Merge pull request #84 from gboeing/dep

Updates for v1.9.2 in prep for v2

environment.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - jupyterlab
-  - osmnx=1.9.1
+  - osmnx=1.9.2
   - pillow
   - pre-commit
   - python=3.11.*

notebooks/00-osmnx-features-demo.ipynb

@@ -78,10 +78,10 @@
    "outputs": [],
    "source": [
     "# convert your MultiDiGraph to an undirected MultiGraph\n",
-    "M = ox.utils_graph.get_undirected(G)\n",
+    "M = ox.convert.to_undirected(G)\n",
     "\n",
     "# convert your MultiDiGraph to a DiGraph without parallel edges\n",
-    "D = ox.utils_graph.get_digraph(G)"
+    "D = ox.convert.to_digraph(G)"
    ]
   },
   {
@@ -214,8 +214,8 @@
    "outputs": [],
    "source": [
     "# impute missing edge speeds and calculate edge travel times with the speed module\n",
-    "G = ox.speed.add_edge_speeds(G)\n",
-    "G = ox.speed.add_edge_travel_times(G)"
+    "G = ox.routing.add_edge_speeds(G)\n",
+    "G = ox.routing.add_edge_travel_times(G)"
    ]
   },
   {
@@ -247,7 +247,7 @@
    "outputs": [],
    "source": [
     "# how long is our route in meters?\n",
-    "edge_lengths = ox.utils_graph.route_to_gdf(G, route)[\"length\"]\n",
+    "edge_lengths = ox.routing.route_to_gdf(G, route)[\"length\"]\n",
     "round(sum(edge_lengths))"
    ]
   },
@@ -455,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/01-overview-osmnx.ipynb

@@ -67,7 +67,7 @@
    "source": [
     "# get the boundary polygon for manhattan, project it, and plot it\n",
     "city = ox.geocode_to_gdf(\"Manhattan, New York, USA\")\n",
-    "city_proj = ox.project_gdf(city)\n",
+    "city_proj = ox.projection.project_gdf(city)\n",
     "ax = city_proj.plot(fc=\"gray\", ec=\"none\")\n",
     "_ = ax.axis(\"off\")"
    ]
@@ -88,7 +88,7 @@
     "]\n",
     "east_bay = ox.geocode_to_gdf(place_names)\n",
     "east_bay.to_file(\"./data/east_bay.gpkg\", driver=\"GPKG\")\n",
-    "east_bay = ox.project_gdf(east_bay)\n",
+    "east_bay = ox.projection.project_gdf(east_bay)\n",
     "ax = east_bay.plot(fc=\"gray\", ec=\"none\")\n",
     "_ = ax.axis(\"off\")"
    ]
@@ -143,10 +143,10 @@
    "outputs": [],
    "source": [
     "# define a bounding box in San Francisco\n",
-    "north, south, east, west = 37.79, 37.78, -122.41, -122.43\n",
+    "bbox = 37.79, 37.78, -122.41, -122.43\n",
     "\n",
     "# create network from that bounding box\n",
-    "G = ox.graph_from_bbox(north, south, east, west, network_type=\"drive_service\")"
+    "G = ox.graph_from_bbox(bbox=bbox, network_type=\"drive_service\")"
    ]
   },
   {
@@ -485,7 +485,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/02-routing-speed-time.ipynb

@@ -59,7 +59,7 @@
    "outputs": [],
    "source": [
     "# randomly sample n points spatially-constrained to the network's geometry\n",
-    "points = ox.utils_geo.sample_points(ox.get_undirected(Gp), n=100)\n",
+    "points = ox.utils_geo.sample_points(ox.convert.to_undirected(Gp), n=100)\n",
     "X = points.x.values\n",
     "Y = points.y.values\n",
     "X0 = X.mean()\n",
@@ -190,7 +190,7 @@
     "# same thing again, but this time pass in a few default speed values (km/hour)\n",
     "# to fill in edges with missing `maxspeed` from OSM\n",
     "hwy_speeds = {\"residential\": 35, \"secondary\": 50, \"tertiary\": 60}\n",
-    "G = ox.add_edge_speeds(G, hwy_speeds)\n",
+    "G = ox.add_edge_speeds(G, hwy_speeds=hwy_speeds)\n",
     "G = ox.add_edge_travel_times(G)"
    ]
   },
@@ -226,10 +226,10 @@
    "outputs": [],
    "source": [
     "# compare the two routes\n",
-    "route1_length = int(sum(ox.utils_graph.route_to_gdf(G, route1, \"length\")[\"length\"]))\n",
-    "route2_length = int(sum(ox.utils_graph.route_to_gdf(G, route2, \"length\")[\"length\"]))\n",
-    "route1_time = int(sum(ox.utils_graph.route_to_gdf(G, route1, \"travel_time\")[\"travel_time\"]))\n",
-    "route2_time = int(sum(ox.utils_graph.route_to_gdf(G, route2, \"travel_time\")[\"travel_time\"]))\n",
+    "route1_length = int(sum(ox.routing.route_to_gdf(G, route1, weight=\"length\")[\"length\"]))\n",
+    "route2_length = int(sum(ox.routing.route_to_gdf(G, route2, weight=\"length\")[\"length\"]))\n",
+    "route1_time = int(sum(ox.routing.route_to_gdf(G, route1, weight=\"travel_time\")[\"travel_time\"]))\n",
+    "route2_time = int(sum(ox.routing.route_to_gdf(G, route2, weight=\"travel_time\")[\"travel_time\"]))\n",
     "print(\"Route 1 is\", route1_length, \"meters and takes\", route1_time, \"seconds.\")\n",
     "print(\"Route 2 is\", route2_length, \"meters and takes\", route2_time, \"seconds.\")"
    ]
@@ -395,7 +395,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/03-graph-place-queries.ipynb

