old_contents: """
  # Hydropy
  
  Analysis of hydrological oriented time series.
  
  This package is especially designed to simplify the collection and analysis of
  hydrology data in an interpretive Python session.  Use HydroPy in a Jupyter
  notebook and save your analysis so that you can recreate your procedures and
  share them with others.  
  
  Hydropy uses the power of Numpy and Pandas to quickly process large datasets.
  
  Matplotlib and Seaborn are built-in to Hydropy, allowing you to create
  publication-ready diagrams quickly and easily.
  
  Try Hydropy in a notebook: hydropy_tutorial.ipynb
  
  Hydropy is available through [Github](https://github.com/stijnvanhoey/hydropy)
  and [PyPI](https://pypi.python.org/pypi/hydropy). The easiest way to use it is
  with the Anaconda scientific distribution. If you have Anaconda, or if you
  already have numpy, pandas, and matplotlib installed, then use Pip to install
  hydropy like this::
  
      $ pip install hydropy
  
  ## Basic Usage::
  
      >>> import numpy as np
      >>> import pandas as pd
  
      >>> import hydropy as hp
      >>> flowdata = pd.read_pickle("./data/FlowData")
      >>> flowdata.head()
  
      >>> myflowserie = hp.HydroAnalysis(flowdata)
      >>> myflowserie.get_year('2009').get_season('summer').plot(figsize=(12,6))
  
  """
  from __future__ import absolute_import, print_function
  
  from .baseflow import (get_baseflow_chapman,
                         get_baseflow_boughton,
                         get_baseflow_ihacres)
  from .storm import selectstorms, plotstorms
  from .flowanalysis import HydroAnalysis
  from .reading_third_party_data import get_usgs
  from .ipython import draw_map
  
  __license__ = 'BSD'
  __title__ = 'hydropy'
  
new_contents: from __future__ import absolute_import, print_function
  """
  # Hydropy
  
  Analysis of hydrological oriented time series.
  
  This package is especially designed to simplify the collection and analysis of
  hydrology data in an interpretive Python session.  Use HydroPy in a Jupyter
  notebook and save your analysis so that you can recreate your procedures and
  share them with others.  
  
  Hydropy uses the power of Numpy and Pandas to quickly process large datasets.
  
  Matplotlib and Seaborn are built-in to Hydropy, allowing you to create
  publication-ready diagrams quickly and easily.
  
  Try Hydropy in a notebook: hydropy_tutorial.ipynb
  
  Hydropy is available through [Github](https://github.com/stijnvanhoey/hydropy)
  and [PyPI](https://pypi.python.org/pypi/hydropy). The easiest way to use it is
  with the Anaconda scientific distribution. If you have Anaconda, or if you
  already have numpy, pandas, and matplotlib installed, then use Pip to install
  hydropy like this::
  
      $ pip install hydropy
  
  ## Basic Usage::
  
      >>> import numpy as np
      >>> import pandas as pd
  
      >>> import hydropy as hp
      >>> flowdata = pd.read_pickle("./data/FlowData")
      >>> flowdata.head()
  
      >>> myflowserie = hp.HydroAnalysis(flowdata)
      >>> myflowserie.get_year('2009').get_season('summer').plot(figsize=(12,6))
  
  """
  
  
  from .baseflow import (get_baseflow_chapman,
                         get_baseflow_boughton,
                         get_baseflow_ihacres)
  from .storm import selectstorms, plotstorms
  from .flowanalysis import HydroAnalysis
  from .reading_third_party_data import get_usgs
  from .ipython import draw_map
  
  __license__ = 'BSD'
  __title__ = 'hydropy'
  
subject: move the __future__ back to the first line