oops, forgot to delete duplicate code

py4DSTEM/process/phase/iterative_mixedstate_multislice_ptychography.py

@@ -1943,50 +1943,3 @@ def visualize(
                 **kwargs,
             )
         return self
-
-    def _return_self_consistency_errors(
-        self,
-        max_batch_size=None,
-    ):
-        """Compute the self-consistency errors for each probe position"""
-
-        xp = self._xp
-        asnumpy = self._asnumpy
-
-        # Batch-size
-        if max_batch_size is None:
-            max_batch_size = self._num_diffraction_patterns
-
-        # Re-initialize fractional positions and vector patches
-        errors = np.array([])
-        positions_px = self._positions_px.copy()
-
-        for start, end in generate_batches(
-            self._num_diffraction_patterns, max_batch=max_batch_size
-        ):
-            # batch indices
-            self._positions_px = positions_px[start:end]
-            self._positions_px_fractional = self._positions_px - xp.round(
-                self._positions_px
-            )
-            (
-                self._vectorized_patch_indices_row,
-                self._vectorized_patch_indices_col,
-            ) = self._extract_vectorized_patch_indices()
-            amplitudes = self._amplitudes[start:end]
-
-            # Overlaps
-            _, _, overlap = self._overlap_projection(self._object, self._probe)
-            fourier_overlap = xp.fft.fft2(overlap)
-            intensity_norm = xp.sqrt(xp.sum(xp.abs(fourier_overlap) ** 2, axis=1))
-
-            # Normalized mean-squared errors
-            batch_errors = xp.sum(
-                xp.abs(amplitudes - intensity_norm) ** 2, axis=(-2, -1)
-            )
-            errors = np.hstack((errors, batch_errors))
-
-        self._positions_px = positions_px.copy()
-        errors /= self._mean_diffraction_intensity
-
-        return asnumpy(errors)

py4DSTEM/process/phase/iterative_mixedstate_ptychography.py

@@ -1606,50 +1606,3 @@ def visualize(
             )
 
         return self
-
-    def _return_self_consistency_errors(
-        self,
-        max_batch_size=None,
-    ):
-        """Compute the self-consistency errors for each probe position"""
-
-        xp = self._xp
-        asnumpy = self._asnumpy
-
-        # Batch-size
-        if max_batch_size is None:
-            max_batch_size = self._num_diffraction_patterns
-
-        # Re-initialize fractional positions and vector patches
-        errors = np.array([])
-        positions_px = self._positions_px.copy()
-
-        for start, end in generate_batches(
-            self._num_diffraction_patterns, max_batch=max_batch_size
-        ):
-            # batch indices
-            self._positions_px = positions_px[start:end]
-            self._positions_px_fractional = self._positions_px - xp.round(
-                self._positions_px
-            )
-            (
-                self._vectorized_patch_indices_row,
-                self._vectorized_patch_indices_col,
-            ) = self._extract_vectorized_patch_indices()
-            amplitudes = self._amplitudes[start:end]
-
-            # Overlaps
-            _, _, overlap = self._overlap_projection(self._object, self._probe)
-            fourier_overlap = xp.fft.fft2(overlap)
-            intensity_norm = xp.sqrt(xp.sum(xp.abs(fourier_overlap) ** 2, axis=1))
-
-            # Normalized mean-squared errors
-            batch_errors = xp.sum(
-                xp.abs(amplitudes - intensity_norm) ** 2, axis=(-2, -1)
-            )
-            errors = np.hstack((errors, batch_errors))
-
-        self._positions_px = positions_px.copy()
-        errors /= self._mean_diffraction_intensity
-
-        return asnumpy(errors)