edzer
commented
1 week ago Since you closed the issue, did you solve it?
Open rociobeatrizc opened 1 week ago
edzer
commented
1 week ago Since you closed the issue, did you solve it?
rociobeatrizc
commented
1 week ago Good evening. Yes, it was a very silly problem. I couldn't set the time dimension correctly, but it was just a matter of adding the dates. The initial situation, when I converted the raster stack into a stars object, was the following:
> jan_stars
stars object with 3 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
CHELSA_bio_01_1981-2010_V2.... -0.95 16.55 28.1 50.30252 71.1 241.7
dimension(s):
from to offset delta refsys point values x/y
x 1 212 13.02 0.008333 WGS 84 FALSE NULL [x]
y 1 146 42.9 -0.008333 WGS 84 FALSE NULL [y]
attributes 1 4 NA NA NA NA CHELSA_bio_01_1981-2010_V2.1,...,CHELSA_bio_14_1981-2010_V2.1 To add the temporal dimension (the same date for each attribute, which are 4) I followed the following steps, which are probably a bit 'rough' but work
# list with the stars object repeated 4 times
l = vector("list", 4)
l[] = list(jan_stars)
# temporary names
names(l) = c("w", "x", "y", "z")
# apply the list of names will result in another dimension: now we have 4 dimensions and 1 attribute
r = do.call("c", l)
r = st_redimension(r)
> r
stars object with 4 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
w.x.y.z -0.95 16.55 28.1 50.30252 71.1 241.7
dimension(s):
from to offset delta refsys point values x/y
x 1 212 13.02 0.008333 WGS 84 FALSE NULL [x]
y 1 146 42.9 -0.008333 WGS 84 FALSE NULL [y]
attributes 1 4 NA NA NA NA CHELSA_bio_01_1981-2010_V2.1,...,CHELSA_bio_14_1981-2010_V2.1
new_dim 1 4 NA NA NA NA w,...,z
# with split, the third dimension, which actually contains the bioclimatic variables, is turned into attributes
> r = split(r, 3)
> r
stars object with 3 dimensions and 4 attributes
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
CHELSA_bio_01_1981-2010_V2.1 -0.95 9.35 12.35 12.03118 15.45 16.65
CHELSA_bio_07_1981-2010_V2.1 16.20 25.30 27.20 25.69690 27.80 29.50
CHELSA_bio_13_1981-2010_V2.1 59.80 84.90 111.20 119.10602 147.40 241.70
CHELSA_bio_14_1981-2010_V2.1 18.90 33.20 43.90 44.37600 53.60 92.80
dimension(s):
from to offset delta refsys point values x/y
x 1 212 13.02 0.008333 WGS 84 FALSE NULL [x]
y 1 146 42.9 -0.008333 WGS 84 FALSE NULL [y]
new_dim 1 4 NA NA NA NA w,...,z
# Finally, let's replace the new_dim with the temporal dimension
r <- st_set_dimensions(.x = r, which = "new_dim", values = as.Date(c("2009-01-01", "2009-01-01", "2009-01-01","2009-01-01")), names = "time")
> r
stars object with 3 dimensions and 4 attributes
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
CHELSA_bio_01_1981-2010_V2.1 -0.95 9.35 12.35 12.03118 15.45 16.65
CHELSA_bio_07_1981-2010_V2.1 16.20 25.30 27.20 25.69690 27.80 29.50
CHELSA_bio_13_1981-2010_V2.1 59.80 84.90 111.20 119.10602 147.40 241.70
CHELSA_bio_14_1981-2010_V2.1 18.90 33.20 43.90 44.37600 53.60 92.80
dimension(s):
from to offset delta refsys point values x/y
x 1 212 13.02 0.008333 WGS 84 FALSE NULL [x]
y 1 146 42.9 -0.008333 WGS 84 FALSE NULL [y]
time 1 4 NA NA Date NA 2009-01-01,...,2009-01-01I apologize if I improperly opened an issue on GitHub with this problem!
edzer
commented
1 week ago Thanks for the clarification!
rociobeatrizc
commented
1 week ago Good evening, I'm reopening the issue because, although the method I wrote above seems to work formally, when I try to select a date, I cannot retrieve the corresponding image. So, I think I might have incorrectly set the temporal dimension. I'll start from the beginning:
I used this function to download three climatic variables (precipitation, temperature, and maximum temperature): for each of these variables, I downloaded the first 4 months of the year.
