OSCR

One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › Raw data ↔ training_analysis.jl, lines 1–63 · score 1.00 · Kv2.2, Kv4.1, Kv1.4, Kv1.6, Kv10.2, Kv12.1
  2. [2] § Methods › Raw data ↔ process_data.jl, lines 1–72 · score 1.00 · Kv2.2, Kv4.1, Kv1.4, Kv1.6, Kv10.2, Kv12.1
  3. [3] § Results › Unified SciML Hodgkin-Huxley model gating function behaviour ↔ training_analysis.jl, lines 1–63 · score 0.95 · Kv10.2, Kv12.1, Kv1.3, Kv10.1, Kv1.5, Kv3.1
  4. [4] § Results › Unified SciML Hodgkin-Huxley model gating function behaviour ↔ src/plotting/aog_recipes.jl, lines 65–124 · score 0.95 · Kv10.2, Kv12.1, Kv1.3, Kv10.1, Kv1.5, Kv3.1
  5. [5] § Results ↔ src/plotting/aog_recipes.jl, lines 65–124 · score 0.90 · Kv3.2, Kv1.4, Kv1.6, Kv2.1, Kv10.1, Kv12.3
  6. [6] § Results ↔ src/plotting/aog_recipes.jl, lines 127–191 · score 0.57 · classical HH model, model predictions, SciML, box, RMSE, protocol

