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# numpy.correlate

`numpy.``correlate`(a, v, mode='valid', old_behavior=False)

  Cross-correlation of two 1-dimensional sequences.

  This function computes the correlation as generally defined in signal processing texts:

      z[k] = sum_n a[n] * conj(v[n+k])

  with a and v sequences being zero-padded where necessary and conj being the conjugate.

  Parameters : | a, v : array_like Input sequences. mode : {‘valid’, ‘same’, ‘full’}, optional Refer to the `convolve` docstring. Note that the default is valid, unlike `convolve`, which uses `full`. old_behavior : bool If True, uses the old behavior from Numeric, (correlate(a,v) == correlate(v,a), and the conjugate is not taken for complex arrays). If False, uses the conventional signal processing definition.

  See also

  `convolve`
    Discrete, linear convolution of two one-dimensional sequences.

  Examples

      >>> np.correlate([1, 2, 3], [0, 1, 0.5])
      array([ 3.5])
      >>> np.correlate([1, 2, 3], [0, 1, 0.5], "same")
      array([ 2. ,  3.5,  3. ])
      >>> np.correlate([1, 2, 3], [0, 1, 0.5], "full")
      array([ 0.5,  2. ,  3.5,  3. ,  0. ])