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cggh / scikit-allel

A Python package for exploring and analysing genetic variation data
MIT License
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Scheduled monthly dependency update for July #399

Closed pyup-bot closed 11 months ago

pyup-bot commented 1 year ago

Update pomegranate from 0.14.8 to 1.0.0.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/pomegranate - Repo: https://github.com/jmschrei/torchegranate

Update cython from 0.29.34 to 0.29.35.

Changelog ### 0.29.35 ``` ==================== Bugs fixed ---------- * A garbage collection enabled subtype of a non-GC extension type could call into the deallocation function of the super type with GC tracking enabled. This could lead to crashes during deallocation if GC was triggered on the type at the same time. (Github issue :issue:`5432`) * Some C compile failures and crashes in CPython 3.12 were resolved. * ``except + nogil`` was syntactically not allowed. ``except +nogil`` (i.e. defining a C++ exception handling function called ``nogil``) is now disallowed to prevent typos. (Github issue :issue:`5430`) * A C compile failure in PyPy 3.10 was resolved. Patch by Matti Picus. (Github issue :issue:`5408`) * Cython modules now use PEP-489 multi-phase init by default in PyPy 3.9 and later. Original patch by Matti Picus. (Github issue :issue:`5413`) * API header files generated by different Cython versions can now be included in the same C file. (Github issue :issue:`5383`) * Function signatures containing a type like `tuple[()]` could not be printed. Patch by Lisandro Dalcin. (Github issue :issue:`5355`) ```
Links - PyPI: https://pypi.org/project/cython - Changelog: https://pyup.io/changelogs/cython/ - Homepage: http://cython.org/

Update numpy from 1.24.3 to 1.25.0.

