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miniconda3/envs/ladir/lib/python3.10/site-packages/scipy-1.15.3.dist-info/INSTALLER

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miniconda3/envs/ladir/lib/python3.10/site-packages/scipy-1.15.3.dist-info/LICENSE.txt

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miniconda3/envs/ladir/lib/python3.10/site-packages/scipy-1.15.3.dist-info/METADATA

@@ -0,0 +1,1082 @@
+Metadata-Version: 2.1
+Name: scipy
+Version: 1.15.3
+Summary: Fundamental algorithms for scientific computing in Python
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+         SUCH DAMAGES.
+         
+           17. Interpretation of Sections 15 and 16.
+         
+           If the disclaimer of warranty and limitation of liability provided
+         above cannot be given local legal effect according to their terms,
+         reviewing courts shall apply local law that most closely approximates
+         an absolute waiver of all civil liability in connection with the
+         Program, unless a warranty or assumption of liability accompanies a
+         copy of the Program in return for a fee.
+         
+                              END OF TERMS AND CONDITIONS
+         
+                     How to Apply These Terms to Your New Programs
+         
+           If you develop a new program, and you want it to be of the greatest
+         possible use to the public, the best way to achieve this is to make it
+         free software which everyone can redistribute and change under these terms.
+         
+           To do so, attach the following notices to the program.  It is safest
+         to attach them to the start of each source file to most effectively
+         state the exclusion of warranty; and each file should have at least
+         the "copyright" line and a pointer to where the full notice is found.
+         
+             <one line to give the program's name and a brief idea of what it does.>
+             Copyright (C) <year>  <name of author>
+         
+             This program is free software: you can redistribute it and/or modify
+             it under the terms of the GNU General Public License as published by
+             the Free Software Foundation, either version 3 of the License, or
+             (at your option) any later version.
+         
+             This program is distributed in the hope that it will be useful,
+             but WITHOUT ANY WARRANTY; without even the implied warranty of
+             MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+             GNU General Public License for more details.
+         
+             You should have received a copy of the GNU General Public License
+             along with this program.  If not, see <http://www.gnu.org/licenses/>.
+         
+         Also add information on how to contact you by electronic and paper mail.
+         
+           If the program does terminal interaction, make it output a short
+         notice like this when it starts in an interactive mode:
+         
+             <program>  Copyright (C) <year>  <name of author>
+             This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+             This is free software, and you are welcome to redistribute it
+             under certain conditions; type `show c' for details.
+         
+         The hypothetical commands `show w' and `show c' should show the appropriate
+         parts of the General Public License.  Of course, your program's commands
+         might be different; for a GUI interface, you would use an "about box".
+         
+           You should also get your employer (if you work as a programmer) or school,
+         if any, to sign a "copyright disclaimer" for the program, if necessary.
+         For more information on this, and how to apply and follow the GNU GPL, see
+         <http://www.gnu.org/licenses/>.
+         
+           The GNU General Public License does not permit incorporating your program
+         into proprietary programs.  If your program is a subroutine library, you
+         may consider it more useful to permit linking proprietary applications with
+         the library.  If this is what you want to do, use the GNU Lesser General
+         Public License instead of this License.  But first, please read
+         <http://www.gnu.org/philosophy/why-not-lgpl.html>.
+         
+         
+         Name: libquadmath
+         Files: scipy.libs/libquadmath*.so
+         Description: dynamically linked to files compiled with gcc
+         Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+         License: LGPL-2.1-or-later
+         
+             GCC Quad-Precision Math Library
+             Copyright (C) 2010-2019 Free Software Foundation, Inc.
+             Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>
+         
+             This file is part of the libquadmath library.
+             Libquadmath is free software; you can redistribute it and/or
+             modify it under the terms of the GNU Library General Public
+             License as published by the Free Software Foundation; either
+             version 2.1 of the License, or (at your option) any later version.
+         
+             Libquadmath is distributed in the hope that it will be useful,
+             but WITHOUT ANY WARRANTY; without even the implied warranty of
+             MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+             Lesser General Public License for more details.
+             https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
+         
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Scientific/Engineering
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: homepage, https://scipy.org/
+Project-URL: documentation, https://docs.scipy.org/doc/scipy/
+Project-URL: source, https://github.com/scipy/scipy
+Project-URL: download, https://github.com/scipy/scipy/releases
+Project-URL: tracker, https://github.com/scipy/scipy/issues
+Requires-Python: >=3.10
+Requires-Dist: numpy<2.5,>=1.23.5
+Provides-Extra: test
+Requires-Dist: pytest; extra == "test"
+Requires-Dist: pytest-cov; extra == "test"
+Requires-Dist: pytest-timeout; extra == "test"
+Requires-Dist: pytest-xdist; extra == "test"
+Requires-Dist: asv; extra == "test"
+Requires-Dist: mpmath; extra == "test"
+Requires-Dist: gmpy2; extra == "test"
+Requires-Dist: threadpoolctl; extra == "test"
+Requires-Dist: scikit-umfpack; extra == "test"
+Requires-Dist: pooch; extra == "test"
+Requires-Dist: hypothesis>=6.30; extra == "test"
+Requires-Dist: array-api-strict<2.1.1,>=2.0; extra == "test"
+Requires-Dist: Cython; extra == "test"
+Requires-Dist: meson; extra == "test"
+Requires-Dist: ninja; sys_platform != "emscripten" and extra == "test"
+Provides-Extra: doc
+Requires-Dist: sphinx<8.0.0,>=5.0.0; extra == "doc"
+Requires-Dist: intersphinx_registry; extra == "doc"
+Requires-Dist: pydata-sphinx-theme>=0.15.2; extra == "doc"
+Requires-Dist: sphinx-copybutton; extra == "doc"
+Requires-Dist: sphinx-design>=0.4.0; extra == "doc"
+Requires-Dist: matplotlib>=3.5; extra == "doc"
+Requires-Dist: numpydoc; extra == "doc"
+Requires-Dist: jupytext; extra == "doc"
+Requires-Dist: myst-nb; extra == "doc"
+Requires-Dist: pooch; extra == "doc"
+Requires-Dist: jupyterlite-sphinx>=0.19.1; extra == "doc"
+Requires-Dist: jupyterlite-pyodide-kernel; extra == "doc"
+Provides-Extra: dev
+Requires-Dist: mypy==1.10.0; extra == "dev"
+Requires-Dist: typing_extensions; extra == "dev"
+Requires-Dist: types-psutil; extra == "dev"
+Requires-Dist: pycodestyle; extra == "dev"
+Requires-Dist: ruff>=0.0.292; extra == "dev"
+Requires-Dist: cython-lint>=0.12.2; extra == "dev"
+Requires-Dist: rich-click; extra == "dev"
+Requires-Dist: doit>=0.36.0; extra == "dev"
+Requires-Dist: pydevtool; extra == "dev"
+Description-Content-Type: text/x-rst
+
+.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg
+  :target: https://scipy.org
+  :width: 110
+  :height: 110
+  :align: left 
+
+.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
+  :target: https://numfocus.org
+
+.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads
+  :target: https://pypi.org/project/scipy/
+
+.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads
+  :target: https://anaconda.org/conda-forge/scipy
+
+.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg
+  :target: https://stackoverflow.com/questions/tagged/scipy
+
+.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue.svg
+  :target: https://www.nature.com/articles/s41592-019-0686-2
+
+SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
+science, and engineering. It includes modules for statistics, optimization,
+integration, linear algebra, Fourier transforms, signal and image processing,
+ODE solvers, and more.
+
+- **Website:** https://scipy.org
+- **Documentation:** https://docs.scipy.org/doc/scipy/
+- **Development version of the documentation:** https://scipy.github.io/devdocs
+- **SciPy development forum:** https://discuss.scientific-python.org/c/contributor/scipy
+- **Stack Overflow:** https://stackoverflow.com/questions/tagged/scipy
+- **Source code:** https://github.com/scipy/scipy
+- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
+- **Bug reports:** https://github.com/scipy/scipy/issues
+- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
+- **Report a security vulnerability:** https://tidelift.com/docs/security
+- **Citing in your work:** https://www.scipy.org/citing-scipy/
+
+SciPy is built to work with
+NumPy arrays, and provides many user-friendly and efficient numerical routines,
+such as routines for numerical integration and optimization. Together, they
+run on all popular operating systems, are quick to install, and are free of
+charge. NumPy and SciPy are easy to use, but powerful enough to be depended
+upon by some of the world's leading scientists and engineers. If you need to
+manipulate numbers on a computer and display or publish the results, give
+SciPy a try!
+
+For the installation instructions, see `our install
+guide <https://scipy.org/install/>`__.
+
+
+Call for Contributions
+----------------------
+
+We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
+first issue" may be a good starting point. Have a look at `our contributing
+guide <https://scipy.github.io/devdocs/dev/index.html>`__.
+
+Writing code isn’t the only way to contribute to SciPy. You can also:
+
+- review pull requests
+- triage issues
+- develop tutorials, presentations, and other educational materials
+- maintain and improve `our website <https://github.com/scipy/scipy.org>`__
+- develop graphic design for our brand assets and promotional materials
+- help with outreach and onboard new contributors
+- write grant proposals and help with other fundraising efforts
+
+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the `forum <https://discuss.scientific-python.org/c/contributor/scipy>`__
+or here, on GitHub, by leaving a comment on a relevant issue that is already
+open.
+
+If you are new to contributing to open source, `this
+guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
+and how to get involved.

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy-1.15.3.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_17_x86_64
+Tag: cp310-cp310-manylinux2014_x86_64
+

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/_mvt.py

@@ -0,0 +1,171 @@
+import math
+import numpy as np
+from scipy import special
+from scipy.stats._qmc import primes_from_2_to
+
+
+def _primes(n):
+    # Defined to facilitate comparison between translation and source
+    # In Matlab, primes(10.5) -> first four primes, primes(11.5) -> first five
+    return primes_from_2_to(math.ceil(n))
+
+
+def _gaminv(a, b):
+    # Defined to facilitate comparison between translation and source
+    # Matlab's `gaminv` is like `special.gammaincinv` but args are reversed
+    return special.gammaincinv(b, a)
+
+
+def _qsimvtv(m, nu, sigma, a, b, rng):
+    """Estimates the multivariate t CDF using randomized QMC
+
+    Parameters
+    ----------
+    m : int
+        The number of points
+    nu : float
+        Degrees of freedom
+    sigma : ndarray
+        A 2D positive semidefinite covariance matrix
+    a : ndarray
+        Lower integration limits
+    b : ndarray
+        Upper integration limits.
+    rng : Generator
+        Pseudorandom number generator
+
+    Returns
+    -------
+    p : float
+        The estimated CDF.
+    e : float
+        An absolute error estimate.
+
+    """
+    # _qsimvtv is a Python translation of the Matlab function qsimvtv,
+    # semicolons and all.
+    #
+    #   This function uses an algorithm given in the paper
+    #      "Comparison of Methods for the Numerical Computation of
+    #       Multivariate t Probabilities", in
+    #      J. of Computational and Graphical Stat., 11(2002), pp. 950-971, by
+    #          Alan Genz and Frank Bretz
+    #
+    #   The primary references for the numerical integration are
+    #    "On a Number-Theoretical Integration Method"
+    #    H. Niederreiter, Aequationes Mathematicae, 8(1972), pp. 304-11.
+    #    and
+    #    "Randomization of Number Theoretic Methods for Multiple Integration"
+    #     R. Cranley & T.N.L. Patterson, SIAM J Numer Anal, 13(1976), pp. 904-14.
+    #
+    #   Alan Genz is the author of this function and following Matlab functions.
+    #          Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
+    #          Email : alangenz@wsu.edu
+    #
+    # Copyright (C) 2013, Alan Genz,  All rights reserved.
+    #
+    # Redistribution and use in source and binary forms, with or without
+    # modification, are permitted provided the following conditions are met:
+    #   1. Redistributions of source code must retain the above copyright
+    #      notice, this list of conditions and the following disclaimer.
+    #   2. Redistributions in binary form must reproduce the above copyright
+    #      notice, this list of conditions and the following disclaimer in
+    #      the documentation and/or other materials provided with the
+    #      distribution.
+    #   3. The contributor name(s) may not be used to endorse or promote
+    #      products derived from this software without specific prior
+    #      written permission.
+    # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+    # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+    # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+    # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+    # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
+    # OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+    # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+    # TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
+    # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+    # Initialization
+    sn = max(1, math.sqrt(nu)); ch, az, bz = _chlrps(sigma, a/sn, b/sn)
+    n = len(sigma); N = 10; P = math.ceil(m/N); on = np.ones(P); p = 0; e = 0
+    ps = np.sqrt(_primes(5*n*math.log(n+4)/4)); q = ps[:, np.newaxis]  # Richtmyer gens.
+
+    # Randomization loop for ns samples
+    c = None; dc = None
+    for S in range(N):
+        vp = on.copy(); s = np.zeros((n, P))
+        for i in range(n):
+            x = np.abs(2*np.mod(q[i]*np.arange(1, P+1) + rng.random(), 1)-1)  # periodizing transform
+            if i == 0:
+                r = on
+                if nu > 0:
+                    r = np.sqrt(2*_gaminv(x, nu/2))
+            else:
+                y = _Phinv(c + x*dc)
+                s[i:] += ch[i:, i-1:i] * y
+            si = s[i, :]; c = on.copy(); ai = az[i]*r - si; d = on.copy(); bi = bz[i]*r - si
+            c[ai <= -9] = 0; tl = abs(ai) < 9; c[tl] = _Phi(ai[tl])
+            d[bi <= -9] = 0; tl = abs(bi) < 9; d[tl] = _Phi(bi[tl])
+            dc = d - c; vp = vp * dc
+        d = (np.mean(vp) - p)/(S + 1); p = p + d; e = (S - 1)*e/(S + 1) + d**2
+    e = math.sqrt(e)  # error estimate is 3 times std error with N samples.
+    return p, e
+
+
+#  Standard statistical normal distribution functions
+def _Phi(z):
+    return special.ndtr(z)
+
+
+def _Phinv(p):
+    return special.ndtri(p)
+
+
+def _chlrps(R, a, b):
+    """
+    Computes permuted and scaled lower Cholesky factor c for R which may be
+    singular, also permuting and scaling integration limit vectors a and b.
+    """
+    ep = 1e-10  # singularity tolerance
+    eps = np.finfo(R.dtype).eps
+
+    n = len(R); c = R.copy(); ap = a.copy(); bp = b.copy(); d = np.sqrt(np.maximum(np.diag(c), 0))
+    for i in range(n):
+        if d[i] > 0:
+            c[:, i] /= d[i]; c[i, :] /= d[i]
+            ap[i] /= d[i]; bp[i] /= d[i]
+    y = np.zeros((n, 1)); sqtp = math.sqrt(2*math.pi)
+
+    for k in range(n):
+        im = k; ckk = 0; dem = 1; s = 0
+        for i in range(k, n):
+            if c[i, i] > eps:
+                cii = math.sqrt(max(c[i, i], 0))
+                if i > 0: s = c[i, :k] @ y[:k]
+                ai = (ap[i]-s)/cii; bi = (bp[i]-s)/cii; de = _Phi(bi)-_Phi(ai)
+                if de <= dem:
+                    ckk = cii; dem = de; am = ai; bm = bi; im = i
+        if im > k:
+            ap[[im, k]] = ap[[k, im]]; bp[[im, k]] = bp[[k, im]]; c[im, im] = c[k, k]
+            t = c[im, :k].copy(); c[im, :k] = c[k, :k]; c[k, :k] = t
+            t = c[im+1:, im].copy(); c[im+1:, im] = c[im+1:, k]; c[im+1:, k] = t
+            t = c[k+1:im, k].copy(); c[k+1:im, k] = c[im, k+1:im].T; c[im, k+1:im] = t.T
+        if ckk > ep*(k+1):
+            c[k, k] = ckk; c[k, k+1:] = 0
+            for i in range(k+1, n):
+                c[i, k] = c[i, k]/ckk; c[i, k+1:i+1] = c[i, k+1:i+1] - c[i, k]*c[k+1:i+1, k].T
+            if abs(dem) > ep:
+                y[k] = (np.exp(-am**2/2) - np.exp(-bm**2/2)) / (sqtp*dem)
+            else:
+                y[k] = (am + bm) / 2
+                if am < -10:
+                    y[k] = bm
+                elif bm > 10:
+                    y[k] = am
+            c[k, :k+1] /= ckk; ap[k] /= ckk; bp[k] /= ckk
+        else:
+            c[k:, k] = 0; y[k] = (ap[k] + bp[k])/2
+        pass
+    return c, ap, bp

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py

@@ -0,0 +1,607 @@
+# DO NOT EDIT THIS FILE!
+# This file was generated by the R script
+#     generate_fisher_exact_results_from_r.R
+# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
+
+
+from collections import namedtuple
+import numpy as np
+
+
+Inf = np.inf
+
+Parameters = namedtuple('Parameters',
+                        ['table', 'confidence_level', 'alternative'])
+RResults = namedtuple('RResults',
+                      ['pvalue', 'conditional_odds_ratio',
+                       'conditional_odds_ratio_ci'])
+data = [
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.04035202926536294,
+                                         2.662846672960251))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.004668988338943325,
+                                         0.895792956493601))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.4153910882532168,
+                                         259.2593661129417))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.09580440012477633,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.08056337526385809,
+                                         1.22704788545557))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.2697004098849359,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.1176691231650079,
+                                         1.787463657995973))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157192,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.003857141267422399,
+                                         2.407369893767229))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.06126482213438735,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         1.451643573543705))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904762,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869288))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904761,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869287))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=2.005657880389071e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(349.2595113327733,
+                                         3630.382605689872))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=5.728437460831947e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(152.4166024390096,
+                                         1425.700792178893))),
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miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat

@@ -0,0 +1,108 @@
+NIST/ITL StRD 
+Dataset Name:   AtmWtAg   (AtmWtAg.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 108) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
+                "The Absolute Isotopic Abundance & Atomic Weight
+                of a Reference Sample of Silver".
+                NBS Journal of Research, 87, pp. 9-19.
+
+
+Data:           1 Factor
+                2 Treatments
+                24 Replicates/Cell
+                48 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Observed Data
+
+
+Model:          3 Parameters (mu, tau_1, tau_2)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+
+Between Instrument  1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
+Within Instrument  46 1.04951729166667E-08 2.28155932971014E-10
+
+                   Certified R-Squared 2.57426544538321E-01
+
+                   Certified Residual
+                   Standard Deviation  1.51048314446410E-05
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument           AgWt
+           1            107.8681568
+           1            107.8681465
+           1            107.8681572
+           1            107.8681785
+           1            107.8681446
+           1            107.8681903
+           1            107.8681526
+           1            107.8681494
+           1            107.8681616
+           1            107.8681587
+           1            107.8681519
+           1            107.8681486
+           1            107.8681419
+           1            107.8681569
+           1            107.8681508
+           1            107.8681672
+           1            107.8681385
+           1            107.8681518
+           1            107.8681662
+           1            107.8681424
+           1            107.8681360
+           1            107.8681333
+           1            107.8681610
+           1            107.8681477
+           2            107.8681079
+           2            107.8681344
+           2            107.8681513
+           2            107.8681197
+           2            107.8681604
+           2            107.8681385
+           2            107.8681642
+           2            107.8681365
+           2            107.8681151
+           2            107.8681082
+           2            107.8681517
+           2            107.8681448
+           2            107.8681198
+           2            107.8681482
+           2            107.8681334
+           2            107.8681609
+           2            107.8681101
+           2            107.8681512
+           2            107.8681469
+           2            107.8681360
+           2            107.8681254
+           2            107.8681261
+           2            107.8681450
+           2            107.8681368

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat

@@ -0,0 +1,85 @@
+NIST/ITL StRD 
+Dataset Name:   SiRstv     (SiRstv.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 85) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Ehrstein, James and Croarkin, M. Carroll.
+                Unpublished NIST dataset.
+
+
+Data:           1 Factor
+                5 Treatments
+                5  Replicates/Cell
+                25 Observations
+                3 Constant Leading Digits
+                Lower Level of Difficulty
+                Observed Data
+
+
+Model:          6 Parameters (mu,tau_1, ... , tau_5)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Instrument  4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00
+Within Instrument  20 2.16636560000000E-01 1.08318280000000E-02
+
+                   Certified R-Squared 1.90999039051129E-01
+
+                   Certified Residual
+                   Standard Deviation  1.04076068334656E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument   Resistance
+           1         196.3052
+           1         196.1240
+           1         196.1890
+           1         196.2569
+           1         196.3403
+           2         196.3042
+           2         196.3825
+           2         196.1669
+           2         196.3257
+           2         196.0422
+           3         196.1303
+           3         196.2005
+           3         196.2889
+           3         196.0343
+           3         196.1811
+           4         196.2795
+           4         196.1748
+           4         196.1494
+           4         196.1485
+           4         195.9885
+           5         196.2119
+           5         196.1051
+           5         196.1850
+           5         196.0052
+           5         196.2090

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs01   (SmLs01.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           2         1.3
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           3         1.5
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           4         1.3
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           5         1.5
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           6         1.3
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           7         1.5
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           8         1.3
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           9         1.5
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat

@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs02   (SmLs02.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           2         1.3
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           3         1.5
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
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+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           8         1.3
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           9         1.5
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
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+           9         1.4
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+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
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+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
+           9         1.6
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+           9         1.4
+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.6
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+           9         1.4
+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs04   (SmLs04.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           2       1000000.3
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           3       1000000.5
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           4       1000000.3
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           5       1000000.5
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           6       1000000.3
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           7       1000000.5
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           8       1000000.3
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           9       1000000.5
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat

@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs05   (SmLs05.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
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+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs07   (SmLs07.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           2    1000000000000.3
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           3    1000000000000.5
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
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+           3    1000000000000.4
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+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           4    1000000000000.3
+           4    1000000000000.2
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+           4    1000000000000.2
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+           4    1000000000000.2
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+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat

@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs08   (SmLs08.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares              F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat

@@ -0,0 +1,97 @@
+NIST/ITL StRD
+Dataset Name:  Norris (Norris.dat)
+
+File Format:   ASCII
+               Certified Values  (lines 31 to 46)
+               Data              (lines 61 to 96)
+
+Procedure:     Linear Least Squares Regression
+
+Reference:     Norris, J., NIST.  
+               Calibration of Ozone Monitors.
+
+Data:          1 Response Variable (y)
+               1 Predictor Variable (x)
+               36 Observations
+               Lower Level of Difficulty
+               Observed Data
+
+Model:         Linear Class
+               2 Parameters (B0,B1)
+
+               y = B0 + B1*x + e
+
+
+
+               Certified Regression Statistics
+
+                                          Standard Deviation
+     Parameter          Estimate             of Estimate
+
+        B0        -0.262323073774029     0.232818234301152
+        B1         1.00211681802045      0.429796848199937E-03
+
+     Residual
+     Standard Deviation   0.884796396144373
+
+     R-Squared            0.999993745883712
+
+
+               Certified Analysis of Variance Table
+
+Source of Degrees of    Sums of             Mean  
+Variation  Freedom      Squares            Squares           F Statistic
+              
+Regression    1     4255954.13232369   4255954.13232369   5436385.54079785
+Residual     34     26.6173985294224   0.782864662630069
+
+                 
+                                          
+                                          
+                                                           
+
+                            
+                                   
+                                                       
+
+
+
+
+Data:       y          x
+           0.1        0.2
+         338.8      337.4
+         118.1      118.2
+         888.0      884.6
+           9.2       10.1
+         228.1      226.5
+         668.5      666.3
+         998.5      996.3
+         449.1      448.6
+         778.9      777.0
+         559.2      558.2
+           0.3        0.4
+           0.1        0.6
+         778.1      775.5
+         668.8      666.9
+         339.3      338.0
+         448.9      447.5
+          10.8       11.6
+         557.7      556.0
+         228.3      228.1
+         998.0      995.8
+         888.8      887.6
+         119.6      120.2
+           0.3        0.3
+           0.6        0.3
+         557.6      556.8
+         339.3      339.1
+         888.0      887.2
+         998.5      999.0
+         778.9      779.0
+          10.2       11.1
+         117.6      118.3
+         228.9      229.2
+         668.4      669.1
+         449.2      448.9
+           0.2        0.5
+                                   

