INFO: Pandarallel will run on 8 workers.
INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.

 Selecting 5.0% vehicles from 124261 vehicles
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/00_traj_1344.5.json.gz
Loading a file takes 54.99s
Creating data frames takes 20.43s
Filling data types and values takes 5.81s

 Processing a file takes 18.87s
Concatenating time series takes 0.47s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/01_traj_2230.5.json.gz
Loading a file takes 55.79s
Creating data frames takes 18.90s
Filling data types and values takes 5.01s

 Processing a file takes 18.30s
Concatenating time series takes 0.28s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/02_traj_3066.0.json.gz
Loading a file takes 49.33s
Creating data frames takes 18.32s
Filling data types and values takes 4.94s

 Processing a file takes 17.73s
Concatenating time series takes 0.31s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/03_traj_3860.5.json.gz
Loading a file takes 48.28s
Creating data frames takes 18.56s
Filling data types and values takes 5.12s

 Processing a file takes 17.97s
Concatenating time series takes 0.38s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/04_traj_4653.5.json.gz
Loading a file takes 52.41s
Creating data frames takes 18.82s
Filling data types and values takes 5.06s

 Processing a file takes 17.52s
Concatenating time series takes 0.45s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/05_traj_5432.0.json.gz
Loading a file takes 50.93s
Creating data frames takes 19.13s
Filling data types and values takes 5.35s

 Processing a file takes 28.74s
Concatenating time series takes 0.45s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/06_traj_6200.5.json.gz
Loading a file takes 59.32s
Creating data frames takes 20.80s
Filling data types and values takes 5.32s

 Processing a file takes 32.61s
Concatenating time series takes 0.58s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/07_traj_6942.5.json.gz
Loading a file takes 60.89s
Creating data frames takes 22.97s
Filling data types and values takes 5.65s

 Processing a file takes 18.38s
Concatenating time series takes 0.68s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/08_traj_9937.5.json.gz
Loading a file takes 54.91s
Creating data frames takes 20.95s
Filling data types and values takes 5.69s

 Processing a file takes 18.53s
Concatenating time series takes 0.49s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/trajectory/09_traj_end.json.gz
Loading a file takes 9.57s
Creating data frames takes 3.18s
Filling data types and values takes 0.61s

 Processing a file takes 4.87s
Concatenating time series takes 0.14s
Saving all the section time series takes 76.15s
Saving all the junction time series takes 10.55s
Saving network entrance and exit takes 0.08s
Aggregating the raw statistics to different intervals...

