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author obache; state Exp; branches; next 1.5; commitid ZlAugJwlzIgMlOox; 1.5 date 2013.04.14.00.28.53; author markd; state Exp; branches; next 1.4; 1.4 date 2010.06.06.02.37.08; author markd; state Exp; branches; next 1.3; 1.3 date 2009.07.25.12.16.45; author markd; state Exp; branches; next 1.2; 1.2 date 2009.06.14.18.05.48; author joerg; state Exp; branches; next 1.1; 1.1 date 2008.12.19.22.14.28; author markd; state Exp; branches 1.1.1.1; next ; 1.1.1.1 date 2008.12.19.22.14.28; author markd; state Exp; branches; next ; desc @@ 1.35 log @py-scipy: updated to 1.17.0 SciPy 1.17.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.17.x branch, and on adding new features on the main branch. @ text @@@comment $NetBSD: PLIST,v 1.34 2025/09/23 17:42:23 adam Exp $ ${PYSITELIB}/${WHEEL_INFODIR}/LICENSE.txt ${PYSITELIB}/${WHEEL_INFODIR}/METADATA ${PYSITELIB}/${WHEEL_INFODIR}/RECORD ${PYSITELIB}/${WHEEL_INFODIR}/WHEEL ${PYSITELIB}/scipy/__config__.py ${PYSITELIB}/scipy/__config__.pyc ${PYSITELIB}/scipy/__config__.pyo ${PYSITELIB}/scipy/__init__.py ${PYSITELIB}/scipy/__init__.pyc ${PYSITELIB}/scipy/__init__.pyo ${PYSITELIB}/scipy/_cyutility.so ${PYSITELIB}/scipy/_distributor_init.py ${PYSITELIB}/scipy/_distributor_init.pyc ${PYSITELIB}/scipy/_distributor_init.pyo ${PYSITELIB}/scipy/_lib/__init__.py ${PYSITELIB}/scipy/_lib/__init__.pyc ${PYSITELIB}/scipy/_lib/__init__.pyo ${PYSITELIB}/scipy/_lib/_array_api.py ${PYSITELIB}/scipy/_lib/_array_api.pyc ${PYSITELIB}/scipy/_lib/_array_api.pyo ${PYSITELIB}/scipy/_lib/_array_api_compat_vendor.py 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${PYSITELIB}/scipy/stats/tests/test_marray.py ${PYSITELIB}/scipy/stats/tests/test_marray.pyc ${PYSITELIB}/scipy/stats/tests/test_marray.pyo ${PYSITELIB}/scipy/stats/tests/test_mgc.py ${PYSITELIB}/scipy/stats/tests/test_mgc.pyc ${PYSITELIB}/scipy/stats/tests/test_mgc.pyo ${PYSITELIB}/scipy/stats/tests/test_morestats.py ${PYSITELIB}/scipy/stats/tests/test_morestats.pyc ${PYSITELIB}/scipy/stats/tests/test_morestats.pyo ${PYSITELIB}/scipy/stats/tests/test_mstats_basic.py ${PYSITELIB}/scipy/stats/tests/test_mstats_basic.pyc ${PYSITELIB}/scipy/stats/tests/test_mstats_basic.pyo ${PYSITELIB}/scipy/stats/tests/test_mstats_extras.py ${PYSITELIB}/scipy/stats/tests/test_mstats_extras.pyc ${PYSITELIB}/scipy/stats/tests/test_mstats_extras.pyo ${PYSITELIB}/scipy/stats/tests/test_multicomp.py ${PYSITELIB}/scipy/stats/tests/test_multicomp.pyc ${PYSITELIB}/scipy/stats/tests/test_multicomp.pyo ${PYSITELIB}/scipy/stats/tests/test_multivariate.py ${PYSITELIB}/scipy/stats/tests/test_multivariate.pyc ${PYSITELIB}/scipy/stats/tests/test_multivariate.pyo ${PYSITELIB}/scipy/stats/tests/test_new_distributions.py ${PYSITELIB}/scipy/stats/tests/test_new_distributions.pyc ${PYSITELIB}/scipy/stats/tests/test_new_distributions.pyo ${PYSITELIB}/scipy/stats/tests/test_odds_ratio.py ${PYSITELIB}/scipy/stats/tests/test_odds_ratio.pyc ${PYSITELIB}/scipy/stats/tests/test_odds_ratio.pyo ${PYSITELIB}/scipy/stats/tests/test_qmc.py ${PYSITELIB}/scipy/stats/tests/test_qmc.pyc ${PYSITELIB}/scipy/stats/tests/test_qmc.pyo ${PYSITELIB}/scipy/stats/tests/test_quantile.py ${PYSITELIB}/scipy/stats/tests/test_quantile.pyc ${PYSITELIB}/scipy/stats/tests/test_quantile.pyo ${PYSITELIB}/scipy/stats/tests/test_rank.py ${PYSITELIB}/scipy/stats/tests/test_rank.pyc ${PYSITELIB}/scipy/stats/tests/test_rank.pyo ${PYSITELIB}/scipy/stats/tests/test_relative_risk.py ${PYSITELIB}/scipy/stats/tests/test_relative_risk.pyc ${PYSITELIB}/scipy/stats/tests/test_relative_risk.pyo ${PYSITELIB}/scipy/stats/tests/test_resampling.py ${PYSITELIB}/scipy/stats/tests/test_resampling.pyc ${PYSITELIB}/scipy/stats/tests/test_resampling.pyo ${PYSITELIB}/scipy/stats/tests/test_sampling.py ${PYSITELIB}/scipy/stats/tests/test_sampling.pyc ${PYSITELIB}/scipy/stats/tests/test_sampling.pyo ${PYSITELIB}/scipy/stats/tests/test_sensitivity_analysis.py ${PYSITELIB}/scipy/stats/tests/test_sensitivity_analysis.pyc ${PYSITELIB}/scipy/stats/tests/test_sensitivity_analysis.pyo ${PYSITELIB}/scipy/stats/tests/test_stats.py ${PYSITELIB}/scipy/stats/tests/test_stats.pyc ${PYSITELIB}/scipy/stats/tests/test_stats.pyo ${PYSITELIB}/scipy/stats/tests/test_survival.py ${PYSITELIB}/scipy/stats/tests/test_survival.pyc ${PYSITELIB}/scipy/stats/tests/test_survival.pyo ${PYSITELIB}/scipy/stats/tests/test_tukeylambda_stats.py ${PYSITELIB}/scipy/stats/tests/test_tukeylambda_stats.pyc ${PYSITELIB}/scipy/stats/tests/test_tukeylambda_stats.pyo ${PYSITELIB}/scipy/stats/tests/test_variation.py ${PYSITELIB}/scipy/stats/tests/test_variation.pyc ${PYSITELIB}/scipy/stats/tests/test_variation.pyo ${PYSITELIB}/scipy/version.py ${PYSITELIB}/scipy/version.pyc ${PYSITELIB}/scipy/version.pyo @ 1.34 log @py-scipy: updated to 1.16.2 Issues closed for 1.16.2 BLD: Windows Arm builds MAINT: specify lower bound for ``pytest`` in ``pyproject.toml`` CI: new GH runners available for macOS 15 and Windows 2025 BUG: signal.periodogram: memory leak Incomplete license terms for sub components since version 1.16.0... BUG: optimize.nnls: memory leak BUG: optimize: missing error checking for slsqp inputs CI: CUDA compilation failures for all cupy tests BUG: signal.lombscargle(precenter=True) modifies input array... CI: macOS conda-forge job failing because of scikit-umfpack upgrade Pull requests for 1.16.2 BLD: Windows Arm64 wheel support CI: validate build on Win-ARM64 CI: bump OpenBLAS and cibuildwheel versions REL, MAINT: prepare for SciPy 1.16.2 BUG: Skip installing gmpy2(test deps) for Win-ARM64 BUG: signal: Avoid unbounded cache for ShortTimeFFT MAINT: ensure Qhull license file gets included in wheels BUG: fix regression for non-integer ``maxiter`` in SLSQP MAINT: Update licensing of bundled software BUG: signal.ShortTimeFFT: Remove ``@@lru_cache`` decorators BUG: optimize: fix the incompatible dtypes in L-BFGS-B BUG: signal.lombscargle: replace in-place input adjustments MAINT: Bump CuPy to 13.6.0 to fix GPU CI MAINT: add lower bound for pytest MAINT, BLD: ignore meson wraplock BUG: fix memory leaks in ``optimize.nnls`` and ``linalg.sqrtm`` CI: pin scikit-umfpack to 0.3.3 to avoid failures, 0.4.2 is broken. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.33 2025/08/20 12:19:23 adam Exp $ d25 3 d31 3 d57 3 a68 3 ${PYSITELIB}/scipy/_lib/_threadsafety.py ${PYSITELIB}/scipy/_lib/_threadsafety.pyc ${PYSITELIB}/scipy/_lib/_threadsafety.pyo a258 3 ${PYSITELIB}/scipy/_lib/decorator.py ${PYSITELIB}/scipy/_lib/decorator.pyc ${PYSITELIB}/scipy/_lib/decorator.pyo a355 3 ${PYSITELIB}/scipy/_lib/tests/test__threadsafety.py ${PYSITELIB}/scipy/_lib/tests/test__threadsafety.pyc ${PYSITELIB}/scipy/_lib/tests/test__threadsafety.pyo d609 1 a658 1 ${PYSITELIB}/scipy/integrate/_lsoda.so a694 1 ${PYSITELIB}/scipy/integrate/_test_odeint_banded.so a725 3 ${PYSITELIB}/scipy/integrate/tests/test_odeint_jac.py ${PYSITELIB}/scipy/integrate/tests/test_odeint_jac.pyc ${PYSITELIB}/scipy/integrate/tests/test_odeint_jac.pyo d788 6 d795 3 a907 1 ${PYSITELIB}/scipy/io/_test_fortran.so d1258 1 a2345 1 ${PYSITELIB}/scipy/sparse/linalg/_eigen/arpack/COPYING d2349 1 a2349 1 ${PYSITELIB}/scipy/sparse/linalg/_eigen/arpack/_arpack.so d2380 3 d2446 1 a2446 4 ${PYSITELIB}/scipy/sparse/linalg/_propack/_cpropack.so ${PYSITELIB}/scipy/sparse/linalg/_propack/_dpropack.so ${PYSITELIB}/scipy/sparse/linalg/_propack/_spropack.so ${PYSITELIB}/scipy/sparse/linalg/_propack/_zpropack.so d2475 3 d2513 3 d2665 11 a2675 2 ${PYSITELIB}/scipy/spatial/transform/_rigid_transform.so ${PYSITELIB}/scipy/spatial/transform/_rotation.so d2682 3 d2896 3 d3278 3 d3326 3 @ 1.33 log @py-scipy: updated to 1.16.1 Issues closed for 1.16.1 BUG: ndimage.median_filter: always returns ``0`` on single element... BUG: optimize.shgo: unbounded cache in Complex.split_edge BUG: ``@@_transition_to_rng`` leads to inconsistent function signatures BUG: stats.beta.entropy: doesn't work with array-valued a, b BUG: signal.tf2sos: ``ComplexWarning`` BUG: signal.freqz: erroneously returns complex-valued frequencies BUG: linalg.sqrtm: incorrect results in 1.16.0 when at least... BUG: CSC matrix multiplication and indptrs BUG: Compilation failure in HiGHs with MSVC DOC: optimize.minimize: erroneously claims COBYLA doesn't support... Pull requests for 1.16.1 BUG: optimize.shgo: Complex cache ``split_edge`` differently CI: add cp314/cp314t nighly wheel builds DOC: ndimage.vectorized_filter: correct ``output`` description REL, MAINT: prep for 1.16.1 BUG: signal.tf2sos: fix a new ``ComplexWarning`` BUG: signal.remez: fix handling of ``weight`` array BUG: fix signature of ``@@_transition_to_rng`` functions (SPEC... BUG: linalg: Fix pointer casting order for sqrtm BUG: fix broadcasting in ``beta.entropy()`` with new infrastructure DOC: optimize.least_squares: minor nit BUG: ndimage.median_filter: single element array handling BUG: sparse: ``multiply()`` should produce CSC output for CSC... BLD: Add bigobj flag for MSVC to fix object file section limit DOC: optimize.minimize: remove outdated limitation for equality... MAINT: sparse: ``multiply()`` uses attribute presence instead... CI: bump to cibuildwheel 3.1.0 for 3.14.0rc1, add cp314t musllinux... CI: disable special-casing of 3.14-dev now that Python 3.14rc1... MAINT: Python 3.14 to classifiers @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.32 2025/02/19 09:23:15 adam Exp $ d2593 1 @ 1.32 log @py-scipy: updated to 1.15.2 Issues closed for 1.15.2 BUG: _lib: crash in uarray on interpreter exit with a free-threaded... BUG: special: Unchecked calls of malloc() and calloc() in specfun.h BUG: Segmentation Fault when passing a special array to ``scipy.cluster.hierarch``... BUG: median_filter on 1D array crashes with memory corruption... BUG: special: Potential memory leak in the function ``besy()``... BUG: ndimage.median_filter: Heap corruption with scipy 1.15 BUG: signal.medfilt and ndimage.median_filter not returning correct... BUG: special: Unchecked malloc in stirling2.h BUG: Matrix multiplication of a coo_matrix with an invalid type... BLD: libhighs.a static library is installed BUG: interpolate test_bary_rational.TestAAA.test_basic_functions... BUG: ``scipy.stats.zmap`` returns incorrect values for complex... BUG: spatial.transform: ``Rotation.from_matrix()`` uses inaccurate... BUG: io.loadmat thinks a file with a lot of zeros is valid BUG: Slicing sparse matrix with ``None`` gives result different... MAINT: scipy.stats: test_regressZEROX SIMD warning for certain... Pull requests for 1.15.2 BUG: immortalize uarray global strings in order to prevent negative... BUG: special: Fix unchecked memory allocations in ``specfun.h`` BUG: cluster: ``cophenet`` intercept invalid linkage matrix count REL, MAINT: prep for 1.15.2 MAINT: stats.Mixture: make return type consistent when ``shape``... MAINT: stats.Mixture: fix inverse functions when mean is undefined BUG: special: Fix unchecked malloc in stirling2.h TST: turn off dtype check due to endianness BUG: sparse: fix selecting wrong dtype for coo coords TST: interpolate: small tolerance bump to TestAAA.test_basic_functions MAINT: Stop installing ``libhighs`` BUG: sparse.linalg.norm: add test for and fix return type BUG: sparse: revert NotImplemented return values in dot and matmul DOC: sparse.linalg: add two recent functions to namespace and... BUG: wrap median_filter stability MAINT: stats.zmap: restore support for complex data MAINT: integrate.cumulative_simpson: bump test tolerance BUG: scipy.spatial: Fix inaccurate orthonormalization in ``Rotation.from_matrix(``... BUG: special: Fix a memory leak in the AMOS function besy(). CI: migrate Linux aarch64 jobs to GitHub Actions, add cp313t... BUG: io.loadmat: throw error on file containing all zeros BUG: sparse: fix spmatrix indexing with None and implicit padding... MAINT: stats: pearsonr SIMD-related shim @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.31 2025/01/05 08:58:47 adam Exp $ d12 1 d22 3 a43 3 ${PYSITELIB}/scipy/_lib/_finite_differences.py ${PYSITELIB}/scipy/_lib/_finite_differences.pyc ${PYSITELIB}/scipy/_lib/_finite_differences.pyo d51 3 d164 3 d176 38 a213 6 ${PYSITELIB}/scipy/_lib/array_api_extra/_funcs.py ${PYSITELIB}/scipy/_lib/array_api_extra/_funcs.pyc ${PYSITELIB}/scipy/_lib/array_api_extra/_funcs.pyo ${PYSITELIB}/scipy/_lib/array_api_extra/_typing.py ${PYSITELIB}/scipy/_lib/array_api_extra/_typing.pyc ${PYSITELIB}/scipy/_lib/array_api_extra/_typing.pyo d263 78 a747 1 ${PYSITELIB}/scipy/interpolate/_bspl.so d1307 1 d1390 3 a1593 1 ${PYSITELIB}/scipy/optimize/_cobyla.so a1602 1 ${PYSITELIB}/scipy/optimize/_cython_nnls.so a1738 1 ${PYSITELIB}/scipy/optimize/_slsqp.so d1742 1 d2003 3 d2026 3 d2035 3 d2051 3 a2592 1 ${PYSITELIB}/scipy/spatial/qhull_src/COPYING.txt d2654 1 d2668 3 d2891 3 a2893 3 ${PYSITELIB}/scipy/special/tests/test_log_softmax.py ${PYSITELIB}/scipy/special/tests/test_log_softmax.pyc ${PYSITELIB}/scipy/special/tests/test_log_softmax.pyo a2980 3 ${PYSITELIB}/scipy/special/tests/test_xsf_cuda.py ${PYSITELIB}/scipy/special/tests/test_xsf_cuda.pyc ${PYSITELIB}/scipy/special/tests/test_xsf_cuda.pyo a2983 56 ${PYSITELIB}/scipy/special/xsf/binom.h ${PYSITELIB}/scipy/special/xsf/cdflib.h ${PYSITELIB}/scipy/special/xsf/cephes/airy.h ${PYSITELIB}/scipy/special/xsf/cephes/besselpoly.h ${PYSITELIB}/scipy/special/xsf/cephes/beta.h ${PYSITELIB}/scipy/special/xsf/cephes/cbrt.h ${PYSITELIB}/scipy/special/xsf/cephes/chbevl.h ${PYSITELIB}/scipy/special/xsf/cephes/chdtr.h ${PYSITELIB}/scipy/special/xsf/cephes/const.h ${PYSITELIB}/scipy/special/xsf/cephes/ellie.h ${PYSITELIB}/scipy/special/xsf/cephes/ellik.h ${PYSITELIB}/scipy/special/xsf/cephes/ellpe.h ${PYSITELIB}/scipy/special/xsf/cephes/ellpk.h ${PYSITELIB}/scipy/special/xsf/cephes/expn.h ${PYSITELIB}/scipy/special/xsf/cephes/gamma.h ${PYSITELIB}/scipy/special/xsf/cephes/hyp2f1.h ${PYSITELIB}/scipy/special/xsf/cephes/hyperg.h ${PYSITELIB}/scipy/special/xsf/cephes/i0.h ${PYSITELIB}/scipy/special/xsf/cephes/i1.h ${PYSITELIB}/scipy/special/xsf/cephes/igam.h ${PYSITELIB}/scipy/special/xsf/cephes/igam_asymp_coeff.h ${PYSITELIB}/scipy/special/xsf/cephes/igami.h ${PYSITELIB}/scipy/special/xsf/cephes/j0.h ${PYSITELIB}/scipy/special/xsf/cephes/j1.h ${PYSITELIB}/scipy/special/xsf/cephes/jv.h ${PYSITELIB}/scipy/special/xsf/cephes/k0.h ${PYSITELIB}/scipy/special/xsf/cephes/k1.h ${PYSITELIB}/scipy/special/xsf/cephes/kn.h ${PYSITELIB}/scipy/special/xsf/cephes/lanczos.h ${PYSITELIB}/scipy/special/xsf/cephes/ndtr.h ${PYSITELIB}/scipy/special/xsf/cephes/poch.h ${PYSITELIB}/scipy/special/xsf/cephes/polevl.h ${PYSITELIB}/scipy/special/xsf/cephes/psi.h ${PYSITELIB}/scipy/special/xsf/cephes/rgamma.h ${PYSITELIB}/scipy/special/xsf/cephes/scipy_iv.h ${PYSITELIB}/scipy/special/xsf/cephes/shichi.h ${PYSITELIB}/scipy/special/xsf/cephes/sici.h ${PYSITELIB}/scipy/special/xsf/cephes/sindg.h ${PYSITELIB}/scipy/special/xsf/cephes/tandg.h ${PYSITELIB}/scipy/special/xsf/cephes/trig.h ${PYSITELIB}/scipy/special/xsf/cephes/unity.h ${PYSITELIB}/scipy/special/xsf/cephes/zeta.h ${PYSITELIB}/scipy/special/xsf/config.h ${PYSITELIB}/scipy/special/xsf/digamma.h ${PYSITELIB}/scipy/special/xsf/error.h ${PYSITELIB}/scipy/special/xsf/evalpoly.h ${PYSITELIB}/scipy/special/xsf/expint.h ${PYSITELIB}/scipy/special/xsf/hyp2f1.h ${PYSITELIB}/scipy/special/xsf/iv_ratio.h ${PYSITELIB}/scipy/special/xsf/lambertw.h ${PYSITELIB}/scipy/special/xsf/loggamma.h ${PYSITELIB}/scipy/special/xsf/sici.h ${PYSITELIB}/scipy/special/xsf/tools.h ${PYSITELIB}/scipy/special/xsf/trig.h ${PYSITELIB}/scipy/special/xsf/wright_bessel.h ${PYSITELIB}/scipy/special/xsf/zlog1.h d3011 3 d3041 3 a3080 1 ${PYSITELIB}/scipy/stats/_mvn.so d3101 4 d3234 3 d3276 3 d3303 3 @ 1.31 log @py-scipy: updated to 1.15.0 1.15.0 Highlights of this release Sparse arrays are now fully functional for 1-D and 2-D arrays. We recommend that all new code use sparse arrays instead of sparse matrices and that developers start to migrate their existing code from sparse matrix to sparse array: Migration from spmatrix to sparray. Both sparse.linalg and sparse.csgraph work with either sparse matrix or sparse array and work internally with sparse array. Sparse arrays now provide basic support for n-D arrays in the COO format including add, subtract, reshape, transpose, matmul, dot, tensordot and others. More functionality is coming in future releases. Preliminary support for free-threaded Python 3.13. New probability distribution features in scipy.stats can be used to improve the speed and accuracy of existing continuous distributions and perform new probability calculations. Several new features support vectorized calculations with Python Array API Standard compatible input (see “Array API Standard Support” below): scipy.differentiate is a new top-level submodule for accurate estimation of derivatives of black box functions. scipy.optimize.elementwise contains new functions for root-finding and minimization of univariate functions. scipy.integrate offers new functions cubature, tanhsinh, and nsum for multivariate integration, univariate integration, and univariate series summation, respectively. scipy.interpolate.AAA adds the AAA algorithm for barycentric rational approximation of real or complex functions. scipy.special adds new functions offering improved Legendre function implementations with a more consistent interface. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.30 2024/08/21 10:11:44 adam Exp $ a1515 1 ${PYSITELIB}/scipy/optimize/_highspy/libhighs.a @ 1.30 log @py-scipy: updated to 1.14.1 Issues closed for 1.14.1 BUG: doccer: \`test_decorator\` fails with Python 3.13 due to... BUG: open_memstream unavailable with glibc >= 2.10 + C99 ENH: 3.13 wheels BUG: spsolve prints "dgstrf info" to stdout on singular matrices BUG: \`special.pro_rad1\`: incorrect results BUG: sparse: \`hstack/vstack\` between a sparse and ndarray breaks... MAINT: \`cluster\`/\`stats\`: array API test failures in main BUG: unable to securely deploy app as scipy 1.14.0 requires write... BUG: signal: crash in \`signaltools\` on free-threaded Python,... CI: documentation build failing? BUG: \`fft.hfftn\` fails on list inputs BUG: Files in SuperLU under LGPL license BUG: io/scipy.sparse.csgraph: refguide check failure in main DOC: \`sampling_tdr.rst\` failing in CircleCI smoke-tutorials... BUG: dtype changed for argument to \`rv_discrete._pmf\` BUG: odr: pickling is not possible DOC: build failing in CI BLD, CI: Cirrus 3.13 wheels? Pull requests for 1.14.1 BLD: make cp313 wheels [wheel build] REL, MAINT: prep for 1.14.1 BUG: special: Fixes for pro_rad1 BUG: special: remove type punning to avoid warnings in LTO builds MAINT: uarray: fix typo in \`small_dynamic_array.h\` MAINT: adapt to array-api-strict 2.0 BLD: Enable \`open_memstream()\` on newer glibc MAINT: Unskip \`scipy.misc.test.test_decorator\` for Python 3.13+ DOC: add release note for 1.14 sparse section about sparse array... BUG: sparse: fix 1D for vstack/hstack and Improve 1D error msgs... BUG: signal: fix crash under free-threaded CPython in medfilt2d BUG: sparse.linalg: Update \`SuperLU\` to fix unfilterable output... DOC: Fix type of \`\`html_sidebars\`\` value in \`\`conf.py\`\` BUG: fft: fix array-like input BUG: sparse: fix failing doctests in io and csgraph that print... DOC: stats: silence the doctest error MAINT: sparse.linalg: update \`SuperLU/colamd.c\` to fix license... BUG: stats: adapt to \`np.floor\` type promotion removal MAINT: \`stats.bartlett\`: ensure statistic is non-negative CI: Update to cibuildwheel 2.20.0 BUG: odr: fix pickling DOC: Don't use Sphinx 8.0.0 until gh-21323 is resolved. BUG: sparse: Fix 1D specialty hstack codes MAINT: special: Accommodate changed integer handling in NumPy... BLD: cp313 wheels on \`manylinux_aarch64\` @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.29 2024/07/31 18:15:38 adam Exp $ d21 3 d103 3 d124 6 d139 3 d157 3 d166 9 d251 3 d257 3 d353 12 d498 3 d538 3 d559 15 d604 3 d628 3 d639 1 d650 4 d690 3 a692 1 ${PYSITELIB}/scipy/interpolate/interpnd.so d711 3 d885 1 d1087 1 a1097 1 ${PYSITELIB}/scipy/io/tests/data/test-8000Hz-le-1ch-10S-20bit-extra.wav d1131 1 a1131 1 ${PYSITELIB}/scipy/linalg.pxd d1151 1 a1180 4 ${PYSITELIB}/scipy/linalg/_interpolative.so ${PYSITELIB}/scipy/linalg/_interpolative_backend.py ${PYSITELIB}/scipy/linalg/_interpolative_backend.pyc ${PYSITELIB}/scipy/linalg/_interpolative_backend.pyo d1182 1 a1341 4 ${PYSITELIB}/scipy/misc/_common.py ${PYSITELIB}/scipy/misc/_common.pyc ${PYSITELIB}/scipy/misc/_common.pyo ${PYSITELIB}/scipy/misc/ascent.dat a1347 14 ${PYSITELIB}/scipy/misc/ecg.dat ${PYSITELIB}/scipy/misc/face.dat ${PYSITELIB}/scipy/misc/tests/__init__.py ${PYSITELIB}/scipy/misc/tests/__init__.pyc 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${PYSITELIB}/scipy/optimize/_highs/src/cython/HConst.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/Highs.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsIO.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsInfo.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsLp.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsLpUtils.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsModelUtils.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsOptions.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsRuntimeOptions.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/HighsStatus.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/SimplexConst.pxd ${PYSITELIB}/scipy/optimize/_highs/src/cython/highs_c_api.pxd a1580 1 ${PYSITELIB}/scipy/optimize/_minpack2.so d1666 3 d1710 3 a1792 3 ${PYSITELIB}/scipy/optimize/tests/test_differentiate.py ${PYSITELIB}/scipy/optimize/tests/test_differentiate.pyc ${PYSITELIB}/scipy/optimize/tests/test_differentiate.pyo a1882 3 ${PYSITELIB}/scipy/signal/_bsplines.py ${PYSITELIB}/scipy/signal/_bsplines.pyc ${PYSITELIB}/scipy/signal/_bsplines.pyo a1916 1 ${PYSITELIB}/scipy/signal/_spectral.so d1920 1 d1922 3 d2013 3 d2407 3 a2427 1 ${PYSITELIB}/scipy/spatial/_ckdtree.pyi a2525 1 ${PYSITELIB}/scipy/spatial/transform/_rotation.pyi d2548 1 a2548 1 ${PYSITELIB}/scipy/special.pxd d2564 3 d2576 3 a2669 43 ${PYSITELIB}/scipy/special/special/binom.h ${PYSITELIB}/scipy/special/special/cephes/airy.h ${PYSITELIB}/scipy/special/special/cephes/besselpoly.h ${PYSITELIB}/scipy/special/special/cephes/beta.h ${PYSITELIB}/scipy/special/special/cephes/cbrt.h ${PYSITELIB}/scipy/special/special/cephes/chbevl.h ${PYSITELIB}/scipy/special/special/cephes/chdtr.h ${PYSITELIB}/scipy/special/special/cephes/const.h ${PYSITELIB}/scipy/special/special/cephes/expn.h ${PYSITELIB}/scipy/special/special/cephes/gamma.h ${PYSITELIB}/scipy/special/special/cephes/hyp2f1.h ${PYSITELIB}/scipy/special/special/cephes/hyperg.h 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${PYSITELIB}/scipy/special/special/config.h ${PYSITELIB}/scipy/special/special/digamma.h ${PYSITELIB}/scipy/special/special/error.h ${PYSITELIB}/scipy/special/special/evalpoly.h ${PYSITELIB}/scipy/special/special/hyp2f1.h ${PYSITELIB}/scipy/special/special/lambertw.h ${PYSITELIB}/scipy/special/special/loggamma.h ${PYSITELIB}/scipy/special/special/tools.h ${PYSITELIB}/scipy/special/special/trig.h ${PYSITELIB}/scipy/special/special/wright_bessel.h ${PYSITELIB}/scipy/special/special/zlog1.h d2756 3 d2849 3 d2855 56 d2941 3 d2959 3 d3003 3 d3012 3 a3035 3 ${PYSITELIB}/scipy/stats/_rvs_sampling.py ${PYSITELIB}/scipy/stats/_rvs_sampling.pyc ${PYSITELIB}/scipy/stats/_rvs_sampling.pyo d3152 3 d3161 3 @ 1.29 log @py-scipy: updated to 1.14.0 SciPy 1.14.0 is the culmination of 3 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.14.x branch, and on adding new features on the main branch. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.28 2024/04/19 19:24:25 adam Exp $ d2668 3 @ 1.28 log @py-scipy: updated to 1.13.0 SciPy 1.13.0 is the culmination of 3 months of hard work. This out-of-band release aims to support NumPy ``2.0.0``, and is backwards compatible to NumPy ``1.22.4``. The version of OpenBLAS used to build the PyPI wheels has been increased to ``0.3.26.dev``. This release requires Python 3.9+ and NumPy 1.22.4 or greater. For running on PyPy, PyPy3 6.0+ is required. ************************** Highlights of this release ************************** - Support for NumPy ``2.0.0``. - Interactive examples have been added to the documentation, allowing users to run the examples locally on embedded Jupyterlite notebooks in their browser. - Preliminary 1D array support for the COO and DOK sparse formats. - Several `scipy.stats` functions have gained support for additional ``axis``, ``nan_policy``, and ``keepdims`` arguments. `scipy.stats` also has several performance and accuracy improvements. ************ New features ************ `scipy.integrate` improvements ============================== - The ``terminal`` attribute of `scipy.integrate.solve_ivp` ``events`` callables now additionally accepts integer values to specify a number of occurrences required for termination, rather than the previous restriction of only accepting a ``bool`` value to terminate on the first registered event. `scipy.io` improvements ======================= - `scipy.io.wavfile.write` has improved ``dtype`` input validation. `scipy.interpolate` improvements ================================ - The Modified Akima Interpolation has been added to ``interpolate.Akima1DInterpolator``, available via the new ``method`` argument. - New method ``BSpline.insert_knot`` inserts a knot into a ``BSpline`` instance. This routine is similar to the module-level `scipy.interpolate.insert` function, and works with the BSpline objects instead of ``tck`` tuples. - ``RegularGridInterpolator`` gained the functionality to compute derivatives in place. For instance, ``RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1))`` evaluates the mixed second derivative, :math:`\partial^2 / \partial x \partial y` at ``xi``. - Performance characteristics of tensor-product spline methods of ``RegularGridInterpolator`` have been changed: evaluations should be significantly faster, while construction might be slower. If you experience issues with construction times, you may need to experiment with optional keyword arguments ``solver`` and ``solver_args``. Previous behavior (fast construction, slow evaluations) can be obtained via `"*_legacy"` methods: ``method="cubic_legacy"`` is exactly equivalent to ``method="cubic"`` in previous releases. See ``gh-19633`` for details. `scipy.signal` improvements =========================== - Many filter design functions now have improved input validation for the sampling frequency (``fs``). `scipy.sparse` improvements =========================== - ``coo_array`` now supports 1D shapes, and has additional 1D support for ``min``, ``max``, ``argmin``, and ``argmax``. The DOK format now has preliminary 1D support as well, though only supports simple integer indices at the time of writing. - Experimental support has been added for ``pydata/sparse`` array inputs to `scipy.sparse.csgraph`. - ``dok_array`` and ``dok_matrix`` now have proper implementations of ``fromkeys``. - ``csr`` and ``csc`` formats now have improved ``setdiag`` performance. `scipy.spatial` improvements ============================ - ``voronoi_plot_2d`` now draws Voronoi edges to infinity more clearly when the aspect ratio is skewed. `scipy.special` improvements ============================ - All Fortran code, namely, ``AMOS``, ``specfun``, and ``cdflib`` libraries that the majority of special functions depend on, is ported to Cython/C. - The function ``factorialk`` now also supports faster, approximate calculation using ``exact=False``. `scipy.stats` improvements ========================== - `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized, improving their performance and the performance of hypothesis tests that depend on them. - ``stats.mannwhitneyu`` should now be faster due to a vectorized statistic calculation, improved caching, improved exploitation of symmetry, and a memory reduction. ``PermutationMethod`` support was also added. - `scipy.stats.mood` now has ``nan_policy`` and ``keepdims`` support. - `scipy.stats.brunnermunzel` now has ``axis`` and ``keepdims`` support. - `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`, `scipy.stats.normaltest`, `scipy.stats.skewtest`, `scipy.stats.kurtosistest`, `scipy.stats.f_oneway`, `scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and `scipy.stats.kstest` have gained ``axis``, ``nan_policy`` and ``keepdims`` support. - `scipy.stats.boxcox_normmax` has gained a ``ymax`` parameter to allow user specification of the maximum value of the transformed data. - `scipy.stats.vonmises` ``pdf`` method has been extended to support ``kappa=0``. The ``fit`` method is also more performant due to the use of non-trivial bounds to solve for ``kappa``. - High order ``moment`` calculations for `scipy.stats.powerlaw` are now more accurate. - The ``fit`` methods of `scipy.stats.gamma` (with ``method='mm'``) and `scipy.stats.loglaplace` are faster and more reliable. - `scipy.stats.goodness_of_fit` now supports the use of a custom ``statistic`` provided by the user. - `scipy.stats.wilcoxon` now supports ``PermutationMethod``, enabling calculation of accurate p-values in the presence of ties and zeros. - `scipy.stats.monte_carlo_test` now has improved robustness in the face of numerical noise. - `scipy.stats.wasserstein_distance_nd` was introduced to compute the Wasserstein-1 distance between two N-D discrete distributions. ******************* Deprecated features ******************* - Complex dtypes in ``PchipInterpolator`` and ``Akima1DInterpolator`` have been deprecated and will raise an error in SciPy 1.15.0. If you are trying to use the real components of the passed array, use ``np.real`` on ``y``. ****************************** Backwards incompatible changes ****************************** ************* Other changes ************* - The second argument of `scipy.stats.moment` has been renamed to ``order`` while maintaining backward compatibility. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.27 2023/10/28 19:57:11 wiz Exp $ d82 3 d103 3 d109 12 d130 3 d142 3 d148 39 d566 1 d604 3 a606 1 ${PYSITELIB}/scipy/interpolate/dfitpack.so d1010 1 d1018 1 d1181 2 d1225 3 a1242 3 ${PYSITELIB}/scipy/linalg/tests/test_misc.py ${PYSITELIB}/scipy/linalg/tests/test_misc.pyc ${PYSITELIB}/scipy/linalg/tests/test_misc.pyo d1406 3 d1670 2 d1708 3 d1729 3 d2307 3 a2330 3 ${PYSITELIB}/scipy/sparse/tests/test_deprecations.py ${PYSITELIB}/scipy/sparse/tests/test_deprecations.pyc ${PYSITELIB}/scipy/sparse/tests/test_deprecations.pyo a2486 1 ${PYSITELIB}/scipy/special/_cdflib.so d2492 1 d2521 3 d2552 1 a2582 1 ${PYSITELIB}/scipy/special/cython_special.pyx d2594 2 d2597 3 d2601 1 d2603 15 d2620 2 d2623 1 d2629 1 d2632 1 d2634 1 d2642 2 d2656 3 d2692 3 d2710 3 d2800 3 a2826 12 ${PYSITELIB}/scipy/stats/_boost/__init__.py ${PYSITELIB}/scipy/stats/_boost/__init__.pyc ${PYSITELIB}/scipy/stats/_boost/__init__.pyo ${PYSITELIB}/scipy/stats/_boost/beta_ufunc.so ${PYSITELIB}/scipy/stats/_boost/binom_ufunc.so ${PYSITELIB}/scipy/stats/_boost/hypergeom_ufunc.so ${PYSITELIB}/scipy/stats/_boost/invgauss_ufunc.so ${PYSITELIB}/scipy/stats/_boost/nbinom_ufunc.so ${PYSITELIB}/scipy/stats/_boost/ncf_ufunc.so ${PYSITELIB}/scipy/stats/_boost/nct_ufunc.so ${PYSITELIB}/scipy/stats/_boost/ncx2_ufunc.so ${PYSITELIB}/scipy/stats/_boost/skewnorm_ufunc.so a2862 3 ${PYSITELIB}/scipy/stats/_generate_pyx.py ${PYSITELIB}/scipy/stats/_generate_pyx.pyc ${PYSITELIB}/scipy/stats/_generate_pyx.pyo d2879 3 a3037 3 ${PYSITELIB}/scipy/stats/tests/test_boost_ufuncs.py ${PYSITELIB}/scipy/stats/tests/test_boost_ufuncs.pyc ${PYSITELIB}/scipy/stats/tests/test_boost_ufuncs.pyo d3077 3 @ 1.27 log @python/wheel.mk: simplify a lot, and switch to 'installer' for installation This follows the recommended bootstrap method (flit_core, build, installer). However, installer installs different files than pip, so update PLISTs for all packages using wheel.mk and bump their PKGREVISIONs. @ text @d1 1 a1 1 @@comment $NetBSD$ d18 3 d34 3 d70 54 d152 3 d266 3 d275 3 a277 3 ${PYSITELIB}/scipy/fft/_fftlog_multimethods.py ${PYSITELIB}/scipy/fft/_fftlog_multimethods.pyc ${PYSITELIB}/scipy/fft/_fftlog_multimethods.pyo d307 3 d319 3 a321 3 ${PYSITELIB}/scipy/fft/tests/test_fft_function.py ${PYSITELIB}/scipy/fft/tests/test_fft_function.pyc ${PYSITELIB}/scipy/fft/tests/test_fft_function.pyo a330 3 ${PYSITELIB}/scipy/fft/tests/test_numpy.py ${PYSITELIB}/scipy/fft/tests/test_numpy.pyc ${PYSITELIB}/scipy/fft/tests/test_numpy.pyo d445 3 d487 3 a512 3 ${PYSITELIB}/scipy/interpolate/_interpnd_info.py ${PYSITELIB}/scipy/interpolate/_interpnd_info.pyc ${PYSITELIB}/scipy/interpolate/_interpnd_info.pyo d516 3 d605 4 a986 1 ${PYSITELIB}/scipy/linalg/_blas_subroutine_wrappers.f a1028 4 ${PYSITELIB}/scipy/linalg/_flinalg.so ${PYSITELIB}/scipy/linalg/_flinalg_py.py ${PYSITELIB}/scipy/linalg/_flinalg_py.pyc ${PYSITELIB}/scipy/linalg/_flinalg_py.pyo a1032 1 ${PYSITELIB}/scipy/linalg/_lapack_subroutine_wrappers.f a1094 3 ${PYSITELIB}/scipy/linalg/flinalg.py ${PYSITELIB}/scipy/linalg/flinalg.pyc ${PYSITELIB}/scipy/linalg/flinalg.pyo d1286 3 a1321 2 ${PYSITELIB}/scipy/optimize/__nnls.pyi ${PYSITELIB}/scipy/optimize/__nnls.so d1326 6 d1339 3 d1348 3 d1379 3 d1460 1 d1624 6 d1645 3 d1654 3 d1764 3 a1771 3 ${PYSITELIB}/scipy/signal/_spectral.py ${PYSITELIB}/scipy/signal/_spectral.pyc ${PYSITELIB}/scipy/signal/_spectral.pyo d1814 3 d1856 3 a1981 3 ${PYSITELIB}/scipy/sparse/csgraph/setup.py ${PYSITELIB}/scipy/sparse/csgraph/setup.pyc ${PYSITELIB}/scipy/sparse/csgraph/setup.pyo d2000 3 a2104 1 ${PYSITELIB}/scipy/sparse/linalg/_isolve/_iterative.so d2163 3 d2209 3 d2232 3 d2238 3 d2250 3 d2259 3 d2406 1 d2474 3 d2509 16 d2531 3 d2657 3 d2672 3 d2690 1 d2714 3 d2815 3 a2823 1 ${PYSITELIB}/scipy/stats/_statlib.so a2841 1 ${PYSITELIB}/scipy/stats/_unuran/unuran.pxd d2850 3 a2885 3 ${PYSITELIB}/scipy/stats/statlib.py ${PYSITELIB}/scipy/stats/statlib.pyc ${PYSITELIB}/scipy/stats/statlib.pyo d2901 1 d2955 3 @ 1.26 log @py-scipy: updated to 1.11.2 SciPy 1.11.2 Issues closed for 1.11.2 special.jn_zeros(281, 6) hangs Complex matrix square root of positive semi-definite matrix BUG: \`loadmat\` fails to load matlab structures with anonymous... BUG: Floating point CSC with int64 indices doesn't work with... BUG: \`scipy.optimize.minimize\` fails when \`dtype=float32\`... DOC: Broken link to installation instructions in README.rst BUG: Build failure with Cython 3.0.0b3 if scipy is already installed BUG: optimize.least_squares with method='trf' yields wrong result... BUG: cKDtree.query no longer accepts DataFrame as input Spalde error with scipy 1.10: 0-th dimension must be fixed BUG: BUG: Compilation of scipy 1.10.1 and 1.11.1 fails with Python... Pull requests for 1.11.2 BUG: Fix error in linalg/_matfuncs_sqrtm.py BUG: sparse.linalg: Cast index arrays to intc before calling... Allow johnsonsu parameters to be floats MAINT: stats: fix NumPy DeprecationWarnings REL, MAINT: prep for 1.11.2 DOC: Fix broken link to installation guide in README BUG: Ensure cKDtree.query does not pass Pandas DataFrame to np.isfinite CI, MAINT: 32-bit Pillow pin BLD: copy \`cython_optimize.pxd\` to build dir BUG: make \`L-BFGS-B\` optimizer work with single precision gradient BUG: io/matlab: Fix loading of mat files containing fn handles... BUG: make Bessel-roots function not hang and not skip roots DOC: linking interp1d docstring to tutorial BUG: lsq trf gives x=1e-10 if x0 is near a bound CI/BLD: create cp312 wheels DOC: Fix installation instructions using venv/pip CI: move the musllinux Cirrus job to GHA, optimize other jobs CI: reduce Cirrus CI usage during wheel builds BUG: interpolate: fix spalde with len(c) < len(t) BUG: pass unused xrtol in fmin_bfgs to _minimize_bfgs BLD: rename \`setup.py\` to \`_setup.py\` to signal it should... 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BUG: segfault in \`scipy.linalg.lu\` on x86_64 windows and macos... BUG: factorial return type inconsistent for 0-dim arrays determinant of a 1x1 matrix returns an array, not a scalar Licensing concern Pull requests for 1.11.1 BUG: Fix work array construction for various weight shapes. REL, MAINT: prep for 1.11.1 BUG: fix handling for \`factorial(..., exact=False)\` for 0-dim... FIX:linalg.lu:Guard against permute_l out of bound behavior MAINT:linalg.det:Return scalars for singleton inputs MAINT: fix unuran licensing SciPy 1.11.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.11.x branch, and on adding new features on the main branch. This release requires Python 3.9+ and NumPy 1.21.6 or greater. For running on PyPy, PyPy3 6.0+ is required. ************************** Highlights of this release ************************** - Several `scipy.sparse` array API improvements, including `sparse.sparray`, a new public base class distinct from the older `sparse.spmatrix` class, proper 64-bit index support, and numerous deprecations paving the way to a modern sparse array experience. - `scipy.stats` added tools for survival analysis, multiple hypothesis testing, sensitivity analysis, and working with censored data. - A new function was added for quasi-Monte Carlo integration, and linear algebra functions ``det`` and ``lu`` now accept nD-arrays. - An ``axes`` argument was added broadly to ``ndimage`` functions, facilitating analysis of stacked image data. ************ New features ************ `scipy.integrate` improvements ============================== - Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration. - For an even number of points, `scipy.integrate.simpson` now calculates a parabolic segment over the last three points which gives improved accuracy over the previous implementation. `scipy.cluster` improvements ============================ - ``disjoint_set`` has a new method ``subset_size`` for providing the size of a particular subset. `scipy.constants` improvements ================================ - The ``quetta``, ``ronna``, ``ronto``, and ``quecto`` SI prefixes were added. `scipy.linalg` improvements =========================== - `scipy.linalg.det` is improved and now accepts nD-arrays. - `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new ``p_indices`` switch the output permutation argument can be 1D ``(n,)`` permutation index instead of the full ``(n, n)`` array. `scipy.ndimage` improvements ============================ - ``axes`` argument was added to ``rank_filter``, ``percentile_filter``, ``median_filter``, ``uniform_filter``, ``minimum_filter``, ``maximum_filter``, and ``gaussian_filter``, which can be useful for processing stacks of image data. `scipy.optimize` improvements ============================= - `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS. - `scipy.optimize.root_scalar` now uses Newton's method to be used without providing ``fprime`` and the ``secant`` method to be used without a second guess. - `scipy.optimize.lsq_linear` now accepts ``bounds`` arguments of type `scipy.optimize.Bounds`. - `scipy.optimize.minimize` ``method='cobyla'`` now supports simple bound constraints. - Users can opt into a new callback interface for most methods of `scipy.optimize.minimize`: If the provided callback callable accepts a single keyword argument, ``intermediate_result``, `scipy.optimize.minimize` now passes both the current solution and the optimal value of the objective function to the callback as an instance of `scipy.optimize.OptimizeResult`. It also allows the user to terminate optimization by raising a ``StopIteration`` exception from the callback function. `scipy.optimize.minimize` will return normally, and the latest solution information is provided in the result object. - `scipy.optimize.curve_fit` now supports an optional ``nan_policy`` argument. - `scipy.optimize.shgo` now has parallelization with the ``workers`` argument, symmetry arguments that can improve performance, class-based design to improve usability, and generally improved performance. `scipy.signal` improvements =========================== - ``istft`` has an improved warning message when the NOLA condition fails. `scipy.sparse` improvements =========================== - A new public base class `scipy.sparse.sparray` was introduced, allowing further extension of the sparse array API (such as the support for 1-dimensional sparse arrays) without breaking backwards compatibility. `isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes, while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse matrix classes. - Division of sparse arrays by a dense array now returns sparse arrays. - `scipy.sparse.isspmatrix` now only returns `True` for the sparse matrices instances. `scipy.sparse.issparse` now has to be used instead to check for instances of sparse arrays or instances of sparse matrices. - Sparse arrays constructed with int64 indices will no longer automatically downcast to int32. - The ``argmin`` and ``argmax`` methods now return the correct result when explicit zeros are present. `scipy.sparse.linalg` improvements ================================== - dividing ``LinearOperator`` by a number now returns a ``_ScaledLinearOperator`` - ``LinearOperator`` now supports right multiplication by arrays - ``lobpcg`` should be more efficient following removal of an extraneous QR decomposition. `scipy.spatial` improvements ============================ - Usage of new C++ backend for additional distance metrics, the majority of which will see substantial performance improvements, though a few minor regressions are known. These are focused on distances between boolean arrays. `scipy.special` improvements ============================ - The factorial functions ``factorial``, ``factorial2`` and ``factorialk`` were made consistent in their behavior (in terms of dimensionality, errors etc.). Additionally, ``factorial2`` can now handle arrays with ``exact=True``, and ``factorialk`` can handle arrays. `scipy.stats` improvements ========================== New Features ------------ - `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices. - `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple experimental groups against the mean of a control group. - `scipy.stats.ecdf` for computing the empirical CDF and complementary CDF (survival function / SF) from uncensored or right-censored data. This function is also useful for survival analysis / Kaplan-Meier estimation. - `scipy.stats.logrank` to compare survival functions underlying samples. - `scipy.stats.false_discovery_control` for adjusting p-values to control the false discovery rate of multiple hypothesis tests using the Benjamini-Hochberg or Benjamini-Yekutieli procedures. - `scipy.stats.CensoredData` to represent censored data. It can be used as input to the ``fit`` method of univariate distributions and to the new ``ecdf`` function. - Filliben's goodness of fit test as ``method='Filliben'`` of `scipy.stats.goodness_of_fit`. - `scipy.stats.ttest_ind` has a new method, ``confidence_interval`` for computing a confidence interval of the difference between means. - `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and `scipy.stats.BootstrapMethod` are new classes to configure resampling and/or Monte Carlo versions of hypothesis tests. 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log @py-scipy: updated to 1.10.1 SciPy 1.10.1 is a bug-fix release with no new features compared to 1.10.0. SciPy 1.10.0 Release Notes ========================== SciPy 1.10.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.10.x branch, and on adding new features on the main branch. This release requires Python 3.8+ and NumPy 1.19.5 or greater. For running on PyPy, PyPy3 6.0+ is required. ************************** Highlights of this release ************************** - A new dedicated datasets submodule (`scipy.datasets`) has been added, and is now preferred over usage of `scipy.misc` for dataset retrieval. - A new `scipy.interpolate.make_smoothing_spline` function was added. This function constructs a smoothing cubic spline from noisy data, using the generalized cross-validation (GCV) criterion to find the tradeoff between smoothness and proximity to data points. - `scipy.stats` has three new distributions, two new hypothesis tests, three new sample statistics, a class for greater control over calculations involving covariance matrices, and many other enhancements. ************ New features ************ `scipy.datasets` introduction ============================= - A new dedicated ``datasets`` submodule has been added. The submodules is meant for datasets that are relevant to other SciPy submodules ands content (tutorials, examples, tests), as well as contain a curated set of datasets that are of wider interest. As of this release, all the datasets from `scipy.misc` have been added to `scipy.datasets` (and deprecated in `scipy.misc`). - The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/) (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage. - Added datasets from ``scipy.misc``: `scipy.datasets.face`, `scipy.datasets.ascent`, `scipy.datasets.electrocardiogram` - Added download and caching functionality: - `scipy.datasets.download_all`: a function to download all the `scipy.datasets` associated files at once. - `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset files from the file system. - ``scipy/datasets/_download_all.py`` can be run as a standalone script for packaging purposes to avoid any external dependency at build or test time. This can be used by SciPy packagers (e.g., for Linux distros) which may have to adhere to rules that forbid downloading sources from external repositories at package build time. `scipy.integrate` improvements ============================== - Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set ``True`` to integrate a complex integrand. `scipy.interpolate` improvements ================================ - `scipy.interpolate.interpn` now supports tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and ``pchip``) - Tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and ``pchip``) in `scipy.interpolate.interpn` and `scipy.interpolate.RegularGridInterpolator` now allow values with trailing dimensions. - `scipy.interpolate.RegularGridInterpolator` has a new fast path for ``method="linear"`` with 2D data, and ``RegularGridInterpolator`` is now easier to subclass - `scipy.interpolate.interp1d` now can take a single value for non-spline methods. - A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`, allowing extrapolation based on the first and last intervals. - A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an implementation of the generalized cross-validation spline smoothing algorithm. The ``lam=None`` (default) mode of this function is a clean-room reimplementation of the classic ``gcvspl.f`` Fortran algorithm for constructing GCV splines. - A new ``method="pchip"`` mode was aded to `scipy.interpolate.RegularGridInterpolator`. This mode constructs an interpolator using tensor products of C1-continuous monotone splines (essentially, a `scipy.interpolate.PchipInterpolator` instance per dimension). `scipy.sparse.linalg` improvements ================================== - The spectral 2-norm is now available in `scipy.sparse.linalg.norm`. - The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius norm) has been improved. - LAPACK wrappers were added for ``trexc`` and ``trsen``. - The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding the following improvements: - a simple tunable restart potentially increases the attainable accuracy for edge cases, - internal postprocessing runs one final exact Rayleigh-Ritz method giving more accurate and orthonormal eigenvectors, - output the computed iterate with the smallest max norm of the residual and drop the history of subsequent iterations, - remove the check for ``LinearOperator`` format input and thus allow a simple function handle of a callable object as an input, - better handling of common user errors with input data, rather than letting the algorithm fail. `scipy.linalg` improvements =========================== - `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted to square arrays. `scipy.ndimage` improvements ============================ - The new `scipy.ndimage.value_indices` function provides a time-efficient method to search for the locations of individual values with an array of image data. - A new ``radius`` argument is supported by `scipy.ndimage.gaussian_filter1d` and `scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter. `scipy.optimize` improvements ============================= - `scipy.optimize.brute` now coerces non-iterable/single-value ``args`` into a tuple. - `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept `scipy.optimize.Bounds` for bounds constraints. - Added a tutorial for `scipy.optimize.milp`. - Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects. - Additional options (``parallel``, ``threads``, ``mip_rel_gap``) can now be passed to `scipy.optimize.linprog` with ``method='highs'``. `scipy.signal` improvements =========================== - The new window function `scipy.signal.windows.lanczos` was added to compute a Lanczos window, also known as a sinc window. `scipy.sparse.csgraph` improvements =================================== - the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and star graphs in particular see a marked performance improvement `scipy.special` improvements ============================ - The new function `scipy.special.powm1`, a ufunc with signature ``powm1(x, y)``, computes ``x**y - 1``. The function avoids the loss of precision that can result when ``y`` is close to 0 or when ``x`` is close to 1. - `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under the hood. `scipy.stats` improvements ========================== - Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for use with any univariate distribution, any combination of known and unknown parameters, and several choices of test statistic (Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling). - Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports multi-sample statistics. Also, the bootstrap distribution is returned in the result object, and the result object can be passed into the function as parameter ``bootstrap_result`` to add additional resamples or change the confidence interval level and type. - Added maximum spacing estimation to `scipy.stats.fit`. - Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`. - Added new sample statistics. - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional and unconditional odds ratios and corresponding confidence intervals for 2x2 contingency tables. - Added `scipy.stats.directional_stats` to compute sample statistics of n-dimensional directional data. - Added `scipy.stats.expectile`, which generalizes the expected value in the same way as quantiles are a generalization of the median. - Added new statistical distributions. - Added `scipy.stats.uniform_direction`, a multivariate distribution to sample uniformly from the surface of a hypersphere. - Added `scipy.stats.random_table`, a multivariate distribution to sample uniformly from m x n contingency tables with provided marginals. - Added `scipy.stats.truncpareto`, the truncated Pareto distribution. - Improved the ``fit`` method of several distributions. - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical solution when ``method='mm'``, which also serves a starting guess to improve the performance of ``method='mle'``. - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum likelihood estimates have been extended to the cases in which location or scale are fixed by the user. - Analytical maximum likelihood estimates have been added for `scipy.stats.powerlaw`. - Improved random variate sampling of several distributions. - Drawing multiple samples from `scipy.stats.matrix_normal`, `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and `scipy.stats.unitary_group` is faster. - The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval ``[-np.pi, np.pi]``. - Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small values of the shape parameter. - Improved the speed and/or accuracy of functions of several statistical distributions. - Added `scipy.stats.Covariance` for better speed, accuracy, and user control in multivariate normal calculations. - `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf`` methods now use the implementations from Boost, improving speed while maintaining accuracy. The calculation of higher-order moments is also faster and more accurate. - `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the implementations from Boost, improving speed and accuracy. - `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for small probability masses. - `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations from Boost, improving speed and accuracy. - Implemented the ``logpdf`` method of `scipy.stats.vonmises` for reliability in extreme tails. - Implemented the ``isf`` method of `scipy.stats.levy` for speed and accuracy. - Improved the robustness of `scipy.stats.studentized_range` for large ``df`` by adding an infinite degree-of-freedom approximation. - Added a parameter ``lower_limit`` to `scipy.stats.multivariate_normal`, allowing the user to change the integration limit from -inf to a desired value. - Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large concentration values. - Enhanced `scipy.stats.gaussian_kde`. - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired marginal distribution of the original kernel density estimate distribution. - The ``cdf`` method of `scipy.stats.gaussian_kde` now accepts a ``lower_limit`` parameter for integrating the PDF over a rectangular region. - Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython, improving speed. - The global interpreter lock is released by the ``pdf`` method of `scipy.stats.gaussian_kde` for improved multithreading performance. - Replaced explicit matrix inversion with Cholesky decomposition for speed and accuracy. - Enhanced the result objects returned by many `scipy.stats` functions - Added a ``confidence_interval`` method to the result object returned by `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`. - The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``, ``chi2_contingency``, ``median_test`` and ``mood`` now return bunch objects rather than plain tuples, allowing attributes to be accessed by name. - Attributes of the result objects returned by ``multiscale_graphcorr``, ``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``, ``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to ``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`. Old attribute names are still allowed for backward compatibility. - `scipy.stats.anderson` now returns the parameters of the fitted distribution in a `scipy.stats._result_classes.FitResult` object. - The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default), ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF plot). - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the location (argmax) at which the statistic is calculated and the variant of the statistic used. - Improved the performance of several `scipy.stats` functions. - Improved the performance of `scipy.stats.cramervonmises_2samp` and `scipy.stats.ks_2samp` with ``method='exact'``. - Improved the performance of `scipy.stats.siegelslopes`. - Improved the performance of `scipy.stats.mstats.hdquantile_sd`. - Improved the performance of `scipy.stats.binned_statistic_dd` for several NumPy statistics, and binned statistics methods now support complex data. - Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`. It replaces ``centered``, which is now deprecated. - Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine` subclasses to improve characteristics of the quasi-random variates. - Added tie correction to `scipy.stats.mood`. - Added tutorials for resampling methods in `scipy.stats`. - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided ``statistic`` is vectorized, so passing the ``vectorized`` argument explicitly is no longer required to take advantage of vectorized statistics. - Improved the speed of `scipy.stats.permutation_test` for permutation types ``'samples'`` and ``'pairings'``. - Added ``axis``, ``nan_policy``, and masked array support to `scipy.stats.jarque_bera`. - Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`. ******************* Deprecated features ******************* - `scipy.misc` module and all the methods in ``misc`` are deprecated in v1.10 and will be completely removed in SciPy v2.0.0. Users are suggested to utilize the `scipy.datasets` module instead for the dataset methods. - `scipy.stats.qmc.LatinHypercube` parameter ``centered`` has been deprecated. It is replaced by the ``scramble`` argument for more consistency with other QMC engines. - `scipy.interpolate.interp2d` class has been deprecated. The docstring of the deprecated routine lists recommended replacements. ******************** Expired Deprecations ******************** - There is an ongoing effort to follow through on long-standing deprecations. - The following previously deprecated features are affected: - Removed ``cond`` & ``rcond`` kwargs in ``linalg.pinv`` - Removed wrappers ``scipy.linalg.blas.{clapack, flapack}`` - Removed ``scipy.stats.NumericalInverseHermite`` and removed ``tol`` & ``max_intervals`` kwargs from ``scipy.stats.sampling.NumericalInverseHermite`` - Removed ``local_search_options`` kwarg frrom ``scipy.optimize.dual_annealing``. ************* Other changes ************* - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided ``statistic`` is vectorized by looking for an ``axis`` parameter in the signature of ``statistic``. If an ``axis`` parameter is present in ``statistic`` but should not be relied on for vectorized calls, users must pass option ``vectorized==False`` explicitly. - `scipy.stats.multivariate_normal` will now raise a ``ValueError`` when the covariance matrix is not positive semidefinite, regardless of which method is called. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.23 2022/10/05 20:54:31 adam Exp $ a7 2 ${PYSITELIB}/scipy/HACKING.rst.txt ${PYSITELIB}/scipy/INSTALL.rst.txt a24 3 ${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.py ${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.pyc ${PYSITELIB}/scipy/_build_utils/gcc_build_bitness.pyo d864 2 d1000 1 d1282 6 a1288 3 ${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.py ${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.pyc ${PYSITELIB}/scipy/optimize/_shgo_lib/triangulation.pyo a1580 3 ${PYSITELIB}/scipy/sparse/_arrays.py ${PYSITELIB}/scipy/sparse/_arrays.pyc ${PYSITELIB}/scipy/sparse/_arrays.pyo d1624 3 d1875 1 d2188 3 d2252 3 d2270 3 d2292 3 d2310 3 d2375 1 d2396 1 d2402 1 d2405 1 d2414 2 d2422 1 d2430 1 d2432 1 @ 1.23 log @py-scipy: updated to 1.9.1 SciPy 1.9.1 is a bug-fix release with no new features compared to 1.9.0. Notably, some important meson build fixes are included. SciPy 1.9.