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/1985#issuecomment-373694632,https://api.github.com/repos/pydata/xarray/issues/1985,373694632,MDEyOklzc3VlQ29tbWVudDM3MzY5NDYzMg==,22245117,2018-03-16T12:09:50Z,2018-03-16T12:09:50Z,CONTRIBUTOR,"Alright, I found the problem.
I'm loading several variables from different files.
All the variables have 1464 snapshots. However, one of the 3D variables has just one snapshot at a different time (I found a bag in my bash script to re-organize the raw data). When I load my dataset using .open_mfdataset, the time dimension has an extra snapshot (length is 1465). However, xarray doesn't like it and when I run functions such as to_netcdf it takes forever (no error).
Thanks @fujiisoup for the help!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,304624171
https://github.com/pydata/xarray/issues/1985#issuecomment-372570107,https://api.github.com/repos/pydata/xarray/issues/1985,372570107,MDEyOklzc3VlQ29tbWVudDM3MjU3MDEwNw==,22245117,2018-03-13T07:21:10Z,2018-03-13T07:21:10Z,CONTRIBUTOR,"I forgot to mention that I'm getting this warning:
/home/idies/anaconda3/lib/python3.5/site-packages/dask/core.py:306: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
elif type_arg is type(key) and arg == key:
However, I don't think it is relevant since I get the same warning when I'm able to run .to_netcdf() on the 3D variable.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,304624171
https://github.com/pydata/xarray/issues/1985#issuecomment-372566304,https://api.github.com/repos/pydata/xarray/issues/1985,372566304,MDEyOklzc3VlQ29tbWVudDM3MjU2NjMwNA==,22245117,2018-03-13T07:01:51Z,2018-03-13T07:01:51Z,CONTRIBUTOR,"The problem occurs when I run the very last line, which is to_netcdf().
Right before, the dataset looks like this:
```python
<xarray.Dataset>
Dimensions: (X: 10, Y: 25, Z: 1, time: 2)
Coordinates:
* time (time) datetime64[ns] 2007-11-15 2007-11-16
* Z (Z) float64 1.0
* X (X) float64 -29.94 -29.89 -29.85 -29.81 -29.76 -29.72 -29.67 ...
* Y (Y) float64 65.01 65.03 65.05 65.07 65.09 65.11 65.13 65.15 ...
Data variables:
drF (time, Z) float64 2.0 2.0
dxF (time, Y, X) float64 2.066e+03 2.066e+03 2.066e+03 2.066e+03 ...
dyF (time, Y, X) float64 2.123e+03 2.123e+03 2.123e+03 2.123e+03 ...
rA (time, Y, X) float64 4.386e+06 4.386e+06 4.386e+06 4.386e+06 ...
fCori (time, Y, X) float64 0.0001322 0.0001322 0.0001322 0.0001322 ...
R_low (time, Y, X) float64 -2.001e+03 -1.989e+03 -1.973e+03 ...
Ro_surf (time, Y, X) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Depth (time, Y, X) float64 2.001e+03 1.989e+03 1.973e+03 1.963e+03 ...
HFacC (time, Z, Y, X) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...
Temp (time, Z, Y, X) float64 dask.array<shape=(2, 1, 25, 10), chunksize=(1, 1, 25, 10)>
```
This is a dask array, right?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,304624171
https://github.com/pydata/xarray/issues/1985#issuecomment-372558850,https://api.github.com/repos/pydata/xarray/issues/1985,372558850,MDEyOklzc3VlQ29tbWVudDM3MjU1ODg1MA==,22245117,2018-03-13T06:19:47Z,2018-03-13T06:23:00Z,CONTRIBUTOR,"I have the same issue if I don't copy the dataset.
Here are the coordinates of my dataset:
```python
<xarray.Dataset>
Dimensions: (X: 960, Xp1: 961, Y: 880, Yp1: 881, Z: 216, Zl: 216, Zp1: 217, Zu: 216, time: 1465)
Coordinates:
* Z (Z) float64 1.0 3.5 7.0 11.5 17.0 23.5 31.0 39.5 49.0 59.5 ...
* Zp1 (Zp1) float64 0.0 2.0 5.0 9.0 14.0 20.0 27.0 35.0 44.0 54.0 ...
* Zu (Zu) float64 2.0 5.0 9.0 14.0 20.0 27.0 35.0 44.0 54.0 65.0 ...
* Zl (Zl) float64 0.0 2.0 5.0 9.0 14.0 20.0 27.0 35.0 44.0 54.0 ...
* X (X) float64 -46.92 -46.83 -46.74 -46.65 -46.57 -46.48 -46.4 ...
* Y (Y) float64 56.81 56.85 56.89 56.93 56.96 57.0 57.04 57.08 ...
* Xp1 (Xp1) float64 -46.96 -46.87 -46.78 -46.7 -46.61 -46.53 ...
* Yp1 (Yp1) float64 56.79 56.83 56.87 56.91 56.95 56.98 57.02 ...
* time (time) datetime64[ns] 2007-09-01 2007-09-01T06:00:00 ...
```
I don't think the horizontal coordinates are the problem because it works fine when I use the same function on 3D variables.
I'm also attaching the function that I use to open the dataset, just in case is helpful:
```python
def load_dataset():
""""""
Load the whole dataset
""""""
# Import grid and fields separately, then merge
gridpath = '/home/idies/workspace/OceanCirculation/exp_ASR/grid_glued.nc'
fldspath = '/home/idies/workspace/OceanCirculation/exp_ASR/result_*/output_glued/*.*_glued.nc'
gridset = xr.open_dataset(gridpath,
drop_variables = ['XU','YU','XV','YV','RC','RF','RU','RL'])
fldsset = xr.open_mfdataset(fldspath,
concat_dim = 'T',
drop_variables = ['diag_levels','iter'])
ds = xr.merge([gridset, fldsset])
# Adjust dimensions creating conflicts
ds = ds.rename({'Z': 'Ztmp'})
ds = ds.rename({'T': 'time', 'Ztmp': 'Z', 'Zmd000216': 'Z'})
ds = ds.squeeze('Zd000001')
for dim in ['Z','Zp1', 'Zu','Zl']:
ds[dim].values = np.fabs(ds[dim].values)
ds[dim].attrs.update({'positive': 'down'})
# Create horizontal vectors (remove zeros due to exch2)
ds['X'].values = ds.XC.where((ds.XC!=0) & (ds.YC!=0)).mean(dim='Y', skipna=True)
ds['Xp1'].values = ds.XG.where((ds.XG!=0) & (ds.YG!=0)).mean(dim='Yp1', skipna=True)
ds['Y'].values = ds.YC.where((ds.XC!=0) & (ds.YC!=0)).mean(dim='X', skipna=True)
ds['Yp1'].values = ds.YG.where((ds.XG!=0) & (ds.YG!=0)).mean(dim='Xp1', skipna=True)
ds = ds.drop(['XC','YC','XG','YG'])
# Create xgcm grid
ds['Z'].attrs.update({'axis': 'Z'})
ds['X'].attrs.update({'axis': 'X'})
ds['Y'].attrs.update({'axis': 'Y'})
for dim in ['Zp1','Zu','Zl','Xp1','Yp1']:
if min(ds[dim].values)<min(ds[dim[0]].values):
ds[dim].attrs.update({'axis': dim[0], 'c_grid_axis_shift': -0.5})
elif min(ds[dim].values)>min(ds[dim[0]].values):
ds[dim].attrs.update({'axis': dim[0], 'c_grid_axis_shift': +0.5})
grid = xgcm.Grid(ds,periodic=False)
return ds, grid
```
I think somewhere I trigger the loading of the whole dataset. Otherwise, I don't understand why it works when I open just one month instead of the whole year.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,304624171