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 1798441216,I_kwDOAMm_X85rMgkA,7978,Clean up colormap code?,3958036,open,0,,,1,2023-07-11T08:49:36Z,2023-07-11T15:47:52Z,,CONTRIBUTOR,,,,"In fixing some bugs with color bars (https://github.com/pydata/xarray/pull/3601), we had to do some clunky workarounds because of limitations of matplotlib's API for modifying colormaps - this prompted a matplotlib issue https://github.com/matplotlib/matplotlib/issues/16296#issuecomment-1629755861. That issue has now been closed, and apparently the limitations are now fixed, so it should be possible to tidy up some of the colorbar code (at least at some point, once the oldest xarray-supported matplotlib includes the new API). I have no time to look into this myself, but opening this issue to flag the new matplotlib features in case someone is looking at refactoring colorbar or colormap code.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7978/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 666523009,MDU6SXNzdWU2NjY1MjMwMDk=,4276,isel with 0d dask array index fails,3958036,closed,0,,,0,2020-07-27T19:14:22Z,2023-03-15T02:48:01Z,2023-03-15T02:48:01Z,CONTRIBUTOR,,,," **What happened**: If a 0d dask array is passed as an argument to `isel()`, an error occurs because dask arrays do not have a `.item()` method. I came across this when trying to use the result of `da.argmax()` from a dask-backed array to select from the DataArray. **What you expected to happen**: `isel()` returns the value at the index contained in the 0d dask array. **Minimal Complete Verifiable Example**: ```python import dask.array as daskarray import numpy as np import xarray as xr a = daskarray.from_array(np.linspace(0., 1.)) da = xr.DataArray(a, dims=""x"") x_selector = da.argmax(dim=...) da_max = da.isel(x_selector) ``` **Anything else we need to know?**: I think the problem is here https://github.com/pydata/xarray/blob/a198218ddabe557adbb04311b3234ec8d20419e7/xarray/core/variable.py#L546-L548 and `k.values.item()` or `int(k.data)` would fix my issue, but I don't know the reason for using `.item()` in the first place, so I'm not sure if either of these would have some undesirable side-effect. May be related to #2511, but from the code snippet above, I think this is a specific issue of 0d dask arrays rather than a generic dask-indexing issue like #2511. I'd like to fix this because it breaks the nice new features of `argmin()` and `argmax()` if the `DataArray` is dask-backed. **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 | packaged by conda-forge | (default, Jun 1 2020, 18:57:50) [GCC 7.5.0] python-bits: 64 OS: Linux OS-release: 5.4.0-42-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.4 xarray: 0.16.0 pandas: 1.0.5 numpy: 1.18.5 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.19.0 distributed: 2.21.0 matplotlib: 3.2.2 cartopy: None seaborn: None numbagg: None pint: 0.13 setuptools: 49.2.0.post20200712 pip: 20.1.1 conda: 4.8.3 pytest: 5.4.3 IPython: 7.15.0 sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4276/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 698577111,MDU6SXNzdWU2OTg1NzcxMTE=,4417,Inconsistency in whether index is created with new dimension coordinate?,3958036,closed,0,,,6,2020-09-10T22:44:54Z,2022-09-13T07:54:32Z,2022-09-13T07:54:32Z,CONTRIBUTOR,,,,"It seems like `set_coords()` doesn't create an index variable. Is there a reason for this? I was surprised that the following code snippets produce different Datasets (first one has empty `indexes`, second one has `x` in `indexes`), even though both Datasets have a 'dimension coordinate' `x`: (1) ``` import numpy as np import xarray as xr ds = xr.Dataset() ds['a'] = ('x', np.linspace(0,1)) ds['b'] = ('x', np.linspace(3,4)) ds = ds.rename(b='x') ds = ds.set_coords('x') print(ds) print('indexes', ds.indexes) ``` (2) ``` import numpy as np import xarray as xr ds = xr.Dataset() ds['a'] = ('x', np.linspace(0,1)) ds['x'] = ('x', np.linspace(3,4)) print(ds) print('indexes', ds.indexes) ``` **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 | packaged by conda-forge | (default, Jun 1 2020, 18:57:50) [GCC 7.5.0] python-bits: 64 OS: Linux OS-release: 5.4.0-47-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.4 xarray: 0.16.0 pandas: 1.1.1 numpy: 1.18.5 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.23.0 distributed: 2.25.0 matplotlib: 3.2.2 cartopy: None seaborn: None numbagg: None pint: 0.13 setuptools: 49.6.0.post20200814 pip: 20.2.3 conda: 4.8.4 pytest: 5.4.3 IPython: 7.15.0 sphinx: 3.2.1
