Auto-sync: 2026-06-25 22:43:25 (part 16)

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/AUTHORS

@@ -0,0 +1,1379 @@
+All people who contributed to SymPy by sending at least a patch or
+more (in the order of the date of their first contribution), except
+those who explicitly didn't want to be mentioned. People with a * next
+to their names are not found in the metadata of the git history. This
+file is generated automatically by running `./bin/authors_update.py`.
+
+There are a total of 1371 authors.
+
+Ondřej Čertík <ondrej@certik.cz>
+Fabian Pedregosa <fabian@fseoane.net>
+Jurjen N.E. Bos <jnebos@gmail.com>
+Mateusz Paprocki <mattpap@gmail.com>
+*Marc-Etienne M.Leveille <protonyc@gmail.com>
+Brian Jorgensen <brian.jorgensen@gmail.com>
+Jason Gedge <inferno1386@gmail.com>
+Robert Schwarz <lethargo@googlemail.com>
+Pearu Peterson <pearu.peterson@gmail.com>
+Fredrik Johansson <fredrik.johansson@gmail.com>
+Chris Wu <chris.wu@gmail.com>
+*Ulrich Hecht <ulrich.hecht@gmail.com>
+Goutham Lakshminarayan <dl.goutham@gmail.com>
+David Lawrence <dmlawrence@gmail.com>
+Jaroslaw Tworek <dev.jrx@gmail.com>
+David Marek <h4wk.cz@gmail.com>
+Bernhard R. Link <brlink@debian.org>
+Andrej Tokarčík <androsis@gmail.com>
+Or Dvory <gidesa@gmail.com>
+Saroj Adhikari <adh.saroj@gmail.com>
+Pauli Virtanen <pav@iki.fi>
+Robert Kern <robert.kern@gmail.com>
+James Aspnes <aspnes@cs.yale.edu>
+Nimish Telang <ntelang@gmail.com>
+Abderrahim Kitouni <a.kitouni@gmail.com>
+Pan Peng <pengpanster@gmail.com>
+Friedrich Hagedorn <friedrich_h@gmx.de>
+Elrond der Elbenfuerst <elrond+sympy.org@samba-tng.org>
+Rizgar Mella <rizgar.mella@gmail.com>
+Felix Kaiser <felix.kaiser@fxkr.net>
+Roberto Nobrega <rwnobrega@gmail.com>
+David Roberts <dvdr18@gmail.com>
+Sebastian Krämer <basti.kr@gmail.com>
+Vinzent Steinberg <vinzent.steinberg@gmail.com>
+Riccardo Gori <goriccardo@gmail.com>
+Case Van Horsen <casevh@gmail.com>
+Stepan Roucka <stepan@roucka.eu>
+Ali Raza Syed <arsyed@gmail.com>
+Stefano Maggiolo <s.maggiolo@gmail.com>
+Robert Cimrman <cimrman3@ntc.zcu.cz>
+Bastian Weber <bastian.weber@gmx-topmail.de>
+Sebastian Krause <sebastian.krause@gmx.de>
+Sebastian Kreft <skreft@gmail.com>
+*Dan <coolg49964@gmail.com>
+Alan Bromborsky <abrombo@verizon.net>
+Boris Timokhin <qoqenator@gmail.com>
+Robert <average.programmer@gmail.com>
+Andy R. Terrel <aterrel@uchicago.edu>
+Hubert Tsang <intsangity@gmail.com>
+Konrad Meyer <konrad.meyer@gmail.com>
+Henrik Johansson <henjo2006@gmail.com>
+Priit Laes <plaes@plaes.org>
+Freddie Witherden <freddie@witherden.org>
+Brian E. Granger <ellisonbg@gmail.com>
+Andrew Straw <strawman@astraw.com>
+Kaifeng Zhu <cafeeee@gmail.com>
+Ted Horst <ted.horst@earthlink.net>
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+Tomasz Buchert <thinred@gmail.com>
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+Jochen Voss <voss@seehuhn.de>
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+Zac Hatfield-Dodds <zac.hatfield.dodds@gmail.com>
+platypus <platypus.computerchip@gmail.com>
+codecruisader <nnisarg55@gmail.com>
+James Whitehead <whiteheadj@gmail.com>
+atharvParlikar <atharvparlikar@gmail.com>
+Ivan Petukhov <satels@gmail.com>
+Augusto Borges <borges.augustoar@gmail.com>
+Han Wei Ang <ang.h.w@u.nus.edu>
+Congxu Yang <u7189828@anu.edu.au>
+Saicharan <62512681+saicharan2804@users.noreply.github.com>
+Arnab Nandi <arnabnandi2002@gmail.com>
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+Pascal Gitz <pascal.gitz@hotmail.ch>
+Ravindu-Hirimuthugoda <ravindu.18@cse.mrt.ac.lk>
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+George Pittock <gpittock4@gmail.com>
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+Tommaso Vaccari <05-gesto-follemente@icloud.com>
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+Abhishek kumar <kumar325571@gmail.com>
+Sam Lubelsky <sammy56lt@gmail.com>
+Henrique Soares <henrique.c.soares@tecnico.ulisboa.pt>
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+Hwayeon Kang <hwayeonniii@gmail.com>
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+Gerald Teschl <gerald.teschl@univie.ac.at>
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+Zhenxu Zhu <xzdlj@outlook.com>
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+João Bravo <joaocgbravo@tecnico.ulisboa.pt>
+Dean Price <dean1357price1357@gmail.com>
+Edward Z. Yang <ezyang@mit.edu>
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+Jean-François B <2589111+jfbu@users.noreply.github.com>
+Zexuan Zhou (Bruce) <zzx498636727@gmail.com>
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+Temiloluwa Yusuf ytemiloluwa@gmail.com ytemiloluwa <ytemiloluwa@gmail.com>
+Davi Laerte <davilae011@gmail.com>
+Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
+Harshit Gupta <harshithigh2001@gmail.com>
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+Kevin McWhirter <klmcw@yahoo.com>
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+Lucas Kletzander <lucas.kletzander@gmail.com>
+Pratyksh Gupta <pratykshgupta9999@gmail.com>
+Leonardo Mangani <leomangani4@gmail.com>
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+Krishnav Bajoria <bajoriakrishnav@gmail.com>
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+Paramjit Singh <paramjit1071@gmail.com>
+João Rodrigues <abcjoao@hotmail.com>
+Alejandro García Prada <114813960+AlexGarciaPrada@users.noreply.github.com>
+Matthew Treinish <mtreinish@kortar.org>
+Clayton Rabideau <claytonrabideau@gmail.com>
+Victoria Koval <bictoriakoval16@gmail.com>
+Voaides Negustor Robert <134785947+voaidesr@users.noreply.github.com>
+Ovsk Mendov <bbb23exposed@gmail.com>
+David Brooks <dave@bcs.co.nz>
+Nicholas Laustrup <124007393+nicklaustrup@users.noreply.github.com>
+Harikrishna Srinivasan <harikrishnasri3@gmail.com>
+Mathis Cros <mathis.cros@telecom-paris.fr>
+Arnav Mummineni <45217840+RCoder01@users.noreply.github.com>
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+KJaybhaye <krushnajaybhaye01@gmail.com>

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/INSTALLER

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

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/LICENSE

@@ -0,0 +1,153 @@
+Copyright (c) 2006-2023 SymPy Development Team
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. 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.
+  c. Neither the name of SymPy 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 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 REGENTS 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, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.
+
+--------------------------------------------------------------------------------
+
+Patches that were taken from the Diofant project (https://github.com/diofant/diofant)
+are licensed as:
+
+Copyright (c) 2006-2018 SymPy Development Team,
+              2013-2023 Sergey B Kirpichev
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. 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.
+  c. Neither the name of Diofant or SymPy 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 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 REGENTS 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, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.
+
+--------------------------------------------------------------------------------
+
+Submodules taken from the multipledispatch project (https://github.com/mrocklin/multipledispatch)
+are licensed as:
+
+Copyright (c) 2014 Matthew Rocklin
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. 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.
+  c. Neither the name of multipledispatch 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 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 REGENTS 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, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.
+
+--------------------------------------------------------------------------------
+
+The files under the directory sympy/parsing/autolev/tests/pydy-example-repo
+are directly copied from PyDy project and are licensed as:
+
+Copyright (c) 2009-2023, PyDy Authors
+All 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 this project 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 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 PYDY AUTHORS 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, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+--------------------------------------------------------------------------------
+
+The files under the directory sympy/parsing/latex 
+are directly copied from latex2sympy project and are licensed as:
+
+Copyright 2016, latex2sympy
+
+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.

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/METADATA

@@ -0,0 +1,34 @@
+Metadata-Version: 2.2
+Name: sympy
+Version: 1.14.0+computecanada
+Summary: Computer algebra system (CAS) in Python
+Description-Content-Type: text/markdown
+Keywords: Math CAS
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Physics
+Classifier: Programming Language :: Python :: 3
+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 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Author: SymPy development team
+Author-email: sympy@googlegroups.com
+Home-page: https://sympy.org
+Project-URL: Source, https://github.com/sympy/sympy
+Requires-Python: >=3.9
+Requires-Dist: mpmath<1.4,>=1.1.0
+Requires-Dist: pytest>=7.1.0; extra == "dev"
+Requires-Dist: hypothesis>=6.70.0; extra == "dev"
+Provides-Extra: dev
+License-File: LICENSE
+License-File: AUTHORS
+
+BSD

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: wheelfile 0.0.8
+Root-Is-Purelib: true
+Tag: py3-none-any
+

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[console_scripts]
+isympy = isympy:main

outputs/audit_venv/lib/python3.11/site-packages/sympy-1.14.0+computecanada.dist-info/top_level.txt

@@ -0,0 +1,2 @@
+isympy
+sympy

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_mathematica.py

@@ -0,0 +1,287 @@
+from sympy.core import (S, pi, oo, symbols, Function, Rational, Integer, Tuple,
+                        Derivative, Eq, Ne, Le, Lt, Gt, Ge)
+from sympy.integrals import Integral
+from sympy.concrete import Sum
+from sympy.functions import (exp, sin, cos, fresnelc, fresnels, conjugate, Max,
+                             Min, gamma, polygamma, loggamma, erf, erfi, erfc,
+                             erf2, expint, erfinv, erfcinv, Ei, Si, Ci, li,
+                             Shi, Chi, uppergamma, beta, subfactorial, erf2inv,
+                             factorial, factorial2, catalan, RisingFactorial,
+                             FallingFactorial, harmonic, atan2, sec, acsc,
+                             hermite, laguerre, assoc_laguerre, jacobi,
+                             gegenbauer, chebyshevt, chebyshevu, legendre,
+                             assoc_legendre, Li, LambertW)
+
+from sympy.printing.mathematica import mathematica_code as mcode
+
+x, y, z, w = symbols('x,y,z,w')
+f = Function('f')
+
+
+def test_Integer():
+    assert mcode(Integer(67)) == "67"
+    assert mcode(Integer(-1)) == "-1"
+
+
+def test_Rational():
+    assert mcode(Rational(3, 7)) == "3/7"
+    assert mcode(Rational(18, 9)) == "2"
+    assert mcode(Rational(3, -7)) == "-3/7"
+    assert mcode(Rational(-3, -7)) == "3/7"
+    assert mcode(x + Rational(3, 7)) == "x + 3/7"
+    assert mcode(Rational(3, 7)*x) == "(3/7)*x"
+
+
+def test_Relational():
+    assert mcode(Eq(x, y)) == "x == y"
+    assert mcode(Ne(x, y)) == "x != y"
+    assert mcode(Le(x, y)) == "x <= y"
+    assert mcode(Lt(x, y)) == "x < y"
+    assert mcode(Gt(x, y)) == "x > y"
+    assert mcode(Ge(x, y)) == "x >= y"
+
+
+def test_Function():
+    assert mcode(f(x, y, z)) == "f[x, y, z]"
+    assert mcode(sin(x) ** cos(x)) == "Sin[x]^Cos[x]"
+    assert mcode(sec(x) * acsc(x)) == "ArcCsc[x]*Sec[x]"
+    assert mcode(atan2(y, x)) == "ArcTan[x, y]"
+    assert mcode(conjugate(x)) == "Conjugate[x]"
+    assert mcode(Max(x, y, z)*Min(y, z)) == "Max[x, y, z]*Min[y, z]"
+    assert mcode(fresnelc(x)) == "FresnelC[x]"
+    assert mcode(fresnels(x)) == "FresnelS[x]"
+    assert mcode(gamma(x)) == "Gamma[x]"
+    assert mcode(uppergamma(x, y)) == "Gamma[x, y]"
+    assert mcode(polygamma(x, y)) == "PolyGamma[x, y]"
+    assert mcode(loggamma(x)) == "LogGamma[x]"
+    assert mcode(erf(x)) == "Erf[x]"
+    assert mcode(erfc(x)) == "Erfc[x]"
+    assert mcode(erfi(x)) == "Erfi[x]"
+    assert mcode(erf2(x, y)) == "Erf[x, y]"
+    assert mcode(expint(x, y)) == "ExpIntegralE[x, y]"
+    assert mcode(erfcinv(x)) == "InverseErfc[x]"
+    assert mcode(erfinv(x)) == "InverseErf[x]"
+    assert mcode(erf2inv(x, y)) == "InverseErf[x, y]"
+    assert mcode(Ei(x)) == "ExpIntegralEi[x]"
+    assert mcode(Ci(x)) == "CosIntegral[x]"
+    assert mcode(li(x)) == "LogIntegral[x]"
+    assert mcode(Si(x)) == "SinIntegral[x]"
+    assert mcode(Shi(x)) == "SinhIntegral[x]"
+    assert mcode(Chi(x)) == "CoshIntegral[x]"
+    assert mcode(beta(x, y)) == "Beta[x, y]"
+    assert mcode(factorial(x)) == "Factorial[x]"
+    assert mcode(factorial2(x)) == "Factorial2[x]"
+    assert mcode(subfactorial(x)) == "Subfactorial[x]"
+    assert mcode(FallingFactorial(x, y)) == "FactorialPower[x, y]"
+    assert mcode(RisingFactorial(x, y)) == "Pochhammer[x, y]"
+    assert mcode(catalan(x)) == "CatalanNumber[x]"
+    assert mcode(harmonic(x)) == "HarmonicNumber[x]"
+    assert mcode(harmonic(x, y)) == "HarmonicNumber[x, y]"
+    assert mcode(Li(x)) == "LogIntegral[x] - LogIntegral[2]"
+    assert mcode(LambertW(x)) == "ProductLog[x]"
+    assert mcode(LambertW(x, -1)) == "ProductLog[-1, x]"
+    assert mcode(LambertW(x, y)) == "ProductLog[y, x]"
+
+
+def test_special_polynomials():
+    assert mcode(hermite(x, y)) == "HermiteH[x, y]"
+    assert mcode(laguerre(x, y)) == "LaguerreL[x, y]"
+    assert mcode(assoc_laguerre(x, y, z)) == "LaguerreL[x, y, z]"
+    assert mcode(jacobi(x, y, z, w)) == "JacobiP[x, y, z, w]"
+    assert mcode(gegenbauer(x, y, z)) == "GegenbauerC[x, y, z]"
+    assert mcode(chebyshevt(x, y)) == "ChebyshevT[x, y]"
+    assert mcode(chebyshevu(x, y)) == "ChebyshevU[x, y]"
+    assert mcode(legendre(x, y)) == "LegendreP[x, y]"
+    assert mcode(assoc_legendre(x, y, z)) == "LegendreP[x, y, z]"
+
+
+def test_Pow():
+    assert mcode(x**3) == "x^3"
+    assert mcode(x**(y**3)) == "x^(y^3)"
+    assert mcode(1/(f(x)*3.5)**(x - y**x)/(x**2 + y)) == \
+        "(3.5*f[x])^(-x + y^x)/(x^2 + y)"
+    assert mcode(x**-1.0) == 'x^(-1.0)'
+    assert mcode(x**Rational(2, 3)) == 'x^(2/3)'
+
+
+def test_Mul():
+    A, B, C, D = symbols('A B C D', commutative=False)
+    assert mcode(x*y*z) == "x*y*z"
+    assert mcode(x*y*A) == "x*y*A"
+    assert mcode(x*y*A*B) == "x*y*A**B"
+    assert mcode(x*y*A*B*C) == "x*y*A**B**C"
+    assert mcode(x*A*B*(C + D)*A*y) == "x*y*A**B**(C + D)**A"
+
+
+def test_constants():
+    assert mcode(S.Zero) == "0"
+    assert mcode(S.One) == "1"
+    assert mcode(S.NegativeOne) == "-1"
+    assert mcode(S.Half) == "1/2"
+    assert mcode(S.ImaginaryUnit) == "I"
+
+    assert mcode(oo) == "Infinity"
+    assert mcode(S.NegativeInfinity) == "-Infinity"
+    assert mcode(S.ComplexInfinity) == "ComplexInfinity"
+    assert mcode(S.NaN) == "Indeterminate"
+
+    assert mcode(S.Exp1) == "E"
+    assert mcode(pi) == "Pi"
+    assert mcode(S.GoldenRatio) == "GoldenRatio"
+    assert mcode(S.TribonacciConstant) == \
+        "(1/3 + (1/3)*(19 - 3*33^(1/2))^(1/3) + " \
+        "(1/3)*(3*33^(1/2) + 19)^(1/3))"
+    assert mcode(2*S.TribonacciConstant) == \
+        "2*(1/3 + (1/3)*(19 - 3*33^(1/2))^(1/3) + " \
+        "(1/3)*(3*33^(1/2) + 19)^(1/3))"
+    assert mcode(S.EulerGamma) == "EulerGamma"
+    assert mcode(S.Catalan) == "Catalan"
+
+
+def test_containers():
+    assert mcode([1, 2, 3, [4, 5, [6, 7]], 8, [9, 10], 11]) == \
+        "{1, 2, 3, {4, 5, {6, 7}}, 8, {9, 10}, 11}"
+    assert mcode((1, 2, (3, 4))) == "{1, 2, {3, 4}}"
+    assert mcode([1]) == "{1}"
+    assert mcode((1,)) == "{1}"
+    assert mcode(Tuple(*[1, 2, 3])) == "{1, 2, 3}"
+
+
+def test_matrices():
+    from sympy.matrices import MutableDenseMatrix, MutableSparseMatrix, \
+        ImmutableDenseMatrix, ImmutableSparseMatrix
+    A = MutableDenseMatrix(
+        [[1, -1, 0, 0],
+         [0, 1, -1, 0],
+         [0, 0, 1, -1],
+         [0, 0, 0, 1]]
+    )
+    B = MutableSparseMatrix(A)
+    C = ImmutableDenseMatrix(A)
+    D = ImmutableSparseMatrix(A)
+
+    assert mcode(C) == mcode(A) == \
+        "{{1, -1, 0, 0}, " \
+        "{0, 1, -1, 0}, " \
+        "{0, 0, 1, -1}, " \
+        "{0, 0, 0, 1}}"
+
+    assert mcode(D) == mcode(B) == \
+        "SparseArray[{" \
+        "{1, 1} -> 1, {1, 2} -> -1, {2, 2} -> 1, {2, 3} -> -1, " \
+        "{3, 3} -> 1, {3, 4} -> -1, {4, 4} -> 1" \
+        "}, {4, 4}]"
+
+    # Trivial cases of matrices
+    assert mcode(MutableDenseMatrix(0, 0, [])) == '{}'
+    assert mcode(MutableSparseMatrix(0, 0, [])) == 'SparseArray[{}, {0, 0}]'
+    assert mcode(MutableDenseMatrix(0, 3, [])) == '{}'
+    assert mcode(MutableSparseMatrix(0, 3, [])) == 'SparseArray[{}, {0, 3}]'
+    assert mcode(MutableDenseMatrix(3, 0, [])) == '{{}, {}, {}}'
+    assert mcode(MutableSparseMatrix(3, 0, [])) == 'SparseArray[{}, {3, 0}]'
+
+def test_NDArray():
+    from sympy.tensor.array import (
+        MutableDenseNDimArray, ImmutableDenseNDimArray,
+        MutableSparseNDimArray, ImmutableSparseNDimArray)
+
+    example = MutableDenseNDimArray(
+        [[[1, 2, 3, 4],
+          [5, 6, 7, 8],
+          [9, 10, 11, 12]],
+         [[13, 14, 15, 16],
+          [17, 18, 19, 20],
+          [21, 22, 23, 24]]]
+    )
+
+    assert mcode(example) == \
+    "{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, " \
+    "{{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}"
+
+    example = ImmutableDenseNDimArray(example)
+
+    assert mcode(example) == \
+    "{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, " \
+    "{{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}"
+
+    example = MutableSparseNDimArray(example)
+
+    assert mcode(example) == \
+    "SparseArray[{" \
+        "{1, 1, 1} -> 1, {1, 1, 2} -> 2, {1, 1, 3} -> 3, " \
+        "{1, 1, 4} -> 4, {1, 2, 1} -> 5, {1, 2, 2} -> 6, " \
+        "{1, 2, 3} -> 7, {1, 2, 4} -> 8, {1, 3, 1} -> 9, " \
+        "{1, 3, 2} -> 10, {1, 3, 3} -> 11, {1, 3, 4} -> 12, " \
+        "{2, 1, 1} -> 13, {2, 1, 2} -> 14, {2, 1, 3} -> 15, " \
+        "{2, 1, 4} -> 16, {2, 2, 1} -> 17, {2, 2, 2} -> 18, " \
+        "{2, 2, 3} -> 19, {2, 2, 4} -> 20, {2, 3, 1} -> 21, " \
+        "{2, 3, 2} -> 22, {2, 3, 3} -> 23, {2, 3, 4} -> 24" \
+        "}, {2, 3, 4}]"
+
+    example = ImmutableSparseNDimArray(example)
+
+    assert mcode(example) == \
+    "SparseArray[{" \
+        "{1, 1, 1} -> 1, {1, 1, 2} -> 2, {1, 1, 3} -> 3, " \
+        "{1, 1, 4} -> 4, {1, 2, 1} -> 5, {1, 2, 2} -> 6, " \
+        "{1, 2, 3} -> 7, {1, 2, 4} -> 8, {1, 3, 1} -> 9, " \
+        "{1, 3, 2} -> 10, {1, 3, 3} -> 11, {1, 3, 4} -> 12, " \
+        "{2, 1, 1} -> 13, {2, 1, 2} -> 14, {2, 1, 3} -> 15, " \
+        "{2, 1, 4} -> 16, {2, 2, 1} -> 17, {2, 2, 2} -> 18, " \
+        "{2, 2, 3} -> 19, {2, 2, 4} -> 20, {2, 3, 1} -> 21, " \
+        "{2, 3, 2} -> 22, {2, 3, 3} -> 23, {2, 3, 4} -> 24" \
+        "}, {2, 3, 4}]"
+
+
+def test_Integral():
+    assert mcode(Integral(sin(sin(x)), x)) == "Hold[Integrate[Sin[Sin[x]], x]]"
+    assert mcode(Integral(exp(-x**2 - y**2),
+                          (x, -oo, oo),
+                          (y, -oo, oo))) == \
+        "Hold[Integrate[Exp[-x^2 - y^2], {x, -Infinity, Infinity}, " \
+        "{y, -Infinity, Infinity}]]"
+
+
+def test_Derivative():
+    assert mcode(Derivative(sin(x), x)) == "Hold[D[Sin[x], x]]"
+    assert mcode(Derivative(x, x)) == "Hold[D[x, x]]"
+    assert mcode(Derivative(sin(x)*y**4, x, 2)) == "Hold[D[y^4*Sin[x], {x, 2}]]"
+    assert mcode(Derivative(sin(x)*y**4, x, y, x)) == "Hold[D[y^4*Sin[x], x, y, x]]"
+    assert mcode(Derivative(sin(x)*y**4, x, y, 3, x)) == "Hold[D[y^4*Sin[x], x, {y, 3}, x]]"
+
+
+def test_Sum():
+    assert mcode(Sum(sin(x), (x, 0, 10))) == "Hold[Sum[Sin[x], {x, 0, 10}]]"
+    assert mcode(Sum(exp(-x**2 - y**2),
+                     (x, -oo, oo),
+                     (y, -oo, oo))) == \
+        "Hold[Sum[Exp[-x^2 - y^2], {x, -Infinity, Infinity}, " \
+        "{y, -Infinity, Infinity}]]"
+
+
+def test_comment():
+    from sympy.printing.mathematica import MCodePrinter
+    assert MCodePrinter()._get_comment("Hello World") == \
+        "(* Hello World *)"
+
+
+def test_userfuncs():
+    # Dictionary mutation test
+    some_function = symbols("some_function", cls=Function)
+    my_user_functions = {"some_function": "SomeFunction"}
+    assert mcode(
+        some_function(z),
+        user_functions=my_user_functions) == \
+        'SomeFunction[z]'
+    assert mcode(
+        some_function(z),
+        user_functions=my_user_functions) == \
+        'SomeFunction[z]'
+
+    # List argument test
+    my_user_functions = \
+        {"some_function": [(lambda x: True, "SomeOtherFunction")]}
+    assert mcode(
+        some_function(z),
+        user_functions=my_user_functions) == \
+        'SomeOtherFunction[z]'

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_numpy.py

@@ -0,0 +1,381 @@
+from sympy.concrete.summations import Sum
+from sympy.core.mod import Mod
+from sympy.core.relational import (Equality, Unequality)
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.special.gamma_functions import polygamma
+from sympy.functions.special.error_functions import (Si, Ci)
+from sympy.matrices import Matrix
+from sympy.matrices.expressions.blockmatrix import BlockMatrix
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions.special import Identity
+from sympy.utilities.lambdify import lambdify
+from sympy import symbols, Min, Max
+
+from sympy.abc import x, i, j, a, b, c, d
+from sympy.core import Pow
+from sympy.codegen.matrix_nodes import MatrixSolve
+from sympy.codegen.numpy_nodes import logaddexp, logaddexp2
+from sympy.codegen.cfunctions import log1p, expm1, hypot, log10, exp2, log2, Sqrt
+from sympy.tensor.array import Array
+from sympy.tensor.array.expressions.array_expressions import ArrayTensorProduct, ArrayAdd, \
+    PermuteDims, ArrayDiagonal
+from sympy.printing.numpy import NumPyPrinter, SciPyPrinter, _numpy_known_constants, \
+    _numpy_known_functions, _scipy_known_constants, _scipy_known_functions
+from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
+
+from sympy.testing.pytest import skip, raises
+from sympy.external import import_module
+
+np = import_module('numpy')
+jax = import_module('jax')
+
+if np:
+    deafult_float_info = np.finfo(np.array([]).dtype)
+    NUMPY_DEFAULT_EPSILON = deafult_float_info.eps
+
+def test_numpy_piecewise_regression():
+    """
+    NumPyPrinter needs to print Piecewise()'s choicelist as a list to avoid
+    breaking compatibility with numpy 1.8. This is not necessary in numpy 1.9+.
+    See gh-9747 and gh-9749 for details.
+    """
+    printer = NumPyPrinter()
+    p = Piecewise((1, x < 0), (0, True))
+    assert printer.doprint(p) == \
+        'numpy.select([numpy.less(x, 0),True], [1,0], default=numpy.nan)'
+    assert printer.module_imports == {'numpy': {'select', 'less', 'nan'}}
+
+def test_numpy_logaddexp():
+    lae = logaddexp(a, b)
+    assert NumPyPrinter().doprint(lae) == 'numpy.logaddexp(a, b)'
+    lae2 = logaddexp2(a, b)
+    assert NumPyPrinter().doprint(lae2) == 'numpy.logaddexp2(a, b)'
+
+
+def test_sum():
+    if not np:
+        skip("NumPy not installed")
+
+    s = Sum(x ** i, (i, a, b))
+    f = lambdify((a, b, x), s, 'numpy')
+
+    a_, b_ = 0, 10
+    x_ = np.linspace(-1, +1, 10)
+    assert np.allclose(f(a_, b_, x_), sum(x_ ** i_ for i_ in range(a_, b_ + 1)))
+
+    s = Sum(i * x, (i, a, b))
+    f = lambdify((a, b, x), s, 'numpy')
+
+    a_, b_ = 0, 10
+    x_ = np.linspace(-1, +1, 10)
+    assert np.allclose(f(a_, b_, x_), sum(i_ * x_ for i_ in range(a_, b_ + 1)))
+
+
+def test_multiple_sums():
+    if not np:
+        skip("NumPy not installed")
+
+    s = Sum((x + j) * i, (i, a, b), (j, c, d))
+    f = lambdify((a, b, c, d, x), s, 'numpy')
+
+    a_, b_ = 0, 10
+    c_, d_ = 11, 21
+    x_ = np.linspace(-1, +1, 10)
+    assert np.allclose(f(a_, b_, c_, d_, x_),
+                       sum((x_ + j_) * i_ for i_ in range(a_, b_ + 1) for j_ in range(c_, d_ + 1)))
+
+
+def test_codegen_einsum():
+    if not np:
+        skip("NumPy not installed")
+
+    M = MatrixSymbol("M", 2, 2)
+    N = MatrixSymbol("N", 2, 2)
+
+    cg = convert_matrix_to_array(M * N)
+    f = lambdify((M, N), cg, 'numpy')
+
+    ma = np.array([[1, 2], [3, 4]])
+    mb = np.array([[1,-2], [-1, 3]])
+    assert (f(ma, mb) == np.matmul(ma, mb)).all()
+
+
+def test_codegen_extra():
+    if not np:
+        skip("NumPy not installed")
+
+    M = MatrixSymbol("M", 2, 2)
+    N = MatrixSymbol("N", 2, 2)
+    P = MatrixSymbol("P", 2, 2)
+    Q = MatrixSymbol("Q", 2, 2)
+    ma = np.array([[1, 2], [3, 4]])
+    mb = np.array([[1,-2], [-1, 3]])
+    mc = np.array([[2, 0], [1, 2]])
+    md = np.array([[1,-1], [4, 7]])
+
+    cg = ArrayTensorProduct(M, N)
+    f = lambdify((M, N), cg, 'numpy')
+    assert (f(ma, mb) == np.einsum(ma, [0, 1], mb, [2, 3])).all()
+
+    cg = ArrayAdd(M, N)
+    f = lambdify((M, N), cg, 'numpy')
+    assert (f(ma, mb) == ma+mb).all()
+
+    cg = ArrayAdd(M, N, P)
+    f = lambdify((M, N, P), cg, 'numpy')
+    assert (f(ma, mb, mc) == ma+mb+mc).all()
+
+    cg = ArrayAdd(M, N, P, Q)
+    f = lambdify((M, N, P, Q), cg, 'numpy')
+    assert (f(ma, mb, mc, md) == ma+mb+mc+md).all()
+
+    cg = PermuteDims(M, [1, 0])
+    f = lambdify((M,), cg, 'numpy')
+    assert (f(ma) == ma.T).all()
+
+    cg = PermuteDims(ArrayTensorProduct(M, N), [1, 2, 3, 0])
+    f = lambdify((M, N), cg, 'numpy')
+    assert (f(ma, mb) == np.transpose(np.einsum(ma, [0, 1], mb, [2, 3]), (1, 2, 3, 0))).all()
+
+    cg = ArrayDiagonal(ArrayTensorProduct(M, N), (1, 2))
+    f = lambdify((M, N), cg, 'numpy')
+    assert (f(ma, mb) == np.diagonal(np.einsum(ma, [0, 1], mb, [2, 3]), axis1=1, axis2=2)).all()
+
+
+def test_relational():
+    if not np:
+        skip("NumPy not installed")
+
+    e = Equality(x, 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [False, True, False])
+
+    e = Unequality(x, 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [True, False, True])
+
+    e = (x < 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [True, False, False])
+
+    e = (x <= 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [True, True, False])
+
+    e = (x > 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [False, False, True])
+
+    e = (x >= 1)
+
+    f = lambdify((x,), e)
+    x_ = np.array([0, 1, 2])
+    assert np.array_equal(f(x_), [False, True, True])
+
+
+def test_mod():
+    if not np:
+        skip("NumPy not installed")
+
+    e = Mod(a, b)
+    f = lambdify((a, b), e)
+
+    a_ = np.array([0, 1, 2, 3])
+    b_ = 2
+    assert np.array_equal(f(a_, b_), [0, 1, 0, 1])
+
+    a_ = np.array([0, 1, 2, 3])
+    b_ = np.array([2, 2, 2, 2])
+    assert np.array_equal(f(a_, b_), [0, 1, 0, 1])
+
+    a_ = np.array([2, 3, 4, 5])
+    b_ = np.array([2, 3, 4, 5])
+    assert np.array_equal(f(a_, b_), [0, 0, 0, 0])
+
+
+def test_pow():
+    if not np:
+        skip('NumPy not installed')
+
+    expr = Pow(2, -1, evaluate=False)
+    f = lambdify([], expr, 'numpy')
+    assert f() == 0.5
+
+
+def test_expm1():
+    if not np:
+        skip("NumPy not installed")
+
+    f = lambdify((a,), expm1(a), 'numpy')
+    assert abs(f(1e-10) - 1e-10 - 5e-21) <= 1e-10 * NUMPY_DEFAULT_EPSILON
+
+
+def test_log1p():
+    if not np:
+        skip("NumPy not installed")
+
+    f = lambdify((a,), log1p(a), 'numpy')
+    assert abs(f(1e-99) - 1e-99) <= 1e-99 * NUMPY_DEFAULT_EPSILON
+
+def test_hypot():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a, b), hypot(a, b), 'numpy')(3, 4) - 5) <= NUMPY_DEFAULT_EPSILON
+
+def test_log10():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a,), log10(a), 'numpy')(100) - 2) <= NUMPY_DEFAULT_EPSILON
+
+
+def test_exp2():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a,), exp2(a), 'numpy')(5) - 32) <= NUMPY_DEFAULT_EPSILON
+
+
+def test_log2():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a,), log2(a), 'numpy')(256) - 8) <= NUMPY_DEFAULT_EPSILON
+
+
+def test_Sqrt():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a,), Sqrt(a), 'numpy')(4) - 2) <= NUMPY_DEFAULT_EPSILON
+
+
+def test_sqrt():
+    if not np:
+        skip("NumPy not installed")
+    assert abs(lambdify((a,), sqrt(a), 'numpy')(4) - 2) <= NUMPY_DEFAULT_EPSILON
+
+
+def test_matsolve():
+    if not np:
+        skip("NumPy not installed")
+
+    M = MatrixSymbol("M", 3, 3)
+    x = MatrixSymbol("x", 3, 1)
+
+    expr = M**(-1) * x + x
+    matsolve_expr = MatrixSolve(M, x) + x
+
+    f = lambdify((M, x), expr)
+    f_matsolve = lambdify((M, x), matsolve_expr)
+
+    m0 = np.array([[1, 2, 3], [3, 2, 5], [5, 6, 7]])
+    assert np.linalg.matrix_rank(m0) == 3
+
+    x0 = np.array([3, 4, 5])
+
+    assert np.allclose(f_matsolve(m0, x0), f(m0, x0))
+
+
+def test_16857():
+    if not np:
+        skip("NumPy not installed")
+
+    a_1 = MatrixSymbol('a_1', 10, 3)
+    a_2 = MatrixSymbol('a_2', 10, 3)
+    a_3 = MatrixSymbol('a_3', 10, 3)
+    a_4 = MatrixSymbol('a_4', 10, 3)
+    A = BlockMatrix([[a_1, a_2], [a_3, a_4]])
+    assert A.shape == (20, 6)
+
+    printer = NumPyPrinter()
+    assert printer.doprint(A) == 'numpy.block([[a_1, a_2], [a_3, a_4]])'
+
+
+def test_issue_17006():
+    if not np:
+        skip("NumPy not installed")
+
+    M = MatrixSymbol("M", 2, 2)
+
+    f = lambdify(M, M + Identity(2))
+    ma = np.array([[1, 2], [3, 4]])
+    mr = np.array([[2, 2], [3, 5]])
+
+    assert (f(ma) == mr).all()
+
+    from sympy.core.symbol import symbols
+    n = symbols('n', integer=True)
+    N = MatrixSymbol("M", n, n)
+    raises(NotImplementedError, lambda: lambdify(N, N + Identity(n)))
+
+def test_jax_tuple_compatibility():
+    if not jax:
+        skip("Jax not installed")
+
+    x, y, z = symbols('x y z')
+    expr = Max(x, y, z) + Min(x, y, z)
+    func = lambdify((x, y, z), expr, 'jax')
+    input_tuple1, input_tuple2 = (1, 2, 3), (4, 5, 6)
+    input_array1, input_array2 = jax.numpy.asarray(input_tuple1), jax.numpy.asarray(input_tuple2)
+    assert np.allclose(func(*input_tuple1), func(*input_array1))
+    assert np.allclose(func(*input_tuple2), func(*input_array2))
+
+def test_numpy_array():
+    p = NumPyPrinter()
+    assert p.doprint(Array([[1, 2], [3, 5]])) == 'numpy.array([[1, 2], [3, 5]])'
+    assert p.doprint(Array([1, 2])) == 'numpy.array([1, 2])'
+    assert p.doprint(Array([[[1, 2, 3]]])) == 'numpy.array([[[1, 2, 3]]])'
+    assert p.doprint(Array([], (0,))) == 'numpy.zeros((0,))'
+    assert p.doprint(Array([], (0, 0))) == 'numpy.zeros((0, 0))'
+    assert p.doprint(Array([], (0, 1))) == 'numpy.zeros((0, 1))'
+    assert p.doprint(Array([], (1, 0))) == 'numpy.zeros((1, 0))'
+    assert p.doprint(Array([1], ())) == 'numpy.array(1)'
+
+def test_numpy_matrix():
+    p = NumPyPrinter()
+    assert p.doprint(Matrix([[1, 2], [3, 5]])) == 'numpy.array([[1, 2], [3, 5]])'
+    assert p.doprint(Matrix([1, 2])) == 'numpy.array([[1], [2]])'
+    assert p.doprint(Matrix(0, 0, [])) == 'numpy.zeros((0, 0))'
+    assert p.doprint(Matrix(0, 1, [])) == 'numpy.zeros((0, 1))'
+    assert p.doprint(Matrix(1, 0, [])) == 'numpy.zeros((1, 0))'
+
+def test_numpy_known_funcs_consts():
+    assert _numpy_known_constants['NaN'] == 'numpy.nan'
+    assert _numpy_known_constants['EulerGamma'] == 'numpy.euler_gamma'
+
+    assert _numpy_known_functions['acos'] == 'numpy.arccos'
+    assert _numpy_known_functions['log'] == 'numpy.log'
+
+def test_scipy_known_funcs_consts():
+    assert _scipy_known_constants['GoldenRatio'] == 'scipy.constants.golden_ratio'
+    assert _scipy_known_constants['Pi'] == 'scipy.constants.pi'
+
+    assert _scipy_known_functions['erf'] == 'scipy.special.erf'
+    assert _scipy_known_functions['factorial'] == 'scipy.special.factorial'
+
+def test_numpy_print_methods():
+    prntr = NumPyPrinter()
+    assert hasattr(prntr, '_print_acos')
+    assert hasattr(prntr, '_print_log')
+
+def test_scipy_print_methods():
+    prntr = SciPyPrinter()
+    assert hasattr(prntr, '_print_acos')
+    assert hasattr(prntr, '_print_log')
+    assert hasattr(prntr, '_print_erf')
+    assert hasattr(prntr, '_print_factorial')
+    assert hasattr(prntr, '_print_chebyshevt')
+    k = Symbol('k', integer=True, nonnegative=True)
+    x = Symbol('x', real=True)
+    assert prntr.doprint(polygamma(k, x)) == "scipy.special.polygamma(k, x)"
+    assert prntr.doprint(Si(x)) == "scipy.special.sici(x)[0]"
+    assert prntr.doprint(Ci(x)) == "scipy.special.sici(x)[1]"

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_octave.py

@@ -0,0 +1,515 @@
+from sympy.core import (S, pi, oo, symbols, Function, Rational, Integer,
+                        Tuple, Symbol, EulerGamma, GoldenRatio, Catalan,
+                        Lambda, Mul, Pow, Mod, Eq, Ne, Le, Lt, Gt, Ge)
+from sympy.codegen.matrix_nodes import MatrixSolve
+from sympy.functions import (arg, atan2, bernoulli, beta, ceiling, chebyshevu,
+                             chebyshevt, conjugate, DiracDelta, exp, expint,
+                             factorial, floor, harmonic, Heaviside, im,
+                             laguerre, LambertW, log, Max, Min, Piecewise,
+                             polylog, re, RisingFactorial, sign, sinc, sqrt,
+                             zeta, binomial, legendre, dirichlet_eta,
+                             riemann_xi)
+from sympy.functions import (sin, cos, tan, cot, sec, csc, asin, acos, acot,
+                             atan, asec, acsc, sinh, cosh, tanh, coth, csch,
+                             sech, asinh, acosh, atanh, acoth, asech, acsch)
+from sympy.testing.pytest import raises, XFAIL
+from sympy.utilities.lambdify import implemented_function
+from sympy.matrices import (eye, Matrix, MatrixSymbol, Identity,
+                            HadamardProduct, SparseMatrix, HadamardPower)
+from sympy.functions.special.bessel import (jn, yn, besselj, bessely, besseli,
+                                            besselk, hankel1, hankel2, airyai,
+                                            airybi, airyaiprime, airybiprime)
+from sympy.functions.special.gamma_functions import (gamma, lowergamma,
+                                                     uppergamma, loggamma,
+                                                     polygamma)
+from sympy.functions.special.error_functions import (Chi, Ci, erf, erfc, erfi,
+                                                     erfcinv, erfinv, fresnelc,
+                                                     fresnels, li, Shi, Si, Li,
+                                                     erf2, Ei)
+from sympy.printing.octave import octave_code, octave_code as mcode
+
+x, y, z = symbols('x,y,z')
+
+
+def test_Integer():
+    assert mcode(Integer(67)) == "67"
+    assert mcode(Integer(-1)) == "-1"
+
+
+def test_Rational():
+    assert mcode(Rational(3, 7)) == "3/7"
+    assert mcode(Rational(18, 9)) == "2"
+    assert mcode(Rational(3, -7)) == "-3/7"
+    assert mcode(Rational(-3, -7)) == "3/7"
+    assert mcode(x + Rational(3, 7)) == "x + 3/7"
+    assert mcode(Rational(3, 7)*x) == "3*x/7"
+
+
+def test_Relational():
+    assert mcode(Eq(x, y)) == "x == y"
+    assert mcode(Ne(x, y)) == "x != y"
+    assert mcode(Le(x, y)) == "x <= y"
+    assert mcode(Lt(x, y)) == "x < y"
+    assert mcode(Gt(x, y)) == "x > y"
+    assert mcode(Ge(x, y)) == "x >= y"
+
+
+def test_Function():
+    assert mcode(sin(x) ** cos(x)) == "sin(x).^cos(x)"
+    assert mcode(sign(x)) == "sign(x)"
+    assert mcode(exp(x)) == "exp(x)"
+    assert mcode(log(x)) == "log(x)"
+    assert mcode(factorial(x)) == "factorial(x)"
+    assert mcode(floor(x)) == "floor(x)"
+    assert mcode(atan2(y, x)) == "atan2(y, x)"
+    assert mcode(beta(x, y)) == 'beta(x, y)'
+    assert mcode(polylog(x, y)) == 'polylog(x, y)'
+    assert mcode(harmonic(x)) == 'harmonic(x)'
+    assert mcode(bernoulli(x)) == "bernoulli(x)"
+    assert mcode(bernoulli(x, y)) == "bernoulli(x, y)"
+    assert mcode(legendre(x, y)) == "legendre(x, y)"
+
+
+def test_Function_change_name():
+    assert mcode(abs(x)) == "abs(x)"
+    assert mcode(ceiling(x)) == "ceil(x)"
+    assert mcode(arg(x)) == "angle(x)"
+    assert mcode(im(x)) == "imag(x)"
+    assert mcode(re(x)) == "real(x)"
+    assert mcode(conjugate(x)) == "conj(x)"
+    assert mcode(chebyshevt(y, x)) == "chebyshevT(y, x)"
+    assert mcode(chebyshevu(y, x)) == "chebyshevU(y, x)"
+    assert mcode(laguerre(x, y)) == "laguerreL(x, y)"
+    assert mcode(Chi(x)) == "coshint(x)"
+    assert mcode(Shi(x)) ==  "sinhint(x)"
+    assert mcode(Ci(x)) == "cosint(x)"
+    assert mcode(Si(x)) ==  "sinint(x)"
+    assert mcode(li(x)) ==  "logint(x)"
+    assert mcode(loggamma(x)) ==  "gammaln(x)"
+    assert mcode(polygamma(x, y)) == "psi(x, y)"
+    assert mcode(RisingFactorial(x, y)) == "pochhammer(x, y)"
+    assert mcode(DiracDelta(x)) == "dirac(x)"
+    assert mcode(DiracDelta(x, 3)) == "dirac(3, x)"
+    assert mcode(Heaviside(x)) == "heaviside(x, 1/2)"
+    assert mcode(Heaviside(x, y)) == "heaviside(x, y)"
+    assert mcode(binomial(x, y)) == "bincoeff(x, y)"
+    assert mcode(Mod(x, y)) == "mod(x, y)"
+
+
+def test_minmax():
+    assert mcode(Max(x, y) + Min(x, y)) == "max(x, y) + min(x, y)"
+    assert mcode(Max(x, y, z)) == "max(x, max(y, z))"
+    assert mcode(Min(x, y, z)) == "min(x, min(y, z))"
+
+
+def test_Pow():
+    assert mcode(x**3) == "x.^3"
+    assert mcode(x**(y**3)) == "x.^(y.^3)"
+    assert mcode(x**Rational(2, 3)) == 'x.^(2/3)'
+    g = implemented_function('g', Lambda(x, 2*x))
+    assert mcode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
+        "(3.5*2*x).^(-x + y.^x)./(x.^2 + y)"
+    # For issue 14160
+    assert mcode(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False),
+                                                evaluate=False)) == '-2*x./(y.*y)'
+
+
+def test_basic_ops():
+    assert mcode(x*y) == "x.*y"
+    assert mcode(x + y) == "x + y"
+    assert mcode(x - y) == "x - y"
+    assert mcode(-x) == "-x"
+
+
+def test_1_over_x_and_sqrt():
+    # 1.0 and 0.5 would do something different in regular StrPrinter,
+    # but these are exact in IEEE floating point so no different here.
+    assert mcode(1/x) == '1./x'
+    assert mcode(x**-1) == mcode(x**-1.0) == '1./x'
+    assert mcode(1/sqrt(x)) == '1./sqrt(x)'
+    assert mcode(x**-S.Half) == mcode(x**-0.5) == '1./sqrt(x)'
+    assert mcode(sqrt(x)) == 'sqrt(x)'
+    assert mcode(x**S.Half) == mcode(x**0.5) == 'sqrt(x)'
+    assert mcode(1/pi) == '1/pi'
+    assert mcode(pi**-1) == mcode(pi**-1.0) == '1/pi'
+    assert mcode(pi**-0.5) == '1/sqrt(pi)'
+
+
+def test_mix_number_mult_symbols():
+    assert mcode(3*x) == "3*x"
+    assert mcode(pi*x) == "pi*x"
+    assert mcode(3/x) == "3./x"
+    assert mcode(pi/x) == "pi./x"
+    assert mcode(x/3) == "x/3"
+    assert mcode(x/pi) == "x/pi"
+    assert mcode(x*y) == "x.*y"
+    assert mcode(3*x*y) == "3*x.*y"
+    assert mcode(3*pi*x*y) == "3*pi*x.*y"
+    assert mcode(x/y) == "x./y"
+    assert mcode(3*x/y) == "3*x./y"
+    assert mcode(x*y/z) == "x.*y./z"
+    assert mcode(x/y*z) == "x.*z./y"
+    assert mcode(1/x/y) == "1./(x.*y)"
+    assert mcode(2*pi*x/y/z) == "2*pi*x./(y.*z)"
+    assert mcode(3*pi/x) == "3*pi./x"
+    assert mcode(S(3)/5) == "3/5"
+    assert mcode(S(3)/5*x) == "3*x/5"
+    assert mcode(x/y/z) == "x./(y.*z)"
+    assert mcode((x+y)/z) == "(x + y)./z"
+    assert mcode((x+y)/(z+x)) == "(x + y)./(x + z)"
+    assert mcode((x+y)/EulerGamma) == "(x + y)/%s" % EulerGamma.evalf(17)
+    assert mcode(x/3/pi) == "x/(3*pi)"
+    assert mcode(S(3)/5*x*y/pi) == "3*x.*y/(5*pi)"
+
+
+def test_mix_number_pow_symbols():
+    assert mcode(pi**3) == 'pi^3'
+    assert mcode(x**2) == 'x.^2'
+    assert mcode(x**(pi**3)) == 'x.^(pi^3)'
+    assert mcode(x**y) == 'x.^y'
+    assert mcode(x**(y**z)) == 'x.^(y.^z)'
+    assert mcode((x**y)**z) == '(x.^y).^z'
+
+
+def test_imag():
+    I = S('I')
+    assert mcode(I) == "1i"
+    assert mcode(5*I) == "5i"
+    assert mcode((S(3)/2)*I) == "3*1i/2"
+    assert mcode(3+4*I) == "3 + 4i"
+    assert mcode(sqrt(3)*I) == "sqrt(3)*1i"
+
+
+def test_constants():
+    assert mcode(pi) == "pi"
+    assert mcode(oo) == "inf"
+    assert mcode(-oo) == "-inf"
+    assert mcode(S.NegativeInfinity) == "-inf"
+    assert mcode(S.NaN) == "NaN"
+    assert mcode(S.Exp1) == "exp(1)"
+    assert mcode(exp(1)) == "exp(1)"
+
+
+def test_constants_other():
+    assert mcode(2*GoldenRatio) == "2*(1+sqrt(5))/2"
+    assert mcode(2*Catalan) == "2*%s" % Catalan.evalf(17)
+    assert mcode(2*EulerGamma) == "2*%s" % EulerGamma.evalf(17)
+
+
+def test_boolean():
+    assert mcode(x & y) == "x & y"
+    assert mcode(x | y) == "x | y"
+    assert mcode(~x) == "~x"
+    assert mcode(x & y & z) == "x & y & z"
+    assert mcode(x | y | z) == "x | y | z"
+    assert mcode((x & y) | z) == "z | x & y"
+    assert mcode((x | y) & z) == "z & (x | y)"
+
+
+def test_KroneckerDelta():
+    from sympy.functions import KroneckerDelta
+    assert mcode(KroneckerDelta(x, y)) == "double(x == y)"
+    assert mcode(KroneckerDelta(x, y + 1)) == "double(x == (y + 1))"
+    assert mcode(KroneckerDelta(2**x, y)) == "double((2.^x) == y)"
+
+
+def test_Matrices():
+    assert mcode(Matrix(1, 1, [10])) == "10"
+    A = Matrix([[1, sin(x/2), abs(x)],
+                [0, 1, pi],
+                [0, exp(1), ceiling(x)]])
+    expected = "[1 sin(x/2) abs(x); 0 1 pi; 0 exp(1) ceil(x)]"
+    assert mcode(A) == expected
+    # row and columns
+    assert mcode(A[:,0]) == "[1; 0; 0]"
+    assert mcode(A[0,:]) == "[1 sin(x/2) abs(x)]"
+    # empty matrices
+    assert mcode(Matrix(0, 0, [])) == '[]'
+    assert mcode(Matrix(0, 3, [])) == 'zeros(0, 3)'
+    # annoying to read but correct
+    assert mcode(Matrix([[x, x - y, -y]])) == "[x x - y -y]"
+
+
+def test_vector_entries_hadamard():
+    # For a row or column, user might to use the other dimension
+    A = Matrix([[1, sin(2/x), 3*pi/x/5]])
+    assert mcode(A) == "[1 sin(2./x) 3*pi./(5*x)]"
+    assert mcode(A.T) == "[1; sin(2./x); 3*pi./(5*x)]"
+
+
+@XFAIL
+def test_Matrices_entries_not_hadamard():
+    # For Matrix with col >= 2, row >= 2, they need to be scalars
+    # FIXME: is it worth worrying about this?  Its not wrong, just
+    # leave it user's responsibility to put scalar data for x.
+    A = Matrix([[1, sin(2/x), 3*pi/x/5], [1, 2, x*y]])
+    expected = ("[1 sin(2/x) 3*pi/(5*x);\n"
+                "1        2        x*y]") # <- we give x.*y
+    assert mcode(A) == expected
+
+
+def test_MatrixSymbol():
+    n = Symbol('n', integer=True)
+    A = MatrixSymbol('A', n, n)
+    B = MatrixSymbol('B', n, n)
+    assert mcode(A*B) == "A*B"
+    assert mcode(B*A) == "B*A"
+    assert mcode(2*A*B) == "2*A*B"
+    assert mcode(B*2*A) == "2*B*A"
+    assert mcode(A*(B + 3*Identity(n))) == "A*(3*eye(n) + B)"
+    assert mcode(A**(x**2)) == "A^(x.^2)"
+    assert mcode(A**3) == "A^3"
+    assert mcode(A**S.Half) == "A^(1/2)"
+
+
+def test_MatrixSolve():
+    n = Symbol('n', integer=True)
+    A = MatrixSymbol('A', n, n)
+    x = MatrixSymbol('x', n, 1)
+    assert mcode(MatrixSolve(A, x)) == "A \\ x"
+
+def test_special_matrices():
+    assert mcode(6*Identity(3)) == "6*eye(3)"
+
+
+def test_containers():
+    assert mcode([1, 2, 3, [4, 5, [6, 7]], 8, [9, 10], 11]) == \
+        "{1, 2, 3, {4, 5, {6, 7}}, 8, {9, 10}, 11}"
+    assert mcode((1, 2, (3, 4))) == "{1, 2, {3, 4}}"
+    assert mcode([1]) == "{1}"
+    assert mcode((1,)) == "{1}"
+    assert mcode(Tuple(*[1, 2, 3])) == "{1, 2, 3}"
+    assert mcode((1, x*y, (3, x**2))) == "{1, x.*y, {3, x.^2}}"
+    # scalar, matrix, empty matrix and empty list
+    assert mcode((1, eye(3), Matrix(0, 0, []), [])) == "{1, [1 0 0; 0 1 0; 0 0 1], [], {}}"
+
+
+def test_octave_noninline():
+    source = mcode((x+y)/Catalan, assign_to='me', inline=False)
+    expected = (
+        "Catalan = %s;\n"
+        "me = (x + y)/Catalan;"
+    ) % Catalan.evalf(17)
+    assert source == expected
+
+
+def test_octave_piecewise():
+    expr = Piecewise((x, x < 1), (x**2, True))
+    assert mcode(expr) == "((x < 1).*(x) + (~(x < 1)).*(x.^2))"
+    assert mcode(expr, assign_to="r") == (
+        "r = ((x < 1).*(x) + (~(x < 1)).*(x.^2));")
+    assert mcode(expr, assign_to="r", inline=False) == (
+        "if (x < 1)\n"
+        "  r = x;\n"
+        "else\n"
+        "  r = x.^2;\n"
+        "end")
+    expr = Piecewise((x**2, x < 1), (x**3, x < 2), (x**4, x < 3), (x**5, True))
+    expected = ("((x < 1).*(x.^2) + (~(x < 1)).*( ...\n"
+                "(x < 2).*(x.^3) + (~(x < 2)).*( ...\n"
+                "(x < 3).*(x.^4) + (~(x < 3)).*(x.^5))))")
+    assert mcode(expr) == expected
+    assert mcode(expr, assign_to="r") == "r = " + expected + ";"
+    assert mcode(expr, assign_to="r", inline=False) == (
+        "if (x < 1)\n"
+        "  r = x.^2;\n"
+        "elseif (x < 2)\n"
+        "  r = x.^3;\n"
+        "elseif (x < 3)\n"
+        "  r = x.^4;\n"
+        "else\n"
+        "  r = x.^5;\n"
+        "end")
+    # Check that Piecewise without a True (default) condition error
+    expr = Piecewise((x, x < 1), (x**2, x > 1), (sin(x), x > 0))
+    raises(ValueError, lambda: mcode(expr))
+
+
+def test_octave_piecewise_times_const():
+    pw = Piecewise((x, x < 1), (x**2, True))
+    assert mcode(2*pw) == "2*((x < 1).*(x) + (~(x < 1)).*(x.^2))"
+    assert mcode(pw/x) == "((x < 1).*(x) + (~(x < 1)).*(x.^2))./x"
+    assert mcode(pw/(x*y)) == "((x < 1).*(x) + (~(x < 1)).*(x.^2))./(x.*y)"
+    assert mcode(pw/3) == "((x < 1).*(x) + (~(x < 1)).*(x.^2))/3"
+
+
+def test_octave_matrix_assign_to():
+    A = Matrix([[1, 2, 3]])
+    assert mcode(A, assign_to='a') == "a = [1 2 3];"
+    A = Matrix([[1, 2], [3, 4]])
+    assert mcode(A, assign_to='A') == "A = [1 2; 3 4];"
+
+
+def test_octave_matrix_assign_to_more():
+    # assigning to Symbol or MatrixSymbol requires lhs/rhs match
+    A = Matrix([[1, 2, 3]])
+    B = MatrixSymbol('B', 1, 3)
+    C = MatrixSymbol('C', 2, 3)
+    assert mcode(A, assign_to=B) == "B = [1 2 3];"
+    raises(ValueError, lambda: mcode(A, assign_to=x))
+    raises(ValueError, lambda: mcode(A, assign_to=C))
+
+
+def test_octave_matrix_1x1():
+    A = Matrix([[3]])
+    B = MatrixSymbol('B', 1, 1)
+    C = MatrixSymbol('C', 1, 2)
+    assert mcode(A, assign_to=B) == "B = 3;"
+    # FIXME?
+    #assert mcode(A, assign_to=x) == "x = 3;"
+    raises(ValueError, lambda: mcode(A, assign_to=C))
+
+
+def test_octave_matrix_elements():
+    A = Matrix([[x, 2, x*y]])
+    assert mcode(A[0, 0]**2 + A[0, 1] + A[0, 2]) == "x.^2 + x.*y + 2"
+    A = MatrixSymbol('AA', 1, 3)
+    assert mcode(A) == "AA"
+    assert mcode(A[0, 0]**2 + sin(A[0,1]) + A[0,2]) == \
+           "sin(AA(1, 2)) + AA(1, 1).^2 + AA(1, 3)"
+    assert mcode(sum(A)) == "AA(1, 1) + AA(1, 2) + AA(1, 3)"
+
+
+def test_octave_boolean():
+    assert mcode(True) == "true"
+    assert mcode(S.true) == "true"
+    assert mcode(False) == "false"
+    assert mcode(S.false) == "false"
+
+
+def test_octave_not_supported():
+    with raises(NotImplementedError):
+        mcode(S.ComplexInfinity)
+    f = Function('f')
+    assert mcode(f(x).diff(x), strict=False) == (
+        "% Not supported in Octave:\n"
+        "% Derivative\n"
+        "Derivative(f(x), x)"
+    )
+
+
+def test_octave_not_supported_not_on_whitelist():
+    from sympy.functions.special.polynomials import assoc_laguerre
+    with raises(NotImplementedError):
+        mcode(assoc_laguerre(x, y, z))
+
+
+def test_octave_expint():
+    assert mcode(expint(1, x)) == "expint(x)"
+    with raises(NotImplementedError):
+        mcode(expint(2, x))
+    assert mcode(expint(y, x), strict=False) == (
+        "% Not supported in Octave:\n"
+        "% expint\n"
+        "expint(y, x)"
+    )
+
+
+def test_trick_indent_with_end_else_words():
+    # words starting with "end" or "else" do not confuse the indenter
+    t1 = S('endless')
+    t2 = S('elsewhere')
+    pw = Piecewise((t1, x < 0), (t2, x <= 1), (1, True))
+    assert mcode(pw, inline=False) == (
+        "if (x < 0)\n"
+        "  endless\n"
+        "elseif (x <= 1)\n"
+        "  elsewhere\n"
+        "else\n"
+        "  1\n"
+        "end")
+
+
+def test_hadamard():
+    A = MatrixSymbol('A', 3, 3)
+    B = MatrixSymbol('B', 3, 3)
+    v = MatrixSymbol('v', 3, 1)
+    h = MatrixSymbol('h', 1, 3)
+    C = HadamardProduct(A, B)
+    n = Symbol('n')
+    assert mcode(C) == "A.*B"
+    assert mcode(C*v) == "(A.*B)*v"
+    assert mcode(h*C*v) == "h*(A.*B)*v"
+    assert mcode(C*A) == "(A.*B)*A"
+    # mixing Hadamard and scalar strange b/c we vectorize scalars
+    assert mcode(C*x*y) == "(x.*y)*(A.*B)"
+
+    # Testing HadamardPower:
+    assert mcode(HadamardPower(A, n)) == "A.**n"
+    assert mcode(HadamardPower(A, 1+n)) == "A.**(n + 1)"
+    assert mcode(HadamardPower(A*B.T, 1+n)) == "(A*B.T).**(n + 1)"
+
+
+def test_sparse():
+    M = SparseMatrix(5, 6, {})
+    M[2, 2] = 10
+    M[1, 2] = 20
+    M[1, 3] = 22
+    M[0, 3] = 30
+    M[3, 0] = x*y
+    assert mcode(M) == (
+        "sparse([4 2 3 1 2], [1 3 3 4 4], [x.*y 20 10 30 22], 5, 6)"
+    )
+
+
+def test_sinc():
+    assert mcode(sinc(x)) == 'sinc(x/pi)'
+    assert mcode(sinc(x + 3)) == 'sinc((x + 3)/pi)'
+    assert mcode(sinc(pi*(x + 3))) == 'sinc(x + 3)'
+
+
+def test_trigfun():
+    for f in (sin, cos, tan, cot, sec, csc, asin, acos, acot, atan, asec, acsc,
+              sinh, cosh, tanh, coth, csch, sech, asinh, acosh, atanh, acoth,
+              asech, acsch):
+        assert octave_code(f(x) == f.__name__ + '(x)')
+
+
+def test_specfun():
+    n = Symbol('n')
+    for f in [besselj, bessely, besseli, besselk]:
+        assert octave_code(f(n, x)) == f.__name__ + '(n, x)'
+    for f in (erfc, erfi, erf, erfinv, erfcinv, fresnelc, fresnels, gamma):
+        assert octave_code(f(x)) == f.__name__ + '(x)'
+    assert octave_code(hankel1(n, x)) == 'besselh(n, 1, x)'
+    assert octave_code(hankel2(n, x)) == 'besselh(n, 2, x)'
+    assert octave_code(airyai(x)) == 'airy(0, x)'
+    assert octave_code(airyaiprime(x)) == 'airy(1, x)'
+    assert octave_code(airybi(x)) == 'airy(2, x)'
+    assert octave_code(airybiprime(x)) == 'airy(3, x)'
+    assert octave_code(uppergamma(n, x)) == '(gammainc(x, n, \'upper\').*gamma(n))'
+    assert octave_code(lowergamma(n, x)) == '(gammainc(x, n).*gamma(n))'
+    assert octave_code(z**lowergamma(n, x)) == 'z.^(gammainc(x, n).*gamma(n))'
+    assert octave_code(jn(n, x)) == 'sqrt(2)*sqrt(pi)*sqrt(1./x).*besselj(n + 1/2, x)/2'
+    assert octave_code(yn(n, x)) == 'sqrt(2)*sqrt(pi)*sqrt(1./x).*bessely(n + 1/2, x)/2'
+    assert octave_code(LambertW(x)) == 'lambertw(x)'
+    assert octave_code(LambertW(x, n)) == 'lambertw(n, x)'
+
+    # Automatic rewrite
+    assert octave_code(Ei(x)) == '(logint(exp(x)))'
+    assert octave_code(dirichlet_eta(x)) == '(((x == 1).*(log(2)) + (~(x == 1)).*((1 - 2.^(1 - x)).*zeta(x))))'
+    assert octave_code(riemann_xi(x)) == '(pi.^(-x/2).*x.*(x - 1).*gamma(x/2).*zeta(x)/2)'
+
+
+def test_MatrixElement_printing():
+    # test cases for issue #11821
+    A = MatrixSymbol("A", 1, 3)
+    B = MatrixSymbol("B", 1, 3)
+    C = MatrixSymbol("C", 1, 3)
+
+    assert mcode(A[0, 0]) == "A(1, 1)"
+    assert mcode(3 * A[0, 0]) == "3*A(1, 1)"
+
+    F = C[0, 0].subs(C, A - B)
+    assert mcode(F) == "(A - B)(1, 1)"
+
+
+def test_zeta_printing_issue_14820():
+    assert octave_code(zeta(x)) == 'zeta(x)'
+    with raises(NotImplementedError):
+        octave_code(zeta(x, y))
+
+
+def test_automatic_rewrite():
+    assert octave_code(Li(x)) == '(logint(x) - logint(2))'
+    assert octave_code(erf2(x, y)) == '(-erf(x) + erf(y))'

