Added mask interface accessible from the UI, with it you can create slices from your masks or your images of the samples, included masking in the main processing, on the spectra creation, and on the 3d surface plot, started with interface for pre processing spectral data
This commit is contained in:
Pixelguy14
parent
e13b5ef0cc
commit
3439a8a4dd
399
app.jl
399
app.jl
@ -1,3 +1,5 @@
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# app.jl
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module App
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# ==Packages ==
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using GenieFramework # Set up Genie development environment.
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@ -6,7 +8,6 @@ using Libz
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using PlotlyBase
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using CairoMakie
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using Colors
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# using julia_mzML_imzML
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using MSI_src # Import the new MSIData library
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using Statistics
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using NaturalSort
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@ -20,9 +21,50 @@ using JSON
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using Dates
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# Bring MSIData into App module's scope
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using .MSI_src: MSIData, OpenMSIData, #=GetSpectrum,=# process_spectrum, IterateSpectra, ImzMLSource, _iterate_spectra_fast, MzMLSource, find_mass, ViridisPalette, get_mz_slice, get_multiple_mz_slices, quantize_intensity, save_bitmap, median_filter, save_bitmap, downsample_spectrum, TrIQ, precompute_analytics, ImportMzmlFile
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using .MSI_src: MSIData, OpenMSIData, process_spectrum, IterateSpectra, ImzMLSource, _iterate_spectra_fast, MzMLSource, find_mass, ViridisPalette, get_mz_slice, get_multiple_mz_slices, quantize_intensity, save_bitmap, median_filter, save_bitmap, downsample_spectrum, TrIQ, precompute_analytics, ImportMzmlFile, generate_colorbar_image, load_and_prepare_mask
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if !@isdefined(increment_image)
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include("./julia_imzML_visual.jl")
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end
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# --- Memory Validation Logging ---
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if get(ENV, "GENIE_ENV", "dev") != "prod"
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function get_rss_mb()
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if !Sys.islinux()
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return 0.0
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end
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try
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pid = getpid()
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cmd = `ps -p $pid -o rss=`
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rss_kb_str = read(cmd, String)
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rss_kb = parse(Int, strip(rss_kb_str))
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return round(rss_kb / 1024, digits=2)
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catch e
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@warn "Could not get RSS via `ps` command. Error: $e"
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return 0.0
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end
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end
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function log_memory_usage(context::String, msi_data_val)
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rss_mb = get_rss_mb()
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msi_data_size_mb = 0
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if msi_data_val !== nothing
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msi_data_size_mb = round(Base.summarysize(msi_data_val) / (1024^2), digits=2)
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end
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gc_time_s = round(GC.time(), digits=3)
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println("--- MEMORY LOG [$(context)] ---")
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println(" Timestamp: $(now())")
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println(" Process RSS: $(rss_mb) MB")
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println(" msi_data size: $(msi_data_size_mb) MB")
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println(" Cumulative GC time: $(gc_time_s) s")
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println("--------------------------")
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end
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else
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log_memory_usage(context::String, msi_data_val) = nothing # No-op for production
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end
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@genietools
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@ -47,6 +89,7 @@ include("./julia_imzML_visual.jl")
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@in triqEnabled=false
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@in SpectraEnabled=false
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@in MFilterEnabled=false
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@in maskEnabled=false
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# Dialogs
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@in warning_msg=false
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@in CompareDialog=false
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@ -160,6 +203,9 @@ include("./julia_imzML_visual.jl")
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@out msg_conversion = ""
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@out btnConvertDisable = true
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# == Pre Processing Variables ==
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@in pre_tab = "stabilization"
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# == Batch Summary Dialog ==
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@in showBatchSummary = false
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@out batch_summary = ""
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@ -271,7 +317,7 @@ include("./julia_imzML_visual.jl")
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margin=attr(l=0,r=0,t=120,b=0,pad=0)
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)
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# Dummy 2D scatter plot
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traceSpectra=PlotlyBase.scatter(x=Vector{Float64}(), y=Vector{Float64}(),marker=attr(size=1, color="blue", opacity=0.1))
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traceSpectra=PlotlyBase.stem(x=Vector{Float64}(), y=Vector{Float64}(),marker=attr(size=1, color="blue", opacity=0.1))
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# Create conection to frontend
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@out plotdata=[traceSpectra]
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@out plotlayout=layoutSpectra
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@ -384,6 +430,7 @@ include("./julia_imzML_visual.jl")
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imgWidth, imgHeight = dims[1], dims[2]
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msi_data = nothing # Ensure data is not held in memory
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log_memory_usage("Fast Load (msi_data cleared)", msi_data)
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btnMetadataDisable = false
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btnStartDisable = false
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btnPlotDisable = false
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@ -443,6 +490,7 @@ include("./julia_imzML_visual.jl")
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selected_folder_main = dataset_name
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msi_data = loaded_data
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log_memory_usage("Full Load", msi_data)
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eTime = round(time() - sTime, digits=3)
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msg = "Active file loaded in $(eTime) seconds. Dataset '$(dataset_name)' is ready for analysis."
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@ -612,7 +660,7 @@ include("./julia_imzML_visual.jl")
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overall_progress = 0.0
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progress_message = "Preparing batch process..."
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# --- CAPTURE CURRENT VALUES HERE (NO []) ---
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# --- CAPTURE CURRENT VALUES HERE ---
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current_selected_files = selected_files
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current_nmass = Nmass
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current_tol = Tol
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@ -620,6 +668,7 @@ include("./julia_imzML_visual.jl")
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current_triq_enabled = triqEnabled
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current_triq_prob = triqProb
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current_mfilter_enabled = MFilterEnabled
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current_mask_enabled = maskEnabled
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current_registry_path = registry_path
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println("starting main process with $(length(current_selected_files)) files")
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@ -635,62 +684,33 @@ include("./julia_imzML_visual.jl")
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return
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end
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println("Nmass value: '$current_nmass'")
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println("Type of current_nmass: $(typeof(current_nmass))")
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masses_str = split(current_nmass, ',', keepempty=false)
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println("Parsed masses strings: $masses_str")
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masses = Float64[]
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try
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masses = [parse(Float64, strip(m)) for m in masses_str]
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println("Parsed masses: $masses")
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masses = [parse(Float64, strip(m)) for m in split(current_nmass, ',', keepempty=false)]
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catch e
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progress_message = "Invalid m/z value(s). Please provide a comma-separated list of numbers. Error: $e"
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warning_msg = true
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println(progress_message)
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return
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end
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println("Masses array: $masses, type: $(typeof(masses))")
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if !(masses isa AbstractArray) || isempty(masses)
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if isempty(masses)
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progress_message = "No valid m/z values found. Please provide comma-separated positive numbers."
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warning_msg = true
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println(progress_message)
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return
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end
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# Check other parameters
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if !(0 < current_tol <= 1)
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progress_message = "Tolerance must be between 0 and 1."
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warning_msg = true
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println(progress_message)
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return
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end
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if !(1 < current_color_level < 257)
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progress_message = "Color levels must be between 2 and 256."
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warning_msg = true
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println(progress_message)
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return
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end
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println("entering batch processing loop with masses: $masses")
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# --- 2. Batch Processing Loop ---
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num_files = length(current_selected_files)
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total_steps = num_files
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current_step = 0
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errors = Dict("load_errors" => String[], "slice_errors" => String[], "io_errors" => String[])
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newly_created_folders = String[]
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files_without_mask = 0
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for (file_idx, file_path) in enumerate(current_selected_files)
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# Update progress for current file
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progress_message = "Processing file $file_idx/$num_files: $(basename(file_path))"
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overall_progress = current_step / total_steps
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# Create parameters for this specific file
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all_params = (
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tolerance = current_tol,
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colorL = current_color_level,
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@ -702,14 +722,12 @@ include("./julia_imzML_visual.jl")
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nFiles = num_files
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)
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# Process the file
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success, error_msg = process_file_safely(file_path, masses, all_params, progress_message, overall_progress)
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success, error_msg = process_file_safely(file_path, masses, all_params, progress_message, overall_progress, use_mask=current_mask_enabled)
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if !success
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push!(errors["load_errors"], error_msg)
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else
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dataset_name = replace(basename(file_path), r"\.imzML$"i => "")
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push!(newly_created_folders, dataset_name)
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push!(newly_created_folders, replace(basename(file_path), r"\.imzML$"i => ""))
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end
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current_step += 1
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end
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@ -717,7 +735,6 @@ include("./julia_imzML_visual.jl")
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# --- 3. Final Report ---
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total_time_end = round(time() - total_time_start, digits=3)
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# Update folder lists in UI
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registry = load_registry(current_registry_path)
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all_folders = sort(collect(keys(registry)), lt=natural)
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img_folders = filter(folder -> get(get(registry, folder, Dict()), "is_imzML", false), all_folders)
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@ -738,8 +755,11 @@ include("./julia_imzML_visual.jl")
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warning_msg = true
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end
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mask_summary = current_mask_enabled ? "\nFiles processed without a mask: $(files_without_mask)" : ""
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batch_summary = """
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Processed $(successful_files)/$(num_files) files successfully.
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$(mask_summary)
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Errors by category:
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• Load failures: $(length(errors["load_errors"]))
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@ -751,6 +771,56 @@ include("./julia_imzML_visual.jl")
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"""
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showBatchSummary = true
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# Update UI to display the last generated image
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if !isempty(newly_created_folders)
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timestamp = string(time_ns())
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folder_path = joinpath("public", selected_folder_main)
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if current_triq_enabled
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triq_files = filter(filename -> startswith(filename, "TrIQ_") && endswith(filename, ".bmp"), readdir(folder_path))
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col_triq_files = filter(filename -> startswith(filename, "colorbar_TrIQ_") && endswith(filename, ".png"), readdir(folder_path))
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if !isempty(triq_files)
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latest_triq = triq_files[argmax([mtime(joinpath(folder_path, f)) for f in triq_files])]
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current_triq = latest_triq
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imgIntT = "/$(selected_folder_main)/$(current_triq)?t=$(timestamp)"
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plotdataImgT, plotlayoutImgT, _, _ = loadImgPlot(imgIntT)
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text_nmass = replace(current_triq, r"TrIQ_|.bmp" => "")
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msgtriq = "TrIQ <i>m/z</i>: $(replace(text_nmass, "_" => "."))"
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if !isempty(col_triq_files)
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latest_col_triq = col_triq_files[argmax([mtime(joinpath(folder_path, f)) for f in col_triq_files])]
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current_col_triq = latest_col_triq
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colorbarT = "/$(selected_folder_main)/$(current_col_triq)?t=$(timestamp)"
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else
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colorbarT = ""
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end
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selectedTab = "tab1"
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end
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else # Not TrIQ enabled, display regular MSI image
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msi_files = filter(filename -> startswith(filename, "MSI_") && endswith(filename, ".bmp"), readdir(folder_path))
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col_msi_files = filter(filename -> startswith(filename, "colorbar_MSI_") && endswith(filename, ".png"), readdir(folder_path))
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if !isempty(msi_files)
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latest_msi = msi_files[argmax([mtime(joinpath(folder_path, f)) for f in msi_files])]
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current_msi = latest_msi
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imgInt = "/$(selected_folder_main)/$(current_msi)?t=$(timestamp)"
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plotdataImg, plotlayoutImg, _, _ = loadImgPlot(imgInt)
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text_nmass = replace(current_msi, r"MSI_|.bmp" => "")
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msgimg = "<i>m/z</i>: $(replace(text_nmass, "_" => "."))"
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if !isempty(col_msi_files)
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latest_col_msi = col_msi_files[argmax([mtime(joinpath(folder_path, f)) for f in col_msi_files])]
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current_col_msi = latest_col_msi
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colorbar = "/$(selected_folder_main)/$(current_col_msi)?t=$(timestamp)"
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else
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colorbar = ""
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end
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selectedTab = "tab0"
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end
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end
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end
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catch e
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println("Error in main process: $e")
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msg = "Batch processing failed: $e"
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@ -766,6 +836,10 @@ include("./julia_imzML_visual.jl")
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SpectraEnabled = true
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overall_progress = 0.0
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println("Done")
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GC.gc()
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if Sys.islinux()
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ccall(:malloc_trim, Int32, (Int32,), 0)
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end
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end
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end
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end
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@ -786,7 +860,9 @@ include("./julia_imzML_visual.jl")
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try
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sTime = time()
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registry = load_registry(registry_path)
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target_path = registry[selected_folder_main]["source_path"]
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entry = registry[selected_folder_main]
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target_path = entry["source_path"]
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if target_path == "unknown (manually added)"
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msg = "Dataset selected contained no route."
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warning_msg = true
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@ -797,22 +873,29 @@ include("./julia_imzML_visual.jl")
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msg = "Reloading $(basename(target_path)) for analysis..."
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full_route = target_path
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msi_data = OpenMSIData(target_path)
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existing_entry = get(registry, selected_folder_main, nothing)
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if existing_entry !== nothing && haskey(get(existing_entry, "metadata", Dict()), "global_min_mz") && existing_entry["metadata"]["global_min_mz"] !== nothing
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println("Injecting cached m/z range to skip Pass 1...")
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msi_data.global_min_mz = existing_entry["metadata"]["global_min_mz"]
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msi_data.global_max_mz = existing_entry["metadata"]["global_max_mz"]
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if haskey(get(entry, "metadata", Dict()), "global_min_mz") && entry["metadata"]["global_min_mz"] !== nothing
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msi_data.global_min_mz = entry["metadata"]["global_min_mz"]
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msi_data.global_max_mz = entry["metadata"]["global_max_mz"]
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else
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precompute_analytics(msi_data)
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end
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end
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plotdata, plotlayout, xSpectraMz, ySpectraMz = meanSpectrumPlot(msi_data, selected_folder_main)
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local mask_path_for_plot::Union{String, Nothing} = nothing
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if maskEnabled && get(entry, "has_mask", false)
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mask_path_for_plot = get(entry, "mask_path", "")
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if !isfile(mask_path_for_plot)
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@warn "Mask not found for plotting: $(mask_path_for_plot). Plotting without mask."
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mask_path_for_plot = nothing
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end
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end
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plotdata, plotlayout, xSpectraMz, ySpectraMz = meanSpectrumPlot(msi_data, selected_folder_main, mask_path=mask_path_for_plot)
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selectedTab = "tab2"
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fTime = time()
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eTime = round(fTime - sTime, digits=3)
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msg = "Plot loaded in $(eTime) seconds"
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log_memory_usage("Mean Plot Generated", msi_data)
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catch e
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msg = "Could not generate mean spectrum plot: $e"
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warning_msg = true
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@ -822,6 +905,10 @@ include("./julia_imzML_visual.jl")
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btnPlotDisable = false
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btnSpectraDisable = false
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btnStartDisable = false
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GC.gc()
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if Sys.islinux()
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ccall(:malloc_trim, Int32, (Int32,), 0)
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end
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end
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end
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end
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@ -842,7 +929,9 @@ include("./julia_imzML_visual.jl")
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try
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sTime = time()
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registry = load_registry(registry_path)
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target_path = registry[selected_folder_main]["source_path"]
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entry = registry[selected_folder_main]
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target_path = entry["source_path"]
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if target_path == "unknown (manually added)"
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msg = "Dataset selected contained no route."
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warning_msg = true
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@ -853,22 +942,29 @@ include("./julia_imzML_visual.jl")
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msg = "Reloading $(basename(target_path)) for analysis..."
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full_route = target_path
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msi_data = OpenMSIData(target_path)
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existing_entry = get(registry, selected_folder_main, nothing)
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if existing_entry !== nothing && haskey(get(existing_entry, "metadata", Dict()), "global_min_mz") && existing_entry["metadata"]["global_min_mz"] !== nothing
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println("Injecting cached m/z range to skip Pass 1...")
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msi_data.global_min_mz = existing_entry["metadata"]["global_min_mz"]
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msi_data.global_max_mz = existing_entry["metadata"]["global_max_mz"]
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if haskey(get(entry, "metadata", Dict()), "global_min_mz") && entry["metadata"]["global_min_mz"] !== nothing
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msi_data.global_min_mz = entry["metadata"]["global_min_mz"]
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msi_data.global_max_mz = entry["metadata"]["global_max_mz"]
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else
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precompute_analytics(msi_data)
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end
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end
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plotdata, plotlayout, xSpectraMz, ySpectraMz = sumSpectrumPlot(msi_data, selected_folder_main)
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local mask_path_for_plot::Union{String, Nothing} = nothing
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if maskEnabled && get(entry, "has_mask", false)
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mask_path_for_plot = get(entry, "mask_path", "")
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if !isfile(mask_path_for_plot)
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@warn "Mask not found for plotting: $(mask_path_for_plot). Plotting without mask."
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mask_path_for_plot = nothing
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end
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end
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plotdata, plotlayout, xSpectraMz, ySpectraMz = sumSpectrumPlot(msi_data, selected_folder_main, mask_path=mask_path_for_plot)
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selectedTab = "tab2"
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fTime = time()
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eTime = round(fTime - sTime, digits=3)
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msg = "Total plot loaded in $(eTime) seconds"
|
||||
log_memory_usage("Sum Plot Generated", msi_data)
|
||||
catch e
|
||||
msg = "Could not generate total spectrum plot: $e"
|
||||
warning_msg = true
|
||||
@ -878,6 +974,10 @@ include("./julia_imzML_visual.jl")
|
||||
btnPlotDisable = false
|
||||
btnSpectraDisable = false
|
||||
btnStartDisable = false
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -899,7 +999,17 @@ include("./julia_imzML_visual.jl")
|
||||
try
|
||||
sTime = time()
|
||||
registry = load_registry(registry_path)
|
||||
target_path = registry[selected_folder_main]["source_path"]
|
||||
|
||||
# Add error handling for registry access
|
||||
if !haskey(registry, selected_folder_main)
|
||||
msg = "Dataset '$selected_folder_main' not found in registry."
|
||||
warning_msg = true
|
||||
return
|
||||
end
|
||||
|
||||
entry = registry[selected_folder_main]
|
||||
target_path = entry["source_path"]
|
||||
|
||||
if target_path == "unknown (manually added)"
|
||||
msg = "Dataset selected contained no route."
|
||||
warning_msg = true
|
||||
@ -910,25 +1020,62 @@ include("./julia_imzML_visual.jl")
|
||||
msg = "Reloading $(basename(target_path)) for analysis..."
|
||||
full_route = target_path
|
||||
msi_data = OpenMSIData(target_path)
|
||||
|
||||
existing_entry = get(registry, selected_folder_main, nothing)
|
||||
if existing_entry !== nothing && haskey(get(existing_entry, "metadata", Dict()), "global_min_mz") && existing_entry["metadata"]["global_min_mz"] !== nothing
|
||||
println("Injecting cached m/z range to skip Pass 1...")
|
||||
msi_data.global_min_mz = existing_entry["metadata"]["global_min_mz"]
|
||||
msi_data.global_max_mz = existing_entry["metadata"]["global_max_mz"]
|
||||
if haskey(get(entry, "metadata", Dict()), "global_min_mz") && entry["metadata"]["global_min_mz"] !== nothing
|
||||
msi_data.global_min_mz = entry["metadata"]["global_min_mz"]
|
||||
msi_data.global_max_mz = entry["metadata"]["global_max_mz"]
|
||||
else
|
||||
precompute_analytics(msi_data)
|
||||
end
|
||||
end
|
||||
|
||||
y = yCoord < 0 ? abs(yCoord) : yCoord
|
||||
plotdata, plotlayout, xSpectraMz, ySpectraMz = xySpectrumPlot(msi_data, xCoord, y, imgWidth, imgHeight, selected_folder_main)
|
||||
xCoord = plotlayout.title == "Spectrum #$(xCoord)" ? xCoord : clamp(xCoord, 1, imgWidth)
|
||||
yCoord = plotlayout.title == "Spectrum #$(xCoord)" ? 0 : -clamp(y, 1, imgHeight)
|
||||
local mask_path_for_plot::Union{String, Nothing} = nothing
|
||||
if maskEnabled && get(entry, "has_mask", false)
|
||||
mask_path_for_plot = get(entry, "mask_path", "")
|
||||
if !isfile(mask_path_for_plot)
|
||||
@warn "Mask not found for plotting: $(mask_path_for_plot). Plotting without mask."
|
||||
mask_path_for_plot = nothing
|
||||
end
|
||||
end
|
||||
|
||||
# Convert to positive coordinates for processing
|
||||
y_positive = yCoord < 0 ? abs(yCoord) : yCoord
|
||||
plotdata, plotlayout, xSpectraMz, ySpectraMz = xySpectrumPlot(msi_data, xCoord, y_positive, imgWidth, imgHeight, selected_folder_main, mask_path=mask_path_for_plot)
|
||||
|
||||
# Update coordinates based on actual plot title
|
||||
# Extract title text from the Dict safely
|
||||
actual_title = if plotlayout.title isa Dict && haskey(plotlayout.title, :text)
|
||||
plotlayout.title[:text]
|
||||
elseif plotlayout.title isa Dict && haskey(plotlayout.title, "text")
|
||||
plotlayout.title["text"]
|
||||
else
|
||||
string(plotlayout.title) # Fallback
|
||||
end
|
||||
|
||||
if occursin("Masked Spectrum at", actual_title)
|
||||
# Extract coordinates from masked spectrum title
|
||||
coords_match = match(r"Masked Spectrum at \((\d+), (\d+)\)", actual_title)
|
||||
if coords_match !== nothing
|
||||
xCoord = parse(Int, coords_match.captures[1])
|
||||
yCoord = -parse(Int, coords_match.captures[2]) # Negative for display
|
||||
end
|
||||
elseif occursin("Spectrum at", actual_title)
|
||||
# Extract coordinates from regular spectrum title
|
||||
coords_match = match(r"Spectrum at \((\d+), (\d+)\)", actual_title)
|
||||
if coords_match !== nothing
|
||||
xCoord = parse(Int, coords_match.captures[1])
|
||||
yCoord = -parse(Int, coords_match.captures[2]) # Negative for display
|
||||
end
|
||||
else
|
||||
# For non-imaging data or fallback, just clamp the coordinates
|
||||
xCoord = clamp(xCoord, 1, imgWidth)
|
||||
yCoord = yCoord < 0 ? yCoord : -clamp(yCoord, 1, imgHeight)
|
||||
end
|
||||
|
||||
selectedTab = "tab2"
|
||||
fTime = time()
|
||||
eTime = round(fTime - sTime, digits=3)
|
||||
msg = "Plot loaded in $(eTime) seconds"
|
||||
log_memory_usage("XY Plot Generated", msi_data)
|
||||
catch e
|
||||
msg = "Could not retrieve spectrum: $e"
|
||||
warning_msg = true
|
||||
@ -938,6 +1085,10 @@ include("./julia_imzML_visual.jl")
|
||||
btnPlotDisable = false
|
||||
btnSpectraDisable = false
|
||||
btnStartDisable = false
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -1213,6 +1364,13 @@ include("./julia_imzML_visual.jl")
|
||||
|
||||
# This handler will now correctly load the first image from the newly selected folder.
|
||||
@onchange selected_folder_main begin
|
||||
msi_data = nothing
|
||||
log_memory_usage("Folder Changed (msi_data cleared)", msi_data)
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
|
||||
if !isempty(selected_folder_main)
|
||||
folder_path = joinpath("public", selected_folder_main)
|
||||
if !isdir(folder_path)
|
||||
@ -1226,6 +1384,7 @@ include("./julia_imzML_visual.jl")
|
||||
plotlayoutImg = layoutImg
|
||||
plotdataImgT = [traceImg]
|
||||
plotlayoutImgT = layoutImg
|
||||
imgWidth, imgHeight = 0, 0
|
||||
return
|
||||
end
|
||||
|
||||
@ -1236,7 +1395,8 @@ include("./julia_imzML_visual.jl")
|
||||
if !isempty(msi_bmp)
|
||||
current_msi = first(msi_bmp)
|
||||
imgInt = "/$(selected_folder_main)/$(current_msi)"
|
||||
plotdataImg, plotlayoutImg, _, _ = loadImgPlot(imgInt)
|
||||
plotdataImg, plotlayoutImg, w, h = loadImgPlot(imgInt)
|
||||
imgWidth, imgHeight = w, h
|
||||
text_nmass = replace(current_msi, r"MSI_|.bmp" => "")
|
||||
msgimg = "<i>m/z</i>: $(replace(text_nmass, "_" => "."))"
|
||||
if !isempty(col_msi_png)
|
||||
@ -1260,7 +1420,11 @@ include("./julia_imzML_visual.jl")
|
||||
if !isempty(triq_bmp)
|
||||
current_triq = first(triq_bmp)
|
||||
imgIntT = "/$(selected_folder_main)/$(current_triq)"
|
||||
plotdataImgT, plotlayoutImgT, _, _ = loadImgPlot(imgIntT)
|
||||
plotdataImgT, plotlayoutImgT, w, h = loadImgPlot(imgIntT)
|
||||
# If no MSI image was loaded, dimensions from TrIQ image are used.
|
||||
if isempty(msi_bmp)
|
||||
imgWidth, imgHeight = w, h
|
||||
end
|
||||
text_nmass = replace(current_triq, r"TrIQ_|.bmp" => "")
|
||||
msgtriq = "TrIQ <i>m/z</i>: $(replace(text_nmass, "_" => "."))"
|
||||
if !isempty(col_triq_png)
|
||||
@ -1276,6 +1440,10 @@ include("./julia_imzML_visual.jl")
|
||||
plotdataImgT = [traceImg]
|
||||
plotlayoutImgT = layoutImg
|
||||
end
|
||||
|
||||
if isempty(msi_bmp) && isempty(triq_bmp)
|
||||
imgWidth, imgHeight = 0, 0
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
@ -1409,31 +1577,51 @@ include("./julia_imzML_visual.jl")
|
||||
|
||||
@async begin
|
||||
try
|
||||
sTime=time()
|
||||
plotdata3d, plotlayout3d=loadSurfacePlot(imgInt)
|
||||
GC.gc() # Trigger garbage collection
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0) # Ensure julia returns the freed memory to OS
|
||||
# --- Get Mask Path ---
|
||||
local mask_path_for_plot::Union{String, Nothing} = nothing
|
||||
if maskEnabled && !isempty(selected_folder_main)
|
||||
registry = load_registry(registry_path)
|
||||
entry = get(registry, selected_folder_main, nothing)
|
||||
if entry !== nothing && get(entry, "has_mask", false)
|
||||
mask_path_candidate = get(entry, "mask_path", "")
|
||||
if isfile(mask_path_candidate)
|
||||
mask_path_for_plot = mask_path_candidate
|
||||
else
|
||||
@warn "Mask enabled but file not found: $(mask_path_candidate). Plotting without mask."
|
||||
end
|
||||
end
|
||||
end
|
||||
# ---
|
||||
|
||||
sTime = time()
|
||||
if mask_path_for_plot !== nothing
|
||||
plotdata3d, plotlayout3d = loadSurfacePlot(imgInt, mask_path_for_plot)
|
||||
else
|
||||
plotdata3d, plotlayout3d = loadSurfacePlot(imgInt)
|
||||
end
|
||||
|
||||
selectedTab = "tab4"
|
||||
fTime = time()
|
||||
eTime = round(fTime - sTime, digits=3)
|
||||
msg = "Plot loaded in $(eTime) seconds"
|
||||
log_memory_usage("Mean Plot Generated", msi_data)
|
||||
catch e
|
||||
msg = "Failed to load and process image: $e"
|
||||
warning_msg = true
|
||||
@error "3D plot generation failed" exception=(e, catch_backtrace())
|
||||
finally
|
||||
progressPlot=false
|
||||
btnPlotDisable=false
|
||||
btnStartDisable=false
|
||||
if msi_data !== nothing
|
||||
# We enable coord search and spectra plot creation
|
||||
btnSpectraDisable=false
|
||||
SpectraEnabled=true
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end # 3d plot for TrIQ
|
||||
|
||||
@onbutton triq3dPlot begin
|
||||
msg = "TrIQ 3D plot selected"
|
||||
@ -1454,27 +1642,47 @@ include("./julia_imzML_visual.jl")
|
||||
|
||||
@async begin
|
||||
try
|
||||
sTime=time()
|
||||
plotdata3d, plotlayout3d=loadSurfacePlot(imgIntT)
|
||||
GC.gc() # Trigger garbage collection
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0) # Ensure julia returns the freed memory to OS
|
||||
# --- Get Mask Path ---
|
||||
local mask_path_for_plot::Union{String, Nothing} = nothing
|
||||
if maskEnabled[] && !isempty(selected_folder_main)
|
||||
registry = load_registry(registry_path)
|
||||
entry = get(registry, selected_folder_main, nothing)
|
||||
if entry !== nothing && get(entry, "has_mask", false)
|
||||
mask_path_candidate = get(entry, "mask_path", "")
|
||||
if isfile(mask_path_candidate)
|
||||
mask_path_for_plot = mask_path_candidate
|
||||
else
|
||||
@warn "Mask enabled but file not found: $(mask_path_candidate). Plotting without mask."
|
||||
end
|
||||
end
|
||||
end
|
||||
# ---
|
||||
|
||||
sTime = time()
|
||||
if mask_path_for_plot !== nothing
|
||||
plotdata3d, plotlayout3d = loadSurfacePlot(imgIntT, mask_path_for_plot)
|
||||
else
|
||||
plotdata3d, plotlayout3d = loadSurfacePlot(imgIntT)
|
||||
end
|
||||
|
||||
selectedTab = "tab4"
|
||||
fTime = time()
|
||||
eTime = round(fTime - sTime, digits=3)
|
||||
msg = "Plot loaded in $(eTime) seconds"
|
||||
log_memory_usage("Mean Plot Generated", msi_data)
|
||||
catch e
|
||||
msg = "Failed to load and process image: $e"
|
||||
warning_msg = true
|
||||
@error "3D TrIQ plot generation failed" exception=(e, catch_backtrace())
|
||||
finally
|
||||
progressPlot=false
|
||||
btnPlotDisable=false
|
||||
btnStartDisable=false
|
||||
if msi_data !== nothing
|
||||
# We enable coord search and spectra plot creation
|
||||
btnSpectraDisable=false
|
||||
SpectraEnabled=true
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -1517,10 +1725,11 @@ include("./julia_imzML_visual.jl")
|
||||
progressPlot=false
|
||||
btnPlotDisable=false
|
||||
btnStartDisable=false
|
||||
if msi_data !== nothing
|
||||
# We enable coord search and spectra plot creation
|
||||
btnSpectraDisable=false
|
||||
SpectraEnabled=true
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -1562,10 +1771,11 @@ include("./julia_imzML_visual.jl")
|
||||
progressPlot=false
|
||||
btnPlotDisable=false
|
||||
btnStartDisable=false
|
||||
if msi_data !== nothing
|
||||
# We enable coord search and spectra plot creation
|
||||
btnSpectraDisable=false
|
||||
SpectraEnabled=true
|
||||
GC.gc()
|
||||
if Sys.islinux()
|
||||
ccall(:malloc_trim, Int32, (Int32,), 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -1579,7 +1789,7 @@ include("./julia_imzML_visual.jl")
|
||||
@onchange Nmass begin
|
||||
if !isempty(xSpectraMz)
|
||||
# Main spectrum trace
|
||||
traceSpectra = PlotlyBase.scatter(
|
||||
traceSpectra = PlotlyBase.stem(
|
||||
x=xSpectraMz,
|
||||
y=ySpectraMz,
|
||||
marker=attr(size=1, color="blue", opacity=0.5),
|
||||
@ -1651,13 +1861,13 @@ include("./julia_imzML_visual.jl")
|
||||
|
||||
@onchange xCoord, yCoord begin
|
||||
if selectedTab == "tab1"
|
||||
plotdataImgT = filter(trace -> !(get(trace, :name, "") in ["Line X", "Line Y"]), plotdataImgT)
|
||||
main_trace = plotdataImgT[1] # The heatmap/image trace
|
||||
trace1, trace2 = crossLinesPlot(xCoord, yCoord, imgWidth, -imgHeight)
|
||||
plotdataImgT = append!(plotdataImgT, [trace1, trace2])
|
||||
plotdataImgT = [main_trace, trace1, trace2] # Fresh array every time
|
||||
elseif selectedTab == "tab0"
|
||||
plotdataImg = filter(trace -> !(get(trace, :name, "") in ["Line X", "Line Y", "Optical"]), plotdataImg)
|
||||
main_trace = plotdataImg[1] # The heatmap/image trace
|
||||
trace1, trace2 = crossLinesPlot(xCoord, yCoord, imgWidth, -imgHeight)
|
||||
plotdataImg = append!(plotdataImg, [trace1, trace2])
|
||||
plotdataImg = [main_trace, trace1, trace2]
|
||||
end
|
||||
end
|
||||
|
||||
@ -1769,6 +1979,7 @@ include("./julia_imzML_visual.jl")
|
||||
end
|
||||
end
|
||||
end
|
||||
log_memory_usage("App Ready", msi_data)
|
||||
warmup_init()
|
||||
end
|
||||
|
||||
|
||||
138
app.jl.html
138
app.jl.html
@ -9,19 +9,16 @@
|
||||
<!-- Left DIV -->
|
||||
<div id="intDivStyle-left" class="st-col col-12 st-module">
|
||||
<q-tabs v-model="left_tab" dense class="text-grey" indicator-color="primary" align="justify">
|
||||
<q-tab name="pre_treatment" label="Pre-Treatment"></q-tab>
|
||||
<q-tab name="generator" label="Slice Generator"></q-tab>
|
||||
<q-tab name="converter" label="Converter"></q-tab>
|
||||
</q-tabs>
|
||||
<q-separator></q-separator>
|
||||
<q-tab-panels v-model="left_tab" animated>
|
||||
<q-tab-panel name="generator">
|
||||
<div class="text-h6">imzML & mzML Data Processor</div>
|
||||
<p>Please make sure the ibd and imzML file are located in the same directory and have the same name.
|
||||
<br>It may take a while to generate the slice / spectrum, please be patient.
|
||||
<br>To generate the contour or surface plots, you have to select the desired slice first using the interface.</p>
|
||||
<q-tab-panel name="pre_treatment">
|
||||
<div class="text-h6">imzML & mzML Data Pre-Treatment</div>
|
||||
<div class="row items-center">
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer col"
|
||||
v-model="full_route" readonly
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer col" v-model="full_route" readonly
|
||||
:label="batch_file_count > 0 ? batch_file_count + ' file(s) in batch' : 'Select an imzML file'"
|
||||
v-on:click="btnSearch=true">
|
||||
<template v-slot:append>
|
||||
@ -29,8 +26,45 @@
|
||||
</template>
|
||||
</q-input>
|
||||
<q-btn class="q-ma-sm" icon="add" v-on:click="btnAddBatch=true" label="Add"></q-btn>
|
||||
<q-btn class="q-ma-sm" icon="clear" v-on:click="clear_batch_btn=true"
|
||||
:disable="batch_file_count === 0" label="Clear"></q-btn>
|
||||
<q-btn class="q-ma-sm" icon="clear" v-on:click="clear_batch_btn=true" :disable="batch_file_count === 0"
|
||||
label="Clear"></q-btn>
|
||||
</div>
|
||||
<br>
|
||||
<br>
|
||||
<q-tabs v-model="pre_tab" dense class="text-grey" indicator-color="primary" align="justify">
|
||||
<q-tab name="stabilization" label="Stabilization"></q-tab>
|
||||
<q-tab name="smoothing" label="Smoothing"></q-tab>
|
||||
<q-tab name="baseline" label="Baseline"></q-tab>
|
||||
<q-tab name="calibration" label="Calibration"></q-tab>
|
||||
<q-tab name="warping" label="Warping"></q-tab>
|
||||
<q-tab name="peak" label="Peak Detection"></q-tab>
|
||||
<q-tab name="binning" label="Binning"></q-tab>
|
||||
</q-tabs>
|
||||
<q-separator></q-separator>
|
||||
<q-tab-panels v-model="pre_tab" animated>
|
||||
<q-tab-panel name="stabilization">
|
||||
<p>HI!</p>
|
||||
</q-tab-panel>
|
||||
</q-tab-panels>
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="generator">
|
||||
<div class="text-h6">imzML & mzML Data Processor</div>
|
||||
<p>Please make sure the ibd and imzML file are located in the same directory and have the same name.