  0%|          | 0/1522 [00:00<?, ?it/s]
  0%|          | 1/1522 [00:00<09:05,  2.79it/s]
  1%|          | 8/1522 [00:00<01:19, 19.12it/s]
  4%|▍         | 63/1522 [00:00<00:09, 156.60it/s]
  7%|▋         | 104/1522 [00:00<00:06, 224.09it/s]
  9%|▉         | 136/1522 [00:00<00:06, 201.94it/s]
 11%|█         | 163/1522 [00:01<00:06, 209.69it/s]
 12%|█▏        | 189/1522 [00:01<00:06, 218.80it/s]
 14%|█▍        | 218/1522 [00:01<00:05, 236.68it/s]
 16%|█▌        | 245/1522 [00:01<00:05, 243.85it/s]
 18%|█▊        | 272/1522 [00:01<00:05, 243.50it/s]
 20%|█▉        | 298/1522 [00:01<00:05, 220.53it/s]
 21%|██        | 322/1522 [00:02<00:17, 70.05it/s] 
 23%|██▎       | 350/1522 [00:02<00:12, 91.47it/s]
 25%|██▌       | 388/1522 [00:02<00:08, 126.98it/s]
 28%|██▊       | 419/1522 [00:02<00:07, 152.93it/s]
 29%|██▉       | 447/1522 [00:02<00:06, 172.55it/s]
 31%|███       | 473/1522 [00:03<00:05, 179.56it/s]
 33%|███▎      | 497/1522 [00:03<00:05, 179.58it/s]
 34%|███▍      | 520/1522 [00:03<00:05, 171.67it/s]
 36%|███▌      | 541/1522 [00:03<00:05, 166.24it/s]
 37%|███▋      | 561/1522 [00:03<00:05, 170.99it/s]
 38%|███▊      | 584/1522 [00:03<00:05, 183.81it/s]
 40%|███▉      | 604/1522 [00:03<00:05, 166.26it/s]
 41%|████▏     | 629/1522 [00:03<00:04, 185.26it/s]
 43%|████▎     | 656/1522 [00:04<00:04, 205.84it/s]
 45%|████▍     | 678/1522 [00:04<00:04, 208.35it/s]
 46%|████▌     | 700/1522 [00:04<00:04, 195.20it/s]
 47%|████▋     | 721/1522 [00:04<00:04, 198.05it/s]
 49%|████▉     | 745/1522 [00:04<00:03, 207.38it/s]
 50%|█████     | 767/1522 [00:04<00:03, 195.40it/s]
 52%|█████▏    | 790/1522 [00:04<00:03, 204.61it/s]
 53%|█████▎    | 811/1522 [00:04<00:03, 193.80it/s]
 55%|█████▌    | 839/1522 [00:04<00:03, 214.63it/s]
 57%|█████▋    | 864/1522 [00:05<00:02, 224.06it/s]
 59%|█████▊    | 891/1522 [00:05<00:02, 235.19it/s]
 60%|██████    | 915/1522 [00:05<00:02, 221.01it/s]
 62%|██████▏   | 941/1522 [00:05<00:02, 230.27it/s]
 64%|██████▎   | 969/1522 [00:05<00:02, 236.65it/s]
 65%|██████▌   | 993/1522 [00:05<00:02, 236.80it/s]
 67%|██████▋   | 1017/1522 [00:05<00:02, 236.90it/s]
 68%|██████▊   | 1041/1522 [00:05<00:02, 222.45it/s]
 70%|██████▉   | 1064/1522 [00:05<00:02, 222.19it/s]
 71%|███████▏  | 1088/1522 [00:06<00:01, 225.75it/s]
 73%|███████▎  | 1111/1522 [00:06<00:01, 222.36it/s]
 75%|███████▍  | 1134/1522 [00:06<00:01, 208.47it/s]
 76%|███████▋  | 1164/1522 [00:06<00:01, 232.38it/s]
 78%|███████▊  | 1188/1522 [00:06<00:01, 234.14it/s]
 80%|███████▉  | 1212/1522 [00:06<00:01, 229.81it/s]
 81%|████████▏ | 1240/1522 [00:06<00:01, 236.35it/s]
 83%|████████▎ | 1270/1522 [00:06<00:01, 250.60it/s]
 85%|████████▌ | 1297/1522 [00:06<00:00, 254.17it/s]
 87%|████████▋ | 1323/1522 [00:07<00:00, 239.28it/s]
 89%|████████▊ | 1348/1522 [00:07<00:00, 233.63it/s]
 90%|█████████ | 1372/1522 [00:08<00:02, 57.71it/s] 
 92%|█████████▏| 1393/1522 [00:08<00:01, 69.66it/s]
 94%|█████████▍| 1428/1522 [00:08<00:00, 99.88it/s]
 95%|█████████▌| 1450/1522 [00:08<00:00, 113.35it/s]
 97%|█████████▋| 1472/1522 [00:08<00:00, 129.74it/s]
 99%|█████████▉| 1504/1522 [00:08<00:00, 160.59it/s]
100%|██████████| 1522/1522 [00:08<00:00, 170.11it/s]
Constructing time series for each modality ...
Saving aggregated time series to /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_015/timeseries/agg_timeseries.pkl
Extracting samples from time series ...
number of samples: 20
Packing the samples into numpy arrays ...