Metadata-Version: 1.1
Name: scinum
Version: 0.2.4
Summary: Scientific numbers with multiple uncertainties and correlation-aware, gaussian propagation.
Home-page: https://github.com/riga/scinum
Author: Marcel Rieger
Author-email: python-scinum@googlegroups.com
License: MIT
Description: |Build Status| |Documentation Status| |Package Status|
        
        scinum provides a simple ``Number`` class that wraps plain floats or
        `NumPy <http://www.numpy.org/>`__ arrays and adds support for multiple
        uncertainties, automatic (gaussian) error propagation, and scientific
        rounding.
        
        Usage
        ~~~~~
        
        The following examples demonstrate the most common use cases. For more
        info, see the `API
        documentation <http://scinum.readthedocs.org/en/latest/?badge=latest>`__.
        
        Number definition
                         
        
        .. code:: python
        
            from scinum import Number, UP, DOWN
        
            num = Number(5, (2, 1))
            print(num)                    # -> 5.00 +2.00-1.00
            print(num.nominal)            # -> 5.0
            print(num.n)                  # -> 5.0 (shorthand)
            print(num.get_uncertainty())  # -> (2.0, 1.0)
            print(num.u())                # -> (2.0, 1.0) (shorthand)
            print(num.u(direction=UP))    # -> 2.0
        
        Multiple uncertainties
                              
        
        .. code:: python
        
            from scinum import Number, ABS, REL
        
            num = Number(2.5, {
                "sourceA": 0.5,                  # absolute 0.5, both up and down
                "sourceB": (1.0, 1.5),           # absolute 1.0 up, 1.5 down
                "sourceC": (REL, 0.1),           # relative 10%, both up and down
                "sourceD": (REL, 0.1, 0.2),      # relative 10% up, 20% down
                "sourceE": (1.0, REL, 0.2),      # absolute 1.0 up, relative 20% down
                "sourceF": (REL, 0.3, ABS, 0.3)  # relative 30% up, absolute 0.3 down
            })
        
        Formatting and rounding
                               
        
        ``Number.str()`` provides some simple formatting tools, including
        ``latex`` and ``root latex`` support, as well as scientific rounding
        rules:
        
        .. code:: python
        
            # output formatting
            n = Number(8848, 10)
            n.str(unit="m")                          # -> "8848.00 +-10.00 m"
            n.str(unit="m", force_asymmetric=True)   # -> "8848.00 +10.00-10.00 m"
            n.str(unit="m", scientific=True)         # -> "8.85 +-0.01 x 1E3 m"
            n.str(unit="m", si=True)                 # -> "8.85 +-0.01 km"
            n.str(unit="m", style="latex")           # -> "$8848.00\;\pm10.00\;m$"
            n.str(unit="m", style="latex", si=True)  # -> "$8.85\;\pm0.01\;km$"
            n.str(unit="m", style="root")            # -> "8848.00 #pm 10.00 m"
            n.str(unit="m", style="root", si=True)   # -> "8.85 #pm 0.01 km"
        
            # output rounding
            n = Number(17.321, {"a": 1.158, "b": 0.453})
            n.str()               # -> '17.32 +1.16-1.16 (a), +0.45-0.45 (b)'
            n.str("%.3f")         # -> '17.321 +1.158-1.158 (a), +0.453-0.453 (b)'
            n.str("publication")  # -> '17.32 +1.16-1.16 (a) +0.45-0.45 (b)'
            n.str("pdg")          # -> '17.3 +1.2-1.2 (a) +0.5-0.5 (b)'
        
        For situations that require more sophisticated rounding and formatting
        rules, you might want to checkout:
        
        -  ```sn.split_value()`` <http://scinum.readthedocs.io/en/latest/#split-value>`__
        -  ```sn.match_precision()`` <http://scinum.readthedocs.io/en/latest/#match-precision>`__
        -  ```sn.round_uncertainty()`` <http://scinum.readthedocs.io/en/latest/#round-uncertainty>`__
        -  ```sn.round_value()`` <http://scinum.readthedocs.io/en/latest/#round-value>`__
        -  ```sn.infer_si_prefix()`` <http://scinum.readthedocs.io/en/latest/#infer-si-prefix>`__
        
        NumPy arrays
                    
        
        .. code:: python
        
            from scinum import Number, ABS, REL
            import numpy as np
        
            num = Number(np.array([3, 4, 5]), 2)
            print(num)
            # [ 3.  4.  5.]
            # + [ 2.  2.  2.]
            # - [ 2.  2.  2.]
        
