Exploration of routing libraries

scripts/sources_sinks_routing/benchmark_shortest_path.py

@@ -0,0 +1,181 @@
+import random
+import time
+import argparse
+
+import numpy as np
+import pandana
+from pandas import DataFrame
+from igraph import Graph
+import geopandas as gpd
+from prettytable import PrettyTable
+
+
+def str_to_int(str_list):
+    return [int(string[6:]) for string in str_list]
+
+
+def get_input_list(source, dest):
+    """Return the input lists for many-to-many shortest path calculation
+    with pandana.Network.shortest_paths
+    >>> list1, list2 = get_input_list(["a", "b", "c"], ["d", "e", "f", "g"])
+    >>> list1
+    ["a", "a", "a", "a", "b", "b", "b", "b", "c", "c", "c", "c"]
+    >>> list2
+    ["d", "e", "f", "g", "d", "e", "f", "g", "d", "e", "f", "g"]
+    """
+    nodes_a = []
+    nodes_b = []
+    for source_node in source:
+        nodes_a.extend([source_node] * len(dest))
+        nodes_b.extend(dest)
+    return nodes_a, nodes_b
+
+
+def shortest_paths_igraph(geodf, source_ensbl, dest_ensbl):
+    """Perform many-to-many shortest path calculation with python-igraph.
+
+    Positional arguments:
+    geodf -- A geodataframe describing the edges. Must contain
+    columns "from_node", "to_node" and "length_km".
+    source_ensbl: A list of node ids for starting nodes.
+    dest_ensbl: A list of node ids for destination nodes.
+
+    Returns:
+    A list of shortest paths (themselves list of edges).
+    """
+    edges = geodf.loc[:, ["from_node", "to_node", "length_km"]]
+    g = Graph.DataFrame(edges, directed=False)
+    dest = g.vs.select(name_in=dest_ensbl)
+
+    shortest_paths = []
+    for source_id in source_ensbl:
+        start = g.vs.find(name=source_id)
+        sp = g.get_shortest_paths(
+            start, dest, weights="length_km", output="epath"
+        )
+        shortest_paths.append(sp)
+
+    return shortest_paths
+
+
+def shortest_paths_pandana(geodf, nodes_geodf, source_ensbl, dest_ensbl):
+    """Perform many-to-many shortest path calculation with pandana.
+
+    Positional arguments:
+    geodf -- A geodataframe describing the edges. Must contain
+    columns "from_node", "to_node" and "length_km".
+    nodes_geodf -- A geodataframe describing the nodes.
+    source_ensbl -- A list of node ids for starting nodes.
+    dest_ensbl -- A list of node ids for destination nodes.
+
+    Returns:
+    A list of shortest paths (themselves list of vertices).
+    """
+    list_of_coords = [
+        (point.x, point.y) for point in nodes_geodf.loc[:, "geometry"]
+    ]
+    nodes = DataFrame(list_of_coords, columns=["x", "y"])
+
+    # Transform source and destination columns into lists of integers
+    # (node ids)
+    from_node = str_to_int(gdf["from_node"])
+    to_node = str_to_int(gdf["to_node"])
+
+    edges = DataFrame(
+        {"from": from_node, "to": to_node, "weight": gdf["length_km"].values}
+    )
+    net = pandana.Network(
+        nodes["x"], nodes["y"], edges["from"], edges["to"], edges[["weight"]]
+    )
+    nodes_a, nodes_b = get_input_list(
+        str_to_int(source_ensbl), str_to_int(dest_ensbl)
+    )
+    return net.shortest_paths(nodes_a, nodes_b, imp_name="weight")
+
+
+def reconstruct_epath(vpath, gdf):
+    """Compute edge path from a vertex path.
+
+    Positional arguments:
+    vpath -- A list of node ids
+    gdf -- A geodataframe describing the edges. Must contain
+    columns "from_node", "to_node" with nodes as "roadn_<nodeid>"
+
+    Returns:
+    A list of edge IDs.