# devtools required
if(!require(devtools)) install.packages("devtools")
library(devtools)
devtools::install_github("HelgeJentsch/ClimDatDownloadR")
library(ClimDatDownloadR)
# shapefile and bounding box
aoi_abruzzo <- st_read("abruzzo.shp") %>% .$geometry
# Bounding Box
abruzzo_bb <- st_bbox(aoi_abruzzo)
WorldClim.HistClim.download(
# save.location = "./",
parameter = c("prec", "temp", "tmax"),
# 4 months, from jan to april
month.var = c(1:4),
# here, 10 arc-minutes are chosen as resolution
resolution = c("10m"),
version.var = c("2.1"),
clipping = TRUE,
clip.shapefile = aoi_abruzzo,
clip.extent = abruzzo_bb,
# buffer = 0,
convert.files.to.asc = FALSE,
stacking.data = TRUE,
keep.raw.zip = FALSE,
delete.raw.data = FALSE,
save.bib.file = TRUE
)As a result, I have 4 .tif files for each variable, each representing a month. This is, for example, the precipitation variable imported as a stack:
# Import raster
# String containing the names of raster files
rastlist <- list.files(path ="my/path/prec/WorldClim_2.1_prec_30s/clipped_2024-10-09_16-35-06", pattern = "wc2.1", full.names = TRUE)
# Using the list of names, all the files are imported into a single raster package
stack_prec <- stack(rastlist)
> stack_prec
class : RasterStack
dimensions : 145, 212, 30740, 4 (nrow, ncol, ncell, nlayers)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 13.01667, 14.78333, 41.68333, 42.89167 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
names : wc2.1_30s_prec_01, wc2.1_30s_prec_02, wc2.1_30s_prec_03, wc2.1_30s_prec_04
min values : 37, 34, 35, 32
max values : 102, 99, 84, 106 I have 4 layers, each corresponding to a month. I would like to create a stars object where, by adding and associating the temporal dimension to each layer, I am able to extract the precipitation corresponding to that month using the date.
These are the steps I followed
# stars object
stars_prec <- st_as_stars(stack_prec)
> stars_prec
stars object with 3 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
wc2.1_30s_prec_01 32 48 53 55.19539 60 106 24480
dimension(s):
from to offset delta refsys values x/y
x 1 212 13.02 0.008333 +proj=longlat +datum=WGS8... NULL [x]
y 1 145 42.89 -0.008333 +proj=longlat +datum=WGS8... NULL [y]
band 1 4 NA NA NA wc2.1_30s_prec_01,...,wc2.1_30s_prec_04 Already at this point, I can see that the problem is that it only considers the first month of precipitation (wc2.1_30s_prec_01) as an 'attribute' and does not include all 4 of them.
To add the temporal dimension, I tried the following, based on an answer here on Stack Overflow.
# add temporal dimension
l = vector("list", 4)
l[] = list(stars_prec)
r = do.call("c", l)
r = st_redimension(r)
# 4 random days of the 4 months
r <- st_set_dimensions(.x = r, which = "new_dim", values = as.Date(c("1980-01-01", "1980-02-01", "1980-03-01","1980-04-01")), names = "time")
> r
stars object with 4 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
wc2.1_30s_prec_01.wc2.1_30s... 32 48 53 55.19539 60 106 97920
dimension(s):
from to offset delta refsys values x/y
x 1 212 13.02 0.008333 +proj=longlat +datum=WGS8... NULL [x]
y 1 145 42.89 -0.008333 +proj=longlat +datum=WGS8... NULL [y]
band 1 4 NA NA NA wc2.1_30s_prec_01,...,wc2.1_30s_prec_04
time 1 4 NA NA Date 1980-01-01,...,1980-04-01 I set the temporal dimension, but the attribute still only includes the precipitation values for the first month.
If I apply the split function on the third dimension, I get this:
> split(r,3)
stars object with 3 dimensions and 4 attributes
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
wc2.1_30s_prec_01 37 44 48 49.30158 51 102 24480
wc2.1_30s_prec_02 34 49 52 53.25581 56 99 24480
wc2.1_30s_prec_03 35 50 53 53.35853 56 84 24480
wc2.1_30s_prec_04 32 61 66 64.86564 70 106 24480
dimension(s):
from to offset delta refsys values x/y
x 1 212 13.02 0.008333 +proj=longlat +datum=WGS8... NULL [x]
y 1 145 42.89 -0.008333 +proj=longlat +datum=WGS8... NULL [y]
time 1 4 NA NA Date 1980-01-01,...,1980-04-01 Each month becomes a separate attribute! I don't know how to solve this problem; I feel like the solution is simple, but I've been stuck for days. I would like to obtain the classic data cube, like this. Many thanks in advance.
Hello from Bologna, Italy! This is the first Issue I open on GitHub :)
I've been trying to solve a problem for days. I downloaded and imported 4 bioclimatic variables from CHELSA for 3 different months: January, February, and March. I created three stacks, one for each month, containing the variables. Here is how the files look:
My goal is to create data cubes, so I need to add the temporal dimension. In this case, since these are climate series, each layer represents a monthly average over a 30-year period. Therefore, I thought of simply assigning a representative date of the month to each stack.
But if I proceed to create a 'stars' object, I get an object with a single attribute and three dimensions.
'split' option
At this point, I tried to convert the 'attributes' dimension into separate attributes using 'split', but I lost one dimension and couldn't find any way to add it back while keeping the four attributes in place.
manually add date
However, if I manually add the date, I'm left with only one attribute and I can no longer recover the other three. I need to keep this information as it will be needed for creating an SDM.
I have the impression that the solution is simple, but I just can't seem to find it. I really hope for some help. Thank you so much in advance!