Changelog ### 1.25.0 ``` The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been work to prepare for the future NumPy 2.0.0 release, resulting in a large number of new and expired deprecation. Highlights are: - Support for MUSL, there are now MUSL wheels. - Support the Fujitsu C/C++ compiler. - Object arrays are now supported in einsum - Support for inplace matrix multiplication (`=`). We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is needed because distutils has been dropped by Python 3.12 and we will be switching to using meson for future builds. The next mainline release will be NumPy 2.0.0. We plan that the 2.0 series will still support downstream projects built against earlier versions of NumPy. The Python versions supported in this release are 3.9-3.11. Deprecations - `np.core.MachAr` is deprecated. It is private API. In names defined in `np.core` should generally be considered private. ([gh-22638](https://github.com/numpy/numpy/pull/22638)) - `np.finfo(None)` is deprecated. ([gh-23011](https://github.com/numpy/numpy/pull/23011)) - `np.round_` is deprecated. Use `np.round` instead. ([gh-23302](https://github.com/numpy/numpy/pull/23302)) - `np.product` is deprecated. Use `np.prod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.cumproduct` is deprecated. Use `np.cumprod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.sometrue` is deprecated. Use `np.any` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.alltrue` is deprecated. Use `np.all` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - Only ndim-0 arrays are treated as scalars. NumPy used to treat all arrays of size 1 (e.g., `np.array([3.14])`) as scalars. In the future, this will be limited to arrays of ndim 0 (e.g., `np.array(3.14)`). The following expressions will report a deprecation warning: python a = np.array([3.14]) float(a) better: a[0] to get the numpy.float or a.item() b = np.array([[3.14]]) c = numpy.random.rand(10) c[0] = b better: c[0] = b[0, 0] ([gh-10615](https://github.com/numpy/numpy/pull/10615)) - `numpy.find_common_type` is now deprecated and its use should be replaced with either `numpy.result_type` or `numpy.promote_types`. Most users leave the second `scalar_types` argument to `find_common_type` as `[]` in which case `np.result_type` and `np.promote_types` are both faster and more robust. When not using `scalar_types` the main difference is that the replacement intentionally converts non-native byte-order to native byte order. Further, `find_common_type` returns `object` dtype rather than failing promotion. This leads to differences when the inputs are not all numeric. Importantly, this also happens for e.g. timedelta/datetime for which NumPy promotion rules are currently sometimes surprising. When the `scalar_types` argument is not `[]` things are more complicated. In most cases, using `np.result_type` and passing the Python values `0`, `0.0`, or `0j` has the same result as using `int`, `float`, or `complex` in `scalar_types`. When `scalar_types` is constructed, `np.result_type` is the correct replacement and it may be passed scalar values like `np.float32(0.0)`. Passing values other than 0, may lead to value-inspecting behavior (which `np.find_common_type` never used and NEP 50 may change in the future). The main possible change in behavior in this case, is when the array types are signed integers and scalar types are unsigned. If you are unsure about how to replace a use of `scalar_types` or when non-numeric dtypes are likely, please do not hesitate to open a NumPy issue to ask for help. ([gh-22539](https://github.com/numpy/numpy/pull/22539)) Expired deprecations - `np.core.machar` and `np.finfo.machar` have been removed. ([gh-22638](https://github.com/numpy/numpy/pull/22638)) - `+arr` will now raise an error when the dtype is not numeric (and positive is undefined). ([gh-22998](https://github.com/numpy/numpy/pull/22998)) - A sequence must now be passed into the stacking family of functions (`stack`, `vstack`, `hstack`, `dstack` and `column_stack`). ([gh-23019](https://github.com/numpy/numpy/pull/23019)) - `np.clip` now defaults to same-kind casting. Falling back to unsafe casting was deprecated in NumPy 1.17. ([gh-23403](https://github.com/numpy/numpy/pull/23403)) - `np.clip` will now propagate `np.nan` values passed as `min` or `max`. Previously, a scalar NaN was usually ignored. This was deprecated in NumPy 1.17. ([gh-23403](https://github.com/numpy/numpy/pull/23403)) - The `np.dual` submodule has been removed. ([gh-23480](https://github.com/numpy/numpy/pull/23480)) - NumPy now always ignores sequence behavior for an array-like (defining one of the array protocols). (Deprecation started NumPy 1.20) ([gh-23660](https://github.com/numpy/numpy/pull/23660)) - The niche `FutureWarning` when casting to a subarray dtype in `astype` or the array creation functions such as `asarray` is now finalized. The behavior is now always the same as if the subarray dtype was wrapped into a single field (which was the workaround, previously). (FutureWarning since NumPy 1.20) ([gh-23666](https://github.com/numpy/numpy/pull/23666)) - `==` and `!=` warnings have been finalized. The `==` and `!=` operators on arrays now always: - raise errors that occur during comparisons such as when the arrays have incompatible shapes (`np.array([1, 2]) == np.array([1, 2, 3])`). - return an array of all `True` or all `False` when values are fundamentally not comparable (e.g. have different dtypes). An example is `np.array(["a"]) == np.array([1])`. This mimics the Python behavior of returning `False` and `True` when comparing incompatible types like `"a" == 1` and `"a" != 1`. For a long time these gave `DeprecationWarning` or `FutureWarning`. ([gh-22707](https://github.com/numpy/numpy/pull/22707)) - Nose support has been removed. NumPy switched to using pytest in 2018 and nose has been unmaintained for many years. We have kept NumPy\'s nose support to avoid breaking downstream projects who might have been using it and not yet switched to pytest or some other testing framework. With the arrival of Python 3.12, unpatched nose will raise an error. It is time to move on. *Decorators removed*: - raises - slow - setastest - skipif - knownfailif - deprecated - parametrize - \_needs_refcount These are not to be confused with pytest versions with similar names, e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize. *Functions removed*: - Tester - import_nose - run_module_suite ([gh-23041](https://github.com/numpy/numpy/pull/23041)) - The `numpy.testing.utils` shim has been removed. Importing from the `numpy.testing.utils` shim has been deprecated since 2019, the shim has now been removed. All imports should be made directly from `numpy.testing`. ([gh-23060](https://github.com/numpy/numpy/pull/23060)) - The environment variable to disable dispatching has been removed. Support for the `NUMPY_EXPERIMENTAL_ARRAY_FUNCTION` environment variable has been removed. This variable disabled dispatching with `__array_function__`. ([gh-23376](https://github.com/numpy/numpy/pull/23376)) - Support for `y=` as an alias of `out=` has been removed. The `fix`, `isposinf` and `isneginf` functions allowed using `y=` as a (deprecated) alias for `out=`. This is no longer supported. ([gh-23376](https://github.com/numpy/numpy/pull/23376)) Compatibility notes - The `busday_count` method now correctly handles cases where the `begindates` is later in time than the `enddates`. Previously, the `enddates` was included, even though the documentation states it is always excluded. ([gh-23229](https://github.com/numpy/numpy/pull/23229)) - When comparing datetimes and timedelta using `np.equal` or `np.not_equal` numpy previously allowed the comparison with `casting="unsafe"`. This operation now fails. Forcing the output dtype using the `dtype` kwarg can make the operation succeed, but we do not recommend it. ([gh-22707](https://github.com/numpy/numpy/pull/22707)) - When loading data from a file handle using `np.load`, if the handle is at the end of file, as can happen when reading multiple arrays by calling `np.load` repeatedly, numpy previously raised `ValueError` if `allow_pickle=False`, and `OSError` if `allow_pickle=True`. Now it raises `EOFError` instead, in both cases. ([gh-23105](https://github.com/numpy/numpy/pull/23105)) `np.pad` with `mode=wrap` pads with strict multiples of original data Code based on earlier version of `pad` that uses `mode="wrap"` will return different results when the padding size is larger than initial array. `np.pad` with `mode=wrap` now always fills the space with strict multiples of original data even if the padding size is larger than the initial array. ([gh-22575](https://github.com/numpy/numpy/pull/22575)) Cython `long_t` and `ulong_t` removed `long_t` and `ulong_t` were aliases for `longlong_t` and `ulonglong_t` and confusing (a remainder from of Python 2). This change may lead to the errors: 'long_t' is not a type identifier 'ulong_t' is not a type identifier We recommend use of bit-sized types such as `cnp.int64_t` or the use of `cnp.intp_t` which is 32 bits on 32 bit systems and 64 bits on 64 bit systems (this is most compatible with indexing). If C `long` is desired, use plain `long` or `npy_long`. `cnp.int_t` is also `long` (NumPy\'s default integer). However, `long` is 32 bit on 64 bit windows and we may wish to adjust this even in NumPy. (Please do not hesitate to contact NumPy developers if you are curious about this.) ([gh-22637](https://github.com/numpy/numpy/pull/22637)) Changed error message and type for bad `axes` argument to `ufunc` The error message and type when a wrong `axes` value is passed to `ufunc(..., axes=[...])` has changed. The message is now more indicative of the problem, and if the value is mismatched an `AxisError` will be raised. A `TypeError` will still be raised for invalidinput types. ([gh-22675](https://github.com/numpy/numpy/pull/22675)) Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where` If the `where` keyword argument of a `numpy.ufunc`{.interpreted-text role="class"} is a subclass of `numpy.ndarray`{.interpreted-text role="class"} or is a duck type that defines `numpy.class.__array_ufunc__`{.interpreted-text role="func"} it can override the behavior of the ufunc using the same mechanism as the input and output arguments. Note that for this to work properly, the `where.__array_ufunc__` implementation will have to unwrap the `where` argument to pass it into the default implementation of the `ufunc` or, for `numpy.ndarray`{.interpreted-text role="class"} subclasses before using `super().__array_ufunc__`. ([gh-23240](https://github.com/numpy/numpy/pull/23240)) Compiling against the NumPy C API is now backwards compatible by default NumPy now defaults to exposing a backwards compatible subset of the C-API. This makes the use of `oldest-supported-numpy` unnecessary. Libraries can override the default minimal version to be compatible with using: define NPY_TARGET_VERSION NPY_1_22_API_VERSION before including NumPy or by passing the equivalent `-D` option to the compiler. The NumPy 1.25 default is `NPY_1_19_API_VERSION`. Because the ``` ### 1.24.4 ``` discovered after the 1.24.3 release. It is the last planned release in the 1.24.x cycle. The Python versions supported by this release are 3.8-3.11. Contributors A total of 4 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Sebastian Berg - Hongyang Peng + Pull requests merged A total of 6 pull requests were merged for this release. - [23720](https://github.com/numpy/numpy/pull/23720): MAINT, BLD: Pin rtools to version 4.0 for Windows builds. - [23739](https://github.com/numpy/numpy/pull/23739): BUG: fix the method for checking local files for 1.24.x - [23760](https://github.com/numpy/numpy/pull/23760): MAINT: Copy rtools installation from install-rtools. - [23761](https://github.com/numpy/numpy/pull/23761): BUG: Fix masked array ravel order for A (and somewhat K) - [23890](https://github.com/numpy/numpy/pull/23890): TYP,DOC: Annotate and document the `metadata` parameter of\... - [23994](https://github.com/numpy/numpy/pull/23994): MAINT: Update rtools installation Checksums MD5 25049e3aee79dde29e7a498d3ad13379 numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl 579b5c357c918feaef4af03af8afb721 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6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835 numpy-1.24.4-cp39-cp39-win32.whl befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8 numpy-1.24.4-cp39-cp39-win_amd64.whl 31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2 numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl 80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463 numpy-1.24.4.tar.gz ```
Links - PyPI: https://pypi.org/project/numpy - Changelog: https://pyup.io/changelogs/numpy/ - Homepage: https://www.numpy.org