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json

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+    }
+  ]
+}

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_resampling.py

@@ -0,0 +1,2009 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal, suppress_warnings
+
+from scipy.conftest import array_api_compatible
+from scipy._lib._util import rng_integers
+from scipy._lib._array_api import array_namespace, is_numpy
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal
+from scipy import stats, special
+from scipy.optimize import root
+
+from scipy.stats import bootstrap, monte_carlo_test, permutation_test, power
+import scipy.stats._resampling as _resampling
+
+
+def test_bootstrap_iv():
+
+    message = "`data` must be a sequence of samples."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(1, np.mean)
+
+    message = "`data` must contain at least one sample."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(tuple(), np.mean)
+
+    message = "each sample in `data` must contain two or more observations..."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3], [1]), np.mean)
+
+    message = ("When `paired is True`, all samples must have the same length ")
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3], [1, 2, 3, 4]), np.mean, paired=True)
+
+    message = "`vectorized` must be `True`, `False`, or `None`."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(1, np.mean, vectorized='ekki')
+
+    message = "`axis` must be an integer."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, axis=1.5)
+
+    message = "could not convert string to float"
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, confidence_level='ni')
+
+    message = "`n_resamples` must be a non-negative integer."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, n_resamples=-1000)
+
+    message = "`n_resamples` must be a non-negative integer."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, n_resamples=1000.5)
+
+    message = "`batch` must be a positive integer or None."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, batch=-1000)
+
+    message = "`batch` must be a positive integer or None."
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, batch=1000.5)
+
+    message = "`method` must be in"
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, method='ekki')
+
+    message = "`bootstrap_result` must have attribute `bootstrap_distribution'"
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, bootstrap_result=10)
+
+    message = "Either `bootstrap_result.bootstrap_distribution.size`"
+    with pytest.raises(ValueError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, n_resamples=0)
+
+    message = "SeedSequence expects int or sequence of ints"
+    with pytest.raises(TypeError, match=message):
+        bootstrap(([1, 2, 3],), np.mean, rng='herring')
+
+
+@pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+@pytest.mark.parametrize("axis", [0, 1, 2])
+def test_bootstrap_batch(method, axis):
+    # for one-sample statistics, batch size shouldn't affect the result
+    np.random.seed(0)
+
+    x = np.random.rand(10, 11, 12)
+    # SPEC-007 leave one call with random_state to ensure it still works
+    res1 = bootstrap((x,), np.mean, batch=None, method=method,
+                     random_state=0, axis=axis, n_resamples=100)
+    np.random.seed(0)
+    res2 = bootstrap((x,), np.mean, batch=10, method=method,
+                     axis=axis, n_resamples=100)
+
+    assert_equal(res2.confidence_interval.low, res1.confidence_interval.low)
+    assert_equal(res2.confidence_interval.high, res1.confidence_interval.high)
+    assert_equal(res2.standard_error, res1.standard_error)
+
+
+@pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+def test_bootstrap_paired(method):
+    # test that `paired` works as expected
+    np.random.seed(0)
+    n = 100
+    x = np.random.rand(n)
+    y = np.random.rand(n)
+
+    def my_statistic(x, y, axis=-1):
+        return ((x-y)**2).mean(axis=axis)
+
+    def my_paired_statistic(i, axis=-1):
+        a = x[i]
+        b = y[i]
+        res = my_statistic(a, b)
+        return res
+
+    i = np.arange(len(x))
+
+    res1 = bootstrap((i,), my_paired_statistic, rng=0)
+    res2 = bootstrap((x, y), my_statistic, paired=True, rng=0)
+
+    assert_allclose(res1.confidence_interval, res2.confidence_interval)
+    assert_allclose(res1.standard_error, res2.standard_error)
+
+
+@pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+@pytest.mark.parametrize("axis", [0, 1, 2])
+@pytest.mark.parametrize("paired", [True, False])
+def test_bootstrap_vectorized(method, axis, paired):
+    # test that paired is vectorized as expected: when samples are tiled,
+    # CI and standard_error of each axis-slice is the same as those of the
+    # original 1d sample
+
+    np.random.seed(0)
+
+    def my_statistic(x, y, z, axis=-1):
+        return x.mean(axis=axis) + y.mean(axis=axis) + z.mean(axis=axis)
+
+    shape = 10, 11, 12
+    n_samples = shape[axis]
+
+    x = np.random.rand(n_samples)
+    y = np.random.rand(n_samples)
+    z = np.random.rand(n_samples)
+    res1 = bootstrap((x, y, z), my_statistic, paired=paired, method=method,
+                     rng=0, axis=0, n_resamples=100)
+    assert (res1.bootstrap_distribution.shape
+            == res1.standard_error.shape + (100,))
+
+    reshape = [1, 1, 1]
+    reshape[axis] = n_samples
+    x = np.broadcast_to(x.reshape(reshape), shape)
+    y = np.broadcast_to(y.reshape(reshape), shape)
+    z = np.broadcast_to(z.reshape(reshape), shape)
+    res2 = bootstrap((x, y, z), my_statistic, paired=paired, method=method,
+                     rng=0, axis=axis, n_resamples=100)
+
+    assert_allclose(res2.confidence_interval.low,
+                    res1.confidence_interval.low)
+    assert_allclose(res2.confidence_interval.high,
+                    res1.confidence_interval.high)
+    assert_allclose(res2.standard_error, res1.standard_error)
+
+    result_shape = list(shape)
+    result_shape.pop(axis)
+
+    assert_equal(res2.confidence_interval.low.shape, result_shape)
+    assert_equal(res2.confidence_interval.high.shape, result_shape)
+    assert_equal(res2.standard_error.shape, result_shape)
+
+
+@pytest.mark.slow
+@pytest.mark.xfail_on_32bit("MemoryError with BCa observed in CI")
+@pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+def test_bootstrap_against_theory(method):
+    # based on https://www.statology.org/confidence-intervals-python/
+    rng = np.random.default_rng(2442101192988600726)
+    data = stats.norm.rvs(loc=5, scale=2, size=5000, random_state=rng)
+    alpha = 0.95
+    dist = stats.t(df=len(data)-1, loc=np.mean(data), scale=stats.sem(data))
+    expected_interval = dist.interval(confidence=alpha)
+    expected_se = dist.std()
+
+    config = dict(data=(data,), statistic=np.mean, n_resamples=5000,
+                  method=method, rng=rng)
+    res = bootstrap(**config, confidence_level=alpha)
+    assert_allclose(res.confidence_interval, expected_interval, rtol=5e-4)
+    assert_allclose(res.standard_error, expected_se, atol=3e-4)
+
+    config.update(dict(n_resamples=0, bootstrap_result=res))
+    res = bootstrap(**config, confidence_level=alpha, alternative='less')
+    assert_allclose(res.confidence_interval.high, dist.ppf(alpha), rtol=5e-4)
+
+    config.update(dict(n_resamples=0, bootstrap_result=res))
+    res = bootstrap(**config, confidence_level=alpha, alternative='greater')
+    assert_allclose(res.confidence_interval.low, dist.ppf(1-alpha), rtol=5e-4)
+
+
+tests_R = {"basic": (23.77, 79.12),
+           "percentile": (28.86, 84.21),
+           "BCa": (32.31, 91.43)}
+
+
+@pytest.mark.parametrize("method, expected", tests_R.items())
+def test_bootstrap_against_R(method, expected):
+    # Compare against R's "boot" library
+    # library(boot)
+
+    # stat <- function (x, a) {
+    #     mean(x[a])
+    # }
+
+    # x <- c(10, 12, 12.5, 12.5, 13.9, 15, 21, 22,
+    #        23, 34, 50, 81, 89, 121, 134, 213)
+
+    # # Use a large value so we get a few significant digits for the CI.
+    # n = 1000000
+    # bootresult = boot(x, stat, n)
+    # result <- boot.ci(bootresult)
+    # print(result)
+    x = np.array([10, 12, 12.5, 12.5, 13.9, 15, 21, 22,
+                  23, 34, 50, 81, 89, 121, 134, 213])
+    res = bootstrap((x,), np.mean, n_resamples=1000000, method=method,
+                    rng=0)
+    assert_allclose(res.confidence_interval, expected, rtol=0.005)
+
+
+tests_against_itself_1samp = {"basic": 1780,
+                              "percentile": 1784,
+                              "BCa": 1784}
+
+
+def test_multisample_BCa_against_R():
+    # Because bootstrap is stochastic, it's tricky to test against reference
+    # behavior. Here, we show that SciPy's BCa CI matches R wboot's BCa CI
+    # much more closely than the other SciPy CIs do.
+
+    # arbitrary skewed data
+    x = [0.75859206, 0.5910282, -0.4419409, -0.36654601,
+         0.34955357, -1.38835871, 0.76735821]
+    y = [1.41186073, 0.49775975, 0.08275588, 0.24086388,
+         0.03567057, 0.52024419, 0.31966611, 1.32067634]
+
+    # a multi-sample statistic for which the BCa CI tends to be different
+    # from the other CIs
+    def statistic(x, y, axis):
+        s1 = stats.skew(x, axis=axis)
+        s2 = stats.skew(y, axis=axis)
+        return s1 - s2
+
+    # compute confidence intervals using each method
+    rng = np.random.default_rng(468865032284792692)
+
+    res_basic = stats.bootstrap((x, y), statistic, method='basic',
+                                batch=100, rng=rng)
+    res_percent = stats.bootstrap((x, y), statistic, method='percentile',
+                                  batch=100, rng=rng)
+    res_bca = stats.bootstrap((x, y), statistic, method='bca',
+                              batch=100, rng=rng)
+
+    # compute midpoints so we can compare just one number for each
+    mid_basic = np.mean(res_basic.confidence_interval)
+    mid_percent = np.mean(res_percent.confidence_interval)
+    mid_bca = np.mean(res_bca.confidence_interval)
+
+    # reference for BCA CI computed using R wboot package:
+    # library(wBoot)
+    # library(moments)
+
+    # x = c(0.75859206, 0.5910282, -0.4419409, -0.36654601,
+    #       0.34955357, -1.38835871,  0.76735821)
+    # y = c(1.41186073, 0.49775975, 0.08275588, 0.24086388,
+    #       0.03567057, 0.52024419, 0.31966611, 1.32067634)
+
+    # twoskew <- function(x1, y1) {skewness(x1) - skewness(y1)}
+    # boot.two.bca(x, y, skewness, conf.level = 0.95,
+    #              R = 9999, stacked = FALSE)
+    mid_wboot = -1.5519
+
+    # compute percent difference relative to wboot BCA method
+    diff_basic = (mid_basic - mid_wboot)/abs(mid_wboot)
+    diff_percent = (mid_percent - mid_wboot)/abs(mid_wboot)
+    diff_bca = (mid_bca - mid_wboot)/abs(mid_wboot)
+
+    # SciPy's BCa CI midpoint is much closer than that of the other methods
+    assert diff_basic < -0.15
+    assert diff_percent > 0.15
+    assert abs(diff_bca) < 0.03
+
+
+def test_BCa_acceleration_against_reference():
+    # Compare the (deterministic) acceleration parameter for a multi-sample
+    # problem against a reference value. The example is from [1], but Efron's
+    # value seems inaccurate. Straightforward code for computing the
+    # reference acceleration (0.011008228344026734) is available at:
+    # https://github.com/scipy/scipy/pull/16455#issuecomment-1193400981
+
+    y = np.array([10, 27, 31, 40, 46, 50, 52, 104, 146])
+    z = np.array([16, 23, 38, 94, 99, 141, 197])
+
+    def statistic(z, y, axis=0):
+        return np.mean(z, axis=axis) - np.mean(y, axis=axis)
+
+    data = [z, y]
+    res = stats.bootstrap(data, statistic)
+
+    axis = -1
+    alpha = 0.95
+    theta_hat_b = res.bootstrap_distribution
+    batch = 100
+    _, _, a_hat = _resampling._bca_interval(data, statistic, axis, alpha,
+                                            theta_hat_b, batch)
+    assert_allclose(a_hat, 0.011008228344026734)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("method, expected",
+                         tests_against_itself_1samp.items())
+def test_bootstrap_against_itself_1samp(method, expected):
+    # The expected values in this test were generated using bootstrap
+    # to check for unintended changes in behavior. The test also makes sure
+    # that bootstrap works with multi-sample statistics and that the
+    # `axis` argument works as expected / function is vectorized.
+    np.random.seed(0)
+
+    n = 100  # size of sample
+    n_resamples = 999  # number of bootstrap resamples used to form each CI
+    confidence_level = 0.9
+
+    # The true mean is 5
+    dist = stats.norm(loc=5, scale=1)
+    stat_true = dist.mean()
+
+    # Do the same thing 2000 times. (The code is fully vectorized.)
+    n_replications = 2000
+    data = dist.rvs(size=(n_replications, n))
+    res = bootstrap((data,),
+                    statistic=np.mean,
+                    confidence_level=confidence_level,
+                    n_resamples=n_resamples,
+                    batch=50,
+                    method=method,
+                    axis=-1)
+    ci = res.confidence_interval
+
+    # ci contains vectors of lower and upper confidence interval bounds
+    ci_contains_true = np.sum((ci[0] < stat_true) & (stat_true < ci[1]))
+    assert ci_contains_true == expected
+
+    # ci_contains_true is not inconsistent with confidence_level
+    pvalue = stats.binomtest(ci_contains_true, n_replications,
+                             confidence_level).pvalue
+    assert pvalue > 0.1
+
+
+tests_against_itself_2samp = {"basic": 892,
+                              "percentile": 890}
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("method, expected",
+                         tests_against_itself_2samp.items())
+def test_bootstrap_against_itself_2samp(method, expected):
+    # The expected values in this test were generated using bootstrap
+    # to check for unintended changes in behavior. The test also makes sure
+    # that bootstrap works with multi-sample statistics and that the
+    # `axis` argument works as expected / function is vectorized.
+    np.random.seed(0)
+
+    n1 = 100  # size of sample 1
+    n2 = 120  # size of sample 2
+    n_resamples = 999  # number of bootstrap resamples used to form each CI
+    confidence_level = 0.9
+
+    # The statistic we're interested in is the difference in means
+    def my_stat(data1, data2, axis=-1):
+        mean1 = np.mean(data1, axis=axis)
+        mean2 = np.mean(data2, axis=axis)
+        return mean1 - mean2
+
+    # The true difference in the means is -0.1
+    dist1 = stats.norm(loc=0, scale=1)
+    dist2 = stats.norm(loc=0.1, scale=1)
+    stat_true = dist1.mean() - dist2.mean()
+
+    # Do the same thing 1000 times. (The code is fully vectorized.)
+    n_replications = 1000
+    data1 = dist1.rvs(size=(n_replications, n1))
+    data2 = dist2.rvs(size=(n_replications, n2))
+    res = bootstrap((data1, data2),
+                    statistic=my_stat,
+                    confidence_level=confidence_level,
+                    n_resamples=n_resamples,
+                    batch=50,
+                    method=method,
+                    axis=-1)
+    ci = res.confidence_interval
+
+    # ci contains vectors of lower and upper confidence interval bounds
+    ci_contains_true = np.sum((ci[0] < stat_true) & (stat_true < ci[1]))
+    assert ci_contains_true == expected
+
+    # ci_contains_true is not inconsistent with confidence_level
+    pvalue = stats.binomtest(ci_contains_true, n_replications,
+                             confidence_level).pvalue
+    assert pvalue > 0.1
+
+
+@pytest.mark.parametrize("method", ["basic", "percentile"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_bootstrap_vectorized_3samp(method, axis):
+    def statistic(*data, axis=0):
+        # an arbitrary, vectorized statistic
+        return sum(sample.mean(axis) for sample in data)
+
+    def statistic_1d(*data):
+        # the same statistic, not vectorized
+        for sample in data:
+            assert sample.ndim == 1
+        return statistic(*data, axis=0)
+
+    np.random.seed(0)
+    x = np.random.rand(4, 5)
+    y = np.random.rand(4, 5)
+    z = np.random.rand(4, 5)
+    res1 = bootstrap((x, y, z), statistic, vectorized=True,
+                     axis=axis, n_resamples=100, method=method, rng=0)
+    res2 = bootstrap((x, y, z), statistic_1d, vectorized=False,
+                     axis=axis, n_resamples=100, method=method, rng=0)
+    assert_allclose(res1.confidence_interval, res2.confidence_interval)
+    assert_allclose(res1.standard_error, res2.standard_error)
+
+
+@pytest.mark.xfail_on_32bit("Failure is not concerning; see gh-14107")
+@pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_bootstrap_vectorized_1samp(method, axis):
+    def statistic(x, axis=0):
+        # an arbitrary, vectorized statistic
+        return x.mean(axis=axis)
+
+    def statistic_1d(x):
+        # the same statistic, not vectorized
+        assert x.ndim == 1
+        return statistic(x, axis=0)
+
+    np.random.seed(0)
+    x = np.random.rand(4, 5)
+    res1 = bootstrap((x,), statistic, vectorized=True, axis=axis,
+                     n_resamples=100, batch=None, method=method,
+                     rng=0)
+    res2 = bootstrap((x,), statistic_1d, vectorized=False, axis=axis,
+                     n_resamples=100, batch=10, method=method,
+                     rng=0)
+    assert_allclose(res1.confidence_interval, res2.confidence_interval)
+    assert_allclose(res1.standard_error, res2.standard_error)
+
+
+@pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+def test_bootstrap_degenerate(method):
+    data = 35 * [10000.]
+    if method == "BCa":
+        with np.errstate(invalid='ignore'):
+            msg = "The BCa confidence interval cannot be calculated"
+            with pytest.warns(stats.DegenerateDataWarning, match=msg):
+                res = bootstrap([data, ], np.mean, method=method)
+                assert_equal(res.confidence_interval, (np.nan, np.nan))
+    else:
+        res = bootstrap([data, ], np.mean, method=method)
+        assert_equal(res.confidence_interval, (10000., 10000.))
+    assert_equal(res.standard_error, 0)
+
+
+@pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+def test_bootstrap_gh15678(method):
+    # Check that gh-15678 is fixed: when statistic function returned a Python
+    # float, method="BCa" failed when trying to add a dimension to the float
+    rng = np.random.default_rng(354645618886684)
+    dist = stats.norm(loc=2, scale=4)
+    data = dist.rvs(size=100, random_state=rng)
+    data = (data,)
+    res = bootstrap(data, stats.skew, method=method, n_resamples=100,
+                    rng=np.random.default_rng(9563))
+    # this always worked because np.apply_along_axis returns NumPy data type
+    ref = bootstrap(data, stats.skew, method=method, n_resamples=100,
+                    rng=np.random.default_rng(9563), vectorized=False)
+    assert_allclose(res.confidence_interval, ref.confidence_interval)
+    assert_allclose(res.standard_error, ref.standard_error)
+    assert isinstance(res.standard_error, np.float64)
+
+
+def test_bootstrap_min():
+    # Check that gh-15883 is fixed: percentileofscore should
+    # behave according to the 'mean' behavior and not trigger nan for BCa
+    rng = np.random.default_rng(1891289180021102)
+    dist = stats.norm(loc=2, scale=4)
+    data = dist.rvs(size=100, random_state=rng)
+    true_min = np.min(data)
+    data = (data,)
+    res = bootstrap(data, np.min, method="BCa", n_resamples=100,
+                    rng=np.random.default_rng(3942))
+    assert true_min == res.confidence_interval.low
+    res2 = bootstrap(-np.array(data), np.max, method="BCa", n_resamples=100,
+                     rng=np.random.default_rng(3942))
+    assert_allclose(-res.confidence_interval.low,
+                    res2.confidence_interval.high)
+    assert_allclose(-res.confidence_interval.high,
+                    res2.confidence_interval.low)
+
+
+@pytest.mark.parametrize("additional_resamples", [0, 1000])
+def test_re_bootstrap(additional_resamples):
+    # Test behavior of parameter `bootstrap_result`
+    rng = np.random.default_rng(8958153316228384)
+    x = rng.random(size=100)
+
+    n1 = 1000
+    n2 = additional_resamples
+    n3 = n1 + additional_resamples
+
+    rng = np.random.default_rng(296689032789913033)
+    res = stats.bootstrap((x,), np.mean, n_resamples=n1, rng=rng,
+                          confidence_level=0.95, method='percentile')
+    res = stats.bootstrap((x,), np.mean, n_resamples=n2, rng=rng,
+                          confidence_level=0.90, method='BCa',
+                          bootstrap_result=res)
+
+    rng = np.random.default_rng(296689032789913033)
+    ref = stats.bootstrap((x,), np.mean, n_resamples=n3, rng=rng,
+                          confidence_level=0.90, method='BCa')
+
+    assert_allclose(res.standard_error, ref.standard_error, rtol=1e-14)
+    assert_allclose(res.confidence_interval, ref.confidence_interval,
+                    rtol=1e-14)
+
+
+@pytest.mark.xfail_on_32bit("Sensible to machine precision")
+@pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+def test_bootstrap_alternative(method):
+    rng = np.random.default_rng(5894822712842015040)
+    dist = stats.norm(loc=2, scale=4)
+    data = (dist.rvs(size=(100), random_state=rng),)
+
+    config = dict(data=data, statistic=np.std, rng=rng, axis=-1)
+    t = stats.bootstrap(**config, confidence_level=0.9)
+
+    config.update(dict(n_resamples=0, bootstrap_result=t))
+    l = stats.bootstrap(**config, confidence_level=0.95, alternative='less')
+    g = stats.bootstrap(**config, confidence_level=0.95, alternative='greater')
+
+    assert_allclose(l.confidence_interval.high, t.confidence_interval.high,
+                    rtol=1e-14)
+    assert_allclose(g.confidence_interval.low, t.confidence_interval.low,
+                    rtol=1e-14)
+    assert np.isneginf(l.confidence_interval.low)
+    assert np.isposinf(g.confidence_interval.high)
+
+    with pytest.raises(ValueError, match='`alternative` must be one of'):
+        stats.bootstrap(**config, alternative='ekki-ekki')
+
+
+def test_jackknife_resample():
+    shape = 3, 4, 5, 6
+    np.random.seed(0)
+    x = np.random.rand(*shape)
+    y = next(_resampling._jackknife_resample(x))
+
+    for i in range(shape[-1]):
+        # each resample is indexed along second to last axis
+        # (last axis is the one the statistic will be taken over / consumed)
+        slc = y[..., i, :]
+        expected = np.delete(x, i, axis=-1)
+
+        assert np.array_equal(slc, expected)
+
+    y2 = np.concatenate(list(_resampling._jackknife_resample(x, batch=2)),
+                        axis=-2)
+    assert np.array_equal(y2, y)
+
+
+@pytest.mark.parametrize("rng_name", ["RandomState", "default_rng"])
+def test_bootstrap_resample(rng_name):
+    rng = getattr(np.random, rng_name, None)
+    if rng is None:
+        pytest.skip(f"{rng_name} not available.")
+    rng1 = rng(0)
+    rng2 = rng(0)
+
+    n_resamples = 10
+    shape = 3, 4, 5, 6
+
+    np.random.seed(0)
+    x = np.random.rand(*shape)
+    y = _resampling._bootstrap_resample(x, n_resamples, rng=rng1)
+
+    for i in range(n_resamples):
+        # each resample is indexed along second to last axis
+        # (last axis is the one the statistic will be taken over / consumed)
+        slc = y[..., i, :]
+
+        js = rng_integers(rng2, 0, shape[-1], shape[-1])
+        expected = x[..., js]
+
+        assert np.array_equal(slc, expected)
+
+
+@pytest.mark.parametrize("score", [0, 0.5, 1])
+@pytest.mark.parametrize("axis", [0, 1, 2])
+def test_percentile_of_score(score, axis):
+    shape = 10, 20, 30
+    np.random.seed(0)
+    x = np.random.rand(*shape)
+    p = _resampling._percentile_of_score(x, score, axis=-1)
+
+    def vectorized_pos(a, score, axis):
+        return np.apply_along_axis(stats.percentileofscore, axis, a, score)
+
+    p2 = vectorized_pos(x, score, axis=-1)/100
+
+    assert_allclose(p, p2, 1e-15)
+
+
+def test_percentile_along_axis():
+    # the difference between _percentile_along_axis and np.percentile is that
+    # np.percentile gets _all_ the qs for each axis slice, whereas
+    # _percentile_along_axis gets the q corresponding with each axis slice
+
+    shape = 10, 20
+    np.random.seed(0)
+    x = np.random.rand(*shape)
+    q = np.random.rand(*shape[:-1]) * 100
+    y = _resampling._percentile_along_axis(x, q)
+
+    for i in range(shape[0]):
+        res = y[i]
+        expected = np.percentile(x[i], q[i], axis=-1)
+        assert_allclose(res, expected, 1e-15)
+
+
+@pytest.mark.parametrize("axis", [0, 1, 2])
+def test_vectorize_statistic(axis):
+    # test that _vectorize_statistic vectorizes a statistic along `axis`
+
+    def statistic(*data, axis):
+        # an arbitrary, vectorized statistic
+        return sum(sample.mean(axis) for sample in data)
+
+    def statistic_1d(*data):
+        # the same statistic, not vectorized
+        for sample in data:
+            assert sample.ndim == 1
+        return statistic(*data, axis=0)
+
+    # vectorize the non-vectorized statistic
+    statistic2 = _resampling._vectorize_statistic(statistic_1d)
+
+    np.random.seed(0)
+    x = np.random.rand(4, 5, 6)
+    y = np.random.rand(4, 1, 6)
+    z = np.random.rand(1, 5, 6)
+
+    res1 = statistic(x, y, z, axis=axis)
+    res2 = statistic2(x, y, z, axis=axis)
+    assert_allclose(res1, res2)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+def test_vector_valued_statistic(method):
+    # Generate 95% confidence interval around MLE of normal distribution
+    # parameters. Repeat 100 times, each time on sample of size 100.
+    # Check that confidence interval contains true parameters ~95 times.
+    # Confidence intervals are estimated and stochastic; a test failure
+    # does not necessarily indicate that something is wrong. More important
+    # than values of `counts` below is that the shapes of the outputs are
+    # correct.
+
+    rng = np.random.default_rng(2196847219)
+    params = 1, 0.5
+    sample = stats.norm.rvs(*params, size=(100, 100), random_state=rng)
+
+    def statistic(data, axis):
+        return np.asarray([np.mean(data, axis),
+                           np.std(data, axis, ddof=1)])
+
+    res = bootstrap((sample,), statistic, method=method, axis=-1,
+                    n_resamples=9999, batch=200)
+
+    counts = np.sum((res.confidence_interval.low.T < params)
+                    & (res.confidence_interval.high.T > params),
+                    axis=0)
+    assert np.all(counts >= 90)
+    assert np.all(counts <= 100)
+    assert res.confidence_interval.low.shape == (2, 100)
+    assert res.confidence_interval.high.shape == (2, 100)
+    assert res.standard_error.shape == (2, 100)
+    assert res.bootstrap_distribution.shape == (2, 100, 9999)
+
+
+@pytest.mark.slow
+@pytest.mark.filterwarnings('ignore::RuntimeWarning')
+def test_vector_valued_statistic_gh17715():
+    # gh-17715 reported a mistake introduced in the extension of BCa to
+    # multi-sample statistics; a `len` should have been `.shape[-1]`. Check
+    # that this is resolved.
+
+    rng = np.random.default_rng(141921000979291141)
+
+    def concordance(x, y, axis):
+        xm = x.mean(axis)
+        ym = y.mean(axis)
+        cov = ((x - xm[..., None]) * (y - ym[..., None])).mean(axis)
+        return (2 * cov) / (x.var(axis) + y.var(axis) + (xm - ym) ** 2)
+
+    def statistic(tp, tn, fp, fn, axis):
+        actual = tp + fp
+        expected = tp + fn
+        return np.nan_to_num(concordance(actual, expected, axis))
+
+    def statistic_extradim(*args, axis):
+        return statistic(*args, axis)[np.newaxis, ...]
+
+    data = [[4, 0, 0, 2],  # (tp, tn, fp, fn)
+            [2, 1, 2, 1],
+            [0, 6, 0, 0],
+            [0, 6, 3, 0],
+            [0, 8, 1, 0]]
+    data = np.array(data).T
+
+    res = bootstrap(data, statistic_extradim, rng=rng, paired=True)
+    ref = bootstrap(data, statistic, rng=rng, paired=True)
+    assert_allclose(res.confidence_interval.low[0],
+                    ref.confidence_interval.low, atol=1e-15)
+    assert_allclose(res.confidence_interval.high[0],
+                    ref.confidence_interval.high, atol=1e-15)
+
+
+def test_gh_20850():
+    rng = np.random.default_rng(2085020850)
+    x = rng.random((10, 2))
+    y = rng.random((11, 2))
+    def statistic(x, y, axis):
+        return stats.ttest_ind(x, y, axis=axis).statistic
+
+    # The shapes do *not* need to be the same along axis
+    stats.bootstrap((x, y), statistic)
+    stats.bootstrap((x.T, y.T), statistic, axis=1)
+    # But even when the shapes *are* the same along axis, the lengths
+    # along other dimensions have to be the same (or `bootstrap` warns).
+    message = "Ignoring the dimension specified by `axis`..."
+    with pytest.warns(FutureWarning, match=message):
+        stats.bootstrap((x, y[:10, 0]), statistic)  # this won't work after 1.16
+    with pytest.warns(FutureWarning, match=message):
+        stats.bootstrap((x, y[:10, 0:1]), statistic)  # this will
+    with pytest.warns(FutureWarning, match=message):
+        stats.bootstrap((x.T, y.T[0:1, :10]), statistic, axis=1)  # this will
+
+
+# --- Test Monte Carlo Hypothesis Test --- #
+
+class TestMonteCarloHypothesisTest:
+    atol = 2.5e-2  # for comparing p-value
+
+    def get_rvs(self, rvs_in, rs, dtype=None, xp=np):
+        return lambda *args, **kwds: xp.asarray(rvs_in(*args, random_state=rs, **kwds),
+                                                dtype=dtype)
+
+    def get_statistic(self, xp):
+        def statistic(x, axis):
+            m = xp.mean(x, axis=axis)
+            v = xp.var(x, axis=axis, correction=1)
+            n = x.shape[axis]
+            return m / (v/n)**0.5
+            # return stats.ttest_1samp(x, popmean=0., axis=axis).statistic)
+        return statistic
+
+    @array_api_compatible
+    def test_input_validation(self, xp):
+        # test that the appropriate error messages are raised for invalid input
+
+        data = xp.asarray([1., 2., 3.])
+        def stat(x, axis=None):
+            return xp.mean(x, axis=axis)
+
+        message = "Array shapes are incompatible for broadcasting."
+        temp = (xp.zeros((2, 5)), xp.zeros((3, 5)))
+        rvs = (stats.norm.rvs, stats.norm.rvs)
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(temp, rvs, lambda x, y, axis: 1, axis=-1)
+
+        message = "`axis` must be an integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, axis=1.5)
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, vectorized=1.5)
+
+        message = "`rvs` must be callable or sequence of callables."
+        with pytest.raises(TypeError, match=message):
+            monte_carlo_test(data, None, stat)
+        with pytest.raises(TypeError, match=message):
+            temp = xp.asarray([[1., 2.], [3., 4.]])
+            monte_carlo_test(temp, [lambda x: x, None], stat)
+
+        message = "If `rvs` is a sequence..."
+        with pytest.raises(ValueError, match=message):
+            temp = xp.asarray([[1., 2., 3.]])
+            monte_carlo_test(temp, [lambda x: x, lambda x: x], stat)
+
+        message = "`statistic` must be callable."
+        with pytest.raises(TypeError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, None)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, n_resamples=-1000)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, n_resamples=1000.5)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, batch=-1000)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, batch=1000.5)
+
+        message = "`alternative` must be in..."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, alternative='ekki')
+
+        # *If* this raises a value error, make sure it has the intended message
+        message = "Signature inspection of statistic"
+        def rvs(size):
+            return xp.asarray(stats.norm.rvs(size=size))
+        try:
+            monte_carlo_test(data, rvs, xp.mean)
+        except ValueError as e:
+            assert str(e).startswith(message)
+
+    @array_api_compatible
+    def test_input_validation_xp(self, xp):
+        def non_vectorized_statistic(x):
+            return xp.mean(x)
+
+        message = "`statistic` must be vectorized..."
+        sample = xp.asarray([1., 2., 3.])
+        if is_numpy(xp):
+            monte_carlo_test(sample, stats.norm.rvs, non_vectorized_statistic)
+            return
+
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(sample, stats.norm.rvs, non_vectorized_statistic)
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(sample, stats.norm.rvs, xp.mean, vectorized=False)
+
+    @pytest.mark.xslow
+    @array_api_compatible
+    def test_batch(self, xp):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `statistic`
+        rng = np.random.default_rng(23492340193)
+        x = xp.asarray(rng.standard_normal(size=10))
+
+        xp_test = array_namespace(x)  # numpy.std doesn't have `correction`
+        def statistic(x, axis):
+            batch_size = 1 if x.ndim == 1 else x.shape[0]
+            statistic.batch_size = max(batch_size, statistic.batch_size)
+            statistic.counter += 1
+            return self.get_statistic(xp_test)(x, axis=axis)
+        statistic.counter = 0
+        statistic.batch_size = 0
+
+        kwds = {'sample': x, 'statistic': statistic,
+                'n_resamples': 1000, 'vectorized': True}
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        res1 = monte_carlo_test(batch=1, **kwds)
+        assert_equal(statistic.counter, 1001)
+        assert_equal(statistic.batch_size, 1)
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        statistic.counter = 0
+        res2 = monte_carlo_test(batch=50, **kwds)
+        assert_equal(statistic.counter, 21)
+        assert_equal(statistic.batch_size, 50)
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        statistic.counter = 0
+        res3 = monte_carlo_test(**kwds)
+        assert_equal(statistic.counter, 2)
+        assert_equal(statistic.batch_size, 1000)
+
+        xp_assert_equal(res1.pvalue, res3.pvalue)
+        xp_assert_equal(res2.pvalue, res3.pvalue)
+
+    @array_api_compatible
+    @pytest.mark.parametrize('axis', range(-3, 3))
+    def test_axis_dtype(self, axis, xp):
+        # test that Nd-array samples are handled correctly for valid values
+        # of the `axis` parameter; also make sure non-default dtype is maintained
+        rng = np.random.default_rng(2389234)
+        size = [2, 3, 4]
+        size[axis] = 100
+
+        # Determine non-default dtype
+        dtype_default = xp.asarray(1.).dtype
+        dtype_str = 'float32'if ("64" in str(dtype_default)) else 'float64'
+        dtype_np = getattr(np, dtype_str)
+        dtype = getattr(xp, dtype_str)
+
+        # ttest_1samp is CPU array-API compatible, but it would be good to
+        # include CuPy in this test. We'll perform ttest_1samp with a
+        # NumPy array, but all the rest with be done with fully array-API
+        # compatible code.
+        x = rng.standard_normal(size=size, dtype=dtype_np)
+        expected = stats.ttest_1samp(x, popmean=0., axis=axis)
+
+        x = xp.asarray(x, dtype=dtype)
+        xp_test = array_namespace(x)  # numpy.std doesn't have `correction`
+        statistic = self.get_statistic(xp_test)
+        rvs = self.get_rvs(stats.norm.rvs, rng, dtype=dtype, xp=xp)
+
+        res = monte_carlo_test(x, rvs, statistic, vectorized=True,
+                               n_resamples=20000, axis=axis)
+
+        ref_statistic = xp.asarray(expected.statistic, dtype=dtype)
+        ref_pvalue = xp.asarray(expected.pvalue, dtype=dtype)
+        xp_assert_close(res.statistic, ref_statistic)
+        xp_assert_close(res.pvalue, ref_pvalue, atol=self.atol)
+
+    @array_api_compatible
+    @pytest.mark.parametrize('alternative', ("two-sided", "less", "greater"))
+    def test_alternative(self, alternative, xp):
+        # test that `alternative` is working as expected
+        rng = np.random.default_rng(65723433)
+
+        x = rng.standard_normal(size=30)
+        ref = stats.ttest_1samp(x, 0., alternative=alternative)
+
+        x = xp.asarray(x)
+        xp_test = array_namespace(x)  # numpy.std doesn't have `correction`
+        statistic = self.get_statistic(xp_test)
+        rvs = self.get_rvs(stats.norm.rvs, rng, xp=xp)
+
+        res = monte_carlo_test(x, rvs, statistic, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue), atol=self.atol)
+
+
+    # Tests below involve statistics that are not yet array-API compatible.
+    # They can be converted when the statistics are converted.
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative', ("less", "greater"))
+    @pytest.mark.parametrize('a', np.linspace(-0.5, 0.5, 5))  # skewness
+    def test_against_ks_1samp(self, alternative, a):
+        # test that monte_carlo_test can reproduce pvalue of ks_1samp
+        rng = np.random.default_rng(65723433)
+
+        x = stats.skewnorm.rvs(a=a, size=30, random_state=rng)
+        expected = stats.ks_1samp(x, stats.norm.cdf, alternative=alternative)
+
+        def statistic1d(x):
+            return stats.ks_1samp(x, stats.norm.cdf, mode='asymp',
+                                  alternative=alternative).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic1d,
+                               n_resamples=1000, vectorized=False,
+                               alternative=alternative)
+
+        assert_allclose(res.statistic, expected.statistic)
+        if alternative == 'greater':
+            assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+        elif alternative == 'less':
+            assert_allclose(1-res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.parametrize('hypotest', (stats.skewtest, stats.kurtosistest))
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    @pytest.mark.parametrize('a', np.linspace(-2, 2, 5))  # skewness
+    def test_against_normality_tests(self, hypotest, alternative, a):
+        # test that monte_carlo_test can reproduce pvalue of normality tests
+        rng = np.random.default_rng(85723405)
+
+        x = stats.skewnorm.rvs(a=a, size=150, random_state=rng)
+        expected = hypotest(x, alternative=alternative)
+
+        def statistic(x, axis):
+            return hypotest(x, axis=axis).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic, vectorized=True,
+                               alternative=alternative)
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.parametrize('a', np.arange(-2, 3))  # skewness parameter
+    def test_against_normaltest(self, a):
+        # test that monte_carlo_test can reproduce pvalue of normaltest
+        rng = np.random.default_rng(12340513)
+
+        x = stats.skewnorm.rvs(a=a, size=150, random_state=rng)
+        expected = stats.normaltest(x)
+
+        def statistic(x, axis):
+            return stats.normaltest(x, axis=axis).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic, vectorized=True,
+                               alternative='greater')
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize('a', np.linspace(-0.5, 0.5, 5))  # skewness
+    def test_against_cramervonmises(self, a):
+        # test that monte_carlo_test can reproduce pvalue of cramervonmises
+        rng = np.random.default_rng(234874135)
+
+        x = stats.skewnorm.rvs(a=a, size=30, random_state=rng)
