BUG: Iris coords bounds indexing broken with multiple ellipsis

[cpelley]

In numpy v1.12.0 it is no longer simply deprecated to index with a double ellipsis, it is now enforced by an IndexError exception raised. Here is the relevant numpy change.

This exposes a bug in iris as iris.util.column_slices_generator can return an ellipsis, then an additional ellipsis is also appended here.

>>> import numpy as np
>>> import iris.coords.DimCoord as DimCoord
>>> np.__version__
'1.12.0'
>>> coord = DimCoord([-1,  0,  1,  2])
>>> coord.guess_bounds()
>>> coord[np.array([0, 3])]
*** IndexError: an index can only have a single ellipsis ('...')

Here is a partial traceback:

...
    coord = super(DimCoord, self).__getitem__(key)
  File "/path/to/iris/coords.py", line 496, in __getitem__
    bounds = bounds[keys + (Ellipsis, )]
IndexError: an index can only have a single ellipsis ('...')

Workaround is to explicitly specify numpy as one of our dependencies and pin its version to 1.11

[cpelley]

This exposes a problem with the travis testing. I notice that the iris travis tests still appear to be using numpy v1.10 but I get numpy v1.12 (the latest) via a conda install iris with the conda forge channel (where one get's iris v1.12 right now). Since iris supports later versions of numpy, I think this exposes a bug and a frailty of testing here.

[marqh]

this is being actively worked on by myself and @djkirkham

i know there are changes staged for the coord API to avoid double ellipsis
suggestions for increasing test coverage in this realm are most welcome, please notify @djkirkham and I of these

[cpelley]

this is being actively worked on by myself and @djkirkham

Great news, let me know if I can help in any way.

[marqh]

Great news, let me know if I can help in any way.

writing a test case for us which catches the failure mode you are interested in would help

the only failure i remember seeing was fixed inside iris.coord not iris.util

but this is exactly here:

  File "/path/to/iris/coords.py", line 496, in __getitem__
    bounds = bounds[keys + (Ellipsis, )]

iirc

[cpelley]

the only failure i remember seeing was fixed inside iris.coord not iris.util

That's right the failure is indeed in iris.coord. I mentioned iris.util.column_slices_generator only as context to where the two ellipsis had come from that's all.

[cpelley]

this is being actively worked on by myself and @djkirkham

@marqh do you have an open PR/issue?

writing a test case for us which catches the failure mode you are interested in would help

See the ticket description. Happy to turn this into a failing unittest for your branch if you want.

[DamienIrving]

Here's an error I got today which might be relevant to this bug (and might be a good test case):

iris.coord_categorisation.add_year(cube, 'time')
cube = cube.aggregated_by(['year'], iris.analysis.MEAN)

IndexError: an index can only have a single ellipsis ('...')

[marqh]

I believe #2440 has fixed this issue, as part of a review of version dependencies

@cpelley @DamienIrving
if you get an opportunity to confirm this, or raise why it is still an issue, that would be helpful

[DamienIrving]

@marqh To confirm I'd need to install iris from source, right? I usually install using conda and have very little sys admin experience, so what would be the easiest way for me to test the source? (Or will the changes made in #2440 be part of the conda install soon anyway?)

[cpelley]

I'll confirm shortly, just have numpy building. @DamienIrving, if you can provide me a small isolated example to run, I can see if I can run it for you?

[cpelley]

Confirmed, example in the ticket description now works.

Cheers

[DamienIrving]

@cpelley If you have any cube with a time axis and try the following you'll get the error:

iris.coord_categorisation.add_year(cube, 'time')
cube = cube.aggregated_by(['year'], iris.analysis.MEAN)

IndexError: an index can only have a single ellipsis ('...')

[cpelley]

@DamienIrving I can confirm the following works for me now with iris master:

>>> import iris.tests.stock
>>> import iris.coord_categorisation
>>> import numpy as np
>>> np.__version__
'1.12.1'
>>> cube = iris.tests.stock.realistic_3d()
>>> iris.coord_categorisation.add_year(cube, 'time')
>>> cube.aggregated_by(['year'], iris.analysis.MEAN)
<iris 'Cube' of air_potential_temperature / (K) (time: 1; grid_latitude: 9; grid_longitude: 11)>

You happy for me to close this issue?

[DamienIrving]

@cpelley Thanks! I'd be happy for you to close the issue.

Do you know how long it might be before this change is available to those who install iris via conda? The inability to create an annual mean timeseries is rather crippling for many of my workflows.