Add assert_equal() and cleanup

src/extra.py

@@ -37,6 +37,13 @@ def assert_allclose(f1, f2, atol, rtol):
         assert_allclose(val, f2[key], atol=atol, rtol=rtol)
 
 
+def assert_equal(f1, f2):
+    if isinstance(f1, Field):
+        return np.testing.assert_equal(f1.val, f2.val)
+    for key, val in f1.items():
+        assert_equal(val, f2[key])
+
+
 def _adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol,
                             only_r_linear):
     needed_cap = op.TIMES | op.ADJOINT_TIMES
@@ -249,7 +256,7 @@ def _linearization_value_consistency(op, loc):
 
 
 def check_jacobian_consistency(op, loc, tol=1e-8, ntries=100, perf_check=True,
-        only_r_differentiable=True):
+                               only_r_differentiable=True):
     """
     Checks the Jacobian of an operator against its finite difference
     approximation.

src/operators/energy_operators.py

@@ -270,14 +270,13 @@ class GaussianEnergy(EnergyOperator):
                 if sampling_dtype != _field_to_dtype(self._mean):
                     raise ValueError("Sampling dtype and mean not compatible")
 
+        self._icov = inverse_covariance
         if inverse_covariance is None:
             self._op = Squared2NormOperator(self._domain).scale(0.5)
             self._met = ScalingOperator(self._domain, 1)
-            self._trivial_invcov = True
         else:
             self._op = QuadraticFormOperator(inverse_covariance)
             self._met = inverse_covariance
-            self._trivial_invcov = False
         if sampling_dtype is not None:
             self._met = SamplingDtypeSetter(self._met, sampling_dtype)