def main():
if len(sys.argv) < 2:
print("Image the first set of visibilites from a visibilities file")
print()
print("usage: {} <file>".format(sys.argv[0]))
sys.exit(1)
else:
path = sys.argv[1]
_, body = next(read_full(path))
cm = correlation_matrix(body, constants.NUM_ANTS)
corr_data = np.abs(cm)
corr_data[np.diag_indices(constants.NUM_ANTS)] = np.min(corr_data)
gains = np.ones((1, constants.NUM_ANTS), dtype=np.complex64)
cal_mat = gains.conj().transpose() * gains
cm = cal_mat * cm
U, V = load_antpos(constants.ANTPOS)
mDuv = constants.C_MS / FRQ / 2.0
normal = []
for i in range(5):
start = time.time()
grid(U, V, cm, mDuv, constants.IMAGE_RES)
end = time.time()
normal.append(end - start)
print("normal: {}".format(np.average(normal)))
fast = []
for i in range(5):
start = time.time()
grid_fast(U, V, cm, mDuv, constants.IMAGE_RES)
end = time.time()
fast.append(end - start)
print("fast: {}".format(np.average(fast)))
def make_image(cm, frequency):
"""
Create an image from the correlation matrix
"""
# No calibration vector applied
gains = np.ones((1, constants.NUM_ANTS), dtype=np.complex64)
cal_mat = gains.conj().transpose() * gains
# Apply supplied calibration vector
cm = cal_mat * cm
U, V = load_antpos(constants.ANTPOS)
mDuv = constants.C_MS / frequency / 2.0
gridvis = grid(U, V, cm, mDuv, constants.IMAGE_RES)
gridvis = np.fft.fftshift(gridvis)
gridvis = np.flipud(np.fliplr(gridvis))
gridvis = np.conjugate(gridvis)
return np.real(np.fft.fftshift(np.fft.fft2(gridvis)))
def test_load_antpos(self):
load_antpos(constants.ANTPOS)