Updated the strategy files with AOflaggers latest versions.

rfistrategies/lofar-default.lua

@@ -2,18 +2,17 @@
  This is the LOFAR strategy. It is almost equal to 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
+  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 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 = 3.0 -- Amount of "extra" smoothing in frequency direction
   local transient_threshold_factor = 1.0 -- decreasing this value makes detection of transient RFI more aggressive
@@ -27,22 +26,21 @@ function execute(input)
   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. 
+  -- so that the statistics ignore any flags in the input data.
   local copy_of_input = input:copy()
-  
-  for ipol,polarization in ipairs(inpPolarizations) do
- 
+
+  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 = math.pow(threshold_factor_step, iteration_count-i)
+    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)
- 
+      aoflagger.sumthreshold(data, sumthr_level, sumthr_level * transient_threshold_factor, true, true)
+
       -- Do timestep & channel flagging
       local chdata = data:copy()
       aoflagger.threshold_timestep_rms(data, 3.5)
@@ -59,21 +57,20 @@ function execute(input)
       tmp:set_mask(data)
       data = tmp
 
-      aoflagger.set_progress((ipol-1)*iteration_count+i, #inpPolarizations*iteration_count )
+      aoflagger.set_progress((ipol - 1) * iteration_count + i, #inpPolarizations * iteration_count)
     end -- end of iterations
 
-    aoflagger.sumthreshold(data, base_threshold, base_threshold*transient_threshold_factor, true, true)
+    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 )
+    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
-

rfistrategies/lofar-lba-wideband.lua

 --[[
  This is a LBA AOFlagger strategy for combined subbands, version 2021-03-30
  Author: André Offringa
-]]--
+]]
 
 aoflagger.require_min_version("3.1")
 
 function execute(input)
-
   --
   -- Generic settings
   --
@@ -17,51 +16,56 @@ function execute(input)
   -- { 'I', 'Q' } to flag only on Stokes I and Q
   local flag_polarizations = input:get_polarizations()
 
-  local base_threshold = 1.2  -- lower means more sensitive detection
+  local base_threshold = 1.2 -- lower means more sensitive detection
   -- How to flag complex values, options are: phase, amplitude, real, imaginary, complex
   -- May have multiple values to perform detection multiple times
   local flag_representations = { "amplitude" }
-  local iteration_count = 5  -- how many iterations to perform?
+  local iteration_count = 5 -- how many iterations to perform?
   local threshold_factor_step = 2.0 -- How much to increase the sensitivity each iteration?
   -- If the following variable is true, the strategy will consider existing flags
   -- as bad data. It will exclude flagged data from detection, and make sure that any existing
   -- flags on input will be flagged on output. If set to false, existing flags are ignored.
   local exclude_original_flags = true
   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()
 
-  if(not exclude_original_flags) then
+  if not exclude_original_flags then
     input:clear_mask()
   end
   -- For collecting statistics. Note that this is done after clear_mask(),
-  -- so that the statistics ignore any flags in the input data. 
+  -- so that the statistics ignore any flags in the input data.
   local copy_of_input = input:copy()
 
   aoflagger.normalize_bandpass(input)
-  
-  for ipol,polarization in ipairs(flag_polarizations) do
- 
+
+  for ipol, polarization in ipairs(flag_polarizations) do
     local pol_data = input:convert_to_polarization(polarization)
     local original_data
 
-    for _,representation in ipairs(flag_representations) do
-
+    for _, representation in ipairs(flag_representations) do
       data = pol_data:convert_to_complex(representation)
       original_data = data:copy()
 
-      for i=1,iteration_count-1 do
-        local threshold_factor = math.pow(threshold_factor_step, iteration_count-i)
+      for i = 1, iteration_count - 1 do
+        local threshold_factor = threshold_factor_step ^ (iteration_count - i)
 
         local sumthr_level = threshold_factor * base_threshold
-        if(exclude_original_flags) then
-          aoflagger.sumthreshold_masked(data, original_data, sumthr_level, sumthr_level*transient_threshold_factor, true, true)
+        if exclude_original_flags then
+          aoflagger.sumthreshold_masked(
+            data,
+            original_data,
+            sumthr_level,
+            sumthr_level * transient_threshold_factor,
+            true,
+            true
+          )
         else
-          aoflagger.sumthreshold(data, sumthr_level, sumthr_level*transient_threshold_factor, true, true)
+          aoflagger.sumthreshold(data, sumthr_level, sumthr_level * transient_threshold_factor, true, true)
         end
 
         -- Do timestep & channel flagging
@@ -72,7 +76,7 @@ function execute(input)
 
         -- High pass filtering steps
         data:set_visibilities(original_data)
-        if(exclude_original_flags) then
+        if exclude_original_flags then
           data:join_mask(original_data)
         end
 
@@ -82,31 +86,40 @@ function execute(input)
         -- the following visualize function will add the current result
         -- to the list of displayable visualizations.
         -- If the script is not running inside rfigui, the call is ignored.
-        aoflagger.visualize(data, "Fit #"..i, i-1)
+        aoflagger.visualize(data, "Fit #" .. i, i - 1)
 
         local tmp = original_data - data
         tmp:set_mask(data)
         data = tmp
 
-        aoflagger.visualize(data, "Residual #"..i, i+iteration_count)
-        aoflagger.set_progress((ipol-1)*iteration_count+i, #flag_polarizations*iteration_count )
+        aoflagger.visualize(data, "Residual #" .. i, i + iteration_count)
+        aoflagger.set_progress((ipol - 1) * iteration_count + i, #flag_polarizations * iteration_count)
       end -- end of iterations
 
-      if(exclude_original_flags) then
-        aoflagger.sumthreshold_masked(data, original_data, base_threshold, base_threshold*transient_threshold_factor, true, true)
+      if exclude_original_flags then
+        aoflagger.sumthreshold_masked(
+          data,
+          original_data,
+          base_threshold,
+          base_threshold * transient_threshold_factor,
+          true,
+          true
+        )
       else
-        aoflagger.sumthreshold(data, base_threshold, base_threshold*transient_threshold_factor, true, true)
+        aoflagger.sumthreshold(data, base_threshold, base_threshold * transient_threshold_factor, true, true)
       end
     end -- end of complex representation iteration
 
-    if(exclude_original_flags) then
+    if exclude_original_flags then
       data:join_mask(original_data)
     end
 
     -- Helper function used below
     function contains(arr, val)
-      for _,v in ipairs(arr) do
-        if v == val then return true end
+      for _, v in ipairs(arr) do
+        if v == val then
+          return true
+        end
       end
       return false
     end
@@ -120,11 +133,11 @@ function execute(input)
       input:join_mask(data)
     end
 
-    aoflagger.visualize(data, "Residual #"..iteration_count, 2*iteration_count)
-    aoflagger.set_progress(ipol, #flag_polarizations )
+    aoflagger.visualize(data, "Residual #" .. iteration_count, 2 * iteration_count)
+    aoflagger.set_progress(ipol, #flag_polarizations)
   end -- end of polarization iterations
 
-  if(exclude_original_flags) then
+  if exclude_original_flags then
     aoflagger.scale_invariant_rank_operator_masked(input, copy_of_input, 0.2, 0.2)
   else
     aoflagger.scale_invariant_rank_operator(input, 0.2, 0.2)
@@ -140,4 +153,3 @@ function execute(input)
   end
   input:flag_nans()
 end
-