issues: 532696790

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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
532696790 MDU6SXNzdWU1MzI2OTY3OTA= 3594 support for units with pint 14808389 open 0     7 2019-12-04T13:49:28Z 2022-08-03T11:44:05Z   MEMBER      

pint's implementation of NEP-18 (see hgrecco/pint#905) is close enough so we can finally start working on the pint support (i.e. make the integration tests pass). This would be the list of tasks to get there: * integration tests: - [x] implement integration tests for DataArray, Dataset and top-level functions (#3238, #3447, #3493) - [x] add tests for Variable as discussed in #3493 (#3654) - [x] clean up the current tests (#3600) - [x] use the standard assert_identical and assert_allclose functions (#3611, #3643, #3654, #3706, #3975) - [x] clean up the TestVariable.test_pad tests * actually get xarray to support units: - [x] top-level functions (#3611) - [x] Variable (#3706) + rolling_window and identical need larger modifications - [x] DataArray (#3643) - [x] Dataset - [x] silence all the UnitStrippedWarnings in the testsuite (#4163) - [ ] try to get nanprod to work with quantities - [x] add support for per variable fill values (#4165) - [x] repr with units (#2773) - [ ] type hierarchy (e.g. for np.maximum(data_array, quantity) vs np.maximum(quantity, data_array)) (#3950) * update the documentation - [x] point to pint-xarray (see #4530) - [x] mention the requirement for UnitRegistry(force_ndarray=True) or UnitRegistry(force_ndarray_like=True) (see https://pint-xarray.readthedocs.io/en/stable/creation.html#attaching-units) - [x] list the known issues (see https://github.com/pydata/xarray/pull/3643#issue-354872657 and https://github.com/pydata/xarray/pull/3643#issuecomment-602225731) (#4530): + pandas (indexing) + bottleneck (bfill, ffill) + scipy (interp) + numbagg (rolling_exp) + numpy.lib.stride_tricks.as_strided: rolling + numpy.vectorize: interpolate_na - [x] ~update the install instructions (we can use standard conda / pip now)~ this should be done by pint-xarray

 
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