apModel/server.R at master · whitwort/apModel · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
library(shiny)
library(ggplot2)
library(reshape)

# Load model into the local environment
source("model.R", local = TRUE)

# Define server logic required to generate the plot
shinyServer(function(input, output) {

  # Capture input variables in a reactive expression
  runArgs   <- reactive({

    # Bind initial state and parameter inputs
    return(list(
        state = vapply( names(state)
                      , function(name) { as.numeric(input[[name]]) }
                      , FUN.VALUE = numeric(1)
                      )

      , parameters = vapply( names(parameters)
                           , function(name) { as.numeric(input[[name]]) }
                           , FUN.VALUE = numeric(1)
                           )

      ))
  })

  # Run the model in a reactive expression
  runModel  <- reactive({

    args  <- runArgs()

    # Simulation time depth
    depth <- (time["end"] - time["start"]) / time["step"]

    # Run the simulation; convert result to a data.frame
    result <- data.frame(solver(
        y     = args$state
      , times = seq(time["start"], input$time.end, by = abs(input$time.end - time["start"]) / depth)
      , func  = model
      , parms = args$parameters
    ))

    return(result)

  })

  # Simulation plot
  output$voltagePlot <- renderPlot({

    p <- ggplot(melt(runModel()[,c("time","v")], id = "time")) +
      geom_line( aes(time, value, colour = variable) )  +
      ylab("mV")

    print(p)

  })
  output$conductancePlot <- renderPlot({

    p <- ggplot(melt(runModel()[,c("time","m","h","n")], id = "time")) +
          geom_line( aes(time, value, colour = variable) )  +
          ylab("[variable]")

    print(p)

  })

})