Update dask[array] from 2023.4.1 to 2023.6.1.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/dask

Update scipy from 1.10.1 to 1.11.1.

Changelog ### 1.11.1 ``` compared to `1.11.0`. In particular, a licensing issue discovered after the release of `1.11.0` has been addressed. Authors ======= * Name (commits) * h-vetinari (1) * Robert Kern (1) * Ilhan Polat (4) * Tyler Reddy (8) A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ``` ### 1.11.0 ``` 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 a new public base class distinct from the older matrix class, proper 64-bit index support, and numerous deprecations paving the way to a modern sparse array experience. - Added three new statistical distributions, and wide-ranging performance and precision improvements to several other statistical distributions. - 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 =========================== - `scipy.sparse` array (not matrix) classes now return a sparse array instead of a dense array when divided by a dense array. - A new public base class `scipy.sparse.sparray` was introduced, allowing `isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes, while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse matrix types. - The behavior of `scipy.sparse.isspmatrix()` was updated to return True for only the sparse matrix types. If you want to check for either sparse arrays or sparse matrices, use `scipy.sparse.issparse()` instead. (Previously, these had identical behavior.) - Sparse arrays constructed with 64-bit indices will no longer automatically downcast to 32-bit. - A new `scipy.sparse.diags_array` function was added, which behaves like the existing `scipy.sparse.diags` function except that it returns a sparse array instead of a sparse matrix. - ``argmin`` and ``argmax`` methods now return the correct result when no implicit 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 / Kaplain-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 confidence intervals. - `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and `scipy.stats.BootstrapMethod` are new classes to configure resampling and/or Monte Carlo versions of hypothesis tests. They can currently be used with `scipy.stats.pearsonr`. Statistical Distributions ------------------------- - Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`. This distribution is the most common analogue of the normal distribution on the unit sphere. - Added the relativistic Breit-Wigner distribution as `scipy.stats.rel_breitwigner`. It is used in high energy physics to model resonances. - Added the Dirichlet multinomial distribution as `scipy.stats.dirichlet_multinomial`. - Improved the speed and precision of several univariate statistical distributions. - `scipy.stats.anglit` ``sf`` - `scipy.stats.beta` ``entropy`` - `scipy.stats.betaprime` ``cdf``, ``sf``, ``ppf`` - `scipy.stats.chi` ``entropy`` - `scipy.stats.chi2` ``entropy`` - `scipy.stats.dgamma` ``entropy``, ``cdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.dweibull` ``entropy``, ``sf``, and ``isf`` - `scipy.stats.exponweib` ``sf`` and ``isf`` - `scipy.stats.f` ``entropy`` - `scipy.stats.foldcauchy` ``sf`` - `scipy.stats.foldnorm` ``cdf`` and ``sf`` - `scipy.stats.gamma` ``entropy`` - `scipy.stats.genexpon` ``ppf``, ``isf``, ``rvs`` - `scipy.stats.gengamma` ``entropy`` - `scipy.stats.geom` ``entropy`` - `scipy.stats.genlogistic` ``entropy``, ``logcdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.genhyperbolic` ``cdf`` and ``sf`` - `scipy.stats.gibrat` ``sf`` and ``isf`` - `scipy.stats.gompertz` ``entropy``, ``sf``. and ``isf`` - `scipy.stats.halflogistic` ``sf``, and ``isf`` - `scipy.stats.halfcauchy` ``sf`` and ``isf`` - `scipy.stats.halfnorm` ``cdf``, ``sf``, and ``isf`` - `scipy.stats.invgamma` ``entropy`` - `scipy.stats.invgauss` ``entropy`` - `scipy.stats.johnsonsb` ``pdf``, ``cdf``, ``sf``, ``ppf``, and ``isf`` - `scipy.stats.johnsonsu` ``pdf``, ``sf``, ``isf``, and ``stats`` - `scipy.stats.lognorm` ``fit`` - `scipy.stats.loguniform` ``entropy``, ``logpdf``, ``pdf``, ``cdf``, ``ppf``, and ``stats`` - `scipy.stats.maxwell` ``sf`` and ``isf`` - `scipy.stats.nakagami` ``entropy`` - `scipy.stats.powerlaw` ``sf`` - `scipy.stats.powerlognorm` ``logpdf``, ``logsf``, ``sf``, and ``isf`` - `scipy.stats.powernorm` ``sf`` and ``isf`` - `scipy.stats.t` ``entropy``, ``logpdf``, and ``pdf`` - `scipy.stats.truncexpon` ``sf``, and ``isf`` - `scipy.stats.truncnorm` ``entropy`` - `scipy.stats.truncpareto` ``fit`` - `scipy.stats.vonmises` ``fit`` - `scipy.stats.multivariate_t` now has ``cdf`` and ``entropy`` methods. - `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and `scipy.stats.invwishart` now have an ``entropy`` method. Other Improvements ------------------ - `scipy.stats.monte_carlo_test` now supports multi-sample statistics. - `scipy.stats.bootstrap` can now produce one-sided confidence intervals. - `scipy.stats.rankdata` performance was improved for ``method=ordinal`` and ``method=dense``. - `scipy.stats.moment` now supports non-central moment calculation. - `scipy.stats.anderson` now supports the ``weibull_min`` distribution. - `scipy.stats.sem` and `scipy.stats.iqr` now support ``axis``, ``nan_policy``, and masked array input. Deprecated features ================= - Multi-Ellipsis sparse matrix indexing has been deprecated and will be removed in SciPy 1.13. - Several methods were deprecated for sparse arrays: ``asfptype``, ``getrow``, ``getcol``, ``get_shape``, ``getmaxprint``, ``set_shape``, ``getnnz``, and ``getformat``. Additionally, the ``.A`` and ``.H`` attributes were deprecated. Sparse matrix types are not affected. - The `scipy.linalg` functions ``tri``, ``triu`` & ``tril`` are deprecated and will be removed in SciPy 1.13. Users are recommended to use the NumPy versions of these functions with identical names. - The `scipy.signal` functions ``bspline``, ``quadratic`` & ``cubic`` are deprecated and will be removed in SciPy 1.13. Users are recommended to use `scipy.interpolate.BSpline` instead. - The ``even`` keyword of `scipy.integrate.simpson` is deprecated and will be removed in SciPy 1.13.0. Users should leave this as the default as this gives improved accuracy compared to the other methods. - Using ``exact=True`` when passing integers in a float array to ``factorial`` is deprecated and will be removed in SciPy 1.13.0. - float128 and object dtypes are deprecated for `scipy.signal.medfilt` and `scipy.signal.order_filter` - The functions ``scipy.signal.{lsim2, impulse2, step2}`` had long been deprecated in documentation only. They now raise a DeprecationWarning and will be removed in SciPy 1.13.0. - Importing window functions directly from `scipy.window` has been soft deprecated since SciPy 1.1.0. They now raise a ``DeprecationWarning`` and will be removed in SciPy 1.13.0. Users should instead import them from `scipy.signal.window` or use the convenience function `scipy.signal.get_window`. Backwards incompatible changes ============================ - The default for the ``legacy`` keyword of `scipy.special.comb` has changed from ``True`` to ``False``, as announced since its introduction. Expired Deprecations ================== There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The ``n`` keyword has been removed from `scipy.stats.moment`. - The ``alpha`` keyword has been removed from `scipy.stats.interval`. - The misspelt ``gilbrat`` distribution has been removed (use `scipy.stats.gibrat`). - The deprecated spelling of the ``kulsinski`` distance metric has been removed (use `scipy.spatial.distance.kulczynski1`). - The ``vertices`` keyword of `scipy.spatial.Delauney.qhull` has