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 141875 vehicles
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/00_traj_1313.5.json.gz
Loading a file takes 50.92s
Creating data frames takes 37.94s
Filling data types and values takes 7.41s

 Processing a file takes 18.59s
Concatenating time series takes 0.45s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/01_traj_2037.5.json.gz
Loading a file takes 56.62s
Creating data frames takes 29.05s
Filling data types and values takes 5.60s

 Processing a file takes 17.65s
Concatenating time series takes 0.20s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/02_traj_2714.5.json.gz
Loading a file takes 55.44s
Creating data frames takes 19.95s
Filling data types and values takes 5.80s

 Processing a file takes 17.42s
Concatenating time series takes 0.30s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/03_traj_3370.0.json.gz
Loading a file takes 50.65s
Creating data frames takes 19.15s
Filling data types and values takes 5.40s

 Processing a file takes 18.26s
Concatenating time series takes 0.36s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/04_traj_4005.0.json.gz
Loading a file takes 58.77s
Creating data frames takes 20.73s
Filling data types and values takes 5.89s

 Processing a file takes 18.28s
Concatenating time series takes 0.43s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/05_traj_4608.5.json.gz
Loading a file takes 55.62s
Creating data frames takes 20.88s
Filling data types and values takes 6.08s

 Processing a file takes 18.94s
Concatenating time series takes 0.42s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/06_traj_5185.0.json.gz
Loading a file takes 58.17s
Creating data frames takes 20.68s
Filling data types and values takes 5.95s

 Processing a file takes 18.73s
Concatenating time series takes 0.66s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/07_traj_5723.5.json.gz
Loading a file takes 58.38s
Creating data frames takes 19.98s
Filling data types and values takes 5.74s

 Processing a file takes 19.44s
Concatenating time series takes 0.41s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/08_traj_6238.5.json.gz
Loading a file takes 58.34s
Creating data frames takes 19.71s
Filling data types and values takes 5.78s

 Processing a file takes 18.46s
Concatenating time series takes 0.57s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/09_traj_6739.5.json.gz
Loading a file takes 53.33s
Creating data frames takes 20.92s
Filling data types and values takes 5.89s

 Processing a file takes 17.97s
Concatenating time series takes 0.45s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/10_traj_7227.0.json.gz
Loading a file takes 60.20s
Creating data frames takes 21.81s
Filling data types and values takes 5.98s

 Processing a file takes 18.37s
Concatenating time series takes 1.02s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/11_traj_8264.5.json.gz
Loading a file takes 55.00s
Creating data frames takes 18.12s
Filling data types and values takes 5.25s

 Processing a file takes 16.44s
Concatenating time series takes 0.39s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/12_traj_14269.0.json.gz
Loading a file takes 44.97s
Creating data frames takes 17.89s
Filling data types and values takes 5.12s
/home/weijiang/Projects/Netsanut/scripts/data/simbarca/time_series_from_traj.py:424: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
  df = pd.concat([df, entering, exiting], ignore_index=True, copy=False)

 Processing a file takes 13.36s
Concatenating time series takes 0.25s
Working on file /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/trajectory/13_traj_end.json.gz
Loading a file takes 26.30s
Creating data frames takes 10.84s
Filling data types and values takes 5.44s

