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    "info": {
        "author": "Marcel Rieger",
        "author_email": "python-scinum@googlegroups.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
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        "description": "![scinum logo](https://raw.githubusercontent.com/riga/scinum/master/logo250.png \"scinum logo\")\n\n[![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://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)\n\nscinum 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.\n\n\n### Usage\n\nThe 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)\n\n\n###### Number definition\n\n```python\nfrom scinum import Number, UP, DOWN\n\nnum = Number(5, (2, 1))\nprint(num)                    # -> 5.00 +2.00-1.00\n\n# get the nominal value\nprint(num.nominal)            # -> 5.0\nprint(num.n)                  # -> 5.0 (shorthand)\nprint(num())                  # -> 5.0 (shorthand)\n\n# get uncertainties\nprint(num.get_uncertainty())  # -> (2.0, 1.0)\nprint(num.u())                # -> (2.0, 1.0) (shorthand)\nprint(num.u(direction=UP))    # -> 2.0\n\n# get shifted values\nprint(num.get())              # -> 5.0 (no shift)\nprint(num.get(UP))            # -> 7.0 (up shift)\nprint(num(UP))                # -> 7.0 (up shift, shorthand)\nprint(num.get(DOWN))          # -> 4.0 (down shift)\nprint(num(DOWN))              # -> 4.0 (down shift, shorthand)\n```\n\n\n###### Multiple uncertainties\n\n```python\nfrom scinum import Number, ABS, REL\n\nnum = Number(2.5, {\n    \"sourceA\": 0.5,                  # absolute 0.5, both up and down\n    \"sourceB\": (1.0, 1.5),           # absolute 1.0 up, 1.5 down\n    \"sourceC\": (REL, 0.1),           # relative 10%, both up and down\n    \"sourceD\": (REL, 0.1, 0.2),      # relative 10% up, 20% down\n    \"sourceE\": (1.0, REL, 0.2),      # absolute 1.0 up, relative 20% down\n    \"sourceF\": (REL, 0.3, ABS, 0.3)  # relative 30% up, absolute 0.3 down\n})\n```\n\n\n###### Formatting and rounding\n\n`Number.str()` provides some simple formatting tools, including `latex` and `root latex` support, as well as scientific rounding rules:\n\n```python\n# output formatting\nn = Number(8848, 10)\nn.str(unit=\"m\")                          # -> \"8848.0 +- 10.0 m\"\nn.str(unit=\"m\", force_asymmetric=True)   # -> \"8848.0 +10.0-10.0 m\"\nn.str(unit=\"m\", scientific=True)         # -> \"8.848 +- 0.01 x 1E3 m\"\nn.str(unit=\"m\", si=True)                 # -> \"8.848 +- 0.01 km\"\nn.str(unit=\"m\", style=\"latex\")           # -> \"$8848.0 \\pm 10.0\\,m$\"\nn.str(unit=\"m\", style=\"latex\", si=True)  # -> \"8.848 \\pm 0.01\\,km\"\nn.str(unit=\"m\", style=\"root\")            # -> \"8848.0 #pm 10.0 m\"\nn.str(unit=\"m\", style=\"root\", si=True)   # -> \"8.848 #pm 0.01 km\"\n\n# output rounding\nn = Number(17.321, {\"a\": 1.158, \"b\": 0.453})\nn.str()               # -> '17.321 +- 1.158 (a) +- 0.453 (b)'\nn.str(\"%.1f\")         # -> '17.3 +- 1.2 (a) +- 0.5 (b)'\nn.str(\"publication\")  # -> '17.32 +- 1.16 (a) +- 0.45 (b)'\nn.str(\"pdg\")          # -> '17.3 +- 1.2 (a) +- 0.5 (b)'\n```\n\nFor situations that require more sophisticated rounding and formatting rules, you might want to checkout:\n\n- [`sn.split_value()`](http://scinum.readthedocs.io/en/latest/#split-value)\n- [`sn.match_precision()`](http://scinum.readthedocs.io/en/latest/#match-precision)\n- [`sn.round_uncertainty()`](http://scinum.readthedocs.io/en/latest/#round-uncertainty)\n- [`sn.round_value()`](http://scinum.readthedocs.io/en/latest/#round-value)\n- [`sn.infer_si_prefix()`](http://scinum.readthedocs.io/en/latest/#infer-si-prefix)\n\n\n###### NumPy arrays\n\n```python\nfrom scinum import Number, ABS, REL\nimport numpy as np\n\nnum = Number(np.array([3, 4, 5]), 2)\nprint(num)\n# [ 3.  4.  5.]\n# + [ 2.  2.  2.]\n# - [ 2.  2.  2.]\n\nnum = Number(np.array([3, 4, 5]), {\n    \"sourceA\": (np.array([0.1, 0.2, 0.3]), REL, 0.5)  # absolute values for up, 50% down\n})\nprint(num)\n# [ 3.  4.  5.]\n# + sourceA [ 0.1  0.2  0.3]\n# - sourceA [ 1.5  2.   2.5]\n```\n\n\n###### Uncertainty propagation\n\n```python\nfrom scinum import Number\n\nnum = Number(5, 1)\nprint(num + 2)  # -> '7.0 +- 1.0'\nprint(num * 3)  # -> '15.0 +- 3.0'\n\nnum2 = Number(2.5, 1.5)\nprint(num + num2)  # -> '7.5 +- 1.80277563773'\nprint(num * num2)  # -> '12.5 +- 7.90569415042'\n\n# add num2 to num and consider their uncertainties to be fully correlated, i.e. rho = 1\nnum.add(num2, rho=1)\nprint(num)  # -> '7.5 +- 2.5'\n```\n\n\n###### Math operations\n\nAs a drop-in replacement for the `math` module, scinum provides an object `ops` that contains math operations that are aware of guassian error propagation.\n\n```python\nfrom scinum import Number, ops\n\nnum = ops.log(Number(5, 2))\nprint(num)  # -> 1.61 (+0.40, -0.40)\n\nnum = ops.exp(ops.tan(Number(5, 2)))\nprint(num)  # -> 0.03 (+0.85, -0.85)\n```\n\n\n###### Custom operations\n\nThere 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:\n\n```python\nfrom scinum import Number, ops\n\n@ops.register\ndef my_op(x):\n    return x * 2 + 1\n\n@my_op.derive\ndef my_op(x):\n    return 2\n\nnum = ops.my_op(Number(5, 2))\nprint(num)  # -> 11.00 (+4.00, -4.00)\n```\n\nPlease 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.\n\n\n### Installation and dependencies\n\nVia [pip](https://pypi.python.org/pypi/scinum)\n\n```bash\npip install scinum\n```\n\nor by simply copying the file into your project.\n\nNumpy is an optional dependency.\n\n\n### Contributing\n\nIf you like to contribute, I'm happy to receive pull requests. Just make sure to add a new test cases and run them via:\n\n```bash\n> python -m unittest tests\n```\n\n\n##### Testing\n\nIn general, tests should be run for different environments:\n\n- Python 2.7\n- Python 3.X (X \u2265 5)\n\n\n##### Docker\n\nTo run tests in a docker container, do:\n\n```bash\ngit clone https://github.com/riga/scinum.git\ncd scinum\n\ndocker run --rm -v `pwd`:/scinum -w /scinum python:3.6 python -m unittest tests\n```\n\n\n### Development\n\n- Source hosted at [GitHub](https://github.com/riga/scinum)\n- Report issues, questions, feature requests on [GitHub Issues](https://github.com/riga/scinum/issues)",
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