+        expected = stats.cramervonmises(x, stats.norm.cdf)
+
+        def statistic1d(x):
+            return stats.cramervonmises(x, stats.norm.cdf).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic1d,
+                               n_resamples=1000, vectorized=False,
+                               alternative='greater')
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('dist_name', ('norm', 'logistic'))
+    @pytest.mark.parametrize('i', range(5))
+    def test_against_anderson(self, dist_name, i):
+        # test that monte_carlo_test can reproduce results of `anderson`. Note:
+        # `anderson` does not provide a p-value; it provides a list of
+        # significance levels and the associated critical value of the test
+        # statistic. `i` used to index this list.
+
+        # find the skewness for which the sample statistic matches one of the
+        # critical values provided by `stats.anderson`
+
+        def fun(a):
+            rng = np.random.default_rng(394295467)
+            x = stats.tukeylambda.rvs(a, size=100, random_state=rng)
+            expected = stats.anderson(x, dist_name)
+            return expected.statistic - expected.critical_values[i]
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            sol = root(fun, x0=0)
+        assert sol.success
+
+        # get the significance level (p-value) associated with that critical
+        # value
+        a = sol.x[0]
+        rng = np.random.default_rng(394295467)
+        x = stats.tukeylambda.rvs(a, size=100, random_state=rng)
+        expected = stats.anderson(x, dist_name)
+        expected_stat = expected.statistic
+        expected_p = expected.significance_level[i]/100
+
+        # perform equivalent Monte Carlo test and compare results
+        def statistic1d(x):
+            return stats.anderson(x, dist_name).statistic
+
+        dist_rvs = self.get_rvs(getattr(stats, dist_name).rvs, rng)
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            res = monte_carlo_test(x, dist_rvs,
+                                   statistic1d, n_resamples=1000,
+                                   vectorized=False, alternative='greater')
+
+        assert_allclose(res.statistic, expected_stat)
+        assert_allclose(res.pvalue, expected_p, atol=2*self.atol)
+
+    def test_p_never_zero(self):
+        # Use biased estimate of p-value to ensure that p-value is never zero
+        # per monte_carlo_test reference [1]
+        rng = np.random.default_rng(2190176673029737545)
+        x = np.zeros(100)
+        res = monte_carlo_test(x, rng.random, np.mean,
+                               vectorized=True, alternative='less')
+        assert res.pvalue == 0.0001
+
+    def test_against_ttest_ind(self):
+        # test that `monte_carlo_test` can reproduce results of `ttest_ind`.
+        rng = np.random.default_rng(219017667302737545)
+        data = rng.random(size=(2, 5)), rng.random(size=7)  # broadcastable
+        rvs = rng.normal, rng.normal
+        def statistic(x, y, axis):
+            return stats.ttest_ind(x, y, axis=axis).statistic
+
+        res = stats.monte_carlo_test(data, rvs, statistic, axis=-1)
+        ref = stats.ttest_ind(data[0], [data[1]], axis=-1)
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, rtol=2e-2)
+
+    def test_against_f_oneway(self):
+        # test that `monte_carlo_test` can reproduce results of `f_oneway`.
+        rng = np.random.default_rng(219017667302737545)
+        data = (rng.random(size=(2, 100)), rng.random(size=(2, 101)),
+                rng.random(size=(2, 102)), rng.random(size=(2, 103)))
+        rvs = rng.normal, rng.normal, rng.normal, rng.normal
+
+        def statistic(*args, axis):
+            return stats.f_oneway(*args, axis=axis).statistic
+
+        res = stats.monte_carlo_test(data, rvs, statistic, axis=-1,
+                                     alternative='greater')
+        ref = stats.f_oneway(*data, axis=-1)
+
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, atol=1e-2)
+
+    @pytest.mark.fail_slow(2)
+    @pytest.mark.xfail_on_32bit("Statistic may not depend on sample order on 32-bit")
+    def test_finite_precision_statistic(self):
+        # Some statistics return numerically distinct values when the values
+        # should be equal in theory. Test that `monte_carlo_test` accounts
+        # for this in some way.
+        rng = np.random.default_rng(2549824598234528)
+        n_resamples = 9999
+        def rvs(size):
+            return 1. * stats.bernoulli(p=0.333).rvs(size=size, random_state=rng)
+
+        x = rvs(100)
+        res = stats.monte_carlo_test(x, rvs, np.var, alternative='less',
+                                     n_resamples=n_resamples)
+        # show that having a tolerance matters
+        c0 = np.sum(res.null_distribution <= res.statistic)
+        c1 = np.sum(res.null_distribution <= res.statistic*(1+1e-15))
+        assert c0 != c1
+        assert res.pvalue == (c1 + 1)/(n_resamples + 1)
+
+
+class TestPower:
+    def test_input_validation(self):
+        # test that the appropriate error messages are raised for invalid input
+        rng = np.random.default_rng(8519895914314711673)
+
+        test = stats.ttest_ind
+        rvs = (rng.normal, rng.normal)
+        n_observations = (10, 12)
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, vectorized=1.5)
+
+        message = "`rvs` must be callable or sequence of callables."
+        with pytest.raises(TypeError, match=message):
+            power(test, None, n_observations)
+        with pytest.raises(TypeError, match=message):
+            power(test, (rng.normal, 'ekki'), n_observations)
+
+        message = "If `rvs` is a sequence..."
+        with pytest.raises(ValueError, match=message):
+            power(test, (rng.normal,), n_observations)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, (10,))
+
+        message = "`significance` must contain floats between 0 and 1."
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, significance=2)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, significance=np.linspace(-1, 1))
+
+        message = "`kwargs` must be a dictionary"
+        with pytest.raises(TypeError, match=message):
+            power(test, rvs, n_observations, kwargs=(1, 2, 3))
+
+        message = "shape mismatch: objects cannot be broadcast"
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, ([10, 11], [12, 13, 14]))
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, ([10, 11], [12, 13]), kwargs={'x': [1, 2, 3]})
+
+        message = "`test` must be callable"
+        with pytest.raises(TypeError, match=message):
+            power(None, rvs, n_observations)
+
+        message = "`n_resamples` must be a positive integer"
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, n_resamples=-10)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, n_resamples=10.5)
+
+        message = "`batch` must be a positive integer"
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, batch=-10)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, batch=10.5)
+
+    @pytest.mark.slow
+    def test_batch(self):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `test`
+        rng = np.random.default_rng(23492340193)
+
+        def test(x, axis):
+            batch_size = 1 if x.ndim == 1 else len(x)
+            test.batch_size = max(batch_size, test.batch_size)
+            test.counter += 1
+            return stats.ttest_1samp(x, 0, axis=axis).pvalue
+        test.counter = 0
+        test.batch_size = 0
+
+        kwds = dict(test=test, n_observations=10, n_resamples=1000)
+
+        rng = np.random.default_rng(23492340193)
+        res1 = power(**kwds, rvs=rng.normal, batch=1)
+        assert_equal(test.counter, 1000)
+        assert_equal(test.batch_size, 1)
+
+        rng = np.random.default_rng(23492340193)
+        test.counter = 0
+        res2 = power(**kwds, rvs=rng.normal, batch=50)
+        assert_equal(test.counter, 20)
+        assert_equal(test.batch_size, 50)
+
+        rng = np.random.default_rng(23492340193)
+        test.counter = 0
+        res3 = power(**kwds, rvs=rng.normal, batch=1000)
+        assert_equal(test.counter, 1)
+        assert_equal(test.batch_size, 1000)
+
+        assert_equal(res1.power, res3.power)
+        assert_equal(res2.power, res3.power)
+
+    @pytest.mark.slow
+    def test_vectorization(self):
+        # Test that `power` is vectorized as expected
+        rng = np.random.default_rng(25495254834552)
+
+        # Single vectorized call
+        popmeans = np.array([0, 0.2])
+        def test(x, alternative, axis=-1):
+            # ensure that popmeans axis is zeroth and orthogonal to the rest
+            popmeans_expanded = np.expand_dims(popmeans, tuple(range(1, x.ndim + 1)))
+            return stats.ttest_1samp(x, popmeans_expanded, alternative=alternative,
+                                     axis=axis)
+
+        # nx and kwargs broadcast against one another
+        nx = np.asarray([10, 15, 20, 50, 100])[:, np.newaxis]
+        kwargs = {'alternative': ['less', 'greater', 'two-sided']}
+
+        # This dimension is added to the beginning
+        significance = np.asarray([0.01, 0.025, 0.05, 0.1])
+        res = stats.power(test, rng.normal, nx, significance=significance,
+                          kwargs=kwargs)
+
+        # Looping over all combinations
+        ref = []
+        for significance_i in significance:
+            for nx_i in nx:
+                for alternative_i in kwargs['alternative']:
+                    for popmean_i in popmeans:
+                        def test2(x, axis=-1):
+                            return stats.ttest_1samp(x, popmean_i, axis=axis,
+                                                     alternative=alternative_i)
+
+                        tmp = stats.power(test2, rng.normal, nx_i,
+                                          significance=significance_i)
+                        ref.append(tmp.power)
+        ref = np.reshape(ref, res.power.shape)
+
+        # Show that results are similar
+        assert_allclose(res.power, ref, rtol=2e-2, atol=1e-2)
+
+    def test_ttest_ind_null(self):
+        # Check that the p-values of `ttest_ind` are uniformly distributed under
+        # the null hypothesis
+        rng = np.random.default_rng(254952548345528)
+
+        test = stats.ttest_ind
+        n_observations = rng.integers(10, 100, size=(2, 10))
+        rvs = rng.normal, rng.normal
+        significance = np.asarray([0.01, 0.05, 0.1])
+        res = stats.power(test, rvs, n_observations, significance=significance)
+        significance = np.broadcast_to(significance[:, np.newaxis], res.power.shape)
+        assert_allclose(res.power, significance, atol=1e-2)
+
+    def test_ttest_1samp_power(self):
+        # Check simulated ttest_1samp power against reference
+        rng = np.random.default_rng(254952548345528)
+
+        # Reference values computed with statmodels
+        # import numpy as np
+        # from statsmodels.stats.power import tt_solve_power
+        # tt_solve_power = np.vectorize(tt_solve_power)
+        # tt_solve_power([0.1, 0.5, 0.9], [[10], [20]], [[[0.01]], [[0.05]]])
+        ref = [[[0.0126515 , 0.10269751, 0.40415802],
+                [0.01657775, 0.29734608, 0.86228288]],
+               [[0.0592903 , 0.29317561, 0.71718121],
+                [0.07094116, 0.56450441, 0.96815163]]]
+
+        kwargs = {'popmean': [0.1, 0.5, 0.9]}
+        n_observations = [[10], [20]]
+        significance = [0.01, 0.05]
+        res = stats.power(stats.ttest_1samp, rng.normal, n_observations,
+                          significance=significance, kwargs=kwargs)
+        assert_allclose(res.power, ref, atol=1e-2)
+
+
+class TestPermutationTest:
+
+    rtol = 1e-14
+
+    def setup_method(self):
+        self.rng = np.random.default_rng(7170559330470561044)
+
+    # -- Input validation -- #
+
+    def test_permutation_test_iv(self):
+
+        def stat(x, y, axis):
+            return stats.ttest_ind((x, y), axis).statistic
+
+        message = "each sample in `data` must contain two or more ..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1]), stat)
+
+        message = "`data` must be a tuple containing at least two samples"
+        with pytest.raises(ValueError, match=message):
+            permutation_test((1,), stat)
+        with pytest.raises(TypeError, match=message):
+            permutation_test(1, stat)
+
+        message = "`axis` must be an integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, axis=1.5)
+
+        message = "`permutation_type` must be in..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat,
+                             permutation_type="ekki")
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, vectorized=1.5)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, n_resamples=-1000)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, n_resamples=1000.5)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, batch=-1000)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, batch=1000.5)
+
+        message = "`alternative` must be in..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, alternative='ekki')
+
+        message = "SeedSequence expects int or sequence of ints"
+        with pytest.raises(TypeError, match=message):
+            permutation_test(([1, 2, 3], [1, 2, 3]), stat, rng='herring')
+
+    # -- Test Parameters -- #
+    # SPEC-007 leave one call with seed to check it still works
+    @pytest.mark.parametrize('random_state', [np.random.RandomState,
+                                              np.random.default_rng])
+    @pytest.mark.parametrize('permutation_type',
+                             ['pairings', 'samples', 'independent'])
+    def test_batch(self, permutation_type, random_state):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `statistic`
+        x = self.rng.random(10)
+        y = self.rng.random(10)
+
+        def statistic(x, y, axis):
+            batch_size = 1 if x.ndim == 1 else len(x)
+            statistic.batch_size = max(batch_size, statistic.batch_size)
+            statistic.counter += 1
+            return np.mean(x, axis=axis) - np.mean(y, axis=axis)
+        statistic.counter = 0
+        statistic.batch_size = 0
+
+        kwds = {'n_resamples': 1000, 'permutation_type': permutation_type,
+                'vectorized': True}
+        res1 = stats.permutation_test((x, y), statistic, batch=1,
+                                      random_state=random_state(0), **kwds)
+        assert_equal(statistic.counter, 1001)
+        assert_equal(statistic.batch_size, 1)
+
+        statistic.counter = 0
+        res2 = stats.permutation_test((x, y), statistic, batch=50,
+                                      random_state=random_state(0), **kwds)
+        assert_equal(statistic.counter, 21)
+        assert_equal(statistic.batch_size, 50)
+
+        statistic.counter = 0
+        res3 = stats.permutation_test((x, y), statistic, batch=1000,
+                                      random_state=random_state(0), **kwds)
+        assert_equal(statistic.counter, 2)
+        assert_equal(statistic.batch_size, 1000)
+
+        assert_equal(res1.pvalue, res3.pvalue)
+        assert_equal(res2.pvalue, res3.pvalue)
+
+    # SPEC-007 leave at least one call with seed to check it still works
+    @pytest.mark.parametrize('random_state', [np.random.RandomState,
+                                              np.random.default_rng])
+    @pytest.mark.parametrize('permutation_type, exact_size',
+                             [('pairings', special.factorial(3)**2),
+                              ('samples', 2**3),
+                              ('independent', special.binom(6, 3))])
+    def test_permutations(self, permutation_type, exact_size, random_state):
+        # make sure that the `permutations` parameter is respected by checking
+        # the size of the null distribution
+        x = self.rng.random(3)
+        y = self.rng.random(3)
+
+        def statistic(x, y, axis):
+            return np.mean(x, axis=axis) - np.mean(y, axis=axis)
+
+        kwds = {'permutation_type': permutation_type,
+                'vectorized': True}
+        res = stats.permutation_test((x, y), statistic, n_resamples=3,
+                                     random_state=random_state(0), **kwds)
+        assert_equal(res.null_distribution.size, 3)
+
+        res = stats.permutation_test((x, y), statistic, **kwds)
+        assert_equal(res.null_distribution.size, exact_size)
+
+    # -- Randomized Permutation Tests -- #
+
+    # To get reasonable accuracy, these next three tests are somewhat slow.
+    # Originally, I had them passing for all combinations of permutation type,
+    # alternative, and RNG, but that takes too long for CI. Instead, split
+    # into three tests, each testing a particular combination of the three
+    # parameters.
+
+    def test_randomized_test_against_exact_both(self):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='both
+
+        alternative, rng = 'less', 0
+
+        nx, ny, permutations = 8, 9, 24000
+        assert special.binom(nx + ny, nx) > permutations
+
+        x = stats.norm.rvs(size=nx)
+        y = stats.norm.rvs(size=ny)
+        data = x, y
+
+        def statistic(x, y, axis):
+            return np.mean(x, axis=axis) - np.mean(y, axis=axis)
+
+        kwds = {'vectorized': True, 'permutation_type': 'independent',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations,
+                               **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=np.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        assert_allclose(res.pvalue, res2.pvalue, atol=1e-2)
+
+    @pytest.mark.slow()
+    def test_randomized_test_against_exact_samples(self):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='samples'
+
+        alternative, rng = 'greater', None
+
+        nx, ny, permutations = 15, 15, 32000
+        assert 2**nx > permutations
+
+        x = stats.norm.rvs(size=nx)
+        y = stats.norm.rvs(size=ny)
+        data = x, y
+
+        def statistic(x, y, axis):
+            return np.mean(x - y, axis=axis)
+
+        kwds = {'vectorized': True, 'permutation_type': 'samples',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations,
+                               **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=np.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        assert_allclose(res.pvalue, res2.pvalue, atol=1e-2)
+
+    def test_randomized_test_against_exact_pairings(self):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='pairings'
+
+        alternative, rng = 'two-sided', self.rng
+
+        nx, ny, permutations = 8, 8, 40000
+        assert special.factorial(nx) > permutations
+
+        x = stats.norm.rvs(size=nx)
+        y = stats.norm.rvs(size=ny)
+        data = [x]
+
+        def statistic1d(x):
+            return stats.pearsonr(x, y)[0]
+
+        statistic = _resampling._vectorize_statistic(statistic1d)
+
+        kwds = {'vectorized': True, 'permutation_type': 'samples',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations,
+                               **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=np.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        assert_allclose(res.pvalue, res2.pvalue, atol=1e-2)
+
+    # -- Independent (Unpaired) Sample Tests -- #
+
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_ks_2samp(self, alternative):
+
+        x = self.rng.normal(size=4, scale=1)
+        y = self.rng.normal(size=5, loc=3, scale=3)
+
+        expected = stats.ks_2samp(x, y, alternative=alternative, mode='exact')
+
+        def statistic1d(x, y):
+            return stats.ks_2samp(x, y, mode='asymp',
+                                  alternative=alternative).statistic
+
+        # ks_2samp is always a one-tailed 'greater' test
+        # it's the statistic that changes (D+ vs D- vs max(D+, D-))
+        res = permutation_test((x, y), statistic1d, n_resamples=np.inf,
+                               alternative='greater', rng=self.rng)
+
+        assert_allclose(res.statistic, expected.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_ansari(self, alternative):
+
+        x = self.rng.normal(size=4, scale=1)
+        y = self.rng.normal(size=5, scale=3)
+
+        # ansari has a different convention for 'alternative'
+        alternative_correspondence = {"less": "greater",
+                                      "greater": "less",
+                                      "two-sided": "two-sided"}
+        alternative_scipy = alternative_correspondence[alternative]
+        expected = stats.ansari(x, y, alternative=alternative_scipy)
+
+        def statistic1d(x, y):
+            return stats.ansari(x, y).statistic
+
+        res = permutation_test((x, y), statistic1d, n_resamples=np.inf,
+                               alternative=alternative, rng=self.rng)
+
+        assert_allclose(res.statistic, expected.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_mannwhitneyu(self, alternative):
+
+        x = stats.uniform.rvs(size=(3, 5, 2), loc=0, random_state=self.rng)
+        y = stats.uniform.rvs(size=(3, 5, 2), loc=0.05, random_state=self.rng)
+
+        expected = stats.mannwhitneyu(x, y, axis=1, alternative=alternative)
+
+        def statistic(x, y, axis):
+            return stats.mannwhitneyu(x, y, axis=axis).statistic
+
+        res = permutation_test((x, y), statistic, vectorized=True,
+                               n_resamples=np.inf, alternative=alternative,
+                               axis=1, rng=self.rng)
+
+        assert_allclose(res.statistic, expected.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    def test_against_cvm(self):
+
+        x = stats.norm.rvs(size=4, scale=1, random_state=self.rng)
+        y = stats.norm.rvs(size=5, loc=3, scale=3, random_state=self.rng)
+
+        expected = stats.cramervonmises_2samp(x, y, method='exact')
+
+        def statistic1d(x, y):
+            return stats.cramervonmises_2samp(x, y,
+                                              method='asymptotic').statistic
+
+        # cramervonmises_2samp has only one alternative, greater
+        res = permutation_test((x, y), statistic1d, n_resamples=np.inf,
+                               alternative='greater', rng=self.rng)
+
+        assert_allclose(res.statistic, expected.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    @pytest.mark.xslow()
+    @pytest.mark.parametrize('axis', (-1, 2))
+    def test_vectorized_nsamp_ptype_both(self, axis):
+        # Test that permutation_test with permutation_type='independent' works
+        # properly for a 3-sample statistic with nd array samples of different
+        # (but compatible) shapes and ndims. Show that exact permutation test
+        # and random permutation tests approximate SciPy's asymptotic pvalues
+        # and that exact and random permutation test results are even closer
+        # to one another (than they are to the asymptotic results).
+
+        # Three samples, different (but compatible) shapes with different ndims
+        rng = np.random.default_rng(6709265303529651545)
+        x = rng.random(size=(3))
+        y = rng.random(size=(1, 3, 2))
+        z = rng.random(size=(2, 1, 4))
+        data = (x, y, z)
+
+        # Define the statistic (and pvalue for comparison)
+        def statistic1d(*data):
+            return stats.kruskal(*data).statistic
+
+        def pvalue1d(*data):
+            return stats.kruskal(*data).pvalue
+
+        statistic = _resampling._vectorize_statistic(statistic1d)
+        pvalue = _resampling._vectorize_statistic(pvalue1d)
+
+        # Calculate the expected results
+        x2 = np.broadcast_to(x, (2, 3, 3))  # broadcast manually because
+        y2 = np.broadcast_to(y, (2, 3, 2))  # _vectorize_statistic doesn't
+        z2 = np.broadcast_to(z, (2, 3, 4))
+        expected_statistic = statistic(x2, y2, z2, axis=axis)
+        expected_pvalue = pvalue(x2, y2, z2, axis=axis)
+
+        # Calculate exact and randomized permutation results
+        kwds = {'vectorized': False, 'axis': axis, 'alternative': 'greater',
+                'permutation_type': 'independent', 'rng': self.rng}
+        res = permutation_test(data, statistic1d, n_resamples=np.inf, **kwds)
+        res2 = permutation_test(data, statistic1d, n_resamples=1000, **kwds)
+
+        # Check results
+        assert_allclose(res.statistic, expected_statistic, rtol=self.rtol)
+        assert_allclose(res.statistic, res2.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected_pvalue, atol=6e-2)
+        assert_allclose(res.pvalue, res2.pvalue, atol=3e-2)
+
+    # -- Paired-Sample Tests -- #
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_wilcoxon(self, alternative):
+
+        x = stats.uniform.rvs(size=(3, 6, 2), loc=0, random_state=self.rng)
+        y = stats.uniform.rvs(size=(3, 6, 2), loc=0.05, random_state=self.rng)
+
+        # We'll check both 1- and 2-sample versions of the same test;
+        # we expect identical results to wilcoxon in all cases.
+        def statistic_1samp_1d(z):
+            # 'less' ensures we get the same of two statistics every time
+            return stats.wilcoxon(z, alternative='less').statistic
+
+        def statistic_2samp_1d(x, y):
+            return stats.wilcoxon(x, y, alternative='less').statistic
+
+        def test_1d(x, y):
+            return stats.wilcoxon(x, y, alternative=alternative)
+
+        test = _resampling._vectorize_statistic(test_1d)
+
+        expected = test(x, y, axis=1)
+        expected_stat = expected[0]
+        expected_p = expected[1]
+
+        kwds = {'vectorized': False, 'axis': 1, 'alternative': alternative,
+                'permutation_type': 'samples', 'rng': self.rng,
+                'n_resamples': np.inf}
+        res1 = permutation_test((x-y,), statistic_1samp_1d, **kwds)
+        res2 = permutation_test((x, y), statistic_2samp_1d, **kwds)
+
+        # `wilcoxon` returns a different statistic with 'two-sided'
+        assert_allclose(res1.statistic, res2.statistic, rtol=self.rtol)
+        if alternative != 'two-sided':
+            assert_allclose(res2.statistic, expected_stat, rtol=self.rtol)
+
+        assert_allclose(res2.pvalue, expected_p, rtol=self.rtol)
+        assert_allclose(res1.pvalue, res2.pvalue, rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_binomtest(self, alternative):
+
+        x = self.rng.integers(0, 2, size=10)
+        x[x == 0] = -1
+        # More naturally, the test would flip elements between 0 and one.
+        # However, permutation_test will flip the _signs_ of the elements.
+        # So we have to work with +1/-1 instead of 1/0.
+
+        def statistic(x, axis=0):
+            return np.sum(x > 0, axis=axis)
+
+        k, n, p = statistic(x), 10, 0.5
+        expected = stats.binomtest(k, n, p, alternative=alternative)
+
+        res = stats.permutation_test((x,), statistic, vectorized=True,
+                                     permutation_type='samples',
+                                     n_resamples=np.inf, rng=self.rng,
+                                     alternative=alternative)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    # -- Exact Association Tests -- #
+
+    def test_against_kendalltau(self):
+
+        x = self.rng.normal(size=6)
+        y = x + self.rng.normal(size=6)
+
+        expected = stats.kendalltau(x, y, method='exact')
+
+        def statistic1d(x):
+            return stats.kendalltau(x, y, method='asymptotic').statistic
+
+        # kendalltau currently has only one alternative, two-sided
+        res = permutation_test((x,), statistic1d, permutation_type='pairings',
+                               n_resamples=np.inf, rng=self.rng)
+
+        assert_allclose(res.statistic, expected.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected.pvalue, rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
+    def test_against_fisher_exact(self, alternative):
+
+        def statistic(x,):
+            return np.sum((x == 1) & (y == 1))
+
+        # x and y are binary random variables with some dependence
+        rng = np.random.default_rng(6235696159000529929)
+        x = (rng.random(7) > 0.6).astype(float)
+        y = (rng.random(7) + 0.25*x > 0.6).astype(float)
+        tab = stats.contingency.crosstab(x, y)[1]
+
+        res = permutation_test((x,), statistic, permutation_type='pairings',
+                               n_resamples=np.inf, alternative=alternative,
+                               rng=rng)
+        res2 = stats.fisher_exact(tab, alternative=alternative)
+
+        assert_allclose(res.pvalue, res2[1])
+
+    @pytest.mark.xslow()
+    @pytest.mark.parametrize('axis', (-2, 1))
+    def test_vectorized_nsamp_ptype_samples(self, axis):
+        # Test that permutation_test with permutation_type='samples' works
+        # properly for a 3-sample statistic with nd array samples of different
+        # (but compatible) shapes and ndims. Show that exact permutation test
+        # reproduces SciPy's exact pvalue and that random permutation test
+        # approximates it.
+
+        x = self.rng.random(size=(2, 4, 3))
+        y = self.rng.random(size=(1, 4, 3))
+        z = self.rng.random(size=(2, 4, 1))
+        x = stats.rankdata(x, axis=axis)
+        y = stats.rankdata(y, axis=axis)
+        z = stats.rankdata(z, axis=axis)
+        y = y[0]  # to check broadcast with different ndim
+        data = (x, y, z)
+
+        def statistic1d(*data):
+            return stats.page_trend_test(data, ranked=True,
+                                         method='asymptotic').statistic
+
+        def pvalue1d(*data):
+            return stats.page_trend_test(data, ranked=True,
+                                         method='exact').pvalue
+
+        statistic = _resampling._vectorize_statistic(statistic1d)
+        pvalue = _resampling._vectorize_statistic(pvalue1d)
+
+        expected_statistic = statistic(*np.broadcast_arrays(*data), axis=axis)
+        expected_pvalue = pvalue(*np.broadcast_arrays(*data), axis=axis)
+
+        # Let's forgive this use of an integer seed, please.
+        kwds = {'vectorized': False, 'axis': axis, 'alternative': 'greater',
+                'permutation_type': 'pairings', 'rng': 0}
+        res = permutation_test(data, statistic1d, n_resamples=np.inf, **kwds)
+        res2 = permutation_test(data, statistic1d, n_resamples=5000, **kwds)
+
+        assert_allclose(res.statistic, expected_statistic, rtol=self.rtol)
+        assert_allclose(res.statistic, res2.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected_pvalue, rtol=self.rtol)
+        assert_allclose(res.pvalue, res2.pvalue, atol=3e-2)
+
+    # -- Test Against External References -- #
+
+    tie_case_1 = {'x': [1, 2, 3, 4], 'y': [1.5, 2, 2.5],
+                  'expected_less': 0.2000000000,
+                  'expected_2sided': 0.4,  # 2*expected_less
+                  'expected_Pr_gte_S_mean': 0.3428571429,  # see note below
+                  'expected_statistic': 7.5,
+                  'expected_avg': 9.142857, 'expected_std': 1.40698}
+    tie_case_2 = {'x': [111, 107, 100, 99, 102, 106, 109, 108],
+                  'y': [107, 108, 106, 98, 105, 103, 110, 105, 104],
+                  'expected_less': 0.1555738379,
+                  'expected_2sided': 0.3111476758,
+                  'expected_Pr_gte_S_mean': 0.2969971205,  # see note below
+                  'expected_statistic': 32.5,
+                  'expected_avg': 38.117647, 'expected_std': 5.172124}
+
+    @pytest.mark.xslow()  # only the second case is slow, really
+    @pytest.mark.parametrize('case', (tie_case_1, tie_case_2))
+    def test_with_ties(self, case):
+        """
+        Results above from SAS PROC NPAR1WAY, e.g.
+
+        DATA myData;
+        INPUT X Y;
+        CARDS;
+        1 1
+        1 2
+        1 3
+        1 4
+        2 1.5
+        2 2
+        2 2.5
+        ods graphics on;
+        proc npar1way AB data=myData;
+            class X;
+            EXACT;
+        run;
+        ods graphics off;
+
+        Note: SAS provides Pr >= |S-Mean|, which is different from our
+        definition of a two-sided p-value.
+
+        """
+
+        x = case['x']
+        y = case['y']
+
+        expected_statistic = case['expected_statistic']
+        expected_less = case['expected_less']
+        expected_2sided = case['expected_2sided']
+        expected_Pr_gte_S_mean = case['expected_Pr_gte_S_mean']
+        expected_avg = case['expected_avg']
+        expected_std = case['expected_std']
+
+        def statistic1d(x, y):
+            return stats.ansari(x, y).statistic
+
+        with np.testing.suppress_warnings() as sup:
+            sup.filter(UserWarning, "Ties preclude use of exact statistic")
+            res = permutation_test((x, y), statistic1d, n_resamples=np.inf,
+                                   alternative='less')
+            res2 = permutation_test((x, y), statistic1d, n_resamples=np.inf,
+                                    alternative='two-sided')
+
+        assert_allclose(res.statistic, expected_statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected_less, atol=1e-10)
+        assert_allclose(res2.pvalue, expected_2sided, atol=1e-10)
+        assert_allclose(res2.null_distribution.mean(), expected_avg, rtol=1e-6)
+        assert_allclose(res2.null_distribution.std(), expected_std, rtol=1e-6)
+
+        # SAS provides Pr >= |S-Mean|; might as well check against that, too
+        S = res.statistic
+        mean = res.null_distribution.mean()
+        n = len(res.null_distribution)
+        Pr_gte_S_mean = np.sum(np.abs(res.null_distribution-mean)
+                               >= np.abs(S-mean))/n
+        assert_allclose(expected_Pr_gte_S_mean, Pr_gte_S_mean)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative, expected_pvalue',
+                             (('less', 0.9708333333333),
+                              ('greater', 0.05138888888889),
+                              ('two-sided', 0.1027777777778)))
+    def test_against_spearmanr_in_R(self, alternative, expected_pvalue):
+        """
+        Results above from R cor.test, e.g.
+
+        options(digits=16)
+        x <- c(1.76405235, 0.40015721, 0.97873798,
+               2.2408932, 1.86755799, -0.97727788)
+        y <- c(2.71414076, 0.2488, 0.87551913,
+               2.6514917, 2.01160156, 0.47699563)
+        cor.test(x, y, method = "spearm", alternative = "t")
+        """
+        # data comes from
+        # np.random.seed(0)
+        # x = stats.norm.rvs(size=6)
+        # y = x + stats.norm.rvs(size=6)
+        x = [1.76405235, 0.40015721, 0.97873798,
+             2.2408932, 1.86755799, -0.97727788]
+        y = [2.71414076, 0.2488, 0.87551913,
+             2.6514917, 2.01160156, 0.47699563]
+        expected_statistic = 0.7714285714285715
+
+        def statistic1d(x):
+            return stats.spearmanr(x, y).statistic
+
+        res = permutation_test((x,), statistic1d, permutation_type='pairings',
+                               n_resamples=np.inf, alternative=alternative)
+
+        assert_allclose(res.statistic, expected_statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected_pvalue, atol=1e-13)
+
+    @pytest.mark.parametrize("batch", (-1, 0))
+    def test_batch_generator_iv(self, batch):
+        with pytest.raises(ValueError, match="`batch` must be positive."):
+            list(_resampling._batch_generator([1, 2, 3], batch))
+
+    batch_generator_cases = [(range(0), 3, []),
+                             (range(6), 3, [[0, 1, 2], [3, 4, 5]]),
+                             (range(8), 3, [[0, 1, 2], [3, 4, 5], [6, 7]])]
+
+    @pytest.mark.parametrize("iterable, batch, expected",
+                             batch_generator_cases)
+    def test_batch_generator(self, iterable, batch, expected):
+        got = list(_resampling._batch_generator(iterable, batch))
+        assert got == expected
+
+    @pytest.mark.fail_slow(2)
+    def test_finite_precision_statistic(self):
+        # Some statistics return numerically distinct values when the values
+        # should be equal in theory. Test that `permutation_test` accounts
+        # for this in some way.
+        x = [1, 2, 4, 3]
+        y = [2, 4, 6, 8]
+
+        def statistic(x, y):
+            return stats.pearsonr(x, y)[0]
+
+        res = stats.permutation_test((x, y), statistic, vectorized=False,
+                                     permutation_type='pairings')
+        r, pvalue, null = res.statistic, res.pvalue, res.null_distribution
+
+        correct_p = 2 * np.sum(null >= r - 1e-14) / len(null)
+        assert pvalue == correct_p == 1/3
+        # Compare against other exact correlation tests using R corr.test
+        # options(digits=16)
+        # x = c(1, 2, 4, 3)
+        # y = c(2, 4, 6, 8)
+        # cor.test(x, y, alternative = "t", method = "spearman")  # 0.333333333
+        # cor.test(x, y, alternative = "t", method = "kendall")  # 0.333333333
+
+
+def test_all_partitions_concatenated():
+    # make sure that _all_paritions_concatenated produces the correct number
+    # of partitions of the data into samples of the given sizes and that
+    # all are unique
+    n = np.array([3, 2, 4], dtype=int)
+    nc = np.cumsum(n)
+
+    all_partitions = set()
+    counter = 0
+    for partition_concatenated in _resampling._all_partitions_concatenated(n):
+        counter += 1
+        partitioning = np.split(partition_concatenated, nc[:-1])
+        all_partitions.add(tuple([frozenset(i) for i in partitioning]))
+
+    expected = np.prod([special.binom(sum(n[i:]), sum(n[i+1:]))
+                        for i in range(len(n)-1)])
+
+    assert_equal(counter, expected)
+    assert_equal(len(all_partitions), expected)
+
+
+@pytest.mark.parametrize('fun_name',
+                         ['bootstrap', 'permutation_test', 'monte_carlo_test'])
+def test_parameter_vectorized(fun_name):
+    # Check that parameter `vectorized` is working as desired for all
+    # resampling functions. Results don't matter; just don't fail asserts.
+    rng = np.random.default_rng(75245098234592)
+    sample = rng.random(size=10)
+
+    def rvs(size):  # needed by `monte_carlo_test`
+        return stats.norm.rvs(size=size, random_state=rng)
+
+    fun_options = {'bootstrap': {'data': (sample,), 'rng': rng,
+                                 'method': 'percentile'},
+                   'permutation_test': {'data': (sample,), 'rng': rng,
+                                        'permutation_type': 'samples'},
+                   'monte_carlo_test': {'sample': sample, 'rvs': rvs}}
+    common_options = {'n_resamples': 100}
+
+    fun = getattr(stats, fun_name)
+    options = fun_options[fun_name]
+    options.update(common_options)
+
+    def statistic(x, axis):
+        assert x.ndim > 1 or np.array_equal(x, sample)
+        return np.mean(x, axis=axis)
+    fun(statistic=statistic, vectorized=None, **options)
+    fun(statistic=statistic, vectorized=True, **options)
+
+    def statistic(x):
+        assert x.ndim == 1
+        return np.mean(x)
+    fun(statistic=statistic, vectorized=None, **options)
+    fun(statistic=statistic, vectorized=False, **options)
+
+
+class TestMonteCarloMethod:
+    def test_rvs_and_random_state(self):
+        message = "Use of `rvs` and `rng` are mutually exclusive."
+        rng = np.random.default_rng(34982345)
+        with pytest.raises(ValueError, match=message):
+            stats.MonteCarloMethod(rvs=rng.random, rng=rng)

