trf #107

behinger · 2024-09-20T13:36:34Z

added TRFComponent
added Tutorial for it

github-actions · 2024-09-20T13:37:50Z

docs/literate/tutorials/TRF.jl

+#
+# ## Simulation
+# we start with the simplest possible design, one condition
+design = SingleSubjectDesign(conditions=Dict(:dummy=>["A"]));


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Suggested change

design = SingleSubjectDesign(conditions=Dict(:dummy=>["A"]));

design = SingleSubjectDesign(conditions = Dict(:dummy => ["A"]));

github-actions · 2024-09-20T13:37:50Z

docs/literate/tutorials/TRF.jl

+brain_response = [LinearModelComponent(basis=p100(),formula=@formula(0~1),β=[1]),
+                  LinearModelComponent(basis=n170(),formula=@formula(0~1),β=[1]),
+                  LinearModelComponent(basis=p300(),formula=@formula(0~1),β=[1])];


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

brain_response = [LinearModelComponent(basis=p100(),formula=@formula(0~1),β=[1]),

LinearModelComponent(basis=n170(),formula=@formula(0~1),β=[1]),

LinearModelComponent(basis=p300(),formula=@formula(0~1),β=[1])];

brain_response = [

LinearModelComponent(basis = p100(), formula = @formula(0 ~ 1), β = [1]),

LinearModelComponent(basis = n170(), formula = @formula(0 ~ 1), β = [1]),

LinearModelComponent(basis = p300(), formula = @formula(0 ~ 1), β = [1]),

];

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+feature = rand(1_000)
+
+# Next we have to nest the response in a `TRFComponent` and add ou
+trf_component = TRFComponent(brain_response,feature);


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

trf_component = TRFComponent(brain_response,feature);

trf_component = TRFComponent(brain_response, feature);

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+trf_component = TRFComponent(brain_response,feature);
+
+# Finally, when simulating, we have only a single "event" (that is, TRF-) onset, the first sample. Therefore, we use  `TRFOnset` to indicate this.
+dat,evts = simulate(design,trf_component,UnfoldSim.TRFOnset());


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Suggested change

dat,evts = simulate(design,trf_component,UnfoldSim.TRFOnset());

dat, evts = simulate(design, trf_component, UnfoldSim.TRFOnset());

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+dat,evts = simulate(design,trf_component,UnfoldSim.TRFOnset());
+
+# Let's plot the feature signal and the TRF response
+f,ax,h =lines(dat)


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Suggested change

f,ax,h =lines(dat)

f, ax, h = lines(dat)

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+dat_l,_ = simulate(design,TRFComponent(brain_response,feature_luminance),UnfoldSim.TRFOnset());
+dat_r,_ = simulate(design,TRFComponent(brain_response,feature_size),UnfoldSim.TRFOnset());


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

dat_l,_ = simulate(design,TRFComponent(brain_response,feature_luminance),UnfoldSim.TRFOnset());

dat_r,_ = simulate(design,TRFComponent(brain_response,feature_size),UnfoldSim.TRFOnset());

dat_l, _ =

simulate(design, TRFComponent(brain_response, feature_luminance), UnfoldSim.TRFOnset());

dat_r, _ =

simulate(design, TRFComponent(brain_response, feature_size), UnfoldSim.TRFOnset());

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+dat_r,_ = simulate(design,TRFComponent(brain_response,feature_size),UnfoldSim.TRFOnset());
+
+# let's plot and compare to the previous plot
+f,ax,h = lines(dat_l .+ dat_r)


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

f,ax,h = lines(dat_l .+ dat_r)

f, ax, h = lines(dat_l .+ dat_r)

github-actions · 2024-09-20T14:11:45Z

docs/literate/tutorials/TRF.jl

+dat_combined,_ = simulate(design,[TRFComponent(brain_response,feature_size),TRFComponent(brain_response,feature_luminance)],UnfoldSim.TRFOnset());
+f,ax,h = lines(dat_combined)


