#outf <- "/mnt/acebulk"
## SEA ICE DURATION
library(raster)
library(tibble)
library(dplyr)
library(aceecostats)
library(dplyr)
outf <- "/home/acebulk"
dp <- file.path(outf, "data/seaiceseason")
db <- dplyr::src_sqlite("/home/acebulk/data/habitat_assessment.sqlite3")
gridarea <- readRDS(file.path(outf,"data/nsidc_south_area.rds"))/1e6
## put a tidy end to the series
maxdate <- ISOdatetime(2017, 9, 1, 0, 0, 0, tz = "GMT")
## load previously calculated sea ice season metrics (seaiceson_southern_2016.Rmd)
library(raster)
day <- brick(file.path(outf, "data/south_daycount.grd"))
listtab <- vector("list", nlayers(day))
dates <- as.POSIXct(seq(as.Date("1979-02-15"), by = "1 year", length = nlayers(day)))
for (i in seq_along(listtab)) {
asub <- i # which(segs == unique(segs)[i])
a_obj <- subset(day, asub)
tab <- tabit(a_obj) %>% rename(days = val) %>% mutate(date = dates[asub[1]])
#filter(dur > 0)
#tab$dur[tab$dur < 30] <- 365
# tab$max<- values(max(a_obj))[tab$cell_]
# tab$mean <- values(mean(a_obj))[tab$cell_]
listtab[[i]] <- tab
print(i)
}
cell_tab <- bind_rows(listtab) %>%
mutate(decade = decade_maker(date)) %>%
filter(date < maxdate) %>%
filter(!is.na(decade))
ucell <- distinct(cell_tab, cell_) %>% mutate(area = raster::extract(gridarea[[1]], cell_))
ucell$ID <- over(spTransform(xyFromCell(gridarea, ucell$cell_, spatial=TRUE), projection(aes_zone)),
aes_zone)$ID
## summ_tab is the mean values over time
summ_tab <- cell_tab %>% inner_join(ucell %>% inner_join(aes_zone@data[, c("ID", "SectorName", "Zone")])) %>%
#mutate(Season = aes_season(date)) %>%
group_by(Zone, decade, SectorName, date) %>%
summarize(days = mean(days)) %>%
ungroup()
summ_tab_nozone <- cell_tab %>% inner_join(ucell %>% inner_join(aes_zone@data[, c("ID", "SectorName", "Zone")])) %>%
#mutate(Season = aes_season(date)) %>%
group_by(decade, SectorName, date) %>%
summarize(days = mean(days)) %>%
ungroup()
#cell_tab <- cell_tab %>% inner_join(ucell %>% inner_join(aes_region@data[, c("index", "SectorName", "Zone", "Shelf")]))
## raw_tab is all the cell values for density plots
raw_tab <- cell_tab %>% inner_join(ucell %>% inner_join(aes_zone@data[, c("ID", "SectorName", "Zone")]))
raw_tab <- raw_tab %>% mutate(season = aes_season(date))
#db$con %>% db_drop_table(table='ice_days_density_tab')
#db$con %>% db_drop_table(table='ice_days_sparkline_tab')
#db$con %>% db_drop_table(table='ice_days_sparkline_tab_nozone')
copy_to(db, raw_tab, "ice_days_density_tab", temporary = FALSE)
copy_to(db, summ_tab, "ice_days_sparkline_tab", temporary = FALSE)
copy_to(db, summ_tab_nozone, "ice_days_sparkline_tab_nozone", temporary = FALSE)
# write_feather(cell_tab, file.path(outf, "seaice_duration_cell_tab.feather"))
# writeRaster(ras, file.path(outf, "seaice_duration_raster.grd"))
# write_feather(summ_tab, file.path(outf, "seaice_duration_summ_tab.feather"))
# write_feather(raw_tab, file.path(outf, "seaice_duration_raw_tab.feather"))
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