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add RSA to docs
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@DominiqueMakowski
DominiqueMakowski committed Jun 7, 2022
1 parent ce541dd commit bdc0961
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4 changes: 4 additions & 0 deletions docs/functions/hrv.rst
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Expand Up @@ -22,6 +22,10 @@ Sub-domains
""""""""""""""""""
.. autofunction:: neurokit2.hrv.hrv_nonlinear

*hrv_rsa()*
""""""""""""""""""
.. autofunction:: neurokit2.hrv.hrv_rsa

*hrv_rqa()*
""""""""""""""""""
.. autofunction:: neurokit2.hrv.hrv_rqa
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118 changes: 72 additions & 46 deletions studies/complexity_eeg/analysis.Rmd
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Expand Up @@ -68,7 +68,7 @@ data_delay <- read.csv("data_delay.csv") |>
```{r delay_perchannel, warning=FALSE, message=FALSE, fig.height=16, fig.width=10}
delay_perchannel <- function(data_delay, dataset="Lemon") {
data <- filter(data_delay, Dataset == dataset) |>
mutate(Score = 1 + Score)
mutate(Score = 1 + Score * 100)
by_channel <- data |>
group_by(Condition, Metric, Area, Channel, Value) |>
Expand All @@ -81,11 +81,11 @@ delay_perchannel <- function(data_delay, dataset="Lemon") {
ggplot(aes(x = Value, y = Score, color = Area)) +
geom_line(aes(group=Channel), alpha = 0.20) +
geom_line(data=by_area, aes(group=Area), size=1) +
geom_vline(xintercept = c(25, 27, 30), linetype = "dashed", size = 0.5) +
geom_vline(xintercept = c(27), linetype = "dashed", size = 0.5) +
facet_wrap(~Condition*Metric, scales = "free_y") +
see::scale_color_flat_d(palette = "rainbow") +
scale_y_log10(expand = c(0, 0)) +
# scale_x_continuous(expand = c(0, 0),
scale_x_continuous(expand = c(0, 0)) +
# limits = c(0, NA),
# breaks=c(5, seq(0, 80, 20)),
# labels=c(5, seq(0, 80, 20))) +
Expand All @@ -105,54 +105,55 @@ p1 / p3 / p4 +
plot_annotation(title = "Optimization of Delay", theme = theme(plot.title = element_text(hjust = 0.5, face = "bold")))
```

#### Per Subject

```{r delay_persubject, warning=FALSE, message=FALSE, fig.height=16, fig.width=10}
delay_persubject <- function(data_delay, dataset="Lemon") {
data <- filter(data_delay, Dataset == dataset)
by_subject <- data |>
group_by(Condition, Metric, Area, Participant, Value) |>
summarise_all(mean)
by_area <- data |>
group_by(Condition, Metric, Area, Value) |>
summarise_all(mean)
by_subject |>
mutate(group = paste0(Participant, Area)) |>
ggplot(aes(x = Value, y = Score, color = Area)) +
geom_line(aes(group=group), alpha = 0.20) +
geom_line(data=by_area, aes(group=Area), size=1) +
# geom_vline(xintercept = 10, linetype = "dashed", size = 0.5) +
facet_wrap(~Condition*Metric, scales = "free_y") +
see::scale_color_flat_d(palette = "rainbow") +
scale_y_log10(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0),
limits = c(0, NA),
breaks=c(5, seq(0, 80, 20)),
labels=c(5, seq(0, 80, 20))) +
labs(title = paste0("Dataset: ", dataset), x = NULL, y = NULL) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
see::theme_modern() +
theme(plot.title = element_text(face = "plain", hjust = 0))
}
p1 <- delay_persubject(data_delay, dataset="Lemon")
# p2 <- delay_persubject(data_delay, dataset="Texas")
p3 <- delay_persubject(data_delay, dataset="SRM")
p4 <- delay_persubject(data_delay, dataset="Wang (2022)")
p1 / p3 / p4 +
plot_layout(heights = c(2, 1, 2)) +
plot_annotation(title = "Optimization of Delay", theme = theme(plot.title = element_text(hjust = 0.5, face = "bold")))
```
<!-- #### Per Subject -->

