Specify lag in numpy.correlate

Question

Matlab\'s cross-correlation function xcorr(x,y,maxlags) has an option maxlag, which returns the cross-correlation sequence over the lag range

Answer 1

This is my implementation of the lead-lag correlation, but it is limited to be 1-D and not guaranteed to be the best in terms of efficient. It uses the scipy.stats.pearsonr to the do the core computation, so also returned is the p value for the coefficient. Please modify to optimize based on this straw man.

def lagcorr(x,y,lag=None,verbose=True):
    '''Compute lead-lag correlations between 2 time series.

    ,: 1-D time series.
    : lag option, could take different forms of :
          if 0 or None, compute ordinary correlation and p-value;
          if positive integer, compute lagged correlation with lag
          upto ;
          if negative integer, compute lead correlation with lead
          upto <-lag>;
          if pass in an list or tuple or array of integers, compute 
          lead/lag correlations at different leads/lags.

    Note: when talking about lead/lag, uses  as a reference.
    Therefore positive lag means  lags  by , computation is
    done by shifting  to the left hand side by  with respect to
    .
    Similarly negative lag means  leads  by , computation is
    done by shifting  to the right hand side by  with respect to
    .

    Return : a (n*2) array, with 1st column the correlation 
    coefficients, 2nd column correpsonding p values.

    Currently only works for 1-D arrays.
    '''

    import numpy
    from scipy.stats import pearsonr

    if len(x)!=len(y):
        raise('Input variables of different lengths.')

    #--------Unify types of -------------
    if numpy.isscalar(lag):
        if abs(lag)>=len(x):
            raise('Maximum lag equal or larger than array.')
        if lag<0:
            lag=-numpy.arange(abs(lag)+1)
        elif lag==0:
            lag=[0,]
        else:
            lag=numpy.arange(lag+1)    
    elif lag is None:
        lag=[0,]
    else:
        lag=numpy.asarray(lag)

    #-------Loop over lags---------------------
    result=[]
    if verbose:
        print '\n#: Computing lagged-correlations at lags:',lag

    for ii in lag:
        if ii<0:
            result.append(pearsonr(x[:ii],y[-ii:]))
        elif ii==0:
            result.append(pearsonr(x,y))
        elif ii>0:
            result.append(pearsonr(x[ii:],y[:-ii]))

    result=numpy.asarray(result)

    return result