Skip to main content

DASPy is an open-source project dedicated to provide a python package for DAS (Distributed Acoustic Sensing) data processing, which comprises classic seismic data processing techniques and Specialized algorithms for DAS applications.

Project description

<img src=”https://raw.github.com/hmz-03/daspy/main/website/logo.png” height=”200” />

[![Supported Python versions](https://img.shields.io/badge/python-3.9%20|%203.10%20|%203.11%20|%203.12-blue)](https://pypi.org/project/DASPy-toolbox/) [![License](https://img.shields.io/pypi/l/daspy-toolbox.svg)](https://opensource.org/license/mit) [![PyPI Version](https://img.shields.io/pypi/v/daspy-toolbox.svg)](https://pypi.org/project/DASPy-toolbox/)

[![DOI](https://img.shields.io/badge/DOI-10.1785/0220240124-blue.svg)](https://doi.org/10.1785/0220240124) [![PyPI Downloads](https://img.shields.io/pypi/dm/daspy-toolbox.svg?label=pypi)](https://pypi.org/project/DASPy-toolbox/) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/daspy-toolbox?label=conda)](https://anaconda.org/conda-forge/daspy-toolbox)

DASPy is an open-source project dedicated to provide a python package for DAS (Distributed Acoustic Sensing) data processing.

The goal of the DASPy project is to lower the bar of DAS data processing. DASPy includes: * Classic seismic data processing techniques, including preprocessing, filter, spectrum analysis, and visualization * Specialized algorithms for DAS applications, including denoising, waveform decomposition, channel attribute analysis, and strain-velocity conversion.

DASPy is licensed under the MIT License. [An English version of DASPy tutorial](https://daspy-tutorial.readthedocs.io/en/latest/), [a Chinese version of DASPy tutorial](https://daspy-tutorial-cn.readthedocs.io/zh-cn/latest/) and [an example of Jupyter notebook](document/example.ipynb) is available. If you have any questions, please contact me via <hmz2018@mail.ustc.edu.cn>.

## Installation DASPy runs on Linux, Windows and Mac OS and on Python 3.9 and up.

### Pip ` pip install daspy-toolbox `

Install the latest version from GitHub:

` pip install git+https://github.com/HMZ-03/DASPy.git `

### Conda

` conda install daspy-toolbox `

or

` conda install conda-forge::daspy-toolbox `

### Manual installation 1. Install dependent packages: numpy, scipy >=1.13, matplotlib, geographiclib, pyproj, h5py, segyio, nptdms, tqdm

  1. Add DASPy into your Python path.

## Getting started ` from daspy import read sec = read() # load example waveform sec.bandpass(1, 15) sec.plot() ` <img src=”./website/waveform.png” height=”500” />

### Contributing

Please see details on how to contribute to the project [here](CONTRIBUTING.md) and [here](CodingStyleGuide.md).

### Reference

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

daspy_toolbox-1.2.3.tar.gz (19.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

daspy_toolbox-1.2.3-py3-none-any.whl (19.2 MB view details)

Uploaded Python 3

File details

Details for the file daspy_toolbox-1.2.3.tar.gz.

File metadata

  • Download URL: daspy_toolbox-1.2.3.tar.gz
  • Upload date:
  • Size: 19.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daspy_toolbox-1.2.3.tar.gz
Algorithm Hash digest
SHA256 07aa0c3fd557fefdcc137e9c440503f9da88805f9aa367580f008dcc469558e4
MD5 c8e6f6231c1ab49ddf9c0947999bb728
BLAKE2b-256 9b185d06a6e6f76e3fddb30605051b61838ff17bb2bebfce7e1ce2a1c2f9996b

See more details on using hashes here.

Provenance

The following attestation bundles were made for daspy_toolbox-1.2.3.tar.gz:

Publisher: workflow.yml on HMZ-03/DASPy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daspy_toolbox-1.2.3-py3-none-any.whl.

File metadata

  • Download URL: daspy_toolbox-1.2.3-py3-none-any.whl
  • Upload date:
  • Size: 19.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daspy_toolbox-1.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 6065f717197c046d7df1c5f4ad0ec3612253a3d4820fc5577288083564920803
MD5 c7dc4ddc62790018a920305f1fa9c5a1
BLAKE2b-256 197b2df44ae3bede13c9a48c3fd41ef020c92e76c79a6b41911c7ffd5aadcde6

See more details on using hashes here.

Provenance

The following attestation bundles were made for daspy_toolbox-1.2.3-py3-none-any.whl:

Publisher: workflow.yml on HMZ-03/DASPy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page