            num = Number(np.array([3, 4, 5]), {
                "sourceA": (np.array([0.1, 0.2, 0.3]), REL, 0.5)  # absolute values for up, 50% down
            })
            print(num)
            # [ 3.  4.  5.]
            # + sourceA [ 0.1  0.2  0.3]
            # - sourceA [ 1.5  2.   2.5]
        
        Uncertainty propagation
                               
        
        .. code:: python
        
            from scinum import Number
        
            num = Number(5, 1)
            print(num + 2)  # -> 7.00 (+1.00, -1.00)
            print(num * 3)  # -> 15.00 (+3.00, -3.00)
        
            num2 = Number(2.5, 1.5)
            print(num + num2)  # -> 7.50 (+1.80, -1.80)
            print(num * num2)  # -> 12.50 (+7.91, -7.91)
        
            # add num2 to num and consider their uncertainties to be fully correlated, i.e. rho = 1
            num.add(num2, rho=1)
            print(num)  # -> 7.5 (+2.50, -2.50)
        
        Math operations
                       
        
        As a drop-in replacement for the ``math`` module, scinum provides an
        object ``ops`` that contains math operations that are aware of guassian
        error propagation.
        
        .. code:: python
        
            from scinum import Number, ops
        
            num = ops.log(Number(5, 2))
            print(num)  # -> 1.61 (+0.40, -0.40)
        
            num = ops.exp(ops.tan(Number(5, 2)))
            print(num)  # -> 0.03 (+0.85, -0.85)
        
        Custom operations
                         
        
        There might be situations where a specific operation is not (yet)
        contained in the ``ops`` object. In this case, you can easily register a
        new one via:
        
        .. code:: python
        
            from scinum import Number, ops
        
            @ops.register
            def my_op(x):
                return x * 2 + 1
        
            @my_op.derive
            def my_op(x):
                return 2
        
            num = ops.my_op(Number(5, 2))
            print(num)  # -> 11.00 (+4.00, -4.00)
        
        Please note that there is no need to register *simple* functions like in
        the particular example above as most of them are just composite
        operations whose propagation rules (derivatives) are already known.
        
        Installation and dependencies
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Via `pip <https://pypi.python.org/pypi/scinum>`__
        
        .. code:: bash
        
            pip install scinum
        
        or by simply copying the file into your project.
        
        Numpy is an optional dependency.
        
        Contributing
        ~~~~~~~~~~~~
        
        If you like to contribute, I'm happy to receive pull requests. Just make
        sure to add a new test cases and run them via:
        
        .. code:: bash
        
            > python -m unittest tests
        
        Testing
        '''''''
        
        In general, tests should be run for different environments:
        
        -  Python 2.7
        -  Python 3.X (X ≥ 5)
        
        Docker
        ''''''
        
        To run tests in a docker container, do:
        
        .. code:: bash
        
            git clone https://github.com/riga/scinum.git
            cd scinum
        
            docker run --rm -v `pwd`:/root/scinum -w /root/scinum python:3.6 python -m unittest tests
        
        Development
        ~~~~~~~~~~~
        
        -  Source hosted at `GitHub <https://github.com/riga/scinum>`__
        -  Report issues, questions, feature requests on `GitHub
           Issues <https://github.com/riga/scinum/issues>`__
        
        Contributors
        ~~~~~~~~~~~~
        
        -  `Marcel R. <https://github.com/riga>`__ (author)
        
        License
        ~~~~~~~
        
        The MIT License (MIT)
        
        Copyright (c) 2017-2018 Marcel Rieger
        
        Permission is hereby granted, free of charge, to any person obtaining a
        copy of this software and associated documentation files (the
        "Software"), to deal in the Software without restriction, including
        without limitation the rights to use, copy, modify, merge, publish,
        distribute, sublicense, and/or sell copies of the Software, and to
        permit persons to whom the Software is furnished to do so, subject to
        the following conditions:
        
        The above copyright notice and this permission notice shall be included
        in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
        OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
        MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
        IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
        CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
        TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
        SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
        .. |Build Status| image:: https://travis-ci.org/riga/scinum.svg?branch=master
           :target: https://travis-ci.org/riga/scinum
        .. |Documentation Status| image:: https://readthedocs.org/projects/scinum/badge/?version=latest
           :target: http://scinum.readthedocs.org/en/latest/?badge=latest
        .. |Package Status| image:: https://badge.fury.io/py/scinum.svg
           :target: https://badge.fury.io/py/scinum
        
Keywords: scientific,numbers,error,systematics,propagation
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