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_precedence.py

@@ -0,0 +1,128 @@
+from sympy.concrete.products import Product
+from sympy.concrete.summations import Sum
+from sympy.core.function import Derivative, Function
+from sympy.core.numbers import Integer, Rational, Float, oo
+from sympy.core.relational import Rel
+from sympy.core.symbol import symbols
+from sympy.functions import sin
+from sympy.integrals.integrals import Integral
+from sympy.series.order import Order
+
+from sympy.printing.precedence import precedence, PRECEDENCE
+
+x, y = symbols("x,y")
+
+
+def test_Add():
+    assert precedence(x + y) == PRECEDENCE["Add"]
+    assert precedence(x*y + 1) == PRECEDENCE["Add"]
+
+
+def test_Function():
+    assert precedence(sin(x)) == PRECEDENCE["Func"]
+
+def test_Derivative():
+    assert precedence(Derivative(x, y)) == PRECEDENCE["Atom"]
+
+def test_Integral():
+    assert precedence(Integral(x, y)) == PRECEDENCE["Atom"]
+
+
+def test_Mul():
+    assert precedence(x*y) == PRECEDENCE["Mul"]
+    assert precedence(-x*y) == PRECEDENCE["Add"]
+
+
+def test_Number():
+    assert precedence(Integer(0)) == PRECEDENCE["Atom"]
+    assert precedence(Integer(1)) == PRECEDENCE["Atom"]
+    assert precedence(Integer(-1)) == PRECEDENCE["Add"]
+    assert precedence(Integer(10)) == PRECEDENCE["Atom"]
+    assert precedence(Rational(5, 2)) == PRECEDENCE["Mul"]
+    assert precedence(Rational(-5, 2)) == PRECEDENCE["Add"]
+    assert precedence(Float(5)) == PRECEDENCE["Atom"]
+    assert precedence(Float(-5)) == PRECEDENCE["Add"]
+    assert precedence(oo) == PRECEDENCE["Atom"]
+    assert precedence(-oo) == PRECEDENCE["Add"]
+
+
+def test_Order():
+    assert precedence(Order(x)) == PRECEDENCE["Atom"]
+
+
+def test_Pow():
+    assert precedence(x**y) == PRECEDENCE["Pow"]
+    assert precedence(-x**y) == PRECEDENCE["Add"]
+    assert precedence(x**-y) == PRECEDENCE["Pow"]
+
+
+def test_Product():
+    assert precedence(Product(x, (x, y, y + 1))) == PRECEDENCE["Atom"]
+
+
+def test_Relational():
+    assert precedence(Rel(x + y, y, "<")) == PRECEDENCE["Relational"]
+
+
+def test_Sum():
+    assert precedence(Sum(x, (x, y, y + 1))) == PRECEDENCE["Atom"]
+
+
+def test_Symbol():
+    assert precedence(x) == PRECEDENCE["Atom"]
+
+
+def test_And_Or():
+    # precedence relations between logical operators, ...
+    assert precedence(x & y) > precedence(x | y)
+    assert precedence(~y) > precedence(x & y)
+    # ... and with other operators (cfr. other programming languages)
+    assert precedence(x + y) > precedence(x | y)
+    assert precedence(x + y) > precedence(x & y)
+    assert precedence(x*y) > precedence(x | y)
+    assert precedence(x*y) > precedence(x & y)
+    assert precedence(~y) > precedence(x*y)
+    assert precedence(~y) > precedence(x - y)
+    # double checks
+    assert precedence(x & y) == PRECEDENCE["And"]
+    assert precedence(x | y) == PRECEDENCE["Or"]
+    assert precedence(~y) == PRECEDENCE["Not"]
+
+
+def test_custom_function_precedence_comparison():
+    """
+    Test cases for custom functions with different precedence values,
+    specifically handling:
+    1. Functions with precedence < PRECEDENCE["Mul"] (50)
+    2. Functions with precedence = Func (70)
+
+    Key distinction:
+    1. Lower precedence functions (45) need parentheses: -2*(x F y)
+    2. Higher precedence functions (70) don't: -2*x F y
+    """
+    class LowPrecedenceF(Function):
+        precedence = PRECEDENCE["Mul"] - 5
+        def _sympystr(self, printer):
+            return f"{printer._print(self.args[0])} F {printer._print(self.args[1])}"
+
+    class HighPrecedenceF(Function):
+        precedence = PRECEDENCE["Func"]
+        def _sympystr(self, printer):
+            return f"{printer._print(self.args[0])} F {printer._print(self.args[1])}"
+
+    def test_low_precedence():
+        expr1 = 2 * LowPrecedenceF(x, y)
+        assert str(expr1) == "2*(x F y)"
+
+        expr2 = -2 * LowPrecedenceF(x, y)
+        assert str(expr2) == "-2*(x F y)"
+
+    def test_high_precedence():
+        expr1 = 2 * HighPrecedenceF(x, y)
+        assert str(expr1) == "2*x F y"
+
+        expr2 = -2 * HighPrecedenceF(x, y)
+        assert str(expr2) == "-2*x F y"
+
+    test_low_precedence()
+    test_high_precedence()

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_preview.py

@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+
+from sympy.core.relational import Eq
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.printing.preview import preview
+
+from io import BytesIO
+
+
+def test_preview():
+    x = Symbol('x')
+    obj = BytesIO()
+    try:
+        preview(x, output='png', viewer='BytesIO', outputbuffer=obj)
+    except RuntimeError:
+        pass  # latex not installed on CI server
+
+
+def test_preview_unicode_symbol():
+    # issue 9107
+    a = Symbol('α')
+    obj = BytesIO()
+    try:
+        preview(a, output='png', viewer='BytesIO', outputbuffer=obj)
+    except RuntimeError:
+        pass  # latex not installed on CI server
+
+
+def test_preview_latex_construct_in_expr():
+    # see PR 9801
+    x = Symbol('x')
+    pw = Piecewise((1, Eq(x, 0)), (0, True))
+    obj = BytesIO()
+    try:
+        preview(pw, output='png', viewer='BytesIO', outputbuffer=obj)
+    except RuntimeError:
+        pass  # latex not installed on CI server

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_pycode.py

@@ -0,0 +1,493 @@
+from sympy import Not
+from sympy.codegen import Assignment
+from sympy.codegen.ast import none
+from sympy.codegen.cfunctions import expm1, log1p
+from sympy.codegen.scipy_nodes import cosm1
+from sympy.codegen.matrix_nodes import MatrixSolve
+from sympy.core import Expr, Mod, symbols, Eq, Le, Gt, zoo, oo, Rational, Pow
+from sympy.core.function import Derivative
+from sympy.core.numbers import pi
+from sympy.core.singleton import S
+from sympy.functions import acos, KroneckerDelta, Piecewise, sign, sqrt, Min, Max, cot, acsch, asec, coth, sec, log, sin, cos, tan, asin, atan, sinh, cosh, tanh, asinh, acosh, atanh
+from sympy.functions.elementary.trigonometric import atan2
+from sympy.logic import And, Or
+from sympy.matrices import SparseMatrix, MatrixSymbol, Identity
+from sympy.printing.codeprinter import PrintMethodNotImplementedError
+from sympy.printing.pycode import (
+    MpmathPrinter, CmathPrinter, PythonCodePrinter, pycode, SymPyPrinter
+)
+from sympy.printing.tensorflow import TensorflowPrinter
+from sympy.printing.numpy import NumPyPrinter, SciPyPrinter
+from sympy.testing.pytest import raises, skip
+from sympy.tensor import IndexedBase, Idx
+from sympy.tensor.array.expressions.array_expressions import ArraySymbol, ArrayDiagonal, ArrayContraction, ZeroArray, OneArray
+from sympy.external import import_module
+from sympy.functions.special.gamma_functions import loggamma
+
+
+
+x, y, z = symbols('x y z')
+p = IndexedBase("p")
+
+
+def test_PythonCodePrinter():
+    prntr = PythonCodePrinter()
+
+    assert not prntr.module_imports
+
+    assert prntr.doprint(x**y) == 'x**y'
+    assert prntr.doprint(Mod(x, 2)) == 'x % 2'
+    assert prntr.doprint(-Mod(x, y)) == '-(x % y)'
+    assert prntr.doprint(Mod(-x, y)) == '(-x) % y'
+    assert prntr.doprint(And(x, y)) == 'x and y'
+    assert prntr.doprint(Or(x, y)) == 'x or y'
+    assert prntr.doprint(1/(x+y)) == '1/(x + y)'
+    assert prntr.doprint(Not(x)) == 'not x'
+    assert not prntr.module_imports
+
+    assert prntr.doprint(pi) == 'math.pi'
+    assert prntr.module_imports == {'math': {'pi'}}
+
+    assert prntr.doprint(x**Rational(1, 2)) == 'math.sqrt(x)'
+    assert prntr.doprint(sqrt(x)) == 'math.sqrt(x)'
+    assert prntr.module_imports == {'math': {'pi', 'sqrt'}}
+
+    assert prntr.doprint(acos(x)) == 'math.acos(x)'
+    assert prntr.doprint(cot(x)) == '(1/math.tan(x))'
+    assert prntr.doprint(coth(x)) == '((math.exp(x) + math.exp(-x))/(math.exp(x) - math.exp(-x)))'
+    assert prntr.doprint(asec(x)) == '(math.acos(1/x))'
+    assert prntr.doprint(acsch(x)) == '(math.log(math.sqrt(1 + x**(-2)) + 1/x))'
+
+    assert prntr.doprint(Assignment(x, 2)) == 'x = 2'
+    assert prntr.doprint(Piecewise((1, Eq(x, 0)),
+                        (2, x>6))) == '((1) if (x == 0) else (2) if (x > 6) else None)'
+    assert prntr.doprint(Piecewise((2, Le(x, 0)),
+                        (3, Gt(x, 0)), evaluate=False)) == '((2) if (x <= 0) else'\
+                                                        ' (3) if (x > 0) else None)'
+    assert prntr.doprint(sign(x)) == '(0.0 if x == 0 else math.copysign(1, x))'
+    assert prntr.doprint(p[0, 1]) == 'p[0, 1]'
+    assert prntr.doprint(KroneckerDelta(x,y)) == '(1 if x == y else 0)'
+
+    assert prntr.doprint((2,3)) == "(2, 3)"
+    assert prntr.doprint([2,3]) == "[2, 3]"
+
+    assert prntr.doprint(Min(x, y)) == "min(x, y)"
+    assert prntr.doprint(Max(x, y)) == "max(x, y)"
+
+
+def test_PythonCodePrinter_standard():
+    prntr = PythonCodePrinter()
+
+    assert prntr.standard == 'python3'
+
+    raises(ValueError, lambda: PythonCodePrinter({'standard':'python4'}))
+
+
+def test_CmathPrinter():
+    p = CmathPrinter()
+
+    assert p.doprint(sqrt(x)) == 'cmath.sqrt(x)'
+    assert p.doprint(log(x)) == 'cmath.log(x)'
+
+    assert p.doprint(sin(x)) == 'cmath.sin(x)'
+    assert p.doprint(cos(x)) == 'cmath.cos(x)'
+    assert p.doprint(tan(x)) == 'cmath.tan(x)'
+
+    assert p.doprint(asin(x)) == 'cmath.asin(x)'
+    assert p.doprint(acos(x)) == 'cmath.acos(x)'
+    assert p.doprint(atan(x)) == 'cmath.atan(x)'
+
+    assert p.doprint(sinh(x)) == 'cmath.sinh(x)'
+    assert p.doprint(cosh(x)) == 'cmath.cosh(x)'
+    assert p.doprint(tanh(x)) == 'cmath.tanh(x)'
+
+    assert p.doprint(asinh(x)) == 'cmath.asinh(x)'
+    assert p.doprint(acosh(x)) == 'cmath.acosh(x)'
+    assert p.doprint(atanh(x)) == 'cmath.atanh(x)'
+
+
+def test_MpmathPrinter():
+    p = MpmathPrinter()
+    assert p.doprint(sign(x)) == 'mpmath.sign(x)'
+    assert p.doprint(Rational(1, 2)) == 'mpmath.mpf(1)/mpmath.mpf(2)'
+
+    assert p.doprint(S.Exp1) == 'mpmath.e'
+    assert p.doprint(S.Pi) == 'mpmath.pi'
+    assert p.doprint(S.GoldenRatio) == 'mpmath.phi'
+    assert p.doprint(S.EulerGamma) == 'mpmath.euler'
+    assert p.doprint(S.NaN) == 'mpmath.nan'
+    assert p.doprint(S.Infinity) == 'mpmath.inf'
+    assert p.doprint(S.NegativeInfinity) == 'mpmath.ninf'
+    assert p.doprint(loggamma(x)) == 'mpmath.loggamma(x)'
+
+
+def test_NumPyPrinter():
+    from sympy.core.function import Lambda
+    from sympy.matrices.expressions.adjoint import Adjoint
+    from sympy.matrices.expressions.diagonal import (DiagMatrix, DiagonalMatrix, DiagonalOf)
+    from sympy.matrices.expressions.funcmatrix import FunctionMatrix
+    from sympy.matrices.expressions.hadamard import HadamardProduct
+    from sympy.matrices.expressions.kronecker import KroneckerProduct
+    from sympy.matrices.expressions.special import (OneMatrix, ZeroMatrix)
+    from sympy.abc import a, b
+    p = NumPyPrinter()
+    assert p.doprint(sign(x)) == 'numpy.sign(x)'
+    A = MatrixSymbol("A", 2, 2)
+    B = MatrixSymbol("B", 2, 2)
+    C = MatrixSymbol("C", 1, 5)
+    D = MatrixSymbol("D", 3, 4)
+    assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
+    assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
+    assert p.doprint(Identity(3)) == "numpy.eye(3)"
+
+    u = MatrixSymbol('x', 2, 1)
+    v = MatrixSymbol('y', 2, 1)
+    assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
+    assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
+
+    assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
+    assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
+    assert p.doprint(FunctionMatrix(4, 5, Lambda((a, b), a + b))) == \
+        "numpy.fromfunction(lambda a, b: a + b, (4, 5))"
+    assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
+    assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
+    assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
+    assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
+    assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
+    assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
+
+    # Workaround for numpy negative integer power errors
+    assert p.doprint(x**-1) == 'x**(-1.0)'
+    assert p.doprint(x**-2) == 'x**(-2.0)'
+
+    expr = Pow(2, -1, evaluate=False)
+    assert p.doprint(expr) == "2**(-1.0)"
+
+    assert p.doprint(S.Exp1) == 'numpy.e'
+    assert p.doprint(S.Pi) == 'numpy.pi'
+    assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
+    assert p.doprint(S.NaN) == 'numpy.nan'
+    assert p.doprint(S.Infinity) == 'numpy.inf'
+    assert p.doprint(S.NegativeInfinity) == '-numpy.inf'
+
+    # Function rewriting operator precedence fix
+    assert p.doprint(sec(x)**2) == '(numpy.cos(x)**(-1.0))**2'
+
+
+def test_issue_18770():
+    numpy = import_module('numpy')
+    if not numpy:
+        skip("numpy not installed.")
+
+    from sympy.functions.elementary.miscellaneous import (Max, Min)
+    from sympy.utilities.lambdify import lambdify
+
+    expr1 = Min(0.1*x + 3, x + 1, 0.5*x + 1)
+    func = lambdify(x, expr1, "numpy")
+    assert (func(numpy.linspace(0, 3, 3)) == [1.0, 1.75, 2.5 ]).all()
+    assert  func(4) == 3
+
+    expr1 = Max(x**2, x**3)
+    func = lambdify(x,expr1, "numpy")
+    assert (func(numpy.linspace(-1, 2, 4)) == [1, 0, 1, 8] ).all()
+    assert func(4) == 64
+
+
+def test_SciPyPrinter():
+    p = SciPyPrinter()
+    expr = acos(x)
+    assert 'numpy' not in p.module_imports
+    assert p.doprint(expr) == 'numpy.arccos(x)'
+    assert 'numpy' in p.module_imports
+    assert not any(m.startswith('scipy') for m in p.module_imports)
+    smat = SparseMatrix(2, 5, {(0, 1): 3})
+    assert p.doprint(smat) == \
+        'scipy.sparse.coo_matrix(([3], ([0], [1])), shape=(2, 5))'
+    assert 'scipy.sparse' in p.module_imports
+
+    assert p.doprint(S.GoldenRatio) == 'scipy.constants.golden_ratio'
+    assert p.doprint(S.Pi) == 'scipy.constants.pi'
+    assert p.doprint(S.Exp1) == 'numpy.e'
+
+
+def test_pycode_reserved_words():
+    s1, s2 = symbols('if else')
+    raises(ValueError, lambda: pycode(s1 + s2, error_on_reserved=True))
+    py_str = pycode(s1 + s2)
+    assert py_str in ('else_ + if_', 'if_ + else_')
+
+
+def test_issue_20762():
+    # Make sure pycode removes curly braces from subscripted variables
+    a_b, b, a_11 = symbols('a_{b} b a_{11}')
+    expr = a_b*b
+    assert pycode(expr) == 'a_b*b'
+    expr = a_11*b
+    assert pycode(expr) == 'a_11*b'
+
+
+def test_sqrt():
+    prntr = PythonCodePrinter()
+    assert prntr._print_Pow(sqrt(x), rational=False) == 'math.sqrt(x)'
+    assert prntr._print_Pow(1/sqrt(x), rational=False) == '1/math.sqrt(x)'
+
+    prntr = PythonCodePrinter({'standard' : 'python3'})
+    assert prntr._print_Pow(sqrt(x), rational=True) == 'x**(1/2)'
+    assert prntr._print_Pow(1/sqrt(x), rational=True) == 'x**(-1/2)'
+
+    prntr = MpmathPrinter()
+    assert prntr._print_Pow(sqrt(x), rational=False) == 'mpmath.sqrt(x)'
+    assert prntr._print_Pow(sqrt(x), rational=True) == \
+        "x**(mpmath.mpf(1)/mpmath.mpf(2))"
+
+    prntr = NumPyPrinter()
+    assert prntr._print_Pow(sqrt(x), rational=False) == 'numpy.sqrt(x)'
+    assert prntr._print_Pow(sqrt(x), rational=True) == 'x**(1/2)'
+
+    prntr = SciPyPrinter()
+    assert prntr._print_Pow(sqrt(x), rational=False) == 'numpy.sqrt(x)'
+    assert prntr._print_Pow(sqrt(x), rational=True) == 'x**(1/2)'
+
+    prntr = SymPyPrinter()
+    assert prntr._print_Pow(sqrt(x), rational=False) == 'sympy.sqrt(x)'
+    assert prntr._print_Pow(sqrt(x), rational=True) == 'x**(1/2)'
+
+
+def test_frac():
+    from sympy.functions.elementary.integers import frac
+
+    expr = frac(x)
+    prntr = NumPyPrinter()
+    assert prntr.doprint(expr) == 'numpy.mod(x, 1)'
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr) == 'numpy.mod(x, 1)'
+
+    prntr = PythonCodePrinter()
+    assert prntr.doprint(expr) == 'x % 1'
+
+    prntr = MpmathPrinter()
+    assert prntr.doprint(expr) == 'mpmath.frac(x)'
+
+    prntr = SymPyPrinter()
+    assert prntr.doprint(expr) == 'sympy.functions.elementary.integers.frac(x)'
+
+
+class CustomPrintedObject(Expr):
+    def _numpycode(self, printer):
+        return 'numpy'
+
+    def _mpmathcode(self, printer):
+        return 'mpmath'
+
+
+def test_printmethod():
+    obj = CustomPrintedObject()
+    assert NumPyPrinter().doprint(obj) == 'numpy'
+    assert MpmathPrinter().doprint(obj) == 'mpmath'
+
+
+def test_codegen_ast_nodes():
+    assert pycode(none) == 'None'
+
+
+def test_issue_14283():
+    prntr = PythonCodePrinter()
+
+    assert prntr.doprint(zoo) == "math.nan"
+    assert prntr.doprint(-oo) == "float('-inf')"
+
+
+def test_NumPyPrinter_print_seq():
+    n = NumPyPrinter()
+
+    assert n._print_seq(range(2)) == '(0, 1,)'
+
+
+def test_issue_16535_16536():
+    from sympy.functions.special.gamma_functions import (lowergamma, uppergamma)
+
+    a = symbols('a')
+    expr1 = lowergamma(a, x)
+    expr2 = uppergamma(a, x)
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr1) == 'scipy.special.gamma(a)*scipy.special.gammainc(a, x)'
+    assert prntr.doprint(expr2) == 'scipy.special.gamma(a)*scipy.special.gammaincc(a, x)'
+
+    p_numpy = NumPyPrinter()
+    p_pycode = PythonCodePrinter({'strict': False})
+
+    for expr in [expr1, expr2]:
+        with raises(NotImplementedError):
+            p_numpy.doprint(expr1)
+        assert "Not supported" in p_pycode.doprint(expr)
+
+
+def test_Integral():
+    from sympy.functions.elementary.exponential import exp
+    from sympy.integrals.integrals import Integral
+
+    single = Integral(exp(-x), (x, 0, oo))
+    double = Integral(x**2*exp(x*y), (x, -z, z), (y, 0, z))
+    indefinite = Integral(x**2, x)
+    evaluateat = Integral(x**2, (x, 1))
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(single) == 'scipy.integrate.quad(lambda x: numpy.exp(-x), 0, numpy.inf)[0]'
+    assert prntr.doprint(double) == 'scipy.integrate.nquad(lambda x, y: x**2*numpy.exp(x*y), ((-z, z), (0, z)))[0]'
+    raises(NotImplementedError, lambda: prntr.doprint(indefinite))
+    raises(NotImplementedError, lambda: prntr.doprint(evaluateat))
+
+    prntr = MpmathPrinter()
+    assert prntr.doprint(single) == 'mpmath.quad(lambda x: mpmath.exp(-x), (0, mpmath.inf))'
+    assert prntr.doprint(double) == 'mpmath.quad(lambda x, y: x**2*mpmath.exp(x*y), (-z, z), (0, z))'
+    raises(NotImplementedError, lambda: prntr.doprint(indefinite))
+    raises(NotImplementedError, lambda: prntr.doprint(evaluateat))
+
+
+def test_fresnel_integrals():
+    from sympy.functions.special.error_functions import (fresnelc, fresnels)
+
+    expr1 = fresnelc(x)
+    expr2 = fresnels(x)
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr1) == 'scipy.special.fresnel(x)[1]'
+    assert prntr.doprint(expr2) == 'scipy.special.fresnel(x)[0]'
+
+    p_numpy = NumPyPrinter()
+    p_pycode = PythonCodePrinter()
+    p_mpmath = MpmathPrinter()
+    for expr in [expr1, expr2]:
+        with raises(NotImplementedError):
+            p_numpy.doprint(expr)
+        with raises(NotImplementedError):
+            p_pycode.doprint(expr)
+
+    assert p_mpmath.doprint(expr1) == 'mpmath.fresnelc(x)'
+    assert p_mpmath.doprint(expr2) == 'mpmath.fresnels(x)'
+
+
+def test_beta():
+    from sympy.functions.special.beta_functions import beta
+
+    expr = beta(x, y)
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr) == 'scipy.special.beta(x, y)'
+
+    prntr = NumPyPrinter()
+    assert prntr.doprint(expr) == '(math.gamma(x)*math.gamma(y)/math.gamma(x + y))'
+
+    prntr = PythonCodePrinter()
+    assert prntr.doprint(expr) == '(math.gamma(x)*math.gamma(y)/math.gamma(x + y))'
+
+    prntr = PythonCodePrinter({'allow_unknown_functions': True})
+    assert prntr.doprint(expr) == '(math.gamma(x)*math.gamma(y)/math.gamma(x + y))'
+
+    prntr = MpmathPrinter()
+    assert prntr.doprint(expr) ==  'mpmath.beta(x, y)'
+
+def test_airy():
+    from sympy.functions.special.bessel import (airyai, airybi)
+
+    expr1 = airyai(x)
+    expr2 = airybi(x)
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr1) == 'scipy.special.airy(x)[0]'
+    assert prntr.doprint(expr2) == 'scipy.special.airy(x)[2]'
+
+    prntr = NumPyPrinter({'strict': False})
+    assert "Not supported" in prntr.doprint(expr1)
+    assert "Not supported" in prntr.doprint(expr2)
+
+    prntr = PythonCodePrinter({'strict': False})
+    assert "Not supported" in prntr.doprint(expr1)
+    assert "Not supported" in prntr.doprint(expr2)
+
+def test_airy_prime():
+    from sympy.functions.special.bessel import (airyaiprime, airybiprime)
+
+    expr1 = airyaiprime(x)
+    expr2 = airybiprime(x)
+
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expr1) == 'scipy.special.airy(x)[1]'
+    assert prntr.doprint(expr2) == 'scipy.special.airy(x)[3]'
+
+    prntr = NumPyPrinter({'strict': False})
+    assert "Not supported" in prntr.doprint(expr1)
+    assert "Not supported" in prntr.doprint(expr2)
+
+    prntr = PythonCodePrinter({'strict': False})
+    assert "Not supported" in prntr.doprint(expr1)
+    assert "Not supported" in prntr.doprint(expr2)
+
+
+def test_numerical_accuracy_functions():
+    prntr = SciPyPrinter()
+    assert prntr.doprint(expm1(x)) == 'numpy.expm1(x)'
+    assert prntr.doprint(log1p(x)) == 'numpy.log1p(x)'
+    assert prntr.doprint(cosm1(x)) == 'scipy.special.cosm1(x)'
+
+def test_array_printer():
+    A = ArraySymbol('A', (4,4,6,6,6))
+    I = IndexedBase('I')
+    i,j,k = Idx('i', (0,1)), Idx('j', (2,3)), Idx('k', (4,5))
+
+    prntr = NumPyPrinter()
+    assert prntr.doprint(ZeroArray(5)) == 'numpy.zeros((5,))'
+    assert prntr.doprint(OneArray(5)) == 'numpy.ones((5,))'
+    assert prntr.doprint(ArrayContraction(A, [2,3])) == 'numpy.einsum("abccd->abd", A)'
+    assert prntr.doprint(I) == 'I'
+    assert prntr.doprint(ArrayDiagonal(A, [2,3,4])) == 'numpy.einsum("abccc->abc", A)'
+    assert prntr.doprint(ArrayDiagonal(A, [0,1], [2,3])) == 'numpy.einsum("aabbc->cab", A)'
+    assert prntr.doprint(ArrayContraction(A, [2], [3])) == 'numpy.einsum("abcde->abe", A)'
+    assert prntr.doprint(Assignment(I[i,j,k], I[i,j,k])) == 'I = I'
+
+    prntr = TensorflowPrinter()
+    assert prntr.doprint(ZeroArray(5)) == 'tensorflow.zeros((5,))'
+    assert prntr.doprint(OneArray(5)) == 'tensorflow.ones((5,))'
+    assert prntr.doprint(ArrayContraction(A, [2,3])) == 'tensorflow.linalg.einsum("abccd->abd", A)'
+    assert prntr.doprint(I) == 'I'
+    assert prntr.doprint(ArrayDiagonal(A, [2,3,4])) == 'tensorflow.linalg.einsum("abccc->abc", A)'
+    assert prntr.doprint(ArrayDiagonal(A, [0,1], [2,3])) == 'tensorflow.linalg.einsum("aabbc->cab", A)'
+    assert prntr.doprint(ArrayContraction(A, [2], [3])) == 'tensorflow.linalg.einsum("abcde->abe", A)'
+    assert prntr.doprint(Assignment(I[i,j,k], I[i,j,k])) == 'I = I'
+
+
+def test_custom_Derivative_methods():
+    class MyPrinter(SciPyPrinter):
+        def _print_Derivative_cosm1(self, args, seq_orders):
+            arg, = args
+            order, = seq_orders
+            return 'my_custom_cosm1(%s, deriv_order=%d)' % (self._print(arg), order)
+
+        def _print_Derivative_atan2(self, args, seq_orders):
+            arg1, arg2 = args
+            ord1, ord2 = seq_orders
+            return 'my_custom_atan2(%s, %s, deriv1=%d, deriv2=%d)' % (
+                self._print(arg1), self._print(arg2), ord1, ord2
+            )
+
+    p = MyPrinter()
+    cosm1_1 = cosm1(x).diff(x, evaluate=False)
+    assert p.doprint(cosm1_1) == 'my_custom_cosm1(x, deriv_order=1)'
+    atan2_2_3 = atan2(x, y).diff(x, 2, y, 3, evaluate=False)
+    assert p.doprint(atan2_2_3) == 'my_custom_atan2(x, y, deriv1=2, deriv2=3)'
+
+    try:
+        p.doprint(expm1(x).diff(x, evaluate=False))
+    except PrintMethodNotImplementedError as e:
+        assert '_print_Derivative_expm1' in repr(e)
+    else:
+        assert False  # should have thrown
+
+    try:
+        p.doprint(Derivative(cosm1(x**2),x))
+    except ValueError as e:
+        assert '_print_Derivative(' in repr(e)
+    else:
+        assert False  # should have thrown

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_python.py

@@ -0,0 +1,203 @@
+from sympy.core.function import (Derivative, Function)
+from sympy.core.numbers import (I, Rational, oo, pi)
+from sympy.core.relational import (Eq, Ge, Gt, Le, Lt, Ne)
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.complexes import (Abs, conjugate)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import sin
+from sympy.integrals.integrals import Integral
+from sympy.matrices.dense import Matrix
+from sympy.series.limits import limit
+
+from sympy.printing.python import python
+
+from sympy.testing.pytest import raises, XFAIL
+
+x, y = symbols('x,y')
+th = Symbol('theta')
+ph = Symbol('phi')
+
+
+def test_python_basic():
+    # Simple numbers/symbols
+    assert python(-Rational(1)/2) == "e = Rational(-1, 2)"
+    assert python(-Rational(13)/22) == "e = Rational(-13, 22)"
+    assert python(oo) == "e = oo"
+
+    # Powers
+    assert python(x**2) == "x = Symbol(\'x\')\ne = x**2"
+    assert python(1/x) == "x = Symbol('x')\ne = 1/x"
+    assert python(y*x**-2) == "y = Symbol('y')\nx = Symbol('x')\ne = y/x**2"
+    assert python(
+        x**Rational(-5, 2)) == "x = Symbol('x')\ne = x**Rational(-5, 2)"
+
+    # Sums of terms
+    assert python(x**2 + x + 1) in [
+        "x = Symbol('x')\ne = 1 + x + x**2",
+        "x = Symbol('x')\ne = x + x**2 + 1",
+        "x = Symbol('x')\ne = x**2 + x + 1", ]
+    assert python(1 - x) in [
+        "x = Symbol('x')\ne = 1 - x",
+        "x = Symbol('x')\ne = -x + 1"]
+    assert python(1 - 2*x) in [
+        "x = Symbol('x')\ne = 1 - 2*x",
+        "x = Symbol('x')\ne = -2*x + 1"]
+    assert python(1 - Rational(3, 2)*y/x) in [
+        "y = Symbol('y')\nx = Symbol('x')\ne = 1 - 3/2*y/x",
+        "y = Symbol('y')\nx = Symbol('x')\ne = -3/2*y/x + 1",
+        "y = Symbol('y')\nx = Symbol('x')\ne = 1 - 3*y/(2*x)"]
+
+    # Multiplication
+    assert python(x/y) == "x = Symbol('x')\ny = Symbol('y')\ne = x/y"
+    assert python(-x/y) == "x = Symbol('x')\ny = Symbol('y')\ne = -x/y"
+    assert python((x + 2)/y) in [
+        "y = Symbol('y')\nx = Symbol('x')\ne = 1/y*(2 + x)",
+        "y = Symbol('y')\nx = Symbol('x')\ne = 1/y*(x + 2)",
+        "x = Symbol('x')\ny = Symbol('y')\ne = 1/y*(2 + x)",
+        "x = Symbol('x')\ny = Symbol('y')\ne = (2 + x)/y",
+        "x = Symbol('x')\ny = Symbol('y')\ne = (x + 2)/y"]
+    assert python((1 + x)*y) in [
+        "y = Symbol('y')\nx = Symbol('x')\ne = y*(1 + x)",
+        "y = Symbol('y')\nx = Symbol('x')\ne = y*(x + 1)", ]
+
+    # Check for proper placement of negative sign
+    assert python(-5*x/(x + 10)) == "x = Symbol('x')\ne = -5*x/(x + 10)"
+    assert python(1 - Rational(3, 2)*(x + 1)) in [
+        "x = Symbol('x')\ne = Rational(-3, 2)*x + Rational(-1, 2)",
+        "x = Symbol('x')\ne = -3*x/2 + Rational(-1, 2)",
+        "x = Symbol('x')\ne = -3*x/2 + Rational(-1, 2)"
+    ]
+
+
+def test_python_keyword_symbol_name_escaping():
+    # Check for escaping of keywords
+    assert python(
+        5*Symbol("lambda")) == "lambda_ = Symbol('lambda')\ne = 5*lambda_"
+    assert (python(5*Symbol("lambda") + 7*Symbol("lambda_")) ==
+            "lambda__ = Symbol('lambda')\nlambda_ = Symbol('lambda_')\ne = 7*lambda_ + 5*lambda__")
+    assert (python(5*Symbol("for") + Function("for_")(8)) ==
+            "for__ = Symbol('for')\nfor_ = Function('for_')\ne = 5*for__ + for_(8)")
+
+
+def test_python_keyword_function_name_escaping():
+    assert python(
+        5*Function("for")(8)) == "for_ = Function('for')\ne = 5*for_(8)"
+
+
+def test_python_relational():
+    assert python(Eq(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = Eq(x, y)"
+    assert python(Ge(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x >= y"
+    assert python(Le(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x <= y"
+    assert python(Gt(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x > y"
+    assert python(Lt(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x < y"
+    assert python(Ne(x/(y + 1), y**2)) in [
+        "x = Symbol('x')\ny = Symbol('y')\ne = Ne(x/(1 + y), y**2)",
+        "x = Symbol('x')\ny = Symbol('y')\ne = Ne(x/(y + 1), y**2)"]
+
+
+def test_python_functions():
+    # Simple
+    assert python(2*x + exp(x)) in "x = Symbol('x')\ne = 2*x + exp(x)"
+    assert python(sqrt(2)) == 'e = sqrt(2)'
+    assert python(2**Rational(1, 3)) == 'e = 2**Rational(1, 3)'
+    assert python(sqrt(2 + pi)) == 'e = sqrt(2 + pi)'
+    assert python((2 + pi)**Rational(1, 3)) == 'e = (2 + pi)**Rational(1, 3)'
+    assert python(2**Rational(1, 4)) == 'e = 2**Rational(1, 4)'
+    assert python(Abs(x)) == "x = Symbol('x')\ne = Abs(x)"
+    assert python(
+        Abs(x/(x**2 + 1))) in ["x = Symbol('x')\ne = Abs(x/(1 + x**2))",
+            "x = Symbol('x')\ne = Abs(x/(x**2 + 1))"]
+
+    # Univariate/Multivariate functions
+    f = Function('f')
+    assert python(f(x)) == "x = Symbol('x')\nf = Function('f')\ne = f(x)"
+    assert python(f(x, y)) == "x = Symbol('x')\ny = Symbol('y')\nf = Function('f')\ne = f(x, y)"
+    assert python(f(x/(y + 1), y)) in [
+        "x = Symbol('x')\ny = Symbol('y')\nf = Function('f')\ne = f(x/(1 + y), y)",
+        "x = Symbol('x')\ny = Symbol('y')\nf = Function('f')\ne = f(x/(y + 1), y)"]
+
+    # Nesting of square roots
+    assert python(sqrt((sqrt(x + 1)) + 1)) in [
+        "x = Symbol('x')\ne = sqrt(1 + sqrt(1 + x))",
+        "x = Symbol('x')\ne = sqrt(sqrt(x + 1) + 1)"]
+
+    # Nesting of powers
+    assert python((((x + 1)**Rational(1, 3)) + 1)**Rational(1, 3)) in [
+        "x = Symbol('x')\ne = (1 + (1 + x)**Rational(1, 3))**Rational(1, 3)",
+        "x = Symbol('x')\ne = ((x + 1)**Rational(1, 3) + 1)**Rational(1, 3)"]
+
+    # Function powers
+    assert python(sin(x)**2) == "x = Symbol('x')\ne = sin(x)**2"
+
+
+@XFAIL
+def test_python_functions_conjugates():
+    a, b = map(Symbol, 'ab')
+    assert python( conjugate(a + b*I) ) == '_     _\na - I*b'
+    assert python( conjugate(exp(a + b*I)) ) == ' _     _\n a - I*b\ne       '
+
+
+def test_python_derivatives():
+    # Simple
+    f_1 = Derivative(log(x), x, evaluate=False)
+    assert python(f_1) == "x = Symbol('x')\ne = Derivative(log(x), x)"
+
+    f_2 = Derivative(log(x), x, evaluate=False) + x
+    assert python(f_2) == "x = Symbol('x')\ne = x + Derivative(log(x), x)"
+
+    # Multiple symbols
+    f_3 = Derivative(log(x) + x**2, x, y, evaluate=False)
+    assert python(f_3) == \
+        "x = Symbol('x')\ny = Symbol('y')\ne = Derivative(x**2 + log(x), x, y)"
+
+    f_4 = Derivative(2*x*y, y, x, evaluate=False) + x**2
+    assert python(f_4) in [
+        "x = Symbol('x')\ny = Symbol('y')\ne = x**2 + Derivative(2*x*y, y, x)",
+        "x = Symbol('x')\ny = Symbol('y')\ne = Derivative(2*x*y, y, x) + x**2"]
+
+
+def test_python_integrals():
+    # Simple
+    f_1 = Integral(log(x), x)
+    assert python(f_1) == "x = Symbol('x')\ne = Integral(log(x), x)"
+
+    f_2 = Integral(x**2, x)
+    assert python(f_2) == "x = Symbol('x')\ne = Integral(x**2, x)"
+
+    # Double nesting of pow
+    f_3 = Integral(x**(2**x), x)
+    assert python(f_3) == "x = Symbol('x')\ne = Integral(x**(2**x), x)"
+
+    # Definite integrals
+    f_4 = Integral(x**2, (x, 1, 2))
+    assert python(f_4) == "x = Symbol('x')\ne = Integral(x**2, (x, 1, 2))"
+
+    f_5 = Integral(x**2, (x, Rational(1, 2), 10))
+    assert python(
+        f_5) == "x = Symbol('x')\ne = Integral(x**2, (x, Rational(1, 2), 10))"
+
+    # Nested integrals
+    f_6 = Integral(x**2*y**2, x, y)
+    assert python(f_6) == "x = Symbol('x')\ny = Symbol('y')\ne = Integral(x**2*y**2, x, y)"
+
+
+def test_python_matrix():
+    p = python(Matrix([[x**2+1, 1], [y, x+y]]))
+    s = "x = Symbol('x')\ny = Symbol('y')\ne = MutableDenseMatrix([[x**2 + 1, 1], [y, x + y]])"
+    assert p == s
+
+def test_python_limits():
+    assert python(limit(x, x, oo)) == 'e = oo'
+    assert python(limit(x**2, x, 0)) == 'e = 0'
+
+def test_issue_20762():
+    # Make sure Python removes curly braces from subscripted variables
+    a_b = Symbol('a_{b}')
+    b = Symbol('b')
+    expr = a_b*b
+    assert python(expr) == "a_b = Symbol('a_{b}')\nb = Symbol('b')\ne = a_b*b"
+
+
+def test_settings():
+    raises(TypeError, lambda: python(x, method="garbage"))

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_rcode.py

@@ -0,0 +1,476 @@
+from sympy.core import (S, pi, oo, Symbol, symbols, Rational, Integer,
+                        GoldenRatio, EulerGamma, Catalan, Lambda, Dummy)
+from sympy.functions import (Piecewise, sin, cos, Abs, exp, ceiling, sqrt,
+                             gamma, sign, Max, Min, factorial, beta)
+from sympy.core.relational import (Eq, Ge, Gt, Le, Lt, Ne)
+from sympy.sets import Range
+from sympy.logic import ITE
+from sympy.codegen import For, aug_assign, Assignment
+from sympy.testing.pytest import raises
+from sympy.printing.rcode import RCodePrinter
+from sympy.utilities.lambdify import implemented_function
+from sympy.tensor import IndexedBase, Idx
+from sympy.matrices import Matrix, MatrixSymbol
+
+from sympy.printing.rcode import rcode
+
+x, y, z = symbols('x,y,z')
+
+
+def test_printmethod():
+    class fabs(Abs):
+        def _rcode(self, printer):
+            return "abs(%s)" % printer._print(self.args[0])
+
+    assert rcode(fabs(x)) == "abs(x)"
+
+
+def test_rcode_sqrt():
+    assert rcode(sqrt(x)) == "sqrt(x)"
+    assert rcode(x**0.5) == "sqrt(x)"
+    assert rcode(sqrt(x)) == "sqrt(x)"
+
+
+def test_rcode_Pow():
+    assert rcode(x**3) == "x^3"
+    assert rcode(x**(y**3)) == "x^(y^3)"
+    g = implemented_function('g', Lambda(x, 2*x))
+    assert rcode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
+        "(3.5*2*x)^(-x + y^x)/(x^2 + y)"
+    assert rcode(x**-1.0) == '1.0/x'
+    assert rcode(x**Rational(2, 3)) == 'x^(2.0/3.0)'
+    _cond_cfunc = [(lambda base, exp: exp.is_integer, "dpowi"),
+                   (lambda base, exp: not exp.is_integer, "pow")]
+    assert rcode(x**3, user_functions={'Pow': _cond_cfunc}) == 'dpowi(x, 3)'
+    assert rcode(x**3.2, user_functions={'Pow': _cond_cfunc}) == 'pow(x, 3.2)'
+
+
+def test_rcode_Max():
+    # Test for gh-11926
+    assert rcode(Max(x,x*x),user_functions={"Max":"my_max", "Pow":"my_pow"}) == 'my_max(x, my_pow(x, 2))'
+
+
+def test_rcode_constants_mathh():
+    assert rcode(exp(1)) == "exp(1)"
+    assert rcode(pi) == "pi"
+    assert rcode(oo) == "Inf"
+    assert rcode(-oo) == "-Inf"
+
+
+def test_rcode_constants_other():
+    assert rcode(2*GoldenRatio) == "GoldenRatio = 1.61803398874989;\n2*GoldenRatio"
+    assert rcode(
+        2*Catalan) == "Catalan = 0.915965594177219;\n2*Catalan"
+    assert rcode(2*EulerGamma) == "EulerGamma = 0.577215664901533;\n2*EulerGamma"
+
+
+def test_rcode_Rational():
+    assert rcode(Rational(3, 7)) == "3.0/7.0"
+    assert rcode(Rational(18, 9)) == "2"
+    assert rcode(Rational(3, -7)) == "-3.0/7.0"
+    assert rcode(Rational(-3, -7)) == "3.0/7.0"
+    assert rcode(x + Rational(3, 7)) == "x + 3.0/7.0"
+    assert rcode(Rational(3, 7)*x) == "(3.0/7.0)*x"
+
+
+def test_rcode_Integer():
+    assert rcode(Integer(67)) == "67"
+    assert rcode(Integer(-1)) == "-1"
+
+
+def test_rcode_functions():
+    assert rcode(sin(x) ** cos(x)) == "sin(x)^cos(x)"
+    assert rcode(factorial(x) + gamma(y)) == "factorial(x) + gamma(y)"
+    assert rcode(beta(Min(x, y), Max(x, y))) == "beta(min(x, y), max(x, y))"
+
+
+def test_rcode_inline_function():
+    x = symbols('x')
+    g = implemented_function('g', Lambda(x, 2*x))
+    assert rcode(g(x)) == "2*x"
+    g = implemented_function('g', Lambda(x, 2*x/Catalan))
+    assert rcode(
+        g(x)) == "Catalan = %s;\n2*x/Catalan" % Catalan.n()
+    A = IndexedBase('A')
+    i = Idx('i', symbols('n', integer=True))
+    g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
+    res=rcode(g(A[i]), assign_to=A[i])
+    ref=(
+        "for (i in 1:n){\n"
+        "   A[i] = (A[i] + 1)*(A[i] + 2)*A[i];\n"
+        "}"
+    )
+    assert res == ref
+
+
+def test_rcode_exceptions():
+    assert rcode(ceiling(x)) == "ceiling(x)"
+    assert rcode(Abs(x)) == "abs(x)"
+    assert rcode(gamma(x)) == "gamma(x)"
+
+
+def test_rcode_user_functions():
+    x = symbols('x', integer=False)
+    n = symbols('n', integer=True)
+    custom_functions = {
+        "ceiling": "myceil",
+        "Abs": [(lambda x: not x.is_integer, "fabs"), (lambda x: x.is_integer, "abs")],
+    }
+    assert rcode(ceiling(x), user_functions=custom_functions) == "myceil(x)"
+    assert rcode(Abs(x), user_functions=custom_functions) == "fabs(x)"
+    assert rcode(Abs(n), user_functions=custom_functions) == "abs(n)"
+
+
+def test_rcode_boolean():
+    assert rcode(True) == "True"
+    assert rcode(S.true) == "True"
+    assert rcode(False) == "False"
+    assert rcode(S.false) == "False"
+    assert rcode(x & y) == "x & y"
+    assert rcode(x | y) == "x | y"
+    assert rcode(~x) == "!x"
+    assert rcode(x & y & z) == "x & y & z"
+    assert rcode(x | y | z) == "x | y | z"
+    assert rcode((x & y) | z) == "z | x & y"
+    assert rcode((x | y) & z) == "z & (x | y)"
+
+def test_rcode_Relational():
+    assert rcode(Eq(x, y)) == "x == y"
+    assert rcode(Ne(x, y)) == "x != y"
+    assert rcode(Le(x, y)) == "x <= y"
+    assert rcode(Lt(x, y)) == "x < y"
+    assert rcode(Gt(x, y)) == "x > y"
+    assert rcode(Ge(x, y)) == "x >= y"
+
+
+def test_rcode_Piecewise():
+    expr = Piecewise((x, x < 1), (x**2, True))
+    res=rcode(expr)
+    ref="ifelse(x < 1,x,x^2)"
+    assert res == ref
+    tau=Symbol("tau")
+    res=rcode(expr,tau)
+    ref="tau = ifelse(x < 1,x,x^2);"
+    assert res == ref
+
+    expr = 2*Piecewise((x, x < 1), (x**2, x<2), (x**3,True))
+    assert rcode(expr) == "2*ifelse(x < 1,x,ifelse(x < 2,x^2,x^3))"
+    res = rcode(expr, assign_to='c')
+    assert res == "c = 2*ifelse(x < 1,x,ifelse(x < 2,x^2,x^3));"
+
+    # Check that Piecewise without a True (default) condition error
+    #expr = Piecewise((x, x < 1), (x**2, x > 1), (sin(x), x > 0))
+    #raises(ValueError, lambda: rcode(expr))
+    expr = 2*Piecewise((x, x < 1), (x**2, x<2))
+    assert(rcode(expr))== "2*ifelse(x < 1,x,ifelse(x < 2,x^2,NA))"
+
+
+def test_rcode_sinc():
+    from sympy.functions.elementary.trigonometric import sinc
+    expr = sinc(x)
+    res = rcode(expr)
+    ref = "(ifelse(x != 0,sin(x)/x,1))"
+    assert res == ref
+
+
+def test_rcode_Piecewise_deep():
+    p = rcode(2*Piecewise((x, x < 1), (x + 1, x < 2), (x**2, True)))
+    assert p == "2*ifelse(x < 1,x,ifelse(x < 2,x + 1,x^2))"
+    expr = x*y*z + x**2 + y**2 + Piecewise((0, x < 0.5), (1, True)) + cos(z) - 1
+    p = rcode(expr)
+    ref="x^2 + x*y*z + y^2 + ifelse(x < 0.5,0,1) + cos(z) - 1"
+    assert p == ref
+
+    ref="c = x^2 + x*y*z + y^2 + ifelse(x < 0.5,0,1) + cos(z) - 1;"
+    p = rcode(expr, assign_to='c')
+    assert p == ref
+
+
+def test_rcode_ITE():
+    expr = ITE(x < 1, y, z)
+    p = rcode(expr)
+    ref="ifelse(x < 1,y,z)"
+    assert p == ref
+
+
+def test_rcode_settings():
+    raises(TypeError, lambda: rcode(sin(x), method="garbage"))
+
+
+def test_rcode_Indexed():
+    n, m, o = symbols('n m o', integer=True)
+    i, j, k = Idx('i', n), Idx('j', m), Idx('k', o)
+    p = RCodePrinter()
+    p._not_r = set()
+
+    x = IndexedBase('x')[j]
+    assert p._print_Indexed(x) == 'x[j]'
+    A = IndexedBase('A')[i, j]
+    assert p._print_Indexed(A) == 'A[i, j]'
+    B = IndexedBase('B')[i, j, k]
+    assert p._print_Indexed(B) == 'B[i, j, k]'
+
+    assert p._not_r == set()
+
+def test_rcode_Indexed_without_looking_for_contraction():
+    len_y = 5
+    y = IndexedBase('y', shape=(len_y,))
+    x = IndexedBase('x', shape=(len_y,))
+    Dy = IndexedBase('Dy', shape=(len_y-1,))
+    i = Idx('i', len_y-1)
+    e=Eq(Dy[i], (y[i+1]-y[i])/(x[i+1]-x[i]))
+    code0 = rcode(e.rhs, assign_to=e.lhs, contract=False)
+    assert code0 == 'Dy[i] = (y[%s] - y[i])/(x[%s] - x[i]);' % (i + 1, i + 1)
+
+
+def test_rcode_loops_matrix_vector():
+    n, m = symbols('n m', integer=True)
+    A = IndexedBase('A')
+    x = IndexedBase('x')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+
+    s = (
+        'for (i in 1:m){\n'
+        '   y[i] = 0;\n'
+        '}\n'
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      y[i] = A[i, j]*x[j] + y[i];\n'
+        '   }\n'
+        '}'
+    )
+    c = rcode(A[i, j]*x[j], assign_to=y[i])
+    assert c == s
+
+
+def test_dummy_loops():
+    # the following line could also be
+    # [Dummy(s, integer=True) for s in 'im']
+    # or [Dummy(integer=True) for s in 'im']
+    i, m = symbols('i m', integer=True, cls=Dummy)
+    x = IndexedBase('x')
+    y = IndexedBase('y')
+    i = Idx(i, m)
+
+    expected = (
+            'for (i_%(icount)i in 1:m_%(mcount)i){\n'
+        '   y[i_%(icount)i] = x[i_%(icount)i];\n'
+        '}'
+    ) % {'icount': i.label.dummy_index, 'mcount': m.dummy_index}
+    code = rcode(x[i], assign_to=y[i])
+    assert code == expected
+
+
+def test_rcode_loops_add():
+    n, m = symbols('n m', integer=True)
+    A = IndexedBase('A')
+    x = IndexedBase('x')
+    y = IndexedBase('y')
+    z = IndexedBase('z')
+    i = Idx('i', m)
+    j = Idx('j', n)
+
+    s = (
+        'for (i in 1:m){\n'
+        '   y[i] = x[i] + z[i];\n'
+        '}\n'
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      y[i] = A[i, j]*x[j] + y[i];\n'
+        '   }\n'
+        '}'
+    )
+    c = rcode(A[i, j]*x[j] + x[i] + z[i], assign_to=y[i])
+    assert c == s
+
+
+def test_rcode_loops_multiple_contractions():
+    n, m, o, p = symbols('n m o p', integer=True)
+    a = IndexedBase('a')
+    b = IndexedBase('b')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+    k = Idx('k', o)
+    l = Idx('l', p)
+
+    s = (
+        'for (i in 1:m){\n'
+        '   y[i] = 0;\n'
+        '}\n'
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      for (k in 1:o){\n'
+        '         for (l in 1:p){\n'
+        '            y[i] = a[i, j, k, l]*b[j, k, l] + y[i];\n'
+        '         }\n'
+        '      }\n'
+        '   }\n'
+        '}'
+    )
+    c = rcode(b[j, k, l]*a[i, j, k, l], assign_to=y[i])
+    assert c == s
+
+
+def test_rcode_loops_addfactor():
+    n, m, o, p = symbols('n m o p', integer=True)
+    a = IndexedBase('a')
+    b = IndexedBase('b')
+    c = IndexedBase('c')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+    k = Idx('k', o)
+    l = Idx('l', p)
+
+    s = (
+        'for (i in 1:m){\n'
+        '   y[i] = 0;\n'
+        '}\n'
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      for (k in 1:o){\n'
+        '         for (l in 1:p){\n'
+        '            y[i] = (a[i, j, k, l] + b[i, j, k, l])*c[j, k, l] + y[i];\n'
+        '         }\n'
+        '      }\n'
+        '   }\n'
+        '}'
+    )
+    c = rcode((a[i, j, k, l] + b[i, j, k, l])*c[j, k, l], assign_to=y[i])
+    assert c == s
+
+
+def test_rcode_loops_multiple_terms():
+    n, m, o, p = symbols('n m o p', integer=True)
+    a = IndexedBase('a')
+    b = IndexedBase('b')
+    c = IndexedBase('c')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+    k = Idx('k', o)
+
+    s0 = (
+        'for (i in 1:m){\n'
+        '   y[i] = 0;\n'
+        '}\n'
+    )
+    s1 = (
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      for (k in 1:o){\n'
+        '         y[i] = b[j]*b[k]*c[i, j, k] + y[i];\n'
+        '      }\n'
+        '   }\n'
+        '}\n'
+    )
+    s2 = (
+        'for (i in 1:m){\n'
+        '   for (k in 1:o){\n'
+        '      y[i] = a[i, k]*b[k] + y[i];\n'
+        '   }\n'
+        '}\n'
+    )
+    s3 = (
+        'for (i in 1:m){\n'
+        '   for (j in 1:n){\n'
+        '      y[i] = a[i, j]*b[j] + y[i];\n'
+        '   }\n'
+        '}\n'
+    )
+    c = rcode(
+        b[j]*a[i, j] + b[k]*a[i, k] + b[j]*b[k]*c[i, j, k], assign_to=y[i])
+
+    ref={}
+    ref[0] = s0 + s1 + s2 + s3[:-1]
+    ref[1] = s0 + s1 + s3 + s2[:-1]
+    ref[2] = s0 + s2 + s1 + s3[:-1]
+    ref[3] = s0 + s2 + s3 + s1[:-1]
+    ref[4] = s0 + s3 + s1 + s2[:-1]
+    ref[5] = s0 + s3 + s2 + s1[:-1]
+
+    assert (c == ref[0] or
+            c == ref[1] or
+            c == ref[2] or
+            c == ref[3] or
+            c == ref[4] or
+            c == ref[5])
+
+
+def test_dereference_printing():
+    expr = x + y + sin(z) + z
+    assert rcode(expr, dereference=[z]) == "x + y + (*z) + sin((*z))"
+
+
+def test_Matrix_printing():
+    # Test returning a Matrix
+    mat = Matrix([x*y, Piecewise((2 + x, y>0), (y, True)), sin(z)])
+    A = MatrixSymbol('A', 3, 1)
+    p = rcode(mat, A)
+    assert p == (
+        "A[0] = x*y;\n"
+        "A[1] = ifelse(y > 0,x + 2,y);\n"
+        "A[2] = sin(z);")
+    # Test using MatrixElements in expressions
+    expr = Piecewise((2*A[2, 0], x > 0), (A[2, 0], True)) + sin(A[1, 0]) + A[0, 0]
+    p = rcode(expr)
+    assert p  == ("ifelse(x > 0,2*A[2],A[2]) + sin(A[1]) + A[0]")
+    # Test using MatrixElements in a Matrix
+    q = MatrixSymbol('q', 5, 1)
+    M = MatrixSymbol('M', 3, 3)
+    m = Matrix([[sin(q[1,0]), 0, cos(q[2,0])],
+        [q[1,0] + q[2,0], q[3, 0], 5],
+        [2*q[4, 0]/q[1,0], sqrt(q[0,0]) + 4, 0]])
+    assert rcode(m, M) == (
+        "M[0] = sin(q[1]);\n"
+        "M[1] = 0;\n"
+        "M[2] = cos(q[2]);\n"
+        "M[3] = q[1] + q[2];\n"
+        "M[4] = q[3];\n"
+        "M[5] = 5;\n"
+        "M[6] = 2*q[4]/q[1];\n"
+        "M[7] = sqrt(q[0]) + 4;\n"
+        "M[8] = 0;")
+
+
+def test_rcode_sgn():
+
+    expr = sign(x) * y
+    assert rcode(expr) == 'y*sign(x)'
+    p = rcode(expr, 'z')
+    assert p  == 'z = y*sign(x);'
+
+    p = rcode(sign(2 * x + x**2) * x + x**2)
+    assert p  == "x^2 + x*sign(x^2 + 2*x)"
+
+    expr = sign(cos(x))
+    p = rcode(expr)
+    assert p == 'sign(cos(x))'
+
+def test_rcode_Assignment():
+    assert rcode(Assignment(x, y + z)) == 'x = y + z;'
+    assert rcode(aug_assign(x, '+', y + z)) == 'x += y + z;'
+
+
+def test_rcode_For():
+    f = For(x, Range(0, 10, 2), [aug_assign(y, '*', x)])
+    sol = rcode(f)
+    assert sol == ("for(x in seq(from=0, to=9, by=2){\n"
+                   "   y *= x;\n"
+                   "}")
+
+
+def test_MatrixElement_printing():
+    # test cases for issue #11821
+    A = MatrixSymbol("A", 1, 3)
+    B = MatrixSymbol("B", 1, 3)
+    C = MatrixSymbol("C", 1, 3)
+
+    assert(rcode(A[0, 0]) == "A[0]")
+    assert(rcode(3 * A[0, 0]) == "3*A[0]")
+
+    F = C[0, 0].subs(C, A - B)
+    assert(rcode(F) == "(A - B)[0]")

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_repr.py

@@ -0,0 +1,382 @@
+from __future__ import annotations
+from typing import Any
+
+from sympy.external.gmpy import GROUND_TYPES
+from sympy.testing.pytest import raises, warns_deprecated_sympy
+from sympy.assumptions.ask import Q
+from sympy.core.function import (Function, WildFunction)
+from sympy.core.numbers import (AlgebraicNumber, Float, Integer, Rational)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Dummy, Symbol, Wild, symbols)
+from sympy.core.sympify import sympify
+from sympy.functions.elementary.complexes import Abs
+from sympy.functions.elementary.miscellaneous import (root, sqrt)
+from sympy.functions.elementary.trigonometric import sin
+from sympy.functions.special.delta_functions import Heaviside
+from sympy.logic.boolalg import (false, true)
+from sympy.matrices.dense import (Matrix, ones)
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.immutable import ImmutableDenseMatrix
+from sympy.combinatorics import Cycle, Permutation
+from sympy.core.symbol import Str
+from sympy.geometry import Point, Ellipse
+from sympy.printing import srepr
+from sympy.polys import ring, field, ZZ, QQ, lex, grlex, Poly
+from sympy.polys.polyclasses import DMP
+from sympy.polys.agca.extensions import FiniteExtension
+
+x, y = symbols('x,y')
+
+# eval(srepr(expr)) == expr has to succeed in the right environment. The right
+# environment is the scope of "from sympy import *" for most cases.
+ENV: dict[str, Any] = {"Str": Str}
+exec("from sympy import *", ENV)
+
+
+def sT(expr, string, import_stmt=None, **kwargs):
+    """
+    sT := sreprTest
+
+    Tests that srepr delivers the expected string and that
+    the condition eval(srepr(expr))==expr holds.
+    """
+    if import_stmt is None:
+        ENV2 = ENV
+    else:
+        ENV2 = ENV.copy()
+        exec(import_stmt, ENV2)
+
+    assert srepr(expr, **kwargs) == string
+    assert eval(string, ENV2) == expr
+
+
+def test_printmethod():
+    class R(Abs):
+        def _sympyrepr(self, printer):
+            return "foo(%s)" % printer._print(self.args[0])
+    assert srepr(R(x)) == "foo(Symbol('x'))"
+
+
+def test_Add():
+    sT(x + y, "Add(Symbol('x'), Symbol('y'))")
+    assert srepr(x**2 + 1, order='lex') == "Add(Pow(Symbol('x'), Integer(2)), Integer(1))"
+    assert srepr(x**2 + 1, order='old') == "Add(Integer(1), Pow(Symbol('x'), Integer(2)))"
+    assert srepr(sympify('x + 3 - 2', evaluate=False), order='none') == "Add(Symbol('x'), Integer(3), Mul(Integer(-1), Integer(2)))"
+
+
+def test_more_than_255_args_issue_10259():
+    from sympy.core.add import Add
+    from sympy.core.mul import Mul
+    for op in (Add, Mul):
+        expr = op(*symbols('x:256'))
+        assert eval(srepr(expr)) == expr
+
+
+def test_Function():
+    sT(Function("f")(x), "Function('f')(Symbol('x'))")
+    # test unapplied Function
+    sT(Function('f'), "Function('f')")
+
+    sT(sin(x), "sin(Symbol('x'))")
+    sT(sin, "sin")
+
+
+def test_Heaviside():
+    sT(Heaviside(x), "Heaviside(Symbol('x'))")
+    sT(Heaviside(x, 1), "Heaviside(Symbol('x'), Integer(1))")
+
+
+def test_Geometry():
+    sT(Point(0, 0), "Point2D(Integer(0), Integer(0))")
+    sT(Ellipse(Point(0, 0), 5, 1),
+       "Ellipse(Point2D(Integer(0), Integer(0)), Integer(5), Integer(1))")
+    # TODO more tests
+
+
+def test_Singletons():
+    sT(S.Catalan, 'Catalan')
+    sT(S.ComplexInfinity, 'zoo')
+    sT(S.EulerGamma, 'EulerGamma')
+    sT(S.Exp1, 'E')
+    sT(S.GoldenRatio, 'GoldenRatio')
+    sT(S.TribonacciConstant, 'TribonacciConstant')
+    sT(S.Half, 'Rational(1, 2)')
+    sT(S.ImaginaryUnit, 'I')
+    sT(S.Infinity, 'oo')
+    sT(S.NaN, 'nan')
+    sT(S.NegativeInfinity, '-oo')
+    sT(S.NegativeOne, 'Integer(-1)')
+    sT(S.One, 'Integer(1)')
+    sT(S.Pi, 'pi')
+    sT(S.Zero, 'Integer(0)')
+    sT(S.Complexes, 'Complexes')
+    sT(S.EmptySequence, 'EmptySequence')
+    sT(S.EmptySet, 'EmptySet')
+    # sT(S.IdentityFunction, 'Lambda(_x, _x)')
+    sT(S.Naturals, 'Naturals')
+    sT(S.Naturals0, 'Naturals0')
+    sT(S.Rationals, 'Rationals')
+    sT(S.Reals, 'Reals')
+    sT(S.UniversalSet, 'UniversalSet')
+
+
+def test_Integer():
+    sT(Integer(4), "Integer(4)")
+
+
+def test_list():
+    sT([x, Integer(4)], "[Symbol('x'), Integer(4)]")
+
+
+def test_Matrix():
+    for cls, name in [(Matrix, "MutableDenseMatrix"), (ImmutableDenseMatrix, "ImmutableDenseMatrix")]:
+        sT(cls([[x**+1, 1], [y, x + y]]),
+           "%s([[Symbol('x'), Integer(1)], [Symbol('y'), Add(Symbol('x'), Symbol('y'))]])" % name)
+
+        sT(cls(), "%s([])" % name)
+
+        sT(cls([[x**+1, 1], [y, x + y]]), "%s([[Symbol('x'), Integer(1)], [Symbol('y'), Add(Symbol('x'), Symbol('y'))]])" % name)
+
+
+def test_empty_Matrix():
+    sT(ones(0, 3), "MutableDenseMatrix(0, 3, [])")
+    sT(ones(4, 0), "MutableDenseMatrix(4, 0, [])")
+    sT(ones(0, 0), "MutableDenseMatrix([])")
+
+
+def test_Rational():
+    sT(Rational(1, 3), "Rational(1, 3)")
+    sT(Rational(-1, 3), "Rational(-1, 3)")
+
+
+def test_Float():
+    sT(Float('1.23', dps=3), "Float('1.22998', precision=13)")
+    sT(Float('1.23456789', dps=9), "Float('1.23456788994', precision=33)")
+    sT(Float('1.234567890123456789', dps=19),
+       "Float('1.234567890123456789013', precision=66)")
+    sT(Float('0.60038617995049726', dps=15),
+       "Float('0.60038617995049726', precision=53)")
+
+    sT(Float('1.23', precision=13), "Float('1.22998', precision=13)")
+    sT(Float('1.23456789', precision=33),
+       "Float('1.23456788994', precision=33)")
+    sT(Float('1.234567890123456789', precision=66),
+       "Float('1.234567890123456789013', precision=66)")
+    sT(Float('0.60038617995049726', precision=53),
+       "Float('0.60038617995049726', precision=53)")
+
+    sT(Float('0.60038617995049726', 15),
+       "Float('0.60038617995049726', precision=53)")
+
+
+def test_Symbol():
+    sT(x, "Symbol('x')")
+    sT(y, "Symbol('y')")
+    sT(Symbol('x', negative=True), "Symbol('x', negative=True)")
+
+
+def test_Symbol_two_assumptions():
+    x = Symbol('x', negative=0, integer=1)
+    # order could vary
+    s1 = "Symbol('x', integer=True, negative=False)"
+    s2 = "Symbol('x', negative=False, integer=True)"
+    assert srepr(x) in (s1, s2)
+    assert eval(srepr(x), ENV) == x
+
+
+def test_Symbol_no_special_commutative_treatment():
+    sT(Symbol('x'), "Symbol('x')")
+    sT(Symbol('x', commutative=False), "Symbol('x', commutative=False)")
+    sT(Symbol('x', commutative=0), "Symbol('x', commutative=False)")
+    sT(Symbol('x', commutative=True), "Symbol('x', commutative=True)")
+    sT(Symbol('x', commutative=1), "Symbol('x', commutative=True)")
+
+
+def test_Wild():
+    sT(Wild('x', even=True), "Wild('x', even=True)")
+
+
+def test_Dummy():
+    d = Dummy('d')
+    sT(d, "Dummy('d', dummy_index=%s)" % str(d.dummy_index))
+
+
+def test_Dummy_assumption():
+    d = Dummy('d', nonzero=True)
+    assert d == eval(srepr(d))
+    s1 = "Dummy('d', dummy_index=%s, nonzero=True)" % str(d.dummy_index)
+    s2 = "Dummy('d', nonzero=True, dummy_index=%s)" % str(d.dummy_index)
+    assert srepr(d) in (s1, s2)
+
+
+def test_Dummy_from_Symbol():
+    # should not get the full dictionary of assumptions
+    n = Symbol('n', integer=True)
+    d = n.as_dummy()
+    assert srepr(d
+        ) == "Dummy('n', dummy_index=%s)" % str(d.dummy_index)
+
+
+def test_tuple():
+    sT((x,), "(Symbol('x'),)")
+    sT((x, y), "(Symbol('x'), Symbol('y'))")
+
+
+def test_WildFunction():
+    sT(WildFunction('w'), "WildFunction('w')")
+
+
+def test_settins():
+    raises(TypeError, lambda: srepr(x, method="garbage"))
+
+
+def test_Mul():
+    sT(3*x**3*y, "Mul(Integer(3), Pow(Symbol('x'), Integer(3)), Symbol('y'))")
+    assert srepr(3*x**3*y, order='old') == "Mul(Integer(3), Symbol('y'), Pow(Symbol('x'), Integer(3)))"
+    assert srepr(sympify('(x+4)*2*x*7', evaluate=False), order='none') == "Mul(Add(Symbol('x'), Integer(4)), Integer(2), Symbol('x'), Integer(7))"
+
+
+def test_AlgebraicNumber():
+    a = AlgebraicNumber(sqrt(2))
+    sT(a, "AlgebraicNumber(Pow(Integer(2), Rational(1, 2)), [Integer(1), Integer(0)])")
+    a = AlgebraicNumber(root(-2, 3))
+    sT(a, "AlgebraicNumber(Pow(Integer(-2), Rational(1, 3)), [Integer(1), Integer(0)])")
+
+
+def test_PolyRing():
+    assert srepr(ring("x", ZZ, lex)[0]) == "PolyRing((Symbol('x'),), ZZ, lex)"
+    assert srepr(ring("x,y", QQ, grlex)[0]) == "PolyRing((Symbol('x'), Symbol('y')), QQ, grlex)"
+    assert srepr(ring("x,y,z", ZZ["t"], lex)[0]) == "PolyRing((Symbol('x'), Symbol('y'), Symbol('z')), ZZ[t], lex)"
+
+
+def test_FracField():
+    assert srepr(field("x", ZZ, lex)[0]) == "FracField((Symbol('x'),), ZZ, lex)"
+    assert srepr(field("x,y", QQ, grlex)[0]) == "FracField((Symbol('x'), Symbol('y')), QQ, grlex)"
+    assert srepr(field("x,y,z", ZZ["t"], lex)[0]) == "FracField((Symbol('x'), Symbol('y'), Symbol('z')), ZZ[t], lex)"
+
+
+def test_PolyElement():
+    R, x, y = ring("x,y", ZZ)
+    assert srepr(3*x**2*y + 1) == "PolyElement(PolyRing((Symbol('x'), Symbol('y')), ZZ, lex), [((2, 1), 3), ((0, 0), 1)])"
+
+
+def test_FracElement():
+    F, x, y = field("x,y", ZZ)
+    assert srepr((3*x**2*y + 1)/(x - y**2)) == "FracElement(FracField((Symbol('x'), Symbol('y')), ZZ, lex), [((2, 1), 3), ((0, 0), 1)], [((1, 0), 1), ((0, 2), -1)])"
+
+
+def test_FractionField():
+    assert srepr(QQ.frac_field(x)) == \
+        "FractionField(FracField((Symbol('x'),), QQ, lex))"
+    assert srepr(QQ.frac_field(x, y, order=grlex)) == \
+        "FractionField(FracField((Symbol('x'), Symbol('y')), QQ, grlex))"
+
+
+def test_PolynomialRingBase():
+    assert srepr(ZZ.old_poly_ring(x)) == \
+        "GlobalPolynomialRing(ZZ, Symbol('x'))"
+    assert srepr(ZZ[x].old_poly_ring(y)) == \
+        "GlobalPolynomialRing(ZZ[x], Symbol('y'))"
+    assert srepr(QQ.frac_field(x).old_poly_ring(y)) == \
+        "GlobalPolynomialRing(FractionField(FracField((Symbol('x'),), QQ, lex)), Symbol('y'))"
+
+
+def test_DMP():
+    p1 = DMP([1, 2], ZZ)
+    p2 = ZZ.old_poly_ring(x)([1, 2])
+    if GROUND_TYPES != 'flint':
+        assert srepr(p1) == "DMP_Python([1, 2], ZZ)"
+        assert srepr(p2) == "DMP_Python([1, 2], ZZ)"
+    else:
+        assert srepr(p1) == "DUP_Flint([1, 2], ZZ)"
+        assert srepr(p2) == "DUP_Flint([1, 2], ZZ)"
+
+
+def test_FiniteExtension():
+    assert srepr(FiniteExtension(Poly(x**2 + 1, x))) == \
+        "FiniteExtension(Poly(x**2 + 1, x, domain='ZZ'))"
+
+
+def test_ExtensionElement():
+    A = FiniteExtension(Poly(x**2 + 1, x))
+    if GROUND_TYPES != 'flint':
+        ans = "ExtElem(DMP_Python([1, 0], ZZ), FiniteExtension(Poly(x**2 + 1, x, domain='ZZ')))"
+    else:
+        ans = "ExtElem(DUP_Flint([1, 0], ZZ), FiniteExtension(Poly(x**2 + 1, x, domain='ZZ')))"
+    assert srepr(A.generator) == ans
+
+def test_BooleanAtom():
+    assert srepr(true) == "true"
+    assert srepr(false) == "false"
+
+
+def test_Integers():
+    sT(S.Integers, "Integers")
+
+
+def test_Naturals():
+    sT(S.Naturals, "Naturals")
+
+
+def test_Naturals0():
+    sT(S.Naturals0, "Naturals0")
+
+
+def test_Reals():
+    sT(S.Reals, "Reals")
+
+
+def test_matrix_expressions():
+    n = symbols('n', integer=True)
+    A = MatrixSymbol("A", n, n)
+    B = MatrixSymbol("B", n, n)
+    sT(A, "MatrixSymbol(Str('A'), Symbol('n', integer=True), Symbol('n', integer=True))")
+    sT(A*B, "MatMul(MatrixSymbol(Str('A'), Symbol('n', integer=True), Symbol('n', integer=True)), MatrixSymbol(Str('B'), Symbol('n', integer=True), Symbol('n', integer=True)))")
+    sT(A + B, "MatAdd(MatrixSymbol(Str('A'), Symbol('n', integer=True), Symbol('n', integer=True)), MatrixSymbol(Str('B'), Symbol('n', integer=True), Symbol('n', integer=True)))")
+
+
+def test_Cycle():
+    # FIXME: sT fails because Cycle is not immutable and calling srepr(Cycle(1, 2))
+    # adds keys to the Cycle dict (GH-17661)
+    #import_stmt = "from sympy.combinatorics import Cycle"
+    #sT(Cycle(1, 2), "Cycle(1, 2)", import_stmt)
+    assert srepr(Cycle(1, 2)) == "Cycle(1, 2)"
+
+
+def test_Permutation():
+    import_stmt = "from sympy.combinatorics import Permutation"
+    sT(Permutation(1, 2)(3, 4), "Permutation([0, 2, 1, 4, 3])", import_stmt, perm_cyclic=False)
+    sT(Permutation(1, 2)(3, 4), "Permutation(1, 2)(3, 4)", import_stmt, perm_cyclic=True)
+
+    with warns_deprecated_sympy():
+        old_print_cyclic = Permutation.print_cyclic
+        Permutation.print_cyclic = False
+        sT(Permutation(1, 2)(3, 4), "Permutation([0, 2, 1, 4, 3])", import_stmt)
+        Permutation.print_cyclic = old_print_cyclic
+
+def test_dict():
+    from sympy.abc import x, y, z
+    d = {}
+    assert srepr(d) == "{}"
+    d = {x: y}
+    assert srepr(d) == "{Symbol('x'): Symbol('y')}"
+    d = {x: y, y: z}
+    assert srepr(d) in (
+        "{Symbol('x'): Symbol('y'), Symbol('y'): Symbol('z')}",
+        "{Symbol('y'): Symbol('z'), Symbol('x'): Symbol('y')}",
+    )
+    d = {x: {y: z}}
+    assert srepr(d) == "{Symbol('x'): {Symbol('y'): Symbol('z')}}"
+
+def test_set():
+    from sympy.abc import x, y
+    s = set()
+    assert srepr(s) == "set()"
+    s = {x, y}
+    assert srepr(s) in ("{Symbol('x'), Symbol('y')}", "{Symbol('y'), Symbol('x')}")
+
+def test_Predicate():
+    sT(Q.even, "Q.even")
+
+def test_AppliedPredicate():
+    sT(Q.even(Symbol('z')), "AppliedPredicate(Q.even, Symbol('z'))")

