diff --git a/STAR_Protocol.ipynb b/STAR_Protocol.ipynb
index cca7a1a..d297860 100644
--- a/STAR_Protocol.ipynb
+++ b/STAR_Protocol.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "6bb9ddd6-e96d-4cdf-8f1b-9a197b46a730",
    "metadata": {},
    "outputs": [],
@@ -13,21 +13,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "11bea910-8418-4248-9a28-2d474c3fe468",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/kenjilee/Library/Caches/pypoetry/virtualenvs/star-protocol-wavemap-S8Yf6mBx-py3.8/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from wavemap_paper.helper_functions import RAND_STATE, set_rand_state\n",
     "set_rand_state(RAND_STATE)"
@@ -35,27 +26,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "4a98efe7-6528-4b31-8226-45d7a50adc51",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "OMP: Info #270: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from umap import umap_ as umap\n",
-    "reducer = umap.UMAP(random_state=RAND_STATE)\n",
+    "reducer = umap.UMAP(n_neighbors=15, random_state=RAND_STATE)\n",
     "mapper = reducer.fit(normWFs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "id": "c01efadf-4699-4f41-86ba-9ad07d935646",
    "metadata": {},
    "outputs": [],
@@ -66,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "id": "63e1754a-bbf3-4382-b033-3ab701c27016",
    "metadata": {},
    "outputs": [
@@ -87,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "id": "849d6319-719d-4f35-8c56-d5545ac9af32",
    "metadata": {
     "tags": []
@@ -111,17 +94,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "id": "1a04d04c-c726-47a3-9d5c-95d6ca58af5a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fbe601ba8e0>"
+       "<matplotlib.collections.PathCollection at 0x7f8eac923fd0>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },