text: # The MIT License (MIT)
  # Copyright (c) 2014-2017 University of Bristol
  # 
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  # 
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  # 
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
  # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
  # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
  # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
  # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
  # OR OTHER DEALINGS IN THE SOFTWARE.
  
  from hyperstream import Tool, StreamInstance, ToolExecutionError
  from hyperstream.utils import check_input_stream_count
  
  import random
  
  
  class Gauss(Tool):
      """
      Gaussian distribution.
  
      mu is the mean, and sigma is the standard deviation.  This is
      slightly faster than the normalvariate() function.
  
      Optionally initialize internal state of the random number generator using seed.
      """
      def __init__(self, mu=0.0, sigma=1.0, seed=None):
          super(Gauss, self).__init__(mu=mu, sigma=sigma, seed=seed)
          random.seed(self.seed)
  
      @check_input_stream_count(0)
      def _execute(self, sources, alignment_stream, interval):
          if alignment_stream is None:
              raise ToolExecutionError("Alignment stream expected")
  
          for ti, _ in alignment_stream.window(interval, force_calculation=True):
              yield StreamInstance(ti, random.gauss(mu=self.mu, sigma=self.sigma))
  