remove commented code

Author: Jammy2211

autoarray/structures/arrays/kernel_2d.py

@@ -700,115 +700,6 @@ def convolve_image_no_blurring_for_mapping(self, image, mask, jax_method="direct
 
         return Array2D(values=convolved_array_1d, mask=mask)
 
-    # def convolve_mapping_matrix(self, mapping_matrix, mask, jax_method="direct", chan_chunk=128):
-    #     """
-    #     mapping_matrix : (N_masked, S)  rows correspond to mask == False in row-major order
-    #     mask           : (H, W) boolean (False = kept, True = masked)
-    #     chan_chunk     : number of source columns processed at once (avoid large workspaces)
-    #     to_f32         : cast inputs to float32 for smaller workspace & faster conv
-    #     returns        : (N_masked, S) convolved, re-masked mapping matrix
-    #     """
-    #     H, W = mask.shape
-    #     S = mapping_matrix.shape[1]
-    #     N = H * W
-    #
-    #     psf_kernel = self.stored_native.array
-    #
-    #     # Indices of kept pixels (False in the mask), STATIC size for jit
-    #     flat_mask = mask.reshape(-1)
-    #     K = mapping_matrix.shape[0]  # number of unmasked pixels
-    #     keep_idx = jnp.where(~flat_mask, size=K, fill_value=0)[0]  # (K,)
-    #
-    #     # Expand to full grid (N, S): zeros everywhere, fill only kept rows
-    #     full = jnp.zeros((N, S), dtype=mapping_matrix.dtype)
-    #     full = full.at[keep_idx, :].set(mapping_matrix)
-    #
-    #     out_cols = []
-    #     kH, kW = psf_kernel.shape
-    #
-    #     for c0 in range(0, S, chan_chunk):
-    #         c1 = min(c0 + chan_chunk, S)
-    #         G = c1 - c0  # groups in this chunk
-    #
-    #         # Treat the G columns as G input channels: images shape [1, G, H, W]
-    #         imgs = full[:, c0:c1].T.reshape(1, G, H, W)
-    #
-    #         # Depthwise/grouped conv: kernel shape [G, 1, kH, kW]
-    #         # Use broadcast_to to avoid actual data replication.
-    #         kernel = jnp.broadcast_to(psf_kernel[None, None, :, :], (G, 1, kH, kW))
-    #
-    #         y = lax.conv_general_dilated(
-    #             imgs,
-    #             kernel,
-    #             window_strides=(1, 1),
-    #             padding="SAME",
-    #             dimension_numbers=("NCHW", "OIHW", "NCHW"),
-    #             feature_group_count=G,  # depthwise: each channel convolved independently
-    #         )  # [1, G, H, W]
-    #
-    #         # Back to (N, G) then re-mask
-    #         y_full = y.reshape(G, N).T
-    #         y_masked = y_full[keep_idx, :]  # (K, G)
-    #         out_cols.append(y_masked)
-    #
-    #     return jnp.concatenate(out_cols, axis=1)  # (K, S)
-    #
-    #
-    # def convolve_mapping_matrix(self, mapping_matrix, mask, jax_method="direct"):
-    #     """
-    #     mapping_matrix: [N_masked, S]  (rows correspond to mask==False in row-major order)
-    #     mask          : [H, W] boolean (False=kept, True=masked)
-    #     returns       : [N_masked, S] convolved, re-masked mapping matrix
-    #     """
-    #     psf_kernel = self.stored_native.array
-    #     H, W = mask.shape
-    #     S = mapping_matrix.shape[1]
-    #     N = H * W
-    #
-    #     # Indices of kept pixels (False in the mask), with STATIC size for jit.
-    #     flat_mask = mask.reshape(-1)
-    #     K = mapping_matrix.shape[0]  # number of unmasked pixels
-    #     keep_idx = jnp.where(~flat_mask, size=K, fill_value=0)[0]  # shape (K,)
-    #
-    #     # Expand to full grid: zeros everywhere, fill only kept rows.
-    #     full = jnp.zeros((N, S), dtype=mapping_matrix.dtype)
-    #     full = full.at[keep_idx, :].set(mapping_matrix)
-    #
-    #     # Batch conv: [S, 1, H, W] * [1, 1, kH, kW] -> [S, 1, H, W]
-    #     images = full.T.reshape(S, 1, H, W)
-    #     kernel = psf_kernel[jnp.newaxis, jnp.newaxis, :, :]
-    #
-    #     convolved = lax.conv_general_dilated(
-    #         images,
-    #         kernel,
-    #         window_strides=(1, 1),
-    #         padding="SAME",
-    #         dimension_numbers=("NCHW", "OIHW", "NCHW"),
-    #     )
-    #
-    #     # Back to masked slim grid
-    #     convolved_full = convolved.reshape(S, N).T
-    #     convolved_masked = convolved_full[keep_idx, :]  # shape (K, S)
-    #
-    #     return convolved_masked
-
-
-    # def convolve_mapping_matrix(self, mapping_matrix, mask, jax_method="direct"):
-
-        # mapping_matrix_list = []
-        #
-        # for i in range(mapping_matrix.shape[1]):
-        #
-        #     blurred_image_2d = self.convolve_image_no_blurring_for_mapping(
-        #         image=mapping_matrix[:,i],
-        #         mask=mask
-        #     )
-        #
-        #     mapping_matrix_list.append(blurred_image_2d.array)
-        #
-        # return jnp.stack(mapping_matrix_list, axis=1)
-
-
     def convolve_mapping_matrix(self, mapping_matrix, mask, jax_method="direct"):
         """For a given 1D array and blurring array, convolve the two using this psf.