Rely on NEP-18 to dispatch to dask in duck_array_ops #5571

TomNicholas · 2021-07-03T19:24:33Z

Removes special-casing for dask in duck_array_ops.py, instead relying on NEP-18 to call it when the input is a dask array.

Probably actually don't need the _dask_or_eager_func() (now _module_func()) helper function at all, because all remaining instances look like pandas_isnull = _module_func("isnull", module=pd), which could just be pandas_isnull = pd.isnull.

Only problem is that I seem to have broken one (parameterized) test: test_duck_array_ops.py::test_min_count[True-True-None-sum-True-bool_-1] fails with

    @pytest.mark.parametrize("dim_num", [1, 2])
    @pytest.mark.parametrize("dtype", [float, int, np.float32, np.bool_])
    @pytest.mark.parametrize("dask", [False, True])
    @pytest.mark.parametrize("func", ["sum", "prod"])
    @pytest.mark.parametrize("aggdim", [None, "x"])
    @pytest.mark.parametrize("contains_nan", [True, False])
    @pytest.mark.parametrize("skipna", [True, False, None])
    def test_min_count(dim_num, dtype, dask, func, aggdim, contains_nan, skipna):
        if dask and not has_dask:
            pytest.skip("requires dask")
    
        da = construct_dataarray(dim_num, dtype, contains_nan=contains_nan, dask=dask)
        min_count = 3
    
        # If using Dask, the function call should be lazy.
        with raise_if_dask_computes():
>           actual = getattr(da, func)(dim=aggdim, skipna=skipna, min_count=min_count)

/home/tegn500/Documents/Work/Code/xarray/xarray/tests/test_duck_array_ops.py:578: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/home/tegn500/Documents/Work/Code/xarray/xarray/core/common.py:56: in wrapped_func
    return self.reduce(func, dim, axis, skipna=skipna, **kwargs)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/dataarray.py:2638: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/variable.py:1725: in reduce
    data = func(self.data, **kwargs)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:328: in f
    return func(values, axis=axis, **kwargs)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/nanops.py:106: in nansum
    a, mask = _replace_nan(a, 0)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/nanops.py:23: in _replace_nan
    mask = isnull(a)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:83: in isnull
    return pandas_isnull(data)
/home/tegn500/Documents/Work/Code/xarray/xarray/core/duck_array_ops.py:40: in f
    return getattr(module, name)(*args, **kwargs)
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/pandas/core/dtypes/missing.py:127: in isna
    return _isna(obj)
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/pandas/core/dtypes/missing.py:166: in _isna
    return _isna_ndarraylike(np.asarray(obj), inf_as_na=inf_as_na)
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/numpy/core/_asarray.py:102: in asarray
    return array(a, dtype, copy=False, order=order)
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:1502: in __array__
    x = self.compute()
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/base.py:285: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/base.py:567: in compute
    results = schedule(dsk, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.tests.CountingScheduler object at 0x7f0804db2310>
dsk = {('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 0): array([False, False, False, False], dtype=object), ('xar...pe=object), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 3): array([nan, False, False, nan], dtype=object)}
keys = [[('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 0), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7'...array-<this-array>-29953318277423606f95b509ad1a9aa7', 2), ('xarray-<this-array>-29953318277423606f95b509ad1a9aa7', 3)]]
kwargs = {}

    def __call__(self, dsk, keys, **kwargs):
        self.total_computes += 1
        if self.total_computes > self.max_computes:
>           raise RuntimeError(
                "Too many computes. Total: %d > max: %d."
                % (self.total_computes, self.max_computes)
            )
E           RuntimeError: Too many computes. Total: 1 > max: 0.

