diff --git a/docs/examples/pace_cyano.ipynb b/docs/examples/pace_cyano.ipynb
index e031dbc..24658b1 100644
--- a/docs/examples/pace_cyano.ipynb
+++ b/docs/examples/pace_cyano.ipynb
@@ -6,12 +6,9 @@
     "id": "yk1OuuFc-_I4"
    },
    "source": [
-    "## What is HyperCoast?\n",
-    "HyperCoast streamlines the processing of hyperspectral data from existing spaceborne and airborne missions (e.g., PACE, EMIT, AVIRIS, NEON, and DESIS) and upcoming hyperspectral missions, such as, SBG and GLIMR.\n",
+    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/HyperCoast/blob/main/docs/examples/pace_cyano.ipynb)\n",
     "\n",
-    "https://hypercoast.org\n",
-    "\n",
-    "<img src=\"https://i.imgur.com/HBMjW6o.gif\" width=\"500\" alt=\"HyperCoast Processing\">"
+    "# Mapping Cyanobacteria with PACE data"
    ]
   },
   {
@@ -20,16 +17,27 @@
     "id": "AvpjFXjN-_I_"
    },
    "source": [
+    "## Install packages\n",
+    "\n",
     "Uncomment the following cell to install the HyperCoast package."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U \"hypercoast[extra]\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "ASucI-Sc-_JD"
    },
    "source": [
-    "Import libraries"
+    "## Import libraries"
    ]
   },
   {
@@ -40,8 +48,8 @@
    },
    "outputs": [],
    "source": [
-    "import hypercoast\n",
-    "import pandas as pd"
+    "import earthaccess\n",
+    "import hypercoast"
    ]
   },
   {
@@ -50,604 +58,84 @@
     "id": "bsEz1U-e-_JE"
    },
    "source": [
-    "Download a sample dataset."
+    "## Download PACE data\n",
+    "\n",
+    "To download and access the PACE AOP data, you will need to create an Earthdata login. You can register for an account at [urs.earthdata.nasa.gov](https://urs.earthdata.nasa.gov). Once you have an account, run the following cell and enter your NASA Earthdata login credentials."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "id": "oY5qbVGL-_JF"
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "filepath = \"./PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
-    "dataset = hypercoast.read_pace(filepath)"
+    "earthaccess.login(persist=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "id": "oIUM0vLh-_JG"
-   },
+   "metadata": {},
    "source": [
-    "Load the HYpoxia field data."
+    "Search for PACE AOP data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 377
-    },
-    "id": "4QV5JjIZ-_JG",
-    "outputId": "82773c91-a525-4c7b-9b4d-4736fac2a743"
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "kml_path = \"./GOM_Hypoxia.kml\"\n",
-    "filepath = \"./Hypoxia_Data_Sheet.xlsx\"\n",
-    "df = pd.read_excel(filepath)\n",
-    "df.head()"
+    "results = hypercoast.search_pace(\n",
+    "    bounding_box=(-83, 25, -81, 28),\n",
+    "    temporal=(\"2024-07-30\", \"2024-08-15\"),\n",
+    "    short_name=\"PACE_OCI_L2_AOP_NRT\",\n",
+    "    count=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download PACE AOP data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 377
-    },
-    "id": "hq6TIsGs-_JH",
-    "outputId": "fe56aa5f-483c-473d-c2d6-f4837a825dda"
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\n",
-    "df_filtered.head()"
+    "hypercoast.download_pace(results[:1], out_dir=\"data\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "id": "csMuk4GW-_JH"
-   },
+   "metadata": {},
    "source": [
-    "Visualize PACE image and field data on an interactive map."