Paper

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The authors' code

Julia · 1,498 lines · 45 KB · no license · 3 matches

  1. prot_renamer = renamer(
  2. :act_m90 => "",
  3. :act_m80 => "",
  4. :act_m70 => "",
  5. :act_m60 => "",
  6. :act_m50 => "",
  7. :act_m40 => "",
  8. :act_m30 => "",
  9. :act_m20 => "",
  10. :act_m10 => "",
  11. :act_0 => "",
  12. :act_p10 => "",
  13. :act_p20 => "",
  14. :act_p30 => "",
  15. :act_p40 => "",
  16. :act_p50 => "",
  17. :act_p60 => "",
  18. :act_p70 => "",
  19. :act_p80 => "",
  20. ##
  21. :dea_m80 => "",
  22. :dea_m70 => "",
  23. :dea_m60 => "",
  24. :dea_m50 => "",
  25. :dea_m40 => "",
  26. :dea_m30 => "",
  27. :dea_m20 => "",
  28. :dea_m10 => "",
  29. :dea_0 => "",
  30. :dea_p10 => "",
  31. :dea_p20 => "",
  32. :dea_p30 => "",
  33. ##
  34. :ina_m40 => "",
  35. :ina_m30 => "",
  36. :ina_m20 => "",
  37. :ina_m10 => "",
  38. :ina_0 => "",
  39. :ina_p10 => "",
  40. :ina_p20 => "",
  41. :ina_p30 => "",
  42. :ina_p40 => "",
  43. :ina_p50 => "",
  44. :ina_p60 => "",
  45. :ina_p70 => "",
  46. ##
  47. :rec_50 => "",
  48. :rec_200 => "",
  49. :rec_350 => "",
  50. :rec_500 => "",
  51. :rec_650 => "",
  52. :rec_800 => "",
  53. :rec_950 => "",
  54. :rec_1100 => "",
  55. :rec_1250 => "",
  56. :rec_1400 => "",
  57. :rec_1550 => "",
  58. :rec_1700 => "",
  59. :rec_1850 => "",
  60. :rec_2000 => "",
  61. :rec_2150 => "",
  62. :rec_2300 => "",
  63. )
  65. base_renamer = renamer(
  66. :kv11 => "Kᵥ1.1",
  67. :kv12 => "Kᵥ1.2",
  68. :kv13 => "Kᵥ1.3",
  69. :kv14 => "Kᵥ1.4",
  70. :kv15 => "Kᵥ1.5",
  71. :kv16 => "Kᵥ1.6",
  72. :kv18 => "Kᵥ1.8",
  73. :kv21 => "Kᵥ2.1",
  74. :kv22 => "Kᵥ2.2",
  75. :kv31 => "Kᵥ3.1",
  76. :kv32 => "Kᵥ3.2",
  77. :kv33 => "Kᵥ3.3",
  78. :kv34 => "Kᵥ3.4",
  79. :kv41 => "Kᵥ4.1",
  80. :kv42 => "Kᵥ4.2",
  81. :kv43 => "Kᵥ4.3",
  82. :kv71 => "Kᵥ7.1",
  83. :kv73 => "Kᵥ7.3",
  84. :kv101 => "Kᵥ10.1",
  85. :kv102 => "Kᵥ10.2",
  86. :kv121 => "Kᵥ12.1",
  87. :kv123 => "Kᵥ12.3",
  88. )
  90. cname_renamer = renamer(
  91. :kv11 => "Kᵥ1.1 (n=51)",
  92. :kv12 => "Kᵥ1.2 (n=38)",
  93. :kv13 => "Kᵥ1.3 (n=67)",
  94. :kv14 => "Kᵥ1.4 (n=42)",
  95. :kv15 => "Kᵥ1.5 (n=47)",
  96. :kv16 => "Kᵥ1.6 (n=26)",
  97. :kv18 => "Kᵥ1.8 (n=9)",
  98. :kv21 => "Kᵥ2.1 (n=18)",
  99. :kv22 => "Kᵥ2.2 (n=21)",
  100. :kv31 => "Kᵥ3.1 (n=22)",
  101. :kv32 => "Kᵥ3.2 (n=28)",
  102. :kv33 => "Kᵥ3.3 (n=45)",
  103. :kv34 => "Kᵥ3.4 (n=26)",
  104. :kv41 => "Kᵥ4.1 (n=16)",
  105. :kv42 => "Kᵥ4.2 (n=12)",
  106. :kv43 => "Kᵥ4.3 (n=20)",
  107. :kv71 => "Kᵥ7.1 (n=11)",
  108. :kv73 => "Kᵥ7.3 (n=2)",
  109. :kv101 => "Kᵥ10.1 (n=20)",
  110. :kv102 => "Kᵥ10.2 (n=10)",
  111. :kv121 => "Kᵥ12.1 (n=24)",
  112. :kv123 => "Kᵥ12.3 (n=10)",
  113. :pooled => "Pooled (n=565)",
  114. )
  116. f_renamer = renamer(
  117. :im1_∞ => L"m_{1,\infty}(V)",
  118. :im2_∞ => L"m_{2,\infty}(V)",
  119. :im1_τ => L"\tau_1(V)",
  120. :im2_τ => L"\tau_2(V)",
  121. )
  124. function rows_cells_cols_prots(df, prot, types; nrows=5, w=100, h=100, data_color=:red, pred_color=:black, alpha=0.3)
  127. df_ch = filter(r -> (in(r.ctype, types) && r.protocol == prot), df)
  128. df_ch[:, :row_id] = repeat([missing], size(df_ch)[1])
  129. @rtransform!(df_ch, :i = string(findfirst(unique(filter(r -> r.ctype == :ctype, df_ch).id) .== :id)))
  131. df_flat = DataFrames.flatten(df_ch, [:t, :data])
  132. df_rec, df_pred = groupby(df_flat, :data_type)
  134. if df_rec[1, :data_type] == :prediction
  135. df_rec, df_pred = df_pred, df_rec
  136. end
  138. plt = data(df_rec) * visual(Lines, alpha=alpha) *
  139. mapping(
  140. :t => "Time (ms)",
  141. :data => "Normalized current (a.u.)",
  142. row=:i => renamer([string(i) => "" for i in 1:1000]),
  143. col=:ctype => base_renamer,
  144. color=:temp => renamer(("15" => "15°C", "25" => "25°C", "35" => "35°C")) => "Data recorded at"
  145. ) +
  146. data(df_pred) * visual(Lines, alpha=alpha, label="uSciML HH Model prediction", color=pred_color) *
  147. mapping(
  148. :t => "Time (ms)",
  149. :data => "Normalized current (a.u.)",
  150. row=:i => renamer([string(i) => "" for i in 1:1000]),
  151. col=:ctype => base_renamer,
  152. )
  154. axis = (width=w, height=h)
  156. pg = paginate(plt, row=nrows)
  157. fg = draw(
  158. pg,
  159. legend=(; framevisible=false, position=:top),
  160. facet=(; linkxaxes=:minimal),
  161. axis=axis,
  162. )
  164. return fg
  165. end
  168. function df_to_boxplot(df; w=100, h=100)
  169. df_flat = DataFrames.flatten(df, [:t, :data])
  171. df1, df2 = groupby(df_flat, :data_type)
  173. dfoi = if df1[1, :data_type] == :prediction
  174. df1
  175. else
  176. df2
  177. end
  178. model_renamer = renamer(
  179. (:SciML_pooled => "Unified SciML",
  180. :SciML_split => "Individual SciML",
  181. :classical_no_ref => "Classical HH, no rand. eff.",
  182. :classical_with_ref => "Classical with rand. eff."
  183. )
  184. )
  185. plt = data(dfoi) * visual(BoxPlot, show_outliers=false) *
  186. mapping(
  187. :model_type => model_renamer => "HH Model Type",
  188. :rmse => "RMSE (protocol traces level)",
  189. color=:model_type => model_renamer => "HH Model Type",
  190. layout=:ctype => cname_renamer
  191. )
  193. axis = (width=w, height=h, xticklabelrotation=pi / 4, xticklabelsvisible=false, limits=(nothing, nothing, -0.005, nothing))
  194. fg = draw(plt,
  195. legend=(; framevisible=false, position=:top),
  196. facet=(; linkxaxes=:none, linkyaxes=:none),
  197. axis=axis,)
  199. fg.figure.content[6].backgroundcolor = (:red, 0.3)
  200. fg.figure.content[19].backgroundcolor = (:red, 0.3)
  201. fg.figure.content[20].backgroundcolor = (:red, 0.3)
  203. return fg
  204. end
  206. function inf_tau_grid(df, flatten_syms, labels, fig_inds, transformer; w=100, h=100, alpha=1.0)
  208. df_flat = DataFrames.flatten(df, flatten_syms)
  210. fig = Figure(; size=(w, h))
  212. yscales = [
  213. identity,
  214. identity,
  215. log10,
  216. log10,
  217. ]
  220. grid_i = nothing
  221. for (sym, label, (i, j), tr) in zip(flatten_syms[1:end-1], labels, fig_inds, transformer)
  222. sub_i = fig[i, j]
  223. plt_i = data(df_flat) * visual(Lines, alpha=alpha) * (
  224. mapping(
  225. :v => (x -> 80 * x) => "Voltage (mV)",
  226. sym => (x -> tr(x)),
  227. color=:temp => (x -> string(x) * " °C") => "Temperature",
  228. row=:ctype => cname_renamer
  229. )
  230. )
  232. if j == 1
  233. grid_i = draw!(sub_i, plt_i, axis=(; ylabel=label, titlevisible=false, yscale=yscales[j]))
  234. elseif j == 4
  235. grid_i = draw!(sub_i, plt_i, axis=(; ylabel=label, yscale=yscales[j]))
  236. else
  237. grid_i = draw!(sub_i, plt_i, axis=(; ylabel=label, titlevisible=false, yscale=yscales[j]))
  238. end
  239. end
  241. legend!(fig[0, :], grid_i; nbanks=3, framevisible=false)
  244. rowsize!(fig.layout, 1, Relative(0.99))
  246. for k in 1:3
  247. colgap!(fig.layout, k, -5)
  248. end
  250. return fig
  251. end
  255. function plot_early_stopping_bands(df; w=100, h=100)
  257. df_unique_its = combine(groupby(df, [:channel, :iteration]),
  258. :rmse_low => (x -> x[1]) => :low,
  259. :rmse_high => (x -> x[1]) => :high,
  260. :rmse_median => (x -> x[1]) => :median)
  262. sort!(df_unique_its, :iteration)
  264. df_unique_mins = vcat([filter(r -> r.median == minimum(g[!, :median]), g) for g in groupby(df_unique_its, :channel)]...)
  266. plt1 = data(df_unique_its) * mapping(:iteration, :low, :high, layout=:channel => cname_renamer) * visual(Band, color=(:black, 0.3), label="95% CI")
