eqcctpro 0.4

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EQCCTPro: powerful seismic event detection toolkit

EQCCTPro is a machine-learning based seismic event detection and processing framework designed for efficient execution on CPU and GPU architectures. It leverages machine learning models to analyze mSEED seismic data files and optimize computational resources based on the system's configuration.

Features

• Supports both CPU and GPU execution
• Configurable parallelism for optimized performance
• Automatic selection of best-use-case configurations
• Efficient handling of large-scale seismic data
• Includes tools for evaluating system performance

Installation

To install the necessary dependencies, create a conda environment using:

conda env create -f environment.yml
conda activate eqcctpro

Usage

Running EQCCTPro

To run EQCCTPro with a specified mSEED input directory and output directory:

from eqcctpro import EQCCTMSeedRunner

eqcct_runner = EQCCTMSeedRunner(
    use_gpu=True,
    intra_threads=1,
    inter_threads=1,
    cpu_id_list=[0,1,2,3,4],
    input_dir='/path/to/mseed',
    output_dir='/path/to/outputs',
    log_filepath='/path/to/outputs/eqcctpro.log',
    P_threshold=0.001,
    S_threshold=0.02,
    p_model_filepath='/path/to/model_p.h5',
    s_model_filepath='/path/to/model_s.h5',
    number_of_concurrent_predictions=5,
    best_usecase_config=True,
    csv_dir='/path/to/csv',
    selected_gpus=[0],
    set_vram_mb=24750,
    specific_stations='AT01, BP01, DG05'
)
eqcct_runner.run_eqcctpro()

Evaluating System Performance

To evaluate the system’s GPU performance:

from eqcctpro import EvaluateSystem

eval_gpu = EvaluateSystem(
    mode='gpu',
    intra_threads=1,
    inter_threads=1,
    input_dir='/path/to/mseed',
    output_dir='/path/to/outputs',
    log_filepath='/path/to/outputs/eqcctpro.log',
    csv_dir='/path/to/csv',
    P_threshold=0.001,
    S_threshold=0.02,
    p_model_filepath='/path/to/model_p.h5',
    s_model_filepath='/path/to/model_s.h5',
    stations2use=2,
    cpu_id_list=[0,1],
    set_vram_mb=24750,
    selected_gpus=[0]
)
eval_gpu.evaluate()

Finding Optimal CPU/GPU Configurations

To determine the best CPU or GPU configuration:

from eqcctpro import OptimalCPUConfigurationFinder, OptimalGPUConfigurationFinder

csv_filepath = '/path/to/csv'

cpu_finder = OptimalCPUConfigurationFinder(csv_filepath)
best_cpu_config = cpu_finder.find_best_overall_usecase()
print(best_cpu_config)

optimal_cpu_config = cpu_finder.find_optimal_for(cpu=3, station_count=2)
print(optimal_cpu_config)

gpu_finder = OptimalGPUConfigurationFinder(csv_filepath)
best_gpu_config = gpu_finder.find_best_overall_usecase()
print(best_gpu_config)

optimal_gpu_config = gpu_finder.find_optimal_for(num_cpus=1, gpu_list=[0], station_count=1)
print(optimal_gpu_config)

Configuration

The environment.yml file specifies the dependencies required to run EQCCTPro. Ensure you have the correct versions installed by using the provided conda environment setup.

License

EQCCTPro is provided under an open-source license. See LICENSE for details.

Contact

For inquiries or issues, please contact [your email or organization link].