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_rust.py

@@ -0,0 +1,363 @@
+from sympy.core import (S, pi, oo, symbols, Rational, Integer,
+                        GoldenRatio, EulerGamma, Catalan, Lambda, Dummy,
+                        Eq, Ne, Le, Lt, Gt, Ge, Mod)
+from sympy.functions import (Piecewise, sin, cos, Abs, exp, ceiling, sqrt,
+                             sign, floor)
+from sympy.logic import ITE
+from sympy.testing.pytest import raises
+from sympy.utilities.lambdify import implemented_function
+from sympy.tensor import IndexedBase, Idx
+from sympy.matrices import MatrixSymbol, SparseMatrix, Matrix
+
+from sympy.printing.codeprinter import rust_code
+
+x, y, z = symbols('x,y,z', integer=False, real=True)
+k, m, n = symbols('k,m,n', integer=True)
+
+
+def test_Integer():
+    assert rust_code(Integer(42)) == "42"
+    assert rust_code(Integer(-56)) == "-56"
+
+
+def test_Relational():
+    assert rust_code(Eq(x, y)) == "x == y"
+    assert rust_code(Ne(x, y)) == "x != y"
+    assert rust_code(Le(x, y)) == "x <= y"
+    assert rust_code(Lt(x, y)) == "x < y"
+    assert rust_code(Gt(x, y)) == "x > y"
+    assert rust_code(Ge(x, y)) == "x >= y"
+
+
+def test_Rational():
+    assert rust_code(Rational(3, 7)) == "3_f64/7.0"
+    assert rust_code(Rational(18, 9)) == "2"
+    assert rust_code(Rational(3, -7)) == "-3_f64/7.0"
+    assert rust_code(Rational(-3, -7)) == "3_f64/7.0"
+    assert rust_code(x + Rational(3, 7)) == "x + 3_f64/7.0"
+    assert rust_code(Rational(3, 7)*x) == "(3_f64/7.0)*x"
+
+
+def test_basic_ops():
+    assert rust_code(x + y) == "x + y"
+    assert rust_code(x - y) == "x - y"
+    assert rust_code(x * y) == "x*y"
+    assert rust_code(x / y) == "x*y.recip()"
+    assert rust_code(-x) == "-x"
+    assert rust_code(2 * x) == "2.0*x"
+    assert rust_code(y + 2) == "y + 2.0"
+    assert rust_code(x + n) == "n as f64 + x"
+
+def test_printmethod():
+    class fabs(Abs):
+        def _rust_code(self, printer):
+            return "%s.fabs()" % printer._print(self.args[0])
+    assert rust_code(fabs(x)) == "x.fabs()"
+    a = MatrixSymbol("a", 1, 3)
+    assert rust_code(a[0,0]) == 'a[0]'
+
+
+def test_Functions():
+    assert rust_code(sin(x) ** cos(x)) == "x.sin().powf(x.cos())"
+    assert rust_code(abs(x)) == "x.abs()"
+    assert rust_code(ceiling(x)) == "x.ceil()"
+    assert rust_code(floor(x)) == "x.floor()"
+
+    # Automatic rewrite
+    assert rust_code(Mod(x, 3)) == 'x - 3.0*((1_f64/3.0)*x).floor()'
+
+
+def test_Pow():
+    assert rust_code(1/x) == "x.recip()"
+    assert rust_code(x**-1) == rust_code(x**-1.0) == "x.recip()"
+    assert rust_code(sqrt(x)) == "x.sqrt()"
+    assert rust_code(x**S.Half) == rust_code(x**0.5) == "x.sqrt()"
+
+    assert rust_code(1/sqrt(x)) == "x.sqrt().recip()"
+    assert rust_code(x**-S.Half) == rust_code(x**-0.5) == "x.sqrt().recip()"
+
+    assert rust_code(1/pi) == "PI.recip()"
+    assert rust_code(pi**-1) == rust_code(pi**-1.0) == "PI.recip()"
+    assert rust_code(pi**-0.5) == "PI.sqrt().recip()"
+
+    assert rust_code(x**Rational(1, 3)) == "x.cbrt()"
+    assert rust_code(2**x) == "x.exp2()"
+    assert rust_code(exp(x)) == "x.exp()"
+    assert rust_code(x**3) == "x.powi(3)"
+    assert rust_code(x**(y**3)) == "x.powf(y.powi(3))"
+    assert rust_code(x**Rational(2, 3)) == "x.powf(2_f64/3.0)"
+
+    g = implemented_function('g', Lambda(x, 2*x))
+    assert rust_code(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
+        "(3.5*2.0*x).powf(-x + y.powf(x))/(x.powi(2) + y)"
+    _cond_cfunc = [(lambda base, exp: exp.is_integer, "dpowi", 1),
+                   (lambda base, exp: not exp.is_integer, "pow", 1)]
+    assert rust_code(x**3, user_functions={'Pow': _cond_cfunc}) == 'x.dpowi(3)'
+    assert rust_code(x**3.2, user_functions={'Pow': _cond_cfunc}) == 'x.pow(3.2)'
+
+
+def test_constants():
+    assert rust_code(pi) == "PI"
+    assert rust_code(oo) == "INFINITY"
+    assert rust_code(S.Infinity) == "INFINITY"
+    assert rust_code(-oo) == "NEG_INFINITY"
+    assert rust_code(S.NegativeInfinity) == "NEG_INFINITY"
+    assert rust_code(S.NaN) == "NAN"
+    assert rust_code(exp(1)) == "E"
+    assert rust_code(S.Exp1) == "E"
+
+
+def test_constants_other():
+    assert rust_code(2*GoldenRatio) == "const GoldenRatio: f64 = %s;\n2.0*GoldenRatio" % GoldenRatio.evalf(17)
+    assert rust_code(
+            2*Catalan) == "const Catalan: f64 = %s;\n2.0*Catalan" % Catalan.evalf(17)
+    assert rust_code(2*EulerGamma) == "const EulerGamma: f64 = %s;\n2.0*EulerGamma" % EulerGamma.evalf(17)
+
+
+def test_boolean():
+    assert rust_code(True) == "true"
+    assert rust_code(S.true) == "true"
+    assert rust_code(False) == "false"
+    assert rust_code(S.false) == "false"
+    assert rust_code(k & m) == "k && m"
+    assert rust_code(k | m) == "k || m"
+    assert rust_code(~k) == "!k"
+    assert rust_code(k & m & n) == "k && m && n"
+    assert rust_code(k | m | n) == "k || m || n"
+    assert rust_code((k & m) | n) == "n || k && m"
+    assert rust_code((k | m) & n) == "n && (k || m)"
+
+
+def test_Piecewise():
+    expr = Piecewise((x, x < 1), (x + 2, True))
+    assert rust_code(expr) == (
+            "if (x < 1.0) {\n"
+            "    x\n"
+            "} else {\n"
+            "    x + 2.0\n"
+            "}")
+    assert rust_code(expr, assign_to="r") == (
+        "r = if (x < 1.0) {\n"
+        "    x\n"
+        "} else {\n"
+        "    x + 2.0\n"
+        "};")
+    assert rust_code(expr, assign_to="r", inline=True) == (
+        "r = if (x < 1.0) { x } else { x + 2.0 };")
+    expr = Piecewise((x, x < 1), (x + 1, x < 5), (x + 2, True))
+    assert rust_code(expr, inline=True) == (
+        "if (x < 1.0) { x } else if (x < 5.0) { x + 1.0 } else { x + 2.0 }")
+    assert rust_code(expr, assign_to="r", inline=True) == (
+        "r = if (x < 1.0) { x } else if (x < 5.0) { x + 1.0 } else { x + 2.0 };")
+    assert rust_code(expr, assign_to="r") == (
+        "r = if (x < 1.0) {\n"
+        "    x\n"
+        "} else if (x < 5.0) {\n"
+        "    x + 1.0\n"
+        "} else {\n"
+        "    x + 2.0\n"
+        "};")
+    expr = 2*Piecewise((x, x < 1), (x + 1, x < 5), (x + 2, True))
+    assert rust_code(expr, inline=True) == (
+        "2.0*if (x < 1.0) { x } else if (x < 5.0) { x + 1.0 } else { x + 2.0 }")
+    expr = 2*Piecewise((x, x < 1), (x + 1, x < 5), (x + 2, True)) - 42
+    assert rust_code(expr, inline=True) == (
+        "2.0*if (x < 1.0) { x } else if (x < 5.0) { x + 1.0 } else { x + 2.0 } - 42.0")
+    # Check that Piecewise without a True (default) condition error
+    expr = Piecewise((x, x < 1), (x**2, x > 1), (sin(x), x > 0))
+    raises(ValueError, lambda: rust_code(expr))
+
+
+def test_dereference_printing():
+    expr = x + y + sin(z) + z
+    assert rust_code(expr, dereference=[z]) == "x + y + (*z) + (*z).sin()"
+
+
+def test_sign():
+    expr = sign(x) * y
+    assert rust_code(expr) == "y*(if (x == 0.0) { 0.0 } else { (x).signum() }) as f64"
+    assert rust_code(expr, assign_to='r') == "r = y*(if (x == 0.0) { 0.0 } else { (x).signum() }) as f64;"
+
+    expr = sign(x + y) + 42
+    assert rust_code(expr) == "(if (x + y == 0.0) { 0.0 } else { (x + y).signum() }) + 42"
+    assert rust_code(expr, assign_to='r') == "r = (if (x + y == 0.0) { 0.0 } else { (x + y).signum() }) + 42;"
+
+    expr = sign(cos(x))
+    assert rust_code(expr) == "(if (x.cos() == 0.0) { 0.0 } else { (x.cos()).signum() })"
+
+
+def test_reserved_words():
+
+    x, y = symbols("x if")
+
+    expr = sin(y)
+    assert rust_code(expr) == "if_.sin()"
+    assert rust_code(expr, dereference=[y]) == "(*if_).sin()"
+    assert rust_code(expr, reserved_word_suffix='_unreserved') == "if_unreserved.sin()"
+
+    with raises(ValueError):
+        rust_code(expr, error_on_reserved=True)
+
+
+def test_ITE():
+    ekpr = ITE(k < 1, m, n)
+    assert rust_code(ekpr) == (
+            "if (k < 1) {\n"
+            "    m\n"
+            "} else {\n"
+            "    n\n"
+            "}")
+
+
+def test_Indexed():
+    n, m, o = symbols('n m o', integer=True)
+    i, j, k = Idx('i', n), Idx('j', m), Idx('k', o)
+
+    x = IndexedBase('x')[j]
+    assert rust_code(x) == "x[j]"
+
+    A = IndexedBase('A')[i, j]
+    assert rust_code(A) == "A[m*i + j]"
+
+    B = IndexedBase('B')[i, j, k]
+    assert rust_code(B) == "B[m*o*i + o*j + k]"
+
+
+def test_dummy_loops():
+    i, m = symbols('i m', integer=True, cls=Dummy)
+    x = IndexedBase('x')
+    y = IndexedBase('y')
+    i = Idx(i, m)
+
+    assert rust_code(x[i], assign_to=y[i]) == (
+        "for i in 0..m {\n"
+        "    y[i] = x[i];\n"
+        "}")
+
+
+def test_loops():
+    m, n = symbols('m n', integer=True)
+    A = IndexedBase('A')
+    x = IndexedBase('x')
+    y = IndexedBase('y')
+    z = IndexedBase('z')
+    i = Idx('i', m)
+    j = Idx('j', n)
+
+    assert rust_code(A[i, j]*x[j], assign_to=y[i]) == (
+        "for i in 0..m {\n"
+        "    y[i] = 0;\n"
+        "}\n"
+        "for i in 0..m {\n"
+        "    for j in 0..n {\n"
+        "        y[i] = A[n*i + j]*x[j] + y[i];\n"
+        "    }\n"
+        "}")
+
+    assert rust_code(A[i, j]*x[j] + x[i] + z[i], assign_to=y[i]) == (
+        "for i in 0..m {\n"
+        "    y[i] = x[i] + z[i];\n"
+        "}\n"
+        "for i in 0..m {\n"
+        "    for j in 0..n {\n"
+        "        y[i] = A[n*i + j]*x[j] + y[i];\n"
+        "    }\n"
+        "}")
+
+
+def test_loops_multiple_contractions():
+    n, m, o, p = symbols('n m o p', integer=True)
+    a = IndexedBase('a')
+    b = IndexedBase('b')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+    k = Idx('k', o)
+    l = Idx('l', p)
+
+    assert rust_code(b[j, k, l]*a[i, j, k, l], assign_to=y[i]) == (
+        "for i in 0..m {\n"
+        "    y[i] = 0;\n"
+        "}\n"
+        "for i in 0..m {\n"
+        "    for j in 0..n {\n"
+        "        for k in 0..o {\n"
+        "            for l in 0..p {\n"
+        "                y[i] = a[%s]*b[%s] + y[i];\n" % (i*n*o*p + j*o*p + k*p + l, j*o*p + k*p + l) +\
+        "            }\n"
+        "        }\n"
+        "    }\n"
+        "}")
+
+
+def test_loops_addfactor():
+    m, n, o, p = symbols('m n o p', integer=True)
+    a = IndexedBase('a')
+    b = IndexedBase('b')
+    c = IndexedBase('c')
+    y = IndexedBase('y')
+    i = Idx('i', m)
+    j = Idx('j', n)
+    k = Idx('k', o)
+    l = Idx('l', p)
+
+    code = rust_code((a[i, j, k, l] + b[i, j, k, l])*c[j, k, l], assign_to=y[i])
+    assert code == (
+        "for i in 0..m {\n"
+        "    y[i] = 0;\n"
+        "}\n"
+        "for i in 0..m {\n"
+        "    for j in 0..n {\n"
+        "        for k in 0..o {\n"
+        "            for l in 0..p {\n"
+        "                y[i] = (a[%s] + b[%s])*c[%s] + y[i];\n" % (i*n*o*p + j*o*p + k*p + l, i*n*o*p + j*o*p + k*p + l, j*o*p + k*p + l) +\
+        "            }\n"
+        "        }\n"
+        "    }\n"
+        "}")
+
+
+def test_settings():
+    raises(TypeError, lambda: rust_code(sin(x), method="garbage"))
+
+
+def test_inline_function():
+    x = symbols('x')
+    g = implemented_function('g', Lambda(x, 2*x))
+    assert rust_code(g(x)) == "2*x"
+
+    g = implemented_function('g', Lambda(x, 2*x/Catalan))
+    assert rust_code(g(x)) == (
+        "const Catalan: f64 = %s;\n2.0*x/Catalan" % Catalan.evalf(17))
+
+    A = IndexedBase('A')
+    i = Idx('i', symbols('n', integer=True))
+    g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
+    assert rust_code(g(A[i]), assign_to=A[i]) == (
+        "for i in 0..n {\n"
+        "    A[i] = (A[i] + 1)*(A[i] + 2)*A[i];\n"
+        "}")
+
+
+def test_user_functions():
+    x = symbols('x', integer=False)
+    n = symbols('n', integer=True)
+    custom_functions = {
+        "ceiling": "ceil",
+        "Abs": [(lambda x: not x.is_integer, "fabs", 4), (lambda x: x.is_integer, "abs", 4)],
+    }
+    assert rust_code(ceiling(x), user_functions=custom_functions) == "x.ceil()"
+    assert rust_code(Abs(x), user_functions=custom_functions) == "fabs(x)"
+    assert rust_code(Abs(n), user_functions=custom_functions) == "abs(n)"
+
+
+def test_matrix():
+    assert rust_code(Matrix([1, 2, 3])) == '[1, 2, 3]'
+    with raises(ValueError):
+        rust_code(Matrix([[1, 2, 3]]))
+
+
+def test_sparse_matrix():
+    # gh-15791
+    with raises(NotImplementedError):
+        rust_code(SparseMatrix([[1, 2, 3]]))

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_smtlib.py

@@ -0,0 +1,553 @@
+import contextlib
+import itertools
+import re
+import typing
+from enum import Enum
+from typing import Callable
+
+import sympy
+from sympy import Add, Implies, sqrt
+from sympy.core import Mul, Pow
+from sympy.core import (S, pi, symbols, Function, Rational, Integer,
+                        Symbol, Eq, Ne, Le, Lt, Gt, Ge)
+from sympy.functions import Piecewise, exp, sin, cos
+from sympy.assumptions.ask import Q
+from sympy.printing.smtlib import smtlib_code
+from sympy.testing.pytest import raises, Failed
+
+x, y, z = symbols('x,y,z')
+
+
+class _W(Enum):
+    DEFAULTING_TO_FLOAT = re.compile("Could not infer type of `.+`. Defaulting to float.", re.IGNORECASE)
+    WILL_NOT_DECLARE = re.compile("Non-Symbol/Function `.+` will not be declared.", re.IGNORECASE)
+    WILL_NOT_ASSERT = re.compile("Non-Boolean expression `.+` will not be asserted. Converting to SMTLib verbatim.", re.IGNORECASE)
+
+
+@contextlib.contextmanager
+def _check_warns(expected: typing.Iterable[_W]):
+    warns: typing.List[str] = []
+    log_warn = warns.append
+    yield log_warn
+
+    errors = []
+    for i, (w, e) in enumerate(itertools.zip_longest(warns, expected)):
+        if not e:
+            errors += [f"[{i}] Received unexpected warning `{w}`."]
+        elif not w:
+            errors += [f"[{i}] Did not receive expected warning `{e.name}`."]
+        elif not e.value.match(w):
+            errors += [f"[{i}] Warning `{w}` does not match expected {e.name}."]
+
+    if errors: raise Failed('\n'.join(errors))
+
+
+def test_Integer():
+    with _check_warns([_W.WILL_NOT_ASSERT] * 2) as w:
+        assert smtlib_code(Integer(67), log_warn=w) == "67"
+        assert smtlib_code(Integer(-1), log_warn=w) == "-1"
+    with _check_warns([]) as w:
+        assert smtlib_code(Integer(67)) == "67"
+        assert smtlib_code(Integer(-1)) == "-1"
+
+
+def test_Rational():
+    with _check_warns([_W.WILL_NOT_ASSERT] * 4) as w:
+        assert smtlib_code(Rational(3, 7), log_warn=w) == "(/ 3 7)"
+        assert smtlib_code(Rational(18, 9), log_warn=w) == "2"
+        assert smtlib_code(Rational(3, -7), log_warn=w) == "(/ -3 7)"
+        assert smtlib_code(Rational(-3, -7), log_warn=w) == "(/ 3 7)"
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT] * 2) as w:
+        assert smtlib_code(x + Rational(3, 7), auto_declare=False, log_warn=w) == "(+ (/ 3 7) x)"
+        assert smtlib_code(Rational(3, 7) * x, log_warn=w) == "(declare-const x Real)\n" \
+                                                              "(* (/ 3 7) x)"
+
+
+def test_Relational():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT] * 12) as w:
+        assert smtlib_code(Eq(x, y), auto_declare=False, log_warn=w) == "(assert (= x y))"
+        assert smtlib_code(Ne(x, y), auto_declare=False, log_warn=w) == "(assert (not (= x y)))"
+        assert smtlib_code(Le(x, y), auto_declare=False, log_warn=w) == "(assert (<= x y))"
+        assert smtlib_code(Lt(x, y), auto_declare=False, log_warn=w) == "(assert (< x y))"
+        assert smtlib_code(Gt(x, y), auto_declare=False, log_warn=w) == "(assert (> x y))"
+        assert smtlib_code(Ge(x, y), auto_declare=False, log_warn=w) == "(assert (>= x y))"
+
+
+def test_AppliedBinaryRelation():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT] * 12) as w:
+        assert smtlib_code(Q.eq(x, y), auto_declare=False, log_warn=w) == "(assert (= x y))"
+        assert smtlib_code(Q.ne(x, y), auto_declare=False, log_warn=w) == "(assert (not (= x y)))"
+        assert smtlib_code(Q.lt(x, y), auto_declare=False, log_warn=w) == "(assert (< x y))"
+        assert smtlib_code(Q.le(x, y), auto_declare=False, log_warn=w) == "(assert (<= x y))"
+        assert smtlib_code(Q.gt(x, y), auto_declare=False, log_warn=w) == "(assert (> x y))"
+        assert smtlib_code(Q.ge(x, y), auto_declare=False, log_warn=w) == "(assert (>= x y))"
+
+    raises(ValueError, lambda: smtlib_code(Q.complex(x), log_warn=w))
+
+
+def test_AppliedPredicate():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT] * 6) as w:
+        assert smtlib_code(Q.positive(x), auto_declare=False, log_warn=w) == "(assert (> x 0))"
+        assert smtlib_code(Q.negative(x), auto_declare=False, log_warn=w) == "(assert (< x 0))"
+        assert smtlib_code(Q.zero(x), auto_declare=False, log_warn=w) == "(assert (= x 0))"
+        assert smtlib_code(Q.nonpositive(x), auto_declare=False, log_warn=w) == "(assert (<= x 0))"
+        assert smtlib_code(Q.nonnegative(x), auto_declare=False, log_warn=w) == "(assert (>= x 0))"
+        assert smtlib_code(Q.nonzero(x), auto_declare=False, log_warn=w) == "(assert (not (= x 0)))"
+
+def test_Function():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(sin(x) ** cos(x), auto_declare=False, log_warn=w) == "(pow (sin x) (cos x))"
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            abs(x),
+            symbol_table={x: int, y: bool},
+            known_types={int: "INTEGER_TYPE"},
+            known_functions={sympy.Abs: "ABSOLUTE_VALUE_OF"},
+            log_warn=w
+        ) == "(declare-const x INTEGER_TYPE)\n" \
+             "(ABSOLUTE_VALUE_OF x)"
+
+    my_fun1 = Function('f1')
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            my_fun1(x),
+            symbol_table={my_fun1: Callable[[bool], float]},
+            log_warn=w
+        ) == "(declare-const x Bool)\n" \
+             "(declare-fun f1 (Bool) Real)\n" \
+             "(f1 x)"
+
+    with _check_warns([]) as w:
+        assert smtlib_code(
+            my_fun1(x),
+            symbol_table={my_fun1: Callable[[bool], bool]},
+            log_warn=w
+        ) == "(declare-const x Bool)\n" \
+             "(declare-fun f1 (Bool) Bool)\n" \
+             "(assert (f1 x))"
+
+        assert smtlib_code(
+            Eq(my_fun1(x, z), y),
+            symbol_table={my_fun1: Callable[[int, bool], bool]},
+            log_warn=w
+        ) == "(declare-const x Int)\n" \
+             "(declare-const y Bool)\n" \
+             "(declare-const z Bool)\n" \
+             "(declare-fun f1 (Int Bool) Bool)\n" \
+             "(assert (= (f1 x z) y))"
+
+        assert smtlib_code(
+            Eq(my_fun1(x, z), y),
+            symbol_table={my_fun1: Callable[[int, bool], bool]},
+            known_functions={my_fun1: "MY_KNOWN_FUN", Eq: '=='},
+            log_warn=w
+        ) == "(declare-const x Int)\n" \
+             "(declare-const y Bool)\n" \
+             "(declare-const z Bool)\n" \
+             "(assert (== (MY_KNOWN_FUN x z) y))"
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT] * 3) as w:
+        assert smtlib_code(
+            Eq(my_fun1(x, z), y),
+            known_functions={my_fun1: "MY_KNOWN_FUN", Eq: '=='},
+            log_warn=w
+        ) == "(declare-const x Real)\n" \
+             "(declare-const y Real)\n" \
+             "(declare-const z Real)\n" \
+             "(assert (== (MY_KNOWN_FUN x z) y))"
+
+
+def test_Pow():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(x ** 3, auto_declare=False, log_warn=w) == "(pow x 3)"
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(x ** (y ** 3), auto_declare=False, log_warn=w) == "(pow x (pow y 3))"
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(x ** Rational(2, 3), auto_declare=False, log_warn=w) == '(pow x (/ 2 3))'
+
+        a = Symbol('a', integer=True)
+        b = Symbol('b', real=True)
+        c = Symbol('c')
+
+        def g(x): return 2 * x
+
+        # if x=1, y=2, then expr=2.333...
+        expr = 1 / (g(a) * 3.5) ** (a - b ** a) / (a ** 2 + b)
+
+    with _check_warns([]) as w:
+        assert smtlib_code(
+            [
+                Eq(a < 2, c),
+                Eq(b > a, c),
+                c & True,
+                Eq(expr, 2 + Rational(1, 3))
+            ],
+            log_warn=w
+        ) == '(declare-const a Int)\n' \
+             '(declare-const b Real)\n' \
+             '(declare-const c Bool)\n' \
+             '(assert (= (< a 2) c))\n' \
+             '(assert (= (> b a) c))\n' \
+             '(assert c)\n' \
+             '(assert (= ' \
+             '(* (pow (* 7.0 a) (+ (pow b a) (* -1 a))) (pow (+ b (pow a 2)) -1)) ' \
+             '(/ 7 3)' \
+             '))'
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Mul(-2, c, Pow(Mul(b, b, evaluate=False), -1, evaluate=False), evaluate=False),
+            log_warn=w
+        ) == '(declare-const b Real)\n' \
+             '(declare-const c Real)\n' \
+             '(* -2 c (pow (* b b) -1))'
+
+
+def test_basic_ops():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(x * y, auto_declare=False, log_warn=w) == "(* x y)"
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(x + y, auto_declare=False, log_warn=w) == "(+ x y)"
+
+    # with _check_warns([_SmtlibWarnings.DEFAULTING_TO_FLOAT, _SmtlibWarnings.DEFAULTING_TO_FLOAT, _SmtlibWarnings.WILL_NOT_ASSERT]) as w:
+    # todo: implement re-write, currently does '(+ x (* -1 y))' instead
+    # assert smtlib_code(x - y, auto_declare=False, log_warn=w) == "(- x y)"
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(-x, auto_declare=False, log_warn=w) == "(* -1 x)"
+
+
+def test_quantifier_extensions():
+    from sympy.logic.boolalg import Boolean
+    from sympy import Interval, Tuple, sympify
+
+    # start For-all quantifier class example
+    class ForAll(Boolean):
+        def _smtlib(self, printer):
+            bound_symbol_declarations = [
+                printer._s_expr(sym.name, [
+                    printer._known_types[printer.symbol_table[sym]],
+                    Interval(start, end)
+                ]) for sym, start, end in self.limits
+            ]
+            return printer._s_expr('forall', [
+                printer._s_expr('', bound_symbol_declarations),
+                self.function
+            ])
+
+        @property
+        def bound_symbols(self):
+            return {s for s, _, _ in self.limits}
+
+        @property
+        def free_symbols(self):
+            bound_symbol_names = {s.name for s in self.bound_symbols}
+            return {
+                s for s in self.function.free_symbols
+                if s.name not in bound_symbol_names
+            }
+
+        def __new__(cls, *args):
+            limits = [sympify(a) for a in args if isinstance(a, (tuple, Tuple))]
+            function = [sympify(a) for a in args if isinstance(a, Boolean)]
+            assert len(limits) + len(function) == len(args)
+            assert len(function) == 1
+            function = function[0]
+
+            if isinstance(function, ForAll): return ForAll.__new__(
+                ForAll, *(limits + function.limits), function.function
+            )
+            inst = Boolean.__new__(cls)
+            inst._args = tuple(limits + [function])
+            inst.limits = limits
+            inst.function = function
+            return inst
+
+    # end For-All Quantifier class example
+
+    f = Function('f')
+    with _check_warns([_W.DEFAULTING_TO_FLOAT]) as w:
+        assert smtlib_code(
+            ForAll((x, -42, +21), Eq(f(x), f(x))),
+            symbol_table={f: Callable[[float], float]},
+            log_warn=w
+        ) == '(assert (forall ( (x Real [-42, 21])) true))'
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT] * 2) as w:
+        assert smtlib_code(
+            ForAll(
+                (x, -42, +21), (y, -100, 3),
+                Implies(Eq(x, y), Eq(f(x), f(y)))
+            ),
+            symbol_table={f: Callable[[float], float]},
+            log_warn=w
+        ) == '(declare-fun f (Real) Real)\n' \
+             '(assert (' \
+             'forall ( (x Real [-42, 21]) (y Real [-100, 3])) ' \
+             '(=> (= x y) (= (f x) (f y)))' \
+             '))'
+
+    a = Symbol('a', integer=True)
+    b = Symbol('b', real=True)
+    c = Symbol('c')
+
+    with _check_warns([]) as w:
+        assert smtlib_code(
+            ForAll(
+                (a, 2, 100), ForAll(
+                    (b, 2, 100),
+                    Implies(a < b, sqrt(a) < b) | c
+                )),
+            log_warn=w
+        ) == '(declare-const c Bool)\n' \
+             '(assert (forall ( (a Int [2, 100]) (b Real [2, 100])) ' \
+             '(or c (=> (< a b) (< (pow a (/ 1 2)) b)))' \
+             '))'
+
+
+def test_mix_number_mult_symbols():
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            1 / pi,
+            known_constants={pi: "MY_PI"},
+            log_warn=w
+        ) == '(pow MY_PI -1)'
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            [
+                Eq(pi, 3.14, evaluate=False),
+                1 / pi,
+            ],
+            known_constants={pi: "MY_PI"},
+            log_warn=w
+        ) == '(assert (= MY_PI 3.14))\n' \
+             '(pow MY_PI -1)'
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Add(S.Zero, S.One, S.NegativeOne, S.Half,
+                S.Exp1, S.Pi, S.GoldenRatio, evaluate=False),
+            known_constants={
+                S.Pi: 'p', S.GoldenRatio: 'g',
+                S.Exp1: 'e'
+            },
+            known_functions={
+                Add: 'plus',
+                exp: 'exp'
+            },
+            precision=3,
+            log_warn=w
+        ) == '(plus 0 1 -1 (/ 1 2) (exp 1) p g)'
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Add(S.Zero, S.One, S.NegativeOne, S.Half,
+                S.Exp1, S.Pi, S.GoldenRatio, evaluate=False),
+            known_constants={
+                S.Pi: 'p'
+            },
+            known_functions={
+                Add: 'plus',
+                exp: 'exp'
+            },
+            precision=3,
+            log_warn=w
+        ) == '(plus 0 1 -1 (/ 1 2) (exp 1) p 1.62)'
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Add(S.Zero, S.One, S.NegativeOne, S.Half,
+                S.Exp1, S.Pi, S.GoldenRatio, evaluate=False),
+            known_functions={Add: 'plus'},
+            precision=3,
+            log_warn=w
+        ) == '(plus 0 1 -1 (/ 1 2) 2.72 3.14 1.62)'
+
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Add(S.Zero, S.One, S.NegativeOne, S.Half,
+                S.Exp1, S.Pi, S.GoldenRatio, evaluate=False),
+            known_constants={S.Exp1: 'e'},
+            known_functions={Add: 'plus'},
+            precision=3,
+            log_warn=w
+        ) == '(plus 0 1 -1 (/ 1 2) e 3.14 1.62)'
+
+
+def test_boolean():
+    with _check_warns([]) as w:
+        assert smtlib_code(x & y, log_warn=w) == '(declare-const x Bool)\n' \
+                                                 '(declare-const y Bool)\n' \
+                                                 '(assert (and x y))'
+        assert smtlib_code(x | y, log_warn=w) == '(declare-const x Bool)\n' \
+                                                 '(declare-const y Bool)\n' \
+                                                 '(assert (or x y))'
+        assert smtlib_code(~x, log_warn=w) == '(declare-const x Bool)\n' \
+                                              '(assert (not x))'
+        assert smtlib_code(x & y & z, log_warn=w) == '(declare-const x Bool)\n' \
+                                                     '(declare-const y Bool)\n' \
+                                                     '(declare-const z Bool)\n' \
+                                                     '(assert (and x y z))'
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT]) as w:
+        assert smtlib_code((x & ~y) | (z > 3), log_warn=w) == '(declare-const x Bool)\n' \
+                                                              '(declare-const y Bool)\n' \
+                                                              '(declare-const z Real)\n' \
+                                                              '(assert (or (> z 3) (and x (not y))))'
+
+    f = Function('f')
+    g = Function('g')
+    h = Function('h')
+    with _check_warns([_W.DEFAULTING_TO_FLOAT]) as w:
+        assert smtlib_code(
+            [Gt(f(x), y),
+             Lt(y, g(z))],
+            symbol_table={
+                f: Callable[[bool], int], g: Callable[[bool], int],
+            }, log_warn=w
+        ) == '(declare-const x Bool)\n' \
+             '(declare-const y Real)\n' \
+             '(declare-const z Bool)\n' \
+             '(declare-fun f (Bool) Int)\n' \
+             '(declare-fun g (Bool) Int)\n' \
+             '(assert (> (f x) y))\n' \
+             '(assert (< y (g z)))'
+
+    with _check_warns([]) as w:
+        assert smtlib_code(
+            [Eq(f(x), y),
+             Lt(y, g(z))],
+            symbol_table={
+                f: Callable[[bool], int], g: Callable[[bool], int],
+            }, log_warn=w
+        ) == '(declare-const x Bool)\n' \
+             '(declare-const y Int)\n' \
+             '(declare-const z Bool)\n' \
+             '(declare-fun f (Bool) Int)\n' \
+             '(declare-fun g (Bool) Int)\n' \
+             '(assert (= (f x) y))\n' \
+             '(assert (< y (g z)))'
+
+    with _check_warns([]) as w:
+        assert smtlib_code(
+            [Eq(f(x), y),
+             Eq(g(f(x)), z),
+             Eq(h(g(f(x))), x)],
+            symbol_table={
+                f: Callable[[float], int],
+                g: Callable[[int], bool],
+                h: Callable[[bool], float]
+            },
+            log_warn=w
+        ) == '(declare-const x Real)\n' \
+             '(declare-const y Int)\n' \
+             '(declare-const z Bool)\n' \
+             '(declare-fun f (Real) Int)\n' \
+             '(declare-fun g (Int) Bool)\n' \
+             '(declare-fun h (Bool) Real)\n' \
+             '(assert (= (f x) y))\n' \
+             '(assert (= (g (f x)) z))\n' \
+             '(assert (= (h (g (f x))) x))'
+
+
+# todo: make smtlib_code support arrays
+# def test_containers():
+#     assert julia_code([1, 2, 3, [4, 5, [6, 7]], 8, [9, 10], 11]) == \
+#            "Any[1, 2, 3, Any[4, 5, Any[6, 7]], 8, Any[9, 10], 11]"
+#     assert julia_code((1, 2, (3, 4))) == "(1, 2, (3, 4))"
+#     assert julia_code([1]) == "Any[1]"
+#     assert julia_code((1,)) == "(1,)"
+#     assert julia_code(Tuple(*[1, 2, 3])) == "(1, 2, 3)"
+#     assert julia_code((1, x * y, (3, x ** 2))) == "(1, x .* y, (3, x .^ 2))"
+#     # scalar, matrix, empty matrix and empty list
+#     assert julia_code((1, eye(3), Matrix(0, 0, []), [])) == "(1, [1 0 0;\n0 1 0;\n0 0 1], zeros(0, 0), Any[])"
+
+def test_smtlib_piecewise():
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Piecewise((x, x < 1),
+                      (x ** 2, True)),
+            auto_declare=False,
+            log_warn=w
+        ) == '(ite (< x 1) x (pow x 2))'
+
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(
+            Piecewise((x ** 2, x < 1),
+                      (x ** 3, x < 2),
+                      (x ** 4, x < 3),
+                      (x ** 5, True)),
+            auto_declare=False,
+            log_warn=w
+        ) == '(ite (< x 1) (pow x 2) ' \
+             '(ite (< x 2) (pow x 3) ' \
+             '(ite (< x 3) (pow x 4) ' \
+             '(pow x 5))))'
+
+    # Check that Piecewise without a True (default) condition error
+    expr = Piecewise((x, x < 1), (x ** 2, x > 1), (sin(x), x > 0))
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        raises(AssertionError, lambda: smtlib_code(expr, log_warn=w))
+
+
+def test_smtlib_piecewise_times_const():
+    pw = Piecewise((x, x < 1), (x ** 2, True))
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(2 * pw, log_warn=w) == '(declare-const x Real)\n(* 2 (ite (< x 1) x (pow x 2)))'
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(pw / x, log_warn=w) == '(declare-const x Real)\n(* (pow x -1) (ite (< x 1) x (pow x 2)))'
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(pw / (x * y), log_warn=w) == '(declare-const x Real)\n(declare-const y Real)\n(* (pow x -1) (pow y -1) (ite (< x 1) x (pow x 2)))'
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        assert smtlib_code(pw / 3, log_warn=w) == '(declare-const x Real)\n(* (/ 1 3) (ite (< x 1) x (pow x 2)))'
+
+
+# todo: make smtlib_code support arrays / matrices ?
+# def test_smtlib_matrix_assign_to():
+#     A = Matrix([[1, 2, 3]])
+#     assert smtlib_code(A, assign_to='a') == "a = [1 2 3]"
+#     A = Matrix([[1, 2], [3, 4]])
+#     assert smtlib_code(A, assign_to='A') == "A = [1 2;\n3 4]"
+
+# def test_julia_matrix_1x1():
+#     A = Matrix([[3]])
+#     B = MatrixSymbol('B', 1, 1)
+#     C = MatrixSymbol('C', 1, 2)
+#     assert julia_code(A, assign_to=B) == "B = [3]"
+#     raises(ValueError, lambda: julia_code(A, assign_to=C))
+
+# def test_julia_matrix_elements():
+#     A = Matrix([[x, 2, x * y]])
+#     assert julia_code(A[0, 0] ** 2 + A[0, 1] + A[0, 2]) == "x .^ 2 + x .* y + 2"
+#     A = MatrixSymbol('AA', 1, 3)
+#     assert julia_code(A) == "AA"
+#     assert julia_code(A[0, 0] ** 2 + sin(A[0, 1]) + A[0, 2]) == \
+#            "sin(AA[1,2]) + AA[1,1] .^ 2 + AA[1,3]"
+#     assert julia_code(sum(A)) == "AA[1,1] + AA[1,2] + AA[1,3]"
+
+def test_smtlib_boolean():
+    with _check_warns([]) as w:
+        assert smtlib_code(True, auto_assert=False, log_warn=w) == 'true'
+        assert smtlib_code(True, log_warn=w) == '(assert true)'
+        assert smtlib_code(S.true, log_warn=w) == '(assert true)'
+        assert smtlib_code(S.false, log_warn=w) == '(assert false)'
+        assert smtlib_code(False, log_warn=w) == '(assert false)'
+        assert smtlib_code(False, auto_assert=False, log_warn=w) == 'false'
+
+
+def test_not_supported():
+    f = Function('f')
+    with _check_warns([_W.DEFAULTING_TO_FLOAT, _W.WILL_NOT_ASSERT]) as w:
+        raises(KeyError, lambda: smtlib_code(f(x).diff(x), symbol_table={f: Callable[[float], float]}, log_warn=w))
+    with _check_warns([_W.WILL_NOT_ASSERT]) as w:
+        raises(KeyError, lambda: smtlib_code(S.ComplexInfinity, log_warn=w))
+
+
+def test_Float():
+    assert smtlib_code(0.0) == "0.0"
+    assert smtlib_code(0.000000000000000003) == '(* 3.0 (pow 10 -18))'
+    assert smtlib_code(5.3) == "5.3"