+    """
+    g = Graph.DataFrame(gdf.loc[:, ["from_node", "to_node"]], directed=False)
+    igraph_nodes = [g.vs.find(name="roadn_" + str(i)).index for i in vpath]
+    epath = g.get_eids(path=igraph_nodes, directed=False)
+    return epath
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="")
+    parser.add_argument("--sizes", nargs="+")
+    parser.add_argument("--nreps", type=int, default=10)
+    parser.add_argument("-r", "--reconstr", action="store_true")
+
+    args = parser.parse_args()
+
+    gdf = gpd.read_file("jamaica_roads.gpkg")
+    nodes_gdf = gpd.read_file("jamaica_roads.gpkg", layer="nodes")
+
+    igraph_times = np.zeros((args.nreps, len(args.sizes)))
+    pandana_times = np.zeros((args.nreps, len(args.sizes)))
+    pandana_reconstruct_times = np.zeros((args.nreps, len(args.sizes)))
+
+    for j, size in enumerate(args.sizes):
+        print(f"Size {j+1} of {len(args.sizes)}")
+        # Sample source and destination ensembles
+        for irep in range(args.nreps):
+            print(f"    Rep {irep + 1} of {args.nreps}")
+            # Some nodes in the "nodes" layer don't seem to have roads
+            # start of end at them. So picking from from_node and to_node
+            # columns.
+            source_ensbl = random.sample(list(gdf["from_node"]), int(size))
+            dest_ensbl = random.sample(list(gdf["to_node"]), int(size))
+
+            print("        Timing igraph")
+            tic = time.time()
+            path = shortest_paths_igraph(gdf, source_ensbl, dest_ensbl)
+            toc = time.time()
+            igraph_times[irep, j] = toc - tic
+
+            print("        Timing pandana")
+            tic = time.time()
+            vpaths = shortest_paths_pandana(
+                gdf, nodes_gdf, source_ensbl, dest_ensbl
+            )
+            toc = time.time()
+            pandana_times[irep, j] = toc - tic
+
+            if args.reconstr:
+                print("        Timing pandana reconstruction step")
+                tic = time.time()
+                epaths = [reconstruct_epath(vpath, gdf) for vpath in vpaths]
+                toc = time.time()
+                pandana_reconstruct_times[irep, j] = toc - tic
+
+    tbl = PrettyTable()
+    tbl.add_column("Size", args.sizes)
+    tbl.add_column("Igraph (avg)", np.mean(igraph_times, axis=0))
+    tbl.add_column("Igraph (std)", np.std(igraph_times, axis=0))
+    tbl.add_column("pandana (avg)", np.mean(pandana_times, axis=0))
+    tbl.add_column("pandana (std)", np.std(pandana_times, axis=0))
+    if args.reconstr:
+        tbl.add_column(
+            "reconstr (avg)", np.mean(pandana_reconstruct_times, axis=0)
+        )
+        tbl.add_column(
+            "reconstr (std)", np.std(pandana_reconstruct_times, axis=0)
+        )
+
+    from prettytable import MARKDOWN
+
+    tbl.float_format = "1.2"
+    tbl.set_style(MARKDOWN)
+    with open("table.txt", "w") as f:
+        f.write(tbl.get_string())
+        f.write("\n")
+    print(tbl)

scripts/sources_sinks_routing/shortest_paths_igraph.py

@@ -0,0 +1,41 @@
+from igraph import Graph
