url <- vapply(seq(as.Date("1993-01-01"), by = "month", length.out = 12), \(.x) bluelink::bluelink_dsn(.x, varname = "ocean_temp", vsicurl = F), "")
dods <- gsub("fileServer", "dodsC", url)
library(reticulate)
py_require("pydap")
py_require("jinja2")
py_require("dask")
import("dask")
py_require("xarray")
xarray <- import("xarray")xarray$open_mfdataset(dods, engine = "pydap", decode_timedelta = FALSE)
<xarray.Dataset> Size: 804GB
Dimensions: (Time: 365, nv: 2, st_ocean: 51, yt_ocean: 1500,
xt_ocean: 3600, st_edges_ocean: 52)
Coordinates:
* xt_ocean (xt_ocean) float64 29kB 0.05 0.15 0.25 ... 359.8 359.9 360.0
* yt_ocean (yt_ocean) float64 12kB -74.95 -74.85 -74.75 ... 74.85 74.95
* st_ocean (st_ocean) float64 408B 2.5 7.5 12.5 ... 3.603e+03 4.509e+03
* Time (Time) datetime64[ns] 3kB 1993-01-01T12:00:00 ... 1993-12...
* nv (nv) float64 16B 1.0 2.0
* st_edges_ocean (st_edges_ocean) float64 416B 0.0 5.0 ... 4.056e+03 5e+03
Data variables:
average_T1 (Time) datetime64[ns] 3kB dask.array<chunksize=(31,), meta=np.ndarray>
average_T2 (Time) datetime64[ns] 3kB dask.array<chunksize=(31,), meta=np.ndarray>
average_DT (Time) float64 3kB dask.array<chunksize=(31,), meta=np.ndarray>
Time_bounds (Time, nv) float64 6kB dask.array<chunksize=(31, 2), meta=np.ndarray>
temp (Time, st_ocean, yt_ocean, xt_ocean) float64 804GB dask.array<chunksize=(31, 51, 1500, 3600), meta=np.ndarray>
Attributes:
filename: TMP/ocean_ofam_1993_01_01.nc.0000
NumFilesInSet: 20
grid_type: regular
grid_tile: N/A
history: Mon May 18 20:07:58 2020: ncrcat -4 --df...
NCO: netCDF Operators version 4.9.2 (Homepage...
catalogue_doi_url: http://dx.doi.org/10.25914/6009627c7af03
acknowledgement: BRAN is made freely available by CSIRO B...
title: BRAN2020
DODS_EXTRA.Unlimited_Dimension: Time