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_str.py

@@ -0,0 +1,1206 @@
+from sympy import MatAdd
+from sympy.algebras.quaternion import Quaternion
+from sympy.assumptions.ask import Q
+from sympy.calculus.accumulationbounds import AccumBounds
+from sympy.combinatorics.partitions import Partition
+from sympy.concrete.summations import (Sum, summation)
+from sympy.core.add import Add
+from sympy.core.containers import (Dict, Tuple)
+from sympy.core.expr import UnevaluatedExpr, Expr
+from sympy.core.function import (Derivative, Function, Lambda, Subs, WildFunction)
+from sympy.core.mul import Mul
+from sympy.core import (Catalan, EulerGamma, GoldenRatio, TribonacciConstant)
+from sympy.core.numbers import (E, Float, I, Integer, Rational, nan, oo, pi, zoo)
+from sympy.core.parameters import _exp_is_pow
+from sympy.core.power import Pow
+from sympy.core.relational import (Eq, Rel, Ne)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Dummy, Symbol, Wild, symbols)
+from sympy.functions.combinatorial.factorials import (factorial, factorial2, subfactorial)
+from sympy.functions.elementary.complexes import Abs
+from sympy.functions.elementary.exponential import exp
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.functions.special.delta_functions import Heaviside
+from sympy.functions.special.zeta_functions import zeta
+from sympy.integrals.integrals import Integral
+from sympy.logic.boolalg import (Equivalent, false, true, Xor)
+from sympy.matrices.dense import Matrix
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions import Identity
+from sympy.matrices.expressions.slice import MatrixSlice
+from sympy.matrices import SparseMatrix
+from sympy.polys.polytools import factor
+from sympy.series.limits import Limit
+from sympy.series.order import O
+from sympy.sets.sets import (Complement, FiniteSet, Interval, SymmetricDifference)
+from sympy.stats import (Covariance, Expectation, Probability, Variance)
+from sympy.stats.rv import RandomSymbol
+from sympy.external import import_module
+from sympy.physics.control.lti import TransferFunction, Series, Parallel, \
+    Feedback, TransferFunctionMatrix, MIMOSeries, MIMOParallel, MIMOFeedback
+from sympy.physics.units import second, joule
+from sympy.polys import (Poly, rootof, RootSum, groebner, ring, field, ZZ, QQ,
+    ZZ_I, QQ_I, lex, grlex)
+from sympy.geometry import Point, Circle, Polygon, Ellipse, Triangle
+from sympy.tensor import NDimArray
+from sympy.tensor.array.expressions.array_expressions import ArraySymbol, ArrayElement
+
+from sympy.testing.pytest import raises, warns_deprecated_sympy
+
+from sympy.printing import sstr, sstrrepr, StrPrinter
+from sympy.physics.quantum.trace import Tr
+
+x, y, z, w, t = symbols('x,y,z,w,t')
+d = Dummy('d')
+
+
+def test_printmethod():
+    class R(Abs):
+        def _sympystr(self, printer):
+            return "foo(%s)" % printer._print(self.args[0])
+    assert sstr(R(x)) == "foo(x)"
+
+    class R(Abs):
+        def _sympystr(self, printer):
+            return "foo"
+    assert sstr(R(x)) == "foo"
+
+
+def test_Abs():
+    assert str(Abs(x)) == "Abs(x)"
+    assert str(Abs(Rational(1, 6))) == "1/6"
+    assert str(Abs(Rational(-1, 6))) == "1/6"
+
+
+def test_Add():
+    assert str(x + y) == "x + y"
+    assert str(x + 1) == "x + 1"
+    assert str(x + x**2) == "x**2 + x"
+    assert str(Add(0, 1, evaluate=False)) == "0 + 1"
+    assert str(Add(0, 0, 1, evaluate=False)) == "0 + 0 + 1"
+    assert str(1.0*x) == "1.0*x"
+    assert str(5 + x + y + x*y + x**2 + y**2) == "x**2 + x*y + x + y**2 + y + 5"
+    assert str(1 + x + x**2/2 + x**3/3) == "x**3/3 + x**2/2 + x + 1"
+    assert str(2*x - 7*x**2 + 2 + 3*y) == "-7*x**2 + 2*x + 3*y + 2"
+    assert str(x - y) == "x - y"
+    assert str(2 - x) == "2 - x"
+    assert str(x - 2) == "x - 2"
+    assert str(x - y - z - w) == "-w + x - y - z"
+    assert str(x - z*y**2*z*w) == "-w*y**2*z**2 + x"
+    assert str(x - 1*y*x*y) == "-x*y**2 + x"
+    assert str(sin(x).series(x, 0, 15)) == "x - x**3/6 + x**5/120 - x**7/5040 + x**9/362880 - x**11/39916800 + x**13/6227020800 + O(x**15)"
+    assert str(Add(Add(-w, x, evaluate=False), Add(-y, z,  evaluate=False),  evaluate=False)) == "(-w + x) + (-y + z)"
+    assert str(Add(Add(-x, -y, evaluate=False), -z, evaluate=False)) == "-z + (-x - y)"
+    assert str(Add(Add(Add(-x, -y, evaluate=False), -z, evaluate=False), -t, evaluate=False)) == "-t + (-z + (-x - y))"
+
+
+def test_Catalan():
+    assert str(Catalan) == "Catalan"
+
+
+def test_ComplexInfinity():
+    assert str(zoo) == "zoo"
+
+
+def test_Derivative():
+    assert str(Derivative(x, y)) == "Derivative(x, y)"
+    assert str(Derivative(x**2, x, evaluate=False)) == "Derivative(x**2, x)"
+    assert str(Derivative(
+        x**2/y, x, y, evaluate=False)) == "Derivative(x**2/y, x, y)"
+
+
+def test_dict():
+    assert str({1: 1 + x}) == sstr({1: 1 + x}) == "{1: x + 1}"
+    assert str({1: x**2, 2: y*x}) in ("{1: x**2, 2: x*y}", "{2: x*y, 1: x**2}")
+    assert sstr({1: x**2, 2: y*x}) == "{1: x**2, 2: x*y}"
+
+
+def test_Dict():
+    assert str(Dict({1: 1 + x})) == sstr({1: 1 + x}) == "{1: x + 1}"
+    assert str(Dict({1: x**2, 2: y*x})) in (
+        "{1: x**2, 2: x*y}", "{2: x*y, 1: x**2}")
+    assert sstr(Dict({1: x**2, 2: y*x})) == "{1: x**2, 2: x*y}"
+
+
+def test_Dummy():
+    assert str(d) == "_d"
+    assert str(d + x) == "_d + x"
+
+
+def test_EulerGamma():
+    assert str(EulerGamma) == "EulerGamma"
+
+
+def test_Exp():
+    assert str(E) == "E"
+    with _exp_is_pow(True):
+        assert str(exp(x)) == "E**x"
+
+
+def test_factorial():
+    n = Symbol('n', integer=True)
+    assert str(factorial(-2)) == "zoo"
+    assert str(factorial(0)) == "1"
+    assert str(factorial(7)) == "5040"
+    assert str(factorial(n)) == "factorial(n)"
+    assert str(factorial(2*n)) == "factorial(2*n)"
+    assert str(factorial(factorial(n))) == 'factorial(factorial(n))'
+    assert str(factorial(factorial2(n))) == 'factorial(factorial2(n))'
+    assert str(factorial2(factorial(n))) == 'factorial2(factorial(n))'
+    assert str(factorial2(factorial2(n))) == 'factorial2(factorial2(n))'
+    assert str(subfactorial(3)) == "2"
+    assert str(subfactorial(n)) == "subfactorial(n)"
+    assert str(subfactorial(2*n)) == "subfactorial(2*n)"
+
+
+def test_Function():
+    f = Function('f')
+    fx = f(x)
+    w = WildFunction('w')
+    assert str(f) == "f"
+    assert str(fx) == "f(x)"
+    assert str(w) == "w_"
+
+
+def test_Geometry():
+    assert sstr(Point(0, 0)) == 'Point2D(0, 0)'
+    assert sstr(Circle(Point(0, 0), 3)) == 'Circle(Point2D(0, 0), 3)'
+    assert sstr(Ellipse(Point(1, 2), 3, 4)) == 'Ellipse(Point2D(1, 2), 3, 4)'
+    assert sstr(Triangle(Point(1, 1), Point(7, 8), Point(0, -1))) == \
+        'Triangle(Point2D(1, 1), Point2D(7, 8), Point2D(0, -1))'
+    assert sstr(Polygon(Point(5, 6), Point(-2, -3), Point(0, 0), Point(4, 7))) == \
+        'Polygon(Point2D(5, 6), Point2D(-2, -3), Point2D(0, 0), Point2D(4, 7))'
+    assert sstr(Triangle(Point(0, 0), Point(1, 0), Point(0, 1)), sympy_integers=True) == \
+        'Triangle(Point2D(S(0), S(0)), Point2D(S(1), S(0)), Point2D(S(0), S(1)))'
+    assert sstr(Ellipse(Point(1, 2), 3, 4), sympy_integers=True) == \
+        'Ellipse(Point2D(S(1), S(2)), S(3), S(4))'
+
+
+def test_GoldenRatio():
+    assert str(GoldenRatio) == "GoldenRatio"
+
+
+def test_Heaviside():
+    assert str(Heaviside(x)) == str(Heaviside(x, S.Half)) == "Heaviside(x)"
+    assert str(Heaviside(x, 1)) == "Heaviside(x, 1)"
+
+
+def test_TribonacciConstant():
+    assert str(TribonacciConstant) == "TribonacciConstant"
+
+
+def test_ImaginaryUnit():
+    assert str(I) == "I"
+
+
+def test_Infinity():
+    assert str(oo) == "oo"
+    assert str(oo*I) == "oo*I"
+
+
+def test_Integer():
+    assert str(Integer(-1)) == "-1"
+    assert str(Integer(1)) == "1"
+    assert str(Integer(-3)) == "-3"
+    assert str(Integer(0)) == "0"
+    assert str(Integer(25)) == "25"
+
+
+def test_Integral():
+    assert str(Integral(sin(x), y)) == "Integral(sin(x), y)"
+    assert str(Integral(sin(x), (y, 0, 1))) == "Integral(sin(x), (y, 0, 1))"
+
+
+def test_Interval():
+    n = (S.NegativeInfinity, 1, 2, S.Infinity)
+    for i in range(len(n)):
+        for j in range(i + 1, len(n)):
+            for l in (True, False):
+                for r in (True, False):
+                    ival = Interval(n[i], n[j], l, r)
+                    assert S(str(ival)) == ival
+
+
+def test_AccumBounds():
+    a = Symbol('a', real=True)
+    assert str(AccumBounds(0, a)) == "AccumBounds(0, a)"
+    assert str(AccumBounds(0, 1)) == "AccumBounds(0, 1)"
+
+
+def test_Lambda():
+    assert str(Lambda(d, d**2)) == "Lambda(_d, _d**2)"
+    # issue 2908
+    assert str(Lambda((), 1)) == "Lambda((), 1)"
+    assert str(Lambda((), x)) == "Lambda((), x)"
+    assert str(Lambda((x, y), x+y)) == "Lambda((x, y), x + y)"
+    assert str(Lambda(((x, y),), x+y)) == "Lambda(((x, y),), x + y)"
+
+
+def test_Limit():
+    assert str(Limit(sin(x)/x, x, y)) == "Limit(sin(x)/x, x, y, dir='+')"
+    assert str(Limit(1/x, x, 0)) == "Limit(1/x, x, 0, dir='+')"
+    assert str(
+        Limit(sin(x)/x, x, y, dir="-")) == "Limit(sin(x)/x, x, y, dir='-')"
+
+
+def test_list():
+    assert str([x]) == sstr([x]) == "[x]"
+    assert str([x**2, x*y + 1]) == sstr([x**2, x*y + 1]) == "[x**2, x*y + 1]"
+    assert str([x**2, [y + x]]) == sstr([x**2, [y + x]]) == "[x**2, [x + y]]"
+
+
+def test_Matrix_str():
+    M = Matrix([[x**+1, 1], [y, x + y]])
+    assert str(M) == "Matrix([[x, 1], [y, x + y]])"
+    assert sstr(M) == "Matrix([\n[x,     1],\n[y, x + y]])"
+    M = Matrix([[1]])
+    assert str(M) == sstr(M) == "Matrix([[1]])"
+    M = Matrix([[1, 2]])
+    assert str(M) == sstr(M) ==  "Matrix([[1, 2]])"
+    M = Matrix()
+    assert str(M) == sstr(M) == "Matrix(0, 0, [])"
+    M = Matrix(0, 1, lambda i, j: 0)
+    assert str(M) == sstr(M) == "Matrix(0, 1, [])"
+
+
+def test_Mul():
+    assert str(x/y) == "x/y"
+    assert str(y/x) == "y/x"
+    assert str(x/y/z) == "x/(y*z)"
+    assert str((x + 1)/(y + 2)) == "(x + 1)/(y + 2)"
+    assert str(2*x/3) == '2*x/3'
+    assert str(-2*x/3) == '-2*x/3'
+    assert str(-1.0*x) == '-1.0*x'
+    assert str(1.0*x) == '1.0*x'
+    assert str(Mul(0, 1, evaluate=False)) == '0*1'
+    assert str(Mul(1, 0, evaluate=False)) == '1*0'
+    assert str(Mul(1, 1, evaluate=False)) == '1*1'
+    assert str(Mul(1, 1, 1, evaluate=False)) == '1*1*1'
+    assert str(Mul(1, 2, evaluate=False)) == '1*2'
+    assert str(Mul(1, S.Half, evaluate=False)) == '1*(1/2)'
+    assert str(Mul(1, 1, S.Half, evaluate=False)) == '1*1*(1/2)'
+    assert str(Mul(1, 1, 2, 3, x, evaluate=False)) == '1*1*2*3*x'
+    assert str(Mul(1, -1, evaluate=False)) == '1*(-1)'
+    assert str(Mul(-1, 1, evaluate=False)) == '-1*1'
+    assert str(Mul(4, 3, 2, 1, 0, y, x, evaluate=False)) == '4*3*2*1*0*y*x'
+    assert str(Mul(4, 3, 2, 1+z, 0, y, x, evaluate=False)) == '4*3*2*(z + 1)*0*y*x'
+    assert str(Mul(Rational(2, 3), Rational(5, 7), evaluate=False)) == '(2/3)*(5/7)'
+    # For issue 14160
+    assert str(Mul(-2, x, Pow(Mul(y,y,evaluate=False), -1, evaluate=False),
+                                                evaluate=False)) == '-2*x/(y*y)'
+    # issue 21537
+    assert str(Mul(x, Pow(1/y, -1, evaluate=False), evaluate=False)) == 'x/(1/y)'
+
+    # Issue 24108
+    from sympy.core.parameters import evaluate
+    with evaluate(False):
+        assert str(Mul(Pow(Integer(2), Integer(-1)), Add(Integer(-1), Mul(Integer(-1), Integer(1))))) == "(-1 - 1*1)/2"
+
+    class CustomClass1(Expr):
+        is_commutative = True
+
+    class CustomClass2(Expr):
+        is_commutative = True
+    cc1 = CustomClass1()
+    cc2 = CustomClass2()
+    assert str(Rational(2)*cc1) == '2*CustomClass1()'
+    assert str(cc1*Rational(2)) == '2*CustomClass1()'
+    assert str(cc1*Float("1.5")) == '1.5*CustomClass1()'
+    assert str(cc2*Rational(2)) == '2*CustomClass2()'
+    assert str(cc2*Rational(2)*cc1) == '2*CustomClass1()*CustomClass2()'
+    assert str(cc1*Rational(2)*cc2) == '2*CustomClass1()*CustomClass2()'
+
+
+def test_NaN():
+    assert str(nan) == "nan"
+
+
+def test_NegativeInfinity():
+    assert str(-oo) == "-oo"
+
+def test_Order():
+    assert str(O(x)) == "O(x)"
+    assert str(O(x**2)) == "O(x**2)"
+    assert str(O(x*y)) == "O(x*y, x, y)"
+    assert str(O(x, x)) == "O(x)"
+    assert str(O(x, (x, 0))) == "O(x)"
+    assert str(O(x, (x, oo))) == "O(x, (x, oo))"
+    assert str(O(x, x, y)) == "O(x, x, y)"
+    assert str(O(x, x, y)) == "O(x, x, y)"
+    assert str(O(x, (x, oo), (y, oo))) == "O(x, (x, oo), (y, oo))"
+
+
+def test_Permutation_Cycle():
+    from sympy.combinatorics import Permutation, Cycle
+
+    # general principle: economically, canonically show all moved elements
+    # and the size of the permutation.
+
+    for p, s in [
+        (Cycle(),
+        '()'),
+        (Cycle(2),
+        '(2)'),
+        (Cycle(2, 1),
+        '(1 2)'),
+        (Cycle(1, 2)(5)(6, 7)(10),
+        '(1 2)(6 7)(10)'),
+        (Cycle(3, 4)(1, 2)(3, 4),
+        '(1 2)(4)'),
+    ]:
+        assert sstr(p) == s
+
+    for p, s in [
+        (Permutation([]),
+        'Permutation([])'),
+        (Permutation([], size=1),
+        'Permutation([0])'),
+        (Permutation([], size=2),
+        'Permutation([0, 1])'),
+        (Permutation([], size=10),
+        'Permutation([], size=10)'),
+        (Permutation([1, 0, 2]),
+        'Permutation([1, 0, 2])'),
+        (Permutation([1, 0, 2, 3, 4, 5]),
+        'Permutation([1, 0], size=6)'),
+        (Permutation([1, 0, 2, 3, 4, 5], size=10),
+        'Permutation([1, 0], size=10)'),
+    ]:
+        assert sstr(p, perm_cyclic=False) == s
+
+    for p, s in [
+        (Permutation([]),
+        '()'),
+        (Permutation([], size=1),
+        '(0)'),
+        (Permutation([], size=2),
+        '(1)'),
+        (Permutation([], size=10),
+        '(9)'),
+        (Permutation([1, 0, 2]),
+        '(2)(0 1)'),
+        (Permutation([1, 0, 2, 3, 4, 5]),
+        '(5)(0 1)'),
+        (Permutation([1, 0, 2, 3, 4, 5], size=10),
+        '(9)(0 1)'),
+        (Permutation([0, 1, 3, 2, 4, 5], size=10),
+        '(9)(2 3)'),
+    ]:
+        assert sstr(p) == s
+
+
+    with warns_deprecated_sympy():
+        old_print_cyclic = Permutation.print_cyclic
+        Permutation.print_cyclic = False
+        assert sstr(Permutation([1, 0, 2])) == 'Permutation([1, 0, 2])'
+        Permutation.print_cyclic = old_print_cyclic
+
+def test_Pi():
+    assert str(pi) == "pi"
+
+
+def test_Poly():
+    assert str(Poly(0, x)) == "Poly(0, x, domain='ZZ')"
+    assert str(Poly(1, x)) == "Poly(1, x, domain='ZZ')"
+    assert str(Poly(x, x)) == "Poly(x, x, domain='ZZ')"
+
+    assert str(Poly(2*x + 1, x)) == "Poly(2*x + 1, x, domain='ZZ')"
+    assert str(Poly(2*x - 1, x)) == "Poly(2*x - 1, x, domain='ZZ')"
+
+    assert str(Poly(-1, x)) == "Poly(-1, x, domain='ZZ')"
+    assert str(Poly(-x, x)) == "Poly(-x, x, domain='ZZ')"
+
+    assert str(Poly(-2*x + 1, x)) == "Poly(-2*x + 1, x, domain='ZZ')"
+    assert str(Poly(-2*x - 1, x)) == "Poly(-2*x - 1, x, domain='ZZ')"
+
+    assert str(Poly(x - 1, x)) == "Poly(x - 1, x, domain='ZZ')"
+    assert str(Poly(2*x + x**5, x)) == "Poly(x**5 + 2*x, x, domain='ZZ')"
+
+    assert str(Poly(3**(2*x), 3**x)) == "Poly((3**x)**2, 3**x, domain='ZZ')"
+    assert str(Poly((x**2)**x)) == "Poly(((x**2)**x), (x**2)**x, domain='ZZ')"
+
+    assert str(Poly((x + y)**3, (x + y), expand=False)
+                ) == "Poly((x + y)**3, x + y, domain='ZZ')"
+    assert str(Poly((x - 1)**2, (x - 1), expand=False)
+                ) == "Poly((x - 1)**2, x - 1, domain='ZZ')"
+
+    assert str(
+        Poly(x**2 + 1 + y, x)) == "Poly(x**2 + y + 1, x, domain='ZZ[y]')"
+    assert str(
+        Poly(x**2 - 1 + y, x)) == "Poly(x**2 + y - 1, x, domain='ZZ[y]')"
+
+    assert str(Poly(x**2 + I*x, x)) == "Poly(x**2 + I*x, x, domain='ZZ_I')"
+    assert str(Poly(x**2 - I*x, x)) == "Poly(x**2 - I*x, x, domain='ZZ_I')"
+
+    assert str(Poly(-x*y*z + x*y - 1, x, y, z)
+               ) == "Poly(-x*y*z + x*y - 1, x, y, z, domain='ZZ')"
+    assert str(Poly(-w*x**21*y**7*z + (1 + w)*z**3 - 2*x*z + 1, x, y, z)) == \
+        "Poly(-w*x**21*y**7*z - 2*x*z + (w + 1)*z**3 + 1, x, y, z, domain='ZZ[w]')"
+
+    assert str(Poly(x**2 + 1, x, modulus=2)) == "Poly(x**2 + 1, x, modulus=2)"
+    assert str(Poly(2*x**2 + 3*x + 4, x, modulus=17)) == "Poly(2*x**2 + 3*x + 4, x, modulus=17)"
+
+
+def test_PolyRing():
+    assert str(ring("x", ZZ, lex)[0]) == "Polynomial ring in x over ZZ with lex order"
+    assert str(ring("x,y", QQ, grlex)[0]) == "Polynomial ring in x, y over QQ with grlex order"
+    assert str(ring("x,y,z", ZZ["t"], lex)[0]) == "Polynomial ring in x, y, z over ZZ[t] with lex order"
+
+
+def test_FracField():
+    assert str(field("x", ZZ, lex)[0]) == "Rational function field in x over ZZ with lex order"
+    assert str(field("x,y", QQ, grlex)[0]) == "Rational function field in x, y over QQ with grlex order"
+    assert str(field("x,y,z", ZZ["t"], lex)[0]) == "Rational function field in x, y, z over ZZ[t] with lex order"
+
+
+def test_PolyElement():
+    Ruv, u,v = ring("u,v", ZZ)
+    Rxyz, x,y,z = ring("x,y,z", Ruv)
+    Rx_zzi, xz = ring("x", ZZ_I)
+
+    assert str(x - x) == "0"
+    assert str(x - 1) == "x - 1"
+    assert str(x + 1) == "x + 1"
+    assert str(x**2) == "x**2"
+
+    assert str((u**2 + 3*u*v + 1)*x**2*y + u + 1) == "(u**2 + 3*u*v + 1)*x**2*y + u + 1"
+    assert str((u**2 + 3*u*v + 1)*x**2*y + (u + 1)*x) == "(u**2 + 3*u*v + 1)*x**2*y + (u + 1)*x"
+    assert str((u**2 + 3*u*v + 1)*x**2*y + (u + 1)*x + 1) == "(u**2 + 3*u*v + 1)*x**2*y + (u + 1)*x + 1"
+    assert str((-u**2 + 3*u*v - 1)*x**2*y - (u + 1)*x - 1) == "-(u**2 - 3*u*v + 1)*x**2*y - (u + 1)*x - 1"
+
+    assert str(-(v**2 + v + 1)*x + 3*u*v + 1) == "-(v**2 + v + 1)*x + 3*u*v + 1"
+    assert str(-(v**2 + v + 1)*x - 3*u*v + 1) == "-(v**2 + v + 1)*x - 3*u*v + 1"
+
+    assert str((1+I)*xz + 2) == "(1 + 1*I)*x + (2 + 0*I)"
+
+
+def test_FracElement():
+    Fuv, u,v = field("u,v", ZZ)
+    Fxyzt, x,y,z,t = field("x,y,z,t", Fuv)
+    Rx_zzi, xz = field("x", QQ_I)
+    i = QQ_I(0, 1)
+
+    assert str(x - x) == "0"
+    assert str(x - 1) == "x - 1"
+    assert str(x + 1) == "x + 1"
+
+    assert str(x/3) == "x/3"
+    assert str(x/z) == "x/z"
+    assert str(x*y/z) == "x*y/z"
+    assert str(x/(z*t)) == "x/(z*t)"
+    assert str(x*y/(z*t)) == "x*y/(z*t)"
+
+    assert str((x - 1)/y) == "(x - 1)/y"
+    assert str((x + 1)/y) == "(x + 1)/y"
+    assert str((-x - 1)/y) == "(-x - 1)/y"
+    assert str((x + 1)/(y*z)) == "(x + 1)/(y*z)"
+    assert str(-y/(x + 1)) == "-y/(x + 1)"
+    assert str(y*z/(x + 1)) == "y*z/(x + 1)"
+
+    assert str(((u + 1)*x*y + 1)/((v - 1)*z - 1)) == "((u + 1)*x*y + 1)/((v - 1)*z - 1)"
+    assert str(((u + 1)*x*y + 1)/((v - 1)*z - t*u*v - 1)) == "((u + 1)*x*y + 1)/((v - 1)*z - u*v*t - 1)"
+
+    assert str((1+i)/xz) == "(1 + 1*I)/x"
+    assert str(((1+i)*xz - i)/xz) == "((1 + 1*I)*x + (0 + -1*I))/x"
+
+
+def test_GaussianInteger():
+    assert str(ZZ_I(1, 0)) == "1"
+    assert str(ZZ_I(-1, 0)) == "-1"
+    assert str(ZZ_I(0, 1)) == "I"
+    assert str(ZZ_I(0, -1)) == "-I"
+    assert str(ZZ_I(0, 2)) == "2*I"
+    assert str(ZZ_I(0, -2)) == "-2*I"
+    assert str(ZZ_I(1, 1)) == "1 + I"
+    assert str(ZZ_I(-1, -1)) == "-1 - I"
+    assert str(ZZ_I(-1, -2)) == "-1 - 2*I"
+
+
+def test_GaussianRational():
+    assert str(QQ_I(1, 0)) == "1"
+    assert str(QQ_I(QQ(2, 3), 0)) == "2/3"
+    assert str(QQ_I(0, QQ(2, 3))) == "2*I/3"
+    assert str(QQ_I(QQ(1, 2), QQ(-2, 3))) == "1/2 - 2*I/3"
+
+
+def test_Pow():
+    assert str(x**-1) == "1/x"
+    assert str(x**-2) == "x**(-2)"
+    assert str(x**2) == "x**2"
+    assert str((x + y)**-1) == "1/(x + y)"
+    assert str((x + y)**-2) == "(x + y)**(-2)"
+    assert str((x + y)**2) == "(x + y)**2"
+    assert str((x + y)**(1 + x)) == "(x + y)**(x + 1)"
+    assert str(x**Rational(1, 3)) == "x**(1/3)"
+    assert str(1/x**Rational(1, 3)) == "x**(-1/3)"
+    assert str(sqrt(sqrt(x))) == "x**(1/4)"
+    # not the same as x**-1
+    assert str(x**-1.0) == 'x**(-1.0)'
+    # see issue #2860
+    assert str(Pow(S(2), -1.0, evaluate=False)) == '2**(-1.0)'
+
+
+def test_sqrt():
+    assert str(sqrt(x)) == "sqrt(x)"
+    assert str(sqrt(x**2)) == "sqrt(x**2)"
+    assert str(1/sqrt(x)) == "1/sqrt(x)"
+    assert str(1/sqrt(x**2)) == "1/sqrt(x**2)"
+    assert str(y/sqrt(x)) == "y/sqrt(x)"
+    assert str(x**0.5) == "x**0.5"
+    assert str(1/x**0.5) == "x**(-0.5)"
+
+
+def test_Rational():
+    n1 = Rational(1, 4)
+    n2 = Rational(1, 3)
+    n3 = Rational(2, 4)
+    n4 = Rational(2, -4)
+    n5 = Rational(0)
+    n7 = Rational(3)
+    n8 = Rational(-3)
+    assert str(n1*n2) == "1/12"
+    assert str(n1*n2) == "1/12"
+    assert str(n3) == "1/2"
+    assert str(n1*n3) == "1/8"
+    assert str(n1 + n3) == "3/4"
+    assert str(n1 + n2) == "7/12"
+    assert str(n1 + n4) == "-1/4"
+    assert str(n4*n4) == "1/4"
+    assert str(n4 + n2) == "-1/6"
+    assert str(n4 + n5) == "-1/2"
+    assert str(n4*n5) == "0"
+    assert str(n3 + n4) == "0"
+    assert str(n1**n7) == "1/64"
+    assert str(n2**n7) == "1/27"
+    assert str(n2**n8) == "27"
+    assert str(n7**n8) == "1/27"
+    assert str(Rational("-25")) == "-25"
+    assert str(Rational("1.25")) == "5/4"
+    assert str(Rational("-2.6e-2")) == "-13/500"
+    assert str(S("25/7")) == "25/7"
+    assert str(S("-123/569")) == "-123/569"
+    assert str(S("0.1[23]", rational=1)) == "61/495"
+    assert str(S("5.1[666]", rational=1)) == "31/6"
+    assert str(S("-5.1[666]", rational=1)) == "-31/6"
+    assert str(S("0.[9]", rational=1)) == "1"
+    assert str(S("-0.[9]", rational=1)) == "-1"
+
+    assert str(sqrt(Rational(1, 4))) == "1/2"
+    assert str(sqrt(Rational(1, 36))) == "1/6"
+
+    assert str((123**25) ** Rational(1, 25)) == "123"
+    assert str((123**25 + 1)**Rational(1, 25)) != "123"
+    assert str((123**25 - 1)**Rational(1, 25)) != "123"
+    assert str((123**25 - 1)**Rational(1, 25)) != "122"
+
+    assert str(sqrt(Rational(81, 36))**3) == "27/8"
+    assert str(1/sqrt(Rational(81, 36))**3) == "8/27"
+
+    assert str(sqrt(-4)) == str(2*I)
+    assert str(2**Rational(1, 10**10)) == "2**(1/10000000000)"
+
+    assert sstr(Rational(2, 3), sympy_integers=True) == "S(2)/3"
+    x = Symbol("x")
+    assert sstr(x**Rational(2, 3), sympy_integers=True) == "x**(S(2)/3)"
+    assert sstr(Eq(x, Rational(2, 3)), sympy_integers=True) == "Eq(x, S(2)/3)"
+    assert sstr(Limit(x, x, Rational(7, 2)), sympy_integers=True) == \
+        "Limit(x, x, S(7)/2, dir='+')"
+
+
+def test_Float():
+    # NOTE dps is the whole number of decimal digits
+    assert str(Float('1.23', dps=1 + 2)) == '1.23'
+    assert str(Float('1.23456789', dps=1 + 8)) == '1.23456789'
+    assert str(
+        Float('1.234567890123456789', dps=1 + 18)) == '1.234567890123456789'
+    assert str(pi.evalf(1 + 2)) == '3.14'
+    assert str(pi.evalf(1 + 14)) == '3.14159265358979'
+    assert str(pi.evalf(1 + 64)) == ('3.141592653589793238462643383279'
+                                     '5028841971693993751058209749445923')
+    assert str(pi.round(-1)) == '0.0'
+    assert str((pi**400 - (pi**400).round(1)).n(2)) == '-0.e+88'
+    assert sstr(Float("100"), full_prec=False, min=-2, max=2) == '1.0e+2'
+    assert sstr(Float("100"), full_prec=False, min=-2, max=3) == '100.0'
+    assert sstr(Float("0.1"), full_prec=False, min=-2, max=3) == '0.1'
+    assert sstr(Float("0.099"), min=-2, max=3) == '9.90000000000000e-2'
+
+
+def test_Relational():
+    assert str(Rel(x, y, "<")) == "x < y"
+    assert str(Rel(x + y, y, "==")) == "Eq(x + y, y)"
+    assert str(Rel(x, y, "!=")) == "Ne(x, y)"
+    assert str(Eq(x, 1) | Eq(x, 2)) == "Eq(x, 1) | Eq(x, 2)"
+    assert str(Ne(x, 1) & Ne(x, 2)) == "Ne(x, 1) & Ne(x, 2)"
+
+
+def test_AppliedBinaryRelation():
+    assert str(Q.eq(x, y)) == "Q.eq(x, y)"
+    assert str(Q.ne(x, y)) == "Q.ne(x, y)"
+
+
+def test_CRootOf():
+    assert str(rootof(x**5 + 2*x - 1, 0)) == "CRootOf(x**5 + 2*x - 1, 0)"
+
+
+def test_RootSum():
+    f = x**5 + 2*x - 1
+
+    assert str(
+        RootSum(f, Lambda(z, z), auto=False)) == "RootSum(x**5 + 2*x - 1)"
+    assert str(RootSum(f, Lambda(
+        z, z**2), auto=False)) == "RootSum(x**5 + 2*x - 1, Lambda(z, z**2))"
+
+
+def test_GroebnerBasis():
+    assert str(groebner(
+        [], x, y)) == "GroebnerBasis([], x, y, domain='ZZ', order='lex')"
+
+    F = [x**2 - 3*y - x + 1, y**2 - 2*x + y - 1]
+
+    assert str(groebner(F, order='grlex')) == \
+        "GroebnerBasis([x**2 - x - 3*y + 1, y**2 - 2*x + y - 1], x, y, domain='ZZ', order='grlex')"
+    assert str(groebner(F, order='lex')) == \
+        "GroebnerBasis([2*x - y**2 - y + 1, y**4 + 2*y**3 - 3*y**2 - 16*y + 7], x, y, domain='ZZ', order='lex')"
+
+def test_set():
+    assert sstr(set()) == 'set()'
+    assert sstr(frozenset()) == 'frozenset()'
+
+    assert sstr({1}) == '{1}'
+    assert sstr(frozenset([1])) == 'frozenset({1})'
+    assert sstr({1, 2, 3}) == '{1, 2, 3}'
+    assert sstr(frozenset([1, 2, 3])) == 'frozenset({1, 2, 3})'
+
+    assert sstr(
+        {1, x, x**2, x**3, x**4}) == '{1, x, x**2, x**3, x**4}'
+    assert sstr(
+        frozenset([1, x, x**2, x**3, x**4])) == 'frozenset({1, x, x**2, x**3, x**4})'
+
+
+def test_SparseMatrix():
+    M = SparseMatrix([[x**+1, 1], [y, x + y]])
+    assert str(M) == "Matrix([[x, 1], [y, x + y]])"
+    assert sstr(M) == "Matrix([\n[x,     1],\n[y, x + y]])"
+
+
+def test_Sum():
+    assert str(summation(cos(3*z), (z, x, y))) == "Sum(cos(3*z), (z, x, y))"
+    assert str(Sum(x*y**2, (x, -2, 2), (y, -5, 5))) == \
+        "Sum(x*y**2, (x, -2, 2), (y, -5, 5))"
+
+
+def test_Symbol():
+    assert str(y) == "y"
+    assert str(x) == "x"
+    e = x
+    assert str(e) == "x"
+
+
+def test_tuple():
+    assert str((x,)) == sstr((x,)) == "(x,)"
+    assert str((x + y, 1 + x)) == sstr((x + y, 1 + x)) == "(x + y, x + 1)"
+    assert str((x + y, (
+        1 + x, x**2))) == sstr((x + y, (1 + x, x**2))) == "(x + y, (x + 1, x**2))"
+
+
+def test_Series_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tf3 = TransferFunction(t*x**2 - t**w*x + w, t - y, y)
+    assert str(Series(tf1, tf2)) == \
+        "Series(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y))"
+    assert str(Series(tf1, tf2, tf3)) == \
+        "Series(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y), TransferFunction(t*x**2 - t**w*x + w, t - y, y))"
+    assert str(Series(-tf2, tf1)) == \
+        "Series(TransferFunction(-x + y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y))"
+
+
+def test_MIMOSeries_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
+    tfm_2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf2]])
+    assert str(MIMOSeries(tfm_1, tfm_2)) == \
+        "MIMOSeries(TransferFunctionMatrix(((TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)), "\
+            "(TransferFunction(x - y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y)))), "\
+                "TransferFunctionMatrix(((TransferFunction(x - y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y)), "\
+                    "(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)))))"
+
+
+def test_TransferFunction_str():
+    tf1 = TransferFunction(x - 1, x + 1, x)
+    assert str(tf1) == "TransferFunction(x - 1, x + 1, x)"
+    tf2 = TransferFunction(x + 1, 2 - y, x)
+    assert str(tf2) == "TransferFunction(x + 1, 2 - y, x)"
+    tf3 = TransferFunction(y, y**2 + 2*y + 3, y)
+    assert str(tf3) == "TransferFunction(y, y**2 + 2*y + 3, y)"
+
+
+def test_Parallel_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tf3 = TransferFunction(t*x**2 - t**w*x + w, t - y, y)
+    assert str(Parallel(tf1, tf2)) == \
+        "Parallel(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y))"
+    assert str(Parallel(tf1, tf2, tf3)) == \
+        "Parallel(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y), TransferFunction(t*x**2 - t**w*x + w, t - y, y))"
+    assert str(Parallel(-tf2, tf1)) == \
+        "Parallel(TransferFunction(-x + y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y))"
+
+
+def test_MIMOParallel_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
+    tfm_2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf2]])
+    assert str(MIMOParallel(tfm_1, tfm_2)) == \
+        "MIMOParallel(TransferFunctionMatrix(((TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)), "\
+            "(TransferFunction(x - y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y)))), "\
+                "TransferFunctionMatrix(((TransferFunction(x - y, x + y, y), TransferFunction(x*y**2 - z, -t**3 + y**3, y)), "\
+                    "(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)))))"
+
+
+def test_Feedback_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tf3 = TransferFunction(t*x**2 - t**w*x + w, t - y, y)
+    assert str(Feedback(tf1*tf2, tf3)) == \
+        "Feedback(Series(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)), " \
+        "TransferFunction(t*x**2 - t**w*x + w, t - y, y), -1)"
+    assert str(Feedback(tf1, TransferFunction(1, 1, y), 1)) == \
+        "Feedback(TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(1, 1, y), 1)"
+
+
+def test_MIMOFeedback_str():
+    tf1 = TransferFunction(x**2 - y**3, y - z, x)
+    tf2 = TransferFunction(y - x, z + y, x)
+    tfm_1 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf2]])
+    tfm_2 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
+    assert (str(MIMOFeedback(tfm_1, tfm_2)) \
+            == "MIMOFeedback(TransferFunctionMatrix(((TransferFunction(-x + y, y + z, x), TransferFunction(x**2 - y**3, y - z, x))," \
+            " (TransferFunction(x**2 - y**3, y - z, x), TransferFunction(-x + y, y + z, x)))), " \
+            "TransferFunctionMatrix(((TransferFunction(x**2 - y**3, y - z, x), " \
+            "TransferFunction(-x + y, y + z, x)), (TransferFunction(-x + y, y + z, x), TransferFunction(x**2 - y**3, y - z, x)))), -1)")
+    assert (str(MIMOFeedback(tfm_1, tfm_2, 1)) \
+            == "MIMOFeedback(TransferFunctionMatrix(((TransferFunction(-x + y, y + z, x), TransferFunction(x**2 - y**3, y - z, x)), " \
+            "(TransferFunction(x**2 - y**3, y - z, x), TransferFunction(-x + y, y + z, x)))), " \
+            "TransferFunctionMatrix(((TransferFunction(x**2 - y**3, y - z, x), TransferFunction(-x + y, y + z, x)), "\
+            "(TransferFunction(-x + y, y + z, x), TransferFunction(x**2 - y**3, y - z, x)))), 1)")
+
+
+def test_TransferFunctionMatrix_str():
+    tf1 = TransferFunction(x*y**2 - z, y**3 - t**3, y)
+    tf2 = TransferFunction(x - y, x + y, y)
+    tf3 = TransferFunction(t*x**2 - t**w*x + w, t - y, y)
+    assert str(TransferFunctionMatrix([[tf1], [tf2]])) == \
+        "TransferFunctionMatrix(((TransferFunction(x*y**2 - z, -t**3 + y**3, y),), (TransferFunction(x - y, x + y, y),)))"
+    assert str(TransferFunctionMatrix([[tf1, tf2], [tf3, tf2]])) == \
+        "TransferFunctionMatrix(((TransferFunction(x*y**2 - z, -t**3 + y**3, y), TransferFunction(x - y, x + y, y)), (TransferFunction(t*x**2 - t**w*x + w, t - y, y), TransferFunction(x - y, x + y, y))))"
+
+
+def test_Quaternion_str_printer():
+    q = Quaternion(x, y, z, t)
+    assert str(q) == "x + y*i + z*j + t*k"
+    q = Quaternion(x,y,z,x*t)
+    assert str(q) == "x + y*i + z*j + t*x*k"
+    q = Quaternion(x,y,z,x+t)
+    assert str(q) == "x + y*i + z*j + (t + x)*k"
+
+
+def test_Quantity_str():
+    assert sstr(second, abbrev=True) == "s"
+    assert sstr(joule, abbrev=True) == "J"
+    assert str(second) == "second"
+    assert str(joule) == "joule"
+
+
+def test_wild_str():
+    # Check expressions containing Wild not causing infinite recursion
+    w = Wild('x')
+    assert str(w + 1) == 'x_ + 1'
+    assert str(exp(2**w) + 5) == 'exp(2**x_) + 5'
+    assert str(3*w + 1) == '3*x_ + 1'
+    assert str(1/w + 1) == '1 + 1/x_'
+    assert str(w**2 + 1) == 'x_**2 + 1'
+    assert str(1/(1 - w)) == '1/(1 - x_)'
+
+
+def test_wild_matchpy():
+    from sympy.utilities.matchpy_connector import WildDot, WildPlus, WildStar
+
+    matchpy = import_module("matchpy")
+
+    if matchpy is None:
+        return
+
+    wd = WildDot('w_')
+    wp = WildPlus('w__')
+    ws = WildStar('w___')
+
+    assert str(wd) == 'w_'
+    assert str(wp) == 'w__'
+    assert str(ws) == 'w___'
+
+    assert str(wp/ws + 2**wd) == '2**w_ + w__/w___'
+    assert str(sin(wd)*cos(wp)*sqrt(ws)) == 'sqrt(w___)*sin(w_)*cos(w__)'
+
+
+def test_zeta():
+    assert str(zeta(3)) == "zeta(3)"
+
+
+def test_issue_3101():
+    e = x - y
+    a = str(e)
+    b = str(e)
+    assert a == b
+
+
+def test_issue_3103():
+    e = -2*sqrt(x) - y/sqrt(x)/2
+    assert str(e) not in ["(-2)*x**1/2(-1/2)*x**(-1/2)*y",
+            "-2*x**1/2(-1/2)*x**(-1/2)*y", "-2*x**1/2-1/2*x**-1/2*w"]
+    assert str(e) == "-2*sqrt(x) - y/(2*sqrt(x))"
+
+
+def test_issue_4021():
+    e = Integral(x, x) + 1
+    assert str(e) == 'Integral(x, x) + 1'
+
+
+def test_sstrrepr():
+    assert sstr('abc') == 'abc'
+    assert sstrrepr('abc') == "'abc'"
+
+    e = ['a', 'b', 'c', x]
+    assert sstr(e) == "[a, b, c, x]"
+    assert sstrrepr(e) == "['a', 'b', 'c', x]"
+
+
+def test_infinity():
+    assert sstr(oo*I) == "oo*I"
+
+
+def test_full_prec():
+    assert sstr(S("0.3"), full_prec=True) == "0.300000000000000"
+    assert sstr(S("0.3"), full_prec="auto") == "0.300000000000000"
+    assert sstr(S("0.3"), full_prec=False) == "0.3"
+    assert sstr(S("0.3")*x, full_prec=True) in [
+        "0.300000000000000*x",
+        "x*0.300000000000000"
+    ]
+    assert sstr(S("0.3")*x, full_prec="auto") in [
+        "0.3*x",
+        "x*0.3"
+    ]
+    assert sstr(S("0.3")*x, full_prec=False) in [
+        "0.3*x",
+        "x*0.3"
+    ]
+
+
+def test_noncommutative():
+    A, B, C = symbols('A,B,C', commutative=False)
+
+    assert sstr(A*B*C**-1) == "A*B*C**(-1)"
+    assert sstr(C**-1*A*B) == "C**(-1)*A*B"
+    assert sstr(A*C**-1*B) == "A*C**(-1)*B"
+    assert sstr(sqrt(A)) == "sqrt(A)"
+    assert sstr(1/sqrt(A)) == "A**(-1/2)"
+
+
+def test_empty_printer():
+    str_printer = StrPrinter()
+    assert str_printer.emptyPrinter("foo") == "foo"
+    assert str_printer.emptyPrinter(x*y) == "x*y"
+    assert str_printer.emptyPrinter(32) == "32"
+
+def test_decimal_printer():
+    dec_printer = StrPrinter(settings={"dps":3})
+    f = Function('f')
+    assert dec_printer.doprint(f(1.329294)) == "f(1.33)"
+
+
+def test_settings():
+    raises(TypeError, lambda: sstr(S(4), method="garbage"))
+
+
+def test_RandomDomain():
+    from sympy.stats import Normal, Die, Exponential, pspace, where
+    X = Normal('x1', 0, 1)
+    assert str(where(X > 0)) == "Domain: (0 < x1) & (x1 < oo)"
+
+    D = Die('d1', 6)
+    assert str(where(D > 4)) == "Domain: Eq(d1, 5) | Eq(d1, 6)"
+
+    A = Exponential('a', 1)
+    B = Exponential('b', 1)
+    assert str(pspace(Tuple(A, B)).domain) == "Domain: (0 <= a) & (0 <= b) & (a < oo) & (b < oo)"
+
+
+def test_FiniteSet():
+    assert str(FiniteSet(*range(1, 51))) == (
+        '{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,'
+        ' 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,'
+        ' 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50}'
+    )
+    assert str(FiniteSet(*range(1, 6))) == '{1, 2, 3, 4, 5}'
+    assert str(FiniteSet(*[x*y, x**2])) == '{x**2, x*y}'
+    assert str(FiniteSet(FiniteSet(FiniteSet(x, y), 5), FiniteSet(x,y), 5)
+               ) == 'FiniteSet(5, FiniteSet(5, {x, y}), {x, y})'
+
+
+def test_Partition():
+    assert str(Partition(FiniteSet(x, y), {z})) == 'Partition({z}, {x, y})'
+
+def test_UniversalSet():
+    assert str(S.UniversalSet) == 'UniversalSet'
+
+
+def test_PrettyPoly():
+    F = QQ.frac_field(x, y)
+    R = QQ[x, y]
+    assert sstr(F.convert(x/(x + y))) == sstr(x/(x + y))
+    assert sstr(R.convert(x + y)) == sstr(x + y)
+
+
+def test_categories():
+    from sympy.categories import (Object, NamedMorphism,
+        IdentityMorphism, Category)
+
+    A = Object("A")
+    B = Object("B")
+
+    f = NamedMorphism(A, B, "f")
+    id_A = IdentityMorphism(A)
+
+    K = Category("K")
+
+    assert str(A) == 'Object("A")'
+    assert str(f) == 'NamedMorphism(Object("A"), Object("B"), "f")'
+    assert str(id_A) == 'IdentityMorphism(Object("A"))'
+
+    assert str(K) == 'Category("K")'
+
+
+def test_Tr():
+    A, B = symbols('A B', commutative=False)
+    t = Tr(A*B)
+    assert str(t) == 'Tr(A*B)'
+
+
+def test_issue_6387():
+    assert str(factor(-3.0*z + 3)) == '-3.0*(1.0*z - 1.0)'
+
+
+def test_MatMul_MatAdd():
+    X, Y = MatrixSymbol("X", 2, 2), MatrixSymbol("Y", 2, 2)
+    assert str(2*(X + Y)) == "2*X + 2*Y"
+
+    assert str(I*X) == "I*X"
+    assert str(-I*X) == "-I*X"
+    assert str((1 + I)*X) == '(1 + I)*X'
+    assert str(-(1 + I)*X) == '(-1 - I)*X'
+    assert str(MatAdd(MatAdd(X, Y), MatAdd(X, Y))) == '(X + Y) + (X + Y)'
+
+
+def test_MatrixSlice():
+    n = Symbol('n', integer=True)
+    X = MatrixSymbol('X', n, n)
+    Y = MatrixSymbol('Y', 10, 10)
+    Z = MatrixSymbol('Z', 10, 10)
+
+    assert str(MatrixSlice(X, (None, None, None), (None, None, None))) == 'X[:, :]'
+    assert str(X[x:x + 1, y:y + 1]) == 'X[x:x + 1, y:y + 1]'
+    assert str(X[x:x + 1:2, y:y + 1:2]) == 'X[x:x + 1:2, y:y + 1:2]'
+    assert str(X[:x, y:]) == 'X[:x, y:]'
+    assert str(X[:x, y:]) == 'X[:x, y:]'
+    assert str(X[x:, :y]) == 'X[x:, :y]'
+    assert str(X[x:y, z:w]) == 'X[x:y, z:w]'
+    assert str(X[x:y:t, w:t:x]) == 'X[x:y:t, w:t:x]'
+    assert str(X[x::y, t::w]) == 'X[x::y, t::w]'
+    assert str(X[:x:y, :t:w]) == 'X[:x:y, :t:w]'
+    assert str(X[::x, ::y]) == 'X[::x, ::y]'
+    assert str(MatrixSlice(X, (0, None, None), (0, None, None))) == 'X[:, :]'
+    assert str(MatrixSlice(X, (None, n, None), (None, n, None))) == 'X[:, :]'
+    assert str(MatrixSlice(X, (0, n, None), (0, n, None))) == 'X[:, :]'
+    assert str(MatrixSlice(X, (0, n, 2), (0, n, 2))) == 'X[::2, ::2]'
+    assert str(X[1:2:3, 4:5:6]) == 'X[1:2:3, 4:5:6]'
+    assert str(X[1:3:5, 4:6:8]) == 'X[1:3:5, 4:6:8]'
+    assert str(X[1:10:2]) == 'X[1:10:2, :]'
+    assert str(Y[:5, 1:9:2]) == 'Y[:5, 1:9:2]'
+    assert str(Y[:5, 1:10:2]) == 'Y[:5, 1::2]'
+    assert str(Y[5, :5:2]) == 'Y[5:6, :5:2]'
+    assert str(X[0:1, 0:1]) == 'X[:1, :1]'
+    assert str(X[0:1:2, 0:1:2]) == 'X[:1:2, :1:2]'
+    assert str((Y + Z)[2:, 2:]) == '(Y + Z)[2:, 2:]'
+
+def test_true_false():
+    assert str(true) == repr(true) == sstr(true) == "True"
+    assert str(false) == repr(false) == sstr(false) == "False"
+
+def test_Equivalent():
+    assert str(Equivalent(y, x)) == "Equivalent(x, y)"
+
+def test_Xor():
+    assert str(Xor(y, x, evaluate=False)) == "x ^ y"
+
+def test_Complement():
+    assert str(Complement(S.Reals, S.Naturals)) == 'Complement(Reals, Naturals)'
+
+def test_SymmetricDifference():
+    assert str(SymmetricDifference(Interval(2, 3), Interval(3, 4),evaluate=False)) == \
+           'SymmetricDifference(Interval(2, 3), Interval(3, 4))'
+
+
+def test_UnevaluatedExpr():
+    a, b = symbols("a b")
+    expr1 = 2*UnevaluatedExpr(a+b)
+    assert str(expr1) == "2*(a + b)"
+
+
+def test_MatrixElement_printing():
+    # test cases for issue #11821
+    A = MatrixSymbol("A", 1, 3)
+    B = MatrixSymbol("B", 1, 3)
+    C = MatrixSymbol("C", 1, 3)
+
+    assert(str(A[0, 0]) == "A[0, 0]")
+    assert(str(3 * A[0, 0]) == "3*A[0, 0]")
+
+    F = C[0, 0].subs(C, A - B)
+    assert str(F) == "(A - B)[0, 0]"
+
+
+def test_MatrixSymbol_printing():
+    A = MatrixSymbol("A", 3, 3)
+    B = MatrixSymbol("B", 3, 3)
+
+    assert str(A - A*B - B) == "A - A*B - B"
+    assert str(A*B - (A+B)) == "-A + A*B - B"
+    assert str(A**(-1)) == "A**(-1)"
+    assert str(A**3) == "A**3"
+
+
+def test_MatrixExpressions():
+    n = Symbol('n', integer=True)
+    X = MatrixSymbol('X', n, n)
+
+    assert str(X) == "X"
+
+    # Apply function elementwise (`ElementwiseApplyFunc`):
+
+    expr = (X.T*X).applyfunc(sin)
+    assert str(expr) == 'Lambda(_d, sin(_d)).(X.T*X)'
+
+    lamda = Lambda(x, 1/x)
+    expr = (n*X).applyfunc(lamda)
+    assert str(expr) == 'Lambda(x, 1/x).(n*X)'
+
+
+def test_Subs_printing():
+    assert str(Subs(x, (x,), (1,))) == 'Subs(x, x, 1)'
+    assert str(Subs(x + y, (x, y), (1, 2))) == 'Subs(x + y, (x, y), (1, 2))'
+
+
+def test_issue_15716():
+    e = Integral(factorial(x), (x, -oo, oo))
+    assert e.as_terms() == ([(e, ((1.0, 0.0), (1,), ()))], [e])
+
+
+def test_str_special_matrices():
+    from sympy.matrices import Identity, ZeroMatrix, OneMatrix
+    assert str(Identity(4)) == 'I'
+    assert str(ZeroMatrix(2, 2)) == '0'
+    assert str(OneMatrix(2, 2)) == '1'
+
+
+def test_issue_14567():
+    assert factorial(Sum(-1, (x, 0, 0))) + y  # doesn't raise an error
+
+
+def test_issue_21823():
+    assert str(Partition([1, 2])) == 'Partition({1, 2})'
+    assert str(Partition({1, 2})) == 'Partition({1, 2})'
+
+
+def test_issue_22689():
+    assert str(Mul(Pow(x,-2, evaluate=False), Pow(3,-1,evaluate=False), evaluate=False)) == "1/(x**2*3)"
+
+
+def test_issue_21119_21460():
+    ss = lambda x: str(S(x, evaluate=False))
+    assert ss('4/2') == '4/2'
+    assert ss('4/-2') == '4/(-2)'
+    assert ss('-4/2') == '-4/2'
+    assert ss('-4/-2') == '-4/(-2)'
+    assert ss('-2*3/-1') == '-2*3/(-1)'
+    assert ss('-2*3/-1/2') == '-2*3/(-1*2)'
+    assert ss('4/2/1') == '4/(2*1)'
+    assert ss('-2/-1/2') == '-2/(-1*2)'
+    assert ss('2*3*4**(-2*3)') == '2*3/4**(2*3)'
+    assert ss('2*3*1*4**(-2*3)') == '2*3*1/4**(2*3)'
+
+
+def test_Str():
+    from sympy.core.symbol import Str
+    assert str(Str('x')) == 'x'
+    assert sstrrepr(Str('x')) == "Str('x')"
+
+
+def test_diffgeom():
+    from sympy.diffgeom import Manifold, Patch, CoordSystem, BaseScalarField
+    x,y = symbols('x y', real=True)
+    m = Manifold('M', 2)
+    assert str(m) == "M"
+    p = Patch('P', m)
+    assert str(p) == "P"
+    rect = CoordSystem('rect', p, [x, y])
+    assert str(rect) == "rect"
+    b = BaseScalarField(rect, 0)
+    assert str(b) == "x"
+
+def test_NDimArray():
+    assert sstr(NDimArray(1.0), full_prec=True) == '1.00000000000000'
+    assert sstr(NDimArray(1.0), full_prec=False) == '1.0'
+    assert sstr(NDimArray([1.0, 2.0]), full_prec=True) == '[1.00000000000000, 2.00000000000000]'
+    assert sstr(NDimArray([1.0, 2.0]), full_prec=False) == '[1.0, 2.0]'
+    assert sstr(NDimArray([], (0,))) == 'ImmutableDenseNDimArray([], (0,))'
+    assert sstr(NDimArray([], (0, 0))) == 'ImmutableDenseNDimArray([], (0, 0))'
+    assert sstr(NDimArray([], (0, 1))) == 'ImmutableDenseNDimArray([], (0, 1))'
+    assert sstr(NDimArray([], (1, 0))) == 'ImmutableDenseNDimArray([], (1, 0))'
+
+def test_Predicate():
+    assert sstr(Q.even) == 'Q.even'
+
+def test_AppliedPredicate():
+    assert sstr(Q.even(x)) == 'Q.even(x)'
+
+def test_printing_str_array_expressions():
+    assert sstr(ArraySymbol("A", (2, 3, 4))) == "A"
+    assert sstr(ArrayElement("A", (2, 1/(1-x), 0))) == "A[2, 1/(1 - x), 0]"
+    M = MatrixSymbol("M", 3, 3)
+    N = MatrixSymbol("N", 3, 3)
+    assert sstr(ArrayElement(M*N, [x, 0])) == "(M*N)[x, 0]"
+
+def test_printing_stats():
+    # issue 24132
+    x = RandomSymbol("x")
+    y = RandomSymbol("y")
+    z1 = Probability(x > 0)*Identity(2)
+    z2 = Expectation(x)*Identity(2)
+    z3 = Variance(x)*Identity(2)
+    z4 = Covariance(x, y) * Identity(2)
+
+    assert str(z1) == "Probability(x > 0)*I"
+    assert str(z2) == "Expectation(x)*I"
+    assert str(z3) == "Variance(x)*I"
+    assert str(z4) ==  "Covariance(x, y)*I"
+    assert z1.is_commutative == False
+    assert z2.is_commutative == False
+    assert z3.is_commutative == False
+    assert z4.is_commutative == False
+    assert z2._eval_is_commutative() == False
+    assert z3._eval_is_commutative() == False
+    assert z4._eval_is_commutative() == False

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_tableform.py

@@ -0,0 +1,182 @@
+from sympy.core.singleton import S
+from sympy.printing.tableform import TableForm
+from sympy.printing.latex import latex
+from sympy.abc import x
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import sin
+from sympy.testing.pytest import raises
+
+from textwrap import dedent
+
+
+def test_TableForm():
+    s = str(TableForm([["a", "b"], ["c", "d"], ["e", 0]],
+        headings="automatic"))
+    assert s == (
+        '  | 1 2\n'
+        '-------\n'
+        '1 | a b\n'
+        '2 | c d\n'
+        '3 | e  '
+    )
+    s = str(TableForm([["a", "b"], ["c", "d"], ["e", 0]],
+        headings="automatic", wipe_zeros=False))
+    assert s == dedent('''\
+          | 1 2
+        -------
+        1 | a b
+        2 | c d
+        3 | e 0''')
+    s = str(TableForm([[x**2, "b"], ["c", x**2], ["e", "f"]],
+            headings=("automatic", None)))
+    assert s == (
+        '1 | x**2 b   \n'
+        '2 | c    x**2\n'
+        '3 | e    f   '
+    )
+    s = str(TableForm([["a", "b"], ["c", "d"], ["e", "f"]],
+            headings=(None, "automatic")))
+    assert s == dedent('''\
+        1 2
+        ---
+        a b
+        c d
+        e f''')
+    s = str(TableForm([[5, 7], [4, 2], [10, 3]],
+            headings=[["Group A", "Group B", "Group C"], ["y1", "y2"]]))
+    assert s == (
+        '        | y1 y2\n'
+        '---------------\n'
+        'Group A | 5  7 \n'
+        'Group B | 4  2 \n'
+        'Group C | 10 3 '
+    )
+    raises(
+        ValueError,
+        lambda:
+        TableForm(
+            [[5, 7], [4, 2], [10, 3]],
+            headings=[["Group A", "Group B", "Group C"], ["y1", "y2"]],
+            alignments="middle")
+    )
+    s = str(TableForm([[5, 7], [4, 2], [10, 3]],
+            headings=[["Group A", "Group B", "Group C"], ["y1", "y2"]],
+            alignments="right"))
+    assert s == dedent('''\
+                | y1 y2
+        ---------------
+        Group A |  5  7
+        Group B |  4  2
+        Group C | 10  3''')
+
+    # other alignment permutations
+    d = [[1, 100], [100, 1]]
+    s = TableForm(d, headings=(('xxx', 'x'), None), alignments='l')
+    assert str(s) == (
+        'xxx | 1   100\n'
+        '  x | 100 1  '
+    )
+    s = TableForm(d, headings=(('xxx', 'x'), None), alignments='lr')
+    assert str(s) == dedent('''\
+    xxx | 1   100
+      x | 100   1''')
+    s = TableForm(d, headings=(('xxx', 'x'), None), alignments='clr')
+    assert str(s) == dedent('''\
+    xxx | 1   100
+     x  | 100   1''')
+
+    s = TableForm(d, headings=(('xxx', 'x'), None))
+    assert str(s) == (
+        'xxx | 1   100\n'
+        '  x | 100 1  '
+    )
+
+    raises(ValueError, lambda: TableForm(d, alignments='clr'))
+
+    #pad
+    s = str(TableForm([[None, "-", 2], [1]], pad='?'))
+    assert s == dedent('''\
+        ? - 2
+        1 ? ?''')
+
+
+def test_TableForm_latex():
+    s = latex(TableForm([[0, x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]],
+            wipe_zeros=True, headings=("automatic", "automatic")))
+    assert s == (
+        '\\begin{tabular}{r l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 &   & $x^{3}$ \\\\\n'
+        '2 & $c$ & $\\frac{1}{4}$ \\\\\n'
+        '3 & $\\sqrt{x}$ & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )
+    s = latex(TableForm([[0, x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]],
+            wipe_zeros=True, headings=("automatic", "automatic"), alignments='l'))
+    assert s == (
+        '\\begin{tabular}{r l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 &   & $x^{3}$ \\\\\n'
+        '2 & $c$ & $\\frac{1}{4}$ \\\\\n'
+        '3 & $\\sqrt{x}$ & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )
+    s = latex(TableForm([[0, x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]],
+            wipe_zeros=True, headings=("automatic", "automatic"), alignments='l'*3))
+    assert s == (
+        '\\begin{tabular}{l l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 &   & $x^{3}$ \\\\\n'
+        '2 & $c$ & $\\frac{1}{4}$ \\\\\n'
+        '3 & $\\sqrt{x}$ & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )
+    s = latex(TableForm([["a", x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]],
+            headings=("automatic", "automatic")))
+    assert s == (
+        '\\begin{tabular}{r l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 & $a$ & $x^{3}$ \\\\\n'
+        '2 & $c$ & $\\frac{1}{4}$ \\\\\n'
+        '3 & $\\sqrt{x}$ & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )
+    s = latex(TableForm([["a", x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]],
+            formats=['(%s)', None], headings=("automatic", "automatic")))
+    assert s == (
+        '\\begin{tabular}{r l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 & (a) & $x^{3}$ \\\\\n'
+        '2 & (c) & $\\frac{1}{4}$ \\\\\n'
+        '3 & (sqrt(x)) & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )
+
+    def neg_in_paren(x, i, j):
+        if i % 2:
+            return ('(%s)' if x < 0 else '%s') % x
+        else:
+            pass  # use default print
+    s = latex(TableForm([[-1, 2], [-3, 4]],
+            formats=[neg_in_paren]*2, headings=("automatic", "automatic")))
+    assert s == (
+        '\\begin{tabular}{r l l}\n'
+        ' & 1 & 2 \\\\\n'
+        '\\hline\n'
+        '1 & -1 & 2 \\\\\n'
+        '2 & (-3) & 4 \\\\\n'
+        '\\end{tabular}'
+    )
+    s = latex(TableForm([["a", x**3], ["c", S.One/4], [sqrt(x), sin(x**2)]]))
+    assert s == (
+        '\\begin{tabular}{l l}\n'
+        '$a$ & $x^{3}$ \\\\\n'
+        '$c$ & $\\frac{1}{4}$ \\\\\n'
+        '$\\sqrt{x}$ & $\\sin{\\left(x^{2} \\right)}$ \\\\\n'
+        '\\end{tabular}'
+    )

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_tensorflow.py

@@ -0,0 +1,493 @@
+import random
+from sympy.core.function import Derivative
+from sympy.core.symbol import symbols
+from sympy import Piecewise
+from sympy.tensor.array.expressions.array_expressions import ArrayTensorProduct, ArrayAdd, \
+    PermuteDims, ArrayDiagonal
+from sympy.core.relational import Eq, Ne, Ge, Gt, Le, Lt
+from sympy.external import import_module
+from sympy.functions import \
+    Abs, ceiling, exp, floor, sign, sin, asin, sqrt, cos, \
+    acos, tan, atan, atan2, cosh, acosh, sinh, asinh, tanh, atanh, \
+    re, im, arg, erf, loggamma, log
+from sympy.codegen.cfunctions import isnan, isinf
+from sympy.matrices import Matrix, MatrixBase, eye, randMatrix
+from sympy.matrices.expressions import \
+    Determinant, HadamardProduct, Inverse, MatrixSymbol, Trace
+from sympy.printing.tensorflow import tensorflow_code
+from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array
+from sympy.utilities.lambdify import lambdify
+from sympy.testing.pytest import skip
+from sympy.testing.pytest import XFAIL
+
+
+tf = tensorflow = import_module("tensorflow")
+
+if tensorflow:
+    # Hide Tensorflow warnings
+    import os
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+
+M = MatrixSymbol("M", 3, 3)
+N = MatrixSymbol("N", 3, 3)
+P = MatrixSymbol("P", 3, 3)
+Q = MatrixSymbol("Q", 3, 3)
+
+x, y, z, t = symbols("x y z t")
+
+if tf is not None:
+    llo = [list(range(i, i+3)) for i in range(0, 9, 3)]
+    m3x3 = tf.constant(llo)
+    m3x3sympy = Matrix(llo)
+
+
+def _compare_tensorflow_matrix(variables, expr, use_float=False):
+    f = lambdify(variables, expr, 'tensorflow')
+    if not use_float:
+        random_matrices = [randMatrix(v.rows, v.cols) for v in variables]
+    else:
+        random_matrices = [randMatrix(v.rows, v.cols)/100. for v in variables]
+
+    graph = tf.Graph()
+    r = None
+    with graph.as_default():
+        random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
+        session = tf.compat.v1.Session(graph=graph)
+        r = session.run(f(*random_variables))
+
+    e = expr.subs(dict(zip(variables, random_matrices)))
+    e = e.doit()
+    if e.is_Matrix:
+        if not isinstance(e, MatrixBase):
+            e = e.as_explicit()
+        e = e.tolist()
+
+    if not use_float:
+        assert (r == e).all()
+    else:
+        r = [i for row in r for i in row]
+        e = [i for row in e for i in row]
+        assert all(
+            abs(a-b) < 10**-(4-int(log(abs(a), 10))) for a, b in zip(r, e))
+
+
+# Creating a custom inverse test.
+# See https://github.com/sympy/sympy/issues/18469
+def _compare_tensorflow_matrix_inverse(variables, expr, use_float=False):
+    f = lambdify(variables, expr, 'tensorflow')
+    if not use_float:
+        random_matrices = [eye(v.rows, v.cols)*4 for v in variables]
+    else:
+        random_matrices = [eye(v.rows, v.cols)*3.14 for v in variables]
+
+    graph = tf.Graph()
+    r = None
+    with graph.as_default():
+        random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
+        session = tf.compat.v1.Session(graph=graph)
+        r = session.run(f(*random_variables))
+
+    e = expr.subs(dict(zip(variables, random_matrices)))
+    e = e.doit()
+    if e.is_Matrix:
+        if not isinstance(e, MatrixBase):
+            e = e.as_explicit()
+        e = e.tolist()
+
+    if not use_float:
+        assert (r == e).all()
+    else:
+        r = [i for row in r for i in row]
+        e = [i for row in e for i in row]
+        assert all(
+            abs(a-b) < 10**-(4-int(log(abs(a), 10))) for a, b in zip(r, e))
+
+
+def _compare_tensorflow_matrix_scalar(variables, expr):
+    f = lambdify(variables, expr, 'tensorflow')
+    random_matrices = [
+        randMatrix(v.rows, v.cols).evalf() / 100 for v in variables]
+
+    graph = tf.Graph()
+    r = None
+    with graph.as_default():
+        random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
+        session = tf.compat.v1.Session(graph=graph)
+        r = session.run(f(*random_variables))
+
+    e = expr.subs(dict(zip(variables, random_matrices)))
+    e = e.doit()
+    assert abs(r-e) < 10**-6
+
+
+def _compare_tensorflow_scalar(
+    variables, expr, rng=lambda: random.randint(0, 10)):
+    f = lambdify(variables, expr, 'tensorflow')
+    rvs = [rng() for v in variables]
+
+    graph = tf.Graph()
+    r = None
+    with graph.as_default():
+        tf_rvs = [eval(tensorflow_code(i)) for i in rvs]
+        session = tf.compat.v1.Session(graph=graph)
+        r = session.run(f(*tf_rvs))
+
+    e = expr.subs(dict(zip(variables, rvs))).evalf().doit()
+    assert abs(r-e) < 10**-6
+
+
+def _compare_tensorflow_relational(
+    variables, expr, rng=lambda: random.randint(0, 10)):
+    f = lambdify(variables, expr, 'tensorflow')
+    rvs = [rng() for v in variables]
+
+    graph = tf.Graph()
+    r = None
+    with graph.as_default():
+        tf_rvs = [eval(tensorflow_code(i)) for i in rvs]
+        session = tf.compat.v1.Session(graph=graph)
+        r = session.run(f(*tf_rvs))
+
+    e = expr.subs(dict(zip(variables, rvs))).doit()
+    assert r == e
+
+
+def test_tensorflow_printing():
+    assert tensorflow_code(eye(3)) == \
+        "tensorflow.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])"
+
+    expr = Matrix([[x, sin(y)], [exp(z), -t]])
+    assert tensorflow_code(expr) == \
+        "tensorflow.Variable(" \
+            "[[x, tensorflow.math.sin(y)]," \
+            " [tensorflow.math.exp(z), -t]])"
+
+
+# This (random) test is XFAIL because it fails occasionally
+# See https://github.com/sympy/sympy/issues/18469
+@XFAIL
+def test_tensorflow_math():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    expr = Abs(x)
+    assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
+    _compare_tensorflow_scalar((x,), expr)
+
+    expr = sign(x)
+    assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
+    _compare_tensorflow_scalar((x,), expr)
+
+    expr = ceiling(x)
+    assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = floor(x)
+    assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = exp(x)
+    assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = sqrt(x)
+    assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = x ** 4
+    assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = cos(x)
+    assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = acos(x)
+    assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(0, 0.95))
+
+    expr = sin(x)
+    assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = asin(x)
+    assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = tan(x)
+    assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = atan(x)
+    assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = atan2(y, x)
+    assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
+    _compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())
+
+    expr = cosh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = acosh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
+
+    expr = sinh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
+
+    expr = asinh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
+
+    expr = tanh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
+    _compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
+
+    expr = atanh(x)
+    assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
+    _compare_tensorflow_scalar(
+        (x,), expr, rng=lambda: random.uniform(-.5, .5))
+
+    expr = erf(x)
+    assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
+    _compare_tensorflow_scalar(
+        (x,), expr, rng=lambda: random.random())
+
+    expr = loggamma(x)
+    assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
+    _compare_tensorflow_scalar(
+        (x,), expr, rng=lambda: random.random())
+
+
+def test_tensorflow_complexes():
+    assert tensorflow_code(re(x)) == "tensorflow.math.real(x)"
+    assert tensorflow_code(im(x)) == "tensorflow.math.imag(x)"
+    assert tensorflow_code(arg(x)) == "tensorflow.math.angle(x)"
+
+
+def test_tensorflow_relational():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    expr = Eq(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.equal(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+    expr = Ne(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.not_equal(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+    expr = Ge(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.greater_equal(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+    expr = Gt(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.greater(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+    expr = Le(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.less_equal(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+    expr = Lt(x, y)
+    assert tensorflow_code(expr) == "tensorflow.math.less(x, y)"
+    _compare_tensorflow_relational((x, y), expr)
+
+
+# This (random) test is XFAIL because it fails occasionally
+# See https://github.com/sympy/sympy/issues/18469
+@XFAIL
+def test_tensorflow_matrices():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    expr = M
+    assert tensorflow_code(expr) == "M"
+    _compare_tensorflow_matrix((M,), expr)
+
+    expr = M + N
+    assert tensorflow_code(expr) == "tensorflow.math.add(M, N)"
+    _compare_tensorflow_matrix((M, N), expr)
+
+    expr = M * N
+    assert tensorflow_code(expr) == "tensorflow.linalg.matmul(M, N)"
+    _compare_tensorflow_matrix((M, N), expr)
+
+    expr = HadamardProduct(M, N)
+    assert tensorflow_code(expr) == "tensorflow.math.multiply(M, N)"
+    _compare_tensorflow_matrix((M, N), expr)
+
+    expr = M*N*P*Q
+    assert tensorflow_code(expr) == \
+        "tensorflow.linalg.matmul(" \
+            "tensorflow.linalg.matmul(" \
+                "tensorflow.linalg.matmul(M, N), P), Q)"
+    _compare_tensorflow_matrix((M, N, P, Q), expr)
+
+    expr = M**3
+    assert tensorflow_code(expr) == \
+        "tensorflow.linalg.matmul(tensorflow.linalg.matmul(M, M), M)"
+    _compare_tensorflow_matrix((M,), expr)
+
+    expr = Trace(M)
+    assert tensorflow_code(expr) == "tensorflow.linalg.trace(M)"
+    _compare_tensorflow_matrix((M,), expr)
+
+    expr = Determinant(M)
+    assert tensorflow_code(expr) == "tensorflow.linalg.det(M)"
+    _compare_tensorflow_matrix_scalar((M,), expr)
+
+    expr = Inverse(M)
+    assert tensorflow_code(expr) == "tensorflow.linalg.inv(M)"
+    _compare_tensorflow_matrix_inverse((M,), expr, use_float=True)
+
+    expr = M.T
+    assert tensorflow_code(expr, tensorflow_version='1.14') == \
+        "tensorflow.linalg.matrix_transpose(M)"
+    assert tensorflow_code(expr, tensorflow_version='1.13') == \
+        "tensorflow.matrix_transpose(M)"
+
+    _compare_tensorflow_matrix((M,), expr)
+
+
+def test_codegen_einsum():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    graph = tf.Graph()
+    with graph.as_default():
+        session = tf.compat.v1.Session(graph=graph)
+
+        M = MatrixSymbol("M", 2, 2)
+        N = MatrixSymbol("N", 2, 2)
+
+        cg = convert_matrix_to_array(M * N)
+        f = lambdify((M, N), cg, 'tensorflow')
+
+        ma = tf.constant([[1, 2], [3, 4]])
+        mb = tf.constant([[1,-2], [-1, 3]])
+        y = session.run(f(ma, mb))
+        c = session.run(tf.matmul(ma, mb))
+        assert (y == c).all()
+
+
+def test_codegen_extra():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    graph = tf.Graph()
+    with graph.as_default():
+        session = tf.compat.v1.Session()
+
+        M = MatrixSymbol("M", 2, 2)
+        N = MatrixSymbol("N", 2, 2)
+        P = MatrixSymbol("P", 2, 2)
+        Q = MatrixSymbol("Q", 2, 2)
+        ma = tf.constant([[1, 2], [3, 4]])
+        mb = tf.constant([[1,-2], [-1, 3]])
+        mc = tf.constant([[2, 0], [1, 2]])
+        md = tf.constant([[1,-1], [4, 7]])
+
+        cg = ArrayTensorProduct(M, N)
+        assert tensorflow_code(cg) == \
+            'tensorflow.linalg.einsum("ab,cd", M, N)'
+        f = lambdify((M, N), cg, 'tensorflow')
+        y = session.run(f(ma, mb))
+        c = session.run(tf.einsum("ij,kl", ma, mb))
+        assert (y == c).all()
+
+        cg = ArrayAdd(M, N)
+        assert tensorflow_code(cg) == 'tensorflow.math.add(M, N)'
+        f = lambdify((M, N), cg, 'tensorflow')
+        y = session.run(f(ma, mb))
+        c = session.run(ma + mb)
+        assert (y == c).all()
+
+        cg = ArrayAdd(M, N, P)
+        assert tensorflow_code(cg) == \
+            'tensorflow.math.add(tensorflow.math.add(M, N), P)'
+        f = lambdify((M, N, P), cg, 'tensorflow')
+        y = session.run(f(ma, mb, mc))
+        c = session.run(ma + mb + mc)
+        assert (y == c).all()
+
+        cg = ArrayAdd(M, N, P, Q)
+        assert tensorflow_code(cg) == \
+            'tensorflow.math.add(' \
+                'tensorflow.math.add(tensorflow.math.add(M, N), P), Q)'
+        f = lambdify((M, N, P, Q), cg, 'tensorflow')
+        y = session.run(f(ma, mb, mc, md))
+        c = session.run(ma + mb + mc + md)
+        assert (y == c).all()
+
+        cg = PermuteDims(M, [1, 0])
+        assert tensorflow_code(cg) == 'tensorflow.transpose(M, [1, 0])'
+        f = lambdify((M,), cg, 'tensorflow')
+        y = session.run(f(ma))
+        c = session.run(tf.transpose(ma))
+        assert (y == c).all()
+
+        cg = PermuteDims(ArrayTensorProduct(M, N), [1, 2, 3, 0])
+        assert tensorflow_code(cg) == \
+            'tensorflow.transpose(' \
+                'tensorflow.linalg.einsum("ab,cd", M, N), [1, 2, 3, 0])'
+        f = lambdify((M, N), cg, 'tensorflow')
+        y = session.run(f(ma, mb))
+        c = session.run(tf.transpose(tf.einsum("ab,cd", ma, mb), [1, 2, 3, 0]))
+        assert (y == c).all()
+
+        cg = ArrayDiagonal(ArrayTensorProduct(M, N), (1, 2))
+        assert tensorflow_code(cg) == \
+            'tensorflow.linalg.einsum("ab,bc->acb", M, N)'
+        f = lambdify((M, N), cg, 'tensorflow')
+        y = session.run(f(ma, mb))
+        c = session.run(tf.einsum("ab,bc->acb", ma, mb))
+        assert (y == c).all()
+
+
+def test_MatrixElement_printing():
+    A = MatrixSymbol("A", 1, 3)
+    B = MatrixSymbol("B", 1, 3)
+    C = MatrixSymbol("C", 1, 3)
+
+    assert tensorflow_code(A[0, 0]) == "A[0, 0]"
+    assert tensorflow_code(3 * A[0, 0]) == "3*A[0, 0]"
+
+    F = C[0, 0].subs(C, A - B)
+    assert tensorflow_code(F) == "(tensorflow.math.add((-1)*B, A))[0, 0]"
+
+
+def test_tensorflow_Derivative():
+    expr = Derivative(sin(x), x)
+    assert tensorflow_code(expr) == \
+        "tensorflow.gradients(tensorflow.math.sin(x), x)[0]"
+
+def test_tensorflow_isnan_isinf():
+    if not tf:
+        skip("TensorFlow not installed")
+
+    # Test for isnan
+    x = symbols("x")
+    # Return 0 if x is of nan value, and 1 otherwise
+    expression = Piecewise((0.0, isnan(x)), (1.0, True))
+    printed_code = tensorflow_code(expression)
+    expected_printed_code = "tensorflow.where(tensorflow.math.is_nan(x), 0.0, 1.0)"
+    assert tensorflow_code(expression) == expected_printed_code, f"Incorrect printed result {printed_code}, expected {expected_printed_code}"
+    for _input, _expected in [(float('nan'), 0.0), (float('inf'), 1.0), (float('-inf'), 1.0), (1.0, 1.0)]:
+        _output = lambdify((x), expression, modules="tensorflow")(x=tf.constant([_input]))
+        assert (_output == _expected).numpy().all()
+
+    # Test for isinf
+    x = symbols("x")
+    # Return 0 if x is of nan value, and 1 otherwise
+    expression = Piecewise((0.0, isinf(x)), (1.0, True))
+    printed_code = tensorflow_code(expression)
+    expected_printed_code = "tensorflow.where(tensorflow.math.is_inf(x), 0.0, 1.0)"
+    assert tensorflow_code(expression) == expected_printed_code, f"Incorrect printed result {printed_code}, expected {expected_printed_code}"
+    for _input, _expected in [(float('inf'), 0.0), (float('-inf'), 0.0), (float('nan'), 1.0), (1.0, 1.0)]:
+        _output = lambdify((x), expression, modules="tensorflow")(x=tf.constant([_input]))
+        assert (_output == _expected).numpy().all()

