new_contents: # -*- coding: utf-8 -*-
  # Copyright (C) 2017, Simone Marsili
  # All rights reserved.
  # License: BSD 3 clause
  # Author: Simone Marsili (simomarsili@gmail.com)
  """
  entropy from discrete data.
  
  The **ndd** module is a simple and minimal Python interface to the
  Nemenman-Schafee-Bialek (NSB) algorithm, a parameter-free, fully Bayesian
  algorithm for entropy estimation from discrete data.
  
  ### Basic usage 
  
  The `ndd.entropy` function takes as input an histogram vecor of counts
  (a list/array-like of integers) and returns a entropy estimate computed as a
  posterior mean (in nats): 
  
  ```python
  >>> counts
  [7, 3, 5, 8, 9, 1, 3, 3, 1, 0, 2, 5, 2, 11, 4, 23, 5, 0, 8, 0]
  >>> import ndd
  >>> entropy_estimate = ndd.entropy(counts)
  >>> entropy_estimate
  2.623634344902917
  ```
  
  The uncertainty in the entropy estimate can be quantified by the posterior
  standard deviation:
  ```python
  >>> entropy_estimate, std = ndd.entropy(counts, return_std=True)
  >>> std
  0.048675500725595504
  ```
  
  ### References
  
  Some refs:
  
  ```
  @article{wolpert1995estimating,
    title={Estimating functions of probability distributions from a finite set of samples},
    author={Wolpert, David H and Wolf, David R},
    journal={Physical Review E},
    volume={52},
    number={6},
    pages={6841},
    year={1995},
    publisher={APS}
  }
  
  @inproceedings{nemenman2002entropy,
    title={Entropy and inference, revisited},
    author={Nemenman, Ilya and Shafee, Fariel and Bialek, William},
    booktitle={Advances in neural information processing systems},
    pages={471--478},
    year={2002}
  }
  
  @article{nemenman2004entropy,
    title={Entropy and information in neural spike trains: Progress on the sampling problem},
    author={Nemenman, Ilya and Bialek, William and van Steveninck, Rob de Ruyter},
    journal={Physical Review E},
    volume={69},
    number={5},
    pages={056111},
    year={2004},
    publisher={APS}
  }
  
  @article{archer2013bayesian,
    title={Bayesian and quasi-Bayesian estimators for mutual information from discrete data},
    author={Archer, Evan and Park, Il Memming and Pillow, Jonathan W},
    journal={Entropy},
    volume={15},
    number={5},
    pages={1738--1755},
    year={2013},
    publisher={Multidisciplinary Digital Publishing Institute}
  }
  ```
  
  and interesting links:
  
  - [Sebastian Nowozin on Bayesian estimators](http://www.nowozin.net/sebastian/blog/estimating-discrete-entropy-part-3.html)
  
  - [Il Memming Park on discrete entropy estimators](https://memming.wordpress.com/2014/02/09/a-guide-to-discrete-entropy-estimators/)
  
  """
  from __future__ import (absolute_import, division,
                          print_function, unicode_literals)
  from builtins import (  # pylint: disable=redefined-builtin, unused-import
      bytes, dict, int, list, object, range, str,
      ascii, chr, hex, input, next, oct, open,
      pow, round, super,
      filter, map, zip)
  import os
  import json
  from ndd.nsb import (entropy, histogram)
  
  path_to_version = os.path.join(os.path.dirname(__file__), 'version.json')
  with open(path_to_version, 'r') as f:
      version_data = json.load(f)
      try:
          __version__ = version_data['number']
      except KeyError:
          # no version number in version.json
          raise KeyError("check version file: no version number")
  
  __copyright__ = "Copyright (C) 2016,2017 Simone Marsili"
  __license__ = "BSD 3 clause"
  __author__ = "Simone Marsili (simo.marsili@gmail.com)"
  __all__ = ["entropy",
             "histogram"]
  
subject: add __init__.py