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/3141#issuecomment-513582194,https://api.github.com/repos/pydata/xarray/issues/3141,513582194,MDEyOklzc3VlQ29tbWVudDUxMzU4MjE5NA==,21049064,2019-07-21T19:45:32Z,2019-07-21T19:45:32Z,NONE,"Would love to! Sorry have been away this weekend. Do i just clone the repo write the code and send in a PR in a new branch? (first PR on a public repo!)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,469633509 https://github.com/pydata/xarray/issues/3004#issuecomment-508995157,https://api.github.com/repos/pydata/xarray/issues/3004,508995157,MDEyOklzc3VlQ29tbWVudDUwODk5NTE1Nw==,21049064,2019-07-07T12:17:45Z,2019-07-07T12:17:45Z,NONE,Perfect thankyou!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,453576041 https://github.com/pydata/xarray/issues/3053#issuecomment-508995115,https://api.github.com/repos/pydata/xarray/issues/3053,508995115,MDEyOklzc3VlQ29tbWVudDUwODk5NTExNQ==,21049064,2019-07-07T12:17:12Z,2019-07-07T12:17:12Z,NONE,Thanks closing!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,462010865 https://github.com/pydata/xarray/issues/3053#issuecomment-508544556,https://api.github.com/repos/pydata/xarray/issues/3053,508544556,MDEyOklzc3VlQ29tbWVudDUwODU0NDU1Ng==,21049064,2019-07-04T17:29:25Z,2019-07-04T17:29:25Z,NONE,"This is the greatest thing since sliced bread thankyou @spencerkclark !! I have been referring to this constantly for the last week :D ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,462010865 https://github.com/pydata/xarray/issues/3018#issuecomment-507630032,https://api.github.com/repos/pydata/xarray/issues/3018,507630032,MDEyOklzc3VlQ29tbWVudDUwNzYzMDAzMg==,21049064,2019-07-02T11:10:33Z,2019-07-02T11:10:33Z,NONE,"This is an awesome addition thankyou! I updated my `xarray` to V:`0.12.2` but I am still getting an error with dataset objects ```python a = np.ones((400)) * 10 a[3:7] = 0.2 a[10:13] = 0.2 p = np.repeat(a, 25).reshape(400, 5, 5) lat = np.arange(0, 5) lon = np.arange(0, 5) time = pd.date_range('2000-01-01', freq='M', periods=p.shape[0]) d = xr.Dataset( {'precip': (['time', 'lat', 'lon'], p)}, coords={ 'lon': lon, 'lat': lat, 'time': time } ) d.groupby('time.month').quantile(q=0.1) ``` gives the error message ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 d.groupby('time.month').quantile(q=0.1) AttributeError: 'DatasetGroupBy' object has no attribute 'quantile' ``` Whereas for the `DataArray` it works fine. ```python d.precip.groupby('time.month').quantile(q=0.1) Out[39]: array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.]) Coordinates: * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12 ``` Is this the expected behaviour? ``` INSTALLED VERSIONS ------------------ commit: None python: 3.7.0 | packaged by conda-forge | (default, Nov 12 2018, 12:34:36) [Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 18.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 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: None h5netcdf: None h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: 1.0.17 cfgrib: 0.9.7 iris: None bottleneck: 1.2.1 dask: 1.2.2 distributed: 1.28.1 matplotlib: 3.1.0 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.0.1 pip: 19.1 conda: None pytest: 4.5.0 IPython: 7.1.1 sphinx: 2.0.1 ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,455262061 https://github.com/pydata/xarray/issues/3053#issuecomment-506730153,https://api.github.com/repos/pydata/xarray/issues/3053,506730153,MDEyOklzc3VlQ29tbWVudDUwNjczMDE1Mw==,21049064,2019-06-28T13:17:04Z,2019-06-28T13:17:42Z,NONE,"Thanks! Your assumption was correct, apologies for the mistake! This might be asking for too much but is there any way I can keep track of the `forecast_horizon` or `initialisation_date`? The only issue I can foresee is that I won't be able to distinguish between the forecasts for the same date, even though those initialised closest to the `valid_time` will likely have more information in them than those initialised a long time before. What I'm asking is can I add a new dimension to my original `ds` so that I have the OPTION to select by either `valid_time`/`time` or by `forecast_horizon`/`initialisation_date` ## So I would be looking for something like: ``` Dimensions: (forecast_horizon: 24, initialisation_date: 12, lat: 36, lon: 45, number: 51, time: 288) Coordinates: * lon (lon) float64 33.5 33.7 33.9 34.1 34.3 ... 