gpr-sidl-inv/config.py

# config.py
import os

class Path_Config:

    #path related to dataset
    path = os.getcwd()
    in_data_dir=path+ '/dataset/in_data/'
    out_data_dir=path+ '/dataset/out_data/'
    INPUT_DATA_FOLDER = './dataset/out_data/'
    INPUT_LABEL_FOLDER = './dataset/eps_label_in_time/'
    dataset_path="./dataset/data.csv"
    labelset_path="./dataset/label.csv"

    #path related to field data and inversion result
    test_file_name='1350HENGDUANMIAN.DZT'
    TEST_FILE = './field_data/'+test_file_name
    CONVERTED_TEST_FILE=TEST_FILE[:-4]+'_RAW.csv'
    CONVERTED_TEST_FILE_img='./IMG/'+test_file_name[:-4]+'_RAW.png'
    PROCESSED_TEST_FILE='./field_data/'+test_file_name[:-4]+'_PROCESSED.csv'
    PROCESSED_TEST_FILE_img= './IMG/'+test_file_name[:-4]+'_PROCESSED.png'
    inversion_time_result_file='./time_result_csv/'+test_file_name[:-4]+'_time_result.csv'
    inversion_time_result_img= './IMG/'+test_file_name[:-4]+'_time_result.png'
    inversion_depth_result_img= './IMG/'+test_file_name[:-4]+'_depth_result.png'

    #path related to training
    field_impulse='./impulse/reflection_impulse_field_standard.csv'
    sim_impulse='./impulse/impulse_simulated_standard.csv'
    train_val_loss='./SAVE/train_val_loss.csv'
    LATEST_MODEL_PATH = 'SAVE/latest_model.pt'
    BEST_MODEL_PATH= 'SAVE/best_model.pt'



class Forward_Model_Config:

    # parameters about forward modeling for dataset
    model_num=5                  # Number of models
    depth=8                      # Model depth unit:M
    air_depth=0.5                # Air layer thickness, unit:M
    grid_length=0.02             # grid length
    surface_width=3              # surface width:M
    layers_range=[2,7]           # layers range
    smooth_cell=12               # Model smoothing parameters
    min_layer_thickness=0.02     # <=grid length
    first_layer_minlenth=0.8     # first layer minlenth
    first_layer_maxlenth=3.5     # first layer maxlenth
    permittivity_range=(3,30)    # the range of permittivity
    first_layer_eps_range=(8,30) # the range of permittivity of first layer
    max_permittivity=30          # the max permittivity
    Time=200e-9                  # Collection time window (within the medium)
    static_time=9e-9             # Collection time window (in the air)
    frequency=0.7e8              # Antenna central frequency
    Twindows=Time+static_time    # total time
    data_per_ns=10               # Sampling rate per nanosecond
    direct_wave_time=35e-9       # Direct wave duration (ns)
    data_length=1000             # Target length after interpolation (unit: grid number)
    filter_threthold=0.0015      # Minimum absolute value threshold for valid data
    root = os.getcwd()           # file path


class Field_data_test_Config:

    # parameters about field data
    distance=100                                        # unit: m
    time_window=200                                     # unit: ns
    extract_time_grid=317                               # Extracted time range (grid size)
    bad_trace=[3900,3901]                               # bad trace range
    detection_distance=1000                             # unit: grid
    time_window_length=Forward_Model_Config.data_length # unit: grid
    refer_wave_idx=920                                  # Reference trace index
    wavelet_range=[215,325]                             # Reference trace location
    static_time=wavelet_range[0]


class Network_train_Config:
    BATCH_SIZE = 10                                         # batch size of training
    LR = 0.001                                              # learning rate
    EPOCHS = 60                                             # max training epoch
    val_loss_min = 50                                       # val_loss_min
    network_layers=[8, 8, 8, 8]                             # network layers
    lr_decrease_rate=0.9                                    # learning rate decrease rate
    dataset_Proportion=0.9                                  # Proportion of training set
    save_period=10                                          # Save interval (unit: epoch)
    num_workers=0                                           # num_workers
    noise_coff=0.03                                         # Add intensity of noise
    shift_distance=53                                       # Convolutional correction distance (unit: grid)
    max_permittivity=Forward_Model_Config.max_permittivity
    data_length=Forward_Model_Config.data_length

class Network_prediction_Config:   
    
    
    initial_params = ([21, 21, 21, 21, 21], [200, 200, 200, 200, 200])  #Set initial layered model
    #example:  initial_params = ([layer1_permittivity, layer2_permittivity, ...], [layer1_thickness, layer2_thickness, ...])
    smooth_window_size = 20     
    ######
    ######
    ######                          
    num_workers= Network_train_Config.num_workers                 
    dz_interval=Forward_Model_Config.grid_length
    network_layers=Network_train_Config.network_layers   
    max_permittvity=Forward_Model_Config.max_permittivity
    num_workers=Network_train_Config.num_workers
    dt=Forward_Model_Config.Time/Forward_Model_Config.data_length
    max_samples=Forward_Model_Config.data_length
    distance=Field_data_test_Config.distance
    time_window=Field_data_test_Config.time_window
    BATCH_SIZE = 1