def cohere(self, x, y, NFFT=<NUM_LIT>, Fs=<NUM_LIT:2>, Fc=<NUM_LIT:0>, detrend=mlab.detrend_none,<EOL>window=mlab.window_hanning, noverlap=<NUM_LIT:0>, pad_to=None,<EOL>sides='<STR_LIT:default>', scale_by_freq=None, **kwargs):
if not self._hold: self.cla()<EOL>cxy, freqs = mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap,<EOL>scale_by_freq)<EOL>freqs += Fc<EOL>self.plot(freqs, cxy, **kwargs)<EOL>self.set_xlabel('<STR_LIT>')<EOL>self.set_ylabel('<STR_LIT>')<EOL>self.grid(True)<EOL>return cxy, freqs<EOL>
call signature::

  cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
         window = mlab.window_hanning, noverlap=0, pad_to=None,
         sides='default', scale_by_freq=None, **kwargs)

cohere the coherence between *x* and *y*.  Coherence is the normalized
cross spectral density:

.. math::

  C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}}

%(PSD)s

  *Fc*: integer
    The center frequency of *x* (defaults to 0), which offsets
    the x extents of the plot to reflect the frequency range used
    when a signal is acquired and then filtered and downsampled to
    baseband.

The return value is a tuple (*Cxy*, *f*), where *f* are the
frequencies of the coherence vector.

kwargs are applied to the lines.

References:

  * Bendat & Piersol -- Random Data: Analysis and Measurement
    Procedures, John Wiley & Sons (1986)

kwargs control the :class:`~matplotlib.lines.Line2D`
properties of the coherence plot:

%(Line2D)s

**Example:**

.. plot:: mpl_examples/pylab_examples/cohere_demo.py