Is your feature request related to a problem?

I would like to be able to expand the dimensions of a dataset, but most of the time, I only want to expand the datasets of a few key variables.

It would be nice if there were some kind of filter mechanism.

Describe the solution you'd like

```python import xarray as xr dataset = xr.Dataset(data_vars={'foo': 1, 'bar': 2}) dataset.expand_dims("zar", include_variables=["foo"])

Only foo is expanded, bar is left alone.

```

Describe alternatives you've considered

Writing my own function. I'll probably do this.

Subclassing. Too confusing and easy to "diverge" from you all when you do decide to implment this.

Additional context

For large datasets, you likely just want some key parameters expanded, and not all parameters expanded.

xarray version: 2022.10.0

What is your issue?

The new nanosecond warning doesn't really point anybody to where they should change their code.

Nor does it really tell them how to fix it.

import xarray as xr import numpy as np xr.DataArray(np.zeros(1, dtype='datetime64[us]')) yields

xarray/core/variable.py:194: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values.

https://github.com/pydata/xarray/blob/f32d354e295c05fb5c5ece7862f77f19d82d5894/xarray/core/variable.py#L194

I think at the very least, the stacklevel should be specified when calling the warn function.

It isn't really pretty, but I've been passing a parameter when I expect to pass up a warning to the end user: eg. https://github.com/vispy/vispy/pull/2405

However, others have not liked that approach.

Is your feature request related to a problem?

I often store a few scalars or arrays in xarray containers.

However, when I want to optionally address their data the code I have to run ```python import xarray as xr dataset = xr.Dataset()

my_variable = dataset.get('my_variable', None) if my_variable is not None: my_variable = my_variable.data else: my_variable = np.asarray(1.0) # the default value I actually want ```

Describe the solution you'd like

```python import xarray as xr dataset = xr.Dataset()

my_variable = dataset.get_data('my_variable', np.asarray(1.0)) ```

Describe alternatives you've considered

No response

Additional context

Thank you!

Is your feature request related to a problem?

I understand that this is a double edge sword. but we were relying on BACKEND_ENTRYPOINTS being a dictionary to a class and that broke in

https://github.com/pydata/xarray/pull/7523

Describe the solution you'd like

Some agreed upon way that we could create a new backend. This would allow users to provide more custom parameters to file creation attributes and other options that are currently not exposed via xarray.

I've used this to overwrite some parameters like netcdf global variables.

I've also used this to add alignment_threshold and alignment_interval to h5netcdf.

I did it through a custom backend because it felt like a contentious feature at the time. (I really do think it helps performance).

Describe alternatives you've considered

A deprecation cycle in the future???

Maybe this could have been acheived with the definition of RELOADABLE_BACKEND_ENTRYPOINTS and leaving the BACKEND_ENTRYPOINTS unchanged in signature.

Additional context

We used this to define the alignment within a file. netcdf4 exposed this as a global variable so we have to somewhat hack around it just before creation time.

I mean, you can probably say:

"Doing this is too complicated, we don't want to give any guarantees on this front."

I would agree with you.....

In the search for the perfect data storage mechanism, I find myself needing to store some of the images I am generating the metadata seperately. It is really useful for me to serialize just the coordinates of my DataArray.

My serialization method of choice is json since it allows me to read the metadata with just a text editor. For that, having the coordinates as a self contained dictionary is really important.

Currently, I convert just the coordinates to a dataset, and serialize that. The code looks something like this:

```python import xarray as xr import numpy as np

Setup an array with coordinates

n = np.zeros(3) coords={'x': np.arange(3)} m = xr.DataArray(n, dims=['x'], coords=coords)

coords_dataset_dict = m.coords.to_dataset().to_dict() coords_dict = coords_dataset_dict['coords']

Read/Write dictionary to JSON file

This works, but I'm essentially creating an emtpy dataset for it

coords_set = xr.Dataset.from_dict(coords_dataset_dict) coords2 = coords_set.coords # so many coords :D m2 = xr.DataArray(np.zeros(shape=m.shape), dims=m.dims, coords=coords2) ```

Would encapsulating this functionality in the Coordinates class be accepted as a PR?

