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implement dpnp.bartlett (#2366)
In this PR, `dpnp.bartlett` is implemented.
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‎CHANGELOG.md

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* Added implementation of `dpnp.hamming` [#2341](https://github.com/IntelPython/dpnp/pull/2341), [#2357](https://github.com/IntelPython/dpnp/pull/2357)
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* Added implementation of `dpnp.hanning` [#2358](https://github.com/IntelPython/dpnp/pull/2358)
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* Added implementation of `dpnp.blackman` [#2363](https://github.com/IntelPython/dpnp/pull/2363)
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* Added implementation of `dpnp.bartlett` [#2366](https://github.com/IntelPython/dpnp/pull/2366)
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### Changed
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‎dpnp/backend/extensions/window/bartlett.hpp

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//*****************************************************************************
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// Copyright (c) 2025, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// - Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// - Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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// THE POSSIBILITY OF SUCH DAMAGE.
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//*****************************************************************************
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#pragma once
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#include "common.hpp"
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#include <sycl/sycl.hpp>
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namespace dpnp::extensions::window::kernels
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{
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template <typename T>
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class BartlettFunctor
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{
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private:
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T *data = nullptr;
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const std::size_t N;
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public:
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BartlettFunctor(T *data, const std::size_t N) : data(data), N(N) {}
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void operator()(sycl::id<1> id) const
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{
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const auto i = id.get(0);
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const T alpha = (N - 1) / T(2);
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data[i] = T(1) - sycl::fabs(i - alpha) / alpha;
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}
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};
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template <typename fnT, typename T>
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struct BartlettFactory
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{
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fnT get()
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{
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if constexpr (std::is_floating_point_v<T>) {
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return window_impl<T, BartlettFunctor>;
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}
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else {
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return nullptr;
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}
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}
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};
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} // namespace dpnp::extensions::window::kernels

‎dpnp/backend/extensions/window/blackman.hpp

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{
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const auto i = id.get(0);
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data[i] = T(0.42) - T(0.5) * sycl::cospi(T(2) * i / (N - 1)) +
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T(0.08) * sycl::cospi(T(4) * i / (N - 1));
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const T alpha = T(2) * i / (N - 1);
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data[i] = T(0.42) - T(0.5) * sycl::cospi(alpha) +
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T(0.08) * sycl::cospi(T(2) * alpha);
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}
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};
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‎dpnp/backend/extensions/window/window_py.cpp

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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include "bartlett.hpp"
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#include "blackman.hpp"
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#include "common.hpp"
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#include "hamming.hpp"
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namespace dpctl_td_ns = dpctl::tensor::type_dispatch;
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static window_fn_ptr_t bartlett_dispatch_vector[dpctl_td_ns::num_types];
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static window_fn_ptr_t blackman_dispatch_vector[dpctl_td_ns::num_types];
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static window_fn_ptr_t hamming_dispatch_vector[dpctl_td_ns::num_types];
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static window_fn_ptr_t hanning_dispatch_vector[dpctl_td_ns::num_types];
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using arrayT = dpctl::tensor::usm_ndarray;
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using event_vecT = std::vector<sycl::event>;
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{
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window_ns::init_window_dispatch_vectors<
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window_ns::kernels::BartlettFactory>(bartlett_dispatch_vector);
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auto bartlett_pyapi = [&](sycl::queue &exec_q, const arrayT &result,
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const event_vecT &depends = {}) {
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return window_ns::py_window(exec_q, result, depends,
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bartlett_dispatch_vector);
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};
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m.def("_bartlett", bartlett_pyapi, "Call Bartlett kernel",
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py::arg("sycl_queue"), py::arg("result"),
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py::arg("depends") = py::list());
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}
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{
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window_ns::init_window_dispatch_vectors<
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window_ns::kernels::BlackmanFactory>(blackman_dispatch_vector);

