Drop dimension for exporting dataarray to geotiff not working

Question

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!

ds.squeeze(dim='lsm_time') solved it for me - I'm new to posting on the forum, @Val feel free to post the solution as answer :) — jstew
– jstew, Commented Feb 11, 2022 at 16:47

Val · Accepted Answer · 2022-02-14 18:35:36Z

1

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})

answered Feb 14, 2022 at 18:35

Val

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