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.9.x branch, and on adding new features on the main branch. This release requires Python 3.8-3.11 and NumPy 1.18.5 or greater. For running on PyPy, PyPy3 6.0+ is required. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.22 2022/04/09 12:15:31 adam Exp $ a26 6 ${PYSITELIB}/scipy/_build_utils/copyfiles.py ${PYSITELIB}/scipy/_build_utils/copyfiles.pyc ${PYSITELIB}/scipy/_build_utils/copyfiles.pyo ${PYSITELIB}/scipy/_build_utils/cythoner.py ${PYSITELIB}/scipy/_build_utils/cythoner.pyc ${PYSITELIB}/scipy/_build_utils/cythoner.pyo d63 3 d180 21 d344 1 d448 1 d475 1 d1038 1 a1794 1 ${PYSITELIB}/scipy/sparse/linalg/_isolve/tests/demo_lgmres.py d2072 2 a2073 2 ${PYSITELIB}/scipy/special/_test_round.pyi ${PYSITELIB}/scipy/special/_test_round.so d2117 1 d2142 1 d2177 1 d2180 2 d2185 1 d2195 3 a2221 4 ${PYSITELIB}/scipy/stats/_hypotests_pythran.py ${PYSITELIB}/scipy/stats/_hypotests_pythran.pyc ${PYSITELIB}/scipy/stats/_hypotests_pythran.pyo ${PYSITELIB}/scipy/stats/_hypotests_pythran.so d2251 3 d2262 7 d2292 4 d2358 1 a2379 1 ${PYSITELIB}/scipy/stats/tests/studentized_range_mpmath_ref.py d2391 3 d2400 1 @ 1.22 log @py-scipy: updated to 1.8.0 SciPy 1.8.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.8.x branch, and on adding new features on the master branch. This release requires Python 3.8+ and NumPy 1.17.3 or greater. For running on PyPy, PyPy3 6.0+ is required. ************************** Highlights of this release ************************** - A sparse array API has been added for early testing and feedback; this work is ongoing, and users should expect minor API refinements over the next few releases. - The sparse SVD library PROPACK is now vendored with SciPy, and an interface is exposed via `scipy.sparse.svds` with ``solver='PROPACK'``. It is currently default-off due to potential issues on Windows that we aim to resolve in the next release, but can be optionally enabled at runtime for friendly testing with an environment variable setting of ``USE_PROPACK=1``. - A new `scipy.stats.sampling` submodule that leverages the ``UNU.RAN`` C library to sample from arbitrary univariate non-uniform continuous and discrete distributions - All namespaces that were private but happened to miss underscores in their names have been deprecated. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.21 2021/11/30 17:00:44 adam Exp $ d11 1 d27 9 d73 3 d151 1 d180 1 d199 3 d223 1 d232 2 d282 1 d326 1 d368 2 d426 3 d455 1 d467 1 a467 1 ${PYSITELIB}/scipy/interpolate/tests/test_regression.py d487 1 d529 1 d705 1 d810 1 d830 1 d883 2 a963 1 ${PYSITELIB}/scipy/linalg/src/lapack_deprecations/LICENSE d971 2 d995 1 a1001 3 ${PYSITELIB}/scipy/misc/_doccer.py ${PYSITELIB}/scipy/misc/_doccer.pyc ${PYSITELIB}/scipy/misc/_doccer.pyo d1015 1 d1071 1 d1103 1 d1128 4 d1156 1 d1191 1 a1191 4 ${PYSITELIB}/scipy/optimize/_lsap.py ${PYSITELIB}/scipy/optimize/_lsap.pyc ${PYSITELIB}/scipy/optimize/_lsap.pyo ${PYSITELIB}/scipy/optimize/_lsap_module.so d1220 3 d1262 1 d1311 1 d1380 1 d1395 1 d1405 1 d1517 1 d1652 1 d1698 1 d1721 1 d1733 1 d1739 1 d1774 1 d1823 1 d1847 1 d1934 1 d1961 1 d2155 1 a2158 3 ${PYSITELIB}/scipy/stats/_bootstrap.py ${PYSITELIB}/scipy/stats/_bootstrap.pyc ${PYSITELIB}/scipy/stats/_bootstrap.pyo d2183 3 d2202 7 d2236 3 d2271 3 a2273 3 ${PYSITELIB}/scipy/stats/_wilcoxon_data.py ${PYSITELIB}/scipy/stats/_wilcoxon_data.pyc ${PYSITELIB}/scipy/stats/_wilcoxon_data.pyo d2318 5 d2334 1 d2336 1 a2336 2 ${PYSITELIB}/scipy/stats/tests/data/stable-cdf-sample-data.npy ${PYSITELIB}/scipy/stats/tests/data/stable-pdf-sample-data.npy d2338 1 d2342 1 a2342 1 ${PYSITELIB}/scipy/stats/tests/test_bootstrap.py d2360 1 @ 1.21 log @py-scipy: updated to 1.7.3 SciPy 1.7.3 is a bug-fix release that provides binary wheels for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels are only available for MacOS version 12.0 and greater, as explained in Issue 14688, linked below. Issues closed for 1.7.3 ----------------------- * Segmentation fault on import of scipy.integrate on Apple M1 ARM... * BUG: ARPACK's eigsh & OpenBLAS from Apple Silicon M1 (arm64)... * four CI failures on pre-release job * Remaining test failures for macOS arm64 wheel * BUG: Segmentation fault caused by scipy.stats.qmc.qmc.update_discrepancy Pull requests for 1.7.3 ----------------------- * BLD: update pyproject.toml for Python 3.10 changes * BUG: out of bounds indexing in stats.qmc.update_discrepancy * MAINT: skip a few failing tests in \`1.7.x\` for macOS arm64 @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.20 2021/11/06 11:53:13 adam Exp $ d56 3 d89 3 d118 1 d150 6 d224 12 d306 1 d311 3 d318 3 d326 7 a332 1 ${PYSITELIB}/scipy/integrate/lsoda.so d352 3 a354 1 ${PYSITELIB}/scipy/integrate/vode.so d366 3 d372 12 d387 3 d391 3 a408 3 ${PYSITELIB}/scipy/interpolate/interpnd_info.py ${PYSITELIB}/scipy/interpolate/interpnd_info.pyc ${PYSITELIB}/scipy/interpolate/interpnd_info.pyo d445 24 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${PYSITELIB}/scipy/sparse/linalg/isolve/tests/test_lsqr.py ${PYSITELIB}/scipy/sparse/linalg/isolve/tests/test_minres.py ${PYSITELIB}/scipy/sparse/linalg/isolve/tests/test_utils.py ${PYSITELIB}/scipy/sparse/linalg/isolve/utils.py ${PYSITELIB}/scipy/sparse/linalg/isolve/utils.pyc ${PYSITELIB}/scipy/sparse/linalg/isolve/utils.pyo d1768 1 d1774 1 d1791 1 d1804 2 d1812 3 d1821 2 d1828 3 a1830 2 ${PYSITELIB}/scipy/spatial/ckdtree.pyi ${PYSITELIB}/scipy/spatial/ckdtree.so d1838 3 a1840 2 ${PYSITELIB}/scipy/spatial/qhull.pyi ${PYSITELIB}/scipy/spatial/qhull.so d1888 2 d1896 3 a1898 2 ${PYSITELIB}/scipy/spatial/transform/rotation.pyi ${PYSITELIB}/scipy/spatial/transform/rotation.so d1910 3 d1933 4 d1952 3 d1982 7 a2010 1 ${PYSITELIB}/scipy/special/orthogonal.pyi d2018 3 a2020 1 ${PYSITELIB}/scipy/special/specfun.so d2033 1 d2044 1 d2078 5 d2094 1 d2132 7 d2145 9 d2157 1 d2178 1 d2183 3 d2189 12 d2204 3 a2206 2 ${PYSITELIB}/scipy/stats/biasedurn.pxd ${PYSITELIB}/scipy/stats/biasedurn.so d2228 3 a2230 1 ${PYSITELIB}/scipy/stats/mvn.so d2234 3 d2240 3 a2242 1 ${PYSITELIB}/scipy/stats/statlib.so d2264 1 d2284 1 d2287 1 @ 1.20 log @py-scipy: updated to 1.7.2 SciPy 1.7.2 is a bug-fix release with no new features compared to 1.7.1. Notably, the release includes wheels for Python 3.10, and wheels are now built with a newer version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided for MacOS x86_64 (thin, not universal2 or arm64 at this time), and Windows/Linux 64-bit. Many wheels are now built with newer versions of manylinux, which may require newer versions of pip. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.19 2021/11/02 18:51:02 adam Exp $ d5 1 a7 1 ${PYSITELIB}/${EGG_INFODIR}/not-zip-safe d1736 9 @ 1.19 log @py-scipy: updated to 1.7.1 SciPy 1.7.1 is a bug-fix release with no new features compared to 1.7.0. 1.7.0: A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added The documentation design was updated to use the same PyData-Sphinx theme as NumPy and other ecosystem libraries. We now vendor and leverage the Boost C++ library to enable numerous improvements for long-standing weaknesses in scipy.stats scipy.stats has six new distributions, eight new (or overhauled) hypothesis tests, a new function for bootstrapping, a class that enables fast random variate sampling and percentile point function evaluation, and many other enhancements. cdist and pdist distance calculations are faster for several metrics, especially weighted cases, thanks to a rewrite to a new C++ backend framework A new class for radial basis function interpolation, RBFInterpolator, was added to address issues with the Rbf class. @ text @d1 7 a7 6 @@comment $NetBSD: PLIST,v 1.18 2021/04/05 19:26:07 tnn Exp $ ${PYSITELIB}/${EGG_FILE}/PKG-INFO ${PYSITELIB}/${EGG_FILE}/SOURCES.txt ${PYSITELIB}/${EGG_FILE}/dependency_links.txt ${PYSITELIB}/${EGG_FILE}/requires.txt ${PYSITELIB}/${EGG_FILE}/top_level.txt @ 1.18 log @py-scipy: update to 1.6.2 Highlights of this release scipy.ndimage improvements: Fixes and ehancements to boundary extension modes for interpolation functions. Support for complex-valued inputs in many filtering and interpolation functions. New grid_mode option for scipy.ndimage.zoom to enable results consistent with scikit-image's rescale. scipy.optimize.linprog has fast, new methods for large, sparse problems from the HiGHS library. scipy.stats improvements including new distributions, a new test, and enhancements to existing distributions and tests Deprecated features scipy.spatial changes Calling KDTree.query with k=None to find all neighbours is deprecated. Use KDTree.query_ball_point instead. distance.wminkowski was deprecated; use distance.minkowski and supply weights with the w keyword instead. Backwards incompatible changes Using scipy.fft as a function aliasing numpy.fft.fft was removed after being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule must be explicitly imported now, in line with other SciPy subpackages. scipy.signal changes The output of decimate, lfilter_zi, lfiltic, sos2tf, and sosfilt_zi have been changed to match numpy.result_type of their inputs. The window function slepian was removed. The frechet_l and frechet_r distributions were removed. @ text @d1 1 a1 1 @@comment $NetBSD$ d31 3 d42 3 a110 1 ${PYSITELIB}/scipy/_lib/tests/test_linear_assignment.py d154 1 d167 3 d202 1 d332 7 d378 1 d668 1 d813 1 d905 1 d924 3 a935 1 ${PYSITELIB}/scipy/optimize/_highs/_mpswriter.so a941 1 ${PYSITELIB}/scipy/optimize/_highs/cython/src/HighsMipSolver.pxd a1032 4 ${PYSITELIB}/scipy/optimize/_shgo_lib/sobol_seq.py ${PYSITELIB}/scipy/optimize/_shgo_lib/sobol_seq.pyc ${PYSITELIB}/scipy/optimize/_shgo_lib/sobol_seq.pyo ${PYSITELIB}/scipy/optimize/_shgo_lib/sobol_vec.gz d1108 3 d1190 3 d1202 3 d1487 1 d1508 1 d1513 1 d1568 1 d1604 3 d1631 6 d1686 1 d1706 1 d1723 1 d1732 9 d1747 3 d1759 6 d1771 3 d1777 14 d1794 3 d1807 2 d1831 3 d1857 2 d1860 1 d1863 1 d1867 1 d1875 1 d1877 1 @ 1.17 log @py-scipy: update to 1.5.2 Done to fix build w/ gfortran 10. "make test" was mostly OK except for three tests that returned nan where inf was expected ... Highlights of this release: wrappers for more than a dozen new LAPACK routines are now available in scipy.linalg.lapack Improved support for leveraging 64-bit integer size from linear algebra backends addition of the probability distribution for two-sided one-sample Kolmogorov-Smirnov tests New features: Too many; see release notes at github. Backwards incompatible changes: The output signatures of ?syevr, ?heevr have been changed from w, v, info to w, v, m, isuppz, info The order of output arguments w, v of {gv, gvd, gvx} is swapped. The output length of scipy.signal.upfirdn has been corrected, resulting outputs may now be shorter for some combinations of up/down ratios and input signal and filter lengths. scipy.signal.resample now supports a domain keyword argument for specification of time or frequency domain input. @ text @d5 1 a9 1 ${PYSITELIB}/scipy/THANKS.txt a31 1 ${PYSITELIB}/scipy/_build_utils/tests/test_circular_imports.py d39 3 d46 3 d101 1 d105 1 d125 1 d265 5 d386 1 d640 1 d646 2 d650 7 d706 1 d793 1 a821 1 ${PYSITELIB}/scipy/ndimage/_ctest_oldapi.so d858 2 a861 2 ${PYSITELIB}/scipy/ndimage/tests/test_ndimage.py ${PYSITELIB}/scipy/ndimage/tests/test_regression.py d907 22 d933 6 d993 3 d1143 1 d1220 1 d1538 1 a1538 3 ${PYSITELIB}/scipy/spatial/transform/rotation.py ${PYSITELIB}/scipy/spatial/transform/rotation.pyc ${PYSITELIB}/scipy/spatial/transform/rotation.pyo d1778 1 @ 1.16 log @py-scipy: updated to 1.4.1 SciPy 1.4.1 is a bug-fix release with no new features compared to 1.4.0. Importantly, it aims to fix a problem where an older version of pybind11 may cause a segmentation fault when imported alongside incompatible libraries. SciPy 1.4.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.4.x branch, and on adding new features on the master branch. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.15 2019/06/14 14:53:29 adam Exp $ a47 3 ${PYSITELIB}/scipy/_lib/_numpy_compat.py ${PYSITELIB}/scipy/_lib/_numpy_compat.pyc ${PYSITELIB}/scipy/_lib/_numpy_compat.pyo d52 2 a76 3 ${PYSITELIB}/scipy/_lib/_version.py ${PYSITELIB}/scipy/_lib/_version.pyc ${PYSITELIB}/scipy/_lib/_version.pyo a89 3 ${PYSITELIB}/scipy/_lib/six.py ${PYSITELIB}/scipy/_lib/six.pyc ${PYSITELIB}/scipy/_lib/six.pyo d92 1 a95 1 ${PYSITELIB}/scipy/_lib/tests/test__version.py d97 1 d265 3 a276 3 ${PYSITELIB}/scipy/integrate/quadrature.py ${PYSITELIB}/scipy/integrate/quadrature.pyc ${PYSITELIB}/scipy/integrate/quadrature.pyo d502 1 d560 1 d631 1 d650 3 a731 3 ${PYSITELIB}/scipy/linalg/linalg_version.py ${PYSITELIB}/scipy/linalg/linalg_version.pyc ${PYSITELIB}/scipy/linalg/linalg_version.pyo d760 1 d791 1 d842 3 a844 3 ${PYSITELIB}/scipy/odr/add_newdocs.py ${PYSITELIB}/scipy/odr/add_newdocs.pyc ${PYSITELIB}/scipy/odr/add_newdocs.pyo d860 1 d934 3 a936 1 ${PYSITELIB}/scipy/optimize/_nnls.so a1045 3 ${PYSITELIB}/scipy/optimize/nnls.py ${PYSITELIB}/scipy/optimize/nnls.pyc ${PYSITELIB}/scipy/optimize/nnls.pyo d1075 1 d1403 3 d1416 1 d1418 1 d1469 1 d1505 3 d1550 1 d1555 1 d1565 1 a1566 3 ${PYSITELIB}/scipy/special/lambertw.py ${PYSITELIB}/scipy/special/lambertw.pyc ${PYSITELIB}/scipy/special/lambertw.pyo d1569 1 d1587 1 d1594 1 d1597 1 d1602 1 d1651 3 d1667 3 a1729 3 ${PYSITELIB}/scipy/stats/vonmises.py ${PYSITELIB}/scipy/stats/vonmises.pyc ${PYSITELIB}/scipy/stats/vonmises.pyo @ 1.15 log @py-scipy: updated to 1.3.0 SciPy 1.3.0 Release Notes SciPy 1.3.0 is the culmination of 5 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been some API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.3.x branch, and on adding new features on the master branch. This release requires Python 3.5+ and NumPy 1.13.3 or greater. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release - Three new stats functions, a rewrite of pearsonr, and an exact computation of the Kolmogorov-Smirnov two-sample test - A new Cython API for bounded scalar-function root-finders in scipy.optimize - Substantial CSR and CSC sparse matrix indexing performance improvements - Added support for interpolation of rotations with continuous angular rate and acceleration in RotationSpline SciPy 1.2.0 Release Notes SciPy 1.2.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.2.