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_sampling.py

@@ -0,0 +1,1445 @@
+import threading
+import pickle
+import pytest
+from copy import deepcopy
+import platform
+import sys
+import math
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal, suppress_warnings
+from scipy.stats.sampling import (
+    TransformedDensityRejection,
+    DiscreteAliasUrn,
+    DiscreteGuideTable,
+    NumericalInversePolynomial,
+    NumericalInverseHermite,
+    RatioUniforms,
+    SimpleRatioUniforms,
+    UNURANError
+)
+from pytest import raises as assert_raises
+from scipy import stats
+from scipy import special
+from scipy.stats import chisquare, cramervonmises
+from scipy.stats._distr_params import distdiscrete, distcont
+from scipy._lib._util import check_random_state
+
+
+# common test data: this data can be shared between all the tests.
+
+
+# Normal distribution shared between all the continuous methods
+class StandardNormal:
+    def pdf(self, x):
+        # normalization constant needed for NumericalInverseHermite
+        return 1./np.sqrt(2.*np.pi) * np.exp(-0.5 * x*x)
+
+    def dpdf(self, x):
+        return 1./np.sqrt(2.*np.pi) * -x * np.exp(-0.5 * x*x)
+
+    def cdf(self, x):
+        return special.ndtr(x)
+
+
+all_methods = [
+    ("TransformedDensityRejection", {"dist": StandardNormal()}),
+    ("DiscreteAliasUrn", {"dist": [0.02, 0.18, 0.8]}),
+    ("DiscreteGuideTable", {"dist": [0.02, 0.18, 0.8]}),
+    ("NumericalInversePolynomial", {"dist": StandardNormal()}),
+    ("NumericalInverseHermite", {"dist": StandardNormal()}),
+    ("SimpleRatioUniforms", {"dist": StandardNormal(), "mode": 0})
+]
+
+if (sys.implementation.name == 'pypy'
+        and sys.implementation.version < (7, 3, 10)):
+    # changed in PyPy for v7.3.10
+    floaterr = r"unsupported operand type for float\(\): 'list'"
+else:
+    floaterr = r"must be real number, not list"
+# Make sure an internal error occurs in UNU.RAN when invalid callbacks are
+# passed. Moreover, different generators throw different error messages.
+# So, in case of an `UNURANError`, we do not validate the error message.
+bad_pdfs_common = [
+    # Negative PDF
+    (lambda x: -x, UNURANError, r"..."),
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # Infinite value returned => Overflow error.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # signature of PDF wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+# same approach for dpdf
+bad_dpdf_common = [
+    # Infinite value returned.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # signature of dPDF wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+# same approach for logpdf
+bad_logpdfs_common = [
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # Infinite value returned => Overflow error.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # signature of logpdf wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+bad_pv_common = [
+    ([], r"must contain at least one element"),
+    ([[1.0, 0.0]], r"wrong number of dimensions \(expected 1, got 2\)"),
+    ([0.2, 0.4, np.nan, 0.8], r"must contain only finite / non-nan values"),
+    ([0.2, 0.4, np.inf, 0.8], r"must contain only finite / non-nan values"),
+    ([0.0, 0.0], r"must contain at least one non-zero value"),
+]
+
+
+# size of the domains is incorrect
+bad_sized_domains = [
+    # > 2 elements in the domain
+    ((1, 2, 3), ValueError, r"must be a length 2 tuple"),
+    # empty domain
+    ((), ValueError, r"must be a length 2 tuple")
+]
+
+# domain values are incorrect
+bad_domains = [
+    ((2, 1), UNURANError, r"left >= right"),
+    ((1, 1), UNURANError, r"left >= right"),
+]
+
+# infinite and nan values present in domain.
+inf_nan_domains = [
+    # left >= right
+    ((10, 10), UNURANError, r"left >= right"),
+    ((np.inf, np.inf), UNURANError, r"left >= right"),
+    ((-np.inf, -np.inf), UNURANError, r"left >= right"),
+    ((np.inf, -np.inf), UNURANError, r"left >= right"),
+    # Also include nans in some of the domains.
+    ((-np.inf, np.nan), ValueError, r"only non-nan values"),
+    ((np.nan, np.inf), ValueError, r"only non-nan values")
+]
+
+# `nan` values present in domain. Some distributions don't support
+# infinite tails, so don't mix the nan values with infinities.
+nan_domains = [
+    ((0, np.nan), ValueError, r"only non-nan values"),
+    ((np.nan, np.nan), ValueError, r"only non-nan values")
+]
+
+
+# all the methods should throw errors for nan, bad sized, and bad valued
+# domains.
+@pytest.mark.parametrize("domain, err, msg",
+                         bad_domains + bad_sized_domains +
+                         nan_domains)  # type: ignore[operator]
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_bad_domain(domain, err, msg, method, kwargs):
+    Method = getattr(stats.sampling, method)
+    with pytest.raises(err, match=msg):
+        Method(**kwargs, domain=domain)
+
+
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_random_state(method, kwargs):
+    Method = getattr(stats.sampling, method)
+
+    # simple seed that works for any version of NumPy
+    seed = 123
+    rng1 = Method(**kwargs, random_state=seed)
+    rng2 = Method(**kwargs, random_state=seed)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+    # global seed
+    np.random.seed(123)
+    rng1 = Method(**kwargs)
+    rvs1 = rng1.rvs(100)
+    np.random.seed(None)
+    rng2 = Method(**kwargs, random_state=123)
+    rvs2 = rng2.rvs(100)
+    assert_equal(rvs1, rvs2)
+
+    # Generator seed for new NumPy
+    # when a RandomState is given, it should take the bitgen_t
+    # member of the class and create a Generator instance.
+    seed1 = np.random.RandomState(np.random.MT19937(123))
+    seed2 = np.random.Generator(np.random.MT19937(123))
+    rng1 = Method(**kwargs, random_state=seed1)
+    rng2 = Method(**kwargs, random_state=seed2)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+
+def test_set_random_state():
+    rng1 = TransformedDensityRejection(StandardNormal(), random_state=123)
+    rng2 = TransformedDensityRejection(StandardNormal())
+    rng2.set_random_state(123)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+    rng = TransformedDensityRejection(StandardNormal(), random_state=123)
+    rvs1 = rng.rvs(100)
+    rng.set_random_state(123)
+    rvs2 = rng.rvs(100)
+    assert_equal(rvs1, rvs2)
+
+
+def test_threading_behaviour():
+    # Test if the API is thread-safe.
+    # This verifies if the lock mechanism and the use of `PyErr_Occurred`
+    # is correct.
+    errors = {"err1": None, "err2": None}
+
+    class Distribution:
+        def __init__(self, pdf_msg):
+            self.pdf_msg = pdf_msg
+
+        def pdf(self, x):
+            if 49.9 < x < 50.0:
+                raise ValueError(self.pdf_msg)
+            return x
+
+        def dpdf(self, x):
+            return 1
+
+    def func1():
+        dist = Distribution('foo')
+        rng = TransformedDensityRejection(dist, domain=(10, 100),
+                                          random_state=12)
+        try:
+            rng.rvs(100000)
+        except ValueError as e:
+            errors['err1'] = e.args[0]
+
+    def func2():
+        dist = Distribution('bar')
+        rng = TransformedDensityRejection(dist, domain=(10, 100),
+                                          random_state=2)
+        try:
+            rng.rvs(100000)
+        except ValueError as e:
+            errors['err2'] = e.args[0]
+
+    t1 = threading.Thread(target=func1)
+    t2 = threading.Thread(target=func2)
+
+    t1.start()
+    t2.start()
+
+    t1.join()
+    t2.join()
+
+    assert errors['err1'] == 'foo'
+    assert errors['err2'] == 'bar'
+
+
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_pickle(method, kwargs):
+    Method = getattr(stats.sampling, method)
+    rng1 = Method(**kwargs, random_state=123)
+    obj = pickle.dumps(rng1)
+    rng2 = pickle.loads(obj)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+
+@pytest.mark.parametrize("size", [None, 0, (0, ), 1, (10, 3), (2, 3, 4, 5),
+                                  (0, 0), (0, 1)])
+def test_rvs_size(size):
+    # As the `rvs` method is present in the base class and shared between
+    # all the classes, we can just test with one of the methods.
+    rng = TransformedDensityRejection(StandardNormal())
+    if size is None:
+        assert np.isscalar(rng.rvs(size))
+    else:
+        if np.isscalar(size):
+            size = (size, )
+        assert rng.rvs(size).shape == size
+
+
+def test_with_scipy_distribution():
+    # test if the setup works with SciPy's rv_frozen distributions
+    dist = stats.norm()
+    urng = np.random.default_rng(0)
+    rng = NumericalInverseHermite(dist, random_state=urng)
+    u = np.linspace(0, 1, num=100)
+    check_cont_samples(rng, dist, dist.stats())
+    assert_allclose(dist.ppf(u), rng.ppf(u))
+    # test if it works with `loc` and `scale`
+    dist = stats.norm(loc=10., scale=5.)
+    rng = NumericalInverseHermite(dist, random_state=urng)
+    check_cont_samples(rng, dist, dist.stats())
+    assert_allclose(dist.ppf(u), rng.ppf(u))
+    # check for discrete distributions
+    dist = stats.binom(10, 0.2)
+    rng = DiscreteAliasUrn(dist, random_state=urng)
+    domain = dist.support()
+    pv = dist.pmf(np.arange(domain[0], domain[1]+1))
+    check_discr_samples(rng, pv, dist.stats())
+
+
+def check_cont_samples(rng, dist, mv_ex, rtol=1e-7, atol=1e-1):
+    rvs = rng.rvs(100000)
+    mv = rvs.mean(), rvs.var()
+    # test the moments only if the variance is finite
+    if np.isfinite(mv_ex[1]):
+        assert_allclose(mv, mv_ex, rtol=rtol, atol=atol)
+    # Cramer Von Mises test for goodness-of-fit
+    rvs = rng.rvs(500)
+    dist.cdf = np.vectorize(dist.cdf)
+    pval = cramervonmises(rvs, dist.cdf).pvalue
+    assert pval > 0.1
+
+
+def check_discr_samples(rng, pv, mv_ex, rtol=1e-3, atol=1e-1):
+    rvs = rng.rvs(100000)
+    # test if the first few moments match
+    mv = rvs.mean(), rvs.var()
+    assert_allclose(mv, mv_ex, rtol=rtol, atol=atol)
+    # normalize
+    pv = pv / pv.sum()
+    # chi-squared test for goodness-of-fit
+    obs_freqs = np.zeros_like(pv)
+    _, freqs = np.unique(rvs, return_counts=True)
+    freqs = freqs / freqs.sum()
+    obs_freqs[:freqs.size] = freqs
+    pval = chisquare(obs_freqs, pv).pvalue
+    assert pval > 0.1
+
+
+def test_warning_center_not_in_domain():
+    # UNURAN will warn if the center provided or the one computed w/o the
+    # domain is outside of the domain
+    msg = "102 : center moved into domain of distribution"
+    with pytest.warns(RuntimeWarning, match=msg):
+        NumericalInversePolynomial(StandardNormal(), center=0, domain=(3, 5))
+    with pytest.warns(RuntimeWarning, match=msg):
+        NumericalInversePolynomial(StandardNormal(), domain=(3, 5))
+
+
+@pytest.mark.parametrize('method', ["SimpleRatioUniforms",
+                                    "NumericalInversePolynomial",
+                                    "TransformedDensityRejection"])
+def test_error_mode_not_in_domain(method):
+    # UNURAN raises an error if the mode is not in the domain
+    # the behavior is different compared to the case that center is not in the
+    # domain. mode is supposed to be the exact value, center can be an
+    # approximate value
+    Method = getattr(stats.sampling, method)
+    msg = "17 : mode not in domain"
+    with pytest.raises(UNURANError, match=msg):
+        Method(StandardNormal(), mode=0, domain=(3, 5))
+
+
+@pytest.mark.parametrize('method', ["NumericalInverseHermite",
+                                    "NumericalInversePolynomial"])
+class TestQRVS:
+    def test_input_validation(self, method):
+        match = "`qmc_engine` must be an instance of..."
+        with pytest.raises(ValueError, match=match):
+            Method = getattr(stats.sampling, method)
+            gen = Method(StandardNormal())
+            gen.qrvs(qmc_engine=0)
+
+        # issues with QMCEngines and old NumPy
+        Method = getattr(stats.sampling, method)
+        gen = Method(StandardNormal())
+
+        match = "`d` must be consistent with dimension of `qmc_engine`."
+        with pytest.raises(ValueError, match=match):
+            gen.qrvs(d=3, qmc_engine=stats.qmc.Halton(2))
+
+    qrngs = [None, stats.qmc.Sobol(1, seed=0), stats.qmc.Halton(3, seed=0)]
+    # `size=None` should not add anything to the shape, `size=1` should
+    sizes = [(None, tuple()), (1, (1,)), (4, (4,)),
+             ((4,), (4,)), ((2, 4), (2, 4))]  # type: ignore
+    # Neither `d=None` nor `d=1` should add anything to the shape
+    ds = [(None, tuple()), (1, tuple()), (3, (3,))]
+
+    @pytest.mark.parametrize('qrng', qrngs)
+    @pytest.mark.parametrize('size_in, size_out', sizes)
+    @pytest.mark.parametrize('d_in, d_out', ds)
+    def test_QRVS_shape_consistency(self, qrng, size_in, size_out,
+                                    d_in, d_out, method):
+        w32 = sys.platform == "win32" and platform.architecture()[0] == "32bit"
+        if w32 and method == "NumericalInversePolynomial":
+            pytest.xfail("NumericalInversePolynomial.qrvs fails for Win "
+                         "32-bit")
+
+        dist = StandardNormal()
+        Method = getattr(stats.sampling, method)
+        gen = Method(dist)
+
+        # If d and qrng.d are inconsistent, an error is raised
+        if d_in is not None and qrng is not None and qrng.d != d_in:
+            match = "`d` must be consistent with dimension of `qmc_engine`."
+            with pytest.raises(ValueError, match=match):
+                gen.qrvs(size_in, d=d_in, qmc_engine=qrng)
+            return
+
+        # Sometimes d is really determined by qrng
+        if d_in is None and qrng is not None and qrng.d != 1:
+            d_out = (qrng.d,)
+
+        shape_expected = size_out + d_out
+
+        qrng2 = deepcopy(qrng)
+        qrvs = gen.qrvs(size=size_in, d=d_in, qmc_engine=qrng)
+        if size_in is not None:
+            assert qrvs.shape == shape_expected
+
+        if qrng2 is not None:
+            uniform = qrng2.random(np.prod(size_in) or 1)
+            qrvs2 = stats.norm.ppf(uniform).reshape(shape_expected)
+            assert_allclose(qrvs, qrvs2, atol=1e-12)
+
+    def test_QRVS_size_tuple(self, method):
+        # QMCEngine samples are always of shape (n, d). When `size` is a tuple,
+        # we set `n = prod(size)` in the call to qmc_engine.random, transform
+        # the sample, and reshape it to the final dimensions. When we reshape,
+        # we need to be careful, because the _columns_ of the sample returned
+        # by a QMCEngine are "independent"-ish, but the elements within the
+        # columns are not. We need to make sure that this doesn't get mixed up
+        # by reshaping: qrvs[..., i] should remain "independent"-ish of
+        # qrvs[..., i+1], but the elements within qrvs[..., i] should be
+        # transformed from the same low-discrepancy sequence.
+
+        dist = StandardNormal()
+        Method = getattr(stats.sampling, method)
+        gen = Method(dist)
+
+        size = (3, 4)
+        d = 5
+        qrng = stats.qmc.Halton(d, seed=0)
+        qrng2 = stats.qmc.Halton(d, seed=0)
+
+        uniform = qrng2.random(np.prod(size))
+
+        qrvs = gen.qrvs(size=size, d=d, qmc_engine=qrng)
+        qrvs2 = stats.norm.ppf(uniform)
+
+        for i in range(d):
+            sample = qrvs[..., i]
+            sample2 = qrvs2[:, i].reshape(size)
+            assert_allclose(sample, sample2, atol=1e-12)
+
+
+class TestTransformedDensityRejection:
+    # Simple Custom Distribution
+    class dist0:
+        def pdf(self, x):
+            return 3/4 * (1-x*x)
+
+        def dpdf(self, x):
+            return 3/4 * (-2*x)
+
+        def cdf(self, x):
+            return 3/4 * (x - x**3/3 + 2/3)
+
+        def support(self):
+            return -1, 1
+
+    # Standard Normal Distribution
+    class dist1:
+        def pdf(self, x):
+            return stats.norm._pdf(x / 0.1)
+
+        def dpdf(self, x):
+            return -x / 0.01 * stats.norm._pdf(x / 0.1)
+
+        def cdf(self, x):
+            return stats.norm._cdf(x / 0.1)
+
+    # pdf with piecewise linear function as transformed density
+    # with T = -1/sqrt with shift. Taken from UNU.RAN test suite
+    # (from file t_tdr_ps.c)
+    class dist2:
+        def __init__(self, shift):
+            self.shift = shift
+
+        def pdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            return 0.5 * y * y
+
+        def dpdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            y = y * y * y
+            return y if (x < 0.) else -y
+
+        def cdf(self, x):
+            x -= self.shift
+            if x <= 0.:
+                return 0.5 / (1. - x)
+            else:
+                return 1. - 0.5 / (1. + x)
+
+    dists = [dist0(), dist1(), dist2(0.), dist2(10000.)]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [0., 4./15.]
+    mv1 = [0., 0.01]
+    mv2 = [0., np.inf]
+    mv3 = [10000., np.inf]
+    mvs = [mv0, mv1, mv2, mv3]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             zip(dists, mvs))
+    def test_basic(self, dist, mv_ex):
+        with suppress_warnings() as sup:
+            # filter the warnings thrown by UNU.RAN
+            sup.filter(RuntimeWarning)
+            rng = TransformedDensityRejection(dist, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # PDF 0 everywhere => bad construction points
+    bad_pdfs = [(lambda x: 0, UNURANError, r"50 : bad construction points.")]
+    bad_pdfs += bad_pdfs_common  # type: ignore[arg-type]
+
+    @pytest.mark.parametrize("pdf, err, msg", bad_pdfs)
+    def test_bad_pdf(self, pdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = pdf
+        dist.dpdf = lambda x: 1  # an arbitrary dPDF
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(dist)
+
+    @pytest.mark.parametrize("dpdf, err, msg", bad_dpdf_common)
+    def test_bad_dpdf(self, dpdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = lambda x: x
+        dist.dpdf = dpdf
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(dist, domain=(1, 10))
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(StandardNormal(), domain=domain)
+
+    @pytest.mark.parametrize("construction_points", [-1, 0, 0.1])
+    def test_bad_construction_points_scalar(self, construction_points):
+        with pytest.raises(ValueError, match=r"`construction_points` must be "
+                                             r"a positive integer."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+    def test_bad_construction_points_array(self):
+        # empty array
+        construction_points = []
+        with pytest.raises(ValueError, match=r"`construction_points` must "
+                                             r"either be a "
+                                             r"scalar or a non-empty array."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points not monotonically increasing
+        construction_points = [1, 1, 1, 1, 1, 1]
+        with pytest.warns(RuntimeWarning, match=r"33 : starting points not "
+                                                r"strictly monotonically "
+                                                r"increasing"):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points containing nans
+        construction_points = [np.nan, np.nan, np.nan]
+        with pytest.raises(UNURANError, match=r"50 : bad construction "
+                                              r"points."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points out of domain
+        construction_points = [-10, 10]
+        with pytest.warns(RuntimeWarning, match=r"50 : starting point out of "
+                                                r"domain"):
+            TransformedDensityRejection(
+                StandardNormal(), domain=(-3, 3),
+                construction_points=construction_points
+            )
+
+    @pytest.mark.parametrize("c", [-1., np.nan, np.inf, 0.1, 1.])
+    def test_bad_c(self, c):
+        msg = r"`c` must either be -0.5 or 0."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(StandardNormal(), c=-1.)
+
+    u = [np.linspace(0, 1, num=1000), [], [[]], [np.nan],
+         [-np.inf, np.nan, np.inf], 0,
+         [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf_hat(self, u):
+        # Increase the `max_squeeze_hat_ratio` so the ppf_hat is more
+        # accurate.
+        rng = TransformedDensityRejection(StandardNormal(),
+                                          max_squeeze_hat_ratio=0.9999)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "invalid value encountered in greater")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "greater_equal")
+            sup.filter(RuntimeWarning, "invalid value encountered in less")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "less_equal")
+            res = rng.ppf_hat(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-3, atol=1e-5)
+        assert res.shape == expected.shape
+
+    def test_bad_dist(self):
+        # Empty distribution
+        class dist:
+            ...
+
+        msg = r"`pdf` required but not found."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(dist)
+
+        # dPDF not present in dist
+        class dist:
+            pdf = lambda x: 1-x*x  # noqa: E731
+
+        msg = r"`dpdf` required but not found."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(dist)
+
+
+class TestDiscreteAliasUrn:
+    # DAU fails on these probably because of large domains and small
+    # computation errors in PMF. Mean/SD match but chi-squared test fails.
+    basic_fail_dists = {
+        'nchypergeom_fisher',  # numerical errors on tails
+        'nchypergeom_wallenius',  # numerical errors on tails
+        'randint'  # fails on 32-bit ubuntu
+    }
+
+    @pytest.mark.parametrize("distname, params", distdiscrete)
+    def test_basic(self, distname, params):
+        if distname in self.basic_fail_dists:
+            msg = ("DAU fails on these probably because of large domains "
+                   "and small computation errors in PMF.")
+            pytest.skip(msg)
+        if not isinstance(distname, str):
+            dist = distname
+        else:
+            dist = getattr(stats, distname)
+        dist = dist(*params)
+        domain = dist.support()
+        if not np.isfinite(domain[1] - domain[0]):
+            # DAU only works with finite domain. So, skip the distributions
+            # with infinite tails.
+            pytest.skip("DAU only works with a finite domain.")
+        k = np.arange(domain[0], domain[1]+1)
+        pv = dist.pmf(k)
+        mv_ex = dist.stats('mv')
+        rng = DiscreteAliasUrn(dist, random_state=42)
+        check_discr_samples(rng, pv, mv_ex)
+
+    # Can't use bad_pmf_common here as we evaluate PMF early on to avoid
+    # unhelpful errors from UNU.RAN.
+    bad_pmf = [
+        # inf returned
+        (lambda x: np.inf, ValueError,
+         r"must contain only finite / non-nan values"),
+        # nan returned
+        (lambda x: np.nan, ValueError,
+         r"must contain only finite / non-nan values"),
+        # all zeros
+        (lambda x: 0.0, ValueError,
+         r"must contain at least one non-zero value"),
+        # Undefined name inside the function
+        (lambda x: foo, NameError,  # type: ignore[name-defined]  # noqa: F821
+         r"name 'foo' is not defined"),
+        # Returning wrong type.
+        (lambda x: [], ValueError,
+         r"setting an array element with a sequence."),
+        # probabilities < 0
+        (lambda x: -x, UNURANError,
+         r"50 : probability < 0"),
+        # signature of PMF wrong
+        (lambda: 1.0, TypeError,
+         r"takes 0 positional arguments but 1 was given")
+    ]
+
+    @pytest.mark.parametrize("pmf, err, msg", bad_pmf)
+    def test_bad_pmf(self, pmf, err, msg):
+        class dist:
+            pass
+        dist.pmf = pmf
+        with pytest.raises(err, match=msg):
+            DiscreteAliasUrn(dist, domain=(1, 10))
+
+    @pytest.mark.parametrize("pv", [[0.18, 0.02, 0.8],
+                                    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]])
+    def test_sampling_with_pv(self, pv):
+        pv = np.asarray(pv, dtype=np.float64)
+        rng = DiscreteAliasUrn(pv, random_state=123)
+        rng.rvs(100_000)
+        pv = pv / pv.sum()
+        variates = np.arange(0, len(pv))
+        # test if the first few moments match
+        m_expected = np.average(variates, weights=pv)
+        v_expected = np.average((variates - m_expected) ** 2, weights=pv)
+        mv_expected = m_expected, v_expected
+        check_discr_samples(rng, pv, mv_expected)
+
+    @pytest.mark.parametrize("pv, msg", bad_pv_common)
+    def test_bad_pv(self, pv, msg):
+        with pytest.raises(ValueError, match=msg):
+            DiscreteAliasUrn(pv)
+
+    # DAU doesn't support infinite tails. So, it should throw an error when
+    # inf is present in the domain.
+    inf_domain = [(-np.inf, np.inf), (np.inf, np.inf), (-np.inf, -np.inf),
+                  (0, np.inf), (-np.inf, 0)]
+
+    @pytest.mark.parametrize("domain", inf_domain)
+    def test_inf_domain(self, domain):
+        with pytest.raises(ValueError, match=r"must be finite"):
+            DiscreteAliasUrn(stats.binom(10, 0.2), domain=domain)
+
+    def test_bad_urn_factor(self):
+        with pytest.warns(RuntimeWarning, match=r"relative urn size < 1."):
+            DiscreteAliasUrn([0.5, 0.5], urn_factor=-1)
+
+    def test_bad_args(self):
+        msg = (r"`domain` must be provided when the "
+               r"probability vector is not available.")
+
+        class dist:
+            def pmf(self, x):
+                return x
+
+        with pytest.raises(ValueError, match=msg):
+            DiscreteAliasUrn(dist)
+
+    def test_gh19359(self):
+        pv = special.softmax(np.ones((1533,)))
+        rng = DiscreteAliasUrn(pv, random_state=42)
+        # check the correctness
+        check_discr_samples(rng, pv, (1532 / 2, (1532**2 - 1) / 12),
+                            rtol=5e-3)
+
+
+class TestNumericalInversePolynomial:
+    # Simple Custom Distribution
+    class dist0:
+        def pdf(self, x):
+            return 3/4 * (1-x*x)
+
+        def cdf(self, x):
+            return 3/4 * (x - x**3/3 + 2/3)
+
+        def support(self):
+            return -1, 1
+
+    # Standard Normal Distribution
+    class dist1:
+        def pdf(self, x):
+            return stats.norm._pdf(x / 0.1)
+
+        def cdf(self, x):
+            return stats.norm._cdf(x / 0.1)
+
+    # Sin 2 distribution
+    #          /  0.05 + 0.45*(1 +sin(2 Pi x))  if |x| <= 1
+    #  f(x) = <
+    #          \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_pinv.c)
+    class dist2:
+        def pdf(self, x):
+            return 0.05 + 0.45 * (1 + np.sin(2*np.pi*x))
+
+        def cdf(self, x):
+            return (0.05*(x + 1) +
+                    0.9*(1. + 2.*np.pi*(1 + x) - np.cos(2.*np.pi*x)) /
+                    (4.*np.pi))
+
+        def support(self):
+            return -1, 1
+
+    # Sin 10 distribution
+    #          /  0.05 + 0.45*(1 +sin(2 Pi x))  if |x| <= 5
+    #  f(x) = <
+    #          \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_pinv.c)
+    class dist3:
+        def pdf(self, x):
+            return 0.2 * (0.05 + 0.45 * (1 + np.sin(2*np.pi*x)))
+
+        def cdf(self, x):
+            return x/10. + 0.5 + 0.09/(2*np.pi) * (np.cos(10*np.pi) -
+                                                   np.cos(2*np.pi*x))
+
+        def support(self):
+            return -5, 5
+
+    dists = [dist0(), dist1(), dist2(), dist3()]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [0., 4./15.]
+    mv1 = [0., 0.01]
+    mv2 = [-0.45/np.pi, 2/3*0.5 - 0.45**2/np.pi**2]
+    mv3 = [-0.45/np.pi, 0.2 * 250/3 * 0.5 - 0.45**2/np.pi**2]
+    mvs = [mv0, mv1, mv2, mv3]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             zip(dists, mvs))
+    def test_basic(self, dist, mv_ex):
+        rng = NumericalInversePolynomial(dist, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("distname, params", distcont)
+    def test_basic_all_scipy_dists(self, distname, params):
+
+        very_slow_dists = ['anglit', 'gausshyper', 'kappa4',
+                           'ksone', 'kstwo', 'levy_l',
+                           'levy_stable', 'studentized_range',
+                           'trapezoid', 'triang', 'vonmises']
+        # for these distributions, some assertions fail due to minor
+        # numerical differences. They can be avoided either by changing
+        # the seed or by increasing the u_resolution.
+        fail_dists = ['chi2', 'fatiguelife', 'gibrat',
+                      'halfgennorm', 'lognorm', 'ncf',
+                      'ncx2', 'pareto', 't']
+        # for these distributions, skip the check for agreement between sample
+        # moments and true moments. We cannot expect them to pass due to the
+        # high variance of sample moments.
+        skip_sample_moment_check = ['rel_breitwigner']
+
+        if distname in very_slow_dists:
+            pytest.skip(f"PINV too slow for {distname}")
+        if distname in fail_dists:
+            pytest.skip(f"PINV fails for {distname}")
+        dist = (getattr(stats, distname)
+                if isinstance(distname, str)
+                else distname)
+        dist = dist(*params)
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            rng = NumericalInversePolynomial(dist, random_state=42)
+        if distname in skip_sample_moment_check:
+            return
+        check_cont_samples(rng, dist, [dist.mean(), dist.var()])
+
+    @pytest.mark.parametrize("pdf, err, msg", bad_pdfs_common)
+    def test_bad_pdf(self, pdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = pdf
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(dist, domain=[0, 5])
+
+    @pytest.mark.parametrize("logpdf, err, msg", bad_logpdfs_common)
+    def test_bad_logpdf(self, logpdf, err, msg):
+        class dist:
+            pass
+        dist.logpdf = logpdf
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(dist, domain=[0, 5])
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(StandardNormal(), domain=domain)
+
+    u = [
+        # test if quantile 0 and 1 return -inf and inf respectively and check
+        # the correctness of the PPF for equidistant points between 0 and 1.
+        np.linspace(0, 1, num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf(self, u):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "invalid value encountered in greater")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "greater_equal")
+            sup.filter(RuntimeWarning, "invalid value encountered in less")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "less_equal")
+            res = rng.ppf(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-11, atol=1e-11)
+        assert res.shape == expected.shape
+
+    x = [np.linspace(-10, 10, num=10000), [], [[]], [np.nan],
+         [-np.inf, np.nan, np.inf], 0,
+         [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-np.inf, 3, 4]]]
+
+    @pytest.mark.parametrize("x", x)
+    def test_cdf(self, x):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "invalid value encountered in greater")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "greater_equal")
+            sup.filter(RuntimeWarning, "invalid value encountered in less")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "less_equal")
+            res = rng.cdf(x)
+            expected = stats.norm.cdf(x)
+        assert_allclose(res, expected, rtol=1e-11, atol=1e-11)
+        assert res.shape == expected.shape
+
+    @pytest.mark.slow
+    def test_u_error(self):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-10)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-10
+        assert mae <= max_error
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-14
+        assert mae <= max_error
+
+    bad_orders = [1, 4.5, 20, np.inf, np.nan]
+    bad_u_resolution = [1e-20, 1e-1, np.inf, np.nan]
+
+    @pytest.mark.parametrize("order", bad_orders)
+    def test_bad_orders(self, order):
+        dist = StandardNormal()
+
+        msg = r"`order` must be an integer in the range \[3, 17\]."
+        with pytest.raises(ValueError, match=msg):
+            NumericalInversePolynomial(dist, order=order)
+
+    @pytest.mark.parametrize("u_resolution", bad_u_resolution)
+    def test_bad_u_resolution(self, u_resolution):
+        msg = r"`u_resolution` must be between 1e-15 and 1e-5."
+        with pytest.raises(ValueError, match=msg):
+            NumericalInversePolynomial(StandardNormal(),
+                                       u_resolution=u_resolution)
+
+    def test_bad_args(self):
+
+        class BadDist:
+            def cdf(self, x):
+                return stats.norm._cdf(x)
+
+        dist = BadDist()
+        msg = r"Either of the methods `pdf` or `logpdf` must be specified"
+        with pytest.raises(ValueError, match=msg):
+            rng = NumericalInversePolynomial(dist)
+
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist)
+        msg = r"`sample_size` must be greater than or equal to 1000."
+        with pytest.raises(ValueError, match=msg):
+            rng.u_error(10)
+
+        class Distribution:
+            def pdf(self, x):
+                return np.exp(-0.5 * x*x)
+
+        dist = Distribution()
+        rng = NumericalInversePolynomial(dist)
+        msg = r"Exact CDF required but not found."
+        with pytest.raises(ValueError, match=msg):
+            rng.u_error()
+
+    def test_logpdf_pdf_consistency(self):
+        # 1. check that PINV works with pdf and logpdf only
+        # 2. check that generated ppf is the same (up to a small tolerance)
+
+        class MyDist:
+            pass
+
+        # create generator from dist with only pdf
+        dist_pdf = MyDist()
+        dist_pdf.pdf = lambda x: math.exp(-x*x/2)
+        rng1 = NumericalInversePolynomial(dist_pdf)
+
+        # create dist with only logpdf
+        dist_logpdf = MyDist()
+        dist_logpdf.logpdf = lambda x: -x*x/2
+        rng2 = NumericalInversePolynomial(dist_logpdf)
+
+        q = np.linspace(1e-5, 1-1e-5, num=100)
+        assert_allclose(rng1.ppf(q), rng2.ppf(q))
+
+
+class TestNumericalInverseHermite:
+    #         /  (1 +sin(2 Pi x))/2  if |x| <= 1
+    # f(x) = <
+    #         \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_hinv.c)
+    class dist0:
+        def pdf(self, x):
+            return 0.5*(1. + np.sin(2.*np.pi*x))
+
+        def dpdf(self, x):
+            return np.pi*np.cos(2.*np.pi*x)
+
+        def cdf(self, x):
+            return (1. + 2.*np.pi*(1 + x) - np.cos(2.*np.pi*x)) / (4.*np.pi)
+
+        def support(self):
+            return -1, 1
+
+    #         /  Max(sin(2 Pi x)),0)Pi/2  if -1 < x <0.5
+    # f(x) = <
+    #         \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_hinv.c)
+    class dist1:
+        def pdf(self, x):
+            if (x <= -0.5):
+                return np.sin((2. * np.pi) * x) * 0.5 * np.pi
+            if (x < 0.):
+                return 0.
+            if (x <= 0.5):
+                return np.sin((2. * np.pi) * x) * 0.5 * np.pi
+
+        def dpdf(self, x):
+            if (x <= -0.5):
+                return np.cos((2. * np.pi) * x) * np.pi * np.pi
+            if (x < 0.):
+                return 0.
+            if (x <= 0.5):
+                return np.cos((2. * np.pi) * x) * np.pi * np.pi
+
+        def cdf(self, x):
+            if (x <= -0.5):
+                return 0.25 * (1 - np.cos((2. * np.pi) * x))
+            if (x < 0.):
+                return 0.5
+            if (x <= 0.5):
+                return 0.75 - 0.25 * np.cos((2. * np.pi) * x)
+
+        def support(self):
+            return -1, 0.5
+
+    dists = [dist0(), dist1()]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [-1/(2*np.pi), 1/3 - 1/(4*np.pi*np.pi)]
+    mv1 = [-1/4, 3/8-1/(2*np.pi*np.pi) - 1/16]
+    mvs = [mv0, mv1]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             zip(dists, mvs))
+    @pytest.mark.parametrize("order", [3, 5])
+    def test_basic(self, dist, mv_ex, order):
+        rng = NumericalInverseHermite(dist, order=order, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            NumericalInverseHermite(StandardNormal(), domain=domain)
+
+    def basic_test_all_scipy_dists(self, distname, shapes):
+        slow_dists = {'ksone', 'kstwo', 'levy_stable', 'skewnorm'}
+        fail_dists = {'beta', 'gausshyper', 'geninvgauss', 'ncf', 'nct',
+                      'norminvgauss', 'genhyperbolic', 'studentized_range',
+                      'vonmises', 'kappa4', 'invgauss', 'wald'}
+
+        if distname in slow_dists:
+            pytest.skip("Distribution is too slow")
+        if distname in fail_dists:
+            # specific reasons documented in gh-13319
+            # https://github.com/scipy/scipy/pull/13319#discussion_r626188955
+            pytest.xfail("Fails - usually due to inaccurate CDF/PDF")
+
+        np.random.seed(0)
+
+        dist = getattr(stats, distname)(*shapes)
+        fni = NumericalInverseHermite(dist)
+
+        x = np.random.rand(10)
+        p_tol = np.max(np.abs(dist.ppf(x)-fni.ppf(x))/np.abs(dist.ppf(x)))
+        u_tol = np.max(np.abs(dist.cdf(fni.ppf(x)) - x))
+
+        assert p_tol < 1e-8
+        assert u_tol < 1e-12
+
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.xslow
+    @pytest.mark.parametrize(("distname", "shapes"), distcont)
+    def test_basic_all_scipy_dists(self, distname, shapes):
+        # if distname == "truncnorm":
+        #     pytest.skip("Tested separately")
+        self.basic_test_all_scipy_dists(distname, shapes)
+
+    @pytest.mark.fail_slow(5)
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    def test_basic_truncnorm_gh17155(self):
+        self.basic_test_all_scipy_dists("truncnorm", (0.1, 2))
+
+    def test_input_validation(self):
+        match = r"`order` must be either 1, 3, or 5."
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite(StandardNormal(), order=2)
+
+        match = "`cdf` required but not found"
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite("norm")
+
+        match = "could not convert string to float"
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite(StandardNormal(),
+                                    u_resolution='ekki')
+
+    rngs = [None, 0, np.random.RandomState(0)]
+    rngs.append(np.random.default_rng(0))  # type: ignore
+    sizes = [(None, tuple()), (8, (8,)), ((4, 5, 6), (4, 5, 6))]
+
+    @pytest.mark.parametrize('rng', rngs)
+    @pytest.mark.parametrize('size_in, size_out', sizes)
+    def test_RVS(self, rng, size_in, size_out):
+        dist = StandardNormal()
+        fni = NumericalInverseHermite(dist)
+
+        rng2 = deepcopy(rng)
+        rvs = fni.rvs(size=size_in, random_state=rng)
+        if size_in is not None:
+            assert rvs.shape == size_out
+
+        if rng2 is not None:
+            rng2 = check_random_state(rng2)
+            uniform = rng2.uniform(size=size_in)
+            rvs2 = stats.norm.ppf(uniform)
+            assert_allclose(rvs, rvs2)
+
+    def test_inaccurate_CDF(self):
+        # CDF function with inaccurate tail cannot be inverted; see gh-13319
+        # https://github.com/scipy/scipy/pull/13319#discussion_r626188955
+        shapes = (2.3098496451481823, 0.6268795430096368)
+        match = ("98 : one or more intervals very short; possibly due to "
+                 "numerical problems with a pole or very flat tail")
+
+        # fails with default tol
+        with pytest.warns(RuntimeWarning, match=match):
+            NumericalInverseHermite(stats.beta(*shapes))
+
+        # no error with coarser tol
+        NumericalInverseHermite(stats.beta(*shapes), u_resolution=1e-8)
+
+    def test_custom_distribution(self):
+        dist1 = StandardNormal()
+        fni1 = NumericalInverseHermite(dist1)
+
+        dist2 = stats.norm()
+        fni2 = NumericalInverseHermite(dist2)
+
+        assert_allclose(fni1.rvs(random_state=0), fni2.rvs(random_state=0))
+
+    u = [
+        # check the correctness of the PPF for equidistant points between
+        # 0.02 and 0.98.
+        np.linspace(0., 1., num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf(self, u):
+        dist = StandardNormal()
+        rng = NumericalInverseHermite(dist, u_resolution=1e-12)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "invalid value encountered in greater")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "greater_equal")
+            sup.filter(RuntimeWarning, "invalid value encountered in less")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "less_equal")
+            res = rng.ppf(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-9, atol=3e-10)
+        assert res.shape == expected.shape
+
+    @pytest.mark.slow
+    def test_u_error(self):
+        dist = StandardNormal()
+        rng = NumericalInverseHermite(dist, u_resolution=1e-10)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-10
+        assert mae <= max_error
+        with suppress_warnings() as sup:
+            # ignore warning about u-resolution being too small.
+            sup.filter(RuntimeWarning)
+            rng = NumericalInverseHermite(dist, u_resolution=1e-14)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-14
+        assert mae <= max_error
+
+
+class TestDiscreteGuideTable:
+    basic_fail_dists = {
+        'nchypergeom_fisher',  # numerical errors on tails
+        'nchypergeom_wallenius',  # numerical errors on tails
+        'randint'  # fails on 32-bit ubuntu
+    }
+
+    def test_guide_factor_gt3_raises_warning(self):
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=7)
+
+    def test_guide_factor_zero_raises_warning(self):
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=0)
+
+    def test_negative_guide_factor_raises_warning(self):
+        # This occurs from the UNU.RAN wrapper automatically.
+        # however it already gives a useful warning
+        # Here we just test that a warning is raised.
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=-1)
+
+    @pytest.mark.parametrize("distname, params", distdiscrete)
+    def test_basic(self, distname, params):
+        if distname in self.basic_fail_dists:
+            msg = ("DGT fails on these probably because of large domains "
+                   "and small computation errors in PMF.")
+            pytest.skip(msg)
+
+        if not isinstance(distname, str):
+            dist = distname
+        else:
+            dist = getattr(stats, distname)
+
+        dist = dist(*params)
+        domain = dist.support()
+
+        if not np.isfinite(domain[1] - domain[0]):
+            # DGT only works with finite domain. So, skip the distributions
+            # with infinite tails.
+            pytest.skip("DGT only works with a finite domain.")
+
+        k = np.arange(domain[0], domain[1]+1)
+        pv = dist.pmf(k)
+        mv_ex = dist.stats('mv')
+        rng = DiscreteGuideTable(dist, random_state=42)
+        check_discr_samples(rng, pv, mv_ex)
+
+    u = [
+        # the correctness of the PPF for equidistant points between 0 and 1.
+        np.linspace(0, 1, num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize('u', u)
+    def test_ppf(self, u):
+        n, p = 4, 0.1
+        dist = stats.binom(n, p)
+        rng = DiscreteGuideTable(dist, random_state=42)
+
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning, "invalid value encountered in greater")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "greater_equal")
+            sup.filter(RuntimeWarning, "invalid value encountered in less")
+            sup.filter(RuntimeWarning, "invalid value encountered in "
+                                       "less_equal")
+
+            res = rng.ppf(u)
+            expected = stats.binom.ppf(u, n, p)
+        assert_equal(res.shape, expected.shape)
+        assert_equal(res, expected)
+
+    @pytest.mark.parametrize("pv, msg", bad_pv_common)
+    def test_bad_pv(self, pv, msg):
+        with pytest.raises(ValueError, match=msg):
+            DiscreteGuideTable(pv)
+
+    # DGT doesn't support infinite tails. So, it should throw an error when
+    # inf is present in the domain.
+    inf_domain = [(-np.inf, np.inf), (np.inf, np.inf), (-np.inf, -np.inf),
+                  (0, np.inf), (-np.inf, 0)]
+
+    @pytest.mark.parametrize("domain", inf_domain)
+    def test_inf_domain(self, domain):
+        with pytest.raises(ValueError, match=r"must be finite"):
+            DiscreteGuideTable(stats.binom(10, 0.2), domain=domain)
+
+
+class TestSimpleRatioUniforms:
+    # pdf with piecewise linear function as transformed density
+    # with T = -1/sqrt with shift. Taken from UNU.RAN test suite
+    # (from file t_srou.c)
+    class dist:
+        def __init__(self, shift):
+            self.shift = shift
+            self.mode = shift
+
+        def pdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            return 0.5 * y * y
+
+        def cdf(self, x):
+            x -= self.shift
+            if x <= 0.:
+                return 0.5 / (1. - x)
+            else:
+                return 1. - 0.5 / (1. + x)
+
+    dists = [dist(0.), dist(10000.)]
+
+    # exact mean and variance of the distributions in the list dists
+    mv1 = [0., np.inf]
+    mv2 = [10000., np.inf]
+    mvs = [mv1, mv2]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             zip(dists, mvs))
+    def test_basic(self, dist, mv_ex):
+        rng = SimpleRatioUniforms(dist, mode=dist.mode, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+        rng = SimpleRatioUniforms(dist, mode=dist.mode,
+                                  cdf_at_mode=dist.cdf(dist.mode),
+                                  random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            SimpleRatioUniforms(StandardNormal(), domain=domain)
+
+    def test_bad_args(self):
+        # pdf_area < 0
+        with pytest.raises(ValueError, match=r"`pdf_area` must be > 0"):
+            SimpleRatioUniforms(StandardNormal(), mode=0, pdf_area=-1)
+
+
+class TestRatioUniforms:
+    def test_rv_generation(self):
+        # use KS test to check distribution of rvs
+        # normal distribution
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        u = np.sqrt(f(0))
+        gen = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12345)
+        assert_equal(stats.kstest(gen.rvs(2500), 'norm')[1] > 0.25, True)
+
+        # exponential distribution
+        gen = RatioUniforms(lambda x: np.exp(-x), umax=1,
+                            vmin=0, vmax=2*np.exp(-1), random_state=12345)
+        assert_equal(stats.kstest(gen.rvs(1000), 'expon')[1] > 0.25, True)
+
+    def test_shape(self):
+        # test shape of return value depending on size parameter
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        u = np.sqrt(f(0))
+
+        gen1 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen2 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen3 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        r1, r2, r3 = gen1.rvs(3), gen2.rvs((3,)), gen3.rvs((3, 1))
+        assert_equal(r1, r2)
+        assert_equal(r2, r3.flatten())
+        assert_equal(r1.shape, (3,))
+        assert_equal(r3.shape, (3, 1))
+
+        gen4 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12)
+        gen5 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12)
+        r4, r5 = gen4.rvs(size=(3, 3, 3)), gen5.rvs(size=27)
+        assert_equal(r4.flatten(), r5)
+        assert_equal(r4.shape, (3, 3, 3))
+
+        gen6 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen7 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen8 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        r6, r7, r8 = gen6.rvs(), gen7.rvs(1), gen8.rvs((1,))
+        assert_equal(r6, r7)
+        assert_equal(r7, r8)
+
+    def test_random_state(self):
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        umax = np.sqrt(f(0))
+        gen1 = RatioUniforms(f, umax=umax, vmin=-v, vmax=v, random_state=1234)
+        r1 = gen1.rvs(10)
+        np.random.seed(1234)
+        gen2 = RatioUniforms(f, umax=umax, vmin=-v, vmax=v)
+        r2 = gen2.rvs(10)
+        assert_equal(r1, r2)
+
+    def test_exceptions(self):
+        f = stats.norm.pdf
+        # need vmin < vmax
+        with assert_raises(ValueError, match="vmin must be smaller than vmax"):
+            RatioUniforms(pdf=f, umax=1, vmin=3, vmax=1)
+        with assert_raises(ValueError, match="vmin must be smaller than vmax"):
+            RatioUniforms(pdf=f, umax=1, vmin=1, vmax=1)
+        # need umax > 0
+        with assert_raises(ValueError, match="umax must be positive"):
+            RatioUniforms(pdf=f, umax=-1, vmin=1, vmax=3)
+        with assert_raises(ValueError, match="umax must be positive"):
+            RatioUniforms(pdf=f, umax=0, vmin=1, vmax=3)