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4417/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 708337538,MDU6SXNzdWU3MDgzMzc1Mzg=,4456,workaround for file with variable and dimension having same name,3958036,closed,0,,,4,2020-09-24T17:10:04Z,2021-12-29T16:55:53Z,2021-12-29T16:55:53Z,CONTRIBUTOR,,,,"Adding a variable that's not a 1d ""dimension coordinate"" with the same name as a dimension is an error. This makes sense. However, if I have a `.nc` file that has such a variable, is there any workaround to get the badly-named variable into `xarray` short of altering the `.nc` file or loading it separately with `netCDF4`? I.e. to make the following work somehow ``` import xarray as xr import netCDF4 f = netCDF4.Dataset() f = netCDF4.Dataset(""test.nc"", ""w"") f.createDimension(""x"", 2) f.createDimension(""y"", 3) f[""y""] = np.ones([2,3]) f[""y""][...] = 1.0 f.close() ds = xr.open_dataset('test.nc') ``` rather than getting the current error `MissingDimensionsError: 'y' has more than 1-dimension and the same name as one of its dimensions ('x', 'y'). xarray disallows such variables because they conflict with the coordinates used to label dimensions.` I think it might be nice to have something like a `rename_vars` argument to `open_dataset()`. Similar to how `drop_vars` ignores a list of variables, `rename_vars` could rename a dict of variables so the example above could do ``` ds = xr.open_dataset(""test.nc"", rename_vars={""y"": ""y_not_dimension""}) ``` and get a Dataset with a dimension `""y""` and a variable `""y_not_dimension""`.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4456/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1022478180,I_kwDOAMm_X8488cdk,5852,Surprising behaviour of Dataset/DataArray.interp() with NaN entries,3958036,open,0,,,0,2021-10-11T09:33:11Z,2021-10-11T09:33:11Z,,CONTRIBUTOR,,,," I think this is due to documented 'undefined behaviour' of `scipy.interpolate.interp1d`, so not really a bug, but I think it would be more user-friendly if xarray gave an error in this case rather than producing an 'incorrect' result. **What happened**: If a DataArray contains a NaN value and is interpolated, output values that do not depend on the entry that was NaN may still be NaN. **What you expected to happen**: The [docs for `scipy.interpolate.interp1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy-interpolate-interp1d) say > Calling interp1d with NaNs present in input values results in undefined behaviour. which explain the output below, and presumably mean it is not fixable on the xarray side (short of some ugly work-around). I think it would be good though to check for NaNs in `DataArray/Dataset.interp()`, and if they are present raise an exception (or possibly a warning?) about 'undefined behaviour'. `scipy.interpolate.interp2d` [has a similar note](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp2d.html#scipy-interpolate-interp2d), while `scipy.interpolate.interpn` [does not mention it](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html#scipy-interpolate-interpn) (but has very limited information). What I'd initially expected was an output would be valid at locations in the array that shouldn't depend on the NaN input: interpolating a 2d DataArray (with dims x and y) in the x-dimension, if only one y-index in the input has a NaN value, that y-index in the output might contain NaNs, but the others should be OK. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr da = xr.DataArray(np.ones([3, 4]), dims=(""x"", ""y"")) da[0, 0] = float(""nan"") newx = np.linspace(0., 3., 5) interp_da = da.interp(x=newx) print(interp_da) ``` On my system, this gives output: ``` array([[nan, 1., 1., 1.], [nan, 1., 1., 1.], [ 1., 1., 1., 1.], [nan, nan, nan, nan], [nan, nan, nan, nan]]) Coordinates: * x (x) float64 0.0 0.75 1.5 2.25 3.0 Dimensions without coordinates: y ``` [Surprisingly, I get the same output even using `method=""nearest""`.] You might expect at least the following, with NaN only at `y=0`: ``` array([[nan, 1., 1., 1.], [nan, 1., 1., 1.], [ 1., 1., 1., 1.], [nan, 1., 1., 1.], [nan, 1., 1., 1.]]) Coordinates: * x (x) float64 0.0 0.75 1.5 2.25 3.0 Dimensions without coordinates: y ``` **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:39:48) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.11.0-37-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: ('en_GB', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.8.0 xarray: 0.19.0 pandas: 1.3.1 numpy: 1.21.1 scipy: 1.7.1 netCDF4: 1.5.7 pydap: None h5netcdf: None h5py: 3.3.0 Nio: None zarr: None cftime: 1.5.0 nc_time_axis: 1.3.1 PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2021.07.2 distributed: 2021.07.2 matplotlib: 3.4.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: 0.17 setuptools: 49.6.0.post20210108 pip: 21.2.4 conda: 4.10.3 pytest: 6.2.4 IPython: 7.26.0 sphinx: 4.1.