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- .to_zarr with datetime64[ns] · 4 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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402327882 | https://github.com/pydata/xarray/issues/2265#issuecomment-402327882 | https://api.github.com/repos/pydata/xarray/issues/2265 | MDEyOklzc3VlQ29tbWVudDQwMjMyNzg4Mg== | NickMortimer 4338975 | 2018-07-04T00:25:33Z | 2018-07-04T00:25:33Z | NONE | @jhamman thanks I'll add to the discussion there and close this issue. |
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.to_zarr with datetime64[ns] 337733183 | |
402216978 | https://github.com/pydata/xarray/issues/2265#issuecomment-402216978 | https://api.github.com/repos/pydata/xarray/issues/2265 | MDEyOklzc3VlQ29tbWVudDQwMjIxNjk3OA== | jhamman 2443309 | 2018-07-03T16:30:06Z | 2018-07-03T16:30:06Z | MEMBER |
@NickMortimer - No, not yet. It has been proposed already though in #2022. |
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.to_zarr with datetime64[ns] 337733183 | |
402155326 | https://github.com/pydata/xarray/issues/2265#issuecomment-402155326 | https://api.github.com/repos/pydata/xarray/issues/2265 | MDEyOklzc3VlQ29tbWVudDQwMjE1NTMyNg== | NickMortimer 4338975 | 2018-07-03T13:22:24Z | 2018-07-03T13:24:04Z | NONE | @spencerkclark yes that helps very much and a great example of how to answer a question! I'm learning so much from this group. Is there a way of appending an xarray dataset onto an existing zarr array? That's why I've been accessing direct through zarr, what I'm trying to do is build a zarr file of all the Argo float profiles and add new ones as they arrive. |
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.to_zarr with datetime64[ns] 337733183 | |
402131750 | https://github.com/pydata/xarray/issues/2265#issuecomment-402131750 | https://api.github.com/repos/pydata/xarray/issues/2265 | MDEyOklzc3VlQ29tbWVudDQwMjEzMTc1MA== | spencerkclark 6628425 | 2018-07-03T12:05:37Z | 2018-07-03T12:05:37Z | MEMBER | When writing datetime objects to disk, xarray encodes them following CF conventions (converting them to numerical values quantifying some units of time since a given reference date). This is to support other backends which do not support writing For accurate roundtripping of Datasets, xarray also includes logic to automatically decode datetimes stored following CF conventions. For that reason, instead of loading in the raw zarr store using zarr's See the following example: ``` In [1]: import numpy as np In [2]: import xarray as xr In [3]: da = xr.DataArray(np.datetime64('2000-01-01'), name='date') In [4]: da Out[4]: <xarray.DataArray 'date' ()> array('2000-01-01T00:00:00.000000000', dtype='datetime64[ns]') In [5]: da.to_dataset().to_zarr('example.zarr') Out[5]: <xarray.backends.zarr.ZarrStore at 0x1109ca190> In [6]: ds = xr.open_zarr('example.zarr') In [7]: ds.date
Out[7]:
<xarray.DataArray 'date' ()>
array('2000-01-01T00:00:00.000000000', dtype='datetime64[ns]')
In [2]: z = zarr.open('example.zarr') In [3]: z['date'][...] Out[3]: array(0) In [4]: z['date'].attrs['units'] Out[4]: u'days since 2000-01-01 00:00:00' In [5]: z['date'].attrs['calendar'] Out[5]: u'proleptic_gregorian' ``` Unlike xarray, zarr does not include logic for automatically decoding CF-encoded datetimes. I hope that helps. |
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.to_zarr with datetime64[ns] 337733183 |
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