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# scipy.signal.correlate

`scipy.signal.``correlate`(in1, in2, mode='full', method='auto')

  Cross-correlate two N-dimensional arrays.

  Cross-correlate in1 and in2, with the output size determined by the mode argument.

  Parameters: | in1 : array_like First input. in2 : array_like Second input. Should have the same number of dimensions as in1. mode : str {‘full’, ‘valid’, ‘same’}, optional A string indicating the size of the output: `full` The output is the full discrete linear cross-correlation of the inputs. (Default) `valid` The output consists only of those elements that do not rely on the zero-padding. In ‘valid’ mode, either in1 or in2 must be at least as large as the other in every dimension. `same` The output is the same size as in1, centered with respect to the ‘full’ output. method : str {‘auto’, ‘direct’, ‘fft’}, optional A string indicating which method to use to calculate the correlation. `direct` The correlation is determined directly from sums, the definition of correlation. `fft` The Fast Fourier Transform is used to perform the correlation more quickly (only available for numerical arrays.) `auto` Automatically chooses direct or Fourier method based on an estimate of which is faster (default). See `convolve` Notes for more detail. New in version 0.19.0.
  Returns: | correlate : array An N-dimensional array containing a subset of the discrete linear cross-correlation of in1 with in2.

  See also

  `choose_conv_method`
    contains more documentation on method.

  Notes

  The correlation z of two d-dimensional arrays x and y is defined as:

      z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])

  This way, if x and y are 1-D arrays and `z = correlate(x, y, 'full')` then

  \[z[k] = (x * y)(k - N + 1) = \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}\]

  for \(k = 0, 1, ..., ||x|| + ||y|| - 2\)

  where \(||x||\) is the length of `x`, \(N = \max(||x||,||y||)\), and \(y_m\) is 0 when m is outside the range of y.

  `method='fft'` only works for numerical arrays as it relies on `fftconvolve`. In certain cases (i.e., arrays of objects or when rounding integers can lose precision), `method='direct'` is always used.

  Examples

  Implement a matched filter using cross-correlation, to recover a signal that has passed through a noisy channel.

      >>> from scipy import signal
      >>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
      >>> sig_noise = sig + np.random.randn(len(sig))
      >>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128

      >>> import matplotlib.pyplot as plt
      >>> clock = np.arange(64, len(sig), 128)
      >>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
      >>> ax_orig.plot(sig)
      >>> ax_orig.plot(clock, sig[clock], 'ro')
      >>> ax_orig.set_title('Original signal')
      >>> ax_noise.plot(sig_noise)
      >>> ax_noise.set_title('Signal with noise')
      >>> ax_corr.plot(corr)
      >>> ax_corr.plot(clock, corr[clock], 'ro')
      >>> ax_corr.axhline(0.5, ls=':')
      >>> ax_corr.set_title('Cross-correlated with rectangular pulse')
      >>> ax_orig.margins(0, 0.1)
      >>> fig.tight_layout()
      >>> fig.show()

  (Source code)