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