41.65 41.85 42.05 42.25 * lat (lat) float64 -5.175 -5.176 -5.177 -5.177 ... -5.2 -5.201 -5.202 * number (number) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50 * initialisation_date (initialisation_date) datetime64[ns] 2018-01-31 ... 2018-12-31 * forecast_horizon (forecast_horizon) timedelta64[ns] 28 days ... 215 days * time (time) datetime64[ns] 2018-04-02 2018-04-03 ... 2018-04-30 Data variables: precip (forecast_horizon, initialisation_date, number, lat, lon, time) float64 0.2684 0.8408 ... 1.7 -0.383 ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,462010865 https://github.com/pydata/xarray/issues/3004#issuecomment-500165601,https://api.github.com/repos/pydata/xarray/issues/3004,500165601,MDEyOklzc3VlQ29tbWVudDUwMDE2NTYwMQ==,21049064,2019-06-08T21:28:34Z,2019-06-08T21:28:34Z,NONE,"The best way I have found so far is: ``` df = rank_norm.to_dataframe() bins = pd.qcut(df['rank_norm'], 5, labels=[1, 2, 3, 4, 5]) output = bins.to_xarray().to_dataset().rename({'rank_norm':'rank_quantile'}) ``` Which returns: ``` Dimensions: (lat: 10, lon: 10, time: 70) Coordinates: * lat (lat) float64 -5.175 -5.125 -5.075 ... -4.825 -4.775 -4.725 * lon (lon) float64 33.52 33.57 33.62 33.67 ... 33.87 33.92 33.97 * time (time) datetime64[ns] 2010-02-28 2010-03-31 ... 2015-11-30 Data variables: rank_quantile (lat, lon, time) int64 2 1 1 1 2 2 1 1 1 ... 1 1 2 2 1 4 2 2 ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,453576041 https://github.com/pydata/xarray/issues/3004#issuecomment-499959555,https://api.github.com/repos/pydata/xarray/issues/3004,499959555,MDEyOklzc3VlQ29tbWVudDQ5OTk1OTU1NQ==,21049064,2019-06-07T16:53:55Z,2019-06-08T21:11:46Z,NONE,"So if I want them separated into 5 percentiles `(0, 20) (20,40) (40,60) (60,80) (80,100)` ``` bins = [-0.01, 20., 40., 60., 80., np.Inf] bin_labels = ['1', '2', '3', '4, '5'] ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,453576041 https://github.com/pydata/xarray/issues/3004#issuecomment-499961306,https://api.github.com/repos/pydata/xarray/issues/3004,499961306,MDEyOklzc3VlQ29tbWVudDQ5OTk2MTMwNg==,21049064,2019-06-07T16:59:12Z,2019-06-07T16:59:12Z,NONE,"Also how do I assign the result of the `xarray.core.groupby.DatasetGroupBy` and the `labels` to a new variable? ``` bin_labels = ['20', '40', '60', '80'] decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins, labels=bin_labels) decile_index_gpby.assign() # assign_coords() ``` Gives me the error message: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins, labels=bin_labels) 2 decile_index_gpby.assign() # assign_coords() ~/miniconda3/lib/python3.7/site-packages/xarray/core/common.py in groupby_bins(self, group, bins, right, labels, precision, include_lowest, squeeze) 529 cut_kwargs={'right': right, 'labels': labels, 530 'precision': precision, --> 531 'include_lowest': include_lowest}) 532 533 def rolling(self, dim=None, min_periods=None, center=False, **dim_kwargs): ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in __init__(self, obj, group, squeeze, grouper, bins, cut_kwargs) 249 250 if bins is not None: --> 251 binned = pd.cut(group.values, bins, **cut_kwargs) 252 new_dim_name = group.name + '_bins' 253 group = DataArray(binned, group.coords, name=new_dim_name) ~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/tile.py in cut(x, bins, right, labels, retbins, precision, include_lowest, duplicates) 239 include_lowest=include_lowest, 240 dtype=dtype, --> 241 duplicates=duplicates) 242 243 return _postprocess_for_cut(fac, bins, retbins, x_is_series, ~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/tile.py in _bins_to_cuts(x, bins, right, labels, precision, include_lowest, dtype, duplicates) 357 else: 358 if len(labels) != len(bins) - 1: --> 359 raise ValueError('Bin labels must be one fewer than ' 360 'the number of bin edges') 361 if not is_categorical_dtype(labels): ValueError: Bin labels must be one fewer than the number of bin edges In [7]: bin_labels = ['20', '40', '60', '80'] ...: decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins, labels=bin_labels) ...: decile_index_gpby.assign() # assign_coords() ...: --------------------------------------------------------------------------- IndexError Traceback (most recent call last) in 1 bin_labels = ['20', '40', '60', '80'] 2 decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins, labels=bin_labels) ----> 3 decile_index_gpby.assign() # assign_coords() ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in assign(self, **kwargs) 772 Dataset.assign 773 """""" --> 774 return self.apply(lambda ds: ds.assign(**kwargs)) 775 776 ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in apply(self, func, args, **kwargs) 684 kwargs.pop('shortcut', None) # ignore shortcut if set (for now) 685 applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped()) --> 686 return self._combine(applied) 687 688 def _combine(self, applied): ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _combine(self, applied) 691 coord, dim, positions = self._infer_concat_args(applied_example) 692 combined = concat(applied, dim) --> 693 combined = _maybe_reorder(combined, dim, positions) 694 if coord is not None: 695 combined[coord.name] = coord ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _maybe_reorder(xarray_obj, dim, positions) 468 469 def _maybe_reorder(xarray_obj, dim, positions): --> 470 order = _inverse_permutation_indices(positions) 471 472 if order is None: ~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _inverse_permutation_indices(positions) 110 positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions] 111 --> 112 indices = nputils.inverse_permutation(np.concatenate(positions)) 113 return indices 114 ~/miniconda3/lib/python3.7/site-packages/xarray/core/nputils.py in inverse_permutation(indices) 58 # use intp instead of int64 because of windows :( 59 inverse_permutation = np.empty(len(indices), dtype=np.intp) ---> 60 inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp) 61 return inverse_permutation 62 IndexError: index 1204 is out of bounds for axis 0 with size 1000 ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,453576041 https://github.com/pydata/xarray/issues/3004#issuecomment-499958420,https://api.github.com/repos/pydata/xarray/issues/3004,499958420,MDEyOklzc3VlQ29tbWVudDQ5OTk1ODQyMA==,21049064,2019-06-07T16:50:36Z,2019-06-07T16:50:36Z,NONE,"Why does the number of bin labels have to be one less than the number of bins? ``` bin_labels = ['20', '40', '60', '80', '100'] decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins, labels=bin_labels) Out[]: ValueError: Bin labels must be one fewer than the number of bin edges ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,453576041 https://github.com/pydata/xarray/issues/2030#issuecomment-497722295,https://api.github.com/repos/pydata/xarray/issues/2030,497722295,MDEyOklzc3VlQ29tbWVudDQ5NzcyMjI5NQ==,21049064,2019-05-31T14:09:35Z,2019-05-31T14:09:35Z,NONE,"@philippjfr How do you change the `fps` in the code that you posted [above](https://github.com/pydata/xarray/issues/2030#issuecomment-377550475) Thanks for your help!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,309965118 https://github.com/pydata/xarray/issues/2547#issuecomment-460600719,https://api.github.com/repos/pydata/xarray/issues/2547,460600719,MDEyOklzc3VlQ29tbWVudDQ2MDYwMDcxOQ==,21049064,2019-02-05T11:15:49Z,2019-02-05T11:15:49Z,NONE,But the original question was answered so thank you very much!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-460600500,https://api.github.com/repos/pydata/xarray/issues/2547,460600500,MDEyOklzc3VlQ29tbWVudDQ2MDYwMDUwMA==,21049064,2019-02-05T11:15:01Z,2019-02-05T11:15:01Z,NONE,"Sorry for the silence! I got pulled away to another project. Unfortunately I wasn't able to finish completing the task in xarray but I found that the easiest way around the problem was to use a combination of two functions: ```python def change_missing_vals_to_9999f(ds, variable): """""" Change the missing values from np.nan to -9999.0f"""""" arr = ds[variable].values # set the values to -9999 arr[np.isnan(arr)] = -9999 # reassign the values back to the array ds[variable] = (ds[variable].dims, arr) return ds def change_missing_data_values(filename): """""" change the values INSIDE the .nc file to -9999.0f """""" assert ( filename.split(""."")