diff --git a/nanshe_ipython.ipynb b/nanshe_ipython.ipynb
index dfceab2..81fcc9a 100644
--- a/nanshe_ipython.ipynb
+++ b/nanshe_ipython.ipynb
@@ -241,7 +241,7 @@
       "source": [
         "from nanshe_workflow.par import halo_block_parallel\n",
         "\n",
-        "from nanshe_workflow.imp2 import extract_f0, wavelet_transform, renormalized_images, normalize_data\n",
+        "from nanshe_workflow.imp2 import extract_f0, wavelet_transform, normalize_data\n",
         "\n",
         "from nanshe_workflow.par import halo_block_generate_dictionary_parallel\n",
         "from nanshe_workflow.imp import block_postprocess_data_parallel\n",
@@ -993,11 +993,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Normalize Data\n",
+        "### Project\n",
         "\n",
         "* `block_frames` (`int`): number of frames to work with in each full frame block (run in parallel).\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)."
+        "* `proj_type` (`str`): type of projection to take."
       ]
     },
     {
@@ -1007,12 +1006,11 @@
       "outputs": [],
       "source": [
         "block_frames = 40\n",
-        "block_space = 300\n",
-        "norm_frames = 100\n",
+        "proj_type = \"max\"\n",
         "\n",
         "\n",
         "with get_executor(client) as executor:\n",
-        "    dask_io_remove(data_basename + postfix_norm + zarr_ext, executor)\n",
+        "    dask_io_remove(data_basename + postfix_dict + zarr_ext, executor)\n",
         "\n",
         "\n",
         "    with open_zarr(data_basename + postfix_wt + zarr_ext, \"r\") as f:\n",
@@ -1027,106 +1025,28 @@
         "                da_imgs_flt.dtype.itemsize >= 4):\n",
         "            da_imgs_flt = da_imgs_flt.astype(np.float32)\n",
         "\n",
-        "        da_imgs_flt_mins = da_imgs_flt.min(\n",
-        "            axis=tuple(irange(1, da_imgs_flt.ndim)),\n",
-        "            keepdims=True\n",
-        "        )\n",
-        "\n",
-        "        da_imgs_flt_shift = da_imgs_flt - da_imgs_flt_mins\n",
-        "\n",
-        "        da_result = renormalized_images(da_imgs_flt_shift)\n",
+        "        da_result = da_imgs\n",
+        "        if proj_type == \"max\":\n",
+        "            da_result = da_result.max(axis=0, keepdims=True)\n",
+        "        elif proj_type == \"std\":\n",
+        "            da_result = da_result.std(axis=0, keepdims=True)\n",
         "\n",
         "        # Store denoised data\n",
-        "        dask_store_zarr(data_basename + postfix_norm + zarr_ext, [\"images\"], [da_result], executor)\n",
-        "\n",
-        "\n",
-        "    zip_zarr(data_basename + postfix_norm + zarr_ext, executor)\n",
-        "\n",
-        "\n",
-        "if __IPYTHON__:\n",
-        "    result_image_stack = LazyZarrDataset(data_basename + postfix_norm + 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": {},
-      "source": [
-        "### Dictionary Learning\n",
-        "\n",
-        "* `n_components` (`int`): number of basis images in the dictionary.\n",
-        "* `batchsize` (`int`): minibatch size to use.\n",
-        "* `iters` (`int`): number of iterations to run before getting dictionary.\n",
-        "* `lambda1` (`float`): weight for L<sup>1</sup> sparisty enforcement on sparse code.\n",
-        "* `lambda2` (`float`): weight for L<sup>2</sup> sparisty enforcement on sparse code.\n",
-        "\n",
-        "<br>\n",
-        "* `block_frames` (`int`): number of frames to work with in each full frame 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": [
-        "n_components = 50\n",
-        "batchsize = 256\n",
-        "iters = 100\n",
-        "lambda1 = 0.2\n",
-        "lambda2 = 0.0\n",
-        "\n",
-        "block_frames = 51\n",
-        "norm_frames = 100\n",
+        "        dask_store_zarr(data_basename + postfix_dict + zarr_ext, [\"images\"], [da_result], executor)\n",
         "\n",
         "\n",
-        "with get_executor(client) as executor:\n",
-        "    dask_io_remove(data_basename + postfix_dict + zarr_ext, executor)\n",
-        "\n",
-        "\n",
-        "result = LazyZarrDataset(data_basename + postfix_norm + zarr_ext, \"images\")\n",
-        "block_shape = (block_frames,) + result.shape[1:]\n",
-        "with open_zarr(data_basename + postfix_dict + zarr_ext, \"w\") as f2:\n",
-        "    new_result = f2.create_dataset(\"images\", shape=(n_components,) + result.shape[1:], dtype=result.dtype, chunks=True)\n",
-        "\n",
-        "    result = par_generate_dictionary(block_shape)(\n",
-        "        result,\n",
-        "        n_components=n_components,\n",
-        "        out=new_result,\n",
-        "        **{\"sklearn.decomposition.dict_learning_online\" : {\n",
-        "                \"n_jobs\" : 1,\n",
-        "                \"n_iter\" : iters,\n",
-        "                \"batch_size\" : batchsize,\n",
-        "                \"alpha\" : lambda1\n",
-        "            }\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "    result_j = f2.create_dataset(\"images_j\", shape=new_result.shape, dtype=numpy.uint16, chunks=True)\n",
-        "    par_norm_layer(num_frames=norm_frames)(result, out=result_j)\n",
-        "\n",
-        "\n",
-        "with get_executor(client) as executor:\n",
         "    zip_zarr(data_basename + postfix_dict + zarr_ext, executor)\n",
         "\n",
         "\n",
         "if __IPYTHON__:\n",
-        "    result_image_stack = LazyZarrDataset(data_basename + postfix_dict + zarr_ext, \"images\")\n",
+        "    result_image_stack = LazyZarrDataset(data_basename + postfix_dict + 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",
-        "    )\n",
-        "    mplsv.time_nav.stime.label.set_text(\"Basis Image\")"
+        "        vmin=result_image_stack.min(),\n",
+        "        vmax=result_image_stack.max()\n",
+        "    )"
       ]
     },
     {