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_theanocode.py

@@ -0,0 +1,639 @@
+"""
+Important note on tests in this module - the Theano printing functions use a
+global cache by default, which means that tests using it will modify global
+state and thus not be independent from each other. Instead of using the "cache"
+keyword argument each time, this module uses the theano_code_ and
+theano_function_ functions defined below which default to using a new, empty
+cache instead.
+"""
+
+import logging
+
+from sympy.external import import_module
+from sympy.testing.pytest import raises, SKIP, warns_deprecated_sympy
+
+theanologger = logging.getLogger('theano.configdefaults')
+theanologger.setLevel(logging.CRITICAL)
+theano = import_module('theano')
+theanologger.setLevel(logging.WARNING)
+
+
+if theano:
+    import numpy as np
+    ts = theano.scalar
+    tt = theano.tensor
+    xt, yt, zt = [tt.scalar(name, 'floatX') for name in 'xyz']
+    Xt, Yt, Zt = [tt.tensor('floatX', (False, False), name=n) for n in 'XYZ']
+else:
+    #bin/test will not execute any tests now
+    disabled = True
+
+import sympy as sy
+from sympy.core.singleton import S
+from sympy.abc import x, y, z, t
+from sympy.printing.theanocode import (theano_code, dim_handling,
+        theano_function)
+
+
+# Default set of matrix symbols for testing - make square so we can both
+# multiply and perform elementwise operations between them.
+X, Y, Z = [sy.MatrixSymbol(n, 4, 4) for n in 'XYZ']
+
+# For testing AppliedUndef
+f_t = sy.Function('f')(t)
+
+
+def theano_code_(expr, **kwargs):
+    """ Wrapper for theano_code that uses a new, empty cache by default. """
+    kwargs.setdefault('cache', {})
+    with warns_deprecated_sympy():
+        return theano_code(expr, **kwargs)
+
+def theano_function_(inputs, outputs, **kwargs):
+    """ Wrapper for theano_function that uses a new, empty cache by default. """
+    kwargs.setdefault('cache', {})
+    with warns_deprecated_sympy():
+        return theano_function(inputs, outputs, **kwargs)
+
+
+def fgraph_of(*exprs):
+    """ Transform SymPy expressions into Theano Computation.
+
+    Parameters
+    ==========
+    exprs
+        SymPy expressions
+
+    Returns
+    =======
+    theano.gof.FunctionGraph
+    """
+    outs = list(map(theano_code_, exprs))
+    ins = theano.gof.graph.inputs(outs)
+    ins, outs = theano.gof.graph.clone(ins, outs)
+    return theano.gof.FunctionGraph(ins, outs)
+
+
+def theano_simplify(fgraph):
+    """ Simplify a Theano Computation.
+
+    Parameters
+    ==========
+    fgraph : theano.gof.FunctionGraph
+
+    Returns
+    =======
+    theano.gof.FunctionGraph
+    """
+    mode = theano.compile.get_default_mode().excluding("fusion")
+    fgraph = fgraph.clone()
+    mode.optimizer.optimize(fgraph)
+    return fgraph
+
+
+def theq(a, b):
+    """ Test two Theano objects for equality.
+
+    Also accepts numeric types and lists/tuples of supported types.
+
+    Note - debugprint() has a bug where it will accept numeric types but does
+    not respect the "file" argument and in this case and instead prints the number
+    to stdout and returns an empty string. This can lead to tests passing where
+    they should fail because any two numbers will always compare as equal. To
+    prevent this we treat numbers as a separate case.
+    """
+    numeric_types = (int, float, np.number)
+    a_is_num = isinstance(a, numeric_types)
+    b_is_num = isinstance(b, numeric_types)
+
+    # Compare numeric types using regular equality
+    if a_is_num or b_is_num:
+        if not (a_is_num and b_is_num):
+            return False
+
+        return a == b
+
+    # Compare sequences element-wise
+    a_is_seq = isinstance(a, (tuple, list))
+    b_is_seq = isinstance(b, (tuple, list))
+
+    if a_is_seq or b_is_seq:
+        if not (a_is_seq and b_is_seq) or type(a) != type(b):
+            return False
+
+        return list(map(theq, a)) == list(map(theq, b))
+
+    # Otherwise, assume debugprint() can handle it
+    astr = theano.printing.debugprint(a, file='str')
+    bstr = theano.printing.debugprint(b, file='str')
+
+    # Check for bug mentioned above
+    for argname, argval, argstr in [('a', a, astr), ('b', b, bstr)]:
+        if argstr == '':
+            raise TypeError(
+                'theano.printing.debugprint(%s) returned empty string '
+                '(%s is instance of %r)'
+                % (argname, argname, type(argval))
+            )
+
+    return astr == bstr
+
+
+def test_example_symbols():
+    """
+    Check that the example symbols in this module print to their Theano
+    equivalents, as many of the other tests depend on this.
+    """
+    assert theq(xt, theano_code_(x))
+    assert theq(yt, theano_code_(y))
+    assert theq(zt, theano_code_(z))
+    assert theq(Xt, theano_code_(X))
+    assert theq(Yt, theano_code_(Y))
+    assert theq(Zt, theano_code_(Z))
+
+
+def test_Symbol():
+    """ Test printing a Symbol to a theano variable. """
+    xx = theano_code_(x)
+    assert isinstance(xx, (tt.TensorVariable, ts.ScalarVariable))
+    assert xx.broadcastable == ()
+    assert xx.name == x.name
+
+    xx2 = theano_code_(x, broadcastables={x: (False,)})
+    assert xx2.broadcastable == (False,)
+    assert xx2.name == x.name
+
+def test_MatrixSymbol():
+    """ Test printing a MatrixSymbol to a theano variable. """
+    XX = theano_code_(X)
+    assert isinstance(XX, tt.TensorVariable)
+    assert XX.broadcastable == (False, False)
+
+@SKIP  # TODO - this is currently not checked but should be implemented
+def test_MatrixSymbol_wrong_dims():
+    """ Test MatrixSymbol with invalid broadcastable. """
+    bcs = [(), (False,), (True,), (True, False), (False, True,), (True, True)]
+    for bc in bcs:
+        with raises(ValueError):
+            theano_code_(X, broadcastables={X: bc})
+
+def test_AppliedUndef():
+    """ Test printing AppliedUndef instance, which works similarly to Symbol. """
+    ftt = theano_code_(f_t)
+    assert isinstance(ftt, tt.TensorVariable)
+    assert ftt.broadcastable == ()
+    assert ftt.name == 'f_t'
+
+
+def test_add():
+    expr = x + y
+    comp = theano_code_(expr)
+    assert comp.owner.op == theano.tensor.add
+
+def test_trig():
+    assert theq(theano_code_(sy.sin(x)), tt.sin(xt))
+    assert theq(theano_code_(sy.tan(x)), tt.tan(xt))
+
+def test_many():
+    """ Test printing a complex expression with multiple symbols. """
+    expr = sy.exp(x**2 + sy.cos(y)) * sy.log(2*z)
+    comp = theano_code_(expr)
+    expected = tt.exp(xt**2 + tt.cos(yt)) * tt.log(2*zt)
+    assert theq(comp, expected)
+
+
+def test_dtype():
+    """ Test specifying specific data types through the dtype argument. """
+    for dtype in ['float32', 'float64', 'int8', 'int16', 'int32', 'int64']:
+        assert theano_code_(x, dtypes={x: dtype}).type.dtype == dtype
+
+    # "floatX" type
+    assert theano_code_(x, dtypes={x: 'floatX'}).type.dtype in ('float32', 'float64')
+
+    # Type promotion
+    assert theano_code_(x + 1, dtypes={x: 'float32'}).type.dtype == 'float32'
+    assert theano_code_(x + y, dtypes={x: 'float64', y: 'float32'}).type.dtype == 'float64'
+
+
+def test_broadcastables():
+    """ Test the "broadcastables" argument when printing symbol-like objects. """
+
+    # No restrictions on shape
+    for s in [x, f_t]:
+        for bc in [(), (False,), (True,), (False, False), (True, False)]:
+            assert theano_code_(s, broadcastables={s: bc}).broadcastable == bc
+
+    # TODO - matrix broadcasting?
+
+def test_broadcasting():
+    """ Test "broadcastable" attribute after applying element-wise binary op. """
+
+    expr = x + y
+
+    cases = [
+        [(), (), ()],
+        [(False,), (False,), (False,)],
+        [(True,), (False,), (False,)],
+        [(False, True), (False, False), (False, False)],
+        [(True, False), (False, False), (False, False)],
+    ]
+
+    for bc1, bc2, bc3 in cases:
+        comp = theano_code_(expr, broadcastables={x: bc1, y: bc2})
+        assert comp.broadcastable == bc3
+
+
+def test_MatMul():
+    expr = X*Y*Z
+    expr_t = theano_code_(expr)
+    assert isinstance(expr_t.owner.op, tt.Dot)
+    assert theq(expr_t, Xt.dot(Yt).dot(Zt))
+
+def test_Transpose():
+    assert isinstance(theano_code_(X.T).owner.op, tt.DimShuffle)
+
+def test_MatAdd():
+    expr = X+Y+Z
+    assert isinstance(theano_code_(expr).owner.op, tt.Elemwise)
+
+
+def test_Rationals():
+    assert theq(theano_code_(sy.Integer(2) / 3), tt.true_div(2, 3))
+    assert theq(theano_code_(S.Half), tt.true_div(1, 2))
+
+def test_Integers():
+    assert theano_code_(sy.Integer(3)) == 3
+
+def test_factorial():
+    n = sy.Symbol('n')
+    assert theano_code_(sy.factorial(n))
+
+def test_Derivative():
+    simp = lambda expr: theano_simplify(fgraph_of(expr))
+    assert theq(simp(theano_code_(sy.Derivative(sy.sin(x), x, evaluate=False))),
+                simp(theano.grad(tt.sin(xt), xt)))
+
+
+def test_theano_function_simple():
+    """ Test theano_function() with single output. """
+    f = theano_function_([x, y], [x+y])
+    assert f(2, 3) == 5
+
+def test_theano_function_multi():
+    """ Test theano_function() with multiple outputs. """
+    f = theano_function_([x, y], [x+y, x-y])
+    o1, o2 = f(2, 3)
+    assert o1 == 5
+    assert o2 == -1
+
+def test_theano_function_numpy():
+    """ Test theano_function() vs Numpy implementation. """
+    f = theano_function_([x, y], [x+y], dim=1,
+                         dtypes={x: 'float64', y: 'float64'})
+    assert np.linalg.norm(f([1, 2], [3, 4]) - np.asarray([4, 6])) < 1e-9
+
+    f = theano_function_([x, y], [x+y], dtypes={x: 'float64', y: 'float64'},
+                         dim=1)
+    xx = np.arange(3).astype('float64')
+    yy = 2*np.arange(3).astype('float64')
+    assert np.linalg.norm(f(xx, yy) - 3*np.arange(3)) < 1e-9
+
+
+def test_theano_function_matrix():
+    m = sy.Matrix([[x, y], [z, x + y + z]])
+    expected = np.array([[1.0, 2.0], [3.0, 1.0 + 2.0 + 3.0]])
+    f = theano_function_([x, y, z], [m])
+    np.testing.assert_allclose(f(1.0, 2.0, 3.0), expected)
+    f = theano_function_([x, y, z], [m], scalar=True)
+    np.testing.assert_allclose(f(1.0, 2.0, 3.0), expected)
+    f = theano_function_([x, y, z], [m, m])
+    assert isinstance(f(1.0, 2.0, 3.0), type([]))
+    np.testing.assert_allclose(f(1.0, 2.0, 3.0)[0], expected)
+    np.testing.assert_allclose(f(1.0, 2.0, 3.0)[1], expected)
+
+def test_dim_handling():
+    assert dim_handling([x], dim=2) == {x: (False, False)}
+    assert dim_handling([x, y], dims={x: 1, y: 2}) == {x: (False, True),
+                                                       y: (False, False)}
+    assert dim_handling([x], broadcastables={x: (False,)}) == {x: (False,)}
+
+def test_theano_function_kwargs():
+    """
+    Test passing additional kwargs from theano_function() to theano.function().
+    """
+    import numpy as np
+    f = theano_function_([x, y, z], [x+y], dim=1, on_unused_input='ignore',
+            dtypes={x: 'float64', y: 'float64', z: 'float64'})
+    assert np.linalg.norm(f([1, 2], [3, 4], [0, 0]) - np.asarray([4, 6])) < 1e-9
+
+    f = theano_function_([x, y, z], [x+y],
+                        dtypes={x: 'float64', y: 'float64', z: 'float64'},
+                        dim=1, on_unused_input='ignore')
+    xx = np.arange(3).astype('float64')
+    yy = 2*np.arange(3).astype('float64')
+    zz = 2*np.arange(3).astype('float64')
+    assert np.linalg.norm(f(xx, yy, zz) - 3*np.arange(3)) < 1e-9
+
+def test_theano_function_scalar():
+    """ Test the "scalar" argument to theano_function(). """
+
+    args = [
+        ([x, y], [x + y], None, [0]),  # Single 0d output
+        ([X, Y], [X + Y], None, [2]),  # Single 2d output
+        ([x, y], [x + y], {x: 0, y: 1}, [1]),  # Single 1d output
+        ([x, y], [x + y, x - y], None, [0, 0]),  # Two 0d outputs
+        ([x, y, X, Y], [x + y, X + Y], None, [0, 2]),  # One 0d output, one 2d
+    ]
+
+    # Create and test functions with and without the scalar setting
+    for inputs, outputs, in_dims, out_dims in args:
+        for scalar in [False, True]:
+
+            f = theano_function_(inputs, outputs, dims=in_dims, scalar=scalar)
+
+            # Check the theano_function attribute is set whether wrapped or not
+            assert isinstance(f.theano_function, theano.compile.function_module.Function)
+
+            # Feed in inputs of the appropriate size and get outputs
+            in_values = [
+                np.ones([1 if bc else 5 for bc in i.type.broadcastable])
+                for i in f.theano_function.input_storage
+            ]
+            out_values = f(*in_values)
+            if not isinstance(out_values, list):
+                out_values = [out_values]
+
+            # Check output types and shapes
+            assert len(out_dims) == len(out_values)
+            for d, value in zip(out_dims, out_values):
+
+                if scalar and d == 0:
+                    # Should have been converted to a scalar value
+                    assert isinstance(value, np.number)
+
+                else:
+                    # Otherwise should be an array
+                    assert isinstance(value, np.ndarray)
+                    assert value.ndim == d
+
+def test_theano_function_bad_kwarg():
+    """
+    Passing an unknown keyword argument to theano_function() should raise an
+    exception.
+    """
+    raises(Exception, lambda : theano_function_([x], [x+1], foobar=3))
+
+
+def test_slice():
+    assert theano_code_(slice(1, 2, 3)) == slice(1, 2, 3)
+
+    def theq_slice(s1, s2):
+        for attr in ['start', 'stop', 'step']:
+            a1 = getattr(s1, attr)
+            a2 = getattr(s2, attr)
+            if a1 is None or a2 is None:
+                if not (a1 is None or a2 is None):
+                    return False
+            elif not theq(a1, a2):
+                return False
+        return True
+
+    dtypes = {x: 'int32', y: 'int32'}
+    assert theq_slice(theano_code_(slice(x, y), dtypes=dtypes), slice(xt, yt))
+    assert theq_slice(theano_code_(slice(1, x, 3), dtypes=dtypes), slice(1, xt, 3))
+
+def test_MatrixSlice():
+    from theano import Constant
+
+    cache = {}
+
+    n = sy.Symbol('n', integer=True)
+    X = sy.MatrixSymbol('X', n, n)
+
+    Y = X[1:2:3, 4:5:6]
+    Yt = theano_code_(Y, cache=cache)
+
+    s = ts.Scalar('int64')
+    assert tuple(Yt.owner.op.idx_list) == (slice(s, s, s), slice(s, s, s))
+    assert Yt.owner.inputs[0] == theano_code_(X, cache=cache)
+    # == doesn't work in theano like it does in SymPy. You have to use
+    # equals.
+    assert all(Yt.owner.inputs[i].equals(Constant(s, i)) for i in range(1, 7))
+
+    k = sy.Symbol('k')
+    theano_code_(k, dtypes={k: 'int32'})
+    start, stop, step = 4, k, 2
+    Y = X[start:stop:step]
+    Yt = theano_code_(Y, dtypes={n: 'int32', k: 'int32'})
+    # assert Yt.owner.op.idx_list[0].stop == kt
+
+def test_BlockMatrix():
+    n = sy.Symbol('n', integer=True)
+    A, B, C, D = [sy.MatrixSymbol(name, n, n) for name in 'ABCD']
+    At, Bt, Ct, Dt = map(theano_code_, (A, B, C, D))
+    Block = sy.BlockMatrix([[A, B], [C, D]])
+    Blockt = theano_code_(Block)
+    solutions = [tt.join(0, tt.join(1, At, Bt), tt.join(1, Ct, Dt)),
+                 tt.join(1, tt.join(0, At, Ct), tt.join(0, Bt, Dt))]
+    assert any(theq(Blockt, solution) for solution in solutions)
+
+@SKIP
+def test_BlockMatrix_Inverse_execution():
+    k, n = 2, 4
+    dtype = 'float32'
+    A = sy.MatrixSymbol('A', n, k)
+    B = sy.MatrixSymbol('B', n, n)
+    inputs = A, B
+    output = B.I*A
+
+    cutsizes = {A: [(n//2, n//2), (k//2, k//2)],
+                B: [(n//2, n//2), (n//2, n//2)]}
+    cutinputs = [sy.blockcut(i, *cutsizes[i]) for i in inputs]
+    cutoutput = output.subs(dict(zip(inputs, cutinputs)))
+
+    dtypes = dict(zip(inputs, [dtype]*len(inputs)))
+    f = theano_function_(inputs, [output], dtypes=dtypes, cache={})
+    fblocked = theano_function_(inputs, [sy.block_collapse(cutoutput)],
+                                dtypes=dtypes, cache={})
+
+    ninputs = [np.random.rand(*x.shape).astype(dtype) for x in inputs]
+    ninputs = [np.arange(n*k).reshape(A.shape).astype(dtype),
+               np.eye(n).astype(dtype)]
+    ninputs[1] += np.ones(B.shape)*1e-5
+
+    assert np.allclose(f(*ninputs), fblocked(*ninputs), rtol=1e-5)
+
+def test_DenseMatrix():
+    t = sy.Symbol('theta')
+    for MatrixType in [sy.Matrix, sy.ImmutableMatrix]:
+        X = MatrixType([[sy.cos(t), -sy.sin(t)], [sy.sin(t), sy.cos(t)]])
+        tX = theano_code_(X)
+        assert isinstance(tX, tt.TensorVariable)
+        assert tX.owner.op == tt.join_
+
+
+def test_cache_basic():
+    """ Test single symbol-like objects are cached when printed by themselves. """
+
+    # Pairs of objects which should be considered equivalent with respect to caching
+    pairs = [
+        (x, sy.Symbol('x')),
+        (X, sy.MatrixSymbol('X', *X.shape)),
+        (f_t, sy.Function('f')(sy.Symbol('t'))),
+    ]
+
+    for s1, s2 in pairs:
+        cache = {}
+        st = theano_code_(s1, cache=cache)
+
+        # Test hit with same instance
+        assert theano_code_(s1, cache=cache) is st
+
+        # Test miss with same instance but new cache
+        assert theano_code_(s1, cache={}) is not st
+
+        # Test hit with different but equivalent instance
+        assert theano_code_(s2, cache=cache) is st
+
+def test_global_cache():
+    """ Test use of the global cache. """
+    from sympy.printing.theanocode import global_cache
+
+    backup = dict(global_cache)
+    try:
+        # Temporarily empty global cache
+        global_cache.clear()
+
+        for s in [x, X, f_t]:
+            with warns_deprecated_sympy():
+                st = theano_code(s)
+                assert theano_code(s) is st
+
+    finally:
+        # Restore global cache
+        global_cache.update(backup)
+
+def test_cache_types_distinct():
+    """
+    Test that symbol-like objects of different types (Symbol, MatrixSymbol,
+    AppliedUndef) are distinguished by the cache even if they have the same
+    name.
+    """
+    symbols = [sy.Symbol('f_t'), sy.MatrixSymbol('f_t', 4, 4), f_t]
+
+    cache = {}  # Single shared cache
+    printed = {}
+
+    for s in symbols:
+        st = theano_code_(s, cache=cache)
+        assert st not in printed.values()
+        printed[s] = st
+
+    # Check all printed objects are distinct
+    assert len(set(map(id, printed.values()))) == len(symbols)
+
+    # Check retrieving
+    for s, st in printed.items():
+        with warns_deprecated_sympy():
+            assert theano_code(s, cache=cache) is st
+
+def test_symbols_are_created_once():
+    """
+    Test that a symbol is cached and reused when it appears in an expression
+    more than once.
+    """
+    expr = sy.Add(x, x, evaluate=False)
+    comp = theano_code_(expr)
+
+    assert theq(comp, xt + xt)
+    assert not theq(comp, xt + theano_code_(x))
+
+def test_cache_complex():
+    """
+    Test caching on a complicated expression with multiple symbols appearing
+    multiple times.
+    """
+    expr = x ** 2 + (y - sy.exp(x)) * sy.sin(z - x * y)
+    symbol_names = {s.name for s in expr.free_symbols}
+    expr_t = theano_code_(expr)
+
+    # Iterate through variables in the Theano computational graph that the
+    # printed expression depends on
+    seen = set()
+    for v in theano.gof.graph.ancestors([expr_t]):
+        # Owner-less, non-constant variables should be our symbols
+        if v.owner is None and not isinstance(v, theano.gof.graph.Constant):
+            # Check it corresponds to a symbol and appears only once
+            assert v.name in symbol_names
+            assert v.name not in seen
+            seen.add(v.name)
+
+    # Check all were present
+    assert seen == symbol_names
+
+
+def test_Piecewise():
+    # A piecewise linear
+    expr = sy.Piecewise((0, x<0), (x, x<2), (1, True))  # ___/III
+    result = theano_code_(expr)
+    assert result.owner.op == tt.switch
+
+    expected = tt.switch(xt<0, 0, tt.switch(xt<2, xt, 1))
+    assert theq(result, expected)
+
+    expr = sy.Piecewise((x, x < 0))
+    result = theano_code_(expr)
+    expected = tt.switch(xt < 0, xt, np.nan)
+    assert theq(result, expected)
+
+    expr = sy.Piecewise((0, sy.And(x>0, x<2)), \
+        (x, sy.Or(x>2, x<0)))
+    result = theano_code_(expr)
+    expected = tt.switch(tt.and_(xt>0,xt<2), 0, \
+        tt.switch(tt.or_(xt>2, xt<0), xt, np.nan))
+    assert theq(result, expected)
+
+
+def test_Relationals():
+    assert theq(theano_code_(sy.Eq(x, y)), tt.eq(xt, yt))
+    # assert theq(theano_code_(sy.Ne(x, y)), tt.neq(xt, yt))  # TODO - implement
+    assert theq(theano_code_(x > y), xt > yt)
+    assert theq(theano_code_(x < y), xt < yt)
+    assert theq(theano_code_(x >= y), xt >= yt)
+    assert theq(theano_code_(x <= y), xt <= yt)
+
+
+def test_complexfunctions():
+    with warns_deprecated_sympy():
+        xt, yt = theano_code_(x, dtypes={x:'complex128'}), theano_code_(y, dtypes={y: 'complex128'})
+    from sympy.functions.elementary.complexes import conjugate
+    from theano.tensor import as_tensor_variable as atv
+    from theano.tensor import complex as cplx
+    with warns_deprecated_sympy():
+        assert theq(theano_code_(y*conjugate(x)), yt*(xt.conj()))
+        assert theq(theano_code_((1+2j)*x), xt*(atv(1.0)+atv(2.0)*cplx(0,1)))
+
+
+def test_constantfunctions():
+    with warns_deprecated_sympy():
+        tf = theano_function_([],[1+1j])
+    assert(tf()==1+1j)
+
+
+def test_Exp1():
+    """
+    Test that exp(1) prints without error and evaluates close to SymPy's E
+    """
+    # sy.exp(1) should yield same instance of E as sy.E (singleton), but extra
+    # check added for sanity
+    e_a = sy.exp(1)
+    e_b = sy.E
+
+    np.testing.assert_allclose(float(e_a), np.e)
+    np.testing.assert_allclose(float(e_b), np.e)
+
+    e = theano_code_(e_a)
+    np.testing.assert_allclose(float(e_a), e.eval())
+
+    e = theano_code_(e_b)
+    np.testing.assert_allclose(float(e_b), e.eval())

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_torch.py

@@ -0,0 +1,531 @@
+import random
+import math
+
+from sympy import symbols, Derivative
+from sympy.printing.pytorch import torch_code
+from sympy import (eye, MatrixSymbol, Matrix)
+from sympy.tensor.array import NDimArray
+from sympy.tensor.array.expressions.array_expressions import (
+    ArrayTensorProduct, ArrayAdd,
+    PermuteDims, ArrayDiagonal, _CodegenArrayAbstract)
+from sympy.utilities.lambdify import lambdify
+from sympy.core.relational import Eq, Ne, Ge, Gt, Le, Lt
+from sympy.functions import \
+    Abs, ceiling, exp, floor, sign, sin, asin, cos, \
+    acos, tan, atan, atan2, cosh, acosh, sinh, asinh, tanh, atanh, \
+    re, im, arg, erf, loggamma, sqrt
+from sympy.testing.pytest import skip
+from sympy.external import import_module
+from sympy.matrices.expressions import \
+    Determinant, HadamardProduct, Inverse, Trace
+from sympy.matrices import randMatrix
+from sympy.matrices import Identity, ZeroMatrix, OneMatrix
+from sympy import conjugate, I
+from sympy import Heaviside, gamma, polygamma
+
+
+
+torch = import_module("torch")
+
+M = MatrixSymbol("M", 3, 3)
+N = MatrixSymbol("N", 3, 3)
+P = MatrixSymbol("P", 3, 3)
+Q = MatrixSymbol("Q", 3, 3)
+
+x, y, z, t = symbols("x y z t")
+
+if torch is not None:
+    llo = [list(range(i, i + 3)) for i in range(0, 9, 3)]
+    m3x3 = torch.tensor(llo, dtype=torch.float64)
+    m3x3sympy = Matrix(llo)
+
+
+def _compare_torch_matrix(variables, expr):
+    f = lambdify(variables, expr, 'torch')
+
+    random_matrices = [randMatrix(i.shape[0], i.shape[1]) for i in variables]
+    random_variables = [torch.tensor(i.tolist(), dtype=torch.float64) for i in random_matrices]
+    r = f(*random_variables)
+    e = expr.subs(dict(zip(variables, random_matrices))).doit()
+
+    if isinstance(e, _CodegenArrayAbstract):
+        e = e.doit()
+
+    if hasattr(e, 'is_number') and e.is_number:
+        if isinstance(r, torch.Tensor) and r.dim() == 0:
+            r = r.item()
+            e = float(e)
+            assert abs(r - e) < 1e-6
+            return
+
+    if e.is_Matrix or isinstance(e, NDimArray):
+        e = torch.tensor(e.tolist(), dtype=torch.float64)
+        assert torch.allclose(r, e, atol=1e-6)
+    else:
+        raise TypeError(f"Cannot compare {type(r)} with {type(e)}")
+
+
+def _compare_torch_scalar(variables, expr, rng=lambda: random.uniform(-5, 5)):
+    f = lambdify(variables, expr, 'torch')
+    rvs = [rng() for v in variables]
+    t_rvs = [torch.tensor(i, dtype=torch.float64) for i in rvs]
+    r = f(*t_rvs)
+    if isinstance(r, torch.Tensor):
+        r = r.item()
+    e = expr.subs(dict(zip(variables, rvs))).doit()
+    assert abs(r - e) < 1e-6
+
+
+def _compare_torch_relational(variables, expr, rng=lambda: random.randint(0, 10)):
+    f = lambdify(variables, expr, 'torch')
+    rvs = [rng() for v in variables]
+    t_rvs = [torch.tensor(i, dtype=torch.float64) for i in rvs]
+    r = f(*t_rvs)
+    e = bool(expr.subs(dict(zip(variables, rvs))).doit())
+    assert r.item() == e
+
+
+def test_torch_math():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = Abs(x)
+    assert torch_code(expr) == "torch.abs(x)"
+    f = lambdify(x, expr, 'torch')
+    ma = torch.tensor([[-1, 2, -3, -4]], dtype=torch.float64)
+    y_abs = f(ma)
+    c = torch.abs(ma)
+    assert torch.all(y_abs == c)
+
+    expr = sign(x)
+    assert torch_code(expr) == "torch.sign(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-10, 10))
+
+    expr = ceiling(x)
+    assert torch_code(expr) == "torch.ceil(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = floor(x)
+    assert torch_code(expr) == "torch.floor(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = exp(x)
+    assert torch_code(expr) == "torch.exp(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-2, 2))
+
+    expr = sqrt(x)
+    assert torch_code(expr) == "torch.sqrt(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = x ** 4
+    assert torch_code(expr) == "torch.pow(x, 4)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = cos(x)
+    assert torch_code(expr) == "torch.cos(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = acos(x)
+    assert torch_code(expr) == "torch.acos(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-0.99, 0.99))
+
+    expr = sin(x)
+    assert torch_code(expr) == "torch.sin(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.random())
+
+    expr = asin(x)
+    assert torch_code(expr) == "torch.asin(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-0.99, 0.99))
+
+    expr = tan(x)
+    assert torch_code(expr) == "torch.tan(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-1.5, 1.5))
+
+    expr = atan(x)
+    assert torch_code(expr) == "torch.atan(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-5, 5))
+
+    expr = atan2(y, x)
+    assert torch_code(expr) == "torch.atan2(y, x)"
+    _compare_torch_scalar((y, x), expr, rng=lambda: random.uniform(-5, 5))
+
+    expr = cosh(x)
+    assert torch_code(expr) == "torch.cosh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-2, 2))
+
+    expr = acosh(x)
+    assert torch_code(expr) == "torch.acosh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(1.1, 5))
+
+    expr = sinh(x)
+    assert torch_code(expr) == "torch.sinh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-2, 2))
+
+    expr = asinh(x)
+    assert torch_code(expr) == "torch.asinh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-5, 5))
+
+    expr = tanh(x)
+    assert torch_code(expr) == "torch.tanh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-2, 2))
+
+    expr = atanh(x)
+    assert torch_code(expr) == "torch.atanh(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-0.9, 0.9))
+
+    expr = erf(x)
+    assert torch_code(expr) == "torch.erf(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(-2, 2))
+
+    expr = loggamma(x)
+    assert torch_code(expr) == "torch.lgamma(x)"
+    _compare_torch_scalar((x,), expr, rng=lambda: random.uniform(0.5, 5))
+
+
+def test_torch_complexes():
+    assert torch_code(re(x)) == "torch.real(x)"
+    assert torch_code(im(x)) == "torch.imag(x)"
+    assert torch_code(arg(x)) == "torch.angle(x)"
+
+
+def test_torch_relational():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = Eq(x, y)
+    assert torch_code(expr) == "torch.eq(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+    expr = Ne(x, y)
+    assert torch_code(expr) == "torch.ne(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+    expr = Ge(x, y)
+    assert torch_code(expr) == "torch.ge(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+    expr = Gt(x, y)
+    assert torch_code(expr) == "torch.gt(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+    expr = Le(x, y)
+    assert torch_code(expr) == "torch.le(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+    expr = Lt(x, y)
+    assert torch_code(expr) == "torch.lt(x, y)"
+    _compare_torch_relational((x, y), expr)
+
+
+def test_torch_matrix():
+    if torch is None:
+        skip("PyTorch not installed")
+
+    expr = M
+    assert torch_code(expr) == "M"
+    f = lambdify((M,), expr, "torch")
+    eye_mat = eye(3)
+    eye_tensor = torch.tensor(eye_mat.tolist(), dtype=torch.float64)
+    assert torch.allclose(f(eye_tensor), eye_tensor)
+
+    expr = M * N
+    assert torch_code(expr) == "torch.matmul(M, N)"
+    _compare_torch_matrix((M, N), expr)
+
+    expr = M ** 3
+    assert torch_code(expr) == "torch.mm(torch.mm(M, M), M)"
+    _compare_torch_matrix((M,), expr)
+
+    expr = M * N * P * Q
+    assert torch_code(expr) == "torch.matmul(torch.matmul(torch.matmul(M, N), P), Q)"
+    _compare_torch_matrix((M, N, P, Q), expr)
+
+    expr = Trace(M)
+    assert torch_code(expr) == "torch.trace(M)"
+    _compare_torch_matrix((M,), expr)
+
+    expr = Determinant(M)
+    assert torch_code(expr) == "torch.det(M)"
+    _compare_torch_matrix((M,), expr)
+
+    expr = HadamardProduct(M, N)
+    assert torch_code(expr) == "torch.mul(M, N)"
+    _compare_torch_matrix((M, N), expr)
+
+    expr = Inverse(M)
+    assert torch_code(expr) == "torch.linalg.inv(M)"
+
+    # For inverse, use a matrix that's guaranteed to be invertible
+    eye_mat = eye(3)
+    eye_tensor = torch.tensor(eye_mat.tolist(), dtype=torch.float64)
+    f = lambdify((M,), expr, "torch")
+    result = f(eye_tensor)
+    expected = torch.linalg.inv(eye_tensor)
+    assert torch.allclose(result, expected)
+
+
+def test_torch_array_operations():
+    if not torch:
+        skip("PyTorch not installed")
+
+    M = MatrixSymbol("M", 2, 2)
+    N = MatrixSymbol("N", 2, 2)
+    P = MatrixSymbol("P", 2, 2)
+    Q = MatrixSymbol("Q", 2, 2)
+
+    ma = torch.tensor([[1., 2.], [3., 4.]], dtype=torch.float64)
+    mb = torch.tensor([[1., -2.], [-1., 3.]], dtype=torch.float64)
+    mc = torch.tensor([[2., 0.], [1., 2.]], dtype=torch.float64)
+    md = torch.tensor([[1., -1.], [4., 7.]], dtype=torch.float64)
+
+    cg = ArrayTensorProduct(M, N)
+    assert torch_code(cg) == 'torch.einsum("ab,cd", M, N)'
+    f = lambdify((M, N), cg, 'torch')
+    y = f(ma, mb)
+    c = torch.einsum("ij,kl", ma, mb)
+    assert torch.allclose(y, c)
+
+    cg = ArrayAdd(M, N)
+    assert torch_code(cg) == 'torch.add(M, N)'
+    f = lambdify((M, N), cg, 'torch')
+    y = f(ma, mb)
+    c = ma + mb
+    assert torch.allclose(y, c)
+
+    cg = ArrayAdd(M, N, P)
+    assert torch_code(cg) == 'torch.add(torch.add(M, N), P)'
+    f = lambdify((M, N, P), cg, 'torch')
+    y = f(ma, mb, mc)
+    c = ma + mb + mc
+    assert torch.allclose(y, c)
+
+    cg = ArrayAdd(M, N, P, Q)
+    assert torch_code(cg) == 'torch.add(torch.add(torch.add(M, N), P), Q)'
+    f = lambdify((M, N, P, Q), cg, 'torch')
+    y = f(ma, mb, mc, md)
+    c = ma + mb + mc + md
+    assert torch.allclose(y, c)
+
+    cg = PermuteDims(M, [1, 0])
+    assert torch_code(cg) == 'M.permute(1, 0)'
+    f = lambdify((M,), cg, 'torch')
+    y = f(ma)
+    c = ma.T
+    assert torch.allclose(y, c)
+
+    cg = PermuteDims(ArrayTensorProduct(M, N), [1, 2, 3, 0])
+    assert torch_code(cg) == 'torch.einsum("ab,cd", M, N).permute(1, 2, 3, 0)'
+    f = lambdify((M, N), cg, 'torch')
+    y = f(ma, mb)
+    c = torch.einsum("ab,cd", ma, mb).permute(1, 2, 3, 0)
+    assert torch.allclose(y, c)
+
+    cg = ArrayDiagonal(ArrayTensorProduct(M, N), (1, 2))
+    assert torch_code(cg) == 'torch.einsum("ab,bc->acb", M, N)'
+    f = lambdify((M, N), cg, 'torch')
+    y = f(ma, mb)
+    c = torch.einsum("ab,bc->acb", ma, mb)
+    assert torch.allclose(y, c)
+
+
+def test_torch_derivative():
+    """Test derivative handling."""
+    expr = Derivative(sin(x), x)
+    assert torch_code(expr) == 'torch.autograd.grad(torch.sin(x), x)[0]'
+
+
+def test_torch_printing_dtype():
+    if not torch:
+        skip("PyTorch not installed")
+
+    # matrix printing with default dtype
+    expr = Matrix([[x, sin(y)], [exp(z), -t]])
+    assert "dtype=torch.float64" in torch_code(expr)
+
+    # explicit dtype
+    assert "dtype=torch.float32" in torch_code(expr, dtype="torch.float32")
+
+    # with requires_grad
+    result = torch_code(expr, requires_grad=True)
+    assert "requires_grad=True" in result
+    assert "dtype=torch.float64" in result
+
+    # both
+    result = torch_code(expr, requires_grad=True, dtype="torch.float32")
+    assert "requires_grad=True" in result
+    assert "dtype=torch.float32" in result
+
+
+def test_requires_grad():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = sin(x) + cos(y)
+    f = lambdify([x, y], expr, 'torch')
+
+    # make sure the gradients flow
+    x_val = torch.tensor(1.0, requires_grad=True)
+    y_val = torch.tensor(2.0, requires_grad=True)
+    result = f(x_val, y_val)
+    assert result.requires_grad
+    result.backward()
+
+    # x_val.grad should be cos(x_val) which is close to cos(1.0)
+    assert abs(x_val.grad.item() - float(cos(1.0).evalf())) < 1e-6
+
+    # y_val.grad should be -sin(y_val) which is close to -sin(2.0)
+    assert abs(y_val.grad.item() - float(-sin(2.0).evalf())) < 1e-6
+
+
+def test_torch_multi_variable_derivatives():
+    if not torch:
+        skip("PyTorch not installed")
+
+    x, y, z = symbols("x y z")
+
+    expr = Derivative(sin(x), x)
+    assert torch_code(expr) == "torch.autograd.grad(torch.sin(x), x)[0]"
+
+    expr = Derivative(sin(x), (x, 2))
+    assert torch_code(
+        expr) == "torch.autograd.grad(torch.autograd.grad(torch.sin(x), x, create_graph=True)[0], x, create_graph=True)[0]"
+
+    expr = Derivative(sin(x * y), x, y)
+    result = torch_code(expr)
+    expected = "torch.autograd.grad(torch.autograd.grad(torch.sin(x*y), x, create_graph=True)[0], y, create_graph=True)[0]"
+    normalized_result = result.replace(" ", "")
+    normalized_expected = expected.replace(" ", "")
+    assert normalized_result == normalized_expected
+
+    expr = Derivative(sin(x), x, x)
+    result = torch_code(expr)
+    expected = "torch.autograd.grad(torch.autograd.grad(torch.sin(x), x, create_graph=True)[0], x, create_graph=True)[0]"
+    assert result == expected
+
+    expr = Derivative(sin(x * y * z), x, (y, 2), z)
+    result = torch_code(expr)
+    expected = "torch.autograd.grad(torch.autograd.grad(torch.autograd.grad(torch.autograd.grad(torch.sin(x*y*z), x, create_graph=True)[0], y, create_graph=True)[0], y, create_graph=True)[0], z, create_graph=True)[0]"
+    normalized_result = result.replace(" ", "")
+    normalized_expected = expected.replace(" ", "")
+    assert normalized_result == normalized_expected
+
+
+def test_torch_derivative_lambdify():
+    if not torch:
+        skip("PyTorch not installed")
+
+    x = symbols("x")
+    y = symbols("y")
+
+    expr = Derivative(x ** 2, x)
+    f = lambdify(x, expr, 'torch')
+    x_val = torch.tensor(2.0, requires_grad=True)
+    result = f(x_val)
+    assert torch.isclose(result, torch.tensor(4.0))
+
+    expr = Derivative(sin(x), (x, 2))
+    f = lambdify(x, expr, 'torch')
+    # Second derivative of sin(x) at x=0 is 0, not -1
+    x_val = torch.tensor(0.0, requires_grad=True)
+    result = f(x_val)
+    assert torch.isclose(result, torch.tensor(0.0), atol=1e-5)
+
+    x_val = torch.tensor(math.pi / 2, requires_grad=True)
+    result = f(x_val)
+    assert torch.isclose(result, torch.tensor(-1.0), atol=1e-5)
+
+    expr = Derivative(x * y ** 2, x, y)
+    f = lambdify((x, y), expr, 'torch')
+    x_val = torch.tensor(2.0, requires_grad=True)
+    y_val = torch.tensor(3.0, requires_grad=True)
+    result = f(x_val, y_val)
+    assert torch.isclose(result, torch.tensor(6.0))
+
+
+def test_torch_special_matrices():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = Identity(3)
+    assert torch_code(expr) == "torch.eye(3)"
+
+    n = symbols("n")
+    expr = Identity(n)
+    assert torch_code(expr) == "torch.eye(n, n)"
+
+    expr = ZeroMatrix(2, 3)
+    assert torch_code(expr) == "torch.zeros((2, 3))"
+
+    m, n = symbols("m n")
+    expr = ZeroMatrix(m, n)
+    assert torch_code(expr) == "torch.zeros((m, n))"
+
+    expr = OneMatrix(2, 3)
+    assert torch_code(expr) == "torch.ones((2, 3))"
+
+    expr = OneMatrix(m, n)
+    assert torch_code(expr) == "torch.ones((m, n))"
+
+
+def test_torch_special_matrices_lambdify():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = Identity(3)
+    f = lambdify([], expr, 'torch')
+    result = f()
+    expected = torch.eye(3)
+    assert torch.allclose(result, expected)
+
+    expr = ZeroMatrix(2, 3)
+    f = lambdify([], expr, 'torch')
+    result = f()
+    expected = torch.zeros((2, 3))
+    assert torch.allclose(result, expected)
+
+    expr = OneMatrix(2, 3)
+    f = lambdify([], expr, 'torch')
+    result = f()
+    expected = torch.ones((2, 3))
+    assert torch.allclose(result, expected)
+
+
+def test_torch_complex_operations():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = conjugate(x)
+    assert torch_code(expr) == "torch.conj(x)"
+
+    # SymPy distributes conjugate over addition and applies specific rules for each term
+    expr = conjugate(sin(x) + I * cos(y))
+    assert torch_code(expr) == "torch.sin(torch.conj(x)) - 1j*torch.cos(torch.conj(y))"
+
+    expr = I
+    assert torch_code(expr) == "1j"
+
+    expr = 2 * I + x
+    assert torch_code(expr) == "x + 2*1j"
+
+    expr = exp(I * x)
+    assert torch_code(expr) == "torch.exp(1j*x)"
+
+
+def test_torch_special_functions():
+    if not torch:
+        skip("PyTorch not installed")
+
+    expr = Heaviside(x)
+    assert torch_code(expr) == "torch.heaviside(x, 1/2)"
+
+    expr = Heaviside(x, 0)
+    assert torch_code(expr) == "torch.heaviside(x, 0)"
+
+    expr = gamma(x)
+    assert torch_code(expr) == "torch.special.gamma(x)"
+
+    expr = polygamma(0, x)  # Use polygamma instead of digamma because sympy will default to that anyway
+    assert torch_code(expr) == "torch.special.digamma(x)"
+
+    expr = gamma(sin(x))
+    assert torch_code(expr) == "torch.special.gamma(torch.sin(x))"

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tests/test_tree.py

@@ -0,0 +1,196 @@
+from sympy.printing.tree import tree
+from sympy.testing.pytest import XFAIL
+
+
+# Remove this flag after making _assumptions cache deterministic.
+@XFAIL
+def test_print_tree_MatAdd():
+    from sympy.matrices.expressions import MatrixSymbol
+    A = MatrixSymbol('A', 3, 3)
+    B = MatrixSymbol('B', 3, 3)
+
+    test_str = [
+        'MatAdd: A + B\n',
+        'algebraic: False\n',
+        'commutative: False\n',
+        'complex: False\n',
+        'composite: False\n',
+        'even: False\n',
+        'extended_negative: False\n',
+        'extended_nonnegative: False\n',
+        'extended_nonpositive: False\n',
+        'extended_nonzero: False\n',
+        'extended_positive: False\n',
+        'extended_real: False\n',
+        'imaginary: False\n',
+        'integer: False\n',
+        'irrational: False\n',
+        'negative: False\n',
+        'noninteger: False\n',
+        'nonnegative: False\n',
+        'nonpositive: False\n',
+        'nonzero: False\n',
+        'odd: False\n',
+        'positive: False\n',
+        'prime: False\n',
+        'rational: False\n',
+        'real: False\n',
+        'transcendental: False\n',
+        'zero: False\n',
+        '+-MatrixSymbol: A\n',
+        '| algebraic: False\n',
+        '| commutative: False\n',
+        '| complex: False\n',
+        '| composite: False\n',
+        '| even: False\n',
+        '| extended_negative: False\n',
+        '| extended_nonnegative: False\n',
+        '| extended_nonpositive: False\n',
+        '| extended_nonzero: False\n',
+        '| extended_positive: False\n',
+        '| extended_real: False\n',
+        '| imaginary: False\n',
+        '| integer: False\n',
+        '| irrational: False\n',
+        '| negative: False\n',
+        '| noninteger: False\n',
+        '| nonnegative: False\n',
+        '| nonpositive: False\n',
+        '| nonzero: False\n',
+        '| odd: False\n',
+        '| positive: False\n',
+        '| prime: False\n',
+        '| rational: False\n',
+        '| real: False\n',
+        '| transcendental: False\n',
+        '| zero: False\n',
+        '| +-Symbol: A\n',
+        '| | commutative: True\n',
+        '| +-Integer: 3\n',
+        '| | algebraic: True\n',
+        '| | commutative: True\n',
+        '| | complex: True\n',
+        '| | extended_negative: False\n',
+        '| | extended_nonnegative: True\n',
+        '| | extended_real: True\n',
+        '| | finite: True\n',
+        '| | hermitian: True\n',
+        '| | imaginary: False\n',
+        '| | infinite: False\n',
+        '| | integer: True\n',
+        '| | irrational: False\n',
+        '| | negative: False\n',
+        '| | noninteger: False\n',
+        '| | nonnegative: True\n',
+        '| | rational: True\n',
+        '| | real: True\n',
+        '| | transcendental: False\n',
+        '| +-Integer: 3\n',
+        '|   algebraic: True\n',
+        '|   commutative: True\n',
+        '|   complex: True\n',
+        '|   extended_negative: False\n',
+        '|   extended_nonnegative: True\n',
+        '|   extended_real: True\n',
+        '|   finite: True\n',
+        '|   hermitian: True\n',
+        '|   imaginary: False\n',
+        '|   infinite: False\n',
+        '|   integer: True\n',
+        '|   irrational: False\n',
+        '|   negative: False\n',
+        '|   noninteger: False\n',
+        '|   nonnegative: True\n',
+        '|   rational: True\n',
+        '|   real: True\n',
+        '|   transcendental: False\n',
+        '+-MatrixSymbol: B\n',
+        '  algebraic: False\n',
+        '  commutative: False\n',
+        '  complex: False\n',
+        '  composite: False\n',
+        '  even: False\n',
+        '  extended_negative: False\n',
+        '  extended_nonnegative: False\n',
+        '  extended_nonpositive: False\n',
+        '  extended_nonzero: False\n',
+        '  extended_positive: False\n',
+        '  extended_real: False\n',
+        '  imaginary: False\n',
+        '  integer: False\n',
+        '  irrational: False\n',
+        '  negative: False\n',
+        '  noninteger: False\n',
+        '  nonnegative: False\n',
+        '  nonpositive: False\n',
+        '  nonzero: False\n',
+        '  odd: False\n',
+        '  positive: False\n',
+        '  prime: False\n',
+        '  rational: False\n',
+        '  real: False\n',
+        '  transcendental: False\n',
+        '  zero: False\n',
+        '  +-Symbol: B\n',
+        '  | commutative: True\n',
+        '  +-Integer: 3\n',
+        '  | algebraic: True\n',
+        '  | commutative: True\n',
+        '  | complex: True\n',
+        '  | extended_negative: False\n',
+        '  | extended_nonnegative: True\n',
+        '  | extended_real: True\n',
+        '  | finite: True\n',
+        '  | hermitian: True\n',
+        '  | imaginary: False\n',
+        '  | infinite: False\n',
+        '  | integer: True\n',
+        '  | irrational: False\n',
+        '  | negative: False\n',
+        '  | noninteger: False\n',
+        '  | nonnegative: True\n',
+        '  | rational: True\n',
+        '  | real: True\n',
+        '  | transcendental: False\n',
+        '  +-Integer: 3\n',
+        '    algebraic: True\n',
+        '    commutative: True\n',
+        '    complex: True\n',
+        '    extended_negative: False\n',
+        '    extended_nonnegative: True\n',
+        '    extended_real: True\n',
+        '    finite: True\n',
+        '    hermitian: True\n',
+        '    imaginary: False\n',
+        '    infinite: False\n',
+        '    integer: True\n',
+        '    irrational: False\n',
+        '    negative: False\n',
+        '    noninteger: False\n',
+        '    nonnegative: True\n',
+        '    rational: True\n',
+        '    real: True\n',
+        '    transcendental: False\n'
+    ]
+
+    assert tree(A + B) == "".join(test_str)
+
+
+def test_print_tree_MatAdd_noassumptions():
+    from sympy.matrices.expressions import MatrixSymbol
+    A = MatrixSymbol('A', 3, 3)
+    B = MatrixSymbol('B', 3, 3)
+
+    test_str = \
+"""MatAdd: A + B
++-MatrixSymbol: A
+| +-Str: A
+| +-Integer: 3
+| +-Integer: 3
++-MatrixSymbol: B
+  +-Str: B
+  +-Integer: 3
+  +-Integer: 3
+"""
+
+    assert tree(A + B, assumptions=False) == test_str

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/theanocode.py

@@ -0,0 +1,571 @@
+"""
+.. deprecated:: 1.8
+
+  ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to
+  Aesara. Use ``sympy.printing.aesaracode`` instead. See
+  :ref:`theanocode-deprecated` for more information.
+
+"""
+from __future__ import annotations
+import math
+from typing import Any
+
+from sympy.external import import_module
+from sympy.printing.printer import Printer
+from sympy.utilities.iterables import is_sequence
+import sympy
+from functools import partial
+
+from sympy.utilities.decorator import doctest_depends_on
+from sympy.utilities.exceptions import sympy_deprecation_warning
+
+
+__doctest_requires__ = {('theano_function',): ['theano']}
+
+
+theano = import_module('theano')
+
+
+if theano:
+    ts = theano.scalar
+    tt = theano.tensor
+    from theano.sandbox import linalg as tlinalg
+
+    mapping = {
+            sympy.Add: tt.add,
+            sympy.Mul: tt.mul,
+            sympy.Abs: tt.abs_,
+            sympy.sign: tt.sgn,
+            sympy.ceiling: tt.ceil,
+            sympy.floor: tt.floor,
+            sympy.log: tt.log,
+            sympy.exp: tt.exp,
+            sympy.sqrt: tt.sqrt,
+            sympy.cos: tt.cos,
+            sympy.acos: tt.arccos,
+            sympy.sin: tt.sin,
+            sympy.asin: tt.arcsin,
+            sympy.tan: tt.tan,
+            sympy.atan: tt.arctan,
+            sympy.atan2: tt.arctan2,
+            sympy.cosh: tt.cosh,
+            sympy.acosh: tt.arccosh,
+            sympy.sinh: tt.sinh,
+            sympy.asinh: tt.arcsinh,
+            sympy.tanh: tt.tanh,
+            sympy.atanh: tt.arctanh,
+            sympy.re: tt.real,
+            sympy.im: tt.imag,
+            sympy.arg: tt.angle,
+            sympy.erf: tt.erf,
+            sympy.gamma: tt.gamma,
+            sympy.loggamma: tt.gammaln,
+            sympy.Pow: tt.pow,
+            sympy.Eq: tt.eq,
+            sympy.StrictGreaterThan: tt.gt,
+            sympy.StrictLessThan: tt.lt,
+            sympy.LessThan: tt.le,
+            sympy.GreaterThan: tt.ge,
+            sympy.And: tt.and_,
+            sympy.Or: tt.or_,
+            sympy.Max: tt.maximum,  # SymPy accept >2 inputs, Theano only 2
+            sympy.Min: tt.minimum,  # SymPy accept >2 inputs, Theano only 2
+            sympy.conjugate: tt.conj,
+            sympy.core.numbers.ImaginaryUnit: lambda:tt.complex(0,1),
+            # Matrices
+            sympy.MatAdd: tt.Elemwise(ts.add),
+            sympy.HadamardProduct: tt.Elemwise(ts.mul),
+            sympy.Trace: tlinalg.trace,
+            sympy.Determinant : tlinalg.det,
+            sympy.Inverse: tlinalg.matrix_inverse,
+            sympy.Transpose: tt.DimShuffle((False, False), [1, 0]),
+    }
+
+
+class TheanoPrinter(Printer):
+    """ Code printer which creates Theano symbolic expression graphs.
+
+    Parameters
+    ==========
+
+    cache : dict
+        Cache dictionary to use. If None (default) will use
+        the global cache. To create a printer which does not depend on or alter
+        global state pass an empty dictionary. Note: the dictionary is not
+        copied on initialization of the printer and will be updated in-place,
+        so using the same dict object when creating multiple printers or making
+        multiple calls to :func:`.theano_code` or :func:`.theano_function` means
+        the cache is shared between all these applications.
+
+    Attributes
+    ==========
+
+    cache : dict
+        A cache of Theano variables which have been created for SymPy
+        symbol-like objects (e.g. :class:`sympy.core.symbol.Symbol` or
+        :class:`sympy.matrices.expressions.MatrixSymbol`). This is used to
+        ensure that all references to a given symbol in an expression (or
+        multiple expressions) are printed as the same Theano variable, which is
+        created only once. Symbols are differentiated only by name and type. The
+        format of the cache's contents should be considered opaque to the user.
+    """
+    printmethod = "_theano"
+
+    def __init__(self, *args, **kwargs):
+        self.cache = kwargs.pop('cache', {})
+        super().__init__(*args, **kwargs)
+
+    def _get_key(self, s, name=None, dtype=None, broadcastable=None):
+        """ Get the cache key for a SymPy object.
+
+        Parameters
+        ==========
+
+        s : sympy.core.basic.Basic
+            SymPy object to get key for.
+
+        name : str
+            Name of object, if it does not have a ``name`` attribute.
+        """
+
+        if name is None:
+            name = s.name
+
+        return (name, type(s), s.args, dtype, broadcastable)
+
+    def _get_or_create(self, s, name=None, dtype=None, broadcastable=None):
+        """
+        Get the Theano variable for a SymPy symbol from the cache, or create it
+        if it does not exist.
+        """
+
+        # Defaults
+        if name is None:
+            name = s.name
+        if dtype is None:
+            dtype = 'floatX'
+        if broadcastable is None:
+            broadcastable = ()
+
+        key = self._get_key(s, name, dtype=dtype, broadcastable=broadcastable)
+
+        if key in self.cache:
+            return self.cache[key]
+
+        value = tt.tensor(name=name, dtype=dtype, broadcastable=broadcastable)
+        self.cache[key] = value
+        return value
+
+    def _print_Symbol(self, s, **kwargs):
+        dtype = kwargs.get('dtypes', {}).get(s)
+        bc = kwargs.get('broadcastables', {}).get(s)
+        return self._get_or_create(s, dtype=dtype, broadcastable=bc)
+
+    def _print_AppliedUndef(self, s, **kwargs):
+        name = str(type(s)) + '_' + str(s.args[0])
+        dtype = kwargs.get('dtypes', {}).get(s)
+        bc = kwargs.get('broadcastables', {}).get(s)
+        return self._get_or_create(s, name=name, dtype=dtype, broadcastable=bc)
+
+    def _print_Basic(self, expr, **kwargs):
+        op = mapping[type(expr)]
+        children = [self._print(arg, **kwargs) for arg in expr.args]
+        return op(*children)
+
+    def _print_Number(self, n, **kwargs):
+        # Integers already taken care of below, interpret as float
+        return float(n.evalf())
+
+    def _print_MatrixSymbol(self, X, **kwargs):
+        dtype = kwargs.get('dtypes', {}).get(X)
+        return self._get_or_create(X, dtype=dtype, broadcastable=(None, None))
+
+    def _print_DenseMatrix(self, X, **kwargs):
+        if not hasattr(tt, 'stacklists'):
+            raise NotImplementedError(
+               "Matrix translation not yet supported in this version of Theano")
+
+        return tt.stacklists([
+            [self._print(arg, **kwargs) for arg in L]
+            for L in X.tolist()
+        ])
+
+    _print_ImmutableMatrix = _print_ImmutableDenseMatrix = _print_DenseMatrix
+
+    def _print_MatMul(self, expr, **kwargs):
+        children = [self._print(arg, **kwargs) for arg in expr.args]
+        result = children[0]
+        for child in children[1:]:
+            result = tt.dot(result, child)
+        return result
+
+    def _print_MatPow(self, expr, **kwargs):
+        children = [self._print(arg, **kwargs) for arg in expr.args]
+        result = 1
+        if isinstance(children[1], int) and children[1] > 0:
+            for i in range(children[1]):
+                result = tt.dot(result, children[0])
+        else:
+            raise NotImplementedError('''Only non-negative integer
+           powers of matrices can be handled by Theano at the moment''')
+        return result
+
+    def _print_MatrixSlice(self, expr, **kwargs):
+        parent = self._print(expr.parent, **kwargs)
+        rowslice = self._print(slice(*expr.rowslice), **kwargs)
+        colslice = self._print(slice(*expr.colslice), **kwargs)
+        return parent[rowslice, colslice]
+
+    def _print_BlockMatrix(self, expr, **kwargs):
+        nrows, ncols = expr.blocks.shape
+        blocks = [[self._print(expr.blocks[r, c], **kwargs)
+                        for c in range(ncols)]
+                        for r in range(nrows)]
+        return tt.join(0, *[tt.join(1, *row) for row in blocks])
+
+
+    def _print_slice(self, expr, **kwargs):
+        return slice(*[self._print(i, **kwargs)
+                        if isinstance(i, sympy.Basic) else i
+                        for i in (expr.start, expr.stop, expr.step)])
+
+    def _print_Pi(self, expr, **kwargs):
+        return math.pi
+
+    def _print_Exp1(self, expr, **kwargs):
+        return ts.exp(1)
+
+    def _print_Piecewise(self, expr, **kwargs):
+        import numpy as np
+        e, cond = expr.args[0].args  # First condition and corresponding value
+
+        # Print conditional expression and value for first condition
+        p_cond = self._print(cond, **kwargs)
+        p_e = self._print(e, **kwargs)
+
+        # One condition only
+        if len(expr.args) == 1:
+            # Return value if condition else NaN
+            return tt.switch(p_cond, p_e, np.nan)
+
+        # Return value_1 if condition_1 else evaluate remaining conditions
+        p_remaining = self._print(sympy.Piecewise(*expr.args[1:]), **kwargs)
+        return tt.switch(p_cond, p_e, p_remaining)
+
+    def _print_Rational(self, expr, **kwargs):
+        return tt.true_div(self._print(expr.p, **kwargs),
+                           self._print(expr.q, **kwargs))
+
+    def _print_Integer(self, expr, **kwargs):
+        return expr.p
+
+    def _print_factorial(self, expr, **kwargs):
+        return self._print(sympy.gamma(expr.args[0] + 1), **kwargs)
+
+    def _print_Derivative(self, deriv, **kwargs):
+        rv = self._print(deriv.expr, **kwargs)
+        for var in deriv.variables:
+            var = self._print(var, **kwargs)
+            rv = tt.Rop(rv, var, tt.ones_like(var))
+        return rv
+
+    def emptyPrinter(self, expr):
+        return expr
+
+    def doprint(self, expr, dtypes=None, broadcastables=None):
+        """ Convert a SymPy expression to a Theano graph variable.
+
+        The ``dtypes`` and ``broadcastables`` arguments are used to specify the
+        data type, dimension, and broadcasting behavior of the Theano variables
+        corresponding to the free symbols in ``expr``. Each is a mapping from
+        SymPy symbols to the value of the corresponding argument to
+        ``theano.tensor.Tensor``.
+
+        See the corresponding `documentation page`__ for more information on
+        broadcasting in Theano.
+
+        .. __: http://deeplearning.net/software/theano/tutorial/broadcasting.html
+
+        Parameters
+        ==========
+
+        expr : sympy.core.expr.Expr
+            SymPy expression to print.
+
+        dtypes : dict
+            Mapping from SymPy symbols to Theano datatypes to use when creating
+            new Theano variables for those symbols. Corresponds to the ``dtype``
+            argument to ``theano.tensor.Tensor``. Defaults to ``'floatX'``
+            for symbols not included in the mapping.
+
+        broadcastables : dict
+            Mapping from SymPy symbols to the value of the ``broadcastable``
+            argument to ``theano.tensor.Tensor`` to use when creating Theano
+            variables for those symbols. Defaults to the empty tuple for symbols
+            not included in the mapping (resulting in a scalar).
+
+        Returns
+        =======
+
+        theano.gof.graph.Variable
+            A variable corresponding to the expression's value in a Theano
+            symbolic expression graph.
+
+        """
+        if dtypes is None:
+            dtypes = {}
+        if broadcastables is None:
+            broadcastables = {}
+
+        return self._print(expr, dtypes=dtypes, broadcastables=broadcastables)
+
+
+global_cache: dict[Any, Any] = {}
+
+
+def theano_code(expr, cache=None, **kwargs):
+    """
+    Convert a SymPy expression into a Theano graph variable.
+
+    .. deprecated:: 1.8
+
+      ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to
+      Aesara. Use ``sympy.printing.aesaracode`` instead. See
+      :ref:`theanocode-deprecated` for more information.
+
+    Parameters
+    ==========
+
+    expr : sympy.core.expr.Expr
+        SymPy expression object to convert.
+
+    cache : dict
+        Cached Theano variables (see :class:`TheanoPrinter.cache
+        <TheanoPrinter>`). Defaults to the module-level global cache.
+
+    dtypes : dict
+        Passed to :meth:`.TheanoPrinter.doprint`.
+
+    broadcastables : dict
+        Passed to :meth:`.TheanoPrinter.doprint`.
+
+    Returns
+    =======
+
+    theano.gof.graph.Variable
+        A variable corresponding to the expression's value in a Theano symbolic
+        expression graph.
+
+    """
+    sympy_deprecation_warning(
+        """
+        sympy.printing.theanocode is deprecated. Theano has been renamed to
+        Aesara. Use sympy.printing.aesaracode instead.""",
+        deprecated_since_version="1.8",
+    active_deprecations_target='theanocode-deprecated')
+
+    if not theano:
+        raise ImportError("theano is required for theano_code")
+
+    if cache is None:
+        cache = global_cache
+
+    return TheanoPrinter(cache=cache, settings={}).doprint(expr, **kwargs)
+
+
+def dim_handling(inputs, dim=None, dims=None, broadcastables=None):
+    r"""
+    Get value of ``broadcastables`` argument to :func:`.theano_code` from
+    keyword arguments to :func:`.theano_function`.
+
+    Included for backwards compatibility.
+
+    Parameters
+    ==========
+
+    inputs
+        Sequence of input symbols.
+
+    dim : int
+        Common number of dimensions for all inputs. Overrides other arguments
+        if given.
+
+    dims : dict
+        Mapping from input symbols to number of dimensions. Overrides
+        ``broadcastables`` argument if given.
+
+    broadcastables : dict
+        Explicit value of ``broadcastables`` argument to
+        :meth:`.TheanoPrinter.doprint`. If not None function will return this value unchanged.
+
+    Returns
+    =======
+    dict
+        Dictionary mapping elements of ``inputs`` to their "broadcastable"
+        values (tuple of ``bool``\ s).
+    """
+    if dim is not None:
+        return dict.fromkeys(inputs, (False,) * dim)
+
+    if dims is not None:
+        maxdim = max(dims.values())
+        return {
+            s: (False,) * d + (True,) * (maxdim - d)
+            for s, d in dims.items()
+        }
+
+    if broadcastables is not None:
+        return broadcastables
+
+    return {}
+
+
+@doctest_depends_on(modules=('theano',))
+def theano_function(inputs, outputs, scalar=False, *,
+        dim=None, dims=None, broadcastables=None, **kwargs):
+    """
+    Create a Theano function from SymPy expressions.
+
+    .. deprecated:: 1.8
+
+      ``sympy.printing.theanocode`` is deprecated. Theano has been renamed to
+      Aesara. Use ``sympy.printing.aesaracode`` instead. See
+      :ref:`theanocode-deprecated` for more information.
+
+    The inputs and outputs are converted to Theano variables using
+    :func:`.theano_code` and then passed to ``theano.function``.
+
+    Parameters
+    ==========
+
+    inputs
+        Sequence of symbols which constitute the inputs of the function.
+
+    outputs
+        Sequence of expressions which constitute the outputs(s) of the
+        function. The free symbols of each expression must be a subset of
+        ``inputs``.
+
+    scalar : bool
+        Convert 0-dimensional arrays in output to scalars. This will return a
+        Python wrapper function around the Theano function object.
+
+    cache : dict
+        Cached Theano variables (see :class:`TheanoPrinter.cache
+        <TheanoPrinter>`). Defaults to the module-level global cache.
+
+    dtypes : dict
+        Passed to :meth:`.TheanoPrinter.doprint`.
+
+    broadcastables : dict
+        Passed to :meth:`.TheanoPrinter.doprint`.
+
+    dims : dict
+        Alternative to ``broadcastables`` argument. Mapping from elements of
+        ``inputs`` to integers indicating the dimension of their associated
+        arrays/tensors. Overrides ``broadcastables`` argument if given.
+
+    dim : int
+        Another alternative to the ``broadcastables`` argument. Common number of
+        dimensions to use for all arrays/tensors.
+        ``theano_function([x, y], [...], dim=2)`` is equivalent to using
+        ``broadcastables={x: (False, False), y: (False, False)}``.
+
+    Returns
+    =======
+    callable
+        A callable object which takes values of ``inputs`` as positional
+        arguments and returns an output array for each of the expressions
+        in ``outputs``. If ``outputs`` is a single expression the function will
+        return a Numpy array, if it is a list of multiple expressions the
+        function will return a list of arrays. See description of the ``squeeze``
+        argument above for the behavior when a single output is passed in a list.
+        The returned object will either be an instance of
+        ``theano.compile.function_module.Function`` or a Python wrapper
+        function around one. In both cases, the returned value will have a
+        ``theano_function`` attribute which points to the return value of
+        ``theano.function``.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y, z
+    >>> from sympy.printing.theanocode import theano_function
+
+    A simple function with one input and one output:
+
+    >>> f1 = theano_function([x], [x**2 - 1], scalar=True)
+    >>> f1(3)
+    8.0
+
+    A function with multiple inputs and one output:
+
+    >>> f2 = theano_function([x, y, z], [(x**z + y**z)**(1/z)], scalar=True)
+    >>> f2(3, 4, 2)
+    5.0
+
+    A function with multiple inputs and multiple outputs:
+
+    >>> f3 = theano_function([x, y], [x**2 + y**2, x**2 - y**2], scalar=True)
+    >>> f3(2, 3)
+    [13.0, -5.0]
+
+    See also
+    ========
+
+    dim_handling
+
+    """
+    sympy_deprecation_warning(
+        """
+        sympy.printing.theanocode is deprecated. Theano has been renamed to Aesara. Use sympy.printing.aesaracode instead""",
+        deprecated_since_version="1.8",
+    active_deprecations_target='theanocode-deprecated')
+
+    if not theano:
+        raise ImportError("theano is required for theano_function")
+
+    # Pop off non-theano keyword args
+    cache = kwargs.pop('cache', {})
+    dtypes = kwargs.pop('dtypes', {})
+
+    broadcastables = dim_handling(
+        inputs, dim=dim, dims=dims, broadcastables=broadcastables,
+    )
+
+    # Print inputs/outputs
+    code = partial(theano_code, cache=cache, dtypes=dtypes,
+                   broadcastables=broadcastables)
+    tinputs = list(map(code, inputs))
+    toutputs = list(map(code, outputs))
+
+    #fix constant expressions as variables
+    toutputs = [output if isinstance(output, theano.Variable) else tt.as_tensor_variable(output) for output in toutputs]
+
+    if len(toutputs) == 1:
+        toutputs = toutputs[0]
+
+    # Compile theano func
+    func = theano.function(tinputs, toutputs, **kwargs)
+
+    is_0d = [len(o.variable.broadcastable) == 0 for o in func.outputs]
+
+    # No wrapper required
+    if not scalar or not any(is_0d):
+        func.theano_function = func
+        return func
+
+    # Create wrapper to convert 0-dimensional outputs to scalars
+    def wrapper(*args):
+        out = func(*args)
+        # out can be array(1.0) or [array(1.0), array(2.0)]
+
+        if is_sequence(out):
+            return [o[()] if is_0d[i] else o for i, o in enumerate(out)]
+        else:
+            return out[()]
+
+    wrapper.__wrapped__ = func
+    wrapper.__doc__ = func.__doc__
+    wrapper.theano_function = func
+    return wrapper

outputs/audit_venv/lib/python3.11/site-packages/sympy/printing/tree.py

@@ -0,0 +1,175 @@
+def pprint_nodes(subtrees):
+    """
+    Prettyprints systems of nodes.
+
+    Examples
+    ========
+
+    >>> from sympy.printing.tree import pprint_nodes
+    >>> print(pprint_nodes(["a", "b1\\nb2", "c"]))
+    +-a
+    +-b1
+    | b2
+    +-c
+
+    """
+    def indent(s, type=1):
+        x = s.split("\n")
+        r = "+-%s\n" % x[0]
+        for a in x[1:]:
+            if a == "":
+                continue
+            if type == 1:
+                r += "| %s\n" % a
+            else:
+                r += "  %s\n" % a
+        return r
+    if not subtrees:
+        return ""
+    f = ""
+    for a in subtrees[:-1]:
+        f += indent(a)
+    f += indent(subtrees[-1], 2)
+    return f
+
+
+def print_node(node, assumptions=True):
+    """
+    Returns information about the "node".
+
+    This includes class name, string representation and assumptions.
+
+    Parameters
+    ==========
+
+    assumptions : bool, optional
+        See the ``assumptions`` keyword in ``tree``
+    """
+    s = "%s: %s\n" % (node.__class__.__name__, str(node))
+
+    if assumptions:
+        d = node._assumptions
+    else:
+        d = None
+
+    if d:
+        for a in sorted(d):
+            v = d[a]
+            if v is None:
+                continue
+            s += "%s: %s\n" % (a, v)
+
+    return s
+
+
+def tree(node, assumptions=True):
+    """
+    Returns a tree representation of "node" as a string.
+
+    It uses print_node() together with pprint_nodes() on node.args recursively.
+
+    Parameters
+    ==========
+
+    assumptions : bool, optional
+        The flag to decide whether to print out all the assumption data
+        (such as ``is_integer`, ``is_real``) associated with the
+        expression or not.
+
+        Enabling the flag makes the result verbose, and the printed
+        result may not be deterministic because of the randomness used
+        in backtracing the assumptions.
+
+    See Also
+    ========
+
+    print_tree
+
+    """
+    subtrees = []
+    for arg in node.args:
+        subtrees.append(tree(arg, assumptions=assumptions))
+    s = print_node(node, assumptions=assumptions) + pprint_nodes(subtrees)
+    return s
+
+
+def print_tree(node, assumptions=True):
+    """
+    Prints a tree representation of "node".
+
+    Parameters
+    ==========
+
+    assumptions : bool, optional
+        The flag to decide whether to print out all the assumption data
+        (such as ``is_integer`, ``is_real``) associated with the
+        expression or not.
+
+        Enabling the flag makes the result verbose, and the printed
+        result may not be deterministic because of the randomness used
+        in backtracing the assumptions.
+
+    Examples
+    ========
+
+    >>> from sympy.printing import print_tree
+    >>> from sympy import Symbol
+    >>> x = Symbol('x', odd=True)
+    >>> y = Symbol('y', even=True)
+
+    Printing with full assumptions information:
+
+    >>> print_tree(y**x)
+    Pow: y**x
+    +-Symbol: y
+    | algebraic: True
+    | commutative: True
+    | complex: True
+    | even: True
+    | extended_real: True
+    | finite: True
+    | hermitian: True
+    | imaginary: False
+    | infinite: False
+    | integer: True
+    | irrational: False
+    | noninteger: False
+    | odd: False
+    | rational: True
+    | real: True
+    | transcendental: False
+    +-Symbol: x
+      algebraic: True
+      commutative: True
+      complex: True
+      even: False
+      extended_nonzero: True
+      extended_real: True
+      finite: True
+      hermitian: True
+      imaginary: False
+      infinite: False
+      integer: True
+      irrational: False
+      noninteger: False
+      nonzero: True
+      odd: True
+      rational: True
+      real: True
+      transcendental: False
+      zero: False
+
+    Hiding the assumptions:
+
+    >>> print_tree(y**x, assumptions=False)
+    Pow: y**x
+    +-Symbol: y
+    +-Symbol: x
+
+    See Also
+    ========
+
+    tree
+
+    """
+    print(tree(node, assumptions=assumptions))

outputs/audit_venv/lib/python3.11/site-packages/sympy/release.py

@@ -0,0 +1 @@
+__version__ = "1.14.0"

outputs/audit_venv/lib/python3.11/site-packages/sympy/sandbox/__init__.py

@@ -0,0 +1,8 @@
+"""
+Sandbox module of SymPy.
+
+This module contains experimental code, use at your own risk!
+
+There is no warranty that this code will still be located here in future
+versions of SymPy.
+"""

outputs/audit_venv/lib/python3.11/site-packages/sympy/sandbox/indexed_integrals.py

