issues

Is your feature request related to a problem?

I ran into the following issue when trying to access a remote dataset. Here is the concrete example that reproduces the error. ```python from pydap.client import open_url from pydap.cas.urs import setup_session import xarray as xr import numpy as np

username = "UsernameHere" password= "PasswordHere" filename = 'Daymet_Daily_V4R1.daymet_v4_daily_na_tmax_2010.nc' hyrax_url = 'https://opendap.earthdata.nasa.gov/collections/C2532426483-ORNL_CLOUD/granules/' url1 = hyrax_url + filename session = setup_session(username, password, check_url=hyrax_url)

ds = xr.open_dataset(url1, engine="pydap", session=session) The last line returns an error:python ValueError: dimensions ('time',) must have the same length as the number of data dimensions, ndim=2 ```

The issue involves the variable time_bnds. I know that because this works: python DS = [] for var in [var for var in tmax_ds.keys() if var not in ['time_bnds']]: DS.append(xr.open_dataset(url1+'?'+var, engine='pydap', session=session)) ds = xr.merge(DS) I also tried passing decode_times=False but continue having the error. The above for loop works but I think unnecessarily too slow (~30 secs).

I tried all this with the newer versions of xarray.__version__ = [2024.2, 2024.3].

Describe the solution you'd like

I think it would be nice to be able to drop the variable I know I don't want. So something like this:

python ds = xr.open_dataset(url1, drop_variables='time_bnds', engine="pydap", session=session) and only create a xarray.dataset with the variables I want. However when I do that <ins>I continue to have the same error as before</ins>, which means that drop_variables is being applied after creating the xarray.dataset.

Describe alternatives you've considered

This is potentially a backend issue with pydap - which does not take a drop_variables option, but since dropping a variable is a one-liner in pydap and takes less than 1milisec, it makes it an desirable feature.

For example I can easily open the dataset and drop the variable with pydap as described below

```python $ dataset = open_url(url1, session=session) # this works $ dataset[tuple([var for var in dataset.keys() if var not in ['time_bnds']])] # this takes < 1ms

<DatasetType with children 'y', 'lon', 'lat', 'time', 'x', 'tmax', 'lambert_conformal_conic', 'yearday'> ```

It looks like it would be a easy implementation on the backend, but at the same time I took a look at pydap_.py

https://github.com/pydata/xarray/blob/b80260781ee19bddee01ef09ac0da31ec12c5152/xarray/backends/pydap_.py#L129-L130

and I feel like it could also be implemented at the xarray level by allowing drop_variables which is already an argument in xarray.open_dataset, to be passed to the PydapDataStore (I guess in both scenarios drop_variables would be passed).

Any thoughts or suggestions? I can certainly lead on this effort as I already will be working on enabling the dap4 implementation within pydap.

This is related to a previous issue (#3668), although the actual problem on that issue is a bit more technically involved and is related with clusters... I decided to open this related issue so that the discussion in #3668 remains visible for other users.

This issue is about the need to implement code that allows to read multiple zarr files. This can be particularly helpful when reading data through an Intake Catalog entry (Intake-xarray plugin), which can allow for a compact way to introduce parallelism when working with multiple zarr files. There are two steps for this, one to work at the xarray level (write a fuction that does that) and then write an option on the Intake-xarray plugin that can use such xarray functionality.

I am more than willing to work on such problem, if nobody's already working on this.

This is associated with #3995 and somewhat mentioned in #3668. This is, emulating xarray.open_mfdataset, it allows xarray to read multiple zarr files from a glob or a list of files pointing to a zarr store. It follows the code for xarray.open_mfdataset and the description to the function is the same, as they do the same thing but for respective files type.