<!-- ```{r delay_persubject, warning=FALSE, message=FALSE, fig.height=16, fig.width=10} -->
<!-- delay_persubject <- function(data_delay, dataset="Lemon") { -->
<!-- data <- filter(data_delay, Dataset == dataset) |> -->
<!-- mutate(Score = 1 + Score) -->

<!-- by_subject <- data |> -->
<!-- group_by(Condition, Metric, Area, Participant, Value) |> -->
<!-- summarise_all(mean) -->
<!-- by_area <- data |> -->
<!-- group_by(Condition, Metric, Area, Value) |> -->
<!-- summarise_all(mean) -->

<!-- by_subject |> -->
<!-- mutate(group = paste0(Participant, Area)) |> -->
<!-- ggplot(aes(x = Value, y = Score, color = Area)) + -->
<!-- geom_line(aes(group=group), alpha = 0.20) + -->
<!-- geom_line(data=by_area, aes(group=Area), size=1) + -->
<!-- geom_vline(xintercept = c(27), linetype = "dashed", size = 0.5) + -->
<!-- facet_wrap(~Condition*Metric, scales = "free_y") + -->
<!-- see::scale_color_flat_d(palette = "rainbow") + -->
<!-- scale_y_log10(expand = c(0, 0)) + -->
<!-- scale_x_continuous(expand = c(0, 0), -->
<!-- limits = c(0, NA), -->
<!-- breaks=c(5, seq(0, 80, 20)), -->
<!-- labels=c(5, seq(0, 80, 20))) + -->
<!-- labs(title = paste0("Dataset: ", dataset), x = NULL, y = NULL) + -->
<!-- guides(colour = guide_legend(override.aes = list(alpha = 1))) + -->
<!-- see::theme_modern() + -->
<!-- theme(plot.title = element_text(face = "plain", hjust = 0)) -->
<!-- } -->

<!-- p1 <- delay_persubject(data_delay, dataset="Lemon") -->
<!-- # p2 <- delay_persubject(data_delay, dataset="Texas") -->
<!-- p3 <- delay_persubject(data_delay, dataset="SRM") -->
<!-- p4 <- delay_persubject(data_delay, dataset="Wang (2022)") -->

<!-- p1 / p3 / p4 + -->
<!-- plot_layout(heights = c(2, 1, 2)) + -->
<!-- plot_annotation(title = "Optimization of Delay", theme = theme(plot.title = element_text(hjust = 0.5, face = "bold"))) -->
<!-- ``` -->



#### 2D Attractors

```{r attractors2D, eval=FALSE, warning=FALSE, message=FALSE, fig.height=16, fig.width=10}
data <- read.csv("data_attractor2D.csv")
data <- read.csv("data_attractor.csv")
data |>
mutate(Delay = Delay/Sampling_Rate*1000) |>
Expand Down Expand Up @@ -197,7 +198,7 @@ p <- data |>
animate(
p +
labs(title = "Delay τ = {closest_state} ms") +
transition_states(Period, transition_length = 0.5, state_length = 0),
transition_states(Freq, transition_length = 0.5, state_length = 0),
width = 600, height = 200, fps=20
)
Expand Down Expand Up @@ -257,3 +258,28 @@ p1 / p3 / p4 +
plot_layout(heights = c(2, 1, 2)) +
plot_annotation(title = "Optimization of Ebmedding Dimension", theme = theme(plot.title = element_text(hjust = 0.5, face = "bold")))
```



<!-- #### 3D Attractors -->

<!-- ```{r attractors3D, eval=FALSE, warning=FALSE, message=FALSE, fig.height=16, fig.width=10} -->
<!-- library(plotly) -->

<!-- data <- read.csv("data_attractor.csv") -->

<!-- plots <- list() -->
<!-- for(i in unique(data$Dataset)) { -->
<!-- plots[[i]] <- plot_ly(data[data$Dataset == i, ], -->
<!-- x = ~x, -->
<!-- y = ~y, -->
<!-- z = ~z, -->
<!-- type = 'scatter3d', -->
<!-- mode = 'lines', -->
<!-- opacity = 0.8, -->
<!-- line = list(width = 0.5, -->
<!-- color = ~c, -->
<!-- colorscale = 'Viridis')) -->
<!-- } -->
<!-- plots[["Lemon"]] -->
<!-- ``` -->

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