been removed (use simplices). - The ``residual`` property of `scipy.sparse.csgraph.maximum_flow` has been removed (use ``flow``). - The ``extradoc`` keyword of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed. - The ``sym_pos`` keyword of `scipy.linalg.solve` has been removed. - The `scipy.optimize.minimize` function now raises an error for ``x0`` with ``x0.ndim > 1``. - In `scipy.stats.mode`, the default value of ``keepdims`` is now ``False``, and support for non-numeric input has been removed. - The function `scipy.signal.lsim` does not support non-uniform time steps anymore. Other changes ============ - Rewrote the source build docs and restructured the contributor guide. - Improved support for cross-compiling with meson build system. - MyST-NB notebook infrastructure has been added to our documentation. Authors ======= * h-vetinari (69) * Oriol Abril-Pla (1) + * Anton Akhmerov (13) * Andrey Akinshin (1) + * alice (1) + * Oren Amsalem (1) * Ross Barnowski (11) * Christoph Baumgarten (2) * Dawson Beatty (1) + * Doron Behar (1) + * Peter Bell (1) * John Belmonte (1) + * boeleman (1) + * Jack Borchanian (1) + * Matt Borland (3) + * Jake Bowhay (40) * Sienna Brent (1) + * Matthew Brett (1) * Evgeni Burovski (38) * Matthias Bussonnier (2) * Maria Cann (1) + * Alfredo Carella (1) + * CJ Carey (18) * Hood Chatham (2) * Anirudh Dagar (3) * Alberto Defendi (1) + * Pol del Aguila (1) + * Hans Dembinski (1) * Dennis (1) + * Vinayak Dev (1) + * Thomas Duvernay (1) * DWesl (4) * Stefan Endres (66) * Evandro (1) + * Tom Eversdijk (2) + * Isuru Fernando (1) * Franz Forstmayr (4) * Joseph Fox-Rabinovitz (1) * Stefano Frazzetto (1) + * Neil Girdhar (1) * Caden Gobat (1) + * Ralf Gommers (146) * GonVas (1) + * Marco Gorelli (1) * Brett Graham (2) + * Matt Haberland (385) * harshvardhan2707 (1) + * Alex Herbert (1) + * Guillaume Horel (1) * Geert-Jan Huizing (1) + * Jakob Jakobson (2) * Julien Jerphanion (5) * jyuv (2) * Rajarshi Karmakar (1) + * Ganesh Kathiresan (3) + * Robert Kern (4) * Andrew Knyazev (3) * Sergey Koposov (1) * Rishi Kulkarni (2) + * Eric Larson (1) * Zoufiné Lauer-Bare (2) + * Antony Lee (3) * Gregory R. Lee (8) * Guillaume Lemaitre (1) + * lilinjie (2) + * Yannis Linardos (1) + * Christian Lorentzen (5) * Loïc Estève (1) * Charlie Marsh (2) + * Boris Martin (1) + * Nicholas McKibben (10) * Melissa Weber Mendonça (57) * Michał Górny (1) + * Jarrod Millman (2) * Stefanie Molin (2) + * Mark W. Mueller (1) + * mustafacevik (1) + * Takumasa N (1) + * nboudrie (1) * Andrew Nelson (111) * Nico Schlömer (4) * Lysandros Nikolaou (2) + * Kyle Oman (1) * OmarManzoor (2) + * Simon Ott (1) + * Geoffrey Oxberry (1) + * Geoffrey M. Oxberry (2) + * Sravya papaganti (1) + * Tirth Patel (2) * Ilhan Polat (32) * Quentin Barthélemy (1) * Matteo Raso (12) + * Tyler Reddy (97) * Lucas Roberts (1) * Pamphile Roy (224) * Jordan Rupprecht (1) + * Atsushi Sakai (11) * Omar Salman (7) + * Leo Sandler (1) + * Ujjwal Sarswat (3) + * Saumya (1) + * Daniel Schmitz (79) * Henry Schreiner (2) + * Dan Schult (3) + * Eli Schwartz (6) * Tomer Sery (2) + * Scott Shambaugh (4) + * Gagandeep Singh (1) * Ethan Steinberg (6) + * stepeos (2) + * Albert Steppi (3) * Strahinja Lukić (1) * Kai Striega (4) * suen-bit (1) + * Tartopohm (2) * Logan Thomas (2) + * Jacopo Tissino (1) + * Matus Valo (10) + * Jacob Vanderplas (2) * Christian Veenhuis (1) + * Isaac Virshup (1) * Stefan van der Walt (14) * Warren Weckesser (63) * windows-server-2003 (1) * Levi John Wolf (3) * Nobel Wong (1) + * Benjamin Yeh (1) + * Rory Yorke (1) * Younes (2) + * Zaikun ZHANG (1) + * Alex Zverianskii (1) + A total of 131 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ```
Links - PyPI: https://pypi.org/project/scipy - Changelog: https://pyup.io/changelogs/scipy/ - Homepage: https://scipy.org/