+import geopandas as gpd
+import matplotlib.pyplot as plt
+
+gdf = gpd.read_file("jamaica_roads.gpkg")
+edges = gdf.loc[:, ["from_node", "to_node", "length_km"]]
+
+g = Graph.DataFrame(edges, directed=False)
+
+start = g.vs.find(name="roadn_0")
+finish = g.vs.find(name="roadn_15891")
+
+# Return list of list of edge IDs
+# Return vpath for comparison with pandana
+sp = g.get_shortest_paths(
+    start.index, finish.index, weights="length_km", output="vpath"
+)
+
+# base = gdf.plot()
+# for path in sp:
+#     # Select linestrings according to edge IDs making the path
+#     # edge ID are 1to1 with row id in dataframe
+#     sub_gdf = gdf.iloc[path, :]
+#     sub_gdf.plot(ax=base, color="green")
+# plt.show()
+
+nodes_gdf = gpd.read_file("jamaica_roads.gpkg", layer="nodes")
+base = gdf.plot()
+for path in sp:
+    nodeslist = [int(g.vs[i]["name"][6:]) for i in path]
+    # Select linestrings according to edge IDs making the path
+    # edge ID are 1to1 with row id in dataframe
+    sub_gdf = nodes_gdf.iloc[nodeslist, :]
+    sub_gdf.plot(ax=base, color="red", markersize=6)
+plt.show()
+
+# edge list
+#[[0, 11345, 11356, 15399, 15357, 15356, 526, 1, 521, 522, 524, 523, 520, 509, 510, 519, 517, 516, 518, 515, 513, 512, 511, 506, 3244, 3245, 3260, 3261, 3255, 3256, 3254, 3253, 3250, 3247, 3249, 3251, 3258, 3259, 3777, 3781, 3785, 3784, 3786, 3766, 3765, 3778, 3805, 3804, 542, 539, 540, 685, 684, 683, 682, 680, 688, 687, 686, 679, 678, 689, 690, 694, 706, 700, 699, 698, 697, 696, 695, 705, 704, 715, 693, 691, 716, 717, 718, 720, 719, 711, 708, 709, 710, 713, 701, 702, 707, 712, 976, 989, 988, 990, 978, 977, 983, 982, 996, 995, 993, 986, 987, 992, 994, 984, 985, 979, 8043, 7988, 981, 997, 15476, 15472, 7944, 15473, 4475, 4476, 4470, 4471, 4472, 4481, 4479, 4473, 4474, 4477, 4478, 7833, 15495, 15510, 15509, 15531, 4573, 4572, 4578, 4579, 4581, 4577, 4576, 4575, 4574, 4571, 4570, 785, 809, 1387, 1390, 1382, 1383, 1397, 1395, 1394, 1398, 1378, 1379, 1396, 1388, 1389, 1399, 1391, 1384, 1385, 1380, 1377, 1376, 1393, 1386, 1369, 4614, 4608, 4607, 4606, 4610, 4609, 4597, 4596, 4598, 4599, 4615, 4595, 4594, 4600, 4601, 4603, 4604, 4605, 1310, 1311, 1334, 4988, 8099, 4925, 4915, 4914, 4917, 4916, 4919, 4918, 4920, 4926, 4937, 4932, 4933, 4938, 4939, 4934, 4936, 7611, 4940, 5028, 5029, 7722, 5041, 5044, 5043, 5039, 5040, 5042, 1139, 1154, 1153, 1149, 1150, 1151, 1146, 1147, 8332, 1155, 5345, 5342, 5341, 5387, 5388, 5389, 5484, 5470, 5471, 5486, 5398, 5397, 8199, 5391, 5396, 5394, 5395, 5399, 5393, 5392, 5400, 5427, 5420, 5415, 5403, 5404, 8266, 5428, 5411, 5413, 5406, 5405, 5407, 5429, 5430, 5408, 5409, 5414, 5416, 5417, 5426, 5424, 5425, 5431, 8299, 5402, 1182, 1179, 1173, 1174, 1178, 1180, 1184, 1168, 1167, 1166, 1165, 16002, 16003, 16004, 16009, 15979, 15978, 15980, 15992, 15987, 15986, 15985, 15996, 15995, 15993, 15984, 15983, 15982, 15974, 15973, 15991, 15990, 8310, 15989, 15998, 15997, 6294, 6292, 6293, 6120, 6208, 6305, 6304, 6302, 6298, 8243, 6297, 6296, 6295, 6303, 6554, 6551, 6552, 6558, 6555, 6550, 6543, 6544, 6548, 6549, 6539, 6540, 6546, 6547]]
+
