ENH: Add code for calculating graph statistics and

docs/notebooks/osmnx/docs/notebooks/osmnx/graphs/RouteID_1cfa272e-cb3d-4ea4-9cac-dc6dc356d7f0.shp/edges.cpg

@@ -0,0 +1 @@
+UTF-8

docs/notebooks/osmnx/docs/notebooks/osmnx/graphs/RouteID_1cfa272e-cb3d-4ea4-9cac-dc6dc356d7f0.shp/edges.prj

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

docs/notebooks/osmnx/docs/notebooks/osmnx/graphs/RouteID_1cfa272e-cb3d-4ea4-9cac-dc6dc356d7f0.shp/nodes.cpg

@@ -0,0 +1 @@
+UTF-8

docs/notebooks/osmnx/docs/notebooks/osmnx/graphs/RouteID_1cfa272e-cb3d-4ea4-9cac-dc6dc356d7f0.shp/nodes.prj

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

docs/notebooks/osmnx/done/ntbk-0.py

@@ -0,0 +1,143 @@
+# %%
+# import libraries
+import osmnx as ox
+import networkx as nx
+import pandas as pd
+from lmr_analyzer import Haversine
+import json
+
+# start timer for the loop
+import time
+
+# %%
+# define the filename
+filename = "route_id_0.csv"  # "docs/notebooks/osmnx/route_id_0_test.csv"
+
+# %%
+# import the csv file and store it in a pd.dataframe
+df = pd.read_csv(filename)
+
+# check the header
+df.head()
+
+# %%
+area_dict = dict()
+
+# %%
+# iterate over the df and calculate the area of each bounding box
+area_dict = dict()
+for i in range(df.shape[0]):
+    area = (
+        Haversine(
+            lat1=df["bbox_north"][i],
+            lon1=df["bbox_west"][i],
+            lat2=df["bbox_south"][i],
+            lon2=df["bbox_west"][i],
+        )
+        * Haversine(
+            lat1=df["bbox_north"][i],
+            lon1=df["bbox_west"][i],
+            lat2=df["bbox_north"][i],
+            lon2=df["bbox_east"][i],
+        )
+    ) * (1000**2)
+    area_dict[df["route_id"][i]] = area
+
+# %%
+graph_dict = dict()
+
+# %%
+# create graphs for each bounding box
+count = 1
+start = time.time()
+end_it = start
+
+for key, value in df.iterrows():
+    start_it = time.time()
+    graph_dict[value["route_id"]] = ox.graph.graph_from_bbox(
+        north=value["bbox_north"],
+        south=value["bbox_south"],
+        east=value["bbox_east"],
+        west=value["bbox_west"],
+        network_type="drive",
+        simplify=True,
+        retain_all=False,
+        truncate_by_edge=False,
+        clean_periphery=True,
+        custom_filter=None,
+    )
+    print(
+        f"\rGraph done {count:4} out of {len(df)}, {(100*count/len(df)):.2f} % - Estimated time remaining: {(end_it-start)*((len(df)-count)/(60*count)):.2f} min",
+        end="",
+        flush=True,
+    )
+    count += 1
+    end_it = time.time()
+
+# %%
+# iterate through the graph_dict and convert the graphs to undirected graphs
+for key, value in graph_dict.items():
+    graph_dict[key] = nx.to_undirected(value)
+
+# %%
+# calculate basic stats for each graph
+stats_dict = dict()
+count = 1
+for key, value in graph_dict.items():
+    stats_dict[key] = ox.basic_stats(value, area=area_dict[key])
+    print(
+        f"\rStats done {count:4} out of {len(df)}, {(100*count/len(df)):.2f} %",
+        end="",
+        flush=True,
+    )
+    count += 1
+
+# %%
+# calculate orientation and entropy for each graph
+entropy_dict = dict()
+
+# %%
+count = 1
+
+start = time.time()
+start_it = start
+
+for key, value in graph_dict.items():
+    graph_dict[key] = ox.bearing.add_edge_bearings(value)
+    entropy_dict[key] = ox.bearing.orientation_entropy(value)
+    end_it = time.time()
+    print(
+        f"\rBearing done {count:4} out of {len(df)}, {(100*count/len(df)):.2f} % - Estimated time remaining: {(end_it-start)*((len(df)-count)/(60*count)):.2f} min",
+        end="",
+        flush=True,
+    )
+    count += 1
+    start_it = time.time()
+
+# %%
+# combine stats and entropy into one dict
+stats_entropy_dict = dict()
+
+# %%
+count = 1
+for key, value in stats_dict.items():
+    stats_entropy_dict[key] = {
+        "street_length_total": value["street_length_total"],
+        "street_density_km": value["street_density_km"],
+        "intersection_count": value["intersection_count"],
+        "intersection_density_km": value["intersection_density_km"],
+        "k_avg": value["k_avg"],
+        "street_density_km": value["street_density_km"],
+        "entropy": entropy_dict[key],
+    }
+    print(
+        f"\rDone {count:4} out of {len(df)}, {(100*count/len(df)):.2f} %",
+        end="",
+        flush=True,
+    )
+    count += 1
+
+# %%
+# save the final dictionary as a json file
+with open(f"stats_entropy_dict{filename[-5]}.json", "w") as fp:
+    json.dump(stats_entropy_dict, fp)