Encodings for object data types are not saved. #40
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When encoding cf variables check if dtype is np.datetime64. fixes akleeman/xray/issues/39
| @@ -249,11 +249,12 @@ def encode_cf_variable(array): | |||
| attributes = array.attributes.copy() | |||
| encoding = array.encoding | |||
|
|
|||
| if isinstance(data, pd.DatetimeIndex): | |||
| if (isinstance(data, pd.DatetimeIndex) or | |||
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I think the right check is np.issubdtype(data.dtype, np.datetime64), which will cover both these cases.
Changed datetime check, copied encoding.
shoyer
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Mar 7, 2014
This allows us to simplify our internal model for XArray (it always cached internally as a base ndarray) and supports some previously tricky aspects involving pandas.Index objects. Noteably: 1. The dtype of arrays stored as pandas.Index objects can now be faithfully saved and restored. Doing math with XArray objects always yields objects with the right dtype, so `ds['latitude'] + 1` has dtype=float, not dtype=object. 2. It's no longer necessary to load index data into memory upon creating a new Dataset. Instead, the index data can be loaded on demand. 3. `var.data` is always an ndarray. `var.index` is always a pandas.Index. Related issues: #17, #39, #40.
keewis
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Jan 17, 2024
* tests for name collisions between variables and children * pass 2/3 tests
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decode_cf_variable will not save encoding for any 'object' dtypes.
When encoding cf variables check if dtype is np.datetime64 as well as DatetimeIndex.
fixes akleeman/xray/issues/39