|
||||
<br>It may take a while to generate the slice / spectrum, please be patient.
|
||||
<br>To generate the contour or surface plots, you have to select the desired slice first using the
|
||||
interface.
|
||||
</p>
|
||||
<div class="row items-center">
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer col" v-model="full_route" readonly
|
||||
:label="batch_file_count > 0 ? batch_file_count + ' file(s) in batch' : 'Select an imzML file'"
|
||||
v-on:click="btnSearch=true">
|
||||
<template v-slot:append>
|
||||
<q-icon name="search" v-on:click="btnSearch=true" class="cursor-pointer" />
|
||||
</template>
|
||||
</q-input>
|
||||
<q-btn class="q-ma-sm" icon="add" v-on:click="btnAddBatch=true" label="Add"></q-btn>
|
||||
<q-btn class="q-ma-sm" icon="clear" v-on:click="clear_batch_btn=true" :disable="batch_file_count === 0"
|
||||
label="Clear"></q-btn>
|
||||
</div>
|
||||
<q-list bordered separator v-if="selected_files.length > 0">
|
||||
<q-item v-for="(file, index) in selected_files" :key="index">
|
||||
@ -47,8 +81,7 @@
|
||||
<div class="row">
|
||||
<div class="st-col col-4 col-sm q-ma-sm">
|
||||
<q-input standout="custom-standout" id="textNmass" v-model="Nmass"
|
||||
label="Mass-to-charge ratio(s) of interest" type="text"
|
||||
:rules="[
|
||||
label="Mass-to-charge ratio(s) of interest" type="text" :rules="[
|
||||
val => !!val || '* Required',
|
||||
val => val.split(',').every(m => !isNaN(parseFloat(m.trim())) && parseFloat(m.trim()) > 0) || 'Need comma-separated positive numbers'
|
||||
]">
|
||||
@ -73,6 +106,8 @@
|
||||
label="Add Median Filter"></q-toggle>
|
||||
<q-toggle id="btnEnableTriq" v-on:click="triqEnabled" v-model="triqEnabled" color="blue"
|
||||
label="Add Threshold Intensity Quantization (TrIQ)"></q-toggle>
|
||||
<q-toggle id="btnEnableMask" v-on:click="maskEnabled" v-model="maskEnabled" color="black"
|
||||
label="Use Mask To Filter Data"></q-toggle>
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="st-col col-4 col-sm-4 q-ma-sm">
|
||||
@ -116,12 +151,12 @@
|
||||
<div class="st-col col-4 col-sm-4 q-ma-sm">
|
||||
<q-input standout="custom-standout" step="1" v-model="xCoord" label="X coord" type="number" :rules="[
|
||||
val => SpectraEnabled ? ( '* Required', val >= 0|| 'Needs to be bigger than 0') : true
|
||||
]" :readonly="!SpectraEnabled" :disable="!SpectraEnabled"></q-input>
|
||||
]"></q-input>
|
||||
</div>
|
||||
<div class="st-col col-4 col-sm-4 q-ma-sm">
|
||||
<q-input standout="custom-standout" step="1" v-model="yCoord" label="Y coord" type="number" :rules="[
|
||||
val => SpectraEnabled ? ( '* Required', val <= 0|| 'Needs to be lower than 0') : true
|
||||
]" :readonly="!SpectraEnabled" :disable="!SpectraEnabled"></q-input>
|
||||
]"></q-input>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@ -201,21 +236,25 @@
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="converter">
|
||||
<div class="text-h6">mzML to imzML Converter</div>
|
||||
<p>Select the .mzML file and the corresponding .txt synchronization file to convert them into an .imzML/.ibd pair.</p>
|
||||
<p>Select the .mzML file and the corresponding .txt synchronization file to convert them into an .imzML/.ibd
|
||||
pair.</p>
|
||||
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer" v-model="mzml_full_route" readonly label="Select your .mzML file" v-on:click="btnSearchMzml=true">
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer" v-model="mzml_full_route" readonly
|
||||
label="Select your .mzML file" v-on:click="btnSearchMzml=true">
|
||||
<template v-slot:append>
|
||||
<q-icon name="search" v-on:click="btnSearchMzml=true" class="cursor-pointer" />
|
||||
</template>
|
||||
</q-input>
|
||||
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer" v-model="sync_full_route" readonly label="Select your .txt sync file" v-on:click="btnSearchSync=true">
|
||||
<q-input standout="custom-standout" class="q-ma-sm cursor-pointer" v-model="sync_full_route" readonly
|
||||
label="Select your .txt sync file" v-on:click="btnSearchSync=true">
|
||||
<template v-slot:append>
|
||||
<q-icon name="search" v-on:click="btnSearchSync=true" class="cursor-pointer" />
|
||||
</template>
|
||||
</q-input>
|
||||
|
||||
<q-btn :loading="progress_conversion" class="q-ma-sm btn-style" :disabled="btnConvertDisable" icon="swap_horiz" v-on:click="convert_process=true" padding="lg" label="Convert File">
|
||||
<q-btn :loading="progress_conversion" class="q-ma-sm btn-style" :disabled="btnConvertDisable"
|
||||
icon="swap_horiz" v-on:click="convert_process=true" padding="lg" label="Convert File">
|
||||
<template v-slot:loading>
|
||||
<q-spinner-hourglass class="on-left" />
|
||||
Converting...
|
||||
@ -235,7 +274,8 @@
|
||||
<!-- Content for Tab 0 -->
|
||||
<h6>Image visualizer</h6>
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset"
|
||||
class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-space></q-space>
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="imgMinus=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="imgPlus=true"></q-btn>
|
||||
@ -257,7 +297,8 @@
|
||||
<!-- Content for Tab 1 -->
|
||||
<h6>TrIQ visualizer</h6>
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset"
|
||||
class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-space></q-space>
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="imgMinusT=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="imgPlusT=true"></q-btn>
|
||||
@ -265,8 +306,8 @@
|
||||
<!-- Triq Image manager -->
|
||||
<div id="image-container-triq" class="row st-col col-12">
|
||||
<div class="col-10 q-pa-none q-ma-none ">
|
||||
<plotly id="plotImgT" :data="plotdataImgT" :layout="plotlayoutImgT"
|
||||
class="q-pa-none q-ma-none sync_data" @click="data_click"></plotly>
|
||||
<plotly id="plotImgT" :data="plotdataImgT" :layout="plotlayoutImgT" class="q-pa-none q-ma-none sync_data"
|
||||
@click="data_click"></plotly>
|
||||
</div>
|
||||
<div class="col-2 q-pa-none q-ma-none ">
|
||||
<q-img id="colorbar-triq" class="q-ma-none q-pa-none" :src="colorbarT"></q-img>
|
||||
@ -277,7 +318,8 @@
|
||||
|
||||
<q-tab-panel name="tab2">
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_main" :options="available_folders" label="Select Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-select v-model="selected_folder_main" :options="available_folders" label="Select Dataset" class="q-ma-sm"
|
||||
style="min-width: 200px;"></q-select>
|
||||
<q-btn-dropdown class="q-ma-sm btn-style" :loading="progressSpectraPlot" :disable="btnSpectraDisable"
|
||||
label="Generate Spectra" icon="play_arrow">
|
||||
<template v-slot:loading>
|
||||
@ -352,9 +394,11 @@
|
||||
<div class="row">
|
||||
<div class="col-6">
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_compare_left" :options="image_available_folders" label="Select Left Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-select v-model="selected_folder_compare_left" :options="image_available_folders"
|
||||
label="Select Left Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-space></q-space>
|
||||
<st-tabs id="tabHeaderCompareLeft" :ids="CompTabIDsLeft" :labels="CompTabLabelsLeft" v-model="CompSelectedTabLeft"></st-tabs>
|
||||
<st-tabs id="tabHeaderCompareLeft" :ids="CompTabIDsLeft" :labels="CompTabLabelsLeft"
|
||||
v-model="CompSelectedTabLeft"></st-tabs>
|
||||
</div>
|
||||
<q-tab-panels v-model="CompSelectedTabLeft">
|
||||
<q-tab-panel name="tab0">
|
||||
@ -362,12 +406,14 @@
|
||||
<!-- Btn image changer -->
|
||||
<div>
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="imgMinusCompLeft=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="imgPlusCompLeft=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style"
|
||||
v-on:click="imgPlusCompLeft=true"></q-btn>
|
||||
</div>
|
||||
<!-- Image manager -->
|
||||
<div id="image-container-compare-left-normal" class="row st-col col-12">
|
||||
<div class="col-10 q-pa-none q-ma-none ">
|
||||
<plotly id="plotImgCompareLeft" :data="plotdataImgCompLeft" :layout="plotlayoutImgCompLeft" class="q-pa-none q-ma-none">
|
||||
<plotly id="plotImgCompareLeft" :data="plotdataImgCompLeft" :layout="plotlayoutImgCompLeft"
|
||||
class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</div>
|
||||
<div class="col-2">
|
||||
@ -382,12 +428,14 @@
|
||||
<!-- Triq Btn image changer -->
|
||||
<div>
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="imgMinusTCompLeft=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="imgPlusTCompLeft=true"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style"
|
||||
v-on:click="imgPlusTCompLeft=true"></q-btn>
|
||||
</div>
|
||||
<!-- Triq Image manager -->
|
||||
<div id="image-container-compare-left-triq" class="row st-col col-12">
|
||||
<div class="col-10 q-pa-none q-ma-none ">
|
||||
<plotly id="plotImgTCompareLeft" :data="plotdataImgTCompLeft" :layout="plotlayoutImgTCompLeft" class="q-pa-none q-ma-none">
|
||||
<plotly id="plotImgTCompareLeft" :data="plotdataImgTCompLeft" :layout="plotlayoutImgTCompLeft"
|
||||
class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</div>
|
||||
<div class="col-2">
|
||||
@ -398,23 +446,28 @@
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab2">
|
||||
<!-- Content for Tab 2 -->
|
||||
<plotly id="plotSpectraCompareLeft" :data="plotdata" :layout="plotlayout" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plotSpectraCompareLeft" :data="plotdata" :layout="plotlayout" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab3">
|
||||
<!-- Content for Tab 3 -->
|
||||
<plotly id="plotTopoCompareLeft" :data="plotdataC" :layout="plotlayoutC" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plotTopoCompareLeft" :data="plotdataC" :layout="plotlayoutC" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab4">
|
||||
<!-- Content for Tab 4 -->
|
||||
<plotly id="plot3dCompareLeft" :data="plotdata3d" :layout="plotlayout3d" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plot3dCompareLeft" :data="plotdata3d" :layout="plotlayout3d" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
</q-tab-panels>
|
||||
</div>
|
||||
<div class="col-6">
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_compare_right" :options="image_available_folders" label="Select Right Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-select v-model="selected_folder_compare_right" :options="image_available_folders"
|
||||
label="Select Right Dataset" class="q-ma-sm" style="min-width: 200px;"></q-select>
|
||||
<q-space></q-space>
|
||||
<st-tabs id="tabHeaderCompareRight" :ids="CompTabIDsRight" :labels="CompTabLabelsRight" v-model="CompSelectedTabRight"></st-tabs>
|
||||
<st-tabs id="tabHeaderCompareRight" :ids="CompTabIDsRight" :labels="CompTabLabelsRight"
|
||||
v-model="CompSelectedTabRight"></st-tabs>
|
||||
</div>
|
||||
<q-tab-panels v-model="CompSelectedTabRight">
|
||||
<q-tab-panel name="tab0">
|
||||
@ -462,15 +515,18 @@
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab2">
|
||||
<!-- Content for Tab 2 -->
|
||||
<plotly id="plotSpectraCompareRight" :data="plotdata" :layout="plotlayout" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plotSpectraCompareRight" :data="plotdata" :layout="plotlayout" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab3">
|
||||
<!-- Content for Tab 3 -->
|
||||
<plotly id="plotTopoCompareRight" :data="plotdataC" :layout="plotlayoutC" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plotTopoCompareRight" :data="plotdataC" :layout="plotlayoutC" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
<q-tab-panel name="tab4">
|
||||
<!-- Content for Tab 4 -->
|
||||
<plotly id="plot3dCompareRight" :data="plotdata3d" :layout="plotlayout3d" class="q-pa-none q-ma-none"></plotly>
|
||||
<plotly id="plot3dCompareRight" :data="plotdata3d" :layout="plotlayout3d" class="q-pa-none q-ma-none">
|
||||
</plotly>
|
||||
</q-tab-panel>
|
||||
</q-tab-panels>
|
||||
</div>
|
||||
@ -504,14 +560,8 @@
|
||||
<q-card-section class="row items-center q-pb-none">
|
||||
<div class="text-h6">Dataset Summary</div>
|
||||
<q-space />
|
||||
<q-select
|
||||
v-model="selected_folder_metadata"
|
||||
:options="available_folders"
|
||||
label="Select Dataset"
|
||||
class="q-ma-sm"
|
||||
style="min-width: 250px;"
|
||||
standout="custom-standout"
|
||||
></q-select>
|
||||
<q-select v-model="selected_folder_metadata" :options="available_folders" label="Select Dataset" class="q-ma-sm"
|
||||
style="min-width: 250px;" standout="custom-standout"></q-select>
|
||||
</q-card-section>
|
||||
|
||||
<q-card-section class="q-pt-none">
|
||||
|
||||
@ -1,32 +1,67 @@
|
||||
# == Search functions ==
|
||||
# Functions that recieve a list to update, and the current direction both as string for
|
||||
# searching in the directory the position the list is going
|
||||
# julia_imzML_visual.jl
|
||||
|
||||
"""
|
||||
increment_image(current_image, image_list)
|
||||
|
||||
Finds the next image in a list.
|
||||
|
||||
# Arguments
|
||||
- `current_image`: The current image file name.
|
||||
- `image_list`: The list of available image file names.
|
||||
|
||||
# Returns
|
||||
- The file name of the next image, or the last image if the current one is the last or not found.
|
||||
- `nothing` if `image_list` is empty.
|
||||
"""
|
||||
function increment_image(current_image, image_list)
|
||||
if isempty(image_list)
|
||||
return nothing
|
||||
end
|
||||
current_index = findfirst(isequal(current_image), image_list)
|
||||
if current_index==nothing || current_index==length(image_list) || current_image ===""
|
||||
return image_list[length(image_list)] # Return the current image if it's the last one or not found
|
||||
if current_index === nothing || current_index == length(image_list) || current_image == ""
|
||||
return image_list[end] # Return the last image if current is not found or is the last
|
||||
else
|
||||
return image_list[current_index + 1] # Move to the next image
|
||||
end
|
||||
end
|
||||
|
||||
"""
|
||||
decrement_image(current_image, image_list)
|
||||
|
||||
Finds the previous image in a list.
|
||||
|
||||
# Arguments
|
||||
- `current_image`: The current image file name.
|
||||
- `image_list`: The list of available image file names.
|
||||
|
||||
# Returns
|
||||
- The file name of the previous image, or the first image if the current one is the first or not found.
|
||||
- `nothing` if `image_list` is empty.
|
||||
"""
|
||||
function decrement_image(current_image, image_list)
|
||||
if isempty(image_list)
|
||||
return nothing
|
||||
end
|
||||
current_index = findfirst(isequal(current_image), image_list)
|
||||
if current_index==nothing || current_index==1 || current_image===""
|
||||
return image_list[1] # Return the current image if it's the first one or not found
|
||||
if current_index === nothing || current_index == 1 || current_image == ""
|
||||
return image_list[1] # Return the first image if current is not found or is the first
|
||||
else
|
||||
return image_list[current_index - 1] # Move to the previous image
|
||||
end
|
||||
end
|
||||
|
||||
## Plot Image functions
|
||||
# Downsample an image matrix to a maximum dimension while preserving aspect ratio
|
||||
"""
|
||||
downsample_image(img_matrix, max_dim::Int)
|
||||
|
||||
Downsamples an image matrix to a maximum dimension while preserving aspect ratio.
|
||||
|
||||
# Arguments
|
||||
- `img_matrix`: The image matrix to downsample.
|
||||
- `max_dim`: The maximum dimension (width or height) for the downsampled image.
|
||||
|
||||
# Returns
|
||||
- The downsampled image matrix.
|
||||
"""
|
||||
function downsample_image(img_matrix, max_dim::Int)
|
||||
h, w = size(img_matrix)
|
||||
if h <= max_dim && w <= max_dim
|
||||
@ -46,9 +81,20 @@ function downsample_image(img_matrix, max_dim::Int)
|
||||
return imresize(img_matrix, (new_h, new_w))
|
||||
end
|
||||
|
||||
# loadImgPlot recieves the local directory of the image as a string,
|
||||
# returns the layout and data for the heatmap plotly plot
|
||||
# this function loads the image into a plot
|
||||
"""
|
||||
loadImgPlot(interfaceImg::String)
|
||||
|
||||
Loads an image and creates a Plotly heatmap.
|
||||
|
||||
# Arguments
|
||||
- `interfaceImg`: The path to the image file relative to the "public" directory.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly trace.
|
||||
- `plotlayout`: The Plotly layout for the plot.
|
||||
- `width`: The width of the loaded image.
|
||||
- `height`: The height of the loaded image.
|
||||
"""
|
||||
function loadImgPlot(interfaceImg::String)
|
||||
# Load the image
|
||||
cleaned_img = replace(interfaceImg, r"\?.*" => "")
|
||||
@ -61,8 +107,8 @@ function loadImgPlot(interfaceImg::String)
|
||||
elevation = Float32.(Array(img_array)) ./ 255.0
|
||||
# Get the X, Y coordinates of the image
|
||||
height, width = size(img_array)
|
||||
X=collect(1:width)
|
||||
Y=collect(1:height)
|
||||
X = 1:width
|
||||
Y = 1:height
|
||||
|
||||
# Create the layout
|
||||
layout = PlotlyBase.Layout(
|
||||
@ -118,9 +164,22 @@ function loadImgPlot(interfaceImg::String)
|
||||
return plotdata, plotlayout, width, height
|
||||
end
|
||||
|
||||
# loadImgPlot recieves the local directory of the image as a string, the local directory o the overlay image
|
||||
# and the transparency its required to have. Returns the layout and data for the heatmap plotly plot
|
||||
# this function loads the image into a plot
|
||||
"""
|
||||
loadImgPlot(interfaceImg::String, overlayImg::String, imgTrans::Float64)
|
||||
|
||||
Loads a main image and overlays a second image on top, creating a Plotly heatmap.
|
||||
|
||||
# Arguments
|
||||
- `interfaceImg`: Path to the main image file.
|
||||
- `overlayImg`: Path to the overlay image file.
|
||||
- `imgTrans`: Transparency level for the overlay image (0.0 to 1.0).
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly trace for the main image.
|
||||
- `plotlayout`: The Plotly layout, including the overlay image.
|
||||
- `width`: The width of the main image.
|
||||
- `height`: The height of the main image.
|
||||
"""
|
||||
function loadImgPlot(interfaceImg::String, overlayImg::String, imgTrans::Float64)
|
||||
timestamp = string(time_ns())
|
||||
# Load the main image
|
||||
@ -134,8 +193,8 @@ function loadImgPlot(interfaceImg::String, overlayImg::String, imgTrans::Float64
|
||||
elevation = Float32.(Array(img_array)) ./ 255.0
|
||||
# Get the X, Y coordinates of the image
|
||||
height, width = size(img_array)
|
||||
X = collect(1:width)
|
||||
Y = collect(1:height)
|
||||
X = 1:width
|
||||
Y = 1:height
|
||||
|
||||
# Create the layout with overlay image
|
||||
layoutImg = PlotlyBase.Layout(
|
||||
@ -188,10 +247,18 @@ function loadImgPlot(interfaceImg::String, overlayImg::String, imgTrans::Float64
|
||||
return plotdata, plotlayout, width, height
|
||||
end
|
||||
|
||||
# loadContourPlot recieves the local directory of the image as a string,
|
||||
# returns the layout and data for the contour plotly plot
|
||||
# this function loads the image and applies a gaussian filter
|
||||
# to smoothen it and loads it into a plot
|
||||
"""
|
||||
loadContourPlot(interfaceImg::String)
|
||||
|
||||
Loads an image, smooths it, and creates a Plotly contour plot.
|
||||
|
||||
# Arguments
|
||||
- `interfaceImg`: Path to the image file.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly contour trace.
|
||||
- `plotlayout`: The Plotly layout for the plot.
|
||||
"""
|
||||
function loadContourPlot(interfaceImg::String)
|
||||
# Load the image
|
||||
cleaned_img=replace(interfaceImg, r"\?.*" => "")
|
||||
@ -258,10 +325,18 @@ function loadContourPlot(interfaceImg::String)
|
||||
return plotdata, plotlayout
|
||||
end
|
||||
|
||||
# loadSurfacePlot recieves the local directory of the image as a string,
|
||||
# returns the layout and data for the surface plotly plot
|
||||
# this function loads the image and applies a gaussian filter
|
||||
# to smoothen it and loads it into a 3D plot
|
||||
"""
|
||||
loadSurfacePlot(interfaceImg::String)
|
||||
|
||||
Loads an image, smooths it, and creates a 3D Plotly surface plot.
|
||||
|
||||
# Arguments
|
||||
- `interfaceImg`: Path to the image file.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly surface trace.
|
||||
- `plotlayout`: The Plotly layout for the 3D plot.
|
||||
"""
|
||||
function loadSurfacePlot(interfaceImg::String)
|
||||
# Load the image
|
||||
cleaned_img=replace(interfaceImg, r"\?.*" => "")
|
||||
@ -347,8 +422,21 @@ function loadSurfacePlot(interfaceImg::String)
|
||||
return plotdata, plotlayout
|
||||
end
|
||||
|
||||
# This function recieves the x and y coords currently selected, and the dimentions of
|
||||
# the image to create two traces that will display in a cross section
|
||||
"""
|
||||
crossLinesPlot(x, y, maxwidth, maxheight)
|
||||
|
||||
Creates two line traces for a crosshair indicator on a plot.
|
||||
|
||||
# Arguments
|
||||
- `x`: The x-coordinate of the crosshair center.
|
||||
- `y`: The y-coordinate of the crosshair center.
|
||||
- `maxwidth`: The width of the plot area.
|
||||
- `maxheight`: The height of the plot area.
|
||||
|
||||
# Returns
|
||||
- `trace1`: The horizontal line trace.
|
||||
- `trace2`: The vertical line trace.
|
||||
"""
|
||||
function crossLinesPlot(x, y, maxwidth, maxheight)
|
||||
# Define the coordinates for the two lines
|
||||
l1_x = [0, maxwidth]
|
||||
@ -363,8 +451,18 @@ function crossLinesPlot(x, y, maxwidth, maxheight)
|
||||
return trace1, trace2
|
||||
end
|
||||
|
||||
# This function is used for giving colorbar values a visual format
|
||||
# that shortens long values giving them scientific notation
|
||||
"""
|
||||
log_tick_formatter(values::Vector{Float64})
|
||||
|
||||
Formats a vector of numbers into strings with a custom scientific notation for use as tick labels.
|
||||
For example, 1000 becomes "100x10¹" and 0.01 becomes "1.0x10⁻²".
|
||||
|
||||
# Arguments
|
||||
- `values`: A vector of `Float64` values to format.
|
||||
|
||||
# Returns
|
||||
- A vector of formatted strings.
|
||||
"""
|
||||
function log_tick_formatter(values::Vector{Float64})
|
||||
# Initialize exponents dictionary
|
||||
exponents = zeros(Int, length(values))
|
||||
@ -387,73 +485,31 @@ function log_tick_formatter(values::Vector{Float64})
|
||||
return map((v, e) -> e == 0 ? "$(round(v, sigdigits=2))" : "$(round(v, sigdigits=2))x10" * Makie.UnicodeFun.to_superscript(e), formValues, exponents)
|
||||
end
|
||||
|
||||
function generate_colorbar_image(slice_data::AbstractMatrix, color_levels::Int, output_path::String; use_triq::Bool=false, triq_prob::Float64=0.98)
|
||||
# 1. Determine bounds based on whether TrIQ is used
|
||||
min_val, max_val = if use_triq
|
||||
MSI_src.get_outlier_thres(slice_data, triq_prob)
|
||||
"""
|
||||
meanSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
|
||||
Generates a plot of the mean spectrum from MSIData.
|
||||
|
||||
# Arguments
|
||||
- `data`: The `MSIData` object.
|
||||
- `dataset_name`: Optional name of the dataset for the plot title.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly trace.
|
||||
- `plotlayout`: The Plotly layout for the plot.
|
||||
- `xSpectraMz`: The m/z values of the spectrum.
|
||||
- `ySpectraMz`: The intensity values of the spectrum.
|
||||
"""
|
||||
function meanSpectrumPlot(data::MSIData, dataset_name::String=""; mask_path::Union{String, Nothing}=nothing)
|
||||
# Determine base title based on mask usage
|
||||
base_title = if mask_path !== nothing
|
||||
"Masked Average Spectrum"
|
||||
else
|
||||
extrema(slice_data)
|
||||
"Average Spectrum"
|
||||
end
|
||||
|
||||
# 2. Replicate the tick calculation logic from plot_slices
|
||||
bins = color_levels
|
||||
levels = range(min_val, stop=max_val, length=bins + 1)
|
||||
level_range = levels[end] - levels[1]
|
||||
title_text = isempty(dataset_name) ? base_title : "$base_title for: $dataset_name"
|
||||
|
||||
if level_range == 0
|
||||
levels = range(min_val - 0.1, stop=max_val + 0.1, length=bins + 1)
|
||||
level_range = 0.2
|
||||
end
|
||||
|
||||
exponent = level_range > 0 ? floor(log10(level_range)) / 3 : 0
|
||||
scale = 10^(3 * exponent)
|
||||
scaled_levels = levels ./ scale
|
||||
|
||||
format_num = level_range > 0 ? floor(log10(level_range)) % 3 : 0
|
||||
labels = if format_num == 0
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
elseif format_num == 1
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
else
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
end
|
||||
|
||||
divisors = 2:7
|
||||
remainders = (bins - 1) .% divisors
|
||||
best_divisor = divisors[findlast(x -> x == minimum(remainders), remainders)]
|
||||
tick_indices = round.(Int, range(1, stop=bins + 1, length=best_divisor + 1))
|
||||
|
||||
if !(1 in tick_indices)
|
||||
pushfirst!(tick_indices, 1)
|
||||
end
|
||||
if !((bins + 1) in tick_indices)
|
||||
push!(tick_indices, bins + 1)
|
||||
end
|
||||
unique!(sort!(tick_indices))
|
||||
|
||||
tick_positions = levels[tick_indices]
|
||||
tick_labels = labels[tick_indices]
|
||||
|
||||
# 3. Create and save the colorbar image
|
||||
fig = Figure(size=(150, 250))
|
||||
Colorbar(fig[1, 1],
|
||||
colormap=cgrad(:viridis, bins, categorical=true),
|
||||
# limits=(min_val, max_val),
|
||||
limits=(levels[1], levels[end]),
|
||||
label=(scale == 1 ? "Intensity" : "Intensity ×10^$(round(Int, 3 * exponent))"),
|
||||
ticks=(tick_positions, tick_labels),
|
||||
labelsize=20,
|
||||
ticklabelsize=16
|
||||
)
|
||||
save(output_path, fig)
|
||||
end
|
||||
|
||||
# meanSpectrumPlot recieves the local directory of the image as a string,
|
||||
# returns the layout and data for the surface plotly plot
|
||||
# this function loads the spectra data and makes a mean to display
|
||||
# its values in the spectrum plot
|
||||
function meanSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
title_text = isempty(dataset_name) ? "Average Spectrum Plot" : "Average Spectrum for: $dataset_name"
|
||||
layout = PlotlyBase.Layout(
|
||||
title=PlotlyBase.attr(
|
||||
text=title_text,
|
||||
@ -477,37 +533,71 @@ function meanSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
)
|
||||
|
||||
# Use the new, efficient function from the backend
|
||||
xSpectraMz, ySpectraMz = get_average_spectrum(data)
|
||||
xSpectraMz, ySpectraMz = get_average_spectrum(data, mask_path=mask_path)
|
||||
|
||||
if isempty(xSpectraMz)
|
||||
if isempty(xSpectraMz) || isempty(ySpectraMz)
|
||||
@warn "Average spectrum is empty."
|
||||
trace = PlotlyBase.scatter(x=Float64[], y=Float64[])
|
||||
trace = PlotlyBase.stem(x=Float64[], y=Float64[])
|
||||
# Update title to indicate empty spectrum
|
||||
layout.title.text = "Empty " * layout.title.text
|
||||
else
|
||||
trace = PlotlyBase.scatter(x=xSpectraMz, y=ySpectraMz, marker=attr(size=1, color="blue", opacity=0.5), name="Average", hoverinfo="x",hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
trace = PlotlyBase.stem(x=xSpectraMz, y=ySpectraMz, marker=attr(size=1, color="blue", opacity=0.5), name="Average", hoverinfo="x", hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
end
|
||||
|
||||
|
||||
plotdata = [trace]
|
||||
plotlayout = layout
|
||||
return plotdata, plotlayout, xSpectraMz, ySpectraMz
|
||||
end
|
||||
|
||||
function xySpectrumPlot(data::MSIData, xCoord::Int, yCoord::Int, imgWidth::Int, imgHeight::Int, dataset_name::String="")
|
||||
"""
|
||||
xySpectrumPlot(data::MSIData, xCoord::Int, yCoord::Int, imgWidth::Int, imgHeight::Int, dataset_name::String="")
|
||||
|
||||
Generates a plot for the spectrum at a specific coordinate (for imaging data) or index (for non-imaging data).
|
||||
|
||||
# Arguments
|
||||
- `data`: The `MSIData` object.
|
||||
- `xCoord`: The x-coordinate or spectrum index.
|
||||
- `yCoord`: The y-coordinate (used for imaging data).
|
||||
- `imgWidth`: The width of the MSI image.
|
||||
- `imgHeight`: The height of the MSI image.
|
||||
- `dataset_name`: Optional name of the dataset for the plot title.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly trace.
|
||||
- `plotlayout`: The Plotly layout for the plot.
|
||||
- `mz`: The m/z values of the spectrum.
|
||||
- `intensity`: The intensity values of the spectrum.
|
||||
"""
|
||||
function xySpectrumPlot(data::MSIData, xCoord::Int, yCoord::Int, imgWidth::Int, imgHeight::Int, dataset_name::String=""; mask_path::Union{String, Nothing}=nothing)
|
||||
local mz::AbstractVector, intensity::AbstractVector
|
||||
local plot_title::String
|
||||
|
||||
is_imaging = data.source isa ImzMLSource
|
||||
|
||||
if is_imaging
|
||||
# For imaging data, use (X, Y) coordinates
|
||||
x = clamp(xCoord, 1, imgWidth)
|
||||
y = clamp(yCoord, 1, imgHeight)
|
||||
|
||||
if mask_path !== nothing
|
||||
mask_matrix = load_and_prepare_mask(mask_path, (imgWidth, imgHeight))
|
||||
if !mask_matrix[y, x]
|
||||
@warn "Coordinate ($x, $y) is outside the specified mask."
|
||||
mz, intensity = Float64[], Float64[]
|
||||
base_title = "Spectrum Outside Mask at ($x, $y)"
|
||||
else
|
||||
process_spectrum(data, Int(x), Int(y)) do recieved_mz, recieved_intensity
|
||||
mz = recieved_mz
|
||||
intensity = recieved_intensity
|
||||
end
|
||||
base_title = "Masked Spectrum at ($x, $y)"
|
||||
end
|
||||
else
|
||||
process_spectrum(data, Int(x), Int(y)) do recieved_mz, recieved_intensity
|
||||
mz = recieved_mz
|
||||
intensity = recieved_intensity
|
||||
end
|
||||
base_title = "Spectrum at ($x, $y)"
|
||||
end
|
||||
else
|
||||
# For non-imaging data, treat xCoord as the spectrum index
|
||||
index = clamp(xCoord, 1, length(data.spectra_metadata))
|
||||
@ -546,7 +636,7 @@ function xySpectrumPlot(data::MSIData, xCoord::Int, yCoord::Int, imgWidth::Int,
|
||||
# Downsample for plotting performance
|
||||
mz_down, int_down = MSI_src.downsample_spectrum(mz, intensity)
|
||||
|
||||
trace = PlotlyBase.scatter(x=mz_down, y=int_down, marker=attr(size=1, color="blue", opacity=0.5), name="Spectrum", hoverinfo="x", hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
trace = PlotlyBase.stem(x=mz_down, y=int_down, marker=attr(size=1, color="blue", opacity=0.5), name="Spectrum", hoverinfo="x", hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
|
||||
plotdata = [trace]
|
||||
plotlayout = layout
|
||||
@ -554,8 +644,31 @@ function xySpectrumPlot(data::MSIData, xCoord::Int, yCoord::Int, imgWidth::Int,
|
||||
return plotdata, plotlayout, mz, intensity
|
||||
end
|
||||
|
||||
function sumSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
title_text = isempty(dataset_name) ? "Total Spectrum Plot" : "Total Spectrum for: $dataset_name"
|
||||
"""
|
||||
sumSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
|
||||
Generates a plot of the total (summed) spectrum from MSIData.
|
||||
|
||||
# Arguments
|
||||
- `data`: The `MSIData` object.
|
||||
- `dataset_name`: Optional name of the dataset for the plot title.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly trace.
|
||||
- `plotlayout`: The Plotly layout for the plot.
|
||||
- `xSpectraMz`: The m/z values of the spectrum.
|
||||
- `ySpectraMz`: The intensity values of the spectrum.
|
||||
"""
|
||||
function sumSpectrumPlot(data::MSIData, dataset_name::String=""; mask_path::Union{String, Nothing}=nothing)
|
||||
# Determine base title based on mask usage
|
||||
base_title = if mask_path !== nothing
|
||||
"Masked Total Spectrum"
|
||||
else
|
||||
"Total Spectrum"
|
||||
end
|
||||
|
||||
title_text = isempty(dataset_name) ? base_title : "$base_title for: $dataset_name"
|
||||
|
||||
layout = PlotlyBase.Layout(
|
||||
title=PlotlyBase.attr(
|
||||
text=title_text,
|
||||
@ -579,13 +692,15 @@ function sumSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
)
|
||||
|
||||
# Use the get_total_spectrum function from the backend
|
||||
xSpectraMz, ySpectraMz = get_total_spectrum(data)
|
||||
xSpectraMz, ySpectraMz, num_spectra = get_total_spectrum(data, mask_path=mask_path)
|
||||
|
||||
if isempty(xSpectraMz)
|
||||
if isempty(xSpectraMz) || isempty(ySpectraMz)
|
||||
@warn "Total spectrum is empty."
