diff --git a/book/labs/lab3b.ipynb b/book/labs/lab3b.ipynb
index 8ababc7..8dd0c37 100644
--- a/book/labs/lab3b.ipynb
+++ b/book/labs/lab3b.ipynb
@@ -76,42 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
-       "                    --jp-widgets-label-color: white;\n",
-       "                    --jp-ui-font-color1: white;\n",
-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\hypercoast\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
+     ]
     }
    ],
    "source": [
@@ -122,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -262,30 +236,20 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading...\n",
-      "From: https://github.com/opengeos/datasets/releases/download/places/Rio_Grande_do_Sul_Brazil.geojson\n",
-      "To: C:\\Users\\C00553~1\\AppData\\Local\\Temp\\3d72a0d1-2c50-4d9c-897d-7cd490880797.geojson\n",
-      "100%|██████████| 61.2k/61.2k [00:00<?, ?B/s]\n"
-     ]
     }
    ],
    "source": [
     "# geemap refers to the geemap library\n",
     "# geojson_to_ee(url) is used to convert a GEOJSON file into a Google Earth Engine object. \n",
     "# The url variable, the GEOJSON file from the URL is converted into a format (an Earth Engine object) that can be used within the Google Earth Engine framework. \n",
-    "roi = geemap.geojson_to_ee(url)\n",
-    "# roi = ee.FeatureCollection(ee.Geometry.BBox(-52.0027, -30.6197, -50.2659, -29.6696))\n",
+    "#roi = geemap.geojson_to_ee(url)\n",
+    "roi = ee.FeatureCollection(ee.Geometry.BBox(-91.9264, 28.6081, -88.3441, 30.6893))\n",
     "# roi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -391,12 +355,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6473a995af9f4a34bf4780abacb8f02f",
+       "model_id": "6b4887b5a0ae4555b09391607e8afceb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map(center=[-29.77865021719509, -53.24534458841491], controls=(WidgetControl(options=['position', 'transparent…"
+       "Map(center=[29.649126101661345, -90.1352500000001], controls=(WidgetControl(options=['position', 'transparent_…"
       ]
      },
      "execution_count": 9,
@@ -420,11 +384,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[-91.9264, 28.6081, -88.3441, 30.6893]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#m.user_roi_coords()"
+    "m.user_roi_coords()"
    ]
   },
   {
@@ -1158,7 +1167,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "hypercoast",
    "language": "python",
    "name": "python3"
   },
@@ -1173,11 +1182,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.14"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f05ea183a4718249d13ada7f166c6bdba1d00716247af5c11c23af8d5923f1"
-   }
   }
  },
  "nbformat": 4,
diff --git a/book/labs/lab4a.ipynb b/book/labs/lab4a.ipynb
index 2519be1..0f4d08b 100644
--- a/book/labs/lab4a.ipynb
+++ b/book/labs/lab4a.ipynb
@@ -15,11 +15,248 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %pip install \"hypercoast[extra]\"\n",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: hypercoast[extra] in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (0.8.0)\n",
+      "Requirement already satisfied: dask in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (2024.9.0)\n",
+      "Requirement already satisfied: earthaccess in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.10.0)\n",
+      "Requirement already satisfied: geopandas in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.0.1)\n",
+      "Requirement already satisfied: h5netcdf in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.3.0)\n",
+      "Requirement already satisfied: hvplot in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.10.0)\n",
+      "Requirement already satisfied: leafmap>=0.37.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.37.3)\n",
+      "Requirement already satisfied: localtileserver in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.10.3)\n",
+      "Requirement already satisfied: mapclassify in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (2.8.0)\n",
+      "Requirement already satisfied: netcdf4 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.7.1)\n",
+      "Requirement already satisfied: numpy<2.0.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.26.4)\n",
+      "Requirement already satisfied: rioxarray in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.17.0)\n",
+      "Requirement already satisfied: s3fs in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (2024.9.0)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.5.2)\n",
+      "Requirement already satisfied: scipy in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.14.1)\n",
+      "Requirement already satisfied: xarray in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (2024.9.0)\n",
+      "Requirement already satisfied: cartopy in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.23.0)\n",
+      "Requirement already satisfied: fiona in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (1.9.4)\n",
+      "Requirement already satisfied: openpyxl in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (3.1.5)\n",
+      "Requirement already satisfied: pyvista[jupyter] in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from hypercoast[extra]) (0.44.1)\n",
+      "Requirement already satisfied: anywidget>=0.9.13 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.9.13)\n",
+      "Requirement already satisfied: bqplot>=0.12.43 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.12.43)\n",
+      "Requirement already satisfied: colour>=0.1.5 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.1.5)\n",
+      "Requirement already satisfied: duckdb>=0.10.3 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (1.0.0)\n",
+      "Requirement already satisfied: folium>=0.14.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.17.0)\n",
+      "Requirement already satisfied: gdown>=5.1.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (5.2.0)\n",
