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3 rows where state = "closed" and user = 5497186 sorted by updated_at descending
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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 |
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178887041 | MDU6SXNzdWUxNzg4ODcwNDE= | 1016 | DataArray coords: tuple vs. list | jonathanstrong 5497186 | closed | 0 | 2 | 2016-09-23T14:08:15Z | 2018-10-31T16:56:47Z | 2018-10-31T16:56:47Z | NONE | is there a reason the ``` this worksxarray.DataArray(np.random.random((3, 3, 3)), dims=('one', 'two', 'three'), coords={ 'one': ['four', 'five', 'six'], } ) this throws the following errorValueError: dimensions ('four',) must have the same length as the number of data dimensions, ndim=0xarray.DataArray(np.random.random((3, 3, 3)), dims=('one', 'two', 'three'), coords={ 'one': ('four', 'five', 'six'), } ) ``` even if there was a clearer error it would help quite a bit. As it stands you are thinking, 'what?! four isn't a dimension!' using 0.8.2 |
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134376872 | MDU6SXNzdWUxMzQzNzY4NzI= | 768 | save/load DataArray to numpy npz functions | jonathanstrong 5497186 | closed | 0 | 11 | 2016-02-17T19:29:31Z | 2016-12-24T11:55:40Z | 2016-12-24T11:55:40Z | NONE | hey - Apologies if this is bad form: I wanted to pass this along but don't have time to do a proper pull request. I have found pickle to be really problematic for serializing data, so wrote these two functions to save to numpy's binary npz format and retrieve it. Generally, the numpy format is much less likely to bomb when attempting to load on another computer because of some unseen dependency. If there's interest, I could probably add this as a serialization method to DataArray in the next month or so. ``` python def to_npz(da, file_or_buffer): if 'dims' in da.dims: raise ValueError('Can\'t use "dims" as a dim name.') if 'values' in da.dims: raise ValueError('Can\'t use "values" as a dim name.') arrays = {} arrays['dims'] = da.dims for dim in da.dims: arrays[dim] = da.indexes[dim] arrays['values'] = da.values np.savez(file_or_buffer, **arrays) def from_npz(file_or_buffer): data = np.load(file_or_buffer) assert hasattr(data, 'keys'), "np.load returned a {}, not a dict-like object".format(type(data)) assert 'dims' in data, 'Can\'t locate "dims" key in file' assert 'values' in data, 'Can\'t locate "values" key in file' for dimname in data['dims']: assert dimname in data, 'Can\'t locate "{}" key in file'.format(dimname) return xray.DataArray(data['values'], dims=data['dims'], coords=dict(zip(data['dims'], [data[dimname] for dimname in data['dims']]))) ``` it's pretty speedy, here is an example for a (3, 4, 5) shaped DataArray:
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completed | xarray 13221727 | issue | ||||||
126205116 | MDU6SXNzdWUxMjYyMDUxMTY= | 716 | pandas date_range as index causes TypeError on repr | jonathanstrong 5497186 | closed | 0 | 2 | 2016-01-12T15:33:20Z | 2016-01-13T22:17:00Z | 2016-01-13T22:17:00Z | NONE | love this library btw, much thanks. ```
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completed | xarray 13221727 | issue |
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