CODEWITHSUNDEEP
pythonmatplotlib
learner
learner

Reputation: 2742

Suppress the plot and only get the result

I am trying to align two signals to get the lag that gives me the best coefficient. I am using the xcorr function in matplotlib. I am only interested in z in the following code.

Is there a way to suppress the plot (I dont want the plot) and get only the result?

from matplotlib.pyplot import xcorr
z = xcorr([1.,2.,3.,4.,5.], [0,0,0,0,1.], normed=False, maxlags=4)
lagsOut = list(z[0])  
corrCoeff = list(z[1])

Thanks

Upvotes: 0

Views: 1590

Answers (3)

Leonard AB
Leonard AB

Reputation: 1579

as I answered here, https://stackoverflow.com/a/47897581/5122657 Sometimes we cannot use numpy.correlate because sometimes we need the maxlags parameter that is only provided by the matplotlib.xcorr. But I understand that if we put complex data type as the arguments into matplotlib.xcorr directly, we will get "casting complex to real datatype" warning when matplotlib tries to draw the plot. Below I modified the code from matplotlib, so it can be used as a standalone function.

<!-- language: python -->

import numpy as np
import matplotlib.pyplot as plt

def xcorr(x, y, maxlags=10):
    Nx = len(x)
    if Nx != len(y):
        raise ValueError('x and y must be equal length')

    c = np.correlate(x, y, mode=2)

    if maxlags is None:
        maxlags = Nx - 1

    if maxlags >= Nx or maxlags < 1:
        raise ValueError('maxlags must be None or strictly positive < %d' % Nx)

    c = c[Nx - 1 - maxlags:Nx + maxlags]

    return c

Upvotes: 0

farenorth
farenorth

Reputation: 10781

Use np.correlate:

import numpy as np
x = [1., 2., 3., 4., 5.]
y = [0, 0, 0, 0, 1.]
corrCoef = np.correlate(x, y, 'full')
lagsOut = np.arange(-len(x)+1, len(x))

Upvotes: 2

loopbackbee
loopbackbee

Reputation: 23322

matplotlib is a plotting module. If you don't want plotting, you're better off just using numpy directly. See numpy.correlate

In case you need anything more from xcorr, you can use inspect.getsource to see what it does. Here's a abridged dump:

def xcorr(self, x, y, normed=True, detrend=mlab.detrend_none,
          usevlines=True, maxlags=10, **kwargs):
        Nx = len(x)
        if Nx != len(y):
            raise ValueError('x and y must be equal length')

        x = detrend(np.asarray(x))
        y = detrend(np.asarray(y))

        c = np.correlate(x, y, mode=2)

        if normed:
            c /= np.sqrt(np.dot(x, x) * np.dot(y, y))

        if maxlags is None:
            maxlags = Nx - 1

        if maxlags >= Nx or maxlags < 1:
            raise ValueError('maglags must be None or strictly '
                             'positive < %d' % Nx)

        lags = np.arange(-maxlags, maxlags + 1)
        c = c[Nx - 1 - maxlags:Nx + maxlags]

        if usevlines:
            a = self.vlines(lags, [0], c, **kwargs)
            b = self.axhline(**kwargs)
        else:

            kwargs.setdefault('marker', 'o')
            kwargs.setdefault('linestyle', 'None')
            a, = self.plot(lags, c, **kwargs)
            b = None
        return lags, c, a, b

Upvotes: 4

Related Questions