diff --git a/notebooks/draw_raster.ipynb b/notebooks/draw_raster.ipynb
new file mode 100644
index 0000000..f84f111
--- /dev/null
+++ b/notebooks/draw_raster.ipynb
@@ -0,0 +1,934 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(27, 30, 130)\n",
+      "3 3\n",
+      "[25 23 22 19 17  8  9 16 14 13 21 20 18 15  0 11 10  7  6  5  4  3  2  1\n",
+      " 24 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
+    "import os\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "from sklearn.cluster import SpectralClustering\n",
+    "\n",
+    "result_dir = '../results'\n",
+    "tasks = ['co_smooth', 'inter_region', 'intra_region']\n",
+    "bin_size = 0.02 # 20 ms\n",
+    "colormap = 'magma'\n",
+    "# get all gt.npy files under result_dir\n",
+    "gt_file = glob.glob(os.path.join(result_dir, '**/gt.npy'), recursive=True)[0]\n",
+    "gt_result = np.load(gt_file)\n",
+    "print(gt_result.shape)\n",
+    "temporal_result_paths = glob.glob(os.path.join(\n",
+    "    result_dir, \n",
+    "    'eval', \n",
+    "    'num_session_1', \n",
+    "    '**/mask_temporal', \n",
+    "    '**/pred.npy'), recursive=True)\n",
+    "\n",
+    "# filder out the temporal result\n",
+    "temporal_result_paths = [result for result in temporal_result_paths if any(task in result for task in tasks)]\n",
+    "\n",
+    "mtm_result_paths = glob.glob(os.path.join(\n",
+    "    result_dir, \n",
+    "    'eval', \n",
+    "    'num_session_1', \n",
+    "    '**/mask_all', \n",
+    "    '**/pred.npy'), recursive=True)\n",
+    "# filter out the mtm result\n",
+    "mtm_result_paths = [result for result in mtm_result_paths if any(task in result for task in tasks)]\n",
+    "print(len(temporal_result_paths), len(mtm_result_paths))\n",
+    "\n",
+    "\n",
+    "clustering = SpectralClustering(n_clusters=2, \n",
+    "                                n_neighbors=5,\n",
+    "                                affinity='nearest_neighbors',\n",
+    "                                assign_labels='discretize',\n",
+    "                                random_state=0)\n",
+    "neuron_range = {\"start\": 26, \"end\": 52}\n",
+    "                \n",
+    "gt_result = gt_result.mean(0)[:, neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "clustering = clustering.fit(gt_result.T)\n",
+    "t_sort = np.argsort(clustering.labels_)\n",
+    "print(t_sort)\n",
+    "gt_mean = gt_result / bin_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Intra-Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/intra_region/pred.npy']\n",
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_temporal/stitch_True/nlb-rtt/intra_region/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the temporal result that inter_region in the path\n",
+    "temporal_result_path = [result for result in temporal_result_paths if 'intra_region' in result]\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'intra_region' in result]\n",
+    "# load the temporal result\n",
+    "temporal_result = np.load(temporal_result_path[0])\n",
+    "temporal_mean = temporal_result.mean(0) / bin_size\n",
+    "temporal_mean = temporal_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(temporal_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], temporal_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(temporal_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(temporal_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Baseline') # (bps: -0.29)\n",
+    "axes[1].set_title('MtM') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.05', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.10', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 2, 10, 4.5, 7.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.3, 0.53, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=2, loc2=4, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 23, 27, 0.7, 2.25\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.55, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inter-Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/intra_region/pred.npy']\n",
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_temporal/stitch_True/nlb-rtt/intra_region/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the temporal result that inter_region in the path\n",
+    "temporal_result_path = [result for result in temporal_result_paths if 'intra_region' in result]\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'intra_region' in result]\n",
+    "# load the temporal result\n",
+    "temporal_result = np.load(temporal_result_path[0])\n",
+    "temporal_mean = temporal_result.mean(0) / bin_size\n",
+    "temporal_mean = temporal_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(temporal_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], temporal_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(temporal_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(temporal_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Baseline') # (bps: -0.29)\n",
+    "axes[1].set_title('MtM') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.143', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.145', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 13, 16, 4.5, 7.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.1, 0.40, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 23, 27, 1.9, 3.29\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.60, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Co-Smooth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/co_smooth/pred.npy']\n",
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_temporal/stitch_True/nlb-rtt/co_smooth/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the temporal result that inter_region in the path\n",
+    "temporal_result_path = [result for result in temporal_result_paths if 'co_smooth' in result]\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'co_smooth' in result]\n",
+    "# load the temporal result\n",
+    "temporal_result = np.load(temporal_result_path[0])\n",
+    "temporal_mean = temporal_result.mean(0) / bin_size\n",
+    "temporal_mean = temporal_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(temporal_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], temporal_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(temporal_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(temporal_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Baseline') # (bps: -0.29)\n",
+    "axes[1].set_title('MtM') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.10', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.12', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 9, 13, 0.5, 2.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.08, 0.40, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 17, 20, 0.2, 1.9\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.65, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=2, loc2=4, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multi-Session MtM v.s. Single-Session MtM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "multi_mtm_result_paths = glob.glob(os.path.join(\n",
+    "    result_dir, \n",
+    "    'eval', \n",
+    "    'num_session_34', \n",
+    "    '**/mask_all', \n",
+    "    '**/pred.npy'), recursive=True)\n",
+    "multi_mtm_result_paths = [result for result in multi_mtm_result_paths if any(task in result for task in tasks)]\n",
+    "multi_mtm_result_paths\n",
+    "print(len(multi_mtm_result_paths), len(mtm_result_paths))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare Co-Smooth Multi-Session MtM with Single-Session MtM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/co_smooth/pred.npy']\n",
+      "['../results/eval/num_session_34/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/co_smooth/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the multi_session mtm result that inter_region in the path\n",
+    "multi_mtm_result_path = [result for result in multi_mtm_result_paths if 'co_smooth' in result]\n",
+    "\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'co_smooth' in result]\n",
+    "# load the multi_session mtm result\n",
+    "multi_mtm_result = np.load(multi_mtm_result_path[0])\n",
+    "multi_mtm_mean = multi_mtm_result.mean(0) / bin_size\n",
+    "multi_mtm_mean = multi_mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(multi_mtm_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], multi_mtm_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(multi_mtm_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(multi_mtm_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Single-Session') # (bps: -0.29)\n",
+    "axes[1].set_title('Multi-Session') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.13', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.16', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 9, 13, 0.5, 2.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.08, 0.40, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 17, 20, 0.2, 1.9\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.65, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=2, loc2=4, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inter-Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/inter_region/pred.npy']\n",
+      "['../results/eval/num_session_34/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/inter_region/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the multi_session mtm result that inter_region in the path\n",
+    "multi_mtm_result_path = [result for result in multi_mtm_result_paths if 'inter_region' in result]\n",
+    "\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'inter_region' in result]\n",
+    "# load the multi_session mtm result\n",
+    "multi_mtm_result = np.load(multi_mtm_result_path[0])\n",
+    "multi_mtm_mean = multi_mtm_result.mean(0) / bin_size\n",
+    "multi_mtm_mean = multi_mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(multi_mtm_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], multi_mtm_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(multi_mtm_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(multi_mtm_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Single-Session') # (bps: -0.29)\n",
+    "axes[1].set_title('Multi-Session') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.14', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.16', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 13, 16, 4.5, 7.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.1, 0.40, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 23, 27, 1.9, 3.29\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.60, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Intra-Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../results/eval/num_session_1/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/intra_region/pred.npy']\n",
+      "['../results/eval/num_session_34/model_NDT1/method_ssl/mask_all/stitch_True/nlb-rtt/intra_region/pred.npy']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the multi_session mtm result that inter_region in the path\n",
+    "multi_mtm_result_path = [result for result in multi_mtm_result_paths if 'intra_region' in result]\n",
+    "\n",
+    "# get the mtm result that inter_region in the path\n",
+    "mtm_result_path = [result for result in mtm_result_paths if 'intra_region' in result]\n",
+    "# load the multi_session mtm result\n",
+    "multi_mtm_result = np.load(multi_mtm_result_path[0])\n",
+    "multi_mtm_mean = multi_mtm_result.mean(0) / bin_size\n",
+    "multi_mtm_mean = multi_mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "# load the mtm result\n",
+    "mtm_result = np.load(mtm_result_path[0])\n",
+    "print(mtm_result_path)\n",
+    "print(multi_mtm_result_path)\n",
+    "mtm_mean = mtm_result.mean(0) / bin_size\n",
+    "mtm_mean = mtm_mean[:,neuron_range[\"start\"]:neuron_range[\"end\"]]\n",
+    "\n",
+    "display = [gt_mean.T[t_sort], mtm_mean.T[t_sort], multi_mtm_mean.T[t_sort]]\n",
+    "minmin = np.min([np.min(gt_mean), np.min(multi_mtm_mean), np.min(mtm_mean)])\n",
+    "maxmax = np.max([np.max(gt_mean), np.max(multi_mtm_mean), np.max(mtm_mean)])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "divider = make_axes_locatable(axes[2])\n",
+    "cbar_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "im1 = axes[0].imshow(display[0], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im2 = axes[1].imshow(display[1], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "im3 = axes[2].imshow(display[2], aspect='auto', cmap=colormap, vmin=minmin, vmax=maxmax, origin='lower')\n",
+    "plt.colorbar(im1, cax=cbar_ax)\n",
+    "cbar_ax.set_title('Firing Rate')\n",
+    "axes[0].set_title('Ground Truth')\n",
+    "axes[2].set_title('Single-Session') # (bps: -0.29)\n",
+    "axes[1].set_title('Multi-Session') # bps: 0.39\n",
+    "axes[0].set_ylabel('Neurons from R2')\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.set_xticks([0, 7.5, 15, 22.5, 30], [0, 0.075, 0.15, 0.225, 0.3])\n",
+    "    ax.set_xlabel('Time (s)')\n",
+    "    if i == 0:\n",
+    "        ax.spines[['right', 'top']].set_visible(False)\n",
+    "    else:\n",
+    "        ax.spines[['right', 'top', 'left']].set_visible(False)\n",
+    "        ax.set_yticks([],[])\n",
+    "\n",
+    "axes[2].text(0, 25.75, 'bps: 0.08', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "axes[1].text(0, 25.75, 'bps: 0.11', bbox=dict(boxstyle=\"square\",\n",
+    "                   ec='white',\n",
+    "                   fc='gainsboro',\n",
+    "                   alpha=.5\n",
+    "                   ))\n",
+    "for i, ax in enumerate(axes):\n",
+    "    # small box to show the zoomed in region\n",
+    "    x1, x2, y1, y2 = 2, 10, 4.5, 7.5\n",
+    "    # draw a rectangle in the main axes\n",
+    "    # left, bottom, width, height\n",
+    "    axins = ax.inset_axes(\n",
+    "        [0.3, 0.53, 0.30, 0.30],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=2, loc2=4, fc=\"none\", ec=\"1\")\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 + 3\n",
+    "    cy = ry + rect.get_height() * 1.25\n",
+    "    ax.annotate('1', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('1', (44, 13), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "for i, ax in enumerate(axes):\n",
+    "    x1, x2, y1, y2 = 23, 27, 0.7, 2.25\n",
+    "    axins = ax.inset_axes(\n",
+    "        # [0.02, 0.55, 0.2, 0.2],\n",
+    "        [0.55, 0.30, .25, .23],\n",
+    "        xlim=(x1, x2), ylim=(y1, y2), xticklabels=[], yticklabels=[])\n",
+    "    axins.imshow(display[i], origin='lower', cmap=colormap, vmin=minmin, vmax=maxmax)\n",
+    "    axins.spines['bottom'].set_color('white')\n",
+    "    axins.spines['top'].set_color('white') \n",
+    "    axins.spines['right'].set_color('white')\n",
+    "    axins.spines['left'].set_color('white')\n",
+    "    mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"1\", linestyle='-')\n",
+    "    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='white', facecolor='none')\n",
+    "    # ax.add_patch(rect)\n",
+    "    rx, ry = rect.get_xy()\n",
+    "    cx = rx + rect.get_width() / 2 \n",
+    "    cy = ry + rect.get_height() * 1.3\n",
+    "    ax.annotate('2', (cx, cy), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    ax.annotate('2', (93, 17), color='w', weight='bold', \n",
+    "                fontsize=12, ha='center', va='center')\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/script/finetune_eval_multi_session.sh b/script/finetune_eval_multi_session.sh
index 4174790..3577961 100644
--- a/script/finetune_eval_multi_session.sh
+++ b/script/finetune_eval_multi_session.sh
@@ -46,7 +46,7 @@ conda activate ibl-fm
 
 cd ../
 
-python src/finetune_eval_multi_session.py --mask_ratio 0.3 \
+python src/finetune_eval_multi_session.py --mask_ratio 0.2 \
                          --mask_mode $MASK_MODE \
                          --model_name $MODEL_NAME \
                          --prompting $PROMPTING \
@@ -55,7 +55,8 @@ python src/finetune_eval_multi_session.py --mask_ratio 0.3 \
                          --base_path $SCRATCH/IBL_foundation_model \
                          --num_train_sessions $NUM_TRAIN_SESSIONS \
                          --test_eid $TEST_EID \
-                         --use_dummy
+                         --use_nlb \
+                         --seed 42
 