|
||||
trace = PlotlyBase.scatter(x=Float64[], y=Float64[])
|
||||
trace = PlotlyBase.stem(x=Float64[], y=Float64[])
|
||||
# Update title to indicate empty spectrum
|
||||
layout.title.text = "Empty " * layout.title.text
|
||||
else
|
||||
trace = PlotlyBase.scatter(x=xSpectraMz, y=ySpectraMz, marker=attr(size=1, color="blue", opacity=0.5), name="Total", hoverinfo="x",hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
trace = PlotlyBase.stem(x=xSpectraMz, y=ySpectraMz, marker=attr(size=1, color="blue", opacity=0.5), name="Total", hoverinfo="x", hovertemplate="<b>m/z</b>: %{x:.4f}<extra></extra>")
|
||||
end
|
||||
|
||||
plotdata = [trace]
|
||||
@ -593,23 +708,25 @@ function sumSpectrumPlot(data::MSIData, dataset_name::String="")
|
||||
return plotdata, plotlayout, xSpectraMz, ySpectraMz
|
||||
end
|
||||
|
||||
"""
|
||||
warmup_init()
|
||||
|
||||
Performs pre-compilation of key functions at application startup to reduce first-use latency.
|
||||
This is run asynchronously and should not block application startup.
|
||||
"""
|
||||
function warmup_init()
|
||||
@async begin
|
||||
println("Pre-compiling functions at startup...")
|
||||
|
||||
# Create a dummy MSIData object to be used for pre-compilation
|
||||
# dummy_source = ImzMLSource("dummy.ibd", Float32, Float32)
|
||||
# dummy_meta = MSI_src.SpectrumMetadata(0,0,"",MSI_src.UNKNOWN, MSI_src.SpectrumAsset(Float32,false,0,0,:mz), MSI_src.SpectrumAsset(Float32,false,0,0,:intensity))
|
||||
# dummy_msi_data = MSIData(dummy_source, [dummy_meta], (1,1), zeros(Int,1,1), 0)
|
||||
|
||||
# Pre-compile functions from btnSearch
|
||||
# try OpenMSIData("dummy.imzML") catch end
|
||||
# try precompute_analytics(dummy_msi_data) catch end
|
||||
|
||||
# Pre-compile functions from mainProcess
|
||||
# try get_mz_slice(dummy_msi_data, 1.0, 1.0) catch end
|
||||
try TrIQ(zeros(10,10), 256, 0.98) catch end
|
||||
try quantize_intensity(zeros(10,10), 256) catch end
|
||||
# Pre-compile image processing and plotting functions
|
||||
try
|
||||
TrIQ(zeros(10, 10), 256, 0.98)
|
||||
catch
|
||||
end
|
||||
try
|
||||
quantize_intensity(zeros(10, 10), 256)
|
||||
catch
|
||||
end
|
||||
|
||||
dummy_bmp_path = joinpath("public", "dummy.bmp")
|
||||
dummy_png_path = joinpath("public", "dummy.png")
|
||||
@ -628,6 +745,17 @@ function warmup_init()
|
||||
end
|
||||
end
|
||||
|
||||
"""
|
||||
load_registry(registry_path)
|
||||
|
||||
Loads the dataset registry from a JSON file.
|
||||
|
||||
# Arguments
|
||||
- `registry_path`: Path to the `registry.json` file.
|
||||
|
||||
# Returns
|
||||
- A dictionary containing the registry data. Returns an empty dictionary if the file doesn't exist or fails to parse.
|
||||
"""
|
||||
function load_registry(registry_path)
|
||||
if isfile(registry_path)
|
||||
try
|
||||
@ -640,6 +768,18 @@ function load_registry(registry_path)
|
||||
return Dict{String,Any}()
|
||||
end
|
||||
|
||||
"""
|
||||
extract_metadata(msi_data::MSIData, source_path::String)
|
||||
|
||||
Extracts key metadata from an `MSIData` object for display.
|
||||
|
||||
# Arguments
|
||||
- `msi_data`: The `MSIData` object.
|
||||
- `source_path`: The path to the source data file.
|
||||
|
||||
# Returns
|
||||
- A dictionary containing summary statistics and metadata.
|
||||
"""
|
||||
function extract_metadata(msi_data::MSIData, source_path::String)
|
||||
df = msi_data.spectrum_stats_df
|
||||
if df === nothing
|
||||
@ -686,16 +826,37 @@ function extract_metadata(msi_data::MSIData, source_path::String)
|
||||
)
|
||||
end
|
||||
|
||||
"""
|
||||
update_registry(registry_path, dataset_name, source_path, metadata=nothing, is_imzML=false)
|
||||
|
||||
Adds or updates an entry in the dataset registry JSON file.
|
||||
|
||||
# Arguments
|
||||
- `registry_path`: Path to the `registry.json` file.
|
||||
- `dataset_name`: The name of the dataset.
|
||||
- `source_path`: The path to the source data file.
|
||||
- `metadata`: Optional dictionary of metadata to store.
|
||||
- `is_imzML`: Boolean indicating if the source is an imzML file.
|
||||
"""
|
||||
function update_registry(registry_path, dataset_name, source_path, metadata=nothing, is_imzML=false)
|
||||
registry = load_registry(registry_path)
|
||||
entry = Dict{String, Any}( # Explicitly type the dictionary to allow mixed value types
|
||||
"source_path" => source_path,
|
||||
"processed_date" => string(now()),
|
||||
"is_imzML" => is_imzML
|
||||
)
|
||||
|
||||
# Get existing entry if it exists, otherwise create new one
|
||||
existing_entry = get(registry, dataset_name, Dict{String,Any}())
|
||||
|
||||
# Start with existing data and update only the basic fields
|
||||
entry = copy(existing_entry)
|
||||
entry["source_path"] = source_path
|
||||
entry["processed_date"] = string(now())
|
||||
entry["is_imzML"] = is_imzML
|
||||
|
||||
# Only update metadata if provided
|
||||
if metadata !== nothing
|
||||
entry["metadata"] = metadata
|
||||
end
|
||||
|
||||
# Note: has_mask and mask_path are preserved from existing_entry if they exist
|
||||
|
||||
registry[dataset_name] = entry
|
||||
|
||||
try
|
||||
@ -707,7 +868,25 @@ function update_registry(registry_path, dataset_name, source_path, metadata=noth
|
||||
end
|
||||
end
|
||||
|
||||
function process_file_safely(file_path, masses, params, progress_message_ref, overall_progress_ref)
|
||||
"""
|
||||
process_file_safely(file_path, masses, params, progress_message_ref, overall_progress_ref)
|
||||
|
||||
Safely processes a single MSI data file, generating and saving m/z slices.
|
||||
|
||||
This function handles loading data, generating slices, saving results as bitmaps,
|
||||
and updating the data registry. It includes error handling and memory cleanup.
|
||||
|
||||
# Arguments
|
||||
- `file_path`: Path to the `.imzML` file.
|
||||
- `masses`: A vector of m/z values to generate slices for.
|
||||
- `params`: A structure or dictionary containing processing parameters.
|
||||
- `progress_message_ref`: A reference to update with progress messages.
|
||||
- `overall_progress_ref`: A reference to update with overall progress.
|
||||
|
||||
# Returns
|
||||
- A tuple `(success::Bool, message::String)`.
|
||||
"""
|
||||
function process_file_safely(file_path, masses, params, progress_message_ref, overall_progress_ref; use_mask::Bool=false)
|
||||
local_msi_data = nothing
|
||||
dataset_name = replace(basename(file_path), r"\.imzML$"i => "")
|
||||
output_dir = joinpath("public", dataset_name)
|
||||
@ -722,15 +901,39 @@ function process_file_safely(file_path, masses, params, progress_message_ref, ov
|
||||
return (false, "Skipped: Not an imzML file")
|
||||
end
|
||||
|
||||
# --- Generate Slices (this will call precompute_analytics if needed) ---
|
||||
progress_message_ref = "Generating $(length(masses)) slices for $(dataset_name)..."
|
||||
slice_dict = get_multiple_mz_slices(local_msi_data, masses, params.tolerance)
|
||||
# --- Get mask path and load mask_matrix only if use_mask is true ---
|
||||
local mask_matrix_for_triq::Union{BitMatrix, Nothing} = nothing
|
||||
mask_path = nothing
|
||||
if use_mask
|
||||
registry = load_registry(params.registry)
|
||||
entry = get(registry, dataset_name, nothing)
|
||||
if entry !== nothing && get(entry, "has_mask", false)
|
||||
mask_path_candidate = get(entry, "mask_path", "")
|
||||
if isfile(mask_path_candidate)
|
||||
mask_path = mask_path_candidate
|
||||
# Load the mask matrix here
|
||||
mask_matrix_for_triq = load_and_prepare_mask(mask_path, (local_msi_data.image_dims[1], local_msi_data.image_dims[2]))
|
||||
println("DEBUG: Using mask: $(mask_path) with dimensions $(size(mask_matrix_for_triq))")
|
||||
else
|
||||
@warn "Mask enabled but file not found: $(mask_path_candidate). Clearing invalid mask entry."
|
||||
# Clear invalid mask entry
|
||||
update_registry_mask_fields(params.registry, dataset_name, false, "")
|
||||
mask_path = nothing
|
||||
end
|
||||
else
|
||||
@warn "Mask enabled but no valid mask entry found for: $(dataset_name)"
|
||||
end
|
||||
end
|
||||
|
||||
# --- Extract metadata *after* it has been computed ---
|
||||
# --- Generate Slices ---
|
||||
progress_message_ref = "Generating $(length(masses)) slices for $(dataset_name)..."
|
||||
slice_dict = get_multiple_mz_slices(local_msi_data, masses, params.tolerance, mask_path=mask_path)
|
||||
|
||||
# --- Extract metadata ---
|
||||
metadata = extract_metadata(local_msi_data, file_path)
|
||||
|
||||
# --- Save Slices ---
|
||||
mkpath(output_dir) # Ensure output directory exists
|
||||
mkpath(output_dir)
|
||||
|
||||
for (mass_idx, mass) in enumerate(masses)
|
||||
progress_message_ref = "File $(params.fileIdx)/$(params.nFiles): Saving slice for m/z=$mass"
|
||||
@ -744,7 +947,7 @@ function process_file_safely(file_path, masses, params, progress_message_ref, ov
|
||||
sliceQuant = zeros(UInt8, size(slice))
|
||||
@warn "No intensity data for m/z = $mass in $(dataset_name)"
|
||||
else
|
||||
sliceQuant = params.triqE ? TrIQ(slice, params.colorL, params.triqP) : quantize_intensity(slice, params.colorL)
|
||||
sliceQuant = params.triqE ? TrIQ(slice, params.colorL, params.triqP, mask_matrix=mask_matrix_for_triq) : quantize_intensity(slice, params.colorL, mask_matrix=mask_matrix_for_triq)
|
||||
if params.medianF
|
||||
sliceQuant = round.(UInt8, median_filter(sliceQuant))
|
||||
end
|
||||
@ -752,7 +955,8 @@ function process_file_safely(file_path, masses, params, progress_message_ref, ov
|
||||
|
||||
save_bitmap(joinpath(output_dir, bitmap_filename), sliceQuant, ViridisPalette)
|
||||
if !all(iszero, slice)
|
||||
generate_colorbar_image(slice, params.colorL, joinpath(output_dir, colorbar_filename); use_triq=params.triqE, triq_prob=params.triqP)
|
||||
generate_colorbar_image(slice, params.colorL, joinpath(output_dir, colorbar_filename);
|
||||
use_triq=params.triqE, triq_prob=params.triqP, mask_path=mask_path)
|
||||
end
|
||||
end
|
||||
|
||||
@ -774,3 +978,149 @@ function process_file_safely(file_path, masses, params, progress_message_ref, ov
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function update_registry_mask_fields(registry_path, dataset_name, has_mask, mask_path)
|
||||
registry = load_registry(registry_path)
|
||||
|
||||
if haskey(registry, dataset_name)
|
||||
registry[dataset_name]["has_mask"] = has_mask
|
||||
registry[dataset_name]["mask_path"] = mask_path
|
||||
else
|
||||
# Create new entry if it doesn't exist
|
||||
registry[dataset_name] = Dict{String,Any}(
|
||||
"has_mask" => has_mask,
|
||||
"mask_path" => mask_path,
|
||||
"source_path" => "",
|
||||
"processed_date" => string(now()),
|
||||
"is_imzML" => false
|
||||
)
|
||||
end
|
||||
|
||||
try
|
||||
open(registry_path, "w") do f
|
||||
JSON.print(f, registry, 4)
|
||||
end
|
||||
catch e
|
||||
@error "Failed to write to registry.json: $e"
|
||||
end
|
||||
end
|
||||
|
||||
"""
|
||||
loadSurfacePlot(interfaceImg::String, mask_path::String)
|
||||
|
||||
Loads an image, smooths it, applies a mask, crops the data to the masked region, and creates a 3D Plotly surface plot.
|
||||
|
||||
# Arguments
|
||||
- `interfaceImg`: Path to the image file.
|
||||
- `mask_path`: Path to a mask file. Areas outside the mask will be removed and the plot axes will be cropped to the masked region.
|
||||
|
||||
# Returns
|
||||
- `plotdata`: A vector containing the Plotly surface trace.
|
||||
- `plotlayout`: The Plotly layout for the 3D plot.
|
||||
"""
|
||||
function loadSurfacePlot(interfaceImg::String, mask_path::String)
|
||||
# Load the image
|
||||
cleaned_img = replace(interfaceImg, r"\?.*" => "")
|
||||
cleaned_img = lstrip(cleaned_img, '/')
|
||||
var = joinpath("./public", cleaned_img)
|
||||
img = load(var)
|
||||
img_gray = Gray.(img) # Convert to grayscale
|
||||
img_array = Array(img_gray)
|
||||
elevation = Float32.(Array(img_array)) ./ 255.0 # Normalize between 0 and 1
|
||||
|
||||
# Smooth the image
|
||||
sigma = 3.0
|
||||
kernel = Kernel.gaussian(sigma)
|
||||
elevation_smoothed = imfilter(elevation, kernel)
|
||||
|
||||
# --- APPLY MASK and CROP DATA ---
|
||||
mask_matrix = load_and_prepare_mask(mask_path, (size(elevation_smoothed, 2), size(elevation_smoothed, 1)))
|
||||
elevation_smoothed[.!mask_matrix] .= NaN32
|
||||
|
||||
non_nan_indices = findall(!isnan, elevation_smoothed)
|
||||
if isempty(non_nan_indices)
|
||||
# Return an empty plot if mask removes everything
|
||||
return [PlotlyBase.surface()], PlotlyBase.Layout(title="Empty plot: Mask covered all data")
|
||||
end
|
||||
|
||||
row_indices = [idx[1] for idx in non_nan_indices]
|
||||
col_indices = [idx[2] for idx in non_nan_indices]
|
||||
|
||||
y_range = minimum(row_indices):maximum(row_indices)
|
||||
x_range = minimum(col_indices):maximum(col_indices)
|
||||
|
||||
cropped_elevation = elevation_smoothed[y_range, x_range]
|
||||
# ---
|
||||
|
||||
# --- DOWNSAMPLE CROPPED DATA ---
|
||||
cropped_elevation = downsample_image(cropped_elevation, 256)
|
||||
# ---
|
||||
|
||||
# Create the X, Y meshgrid coordinates for the CROPPED data
|
||||
x_coords = x_range
|
||||
y_coords = y_range
|
||||
X = repeat(reshape(x_coords, 1, length(x_coords)), length(y_coords), 1)
|
||||
Y = repeat(reshape(y_coords, length(y_coords), 1), 1, length(x_coords))
|
||||
|
||||
# Define tick values and text for colorbars, ignoring NaNs
|
||||
non_nan_values = filter(!isnan, cropped_elevation)
|
||||
min_val = isempty(non_nan_values) ? 0.0 : minimum(non_nan_values)
|
||||
max_val = isempty(non_nan_values) ? 1.0 : maximum(non_nan_values)
|
||||
tickV = range(min_val, stop=max_val, length=8)
|
||||
tickT = log_tick_formatter(collect(tickV))
|
||||
|
||||
# Transpose the elevation_smoothed array if Y axis is longer than X axis to fix chopping
|
||||
cropped_elevation = transpose(cropped_elevation)
|
||||
if size(cropped_elevation, 1) < size(cropped_elevation, 2)
|
||||
Y = -Y
|
||||
else
|
||||
X = -X
|
||||
end
|
||||
|
||||
# Calculate the number of ticks and aspect ratio for the 3d plot
|
||||
x_nticks = min(20, length(x_coords))
|
||||
y_nticks = min(20, length(y_coords))
|
||||
z_nticks = 5
|
||||
aspect_ratio = attr(x=1, y=length(y_coords) / length(x_coords), z=0.5)
|
||||
|
||||
# Define the layout for the 3D plot
|
||||
layout3D = PlotlyBase.Layout(
|
||||
title=PlotlyBase.attr(
|
||||
text="3D surface plot of $cleaned_img (masked, cropped, downsampled)",
|
||||
font=PlotlyBase.attr(
|
||||
family="Roboto, Lato, sans-serif",
|
||||
size=18,
|
||||
color="black"
|
||||
)
|
||||
),
|
||||
scene=attr(
|
||||
xaxis_nticks=x_nticks,
|
||||
yaxis_nticks=y_nticks,
|
||||
zaxis_nticks=z_nticks,
|
||||
camera=attr(eye=attr(x=0, y=1, z=0.5)),
|
||||
aspectratio=aspect_ratio
|
||||
),
|
||||
margin=attr(l=0, r=0, t=120, b=0, pad=0)
|
||||
)
|
||||
|
||||
trace3D = PlotlyBase.surface(
|
||||
x=X[1, :],
|
||||
y=Y[:, 1],
|
||||
z=cropped_elevation,
|
||||
contours_z=attr(
|
||||
show=true,
|
||||
usecolormap=true,
|
||||
highlightcolor="limegreen",
|
||||
project_z=true
|
||||
),
|
||||
colorscale="Viridis",
|
||||
colorbar=attr(
|
||||
tickvals=tickV,
|
||||
ticktext=tickT,
|
||||
nticks=8
|
||||
)
|
||||
)
|
||||
plotdata = [trace3D]
|
||||
plotlayout = layout3D
|
||||
return plotdata, plotlayout
|
||||
end
|
||||
13
mask.jl
13
mask.jl
@ -9,14 +9,12 @@ using Statistics, NaturalSort, LinearAlgebra, StipplePlotly
|
||||
using Base.Filesystem: mv
|
||||
|
||||
using MSI_src
|
||||
using .MSI_src: MSIData
|
||||
using .MSI_src: MSIData, process_image_pipeline
|
||||
|
||||
# Plot Handling
|
||||
include("./julia_imzML_visual.jl")
|
||||
|
||||
# Image Processing Pipeline
|
||||
include("src/ImageProcessing.jl")
|
||||
using .ImageProcessing
|
||||
using ImageBinarization
|
||||
|
||||
function load_and_binarize_mask(path)
|
||||
@ -46,7 +44,7 @@ function alter_image(img_path, otsu_scale, noise_size_percent, hole_size_percent
|
||||
gray_img = Float32.(ensure_grayscale(original_img))
|
||||
|
||||
binary, noise_removed, holes_filled, smoothed =
|
||||
ImageProcessing.process_image_pipeline(gray_img;
|
||||
process_image_pipeline(gray_img;
|
||||
otsu_scale=otsu_scale, noise_size_percent=noise_size_percent,
|
||||
hole_size_percent=hole_size_percent, smoothing=smoothing_level)
|
||||
|
||||
@ -250,7 +248,6 @@ end
|
||||
is_browsing_slices = true
|
||||
is_editing_mask = false
|
||||
folder_path = joinpath("public", selected_folder_main)
|
||||
println("Selected folder: $selected_folder_main")
|
||||
if !isdir(folder_path)
|
||||
imgInt = ""
|
||||
msgimg = "Folder not found."
