Merge afa239e into 45ba570

Author: vtavana

CHANGELOG.md

@@ -41,7 +41,7 @@ In addition, this release completes implementation of `dpnp.fft` module and adds
 * Added implementation of `dpnp.resize` and `dpnp.rot90` functions [#2030](https://github.com/IntelPython/dpnp/pull/2030)
 * Added implementation of `dpnp.require` function [#2036](https://github.com/IntelPython/dpnp/pull/2036)
 
-### Change
+### Changed
 
 * Extended pre-commit pylint check to `dpnp.fft` module [#1860](https://github.com/IntelPython/dpnp/pull/1860)
 * Reworked `vm` vector math backend to reuse `dpctl.tensor` functions around unary and binary functions [#1868](https://github.com/IntelPython/dpnp/pull/1868)
@@ -105,6 +105,7 @@ In addition, this release completes implementation of `dpnp.fft` module and adds
 * Updated `dpnp.fft` backend to depend on `INTEL_MKL_VERSION` flag to ensures that the appropriate code segment is executed based on the version of OneMKL [#2035](https://github.com/IntelPython/dpnp/pull/2035)
 * Use `dpctl::tensor::alloc_utils::sycl_free_noexcept` instead of `sycl::free` in `host_task` tasks associated with life-time management of temporary USM allocations [#2058](https://github.com/IntelPython/dpnp/pull/2058)
 * Improved implementation of `dpnp.kron` to avoid unnecessary copy for non-contiguous arrays [#2059](https://github.com/IntelPython/dpnp/pull/2059)
+* Updated the test suit for `dpnp.fft` module [#2071](https://github.com/IntelPython/dpnp/pull/2071)
 
 ### Fixed
 

pyproject.toml

@@ -16,6 +16,7 @@ exclude-protected = ["_create_from_usm_ndarray"]
 
 [tool.pylint.design]
 max-args = 11
+max-positional-arguments = 9
 max-locals = 30
 max-branches = 15
 max-returns = 8

tests/test_fft.py

@@ -564,10 +564,13 @@ def test_fftn_out(self, axes, s):
         assert_dtype_allclose(iresult, iexpected, check_only_type_kind=True)
 
     def test_negative_s(self):
-        # stock NumPy 2.0, if s is -1, the whole input is used (no padding/trimming).
-        a_np = numpy.empty((3, 4, 5), dtype=numpy.complex64)
+        x1 = numpy.random.uniform(-10, 10, 60)
+        x2 = numpy.random.uniform(-10, 10, 60)
+        a_np = numpy.array(x1 + 1j * x2, dtype=numpy.complex64).reshape(3, 4, 5)
         a = dpnp.array(a_np)
 
+        # For dpnp and stock NumPy 2.0, if s is -1, the whole input is used
+        # (no padding or trimming).
         result = dpnp.fft.fftn(a, s=(-1, -1), axes=(0, 2))
         expected = numpy.fft.fftn(a_np, s=(3, 5), axes=(0, 2))
         assert_dtype_allclose(result, expected, check_only_type_kind=True)