Cross correlation function

uncertise/distributions.py

@@ -120,27 +120,19 @@ def basic_stats(cdfmat, var_span, verbose=False):
 
     return median, max, mean, min
 
-def cross_correlation(x, y, normed=True, detrend=False, maxlags=10):
-''' Cross correlation of two signals of equal length
+def cross_correlation(x, y, maxlags=10):
+    ''' Cross correlation of two signals of equal length
     Returns the coefficients when normed=True
     Returns inner products when normed=False
     Usage: lags, c = xcorr(x,y,maxlags=len(x)-1)
-    Optional detrending e.g. mlab.detrend_mean'''
+    c_{av}[k] = sum_n a[n+k] * conj(v[n]) '''
 
     Nx = len(x)
     if Nx != len(y):
         raise ValueError('x and y must be equal length')
 
-    if detrend:
-        import matplotlib.mlab as mlab
-        x = mlab.detrend_mean(np.asarray(x))  # can set your preferences here
-        y = mlab.detrend_mean(np.asarray(y))
-
     c = np.correlate(x, y, mode='full')
 
-    if normed:
-        n = np.sqrt(np.dot(x, x) * np.dot(y, y))  # this is the transformation function
-        c = np.true_divide(c, n)
 
     if maxlags is None:
         maxlags = Nx - 1
@@ -151,4 +143,4 @@ def cross_correlation(x, y, normed=True, detrend=False, maxlags=10):
 
     lags = np.arange(-maxlags, maxlags + 1)
     c = c[Nx - 1 - maxlags:Nx + maxlags]
-    return lags, c
+    return lags, c
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