+      "Requirement already satisfied: geojson>=3.1.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (3.1.0)\n",
+      "Requirement already satisfied: ipyevents>=2.0.2 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (2.0.2)\n",
+      "Requirement already satisfied: ipyfilechooser>=0.6.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.6.0)\n",
+      "Requirement already satisfied: ipyleaflet>=0.18.2 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.19.2)\n",
+      "Requirement already satisfied: ipyvuetify>=1.9.4 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (1.10.0)\n",
+      "Requirement already satisfied: ipywidgets>=7.6.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (8.1.5)\n",
+      "Requirement already satisfied: matplotlib>=3.7.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (3.9.2)\n",
+      "Requirement already satisfied: pandas>=2.0.3 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (2.2.2)\n",
+      "Requirement already satisfied: plotly>=5.15.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (5.24.1)\n",
+      "Requirement already satisfied: pyshp>=2.3.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (2.3.1)\n",
+      "Requirement already satisfied: pystac-client>=0.8.2 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.8.3)\n",
+      "Requirement already satisfied: python-box>=7.2.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (7.2.0)\n",
+      "Requirement already satisfied: scooby>=0.10.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (0.10.0)\n",
+      "Requirement already satisfied: whiteboxgui>=2.3.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (2.3.0)\n",
+      "Requirement already satisfied: xyzservices>=2024.6.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from leafmap>=0.37.1->hypercoast[extra]) (2024.9.0)\n",
+      "Requirement already satisfied: shapely>=1.7 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from cartopy->hypercoast[extra]) (2.0.2)\n",
+      "Requirement already satisfied: packaging>=20 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from cartopy->hypercoast[extra]) (24.1)\n",
+      "Requirement already satisfied: pyproj>=3.3.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from cartopy->hypercoast[extra]) (3.6.1)\n",
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+      "Requirement already satisfied: importlib-resources>=6.3.2 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from earthaccess->hypercoast[extra]) (6.4.5)\n",
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+      "Requirement already satisfied: requests>=2.26 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from earthaccess->hypercoast[extra]) (2.32.3)\n",
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+      "Requirement already satisfied: typing_extensions>=4.10.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from earthaccess->hypercoast[extra]) (4.12.2)\n",
+      "Requirement already satisfied: aiobotocore<3.0.0,>=2.5.4 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from s3fs->hypercoast[extra]) (2.15.0)\n",
+      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from s3fs->hypercoast[extra]) (3.10.5)\n",
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+      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (1.5.0)\n",
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+      "Requirement already satisfied: executing>=1.2.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->leafmap>=0.37.1->hypercoast[extra]) (2.1.0)\n",
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+      "Requirement already satisfied: pure-eval in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->leafmap>=0.37.1->hypercoast[extra]) (0.2.3)\n",
+      "Requirement already satisfied: fqdn in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (1.5.1)\n",
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+      "Requirement already satisfied: jsonpointer>1.13 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (3.0.0)\n",
+      "Requirement already satisfied: uri-template in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (1.3.0)\n",
+      "Requirement already satisfied: webcolors>=24.6.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (24.8.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (1.17.1)\n",
+      "Requirement already satisfied: pycparser in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (2.22)\n",
+      "Requirement already satisfied: arrow>=0.15.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (1.3.0)\n",
+      "Requirement already satisfied: types-python-dateutil>=2.8.10 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hypercoast\\lib\\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.24.0->jupyter-server-proxy->pyvista[jupyter]; extra == \"extra\"->hypercoast[extra]) (2.9.0.20240906)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"hypercoast[extra]\"\n",
     "# %pip install -U localtileserver"
    ]
   },
@@ -32,9 +269,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\hypercoast\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
+     ]
+    }
+   ],
    "source": [
     "import earthaccess\n",
     "import hypercoast\n",
@@ -190,11 +436,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filepath = \"data/PACE_OCI.20241010T183444.L2.OC_AOP.V2_0.NRT.nc\"\n",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filepath = \"PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
     "plot = hypercoast.view_pace_pixel_locations(filepath, step=20)"
    ]
   },
@@ -207,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,13 +472,6 @@
     "# dataset"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image](https://github.com/user-attachments/assets/56b1fae3-9acf-4ee1-8dc9-7f6784bedf88)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -523,7 +773,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "hypercoast",
    "language": "python",
    "name": "python3"
   },