def generator(src_dm, inds):
res = None
R = src_dm.bandlims.get_obj((0, 0))[1]
A = src_dm.bandlims.get_obj((1, 1))[1]
nr = src_dm.ns.get_obj((0, 0))
na = src_dm.ns.get_obj((1, 1))
dr = R / nr
da = A / na
S = 1.0 / dr
P = 1.0 / da
r = inds[0]
a = inds[1]
phi = 1.0 + 0.0j
ampl = 1.0
symb = 'f'
sfc = sgl.expfc(S, r, phi, ampl, symb)
pfc = sgl.expfc(P, a, phi, ampl, symb)
res = [lambda x: sfc([x[0]]) * pfc([x[1]])]
return res
def generator(src_dm, ind):
res = None
F = src_dm.bandlims.get_obj((0, 0))[1]
n = src_dm.ns.get_obj((0, 0))
df = F / n
T = 1.0 / df
f = ind[0]
phi = 1.0 + 0.0j
ampl = 1.0
symb = 't'
res = [sgl.expfc(T, f, phi, ampl, symb)]
return res
def generator(src_dm, ind):
res = None
T = src_dm.bandlims.get_obj((0, 0))[1]
n = src_dm.ns.get_obj((0, 0))
dt = T / n
F = 1.0 / dt
t = ind[0]
phi = 1.0 + 0.0j
ampl = 1.0
symb = 'f'
res = [sgl.expfc(F, t, phi, ampl, symb)]
return res
coeffs = np.zeros(shape=(n1, n2), dtype=np.complex64)
#Here tt - tt means that the signal is a linear combination of time domain base
#functions and coordinatization is also done in this space. Base functions are app-
#roximated currently by step functions.
bsgen = pdtab.get_bsgen(symb_tt, symb_tt)
#Coefficients are set in a way that the time domain signal form will be a planar
#wave with frequencies (2,1).
phase = 1.0 + 0.0j
ampl = 1.0
f1 = 2
f2 = 1
exp1 = su.expfc(T1, f1, phase, ampl, 't')
exp2 = su.expfc(T2, f2, phase, ampl, 't')
coeffs = np.zeros(shape=(n1, n2), dtype=np.complex64)
for i in range(0, n1):
for k in range(0, n2):
coeffs[i, k] = exp1([i * T1 / n1]) * exp2([k * T2 / n2])
sgl_tt_tt = sp.Signal(labels, coeffs, bsgen)
#Vectorization is needed for the plot. Plotting is done by a simple Matplotlib
#wrapper.
vsign = sp.sigvec(sgl_tt_tt.fcval_at, dm_tt, dm_tt)[1]
vw.plot_heatvec(dm_tt, vsign)