 cd script
 
diff --git a/src/configs/finetune_nlb_trainer.yaml b/src/configs/finetune_nlb_trainer.yaml
new file mode 100644
index 0000000..0f5123f
--- /dev/null
+++ b/src/configs/finetune_nlb_trainer.yaml
@@ -0,0 +1,130 @@
+seed: 42
+
+savestring: test
+wandb_project: multi-session
+log_to_wandb: false
+
+verbosity: 0
+
+# wandb configuration
+wandb:
+  use: true
+  entity: null
+  project: multi-session
+  run_name: 671c7ea7
+
+# Logging directories
+dirs:
+  checkpoint_dir: checkpoints  # save model state dicts (todo optimizer states)
+  log_dir: results  # save tensorboard logs
+  dataset_cache_dir: checkpoints/datasets_cache  # save dataset cache
+  # dataset_dir: /home/ppwang/neural-data-transformers/data/lfads_lorenz.h5
+  # pretrained_model_path: checkpoints/models/ndt1/ssl/temporal/model_best_multi-session.pt
+  dataset_dir: ibl-foundation-model/671c7ea7-6726-4fbe-adeb-f89c2c8e489b
+  behav_dir:  671c7ea7-6726-4fbe-adeb-f89c2c8e489b
+  huggingface_org: ibl-foundation-model
+  pretrain_model_path: /scratch/yl6624/IBL_foundation_model/results_/train/num_session_34/model_NDT1/method_ssl/mask_all/stitch_True/model_best.pt
+  
+
+
+# Training configuration
+training:
+  num_epochs: 1000
+  train_batch_size: 16
+  test_batch_size: 16  
+  shuffle_test_dataloader: false    # Shuffle test dataloader between epochs
+
+  save_plot_every_n_epochs: 10  # Plot the model output every n epochs
+  save_every: 50  # Save checkpoint
+  eval_every: null  # Eval model
+
+  dummy: true  # Use dummy data
+
+
+# Model configuration. 
+# Will be passed to the model __init__  method if a model is not passed to the Trainer __init__ method.
+model: 
+  model_class: null   # Any registered model class name. 
+
+# Data configuration.
+data:
+  # dataset_name: lorenz # Any registered dataset name.
+  dataset_name: ibl # Any registered dataset name.
+  dataset_class: ssl # Any registered dataset class name.
+
+  # Load raw dataset if a dataset is not passed to the Trainer __init__ method. 
+  hf_dataset_name: null   # from huggingface
+  json_dataset_name: null # from json file
+
+  train_name: train   # name of the train split in the raw datasete
+  test_name: test     # name of the test split in the raw datasete
+  train_len: null     # used length of the train dataset. null to use all
+  test_len: null      # used length of the test dataset. null to use all
+
+  LOG_EPSILON: 1.e-7 # epsilon for log transformation, to prevent log(0)
+  use_lograte: True # use lograte
+
+  max_time_length: 30    # max_time_length has to be a multiple of time patch size
+  max_space_length: 668   # max_space_length has to be a multiple of space patch size
+  patching: true # patching the neurons
+  sort_by_depth: false
+  brain_region: all
+
+  include_behav: false # include behavior data
+  target: null
+
+  load_meta: true
+
+  num_sessions: 40
+  train_session_eid: ['72cb5550-43b4-4ef0-add5-e4adfdfb5e02', '51e53aff-1d5d-4182-a684-aba783d50ae5', 'd57df551-6dcb-4242-9c72-b806cff5613a', 'e2b845a1-e313-4a08-bc61-a5f662ed295e', 'd2832a38-27f6-452d-91d6-af72d794136c'] 
+  test_session_eid: [] 
+
+  split_method: predefined # random_split/session_based/predefined
+
+  use_aligned_test: False
+
+  sort_by_depth: false
+  sort_by_region: false
+  brain_region: all
+
+  spike_augmentation: false
+
+  use_re: true
+
+# Method configuration. Contains kwargs that are specific to the training method.
+method:
+
+  # Passed to the model __init__ method together with the model config
+  model_kwargs: 
+    method_name: ssl #ssl 
+
+    use_lograte: true
+    loss: poisson_nll  # poisson_nll # mse/other distirbutions (todo)
+    output_size: 2
+    clf: false
+    reg: false
+
+  # Passed to the Dataset __init__ method together with the raw dataset. 
+  dataset_kwargs: {}
+
+  # Passed to the DataLoader __init__ method.
+  dataloader_kwargs:
+    # Contains which keys to pad, along which dimension with which value
+    pad_dict: 
+      spikes:
+          dim: 0
+          side: right
+          value: 0
+          truncate: null
+          min_length: null
+      
+
+optimizer:
+  gradient_accumulation_steps: 1
+  lr: 1.e-4
+  wd: 0.01
+  eps: 1.e-8
+  warmup_pct: 0.15 # cosine/linear
+  gamma: 0.95     # step
+  div_factor: 10  # cosine
+  scheduler: cosine # step/cosine/linear
\ No newline at end of file
diff --git a/src/configs/finetune_sessions_trainer.yaml b/src/configs/finetune_sessions_trainer.yaml
index 9563826..fe3b221 100644
--- a/src/configs/finetune_sessions_trainer.yaml
+++ b/src/configs/finetune_sessions_trainer.yaml
@@ -8,7 +8,7 @@ verbosity: 0
 
 # wandb configuration
 wandb:
-  use: true
+  use: false
   entity: null
   project: multi-session
   run_name: 671c7ea7
@@ -63,7 +63,7 @@ data:
   LOG_EPSILON: 1.e-7 # epsilon for log transformation, to prevent log(0)
   use_lograte: True # use lograte
 
