nc_proxy #499

dblodgett-usgs · 2022-01-11T04:15:03Z

I will refine this description tomorrow, but I'm excited to share progress.

As of January 10th, this needs a lot more testing, but the core of it is shown in the reprex below. The stars_proxy is now nearly 1:1 with the regular stars_proxy. I think they could likely be combined with a little effort on both sides.

Most of the heavy lifting is done in read_ncdf by updating the NetCDF request based on the nc_proxy dimensions passed in. I found a number of edge case bugs that are included and should help clean up some of the regular stars_proxy methods.

library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

# We can get a proxy object from an OPeNDAP source:
f <- "https://cida.usgs.gov/thredds/dodsC/bcsd_obs"

nc <- stars::read_ncdf(f, proxy = TRUE)
#> no 'var' specified, using pr, prate, tas, tasmax, tasmin, wind
#> other available variables:
#>  longitude, latitude, time, longitude_bnds, latitude_bnds
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.

# the print method shows what's available.
nc
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs"
#> 
#> Available nc variables:
#> pr
#> prate
#> tas
#> tasmax
#> tasmin
#> wind
#> 
#> dimension(s):
#>           from  to  offset delta  refsys point                    values x/y
#> longitude    1 462 -124.75 0.125  WGS 84    NA                      NULL [x]
#> latitude     1 222  25.125 0.125  WGS 84    NA                      NULL [y]
#> time         1 600      NA    NA POSIXct    NA 1950-01-31,...,1999-12-31

# the plot method plots some number of timesteps.
plot(nc, max_times = 9)
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> Will return stars object with 923076 cells.

# we can use subsetting as normal.
plot(nc["tasmax"], max_times = 1)
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> Will return stars object with 102564 cells.

states <- sf::read_sf("https://reference.geoconnex.us/collections/states/items")

states <- dplyr::filter(states, !STUSPS %in% c("AS", "AK", "HI", "PR", "VI", "GU", "MP"))


IA <- dplyr::filter(states, STUSPS == "IA")
plot(sf::st_geometry(IA))
plot(sf::st_geometry(states), add = TRUE)

# We can use the sf selector...
nc_crop <- nc[sf::st_bbox(IA)]

# Or just use st_crop
nc_crop <- st_crop(nc, sf::st_bbox(IA), collect = FALSE)

# Other options cause a call list, but the above don't...
attr(nc_crop, "call_list")
#> NULL

# Plot one time step of just the cropped proxy pulls data
plot(nc_crop[,,,1], add = TRUE)
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> Will return stars object with 1378 cells.

# We can also just subset directly...
plot(sf::st_geometry(states))
plot(nc["wind", 100:300, 100:200, 25], add = TRUE)
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> Will return stars object with 20301 cells.

# Normal stuff works too...
f <- system.file("nc/reduced.nc", package = "stars")

nc <- read_ncdf(f, proxy = TRUE)
#> no 'var' specified, using sst, anom, err, ice
#> other available variables:
#>  lon, lat, zlev, time
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.

nc["sst"]
#> netcdf source stars proxy object from:
#> [1] "[...]/reduced.nc"
#> 
#> Available nc variables:
#> sst
#> 
#> dimension(s):
#>      from  to offset delta  refsys point         values x/y
#> lon     1 180     -1     2  WGS 84    NA           NULL [x]
#> lat     1  90    -90     2  WGS 84    NA           NULL [y]
#> zlev    1   1     NA    NA      NA    NA              0    
#> time    1   1     NA    NA POSIXct    NA 1981-12-31 UTC

nc[[5]] <- nc[[4]]

nc
#> netcdf source stars proxy object from:
#> [1] "[...]/reduced.nc"
#> 
#> Available nc variables:
#> sst
#> anom
#> err
#> ice
#> 
#> 
#> dimension(s):
#>      from  to offset delta  refsys point         values x/y
#> lon     1 180     -1     2  WGS 84    NA           NULL [x]
#> lat     1  90    -90     2  WGS 84    NA           NULL [y]
#> zlev    1   1     NA    NA      NA    NA              0    
#> time    1   1     NA    NA POSIXct    NA 1981-12-31 UTC

c(nc["sst"], nc["ice"])
#> netcdf source stars proxy object from:
#> [1] "[...]/reduced.nc"
#> 
#> Available nc variables:
#> sst
#> ice
#> 
#> dimension(s):
#>      from  to offset delta  refsys point         values x/y
#> lon     1 180     -1     2  WGS 84    NA           NULL [x]
#> lat     1  90    -90     2  WGS 84    NA           NULL [y]
#> zlev    1   1     NA    NA      NA    NA              0    
#> time    1   1     NA    NA POSIXct    NA 1981-12-31 UTC

^{Created on 2022-01-10 by the reprex package (v2.0.1)}

… Start playing with vignette.