+    "## Read PACE data\n",
+    "\n",
+    "Read PACE AOP data as an `xarray.Dataset`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
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-     ]
-    },
-    "id": "IMtsAbVj-_JH",
-    "outputId": "179c7bd8-2ea9-499d-a628-26bd77cf9f89"
+    "id": "oY5qbVGL-_JF"
    },
    "outputs": [],
    "source": [
-    "m = hypercoast.Map()\n",
-    "m.add_basemap(\"Hybrid\")\n",
-    "wavelengths = [450, 550, 650]\n",
-    "m.add_pace(\n",
-    "    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n",
-    ")\n",
-    "m.add(\"spectral\")\n",
-    "style = {\"weight\": 2, \"color\": \"red\"}\n",
-    "m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\n",
-    "m.add_points_from_xy(\n",
-    "    df_filtered,\n",
-    "    x=\"Lon\",\n",
-    "    y=\"Lat\",\n",
-    "    max_cluster_radius=50,\n",
-    "    layer_name=\"Hypoxia Data Points\",\n",
-    ")\n",
-    "m.set_center(-91.46118, 28.89758, zoom=8)\n",
-    "m\n",
-    "dataset"
+    "filepath = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
+    "dataset = hypercoast.read_pace(filepath)\n",
+    "# dataset"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "id": "3EK3XYY2-_JI"
-   },
+   "metadata": {},
    "source": [
-    "![](https://i.imgur.com/JdgotIT.gif)"
+    "![image](https://github.com/user-attachments/assets/56b1fae3-9acf-4ee1-8dc9-7f6784bedf88)"
    ]
   },
   {
@@ -656,7 +144,7 @@
     "id": "NYo6cw13-_JI"
    },
    "source": [
-    "Compute band ratios."
+    "## Compute band ratios"
    ]
   },
   {
@@ -672,7 +160,8 @@
     "    (da.sel(wavelength=650) > da.sel(wavelength=620))\n",
     "    & (da.sel(wavelength=701) > da.sel(wavelength=681))\n",
     "    & (da.sel(wavelength=701) > da.sel(wavelength=450))\n",
-    ")"
+    ")\n",
+    "# data"
    ]
   },
   {
@@ -692,7 +181,7 @@
     "id": "EuKeNoNp-_JJ"
    },
    "source": [
-    "Visualize the selected region based on band ratios."
+    "## Visualize the selected region based on band ratios"
    ]
   },
   {
@@ -751,7 +240,7 @@
     "id": "7WHdt4El-_JJ"
    },
    "source": [
-    "## What you can do next about Cyanobacteria using PACE???\n",
+    "## Cyanobacteria and Spectral Angle Mapper\n",
     "\n",
     "Spectral Angle Mapper: Spectral similarity\n",
     "Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels\n",
@@ -762,17 +251,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## K-means applied to the whole image"
+   ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jDrdL13RcLFm"
+   },
+   "outputs": [],
    "source": [
-    "# K-means applied to whole image"
+    "import numpy as np\n",
+    "from sklearn.cluster import KMeans"
    ]
   },
   {
@@ -823,20 +317,6 @@
     "print(\"Size of each dimension:\", da.sizes)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jDrdL13RcLFm"
-   },
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.cluster import KMeans"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -914,6 +394,18 @@
     "cluster_labels = labels.reshape(da.shape[:-1])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -927,11 +419,6 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as mcolors\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "\n",
     "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -946,7 +433,7 @@
     "\n",
     "# Create a figure and axis with the correct map projection\n",
     "fig, ax = plt.subplots(\n",
-    "    figsize=(12, 10), dpi=400, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+    "    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
     ")\n",
     "\n",
     "# Plot the K-means classification results on the map\n",
@@ -993,7 +480,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Keans applied to selected pixels"
+    "## Keans applied to selected pixels"
    ]
   },
   {
@@ -1004,12 +491,6 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as mcolors\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import numpy as np\n",
-    "\n",
     "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1034,7 +515,7 @@
     "\n",
     "# Create a figure and axis with the correct map projection\n",
     "fig, ax = plt.subplots(\n",
-    "    figsize=(12, 10), dpi=400, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+    "    figsize=(6, 4), dpi=200, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
     ")\n",
     "\n",
     "# Plot the filtered K-means classification results on the map\n",
@@ -1081,7 +562,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PCA + SAM applied to whole image"
+    "## PCA + SAM applied to whole image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
    ]
   },
   {
@@ -1129,8 +619,6 @@
    },