  267. plt2 = data(df_unique_its) * mapping(:iteration, :median, layout=:channel => cname_renamer) * visual(Lines, color=(:black, 0.8), label="Median")
  268. plt4 = data(df_unique_mins) * mapping(:iteration, layout=:channel => cname_renamer) * visual(VLines, color=(:red, 0.8), label="Minimal Validation RMSE")
  270. axis = (width=w,
  271. height=h,
  272. ylabel="Validation RMSE",
  273. xticks=0:30:300,
  274. xticklabelrotation=pi / 2
  275. )
  276. fg = draw(plt1 + plt2 + plt4,
  277. legend=(; framevisible=false, position=:top),
  278. axis=axis,
  279. facet=(; linkxaxes=:none)
  280. )
  282. end
  285. function make_big_grid_bands(df, fb, sb, syms, labels, idx1, idx2, tr; w=100, h=100, alpha=1.0)
  287. fig = Figure(; size=(w, h))
  289. _, grid_i = inf_tau_grid_bands!(
  290. fig,
  291. filter(:ctype => (x -> in(x, fb)), df),
  292. syms,
  293. labels,
  294. idx1,
  295. tr,
  296. alpha=0.2)
  298. _, grid_i = inf_tau_grid_bands!(
  299. fig,
  300. filter(:ctype => (x -> in(x, sb)), df),
  301. syms,
  302. labels,
  303. idx2,
  304. tr,
  305. alpha=0.2)
  309. legend!(fig[0, :], grid_i; nbanks=3, framevisible=false)
  311. Label(fig[1, 1], labels[1], padding=(0, 0, -20, 0))
  312. Label(fig[1, 2], labels[2], padding=(0, 0, -20, 0))
  313. Label(fig[1, 3], labels[3], padding=(0, 0, -20, 0))
  314. Label(fig[1, 4], labels[4], padding=(0, 0, -20, 0))
  315. Label(fig[1, 5], labels[1], padding=(0, 0, -20, 0))
  316. Label(fig[1, 6], labels[2], padding=(0, 0, -20, 0))
  317. Label(fig[1, 7], labels[3], padding=(0, 0, -20, 0))
  318. Label(fig[1, 8], labels[4], padding=(0, 0, -20, 0))
  321. rowsize!(fig.layout, 1, Relative(0.03))
  322. rowsize!(fig.layout, 2, Relative(0.93))
  324. for i in 1:8
  325. colsize!(fig.layout, i, Relative(0.125))
  326. end
  328. for k in 1:3
  329. colgap!(fig.layout, k, -5)
  330. end
  331. for k in 5:7
  332. colgap!(fig.layout, k, -5)
  333. end
  335. return fig
  336. end
  339. function inf_tau_grid_bands!(fig, df, syms, labels, fig_inds, transformer; w=100, h=100, alpha=1.0)
  341. tr = NamedTuple{Tuple(syms[1:end-1])}(transformer)
  343. dfs = map(syms[1:end-1]) do sym
  345. v = []
  346. temp = []
  347. q025 = []
  348. q975 = []
  349. meds = []
  350. ctype = []
  352. for g in groupby(df, [:ctype, :temp])
  354. mat_sym = cat(g[!, sym]..., dims=2)
  356. q025_i = tr[sym].(quantile.(eachrow(mat_sym), 0.025))
  357. q975_i = tr[sym].(quantile.(eachrow(mat_sym), 0.975))
  358. med_i = tr[sym].(median.(eachrow(mat_sym)))
  360. push!(v, g[1, :v])
  361. push!(temp, g[1, :temp])
  362. push!(ctype, g[1, :ctype])
  363. push!(q025, q025_i)
  364. push!(q975, q975_i)
  365. push!(meds, med_i)
  366. end
  368. t = (v=v, low=q025, high=q975, median=meds, temp=temp, channel=ctype)
  369. DataFrame(t)
  370. end
  372. yscales = [
  373. identity,
  374. identity,
  375. log10,
  376. log10,
  377. identity,
  378. identity,
  379. log10,
  380. log10,
  381. ]
  383. grid_i = nothing
  384. for (sym, label, (i, j), df_i) in zip(syms[1:end-1], labels, fig_inds, dfs)
  386. df_flat = DataFrames.flatten(df_i, [:v, :low, :high, :median])
  388. sub_i = fig[i, j]
  390. plt_i_1 = data(df_flat) *
  391. mapping(:v => (x -> 80 * x) => "V (mV)", :low, :high, row=:channel => cname_renamer, color=:temp => (x -> string(x) * " °C") => "Temperature") *
  392. visual(Band, alpha=alpha)
  394. plt_i_2 = data(df_flat) *
  395. mapping(:v => (x -> 80 * x) => "V (mV)", :median, row=:channel => cname_renamer, color=:temp => (x -> string(x) * " °C") => "Temperature") *
  396. visual(Lines)
  397. plt_i = plt_i_1 + plt_i_2
  399. if j in [1; 5]
  400. grid_i = draw!(sub_i, plt_i,
  401. axis=(;
  402. limits=(nothing, nothing, -0.1, 1.1),
  403. titlevisible=false,
  404. ylabelvisible=false,
  405. yscale=yscales[j],
  406. xticklabelrotation=pi / 2,
  407. xticks=-90:45:90))
  408. elseif j in [2; 6]
  409. grid_i = draw!(sub_i, plt_i,
  410. axis=(;
  411. limits=(nothing, nothing, -0.1, 1.1),
  412. titlevisible=false,
  413. ylabelvisible=false,
  414. yticklabelsvisible=false,
  415. yscale=yscales[j],
  416. xticklabelrotation=pi / 2,
  417. xticks=-90:45:90))
  418. elseif j in [3; 7]
  419. grid_i = draw!(sub_i, plt_i,
  420. axis=(;
  421. limits=(nothing, nothing, 10^-2.5, 10^4.5),
  422. titlevisible=false,
  423. ylabelvisible=false,
  424. yscale=yscales[j],
  425. yticks=[10^-2; 10^1; 10^4],
  426. ytickformat=values -> ["10⁻²"; "10¹"; "10⁴"],
  427. xticklabelrotation=pi / 2,
  428. xticks=-90:45:90))
  429. elseif j in [4; 8]
  430. grid_i = draw!(sub_i, plt_i,
  431. axis=(;
  432. limits=(nothing, nothing, 10^-2.5, 10^4.5),
  433. ylabelvisible=false,
  434. yscale=yscales[j],
  435. yticklabelsvisible=false,
  436. yticks=[10^-2; 10^1; 10^4],
  437. ytickformat=values -> ["10⁻²"; "10¹"; "10⁴"],
  438. xticklabelrotation=pi / 2,
  439. xticks=-90:45:90))
  440. end
  441. end
  443. rowlabels = filter(fig.content) do x
  444. x isa Label && x.layoutobservables.gridcontent[].side === Right()
  445. end
  447. for label in rowlabels
  448. label.fontsize = 12
  449. end
  451. return fig, grid_i
  452. end
  455. function df_to_pairplot(df, names;
  456. w=100,
  457. h=100,
  458. cgap=10,
  459. rgap=10,
  460. labelfontsize=10,
  461. ticklabelsize=10,
  462. contour_lims=(nothing, nothing, nothing, nothing),
  463. hist_lims=(nothing, nothing, nothing, nothing),
  464. cmap=:bluesreds,
  465. alpha=0.5,
  466. nbins=20
  467. )
  469. fig = Figure(size=(w, h))
  470. gs = GridLayout(fig[1, 1])
  472. pairplot(
  473. gs,
  474. df[!, names] => (
  475. PairPlots.Scatter(color=df[!, :rmse], markersize=6, colormap=cmap, alpha=alpha),
  476. PairPlots.MarginHist(color=(:grey, 0.3)),
  477. PairPlots.MarginDensity(),
  478. ),
  479. bins=Dict(
  480. :η₁ => nbins,
  481. :η₂ => nbins,
  482. :η₃ => nbins,
  483. :η₄ => nbins,
  484. :η₅ => nbins,
  485. :η₆ => nbins,
  486. :η₇ => nbins,
  487. :η₈ => nbins,
  488. ),
  489. topright=true,
  490. bottomleft=false,
  491. labels=Dict(
  492. :η₁ => "",
  493. :η₂ => "",
  494. :η₃ => "",
  495. :η₄ => "",
  496. :η₅ => "",
  497. :η₆ => "",
  498. :η₇ => "",
  499. :η₈ => "",
  500. :η₉ => "",
  501. )
  502. )
  503. pairplot(
  504. gs,
  505. df[!, names] => (
  506. PairPlots.Contour(),
  507. PairPlots.MarginHist(color=(:grey, 0.3)),
  508. PairPlots.MarginDensity(),
  509. ),
  510. bins=Dict(
  511. :η₁ => nbins,
  512. :η₂ => nbins,
  513. :η₃ => nbins,
  514. :η₄ => nbins,
  515. :η₅ => nbins,
  516. :η₆ => nbins,
  517. :η₇ => nbins,
  518. :η₈ => nbins,
  519. ),
  520. )
  523. ###### this is hacky but didn't find other options
  524. for ax in fig.content
  525. ax.xticklabelrotation = pi / 3
  526. ax.yticklabelrotation = 0
  527. ax.ylabelsize = labelfontsize
  528. ax.xlabelsize = labelfontsize
  529. ax.yticklabelsize = ticklabelsize
  530. ax.xticklabelsize = ticklabelsize
  532. ax.ylabelpadding = -10
  533. ax.xlabelpadding = -10
  535. if ax.limits[][2] === nothing
  536. ax.limits = contour_lims
  537. else
  538. ax.limits = hist_lims
  539. end
  540. end
  542. Colorbar(fig[1, 2], limits=(minimum(df[!, :rmse]), maximum(df[!, :rmse])), colormap=cmap, label="RMSE")
  544. rowgap!(gs, rgap)
  545. colgap!(gs, cgap)
  547. return fig
  548. end
  551. function df_to_stuff_v_temp(df, fb, sb, f_syms; w=100, h=100)
  553. fig = Figure(size=(w, h))
  555. batches = [fb, sb]
  557. for i in 1:2
  558. batch = batches[i]
  560. df_flat = DataFrames.flatten(filter(:ctype => (x -> in(x, batch)), df), f_syms)
  562. sub_i = fig[1, i]
