add logging instead of warning

python/lsst/meas/algorithms/gp_interpolation.py

@@ -9,7 +9,7 @@
 from jax import jit
 import jax.numpy as jnp
 
-import warnings
+import logging
 
 __all__ = ["interpolateOverDefectsGP"]
 
@@ -276,7 +276,9 @@ class InterpolateOverDefectGaussianProcess:
         The threshold for sub-dividing the bad pixel array to avoid memory error. Default is 20000.
     correlation_length_cut : `int`, optional
         The factor by which to dilate the bounding box around defects. Default is 5.
-
+    log : `lsst.log.Log`, `logging.Logger` or `None`, optional
+        Logger object used to write out messages. If `None` a default
+        logger will be used.
     """
 
     def __init__(
@@ -289,6 +291,7 @@ def __init__(
         threshold_dynamic_binning=1000,
         threshold_subdivide=20000,
         correlation_length_cut=5,
+        log=None,
     ):
         if method == "jax":
             self.GaussianProcess = GaussianProcessJax
@@ -297,6 +300,7 @@ def __init__(
         else:
             raise ValueError("Invalid method. Must be 'jax' or 'treegp'.")
 
+        self.log = log or logging.getLogger(__name__)
         self.bin_spacing = bin_spacing
         self.threshold_subdivide = threshold_subdivide
         self.threshold_dynamic_binning = threshold_dynamic_binning
@@ -397,7 +401,7 @@ def interpolate_sub_masked_image(self, sub_masked_image):
         )
         # Do nothing if bad pixel is None.
         if bad_pixel.size == 0 or good_pixel.size == 0:
-            warnings.warn("No bad or good pixels found. No interpolation performed.")
+            self.log.info("No bad or good pixels found. No interpolation performed.")
             return sub_masked_image
         # Do GP interpolation if bad pixel found.
         else:
@@ -411,7 +415,7 @@ def interpolate_sub_masked_image(self, sub_masked_image):
                 filter_finite = np.isfinite(good_pixel[:, 2:]).T[0]
                 good_pixel = good_pixel[filter_finite]
                 if good_pixel.size == 0:
-                    warnings.warn(
+                    self.log.info(
                         "No bad or good pixels found. No interpolation performed."
                     )
                     return sub_masked_image
@@ -421,7 +425,7 @@ def interpolate_sub_masked_image(self, sub_masked_image):
             try:
                 good_pixel = self._good_pixel_binning(copy.deepcopy(good_pixel))
             except Exception:
-                warnings.warn(
+                self.log.info(
                     "Binning failed, use original good pixel array in interpolation."
                 )
 
@@ -461,6 +465,7 @@ def interpolateOverDefectsGP(
     bin_spacing=25,
     threshold_dynamic_binning=1000,
     threshold_subdivide=20000,
+    log=None,
 ):
     """
     Interpolate over defects in an image using Gaussian Process interpolation.
@@ -485,6 +490,9 @@ def interpolateOverDefectsGP(
         The threshold for dynamic binning. Default is 1000.
     threshold_subdivide : `int`, optional
         The threshold for subdividing defects. Default is 20000.
+    log : `lsst.log.Log`, `logging.Logger` or `None`, optional
+        Logger object used to write out messages. If `None` a default
+        logger will be used.
 
     Returns
     -------
@@ -496,8 +504,9 @@ def interpolateOverDefectsGP(
         If no defects are found in the image.
 
     """
+    log = log or logging.getLogger(__name__)
     if badList == [] or badList is None:
-        warnings.warn("WARNING: no defects found. No interpolation performed.")
+        log.info("No defects found. No interpolation performed.")
         return
     gp = InterpolateOverDefectGaussianProcess(
         image,
@@ -507,5 +516,6 @@ def interpolateOverDefectsGP(
         bin_spacing=bin_spacing,
         threshold_dynamic_binning=threshold_dynamic_binning,
         threshold_subdivide=threshold_subdivide,
+        log=log,
     )
     gp.interpolate_over_defects()