Metadata-Version: 2.1
Name: scinum
Version: 0.2.6
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: <img src="https://raw.githubusercontent.com/riga/scinum/master/logo.png" alt="scinum logo" width="250"/>
        
        [![Build Status](https://travis-ci.org/riga/scinum.svg?branch=master)](https://travis-ci.org/riga/scinum) [![Documentation Status](https://readthedocs.org/projects/scinum/badge/?version=latest)](http://scinum.readthedocs.org/en/latest/?badge=latest) [![Package Status](https://badge.fury.io/py/scinum.svg)](https://badge.fury.io/py/scinum)
        
        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
        
        ```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
        
        ```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:
        
        ```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
        
        ```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
        
        ```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.
        
        ```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:
        
        ```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)
        
        ```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:
        
        ```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:
        
        ```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.
        
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
Requires-Python: >=2.7
Description-Content-Type: text/markdown