It would add 2 functions that would look like: ```python def to_dict(self): # offload the heavy lifting to the Dataset class return self.to_dataset().to_dict()['coords']

def from_dict(self, d): # Offload the heavy lifting again to the Dataset class d_dataset = {'dims': [], 'attrs': [], 'coords': d} return Dataset.from_dict(d_dataset).coords ```

Is your feature request related to a problem? Please describe. Feature request: It seems that it should be possible to append to netcdf4 stores along the unlimited dimensions. Is there an example of this?

Describe the solution you'd like I would like the following code to be valid: ```python from xarray.tests.test_dataset import create_append_test_data ds, ds_to_append, ds_with_new_var = create_append_test_data()

filename = 'test_dataset.nc'

Choose any one of

engine : {'netcdf4', 'scipy', 'h5netcdf'}

engine = 'netcdf4' ds.to_netcdf(filename, mode='w', unlimited_dims=['time'], engine=engine) ds_to_append.to_netcdf(filename, mode='a', unlimited_dims=['time'], engine=engine) ```

Describe alternatives you've considered I guess you could use zarr, but the fact that it creates multiple files is a problem.

Additional context xarray version: 0.16.0

Code Sample

```python import numpy as np import xarray as xr

a = xr.DataArray(np.zeros((3, 3)), dims=('y', 'x')) a.attrs['my_array'] = np.arange(6, dtype='uint8').reshape(2, 3)

a.to_netcdf('a.nc') b = xr.open_dataarray('a.nc') b.load() assert np.all(b == a) print('all arrays equal')

assert b.dtype == a.dtype print('dtypes equal')

print(a.my_array.shape) print(b.my_array.shape) assert a.my_array.shape == b.my_array.shape ```

Problem description

I have some metadata that is in the form of numpy arrays. I would think that it should round trip with netcdf.

Expected Output

equal shapes inside the metadata

Output of xr.show_versions()

Code Sample, a copy-pastable example if possible

```python

%%

import xarray as xa import numpy as np

n = np.zeros((3, 2))

data = xa.DataArray(n, dims=['y', 'x'], coords={'y':range(3), 'x':range(2)})

data = data.assign_coords(z=xa.DataArray(np.arange(6).reshape((3, 2)), dims=['y', 'x']))

print('Original Data') print('=============') print(data)

%%

my_slice = data[0, 1] print("Sliced data") print("===========") print("z coordinate remembers it's own x value") print(f'x = {my_slice.z.x}')

%%

expanded_slice = data[0, 1].expand_dims('x') print("expanded slice") print("==============") print("forgot that 'z' had 'x' coordinates") print("but remembered it had a 'y' coordinate") print(f"z = {expanded_slice.z}") print(expanded_slice.z.x) ```

Output: Original Data ============= <xarray.DataArray (y: 3, x: 2)> array([[0., 0.], [0., 0.], [0., 0.]]) Coordinates: * y (y) int32 0 1 2 * x (x) int32 0 1 z (y, x) int32 0 1 2 3 4 5 Sliced data =========== z coordinate remembers it's own x value x = <xarray.DataArray 'x' ()> array(1) Coordinates: y int32 0 x int32 1 z int32 1 expanded slice ============== forgot that 'z' had 'x' coordinates but remembered it had a 'y' coordinate z = <xarray.DataArray 'z' ()> array(1) Coordinates: y int32 0 z int32 1 AttributeError: 'DataArray' object has no attribute 'x'

Problem description

The coordinate used to have an explicit dimension. When we expanded the dimension, that information should not be erased. Note that information about other coordinates are maintained.

The challenge

The coordinates probably have fewer dimensions than the original data. I'm not sure about xarray's model, but a few challenges come to mind: 1. is the relative order of dimensions maintained between data in the same dataset/dataarray? 2. Can coordinates have MORE dimensions than the array itself?

The answer to these two questions might make or break If not, then this becomes a very difficult problem to solve since we don't know where to insert this new dimension in the coordinate array.

Output of xr.show_versions()