Here is the path animation of the below script I generated differently: (It took around 3 hrs... for the below script to run, taking one picture at each step which I used to generate this smooth video) https://www.youtube.com/watch?v=-ObZcSWGhdM
Slow render of a graph path using python matplotlib animation
I am computing shortest paths of graphs using Python Networkx and Matplotlib to animating paths. I'm focused on animating path for the route inspection problem in particular here. My goal is to smoothly render such animation to visually verify the path and ultimately save the animation in mp4 via FFmpeg (but that's another topic).
My issue is that the animation is not smooth at all for the graph and path I'm currently working on: 418 nodes, 558 edges and a path of 783 steps.
Given that I have a fast computer, I believe my code can clearly be improved or, unlikely, I have reached the limit of matplotlib for my use-case.
The below code runs correctly but as you may see, it draws the path slowly and slower at each path step. I think it takes me around 30' to draw 300 steps.
I have seen that the code runs faster if I'm now displaying the nodes labels so they are not displayed with this code.
Because I only want to change the color of edges and nodes, there might be a more efficient approach than mine as I believe I'm drawing the entire graph at each step. I don't know how to just change the color of nodes/edges without redrawing everything as I'm new to all of that.
Code:
# Import libraries
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
# Define Graph
G = nx.Graph()
G.add_edges_from([(1, 3, {'weight': 99}), (2, 3, {'weight': 73}), (3, 4, {'weight': 45}), (4, 104, {'weight': 166}), (4, 6, {'weight': 89}), (6, 103, {'weight': 127}), (103, 104, {'weight': 76}), (104, 106, {'weight': 45}), (106, 108, {'weight': 54}), (108, 110, {'weight': 49}), (110, 115, {'weight': 39}), (110, 111, {'weight': 85}), (111, 113, {'weight': 49}), (111, 114, {'weight': 31}), (114, 115, {'weight': 82}), (108, 109, {'weight': 203}), (106, 107, {'weight': 218}), (104, 105, {'weight': 229}), (105, 107, {'weight': 47}), (107, 109, {'weight': 69}), (5, 6, {'weight': 222}), (5, 8, {'weight': 49}), (7, 8, {'weight': 28}), (8, 13, {'weight': 107}), (13, 14, {'weight': 80}), (12, 13, {'weight': 80}), (14, 15, {'weight': 81}), (15, 10, {'weight': 63}), (9, 10, {'weight': 33}), (10, 11, {'weight': 127}), (6, 11, {'weight': 96}), (11, 17, {'weight': 68}), (26, 103, {'weight': 213}), (15, 16, {'weight': 69}), (16, 17, {'weight': 62}), (17, 25, {'weight': 76}), (16, 23, {'weight': 102}), (23, 24, {'weight': 31}), (24, 31, {'weight': 25}), (22, 23, {'weight': 75}), (22, 15, {'weight': 112}), (13, 20, {'weight': 124}), (14, 21, {'weight': 116}), (20, 21, {'weight': 79}), (21, 22, {'weight': 77}), (18, 19, {'weight': 74}), (19, 20, {'weight': 80}), (12, 19, {'weight': 119}), (35, 22, {'weight': 99}), (34, 35, {'weight': 15}), (33, 34, {'weight': 137}), (32, 33, {'weight': 46}), (29, 32, {'weight': 39}), (29, 100, {'weight': 181}), (100, 101, {'weight': 51}), (27, 101, {'weight': 162}), (27, 28, {'weight': 31}), (26, 27, {'weight': 36}), (25, 26, {'weight': 19}), (25, 30, {'weight': 30}), (28, 30, {'weight': 20}), (28, 29, {'weight': 50}), (32, 51, {'weight': 197}), (50, 51, {'weight': 99}), (33, 50, {'weight': 188}), (46, 50, {'weight': 150}), (44, 46, {'weight': 75}), (44, 45, {'weight': 52}), (35, 44, {'weight': 114}), (43, 46, {'weight': 71}), (35, 36, {'weight': 28}), (36, 37, {'weight': 16}), (37, 38, {'weight': 123}), (38, 40, {'weight': 95}), (40, 41, 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# Define nodes gps coords for plotting
