pd.read_csv(path_to_spin_file, comment="#")
 
+    # Compute times by combining seconds and subseconds fields
+    repoint_df["repoint_start_met"] = (
+        repoint_df["repoint_start_sec"] + repoint_df["repoint_start_subsec"] / 1e3
+    )
+    repoint_df["repoint_end_met"] = (
+        repoint_df["repoint_end_sec"] + repoint_df["repoint_end_subsec"] / 1e3
+    )
+
     return repoint_df
 
 
@@ -68,8 +87,12 @@ def interpolate_repoint_data(
     repoint_df : pandas.DataFrame
         Repoint table data interpolated such that there is one row
         for each of the queried MET times. Output columns are:
-            - `repoint_start_time`
-            - `repoint_end_time`
+            - `repoint_start_sec`
+            - `repoint_start_subsec`
+            - `repoint_start_met`
+            - `repoint_end_sec`
+            - `repoint_end_subsec`
+            - `repoint_end_met`
             - `repoint_id`
             - `repoint_in_progress`
 
@@ -84,29 +107,29 @@ def interpolate_repoint_data(
     query_met_times = np.atleast_1d(query_met_times)
 
     # Make sure no query times are before the first repoint in the dataframe.
-    repoint_df_start_time = repoint_df["repoint_start_time"].values[0]
-    if np.any(query_met_times < repoint_df_start_time):
-        bad_times = query_met_times[query_met_times < repoint_df_start_time]
+    repoint_df_start_met = repoint_df["repoint_start_met"].values[0]
+    if np.any(query_met_times < repoint_df_start_met):
+        bad_times = query_met_times[query_met_times < repoint_df_start_met]
         raise ValueError(
             f"{bad_times.size} query times are before the first repoint start "
-            f" time in the repoint table. {bad_times=}, {repoint_df_start_time=}"
+            f" time in the repoint table. {bad_times=}, {repoint_df_start_met=}"
         )
     # Make sure that no query times are after the valid range of the dataframe.
     # We approximate the end time of the table by adding 24 hours to the last
     # known repoint start time.
-    repoint_df_end_time = repoint_df["repoint_start_time"].values[-1] + 24 * 60 * 60
-    if np.any(query_met_times >= repoint_df_end_time):
-        bad_times = query_met_times[query_met_times >= repoint_df_end_time]
+    repoint_df_end_met = repoint_df["repoint_start_met"].values[-1] + 24 * 60 * 60
+    if np.any(query_met_times >= repoint_df_end_met):
+        bad_times = query_met_times[query_met_times >= repoint_df_end_met]
         raise ValueError(
             f"{bad_times.size} query times are after the valid time of the "
             f"pointing table. The valid end time is 24-hours after the last "
-            f"repoint_start_time. {bad_times=}, {repoint_df_end_time=}"
+            f"repoint_start_time. {bad_times=}, {repoint_df_end_met=}"
         )
 
     # Find the row index for each queried MET time such that:
     # repoint_start_time[i] <= MET < repoint_start_time[i+1]
     row_indices = (
-        np.searchsorted(repoint_df["repoint_start_time"], query_met_times, side="right")
+        np.searchsorted(repoint_df["repoint_start_met"], query_met_times, side="right")
         - 1
     )
     out_df = repoint_df.iloc[row_indices]
@@ -115,6 +138,6 @@ def interpolate_repoint_data(
     # The table already has the correct row for each query time, so we
     # only need to check if the query time is less than the repoint end time to
     # get the same result as `repoint_start_time <= query_met_times < repoint_end_time`.
-    out_df["repoint_in_progress"] = query_met_times < out_df["repoint_end_time"].values
+    out_df["repoint_in_progress"] = query_met_times < out_df["repoint_end_met"].values
 
     return out_df

imap_processing/tests/conftest.py

@@ -590,8 +590,10 @@ def generate_repoint_data(
         repoint_end_met = repoint_start_times + 15 * 60
     repoint_df = pd.DataFrame.from_dict(
         {
-            "repoint_start_time": repoint_start_times,
-            "repoint_end_time": np.array(repoint_end_met),
+            "repoint_start_sec": repoint_start_times.astype(int),
+            "repoint_start_subsec": ((repoint_start_times % 1.0) * 1e3).astype(int),
+            "repoint_end_sec": repoint_end_met.astype(int),
+            "repoint_end_subsec": ((repoint_end_met % 1.0) * 1e3).astype(int),
             "repoint_id": np.arange(repoint_start_times.size, dtype=int)
             + repoint_id_start,
         }

imap_processing/tests/spice/test_data/fake_repoint_data.csv

@@ -1,5 +1,5 @@
 # This is a fake csv file for the sole purpose of testing repoint module functions
-repoint_start_time,repoint_end_time,repoint_id
-0,5,0
-15,20,1
-25,30,2
+repoint_start_sec,repoint_start_subsec,repoint_end_sec,repoint_end_subsec,repoint_id
+0,100,5,100,0
+15,200,20,200,1
+25,300,30,300,2

imap_processing/tests/spice/test_repoint.py

@@ -20,8 +20,12 @@ def test_get_repoint_data(fake_repoint_data):
     repoint_df = get_repoint_data()
     assert isinstance(repoint_df, pd.DataFrame)
     assert set(repoint_df.columns) == {
-        "repoint_start_time",
-        "repoint_end_time",
+        "repoint_start_sec",
+        "repoint_start_subsec",
+        "repoint_start_met",
+        "repoint_end_sec",
+        "repoint_end_subsec",
+        "repoint_end_met",
         "repoint_id",
     }
 
@@ -36,10 +40,14 @@ def test_spin_data_no_table():
 
 def test_interpolate_repoint_data(fake_repoint_data):
     """Test coverage for get_repoint_data function."""
-    query_times = np.array([0, 6, 32])
+    query_times = np.array([0.1, 6, 32])
     expected_vals = {
-        "repoint_start_time": np.array([0, 0, 25]),
-        "repoint_end_time": np.array([5, 5, 30]),
+        "repoint_start_sec": np.array([0, 0, 25]),
+        "repoint_start_subsec": np.array([100, 100, 300]),
+        "repoint_start_met": np.array([0.1, 0.1, 25.3]),
+        "repoint_end_sec": np.array([5, 5, 30]),
+        "repoint_end_subsec": np.array([100, 100, 300]),
+        "repoint_end_met": np.array([5.1, 5.1, 30.3]),
         "repoint_id": np.array([0, 0, 2]),
         "repoint_in_progress": np.array([True, False, False]),
     }
@@ -57,7 +65,7 @@ def test_interpolate_repoint_data(fake_repoint_data):
             "1 query times are before",
         ),  # Query times before start of table
         (
-            np.array([0, 24 * 60 * 60 + 26]),
+            np.array([1, 24 * 60 * 60 + 26]),
             "1 query times are after",
         ),  # Query times after end of valid range
     ],
@@ -81,12 +89,12 @@ def test_interpolate_repoint_data_with_use_fake_fixture(use_fake_repoint_data_fo
 
     # Expected repoint_start_times are the seeded start times array repeated twice
     np.testing.assert_array_equal(
-        repoint_df["repoint_start_time"].values,
+        repoint_df["repoint_start_met"].values,
         np.concat([repoint_start_times, repoint_start_times]),
     )
     # Expected repoint_end_times are the seeded start times + 15 minutes
     np.testing.assert_array_equal(
-        repoint_df["repoint_end_time"].values,
+        repoint_df["repoint_end_met"].values,
         np.concat([repoint_start_times + 15 * 60, repoint_start_times + 15 * 60]),
     )
     # Expected repoint_id