Mercurial > repos > PredictPDPS / file comparison
comparison: src/Stats.jl
src/Stats.jl
- changeset 34
- aca9c90f151c
- child 41
- 3e54b2b54029
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| 33:e091766f556d | 34:aca9c90f151c |
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| 1 __precompile__() | |
| 2 | |
| 3 module Stats | |
| 4 | |
| 5 | |
| 6 ######################## | |
| 7 # Load external modules | |
| 8 ######################## | |
| 9 | |
| 10 using CSV, DataFrames | |
| 11 using Statistics | |
| 12 | |
| 13 export calculate_statistics | |
| 14 | |
| 15 function calculate_statistics() | |
| 16 ImName = ("lighthouse200x300", "shepplogan256x256", "brainphantom256x256") | |
| 17 AlgName = ("dualscaling", "greedy","noprediction","primalonly","proximal","rotation","zerodual") | |
| 18 mystart = 41 # Corresponds to the 500th iterate | |
| 19 | |
| 20 # Define an array to store results | |
| 21 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[]) | |
| 22 | |
| 23 for imname in ImName | |
| 24 for algname in AlgName | |
| 25 directory_path = "./img/" | |
| 26 files = readdir(directory_path) | |
| 27 filtered_files = filter(file -> startswith(file, "$(imname)_pdps_known_$(algname)") && endswith(file, ".txt"), files) | |
| 28 | |
| 29 for file in filtered_files | |
| 30 filename = directory_path * file | |
| 31 data = CSV.File(filename, delim='\t', header=2) |> DataFrame | |
| 32 | |
| 33 # Extract α from filename | |
| 34 α, _, _ = extract_parameters(filename) | |
| 35 | |
| 36 | |
| 37 # Extract SSIM and PSNR columns starting from 1st iteration | |
| 38 ssim_values1 = Float64.(data[:, :ssim]) | |
| 39 psnr_values1 = Float64.(data[:, :psnr]) | |
| 40 | |
| 41 # Extract SSIM and PSNR columns starting from 500th iteration | |
| 42 ssim_values500 = Float64.(data[mystart:end, :ssim]) | |
| 43 psnr_values500 = Float64.(data[mystart:end, :psnr]) | |
| 44 | |
| 45 # Calculate mean and confidence intervals | |
| 46 ssim_mean1 = round(mean(ssim_values1), digits=4) | |
| 47 psnr_mean1 = round(mean(psnr_values1), digits=4) | |
| 48 | |
| 49 ssim_mean500 = round(mean(ssim_values500), digits=4) | |
| 50 psnr_mean500 = round(mean(psnr_values500), digits=4) | |
| 51 ssim_std500 = round(std(ssim_values500), digits=4) | |
| 52 psnr_std500 = round(std(psnr_values500), digits=4) | |
| 53 n = length(ssim_values500) | |
| 54 | |
| 55 ssim_ci_lower = round(ssim_mean500 - 1.96 * ssim_std500 / sqrt(n), digits=4) | |
| 56 ssim_ci_upper = round(ssim_mean500 + 1.96 * ssim_std500 / sqrt(n), digits=4) | |
| 57 psnr_ci_lower = round(psnr_mean500 - 1.96 * psnr_std500 / sqrt(n), digits=4) | |
| 58 psnr_ci_upper = round(psnr_mean500 + 1.96 * psnr_std500 / sqrt(n), digits=4) | |
| 59 | |
| 60 ssim_ci = "$(ssim_ci_lower) - $(ssim_ci_upper)" | |
| 61 psnr_ci = "$(psnr_ci_lower) - $(psnr_ci_upper)" | |
| 62 experiment = "$(imname)" | |
| 63 algorithm = "$(algname)" | |
| 64 | |
| 65 # Append results to DataFrame | |
| 66 push!(results, (experiment, α, algorithm, psnr_mean1, psnr_mean500, psnr_ci, ssim_mean1, ssim_mean500, ssim_ci)) | |
| 67 end | |
| 68 end | |
| 69 end | |
| 70 sort!(results, [:experiment, :α]) | |
| 71 csv_path = "./img/summarystats.csv" | |
| 72 if isfile(csv_path) | |
| 73 rm(csv_path) | |
| 74 end | |
| 75 CSV.write(csv_path, results) | |
| 76 end | |
| 77 | |
| 78 end |