nodes_coords = [[2.2562154, 48.9010192], [2.2552395, 48.9016349], [2.2561504, 48.9019072], [2.256213, 48.9023117], [2.2533483, 48.9022124], [2.2560583, 48.9031052], [2.2526502, 48.9024669], [2.2529865, 48.9025795], [2.2539978, 48.9033062], [2.254378, 48.90346], [2.2559377, 48.9039617], [2.2512099, 48.9031913], [2.2522591, 48.9034114], [2.2532497, 48.9037264], [2.2542639, 48.9040196], [2.255091, 48.9043102], [2.2558433, 48.904568], [2.2496848, 48.903958], [2.2506347, 48.9041945], [2.2516522, 48.9044511], [2.252662, 48.9046991], [2.2536487, 48.9049375], [2.2546109, 48.9051719], [2.2549951, 48.905281], [2.2557699, 48.9052503], [2.2560158, 48.9052059], [2.2562064, 48.9055009], [2.2558068, 48.9056019], [2.2557576, 48.9060545], [2.2555949, 48.9054919], [2.2550966, 48.9054932], [2.2556871, 48.9064052], [2.2550969, 48.9062517], [2.2533605, 48.9057877], [2.2531638, 48.9057675], [2.2527857, 48.9058181], [2.2527457, 48.9056766], [2.2511718, 48.9052887], [2.2497184, 48.9049283], [2.2507196, 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48.9064429], [2.2724316, 48.9062142], [2.2713243, 48.905923], [2.270826, 48.905793], [2.2693245, 48.9053044], [2.2691194, 48.9050719], [2.2689375, 48.9049471], [2.26872, 48.9049211], [2.2686013, 48.9049653], [2.2683007, 48.9049965], [2.2679048, 48.9055527], [2.2679447, 48.9058675], [2.2681243, 48.9060031], [2.2687562, 48.9062129], [2.2690023, 48.9059987], [2.2709032, 48.9062137], [2.2719633, 48.9064763], [2.2729046, 48.9066791], [2.2735216, 48.906783], [2.2733397, 48.9070794], [2.2695981, 48.9065442], [2.2703734, 48.9068299], [2.2713859, 48.9071938], [2.2721611, 48.9075058], [2.2726171, 48.9076682], [2.2743091, 48.908283], [2.2741894, 48.9090284], [2.2736517, 48.9089442], [2.2719104, 48.9084966], [2.2705944, 48.9077431], [2.2699098, 48.9073435], [2.2692446, 48.9069632], [2.2685289, 48.9065601], [2.2672885, 48.9059808], [2.2665336, 48.9061732], [2.2663407, 48.9064377], [2.2674448, 48.9068071], [2.2670989, 48.9072378], [2.2661648, 48.9069225], [2.2658755, 48.9073837], [2.2646118, 48.9072657], [2.2640264, 48.9071848], [2.2638568, 48.9071498], [2.2667601, 48.9076832], [2.2670129, 48.9077859], [2.267392, 48.9079083], [2.2677347, 48.9080464], [2.2682086, 48.9082068], [2.2685359, 48.9077867], [2.2692974, 48.9080752], [2.2702284, 48.9078244], [2.2704685, 48.9082794], [2.2730951, 48.9094461], [2.2743286, 48.9094969], [2.2739111, 48.9098387], [2.2743133, 48.9100381], [2.273694, 48.910819], [2.2731767, 48.910479], [2.272831, 48.9108932], [2.2727534, 48.9111977], [2.2727417, 48.911445], [2.2722382, 48.9116514], [2.2721749, 48.9109912], [2.2716607, 48.9110588], [2.2726719, 48.9098062], [2.2723442, 48.9101019], [2.2721867, 48.9102008], [2.2712374, 48.9092823], [2.2707741, 48.9091386], [2.2699911, 48.9088482], [2.269676, 48.9087415], [2.2695043, 48.9086658], [2.2691657, 48.9085576], [2.2690081, 48.9084865], [2.2686766, 48.9083768], [2.2684112, 48.9090522], [2.2682322, 48.9093352], [2.2679432, 48.9091509], [2.2673895, 48.9089533], [2.2666618, 48.9087454], [2.2657314, 48.9080209], [2.2652728, 48.9079841], [2.2642436, 48.9078518], [2.2641038, 48.9083003], [2.2645233, 48.9083297], [2.2654909, 48.9087377], [2.2650826, 48.9087598], [2.2648477, 48.9087818], [2.2646743, 48.9094215], [2.2653231, 48.9091972], [2.2663454, 48.9095824], [2.2675951, 48.9099931], [2.2679669, 48.9101127], [2.2684889, 48.9102634], [2.2689398, 48.9104142], [2.2691929, 48.9103674], [2.2704031, 48.9102634], [2.268133, 48.9106117], [2.266116, 48.9105857], [2.2654515, 48.9103986], [2.2655702, 48.910045], [2.2649532, 48.910279], [2.2643667, 48.9097486], [2.2642828, 48.9102412], [2.2647414, 48.9104875], [2.2653231, 48.9108514], [2.2658544, 48.9111381], [2.2659903, 48.9118675], [2.2666964, 48.911759], [2.2674906, 48.911633], [2.2681547, 48.911525], [2.2675522, 48.9122404], [2.2667854, 48.9125239], [2.2662651, 48.9127309], [2.2663336, 48.9132483], [2.2654573, 48.9138647], [2.2654093, 48.9134058], [2.2648206, 48.9134688], [2.264711, 48.9126499], [2.2655463, 48.9119615], [2.2651766, 48.9120335], [2.2650328, 48.912029], [2.2643334, 48.9109694], [2.264285, 48.9113353], [2.2635177, 48.9114514], [2.2644235, 48.912083], [2.2637089, 48.9121768], [2.2638868, 48.9131596], [2.2633592, 48.9136496], [2.2625931, 48.914287], [2.2616662, 48.9143797], [2.2628752, 48.9136949], [2.262475, 48.9137271], [2.2631873, 48.912824], [2.2626075, 48.9123158], [2.2622102, 48.912382], [2.261987, 48.9129747], [2.2615453, 48.9137895], [2.2597952, 48.9137838], [2.2618976, 48.9124362], [2.2616237, 48.9124767], [2.2613122, 48.9127984], [2.260833, 48.9127939], [2.2620653, 48.9118306], [2.2619261, 48.9115686], [2.2626904, 48.9111419], [2.2631283, 48.9103252], [2.2631915, 48.9096595], [2.264121, 48.9094753], [2.2638475, 48.9094188], [2.2639647, 48.9088819], [2.263549, 48.9089396], [2.2632847, 48.9089795], [2.2634289, 48.9085191], [2.2631399, 48.9084874], [2.2633015, 48.9079316], [2.2630093, 48.9078815], [2.2630574, 48.9076736], [2.2626826, 48.9077091], [2.2622566, 48.9077451], [2.2619322, 48.9077271], [2.2611271, 48.9075653], [2.2608027, 48.9076911], [2.2637496, 48.907593], [2.2635595, 48.9079466], [2.2637069, 48.9079082], [2.2622918, 48.9090983], [2.26203, 48.9092935], [2.2619401, 48.9090983], [2.2618736, 48.9091266], [2.2616389, 48.9100996], [2.2615685, 48.9104721], [2.2608181, 48.9105465], [2.2610331, 48.9112761], [2.2611308, 48.9116434], [2.261205, 48.9121854], [2.2614834, 48.9123754], [2.2606032, 48.9121648], [2.2606618, 48.9125244], [2.260271, 48.9121288], [2.2602827, 48.911443], [2.2598567, 48.9114096], [2.2598059, 48.9107931], [2.259802, 48.9106698], [2.2590789, 48.9107418], [2.2587819, 48.9128198], [2.2597746, 48.9126914], [2.2599935, 48.9132462], [2.2596456, 48.9134491], [2.2589304, 48.9143764], [2.2604515, 48.9144591], [2.2584497, 48.913765], [2.2579142, 48.9137599], [2.2586938, 48.9091509], [2.2581521, 48.9084752], [2.2588707, 48.9077377], [2.2590973, 48.9077195], [2.2594786, 48.9076941], [2.2597218, 48.9075633], [2.2593184, 48.9074253], [2.2600977, 48.9074689], [2.2608768, 48.9082346], [2.2606398, 48.9081958], [2.2600977, 48.9083957], [2.2598916, 48.9085872], [2.2593847, 48.9080465], [2.2595223, 48.9082404], [2.2592447, 48.9084144], [2.2594851, 48.9084943], [2.2595212, 48.9086655], [2.2604166, 48.9091216], [2.2601929, 48.9091362], [2.2599347, 48.9088928], [2.260161, 48.908944], [2.2603271, 48.9088699], [2.2747081, 48.9069078], [2.2584055, 48.9093463], [2.2557686, 48.912472], [2.2590996, 48.9081554]]