+# vertex list
+#[0, 538, 539, 9029, 8992, 8991, 10470, 1, 10466, 10467, 10469, 10468, 10465, 10455, 10456, 10464, 10462, 10461, 10463, 10460, 10459, 10458, 10457, 10452, 12918, 12919, 12932, 12933, 12928, 12929, 12927, 12926, 12923, 12920, 12922, 12924, 12930, 12931, 13398, 13400, 13404, 13403, 13405, 13388, 13387, 13399, 13422, 13421, 10485, 10482, 10483, 10613, 10612, 10611, 10610, 10609, 10616, 10615, 10614, 10608, 10607, 10617, 10618, 10621, 10632, 10627, 10626, 10625, 10624, 10623, 10622, 10631, 10630, 10640, 10620, 10619, 10641, 10642, 10643, 10645, 10644, 10637, 10634, 10635, 10636, 10639, 10628, 10629, 10633, 10638, 10876, 10888, 10887, 10889, 10878, 10877, 10882, 10881, 10894, 10893, 10891, 10885, 10886, 10890, 10892, 10883, 10884, 10879, 24, 235, 10880, 10895, 9099, 9095, 231, 9096, 14025, 14026, 14020, 14021, 14022, 14030, 14029, 14023, 14024, 14027, 14028, 221, 9115, 9129, 9128, 9148, 14113, 14112, 14118, 14119, 14120, 14117, 14116, 14115, 14114, 14111, 14110, 10703, 10725, 11245, 11248, 11240, 11241, 11254, 11252, 11251, 11255, 11237, 11238, 11253, 11246, 11247, 11256, 11249, 11242, 11243, 11239, 11236, 11235, 11250, 11244, 11229, 14150, 14145, 14144, 14143, 14147, 14146, 14135, 14134, 14136, 14137, 14151, 14133, 14132, 14138, 14139, 14140, 14141, 14142, 11176, 11177, 11198, 14488, 245, 14430, 14421, 14420, 14423, 14422, 14425, 14424, 14426, 14431, 14441, 14437, 14438, 14442, 14443, 14439, 14440, 201, 14444, 14523, 14524, 211, 14535, 14538, 14537, 14533, 14534, 14536, 11021, 11035, 11034, 11030, 11031, 11032, 11028, 11029, 266, 11036, 14809, 14806, 14805, 14847, 14848, 14849, 14934, 14921, 14922, 14936, 14857, 14856, 254, 14850, 14855, 14853, 14854, 14858, 14852, 14851, 14859, 14883, 14877, 14872, 14861, 14862, 260, 14884, 14869, 14870, 14864, 14863, 14865, 14885, 14886, 14866, 14867, 14871, 14873, 14874, 14882, 14880, 14881, 14887, 263, 14860, 11060, 11058, 11052, 11053, 11057, 11059, 11062, 11048, 11047, 11046, 11045, 9572, 9573, 9574, 9579, 9551, 9550, 9552, 9563, 9559, 9558, 9557, 9567, 9566, 9564, 9556, 9555, 9554, 9547, 9546, 9562, 9561, 264, 9560, 9569, 9568, 15663, 15661, 15662, 15506, 15586, 15673, 15672, 15670, 15667, 258, 15666, 15665, 15664, 15671, 15898, 15895, 15896, 15900, 15899, 15894, 15888, 15889, 15892, 15893, 15884, 15885, 15890, 15891]

scripts/sources_sinks_routing/shortest_paths_pandana.py

@@ -0,0 +1,89 @@
+import pandana
+from igraph import Graph
+import geopandas as gpd
+from pandas import DataFrame
+import matplotlib.pyplot as plt
+import numpy as np
+
+gdf = gpd.read_file("jamaica_roads.gpkg")
+
+from_node = [int(node_id[6:]) for node_id in gdf["from_node"]]
+to_node = [int(node_id[6:]) for node_id in gdf["to_node"]]
+
+nids, idxs = np.unique(from_node + to_node, return_index=True)
+x = []
+y = []
+for nid, idx in zip(nids, idxs):
+    linestr = gdf.loc[idx % len(from_node), "geometry"]
+    coords = linestr.coords[int(-1 * (idx // len(from_node)))]
+    x.append(coords[0])
+    y.append(coords[1])
+
+nodes = DataFrame({"x": x, "y": y}, index=nids)
+edges = DataFrame(