  564. plt = data(df_flat) * mapping(:v => "V (mV)",
  565. [:m1_∞ => "" :m2_∞ => "" :m1_τ => "" :m2_τ => ""],
  566. col=dims(2) => renamer([L"m_{1,\infty}(V)" L"m_{2,\infty}(V)" L"\tau_1(V)" L"\tau_2(V)"]),
  567. row=:ctype => base_renamer,
  568. color=:temp => "Temperature °C",
  569. group=:temp => nonnumeric
  570. ) *
  571. visual(Lines, linewidth=4) +
  572. data((x=[-80; 80], y=[-1.1; 1.1])) * mapping(:x, :y) * visual(Lines, alpha=0.0)
  574. axis = (;
  575. limits=(-88, 88, -0.1, nothing),
  576. xticks=-80:40:80,
  577. xticklabelrotation=pi / 2,
  578. ylabel=""
  579. )
  580. draw!(
  581. sub_i,
  582. plt,
  583. axis=axis,
  584. facet=(; linkyaxes=:none))
  585. end
  587. Colorbar(fig[1, 3],
  588. limits=(minimum(df[!, :temp]), maximum(df[!, :temp])),
  589. label="Temperature °C",
  590. colormap=cgrad(:viridis, 5, categorical=true)
  591. )
  593. return fig
  595. end
  599. function protocol_vpcs(df, model, subs)
  601. gdf = groupby(df, :protocol)
  603. vpc_objs = []
  604. vpc_keys = []
  605. for g in gdf[subs]
  607. println(g[1, :protocol])
  609. pop = [collect(g[!, :subject])...]
  610. p = g[1, :params]
  611. ebes = [collect(g[!, :ebe])...]
  613. fpm = fit(
  614. model,
  615. pop,
  616. p,
  617. MAP(FOCE()),
  618. optim_options=(;
  619. iterations=0,
  620. show_trace=false,
  621. ),
  622. init_randeffs=ebes,
  623. constantcoef=filter(i -> i !== :σ, keys(p)),
  624. )
  626. _vpc = vpc(fpm)
  627. push!(vpc_objs, _vpc)
  628. push!(vpc_keys, g[1, :protocol])
  629. end
  631. return NamedTuple{Tuple(vpc_keys)}(vpc_objs)
  632. end
  635. function vpcs_nt_to_plot(vpc_nt; w=100, h=100, prot="act")
  637. all_df = DataFrame(repeat([[],], 10),
  638. [:data_τ, :data_time, :I_obs, :sim_τ, :sim_time, :lower, :upper, :lower_error, :upper_error, :protocol]
  639. )
  641. ks_prot = [i for i in keys(vpc_nt) if string(i)[1:3] == prot]
  643. map(ks_prot) do k
  645. vpc_i = vpc_nt[k]
  647. data_i = vpc_i.popvpc.data_quantiles
  648. sim_i = vpc_i.simulated_quantiles
  650. df_i = DataFrame(
  651. [data_i[!, :τ], data_i[!, :time], data_i[!, :I_obs], sim_i[!, :τ], sim_i[!, :time], sim_i[!, :lower], sim_i[!, :upper]],
  652. [:data_τ, :data_time, :I_obs, :sim_τ, :sim_time, :lower, :upper]
  653. )
  654. @rtransform!(df_i, :lower_error = :I_obs < :lower ? :I_obs : :lower)
  655. @rtransform!(df_i, :upper_error = :I_obs > :upper ? :I_obs : :upper)
  656. @rtransform!(df_i, :protocol = k)
  658. append!(all_df, df_i)
  659. end
  661. plt = data(all_df) * mapping(:sim_time, :I_obs, group=:data_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Lines) +
  662. data(all_df) * mapping(:sim_time, :lower, :upper, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.5, color=:lightblue) +
  663. data(all_df) * mapping(:sim_time, :lower_error, :lower, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.5, color=:red, overdraw=false) +
  664. data(all_df) * mapping(:sim_time, :upper, :upper_error, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.5, color=:red)
  666. axis = (width=w, height=h, xlabel="Time (ms)", ylabel=L"\frac{I}{I_{max}}")
  667. draw(plt, axis=axis)
  669. end
  671. function cust_gof(insp; cmap=:binary, hbins=100, cbins=1000, w=600, h=600,
  672. xy_lw=nothing,
  673. loess_lw=nothing,
  674. ols_lw=nothing,
  675. xy_col=nothing,
  676. loess_col=nothing,
  677. ols_col=nothing,
  678. )
  680. data = [i.observations.I_obs for i in insp.o.data]
  681. data = vcat(data...)
  682. m_id = .!ismissing.(data)
  683. data = Vector{Float64}(data[m_id])
  685. pred = [i.pred.I_obs for i in insp.pred]
  686. pred = vcat(pred...)
  687. pred = pred[m_id]
  689. ipred = [i.ipred.I_obs for i in insp.pred]
  690. ipred = vcat(ipred...)
  691. ipred = ipred[m_id]
  693. time = [i.subject.time for i in insp.wres]
  694. time = vcat(time...)
  695. time = time[m_id]
  697. wres = [i.wres.I_obs for i in insp.wres]
  698. wres = vcat(wres...)
  699. wres = Vector{Float64}(wres[m_id])
  701. iwres = [i.iwres.I_obs for i in insp.wres]
  702. iwres = vcat(iwres...)
  703. iwres = Vector{Float64}(iwres[m_id])
  706. loess_elem = [LineElement(color=loess_col, linewidth=loess_lw),]
  707. ols_elem = [LineElement(color=ols_col, linewidth=ols_lw),]
  708. xy_elem = [LineElement(color=xy_col, linewidth=xy_lw),]
  710. h1 = fit(Histogram, (pred, data), nbins=hbins)
  711. h2 = fit(Histogram, (ipred, data), nbins=hbins)
  712. h3 = fit(Histogram, (time, wres), nbins=hbins)
  713. h4 = fit(Histogram, (ipred, iwres), nbins=hbins)
  715. crange_min = 1
  716. crange_max = log10(1 + maximum([
  717. maximum(h1.weights);
  718. maximum(h2.weights);
  719. maximum(h3.weights);
  720. maximum(h4.weights)
  721. ]))
  723. f = Figure(; size=(w, h))
  725. ax1 = Axis(
  726. f[1, 1],
  727. limits=(-0.05, 1.05, -0.05, 1.05),
  728. xlabel="Population Predictions",
  729. ylabel="Observations",
  730. xticklabelrotation=pi / 4
  731. )
  732. contour!(
  733. ax1,
  734. collect(h1.edges[1][1:end-1]),
  735. collect(h1.edges[2])[1:end-1],
  736. log10.(Float64.(h1.weights) .+ 1),
  737. colormap=cmap,
  738. colorrange=(crange_min, crange_max),
  739. levels=cbins)
  740. observations_vs_predictions!(
  741. ax1,
  742. insp,
  743. markersize=0,
  744. ols_color=ols_col,
  745. ols_linewidth=ols_lw,
  746. loess_color=loess_col,
  747. loess_linewidth=loess_lw,
  748. )
  751. ax2 = Axis(
  752. f[1, 2],
  753. limits=(-0.05, 1.05, -0.05, 1.05),
  754. xlabel="Individual Predictions",
  755. ylabel="Observations",
  756. xticklabelrotation=pi / 4
  757. )
  758. contour!(
  759. ax2,
  760. collect(h2.edges[1][1:end-1]),
  761. collect(h2.edges[2])[1:end-1],
  762. log10.(Float64.(h2.weights) .+ 1),
  763. colormap=cmap,
  764. colorrange=(crange_min, crange_max),
  765. levels=cbins)
  766. observations_vs_ipredictions!(
  767. ax2,
  768. insp,
  769. markersize=0,
  770. ols_color=ols_col,
  771. ols_linewidth=ols_lw,
  772. loess_color=loess_col,
  773. loess_linewidth=loess_lw,
  774. )
  777. ax3 = Axis(
  778. f[2, 1],
  779. limits=(nothing, nothing, -10, 10),
  780. ylabel="Weighted population residuals",
  781. xlabel="Time (ms)",
  782. xticklabelrotation=pi / 4
  783. )
  784. contour!(
  785. ax3,
  786. collect(h3.edges[1][1:end-1]),
  787. collect(h3.edges[2])[1:end-1],
  788. log10.(Float64.(h3.weights) .+ 1),
  789. colormap=cmap,
  790. colorrange=(crange_min, crange_max),
  791. levels=cbins)
  792. wresiduals_vs_time!(
  793. ax3,
  794. insp,
  795. markersize=0,
  796. ols_color=ols_col,
  797. ols_linewidth=ols_lw,
  798. loess_color=loess_col,
  799. loess_linewidth=loess_lw,
  800. )
  803. ax4 = Axis(
  804. f[2, 2],
  805. limits=(nothing, nothing, -10, 10),
  806. ylabel="Weighted individual residuals",
  807. xlabel="Individual predictions",
  808. xticklabelrotation=pi / 4
  809. )
  810. contour!(
  811. ax4,
  812. collect(h4.edges[1][1:end-1]),
  813. collect(h4.edges[2])[1:end-1],
  814. log10.(Float64.(h4.weights) .+ 1),
  815. colormap=cmap,
  816. colorrange=(crange_min, crange_max),
  817. levels=cbins)
  818. iwresiduals_vs_ipredictions!(
  819. ax4,
  820. insp,
  821. markersize=0,
  822. ols_color=ols_col,
  823. ols_linewidth=ols_lw,
  824. loess_color=loess_col,
  825. loess_linewidth=loess_lw,
  826. )
  829. Legend(f[0, :],
  830. [loess_elem, ols_elem, xy_elem],
  831. ["LOESS", "OLS", L"y=x"],
  832. framevisible=false,
  833. nbanks=3,
  834. )
  836. Colorbar(f[1:2, 3], limits=(crange_min, crange_max), colormap=cmap, label="log₁₀(# of points + 1)")
  838. rowsize!(f.layout, 0, Relative(0.05))
  840. return f
