## Tests of rgl_gaussian_2D()
#library(rgl)
library(viridisLite)
data(gaussplot_sample_data)
samp_dat <-
gaussplot_sample_data[,1:3]
bad_data1 <- cbind(samp_dat[,c(1,3)], yvalz = rnorm(nrow(samp_dat)))
bad_data2 <- cbind(samp_dat[,c(1,2)], Z_values = rnorm(nrow(samp_dat)))
test_that("rgl_gaussian_2D() fails when nonsense is supplied", {
expect_error(rgl_gaussian_2D("steve"))
expect_error(rgl_gaussian_2D(c("a", "b", "c")))
expect_error(rgl_gaussian_2D())
expect_error(rgl_gaussian_2D(samp_dat))
expect_error(rgl_gaussian_2D(bad_data1))
expect_error(rgl_gaussian_2D(bad_data2))
expect_error(rgl_gaussian_2D(samp_dat, maxiter = "a"))
})
gauss_fit_ue <-
fit_gaussian_2D(samp_dat,
method = "elliptical",
constrain_orientation = "unconstrained")
gauss_fit_ce <-
fit_gaussian_2D(samp_dat,
method = "elliptical",
constrain_orientation = 0)
gauss_fit_uel <-
fit_gaussian_2D(samp_dat,
method = "elliptical_log",
constrain_orientation = "unconstrained")
gauss_fit_cel <-
fit_gaussian_2D(samp_dat,
method = "elliptical_log",
constrain_orientation = -1)
gauss_fit_cir <-
fit_gaussian_2D(samp_dat,
method = "circular")
## predict one data set
## Generate a grid of x- and y- values on which to predict
grid <-
expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
Y_values = seq(from = -1, to = 4, by = 0.1))
## Predict the values using predict_gaussian_2D
gauss_data <-
predict_gaussian_2D(
fit_object = gauss_fit_cel,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
#test that plotting works?
#pdf(file = NULL)
if (interactive()) { rgl_gaussian_2D(gauss_data)}
#dev.off()
test_that("rgl_gaussian_2D() fails when bad args are supplied", {
expect_error(rgl_gaussian_2D(gauss_data,
viridis_dir = "increasing"))
expect_error(rgl_gaussian_2D(gauss_data,
viridis_dir = "1"))
expect_error(rgl_gaussian_2D(gauss_data,
viridis_dir = 10))
})
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