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_sensitivity_analysis.py

@@ -0,0 +1,310 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_less
+import pytest
+
+from scipy import stats
+from scipy.stats import sobol_indices
+from scipy.stats._resampling import BootstrapResult
+from scipy.stats._sensitivity_analysis import (
+    BootstrapSobolResult, f_ishigami, sample_AB, sample_A_B
+)
+
+
+@pytest.fixture(scope='session')
+def ishigami_ref_indices():
+    """Reference values for Ishigami from Saltelli2007.
+
+    Chapter 4, exercise 5 pages 179-182.
+    """
+    a = 7.
+    b = 0.1
+
+    var = 0.5 + a**2/8 + b*np.pi**4/5 + b**2*np.pi**8/18
+    v1 = 0.5 + b*np.pi**4/5 + b**2*np.pi**8/50
+    v2 = a**2/8
+    v3 = 0
+    v12 = 0
+    # v13: mistake in the book, see other derivations e.g. in 10.1002/nme.4856
+    v13 = b**2*np.pi**8*8/225
+    v23 = 0
+
+    s_first = np.array([v1, v2, v3])/var
+    s_second = np.array([
+        [0., 0., v13],
+        [v12, 0., v23],
+        [v13, v23, 0.]
+    ])/var
+    s_total = s_first + s_second.sum(axis=1)
+
+    return s_first, s_total
+
+
+def f_ishigami_vec(x):
+    """Output of shape (2, n)."""
+    res = f_ishigami(x)
+    return res, res
+
+
+class TestSobolIndices:
+
+    dists = [
+        stats.uniform(loc=-np.pi, scale=2*np.pi)  # type: ignore[attr-defined]
+    ] * 3
+
+    def test_sample_AB(self):
+        # (d, n)
+        A = np.array(
+            [[1, 4, 7, 10],
+             [2, 5, 8, 11],
+             [3, 6, 9, 12]]
+        )
+        B = A + 100
+        # (d, d, n)
+        ref = np.array(
+            [[[101, 104, 107, 110],
+              [2, 5, 8, 11],
+              [3, 6, 9, 12]],
+             [[1, 4, 7, 10],
+              [102, 105, 108, 111],
+              [3, 6, 9, 12]],
+             [[1, 4, 7, 10],
+              [2, 5, 8, 11],
+              [103, 106, 109, 112]]]
+        )
+        AB = sample_AB(A=A, B=B)
+        assert_allclose(AB, ref)
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail_on_32bit("Can't create large array for test")
+    @pytest.mark.parametrize(
+        'func',
+        [f_ishigami, pytest.param(f_ishigami_vec, marks=pytest.mark.slow)],
+        ids=['scalar', 'vector']
+    )
+    def test_ishigami(self, ishigami_ref_indices, func):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=func, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        if func.__name__ == 'f_ishigami_vec':
+            ishigami_ref_indices = [
+                    [ishigami_ref_indices[0], ishigami_ref_indices[0]],
+                    [ishigami_ref_indices[1], ishigami_ref_indices[1]]
+            ]
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+        assert res._bootstrap_result is None
+        bootstrap_res = res.bootstrap(n_resamples=99)
+        assert isinstance(bootstrap_res, BootstrapSobolResult)
+        assert isinstance(res._bootstrap_result, BootstrapResult)
+
+        assert res._bootstrap_result.confidence_interval.low.shape[0] == 2
+        assert res._bootstrap_result.confidence_interval.low[1].shape \
+               == res.first_order.shape
+
+        assert bootstrap_res.first_order.confidence_interval.low.shape \
+               == res.first_order.shape
+        assert bootstrap_res.total_order.confidence_interval.low.shape \
+               == res.total_order.shape
+
+        assert_array_less(
+            bootstrap_res.first_order.confidence_interval.low, res.first_order
+        )
+        assert_array_less(
+            res.first_order, bootstrap_res.first_order.confidence_interval.high
+        )
+        assert_array_less(
+            bootstrap_res.total_order.confidence_interval.low, res.total_order
+        )
+        assert_array_less(
+            res.total_order, bootstrap_res.total_order.confidence_interval.high
+        )
+
+        # call again to use previous results and change a param
+        assert isinstance(
+            res.bootstrap(confidence_level=0.9, n_resamples=99),
+            BootstrapSobolResult
+        )
+        assert isinstance(res._bootstrap_result, BootstrapResult)
+
+    def test_func_dict(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        n = 4096
+        dists = [
+            stats.uniform(loc=-np.pi, scale=2*np.pi),
+            stats.uniform(loc=-np.pi, scale=2*np.pi),
+            stats.uniform(loc=-np.pi, scale=2*np.pi)
+        ]
+
+        A, B = sample_A_B(n=n, dists=dists, rng=rng)
+        AB = sample_AB(A=A, B=B)
+
+        func = {
+            'f_A': f_ishigami(A).reshape(1, -1),
+            'f_B': f_ishigami(B).reshape(1, -1),
+            'f_AB': f_ishigami(AB).reshape((3, 1, -1))
+        }
+
+        # preserve use of old random_state during SPEC 7 transition
+        res = sobol_indices(
+            func=func, n=n,
+            dists=dists,
+            rng=rng
+        )
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+
+        res = sobol_indices(
+            func=func, n=n,
+            rng=rng
+        )
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        # Ideally should be exactly equal but since f_ishigami
+        # uses floating point operations, so exact equality
+        # might not be possible (due to flakiness in computation).
+        # So, assert_allclose is used with default parameters
+        # Regression test for https://github.com/scipy/scipy/issues/21383
+        assert_allclose(f_ishigami(A).reshape(1, -1), func['f_A'])
+        assert_allclose(f_ishigami(B).reshape(1, -1), func['f_B'])
+        assert_allclose(f_ishigami(AB).reshape((3, 1, -1)), func['f_AB'])
+
+    def test_method(self, ishigami_ref_indices):
+        def jansen_sobol(f_A, f_B, f_AB):
+            """Jansen for S and Sobol' for St.
+
+            From Saltelli2010, table 2 formulations (c) and (e)."""
+            var = np.var([f_A, f_B], axis=(0, -1))
+
+            s = (var - 0.5*np.mean((f_B - f_AB)**2, axis=-1)) / var
+            st = np.mean(f_A*(f_A - f_AB), axis=-1) / var
+
+            return s.T, st.T
+
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami, n=4096,
+            dists=self.dists,
+            method=jansen_sobol,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+        def jansen_sobol_typed(
+            f_A: np.ndarray, f_B: np.ndarray, f_AB: np.ndarray
+        ) -> tuple[np.ndarray, np.ndarray]:
+            return jansen_sobol(f_A, f_B, f_AB)
+
+        _ = sobol_indices(
+            func=f_ishigami, n=8,
+            dists=self.dists,
+            method=jansen_sobol_typed,
+            rng=rng
+        )
+
+    def test_normalization(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=lambda x: f_ishigami(x) + 1000, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+    def test_constant_function(self, ishigami_ref_indices):
+
+        def f_ishigami_vec_const(x):
+            """Output of shape (3, n)."""
+            res = f_ishigami(x)
+            return res, res * 0 + 10, res
+
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami_vec_const, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        ishigami_vec_indices = [
+                [ishigami_ref_indices[0], [0, 0, 0], ishigami_ref_indices[0]],
+                [ishigami_ref_indices[1], [0, 0, 0], ishigami_ref_indices[1]]
+        ]
+
+        assert_allclose(res.first_order, ishigami_vec_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_vec_indices[1], atol=1e-2)
+
+    @pytest.mark.xfail_on_32bit("Can't create large array for test")
+    def test_more_converged(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami, n=2**19,  # 524288
+            dists=self.dists,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-4)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-4)
+
+    def test_raises(self):
+
+        message = r"Each distribution in `dists` must have method `ppf`"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, dists="uniform")
+
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, dists=[lambda x: x])
+
+        message = r"The balance properties of Sobol'"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=7, func=f_ishigami, dists=[stats.uniform()])
+
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=4.1, func=f_ishigami, dists=[stats.uniform()])
+
+        message = r"'toto' is not a valid 'method'"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, method='toto')
+
+        message = r"must have the following signature"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, method=lambda x: x)
+
+        message = r"'dists' must be defined when 'func' is a callable"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami)
+
+        def func_wrong_shape_output(x):
+            return x.reshape(-1, 1)
+
+        message = r"'func' output should have a shape"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(
+                n=2, func=func_wrong_shape_output, dists=[stats.uniform()]
+            )
+
+        message = r"When 'func' is a dictionary"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(
+                n=2, func={'f_A': [], 'f_AB': []}, dists=[stats.uniform()]
+            )
+
+        with pytest.raises(ValueError, match=message):
+            # f_B malformed
+            sobol_indices(
+                n=2,
+                func={'f_A': [1, 2], 'f_B': [3], 'f_AB': [5, 6, 7, 8]},
+            )
+
+        with pytest.raises(ValueError, match=message):
+            # f_AB malformed
+            sobol_indices(
+                n=2,
+                func={'f_A': [1, 2], 'f_B': [3, 4], 'f_AB': [5, 6, 7]},
+            )

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_survival.py

@@ -0,0 +1,466 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy import stats
+from scipy.stats import _survival
+
+
+def _kaplan_meier_reference(times, censored):
+    # This is a very straightforward implementation of the Kaplan-Meier
+    # estimator that does almost everything differently from the implementation
+    # in stats.ecdf.
+
+    # Begin by sorting the raw data. Note that the order of death and loss
+    # at a given time matters: death happens first. See [2] page 461:
+    # "These conventions may be paraphrased by saying that deaths recorded as
+    # of an age t are treated as if they occurred slightly before t, and losses
+    # recorded as of an age t are treated as occurring slightly after t."
+    # We implement this by sorting the data first by time, then by `censored`,
+    # (which is 0 when there is a death and 1 when there is only a loss).
+    dtype = [('time', float), ('censored', int)]
+    data = np.array([(t, d) for t, d in zip(times, censored)], dtype=dtype)
+    data = np.sort(data, order=('time', 'censored'))
+    times = data['time']
+    died = np.logical_not(data['censored'])
+
+    m = times.size
+    n = np.arange(m, 0, -1)  # number at risk
+    sf = np.cumprod((n - died) / n)
+
+    # Find the indices of the *last* occurrence of unique times. The
+    # corresponding entries of `times` and `sf` are what we want.
+    _, indices = np.unique(times[::-1], return_index=True)
+    ref_times = times[-indices - 1]
+    ref_sf = sf[-indices - 1]
+    return ref_times, ref_sf
+
+
+class TestSurvival:
+
+    @staticmethod
+    def get_random_sample(rng, n_unique):
+        # generate random sample
+        unique_times = rng.random(n_unique)
+        # convert to `np.int32` to resolve `np.repeat` failure in 32-bit CI
+        repeats = rng.integers(1, 4, n_unique).astype(np.int32)
+        times = rng.permuted(np.repeat(unique_times, repeats))
+        censored = rng.random(size=times.size) > rng.random()
+        sample = stats.CensoredData.right_censored(times, censored)
+        return sample, times, censored
+
+    def test_input_validation(self):
+        message = '`sample` must be a one-dimensional sequence.'
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf([[1]])
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf(1)
+
+        message = '`sample` must not contain nan'
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf([np.nan])
+
+        message = 'Currently, only uncensored and right-censored data...'
+        with pytest.raises(NotImplementedError, match=message):
+            stats.ecdf(stats.CensoredData.left_censored([1], censored=[True]))
+
+        message = 'method` must be one of...'
+        res = stats.ecdf([1, 2, 3])
+        with pytest.raises(ValueError, match=message):
+            res.cdf.confidence_interval(method='ekki-ekki')
+        with pytest.raises(ValueError, match=message):
+            res.sf.confidence_interval(method='shrubbery')
+
+        message = 'confidence_level` must be a scalar between 0 and 1'
+        with pytest.raises(ValueError, match=message):
+            res.cdf.confidence_interval(-1)
+        with pytest.raises(ValueError, match=message):
+            res.sf.confidence_interval([0.5, 0.6])
+
+        message = 'The confidence interval is undefined at some observations.'
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.cdf.confidence_interval()
+
+        message = 'Confidence interval bounds do not implement...'
+        with pytest.raises(NotImplementedError, match=message):
+            ci.low.confidence_interval()
+        with pytest.raises(NotImplementedError, match=message):
+            ci.high.confidence_interval()
+
+    def test_edge_cases(self):
+        res = stats.ecdf([])
+        assert_equal(res.cdf.quantiles, [])
+        assert_equal(res.cdf.probabilities, [])
+
+        res = stats.ecdf([1])
+        assert_equal(res.cdf.quantiles, [1])
+        assert_equal(res.cdf.probabilities, [1])
+
+    def test_unique(self):
+        # Example with unique observations; `stats.ecdf` ref. [1] page 80
+        sample = [6.23, 5.58, 7.06, 6.42, 5.20]
+        res = stats.ecdf(sample)
+        ref_x = np.sort(np.unique(sample))
+        ref_cdf = np.arange(1, 6) / 5
+        ref_sf = 1 - ref_cdf
+        assert_equal(res.cdf.quantiles, ref_x)
+        assert_equal(res.cdf.probabilities, ref_cdf)
+        assert_equal(res.sf.quantiles, ref_x)
+        assert_equal(res.sf.probabilities, ref_sf)
+
+    def test_nonunique(self):
+        # Example with non-unique observations; `stats.ecdf` ref. [1] page 82
+        sample = [0, 2, 1, 2, 3, 4]
+        res = stats.ecdf(sample)
+        ref_x = np.sort(np.unique(sample))
+        ref_cdf = np.array([1/6, 2/6, 4/6, 5/6, 1])
+        ref_sf = 1 - ref_cdf
+        assert_equal(res.cdf.quantiles, ref_x)
+        assert_equal(res.cdf.probabilities, ref_cdf)
+        assert_equal(res.sf.quantiles, ref_x)
+        assert_equal(res.sf.probabilities, ref_sf)
+
+    def test_evaluate_methods(self):
+        # Test CDF and SF `evaluate` methods
+        rng = np.random.default_rng(1162729143302572461)
+        sample, _, _ = self.get_random_sample(rng, 15)
+        res = stats.ecdf(sample)
+        x = res.cdf.quantiles
+        xr = x + np.diff(x, append=x[-1]+1)/2  # right shifted points
+
+        assert_equal(res.cdf.evaluate(x), res.cdf.probabilities)
+        assert_equal(res.cdf.evaluate(xr), res.cdf.probabilities)
+        assert_equal(res.cdf.evaluate(x[0]-1), 0)  # CDF starts at 0
+        assert_equal(res.cdf.evaluate([-np.inf, np.inf]), [0, 1])
+
+        assert_equal(res.sf.evaluate(x), res.sf.probabilities)
+        assert_equal(res.sf.evaluate(xr), res.sf.probabilities)
+        assert_equal(res.sf.evaluate(x[0]-1), 1)  # SF starts at 1
+        assert_equal(res.sf.evaluate([-np.inf, np.inf]), [1, 0])
+
+    # ref. [1] page 91
+    t1 = [37, 43, 47, 56, 60, 62, 71, 77, 80, 81]  # times
+    d1 = [0, 0, 1, 1, 0, 0, 0, 1, 1, 1]  # 1 means deaths (not censored)
+    r1 = [1, 1, 0.875, 0.75, 0.75, 0.75, 0.75, 0.5, 0.25, 0]  # reference SF
+
+    # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+    t2 = [8, 12, 26, 14, 21, 27, 8, 32, 20, 40]
+    d2 = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0]
+    r2 = [0.9, 0.788, 0.675, 0.675, 0.54, 0.405, 0.27, 0.27, 0.27]
+    t3 = [33, 28, 41, 48, 48, 25, 37, 48, 25, 43]
+    d3 = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+    r3 = [1, 0.875, 0.75, 0.75, 0.6, 0.6, 0.6]
+
+    # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/bs704_survival4.html
+    t4 = [24, 3, 11, 19, 24, 13, 14, 2, 18, 17,
+          24, 21, 12, 1, 10, 23, 6, 5, 9, 17]
+    d4 = [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1]
+    r4 = [0.95, 0.95, 0.897, 0.844, 0.844, 0.844, 0.844, 0.844, 0.844,
+          0.844, 0.76, 0.676, 0.676, 0.676, 0.676, 0.507, 0.507]
+
+    # https://www.real-statistics.com/survival-analysis/kaplan-meier-procedure/confidence-interval-for-the-survival-function/
+    t5 = [3, 5, 8, 10, 5, 5, 8, 12, 15, 14, 2, 11, 10, 9, 12, 5, 8, 11]
+    d5 = [1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1]
+    r5 = [0.944, 0.889, 0.722, 0.542, 0.542, 0.542, 0.361, 0.181, 0.181, 0.181]
+
+    @pytest.mark.parametrize("case", [(t1, d1, r1), (t2, d2, r2), (t3, d3, r3),
+                                      (t4, d4, r4), (t5, d5, r5)])
+    def test_right_censored_against_examples(self, case):
+        # test `ecdf` against other implementations on example problems
+        times, died, ref = case
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        assert_allclose(res.sf.probabilities, ref, atol=1e-3)
+        assert_equal(res.sf.quantiles, np.sort(np.unique(times)))
+
+        # test reference implementation against other implementations
+        res = _kaplan_meier_reference(times, np.logical_not(died))
+        assert_equal(res[0], np.sort(np.unique(times)))
+        assert_allclose(res[1], ref, atol=1e-3)
+
+    @pytest.mark.parametrize('seed', [182746786639392128, 737379171436494115,
+                                      576033618403180168, 308115465002673650])
+    def test_right_censored_against_reference_implementation(self, seed):
+        # test `ecdf` against reference implementation on random problems
+        rng = np.random.default_rng(seed)
+        n_unique = rng.integers(10, 100)
+        sample, times, censored = self.get_random_sample(rng, n_unique)
+        res = stats.ecdf(sample)
+        ref = _kaplan_meier_reference(times, censored)
+        assert_allclose(res.sf.quantiles, ref[0])
+        assert_allclose(res.sf.probabilities, ref[1])
+
+        # If all observations are uncensored, the KM estimate should match
+        # the usual estimate for uncensored data
+        sample = stats.CensoredData(uncensored=times)
+        res = _survival._ecdf_right_censored(sample)  # force Kaplan-Meier
+        ref = stats.ecdf(times)
+        assert_equal(res[0], ref.sf.quantiles)
+        assert_allclose(res[1], ref.cdf.probabilities, rtol=1e-14)
+        assert_allclose(res[2], ref.sf.probabilities, rtol=1e-14)
+
+    def test_right_censored_ci(self):
+        # test "greenwood" confidence interval against example 4 (URL above).
+        times, died = self.t4, self.d4
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        ref_allowance = [0.096, 0.096, 0.135, 0.162, 0.162, 0.162, 0.162,
+                         0.162, 0.162, 0.162, 0.214, 0.246, 0.246, 0.246,
+                         0.246, 0.341, 0.341]
+
+        sf_ci = res.sf.confidence_interval()
+        cdf_ci = res.cdf.confidence_interval()
+        allowance = res.sf.probabilities - sf_ci.low.probabilities
+
+        assert_allclose(allowance, ref_allowance, atol=1e-3)
+        assert_allclose(sf_ci.low.probabilities,
+                        np.clip(res.sf.probabilities - allowance, 0, 1))
+        assert_allclose(sf_ci.high.probabilities,
+                        np.clip(res.sf.probabilities + allowance, 0, 1))
+        assert_allclose(cdf_ci.low.probabilities,
+                        np.clip(res.cdf.probabilities - allowance, 0, 1))
+        assert_allclose(cdf_ci.high.probabilities,
+                        np.clip(res.cdf.probabilities + allowance, 0, 1))
+
+        # test "log-log" confidence interval against Mathematica
+        # e = {24, 3, 11, 19, 24, 13, 14, 2, 18, 17, 24, 21, 12, 1, 10, 23, 6, 5,
+        #      9, 17}
+        # ci = {1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0}
+        # R = EventData[e, ci]
+        # S = SurvivalModelFit[R]
+        # S["PointwiseIntervals", ConfidenceLevel->0.95,
+        #   ConfidenceTransform->"LogLog"]
+
+        ref_low = [0.694743, 0.694743, 0.647529, 0.591142, 0.591142, 0.591142,
+                   0.591142, 0.591142, 0.591142, 0.591142, 0.464605, 0.370359,
+                   0.370359, 0.370359, 0.370359, 0.160489, 0.160489]
+        ref_high = [0.992802, 0.992802, 0.973299, 0.947073, 0.947073, 0.947073,
+                    0.947073, 0.947073, 0.947073, 0.947073, 0.906422, 0.856521,
+                    0.856521, 0.856521, 0.856521, 0.776724, 0.776724]
+        sf_ci = res.sf.confidence_interval(method='log-log')
+        assert_allclose(sf_ci.low.probabilities, ref_low, atol=1e-6)
+        assert_allclose(sf_ci.high.probabilities, ref_high, atol=1e-6)
+
+    def test_right_censored_ci_example_5(self):
+        # test "exponential greenwood" confidence interval against example 5
+        times, died = self.t5, self.d5
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        lower = np.array([0.66639, 0.624174, 0.456179, 0.287822, 0.287822,
+                          0.287822, 0.128489, 0.030957, 0.030957, 0.030957])
+        upper = np.array([0.991983, 0.970995, 0.87378, 0.739467, 0.739467,
+                          0.739467, 0.603133, 0.430365, 0.430365, 0.430365])
+
+        sf_ci = res.sf.confidence_interval(method='log-log')
+        cdf_ci = res.cdf.confidence_interval(method='log-log')
+
+        assert_allclose(sf_ci.low.probabilities, lower, atol=1e-5)
+        assert_allclose(sf_ci.high.probabilities, upper, atol=1e-5)
+        assert_allclose(cdf_ci.low.probabilities, 1-upper, atol=1e-5)
+        assert_allclose(cdf_ci.high.probabilities, 1-lower, atol=1e-5)
+
+        # Test against R's `survival` library `survfit` function, 90%CI
+        # library(survival)
+        # options(digits=16)
+        # time = c(3, 5, 8, 10, 5, 5, 8, 12, 15, 14, 2, 11, 10, 9, 12, 5, 8, 11)
+        # status = c(1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1)
+        # res = survfit(Surv(time, status)
+        # ~1, conf.type = "log-log", conf.int = 0.90)
+        # res$time; res$lower; res$upper
+        low = [0.74366748406861172, 0.68582332289196246, 0.50596835651480121,
+               0.32913131413336727, 0.32913131413336727, 0.32913131413336727,
+               0.15986912028781664, 0.04499539918147757, 0.04499539918147757,
+               0.04499539918147757]
+        high = [0.9890291867238429, 0.9638835422144144, 0.8560366823086629,
+                0.7130167643978450, 0.7130167643978450, 0.7130167643978450,
+                0.5678602982997164, 0.3887616766886558, 0.3887616766886558,
+                0.3887616766886558]
+        sf_ci = res.sf.confidence_interval(method='log-log',
+                                           confidence_level=0.9)
+        assert_allclose(sf_ci.low.probabilities, low)
+        assert_allclose(sf_ci.high.probabilities, high)
+
+        # And with conf.type = "plain"
+        low = [0.8556383113628162, 0.7670478794850761, 0.5485720663578469,
+               0.3441515412527123, 0.3441515412527123, 0.3441515412527123,
+               0.1449184105424544, 0., 0., 0.]
+        high = [1., 1., 0.8958723780865975, 0.7391817920806210,
+                0.7391817920806210, 0.7391817920806210, 0.5773038116797676,
+                0.3642270254596720, 0.3642270254596720, 0.3642270254596720]
+        sf_ci = res.sf.confidence_interval(confidence_level=0.9)
+        assert_allclose(sf_ci.low.probabilities, low)
+        assert_allclose(sf_ci.high.probabilities, high)
+
+    def test_right_censored_ci_nans(self):
+        # test `ecdf` confidence interval on a problem that results in NaNs
+        times, died = self.t1, self.d1
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+
+        # Reference values generated with Matlab
+        # format long
+        # t = [37 43 47 56 60 62 71 77 80 81];
+        # d = [0 0 1 1 0 0 0 1 1 1];
+        # censored = ~d1;
+        # [f, x, flo, fup] = ecdf(t, 'Censoring', censored, 'Alpha', 0.05);
+        x = [37, 47, 56, 77, 80, 81]
+        flo = [np.nan, 0, 0, 0.052701464070711, 0.337611126231790, np.nan]
+        fup = [np.nan, 0.35417230377, 0.5500569798, 0.9472985359, 1.0, np.nan]
+        i = np.searchsorted(res.cdf.quantiles, x)
+
+        message = "The confidence interval is undefined at some observations"
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.cdf.confidence_interval()
+
+        # Matlab gives NaN as the first element of the CIs. Mathematica agrees,
+        # but R's survfit does not. It makes some sense, but it's not what the
+        # formula gives, so skip that element.
+        assert_allclose(ci.low.probabilities[i][1:], flo[1:])
+        assert_allclose(ci.high.probabilities[i][1:], fup[1:])
+
+        # [f, x, flo, fup] = ecdf(t, 'Censoring', censored, 'Function',
+        #                        'survivor', 'Alpha', 0.05);
+        flo = [np.nan, 0.64582769623, 0.449943020228, 0.05270146407, 0, np.nan]
+        fup = [np.nan, 1.0, 1.0, 0.947298535929289, 0.662388873768210, np.nan]
+        i = np.searchsorted(res.cdf.quantiles, x)
+
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.sf.confidence_interval()
+
+        assert_allclose(ci.low.probabilities[i][1:], flo[1:])
+        assert_allclose(ci.high.probabilities[i][1:], fup[1:])
+
+        # With the same data, R's `survival` library `survfit` function
+        # doesn't produce the leading NaN
+        # library(survival)
+        # options(digits=16)
+        # time = c(37, 43, 47, 56, 60, 62, 71, 77, 80, 81)
+        # status = c(0, 0, 1, 1, 0, 0, 0, 1, 1, 1)
+        # res = survfit(Surv(time, status)
+        # ~1, conf.type = "plain", conf.int = 0.95)
+        # res$time
+        # res$lower
+        # res$upper
+        low = [1., 1., 0.64582769623233816, 0.44994302022779326,
+               0.44994302022779326, 0.44994302022779326, 0.44994302022779326,
+               0.05270146407071086, 0., np.nan]
+        high = [1., 1., 1., 1., 1., 1., 1., 0.9472985359292891,
+                0.6623888737682101, np.nan]
+        assert_allclose(ci.low.probabilities, low)
+        assert_allclose(ci.high.probabilities, high)
+
+        # It does with conf.type="log-log", as do we
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.sf.confidence_interval(method='log-log')
+        low = [np.nan, np.nan, 0.38700001403202522, 0.31480711370551911,
+               0.31480711370551911, 0.31480711370551911, 0.31480711370551911,
+               0.08048821148507734, 0.01049958986680601, np.nan]
+        high = [np.nan, np.nan, 0.9813929658789660, 0.9308983170906275,
+                0.9308983170906275, 0.9308983170906275, 0.9308983170906275,
+                0.8263946341076415, 0.6558775085110887, np.nan]
+        assert_allclose(ci.low.probabilities, low)
+        assert_allclose(ci.high.probabilities, high)
+
+    def test_right_censored_against_uncensored(self):
+        rng = np.random.default_rng(7463952748044886637)
+        sample = rng.integers(10, 100, size=1000)
+        censored = np.zeros_like(sample)
+        censored[np.argmax(sample)] = True
+        res = stats.ecdf(sample)
+        ref = stats.ecdf(stats.CensoredData.right_censored(sample, censored))
+        assert_equal(res.sf.quantiles, ref.sf.quantiles)
+        assert_equal(res.sf._n, ref.sf._n)
+        assert_equal(res.sf._d[:-1], ref.sf._d[:-1])  # difference @ [-1]
+        assert_allclose(res.sf._sf[:-1], ref.sf._sf[:-1], rtol=1e-14)
+
+    def test_plot_iv(self):
+        rng = np.random.default_rng(1769658657308472721)
+        n_unique = rng.integers(10, 100)
+        sample, _, _ = self.get_random_sample(rng, n_unique)
+        res = stats.ecdf(sample)
+
+        try:
+            import matplotlib.pyplot as plt  # noqa: F401
+            res.sf.plot()  # no other errors occur
+        except (ModuleNotFoundError, ImportError):
+            message = r"matplotlib must be installed to use method `plot`."
+            with pytest.raises(ModuleNotFoundError, match=message):
+                res.sf.plot()
+
+
+class TestLogRank:
+
+    @pytest.mark.parametrize(
+        "x, y, statistic, pvalue",
+        # Results validate with R
+        # library(survival)
+        # options(digits=16)
+        #
+        # futime_1 <- c(8, 12, 26, 14, 21, 27, 8, 32, 20, 40)
+        # fustat_1 <- c(1, 1, 1, 1, 1, 1, 0, 0, 0, 0)
+        # rx_1 <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+        #
+        # futime_2 <- c(33, 28, 41, 48, 48, 25, 37, 48, 25, 43)
+        # fustat_2 <- c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0)
+        # rx_2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
+        #
+        # futime <- c(futime_1, futime_2)
+        # fustat <- c(fustat_1, fustat_2)
+        # rx <- c(rx_1, rx_2)
+        #
+        # survdiff(formula = Surv(futime, fustat) ~ rx)
+        #
+        # Also check against another library which handle alternatives
+        # library(nph)
+        # logrank.test(futime, fustat, rx, alternative = "two.sided")
+        # res["test"]
+        [(
+              # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+              # uncensored, censored
+              [[8, 12, 26, 14, 21, 27], [8, 32, 20, 40]],
+              [[33, 28, 41], [48, 48, 25, 37, 48, 25, 43]],
+              # chi2, ["two-sided", "less", "greater"]
+              6.91598157449,
+              [0.008542873404, 0.9957285632979385, 0.004271436702061537]
+         ),
+         (
+              # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+              [[19, 6, 5, 4], [20, 19, 17, 14]],
+              [[16, 21, 7], [21, 15, 18, 18, 5]],
+              0.835004855038,
+              [0.3608293039, 0.8195853480676912, 0.1804146519323088]
+         ),
+         (
+              # Bland, Altman, "The logrank test", BMJ, 2004
+              # https://www.bmj.com/content/328/7447/1073.short
+              [[6, 13, 21, 30, 37, 38, 49, 50, 63, 79, 86, 98, 202, 219],
+               [31, 47, 80, 82, 82, 149]],
+              [[10, 10, 12, 13, 14, 15, 16, 17, 18, 20, 24, 24, 25, 28, 30,
+                33, 35, 37, 40, 40, 46, 48, 76, 81, 82, 91, 112, 181],
+               [34, 40, 70]],
+              7.49659416854,
+              [0.006181578637, 0.003090789318730882, 0.9969092106812691]
+         )]
+    )
+    def test_log_rank(self, x, y, statistic, pvalue):
+        x = stats.CensoredData(uncensored=x[0], right=x[1])
+        y = stats.CensoredData(uncensored=y[0], right=y[1])
+
+        for i, alternative in enumerate(["two-sided", "less", "greater"]):
+            res = stats.logrank(x=x, y=y, alternative=alternative)
+
+            # we return z and use the normal distribution while other framework
+            # return z**2. The p-value are directly comparable, but we have to
+            # square the statistic
+            assert_allclose(res.statistic**2, statistic, atol=1e-10)
+            assert_allclose(res.pvalue, pvalue[i], atol=1e-10)
+
+    def test_raises(self):
+        sample = stats.CensoredData([1, 2])
+
+        msg = r"`y` must be"
+        with pytest.raises(ValueError, match=msg):
+            stats.logrank(x=sample, y=[[1, 2]])
+
+        msg = r"`x` must be"
+        with pytest.raises(ValueError, match=msg):
+            stats.logrank(x=[[1, 2]], y=sample)