[-1] == ""nc"" ), ""This function only works with .nc files. Filename: {}"".format(filename) print(""** Processing {} **"").format(filename) # ONLY OPEN THE DATASET ONCE ds = xr.open_dataset(filename) variables = ds.data_vars.keys() for variable in variables: print(""** Working on variable {} **"".format(variable)) ds = change_missing_vals_to_9999f(ds, variable) # ds.map(change_missing_vals_to_9999f, variable) # rewrite to netcdf file ds.to_netcdf(filename) print(""** Written variables {} to filename {} **"").format(variables, filename) return ``` and then another function using the `NCO` command: ``` def change_nc_FillValue(filename): """""" use the NCO command to change the fillvalue metadata in the .nc files"""""" command = ""ncatted -a _FillValue,,m,f,-9999.0 {}"".format(filename) os.system(command) print(""** _FillValue changed on {} file **"".format(filename)) return ``` RUN HERE: ``` @click.command() @click.argument(""filename"", type=str) def main(filename): """""" Run the two commands a) change the Values INSIDE the .nc file [python, numpy, xarray] b) change the associated METADATA for the .nc file headers [nco] """""" change_missing_data_values(filename) change_nc_FillValue(filename) print(""**** PROCESS DONE FOR {} ****"").format(filename) return ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-436688702,https://api.github.com/repos/pydata/xarray/issues/2547,436688702,MDEyOklzc3VlQ29tbWVudDQzNjY4ODcwMg==,21049064,2018-11-07T16:36:09Z,2018-11-07T16:46:01Z,NONE,"@spencerkclark Thanks very much that is awesome! One final Q: How do I set the `fill_value` not to `nan` but to `-1` (this is what I need for the Land Surface Model)? ### The current output of `ncdump -h Rg_dummy.nc` is: ``` ... variables: double time(time) ; time:_FillValue = NaN ; time:standard_name = ""time"" ; time:units = ""day as %Y%m%d.%f"" ; time:calendar = ""proleptic_gregorian"" ; short Rg(time, y, x) ; Rg:_FillValue = NaN ; Rg:long_name = ""HWSD sub sum content"" ; Rg:units = ""percent wt"" ; Rg:valid_range = 97., 103. ; double latitude(y, x) ; latitude:_FillValue = -99999. ; double longitude(y, x) ; longitude:_FillValue = -99999. ; ``` ### What I want is: `ncdump -h Rg_dummy.nc` ``` ... variables: double time(time) ; time:_FillValue = -1s ; time:standard_name = ""time"" ; time:units = ""day as %Y%m%d.%f"" ; time:calendar = ""proleptic_gregorian"" ; short Rg(time, y, x) ; Rg:_FillValue = -1s ; Rg:long_name = ""HWSD sub sum content"" ; Rg:units = ""percent wt"" ; Rg:valid_range = 97., 103. ; double latitude(y, x) ; latitude:_FillValue = -99999. ; double longitude(y, x) ; longitude:_FillValue = -99999. ; ``` I want to do something like: ``` ds2.to_netcdf(filename, set_fill_value=-1) ``` I saw these: [Issue #1598](https://github.com/pydata/xarray/issues/1598) [Issue #1865](https://github.com/pydata/xarray/issues/1865) But failed to understand where/how to use them Thank you so much for helping me out with xarray. It's crazy powerful. It's also just very big!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-436668695,https://api.github.com/repos/pydata/xarray/issues/2547,436668695,MDEyOklzc3VlQ29tbWVudDQzNjY2ODY5NQ==,21049064,2018-11-07T15:45:22Z,2018-11-07T15:46:15Z,NONE,"That all worked great until I tried to write out to a .nc file. ```python data_dir = ""./"" filename = ""Rg_dummy.nc"" # get the datetime range times = pd.date_range(""2000-01-01"", ""2000-12-31"", name=""time"") var = ""Rg"" copyfile(data_dir + filename, ""temp.nc"") ds = xr.open_dataset(""temp.nc"") print(""Temporary Data read to Python"") # FORWARD FILL FROM THE ORIGINAL DATA to new timesteps ds['time'] = np.array([times[0]]) ds.reindex({""time"":times}) ds.ffill(""time"") ds.to_netcdf(filename, format=""NETCDF3_CLASSIC"") print(filename, ""Written!"") # remove temporary file os.remove(data_dir+""temp.nc"") print(""Temporary Data Removed"") del ds ``` I get the following Error message: ``` Temporary Data read to Python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in () 15 ds.ffill(""time"") 16 ---> 17 ds.to_netcdf(filename, format=""NETCDF3_CLASSIC"") 18 print(filename, ""Written!"") 