move

shopt/analyticLBFGS.jl

@@ -0,0 +1,101 @@
+include("radialProfiles.jl")
+
+#=
+Functions for Cost and Gradient used in the Optimize step with LBFGS
+NB: Reparameterization for [s, g1, g2] via [σ, e1, e2] to constraint update steps inside R+ x B_2(r)
+=#
+
+function cost(params; r = r, c= c, starL=starCatalog[iteration], radial=fGaussian, AnalyticStampSize=AnalyticStampSize, get_middle_nxn=get_middle_nxn) 
+  Totalcost = 0
+  σ = params[1]
+  s_guess = σ^2
+  e1_guess = params[2]
+  e2_guess = params[3]
+  ellipticity = sqrt((e1_guess)^2 + (e2_guess)^2)
+  normG = sqrt(1 + 0.5*( (1/ellipticity^2) - sqrt( (4/ellipticity^2)+ (1/ellipticity^4)  )  ))
+  ratio = ellipticity/normG
+  g1_guess = e1_guess/ratio
+  g2_guess = e2_guess/ratio
+
+  starL = get_middle_nxn(starL, AnalyticStampSize)
+  r = AnalyticStampSize
+  c = AnalyticStampSize
+
+  sum = 0
+  for u in 1:r
+    for v in 1:c
+      try
+        sum +=  radial(u,v, g1_guess, g2_guess, s_guess, r/2,c/2)
+      catch
+        sum += 0
+      end
+    end
+  end
+  A_guess = 1/sum
+
+  for u in 1:r
+    for v in 1:c
+      if isnan(starL[u,v])
+        Totalcost += 0
+      else
+        Totalcost += 0.5*(A_guess*radial(u, v, g1_guess, g2_guess, s_guess, r/2, c/2) - starL[u, v])^2
+      end
+    end
+  end
+  return Totalcost
+end
+
+
+function costD(params; r=r, c=c, starL=pixelGridFits[iteration], radial=fGaussian, AnalyticStampSize=AnalyticStampSize, get_middle_nxn=get_middle_nxn) 
+  Totalcost = 0
+  σ = params[1]
+  s_guess = σ^2
+  e1_guess = params[2]
+  e2_guess = params[3]
+  ellipticity = sqrt((e1_guess)^2 + (e2_guess)^2)
+  normG = sqrt(1 + 0.5*( (1/ellipticity^2) - sqrt( (4/ellipticity^2)+ (1/ellipticity^4)  )  ))
+  ratio = ellipticity/normG
+  g1_guess = e1_guess/ratio
+  g2_guess = e2_guess/ratio
+
+  starL = get_middle_nxn(starL, AnalyticStampSize)
+  r = AnalyticStampSize
+  c = AnalyticStampSize
+
+  sum = 0
+  for u in 1:r
+    for v in 1:c
+      try
+        sum +=  radial(u,v, g1_guess, g2_guess, s_guess, r/2,c/2)
+      catch
+        sum += 0
+      end
+    end
+  end
+  A_guess = 1/sum
+
+  for u in 1:r
+    for v in 1:c
+      if isnan(starL[u,v])
+        Totalcost += 0
+      else
+        Totalcost += 0.5*(A_guess*radial(u, v, g1_guess, g2_guess, s_guess, r/2, c/2) - starL[u, v])^2
+      end
+    end
+  end
+  return Totalcost
+end
+
+function g!(storage, p)
+    grad_cost = ForwardDiff.gradient(cost, p)
+    storage[1] = grad_cost[1]
+    storage[2] = grad_cost[2]
+    storage[3] = grad_cost[3]
+end
+
+function gD!(storage, p)
+  grad_cost = ForwardDiff.gradient(costD, p)
+  storage[1] = grad_cost[1]
+  storage[2] = grad_cost[2]
+  storage[3] = grad_cost[3]
+end

shopt/argparser.jl

@@ -0,0 +1,14 @@
+#=
+Function to parse arguments from the command line
+=#
+
+function process_arguments(args)
+  fancyPrint("Parsing Arguments")
+  configdir = args[1]
+  println("━ Config Directory: ", configdir)
+  outdir = args[2]
+  println("━ Output Directory: ", outdir)
+  catalog = args[3]
+  println("━ Catalog: ", catalog)
+end
+

shopt/chisq.jl

@@ -0,0 +1,12 @@
+function chisq_cost(params; starL=starCatalog[iteration], weight_map=errVignets[iteration])    
+  pixel_values = params
+  chisq = sum(x -> isfinite(x) ? x : 0, (pixel_values .- vec(starL)) .^ 2 ./ vec(weight_map))
+  return chisq 
+end 
+
+
+function chisq_g!(storage, p)
+  chisq_grad_cost = ForwardDiff.gradient(chisq_cost, p)
+  storage[1:length(chisq_grad_cost)] = chisq_grad_cost
+end
+