|
||||
@ -675,14 +672,16 @@ end
|
||||
reg_path = abspath(joinpath(@__DIR__, "public", "registry.json"))
|
||||
registry = isfile(reg_path) ? JSON.parsefile(reg_path) : Dict{String, Any}()
|
||||
|
||||
full_mask_path = abspath(final_path)
|
||||
|
||||
# Update the registry entry
|
||||
if haskey(registry, selected_folder_main)
|
||||
registry[selected_folder_main]["mask_path"] = "/css/masks/$(final_mask_name)"
|
||||
registry[selected_folder_main]["mask_path"] = full_mask_path
|
||||
registry[selected_folder_main]["has_mask"] = true
|
||||
else
|
||||
# Create a new entry if folder doesn't exist in registry
|
||||
registry[selected_folder_main] = Dict(
|
||||
"mask_path" => "/css/masks/$(final_mask_name)",
|
||||
"mask_path" => full_mask_path,
|
||||
"has_mask" => true,
|
||||
"is_imzML" => true,
|
||||
"processed_date" => string(Dates.now())
|
||||
|
||||
92
mask.jl.html
92
mask.jl.html
@ -14,35 +14,50 @@
|
||||
<!-- Step 1: Select Slice -->
|
||||
<div class="text-subtitle1 q-mt-md">Step 1: Select Slice</div>
|
||||
<div class="row items-center">
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset" class="q-ma-sm col" standout="custom-standout" :disable="is_editing_mask"></q-select>
|
||||
<q-select v-model="selected_folder_main" :options="image_available_folders" label="Select Dataset"
|
||||
class="q-ma-sm col" standout="custom-standout" :disable="is_editing_mask"></q-select>
|
||||
</div>
|
||||
<p class="text-center" v-html="msgimg"></p>
|
||||
|
||||
<!-- Step 2: Provide Mask Input -->
|
||||
<div class="text-subtitle1 q-mt-md">Step 2: Create/Upload Mask</div>
|
||||
<div class="row justify-around">
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_use_slice_as_mask = true" label="Use Current Slice" :disable="!is_browsing_slices || !imgInt"></q-btn>
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_upload_mask = true" label="Upload to Create Mask" :disable="!is_browsing_slices || !imgInt"></q-btn>
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_change_slice = true" label="Change Mask Blueprint" :disable="!is_editing_mask"></q-btn>
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_use_slice_as_mask = true" label="Use Current Slice"
|
||||
:disable="!is_browsing_slices || !imgInt"></q-btn>
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_upload_mask = true" label="Upload to Create Mask"
|
||||
:disable="!is_browsing_slices || !imgInt"></q-btn>
|
||||
<q-btn class="q-ma-sm btn-style" v-on:click="btn_change_slice = true" label="Change Mask Blueprint"
|
||||
:disable="!is_editing_mask"></q-btn>
|
||||
</div>
|
||||
|
||||
<!-- Step 3: Automated Processing & Overlay -->
|
||||
<div class="text-subtitle1 q-mt-md">Step 3: Adjust & Verify</div>
|
||||
<div class="q-mt-sm">
|
||||
<p class="text-caption text-center">Adjust automated processing for the initial mask:</p>
|
||||
<q-slider color="primary" label-always v-model="otsu_scale" :label-value="'Otsu Scale: ' + otsu_scale.toFixed(2)" :step="0.01" :min="0.1" :max="2.0" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="noise_size_percent" :label-value="'Noise Size: ' + (noise_size_percent * 100).toFixed(3) + '%'" :step="0.001" :min="0.001" :max="0.5" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="hole_size_percent" :label-value="'Hole Size: ' + (hole_size_percent * 100).toFixed(4) + '%'" :step="0.0005" :min="0.0005" :max="0.5" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="smoothing_level" :label-value="'Smoothing: ' + smoothing_level + 'px'" :step="2" :min="1" :max="9" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="otsu_scale"
|
||||
:label-value="'Otsu Scale: ' + otsu_scale.toFixed(2)" :step="0.01" :min="0.1" :max="2.0"
|
||||
:disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="noise_size_percent"
|
||||
:label-value="'Noise Size: ' + (noise_size_percent * 100).toFixed(3) + '%'" :step="0.001"
|
||||
:min="0.001" :max="0.5" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="hole_size_percent"
|
||||
:label-value="'Hole Size: ' + (hole_size_percent * 100).toFixed(4) + '%'" :step="0.0005"
|
||||
:min="0.0005" :max="0.5" :disable="!is_editing_mask"></q-slider>
|
||||
<q-slider color="primary" label-always v-model="smoothing_level"
|
||||
:label-value="'Smoothing: ' + smoothing_level + 'px'" :step="2" :min="1" :max="9"
|
||||
:disable="!is_editing_mask"></q-slider>
|
||||
</div>
|
||||
<div class="q-mt-md">
|
||||
<p class="text-caption text-center">Adjust slice overlay transparency:</p>
|
||||
<q-slider color="black" v-model="imgTrans" :min="0.0" :max="1.0" :step="0.05" label-always :label-value="'Transparency: ' + imgTrans.toFixed(2)" :disable="!is_editing_mask" />
|
||||
<q-slider color="black" v-model="imgTrans" :min="0.0" :max="1.0" :step="0.05" label-always
|
||||
:label-value="'Transparency: ' + imgTrans.toFixed(2)" :disable="!is_editing_mask" />
|
||||
</div>
|
||||
|
||||
<p class="q-mt-md" :class="{'text-negative': mask_editor_warning}">{{ mask_editor_message }}</p>
|
||||
<div class="row">
|
||||
<q-btn :loading="progress" class="q-ma-sm btn-style" :disable="!is_editing_mask" v-on:click="btn_save_final_mask = !btn_save_final_mask" padding="lg" icon="save" label="Save Final Mask"/>
|
||||
<q-btn :loading="progress" class="q-ma-sm btn-style" :disable="!is_editing_mask"
|
||||
v-on:click="btn_save_final_mask = !btn_save_final_mask" padding="lg" icon="save"
|
||||
label="Save Final Mask" />
|
||||
<q-btn class="q-ma-sm btn-style" icon="arrow_back" label="Return" href="/"></q-btn>
|
||||
</div>
|
||||
</div>
|
||||
@ -53,8 +68,10 @@
|
||||
<div id="intDivStyle-right" class="st-col col-12 st-module">
|
||||
<div class="text-h6">Mask Preview</div>
|
||||
<div class="row items-center">
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="btn_img_minus=true" :disable="is_editing_mask"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="btn_img_plus=true" :disable="is_editing_mask"></q-btn>
|
||||
<q-btn icon="arrow_back" class="q-my-sm btn-style" v-on:click="btn_img_minus=true"
|
||||
:disable="is_editing_mask"></q-btn>
|
||||
<q-btn icon="arrow_forward" class="q-my-sm on-right btn-style" v-on:click="btn_img_plus=true"
|
||||
:disable="is_editing_mask"></q-btn>
|
||||
</div>
|
||||
|
||||
<div v-if="!show_verification_plot" class="text-center text-grey q-pa-xl">
|
||||
@ -64,9 +81,11 @@
|
||||
</div>
|
||||
|
||||
<div v-if="show_verification_plot">
|
||||
<plotly :data="plotdata_verify" :layout="plotlayout_verify" class="q-pa-none q-ma-none pixelated-plot"></plotly>
|
||||
<plotly :data="plotdata_verify" :layout="plotlayout_verify" class="q-pa-none q-ma-none pixelated-plot">
|
||||
</plotly>
|
||||
<div class="q-mt-md text-center">
|
||||
<q-btn color="primary" class="btn-style" label="Edit Manually" v-on:click="btn_edit_manually=true" />
|
||||
<q-btn color="primary" class="btn-style" label="Edit Manually"
|
||||
v-on:click="btn_edit_manually=true" />
|
||||
</div>
|
||||
</div>
|
||||
<p class="text-center" v-html="msgimg"></p>
|
||||
@ -87,29 +106,39 @@
|
||||
<div class="q-pa-md q-gutter-sm row justify-center items-center">
|
||||
<!-- Tool Selection -->
|
||||
<q-btn-group>
|
||||
<q-btn label="Brush" v-on:click="current_tool = 'brush'" :color="current_tool === 'brush' ? 'primary' : 'white'" text-color="black"/>
|
||||
<q-btn label="Eraser" v-on:click="current_tool = 'eraser'" :color="current_tool === 'eraser' ? 'primary' : 'white'" text-color="black"/>
|
||||
<q-btn label="Bucket" v-on:click="current_tool = 'bucket'" :color="current_tool === 'bucket' ? 'primary' : 'white'" text-color="black"/>
|
||||
<q-btn label="Drag" v-on:click="current_tool = 'drag'" :color="current_tool === 'drag' ? 'primary' : 'white'" text-color="black"/>
|
||||
<q-btn label="Brush" v-on:click="current_tool = 'brush'"
|
||||
:color="current_tool === 'brush' ? 'primary' : 'white'" text-color="black" />
|
||||
<q-btn label="Eraser" v-on:click="current_tool = 'eraser'"
|
||||
:color="current_tool === 'eraser' ? 'primary' : 'white'" text-color="black" />
|
||||
<q-btn label="Bucket" v-on:click="current_tool = 'bucket'"
|
||||
:color="current_tool === 'bucket' ? 'primary' : 'white'" text-color="black" />
|
||||
<q-btn label="Drag" v-on:click="current_tool = 'drag'"
|
||||
:color="current_tool === 'drag' ? 'primary' : 'white'" text-color="black" />
|
||||
</q-btn-group>
|
||||
|
||||
<q-separator vertical inset />
|
||||
|
||||
<!-- Tool Settings -->
|
||||
<q-input dense filled label="Brush Size" type="number" v-model.number="brush_size" style="max-width: 120px" :min="1"/>
|
||||
<q-input dense filled label="Zoom" type="number" step="0.1" v-model.number="editor_scale" style="max-width: 120px" :min="0.1"/>
|
||||
<q-input dense filled label="Brush Size" type="number" v-model.number="brush_size"
|
||||
style="max-width: 120px" :min="1" />
|
||||
<q-input dense filled label="Zoom" type="number" step="0.1" v-model.number="editor_scale"
|
||||
style="max-width: 120px" :min="0.1" />
|
||||
|
||||
<q-separator vertical inset />
|
||||
|
||||
<!-- Image Manipulation -->
|
||||
<q-input dense filled label="Rotate (deg)" type="number" v-model.number="rotate_degrees" style="max-width: 150px" :step="90"/>
|
||||
<q-select dense filled label="Flip" v-model="flip_direction" :options="['horizontal', 'vertical']" style="max-width: 150px"/>
|
||||
<q-input dense filled label="Rotate (deg)" type="number" v-model.number="rotate_degrees"
|
||||
style="max-width: 150px" :step="90" />
|
||||
<q-select dense filled label="Flip" v-model="flip_direction" :options="['horizontal', 'vertical']"
|
||||
style="max-width: 150px" />
|
||||
<q-btn label="Apply Flip" v-on:click="btn_flip_mask = !btn_flip_mask" />
|
||||
</div>
|
||||
|
||||
<!-- Drag Tool Controls -->
|
||||
<div v-if="current_tool === 'drag'" class="q-pa-sm q-gutter-sm row justify-center items-center" style="border: 1px solid #ccc; border-radius: 4px;">
|
||||
<q-input dense filled label="Move Pixels" type="number" v-model.number="move_pixels" style="max-width: 120px" :min="1"/>
|
||||
<div v-if="current_tool === 'drag'" class="q-pa-sm q-gutter-sm row justify-center items-center"
|
||||
style="border: 1px solid #ccc; border-radius: 4px;">
|
||||
<q-input dense filled label="Move Pixels" type="number" v-model.number="move_pixels"
|
||||
style="max-width: 120px" :min="1" />
|
||||
<q-btn icon="arrow_upward" v-on:click="move_mask_payload = { direction: 'up', t: Date.now() }" />
|
||||
<q-btn icon="arrow_downward" v-on:click="move_mask_payload = { direction: 'down', t: Date.now() }" />
|
||||
<q-btn icon="arrow_back" v-on:click="move_mask_payload = { direction: 'left', t: Date.now() }" />
|
||||
@ -118,21 +147,16 @@
|
||||
|
||||
<!-- Interactive Canvas -->
|
||||
<div class="row justify-center q-pa-md">
|
||||
<div style="position: relative; display: inline-block; overflow: auto; max-width: 100%;"> <!-- Added for scrolling very large images -->
|
||||
<canvas id="maskCanvas"
|
||||
:width="canvas_width"
|
||||
:height="canvas_height"
|
||||
:style="{
|
||||
<div style="position: relative; display: inline-block; overflow: auto; max-width: 100%;">
|
||||
<!-- Added for scrolling very large images -->
|
||||
<canvas id="maskCanvas" :width="canvas_width" :height="canvas_height" :style="{
|
||||
width: canvas_width * editor_scale + 'px',
|
||||
height: canvas_height * editor_scale + 'px',
|
||||
border: '2px solid #ccc',
|
||||
cursor: current_tool === 'drag' ? 'move' : 'crosshair',
|
||||
imageRendering: 'pixelated'
|
||||
}"
|
||||
v-on:mousedown.prevent="startDrawing"
|
||||
v-on:mousemove.prevent="draw"
|
||||
v-on:mouseup.prevent="stopDrawing()"
|
||||
v-on:mouseleave.prevent="stopDrawing()">
|
||||
}" v-on:mousedown.prevent="startDrawing" v-on:mousemove.prevent="draw"
|
||||
v-on:mouseup.prevent="stopDrawing()" v-on:mouseleave.prevent="stopDrawing()">
|
||||
</canvas>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -1,16 +1,80 @@
|
||||
module ImageProcessing
|
||||
# src/ImageProcessing
|
||||
|
||||
using Images
|
||||
using ImageBinarization
|
||||
using ImageMorphology
|
||||
using ImageComponentAnalysis
|
||||
using Colors # For converting to grayscale
|
||||
|
||||
export process_image_pipeline
|
||||
export load_and_prepare_mask # Export the new function
|
||||
|
||||
"""
|
||||
load_and_prepare_mask(mask_path::String, target_dims::Tuple{Int, Int})
|
||||
|
||||
Loads a PNG image mask, converts it to a binary (Boolean) matrix,
|
||||
and resizes it to the specified `target_dims`. White pixels in the mask
|
||||
are considered `true` (part of the ROI), and black pixels are `false`.
|
||||
|
||||
# Arguments
|
||||
- `mask_path`: Absolute path to the PNG mask file.
|
||||
- `target_dims`: A tuple `(width, height)` representing the desired output dimensions.
|
||||
|
||||
# Returns
|
||||
- A `BitMatrix` of `target_dims` where `true` indicates the ROI.
