Add inject_points for projecting external points onto the network

docs/source/api.rst

@@ -41,6 +41,7 @@ A subset of functions dealing with network nodes:
    consolidate_nodes
    remove_interstitial_nodes
    induce_nodes
+   inject_points
 
 Face artifact detection
 -----------------------

docs/source/simple_preprocessing.ipynb

@@ -524,8 +524,76 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The figures above are self-explanatory. However, remember that the extended network is not topologically correct and is not suitable for network analysis directly. Use `induce_nodes` to fix it if needed.\n",
+    "The figures above are self-explanatory. However, remember that the extended network is not topologically correct and is not suitable for network analysis directly. Use `induce_nodes` to fix it if needed.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inject points\n",
+    "\n",
+    "Sometimes you need a node at a specific external location, for example a point of interest sitting near (but not on) the network. `neatnet.split` and `neatnet.inject_points` both add a node, but they treat an off-line point differently.\n",
+    "\n",
+    "`split` snaps the line onto the point, so a point that is not already on the line forces the line to bend through it. `inject_points` projects the point onto the nearest line and splits it at that projection, leaving the original linework unchanged. Both require the network and the points to share a CRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "metadata": {},
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 2 Axes>"
+      ]
+     }
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from shapely.geometry import Point\n",
+    "\n",
+    "streets = gpd.GeoDataFrame(geometry=[LineString([(0, 0), (10, 0)])], crs=\"EPSG:3857\")\n",
+    "points = gpd.GeoDataFrame(geometry=[Point(5, 2)], crs=\"EPSG:3857\")\n",
+    "\n",
+    "injected = neatnet.inject_points(streets, points, snap_radius=5)\n",
+    "forced = neatnet.split(points.geometry, streets, streets.crs, eps=5)\n",
     "\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 3))\n",
+    "\n",
+    "# inject_points: the point is projected onto the line; the linework is kept\n",
+    "streets.plot(ax=ax1, color=\"lightgray\", linewidth=4)\n",
+    "injected.plot(ax=ax1, linewidth=1.5)\n",
+    "points.plot(ax=ax1, color=\"red\", zorder=5)\n",
+    "ax1.plot([5, 5], [2, 0], color=\"gray\", linestyle=\"--\", linewidth=1)\n",
+    "ax1.scatter([5], [0], color=\"black\", zorder=6)\n",
+    "ax1.set_title(\"inject_points: linework kept, just subdivided\")\n",
+    "ax1.set_axis_off()\n",
+    "\n",
+    "# split: the line is snapped onto the point, bending through it\n",
+    "streets.plot(ax=ax2, color=\"lightgray\", linewidth=4)\n",
+    "forced.plot(ax=ax2, linewidth=1.5)\n",
+    "points.plot(ax=ax2, color=\"red\", zorder=5)\n",
+    "ax2.set_title(\"split: line forced through the point\")\n",
+    "ax2.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On the left, `inject_points` projected the point down onto the line and added a node there (black), keeping the line straight. On the right, `split` pulled the line up to the point, leaving a kink. Use `inject_points` when points lie near the line and you want to preserve its shape; points farther than `snap_radius` are ignored."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "For more details and further options, see the [API documentation](api.rst)."
    ]
   }

neatnet/__init__.py

@@ -8,6 +8,7 @@
     consolidate_nodes,
     fix_topology,
     induce_nodes,
+    inject_points,
     remove_interstitial_nodes,
     split,
 )