diff --git a/brahmap/core/noise_ops_toeplitz.py b/brahmap/core/noise_ops_toeplitz.py
index 80c90bd..a838abd 100644
--- a/brahmap/core/noise_ops_toeplitz.py
+++ b/brahmap/core/noise_ops_toeplitz.py
@@ -161,18 +161,25 @@ def __init__(
             dtype=dtype,
         )
 
-        self.__precond_atol = precond_atol
-        self.__precond_maxiter = precond_maxiter
-        self.__precond_callback = precond_callback
+        self.precond_atol = precond_atol
+        self.precond_maxiter = precond_maxiter
+        self.precond_callback = precond_callback
 
-        self.__last_num_iterations = 0
+        self.__previous_num_iterations = 0
+
+        super(InvNoiseCovLO_Toeplitz01, self).__init__(
+            nargin=size,
+            matvec=self._mult,
+            input_type=input_type,
+            dtype=dtype,
+        )
 
         if precond_op is None:
-            self.__precond_op = None
+            self.precond_op = None
         elif isinstance(precond_op, LinearOperator) or isinstance(
             precond_op, np.ndarray
         ):
-            self.__precond_op = precond_op
+            self.precond_op = precond_op
         elif precond_op in ["Strang", "TChan", "RChan", "KK2"]:
             if input_type == "power_spectrum":
                 cov = np.fft.ifft(input).real[:size]
@@ -204,7 +211,7 @@ def __init__(
                 new_cov[0] = 0
                 new_cov = cov - new_cov
 
-            self.__precond_op = InvNoiseCovLO_Circulant(
+            self.precond_op = InvNoiseCovLO_Circulant(
                 size=size,
                 input=new_cov,
                 input_type="covariance",
@@ -217,29 +224,44 @@ def __init__(
                 message="Invalid preconditioner operator provided!",
             )
 
-        super(InvNoiseCovLO_Toeplitz01, self).__init__(
-            nargin=size,
-            matvec=self._mult,
-            input_type=input_type,
-            dtype=dtype,
-        )
+    @property
+    def precond_op(self):
+        return self.__precond_op
+
+    @precond_op.setter
+    def precond_op(self, operator: LinearOperator):
+        if operator is not None:
+            MPI_RAISE_EXCEPTION(
+                condition=(self.shape != operator.shape),
+                exception=ValueError,
+                message=f"The shape of the input operator {operator.shape} is not compatible with the shape of inverse Toeplitz operator {self.shape}",
+            )
+        self.__precond_op = operator
 
     @property
     def diag(self) -> np.ndarray:
-        factor = 1.0
-        return factor * np.ones(self.size, dtype=self.dtype)
+        try:
+            diag_arr = getattr(self, "__diag")
+        except AttributeError:
+            factor = 1.0
+            diag_arr = factor * np.ones(self.size, dtype=self.dtype)
+        return diag_arr
+
+    @diag.setter
+    def diag(self, diag: np.ndarray):
+        self.__diag = diag
 
     @property
-    def get_last_num_iterations(self) -> int:
-        return self.__last_num_iterations
+    def previous_num_iterations(self) -> int:
+        return self.__previous_num_iterations
 
     def get_inverse(self):
         return self.__toeplitz_op
 
     def __callback(self, x, r, norm_residual):
-        self.__last_num_iterations += 1
-        if self.__precond_callback is not None:
-            self.__precond_callback(x, r, norm_residual)
+        self.__previous_num_iterations += 1
+        if self.precond_callback is not None:
+            self.precond_callback(x, r, norm_residual)
 
     def _mult(self, vec: np.ndarray):
         MPI_RAISE_EXCEPTION(
@@ -248,7 +270,7 @@ def _mult(self, vec: np.ndarray):
             message=f"Dimensions of `vec` is not compatible with the dimensions of this `InvNoiseCovLO_Toeplitz` instance.\nShape of `InvNoiseCovLO_Toeplitz` instance: {self.shape}\nShape of `vec`: {vec.shape}",
         )
 
-        self.__last_num_iterations = 0
+        self.__previous_num_iterations = 0
 
         if vec.dtype != self.dtype:
             if MPI_UTILS.rank == 0:
@@ -261,9 +283,9 @@ def _mult(self, vec: np.ndarray):
         prod, _ = cg(
             A=self.__toeplitz_op,
             b=vec,
-            atol=self.__precond_atol,
-            maxiter=self.__precond_maxiter,
-            M=self.__precond_op,
+            atol=self.precond_atol,
+            maxiter=self.precond_maxiter,
+            M=self.precond_op,
             callback=self.__callback,
             parallel=False,
         )
diff --git a/brahmap/core/process_time_samples.py b/brahmap/core/process_time_samples.py
index 1ccc285..1f4b8c5 100644
--- a/brahmap/core/process_time_samples.py
+++ b/brahmap/core/process_time_samples.py
@@ -304,21 +304,14 @@ def bad_pixels(self):
 
     def get_hit_counts(self):
         """Returns hit counts of the pixel indices"""
-        return self.hit_counts
-        # hit_counts_newidx = np.zeros(self.new_npix, dtype=int)
-        # for idx in range(self.nsamples):
-        #     hit_counts_newidx[self.pointings[idx]] += self.pointings_flag[idx]
-
-        # MPI_UTILS.comm.Allreduce(MPI.IN_PLACE, hit_counts_newidx, MPI.SUM)
-
-        # hit_counts = np.ma.masked_array(
-        #     data=np.zeros(self.npix),
-        #     mask=np.logical_not(self.pixel_flag),
-        #     fill_value=-1.6375e30,
-        # )
+        hit_counts = np.ma.masked_array(
+            data=np.zeros(self.npix),
+            mask=np.logical_not(self.pixel_flag),
+            fill_value=-1.6375e30,
+        )
 
-        # hit_counts[~hit_counts.mask] = hit_counts_newidx
-        # return hit_counts
+        hit_counts[~hit_counts.mask] = self.hit_counts
+        return hit_counts
 
     def _compute_weights(self, pol_angles: np.ndarray, noise_weights: np.ndarray):
         self.hit_counts = np.zeros(self.npix, dtype=self.pointings.dtype)