-  max_time_length: 100    # max_time_length has to be a multiple of time patch size
+  max_time_length: 30    # max_time_length has to be a multiple of time patch size
   max_space_length: 668   # max_space_length has to be a multiple of space patch size
   patching: true # patching the neurons
   sort_by_depth: false
diff --git a/src/configs/ndt1_stitching.yaml b/src/configs/ndt1_stitching.yaml
index 828bad9..fad0f15 100644
--- a/src/configs/ndt1_stitching.yaml
+++ b/src/configs/ndt1_stitching.yaml
@@ -11,7 +11,7 @@ encoder:
   masker:
     force_active: true         
     mode: all      # masking mode, e.g. all, temporal
-    ratio: 0.3          # ratio of data to predict
+    ratio: 0.2          # ratio of data to predict
     zero_ratio: 1.0     # of the data to predict, ratio of zeroed out
     random_ratio: 1.0   # of the not zeroed, ratio of randomly replaced
     expand_prob: 0.0    # probability of expanding the mask in ``temporal`` mode
diff --git a/src/configs/ndt1_stitching_prompting.yaml b/src/configs/ndt1_stitching_prompting.yaml
index 4ee9410..5af66b0 100644
--- a/src/configs/ndt1_stitching_prompting.yaml
+++ b/src/configs/ndt1_stitching_prompting.yaml
@@ -11,7 +11,7 @@ encoder:
   masker:
     force_active: true         
     mode: all      # masking mode
-    ratio: 0.3          # ratio of data to predict
+    ratio: 0.2          # ratio of data to predict
     zero_ratio: 1.0     # of the data to predict, ratio of zeroed out
     random_ratio: 1.0   # of the not zeroed, ratio of randomly replaced
     expand_prob: 0.0    # probability of expanding the mask in ``temporal`` mode
diff --git a/src/configs/ndt1_stitching_prompting_temporal.yaml b/src/configs/ndt1_stitching_prompting_temporal.yaml
new file mode 100644
index 0000000..0872858
--- /dev/null
+++ b/src/configs/ndt1_stitching_prompting_temporal.yaml
@@ -0,0 +1,108 @@
+model_class: NDT1
+# NDT1 stitching mask scheme all: all masking/stitching
+
+encoder:
+
+  from_pt: null
+
+  stitching: true
+
+  # Mask spikes
+  masker:
+    force_active: true         
+    mode: temporal      # masking mode
+    ratio: 0.2          # ratio of data to predict
+    zero_ratio: 1.0     # of the data to predict, ratio of zeroed out
+    random_ratio: 1.0   # of the not zeroed, ratio of randomly replaced
+    expand_prob: 0.0    # probability of expanding the mask in ``temporal`` mode
+    max_timespan: 1     # max span of mask if expanded
+    channels: null
+    # [579, 21, 486, 316, 521, 71, 32, 561, 445, 470, 205, 41, 230, 306, 592, 148, 484, 592, 181, 94, 120, 601, 414, 497, 263, 447, 16, 537, 396, 323, 68, 444, 325, 137, 223, 434, 131, 363, 280, 419, 332, 218, 130, 580, 381, 560, 576, 145, 280, 234, 258, 37, 9, 324, 199, 610, 529, 196, 164, 348]     # neurons to mask in ``co-smooth`` mode
+    timesteps: null             # time steps to mask in ``forward-pred`` mode
+    mask_regions: ['all']       # brain regions to mask in ``inter-region`` mode
+    target_regions: ['all']    # brain regions to predict in ``intra-region`` mode
+    n_mask_regions: 1       # number of regions to choose from the list of mask_regions or target_regions
+    
+  # Context available for each timestep
+  context:
+    forward: -1
+    backward: -1
+
+  # Normalize and add noise
+  norm_and_noise:
+    active: false
+    smooth_sd: 2                # gaussian smoohing  
+    norm: "zscore"              # which normalization layer to use (null/layernorm/scalenorm/zscore)
+    eps: 1.e-7                  # avoid dividing by zero when normalizing padded spikes     
+    white_noise_sd: 1.0         # gaussian noise added to the inputs  1.0 originally
+    constant_offset_sd: 0.2     # gaussian noise added to the inputs but contsnat in the time dimension 0.2 originally
+    
+
+  # Embedding layer
+  embedder:
+    n_channels: 668       # number of neurons recorded 
+    n_blocks: 24          # number of blocks of experiments
+    n_dates: 24           # number of days of experiments
+    max_F: 100           # max feature len in timesteps
+
+    mode: linear          # linear/embed/identity
+    mult: 2               # embedding multiplier. hiddden_sizd = n_channels * mult
+    adapt: false          # adapt the embedding layer for each day
+    pos: true             # embed position 
+    act: softsign         # activation for the embedding layers
+    scale: 1              # scale the embedding multiplying by this number
+    bias: true            # use bias in the embedding layer
+    dropout: 0.2          # dropout in embedding layer
+    
+    fixup_init: false     # modify weight initialization
+    init_range: 0.1       # initialization range for embeddings
+    spike_log_init: false # special initialization 
+    max_spikes: 0         # max number of spikes in a single time bin
+
+    tokenize_binary_mask: false
+    use_prompt: true
+    use_session: true
+
+    stack:
+      active: false        # wether to stack consecutive timesteps
+      size: 32            # number of consecutive timesteps to stack
+      stride: 4           # stacking stride
+
+
+  # Transformer
+  transformer:
+    n_layers: 5           # number of transformer layers
+    hidden_size: 512     # hidden space of the transformer
+    use_scalenorm: false  # use scalenorm  instead of layernorm
+    use_rope: false       # use rotary postional encoding
+    rope_theta: 10000.0   # rope angle of rotation
+
+
+    n_heads: 8            # number of attentiomn heads
+    attention_bias: true  # learn bias in the attention layers
+
+    act: gelu             # activiation function in mlp layers
+    inter_size: 1024      # intermediate dimension in the mlp layers
+    mlp_bias: true        # learn bias in the mlp layers
+    
+    dropout: 0.4          # dropout in transformer layers
+    fixup_init: true      # modify weight initialization
+
+  # Projection to factor space
+  factors:  
+    active: false  # project from hidden_size to factors
+    size: 8                  # factors size  
+    act: relu                 # activation function after projecting to factors
+    bias: true                # use bias in projection to factors
+    dropout: 0.0              # dropout in projection to factors
+    fixup_init: false         # modify weight initialization
+    init_range: 0.1           # initialization range for factors projetion
+    
+decoder:
+  from_pt: null
+
+
+
+
+
+
diff --git a/src/configs/ndt1_stitching_temporal.yaml b/src/configs/ndt1_stitching_temporal.yaml
new file mode 100644
index 0000000..8beb86f
--- /dev/null
+++ b/src/configs/ndt1_stitching_temporal.yaml
@@ -0,0 +1,108 @@
+model_class: NDT1
+
+# NDT1 stitching baseline: temporal masking/stitching
+encoder:
+
+  from_pt: null
+
+  stitching: true
+
+  # Mask spikes
+  masker:
+    force_active: true         
+    mode: temporal      # masking mode, e.g. all, temporal
+    ratio: 0.2          # ratio of data to predict
+    zero_ratio: 1.0     # of the data to predict, ratio of zeroed out
+    random_ratio: 1.0   # of the not zeroed, ratio of randomly replaced
+    expand_prob: 0.0    # probability of expanding the mask in ``temporal`` mode
+    max_timespan: 1     # max span of mask if expanded
+    channels: null
+    # [579, 21, 486, 316, 521, 71, 32, 561, 445, 470, 205, 41, 230, 306, 592, 148, 484, 592, 181, 94, 120, 601, 414, 497, 263, 447, 16, 537, 396, 323, 68, 444, 325, 137, 223, 434, 131, 363, 280, 419, 332, 218, 130, 580, 381, 560, 576, 145, 280, 234, 258, 37, 9, 324, 199, 610, 529, 196, 164, 348]     # neurons to mask in ``co-smooth`` mode
+    timesteps: null             # time steps to mask in ``forward-pred`` mode
+    mask_regions: ['all']       # brain regions to mask in ``inter-region`` mode
+    target_regions: ['all']    # brain regions to predict in ``intra-region`` mode
+    n_mask_regions: 1       # number of regions to choose from the list of mask_regions or target_regions
+    
+  # Context available for each timestep
+  context:
+    forward: -1
+    backward: -1
+
+  # Normalize and add noise
+  norm_and_noise:
+    active: false
+    smooth_sd: 2                # gaussian smoohing  
+    norm: "zscore"              # which normalization layer to use (null/layernorm/scalenorm/zscore)
+    eps: 1.e-7                  # avoid dividing by zero when normalizing padded spikes     
+    white_noise_sd: 1.0         # gaussian noise added to the inputs  1.0 originally
+    constant_offset_sd: 0.2     # gaussian noise added to the inputs but contsnat in the time dimension 0.2 originally
+    
+
+  # Embedding layer
+  embedder:
+    n_channels: 668       # number of neurons recorded 
+    n_blocks: 24          # number of blocks of experiments
+    n_dates: 24           # number of days of experiments
+    max_F: 100           # max feature len in timesteps
+
+    mode: linear          # linear/embed/identity
+    mult: 2               # embedding multiplier. hiddden_sizd = n_channels * mult
+    adapt: false          # adapt the embedding layer for each day
+    pos: true             # embed position 
+    act: softsign         # activation for the embedding layers
+    scale: 1              # scale the embedding multiplying by this number
+    bias: true            # use bias in the embedding layer
+    dropout: 0.2          # dropout in embedding layer
+    
+    fixup_init: false     # modify weight initialization
+    init_range: 0.1       # initialization range for embeddings
+    spike_log_init: false # special initialization 
+    max_spikes: 0         # max number of spikes in a single time bin
+
+    tokenize_binary_mask: false
+    use_prompt: false
+    use_session: true
+
+    stack:
+      active: false        # wether to stack consecutive timesteps
+      size: 32            # number of consecutive timesteps to stack
+      stride: 4           # stacking stride
+
+
+  # Transformer
+  transformer:
+    n_layers: 5           # number of transformer layers
+    hidden_size: 512     # hidden space of the transformer
+    use_scalenorm: false  # use scalenorm  instead of layernorm
+    use_rope: false       # use rotary postional encoding
+    rope_theta: 10000.0   # rope angle of rotation
+
+
+    n_heads: 8            # number of attentiomn heads
+    attention_bias: true  # learn bias in the attention layers
+
+    act: gelu             # activiation function in mlp layers
+    inter_size: 1024      # intermediate dimension in the mlp layers
+    mlp_bias: true        # learn bias in the mlp layers
+    
+    dropout: 0.4          # dropout in transformer layers
+    fixup_init: true      # modify weight initialization
+
+  # Projection to factor space
+  factors:  
+    active: false  # project from hidden_size to factors
+    size: 8                  # factors size  
+    act: relu                 # activation function after projecting to factors
+    bias: true                # use bias in projection to factors
+    dropout: 0.0              # dropout in projection to factors
+    fixup_init: false         # modify weight initialization
+    init_range: 0.1           # initialization range for factors projetion
+    
+decoder:
+  from_pt: null
+
+
+
+
+
+
diff --git a/src/configs/trainer_nlb.yaml b/src/configs/trainer_nlb.yaml
new file mode 100644
index 0000000..67fc4c6
--- /dev/null
+++ b/src/configs/trainer_nlb.yaml
@@ -0,0 +1,122 @@
+seed: 42
+
+savestring: test
+wandb_project: ibl-ndt1-100
+log_to_wandb: false
+
+verbosity: 0
+
+# wandb configuration
+wandb:
+  use: true
+  entity: null
+  project: ibl-ndt1-100
+  run_name: 671c7ea7
+
+# Logging directories
+dirs:
+  checkpoint_dir: checkpoints  # save model state dicts (todo optimizer states)
+  log_dir: results  # save tensorboard logs
+  dataset_cache_dir: checkpoints/datasets_cache  # save dataset cache
+  # dataset_dir: /home/ppwang/neural-data-transformers/data/lfads_lorenz.h5
+  # pretrained_model_path: checkpoints/models/ndt1/ssl/temporal/model_best_multi-session.pt
+  pretrained_model_path: /scratch/yl6624/IBL_foundation_model/results_/train/num_session_34/model_NDT1/method_ssl/mask_all/stitch_True/model_best.pt
+  dataset_dir: ibl-foundation-model/671c7ea7-6726-4fbe-adeb-f89c2c8e489b_aligned
+  behav_dir:  data/671c7ea7-6726-4fbe-adeb-f89c2c8e489b_aligned
+  huggingface_org: ibl-foundation-model
+  
+
+
+# Training configuration
+training:
+  num_epochs: 1000
+  train_batch_size: 16
+  test_batch_size: 16  
+  shuffle_test_dataloader: false    # Shuffle test dataloader between epochs
+
+  save_plot_every_n_epochs: 5  # Plot the model output every n epochs
+  save_every: 100  # Save checkpoint
+  eval_every: null  # Eval model
+
+
+
+# Model configuration. 
+# Will be passed to the model __init__  method if a model is not passed to the Trainer __init__ method.
+model: 
+  model_class: null   # Any registered model class name. 
+
+# Data configuration.
+data:
+  # dataset_name: lorenz # Any registered dataset name.
+  dataset_name: ibl # Any registered dataset name.
+  dataset_class: ssl # Any registered dataset class name.
+
+  # Load raw dataset if a dataset is not passed to the Trainer __init__ method. 
+  hf_dataset_name: null   # from huggingface
+  json_dataset_name: null # from json file
+
+  train_name: train   # name of the train split in the raw datasete
+  test_name: test     # name of the test split in the raw datasete
+  train_len: null     # used length of the train dataset. null to use all
+  test_len: null      # used length of the test dataset. null to use all
+
+  LOG_EPSILON: 1.e-7 # epsilon for log transformation, to prevent log(0)
+  use_lograte: True # use lograte
+
+  max_time_length: 30    # max_time_length has to be a multiple of time patch size
+  max_space_length: 668   # max_space_length has to be a multiple of space patch size
+  patching: true # patching the neurons
+  sort_by_depth: false
+  sort_by_region: false
+  brain_region: all
+  spike_augmentation: false
+
+  include_behav: false # include behavior data
+  target: null
+
+  load_meta: true
+
+  num_sessions: 3
+  test_session_eid: ["671c7ea7-6726-4fbe-adeb-f89c2c8e489b"] 
+
+  split_method: session_based # random_split/session_based
+
+  use_aligned_test: False
+
+# Method configuration. Contains kwargs that are specific to the training method.
+method:
+
+  # Passed to the model __init__ method together with the model config
+  model_kwargs: 
+    method_name: ssl #ssl 
+
+    use_lograte: true
+    loss: poisson_nll  # poisson_nll # mse/other distirbutions (todo)
+    output_size: 2
+    clf: false
+    reg: false
+
+  # Passed to the Dataset __init__ method together with the raw dataset. 
+  dataset_kwargs: {}
+
+  # Passed to the DataLoader __init__ method.
+  dataloader_kwargs:
+    # Contains which keys to pad, along which dimension with which value
+    pad_dict: 
+      spikes:
+          dim: 0
+          side: right
+          value: 0
+          truncate: null
+          min_length: null
+      
+
+optimizer:
+  gradient_accumulation_steps: 1
+  lr: 1.e-4
+  wd: 0.01
+  eps: 1.e-8
+  warmup_pct: 0.15 # cosine/linear
+  gamma: 0.95     # step
+  div_factor: 10  # cosine
+  scheduler: cosine # step/cosine/linear
diff --git a/src/finetune_eval_multi_session.py b/src/finetune_eval_multi_session.py
index a2a4acd..198f1a5 100644
--- a/src/finetune_eval_multi_session.py
+++ b/src/finetune_eval_multi_session.py
@@ -2,6 +2,7 @@
 from math import ceil
 from datasets import load_dataset, load_from_disk, concatenate_datasets, load_dataset_builder
 from utils.dataset_utils import get_user_datasets, load_ibl_dataset, split_both_dataset
+from utils.nlb_data_utils import load_nlb_dataset
 import argparse
 from datasets import load_dataset, load_from_disk, concatenate_datasets
 from utils.dataset_utils import load_ibl_dataset
@@ -34,9 +35,12 @@
 ap.add_argument("--base_path", type=str, default='/mnt/home/yzhang1/ceph')
 ap.add_argument("--num_train_sessions", type=int, default=1)
 ap.add_argument('--use_dummy', action='store_true')
+ap.add_argument('--use_nlb', action='store_true')
+ap.add_argument('--use_leaderboard', action='store_true')
+ap.add_argument('--seed', type=int, default=42)
 args = ap.parse_args()
 
-eid = args.test_eid
+eid = args.test_eid if not args.use_nlb else "nlb-rtt"
 base_path = args.base_path
 model_acroynm = args.model_name.lower()
 num_train_sessions = args.num_train_sessions
@@ -44,27 +48,38 @@
 
 if args.prompting == "True":
     if args.model_name == 'NDT1':
-        kwargs = {
-            "model": f"include:src/configs/{model_acroynm}_stitching_prompting.yaml"
-        }
+        if args.mask_mode == "all":
+            kwargs = {
+                "model": f"include:src/configs/{model_acroynm}_stitching_prompting.yaml"
+            }
+        elif args.mask_mode == "temporal":
+            kwargs = {
+                "model": f"include:src/configs/{model_acroynm}_stitching_prompting_temporal.yaml"
+            }
     elif args.model_name == 'NDT2':
         kwargs = {
             "model": f"include:src/configs/{model_acroynm}_prompting.yaml"
         }
 else:
     if args.model_name == 'NDT1':
-        kwargs = {
-            "model": f"include:src/configs/{model_acroynm}_stitching.yaml"
-        }
+        if args.mask_mode == "all":
+            kwargs = {
+                "model": f"include:src/configs/{model_acroynm}_stitching.yaml"
+            }
+        elif args.mask_mode == "temporal":
+            kwargs = {
+                "model": f"include:src/configs/{model_acroynm}_stitching_temporal.yaml"
+            }
     elif args.model_name == 'NDT2':
         kwargs = {
             "model": f"include:src/configs/{model_acroynm}.yaml"
         }
 
+trainer_config_path = f"src/configs/finetune_nlb_trainer.yaml" if args.use_nlb else f"src/configs/finetune_sessions_trainer.yaml"
 config = config_from_kwargs(kwargs)
-config = update_config("src/configs/finetune_sessions_trainer.yaml", config)
+config = update_config(trainer_config_path, config)
 
-set_seed(config.seed)
+set_seed(args.seed)
 # Shared variable to signal the dummy load to stop
 stop_dummy_load = threading.Event()
 if args.use_dummy:
@@ -76,17 +91,23 @@
     if args.train == "True":
         print('Start model training.')
         print('=====================')
-        train_dataset, val_dataset, test_dataset, meta_data = load_ibl_dataset(config.dirs.dataset_cache_dir, 
-                            config.dirs.huggingface_org,
-                            eid=None,
-                            num_sessions=config.data.num_sessions,
-                            split_method=config.data.split_method,
-                            train_session_eid=[eid],
-                            test_session_eid=config.data.test_session_eid,
-                            batch_size=config.training.train_batch_size,
-                            seed=config.seed)
+        if args.use_nlb:
+            if args.use_leaderboard:
+                from utils.nlb_data_utils import load_nlb_dataset_test as load_nlb_dataset
+            train_dataset, val_dataset, meta_data = load_nlb_dataset("data/000129/sub-Indy", 20)
+        else:
+            train_dataset, val_dataset, test_dataset, meta_data = load_ibl_dataset(config.dirs.dataset_cache_dir, 
+                                config.dirs.huggingface_org,
+                                eid=None,
+                                num_sessions=config.data.num_sessions,
+                                split_method=config.data.split_method,
+                                train_session_eid=[eid],
+                                test_session_eid=config.data.test_session_eid,
+                                batch_size=config.training.train_batch_size,
+                                seed=config.seed)
 