…s_stars method.

…blodgett-usgs-main

edzer · 2022-01-12T13:06:29Z

Looks great, and works for me too!

dblodgett-usgs · 2022-01-12T14:10:45Z

It's coming along. I found some snags when testing the call_list stuff a bit last night. Think I have a solution there. Will update with more testing and examples in a bit.

dblodgett-usgs · 2022-01-12T14:48:04Z

library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

# Load up the same file from two sources.
nc_gdal <- read_stars(system.file("nc/bcsd_obs_1999.nc", package = "stars"),
                 proxy = TRUE)

nc_nc <- read_ncdf(system.file("nc/bcsd_obs_1999.nc", package = "stars"),
                proxy = TRUE)
#> no 'var' specified, using pr, tas
#> other available variables:
#>  latitude, longitude, time
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.

sf::st_crs(nc_gdal) <- sf::st_crs(nc_nc)

nc_sf <- sf::st_transform(
    read_sf(system.file("gpkg/nc.gpkg", package="sf")),
    sf::st_crs(nc_nc))

# Crop with collect = TRUE adds to the call_list
(nc_gdal2 <- st_crop(nc_gdal, nc_sf[10, ], collect = TRUE))
#> stars_proxy object with 2 attributes in 2 file(s):
#> $pr
#> [1] "[...]/bcsd_obs_1999.nc:pr"
#> 
#> $tas
#> [1] "[...]/bcsd_obs_1999.nc:tas"
#> 
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000013397a60>
(nc_nc2 <- st_crop(nc_nc, nc_sf[10, ], collect = TRUE))
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000022140550>

# Convert to stars and plot.
(nc_gdal3 <- st_as_stars(nc_gdal2))

#> stars object with 3 dimensions and 2 attributes
#> attribute(s):
#>                Min.   1st Qu.   Median     Mean   3rd Qu.      Max. NA's
#> pr [mm/m] 38.669998 56.425001 77.11500 88.78302 113.44250 227.21001   48
#> tas [C]    4.377258  6.257742 13.77183 13.97364  20.12717  25.98855   48
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31
(nc_nc3 <- st_as_stars(nc_nc2))
#> Will return stars object with 144 cells.
#> stars object with 3 dimensions and 2 attributes
#> attribute(s):
#>                Min.   1st Qu.   Median     Mean   3rd Qu.      Max. NA's
#> pr [mm/m] 38.669998 56.425001 77.11500 88.78302 113.44250 227.21001   48
#> tas [C]    4.377258  6.257742 13.77183 13.97364  20.12717  25.98855   48
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude    1  4  -80.5 0.125  WGS 84    NA                      NULL [x]
#> latitude     1  3  36.25 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31

plot(nc_sf$geom[10])
plot(nc_gdal3["pr",,,1], add = TRUE)

plot(nc_sf$geom[10])
plot(nc_nc3["pr",,,1], add = TRUE)

# Crop with collect = FALSE does not add to the call list.  
(nc_gdal2 <- st_crop(nc_gdal, nc_sf[10, ], collect = FALSE))
#> stars_proxy object with 2 attributes in 2 file(s):
#> $pr
#> [1] "[...]/bcsd_obs_1999.nc:pr"
#> 
#> $tas
#> [1] "[...]/bcsd_obs_1999.nc:tas"
#> 
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31
(nc_nc2 <- st_crop(nc_nc, nc_sf[10, ], collect = FALSE))
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31

(nc_gdal3 <- st_as_stars(nc_gdal2))

#> stars object with 3 dimensions and 2 attributes
#> attribute(s):
#>                Min.   1st Qu.   Median     Mean   3rd Qu.      Max.
#> pr [mm/m] 38.560001 58.087500 76.69500 89.63785 114.58000 227.21001
#> tas [C]    4.377258  6.216976 13.72325 13.99577  20.12717  25.98855
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31
(nc_nc3 <- st_as_stars(nc_nc2))
#> Will return stars object with 144 cells.
#> stars object with 3 dimensions and 2 attributes
#> attribute(s):
#>                Min.   1st Qu.   Median     Mean   3rd Qu.      Max.
#> pr [mm/m] 38.560001 58.087500 76.69500 89.63785 114.58000 227.21001
#> tas [C]    4.377258  6.216976 13.72325 13.99577  20.12717  25.98855
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude    1  4  -80.5 0.125  WGS 84    NA                      NULL [x]
#> latitude     1  3  36.25 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31

plot(nc_sf$geom[10])
plot(nc_gdal3["pr",,,1], add = TRUE)

plot(nc_sf$geom[10])
plot(nc_nc3["pr",,,1], add = TRUE)