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5852/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 940702754,MDU6SXNzdWU5NDA3MDI3NTQ=,5589,Call .compute() in all plot methods?,3958036,open,0,,,3,2021-07-09T12:03:30Z,2021-07-09T15:57:27Z,,CONTRIBUTOR,,,,"I noticed what I think might be a performance bug: should `.compute()` be called on the input data in all the plotting methods (e.g. `plot.pcolormesh()`) like it is in `.plot()` here https://github.com/pydata/xarray/blob/bf27e2c1fd81f5e0afe1ef91c13f651db54b44bb/xarray/plot/plot.py#L166 See also discussion in https://github.com/pydata/xarray/issues/2390. I was making plots from a large dataset of a quantity that is the output of quite a bit of computation. A script which made an animation of the full time-series (a couple of thousand time points) actually ran significantly faster than a script that made pcolormesh plots of just 3 time points (~2hrs compared to ~5hrs). The difference I can think of is that the animation script called `.values` before the animation function, but the plotting script called `xarray.plot.pcolormesh()` without calling `.values`/`.load()`/`.compute()` first. A modified version of the script that calls `.load()` before any plot calls reduced the run time to 30 mins even though I plotted 18 time points, not just 3. 2d plots might all be covered by adding a `darray = darray.compute()` call in `newplotfunc()`?https://github.com/pydata/xarray/blob/bf27e2c1fd81f5e0afe1ef91c13f651db54b44bb/xarray/plot/plot.py#L609 I guess the 1d plot functions would all need to be modified individually.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5589/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 609907735,MDExOlB1bGxSZXF1ZXN0NDExNDIxODIz,4017,Combining attrs of member DataArrays of Datasets,3958036,closed,0,,,0,2020-04-30T12:23:10Z,2021-05-05T16:37:25Z,2021-05-05T16:37:25Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4017,"While looking at #4009, I noticed that the `combine_attrs` kwarg to `concat` or `merge` only affects the global attrs of the `Dataset`, not the attrs of `DataArray` or `Variable` members of the `Dataset`s being combined. I think this is a bug. So far this PR adds tests that reproduce the issue in #4009, and the issue described above. Fixing should be fairly simple: for #4009 pass `combine_attrs=""drop""` to `combine_by_coords` and `combine_nested` in `open_mfdataset`; for this issue insert the `combine_attrs` handling in an appropriate place - possibly in `merge.unique_variable`. I'll update with fixes when I get a chance. - [ ] Closes #4009 - [x] Tests added - [ ] Passes `isort -rc . && black . && mypy . && flake8` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4017/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 847489988,MDExOlB1bGxSZXF1ZXN0NjA2NTAyMzA4,5101,Surface plots,3958036,closed,0,,,8,2021-03-31T22:58:20Z,2021-05-03T13:05:59Z,2021-05-03T13:05:02Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5101,"`.plot.surface()` method that wraps `matplotlib`'s `plot_surface()` method from the 3d toolkit. * works with facet grids * disables all the automatic color-map, etc. because it doesn't necessarily make sense for surface plots - the default (in matplotlib, and as implemented now) is not to color the surface. xarray's auto-selection stuff would interfere with `plot_surface()`'s kwargs. I'm not sure if there's somewhere good to note the new `surface()` method in the docs to make it more discoverable? Maybe in one of the examples? - [x] Closes #5084 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5101/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 842583817,MDU6SXNzdWU4NDI1ODM4MTc=,5084,plot_surface() wrapper,3958036,closed,0,,,2,2021-03-27T19:16:09Z,2021-05-03T13:05:02Z,2021-05-03T13:05:02Z,CONTRIBUTOR,,,,"Is there an xarray way to make a surface plot, like matplotlib's `plot_surface()`? I didn't see one on a quick skim, but expect it should be fairly easy to add, following the style for `contour()`, `pcolormesh()`, etc.? For the matplotlib version, see https://matplotlib.org/stable/gallery/mplot3d/surface3d.html.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5084/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 857378504,MDExOlB1bGxSZXF1ZXN0NjE0ODA5NTg1,5153,cumulative_integrate() method,3958036,closed,0,,,8,2021-04-13T22:53:58Z,2021-05-02T10:34:23Z,2021-05-01T20:01:31Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5153,"Provides the functionality of `scipy.integrate.cumulative_trapezoid`. - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst`","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5153/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 823059252,MDU6SXNzdWU4MjMwNTkyNTI=,5002,Dataset.plot.quiver() docs slightly misleading,3958036,closed,0,,,3,2021-03-05T12:54:19Z,2021-05-01T17:38:39Z,2021-05-01T17:38:39Z,CONTRIBUTOR,,,,"In the docs for `Dataset.plot.quiver()` http://xarray.pydata.org/en/latest/generated/xarray.Dataset.plot.quiver.html the `u` and `v` arguments are labelled as 'optional'. They are required for quiver plots though, so this is slightly confusing. I guess it is like this because the docs are created from a generic `_dsplot()` docstring, so don't know if it's fixable in a sensible way...","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5002/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 847006334,MDU6SXNzdWU4NDcwMDYzMzQ=,5097,2d plots may fail for some choices of `x` and `y`,3958036,closed,0,,,1,2021-03-31T17:26:34Z,2021-04-22T07:16:17Z,2021-04-22T07:16:17Z,CONTRIBUTOR,,,,"**What happened**: When making a 2d plot with a 1d `x` argument and a 2d `y`, if the two