@@ -0,0 +1,72 @@
+from sympy.tensor import Indexed
+from sympy.core.containers import Tuple
+from sympy.core.symbol import Dummy
+from sympy.core.sympify import sympify
+from sympy.integrals.integrals import Integral
+
+
+class IndexedIntegral(Integral):
+    """
+    Experimental class to test integration by indexed variables.
+
+    Usage is analogue to ``Integral``, it simply adds awareness of
+    integration over indices.
+
+    Contraction of non-identical index symbols referring to the same
+    ``IndexedBase`` is not yet supported.
+
+    Examples
+    ========
+
+    >>> from sympy.sandbox.indexed_integrals import IndexedIntegral
+    >>> from sympy import IndexedBase, symbols
+    >>> A = IndexedBase('A')
+    >>> i, j = symbols('i j', integer=True)
+    >>> ii = IndexedIntegral(A[i], A[i])
+    >>> ii
+    Integral(_A[i], _A[i])
+    >>> ii.doit()
+    A[i]**2/2
+
+    If the indices are different, indexed objects are considered to be
+    different variables:
+
+    >>> i2 = IndexedIntegral(A[j], A[i])
+    >>> i2
+    Integral(A[j], _A[i])
+    >>> i2.doit()
+    A[i]*A[j]
+    """
+
+    def __new__(cls, function, *limits, **assumptions):
+        repl, limits = IndexedIntegral._indexed_process_limits(limits)
+        function = sympify(function)
+        function = function.xreplace(repl)
+        obj = Integral.__new__(cls, function, *limits, **assumptions)
+        obj._indexed_repl = repl
+        obj._indexed_reverse_repl = {val: key for key, val in repl.items()}
+        return obj
+
+    def doit(self):
+        res = super().doit()
+        return res.xreplace(self._indexed_reverse_repl)
+
+    @staticmethod
+    def _indexed_process_limits(limits):
+        repl = {}
+        newlimits = []
+        for i in limits:
+            if isinstance(i, (tuple, list, Tuple)):
+                v = i[0]
+                vrest = i[1:]
+            else:
+                v = i
+                vrest = ()
+            if isinstance(v, Indexed):
+                if v not in repl:
+                    r = Dummy(str(v))
+                    repl[v] = r
+                newlimits.append((r,)+vrest)
+            else:
+                newlimits.append(i)
+        return repl, newlimits

outputs/audit_venv/lib/python3.11/site-packages/sympy/sandbox/tests/test_indexed_integrals.py

@@ -0,0 +1,25 @@
+from sympy.sandbox.indexed_integrals import IndexedIntegral
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.tensor.indexed import (Idx, IndexedBase)
+
+
+def test_indexed_integrals():
+    A = IndexedBase('A')
+    i, j = symbols('i j', integer=True)
+    a1, a2 = symbols('a1:3', cls=Idx)
+    assert isinstance(a1, Idx)
+
+    assert IndexedIntegral(1, A[i]).doit() == A[i]
+    assert IndexedIntegral(A[i], A[i]).doit() == A[i] ** 2 / 2
+    assert IndexedIntegral(A[j], A[i]).doit() == A[i] * A[j]
+    assert IndexedIntegral(A[i] * A[j], A[i]).doit() == A[i] ** 2 * A[j] / 2
+    assert IndexedIntegral(sin(A[i]), A[i]).doit() == -cos(A[i])
+    assert IndexedIntegral(sin(A[j]), A[i]).doit() == sin(A[j]) * A[i]
+
+    assert IndexedIntegral(1, A[a1]).doit() == A[a1]
+    assert IndexedIntegral(A[a1], A[a1]).doit() == A[a1] ** 2 / 2
+    assert IndexedIntegral(A[a2], A[a1]).doit() == A[a1] * A[a2]
+    assert IndexedIntegral(A[a1] * A[a2], A[a1]).doit() == A[a1] ** 2 * A[a2] / 2
+    assert IndexedIntegral(sin(A[a1]), A[a1]).doit() == -cos(A[a1])
+    assert IndexedIntegral(sin(A[a2]), A[a1]).doit() == sin(A[a2]) * A[a1]

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/__init__.py

@@ -0,0 +1,23 @@
+"""A module that handles series: find a limit, order the series etc.
+"""
+from .order import Order
+from .limits import limit, Limit
+from .gruntz import gruntz
+from .series import series
+from .approximants import approximants
+from .residues import residue
+from .sequences import SeqPer, SeqFormula, sequence, SeqAdd, SeqMul
+from .fourier import fourier_series
+from .formal import fps
+from .limitseq import difference_delta, limit_seq
+
+from sympy.core.singleton import S
+EmptySequence = S.EmptySequence
+
+O = Order
+
+__all__ = ['Order', 'O', 'limit', 'Limit', 'gruntz', 'series', 'approximants',
+        'residue', 'EmptySequence', 'SeqPer', 'SeqFormula', 'sequence',
+        'SeqAdd', 'SeqMul', 'fourier_series', 'fps', 'difference_delta',
+        'limit_seq'
+        ]

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/acceleration.py

@@ -0,0 +1,101 @@
+"""
+Convergence acceleration / extrapolation methods for series and
+sequences.
+
+References:
+Carl M. Bender & Steven A. Orszag, "Advanced Mathematical Methods for
+Scientists and Engineers: Asymptotic Methods and Perturbation Theory",
+Springer 1999. (Shanks transformation: pp. 368-375, Richardson
+extrapolation: pp. 375-377.)
+"""
+
+from sympy.core.numbers import Integer
+from sympy.core.singleton import S
+from sympy.functions.combinatorial.factorials import factorial
+
+
+def richardson(A, k, n, N):
+    """
+    Calculate an approximation for lim k->oo A(k) using Richardson
+    extrapolation with the terms A(n), A(n+1), ..., A(n+N+1).
+    Choosing N ~= 2*n often gives good results.
+
+    Examples
+    ========
+
+    A simple example is to calculate exp(1) using the limit definition.
+    This limit converges slowly; n = 100 only produces two accurate
+    digits:
+
+        >>> from sympy.abc import n
+        >>> e = (1 + 1/n)**n
+        >>> print(round(e.subs(n, 100).evalf(), 10))
+        2.7048138294
+
+    Richardson extrapolation with 11 appropriately chosen terms gives
+    a value that is accurate to the indicated precision:
+
+        >>> from sympy import E
+        >>> from sympy.series.acceleration import richardson
+        >>> print(round(richardson(e, n, 10, 20).evalf(), 10))
+        2.7182818285
+        >>> print(round(E.evalf(), 10))
+        2.7182818285
+
+    Another useful application is to speed up convergence of series.
+    Computing 100 terms of the zeta(2) series 1/k**2 yields only
+    two accurate digits:
+
+        >>> from sympy.abc import k, n
+        >>> from sympy import Sum
+        >>> A = Sum(k**-2, (k, 1, n))
+        >>> print(round(A.subs(n, 100).evalf(), 10))
+        1.6349839002
+
+    Richardson extrapolation performs much better:
+
+        >>> from sympy import pi
+        >>> print(round(richardson(A, n, 10, 20).evalf(), 10))
+        1.6449340668
+        >>> print(round(((pi**2)/6).evalf(), 10))     # Exact value
+        1.6449340668
+
+    """
+    s = S.Zero
+    for j in range(0, N + 1):
+        s += (A.subs(k, Integer(n + j)).doit() * (n + j)**N *
+              S.NegativeOne**(j + N) / (factorial(j) * factorial(N - j)))
+    return s
+
+
+def shanks(A, k, n, m=1):
+    """
+    Calculate an approximation for lim k->oo A(k) using the n-term Shanks
+    transformation S(A)(n). With m > 1, calculate the m-fold recursive
+    Shanks transformation S(S(...S(A)...))(n).
+
+    The Shanks transformation is useful for summing Taylor series that
+    converge slowly near a pole or singularity, e.g. for log(2):
+
+        >>> from sympy.abc import k, n
+        >>> from sympy import Sum, Integer
+        >>> from sympy.series.acceleration import shanks
+        >>> A = Sum(Integer(-1)**(k+1) / k, (k, 1, n))
+        >>> print(round(A.subs(n, 100).doit().evalf(), 10))
+        0.6881721793
+        >>> print(round(shanks(A, n, 25).evalf(), 10))
+        0.6931396564
+        >>> print(round(shanks(A, n, 25, 5).evalf(), 10))
+        0.6931471806
+
+    The correct value is 0.6931471805599453094172321215.
+    """
+    table = [A.subs(k, Integer(j)).doit() for j in range(n + m + 2)]
+    table2 = table.copy()
+
+    for i in range(1, m + 1):
+        for j in range(i, n + m + 1):
+            x, y, z = table[j - 1], table[j], table[j + 1]
+            table2[j] = (z*x - y**2) / (z + x - 2*y)
+        table = table2.copy()
+    return table[n]

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/approximants.py

@@ -0,0 +1,103 @@
+from sympy.core.singleton import S
+from sympy.core.symbol import Symbol
+from sympy.polys.polytools import lcm
+from sympy.utilities import public
+
+@public
+def approximants(l, X=Symbol('x'), simplify=False):
+    """
+    Return a generator for consecutive Pade approximants for a series.
+    It can also be used for computing the rational generating function of a
+    series when possible, since the last approximant returned by the generator
+    will be the generating function (if any).
+
+    Explanation
+    ===========
+
+    The input list can contain more complex expressions than integer or rational
+    numbers; symbols may also be involved in the computation. An example below
+    show how to compute the generating function of the whole Pascal triangle.
+
+    The generator can be asked to apply the sympy.simplify function on each
+    generated term, which will make the computation slower; however it may be
+    useful when symbols are involved in the expressions.
+
+    Examples
+    ========
+
+    >>> from sympy.series import approximants
+    >>> from sympy import lucas, fibonacci, symbols, binomial
+    >>> g = [lucas(k) for k in range(16)]
+    >>> [e for e in approximants(g)]
+    [2, -4/(x - 2), (5*x - 2)/(3*x - 1), (x - 2)/(x**2 + x - 1)]
+
+    >>> h = [fibonacci(k) for k in range(16)]
+    >>> [e for e in approximants(h)]
+    [x, -x/(x - 1), (x**2 - x)/(2*x - 1), -x/(x**2 + x - 1)]
+
+    >>> x, t = symbols("x,t")
+    >>> p=[sum(binomial(k,i)*x**i for i in range(k+1)) for k in range(16)]
+    >>> y = approximants(p, t)
+    >>> for k in range(3): print(next(y))
+    1
+    (x + 1)/((-x - 1)*(t*(x + 1) + (x + 1)/(-x - 1)))
+    nan
+
+    >>> y = approximants(p, t, simplify=True)
+    >>> for k in range(3): print(next(y))
+    1
+    -1/(t*(x + 1) - 1)
+    nan
+
+    See Also
+    ========
+
+    sympy.concrete.guess.guess_generating_function_rational
+    mpmath.pade
+    """
+    from sympy.simplify import simplify as simp
+    from sympy.simplify.radsimp import denom
+    p1, q1 = [S.One], [S.Zero]
+    p2, q2 = [S.Zero], [S.One]
+    while len(l):
+        b = 0
+        while l[b]==0:
+            b += 1
+            if b == len(l):
+                return
+        m = [S.One/l[b]]
+        for k in range(b+1, len(l)):
+            s = 0
+            for j in range(b, k):
+                s -= l[j+1] * m[b-j-1]
+            m.append(s/l[b])
+        l = m
+        a, l[0] = l[0], 0
+        p = [0] * max(len(p2), b+len(p1))
+        q = [0] * max(len(q2), b+len(q1))
+        for k in range(len(p2)):
+            p[k] = a*p2[k]
+        for k in range(b, b+len(p1)):
+            p[k] += p1[k-b]
+        for k in range(len(q2)):
+            q[k] = a*q2[k]
+        for k in range(b, b+len(q1)):
+            q[k] += q1[k-b]
+        while p[-1]==0: p.pop()
+        while q[-1]==0: q.pop()
+        p1, p2 = p2, p
+        q1, q2 = q2, q
+
+        # yield result
+        c = 1
+        for x in p:
+            c = lcm(c, denom(x))
+        for x in q:
+            c = lcm(c, denom(x))
+        out = ( sum(c*e*X**k for k, e in enumerate(p))
+              / sum(c*e*X**k for k, e in enumerate(q)) )
+        if simplify:
+            yield(simp(out))
+        else:
+            yield out
+    return

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/aseries.py

@@ -0,0 +1,10 @@
+from sympy.core.sympify import sympify
+
+
+def aseries(expr, x=None, n=6, bound=0, hir=False):
+    """
+    See the docstring of Expr.aseries() for complete details of this wrapper.
+
+    """
+    expr = sympify(expr)
+    return expr.aseries(x, n, bound, hir)

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/benchmarks/bench_limit.py

@@ -0,0 +1,9 @@
+from sympy.core.numbers import oo
+from sympy.core.symbol import Symbol
+from sympy.series.limits import limit
+
+x = Symbol('x')
+
+
+def timeit_limit_1x():
+    limit(1/x, x, oo)

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/benchmarks/bench_order.py

@@ -0,0 +1,10 @@
+from sympy.core.add import Add
+from sympy.core.symbol import Symbol
+from sympy.series.order import O
+
+x = Symbol('x')
+l = [x**i for i in range(1000)]
+l.append(O(x**1001))
+
+def timeit_order_1x():
+    Add(*l)

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/formal.py

@@ -0,0 +1,1863 @@
+"""Formal Power Series"""
+
+from collections import defaultdict
+
+from sympy.core.numbers import (nan, oo, zoo)
+from sympy.core.add import Add
+from sympy.core.expr import Expr
+from sympy.core.function import Derivative, Function, expand
+from sympy.core.mul import Mul
+from sympy.core.numbers import Rational
+from sympy.core.relational import Eq
+from sympy.sets.sets import Interval
+from sympy.core.singleton import S
+from sympy.core.symbol import Wild, Dummy, symbols, Symbol
+from sympy.core.sympify import sympify
+from sympy.discrete.convolutions import convolution
+from sympy.functions.combinatorial.factorials import binomial, factorial, rf
+from sympy.functions.combinatorial.numbers import bell
+from sympy.functions.elementary.integers import floor, frac, ceiling
+from sympy.functions.elementary.miscellaneous import Min, Max
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.series.limits import Limit
+from sympy.series.order import Order
+from sympy.series.sequences import sequence
+from sympy.series.series_class import SeriesBase
+from sympy.utilities.iterables import iterable
+
+
+
+def rational_algorithm(f, x, k, order=4, full=False):
+    """
+    Rational algorithm for computing
+    formula of coefficients of Formal Power Series
+    of a function.
+
+    Explanation
+    ===========
+
+    Applicable when f(x) or some derivative of f(x)
+    is a rational function in x.
+
+    :func:`rational_algorithm` uses :func:`~.apart` function for partial fraction
+    decomposition. :func:`~.apart` by default uses 'undetermined coefficients
+    method'. By setting ``full=True``, 'Bronstein's algorithm' can be used
+    instead.
+
+    Looks for derivative of a function up to 4'th order (by default).
+    This can be overridden using order option.
+
+    Parameters
+    ==========
+
+    x : Symbol
+    order : int, optional
+        Order of the derivative of ``f``, Default is 4.
+    full : bool
+
+    Returns
+    =======
+
+    formula : Expr
+    ind : Expr
+        Independent terms.
+    order : int
+    full : bool
+
+    Examples
+    ========
+
+    >>> from sympy import log, atan
+    >>> from sympy.series.formal import rational_algorithm as ra
+    >>> from sympy.abc import x, k
+
+    >>> ra(1 / (1 - x), x, k)
+    (1, 0, 0)
+    >>> ra(log(1 + x), x, k)
+    (-1/((-1)**k*k), 0, 1)
+
+    >>> ra(atan(x), x, k, full=True)
+    ((-I/(2*(-I)**k) + I/(2*I**k))/k, 0, 1)
+
+    Notes
+    =====
+
+    By setting ``full=True``, range of admissible functions to be solved using
+    ``rational_algorithm`` can be increased. This option should be used
+    carefully as it can significantly slow down the computation as ``doit`` is
+    performed on the :class:`~.RootSum` object returned by the :func:`~.apart`
+    function. Use ``full=False`` whenever possible.
+
+    See Also
+    ========
+
+    sympy.polys.partfrac.apart
+
+    References
+    ==========
+
+    .. [1] Formal Power Series - Dominik Gruntz, Wolfram Koepf
+    .. [2] Power Series in Computer Algebra - Wolfram Koepf
+
+    """
+    from sympy.polys import RootSum, apart
+    from sympy.integrals import integrate
+
+    diff = f
+    ds = []  # list of diff
+
+    for i in range(order + 1):
+        if i:
+            diff = diff.diff(x)
+
+        if diff.is_rational_function(x):
+            coeff, sep = S.Zero, S.Zero
+
+            terms = apart(diff, x, full=full)
+            if terms.has(RootSum):
+                terms = terms.doit()
+
+            for t in Add.make_args(terms):
+                num, den = t.as_numer_denom()
+                if not den.has(x):
+                    sep += t
+                else:
+                    if isinstance(den, Mul):
+                        # m*(n*x - a)**j -> (n*x - a)**j
+                        ind = den.as_independent(x)
+                        den = ind[1]
+                        num /= ind[0]
+
+                    # (n*x - a)**j -> (x - b)
+                    den, j = den.as_base_exp()
+                    a, xterm = den.as_coeff_add(x)
+
+                    # term -> m/x**n
+                    if not a:
+                        sep += t
+                        continue
+
+                    xc = xterm[0].coeff(x)
+                    a /= -xc
+                    num /= xc**j
+
+                    ak = ((-1)**j * num *
+                          binomial(j + k - 1, k).rewrite(factorial) /
+                          a**(j + k))
+                    coeff += ak
+
+            # Hacky, better way?
+            if coeff.is_zero:
+                return None
+            if (coeff.has(x) or coeff.has(zoo) or coeff.has(oo) or
+                    coeff.has(nan)):
+                return None
+
+            for j in range(i):
+                coeff = (coeff / (k + j + 1))
+                sep = integrate(sep, x)
+                sep += (ds.pop() - sep).limit(x, 0)  # constant of integration
+            return (coeff.subs(k, k - i), sep, i)
+
+        else:
+            ds.append(diff)
+
+    return None
+
+
+def rational_independent(terms, x):
+    """
+    Returns a list of all the rationally independent terms.
+
+    Examples
+    ========
+
+    >>> from sympy import sin, cos
+    >>> from sympy.series.formal import rational_independent
+    >>> from sympy.abc import x
+
+    >>> rational_independent([cos(x), sin(x)], x)
+    [cos(x), sin(x)]
+    >>> rational_independent([x**2, sin(x), x*sin(x), x**3], x)
+    [x**3 + x**2, x*sin(x) + sin(x)]
+    """
+    if not terms:
+        return []
+
+    ind = terms[0:1]
+
+    for t in terms[1:]:
+        n = t.as_independent(x)[1]
+        for i, term in enumerate(ind):
+            d = term.as_independent(x)[1]
+            q = (n / d).cancel()
+            if q.is_rational_function(x):
+                ind[i] += t
+                break
+        else:
+            ind.append(t)
+    return ind
+
+
+def simpleDE(f, x, g, order=4):
+    r"""
+    Generates simple DE.
+
+    Explanation
+    ===========
+
+    DE is of the form
+
+    .. math::
+        f^k(x) + \sum\limits_{j=0}^{k-1} A_j f^j(x) = 0
+
+    where :math:`A_j` should be rational function in x.
+
+    Generates DE's upto order 4 (default). DE's can also have free parameters.
+
+    By increasing order, higher order DE's can be found.
+
+    Yields a tuple of (DE, order).
+    """
+    from sympy.solvers.solveset import linsolve
+
+    a = symbols('a:%d' % (order))
+
+    def _makeDE(k):
+        eq = f.diff(x, k) + Add(*[a[i]*f.diff(x, i) for i in range(0, k)])
+        DE = g(x).diff(x, k) + Add(*[a[i]*g(x).diff(x, i) for i in range(0, k)])
+        return eq, DE
+
+    found = False
+    for k in range(1, order + 1):
+        eq, DE = _makeDE(k)
+        eq = eq.expand()
+        terms = eq.as_ordered_terms()
+        ind = rational_independent(terms, x)
+        if found or len(ind) == k:
+            sol = dict(zip(a, (i for s in linsolve(ind, a[:k]) for i in s)))
+            if sol:
+                found = True
+                DE = DE.subs(sol)
+            DE = DE.as_numer_denom()[0]
+            DE = DE.factor().as_coeff_mul(Derivative)[1][0]
+            yield DE.collect(Derivative(g(x))), k
+
+
+def exp_re(DE, r, k):
+    """Converts a DE with constant coefficients (explike) into a RE.
+
+    Explanation
+    ===========
+
+    Performs the substitution:
+
+    .. math::
+        f^j(x) \\to r(k + j)
+
+    Normalises the terms so that lowest order of a term is always r(k).
+
+    Examples
+    ========
+
+    >>> from sympy import Function, Derivative
+    >>> from sympy.series.formal import exp_re
+    >>> from sympy.abc import x, k
+    >>> f, r = Function('f'), Function('r')
+
+    >>> exp_re(-f(x) + Derivative(f(x)), r, k)
+    -r(k) + r(k + 1)
+    >>> exp_re(Derivative(f(x), x) + Derivative(f(x), (x, 2)), r, k)
+    r(k) + r(k + 1)
+
+    See Also
+    ========
+
+    sympy.series.formal.hyper_re
+    """
+    RE = S.Zero
+
+    g = DE.atoms(Function).pop()
+
+    mini = None
+    for t in Add.make_args(DE):
+        coeff, d = t.as_independent(g)
+        if isinstance(d, Derivative):
+            j = d.derivative_count
+        else:
+            j = 0
+        if mini is None or j < mini:
+            mini = j
+        RE += coeff * r(k + j)
+    if mini:
+        RE = RE.subs(k, k - mini)
+    return RE
+
+
+def hyper_re(DE, r, k):
+    """
+    Converts a DE into a RE.
+
+    Explanation
+    ===========
+
+    Performs the substitution:
+
+    .. math::
+        x^l f^j(x) \\to (k + 1 - l)_j . a_{k + j - l}
+
+    Normalises the terms so that lowest order of a term is always r(k).
+
+    Examples
+    ========
+
+    >>> from sympy import Function, Derivative
+    >>> from sympy.series.formal import hyper_re
+    >>> from sympy.abc import x, k
+    >>> f, r = Function('f'), Function('r')
+
+    >>> hyper_re(-f(x) + Derivative(f(x)), r, k)
+    (k + 1)*r(k + 1) - r(k)
+    >>> hyper_re(-x*f(x) + Derivative(f(x), (x, 2)), r, k)
+    (k + 2)*(k + 3)*r(k + 3) - r(k)
+
+    See Also
+    ========
+
+    sympy.series.formal.exp_re
+    """
+    RE = S.Zero
+
+    g = DE.atoms(Function).pop()
+    x = g.atoms(Symbol).pop()
+
+    mini = None
+    for t in Add.make_args(DE.expand()):
+        coeff, d = t.as_independent(g)
+        c, v = coeff.as_independent(x)
+        l = v.as_coeff_exponent(x)[1]
+        if isinstance(d, Derivative):
+            j = d.derivative_count
+        else:
+            j = 0
+        RE += c * rf(k + 1 - l, j) * r(k + j - l)
+        if mini is None or j - l < mini:
+            mini = j - l
+
+    RE = RE.subs(k, k - mini)
+
+    m = Wild('m')
+    return RE.collect(r(k + m))
+
+
+def _transformation_a(f, x, P, Q, k, m, shift):
+    f *= x**(-shift)
+    P = P.subs(k, k + shift)
+    Q = Q.subs(k, k + shift)
+    return f, P, Q, m
+
+
+def _transformation_c(f, x, P, Q, k, m, scale):
+    f = f.subs(x, x**scale)
+    P = P.subs(k, k / scale)
+    Q = Q.subs(k, k / scale)
+    m *= scale
+    return f, P, Q, m
+
+
+def _transformation_e(f, x, P, Q, k, m):
+    f = f.diff(x)
+    P = P.subs(k, k + 1) * (k + m + 1)
+    Q = Q.subs(k, k + 1) * (k + 1)
+    return f, P, Q, m
+
+
+def _apply_shift(sol, shift):
+    return [(res, cond + shift) for res, cond in sol]
+
+
+def _apply_scale(sol, scale):
+    return [(res, cond / scale) for res, cond in sol]
+
+
+def _apply_integrate(sol, x, k):
+    return [(res / ((cond + 1)*(cond.as_coeff_Add()[1].coeff(k))), cond + 1)
+            for res, cond in sol]
+
+
+def _compute_formula(f, x, P, Q, k, m, k_max):
+    """Computes the formula for f."""
+    from sympy.polys import roots
+
+    sol = []
+    for i in range(k_max + 1, k_max + m + 1):
+        if (i < 0) == True:
+            continue
+        r = f.diff(x, i).limit(x, 0) / factorial(i)
+        if r.is_zero:
+            continue
+
+        kterm = m*k + i
+        res = r
+
+        p = P.subs(k, kterm)
+        q = Q.subs(k, kterm)
+        c1 = p.subs(k, 1/k).leadterm(k)[0]
+        c2 = q.subs(k, 1/k).leadterm(k)[0]
+        res *= (-c1 / c2)**k
+
+        res *= Mul(*[rf(-r, k)**mul for r, mul in roots(p, k).items()])
+        res /= Mul(*[rf(-r, k)**mul for r, mul in roots(q, k).items()])
+
+        sol.append((res, kterm))
+
+    return sol
+
+
+def _rsolve_hypergeometric(f, x, P, Q, k, m):
+    """
+    Recursive wrapper to rsolve_hypergeometric.
+
+    Explanation
+    ===========
+
+    Returns a Tuple of (formula, series independent terms,
+    maximum power of x in independent terms) if successful
+    otherwise ``None``.
+
+    See :func:`rsolve_hypergeometric` for details.
+    """
+    from sympy.polys import lcm, roots
+    from sympy.integrals import integrate
+
+    # transformation - c
+    proots, qroots = roots(P, k), roots(Q, k)
+    all_roots = dict(proots)
+    all_roots.update(qroots)
+    scale = lcm([r.as_numer_denom()[1] for r, t in all_roots.items()
+                 if r.is_rational])
+    f, P, Q, m = _transformation_c(f, x, P, Q, k, m, scale)
+
+    # transformation - a
+    qroots = roots(Q, k)
+    if qroots:
+        k_min = Min(*qroots.keys())
+    else:
+        k_min = S.Zero
+    shift = k_min + m
+    f, P, Q, m = _transformation_a(f, x, P, Q, k, m, shift)
+
+    l = (x*f).limit(x, 0)
+    if not isinstance(l, Limit) and l != 0:  # Ideally should only be l != 0
+        return None
+
+    qroots = roots(Q, k)
+    if qroots:
+        k_max = Max(*qroots.keys())
+    else:
+        k_max = S.Zero
+
+    ind, mp = S.Zero, -oo
+    for i in range(k_max + m + 1):
+        r = f.diff(x, i).limit(x, 0) / factorial(i)
+        if r.is_finite is False:
+            old_f = f
+            f, P, Q, m = _transformation_a(f, x, P, Q, k, m, i)
+            f, P, Q, m = _transformation_e(f, x, P, Q, k, m)
+            sol, ind, mp = _rsolve_hypergeometric(f, x, P, Q, k, m)
+            sol = _apply_integrate(sol, x, k)
+            sol = _apply_shift(sol, i)
+            ind = integrate(ind, x)
+            ind += (old_f - ind).limit(x, 0)  # constant of integration
+            mp += 1
+            return sol, ind, mp
+        elif r:
+            ind += r*x**(i + shift)
+            pow_x = Rational((i + shift), scale)
+            if pow_x > mp:
+                mp = pow_x  # maximum power of x
+    ind = ind.subs(x, x**(1/scale))
+
+    sol = _compute_formula(f, x, P, Q, k, m, k_max)
+    sol = _apply_shift(sol, shift)
+    sol = _apply_scale(sol, scale)
+
+    return sol, ind, mp
+
+
+def rsolve_hypergeometric(f, x, P, Q, k, m):
+    """
+    Solves RE of hypergeometric type.
+
+    Explanation
+    ===========
+
+    Attempts to solve RE of the form
+
+    Q(k)*a(k + m) - P(k)*a(k)
+
+    Transformations that preserve Hypergeometric type:
+
+        a. x**n*f(x): b(k + m) = R(k - n)*b(k)
+        b. f(A*x): b(k + m) = A**m*R(k)*b(k)
+        c. f(x**n): b(k + n*m) = R(k/n)*b(k)
+        d. f(x**(1/m)): b(k + 1) = R(k*m)*b(k)
+        e. f'(x): b(k + m) = ((k + m + 1)/(k + 1))*R(k + 1)*b(k)
+
+    Some of these transformations have been used to solve the RE.
+
+    Returns
+    =======
+
+    formula : Expr
+    ind : Expr
+        Independent terms.
+    order : int
+
+    Examples
+    ========
+
+    >>> from sympy import exp, ln, S
+    >>> from sympy.series.formal import rsolve_hypergeometric as rh
+    >>> from sympy.abc import x, k
+
+    >>> rh(exp(x), x, -S.One, (k + 1), k, 1)
+    (Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
+
+    >>> rh(ln(1 + x), x, k**2, k*(k + 1), k, 1)
+    (Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
+     Eq(Mod(k, 1), 0)), (0, True)), x, 2)
+
+    References
+    ==========
+
+    .. [1] Formal Power Series - Dominik Gruntz, Wolfram Koepf
+    .. [2] Power Series in Computer Algebra - Wolfram Koepf
+    """
+    result = _rsolve_hypergeometric(f, x, P, Q, k, m)
+
+    if result is None:
+        return None
+
+    sol_list, ind, mp = result
+
+    sol_dict = defaultdict(lambda: S.Zero)
+    for res, cond in sol_list:
+        j, mk = cond.as_coeff_Add()
+        c = mk.coeff(k)
+
+        if j.is_integer is False:
+            res *= x**frac(j)
+            j = floor(j)
+
+        res = res.subs(k, (k - j) / c)
+        cond = Eq(k % c, j % c)
+        sol_dict[cond] += res  # Group together formula for same conditions
+
+    sol = [(res, cond) for cond, res in sol_dict.items()]
+    sol.append((S.Zero, True))
+    sol = Piecewise(*sol)
+
+    if mp is -oo:
+        s = S.Zero
+    elif mp.is_integer is False:
+        s = ceiling(mp)
+    else:
+        s = mp + 1
+
+    #  save all the terms of
+    #  form 1/x**k in ind
+    if s < 0:
+        ind += sum(sequence(sol * x**k, (k, s, -1)))
+        s = S.Zero
+
+    return (sol, ind, s)
+
+
+def _solve_hyper_RE(f, x, RE, g, k):
+    """See docstring of :func:`rsolve_hypergeometric` for details."""
+    terms = Add.make_args(RE)
+
+    if len(terms) == 2:
+        gs = list(RE.atoms(Function))
+        P, Q = map(RE.coeff, gs)
+        m = gs[1].args[0] - gs[0].args[0]
+        if m < 0:
+            P, Q = Q, P
+            m = abs(m)
+        return rsolve_hypergeometric(f, x, P, Q, k, m)
+
+
+def _solve_explike_DE(f, x, DE, g, k):
+    """Solves DE with constant coefficients."""
+    from sympy.solvers import rsolve
+
+    for t in Add.make_args(DE):
+        coeff, d = t.as_independent(g)
+        if coeff.free_symbols:
+            return
+
+    RE = exp_re(DE, g, k)
+
+    init = {}
+    for i in range(len(Add.make_args(RE))):
+        if i:
+            f = f.diff(x)
+        init[g(k).subs(k, i)] = f.limit(x, 0)
+
+    sol = rsolve(RE, g(k), init)
+
+    if sol:
+        return (sol / factorial(k), S.Zero, S.Zero)
+
+
+def _solve_simple(f, x, DE, g, k):
+    """Converts DE into RE and solves using :func:`rsolve`."""
+    from sympy.solvers import rsolve
+
+    RE = hyper_re(DE, g, k)
+
+    init = {}
+    for i in range(len(Add.make_args(RE))):
+        if i:
+            f = f.diff(x)
+        init[g(k).subs(k, i)] = f.limit(x, 0) / factorial(i)
+
+    sol = rsolve(RE, g(k), init)
+
+    if sol:
+        return (sol, S.Zero, S.Zero)
+
+
+def _transform_explike_DE(DE, g, x, order, syms):
+    """Converts DE with free parameters into DE with constant coefficients."""
+    from sympy.solvers.solveset import linsolve
+
+    eq = []
+    highest_coeff = DE.coeff(Derivative(g(x), x, order))
+    for i in range(order):
+        coeff = DE.coeff(Derivative(g(x), x, i))
+        coeff = (coeff / highest_coeff).expand().collect(x)
+        eq.extend(Add.make_args(coeff))
+    temp = []
+    for e in eq:
+        if e.has(x):
+            break
+        elif e.has(Symbol):
+            temp.append(e)
+    else:
+        eq = temp
+    if eq:
+        sol = dict(zip(syms, (i for s in linsolve(eq, list(syms)) for i in s)))
+        if sol:
+            DE = DE.subs(sol)
+            DE = DE.factor().as_coeff_mul(Derivative)[1][0]
+            DE = DE.collect(Derivative(g(x)))
+    return DE
+
+
+def _transform_DE_RE(DE, g, k, order, syms):
+    """Converts DE with free parameters into RE of hypergeometric type."""
+    from sympy.solvers.solveset import linsolve
+
+    RE = hyper_re(DE, g, k)
+
+    eq = [RE.coeff(g(k + i)) for i in range(1, order)]
+    sol = dict(zip(syms, (i for s in linsolve(eq, list(syms)) for i in s)))
+    if sol:
+        m = Wild('m')
+        RE = RE.subs(sol)
+        RE = RE.factor().as_numer_denom()[0].collect(g(k + m))
+        RE = RE.as_coeff_mul(g)[1][0]
+        for i in range(order):  # smallest order should be g(k)
+            if RE.coeff(g(k + i)) and i:
+                RE = RE.subs(k, k - i)
+                break
+    return RE
+
+
+def solve_de(f, x, DE, order, g, k):
+    """
+    Solves the DE.
+
+    Explanation
+    ===========
+
+    Tries to solve DE by either converting into a RE containing two terms or
+    converting into a DE having constant coefficients.
+
+    Returns
+    =======
+
+    formula : Expr
+    ind : Expr
+        Independent terms.
+    order : int
+
+    Examples
+    ========
+
+    >>> from sympy import Derivative as D, Function
+    >>> from sympy import exp, ln
+    >>> from sympy.series.formal import solve_de
+    >>> from sympy.abc import x, k
+    >>> f = Function('f')
+
+    >>> solve_de(exp(x), x, D(f(x), x) - f(x), 1, f, k)
+    (Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
+
+    >>> solve_de(ln(1 + x), x, (x + 1)*D(f(x), x, 2) + D(f(x)), 2, f, k)
+    (Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
+     Eq(Mod(k, 1), 0)), (0, True)), x, 2)
+    """
+    sol = None
+    syms = DE.free_symbols.difference({g, x})
+
+    if syms:
+        RE = _transform_DE_RE(DE, g, k, order, syms)
+    else:
+        RE = hyper_re(DE, g, k)
+    if not RE.free_symbols.difference({k}):
+        sol = _solve_hyper_RE(f, x, RE, g, k)
+
+    if sol:
+        return sol
+
+    if syms:
+        DE = _transform_explike_DE(DE, g, x, order, syms)
+    if not DE.free_symbols.difference({x}):
+        sol = _solve_explike_DE(f, x, DE, g, k)
+
+    if sol:
+        return sol
+
+
+def hyper_algorithm(f, x, k, order=4):
+    """
+    Hypergeometric algorithm for computing Formal Power Series.
+
+    Explanation
+    ===========
+
+    Steps:
+        * Generates DE
+        * Convert the DE into RE
+        * Solves the RE
+
+    Examples
+    ========
+
+    >>> from sympy import exp, ln
+    >>> from sympy.series.formal import hyper_algorithm
+
+    >>> from sympy.abc import x, k
+
+    >>> hyper_algorithm(exp(x), x, k)
+    (Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
+
+    >>> hyper_algorithm(ln(1 + x), x, k)
+    (Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
+     Eq(Mod(k, 1), 0)), (0, True)), x, 2)
+
+    See Also
+    ========
+
+    sympy.series.formal.simpleDE
+    sympy.series.formal.solve_de
+    """
+    g = Function('g')
+
+    des = []  # list of DE's
+    sol = None
+    for DE, i in simpleDE(f, x, g, order):
+        if DE is not None:
+            sol = solve_de(f, x, DE, i, g, k)
+        if sol:
+            return sol
+        if not DE.free_symbols.difference({x}):
+            des.append(DE)
+
+    # If nothing works
+    # Try plain rsolve
+    for DE in des:
+        sol = _solve_simple(f, x, DE, g, k)
+        if sol:
+            return sol
+
+
+def _compute_fps(f, x, x0, dir, hyper, order, rational, full):
+    """Recursive wrapper to compute fps.
+
+    See :func:`compute_fps` for details.
+    """
+    if x0 in [S.Infinity, S.NegativeInfinity]:
+        dir = S.One if x0 is S.Infinity else -S.One
+        temp = f.subs(x, 1/x)
+        result = _compute_fps(temp, x, 0, dir, hyper, order, rational, full)
+        if result is None:
+            return None
+        return (result[0], result[1].subs(x, 1/x), result[2].subs(x, 1/x))
+    elif x0 or dir == -S.One:
+        if dir == -S.One:
+            rep = -x + x0
+            rep2 = -x
+            rep2b = x0
+        else:
+            rep = x + x0
+            rep2 = x
+            rep2b = -x0
+        temp = f.subs(x, rep)
+        result = _compute_fps(temp, x, 0, S.One, hyper, order, rational, full)
+        if result is None:
+            return None
+        return (result[0], result[1].subs(x, rep2 + rep2b),
+                result[2].subs(x, rep2 + rep2b))
+
+    if f.is_polynomial(x):
+        k = Dummy('k')
+        ak = sequence(Coeff(f, x, k), (k, 1, oo))
+        xk = sequence(x**k, (k, 0, oo))
+        ind = f.coeff(x, 0)
+        return ak, xk, ind
+
+    #  Break instances of Add
+    #  this allows application of different
+    #  algorithms on different terms increasing the
+    #  range of admissible functions.
+    if isinstance(f, Add):
+        result = False
+        ak = sequence(S.Zero, (0, oo))
+        ind, xk = S.Zero, None
+        for t in Add.make_args(f):
+            res = _compute_fps(t, x, 0, S.One, hyper, order, rational, full)
+            if res:
+                if not result:
+                    result = True
+                    xk = res[1]
+                if res[0].start > ak.start:
+                    seq = ak
+                    s, f = ak.start, res[0].start
+                else:
+                    seq = res[0]
+                    s, f = res[0].start, ak.start
+                save = Add(*[z[0]*z[1] for z in zip(seq[0:(f - s)], xk[s:f])])
+                ak += res[0]
+                ind += res[2] + save
+            else:
+                ind += t
+        if result:
+            return ak, xk, ind
+        return None
+
+    # The symbolic term - symb, if present, is being separated from the function
+    # Otherwise symb is being set to S.One
+    syms = f.free_symbols.difference({x})
+    (f, symb) = expand(f).as_independent(*syms)
+
+    result = None
+
+    # from here on it's x0=0 and dir=1 handling
+    k = Dummy('k')
+    if rational:
+        result = rational_algorithm(f, x, k, order, full)
+
+    if result is None and hyper:
+        result = hyper_algorithm(f, x, k, order)
+
+    if result is None:
+        return None
+
+    from sympy.simplify.powsimp import powsimp
+    if symb.is_zero:
+        symb = S.One
+    else:
+        symb = powsimp(symb)
+    ak = sequence(result[0], (k, result[2], oo))
+    xk_formula = powsimp(x**k * symb)
+    xk = sequence(xk_formula, (k, 0, oo))
+    ind = powsimp(result[1] * symb)
+
+    return ak, xk, ind
+
+
+def compute_fps(f, x, x0=0, dir=1, hyper=True, order=4, rational=True,
+                full=False):
+    """
+    Computes the formula for Formal Power Series of a function.
+
+    Explanation
+    ===========
+
+    Tries to compute the formula by applying the following techniques
+    (in order):
+
+    * rational_algorithm
+    * Hypergeometric algorithm
+
+    Parameters
+    ==========
+
+    x : Symbol
+    x0 : number, optional
+        Point to perform series expansion about. Default is 0.
+    dir : {1, -1, '+', '-'}, optional
+        If dir is 1 or '+' the series is calculated from the right and
+        for -1 or '-' the series is calculated from the left. For smooth
+        functions this flag will not alter the results. Default is 1.
+    hyper : {True, False}, optional
+        Set hyper to False to skip the hypergeometric algorithm.
+        By default it is set to False.
+    order : int, optional
+        Order of the derivative of ``f``, Default is 4.
+    rational : {True, False}, optional
+        Set rational to False to skip rational algorithm. By default it is set
+        to True.
+    full : {True, False}, optional
+        Set full to True to increase the range of rational algorithm.
+        See :func:`rational_algorithm` for details. By default it is set to
+        False.
+
+    Returns
+    =======
+
+    ak : sequence
+        Sequence of coefficients.
+    xk : sequence
+        Sequence of powers of x.
+    ind : Expr
+        Independent terms.
+    mul : Pow
+        Common terms.
+
+    See Also
+    ========
+
+    sympy.series.formal.rational_algorithm
+    sympy.series.formal.hyper_algorithm
+    """
+    f = sympify(f)
+    x = sympify(x)
+
+    if not f.has(x):
+        return None
+
+    x0 = sympify(x0)
+
+    if dir == '+':
+        dir = S.One
+    elif dir == '-':
+        dir = -S.One
+    elif dir not in [S.One, -S.One]:
+        raise ValueError("Dir must be '+' or '-'")
+    else:
+        dir = sympify(dir)
+
+    return _compute_fps(f, x, x0, dir, hyper, order, rational, full)
+
+
+class Coeff(Function):
+    """
+    Coeff(p, x, n) represents the nth coefficient of the polynomial p in x
+    """
+    @classmethod
+    def eval(cls, p, x, n):
+        if p.is_polynomial(x) and n.is_integer:
+            return p.coeff(x, n)
+
+
+class FormalPowerSeries(SeriesBase):
+    """
+    Represents Formal Power Series of a function.
+
+    Explanation
+    ===========
+
+    No computation is performed. This class should only to be used to represent
+    a series. No checks are performed.
+
+    For computing a series use :func:`fps`.
+
+    See Also
+    ========
+
+    sympy.series.formal.fps
+    """
+    def __new__(cls, *args):
+        args = map(sympify, args)
+        return Expr.__new__(cls, *args)
+
+    def __init__(self, *args):
+        ak = args[4][0]
+        k = ak.variables[0]
+        self.ak_seq = sequence(ak.formula, (k, 1, oo))
+        self.fact_seq = sequence(factorial(k), (k, 1, oo))
+        self.bell_coeff_seq = self.ak_seq * self.fact_seq
+        self.sign_seq = sequence((-1, 1), (k, 1, oo))
+
+    @property
+    def function(self):
+        return self.args[0]
+
+    @property
+    def x(self):
+        return self.args[1]
+
+    @property
+    def x0(self):
+        return self.args[2]
+
+    @property
+    def dir(self):
+        return self.args[3]
+
+    @property
+    def ak(self):
+        return self.args[4][0]
+
+    @property
+    def xk(self):
+        return self.args[4][1]
+
+    @property
+    def ind(self):
+        return self.args[4][2]
+
+    @property
+    def interval(self):
+        return Interval(0, oo)
+
+    @property
+    def start(self):
+        return self.interval.inf
+
+    @property
+    def stop(self):
+        return self.interval.sup
+
+    @property
+    def length(self):
+        return oo
+
+    @property
+    def infinite(self):
+        """Returns an infinite representation of the series"""
+        from sympy.concrete import Sum
+        ak, xk = self.ak, self.xk
+        k = ak.variables[0]
+        inf_sum = Sum(ak.formula * xk.formula, (k, ak.start, ak.stop))
+
+        return self.ind + inf_sum
+
+    def _get_pow_x(self, term):
+        """Returns the power of x in a term."""
+        xterm, pow_x = term.as_independent(self.x)[1].as_base_exp()
+        if not xterm.has(self.x):
+            return S.Zero
+        return pow_x
+
+    def polynomial(self, n=6):
+        """
+        Truncated series as polynomial.
+
+        Explanation
+        ===========
+
+        Returns series expansion of ``f`` upto order ``O(x**n)``
+        as a polynomial(without ``O`` term).
+        """
+        terms = []
+        sym = self.free_symbols
+        for i, t in enumerate(self):
+            xp = self._get_pow_x(t)
+            if xp.has(*sym):
+                xp = xp.as_coeff_add(*sym)[0]
+            if xp >= n:
+                break
+            elif xp.is_integer is True and i == n + 1:
+                break
+            elif t is not S.Zero:
+                terms.append(t)
+
+        return Add(*terms)
+
+    def truncate(self, n=6):
+        """
+        Truncated series.
+
+        Explanation
+        ===========
+
+        Returns truncated series expansion of f upto
+        order ``O(x**n)``.
+
+        If n is ``None``, returns an infinite iterator.
+        """
+        if n is None:
+            return iter(self)
+
+        x, x0 = self.x, self.x0
+        pt_xk = self.xk.coeff(n)
+        if x0 is S.NegativeInfinity:
+            x0 = S.Infinity
+
+        return self.polynomial(n) + Order(pt_xk, (x, x0))
+
+    def zero_coeff(self):
+        return self._eval_term(0)
+
+    def _eval_term(self, pt):
+        try:
+            pt_xk = self.xk.coeff(pt)
+            pt_ak = self.ak.coeff(pt).simplify()  # Simplify the coefficients
+        except IndexError:
+            term = S.Zero
+        else:
+            term = (pt_ak * pt_xk)
+
+        if self.ind:
+            ind = S.Zero
+            sym = self.free_symbols
+            for t in Add.make_args(self.ind):
+                pow_x = self._get_pow_x(t)
+                if pow_x.has(*sym):
+                    pow_x = pow_x.as_coeff_add(*sym)[0]
+                if pt == 0 and pow_x < 1:
+                    ind += t
+                elif pow_x >= pt and pow_x < pt + 1:
+                    ind += t
+            term += ind
+
+        return term.collect(self.x)
+
+    def _eval_subs(self, old, new):
+        x = self.x
+        if old.has(x):
+            return self
+
+    def _eval_as_leading_term(self, x, logx, cdir):
+        for t in self:
+            if t is not S.Zero:
+                return t
+
+    def _eval_derivative(self, x):
+        f = self.function.diff(x)
+        ind = self.ind.diff(x)
+
+        pow_xk = self._get_pow_x(self.xk.formula)
+        ak = self.ak
+        k = ak.variables[0]
+        if ak.formula.has(x):
+            form = []
+            for e, c in ak.formula.args:
+                temp = S.Zero
+                for t in Add.make_args(e):
+                    pow_x = self._get_pow_x(t)
+                    temp += t * (pow_xk + pow_x)
+                form.append((temp, c))
+            form = Piecewise(*form)
+            ak = sequence(form.subs(k, k + 1), (k, ak.start - 1, ak.stop))
+        else:
+            ak = sequence((ak.formula * pow_xk).subs(k, k + 1),
+                          (k, ak.start - 1, ak.stop))
+
+        return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
+
+    def integrate(self, x=None, **kwargs):
+        """
+        Integrate Formal Power Series.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, sin, integrate
+        >>> from sympy.abc import x
+        >>> f = fps(sin(x))
+        >>> f.integrate(x).truncate()
+        -1 + x**2/2 - x**4/24 + O(x**6)
+        >>> integrate(f, (x, 0, 1))
+        1 - cos(1)
+        """
+        from sympy.integrals import integrate
+
+        if x is None:
+            x = self.x
+        elif iterable(x):
+            return integrate(self.function, x)
+
+        f = integrate(self.function, x)
+        ind = integrate(self.ind, x)
+        ind += (f - ind).limit(x, 0)  # constant of integration
+
+        pow_xk = self._get_pow_x(self.xk.formula)
+        ak = self.ak
+        k = ak.variables[0]
+        if ak.formula.has(x):
+            form = []
+            for e, c in ak.formula.args:
+                temp = S.Zero
+                for t in Add.make_args(e):
+                    pow_x = self._get_pow_x(t)
+                    temp += t / (pow_xk + pow_x + 1)
+                form.append((temp, c))
+            form = Piecewise(*form)
+            ak = sequence(form.subs(k, k - 1), (k, ak.start + 1, ak.stop))
+        else:
+            ak = sequence((ak.formula / (pow_xk + 1)).subs(k, k - 1),
+                          (k, ak.start + 1, ak.stop))
+
+        return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
+
+    def product(self, other, x=None, n=6):
+        """
+        Multiplies two Formal Power Series, using discrete convolution and
+        return the truncated terms upto specified order.
+
+        Parameters
+        ==========
+
+        n : Number, optional
+            Specifies the order of the term up to which the polynomial should
+            be truncated.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, sin, exp
+        >>> from sympy.abc import x
+        >>> f1 = fps(sin(x))
+        >>> f2 = fps(exp(x))
+
+        >>> f1.product(f2, x).truncate(4)
+        x + x**2 + x**3/3 + O(x**4)
+
+        See Also
+        ========
+
+        sympy.discrete.convolutions
+        sympy.series.formal.FormalPowerSeriesProduct
+
+        """
+
+        if n is None:
+            return iter(self)
+
+        other = sympify(other)
+
+        if not isinstance(other, FormalPowerSeries):
+            raise ValueError("Both series should be an instance of FormalPowerSeries"
+                             " class.")
+
+        if self.dir != other.dir:
+            raise ValueError("Both series should be calculated from the"
+                             " same direction.")
+        elif self.x0 != other.x0:
+            raise ValueError("Both series should be calculated about the"
+                             " same point.")
+
+        elif self.x != other.x:
+            raise ValueError("Both series should have the same symbol.")
+
+        return FormalPowerSeriesProduct(self, other)
+
+    def coeff_bell(self, n):
+        r"""
+        self.coeff_bell(n) returns a sequence of Bell polynomials of the second kind.
+        Note that ``n`` should be a integer.
+
+        The second kind of Bell polynomials (are sometimes called "partial" Bell
+        polynomials or incomplete Bell polynomials) are defined as
+
+        .. math::
+            B_{n,k}(x_1, x_2,\dotsc x_{n-k+1}) =
+                \sum_{j_1+j_2+j_2+\dotsb=k \atop j_1+2j_2+3j_2+\dotsb=n}
+                \frac{n!}{j_1!j_2!\dotsb j_{n-k+1}!}
+                \left(\frac{x_1}{1!} \right)^{j_1}
+                \left(\frac{x_2}{2!} \right)^{j_2} \dotsb
+                \left(\frac{x_{n-k+1}}{(n-k+1)!} \right) ^{j_{n-k+1}}.
+
+        * ``bell(n, k, (x1, x2, ...))`` gives Bell polynomials of the second kind,
+          `B_{n,k}(x_1, x_2, \dotsc, x_{n-k+1})`.
+
+        See Also
+        ========
+
+        sympy.functions.combinatorial.numbers.bell
+
+        """
+
+        inner_coeffs = [bell(n, j, tuple(self.bell_coeff_seq[:n-j+1])) for j in range(1, n+1)]
+
+        k = Dummy('k')
+        return sequence(tuple(inner_coeffs), (k, 1, oo))
+
+    def compose(self, other, x=None, n=6):
+        r"""
+        Returns the truncated terms of the formal power series of the composed function,
+        up to specified ``n``.
+
+        Explanation
+        ===========
+
+        If ``f`` and ``g`` are two formal power series of two different functions,
+        then the coefficient sequence ``ak`` of the composed formal power series `fp`
+        will be as follows.
+
+        .. math::
+            \sum\limits_{k=0}^{n} b_k B_{n,k}(x_1, x_2, \dotsc, x_{n-k+1})
+
+        Parameters
+        ==========
+
+        n : Number, optional
+            Specifies the order of the term up to which the polynomial should
+            be truncated.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, sin, exp
+        >>> from sympy.abc import x
+        >>> f1 = fps(exp(x))
+        >>> f2 = fps(sin(x))
+
+        >>> f1.compose(f2, x).truncate()
+        1 + x + x**2/2 - x**4/8 - x**5/15 + O(x**6)
+
+        >>> f1.compose(f2, x).truncate(8)
+        1 + x + x**2/2 - x**4/8 - x**5/15 - x**6/240 + x**7/90 + O(x**8)
+
+        See Also
+        ========
+
+        sympy.functions.combinatorial.numbers.bell
+        sympy.series.formal.FormalPowerSeriesCompose
+
+        References
+        ==========
+
+        .. [1] Comtet, Louis: Advanced combinatorics; the art of finite and infinite expansions. Reidel, 1974.
+
+        """
+
+        if n is None:
+            return iter(self)
+
+        other = sympify(other)
+
+        if not isinstance(other, FormalPowerSeries):
+            raise ValueError("Both series should be an instance of FormalPowerSeries"
+                             " class.")
+
+        if self.dir != other.dir:
+            raise ValueError("Both series should be calculated from the"
+                             " same direction.")
+        elif self.x0 != other.x0:
+            raise ValueError("Both series should be calculated about the"
+                             " same point.")
+
+        elif self.x != other.x:
+            raise ValueError("Both series should have the same symbol.")
+
+        if other._eval_term(0).as_coeff_mul(other.x)[0] is not S.Zero:
+            raise ValueError("The formal power series of the inner function should not have any "
+                "constant coefficient term.")
+
+        return FormalPowerSeriesCompose(self, other)
+
+    def inverse(self, x=None, n=6):
+        r"""
+        Returns the truncated terms of the inverse of the formal power series,
+        up to specified ``n``.
+
+        Explanation
+        ===========
+
+        If ``f`` and ``g`` are two formal power series of two different functions,
+        then the coefficient sequence ``ak`` of the composed formal power series ``fp``
+        will be as follows.
+
+        .. math::
+            \sum\limits_{k=0}^{n} (-1)^{k} x_0^{-k-1} B_{n,k}(x_1, x_2, \dotsc, x_{n-k+1})
+
+        Parameters
+        ==========
+
+        n : Number, optional
+            Specifies the order of the term up to which the polynomial should
+            be truncated.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, exp, cos
+        >>> from sympy.abc import x
+        >>> f1 = fps(exp(x))
+        >>> f2 = fps(cos(x))
+
+        >>> f1.inverse(x).truncate()
+        1 - x + x**2/2 - x**3/6 + x**4/24 - x**5/120 + O(x**6)
+
+        >>> f2.inverse(x).truncate(8)
+        1 + x**2/2 + 5*x**4/24 + 61*x**6/720 + O(x**8)
+
+        See Also
+        ========
+
+        sympy.functions.combinatorial.numbers.bell
+        sympy.series.formal.FormalPowerSeriesInverse
+
+        References
+        ==========
+
+        .. [1] Comtet, Louis: Advanced combinatorics; the art of finite and infinite expansions. Reidel, 1974.
+
+        """
+
+        if n is None:
+            return iter(self)
+
+        if self._eval_term(0).is_zero:
+            raise ValueError("Constant coefficient should exist for an inverse of a formal"
+                " power series to exist.")
+
+        return FormalPowerSeriesInverse(self)
+
+    def __add__(self, other):
+        other = sympify(other)
+
+        if isinstance(other, FormalPowerSeries):
+            if self.dir != other.dir:
+                raise ValueError("Both series should be calculated from the"
+                                 " same direction.")
+            elif self.x0 != other.x0:
+                raise ValueError("Both series should be calculated about the"
+                                 " same point.")
+
+            x, y = self.x, other.x
+            f = self.function + other.function.subs(y, x)
+
+            if self.x not in f.free_symbols:
+                return f
+
+            ak = self.ak + other.ak
+            if self.ak.start > other.ak.start:
+                seq = other.ak
+                s, e = other.ak.start, self.ak.start
+            else:
+                seq = self.ak
+                s, e = self.ak.start, other.ak.start
+            save = Add(*[z[0]*z[1] for z in zip(seq[0:(e - s)], self.xk[s:e])])
+            ind = self.ind + other.ind + save
+
+            return self.func(f, x, self.x0, self.dir, (ak, self.xk, ind))
+
+        elif not other.has(self.x):
+            f = self.function + other
+            ind = self.ind + other
+
+            return self.func(f, self.x, self.x0, self.dir,
+                             (self.ak, self.xk, ind))
+
+        return Add(self, other)
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __neg__(self):
+        return self.func(-self.function, self.x, self.x0, self.dir,
+                         (-self.ak, self.xk, -self.ind))
+
+    def __sub__(self, other):
+        return self.__add__(-other)
+
+    def __rsub__(self, other):
+        return (-self).__add__(other)
+
+    def __mul__(self, other):
+        other = sympify(other)
+
+        if other.has(self.x):
+            return Mul(self, other)
+
+        f = self.function * other
+        ak = self.ak.coeff_mul(other)
+        ind = self.ind * other
+
+        return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+
+class FiniteFormalPowerSeries(FormalPowerSeries):
+    """Base Class for Product, Compose and Inverse classes"""
+
+    def __init__(self, *args):
+        pass
+
+    @property
+    def ffps(self):
+        return self.args[0]
+
+    @property
+    def gfps(self):
+        return self.args[1]
+
+    @property
+    def f(self):
+        return self.ffps.function
+
+    @property
+    def g(self):
+        return self.gfps.function
+
+    @property
+    def infinite(self):
+        raise NotImplementedError("No infinite version for an object of"
+                     " FiniteFormalPowerSeries class.")
+
+    def _eval_terms(self, n):
+        raise NotImplementedError("(%s)._eval_terms()" % self)
+
+    def _eval_term(self, pt):
+        raise NotImplementedError("By the current logic, one can get terms"
+                                   "upto a certain order, instead of getting term by term.")
+
+    def polynomial(self, n):
+        return self._eval_terms(n)
+
+    def truncate(self, n=6):
+        ffps = self.ffps
+        pt_xk = ffps.xk.coeff(n)
+        x, x0 = ffps.x, ffps.x0
+
+        return self.polynomial(n) + Order(pt_xk, (x, x0))
+
+    def _eval_derivative(self, x):
+        raise NotImplementedError
+
+    def integrate(self, x):
+        raise NotImplementedError
+
+
+class FormalPowerSeriesProduct(FiniteFormalPowerSeries):
+    """Represents the product of two formal power series of two functions.
+
+    Explanation
+    ===========
+
+    No computation is performed. Terms are calculated using a term by term logic,
+    instead of a point by point logic.
+
+    There are two differences between a :obj:`FormalPowerSeries` object and a
+    :obj:`FormalPowerSeriesProduct` object. The first argument contains the two
+    functions involved in the product. Also, the coefficient sequence contains
+    both the coefficient sequence of the formal power series of the involved functions.
+
+    See Also
+    ========
+
+    sympy.series.formal.FormalPowerSeries
+    sympy.series.formal.FiniteFormalPowerSeries
+
+    """
+
+    def __init__(self, *args):
+        ffps, gfps = self.ffps, self.gfps
+
+        k = ffps.ak.variables[0]
+        self.coeff1 = sequence(ffps.ak.formula, (k, 0, oo))
+
+        k = gfps.ak.variables[0]
+        self.coeff2 = sequence(gfps.ak.formula, (k, 0, oo))
+
+    @property
+    def function(self):
+        """Function of the product of two formal power series."""
+        return self.f * self.g
+
+    def _eval_terms(self, n):
+        """
+        Returns the first ``n`` terms of the product formal power series.
+        Term by term logic is implemented here.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, sin, exp
+        >>> from sympy.abc import x
+        >>> f1 = fps(sin(x))
+        >>> f2 = fps(exp(x))
+        >>> fprod = f1.product(f2, x)
+
+        >>> fprod._eval_terms(4)
+        x**3/3 + x**2 + x
+
+        See Also
+        ========
+
+        sympy.series.formal.FormalPowerSeries.product
+
+        """
+        coeff1, coeff2 = self.coeff1, self.coeff2
+
+        aks = convolution(coeff1[:n], coeff2[:n])
+
+        terms = []
+        for i in range(0, n):
+            terms.append(aks[i] * self.ffps.xk.coeff(i))
+
+        return Add(*terms)
+
+
+class FormalPowerSeriesCompose(FiniteFormalPowerSeries):
+    """
+    Represents the composed formal power series of two functions.
+
+    Explanation
+    ===========
+
+    No computation is performed. Terms are calculated using a term by term logic,
+    instead of a point by point logic.
+
+    There are two differences between a :obj:`FormalPowerSeries` object and a
+    :obj:`FormalPowerSeriesCompose` object. The first argument contains the outer
+    function and the inner function involved in the omposition. Also, the
+    coefficient sequence contains the generic sequence which is to be multiplied
+    by a custom ``bell_seq`` finite sequence. The finite terms will then be added up to
+    get the final terms.
+
+    See Also
+    ========
+
+    sympy.series.formal.FormalPowerSeries
+    sympy.series.formal.FiniteFormalPowerSeries
+
+    """
+
+    @property
+    def function(self):
+        """Function for the composed formal power series."""
+        f, g, x = self.f, self.g, self.ffps.x
+        return f.subs(x, g)
+
+    def _eval_terms(self, n):
+        """
+        Returns the first `n` terms of the composed formal power series.
+        Term by term logic is implemented here.
+
+        Explanation
+        ===========
+
+        The coefficient sequence of the :obj:`FormalPowerSeriesCompose` object is the generic sequence.
+        It is multiplied by ``bell_seq`` to get a sequence, whose terms are added up to get
+        the final terms for the polynomial.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, sin, exp
+        >>> from sympy.abc import x
+        >>> f1 = fps(exp(x))
+        >>> f2 = fps(sin(x))
+        >>> fcomp = f1.compose(f2, x)
+
+        >>> fcomp._eval_terms(6)
+        -x**5/15 - x**4/8 + x**2/2 + x + 1
+
+        >>> fcomp._eval_terms(8)
+        x**7/90 - x**6/240 - x**5/15 - x**4/8 + x**2/2 + x + 1
+
+        See Also
+        ========
+
+        sympy.series.formal.FormalPowerSeries.compose
+        sympy.series.formal.FormalPowerSeries.coeff_bell
+
+        """
+
+        ffps, gfps = self.ffps, self.gfps
+        terms = [ffps.zero_coeff()]
+
+        for i in range(1, n):
+            bell_seq = gfps.coeff_bell(i)
+            seq = (ffps.bell_coeff_seq * bell_seq)
+            terms.append(Add(*(seq[:i])) / ffps.fact_seq[i-1] * ffps.xk.coeff(i))
+
+        return Add(*terms)
+
+
+class FormalPowerSeriesInverse(FiniteFormalPowerSeries):
+    """
+    Represents the Inverse of a formal power series.
+
+    Explanation
+    ===========
+
+    No computation is performed. Terms are calculated using a term by term logic,
+    instead of a point by point logic.
+
+    There is a single difference between a :obj:`FormalPowerSeries` object and a
+    :obj:`FormalPowerSeriesInverse` object. The coefficient sequence contains the
+    generic sequence which is to be multiplied by a custom ``bell_seq`` finite sequence.
+    The finite terms will then be added up to get the final terms.
+
+    See Also
+    ========
+
+    sympy.series.formal.FormalPowerSeries
+    sympy.series.formal.FiniteFormalPowerSeries
+
+    """
+    def __init__(self, *args):
+        ffps = self.ffps
+        k = ffps.xk.variables[0]
+
+        inv = ffps.zero_coeff()
+        inv_seq = sequence(inv ** (-(k + 1)), (k, 1, oo))
+        self.aux_seq = ffps.sign_seq * ffps.fact_seq * inv_seq
+
+    @property
+    def function(self):
+        """Function for the inverse of a formal power series."""
+        f = self.f
+        return 1 / f
+
+    @property
+    def g(self):
+        raise ValueError("Only one function is considered while performing"
+                        "inverse of a formal power series.")
+
+    @property
+    def gfps(self):
+        raise ValueError("Only one function is considered while performing"
+                        "inverse of a formal power series.")
+
+    def _eval_terms(self, n):
+        """
+        Returns the first ``n`` terms of the composed formal power series.
+        Term by term logic is implemented here.
+
+        Explanation
+        ===========
+
+        The coefficient sequence of the `FormalPowerSeriesInverse` object is the generic sequence.
+        It is multiplied by ``bell_seq`` to get a sequence, whose terms are added up to get
+        the final terms for the polynomial.
+
+        Examples
+        ========
+
+        >>> from sympy import fps, exp, cos
+        >>> from sympy.abc import x
+        >>> f1 = fps(exp(x))
+        >>> f2 = fps(cos(x))
+        >>> finv1, finv2 = f1.inverse(), f2.inverse()
+
+        >>> finv1._eval_terms(6)
+        -x**5/120 + x**4/24 - x**3/6 + x**2/2 - x + 1
+
+        >>> finv2._eval_terms(8)
+        61*x**6/720 + 5*x**4/24 + x**2/2 + 1
+
+        See Also
+        ========
+
+        sympy.series.formal.FormalPowerSeries.inverse
+        sympy.series.formal.FormalPowerSeries.coeff_bell
+
+        """
+        ffps = self.ffps
+        terms = [ffps.zero_coeff()]
+
+        for i in range(1, n):
+            bell_seq = ffps.coeff_bell(i)
+            seq = (self.aux_seq * bell_seq)
+            terms.append(Add(*(seq[:i])) / ffps.fact_seq[i-1] * ffps.xk.coeff(i))
+
+        return Add(*terms)
+
+
+def fps(f, x=None, x0=0, dir=1, hyper=True, order=4, rational=True, full=False):
+    """
+    Generates Formal Power Series of ``f``.
+
+    Explanation
+    ===========
+
+    Returns the formal series expansion of ``f`` around ``x = x0``
+    with respect to ``x`` in the form of a ``FormalPowerSeries`` object.
+
+    Formal Power Series is represented using an explicit formula
+    computed using different algorithms.
+
+    See :func:`compute_fps` for the more details regarding the computation
+    of formula.
+
+    Parameters
+    ==========
+
+    x : Symbol, optional
+        If x is None and ``f`` is univariate, the univariate symbols will be
+        supplied, otherwise an error will be raised.
+    x0 : number, optional
+        Point to perform series expansion about. Default is 0.
+    dir : {1, -1, '+', '-'}, optional
+        If dir is 1 or '+' the series is calculated from the right and
+        for -1 or '-' the series is calculated from the left. For smooth
+        functions this flag will not alter the results. Default is 1.
+    hyper : {True, False}, optional
+        Set hyper to False to skip the hypergeometric algorithm.
+        By default it is set to False.
+    order : int, optional
+        Order of the derivative of ``f``, Default is 4.
+    rational : {True, False}, optional
+        Set rational to False to skip rational algorithm. By default it is set
+        to True.
+    full : {True, False}, optional
+        Set full to True to increase the range of rational algorithm.
+        See :func:`rational_algorithm` for details. By default it is set to
+        False.
+
+    Examples
+    ========
+
+    >>> from sympy import fps, ln, atan, sin
+    >>> from sympy.abc import x, n
+
+    Rational Functions
+
+    >>> fps(ln(1 + x)).truncate()
+    x - x**2/2 + x**3/3 - x**4/4 + x**5/5 + O(x**6)
+
+    >>> fps(atan(x), full=True).truncate()
+    x - x**3/3 + x**5/5 + O(x**6)
+
+    Symbolic Functions
+
+    >>> fps(x**n*sin(x**2), x).truncate(8)
+    -x**(n + 6)/6 + x**(n + 2) + O(x**(n + 8))
+
+    See Also
+    ========
+
+    sympy.series.formal.FormalPowerSeries
+    sympy.series.formal.compute_fps
+    """
+    f = sympify(f)
+
+    if x is None:
+        free = f.free_symbols
+        if len(free) == 1:
+            x = free.pop()
+        elif not free:
+            return f
+        else:
+            raise NotImplementedError("multivariate formal power series")
+
+    result = compute_fps(f, x, x0, dir, hyper, order, rational, full)
+
+    if result is None:
+        return f
+
+    return FormalPowerSeries(f, x, x0, dir, result)