+    {"from": from_node, "to": to_node, "weight": gdf["length_km"].values}
+)
+net = pandana.Network(
+    nodes["x"], nodes["y"], edges["from"], edges["to"], edges[["weight"]]
+)
+
+start_node_idx = 15891
+sp = net.shortest_path(0, start_node_idx, imp_name="weight")
+edges = gdf.loc[:, ["from_node", "to_node"]]
+g = Graph.DataFrame(edges, directed=False)
+igraph_nodes = [g.vs.find(name="roadn_"+str(i)).index for i in sp]
+epath = g.get_eids(path=igraph_nodes, directed=False)
+
+base = gdf.plot()
+for path in sp:
+    # Select linestrings according to edge IDs making the path
+    # edge ID are 1to1 with row id in dataframe
+    sub_gdf = gdf.iloc[epath, :]
+    sub_gdf.plot(ax=base, color="green", linewidth=5)
+plt.show()
+
+# edge list
+# [8649, 8646, 8643, 8642, 8592, 10194, 10192, 10193, 10187, 10190, 10189, 10188, 10186, 10177, 10191, 10184, 10182, 10183, 10185, 10180, 10179, 10178, 10181, 12907, 12897, 12911, 12912, 12913, 12906, 12909, 12905, 12904, 12900, 12899, 12901, 12908, 12910, 13394, 13397, 13406, 13403, 13404, 13407, 13387, 13395, 13427, 13428, 13426, 10211, 10208, 10351, 10346, 10350, 10345, 10344, 10349, 10348, 10354, 10347, 10343, 10352, 10353, 10356, 10365, 10364, 10361, 10360, 10359, 10358, 10357, 10367, 10363, 10374, 10379, 10355, 10380, 10375, 10376, 10378, 10381, 10377, 10370, 10368, 10369, 10372, 10373, 10362, 10366, 10371, 10632, 10646, 10641, 10650, 10642, 10633, 10649, 10637, 10651, 10648, 10652, 10644, 10640, 10643, 10645, 10647, 10638, 10639, 10634, 10635, 10636, 10654, 10653, 8735, 8731, 8732, 14077, 14084, 14078, 14074, 14075, 14086, 14085, 14082, 14076, 14083, 14079, 14080, 14081, 8811, 8764, 8778, 14180, 14179, 14185, 14186, 14187, 14188, 14184, 14183, 14182, 14181, 14178, 14177, 10462, 11025, 11027, 11028, 11021, 11037, 11035, 11032, 11040, 11036, 11019, 11033, 11034, 11026, 11038, 11039, 11029, 11022, 11023, 11020, 11018, 11031, 11030, 11024, 14221, 14220, 14213, 14212, 14216, 14214, 14218, 14202, 14203, 14204, 14225, 14222, 14201, 14205, 14208, 14207, 14209, 14211, 14210, 10958, 10976, 14576, 14578, 14577, 14517, 14504, 14513, 14505, 14510, 14506, 14507, 14512, 14524, 14526, 14522, 14525, 14527, 14528, 14523, 14529, 14530, 14624, 14607, 14628, 14629, 14632, 14634, 14631, 14627, 14630, 14633, 10813, 10810, 10809, 10806, 10807, 10812, 10805, 10817, 10818, 14926, 14917, 14914, 14958, 14959, 14960, 15055, 15054, 15038, 15059, 15060, 14966, 14965, 14961, 14970, 14964, 14963, 14967, 14968, 14962, 14969, 15003, 14991, 14984, 14980, 14972, 14992, 14993, 15004, 14978, 14985, 14973, 14974, 15002, 14994, 14996, 14975, 14979, 14987, 14981, 14995, 14990, 14989, 14997, 14998, 14999, 14971, 10837, 10835, 10829, 10834, 10836, 10839, 10841, 10825, 10833, 10824, 10827, 9219, 9220, 9227, 9226, 9197, 9198, 9216, 9207, 9203, 9202, 9214, 9225, 9210, 9208, 9201, 9200, 9211, 9195, 9212, 9206, 9205, 9204, 9232, 9215, 15817, 15818, 15815, 15816, 15814, 15829, 15991, 15990, 15996, 15993, 15992, 15999, 15997, 15994, 16000, 15995, 15987, 15986, 15985, 16001, 15998, 15988, 15989, 15984, 16009, 16012, 16013, 16010, 16011, 16007, 16005, 16006, 16004, 16003, 16002, 16008, 16098, 16100, 16105, 16102, 16083, 16085, 16086, 16088, 16087, 16099, 16091, 16173, 16174, 16179, 16177, 16171, 16178, 16168, 16172, 16176, 16169]