# Also works with a slightly deeper call list.
(nc_gdal2 <- st_crop(nc_gdal, nc_sf[10, ]))
#> stars_proxy object with 2 attributes in 2 file(s):
#> $pr
#> [1] "[...]/bcsd_obs_1999.nc:pr"
#> 
#> $tas
#> [1] "[...]/bcsd_obs_1999.nc:tas"
#> 
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000029aaf038>
(nc_nc2 <- st_crop(nc_nc, nc_sf[10, ]))
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000029fda550>

# NOTE: the first call in the call list will read multiple variables
# then this subset will be applied.
(nc_gdal2 <- nc_gdal2["pr", , , 1])
#> stars_proxy object with 2 attributes in 2 file(s):
#> $pr
#> [1] "[...]/bcsd_obs_1999.nc:pr"
#> 
#> $tas
#> [1] "[...]/bcsd_obs_1999.nc:tas"
#> 
#> dimension(s):
#>      from to offset  delta  refsys point                    values x/y
#> x      37 40    -85  0.125  WGS 84    NA                      NULL [x]
#> y       5  7 37.125 -0.125  WGS 84    NA                      NULL [y]
#> time    1 12     NA     NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000029aaf038>
#> 
#> [[2]]
#> x[i = i, , , 1, drop = drop, crop = crop]
#> attr(,".Environment")
#> <environment: 0x000000002a51fb20>
(nc_nc2 <- nc_nc2["pr", , , 1])
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x0000000029fda550>
#> 
#> [[2]]
#> x[i = i, , , 1, drop = drop, crop = crop]
#> attr(,".Environment")
#> <environment: 0x000000002a9d62a0>

plot(nc_sf$geom[10])
plot(nc_gdal2, add = TRUE)

plot(nc_sf$geom[10])
plot(nc_nc2, add = TRUE)
#> Plotting first variable only.
#> Will return stars object with 144 cells.

nc_nc
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude    1 81    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude     1 33     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31

## Look at order of call_list operations.

# This will put two calls in the call list and modify dimensions.
(nc2 <- st_crop(nc_nc, nc_sf[10, ], collect = TRUE))
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x000000002a7cb068>

(nc2 <- nc2["pr", , , 1])
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> tas
#> 
#> dimension(s):
#>           from to offset delta  refsys point                    values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA                      NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA                      NULL [y]
#> time         1 12     NA    NA POSIXct    NA 1999-01-31,...,1999-12-31    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x000000002a7cb068>
#> 
#> [[2]]
#> x[i = i, , , 1, drop = drop, crop = crop]
#> attr(,".Environment")
#> <environment: 0x000000002aceb400>

# This will put one call in the call list and modify dimensions more.
(nc3 <- nc_nc["pr", , , 1])
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> 
#> dimension(s):
#>           from to offset delta  refsys point         values x/y
#> longitude    1 81    -85 0.125  WGS 84    NA           NULL [x]
#> latitude     1 33     33 0.125  WGS 84    NA           NULL [y]
#> time         1  1     NA    NA POSIXct    NA 1999-01-31 UTC

(nc3 <- st_crop(nc3, nc_sf[10, ], collect = TRUE))
#> netcdf source stars proxy object from:
#> [1] "[...]/bcsd_obs_1999.nc"
#> 
#> Available nc variables:
#> pr
#> 
#> dimension(s):
#>           from to offset delta  refsys point         values x/y
#> longitude   37 40    -85 0.125  WGS 84    NA           NULL [x]
#> latitude    27 29     33 0.125  WGS 84    NA           NULL [y]
#> time         1  1     NA    NA POSIXct    NA 1999-01-31 UTC    
#> 
#> call_list:
#> [[1]]
#> st_crop(x = x, y = y, crop = crop, epsilon = epsilon)
#> attr(,".Environment")
#> <environment: 0x000000002b51a060>

# Same outcome but nc3 reads much less data.
(nc2 <- st_as_stars(nc2))
#> Will return stars object with 144 cells.
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#>             Min.  1st Qu. Median   Mean 3rd Qu.   Max. NA's
#> pr [mm/m] 122.24 122.9675 123.83 125.84 127.805 134.97    4
#> dimension(s):
#>           from to offset delta  refsys point         values x/y
#> longitude    1  4  -80.5 0.125  WGS 84    NA           NULL [x]
#> latitude     1  3  36.25 0.125  WGS 84    NA           NULL [y]
#> time         1  1     NA    NA POSIXct    NA 1999-01-31 UTC
    