dimensions have the same size and are in the wrong order, no plot is produced - the third plot in the MCVE is blank. **What you expected to happen**: All three plots in the MCVE should be identical. **Minimal Complete Verifiable Example**: ```python from matplotlib import pyplot as plt import numpy as np import xarray as xr ds = xr.Dataset({""z"": ([""x"", ""y""], np.random.rand(4,4))}) x2d, y2d = np.meshgrid(ds[""x""], ds[""y""]) ds = ds.assign_coords(x2d=([""x"", ""y""], x2d.T), y2d=([""x"", ""y""], y2d.T)) fig, axes = plt.subplots(1,3) h0 = ds[""z""].plot.pcolormesh(x=""y2d"", y=""x2d"", ax=axes[0]) h1 = ds[""z""].plot.pcolormesh(x=""y"", y=""x"", ax=axes[1]) h2 = ds[""z""].plot.pcolormesh(x=""y"", y=""x2d"", ax=axes[2]) plt.show() ``` result: ![test2d](https://user-images.githubusercontent.com/3958036/113184368-fcb7b600-924c-11eb-8479-9ffd2a856b64.png) **Anything else we need to know?**: The bug is present in both the 0.17.0 release and current `master`. I came across this while starting to work on #5084. I think the problem is here https://github.com/pydata/xarray/blob/ddc352faa6de91f266a1749773d08ae8d6f09683/xarray/plot/plot.py#L678-L684 as the check `xval.shape[0] == yval.shape[0]` doesn't work if the single dimension of x is actually the second dimension of y, but happened to have the same size as the first dimension of y? I think it needs to check the actual dimensions of `x` and `y`. Why don't we just do something like ``` xval = xval.broadcast_like(darray) yval = yval.broadcast_like(darray) ``` if either coordinate is 2d before using `.values` to convert to numpy arrays? **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.6 | packaged by conda-forge | (default, Oct 7 2020, 19:08:05) [GCC 7.5.0] python-bits: 64 OS: Linux OS-release: 5.4.0-70-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.17.0 pandas: 1.1.5 numpy: 1.19.4 scipy: 1.5.3 netCDF4: 1.5.5.1 pydap: None h5netcdf: None h5py: 3.1.0 Nio: None zarr: None cftime: 1.3.0 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.3 cartopy: None seaborn: None numbagg: None pint: 0.16.1 setuptools: 49.6.0.post20201009 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5097/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 847199398,MDExOlB1bGxSZXF1ZXN0NjA2MjMwMjE1,5099,Use broadcast_like for 2d plot coordinates,3958036,closed,0,,,3,2021-03-31T19:34:32Z,2021-04-22T07:16:17Z,2021-04-22T07:16:17Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5099,"Use broadcast_like if either `x` or `y` inputs are 2d to ensure that both have dimensions in the same order as the DataArray being plotted. Convert to numpy arrays after possibly using broadcast_like. Simplifies code, and fixes #5097 (bug when dimensions have the same size). @dcherian > IIRC the ""resolving intervals"" section later will break and is somewhat annoying to fix. This is why the current ugly code exists. This change seems to 'just work', and unit tests pass. Is there some extra check that needs doing to make sure ""resolving intervals"" is behaving correctly? I can't think of a unit test that would have caught #5097, since even when the bug happens, a plot is produced without errors or warnings. If anyone has an idea, suggestions/pushes welcome! - [x] Closes #5097 - [ ] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5099/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 823290488,MDExOlB1bGxSZXF1ZXN0NTg1Nzc0MjY4,5003,Add Dataset.plot.streamplot() method,3958036,closed,0,,,2,2021-03-05T17:41:49Z,2021-03-30T16:41:08Z,2021-03-30T16:41:07Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5003,"Since @dcherian added Quiver plots in #4407, it's fairly simple to extend the functionality to `streamplot()`. For example (copying from @dcherian's unit test setup) ``` import xarray as xr from matplotlib import pyplot as plt das = [ xr.DataArray( np.random.randn(3, 3), dims=[""x"", ""y""], coords=[range(k) for k in [3, 3]], ) for _ in [1, 2] ] ds = xr.Dataset({""u"": das[0], ""v"": das[1]}) ds[""mag""] = np.hypot(ds.u, ds.v) ds.plot.streamplot(x=""x"",y=""y"",u=""u"",v=""v"", hue=""mag"") plt.show() ``` ![streamplot](https://user-images.githubusercontent.com/3958036/110155196-311a8c80-7ddd-11eb-99ea-06232cffcc30.png) - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5003/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 698263021,MDU6SXNzdWU2OTgyNjMwMjE=,4415,Adding new DataArray to a Dataset removes attrs of existing coord,3958036,closed,0,,,3,2020-09-10T17:21:32Z,2020-09-10T17:38:08Z,2020-09-10T17:38:08Z,CONTRIBUTOR,,,," **Minimal Complete Verifiable Example**: ``` import numpy as np import xarray as xr ds = xr.Dataset() ds[""a""] = xr.DataArray(np.linspace(0., 1.), dims=""x"") ds[""x""] = xr.DataArray(np.linspace(0., 2., len(ds[""x""])), dims=""x"") ds[""x""].attrs[""foo""] = ""bar"" print(ds[""x""]) ds[""b""] = xr.DataArray(np.linspace(0., 1.), dims=""x"") print(ds[""x""]) ``` **What happened**: Attribute `""foo""` is present at the first print, but missing in the second, after adding `b` to `ds`.