19 /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute) 1148 engine=engine, encoding=encoding, 1149 unlimited_dims=unlimited_dims, -> 1150 compute=compute) 1151 1152 def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None, /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/api.pyc in to_netcdf(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims, compute) 721 try: 722 dataset.dump_to_store(store, sync=sync, encoding=encoding, --> 723 unlimited_dims=unlimited_dims, compute=compute) 724 if path_or_file is None: 725 return target.getvalue() /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims, compute) 1073 1074 store.store(variables, attrs, check_encoding, -> 1075 unlimited_dims=unlimited_dims) 1076 if sync: 1077 store.sync(compute=compute) /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in store(self, variables, attributes, check_encoding_set, unlimited_dims) 366 self.set_dimensions(variables, unlimited_dims=unlimited_dims) 367 self.set_variables(variables, check_encoding_set, --> 368 unlimited_dims=unlimited_dims) 369 370 def set_attributes(self, attributes): /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in set_variables(self, *args, **kwargs) 405 def set_variables(self, *args, **kwargs): 406 with self.ensure_open(autoclose=False): --> 407 super(NetCDF4DataStore, self).set_variables(*args, **kwargs) 408 409 def encode_variable(self, variable): /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in set_variables(self, variables, check_encoding_set, unlimited_dims) 403 check = vn in check_encoding_set 404 target, source = self.prepare_variable( --> 405 name, v, check, unlimited_dims=unlimited_dims) 406 407 self.writer.add(source, target) /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in prepare_variable(self, name, variable, check_encoding, unlimited_dims) 451 least_significant_digit=encoding.get( 452 'least_significant_digit'), --> 453 fill_value=fill_value) 454 _disable_auto_decode_variable(nc4_var) 455 netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.createVariable() netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__init__() TypeError: illegal primitive data type, must be one of ['i8', 'f4', 'f8', 'S1', 'i2', 'i4', 'u8', 'u4', 'u1', 'u2', 'i1'], got datetime64[ns] ``` and if I try with the default netcdf writing options ```python ds.to_netcdf(filename) print(filename, ""Written!"") ``` I get this error message: ``` Temporary Data read to Python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 15 ds.ffill(""time"") 16 ---> 17 ds.to_netcdf(filename) 18 print(filename, ""Written!"") 19 /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute) 1148 engine=engine, encoding=encoding, 1149 unlimited_dims=unlimited_dims, -> 1150 compute=compute) 1151 1152 def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None, /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/api.pyc in to_netcdf(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims, compute) 721 try: 722 dataset.dump_to_store(store, sync=sync, encoding=encoding, --> 723 unlimited_dims=unlimited_dims, compute=compute) 724 if path_or_file is None: 725 return target.getvalue() /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims, compute) 1073 1074 store.store(variables, attrs, check_encoding, -> 1075 unlimited_dims=unlimited_dims) 1076 if sync: 1077 store.sync(compute=compute) /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in store(self, variables, attributes, check_encoding_set, unlimited_dims) 366 self.set_dimensions(variables, unlimited_dims=unlimited_dims) 367 self.set_variables(variables, check_encoding_set, --> 368 unlimited_dims=unlimited_dims) 369 370 def set_attributes(self, attributes): /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in set_variables(self, *args, **kwargs) 405 def set_variables(self, *args, **kwargs): 406 with self.ensure_open(autoclose=False): --> 407 super(NetCDF4DataStore, self).set_variables(*args, **kwargs) 408 409 def encode_variable(self, variable): /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in set_variables(self, variables, check_encoding_set, unlimited_dims) 403 check = vn in check_encoding_set 404 target, source = self.prepare_variable( --> 405 name, v, check, unlimited_dims=unlimited_dims) 406 407 