Forjulia 1.12

Project.toml

@@ -1,7 +1,7 @@
 name = "HiQGA"
 uuid = "01574e87-63b6-4466-925a-334cf7e21b6b"
+version = "0.4.14"
 authors = ["richardt94 <29562583+richardt94@users.noreply.github.com>"]
-version = "0.4.13"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -14,15 +14,12 @@ Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 DistributedArrays = "aaf54ef3-cdf8-58ed-94cc-d582ad619b94"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FastGaussQuadrature = "442a2c76-b920-505d-bb47-c5924d526838"
-FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
-ImageDistances = "51556ac3-7006-55f5-8cb3-34580c88182d"
 ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
 Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 KernelDensitySJ = "49dc5b4e-6806-4504-b2cc-a81764e7a38b"
-LazyGrids = "7031d0ef-c40d-4431-b2f8-61a8d2f650db"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearMaps = "7a12625a-238d-50fd-b39a-03d52299707e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
@@ -53,16 +50,13 @@ Distances = "0.10.7"
 Distributed = "1.7"
 DistributedArrays = "0.6.7"
 Distributions = "0.25.58"
-FastGaussQuadrature = "0.4.9"
-FileIO = "1.10"
+FastGaussQuadrature = "1.0.2"
 Glob = "1.3.1"
-ImageDistances = "0.2.15"
-ImageIO = "0.5.9"
-Images = "0.25.2"
-Interpolations = "0.14.7"
-KernelDensity = "0.6.5"
+ImageIO = "0.6.9"
+Images = "0.26.2"
+Interpolations = "0.15.1"
+KernelDensity = "0.6.9"
 KernelDensitySJ = "0.2.1"
-LazyGrids = "0.5.0"
 LinearAlgebra = "1.7"
 LinearMaps = "3.5.1"
 NearestNeighbors = "0.4.10"
@@ -78,7 +72,7 @@ Roots = "2.0.0"
 SparseArrays = "1.7"
 SpecialFunctions = "1.6, 2"
 Statistics = "1.7"
-StatsBase = "0.33.16"
+StatsBase = "0.34.5"
 Test = "1.7"
 WriteVTK = "1.18.0"
-julia = "1.7 - 1.10.8"
+julia = "~1.7, ~1.8, ~1.9, ~1.10, ~1.12"

src/AEMnoNuisanceGradientInversionTools.jl

@@ -13,7 +13,7 @@ export plotconvandlast, loopacrossAEMsoundings
 # for deterministic inversions, read in
 function compress(soundings, zall; prefix="", rmfile=true, isfirstparalleliteration=false)
     fname = soundings[1].sounding_string*"_gradientinv.dat"    
-    !isfile(fname) && throw(AssertionError("file does not exist perhaps soundings already zipped?"))
+    !isfile(fname) && throw(AssertionError("file $fname does not exist perhaps soundings already zipped?"))
     fout = prefix == "" ? "zipped.dat" : prefix*"_zipped.dat"
     isfirstparalleliteration && isfile(fout) && throw(AssertionError("Zipped file "*fout*" exists, will not overwrite!"))
     for (i, s) in enumerate(soundings)

src/CommonToAll.jl

@@ -1904,7 +1904,7 @@ function calchighalt(fname_dat, windownum_start, windownum_end;
     end
     nwindows = windownum_end - windownum_start + 1
     if !isnothing(times)
-        @assert length(times) = nwindows
+        @assert length(times) == nwindows
         xlab = "time "*time_units
         dologx = true
     else
@@ -1919,13 +1919,13 @@ function calchighalt(fname_dat, windownum_start, windownum_end;
     ax.semilogy(times, bias, label="bias calculated")
     ax.semilogy(times, sdev, label="sdev calculated")
     if !isnothing(agreednoise)
-        ax.semilogy(times, agreedbias, label="bias agreed")
-        ax.semilogy(times, agreedsdev, label="sdev agreed")
+        ax.semilogy(agreedbias[:,1], agreedbias[:,2], label="bias agreed")
+        ax.semilogy(agreednoise[:,1], agreednoise[:,2], label="sdev agreed")
     end
     ax.set_title(suffix*" noise")
     ax.set_xlabel(xlab)
     ax.set_ylabel(amp_units)
-    dologx && ax.set_xcale("log")
+    dologx && ax.set_xscale("log")
     nicenup(f, fsize=fontsize)
     !isempty(suffix) && (suffix = "_"*suffix)
     savefig(fname_dat[1:end-4]*suffix*".png")

src/GradientInversion.jl

@@ -1,4 +1,4 @@
-using LinearMaps, SparseArrays, PositiveFactorizations, LazyGrids
+using LinearMaps, SparseArrays, PositiveFactorizations
 using Roots:find_zero
 using .AbstractOperator, .GP, Optim
 import .AbstractOperator.setnuforinvtype
@@ -342,7 +342,7 @@ function gradientinv(   m::AbstractVector,
         show_trace = debuglevel > 0 ? true : false
         if usebox
             res = optimize(f, nubounds[:,1], nubounds[:,2], nu, Fminbox(BFGS(linesearch=Optim.LineSearches.BackTracking(order=3))), 
-                Optim.Options(;show_trace, outer_f_tol=f_abstol, f_tol=f_abstol, outer_iterations = boxiters, iterations=ntriesnu)) 
+                Optim.Options(;show_trace, outer_f_abstol=f_abstol, f_abstol=f_abstol, outer_iterations = boxiters, iterations=ntriesnu)) 
         else
             @warn "will not be stable if nuisance outside bounds! set usebox=true"   
             res = optimize(f, nu, BFGS(), 
@@ -460,4 +460,4 @@ function makeinverseR1(n; η=1)
     Dinv = Int1Dop(η)
     Dtinv = Int1Dtop(Dinv)
     LinearMap(Dinv, Dtinv, n)
-end
+end

src/ImageRegression.jl

@@ -2,7 +2,7 @@ module ImageRegression
 import ..AbstractOperator.get_misfit
 using ..AbstractOperator
 using PyPlot, LinearAlgebra, Statistics
-using Random, Images, ImageIO, FileIO, ..CommonToAll
+using Random, Images, ImageIO, ..CommonToAll
 
 import ..Model, ..Options, ..OptionsStat, ..OptionsNonstat
 

test/verify_loop_td.jl

@@ -1,4 +1,4 @@
-using PyPlot, HiQGA.transD_GP, Random, SpecialFunctions
+using PyPlot, HiQGA.transD_GP, Random, HiQGA.transD_GP.AEM_VMD_HMD.SpecialFunctions
 ## model
 zfixed   = [-1e5,   0,    ]
 rho      = [1e20,   1,  ]