Mercurial > repos > PredictPDPS / changeset

--- a/Manifest.toml	Thu Apr 25 11:08:03 2024 -0500
+++ b/Manifest.toml	Thu Apr 25 11:16:24 2024 -0500
@@ -25,7 +25,7 @@
 deps = ["DelimitedFiles", "Printf"]
 path = "../AlgTools"
 uuid = "c46e2e78-5339-41fd-a966-983ff60ab8e7"
-version = "0.1.0"
+version = "0.1.1"
 
 [[ArgTools]]
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
@@ -40,12 +40,6 @@
 [[Artifacts]]
 uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
 
-[[AssetRegistry]]
-deps = ["Distributed", "JSON", "Pidfile", "SHA", "Test"]
-git-tree-sha1 = "b25e88db7944f98789130d7b503276bc34bc098e"
-uuid = "bf4720bc-e11a-5d0c-854e-bdca1663c893"
-version = "0.1.0"
-
 [[AxisAlgorithms]]
 deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"]
 git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7"
@@ -66,12 +60,6 @@
 uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
 version = "0.1.8"
 
-[[Blink]]
-deps = ["Base64", "Distributed", "HTTP", "JSExpr", "JSON", "Lazy", "Logging", "MacroTools", "Mustache", "Mux", "Pkg", "Reexport", "Sockets", "WebIO"]
-git-tree-sha1 = "bc93511973d1f949d45b0ea17878e6cb0ad484a1"
-uuid = "ad839575-38b3-5650-b840-f874b8c74a25"
-version = "0.12.9"
-
 [[BufferedStreams]]
 git-tree-sha1 = "4ae47f9a4b1dc19897d3743ff13685925c5202ec"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -287,12 +275,6 @@
 uuid = "2e619515-83b5-522b-bb60-26c02a35a201"
 version = "2.5.0+0"
 
-[[EzXML]]
-deps = ["Printf", "XML2_jll"]
-git-tree-sha1 = "380053d61bb9064d6aa4a9777413b40429c79901"
-uuid = "8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615"
-version = "1.2.0"
-
 [[FFMPEG_jll]]
 deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
 git-tree-sha1 = "ab3f7e1819dba9434a3a5126510c8fda3a4e7000"
@@ -356,12 +338,6 @@
 uuid = "559328eb-81f9-559d-9380-de523a88c83c"
 version = "1.0.10+0"
 
-[[FunctionalCollections]]
-deps = ["Test"]
-git-tree-sha1 = "04cb9cfaa6ba5311973994fe3496ddec19b6292a"
-uuid = "de31a74c-ac4f-5751-b3fd-e18cd04993ca"
-version = "0.5.0"
-
 [[Future]]
 deps = ["Random"]
 uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
@@ -450,12 +426,6 @@
 uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566"
 version = "2.8.1+1"
 
-[[Hiccup]]
-deps = ["MacroTools", "Test"]
-git-tree-sha1 = "6187bb2d5fcbb2007c39e7ac53308b0d371124bd"
-uuid = "9fb69e20-1954-56bb-a84f-559cc56a8ff7"
-version = "0.2.2"
-
 [[Hwloc_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "ca0f6bf568b4bfc807e7537f081c81e35ceca114"
@@ -547,10 +517,10 @@
 version = "0.3.8"
 
 [[ImageTools]]
-deps = ["AlgTools", "ColorTypes", "FileIO", "GR", "OffsetArrays", "Printf", "Setfield"]
+deps = ["AlgTools", "ColorTypes", "ColorVectorSpace", "FileIO", "GR", "OffsetArrays", "Printf", "Setfield", "TestImages"]
 path = "../ImageTools"
 uuid = "b548cc0d-4ade-417e-bf62-0e39f9d2eee9"
-version = "0.1.0"
+version = "0.1.1"
 