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release - 1-D root finding improvements with a new solver, toms748, and a new unified interface, root_scalar - New dual_annealing optimization method that combines stochastic and local deterministic searching - A new optimization algorithm, shgo (simplicial homology global optimization) for derivative free optimization problems - A new category of quaternion-based transformations are available in scipy.spatial.transform @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.14 2018/05/14 06:39:32 adam Exp $ d22 6 d31 3 d51 3 d64 11 d84 3 d107 3 d147 48 a197 1 ${PYSITELIB}/scipy/fftpack/_fftpack.so d218 1 d248 3 d267 3 d289 1 a292 1 ${PYSITELIB}/scipy/integrate/tests/test_ivp.py d375 2 d630 1 a881 3 ${PYSITELIB}/scipy/optimize/_hungarian.py ${PYSITELIB}/scipy/optimize/_hungarian.pyc ${PYSITELIB}/scipy/optimize/_hungarian.pyo d898 4 d1002 1 a1074 1 ${PYSITELIB}/scipy/optimize/tests/test_hungarian.py d1077 1 d1120 1 d1217 1 d1221 1 d1236 1 d1238 1 d1374 1 d1468 3 d1482 1 d1488 3 d1532 3 d1583 2 d1586 1 d1594 1 d1599 1 @ 1.14 log @py-scipy: updated to 1.1.0 SciPy 1.1.0 is the culmination of 7 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.1.x branch, and on adding new features on the master branch. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.13 2017/11/02 09:40:29 adam Exp $ a5 1 ${PYSITELIB}/scipy/BENTO_BUILD.txt d22 3 d61 3 a227 1 ${PYSITELIB}/scipy/interpolate/_interpolate.so a245 3 ${PYSITELIB}/scipy/interpolate/interpolate_wrapper.py ${PYSITELIB}/scipy/interpolate/interpolate_wrapper.pyc ${PYSITELIB}/scipy/interpolate/interpolate_wrapper.pyo a266 1 ${PYSITELIB}/scipy/interpolate/tests/test_interpolate_wrapper.py d293 1 d301 1 a700 3 ${PYSITELIB}/scipy/misc/pilutil.py ${PYSITELIB}/scipy/misc/pilutil.pyc ${PYSITELIB}/scipy/misc/pilutil.pyo a704 25 ${PYSITELIB}/scipy/misc/tests/data/3x3x3.png ${PYSITELIB}/scipy/misc/tests/data/3x3x4.png ${PYSITELIB}/scipy/misc/tests/data/3x4x3.png ${PYSITELIB}/scipy/misc/tests/data/3x4x4.png ${PYSITELIB}/scipy/misc/tests/data/3x5x3.png ${PYSITELIB}/scipy/misc/tests/data/3x5x4.png ${PYSITELIB}/scipy/misc/tests/data/4x3x3.png ${PYSITELIB}/scipy/misc/tests/data/4x3x4.png ${PYSITELIB}/scipy/misc/tests/data/4x4x3.png ${PYSITELIB}/scipy/misc/tests/data/4x4x4.png ${PYSITELIB}/scipy/misc/tests/data/4x5x3.png ${PYSITELIB}/scipy/misc/tests/data/4x5x4.png ${PYSITELIB}/scipy/misc/tests/data/5x3x3.png ${PYSITELIB}/scipy/misc/tests/data/5x3x4.png ${PYSITELIB}/scipy/misc/tests/data/5x4x3.png ${PYSITELIB}/scipy/misc/tests/data/5x4x4.png ${PYSITELIB}/scipy/misc/tests/data/5x5x3.png ${PYSITELIB}/scipy/misc/tests/data/5x5x4.png ${PYSITELIB}/scipy/misc/tests/data/blocks2bit.png ${PYSITELIB}/scipy/misc/tests/data/box1.png ${PYSITELIB}/scipy/misc/tests/data/foo3x5x4indexed.png ${PYSITELIB}/scipy/misc/tests/data/icon.png ${PYSITELIB}/scipy/misc/tests/data/icon_mono.png ${PYSITELIB}/scipy/misc/tests/data/icon_mono_flat.png ${PYSITELIB}/scipy/misc/tests/data/pattern4bit.png a706 1 ${PYSITELIB}/scipy/misc/tests/test_pilutil.py a729 3 ${PYSITELIB}/scipy/ndimage/io.py ${PYSITELIB}/scipy/ndimage/io.pyc ${PYSITELIB}/scipy/ndimage/io.pyo a747 1 ${PYSITELIB}/scipy/ndimage/tests/test_io.py d749 1 d771 1 d778 1 d789 3 d806 9 d857 16 d937 7 d947 1 a947 1 ${PYSITELIB}/scipy/optimize/lbfgsb/README d974 2 a975 1 ${PYSITELIB}/scipy/optimize/tests/test__linprog_ip_clean_inputs.py d979 1 d982 1 d984 1 d1066 1 d1100 3 d1328 1 a1328 1 ${PYSITELIB}/scipy/spatial/qhull/COPYING.txt d1351 2 d1371 15 d1525 3 d1531 3 d1584 2 @ 1.13 log @py-scipy: updated to 1.0.0 Some of the highlights of this release are: * Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. * A set of new ODE solvers and a unified interface to them (scipy.integrate.solve_ivp). * Two new trust region optimizers and a new linear programming method, with improved performance compared to what scipy.optimize offered previously. Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.12 2017/06/24 08:19:40 adam Exp $ d73 1 d565 3 d675 1 d697 1 d741 3 d782 1 d808 6 d818 3 d887 31 d973 3 d983 1 d1017 1 d1075 9 a1083 3 ${PYSITELIB}/scipy/signal/windows.py ${PYSITELIB}/scipy/signal/windows.pyc ${PYSITELIB}/scipy/signal/windows.pyo d1249 1 d1384 3 d1393 3 d1399 3 d1454 1 d1457 1 d1471 1 @ 1.12 log @SciPy 0.19.1 is a bug-fix release with no new features compared to 0.19.0. The most important change is a fix for a severe memory leak in integrate.quad. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.11 2017/03/13 19:42:22 adam Exp $ d23 3 d33 1 d59 1 d66 1 d68 1 d74 1 d79 1 d87 1 d94 3 d109 1 a119 3 ${PYSITELIB}/scipy/fftpack/fftpack_version.py ${PYSITELIB}/scipy/fftpack/fftpack_version.pyc ${PYSITELIB}/scipy/fftpack/fftpack_version.pyo d133 1 d153 24 d197 1 d203 1 d256 1 d277 1 d287 1 d312 1 d345 1 d476 1 d496 1 d498 2 a499 1 ${PYSITELIB}/scipy/io/tests/data/fortran-sf8-10x1x11.dat d501 5 a505 5 ${PYSITELIB}/scipy/io/tests/data/fortran-sf8-22x10x15.dat ${PYSITELIB}/scipy/io/tests/data/fortran-sf8-5x1x1.dat ${PYSITELIB}/scipy/io/tests/data/fortran-sf8-5x3x1.dat ${PYSITELIB}/scipy/io/tests/data/fortran-sf8-7x1x1.dat ${PYSITELIB}/scipy/io/tests/data/fortran-si4-10x1x11.dat d507 3 a509 4 ${PYSITELIB}/scipy/io/tests/data/fortran-si4-22x10x15.dat ${PYSITELIB}/scipy/io/tests/data/fortran-si4-5x1x1.dat ${PYSITELIB}/scipy/io/tests/data/fortran-si4-5x3x1.dat ${PYSITELIB}/scipy/io/tests/data/fortran-si4-7x1x1.dat d552 1 a560 1 ${PYSITELIB}/scipy/linalg/_calc_lwork.so a563 3 ${PYSITELIB}/scipy/linalg/_cython_wrapper_generators.py ${PYSITELIB}/scipy/linalg/_cython_wrapper_generators.pyc ${PYSITELIB}/scipy/linalg/_cython_wrapper_generators.pyo d577 3 d593 3 a608 3 ${PYSITELIB}/scipy/linalg/calc_lwork.py ${PYSITELIB}/scipy/linalg/calc_lwork.pyc ${PYSITELIB}/scipy/linalg/calc_lwork.pyo d655 3 d678 1 d699 1 d760 1 d789 1 d809 3 d848 3 d858 7 d871 6 d890 1 d914 1 d917 1 d919 1 d938 2 d997 1 a1051 3 ${PYSITELIB}/scipy/sparse/csgraph/_components.py ${PYSITELIB}/scipy/sparse/csgraph/_components.pyc ${PYSITELIB}/scipy/sparse/csgraph/_components.pyo d1066 1 a1068 1 ${PYSITELIB}/scipy/sparse/csgraph/tests/test_graph_components.py d1107 1 d1121 1 d1126 1 d1137 1 d1148 1 d1159 3 d1181 1 d1183 1 d1198 1 d1216 1 d1251 1 d1255 1 d1301 3 a1347 3 ${PYSITELIB}/scipy/special/generate_ufuncs.py ${PYSITELIB}/scipy/special/generate_ufuncs.pyc ${PYSITELIB}/scipy/special/generate_ufuncs.pyo d1364 1 d1377 1 d1385 1 d1458 1 @ 1.11 log @SciPy 0.19.0 is the culmination of 7 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.19.x branch, and on adding new features on the master branch. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.10 2016/08/19 10:54:12 wiz Exp $ d1130 2 @ 1.10 log @Updated py-scipy to 0.18.0. Test failures reported upstream. ========================== SciPy 0.18.0 Release Notes ========================== .. contents:: SciPy 0.18.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.19.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater. Highlights of this release include: - A new ODE solver for two-point boundary value problems, `scipy.optimize.solve_bvp`. - A new class, `CubicSpline`, for cubic spline interpolation of data. - N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`. - Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`. - Support for discrete-time linear systems, `scipy.signal.dlti`. New features ============ `scipy.integrate` improvements ------------------------------ A solver of two-point boundary value problems for ODE systems has been implemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated boundary conditions, unknown parameters and certain singular terms. It finds a C1 continious solution using a fourth-order collocation algorithm. `scipy.interpolate` improvements -------------------------------- Cubic spline interpolation is now available via `scipy.interpolate.CubicSpline`. This class represents a piecewise cubic polynomial passing through given points and C2 continuous. It is represented in the standard polynomial basis on each segment. A representation of n-dimensional tensor product piecewise polynomials is available as the `scipy.interpolate.NdPPoly` class. Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be evaluated on periodic domains. Use ``extrapolate="periodic"`` keyword argument for this. `scipy.fftpack` improvements ---------------------------- `scipy.fftpack.next_fast_len` function computes the next "regular" number for FFTPACK. Padding the input to this length can give significant performance increase for `scipy.fftpack.fft`. `scipy.signal` improvements --------------------------- Resampling using polyphase filtering has been implemented in the function `scipy.signal.resample_poly`. This method upsamples a signal, applies a zero-phase low-pass FIR filter, and downsamples using `scipy.signal.upfirdn` (which is also new in 0.18.0). This method can be faster than FFT-based filtering provided by `scipy.signal.resample` for some signals. `scipy.signal.firls`, which constructs FIR filters using least-squares error minimization, was added. `scipy.signal.sosfiltfilt`, which does forward-backward filtering like `scipy.signal.filtfilt` but for second-order sections, was added. Discrete-time linear systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `scipy.signal.dlti` provides an implementation of discrete-time linear systems. Accordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` classes have learned a the new keyword, `dt`, which can be used to create discrete-time instances of the corresponding system representation. `scipy.sparse` improvements --------------------------- The functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in `scipy.sparse` have had their signatures augmented with additional arguments and functionality so as to improve compatibility with analogously defined functions in `numpy`. Sparse matrices now have a `count_nonzero` method, which counts the number of nonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety, which return the number of stored entries (the length of the data attribute), this method counts the actual number of non-zero entries in data. `scipy.optimize` improvements ----------------------------- The implementation of Nelder-Mead minimization, `scipy.minimize(..., method="Nelder-Mead")`, obtained a new keyword, `initial_simplex`, which can be used to specify the initial simplex for the optimization process. Initial step size selection in CG and BFGS minimizers has been improved. We expect that this change will improve numeric stability of optimization in some cases. See pull request gh-5536 for details. Handling of infinite bounds in SLSQP optimization has been improved. We expect that this change will improve numeric stability of optimization in the some cases. See pull request gh-6024 for details. A large suite of global optimization benchmarks has been added to ``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 for details. Nelder-Mead and Powell minimization will now only set defaults for maximum iterations or function evaluations if neither limit is set by the caller. In some cases with a slow converging function and only 1 limit set, the minimization may continue for longer than with previous versions and so is more likely to reach convergence. See issue gh-5966. `scipy.stats` improvements -------------------------- Trapezoidal distribution has been implemented as `scipy.stats.trapz`. Skew normal distribution has been implemented as `scipy.stats.skewnorm`. Burr type XII distribution has been implemented as `scipy.stats.burr12`. Three- and four-parameter kappa distributions have been implemented as `scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively. New `scipy.stats.iqr` function computes the interquartile region of a distribution. Random matrices ~~~~~~~~~~~~~~~ `scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide generators of random matrices in the SO(N) and O(N) groups, respectively. They generate matrices in the Haar distribution, the only uniform distribution on these group manifolds. `scipy.stats.random_correlation` provides a generator for random correlation matrices, given specified eigenvalues. `scipy.linalg` improvements --------------------------- `scipy.linalg.svd` gained a new keyword argument, ``lapack_driver``. Available drivers are ``gesdd`` (default) and ``gesvd``. `scipy.linalg.lapack.ilaver` returns the version of the LAPACK library SciPy links to. `scipy.spatial` improvements ---------------------------- Boolean distances, `scipy.spatial.pdist`, have been sped up. Improvements vary by the function and the input size. In many cases, one can expect a speed-up of x2--x10. New class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on the surface of a sphere. See pull request gh-5232 for details. `scipy.cluster` improvements ---------------------------- A new clustering algorithm, the nearest neighbor chain algorithm, has been implemented for `scipy.cluster.hierarchy.linkage`. As a result, one can expect a significant algorithmic improvement (:math:`O(N^2)` instead of :math:`O(N^3)`) for several linkage methods. `scipy.special` improvements ---------------------------- The new function `scipy.special.loggamma` computes the principal branch of the logarithm of the Gamma function. For real input, ``loggamma`` is compatible with `scipy.special.gammaln`. For complex input, it has more consistent behavior in the complex plane and should be preferred over ``gammaln``. Vectorized forms of spherical Bessel functions have been implemented as `scipy.special.spherical_jn`, `scipy.special.spherical_kn`, `scipy.special.spherical_in` and `scipy.special.spherical_yn`. They are recommended for use over ``sph_*`` functions, which are now deprecated. Several special functions have been extended to the complex domain and/or have seen domain/stability improvements. This includes `spence`, `digamma`, `log1p` and several others. Deprecated features =================== The cross-class properties of `lti` systems have been deprecated. The following properties/setters will raise a `DeprecationWarning`: Name - (accessing/setting raises warning) - (setting raises warning) * StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`) * TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`) * ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - () Spherical Bessel functions, ``sph_in``, ``sph_jn``, ``sph_kn``, ``sph_yn``, ``sph_jnyn`` and ``sph_inkn`` have been deprecated in favor of `scipy.special.spherical_jn` and ``spherical_kn``, ``spherical_yn``, ``spherical_in``. The following functions in `scipy.constants` are deprecated: ``C2K``, ``K2C``, ``C2F``, ``F2C``, ``F2K`` and ``K2F``. They are superceded by a new function `scipy.constants.convert_temperature` that