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noisepy-seis

Ambient-Noise Seismology Package

https://github.com/noisepy/noisepy

Science Score: 59.0%

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    Found 16 DOI reference(s) in README
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    Links to: zenodo.org
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    10 of 28 committers (35.7%) from academic institutions
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    Low similarity (17.2%) to scientific vocabulary

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Repository

Ambient-Noise Seismology Package

Basic Info
Statistics
  • Stars: 194
  • Watchers: 17
  • Forks: 77
  • Open Issues: 6
  • Releases: 47
Created over 7 years ago · Last pushed 11 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Codeowners

README.md

About NoisePy

NoisePy is a Python package designed for fast and easy computation of ambient noise cross-correlation functions. It provides additional functionality for noise monitoring and surface wave dispersion analysis.

Documentation Status Build Status Codecov DOI

Major updates coming

NoisePy is going through a major refactoring to make this package easier to develop and deploy. Submit an issue, fork the repository and create pull requests to contribute.

Installation

The nature of NoisePy being composed of python scripts allows flexible package installation, which is essentially to build dependent libraries the scripts and related functions live upon. We recommend using conda or pip to install.

Note the order of the command lines below matters

With Conda and pip

bash conda create -n noisepy -y python=3.10 pip conda activate noisepy pip install noisepy-seis

To add jupyter dependencies, install them pip install ipykernel notebook python -m ipykernel install --user --name noisepy

With Conda and pip and MPI support

bash conda create -n noisepy -y python=3.10 pip mpi4py conda activate noisepy pip install noisepy-seis[mpi]

With virtual environment

bash python -m venv noisepy source noisepy/bin/activate pip install noisepy-seis

With virtual environment and MPI support

An MPI installation is required. E.g. for macOS using brew : bash brew install open-mpi

bash python -m venv noisepy source noisepy/bin/activate pip install noisepy-seis[mpi]

Functionality

Here is a list of features of the package: * download continous noise data based: + on webservices using obspy's core functions of get_station and get_waveforms + on AWS S3 bucket calls, with a test on the SCEDC AWS Open Dataset. * save seismic data in ASDF format, which convinently assembles meta, wavefrom and auxililary data into one single file (Tutorials on reading/writing ASDF files) * offers scripts to precondition data sets before cross correlations. This involves working with gappy data from various formats (SAC/miniSEED) and storing it on local in ASDF. * performs fast and easy cross-correlation with functionality to run in parallel through MPI * Applications module: + Ambient noise monitoring: measure dv/v using a wide variety of techniques in time, fourier, and wavelet domain (Yuan et al., 2021) + Surface wave dispersion: construct dispersion images using conventional techniques.

Usage

To run the code on a single core, open the terminal and activate the noisepy environment before run following commands. To run on institutional clusters, see installation notes for individual packages on the module list of the cluster.

Deploy using Docker

We use I/O on disk, so users need root access to the file system. To install rootless docker, see instructions here. bash docker pull ghcr.io/noisepy/noisepy:latest docker run -v ~/tmp:/tmp ghcr.io/noisepy/noisepy:latest cross_correlate --path /tmp

Tutorials

Short tutorials on how to use NoisePy can be is available here and can be run directly in Colab. These tutorials present simple examples of how NoisePy might work. We strongly encourage you to download the NoisePy package and play it on your own! If you have any comments and/or suggestions during running the codes, please do not hesitate to contact us through email or open an issue in this github page!

Chengxin Jiang (chengxinjiang@gmail.com) Marine Denolle (mdenolle@uw.edu) Yiyu Ni (niyiyu@uw.edu)

Taxonomy

Taxonomy of the NoisePy variables.

  • station refers to the site that has the seismic instruments that records ground shaking.
  • channel refers to the direction of ground motion investigated for 3 component seismometers. For DAS project, it may refers to the single channel sensors.
  • ista is the index name for looping over stations
  • cc_len correlation length, basic window length in seconds
  • step is the window that get skipped when sliding windows in seconds
  • smooth_N number of points for smoothing the time or frequency domain discrete arrays.
  • maxlag maximum length in seconds saved in files in each side of the correlation (save on storage)
  • substack, substack_windows boolean, number of window over which to substack the correlation (to save storage or do monitoring).
  • time_chunk, nchunk refers to the time unit that defined a single job. for instace, cc_len is the correlation length (e.g., 1 hour, 30 min), the overall duration of the experiment is the total length (1 month, 1 year, ...). The time chunk could be 1 day: the code would loop through each cc_len window in a for loop. But each day will be sent as a thread.

Acknowledgements

Thanks to our contributors so far!

Contributors

Use this reference when publishing on your work with noisepy

Main code:

Algorithms used: * (data pre-processing) Seats, K. J., Jesse F. L., and German A. P. Improved ambient noise correlation functions using Welch s method. Geophysical Journal International 188, no. 2 (2012): 513-523. https://doi.org/10.1111/j.1365-246X.2011.05263.x

This research received software engineering support from the University of Washingtons Scientific Software Engineering Center (SSEC) supported by Schmidt Futures, as part of the Virtual Institute for Scientific Software (VISS). We would like to acknowledge Carlos Garcia Jurado Suarez and Nicholas Rich for their collaboration and contributions to the software.

Owner

  • Name: noisepy
  • Login: noisepy
  • Kind: organization

GitHub Events

Total
  • Create event: 13
  • Release event: 7
  • Issues event: 26
  • Watch event: 30
  • Delete event: 11
  • Issue comment event: 29
  • Push event: 35
  • Pull request review event: 3
  • Pull request event: 13
  • Fork event: 9
Last Year
  • Create event: 13
  • Release event: 7
  • Issues event: 26
  • Watch event: 30
  • Delete event: 11
  • Issue comment event: 29
  • Push event: 35
  • Pull request review event: 3
  • Pull request event: 13
  • Fork event: 9

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 564
  • Total Committers: 28
  • Avg Commits per committer: 20.143
  • Development Distribution Score (DDS): 0.746
Past Year
  • Commits: 303
  • Committers: 16
  • Avg Commits per committer: 18.938
  • Development Distribution Score (DDS): 0.696
Top Committers
Name Email Commits
chengxin c****g@g****m 143
carlosgjs c****s 92
mdenolle m****e@f****u 89
Carlos Garcia Jurado Suarez c****g@u****u 69
Marine Denolle m****e@u****u 44
nrich20 b****0@u****u 42
Yiyu Ni n****u@u****u 20
chengxin jiang c****n@c****l 16
Kuan-Fu Feng 5****g 8
Anuj Sinha s****0@g****m 6
Kuan-Fu Feng k****g@g****m 5
IshikaKhandelwal I****2@g****m 4
IshikaKhandelwal 6****l 4
nrich20 1****0 4
anujsinha3 j****0@g****m 2
Laura l****t@p****t 2
chengxin c****n@d****u 2
savardge g****d@g****m 2
xtyangpsp s****g@g****m 1
Don Setiawan l****s@u****u 1
Minho Choi 3****u 1
Yantao Luo 1****o 1
dependabot[bot] 4****] 1
savardge g****d@o****m 1
marine denolle m****e@m****l 1
lermert l****t@s****h 1
Natasha Toghramadjian n****n@g****u 1
Chengxin Jiang c****n@e****u 1
Committer Domains (Top 20 + Academic)
uw.edu: 5 es09174.anu.edu.au: 1 g.harvard.edu: 1 sed.ethz.ch: 1 dhcp-140-247-105-101.fas.harvard.edu: 1 posteo.net: 1 fas.harvard.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 55
  • Total pull requests: 111
  • Average time to close issues: 2 months
  • Average time to close pull requests: 3 days
  • Total issue authors: 18
  • Total pull request authors: 12
  • Average comments per issue: 0.8
  • Average comments per pull request: 0.78
  • Merged pull requests: 99
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 7
  • Pull requests: 5
  • Average time to close issues: 6 days
  • Average time to close pull requests: about 1 hour
  • Issue authors: 6
  • Pull request authors: 1
  • Average comments per issue: 0.86
  • Average comments per pull request: 0.8
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • carlosgjs (29)
  • mdenolle (10)
  • SeismoFelix (4)
  • niyiyu (3)
  • kuanfufeng (3)
  • ValentinCassayre (2)
  • IshikaKhandelwal (2)
  • nqdu (1)
  • CharlesH-Coding (1)
  • lsetiawan (1)
  • jollyjokers (1)
  • Javitsg (1)
  • ONKSBGI (1)
  • doub1emint (1)
  • GDiaferia (1)
Pull Request Authors
  • carlosgjs (50)
  • niyiyu (35)
  • kuanfufeng (15)
  • IshikaKhandelwal (10)
  • mdenolle (8)
  • anujsinha3 (4)
  • koepflma (4)
  • dependabot[bot] (3)
  • YantaoLuo (2)
  • nrich20 (2)
  • Paspachu (1)
  • lsetiawan (1)
  • LevCarlo (1)
Top Labels
Issue Labels
enhancement (15) bug (3) wontfix (1) good first issue (1) help wanted (1)
Pull Request Labels
dependencies (3) github_actions (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 117 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 41
  • Total maintainers: 3
pypi.org: noisepy-seis

A High-performance Computing Python Package for Ambient Noise Analysis

  • Homepage: https://github.com/noisepy/NoisePy
  • Documentation: https://noisepy-seis.readthedocs.io/
  • License: MIT License Copyright (c) 2019 Marine Denolle & Chengxin Jiang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 0.9.91
    published 11 months ago
  • Versions: 41
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 117 Last month
Rankings
Forks count: 5.8%
Stargazers count: 7.0%
Dependent packages count: 7.0%
Average: 12.5%
Dependent repos count: 30.4%
Maintainers (3)
carlosgjs niyiyu mdenolle
Last synced: 6 months ago

Dependencies

.github/actions/setup/action.yaml actions
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  • mpi4py/setup-mpi v1 composite
.github/workflows/notebooks.yml actions
  • ./.github/actions/setup * composite
  • actions/checkout v3 composite
  • actions/configure-pages v3 composite
  • actions/deploy-pages v2 composite
  • actions/upload-artifact v3 composite
  • actions/upload-pages-artifact v1 composite
.github/workflows/precommit.yaml actions
  • actions/checkout v3.3.0 composite
  • actions/setup-python v4.5.0 composite
.github/workflows/release.yaml actions
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/setup-python v4.5.0 composite
  • actions/upload-artifact v3 composite
  • docker/build-push-action v4 composite
  • docker/login-action v2 composite
  • docker/metadata-action v4 composite
  • docker/setup-buildx-action v2 composite
  • mpi4py/setup-mpi v1 composite
  • pypa/gh-action-pypi-publish v1.8.5 composite
.github/workflows/test.yaml actions
  • ./.github/actions/setup * composite
  • actions/checkout v3.3.0 composite
  • codecov/codecov-action v3 composite
Dockerfile docker
  • python ${PYTHON_VERSION} build
script/write_speed/Dockerfile docker
  • python ${PYTHON_VERSION} build
pyproject.toml pypi
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  • PyYAML ==6.0
  • aiobotocore ==2.5.2
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  • pandas >=1.5.3,<2.0.0
  • psutil >=5.9.5,<6.0.0
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tutorials/requirements.txt pypi
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