  841. end
  843. function plot_all_vpcs(vpc_nt; w=100, h=100, text=repeat([[100; 0.5; "asd"]], 58))
  845. all_df = DataFrame(repeat([[],], 10),
  846. [:data_τ, :data_time, :I_obs, :sim_τ, :sim_time, :lower, :upper, :lower_error, :upper_error, :protocol]
  847. )
  849. map(keys(vpc_nt)) do k
  851. vpc_i = vpc_nt[k]
  853. data_i = vpc_i.popvpc.data_quantiles
  854. sim_i = vpc_i.simulated_quantiles
  856. df_i = DataFrame(
  857. [data_i[!, :τ], data_i[!, :time], data_i[!, :I_obs], sim_i[!, :τ], sim_i[!, :time], sim_i[!, :lower], sim_i[!, :upper]],
  858. [:data_τ, :data_time, :I_obs, :sim_τ, :sim_time, :lower, :upper]
  859. )
  860. @rtransform!(df_i, :lower_error = :I_obs < :lower ? :I_obs : :lower)
  861. @rtransform!(df_i, :upper_error = :I_obs > :upper ? :I_obs : :upper)
  862. @rtransform!(df_i, :protocol = k)
  864. append!(all_df, df_i)
  865. end
  867. f = Figure(size=(w, h))
  869. for (i, pro) in zip(1:4, ["act", "dea", "ina", "rec"])
  871. df_prot = filter(r -> occursin(pro, string(r.protocol)), all_df)
  873. plt = data(df_prot) * mapping(:sim_time => "", :I_obs, group=:data_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Lines) +
  874. data(df_prot) * mapping(:sim_time => "", :lower, :upper, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.75, color=:lightblue) +
  875. data(df_prot) * mapping(:sim_time => "", :lower_error, :lower, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.5, color=:red) +
  876. data(df_prot) * mapping(:sim_time => "", :upper, :upper_error, group=:sim_τ => nonnumeric, layout=:protocol => prot_renamer) * visual(Band, alpha=0.5, color=:red)
  878. with_theme(Theme(colgap=1, rowgap=-3)) do
  879. draw!(
  880. f[i, 1],
  881. plt,
  882. axis=(;
  883. yticklabelsize=12,
  884. xticklabelsize=12,
  885. yticks=[0.1, 0.5, 0.9]
  886. )
  887. )
  888. end
  889. end
  891. Label(f[1, 2], "Activation", rotation=-pi / 2)
  892. Label(f[2, 2], "Deactivation", rotation=-pi / 2)
  893. Label(f[3, 2], "Inactivation", rotation=-pi / 2)
  894. Label(f[4, 2], "Recovery", rotation=-pi / 2)
  896. Label(f[:, 0], L"\frac{I}{I_{max}}", rotation=pi / 2, fontsize=14)
  897. Label(f[5, :], "Time (ms)", fontsize=14)
  899. data_elem = LineElement(color=:black, linewidth=2)
  900. quant_elem = PolyElement(color=(:lightblue, 0.75))
  901. outlier_elem = PolyElement(color=(:red, 0.5))
  903. Legend(f[0, :],
  904. [data_elem, quant_elem, outlier_elem],
  905. [
  906. "Observed quantiles",
  907. "Simulated 95% CI, τ = [0.1, 0.5, 0.9]",
  908. "Outliers"
  909. ],
  910. framevisible=false,
  911. nbanks=3,
  912. )
  914. for i in 1:4
  915. rowsize!(f.layout, i, Relative(0.25))
  916. end
  918. colsize!(f.layout, 0, Relative(0.005))
  919. colsize!(f.layout, 1, Relative(0.99))
  920. colsize!(f.layout, 2, Relative(0.005))
  922. rowgap!(f.layout, 1)
  923. rowgap!(f.layout, 1, 10)
  925. colgap!(f.layout, 2, 7.5)
  927. all_axes = [i for i in f.content if i isa Axis]
  929. for (ax_i, text_i) in zip(all_axes, text)
  930. text!(ax_i, text_i[1], text_i[2], text=text_i[3], fontsize=12, rotation=-pi / 2)
  931. end
  933. return f
  934. end
  937. function example_boxplot_with_traces(df, df_unst; w=100, h=100)
  939. df_flat = DataFrames.flatten(df, [:t, :data])
  941. df1, df2 = groupby(df_flat, :data_type)
  943. dfoi = if df1[1, :data_type] == :prediction
  944. df1
  945. else
  946. df2
  947. end
  948. ddata = if df1[1, :data_type] == :prediction
  949. df2
  950. else
  951. df1
  952. end
  954. model_renamer = renamer(
  955. (:SciML_pooled => "Unified SciML",
  956. :SciML_split => "Individual SciML",
  957. :classical_no_ref => "Classical HH, no rand. eff.",
  958. :classical_with_ref => "Classical with rand. eff."
  959. )
  960. )
  961. null_renamer = renamer(
  962. (:SciML_pooled => "",
  963. :SciML_split => "",
  964. :classical_no_ref => "",
  965. :classical_with_ref => ""
  966. )
  967. )
  969. f = Figure(size=(w, h))
  971. p1 = data(filter(r -> r.ctype == :kv11, dfoi)) * visual(BoxPlot, show_outliers=false) *
  972. mapping(
  973. :model_type => model_renamer => "HH Model Type",
  974. :rmse => "RMSE",
  975. color=:model_type => model_renamer => "HH Model Type",
  976. layout=:ctype => cname_renamer
  977. )
  979. p2 = data(filter(r -> !ismissing(r.I_obs), df_unst)) * visual(Lines, alpha=0.5, linewidth=2) *
  980. mapping(
  981. :time => "Time (ms)",
  982. :I_obs_ipred => L"\frac{I}{I_{max}}",
  983. color=:model_type,
  984. col=:model_type => null_renamer,
  985. group=:id => nonnumeric
  986. )
  987. p3 = data(filter(r -> !ismissing(r.I_obs), df_unst)) * visual(Lines, color=(:black, 0.5), linewidth=2) *
  988. mapping(
  989. :time => "Time (ms)",
  990. :I_obs => L"\frac{I}{I_{max}}",
  991. col=:model_type => null_renamer,
  992. group=:id => nonnumeric
  993. )
  995. gr1 = draw!(
  996. f[1, 5],
  997. p1,
  998. axis=(
  999. xticklabelsvisible=false,
  1000. ),
  1001. )
  1003. gr2 = draw!(
  1004. f[1, 1:4],
  1005. p2 + p3,
  1006. )
  1008. legend!(
  1009. f[0, :],
  1010. gr1;
  1011. framevisible=false, position=:top, nbanks=4
  1012. )
  1014. rowsize!(f.layout, 1, Relative(0.92))
  1016. return f
  1017. end
  1021. function df_temp_to_plot(df, fb, sb; w=100, h=100)
  1023. fig = Figure(size=(w, h))
  1024. gr_i = nothing
  1025. for (i, b) in zip(1:2, [fb, sb])
  1026. plt =
  1027. data(filter(r -> r.ctype in b, df)) * mapping(
  1028. :v => "",
  1029. [:τ1_Q10 => "" :τ2_Q10 => ""],
  1030. col=dims(2) => renamer([L"\tau_1(V)" L"\tau_2(V)"]),
  1031. row=:ctype => base_renamer,
  1032. color=:T_ref => renamer((15 => L"\frac{f_{T=15}}{f_{T=25}}", 25 => L"\frac{f_{T=25}}{f_{T=35}}")) => "Ref. Temp.",
  1033. group=:T_ref => nonnumeric
  1034. ) *
  1035. visual(Lines, linewidth=2)
  1037. gr_i = draw!(
  1038. fig[1, i],
  1039. plt,
  1040. axis=(
  1041. limits=(-20, 80, nothing, nothing),
  1042. xticks=-80:40:80,
  1043. xticklabelrotation=pi / 2,
  1044. ),
  1045. facet=(; linkyaxes=:none)
  1046. )
  1047. end
  1049. Label(fig[2, :], "Voltage (mV)")
  1050. Label(fig[:, 0], L"Q_{10}(V)", rotation=pi / 2)
  1052. legend!(fig[0, :], gr_i; title="asd", framevisible=false, nbanks=2)
  1054. rowsize!(fig.layout, 1, Relative(0.95))
  1056. return fig
  1058. end
  1061. function df_temp_to_plot_ipreds(df, fb, sb; w=100, h=100)
  1063. fig = Figure(size=(w, h))
  1064. gr_i = nothing
  1065. for (i, b) in zip(1:2, [fb, sb])
  1066. plt =
  1067. data(filter(r -> r.ctype in b, df)) * mapping(
  1068. :v => "",
  1069. [:m1_Q10 => "" :m2_Q10 => "" :τ1_Q10 => "" :τ2_Q10 => ""],
  1070. col=dims(2) => renamer([L"m_{1,\infty}(V)" L"m_{2,\infty}(V)" L"\tau_1(V)" L"\tau_2(V)"]),
  1071. row=:ctype => base_renamer,
  1072. color=:T_ref => renamer((15 => L"\frac{f_{T=15}}{f_{T=25}}", 25 => L"\frac{f_{T=25}}{f_{T=35}}")) => "Ref. Temp.",
  1073. group=:T_ref => nonnumeric
  1074. ) *
  1075. visual(Lines, linewidth=2, alpha=0.5)
  1077. gr_i = draw!(
  1078. fig[1, i],
  1079. plt,
  1080. axis=(
  1081. limits=(-80, 80, nothing, nothing),
  1082. xticks=-80:40:80,
  1083. xticklabelrotation=pi / 2,
  1084. ),
  1085. )
  1086. end
  1088. Label(fig[2, :], "Voltage (mV)")
  1089. Label(fig[:, 0], L"Q_{10}(V)", rotation=pi / 2)
  1091. legend!(fig[0, :], gr_i; title="asd", framevisible=false, nbanks=2)
  1093. rowsize!(fig.layout, 1, Relative(0.95))
  1095. return fig
  1097. end
  1100. function plot_data_processing(hd; w=100, h=100)
  1102. ###cols
  1103. ### 1 raw 2 MAPE 3 smoothing 4 baseline 5 normalize 6 rescale 7 artefacts 8 downsample
  1104. ###