-        log_dir = os.path.join(base_path, config.dirs.log_dir, 
+        log_dir = os.path.join(base_path, 
+                            config.dirs.log_dir, 
                             "finetune", 
                             "num_session_{}".format(num_train_sessions),
                             "model_{}".format(config.model.model_class), 
@@ -95,8 +116,7 @@
                             "stitch_{}".format(config.model.encoder.stitching),
                             "{}".format(eid)
                             )
-        if not os.path.exists(log_dir):
-            os.makedirs(log_dir)
+        os.makedirs(log_dir, exist_ok=True)
         print(meta_data)
 
         if config.wandb.use:
@@ -132,6 +152,7 @@
                                 sort_by_depth=config.data.sort_by_depth,
                                 sort_by_region=config.data.sort_by_region,
                                 stitching=config.model.encoder.stitching,
+                                use_nlb=args.use_nlb,
                                 shuffle=True)
         
         val_dataloader = make_loader(val_dataset, 
@@ -146,6 +167,7 @@
                                 sort_by_depth=config.data.sort_by_depth,
                                 sort_by_region=config.data.sort_by_region,
                                 stitching=config.model.encoder.stitching,
+                                use_nlb=args.use_nlb,
                                 shuffle=False)
         
         # Initialize the accelerator
@@ -160,12 +182,23 @@
         model = accelerator.prepare(model)
         # load pretrain model
         pretrain_model_path = f'{base_path}/results/train/num_session_{num_train_sessions}/model_{config.model.model_class}/method_{config.method.model_kwargs.method_name}/mask_{args.mask_mode}/stitch_{config.model.encoder.stitching}/model_best.pt'
-        if num_train_sessions > 1:
+        if num_train_sessions > 1 and not args.use_nlb:
             print('Load pretrain model from:', pretrain_model_path)
             # load weights that can be found in the pretrain model
             model.load_state_dict(torch.load(pretrain_model_path)['model'].state_dict(), strict=False)
-        else:
+        elif not args.use_nlb:
             print('Train from scratch.')
+        if args.use_nlb and config.dirs.pretrain_model_path and num_train_sessions > 1:
+            pretrain_model_path = config.dirs.pretrain_model_path
+            if args.mask_mode == "temporal":
+                print("load pretrained 34-session IBL temporal model, start finetune for NLB RTT data")
+                pretrain_model_path = pretrain_model_path.replace("mask_all", "mask_temporal")
+            else:
+                print("load pretrained 34-session IBL MtM model, start finetune for NLB RTT data")
+            model.load_state_dict(torch.load(pretrain_model_path)['model'].state_dict(), strict=False)
+            print("load pretrained 34-session IBL model, start finetune for NLB RTT data")
+        elif args.use_nlb:
+            print("NLB RTT data, train from scratch.")
         
         optimizer = torch.optim.AdamW(model.parameters(), lr=config.optimizer.lr, weight_decay=config.optimizer.wd, eps=config.optimizer.eps)
         lr_scheduler = OneCycleLR(
@@ -217,12 +250,12 @@
             
         n_time_steps = 100
         
-        co_smooth = True
-        forward_pred = True
+        co_smooth = False
+        forward_pred = False
         inter_region = True
-        intra_region = True
-        choice_decoding = True
-        continuous_decoding = True
+        intra_region = False
+        choice_decoding = True if not args.use_nlb else False
+        continuous_decoding = True if not args.use_nlb else False
         
         print(mask_name)
         
@@ -234,20 +267,47 @@
         configs = {
             'model_config': model_config,
             'model_path': f'{base_path}/results/finetune/num_session_{num_train_sessions}/model_NDT1/method_ssl/{mask_name}/stitch_True/{eid}/model_best.pt',
-            'trainer_config': f'src/configs/trainer_{model_acroynm}.yaml',
+            'trainer_config': f'src/configs/trainer_{model_acroynm}.yaml' if not args.use_nlb else f'src/configs/trainer_nlb.yaml',
             'dataset_path': None, 
             'test_size': 0.2,
             'seed': 42,
             'mask_name': mask_name,
             'eid': eid,
             'stitching': True,
-            'num_sessions': 1 
+            'num_sessions': 1 ,
+            'use_nlb': args.use_nlb,
+            'use_leaderboard': args.use_leaderboard,
         }  
         
         
         # load your model and dataloader
         model, accelerator, dataset, dataloader = load_model_data_local(**configs)
-        
+        from utils.eval_utils import co_smoothing_eval_nlb as co_smoothing_eval
+        if args.use_nlb:
+            from utils.eval_utils import co_smoothing_eval_nlb as co_smoothing_eval
+            print("NLB RTT data, save h5")
+            co_smoothing_configs = {
+                'subtract': 'task',
+                'onset_alignment': [40],
+                'method_name': mask_name, 
+                'save_path': f'{base_path}/results/eval/num_session_{num_train_sessions}/model_NDT1/method_ssl/{mask_name}/stitch_True/{eid}/co_smooth',
+                'mode': 'save_h5',
+                'n_time_steps': n_time_steps,    
+                'held_out_list': None,
+                'is_aligned': True,
+                'target_regions': ['all'],
+                'n_jobs': 8,
+            }
+            results, gt_result, pred_result = co_smoothing_eval(model, 
+                            accelerator, 
+                            dataloader, 
+                            dataset, 
+                            **co_smoothing_configs)
+            print(results)
+
+            print(f"co_smooth shape of gt_result: {gt_result.shape}")
+            print(f"co_smooth shape of pred_result: {pred_result.shape}")
+            wandb.log(results)
         # co-smoothing
         if co_smooth:
             print('Start co-smoothing:')
@@ -263,12 +323,15 @@
                 'n_jobs': 8
             }
         
-            results = co_smoothing_eval(model, 
+            results, gt_result, pred_result = co_smoothing_eval(model, 
                             accelerator, 
                             dataloader, 
                             dataset, 
                             **co_smoothing_configs)
             print(results)
+
+            print(f"co_smooth shape of gt_result: {gt_result.shape}")
+            print(f"co_smooth shape of pred_result: {pred_result.shape}")
             wandb.log(results)
         
         # forward prediction
@@ -281,18 +344,20 @@
                 'save_path': f'{base_path}/results/eval/num_session_{num_train_sessions}/model_NDT1/method_ssl/{mask_name}/stitch_True/{eid}/forward_pred',
                 'mode': 'forward_pred',
                 'n_time_steps': n_time_steps,    
-                'held_out_list': list(range(90, 100)), # NLB uses 200 ms for fp
+                'held_out_list': list(range(90, 100)) if not args.use_nlb else list(range(27,30)),       # NLB uses 200 ms for fp
                 'is_aligned': True,
                 'target_regions': None,
                 'n_jobs': 8
             }
         
-            results = co_smoothing_eval(model, 
+            results, gt_result, pred_result = co_smoothing_eval(model, 
                             accelerator, 
                             dataloader, 
                             dataset, 
                             **co_smoothing_configs)
             print(results)
+            print(f"forward_pred shape of gt_result: {gt_result.shape}")
+            print(f"forward_pred shape of pred_result: {pred_result.shape}")
             wandb.log(results)
             
         
@@ -312,12 +377,14 @@
                 'n_jobs': 8
             }
         
-            results = co_smoothing_eval(model, 
+            results, gt_result, pred_result = co_smoothing_eval(model, 
                             accelerator, 
                             dataloader, 
                             dataset, 
                             **co_smoothing_configs)
             print(results)
+            print(f"inter_region shape of gt_result: {gt_result.shape}")
+            print(f"inter_region shape of pred_result: {pred_result.shape}")
             wandb.log(results)
 
         
@@ -337,12 +404,14 @@
                 'n_jobs': 8
             }
         
-            results = co_smoothing_eval(model, 
+            results, gt_result, pred_result = co_smoothing_eval(model, 
                             accelerator, 
                             dataloader, 
                             dataset, 
                             **co_smoothing_configs)
             print(results)
+            print(f"intra_region shape of gt_result: {gt_result.shape}")
+            print(f"intra_region shape of pred_result: {pred_result.shape}")
             wandb.log(results)
         
         
diff --git a/src/loader/base.py b/src/loader/base.py
index 3521116..551ed54 100644
--- a/src/loader/base.py
+++ b/src/loader/base.py
@@ -265,6 +265,7 @@ def __init__(
         brain_region = 'all',
         dataset_name = "ibl",
         stitching = False,
+        use_nlb = False,
     ) -> None:
         self.dataset = dataset
         self.target = target
@@ -280,6 +281,7 @@ def __init__(
         self.load_meta = load_meta
         self.dataset_name = dataset_name
         self.stitching = stitching
+        self.use_nlb = use_nlb
 
     def _preprocess_h5_data(self, data, idx):
         spike_data, rates, _, _ = data
@@ -399,7 +401,7 @@ def _preprocess_ibl_data(self, data):
         # add attention mask
         time_attn_mask = _attention_mask(self.max_time_length, pad_time_length).astype(np.int64)
         binned_spikes_data = binned_spikes_data.astype(np.float32)
-        
+
         return {
             "spikes_data": binned_spikes_data,
             "time_attn_mask": time_attn_mask,
@@ -411,6 +413,103 @@ def _preprocess_ibl_data(self, data):
             "neuron_regions": list(neuron_regions),
             "eid": data['eid']
         }
+
+    def _preprocess_nlb_data(self, data, idx):
+        binned_spikes_data = data['spikes'][idx]
+
+        if self.target is not None:
+            target_behavior = np.array(data[self.target]).astype(np.float32)
+            if self.target == 'choice':
+                assert target_behavior != 0, "Invalid value for choice."
+                target_behavior = np.array([0., 1.]) if target_behavior == 1 else np.array([1., 0.])
+                target_behavior = target_behavior.astype(np.float32)
+        else:
+            target_behavior = np.array([np.nan])
+
+        pad_time_length, pad_space_length = 0, 0
+
+        num_time_steps, num_neurons = binned_spikes_data.shape
+
+        if self.load_meta:
+            neuron_idxs = np.arange(num_neurons)
+            assert (self.sort_by_depth and self.sort_by_region) == False, "Can only sort either by depth or neuron."
+            if self.sort_by_depth:
+                sorted_neuron_idxs = [x for _, x in sorted(zip(neuron_depths, neuron_idxs))]
+            elif self.sort_by_region:
+                sorted_neuron_idxs = [x for _, x in sorted(zip(neuron_regions, neuron_idxs))]
+            else:
+                sorted_neuron_idxs = neuron_idxs.copy()
+            binned_spikes_data = binned_spikes_data[:,sorted_neuron_idxs]
+            # neuron_depths = neuron_depths[sorted_neuron_idxs]
+        # make neuron regions a list of strings, first 0.2 be m1, next 0.2 be m2, etc.
+        # neuron_regions = np.array([f'r{i+1}' for i in range(5) for _ in range(num_neurons//5)])
+        neuron_regions = np.array(["xx" for i in range(num_neurons)])
+        neuron_regions[:98] = "HI"
+        neuron_regions[98:] = "HO"
+        # neuron_regions from np.array to list of strings
+        neuron_regions = list(neuron_regions)
+
+        # pad along time dimension
+        if num_time_steps > self.max_time_length:
+            binned_spikes_data = binned_spikes_data[:self.max_time_length]
+        else: 
+            if self.pad_to_right:
+                pad_time_length = self.max_time_length - num_time_steps
+                binned_spikes_data = _pad_seq_right_to_n(binned_spikes_data, self.max_time_length, self.pad_value)
+            else:
+                pad_time_length = num_time_steps - self.max_time_length
+                binned_spikes_data = _pad_seq_left_to_n(binned_spikes_data, self.max_time_length, self.pad_value)
+
+        if not self.stitching:
+            # pad along space dimension
+            if num_neurons > self.max_space_length:
+                binned_spikes_data = binned_spikes_data[:,:self.max_space_length]
+                neuron_depths = neuron_depths[:self.max_space_length]
+                neuron_regions = neuron_regions[:self.max_space_length]
+            else: 
+                if self.pad_to_right:
+                    pad_space_length = self.max_space_length - num_neurons
+                    binned_spikes_data = _pad_seq_right_to_n(binned_spikes_data.T, self.max_space_length, self.pad_value)
+                    # binned_spikes_data = _wrap_pad_neuron_up_to_n(binned_spikes_data, self.max_space_length).T
+                    neuron_depths = _pad_seq_right_to_n(neuron_depths, self.max_space_length, np.nan)
+                    neuron_regions = _pad_seq_right_to_n(neuron_regions, self.max_space_length, np.nan)
+                else:
+                    pad_space_length = num_neurons - self.max_space_length
+                    binned_spikes_data = _pad_seq_left_to_n(binned_spikes_data.T, self.max_space_length, self.pad_value)
+                    neuron_depths = _pad_seq_left_to_n(neuron_depths, self.max_space_length, np.nan)
+                    neuron_regions = _pad_seq_left_to_n(neuron_regions, self.max_space_length, np.nan)
+                binned_spikes_data = binned_spikes_data.T
+            spikes_spacestamps = np.arange(self.max_space_length).astype(np.int64)
+            space_attn_mask = _attention_mask(self.max_space_length, pad_space_length).astype(np.int64)
+        else:
+            spikes_spacestamps = np.arange(num_neurons).astype(np.int64)
+            space_attn_mask = _attention_mask(num_neurons, 0).astype(np.int64)
+                
+        spikes_timestamps = np.arange(self.max_time_length).astype(np.int64)
+        
+        # add attention mask
+        time_attn_mask = _attention_mask(self.max_time_length, pad_time_length).astype(np.int64)
+        binned_spikes_data = binned_spikes_data.astype(np.float32)
+        # print(type({'nlb-rtt'}))
+        # print(type(binned_spikes_data))
+        # print(type(time_attn_mask))
+        # print(type(space_attn_mask))
+        # print(type(spikes_timestamps))
+        # print(type(spikes_spacestamps))
+        # print(type(target_behavior))
+        # print(type(list(neuron_regions)))
+        return {
+            "spikes_data": binned_spikes_data,
+            "time_attn_mask": time_attn_mask,
+            "space_attn_mask": space_attn_mask,
+            "spikes_timestamps": spikes_timestamps,
+            "spikes_spacestamps": spikes_spacestamps,
+            "target": target_behavior,
+            # "neuron_depths": neuron_depths, 
+            "neuron_regions": list(neuron_regions),
+            "eid": 'nlb-rtt',
+            "trial_id": data['trial_id'][idx]
+        }
     
