Metadata-Version: 1.0
Name: uncertainties
Version: 1.0.2
Summary: Transparent calculations with uncertainties on the quantities involved (aka "error propagation") ; calculation of derivatives
Home-page: UNKNOWN
Author: Eric O. LEBIGOT (EOL)
Author-email: eric.lebigot@normalesup.org
License: This software is released under the GNU General Public License version
2, for private or academic use (http://creativecommons.org/licenses/GPL/2.0/).
It is possible to use this software under another license (in particular
for commercial uses), which must then be obtained from the original author.
Description: ``uncertainties`` allows calculation such as (0.2 +- 0.01)**2 = 0.04 +- 0.004
        to be performed transparently.
        
        **Correlations** between expressions are correctly taken into account 
        (``x-x`` is exactly zero, for instance).
        
        Basic examples::
        
            # Mathematical operations:
            x = uncertainties.Number_with_uncert((0.20, 0.01))  # x = 0.20+-0.01
            x = uncertainties.Number_with_uncert("0.20(1)")  # Other representation
            print x**2  # Prints "0.04+-0.004"
            print math.sin(x**2)  # Prints "0.0399...+-0.00399..."
        
            # Access to the value at the origin, and to the uncertainty:
            y = x**2
            print y  # Prints "0.04+-0.004"
            print y.origin_value  # Prints "0.04"
            print y.std_dev()  # Prints "0.004..."
        
            print y.derivatives[x]  # Partial derivative: prints "2"
        
            print y - x*x  # Exactly zero: correlations taken into account
        
        **Almost all mathematical operations** are supported, including most 
        functions from the standard ``math`` module and functions from the 
        third-party numpy_ module (fast operations on arrays and matrices).  
        There is no restriction on the complexity of the expressions, or on the 
        number of variables involved.
        
        Another possible use of this module is the calculation of **partial 
        derivatives** of mathematical functions.
        
        More examples and additional information can be obtained with ``pydoc 
        uncertainties`` after installation.
        
        Please send feature requests, bug reports, or compliments to the author.
        .. _numpy: http://numpy.scipy.org/
        
Keywords: error propagation,uncertainties,uncertainty calculation,standard deviation,derivatives,partial derivatives,differentiation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.5
Classifier: Programming Language :: Python :: 2.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
