TST/MAINT: signal.symiirorder2: r, omega, precision are floats; use default dtypes #22783
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ev-br
reviewed
Apr 2, 2025
| rsq = r * r | ||
| a2 = 2 * r * np.cos(omega) | ||
| a2 = 2 * r * math.cos(omega) |
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Despite what the docs say, I wouldn't be surprised if historic usage allows 0D arrays or np.float64s. This part of the codebase is straight from the multipack era. Let's see if tests complain.
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I've slightly extended the scope of this PR; see updated initial post |
ev-br
reviewed
Apr 3, 2025
| @pytest.mark.parametrize('dtyp', ['float32', 'float64']) | ||
| def test_symiir2_values(self, dtyp, xp): | ||
| rng = np.random.RandomState(1234) | ||
| s = rng.uniform(size=16).astype(dtyp) | ||
| s = xp.asarray(s) | ||
| dtyp = getattr(xp, dtyp) | ||
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| # cupy returns f64 for f32 inputs |
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This one is a bit funny: I'm pretty sure CuPy's version was made to follow SciPy's, and now we're adjusting SciPy's to match CuPy's :-).
ev-br
approved these changes
Apr 3, 2025
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scipy.signal.symiirorder2are floats, as per the documentation.This PR undoes an improper wrapping in
xp.asarrayin the test.This fixes a regression vs. array-api-strict git tip, which has started crashing on
__mul__and other ops vs. np.float64 types, on the linea2 = 2 * r * np.cos(omega)because r was anarray_api_strict.Array.symiirorder1andsymiirorder2now return float32 when fed with int inputs, accordingly to the generalxp_promotepolicy.