     def __len__(self):
         if "ibl" in self.dataset_name:
@@ -420,6 +519,8 @@ def __len__(self):
             return len(self.dataset[0])
     
     def __getitem__(self, idx):
+        if self.use_nlb:
+            return self._preprocess_nlb_data(self.dataset, idx)
         if "ibl" in self.dataset_name:
             return self._preprocess_ibl_data(self.dataset[idx])
         else:
diff --git a/src/loader/make_loader.py b/src/loader/make_loader.py
index ebdeb03..6c5236c 100644
--- a/src/loader/make_loader.py
+++ b/src/loader/make_loader.py
@@ -15,6 +15,7 @@ def make_loader(dataset,
                  load_meta=False,
                  dataset_name = "ibl",
                  stitching = False,
+                 use_nlb=False,
                  shuffle = True):
     
     dataset = BaseDataset(dataset=dataset, 
@@ -29,7 +30,8 @@ def make_loader(dataset,
                           sort_by_region = sort_by_region,
                           brain_region = brain_region,
                           load_meta=load_meta,
-                          stitching=stitching
+                          stitching=stitching,
+                          use_nlb=use_nlb,
             )
     print(f"len(dataset): {len(dataset)}")
     if stitching:
diff --git a/src/models/ndt1.py b/src/models/ndt1.py
index 522affa..f73cb83 100644
--- a/src/models/ndt1.py
+++ b/src/models/ndt1.py
@@ -153,6 +153,7 @@ def __init__(self, hidden_size, n_channels, config: DictConfig):
         self.use_session = config.use_session
         if self.use_session:
             self.eid_lookup = include_eids
+            self.eid_lookup[-1] = "nlb-rtt"
             self.eid_to_indx = {r: i for i,r in enumerate(self.eid_lookup)}
             self.embed_session = nn.Embedding(len(self.eid_lookup), hidden_size) 
 
diff --git a/src/trainer/base.py b/src/trainer/base.py
index 68688bb..0cfa8d9 100644
--- a/src/trainer/base.py
+++ b/src/trainer/base.py
@@ -47,6 +47,7 @@ def __init__(
             self.masking_schemes += ['intra-region', 'inter-region']
 
         if self.masking_mode in ["combined", "all"]:
+            print("holy shit")
             print("(train) switch between masking modes: ", self.masking_schemes)
 
     def train(self):
@@ -160,6 +161,7 @@ def train_epoch(self, epoch):
     
     def _forward_model_outputs(self, batch, masking_mode):
         batch = move_batch_to_device(batch, self.accelerator.device)
+
         return self.model(
             batch['spikes_data'], 
             time_attn_mask=batch['time_attn_mask'],
diff --git a/src/utils/eval_utils.py b/src/utils/eval_utils.py
index 4904c53..1018302 100644
--- a/src/utils/eval_utils.py
+++ b/src/utils/eval_utils.py
@@ -2,6 +2,7 @@
 from accelerate import Accelerator
 from loader.make_loader import make_loader
 from utils.dataset_utils import load_ibl_dataset
+from utils.nlb_data_utils import load_nlb_dataset
 from utils.utils import set_seed, move_batch_to_device, plot_gt_pred, metrics_list, plot_avg_rate_and_spike, \
     plot_rate_and_spike
 from utils.config_utils import config_from_kwargs, update_config
@@ -20,6 +21,7 @@
 import os
 from trainer.make import make_trainer
 from pathlib import Path
+from nlb_tools.make_tensors import save_to_h5
 
 NAME2MODEL = {"NDT1": NDT1, "STPatch": STPatch, "iTransformer": iTransformer}
 
@@ -44,6 +46,8 @@ def load_model_data_local(**kwargs):
     eid = kwargs['eid']
     stitching = kwargs['stitching']
     num_sessions = kwargs['num_sessions']
+    use_nlb = kwargs['use_nlb']
+    use_leaderboard = kwargs['use_leaderboard']
 
     # set seed
     set_seed(seed)
@@ -55,17 +59,25 @@ def load_model_data_local(**kwargs):
     config.model.encoder.masker.mode = mask_mode
 
     accelerator = Accelerator()
-
-    _,_,_, meta_data = load_ibl_dataset(
-                            cache_dir=config.dirs.dataset_cache_dir,
-                            user_or_org_name=config.dirs.huggingface_org,
-                            num_sessions=1,
-                            split_method="predefined",
-                            test_session_eid=[],
-                            batch_size=config.training.train_batch_size,
-                            seed=seed,
-                            eid=eid
-                        )
+    if use_nlb:
+        if use_leaderboard:
+            from utils.nlb_data_utils import load_nlb_dataset_test as load_nlb_dataset
+        else:
+            from utils.nlb_data_utils import load_nlb_dataset
+        print(use_leaderboard)
+        train_dataset, eval_dataset, meta_data = load_nlb_dataset("data/000129/sub-Indy", 20)
+        dataset = [train_dataset, eval_dataset]
+    else:  
+        _, _, _, meta_data = load_ibl_dataset(
+                                cache_dir=config.dirs.dataset_cache_dir,
+                                user_or_org_name=config.dirs.huggingface_org,
+                                num_sessions=1,
+                                split_method="predefined",
+                                test_session_eid=[],
+                                batch_size=config.training.train_batch_size,
+                                seed=seed,
+                                eid=eid
+                            )
     print(meta_data)
 
     model_class = NAME2MODEL[config.model.model_class]
@@ -85,9 +97,12 @@ def load_model_data_local(**kwargs):
     model = accelerator.prepare(model)
 
     # load the dataset
-    dataset = load_dataset(f'ibl-foundation-model/{eid}_aligned', cache_dir=config.dirs.dataset_cache_dir)["test"]
-
-    n_neurons = len(dataset['cluster_regions'][0])
+    if not use_nlb:
+        dataset = load_dataset(f'ibl-foundation-model/{eid}_aligned', cache_dir=config.dirs.dataset_cache_dir)["test"]
+        n_neurons = len(dataset['cluster_regions'][0])
+    else:
+        print(meta_data)
+        n_neurons = meta_data['num_neurons'][0]
 
     if config.model.model_class in ["NDT1", "iTransformer"]:
         max_space_length = n_neurons  
@@ -99,15 +114,17 @@ def load_model_data_local(**kwargs):
     print('encoder max space length:', max_space_length)
 
     dataloader = make_loader(
-        dataset,
+        dataset if not use_nlb else dataset[1],
         target=config.data.target,
-        batch_size=len(dataset),
+        batch_size=dataset[1]['spikes'].shape[0] if use_nlb else len(dataset),
         pad_to_right=True,
         pad_value=-1.,
         max_time_length=config.data.max_time_length,
         max_space_length=max_space_length,
         dataset_name=config.data.dataset_name,
         load_meta=config.data.load_meta,
+        use_nlb=use_nlb,
+        stitching=config.model.encoder.stitching,
         shuffle=False,
     )
 
@@ -587,6 +604,719 @@ def co_smoothing_eval(
         f"{mode}_std_r2_trial": np.nanstd(r2_all[:, 1])
     }
 