@@ -121,8 +121,8 @@
     ")\n",
     "\n",
     "# the city is a very small part of the county\n",
-    "alameda1 = ox.project_gdf(alameda1)\n",
-    "alameda2 = ox.project_gdf(alameda2)\n",
+    "alameda1 = ox.projection.project_gdf(alameda1)\n",
+    "alameda2 = ox.projection.project_gdf(alameda2)\n",
     "alameda2.area.iloc[0] / alameda1.area.iloc[0]"
    ]
   },
@@ -173,7 +173,7 @@
    "source": [
     "# you can pass multiple queries with mixed types (dicts and strings)\n",
     "mx_gt_tx = ox.geocode_to_gdf([{\"country\": \"Mexico\"}, \"Guatemala\", {\"state\": \"Texas\"}])\n",
-    "mx_gt_tx = ox.project_gdf(mx_gt_tx)\n",
+    "mx_gt_tx = ox.projection.project_gdf(mx_gt_tx)\n",
     "ax = mx_gt_tx.plot(fc=\"gray\", ec=\"w\")\n",
     "_ = ax.axis(\"off\")"
    ]
@@ -192,7 +192,7 @@
    "outputs": [],
    "source": [
     "france = ox.geocode_to_gdf(\"France\")\n",
-    "france = ox.project_gdf(france)\n",
+    "france = ox.projection.project_gdf(france)\n",
     "ax = france.plot(fc=\"gray\", ec=\"none\")\n",
     "_ = ax.axis(\"off\")"
    ]
@@ -203,7 +203,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "france = ox.project_gdf(ox.geocode_to_gdf(\"R1403916\", by_osmid=True))\n",
+    "france = ox.projection.project_gdf(ox.geocode_to_gdf(\"R1403916\", by_osmid=True))\n",
     "ax = france.plot(fc=\"gray\", ec=\"none\")\n",
     "_ = ax.axis(\"off\")"
    ]
@@ -343,7 +343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/04-simplify-graph-consolidate-nodes.ipynb