/home/tegn500/Documents/Work/Code/xarray/xarray/tests/__init__.py:118: RuntimeError

Closes UserWarning when wrapping pint & dask arrays together #5559
Tests added
Passes pre-commit run --all-files
User visible changes (including notable bug fixes) are documented in whats-new.rst

pep8speaks · 2021-07-03T19:24:40Z

Hello @TomNicholas! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2021-09-29 17:13:58 UTC

TomNicholas · 2021-07-03T19:31:44Z

Right I understand the failure now - everywhere where _dask_or_eager_func was dispatching numpy->dask is working fine via NEP-18, but my change made pandas.isnull no longer dispatch to dask.isnull, which then computes (and raises because we want it to be lazy). The solution is to special-case that one use of pandas (and perhaps any other uses of pandas?).

github-actions · 2021-07-03T19:50:27Z

Unit Test Results

        6 files ±0         6 suites ±0 56m 27s ⏱️ ±0s
16 228 tests ±0 14 493 ✔️ ±0 1 735 💤 ±0 0 ❌ ±0
90 564 runs ±0 82 384 ✔️ ±0 8 180 💤 ±0 0 ❌ ±0

Results for commit ebfc6a3. ± Comparison against base commit ebfc6a3.

♻️ This comment has been updated with latest results.

TomNicholas · 2021-07-03T20:49:50Z

A net-negative pull request 🤯

max-sixty · 2021-07-03T21:11:46Z

Looks great! I don't know the details of this code well, but at the conceptual level it looks good!

A net-negative pull request 🤯

The best type!

xarray/core/duck_array_ops.py

dcherian · 2021-07-03T23:55:08Z

xarray/core/duck_array_ops.py

+from numpy import take, tensordot, transpose, unravel_index  # noqa
+from numpy import where as _where
+from numpy import zeros_like  # noqa
+from numpy.ma import masked_invalid  # noqa


do masked arrays support NEP-18?

Hmm, I don't see an __array_function__ method in MaskedArray, but it does inherit from ndarray?

It does have an __array_function__ attribute

right, it does support NEP-18.

However, for some functions (e.g. np.median) it returns a strange MaskedConstant object if the result would be a constant. Not sure if it needs special care, but something to be aware of.

keewis · 2021-07-14T22:32:30Z

xarray/core/duck_array_ops.py

+from numpy import take, tensordot, transpose, unravel_index  # noqa
+from numpy import where as _where
+from numpy import zeros_like  # noqa
+from numpy.ma import masked_invalid  # noqa


right, it does support NEP-18.

However, for some functions (e.g. np.median) it returns a strange MaskedConstant object if the result would be a constant. Not sure if it needs special care, but something to be aware of.

keewis · 2021-07-14T22:34:30Z

xarray/core/duck_array_ops.py

-zeros_like = _dask_or_eager_func("zeros_like")
+# Requires special-casing because pandas won't automatically dispatch to dask.isnull via NEP-18
+def _dask_or_eager_isnull(obj):
+    if is_duck_dask_array(obj):


does dask_array.isnull dispatch for duck dask arrays, or does this also create e.g. dask(pint(dask)) objects?

It does this:

d = dask_array.array([1, 2, 3]) q = pint.Quantity(d, units='m') dask.array.isnull(q)

raises warnings

/home/tegn500/miniconda3/envs/xarray-testing-min-all-deps-py37/lib/python3.7/site-packages/dask/array/core.py:2756: UserWarning: Passing an object to dask.array.from_array which is already a Dask collection. This can lead to unexpected behavior. "Passing an object to dask.array.from_array which is already a " /home/tegn500/miniconda3/envs/xarray-testing-min-all-deps-py37/lib/python3.7/site-packages/numpy/core/_asarray.py:85: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray. return array(a, dtype, copy=False, order=order)

and returns the result

dask.array<_asarray_isnull, shape=(3,), dtype=bool, chunksize=(3,), chunktype=numpy.ndarray>

I think that's a sensible result of calling isnull on a chunked pint array?