 [[ImageTransformations]]
 deps = ["AxisAlgorithms", "ColorVectorSpace", "CoordinateTransformations", "ImageBase", "ImageCore", "Interpolations", "OffsetArrays", "Rotations", "StaticArrays"]
@@ -655,12 +625,6 @@
 uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210"
 version = "1.5.0"
 
-[[JSExpr]]
-deps = ["JSON", "MacroTools", "Observables", "WebIO"]
-git-tree-sha1 = "b413a73785b98474d8af24fd4c8a975e31df3658"
-uuid = "97c1335a-c9c5-57fe-bc5d-ec35cebe8660"
-version = "0.5.4"
-
 [[JSON]]
 deps = ["Dates", "Mmap", "Parsers", "Unicode"]
 git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a"
@@ -679,12 +643,6 @@
 uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
 version = "3.0.2+0"
 
-[[Kaleido_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "43032da5832754f58d14a91ffbe86d5f176acda9"
-uuid = "f7e6163d-2fa5-5f23-b69c-1db539e41963"
-version = "0.2.1+0"
-
 [[LAME_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
 git-tree-sha1 = "f6250b16881adf048549549fba48b1161acdac8c"
@@ -714,12 +672,6 @@
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 version = "1.3.1"
 
-[[Lazy]]
-deps = ["MacroTools"]
-git-tree-sha1 = "1370f8202dac30758f3c345f9909b97f53d87d3f"
-uuid = "50d2b5c4-7a5e-59d5-8109-a42b560f39c0"
-version = "0.15.1"
-
 [[LazyArtifacts]]
 deps = ["Artifacts", "Pkg"]
 uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
@@ -732,12 +684,12 @@
 [[LibCURL]]
 deps = ["LibCURL_jll", "MozillaCACerts_jll"]
 uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
-version = "0.6.4"
+version = "0.6.3"
 
 [[LibCURL_jll]]
 deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
 uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
-version = "8.4.0+0"
+version = "7.84.0+0"
 
 [[LibGit2]]
 deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
@@ -746,7 +698,7 @@
 [[LibSSH2_jll]]
 deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
 uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
-version = "1.11.0+1"
+version = "1.10.2+0"
 
 [[Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
@@ -921,18 +873,6 @@
 uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
 version = "2022.10.11"
 
-[[Mustache]]
-deps = ["Printf", "Tables"]
-git-tree-sha1 = "a7cefa21a2ff993bff0456bf7521f46fc077ddf1"
-uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70"
-version = "1.0.19"
-
-[[Mux]]
-deps = ["AssetRegistry", "Base64", "HTTP", "Hiccup", "MbedTLS", "Pkg", "Sockets"]
-git-tree-sha1 = "7295d849103ac4fcbe3b2e439f229c5cc77b9b69"
-uuid = "a975b10e-0019-58db-a62f-e48ff68538c9"
-version = "1.0.2"
-
 [[NaNMath]]
 deps = ["OpenLibm_jll"]
 git-tree-sha1 = "0877504529a3e5c3343c6f8b4c0381e57e4387e4"
@@ -955,11 +895,6 @@
 uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
 version = "1.2.0"
 
-[[Observables]]
-git-tree-sha1 = "7438a59546cf62428fc9d1bc94729146d37a7225"
-uuid = "510215fc-4207-5dde-b226-833fc4488ee2"
-version = "0.5.5"
-
 [[OffsetArrays]]
 git-tree-sha1 = "e64b4f5ea6b7389f6f046d13d4896a8f9c1ba71e"
 uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
@@ -1073,12 +1008,6 @@
 uuid = "54e51dfa-9dd7-5231-aa84-a4037b83483a"
 version = "0.3.6"
 