@@ -194,7 +194,7 @@
    "outputs": [],
    "source": [
     "# simplify network with strict mode turned off\n",
-    "G3 = ox.simplify_graph(G.copy(), strict=False)\n",
+    "G3 = ox.simplify_graph(G.copy(), edge_attrs_differ=[\"osmid\"])\n",
     "fig, ax = ox.plot_graph(G3, node_color=\"r\")"
    ]
   },
@@ -206,89 +206,7 @@
     "\n",
     "This is related to simplification. OSMnx by default (with clean_periphery parameter equal to True) buffers the area you request by 0.5km, and then retrieves the street network within this larger, buffered area. Then it simplifies the topology so that nodes represent intersections of streets (rather than including all the interstitial OSM nodes). Then it calculates the (undirected) degree of each node in this larger network. Next it truncates this network by the actual area you requested (either by bounding box, or by polygon). Finally it saves a dictionary of node degree values as a graph attribute.\n",
     "\n",
-    "This has two primary benefits. First, it cleans up stray false edges around the periphery. If clean_periphery=False, peripheral non-intersection nodes within the requested area appear to be cul-de-sacs, as the rest of the edge leading to an intersection outside the area is ignored. If clean_periphery=True, the larger graph is first created, allowing simplification of such edges to their true intersections, allowing their entirety to be pruned after truncating down to the actual requested area. Second, it gives accurate node degrees by both a) counting node neighbors even if they fall outside the retained network (so you don't claim a degree-4 node is degree-2 because only 2 of its neighbors lie within the area), and b) not counting all those stray false edges' terminus nodes as cul-de-sacs that otherwise grossly inflate the count of nodes with degree=1, even though these nodes are really just interstitial nodes in the middle of a chopped-off street segment between intersections.\n",
-    "\n",
-    "See two examples below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get some bbox\n",
-    "bbox = ox.utils_geo.bbox_from_point((45.518698, -122.679964), dist=300)\n",
-    "north, south, east, west = bbox"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = ox.graph_from_bbox(north, south, east, west, network_type=\"drive\", clean_periphery=False)\n",
-    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# the node degree distribution for this graph has many false cul-de-sacs\n",
-    "k = dict(G.degree())\n",
-    "{n: list(k.values()).count(n) for n in range(max(k.values()) + 1)}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Above, notice all the peripheral stray edge stubs. Below, notice these are cleaned up and that the node degrees are accurate with regards to the wider street network that may extend beyond the limits of the requested area."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = ox.graph_from_bbox(north, south, east, west, network_type=\"drive\")\n",
-    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# the streets per node distribution for this cleaned up graph is more accurate\n",
-    "# dict keys = count of streets emanating from the node (ie, intersections and dead-ends)\n",
-    "# dict vals = number of nodes with that count\n",
-    "k = nx.get_node_attributes(G, \"street_count\")\n",
-    "{n: list(k.values()).count(n) for n in range(max(k.values()) + 1)}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A final example. Compare the network below to the ones in the section above. It has the stray peripheral edges cleaned up. Also notice toward the bottom left, two interstitial nodes remain in that east-west street. Why? These are actually intersections, but their (southbound) edges were removed because these edges' next intersections were south of the requested area's boundaries. However, OSMnx correctly kept these nodes in the graph because they are in fact intersections and should be counted in measures of intersection density, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "location_point = (33.299896, -111.831638)\n",
-    "G = ox.graph_from_point(location_point, dist=500, simplify=True)\n",
-    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
+    "This has two primary benefits. First, it cleans up stray false edges around the periphery. If clean_periphery=False, peripheral non-intersection nodes within the requested area appear to be cul-de-sacs, as the rest of the edge leading to an intersection outside the area is ignored. If clean_periphery=True, the larger graph is first created, allowing simplification of such edges to their true intersections, allowing their entirety to be pruned after truncating down to the actual requested area. Second, it gives accurate node degrees by both a) counting node neighbors even if they fall outside the retained network (so you don't claim a degree-4 node is degree-2 because only 2 of its neighbors lie within the area), and b) not counting all those stray false edges' terminus nodes as cul-de-sacs that otherwise grossly inflate the count of nodes with degree=1, even though these nodes are really just interstitial nodes in the middle of a chopped-off street segment between intersections."
    ]
   },
   {
@@ -316,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/05-save-load-networks.ipynb

@@ -138,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Save .osm XML files\n",
+    "## Save OSM XML files\n",
     "\n",
     "To save your graph to disk as a .osm formatted XML file, ensure that you created the graph with `ox.settings.all_oneway=True` for `save_graph_xml` to work properly. See docstring for details.\n",
     "\n",
@@ -154,7 +154,7 @@
     "# save graph to disk as .osm xml file\n",
     "ox.settings.all_oneway = True\n",
     "ox.settings.log_console = True\n",
-    "G = ox.graph_from_place(\"Piedmont, California, USA\", network_type=\"drive\")\n",
+    "G = ox.graph_from_place(\"Piedmont, California, USA\", network_type=\"drive\", simplify=False)\n",
     "ox.save_graph_xml(G, filepath=\"./data/piedmont.osm\")"
    ]
   },
@@ -183,7 +183,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/06-stats-indicators-centrality.ipynb

@@ -62,7 +62,7 @@
     "# get the street network for a place, and its area in square meters\n",
     "place = \"Piedmont, California, USA\"\n",
     "gdf = ox.geocode_to_gdf(place)\n",
-    "area = ox.project_gdf(gdf).unary_union.area\n",
+    "area = ox.projection.project_gdf(gdf).unary_union.area\n",
     "G = ox.graph_from_place(place, network_type=\"drive\")"
    ]
   },
@@ -119,7 +119,7 @@
    "outputs": [],
    "source": [
     "# calculate betweenness with a digraph of G (ie, no parallel edges)\n",
-    "bc = nx.betweenness_centrality(ox.get_digraph(G), weight=\"length\")\n",
+    "bc = nx.betweenness_centrality(ox.convert.to_digraph(G), weight=\"length\")\n",
     "max_node, max_bc = max(bc.items(), key=lambda x: x[1])\n",
     "max_node, max_bc"
    ]
@@ -193,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/07-plot-graph-over-shape.ipynb

@@ -115,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

notebooks/08-custom-filters-infrastructure.ipynb

@@ -138,7 +138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,