text: '''pyblock is a python module for analysis of correlated data.
  
  .. toctree::
     :maxdepth: 1
     :glob:
  
     pyblock.*
  
  :mod:`pyblock.blocking` implements the reblocking algorithm [1]_ and an
  algorithm [2]_, [3]_ for suggesting the most appropriate block size (and thus
  estimate of the standard error in the data set) for data contained within
  :mod:`numpy` arrays.  :mod:`pyblock.pd_utils` provides a nice wrapper around
  this using :mod:`pandas`, and it is highly recommended to use this if possible.
  
  :mod:`pyblock.error` contains functions for simple error propagation and
  formatting of output of a value and it's associated error.
  
  References
  ----------
  .. [1]  "Error estimates on averages of correlated data", H. Flyvbjerg and
     H.G. Petersen, J. Chem. Phys. 91, 461 (1989).
  .. [2] "Monte Carlo errors with less errors", U. Wolff, Comput. Phys. Commun.
         156, 143 (2004) and arXiv:hep-lat/0306017.
  .. [3] "Strategies for improving the efficiency of quantum Monte Carlo
         calculations", R. M. Lee, G. J. Conduit, N. Nemec, P. Lopez Rios, and N.
         D.  Drummond, Phys. Rev. E. 83, 066706 (2011).
  '''
  
  # copyright: (c) 2014 James Spencer
  # license: modified BSD license; see LICENSE for further details.
  
  import warnings
  # For convenience, import all submodules so the user need only import pyblock.
  import pyblock.error
  import pyblock.blocking
  try:
      import pyblock.pd_utils
  except ImportError:
      warnings.warn('Pandas wrappers disabled: pandas not available.')
  try:
      import pyblock.plot
  except ImportError:
      warnings.warn('Plotting disabled: matplotlib not available.')
  