  1106. downsampler(t, s, nbins) = M4downsample(t, s, nbins)
  1107. denoiser(x) = denoise(x, factor=1.0)[1]
  1108. MAPE(x, y) = mean(abs.((x - y) ./ x))
  1109. fig = Figure(size=(w, h))
  1113. act_50 = hd["acquisition"]["timeseries"]["Activation"]["repetitions"]["repetition2"]["data"][end-3, :]
  1114. act_70 = hd["acquisition"]["timeseries"]["Activation"]["repetitions"]["repetition2"]["data"][end-1, :]
  1115. act_80 = hd["acquisition"]["timeseries"]["Activation"]["repetitions"]["repetition2"]["data"][end, :]
  1116. act_t = LinRange(0, 700, length(act_50))
  1118. dea = hd["acquisition"]["timeseries"]["Deactivation"]["repetitions"]["repetition1"]["data"][1, :]
  1119. dea_t = LinRange(0, 700, length(dea))
  1121. ina = hd["acquisition"]["timeseries"]["Inactivation"]["repetitions"]["repetition1"]["data"][end, :]
  1122. ina_t = LinRange(0, 1750, length(ina))
  1124. rec_k = keys(hd["acquisition"]["timeseries"]["Recovery"]["repetitions"]["repetition1"]["data"])
  1125. rec = [hd["acquisition"]["timeseries"]["Recovery"]["repetitions"]["repetition1"]["data"][k]["data"][:] for k in rec_k]
  1126. rec_idx = sortperm(length.(rec))
  1127. rec = rec[rec_idx]
  1128. rec_t = [LinRange(0, 100 + 1500 + 50 + 150 * (i - 1) + 200 + 100, length(rec[i])) for i in 1:16]
  1130. ### =====================
  1132. ax11 = Axis(fig[1, 1], title="Raw data", limits=(-35, 800, -0.1, 1.1), ygridvisible=false)
  1133. ax21 = Axis(fig[2, 1], limits=(-35, 800, -0.1, 1.1), ygridvisible=false)
  1134. ax31 = Axis(fig[3, 1], limits=(-85, 1800, -0.1, 1.1), ygridvisible=false)
  1135. ax41 = Axis(fig[4, 1], limits=(-210, 4300, -0.1, 1.1), ygridvisible=false)
  1137. lines!(ax11, act_t, act_50, color=:blue)
  1138. lines!(ax11, act_t, act_70, color=:red)
  1139. lines!(ax11, act_t, act_80, color=:black)
  1141. lines!(ax21, dea_t, dea, color=:black)
  1143. lines!(ax31, ina_t, ina, color=:black)
  1145. map(zip(rec_t, rec)) do (t, r)
  1146. lines!(ax41, t, r, color=(:black, 0.3))
  1147. end
  1149. hidedecorations!(ax11)
  1150. hidedecorations!(ax21)
  1151. hidedecorations!(ax31)
  1152. hidedecorations!(ax41)
  1154. ### =====================
  1156. ax12 = Axis(fig[1, 2], title="Set baseline", limits=(-35, 800, -0.1, 1.1))
  1157. ax22 = Axis(fig[2, 2], limits=(-35, 800, -0.1, 1.1))
  1158. ax32 = Axis(fig[3, 2], limits=(-85, 1800, -0.1, 1.1))
  1159. ax42 = Axis(fig[4, 2], limits=(-210, 4300, -0.1, 1.1))
  1161. act_50 .-= mean(act_50[act_t.<40])
  1162. act_70 .-= mean(act_70[act_t.<40])
  1163. act_80 .-= mean(act_80[act_t.<40])
  1164. lines!(ax12, act_t, act_50, color=:blue)
  1165. lines!(ax12, act_t, act_70, color=:red)
  1166. lines!(ax12, act_t, act_80, color=:black)
  1168. dea .-= mean(dea[dea_t.<40])
  1169. lines!(ax22, dea_t, dea, color=:black)
  1171. ina .-= mean(ina[ina_t.<40])
  1172. lines!(ax32, ina_t, ina, color=:black)
  1174. map(zip(rec_t, rec)) do (t, r)
  1175. r .-= mean(r[t.<40])
  1176. lines!(ax42, t, r, color=(:black, 0.3))
  1177. end
  1179. hidedecorations!(ax12)
  1180. hidedecorations!(ax22)
  1181. hidedecorations!(ax32)
  1182. hidedecorations!(ax42)
  1184. ### =====================
  1186. ax13 = Axis(fig[1, 3], title="Smoothing", limits=(-35, 800, -0.1, 1.1))
  1187. ax23 = Axis(fig[2, 3], limits=(-35, 800, -0.1, 1.1))
  1188. ax33 = Axis(fig[3, 3], limits=(-85, 1800, -0.1, 1.1))
  1189. ax43 = Axis(fig[4, 3], limits=(-210, 4300, -0.1, 1.1))
  1191. act_50[1000:5991] .= denoise(act_50[1000:5991])[1]
  1192. act_70[1000:5991] .= denoise(act_70[1000:5991])[1]
  1193. act_80[1000:5991] .= denoise(act_80[1000:5991])[1]
  1194. lines!(ax13, act_t, act_50, color=:blue)
  1195. lines!(ax13, act_t, act_70, color=:red)
  1196. lines!(ax13, act_t, act_80, color=:black)
  1198. dea[1000:3991] .= denoise(dea[1000:3991])[1]
  1199. dea[4100:5991] .= denoise(dea[4100:5991])[1]
  1200. lines!(ax23, dea_t, dea, color=:black)
  1202. ina[1000:15990] .= denoise(ina[1000:15990])[1]
  1203. ina[16100:16981] .= denoise(ina[16100:16981])[1]
  1204. lines!(ax33, ina_t, ina, color=:black)
  1206. map(zip(rec_t, rec)) do (t, r)
  1207. r[1000:15990] .= denoise(r[1000:15990])[1]
  1208. lines!(ax43, t, r, color=(:black, 0.3))
  1209. end
  1211. hidedecorations!(ax13)
  1212. hidedecorations!(ax23)
  1213. hidedecorations!(ax33)
  1214. hidedecorations!(ax43)
  1216. ### =====================
  1218. ax14 = Axis(fig[1, 4], title="Normalization + Rescaling", limits=(-35, 800, -0.1, 1.1))
  1219. ax24 = Axis(fig[2, 4], limits=(-35, 800, -0.1, 1.1))
  1220. ax34 = Axis(fig[3, 4], limits=(-85, 1800, -0.1, 1.1))
  1221. ax44 = Axis(fig[4, 4], limits=(-210, 4300, -0.1, 1.1))
  1223. act_80_max = maximum(act_80[1000:5991])
  1224. act_70_max = maximum(act_70[1000:5991])
  1225. act_50_max = maximum(act_50[1000:5991])
  1226. act_50 ./= act_80_max
  1227. act_70 ./= act_80_max
  1228. act_80 ./= act_80_max
  1229. lines!(ax14, act_t, act_50, color=:blue)
  1230. lines!(ax14, act_t, act_70, color=:red)
  1231. lines!(ax14, act_t, act_80, color=:black)
  1233. dea_max = maximum(dea[1000:3991])
  1234. dea = dea * (act_70_max / dea_max) / act_80_max
  1235. lines!(ax24, dea_t, dea, color=:black, label="Deactivation")
  1236. lines!(ax24, act_t, act_70, color=:red, label="Activation +70mV")
  1238. ina_max = maximum(ina[1000:15990])
  1239. ina = ina * (act_70_max / ina_max) / act_80_max
  1240. lines!(ax34, ina_t, ina, color=:black, label="Inactivation +70mV")
  1241. lines!(ax34, act_t, act_70, color=:red, label="Activation +70mV")
  1243. rec = map(rec) do r
  1244. r_max = maximum(r[1000:15990])
  1245. r = r * (act_50_max / r_max) / act_80_max
  1246. end
  1247. map(zip(rec_t, rec)) do (t, r)
  1248. lines!(ax44, t, r, color=(:black, 0.3), label="Recovery")
  1249. end
  1250. lines!(ax44, act_t, act_50, color=:blue, label="Activation +50mV")
  1252. hidedecorations!(ax14)
  1253. hidedecorations!(ax24)
  1254. hidedecorations!(ax34)
  1255. hidedecorations!(ax44)
  1257. ### =====================
  1259. ax15 = Axis(fig[1, 5], title="Exclude artifacts", limits=(-35, 800, -0.1, 1.1))
  1260. ax25 = Axis(fig[2, 5], limits=(-35, 800, -0.1, 1.1))
  1261. ax35 = Axis(fig[3, 5], limits=(-85, 1800, -0.1, 1.1))
  1262. ax45 = Axis(fig[4, 5], limits=(-210, 4300, -0.1, 1.1))
  1265. act_t = Vector{Union{Float64,Missing}}(act_t)
  1266. act_50 = Vector{Union{Float64,Missing}}(act_50)
  1267. act_70 = Vector{Union{Float64,Missing}}(act_70)
  1268. act_80 = Vector{Union{Float64,Missing}}(act_80)
  1269. act_50[386:399] .= missing
  1270. act_50[491:499] .= missing
  1271. act_50[981:999] .= missing
  1272. act_50[5992:6099] .= missing
  1273. act_70[386:399] .= missing
  1274. act_70[491:499] .= missing
  1275. act_70[981:999] .= missing
  1276. act_70[5992:6099] .= missing
  1277. act_80[386:399] .= missing
  1278. act_80[491:499] .= missing
  1279. act_80[981:999] .= missing
  1280. act_80[5992:6099] .= missing
  1281. act_t[386:399] .= missing
  1282. act_t[491:499] .= missing
  1283. act_t[981:999] .= missing
  1284. act_t[5992:6099] .= missing
  1285. lines!(ax15, collect(skipmissing(act_t)), collect(skipmissing(act_50)), color=:blue)
  1286. lines!(ax15, collect(skipmissing(act_t)), collect(skipmissing(act_70)), color=:red)
  1287. lines!(ax15, collect(skipmissing(act_t)), collect(skipmissing(act_80)), color=:black)
  1289. dea_t = Vector{Union{Float64,Missing}}(dea_t)
  1290. dea = Vector{Union{Float64,Missing}}(dea)
  1291. dea_t[386:399] .= missing
  1292. dea_t[491:499] .= missing
  1293. dea_t[981:999] .= missing
  1294. dea_t[3992:4099] .= missing
  1295. dea_t[5992:6099] .= missing
