def get_sparse_matrix(M,N,frac=0.1): 'return a MxN sparse matrix with frac elements randomly filled' data = zeros((M,N))*0. for i in range(int(M*N*frac)): x = random.randint(0,M-1) y = random.randint(0,N-1) data[x,y] = rand() return data
def get_sparse_matrix(M, N, frac=0.1): 'return a MxN sparse matrix with frac elements randomly filled' data = zeros((M, N)) * 0. for i in range(int(M * N * frac)): x = random.randint(0, M - 1) y = random.randint(0, N - 1) data[x, y] = rand() return data
def fftsurr(x): """ Compute an FFT phase randomized surrogate of x """ z = fft(x) a = 2.*pi*1j phase = a*rand(len(x)) z = z*exp(phase) return inverse_fft(z).real
def fftsurr(x, detrend=detrend_none, window=window_none): """ Compute an FFT phase randomized surrogate of x """ x = window(detrend(x)) z = fft(x) a = 2.*pi*1j phase = a*rand(len(x)) z = z*exp(phase) return inverse_fft(z).real
def fftsurr(x, detrend=detrend_none, window=window_none): """ Compute an FFT phase randomized surrogate of x """ x = window(detrend(x)) z = fft(x) a = 2. * pi * 1j phase = a * rand(len(x)) z = z * exp(phase) return inverse_fft(z).real