|
||||
"""
|
||||
function load_and_prepare_mask(mask_path::String, target_dims::Tuple{Int, Int})
|
||||
if !isfile(mask_path)
|
||||
error("Mask file not found: $(mask_path)")
|
||||
end
|
||||
|
||||
# Load the image
|
||||
img = Images.load(mask_path)
|
||||
|
||||
# Convert to grayscale if it's a color image
|
||||
gray_img = Gray.(img)
|
||||
|
||||
# Binarize using Otsu's method - white regions become 'true'
|
||||
binary_img = binarize(gray_img, Otsu())
|
||||
|
||||
# Resize to target dimensions
|
||||
resized_img = imresize(binary_img, (target_dims[2], target_dims[1]))
|
||||
|
||||
# Ensure the output is a BitMatrix, as imresize can change the type
|
||||
return resized_img .> 0.5
|
||||
end
|
||||
|
||||
# ===================================================================
|
||||
# CORE PROCESSING PIPELINE
|
||||
# ===================================================================
|
||||
|
||||
"""
|
||||
process_image_pipeline(gray_img; otsu_scale=1.0, noise_size_percent=0.1, hole_size_percent=0.05, smoothing=2)
|
||||
|
||||
Applies a multi-step image processing pipeline to a grayscale image to segment regions of interest.
|
||||
|
||||
The pipeline consists of:
|
||||
1. **Binarization**: An adjusted Otsu's threshold is used to create a binary image.
|
||||
2. **Noise Removal**: Small white regions (noise) are removed using an area opening operation.
|
||||
3. **Hole Filling**: Small black regions (holes) within larger objects are filled.
|
||||
4. **Edge Smoothing**: The edges of the final regions are smoothed using a morphological closing operation.
|
||||
|
||||
# Arguments
|
||||
- `gray_img`: The input grayscale image (`Matrix{<:Gray}`).
|
||||
|
||||
# Keyword Arguments
|
||||
- `otsu_scale`: A factor to scale the automatically determined Otsu threshold. Values > 1.0 make the threshold stricter (less white), < 1.0 make it more lenient (more white). Default: `1.0`.
|
||||
- `noise_size_percent`: The percentage of the total image area used as a threshold to remove small white noise components. Default: `0.1`.
|
||||
- `hole_size_percent`: The percentage of the total image area used as a threshold to fill black holes in white components. Default: `0.05`.
|
||||
- `smoothing`: The size of the kernel for the final edge smoothing (closing) operation. Default: `2`.
|
||||
|
||||
# Returns
|
||||
- A tuple containing four images representing the intermediate steps of the pipeline:
|
||||
1. `binary_img`: The result of the initial binarization.
|
||||
2. `noise_removed_img`: The image after noise removal.
|
||||
3. `holes_filled_img`: The image after filling holes.
|
||||
4. `smoothed_img`: The final smoothed image.
|
||||
"""
|
||||
function process_image_pipeline(gray_img;
|
||||
otsu_scale=1.0,
|
||||
noise_size_percent=0.1,
|
||||
@ -43,5 +107,3 @@ function process_image_pipeline(gray_img;
|
||||
# --- Return all intermediate steps for visualization ---
|
||||
return binary_img, noise_removed_img, holes_filled_img, smoothed_img
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
122
src/MSIData.jl
122
src/MSIData.jl
@ -143,6 +143,45 @@ mutable struct MSIData
|
||||
end
|
||||
end
|
||||
|
||||
"""
|
||||
get_masked_spectrum_indices(data::MSIData, mask_matrix::BitMatrix) -> Set{Int}
|
||||
|
||||
Converts a 2D boolean mask matrix (e.g., loaded from a PNG) into a `Set` of linear
|
||||
spectrum indices that fall within the `true` regions of the mask.
|
||||
|
||||
# Arguments
|
||||
- `data`: The `MSIData` object containing the `coordinate_map`.
|
||||
- `mask_matrix`: A `BitMatrix` where `true` indicates a pixel is part of the ROI.
|
||||
|
||||
# Returns
|
||||
- A `Set{Int}` containing the linear indices of spectra within the mask.
|
||||
"""
|
||||
function get_masked_spectrum_indices(data::MSIData, mask_matrix::BitMatrix)
|
||||
if data.coordinate_map === nothing
|
||||
error("Coordinate map not available. Cannot apply mask to non-imaging data.")
|
||||
end
|
||||
|
||||
width, height = data.image_dims
|
||||
mask_height, mask_width = size(mask_matrix)
|
||||
|
||||
if mask_width != width || mask_height != height
|
||||
error("Mask dimensions ($(mask_width)x$(mask_height)) do not match image dimensions ($(width)x$(height)).")
|
||||
end
|
||||
|
||||
masked_indices = Set{Int}()
|
||||
for y in 1:height
|
||||
for x in 1:width
|
||||
if mask_matrix[y, x] # If this pixel is part of the mask
|
||||
idx = data.coordinate_map[x, y]
|
||||
if idx != 0 # Ensure there's an actual spectrum at this coordinate
|
||||
push!(masked_indices, idx)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
return masked_indices
|
||||
end
|
||||
|
||||
|
||||
# --- Internal function for reading binary data --- #
|
||||
|
||||
@ -513,7 +552,7 @@ It uses a fast, two-pass approach:
|
||||
This function is highly optimized for `.imzML` by leveraging direct binary
|
||||
reading and optimized binning logic. It is called by `get_total_spectrum`.
|
||||
"""
|
||||
function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000, masked_indices::Union{Set{Int}, Nothing}=nothing)
|
||||
println("Calculating total spectrum for imzML (2-pass method)...")
|
||||
total_start_time = time_ns()
|
||||
|
||||
@ -529,7 +568,7 @@ function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
println(" Pass 1: Finding global m/z range...")
|
||||
g_min_mz = Inf
|
||||
g_max_mz = -Inf
|
||||
_iterate_spectra_fast(msi_data) do idx, mz, _
|
||||
_iterate_spectra_fast(msi_data, masked_indices === nothing ? nothing : collect(masked_indices)) do idx, mz, _
|
||||
if !isempty(mz)
|
||||
local_min, local_max = extrema(mz)
|
||||
g_min_mz = min(g_min_mz, local_min)
|
||||
@ -539,7 +578,7 @@ function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
pass1_duration = (time_ns() - pass1_start_time) / 1e9
|
||||
if !isfinite(g_min_mz)
|
||||
@warn "Could not determine a valid m/z range for imzML. All spectra might be empty."
|
||||
return (Float64[], Float64[])
|
||||
return (Float64[], Float64[], 0)
|
||||
end
|
||||
|
||||
# Use and cache the result
|
||||
@ -560,7 +599,9 @@ function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
# 3. Second Pass: Optimized binning
|
||||
pass2_start_time = time_ns()
|
||||
println(" Pass 2: Summing intensities into $num_bins bins...")
|
||||
_iterate_spectra_fast(msi_data) do idx, mz, intensity
|
||||
num_spectra_processed = 0
|
||||
_iterate_spectra_fast(msi_data, masked_indices === nothing ? nothing : collect(masked_indices)) do idx, mz, intensity
|
||||
num_spectra_processed += 1
|
||||
if isempty(mz)
|
||||
return
|
||||
end
|
||||
@ -594,7 +635,7 @@ function get_total_spectrum_imzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
println("----------------------------------------\n")
|
||||
|
||||
println("Total spectrum calculation complete for imzML.")
|
||||
return (collect(mz_bins), intensity_sum)
|
||||
return (collect(mz_bins), intensity_sum, num_spectra_processed)
|
||||
end
|
||||
|
||||
"""
|
||||
@ -608,7 +649,7 @@ It uses a two-pass approach analogous to the `imzML` implementation:
|
||||
|
||||
This function is called by `get_total_spectrum`.
|
||||
"""
|
||||
function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000, masked_indices::Union{Set{Int}, Nothing}=nothing)
|
||||
println("Calculating total spectrum for mzML (2-pass method)...")
|
||||
total_start_time = time_ns()
|
||||
|
||||
@ -624,7 +665,7 @@ function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
println(" Pass 1: Finding global m/z range...")
|
||||
g_min_mz = Inf
|
||||
g_max_mz = -Inf
|
||||
_iterate_spectra_fast(msi_data) do idx, mz, intensity
|
||||
_iterate_spectra_fast(msi_data, masked_indices === nothing ? nothing : collect(masked_indices)) do idx, mz, intensity
|
||||
if !isempty(mz)
|
||||
local_min, local_max = extrema(mz)
|
||||
g_min_mz = min(g_min_mz, local_min)
|
||||
@ -635,7 +676,7 @@ function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
|
||||
if !isfinite(g_min_mz)
|
||||
@warn "Could not determine a valid m/z range for mzML. All spectra might be empty."
|
||||
return (Float64[], Float64[])
|
||||
return (Float64[], Float64[], 0)
|
||||
end
|
||||
|
||||
# Use and cache the result
|
||||
@ -658,7 +699,9 @@ function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
inv_bin_step = 1.0 / bin_step # Precompute reciprocal
|
||||
min_mz = global_min_mz
|
||||
|
||||
_iterate_spectra_fast(msi_data) do idx, mz, intensity
|
||||
num_spectra_processed = 0
|
||||
_iterate_spectra_fast(msi_data, masked_indices === nothing ? nothing : collect(masked_indices)) do idx, mz, intensity
|
||||
num_spectra_processed += 1
|
||||
if isempty(mz)
|
||||
return
|
||||
end
|
||||
@ -687,7 +730,7 @@ function get_total_spectrum_mzml(msi_data::MSIData; num_bins::Int=2000)
|
||||
println("----------------------------------------\n")
|
||||
|
||||
println("Total spectrum calculation complete for mzML.")
|
||||
return (collect(mz_bins), intensity_sum)
|
||||
return (collect(mz_bins), intensity_sum, num_spectra_processed)
|
||||
end
|
||||
|
||||
"""
|
||||
@ -699,11 +742,18 @@ This function dispatches to a specialized implementation based on the file type
|
||||
|
||||
Returns a tuple containing two vectors: the binned m/z axis and the summed intensities.
|
||||
"""
|
||||
function get_total_spectrum(msi_data::MSIData; num_bins::Int=2000)
|
||||
function get_total_spectrum(msi_data::MSIData; num_bins::Int=2000, mask_path::Union{String, Nothing}=nothing)::Tuple{Vector{Float64}, Vector{Float64}, Int}
|
||||
local masked_indices::Union{Set{Int}, Nothing} = nothing
|
||||
if mask_path !== nothing
|
||||
mask_matrix = load_and_prepare_mask(mask_path, msi_data.image_dims)
|
||||
masked_indices = get_masked_spectrum_indices(msi_data, mask_matrix)
|
||||
println("Calculating total spectrum for masked region (mask from: $(mask_path))")
|
||||
end
|
||||
|
||||
if msi_data.source isa ImzMLSource
|
||||
return get_total_spectrum_imzml(msi_data, num_bins=num_bins)
|
||||
return get_total_spectrum_imzml(msi_data, num_bins=num_bins, masked_indices=masked_indices)
|
||||
else # MzMLSource
|
||||
return get_total_spectrum_mzml(msi_data, num_bins=num_bins)
|
||||
return get_total_spectrum_mzml(msi_data, num_bins=num_bins, masked_indices=masked_indices)
|
||||
end
|
||||
end
|
||||
|
||||
@ -715,14 +765,15 @@ This is effectively the total spectrum divided by the number of spectra.
|
||||
|
||||
Returns a tuple containing two vectors: the binned m/z axis and the averaged intensities.
|
||||
"""
|
||||
function get_average_spectrum(msi_data::MSIData; num_bins::Int=2000)
|
||||
mz_bins, intensity_sum = get_total_spectrum(msi_data, num_bins=num_bins)
|
||||
function get_average_spectrum(msi_data::MSIData; num_bins::Int=2000, mask_path::Union{String, Nothing}=nothing)::Tuple{Vector{Float64}, Vector{Float64}}
|
||||
mz_bins, intensity_sum, num_spectra_processed = get_total_spectrum(msi_data, num_bins=num_bins, mask_path=mask_path)
|
||||
|
||||
if isempty(intensity_sum)
|
||||
return (mz_bins, intensity_sum)
|
||||
if isempty(intensity_sum) || num_spectra_processed == 0
|
||||
@warn "Cannot calculate average spectrum: no spectra were processed."
|
||||
return (mz_bins, Float64[])
|
||||
end
|
||||
num_spectra = length(msi_data.spectra_metadata)
|
||||
average_intensity = intensity_sum ./ num_spectra
|
||||
|
||||
average_intensity = intensity_sum ./ num_spectra_processed
|
||||
|
||||
return (mz_bins, average_intensity)
|
||||
end
|
||||
@ -855,13 +906,16 @@ is significantly faster for bulk processing than reading spectra one by one.
|
||||
- `data`: The `MSIData` object.
|
||||
- `source`: The `ImzMLSource`.
|
||||
"""
|
||||
function _iterate_uncompressed_fast(f::Function, data::MSIData, source::ImzMLSource)
|
||||
function _iterate_uncompressed_fast(f::Function, data::MSIData, source::ImzMLSource, indices_to_iterate::Union{AbstractVector{Int}, Nothing})
|
||||
# Optimized path for uncompressed data using buffer reuse
|
||||
max_points = maximum(meta -> meta.mz_asset.encoded_length, data.spectra_metadata)
|
||||
mz_buffer = Vector{source.mz_format}(undef, max_points)
|
||||
int_buffer = Vector{source.intensity_format}(undef, max_points)
|
||||
|
||||
for i in 1:length(data.spectra_metadata)
|
||||
# Determine which indices to iterate over
|
||||
spectrum_indices = (indices_to_iterate === nothing) ? (1:length(data.spectra_metadata)) : indices_to_iterate
|
||||
|
||||
for i in spectrum_indices
|
||||
meta = data.spectra_metadata[i]
|
||||
nPoints = meta.mz_asset.encoded_length
|
||||
|
||||
@ -904,10 +958,14 @@ and will be slower and allocate more memory than `_iterate_uncompressed_fast`.
|
||||
- `data`: The `MSIData` object.
|
||||
- `source`: The `ImzMLSource`.
|
||||
"""
|
||||
function _iterate_compressed_fast(f::Function, data::MSIData, source::ImzMLSource)
|
||||
function _iterate_compressed_fast(f::Function, data::MSIData, source::ImzMLSource, indices_to_iterate::Union{AbstractVector{Int}, Nothing})
|
||||
# Path for datasets containing at least one compressed spectrum.
|
||||
# This path reads and decompresses each spectrum individually.
|
||||
for i in 1:length(data.spectra_metadata)
|
||||
|
||||
# Determine which indices to iterate over
|
||||
spectrum_indices = (indices_to_iterate === nothing) ? (1:length(data.spectra_metadata)) : indices_to_iterate
|
||||
|
||||
for i in spectrum_indices
|
||||
meta = data.spectra_metadata[i]
|
||||
|
||||
if meta.mz_asset.encoded_length == 0 && meta.int_asset.encoded_length == 0
|
||||
@ -937,7 +995,7 @@ This function acts as a dispatcher:
|
||||
- If all spectra are uncompressed, it uses a highly optimized path that reuses pre-allocated
|
||||
buffers to minimize memory allocations and overhead.
|
||||
"""
|
||||
function _iterate_spectra_fast_impl(f::Function, data::MSIData, source::ImzMLSource)
|
||||
function _iterate_spectra_fast_impl(f::Function, data::MSIData, source::ImzMLSource, indices_to_iterate::Union{AbstractVector{Int}, Nothing})
|
||||
if isempty(data.spectra_metadata)
|
||||
return
|
||||
end
|
||||
@ -947,9 +1005,9 @@ function _iterate_spectra_fast_impl(f::Function, data::MSIData, source::ImzMLSou
|
||||
data.spectra_metadata)
|
||||
|
||||
if any_compressed
|
||||
_iterate_compressed_fast(f, data, source)
|
||||
_iterate_compressed_fast(f, data, source, indices_to_iterate)
|
||||
else
|
||||
_iterate_uncompressed_fast(f, data, source)
|
||||
_iterate_uncompressed_fast(f, data, source, indices_to_iterate)
|
||||
end
|
||||
end
|
||||
|
||||
@ -961,12 +1019,16 @@ through each spectrum, decodes the Base64 data on the fly, and calls the
|
||||
provided function. It bypasses the `GetSpectrum` cache to avoid storing
|
||||
all decoded spectra in memory.
|
||||
"""
|
||||
function _iterate_spectra_fast_impl(f::Function, data::MSIData, source::MzMLSource)
|
||||
function _iterate_spectra_fast_impl(f::Function, data::MSIData, source::MzMLSource, indices_to_iterate::Union{AbstractVector{Int}, Nothing})
|
||||
# This implementation is for mzML. It iterates through each spectrum,
|
||||
# decodes it, and then calls the function `f`. It's less performant than
|
||||
# the imzML version because we cannot pre-allocate buffers of a known size,
|
||||
# but it is correct and still faster than using GetSpectrum due to bypassing the cache.
|
||||
for i in 1:length(data.spectra_metadata)
|
||||
|
||||
# Determine which indices to iterate over
|
||||
spectrum_indices = (indices_to_iterate === nothing) ? (1:length(data.spectra_metadata)) : indices_to_iterate
|
||||
|
||||
for i in spectrum_indices
|
||||
meta = data.spectra_metadata[i]
|
||||
|
||||
mz = read_binary_vector(source.file_handle, meta.mz_asset)
|
||||
@ -986,7 +1048,7 @@ It dispatches to a specialized implementation based on the data source type
|
||||
- `f`: A function to call for each spectrum, with signature `f(index, mz, intensity)`.
|
||||
- `data`: The `MSIData` object.