  1296. dea[386:399] .= missing
  1297. dea[491:499] .= missing
  1298. dea[981:999] .= missing
  1299. dea[3992:4099] .= missing
  1300. dea[5992:6099] .= missing
  1301. lines!(ax25, collect(skipmissing(dea_t)), collect(skipmissing(dea)), color=:black)
  1303. ina_t = Vector{Union{Float64,Missing}}(ina_t)
  1304. ina = Vector{Union{Float64,Missing}}(ina)
  1305. ina_t[386:399] .= missing
  1306. ina_t[491:499] .= missing
  1307. ina_t[981:999] .= missing
  1308. ina_t[15991:16099] .= missing
  1309. ina_t[16982:17099] .= missing
  1310. ina[386:399] .= missing
  1311. ina[491:499] .= missing
  1312. ina[981:999] .= missing
  1313. ina[15991:16099] .= missing
  1314. ina[16982:17099] .= missing
  1315. lines!(ax35, collect(skipmissing(ina_t)), collect(skipmissing(ina)), color=:black)
  1318. rec_t = map(rec_t) do t
  1319. li = length(t)
  1320. t = Vector{Union{Float64,Missing}}(t)
  1321. t[386:399] .= missing
  1322. t[491:499] .= missing
  1323. t[981:999] .= missing
  1324. t[15991:16099] .= missing
  1325. t[(li-3019):(li-2996)] .= missing
  1326. t[(li-999):(li-901)] .= missing
  1327. t
  1328. end
  1329. rec = map(rec) do r
  1330. li = length(r)
  1331. r = Vector{Union{Float64,Missing}}(r)
  1332. r[386:399] .= missing
  1333. r[491:499] .= missing
  1334. r[981:999] .= missing
  1335. r[15991:16099] .= missing
  1336. r[(li-3019):(li-2996)] .= missing
  1337. r[(li-999):(li-901)] .= missing
  1338. r
  1339. end
  1340. map(zip(rec_t, rec)) do (t, r)
  1341. lines!(ax45, collect(skipmissing(t)), collect(skipmissing(r)), color=(:black, 0.3))
  1342. end
  1344. hidedecorations!(ax15)
  1345. hidedecorations!(ax25)
  1346. hidedecorations!(ax35)
  1347. hidedecorations!(ax45)
  1349. ### =====================
  1351. ax16 = Axis(fig[1, 6], title="MAPE exclusion", limits=(-35, 800, -0.1, 1.1))
  1352. ax26 = Axis(fig[2, 6], limits=(-35, 800, -0.1, 1.1))
  1353. ax36 = Axis(fig[3, 6], limits=(-85, 1800, -0.1, 1.1))
  1354. ax46 = Axis(fig[4, 6], limits=(-210, 4300, -0.1, 1.1))
  1356. dea_i = (collect(skipmissing(dea_t)) .> 100) .&& (collect(skipmissing(dea_t)) .< 300)
  1357. act_i = (collect(skipmissing(act_t)) .> 100) .&& (collect(skipmissing(act_t)) .< 300)
  1358. dea_MAPE = MAPE(collect(skipmissing(act_70))[act_i], collect(skipmissing(dea))[dea_i])
  1359. text!(ax26, 100, 0.0, text="MAPE=" * string(dea_MAPE)[1:5])
  1360. lines!(ax26, collect(skipmissing(dea_t))[dea_i], collect(skipmissing(dea))[dea_i], color=:black)
  1361. lines!(ax26, collect(skipmissing(act_t))[act_i], collect(skipmissing(act_70))[act_i], color=:red)
  1364. ina_i = (collect(skipmissing(ina_t)) .> 100) .&& (collect(skipmissing(ina_t)) .< 500)
  1365. act_i = (collect(skipmissing(act_t)) .> 100) .&& (collect(skipmissing(act_t)) .< 500)
  1366. ina_MAPE = MAPE(act_70[1000:5990], ina[1000:5990])
  1367. text!(ax36, 100, 0.0, text="MAPE=" * string(ina_MAPE)[1:5])
  1368. lines!(ax36, collect(skipmissing(ina_t))[ina_i], collect(skipmissing(ina))[ina_i], color=:black)
  1369. lines!(ax36, collect(skipmissing(act_t))[act_i], collect(skipmissing(act_70))[act_i], color=:red)
  1371. map(zip(rec_t, rec)) do (t, r)
  1372. r_i = (collect(skipmissing(t)) .> 100) .&& (collect(skipmissing(t)) .< 500)
  1373. lines!(ax46, collect(skipmissing(t))[r_i], collect(skipmissing(r))[r_i], color=(:black, 0.3))
  1374. end
  1375. lines!(ax46, collect(skipmissing(act_t))[act_i], collect(skipmissing(act_50))[act_i], color=:blue)
  1378. r_MAPEs = [MAPE(act_50[1000:5991], i[1000:5991]) < 0.05 for i in rec]
  1379. last_vals = [i[15990] for i in rec]
  1380. med = median(last_vals)
  1381. sd = std(last_vals)
  1382. last_vals = [(abs(i - med) / sd) < 1 for i in last_vals]
  1383. r_incl = [i for i in 1:length(r_MAPEs) if r_MAPEs[i] && last_vals[i]]
  1384. rec_t = rec_t[r_incl]
  1385. rec = rec[r_incl]
  1388. hidedecorations!(ax16)
  1389. hidespines!(ax16)
  1390. hidedecorations!(ax26)
  1391. hidedecorations!(ax36)
  1392. hidedecorations!(ax46)
  1394. ### =====================
  1396. ax17 = Axis(fig[1, 7], title="Down-sampling", limits=(-35, 800, -0.1, 1.1))
  1397. ax27 = Axis(fig[2, 7], limits=(-35, 800, -0.1, 1.1))
  1398. ax37 = Axis(fig[3, 7], limits=(-85, 1800, -0.1, 1.1))
  1399. ax47 = Axis(fig[4, 7], limits=(-210, 4300, -0.1, 1.1))
  1401. act_t_d = [
  1402. act_t[icr(1, 385, 3)];
  1403. act_t[icr(400, 490, 3)];
  1404. act_t[icr(500, 980, 3)];
  1405. downsampler(act_t[1000:5991], act_50[1000:5991], 30)[1];
  1406. act_t[icr(6100, 6990, 3)]
  1407. ]
  1408. act_50_d = [
  1409. act_50[icr(1, 385, 3)];
  1410. act_50[icr(400, 490, 3)];
  1411. act_50[icr(500, 980, 3)];
  1412. downsampler(act_t[1000:5991], act_50[1000:5991], 30)[2];
  1413. act_50[icr(6100, 6990, 3)]
  1414. ]
  1415. act_70_d = [
  1416. act_70[icr(1, 385, 3)];
  1417. act_70[icr(400, 490, 3)];
  1418. act_70[icr(500, 980, 3)];
  1419. downsampler(act_t[1000:5991], act_70[1000:5991], 30)[2];
  1420. act_70[icr(6100, 6990, 3)]
  1421. ]
  1422. act_80_d = [
  1423. act_80[icr(1, 385, 3)];
  1424. act_80[icr(400, 490, 3)];
  1425. act_80[icr(500, 980, 3)];
  1426. downsampler(act_t[1000:5991], act_80[1000:5991], 30)[2];
  1427. act_80[icr(6100, 6990, 3)]
  1428. ]
  1429. lines!(ax17, collect(skipmissing(act_t_d)), collect(skipmissing(act_50_d)), color=:blue, label="Activation +50mV")
  1430. lines!(ax17, collect(skipmissing(act_t_d)), collect(skipmissing(act_70_d)), color=:red, label="Activation +70mV")
  1431. lines!(ax17, collect(skipmissing(act_t_d)), collect(skipmissing(act_80_d)), color=:black, label="Activation +80mV")
  1434. ina_t_d = [
  1435. ina_t[icr(1, 385, 3)];
  1436. ina_t[icr(400, 490, 3)];
  1437. ina_t[icr(500, 980, 3)];
  1438. downsampler(ina_t[1000:15990], ina[1000:15990], 90)[1];
  1439. downsampler(ina_t[16100:16981], ina[16100:16981], 6)[1];
  1440. ina_t[icr(17100, 17490, 3)]
  1441. ]
  1442. ina_d = [
  1443. ina[icr(1, 385, 3)];
  1444. ina[icr(400, 490, 3)];
  1445. ina[icr(500, 980, 3)];
  1446. downsampler(ina[1000:15990], ina[1000:15990], 90)[1];
  1447. downsampler(ina[16100:16981], ina[16100:16981], 6)[1];
  1448. ina[icr(17100, 17490, 3)]
  1449. ]
  1451. lines!(ax37, collect(skipmissing(ina_t_d)), collect(skipmissing(ina_d)), color=:black)
  1453. map(zip(rec_t, rec)) do (t, r)
  1454. li = length(r)
  1455. t_d = [
  1456. t[icr(1, 385, 3)];
  1457. t[icr(400, 490, 3)];
  1458. t[icr(500, 980, 3)];
  1459. downsampler(t[1000:15990], r[1000:15990], 90)[1];
  1460. t[icr(16100, li - 3020, 3)];
  1461. downsampler(
  1462. t[(li-2995):(li-1000)],
  1463. r[(li-2995):(li-1000)],
  1464. 12)[1]
  1465. t[icr(li - 900, li, 3)]
  1466. ]
  1467. r_d = [
  1468. r[icr(1, 385, 3)];
  1469. r[icr(400, 490, 3)];
  1470. r[icr(500, 980, 3)];
  1471. downsampler(t[1000:15990], r[1000:15990], 90)[2];
  1472. r[icr(16100, li - 3020, 3)];
  1473. downsampler(
  1474. r[(li-2995):(li-1000)],
  1475. r[(li-2995):(li-1000)],
  1476. 12)[2]
  1477. r[icr(li - 900, li, 3)]
  1478. ]
  1479. lines!(ax47, collect(skipmissing(t_d)), collect(skipmissing(r_d)), color=(:black, 0.3))
  1480. end
  1482. hidedecorations!(ax17)
  1483. hidedecorations!(ax27)
  1484. hidespines!(ax27)
  1485. hidedecorations!(ax37)
  1486. hidedecorations!(ax47)
  1488. Legend(fig[1, 8], ax17, framevisible=false)
  1489. Legend(fig[2, 8], ax24, framevisible=false)
  1490. Legend(fig[3, 8], ax34, framevisible=false)
  1491. Legend(fig[4, 8], ax44, framevisible=false, unique=true)
  1493. for i in 1:4
  1494. rowsize!(fig.layout, i, Relative(0.25))
  1495. end
  1497. return fig
  1498. end