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_tukeylambda_stats.py

@@ -0,0 +1,85 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+
+from scipy.stats._tukeylambda_stats import (tukeylambda_variance,
+                                            tukeylambda_kurtosis)
+
+
+def test_tukeylambda_stats_known_exact():
+    """Compare results with some known exact formulas."""
+    # Some exact values of the Tukey Lambda variance and kurtosis:
+    # lambda   var      kurtosis
+    #   0     pi**2/3     6/5     (logistic distribution)
+    #  0.5    4 - pi    (5/3 - pi/2)/(pi/4 - 1)**2 - 3
+    #   1      1/3       -6/5     (uniform distribution on (-1,1))
+    #   2      1/12      -6/5     (uniform distribution on (-1/2, 1/2))
+
+    # lambda = 0
+    var = tukeylambda_variance(0)
+    assert_allclose(var, np.pi**2 / 3, atol=1e-12)
+    kurt = tukeylambda_kurtosis(0)
+    assert_allclose(kurt, 1.2, atol=1e-10)
+
+    # lambda = 0.5
+    var = tukeylambda_variance(0.5)
+    assert_allclose(var, 4 - np.pi, atol=1e-12)
+    kurt = tukeylambda_kurtosis(0.5)
+    desired = (5./3 - np.pi/2) / (np.pi/4 - 1)**2 - 3
+    assert_allclose(kurt, desired, atol=1e-10)
+
+    # lambda = 1
+    var = tukeylambda_variance(1)
+    assert_allclose(var, 1.0 / 3, atol=1e-12)
+    kurt = tukeylambda_kurtosis(1)
+    assert_allclose(kurt, -1.2, atol=1e-10)
+
+    # lambda = 2
+    var = tukeylambda_variance(2)
+    assert_allclose(var, 1.0 / 12, atol=1e-12)
+    kurt = tukeylambda_kurtosis(2)
+    assert_allclose(kurt, -1.2, atol=1e-10)
+
+
+def test_tukeylambda_stats_mpmath():
+    """Compare results with some values that were computed using mpmath."""
+    a10 = dict(atol=1e-10, rtol=0)
+    a12 = dict(atol=1e-12, rtol=0)
+    data = [
+        # lambda        variance              kurtosis
+        [-0.1, 4.78050217874253547, 3.78559520346454510],
+        [-0.0649, 4.16428023599895777, 2.52019675947435718],
+        [-0.05, 3.93672267890775277, 2.13129793057777277],
+        [-0.001, 3.30128380390964882, 1.21452460083542988],
+        [0.001, 3.27850775649572176, 1.18560634779287585],
+        [0.03125, 2.95927803254615800, 0.804487555161819980],
+        [0.05, 2.78281053405464501, 0.611604043886644327],
+        [0.0649, 2.65282386754100551, 0.476834119532774540],
+        [1.2, 0.242153920578588346, -1.23428047169049726],
+        [10.0, 0.00095237579757703597, 2.37810697355144933],
+        [20.0, 0.00012195121951131043, 7.37654321002709531],
+    ]
+
+    for lam, var_expected, kurt_expected in data:
+        var = tukeylambda_variance(lam)
+        assert_allclose(var, var_expected, **a12)
+        kurt = tukeylambda_kurtosis(lam)
+        assert_allclose(kurt, kurt_expected, **a10)
+
+    # Test with vector arguments (most of the other tests are for single
+    # values).
+    lam, var_expected, kurt_expected = zip(*data)
+    var = tukeylambda_variance(lam)
+    assert_allclose(var, var_expected, **a12)
+    kurt = tukeylambda_kurtosis(lam)
+    assert_allclose(kurt, kurt_expected, **a10)
+
+
+def test_tukeylambda_stats_invalid():
+    """Test values of lambda outside the domains of the functions."""
+    lam = [-1.0, -0.5]
+    var = tukeylambda_variance(lam)
+    assert_equal(var, np.array([np.nan, np.inf]))
+
+    lam = [-1.0, -0.25]
+    kurt = tukeylambda_kurtosis(lam)
+    assert_equal(kurt, np.array([np.nan, np.inf]))

miniconda3/envs/ladir/lib/python3.10/site-packages/scipy/stats/tests/test_variation.py

@@ -0,0 +1,214 @@
+import math
+
+import numpy as np
+import pytest
+from numpy.testing import suppress_warnings
+
+from scipy.stats import variation
+from scipy._lib._util import AxisError
+from scipy.conftest import array_api_compatible
+from scipy._lib._array_api import is_numpy
+from scipy._lib._array_api_no_0d import xp_assert_equal, xp_assert_close
+from scipy.stats._axis_nan_policy import (too_small_nd_omit, too_small_nd_not_omit,
+                                          SmallSampleWarning)
+
+pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends")]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+class TestVariation:
+    """
+    Test class for scipy.stats.variation
+    """
+
+    def test_ddof(self, xp):
+        x = xp.arange(9.0)
+        xp_assert_close(variation(x, ddof=1), xp.asarray(math.sqrt(60/8)/4))
+
+    @pytest.mark.parametrize('sgn', [1, -1])
+    def test_sign(self, sgn, xp):
+        x = xp.asarray([1., 2., 3., 4., 5.])
+        v = variation(sgn*x)
+        expected = xp.asarray(sgn*math.sqrt(2)/3)
+        xp_assert_close(v, expected, rtol=1e-10)
+
+    def test_scalar(self, xp):
+        # A scalar is treated like a 1-d sequence with length 1.
+        xp_assert_equal(variation(4.0), 0.0)
+
+    @pytest.mark.parametrize('nan_policy, expected',
+                             [('propagate', np.nan),
+                              ('omit', np.sqrt(20/3)/4)])
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_variation_nan(self, nan_policy, expected, xp):
+        x = xp.arange(10.)
+        x[9] = xp.nan
+        xp_assert_close(variation(x, nan_policy=nan_policy), expected)
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_nan_policy_raise(self, xp):
+        x = xp.asarray([1.0, 2.0, xp.nan, 3.0])
+        with pytest.raises(ValueError, match='input contains nan'):
+            variation(x, nan_policy='raise')
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_bad_nan_policy(self, xp):
+        with pytest.raises(ValueError, match='must be one of'):
+            variation([1, 2, 3], nan_policy='foobar')
+
+    @skip_xp_backends(np_only=True,
+                      reason='`keepdims` only supports NumPy backend')
+    def test_keepdims(self, xp):
+        x = xp.reshape(xp.arange(10), (2, 5))
+        y = variation(x, axis=1, keepdims=True)
+        expected = np.array([[np.sqrt(2)/2],
+                             [np.sqrt(2)/7]])
+        xp_assert_close(y, expected)
+
+    @skip_xp_backends(np_only=True,
+                      reason='`keepdims` only supports NumPy backend')
+    @pytest.mark.parametrize('axis, expected',
+                             [(0, np.empty((1, 0))),
+                              (1, np.full((5, 1), fill_value=np.nan))])
+    def test_keepdims_size0(self, axis, expected, xp):
+        x = xp.zeros((5, 0))
+        if axis == 1:
+            with pytest.warns(SmallSampleWarning, match=too_small_nd_not_omit):
+                y = variation(x, axis=axis, keepdims=True)
+        else:
+            y = variation(x, axis=axis, keepdims=True)
+        xp_assert_equal(y, expected)
+
+    @skip_xp_backends(np_only=True,
+                      reason='`keepdims` only supports NumPy backend')
+    @pytest.mark.parametrize('incr, expected_fill', [(0, np.inf), (1, np.nan)])
+    def test_keepdims_and_ddof_eq_len_plus_incr(self, incr, expected_fill, xp):
+        x = xp.asarray([[1, 1, 2, 2], [1, 2, 3, 3]])
+        y = variation(x, axis=1, ddof=x.shape[1] + incr, keepdims=True)
+        xp_assert_equal(y, xp.full((2, 1), fill_value=expected_fill))
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_propagate_nan(self, xp):
+        # Check that the shape of the result is the same for inputs
+        # with and without nans, cf gh-5817
+        a = xp.reshape(xp.arange(8, dtype=float), (2, -1))
+        a[1, 0] = xp.nan
+        v = variation(a, axis=1, nan_policy="propagate")
+        xp_assert_close(v, [math.sqrt(5/4)/1.5, xp.nan], atol=1e-15)
+
+    @skip_xp_backends(np_only=True, reason='Python list input uses NumPy backend')
+    def test_axis_none(self, xp):
+        # Check that `variation` computes the result on the flattened
+        # input when axis is None.
+        y = variation([[0, 1], [2, 3]], axis=None)
+        xp_assert_close(y, math.sqrt(5/4)/1.5)
+
+    def test_bad_axis(self, xp):
+        # Check that an invalid axis raises np.exceptions.AxisError.
+        x = xp.asarray([[1, 2, 3], [4, 5, 6]])
+        with pytest.raises((AxisError, IndexError)):
+            variation(x, axis=10)
+
+    def test_mean_zero(self, xp):
+        # Check that `variation` returns inf for a sequence that is not
+        # identically zero but whose mean is zero.
+        x = xp.asarray([10., -3., 1., -4., -4.])
+        y = variation(x)
+        xp_assert_equal(y, xp.asarray(xp.inf))
+
+        x2 = xp.stack([x, -10.*x])
+        y2 = variation(x2, axis=1)
+        xp_assert_equal(y2, xp.asarray([xp.inf, xp.inf]))
+
+    @pytest.mark.parametrize('x', [[0.]*5, [1, 2, np.inf, 9]])
+    def test_return_nan(self, x, xp):
+        x = xp.asarray(x)
+        # Test some cases where `variation` returns nan.
+        y = variation(x)
+        xp_assert_equal(y, xp.asarray(xp.nan, dtype=x.dtype))
+
+    @pytest.mark.parametrize('axis, expected',
+                             [(0, []), (1, [np.nan]*3), (None, np.nan)])
+    def test_2d_size_zero_with_axis(self, axis, expected, xp):
+        x = xp.empty((3, 0))
+        with suppress_warnings() as sup:
+            # torch
+            sup.filter(UserWarning, "std*")
+            if axis != 0:
+                if is_numpy(xp):
+                    with pytest.warns(SmallSampleWarning, match="See documentation..."):
+                        y = variation(x, axis=axis)
+                else:
+                    y = variation(x, axis=axis)
+            else:
+                y = variation(x, axis=axis)
+        xp_assert_equal(y, xp.asarray(expected))
+
+    def test_neg_inf(self, xp):
+        # Edge case that produces -inf: ddof equals the number of non-nan
+        # values, the values are not constant, and the mean is negative.
+        x1 = xp.asarray([-3., -5.])
+        xp_assert_equal(variation(x1, ddof=2), xp.asarray(-xp.inf))
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_neg_inf_nan(self, xp):
+        x2 = xp.asarray([[xp.nan, 1, -10, xp.nan],
+                         [-20, -3, xp.nan, xp.nan]])
+        xp_assert_equal(variation(x2, axis=1, ddof=2, nan_policy='omit'),
+                        [-xp.inf, -xp.inf])
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    @pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
+    def test_combined_edge_cases(self, nan_policy, xp):
+        x = xp.array([[0, 10, xp.nan, 1],
+                      [0, -5, xp.nan, 2],
+                      [0, -5, xp.nan, 3]])
+        if nan_policy == 'omit':
+            with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+                y = variation(x, axis=0, nan_policy=nan_policy)
+        else:
+            y = variation(x, axis=0, nan_policy=nan_policy)
+        xp_assert_close(y, [xp.nan, xp.inf, xp.nan, math.sqrt(2/3)/2])
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    @pytest.mark.parametrize(
+        'ddof, expected',
+        [(0, [np.sqrt(1/6), np.sqrt(5/8), np.inf, 0, np.nan, 0.0, np.nan]),
+         (1, [0.5, np.sqrt(5/6), np.inf, 0, np.nan, 0, np.nan]),
+         (2, [np.sqrt(0.5), np.sqrt(5/4), np.inf, np.nan, np.nan, 0, np.nan])]
+    )
+    def test_more_nan_policy_omit_tests(self, ddof, expected, xp):
+        # The slightly strange formatting in the follow array is my attempt to
+        # maintain a clean tabular arrangement of the data while satisfying
+        # the demands of pycodestyle.  Currently, E201 and E241 are not
+        # disabled by the `noqa` annotation.
+        nan = xp.nan
+        x = xp.asarray([[1.0, 2.0, nan, 3.0],
+                        [0.0, 4.0, 3.0, 1.0],
+                        [nan, -.5, 0.5, nan],
+                        [nan, 9.0, 9.0, nan],
+                        [nan, nan, nan, nan],
+                        [3.0, 3.0, 3.0, 3.0],
+                        [0.0, 0.0, 0.0, 0.0]])
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            v = variation(x, axis=1, ddof=ddof, nan_policy='omit')
+        xp_assert_close(v, expected)
+
+    @skip_xp_backends(np_only=True,
+                      reason='`nan_policy` only supports NumPy backend')
+    def test_variation_ddof(self, xp):
+        # test variation with delta degrees of freedom
+        # regression test for gh-13341
+        a = xp.asarray([1., 2., 3., 4., 5.])
+        nan_a = xp.asarray([1, 2, 3, xp.nan, 4, 5, xp.nan])
+        y = variation(a, ddof=1)
+        nan_y = variation(nan_a, nan_policy="omit", ddof=1)
+        xp_assert_close(y, math.sqrt(5/2)/3)
+        assert y == nan_y

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/METADATA

@@ -0,0 +1,268 @@
+Metadata-Version: 2.4
+Name: sentry-sdk
+Version: 2.54.0
+Summary: Python client for Sentry (https://sentry.io)
+Home-page: https://github.com/getsentry/sentry-python
+Author: Sentry Team and Contributors
+Author-email: hello@sentry.io
+License: MIT
+Project-URL: Documentation, https://docs.sentry.io/platforms/python/
+Project-URL: Changelog, https://github.com/getsentry/sentry-python/blob/master/CHANGELOG.md
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: urllib3>=1.26.11
+Requires-Dist: certifi
+Provides-Extra: aiohttp
+Requires-Dist: aiohttp>=3.5; extra == "aiohttp"
+Provides-Extra: anthropic
+Requires-Dist: anthropic>=0.16; extra == "anthropic"
+Provides-Extra: arq
+Requires-Dist: arq>=0.23; extra == "arq"
+Provides-Extra: asyncpg
+Requires-Dist: asyncpg>=0.23; extra == "asyncpg"
+Provides-Extra: beam
+Requires-Dist: apache-beam>=2.12; extra == "beam"
+Provides-Extra: bottle
+Requires-Dist: bottle>=0.12.13; extra == "bottle"
+Provides-Extra: celery
+Requires-Dist: celery>=3; extra == "celery"
+Provides-Extra: celery-redbeat
+Requires-Dist: celery-redbeat>=2; extra == "celery-redbeat"
+Provides-Extra: chalice
+Requires-Dist: chalice>=1.16.0; extra == "chalice"
+Provides-Extra: clickhouse-driver
+Requires-Dist: clickhouse-driver>=0.2.0; extra == "clickhouse-driver"
+Provides-Extra: django
+Requires-Dist: django>=1.8; extra == "django"
+Provides-Extra: falcon
+Requires-Dist: falcon>=1.4; extra == "falcon"
+Provides-Extra: fastapi
+Requires-Dist: fastapi>=0.79.0; extra == "fastapi"
+Provides-Extra: flask
+Requires-Dist: flask>=0.11; extra == "flask"
+Requires-Dist: blinker>=1.1; extra == "flask"
+Requires-Dist: markupsafe; extra == "flask"
+Provides-Extra: grpcio
+Requires-Dist: grpcio>=1.21.1; extra == "grpcio"
+Requires-Dist: protobuf>=3.8.0; extra == "grpcio"
+Provides-Extra: http2
+Requires-Dist: httpcore[http2]==1.*; extra == "http2"
+Provides-Extra: httpx
+Requires-Dist: httpx>=0.16.0; extra == "httpx"
+Provides-Extra: huey
+Requires-Dist: huey>=2; extra == "huey"
+Provides-Extra: huggingface-hub
+Requires-Dist: huggingface_hub>=0.22; extra == "huggingface-hub"
+Provides-Extra: langchain
+Requires-Dist: langchain>=0.0.210; extra == "langchain"
+Provides-Extra: langgraph
+Requires-Dist: langgraph>=0.6.6; extra == "langgraph"
+Provides-Extra: launchdarkly
+Requires-Dist: launchdarkly-server-sdk>=9.8.0; extra == "launchdarkly"
+Provides-Extra: litellm
+Requires-Dist: litellm>=1.77.5; extra == "litellm"
+Provides-Extra: litestar
+Requires-Dist: litestar>=2.0.0; extra == "litestar"
+Provides-Extra: loguru
+Requires-Dist: loguru>=0.5; extra == "loguru"
+Provides-Extra: mcp
+Requires-Dist: mcp>=1.15.0; extra == "mcp"
+Provides-Extra: openai
+Requires-Dist: openai>=1.0.0; extra == "openai"
+Requires-Dist: tiktoken>=0.3.0; extra == "openai"
+Provides-Extra: openfeature
+Requires-Dist: openfeature-sdk>=0.7.1; extra == "openfeature"
+Provides-Extra: opentelemetry
+Requires-Dist: opentelemetry-distro>=0.35b0; extra == "opentelemetry"
+Provides-Extra: opentelemetry-experimental
+Requires-Dist: opentelemetry-distro; extra == "opentelemetry-experimental"
+Provides-Extra: opentelemetry-otlp
+Requires-Dist: opentelemetry-distro[otlp]>=0.35b0; extra == "opentelemetry-otlp"
+Provides-Extra: pure-eval
+Requires-Dist: pure_eval; extra == "pure-eval"
+Requires-Dist: executing; extra == "pure-eval"
+Requires-Dist: asttokens; extra == "pure-eval"
+Provides-Extra: pydantic-ai
+Requires-Dist: pydantic-ai>=1.0.0; extra == "pydantic-ai"
+Provides-Extra: pymongo
+Requires-Dist: pymongo>=3.1; extra == "pymongo"
+Provides-Extra: pyspark
+Requires-Dist: pyspark>=2.4.4; extra == "pyspark"
+Provides-Extra: quart
+Requires-Dist: quart>=0.16.1; extra == "quart"
+Requires-Dist: blinker>=1.1; extra == "quart"
+Provides-Extra: rq
+Requires-Dist: rq>=0.6; extra == "rq"
+Provides-Extra: sanic
+Requires-Dist: sanic>=0.8; extra == "sanic"
+Provides-Extra: sqlalchemy
+Requires-Dist: sqlalchemy>=1.2; extra == "sqlalchemy"
+Provides-Extra: starlette
+Requires-Dist: starlette>=0.19.1; extra == "starlette"
+Provides-Extra: starlite
+Requires-Dist: starlite>=1.48; extra == "starlite"
+Provides-Extra: statsig
+Requires-Dist: statsig>=0.55.3; extra == "statsig"
+Provides-Extra: tornado
+Requires-Dist: tornado>=6; extra == "tornado"
+Provides-Extra: unleash
+Requires-Dist: UnleashClient>=6.0.1; extra == "unleash"
+Provides-Extra: google-genai
+Requires-Dist: google-genai>=1.29.0; extra == "google-genai"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: project-url
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+<a href="https://sentry.io/?utm_source=github&utm_medium=logo" target="_blank">
+  <img src="https://sentry-brand.storage.googleapis.com/github-banners/github-sdk-python.png" alt="Sentry for Python">
+</a>
+<div align="center">
+
+_Bad software is everywhere, and we're tired of it. Sentry is on a mission to help developers write better software faster, so we can get back to enjoying technology. If you want to join us
+[<kbd>**Check out our open positions**</kbd>](https://sentry.io/careers/)_.
+
+[![Discord](https://img.shields.io/discord/621778831602221064?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.com/invite/Ww9hbqr)
+[![X Follow](https://img.shields.io/twitter/follow/sentry?label=sentry&style=social)](https://x.com/intent/follow?screen_name=sentry)
+[![PyPi page link -- version](https://img.shields.io/pypi/v/sentry-sdk.svg)](https://pypi.python.org/pypi/sentry-sdk)
+<img src="https://img.shields.io/badge/python-3.7 | 3.8 | 3.9 | 3.10 | 3.11 | 3.12 | 3.13 | 3.14-blue.svg" alt="python">
+[![Build Status](https://github.com/getsentry/sentry-python/actions/workflows/ci.yml/badge.svg)](https://github.com/getsentry/sentry-python/actions/workflows/ci.yml)
+
+<br/>
+
+</div>
+
+
+# Official Sentry SDK for Python
+
+Welcome to the official Python SDK for **[Sentry](http://sentry.io/)**.
+
+
+## 📦 Getting Started
+
+### Prerequisites
+
+You need a Sentry [account](https://sentry.io/signup/) and [project](https://docs.sentry.io/product/projects/).
+
+### Installation
+
+Getting Sentry into your project is straightforward. Just run this command in your terminal:
+
+```bash
+pip install --upgrade sentry-sdk
+```
+
+### Basic Configuration
+
+Here's a quick configuration example to get Sentry up and running:
+
+```python
+import sentry_sdk
+
+sentry_sdk.init(
+    "https://12927b5f211046b575ee51fd8b1ac34f@o1.ingest.sentry.io/1",  # Your DSN here
+
+    # Set traces_sample_rate to 1.0 to capture 100%
+    # of traces for performance monitoring.
+    traces_sample_rate=1.0,
+)
+```
+
+With this configuration, Sentry will monitor for exceptions and performance issues.
+
+### Quick Usage Example
+
+To generate some events that will show up in Sentry, you can log messages or capture errors:
+
+```python
+import sentry_sdk
+sentry_sdk.init(...)  # same as above
+
+sentry_sdk.capture_message("Hello Sentry!")  # You'll see this in your Sentry dashboard.
+
+raise ValueError("Oops, something went wrong!")  # This will create an error event in Sentry.
+```
+
+
+## 📚 Documentation
+
+For more details on advanced usage, integrations, and customization, check out the full documentation on [https://docs.sentry.io](https://docs.sentry.io/).
+
+
+## 🧩 Integrations
+
+Sentry integrates with a ton of popular Python libraries and frameworks, including [FastAPI](https://docs.sentry.io/platforms/python/integrations/fastapi/), [Django](https://docs.sentry.io/platforms/python/integrations/django/), [Celery](https://docs.sentry.io/platforms/python/integrations/celery/), [OpenAI](https://docs.sentry.io/platforms/python/integrations/openai/) and many, many more.  Check out the [full list of integrations](https://docs.sentry.io/platforms/python/integrations/) to get the full picture.
+
+
+## 🚧 Migrating Between Versions?
+
+### From `1.x` to `2.x`
+
+If you're using the older `1.x` version of the SDK, now's the time to upgrade to `2.x`. It includes significant upgrades and new features. Check our [migration guide](https://docs.sentry.io/platforms/python/migration/1.x-to-2.x) for assistance.
+
+### From `raven-python`
+
+Using the legacy `raven-python` client? It's now in maintenance mode, and we recommend migrating to the new SDK for an improved experience. Get all the details in our [migration guide](https://docs.sentry.io/platforms/python/migration/raven-to-sentry-sdk/).
+
+
+## 🙌 Want to Contribute?
+
+We'd love your help in improving the Sentry SDK! Whether it's fixing bugs, adding features, writing new integrations, or enhancing documentation, every contribution is valuable.
+
+For details on how to contribute, please read our [contribution guide](CONTRIBUTING.md) and explore the [open issues](https://github.com/getsentry/sentry-python/issues).
+
+
+## 🛟 Need Help?
+
+If you encounter issues or need help setting up or configuring the SDK, don't hesitate to reach out to the [Sentry Community on Discord](https://discord.com/invite/Ww9hbqr). There is a ton of great people there ready to help!
+
+
+## 🔗 Resources
+
+Here are all resources to help you make the most of Sentry:
+
+- [Documentation](https://docs.sentry.io/platforms/python/) - Official documentation to get started.
+- [Discord](https://discord.com/invite/Ww9hbqr) - Join our Discord community.
+- [X/Twitter](https://x.com/intent/follow?screen_name=sentry) -  Follow us on X (Twitter) for updates.
+- [Stack Overflow](https://stackoverflow.com/questions/tagged/sentry) - Questions and answers related to Sentry.
+
+<a name="license"></a>
+## 📃 License
+
+The SDK is open-source and available under the MIT license. Check out the [LICENSE](LICENSE) file for more information.
+
+
+## 😘 Contributors
+
+Thanks to everyone who has helped improve the SDK!
+
+<a href="https://github.com/getsentry/sentry-python/graphs/contributors">
+  <img src="https://contributors-img.web.app/image?repo=getsentry/sentry-python" />
+</a>

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+sentry_sdk/profiler/__pycache__/utils.cpython-310.pyc,,
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+sentry_sdk/scope.py,sha256=HtA_ivY_Syuw2sxXiXCgp1PsRg46CetAuVeth_slfzk,68615
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+sentry_sdk/serializer.py,sha256=W3HrcoJ9iMzHa6j9Nmu5Y7Qopbkon3ovxuPxRM5R4pU,13199
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+sentry_sdk/spotlight.py,sha256=0bJrMpoT47xS2LJzpDfUZ5z5YEhdUla78r0YDtVm2Zo,12191
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+sentry_sdk/types.py,sha256=A92AqvfrGQZ9KY6FaUjKfL9F1HK7Ui3heQilVzfzYCs,1269
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miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (82.0.0)
+Root-Is-Purelib: true
+Tag: py2-none-any
+Tag: py3-none-any
+

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[opentelemetry_propagator]
+sentry = sentry_sdk.integrations.opentelemetry:SentryPropagator

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/licenses/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Functional Software, Inc. dba Sentry
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk-2.54.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+sentry_sdk

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/__init__.py

@@ -0,0 +1,69 @@
+from sentry_sdk import profiler
+from sentry_sdk import metrics
+from sentry_sdk.scope import Scope
+from sentry_sdk.transport import Transport, HttpTransport
+from sentry_sdk.client import Client
+
+from sentry_sdk.api import *  # noqa
+from sentry_sdk.consts import VERSION
+
+__all__ = [  # noqa
+    "Hub",
+    "Scope",
+    "Client",
+    "Transport",
+    "HttpTransport",
+    "VERSION",
+    "integrations",
+    # From sentry_sdk.api
+    "init",
+    "add_attachment",
+    "add_breadcrumb",
+    "capture_event",
+    "capture_exception",
+    "capture_message",
+    "configure_scope",
+    "continue_trace",
+    "flush",
+    "get_baggage",
+    "get_client",
+    "get_global_scope",
+    "get_isolation_scope",
+    "get_current_scope",
+    "get_current_span",
+    "get_traceparent",
+    "is_initialized",
+    "isolation_scope",
+    "last_event_id",
+    "new_scope",
+    "push_scope",
+    "remove_attribute",
+    "set_attribute",
+    "set_context",
+    "set_extra",
+    "set_level",
+    "set_measurement",
+    "set_tag",
+    "set_tags",
+    "set_user",
+    "start_span",
+    "start_transaction",
+    "trace",
+    "monitor",
+    "logger",
+    "metrics",
+    "profiler",
+    "start_session",
+    "end_session",
+    "set_transaction_name",
+    "update_current_span",
+]
+
+# Initialize the debug support after everything is loaded
+from sentry_sdk.debug import init_debug_support
+
+init_debug_support()
+del init_debug_support
+
+# circular imports
+from sentry_sdk.hub import Hub