(nc3 <- st_as_stars(nc3))
#> Will return stars object with 12 cells.
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#>             Min.  1st Qu. Median   Mean 3rd Qu.   Max. NA's
#> pr [mm/m] 122.24 122.9675 123.83 125.84 127.805 134.97    4
#> dimension(s):
#>           from to offset delta  refsys point         values x/y
#> longitude    1  4  -80.5 0.125  WGS 84    NA           NULL [x]
#> latitude     1  3  36.25 0.125  WGS 84    NA           NULL [y]
#> time         1  1     NA    NA POSIXct    NA 1999-01-31 UTC

^{Created on 2022-01-12 by the reprex package (v2.0.1)}

dblodgett-usgs · 2022-01-21T18:36:57Z

I started working with curvilinear coordinates and am realizing there's not much (any?) curvilinear handling in stars_proxy. I want to start building that out for nc_proxy. Is that something you have plans for @edzer ?

edzer · 2022-01-21T21:11:00Z

Is that something you have plans for @edzer ?

Absolutely. The low hanging fruit here is where we do read the long and lat matrices in memory - not proxy those -, then proxy the array, and read selections of that in memory when needed (or else loop over chunks).

dblodgett-usgs · 2022-01-22T01:41:27Z

OK -- that's what I was thinking it would do. I'll put some time into it when I get a chance.

…h Z and T coordinates.

…d some debugging of coordinate/dimension handling

dblodgett-usgs · 2022-01-23T03:26:33Z

A little progress -- I can proxy the stageIV precip attributes now. st_as_stars.stars isn't much now that I factored out a stand along add_curvilinear() function.

edzer · 2022-03-08T09:11:42Z

David, LGTM - is this ready to go?

dblodgett-usgs · 2022-03-08T14:04:52Z

I certainly have more I would like to accomplish, but I think this is a positive step, so if you are comfortable merging with somewhat limited functionality compared to the long term, yes.

dblodgett-usgs and others added 24 commits December 21, 2021 11:06

read_ncdf can now return a proxy object. fixes #318

04f78dc

testing and some refactoring

d48ca14

print method for netcdf proxy

0e11ad6

update print method for nc_proxy and nc_request S3 classes

cd7015d

move add_units into package environment and call from print.nc_proxy.…

a56148e

… Start playing with vignette.

create ncproxy.R for nc_proxy methods and get ready to implement st_a…

f1a7523

…s_stars method.

st_as_stars method for nc_proxy

3e5c488

add downsample to read_ncdf for st_as_stars

7e49994

rudimentary plot method for nc_proxy

bb10d33

nc_proxy vignette update with list of methods

6e2ec6f

move nc_request into attributes of main stars list

5fefec0

"[.ncproxy"

d310159

tests and some more basic methods for nc_proxy

7fb912a

make sure new nc_request structure works end to end.

e97bde6

fix up plot method

e5c161a

fix up some issues with coordinate variables and dimension ids

4b18ae5

Merge branch 'main' of https://github.com/dblodgett-usgs/stars into d…

10056a0

…blodgett-usgs-main

method signature cleanup

f823f4c

Merge branch 'main' of https://github.com/dblodgett-usgs/stars into d…

f6d7e4d

…blodgett-usgs-main

handle all dimensions in [.stars_proxy; #496

467ffe5

documentation

d3a1ffb

remove need for nc_request "[" and st_crop methods work now.

406947c

debugging nc_proxy crop methods

91362b3

Testing cleanup

55d694d

subsetting tests for nc_proxy

391096d

dblodgett-usgs added 2 commits January 21, 2022 20:50

Fixes a minor issue with curvilinear coordinates not coming along wit…

5e92828

…h Z and T coordinates.

factored out a add_curvilinear() function from st_as_stars.stars() an…

21c592f

…d some debugging of coordinate/dimension handling

dblodgett-usgs and others added 3 commits February 23, 2022 08:17

add better handling for lon 0-360 coords fixes #511

bde2da8

Merge branch 'main' of github.com:r-spatial/stars into main

4fe94f8

Merge branch 'main' into main

d7aaa65

edzer merged commit e99abcb into r-spatial:main Mar 8, 2022

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nc_proxy #499

nc_proxy #499

dblodgett-usgs commented Jan 11, 2022

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edzer commented Mar 8, 2022

dblodgett-usgs commented Mar 8, 2022

nc_proxy #499

nc_proxy #499

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dblodgett-usgs commented Jan 11, 2022

edzer commented Jan 12, 2022

dblodgett-usgs commented Jan 12, 2022

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