def test_betax1(self):
bx, by = wave.betaxy((1, 19), (1, 2))
bx1 = wave.betax1(19, (1, 2))
self.allclose(bx[0][0], bx1[0])
out = np.empty_like(bx1)
bx1 = wave.betax1(19, (1, 2), out=out)
self.allclose(bx[0][0], out[0])
bx, by = wave.betaxy((1, 19), 2)
bx1 = wave.betax1(19, 2)
self.allclose(bx[0], bx1)
def test_betaxy(self):
bx, by = wave.betaxy(self.shape, self.ks)
self.allclose(self.betax, bx)
self.allclose(self.betay, by)
out = np.empty_like(bx), np.empty_like(by)
wave.betaxy(self.shape, self.ks, out=out)
self.allclose(self.betax, out[0])
self.allclose(self.betay, out[1])
bx, by = wave.betaxy(self.shape, self.ks[1])
self.allclose(self.betax[1], bx)
self.allclose(self.betay[1], by)
self.isfloat(bx)
self.isfloat(by)
def mean_betaphi(field, k0):
"""Calculates mean beta and phi of a given field."""
b = blackman(field.shape[-2:])
f = fft2(field * b) #filter it with blackman..
betax, betay = betaxy(field.shape[-2:], k0)
beta, phi = _fft_betaphi(f, betax, betay)
return beta, phi
def fft_betaxy(shape, k0):
bx, by = betaxy(shape[-2:], np.asarray(k0, FDTYPE)[..., None])
return bx, by #np.broadcast_to(bx,shape),np.broadcast_to(by,shape)