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_batcher.py

@@ -0,0 +1,139 @@
+import os
+import random
+import threading
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING, TypeVar, Generic
+
+from sentry_sdk.utils import format_timestamp, safe_repr, serialize_attribute
+from sentry_sdk.envelope import Envelope, Item, PayloadRef
+
+if TYPE_CHECKING:
+    from typing import Optional, Callable, Any
+
+T = TypeVar("T")
+
+
+class Batcher(Generic[T]):
+    MAX_BEFORE_FLUSH = 100
+    MAX_BEFORE_DROP = 1_000
+    FLUSH_WAIT_TIME = 5.0
+
+    TYPE = ""
+    CONTENT_TYPE = ""
+
+    def __init__(
+        self,
+        capture_func: "Callable[[Envelope], None]",
+        record_lost_func: "Callable[..., None]",
+    ) -> None:
+        self._buffer: "list[T]" = []
+        self._capture_func = capture_func
+        self._record_lost_func = record_lost_func
+        self._running = True
+        self._lock = threading.Lock()
+
+        self._flush_event: "threading.Event" = threading.Event()
+
+        self._flusher: "Optional[threading.Thread]" = None
+        self._flusher_pid: "Optional[int]" = None
+
+    def _ensure_thread(self) -> bool:
+        """For forking processes we might need to restart this thread.
+        This ensures that our process actually has that thread running.
+        """
+        if not self._running:
+            return False
+
+        pid = os.getpid()
+        if self._flusher_pid == pid:
+            return True
+
+        with self._lock:
+            # Recheck to make sure another thread didn't get here and start the
+            # the flusher in the meantime
+            if self._flusher_pid == pid:
+                return True
+
+            self._flusher_pid = pid
+
+            self._flusher = threading.Thread(target=self._flush_loop)
+            self._flusher.daemon = True
+
+            try:
+                self._flusher.start()
+            except RuntimeError:
+                # Unfortunately at this point the interpreter is in a state that no
+                # longer allows us to spawn a thread and we have to bail.
+                self._running = False
+                return False
+
+        return True
+
+    def _flush_loop(self) -> None:
+        while self._running:
+            self._flush_event.wait(self.FLUSH_WAIT_TIME + random.random())
+            self._flush_event.clear()
+            self._flush()
+
+    def add(self, item: "T") -> None:
+        if not self._ensure_thread() or self._flusher is None:
+            return None
+
+        with self._lock:
+            if len(self._buffer) >= self.MAX_BEFORE_DROP:
+                self._record_lost(item)
+                return None
+
+            self._buffer.append(item)
+            if len(self._buffer) >= self.MAX_BEFORE_FLUSH:
+                self._flush_event.set()
+
+    def kill(self) -> None:
+        if self._flusher is None:
+            return
+
+        self._running = False
+        self._flush_event.set()
+        self._flusher = None
+
+    def flush(self) -> None:
+        self._flush()
+
+    def _add_to_envelope(self, envelope: "Envelope") -> None:
+        envelope.add_item(
+            Item(
+                type=self.TYPE,
+                content_type=self.CONTENT_TYPE,
+                headers={
+                    "item_count": len(self._buffer),
+                },
+                payload=PayloadRef(
+                    json={
+                        "items": [
+                            self._to_transport_format(item) for item in self._buffer
+                        ]
+                    }
+                ),
+            )
+        )
+
+    def _flush(self) -> "Optional[Envelope]":
+        envelope = Envelope(
+            headers={"sent_at": format_timestamp(datetime.now(timezone.utc))}
+        )
+        with self._lock:
+            if len(self._buffer) == 0:
+                return None
+
+            self._add_to_envelope(envelope)
+            self._buffer.clear()
+
+        self._capture_func(envelope)
+        return envelope
+
+    def _record_lost(self, item: "T") -> None:
+        pass
+
+    @staticmethod
+    def _to_transport_format(item: "T") -> "Any":
+        pass

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_compat.py

@@ -0,0 +1,94 @@
+import sys
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Any
+    from typing import TypeVar
+
+    T = TypeVar("T")
+
+
+PY37 = sys.version_info[0] == 3 and sys.version_info[1] >= 7
+PY38 = sys.version_info[0] == 3 and sys.version_info[1] >= 8
+PY310 = sys.version_info[0] == 3 and sys.version_info[1] >= 10
+PY311 = sys.version_info[0] == 3 and sys.version_info[1] >= 11
+
+
+def with_metaclass(meta: "Any", *bases: "Any") -> "Any":
+    class MetaClass(type):
+        def __new__(metacls: "Any", name: "Any", this_bases: "Any", d: "Any") -> "Any":
+            return meta(name, bases, d)
+
+    return type.__new__(MetaClass, "temporary_class", (), {})
+
+
+def check_uwsgi_thread_support() -> bool:
+    # We check two things here:
+    #
+    # 1. uWSGI doesn't run in threaded mode by default -- issue a warning if
+    #    that's the case.
+    #
+    # 2. Additionally, if uWSGI is running in preforking mode (default), it needs
+    #    the --py-call-uwsgi-fork-hooks option for the SDK to work properly. This
+    #    is because any background threads spawned before the main process is
+    #    forked are NOT CLEANED UP IN THE CHILDREN BY DEFAULT even if
+    #    --enable-threads is on. One has to explicitly provide
+    #    --py-call-uwsgi-fork-hooks to force uWSGI to run regular cpython
+    #    after-fork hooks that take care of cleaning up stale thread data.
+    try:
+        from uwsgi import opt  # type: ignore
+    except ImportError:
+        return True
+
+    from sentry_sdk.consts import FALSE_VALUES
+
+    def enabled(option: str) -> bool:
+        value = opt.get(option, False)
+        if isinstance(value, bool):
+            return value
+
+        if isinstance(value, bytes):
+            try:
+                value = value.decode()
+            except Exception:
+                pass
+
+        return value and str(value).lower() not in FALSE_VALUES
+
+    # When `threads` is passed in as a uwsgi option,
+    # `enable-threads` is implied on.
+    threads_enabled = "threads" in opt or enabled("enable-threads")
+    fork_hooks_on = enabled("py-call-uwsgi-fork-hooks")
+    lazy_mode = enabled("lazy-apps") or enabled("lazy")
+
+    if lazy_mode and not threads_enabled:
+        from warnings import warn
+
+        warn(
+            Warning(
+                "IMPORTANT: "
+                "We detected the use of uWSGI without thread support. "
+                "This might lead to unexpected issues. "
+                'Please run uWSGI with "--enable-threads" for full support.'
+            )
+        )
+
+        return False
+
+    elif not lazy_mode and (not threads_enabled or not fork_hooks_on):
+        from warnings import warn
+
+        warn(
+            Warning(
+                "IMPORTANT: "
+                "We detected the use of uWSGI in preforking mode without "
+                "thread support. This might lead to crashing workers. "
+                'Please run uWSGI with both "--enable-threads" and '
+                '"--py-call-uwsgi-fork-hooks" for full support.'
+            )
+        )
+
+        return False
+
+    return True

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_init_implementation.py

@@ -0,0 +1,79 @@
+import warnings
+
+from typing import TYPE_CHECKING
+
+import sentry_sdk
+
+if TYPE_CHECKING:
+    from typing import Any, ContextManager, Optional
+
+    import sentry_sdk.consts
+
+
+class _InitGuard:
+    _CONTEXT_MANAGER_DEPRECATION_WARNING_MESSAGE = (
+        "Using the return value of sentry_sdk.init as a context manager "
+        "and manually calling the __enter__ and __exit__ methods on the "
+        "return value are deprecated. We are no longer maintaining this "
+        "functionality, and we will remove it in the next major release."
+    )
+
+    def __init__(self, client: "sentry_sdk.Client") -> None:
+        self._client = client
+
+    def __enter__(self) -> "_InitGuard":
+        warnings.warn(
+            self._CONTEXT_MANAGER_DEPRECATION_WARNING_MESSAGE,
+            stacklevel=2,
+            category=DeprecationWarning,
+        )
+
+        return self
+
+    def __exit__(self, exc_type: "Any", exc_value: "Any", tb: "Any") -> None:
+        warnings.warn(
+            self._CONTEXT_MANAGER_DEPRECATION_WARNING_MESSAGE,
+            stacklevel=2,
+            category=DeprecationWarning,
+        )
+
+        c = self._client
+        if c is not None:
+            c.close()
+
+
+def _check_python_deprecations() -> None:
+    # Since we're likely to deprecate Python versions in the future, I'm keeping
+    # this handy function around. Use this to detect the Python version used and
+    # to output logger.warning()s if it's deprecated.
+    pass
+
+
+def _init(*args: "Optional[str]", **kwargs: "Any") -> "ContextManager[Any]":
+    """Initializes the SDK and optionally integrations.
+
+    This takes the same arguments as the client constructor.
+    """
+    client = sentry_sdk.Client(*args, **kwargs)
+    sentry_sdk.get_global_scope().set_client(client)
+    _check_python_deprecations()
+    rv = _InitGuard(client)
+    return rv
+
+
+if TYPE_CHECKING:
+    # Make mypy, PyCharm and other static analyzers think `init` is a type to
+    # have nicer autocompletion for params.
+    #
+    # Use `ClientConstructor` to define the argument types of `init` and
+    # `ContextManager[Any]` to tell static analyzers about the return type.
+
+    class init(sentry_sdk.consts.ClientConstructor, _InitGuard):  # noqa: N801
+        pass
+
+else:
+    # Alias `init` for actual usage. Go through the lambda indirection to throw
+    # PyCharm off of the weakly typed signature (it would otherwise discover
+    # both the weakly typed signature of `_init` and our faked `init` type).
+
+    init = (lambda: _init)()

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_log_batcher.py

@@ -0,0 +1,56 @@
+from typing import TYPE_CHECKING
+
+from sentry_sdk._batcher import Batcher
+from sentry_sdk.utils import serialize_attribute
+from sentry_sdk.envelope import Envelope, Item, PayloadRef
+
+if TYPE_CHECKING:
+    from typing import Any
+    from sentry_sdk._types import Log
+
+
+class LogBatcher(Batcher["Log"]):
+    MAX_BEFORE_FLUSH = 100
+    MAX_BEFORE_DROP = 1_000
+    FLUSH_WAIT_TIME = 5.0
+
+    TYPE = "log"
+    CONTENT_TYPE = "application/vnd.sentry.items.log+json"
+
+    @staticmethod
+    def _to_transport_format(item: "Log") -> "Any":
+        if "sentry.severity_number" not in item["attributes"]:
+            item["attributes"]["sentry.severity_number"] = item["severity_number"]
+        if "sentry.severity_text" not in item["attributes"]:
+            item["attributes"]["sentry.severity_text"] = item["severity_text"]
+
+        res = {
+            "timestamp": int(item["time_unix_nano"]) / 1.0e9,
+            "trace_id": item.get("trace_id", "00000000-0000-0000-0000-000000000000"),
+            "span_id": item.get("span_id"),
+            "level": str(item["severity_text"]),
+            "body": str(item["body"]),
+            "attributes": {
+                k: serialize_attribute(v) for (k, v) in item["attributes"].items()
+            },
+        }
+
+        return res
+
+    def _record_lost(self, item: "Log") -> None:
+        # Construct log envelope item without sending it to report lost bytes
+        log_item = Item(
+            type=self.TYPE,
+            content_type=self.CONTENT_TYPE,
+            headers={
+                "item_count": 1,
+            },
+            payload=PayloadRef(json={"items": [self._to_transport_format(item)]}),
+        )
+
+        self._record_lost_func(
+            reason="queue_overflow",
+            data_category="log_item",
+            item=log_item,
+            quantity=1,
+        )

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_lru_cache.py

@@ -0,0 +1,43 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Any
+
+
+_SENTINEL = object()
+
+
+class LRUCache:
+    def __init__(self, max_size: int) -> None:
+        if max_size <= 0:
+            raise AssertionError(f"invalid max_size: {max_size}")
+        self.max_size = max_size
+        self._data: "dict[Any, Any]" = {}
+        self.hits = self.misses = 0
+        self.full = False
+
+    def set(self, key: "Any", value: "Any") -> None:
+        current = self._data.pop(key, _SENTINEL)
+        if current is not _SENTINEL:
+            self._data[key] = value
+        elif self.full:
+            self._data.pop(next(iter(self._data)))
+            self._data[key] = value
+        else:
+            self._data[key] = value
+        self.full = len(self._data) >= self.max_size
+
+    def get(self, key: "Any", default: "Any" = None) -> "Any":
+        try:
+            ret = self._data.pop(key)
+        except KeyError:
+            self.misses += 1
+            ret = default
+        else:
+            self.hits += 1
+            self._data[key] = ret
+
+        return ret
+
+    def get_all(self) -> "list[tuple[Any, Any]]":
+        return list(self._data.items())

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_metrics_batcher.py

@@ -0,0 +1,46 @@
+from typing import TYPE_CHECKING
+
+from sentry_sdk._batcher import Batcher
+from sentry_sdk.utils import serialize_attribute
+from sentry_sdk.envelope import Item
+
+if TYPE_CHECKING:
+    from typing import Any
+    from sentry_sdk._types import Metric
+
+
+class MetricsBatcher(Batcher["Metric"]):
+    MAX_BEFORE_FLUSH = 1000
+    MAX_BEFORE_DROP = 10_000
+    FLUSH_WAIT_TIME = 5.0
+
+    TYPE = "trace_metric"
+    CONTENT_TYPE = "application/vnd.sentry.items.trace-metric+json"
+
+    @staticmethod
+    def _to_transport_format(item: "Metric") -> "Any":
+        res = {
+            "timestamp": item["timestamp"],
+            "trace_id": item["trace_id"],
+            "name": item["name"],
+            "type": item["type"],
+            "value": item["value"],
+            "attributes": {
+                k: serialize_attribute(v) for (k, v) in item["attributes"].items()
+            },
+        }
+
+        if item.get("span_id") is not None:
+            res["span_id"] = item["span_id"]
+
+        if item.get("unit") is not None:
+            res["unit"] = item["unit"]
+
+        return res
+
+    def _record_lost(self, item: "Metric") -> None:
+        self._record_lost_func(
+            reason="queue_overflow",
+            data_category="trace_metric",
+            quantity=1,
+        )

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_queue.py

@@ -0,0 +1,289 @@
+"""
+A fork of Python 3.6's stdlib queue (found in Pythons 'cpython/Lib/queue.py')
+with Lock swapped out for RLock to avoid a deadlock while garbage collecting.
+
+https://github.com/python/cpython/blob/v3.6.12/Lib/queue.py
+
+
+See also
+https://codewithoutrules.com/2017/08/16/concurrency-python/
+https://bugs.python.org/issue14976
+https://github.com/sqlalchemy/sqlalchemy/blob/4eb747b61f0c1b1c25bdee3856d7195d10a0c227/lib/sqlalchemy/queue.py#L1
+
+We also vendor the code to evade eventlet's broken monkeypatching, see
+https://github.com/getsentry/sentry-python/pull/484
+
+
+Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Python Software Foundation;
+
+All Rights Reserved
+
+
+PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+--------------------------------------------
+
+1. This LICENSE AGREEMENT is between the Python Software Foundation
+("PSF"), and the Individual or Organization ("Licensee") accessing and
+otherwise using this software ("Python") in source or binary form and
+its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, PSF hereby
+grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+analyze, test, perform and/or display publicly, prepare derivative works,
+distribute, and otherwise use Python alone or in any derivative version,
+provided, however, that PSF's License Agreement and PSF's notice of copyright,
+i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Python Software Foundation;
+All Rights Reserved" are retained in Python alone or in any derivative version
+prepared by Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python.
+
+4. PSF is making Python available to Licensee on an "AS IS"
+basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between PSF and
+Licensee.  This License Agreement does not grant permission to use PSF
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using Python, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+"""
+
+import threading
+
+from collections import deque
+from time import time
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Any
+
+__all__ = ["EmptyError", "FullError", "Queue"]
+
+
+class EmptyError(Exception):
+    "Exception raised by Queue.get(block=0)/get_nowait()."
+
+    pass
+
+
+class FullError(Exception):
+    "Exception raised by Queue.put(block=0)/put_nowait()."
+
+    pass
+
+
+class Queue:
+    """Create a queue object with a given maximum size.
+
+    If maxsize is <= 0, the queue size is infinite.
+    """
+
+    def __init__(self, maxsize=0):
+        self.maxsize = maxsize
+        self._init(maxsize)
+
+        # mutex must be held whenever the queue is mutating.  All methods
+        # that acquire mutex must release it before returning.  mutex
+        # is shared between the three conditions, so acquiring and
+        # releasing the conditions also acquires and releases mutex.
+        self.mutex = threading.RLock()
+
+        # Notify not_empty whenever an item is added to the queue; a
+        # thread waiting to get is notified then.
+        self.not_empty = threading.Condition(self.mutex)
+
+        # Notify not_full whenever an item is removed from the queue;
+        # a thread waiting to put is notified then.
+        self.not_full = threading.Condition(self.mutex)
+
+        # Notify all_tasks_done whenever the number of unfinished tasks
+        # drops to zero; thread waiting to join() is notified to resume
+        self.all_tasks_done = threading.Condition(self.mutex)
+        self.unfinished_tasks = 0
+
+    def task_done(self):
+        """Indicate that a formerly enqueued task is complete.
+
+        Used by Queue consumer threads.  For each get() used to fetch a task,
+        a subsequent call to task_done() tells the queue that the processing
+        on the task is complete.
+
+        If a join() is currently blocking, it will resume when all items
+        have been processed (meaning that a task_done() call was received
+        for every item that had been put() into the queue).
+
+        Raises a ValueError if called more times than there were items
+        placed in the queue.
+        """
+        with self.all_tasks_done:
+            unfinished = self.unfinished_tasks - 1
+            if unfinished <= 0:
+                if unfinished < 0:
+                    raise ValueError("task_done() called too many times")
+                self.all_tasks_done.notify_all()
+            self.unfinished_tasks = unfinished
+
+    def join(self):
+        """Blocks until all items in the Queue have been gotten and processed.
+
+        The count of unfinished tasks goes up whenever an item is added to the
+        queue. The count goes down whenever a consumer thread calls task_done()
+        to indicate the item was retrieved and all work on it is complete.
+
+        When the count of unfinished tasks drops to zero, join() unblocks.
+        """
+        with self.all_tasks_done:
+            while self.unfinished_tasks:
+                self.all_tasks_done.wait()
+
+    def qsize(self):
+        """Return the approximate size of the queue (not reliable!)."""
+        with self.mutex:
+            return self._qsize()
+
+    def empty(self):
+        """Return True if the queue is empty, False otherwise (not reliable!).
+
+        This method is likely to be removed at some point.  Use qsize() == 0
+        as a direct substitute, but be aware that either approach risks a race
+        condition where a queue can grow before the result of empty() or
+        qsize() can be used.
+
+        To create code that needs to wait for all queued tasks to be
+        completed, the preferred technique is to use the join() method.
+        """
+        with self.mutex:
+            return not self._qsize()
+
+    def full(self):
+        """Return True if the queue is full, False otherwise (not reliable!).
+
+        This method is likely to be removed at some point.  Use qsize() >= n
+        as a direct substitute, but be aware that either approach risks a race
+        condition where a queue can shrink before the result of full() or
+        qsize() can be used.
+        """
+        with self.mutex:
+            return 0 < self.maxsize <= self._qsize()
+
+    def put(self, item, block=True, timeout=None):
+        """Put an item into the queue.
+
+        If optional args 'block' is true and 'timeout' is None (the default),
+        block if necessary until a free slot is available. If 'timeout' is
+        a non-negative number, it blocks at most 'timeout' seconds and raises
+        the FullError exception if no free slot was available within that time.
+        Otherwise ('block' is false), put an item on the queue if a free slot
+        is immediately available, else raise the FullError exception ('timeout'
+        is ignored in that case).
+        """
+        with self.not_full:
+            if self.maxsize > 0:
+                if not block:
+                    if self._qsize() >= self.maxsize:
+                        raise FullError()
+                elif timeout is None:
+                    while self._qsize() >= self.maxsize:
+                        self.not_full.wait()
+                elif timeout < 0:
+                    raise ValueError("'timeout' must be a non-negative number")
+                else:
+                    endtime = time() + timeout
+                    while self._qsize() >= self.maxsize:
+                        remaining = endtime - time()
+                        if remaining <= 0.0:
+                            raise FullError()
+                        self.not_full.wait(remaining)
+            self._put(item)
+            self.unfinished_tasks += 1
+            self.not_empty.notify()
+
+    def get(self, block=True, timeout=None):
+        """Remove and return an item from the queue.
+
+        If optional args 'block' is true and 'timeout' is None (the default),
+        block if necessary until an item is available. If 'timeout' is
+        a non-negative number, it blocks at most 'timeout' seconds and raises
+        the EmptyError exception if no item was available within that time.
+        Otherwise ('block' is false), return an item if one is immediately
+        available, else raise the EmptyError exception ('timeout' is ignored
+        in that case).
+        """
+        with self.not_empty:
+            if not block:
+                if not self._qsize():
+                    raise EmptyError()
+            elif timeout is None:
+                while not self._qsize():
+                    self.not_empty.wait()
+            elif timeout < 0:
+                raise ValueError("'timeout' must be a non-negative number")
+            else:
+                endtime = time() + timeout
+                while not self._qsize():
+                    remaining = endtime - time()
+                    if remaining <= 0.0:
+                        raise EmptyError()
+                    self.not_empty.wait(remaining)
+            item = self._get()
+            self.not_full.notify()
+            return item
+
+    def put_nowait(self, item):
+        """Put an item into the queue without blocking.
+
+        Only enqueue the item if a free slot is immediately available.
+        Otherwise raise the FullError exception.
+        """
+        return self.put(item, block=False)
+
+    def get_nowait(self):
+        """Remove and return an item from the queue without blocking.
+
+        Only get an item if one is immediately available. Otherwise
+        raise the EmptyError exception.
+        """
+        return self.get(block=False)
+
+    # Override these methods to implement other queue organizations
+    # (e.g. stack or priority queue).
+    # These will only be called with appropriate locks held
+
+    # Initialize the queue representation
+    def _init(self, maxsize):
+        self.queue: "Any" = deque()
+
+    def _qsize(self):
+        return len(self.queue)
+
+    # Put a new item in the queue
+    def _put(self, item):
+        self.queue.append(item)
+
+    # Get an item from the queue
+    def _get(self):
+        return self.queue.popleft()

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_span_batcher.py

@@ -0,0 +1,128 @@
+import threading
+from collections import defaultdict
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING
+
+from sentry_sdk._batcher import Batcher
+from sentry_sdk.consts import SPANSTATUS
+from sentry_sdk.envelope import Envelope, Item, PayloadRef
+from sentry_sdk.utils import format_timestamp, serialize_attribute, safe_repr
+
+if TYPE_CHECKING:
+    from typing import Any, Callable, Optional
+    from sentry_sdk.traces import StreamedSpan
+    from sentry_sdk._types import SerializedAttributeValue
+
+
+class SpanBatcher(Batcher["StreamedSpan"]):
+    # TODO[span-first]: size-based flushes
+    # TODO[span-first]: adjust flush/drop defaults
+    MAX_BEFORE_FLUSH = 1000
+    MAX_BEFORE_DROP = 5000
+    FLUSH_WAIT_TIME = 5.0
+
+    TYPE = "span"
+    CONTENT_TYPE = "application/vnd.sentry.items.span.v2+json"
+
+    def __init__(
+        self,
+        capture_func: "Callable[[Envelope], None]",
+        record_lost_func: "Callable[..., None]",
+    ) -> None:
+        # Spans from different traces cannot be emitted in the same envelope
+        # since the envelope contains a shared trace header. That's why we bucket
+        # by trace_id, so that we can then send the buckets each in its own
+        # envelope.
+        # trace_id -> span buffer
+        self._span_buffer: dict[str, list["StreamedSpan"]] = defaultdict(list)
+        self._capture_func = capture_func
+        self._record_lost_func = record_lost_func
+        self._running = True
+        self._lock = threading.Lock()
+
+        self._flush_event: "threading.Event" = threading.Event()
+
+        self._flusher: "Optional[threading.Thread]" = None
+        self._flusher_pid: "Optional[int]" = None
+
+    def get_size(self) -> int:
+        # caller is responsible for locking before checking this
+        return sum(len(buffer) for buffer in self._span_buffer.values())
+
+    def add(self, span: "StreamedSpan") -> None:
+        if not self._ensure_thread() or self._flusher is None:
+            return None
+
+        with self._lock:
+            size = self.get_size()
+            if size >= self.MAX_BEFORE_DROP:
+                self._record_lost_func(
+                    reason="queue_overflow",
+                    data_category="span",
+                    quantity=1,
+                )
+                return None
+
+            self._span_buffer[span.trace_id].append(span)
+            if size + 1 >= self.MAX_BEFORE_FLUSH:
+                self._flush_event.set()
+
+    @staticmethod
+    def _to_transport_format(item: "StreamedSpan") -> "Any":
+        # TODO[span-first]
+        res: "dict[str, Any]" = {
+            "span_id": item.span_id,
+            "name": item._name,
+            "status": item._status,
+        }
+
+        if item._attributes:
+            res["attributes"] = {
+                k: serialize_attribute(v) for (k, v) in item._attributes.items()
+            }
+
+        return res
+
+    def _flush(self) -> None:
+        with self._lock:
+            if len(self._span_buffer) == 0:
+                return None
+
+            envelopes = []
+            for trace_id, spans in self._span_buffer.items():
+                if spans:
+                    # TODO[span-first]
+                    # dsc = spans[0].dynamic_sampling_context()
+                    dsc = None
+
+                    envelope = Envelope(
+                        headers={
+                            "sent_at": format_timestamp(datetime.now(timezone.utc)),
+                            "trace": dsc,
+                        }
+                    )
+
+                    envelope.add_item(
+                        Item(
+                            type="span",
+                            content_type="application/vnd.sentry.items.span.v2+json",
+                            headers={
+                                "item_count": len(spans),
+                            },
+                            payload=PayloadRef(
+                                json={
+                                    "items": [
+                                        self._to_transport_format(span)
+                                        for span in spans
+                                    ]
+                                }
+                            ),
+                        )
+                    )
+
+                    envelopes.append(envelope)
+
+            self._span_buffer.clear()
+
+        for envelope in envelopes:
+            self._capture_func(envelope)

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_types.py

@@ -0,0 +1,362 @@
+from typing import TYPE_CHECKING, TypeVar, Union
+
+
+# Re-exported for compat, since code out there in the wild might use this variable.
+MYPY = TYPE_CHECKING
+
+
+SENSITIVE_DATA_SUBSTITUTE = "[Filtered]"
+BLOB_DATA_SUBSTITUTE = "[Blob substitute]"
+
+
+class AnnotatedValue:
+    """
+    Meta information for a data field in the event payload.
+    This is to tell Relay that we have tampered with the fields value.
+    See:
+    https://github.com/getsentry/relay/blob/be12cd49a0f06ea932ed9b9f93a655de5d6ad6d1/relay-general/src/types/meta.rs#L407-L423
+    """
+
+    __slots__ = ("value", "metadata")
+
+    def __init__(self, value: "Optional[Any]", metadata: "Dict[str, Any]") -> None:
+        self.value = value
+        self.metadata = metadata
+
+    def __eq__(self, other: "Any") -> bool:
+        if not isinstance(other, AnnotatedValue):
+            return False
+
+        return self.value == other.value and self.metadata == other.metadata
+
+    def __str__(self: "AnnotatedValue") -> str:
+        return str({"value": str(self.value), "metadata": str(self.metadata)})
+
+    def __len__(self: "AnnotatedValue") -> int:
+        if self.value is not None:
+            return len(self.value)
+        else:
+            return 0
+
+    @classmethod
+    def removed_because_raw_data(cls) -> "AnnotatedValue":
+        """The value was removed because it could not be parsed. This is done for request body values that are not json nor a form."""
+        return AnnotatedValue(
+            value="",
+            metadata={
+                "rem": [  # Remark
+                    [
+                        "!raw",  # Unparsable raw data
+                        "x",  # The fields original value was removed
+                    ]
+                ]
+            },
+        )
+
+    @classmethod
+    def removed_because_over_size_limit(cls, value: "Any" = "") -> "AnnotatedValue":
+        """
+        The actual value was removed because the size of the field exceeded the configured maximum size,
+        for example specified with the max_request_body_size sdk option.
+        """
+        return AnnotatedValue(
+            value=value,
+            metadata={
+                "rem": [  # Remark
+                    [
+                        "!config",  # Because of configured maximum size
+                        "x",  # The fields original value was removed
+                    ]
+                ]
+            },
+        )
+
+    @classmethod
+    def substituted_because_contains_sensitive_data(cls) -> "AnnotatedValue":
+        """The actual value was removed because it contained sensitive information."""
+        return AnnotatedValue(
+            value=SENSITIVE_DATA_SUBSTITUTE,
+            metadata={
+                "rem": [  # Remark
+                    [
+                        "!config",  # Because of SDK configuration (in this case the config is the hard coded removal of certain django cookies)
+                        "s",  # The fields original value was substituted
+                    ]
+                ]
+            },
+        )
+
+
+T = TypeVar("T")
+Annotated = Union[AnnotatedValue, T]
+
+
+if TYPE_CHECKING:
+    from collections.abc import Container, MutableMapping, Sequence
+
+    from datetime import datetime
+
+    from types import TracebackType
+    from typing import Any
+    from typing import Callable
+    from typing import Dict
+    from typing import Mapping
+    from typing import NotRequired
+    from typing import Optional
+    from typing import Tuple
+    from typing import Type
+    from typing_extensions import Literal, TypedDict
+
+    class SDKInfo(TypedDict):
+        name: str
+        version: str
+        packages: "Sequence[Mapping[str, str]]"
+
+    # "critical" is an alias of "fatal" recognized by Relay
+    LogLevelStr = Literal["fatal", "critical", "error", "warning", "info", "debug"]
+
+    DurationUnit = Literal[
+        "nanosecond",
+        "microsecond",
+        "millisecond",
+        "second",
+        "minute",
+        "hour",
+        "day",
+        "week",
+    ]
+
+    InformationUnit = Literal[
+        "bit",
+        "byte",
+        "kilobyte",
+        "kibibyte",
+        "megabyte",
+        "mebibyte",
+        "gigabyte",
+        "gibibyte",
+        "terabyte",
+        "tebibyte",
+        "petabyte",
+        "pebibyte",
+        "exabyte",
+        "exbibyte",
+    ]
+
+    FractionUnit = Literal["ratio", "percent"]
+    MeasurementUnit = Union[DurationUnit, InformationUnit, FractionUnit, str]
+
+    MeasurementValue = TypedDict(
+        "MeasurementValue",
+        {
+            "value": float,
+            "unit": NotRequired[Optional[MeasurementUnit]],
+        },
+    )
+
+    Event = TypedDict(
+        "Event",
+        {
+            "breadcrumbs": Annotated[
+                dict[Literal["values"], list[dict[str, Any]]]
+            ],  # TODO: We can expand on this type
+            "check_in_id": str,
+            "contexts": dict[str, dict[str, object]],
+            "dist": str,
+            "duration": Optional[float],
+            "environment": Optional[str],
+            "errors": list[dict[str, Any]],  # TODO: We can expand on this type
+            "event_id": str,
+            "exception": dict[
+                Literal["values"], list[dict[str, Any]]
+            ],  # TODO: We can expand on this type
+            "extra": MutableMapping[str, object],
+            "fingerprint": list[str],
+            "level": LogLevelStr,
+            "logentry": Mapping[str, object],
+            "logger": str,
+            "measurements": dict[str, MeasurementValue],
+            "message": str,
+            "modules": dict[str, str],
+            "monitor_config": Mapping[str, object],
+            "monitor_slug": Optional[str],
+            "platform": Literal["python"],
+            "profile": object,  # Should be sentry_sdk.profiler.Profile, but we can't import that here due to circular imports
+            "release": Optional[str],
+            "request": dict[str, object],
+            "sdk": Mapping[str, object],
+            "server_name": str,
+            "spans": Annotated[list[dict[str, object]]],
+            "stacktrace": dict[
+                str, object
+            ],  # We access this key in the code, but I am unsure whether we ever set it
+            "start_timestamp": datetime,
+            "status": Optional[str],
+            "tags": MutableMapping[
+                str, str
+            ],  # Tags must be less than 200 characters each
+            "threads": dict[
+                Literal["values"], list[dict[str, Any]]
+            ],  # TODO: We can expand on this type
+            "timestamp": Optional[datetime],  # Must be set before sending the event
+            "transaction": str,
+            "transaction_info": Mapping[str, Any],  # TODO: We can expand on this type
+            "type": Literal["check_in", "transaction"],
+            "user": dict[str, object],
+            "_dropped_spans": int,
+        },
+        total=False,
+    )
+
+    ExcInfo = Union[
+        tuple[Type[BaseException], BaseException, Optional[TracebackType]],
+        tuple[None, None, None],
+    ]
+
+    # TODO: Make a proper type definition for this (PRs welcome!)
+    Hint = Dict[str, Any]
+
+    AttributeValue = (
+        str
+        | bool
+        | float
+        | int
+        | list[str]
+        | list[bool]
+        | list[float]
+        | list[int]
+        | tuple[str, ...]
+        | tuple[bool, ...]
+        | tuple[float, ...]
+        | tuple[int, ...]
+    )
+    Attributes = dict[str, AttributeValue]
+
+    SerializedAttributeValue = TypedDict(
+        # https://develop.sentry.dev/sdk/telemetry/attributes/#supported-types
+        "SerializedAttributeValue",
+        {
+            "type": Literal[
+                "string",
+                "boolean",
+                "double",
+                "integer",
+                "array",
+            ],
+            "value": AttributeValue,
+        },
+    )
+
+    Log = TypedDict(
+        "Log",
+        {
+            "severity_text": str,
+            "severity_number": int,
+            "body": str,
+            "attributes": Attributes,
+            "time_unix_nano": int,
+            "trace_id": Optional[str],
+            "span_id": Optional[str],
+        },
+    )
+
+    MetricType = Literal["counter", "gauge", "distribution"]
+    MetricUnit = Union[DurationUnit, InformationUnit, str]
+
+    Metric = TypedDict(
+        "Metric",
+        {
+            "timestamp": float,
+            "trace_id": Optional[str],
+            "span_id": Optional[str],
+            "name": str,
+            "type": MetricType,
+            "value": float,
+            "unit": Optional[MetricUnit],
+            "attributes": Attributes,
+        },
+    )
+
+    MetricProcessor = Callable[[Metric, Hint], Optional[Metric]]
+
+    # TODO: Make a proper type definition for this (PRs welcome!)
+    Breadcrumb = Dict[str, Any]
+
+    # TODO: Make a proper type definition for this (PRs welcome!)
+    BreadcrumbHint = Dict[str, Any]
+
+    # TODO: Make a proper type definition for this (PRs welcome!)
+    SamplingContext = Dict[str, Any]
+
+    EventProcessor = Callable[[Event, Hint], Optional[Event]]
+    ErrorProcessor = Callable[[Event, ExcInfo], Optional[Event]]
+    BreadcrumbProcessor = Callable[[Breadcrumb, BreadcrumbHint], Optional[Breadcrumb]]
+    TransactionProcessor = Callable[[Event, Hint], Optional[Event]]
+    LogProcessor = Callable[[Log, Hint], Optional[Log]]
+
+    TracesSampler = Callable[[SamplingContext], Union[float, int, bool]]
+
+    # https://github.com/python/mypy/issues/5710
+    NotImplementedType = Any
+
+    EventDataCategory = Literal[
+        "default",
+        "error",
+        "crash",
+        "transaction",
+        "security",
+        "attachment",
+        "session",
+        "internal",
+        "profile",
+        "profile_chunk",
+        "monitor",
+        "span",
+        "log_item",
+        "log_byte",
+        "trace_metric",
+    ]
+    SessionStatus = Literal["ok", "exited", "crashed", "abnormal"]
+
+    ContinuousProfilerMode = Literal["thread", "gevent", "unknown"]
+    ProfilerMode = Union[ContinuousProfilerMode, Literal["sleep"]]
+
+    MonitorConfigScheduleType = Literal["crontab", "interval"]
+    MonitorConfigScheduleUnit = Literal[
+        "year",
+        "month",
+        "week",
+        "day",
+        "hour",
+        "minute",
+        "second",  # not supported in Sentry and will result in a warning
+    ]
+
+    MonitorConfigSchedule = TypedDict(
+        "MonitorConfigSchedule",
+        {
+            "type": MonitorConfigScheduleType,
+            "value": Union[int, str],
+            "unit": MonitorConfigScheduleUnit,
+        },
+        total=False,
+    )
+
+    MonitorConfig = TypedDict(
+        "MonitorConfig",
+        {
+            "schedule": MonitorConfigSchedule,
+            "timezone": str,
+            "checkin_margin": int,
+            "max_runtime": int,
+            "failure_issue_threshold": int,
+            "recovery_threshold": int,
+        },
+        total=False,
+    )
+
+    HttpStatusCodeRange = Union[int, Container[int]]
+
+    class TextPart(TypedDict):
+        type: Literal["text"]
+        content: str