full output ``` array([0. , 0.040816, 0.081633, 0.122449, 0.163265, 0.204082, 0.244898, 0.285714, 0.326531, 0.367347, 0.408163, 0.44898 , 0.489796, 0.530612, 0.571429, 0.612245, 0.653061, 0.693878, 0.734694, 0.77551 , 0.816327, 0.857143, 0.897959, 0.938776, 0.979592, 1.020408, 1.061224, 1.102041, 1.142857, 1.183673, 1.22449 , 1.265306, 1.306122, 1.346939, 1.387755, 1.428571, 1.469388, 1.510204, 1.55102 , 1.591837, 1.632653, 1.673469, 1.714286, 1.755102, 1.795918, 1.836735, 1.877551, 1.918367, 1.959184, 2. ]) Coordinates: * x (x) float64 0.0 0.04082 0.08163 0.1224 ... 1.878 1.918 1.959 2.0 Attributes: foo: bar array([0. , 0.040816, 0.081633, 0.122449, 0.163265, 0.204082, 0.244898, 0.285714, 0.326531, 0.367347, 0.408163, 0.44898 , 0.489796, 0.530612, 0.571429, 0.612245, 0.653061, 0.693878, 0.734694, 0.77551 , 0.816327, 0.857143, 0.897959, 0.938776, 0.979592, 1.020408, 1.061224, 1.102041, 1.142857, 1.183673, 1.22449 , 1.265306, 1.306122, 1.346939, 1.387755, 1.428571, 1.469388, 1.510204, 1.55102 , 1.591837, 1.632653, 1.673469, 1.714286, 1.755102, 1.795918, 1.836735, 1.877551, 1.918367, 1.959184, 2. ]) Coordinates: * x (x) float64 0.0 0.04082 0.08163 0.1224 ... 1.878 1.918 1.959 2.0 ```
**What you expected to happen**: Coordinate `x` should be unchanged.
full expected output ``` array([0. , 0.040816, 0.081633, 0.122449, 0.163265, 0.204082, 0.244898, 0.285714, 0.326531, 0.367347, 0.408163, 0.44898 , 0.489796, 0.530612, 0.571429, 0.612245, 0.653061, 0.693878, 0.734694, 0.77551 , 0.816327, 0.857143, 0.897959, 0.938776, 0.979592, 1.020408, 1.061224, 1.102041, 1.142857, 1.183673, 1.22449 , 1.265306, 1.306122, 1.346939, 1.387755, 1.428571, 1.469388, 1.510204, 1.55102 , 1.591837, 1.632653, 1.673469, 1.714286, 1.755102, 1.795918, 1.836735, 1.877551, 1.918367, 1.959184, 2. ]) Coordinates: * x (x) float64 0.0 0.04082 0.08163 0.1224 ... 1.878 1.918 1.959 2.0 Attributes: foo: bar array([0. , 0.040816, 0.081633, 0.122449, 0.163265, 0.204082, 0.244898, 0.285714, 0.326531, 0.367347, 0.408163, 0.44898 , 0.489796, 0.530612, 0.571429, 0.612245, 0.653061, 0.693878, 0.734694, 0.77551 , 0.816327, 0.857143, 0.897959, 0.938776, 0.979592, 1.020408, 1.061224, 1.102041, 1.142857, 1.183673, 1.22449 , 1.265306, 1.306122, 1.346939, 1.387755, 1.428571, 1.469388, 1.510204, 1.55102 , 1.591837, 1.632653, 1.673469, 1.714286, 1.755102, 1.795918, 1.836735, 1.877551, 1.918367, 1.959184, 2. ]) Coordinates: * x (x) float64 0.0 0.04082 0.08163 0.1224 ... 1.878 1.918 1.959 2.0 Attributes: foo: bar ```
**Anything else we need to know?**: **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 | packaged by conda-forge | (default, Jun 1 2020, 18:57:50) [GCC 7.5.0] python-bits: 64 OS: Linux OS-release: 5.4.0-47-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.4 xarray: 0.16.0 pandas: 1.1.1 numpy: 1.18.5 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.23.0 distributed: 2.25.0 matplotlib: 3.2.2 cartopy: None seaborn: None numbagg: None pint: 0.13 setuptools: 49.6.0.post20200814 pip: 20.2.3 conda: 4.8.4 pytest: 5.4.3 IPython: 7.15.0 sphinx: 3.2.1
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4415/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 668515620,MDU6SXNzdWU2Njg1MTU2MjA=,4289,title bar of docs displays incorrect version,3958036,closed,0,,,5,2020-07-30T09:00:43Z,2020-08-18T22:32:51Z,2020-08-18T22:32:51Z,CONTRIBUTOR,,,," **What happened**: The browser title bar displays an incorrect version when viewing the docs online. See below - title bar says 0.15.1 but actual version in URL is 0.16.0. ![docs-snapshot](https://user-images.githubusercontent.com/3958036/88903135-14b4e100-d24b-11ea-9380-3da51c8e3b99.png) `http://xarray.pydata.org/en/stable/` also displays 0.15.1 in the title bar, but I guess is actually showing 0.16.0 docs (?), which is confusing!","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4289/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 671222189,MDExOlB1bGxSZXF1ZXN0NDYxNDQzNzcx,4298,Fix docstring for missing_dims argument to isel methods,3958036,closed,0,,,1,2020-08-01T21:40:27Z,2020-08-03T20:23:29Z,2020-08-03T20:23:28Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4298,"Incorrect value ""exception"" was given in the description of `missing_dims` argument - ""exception"" was renamed to ""raise"". - [x] Passes `isort . && black . && mypy . && flake8` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4298/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 594594646,MDExOlB1bGxSZXF1ZXN0Mzk5MjAwODg3,3936,Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods,3958036,closed,0,,,27,2020-04-05T18:52:52Z,2020-06-29T20:22:49Z,2020-06-29T19:36:26Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3936,"These return dicts of the indices of the minimum or maximum of a DataArray over several dimensions. Inspired by @fujiisoup's work in #1469. With #3871, replaces #1469. Provides a simpler solution to #3160. Implemented so that ``` da.isel(da.argmin(list_of_dim)) == da.min(list_of_dim) da.isel(da.argmax(list_of_dim)) == da.max(list_of_dim) ``` - [x] Closes #1469 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3936/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 644485140,MDExOlB1bGxSZXF1ZXN0NDM5MDk4NTcz,4173,Fix 4009,3958036,closed,0,,,1,2020-06-24T09:59:28Z,2020-06-24T18:22:20Z,2020-06-24T18:22:19Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4173,"Don't know if/when I'll have time to finish #4017, so pulling out the fix for #4009 into a separate PR here that is ready to merge. - [x] Closes #4009 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4173/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 595882590,MDU6SXNzdWU1OTU4ODI1OTA=,3948,Releasing memory?,3958036,closed,0,,,6,2020-04-07T13:49:07Z,2020-04-07T14:18:36Z,2020-04-07T14:18:36Z,CONTRIBUTOR,,,,"Once `xarray` (or `dask`) has loaded some array into memory, is there any way to force the memory to be released again? Or should this never be necessary? For example, what would be the best workflow for this case: I have several large arrays on disk. Each will fit into memory individually. I want to do some analysis on each array (which produces small results), and keep the results in memory, but I do not need the large arrays any more after the analysis. I'm wondering if some sort of `release()` method would be useful, so that I could say explicitly ""can drop this DataArray from memory, even though the user might have modified it"". My proposed workflow for the case above would then be something like: ``` da1 = ds[""variable1""] result1 = do_some_work(da1) # may load large parts of da1 into memory da1.release() # any changes to da1 not already saved to disk are lost, but do not want da1 any more da2 = ds[""variable2""] result2 = do_some_work(da2) # may load large parts of da2 into memory da2.release() # any changes to da2 not already saved to disk are lost, but do not want da1 any more ... etc. ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3948/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 585868107,MDExOlB1bGxSZXF1ZXN0MzkyMTExMTI4,3877,"Control attrs of result in `merge()`, `concat()`, `combine_by_coords()` and `combine_nested()`",3958036,closed,0,,,7,2020-03-23T01:32:59Z,2020-04-05T20:44:47Z,2020-03-24T20:40:18Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3877,"`combine_attrs` argument for `merge()`, `concat()`, `combine_by_coords()` and `combine_nested()` controls what attributes the result is given. Defaults maintain the current behaviour. Possible values (named following `compat` arguments) are: - 'drop': empty attrs on returned Dataset. - 'identical': all attrs must be the same on every object. - 'no_conflicts': attrs from all objects are combined, any that have the same name must also have the same value. - 'override': skip comparing and copy attrs from the first dataset to the result. - [x] Closes #3865 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [ ] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3877/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 583220835,MDU6SXNzdWU1ODMyMjA4MzU=,3866,Allow `isel` to ignore missing dimensions?,3958036,closed,0,,,0,2020-03-17T18:41:13Z,2020-04-03T19:47:08Z,2020-04-03T19:47:08Z,CONTRIBUTOR,,,,"Sometimes it would be nice for `isel()` to be able to ignore a dimension if it is missing in the Dataset/DataArray. E.g. ``` ds = Dataset() ds.isel(t=0) # currently raises an exception ds.isel(t=0, ignore_missing=True) # would be nice if this was allowed, just returning ds ``` For example, when writing a function can be called on variables with different combinations of dimensions. I think it should be fairly easy to implement, just add the argument to the condition here https://github.com/pydata/xarray/blob/65a5bff79479c4b56d6f733236fe544b7f4120a8/xarray/core/variable.py#L1059-L1062 the only downside would be increased complexity of adding another argument to the API for an issue where a workaround is not hard (at least in the case I have at the moment), just a bit clumsy.