‎dpnp/dpnp_iface_window.py

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import dpnp
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import dpnp.backend.extensions.window._window_impl as wi
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__all__ = ["blackman", "hamming", "hanning"]
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__all__ = ["bartlett", "blackman", "hamming", "hanning"]
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def _call_window_kernel(
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return result
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def bartlett(M, device=None, usm_type=None, sycl_queue=None):
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r"""
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Return the Bartlett window.
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The Bartlett window is very similar to a triangular window, except that the
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end points are at zero. It is often used in signal processing for tapering
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a signal, without generating too much ripple in the frequency domain.
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For full documentation refer to :obj:`numpy.bartlett`.
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Parameters
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----------
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M : int
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Number of points in the output window. If zero or less, an empty array
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is returned.
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device : {None, string, SyclDevice, SyclQueue, Device}, optional
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An array API concept of device where the output array is created.
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`device` can be ``None``, a oneAPI filter selector string, an instance
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of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL
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device, an instance of :class:`dpctl.SyclQueue`, or a
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:class:`dpctl.tensor.Device` object returned by
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:attr:`dpnp.ndarray.device`.
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Default: ``None``.
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usm_type : {None, "device", "shared", "host"}, optional
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The type of SYCL USM allocation for the output array.
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Default: ``None``.
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sycl_queue : {None, SyclQueue}, optional
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A SYCL queue to use for output array allocation and copying. The
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`sycl_queue` can be passed as ``None`` (the default), which means
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to get the SYCL queue from `device` keyword if present or to use
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a default queue.
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Default: ``None``.
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Returns
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-------
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out : dpnp.ndarray of shape (M,)
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The triangular window, with the maximum value normalized to one
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(the value one appears only if the number of samples is odd), with the
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first and last samples equal to zero.
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See Also
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--------
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:obj:`dpnp.blackman` : Return the Blackman window.
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:obj:`dpnp.hamming` : Return the Hamming window.
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:obj:`dpnp.hanning` : Return the Hanning window.
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:obj:`dpnp.kaiser` : Return the Kaiser window.
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Notes
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-----
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The Bartlett window is defined as
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.. math:: w(n) = \frac{2}{M-1} \left(\frac{M-1}{2} -
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\left|n - \frac{M-1}{2}\right|\right)
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\qquad 0 \leq n \leq M-1
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Examples
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--------
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>>> import dpnp as np
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>>> np.bartlett(12)
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array([0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273,
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0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636,
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0.18181818, 0. ])
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Creating the output array on a different device or with a
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specified usm_type:
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>>> x = np.bartlett(4) # default case
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>>> x, x.device, x.usm_type
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(array([0. , 0.66666667, 0.66666667, 0. ]),
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Device(level_zero:gpu:0),
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'device')
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>>> y = np.bartlett(4, device="cpu")
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>>> y, y.device, y.usm_type
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(array([0. , 0.66666667, 0.66666667, 0. ]),
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Device(opencl:cpu:0),
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'device')
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>>> z = np.bartlett(4, usm_type="host")
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>>> z, z.device, z.usm_type
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(array([0. , 0.66666667, 0.66666667, 0. ]),
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Device(level_zero:gpu:0),
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'host')
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"""
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return _call_window_kernel(
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M, wi._bartlett, device=device, usm_type=usm_type, sycl_queue=sycl_queue
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)
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def blackman(M, device=None, usm_type=None, sycl_queue=None):
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r"""
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Return the Blackman window.

‎dpnp/tests/test_sycl_queue.py

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"func, arg, kwargs",
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[
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pytest.param("arange", [-25.7], {"stop": 10**8, "step": 15}),
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pytest.param("bartlett", [10], {}),
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pytest.param("blackman", [10], {}),
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pytest.param("eye", [4, 2], {}),
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pytest.param("empty", [(2, 2)], {}),

‎dpnp/tests/test_usm_type.py

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"func, arg, kwargs",
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[
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pytest.param("arange", [-25.7], {"stop": 10**8, "step": 15}),
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pytest.param("bartlett", [10], {}),
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pytest.param("blackman", [10], {}),
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pytest.param("eye", [4, 2], {}),
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pytest.param("empty", [(3, 4)], {}),

‎dpnp/tests/test_window.py

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from .helper import assert_dtype_allclose
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@pytest.mark.parametrize("func", ["blackman", "hamming", "hanning"])
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@pytest.mark.parametrize("func", ["bartlett", "blackman", "hamming", "hanning"])
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@pytest.mark.parametrize(
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"M",
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[
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assert_dtype_allclose(result, expected)
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@pytest.mark.parametrize("func", ["blackman", "hamming", "hanning"])
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@pytest.mark.parametrize("func", ["bartlett", "blackman", "hamming", "hanning"])
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@pytest.mark.parametrize(
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"M",
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[

‎dpnp/tests/third_party/cupy/math_tests/test_window.py

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*testing.product(
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{
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"m": [0, 1, -1, 1024],
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# TODO: add ["bartlett"] when supported
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"name": ["blackman", "hamming", "hanning"],
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"name": ["bartlett", "blackman", "hamming", "hanning"],
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}
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)
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)

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