self.writer.add(source, target) /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in prepare_variable(self, name, variable, check_encoding, unlimited_dims) 418 unlimited_dims=None): 419 datatype = _get_datatype(variable, self.format, --> 420 raise_on_invalid_encoding=check_encoding) 421 attrs = variable.attrs.copy() 422 /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in _get_datatype(var, nc_format, raise_on_invalid_encoding) 99 def _get_datatype(var, nc_format='NETCDF4', raise_on_invalid_encoding=False): 100 if nc_format == 'NETCDF4': --> 101 datatype = _nc4_dtype(var) 102 else: 103 if 'dtype' in var.encoding: /home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in _nc4_dtype(var) 122 else: 123 raise ValueError('unsupported dtype for netCDF4 variable: {}' --> 124 .format(var.dtype)) 125 return dtype 126 ValueError: unsupported dtype for netCDF4 variable: datetime64[ns] ``` ### Version information If this is useful: ``` In [230]: xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.15.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-696.18.7.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None xarray: 0.10.7 pandas: 0.23.0 numpy: 1.11.3 scipy: 1.1.0 netCDF4: 1.4.0 h5netcdf: None h5py: None Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: None distributed: None matplotlib: 1.5.1 cartopy: None seaborn: None setuptools: 39.1.0 pip: 18.1 conda: None pytest: None IPython: 5.7.0 sphinx: None ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-436606451,https://api.github.com/repos/pydata/xarray/issues/2547,436606451,MDEyOklzc3VlQ29tbWVudDQzNjYwNjQ1MQ==,21049064,2018-11-07T12:24:36Z,2018-11-07T12:25:02Z,NONE,"The ds.time[0] won't let me set it's value to a datetime. Instead it returns a float: ```python In [19]: ds.time[0].item() Out[19]: 10509.0 ``` And none of the following work: ```python # doesn't change the time value ds.time[0].values = times[0] # returns an error because I can't assign to a function call ds.time[0].item() = times[0] # returns ValueError: replacement data must match the Variable's shape ds['time'].values = np.array(times[0]) ``` Thanks for your help!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-436400765,https://api.github.com/repos/pydata/xarray/issues/2547,436400765,MDEyOklzc3VlQ29tbWVudDQzNjQwMDc2NQ==,21049064,2018-11-06T20:40:20Z,2018-11-06T20:41:25Z,NONE,"Data: [netcdf_files.zip](https://github.com/pydata/xarray/files/2554979/netcdf_files.zip) Code below: ```python import numpy as np import pandas as pd import xarray as xr from shutil import copyfile import os data_dir = ""./"" filename = ""Rg_dummy.nc"" # get the datetime range times = pd.date_range(""2000-01-01"", ""2000-12-31"", name=""time"") var = ""Rg"" copyfile(filename, ""temp.nc"") ds = xr.open_dataset(""temp.nc"") print(""Temporary Data read to Python"") # FORWARD FILL FROM THE ORIGINAL DATA to new timesteps ds.reindex({""time"":times}) ds.ffill(""time"") # ds.to_netcdf(filename, format=""NETCDF3_CLASSIC"") # print(filename, ""Written!"") # remove temporary file os.remove(data_dir+""temp.nc"") print(""Temporary Data Removed"") del ds ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/2547#issuecomment-436383962,https://api.github.com/repos/pydata/xarray/issues/2547,436383962,MDEyOklzc3VlQ29tbWVudDQzNjM4Mzk2Mg==,21049064,2018-11-06T19:45:46Z,2018-11-06T19:50:00Z,NONE,"Thanks for your help! you definitely understood me correctly! This doesn't seem to work as it fills my`Rg` arrays with nan values. ```python ds = ds.reindex({""time"":pd.date_range(""2000-01-01"",""2000-12-31"")}) ds = ds.ffill(""time"") ``` ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,377947810 https://github.com/pydata/xarray/issues/1471#issuecomment-433952128,https://api.github.com/repos/pydata/xarray/issues/1471,433952128,MDEyOklzc3VlQ29tbWVudDQzMzk1MjEyOA==,21049064,2018-10-29T15:21:34Z,2018-10-29T15:21:34Z,NONE,"@smartass101 & @shoyer what would be the code for working with a `pandas.MultiIndex` object in this use case? Could you show how it would work related to your example above: ``` Dimensions: (num: 21, ar:2) # <-- note that MB is still of dims {'num': 19} only Coordinates: # <-- mostly unions as done by concat * num (num) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 B