# Define path we want to aniate as a list of nodes to travel through
path = [102, 125, 126, 124, 129, 130, 131, 123, 109, 123, 124, 126, 125, 400, 398, 397, 405, 406, 403, 404, 412, 413, 414, 402, 403, 406, 408, 409, 408, 407, 418, 405, 418, 394, 393, 409, 412, 413, 411, 416, 411, 410, 401, 410, 120, 369, 368, 359, 368, 366, 358, 357, 356, 355, 354, 353, 364, 365, 364, 353, 354, 355, 356, 357, 358, 357, 363, 242, 241, 351, 350, 349, 348, 349, 350, 287, 288, 287, 286, 281, 286, 285, 280, 281, 282, 240, 282, 283, 284, 283, 350, 351, 352, 351, 241, 240, 239, 238, 235, 234, 233, 211, 210, 209, 191, 116, 190, 188, 190, 116, 192, 193, 194, 205, 194, 195, 174, 173, 169, 168, 165, 163, 160, 163, 165, 166, 165, 186, 165, 150, 149, 153, 152, 153, 154, 163, 164, 161, 164, 167, 162, 167, 166, 170, 171, 170, 169, 168, 172, 173, 174, 175, 176, 178, 180, 178, 179, 178, 176, 177, 198, 200, 199, 200, 198, 197, 176, 175, 196, 197, 201, 200, 201, 202, 203, 202, 196, 195, 203, 204, 205, 206, 207, 192, 116, 191, 209, 210, 211, 212, 236, 235, 238, 237, 243, 237, 236, 212, 213, 214, 215, 216, 215, 221, 220, 231, 248, 231, 220, 213, 232, 231, 232, 245, 232, 213, 212, 211, 210, 209, 208, 207, 206, 193, 172, 187, 188, 189, 188, 187, 186, 187, 186, 185, 150, 154, 158, 159, 158, 157, 156, 155, 156, 152, 151, 148, 147, 146, 147, 148, 149, 148, 184, 185, 189, 183, 184, 182, 181, 182, 144, 145, 144, 143, 144, 142, 141, 142, 181, 183, 136, 137, 136, 135, 134, 135, 140, 139, 138, 139, 140, 122, 121, 114, 111, 113, 111, 110, 115, 114, 121, 141, 140, 135, 133, 132, 239, 243, 244, 279, 278, 246, 278, 277, 291, 277, 247, 248, 249, 273, 249, 230, 231, 230, 221, 222, 216, 217, 218, 415, 218, 219, 218, 217, 223, 222, 223, 224, 223, 229, 230, 229, 228, 224, 225, 227, 228, 229, 250, 271, 272, 273, 274, 275, 274, 247, 246, 245, 244, 279, 290, 289, 290, 291, 292, 276, 292, 293, 294, 293, 272, 271, 270, 295, 270, 269, 251, 269, 268, 267, 228, 227, 226, 227, 252, 264, 252, 254, 253, 254, 255, 254, 257, 265, 257, 258, 259, 256, 259, 260, 261, 262, 263, 262, 266, 265, 264, 267, 268, 296, 266, 296, 295, 294, 297, 298, 299, 300, 299, 301, 303, 302, 303, 346, 351, 352, 366, 352, 117, 347, 346, 345, 346, 371, 370, 371, 372, 367, 369, 120, 401, 402, 362, 361, 126, 127, 360, 127, 128, 242, 130, 132, 239, 280, 285, 289, 301, 304, 321, 320, 319, 305, 319, 307, 308, 312, 308, 309, 311, 309, 310, 309, 308, 307, 306, 307, 319, 320, 316, 314, 313, 314, 316, 317, 318, 317, 328, 333, 328, 331, 334, 335, 332, 331, 334, 326, 327, 326, 324, 326, 325, 323, 322, 323, 325, 320, 316, 315, 329, 330, 390, 330, 337, 338, 337, 341, 337, 332, 335, 339, 336, 339, 340, 341, 342, 387, 388, 391, 392, 391, 388, 389, 388, 387, 342, 112, 342, 341, 340, 376, 112, 375, 374, 344, 374, 373, 372, 373, 345, 343, 375, 343, 376, 375, 378, 386, 378, 377, 379, 380, 118, 381, 118, 386, 385, 66, 64, 63, 64, 65, 64, 66, 67, 62, 67, 68, 70, 78, 79, 80, 81, 80, 83, 85, 84, 78, 70, 71, 72, 74, 72, 73, 72, 71, 66, 385, 75, 76, 75, 381, 382, 383, 382, 119, 384, 77, 81, 82, 83, 85, 86, 87, 86, 59, 58, 59, 57, 59, 60, 59, 61, 87, 82, 88, 93, 416, 393, 394, 97, 94, 95, 92, 93, 88, 89, 90, 55, 61, 59, 69, 417, 69, 68, 70, 71, 76, 77, 384, 383, 372, 367, 366, 358, 359, 360, 361, 362, 400, 