-[[Pidfile]]
-deps = ["FileWatching", "Test"]
-git-tree-sha1 = "2d8aaf8ee10df53d0dfb9b8ee44ae7c04ced2b03"
-uuid = "fa939f87-e72e-5be4-a000-7fc836dbe307"
-version = "1.3.0"
-
 [[Pixman_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"]
 git-tree-sha1 = "64779bc4c9784fee475689a1752ef4d5747c5e87"
@@ -1096,36 +1025,6 @@
 uuid = "eebad327-c553-4316-9ea0-9fa01ccd7688"
 version = "0.3.3"
 
-[[PlotlyBase]]
-deps = ["ColorSchemes", "Dates", "DelimitedFiles", "DocStringExtensions", "JSON", "LaTeXStrings", "Logging", "Parameters", "Pkg", "REPL", "Requires", "Statistics", "UUIDs"]
-git-tree-sha1 = "56baf69781fc5e61607c3e46227ab17f7040ffa2"
-uuid = "a03496cd-edff-5a9b-9e67-9cda94a718b5"
-version = "0.8.19"
-
-[[PlotlyJS]]
-deps = ["Base64", "Blink", "DelimitedFiles", "JSExpr", "JSON", "Kaleido_jll", "Markdown", "Pkg", "PlotlyBase", "PlotlyKaleido", "REPL", "Reexport", "Requires", "WebIO"]
-git-tree-sha1 = "e62d886d33b81c371c9d4e2f70663c0637f19459"
-uuid = "f0f68f2c-4968-5e81-91da-67840de0976a"
-version = "0.18.13"
-
-    [PlotlyJS.extensions]
-    CSVExt = "CSV"
-    DataFramesExt = ["DataFrames", "CSV"]
-    IJuliaExt = "IJulia"
-    JSON3Ext = "JSON3"
-
-    [PlotlyJS.weakdeps]
-    CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-    IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
-    JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
-
-[[PlotlyKaleido]]
-deps = ["Base64", "JSON", "Kaleido_jll"]
-git-tree-sha1 = "2650cd8fb83f73394996d507b3411a7316f6f184"
-uuid = "f2990250-8cf9-495f-b13a-cce12b45703c"
-version = "2.2.4"
-
 [[PoissonRandom]]
 deps = ["Random"]
 git-tree-sha1 = "a0f1159c33f846aa77c3f30ebbc69795e5327152"
@@ -1496,24 +1395,6 @@
 uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
 version = "1.4.2"
 
-[[WebIO]]
-deps = ["AssetRegistry", "Base64", "Distributed", "FunctionalCollections", "JSON", "Logging", "Observables", "Pkg", "Random", "Requires", "Sockets", "UUIDs", "WebSockets", "Widgets"]
-git-tree-sha1 = "0eef0765186f7452e52236fa42ca8c9b3c11c6e3"
-uuid = "0f1e0344-ec1d-5b48-a673-e5cf874b6c29"
-version = "0.8.21"
-
-[[WebSockets]]
-deps = ["Base64", "Dates", "HTTP", "Logging", "Sockets"]
-git-tree-sha1 = "4162e95e05e79922e44b9952ccbc262832e4ad07"
-uuid = "104b5d7c-a370-577a-8038-80a2059c5097"
-version = "1.6.0"
-
-[[Widgets]]
-deps = ["Colors", "Dates", "Observables", "OrderedCollections"]
-git-tree-sha1 = "fcdae142c1cfc7d89de2d11e08721d0f2f86c98a"
-uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
-version = "0.6.6"
-
 [[WoodburyMatrices]]
 deps = ["LinearAlgebra", "SparseArrays"]
 git-tree-sha1 = "5f24e158cf4cee437052371455fe361f526da062"
@@ -1525,12 +1406,6 @@
 uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
-[[XLSX]]
-deps = ["Artifacts", "Dates", "EzXML", "Printf", "Tables", "ZipFile"]
-git-tree-sha1 = "319b05e790046f18f12b8eae542546518ef1a88f"
-uuid = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"
-version = "0.10.1"
-
 [[XML2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
 git-tree-sha1 = "532e22cf7be8462035d092ff21fada7527e2c488"
@@ -1693,12 +1568,6 @@
 uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
 version = "1.5.0+0"
 
