Merge pull request #53 from s-ccs/tests

Tests

Project.toml

@@ -36,21 +36,28 @@ UnfoldSim = "ed8ae6d2-84d3-44c6-ab46-0baf21700804"
 WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"
 
 [compat]
-WGLMakie = "0.10"
-ProgressBars = "1"
+BenchmarkTools = "1"
+CairoMakie = "0.12"
+CSV = "0.10"
+ComputationalResources = "0.3"
+DataFrames = "1"
 Distributions = "0.25"
-Revise = "3"
-Observables = "0.5"
+DSP = "0.7"
+FileIO = "1"
 HDF5 = "0.17"
-Statistics = "1"
-DataFrames = "1"
-ComputationalResources = "0.3"
 ImageFiltering = "0.7"
-BenchmarkTools = "1"
-CSV = "0.10"
+Images = "0.26"
+JLD2 = "0.5"
+JuliaFormatter = "1"
+Makie = "0.21, 0.22"
+Observables = "0.5"
+ProgressBars = "1"
+Revise = "3"
+Statistics = "1"
 StatsBase = "0.34"
-Unfold = "0.7"
-DSP = "0.7"
-JuliaFormatter = "2"
-Makie = "0.21"
 TopoPlots = "0.2"
+Unfold = "0.7"
+UnfoldMakie = "0.5"
+UnfoldSim = "0.3"
+WGLMakie = "0.10"
+

docs/Project.toml

@@ -1,5 +1,6 @@
 [deps]
 AlgebraOfGraphics = "cbdf2221-f076-402e-a563-3d30da359d67"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
@@ -8,7 +9,10 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
+ERPgnostics = "b811cc8a-7213-4a66-9205-28465b3b3ba1"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MakieThemes = "e296ed71-da82-5faf-88ab-0034a9761098"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -18,3 +22,8 @@ TopoPlots = "2bdbdf9c-dbd8-403f-947b-1a4e0dd41a7a"
 Unfold = "181c99d8-e21b-4ff3-b70b-c233eddec679"
 UnfoldMakie = "69a5ce3b-64fb-4f22-ae69-36dd4416af2a"
 UnfoldSim = "ed8ae6d2-84d3-44c6-ab46-0baf21700804"
+
+[compat]
+DSP = "0.7"
+JLD2 = "0.5"
+TopoPlots = "0.2"

docs/example_data.jl

@@ -0,0 +1,40 @@
+using UnfoldSim
+using TopoPlots
+using Unfold
+using Random
+using CSV
+
+"""
+    example_data(String) 
+
+Creates example data. Currently, 1 dataset is available.
+
+Datasets:
+- `pattern_detection_values` (default) - Dataframe with 2 fields:\\
+    sorting conditions, estimate (pattern detection values).\\
+
+
+**Return Value:** `DataFrame`.
+"""
+function example_data(example = "pattern_detection_values"; mode = 1)
+    if mode == 1
+        datapath = dirname(dirname(Base.current_project())) * "/data/evts_d.csv"
+    else
+        datapath = dirname(Base.current_project()) * "/data/evts_d.csv"
+    end
+    if example == "pattern_detection_values"
+        evts_d = CSV.read(datapath, DataFrame)
+        pattern_detection_values = stack(evts_d)
+        rename!(pattern_detection_values, :variable => :condition, :value => :estimate)
+        evts_d = nothing
+        return pattern_detection_values
+    elseif example == "pattern_detection_values_32"
+        evts_d = CSV.read(datapath, DataFrame)
+        pattern_detection_values = stack(evts_d[1:32, :])
+        rename!(pattern_detection_values, :variable => :condition, :value => :estimate)
+        evts_d = nothing
+        return pattern_detection_values
+    else
+        error("unknown example data")
+    end
+end

docs/literate/intro/gnostics.jl

@@ -0,0 +1,26 @@
+using CairoMakie
+using DataFrames
+using UnfoldMakie
+using JLD2
+using ERPgnostics
+CairoMakie.activate!()
+
+# Data input
+path = dirname(dirname(Base.current_project()))
+include(path * "/docs/example_data.jl")
+evts = DataFrame(CSV.File(path * "/data/events.csv"))
+positions_128 = JLD2.load_object(path * "/data/positions_128.jld2")
+erps_fix_32 = JLD2.load_object(path * "/data/erps_fix_32.jld2")
+pattern_detection_values_32 = example_data("pattern_detection_values_32");
+desired_conditions = ["duration", "fix_avgpos_x", "fix_avgpos_y", "fix_avgpupilsize"]
+
+# Plotting
+
+inter_toposeries_image(
+    filter(row -> row.condition in desired_conditions, pattern_detection_values_32),
+    evts,
+    erps_fix_32,
+    1:151;
+    positions = positions_128[1:32],
+    figure_configs = (; size = (1500, 700)),
+)

docs/literate/intro/toposeries.jl

@@ -0,0 +1,35 @@
+# # Interactive topoplots
+
+# These topoplots series are different: instead fo voltage they represent pattern detection value (here - entropy) for different sorting variables.
+
+# By clicking on the markers you can see the channel name and sorted variabel in title.
+
+using CairoMakie
+using DataFrames
+using UnfoldMakie
+using JLD2
+using ERPgnostics
+CairoMakie.activate!()
+
+# Data input
+path = dirname(dirname(Base.current_project()))
+include(path * "/docs/example_data.jl")
+
+positions_128 = JLD2.load_object(path * "/data/positions_128.jld2")
+pattern_detection_values = example_data();
+
+# Interactive topoplot series with one row
+
+desired_conditions = ["duration", "fix_avgpos_x", "fix_avgpos_y", "fix_avgpupilsize"]
+inter_toposeries(
+    filter(row -> row.condition in desired_conditions, pattern_detection_values);
+    positions = positions_128,
+)
+
+# Interactive topoplot series with multiple rows
+inter_toposeries(
+    pattern_detection_values;
+    positions = positions_128,
+    toposeries_configs = (; nrows = 4),
+    figure_configs = (; size = (1500, 1200)),
+)