    "outputs": [],
    "source": [
-    "from sklearn.decomposition import PCA\n",
-    "\n",
     "# Apply PCA to reduce dimensionality\n",
     "pca = PCA(n_components=3)\n",
     "pca_data = pca.fit_transform(reshaped_data_no_nan)\n",
@@ -1209,11 +697,6 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "\n",
     "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1279,12 +762,6 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "import numpy as np\n",
-    "\n",
     "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1348,12 +825,6 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "import numpy as np\n",
-    "\n",
     "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1423,7 +894,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# SAM"
+    "## Spectral Angle Mapper (SAM)"
    ]
   },
   {
@@ -1432,13 +903,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xarray as xr\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.colors as mcolors\n",
-    "from scipy.interpolate import interp1d\n",
-    "import hypercoast"
+    "from scipy.interpolate import interp1d"
    ]
   },
   {
@@ -1448,20 +917,11 @@
    "outputs": [],
    "source": [
     "# Load the netCDF data\n",
-    "file_path = \"./PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
+    "file_path = \"data/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
     "dataset = hypercoast.read_pace(file_path)\n",
     "da = dataset[\"Rrs\"]  # Assuming 'Rrs' contains the reflectance data"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(dataset)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1469,15 +929,7 @@
    "outputs": [],
    "source": [
     "# Extract PACE wavelengths\n",
-    "pace_wavelengths = da[\"wavelength\"].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "pace_wavelengths = da[\"wavelength\"].values\n",
     "print(pace_wavelengths)"
    ]
   },
@@ -1504,6 +956,23 @@
     "    return resampled_spectra"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download the SAM spectral library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/SAM_spectral_library.zip\"\n",
+    "hypercoast.download_file(url)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1639,12 +1108,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "import numpy as np\n",
-    "\n",
     "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1710,12 +1173,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.colors as mcolors\n",
-    "import numpy as np\n",
-    "\n",
     "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n",
     "# Also assume that 'da' has the original latitude and longitude data\n",
     "latitudes = da.coords[\"latitude\"].values\n",
@@ -1812,7 +1269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/hypercoast/pace.py b/hypercoast/pace.py
index d9cd63a..446a679 100644
--- a/hypercoast/pace.py
+++ b/hypercoast/pace.py
@@ -592,8 +592,8 @@ def grid_pace_bgc(
     Args:
         dataset (xr.Dataset): The input dataset containing PACE BGC data with latitude and longitude coordinates.
         variable (str, optional): The variable within the dataset to grid. Can be
-           one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags.
-           Defaults to "chlor_a".
+            one of chlor_a, carbon_phyto, poc, chlor_a_unc, carbon_phyto_unc, and l2_flags.
+            Defaults to "chlor_a".
         method (str, optional): The interpolation method to use. Options include "nearest", "linear", and "cubic".
             Defaults to "nearest".
         **kwargs (Any): Additional keyword arguments to pass to the xr.Dataset creation.
diff --git a/mkdocs.yml b/mkdocs.yml
index 72ac7a6..75ecb95 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -60,7 +60,6 @@ plugins:
                   "image_slicing.ipynb",
                   "temperature.ipynb",
                   "pace_oci_l1.ipynb",
-                  "pace_cyano.ipynb",
               ]
 
 markdown_extensions:
@@ -105,6 +104,7 @@ nav:
           - examples/temperature.ipynb
           - examples/pace_oci_l1.ipynb
           - examples/pace_oci_l2.ipynb
+          - examples/pace_cyano.ipynb
           - examples/multispectral.ipynb
           - examples/pca.ipynb
           - examples/field_data.ipynb
diff --git a/pyproject.toml b/pyproject.toml
index 72a12aa..f6ad0b0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,7 @@ extra = [
     "geopandas",
     "openpyxl",
     "pyvista[jupyter]",
+    "scikit-learn",
 ]
 
 
diff --git a/requirements_dev.txt b/requirements_dev.txt
index f89ef3a..705bd29 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -30,6 +30,7 @@ pytest
 pytest-runner
 pyvista
 reuse>=4.0.3
+scikit-learn
 sphinx
 twine
 watchdog