I want to export a single date and variable fwi of a xarray dataset ds to a tif file. However, my dataarray has too many dimensions and I cannot figure out how to effectively remove the dimension lsm_time.
ds
<xarray.Dataset>
Dimensions: (time: 2436, y: 28, x: 58, lsm_time: 1)
Coordinates:
* time (time) datetime64[ns] 2015-05-02T12:00:00 ... 2021-12-31T12:...
step <U8 '12:00:00'
surface float64 0.0
* y (y) float64 36.01 36.11 36.21 36.31 ... 38.41 38.51 38.61 38.71
* x (x) float64 -7.64 -7.54 -7.44 -7.34 ... -2.24 -2.14 -2.04 -1.94
spatial_ref int32 0
Dimensions without coordinates: lsm_time
Data variables:
ffmc (time, y, x, lsm_time) float32 nan nan nan ... 88.93 88.53
dmc (time, y, x, lsm_time) float32 nan nan nan ... 6.511 7.908 8.45
dc (time, y, x, lsm_time) float32 nan nan nan ... 406.2 428.5
isi (time, y, x, lsm_time) float32 nan nan nan ... 3.872 3.852
bui (time, y, x, lsm_time) float32 nan nan nan ... 15.08 16.11
fwi (time, y, x, lsm_time) float32 nan nan nan ... 5.303 5.486
Exporting the dataarray raises the error TooManyDimensions:
ds.fwi.sel(time="07-14-2021").rio.to_raster("file_out.tif")
raise TooManyDimensions(
rioxarray.exceptions.TooManyDimensions: Only 2D and 3D data arrays supported. Data variable: fwi
I already dropped the dimension lsm_time in a previous step, when I masked the dataset ds with a land sea mask lsm (single date), and had to duplicate/add the time dimension of the dataset ds. So maybe this is an error resulting from this data handling.. However, I could figure out how to mask otherwise.
lsm
Dimensions: (x: 58, y: 28, time: 1)
Coordinates:
* x (x) float64 -7.64 -7.54 -7.44 -7.34 ... -2.24 -2.14 -2.04 -1.94
* y (y) float64 36.01 36.11 36.21 36.31 ... 38.41 38.51 38.61 38.71
* time (time) datetime64[ns] 2013-11-29
Data variables:
spatial_ref int32 0
lsm (time, y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 0.9996
lsm = lsm.expand_dims({"new_time" : ds.time})
lsm = lsm.rename({"time":"lsm_time"}).rename({"new_time" : "time"}).drop("lsm_time") #issue here: drop_dims removes variable..
ds = ds.where(lsm>=threshold)
So I already applied .drop("lsm_time")
But, it is still
print(ds.fwi.sel(time="07-14-2021").dims)
> ('time', 'y', 'x', 'lsm_time')
When trying .drop_dims, it removes all data variables.
ds.drop_dims('lsm_time')
<xarray.Dataset>
Dimensions: (time: 2436, y: 28, x: 58)
Coordinates:
* time (time) datetime64[ns] 2015-05-02T12:00:00 ... 2021-12-31T12:...
step <U8 '12:00:00'
surface float64 0.0
* y (y) float64 36.01 36.11 36.21 36.31 ... 38.41 38.51 38.61 38.71
* x (x) float64 -7.64 -7.54 -7.44 -7.34 ... -2.24 -2.14 -2.04 -1.94
spatial_ref int32 0
Data variables:
*empty*
What am I missing or what did I do wrong? Thanks for any help!
Dimensions with size 1 can be removed using the .squeeze method.
Conversely, you can add a dimension of size 1 using .expand_dims
import xarray as xr
x = xr.tutorial.load_dataset("rasm")
y = x.isel(time=slice(0,1))
y.dims
# Frozen({'time': 1, 'y': 205, 'x': 275})
y.squeeze().dims
# Frozen({'y': 205, 'x': 275})
y.squeeze().expand_dims("newdim").dims
# Frozen({'y': 205, 'x': 275, 'newdim': 1})