Metadata-Version: 2.1
Name: scinum
Version: 1.4.1
Summary: Scientific numbers with multiple uncertainties and correlation-aware, gaussian propagation and numpy
Home-page: https://github.com/riga/scinum
Author: Marcel Rieger
Author-email: python-scinum@googlegroups.com
License: BSD-3-Clause
Description: ![scinum logo](https://raw.githubusercontent.com/riga/scinum/master/logo250.png "scinum logo")
        
        [![Lint and test](https://github.com/riga/scinum/actions/workflows/lint_and_test.yml/badge.svg)](https://github.com/riga/scinum/actions/workflows/lint_and_test.yml) [![Documentation Status](https://readthedocs.org/projects/scinum/badge/?version=latest)](http://scinum.readthedocs.org/en/latest/?badge=latest) [![Package Status](https://img.shields.io/pypi/v/scinum.svg?style=flat)](https://pypi.python.org/pypi/scinum) [![License](https://img.shields.io/github/license/riga/scinum.svg)](https://github.com/riga/scinum/blob/master/LICENSE) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/riga/scinum/master?filepath=example.ipynb)
        
        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) or open the [example.ipynb](https://github.com/riga/scinum/blob/master/example.ipynb) notebook on binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/riga/scinum/master?filepath=example.ipynb)
        
        
        ###### Number definition
        
        ```python
        from scinum import Number, UP, DOWN
        
        Number.default_format = "%.2f"
        
        num = Number(5, (2, 1))
        print(num)                    # -> 5.00 +2.00-1.00
        
        # get the nominal value
        print(num.nominal)            # -> 5.0
        print(num.n)                  # -> 5.0 (shorthand)
        print(num())                  # -> 5.0 (shorthand)
        
        # get uncertainties
        print(num.get_uncertainty())  # -> (2.0, 1.0)
        print(num.u())                # -> (2.0, 1.0) (shorthand)
        print(num.u(direction=UP))    # -> 2.0
        
        # get shifted values
        print(num.get())              # -> 5.0 (no shift)
        print(num.get(UP))            # -> 7.0 (up shift)
        print(num(UP))                # -> 7.0 (up shift, shorthand)
        print(num.get(DOWN))          # -> 4.0 (down shift)
        print(num(DOWN))              # -> 4.0 (down shift, shorthand)
        ```
        
        
        ###### 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
        })
        ```
        
        
        ###### Correlation handling
        
        When two numbers are combined by means of an operator, the correlation between equally named uncertainties is assumed to be 1.
        The example above shows how to configure this correlation coefficient `rho` when used with explicit operator methods defined on a number, such as `num.add()` or `num.mul()`.
        
        However, it is probably more convenient to use `Correlation` objects:
        
        ```python
        from scinum import Number, Correlation
        
        num = Number(2, 5)
        print(num * num)  # -> '4.0 +- 20.0', fully correlated by default
        # same as
        # print(num**2)
        # print(num.pow(2, inplace=False))
        
        print(num * Correlation(0) * num)  # -> '4.0 +- 14.14', no correlation
        # same as
        # print(num.pow(2, rho=0, inplace=False))
        ```
        
        The correlation object is combined with a number through multiplication, resulting in a `DeferredResult` object.
        The deferred result is used to resolve the actual uncertainty combination once it is applied to another number instance which happens in a second step.
        Internally, the above example is handled as
        
        ```python
        deferred = num * Correlation(0)
        print(deferred * num)
        ```
        
        and similarly, adding two numbers without correlation can be expressed as
        
        ```python
        (num * Correlation(0)) + num
        ```
        
        When combining numbers with multiple, named uncertainties, correlation coefficients can be controlled per uncertainty by passing names to the `Correlation` constructor.
        
        ```python
        Correlation(1, sourceA=0)  # zero correlation for sourceA, all others default to 1
        Correlation(sourceA=0)     # zero correlation for sourceA, no default
        ```
        
        ###### 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")                          # -> "8848.0 +- 10.0 m"
        n.str(unit="m", force_asymmetric=True)   # -> "8848.0 +10.0-10.0 m"
        n.str(unit="m", scientific=True)         # -> "8.848 +- 0.01 x 1E3 m"
        n.str(unit="m", si=True)                 # -> "8.848 +- 0.01 km"
        n.str(unit="m", style="latex")           # -> "$8848.0 \pm 10.0\,m$"
        n.str(unit="m", style="latex", si=True)  # -> "8.848 \pm 0.01\,km"
        n.str(unit="m", style="root")            # -> "8848.0 #pm 10.0 m"
        n.str(unit="m", style="root", si=True)   # -> "8.848 #pm 0.01 km"
        
        # output rounding
        n = Number(17.321, {"a": 1.158, "b": 0.453})
        n.str()               # -> '17.321 +- 1.158 (a) +- 0.453 (b)'
        n.str("%.1f")         # -> '17.3 +- 1.2 (a) +- 0.5 (b)'
        n.str("publication")  # -> '17.32 +- 1.16 (a) +- 0.45 (b)'
        n.str("pdg")          # -> '17.3 +- 1.2 (a) +- 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)
        
        
        ###### Uncertainty propagation
        
        ```python
        from scinum import Number
        
        num = Number(5, 1)
        print(num + 2)  # -> '7.0 +- 1.0'
        print(num * 3)  # -> '15.0 +- 3.0'
        
        num2 = Number(2.5, 1.5)
        print(num + num2)  # -> '7.5 +- 2.5'
        print(num * num2)  # -> '12.5 +- 10.0'
        
        # add num2 to num and consider their uncertainties to be fully uncorrelated, i.e. rho = 0
        num.add(num2, rho=0)
        print(num)  # -> '7.5 +- 1.80277563773'
        ```
        
        
        ###### 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)  # -> 1.60943791243 +- 0.4
        
        num = ops.exp(ops.tan(Number(5, 2)))
        print(num)  # -> 0.0340299245972 +- 0.845839754815
        print(num.str("%.2f"))  # -> 0.03 +- 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.
        
        
        ###### 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]
        ```
        
        
        ### 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`:/scinum -w /scinum python:3.8 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)
        
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 :: BSD License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Requires-Python: >=2.7
Description-Content-Type: text/markdown
Provides-Extra: docs
