Metadata-Version: 1.0
Name: uncertainties
Version: 1.2.4
Summary: Transparent calculations with uncertainties on the quantities involved (aka "error propagation") ; calculation of derivatives
Home-page: http://pypi.python.org/pypi/uncertainties/
Author: Eric O. LEBIGOT (EOL)
Author-email: eric.lebigot@normalesup.org
License: This software is released under a dual license.  (1) The GNU General Public License version 2.  (2) Any other license, as long as it is obtained from the original author.
Description: ``uncertainties`` allows calculations such as (2 +/- 0.1)*2 = 4
        +/- 0.2 to be performed transparently; much more complex mathematical
        expressions involving numbers with uncertainties can also be evaluated
        directly.
        
        **Correlations** between expressions are correctly taken into account.
        ``x-x`` is thus exactly zero, for instance (most implementations
        found on the web yield a non-zero uncertainty for ``x-x``, which is
        incorrect).
        
        Whatever the complexity of the calculation, the number of steps
        involved, or the correlations between variables, uncertainties
        produced by this program are what is predicted by `error propagation
        theory`_.
        
        
        Basic examples::
        
            from uncertainties import NumberWithUncert
            from uncertainties.umath import *  # sin(), etc.
        
            # Mathematical operations:
            x = NumberWithUncert((0.20, 0.01))  # x = 0.20+/-0.01
            x = NumberWithUncert("0.20(1)")  # Other representation
            print x**2  # Square: prints "0.04+/-0.004"
            print sin(x**2)  # Prints "0.0399...+/-0.00399..."
        
            print x.position_in_sigmas(0.17)  # Prints "-3.0": deviation of -3 sigmas
        
            # Access to the nominal value, and to the uncertainty:
            square = x**2  # Square
            print square  # Prints "0.04+/-0.004"  
            print square.nominal_value  # Prints "0.04"
            print square.std_dev()  # Prints "0.004..."
        
            print square.derivatives[x]  # Partial derivative: prints "0.4" (= 2*0.20)
        
            print square - x*x  # Exactly zero: correlations taken into account
        
        The Python_ (or IPython_) shell can thus be used as a powerful
        calculator that handles quantities with uncertainties (``print``
        statements are optional, which is convenient).
        
        **Almost all mathematical operations** are supported, including most 
        functions from the standard math_ module (sin,...) and functions from the 
        third-party numpy_ module (fast operations on arrays and matrices). 
        Comparison operators (``>``, ``==``, etc.) are supported too.  There is 
        no restriction on the complexity of mathematical expressions, or on the 
        number of variables involved (x-sin(x)+y**2-tan(y*x) can for example be 
        calculated, whether x and y are quantities with uncertainties or not).
        
        Another possible use of this module is the calculation of **partial
        derivatives** of mathematical functions (they are used by `error
        propagation theory`_, and are thus automatically calculated by this
        module).
        
        Additional examples and information can be obtained with ``pydoc 
        uncertainties`` and ``pydoc uncertainties.umath`` after installation.
        
        *Installation*: ``sudo easy_install uncertainties`` might be
        sufficient, depending on your installation (this does not require any
        manual download, but requires setuptools_).  For additional
        installation methods, download the source archive, and see the
        ``README.txt`` file that it contains.
        
        *User feedback*:
        
        - "A gift of the gods for the work I'm doing"
        - "Holy f*** this would have saved me so much f***ing time last semester"    (reddit_)
        - "Very useful" (reddit_)
        
        Please send feature requests, bug reports, or feedback to
        `Eric O. LEBIGOT (EOL)`_.
        
        *Version history* (main changes only):
        
        - 1.2.2: values with uncertainties are now output like 3+/-1, in order          to avoid confusing 3+-1 with 3+(-1).
        - 1.2: a new function, ``wrap()``, is exposed, which allows non-Python        functions (e.g. Fortran or C used through a module such as Scipy) to        handle numbers with uncertainties.
        - 1.1: mathematical functions (such as cosine, etc.) are in a new        uncertainties.umath module;        they do not override functions from the math module anymore.
        - 1.0.12: main class (``Number_with_uncert``) renamed ``NumberWithUncert``           so as to follow `PEP 8`_.
        - 1.0.11: ``origin_value`` renamed more appropriately as           ``nominal_value``.
        - 1.0.9: ``correlations()`` renamed more appropriately as          ``covariance_matrix()``.
        
        .. _Python: http://docs.python.org/tutorial/interpreter.html
        .. _IPython: http://ipython.scipy.org/
        .. _numpy: http://numpy.scipy.org/
        .. _math: http://docs.python.org/library/math.html
        .. _PEP 8: http://www.python.org/dev/peps/pep-0008/
        .. _error propagation theory: http://en.wikipedia.org/wiki/Propagation_of_uncertainty
        .. _setuptools: http://pypi.python.org/pypi/setuptools
        .. _Eric O. LEBIGOT (EOL): mailto:eric.lebigot@normalesup.org
        .. _reddit: http://www.reddit.com/r/Python/comments/am84v/now_you_can_do_calculations_with_uncertainties_5/
        
Keywords: error propagation,uncertainties,uncertainty calculations,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
