Use PmP-traveltime-Net to Predict PmP Traveltime
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* Device configuration, Hyper-parameters setting, paths setting for data and result folders:

.. prompt:: python

  import torch
  import os
  import PmP_traveltime_Net as PTN

  # Device configuration
  cuda = torch.cuda.is_available()
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

  # Hyper-parameters
  batch_size = 128
  num_epochs = 200
  learning_rate = 5e-4

  # Paths for different folders, data and result folders
  datadir="/home/pmpboy/Github/Data"
  wdir="/home/pmpboy/Github/Result/Train_PTN_result"
  if not os.path.exists(wdir):
      os.makedirs(wdir)

* read in the training data:

.. prompt:: python

  train_loader, test_loader = PTN.readin_data_train(datadir,"TrainData_PmP_traveltime_Net",batch_size)

* train PmP-traveltime-Net:

.. prompt:: python

  PTN.NetTrain(wdir,"train_PTN_log","net_PTN_model",train_loader,learning_rate,num_epochs,batch_size,device)

We will see such output during training PmP-traveltime-Net:

.. prompt::

  Epoch [1/200],  Step [1/63],  Loss: 1.060057
  Epoch [1/200],  Step [51/63],  Loss: 0.161801
  Epoch [2/200],  Step [1/63],  Loss: 0.125822
  Epoch [2/200],  Step [51/63],  Loss: 0.120287
  Epoch [3/200],  Step [1/63],  Loss: 0.093617
  Epoch [3/200],  Step [51/63],  Loss: 0.084045
  Epoch [4/200],  Step [1/63],  Loss: 0.073481
  Epoch [4/200],  Step [51/63],  Loss: 0.077970
  Epoch [5/200],  Step [1/63],  Loss: 0.104802
  ......

* model evaluation on test data:

.. prompt:: python

  PTN.netevalu(wdir,"net_PTN_model","predict_PTN_file",test_loader,device)

* quickly visualize the result:

.. prompt:: python

  PTN.plot_modeva(wdir,"train_PTN_log","predict_PTN_file","plot_PTN_modevalu")

.. figure:: /photos/plot_PTN_modevalu.png
   :alt: Quickly Visualize the Performance of Trained PmPNet.
   :width: 100.0%
   :align: center

**Apply the pre-trained PmP-traveltime-Net to a certain year data**

* read in the real data:

.. prompt:: python

  test_loader = PTN.readin_data_real(datadir,"ValidationData_2015",batch_size)

* give PmP traveltime prediction on real data:

.. prompt:: python

  PTN.netpredict(datadir,"ValidationData_2015",wdir,"net_PTN_model","predict_PTN_file_2015",test_loader,device)

* quickly visualize the result:

.. prompt:: python

  PTN.plot_predict(wdir,"predict_PTN_file_2015","plot_PTN_predict_2015")

.. figure:: /photos/plot_PTN_predict_2015.png
   :alt: Quickly Visualize the Performance of Trained PmPNet.
   :width: 60.0%
   :align: center