def create_img(metadata):
"""
Constructs a single image
"""
metadata.sort(key=lambda x: x[1])
data = []
for i in range(len(subbands)):
sb = []
for v in metadata[i * config.inttime * 2:(i + 1) * config.inttime * 2]:
f = open(v[3], 'r')
f.seek(v[4])
dat = parse_data(f.read(LEN_BDY))
dat[(dat < -1) |
(dat > 1)] = 0 # To filter out outlier visibilities
sb.append(dat)
data.append(np.array(sb).mean(axis=0))
return np.rot90(
np.real(
gfft.gfft(np.ravel(data),
in_ax,
out_ax,
enforce_hermitian_symmetry=True,
verbose=False,
W=config.window,
alpha=config.alpha)), 1) * mask
dx = 1.0 / res
out_ax = [(dx, res), (dx, res)]
L = np.linspace(-1, 1, res)
M = np.linspace(-1, 1, res)
mask = np.ones((res, res))
xv, yv = np.meshgrid(L, M)
image = data.copy()
im = image.mean(axis=(-1, -2))
im = np.rot90(
np.real(
gfft.gfft(np.ravel(im), [u, v],
out_ax,
enforce_hermitian_symmetry=True,
verbose=True,
W=6,
alpha=1.5)), 1) * mask
plt.imsave('image.png',
im,
vmin=np.nanmean(im) - 3 * np.nanstd(im),
vmax=np.nanmean(im) + 3 * np.nanstd(im),
origin='lower')
plt.figure(figsize=(16, 16), facecolor='white')
plt.imshow(im,
vmin=np.nanmean(im) - 3 * np.nanstd(im),
vmax=np.nanmean(im) + 3 * np.nanstd(im),
origin='lower')
plt.title('SB320-202012281240-lba_outer.vis direct imaging')