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/fourier.py

@@ -0,0 +1,811 @@
+"""Fourier Series"""
+
+from sympy.core.numbers import (oo, pi)
+from sympy.core.symbol import Wild
+from sympy.core.expr import Expr
+from sympy.core.add import Add
+from sympy.core.containers import Tuple
+from sympy.core.singleton import S
+from sympy.core.symbol import Dummy, Symbol
+from sympy.core.sympify import sympify
+from sympy.functions.elementary.trigonometric import sin, cos, sinc
+from sympy.series.series_class import SeriesBase
+from sympy.series.sequences import SeqFormula
+from sympy.sets.sets import Interval
+from sympy.utilities.iterables import is_sequence
+
+
+__doctest_requires__ = {('fourier_series',): ['matplotlib']}
+
+
+def fourier_cos_seq(func, limits, n):
+    """Returns the cos sequence in a Fourier series"""
+    from sympy.integrals import integrate
+    x, L = limits[0], limits[2] - limits[1]
+    cos_term = cos(2*n*pi*x / L)
+    formula = 2 * cos_term * integrate(func * cos_term, limits) / L
+    a0 = formula.subs(n, S.Zero) / 2
+    return a0, SeqFormula(2 * cos_term * integrate(func * cos_term, limits)
+                          / L, (n, 1, oo))
+
+
+def fourier_sin_seq(func, limits, n):
+    """Returns the sin sequence in a Fourier series"""
+    from sympy.integrals import integrate
+    x, L = limits[0], limits[2] - limits[1]
+    sin_term = sin(2*n*pi*x / L)
+    return SeqFormula(2 * sin_term * integrate(func * sin_term, limits)
+                      / L, (n, 1, oo))
+
+
+def _process_limits(func, limits):
+    """
+    Limits should be of the form (x, start, stop).
+    x should be a symbol. Both start and stop should be bounded.
+
+    Explanation
+    ===========
+
+    * If x is not given, x is determined from func.
+    * If limits is None. Limit of the form (x, -pi, pi) is returned.
+
+    Examples
+    ========
+
+    >>> from sympy.series.fourier import _process_limits as pari
+    >>> from sympy.abc import x
+    >>> pari(x**2, (x, -2, 2))
+    (x, -2, 2)
+    >>> pari(x**2, (-2, 2))
+    (x, -2, 2)
+    >>> pari(x**2, None)
+    (x, -pi, pi)
+    """
+    def _find_x(func):
+        free = func.free_symbols
+        if len(free) == 1:
+            return free.pop()
+        elif not free:
+            return Dummy('k')
+        else:
+            raise ValueError(
+                " specify dummy variables for %s. If the function contains"
+                " more than one free symbol, a dummy variable should be"
+                " supplied explicitly e.g. FourierSeries(m*n**2, (n, -pi, pi))"
+                % func)
+
+    x, start, stop = None, None, None
+    if limits is None:
+        x, start, stop = _find_x(func), -pi, pi
+    if is_sequence(limits, Tuple):
+        if len(limits) == 3:
+            x, start, stop = limits
+        elif len(limits) == 2:
+            x = _find_x(func)
+            start, stop = limits
+
+    if not isinstance(x, Symbol) or start is None or stop is None:
+        raise ValueError('Invalid limits given: %s' % str(limits))
+
+    unbounded = [S.NegativeInfinity, S.Infinity]
+    if start in unbounded or stop in unbounded:
+        raise ValueError("Both the start and end value should be bounded")
+
+    return sympify((x, start, stop))
+
+
+def finite_check(f, x, L):
+
+    def check_fx(exprs, x):
+        return x not in exprs.free_symbols
+
+    def check_sincos(_expr, x, L):
+        if isinstance(_expr, (sin, cos)):
+            sincos_args = _expr.args[0]
+
+            if sincos_args.match(a*(pi/L)*x + b) is not None:
+                return True
+            else:
+                return False
+
+    from sympy.simplify.fu import TR2, TR1, sincos_to_sum
+    _expr = sincos_to_sum(TR2(TR1(f)))
+    add_coeff = _expr.as_coeff_add()
+
+    a = Wild('a', properties=[lambda k: k.is_Integer, lambda k: k != S.Zero, ])
+    b = Wild('b', properties=[lambda k: x not in k.free_symbols, ])
+
+    for s in add_coeff[1]:
+        mul_coeffs = s.as_coeff_mul()[1]
+        for t in mul_coeffs:
+            if not (check_fx(t, x) or check_sincos(t, x, L)):
+                return False, f
+
+    return True, _expr
+
+
+class FourierSeries(SeriesBase):
+    r"""Represents Fourier sine/cosine series.
+
+    Explanation
+    ===========
+
+    This class only represents a fourier series.
+    No computation is performed.
+
+    For how to compute Fourier series, see the :func:`fourier_series`
+    docstring.
+
+    See Also
+    ========
+
+    sympy.series.fourier.fourier_series
+    """
+    def __new__(cls, *args):
+        args = map(sympify, args)
+        return Expr.__new__(cls, *args)
+
+    @property
+    def function(self):
+        return self.args[0]
+
+    @property
+    def x(self):
+        return self.args[1][0]
+
+    @property
+    def period(self):
+        return (self.args[1][1], self.args[1][2])
+
+    @property
+    def a0(self):
+        return self.args[2][0]
+
+    @property
+    def an(self):
+        return self.args[2][1]
+
+    @property
+    def bn(self):
+        return self.args[2][2]
+
+    @property
+    def interval(self):
+        return Interval(0, oo)
+
+    @property
+    def start(self):
+        return self.interval.inf
+
+    @property
+    def stop(self):
+        return self.interval.sup
+
+    @property
+    def length(self):
+        return oo
+
+    @property
+    def L(self):
+        return abs(self.period[1] - self.period[0]) / 2
+
+    def _eval_subs(self, old, new):
+        x = self.x
+        if old.has(x):
+            return self
+
+    def truncate(self, n=3):
+        """
+        Return the first n nonzero terms of the series.
+
+        If ``n`` is None return an iterator.
+
+        Parameters
+        ==========
+
+        n : int or None
+            Amount of non-zero terms in approximation or None.
+
+        Returns
+        =======
+
+        Expr or iterator :
+            Approximation of function expanded into Fourier series.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x, (x, -pi, pi))
+        >>> s.truncate(4)
+        2*sin(x) - sin(2*x) + 2*sin(3*x)/3 - sin(4*x)/2
+
+        See Also
+        ========
+
+        sympy.series.fourier.FourierSeries.sigma_approximation
+        """
+        if n is None:
+            return iter(self)
+
+        terms = []
+        for t in self:
+            if len(terms) == n:
+                break
+            if t is not S.Zero:
+                terms.append(t)
+
+        return Add(*terms)
+
+    def sigma_approximation(self, n=3):
+        r"""
+        Return :math:`\sigma`-approximation of Fourier series with respect
+        to order n.
+
+        Explanation
+        ===========
+
+        Sigma approximation adjusts a Fourier summation to eliminate the Gibbs
+        phenomenon which would otherwise occur at discontinuities.
+        A sigma-approximated summation for a Fourier series of a T-periodical
+        function can be written as
+
+        .. math::
+            s(\theta) = \frac{1}{2} a_0 + \sum _{k=1}^{m-1}
+            \operatorname{sinc} \Bigl( \frac{k}{m} \Bigr) \cdot
+            \left[ a_k \cos \Bigl( \frac{2\pi k}{T} \theta \Bigr)
+            + b_k \sin \Bigl( \frac{2\pi k}{T} \theta \Bigr) \right],
+
+        where :math:`a_0, a_k, b_k, k=1,\ldots,{m-1}` are standard Fourier
+        series coefficients and
+        :math:`\operatorname{sinc} \Bigl( \frac{k}{m} \Bigr)` is a Lanczos
+        :math:`\sigma` factor (expressed in terms of normalized
+        :math:`\operatorname{sinc}` function).
+
+        Parameters
+        ==========
+
+        n : int
+            Highest order of the terms taken into account in approximation.
+
+        Returns
+        =======
+
+        Expr :
+            Sigma approximation of function expanded into Fourier series.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x, (x, -pi, pi))
+        >>> s.sigma_approximation(4)
+        2*sin(x)*sinc(pi/4) - 2*sin(2*x)/pi + 2*sin(3*x)*sinc(3*pi/4)/3
+
+        See Also
+        ========
+
+        sympy.series.fourier.FourierSeries.truncate
+
+        Notes
+        =====
+
+        The behaviour of
+        :meth:`~sympy.series.fourier.FourierSeries.sigma_approximation`
+        is different from :meth:`~sympy.series.fourier.FourierSeries.truncate`
+        - it takes all nonzero terms of degree smaller than n, rather than
+        first n nonzero ones.
+
+        References
+        ==========
+
+        .. [1] https://en.wikipedia.org/wiki/Gibbs_phenomenon
+        .. [2] https://en.wikipedia.org/wiki/Sigma_approximation
+        """
+        terms = [sinc(pi * i / n) * t for i, t in enumerate(self[:n])
+                 if t is not S.Zero]
+        return Add(*terms)
+
+    def shift(self, s):
+        """
+        Shift the function by a term independent of x.
+
+        Explanation
+        ===========
+
+        f(x) -> f(x) + s
+
+        This is fast, if Fourier series of f(x) is already
+        computed.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x**2, (x, -pi, pi))
+        >>> s.shift(1).truncate()
+        -4*cos(x) + cos(2*x) + 1 + pi**2/3
+        """
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        a0 = self.a0 + s
+        sfunc = self.function + s
+
+        return self.func(sfunc, self.args[1], (a0, self.an, self.bn))
+
+    def shiftx(self, s):
+        """
+        Shift x by a term independent of x.
+
+        Explanation
+        ===========
+
+        f(x) -> f(x + s)
+
+        This is fast, if Fourier series of f(x) is already
+        computed.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x**2, (x, -pi, pi))
+        >>> s.shiftx(1).truncate()
+        -4*cos(x + 1) + cos(2*x + 2) + pi**2/3
+        """
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        an = self.an.subs(x, x + s)
+        bn = self.bn.subs(x, x + s)
+        sfunc = self.function.subs(x, x + s)
+
+        return self.func(sfunc, self.args[1], (self.a0, an, bn))
+
+    def scale(self, s):
+        """
+        Scale the function by a term independent of x.
+
+        Explanation
+        ===========
+
+        f(x) -> s * f(x)
+
+        This is fast, if Fourier series of f(x) is already
+        computed.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x**2, (x, -pi, pi))
+        >>> s.scale(2).truncate()
+        -8*cos(x) + 2*cos(2*x) + 2*pi**2/3
+        """
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        an = self.an.coeff_mul(s)
+        bn = self.bn.coeff_mul(s)
+        a0 = self.a0 * s
+        sfunc = self.args[0] * s
+
+        return self.func(sfunc, self.args[1], (a0, an, bn))
+
+    def scalex(self, s):
+        """
+        Scale x by a term independent of x.
+
+        Explanation
+        ===========
+
+        f(x) -> f(s*x)
+
+        This is fast, if Fourier series of f(x) is already
+        computed.
+
+        Examples
+        ========
+
+        >>> from sympy import fourier_series, pi
+        >>> from sympy.abc import x
+        >>> s = fourier_series(x**2, (x, -pi, pi))
+        >>> s.scalex(2).truncate()
+        -4*cos(2*x) + cos(4*x) + pi**2/3
+        """
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        an = self.an.subs(x, x * s)
+        bn = self.bn.subs(x, x * s)
+        sfunc = self.function.subs(x, x * s)
+
+        return self.func(sfunc, self.args[1], (self.a0, an, bn))
+
+    def _eval_as_leading_term(self, x, logx, cdir):
+        for t in self:
+            if t is not S.Zero:
+                return t
+
+    def _eval_term(self, pt):
+        if pt == 0:
+            return self.a0
+        return self.an.coeff(pt) + self.bn.coeff(pt)
+
+    def __neg__(self):
+        return self.scale(-1)
+
+    def __add__(self, other):
+        if isinstance(other, FourierSeries):
+            if self.period != other.period:
+                raise ValueError("Both the series should have same periods")
+
+            x, y = self.x, other.x
+            function = self.function + other.function.subs(y, x)
+
+            if self.x not in function.free_symbols:
+                return function
+
+            an = self.an + other.an
+            bn = self.bn + other.bn
+            a0 = self.a0 + other.a0
+
+            return self.func(function, self.args[1], (a0, an, bn))
+
+        return Add(self, other)
+
+    def __sub__(self, other):
+        return self.__add__(-other)
+
+
+class FiniteFourierSeries(FourierSeries):
+    r"""Represents Finite Fourier sine/cosine series.
+
+    For how to compute Fourier series, see the :func:`fourier_series`
+    docstring.
+
+    Parameters
+    ==========
+
+    f : Expr
+        Expression for finding fourier_series
+
+    limits : ( x, start, stop)
+        x is the independent variable for the expression f
+        (start, stop) is the period of the fourier series
+
+    exprs: (a0, an, bn) or Expr
+        a0 is the constant term a0 of the fourier series
+        an is a dictionary of coefficients of cos terms
+         an[k] = coefficient of cos(pi*(k/L)*x)
+        bn is a dictionary of coefficients of sin terms
+         bn[k] = coefficient of sin(pi*(k/L)*x)
+
+        or exprs can be an expression to be converted to fourier form
+
+    Methods
+    =======
+
+    This class is an extension of FourierSeries class.
+    Please refer to sympy.series.fourier.FourierSeries for
+    further information.
+
+    See Also
+    ========
+
+    sympy.series.fourier.FourierSeries
+    sympy.series.fourier.fourier_series
+    """
+
+    def __new__(cls, f, limits, exprs):
+        f = sympify(f)
+        limits = sympify(limits)
+        exprs = sympify(exprs)
+
+        if not (isinstance(exprs, Tuple) and len(exprs) == 3):  # exprs is not of form (a0, an, bn)
+            # Converts the expression to fourier form
+            c, e = exprs.as_coeff_add()
+            from sympy.simplify.fu import TR10
+            rexpr = c + Add(*[TR10(i) for i in e])
+            a0, exp_ls = rexpr.expand(trig=False, power_base=False, power_exp=False, log=False).as_coeff_add()
+
+            x = limits[0]
+            L = abs(limits[2] - limits[1]) / 2
+
+            a = Wild('a', properties=[lambda k: k.is_Integer, lambda k: k is not S.Zero, ])
+            b = Wild('b', properties=[lambda k: x not in k.free_symbols, ])
+
+            an = {}
+            bn = {}
+
+            # separates the coefficients of sin and cos terms in dictionaries an, and bn
+            for p in exp_ls:
+                t = p.match(b * cos(a * (pi / L) * x))
+                q = p.match(b * sin(a * (pi / L) * x))
+                if t:
+                    an[t[a]] = t[b] + an.get(t[a], S.Zero)
+                elif q:
+                    bn[q[a]] = q[b] + bn.get(q[a], S.Zero)
+                else:
+                    a0 += p
+
+            exprs = Tuple(a0, an, bn)
+
+        return Expr.__new__(cls, f, limits, exprs)
+
+    @property
+    def interval(self):
+        _length = 1 if self.a0 else 0
+        _length += max(set(self.an.keys()).union(set(self.bn.keys()))) + 1
+        return Interval(0, _length)
+
+    @property
+    def length(self):
+        return self.stop - self.start
+
+    def shiftx(self, s):
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        _expr = self.truncate().subs(x, x + s)
+        sfunc = self.function.subs(x, x + s)
+
+        return self.func(sfunc, self.args[1], _expr)
+
+    def scale(self, s):
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        _expr = self.truncate() * s
+        sfunc = self.function * s
+
+        return self.func(sfunc, self.args[1], _expr)
+
+    def scalex(self, s):
+        s, x = sympify(s), self.x
+
+        if x in s.free_symbols:
+            raise ValueError("'%s' should be independent of %s" % (s, x))
+
+        _expr = self.truncate().subs(x, x * s)
+        sfunc = self.function.subs(x, x * s)
+
+        return self.func(sfunc, self.args[1], _expr)
+
+    def _eval_term(self, pt):
+        if pt == 0:
+            return self.a0
+
+        _term = self.an.get(pt, S.Zero) * cos(pt * (pi / self.L) * self.x) \
+                + self.bn.get(pt, S.Zero) * sin(pt * (pi / self.L) * self.x)
+        return _term
+
+    def __add__(self, other):
+        if isinstance(other, FourierSeries):
+            return other.__add__(fourier_series(self.function, self.args[1],\
+                                                finite=False))
+        elif isinstance(other, FiniteFourierSeries):
+            if self.period != other.period:
+                raise ValueError("Both the series should have same periods")
+
+            x, y = self.x, other.x
+            function = self.function + other.function.subs(y, x)
+
+            if self.x not in function.free_symbols:
+                return function
+
+            return fourier_series(function, limits=self.args[1])
+
+
+def fourier_series(f, limits=None, finite=True):
+    r"""Computes the Fourier trigonometric series expansion.
+
+    Explanation
+    ===========
+
+    Fourier trigonometric series of $f(x)$ over the interval $(a, b)$
+    is defined as:
+
+    .. math::
+        \frac{a_0}{2} + \sum_{n=1}^{\infty}
+        (a_n \cos(\frac{2n \pi x}{L}) + b_n \sin(\frac{2n \pi x}{L}))
+
+    where the coefficients are:
+
+    .. math::
+        L = b - a
+
+    .. math::
+        a_0 = \frac{2}{L} \int_{a}^{b}{f(x) dx}
+
+    .. math::
+        a_n = \frac{2}{L} \int_{a}^{b}{f(x) \cos(\frac{2n \pi x}{L}) dx}
+
+    .. math::
+        b_n = \frac{2}{L} \int_{a}^{b}{f(x) \sin(\frac{2n \pi x}{L}) dx}
+
+    The condition whether the function $f(x)$ given should be periodic
+    or not is more than necessary, because it is sufficient to consider
+    the series to be converging to $f(x)$ only in the given interval,
+    not throughout the whole real line.
+
+    This also brings a lot of ease for the computation because
+    you do not have to make $f(x)$ artificially periodic by
+    wrapping it with piecewise, modulo operations,
+    but you can shape the function to look like the desired periodic
+    function only in the interval $(a, b)$, and the computed series will
+    automatically become the series of the periodic version of $f(x)$.
+
+    This property is illustrated in the examples section below.
+
+    Parameters
+    ==========
+
+    limits : (sym, start, end), optional
+        *sym* denotes the symbol the series is computed with respect to.
+
+        *start* and *end* denotes the start and the end of the interval
+        where the fourier series converges to the given function.
+
+        Default range is specified as $-\pi$ and $\pi$.
+
+    Returns
+    =======
+
+    FourierSeries
+        A symbolic object representing the Fourier trigonometric series.
+
+    Examples
+    ========
+
+    Computing the Fourier series of $f(x) = x^2$:
+
+    >>> from sympy import fourier_series, pi
+    >>> from sympy.abc import x
+    >>> f = x**2
+    >>> s = fourier_series(f, (x, -pi, pi))
+    >>> s1 = s.truncate(n=3)
+    >>> s1
+    -4*cos(x) + cos(2*x) + pi**2/3
+
+    Shifting of the Fourier series:
+
+    >>> s.shift(1).truncate()
+    -4*cos(x) + cos(2*x) + 1 + pi**2/3
+    >>> s.shiftx(1).truncate()
+    -4*cos(x + 1) + cos(2*x + 2) + pi**2/3
+
+    Scaling of the Fourier series:
+
+    >>> s.scale(2).truncate()
+    -8*cos(x) + 2*cos(2*x) + 2*pi**2/3
+    >>> s.scalex(2).truncate()
+    -4*cos(2*x) + cos(4*x) + pi**2/3
+
+    Computing the Fourier series of $f(x) = x$:
+
+    This illustrates how truncating to the higher order gives better
+    convergence.
+
+    .. plot::
+        :context: reset
+        :format: doctest
+        :include-source: True
+
+        >>> from sympy import fourier_series, pi, plot
+        >>> from sympy.abc import x
+        >>> f = x
+        >>> s = fourier_series(f, (x, -pi, pi))
+        >>> s1 = s.truncate(n = 3)
+        >>> s2 = s.truncate(n = 5)
+        >>> s3 = s.truncate(n = 7)
+        >>> p = plot(f, s1, s2, s3, (x, -pi, pi), show=False, legend=True)
+
+        >>> p[0].line_color = (0, 0, 0)
+        >>> p[0].label = 'x'
+        >>> p[1].line_color = (0.7, 0.7, 0.7)
+        >>> p[1].label = 'n=3'
+        >>> p[2].line_color = (0.5, 0.5, 0.5)
+        >>> p[2].label = 'n=5'
+        >>> p[3].line_color = (0.3, 0.3, 0.3)
+        >>> p[3].label = 'n=7'
+
+        >>> p.show()
+
+    This illustrates how the series converges to different sawtooth
+    waves if the different ranges are specified.
+
+    .. plot::
+        :context: close-figs
+        :format: doctest
+        :include-source: True
+
+        >>> s1 = fourier_series(x, (x, -1, 1)).truncate(10)
+        >>> s2 = fourier_series(x, (x, -pi, pi)).truncate(10)
+        >>> s3 = fourier_series(x, (x, 0, 1)).truncate(10)
+        >>> p = plot(x, s1, s2, s3, (x, -5, 5), show=False, legend=True)
+
+        >>> p[0].line_color = (0, 0, 0)
+        >>> p[0].label = 'x'
+        >>> p[1].line_color = (0.7, 0.7, 0.7)
+        >>> p[1].label = '[-1, 1]'
+        >>> p[2].line_color = (0.5, 0.5, 0.5)
+        >>> p[2].label = '[-pi, pi]'
+        >>> p[3].line_color = (0.3, 0.3, 0.3)
+        >>> p[3].label = '[0, 1]'
+
+        >>> p.show()
+
+    Notes
+    =====
+
+    Computing Fourier series can be slow
+    due to the integration required in computing
+    an, bn.
+
+    It is faster to compute Fourier series of a function
+    by using shifting and scaling on an already
+    computed Fourier series rather than computing
+    again.
+
+    e.g. If the Fourier series of ``x**2`` is known
+    the Fourier series of ``x**2 - 1`` can be found by shifting by ``-1``.
+
+    See Also
+    ========
+
+    sympy.series.fourier.FourierSeries
+
+    References
+    ==========
+
+    .. [1] https://mathworld.wolfram.com/FourierSeries.html
+    """
+    f = sympify(f)
+
+    limits = _process_limits(f, limits)
+    x = limits[0]
+
+    if x not in f.free_symbols:
+        return f
+
+    if finite:
+        L = abs(limits[2] - limits[1]) / 2
+        is_finite, res_f = finite_check(f, x, L)
+        if is_finite:
+            return FiniteFourierSeries(f, limits, res_f)
+
+    n = Dummy('n')
+    center = (limits[1] + limits[2]) / 2
+    if center.is_zero:
+        neg_f = f.subs(x, -x)
+        if f == neg_f:
+            a0, an = fourier_cos_seq(f, limits, n)
+            bn = SeqFormula(0, (1, oo))
+            return FourierSeries(f, limits, (a0, an, bn))
+        elif f == -neg_f:
+            a0 = S.Zero
+            an = SeqFormula(0, (1, oo))
+            bn = fourier_sin_seq(f, limits, n)
+            return FourierSeries(f, limits, (a0, an, bn))
+    a0, an = fourier_cos_seq(f, limits, n)
+    bn = fourier_sin_seq(f, limits, n)
+    return FourierSeries(f, limits, (a0, an, bn))

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/gruntz.py

@@ -0,0 +1,701 @@
+"""
+Limits
+======
+
+Implemented according to the PhD thesis
+https://www.cybertester.com/data/gruntz.pdf, which contains very thorough
+descriptions of the algorithm including many examples.  We summarize here
+the gist of it.
+
+All functions are sorted according to how rapidly varying they are at
+infinity using the following rules. Any two functions f and g can be
+compared using the properties of L:
+
+L=lim  log|f(x)| / log|g(x)|           (for x -> oo)
+
+We define >, < ~ according to::
+
+    1. f > g .... L=+-oo
+
+        we say that:
+        - f is greater than any power of g
+        - f is more rapidly varying than g
+        - f goes to infinity/zero faster than g
+
+    2. f < g .... L=0
+
+        we say that:
+        - f is lower than any power of g
+
+    3. f ~ g .... L!=0, +-oo
+
+        we say that:
+        - both f and g are bounded from above and below by suitable integral
+          powers of the other
+
+Examples
+========
+::
+    2 < x < exp(x) < exp(x**2) < exp(exp(x))
+    2 ~ 3 ~ -5
+    x ~ x**2 ~ x**3 ~ 1/x ~ x**m ~ -x
+    exp(x) ~ exp(-x) ~ exp(2x) ~ exp(x)**2 ~ exp(x+exp(-x))
+    f ~ 1/f
+
+So we can divide all the functions into comparability classes (x and x^2
+belong to one class, exp(x) and exp(-x) belong to some other class). In
+principle, we could compare any two functions, but in our algorithm, we
+do not compare anything below the class 2~3~-5 (for example log(x) is
+below this), so we set 2~3~-5 as the lowest comparability class.
+
+Given the function f, we find the list of most rapidly varying (mrv set)
+subexpressions of it. This list belongs to the same comparability class.
+Let's say it is {exp(x), exp(2x)}. Using the rule f ~ 1/f we find an
+element "w" (either from the list or a new one) from the same
+comparability class which goes to zero at infinity. In our example we
+set w=exp(-x) (but we could also set w=exp(-2x) or w=exp(-3x) ...). We
+rewrite the mrv set using w, in our case {1/w, 1/w^2}, and substitute it
+into f. Then we expand f into a series in w::
+
+    f = c0*w^e0 + c1*w^e1 + ... + O(w^en),       where e0<e1<...<en, c0!=0
+
+but for x->oo, lim f = lim c0*w^e0, because all the other terms go to zero,
+because w goes to zero faster than the ci and ei. So::
+
+    for e0>0, lim f = 0
+    for e0<0, lim f = +-oo   (the sign depends on the sign of c0)
+    for e0=0, lim f = lim c0
+
+We need to recursively compute limits at several places of the algorithm, but
+as is shown in the PhD thesis, it always finishes.
+
+Important functions from the implementation:
+
+compare(a, b, x) compares "a" and "b" by computing the limit L.
+mrv(e, x) returns list of most rapidly varying (mrv) subexpressions of "e"
+rewrite(e, Omega, x, wsym) rewrites "e" in terms of w
+leadterm(f, x) returns the lowest power term in the series of f
+mrv_leadterm(e, x) returns the lead term (c0, e0) for e
+limitinf(e, x) computes lim e  (for x->oo)
+limit(e, z, z0) computes any limit by converting it to the case x->oo
+
+All the functions are really simple and straightforward except
+rewrite(), which is the most difficult/complex part of the algorithm.
+When the algorithm fails, the bugs are usually in the series expansion
+(i.e. in SymPy) or in rewrite.
+
+This code is almost exact rewrite of the Maple code inside the Gruntz
+thesis.
+
+Debugging
+---------
+
+Because the gruntz algorithm is highly recursive, it's difficult to
+figure out what went wrong inside a debugger. Instead, turn on nice
+debug prints by defining the environment variable SYMPY_DEBUG. For
+example:
+
+[user@localhost]: SYMPY_DEBUG=True ./bin/isympy
+
+In [1]: limit(sin(x)/x, x, 0)
+limitinf(_x*sin(1/_x), _x) = 1
++-mrv_leadterm(_x*sin(1/_x), _x) = (1, 0)
+| +-mrv(_x*sin(1/_x), _x) = set([_x])
+| | +-mrv(_x, _x) = set([_x])
+| | +-mrv(sin(1/_x), _x) = set([_x])
+| |   +-mrv(1/_x, _x) = set([_x])
+| |     +-mrv(_x, _x) = set([_x])
+| +-mrv_leadterm(exp(_x)*sin(exp(-_x)), _x, set([exp(_x)])) = (1, 0)
+|   +-rewrite(exp(_x)*sin(exp(-_x)), set([exp(_x)]), _x, _w) = (1/_w*sin(_w), -_x)
+|     +-sign(_x, _x) = 1
+|     +-mrv_leadterm(1, _x) = (1, 0)
++-sign(0, _x) = 0
++-limitinf(1, _x) = 1
+
+And check manually which line is wrong. Then go to the source code and
+debug this function to figure out the exact problem.
+
+"""
+from functools import reduce
+
+from sympy.core import Basic, S, Mul, PoleError
+from sympy.core.cache import cacheit
+from sympy.core.function import AppliedUndef
+from sympy.core.intfunc import ilcm
+from sympy.core.numbers import I, oo
+from sympy.core.symbol import Dummy, Wild
+from sympy.core.traversal import bottom_up
+
+from sympy.functions import log, exp, sign as _sign
+from sympy.series.order import Order
+from sympy.utilities.misc import debug_decorator as debug
+from sympy.utilities.timeutils import timethis
+
+timeit = timethis('gruntz')
+
+
+def compare(a, b, x):
+    """Returns "<" if a<b, "=" for a == b, ">" for a>b"""
+    # log(exp(...)) must always be simplified here for termination
+    la, lb = log(a), log(b)
+    if isinstance(a, Basic) and (isinstance(a, exp) or (a.is_Pow and a.base == S.Exp1)):
+        la = a.exp
+    if isinstance(b, Basic) and (isinstance(b, exp) or (b.is_Pow and b.base == S.Exp1)):
+        lb = b.exp
+
+    c = limitinf(la/lb, x)
+    if c == 0:
+        return "<"
+    elif c.is_infinite:
+        return ">"
+    else:
+        return "="
+
+
+class SubsSet(dict):
+    """
+    Stores (expr, dummy) pairs, and how to rewrite expr-s.
+
+    Explanation
+    ===========
+
+    The gruntz algorithm needs to rewrite certain expressions in term of a new
+    variable w. We cannot use subs, because it is just too smart for us. For
+    example::
+
+        > Omega=[exp(exp(_p - exp(-_p))/(1 - 1/_p)), exp(exp(_p))]
+        > O2=[exp(-exp(_p) + exp(-exp(-_p))*exp(_p)/(1 - 1/_p))/_w, 1/_w]
+        > e = exp(exp(_p - exp(-_p))/(1 - 1/_p)) - exp(exp(_p))
+        > e.subs(Omega[0],O2[0]).subs(Omega[1],O2[1])
+        -1/w + exp(exp(p)*exp(-exp(-p))/(1 - 1/p))
+
+    is really not what we want!
+
+    So we do it the hard way and keep track of all the things we potentially
+    want to substitute by dummy variables. Consider the expression::
+
+        exp(x - exp(-x)) + exp(x) + x.
+
+    The mrv set is {exp(x), exp(-x), exp(x - exp(-x))}.
+    We introduce corresponding dummy variables d1, d2, d3 and rewrite::
+
+        d3 + d1 + x.
+
+    This class first of all keeps track of the mapping expr->variable, i.e.
+    will at this stage be a dictionary::
+
+        {exp(x): d1, exp(-x): d2, exp(x - exp(-x)): d3}.
+
+    [It turns out to be more convenient this way round.]
+    But sometimes expressions in the mrv set have other expressions from the
+    mrv set as subexpressions, and we need to keep track of that as well. In
+    this case, d3 is really exp(x - d2), so rewrites at this stage is::
+
+        {d3: exp(x-d2)}.
+
+    The function rewrite uses all this information to correctly rewrite our
+    expression in terms of w. In this case w can be chosen to be exp(-x),
+    i.e. d2. The correct rewriting then is::
+
+        exp(-w)/w + 1/w + x.
+    """
+    def __init__(self):
+        self.rewrites = {}
+
+    def __repr__(self):
+        return super().__repr__() + ', ' + self.rewrites.__repr__()
+
+    def __getitem__(self, key):
+        if key not in self:
+            self[key] = Dummy()
+        return dict.__getitem__(self, key)
+
+    def do_subs(self, e):
+        """Substitute the variables with expressions"""
+        for expr, var in self.items():
+            e = e.xreplace({var: expr})
+        return e
+
+    def meets(self, s2):
+        """Tell whether or not self and s2 have non-empty intersection"""
+        return set(self.keys()).intersection(list(s2.keys())) != set()
+
+    def union(self, s2, exps=None):
+        """Compute the union of self and s2, adjusting exps"""
+        res = self.copy()
+        tr = {}
+        for expr, var in s2.items():
+            if expr in self:
+                if exps:
+                    exps = exps.xreplace({var: res[expr]})
+                tr[var] = res[expr]
+            else:
+                res[expr] = var
+        for var, rewr in s2.rewrites.items():
+            res.rewrites[var] = rewr.xreplace(tr)
+        return res, exps
+
+    def copy(self):
+        """Create a shallow copy of SubsSet"""
+        r = SubsSet()
+        r.rewrites = self.rewrites.copy()
+        for expr, var in self.items():
+            r[expr] = var
+        return r
+
+
+@debug
+def mrv(e, x):
+    """Returns a SubsSet of most rapidly varying (mrv) subexpressions of 'e',
+       and e rewritten in terms of these"""
+    from sympy.simplify.powsimp import powsimp
+    e = powsimp(e, deep=True, combine='exp')
+    if not isinstance(e, Basic):
+        raise TypeError("e should be an instance of Basic")
+    if not e.has(x):
+        return SubsSet(), e
+    elif e == x:
+        s = SubsSet()
+        return s, s[x]
+    elif e.is_Mul or e.is_Add:
+        i, d = e.as_independent(x)  # throw away x-independent terms
+        if d.func != e.func:
+            s, expr = mrv(d, x)
+            return s, e.func(i, expr)
+        a, b = d.as_two_terms()
+        s1, e1 = mrv(a, x)
+        s2, e2 = mrv(b, x)
+        return mrv_max1(s1, s2, e.func(i, e1, e2), x)
+    elif e.is_Pow and e.base != S.Exp1:
+        e1 = S.One
+        while e.is_Pow:
+            b1 = e.base
+            e1 *= e.exp
+            e = b1
+        if b1 == 1:
+            return SubsSet(), b1
+        if e1.has(x):
+            return mrv(exp(e1*log(b1)), x)
+        else:
+            s, expr = mrv(b1, x)
+            return s, expr**e1
+    elif isinstance(e, log):
+        s, expr = mrv(e.args[0], x)
+        return s, log(expr)
+    elif isinstance(e, exp) or (e.is_Pow and e.base == S.Exp1):
+        # We know from the theory of this algorithm that exp(log(...)) may always
+        # be simplified here, and doing so is vital for termination.
+        if isinstance(e.exp, log):
+            return mrv(e.exp.args[0], x)
+        # if a product has an infinite factor the result will be
+        # infinite if there is no zero, otherwise NaN; here, we
+        # consider the result infinite if any factor is infinite
+        li = limitinf(e.exp, x)
+        if any(_.is_infinite for _ in Mul.make_args(li)):
+            s1 = SubsSet()
+            e1 = s1[e]
+            s2, e2 = mrv(e.exp, x)
+            su = s1.union(s2)[0]
+            su.rewrites[e1] = exp(e2)
+            return mrv_max3(s1, e1, s2, exp(e2), su, e1, x)
+        else:
+            s, expr = mrv(e.exp, x)
+            return s, exp(expr)
+    elif isinstance(e, AppliedUndef):
+        raise ValueError("MRV set computation for UndefinedFunction is not allowed")
+    elif e.is_Function:
+        l = [mrv(a, x) for a in e.args]
+        l2 = [s for (s, _) in l if s != SubsSet()]
+        if len(l2) != 1:
+            # e.g. something like BesselJ(x, x)
+            raise NotImplementedError("MRV set computation for functions in"
+                                      " several variables not implemented.")
+        s, ss = l2[0], SubsSet()
+        args = [ss.do_subs(x[1]) for x in l]
+        return s, e.func(*args)
+    elif e.is_Derivative:
+        raise NotImplementedError("MRV set computation for derivatives"
+                                  " not implemented yet.")
+    raise NotImplementedError(
+        "Don't know how to calculate the mrv of '%s'" % e)
+
+
+def mrv_max3(f, expsf, g, expsg, union, expsboth, x):
+    """
+    Computes the maximum of two sets of expressions f and g, which
+    are in the same comparability class, i.e. max() compares (two elements of)
+    f and g and returns either (f, expsf) [if f is larger], (g, expsg)
+    [if g is larger] or (union, expsboth) [if f, g are of the same class].
+    """
+    if not isinstance(f, SubsSet):
+        raise TypeError("f should be an instance of SubsSet")
+    if not isinstance(g, SubsSet):
+        raise TypeError("g should be an instance of SubsSet")
+    if f == SubsSet():
+        return g, expsg
+    elif g == SubsSet():
+        return f, expsf
+    elif f.meets(g):
+        return union, expsboth
+
+    c = compare(list(f.keys())[0], list(g.keys())[0], x)
+    if c == ">":
+        return f, expsf
+    elif c == "<":
+        return g, expsg
+    else:
+        if c != "=":
+            raise ValueError("c should be =")
+        return union, expsboth
+
+
+def mrv_max1(f, g, exps, x):
+    """Computes the maximum of two sets of expressions f and g, which
+    are in the same comparability class, i.e. mrv_max1() compares (two elements of)
+    f and g and returns the set, which is in the higher comparability class
+    of the union of both, if they have the same order of variation.
+    Also returns exps, with the appropriate substitutions made.
+    """
+    u, b = f.union(g, exps)
+    return mrv_max3(f, g.do_subs(exps), g, f.do_subs(exps),
+                    u, b, x)
+
+
+@debug
+@cacheit
+@timeit
+def sign(e, x):
+    """
+    Returns a sign of an expression e(x) for x->oo.
+
+    ::
+
+        e >  0 for x sufficiently large ...  1
+        e == 0 for x sufficiently large ...  0
+        e <  0 for x sufficiently large ... -1
+
+    The result of this function is currently undefined if e changes sign
+    arbitrarily often for arbitrarily large x (e.g. sin(x)).
+
+    Note that this returns zero only if e is *constantly* zero
+    for x sufficiently large. [If e is constant, of course, this is just
+    the same thing as the sign of e.]
+    """
+    if not isinstance(e, Basic):
+        raise TypeError("e should be an instance of Basic")
+
+    if e.is_positive:
+        return 1
+    elif e.is_negative:
+        return -1
+    elif e.is_zero:
+        return 0
+
+    elif not e.has(x):
+        from sympy.simplify import logcombine
+        e = logcombine(e)
+        return _sign(e)
+    elif e == x:
+        return 1
+    elif e.is_Mul:
+        a, b = e.as_two_terms()
+        sa = sign(a, x)
+        if not sa:
+            return 0
+        return sa * sign(b, x)
+    elif isinstance(e, exp):
+        return 1
+    elif e.is_Pow:
+        if e.base == S.Exp1:
+            return 1
+        s = sign(e.base, x)
+        if s == 1:
+            return 1
+        if e.exp.is_Integer:
+            return s**e.exp
+    elif isinstance(e, log) and e.args[0].is_positive:
+        return sign(e.args[0] - 1, x)
+
+    # if all else fails, do it the hard way
+    c0, e0 = mrv_leadterm(e, x)
+    return sign(c0, x)
+
+
+@debug
+@timeit
+@cacheit
+def limitinf(e, x):
+    """Limit e(x) for x-> oo."""
+    # rewrite e in terms of tractable functions only
+
+    old = e
+    if not e.has(x):
+        return e  # e is a constant
+    from sympy.simplify.powsimp import powdenest
+    from sympy.calculus.util import AccumBounds
+    if e.has(Order):
+        e = e.expand().removeO()
+    if not x.is_positive or x.is_integer:
+        # We make sure that x.is_positive is True and x.is_integer is None
+        # so we get all the correct mathematical behavior from the expression.
+        # We need a fresh variable.
+        p = Dummy('p', positive=True)
+        e = e.subs(x, p)
+        x = p
+    e = e.rewrite('tractable', deep=True, limitvar=x)
+    e = powdenest(e)
+    if isinstance(e, AccumBounds):
+        if mrv_leadterm(e.min, x) != mrv_leadterm(e.max, x):
+            raise NotImplementedError
+        c0, e0 = mrv_leadterm(e.min, x)
+    else:
+        c0, e0 = mrv_leadterm(e, x)
+    sig = sign(e0, x)
+    if sig == 1:
+        return S.Zero  # e0>0: lim f = 0
+    elif sig == -1:  # e0<0: lim f = +-oo (the sign depends on the sign of c0)
+        if c0.match(I*Wild("a", exclude=[I])):
+            return c0*oo
+        s = sign(c0, x)
+        # the leading term shouldn't be 0:
+        if s == 0:
+            raise ValueError("Leading term should not be 0")
+        return s*oo
+    elif sig == 0:
+        if c0 == old:
+            c0 = c0.cancel()
+        return limitinf(c0, x)  # e0=0: lim f = lim c0
+    else:
+        raise ValueError("{} could not be evaluated".format(sig))
+
+
+def moveup2(s, x):
+    r = SubsSet()
+    for expr, var in s.items():
+        r[expr.xreplace({x: exp(x)})] = var
+    for var, expr in s.rewrites.items():
+        r.rewrites[var] = s.rewrites[var].xreplace({x: exp(x)})
+    return r
+
+
+def moveup(l, x):
+    return [e.xreplace({x: exp(x)}) for e in l]
+
+
+@debug
+@timeit
+@cacheit
+def mrv_leadterm(e, x):
+    """Returns (c0, e0) for e."""
+    Omega = SubsSet()
+    if not e.has(x):
+        return (e, S.Zero)
+    if Omega == SubsSet():
+        Omega, exps = mrv(e, x)
+    if not Omega:
+        # e really does not depend on x after simplification
+        return exps, S.Zero
+    if x in Omega:
+        # move the whole omega up (exponentiate each term):
+        Omega_up = moveup2(Omega, x)
+        exps_up = moveup([exps], x)[0]
+        # NOTE: there is no need to move this down!
+        Omega = Omega_up
+        exps = exps_up
+    #
+    # The positive dummy, w, is used here so log(w*2) etc. will expand;
+    # a unique dummy is needed in this algorithm
+    #
+    # For limits of complex functions, the algorithm would have to be
+    # improved, or just find limits of Re and Im components separately.
+    #
+    w = Dummy("w", positive=True)
+    f, logw = rewrite(exps, Omega, x, w)
+
+    # Ensure expressions of the form exp(log(...)) don't get simplified automatically in the previous steps.
+    # see: https://github.com/sympy/sympy/issues/15323#issuecomment-478639399
+    f = f.replace(lambda f: f.is_Pow and f.has(x), lambda f: exp(log(f.base)*f.exp))
+
+    try:
+        lt = f.leadterm(w, logx=logw)
+    except (NotImplementedError, PoleError, ValueError):
+        n0 = 1
+        _series = Order(1)
+        incr = S.One
+        while _series.is_Order:
+            _series = f._eval_nseries(w, n=n0+incr, logx=logw)
+            incr *= 2
+        series = _series.expand().removeO()
+        try:
+            lt = series.leadterm(w, logx=logw)
+        except (NotImplementedError, PoleError, ValueError):
+            lt = f.as_coeff_exponent(w)
+            if lt[0].has(w):
+                base = f.as_base_exp()[0].as_coeff_exponent(w)
+                ex = f.as_base_exp()[1]
+                lt = (base[0]**ex, base[1]*ex)
+    return (lt[0].subs(log(w), logw), lt[1])
+
+
+def build_expression_tree(Omega, rewrites):
+    r""" Helper function for rewrite.
+
+    We need to sort Omega (mrv set) so that we replace an expression before
+    we replace any expression in terms of which it has to be rewritten::
+
+        e1 ---> e2 ---> e3
+                 \
+                  -> e4
+
+    Here we can do e1, e2, e3, e4 or e1, e2, e4, e3.
+    To do this we assemble the nodes into a tree, and sort them by height.
+
+    This function builds the tree, rewrites then sorts the nodes.
+    """
+    class Node:
+        def __init__(self):
+            self.before = []
+            self.expr = None
+            self.var = None
+        def ht(self):
+            return reduce(lambda x, y: x + y,
+                          [x.ht() for x in self.before], 1)
+    nodes = {}
+    for expr, v in Omega:
+        n = Node()
+        n.var = v
+        n.expr = expr
+        nodes[v] = n
+    for _, v in Omega:
+        if v in rewrites:
+            n = nodes[v]
+            r = rewrites[v]
+            for _, v2 in Omega:
+                if r.has(v2):
+                    n.before.append(nodes[v2])
+
+    return nodes
+
+
+@debug
+@timeit
+def rewrite(e, Omega, x, wsym):
+    """e(x) ... the function
+    Omega ... the mrv set
+    wsym ... the symbol which is going to be used for w
+
+    Returns the rewritten e in terms of w and log(w). See test_rewrite1()
+    for examples and correct results.
+    """
+
+    from sympy import AccumBounds
+    if not isinstance(Omega, SubsSet):
+        raise TypeError("Omega should be an instance of SubsSet")
+    if len(Omega) == 0:
+        raise ValueError("Length cannot be 0")
+    # all items in Omega must be exponentials
+    for t in Omega.keys():
+        if not isinstance(t, exp):
+            raise ValueError("Value should be exp")
+    rewrites = Omega.rewrites
+    Omega = list(Omega.items())
+
+    nodes = build_expression_tree(Omega, rewrites)
+    Omega.sort(key=lambda x: nodes[x[1]].ht(), reverse=True)
+
+    # make sure we know the sign of each exp() term; after the loop,
+    # g is going to be the "w" - the simplest one in the mrv set
+    for g, _ in Omega:
+        sig = sign(g.exp, x)
+        if sig != 1 and sig != -1 and not sig.has(AccumBounds):
+            raise NotImplementedError('Result depends on the sign of %s' % sig)
+    if sig == 1:
+        wsym = 1/wsym  # if g goes to oo, substitute 1/w
+    # O2 is a list, which results by rewriting each item in Omega using "w"
+    O2 = []
+    denominators = []
+    for f, var in Omega:
+        c = limitinf(f.exp/g.exp, x)
+        if c.is_Rational:
+            denominators.append(c.q)
+        arg = f.exp
+        if var in rewrites:
+            if not isinstance(rewrites[var], exp):
+                raise ValueError("Value should be exp")
+            arg = rewrites[var].args[0]
+        O2.append((var, exp((arg - c*g.exp))*wsym**c))
+
+    # Remember that Omega contains subexpressions of "e". So now we find
+    # them in "e" and substitute them for our rewriting, stored in O2
+
+    # the following powsimp is necessary to automatically combine exponentials,
+    # so that the .xreplace() below succeeds:
+    # TODO this should not be necessary
+    from sympy.simplify.powsimp import powsimp
+    f = powsimp(e, deep=True, combine='exp')
+    for a, b in O2:
+        f = f.xreplace({a: b})
+
+    for _, var in Omega:
+        assert not f.has(var)
+
+    # finally compute the logarithm of w (logw).
+    logw = g.exp
+    if sig == 1:
+        logw = -logw  # log(w)->log(1/w)=-log(w)
+
+    # Some parts of SymPy have difficulty computing series expansions with
+    # non-integral exponents. The following heuristic improves the situation:
+    exponent = reduce(ilcm, denominators, 1)
+    f = f.subs({wsym: wsym**exponent})
+    logw /= exponent
+
+    # bottom_up function is required for a specific case - when f is
+    # -exp(p/(p + 1)) + exp(-p**2/(p + 1) + p). No current simplification
+    # methods reduce this to 0 while not expanding polynomials.
+    f = bottom_up(f, lambda w: getattr(w, 'normal', lambda: w)())
+
+    return f, logw
+
+
+def gruntz(e, z, z0, dir="+"):
+    """
+    Compute the limit of e(z) at the point z0 using the Gruntz algorithm.
+
+    Explanation
+    ===========
+
+    ``z0`` can be any expression, including oo and -oo.
+
+    For ``dir="+"`` (default) it calculates the limit from the right
+    (z->z0+) and for ``dir="-"`` the limit from the left (z->z0-). For infinite z0
+    (oo or -oo), the dir argument does not matter.
+
+    This algorithm is fully described in the module docstring in the gruntz.py
+    file. It relies heavily on the series expansion. Most frequently, gruntz()
+    is only used if the faster limit() function (which uses heuristics) fails.
+    """
+    if not z.is_symbol:
+        raise NotImplementedError("Second argument must be a Symbol")
+
+    # convert all limits to the limit z->oo; sign of z is handled in limitinf
+    r = None
+    if z0 in (oo, I*oo):
+        e0 = e
+    elif z0 in (-oo, -I*oo):
+        e0 = e.subs(z, -z)
+    else:
+        if str(dir) == "-":
+            e0 = e.subs(z, z0 - 1/z)
+        elif str(dir) == "+":
+            e0 = e.subs(z, z0 + 1/z)
+        else:
+            raise NotImplementedError("dir must be '+' or '-'")
+
+    r = limitinf(e0, z)
+
+    # This is a bit of a heuristic for nice results... we always rewrite
+    # tractable functions in terms of familiar intractable ones.
+    # It might be nicer to rewrite the exactly to what they were initially,
+    # but that would take some work to implement.
+    return r.rewrite('intractable', deep=True)

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/kauers.py

@@ -0,0 +1,51 @@
+def finite_diff(expression, variable, increment=1):
+    """
+    Takes as input a polynomial expression and the variable used to construct
+    it and returns the difference between function's value when the input is
+    incremented to 1 and the original function value. If you want an increment
+    other than one supply it as a third argument.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y, z
+    >>> from sympy.series.kauers import finite_diff
+    >>> finite_diff(x**2, x)
+    2*x + 1
+    >>> finite_diff(y**3 + 2*y**2 + 3*y + 4, y)
+    3*y**2 + 7*y + 6
+    >>> finite_diff(x**2 + 3*x + 8, x, 2)
+    4*x + 10
+    >>> finite_diff(z**3 + 8*z, z, 3)
+    9*z**2 + 27*z + 51
+    """
+    expression = expression.expand()
+    expression2 = expression.subs(variable, variable + increment)
+    expression2 = expression2.expand()
+    return expression2 - expression
+
+def finite_diff_kauers(sum):
+    """
+    Takes as input a Sum instance and returns the difference between the sum
+    with the upper index incremented by 1 and the original sum. For example,
+    if S(n) is a sum, then finite_diff_kauers will return S(n + 1) - S(n).
+
+    Examples
+    ========
+
+    >>> from sympy.series.kauers import finite_diff_kauers
+    >>> from sympy import Sum
+    >>> from sympy.abc import x, y, m, n, k
+    >>> finite_diff_kauers(Sum(k, (k, 1, n)))
+    n + 1
+    >>> finite_diff_kauers(Sum(1/k, (k, 1, n)))
+    1/(n + 1)
+    >>> finite_diff_kauers(Sum((x*y**2), (x, 1, n), (y, 1, m)))
+    (m + 1)**2*(n + 1)
+    >>> finite_diff_kauers(Sum((x*y), (x, 1, m), (y, 1, n)))
+    (m + 1)*(n + 1)
+    """
+    function = sum.function
+    for l in sum.limits:
+        function = function.subs(l[0], l[- 1] + 1)
+    return function

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/limits.py

@@ -0,0 +1,394 @@
+from sympy.calculus.accumulationbounds import AccumBounds
+from sympy.core import S, Symbol, Add, sympify, Expr, PoleError, Mul
+from sympy.core.exprtools import factor_terms
+from sympy.core.numbers import Float, _illegal
+from sympy.core.function import AppliedUndef
+from sympy.core.symbol import Dummy
+from sympy.functions.combinatorial.factorials import factorial
+from sympy.functions.elementary.complexes import (Abs, sign, arg, re)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.special.gamma_functions import gamma
+from sympy.polys import PolynomialError, factor
+from sympy.series.order import Order
+from .gruntz import gruntz
+
+def limit(e, z, z0, dir="+"):
+    """Computes the limit of ``e(z)`` at the point ``z0``.
+
+    Parameters
+    ==========
+
+    e : expression, the limit of which is to be taken
+
+    z : symbol representing the variable in the limit.
+        Other symbols are treated as constants. Multivariate limits
+        are not supported.
+
+    z0 : the value toward which ``z`` tends. Can be any expression,
+        including ``oo`` and ``-oo``.
+
+    dir : string, optional (default: "+")
+        The limit is bi-directional if ``dir="+-"``, from the right
+        (z->z0+) if ``dir="+"``, and from the left (z->z0-) if
+        ``dir="-"``. For infinite ``z0`` (``oo`` or ``-oo``), the ``dir``
+        argument is determined from the direction of the infinity
+        (i.e., ``dir="-"`` for ``oo``).
+
+    Examples
+    ========
+
+    >>> from sympy import limit, sin, oo
+    >>> from sympy.abc import x
+    >>> limit(sin(x)/x, x, 0)
+    1
+    >>> limit(1/x, x, 0) # default dir='+'
+    oo
+    >>> limit(1/x, x, 0, dir="-")
+    -oo
+    >>> limit(1/x, x, 0, dir='+-')
+    zoo
+    >>> limit(1/x, x, oo)
+    0
+
+    Notes
+    =====
+
+    First we try some heuristics for easy and frequent cases like "x", "1/x",
+    "x**2" and similar, so that it's fast. For all other cases, we use the
+    Gruntz algorithm (see the gruntz() function).
+
+    See Also
+    ========
+
+     limit_seq : returns the limit of a sequence.
+    """
+
+    return Limit(e, z, z0, dir).doit(deep=False)
+
+
+def heuristics(e, z, z0, dir):
+    """Computes the limit of an expression term-wise.
+    Parameters are the same as for the ``limit`` function.
+    Works with the arguments of expression ``e`` one by one, computing
+    the limit of each and then combining the results. This approach
+    works only for simple limits, but it is fast.
+    """
+
+    rv = None
+    if z0 is S.Infinity:
+        rv = limit(e.subs(z, 1/z), z, S.Zero, "+")
+        if isinstance(rv, Limit):
+            return
+    elif (e.is_Mul or e.is_Add or e.is_Pow or (e.is_Function and not isinstance(e, AppliedUndef))):
+        r = []
+        from sympy.simplify.simplify import together
+        for a in e.args:
+            l = limit(a, z, z0, dir)
+            if l.has(S.Infinity) and l.is_finite is None:
+                if isinstance(e, Add):
+                    m = factor_terms(e)
+                    if not isinstance(m, Mul): # try together
+                        m = together(m)
+                    if not isinstance(m, Mul): # try factor if the previous methods failed
+                        m = factor(e)
+                    if isinstance(m, Mul):
+                        return heuristics(m, z, z0, dir)
+                    return
+                return
+            elif isinstance(l, Limit):
+                return
+            elif l is S.NaN:
+                return
+            else:
+                r.append(l)
+        if r:
+            rv = e.func(*r)
+            if rv is S.NaN and e.is_Mul and any(isinstance(rr, AccumBounds) for rr in r):
+                r2 = []
+                e2 = []
+                for ii, rval in enumerate(r):
+                    if isinstance(rval, AccumBounds):
+                        r2.append(rval)
+                    else:
+                        e2.append(e.args[ii])
+
+                if len(e2) > 0:
+                    e3 = Mul(*e2).simplify()
+                    l = limit(e3, z, z0, dir)
+                    rv = l * Mul(*r2)
+
+            if rv is S.NaN:
+                try:
+                    from sympy.simplify.ratsimp import ratsimp
+                    rat_e = ratsimp(e)
+                except PolynomialError:
+                    return
+                if rat_e is S.NaN or rat_e == e:
+                    return
+                return limit(rat_e, z, z0, dir)
+    return rv
+
+
+class Limit(Expr):
+    """Represents an unevaluated limit.
+
+    Examples
+    ========
+
+    >>> from sympy import Limit, sin
+    >>> from sympy.abc import x
+    >>> Limit(sin(x)/x, x, 0)
+    Limit(sin(x)/x, x, 0, dir='+')
+    >>> Limit(1/x, x, 0, dir="-")
+    Limit(1/x, x, 0, dir='-')
+
+    """
+
+    def __new__(cls, e, z, z0, dir="+"):
+        e = sympify(e)
+        z = sympify(z)
+        z0 = sympify(z0)
+
+        if z0 in (S.Infinity, S.ImaginaryUnit*S.Infinity):
+            dir = "-"
+        elif z0 in (S.NegativeInfinity, S.ImaginaryUnit*S.NegativeInfinity):
+            dir = "+"
+
+        if(z0.has(z)):
+            raise NotImplementedError("Limits approaching a variable point are"
+                    " not supported (%s -> %s)" % (z, z0))
+        if isinstance(dir, str):
+            dir = Symbol(dir)
+        elif not isinstance(dir, Symbol):
+            raise TypeError("direction must be of type basestring or "
+                    "Symbol, not %s" % type(dir))
+        if str(dir) not in ('+', '-', '+-'):
+            raise ValueError("direction must be one of '+', '-' "
+                    "or '+-', not %s" % dir)
+
+        obj = Expr.__new__(cls)
+        obj._args = (e, z, z0, dir)
+        return obj
+
+
+    @property
+    def free_symbols(self):
+        e = self.args[0]
+        isyms = e.free_symbols
+        isyms.difference_update(self.args[1].free_symbols)
+        isyms.update(self.args[2].free_symbols)
+        return isyms
+
+
+    def pow_heuristics(self, e):
+        _, z, z0, _ = self.args
+        b1, e1 = e.base, e.exp
+        if not b1.has(z):
+            res = limit(e1*log(b1), z, z0)
+            return exp(res)
+
+        ex_lim = limit(e1, z, z0)
+        base_lim = limit(b1, z, z0)
+
+        if base_lim is S.One:
+            if ex_lim in (S.Infinity, S.NegativeInfinity):
+                res = limit(e1*(b1 - 1), z, z0)
+                return exp(res)
+        if base_lim is S.NegativeInfinity and ex_lim is S.Infinity:
+            return S.ComplexInfinity
+
+
+    def doit(self, **hints):
+        """Evaluates the limit.
+
+        Parameters
+        ==========
+
+        deep : bool, optional (default: True)
+            Invoke the ``doit`` method of the expressions involved before
+            taking the limit.
+
+        hints : optional keyword arguments
+            To be passed to ``doit`` methods; only used if deep is True.
+        """
+
+        e, z, z0, dir = self.args
+
+        if str(dir) == '+-':
+            r = limit(e, z, z0, dir='+')
+            l = limit(e, z, z0, dir='-')
+            if isinstance(r, Limit) and isinstance(l, Limit):
+                if r.args[0] == l.args[0]:
+                    return self
+            if r == l:
+                return l
+            if r.is_infinite and l.is_infinite:
+                return S.ComplexInfinity
+            raise ValueError("The limit does not exist since "
+                             "left hand limit = %s and right hand limit = %s"
+                             % (l, r))
+
+        if z0 is S.ComplexInfinity:
+            raise NotImplementedError("Limits at complex "
+                                    "infinity are not implemented")
+
+        if z0.is_infinite:
+            cdir = sign(z0)
+            cdir = cdir/abs(cdir)
+            e = e.subs(z, cdir*z)
+            dir = "-"
+            z0 = S.Infinity
+
+        if hints.get('deep', True):
+            e = e.doit(**hints)
+            z = z.doit(**hints)
+            z0 = z0.doit(**hints)
+
+        if e == z:
+            return z0
+
+        if not e.has(z):
+            return e
+
+        if z0 is S.NaN:
+            return S.NaN
+
+        if e.has(*_illegal):
+            return self
+
+        if e.is_Order:
+            return Order(limit(e.expr, z, z0), *e.args[1:])
+
+        cdir = S.Zero
+        if str(dir) == "+":
+            cdir = S.One
+        elif str(dir) == "-":
+            cdir = S.NegativeOne
+
+        def set_signs(expr):
+            if not expr.args:
+                return expr
+            newargs = tuple(set_signs(arg) for arg in expr.args)
+            if newargs != expr.args:
+                expr = expr.func(*newargs)
+            abs_flag = isinstance(expr, Abs)
+            arg_flag = isinstance(expr, arg)
+            sign_flag = isinstance(expr, sign)
+            if abs_flag or sign_flag or arg_flag:
+                try:
+                    sig = limit(expr.args[0], z, z0, dir)
+                    if sig.is_zero:
+                        sig = limit(1/expr.args[0], z, z0, dir)
+                except NotImplementedError:
+                    return expr
+                else:
+                    if sig.is_extended_real:
+                        if (sig < 0) == True:
+                            return (-expr.args[0] if abs_flag else
+                                    S.NegativeOne if sign_flag else S.Pi)
+                        elif (sig > 0) == True:
+                            return (expr.args[0] if abs_flag else
+                                    S.One if sign_flag else S.Zero)
+            return expr
+
+        if e.has(Float):
+            # Convert floats like 0.5 to exact SymPy numbers like S.Half, to
+            # prevent rounding errors which can lead to unexpected execution
+            # of conditional blocks that work on comparisons
+            # Also see comments in https://github.com/sympy/sympy/issues/19453
+            from sympy.simplify.simplify import nsimplify
+            e = nsimplify(e)
+        e = set_signs(e)
+
+
+        if e.is_meromorphic(z, z0):
+            if z0 is S.Infinity:
+                newe = e.subs(z, 1/z)
+                # cdir changes sign as oo- should become 0+
+                cdir = -cdir
+            else:
+                newe = e.subs(z, z + z0)
+            try:
+                coeff, ex = newe.leadterm(z, cdir=cdir)
+            except ValueError:
+                pass
+            else:
+                if ex > 0:
+                    return S.Zero
+                elif ex == 0:
+                    return coeff
+                if cdir == 1 or not(int(ex) & 1):
+                    return S.Infinity*sign(coeff)
+                elif cdir == -1:
+                    return S.NegativeInfinity*sign(coeff)
+                else:
+                    return S.ComplexInfinity
+
+        if z0 is S.Infinity:
+            if e.is_Mul:
+                e = factor_terms(e)
+            dummy = Dummy('z', positive=z.is_positive, negative=z.is_negative, real=z.is_real)
+            newe = e.subs(z, 1/dummy)
+            # cdir changes sign as oo- should become 0+
+            cdir = -cdir
+            newz = dummy
+        else:
+            newe = e.subs(z, z + z0)
+            newz = z
+        try:
+            coeff, ex = newe.leadterm(newz, cdir=cdir)
+        except (ValueError, NotImplementedError, PoleError):
+            # The NotImplementedError catching is for custom functions
+            from sympy.simplify.powsimp import powsimp
+            e = powsimp(e)
+            if e.is_Pow:
+                r = self.pow_heuristics(e)
+                if r is not None:
+                    return r
+            try:
+                coeff = newe.as_leading_term(newz, cdir=cdir)
+                if coeff != newe and (coeff.has(exp) or coeff.has(S.Exp1)):
+                    return gruntz(coeff, newz, 0, "-" if re(cdir).is_negative else "+")
+            except (ValueError, NotImplementedError, PoleError):
+                pass
+        else:
+            if isinstance(coeff, AccumBounds) and ex == S.Zero:
+                return coeff
+            if coeff.has(S.Infinity, S.NegativeInfinity, S.ComplexInfinity, S.NaN):
+                return self
+            if not coeff.has(newz):
+                if ex.is_positive:
+                    return S.Zero
+                elif ex == 0:
+                    return coeff
+                elif ex.is_negative:
+                    if cdir == 1:
+                        return S.Infinity*sign(coeff)
+                    elif cdir == -1:
+                        return S.NegativeInfinity*sign(coeff)*S.NegativeOne**(S.One + ex)
+                    else:
+                        return S.ComplexInfinity
+                else:
+                    raise NotImplementedError("Not sure of sign of %s" % ex)
+
+        # gruntz fails on factorials but works with the gamma function
+        # If no factorial term is present, e should remain unchanged.
+        # factorial is defined to be zero for negative inputs (which
+        # differs from gamma) so only rewrite for non-negative z0.
+        if z0.is_extended_nonnegative:
+            e = e.rewrite(factorial, gamma)
+
+        l = None
+
+        try:
+            r = gruntz(e, z, z0, dir)
+            if r is S.NaN or l is S.NaN:
+                raise PoleError()
+        except (PoleError, ValueError):
+            if l is not None:
+                raise
+            r = heuristics(e, z, z0, dir)
+            if r is None:
+                return self
+
+        return r

