def detrend_linear(x): "Return x minus best fit line; 'linear' detrending " # I'm going to regress x on xx=range(len(x)) and return x - # (b*xx+a). Now that I have polyfit working, I could convert the # code here, but if it ain't broke, don't fix it! xx = arange(float(len(x))) X = transpose(array([xx]+[x])) C = cov(X) b = C[0,1]/C[0,0] a = mean(x) - b*mean(xx) return x-(b*xx+a)
def detrend_linear(x): "Return x minus best fit line; 'linear' detrending " # I'm going to regress x on xx=range(len(x)) and return x - # (b*xx+a). Now that I have polyfit working, I could convert the # code here, but if it ain't broke, don't fix it! xx = arange(float(len(x))) X = transpose(array([xx] + [x])) C = cov(X) b = C[0, 1] / C[0, 0] a = mean(x) - b * mean(xx) return x - (b * xx + a)
def corrcoef(*args): """ corrcoef(X) where X is a matrix returns a matrix of correlation coefficients for each numrows observations and numcols variables. corrcoef(x,y) where x and y are vectors returns the matrix or correlation coefficients for x and y. Numeric arrays can be real or complex The correlation matrix is defined from the covariance matrix C as r(i,j) = C[i,j] / sqrt(C[i,i]*C[j,j]) """ if len(args)==2: X = transpose(array([args[0]]+[args[1]])) elif len(args)==1: X = args[0] else: raise RuntimeError, 'Only expecting 1 or 2 arguments' C = cov(X) if len(args)==2: d = resize(diagonal(C), (2,1)) denom = numerix.mlab.sqrt(matrixmultiply(d,transpose(d))) else: dc = diagonal(C) N = len(dc) shape = N,N vi = resize(dc, shape) denom = numerix.mlab.sqrt(vi*transpose(vi)) # element wise multiplication r = divide(C,denom) try: return r.real except AttributeError: return r
def corrcoef(*args): """ corrcoef(X) where X is a matrix returns a matrix of correlation coefficients for each numrows observations and numcols variables. corrcoef(x,y) where x and y are vectors returns the matrix or correlation coefficients for x and y. Numeric arrays can be real or complex The correlation matrix is defined from the covariance matrix C as r(i,j) = C[i,j] / sqrt(C[i,i]*C[j,j]) """ if len(args) == 2: X = transpose(array([args[0]] + [args[1]])) elif len(args) == 1: X = args[0] else: raise RuntimeError, 'Only expecting 1 or 2 arguments' C = cov(X) if len(args) == 2: d = resize(diagonal(C), (2, 1)) denom = numerix.mlab.sqrt(matrixmultiply(d, transpose(d))) else: dc = diagonal(C) N = len(dc) shape = N, N vi = resize(dc, shape) denom = numerix.mlab.sqrt(vi * transpose(vi)) # element wise multiplication r = divide(C, denom) try: return r.real except AttributeError: return r