Create coordinates following defined trajectory

Manually create data points corresponding to piecewise polynomial.

Use create_path() to exert full control over trajectory shape. Starting with scalar values defined by nodes, create_path() integrates twice to create continuously differentiable function.

create_path() is the underlying function to meandr().

If you are trying to generate random curve, it is strongly recommended to use meandr().

create_path(
  n_points = 100,
  nodes = c(1, -1, -1, 2, 0),
  node_int = NULL,
  scale = 1
)

Arguments

n_points	An integer. Controls output "resolution". (Underlying calculus is unaffected).
nodes	A numeric vector corresponding to 2nd derivative values. This determines the overall shape of the function.
node_int	A numeric vector assigning x-values for nodes. Automatically placed at equal intervals if NULL.
scale	A number. Adjusts all y-values so that max(y) = scale.

Value

A tibble containing coordinates of resulting function.

Examples

# quadratic increase followed by linear slope
create_path(nodes = c(1, 0))
#> Creating even-spaced node intervals at 0%, 50%
#> # A tibble: 100 x 2
#>         t        f
#>     <dbl>    <dbl>
#>  1 0.01   0.000133
#>  2 0.02   0.000533
#>  3 0.03   0.00120 
#>  4 0.04   0.00213 
#>  5 0.05   0.00333 
#>  6 0.06   0.0048  
#>  7 0.0700 0.00653 
#>  8 0.08   0.00853 
#>  9 0.09   0.0108  
#> 10 0.10   0.0133  
#> # ... with 90 more rows

# resembles sinusoid
create_path(nodes = c(1, -1, 1, -1, 1), node_int = c(0, 0.125, 0.375, 0.625, 0.875))
#> # A tibble: 100 x 2
#>         t      f
#>     <dbl>  <dbl>
#>  1 0.01   0.0032
#>  2 0.02   0.0128
#>  3 0.03   0.0288
#>  4 0.04   0.0512
#>  5 0.05   0.08  
#>  6 0.06   0.115 
#>  7 0.0700 0.157 
#>  8 0.08   0.205 
#>  9 0.09   0.259 
#> 10 0.10   0.320 
#> # ... with 90 more rows