+def co_smoothing_eval_nlb(
+        model,
+        accelerator,
+        test_dataloader,
+        dataset_list,
+        n=1,
+        **kwargs
+):
+    assert n == 1, 'only support n=1 now'
+
+    for batch in test_dataloader:
+        break
+
+    method_name = kwargs['method_name']
+    mode = kwargs['mode']
+    is_aligned = kwargs['is_aligned']
+    target_regions = kwargs['target_regions']
+    n_jobs = kwargs['n_jobs']
+
+    # hack to accommodate NDT2 - fix later 
+    if sum(batch['space_attn_mask'][0] == 0) == 0:
+        tot_num_neurons = batch['space_attn_mask'].size()[-1]
+    else:
+        tot_num_neurons = (batch['space_attn_mask'][0] == 0).nonzero().min().item() 
+    # uuids_list = np.array(test_dataset['cluster_uuids'][0])[:tot_num_neurons]
+    # region_list = np.array(test_dataset['cluster_regions'])[0][:tot_num_neurons]
+    region_list = np.array(batch['neuron_regions'])[:,0]
+    uuids_list = np.array([f'n_{i}' for i in range(len(region_list))])
+    T = kwargs['n_time_steps']
+    N = batch['spikes_data'].shape[2]
+
+    # if is_aligned:
+        
+    #     # prepare the condition matrix
+    #     b_list = []
+    
+    #     # choice
+    #     choice = np.array(test_dataset['choice'])
+    #     choice = np.tile(np.reshape(choice, (choice.shape[0], 1)), (1, T))
+    #     b_list.append(choice)
+    
+    #     # reward
+    #     reward = np.array(test_dataset['reward'])
+    #     reward = np.tile(np.reshape(reward, (reward.shape[0], 1)), (1, T))
+    #     b_list.append(reward)
+    
+    #     # block
+    #     block = np.array(test_dataset['block'])
+    #     block = np.tile(np.reshape(block, (block.shape[0], 1)), (1, T))
+    #     b_list.append(block)
+    
+    #     behavior_set = np.stack(b_list, axis=-1)
+    
+    #     var_name2idx = {'block': [2],
+    #                     'choice': [0],
+    #                     'reward': [1],
+    #                     'wheel': [3],
+    #                     }
+    #     var_value2label = {'block': {(0.2,): "p(left)=0.2",
+    #                                  (0.5,): "p(left)=0.5",
+    #                                  (0.8,): "p(left)=0.8", },
+    #                        'choice': {(-1.0,): "right",
+    #                                   (1.0,): "left"},
+    #                        'reward': {(0.,): "no reward",
+    #                                   (1.,): "reward", }}
+    #     var_tasklist = ['block', 'choice', 'reward']
+    #     var_behlist = []
+    if mode == "save_h5":
+        train_dataset, eval_dataset = dataset_list[0], dataset_list[1]
+        print(len(dataset_list))
+        train_rates_heldin, train_rates_heldout = [], []
+        train_dataloader = make_loader(
+            train_dataset,
+            target=None,
+            batch_size=train_dataset['spikes'].shape[0],
+            pad_to_right=True,
+            pad_value=-1.,
+            max_time_length=30,
+            max_space_length=668,
+            dataset_name="ibl",
+            load_meta=True,
+            use_nlb=True,
+            stitching=True,
+            shuffle=False,
+        )
+        eval_dataloader = make_loader(
+            eval_dataset,
+            target=None,
+            batch_size=eval_dataset['spikes'].shape[0],
+            pad_to_right=True,
+            pad_value=-1.,
+            max_time_length=30,
+            max_space_length=668,
+            dataset_name="ibl",
+            load_meta=True,
+            use_nlb=True,
+            stitching=True,
+            shuffle=False,
+        )
+        if 'all' in target_regions:
+            target_regions = list(np.unique(region_list))
+        held_out_list = kwargs['held_out_list']
+        assert held_out_list is None, 'inter_region does LOO for all neurons in the target region'
+        # mask heldout neurons region
+        region="HO"
+        hd = np.argwhere(region_list==region).flatten()
+        held_out_list = np.arange(len(hd))
+        held_out_list = [held_out_list]   
+        hd = np.array([held_out_list]).flatten()
+        heldout_ids = np.argwhere(region_list==region).flatten()
+
+        heldin_ids = np.argwhere(region_list=="HI").flatten()
+
+        for batch in train_dataloader:
+            batch = move_batch_to_device(batch, accelerator.device)
+            gt_spike_data = batch['spikes_data'].clone()
+            mask_result = heldout_mask(
+                batch['spikes_data'].clone(),
+                mode='inter_region',
+                heldout_idxs=hd,
+                target_regions=[region],
+                neuron_regions=region_list
+            )              
+            trial_id = batch['trial_id']
+            # sort the trial_id
+            trial_id = np.argsort(trial_id.cpu().numpy())
+            gt_spike_data = gt_spike_data[trial_id]
+            # sort mask_result['spikes'] based on trial_id
+            mask_result['spikes'] = mask_result['spikes'][trial_id]
+            batch['time_attn_mask'] = batch['time_attn_mask'][trial_id]
+            batch['space_attn_mask'] = batch['space_attn_mask'][trial_id]
+            batch['spikes_timestamps'] = batch['spikes_timestamps'][trial_id]
+            batch['spikes_spacestamps'] = batch['spikes_spacestamps'][trial_id]
+            batch['target'] = batch['target'][trial_id]
+            mask_result['eval_mask'] = mask_result['eval_mask'][trial_id]
+
+            masking_mode = 'inter-region' if model.use_prompt else model.encoder.masker.mode
+            model.encoder.mask = False
+            outputs = model(
+                mask_result['spikes'],
+                time_attn_mask=batch['time_attn_mask'],
+                space_attn_mask=batch['space_attn_mask'],
+                spikes_timestamps=batch['spikes_timestamps'], 
+                spikes_spacestamps=batch['spikes_spacestamps'], 
+                targets = batch['target'],
+                neuron_regions=batch['neuron_regions'],
+                eval_mask=mask_result['eval_mask'],
+                masking_mode=masking_mode,
+                num_neuron=batch['spikes_data'].shape[2],
+                eid=batch['eid'][0]  # each batch consists of data from the same eid
+            )
+        train_heldin_gt_spike = gt_spike_data.detach().cpu().numpy()
+        train_heldin_gt_spike = train_heldin_gt_spike[:, :, heldin_ids]
+        
+        outputs.preds = torch.exp(outputs.preds)
+        pred_spikes = outputs.preds.detach().cpu().numpy()
+        train_heldout_pred_spike = pred_spikes[:, :, heldout_ids]
+        train_heldin_pred_spike = pred_spikes[:, :, heldin_ids]
+        
+        for batch in test_dataloader:
+            batch = move_batch_to_device(batch, accelerator.device)
+            gt_spike_data = batch['spikes_data'].clone()
+            mask_result = heldout_mask(
+                batch['spikes_data'].clone(),
+                mode='inter_region',
+                heldout_idxs=hd,
+                target_regions=[region],
+                neuron_regions=region_list
+            )              
+            trial_id = batch['trial_id']
+            # sort the trial_id
+            trial_id = np.argsort(trial_id.cpu().numpy())
+            gt_spike_data = gt_spike_data[trial_id]
+            # sort mask_result['spikes'] based on trial_id
+            mask_result['spikes'] = mask_result['spikes'][trial_id]
+            batch['time_attn_mask'] = batch['time_attn_mask'][trial_id]
+            batch['space_attn_mask'] = batch['space_attn_mask'][trial_id]
+            batch['spikes_timestamps'] = batch['spikes_timestamps'][trial_id]
+            batch['spikes_spacestamps'] = batch['spikes_spacestamps'][trial_id]
+            batch['target'] = batch['target'][trial_id]
+            
+            mask_result['eval_mask'] = mask_result['eval_mask'][trial_id]
+            masking_mode = 'inter-region' if model.use_prompt else model.encoder.masker.mode
+            model.encoder.mask = False
+            outputs = model(
+                mask_result['spikes'],
+                time_attn_mask=batch['time_attn_mask'],
+                space_attn_mask=batch['space_attn_mask'],
+                spikes_timestamps=batch['spikes_timestamps'], 
+                spikes_spacestamps=batch['spikes_spacestamps'], 
+                targets = batch['target'],
+                neuron_regions=batch['neuron_regions'],
+                eval_mask=mask_result['eval_mask'],
+                masking_mode=masking_mode,
+                num_neuron=batch['spikes_data'].shape[2],
+                eid=batch['eid'][0]  # each batch consists of data from the same eid
+            )
+        eval_heldin_gt_spike = gt_spike_data.detach().cpu().numpy()
+        eval_heldin_gt_spike = eval_heldin_gt_spike[:, :, heldin_ids]
+        eval_heldout_gt_spike = gt_spike_data.detach().cpu().numpy()
+        eval_heldout_gt_spike = eval_heldout_gt_spike[:, :, heldout_ids]
+
+        outputs.preds = torch.exp(outputs.preds)
+        pred_spikes = outputs.preds.detach().cpu().numpy()
+        eval_heldout_pred_spike = pred_spikes[:, :, heldout_ids]
+        eval_heldin_pred_spike = pred_spikes[:, :, heldin_ids]
+        
+        print(f"heldin_ids: {heldin_ids}, heldout_ids: {heldout_ids}")
+
+        print(train_heldin_pred_spike.shape,train_heldout_pred_spike.shape)
+        print(eval_heldin_pred_spike.shape,eval_heldout_pred_spike.shape)
+        print(train_heldin_gt_spike.shape,eval_heldin_gt_spike.shape)
+        plt.figure(figsize=(10, 10))
+        plt.subplot(2, 2, 1)
+        plt.imshow(train_heldin_gt_spike.mean(0).T, aspect='auto')
+        plt.title('train heldin gt')
+        plt.subplot(2, 2, 2)
+        plt.imshow(train_heldin_pred_spike.mean(0).T, aspect='auto')
+        plt.title('train heldin pred')
+        plt.subplot(2, 2, 3)
+        plt.imshow(eval_heldin_gt_spike.mean(0).T, aspect='auto')
+        plt.title('eval heldin gt')
+        plt.subplot(2, 2, 4)
+        plt.imshow(eval_heldin_pred_spike.mean(0).T, aspect='auto')
+        plt.title('eval heldin pred')
+        os.makedirs(kwargs['save_path'], exist_ok=True)
+        plt.savefig(os.path.join(kwargs['save_path'], f'train.png'), dpi=200)
+        
+        output_dict  = {
+            "mc_rtt_20": {
+                'train_rates_heldin': train_heldin_pred_spike,
+                'train_rates_heldout': train_heldout_pred_spike,
+                'eval_rates_heldin': eval_heldin_gt_spike,
+                'eval_rates_heldout': eval_heldout_pred_spike
+            }
+        }
+        
+        save_to_h5(output_dict, os.path.join(kwargs['save_path'], '..',f'submission.h5'))
+        # calculate bps for eval_heldout_pred_spike
+        bps = bits_per_spike(eval_heldout_pred_spike, eval_heldout_gt_spike)
+        print(eval_heldout_gt_spike[0])
+        print(f"eval heldout bps: {bps}")
+        bps = bits_per_spike(eval_heldin_pred_spike, eval_heldin_gt_spike)
+        print(f"eval heldin bps: {bps}")
+        exit()
+    elif mode == 'per_neuron':
+        gt_result_list, pred_result_list = [], []
+        bps_result_list, r2_result_list = [float('nan')] * tot_num_neurons, [np.array([np.nan, np.nan])] * N
+        population_bps_result_list = [float('nan')] * tot_num_neurons
+        bps_per_region = []; region = 'CA1'
+        # loop through all the neurons
+        counter = 0
+        for n_i in tqdm(range(0, tot_num_neurons+n_jobs, n_jobs)):    
+            if counter >= tot_num_neurons:
+                break
+            gt_spikes_lst, mask_spikes_lst, eval_mask_lst = [], [], []
+            time_attn_mask_lst, space_attn_mask_lst, spikes_timestamps_lst, spikes_spacestamps_lst, targets_lst, neuron_regions_lst = [], [], [], [], [], []
+            model.eval()
+            with torch.no_grad():
+                for batch in test_dataloader:
+                    batch = move_batch_to_device(batch, accelerator.device)
+                    gt_spike_data = batch['spikes_data'].clone()
+                    for i in range(n_jobs):
+                        counter += 1
+                        if counter <= tot_num_neurons:
+                            mask_result = heldout_mask(
+                                batch['spikes_data'].clone(),
+                                mode='manual',
+                                heldout_idxs=np.array([n_i+i])
+                            )
+                            # print("heldout_idxs:",np.array([n_i+i]))
+                            mask_spikes_lst.append(mask_result['spikes'])
+                            eval_mask_lst.append(mask_result['eval_mask'])
+                            gt_spikes_lst.append(gt_spike_data)
+                            time_attn_mask_lst.append(batch['time_attn_mask'])
+                            space_attn_mask_lst.append(batch['space_attn_mask'])
+                            spikes_timestamps_lst.append(batch['spikes_timestamps'])
+                            spikes_spacestamps_lst.append(batch['spikes_spacestamps'])
+                            targets_lst.append(batch['target'])
+                            neuron_regions_lst.append(batch['neuron_regions'])
+                        else:
+                            break
+
+                    masking_mode = 'neuron' if model.use_prompt else model.encoder.masker.mode
+                    model.encoder.mask = False
+                    
+                    outputs = model(
+                        torch.cat(mask_spikes_lst, 0),
+                        time_attn_mask=torch.cat(time_attn_mask_lst, 0),
+                        space_attn_mask=torch.cat(space_attn_mask_lst, 0),
+                        spikes_timestamps=torch.cat(spikes_timestamps_lst, 0), 
+                        spikes_spacestamps=torch.cat(spikes_spacestamps_lst, 0), 
+                        targets = torch.cat(targets_lst, 0),
+                        neuron_regions=np.stack(neuron_regions_lst),
+                        eval_mask=torch.cat(eval_mask_lst, 0),
+                        masking_mode = masking_mode,
+                        num_neuron=batch['spikes_data'].shape[2],
+                        eid=batch['eid'][0]  # each batch consists of data from the same eid
+                    )
+            outputs.preds = torch.exp(outputs.preds)
+    
+            gt_spikes = torch.cat(gt_spikes_lst, 0).detach().cpu().numpy()
+
+            pred_spikes = outputs.preds.detach().cpu().numpy()
+            print(f"gt_spikes shape: {gt_spikes.shape}, pred_spikes shape: {pred_spikes.shape}")
+            tot_num_trials = len(batch['spikes_data'])
+            print(f"tot_num_trials: {tot_num_trials}, tot_num_neurons: {tot_num_neurons}")
+            # compute co-bps
+            for i in range(n_jobs):
+                if n_i+i < tot_num_neurons:
+                    # polulatio neuron bps
+                    gt_held_out = gt_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :,:]
+                    pred_held_out = pred_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :,:]
+                    bps = bits_per_spike(pred_held_out, gt_held_out)
+                    if np.isinf(bps):
+                        bps = np.nan
+                    population_bps_result_list[n_i+i] = bps
+
+                    # held_out neuron bps, ours
+                    gt_held_out = gt_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, [n_i+i]]
+                    pred_held_out = pred_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, [n_i+i]]
+                    
+                    # print(gt_held_out.shape, pred_held_out.shape)
+                    bps = bits_per_spike(pred_held_out, gt_held_out)
+                    if np.isinf(bps):
+                        bps = np.nan
+                    bps_result_list[n_i+i] = bps
+                    gt_result_list.append(gt_held_out)
+                    pred_result_list.append(pred_held_out)
+
+                    bps_per_region.append(bps)
+        
+                    # compute R2
+                    if False:
+                        X = behavior_set  # [#trials, #timesteps, #variables]
+                        _r2_psth, _r2_trial = viz_single_cell(X, gt_held_out.squeeze(), pred_held_out.squeeze(),
+                                                              var_name2idx, var_tasklist, var_value2label, var_behlist,
+                                                              subtract_psth=kwargs['subtract'],
+                                                              aligned_tbins=kwargs['onset_alignment'],
+                                                              neuron_idx=uuids_list[n_i+i][:4],
+                                                              neuron_region=region_list[n_i+i],
+                                                              method=method_name, save_path=kwargs['save_path'])
+                        r2_result_list[n_i+i] = np.array([_r2_psth, _r2_trial])
+                    else:
+                        r2 = viz_single_cell_unaligned(
+                            gt_held_out.squeeze(), pred_held_out.squeeze(), 
+                            neuron_idx=uuids_list[n_i+i][:4],
+                            neuron_region=region_list[n_i+i],
+                            method=method_name, save_path=kwargs['save_path']
+                        )
+                        r2_result_list[n_i+i] = r2
+                else:
+                    break
+        population_bps_result_list = np.array(population_bps_result_list)
+        print(f'{region} bps: ', np.nanmean(bps_per_region))
+        print("pop_bps shape: ", population_bps_result_list.shape)
+        print(f"{mode} pop_bps: {np.nanmean(population_bps_result_list)}")
+   
+    elif mode == 'forward_pred':
+
+        held_out_list = kwargs['held_out_list']
+
+        assert held_out_list is not None, 'forward_pred requires specific target time points to predict'
+        target_regions = neuron_regions = None
+        held_out_list = [held_out_list]
+
+        bps_result_list, r2_result_list = [float('nan')] * tot_num_neurons, [np.array([np.nan, np.nan])] * N
+        population_bps_result_list = [float('nan')] * tot_num_neurons
+        gt_result_list, pred_result_list = [], []
+        for hd_idx in held_out_list:
+           
+            hd = np.array([hd_idx])
+
+            model.eval()
+            with torch.no_grad():
+                for batch in test_dataloader:
+                    batch = move_batch_to_device(batch, accelerator.device)
+                    gt_spike_data = batch['spikes_data'].clone()
+                    mask_result = heldout_mask(
+                        batch['spikes_data'].clone(),
+                        mode=mode,
+                        heldout_idxs=hd,