+
+# vertex list
+## [    0,   538,   539,  9029,  8992,  8991, 10470,     1, 10466,
+#       10467, 10469, 10468, 10465, 10455, 10456, 10464, 10462, 10461,
+#       10463, 10460, 10459, 10458, 10457, 10452, 12918, 12919, 12932,
+#       12933, 12928, 12929, 12927, 12926, 12923, 12920, 12922, 12924,
+#       12930, 12931, 13398, 13400, 13404, 13403, 13405, 13388, 13387,
+#       13399, 13422, 13421, 10485, 10482, 10483, 10613, 10612, 10611,
+#       10610, 10609, 10616, 10615, 10614, 10608, 10607, 10617, 10618,
+#       10621, 10632, 10627, 10626, 10625, 10624, 10623, 10622, 10631,
+#       10630, 10640, 10620, 10619, 10641, 10642, 10643, 10645, 10644,
+#       10637, 10634, 10635, 10636, 10639, 10628, 10629, 10633, 10638,
+#       10876, 10888, 10887, 10889, 10878, 10877, 10882, 10881, 10894,
+#       10893, 10891, 10885, 10886, 10890, 10892, 10883, 10884, 10879,
+#          24,   235, 10880, 10895,  9099,  9095,   231,  9096, 14025,
+#       14026, 14020, 14021, 14022, 14030, 14029, 14023, 14024, 14027,
+#       14028,   221,  9115,  9129,  9128,  9148, 14113, 14112, 14118,
+#       14119, 14120, 14117, 14116, 14115, 14114, 14111, 14110, 10703,
+#       10725, 11245, 11248, 11240, 11241, 11254, 11252, 11251, 11255,
+#       11237, 11238, 11253, 11246, 11247, 11256, 11249, 11242, 11243,
+#       11239, 11236, 11235, 11250, 11244, 11229, 14150, 14145, 14144,
+#       14143, 14147, 14146, 14135, 14134, 14136, 14137, 14151, 14133,
+#       14132, 14138, 14139, 14140, 14141, 14142, 11176, 11177, 11198,
+#       14488,   245, 14430, 14421, 14420, 14423, 14422, 14425, 14424,
+#       14426, 14431, 14441, 14437, 14438, 14442, 14443, 14439, 14440,
+#         201, 14444, 14523, 14524,   211, 14535, 14538, 14537, 14533,
+#       14534, 14536, 11021, 11035, 11034, 11030, 11031, 11032, 11028,
+#       11029,   266, 11036, 14809, 14806, 14805, 14847, 14848, 14849,
+#       14934, 14921, 14922, 14936, 14857, 14856,   254, 14850, 14855,
+#       14853, 14854, 14858, 14852, 14851, 14859, 14883, 14877, 14872,
+#       14861, 14862,   260, 14884, 14869, 14870, 14864, 14863, 14865,
+#       14885, 14886, 14866, 14867, 14871, 14873, 14874, 14882, 14880,
+#       14881, 14887,   263, 14860, 11060, 11058, 11052, 11053, 11057,
+#       11059, 11062, 11048, 11047, 11046, 11045,  9572,  9573,  9574,
+#        9579,  9551,  9550,  9552,  9563,  9559,  9558,  9557,  9567,
+#        9566,  9564,  9556,  9555,  9554,  9547,  9546,  9562,  9561,
+#         264,  9560,  9569,  9568, 15663, 15661, 15662, 15506, 15586,
+#       15673, 15821, 15820, 15824, 15823, 15822, 15828, 15826, 15825,
+#       15827, 15817, 15816, 15815, 15814, 15829, 15819, 15818, 15813,
+#       15812, 15836, 15839, 15838, 15837, 15834, 15833, 15832, 15831,
+#         506,   507, 15830, 15835, 15920, 15921, 15923, 15906, 15905,
+#       15908, 15909, 15911, 15910, 15915, 15914, 15987, 15988, 15989,
+#       15985, 15984, 15980, 15979, 15986, 15982, 15981])