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 1636431481,I_kwDOAMm_X85hifZ5,7661,Dark mode documentation not readable ,22488770,closed,0,,,0,2023-03-22T20:11:06Z,2023-03-27T18:14:27Z,2023-03-27T18:14:27Z,CONTRIBUTOR,,,,"### What is your issue? When opening the xarray documentation website, it defaults to dark mode as my system is using dark mode. However, much of the text is not readable as it remains black on the dark background, see screenshots for comparison of the homepage in dark vs light mode: ![Screenshot 2023-03-23 at 9 08 38 AM](https://user-images.githubusercontent.com/22488770/227026282-1264a611-e400-4ddc-a289-818cad3b5fd7.png) ![Screenshot 2023-03-23 at 9 08 52 AM](https://user-images.githubusercontent.com/22488770/227026306-a6ca3cd9-cc83-4028-8318-dc611311fa74.png) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7661/reactions"", ""total_count"": 5, ""+1"": 5, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 707752322,MDU6SXNzdWU3MDc3NTIzMjI=,4455,Document units for polyfit when dimension is time,22488770,open,0,,,2,2020-09-23T23:51:02Z,2021-06-23T11:12:55Z,,CONTRIBUTOR,,,," **Is your feature request related to a problem? Please describe.** I think (please correct me if I'm wrong) when using polyfit with dim='time' the units of the output slope are [data_units]/ns, but this not explained in the docstring or in the documentation on the webpage. I figured this out eventually but it could be confusing for new users. **Describe the solution you'd like** Mention in the documentation and/or docstring that the units for time will be ns ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4455/reactions"", ""total_count"": 5, ""+1"": 5, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 702373263,MDU6SXNzdWU3MDIzNzMyNjM=,4427,assign_coords with datetime64[us] changes dtype to datetime64[ns],22488770,closed,0,,,3,2020-09-16T01:14:11Z,2020-09-30T00:49:35Z,2020-09-30T00:49:35Z,CONTRIBUTOR,,,," **What happened**: When using xr.DataArray.assign_coords() to assign a new coordinate to the time dimension that is an array with dtype datetime64[us], after assigning, the dtype is datetime64[ns], resulting in the wrong dates, since the dates I am using are outside the valid range for the [ns] units. **What you expected to happen**: Preserve dtype of array when assigning as a coordinate. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr import cftime tmp = np.random.random(12) da = xr.DataArray(tmp, dims='time') times=list() for mth in np.arange(1, 13): times.append(cftime.DatetimeNoLeap(1250, mth, 1)) times64 = np.array([np.datetime64(t, 'us') for t in times]) da = da.assign_coords({'time': times64}) ``` which gives for the original array: ```python In [49]: times64 Out[49]: array(['1250-01-01T00:00:00.000000', '1250-02-01T00:00:00.000000', '1250-03-01T00:00:00.000000', '1250-04-01T00:00:00.000000', '1250-05-01T00:00:00.000000', '1250-06-01T00:00:00.000000', '1250-07-01T00:00:00.000000', '1250-08-01T00:00:00.000000', '1250-09-01T00:00:00.000000', '1250-10-01T00:00:00.000000', '1250-11-01T00:00:00.000000', '1250-12-01T00:00:00.000000'], dtype='datetime64[us]') ``` and for the array after assigning: ```python In [51]: da.time Out[51]: array(['1834-07-22T23:34:33.709551616', '1834-08-22T23:34:33.709551616', '1834-09-19T23:34:33.709551616', '1834-10-20T23:34:33.709551616', '1834-11-19T23:34:33.709551616', '1834-12-20T23:34:33.709551616', '1835-01-19T23:34:33.709551616', '1835-02-19T23:34:33.709551616', '1835-03-22T23:34:33.709551616', '1835-04-21T23:34:33.709551616', '1835-05-22T23:34:33.709551616', '1835-06-21T23:34:33.709551616'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1834-07-22T23:34:33.709551616 ... 1835-06-... ``` **Anything else we need to know?**: **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:37:09) [Clang 10.0.1 ] python-bits: 64 OS: Darwin OS-release: 18.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.3 xarray: 0.16.0 pandas: 1.1.0 numpy: 1.19.1 scipy: 1.4.1 netCDF4: 1.5.3 pydap: installed h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.21.0 distributed: 2.22.0 matplotlib: 3.1.2 cartopy: 0.17.0 seaborn: None numbagg: None pint: None setuptools: 49.3.1.post20200810 pip: 20.2.2 conda: None pytest: None IPython: 7.17.0 sphinx: 3.2.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4427/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 506914634,MDU6SXNzdWU1MDY5MTQ2MzQ=,3398,Mean called on groupby object adds dimensions to undesired variables,22488770,closed,0,,,3,2019-10-14T23:03:04Z,2019-10-16T14:30:38Z,2019-10-16T14:30:38Z,CONTRIBUTOR,,,,"#### MCVE Code Sample ```python import numpy as np import xarray as xr import cftime # create time coordinate tdays = np.arange(0, 730) time = cftime.num2date(tdays, 'days since 0001-01-01 00:00:00', calendar='noleap') # create spatial coordinate lev = np.arange(100) # Create dummy data x = np.random.rand(time.size, lev.size) y = np.random.rand(lev.size) # Create sample Dataset ds = xr.Dataset({'sample_data': (['time', 'lev'], x), 'independent_data': (['lev'], y)}, coords={'time': (['time'], time), 'lev': (['lev'], lev)}) # Perform groupby and mean ds2 = ds.groupby('time.month').mean(dim='time') ``` #### Actual Output ```python Dimensions: (lev: 100, month: 12) Coordinates: * lev (lev) int64 0 1 2 3 4 5 6 7 8 ... 92 93 94 95 96 97 98 99 * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12 Data variables: sample_data (month, lev) float64 0.5143 0.554 0.5027 ... 0.5246 0.5435 independent_data (month, lev) float64 0.01667 0.4687 ... 0.1015 0.7459 ``` #### Expected Output ```python Dimensions: (lev: 100, month: 12) Coordinates: * lev (lev) int64 0 1 2 3 4 5 6 7 8 ... 92 93 94 95 96 97 98 99 * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12 Data variables: sample_data (month, lev) float64 0.5143 0.554 0.5027 ... 0.5246 0.5435 independent_data (lev) float64 0.01667 0.4687 ... 0.1015 0.7459 ``` #### Problem Description The variable independent_data above initially has no time dimension but, after performing groupby('time.month').mean(dim='time') on the Dataset, it now has a month dimension that is meaningless. Preferably, it should leave the independent_data variable untouched. #### Output of ``xr.show_versions()``
INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 (default, Mar 27 2019, 16:54:48) [Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 18.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.12.2 pandas: 0.24.2 numpy: 1.16.4 scipy: 1.3.0 netCDF4: 1.5.1.2 pydap: installed h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.1.0 cartopy: 0.17.0 seaborn: None numbagg: None setuptools: 41.0.1 pip: 19.1.1 conda: None pytest: None IPython: 7.2.0 sphinx: 2.1.2
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