399, 398, 397, 396, 399, 396, 395, 98, 99, 98, 97, 94, 93, 92, 91, 96, 91, 56, 51, 56, 55, 53, 54, 53, 52, 53, 49, 48, 49, 50, 46, 47, 46, 43, 46, 44, 45, 44, 35, 36, 40, 42, 40, 41, 40, 38, 39, 38, 37, 36, 37, 38, 20, 19, 18, 19, 12, 13, 20, 21, 14, 21, 22, 35, 34, 33, 50, 51, 32, 99, 100, 101, 27, 28, 29, 28, 30, 31, 30, 25, 26, 27, 26, 103, 26, 25, 17, 11, 17, 16, 23, 22, 15, 10, 9, 10, 11, 6, 5, 8, 7, 8, 13, 14, 15, 16, 23, 24, 31, 34, 33, 32, 29, 100, 101, 102, 105, 107, 106, 107, 109, 108, 110, 108, 106, 104, 103, 6, 4, 3, 2, 3, 1, 3, 4, 104, 105, 102]
#path = [102, 125, 126, 127, 128, 242] # simpler path for testing
# SIMPLE graph definition (for testing)
#G = nx.Graph()
#G.add_edges_from([(1,2, {'weight': 1}), (3,4, {'weight': 1}), (2,5, {'weight': 1}), (4,5, {'weight': 1}), (6,7, {'weight': 1}), (8,9, {'weight': 1}), (4,7, {'weight': 1}), (1,7, {'weight': 1}), (3,5, {'weight': 1}), (2,7, {'weight': 1}), (5,8, {'weight': 1}), (2,9, {'weight': 1}), (5,1, {'weight': 1})])
#nodes_coords = [[1,2], [1,5], [6,5], [3,5], [4,1], [6,2], [2,8], [3,3], [2,5]]
#path = [6, 7, 2, 9, 8, 5, 2, 1, 5, 3, 4, 7, 1, 5, 4]
# Define the list of edges and store nodes coords as a parameter
edges = [e for e in G.edges]
for u, v in enumerate(nodes_coords):
G.nodes[u+1]['pos'] = (v[0], v[1])
# Draw the graph before animating the path
edges_color = ["#d9d9d9" for u in G.edges]
nodes_color = ["#a7a7a7" for u in G.nodes]
nx.draw(G, nx.get_node_attributes(G, 'pos'), node_size=140, font_size=6, width=2, node_color=nodes_color, edge_color=edges_color)
#nx.draw(G, nx.get_node_attributes(G, 'pos'), with_labels=True, node_size=140, font_size=6, width=2, node_color=nodes_color, edge_color=edges_color) # with nodes labels -> much slower
fig = plt.gcf()
# Animation function
def animate(frame):
global nodes_color, edges_color
# Color in red the currently visited path node
nodes_color = ["red" if (u == path[frame]) else "#a7a7a7" for u in G.nodes]
# Set the new visited edge of the path in green (cumulative coloring of what has been visited)
if (frame > 0):
try:
edges_color[edges.index((min(path[frame-1],path[frame]), max(path[frame-1],path[frame])))] = "green"
except ValueError:
edges_color[edges.index((max(path[frame-1],path[frame]), min(path[frame-1],path[frame])))] = "green"
else:
time.sleep(3) # to allow me to increase the the matplotlib graph window before starting to draw the path
edges_color = ["#d9d9d9" for u in G.edges] # all edges in grey before showing the circuit in green
nx.draw(G, nx.get_node_attributes(G, 'pos'), node_size=140, font_size=6, width=2, node_color=nodes_color, edge_color=edges_color)
#plt.savefig("graph-" + str(frame) + ".jpg", quality=100, optimize=True,) # save image of the current graph - for testing
#nx.draw(G, nx.get_node_attributes(G, 'pos'), with_labels=True, node_size=140, font_size=6, width=2, node_color=nodes_color, edge_color=edges_color) # with nodes labels -> much slower
anim = animation.FuncAnimation(fig, animate, frames=len(path), interval=200, blit=False, repeat=False, cache_frame_data=False)
plt.show()
If you want to test with a simpler graph/path, you can comment the first block "Define Graph" and uncomment the next one "SIMPLE graph definition (for testing)".
Here is how smooth it looks with this small test graph and path:
For the big graph/path, I cannot save it to show you.