I have two xarray’s datasets as follows:

First dataset:

```
<xarray.Dataset>
Dimensions: (cluster_labels: 300, time: 8784)
Coordinates:
* time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00
* cluster_labels (cluster_labels) int64 0 1 2 3 4 ... 295 296 297 298 299
reference_time datetime64[ns] ...
Data variables: (12/17)
t (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
u (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
v (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
q (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
p (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
precip_lapse_rate (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
... ...
cse (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
LW (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
SW_diffuse (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
cos_illumination (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
SW_direct (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.ndarray>
SW (cluster_labels, time) float64 dask.array<chunksize=(10, 8784), meta=np.n
```

darray>

Second dataset:

```
<xarray.Dataset>
Dimensions: (y: 530, x: 855)
Coordinates:
* y (y) float64 31.62 31.61 31.6 31.6 ... 27.23 27.22 27.21
* x (x) float64 49.85 49.85 49.86 49.87 ... 56.95 56.95 56.96
Data variables:
elevation (y, x) int16 ...
slope (y, x) float64 ...
aspect (y, x) float64 ...
aspect_cos (y, x) float64 ...
aspect_sin (y, x) float64 ...
svf (y, x) float64 ...
cluster_labels (y, x) int32 ...
```

I want to add `x`

and `y`

coordinates from the second dataset to the first dataset, for do this, there is a variable name `cluster_labels`

in the second dataset and also `cluster_labels`

as a coordinate in the first dataset, so I used it by below code:

```
first_dataset.sel(cluster_labels=second_dataset.cluster_labels)
```

This line did successfully and I can create a new dataset same as follow:

```
<xarray.Dataset>
Dimensions: (y: 530, x: 855, time: 8784)
Coordinates:
* time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00
cluster_labels (y, x) int64 96 235 130 130 104 176 ... 34 34 16 266 266
reference_time datetime64[ns] ...
* y (y) float64 31.62 31.61 31.6 31.6 ... 27.23 27.22 27.21
* x (x) float64 49.85 49.85 49.86 49.87 ... 56.95 56.95 56.96
Data variables: (12/17)
t (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
u (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
v (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
q (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
p (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
precip_lapse_rate (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
... ...
cse (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
LW (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
SW_diffuse (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
cos_illumination (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
SW_direct (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
SW (y, x, time) float64 dask.array<chunksize=(530, 855, 8784), meta=np.ndarray>
```

But when I want to plot 2D based on x, y in a single time:

```
df.sel(time='2000-01-01T00:00:00.000000000')['t']
```

it used all of the ram and then crashed, How can I plot any time of the merged dataset?