def flag(self, vis, vis_mask, li, gi, tf, ts, **kwargs):
"""Function that does the actual flag."""
# if all have been masked, no need to flag again
if vis_mask.all():
return
first_threshold = self.params['first_threshold']
exp_factor = self.params['exp_factor']
distribution = self.params['distribution']
max_threshold_len = self.params['max_threshold_len']
sensitivity = self.params['sensitivity']
min_connected = self.params['min_connected']
flag_direction = self.params['flag_direction']
tk_size = self.params['tk_size']
fk_size = self.params['fk_size']
threshold_num = max(0, int(self.params['threshold_num']))
vis_abs = np.abs(vis) # operate only on the amplitude
# first round
# first complete masked vals due to ns by interpolate
itp = interpolate.Interpolate(vis_abs, vis_mask)
background = itp.fit()
# Gaussian fileter
gf = gaussian_filter.GaussianFilter(background, time_kernal_size=tk_size, freq_kernal_size=fk_size)
background = gf.fit()
# sum-threshold
vis_diff = vis_abs - background
# an initial run of N = 1 only to remove extremely high amplitude RFI
st = sum_threshold.SumThreshold(vis_diff, vis_mask, first_threshold, exp_factor, distribution, 1, min_connected)
st.execute(sensitivity, flag_direction)
# if all have been masked, no need to flag again
if st.vis_mask.all():
vis_mask[:] = st.vis_mask
return
# next rounds
for i in xrange(threshold_num):
# Gaussian fileter
gf = gaussian_filter.GaussianFilter(vis_diff, st.vis_mask, time_kernal_size=tk_size, freq_kernal_size=fk_size)
background = gf.fit()
# sum-threshold
vis_diff = vis_diff - background
st = sum_threshold.SumThreshold(vis_diff, st.vis_mask, first_threshold, exp_factor, distribution, max_threshold_len, min_connected)
st.execute(sensitivity, flag_direction)
# if all have been masked, no need to flag again
if st.vis_mask.all():
break
# replace vis_mask with the flagged mask
vis_mask[:] = st.vis_mask
def flag(self, vis, vis_mask, li, gi, fb, ts, **kwargs):
"""Function that does the actual flag."""
# if all have been masked, no need to flag again
if vis_mask.all():
return
first_threshold = self.params['first_threshold']
exp_factor = self.params['exp_factor']
distribution = self.params['distribution']
max_threshold_len = self.params['max_threshold_len']
sensitivity = self.params['sensitivity']
min_connected = self.params['min_connected']
flag_direction = self.params['flag_direction']
vis_abs = np.abs(vis) # operate only on the amplitude
# first complete masked vals due to ns by interpolate
itp = interpolate.Interpolate(vis_abs, vis_mask)
background = itp.fit()
background1 = background.copy()
# nt, nf = background.shape
# for fi in range(nf):
# background[:, fi] = multiscale.median_wavelet_smooth(background[:, fi], level=4)
# for ti in range(nt):
# if fb[0] == fb[1]:
# background[ti, :] = multiscale.median_wavelet_smooth(background[ti, :], level=2)
# else:
# background[ti, :] = multiscale.median_wavelet_smooth(background[ti, :], level=3)
background[:] = multiscale.median_wavelet_smooth(background, level=2)
# sum-threshold
# vis_diff = vis_abs - background
vis_diff = background1 - background
# an initial run of N = 1 only to remove extremely high amplitude RFI
st = sum_threshold.SumThreshold(vis_diff, vis_mask, first_threshold,
exp_factor, distribution, 1,
min_connected)
st.execute(sensitivity, flag_direction)
# if all have been masked, no need to flag again
if st.vis_mask.all():
vis_mask[:] = st.vis_mask
return
vis_diff = np.where(st.vis_mask, 0, vis_diff)
st = sum_threshold.SumThreshold(vis_diff, st.vis_mask, first_threshold,
exp_factor, distribution,
max_threshold_len, min_connected)
st.execute(sensitivity, flag_direction)
vis_mask[:] = st.vis_mask