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        "author": "Nick Amin",
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        "description": "## pyerrors\n\n[![PyPI version](https://badge.fury.io/py/pyerrors.svg)](https://badge.fury.io/py/pyerrors)\n\n### Description\n\nThis is a simple error propagation library that does not handle correlations. However, in most cases, clever\nwriting can eliminate the need for properly handling correlations. The unit tests in\n`tests.py` show most of the features of the library. In a nutshell, you can do\n```python\n>>> from pyerrors import E\n>>> a = E(10) # instantiates an error object with poisson error by default (10.0 \u00b1 3.16)\n>>> b = E(10,2) # specified error (10.0 \u00b1 2.0)\n>>> (4.0*a-2*b)/3\n6.66666666667 \u00b1 4.42216638714\n>>> ((4.0*a-2*b)/3).round(2)\n6.67 \u00b1 4.42\n>>> # unpack as a 2-tuple, or by index\n>>> v,e = E(10)\n>>> print v,e\n10.0 3.16227766017\n>>> # sum\n>>> print sum([E(i) for i in range(10)])\n45.0 \u00b1 6.7082039325\n>>> # nice repr when using numpy inputs\n>>> import numpy as np\n>>> counts = np.histogram(np.random.normal(0,1,100),bins=np.linspace(-4,4,8))[0]\n>>> va = E(counts)\n>>> print va\n[ 0.00 \u00b1 0.00    2.00 \u00b1 1.41   21.00 \u00b1 4.58   47.00 \u00b1 6.86   27.00 \u00b1 5.20    3.00 \u00b1 1.73    0.00 \u00b1 0.00]\n>>> # but numpy requires some coersion afterwards (see the relevant test case in `tests.py` for details)\n>>> print sum(va.to_list())\n100.0 \u00b1 10.0\n```\n\n### Install\n`pip install pyerrors`\n\n### Testing\n`python setup.py test`\n\n### TODO\n\n\n",
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