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.10.1"
-
 [[Zlib_jll]]
 deps = ["Libdl"]
 uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
@@ -1790,7 +1659,7 @@
 [[nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
-version = "1.52.0+1"
+version = "1.48.0+0"
 
 [[oneTBB_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]

--- a/Project.toml	Thu Apr 25 11:08:03 2024 -0500
+++ b/Project.toml	Thu Apr 25 11:16:24 2024 -0500
@@ -22,7 +22,6 @@
 MAT = "23992714-dd62-5051-b70f-ba57cb901cac"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 PerceptualColourMaps = "54e51dfa-9dd7-5231-aa84-a4037b83483a"
-PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a"
 PoissonRandom = "e409e4f3-bfea-5376-8464-e040bb5c01ab"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 QuartzImageIO = "dca85d43-d64c-5e67-8c65-017450d5d020"
@@ -32,4 +31,3 @@
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
-XLSX = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"

--- a/src/PET/PET.jl	Thu Apr 25 11:08:03 2024 -0500
+++ b/src/PET/PET.jl	Thu Apr 25 11:16:24 2024 -0500
@@ -37,7 +37,7 @@
 include("AlgorithmProximal.jl")
 include("AlgorithmRotation.jl")
 include("AlgorithmZeroDual.jl")
-include("PlotResults.jl")
+#include("PlotResults.jl")
 
 import .AlgorithmDualScaling
 import .AlgorithmGreedy
@@ -50,7 +50,7 @@
 using .Radon: backproject!
 using .ImGenerate
 using .OpticalFlow: DisplacementFull, DisplacementConstant
-using .PlotResults
+#using .PlotResults
 
 
 ##############
@@ -61,8 +61,8 @@
        demo_petS4, demo_petS5, demo_petS6, demo_petS7,
        demo_petB1, demo_petB2, demo_petB3, 
        demo_petB4, demo_petB5, demo_petB6, demo_petB7,
-       batchrun_shepplogan, batchrun_brainphantom, batchrun_pet,
-       plot_pet
+       batchrun_shepplogan, batchrun_brainphantom, batchrun_pet
+       #plot_pet
 
 ###################################
 # Parameterisation and experiments
@@ -557,14 +557,14 @@
 # Plotting SSIM and PSNR
 #########################
 
-function plot_pet(kwargs...)
-    ssim_plot("shepplogan")
-    psnr_plot("shepplogan")
-    fv_plot("shepplogan")
-    ssim_plot("brainphantom")
-    psnr_plot("brainphantom")
-    fv_plot("brainphantom")
-end
+#function plot_pet(kwargs...)
+#    ssim_plot("shepplogan")
+#    psnr_plot("shepplogan")
+#    fv_plot("shepplogan")
+#    ssim_plot("brainphantom")
+#    psnr_plot("brainphantom")
+#    fv_plot("brainphantom")
+#end
 
 
 ###############

--- a/src/PlotResults.jl	Thu Apr 25 11:08:03 2024 -0500
+++ b/src/PlotResults.jl	Thu Apr 25 11:16:24 2024 -0500
@@ -1,5 +1,3 @@
-__precompile__()
-
 module PlotResults
 
 
@@ -7,13 +5,12 @@
 # Load external modules
 ########################
 
-using DelimitedFiles, CSV, DataFrames
+using CSV, DataFrames
 using PlotlyJS
 using Colors
-using XLSX: writetable
 using Statistics
 
