8000 TST: add some tests of np.log for complex input. by WarrenWeckesser · Pull Request #24448 · numpy/numpy · GitHub
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TST: add some tests of np.log for complex input. #24448

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Merged
merged 3 commits into from
Jun 5, 2024
Merged

TST: add some tests of np.log for complex input. #24448

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8b28d4f
TST: add some tests of np.log for complex input.
WarrenWeckesser Aug 18, 2023
d93e5eb
BLD: blocklist complex log function clog on musl.
WarrenWeckesser Jun 4, 2024
9cdefc3
BLD: blocklist complex log functions clog* on cygwin.
WarrenWeckesser Jun 5, 2024
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18 changes: 18 additions & 0 deletions numpy/_core/src/common/npy_config.h
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Original file line number Diff line number Diff line change
Expand Up @@ -126,6 +126,14 @@
#undef HAVE_CPOWL
#undef HAVE_CEXPL

/*
* cygwin uses newlib, which has naive implementations of the
* complex log functions.
*/
#undef HAVE_CLOG
#undef HAVE_CLOGF
#undef HAVE_CLOGL

#include <cygwin/version.h>
#if CYGWIN_VERSION_DLL_MAJOR < 3003
// rather than blocklist cabsl, hypotl, modfl, sqrtl, error out
Expand Down Expand Up @@ -182,6 +190,16 @@
#undef HAVE_CACOSHF
#undef HAVE_CACOSHL

/*
* musl's clog is low precision for some inputs. As of MUSL 1.2.5,
* the first comment in clog.c is "// FIXME".
* See https://github.com/numpy/numpy/pull/24416#issuecomment-1678208628
* and https://github.com/numpy/numpy/pull/24448
*/
#undef HAVE_CLOG
#undef HAVE_CLOGF
#undef HAVE_CLOGL

#endif /* defined(__GLIBC) */
#endif /* defined(HAVE_FEATURES_H) */

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30 changes: 30 additions & 0 deletions numpy/_core/tests/test_umath.py
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Expand Up @@ -1404,6 +1404,36 @@ def test_log_strides(self):
assert_array_almost_equal_nulp(np.log(x_f64[::jj]), y_true[::jj], nulp=2)
assert_array_almost_equal_nulp(np.log(x_special[::jj]), y_special[::jj], nulp=2)

# Reference values were computed with mpmath, with mp.dps = 200.
@pytest.mark.parametrize(
'z, wref',
[(1 + 1e-12j, 5e-25 + 1e-12j),
(1.000000000000001 + 3e-08j,
1.5602230246251546e-15 + 2.999999999999996e-08j),
(0.9999995000000417 + 0.0009999998333333417j,
7.831475869017683e-18 + 0.001j),
(0.9999999999999996 + 2.999999999999999e-08j,
5.9107901499372034e-18 + 3e-08j),
(0.99995000042 - 0.009999833j,
-7.015159763822903e-15 - 0.009999999665816696j)],
)
def test_log_precision_float64(self, z, wref):
w = np.log(z)
assert_allclose(w, wref, rtol=1e-15)

# Reference values were computed with mpmath, with mp.dps = 200.
@pytest.mark.parametrize(
'z, wref',
[(np.complex64(1.0 + 3e-6j), np.complex64(4.5e-12+3e-06j)),
(np.complex64(1.0 - 2e-5j), np.complex64(1.9999999e-10 - 2e-5j)),
(np.complex64(0.9999999 + 1e-06j),
np.complex64(-1.192088e-07+1.0000001e-06j))],
)
def test_log_precision_float32(self, z, wref):
w = np.log(z)
assert_allclose(w, wref, rtol=1e-6)


class TestExp:
def test_exp_values(self):
x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
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