eqcctpro 0.4.5

EQCCTPro: A powerful seismic event detection toolkit

EQCCTPro: powerful seismic event detection toolkit

EQCCTPro is a high-performace seismic event detection and processing framework that leverages EQCCT to process seismic data efficiently. It enables users to fully leverage the computational ability of their computing resources for maximum performance for simultaneous seismic waveform processing, achieving real-time performance by identifying and utilizing the optimal computational configurations for their hardware. More information about the development, capabilities, and real-world applications about EQCCTPro can be read about in our research publication here.

Features

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

Installation

To install EQCCTPro, there are two installation approaches:

1. Install EQCCTPro out the box with no sample waveform data to test the application with (experts)
2. Install EQCCTPro with the sample waveform data as provided from the Github folder (first-time users)

It is highly recommended you pull the EQCCTPro folder to gain access to the sample waveform data and code to help you get acquainted with EQCCTPro and its capabilites.

However, if you wish to install only the EQCCTPro Python package and use it out of the box (method 1), run:

pip install eqcctpro

You must have at least Python verison 3.10.14 for the application to run.

You can install Python 3.10.14 using either traditional methods or do the following commands:

[skevofilaxc] conda create --name yourenvironemntname python=3.10.14 -y
[skevofilaxc] conda activate yourenvironemntname 
[skevofilaxc] python3 --version
Python 3.10.14 (it should return)
[skevofilaxc] pip install eqcctpro

You will have access to EQCCTPro and its capabilities, however, it is highly recommended you pull the EQCCTPro folder to gain access to the sample waveform data to help you get acquainted with EQCCTPro's functionality. You can pull the EQCCTPro folder by running the following commands:

[skevofilaxc] mkdir my_work_directory
[skevofilaxc] cd my_work_directory
[skevofilaxc] git clone --depth 1 --filter=tree:0 https://github.com/ut-beg-texnet/eqcct.git --sparse
[skevofilaxc] cd eqcct
[skevofilaxc] git sparse-checkout set eqcctpro

If you wish to install EQCCTPro with the sample waveform data as originally intended for first-time users, and or are having trouble installing Python 3.10.14, there has been a precreated conda environment under the EQCCTPro folder that will install the necessary packages and dependencies needed for EQCCTPro to run (method 2).

You can pull the EQCCTPro folder, create the precreated conda environment, and activate it as originally intended for first-time users using the following commands:

[skevofilaxc] mkdir my_work_directory
[skevofilaxc] cd my_work_directory
[skevofilaxc] git clone --depth 1 --filter=tree:0 https://github.com/ut-beg-texnet/eqcct.git --sparse
[skevofilaxc] cd eqcct
[skevofilaxc] git sparse-checkout set eqcctpro
[skevofilaxc] conda env create -f environment.yml
[skevofilaxc] conda activate eqcctpro

After creating and activating the conda environment, install the EQCCTPro Python package using the following command:

[skevofilaxc] pip install eqcctpro

The pip package will install the remaining packages needed for EQCCTPro to work. More information on the eqcctpro pip package can be found at our PyPi project link here (EQCCTPro).

Creating a Test Workspace Environment

It's highly suggested to create a workspace environment to first understand how eqcctpro works. Sample seismic waveform data from 50 TexNet stations have provided in the eqcctpro repository under the sample_1_minute_data.zip file.

After downloading the .zip file, either individually or through the git pull methods, run the following command to unzip it:

[skevofilaxc] unzip sample_1_minute_data.zip

It's contents will look like:

[skevofilaxc sample_1_minute_data]$ ls
AT01  CF01  DG05  EF54  EF76   HBVL  MB09  MB21   MID02  ODSA  PB16  PB25  PB35  PB52  PH02  SM03  WB11
BB01  CT02  DG09  EF63  FOAK4  HNDO  MB13  MB25   MID03  PB04  PB17  PB26  PB39  PB54  PL01  SMWD  WB12
BP01  DB02  EF02  EF75  FW13   MB06  MB19  MID01  MO01   PB11  PB18  PB34  PB42  PECS  SM02  WB06

Where each subdirectory is named after station code, and is made up of mSEED files of different poses:

[skevofilaxc PB35]$ ls
TX.PB35.00.HH1__20241215T115800Z__20241215T120100Z.mseed  TX.PB35.00.HHZ__20241215T115800Z__20241215T120100Z.mseed
TX.PB35.00.HH2__20241215T115800Z__20241215T120100Z.mseed

EQCCT only needs one pose for the detection to occur, however more poses allow for better detection of the direction of the P and S waves.

