In r-gris/nf:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

The approaches required for Spatial data include the following.

• decompose to tables, at minimum as the fortify and attributes
• then decompose objects to linked branches/vertices
• then normalized those to de-duplicated vertices

Maybe this ?can be done more directly, to get the entire table of vertices classified by branch

This has to be able to be done dynamically, since a projection transformation or choice of vertex attributes can change the definition of "unique".

• straight composition from raster (no duplicated corner coords) and edge based models from paths
• segment- or edge-based forms are needed to drive the triangulation, and visualization

extract_ always works with the geometry _join always works with the attributes

apply_sp <- function(x) { index <- vector("list", nrow(x)) for (i in seq_along(index)) index[[i]] <- sptable(x[i, ]) %>% mutate(object_ = i) bind_rows(index) }

New approach for gris.

library(spbabel) ## devtools::install_github("mdsumner/spbabel")
library(dplyr)
source("R/grist.r")
library(maptools)
data(wrld_simpl)
#install.packages("rworldxtra")
library(rworldxtra)
data(countriesHigh)
# library(spdplyr)
# d1 <- countriesHigh %>% filter(SOVEREIGNT == "Antarctica")
# map1 <- sptable(d1) 
# 
# gg <- gris2(d1, map1)
# gg$s <- grist(gg)
#split(map1, map1$branch_)

d <- countriesHigh
d1<- as.data.frame(d) %>% mutate(object_ = row_number())
map1 <- sptable(d) 

x <- gris2(d1, map1)

verts <- with(x, o %>% filter(NAME == "Japan") %>% select(object_) %>% inner_join(b) %>% inner_join(bXv) %>% inner_join(v))
library(ggplot2)
ggplot(verts) + aes(x = x_, y = y_, fill = branch_, group = branch_) + geom_polygon()


## trips should be indexing from the original data frame
load("~/AMP2015/Data/tracks/EAnt_SES.RData")

library(trip)
library(dplyr)
d <- as_data_frame(ses_tracks) %>% mutate(vertex_ = row_number())

tr <- 
  d %>% select(ref, gmt, vertex_) %>% distinct(ref, gmt, .keep_all = TRUE) %>%  arrange(ref, gmt)

#library(magrittr)
library(ggplot2)
plot_tr <- function(x) {
  dd <- tr %>% inner_join(d %>% select(LON, LAT, vertex_), "vertex_")
  ggplot(dd[1:1000, ]) + aes(x = LON, y = LAT, group = ref, colour = ref) + geom_line()

}



## say we add the segments, then subset
x$s <- grist(x)
xa <- list(o =x$o %>% filter(NAME == "Japan"))
xa$b <- inner_join(x$b, xa$o %>% select(object_))
xa$bXv <- inner_join(x$bXv, xa$b %>% select(branch_))
xa$v <- inner_join(x$v, xa$bXv %>% select(vertex_)) %>% distinct()
xa$s <- inner_join(x$s, xa$b %>% select(branch_))

verts <- with(xa, o %>% select(object_) %>% inner_join(b) %>% inner_join(bXv) %>% inner_join(v))
library(ggplot2)
ggplot(verts) + aes(x = x_, y = y_, fill = branch_, group = branch_) + geom_polygon()

p <- xa$v %>% dplyr::select(x_, y_) %>% as.matrix()
S <- seg2struct(xa) %>% as.matrix()

p <- x$v %>% dplyr::select(x_, y_) %>% as.matrix()
p <- gris:::llh2xyz(cbind(p, 0))
S <- seg2struct(x) %>% as.matrix()

library(rgl)
lines3d(cbind(p, 0)[t(S), ])
tr <- RTriangle::triangulate(p = RTriangle::pslg(xa$v %>% dplyr::select(x_, y_) %>% as.matrix(), 
                                                 S = seg2struct(xa) %>% as.matrix()),                                               a = 0.1)

plot(tr$P, pch = ".")
apply(tr$T, 1, function(a) lines(tr$P[a, ]))

Try conversion from other formats.

library(spatstat)
  # polygon with hole
  ho <- owin(poly=list(list(x=c(0,1,1,0), y=c(0,0,1,1)),
                       list(x=c(0.6,0.4,0.4,0.6), y=c(0.2,0.2,0.4,0.4))))

as_df_bdry <- function(x) {
  bind_rows(lapply(x$bdry, function(xb) data_frame(x_ = xb$x, y_ = xb$y)), .id = "branch_")
}


as_df_bdry(ho)
as_df_branch_bdry(ho)

r-gris/nf documentation built on May 26, 2019, 1:33 p.m.