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Install this package:

emerge -a dev-python/uncertainties

If the package is masked, you can unmask it using the autounmask tool or standard emerge options:

autounmask dev-python/uncertainties

Or alternatively:

emerge --autounmask-write -a dev-python/uncertainties

Package Information

Description:

uncertainties is a Python module, which allows calculations such as . (0.2 +/- 0.01) * 2 = 0.4 +/- 0.02 . to be performed transparently; much more complex mathematical expressions involving numbers with uncertainties can also be evaluated transparently. Correlations between expressions are correctly taken into account; x-x is thus exactly zero, for instance. The uncertainties produced by this module are what is predicted by error propagation theory.

Homepage:

https://pythonhosted.org/uncertainties/ https://github.com/lmfit/uncertainties/ https://pypi.org/project/uncertainties/

License:

BSD

Versions

Metadata

Description

• uncertainties is a Python module, which allows calculations such as . (0.2 +/- 0.01) * 2 = 0.4 +/- 0.02 . to be performed transparently; much more complex mathematical expressions involving numbers with uncertainties can also be evaluated transparently. Correlations between expressions are correctly taken into account; x-x is thus exactly zero, for instance. The uncertainties produced by this module are what is predicted by error propagation theory.

Maintainers

• Gentoo Science Project (sci@gentoo.org) - Type: project
• Python (python@gentoo.org) - Type: project

Upstream

• Remote IDs:
  • github: lmfit/uncertainties
  • pypi: uncertainties

<pkgmetadata>
	<maintainer type="project">
		<email>sci@gentoo.org</email>
		<name>Gentoo Science Project</name>
	</maintainer>
	<maintainer type="project">
		<email>python@gentoo.org</email>
		<name>Python</name>
	</maintainer>
	<longdescription lang="en">
 uncertainties is a Python module, which allows calculations such as
 .
   (0.2 +/- 0.01) * 2 = 0.4 +/- 0.02
 .
 to be performed transparently; much more complex mathematical expressions
 involving numbers with uncertainties can also be evaluated transparently.
 Correlations between expressions are correctly taken into account; x-x is
 thus exactly zero, for instance. The uncertainties produced by this module
 are what is predicted by error propagation theory.    
  </longdescription>
	<upstream>
		<remote-id type="github">lmfit/uncertainties</remote-id>
		<remote-id type="pypi">uncertainties</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

• [Error] Missing md5-cache for ebuild uncertainties-3.2.4

Manifest