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

dat_combined,_ = simulate(design,[TRFComponent(brain_response,feature_size),TRFComponent(brain_response,feature_luminance)],UnfoldSim.TRFOnset());

f,ax,h = lines(dat_combined)

dat_combined, _ = simulate(

design,

[

TRFComponent(brain_response, feature_size),

TRFComponent(brain_response, feature_luminance),

],

UnfoldSim.TRFOnset(),

);

f, ax, h = lines(dat_combined)

github-actions · 2024-09-20T14:11:46Z

docs/literate/tutorials/TRF.jl

+# where you can see that the results are equivalent.
+
+# ## Another cool feature is to modulate the feature vector based on the design
+design_mod = SingleSubjectDesign(conditions=Dict(:condition=>["A","B"]));


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

design_mod = SingleSubjectDesign(conditions=Dict(:condition=>["A","B"]));

design_mod = SingleSubjectDesign(conditions = Dict(:condition => ["A", "B"]));

github-actions · 2024-09-20T14:11:46Z

docs/literate/tutorials/TRF.jl

+design_mod = SingleSubjectDesign(conditions=Dict(:condition=>["A","B"]));
+
+# Let's take only a single component for simplicity. Note how the `formula` has been changed. The β allows to control the amplitude. In this linear model component, the default contrast-function is `Dummy` (also known as `Reference` coding), which means, the second beta indicated a "difference"
+brain_response_mod = LinearModelComponent(basis=p100(),formula=@formula(0~1+condition),β=[1,1]);


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

brain_response_mod = LinearModelComponent(basis=p100(),formula=@formula(0~1+condition),β=[1,1]);

brain_response_mod =

LinearModelComponent(basis = p100(), formula = @formula(0 ~ 1 + condition), β = [1, 1]);

github-actions · 2024-09-20T14:11:46Z

docs/literate/tutorials/TRF.jl

+feature_mod = rand(1000,2)
+feature_mod[:,2] .= 0
+feature_mod[500:600,2] .= 1;


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

feature_mod = rand(1000,2)

feature_mod[:,2] .= 0

feature_mod[500:600,2] .= 1;

feature_mod = rand(1000, 2)

feature_mod[:, 2] .= 0

feature_mod[500:600, 2] .= 1;

github-actions · 2024-09-20T14:11:46Z

docs/literate/tutorials/TRF.jl

+# As visible, the first column has again a random signal, indicating e.g. luminance changes. The second temporal feature indicates some offset (a colorchange?) between 500 and 600 samples.
+
+
+dat_mod,_ = simulate(design_mod,TRFComponent([brain_response_mod],feature_mod),UnfoldSim.TRFOnset());


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

dat_mod,_ = simulate(design_mod,TRFComponent([brain_response_mod],feature_mod),UnfoldSim.TRFOnset());

dat_mod, _ = simulate(

design_mod,

TRFComponent([brain_response_mod], feature_mod),

UnfoldSim.TRFOnset(),

);

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

github-actions · 2024-11-04T10:50:38Z

src/onset.jl

    widths =
-        UnfoldSim.generate_designmatrix(o.width_formula, events, o.width_contrasts) *
+        generate_designmatrix(o.width_formula, events, o.width_contrasts) *
        o.width_β


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

widths =

UnfoldSim.generate_designmatrix(o.width_formula, events, o.width_contrasts) *

generate_designmatrix(o.width_formula, events, o.width_contrasts) *

o.width_β

widths = generate_designmatrix(o.width_formula, events, o.width_contrasts) * o.width_β

github-actions · 2024-11-04T10:50:38Z

src/onset.jl

+        generate_designmatrix(o.offset_formula, events, o.offset_contrasts) *
        o.offset_β


