Skip to main content

An InSAR postprocessing tool

Project description

decorrelation

Documentation

InSAR postprocessing tool

Install

Install cupy and dask_cuda first, then:

With conda:

conda install -c conda-forge decorrelation

With pip:

pip install decorrelation

In development mode:

git clone git@github.com:kanglcn/decorrelation.git ./decorrelation
cd ./decorrelation
pip install -e '.[dev]'

How to use

import decorrelation as dc

This package provide functions for InSAR post-processing which refers as processing after SAR images co-registration and geocoding. The functions include PS/DS identification, coherence matrix estimation, phase linking etc.

Most of the python functions in this package provide 2 kind of API, the array-based API and the file-based API. The inputs of array-based functions generally are numpy or cupy arrays. The inputs of file-based functions are string of path to the array stored in disk. The file-based functions make use of dask package to decrease the memory usage and parallelize the job. However, their is performance cost for using dask, if no parallelization is needed and the memory fits the data, the array-based API is recommended.

CLI is also provided and is almost the same as the file-based API. The only difference between them is the CLI can not directly show the plot.

Please refer to the Documentation for detailed usage.

Warning!!! This package is under intensive development. API is subjected to change without any noticement.

Contact us

  • Most discussion happens on GitHub. Feel free to open an issue or comment on any open issue or pull request.
  • use github discussions to ask questions or leave comments.

Contribution

  • Pull requests are welcomed! Before making a pull request, please open an issue to talk about it.
  • We have notice many excellent open-source packages are rarely paid attention to due to lack of documentation. The package is developed with the nbdev, a notebook-driven development platform. Developers only need to simply write notebooks with lightweight markup and get high-quality documentation, tests, continuous integration, and packaging automatically.

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

decorrelation-0.3.2.tar.gz (33.0 kB view details)

Uploaded Source

Built Distribution

decorrelation-0.3.2-py3-none-any.whl (35.7 kB view details)

Uploaded Python 3

File details

Details for the file decorrelation-0.3.2.tar.gz.

File metadata

  • Download URL: decorrelation-0.3.2.tar.gz
  • Upload date:
  • Size: 33.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.5

File hashes

Hashes for decorrelation-0.3.2.tar.gz
Algorithm Hash digest
SHA256 43a69b0746b9e56e2cdae9129241d469fefe60ddc5a635cabb2317eefa8bfec5
MD5 73c40d655415f4e3c6f5928df7a3e6b6
BLAKE2b-256 75d5ccc9faa6c71eb1033c49f53c6df73137319c9f9564b43b4959e708fe8173

See more details on using hashes here.

File details

Details for the file decorrelation-0.3.2-py3-none-any.whl.

File metadata

File hashes

Hashes for decorrelation-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e8769caca48ba9ff52e98a7809e288eaa964db959b1e3d157b6906c6b38efdac
MD5 9117f54221faf3e021ed9e33a5fd0189
BLAKE2b-256 77458d1b9c6d9d9699455bd59edebebdd1786823b8728906b33a43fcafc21d97

See more details on using hashes here.

Supported by

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