def __new__(self, M=10, beta=1, phi=0, normalised=True, inverted=False):
if not inverted:
if phi == 0:
wc = np.kaiser(M, beta) # Window coefficients
win = wc / sum(wc) # Normalised window
else:
wc = np.kaiser(M, beta) # Window coefficients
m = np.arange(0, M) # Create M indeces from 0 to 1
a = exp(-1j * 2 * pi * m * phi) # Steering vector
ws = dot(wc, a) # Normalisation factor
win = a * wc / ws # Steered and normalised window
else:
if phi == 0:
wc = 1 / np.kaiser(M, beta) # Window coefficients
win = wc / sum(wc) # Normalised window
else:
wc = 1 / np.kaiser(M, beta) # Window coefficients
m = np.arange(0, M) # Create M indeces from 0 to 1
a = exp(-1j * 2 * pi * m * phi) # Steering vector
ws = dot(wc, a) # Normalisation factor
win = a * wc / ws # Steered and normalised window
w = np.Ndarray.__new__(self, win)
# axes=('M',),
# desc = 'Kaiser (beta=%d, phi=%d)'%(beta,phi))
# shape_desc=('M','1'))
return w
def __new__(self, M=10, phi=0, normalised=True):
# Create the window
if phi == 0:
win = np.ones((M, ), dtype=None) / M
else:
wc = np.ones(M, dtype=complex) # Window coefficients
m = np.arange(0, M) # Create M indeces from 0 to 1
a = exp(-1j * 2 * pi * m * phi) # Steering vector
ws = dot(wc, a) # Normalisation factor
win = a * wc / ws # Steered and normalised window
w = np.Ndarray.__new__(self, win)
# axes=('M',),
# desc = 'Rectangular (phi=%d)'%phi)
# desc='Rectangular (phi=%d)'%phi,
# shape_desc=('M','1'))
return w
def __new__(self, M=10, a=0.54, phi=0, normalised=True):
# Create the window
if phi == 0:
wc = a + (1 - a) * np.cos(2 * pi * np.linspace(-0.5, 0.5, M))
win = wc / sum(wc) # Normalised window
else:
n = np.linspace(-0.5, 0.5, M)
wc = a + (1 - a) * np.cos(2 * pi * n) # Window coefficients
m = np.arange(0, M) # Create M indeces from 0 to 1
aa = exp(-1j * 2 * pi * m * phi) # Steering vector
ws = dot(wc, aa) # Normalisation factor
win = aa * wc / ws # Steered and normalised window
w = np.Ndarray.__new__(self, win)
# axes=('M',),
# desc = 'Rectangular (phi=%d)'%phi)
# desc='Rectangular (phi=%d)'%phi,
# shape_desc=('M','1'))
return w
def get_stationary2(A):
P = np.linalg.matrix_power(A, 10000)
P_next = np.dot(P, A)
while not np.allclose(P, P_next):
P_next = np.dot(P_next, A)
return P_next[0]