|
||||
"""
|
||||
function _iterate_spectra_fast(f::Function, data::MSIData)
|
||||
function _iterate_spectra_fast(f::Function, data::MSIData, indices_to_iterate::Union{AbstractVector{Int}, Nothing}=nothing)
|
||||
# Dispatch to the correct implementation based on the source type
|
||||
_iterate_spectra_fast_impl(f, data, data.source)
|
||||
_iterate_spectra_fast_impl(f, data, data.source, indices_to_iterate)
|
||||
end
|
||||
@ -14,7 +14,10 @@ export OpenMSIData,
|
||||
get_average_spectrum,
|
||||
LoadMzml,
|
||||
precompute_analytics,
|
||||
process_spectrum
|
||||
process_spectrum,
|
||||
generate_colorbar_image,
|
||||
process_image_pipeline,
|
||||
load_and_prepare_mask
|
||||
|
||||
# Export the public Preprocessing API
|
||||
export FeatureMatrix,
|
||||
@ -43,6 +46,7 @@ include("mzML.jl")
|
||||
include("imzML.jl")
|
||||
include("MzmlConverter.jl")
|
||||
include("Preprocessing.jl")
|
||||
include("ImageProcessing.jl")
|
||||
|
||||
|
||||
# --- Main Entry Point --- #
|
||||
|
||||
187
src/imzML.jl
187
src/imzML.jl
@ -744,10 +744,17 @@ This is a performant function that iterates through spectra once.
|
||||
# Returns
|
||||
- A `Matrix{Float64}` representing the intensity slice.
|
||||
"""
|
||||
function get_mz_slice(data::MSIData, mass::Real, tolerance::Real)
|
||||
function get_mz_slice(data::MSIData, mass::Real, tolerance::Real; mask_path::Union{String, Nothing}=nothing)
|
||||
width, height = data.image_dims
|
||||
slice_matrix = zeros(Float64, height, width)
|
||||
|
||||
local masked_indices::Union{Set{Int}, Nothing} = nothing
|
||||
if mask_path !== nothing
|
||||
mask_matrix = load_and_prepare_mask(mask_path, (width, height))
|
||||
masked_indices = get_masked_spectrum_indices(data, mask_matrix)
|
||||
println("Applying mask from: $(mask_path), found $(length(masked_indices)) spectra in ROI.")
|
||||
end
|
||||
|
||||
# INTELLIGENT LOADING: Ensure analytics are computed for filtering.
|
||||
if data.spectrum_stats_df === nothing || !hasproperty(data.spectrum_stats_df, :MinMZ)
|
||||
println("Per-spectrum metadata not found. Running one-time analytics computation...")
|
||||
@ -761,7 +768,9 @@ function get_mz_slice(data::MSIData, mass::Real, tolerance::Real)
|
||||
|
||||
# 1. Find all candidate spectra first for efficient filtering
|
||||
candidate_indices = Set{Int}()
|
||||
for i in 1:length(data.spectra_metadata)
|
||||
indices_to_check = masked_indices === nothing ? (1:length(data.spectra_metadata)) : masked_indices
|
||||
|
||||
for i in indices_to_check
|
||||
spec_min_mz = stats_df.MinMZ[i]
|
||||
spec_max_mz = stats_df.MaxMZ[i]
|
||||
if target_max >= spec_min_mz && target_min <= spec_max_mz
|
||||
@ -769,23 +778,20 @@ function get_mz_slice(data::MSIData, mass::Real, tolerance::Real)
|
||||
end
|
||||
end
|
||||
|
||||
println("Found $(length(candidate_indices)) candidate spectra (filtered from $(length(data.spectra_metadata)))")
|
||||
println("Found $(length(candidate_indices)) candidate spectra (filtered from $(length(indices_to_check)) initial spectra)")
|
||||
|
||||
# 2. Iterate using the optimized, low-allocation iterator
|
||||
results_count = 0
|
||||
_iterate_spectra_fast(data) do idx, mz_array, intensity_array
|
||||
# Process only the spectra that are candidates
|
||||
if idx in candidate_indices
|
||||
_iterate_spectra_fast(data, collect(candidate_indices)) do idx, mz_array, intensity_array
|
||||
meta = data.spectra_metadata[idx]
|
||||
intensity = find_mass(mz_array, intensity_array, mass, tolerance)
|
||||
if intensity > 0.0
|
||||
meta = data.spectra_metadata[idx]
|
||||
if 1 <= meta.x <= width && 1 <= meta.y <= height
|
||||
slice_matrix[meta.y, meta.x] = intensity
|
||||
results_count += 1
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
println("Populated $results_count pixels with intensity data")
|
||||
replace!(slice_matrix, NaN => 0.0)
|
||||
@ -802,7 +808,7 @@ This is a highly performant function that iterates through the full dataset only
|
||||
# Returns
|
||||
- A `Dict{Real, Matrix{Float64}}` mapping each mass to its intensity slice matrix.
|
||||
"""
|
||||
function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, tolerance::Real)
|
||||
function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, tolerance::Real; mask_path::Union{String, Nothing}=nothing)
|
||||
width, height = data.image_dims
|
||||
|
||||
# 1. Initialize a dictionary to hold the output slice matrices
|
||||
@ -811,6 +817,13 @@ function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, t
|
||||
slice_dict[mass] = zeros(Float64, height, width)
|
||||
end
|
||||
|
||||
local masked_indices::Union{Set{Int}, Nothing} = nothing
|
||||
if mask_path !== nothing
|
||||
mask_matrix = load_and_prepare_mask(mask_path, (width, height))
|
||||
masked_indices = get_masked_spectrum_indices(data, mask_matrix)
|
||||
println("Applying mask from: $(mask_path), found $(length(masked_indices)) spectra in ROI.")
|
||||
end
|
||||
|
||||
# 2. Ensure analytics are computed for filtering.
|
||||
if data.spectrum_stats_df === nothing || !hasproperty(data.spectrum_stats_df, :MinMZ)
|
||||
println("Per-spectrum metadata not found. Running one-time analytics computation...")
|
||||
@ -820,12 +833,13 @@ function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, t
|
||||
println("Filtering candidate spectra for $(length(masses)) m/z values...")
|
||||
stats_df = data.spectrum_stats_df
|
||||
candidate_indices = Set{Int}()
|
||||
indices_to_check = masked_indices === nothing ? (1:length(data.spectra_metadata)) : masked_indices
|
||||
|
||||
# 3. Find all spectra that could contain *any* of the requested masses.
|
||||
for mass in masses
|
||||
target_min = mass - tolerance
|
||||
target_max = mass + tolerance
|
||||
for i in 1:length(data.spectra_metadata)
|
||||
for i in indices_to_check
|
||||
# If already a candidate, no need to check again
|
||||
if i in candidate_indices
|
||||
continue
|
||||
@ -841,9 +855,7 @@ function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, t
|
||||
println("Found $(length(candidate_indices)) total candidate spectra.")
|
||||
|
||||
# 4. Iterate through the data a single time using the optimized iterator.
|
||||
_iterate_spectra_fast(data) do idx, mz_array, intensity_array
|
||||
# Process only the spectra that are candidates
|
||||
if idx in candidate_indices
|
||||
_iterate_spectra_fast(data, collect(candidate_indices)) do idx, mz_array, intensity_array
|
||||
meta = data.spectra_metadata[idx]
|
||||
# For this single spectrum, check all masses of interest
|
||||
for mass in masses
|
||||
@ -859,7 +871,6 @@ function get_multiple_mz_slices(data::MSIData, masses::AbstractVector{<:Real}, t
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# 5. Clean up and return
|
||||
for mass in masses
|
||||
@ -878,30 +889,15 @@ Generates and saves a plot of a single image slice for a given m/z value.
|
||||
This function closely imitates the logic of the original `GetSlice` but uses
|
||||
the modern `MSIData` access patterns and robust peak finding.
|
||||
"""
|
||||
function plot_slice(msi_data::MSIData, mass::Real, tolerance::Real, output_dir::String; stage_name="slice_mz_$(mass)", bins=256)
|
||||
function plot_slice(msi_data::MSIData, mass::Real, tolerance::Real, output_dir::String;
|
||||
stage_name="slice_mz_$(mass)", bins=256, mask_path::Union{String, Nothing}=nothing)
|
||||
|
||||
# 1. Create an empty image for the slice, with dimensions matching plotting expectations
|
||||
width, height = msi_data.image_dims
|
||||
slice_matrix = zeros(Float64, height, width)
|
||||
|
||||
# 2. Iterate through each spectrum to build the slice
|
||||
# 1. Generate the slice, applying the mask if provided.
|
||||
println("Generating slice for m/z $mass...")
|
||||
_iterate_spectra_fast(msi_data) do spec_idx, mz_array, intensity_array
|
||||
meta = msi_data.spectra_metadata[spec_idx]
|
||||
|
||||
# Find the peak intensity using the modern, robust find_mass
|
||||
intensity = find_mass(mz_array, intensity_array, mass, tolerance)
|
||||
|
||||
if intensity > 0.0
|
||||
# Populate the matrix using (y, x) indexing
|
||||
if 1 <= meta.x <= width && 1 <= meta.y <= height
|
||||
slice_matrix[meta.y, meta.x] = intensity
|
||||
end
|
||||
end
|
||||
end
|
||||
slice_matrix = get_mz_slice(msi_data, mass, tolerance, mask_path=mask_path)
|
||||
println("Slice generation complete.")
|
||||
|
||||
# 3. Plot the resulting slice matrix using CairoMakie
|
||||
# 2. Plot the resulting slice matrix using CairoMakie
|
||||
println("Plotting slice...")
|
||||
|
||||
fig = Figure(size = (600, 500))
|
||||
@ -925,7 +921,7 @@ function plot_slice(msi_data::MSIData, mass::Real, tolerance::Real, output_dir::
|
||||
Colorbar(fig[1, 2], hm, label="Intensity")
|
||||
colgap!(fig.layout, 5)
|
||||
|
||||
# 4. Save the plot
|
||||
# 3. Save the plot
|
||||
mkpath(output_dir)
|
||||
filename = "$(stage_name).png"
|
||||
save_path = joinpath(output_dir, filename)
|
||||
@ -957,9 +953,9 @@ is used to exclude outliers before normalization.
|
||||
# Returns
|
||||
- A tuple `(low, high)` representing the calculated lower and upper intensity bounds.
|
||||
"""
|
||||
function get_outlier_thres(img::AbstractMatrix{<:Real}, prob::Real=0.98)
|
||||
function get_outlier_thres(img::AbstractVector{<:Real}, prob::Real=0.98)
|
||||
# DO NOT filter zeros. Use all pixel values like R does.
|
||||
int_values = vec(img)
|
||||
int_values = img
|
||||
low = minimum(int_values)
|
||||
upp = maximum(int_values)
|
||||
|
||||
@ -1001,6 +997,10 @@ function get_outlier_thres(img::AbstractMatrix{<:Real}, prob::Real=0.98)
|
||||
return (low, actual_threshold)
|
||||
end
|
||||
|
||||
function get_outlier_thres(img::AbstractMatrix{<:Real}, prob::Real=0.98)
|
||||
return get_outlier_thres(vec(img), prob)
|
||||
end
|
||||
|
||||
"""
|
||||
set_pixel_depth(img, bounds, depth)
|
||||
|
||||
@ -1063,11 +1063,26 @@ within these bounds.
|
||||
# Returns
|
||||
- A new image matrix with intensities quantized to the specified depth within the TrIQ bounds.
|
||||
"""
|
||||
function TrIQ(pixMap::AbstractMatrix{<:Real}, depth::Integer, prob::Real=0.98)
|
||||
# Compute new dynamic range
|
||||
bounds = get_outlier_thres(pixMap, prob)
|
||||
function TrIQ(pixMap::AbstractMatrix{<:Real}, depth::Integer, prob::Real=0.98; mask_matrix::Union{BitMatrix, Nothing}=nothing)
|
||||
local values_for_thres
|
||||
if mask_matrix !== nothing
|
||||
# Extract values only from the masked region for threshold calculation
|
||||
values_for_thres = pixMap[mask_matrix]
|
||||
# Filter out only zeros created by the mask.
|
||||
filter!(x -> x != 0, values_for_thres)
|
||||
else
|
||||
values_for_thres = pixMap
|
||||
end
|
||||
|
||||
# Set intensity dynamic range
|
||||
# If the relevant values are empty or all zero, return a zero matrix
|
||||
if isempty(values_for_thres) || all(iszero, values_for_thres)
|
||||
bounds = (0.0, 0.0)
|
||||
else
|
||||
# Compute new dynamic range from the (potentially filtered) values
|
||||
bounds = get_outlier_thres(values_for_thres, prob)
|
||||
end
|
||||
|
||||
# Set intensity dynamic range for the *entire* pixMap, using bounds derived from masked data
|
||||
return set_pixel_depth(pixMap, bounds, depth)
|
||||
end
|
||||
|
||||
@ -1107,8 +1122,17 @@ Linearly scales the intensity values in a slice to a specified number of levels.
|
||||
The output is an array of `UInt8` values.
|
||||
This is a modernized version of `IntQuantCl`.
|
||||
"""
|
||||
function quantize_intensity(slice::AbstractMatrix{<:Real}, levels::Integer=256)
|
||||
function quantize_intensity(slice::AbstractMatrix{<:Real}, levels::Integer=256; mask_matrix::Union{BitMatrix, Nothing}=nothing)
|
||||
local max_val
|
||||
if mask_matrix !== nothing
|
||||
masked_slice = slice[mask_matrix]
|
||||
# Filter out zeros that might have been introduced by masking, if any
|
||||
filter!(x -> x != 0, masked_slice)
|
||||
max_val = isempty(masked_slice) ? 0.0 : maximum(masked_slice)
|
||||
else
|
||||
max_val = maximum(slice)
|
||||
end
|
||||
|
||||
if max_val <= 0
|
||||
return zeros(UInt8, size(slice))
|
||||
end
|
||||
@ -1208,7 +1232,7 @@ function display_statistics(slices::AbstractArray{<:AbstractMatrix{<:Real}, 2},
|
||||
stats_to_calc = Dict{String, Function}("Mean" => mean)
|
||||
all_dfs = Dict{String, DataFrame}()
|
||||
|
||||
for (stat_name, stat_func) in stats_to_to_calc
|
||||
for (stat_name, stat_func) in stats_to_calc
|
||||
# Pre-allocate matrix for better performance
|
||||
stat_matrix = zeros(Float64, n_files, n_masses)
|
||||
|
||||
@ -1498,3 +1522,80 @@ end
|
||||
|
||||
# Viridis color palette (256 colors) - defined as constant
|
||||
const ViridisPalette = generate_palette(ColorSchemes.viridis)
|
||||
|
||||
function generate_colorbar_image(slice_data::AbstractMatrix, color_levels::Int, output_path::String; use_triq::Bool=false, triq_prob::Float64=0.98, mask_path::Union{String, Nothing}=nothing)
|
||||
# 1. Determine bounds based on whether TrIQ is used and if a mask is applied
|
||||
local data_for_bounds
|
||||
if mask_path !== nothing
|
||||
height, width = size(slice_data)
|
||||
mask_matrix = load_and_prepare_mask(mask_path, (width, height))
|
||||
# Extract only the non-zero values within the mask to accurately calculate the range
|
||||
data_for_bounds = slice_data[mask_matrix]
|
||||
filter!(x -> x > 0, data_for_bounds)
|
||||
else
|
||||
data_for_bounds = vec(slice_data)
|
||||
end
|
||||
|
||||
# Handle cases where data_for_bounds might be empty after filtering
|
||||
if isempty(data_for_bounds)
|
||||
min_val, max_val = 0.0, 1.0 # Default range if no data in ROI
|
||||
else
|
||||
min_val, max_val = if use_triq
|
||||
get_outlier_thres(data_for_bounds, triq_prob)
|
||||
else
|
||||
extrema(data_for_bounds)
|
||||
end
|
||||
end
|
||||
|
||||
# 2. Replicate the tick calculation logic from plot_slices
|
||||
bins = color_levels
|
||||
levels = range(min_val, stop=max_val, length=bins + 1)
|
||||
level_range = levels[end] - levels[1]
|
||||
|
||||
if level_range == 0
|
||||
levels = range(min_val - 0.1, stop=max_val + 0.1, length=bins + 1)
|
||||
level_range = 0.2
|
||||
end
|
||||
|
||||
exponent = level_range > 0 ? floor(log10(level_range)) / 3 : 0
|
||||
scale = 10^(3 * exponent)
|
||||
scaled_levels = levels ./ scale
|
||||
|
||||
format_num = level_range > 0 ? floor(log10(level_range)) % 3 : 0
|
||||
labels = if format_num == 0
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
elseif format_num == 1
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
else
|
||||
[ @sprintf("%3.2f", lvl) for lvl in scaled_levels]
|
||||
end
|
||||
|
||||
divisors = 2:7
|
||||
remainders = (bins - 1) .% divisors
|
||||
best_divisor = divisors[findlast(x -> x == minimum(remainders), remainders)]
|
||||
tick_indices = round.(Int, range(1, stop=bins + 1, length=best_divisor + 1))
|
||||
|
||||
if !(1 in tick_indices)
|
||||
pushfirst!(tick_indices, 1)
|
||||
end
|
||||
if !((bins + 1) in tick_indices)
|
||||
push!(tick_indices, bins + 1)
|
||||
end
|
||||
unique!(sort!(tick_indices))
|
||||
|
||||
tick_positions = levels[tick_indices]
|
||||
tick_labels = labels[tick_indices]
|
||||
|
||||
# 3. Create and save the colorbar image
|
||||
fig = Figure(size=(150, 250))
|
||||
Colorbar(fig[1, 1],
|
||||
colormap=cgrad(:viridis, bins, categorical=true),
|
||||
# limits=(min_val, max_val),
|
||||
limits=(levels[1], levels[end]),
|
||||
label=(scale == 1 ? "Intensity" : "Intensity ×10^$(round(Int, 3 * exponent))"),
|
||||
ticks=(tick_positions, tick_labels),
|
||||
labelsize=20,
|
||||
ticklabelsize=16
|
||||
)
|
||||
save(output_path, fig)
|
||||
end
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user