-export fv_plot, ssim_plot, psnr_plot, calculate_statistics
+export fv_plot, ssim_plot, psnr_plot
 
 global mystart = 38
 global myend = 135
@@ -656,66 +653,4 @@
 end
 
 
-function calculate_statistics()
-    ImName = ("lighthouse200x300", "shepplogan256x256", "brainphantom256x256")
-    AlgName = ("dualscaling", "greedy","noprediction","primalonly","proximal","rotation","zerodual")
-    mystart = 41 # Corresponds to the 500th iterate
-    
-    # Define an array to store results
-    results = DataFrame(experiment = String[], α = Float64[], algorithm = String[], psnr_mean1 = Float64[], psnr_mean500 = Float64[], psnr_ci = String[], ssim_mean1 = Float64[], ssim_mean500 = Float64[], ssim_ci = String[])
-
-    for imname in ImName
-        for algname in AlgName
-            directory_path = "./img/"
-            files = readdir(directory_path)
-            filtered_files = filter(file -> startswith(file, "$(imname)_pdps_known_$(algname)") && endswith(file, ".txt"), files)
-
-            for file in filtered_files
-                filename = directory_path * file
-                data = CSV.File(filename, delim='\t', header=2) |> DataFrame
-
-                # Extract α from filename
-                α, _, _ = extract_parameters(filename)
-
-
-                # Extract SSIM and PSNR columns starting from 1st iteration
-                ssim_values1 = Float64.(data[:, :ssim])
-                psnr_values1 = Float64.(data[:, :psnr])
-
-                # Extract SSIM and PSNR columns starting from 500th iteration
-                ssim_values500 = Float64.(data[mystart:end, :ssim])
-                psnr_values500 = Float64.(data[mystart:end, :psnr])
-
-                # Calculate mean and confidence intervals
-                ssim_mean1 = round(mean(ssim_values1), digits=4)
-                psnr_mean1 = round(mean(psnr_values1), digits=4)
-
-                ssim_mean500 = round(mean(ssim_values500), digits=4)
-                psnr_mean500 = round(mean(psnr_values500), digits=4)
-                ssim_std500 = round(std(ssim_values500), digits=4)
-                psnr_std500 = round(std(psnr_values500), digits=4)
-                n = length(ssim_values500)
-
-                ssim_ci_lower = round(ssim_mean500 - 1.96 * ssim_std500 / sqrt(n), digits=4)
-                ssim_ci_upper = round(ssim_mean500 + 1.96 * ssim_std500 / sqrt(n), digits=4)
-                psnr_ci_lower = round(psnr_mean500 - 1.96 * psnr_std500 / sqrt(n), digits=4)
-                psnr_ci_upper = round(psnr_mean500 + 1.96 * psnr_std500 / sqrt(n), digits=4)
-
-                ssim_ci = "$(ssim_ci_lower) - $(ssim_ci_upper)"
-                psnr_ci = "$(psnr_ci_lower) - $(psnr_ci_upper)"
-                experiment = "$(imname)"
-                algorithm = "$(algname)"
-
-                # Append results to DataFrame
-                push!(results, (experiment, α, algorithm, psnr_mean1, psnr_mean500, psnr_ci, ssim_mean1, ssim_mean500, ssim_ci))
-            end
-        end
-    end
-    sort!(results, [:experiment, :α])
-    excel_path = "./img/summarystats.xlsx"
-        if isfile(excel_path)
-            rm(excel_path)
-        end
-            writetable(excel_path, sheetname="Experiments", results)
-end
 end # Module
\ No newline at end of file

--- a/src/PredictPDPS.jl	Thu Apr 25 11:08:03 2024 -0500
+++ b/src/PredictPDPS.jl	Thu Apr 25 11:16:24 2024 -0500
@@ -22,7 +22,7 @@
 using AlgTools.StructTools
 using AlgTools.LinkedLists
 using AlgTools.Comms
-using ImageTools.Visualise: secs_ns, grayimg, do_visualise 
+using ImageTools.Visualise: secs_ns, grayimg, do_visualise
 using ImageTools.ImFilter: gaussian
 
 #####################
@@ -40,7 +40,8 @@
 include("AlgorithmBothNL.jl")
 include("AlgorithmFB.jl")
 include("AlgorithmFBDual.jl")
-include("PlotResults.jl")
+include("Stats.jl")
+#include("PlotResults.jl")
 