can perform all those conversions plus to/from the Rankine temperature scale. Backwards incompatible changes ============================== `scipy.optimize` ---------------- The convergence criterion for ``optimize.bisect``, ``optimize.brentq``, ``optimize.brenth``, and ``optimize.ridder`` now works the same as ``numpy.allclose``. `scipy.ndimage` --------------- The offset in ``ndimage.iterpolation.affine_transform`` is now consistently added after the matrix is applied, independent of if the matrix is specified using a one-dimensional or a two-dimensional array. `scipy.stats` ------------- ``stats.ks_2samp`` used to return nonsensical values if the input was not real or contained nans. It now raises an exception for such inputs. Several deprecated methods of `scipy.stats` distributions have been removed: ``est_loc_scale``, ``vecfunc``, ``veccdf`` and ``vec_generic_moment``. Deprecated functions ``nanmean``, ``nanstd`` and ``nanmedian`` have been removed from `scipy.stats`. These functions were deprecated in scipy 0.15.0 in favor of their `numpy` equivalents. A bug in the ``rvs()`` method of the distributions in `scipy.stats` has been fixed. When arguments to ``rvs()`` were given that were shaped for broadcasting, in many cases the returned random samples were not random. A simple example of the problem is ``stats.norm.rvs(loc=np.zeros(10))``. Because of the bug, that call would return 10 identical values. The bug only affected code that relied on the broadcasting of the shape, location and scale parameters. The ``rvs()`` method also accepted some arguments that it should not have. There is a potential for backwards incompatibility in cases where ``rvs()`` accepted arguments that are not, in fact, compatible with broadcasting. An example is stats.gamma.rvs([2, 5, 10, 15], size=(2,2)) The shape of the first argument is not compatible with the requested size, but the function still returned an array with shape (2, 2). In scipy 0.18, that call generates a ``ValueError``. `scipy.io` ---------- `scipy.io.netcdf` masking now gives precedence to the ``_FillValue`` attribute over the ``missing_value`` attribute, if both are given. Also, data are only treated as missing if they match one of these attributes exactly: values that differ by roundoff from ``_FillValue`` or ``missing_value`` are no longer treated as missing values. `scipy.interpolate` ------------------- `scipy.interpolate.PiecewisePolynomial` class has been removed. It has been deprecated in scipy 0.14.0, and `scipy.interpolate.BPoly.from_derivatives` serves as a drop-in replacement. Other changes ============= Scipy now uses ``setuptools`` for its builds instead of plain distutils. This fixes usage of ``install_requires='scipy'`` in the ``setup.py`` files of projects that depend on Scipy (see Numpy issue gh-6551 for details). It potentially affects the way that build/install methods for Scipy itself behave though. Please report any unexpected behavior on the Scipy issue tracker. PR `#6240 `__ changes the interpretation of the `maxfun` option in `L-BFGS-B` based routines in the `scipy.optimize` module. An `L-BFGS-B` search consists of multiple iterations, with each iteration consisting of one or more function evaluations. Whereas the old search strategy terminated immediately upon reaching `maxfun` function evaluations, the new strategy allows the current iteration to finish despite reaching `maxfun`. The bundled copy of Qhull in the `scipy.spatial` subpackage has been upgraded to version 2015.2. The bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been upgraded to arpack-ng 3.3.0. 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${PLIST.py2x}${PYSITELIB}/scipy/weave/vtk_spec.py ${PLIST.py2x}${PYSITELIB}/scipy/weave/vtk_spec.pyc ${PLIST.py2x}${PYSITELIB}/scipy/weave/vtk_spec.pyo ${PLIST.py2x}${PYSITELIB}/scipy/weave/weave_version.py ${PLIST.py2x}${PYSITELIB}/scipy/weave/weave_version.pyc ${PLIST.py2x}${PYSITELIB}/scipy/weave/weave_version.pyo @ 1.9 log @Update py-scipy to 0.17.0 SciPy 0.17.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.17.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater. Release highlights: * New functions for linear and nonlinear least squares optimization with constraints: scipy.optimize.lsq_linear and scipy.optimize.least_squares * Support for fitting with bounds in scipy.optimize.curve_fit. * Significant improvements to scipy.stats, providing many functions with better handing of inputs which have NaNs or are empty, improved documentation, and consistent behavior between scipy.stats and scipy.stats.mstats. * Significant performance improvements and new functionality in scipy.spatial.cKDTree. SciPy 0.16.0 is the culmination of 7 months of hard work. Highlights of this release include: * A Cython API for BLAS/LAPACK in scipy.linalg * A new benchmark suite. 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${PYSITELIB}/scipy/stats/rv.py ${PYSITELIB}/scipy/stats/rv.pyc ${PYSITELIB}/scipy/stats/rv.pyo d1248 12 @ 1.7 log @Update to 0.15.1 Upstream changes: SciPy 0.15.1 is a bug-fix release with no new features compared to 0.15.0. Issues fixed - ------------ * `#4413 `__: BUG: Tests too strict, f2py doesn't have to overwrite this array * `#4417 `__: BLD: avoid using NPY_API_VERSION to check not using deprecated... * `#4418 `__: Restore and deprecate scipy.linalg.calc_work SciPy 0.15.0 Release Notes ========================== .. contents:: SciPy 0.15.0 is the culmination of 6 months of hard work. It contains several new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.16.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater. New features ============ Linear Programming Interface - ---------------------------- The new function `scipy.optimize.linprog` provides a generic linear programming similar to the way `scipy.optimize.minimize` provides a generic interface to nonlinear programming optimizers. Currently the only method supported is *simplex* which provides a two-phase, dense-matrix-based simplex algorithm. Callbacks functions are supported, allowing the user to monitor the progress of the algorithm. Differential evolution, a global optimizer - ------------------------------------------ A new `scipy.optimize.differential_evolution` function has been added to the ``optimize`` module. Differential Evolution is an algorithm used for finding the global minimum of multivariate functions. It is stochastic in nature (does not use gradient methods), and can search large areas of candidate space, but often requires larger numbers of function evaluations than conventional gradient based techniques. ``scipy.signal`` improvements - ----------------------------- The function `scipy.signal.max_len_seq` was added, which computes a Maximum Length Sequence (MLS) signal. ``scipy.integrate`` improvements - -------------------------------- It is now possible to use `scipy.integrate` routines to integrate multivariate ctypes functions, thus avoiding callbacks to Python and providing better performance. ``scipy.linalg`` improvements - ----------------------------- The function `scipy.linalg.orthogonal_procrustes` for solving the procrustes linear algebra problem was added. BLAS level 2 functions ``her``, ``syr``, ``her2`` and ``syr2`` are now wrapped in ``scipy.linalg``. ``scipy.sparse`` improvements - ----------------------------- `scipy.sparse.linalg.svds` can now take a ``LinearOperator`` as its main input. ``scipy.special`` improvements - ------------------------------ Values of ellipsoidal harmonic (i.e. Lame) functions and associated normalization constants can be now computed using ``ellip_harm``, ``ellip_harm_2``, and ``ellip_normal``. New convenience functions ``entr``, ``rel_entr`` ``kl_div``, ``huber``, and ``pseudo_huber`` were added. ``scipy.sparse.csgraph`` improvements - ------------------------------------- Routines ``reverse_cuthill_mckee`` and ``maximum_bipartite_matching`` for computing reorderings of sparse graphs were added. ``scipy.stats`` improvements - ---------------------------- Added a Dirichlet multivariate distribution, `scipy.stats.dirichlet`. The new function `scipy.stats.median_test` computes Mood's median test. The new function `scipy.stats.combine_pvalues` implements Fisher's and Stouffer's methods for combining p-values. `scipy.stats.describe` returns a namedtuple rather than a tuple, allowing users to access results by index or by name. Deprecated features =================== The `scipy.weave` module is deprecated. It was the only module never ported to Python 3.x, and is not recommended to be used for new code - use Cython instead. In order to support existing code, ``scipy.weave`` has been packaged separately: https://github.com/scipy/weave. It is a pure Python package, and can easily be installed with ``pip install weave``. `scipy.special.bessel_diff_formula` is deprecated. It is a private function, and therefore will be removed from the public API in a following release. ``scipy.stats.nanmean``, ``nanmedian`` and ``nanstd`` functions are deprecated in favor of their numpy equivalents. Backwards incompatible changes ============================== scipy.ndimage - ------------- The functions `scipy.ndimage.minimum_positions`, `scipy.ndimage.maximum_positions`` and `scipy.ndimage.extrema` return positions as ints instead of floats. scipy.integrate - --------------- The format of banded Jacobians in `scipy.integrate.ode` solvers is changed. Note that the previous documentation of this feature was erroneous. SciPy 0.14.1 Release Notes ========================== SciPy 0.14.1 is a bug-fix release with no new features compared to 0.14.0. Issues closed - ------------- - - `#3630 `__: NetCDF reading results in a segfault - - `#3631 `__: SuperLU object not working as expected for complex matrices - - `#3733 `__: segfault from map_coordinates - - `#3780 `__: Segfault when using CSR/CSC matrix and uint32/uint64 - - `#3781 `__: BUG: sparse: fix omitted types in sparsetools typemaps - - `#3802 `__: 0.14.0 API breakage: _gen generators are missing from scipy.stats.distributions API - - `#3805 `__: ndimage test failures with numpy 1.10 - - `#3812 `__: == sometimes wrong on csr_matrix - - `#3853 `__: Many scipy.sparse test errors/failures with numpy 1.9.0b2 - - `#4084 `__: fix exception declarations for Cython 0.21.1 compatibility - - `#4093 `__: BUG: fitpack: avoid a memory error in splev(x, tck, der=k) - - `#4104 `__: BUG: Workaround SGEMV segfault in Accelerate (maintenance 0.14.x) - - `#4143 `__: BUG: fix ndimage functions for large data - - `#4149 `__: Bug in expm for integer arrays - - `#4154 `__: Backport gh-4041 for 0.14.1 (Ensure that the 'size' argument of PIL's 'resize' method is a tuple) - - `#4163 `__: Backport #4142 (ZeroDivisionError in scipy.sparse.linalg.lsqr) - - `#4164 `__: Backport gh-4153 (remove use of deprecated numpy API in lib/lapack/ f2py wrapper) - - `#4180 `__: backport pil resize support tuple fix - - `#4168 `__: Lots of arpack test failures on windows 32 bits with numpy 1.9.1 - - `#4203 `__: Matrix multiplication in 0.14.x is more than 10x slower compared... - - `#4218 `__: attempt to make ndimage interpolation compatible with numpy relaxed... - - `#4225 `__: BUG: off-by-one error in PPoly shape checks - - `#4248 `__: BUG: optimize: fix issue with incorrect use of closure for slsqp. SciPy 0.14.0 Release Notes ========================== .. contents:: SciPy 0.14.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.14.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.5.1 or greater. New features ============ ``scipy.interpolate`` improvements ---------------------------------- A new wrapper function `scipy.interpolate.interpn` for interpolation on regular grids has been added. `interpn` supports linear and nearest-neighbor interpolation in arbitrary dimensions and spline interpolation in two dimensions. Faster implementations of piecewise polynomials in power and Bernstein polynomial bases have been added as `scipy.interpolate.PPoly` and `scipy.interpolate.BPoly`. New users should use these in favor of `scipy.interpolate.PiecewisePolynomial`. `scipy.interpolate.interp1d` now accepts non-monotonic inputs and sorts them. If performance is critical, sorting can be turned off by using the new ``assume_sorted`` keyword. Functionality for evaluation of bivariate spline derivatives in ``scipy.interpolate`` has been added. The new class `scipy.interpolate.Akima1DInterpolator` implements the piecewise cubic polynomial interpolation scheme devised by H. Akima. Functionality for fast interpolation on regular, unevenly spaced grids in arbitrary dimensions has been added as `scipy.interpolate.RegularGridInterpolator` . ``scipy.linalg`` improvements ----------------------------- The new function `scipy.linalg.dft` computes the matrix of the discrete Fourier transform. A condition number estimation function for matrix exponential, `scipy.linalg.expm_cond`, has been added. ``scipy.optimize`` improvements ------------------------------- A set of benchmarks for optimize, which can be run with ``optimize.bench()``, has been added. `scipy.optimize.curve_fit` now has more controllable error estimation via the ``absolute_sigma`` keyword. Support for passing custom minimization methods to ``optimize.minimize()`` and ``optimize.minimize_scalar()`` has been added, currently useful especially for combining ``optimize.basinhopping()`` with custom local optimizer routines. ``scipy.stats`` improvements ---------------------------- A new class `scipy.stats.multivariate_normal` with functionality for multivariate normal random variables has been added. A lot of work on the ``scipy.stats`` distribution framework has been done. Moment calculations (skew and kurtosis mainly) are fixed and verified, all examples are now runnable, and many small accuracy and performance improvements for individual distributions were merged. The new function `scipy.stats.anderson_ksamp` computes the k-sample Anderson-Darling test for the null hypothesis that k samples come from the same parent population. ``scipy.signal`` improvements ----------------------------- ``scipy.signal.iirfilter`` and related functions to design Butterworth, Chebyshev, elliptical and Bessel IIR filters now all use pole-zero ("zpk") format internally instead of using transformations to numerator/denominator format. The accuracy of the produced filters, especially high-order ones, is improved significantly as a result. The new function `scipy.signal.vectorstrength` computes the vector strength, a measure of phase synchrony, of a set of events. ``scipy.special`` improvements ------------------------------ The functions `scipy.special.boxcox` and `scipy.special.boxcox1p`, which