You are now set up for testing.

Usage

There are three main capabilities of EQCCTPro:

1. Process mSEED data from singular or multiple seismic stations using either CPUs or GPUs
2. Evaluate your system to identify the optimal parallelization configurations needed to get the minimum runtime performance out of your system
3. Identify and return back the optimal parallelization configurations for both specific and general-use usecases for both CPU (a) and GPU applications (b)

These capabilities are achieved by the following functions in order respect to the above descriptions: EQCCTMSeedRunner (1), EvaluateSystem (2), OptimalCPUConfigurationFinder (3a), OptimalGPUConfigurationFinder (3b).

Processing mSEED data using EQCCTPro (EQCCTMSeedRunner)

To use EQCCTPro to process mSEED from various seismic stations, use the EQCCTMSeedRunner class. EQCCTMSeedRunner enables users to process multiple mSEED from a given input directory. The input directory is made up of station directories such as:

[skevofilaxc sample_1_minute_data]$ ls
AT01  CF01  DG05  EF54  EF76   HBVL  MB09  MB21   MID02  ODSA  PB16  PB25  PB35  PB52  PH02  SM03  WB11
BB01  CT02  DG09  EF63  FOAK4  HNDO  MB13  MB25   MID03  PB04  PB17  PB26  PB39  PB54  PL01  SMWD  WB12
BP01  DB02  EF02  EF75  FW13   MB06  MB19  MID01  MO01   PB11  PB18  PB34  PB42  PECS  SM02  WB06

Where each subdirectory is named after station code. If you wish to use create your own input directory with custom information, please follow the above naming convention. Otherwise, EQCCTPro will not work.

Within each subdirectory, such as PB35, it is made up of mSEED files of different poses (EX. N, E, Z):

[skevofilaxc PB35]$ ls
TX.PB35.00.HH1__20241215T115800Z__20241215T120100Z.mseed  TX.PB35.00.HHZ__20241215T115800Z__20241215T120100Z.mseed
TX.PB35.00.HH2__20241215T115800Z__20241215T120100Z.mseed

EQCCT only needs one pose for the detection to occur, however more poses allow for better detection of the direction of the P and S waves.

After setting up or utilizing the provided sample waveform directory, and install eqcctpro, import EQCCTMseedRunner as show below:

from eqcctpro import EQCCTMSeedRunner

eqcct_runner = EQCCTMSeedRunner(
    use_gpu=False,
    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()

EQCCTMseedRunner has multiple input paramters that need to be configured and are defined below:

