I am working with a multi-dimensional object (more than just time,lat,lon which is what i am used to).

It is a forecast produced from a weather model and so has the complexity of having an initialisation_date as well as a forecast_horizon.

I have a valid_time coordinate which defines the true time that the forecast refers to.

I want to be able to select these valid_time objects from my xr.Dataset object but I don't know how to select from a coordinate that is not also a dimension.

MCVE Code Sample

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

initialisation_date = pd.date_range('2018-01-01','2018-12-31',freq='M') number = [i for i in range(0, 51)] # corresponds to model number (ensemble of model runs) lat = np.linspace(-5.175003, -5.202, 36) lon = np.linspace(33.5, 42.25, 45) forecast_horizon = np.array( [ 2419200000000000, 2592000000000000, 2678400000000000, 5097600000000000, 5270400000000000, 5356800000000000, 7689600000000000, 7776000000000000, 7862400000000000, 7948800000000000, 10368000000000000, 10454400000000000, 10540800000000000, 10627200000000000, 12960000000000000, 13046400000000000, 13219200000000000, 15638400000000000, 15724800000000000, 15811200000000000, 15897600000000000, 18316800000000000, 18489600000000000, 18576000000000000 ], dtype='timedelta64[ns]' ) valid_time = initialisation_date[:, np.newaxis] + forecast_horizon precip = np.random.normal( 0, 1, size=(len(number), len(initialisation_date), len(forecast_horizon), len(lat), len(lon)) )

ds = xr.Dataset( {'precip': (['number', 'initialisation_date', 'forecast_horizon', 'lat', 'lon'], precip)}, coords={ 'lon': lon, 'lat': lat, 'initialisation_date': initialisation_date, 'number': number, 'forecast_horizon': forecast_horizon, 'valid_time': (['initialisation_date', 'step'], valid_time) } )

Out[]: <xarray.Dataset> Dimensions: (forecast_horizon: 24, initialisation_date: 12, lat: 36, lon: 45, number: 51, step: 24) Coordinates: * lon (lon) float64 33.5 33.7 33.9 34.1 ... 41.85 42.05 42.25 * lat (lat) float64 -5.175 -5.176 -5.177 ... -5.201 -5.202 * initialisation_date (initialisation_date) datetime64[ns] 2018-01-31 ... 2018-12-31 * number (number) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50 * forecast_horizon (forecast_horizon) timedelta64[ns] 28 days ... 215 days valid_time (initialisation_date, step) datetime64[ns] 2018-02-28 ... 2019-08-03 Dimensions without coordinates: step Data variables: precip (number, initialisation_date, forecast_horizon, lat, lon) float64 1.373 ... 1.138 ```

I try to select all the March, April, May months from the valid_time coordinate using .sel[]

```python

select March April May from the valid_time

ds.sel(valid_time=np.isin(ds['valid_time.month'], [3,4,5])) ```

Error Message: ``` <ipython-input-151-132375b92854> in <module> ----> 1 ds.sel(valid_time=np.isin(ds['valid_time.month'], [3,4,5]))

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/dataset.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs) 1729 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, 'sel') 1730 pos_indexers, new_indexes = remap_label_indexers( -> 1731 self, indexers=indexers, method=method, tolerance=tolerance) 1732 result = self.isel(indexers=pos_indexers, drop=drop) 1733 return result._overwrite_indexes(new_indexes)

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/coordinates.py in remap_label_indexers(obj, indexers, method, tolerance, **indexers_kwargs) 315 316 pos_indexers, new_indexes = indexing.remap_label_indexers( --> 317 obj, v_indexers, method=method, tolerance=tolerance 318 ) 319 # attach indexer's coordinate to pos_indexers

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in remap_label_indexers(data_obj, indexers, method, tolerance) 237 new_indexes = {} 238 --> 239 dim_indexers = get_dim_indexers(data_obj, indexers) 240 for dim, label in dim_indexers.items(): 241 try:

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in get_dim_indexers(data_obj, indexers) 205 if invalid: 206 raise ValueError("dimensions or multi-index levels %r do not exist" --> 207 % invalid) 208 209 level_indexers = defaultdict(dict)

ValueError: dimensions or multi-index levels ['valid_time'] do not exist ```

I have also tried creating a new variable of the month object from valid_time and copying it to the same shape as the precip variable ```python

create months array of shape (51, 12, 24, 36, 45)

months = ds['valid_time.month'].values

m = np.repeat(months[np.newaxis, :, :], 51, axis=0) m = np.repeat(m[:, :, :, np.newaxis], 36, axis=3) m = np.repeat(m[:, :, :, :, np.newaxis], 45, axis=4) assert (m[0, :, :, 0, 0] == m[50, :, :, 4, 2]).all(), f"The matrices have not been copied to the correct dimensions"

ds['month'] = (['number', 'initialisation_date', 'forecast_horizon', 'lat', 'lon'], m) ds.where(np.isin(ds['month'], [3,4,5])).dropna(how='all', dim='forecast_horizon') ```

Problem Description

I want to be able to select all of the forecasts that correspond to the valid_time I select.

I think that an issue might be that the result from that query will be an irregular grid, because we will have different initialisation_date and forecast_horizon combinations that match the query. Is that the case

I want to select from a coordinate object that isn't a dimension. How can I go about doing this?

Expected Output

For example. I want to return the lat-lon arrays for the valid_time = 2018-04-01.

The returning combinations should be 51 realisations of a (36 x 45) (lat, lon) grid of forecast values.

So 3 possible forecasts matching this criteria: - initialisation_date=2018-01-01 at a forecast_horizon=3 months - initialisation_date=2018-02-01 at a forecast_horizon=2 months - initialisation_date=2018-03-01 at a forecast_horizon=1 months

Output of xr.show_versions()

# Paste the output here xr.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.7.0 | packaged by conda-forge | (default, Nov 12 2018, 12:34:36) [Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 18.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.4 scipy: 1.3.0 netCDF4: 1.5.1.2 pydap: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: 1.0.17 cfgrib: 0.9.7 iris: None bottleneck: 1.2.1 dask: 1.2.2 distributed: 1.28.1 matplotlib: 3.1.0 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.1 pip: 19.1 conda: None pytest: 4.5.0 IPython: 7.1.1 sphinx: 2.0.1

Thanks so much for your package! Working with these multidimensional datasets would be a pain without xarray. I really appreciate the help that I have got from github / stackExchange by the community in the past. Thanks team!