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3866/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 591471233,MDExOlB1bGxSZXF1ZXN0Mzk2NjM5NjM2,3923,Add missing_dims argument allowing isel() to ignore missing dimensions,3958036,closed,0,,,5,2020-03-31T22:19:54Z,2020-04-03T19:47:08Z,2020-04-03T19:47:08Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3923," Note: only added to `DataArray.isel()` and `Variable.isel()`. A `Dataset` should include all dimensions, so presumably it should always be an error to pass a non-existent dimension when slicing a `Dataset`? - [x] Closes #3866 - [x] Tests added - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3923/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 587280307,MDExOlB1bGxSZXF1ZXN0MzkzMjU2NTgx,3887,Rename ordered_dict_intersection -> compat_dict_intersection,3958036,closed,0,,,4,2020-03-24T21:08:26Z,2020-03-24T22:59:07Z,2020-03-24T22:59:07Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3887,"`xarray` does not use `OrderedDicts` any more, so name did not make sense. As suggested here https://github.com/pydata/xarray/pull/3877#discussion_r396620551. - [x] Test~~s~~ ~~added~~ updated - [x] Passes `isort -rc . && black . && mypy . && flake8` - [x] Fully documented, including `whats-new.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3887/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 583080947,MDU6SXNzdWU1ODMwODA5NDc=,3865,`merge` drops attributes,3958036,closed,0,,,1,2020-03-17T15:06:18Z,2020-03-24T20:40:18Z,2020-03-24T20:40:18Z,CONTRIBUTOR,,,," `xarray.merge()` drops the `attrs` of `Dataset`s being merged. They should be kept, at least if they are compatible #### MCVE Code Sample ```python # Your code here import xarray as xr ds1 = xr.Dataset() ds1.attrs['a'] = 42 ds2 = xr.Dataset() ds2.attrs['a'] = 42 merged = xr.merge([ds1, ds2]) print(merged) ``` the result is ``` Dimensions: () Data variables: *empty* ``` #### Expected Output ``` Dimensions: () Data variables: *empty* Attributes: a: 42 ``` #### Problem Description The current behaviour means I have to check and copy `attrs` to the result of `merge` by hand, even if the `attrs` of the inputs were identical or not conflicting. I'm happy to attempt a PR to fix this. Proposal (following pattern of `compat` arguments): * add a `combine_attrs` argument to `xarray.merge` * `combine_attrs = 'drop'` do not copy `attrs` (current behaviour) * `combine_attrs = 'identical'` if `attrs` of all inputs are identical (using `dict_equiv`) then copy the `attrs` to the result, otherwise raise an exception * `combine_attrs = 'no_conflicts'` merge the `attrs` of all inputs, as long as any keys shared by more than one input have the same value (if not raise an exception) [I propose this is the default behaviour] * `override` copy the `attrs` from the first input, to the result This proposal should also allow `combine_by_coords`, etc. to preserve attributes. These should probably also take a `combine_attrs` argument, which would be passed through to `merge`. #### Versions Current `master` of `pydata/xarray` on 17/3/2020
Output of `xr.show_versions()` INSTALLED VERSIONS ------------------ commit: None python: 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] python-bits: 64 OS: Linux OS-release: 5.3.0-40-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.2 libnetcdf: 4.6.3 xarray: 0.15.0 pandas: 1.0.2 numpy: 1.18.1 scipy: 1.3.0 netCDF4: 1.5.1.2 pydap: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.12.0 distributed: None matplotlib: 3.1.1 cartopy: None seaborn: None numbagg: None setuptools: 45.2.0 pip: 9.0.1 conda: None pytest: 4.4.1 IPython: 7.8.0 sphinx: 1.8.