Adapting tasks to work with new infrastructure. Includes new template…

… generation and configs. Removing template size configs because the template size is dynamic (depends on instrument parameters). DonutStamp now uses the new Image class. All rotation and transposition is handled inside the DonutStamp class when creating that object. Most tests work now, although AuxTel/Latiss tests needs to be done on USDF.

python/lsst/ts/wep/estimation/tie.py

@@ -19,7 +19,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-__all__ = ["TIEAlgorithm"]
+__all__ = ["TieAlgorithm"]
 
 import warnings
 from typing import Iterable, Optional, Union
@@ -30,7 +30,7 @@
 from lsst.ts.wep.utils import DefocalType, createZernikeBasis, createZernikeGradBasis
 
 
-class TIEAlgorithm(WfAlgorithm):
+class TieAlgorithm(WfAlgorithm):
     """Wavefront estimation algorithm class for the TIE solver.
 
     Parameters
@@ -465,7 +465,7 @@ def _fftSolve(
         # TODO: Implement the fft solver
         raise NotImplementedError("The fft solver is not yet implemented.")
 
-    def estimateWf(
+    def estimateZk(
         self,
         I1: Image,
         I2: Image,  # type: ignore[override]

python/lsst/ts/wep/estimation/wfAlgorithm.py

@@ -108,7 +108,7 @@ def _validateInputs(
             raise TypeError("instrument must be an Instrument.")
 
     @abstractmethod
-    def estimateWf(
+    def estimateZk(
         self,
         I1: Image,
         I2: Optional[Image],

python/lsst/ts/wep/estimation/wfAlgorithmFactory.py

@@ -23,7 +23,7 @@
 
 from typing import Union
 
-from lsst.ts.wep.estimation.tie import TIEAlgorithm
+from lsst.ts.wep.estimation.tie import TieAlgorithm
 from lsst.ts.wep.estimation.wfAlgorithm import WfAlgorithm
 from lsst.ts.wep.utils import WfAlgorithmName, configClass
 
@@ -56,6 +56,6 @@ def createWfAlgorithm(
 
         # Return the configured algorithm
         if algoName == WfAlgorithmName.TIE:
-            return configClass(algoConfig, TIEAlgorithm)
+            return configClass(algoConfig, TieAlgorithm)
         else:
             raise ValueError(f"{algoName} not supported.")

python/lsst/ts/wep/estimation/wfEstimator.py

@@ -141,12 +141,12 @@ def units(self, value: str) -> None:
             )
         self._units = value
 
-    def estimateWf(
+    def estimateZk(
         self,
         I1: Image,
         I2: Optional[Image] = None,
     ) -> np.ndarray:
-        """Estimate the wavefront from the stamp or pair of stamps.
+        """Estimate Zernike coefficients of the wavefront from the stamp(s).
 
         Parameters
         ----------
@@ -168,8 +168,8 @@ def estimateWf(
         ValueError
             If I1 and I2 are on the same side of focus.
         """
-        # Estimated wavefront (in meters)
-        zk = self.algo.estimateWf(I1, I2, self.jmax, self.instrument)
+        # Estimate wavefront Zernike coefficients (in meters)
+        zk = self.algo.estimateZk(I1, I2, self.jmax, self.instrument)
 
         # Convert to desired units
         if self.units == "m":

python/lsst/ts/wep/image.py

@@ -43,17 +43,17 @@ class Image(object):
         Whether the image is intra- or extra-focal.
         Can be specified using a DefocalType Enum or the corresponding string.
     bandLabel : BandLabel or str, optional
-        Photometric band for the exposure.
-        Can be specified using a BandLabel Enum or the corresponding string.
+            Photometric band for the exposure. Can be specified using a
+            BandLabel Enum or the corresponding string. If None, BandLabel.REF
+            is used. The empty string "" also maps to BandLabel.REF.
         (the default is BandLabel.REF)
     planeType : PlaneType or str, optional
         Whether the image is on the image plane or the pupil plane.
         Can be specified using a PlaneType Enum, or the corresponding string.
         (the default is PlaneType.Image)
     blendOffsets : np.ndarray or tuple or list, optional
-        Positions of blended donuts relative to location of center donut,
-        in pixels. Must be provided in the format [dxList, dyList].
-        The lengths of dxList and dyList must be the same.
+        Positions of blended donuts relative to central donut, in pixels.
+        Must be provided in the format [[dx1, dy1], [dx2, dy2], ...].
         (the default is an empty array, i.e. no blends)
     mask : np.ndarray, optional
         The mask for the image. Mask creation is meant to be handled by the
@@ -67,7 +67,7 @@ def __init__(
         defocalType: Union[DefocalType, str],
         bandLabel: Union[BandLabel, str] = BandLabel.REF,
         planeType: Union[PlaneType, str] = PlaneType.Image,
-        blendOffsets: Union[np.ndarray, tuple, list] = np.zeros((2, 0)),
+        blendOffsets: Union[np.ndarray, tuple, list, None] = None,
         mask: Optional[np.ndarray] = None,
     ) -> None:
         self.image = image
@@ -103,7 +103,7 @@ def image(self, value: np.ndarray) -> None:
             raise TypeError("image must be a numpy array.")
         if len(value.shape) != 2 or value.shape[0] != value.shape[1]:
             raise ValueError("The image array must be square.")
-        self._image = value
+        self._image = value.copy()
 
     @property
     def fieldAngle(self) -> np.ndarray:
@@ -164,21 +164,24 @@ def bandLabel(self) -> BandLabel:
         return self._bandLabel
 
     @bandLabel.setter
-    def bandLabel(self, value: Union[BandLabel, str]) -> None:
+    def bandLabel(self, value: Union[BandLabel, str, None]) -> None:
         """Set the band label.
 
         Parameters
         ----------
-        value : BandLabel or str
-            Photometric band for the exposure.
-            Can be specified using a BandLabel Enum or the corresponding string.
+        value : BandLabel or str or None
+            Photometric band for the exposure. Can be specified using a
+            BandLabel Enum or the corresponding string. If None, BandLabel.REF
+            is used. The empty string "" also maps to BandLabel.REF.
 
         Raises
         ------
         TypeError
             The provided value is not a BandLabel Enum or string.
         """
-        if isinstance(value, str) or isinstance(value, BandLabel):
+        if value is None or value == "":
+            self._bandLabel = BandLabel.REF
+        elif isinstance(value, str) or isinstance(value, BandLabel):
             self._bandLabel = BandLabel(value)
         else:
             raise TypeError(
@@ -219,27 +222,35 @@ def blendOffsets(self) -> Union[np.ndarray, None]:
         return self._blendOffsets
 
     @blendOffsets.setter
-    def blendOffsets(self, value: Union[np.ndarray, tuple, list]) -> None:
+    def blendOffsets(self, value: Union[np.ndarray, tuple, list, None]) -> None:
         """Set the blend offsets array for the image.
 
         Parameters
         ----------
-        value : np.ndarray or tuple or list
-            Positions of blended donuts relative to location of center donut,
-            in pixels. Must be provided in the format [dxList, dyList].
-            The lengths of dxList and dyList must be the same.
+        value : np.ndarray or tuple or list or None
+            Positions of blended donuts relative to central donut, in pixels.
+            Must be provided in the format [[dx1, dy1], [dx2, dy2], ...].
+            If None, an empty array is populated for you.
 
         Raises
         ------
         ValueError
             If the provided value does not have the correct shape
         """
-        value = np.array(value, dtype=float)
-        if value.shape[0] != 2 or len(value.shape) != 2:
+        # If None, populate an empty array with the correct shape
+        if value is None:
+            value = np.zeros((0, 2))
+
+        # Convert to float array
+        value = np.atleast_2d(value).astype(float)
+
+        # Check shape
+        if value.shape[1] != 2 or len(value.shape) != 2:
             raise ValueError(
-                "blendOffsets must have shape (2, N), "
+                "blendOffsets must have shape (N, 2), "
                 "where N is the number of blends you wish to mask."
             )
+
         self._blendOffsets = value
 
     @property
@@ -266,9 +277,11 @@ def mask(self, value: Optional[np.ndarray]) -> None:
             If the mask is an array and does not match the shape of the image
         """
         if value is not None and not isinstance(value, np.ndarray):
-            raise TypeError("mask must be an array, or None.")
+            raise TypeError("mask must be an array or None.")
         elif isinstance(value, np.ndarray) and value.shape != self.image.shape:
             raise ValueError("mask must have the same shape as self.image.")
+        elif isinstance(value, np.ndarray):
+            value = value.copy()
         self._mask = value
 
     def copy(self) -> Self: