I am learning numpy/scipy, coming from a MATLAB background. The xcorr function in Matlab has an optional argument "maxlag" that limits the lag range from –maxlag to maxlag. This is very useful if you are looking at the cross-correlation between two very long time series but are only interested in the correlation within a certain time range. The performance increases are enormous considering that cross-correlation is incredibly expensive to compute.
In numpy/scipy it seems there are several options for computing cross-correlation. numpy.correlate, numpy.convolve, scipy.signal.fftconvolve. If someone wishes to explain the difference between these, I'd be happy to hear, but mainly what is troubling me is that none of them have a maxlag feature. This means that even if I only want to see correlations between two time series with lags between -100 and +100 ms, for example, it will still calculate the correlation for every lag between -20000 and +20000 ms (which is the length of the time series). This gives a 200x performance hit! Do I have to recode the cross-correlation function by hand to include this feature?
Here are a couple functions to compute auto- and cross-correlation with limited lags. The order of multiplication (and conjugation, in the complex case) was chosen to match the corresponding behavior of numpy.correlate.
import numpy as np
from numpy.lib.stride_tricks import as_strided
def _check_arg(x, xname):
x = np.asarray(x)
if x.ndim != 1:
raise ValueError('%s must be one-dimensional.' % xname)
return x
def autocorrelation(x, maxlag):
"""
Autocorrelation with a maximum number of lags.
`x` must be a one-dimensional numpy array.
This computes the same result as
numpy.correlate(x, x, mode='full')[len(x)-1:len(x)+maxlag]
The return value has length maxlag + 1.
"""
x = _check_arg(x, 'x')
p = np.pad(x.conj(), maxlag, mode='constant')
T = as_strided(p[maxlag:], shape=(maxlag+1, len(x) + maxlag),
strides=(-p.strides[0], p.strides[0]))
return T.dot(p[maxlag:].conj())
def crosscorrelation(x, y, maxlag):
"""
Cross correlation with a maximum number of lags.
`x` and `y` must be one-dimensional numpy arrays with the same length.
This computes the same result as
numpy.correlate(x, y, mode='full')[len(a)-maxlag-1:len(a)+maxlag]
The return vaue has length 2*maxlag + 1.
"""
x = _check_arg(x, 'x')
y = _check_arg(y, 'y')
py = np.pad(y.conj(), 2*maxlag, mode='constant')
T = as_strided(py[2*maxlag:], shape=(2*maxlag+1, len(y) + 2*maxlag),
strides=(-py.strides[0], py.strides[0]))
px = np.pad(x, maxlag, mode='constant')
return T.dot(px)
For example,
In [367]: x = np.array([2, 1.5, 0, 0, -1, 3, 2, -0.5])
In [368]: autocorrelation(x, 3)
Out[368]: array([ 20.5, 5. , -3.5, -1. ])
In [369]: np.correlate(x, x, mode='full')[7:11]
Out[369]: array([ 20.5, 5. , -3.5, -1. ])
In [370]: y = np.arange(8)
In [371]: crosscorrelation(x, y, 3)
Out[371]: array([ 5. , 23.5, 32. , 21. , 16. , 12.5, 9. ])
In [372]: np.correlate(x, y, mode='full')[4:11]
Out[372]: array([ 5. , 23.5, 32. , 21. , 16. , 12.5, 9. ])
(It will be nice to have such a feature in numpy itself.)
matplotlib.pyplot provides matlab like syntax for computating and plotting of cross correlation , auto correlation etc.
You can use xcorr which allows to define the maxlags parameter.
import matplotlib.pyplot as plt
import numpy as np
data = np.arange(0,2*np.pi,0.01)
y1 = np.sin(data)
y2 = np.cos(data)
coeff = plt.xcorr(y1,y2,maxlags=10)
print(*coeff)
[-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
8 9 10] [ -9.81991753e-02 -8.85505028e-02 -7.88613080e-02 -6.91325329e-02
-5.93651264e-02 -4.95600447e-02 -3.97182508e-02 -2.98407146e-02
-1.99284126e-02 -9.98232812e-03 -3.45104289e-06 9.98555430e-03
1.99417667e-02 2.98641953e-02 3.97518558e-02 4.96037706e-02
5.94189688e-02 6.91964864e-02 7.89353663e-02 8.86346584e-02
9.82934198e-02] <matplotlib.collections.LineCollection object at 0x00000000074A9E80> Line2D(_line0)
Until numpy implements the maxlag argument, you can use the function ucorrelate from the pycorrelate package. ucorrelate operates on numpy arrays and has a maxlag keyword. It implements the correlation from using a for-loop and optimizes the execution speed with numba.
Example - autocorrelation with 3 time lags:
import numpy as np
import pycorrelate as pyc
x = np.array([2, 1.5, 0, 0, -1, 3, 2, -0.5])
c = pyc.ucorrelate(x, x, maxlag=3)
c
Result:
Out[1]: array([20, 5, -3])
The pycorrelate documentation contains a notebook showing perfect match between pycorrelate.ucorrelate and numpy.correlate:
I think I have found a solution, as I was facing the same problem:
If you have two vectors x and y of any length N, and want a cross-correlation with a window of fixed len m, you can do:
x = <some_data>
y = <some_data>
# Trim your variables
x_short = x[window:]
y_short = y[window:]
# do two xcorrelations, lagging x and y respectively
left_xcorr = np.correlate(x, y_short) #defaults to 'valid'
right_xcorr = np.correlate(x_short, y) #defaults to 'valid'
# combine the xcorrelations
# note the first value of right_xcorr is the same as the last of left_xcorr
xcorr = np.concatenate(left_xcorr, right_xcorr[1:])
Remember you might need to normalise the variables if you want a bounded correlation
Here is another answer, sourced from here, seems faster on the margin than np.correlate and has the benefit of returning a normalised correlation:
def rolling_window(self, a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
def xcorr(self, x,y):
N=len(x)
M=len(y)
meany=np.mean(y)
stdy=np.std(np.asarray(y))
tmp=self.rolling_window(np.asarray(x),M)
c=np.sum((y-meany)*(tmp-np.reshape(np.mean(tmp,-1),(N-M+1,1))),-1)/(M*np.std(tmp,-1)*stdy)
return c
as I answered here, https://stackoverflow.com/a/47897581/5122657
matplotlib.xcorr has the maxlags param. It is actually a wrapper of the numpy.correlate, so there is no performance saving. Nevertheless it gives exactly the same result given by Matlab's cross-correlation function. Below I edited the code from matplotlib so that it will return only the correlation. The reason is that if we use matplotlib.corr as it is, it will return the plot as well. The problem is, if we put complex data type as the arguments into it, we will get "casting complex to real datatype" warning when matplotlib tries to draw the plot.
<!-- 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
来源：https://stackoverflow.com/questions/30677241/how-to-limit-cross-correlation-window-width-in-numpy