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3865/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 533996523,MDExOlB1bGxSZXF1ZXN0MzQ5OTc0NjUz,3601,Fix contourf set under,3958036,closed,0,,,5,2019-12-06T13:47:37Z,2020-02-24T20:20:09Z,2020-02-24T20:20:08Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3601,"Copies the `cmap._rgba_bad`, `cmap._rgba_under`, and `cmap._rgba_over` values to `new_cmap`, in case they have been set to non-default values. Allows the user to customize plots more by using matplotlib methods on a `cmap` before passing as an argument to xarray's plotting methods. Previously these settings were overridden by defaults when creating the `cmap` actually used to make the plot. I'm not a fan of copying attributes one-by-one like this, but I guess this is an issue with matplotlib's API, unless there's a nicer way to convert a `cmap` to a discrete `cmap` than `mpl.colors.from_levels_and_colors()`. - [x] Closes #3590 - [x] Tests added - [x] Passes `black . && mypy . && flake8` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3601/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 532165408,MDU6SXNzdWU1MzIxNjU0MDg=,3590,cmap.set_under() does not work as expected,3958036,closed,0,,,5,2019-12-03T18:04:07Z,2020-02-24T20:20:07Z,2020-02-24T20:20:07Z,CONTRIBUTOR,,,,"When using matplotlib, the `set_under()` method can be used to set values below the range of a colormap to a certain color, for example ``` import matplotlib from matplotlib import pyplot import numpy dat = numpy.linspace(0, 1)[numpy.newaxis, :]*numpy.linspace(0, 1)[:, numpy.newaxis] cmap = matplotlib.cm.viridis #cmap.set_under('w') pyplot.contourf(dat, vmin=.3, cmap=cmap) pyplot.colorbar() pyplot.show() ``` produces ![matplotlib_no-set_under](https://user-images.githubusercontent.com/3958036/70076161-195ec200-15f6-11ea-94ab-59f1b38e9d25.png) while uncommenting the `cmap.set_under()` call produces ![matplotlib](https://user-images.githubusercontent.com/3958036/70076218-36939080-15f6-11ea-9601-93d3a52b6e20.png) However, using `xarray` to do the same thing, ``` import matplotlib from matplotlib import pyplot from xarray import DataArray import numpy da = DataArray(numpy.linspace(0, 1)[numpy.newaxis, :]*numpy.linspace(0, 1)[:, numpy.newaxis]) cmap = matplotlib.cm.viridis cmap.set_under('w') da.plot.contourf(vmin=.3, cmap=cmap) pyplot.show() ``` produces ![xarray](https://user-images.githubusercontent.com/3958036/70076465-bc174080-15f6-11ea-9f59-37f20569cf6e.png) where it seems the call to `cmap.set_under('w')` had no effect. Expected behaviour would be output like the second plot. #### Output of ``xr.show_versions()``
``` In [2]: xarray.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] python-bits: 64 OS: Linux OS-release: 5.0.0-37-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.0 libnetcdf: 4.6.0 xarray: 0.14.1 pandas: 0.24.2 numpy: 1.16.3 scipy: 1.2.1 netCDF4: 1.3.1 pydap: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.1.0 distributed: None matplotlib: 3.1.1 cartopy: None seaborn: None numbagg: None setuptools: 41.0.1 pip: 19.3.1 conda: None pytest: 4.4.1 IPython: 7.6.1 sphinx: None ```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3590/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 507468507,MDU6SXNzdWU1MDc0Njg1MDc=,3401,_get_scheduler() exception if dask.multiprocessing missing,3958036,closed,0,,,0,2019-10-15T20:35:14Z,2019-10-21T00:17:48Z,2019-10-21T00:17:48Z,CONTRIBUTOR,,,,"These lines were recently changed in #3358 https://github.com/pydata/xarray/blob/3f9069ba376afa35c0ca83b09a6126dd24cb8127/xarray/backends/locks.py#L87-L92 If the 'cloudpickle' package is not installed, then `dask.multiprocessing` is not available. The `try/except` that used to be wrapped around `if actual_get is dask.multiprocessing.get` meant that `_get_scheduler()` worked in that case, returning `""threaded""` (I assume this was the expected behaviour). After #3358, `_get_scheduler()` raised an `AttributeError: module 'dask' has no attribute 'multiprocessing'` until I installed 'cloudpickle'. Suggest either reverting the changes that removed the `try/except` or making 'cloudpickle' a dependency. -------------------------- To reproduce: 1. check 'cloudpickle' is not installed, but 'dask' is 2. execute the following commands ``` >>> import xarray >>> xarray.backends.api._get_scheduler() ``` Expected result: `""threaded""` Actual result: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 xarray.backends.api._get_scheduler() ~/.local/lib/python3.6/site-packages/xarray/backends/locks.py in _get_scheduler(get, collection) 87 pass 88 ---> 89 if actual_get is dask.multiprocessing.get: 90 return ""multiprocessing"" 91 else: AttributeError: module 'dask' has no attribute 'multiprocessing' ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3401/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue