placed sklearn import in a try block

SlideRuleEarth · Dec 23, 2022 · e44818a · e44818a
1 parent 675b197
commit e44818a
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Showing 2 changed files with 27 additions and 17 deletions.
diff --git a/examples/arcticdem.ipynb b/examples/arcticdem.ipynb
@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "icesat2.init(\"localhost\", verbose=True, organization=None)"
+    "icesat2.init(\"slideruleearth.io\", verbose=True)"
    ]
   },
   {

diff --git a/sliderule/icesat2.py b/sliderule/icesat2.py
@@ -41,7 +41,6 @@
 import geopandas
 from shapely.geometry.multipolygon import MultiPolygon
 from shapely.geometry import Polygon
-from sklearn.cluster import KMeans
 import sliderule
 
 ###############################################################################
@@ -51,6 +50,14 @@
 # create logger
 logger = logging.getLogger(__name__)
 
+# import cluster support
+clustering_enabled = False
+try:
+    from sklearn.cluster import KMeans
+    clustering_enabled = True
+except:
+    logger.warning("Unable to import sklearn... clustering support disabled")
+
 # profiling times for each major function
 profiles = {}
 
@@ -1327,22 +1334,25 @@ def toregion(source, tolerance=0.0, cellsize=0.01, n_clusters=1):
         # generate clusters
         clusters = []
         if n_clusters > 1:
-            # pull out centroids of each geometry object
-            if "CenLon" in gdf and "CenLat" in gdf:
-                X = numpy.column_stack((gdf["CenLon"], gdf["CenLat"]))
+            if clustering_enabled:
+                # pull out centroids of each geometry object
+                if "CenLon" in gdf and "CenLat" in gdf:
+                    X = numpy.column_stack((gdf["CenLon"], gdf["CenLat"]))
+                else:
+                    s = gdf.centroid
+                    X = numpy.column_stack((s.x, s.y))
+                # run k means clustering algorithm against polygons in gdf
+                kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=5, max_iter=400)
+                y_kmeans = kmeans.fit_predict(X)
+                k = geopandas.pd.DataFrame(y_kmeans, columns=['cluster'])
+                gdf = gdf.join(k)
+                # build polygon for each cluster
+                for n in range(n_clusters):
+                    c_gdf = gdf[gdf["cluster"] == n]
+                    c_poly = __gdf2poly(c_gdf)
+                    clusters.append(c_poly)
             else:
-                s = gdf.centroid
-                X = numpy.column_stack((s.x, s.y))
-            # run k means clustering algorithm against polygons in gdf
-            kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=5, max_iter=400)
-            y_kmeans = kmeans.fit_predict(X)
-            k = geopandas.pd.DataFrame(y_kmeans, columns=['cluster'])
-            gdf = gdf.join(k)
-            # build polygon for each cluster
-            for n in range(n_clusters):
-                c_gdf = gdf[gdf["cluster"] == n]
-                c_poly = __gdf2poly(c_gdf)
-                clusters.append(c_poly)
+                raise sliderule.FatalError("Clustering support not enabled; unable to import sklearn package")
 
     # update timing profiles
     profiles[toregion.__name__] = time.perf_counter() - tstart