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--[[
This is the JVLA strategy, version 2020-06-14
It is based on the generic "minimal" AOFlagger strategy, version 2020-06-14
Author: André Offringa
]]
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 collecting statistics. Note that this is done after clear_mask(),
-- so that the statistics ignore any flags in the input data.
local copy_of_input = input:copy()
for ipol, polarization in ipairs(inpPolarizations) do
local data = input:convert_to_polarization(polarization)
data = data:convert_to_complex(representation)
local original_data = 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(data, sumthr_level, sumthr_level * transient_threshold_factor, true, true)
-- Do timestep & channel flagging
aoflagger.threshold_timestep_rms(data, 3.5)
-- High pass filtering steps
data:set_visibilities(original_data)
local resized_data = aoflagger.downsample(data, 3, frequency_resize_factor, true)
aoflagger.low_pass_filter(resized_data, 21, 31, 2.5, 1.0)
aoflagger.upsample(resized_data, data, 3, frequency_resize_factor)
local tmp = original_data - data
tmp:set_mask(data)
data = tmp
aoflagger.set_progress((ipol - 1) * iteration_count + i, #inpPolarizations * iteration_count)
end -- end of iterations
aoflagger.normalize_subbands(data, 48)
aoflagger.sumthreshold(data, base_threshold, base_threshold * transient_threshold_factor, true, true)
if input:is_complex() then
data = data:convert_to_complex("complex")
end
input:set_polarization_data(polarization, 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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