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/_werkzeug.py

@@ -0,0 +1,96 @@
+"""
+Copyright (c) 2007 by the Pallets team.
+
+Some rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE AND DOCUMENTATION IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,
+BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+THIS SOFTWARE AND DOCUMENTATION, EVEN IF ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE.
+"""
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Dict
+    from typing import Iterator
+    from typing import Tuple
+
+
+#
+# `get_headers` comes from `werkzeug.datastructures.EnvironHeaders`
+# https://github.com/pallets/werkzeug/blob/0.14.1/werkzeug/datastructures.py#L1361
+#
+# We need this function because Django does not give us a "pure" http header
+# dict. So we might as well use it for all WSGI integrations.
+#
+def _get_headers(environ: "Dict[str, str]") -> "Iterator[Tuple[str, str]]":
+    """
+    Returns only proper HTTP headers.
+    """
+    for key, value in environ.items():
+        key = str(key)
+        if key.startswith("HTTP_") and key not in (
+            "HTTP_CONTENT_TYPE",
+            "HTTP_CONTENT_LENGTH",
+        ):
+            yield key[5:].replace("_", "-").title(), value
+        elif key in ("CONTENT_TYPE", "CONTENT_LENGTH"):
+            yield key.replace("_", "-").title(), value
+
+
+#
+# `get_host` comes from `werkzeug.wsgi.get_host`
+# https://github.com/pallets/werkzeug/blob/1.0.1/src/werkzeug/wsgi.py#L145
+#
+def get_host(environ: "Dict[str, str]", use_x_forwarded_for: bool = False) -> str:
+    """
+    Return the host for the given WSGI environment.
+    """
+    if use_x_forwarded_for and "HTTP_X_FORWARDED_HOST" in environ:
+        rv = environ["HTTP_X_FORWARDED_HOST"]
+        if environ["wsgi.url_scheme"] == "http" and rv.endswith(":80"):
+            rv = rv[:-3]
+        elif environ["wsgi.url_scheme"] == "https" and rv.endswith(":443"):
+            rv = rv[:-4]
+    elif environ.get("HTTP_HOST"):
+        rv = environ["HTTP_HOST"]
+        if environ["wsgi.url_scheme"] == "http" and rv.endswith(":80"):
+            rv = rv[:-3]
+        elif environ["wsgi.url_scheme"] == "https" and rv.endswith(":443"):
+            rv = rv[:-4]
+    elif environ.get("SERVER_NAME"):
+        rv = environ["SERVER_NAME"]
+        if (environ["wsgi.url_scheme"], environ["SERVER_PORT"]) not in (
+            ("https", "443"),
+            ("http", "80"),
+        ):
+            rv += ":" + environ["SERVER_PORT"]
+    else:
+        # In spite of the WSGI spec, SERVER_NAME might not be present.
+        rv = "unknown"
+
+    return rv

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/ai/__init__.py

@@ -0,0 +1,8 @@
+from .utils import (
+    set_data_normalized,
+    GEN_AI_MESSAGE_ROLE_MAPPING,
+    GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING,
+    normalize_message_role,
+    normalize_message_roles,
+    set_conversation_id,
+)  # noqa: F401

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/ai/_openai_completions_api.py

@@ -0,0 +1,66 @@
+from collections.abc import Iterable
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from sentry_sdk._types import TextPart
+    from typing import Union
+
+    from openai.types.chat import (
+        ChatCompletionMessageParam,
+        ChatCompletionSystemMessageParam,
+        ChatCompletionContentPartParam,
+    )
+
+
+def _is_system_instruction(message: "ChatCompletionMessageParam") -> bool:
+    return isinstance(message, dict) and message.get("role") == "system"
+
+
+def _get_system_instructions(
+    messages: "Iterable[ChatCompletionMessageParam]",
+) -> "list[ChatCompletionMessageParam]":
+    if not isinstance(messages, Iterable):
+        return []
+
+    return [message for message in messages if _is_system_instruction(message)]
+
+
+def _get_text_items(
+    content: "Union[str, Iterable[ChatCompletionContentPartParam]]",
+) -> "list[str]":
+    if isinstance(content, str):
+        return [content]
+
+    if not isinstance(content, Iterable):
+        return []
+
+    text_items = []
+    for part in content:
+        if isinstance(part, dict) and part.get("type") == "text":
+            text = part.get("text", None)
+            if text is not None:
+                text_items.append(text)
+
+    return text_items
+
+
+def _transform_system_instructions(
+    system_instructions: "list[ChatCompletionSystemMessageParam]",
+) -> "list[TextPart]":
+    instruction_text_parts: "list[TextPart]" = []
+
+    for instruction in system_instructions:
+        if not isinstance(instruction, dict):
+            continue
+
+        content = instruction.get("content")
+        if content is None:
+            continue
+
+        text_parts: "list[TextPart]" = [
+            {"type": "text", "content": text} for text in _get_text_items(content)
+        ]
+        instruction_text_parts += text_parts
+
+    return instruction_text_parts

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/ai/_openai_responses_api.py

@@ -0,0 +1,22 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Union
+
+    from openai.types.responses import ResponseInputParam, ResponseInputItemParam
+
+
+def _is_system_instruction(message: "ResponseInputItemParam") -> bool:
+    if not isinstance(message, dict) or not message.get("role") == "system":
+        return False
+
+    return "type" not in message or message["type"] == "message"
+
+
+def _get_system_instructions(
+    messages: "Union[str, ResponseInputParam]",
+) -> "list[ResponseInputItemParam]":
+    if not isinstance(messages, list):
+        return []
+
+    return [message for message in messages if _is_system_instruction(message)]

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/ai/monitoring.py

@@ -0,0 +1,141 @@
+import inspect
+import sys
+from functools import wraps
+
+from sentry_sdk.consts import SPANDATA
+import sentry_sdk.utils
+from sentry_sdk import start_span
+from sentry_sdk.tracing import Span
+from sentry_sdk.utils import ContextVar, reraise, capture_internal_exceptions
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Optional, Callable, Awaitable, Any, Union, TypeVar
+
+    F = TypeVar("F", bound=Union[Callable[..., Any], Callable[..., Awaitable[Any]]])
+
+_ai_pipeline_name = ContextVar("ai_pipeline_name", default=None)
+
+
+def set_ai_pipeline_name(name: "Optional[str]") -> None:
+    _ai_pipeline_name.set(name)
+
+
+def get_ai_pipeline_name() -> "Optional[str]":
+    return _ai_pipeline_name.get()
+
+
+def ai_track(description: str, **span_kwargs: "Any") -> "Callable[[F], F]":
+    def decorator(f: "F") -> "F":
+        def sync_wrapped(*args: "Any", **kwargs: "Any") -> "Any":
+            curr_pipeline = _ai_pipeline_name.get()
+            op = span_kwargs.pop("op", "ai.run" if curr_pipeline else "ai.pipeline")
+
+            with start_span(name=description, op=op, **span_kwargs) as span:
+                for k, v in kwargs.pop("sentry_tags", {}).items():
+                    span.set_tag(k, v)
+                for k, v in kwargs.pop("sentry_data", {}).items():
+                    span.set_data(k, v)
+                if curr_pipeline:
+                    span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, curr_pipeline)
+                    return f(*args, **kwargs)
+                else:
+                    _ai_pipeline_name.set(description)
+                    try:
+                        res = f(*args, **kwargs)
+                    except Exception as e:
+                        exc_info = sys.exc_info()
+                        with capture_internal_exceptions():
+                            event, hint = sentry_sdk.utils.event_from_exception(
+                                e,
+                                client_options=sentry_sdk.get_client().options,
+                                mechanism={"type": "ai_monitoring", "handled": False},
+                            )
+                            sentry_sdk.capture_event(event, hint=hint)
+                        reraise(*exc_info)
+                    finally:
+                        _ai_pipeline_name.set(None)
+                    return res
+
+        async def async_wrapped(*args: "Any", **kwargs: "Any") -> "Any":
+            curr_pipeline = _ai_pipeline_name.get()
+            op = span_kwargs.pop("op", "ai.run" if curr_pipeline else "ai.pipeline")
+
+            with start_span(name=description, op=op, **span_kwargs) as span:
+                for k, v in kwargs.pop("sentry_tags", {}).items():
+                    span.set_tag(k, v)
+                for k, v in kwargs.pop("sentry_data", {}).items():
+                    span.set_data(k, v)
+                if curr_pipeline:
+                    span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, curr_pipeline)
+                    return await f(*args, **kwargs)
+                else:
+                    _ai_pipeline_name.set(description)
+                    try:
+                        res = await f(*args, **kwargs)
+                    except Exception as e:
+                        exc_info = sys.exc_info()
+                        with capture_internal_exceptions():
+                            event, hint = sentry_sdk.utils.event_from_exception(
+                                e,
+                                client_options=sentry_sdk.get_client().options,
+                                mechanism={"type": "ai_monitoring", "handled": False},
+                            )
+                            sentry_sdk.capture_event(event, hint=hint)
+                        reraise(*exc_info)
+                    finally:
+                        _ai_pipeline_name.set(None)
+                    return res
+
+        if inspect.iscoroutinefunction(f):
+            return wraps(f)(async_wrapped)  # type: ignore
+        else:
+            return wraps(f)(sync_wrapped)  # type: ignore
+
+    return decorator
+
+
+def record_token_usage(
+    span: "Span",
+    input_tokens: "Optional[int]" = None,
+    input_tokens_cached: "Optional[int]" = None,
+    input_tokens_cache_write: "Optional[int]" = None,
+    output_tokens: "Optional[int]" = None,
+    output_tokens_reasoning: "Optional[int]" = None,
+    total_tokens: "Optional[int]" = None,
+) -> None:
+    # TODO: move pipeline name elsewhere
+    ai_pipeline_name = get_ai_pipeline_name()
+    if ai_pipeline_name:
+        span.set_data(SPANDATA.GEN_AI_PIPELINE_NAME, ai_pipeline_name)
+
+    if input_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_INPUT_TOKENS, input_tokens)
+
+    if input_tokens_cached is not None:
+        span.set_data(
+            SPANDATA.GEN_AI_USAGE_INPUT_TOKENS_CACHED,
+            input_tokens_cached,
+        )
+
+    if input_tokens_cache_write is not None:
+        span.set_data(
+            SPANDATA.GEN_AI_USAGE_INPUT_TOKENS_CACHE_WRITE,
+            input_tokens_cache_write,
+        )
+
+    if output_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens)
+
+    if output_tokens_reasoning is not None:
+        span.set_data(
+            SPANDATA.GEN_AI_USAGE_OUTPUT_TOKENS_REASONING,
+            output_tokens_reasoning,
+        )
+
+    if total_tokens is None and input_tokens is not None and output_tokens is not None:
+        total_tokens = input_tokens + output_tokens
+
+    if total_tokens is not None:
+        span.set_data(SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS, total_tokens)

miniconda3/envs/ladir/lib/python3.10/site-packages/sentry_sdk/ai/utils.py

@@ -0,0 +1,727 @@
+import inspect
+import json
+from collections import deque
+from copy import deepcopy
+from sys import getsizeof
+from typing import TYPE_CHECKING
+
+from sentry_sdk._types import BLOB_DATA_SUBSTITUTE
+
+if TYPE_CHECKING:
+    from typing import Any, Callable, Dict, List, Optional, Tuple
+
+    from sentry_sdk.tracing import Span
+
+import sentry_sdk
+from sentry_sdk.utils import logger
+
+MAX_GEN_AI_MESSAGE_BYTES = 20_000  # 20KB
+# Maximum characters when only a single message is left after bytes truncation
+MAX_SINGLE_MESSAGE_CONTENT_CHARS = 10_000
+
+
+class GEN_AI_ALLOWED_MESSAGE_ROLES:
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+    TOOL = "tool"
+
+
+GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING = {
+    GEN_AI_ALLOWED_MESSAGE_ROLES.SYSTEM: ["system"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.USER: ["user", "human"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.ASSISTANT: ["assistant", "ai"],
+    GEN_AI_ALLOWED_MESSAGE_ROLES.TOOL: ["tool", "tool_call"],
+}
+
+GEN_AI_MESSAGE_ROLE_MAPPING = {}
+for target_role, source_roles in GEN_AI_MESSAGE_ROLE_REVERSE_MAPPING.items():
+    for source_role in source_roles:
+        GEN_AI_MESSAGE_ROLE_MAPPING[source_role] = target_role
+
+
+def parse_data_uri(url: str) -> "Tuple[str, str]":
+    """
+    Parse a data URI and return (mime_type, content).
+
+    Data URI format (RFC 2397): data:[<mediatype>][;base64],<data>
+
+    Examples:
+        data:image/jpeg;base64,/9j/4AAQ... → ("image/jpeg", "/9j/4AAQ...")
+        data:text/plain,Hello → ("text/plain", "Hello")
+        data:;base64,SGVsbG8= → ("", "SGVsbG8=")
+
+    Raises:
+        ValueError: If the URL is not a valid data URI (missing comma separator)
+    """
+    if "," not in url:
+        raise ValueError("Invalid data URI: missing comma separator")
+
+    header, content = url.split(",", 1)
+
+    # Extract mime type from header
+    # Format: "data:<mime>[;param1][;param2]..." e.g. "data:image/jpeg;base64"
+    # Remove "data:" prefix, then take everything before the first semicolon
+    if header.startswith("data:"):
+        mime_part = header[5:]  # Remove "data:" prefix
+    else:
+        mime_part = header
+
+    mime_type = mime_part.split(";")[0]
+
+    return mime_type, content
+
+
+def get_modality_from_mime_type(mime_type: str) -> str:
+    """
+    Infer the content modality from a MIME type string.
+
+    Args:
+        mime_type: A MIME type string (e.g., "image/jpeg", "audio/mp3")
+
+    Returns:
+        One of: "image", "audio", "video", or "document"
+        Defaults to "image" for unknown or empty MIME types.
+
+    Examples:
+        "image/jpeg" -> "image"
+        "audio/mp3" -> "audio"
+        "video/mp4" -> "video"
+        "application/pdf" -> "document"
+        "text/plain" -> "document"
+    """
+    if not mime_type:
+        return "image"  # Default fallback
+
+    mime_lower = mime_type.lower()
+    if mime_lower.startswith("image/"):
+        return "image"
+    elif mime_lower.startswith("audio/"):
+        return "audio"
+    elif mime_lower.startswith("video/"):
+        return "video"
+    elif mime_lower.startswith("application/") or mime_lower.startswith("text/"):
+        return "document"
+    else:
+        return "image"  # Default fallback for unknown types
+
+
+def transform_openai_content_part(
+    content_part: "Dict[str, Any]",
+) -> "Optional[Dict[str, Any]]":
+    """
+    Transform an OpenAI/LiteLLM content part to Sentry's standardized format.
+
+    This handles the OpenAI image_url format used by OpenAI and LiteLLM SDKs.
+
+    Input format:
+    - {"type": "image_url", "image_url": {"url": "..."}}
+    - {"type": "image_url", "image_url": "..."} (string shorthand)
+
+    Output format (one of):
+    - {"type": "blob", "modality": "image", "mime_type": "...", "content": "..."}
+    - {"type": "uri", "modality": "image", "mime_type": "", "uri": "..."}
+
+    Args:
+        content_part: A dictionary representing a content part from OpenAI/LiteLLM
+
+    Returns:
+        A transformed dictionary in standardized format, or None if the format
+        is not OpenAI image_url format or transformation fails.
+    """
+    if not isinstance(content_part, dict):
+        return None
+
+    block_type = content_part.get("type")
+
+    if block_type != "image_url":
+        return None
+
+    image_url_data = content_part.get("image_url")
+    if isinstance(image_url_data, str):
+        url = image_url_data
+    elif isinstance(image_url_data, dict):
+        url = image_url_data.get("url", "")
+    else:
+        return None
+
+    if not url:
+        return None
+
+    # Check if it's a data URI (base64 encoded)
+    if url.startswith("data:"):
+        try:
+            mime_type, content = parse_data_uri(url)
+            return {
+                "type": "blob",
+                "modality": get_modality_from_mime_type(mime_type),
+                "mime_type": mime_type,
+                "content": content,
+            }
+        except ValueError:
+            # If parsing fails, return as URI
+            return {
+                "type": "uri",
+                "modality": "image",
+                "mime_type": "",
+                "uri": url,
+            }
+    else:
+        # Regular URL
+        return {
+            "type": "uri",
+            "modality": "image",
+            "mime_type": "",
+            "uri": url,
+        }
+
+
+def transform_anthropic_content_part(
+    content_part: "Dict[str, Any]",
+) -> "Optional[Dict[str, Any]]":
+    """
+    Transform an Anthropic content part to Sentry's standardized format.
+
+    This handles the Anthropic image and document formats with source dictionaries.
+
+    Input format:
+    - {"type": "image", "source": {"type": "base64", "media_type": "...", "data": "..."}}
+    - {"type": "image", "source": {"type": "url", "media_type": "...", "url": "..."}}
+    - {"type": "image", "source": {"type": "file", "media_type": "...", "file_id": "..."}}
+    - {"type": "document", "source": {...}} (same source formats)
+
+    Output format (one of):
+    - {"type": "blob", "modality": "...", "mime_type": "...", "content": "..."}
+    - {"type": "uri", "modality": "...", "mime_type": "...", "uri": "..."}
+    - {"type": "file", "modality": "...", "mime_type": "...", "file_id": "..."}
+
+    Args:
+        content_part: A dictionary representing a content part from Anthropic
+
+    Returns:
+        A transformed dictionary in standardized format, or None if the format
+        is not Anthropic format or transformation fails.
+    """
+    if not isinstance(content_part, dict):
+        return None
+
+    block_type = content_part.get("type")
+
+    if block_type not in ("image", "document") or "source" not in content_part:
+        return None
+
+    source = content_part.get("source")
+    if not isinstance(source, dict):
+        return None
+
+    source_type = source.get("type")
+    media_type = source.get("media_type", "")
+    modality = (
+        "document"
+        if block_type == "document"
+        else get_modality_from_mime_type(media_type)
+    )
+
+    if source_type == "base64":
+        return {
+            "type": "blob",
+            "modality": modality,
+            "mime_type": media_type,
+            "content": source.get("data", ""),
+        }
+    elif source_type == "url":
+        return {
+            "type": "uri",
+            "modality": modality,
+            "mime_type": media_type,
+            "uri": source.get("url", ""),
+        }
+    elif source_type == "file":
+        return {
+            "type": "file",
+            "modality": modality,
+            "mime_type": media_type,
+            "file_id": source.get("file_id", ""),
+        }
+
+    return None
+
+
+def transform_google_content_part(
+    content_part: "Dict[str, Any]",
+) -> "Optional[Dict[str, Any]]":
+    """
+    Transform a Google GenAI content part to Sentry's standardized format.
+
+    This handles the Google GenAI inline_data and file_data formats.
+
+    Input format:
+    - {"inline_data": {"mime_type": "...", "data": "..."}}
+    - {"file_data": {"mime_type": "...", "file_uri": "..."}}
+
+    Output format (one of):
+    - {"type": "blob", "modality": "...", "mime_type": "...", "content": "..."}
+    - {"type": "uri", "modality": "...", "mime_type": "...", "uri": "..."}
+
+    Args:
+        content_part: A dictionary representing a content part from Google GenAI
+
+    Returns:
+        A transformed dictionary in standardized format, or None if the format
+        is not Google format or transformation fails.
+    """
+    if not isinstance(content_part, dict):
+        return None
+
+    # Handle Google inline_data format
+    if "inline_data" in content_part:
+        inline_data = content_part.get("inline_data")
+        if isinstance(inline_data, dict):
+            mime_type = inline_data.get("mime_type", "")
+            return {
+                "type": "blob",
+                "modality": get_modality_from_mime_type(mime_type),
+                "mime_type": mime_type,
+                "content": inline_data.get("data", ""),
+            }
+        return None
+
+    # Handle Google file_data format
+    if "file_data" in content_part:
+        file_data = content_part.get("file_data")
+        if isinstance(file_data, dict):
+            mime_type = file_data.get("mime_type", "")
+            return {
+                "type": "uri",
+                "modality": get_modality_from_mime_type(mime_type),
+                "mime_type": mime_type,
+                "uri": file_data.get("file_uri", ""),
+            }
+        return None
+
+    return None
+
+
+def transform_generic_content_part(
+    content_part: "Dict[str, Any]",
+) -> "Optional[Dict[str, Any]]":
+    """
+    Transform a generic/LangChain-style content part to Sentry's standardized format.
+
+    This handles generic formats where the type indicates the modality and
+    the data is provided via direct base64, url, or file_id fields.
+
+    Input format:
+    - {"type": "image", "base64": "...", "mime_type": "..."}
+    - {"type": "audio", "url": "...", "mime_type": "..."}
+    - {"type": "video", "base64": "...", "mime_type": "..."}
+    - {"type": "file", "file_id": "...", "mime_type": "..."}
+
+    Output format (one of):
+    - {"type": "blob", "modality": "...", "mime_type": "...", "content": "..."}
+    - {"type": "uri", "modality": "...", "mime_type": "...", "uri": "..."}
+    - {"type": "file", "modality": "...", "mime_type": "...", "file_id": "..."}
+
+    Args:
+        content_part: A dictionary representing a content part in generic format
+
+    Returns:
+        A transformed dictionary in standardized format, or None if the format
+        is not generic format or transformation fails.
+    """
+    if not isinstance(content_part, dict):
+        return None
+
+    block_type = content_part.get("type")
+
+    if block_type not in ("image", "audio", "video", "file"):
+        return None
+
+    # Ensure it's not Anthropic format (which also uses type: "image")
+    if "source" in content_part:
+        return None
+
+    mime_type = content_part.get("mime_type", "")
+    modality = block_type if block_type != "file" else "document"
+
+    # Check for base64 encoded content
+    if "base64" in content_part:
+        return {
+            "type": "blob",
+            "modality": modality,
+            "mime_type": mime_type,
+            "content": content_part.get("base64", ""),
+        }
+    # Check for URL reference
+    elif "url" in content_part:
+        return {
+            "type": "uri",
+            "modality": modality,
+            "mime_type": mime_type,
+            "uri": content_part.get("url", ""),
+        }
+    # Check for file_id reference
+    elif "file_id" in content_part:
+        return {
+            "type": "file",
+            "modality": modality,
+            "mime_type": mime_type,
+            "file_id": content_part.get("file_id", ""),
+        }
+
+    return None
+
+
+def transform_content_part(
+    content_part: "Dict[str, Any]",
+) -> "Optional[Dict[str, Any]]":
+    """
+    Transform a content part from various AI SDK formats to Sentry's standardized format.
+
+    This is a heuristic dispatcher that detects the format and delegates to the
+    appropriate SDK-specific transformer. For direct SDK integration, prefer using
+    the specific transformers directly:
+    - transform_openai_content_part() for OpenAI/LiteLLM
+    - transform_anthropic_content_part() for Anthropic
+    - transform_google_content_part() for Google GenAI
+    - transform_generic_content_part() for LangChain and other generic formats
+
+    Detection order:
+    1. OpenAI: type == "image_url"
+    2. Google: "inline_data" or "file_data" keys present
+    3. Anthropic: type in ("image", "document") with "source" key
+    4. Generic: type in ("image", "audio", "video", "file") with base64/url/file_id
+
+    Output format (one of):
+    - {"type": "blob", "modality": "...", "mime_type": "...", "content": "..."}
+    - {"type": "uri", "modality": "...", "mime_type": "...", "uri": "..."}
+    - {"type": "file", "modality": "...", "mime_type": "...", "file_id": "..."}
+
+    Args:
+        content_part: A dictionary representing a content part from an AI SDK
+
+    Returns:
+        A transformed dictionary in standardized format, or None if the format
+        is unrecognized or transformation fails.
+    """
+    if not isinstance(content_part, dict):
+        return None
+
+    # Try OpenAI format first (most common, clear indicator)
+    result = transform_openai_content_part(content_part)
+    if result is not None:
+        return result
+
+    # Try Google format (unique keys make it easy to detect)
+    result = transform_google_content_part(content_part)
+    if result is not None:
+        return result
+
+    # Try Anthropic format (has "source" key)
+    result = transform_anthropic_content_part(content_part)
+    if result is not None:
+        return result
+
+    # Try generic format as fallback
+    result = transform_generic_content_part(content_part)
+    if result is not None:
+        return result
+
+    # Unrecognized format
+    return None
+
+
+def transform_message_content(content: "Any") -> "Any":
+    """
+    Transform message content, handling both string content and list of content blocks.
+
+    For list content, each item is transformed using transform_content_part().
+    Items that cannot be transformed (return None) are kept as-is.
+
+    Args:
+        content: Message content - can be a string, list of content blocks, or other
+
+    Returns:
+        - String content: returned as-is
+        - List content: list with each transformable item converted to standardized format
+        - Other: returned as-is
+    """
+    if isinstance(content, str):
+        return content
+
+    if isinstance(content, (list, tuple)):
+        transformed = []
+        for item in content:
+            if isinstance(item, dict):
+                result = transform_content_part(item)
+                # If transformation succeeded, use the result; otherwise keep original
+                transformed.append(result if result is not None else item)
+            else:
+                transformed.append(item)
+        return transformed
+
+    return content
+
+
+def _normalize_data(data: "Any", unpack: bool = True) -> "Any":
+    # convert pydantic data (e.g. OpenAI v1+) to json compatible format
+    if hasattr(data, "model_dump"):
+        # Check if it's a class (type) rather than an instance
+        # Model classes can be passed as arguments (e.g., for schema definitions)
+        if inspect.isclass(data):
+            return f"<ClassType: {data.__name__}>"
+
+        try:
+            return _normalize_data(data.model_dump(), unpack=unpack)
+        except Exception as e:
+            logger.warning("Could not convert pydantic data to JSON: %s", e)
+            return data if isinstance(data, (int, float, bool, str)) else str(data)
+
+    if isinstance(data, list):
+        if unpack and len(data) == 1:
+            return _normalize_data(data[0], unpack=unpack)  # remove empty dimensions
+        return list(_normalize_data(x, unpack=unpack) for x in data)
+
+    if isinstance(data, dict):
+        return {k: _normalize_data(v, unpack=unpack) for (k, v) in data.items()}
+
+    return data if isinstance(data, (int, float, bool, str)) else str(data)
+
+
+def set_data_normalized(
+    span: "Span", key: str, value: "Any", unpack: bool = True
+) -> None:
+    normalized = _normalize_data(value, unpack=unpack)
+    if isinstance(normalized, (int, float, bool, str)):
+        span.set_data(key, normalized)
+    else:
+        span.set_data(key, json.dumps(normalized))
+
+
+def normalize_message_role(role: str) -> str:
+    """
+    Normalize a message role to one of the 4 allowed gen_ai role values.
+    Maps "ai" -> "assistant" and keeps other standard roles unchanged.
+    """
+    return GEN_AI_MESSAGE_ROLE_MAPPING.get(role, role)
+
+
+def normalize_message_roles(messages: "list[dict[str, Any]]") -> "list[dict[str, Any]]":
+    """
+    Normalize roles in a list of messages to use standard gen_ai role values.
+    Creates a deep copy to avoid modifying the original messages.
+    """
+    normalized_messages = []
+    for message in messages:
+        if not isinstance(message, dict):
+            normalized_messages.append(message)
+            continue
+        normalized_message = message.copy()
+        if "role" in message:
+            normalized_message["role"] = normalize_message_role(message["role"])
+        normalized_messages.append(normalized_message)
+
+    return normalized_messages
+
+
+def get_start_span_function() -> "Callable[..., Any]":
+    current_span = sentry_sdk.get_current_span()
+    transaction_exists = (
+        current_span is not None and current_span.containing_transaction is not None
+    )
+    return sentry_sdk.start_span if transaction_exists else sentry_sdk.start_transaction
+
+
+def _truncate_single_message_content_if_present(
+    message: "Dict[str, Any]", max_chars: int
+) -> "Dict[str, Any]":
+    """
+    Truncate a message's content to at most `max_chars` characters and append an
+    ellipsis if truncation occurs.
+    """
+    if not isinstance(message, dict) or "content" not in message:
+        return message
+    content = message["content"]
+
+    if not isinstance(content, str) or len(content) <= max_chars:
+        return message
+
+    message["content"] = content[:max_chars] + "..."
+    return message
+
+
+def _find_truncation_index(messages: "List[Dict[str, Any]]", max_bytes: int) -> int:
+    """
+    Find the index of the first message that would exceed the max bytes limit.
+    Compute the individual message sizes, and return the index of the first message from the back
+    of the list that would exceed the max bytes limit.
+    """
+    running_sum = 0
+    for idx in range(len(messages) - 1, -1, -1):
+        size = len(json.dumps(messages[idx], separators=(",", ":")).encode("utf-8"))
+        running_sum += size
+        if running_sum > max_bytes:
+            return idx + 1
+
+    return 0
+
+
+def redact_blob_message_parts(
+    messages: "List[Dict[str, Any]]",
+) -> "List[Dict[str, Any]]":
+    """
+    Redact blob message parts from the messages by replacing blob content with "[Filtered]".
+
+    This function creates a deep copy of messages that contain blob content to avoid
+    mutating the original message dictionaries. Messages without blob content are
+    returned as-is to minimize copying overhead.
+
+    e.g:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "blob",
+                "modality": "image",
+                "mime_type": "image/jpeg",
+                "content": "data:image/jpeg;base64,..."
+            }
+        ]
+    }
+    becomes:
+    {
+        "role": "user",
+        "content": [
+            {
+                "text": "How many ponies do you see in the image?",
+                "type": "text"
+            },
+            {
+                "type": "blob",
+                "modality": "image",
+                "mime_type": "image/jpeg",
+                "content": "[Filtered]"
+            }
+        ]
+    }
+    """
+
+    # First pass: check if any message contains blob content
+    has_blobs = False
+    for message in messages:
+        if not isinstance(message, dict):
+            continue
+        content = message.get("content")
+        if isinstance(content, list):
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "blob":
+                    has_blobs = True
+                    break
+        if has_blobs:
+            break
+
+    # If no blobs found, return original messages to avoid unnecessary copying
+    if not has_blobs:
+        return messages
+
+    # Deep copy messages to avoid mutating the original
+    messages_copy = deepcopy(messages)
+
+    # Second pass: redact blob content in the copy
+    for message in messages_copy:
+        if not isinstance(message, dict):
+            continue
+
+        content = message.get("content")
+        if isinstance(content, list):
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "blob":
+                    item["content"] = BLOB_DATA_SUBSTITUTE
+
+    return messages_copy
+
+
+def truncate_messages_by_size(
+    messages: "List[Dict[str, Any]]",
+    max_bytes: int = MAX_GEN_AI_MESSAGE_BYTES,
+    max_single_message_chars: int = MAX_SINGLE_MESSAGE_CONTENT_CHARS,
+) -> "Tuple[List[Dict[str, Any]], int]":
+    """
+    Returns a truncated messages list, consisting of
+    - the last message, with its content truncated to `max_single_message_chars` characters,
+      if the last message's size exceeds `max_bytes` bytes; otherwise,
+    - the maximum number of messages, starting from the end of the `messages` list, whose total
+      serialized size does not exceed `max_bytes` bytes.
+
+    In the single message case, the serialized message size may exceed `max_bytes`, because
+    truncation is based only on character count in that case.
+    """
+    serialized_json = json.dumps(messages, separators=(",", ":"))
+    current_size = len(serialized_json.encode("utf-8"))
+
+    if current_size <= max_bytes:
+        return messages, 0
+
+    truncation_index = _find_truncation_index(messages, max_bytes)
+    if truncation_index < len(messages):
+        truncated_messages = messages[truncation_index:]
+    else:
+        truncation_index = len(messages) - 1
+        truncated_messages = messages[-1:]
+
+    if len(truncated_messages) == 1:
+        truncated_messages[0] = _truncate_single_message_content_if_present(
+            deepcopy(truncated_messages[0]), max_chars=max_single_message_chars
+        )
+
+    return truncated_messages, truncation_index
+
+
+def truncate_and_annotate_messages(
+    messages: "Optional[List[Dict[str, Any]]]",
+    span: "Any",
+    scope: "Any",
+    max_single_message_chars: int = MAX_SINGLE_MESSAGE_CONTENT_CHARS,
+) -> "Optional[List[Dict[str, Any]]]":
+    if not messages:
+        return None
+
+    messages = redact_blob_message_parts(messages)
+
+    truncated_message = _truncate_single_message_content_if_present(
+        deepcopy(messages[-1]), max_chars=max_single_message_chars
+    )
+    if len(messages) > 1:
+        scope._gen_ai_original_message_count[span.span_id] = len(messages)
+
+    return [truncated_message]
+
+
+def truncate_and_annotate_embedding_inputs(
+    messages: "Optional[List[Dict[str, Any]]]",
+    span: "Any",
+    scope: "Any",
+    max_bytes: int = MAX_GEN_AI_MESSAGE_BYTES,
+) -> "Optional[List[Dict[str, Any]]]":
+    if not messages:
+        return None
+
+    messages = redact_blob_message_parts(messages)
+
+    truncated_messages, removed_count = truncate_messages_by_size(messages, max_bytes)
+    if removed_count > 0:
+        scope._gen_ai_original_message_count[span.span_id] = len(messages)
+
+    return truncated_messages
+
+
+def set_conversation_id(conversation_id: str) -> None:
+    """
+    Set the conversation_id in the scope.
+    """
+    scope = sentry_sdk.get_current_scope()
+    scope.set_conversation_id(conversation_id)