Add filter condition

hypercoast/pace.py

@@ -976,6 +976,7 @@ def apply_sam(
     n_components: int = 3,
     n_clusters: int = 6,
     random_state: int = 0,
+    filter_condition: Optional[Callable[[xr.DataArray], xr.DataArray]] = None,
     plot: bool = True,
     figsize: tuple[int, int] = (8, 6),
     extent: list[float] = None,
@@ -991,6 +992,7 @@ def apply_sam(
         n_components (int, optional): Number of principal components to compute. Defaults to 3.
         n_clusters (int, optional): Number of clusters for K-means. Defaults to 6.
         random_state (int, optional): Random state for K-means. Defaults to 0.
+        filter_condition (Callable[[xr.DataArray], xr.DataArray], optional): A function to filter the data. Defaults to None.
         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).
         extent (List[float], optional): The extent to zoom in to the specified region. Defaults to None.
@@ -1058,6 +1060,9 @@ def spectral_angle_mapper(pixel, reference):
     best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match
     best_match_full = best_match_full.reshape(original_shape)
 
+    if filter_condition is not None:
+        best_match_full = np.where(filter_condition, best_match_full, np.nan)
+
     latitudes = da.coords["latitude"].values
     longitudes = da.coords["longitude"].values