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/7790#issuecomment-1532152709,https://api.github.com/repos/pydata/xarray/issues/7790,1532152709,IC_kwDOAMm_X85bUsuF,14983768,2023-05-02T21:07:27Z,2023-05-02T21:09:10Z,NONE,"@kmuehlbauer - genius! Yes. That pull request should fix this issue exactly! And it explains why I see this issue and you don't - with undefined behavior anything can happen. Since we are on different OSes, our systems behave differently. I just double checked with pandas and this fix will do the right thing: ```python import pandas as pd print(pd.to_timedelta([np.nan, 0],""ns"") + np.datetime64('1970-01-01')) ``` ``` DatetimeIndex(['NaT', '1970-01-01'], dtype='datetime64[ns]', freq=None) ``` I see that the pull request with the fix has been sitting since December of last year. Is there some way to somehow get someone to look at that pull request who can merge it?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1530347592,https://api.github.com/repos/pydata/xarray/issues/7790,1530347592,IC_kwDOAMm_X85bN0BI,14983768,2023-05-01T21:43:08Z,2023-05-01T21:43:56Z,NONE,"Ah hah! Well, I don't know why this is working for you @kmuehlbauer, but I can see why it is not working for me. I've been debugging through the code and it looks like the problem is the `_decode_datetime_with_pandas` function. For me, it's converting a float NaN into an integer, which results in a zero value. It all starts in the `open_zarr` function, which by default sets the `use_cftime` parameter to None by default: https://github.com/pydata/xarray/blob/25d9a28e12141b9b5e4a79454eb76ddd2ee2bc4d/xarray/backends/zarr.py#L701-L817 There's a bunch of stuff that gets called, but eventually we get to the function `decode_cf_datetime`, which ironically (given the name) also takes this `use_cftime` parameter, which is still None. Because `use_cftime` is None, the function calls `_decode_datetime_with_pandas`: https://github.com/pydata/xarray/blob/25d9a28e12141b9b5e4a79454eb76ddd2ee2bc4d/xarray/coding/times.py#L265-L289 and then, in `_decode_datetime_with_pandas`, the code casts a float NaN value to zero: https://github.com/pydata/xarray/blob/979b99831f5d34d33120312a15dad3e6a0830f32/xarray/coding/times.py#L216-L262 In line 254, `flat_num_dates` is `array([ nan, 1.6726176e+18])`. After line 254, `flat_nuM-dates_ns_int` is `array([ 0, 1672617600000000000])`. ","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1530186148,https://api.github.com/repos/pydata/xarray/issues/7790,1530186148,IC_kwDOAMm_X85bNMmk,14983768,2023-05-01T20:25:34Z,2023-05-01T20:25:34Z,NONE,"@kmuehlbauer - I ran https://github.com/pydata/xarray/issues/7790#issuecomment-1529894939 and I get an incorrect fill value: ``` ****************** Created with fill value 1900-01-01 array([ 'NaT', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] NaT 2023-01-02 ****************** Read back out of the zarr store with xarray array(['1970-01-01T00:00:00.000000000', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1970-01-01 2023-01-02 {} {'chunks': (2,), 'preferred_chunks': {'time': 2}, 'compressor': Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0), 'filters': None, '_FillValue': -2208988800000000000, 'units': 'nanoseconds since 1970-01-01', 'calendar': 'proleptic_gregorian', 'dtype': dtype('int64')} ****************** Read back out of the zarr store with zarr [-2208988800000000000 1672617600000000000] ``` and here is my show_versions, since it may have changed because I've added some new libraries. It looks like my ipython version is slightly different, but I can't see how that would affect things. ``` INSTALLED VERSIONS ------------------ commit: None python: 3.11.3 | packaged by conda-forge | (main, Apr 6 2023, 08:58:31) [Clang 14.0.6 ] python-bits: 64 OS: Darwin OS-release: 22.4.0 machine: arm64 processor: arm byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: None libnetcdf: None xarray: 2023.4.2 pandas: 2.0.1 numpy: 1.24.3 scipy: None netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: 2.14.2 cftime: None nc_time_axis: None PseudoNetCDF: None iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 67.7.2 pip: 23.1.2 conda: None pytest: None mypy: None IPython: 8.13.1 sphinx: None ``` ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1530056660,https://api.github.com/repos/pydata/xarray/issues/7790,1530056660,IC_kwDOAMm_X85bMs_U,14983768,2023-05-01T18:37:47Z,2023-05-01T18:39:21Z,NONE,"Oops! Yes. You are right. I had some cross-wording on the variable names. So I started a new notebook. Unfortunately, I think you may have also gotten some wires crossed? You set the time fill value to 1900-01-01, but then use NaT in the actual array? Here is a fresh notebook with a stand-alone cell with everything that I think you were doing, but I'm not 100%. The fill value is still wrong when it gets read out, but it is at least different? The fill value is now set to the units for some reason. This seems like progress? ```python import numpy as np import xarray as xr import zarr # Create a time array with one fill value, NaT time = np.array([np.datetime64(""NaT"", ""ns""), '2023-01-02 00:00:00.00000000'], dtype='M8[ns]') # Create xarray with this fill value xr_time_array = xr.DataArray(data=time,dims=['time'],name='time') xr_ds = xr.Dataset(dict(time=xr_time_array)) print(""**********************"") print(""xarray created with NaT fill value"") print(""----------------------"") print(xr_ds[""time""]) # Save as zarr location_with_units = ""xarray_and_units.zarr"" encoding = { ""time"":{""_FillValue"":np.datetime64(""NaT"",""ns""),""dtype"":np.int64,""units"":""nanoseconds since 1970-01-01""} } xr_ds.to_zarr(location_with_units,mode=""w"",encoding=encoding) # Read it back out again xr_read = xr.open_zarr(location_with_units) print(""**********************"") print(""xarray created read with NaT fill value"") print(""----------------------"") print(xr_read[""time""]) print(xr_read[""time""].attrs) print(xr_read[""time""].encoding) ``` ``` ********************** xarray created with NaT fill value ---------------------- array([ 'NaT', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] NaT 2023-01-02 ********************** xarray created read with NaT fill value ---------------------- array(['1970-01-01T00:00:00.000000000', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1970-01-01 2023-01-02 {} {'chunks': (2,), 'preferred_chunks': {'time': 2}, 'compressor': Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0), 'filters': None, '_FillValue': -9223372036854775808, 'units': 'nanoseconds since 1970-01-01', 'calendar': 'proleptic_gregorian', 'dtype': dtype('int64')} ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1527948787,https://api.github.com/repos/pydata/xarray/issues/7790,1527948787,IC_kwDOAMm_X85bEqXz,14983768,2023-04-28T18:39:01Z,2023-04-28T18:39:01Z,NONE,Where in the code is the time array being _decoded_? That seems to be where a lot of the issue is?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1527918654,https://api.github.com/repos/pydata/xarray/issues/7790,1527918654,IC_kwDOAMm_X85bEjA-,14983768,2023-04-28T18:08:16Z,2023-04-28T18:08:16Z,NONE,"The zarr store does indeed use an integer in this case according to the .zmetadata file: ``` { ""metadata"": { "".zattrs"": {}, "".zgroup"": { ""zarr_format"": 2 }, ""time/.zarray"": { ""chunks"": [ 2 ], ""compressor"": { ""blocksize"": 0, ""clevel"": 5, ""cname"": ""lz4"", ""id"": ""blosc"", ""shuffle"": 1 }, ""dtype"": "" array(['1900-01-01T00:00:00.000000000', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1900-01-01 2023-01-02 ****************** array(['2023-01-02T00:00:00.000000000', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2023-01-02 2023-01-02 {'chunks': (2,), 'preferred_chunks': {'time': 2}, 'compressor': Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0), 'filters': None, '_FillValue': -9.223372036854776e+18, 'units': 'days since 2023-01-02 00:00:00', 'calendar': 'proleptic_gregorian', 'dtype': dtype('float64')} ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1525774670,https://api.github.com/repos/pydata/xarray/issues/7790,1525774670,IC_kwDOAMm_X85a8XlO,14983768,2023-04-27T14:13:58Z,2023-04-27T14:13:58Z,NONE,"Interestingly, xarray is also perfectly happy to read a numpy.datetime64 array out of a zarr store as long as the xarray metadata is present. xarray even helpfully creates an '_FillValue"" attribute for the array so there is no confusion: ``` # Create a zarr store directly with numpy.datetime64 type location_zarr_direct = ""from_zarr.zarr"" root = zarr.open(location_zarr_direct,mode='w') z_time_array = root.create_dataset( ""time"",data=time,shape=time.shape,chunks=time.shape,dtype=time.dtype, fill_value=time_fill_value ) # Add xarray metadata z_time_array.attrs[""_ARRAY_DIMENSIONS""] = [""time""] zarr.convenience.consolidate_metadata(location_zarr_direct) # Use xarray to read this data out xr_read_from_zarr = xr.open_zarr(location_zarr_direct) print(xr_read_from_zarr[""time""]) ``` ``` array([ 'NaT', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] NaT 2023-01-02 Attributes: _FillValue: NaT ``` So I am extremely confused as to why xarray encodes time arrays so strangely when it creates the zarr store itself! (Hence https://github.com/pydata/xarray/discussions/7776)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922 https://github.com/pydata/xarray/issues/7790#issuecomment-1525766244,https://api.github.com/repos/pydata/xarray/issues/7790,1525766244,IC_kwDOAMm_X85a8Vhk,14983768,2023-04-27T14:08:37Z,2023-04-27T14:08:37Z,NONE,"Ah! Okay. I did not know about the `.encoding` option, which does indeed have the fill value. Thank you. Interestingly, -9.223372036854776e+18 is just the float equivalent of numpy.datetime64('NaT'): ```python float(np.datetime64('NaT').view('i8')) ``` ``` -9.223372036854776e+18 ``` And I know this isn't an issue with zarr and NaT because I can create the zarr store directly with the zarr library and it's perfectly happy: ```python # Create a zarr store directly with numpy.datetime64 type location_zarr_direct = ""from_zarr.zarr"" root = zarr.open(location_zarr_direct,mode='w') z_time_array = root.create_dataset( ""time"",data=time,shape=time.shape,chunks=time.shape,dtype=time.dtype, fill_value=time_fill_value ) zarr.convenience.consolidate_metadata(location_zarr_direct) # Read it back out again read_zarr = zarr.open(location_zarr_direct,mode='r') print(read_zarr[""time""][:]) ``` ``` [ 'NaT' '2023-01-02T00:00:00.000000000'] ``` ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1685803922