richness_covariance | Python module computes the covariance contribution

by joergdietrich Python Version: Current License: MIT

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kandi X-RAY | richness_covariance Summary

richness_covariance is a Python library. richness_covariance has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However richness_covariance build file is not available. You can download it from GitHub.

This Python module computes the covariance contribution of the finite richness binnin in the redMaPPer mass calibration. It requires for computation of the NFW Delta Sigma profiles, Diemer's colossus for the DK15 M-c relation, and the tqdm progress meter. Outputs are ndarray covariance matrices in units of (Msun/Mpc^2)^2. All quantities are physical without factors of h. They are saved as Python pickles in the directory output.

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richness_covariance has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

richness_covariance has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of richness_covariance is current.

Quality

richness_covariance has no bugs reported.

Security

richness_covariance has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

richness_covariance is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

richness_covariance releases are not available. You will need to build from source code and install.

richness_covariance has no build file. You will be need to create the build yourself to build the component from source.

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richness_covariance Key Features

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richness_covariance Examples and Code Snippets

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Vulnerabilities

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Install richness_covariance

You can download it from GitHub.
You can use richness_covariance like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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