Add apply_pac function

hypercoast/pace.py

@@ -899,3 +899,65 @@ def apply_kmeans(
         plt.show()
 
     return cluster_labels, latitudes, longitudes
+
+
+def apply_pca(
+    dataset: Union[xr.Dataset, str],
+    n_components: int = 3,
+    plot: bool = True,
+    figsize: tuple[int, int] = (8, 6),
+    x_component: int = 0,
+    y_component: int = 1,
+    color: str = "blue",
+    title: str = "PCA of Spectral Data",
+    **kwargs,
+) -> np.ndarray:
+    """
+    Applies Principal Component Analysis (PCA) to the dataset and optionally plots the results.
+
+    Args:
+        dataset (xr.Dataset | str): The dataset containing the PACE data or the file path to the dataset.
+        n_components (int, optional): Number of principal components to compute. Defaults to 3.
+        plot (bool, optional): Whether to plot the data. Defaults to True.
+        figsize (tuple[int, int], optional): Figure size for the plot. Defaults to (8, 6).
+        x_component (int, optional): The principal component to plot on the x-axis. Defaults to 0.
+        y_component (int, optional): The principal component to plot on the y-axis. Defaults to 1.
+        color (str, optional): Color of the scatter plot points. Defaults to "blue".
+        title (str, optional): Title for the plot. Defaults to "PCA of Spectral Data".
+        **kwargs: Additional keyword arguments to pass to the `plt.scatter` function.
+
+    Returns:
+        np.ndarray: The PCA-transformed data.
+    """
+    from sklearn.decomposition import PCA
+
+    if isinstance(dataset, str):
+        dataset = read_pace(dataset)
+    elif not isinstance(dataset, xr.Dataset):
+        raise ValueError("dataset must be an xarray Dataset")
+
+    da = dataset["Rrs"]
+
+    # Reshape data to (n_pixels, n_bands)
+    reshaped_data = da.values.reshape(-1, da.shape[-1])
+
+    # Handle NaNs by removing them
+    reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]
+
+    # Apply PCA to reduce dimensionality
+    pca = PCA(n_components=n_components)
+    pca_data = pca.fit_transform(reshaped_data_no_nan)
+
+    if plot:
+        plt.figure(figsize=figsize)
+        if "s" not in kwargs:
+            kwargs["s"] = 1
+        plt.scatter(
+            pca_data[:, x_component], pca_data[:, y_component], c=color, **kwargs
+        )
+        plt.title(title)
+        plt.xlabel(f"Principal Component {x_component + 1}")
+        plt.ylabel(f"Principal Component {y_component + 1}")
+        plt.show()
+
+    return pca_data