html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1773#issuecomment-350666998,https://api.github.com/repos/pydata/xarray/issues/1773,350666998,MDEyOklzc3VlQ29tbWVudDM1MDY2Njk5OA==,10050469,2017-12-11T09:23:00Z,2017-12-11T09:23:00Z,MEMBER,"Is this what you want?

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

DomainSpaceTimeSize = 5 # using cartesian 4D
SpaceTimeDensity = [100, 5] # 100 divisions in space 5 in time

x_coord = np.linspace(-DomainSpaceTimeSize, +DomainSpaceTimeSize, SpaceTimeDensity[0])
y_coord = np.linspace(-DomainSpaceTimeSize, +DomainSpaceTimeSize, SpaceTimeDensity[0])
z_coord = np.linspace(-DomainSpaceTimeSize, +DomainSpaceTimeSize, SpaceTimeDensity[0])
t_coord = np.linspace(0, +DomainSpaceTimeSize, SpaceTimeDensity[1])

scale_func = lambda x, y, z, t: np.cos(1*x+2*y+3*z-4*t)

data = scale_func(*np.meshgrid(x_coord, y_coord, z_coord, t_coord))

da = xr.DataArray(data, dims=['x', 'y', 'z', 't'], 
                  coords={'x':x_coord, 'y':y_coord, 'z':z_coord, 't':t_coord})

da.sel(x=0.0, y=1.0, z=0.0, t=3.0, method='nearest')
<xarray.DataArray ()>
array(-0.8039436986070311)
Coordinates:
    x        float64 0.05051
    z        float64 0.05051
    t        float64 2.5
    y        float64 0.9596
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,280899335