DeepScanWorkFlow/2-Run_EqT.py at main · SigProSeismology/DeepScanWorkFlow · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on March 2023

@authors: Hamzeh Mohammadigheymasi, Xiao Zhuowei
"""

"""
Runs EQT on the prepared data from the step 1- Data_preparation, and detect P and S phases for all the stationsConvert the OnDisk data format from the SeisComp format to the EQT format
"""

import warnings
warnings.filterwarnings("ignore")
from tensorflow.python.util import deprecation
deprecation._PRINT_DEPRECATION_WARNINGS = False
import sys
sys.path.append('./src/S_EqT_codes/src/EqT_libs')
sys.path.append('./src/S_EqT_codes/src')
from misc import convert_csv_to_real, xml2REAL_sta
from hdf5_maker import preprocessor
from predictor import predictor
from EqT_utils import DataGeneratorPrediction, picker, generate_arrays_from_file
from EqT_utils import f1, SeqSelfAttention, FeedForward, LayerNormalization
from keras.models import load_model
from keras.optimizers import Adam
import json
import os
import argparse
from pathlib import Path
import tensorflow as tf

def set_gpu_memory_fraction(gpu_fraction):
    gpus = tf.config.list_physical_devices('GPU')
    if gpus:
        try:
            tf.config.set_logical_device_configuration(
                gpus[0],
                [tf.config.LogicalDeviceConfiguration(memory_limit=gpus[0].memory_limit * gpu_fraction)])
            logical_gpus = tf.config.list_logical_devices('GPU')
            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
        except RuntimeError as e:
            # Virtual devices must be set at program startup
            print(e)

def convert(cfgs):
    mseed_dir = cfgs['EqT']['mseed_dir']
    sta_json_path = cfgs['EqT']['sta_json_path']
    overlap = cfgs['EqT']['overlap']
    n_processor = cfgs['EqT']['n_processor']

    preprocessor(mseed_dir=mseed_dir,
                stations_json=sta_json_path,
                overlap=overlap,n_processor=n_processor)
    return 1

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='2-run_EqT')
    parser.add_argument('--config-file', dest='config_file', type=str, help='Configuration file path',
                        default='./Configuration_Parameters.json')
    args = parser.parse_args()
    with open(args.config_file, 'r') as f:
        cfgs = json.load(f)
    task_dir = './' + cfgs['Project'] + '/'
    os.chdir(task_dir)
    # earthquake detection and phase picking by the EqT model
    os.environ['CUDA_VISIBLE_DEVICES']  = cfgs['EqT']['gpuid']
    set_gpu_memory_fraction(0.5)
    convert(cfgs)
    print(' *** Loading the model ...', flush=True)
    model = load_model(cfgs['EqT']['model_path'],
                       custom_objects={'SeqSelfAttention': SeqSelfAttention,
                                       'FeedForward': FeedForward,
                                       'LayerNormalization': LayerNormalization,
                                       'f1': f1
                                        })
    model.compile(loss = ['binary_crossentropy', 'binary_crossentropy', 'binary_crossentropy'],
                  loss_weights = [0.03, 0.40, 0.58],
                  optimizer = Adam(lr = 0.001),
                  metrics = [f1])
    print('Done Loading Model')
    predictor(input_dir= cfgs['EqT']['mseed_dir'] + '_processed_hdfs/',
            input_model=model,
            output_dir=cfgs['EqT']['det_res'],
            estimate_uncertainty=False,
            output_probabilities=False,
            number_of_sampling=cfgs['EqT']['number_of_sampling'],
            loss_weights=[0.02, 0.40, 0.58],
            detection_threshold=cfgs['EqT']['EQ_threshold'],
            P_threshold=cfgs['EqT']['P_threshold'],
            S_threshold=cfgs['EqT']['S_threshold'],
            number_of_plots=0,
            plot_mode='time',
            batch_size=500,gpuid=0,
            number_of_cpus=4,
            keepPS=True,
            spLimit=60)
    # convert xml and csv files for S-EqT and REAL
    xml2REAL_sta(cfgs)
    det_folder = Path(cfgs['EqT']['det_res'])
    for outfolder in det_folder.glob('*_outputs'):
        csv_name = str(outfolder) + '/X_prediction_results.csv'
        state = convert_csv_to_real(csv_name, cfgs)
        if state == 0:
            print('Empty {}'.format(outfolder.name))
        else:
            print('Success On {}'.format(outfolder.name))