Ciao Sarah
> cross_correlate, but given two arrays, that function returns a single
> scalar value (whereas I was expecting a list of correlation
> coefficients corresponding to how well the two signals match on
> successive lags).
That's not really a mpl question, more a 'numerix' one: what does your numerix
stand for ? numpy ? numarray ?
For numpy, I guess that the corresponding function is 'correlate', which you
must use with the 'full' parameter:
>>>corxy = N.correlate(x, y, 'full')
Unless I'm mistaken, the lags are then 1-n, 2-n,..., -1, 0, 1, ..., n-1
Note that you may want to use anomalies (ie, x-x.mean() and y-y.mean()), and
divide by the variances to get proper coefficients
A second possibility is to use FFTs,
>>>Fx = N.fft.fft(x, npad, axis=axis)
>>>Fy = N.fft.fft(y, npad, axis=axis)
>>>iFxy = N.fft.ifft(Fx.conj()*Fy,n=npad,axis=axis).real
with npad=x.size+y.size
The lags are then 0,1,2,...,n,1-n,...,-1
If you're interested, you can use these two functions that I had written
#..............................................................................
def ccf(x, y, axis=None):
"""Computes the cross-correlation function of two series `x` and `y`.
Note that the computations are performed on anomalies (deviations from
average).
Returns the values of the cross-correlation at different lags.
Lags are given as [0,1,2,...,n,n-1,n-2,...,-2,-1].
:Parameters:
`x` : 1D MaskedArray
Time series.
`y` : 1D MaskedArray
Time series.
`axis` : integer *[None]*
Axis along which to compute (0 for rows, 1 for cols).
If `None`, the array is flattened first.
"""
assert(x.ndim == y.ndim, "Inconsistent shape !")
# assert(x.shape == y.shape, "Inconsistent shape !")
if axis is None:
if x.ndim > 1:
x = x.ravel()
y = y.ravel()
npad = x.size + y.size
xanom = (x - x.mean(axis=None))
yanom = (y - y.mean(axis=None))
Fx = N.fft.fft(xanom, npad, )
Fy = N.fft.fft(yanom, npad, )
iFxy = N.fft.ifft(Fx.conj()*Fy).real
varxy = N.sqrt(N.inner(xanom,xanom) * N.inner(yanom,yanom))
else:
npad = x.shape[axis] + y.shape[axis]
if axis == 1:
if x.shape[0] != y.shape[0]:
raise ValueError, "Arrays should have the same length!"
xanom = (x - x.mean(axis=1)[:,None])
yanom = (y - y.mean(axis=1)[:,None])
varxy = N.sqrt((xanom*xanom).sum(1) * (yanom*yanom).sum(1))
[:,None]
else:
if x.shape[1] != y.shape[1]:
raise ValueError, "Arrays should have the same width!"
xanom = (x - x.mean(axis=0))
yanom = (y - y.mean(axis=0))
varxy = N.sqrt((xanom*xanom).sum(0) * (yanom*yanom).sum(0))
Fx = N.fft.fft(xanom, npad, axis=axis)
Fy = N.fft.fft(yanom, npad, axis=axis)
iFxy = N.fft.ifft(Fx.conj()*Fy,n=npad,axis=axis).real
#
return iFxy/varxy
#..............................................................................
def ccf1d(x,y):
"""Computes the crosscorrelation of two flat arrays `x` and `y`, with the
numpy.correlate function.
Note that the computations are performed on anomalies (deviations from
average).
"""
if x.ndim > 1:
x = x.ravel()
if y.ndim > 1:
y = y.ravel()
(xanom, yanom) = (x-x.mean(), y-y.mean())
corxy = N.correlate(xanom, yanom, 'full')
n = min(x.size, y.size)
# return N.r_[ xc[len(yf)-1:], 0, xc[:len(yf)-1] ]
corxy = N.r_[ corxy[:n][::-1], 0, corxy[n:][::-1] ]
varxy = N.sqrt(N.inner(xanom,xanom) * N.inner(yanom,yanom))
return corxy/varxy
>
> So ... does matplotlib have something akin to what I'm after or is
> there an extention that might have it?
>
> Very many thanks,
>
> Sarah
>
> (*)
>
> http://www.mathworks.com/access/helpdesk/help/toolbox/garch/index.html?/acc
>ess/helpdesk/help/toolbox/garch/crosscorr.html
> http://www.mathworks.com/access/helpdesk/help/toolbox/signal/index.html?/ac
>cess/helpdesk/help/toolbox/signal/xcorr.html
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