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--[[
This is the generic "minimal" AOFlagger strategy, version 2020-06-14
Author: André Offringa
It is functionally equal to the default generic strategy, but removes some of the
tweaking parameters and visualization. It is therefore less easy to tweak, but smaller
and possibly slightly faster.
]]
aoflagger.require_min_version("3.0")
function execute(input)
--
-- Generic settings
--
local base_threshold = 1.0 -- lower means more sensitive detection
-- How to flag complex values, options are: phase, amplitude, real, imaginary, complex
local representation = "amplitude"
local iteration_count = 3 -- how many iterations to perform?
local threshold_factor_step = 2.0 -- How much to increase the sensitivity each iteration?
local frequency_resize_factor = 1.0 -- Amount of "extra" smoothing in frequency direction
local transient_threshold_factor = 1.0 -- decreasing this value makes detection of transient RFI more aggressive
--
-- End of generic settings
--
local inpPolarizations = input:get_polarizations()
input:clear_mask()
for ipol, polarization in ipairs(inpPolarizations) do
local converted_data = input:convert_to_polarization(polarization):convert_to_complex(representation)
local converted_copy = converted_data:copy()
for i = 1, iteration_count - 1 do
local threshold_factor = threshold_factor_step ^ (iteration_count - i)
local sumthr_level = threshold_factor * base_threshold
aoflagger.sumthreshold(converted_data, sumthr_level, sumthr_level * transient_threshold_factor, true, true)
-- Do timestep & channel flagging
local chdata = converted_data:copy()
aoflagger.threshold_timestep_rms(converted_data, 3.5)
aoflagger.threshold_channel_rms(chdata, 3.0 * threshold_factor, true)
converted_data:join_mask(chdata)
-- High pass filtering steps
converted_data:set_visibilities(converted_copy)
local resized_data = aoflagger.downsample(converted_data, 3, frequency_resize_factor, true)
aoflagger.low_pass_filter(resized_data, 21, 31, 2.5, 5.0)
aoflagger.upsample(resized_data, converted_data, 3, frequency_resize_factor)
local tmp = converted_copy - converted_data
tmp:set_mask(converted_data)
converted_data = tmp
aoflagger.set_progress((ipol - 1) * iteration_count + i, #inpPolarizations * iteration_count)
end -- end of iterations
aoflagger.sumthreshold(converted_data, base_threshold, base_threshold * transient_threshold_factor, true, true)
if input:is_complex() then
converted_data = converted_data:convert_to_complex("complex")
end
input:set_polarization_data(polarization, converted_data)
aoflagger.set_progress(ipol, #inpPolarizations)
end -- end of polarization iterations
aoflagger.scale_invariant_rank_operator(input, 0.2, 0.2)
aoflagger.threshold_timestep_rms(input, 4.0)
input:flag_nans()
end
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