xarray/core/duck_array_ops.py

xarray/tests/test_units.py

xarray/core/duck_array_ops.py

…nvalid

TomNicholas · 2021-07-22T20:31:35Z

Now there is one more mystery: why do some of the tests for padding fail with output that is 0.5 away from what's expected??:

_____________ TestVariableWithDask.test_pad[xr_arg3-np_arg3-mean] ______________
[gw3] linux -- Python 3.7.10 /usr/share/miniconda/envs/xarray-tests/bin/python

self = <xarray.tests.test_variable.TestVariableWithDask object at 0x7f10bd848990>
mode = 'mean', xr_arg = {'x': (3, 1), 'z': (2, 0)}
np_arg = ((3, 1), (0, 0), (2, 0))

    @pytest.mark.parametrize(
        "mode",
        [
            "mean",
            pytest.param(
                "median",
                marks=pytest.mark.xfail(reason="median is not implemented by Dask"),
            ),
            pytest.param(
                "reflect", marks=pytest.mark.xfail(reason="dask.array.pad bug")
            ),
            "edge",
            pytest.param(
                "linear_ramp",
                marks=pytest.mark.xfail(
                    reason="pint bug: https://github.com/hgrecco/pint/issues/1026"
                ),
            ),
            "maximum",
            "minimum",
            "symmetric",
            "wrap",
        ],
    )
    @pytest.mark.parametrize("xr_arg, np_arg", _PAD_XR_NP_ARGS)
    @pytest.mark.filterwarnings(
        r"ignore:dask.array.pad.+? converts integers to floats."
    )
    def test_pad(self, mode, xr_arg, np_arg):
        data = np.arange(4 * 3 * 2).reshape(4, 3, 2)
        v = self.cls(["x", "y", "z"], data)
    
        actual = v.pad(mode=mode, **xr_arg)
        expected = np.pad(data, np_arg, mode=mode)
   
>       assert_array_equal(actual, expected)
E       AssertionError: 
E       Arrays are not equal
E       
E       Mismatched elements: 48 / 96 (50%)
E       Max absolute difference: 0.5
E       Max relative difference: 0.25
E        x: array([[[ 9.5,  9.5,  9. , 10. ],
E               [11.5, 11.5, 11. , 12. ],
E               [13.5, 13.5, 13. , 14. ]],...
E        y: array([[[10, 10,  9, 10],
E               [12, 12, 11, 12],
E               [14, 14, 13, 14]],...

dcherian · 2021-07-23T21:51:46Z

for padding fail with output that is 0.5 away from what's expected

We work around some dask bugs with dask_array_compat.pad I think you'll need to revert to that and not use np.pad until we can bump dask.

EDIT: though I don't see this kind of thing being fixed there.

Illviljan · 2021-08-21T16:23:26Z

The issue mentioned in dask_array_compat.pad was closed around may 2020 (dask/dask#6213).
The min version of dask can be bumped to 2.24 tomorrow (from 2.15 and 2.9), maybe that's enough to fix the errors?

Illviljan · 2021-08-25T23:42:08Z

@TomNicholas try merging main now. Let's see if there are any errors left.

TomNicholas · 2021-08-26T00:46:35Z

@Illviljan that actually solved those padding errors! Awesome!

The tests still fail because of something going on with np.around in the doctests though 😕

dcherian · 2021-08-26T01:07:39Z

I think you just need to fix the spaces in the expected output

Illviljan · 2021-08-26T04:50:04Z

___________ [doctest] xarray.core._typed_ops.DataArrayOpsMixin.round ___________
EXAMPLE LOCATION UNKNOWN, not showing all tests of that example
??? >>> np.around([0.37, 1.64])
Expected:
    array([0.,  2.])
Got:
    array([0., 2.])

/home/runner/work/xarray/xarray/xarray/core/_typed_ops.py:None: DocTestFailure

The expected had 2 spaces after the comma for some reason. I think the actual output makes more sense now so I think it's fine to just accept this difference and change the expected output.