 
 # Additional
@@ -72,7 +73,8 @@
 
 using .ImGenerate
 using .OpticalFlow: DisplacementFull, DisplacementConstant
-using .PlotResults
+using .Stats
+#using .PlotResults
 using .PET
 
 ##############
@@ -85,14 +87,15 @@
        demo_unknown1,demo_unknown2,demo_unknown3,
        batchrun_denoising,
        batchrun_predictors,
-       demo_denoising1, demo_denoising2, demo_denoising3, 
+       demo_denoising1, demo_denoising2, demo_denoising3,
        demo_denoising4, demo_denoising5, demo_denoising6, demo_denoising7,
-       demo_petS1, demo_petS2, demo_petS3, 
+       demo_petS1, demo_petS2, demo_petS3,
        demo_petS4, demo_petS5, demo_petS6, demo_petS7,
-       demo_petB1, demo_petB2, demo_petB3, 
+       demo_petB1, demo_petB2, demo_petB3,
        demo_petB4, demo_petB5, demo_petB6, demo_petB7,
        batchrun_shepplogan, batchrun_brainphantom, batchrun_pet,
-       plot_denoising, plot_pet, calculate_statistics
+       calculate_statistics
+       #plot_denoising, plot_pet,
        
 ###################################
 # Parameterisation and experiments
@@ -218,17 +221,17 @@
     Experiment(AlgorithmDualScaling, DisplacementConstant, lighthouse,
                p_known₀_denoising),
     Experiment(AlgorithmGreedy, DisplacementConstant, lighthouse,
-               p_known₀_denoising),  
+               p_known₀_denoising),
     Experiment(AlgorithmNoPrediction, DisplacementConstant, lighthouse,
-               p_known₀_denoising),  
+               p_known₀_denoising),
     Experiment(AlgorithmPrimalOnly, DisplacementConstant, lighthouse,
-               p_known₀_denoising),                
+               p_known₀_denoising),
     Experiment(AlgorithmProximal, DisplacementConstant, lighthouse,
                p_known₀_denoising ⬿ (phantom_ρ = 100,)),
     Experiment(AlgorithmRotation, DisplacementConstant, lighthouse,
                p_known₀_denoising),
     Experiment(AlgorithmZeroDual, DisplacementConstant, lighthouse,
-               p_known₀_denoising),  
+               p_known₀_denoising),
 )
 
 const denoising_experiments_all = Iterators.flatten((
@@ -302,7 +305,7 @@
             wp("sigma", (e_params.σ₀ == 1 ? "" : "$(e_params.σ₀)") * "\\sigma_{\\max}")
         end
     end
-end                
+end
 
 function run_experiments(;visualise=true,
                           recalculate=true,
@@ -458,7 +461,7 @@
 
         d = take!(datachannel)
 
-        for iter=1:params.maxiter 
+        for iter=1:params.maxiter
             dnext = take!(datachannel)
             st = step(d.b_noisy, d.v, dnext.b_noisy) do calc_objective
                 stn = st
@@ -484,7 +487,7 @@
                     entry = LogEntry(iter, tm, value,
                                      #sc*d.v_cumul_true[1],
                                      #sc*d.v_cumul_true[2],
-                                     #sc*v[1], sc*v[2], 
+                                     #sc*v[1], sc*v[2],
                                      assess_psnr(x, d.b_true),
                                      assess_ssim(x, d.b_true),
                                      #assess_psnr(d.b_noisy, d.b_true),
@@ -585,7 +588,7 @@
                 GR.polyline(x, y)
             end
         end
-        GR.updatews() 
+        GR.updatews()
     end
 end
 
