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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
116869436 MDU6SXNzdWUxMTY4Njk0MzY= 656 unexpected behavior when assignment used incorrectly jfburkhart 109530 closed 0     1 2015-11-13T22:51:12Z 2016-01-12T18:50:26Z 2016-01-12T18:50:26Z NONE      

An issue came up in which unexpected behavior was resulting from the use of the assignment operator with an xray.Dataset. You can see below in the first simple example, that an attribute can be created and attached to an xray.Dataset object, that the xray is not aware of:

``` python In [1]: import xray

In [2]: ds = xray.Dataset()

In [3]: ds.x = 1

In [4]: ds Out[4]: <xray.Dataset> Dimensions: () Coordinates: empty Data variables: empty

In [5]: ds.x Out[5]: 1 ```

It can be seen also in a bit more detail below, showing that one could easily mistake the assignment for doing something, when in fact it seems to do nothing to the expected variable:

``` python In [27]: ds = xray.Dataset( {'my_ones': (('dim1',), np.ones((100)))}, {'dim1':np.arange(0,100)})

In [28]: ds Out[28]: <xray.Dataset> Dimensions: (dim1: 100) Coordinates: * dim1 (dim1) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... Data variables: my_ones (dim1) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...

In [29]: ds['new_var'] = np.random.rand(100)

In [30]: ds Out[30]: <xray.Dataset> Dimensions: (dim1: 100) Coordinates: * dim1 (dim1) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... Data variables: my_ones (dim1) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... new_var (dim1) float64 0.008922 0.4298 0.878 0.3581 0.05668 0.4459 ...

In [31]: ds.my_ones = np.random.rand(100)

In [32]: ds Out[32]: <xray.Dataset> Dimensions: (dim1: 100) Coordinates: * dim1 (dim1) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... Data variables: my_ones (dim1) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... new_var (dim1) float64 0.008922 0.4298 0.878 0.3581 0.05668 0.4459 ... ```

See here, my_ones are still 1.0s even though we assigned them random numbers above!

Now let's make a real attribute:

``` python In [33]: ds.attrs['my_real_attribute'] = ds.my_ones

In [34]: ds Out[34]: <xray.Dataset> Dimensions: (dim1: 100) Coordinates: * dim1 (dim1) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... Data variables: my_ones (dim1) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... new_var (dim1) float64 0.008922 0.4298 0.878 0.3581 0.05668 0.4459 ... Attributes: my_real_attribute: [ 0.8999457 0.52066577 0.08915267 0.0541855 0.45131377 0.09501768 0.95209237 0.89464241 0.36151148 0.10759526 0.91542506 0.22980835 0.17357796 0.19259669 0.17985614 0.20401482 0.46921508 0.27108936 0.2420025 0.75083791 0.94271824 0.89902967 0.29649179 0.70362153 0.73475068 0.3591411 0.79941874 0.18079561 0.23058618 0.84660496 0.82993496 0.50236557 0.09758405 0.82851117 0.85434362 0.33170515 0.11670085 0.37135022 0.8647876 0.18162848 0.5675491... ```

And we see it shows up. Somehow ds.my_ones persists, but it is not an attribute and not in fact a variable... strange!

 
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   completed xarray 13221727 issue

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