parameters: (samples, high=2*pi, low=0, axis=None)
return_statement: return ((high - low)/2.0/pi) * sqrt(-2*log(R))
docstring: Compute the circular standard deviation for samples assumed to be in the
      range [low to high].
  
      Parameters
      ----------
      samples : array_like
          Input array.
      low : float or int, optional
          Low boundary for circular standard deviation range.  Default is 0.
      high : float or int, optional
          High boundary for circular standard deviation range.
          Default is ``2*pi``.
      axis : int, optional
          Axis along which standard deviations are computed.  The default is
          to compute the standard deviation of the flattened array.
  
      Returns
      -------
      circstd : float
          Circular standard deviation.
  
      Notes
      -----
      This uses a definition of circular standard deviation that in the limit of
      small angles returns a number close to the 'linear' standard deviation.
  
      Examples
      --------
      >>> from scipy.stats import circstd
      >>> circstd([0, 0.1*np.pi/2, 0.001*np.pi, 0.03*np.pi/2])
      0.063564063306
docstring_summary: Compute the circular standard deviation for samples assumed to be in the
      range [low to high].
docstring_tokens: Compute the circular standard deviation for samples assumed to be in the range [ low to high ] .
function: def circstd(samples, high=2*pi, low=0, axis=None):
      """
      Compute the circular standard deviation for samples assumed to be in the
      range [low to high].
  
      Parameters
      ----------
      samples : array_like
          Input array.
      low : float or int, optional
          Low boundary for circular standard deviation range.  Default is 0.
      high : float or int, optional
          High boundary for circular standard deviation range.
          Default is ``2*pi``.
      axis : int, optional
          Axis along which standard deviations are computed.  The default is
          to compute the standard deviation of the flattened array.
  
      Returns
      -------
      circstd : float
          Circular standard deviation.
  
      Notes
      -----
      This uses a definition of circular standard deviation that in the limit of
      small angles returns a number close to the 'linear' standard deviation.
  
      Examples
      --------
      >>> from scipy.stats import circstd
      >>> circstd([0, 0.1*np.pi/2, 0.001*np.pi, 0.03*np.pi/2])
      0.063564063306
  
      """
      samples, ang = _circfuncs_common(samples, high, low)
      S = sin(ang).mean(axis=axis)
      C = cos(ang).mean(axis=axis)
      R = hypot(S, C)
      return ((high - low)/2.0/pi) * sqrt(-2*log(R))
function_tokens: def circstd ( samples , high = 2 * pi , low = 0 , axis = None ) : samples , ang = _circfuncs_common ( samples , high , low ) S = sin ( ang ) . mean ( axis = axis ) C = cos ( ang ) . mean ( axis = axis ) R = hypot ( S , C ) return ( ( high - low ) / 2.0 / pi ) * sqrt ( - 2 * log ( R ) )