aog_recipes.jl at commit 26dc63b, no license · at the source

Overview

Authors: Domas Linkevicius1,2, Angus Chadwick1, Melanie I. Stefan3, David C. Sterratt1
ORCID iDs: Domas Linkevicius, David C. Sterratt
  1. Institute for Machine Learning, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
  2. Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan
  3. Faculty of Medicine, Medical School Berlin, Berlin, Germany
Institutions: University of Edinburgh (United Kingdom); MSB Medical School Berlin (Germany)
Journal: PLoS computational biology, volume 22, issue 4, article e1013078
Dates: received 24 April 2025; accepted 2 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1013078 · PMID 42044162 · PMCID PMC13143182 · OpenAlex W4409841817
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.24.650426
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics
MeSH: Models, Biological*, Potassium Channels, Voltage-Gated*, Animals, Computational Biology, Humans, Ion Channel Gating, Kinetics, Machine Learning, Models, Neurological, Neurons, Nonlinear Dynamics (* major topic)
Journal subjects: Biology and Life Sciences, Biophysics, Ion Channels, Ion Channel Gating, Physical Sciences, Physics, Physiology, Electrophysiology, Neurophysiology, Neuroscience, Biochemistry, Proteins, Computer and Information Sciences, Neural Networks, Voltage-Gated Ion Channels, Potassium Channels, Mathematics, Optimization, Artificial Intelligence, Machine Learning, Data Management, Data Processing
Topic: Semiconductor materials and devices (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: UK Research and Innovation (EP/S02431X/1)
Citations: cited by 1 paper (Europe PMC); 52 references in the paper

Abstract

Ion channels are essential for signal processing and propagation in neural cells. Voltage-gated ion channels permeable to potassium (Kv) form one of the most prominent channel families. Techniques used to model the voltage-dependent gating of Kv channels date back to Hodgkin and Huxley (1952). Different Kv types can display radically different kinetic properties, requiring different mathematical models. However, the construction of Hodgkin-Huxley-like (HH-like) models is generally complex and time consuming due to the number of parameters, their tuning and having to choose functional forms to model gating. In addition to the between-Kv type heterogeneity, there can be significant within-Kv type kinetic heterogeneity between different cells with genetically identical channels. Since HH-like models do not account for such variability, extensions to it are necessary. We use scientific machine learning (SciML), the integration of machine learning methodologies with existing scientific models, and non-linear mixed effects (NLME) modelling to bypass the limitations of HH-like modelling. NLME is a modelling methodology that takes into account both within- and between-subject variability. These tools allowed us to complement the HH-like modelling and construct a unified SciML HH-like model that fits the recordings from 20 different Kv types. The unified SciML HH-like model produced closer fits to the data compared to a set of seven previous HH-like models and was able to represent the highly heterogeneous data from different cells. Our model may be the first step in producing a SciML foundation model for ion channels that would be capable of modelling the gating kinetics of any ion channel type.

Reproduced under the paper's license (CC BY), from the paper cited above.

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dom-linkevicius/SciMLHHModels.jl

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 26dc63b6cd5de79536731f072cf6c4d28328bb00, 23 February 2026
Languages: Julia (15)
Size: 1,368 files, 15 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (Project.toml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Makie (3 files), DataFrames.jl (1 file), Distributions.jl (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/dom-linkevicius/SciMLHHModels.jl

modeldb:229585

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Found in: the text, “Introduction”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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At the source: modeldb.science/229585

francescoalemanno/KissSmoothing.jl

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4f2110c57e5d0868d4f35ca4dd487f73a296ae68, 5 June 2023
Languages: Julia (2)
Size: 15 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Smoothing.”
Holds: README, license file, environment (Project.toml), tests, continuous integration
Not found: CITATION.cff, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/francescoalemanno/KissSmoothing.jl

njohner/Kv-kinetic-models

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 18e0352b9f4d1778c4fe71c5a1d3f29d20714c11, 2 April 2018
Languages: NEURON (188), Python (1)
Size: 506 files, 189 scripts
Software Heritage: not archived
Found in: the text, “Discussion”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (188 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
190 files
At the source: github.com/njohner/Kv-kinetic-models

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 206 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All of the data is freely publicly available at https://channelpedia.epfl.ch/. The specific raw data used in this study is available at https://doi.org/10.7488/ds/8052 and the code is available at https://github.com/dom-linkevicius/SciMLHHModels.jl.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 MeSH terms, 1 funder, 49 references.

Cite

This paper

Linkevicius, D., Chadwick, A., Stefan, M. I., & Sterratt, D. C. (2026). One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling. PLoS computational biology, 22(4), e1013078. https://doi.org/10.1371/journal.pcbi.1013078

BibTeX

@article{linkevicius2026one,
author = {Linkevicius, Domas and Chadwick, Angus and Stefan, Melanie I. and Sterratt, David C.},
title = {{One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling}},
journal = {PLoS computational biology},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e1013078},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013078},
url = {https://doi.org/10.1371/journal.pcbi.1013078},
pmid = {42044162},
pmcid = {PMC13143182}
}

RIS

TY - JOUR
AU - Linkevicius, Domas
AU - Chadwick, Angus
AU - Stefan, Melanie I.
AU - Sterratt, David C.
TI - One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/04/27
VL - 22
IS - 4
SP - e1013078
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013078
UR - https://doi.org/10.1371/journal.pcbi.1013078
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1013078",
"type": "article-journal",
"title": "One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Linkevicius",
"given": "Domas"
},
{
"family": "Chadwick",
"given": "Angus"
},
{
"family": "Stefan",
"given": "Melanie I."
},
{
"family": "Sterratt",
"given": "David C."
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "4",
"page": "e1013078",
"DOI": "10.1371/journal.pcbi.1013078",
"PMID": "42044162",
"PMCID": "PMC13143182",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1013078",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
27
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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