Illviljan · 2021-08-26T18:16:51Z

Seems to be a problem in numpy np.around fails the doctest, see numpy/numpy#19759.

I wonder if the docstring can be ignored if it is copied from somewhere else?

I think an easy workaround is simply replacing the docstring:

xarray/xarray/core/duck_array_ops.py

Line 75 in 4fd81b5

around = _dask_or_eager_func("around")

around = _dask_or_eager_func("around")
# np.around has failing doctests, overwrite it so they pass:
around.__doc__ = """
    Evenly round to the given number of decimals.

    Parameters
    ----------
    a : array_like
        Input data.
    decimals : int, optional
        Number of decimal places to round to (default: 0).  If
        decimals is negative, it specifies the number of positions to
        the left of the decimal point.
    out : ndarray, optional
        Alternative output array in which to place the result. It must have
        the same shape as the expected output, but the type of the output
        values will be cast if necessary. See :ref:`ufuncs-output-type` for more
        details.

    Returns
    -------
    rounded_array : ndarray
        An array of the same type as `a`, containing the rounded values.
        Unless `out` was specified, a new array is created.  A reference to
        the result is returned.

        The real and imaginary parts of complex numbers are rounded
        separately.  The result of rounding a float is a float.

    See Also
    --------
    ndarray.round : equivalent method

    ceil, fix, floor, rint, trunc


    Notes
    -----
    For values exactly halfway between rounded decimal values, NumPy
    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
    -0.5 and 0.5 round to 0.0, etc.

    ``np.around`` uses a fast but sometimes inexact algorithm to round
    floating-point datatypes. For positive `decimals` it is equivalent to
    ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has
    error due to the inexact representation of decimal fractions in the IEEE
    floating point standard [1]_ and errors introduced when scaling by powers
    of ten. For instance, note the extra "1" in the following:

        >>> np.round(56294995342131.5, 3)
        56294995342131.51

    If your goal is to print such values with a fixed number of decimals, it is
    preferable to use numpy's float printing routines to limit the number of
    printed decimals:

        >>> np.format_float_positional(56294995342131.5, precision=3)
        '56294995342131.5'

    The float printing routines use an accurate but much more computationally
    demanding algorithm to compute the number of digits after the decimal
    point.

    Alternatively, Python's builtin `round` function uses a more accurate
    but slower algorithm for 64-bit floating point values:

        >>> round(56294995342131.5, 3)
        56294995342131.5
        >>> np.round(16.055, 2), round(16.055, 2)  # equals 16.0549999999999997
        (16.06, 16.05)


    References
    ----------
    .. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
           https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
    .. [2] "How Futile are Mindless Assessments of
           Roundoff in Floating-Point Computation?", William Kahan,
           https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf

    Examples
    --------
    >>> np.around([0.37, 1.64])
    array([0., 2.])
    >>> np.around([0.37, 1.64], decimals=1)
    array([0.4, 1.6])
    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
    array([0., 2., 2., 4., 4.])
    >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
    array([ 1,  2,  3, 11])
    >>> np.around([1,2,3,11], decimals=-1)
    array([ 0,  0,  0, 10])

    """

Illviljan · 2021-08-27T16:55:12Z

Saw dask does similar fixes too:
https://github.com/dask/dask/blob/85f0b14bd36a5135ce51aeee067b6207374b00c4/dask/array/wrap.py#L183

Here's a version inspired by that one:

around = _dask_or_eager_func("around")
# np.around has failing doctests, overwrite it so they pass:
# https://github.com/numpy/numpy/issues/19759
around.__doc__ = test.__doc__.replace(
    "array([0.,  2.])",
    "array([0., 2.])",
)
around.__doc__ = test.__doc__.replace(
    "array([0.4,  1.6])",
    "array([0.4, 1.6])",
)
around.__doc__ = test.__doc__.replace(
    "array([0.,  2.,  2.,  4.,  4.])",
    "array([0., 2., 2., 4., 4.])",
)

xarray/core/duck_array_ops.py

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

dcherian · 2021-08-27T17:42:32Z

Thanks @Illviljan

xarray/core/duck_array_ops.py

Illviljan · 2021-08-27T18:34:42Z

I must say it gets kind of annoying having so pedantic doctests and doc generation when upstream modules aren't as picky.

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

Illviljan · 2021-08-27T19:08:38Z

I don't understand why doctests doesn't go through the if path, is it using tricks that can make around.__doc__ == None?

Anyway I think just ignoring these typing errors might be easier.

Readthedocs error:

--------
/home/docs/checkouts/readthedocs.org/user_builds/xray/checkouts/5571/xarray/core/_typed_ops.py:docstring of xarray.core._typed_ops.DataArrayOpsMixin.round:65: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/docs/checkouts/readthedocs.org/user_builds/xray/checkouts/5571/xarray/core/_typed_ops.py:docstring of xarray.core._typed_ops.DataArrayOpsMixin.round:65: WARNING: Unexpected section title or transition.

xarray/core/duck_array_ops.py

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

TomNicholas · 2021-08-27T21:36:48Z

Doctests passed! Thanks so much @Illviljan !

dcherian

LGTM though maybe we should merge main again before merging..

TomNicholas · 2021-09-29T17:48:31Z

Thanks for the reminder @dcherian - I merged main and all the tests pass so I'll merge this PR now!

TomNicholas · 2021-09-30T21:39:32Z

The what's new entry for this went in under the wrong edition - I fixed it in ebfc6a3

* basic test for the mean * minimum to get mean working * don't even need to call dask specifically * remove reference to dask when dispatching to modules * fixed special case of pandas vs dask isnull * removed _dask_or_eager_func completely * noqa * pre-commit * what's new * linting * properly import dask for test * fix iris conversion error by rolling back treatment of np.ma.masked_invalid * linting * Update xarray/core/duck_array_ops.py Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com> * Update xarray/core/duck_array_ops.py Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com> * Update xarray/core/duck_array_ops.py Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com> Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com> Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

TomNicholas added 4 commits July 3, 2021 14:57

basic test for the mean

895c262

minimum to get mean working

ec5fb50

don't even need to call dask specifically

78a3cee

remove reference to dask when dispatching to modules

bf8600e

TomNicholas added 5 commits July 3, 2021 15:58

fixed special case of pandas vs dask isnull

b64732a

removed _dask_or_eager_func completely

7405c02

noqa

1c394ee

pre-commit

895d44a

what's new

6b711a2

TomNicholas added the needs review label Jul 3, 2021

dcherian reviewed Jul 3, 2021

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linting

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Release v0.19? #5588

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properly import dask for test

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ENH: Adding __array_ufunc__ capability to MaskedArrays numpy/numpy#16022

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merged main

59278ef

fix iris conversion error by rolling back treatment of np.ma.masked_i…

7cd65cd

…nvalid

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Blog post to show off pint-xarray integration? xarray-contrib/pint-xarray#114

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Bump minimum version requirements for dask from 2.15 to 2.24 #5726

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TomNicholas added 2 commits August 25, 2021 19:47

Merge branch 'main' into duck_array_ops_dont_treat_dask_special

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np.around's footnote [2] is not referenced. numpy/numpy#19765

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Update xarray/core/duck_array_ops.py

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Merge branch 'main' into duck_array_ops_dont_treat_dask_special

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Release v0.20? #5889

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Rely on NEP-18 to dispatch to dask in duck_array_ops #5571

Rely on NEP-18 to dispatch to dask in duck_array_ops #5571

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Rely on NEP-18 to dispatch to dask in duck_array_ops #5571

Rely on NEP-18 to dispatch to dask in duck_array_ops #5571

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Comment last updated at 2021-09-29 17:13:58 UTC

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