@@ -593,11 +596,11 @@
 # Plotting SSIM and PSNR
 #########################
 
-function plot_denoising(kwargs...)
-    ssim_plot("lighthouse")
-    psnr_plot("lighthouse")
-    fv_plot("lighthouse")
-end
+#function plot_denoising(kwargs...)
+#    ssim_plot("lighthouse")
+#    psnr_plot("lighthouse")
+#    fv_plot("lighthouse")
+#end
 
 
 ###############

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/Stats.jl	Thu Apr 25 11:16:24 2024 -0500
@@ -0,0 +1,78 @@
+__precompile__()
+
+module Stats
+
+
+########################
+# Load external modules
+########################
+
+using CSV, DataFrames
+using Statistics
+
+export calculate_statistics
+
+function calculate_statistics()
+    ImName = ("lighthouse200x300", "shepplogan256x256", "brainphantom256x256")
+    AlgName = ("dualscaling", "greedy","noprediction","primalonly","proximal","rotation","zerodual")
+    mystart = 41 # Corresponds to the 500th iterate
+    
+    # Define an array to store results
+    results = DataFrame(experiment = String[], α = Float64[], algorithm = String[], psnr_mean1 = Float64[], psnr_mean500 = Float64[], psnr_ci = String[], ssim_mean1 = Float64[], ssim_mean500 = Float64[], ssim_ci = String[])
+
+    for imname in ImName
+        for algname in AlgName
+            directory_path = "./img/"
+            files = readdir(directory_path)
+            filtered_files = filter(file -> startswith(file, "$(imname)_pdps_known_$(algname)") && endswith(file, ".txt"), files)
+
+            for file in filtered_files
+                filename = directory_path * file
+                data = CSV.File(filename, delim='\t', header=2) |> DataFrame
+
+                # Extract α from filename
+                α, _, _ = extract_parameters(filename)
+
+
+                # Extract SSIM and PSNR columns starting from 1st iteration
+                ssim_values1 = Float64.(data[:, :ssim])
+                psnr_values1 = Float64.(data[:, :psnr])
+
+                # Extract SSIM and PSNR columns starting from 500th iteration
+                ssim_values500 = Float64.(data[mystart:end, :ssim])
+                psnr_values500 = Float64.(data[mystart:end, :psnr])
+
+                # Calculate mean and confidence intervals
+                ssim_mean1 = round(mean(ssim_values1), digits=4)
+                psnr_mean1 = round(mean(psnr_values1), digits=4)
+
+                ssim_mean500 = round(mean(ssim_values500), digits=4)
+                psnr_mean500 = round(mean(psnr_values500), digits=4)
+                ssim_std500 = round(std(ssim_values500), digits=4)
+                psnr_std500 = round(std(psnr_values500), digits=4)
+                n = length(ssim_values500)
+
+                ssim_ci_lower = round(ssim_mean500 - 1.96 * ssim_std500 / sqrt(n), digits=4)
+                ssim_ci_upper = round(ssim_mean500 + 1.96 * ssim_std500 / sqrt(n), digits=4)
+                psnr_ci_lower = round(psnr_mean500 - 1.96 * psnr_std500 / sqrt(n), digits=4)
+                psnr_ci_upper = round(psnr_mean500 + 1.96 * psnr_std500 / sqrt(n), digits=4)
+
+                ssim_ci = "$(ssim_ci_lower) - $(ssim_ci_upper)"
+                psnr_ci = "$(psnr_ci_lower) - $(psnr_ci_upper)"
+                experiment = "$(imname)"
+                algorithm = "$(algname)"
+
+                # Append results to DataFrame
+                push!(results, (experiment, α, algorithm, psnr_mean1, psnr_mean500, psnr_ci, ssim_mean1, ssim_mean500, ssim_ci))
+            end
+        end
+    end
+    sort!(results, [:experiment, :α])
+    csv_path = "./img/summarystats.csv"
+    if isfile(csv_path)
+    	rm(csv_path)
+    end
+        CSV.write(csv_path, results)
+end
+
+end