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

generate_designmatrix(o.offset_formula, events, o.offset_contrasts) *

o.offset_β

generate_designmatrix(o.offset_formula, events, o.offset_contrasts) * o.offset_β

github-actions · 2024-11-04T10:50:38Z

src/onset.jl

+        generate_designmatrix(o.offset_formula, events, o.offset_contrasts) *
        o.offset_β


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

generate_designmatrix(o.offset_formula, events, o.offset_contrasts) *

o.offset_β

generate_designmatrix(o.offset_formula, events, o.offset_contrasts) * o.offset_β

behinger added 2 commits September 20, 2024 13:35

initial version of TRF simulation

59f5364

merge fini

5726ec2

github-actions bot reviewed Sep 20, 2024

View reviewed changes

improved tutorial

4f93a54

github-actions bot reviewed Sep 20, 2024

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behinger added 2 commits September 20, 2024 14:39

fix doilink

26ad7fb

maybe fix the interonset problem?

8ea515b

behinger changed the base branch from main to v4.0 November 4, 2024 10:48

Apply suggestions from code review

73425fa

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

behinger requested a review from jschepers November 4, 2024 10:49

github-actions bot reviewed Nov 4, 2024

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trf #107

trf #107

behinger commented Sep 20, 2024 •

edited

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	design = SingleSubjectDesign(conditions=Dict(:dummy=>["A"]));
	design = SingleSubjectDesign(conditions = Dict(:dummy => ["A"]));

	trf_component = TRFComponent(brain_response,feature);
	trf_component = TRFComponent(brain_response, feature);

	dat,evts = simulate(design,trf_component,UnfoldSim.TRFOnset());
	dat, evts = simulate(design, trf_component, UnfoldSim.TRFOnset());

		dat_l,_ = simulate(design,TRFComponent(brain_response,feature_luminance),UnfoldSim.TRFOnset());
		dat_r,_ = simulate(design,TRFComponent(brain_response,feature_size),UnfoldSim.TRFOnset());

-dat_l,_ = simulate(design,TRFComponent(brain_response,feature_luminance),UnfoldSim.TRFOnset());
-dat_r,_ = simulate(design,TRFComponent(brain_response,feature_size),UnfoldSim.TRFOnset());
+dat_l, _ =
+    simulate(design, TRFComponent(brain_response, feature_luminance), UnfoldSim.TRFOnset());
+dat_r, _ =
+    simulate(design, TRFComponent(brain_response, feature_size), UnfoldSim.TRFOnset());

	f,ax,h = lines(dat_l .+ dat_r)
	f, ax, h = lines(dat_l .+ dat_r)

		dat_combined,_ = simulate(design,[TRFComponent(brain_response,feature_size),TRFComponent(brain_response,feature_luminance)],UnfoldSim.TRFOnset());
		f,ax,h = lines(dat_combined)

	design_mod = SingleSubjectDesign(conditions=Dict(:condition=>["A","B"]));
	design_mod = SingleSubjectDesign(conditions = Dict(:condition => ["A", "B"]));

	brain_response_mod = LinearModelComponent(basis=p100(),formula=@formula(0~1+condition),β=[1,1]);
	brain_response_mod =
	LinearModelComponent(basis = p100(), formula = @formula(0 ~ 1 + condition), β = [1, 1]);

		# As visible, the first column has again a random signal, indicating e.g. luminance changes. The second temporal feature indicates some offset (a colorchange?) between 500 and 600 samples.


		dat_mod,_ = simulate(design_mod,TRFComponent([brain_response_mod],feature_mod),UnfoldSim.TRFOnset());

-dat_mod,_ = simulate(design_mod,TRFComponent([brain_response_mod],feature_mod),UnfoldSim.TRFOnset());
+dat_mod, _ = simulate(
+    design_mod,
+    TRFComponent([brain_response_mod], feature_mod),
+    UnfoldSim.TRFOnset(),
+);

		generate_designmatrix(o.offset_formula, events, o.offset_contrasts) *
		o.offset_β

trf #107

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trf #107

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behinger commented Sep 20, 2024 •

edited

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