Remove deprecated min_size parameter from quadtree_on_points (#843)

Closes #798.

Authors:
  - Mark Harris (https://github.com/harrism)

Approvers:
  - Paul Taylor (https://github.com/trxcllnt)
  - H. Thomson Comer (https://github.com/thomcom)

URL: #843

docs/source/user_guide/cuspatial_api_examples.ipynb

@@ -1109,7 +1109,7 @@
     "    y_max,\n",
     "    scale,\n",
     "    max_depth,\n",
-    "    min_size\n",
+    "    max_size\n",
     ")\n",
     "```\n",
     "to create the quadtree object that is used by the `quadtree_point_in_polygon`  \n",
@@ -1130,13 +1130,13 @@
     "of `10m` points and `max_depth = 7`, $\\frac{10^6}{4^7}$ points will be most  \n",
     "efficiently packed into the leaves of the quad tree.\n",
     "\n",
-    "`min_size`: The minimum number of points for a non-leaf quadtree node. As the  \n",
-    "quadtree is generated, a leaf node containing usable index points will be created  \n",
-    "as points are added. If the number of points in this leaf exceeds `min_size`, the  \n",
-    "leaf will be subdivided, with four new leaves added and the original node removed  \n",
-    "from the set of leaves. This number is probably optimized in most datasets by  \n",
-    "making it a significant fraction of the optimal leaf size computation from above.  \n",
-    "Consider $10,000,000 / 4^7 / 4 = 153$.\n",
+    "`max_size`: The maximum number of points allowed in an internal node before it is\n",
+    "split into four leaf notes. As the  quadtree is generated, a leaf node containing\n",
+    "usable index points will be created as points are added. If the number of points\n",
+    "in this leaf exceeds `max_size`, the leaf will be subdivided, with four new\n",
+    "leaves added and the original node removed from the set of leaves. This number is\n",
+    "probably optimized in most datasets by making it a significant fraction of the\n",
+    "optimal leaf size computation from above. Consider $10,000,000 / 4^7 / 4 = 153$.\n",
     "\n",
     "### [cuspatial.quadtree_on_points](https://docs.rapids.ai/api/cuspatial/nightly/api_docs/spatial.html#cuspatial.quadtree_on_points)"
    ]
@@ -1172,7 +1172,7 @@
     "y_points = (cupy.random.random(10000000) - 0.5) * 180\n",
     "scale = 5\n",
     "max_depth = 7\n",
-    "min_size = 125\n",
+    "max_size = 125\n",
     "point_indices, quadtree = cuspatial.quadtree_on_points(x_points,\n",
     "                                                       y_points,\n",
     "                                                       polygons.x.min(),\n",
@@ -1181,7 +1181,7 @@
     "                                                       polygons.y.max(),\n",
     "                                                       scale,\n",
     "                                                       max_depth,\n",
-    "                                                       min_size)\n",
+    "                                                       max_size)\n",
     "print(point_indices.head())\n",
     "print(quadtree.head())"
    ]
@@ -1307,7 +1307,7 @@
     "                                                       polygons.y.max(),\n",
     "                                                       scale,\n",
     "                                                       max_depth,\n",
-    "                                                       min_size)\n",
+    "                                                       max_size)\n",
     "poly_bboxes = cuspatial.linestring_bounding_boxes(\n",
     "    polygons.ring_offset,\n",
     "    polygons.x,\n",

python/cuspatial/cuspatial/core/spatial/indexing.py

@@ -12,17 +12,7 @@
 
 
 def quadtree_on_points(
-    xs,
-    ys,
-    x_min,
-    x_max,
-    y_min,
-    y_max,
-    scale,
-    max_depth,
-    max_size,
-    # Deprecated, renamed to `max_size`
-    min_size=None,
+    xs, ys, x_min, x_max, y_min, y_max, scale, max_depth, max_size
 ):
     """Construct a quadtree from a set of points for a given area-of-interest
     bounding box.
@@ -171,15 +161,6 @@ def quadtree_on_points(
         Length: 120, dtype: int32
     """
 
-    if min_size is not None and max_size is None:
-        max_size = min_size
-        min_size = None
-        warnings.warn(
-            "Deprecation warning: `min_size` argument has been renamed to "
-            "`max_size`. Support for the old name will be removed in the "
-            "next version."
-        )
-
     xs, ys = normalize_point_columns(as_column(xs), as_column(ys))
     x_min, x_max, y_min, y_max = (
         min(x_min, x_max),