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"author": "Harold Parks, National Research Council Canada",
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"Framework :: IPython",
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"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Topic :: Scientific/Engineering :: Physics"
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"description": "# MetroloPy\n\ntools for dealing with physical quantities: uncertainty propagation and unit conversion\n\n---\n\nMetroloPy is a pure python package and requires Python 3 and the SciPy stack (NumPy, SciPy, Pandas, and IPython). It looks best in a Jupyter Notebook.\n\nInstall MetroloPy with pip:\n\n```\n$ pip install metrolopy\n```\n\nPhysical quantities can then be represented in Python as `gummy` objects with an uncertainty and (or) a unit:\n\n>>> import metrolopy as uc\n>>> a = uc.gummy(1.2345,u=0.0234,unit='cm')\n>>> a\n1.234(23) cm\n\n>>> b = uc.gummy(3.034,u=0.174,unit='mm')\n>>> f = uc.gummy(uc.UniformDist(center=0.9345,half_width=0.096),unit='N')\n>>> p = f/(a*b)\n>>> p\n2.50(21) N/cm2\n\n>>> p.unit = 'kPa'\n>>> p.uunit = '%'\n>>> p\n25.0 kPa ± 8.5%\n
\n\nMetroloPy can do much more including Monte-Carlo uncertainty propagation, generating uncertainty budget tables, and curve fitting. It can also handle expanded uncertainties, degrees of freedom, correlated quantities, and complex valued quantities. See:\n\n* [a tutorial](https://nrc-cnrc.github.io/MetroloPy/_build/html/_static/tutorial.html) (or download the tutorial as Jupyter notebook)\n* [the documentation](https://nrc-cnrc.github.io/MetroloPy/)\n* [the issues page on GitHub](https://github.com/nrc-cnrc/Metrolopy/issues)\n\n\n",
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