+                        target_regions=target_regions,
+                        neuron_regions=region_list
+                    )                
+                    
+                    masking_mode = 'causal' if model.use_prompt else model.encoder.masker.mode
+                    model.encoder.mask = False
+                    
+                    outputs = model(
+                        mask_result['spikes'],
+                        time_attn_mask=batch['time_attn_mask'],
+                        space_attn_mask=batch['space_attn_mask'],
+                        spikes_timestamps=batch['spikes_timestamps'], 
+                        spikes_spacestamps=batch['spikes_spacestamps'], 
+                        targets = batch['target'],
+                        neuron_regions=batch['neuron_regions'],
+                        eval_mask=mask_result['eval_mask'],
+                        masking_mode=masking_mode,
+                        num_neuron=batch['spikes_data'].shape[2],
+                        eid=batch['eid'][0]  # each batch consists of data from the same eid
+                    )
+            outputs.preds = torch.exp(outputs.preds)
+        
+            gt_spikes = gt_spike_data.detach().cpu().numpy()
+            pred_spikes = outputs.preds.detach().cpu().numpy()
+    
+            target_neuron_idxs = np.arange(tot_num_neurons)
+            target_time_idxs = held_out_list[0]
+
+            # compute co-bps
+            gt_held_out = gt_spikes[:, target_time_idxs][:,:,target_neuron_idxs]
+            pred_held_out = pred_spikes[:, target_time_idxs][:,:,target_neuron_idxs]
+    
+            for n_i in tqdm(range(len(target_neuron_idxs)), desc='co-bps'): 
+                pop_bps = bits_per_spike(pred_held_out, gt_held_out)
+                population_bps_result_list[target_neuron_idxs[n_i]] = pop_bps if not np.isinf(pop_bps) else np.nan
+                bps = bits_per_spike(pred_held_out[:,:,[n_i]], gt_held_out[:,:,[n_i]])
+                gt_result_list.append(gt_held_out[:,:,[n_i]])
+                pred_result_list.append(pred_held_out[:,:,[n_i]])
+                if np.isinf(bps):
+                    bps = np.nan
+                bps_result_list[target_neuron_idxs[n_i]] = bps
+
+            # pop_bps = bits_per_spike(pred_held_out, gt_held_out)
+            # if np.isinf(pop_bps):
+            #     pop_bps = np.nan
+            # population_bps_result_list[target_neuron_idxs[n_i]] = pop_bps
+
+            # compute R2
+            ys = gt_spikes[:, target_time_idxs]
+            y_preds = pred_spikes[:, target_time_idxs]
+    
+            # choose the neuron to plot
+            idxs = target_neuron_idxs
+    
+            for i in tqdm(range(idxs.shape[0]), desc='R2'):
+                if False:
+                    X = behavior_set[:, target_time_idxs, :]  # [#trials, #timesteps, #variables]
+                    _r2_psth, _r2_trial = viz_single_cell(X, ys[:, :, idxs[i]], y_preds[:, :, idxs[i]],
+                                                          var_name2idx, var_tasklist, var_value2label, var_behlist,
+                                                          subtract_psth=kwargs['subtract'],
+                                                          aligned_tbins=[],
+                                                          neuron_idx=uuids_list[idxs[i]][:4],
+                                                          neuron_region=region_list[idxs[i]],
+                                                          method=method_name, save_path=kwargs['save_path']);
+                    r2_result_list[idxs[i]] = np.array([_r2_psth, _r2_trial])
+                else:
+                    r2 = viz_single_cell_unaligned(
+                        ys[:, :, idxs[i]], y_preds[:, :, idxs[i]], 
+                        neuron_idx=uuids_list[idxs[i]][:4],
+                        neuron_region=region_list[idxs[i]],
+                        method=method_name, save_path=kwargs['save_path']
+                    )
+                    r2_result_list[idxs[i]] = r2
+        print(f"{mode} pop_bps: {np.nanmean(population_bps_result_list)}")
+
+    elif mode == 'inter_region':
+
+        if 'all' in target_regions:
+            target_regions = list(np.unique(region_list))
+            
+        held_out_list = kwargs['held_out_list']
+
+        assert held_out_list is None, 'inter_region does LOO for all neurons in the target region'
+
+        bps_result_list, r2_result_list = [float('nan')] * tot_num_neurons, [np.array([np.nan, np.nan])] * N
+        population_bps_result_list = [float('nan')] * tot_num_neurons
+        gt_result_list, pred_result_list = [], []
+        y_list, y_pred_list, y_residual_list = [], [], []
+        for region in tqdm(target_regions, desc='region'):
+            print(region)
+            hd = np.argwhere(region_list==region).flatten() 
+            held_out_list = np.arange(len(hd))
+            held_out_list = [held_out_list]   
+            hd = np.array([held_out_list]).flatten()
+            print(f"hd: {hd}, target_regions: {target_regions}, region_list: {region_list}")
+            exit()
+            model.eval()
+            with torch.no_grad():
+                for batch in test_dataloader:
+                    batch = move_batch_to_device(batch, accelerator.device)
+                    gt_spike_data = batch['spikes_data'].clone()
+                    mask_result = heldout_mask(
+                        batch['spikes_data'].clone(),
+                        mode=mode,
+                        heldout_idxs=hd,
+                        target_regions=[region],
+                        neuron_regions=region_list
+                    )              
+
+                    masking_mode = 'inter-region' if model.use_prompt else model.encoder.masker.mode
+                    model.encoder.mask = False
+                    
+                    outputs = model(
+                        mask_result['spikes'],
+                        time_attn_mask=batch['time_attn_mask'],
+                        space_attn_mask=batch['space_attn_mask'],
+                        spikes_timestamps=batch['spikes_timestamps'], 
+                        spikes_spacestamps=batch['spikes_spacestamps'], 
+                        targets = batch['target'],
+                        neuron_regions=batch['neuron_regions'],
+                        eval_mask=mask_result['eval_mask'],
+                        masking_mode=masking_mode,
+                        num_neuron=batch['spikes_data'].shape[2],
+                        eid=batch['eid'][0]  # each batch consists of data from the same eid
+                    )
+            outputs.preds = torch.exp(outputs.preds)
+        
+            gt_spikes = gt_spike_data.detach().cpu().numpy()
+            pred_spikes = outputs.preds.detach().cpu().numpy()
+    
+            target_neuron_idxs = mask_result['heldout_idxs']
+            target_time_idxs = np.arange(gt_spikes.shape[1])
+
+            # compute co-bps
+            gt_held_out = gt_spikes[:, target_time_idxs][:,:,target_neuron_idxs]
+            pred_held_out = pred_spikes[:, target_time_idxs][:,:,target_neuron_idxs]
+
+            bps_per_region = []
+            for n_i in range(len(target_neuron_idxs)): 
+                pop_bps = bits_per_spike(pred_held_out[:,:,], gt_held_out[:,:,])
+                population_bps_result_list[target_neuron_idxs[n_i]] = pop_bps if not np.isinf(pop_bps) else np.nan
+                bps = bits_per_spike(pred_held_out[:,:,[n_i]], gt_held_out[:,:,[n_i]])
+                if np.isinf(bps):
+                    bps = np.nan
+                bps_result_list[target_neuron_idxs[n_i]] = bps
+                bps_per_region.append(bps)
+            print(pred_held_out.shape)
+            print(gt_held_out.shape)
+            bps_per_region = [bits_per_spike(pred_held_out, gt_held_out)]
+            print(f'{region} bps: ', np.nanmean(bps_per_region))
+            # compute R2
+            ys = gt_spikes[:, target_time_idxs]
+            y_preds = pred_spikes[:, target_time_idxs]
+    
+            # choose the neuron to plot
+            idxs = target_neuron_idxs
+            for i in range(idxs.shape[0]):
+                gt_result_list.append(ys[:, :, idxs[i]])
+                pred_result_list.append(y_preds[:, :, idxs[i]])
+                if False:
+                    X = behavior_set[:, target_time_idxs, :]  # [#trials, #timesteps, #variables]
+                    _r2_psth, _r2_trial = viz_single_cell(X, ys[:, :, idxs[i]], y_preds[:, :, idxs[i]],
+                                                          var_name2idx, var_tasklist, var_value2label, var_behlist,
+                                                          subtract_psth=kwargs['subtract'],
+                                                          aligned_tbins=[],
+                                                          neuron_idx=uuids_list[idxs[i]][:4],
+                                                          neuron_region=region_list[idxs[i]],
+                                                          method=method_name, save_path=kwargs['save_path']);
+                    r2_result_list[idxs[i]] = np.array([_r2_psth, _r2_trial])
+                else:
+                    r2 = viz_single_cell_unaligned(
+                        ys[:, :, idxs[i]], y_preds[:, :, idxs[i]], 
+                        neuron_idx=uuids_list[idxs[i]][:4],
+                        neuron_region=region_list[idxs[i]],
+                        method=method_name, save_path=kwargs['save_path']
+                    )
+                    r2_result_list[idxs[i]] = r2
+        print(f"{mode} pop_bps: {np.nanmean(population_bps_result_list)}")
+              
+    elif mode == 'intra_region':
+
+        if 'all' in target_regions:
+            target_regions = list(np.unique(region_list))
+            
+        held_out_list = kwargs['held_out_list']
+        assert held_out_list is None, 'intra_region does LOO for all neurons in the target region'
+
+        bps_result_list, r2_result_list = [float('nan')] * tot_num_neurons, [np.array([np.nan, np.nan])] * N
+        population_bps_result_list = [float('nan')] * tot_num_neurons
+        gt_result_list, pred_result_list = [], []
+        y_list, y_pred_list, y_residual_list = [], [], []
+        for region in tqdm(target_regions, desc='region'):
+            print(region)
+            target_neuron_idxs = np.argwhere(region_list==region).flatten() 
+            held_out_list = list(range(0, len(target_neuron_idxs)+n_jobs, n_jobs))
+            bps_per_region = []
+
+            for hd_idx in held_out_list:
+                
+                if hd_idx >= len(target_neuron_idxs):
+                    break
+
+                gt_spikes_lst, mask_spikes_lst, eval_mask_lst, heldout_idxs_lst = [], [], [], []
+                time_attn_mask_lst, space_attn_mask_lst, spikes_timestamps_lst, spikes_spacestamps_lst, targets_lst, neuron_regions_lst = [], [], [], [], [], []
+    
+                model.eval()
+                with torch.no_grad():
+                    for batch in test_dataloader:
+                        batch = move_batch_to_device(batch, accelerator.device)
+                        gt_spike_data = batch['spikes_data'].clone()
+                        for i in range(n_jobs):
+                            if hd_idx+i < len(target_neuron_idxs):
+                                mask_result = heldout_mask(
+                                    batch['spikes_data'].clone(),
+                                    mode=mode,
+                                    heldout_idxs=np.array([hd_idx+i]).flatten(),
+                                    target_regions=[region],
+                                    neuron_regions=region_list
+                                )   
+                                mask_spikes_lst.append(mask_result['spikes'])
+                                eval_mask_lst.append(mask_result['eval_mask'])
+                                heldout_idxs_lst.append(mask_result['heldout_idxs'])
+                                gt_spikes_lst.append(gt_spike_data)
+                                time_attn_mask_lst.append(batch['time_attn_mask'])
+                                space_attn_mask_lst.append(batch['space_attn_mask'])
+                                spikes_timestamps_lst.append(batch['spikes_timestamps'])
+                                spikes_spacestamps_lst.append(batch['spikes_spacestamps'])
+                                targets_lst.append(batch['target'])
+                                neuron_regions_lst.append(batch['neuron_regions'])
+                            else:
+                                break
+
+                        masking_mode = 'intra-region' if model.use_prompt else model.encoder.masker.mode
+                        model.encoder.mask = False
+                        
+                        outputs = model(
+                            torch.cat(mask_spikes_lst, 0),
+                            time_attn_mask=torch.cat(time_attn_mask_lst, 0),
+                            space_attn_mask=torch.cat(space_attn_mask_lst, 0),
+                            spikes_timestamps=torch.cat(spikes_timestamps_lst, 0), 
+                            spikes_spacestamps=torch.cat(spikes_spacestamps_lst, 0), 
+                            targets = torch.cat(targets_lst, 0),
+                            neuron_regions=np.stack(neuron_regions_lst),
+                            eval_mask=torch.cat(eval_mask_lst, 0),
+                            masking_mode=masking_mode,
+                            num_neuron=batch['spikes_data'].shape[2],
+                            eid=batch['eid'][0]  # each batch consists of data from the same eid
+                        )
+                outputs.preds = torch.exp(outputs.preds)
+            
+                gt_spikes = torch.cat(gt_spikes_lst, 0).detach().cpu().numpy()
+                pred_spikes = outputs.preds.detach().cpu().numpy()
+                tot_num_trials = len(batch['spikes_data'])
+
+                heldout_idxs = np.stack(heldout_idxs_lst).flatten()
+                for i in range(len(heldout_idxs)):
+                    gt_held_out = gt_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, ]
+                    pred_held_out = pred_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, ]
+                    pop_bps = bits_per_spike(pred_held_out, gt_held_out)
+                    if np.isinf(pop_bps):
+                        pop_bps = np.nan
+                    population_bps_result_list[heldout_idxs[i]] = pop_bps
+                    gt_held_out = gt_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, [heldout_idxs[i]]]
+                    pred_held_out = pred_spikes[i*tot_num_trials:(i+1)*tot_num_trials, :, [heldout_idxs[i]]]
+    
+                    bps = bits_per_spike(pred_held_out, gt_held_out)
+                    if np.isinf(bps):
+                        bps = np.nan
+                    bps_result_list[heldout_idxs[i]] = bps
+                    gt_result_list.append(gt_held_out.squeeze())
+                    pred_result_list.append(pred_held_out.squeeze())
+
+                    if False:
+                        X = behavior_set  # [#trials, #timesteps, #variables]
+                        _r2_psth, _r2_trial = viz_single_cell(X, gt_held_out.squeeze(), pred_held_out.squeeze(),
+                                                              var_name2idx, var_tasklist, var_value2label, var_behlist,
+                                                              subtract_psth=kwargs['subtract'],
+                                                              aligned_tbins=[],
+                                                              neuron_idx=uuids_list[heldout_idxs[i]][:4],
+                                                              neuron_region=region_list[heldout_idxs[i]],
+                                                              method=method_name, save_path=kwargs['save_path']);
+                        r2_result_list[heldout_idxs[i]] = np.array([_r2_psth, _r2_trial])
+                    else:
+                        r2 = viz_single_cell_unaligned(
+                            gt_held_out.squeeze(), pred_held_out.squeeze(),
+                            neuron_idx=uuids_list[heldout_idxs[i]][:4],
+                            neuron_region=region_list[heldout_idxs[i]],
+                            method=method_name, save_path=kwargs['save_path']
+                        )
+                        r2_result_list[heldout_idxs[i]] = r2
+            bps_per_region = [bits_per_spike(
+                    np.array(pred_result_list).transpose(1,2,0), 
+                    np.array(gt_result_list).transpose(1,2,0)
+                )]
+            print(print(f'{region} bps: ', np.nanmean(bps_per_region)))
+        print(f"{mode} pop_bps: {np.nanmean(population_bps_result_list)}")
+    else:
+        raise NotImplementedError('mode not implemented')
+
+    # save co-bps
+    os.makedirs(kwargs['save_path'], exist_ok=True)
+    bps_all = np.array(bps_result_list)
+    print("bps_all shape: ", bps_all.shape)
+    bps_mean = np.nanmean(bps_all)
+    print("bps_mean: ", bps_mean)
+
+    bps_std = np.nanstd(bps_all)
+    plt.hist(bps_all, bins=30, alpha=0.75, color='red', edgecolor='black')
+    plt.xlabel('bits per spike')
+    plt.ylabel('count')
+    plt.title('Co-bps distribution\n mean: {:.2f}, std: {:.2f}\n # non-zero neuron: {}'.format(bps_mean, bps_std, len(bps_all)));
+    plt.savefig(os.path.join(kwargs['save_path'], f'bps.png'), dpi=200)
+    np.save(os.path.join(kwargs['save_path'], f'bps.npy'), bps_all)
+    
+    # save R2
+    r2_all = np.array(r2_result_list)
+    np.save(os.path.join(kwargs['save_path'], f'r2.npy'), r2_all)
+    print(f"mean bps: {bps_mean}, std bps: {bps_std}")
+    gt_result = np.stack(gt_result_list,-1).squeeze()
+    pred_result = np.stack(pred_result_list,-1).squeeze()
+    # save gt and pred
+    np.save(os.path.join(kwargs['save_path'], f'gt.npy'), gt_result)
+    np.save(os.path.join(kwargs['save_path'], f'pred.npy'), pred_result)
+    return {
+        f"{mode}_mean_bps": bps_mean,
+        f"{mode}_std_bps": bps_std,
+        f"{mode}_mean_r2_psth": np.nanmean(r2_all[:, 0]) if len(r2_all.shape)> 1 else np.nan,
+        f"{mode}_std_r2_psth": np.nanstd(r2_all[:, 0]) if len(r2_all.shape)> 1 else np.nan,
+        f"{mode}_mean_r2_trial": np.nanmean(r2_all[:, 1]) if len(r2_all.shape)> 1 else np.nanmean(r2_all),
+        f"{mode}_std_r2_trial": np.nanstd(r2_all[:, 1]) if len(r2_all.shape)> 1 else np.nanstd(r2_all)
+    }, gt_result, pred_result
 