docs/make.jl

@@ -34,7 +34,9 @@ makedocs(;
         assets = String[],
     ),
     pages = [
-        "ERPgnostics Documentation" => "index.md",
+        "ERPgnostics highlights" => "index.md",
+        "Plotting" => "generated/intro/toposeries.md",
+        "Diagnostics" => "generated/intro/gnostics.md",
         "API / DocStrings" => "api.md",
     ],
 )

src/ERPgnostics.jl

@@ -10,7 +10,7 @@ using Statistics, StatsBase, Distributions
 using HDF5, FileIO
 using Printf
 using Images
-using TopoPlots 
+using TopoPlots
 using ImageFiltering
 using ComputationalResources
 using Observables
@@ -22,18 +22,22 @@ using ProgressBars
 
 include("interactive_heatmap.jl")
 include("interactive_topoplots.jl")
-include("pattern_detection_mean_filter.jl")
-include("pattern_detection_probability.jl")
+include("pattern_detection.jl")
 include("pattern_simulation.jl")
+include("filters.jl")
 
 export inter_toposeries # or better toposeries_inter
 export inter_toposeries_image
 
 export inter_heatmap
 export inter_heatmap_image
 
-export mult_chan_pattern_detector_probability
-export mult_chan_pattern_detector_probability_meanfilter
 
 export simulate_6patterns
+
+export pattern_detector
+export complex_pattern_detector
+
+export slow_filter
+export mean_filter
 end

src/filters.jl

@@ -0,0 +1,47 @@
+
+function slow_filter(data_init)
+    return UnfoldMakie.imfilter(data_init, UnfoldMakie.Kernel.gaussian((0, max(50, 0))))
+end
+
+function fast_filter!(dat_filtered, kernel, dat) # broken
+    #r = Images.ImageFiltering.ComputationalResources.CPU1(Images.ImageFiltering.FIR())
+    DSP.filt!(dat_filtered, kernel[1].data.parent, dat)
+    return dat_filtered
+end
+
+function mean_filter(dat; output_dim = 20)
+    mean_filter!(similar(dat, output_dim, size(dat, 2)), dat)
+end
+
+function mean_filter!(dat_filtered, dat)
+    n_out = size(dat_filtered, 1)
+    dat_nrows = size(dat, 1)
+    bins = Int.(round.(collect(range(1, stop = dat_nrows, length = n_out + 1))))
+    bins[1] = 1
+    bins[end] = dat_nrows
+    for b = 1:length(bins)-1
+        bin_start = bins[b]
+        bin_stop = bins[b+1]
+        dat_filtered[b, :] .= mean(@view(dat[bin_start:bin_stop, :]), dims = 1)[1, :]
+    end
+    return dat_filtered
+end
+
+#= 
+function range_mean(dat_filt)
+    a = extrema(dat_filt, dims = 2)
+    b = last.(a) .- first.(a)
+    return mean(b)
+end =#
+
+entropy_robust(img::AbstractArray; kind = :shannon, nbins = 256) =
+    entropy_robust(Images.ImageQualityIndexes._log(kind), img; nbins = nbins)
+function entropy_robust(logᵦ::Log, img; nbins = 256) where {Log<:Function}
+    img_trimmed = collect(trim(img[:], prop = 0.1))
+    _, counts = Images.ImageContrastAdjustment.build_histogram(img_trimmed, nbins)
+    n = length(img)
+    _zero = zero(first(counts) / n)
+    -sum(counts) do c
+        c > 0 ? c / n * logᵦ(c / n) : _zero
+    end
+end

src/interactive_heatmap.jl

@@ -36,8 +36,8 @@ function inter_heatmap(pattern_detection_values::DataFrame)
         if event.button == Mouse.left && event.action == Mouse.press
             plot, _ = pick(ax.scene)
             a = DataInspector(plot)
-            pos = Makie.position_on_plot(plot, -1, apply_transform = false)[Vec(1, 2)]
-            b = Makie._pixelated_getindex(plot[1][], plot[2][], plot[3][], pos, true)
+            pos = WGLMakie.position_on_plot(plot, -1, apply_transform = false)[Vec(1, 2)]
+            b = WGLMakie._pixelated_getindex(plot[1][], plot[2][], plot[3][], pos, true)
             chan_i[], var_i[] = b[1], b[2]
         end
     end
@@ -118,8 +118,8 @@ function inter_heatmap_image(
     on(events(f).mousebutton, priority = 1) do event
         if event.button == Mouse.left && event.action == Mouse.press
             plot, _ = pick(ax.scene)
-            pos = Makie.position_on_plot(plot, -1, apply_transform = false)[Vec(1, 2)]
-            b = Makie._pixelated_getindex(plot[1][], plot[2][], plot[3][], pos, true)
+            pos = WGLMakie.position_on_plot(plot, -1, apply_transform = false)[Vec(1, 2)]
+            b = WGLMakie._pixelated_getindex(plot[1][], plot[2][], plot[3][], pos, true)
             chan_i[], var_i[] = b[1], b[2]
             #a = DataInspector(plot)
         end