Update pandas from 2.0.1 to 2.0.3.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/pandas

Update scikit-learn from 1.2.2 to 1.3.0.

Changelog ### 1.3.0 ``` We're happy to announce the 1.3.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.3.html This version supports Python versions 3.8 to 3.11. You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds can be installed using: conda install -c conda-forge scikit-learn ```
Links - PyPI: https://pypi.org/project/scikit-learn - Changelog: https://pyup.io/changelogs/scikit-learn/ - Homepage: http://scikit-learn.org

Update h5py from 3.8.0 to 3.9.0.

Changelog ### 3.9.0 ``` None ```
Links - PyPI: https://pypi.org/project/h5py - Changelog: https://pyup.io/changelogs/h5py/

Update zarr from 2.14.2 to 2.15.0.

Changelog ### 2.15.0 ``` ------ Enhancements ~~~~~~~~~~~~ * Implement more extensive fallback of getitem/setitem for orthogonal indexing. By :user:`Andreas Albert <AndreasAlbertQC>` :issue:`1029`. * Getitems supports ``meta_array``. By :user:`Mads R. B. Kristensen <madsbk>` :issue:`1131`. * ``open_array()`` now takes the ``meta_array`` argument. By :user:`Mads R. B. Kristensen <madsbk>` :issue:`1396`. Maintenance ~~~~~~~~~~~ * Remove ``codecov`` from GitHub actions. By :user:`John A. Kirkham <jakirkham>` :issue:`1391`. * Replace ``np.product`` with ``np.prod`` due to deprecation. By :user:`James Bourbeau <jrbourbeau>` :issue:`1405`. * Activate Py 3.11 builds. By :user:`Joe Hamman <jhamman>` :issue:`1415`. Documentation ~~~~~~~~~~~~~ * Add API reference for V3 Implementation in the docs. By :user:`Sanket Verma <MSanKeys963>` :issue:`1345`. Bug fixes ~~~~~~~~~ * Fix the conda-forge error. Read :issue:`1347` for detailed info. By :user:`Josh Moore <joshmoore>` :issue:`1364` and :issue:`1367`. * Fix ``ReadOnlyError`` when opening V3 store via fsspec reference file system. By :user:`Joe Hamman <jhamman>` :issue:`1383`. * Fix ``normalize_fill_value`` for structured arrays. By :user:`Alan Du <alanhdu>` :issue:`1397`. .. _release_2.14.2: ```
Links - PyPI: https://pypi.org/project/zarr - Changelog: https://pyup.io/changelogs/zarr/

Update ipython from 8.13.0 to 8.14.0.

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Links - PyPI: https://pypi.org/project/ipython - Changelog: https://pyup.io/changelogs/ipython/ - Homepage: https://ipython.org
pyup-bot commented 11 months ago

Closing this in favor of #400