diff --git a/notebooks/REMI/REMI_Digitize_and_Transform.ipynb b/notebooks/REMI/REMI_Digitize_and_Transform.ipynb
index 536ec91a447e39eebb24bf5270c3eb9c6a9c0952..46e73f69daa59ec81568c4ccdbb301168cc5e67f 100644
--- a/notebooks/REMI/REMI_Digitize_and_Transform.ipynb
+++ b/notebooks/REMI/REMI_Digitize_and_Transform.ipynb
@@ -82,8 +82,12 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import LogNorm\n",
+    "from matplotlib.patches import Circle\n",
     "from threadpoolctl import threadpool_limits\n",
     "\n",
+    "import tabulate\n",
+    "from IPython.display import Latex, Markdown, display\n",
+    "\n",
     "import h5py\n",
     "\n",
     "import pasha as psh\n",
@@ -161,6 +165,84 @@
     "print_leaf(remi.tree)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discr_table = []\n",
+    "\n",
+    "if quad_anode:\n",
+    "    signals = ['u1', 'u2', 'v1', 'v2', 'mcp']\n",
+    "    wire_angles = [np.pi*(3/4), np.pi*(1/4)]\n",
+    "else:\n",
+    "    signals = ['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']\n",
+    "    wire_angles = [np.pi*(3/4), np.pi*(3/4+1/3), np.pi*(3/4+2/3)]\n",
+    "    \n",
+    "N = 15\n",
+    "shifts = np.linspace(-0.4, 0.4, N)\n",
+    "\n",
+    "for i, (det_name, cur_det) in enumerate(remi['detector'].items()):\n",
+    "    fig = plt.figure(num=34333+i, figsize=(9, 5))\n",
+    "    fig.text(0.5, 1.0, det_name, ha='center', va='top', size='xx-large')\n",
+    "    ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "    ax.add_patch(Circle((0,0), 1, ec='black', fc='none', lw=2))\n",
+    "    ax.set_xlim(-1.5*(9/5), 1.5*(9/5))\n",
+    "    ax.set_ylim(-1.5, 1.5)\n",
+    "\n",
+    "    _, params = remi.get_discriminator(cur_det['channels'])\n",
+    "    discr_header = params[0].keys()\n",
+    "\n",
+    "    for channel_idx in range(len(signals)):\n",
+    "        index = cur_det['indices'].index(channel_idx)\n",
+    "        discr_table.append((det_name, signals[channel_idx],\n",
+    "                            cur_det['channels'][index],\n",
+    "                            remi['digitizer']['discriminator'],\n",
+    "                            *params[index].values()))\n",
+    "\n",
+    "    for j, start_angle in enumerate(wire_angles):\n",
+    "        x1 = np.cos(start_angle+np.pi/4)\n",
+    "        x2 = np.cos(start_angle+5*np.pi/4)\n",
+    "\n",
+    "        y1 = np.sin(start_angle+np.pi/4)\n",
+    "        y2 = np.sin(start_angle+5*np.pi/4)\n",
+    "\n",
+    "        channel = cur_det['channels'][cur_det['indices'].index(2*j)]\n",
+    "        ax.text(x1*1.2, y1*1.2, f'{signals[2*j]}\\n{channel}',\n",
+    "                c=f'C{j}', fontsize='xx-large', va='center', ha='center')\n",
+    "\n",
+    "        channel = cur_det['channels'][cur_det['indices'].index(2*j+1)]\n",
+    "        ax.text(x2*1.2, y2*1.2, f'{signals[2*j+1]}\\n{channel}',\n",
+    "                c=f'C{j}', fontsize='xx-large', va='center', ha='center')\n",
+    "\n",
+    "        for k, shift in enumerate(shifts):\n",
+    "            x1 = np.cos(start_angle+np.pi/4+shifts[k])\n",
+    "            x2 = np.cos(start_angle+5*np.pi/4+shifts[N-k-1])\n",
+    "\n",
+    "            y1 = np.sin(start_angle+np.pi/4+shifts[k])\n",
+    "            y2 = np.sin(start_angle+5*np.pi/4+shifts[N-k-1])\n",
+    "\n",
+    "            ax.plot([x1, x2], [y1, y2], c=f'C{j}')\n",
+    "\n",
+    "    mcp_angle = np.pi/6\n",
+    "    channel = cur_det['channels'][cur_det['indices'].index(6)]\n",
+    "    ax.text(1.4*np.cos(mcp_angle), 1.2*np.sin(mcp_angle), f'mcp\\n{channel}',\n",
+    "            c='k', fontsize='xx-large', va='center', ha='center')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Latex(tabulate.tabulate(\n",
+    "    discr_table, tablefmt='latex', headers=['', '', '', 'discriminator', *discr_header])))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},