Mercurial > repos > PredictPDPS / changeset
changeset
added plotting and table generator
Wed, 24 Apr 2024 17:04:57 +0300
- author
- Neil Dizon <neil.dizon@helsinki.fi>
- date
- Wed, 24 Apr 2024 17:04:57 +0300
- changeset 32
- 88632284396f
- parent 31
- cbedcfcc0557
- child 33
- e091766f556d
added plotting and table generator
| src/PlotResults.jl | file | annotate | diff | comparison | revisions |
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/src/PlotResults.jl Wed Apr 24 17:04:57 2024 +0300 @@ -0,0 +1,721 @@ +__precompile__() + +module PlotResults + + +######################## +# Load external modules +######################## + +using DelimitedFiles, CSV, DataFrames +using PlotlyJS +using Colors +using XLSX: writetable +using Statistics + +export fv_plot, ssim_plot, psnr_plot, calculate_statistics + +global mystart = 38 +global myend = 135 + +function fv_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)200x300_pdps_known_fv_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dot", name="proxi (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dashdot", name="primo (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dash", name="greed (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdashdot", name="rotat (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="solid", name="dusca (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + ##################################################### + zerodual = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_zerodual") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :function_value]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdash", name="zerod (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + layout = Layout(yaxis_type="log",legend_title_text="Function values") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine;zerodual], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine;zerodual],layout) +end + + +######################################################### +# FUNCTION FOR SSIM +######################################################### +function ssim_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)200x300_pdps_known_ssim_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dot", name="proxi (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dashdot", name="primo (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dash", name="greed (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdashdot", name="rotat (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="solid", name="dusca (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + ##################################################### + zerodual = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_zerodual") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :ssim]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdash", name="zerod (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + layout = Layout(yaxis_type="log", legend_title_text="SSIM") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine;zerodual], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine;zerodual],layout) +end + + + +######################################################### +# FUNCTION FOR PSNR +######################################################### +function psnr_plot(name :: String, save_plot::Bool=true) + save_path = "./img/$(name)200x300_pdps_known_psnr_plot.html" + ################################################# + orig = Vector{GenericTrace{Dict{Symbol, Any}}}() + ################################################# + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_proximal") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dot", name="proxi (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(orig, trace) + end + + ##################################################### + identity = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_primalonly") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dashdot", name="primo (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + adhoc = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_greedy") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="dash", name="greed (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + ##################################################### + rotation = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_rotation") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdashdot", name="rotat (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + ##################################################### + affine = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_dualscaling") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="solid", name="dusca (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + ##################################################### + zerodual = Vector{GenericTrace{Dict{Symbol, Any}}}() + ##################################################### + directory_path = "./img/" + files = readdir(directory_path) + filtered_files = filter(file -> startswith(file, "$(name)200x300_pdps_known_zerodual") && endswith(file, "0.txt"), files) + + # Define an array of line styles and colors + # line_styles = ["solid", "dash", "dot", "dashdot", "longdash"] + line_colors = distinguishable_colors(15) + + for (index,file) in enumerate(filtered_files) + filename = directory_path*file + #data = readdlm(filename, '\t', skipstart=1) + data = CSV.File(filename, delim='\t'; header = 2) |> DataFrame + + # Extract the columns you want to plot + X = Int64.(data[mystart:myend,:iter]) + Y = Float64.(data[mystart:myend, :psnr]) + + #line_style = line_styles[i] + line_color = line_colors[index] + # Extract parameters for legend + α, τ₀, σ₀ = extract_parameters(filename) + + trace = PlotlyJS.scatter(;x=X, y=Y, mode="lines", hovertemplate="%{x:.0f},%{y:.3f}", + line_color=line_color, line_dash="longdash", name="zerod (α=$α, τ₀=$τ₀, σ₀=$σ₀)") + push!(identity, trace) + end + + + layout = Layout(yaxis_type="log", legend_title_text="PSNR") # Set legend title + + if save_plot && !isempty(save_path) + plotlyjs = plot([orig;identity;adhoc;rotation;affine; zerodual], layout) + open(save_path, "w") do io + PlotlyBase.to_html(io, plotlyjs.plot) + end + elseif save_plot + println("Please provide a valid save path.") + end + + return plot([orig;identity;adhoc;rotation;affine;zerodual],layout) +end + +###################### +# Parameter extraction +###################### +function extract_parameters(filename :: String) + # Extracting parameters + params_line = readlines(filename)[1] + + # Split the line by commas and trim each part + params_parts = map(strip, split(params_line, ',')) + + # Initialize variables to store parameter values + α_value, τ₀_value, σ₀_value = missing, missing, missing + + # Look for specific substrings to identify the values of α, τ₀, and σ₀ + for param_part in params_parts + if contains(param_part, "α = ") + α_value = parse(Float64, split(param_part, '=')[2]) + elseif contains(param_part, "τ₀ = ") + τ₀_value = parse(Float64, split(param_part, '=')[2]) + elseif contains(param_part, "σ₀ = ") + σ₀_value = parse(Float64, split(param_part, '=')[2]) + end + end + + # Assign the values to α, τ₀, and σ₀ + α = α_value + τ₀ = τ₀_value + σ₀ = σ₀_value + return α, τ₀, σ₀ +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
