parameters: (signal, divisor)
return_statement: return quot, rem
docstring: Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
  
      Returns the quotient and remainder such that
      ``signal = convolve(divisor, quotient) + remainder``
  
      Parameters
      ----------
      signal : array_like
          Signal data, typically a recorded signal
      divisor : array_like
          Divisor data, typically an impulse response or filter that was
          applied to the original signal
  
      Returns
      -------
      quotient : ndarray
          Quotient, typically the recovered original signal
      remainder : ndarray
          Remainder
  
      Examples
      --------
      Deconvolve a signal that's been filtered:
  
      >>> from scipy import signal
      >>> original = [0, 1, 0, 0, 1, 1, 0, 0]
      >>> impulse_response = [2, 1]
      >>> recorded = signal.convolve(impulse_response, original)
      >>> recorded
      array([0, 2, 1, 0, 2, 3, 1, 0, 0])
      >>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
      >>> recovered
      array([ 0.,  1.,  0.,  0.,  1.,  1.,  0.,  0.])
  
      See Also
      --------
      numpy.polydiv : performs polynomial division (same operation, but
                      also accepts poly1d objects)
docstring_summary: Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
docstring_tokens: Deconvolves divisor out of signal using inverse filtering .
function: def deconvolve(signal, divisor):
      """Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
  
      Returns the quotient and remainder such that
      ``signal = convolve(divisor, quotient) + remainder``
  
      Parameters
      ----------
      signal : array_like
          Signal data, typically a recorded signal
      divisor : array_like
          Divisor data, typically an impulse response or filter that was
          applied to the original signal
  
      Returns
      -------
      quotient : ndarray
          Quotient, typically the recovered original signal
      remainder : ndarray
          Remainder
  
      Examples
      --------
      Deconvolve a signal that's been filtered:
  
      >>> from scipy import signal
      >>> original = [0, 1, 0, 0, 1, 1, 0, 0]
      >>> impulse_response = [2, 1]
      >>> recorded = signal.convolve(impulse_response, original)
      >>> recorded
      array([0, 2, 1, 0, 2, 3, 1, 0, 0])
      >>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
      >>> recovered
      array([ 0.,  1.,  0.,  0.,  1.,  1.,  0.,  0.])
  
      See Also
      --------
      numpy.polydiv : performs polynomial division (same operation, but
                      also accepts poly1d objects)
  
      """
      num = atleast_1d(signal)
      den = atleast_1d(divisor)
      N = len(num)
      D = len(den)
      if D > N:
          quot = []
          rem = num
      else:
          input = zeros(N - D + 1, float)
          input[0] = 1
          quot = lfilter(num, den, input)
          rem = num - convolve(den, quot, mode='full')
      return quot, rem
function_tokens: def deconvolve ( signal , divisor ) : num = atleast_1d ( signal ) den = atleast_1d ( divisor ) N = len ( num ) D = len ( den ) if D > N : quot = [ ] rem = num else : input = zeros ( N - D + 1 , float ) input [ 0 ] = 1 quot = lfilter ( num , den , input ) rem = num - convolve ( den , quot , mode = 'full' ) return quot , rem