moving forward in passing current unit tests

python/lsst/meas/algorithms/gp_interpolation.py

@@ -79,12 +79,12 @@ def jax_rbf_h(x1, x2, sigma, correlation_length):
     return K
 
 def updateMaskFromArray(mask, bad_pixel, interpBit):
-        x0 = mask.getX0()
-        y0 = mask.getY0()
-        for row in bad_pixel:
-            x = int(row[0] - x0)
-            y = int(row[1] - y0)
-            mask.array[x, y] |= interpBit
+    x0 = mask.getX0()
+    y0 = mask.getY0()
+    for row in bad_pixel:
+        x = int(row[0] - x0)
+        y = int(row[1] - y0)
+        mask.array[y, x] |= interpBit
 
 class GaussianProcessJax:
     def __init__(self, std=1.0, correlation_length=1.0, white_noise=0.0, mean=0.0):
@@ -180,10 +180,10 @@ def fit(self, x_good, y_good):
         self.gp = treegp.GPInterpolation(
             kernel=KERNEL,
             optimizer="none",
-            normalize=True,
+            normalize=False,
             white_noise=self.white_noise,
         )
-        self.gp.initialize(x_good, y_good)
+        self.gp.initialize(x_good, y_good - self.mean)
         self.gp.solve()
 
     def predict(self, x_bad):
@@ -198,7 +198,7 @@ def predict(self, x_bad):
 
         """
         y_pred = self.gp.predict(x_bad)
-        return y_pred
+        return y_pred + self.mean
 
 
 class InterpolateOverDefectGaussianProcess:
@@ -330,6 +330,13 @@ def interpolate_sub_masked_image(self, sub_masked_image):
                 np.mean(sub_image_array[np.isfinite(sub_image_array)])
             )
             kernel_amplitude = np.std(good_pixel[:, 2:])
+            if np.isnan(kernel_amplitude):
+                filtre_nan = np.isnan(good_pixel[:, 2:])
+                good_pixel = good_pixel[~filtre_nan]
+                if good_pixel.size == 0:
+                    warnings.warn('No bad or good pixels found. No interpolation performed.')
+                    return sub_masked_image
+                kernel_amplitude = np.std(good_pixel[:, 2:])
             try:
                 good_pixel = self._good_pixel_binning(copy.deepcopy(good_pixel))
             except:
@@ -371,19 +378,20 @@ def interpolateOverDefectsGP(image, fwhm, badList, method="treegp", bin_spacing=
     Returns:
         None
     """
-    # import pickle
-    # dic = {'image': image,
-    #        'fwhm': fwhm,
-    #         'badList': badList,
-    #        'method': method,
-    #        'bin_spacing': bin_spacing,
-    #        'threshold_subdivide':
-    #        threshold_subdivide}
-    # file_name_out = get_name_pkl()
-    # fileout = open(file_name_out, 'wb')
-    # print(f'JE PASSE PAR LA | {file_name_out}')
-    # pickle.dump(dic, fileout)
-    # fileout.close()
+    import pickle
+    dic = {
+        'image': image,
+        'fwhm': fwhm,
+        'badList': badList,
+        'method': method,
+        'bin_spacing': bin_spacing,
+        'threshold_subdivide':threshold_subdivide,
+        }
+    file_name_out = get_name_pkl()
+    fileout = open(file_name_out, 'wb')
+    print(f'JE PASSE PAR LA | {file_name_out}')
+    pickle.dump(dic, fileout)
+    fileout.close()
     if badList == [] or badList is None:
         warnings.warn('WARNING: no defects found. No interpolation performed.')
         return