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Parametrize over different reduction operations #5

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Aug 22, 2024
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Parametrize over different reduction operations #5

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parametrize over different reduction operations
TomNicholas Aug 22, 2024
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22 changes: 8 additions & 14 deletions xarray_array_testing/reduction.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
from contextlib import nullcontext

import hypothesis.strategies as st
import pytest
import xarray.testing.strategies as xrst
from hypothesis import given

Expand All @@ -12,22 +13,15 @@ class ReductionTests(DuckArrayTestMixin):
def expected_errors(op, **parameters):
return nullcontext()

@pytest.mark.parametrize("op", ["mean", "sum", "prod", "std", "var"])
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One of "min" or "max" would be good for checking that dtype is preserved. I'm not sure "std" adds much over "var".

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These were really just chosen in order to cover some known bugs with cubed. Specifically that cubed doesn't yet implement std or var, and the array API standard only requires mean to work with floats, not integers.

Eventually we would want a test suite that covers everything, this is just meant to be a proof-of-principle for discussing overall design.

@given(st.data())
def test_variable_mean(self, data):
def test_variable_mean(self, op, data):
variable = data.draw(xrst.variables(array_strategy_fn=self.array_strategy_fn))

with self.expected_errors("mean", variable=variable):
actual = variable.mean().data
expected = self.xp.mean(variable.data)

self.assert_equal(actual, expected)

@given(st.data())
def test_variable_prod(self, data):
variable = data.draw(xrst.variables(array_strategy_fn=self.array_strategy_fn))

with self.expected_errors("prod", variable=variable):
actual = variable.prod().data
expected = self.xp.prod(variable.data)
with self.expected_errors(op, variable=variable):
# compute using xr.Variable.<OP>()
actual = getattr(variable, op)().data
# compute using xp.<OP>(array)
expected = getattr(self.xp, op)(variable.data)

self.assert_equal(actual, expected)