effective_uvwlen as Field

demo/basic_polarization.py

@@ -53,7 +53,7 @@ def main():
     polmode = obs.polarization.has_crosshanded()
 
     # TODO Add mode with independent noise learning
-    effuv = obs.effective_uvwlen()
+    effuv = obs.effective_uvwlen().val[0]
     dom = ift.RGSpace((npix_wgts := 2500), 2 * np.max(effuv) / npix_wgts)
     if not polmode:
         assert obs.nfreq == obs.npol == 1

demo/imaging_with_automatic_weighting.py

@@ -64,7 +64,7 @@ def main():
         sky = diffuse
     # TODO Add mode with independent noise learning
     npix = 2500
-    effuv = obs.effective_uvwlen()
+    effuv = obs.effective_uvwlen().val[0]
     assert obs.nfreq == obs.npol == 1
     dom = ift.RGSpace(npix, 2 * np.max(effuv) / npix)
     logwgt = ift.SimpleCorrelatedField(

resolve/data/observation.py

@@ -458,7 +458,9 @@ class Observation(BaseObservation):
         return out
 
     def effective_uvwlen(self):
-        return np.outer(self.uvwlen(), self._freq / SPEEDOFLIGHT)
+        arr = np.outer(self.uvwlen(), self._freq / SPEEDOFLIGHT)
+        arr = np.broadcast_to(arr[None], self._dom.shape)
+        return ift.makeField(self._dom, arr)
 
     def uvwlen(self):
         return np.linalg.norm(self.uvw, axis=1)

test/test_general.py

@@ -102,7 +102,7 @@ def test_varcov_imaging_likelihood(obs):
 @pmp("obs", OBS)
 @pmp("noisemodel", range(2))
 def test_weighting_methods(obs, noisemodel):
-    efflen = obs.effective_uvwlen()
+    efflen = obs.effective_uvwlen().val[0]
     npix = 2500
     dom = ift.RGSpace(npix, 2 * max(efflen) / npix)
     baseline_distributor = ift.LinearInterpolator(dom, efflen.T)