compute the Box-Cox transformation, have been added. ``scipy.sparse`` improvements ----------------------------- - Significant performance improvement in CSR, CSC, and DOK indexing speed. - When using Numpy >= 1.9 (to be released in MM 2014), sparse matrices function correctly when given to arguments of ``np.dot``, ``np.multiply`` and other ufuncs. With earlier Numpy and Scipy versions, the results of such operations are undefined and usually unexpected. - Sparse matrices are no longer limited to ``2^31`` nonzero elements. They automatically switch to using 64-bit index data type for matrices containing more elements. User code written assuming the sparse matrices use int32 as the index data type will continue to work, except for such large matrices. Code dealing with larger matrices needs to accept either int32 or int64 indices. Deprecated features =================== ``anneal`` ---------- The global minimization function `scipy.optimize.anneal` is deprecated. All users should use the `scipy.optimize.basinhopping` function instead. ``scipy.stats`` --------------- ``randwcdf`` and ``randwppf`` functions are deprecated. All users should use distribution-specific ``rvs`` methods instead. Probability calculation aliases ``zprob``, ``fprob`` and ``ksprob`` are deprecated. Use instead the ``sf`` methods of the corresponding distributions or the ``special`` functions directly. ``scipy.interpolate`` --------------------- ``PiecewisePolynomial`` class is deprecated. Backwards incompatible changes ============================== scipy.special.lpmn ------------------ ``lpmn`` no longer accepts complex-valued arguments. A new function ``clpmn`` with uniform complex analytic behavior has been added, and it should be used instead. scipy.sparse.linalg ------------------- Eigenvectors in the case of generalized eigenvalue problem are normalized to unit vectors in 2-norm, rather than following the LAPACK normalization convention. The deprecated UMFPACK wrapper in ``scipy.sparse.linalg`` has been removed due to license and install issues. If available, ``scikits.umfpack`` is still used transparently in the ``spsolve`` and ``factorized`` functions. Otherwise, SuperLU is used instead in these functions. scipy.stats ----------- The deprecated functions ``glm``, ``oneway`` and ``cmedian`` have been removed from ``scipy.stats``. ``stats.scoreatpercentile`` now returns an array instead of a list of percentiles. scipy.interpolate ----------------- The API for computing derivatives of a monotone piecewise interpolation has changed: if `p` is a ``PchipInterpolator`` object, `p.derivative(der)` returns a callable object representing the derivative of `p`. For in-place derivatives use the second argument of the `__call__` method: `p(0.1, der=2)` evaluates the second derivative of `p` at `x=0.1`. The method `p.derivatives` has been removed. SciPy 0.13.3 Release Notes SciPy 0.13.3 is a bug-fix release with no new features compared to 0.13.2. Both the weave and the ndimage.label bugs were severe regressions in 0.13.0, hence this release. Issues fixed 3148: fix a memory leak in ndimage.label. 3216: fix weave issue with too long file names for MSVC. Other changes Update Sphinx theme used for html docs so >>> in examples can be toggled. SciPy 0.13.2 Release Notes SciPy 0.13.2 is a bug-fix release with no new features compared to 0.13.1. Issues fixed 3096: require Cython 0.19, earlier versions have memory leaks in fused types 3079: ndimage.label fix swapped 64-bitness test 3108: optimize.fmin_slsqp constraint violation SciPy 0.13.1 Release Notes SciPy 0.13.1 is a bug-fix release with no new features compared to 0.13.0. The only changes are several fixes in ndimage, one of which was a serious regression in ndimage.label (Github issue 3025), which gave incorrect results in 0.13.0. Issues fixed 3025: ndimage.label returns incorrect results in scipy 0.13.0 1992: ndimage.label return type changed from int32 to uint32 1992: ndimage.find_objects doesn't work with int32 input in some cases SciPy 0.13.0 Release Notes ========================== .. contents:: SciPy 0.13.0 is the culmination of 7 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.13.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater. Highlights of this release are: - support for fancy indexing and boolean comparisons with sparse matrices - interpolative decompositions and matrix functions in the linalg module - two new trust-region solvers for unconstrained minimization New features ============ ``scipy.integrate`` improvements -------------------------------- N-dimensional numerical integration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A new function `scipy.integrate.nquad`, which provides N-dimensional integration functionality with a more flexible interface than ``dblquad`` and ``tplquad``, has been added. ``dopri*`` improvements ^^^^^^^^^^^^^^^^^^^^^^^ The intermediate results from the ``dopri`` family of ODE solvers can now be accessed by a *solout* callback function. ``scipy.linalg`` improvements ----------------------------- Interpolative decompositions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Scipy now includes a new module `scipy.linalg.interpolative` containing routines for computing interpolative matrix decompositions (ID). This feature is based on the ID software package by P.G. Martinsson, V. Rokhlin, Y. Shkolnisky, and M. Tygert, previously adapted for Python in the PymatrixId package by K.L. Ho. Polar decomposition ^^^^^^^^^^^^^^^^^^^ A new function `scipy.linalg.polar`, to compute the polar decomposition of a matrix, was added. BLAS level 3 functions ^^^^^^^^^^^^^^^^^^^^^^ The BLAS functions ``symm``, ``syrk``, ``syr2k``, ``hemm``, ``herk`` and ``her2k`` are now wrapped in `scipy.linalg`. Matrix functions ^^^^^^^^^^^^^^^^ Several matrix function algorithms have been implemented or updated following detailed descriptions in recent papers of Nick Higham and his co-authors. These include the matrix square root (``sqrtm``), the matrix logarithm (``logm``), the matrix exponential (``expm``) and its Frechet derivative (``expm_frechet``), and fractional matrix powers (``fractional_matrix_power``). ``scipy.optimize`` improvements ------------------------------- Trust-region unconstrained minimization algorithms ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The ``minimize`` function gained two trust-region solvers for unconstrained minimization: ``dogleg`` and ``trust-ncg``. ``scipy.sparse`` improvements ----------------------------- Boolean comparisons and sparse matrices ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ All sparse matrix types now support boolean data, and boolean operations. Two sparse matrices `A` and `B` can be compared in all the expected ways `A < B`, `A >= B`, `A != B`, producing similar results as dense Numpy arrays. Comparisons with dense matrices and scalars are also supported. CSR and CSC fancy indexing ^^^^^^^^^^^^^^^^^^^^^^^^^^ Compressed sparse row and column sparse matrix types now support fancy indexing with boolean matrices, slices, and lists. So where A is a (CSC or CSR) sparse matrix, you can do things like:: >>> A[A > 0.5] = 1 # since Boolean sparse matrices work >>> A[:2, :3] = 2 >>> A[[1,2], 2] = 3 ``scipy.sparse.linalg`` improvements ------------------------------------ The new function ``onenormest`` provides a lower bound of the 1-norm of a linear operator and has been implemented according to Higham and Tisseur (2000). This function is not only useful for sparse matrices, but can also be used to estimate the norm of products or powers of dense matrices without explictly building the intermediate matrix. The multiplicative action of the matrix exponential of a linear operator (``expm_multiply``) has been implemented following the description in Al-Mohy and Higham (2011). Abstract linear operators (`scipy.sparse.linalg.LinearOperator`) can now be multiplied, added to each other, and exponentiated, producing new linear operators. This enables easier construction of composite linear operations. ``scipy.spatial`` improvements ------------------------------ The vertices of a `ConvexHull` can now be accessed via the `vertices` attribute, which gives proper orientation in 2-D. ``scipy.signal`` improvements ----------------------------- The cosine window function `scipy.signal.cosine` was added. ``scipy.special`` improvements ------------------------------ New functions `scipy.special.xlogy` and `scipy.special.xlog1py` were added. These functions can simplify and speed up code that has to calculate ``x * log(y)`` and give 0 when ``x == 0``. ``scipy.io`` improvements ------------------------- Unformatted Fortran file reader ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The new class `scipy.io.FortranFile` facilitates reading unformatted sequential files written by Fortran code. ``scipy.io.wavfile`` enhancements ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `scipy.io.wavfile.write` now accepts a file buffer. Previously it only accepted a filename. `scipy.io.wavfile.read` and `scipy.io.wavfile.write` can now handle floating point WAV files. ``scipy.interpolate`` improvements ---------------------------------- B-spline derivatives and antiderivatives ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `scipy.interpolate.splder` and `scipy.interpolate.splantider` functions for computing B-splines that represent derivatives and antiderivatives of B-splines were added. These functions are also available in the class-based FITPACK interface as ``UnivariateSpline.derivative`` and ``UnivariateSpline.antiderivative``. ``scipy.stats`` improvements ---------------------------- Distributions now allow using keyword parameters in addition to positional parameters in all methods. The function `scipy.stats.power_divergence` has been added for the Cressie-Read power divergence statistic and goodness of fit test. Included in this family of statistics is the "G-test" (http://en.wikipedia.org/wiki/G-test). `scipy.stats.mood` now accepts multidimensional input. An option was added to `scipy.stats.wilcoxon` for continuity correction. `scipy.stats.chisquare` now has an `axis` argument. `scipy.stats.mstats.chisquare` now has `axis` and `ddof` arguments. Deprecated features =================== ``expm2`` and ``expm3`` ----------------------- The matrix exponential functions `scipy.linalg.expm2` and `scipy.linalg.expm3` are deprecated. All users should use the numerically more robust `scipy.linalg.expm` function instead. ``scipy.stats`` functions ------------------------- `scipy.stats.oneway` is deprecated; `scipy.stats.f_oneway` should be used instead. `scipy.stats.glm` is deprecated. `scipy.stats.ttest_ind` is an equivalent function; more full-featured general (and generalized) linear model implementations can be found in statsmodels. `scipy.stats.cmedian` is deprecated; ``numpy.median`` should be used instead. Backwards incompatible changes ============================== LIL matrix assignment --------------------- Assigning values to LIL matrices with two index arrays now works similarly as assigning into ndarrays:: >>> x = lil_matrix((3, 3)) >>> x[[0,1,2],[0,1,2]]=[0,1,2] >>> x.todense() matrix([[ 0., 0., 0.], [ 0., 1., 0.], [ 0., 0., 2.]]) rather than giving the result:: >>> x.todense() matrix([[ 0., 1., 2.], [ 0., 1., 2.], [ 0., 1., 2.]]) Users relying on the previous behavior will need to revisit their code. The previous behavior is obtained by ``x[numpy.ix_([0,1,2],[0,1,2])] = ...`. Deprecated ``radon`` function removed ------------------------------------- The ``misc.radon`` function, which was deprecated in scipy 0.11.0, has been removed. Users can find a more full-featured ``radon`` function in scikit-image. Removed deprecated keywords ``xa`` and ``xb`` from ``stats.distributions`` -------------------------------------------------------------------------- The keywords ``xa`` and ``xb``, which were deprecated since 0.11.0, have been removed from the distributions in ``scipy.stats``. Changes to MATLAB file readers / writers ---------------------------------------- The major change is that 1D arrays in numpy now become row vectors (shape 1, N) when saved to a MATLAB 5 format file. Previously 1D arrays saved as column vectors (N, 1). This is to harmonize the behavior of writing MATLAB 4 and 5 formats, and adapt to the defaults of numpy and MATLAB - for example ``np.atleast_2d`` returns 1D arrays as row vectors. Trying to save arrays of greater than 2 dimensions in MATLAB 4 format now raises an error instead of silently reshaping the array as 2D. ``scipy.io.loadmat('afile')`` used to look for `afile` on the Python system path (``sys.path``); now ``loadmat`` only looks in the current directory for a relative path filename. Other changes ============= Security fix: ``scipy.weave`` previously used temporary directories in an insecure manner under certain circumstances. Cython is now required to build *unreleased* versions of scipy. The C files generated from Cython sources are not included in the git repo anymore. They are however still shipped in source releases. The code base received a fairly large PEP8 cleanup. A ``tox pep8`` command has been added; new code should pass this test command. 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${PYSITELIB}/scipy/weave/blitz/blitz/generate @@dirrm ${PYSITELIB}/scipy/weave/blitz/blitz/array @@dirrm ${PYSITELIB}/scipy/weave/blitz/blitz @@dirrm ${PYSITELIB}/scipy/weave/blitz @@dirrm ${PYSITELIB}/scipy/weave @@dirrm ${PYSITELIB}/scipy/stsci @@dirrm ${PYSITELIB}/scipy/stats/tests @@dirrm ${PYSITELIB}/scipy/stats @@dirrm ${PYSITELIB}/scipy/special/tests @@dirrm ${PYSITELIB}/scipy/special @@dirrm ${PYSITELIB}/scipy/sparse/tests @@dirrm ${PYSITELIB}/scipy/sparse @@dirrm ${PYSITELIB}/scipy/signal/tests @@dirrm ${PYSITELIB}/scipy/signal @@dirrm ${PYSITELIB}/scipy/sandbox @@dirrm ${PYSITELIB}/scipy/optimize/tests @@dirrm ${PYSITELIB}/scipy/optimize @@dirrm ${PYSITELIB}/scipy/odr/tests @@dirrm ${PYSITELIB}/scipy/odr @@dirrm ${PYSITELIB}/scipy/ndimage/tests @@dirrm ${PYSITELIB}/scipy/ndimage @@dirrm ${PYSITELIB}/scipy/misc/tests/data @@dirrm ${PYSITELIB}/scipy/misc/tests @@dirrm ${PYSITELIB}/scipy/misc @@dirrm ${PYSITELIB}/scipy/maxentropy/tests @@dirrm 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1.1.1.1 log @Initial import of py-scipy 0.6.0 SciPy is an open source library of scientific tools for Python. SciPy supplements the popular Numeric module, gathering a variety of high level science and engineering modules together as a single package. SciPy includes modules for graphics and plotting, optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and others. @ text @@