Starting with a hrrr file downloaded from ncep.
read into a xarray.Dataset like...
ds: xr.Dataset = xr.open_dataset(file, engine="pynio")
Dataset<xarray.Dataset>
Dimensions: (ygrid_0: 1059, xgrid_0: 1799, lv_HYBL0: 50,
lv_HTGL1: 2, lv_HTGL2: 2, lv_TMPL3: 2,
lv_SPDL4: 3, lv_HTGL5: 2, lv_HTGL6: 2,
lv_DBLL7: 2, lv_HTGL8: 2, lv_HTGL9: 3)
Coordinates:
* lv_HTGL6 (lv_HTGL6) float32 1e+03 4e+03
* lv_TMPL3 (lv_TMPL3) float32 253.0 263.0
* lv_HTGL1 (lv_HTGL1) float32 10.0 80.0
* lv_HYBL0 (lv_HYBL0) float32 1.0 2.0 3.0 ... 49.0 50.0
gridlat_0 (ygrid_0, xgrid_0) float32 ...
gridlon_0 (ygrid_0, xgrid_0) float32 ...
Dimensions without coordinates: ygrid_0, xgrid_0, lv_HTGL2, lv_SPDL4, lv_HTGL5,
lv_DBLL7, lv_HTGL8, lv_HTGL9
Data variables: (12/149)
TMP_P0_L1_GLC0 (ygrid_0, xgrid_0) float32 ...
TMP_P0_L103_GLC0 (ygrid_0, xgrid_0) float32 ...
TMP_P0_L105_GLC0 (lv_HYBL0, ygrid_0, xgrid_0) float32 ...
POT_P0_L103_GLC0 (ygrid_0, xgrid_0) float32 ...
DPT_P0_L103_GLC0 (ygrid_0, xgrid_0) float32 ...
LHTFL_P0_L1_GLC0 (ygrid_0, xgrid_0) float32 ...
... ...
lv_HTGL5_l0 (lv_HTGL5) float32 ...
lv_SPDL4_l1 (lv_SPDL4) float32 ...
lv_SPDL4_l0 (lv_SPDL4) float32 ...
lv_HTGL2_l1 (lv_HTGL2) float32 ...
lv_HTGL2_l0 (lv_HTGL2) float32 ...
gridrot_0 (ygrid_0, xgrid_0) float32 ...
for the time being I am only concerned with Variables that contain these 3 common Coordinates [lv_HYBL0, gridlat_0, gridlon_0]
I can manually select/index those Variables that have the Coordinates that I want, like....
ds[["TMP_P0_L105_GLC0",...]]
but I would prefer a more abstract method. In pandas I would do some sort of bool indexing along the lines of ... ds[ds.variables[ds.coords.isin(["gridlat_0","gridlon_0","lv_HYBL0"])]]
this unfortunately does not work.
How can I select Variables based on a condition where the Variable is tied to a Coordinate?
You can still do something similar. You can filter a dataset’s variables using a list of keys, and determine the dimensions by testing the elements of each array’s dims attribute, which is a tuple.
In this case:
required_dims = ['lv_HYBL0', 'gridlat_0', 'gridlon_0']
#sorted tuple
required_dims = tuple(sorted(required_dims))
subset = ds[[
k for k, v in ds.data_vars.items()
if tuple(sorted(v.dims)) == required_dims
]]