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/limitseq.py

@@ -0,0 +1,257 @@
+"""Limits of sequences"""
+
+from sympy.calculus.accumulationbounds import AccumulationBounds
+from sympy.core.add import Add
+from sympy.core.function import PoleError
+from sympy.core.power import Pow
+from sympy.core.singleton import S
+from sympy.core.symbol import Dummy
+from sympy.core.sympify import sympify
+from sympy.functions.combinatorial.numbers import fibonacci
+from sympy.functions.combinatorial.factorials import factorial, subfactorial
+from sympy.functions.special.gamma_functions import gamma
+from sympy.functions.elementary.complexes import Abs
+from sympy.functions.elementary.miscellaneous import Max, Min
+from sympy.functions.elementary.trigonometric import cos, sin
+from sympy.series.limits import Limit
+
+
+def difference_delta(expr, n=None, step=1):
+    """Difference Operator.
+
+    Explanation
+    ===========
+
+    Discrete analog of differential operator. Given a sequence x[n],
+    returns the sequence x[n + step] - x[n].
+
+    Examples
+    ========
+
+    >>> from sympy import difference_delta as dd
+    >>> from sympy.abc import n
+    >>> dd(n*(n + 1), n)
+    2*n + 2
+    >>> dd(n*(n + 1), n, 2)
+    4*n + 6
+
+    References
+    ==========
+
+    .. [1] https://reference.wolfram.com/language/ref/DifferenceDelta.html
+    """
+    expr = sympify(expr)
+
+    if n is None:
+        f = expr.free_symbols
+        if len(f) == 1:
+            n = f.pop()
+        elif len(f) == 0:
+            return S.Zero
+        else:
+            raise ValueError("Since there is more than one variable in the"
+                             " expression, a variable must be supplied to"
+                             " take the difference of %s" % expr)
+    step = sympify(step)
+    if step.is_number is False or step.is_finite is False:
+        raise ValueError("Step should be a finite number.")
+
+    if hasattr(expr, '_eval_difference_delta'):
+        result = expr._eval_difference_delta(n, step)
+        if result:
+            return result
+
+    return expr.subs(n, n + step) - expr
+
+
+def dominant(expr, n):
+    """Finds the dominant term in a sum, that is a term that dominates
+    every other term.
+
+    Explanation
+    ===========
+
+    If limit(a/b, n, oo) is oo then a dominates b.
+    If limit(a/b, n, oo) is 0 then b dominates a.
+    Otherwise, a and b are comparable.
+
+    If there is no unique dominant term, then returns ``None``.
+
+    Examples
+    ========
+
+    >>> from sympy import Sum
+    >>> from sympy.series.limitseq import dominant
+    >>> from sympy.abc import n, k
+    >>> dominant(5*n**3 + 4*n**2 + n + 1, n)
+    5*n**3
+    >>> dominant(2**n + Sum(k, (k, 0, n)), n)
+    2**n
+
+    See Also
+    ========
+
+    sympy.series.limitseq.dominant
+    """
+    terms = Add.make_args(expr.expand(func=True))
+    term0 = terms[-1]
+    comp = [term0]  # comparable terms
+    for t in terms[:-1]:
+        r = term0/t
+        e = r.gammasimp()
+        if e == r:
+            e = r.factor()
+        l = limit_seq(e, n)
+        if l is None:
+            return None
+        elif l.is_zero:
+            term0 = t
+            comp = [term0]
+        elif l not in [S.Infinity, S.NegativeInfinity]:
+            comp.append(t)
+    if len(comp) > 1:
+        return None
+    return term0
+
+
+def _limit_inf(expr, n):
+    try:
+        return Limit(expr, n, S.Infinity).doit(deep=False)
+    except (NotImplementedError, PoleError):
+        return None
+
+
+def _limit_seq(expr, n, trials):
+    from sympy.concrete.summations import Sum
+
+    for i in range(trials):
+        if not expr.has(Sum):
+            result = _limit_inf(expr, n)
+            if result is not None:
+                return result
+
+        num, den = expr.as_numer_denom()
+        if not den.has(n) or not num.has(n):
+            result = _limit_inf(expr.doit(), n)
+            if result is not None:
+                return result
+            return None
+
+        num, den = (difference_delta(t.expand(), n) for t in [num, den])
+        expr = (num / den).gammasimp()
+
+        if not expr.has(Sum):
+            result = _limit_inf(expr, n)
+            if result is not None:
+                return result
+
+        num, den = expr.as_numer_denom()
+
+        num = dominant(num, n)
+        if num is None:
+            return None
+
+        den = dominant(den, n)
+        if den is None:
+            return None
+
+        expr = (num / den).gammasimp()
+
+
+def limit_seq(expr, n=None, trials=5):
+    """Finds the limit of a sequence as index ``n`` tends to infinity.
+
+    Parameters
+    ==========
+
+    expr : Expr
+        SymPy expression for the ``n-th`` term of the sequence
+    n : Symbol, optional
+        The index of the sequence, an integer that tends to positive
+        infinity. If None, inferred from the expression unless it has
+        multiple symbols.
+    trials: int, optional
+        The algorithm is highly recursive. ``trials`` is a safeguard from
+        infinite recursion in case the limit is not easily computed by the
+        algorithm. Try increasing ``trials`` if the algorithm returns ``None``.
+
+    Admissible Terms
+    ================
+
+    The algorithm is designed for sequences built from rational functions,
+    indefinite sums, and indefinite products over an indeterminate n. Terms of
+    alternating sign are also allowed, but more complex oscillatory behavior is
+    not supported.
+
+    Examples
+    ========
+
+    >>> from sympy import limit_seq, Sum, binomial
+    >>> from sympy.abc import n, k, m
+    >>> limit_seq((5*n**3 + 3*n**2 + 4) / (3*n**3 + 4*n - 5), n)
+    5/3
+    >>> limit_seq(binomial(2*n, n) / Sum(binomial(2*k, k), (k, 1, n)), n)
+    3/4
+    >>> limit_seq(Sum(k**2 * Sum(2**m/m, (m, 1, k)), (k, 1, n)) / (2**n*n), n)
+    4
+
+    See Also
+    ========
+
+    sympy.series.limitseq.dominant
+
+    References
+    ==========
+
+    .. [1] Computing Limits of Sequences - Manuel Kauers
+    """
+
+    from sympy.concrete.summations import Sum
+    if n is None:
+        free = expr.free_symbols
+        if len(free) == 1:
+            n = free.pop()
+        elif not free:
+            return expr
+        else:
+            raise ValueError("Expression has more than one variable. "
+                             "Please specify a variable.")
+    elif n not in expr.free_symbols:
+        return expr
+
+    expr = expr.rewrite(fibonacci, S.GoldenRatio)
+    expr = expr.rewrite(factorial, subfactorial, gamma)
+    n_ = Dummy("n", integer=True, positive=True)
+    n1 = Dummy("n", odd=True, positive=True)
+    n2 = Dummy("n", even=True, positive=True)
+
+    # If there is a negative term raised to a power involving n, or a
+    # trigonometric function, then consider even and odd n separately.
+    powers = (p.as_base_exp() for p in expr.atoms(Pow))
+    if (any(b.is_negative and e.has(n) for b, e in powers) or
+            expr.has(cos, sin)):
+        L1 = _limit_seq(expr.xreplace({n: n1}), n1, trials)
+        if L1 is not None:
+            L2 = _limit_seq(expr.xreplace({n: n2}), n2, trials)
+            if L1 != L2:
+                if L1.is_comparable and L2.is_comparable:
+                    return AccumulationBounds(Min(L1, L2), Max(L1, L2))
+                else:
+                    return None
+    else:
+        L1 = _limit_seq(expr.xreplace({n: n_}), n_, trials)
+    if L1 is not None:
+        return L1
+    else:
+        if expr.is_Add:
+            limits = [limit_seq(term, n, trials) for term in expr.args]
+            if any(result is None for result in limits):
+                return None
+            else:
+                return Add(*limits)
+        # Maybe the absolute value is easier to deal with (though not if
+        # it has a Sum). If it tends to 0, the limit is 0.
+        elif not expr.has(Sum):
+            lim = _limit_seq(Abs(expr.xreplace({n: n_})), n_, trials)
+            if lim is not None and lim.is_zero:
+                return S.Zero

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/order.py

@@ -0,0 +1,522 @@
+from sympy.core import S, sympify, Expr, Dummy, Add, Mul
+from sympy.core.cache import cacheit
+from sympy.core.containers import Tuple
+from sympy.core.function import Function, PoleError, expand_power_base, expand_log
+from sympy.core.sorting import default_sort_key
+from sympy.functions.elementary.exponential import exp, log
+from sympy.sets.sets import Complement
+from sympy.utilities.iterables import uniq, is_sequence
+
+
+class Order(Expr):
+    r""" Represents the limiting behavior of some function.
+
+    Explanation
+    ===========
+
+    The order of a function characterizes the function based on the limiting
+    behavior of the function as it goes to some limit. Only taking the limit
+    point to be a number is currently supported. This is expressed in
+    big O notation [1]_.
+
+    The formal definition for the order of a function `g(x)` about a point `a`
+    is such that `g(x) = O(f(x))` as `x \rightarrow a` if and only if there
+    exists a `\delta > 0` and an `M > 0` such that `|g(x)| \leq M|f(x)|` for
+    `|x-a| < \delta`.  This is equivalent to `\limsup_{x \rightarrow a}
+    |g(x)/f(x)| < \infty`.
+
+    Let's illustrate it on the following example by taking the expansion of
+    `\sin(x)` about 0:
+
+    .. math ::
+        \sin(x) = x - x^3/3! + O(x^5)
+
+    where in this case `O(x^5) = x^5/5! - x^7/7! + \cdots`. By the definition
+    of `O`, there is a `\delta > 0` and an `M` such that:
+
+    .. math ::
+        |x^5/5! - x^7/7! + ....| <= M|x^5| \text{ for } |x| < \delta
+
+    or by the alternate definition:
+
+    .. math ::
+        \lim_{x \rightarrow 0} | (x^5/5! - x^7/7! + ....) / x^5| < \infty
+
+    which surely is true, because
+
+    .. math ::
+        \lim_{x \rightarrow 0} | (x^5/5! - x^7/7! + ....) / x^5| = 1/5!
+
+
+    As it is usually used, the order of a function can be intuitively thought
+    of representing all terms of powers greater than the one specified. For
+    example, `O(x^3)` corresponds to any terms proportional to `x^3,
+    x^4,\ldots` and any higher power. For a polynomial, this leaves terms
+    proportional to `x^2`, `x` and constants.
+
+    Examples
+    ========
+
+    >>> from sympy import O, oo, cos, pi
+    >>> from sympy.abc import x, y
+
+    >>> O(x + x**2)
+    O(x)
+    >>> O(x + x**2, (x, 0))
+    O(x)
+    >>> O(x + x**2, (x, oo))
+    O(x**2, (x, oo))
+
+    >>> O(1 + x*y)
+    O(1, x, y)
+    >>> O(1 + x*y, (x, 0), (y, 0))
+    O(1, x, y)
+    >>> O(1 + x*y, (x, oo), (y, oo))
+    O(x*y, (x, oo), (y, oo))
+
+    >>> O(1) in O(1, x)
+    True
+    >>> O(1, x) in O(1)
+    False
+    >>> O(x) in O(1, x)
+    True
+    >>> O(x**2) in O(x)
+    True
+
+    >>> O(x)*x
+    O(x**2)
+    >>> O(x) - O(x)
+    O(x)
+    >>> O(cos(x))
+    O(1)
+    >>> O(cos(x), (x, pi/2))
+    O(x - pi/2, (x, pi/2))
+
+    References
+    ==========
+
+    .. [1] `Big O notation <https://en.wikipedia.org/wiki/Big_O_notation>`_
+
+    Notes
+    =====
+
+    In ``O(f(x), x)`` the expression ``f(x)`` is assumed to have a leading
+    term.  ``O(f(x), x)`` is automatically transformed to
+    ``O(f(x).as_leading_term(x),x)``.
+
+        ``O(expr*f(x), x)`` is ``O(f(x), x)``
+
+        ``O(expr, x)`` is ``O(1)``
+
+        ``O(0, x)`` is 0.
+
+    Multivariate O is also supported:
+
+        ``O(f(x, y), x, y)`` is transformed to
+        ``O(f(x, y).as_leading_term(x,y).as_leading_term(y), x, y)``
+
+    In the multivariate case, it is assumed the limits w.r.t. the various
+    symbols commute.
+
+    If no symbols are passed then all symbols in the expression are used
+    and the limit point is assumed to be zero.
+
+    """
+
+    is_Order = True
+
+    __slots__ = ()
+
+    @cacheit
+    def __new__(cls, expr, *args, **kwargs):
+        expr = sympify(expr)
+
+        if not args:
+            if expr.is_Order:
+                variables = expr.variables
+                point = expr.point
+            else:
+                variables = list(expr.free_symbols)
+                point = [S.Zero]*len(variables)
+        else:
+            args = list(args if is_sequence(args) else [args])
+            variables, point = [], []
+            if is_sequence(args[0]):
+                for a in args:
+                    v, p = list(map(sympify, a))
+                    variables.append(v)
+                    point.append(p)
+            else:
+                variables = list(map(sympify, args))
+                point = [S.Zero]*len(variables)
+
+        if not all(v.is_symbol for v in variables):
+            raise TypeError('Variables are not symbols, got %s' % variables)
+
+        if len(list(uniq(variables))) != len(variables):
+            raise ValueError('Variables are supposed to be unique symbols, got %s' % variables)
+
+        if expr.is_Order:
+            expr_vp = dict(expr.args[1:])
+            new_vp = dict(expr_vp)
+            vp = dict(zip(variables, point))
+            for v, p in vp.items():
+                if v in new_vp.keys():
+                    if p != new_vp[v]:
+                        raise NotImplementedError(
+                            "Mixing Order at different points is not supported.")
+                else:
+                    new_vp[v] = p
+            if set(expr_vp.keys()) == set(new_vp.keys()):
+                return expr
+            else:
+                variables = list(new_vp.keys())
+                point = [new_vp[v] for v in variables]
+
+        if expr is S.NaN:
+            return S.NaN
+
+        if any(x in p.free_symbols for x in variables for p in point):
+            raise ValueError('Got %s as a point.' % point)
+
+        if variables:
+            if any(p != point[0] for p in point):
+                raise NotImplementedError(
+                    "Multivariable orders at different points are not supported.")
+            if point[0] in (S.Infinity, S.Infinity*S.ImaginaryUnit):
+                s = {k: 1/Dummy() for k in variables}
+                rs = {1/v: 1/k for k, v in s.items()}
+                ps = [S.Zero for p in point]
+            elif point[0] in (S.NegativeInfinity, S.NegativeInfinity*S.ImaginaryUnit):
+                s = {k: -1/Dummy() for k in variables}
+                rs = {-1/v: -1/k for k, v in s.items()}
+                ps = [S.Zero for p in point]
+            elif point[0] is not S.Zero:
+                s = {k: Dummy() + point[0] for k in variables}
+                rs = {(v - point[0]).together(): k - point[0] for k, v in s.items()}
+                ps = [S.Zero for p in point]
+            else:
+                s = ()
+                rs = ()
+                ps = list(point)
+
+            expr = expr.subs(s)
+
+            if expr.is_Add:
+                expr = expr.factor()
+
+            if s:
+                args = tuple([r[0] for r in rs.items()])
+            else:
+                args = tuple(variables)
+
+            if len(variables) > 1:
+                # XXX: better way?  We need this expand() to
+                # workaround e.g: expr = x*(x + y).
+                # (x*(x + y)).as_leading_term(x, y) currently returns
+                # x*y (wrong order term!).  That's why we want to deal with
+                # expand()'ed expr (handled in "if expr.is_Add" branch below).
+                expr = expr.expand()
+
+            old_expr = None
+            while old_expr != expr:
+                old_expr = expr
+                if expr.is_Add:
+                    lst = expr.extract_leading_order(args)
+                    expr = Add(*[f.expr for (e, f) in lst])
+
+                elif expr:
+                    try:
+                        expr = expr.as_leading_term(*args)
+                    except PoleError:
+                        if isinstance(expr, Function) or\
+                                all(isinstance(arg, Function) for arg in expr.args):
+                            # It is not possible to simplify an expression
+                            # containing only functions (which raise error on
+                            # call to leading term) further
+                            pass
+                        else:
+                            orders = []
+                            pts = tuple(zip(args, ps))
+                            for arg in expr.args:
+                                try:
+                                    lt = arg.as_leading_term(*args)
+                                except PoleError:
+                                    lt = arg
+                                if lt not in args:
+                                    order = Order(lt)
+                                else:
+                                    order = Order(lt, *pts)
+                                orders.append(order)
+                            if expr.is_Add:
+                                new_expr = Order(Add(*orders), *pts)
+                                if new_expr.is_Add:
+                                    new_expr = Order(Add(*[a.expr for a in new_expr.args]), *pts)
+                                expr = new_expr.expr
+                            elif expr.is_Mul:
+                                expr = Mul(*[a.expr for a in orders])
+                            elif expr.is_Pow:
+                                e = expr.exp
+                                b = expr.base
+                                expr = exp(e * log(b))
+
+                    # It would probably be better to handle this somewhere
+                    # else. This is needed for a testcase in which there is a
+                    # symbol with the assumptions zero=True.
+                    if expr.is_zero:
+                        expr = S.Zero
+                    else:
+                        expr = expr.as_independent(*args, as_Add=False)[1]
+
+                    expr = expand_power_base(expr)
+                    expr = expand_log(expr)
+
+                    if len(args) == 1:
+                        # The definition of O(f(x)) symbol explicitly stated that
+                        # the argument of f(x) is irrelevant.  That's why we can
+                        # combine some power exponents (only "on top" of the
+                        # expression tree for f(x)), e.g.:
+                        # x**p * (-x)**q -> x**(p+q) for real p, q.
+                        x = args[0]
+                        margs = list(Mul.make_args(
+                            expr.as_independent(x, as_Add=False)[1]))
+
+                        for i, t in enumerate(margs):
+                            if t.is_Pow:
+                                b, q = t.args
+                                if b in (x, -x) and q.is_real and not q.has(x):
+                                    margs[i] = x**q
+                                elif b.is_Pow and not b.exp.has(x):
+                                    b, r = b.args
+                                    if b in (x, -x) and r.is_real:
+                                        margs[i] = x**(r*q)
+                                elif b.is_Mul and b.args[0] is S.NegativeOne:
+                                    b = -b
+                                    if b.is_Pow and not b.exp.has(x):
+                                        b, r = b.args
+                                        if b in (x, -x) and r.is_real:
+                                            margs[i] = x**(r*q)
+
+                        expr = Mul(*margs)
+
+            expr = expr.subs(rs)
+
+        if expr.is_Order:
+            expr = expr.expr
+
+        if not expr.has(*variables) and not expr.is_zero:
+            expr = S.One
+
+        # create Order instance:
+        vp = dict(zip(variables, point))
+        variables.sort(key=default_sort_key)
+        point = [vp[v] for v in variables]
+        args = (expr,) + Tuple(*zip(variables, point))
+        obj = Expr.__new__(cls, *args)
+        return obj
+
+    def _eval_nseries(self, x, n, logx, cdir=0):
+        return self
+
+    @property
+    def expr(self):
+        return self.args[0]
+
+    @property
+    def variables(self):
+        if self.args[1:]:
+            return tuple(x[0] for x in self.args[1:])
+        else:
+            return ()
+
+    @property
+    def point(self):
+        if self.args[1:]:
+            return tuple(x[1] for x in self.args[1:])
+        else:
+            return ()
+
+    @property
+    def free_symbols(self):
+        return self.expr.free_symbols | set(self.variables)
+
+    def _eval_power(b, e):
+        if e.is_Number and e.is_nonnegative:
+            return b.func(b.expr ** e, *b.args[1:])
+        if e == O(1):
+            return b
+        return
+
+    def as_expr_variables(self, order_symbols):
+        if order_symbols is None:
+            order_symbols = self.args[1:]
+        else:
+            if (not all(o[1] == order_symbols[0][1] for o in order_symbols) and
+                    not all(p == self.point[0] for p in self.point)):  # pragma: no cover
+                raise NotImplementedError('Order at points other than 0 '
+                    'or oo not supported, got %s as a point.' % self.point)
+            if order_symbols and order_symbols[0][1] != self.point[0]:
+                raise NotImplementedError(
+                        "Multiplying Order at different points is not supported.")
+            order_symbols = dict(order_symbols)
+            for s, p in dict(self.args[1:]).items():
+                if s not in order_symbols.keys():
+                    order_symbols[s] = p
+            order_symbols = sorted(order_symbols.items(), key=lambda x: default_sort_key(x[0]))
+        return self.expr, tuple(order_symbols)
+
+    def removeO(self):
+        return S.Zero
+
+    def getO(self):
+        return self
+
+    @cacheit
+    def contains(self, expr):
+        r"""
+        Return True if expr belongs to Order(self.expr, \*self.variables).
+        Return False if self belongs to expr.
+        Return None if the inclusion relation cannot be determined
+        (e.g. when self and expr have different symbols).
+        """
+        expr = sympify(expr)
+        if expr.is_zero:
+            return True
+        if expr is S.NaN:
+            return False
+        point = self.point[0] if self.point else S.Zero
+        if expr.is_Order:
+            if (any(p != point for p in expr.point) or
+                   any(p != point for p in self.point)):
+                return None
+            if expr.expr == self.expr:
+                # O(1) + O(1), O(1) + O(1, x), etc.
+                return all(x in self.args[1:] for x in expr.args[1:])
+            if expr.expr.is_Add:
+                return all(self.contains(x) for x in expr.expr.args)
+            if self.expr.is_Add and point.is_zero:
+                return any(self.func(x, *self.args[1:]).contains(expr)
+                            for x in self.expr.args)
+            if self.variables and expr.variables:
+                common_symbols = tuple(
+                    [s for s in self.variables if s in expr.variables])
+            elif self.variables:
+                common_symbols = self.variables
+            else:
+                common_symbols = expr.variables
+            if not common_symbols:
+                return None
+            if (self.expr.is_Pow and len(self.variables) == 1
+                and self.variables == expr.variables):
+                    symbol = self.variables[0]
+                    other = expr.expr.as_independent(symbol, as_Add=False)[1]
+                    if (other.is_Pow and other.base == symbol and
+                        self.expr.base == symbol):
+                            if point.is_zero:
+                                rv = (self.expr.exp - other.exp).is_nonpositive
+                            if point.is_infinite:
+                                rv = (self.expr.exp - other.exp).is_nonnegative
+                            if rv is not None:
+                                return rv
+
+            from sympy.simplify.powsimp import powsimp
+            r = None
+            ratio = self.expr/expr.expr
+            ratio = powsimp(ratio, deep=True, combine='exp')
+            for s in common_symbols:
+                from sympy.series.limits import Limit
+                l = Limit(ratio, s, point).doit(heuristics=False)
+                if not isinstance(l, Limit):
+                    l = l != 0
+                else:
+                    l = None
+                if r is None:
+                    r = l
+                else:
+                    if r != l:
+                        return
+            return r
+
+        if self.expr.is_Pow and len(self.variables) == 1:
+            symbol = self.variables[0]
+            other = expr.as_independent(symbol, as_Add=False)[1]
+            if (other.is_Pow and other.base == symbol and
+                self.expr.base == symbol):
+                    if point.is_zero:
+                        rv = (self.expr.exp - other.exp).is_nonpositive
+                    if point.is_infinite:
+                        rv = (self.expr.exp - other.exp).is_nonnegative
+                    if rv is not None:
+                        return rv
+
+        obj = self.func(expr, *self.args[1:])
+        return self.contains(obj)
+
+    def __contains__(self, other):
+        result = self.contains(other)
+        if result is None:
+            raise TypeError('contains did not evaluate to a bool')
+        return result
+
+    def _eval_subs(self, old, new):
+        if old in self.variables:
+            newexpr = self.expr.subs(old, new)
+            i = self.variables.index(old)
+            newvars = list(self.variables)
+            newpt = list(self.point)
+            if new.is_symbol:
+                newvars[i] = new
+            else:
+                syms = new.free_symbols
+                if len(syms) == 1 or old in syms:
+                    if old in syms:
+                        var = self.variables[i]
+                    else:
+                        var = syms.pop()
+                    # First, try to substitute self.point in the "new"
+                    # expr to see if this is a fixed point.
+                    # E.g.  O(y).subs(y, sin(x))
+                    from sympy import limit
+                    if new.has(Order) and limit(new.getO().expr, var, new.getO().point[0]) == self.point[i]:
+                        point = new.getO().point[0]
+                        return Order(newexpr, *zip([var], [point]))
+                    else:
+                        point = new.subs(var, self.point[i])
+                    if point != self.point[i]:
+                        from sympy.solvers.solveset import solveset
+                        d = Dummy()
+                        sol = solveset(old - new.subs(var, d), d)
+                        if isinstance(sol, Complement):
+                            e1 = sol.args[0]
+                            e2 = sol.args[1]
+                            sol = set(e1) - set(e2)
+                        res = [dict(zip((d, ), sol))]
+                        point = d.subs(res[0]).limit(old, self.point[i])
+                    newvars[i] = var
+                    newpt[i] = point
+                elif old not in syms:
+                    del newvars[i], newpt[i]
+                    if not syms and new == self.point[i]:
+                        newvars.extend(syms)
+                        newpt.extend([S.Zero]*len(syms))
+                else:
+                    return
+            return Order(newexpr, *zip(newvars, newpt))
+
+    def _eval_conjugate(self):
+        expr = self.expr._eval_conjugate()
+        if expr is not None:
+            return self.func(expr, *self.args[1:])
+
+    def _eval_derivative(self, x):
+        return self.func(self.expr.diff(x), *self.args[1:]) or self
+
+    def _eval_transpose(self):
+        expr = self.expr._eval_transpose()
+        if expr is not None:
+            return self.func(expr, *self.args[1:])
+
+    def __neg__(self):
+        return self
+
+O = Order

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/residues.py

@@ -0,0 +1,73 @@
+"""
+This module implements the Residue function and related tools for working
+with residues.
+"""
+
+from sympy.core.mul import Mul
+from sympy.core.singleton import S
+from sympy.core.sympify import sympify
+from sympy.utilities.timeutils import timethis
+
+
+@timethis('residue')
+def residue(expr, x, x0):
+    """
+    Finds the residue of ``expr`` at the point x=x0.
+
+    The residue is defined as the coefficient of ``1/(x-x0)`` in the power series
+    expansion about ``x=x0``.
+
+    Examples
+    ========
+
+    >>> from sympy import Symbol, residue, sin
+    >>> x = Symbol("x")
+    >>> residue(1/x, x, 0)
+    1
+    >>> residue(1/x**2, x, 0)
+    0
+    >>> residue(2/sin(x), x, 0)
+    2
+
+    This function is essential for the Residue Theorem [1].
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Residue_theorem
+    """
+    # The current implementation uses series expansion to
+    # calculate it. A more general implementation is explained in
+    # the section 5.6 of the Bronstein's book {M. Bronstein:
+    # Symbolic Integration I, Springer Verlag (2005)}. For purely
+    # rational functions, the algorithm is much easier. See
+    # sections 2.4, 2.5, and 2.7 (this section actually gives an
+    # algorithm for computing any Laurent series coefficient for
+    # a rational function). The theory in section 2.4 will help to
+    # understand why the resultant works in the general algorithm.
+    # For the definition of a resultant, see section 1.4 (and any
+    # previous sections for more review).
+
+    from sympy.series.order import Order
+    from sympy.simplify.radsimp import collect
+    expr = sympify(expr)
+    if x0 != 0:
+        expr = expr.subs(x, x + x0)
+    for n in (0, 1, 2, 4, 8, 16, 32):
+        s = expr.nseries(x, n=n)
+        if not s.has(Order) or s.getn() >= 0:
+            break
+    s = collect(s.removeO(), x)
+    if s.is_Add:
+        args = s.args
+    else:
+        args = [s]
+    res = S.Zero
+    for arg in args:
+        c, m = arg.as_coeff_mul(x)
+        m = Mul(*m)
+        if not (m in (S.One, x) or (m.is_Pow and m.exp.is_Integer)):
+            raise NotImplementedError('term of unexpected form: %s' % m)
+        if m == 1/x:
+            res += c
+    return res

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/sequences.py

@@ -0,0 +1,1239 @@
+from sympy.core.basic import Basic
+from sympy.core.cache import cacheit
+from sympy.core.containers import Tuple
+from sympy.core.decorators import call_highest_priority
+from sympy.core.parameters import global_parameters
+from sympy.core.function import AppliedUndef, expand
+from sympy.core.mul import Mul
+from sympy.core.numbers import Integer
+from sympy.core.relational import Eq
+from sympy.core.singleton import S, Singleton
+from sympy.core.sorting import ordered
+from sympy.core.symbol import Dummy, Symbol, Wild
+from sympy.core.sympify import sympify
+from sympy.matrices import Matrix
+from sympy.polys import lcm, factor
+from sympy.sets.sets import Interval, Intersection
+from sympy.tensor.indexed import Idx
+from sympy.utilities.iterables import flatten, is_sequence, iterable
+
+
+###############################################################################
+#                            SEQUENCES                                        #
+###############################################################################
+
+
+class SeqBase(Basic):
+    """Base class for sequences"""
+
+    is_commutative = True
+    _op_priority = 15
+
+    @staticmethod
+    def _start_key(expr):
+        """Return start (if possible) else S.Infinity.
+
+        adapted from Set._infimum_key
+        """
+        try:
+            start = expr.start
+        except NotImplementedError:
+            start = S.Infinity
+        return start
+
+    def _intersect_interval(self, other):
+        """Returns start and stop.
+
+        Takes intersection over the two intervals.
+        """
+        interval = Intersection(self.interval, other.interval)
+        return interval.inf, interval.sup
+
+    @property
+    def gen(self):
+        """Returns the generator for the sequence"""
+        raise NotImplementedError("(%s).gen" % self)
+
+    @property
+    def interval(self):
+        """The interval on which the sequence is defined"""
+        raise NotImplementedError("(%s).interval" % self)
+
+    @property
+    def start(self):
+        """The starting point of the sequence. This point is included"""
+        raise NotImplementedError("(%s).start" % self)
+
+    @property
+    def stop(self):
+        """The ending point of the sequence. This point is included"""
+        raise NotImplementedError("(%s).stop" % self)
+
+    @property
+    def length(self):
+        """Length of the sequence"""
+        raise NotImplementedError("(%s).length" % self)
+
+    @property
+    def variables(self):
+        """Returns a tuple of variables that are bounded"""
+        return ()
+
+    @property
+    def free_symbols(self):
+        """
+        This method returns the symbols in the object, excluding those
+        that take on a specific value (i.e. the dummy symbols).
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n, m
+        >>> SeqFormula(m*n**2, (n, 0, 5)).free_symbols
+        {m}
+        """
+        return ({j for i in self.args for j in i.free_symbols
+                   .difference(self.variables)})
+
+    @cacheit
+    def coeff(self, pt):
+        """Returns the coefficient at point pt"""
+        if pt < self.start or pt > self.stop:
+            raise IndexError("Index %s out of bounds %s" % (pt, self.interval))
+        return self._eval_coeff(pt)
+
+    def _eval_coeff(self, pt):
+        raise NotImplementedError("The _eval_coeff method should be added to"
+                                  "%s to return coefficient so it is available"
+                                  "when coeff calls it."
+                                  % self.func)
+
+    def _ith_point(self, i):
+        """Returns the i'th point of a sequence.
+
+        Explanation
+        ===========
+
+        If start point is negative infinity, point is returned from the end.
+        Assumes the first point to be indexed zero.
+
+        Examples
+        =========
+
+        >>> from sympy import oo
+        >>> from sympy.series.sequences import SeqPer
+
+        bounded
+
+        >>> SeqPer((1, 2, 3), (-10, 10))._ith_point(0)
+        -10
+        >>> SeqPer((1, 2, 3), (-10, 10))._ith_point(5)
+        -5
+
+        End is at infinity
+
+        >>> SeqPer((1, 2, 3), (0, oo))._ith_point(5)
+        5
+
+        Starts at negative infinity
+
+        >>> SeqPer((1, 2, 3), (-oo, 0))._ith_point(5)
+        -5
+        """
+        if self.start is S.NegativeInfinity:
+            initial = self.stop
+        else:
+            initial = self.start
+
+        if self.start is S.NegativeInfinity:
+            step = -1
+        else:
+            step = 1
+
+        return initial + i*step
+
+    def _add(self, other):
+        """
+        Should only be used internally.
+
+        Explanation
+        ===========
+
+        self._add(other) returns a new, term-wise added sequence if self
+        knows how to add with other, otherwise it returns ``None``.
+
+        ``other`` should only be a sequence object.
+
+        Used within :class:`SeqAdd` class.
+        """
+        return None
+
+    def _mul(self, other):
+        """
+        Should only be used internally.
+
+        Explanation
+        ===========
+
+        self._mul(other) returns a new, term-wise multiplied sequence if self
+        knows how to multiply with other, otherwise it returns ``None``.
+
+        ``other`` should only be a sequence object.
+
+        Used within :class:`SeqMul` class.
+        """
+        return None
+
+    def coeff_mul(self, other):
+        """
+        Should be used when ``other`` is not a sequence. Should be
+        defined to define custom behaviour.
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n
+        >>> SeqFormula(n**2).coeff_mul(2)
+        SeqFormula(2*n**2, (n, 0, oo))
+
+        Notes
+        =====
+
+        '*' defines multiplication of sequences with sequences only.
+        """
+        return Mul(self, other)
+
+    def __add__(self, other):
+        """Returns the term-wise addition of 'self' and 'other'.
+
+        ``other`` should be a sequence.
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n
+        >>> SeqFormula(n**2) + SeqFormula(n**3)
+        SeqFormula(n**3 + n**2, (n, 0, oo))
+        """
+        if not isinstance(other, SeqBase):
+            raise TypeError('cannot add sequence and %s' % type(other))
+        return SeqAdd(self, other)
+
+    @call_highest_priority('__add__')
+    def __radd__(self, other):
+        return self + other
+
+    def __sub__(self, other):
+        """Returns the term-wise subtraction of ``self`` and ``other``.
+
+        ``other`` should be a sequence.
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n
+        >>> SeqFormula(n**2) - (SeqFormula(n))
+        SeqFormula(n**2 - n, (n, 0, oo))
+        """
+        if not isinstance(other, SeqBase):
+            raise TypeError('cannot subtract sequence and %s' % type(other))
+        return SeqAdd(self, -other)
+
+    @call_highest_priority('__sub__')
+    def __rsub__(self, other):
+        return (-self) + other
+
+    def __neg__(self):
+        """Negates the sequence.
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n
+        >>> -SeqFormula(n**2)
+        SeqFormula(-n**2, (n, 0, oo))
+        """
+        return self.coeff_mul(-1)
+
+    def __mul__(self, other):
+        """Returns the term-wise multiplication of 'self' and 'other'.
+
+        ``other`` should be a sequence. For ``other`` not being a
+        sequence see :func:`coeff_mul` method.
+
+        Examples
+        ========
+
+        >>> from sympy import SeqFormula
+        >>> from sympy.abc import n
+        >>> SeqFormula(n**2) * (SeqFormula(n))
+        SeqFormula(n**3, (n, 0, oo))
+        """
+        if not isinstance(other, SeqBase):
+            raise TypeError('cannot multiply sequence and %s' % type(other))
+        return SeqMul(self, other)
+
+    @call_highest_priority('__mul__')
+    def __rmul__(self, other):
+        return self * other
+
+    def __iter__(self):
+        for i in range(self.length):
+            pt = self._ith_point(i)
+            yield self.coeff(pt)
+
+    def __getitem__(self, index):
+        if isinstance(index, int):
+            index = self._ith_point(index)
+            return self.coeff(index)
+        elif isinstance(index, slice):
+            start, stop = index.start, index.stop
+            if start is None:
+                start = 0
+            if stop is None:
+                stop = self.length
+            return [self.coeff(self._ith_point(i)) for i in
+                    range(start, stop, index.step or 1)]
+
+    def find_linear_recurrence(self,n,d=None,gfvar=None):
+        r"""
+        Finds the shortest linear recurrence that satisfies the first n
+        terms of sequence of order `\leq` ``n/2`` if possible.
+        If ``d`` is specified, find shortest linear recurrence of order
+        `\leq` min(d, n/2) if possible.
+        Returns list of coefficients ``[b(1), b(2), ...]`` corresponding to the
+        recurrence relation ``x(n) = b(1)*x(n-1) + b(2)*x(n-2) + ...``
+        Returns ``[]`` if no recurrence is found.
+        If gfvar is specified, also returns ordinary generating function as a
+        function of gfvar.
+
+        Examples
+        ========
+
+        >>> from sympy import sequence, sqrt, oo, lucas
+        >>> from sympy.abc import n, x, y
+        >>> sequence(n**2).find_linear_recurrence(10, 2)
+        []
+        >>> sequence(n**2).find_linear_recurrence(10)
+        [3, -3, 1]
+        >>> sequence(2**n).find_linear_recurrence(10)
+        [2]
+        >>> sequence(23*n**4+91*n**2).find_linear_recurrence(10)
+        [5, -10, 10, -5, 1]
+        >>> sequence(sqrt(5)*(((1 + sqrt(5))/2)**n - (-(1 + sqrt(5))/2)**(-n))/5).find_linear_recurrence(10)
+        [1, 1]
+        >>> sequence(x+y*(-2)**(-n), (n, 0, oo)).find_linear_recurrence(30)
+        [1/2, 1/2]
+        >>> sequence(3*5**n + 12).find_linear_recurrence(20,gfvar=x)
+        ([6, -5], 3*(5 - 21*x)/((x - 1)*(5*x - 1)))
+        >>> sequence(lucas(n)).find_linear_recurrence(15,gfvar=x)
+        ([1, 1], (x - 2)/(x**2 + x - 1))
+        """
+        from sympy.simplify import simplify
+        x = [simplify(expand(t)) for t in self[:n]]
+        lx = len(x)
+        if d is None:
+            r = lx//2
+        else:
+            r = min(d,lx//2)
+        coeffs = []
+        for l in range(1, r+1):
+            l2 = 2*l
+            mlist = []
+            for k in range(l):
+                mlist.append(x[k:k+l])
+            m = Matrix(mlist)
+            if m.det() != 0:
+                y = simplify(m.LUsolve(Matrix(x[l:l2])))
+                if lx == l2:
+                    coeffs = flatten(y[::-1])
+                    break
+                mlist = []
+                for k in range(l,lx-l):
+                    mlist.append(x[k:k+l])
+                m = Matrix(mlist)
+                if m*y == Matrix(x[l2:]):
+                    coeffs = flatten(y[::-1])
+                    break
+        if gfvar is None:
+            return coeffs
+        else:
+            l = len(coeffs)
+            if l == 0:
+                return [], None
+            else:
+                n, d = x[l-1]*gfvar**(l-1), 1 - coeffs[l-1]*gfvar**l
+                for i in range(l-1):
+                    n += x[i]*gfvar**i
+                    for j in range(l-i-1):
+                        n -= coeffs[i]*x[j]*gfvar**(i+j+1)
+                    d -= coeffs[i]*gfvar**(i+1)
+                return coeffs, simplify(factor(n)/factor(d))
+
+class EmptySequence(SeqBase, metaclass=Singleton):
+    """Represents an empty sequence.
+
+    The empty sequence is also available as a singleton as
+    ``S.EmptySequence``.
+
+    Examples
+    ========
+
+    >>> from sympy import EmptySequence, SeqPer
+    >>> from sympy.abc import x
+    >>> EmptySequence
+    EmptySequence
+    >>> SeqPer((1, 2), (x, 0, 10)) + EmptySequence
+    SeqPer((1, 2), (x, 0, 10))
+    >>> SeqPer((1, 2)) * EmptySequence
+    EmptySequence
+    >>> EmptySequence.coeff_mul(-1)
+    EmptySequence
+    """
+
+    @property
+    def interval(self):
+        return S.EmptySet
+
+    @property
+    def length(self):
+        return S.Zero
+
+    def coeff_mul(self, coeff):
+        """See docstring of SeqBase.coeff_mul"""
+        return self
+
+    def __iter__(self):
+        return iter([])
+
+
+class SeqExpr(SeqBase):
+    """Sequence expression class.
+
+    Various sequences should inherit from this class.
+
+    Examples
+    ========
+
+    >>> from sympy.series.sequences import SeqExpr
+    >>> from sympy.abc import x
+    >>> from sympy import Tuple
+    >>> s = SeqExpr(Tuple(1, 2, 3), Tuple(x, 0, 10))
+    >>> s.gen
+    (1, 2, 3)
+    >>> s.interval
+    Interval(0, 10)
+    >>> s.length
+    11
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqPer
+    sympy.series.sequences.SeqFormula
+    """
+
+    @property
+    def gen(self):
+        return self.args[0]
+
+    @property
+    def interval(self):
+        return Interval(self.args[1][1], self.args[1][2])
+
+    @property
+    def start(self):
+        return self.interval.inf
+
+    @property
+    def stop(self):
+        return self.interval.sup
+
+    @property
+    def length(self):
+        return self.stop - self.start + 1
+
+    @property
+    def variables(self):
+        return (self.args[1][0],)
+
+
+class SeqPer(SeqExpr):
+    """
+    Represents a periodic sequence.
+
+    The elements are repeated after a given period.
+
+    Examples
+    ========
+
+    >>> from sympy import SeqPer, oo
+    >>> from sympy.abc import k
+
+    >>> s = SeqPer((1, 2, 3), (0, 5))
+    >>> s.periodical
+    (1, 2, 3)
+    >>> s.period
+    3
+
+    For value at a particular point
+
+    >>> s.coeff(3)
+    1
+
+    supports slicing
+
+    >>> s[:]
+    [1, 2, 3, 1, 2, 3]
+
+    iterable
+
+    >>> list(s)
+    [1, 2, 3, 1, 2, 3]
+
+    sequence starts from negative infinity
+
+    >>> SeqPer((1, 2, 3), (-oo, 0))[0:6]
+    [1, 2, 3, 1, 2, 3]
+
+    Periodic formulas
+
+    >>> SeqPer((k, k**2, k**3), (k, 0, oo))[0:6]
+    [0, 1, 8, 3, 16, 125]
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqFormula
+    """
+
+    def __new__(cls, periodical, limits=None):
+        periodical = sympify(periodical)
+
+        def _find_x(periodical):
+            free = periodical.free_symbols
+            if len(periodical.free_symbols) == 1:
+                return free.pop()
+            else:
+                return Dummy('k')
+
+        x, start, stop = None, None, None
+        if limits is None:
+            x, start, stop = _find_x(periodical), 0, S.Infinity
+        if is_sequence(limits, Tuple):
+            if len(limits) == 3:
+                x, start, stop = limits
+            elif len(limits) == 2:
+                x = _find_x(periodical)
+                start, stop = limits
+
+        if not isinstance(x, (Symbol, Idx)) or start is None or stop is None:
+            raise ValueError('Invalid limits given: %s' % str(limits))
+
+        if start is S.NegativeInfinity and stop is S.Infinity:
+                raise ValueError("Both the start and end value"
+                                 "cannot be unbounded")
+
+        limits = sympify((x, start, stop))
+
+        if is_sequence(periodical, Tuple):
+            periodical = sympify(tuple(flatten(periodical)))
+        else:
+            raise ValueError("invalid period %s should be something "
+                             "like e.g (1, 2) " % periodical)
+
+        if Interval(limits[1], limits[2]) is S.EmptySet:
+            return S.EmptySequence
+
+        return Basic.__new__(cls, periodical, limits)
+
+    @property
+    def period(self):
+        return len(self.gen)
+
+    @property
+    def periodical(self):
+        return self.gen
+
+    def _eval_coeff(self, pt):
+        if self.start is S.NegativeInfinity:
+            idx = (self.stop - pt) % self.period
+        else:
+            idx = (pt - self.start) % self.period
+        return self.periodical[idx].subs(self.variables[0], pt)
+
+    def _add(self, other):
+        """See docstring of SeqBase._add"""
+        if isinstance(other, SeqPer):
+            per1, lper1 = self.periodical, self.period
+            per2, lper2 = other.periodical, other.period
+
+            per_length = lcm(lper1, lper2)
+
+            new_per = []
+            for x in range(per_length):
+                ele1 = per1[x % lper1]
+                ele2 = per2[x % lper2]
+                new_per.append(ele1 + ele2)
+
+            start, stop = self._intersect_interval(other)
+            return SeqPer(new_per, (self.variables[0], start, stop))
+
+    def _mul(self, other):
+        """See docstring of SeqBase._mul"""
+        if isinstance(other, SeqPer):
+            per1, lper1 = self.periodical, self.period
+            per2, lper2 = other.periodical, other.period
+
+            per_length = lcm(lper1, lper2)
+
+            new_per = []
+            for x in range(per_length):
+                ele1 = per1[x % lper1]
+                ele2 = per2[x % lper2]
+                new_per.append(ele1 * ele2)
+
+            start, stop = self._intersect_interval(other)
+            return SeqPer(new_per, (self.variables[0], start, stop))
+
+    def coeff_mul(self, coeff):
+        """See docstring of SeqBase.coeff_mul"""
+        coeff = sympify(coeff)
+        per = [x * coeff for x in self.periodical]
+        return SeqPer(per, self.args[1])
+
+
+class SeqFormula(SeqExpr):
+    """
+    Represents sequence based on a formula.
+
+    Elements are generated using a formula.
+
+    Examples
+    ========
+
+    >>> from sympy import SeqFormula, oo, Symbol
+    >>> n = Symbol('n')
+    >>> s = SeqFormula(n**2, (n, 0, 5))
+    >>> s.formula
+    n**2
+
+    For value at a particular point
+
+    >>> s.coeff(3)
+    9
+
+    supports slicing
+
+    >>> s[:]
+    [0, 1, 4, 9, 16, 25]
+
+    iterable
+
+    >>> list(s)
+    [0, 1, 4, 9, 16, 25]
+
+    sequence starts from negative infinity
+
+    >>> SeqFormula(n**2, (-oo, 0))[0:6]
+    [0, 1, 4, 9, 16, 25]
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqPer
+    """
+
+    def __new__(cls, formula, limits=None):
+        formula = sympify(formula)
+
+        def _find_x(formula):
+            free = formula.free_symbols
+            if len(free) == 1:
+                return free.pop()
+            elif not free:
+                return Dummy('k')
+            else:
+                raise ValueError(
+                    " specify dummy variables for %s. If the formula contains"
+                    " more than one free symbol, a dummy variable should be"
+                    " supplied explicitly e.g., SeqFormula(m*n**2, (n, 0, 5))"
+                    % formula)
+
+        x, start, stop = None, None, None
+        if limits is None:
+            x, start, stop = _find_x(formula), 0, S.Infinity
+        if is_sequence(limits, Tuple):
+            if len(limits) == 3:
+                x, start, stop = limits
+            elif len(limits) == 2:
+                x = _find_x(formula)
+                start, stop = limits
+
+        if not isinstance(x, (Symbol, Idx)) or start is None or stop is None:
+            raise ValueError('Invalid limits given: %s' % str(limits))
+
+        if start is S.NegativeInfinity and stop is S.Infinity:
+                raise ValueError("Both the start and end value "
+                                 "cannot be unbounded")
+        limits = sympify((x, start, stop))
+
+        if Interval(limits[1], limits[2]) is S.EmptySet:
+            return S.EmptySequence
+
+        return Basic.__new__(cls, formula, limits)
+
+    @property
+    def formula(self):
+        return self.gen
+
+    def _eval_coeff(self, pt):
+        d = self.variables[0]
+        return self.formula.subs(d, pt)
+
+    def _add(self, other):
+        """See docstring of SeqBase._add"""
+        if isinstance(other, SeqFormula):
+            form1, v1 = self.formula, self.variables[0]
+            form2, v2 = other.formula, other.variables[0]
+            formula = form1 + form2.subs(v2, v1)
+            start, stop = self._intersect_interval(other)
+            return SeqFormula(formula, (v1, start, stop))
+
+    def _mul(self, other):
+        """See docstring of SeqBase._mul"""
+        if isinstance(other, SeqFormula):
+            form1, v1 = self.formula, self.variables[0]
+            form2, v2 = other.formula, other.variables[0]
+            formula = form1 * form2.subs(v2, v1)
+            start, stop = self._intersect_interval(other)
+            return SeqFormula(formula, (v1, start, stop))
+
+    def coeff_mul(self, coeff):
+        """See docstring of SeqBase.coeff_mul"""
+        coeff = sympify(coeff)
+        formula = self.formula * coeff
+        return SeqFormula(formula, self.args[1])
+
+    def expand(self, *args, **kwargs):
+        return SeqFormula(expand(self.formula, *args, **kwargs), self.args[1])
+
+class RecursiveSeq(SeqBase):
+    """
+    A finite degree recursive sequence.
+
+    Explanation
+    ===========
+
+    That is, a sequence a(n) that depends on a fixed, finite number of its
+    previous values. The general form is
+
+        a(n) = f(a(n - 1), a(n - 2), ..., a(n - d))
+
+    for some fixed, positive integer d, where f is some function defined by a
+    SymPy expression.
+
+    Parameters
+    ==========
+
+    recurrence : SymPy expression defining recurrence
+        This is *not* an equality, only the expression that the nth term is
+        equal to. For example, if :code:`a(n) = f(a(n - 1), ..., a(n - d))`,
+        then the expression should be :code:`f(a(n - 1), ..., a(n - d))`.
+
+    yn : applied undefined function
+        Represents the nth term of the sequence as e.g. :code:`y(n)` where
+        :code:`y` is an undefined function and `n` is the sequence index.
+
+    n : symbolic argument
+        The name of the variable that the recurrence is in, e.g., :code:`n` if
+        the recurrence function is :code:`y(n)`.
+
+    initial : iterable with length equal to the degree of the recurrence
+        The initial values of the recurrence.
+
+    start : start value of sequence (inclusive)
+
+    Examples
+    ========
+
+    >>> from sympy import Function, symbols
+    >>> from sympy.series.sequences import RecursiveSeq
+    >>> y = Function("y")
+    >>> n = symbols("n")
+    >>> fib = RecursiveSeq(y(n - 1) + y(n - 2), y(n), n, [0, 1])
+
+    >>> fib.coeff(3) # Value at a particular point
+    2
+
+    >>> fib[:6] # supports slicing
+    [0, 1, 1, 2, 3, 5]
+
+    >>> fib.recurrence # inspect recurrence
+    Eq(y(n), y(n - 2) + y(n - 1))
+
+    >>> fib.degree # automatically determine degree
+    2
+
+    >>> for x in zip(range(10), fib): # supports iteration
+    ...     print(x)
+    (0, 0)
+    (1, 1)
+    (2, 1)
+    (3, 2)
+    (4, 3)
+    (5, 5)
+    (6, 8)
+    (7, 13)
+    (8, 21)
+    (9, 34)
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqFormula
+
+    """
+
+    def __new__(cls, recurrence, yn, n, initial=None, start=0):
+        if not isinstance(yn, AppliedUndef):
+            raise TypeError("recurrence sequence must be an applied undefined function"
+                            ", found `{}`".format(yn))
+
+        if not isinstance(n, Basic) or not n.is_symbol:
+            raise TypeError("recurrence variable must be a symbol"
+                            ", found `{}`".format(n))
+
+        if yn.args != (n,):
+            raise TypeError("recurrence sequence does not match symbol")
+
+        y = yn.func
+
+        k = Wild("k", exclude=(n,))
+        degree = 0
+
+        # Find all applications of y in the recurrence and check that:
+        #   1. The function y is only being used with a single argument; and
+        #   2. All arguments are n + k for constant negative integers k.
+
+        prev_ys = recurrence.find(y)
+        for prev_y in prev_ys:
+            if len(prev_y.args) != 1:
+                raise TypeError("Recurrence should be in a single variable")
+
+            shift = prev_y.args[0].match(n + k)[k]
+            if not (shift.is_constant() and shift.is_integer and shift < 0):
+                raise TypeError("Recurrence should have constant,"
+                                " negative, integer shifts"
+                                " (found {})".format(prev_y))
+
+            if -shift > degree:
+                degree = -shift
+
+        if not initial:
+            initial = [Dummy("c_{}".format(k)) for k in range(degree)]
+
+        if len(initial) != degree:
+            raise ValueError("Number of initial terms must equal degree")
+
+        degree = Integer(degree)
+        start = sympify(start)
+
+        initial = Tuple(*(sympify(x) for x in initial))
+
+        seq = Basic.__new__(cls, recurrence, yn, n, initial, start)
+
+        seq.cache = {y(start + k): init for k, init in enumerate(initial)}
+        seq.degree = degree
+
+        return seq
+
+    @property
+    def _recurrence(self):
+        """Equation defining recurrence."""
+        return self.args[0]
+
+    @property
+    def recurrence(self):
+        """Equation defining recurrence."""
+        return Eq(self.yn, self.args[0])
+
+    @property
+    def yn(self):
+        """Applied function representing the nth term"""
+        return self.args[1]
+
+    @property
+    def y(self):
+        """Undefined function for the nth term of the sequence"""
+        return self.yn.func
+
+    @property
+    def n(self):
+        """Sequence index symbol"""
+        return self.args[2]
+
+    @property
+    def initial(self):
+        """The initial values of the sequence"""
+        return self.args[3]
+
+    @property
+    def start(self):
+        """The starting point of the sequence. This point is included"""
+        return self.args[4]
+
+    @property
+    def stop(self):
+        """The ending point of the sequence. (oo)"""
+        return S.Infinity
+
+    @property
+    def interval(self):
+        """Interval on which sequence is defined."""
+        return (self.start, S.Infinity)
+
+    def _eval_coeff(self, index):
+        if index - self.start < len(self.cache):
+            return self.cache[self.y(index)]
+
+        for current in range(len(self.cache), index + 1):
+            # Use xreplace over subs for performance.
+            # See issue #10697.
+            seq_index = self.start + current
+            current_recurrence = self._recurrence.xreplace({self.n: seq_index})
+            new_term = current_recurrence.xreplace(self.cache)
+
+            self.cache[self.y(seq_index)] = new_term
+
+        return self.cache[self.y(self.start + current)]
+
+    def __iter__(self):
+        index = self.start
+        while True:
+            yield self._eval_coeff(index)
+            index += 1
+
+
+def sequence(seq, limits=None):
+    """
+    Returns appropriate sequence object.
+
+    Explanation
+    ===========
+
+    If ``seq`` is a SymPy sequence, returns :class:`SeqPer` object
+    otherwise returns :class:`SeqFormula` object.
+
+    Examples
+    ========
+
+    >>> from sympy import sequence
+    >>> from sympy.abc import n
+    >>> sequence(n**2, (n, 0, 5))
+    SeqFormula(n**2, (n, 0, 5))
+    >>> sequence((1, 2, 3), (n, 0, 5))
+    SeqPer((1, 2, 3), (n, 0, 5))
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqPer
+    sympy.series.sequences.SeqFormula
+    """
+    seq = sympify(seq)
+
+    if is_sequence(seq, Tuple):
+        return SeqPer(seq, limits)
+    else:
+        return SeqFormula(seq, limits)
+
+
+###############################################################################
+#                            OPERATIONS                                       #
+###############################################################################
+
+
+class SeqExprOp(SeqBase):
+    """
+    Base class for operations on sequences.
+
+    Examples
+    ========
+
+    >>> from sympy.series.sequences import SeqExprOp, sequence
+    >>> from sympy.abc import n
+    >>> s1 = sequence(n**2, (n, 0, 10))
+    >>> s2 = sequence((1, 2, 3), (n, 5, 10))
+    >>> s = SeqExprOp(s1, s2)
+    >>> s.gen
+    (n**2, (1, 2, 3))
+    >>> s.interval
+    Interval(5, 10)
+    >>> s.length
+    6
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqAdd
+    sympy.series.sequences.SeqMul
+    """
+    @property
+    def gen(self):
+        """Generator for the sequence.
+
+        returns a tuple of generators of all the argument sequences.
+        """
+        return tuple(a.gen for a in self.args)
+
+    @property
+    def interval(self):
+        """Sequence is defined on the intersection
+        of all the intervals of respective sequences
+        """
+        return Intersection(*(a.interval for a in self.args))
+
+    @property
+    def start(self):
+        return self.interval.inf
+
+    @property
+    def stop(self):
+        return self.interval.sup
+
+    @property
+    def variables(self):
+        """Cumulative of all the bound variables"""
+        return tuple(flatten([a.variables for a in self.args]))
+
+    @property
+    def length(self):
+        return self.stop - self.start + 1
+
+
+class SeqAdd(SeqExprOp):
+    """Represents term-wise addition of sequences.
+
+    Rules:
+        * The interval on which sequence is defined is the intersection
+          of respective intervals of sequences.
+        * Anything + :class:`EmptySequence` remains unchanged.
+        * Other rules are defined in ``_add`` methods of sequence classes.
+
+    Examples
+    ========
+
+    >>> from sympy import EmptySequence, oo, SeqAdd, SeqPer, SeqFormula
+    >>> from sympy.abc import n
+    >>> SeqAdd(SeqPer((1, 2), (n, 0, oo)), EmptySequence)
+    SeqPer((1, 2), (n, 0, oo))
+    >>> SeqAdd(SeqPer((1, 2), (n, 0, 5)), SeqPer((1, 2), (n, 6, 10)))
+    EmptySequence
+    >>> SeqAdd(SeqPer((1, 2), (n, 0, oo)), SeqFormula(n**2, (n, 0, oo)))
+    SeqAdd(SeqFormula(n**2, (n, 0, oo)), SeqPer((1, 2), (n, 0, oo)))
+    >>> SeqAdd(SeqFormula(n**3), SeqFormula(n**2))
+    SeqFormula(n**3 + n**2, (n, 0, oo))
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqMul
+    """
+
+    def __new__(cls, *args, **kwargs):
+        evaluate = kwargs.get('evaluate', global_parameters.evaluate)
+
+        # flatten inputs
+        args = list(args)
+
+        # adapted from sympy.sets.sets.Union
+        def _flatten(arg):
+            if isinstance(arg, SeqBase):
+                if isinstance(arg, SeqAdd):
+                    return sum(map(_flatten, arg.args), [])
+                else:
+                    return [arg]
+            if iterable(arg):
+                return sum(map(_flatten, arg), [])
+            raise TypeError("Input must be Sequences or "
+                            " iterables of Sequences")
+        args = _flatten(args)
+
+        args = [a for a in args if a is not S.EmptySequence]
+
+        # Addition of no sequences is EmptySequence
+        if not args:
+            return S.EmptySequence
+
+        if Intersection(*(a.interval for a in args)) is S.EmptySet:
+            return S.EmptySequence
+
+        # reduce using known rules
+        if evaluate:
+            return SeqAdd.reduce(args)
+
+        args = list(ordered(args, SeqBase._start_key))
+
+        return Basic.__new__(cls, *args)
+
+    @staticmethod
+    def reduce(args):
+        """Simplify :class:`SeqAdd` using known rules.
+
+        Iterates through all pairs and ask the constituent
+        sequences if they can simplify themselves with any other constituent.
+
+        Notes
+        =====
+
+        adapted from ``Union.reduce``
+
+        """
+        new_args = True
+        while new_args:
+            for id1, s in enumerate(args):
+                new_args = False
+                for id2, t in enumerate(args):
+                    if id1 == id2:
+                        continue
+                    new_seq = s._add(t)
+                    # This returns None if s does not know how to add
+                    # with t. Returns the newly added sequence otherwise
+                    if new_seq is not None:
+                        new_args = [a for a in args if a not in (s, t)]
+                        new_args.append(new_seq)
+                        break
+                if new_args:
+                    args = new_args
+                    break
+
+        if len(args) == 1:
+            return args.pop()
+        else:
+            return SeqAdd(args, evaluate=False)
+
+    def _eval_coeff(self, pt):
+        """adds up the coefficients of all the sequences at point pt"""
+        return sum(a.coeff(pt) for a in self.args)
+
+
+class SeqMul(SeqExprOp):
+    r"""Represents term-wise multiplication of sequences.
+
+    Explanation
+    ===========
+
+    Handles multiplication of sequences only. For multiplication
+    with other objects see :func:`SeqBase.coeff_mul`.
+
+    Rules:
+        * The interval on which sequence is defined is the intersection
+          of respective intervals of sequences.
+        * Anything \* :class:`EmptySequence` returns :class:`EmptySequence`.
+        * Other rules are defined in ``_mul`` methods of sequence classes.
+
+    Examples
+    ========
+
+    >>> from sympy import EmptySequence, oo, SeqMul, SeqPer, SeqFormula
+    >>> from sympy.abc import n
+    >>> SeqMul(SeqPer((1, 2), (n, 0, oo)), EmptySequence)
+    EmptySequence
+    >>> SeqMul(SeqPer((1, 2), (n, 0, 5)), SeqPer((1, 2), (n, 6, 10)))
+    EmptySequence
+    >>> SeqMul(SeqPer((1, 2), (n, 0, oo)), SeqFormula(n**2))
+    SeqMul(SeqFormula(n**2, (n, 0, oo)), SeqPer((1, 2), (n, 0, oo)))
+    >>> SeqMul(SeqFormula(n**3), SeqFormula(n**2))
+    SeqFormula(n**5, (n, 0, oo))
+
+    See Also
+    ========
+
+    sympy.series.sequences.SeqAdd
+    """
+
+    def __new__(cls, *args, **kwargs):
+        evaluate = kwargs.get('evaluate', global_parameters.evaluate)
+
+        # flatten inputs
+        args = list(args)
+
+        # adapted from sympy.sets.sets.Union
+        def _flatten(arg):
+            if isinstance(arg, SeqBase):
+                if isinstance(arg, SeqMul):
+                    return sum(map(_flatten, arg.args), [])
+                else:
+                    return [arg]
+            elif iterable(arg):
+                return sum(map(_flatten, arg), [])
+            raise TypeError("Input must be Sequences or "
+                            " iterables of Sequences")
+        args = _flatten(args)
+
+        # Multiplication of no sequences is EmptySequence
+        if not args:
+            return S.EmptySequence
+
+        if Intersection(*(a.interval for a in args)) is S.EmptySet:
+            return S.EmptySequence
+
+        # reduce using known rules
+        if evaluate:
+            return SeqMul.reduce(args)
+
+        args = list(ordered(args, SeqBase._start_key))
+
+        return Basic.__new__(cls, *args)
+
+    @staticmethod
+    def reduce(args):
+        """Simplify a :class:`SeqMul` using known rules.
+
+        Explanation
+        ===========
+
+        Iterates through all pairs and ask the constituent
+        sequences if they can simplify themselves with any other constituent.
+
+        Notes
+        =====
+
+        adapted from ``Union.reduce``
+
+        """
+        new_args = True
+        while new_args:
+            for id1, s in enumerate(args):
+                new_args = False
+                for id2, t in enumerate(args):
+                    if id1 == id2:
+                        continue
+                    new_seq = s._mul(t)
+                    # This returns None if s does not know how to multiply
+                    # with t. Returns the newly multiplied sequence otherwise
+                    if new_seq is not None:
+                        new_args = [a for a in args if a not in (s, t)]
+                        new_args.append(new_seq)
+                        break
+                if new_args:
+                    args = new_args
+                    break
+
+        if len(args) == 1:
+            return args.pop()
+        else:
+            return SeqMul(args, evaluate=False)
+
+    def _eval_coeff(self, pt):
+        """multiplies the coefficients of all the sequences at point pt"""
+        val = 1
+        for a in self.args:
+            val *= a.coeff(pt)
+        return val

outputs/audit_venv/lib/python3.11/site-packages/sympy/series/series.py

@@ -0,0 +1,63 @@
+from sympy.core.sympify import sympify
+
+
+def series(expr, x=None, x0=0, n=6, dir="+"):
+    """Series expansion of expr around point `x = x0`.
+
+    Parameters
+    ==========
+
+    expr : Expression
+           The expression whose series is to be expanded.
+
+    x : Symbol
+        It is the variable of the expression to be calculated.
+
+    x0 : Value
+         The value around which ``x`` is calculated. Can be any value
+         from ``-oo`` to ``oo``.
+
+    n : Value
+        The number of terms upto which the series is to be expanded.
+
+    dir : String, optional
+          The series-expansion can be bi-directional. If ``dir="+"``,
+          then (x->x0+). If ``dir="-"``, then (x->x0-). For infinite
+          ``x0`` (``oo`` or ``-oo``), the ``dir`` argument is determined
+          from the direction of the infinity (i.e., ``dir="-"`` for
+          ``oo``).
+
+    Examples
+    ========
+
+    >>> from sympy import series, tan, oo
+    >>> from sympy.abc import x
+    >>> f = tan(x)
+    >>> series(f, x, 2, 6, "+")
+    tan(2) + (1 + tan(2)**2)*(x - 2) + (x - 2)**2*(tan(2)**3 + tan(2)) +
+    (x - 2)**3*(1/3 + 4*tan(2)**2/3 + tan(2)**4) + (x - 2)**4*(tan(2)**5 +
+    5*tan(2)**3/3 + 2*tan(2)/3) + (x - 2)**5*(2/15 + 17*tan(2)**2/15 +
+    2*tan(2)**4 + tan(2)**6) + O((x - 2)**6, (x, 2))
+
+    >>> series(f, x, 2, 3, "-")
+    tan(2) + (2 - x)*(-tan(2)**2 - 1) + (2 - x)**2*(tan(2)**3 + tan(2))
+    + O((x - 2)**3, (x, 2))
+
+    >>> series(f, x, 2, oo, "+")
+    Traceback (most recent call last):
+    ...
+    TypeError: 'Infinity' object cannot be interpreted as an integer
+
+    Returns
+    =======
+
+    Expr
+        Series expansion of the expression about x0
+
+    See Also
+    ========
+
+    sympy.core.expr.Expr.series: See the docstring of Expr.series() for complete details of this wrapper.
+    """
+    expr = sympify(expr)
+    return expr.series(x, x0, n, dir)