In stormwaterheatmap/s8Regressions: What the Package Does (One Line, Title Case)

library(tidync)
require(sf)
require(tidyverse)
library(ncdfgeom)
library(ncdf4) # package for netcdf manipulation
library(raster) # package for raster manipulation
library(rgdal) # package for geospatial analysis
library(ggplot2) # package for plotting

type = "rail"

file <- here::here('data-raw',paste0('Vulcan_v3_US_annual_1km_',type,'_mn.nc4'))

co_data <- tidync(file)

# ncdf4 -------------------------------------------------------------------


nc_data <- nc_open(file)
# # Save the print(nc) dump to a text file
# {
#   sink('vulcan_metadata.txt')
#   print(nc_data)
#   sink()
# }
# sumary <- summary(nc_data)
# lon <- ncvar_get(nc_data, "lon")
# lat <- ncvar_get(nc_data, "lat", verbose = F)
# t <- ncvar_get(nc_data, "time")
#co.array <- ncvar_get(nc_data, "carbon_emissions") # store the data in a 3-dimensional array
cobrick = brick(file,varname = "carbon_emissions")
costack<-stack(cobrick)



domain <- st_read(here::here('data-raw','extent3.shp'))





raster.sub <- crop(cobrick, extent(domain))



library(tmap)
tmap::tmap_mode("view")

qtm(raster.sub)
tm_shape(raster.sub)+tm_raster(palette = "Greens",style="kmeans")
#writeRaster(costack, filename="costack.tif", format="GTiff",overwrite=TRUE)
writeRaster(raster.sub, filename=here::here('data',paste0("Vulcan_",type,".tif")), format="GTiff",overwrite=TRUE)

tm_shape(raster.sub)+tm_raster(palette = "Greens",style="kmeans")

raster ------------------------------------------------------------------

rast <- raster(file,varname = "carbon_emissions") tmpin <- stack(file) nlayers(tmpin)

print(co_data)

d.slice <- co_data %>% hyper_filter(x = x >579000 & x < 1603056,

                                                                    y = y >511000 & y <  1350425.0625)

print(d.slice$grid)

dim(d.slice[[1]]) aa <- (d.slice[[6]])

co_data_out <- d.slice %>% hyper_array() aa <- co_data_out$carbon_emissions

aaa <- raster(aa)

co_data_out <- d.slice %>% hyper_tibble() src_crs <- co_data %>% activate(carbon_emissions) crs.tib <- d.slice %>% hyper_tibble()

get geometry

stack.nc = stack(file) brick.nc = brick(file) raster.nc <- raster(file)

sla.df = raster::as.data.frame(raster.nc, xy = TRUE) xvals <- sla.df$x yvals <- sla.df$y plot(xvals,yvals)

oisstfile <- system.file("nc/reduced.nc", package = "stars") oisst <- tidync(oisstfile) print(oisst) print(co_data) time_ex <- oisst %>% activate("D3") %>% hyper_array() (oisst_data <- oisst %>% hyper_array())

stormwaterheatmap/s8Regressions documentation built on Dec. 23, 2021, 6:38 a.m.