diff --git a/nanshe_ipython.ipynb b/nanshe_ipython.ipynb
index dfceab2..b3232b7 100644
--- a/nanshe_ipython.ipynb
+++ b/nanshe_ipython.ipynb
@@ -51,6 +51,7 @@
         "postfix_sub = \"_sub\"\n",
         "postfix_f_f0 = \"_f_f0\"\n",
         "postfix_wt = \"_wt\"\n",
+        "postfix_diff = \"_diff\"\n",
         "postfix_norm = \"_norm\"\n",
         "postfix_dict = \"_dict\"\n",
         "postfix_post = \"_post\"\n",
@@ -989,6 +990,65 @@
         "    )"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Difference\n",
+        "\n",
+        "* `block_space` (`int`): extent of each spatial dimension for each block (run in parallel).\n",
+        "* `norm_frames` (`int`): number of frames for use during normalization of each full frame block (run in parallel)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "\n",
+        "\n",
+        "block_frames = 40\n",
+        "norm_frames = 100\n",
+        "\n",
+        "\n",
+        "with get_executor(client) as executor:\n",
+        "    dask_io_remove(data_basename + postfix_diff + zarr_ext, executor)\n",
+        "\n",
+        "\n",
+        "    with open_zarr(data_basename + postfix_wt + zarr_ext, \"r\") as f:\n",
+        "        # Load and prep data for computation.\n",
+        "        imgs = f[\"images\"]\n",
+        "        da_imgs = da.from_array(\n",
+        "            imgs, chunks=(block_frames,) + (imgs.ndim - 1) * (block_space,)\n",
+        "        )\n",
+        "\n",
+        "        da_imgs_flt = da_imgs\n",
+        "        if not (issubclass(da_imgs_flt.dtype.type, np.floating) and \n",
+        "                da_imgs_flt.dtype.itemsize >= 4):\n",
+        "            da_imgs_flt = da_imgs_flt.astype(np.float32)\n",
+        "\n",
+        "        da_result = da_imgs_flt[1:] - da_imgs_flt[:-1]\n",
+        "\n",
+        "        # Store denoised data\n",
+        "        dask_store_zarr(data_basename + postfix_diff + zarr_ext, [\"images\"], [da_result], executor)\n",
+        "\n",
+        "\n",
+        "    zip_zarr(data_basename + postfix_diff + zarr_ext, executor)\n",
+        "\n",
+        "\n",
+        "if __IPYTHON__:\n",
+        "    result_image_stack = LazyZarrDataset(data_basename + postfix_diff + zarr_ext, \"images\")\n",
+        "\n",
+        "    mplsv = plt.figure(FigureClass=MPLViewer)\n",
+        "    mplsv.set_images(\n",
+        "        result_image_stack,\n",
+        "        vmin=par_compute_min_projection(num_frames=norm_frames)(result_image_stack).min(),\n",
+        "        vmax=par_compute_max_projection(num_frames=norm_frames)(result_image_stack).max()\n",
+        "    )"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -1015,7 +1075,7 @@
         "    dask_io_remove(data_basename + postfix_norm + zarr_ext, executor)\n",
         "\n",
         "\n",
-        "    with open_zarr(data_basename + postfix_wt + zarr_ext, \"r\") as f:\n",
+        "    with open_zarr(data_basename + postfix_diff + zarr_ext, \"r\") as f:\n",
         "        # Load and prep data for computation.\n",
         "        imgs = f[\"images\"]\n",
         "        da_imgs = da.from_array(\n",