Add drift_pars kwarg to get_aca_offsets and get_fid_offset

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.9.0
+  rev: v0.9.9
   hooks:
     # Run the linter.
     - id: ruff

chandra_aca/drift.py

@@ -171,7 +171,11 @@ def calc(self, times, t_ccd):
         return out[0] if is_scalar else out
 
 
-def get_fid_offset(time: CxoTimeLike, t_ccd: float) -> tuple:
+def get_fid_offset(
+    time: CxoTimeLike,
+    t_ccd: float,
+    drift_pars: dict | None = None,
+) -> tuple:
     """
     Compute the fid light offset values for a given time and temperature.
 
@@ -183,6 +187,8 @@ def get_fid_offset(time: CxoTimeLike, t_ccd: float) -> tuple:
         Time for offset calculation.
     t_ccd : float
         ACA CCD temperature in degrees Celsius.
+    drift_pars : dict, optional
+        ACA drift model parameters.  If not supplied, the default parameters are used.
 
     Returns
     -------
@@ -201,13 +207,15 @@ def get_fid_offset(time: CxoTimeLike, t_ccd: float) -> tuple:
     2022-11 aimpoint drift model and the FEB07 fid characteristics.
     See https://github.com/sot/fid_drift_mon/blob/master/fid_offset_coeff.ipynb
     """
+    if drift_pars is None:
+        drift_pars = DRIFT_PARS
 
     # Clip the time to the minimum time in the drift model
     time = CxoTime(time).secs.clip(CxoTime("2012:001:12:00:00.000").secs, None)
 
     # Define model instances using calibrated parameters
-    drift_y = AcaDriftModel(**DRIFT_PARS["dy"])
-    drift_z = AcaDriftModel(**DRIFT_PARS["dz"])
+    drift_y = AcaDriftModel(**drift_pars["dy"])
+    drift_z = AcaDriftModel(**drift_pars["dz"])
 
     # Compute the predicted asol DY/DZ based on time and ACA CCD temperature
     # via the predictive model calibrated in the fit_aimpoint_drift notebook
@@ -222,7 +230,15 @@ def get_fid_offset(time: CxoTimeLike, t_ccd: float) -> tuple:
     return dy_pred + y_offset, dz_pred + z_offset
 
 
-def get_aca_offsets(detector, chip_id, chipx, chipy, time, t_ccd):
+def get_aca_offsets(
+    detector,
+    chip_id,
+    chipx,
+    chipy,
+    time,
+    t_ccd,
+    drift_pars: dict | None = None,
+):
     """
     Compute the dynamical ACA offset values for the provided inputs.
 
@@ -242,14 +258,19 @@ def get_aca_offsets(detector, chip_id, chipx, chipy, time, t_ccd):
         time(s) of observation (any Chandra.Time compatible format)
     t_ccd
         ACA CCD temperature(s) (degC)
+    drift_pars : dict, optional
+        ACA drift model parameters.  If not supplied, the default parameters are used.
 
     Returns
     -------
     aca_offset_y, aca_offset_z (arcsec)
     """
+    if drift_pars is None:
+        drift_pars = DRIFT_PARS
+
     # Define model instances using calibrated parameters
-    drift_y = AcaDriftModel(**DRIFT_PARS["dy"])
-    drift_z = AcaDriftModel(**DRIFT_PARS["dz"])
+    drift_y = AcaDriftModel(**drift_pars["dy"])
+    drift_z = AcaDriftModel(**drift_pars["dz"])
 
     try:
         asol_to_chip = ASOL_TO_CHIP[detector, chip_id]

chandra_aca/tests/test_drift.py

@@ -4,9 +4,14 @@
 import astropy.table as tbl
 import numpy as np
 import pytest
+from ska_helpers.utils import LazyDict
 
 from chandra_aca import drift
 
+# Drift pars for testing that may reflect a new model in a non-flight testing repo.
+# See test_get_aca_offsets() for how to test a new drift model.
+DRIFT_PARS_TEST = LazyDict(drift.load_drift_pars)
+
 
 def test_get_aca_offsets_legacy():
     """
@@ -53,13 +58,38 @@ def test_get_aca_offsets_legacy():
 
 
 @pytest.mark.parametrize("kwargs", kwargs_list)
-def test_get_aca_offsets(kwargs, monkeypatch):
-    """Regression test that ACA offsets match the original flight values from 2022-11
-    analysis to expected precision."""
-    monkeypatch.setenv("CHANDRA_MODELS_DEFAULT_VERSION", "3.48")
+@pytest.mark.parametrize("use_drift_pars", [True, False])
+def test_get_aca_offsets(kwargs, use_drift_pars):
+    """Regression test that ACA offsets match expected.
+
+    Expected values are the original flight values from 2022-11 analysis to expected
+    precision. The chandra_models version is (by default) current flight, so this test
+    ensures that any model updates do not introduce a regression.
+
+    Testing a new drift model here can be done with environment variables::
+
+      env CHANDRA_MODELS_REPO_DIR=$HOME/git/chandra_models \
+          CHANDRA_MODELS_DEFAULT_VERSION=aimpoint-drift-update  \
+      pytest -k test_drift
+
+    When a new drift model is released, the `aimpoint_regression_data.ecsv` file can
+    optionally be updated with the new values from the analysis notebook.
+    """
     kwargs = kwargs.copy()
     aca_offset_y = kwargs.pop("aca_offset_y")
     aca_offset_z = kwargs.pop("aca_offset_z")
+
+    # Test explicitly providing drift_pars. This should be exactly the same as the
+    # default handling.
+    if use_drift_pars:
+        kwargs["drift_pars"] = DRIFT_PARS_TEST
+        # Print info for use with `-s` flag to see what model is being used.
+        info = DRIFT_PARS_TEST["info"]
+        repo_path = info["repo_path"]
+        version = info["version"]
+        md5 = info["md5"]
+        print(f"{repo_path=} {version=} {md5=}")
+
     offsets = drift.get_aca_offsets(**kwargs)
     dy = offsets[0] - aca_offset_y
     dz = offsets[1] - aca_offset_z
@@ -70,19 +100,21 @@ def test_get_aca_offsets(kwargs, monkeypatch):
 
 
 @pytest.mark.parametrize(
-    "t, t_ccd, expected_dy, expected_dz",
+    "date, t_ccd, expected_dy, expected_dz",
     [
         ("2018:284", -10.0, -7.50, -5.30),
         ("2018:286", -10.0, 5.01, 0.75),
         ("2022:293", -10, 9.46, 1.39),
         ("2022:295", -10, 17.44, 2.90),
     ],
 )
-def test_get_fid_offset(t, t_ccd, expected_dy, expected_dz):
+@pytest.mark.parametrize("use_drift_pars", [True, False])
+def test_get_fid_offset(date, t_ccd, expected_dy, expected_dz, use_drift_pars):
     """
     Test that the get_fid_offset function returns expected values for a few inputs.
     """
-    dy, dz = drift.get_fid_offset(t, t_ccd)
+    kwargs = {"drift_pars": DRIFT_PARS_TEST} if use_drift_pars else {}
+    dy, dz = drift.get_fid_offset(date, t_ccd, **kwargs)
     assert np.isclose(dy, expected_dy, atol=0.01)
     assert np.isclose(dz, expected_dz, atol=0.01)