 def draw_threshold_table(
         mask_methods: list,
diff --git a/src/utils/nlb_data_utils.py b/src/utils/nlb_data_utils.py
new file mode 100644
index 0000000..3371610
--- /dev/null
+++ b/src/utils/nlb_data_utils.py
@@ -0,0 +1,114 @@
+
+import numpy as np
+import pandas as pd
+import torch
+
+from nlb_tools.make_tensors import (
+        make_train_input_tensors,
+        make_eval_input_tensors,
+        make_eval_target_tensors,
+        PARAMS,
+        _prep_mask,
+        _prep_behavior,
+        make_stacked_array,
+        make_jagged_array
+    )
+from nlb_tools.nwb_interface import NWBDataset
+# ## Load dataset
+# dataset = NWBDataset("data/000129/sub-Indy", "*train", split_heldout=False)
+# # resample to 20 ms
+# dataset.resample(6)
+params = PARAMS["mc_rtt"].copy()
+spike_params = {'align_field': 'start_time', 'align_range': (0, 600), 'allow_overlap': True}
+make_params = params['make_params'].copy()
+
+behavior_source = params['behavior_source']
+behavior_fields = ["cursor_pos", "finger_pos","finger_vel","target_pos"]
+
+def create_rtt_dataset(dataset, trial_mask, behavior_make_params):
+    # Retrieve neural data from indicated source
+    data_dict = make_stacked_array(dataset, ["spikes", "heldout_spikes"], spike_params, trial_mask)
+    # Retrieve behavior data from indicated source
+    data_behavior_dict = {}
+    if behavior_source == 'data':
+        for behavior_field in behavior_fields:
+            data_behavior_dict[behavior_field] = make_jagged_array(dataset, [behavior_field], behavior_make_params, trial_mask)[0][behavior_field]
+
+    data_dict = {**data_dict, **data_behavior_dict}
+    data_dict['spikes'] = np.concatenate([data_dict['spikes'], data_dict['heldout_spikes']], axis=2)
+    trial_id = np.arange(data_dict['spikes'].shape[0])
+    data_dict['trial_id'] = trial_id
+    return data_dict
+
+def load_nlb_dataset(dataset_path, bin_size_ms=6):
+    dataset = NWBDataset(dataset_path, "*train", split_heldout=True)
+    # resample to bin_size_ms
+    dataset.resample(bin_size_ms)
+    # Prep mask
+    train_trial_mask = _prep_mask(dataset, "train")
+    val_trial_mask = _prep_mask(dataset, "val")
+    # select 10% of val trials for test
+    # test_trial_mask = np.zeros_like(val_trial_mask)
+    # test_trial_mask[np.random.choice(np.where(val_trial_mask)[0], int(val_trial_mask.sum() * 0.1), replace=False)] = True
+    # val_trial_mask[test_trial_mask] = False
+    
+    # Prep behavior
+    behavior_make_params = _prep_behavior(dataset, params.get('lag', None), make_params)
+
+    # Create datasets
+    train_dataset = create_rtt_dataset(dataset, train_trial_mask, behavior_make_params)
+    val_dataset = create_rtt_dataset(dataset, val_trial_mask, behavior_make_params)
+    # test_dataset = create_rtt_dataset(dataset, test_trial_mask, behavior_make_params)
+
+    print(f"trials number: train {train_trial_mask.sum()}, val {val_trial_mask.sum()}")
+    meta_data = {
+        "num_neurons":[train_dataset['spikes'].shape[2]],
+        "num_sessions":1,
+        "eids":{"nlb-rtt"},
+    }
+    return train_dataset, val_dataset, meta_data
+
+def load_nlb_dataset_test(dataset_path, bin_size_ms=6):
+    dataset = NWBDataset(dataset_path, split_heldout=True)
+
+    train_split = ['train', 'val']
+    # resample to bin_size_ms
+    dataset.resample(bin_size_ms)
+    train_dict = make_train_input_tensors(dataset, dataset_name='mc_rtt', trial_split=train_split, save_file=False)
+    # Unpack data
+    train_spikes_heldin = train_dict['train_spikes_heldin']
+    train_spikes_heldout = train_dict['train_spikes_heldout']
+
+    # Print 3d array shape: trials x time x channel
+    train_spikes = np.concatenate([train_spikes_heldin, train_spikes_heldout], axis=2)
+    trial_id = np.arange(train_spikes_heldin.shape[0])
+    print(train_spikes.shape)
+    train_data_dict = {
+        "spikes": train_spikes,
+        "trial_id": trial_id
+    }
+
+    ## Make eval data
+    # Split for evaluation is same as phase name
+    eval_split = 'test'
+    # Make data tensors
+    eval_dict = make_eval_input_tensors(dataset, dataset_name='mc_rtt', trial_split=eval_split, save_file=False)
+
+    eval_spikes_heldin = eval_dict['eval_spikes_heldin']
+    # fill in eval_spikes_heldout
+    # eval_spikes_heldout.shape[0], train_spikes_heldout.shape[1], train_spikes_heldout.shape[2]
+    eval_spikes_heldout = np.zeros((eval_spikes_heldin.shape[0], train_spikes_heldout.shape[1], train_spikes_heldout.shape[2]))
+    eval_spikes = np.concatenate([eval_spikes_heldin, eval_spikes_heldout], axis=2)
+    trial_id = np.arange(eval_spikes_heldin.shape[0])
+    eval_data_dict = {
+        "spikes": eval_spikes,
+        "trial_id": trial_id
+    }
+
+    meta_data = {
+        "num_neurons":[train_spikes.shape[2]],
+        "num_sessions":1,
+        "eids":{"nlb-rtt"},
+    }
+    return train_data_dict, eval_data_dict, meta_data
+
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..9b223e1
--- /dev/null
+++ b/test.py
@@ -0,0 +1,75 @@
+from nlb_tools.make_tensors import (
+        make_train_input_tensors,
+        make_eval_input_tensors,
+        make_eval_target_tensors,
+        PARAMS,
+        _prep_mask,
+        _prep_behavior,
+        make_stacked_array,
+        make_jagged_array
+    )
+from nlb_tools.nwb_interface import NWBDataset
+import numpy as np 
+from utils.nlb_data_utils import load_nlb_dataset_test
+
+train_dataset, eval_dataset, meta_data = load_nlb_dataset_test('data/000129/sub-Indy', 20)
+
+dataset = NWBDataset('data/000129/sub-Indy', "*train", split_heldout=True)
+# # resample to bin_size_ms
+# dataset.resample(20)
+# print(dataset.data['heldout_spikes'].shape)
+# print(dataset.data['spikes'].shape)
+# # exit()
+# # Prep mask
+# train_trial_mask = _prep_mask(dataset, "train")
+# val_trial_mask = _prep_mask(dataset, "val")
+# test_trial_mask = _prep_mask(dataset, "eval")
+# print(f"trials number: train {train_trial_mask.sum()}, val {val_trial_mask.sum()}, test {test_trial_mask.sum()}")
+# exit()
+# # select 10% of val trials for test
+# test_trial_mask = np.zeros_like(val_trial_mask)
+# test_trial_mask[np.random.choice(np.where(val_trial_mask)[0], int(val_trial_mask.sum() * 0.1), replace=False)] = True
+# val_trial_mask[test_trial_mask] = False
+
+## Dataset preparation
+
+# Choose the phase here, either 'val' for the Validation phase or 'test' for the Test phase
+# Note terminology overlap with 'train', 'val', and 'test' data splits -
+# the phase name corresponds to the data split that predictions are evaluated on
+phase = 'val'
+
+# Choose bin width and resample
+bin_width = 20
+dataset.resample(bin_width)
+
+# Create suffix for group naming later
+suffix = '' if (bin_width == 5) else f'_{int(bin_width)}'
+print(suffix)
+
+## Make train data
+
+# Create input tensors, returned in dict form
+train_split = 'train' if (phase == 'val') else ['train', 'val']
+train_dict = make_train_input_tensors(dataset, dataset_name='mc_rtt', trial_split=train_split, save_file=False)
+
+# Show fields of returned dict
+print(train_dict.keys())
+
+# Unpack data
+train_spikes_heldin = train_dict['train_spikes_heldin']
+train_spikes_heldout = train_dict['train_spikes_heldout']
+
+# Print 3d array shape: trials x time x channel
+print(train_spikes_heldin.shape)
+print(train_spikes_heldout.shape)
+
+## Make eval data
+
+# Split for evaluation is same as phase name
+eval_split = phase
+# Make data tensors
+eval_dict = make_eval_input_tensors(dataset, dataset_name='mc_rtt', trial_split=eval_split, save_file=False)
+print(eval_dict.keys()) # only includes 'eval_spikes_heldout' if available
+eval_spikes_heldin = eval_dict['eval_spikes_heldin']
+
+print(eval_spikes_heldin.shape)
\ No newline at end of file