 Processing a file takes 6.23s
Concatenating time series takes 0.07s
Saving all the section time series takes 77.01s
Saving all the junction time series takes 11.37s
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<12:01,  2.11it/s]
  1%|          | 12/1522 [00:00<00:58, 25.70it/s]
  3%|▎         | 44/1522 [00:00<00:15, 95.64it/s]
  4%|▍         | 66/1522 [00:00<00:11, 125.40it/s]
  6%|▌         | 85/1522 [00:00<00:11, 126.32it/s]
  7%|▋         | 105/1522 [00:01<00:09, 144.07it/s]
  8%|▊         | 124/1522 [00:01<00:09, 145.85it/s]
  9%|▉         | 142/1522 [00:01<00:09, 145.07it/s]
 10%|█         | 158/1522 [00:01<00:10, 131.60it/s]
 11%|█▏        | 173/1522 [00:01<00:10, 132.58it/s]
 12%|█▏        | 188/1522 [00:01<00:09, 136.87it/s]
 13%|█▎        | 203/1522 [00:01<00:10, 130.32it/s]
 14%|█▍        | 217/1522 [00:01<00:10, 128.73it/s]
 15%|█▌        | 232/1522 [00:02<00:09, 133.41it/s]
 16%|█▋        | 249/1522 [00:02<00:09, 135.90it/s]
 17%|█▋        | 263/1522 [00:02<00:09, 133.80it/s]
 18%|█▊        | 281/1522 [00:02<00:08, 139.70it/s]
 20%|██        | 309/1522 [00:02<00:06, 177.46it/s]
 22%|██▏       | 328/1522 [00:03<00:23, 49.79it/s] 
 23%|██▎       | 353/1522 [00:03<00:17, 68.65it/s]
 26%|██▌       | 396/1522 [00:03<00:10, 110.85it/s]
 28%|██▊       | 421/1522 [00:03<00:08, 130.88it/s]
 29%|██▉       | 446/1522 [00:03<00:07, 148.12it/s]
 31%|███       | 469/1522 [00:04<00:06, 163.92it/s]
 32%|███▏      | 492/1522 [00:04<00:06, 159.19it/s]
 34%|███▎      | 513/1522 [00:04<00:06, 165.09it/s]
 35%|███▌      | 533/1522 [00:04<00:05, 167.44it/s]
 36%|███▋      | 553/1522 [00:04<00:05, 161.90it/s]
 38%|███▊      | 571/1522 [00:04<00:05, 162.02it/s]
 39%|███▉      | 593/1522 [00:04<00:05, 176.48it/s]
 40%|████      | 613/1522 [00:04<00:05, 173.33it/s]
 42%|████▏     | 640/1522 [00:05<00:04, 197.22it/s]
 43%|████▎     | 661/1522 [00:05<00:04, 189.59it/s]
 45%|████▍     | 682/1522 [00:05<00:04, 189.70it/s]
 46%|████▌     | 702/1522 [00:05<00:04, 187.58it/s]
 47%|████▋     | 722/1522 [00:05<00:04, 188.74it/s]
 49%|████▉     | 742/1522 [00:05<00:04, 191.00it/s]
 50%|█████     | 763/1522 [00:05<00:03, 193.82it/s]
 51%|█████▏    | 783/1522 [00:05<00:04, 184.42it/s]
 53%|█████▎    | 803/1522 [00:05<00:03, 187.09it/s]
 54%|█████▍    | 822/1522 [00:05<00:03, 184.66it/s]
 55%|█████▌    | 842/1522 [00:06<00:03, 188.17it/s]
 57%|█████▋    | 861/1522 [00:06<00:03, 186.26it/s]
 58%|█████▊    | 881/1522 [00:06<00:03, 183.31it/s]
 59%|█████▉    | 902/1522 [00:06<00:03, 188.63it/s]
 61%|██████    | 922/1522 [00:06<00:03, 189.79it/s]
 62%|██████▏   | 942/1522 [00:06<00:03, 179.07it/s]
 63%|██████▎   | 965/1522 [00:06<00:02, 188.87it/s]
 65%|██████▌   | 992/1522 [00:06<00:02, 206.02it/s]
 67%|██████▋   | 1015/1522 [00:06<00:02, 208.24it/s]
 68%|██████▊   | 1036/1522 [00:07<00:02, 195.79it/s]
 69%|██████▉   | 1056/1522 [00:07<00:02, 191.19it/s]
 71%|███████   | 1078/1522 [00:07<00:02, 156.06it/s]
 73%|███████▎  | 1107/1522 [00:07<00:02, 181.69it/s]
 74%|███████▍  | 1127/1522 [00:07<00:02, 172.71it/s]
 75%|███████▌  | 1146/1522 [00:07<00:02, 168.02it/s]
 77%|███████▋  | 1169/1522 [00:07<00:01, 180.83it/s]
 78%|███████▊  | 1188/1522 [00:07<00:01, 173.89it/s]
 79%|███████▉  | 1209/1522 [00:08<00:01, 182.27it/s]
 81%|████████  | 1229/1522 [00:08<00:01, 186.28it/s]
 82%|████████▏ | 1255/1522 [00:08<00:01, 204.80it/s]
 84%|████████▍ | 1276/1522 [00:08<00:01, 200.29it/s]
 85%|████████▌ | 1297/1522 [00:08<00:01, 192.50it/s]
 87%|████████▋ | 1318/1522 [00:08<00:01, 191.64it/s]
 88%|████████▊ | 1338/1522 [00:08<00:01, 183.82it/s]
 89%|████████▉ | 1359/1522 [00:08<00:00, 187.16it/s]
 91%|█████████ | 1378/1522 [00:10<00:03, 45.08it/s] 
 91%|█████████▏| 1392/1522 [00:10<00:02, 50.69it/s]
 93%|█████████▎| 1417/1522 [00:10<00:01, 71.62it/s]
 94%|█████████▍| 1437/1522 [00:10<00:00, 88.32it/s]
 96%|█████████▌| 1457/1522 [00:10<00:00, 102.52it/s]
 97%|█████████▋| 1474/1522 [00:10<00:00, 109.49it/s]
 98%|█████████▊| 1497/1522 [00:10<00:00, 132.59it/s]
100%|██████████| 1522/1522 [00:10<00:00, 139.91it/s]
Constructing time series for each modality ...
Saving aggregated time series to /home/weijiang/Projects/Netsanut/datasets/simbarca/simulation_sessions/session_045/timeseries/agg_timeseries.pkl
Extracting samples from time series ...
number of samples: 20
Packing the samples into numpy arrays ...