• use_gpu (bool): True or False
  • Tells Ray to use either the GPU(s) (True) or CPUs (False) on your computer to process the waveforms in the entire workflow
  • Further specification of which GPU(s) and CPU(s) are provided in the parameters below
• intra_threads (int): default = 1
  • Controls how many intra-parallelism threads Tensorflow can use
• inter_threads (int): default = 1
  • Controls how many inter-parallelism threads Tensorflow can use
• cpu_id_list (list): default = [1]
  • List that defines which specific CPU cores that sched_setaffinity will allocate for executing the current EQCCTPro process.
  • Allows for specific allocation and limitation of CPUs for a given EQCCTPro process
    • "I want this program to run only on these specific cores."
• input_dir (str)
  • Directory path to the the mSEED directory
  • EX. /home/skevofilaxc/my_work_directory/eqcct/eqcctpro/sample_1_minute_data
• output_dir (str)
  • Directory path to where the output picks and logs will be sent
  • Doesn't need to exist, will be created if doesn't exist
  • Recommended to be in the same working directory as the input directory for convience
• log_filepath (str)
  • Filepath to where the EQCCTPro log will be written to and stored
  • Doesn't need to exist, will be created if doesn't exist
  • Recommended to be in the output directory and called eqcctpro.log, however the name can be changed for your own purposes
• P_threshold (float): default = 0.001
  • Threshold in which the P probabilities above it will be considered as P arrival
• S_threshold (float): default = 0.02
  • Threshold in which the S probabilities above it will be considered as S arrival
• p_model_filepath (str)
  • Filepath to where the P EQCCT detection model is stored
• s_model_filepath (str)
  • Filepath to where the S EQCCT detection model is stored
• number_of_concurrent_predictions (int)
  • The number of concurrent EQCCT detection tasks that can happen simultaneously on a given number of resources
  • EX. if number_of_concurrent_predictions = 5, there will be up to 5 EQCCT instances analyzing 5 different waveforms at the sametime
  • Best to use the optimal amount for your hardware, which can be identified using EvaluateSystem (below)
• best_usecase_config (bool): default = False
  • If True, will override inputted cpu_id_list, number_of_concurrent_predictions, intra_threads, inter_threads values for the best overall usecase configurations
  • Best overall usecase configurations are defined as the best overall input configurations that minimize runtime while doing the most amount of processing with your available hardware
  • Can only be used if EvaluateSystem has been run
• csv_dir (str)
  • Directory path containing the CSV's outputted by EvaluateSystem that contain the trial data that will be used to find the best_usecase_config
  • Script will look for specific files, will only exist if EvaluateSystem has been run
• selected_gpus (list): default = None
  • List of GPU IDs on your computer you want to use if use_gpu = True
  • None existing GPU IDs will cause the code to exit
• set_vram_mb (float)
  • Value of the maximum amount of VRAM EQCCTPro can use
  • Must be a real value that is based on your hardware's physical memory space, if it exceeds the space the code will break due to OutOfMemoryError
• specific_stations (str): default = None
  • String that contains the "list" of stations you want to only analyze
  • EX. Out of the 50 sample stations in sample_1_minute_data, if I only want to analyze AT01, BP01, DG05, then specific_stations='AT01, BP01, DG05'.
  • Removes the need to move station directories around to be used as input, can contain all stations in one directory for access
• cpu_id_list (list): default = [1]
  • List that defines which specific CPU cores that sched_setaffinity will allocate for executing the current EQCCTPro process.
  • Allows for specific allocation and limitation of CPUs for a given EQCCTPro process
    • "I want this program to run only on these specific cores."

Evaluating Your Systems Runtime Performance Capabilites

To evaluate your system’s runtime performance capabilites for both your CPU(s) and GPU(s), the EvaluateSystem class allows you to autonomously evaluate your system:

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()

EvaluateSystem will iterate through different combinations of CPU(s), Concurrent Predictions, and Workloads (stations), as well as GPU(s), and the amount of VRAM (MB) each Concurrent Prediction can use. EvaluateSystem will take time, depending on the number of CPU/GPUs, the amount of VRAM available, and the total workload that needs to be tested. However, after doing the testing once for most if not all usecases, the trial data will be available and can be used to identify the optimal input parallelization configurations for EQCCTMSeedRunner to use to get the maximum amount of processing out of your system in the shortest amonut of time.

The following input parameters need to be configurated for EvaluateSystem to evaluate your system based on your desired utilization of EQCCTPro:

