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

Create test data

ds = xr.Dataset() ds.coords['lon'] = np.arange(-120,-60) ds.coords['lat'] = np.arange(30,50) ds.coords['time'] = pd.date_range('2018-01-01','2018-01-30') ds['AirTemp'] = xr.DataArray(np.ones((ds.lat.size,ds.lon.size,ds.time.size)), dims=['lat','lon','time'])

target_lat = [36.83] target_lon = [-110] target_time = [np.datetime64('2019-06-01')]

Nearest pulls a date too far away

ds.sel(lat=target_lat, lon=target_lon, time=target_time, method='nearest')

Adding tolerance for lat long, but also applied to time

ds.sel(lat=target_lat, lon=target_lon, time=target_time, method='nearest', tolerance=0.5)

Ideally tolerance could accept a dictionary but currently fails

ds.sel(lat=target_lat, lon=target_lon, time=target_time, method='nearest', tolerance={'lat':0.5, 'lon':0.5, 'time':np.timedelta64(1,'D')})

```

Expected Output

A dataset with nearest values to tolerances on each dim.

Problem Description

I would like to add the ability of tolerance to accept a dictionary for multiple tolerance values for different dimensions. Before I try implementing it, I wanted to 1) check it doesn't already exist or someone isn't working on it, and 2) get suggestions for how to proceed.

Output of xr.show_versions()

Code Sample, a copy-pastable example if possible

```python import xarray as xr import dask

Dummy data that on disk is about ~200GB

da = xr.DataArray(dask.array.random.normal(0, 1, size=(12,408,1367,304,448), chunks=(1, 1, 1, 304, 448)), dims=('ensemble', 'init_time', 'fore_time', 'x', 'y'))

Perform some calculation on the dask data

da_sum = da.sum(dim='x').sum(dim='y')(2525)/(10**6)

Write to multiple files

c_e, datasets = zip(*da_sum.to_dataset(name='sic').groupby('ensemble')) paths = ['file_%s.nc' % e for e in c_e] xr.save_mfdataset(datasets, paths)

```

Problem description

Results in a MemoryError, when dask should handle writing this OOM DataArray to multiple within-memory-sized netcdf files. Related SO post here

Expected Output

12 netcdf files (grouped by the ensemble dim).

Output of xr.show_versions()

I love xarray for analysis but getting my data into xarray often takes a lot more time than I think it should. I am a hydrologist and very often hydro data is poorly stored/formatted, which means I need to do multiple merge/conact/combine_first operations etc. to get to a nice xarray dataset format. I think having more examples for importing different types of data would be helpful (for me and possibly others), instead of my current approach, which often entails trial and error.

I can start off by providing an example of importing funky hydrology data that hopefully would be general enough for others to use. Maybe we can compile other examples as well. With the end goal of adding to the readthedocs.

@klapo @jhamman

@jhamman started a backend for RasterIO that I have been working on. There are two issues I am stuck on that I could use some help:

1) Lat/long coords are not being decoded correctly (missing from output dataset). Lat/lon projection are correctly calculated and added here (https://github.com/NicWayand/xray/blob/feature/rasterio/xarray/backends/rasterio_.py#L117). But, it appears (with my limited knowledge of xarray) that the lat/long coords contained within obj are lost at this line (https://github.com/NicWayand/xray/blob/feature/rasterio/xarray/conventions.py#L930).

2) Lazy-loading needs to be enabled. How can I setup/test this? Are there examples from other backends I could follow?

790

Thought it would be helpful to users to know that timezones are not handled here, rather than googling and finding this: https://github.com/pydata/xarray/issues/552

I would like to have multiple functions applied during a open_mfdataset call.

Using one works great:

Python ds = xr.open_mfdataset(files,concat_dim='time',engine='pynio', preprocess=lambda x: x.load())

Does the current behavior include multiple calls? (apologizes if this is defined somewhere, I couldn't find any multiple calls examples)

Something like:

Python ds = xr.open_mfdataset(files,concat_dim='time',engine='pynio', preprocess=[lambda x: x.load(),lambda y: y['time']=100])