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()``
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
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309100522,MDU6SXNzdWUzMDkxMDA1MjI=,2018,MemoryError when using save_mfdataset(),1117224,closed,0,,,1,2018-03-27T19:22:28Z,2020-03-28T07:51:17Z,2020-03-28T07:51:17Z,NONE,,,,"#### 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')*(25*25)/(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](https://stackoverflow.com/questions/49501206/killed-trying-to-use-save-mfdataset?noredirect=1#comment86015596_49501206)
#### Expected Output
12 netcdf files (grouped by the ensemble dim).
#### Output of ``xr.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.12
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
xarray: 0.10.2
pandas: 0.22.0
numpy: 1.14.1
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: 0.5.0
h5py: 2.7.1
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.1
distributed: 1.21.1
matplotlib: 2.2.2
cartopy: None
seaborn: 0.8.1
setuptools: 38.5.1
pip: 9.0.1
conda: None
pytest: None
IPython: 6.2.1
sphinx: None
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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
186326698,MDExOlB1bGxSZXF1ZXN0OTE2Mzk0OTY=,1070,Feature/rasterio,1117224,closed,0,,,11,2016-10-31T16:14:55Z,2017-05-22T08:47:40Z,2017-05-22T08:47:40Z,NONE,,0,pydata/xarray/pulls/1070,"@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 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1070/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
170688064,MDExOlB1bGxSZXF1ZXN0ODA5ODgxNzA=,961,Update time-series.rst,1117224,closed,0,,,3,2016-08-11T16:26:58Z,2017-04-03T05:31:06Z,2017-04-03T05:31:06Z,NONE,,0,pydata/xarray/pulls/961,"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
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/961/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
171504099,MDU6SXNzdWUxNzE1MDQwOTk=,970,Multiple preprocessing functions in open_mfdataset?,1117224,closed,0,,,3,2016-08-16T20:01:22Z,2016-08-17T07:01:02Z,2016-08-16T21:46:43Z,NONE,,,,"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])
```
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