• mode (str)
  • Can be either cpu or gpu
  • Tells EvaluateSystem which configuration trials should it iterate through
• intra_threads (int): default = 1
  • Controls how many intra-parallelism threads Tensorflow can use
• inter_threads (int): default = 1
  • Controls how many inter-parallelism threads Tensorflow can use
• input_dir (str)
  • Directory path to the the mSEED directory
  • EX. /home/skevofilaxc/my_work_directory/eqcct/eqcctpro/sample_1_minute_data
• output_dir (str)
  • Directory path to where the output picks and logs will be sent
  • Doesn't need to exist, will be created if doesn't exist
  • Recommended to be in the same working directory as the input directory for convience
• log_filepath (str)
  • Filepath to where the EQCCTPro log will be written to and stored
  • Doesn't need to exist, will be created if doesn't exist
  • Recommended to be in the output directory and called eqcctpro.log, however the name can be changed for your own purposes
• csv_dir (str)
  • Directory path where the CSV's outputted by EvaluateSystem will be saved
  • Doesn't need to exist, will be created if doesn't exist
• P_threshold (float): default = 0.001
  • Threshold in which the P probabilities above it will be considered as P arrival
• S_threshold (float): default = 0.02
  • Threshold in which the S probabilities above it will be considered as S arrival
• p_model_filepath (str)
  • Filepath to where the P EQCCT detection model is stored
• s_model_filepath (str)
  • Filepath to where the S EQCCT detection model is stored
• stations2use (int): default = None
  • Controls the maximum amount of stations EvaluateSystem can use in its trial iterations
  • Sample data has been provided so that the maximum is 50, however, if using custom data, configure for your specific usecase
• cpu_id_list (list): default = [1]
  • List that defines which specific CPU cores that sched_setaffinity will allocate for executing the current EQCCTPro process and is the maximum amount of cores EvaluteSystem can use in its trial iterations
  • Allows for specific allocation and limitation of CPUs for a given EQCCTPro process
    • "I want this program to run only on these specific cores."
  • Must be at least 1 CPU if using GPUs (Ray needs CPUs to manage the Raylets (concurrent tasks), however the processing of the waveform is done on the GPU)
• set_vram_mb (float)
  • Value of the maximum amount of VRAM EQCCTPro can use
  • Must be a real value that is based on your hardware's physical memory space, if it exceeds the space the code will break due to OutOfMemoryError
• selected_gpus (list): default = None
  • List of GPU IDs on your computer you want to use if mode = 'gpu'
  • Non-existing GPU IDs will cause the code to exit

Finding Optimal CPU/GPU Configurations

After running EvalutateSystem, you can use either the OptimalCPUConfigurationFinder or the OptimalGPUConfigurationFinder determine the best CPU or GPU configurations (respectively) for your specific usecase:

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)

Both OptimalCPUConfigurationFinder and OptimalGPUConfigurationFinder each have two usecases:

1. find_best_overall_usecase

• Returns the best overall usecase configuration
  • Uses middle 50% of CPUs for moderate, balanced CPU usage, with the maximum amount of stations processed with the minimum runtime

2. find_optimal_for

• Return the paralleliztion configurations (EX. concurrent predictions, intra/inter thread counts, vram, etc.) for a given number of CPU(s)/GPU(s) and stations
  • Enables users to quickly identify which input parameters should be used for the given amount of resources and workload they have for the minimum runtime possible on their computer

A input CSV directory path must be passed for the classes to use as a reference point:

• csv_filepath (str)
  • Directory path where the CSV's outputted by EvaluateSystem are

Using OptimalCPUConfigurationFinder.find_best_overall_usecase(), no input parameters are needed. It will return back the best usecase parameters.

For OptimalCPUConfigurationFinder.find_optimal_for(), the function requires two input parameters:

• cpu (int)
  • The number of CPU(s) you want to use in your application
• station_count (int)
  • The number of station(s) you want to use in your application

OptimalCPUConfigurationFinder.find_optimal_for() will return back a trial data point containing the mimimum runtime based on your input paramters

Similar to OptimalCPUConfigurationFinder.find_best_overall_usecase(), OptimalGPUConfigurationFinder.find_best_overall_usecase() will return back the best usecase parameters and no input parameters are needed.

For OptimalGPUConfigurationFinder.find_optimal_for(), the function requires three input parameters:

• cpu (int)
  • The number of CPU(s) you want to use in your application
• gpu_list (list)
  • The specific GPU ID(s) you want to use in your application
  • Useful if you have multiple GPUs available and want to use/dedicate a specific one to using EQCCTPro
• station_count (int)
  • The number of station(s) you want to use in your application

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 [e-mail] or [e-mail].