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Fixing QuadPrecision scalar dtype exposure #234

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Merged
merged 1 commit into from
Nov 25, 2025
+24 −1
Merged

Fixing QuadPrecision scalar dtype exposure #234

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8 changes: 8 additions & 0 deletions quaddtype/numpy_quaddtype/src/scalar.c
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Original file line number Diff line number Diff line change
Expand Up @@ -605,7 +605,15 @@ static PyMethodDef QuadPrecision_methods[] = {
{NULL, NULL, 0, NULL} /* Sentinel */
};

static PyObject *
QuadPrecision_get_dtype(QuadPrecisionObject *self, void *NPY_UNUSED(closure))
{
QuadPrecDTypeObject *dtype = new_quaddtype_instance(self->backend);
return (PyObject *)dtype;
}

static PyGetSetDef QuadPrecision_getset[] = {
{"dtype", (getter)QuadPrecision_get_dtype, NULL, "Data type descriptor for this scalar", NULL},
{"real", (getter)QuadPrecision_get_real, NULL, "Real part of the scalar", NULL},
{"imag", (getter)QuadPrecision_get_imag, NULL, "Imaginary part of the scalar (always 0 for real types)", NULL},
{NULL} /* Sentinel */
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17 changes: 16 additions & 1 deletion quaddtype/tests/test_quaddtype.py
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Expand Up @@ -4292,4 +4292,19 @@ def test_as_integer_ratio_compatibility_with_float(self, value):
# The ratios should be equal
quad_ratio = quad_num / quad_denom
float_ratio = float_num / float_denom
assert abs(quad_ratio - float_ratio) < 1e-15
assert abs(quad_ratio - float_ratio) < 1e-15

def test_quadprecision_scalar_dtype_expose():
quad_ld = QuadPrecision("1e100", backend="longdouble")
quad_sleef = QuadPrecision("1e100", backend="sleef")
assert quad_ld.dtype == QuadPrecDType(backend='longdouble')
assert np.dtype(quad_ld) == QuadPrecDType(backend='longdouble')
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@seberg seberg Nov 25, 2025

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Of course scalar.dtype should work and I guess since you are parametric you have to make it work.

Butt the protocol is the exact opposite of this assert. It specifically wants to disable this in the future because it makes no sense to say np.ones(..., dtype=scalar), the user should write dtype=scalar.dtype.

So in other words: The attribute is right, the __numpy_dtype__ protocol is just unrelated.

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@SwayamInSync SwayamInSync Nov 25, 2025

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oh I see, so we don't have to implement __numpy_dtype__
I misinterpret that, I thought in future had to expose both .dtype and __numpy_dtype__

In current scenarios atleast for testing, I just set np.longdouble = QuadPrecision #(scalar) as we have different names for scalar and dtype and this works correctly.

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@seberg seberg Nov 25, 2025

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What we could try to have is a function to map from the scalar type to the DType class cleanly, I guess (this exists in C, but not Python).
I used to want np.dtype[scalar_type] for this, but while what typing does is conceptually the same, it's not compatible :(.

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@ngoldbaum ngoldbaum Nov 25, 2025

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We can always add np.dtype.from_scalar.


#todo: Uncomment them when 232 is merged
# assert quad_ld.dtype.backend == 1
# assert np.dtype(quad_ld).backend == 1

assert quad_sleef.dtype == QuadPrecDType(backend='sleef')
assert np.dtype(quad_sleef) == QuadPrecDType(backend='sleef')
# assert quad_sleef.dtype.backend == 0
# assert np.dtype(quad_sleef).backend == 0