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lsst · Sep 23, 2024 · 4719d5a · 4719d5a
1 parent dc5719f
commit 4719d5a
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Showing 2 changed files with 34 additions and 17 deletions.
diff --git a/python/lsst/meas/algorithms/gp_interpolation.py b/python/lsst/meas/algorithms/gp_interpolation.py
@@ -32,7 +32,11 @@
 
 import logging
 
-__all__ = ["InterpolateOverDefectGaussianProcess", "GaussianProcessJax", "GaussianProcessTreegp"]
+__all__ = [
+    "InterpolateOverDefectGaussianProcess",
+    "GaussianProcessJax",
+    "GaussianProcessTreegp",
+]
 
 
 def updateMaskFromArray(mask, bad_pixel, interpBit):
@@ -191,6 +195,7 @@ class GaussianProcessJax:
         Mean value of the Gaussian Process. Default is 0.0.
 
     """
+
     def __init__(self, std=1.0, correlation_length=1.0, white_noise=0.0, mean=0.0):
         self.std = std
         self.correlation_length = correlation_length
@@ -217,7 +222,9 @@ def fit(self, x_train, y_train):
         self._alpha = jax_get_alpha(y, kernel)
 
     def predict(self, x_predict):
-        kernel_rect = jax_rbf_kernel(x_predict, self._x, self.std, self.correlation_length)
+        kernel_rect = jax_rbf_kernel(
+            x_predict, self._x, self.std, self.correlation_length
+        )
         y_pred = jax_get_gp_predict(kernel_rect, self._alpha)
         return y_pred + self.mean
 

diff --git a/tests/test_gp_interp.py b/tests/test_gp_interp.py
@@ -27,7 +27,11 @@
 import lsst.utils.tests
 import lsst.geom
 import lsst.afw.image as afwImage
-from lsst.meas.algorithms import InterpolateOverDefectGaussianProcess, GaussianProcessTreegp
+from lsst.meas.algorithms import (
+    InterpolateOverDefectGaussianProcess,
+    GaussianProcessTreegp,
+)
+
 
 def rbf_kernel(x1, x2, sigma, correlation_length):
     """
@@ -62,7 +66,7 @@ def setUp(self):
 
         npoints = 1000
         self.std = 100
-        self.correlation_length = 10.
+        self.correlation_length = 10.0
         self.white_noise = 1e-5
 
         x1 = np.random.uniform(0, 99, npoints)
@@ -71,7 +75,7 @@ def setUp(self):
 
         kernel = rbf_kernel(coord1, coord1, self.std, self.correlation_length)
         kernel += np.eye(npoints) * self.white_noise**2
-        
+
         # Data augmentation. Create a gaussian random field
         # on a 100 * 100 is to slow. So generate 1e3 points
         # and then interpolate it with a GP to do data augmentation.
@@ -84,8 +88,12 @@ def setUp(self):
         x2, x1 = np.meshgrid(x2, x1)
         coord2 = np.array([x1.reshape(-1), x2.reshape(-1)]).T
 
-        tgp = GaussianProcessTreegp(std=self.std, correlation_length=self.correlation_length,
-                                    white_noise=self.white_noise, mean=0.0)
+        tgp = GaussianProcessTreegp(
+            std=self.std,
+            correlation_length=self.correlation_length,
+            white_noise=self.white_noise,
+            mean=0.0,
+        )
         tgp.fit(coord1, z1)
         z2 = tgp.predict(coord2)
         z2 = z2.reshape(100, 121)
@@ -144,16 +152,18 @@ def test_interpolation(self, method: str):
             Code used to solve gaussian process.
         """
 
-        gp = InterpolateOverDefectGaussianProcess(self.maskedimage, 
-                                                  defects=["BAD", "SAT", "CR", "EDGE"],
-                                                  method=method,
-                                                  fwhm=self.correlation_length,
-                                                  bin_image=False,
-                                                  bin_spacing=30,
-                                                  threshold_dynamic_binning=1000,
-                                                  threshold_subdivide=20000,
-                                                  correlation_length_cut=5,
-                                                  log=None,)
+        gp = InterpolateOverDefectGaussianProcess(
+            self.maskedimage,
+            defects=["BAD", "SAT", "CR", "EDGE"],
+            method=method,
+            fwhm=self.correlation_length,
+            bin_image=False,
+            bin_spacing=30,
+            threshold_dynamic_binning=1000,
+            threshold_subdivide=20000,
+            correlation_length_cut=5,
+            log=None,
+        )
 
         gp.run()