id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
481761508,MDU6SXNzdWU0ODE3NjE1MDg=,3223,Feature request for multiple tolerance values when using nearest method and sel(),1117224,open,0,,,4,2019-08-16T19:53:31Z,2024-04-29T23:21:04Z,,NONE,,,,"

```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()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 | packaged by conda-forge | (default, Feb 20 2019, 02:51:38) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.9.184-0.1.ac.235.83.329.metal1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.11.3
pandas: 0.24.1
numpy: 1.15.4
scipy: 1.2.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: 2.9.0
Nio: 1.5.5
zarr: 2.2.0
cftime: 1.0.3.4
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
cyordereddict: None
dask: 1.1.2
distributed: 1.26.0
matplotlib: 3.0.3
cartopy: 0.17.0
seaborn: 0.9.0
setuptools: 40.8.0
pip: 19.0.3
conda: None
pytest: None
IPython: 7.3.0
sphinx: None

</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3223/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
225536793,MDU6SXNzdWUyMjU1MzY3OTM=,1391,Adding Example/Tutorial of importing data to Xarray (Merge/conact/etc),1117224,open,0,1197350,,11,2017-05-01T21:50:33Z,2019-07-12T19:43:30Z,,NONE,,,,"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 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1391/reactions"", ""total_count"": 7, ""+1"": 7, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue