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ckpts/universal/global_step80/zero/12.attention.query_key_value.weight/exp_avg.pt

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ckpts/universal/global_step80/zero/12.attention.query_key_value.weight/exp_avg_sq.pt

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ckpts/universal/global_step80/zero/20.input_layernorm.weight/fp32.pt

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ckpts/universal/global_step80/zero/7.mlp.dense_h_to_4h.weight/exp_avg.pt

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ckpts/universal/global_step80/zero/7.mlp.dense_h_to_4h.weight/fp32.pt

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venv/lib/python3.10/site-packages/sympy/integrals/__init__.py

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+"""Integration functions that integrate a SymPy expression.
+
+    Examples
+    ========
+
+    >>> from sympy import integrate, sin
+    >>> from sympy.abc import x
+    >>> integrate(1/x,x)
+    log(x)
+    >>> integrate(sin(x),x)
+    -cos(x)
+"""
+from .integrals import integrate, Integral, line_integrate
+from .transforms import (mellin_transform, inverse_mellin_transform,
+                        MellinTransform, InverseMellinTransform,
+                        laplace_transform, inverse_laplace_transform,
+                        LaplaceTransform, InverseLaplaceTransform,
+                        fourier_transform, inverse_fourier_transform,
+                        FourierTransform, InverseFourierTransform,
+                        sine_transform, inverse_sine_transform,
+                        SineTransform, InverseSineTransform,
+                        cosine_transform, inverse_cosine_transform,
+                        CosineTransform, InverseCosineTransform,
+                        hankel_transform, inverse_hankel_transform,
+                        HankelTransform, InverseHankelTransform)
+from .singularityfunctions import singularityintegrate
+
+__all__ = [
+    'integrate', 'Integral', 'line_integrate',
+
+    'mellin_transform', 'inverse_mellin_transform', 'MellinTransform',
+    'InverseMellinTransform', 'laplace_transform',
+    'inverse_laplace_transform', 'LaplaceTransform',
+    'InverseLaplaceTransform', 'fourier_transform',
+    'inverse_fourier_transform', 'FourierTransform',
+    'InverseFourierTransform', 'sine_transform', 'inverse_sine_transform',
+    'SineTransform', 'InverseSineTransform', 'cosine_transform',
+    'inverse_cosine_transform', 'CosineTransform', 'InverseCosineTransform',
+    'hankel_transform', 'inverse_hankel_transform', 'HankelTransform',
+    'InverseHankelTransform',
+
+    'singularityintegrate',
+]

venv/lib/python3.10/site-packages/sympy/integrals/benchmarks/bench_integrate.py

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+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.trigonometric import sin
+from sympy.integrals.integrals import integrate
+
+x = Symbol('x')
+
+
+def bench_integrate_sin():
+    integrate(sin(x), x)
+
+
+def bench_integrate_x1sin():
+    integrate(x**1*sin(x), x)
+
+
+def bench_integrate_x2sin():
+    integrate(x**2*sin(x), x)
+
+
+def bench_integrate_x3sin():
+    integrate(x**3*sin(x), x)

venv/lib/python3.10/site-packages/sympy/integrals/benchmarks/bench_trigintegrate.py

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+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.trigonometric import sin
+from sympy.integrals.trigonometry import trigintegrate
+
+x = Symbol('x')
+
+
+def timeit_trigintegrate_sin3x():
+    trigintegrate(sin(x)**3, x)
+
+
+def timeit_trigintegrate_x2():
+    trigintegrate(x**2, x)  # -> None

venv/lib/python3.10/site-packages/sympy/integrals/deltafunctions.py

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+from sympy.core.mul import Mul
+from sympy.core.singleton import S
+from sympy.core.sorting import default_sort_key
+from sympy.functions import DiracDelta, Heaviside
+from .integrals import Integral, integrate
+
+
+def change_mul(node, x):
+    """change_mul(node, x)
+
+       Rearranges the operands of a product, bringing to front any simple
+       DiracDelta expression.
+
+       Explanation
+       ===========
+
+       If no simple DiracDelta expression was found, then all the DiracDelta
+       expressions are simplified (using DiracDelta.expand(diracdelta=True, wrt=x)).
+
+       Return: (dirac, new node)
+       Where:
+         o dirac is either a simple DiracDelta expression or None (if no simple
+           expression was found);
+         o new node is either a simplified DiracDelta expressions or None (if it
+           could not be simplified).
+
+       Examples
+       ========
+
+       >>> from sympy import DiracDelta, cos
+       >>> from sympy.integrals.deltafunctions import change_mul
+       >>> from sympy.abc import x, y
+       >>> change_mul(x*y*DiracDelta(x)*cos(x), x)
+       (DiracDelta(x), x*y*cos(x))
+       >>> change_mul(x*y*DiracDelta(x**2 - 1)*cos(x), x)
+       (None, x*y*cos(x)*DiracDelta(x - 1)/2 + x*y*cos(x)*DiracDelta(x + 1)/2)
+       >>> change_mul(x*y*DiracDelta(cos(x))*cos(x), x)
+       (None, None)
+
+       See Also
+       ========
+
+       sympy.functions.special.delta_functions.DiracDelta
+       deltaintegrate
+    """
+
+    new_args = []
+    dirac = None
+
+    #Sorting is needed so that we consistently collapse the same delta;
+    #However, we must preserve the ordering of non-commutative terms
+    c, nc = node.args_cnc()
+    sorted_args = sorted(c, key=default_sort_key)
+    sorted_args.extend(nc)
+
+    for arg in sorted_args:
+        if arg.is_Pow and isinstance(arg.base, DiracDelta):
+            new_args.append(arg.func(arg.base, arg.exp - 1))
+            arg = arg.base
+        if dirac is None and (isinstance(arg, DiracDelta) and arg.is_simple(x)):
+            dirac = arg
+        else:
+            new_args.append(arg)
+    if not dirac:  # there was no simple dirac
+        new_args = []
+        for arg in sorted_args:
+            if isinstance(arg, DiracDelta):
+                new_args.append(arg.expand(diracdelta=True, wrt=x))
+            elif arg.is_Pow and isinstance(arg.base, DiracDelta):
+                new_args.append(arg.func(arg.base.expand(diracdelta=True, wrt=x), arg.exp))
+            else:
+                new_args.append(arg)
+        if new_args != sorted_args:
+            nnode = Mul(*new_args).expand()
+        else:  # if the node didn't change there is nothing to do
+            nnode = None
+        return (None, nnode)
+    return (dirac, Mul(*new_args))
+
+
+def deltaintegrate(f, x):
+    """
+    deltaintegrate(f, x)
+
+    Explanation
+    ===========
+
+    The idea for integration is the following:
+
+    - If we are dealing with a DiracDelta expression, i.e. DiracDelta(g(x)),
+      we try to simplify it.
+
+      If we could simplify it, then we integrate the resulting expression.
+      We already know we can integrate a simplified expression, because only
+      simple DiracDelta expressions are involved.
+
+      If we couldn't simplify it, there are two cases:
+
+      1) The expression is a simple expression: we return the integral,
+         taking care if we are dealing with a Derivative or with a proper
+         DiracDelta.
+
+      2) The expression is not simple (i.e. DiracDelta(cos(x))): we can do
+         nothing at all.
+
+    - If the node is a multiplication node having a DiracDelta term:
+
+      First we expand it.
+
+      If the expansion did work, then we try to integrate the expansion.
+
+      If not, we try to extract a simple DiracDelta term, then we have two
+      cases:
+
+      1) We have a simple DiracDelta term, so we return the integral.
+
+      2) We didn't have a simple term, but we do have an expression with
+         simplified DiracDelta terms, so we integrate this expression.
+
+    Examples
+    ========
+
+        >>> from sympy.abc import x, y, z
+        >>> from sympy.integrals.deltafunctions import deltaintegrate
+        >>> from sympy import sin, cos, DiracDelta
+        >>> deltaintegrate(x*sin(x)*cos(x)*DiracDelta(x - 1), x)
+        sin(1)*cos(1)*Heaviside(x - 1)
+        >>> deltaintegrate(y**2*DiracDelta(x - z)*DiracDelta(y - z), y)
+        z**2*DiracDelta(x - z)*Heaviside(y - z)
+
+    See Also
+    ========
+
+    sympy.functions.special.delta_functions.DiracDelta
+    sympy.integrals.integrals.Integral
+    """
+    if not f.has(DiracDelta):
+        return None
+
+    # g(x) = DiracDelta(h(x))
+    if f.func == DiracDelta:
+        h = f.expand(diracdelta=True, wrt=x)
+        if h == f:  # can't simplify the expression
+            #FIXME: the second term tells whether is DeltaDirac or Derivative
+            #For integrating derivatives of DiracDelta we need the chain rule
+            if f.is_simple(x):
+                if (len(f.args) <= 1 or f.args[1] == 0):
+                    return Heaviside(f.args[0])
+                else:
+                    return (DiracDelta(f.args[0], f.args[1] - 1) /
+                        f.args[0].as_poly().LC())
+        else:  # let's try to integrate the simplified expression
+            fh = integrate(h, x)
+            return fh
+    elif f.is_Mul or f.is_Pow:  # g(x) = a*b*c*f(DiracDelta(h(x)))*d*e
+        g = f.expand()
+        if f != g:  # the expansion worked
+            fh = integrate(g, x)
+            if fh is not None and not isinstance(fh, Integral):
+                return fh
+        else:
+            # no expansion performed, try to extract a simple DiracDelta term
+            deltaterm, rest_mult = change_mul(f, x)
+
+            if not deltaterm:
+                if rest_mult:
+                    fh = integrate(rest_mult, x)
+                    return fh
+            else:
+                from sympy.solvers import solve
+                deltaterm = deltaterm.expand(diracdelta=True, wrt=x)
+                if deltaterm.is_Mul:  # Take out any extracted factors
+                    deltaterm, rest_mult_2 = change_mul(deltaterm, x)
+                    rest_mult = rest_mult*rest_mult_2
+                point = solve(deltaterm.args[0], x)[0]
+
+                # Return the largest hyperreal term left after
+                # repeated integration by parts.  For example,
+                #
+                #   integrate(y*DiracDelta(x, 1),x) == y*DiracDelta(x,0),  not 0
+                #
+                # This is so Integral(y*DiracDelta(x).diff(x),x).doit()
+                # will return y*DiracDelta(x) instead of 0 or DiracDelta(x),
+                # both of which are correct everywhere the value is defined
+                # but give wrong answers for nested integration.
+                n = (0 if len(deltaterm.args)==1 else deltaterm.args[1])
+                m = 0
+                while n >= 0:
+                    r = S.NegativeOne**n*rest_mult.diff(x, n).subs(x, point)
+                    if r.is_zero:
+                        n -= 1
+                        m += 1
+                    else:
+                        if m == 0:
+                            return r*Heaviside(x - point)
+                        else:
+                            return r*DiracDelta(x,m-1)
+                # In some very weak sense, x=0 is still a singularity,
+                # but we hope will not be of any practical consequence.
+                return S.Zero
+    return None

venv/lib/python3.10/site-packages/sympy/integrals/heurisch.py

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+from __future__ import annotations
+
+from itertools import permutations
+from functools import reduce
+
+from sympy.core.add import Add
+from sympy.core.basic import Basic
+from sympy.core.mul import Mul
+from sympy.core.symbol import Wild, Dummy, Symbol
+from sympy.core.basic import sympify
+from sympy.core.numbers import Rational, pi, I
+from sympy.core.relational import Eq, Ne
+from sympy.core.singleton import S
+from sympy.core.sorting import ordered
+from sympy.core.traversal import iterfreeargs
+
+from sympy.functions import exp, sin, cos, tan, cot, asin, atan
+from sympy.functions import log, sinh, cosh, tanh, coth, asinh
+from sympy.functions import sqrt, erf, erfi, li, Ei
+from sympy.functions import besselj, bessely, besseli, besselk
+from sympy.functions import hankel1, hankel2, jn, yn
+from sympy.functions.elementary.complexes import Abs, re, im, sign, arg
+from sympy.functions.elementary.exponential import LambertW
+from sympy.functions.elementary.integers import floor, ceiling
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.special.delta_functions import Heaviside, DiracDelta
+
+from sympy.simplify.radsimp import collect
+
+from sympy.logic.boolalg import And, Or
+from sympy.utilities.iterables import uniq
+
+from sympy.polys import quo, gcd, lcm, factor_list, cancel, PolynomialError
+from sympy.polys.monomials import itermonomials
+from sympy.polys.polyroots import root_factors
+
+from sympy.polys.rings import PolyRing
+from sympy.polys.solvers import solve_lin_sys
+from sympy.polys.constructor import construct_domain
+
+from sympy.integrals.integrals import integrate
+
+
+def components(f, x):
+    """
+    Returns a set of all functional components of the given expression
+    which includes symbols, function applications and compositions and
+    non-integer powers. Fractional powers are collected with
+    minimal, positive exponents.
+
+    Examples
+    ========
+
+    >>> from sympy import cos, sin
+    >>> from sympy.abc import x
+    >>> from sympy.integrals.heurisch import components
+
+    >>> components(sin(x)*cos(x)**2, x)
+    {x, sin(x), cos(x)}
+
+    See Also
+    ========
+
+    heurisch
+    """
+    result = set()
+
+    if f.has_free(x):
+        if f.is_symbol and f.is_commutative:
+            result.add(f)
+        elif f.is_Function or f.is_Derivative:
+            for g in f.args:
+                result |= components(g, x)
+
+            result.add(f)
+        elif f.is_Pow:
+            result |= components(f.base, x)
+
+            if not f.exp.is_Integer:
+                if f.exp.is_Rational:
+                    result.add(f.base**Rational(1, f.exp.q))
+                else:
+                    result |= components(f.exp, x) | {f}
+        else:
+            for g in f.args:
+                result |= components(g, x)
+
+    return result
+
+# name -> [] of symbols
+_symbols_cache: dict[str, list[Dummy]] = {}
+
+
+# NB @cacheit is not convenient here
+def _symbols(name, n):
+    """get vector of symbols local to this module"""
+    try:
+        lsyms = _symbols_cache[name]
+    except KeyError:
+        lsyms = []
+        _symbols_cache[name] = lsyms
+
+    while len(lsyms) < n:
+        lsyms.append( Dummy('%s%i' % (name, len(lsyms))) )
+
+    return lsyms[:n]
+
+
+def heurisch_wrapper(f, x, rewrite=False, hints=None, mappings=None, retries=3,
+                     degree_offset=0, unnecessary_permutations=None,
+                     _try_heurisch=None):
+    """
+    A wrapper around the heurisch integration algorithm.
+
+    Explanation
+    ===========
+
+    This method takes the result from heurisch and checks for poles in the
+    denominator. For each of these poles, the integral is reevaluated, and
+    the final integration result is given in terms of a Piecewise.
+
+    Examples
+    ========
+
+    >>> from sympy import cos, symbols
+    >>> from sympy.integrals.heurisch import heurisch, heurisch_wrapper
+    >>> n, x = symbols('n x')
+    >>> heurisch(cos(n*x), x)
+    sin(n*x)/n
+    >>> heurisch_wrapper(cos(n*x), x)
+    Piecewise((sin(n*x)/n, Ne(n, 0)), (x, True))
+
+    See Also
+    ========
+
+    heurisch
+    """
+    from sympy.solvers.solvers import solve, denoms
+    f = sympify(f)
+    if not f.has_free(x):
+        return f*x
+
+    res = heurisch(f, x, rewrite, hints, mappings, retries, degree_offset,
+                   unnecessary_permutations, _try_heurisch)
+    if not isinstance(res, Basic):
+        return res
+
+    # We consider each denominator in the expression, and try to find
+    # cases where one or more symbolic denominator might be zero. The
+    # conditions for these cases are stored in the list slns.
+    #
+    # Since denoms returns a set we use ordered. This is important because the
+    # ordering of slns determines the order of the resulting Piecewise so we
+    # need a deterministic order here to make the output deterministic.
+    slns = []
+    for d in ordered(denoms(res)):
+        try:
+            slns += solve([d], dict=True, exclude=(x,))
+        except NotImplementedError:
+            pass
+    if not slns:
+        return res
+    slns = list(uniq(slns))
+    # Remove the solutions corresponding to poles in the original expression.
+    slns0 = []
+    for d in denoms(f):
+        try:
+            slns0 += solve([d], dict=True, exclude=(x,))
+        except NotImplementedError:
+            pass
+    slns = [s for s in slns if s not in slns0]
+    if not slns:
+        return res
+    if len(slns) > 1:
+        eqs = []
+        for sub_dict in slns:
+            eqs.extend([Eq(key, value) for key, value in sub_dict.items()])
+        slns = solve(eqs, dict=True, exclude=(x,)) + slns
+    # For each case listed in the list slns, we reevaluate the integral.
+    pairs = []
+    for sub_dict in slns:
+        expr = heurisch(f.subs(sub_dict), x, rewrite, hints, mappings, retries,
+                        degree_offset, unnecessary_permutations,
+                        _try_heurisch)
+        cond = And(*[Eq(key, value) for key, value in sub_dict.items()])
+        generic = Or(*[Ne(key, value) for key, value in sub_dict.items()])
+        if expr is None:
+            expr = integrate(f.subs(sub_dict),x)
+        pairs.append((expr, cond))
+    # If there is one condition, put the generic case first. Otherwise,
+    # doing so may lead to longer Piecewise formulas
+    if len(pairs) == 1:
+        pairs = [(heurisch(f, x, rewrite, hints, mappings, retries,
+                              degree_offset, unnecessary_permutations,
+                              _try_heurisch),
+                              generic),
+                 (pairs[0][0], True)]
+    else:
+        pairs.append((heurisch(f, x, rewrite, hints, mappings, retries,
+                              degree_offset, unnecessary_permutations,
+                              _try_heurisch),
+                              True))
+    return Piecewise(*pairs)
+
+class BesselTable:
+    """
+    Derivatives of Bessel functions of orders n and n-1
+    in terms of each other.
+
+    See the docstring of DiffCache.
+    """
+
+    def __init__(self):
+        self.table = {}
+        self.n = Dummy('n')
+        self.z = Dummy('z')
+        self._create_table()
+
+    def _create_table(t):
+        table, n, z = t.table, t.n, t.z
+        for f in (besselj, bessely, hankel1, hankel2):
+            table[f] = (f(n-1, z) - n*f(n, z)/z,
+                        (n-1)*f(n-1, z)/z - f(n, z))
+
+        f = besseli
+        table[f] = (f(n-1, z) - n*f(n, z)/z,
+                    (n-1)*f(n-1, z)/z + f(n, z))
+        f = besselk
+        table[f] = (-f(n-1, z) - n*f(n, z)/z,
+                    (n-1)*f(n-1, z)/z - f(n, z))
+
+        for f in (jn, yn):
+            table[f] = (f(n-1, z) - (n+1)*f(n, z)/z,
+                        (n-1)*f(n-1, z)/z - f(n, z))
+
+    def diffs(t, f, n, z):
+        if f in t.table:
+            diff0, diff1 = t.table[f]
+            repl = [(t.n, n), (t.z, z)]
+            return (diff0.subs(repl), diff1.subs(repl))
+
+    def has(t, f):
+        return f in t.table
+
+_bessel_table = None
+
+class DiffCache:
+    """
+    Store for derivatives of expressions.
+
+    Explanation
+    ===========
+
+    The standard form of the derivative of a Bessel function of order n
+    contains two Bessel functions of orders n-1 and n+1, respectively.
+    Such forms cannot be used in parallel Risch algorithm, because
+    there is a linear recurrence relation between the three functions
+    while the algorithm expects that functions and derivatives are
+    represented in terms of algebraically independent transcendentals.
+
+    The solution is to take two of the functions, e.g., those of orders
+    n and n-1, and to express the derivatives in terms of the pair.
+    To guarantee that the proper form is used the two derivatives are
+    cached as soon as one is encountered.
+
+    Derivatives of other functions are also cached at no extra cost.
+    All derivatives are with respect to the same variable `x`.
+    """
+
+    def __init__(self, x):
+        self.cache = {}
+        self.x = x
+
+        global _bessel_table
+        if not _bessel_table:
+            _bessel_table = BesselTable()
+
+    def get_diff(self, f):
+        cache = self.cache
+
+        if f in cache:
+            pass
+        elif (not hasattr(f, 'func') or
+            not _bessel_table.has(f.func)):
+            cache[f] = cancel(f.diff(self.x))
+        else:
+            n, z = f.args
+            d0, d1 = _bessel_table.diffs(f.func, n, z)
+            dz = self.get_diff(z)
+            cache[f] = d0*dz
+            cache[f.func(n-1, z)] = d1*dz
+
+        return cache[f]
+
+def heurisch(f, x, rewrite=False, hints=None, mappings=None, retries=3,
+             degree_offset=0, unnecessary_permutations=None,
+             _try_heurisch=None):
+    """
+    Compute indefinite integral using heuristic Risch algorithm.
+
+    Explanation
+    ===========
+
+    This is a heuristic approach to indefinite integration in finite
+    terms using the extended heuristic (parallel) Risch algorithm, based
+    on Manuel Bronstein's "Poor Man's Integrator".
+
+    The algorithm supports various classes of functions including
+    transcendental elementary or special functions like Airy,
+    Bessel, Whittaker and Lambert.
+
+    Note that this algorithm is not a decision procedure. If it isn't
+    able to compute the antiderivative for a given function, then this is
+    not a proof that such a functions does not exist.  One should use
+    recursive Risch algorithm in such case.  It's an open question if
+    this algorithm can be made a full decision procedure.
+
+    This is an internal integrator procedure. You should use top level
+    'integrate' function in most cases, as this procedure needs some
+    preprocessing steps and otherwise may fail.
+
+    Specification
+    =============
+
+     heurisch(f, x, rewrite=False, hints=None)
+
+       where
+         f : expression
+         x : symbol
+
+         rewrite -> force rewrite 'f' in terms of 'tan' and 'tanh'
+         hints   -> a list of functions that may appear in anti-derivate
+
+          - hints = None          --> no suggestions at all
+          - hints = [ ]           --> try to figure out
+          - hints = [f1, ..., fn] --> we know better
+
+    Examples
+    ========
+
+    >>> from sympy import tan
+    >>> from sympy.integrals.heurisch import heurisch
+    >>> from sympy.abc import x, y
+
+    >>> heurisch(y*tan(x), x)
+    y*log(tan(x)**2 + 1)/2
+
+    See Manuel Bronstein's "Poor Man's Integrator":
+
+    References
+    ==========
+
+    .. [1] https://www-sop.inria.fr/cafe/Manuel.Bronstein/pmint/index.html
+
+    For more information on the implemented algorithm refer to:
+
+    .. [2] K. Geddes, L. Stefanus, On the Risch-Norman Integration
+       Method and its Implementation in Maple, Proceedings of
+       ISSAC'89, ACM Press, 212-217.
+
+    .. [3] J. H. Davenport, On the Parallel Risch Algorithm (I),
+       Proceedings of EUROCAM'82, LNCS 144, Springer, 144-157.
+
+    .. [4] J. H. Davenport, On the Parallel Risch Algorithm (III):
+       Use of Tangents, SIGSAM Bulletin 16 (1982), 3-6.
+
+    .. [5] J. H. Davenport, B. M. Trager, On the Parallel Risch
+       Algorithm (II), ACM Transactions on Mathematical
+       Software 11 (1985), 356-362.
+
+    See Also
+    ========
+
+    sympy.integrals.integrals.Integral.doit
+    sympy.integrals.integrals.Integral
+    sympy.integrals.heurisch.components
+    """
+    f = sympify(f)
+
+    # There are some functions that Heurisch cannot currently handle,
+    # so do not even try.
+    # Set _try_heurisch=True to skip this check
+    if _try_heurisch is not True:
+        if f.has(Abs, re, im, sign, Heaviside, DiracDelta, floor, ceiling, arg):
+            return
+
+    if not f.has_free(x):
+        return f*x
+
+    if not f.is_Add:
+        indep, f = f.as_independent(x)
+    else:
+        indep = S.One
+
+    rewritables = {
+        (sin, cos, cot): tan,
+        (sinh, cosh, coth): tanh,
+    }
+
+    if rewrite:
+        for candidates, rule in rewritables.items():
+            f = f.rewrite(candidates, rule)
+    else:
+        for candidates in rewritables.keys():
+            if f.has(*candidates):
+                break
+        else:
+            rewrite = True
+
+    terms = components(f, x)
+    dcache = DiffCache(x)
+
+    if hints is not None:
+        if not hints:
+            a = Wild('a', exclude=[x])
+            b = Wild('b', exclude=[x])
+            c = Wild('c', exclude=[x])
+
+            for g in set(terms):  # using copy of terms
+                if g.is_Function:
+                    if isinstance(g, li):
+                        M = g.args[0].match(a*x**b)
+
+                        if M is not None:
+                            terms.add( x*(li(M[a]*x**M[b]) - (M[a]*x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
+                            #terms.add( x*(li(M[a]*x**M[b]) - (x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
+                            #terms.add( x*(li(M[a]*x**M[b]) - x*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
+                            #terms.add( li(M[a]*x**M[b]) - Ei((M[b]+1)*log(M[a]*x**M[b])/M[b]) )
+
+                    elif isinstance(g, exp):
+                        M = g.args[0].match(a*x**2)
+
+                        if M is not None:
+                            if M[a].is_positive:
+                                terms.add(erfi(sqrt(M[a])*x))
+                            else: # M[a].is_negative or unknown
+                                terms.add(erf(sqrt(-M[a])*x))
+
+                        M = g.args[0].match(a*x**2 + b*x + c)
+
+                        if M is not None:
+                            if M[a].is_positive:
+                                terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))*
+                                          erfi(sqrt(M[a])*x + M[b]/(2*sqrt(M[a]))))
+                            elif M[a].is_negative:
+                                terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))*
+                                          erf(sqrt(-M[a])*x - M[b]/(2*sqrt(-M[a]))))
+
+                        M = g.args[0].match(a*log(x)**2)
+
+                        if M is not None:
+                            if M[a].is_positive:
+                                terms.add(erfi(sqrt(M[a])*log(x) + 1/(2*sqrt(M[a]))))
+                            if M[a].is_negative:
+                                terms.add(erf(sqrt(-M[a])*log(x) - 1/(2*sqrt(-M[a]))))
+
+                elif g.is_Pow:
+                    if g.exp.is_Rational and g.exp.q == 2:
+                        M = g.base.match(a*x**2 + b)
+
+                        if M is not None and M[b].is_positive:
+                            if M[a].is_positive:
+                                terms.add(asinh(sqrt(M[a]/M[b])*x))
+                            elif M[a].is_negative:
+                                terms.add(asin(sqrt(-M[a]/M[b])*x))
+
+                        M = g.base.match(a*x**2 - b)
+
+                        if M is not None and M[b].is_positive:
+                            if M[a].is_positive:
+                                dF = 1/sqrt(M[a]*x**2 - M[b])
+                                F = log(2*sqrt(M[a])*sqrt(M[a]*x**2 - M[b]) + 2*M[a]*x)/sqrt(M[a])
+                                dcache.cache[F] = dF  # hack: F.diff(x) doesn't automatically simplify to f
+                                terms.add(F)
+                            elif M[a].is_negative:
+                                terms.add(-M[b]/2*sqrt(-M[a])*
+                                           atan(sqrt(-M[a])*x/sqrt(M[a]*x**2 - M[b])))
+
+        else:
+            terms |= set(hints)
+
+    for g in set(terms):  # using copy of terms
+        terms |= components(dcache.get_diff(g), x)
+
+    # XXX: The commented line below makes heurisch more deterministic wrt
+    # PYTHONHASHSEED and the iteration order of sets. There are other places
+    # where sets are iterated over but this one is possibly the most important.
+    # Theoretically the order here should not matter but different orderings
+    # can expose potential bugs in the different code paths so potentially it
+    # is better to keep the non-determinism.
+    #
+    # terms = list(ordered(terms))
+
+    # TODO: caching is significant factor for why permutations work at all. Change this.
+    V = _symbols('x', len(terms))
+
+
+    # sort mapping expressions from largest to smallest (last is always x).
+    mapping = list(reversed(list(zip(*ordered(                          #
+        [(a[0].as_independent(x)[1], a) for a in zip(terms, V)])))[1])) #
+    rev_mapping = {v: k for k, v in mapping}                            #
+    if mappings is None:                                                #
+        # optimizing the number of permutations of mapping              #
+        assert mapping[-1][0] == x # if not, find it and correct this comment
+        unnecessary_permutations = [mapping.pop(-1)]
+        mappings = permutations(mapping)
+    else:
+        unnecessary_permutations = unnecessary_permutations or []
+
+    def _substitute(expr):
+        return expr.subs(mapping)
+
+    for mapping in mappings:
+        mapping = list(mapping)
+        mapping = mapping + unnecessary_permutations
+        diffs = [ _substitute(dcache.get_diff(g)) for g in terms ]
+        denoms = [ g.as_numer_denom()[1] for g in diffs ]
+        if all(h.is_polynomial(*V) for h in denoms) and _substitute(f).is_rational_function(*V):
+            denom = reduce(lambda p, q: lcm(p, q, *V), denoms)
+            break
+    else:
+        if not rewrite:
+            result = heurisch(f, x, rewrite=True, hints=hints,
+                unnecessary_permutations=unnecessary_permutations)
+
+            if result is not None:
+                return indep*result
+        return None
+
+    numers = [ cancel(denom*g) for g in diffs ]
+    def _derivation(h):
+        return Add(*[ d * h.diff(v) for d, v in zip(numers, V) ])
+
+    def _deflation(p):
+        for y in V:
+            if not p.has(y):
+                continue
+
+            if _derivation(p) is not S.Zero:
+                c, q = p.as_poly(y).primitive()
+                return _deflation(c)*gcd(q, q.diff(y)).as_expr()
+
+        return p
+
+    def _splitter(p):
+        for y in V:
+            if not p.has(y):
+                continue
+
+            if _derivation(y) is not S.Zero:
+                c, q = p.as_poly(y).primitive()
+
+                q = q.as_expr()
+
+                h = gcd(q, _derivation(q), y)
+                s = quo(h, gcd(q, q.diff(y), y), y)
+
+                c_split = _splitter(c)
+
+                if s.as_poly(y).degree() == 0:
+                    return (c_split[0], q * c_split[1])
+
+                q_split = _splitter(cancel(q / s))
+
+                return (c_split[0]*q_split[0]*s, c_split[1]*q_split[1])
+
+        return (S.One, p)
+
+    special = {}
+
+    for term in terms:
+        if term.is_Function:
+            if isinstance(term, tan):
+                special[1 + _substitute(term)**2] = False
+            elif isinstance(term, tanh):
+                special[1 + _substitute(term)] = False
+                special[1 - _substitute(term)] = False
+            elif isinstance(term, LambertW):
+                special[_substitute(term)] = True
+
+    F = _substitute(f)
+
+    P, Q = F.as_numer_denom()
+
+    u_split = _splitter(denom)
+    v_split = _splitter(Q)
+
+    polys = set(list(v_split) + [ u_split[0] ] + list(special.keys()))
+
+    s = u_split[0] * Mul(*[ k for k, v in special.items() if v ])
+    polified = [ p.as_poly(*V) for p in [s, P, Q] ]
+
+    if None in polified:
+        return None
+
+    #--- definitions for _integrate
+    a, b, c = [ p.total_degree() for p in polified ]
+
+    poly_denom = (s * v_split[0] * _deflation(v_split[1])).as_expr()
+
+    def _exponent(g):
+        if g.is_Pow:
+            if g.exp.is_Rational and g.exp.q != 1:
+                if g.exp.p > 0:
+                    return g.exp.p + g.exp.q - 1
+                else:
+                    return abs(g.exp.p + g.exp.q)
+            else:
+                return 1
+        elif not g.is_Atom and g.args:
+            return max([ _exponent(h) for h in g.args ])
+        else:
+            return 1
+
+    A, B = _exponent(f), a + max(b, c)
+
+    if A > 1 and B > 1:
+        monoms = tuple(ordered(itermonomials(V, A + B - 1 + degree_offset)))
+    else:
+        monoms = tuple(ordered(itermonomials(V, A + B + degree_offset)))
+
+    poly_coeffs = _symbols('A', len(monoms))
+
+    poly_part = Add(*[ poly_coeffs[i]*monomial
+        for i, monomial in enumerate(monoms) ])
+
+    reducibles = set()
+
+    for poly in ordered(polys):
+        coeff, factors = factor_list(poly, *V)
+        reducibles.add(coeff)
+        for fact, mul in factors:
+            reducibles.add(fact)
+
+    def _integrate(field=None):
+        atans = set()
+        pairs = set()
+
+        if field == 'Q':
+            irreducibles = set(reducibles)
+        else:
+            setV = set(V)
+            irreducibles = set()
+            for poly in ordered(reducibles):
+                zV = setV & set(iterfreeargs(poly))
+                for z in ordered(zV):
+                    s = set(root_factors(poly, z, filter=field))
+                    irreducibles |= s
+                    break
+
+        log_part, atan_part = [], []
+
+        for poly in ordered(irreducibles):
+            m = collect(poly, I, evaluate=False)
+            y = m.get(I, S.Zero)
+            if y:
+                x = m.get(S.One, S.Zero)
+                if x.has(I) or y.has(I):
+                    continue  # nontrivial x + I*y
+                pairs.add((x, y))
+                irreducibles.remove(poly)
+
+        while pairs:
+            x, y = pairs.pop()
+            if (x, -y) in pairs:
+                pairs.remove((x, -y))
+                # Choosing b with no minus sign
+                if y.could_extract_minus_sign():
+                    y = -y
+                irreducibles.add(x*x + y*y)
+                atans.add(atan(x/y))
+            else:
+                irreducibles.add(x + I*y)
+
+
+        B = _symbols('B', len(irreducibles))
+        C = _symbols('C', len(atans))
+
+        # Note: the ordering matters here
+        for poly, b in reversed(list(zip(ordered(irreducibles), B))):
+            if poly.has(*V):
+                poly_coeffs.append(b)
+                log_part.append(b * log(poly))
+
+        for poly, c in reversed(list(zip(ordered(atans), C))):
+            if poly.has(*V):
+                poly_coeffs.append(c)
+                atan_part.append(c * poly)
+
+        # TODO: Currently it's better to use symbolic expressions here instead
+        # of rational functions, because it's simpler and FracElement doesn't
+        # give big speed improvement yet. This is because cancellation is slow
+        # due to slow polynomial GCD algorithms. If this gets improved then
+        # revise this code.
+        candidate = poly_part/poly_denom + Add(*log_part) + Add(*atan_part)
+        h = F - _derivation(candidate) / denom
+        raw_numer = h.as_numer_denom()[0]
+
+        # Rewrite raw_numer as a polynomial in K[coeffs][V] where K is a field
+        # that we have to determine. We can't use simply atoms() because log(3),
+        # sqrt(y) and similar expressions can appear, leading to non-trivial
+        # domains.
+        syms = set(poly_coeffs) | set(V)
+        non_syms = set()
+
+        def find_non_syms(expr):
+            if expr.is_Integer or expr.is_Rational:
+                pass # ignore trivial numbers
+            elif expr in syms:
+                pass # ignore variables
+            elif not expr.has_free(*syms):
+                non_syms.add(expr)
+            elif expr.is_Add or expr.is_Mul or expr.is_Pow:
+                list(map(find_non_syms, expr.args))
+            else:
+                # TODO: Non-polynomial expression. This should have been
+                # filtered out at an earlier stage.
+                raise PolynomialError
+
+        try:
+            find_non_syms(raw_numer)
+        except PolynomialError:
+            return None
+        else:
+            ground, _ = construct_domain(non_syms, field=True)
+
+        coeff_ring = PolyRing(poly_coeffs, ground)
+        ring = PolyRing(V, coeff_ring)
+        try:
+            numer = ring.from_expr(raw_numer)
+        except ValueError:
+            raise PolynomialError
+        solution = solve_lin_sys(numer.coeffs(), coeff_ring, _raw=False)
+
+        if solution is None:
+            return None
+        else:
+            return candidate.xreplace(solution).xreplace(
+                dict(zip(poly_coeffs, [S.Zero]*len(poly_coeffs))))
+
+    if all(isinstance(_, Symbol) for _ in V):
+        more_free = F.free_symbols - set(V)
+    else:
+        Fd = F.as_dummy()
+        more_free = Fd.xreplace(dict(zip(V, (Dummy() for _ in V)))
+            ).free_symbols & Fd.free_symbols
+    if not more_free:
+        # all free generators are identified in V
+        solution = _integrate('Q')
+
+        if solution is None:
+            solution = _integrate()
+    else:
+        solution = _integrate()
+
+    if solution is not None:
+        antideriv = solution.subs(rev_mapping)
+        antideriv = cancel(antideriv).expand()
+
+        if antideriv.is_Add:
+            antideriv = antideriv.as_independent(x)[1]
+
+        return indep*antideriv
+    else:
+        if retries >= 0:
+            result = heurisch(f, x, mappings=mappings, rewrite=rewrite, hints=hints, retries=retries - 1, unnecessary_permutations=unnecessary_permutations)
+
+            if result is not None:
+                return indep*result
+
+        return None

venv/lib/python3.10/site-packages/sympy/integrals/integrals.py

@@ -0,0 +1,1633 @@
+from typing import Tuple as tTuple
+
+from sympy.concrete.expr_with_limits import AddWithLimits
+from sympy.core.add import Add
+from sympy.core.basic import Basic
+from sympy.core.containers import Tuple
+from sympy.core.expr import Expr
+from sympy.core.exprtools import factor_terms
+from sympy.core.function import diff
+from sympy.core.logic import fuzzy_bool
+from sympy.core.mul import Mul
+from sympy.core.numbers import oo, pi
+from sympy.core.relational import Ne
+from sympy.core.singleton import S
+from sympy.core.symbol import (Dummy, Symbol, Wild)
+from sympy.core.sympify import sympify
+from sympy.functions import Piecewise, sqrt, piecewise_fold, tan, cot, atan
+from sympy.functions.elementary.exponential import log
+from sympy.functions.elementary.integers import floor
+from sympy.functions.elementary.complexes import Abs, sign
+from sympy.functions.elementary.miscellaneous import Min, Max
+from .rationaltools import ratint
+from sympy.matrices import MatrixBase
+from sympy.polys import Poly, PolynomialError
+from sympy.series.formal import FormalPowerSeries
+from sympy.series.limits import limit
+from sympy.series.order import Order
+from sympy.tensor.functions import shape
+from sympy.utilities.exceptions import sympy_deprecation_warning
+from sympy.utilities.iterables import is_sequence
+from sympy.utilities.misc import filldedent
+
+
+class Integral(AddWithLimits):
+    """Represents unevaluated integral."""
+
+    __slots__ = ()
+
+    args: tTuple[Expr, Tuple]
+
+    def __new__(cls, function, *symbols, **assumptions):
+        """Create an unevaluated integral.
+
+        Explanation
+        ===========
+
+        Arguments are an integrand followed by one or more limits.
+
+        If no limits are given and there is only one free symbol in the
+        expression, that symbol will be used, otherwise an error will be
+        raised.
+
+        >>> from sympy import Integral
+        >>> from sympy.abc import x, y
+        >>> Integral(x)
+        Integral(x, x)
+        >>> Integral(y)
+        Integral(y, y)
+
+        When limits are provided, they are interpreted as follows (using
+        ``x`` as though it were the variable of integration):
+
+            (x,) or x - indefinite integral
+            (x, a) - "evaluate at" integral is an abstract antiderivative
+            (x, a, b) - definite integral
+
+        The ``as_dummy`` method can be used to see which symbols cannot be
+        targeted by subs: those with a prepended underscore cannot be
+        changed with ``subs``. (Also, the integration variables themselves --
+        the first element of a limit -- can never be changed by subs.)
+
+        >>> i = Integral(x, x)
+        >>> at = Integral(x, (x, x))
+        >>> i.as_dummy()
+        Integral(x, x)
+        >>> at.as_dummy()
+        Integral(_0, (_0, x))
+
+        """
+
+        #This will help other classes define their own definitions
+        #of behaviour with Integral.
+        if hasattr(function, '_eval_Integral'):
+            return function._eval_Integral(*symbols, **assumptions)
+
+        if isinstance(function, Poly):
+            sympy_deprecation_warning(
+                """
+                integrate(Poly) and Integral(Poly) are deprecated. Instead,
+                use the Poly.integrate() method, or convert the Poly to an
+                Expr first with the Poly.as_expr() method.
+                """,
+                deprecated_since_version="1.6",
+                active_deprecations_target="deprecated-integrate-poly")
+
+        obj = AddWithLimits.__new__(cls, function, *symbols, **assumptions)
+        return obj
+
+    def __getnewargs__(self):
+        return (self.function,) + tuple([tuple(xab) for xab in self.limits])
+
+    @property
+    def free_symbols(self):
+        """
+        This method returns the symbols that will exist when the
+        integral is evaluated. This is useful if one is trying to
+        determine whether an integral depends on a certain
+        symbol or not.
+
+        Examples
+        ========
+
+        >>> from sympy import Integral
+        >>> from sympy.abc import x, y
+        >>> Integral(x, (x, y, 1)).free_symbols
+        {y}
+
+        See Also
+        ========
+
+        sympy.concrete.expr_with_limits.ExprWithLimits.function
+        sympy.concrete.expr_with_limits.ExprWithLimits.limits
+        sympy.concrete.expr_with_limits.ExprWithLimits.variables
+        """
+        return super().free_symbols
+
+    def _eval_is_zero(self):
+        # This is a very naive and quick test, not intended to do the integral to
+        # answer whether it is zero or not, e.g. Integral(sin(x), (x, 0, 2*pi))
+        # is zero but this routine should return None for that case. But, like
+        # Mul, there are trivial situations for which the integral will be
+        # zero so we check for those.
+        if self.function.is_zero:
+            return True
+        got_none = False
+        for l in self.limits:
+            if len(l) == 3:
+                z = (l[1] == l[2]) or (l[1] - l[2]).is_zero
+                if z:
+                    return True
+                elif z is None:
+                    got_none = True
+        free = self.function.free_symbols
+        for xab in self.limits:
+            if len(xab) == 1:
+                free.add(xab[0])
+                continue
+            if len(xab) == 2 and xab[0] not in free:
+                if xab[1].is_zero:
+                    return True
+                elif xab[1].is_zero is None:
+                    got_none = True
+            # take integration symbol out of free since it will be replaced
+            # with the free symbols in the limits
+            free.discard(xab[0])
+            # add in the new symbols
+            for i in xab[1:]:
+                free.update(i.free_symbols)
+        if self.function.is_zero is False and got_none is False:
+            return False
+
+    def transform(self, x, u):
+        r"""
+        Performs a change of variables from `x` to `u` using the relationship
+        given by `x` and `u` which will define the transformations `f` and `F`
+        (which are inverses of each other) as follows:
+
+        1) If `x` is a Symbol (which is a variable of integration) then `u`
+           will be interpreted as some function, f(u), with inverse F(u).
+           This, in effect, just makes the substitution of x with f(x).
+
+        2) If `u` is a Symbol then `x` will be interpreted as some function,
+           F(x), with inverse f(u). This is commonly referred to as
+           u-substitution.
+
+        Once f and F have been identified, the transformation is made as
+        follows:
+
+        .. math:: \int_a^b x \mathrm{d}x \rightarrow \int_{F(a)}^{F(b)} f(x)
+                  \frac{\mathrm{d}}{\mathrm{d}x}
+
+        where `F(x)` is the inverse of `f(x)` and the limits and integrand have
+        been corrected so as to retain the same value after integration.
+
+        Notes
+        =====
+
+        The mappings, F(x) or f(u), must lead to a unique integral. Linear
+        or rational linear expression, ``2*x``, ``1/x`` and ``sqrt(x)``, will
+        always work; quadratic expressions like ``x**2 - 1`` are acceptable
+        as long as the resulting integrand does not depend on the sign of
+        the solutions (see examples).
+
+        The integral will be returned unchanged if ``x`` is not a variable of
+        integration.
+
+        ``x`` must be (or contain) only one of of the integration variables. If
+        ``u`` has more than one free symbol then it should be sent as a tuple
+        (``u``, ``uvar``) where ``uvar`` identifies which variable is replacing
+        the integration variable.
+        XXX can it contain another integration variable?
+
+        Examples
+        ========
+
+        >>> from sympy.abc import a, x, u
+        >>> from sympy import Integral, cos, sqrt
+
+        >>> i = Integral(x*cos(x**2 - 1), (x, 0, 1))
+
+        transform can change the variable of integration
+
+        >>> i.transform(x, u)
+        Integral(u*cos(u**2 - 1), (u, 0, 1))
+
+        transform can perform u-substitution as long as a unique
+        integrand is obtained:
+
+        >>> i.transform(x**2 - 1, u)
+        Integral(cos(u)/2, (u, -1, 0))
+
+        This attempt fails because x = +/-sqrt(u + 1) and the
+        sign does not cancel out of the integrand:
+
+        >>> Integral(cos(x**2 - 1), (x, 0, 1)).transform(x**2 - 1, u)
+        Traceback (most recent call last):
+        ...
+        ValueError:
+        The mapping between F(x) and f(u) did not give a unique integrand.
+
+        transform can do a substitution. Here, the previous
+        result is transformed back into the original expression
+        using "u-substitution":
+
+        >>> ui = _
+        >>> _.transform(sqrt(u + 1), x) == i
+        True
+
+        We can accomplish the same with a regular substitution:
+
+        >>> ui.transform(u, x**2 - 1) == i
+        True
+
+        If the `x` does not contain a symbol of integration then
+        the integral will be returned unchanged. Integral `i` does
+        not have an integration variable `a` so no change is made:
+
+        >>> i.transform(a, x) == i
+        True
+
+        When `u` has more than one free symbol the symbol that is
+        replacing `x` must be identified by passing `u` as a tuple:
+
+        >>> Integral(x, (x, 0, 1)).transform(x, (u + a, u))
+        Integral(a + u, (u, -a, 1 - a))
+        >>> Integral(x, (x, 0, 1)).transform(x, (u + a, a))
+        Integral(a + u, (a, -u, 1 - u))
+
+        See Also
+        ========
+
+        sympy.concrete.expr_with_limits.ExprWithLimits.variables : Lists the integration variables
+        as_dummy : Replace integration variables with dummy ones
+        """
+        d = Dummy('d')
+
+        xfree = x.free_symbols.intersection(self.variables)
+        if len(xfree) > 1:
+            raise ValueError(
+                'F(x) can only contain one of: %s' % self.variables)
+        xvar = xfree.pop() if xfree else d
+
+        if xvar not in self.variables:
+            return self
+
+        u = sympify(u)
+        if isinstance(u, Expr):
+            ufree = u.free_symbols
+            if len(ufree) == 0:
+                raise ValueError(filldedent('''
+                f(u) cannot be a constant'''))
+            if len(ufree) > 1:
+                raise ValueError(filldedent('''
+                When f(u) has more than one free symbol, the one replacing x
+                must be identified: pass f(u) as (f(u), u)'''))
+            uvar = ufree.pop()
+        else:
+            u, uvar = u
+            if uvar not in u.free_symbols:
+                raise ValueError(filldedent('''
+                Expecting a tuple (expr, symbol) where symbol identified
+                a free symbol in expr, but symbol is not in expr's free
+                symbols.'''))
+            if not isinstance(uvar, Symbol):
+                # This probably never evaluates to True
+                raise ValueError(filldedent('''
+                Expecting a tuple (expr, symbol) but didn't get
+                a symbol; got %s''' % uvar))
+
+        if x.is_Symbol and u.is_Symbol:
+            return self.xreplace({x: u})
+
+        if not x.is_Symbol and not u.is_Symbol:
+            raise ValueError('either x or u must be a symbol')
+
+        if uvar == xvar:
+            return self.transform(x, (u.subs(uvar, d), d)).xreplace({d: uvar})
+
+        if uvar in self.limits:
+            raise ValueError(filldedent('''
+            u must contain the same variable as in x
+            or a variable that is not already an integration variable'''))
+
+        from sympy.solvers.solvers import solve
+        if not x.is_Symbol:
+            F = [x.subs(xvar, d)]
+            soln = solve(u - x, xvar, check=False)
+            if not soln:
+                raise ValueError('no solution for solve(F(x) - f(u), x)')
+            f = [fi.subs(uvar, d) for fi in soln]
+        else:
+            f = [u.subs(uvar, d)]
+            from sympy.simplify.simplify import posify
+            pdiff, reps = posify(u - x)
+            puvar = uvar.subs([(v, k) for k, v in reps.items()])
+            soln = [s.subs(reps) for s in solve(pdiff, puvar)]
+            if not soln:
+                raise ValueError('no solution for solve(F(x) - f(u), u)')
+            F = [fi.subs(xvar, d) for fi in soln]
+
+        newfuncs = {(self.function.subs(xvar, fi)*fi.diff(d)
+                        ).subs(d, uvar) for fi in f}
+        if len(newfuncs) > 1:
+            raise ValueError(filldedent('''
+            The mapping between F(x) and f(u) did not give
+            a unique integrand.'''))
+        newfunc = newfuncs.pop()
+
+        def _calc_limit_1(F, a, b):
+            """
+            replace d with a, using subs if possible, otherwise limit
+            where sign of b is considered
+            """
+            wok = F.subs(d, a)
+            if wok is S.NaN or wok.is_finite is False and a.is_finite:
+                return limit(sign(b)*F, d, a)
+            return wok
+
+        def _calc_limit(a, b):
+            """
+            replace d with a, using subs if possible, otherwise limit
+            where sign of b is considered
+            """
+            avals = list({_calc_limit_1(Fi, a, b) for Fi in F})
+            if len(avals) > 1:
+                raise ValueError(filldedent('''
+                The mapping between F(x) and f(u) did not
+                give a unique limit.'''))
+            return avals[0]
+
+        newlimits = []
+        for xab in self.limits:
+            sym = xab[0]
+            if sym == xvar:
+                if len(xab) == 3:
+                    a, b = xab[1:]
+                    a, b = _calc_limit(a, b), _calc_limit(b, a)
+                    if fuzzy_bool(a - b > 0):
+                        a, b = b, a
+                        newfunc = -newfunc
+                    newlimits.append((uvar, a, b))
+                elif len(xab) == 2:
+                    a = _calc_limit(xab[1], 1)
+                    newlimits.append((uvar, a))
+                else:
+                    newlimits.append(uvar)
+            else:
+                newlimits.append(xab)
+
+        return self.func(newfunc, *newlimits)
+
+    def doit(self, **hints):
+        """
+        Perform the integration using any hints given.
+
+        Examples
+        ========
+
+        >>> from sympy import Piecewise, S
+        >>> from sympy.abc import x, t
+        >>> p = x**2 + Piecewise((0, x/t < 0), (1, True))
+        >>> p.integrate((t, S(4)/5, 1), (x, -1, 1))
+        1/3
+
+        See Also
+        ========
+
+        sympy.integrals.trigonometry.trigintegrate
+        sympy.integrals.heurisch.heurisch
+        sympy.integrals.rationaltools.ratint
+        as_sum : Approximate the integral using a sum
+        """
+        if not hints.get('integrals', True):
+            return self
+
+        deep = hints.get('deep', True)
+        meijerg = hints.get('meijerg', None)
+        conds = hints.get('conds', 'piecewise')
+        risch = hints.get('risch', None)
+        heurisch = hints.get('heurisch', None)
+        manual = hints.get('manual', None)
+        if len(list(filter(None, (manual, meijerg, risch, heurisch)))) > 1:
+            raise ValueError("At most one of manual, meijerg, risch, heurisch can be True")
+        elif manual:
+            meijerg = risch = heurisch = False
+        elif meijerg:
+            manual = risch = heurisch = False
+        elif risch:
+            manual = meijerg = heurisch = False
+        elif heurisch:
+            manual = meijerg = risch = False
+        eval_kwargs = {"meijerg": meijerg, "risch": risch, "manual": manual, "heurisch": heurisch,
+            "conds": conds}
+
+        if conds not in ('separate', 'piecewise', 'none'):
+            raise ValueError('conds must be one of "separate", "piecewise", '
+                             '"none", got: %s' % conds)
+
+        if risch and any(len(xab) > 1 for xab in self.limits):
+            raise ValueError('risch=True is only allowed for indefinite integrals.')
+
+        # check for the trivial zero
+        if self.is_zero:
+            return S.Zero
+
+        # hacks to handle integrals of
+        # nested summations
+        from sympy.concrete.summations import Sum
+        if isinstance(self.function, Sum):
+            if any(v in self.function.limits[0] for v in self.variables):
+                raise ValueError('Limit of the sum cannot be an integration variable.')
+            if any(l.is_infinite for l in self.function.limits[0][1:]):
+                return self
+            _i = self
+            _sum = self.function
+            return _sum.func(_i.func(_sum.function, *_i.limits).doit(), *_sum.limits).doit()
+
+        # now compute and check the function
+        function = self.function
+        if deep:
+            function = function.doit(**hints)
+        if function.is_zero:
+            return S.Zero
+
+        # hacks to handle special cases
+        if isinstance(function, MatrixBase):
+            return function.applyfunc(
+                lambda f: self.func(f, *self.limits).doit(**hints))
+
+        if isinstance(function, FormalPowerSeries):
+            if len(self.limits) > 1:
+                raise NotImplementedError
+            xab = self.limits[0]
+            if len(xab) > 1:
+                return function.integrate(xab, **eval_kwargs)
+            else:
+                return function.integrate(xab[0], **eval_kwargs)
+
+        # There is no trivial answer and special handling
+        # is done so continue
+
+        # first make sure any definite limits have integration
+        # variables with matching assumptions
+        reps = {}
+        for xab in self.limits:
+            if len(xab) != 3:
+                # it makes sense to just make
+                # all x real but in practice with the
+                # current state of integration...this
+                # doesn't work out well
+                # x = xab[0]
+                # if x not in reps and not x.is_real:
+                #     reps[x] = Dummy(real=True)
+                continue
+            x, a, b = xab
+            l = (a, b)
+            if all(i.is_nonnegative for i in l) and not x.is_nonnegative:
+                d = Dummy(positive=True)
+            elif all(i.is_nonpositive for i in l) and not x.is_nonpositive:
+                d = Dummy(negative=True)
+            elif all(i.is_real for i in l) and not x.is_real:
+                d = Dummy(real=True)
+            else:
+                d = None
+            if d:
+                reps[x] = d
+        if reps:
+            undo = {v: k for k, v in reps.items()}
+            did = self.xreplace(reps).doit(**hints)
+            if isinstance(did, tuple):  # when separate=True
+                did = tuple([i.xreplace(undo) for i in did])
+            else:
+                did = did.xreplace(undo)
+            return did
+
+        # continue with existing assumptions
+        undone_limits = []
+        # ulj = free symbols of any undone limits' upper and lower limits
+        ulj = set()
+        for xab in self.limits:
+            # compute uli, the free symbols in the
+            # Upper and Lower limits of limit I
+            if len(xab) == 1:
+                uli = set(xab[:1])
+            elif len(xab) == 2:
+                uli = xab[1].free_symbols
+            elif len(xab) == 3:
+                uli = xab[1].free_symbols.union(xab[2].free_symbols)
+            # this integral can be done as long as there is no blocking
+            # limit that has been undone. An undone limit is blocking if
+            # it contains an integration variable that is in this limit's
+            # upper or lower free symbols or vice versa
+            if xab[0] in ulj or any(v[0] in uli for v in undone_limits):
+                undone_limits.append(xab)
+                ulj.update(uli)
+                function = self.func(*([function] + [xab]))
+                factored_function = function.factor()
+                if not isinstance(factored_function, Integral):
+                    function = factored_function
+                continue
+
+            if function.has(Abs, sign) and (
+                (len(xab) < 3 and all(x.is_extended_real for x in xab)) or
+                (len(xab) == 3 and all(x.is_extended_real and not x.is_infinite for
+                 x in xab[1:]))):
+                    # some improper integrals are better off with Abs
+                    xr = Dummy("xr", real=True)
+                    function = (function.xreplace({xab[0]: xr})
+                        .rewrite(Piecewise).xreplace({xr: xab[0]}))
+            elif function.has(Min, Max):
+                function = function.rewrite(Piecewise)
+            if (function.has(Piecewise) and
+                not isinstance(function, Piecewise)):
+                    function = piecewise_fold(function)
+            if isinstance(function, Piecewise):
+                if len(xab) == 1:
+                    antideriv = function._eval_integral(xab[0],
+                        **eval_kwargs)
+                else:
+                    antideriv = self._eval_integral(
+                        function, xab[0], **eval_kwargs)
+            else:
+                # There are a number of tradeoffs in using the
+                # Meijer G method. It can sometimes be a lot faster
+                # than other methods, and sometimes slower. And
+                # there are certain types of integrals for which it
+                # is more likely to work than others. These
+                # heuristics are incorporated in deciding what
+                # integration methods to try, in what order. See the
+                # integrate() docstring for details.
+                def try_meijerg(function, xab):
+                    ret = None
+                    if len(xab) == 3 and meijerg is not False:
+                        x, a, b = xab
+                        try:
+                            res = meijerint_definite(function, x, a, b)
+                        except NotImplementedError:
+                            _debug('NotImplementedError '
+                                'from meijerint_definite')
+                            res = None
+                        if res is not None:
+                            f, cond = res
+                            if conds == 'piecewise':
+                                u = self.func(function, (x, a, b))
+                                # if Piecewise modifies cond too
+                                # much it may not be recognized by
+                                # _condsimp pattern matching so just
+                                # turn off all evaluation
+                                return Piecewise((f, cond), (u, True),
+                                    evaluate=False)
+                            elif conds == 'separate':
+                                if len(self.limits) != 1:
+                                    raise ValueError(filldedent('''
+                                        conds=separate not supported in
+                                        multiple integrals'''))
+                                ret = f, cond
+                            else:
+                                ret = f
+                    return ret
+
+                meijerg1 = meijerg
+                if (meijerg is not False and
+                        len(xab) == 3 and xab[1].is_extended_real and xab[2].is_extended_real
+                        and not function.is_Poly and
+                        (xab[1].has(oo, -oo) or xab[2].has(oo, -oo))):
+                    ret = try_meijerg(function, xab)
+                    if ret is not None:
+                        function = ret
+                        continue
+                    meijerg1 = False
+                # If the special meijerg code did not succeed in
+                # finding a definite integral, then the code using
+                # meijerint_indefinite will not either (it might
+                # find an antiderivative, but the answer is likely
+                # to be nonsensical). Thus if we are requested to
+                # only use Meijer G-function methods, we give up at
+                # this stage. Otherwise we just disable G-function
+                # methods.
+                if meijerg1 is False and meijerg is True:
+                    antideriv = None
+                else:
+                    antideriv = self._eval_integral(
+                        function, xab[0], **eval_kwargs)
+                    if antideriv is None and meijerg is True:
+                        ret = try_meijerg(function, xab)
+                        if ret is not None:
+                            function = ret
+                            continue
+
+            final = hints.get('final', True)
+            # dotit may be iterated but floor terms making atan and acot
+            # continuous should only be added in the final round
+            if (final and not isinstance(antideriv, Integral) and
+                antideriv is not None):
+                for atan_term in antideriv.atoms(atan):
+                    atan_arg = atan_term.args[0]
+                    # Checking `atan_arg` to be linear combination of `tan` or `cot`
+                    for tan_part in atan_arg.atoms(tan):
+                        x1 = Dummy('x1')
+                        tan_exp1 = atan_arg.subs(tan_part, x1)
+                        # The coefficient of `tan` should be constant
+                        coeff = tan_exp1.diff(x1)
+                        if x1 not in coeff.free_symbols:
+                            a = tan_part.args[0]
+                            antideriv = antideriv.subs(atan_term, Add(atan_term,
+                                sign(coeff)*pi*floor((a-pi/2)/pi)))
+                    for cot_part in atan_arg.atoms(cot):
+                        x1 = Dummy('x1')
+                        cot_exp1 = atan_arg.subs(cot_part, x1)
+                        # The coefficient of `cot` should be constant
+                        coeff = cot_exp1.diff(x1)
+                        if x1 not in coeff.free_symbols:
+                            a = cot_part.args[0]
+                            antideriv = antideriv.subs(atan_term, Add(atan_term,
+                                sign(coeff)*pi*floor((a)/pi)))
+
+            if antideriv is None:
+                undone_limits.append(xab)
+                function = self.func(*([function] + [xab])).factor()
+                factored_function = function.factor()
+                if not isinstance(factored_function, Integral):
+                    function = factored_function
+                continue
+            else:
+                if len(xab) == 1:
+                    function = antideriv
+                else:
+                    if len(xab) == 3:
+                        x, a, b = xab
+                    elif len(xab) == 2:
+                        x, b = xab
+                        a = None
+                    else:
+                        raise NotImplementedError
+
+                    if deep:
+                        if isinstance(a, Basic):
+                            a = a.doit(**hints)
+                        if isinstance(b, Basic):
+                            b = b.doit(**hints)
+
+                    if antideriv.is_Poly:
+                        gens = list(antideriv.gens)
+                        gens.remove(x)
+
+                        antideriv = antideriv.as_expr()
+
+                        function = antideriv._eval_interval(x, a, b)
+                        function = Poly(function, *gens)
+                    else:
+                        def is_indef_int(g, x):
+                            return (isinstance(g, Integral) and
+                                    any(i == (x,) for i in g.limits))
+
+                        def eval_factored(f, x, a, b):
+                            # _eval_interval for integrals with
+                            # (constant) factors
+                            # a single indefinite integral is assumed
+                            args = []
+                            for g in Mul.make_args(f):
+                                if is_indef_int(g, x):
+                                    args.append(g._eval_interval(x, a, b))
+                                else:
+                                    args.append(g)
+                            return Mul(*args)
+
+                        integrals, others, piecewises = [], [], []
+                        for f in Add.make_args(antideriv):
+                            if any(is_indef_int(g, x)
+                                   for g in Mul.make_args(f)):
+                                integrals.append(f)
+                            elif any(isinstance(g, Piecewise)
+                                     for g in Mul.make_args(f)):
+                                piecewises.append(piecewise_fold(f))
+                            else:
+                                others.append(f)
+                        uneval = Add(*[eval_factored(f, x, a, b)
+                                       for f in integrals])
+                        try:
+                            evalued = Add(*others)._eval_interval(x, a, b)
+                            evalued_pw = piecewise_fold(Add(*piecewises))._eval_interval(x, a, b)
+                            function = uneval + evalued + evalued_pw
+                        except NotImplementedError:
+                            # This can happen if _eval_interval depends in a
+                            # complicated way on limits that cannot be computed
+                            undone_limits.append(xab)
+                            function = self.func(*([function] + [xab]))
+                            factored_function = function.factor()
+                            if not isinstance(factored_function, Integral):
+                                function = factored_function
+        return function
+
+    def _eval_derivative(self, sym):
+        """Evaluate the derivative of the current Integral object by
+        differentiating under the integral sign [1], using the Fundamental
+        Theorem of Calculus [2] when possible.
+
+        Explanation
+        ===========
+
+        Whenever an Integral is encountered that is equivalent to zero or
+        has an integrand that is independent of the variable of integration
+        those integrals are performed. All others are returned as Integral
+        instances which can be resolved with doit() (provided they are integrable).
+
+        References
+        ==========
+
+        .. [1] https://en.wikipedia.org/wiki/Differentiation_under_the_integral_sign
+        .. [2] https://en.wikipedia.org/wiki/Fundamental_theorem_of_calculus
+
+        Examples
+        ========
+
+        >>> from sympy import Integral
+        >>> from sympy.abc import x, y
+        >>> i = Integral(x + y, y, (y, 1, x))
+        >>> i.diff(x)
+        Integral(x + y, (y, x)) + Integral(1, y, (y, 1, x))
+        >>> i.doit().diff(x) == i.diff(x).doit()
+        True
+        >>> i.diff(y)
+        0
+
+        The previous must be true since there is no y in the evaluated integral:
+
+        >>> i.free_symbols
+        {x}
+        >>> i.doit()
+        2*x**3/3 - x/2 - 1/6
+
+        """
+
+        # differentiate under the integral sign; we do not
+        # check for regularity conditions (TODO), see issue 4215
+
+        # get limits and the function
+        f, limits = self.function, list(self.limits)
+
+        # the order matters if variables of integration appear in the limits
+        # so work our way in from the outside to the inside.
+        limit = limits.pop(-1)
+        if len(limit) == 3:
+            x, a, b = limit
+        elif len(limit) == 2:
+            x, b = limit
+            a = None
+        else:
+            a = b = None
+            x = limit[0]
+
+        if limits:  # f is the argument to an integral
+            f = self.func(f, *tuple(limits))
+
+        # assemble the pieces
+        def _do(f, ab):
+            dab_dsym = diff(ab, sym)
+            if not dab_dsym:
+                return S.Zero
+            if isinstance(f, Integral):
+                limits = [(x, x) if (len(l) == 1 and l[0] == x) else l
+                          for l in f.limits]
+                f = self.func(f.function, *limits)
+            return f.subs(x, ab)*dab_dsym
+
+        rv = S.Zero
+        if b is not None:
+            rv += _do(f, b)
+        if a is not None:
+            rv -= _do(f, a)
+        if len(limit) == 1 and sym == x:
+            # the dummy variable *is* also the real-world variable
+            arg = f
+            rv += arg
+        else:
+            # the dummy variable might match sym but it's
+            # only a dummy and the actual variable is determined
+            # by the limits, so mask off the variable of integration
+            # while differentiating
+            u = Dummy('u')
+            arg = f.subs(x, u).diff(sym).subs(u, x)
+            if arg:
+                rv += self.func(arg, (x, a, b))
+        return rv
+
+    def _eval_integral(self, f, x, meijerg=None, risch=None, manual=None,
+                       heurisch=None, conds='piecewise',final=None):
+        """
+        Calculate the anti-derivative to the function f(x).
+
+        Explanation
+        ===========
+
+        The following algorithms are applied (roughly in this order):
+
+        1. Simple heuristics (based on pattern matching and integral table):
+
+           - most frequently used functions (e.g. polynomials, products of
+             trig functions)
+
+        2. Integration of rational functions:
+
+           - A complete algorithm for integrating rational functions is
+             implemented (the Lazard-Rioboo-Trager algorithm).  The algorithm
+             also uses the partial fraction decomposition algorithm
+             implemented in apart() as a preprocessor to make this process
+             faster.  Note that the integral of a rational function is always
+             elementary, but in general, it may include a RootSum.
+
+        3. Full Risch algorithm:
+
+           - The Risch algorithm is a complete decision
+             procedure for integrating elementary functions, which means that
+             given any elementary function, it will either compute an
+             elementary antiderivative, or else prove that none exists.
+             Currently, part of transcendental case is implemented, meaning
+             elementary integrals containing exponentials, logarithms, and
+             (soon!) trigonometric functions can be computed.  The algebraic
+             case, e.g., functions containing roots, is much more difficult
+             and is not implemented yet.
+
+           - If the routine fails (because the integrand is not elementary, or
+             because a case is not implemented yet), it continues on to the
+             next algorithms below.  If the routine proves that the integrals
+             is nonelementary, it still moves on to the algorithms below,
+             because we might be able to find a closed-form solution in terms
+             of special functions.  If risch=True, however, it will stop here.
+
+        4. The Meijer G-Function algorithm:
+
+           - This algorithm works by first rewriting the integrand in terms of
+             very general Meijer G-Function (meijerg in SymPy), integrating
+             it, and then rewriting the result back, if possible.  This
+             algorithm is particularly powerful for definite integrals (which
+             is actually part of a different method of Integral), since it can
+             compute closed-form solutions of definite integrals even when no
+             closed-form indefinite integral exists.  But it also is capable
+             of computing many indefinite integrals as well.
+
+           - Another advantage of this method is that it can use some results
+             about the Meijer G-Function to give a result in terms of a
+             Piecewise expression, which allows to express conditionally
+             convergent integrals.
+
+           - Setting meijerg=True will cause integrate() to use only this
+             method.
+
+        5. The "manual integration" algorithm:
+
+           - This algorithm tries to mimic how a person would find an
+             antiderivative by hand, for example by looking for a
+             substitution or applying integration by parts. This algorithm
+             does not handle as many integrands but can return results in a
+             more familiar form.
+
+           - Sometimes this algorithm can evaluate parts of an integral; in
+             this case integrate() will try to evaluate the rest of the
+             integrand using the other methods here.
+
+           - Setting manual=True will cause integrate() to use only this
+             method.
+
+        6. The Heuristic Risch algorithm:
+
+           - This is a heuristic version of the Risch algorithm, meaning that
+             it is not deterministic.  This is tried as a last resort because
+             it can be very slow.  It is still used because not enough of the
+             full Risch algorithm is implemented, so that there are still some
+             integrals that can only be computed using this method.  The goal
+             is to implement enough of the Risch and Meijer G-function methods
+             so that this can be deleted.
+
+             Setting heurisch=True will cause integrate() to use only this
+             method. Set heurisch=False to not use it.
+
+        """
+
+        from sympy.integrals.risch import risch_integrate, NonElementaryIntegral
+        from sympy.integrals.manualintegrate import manualintegrate
+
+        if risch:
+            try:
+                return risch_integrate(f, x, conds=conds)
+            except NotImplementedError:
+                return None
+
+        if manual:
+            try:
+                result = manualintegrate(f, x)
+                if result is not None and result.func != Integral:
+                    return result
+            except (ValueError, PolynomialError):
+                pass
+
+        eval_kwargs = {"meijerg": meijerg, "risch": risch, "manual": manual,
+            "heurisch": heurisch, "conds": conds}
+
+        # if it is a poly(x) then let the polynomial integrate itself (fast)
+        #
+        # It is important to make this check first, otherwise the other code
+        # will return a SymPy expression instead of a Polynomial.
+        #
+        # see Polynomial for details.
+        if isinstance(f, Poly) and not (manual or meijerg or risch):
+            # Note: this is deprecated, but the deprecation warning is already
+            # issued in the Integral constructor.
+            return f.integrate(x)
+
+        # Piecewise antiderivatives need to call special integrate.
+        if isinstance(f, Piecewise):
+            return f.piecewise_integrate(x, **eval_kwargs)
+
+        # let's cut it short if `f` does not depend on `x`; if
+        # x is only a dummy, that will be handled below
+        if not f.has(x):
+            return f*x
+
+        # try to convert to poly(x) and then integrate if successful (fast)
+        poly = f.as_poly(x)
+        if poly is not None and not (manual or meijerg or risch):
+            return poly.integrate().as_expr()
+
+        if risch is not False:
+            try:
+                result, i = risch_integrate(f, x, separate_integral=True,
+                    conds=conds)
+            except NotImplementedError:
+                pass
+            else:
+                if i:
+                    # There was a nonelementary integral. Try integrating it.
+
+                    # if no part of the NonElementaryIntegral is integrated by
+                    # the Risch algorithm, then use the original function to
+                    # integrate, instead of re-written one
+                    if result == 0:
+                        return NonElementaryIntegral(f, x).doit(risch=False)
+                    else:
+                        return result + i.doit(risch=False)
+                else:
+                    return result
+
+        # since Integral(f=g1+g2+...) == Integral(g1) + Integral(g2) + ...
+        # we are going to handle Add terms separately,
+        # if `f` is not Add -- we only have one term
+
+        # Note that in general, this is a bad idea, because Integral(g1) +
+        # Integral(g2) might not be computable, even if Integral(g1 + g2) is.
+        # For example, Integral(x**x + x**x*log(x)).  But many heuristics only
+        # work term-wise.  So we compute this step last, after trying
+        # risch_integrate.  We also try risch_integrate again in this loop,
+        # because maybe the integral is a sum of an elementary part and a
+        # nonelementary part (like erf(x) + exp(x)).  risch_integrate() is
+        # quite fast, so this is acceptable.
+        from sympy.simplify.fu import sincos_to_sum
+        parts = []
+        args = Add.make_args(f)
+        for g in args:
+            coeff, g = g.as_independent(x)
+
+            # g(x) = const
+            if g is S.One and not meijerg:
+                parts.append(coeff*x)
+                continue
+
+            # g(x) = expr + O(x**n)
+            order_term = g.getO()
+
+            if order_term is not None:
+                h = self._eval_integral(g.removeO(), x, **eval_kwargs)
+
+                if h is not None:
+                    h_order_expr = self._eval_integral(order_term.expr, x, **eval_kwargs)
+
+                    if h_order_expr is not None:
+                        h_order_term = order_term.func(
+                            h_order_expr, *order_term.variables)
+                        parts.append(coeff*(h + h_order_term))
+                        continue
+
+                # NOTE: if there is O(x**n) and we fail to integrate then
+                # there is no point in trying other methods because they
+                # will fail, too.
+                return None
+
+            #               c
+            # g(x) = (a*x+b)
+            if g.is_Pow and not g.exp.has(x) and not meijerg:
+                a = Wild('a', exclude=[x])
+                b = Wild('b', exclude=[x])
+
+                M = g.base.match(a*x + b)
+
+                if M is not None:
+                    if g.exp == -1:
+                        h = log(g.base)
+                    elif conds != 'piecewise':
+                        h = g.base**(g.exp + 1) / (g.exp + 1)
+                    else:
+                        h1 = log(g.base)
+                        h2 = g.base**(g.exp + 1) / (g.exp + 1)
+                        h = Piecewise((h2, Ne(g.exp, -1)), (h1, True))
+
+                    parts.append(coeff * h / M[a])
+                    continue
+
+            #        poly(x)
+            # g(x) = -------
+            #        poly(x)
+            if g.is_rational_function(x) and not (manual or meijerg or risch):
+                parts.append(coeff * ratint(g, x))
+                continue
+
+            if not (manual or meijerg or risch):
+                # g(x) = Mul(trig)
+                h = trigintegrate(g, x, conds=conds)
+                if h is not None:
+                    parts.append(coeff * h)
+                    continue
+
+                # g(x) has at least a DiracDelta term
+                h = deltaintegrate(g, x)
+                if h is not None:
+                    parts.append(coeff * h)
+                    continue
+
+                from .singularityfunctions import singularityintegrate
+                # g(x) has at least a Singularity Function term
+                h = singularityintegrate(g, x)
+                if h is not None:
+                    parts.append(coeff * h)
+                    continue
+
+                # Try risch again.
+                if risch is not False:
+                    try:
+                        h, i = risch_integrate(g, x,
+                            separate_integral=True, conds=conds)
+                    except NotImplementedError:
+                        h = None
+                    else:
+                        if i:
+                            h = h + i.doit(risch=False)
+
+                        parts.append(coeff*h)
+                        continue
+
+                # fall back to heurisch
+                if heurisch is not False:
+                    from sympy.integrals.heurisch import (heurisch as heurisch_,
+                                                          heurisch_wrapper)
+                    try:
+                        if conds == 'piecewise':
+                            h = heurisch_wrapper(g, x, hints=[])
+                        else:
+                            h = heurisch_(g, x, hints=[])
+                    except PolynomialError:
+                        # XXX: this exception means there is a bug in the
+                        # implementation of heuristic Risch integration
+                        # algorithm.
+                        h = None
+            else:
+                h = None
+
+            if meijerg is not False and h is None:
+                # rewrite using G functions
+                try:
+                    h = meijerint_indefinite(g, x)
+                except NotImplementedError:
+                    _debug('NotImplementedError from meijerint_definite')
+                if h is not None:
+                    parts.append(coeff * h)
+                    continue
+
+            if h is None and manual is not False:
+                try:
+                    result = manualintegrate(g, x)
+                    if result is not None and not isinstance(result, Integral):
+                        if result.has(Integral) and not manual:
+                            # Try to have other algorithms do the integrals
+                            # manualintegrate can't handle,
+                            # unless we were asked to use manual only.
+                            # Keep the rest of eval_kwargs in case another
+                            # method was set to False already
+                            new_eval_kwargs = eval_kwargs
+                            new_eval_kwargs["manual"] = False
+                            new_eval_kwargs["final"] = False
+                            result = result.func(*[
+                                arg.doit(**new_eval_kwargs) if
+                                arg.has(Integral) else arg
+                                for arg in result.args
+                            ]).expand(multinomial=False,
+                                      log=False,
+                                      power_exp=False,
+                                      power_base=False)
+                        if not result.has(Integral):
+                            parts.append(coeff * result)
+                            continue
+                except (ValueError, PolynomialError):
+                    # can't handle some SymPy expressions
+                    pass
+
+            # if we failed maybe it was because we had
+            # a product that could have been expanded,
+            # so let's try an expansion of the whole
+            # thing before giving up; we don't try this
+            # at the outset because there are things
+            # that cannot be solved unless they are
+            # NOT expanded e.g., x**x*(1+log(x)). There
+            # should probably be a checker somewhere in this
+            # routine to look for such cases and try to do
+            # collection on the expressions if they are already
+            # in an expanded form
+            if not h and len(args) == 1:
+                f = sincos_to_sum(f).expand(mul=True, deep=False)
+                if f.is_Add:
+                    # Note: risch will be identical on the expanded
+                    # expression, but maybe it will be able to pick out parts,
+                    # like x*(exp(x) + erf(x)).
+                    return self._eval_integral(f, x, **eval_kwargs)
+
+            if h is not None:
+                parts.append(coeff * h)
+            else:
+                return None
+
+        return Add(*parts)
+
+    def _eval_lseries(self, x, logx=None, cdir=0):
+        expr = self.as_dummy()
+        symb = x
+        for l in expr.limits:
+            if x in l[1:]:
+                symb = l[0]
+                break
+        for term in expr.function.lseries(symb, logx):
+            yield integrate(term, *expr.limits)
+
+    def _eval_nseries(self, x, n, logx=None, cdir=0):
+        expr = self.as_dummy()
+        symb = x
+        for l in expr.limits:
+            if x in l[1:]:
+                symb = l[0]
+                break
+        terms, order = expr.function.nseries(
+            x=symb, n=n, logx=logx).as_coeff_add(Order)
+        order = [o.subs(symb, x) for o in order]
+        return integrate(terms, *expr.limits) + Add(*order)*x
+
+    def _eval_as_leading_term(self, x, logx=None, cdir=0):
+        series_gen = self.args[0].lseries(x)
+        for leading_term in series_gen:
+            if leading_term != 0:
+                break
+        return integrate(leading_term, *self.args[1:])
+
+    def _eval_simplify(self, **kwargs):
+        expr = factor_terms(self)
+        if isinstance(expr, Integral):
+            from sympy.simplify.simplify import simplify
+            return expr.func(*[simplify(i, **kwargs) for i in expr.args])
+        return expr.simplify(**kwargs)
+
+    def as_sum(self, n=None, method="midpoint", evaluate=True):
+        """
+        Approximates a definite integral by a sum.
+
+        Parameters
+        ==========
+
+        n :
+            The number of subintervals to use, optional.
+        method :
+            One of: 'left', 'right', 'midpoint', 'trapezoid'.
+        evaluate : bool
+            If False, returns an unevaluated Sum expression. The default
+            is True, evaluate the sum.
+
+        Notes
+        =====
+
+        These methods of approximate integration are described in [1].
+
+        Examples
+        ========
+
+        >>> from sympy import Integral, sin, sqrt
+        >>> from sympy.abc import x, n
+        >>> e = Integral(sin(x), (x, 3, 7))
+        >>> e
+        Integral(sin(x), (x, 3, 7))
+
+        For demonstration purposes, this interval will only be split into 2
+        regions, bounded by [3, 5] and [5, 7].
+
+        The left-hand rule uses function evaluations at the left of each
+        interval:
+
+        >>> e.as_sum(2, 'left')
+        2*sin(5) + 2*sin(3)
+
+        The midpoint rule uses evaluations at the center of each interval:
+
+        >>> e.as_sum(2, 'midpoint')
+        2*sin(4) + 2*sin(6)
+
+        The right-hand rule uses function evaluations at the right of each
+        interval:
+
+        >>> e.as_sum(2, 'right')
+        2*sin(5) + 2*sin(7)
+
+        The trapezoid rule uses function evaluations on both sides of the
+        intervals. This is equivalent to taking the average of the left and
+        right hand rule results:
+
+        >>> e.as_sum(2, 'trapezoid')
+        2*sin(5) + sin(3) + sin(7)
+        >>> (e.as_sum(2, 'left') + e.as_sum(2, 'right'))/2 == _
+        True
+
+        Here, the discontinuity at x = 0 can be avoided by using the
+        midpoint or right-hand method:
+
+        >>> e = Integral(1/sqrt(x), (x, 0, 1))
+        >>> e.as_sum(5).n(4)
+        1.730
+        >>> e.as_sum(10).n(4)
+        1.809
+        >>> e.doit().n(4)  # the actual value is 2
+        2.000
+
+        The left- or trapezoid method will encounter the discontinuity and
+        return infinity:
+
+        >>> e.as_sum(5, 'left')
+        zoo
+
+        The number of intervals can be symbolic. If omitted, a dummy symbol
+        will be used for it.
+
+        >>> e = Integral(x**2, (x, 0, 2))
+        >>> e.as_sum(n, 'right').expand()
+        8/3 + 4/n + 4/(3*n**2)
+
+        This shows that the midpoint rule is more accurate, as its error
+        term decays as the square of n:
+
+        >>> e.as_sum(method='midpoint').expand()
+        8/3 - 2/(3*_n**2)
+
+        A symbolic sum is returned with evaluate=False:
+
+        >>> e.as_sum(n, 'midpoint', evaluate=False)
+        2*Sum((2*_k/n - 1/n)**2, (_k, 1, n))/n
+
+        See Also
+        ========
+
+        Integral.doit : Perform the integration using any hints
+
+        References
+        ==========
+
+        .. [1] https://en.wikipedia.org/wiki/Riemann_sum#Riemann_summation_methods
+        """
+
+        from sympy.concrete.summations import Sum
+        limits = self.limits
+        if len(limits) > 1:
+            raise NotImplementedError(
+                "Multidimensional midpoint rule not implemented yet")
+        else:
+            limit = limits[0]
+            if (len(limit) != 3 or limit[1].is_finite is False or
+                limit[2].is_finite is False):
+                raise ValueError("Expecting a definite integral over "
+                                  "a finite interval.")
+        if n is None:
+            n = Dummy('n', integer=True, positive=True)
+        else:
+            n = sympify(n)
+        if (n.is_positive is False or n.is_integer is False or
+            n.is_finite is False):
+            raise ValueError("n must be a positive integer, got %s" % n)
+        x, a, b = limit
+        dx = (b - a)/n
+        k = Dummy('k', integer=True, positive=True)
+        f = self.function
+
+        if method == "left":
+            result = dx*Sum(f.subs(x, a + (k-1)*dx), (k, 1, n))
+        elif method == "right":
+            result = dx*Sum(f.subs(x, a + k*dx), (k, 1, n))
+        elif method == "midpoint":
+            result = dx*Sum(f.subs(x, a + k*dx - dx/2), (k, 1, n))
+        elif method == "trapezoid":
+            result = dx*((f.subs(x, a) + f.subs(x, b))/2 +
+                Sum(f.subs(x, a + k*dx), (k, 1, n - 1)))
+        else:
+            raise ValueError("Unknown method %s" % method)
+        return result.doit() if evaluate else result
+
+    def principal_value(self, **kwargs):
+        """
+        Compute the Cauchy Principal Value of the definite integral of a real function in the given interval
+        on the real axis.
+
+        Explanation
+        ===========
+
+        In mathematics, the Cauchy principal value, is a method for assigning values to certain improper
+        integrals which would otherwise be undefined.
+
+        Examples
+        ========
+
+        >>> from sympy import Integral, oo
+        >>> from sympy.abc import x
+        >>> Integral(x+1, (x, -oo, oo)).principal_value()
+        oo
+        >>> f = 1 / (x**3)
+        >>> Integral(f, (x, -oo, oo)).principal_value()
+        0
+        >>> Integral(f, (x, -10, 10)).principal_value()
+        0
+        >>> Integral(f, (x, -10, oo)).principal_value() + Integral(f, (x, -oo, 10)).principal_value()
+        0
+
+        References
+        ==========
+
+        .. [1] https://en.wikipedia.org/wiki/Cauchy_principal_value
+        .. [2] https://mathworld.wolfram.com/CauchyPrincipalValue.html
+        """
+        if len(self.limits) != 1 or len(list(self.limits[0])) != 3:
+            raise ValueError("You need to insert a variable, lower_limit, and upper_limit correctly to calculate "
+                             "cauchy's principal value")
+        x, a, b = self.limits[0]
+        if not (a.is_comparable and b.is_comparable and a <= b):
+            raise ValueError("The lower_limit must be smaller than or equal to the upper_limit to calculate "
+                             "cauchy's principal value. Also, a and b need to be comparable.")
+        if a == b:
+            return S.Zero
+
+        from sympy.calculus.singularities import singularities
+
+        r = Dummy('r')
+        f = self.function
+        singularities_list = [s for s in singularities(f, x) if s.is_comparable and a <= s <= b]
+        for i in singularities_list:
+            if i in (a, b):
+                raise ValueError(
+                    'The principal value is not defined in the given interval due to singularity at %d.' % (i))
+        F = integrate(f, x, **kwargs)
+        if F.has(Integral):
+            return self
+        if a is -oo and b is oo:
+            I = limit(F - F.subs(x, -x), x, oo)
+        else:
+            I = limit(F, x, b, '-') - limit(F, x, a, '+')
+        for s in singularities_list:
+            I += limit(((F.subs(x, s - r)) - F.subs(x, s + r)), r, 0, '+')
+        return I
+
+
+
+def integrate(*args, meijerg=None, conds='piecewise', risch=None, heurisch=None, manual=None, **kwargs):
+    """integrate(f, var, ...)
+
+    .. deprecated:: 1.6
+
+       Using ``integrate()`` with :class:`~.Poly` is deprecated. Use
+       :meth:`.Poly.integrate` instead. See :ref:`deprecated-integrate-poly`.
+
+    Explanation
+    ===========
+
+    Compute definite or indefinite integral of one or more variables
+    using Risch-Norman algorithm and table lookup. This procedure is
+    able to handle elementary algebraic and transcendental functions
+    and also a huge class of special functions, including Airy,
+    Bessel, Whittaker and Lambert.
+
+    var can be:
+
+    - a symbol                   -- indefinite integration
+    - a tuple (symbol, a)        -- indefinite integration with result
+                                    given with ``a`` replacing ``symbol``
+    - a tuple (symbol, a, b)     -- definite integration
+
+    Several variables can be specified, in which case the result is
+    multiple integration. (If var is omitted and the integrand is
+    univariate, the indefinite integral in that variable will be performed.)
+
+    Indefinite integrals are returned without terms that are independent
+    of the integration variables. (see examples)
+
+    Definite improper integrals often entail delicate convergence
+    conditions. Pass conds='piecewise', 'separate' or 'none' to have
+    these returned, respectively, as a Piecewise function, as a separate
+    result (i.e. result will be a tuple), or not at all (default is
+    'piecewise').
+
+    **Strategy**
+
+    SymPy uses various approaches to definite integration. One method is to
+    find an antiderivative for the integrand, and then use the fundamental
+    theorem of calculus. Various functions are implemented to integrate
+    polynomial, rational and trigonometric functions, and integrands
+    containing DiracDelta terms.
+
+    SymPy also implements the part of the Risch algorithm, which is a decision
+    procedure for integrating elementary functions, i.e., the algorithm can
+    either find an elementary antiderivative, or prove that one does not
+    exist.  There is also a (very successful, albeit somewhat slow) general
+    implementation of the heuristic Risch algorithm.  This algorithm will
+    eventually be phased out as more of the full Risch algorithm is
+    implemented. See the docstring of Integral._eval_integral() for more
+    details on computing the antiderivative using algebraic methods.
+
+    The option risch=True can be used to use only the (full) Risch algorithm.
+    This is useful if you want to know if an elementary function has an
+    elementary antiderivative.  If the indefinite Integral returned by this
+    function is an instance of NonElementaryIntegral, that means that the
+    Risch algorithm has proven that integral to be non-elementary.  Note that
+    by default, additional methods (such as the Meijer G method outlined
+    below) are tried on these integrals, as they may be expressible in terms
+    of special functions, so if you only care about elementary answers, use
+    risch=True.  Also note that an unevaluated Integral returned by this
+    function is not necessarily a NonElementaryIntegral, even with risch=True,
+    as it may just be an indication that the particular part of the Risch
+    algorithm needed to integrate that function is not yet implemented.
+
+    Another family of strategies comes from re-writing the integrand in
+    terms of so-called Meijer G-functions. Indefinite integrals of a
+    single G-function can always be computed, and the definite integral
+    of a product of two G-functions can be computed from zero to
+    infinity. Various strategies are implemented to rewrite integrands
+    as G-functions, and use this information to compute integrals (see
+    the ``meijerint`` module).
+
+    The option manual=True can be used to use only an algorithm that tries
+    to mimic integration by hand. This algorithm does not handle as many
+    integrands as the other algorithms implemented but may return results in
+    a more familiar form. The ``manualintegrate`` module has functions that
+    return the steps used (see the module docstring for more information).
+
+    In general, the algebraic methods work best for computing
+    antiderivatives of (possibly complicated) combinations of elementary
+    functions. The G-function methods work best for computing definite
+    integrals from zero to infinity of moderately complicated
+    combinations of special functions, or indefinite integrals of very
+    simple combinations of special functions.
+
+    The strategy employed by the integration code is as follows:
+
+    - If computing a definite integral, and both limits are real,
+      and at least one limit is +- oo, try the G-function method of
+      definite integration first.
+
+    - Try to find an antiderivative, using all available methods, ordered
+      by performance (that is try fastest method first, slowest last; in
+      particular polynomial integration is tried first, Meijer
+      G-functions second to last, and heuristic Risch last).
+
+    - If still not successful, try G-functions irrespective of the
+      limits.
+
+    The option meijerg=True, False, None can be used to, respectively:
+    always use G-function methods and no others, never use G-function
+    methods, or use all available methods (in order as described above).
+    It defaults to None.
+
+    Examples
+    ========
+
+    >>> from sympy import integrate, log, exp, oo
+    >>> from sympy.abc import a, x, y
+
+    >>> integrate(x*y, x)
+    x**2*y/2
+
+    >>> integrate(log(x), x)
+    x*log(x) - x
+
+    >>> integrate(log(x), (x, 1, a))
+    a*log(a) - a + 1
+
+    >>> integrate(x)
+    x**2/2
+
+    Terms that are independent of x are dropped by indefinite integration:
+
+    >>> from sympy import sqrt
+    >>> integrate(sqrt(1 + x), (x, 0, x))
+    2*(x + 1)**(3/2)/3 - 2/3
+    >>> integrate(sqrt(1 + x), x)
+    2*(x + 1)**(3/2)/3
+
+    >>> integrate(x*y)
+    Traceback (most recent call last):
+    ...
+    ValueError: specify integration variables to integrate x*y
+
+    Note that ``integrate(x)`` syntax is meant only for convenience
+    in interactive sessions and should be avoided in library code.
+
+    >>> integrate(x**a*exp(-x), (x, 0, oo)) # same as conds='piecewise'
+    Piecewise((gamma(a + 1), re(a) > -1),
+        (Integral(x**a*exp(-x), (x, 0, oo)), True))
+
+    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='none')
+    gamma(a + 1)
+
+    >>> integrate(x**a*exp(-x), (x, 0, oo), conds='separate')
+    (gamma(a + 1), re(a) > -1)
+
+    See Also
+    ========
+
+    Integral, Integral.doit
+
+    """
+    doit_flags = {
+        'deep': False,
+        'meijerg': meijerg,
+        'conds': conds,
+        'risch': risch,
+        'heurisch': heurisch,
+        'manual': manual
+        }
+    integral = Integral(*args, **kwargs)
+
+    if isinstance(integral, Integral):
+        return integral.doit(**doit_flags)
+    else:
+        new_args = [a.doit(**doit_flags) if isinstance(a, Integral) else a
+            for a in integral.args]
+        return integral.func(*new_args)
+
+
+def line_integrate(field, curve, vars):
+    """line_integrate(field, Curve, variables)
+
+    Compute the line integral.
+
+    Examples
+    ========
+
+    >>> from sympy import Curve, line_integrate, E, ln
+    >>> from sympy.abc import x, y, t
+    >>> C = Curve([E**t + 1, E**t - 1], (t, 0, ln(2)))
+    >>> line_integrate(x + y, C, [x, y])
+    3*sqrt(2)
+
+    See Also
+    ========
+
+    sympy.integrals.integrals.integrate, Integral
+    """
+    from sympy.geometry import Curve
+    F = sympify(field)
+    if not F:
+        raise ValueError(
+            "Expecting function specifying field as first argument.")
+    if not isinstance(curve, Curve):
+        raise ValueError("Expecting Curve entity as second argument.")
+    if not is_sequence(vars):
+        raise ValueError("Expecting ordered iterable for variables.")
+    if len(curve.functions) != len(vars):
+        raise ValueError("Field variable size does not match curve dimension.")
+
+    if curve.parameter in vars:
+        raise ValueError("Curve parameter clashes with field parameters.")
+
+    # Calculate derivatives for line parameter functions
+    # F(r) -> F(r(t)) and finally F(r(t)*r'(t))
+    Ft = F
+    dldt = 0
+    for i, var in enumerate(vars):
+        _f = curve.functions[i]
+        _dn = diff(_f, curve.parameter)
+        # ...arc length
+        dldt = dldt + (_dn * _dn)
+        Ft = Ft.subs(var, _f)
+    Ft = Ft * sqrt(dldt)
+
+    integral = Integral(Ft, curve.limits).doit(deep=False)
+    return integral
+
+
+### Property function dispatching ###
+
+@shape.register(Integral)
+def _(expr):
+    return shape(expr.function)
+
+# Delayed imports
+from .deltafunctions import deltaintegrate
+from .meijerint import meijerint_definite, meijerint_indefinite, _debug
+from .trigonometry import trigintegrate

venv/lib/python3.10/site-packages/sympy/integrals/intpoly.py

@@ -0,0 +1,1302 @@
+"""
+Module to implement integration of uni/bivariate polynomials over
+2D Polytopes and uni/bi/trivariate polynomials over 3D Polytopes.
+
+Uses evaluation techniques as described in Chin et al. (2015) [1].
+
+
+References
+===========
+
+.. [1] Chin, Eric B., Jean B. Lasserre, and N. Sukumar. "Numerical integration
+of homogeneous functions on convex and nonconvex polygons and polyhedra."
+Computational Mechanics 56.6 (2015): 967-981
+
+PDF link : http://dilbert.engr.ucdavis.edu/~suku/quadrature/cls-integration.pdf
+"""
+
+from functools import cmp_to_key
+
+from sympy.abc import x, y, z
+from sympy.core import S, diff, Expr, Symbol
+from sympy.core.sympify import _sympify
+from sympy.geometry import Segment2D, Polygon, Point, Point2D
+from sympy.polys.polytools import LC, gcd_list, degree_list, Poly
+from sympy.simplify.simplify import nsimplify
+
+
+def polytope_integrate(poly, expr=None, *, clockwise=False, max_degree=None):
+    """Integrates polynomials over 2/3-Polytopes.
+
+    Explanation
+    ===========
+
+    This function accepts the polytope in ``poly`` and the function in ``expr``
+    (uni/bi/trivariate polynomials are implemented) and returns
+    the exact integral of ``expr`` over ``poly``.
+
+    Parameters
+    ==========
+
+    poly : The input Polygon.
+
+    expr : The input polynomial.
+
+    clockwise : Binary value to sort input points of 2-Polytope clockwise.(Optional)
+
+    max_degree : The maximum degree of any monomial of the input polynomial.(Optional)
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy import Point, Polygon
+    >>> from sympy.integrals.intpoly import polytope_integrate
+    >>> polygon = Polygon(Point(0, 0), Point(0, 1), Point(1, 1), Point(1, 0))
+    >>> polys = [1, x, y, x*y, x**2*y, x*y**2]
+    >>> expr = x*y
+    >>> polytope_integrate(polygon, expr)
+    1/4
+    >>> polytope_integrate(polygon, polys, max_degree=3)
+    {1: 1, x: 1/2, y: 1/2, x*y: 1/4, x*y**2: 1/6, x**2*y: 1/6}
+    """
+    if clockwise:
+        if isinstance(poly, Polygon):
+            poly = Polygon(*point_sort(poly.vertices), evaluate=False)
+        else:
+            raise TypeError("clockwise=True works for only 2-Polytope"
+                            "V-representation input")
+
+    if isinstance(poly, Polygon):
+        # For Vertex Representation(2D case)
+        hp_params = hyperplane_parameters(poly)
+        facets = poly.sides
+    elif len(poly[0]) == 2:
+        # For Hyperplane Representation(2D case)
+        plen = len(poly)
+        if len(poly[0][0]) == 2:
+            intersections = [intersection(poly[(i - 1) % plen], poly[i],
+                                          "plane2D")
+                             for i in range(0, plen)]
+            hp_params = poly
+            lints = len(intersections)
+            facets = [Segment2D(intersections[i],
+                                intersections[(i + 1) % lints])
+                      for i in range(lints)]
+        else:
+            raise NotImplementedError("Integration for H-representation 3D"
+                                      "case not implemented yet.")
+    else:
+        # For Vertex Representation(3D case)
+        vertices = poly[0]
+        facets = poly[1:]
+        hp_params = hyperplane_parameters(facets, vertices)
+
+        if max_degree is None:
+            if expr is None:
+                raise TypeError('Input expression must be a valid SymPy expression')
+            return main_integrate3d(expr, facets, vertices, hp_params)
+
+    if max_degree is not None:
+        result = {}
+        if expr is not None:
+            f_expr = []
+            for e in expr:
+                _ = decompose(e)
+                if len(_) == 1 and not _.popitem()[0]:
+                    f_expr.append(e)
+                elif Poly(e).total_degree() <= max_degree:
+                    f_expr.append(e)
+            expr = f_expr
+
+        if not isinstance(expr, list) and expr is not None:
+            raise TypeError('Input polynomials must be list of expressions')
+
+        if len(hp_params[0][0]) == 3:
+            result_dict = main_integrate3d(0, facets, vertices, hp_params,
+                                           max_degree)
+        else:
+            result_dict = main_integrate(0, facets, hp_params, max_degree)
+
+        if expr is None:
+            return result_dict
+
+        for poly in expr:
+            poly = _sympify(poly)
+            if poly not in result:
+                if poly.is_zero:
+                    result[S.Zero] = S.Zero
+                    continue
+                integral_value = S.Zero
+                monoms = decompose(poly, separate=True)
+                for monom in monoms:
+                    monom = nsimplify(monom)
+                    coeff, m = strip(monom)
+                    integral_value += result_dict[m] * coeff
+                result[poly] = integral_value
+        return result
+
+    if expr is None:
+        raise TypeError('Input expression must be a valid SymPy expression')
+
+    return main_integrate(expr, facets, hp_params)
+
+
+def strip(monom):
+    if monom.is_zero:
+        return S.Zero, S.Zero
+    elif monom.is_number:
+        return monom, S.One
+    else:
+        coeff = LC(monom)
+        return coeff, monom / coeff
+
+def _polynomial_integrate(polynomials, facets, hp_params):
+    dims = (x, y)
+    dim_length = len(dims)
+    integral_value = S.Zero
+    for deg in polynomials:
+        poly_contribute = S.Zero
+        facet_count = 0
+        for hp in hp_params:
+            value_over_boundary = integration_reduction(facets,
+                                                        facet_count,
+                                                        hp[0], hp[1],
+                                                        polynomials[deg],
+                                                        dims, deg)
+            poly_contribute += value_over_boundary * (hp[1] / norm(hp[0]))
+            facet_count += 1
+        poly_contribute /= (dim_length + deg)
+        integral_value += poly_contribute
+
+    return integral_value
+
+
+def main_integrate3d(expr, facets, vertices, hp_params, max_degree=None):
+    """Function to translate the problem of integrating uni/bi/tri-variate
+    polynomials over a 3-Polytope to integrating over its faces.
+    This is done using Generalized Stokes' Theorem and Euler's Theorem.
+
+    Parameters
+    ==========
+
+    expr :
+        The input polynomial.
+    facets :
+        Faces of the 3-Polytope(expressed as indices of `vertices`).
+    vertices :
+        Vertices that constitute the Polytope.
+    hp_params :
+        Hyperplane Parameters of the facets.
+    max_degree : optional
+        Max degree of constituent monomial in given list of polynomial.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import main_integrate3d, \
+    hyperplane_parameters
+    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
+                (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
+                [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
+                [3, 1, 0, 2], [0, 4, 6, 2]]
+    >>> vertices = cube[0]
+    >>> faces = cube[1:]
+    >>> hp_params = hyperplane_parameters(faces, vertices)
+    >>> main_integrate3d(1, faces, vertices, hp_params)
+    -125
+    """
+    result = {}
+    dims = (x, y, z)
+    dim_length = len(dims)
+    if max_degree:
+        grad_terms = gradient_terms(max_degree, 3)
+        flat_list = [term for z_terms in grad_terms
+                     for x_term in z_terms
+                     for term in x_term]
+
+        for term in flat_list:
+            result[term[0]] = 0
+
+        for facet_count, hp in enumerate(hp_params):
+            a, b = hp[0], hp[1]
+            x0 = vertices[facets[facet_count][0]]
+
+            for i, monom in enumerate(flat_list):
+                #  Every monomial is a tuple :
+                #  (term, x_degree, y_degree, z_degree, value over boundary)
+                expr, x_d, y_d, z_d, z_index, y_index, x_index, _ = monom
+                degree = x_d + y_d + z_d
+                if b.is_zero:
+                    value_over_face = S.Zero
+                else:
+                    value_over_face = \
+                        integration_reduction_dynamic(facets, facet_count, a,
+                                                      b, expr, degree, dims,
+                                                      x_index, y_index,
+                                                      z_index, x0, grad_terms,
+                                                      i, vertices, hp)
+                monom[7] = value_over_face
+                result[expr] += value_over_face * \
+                    (b / norm(a)) / (dim_length + x_d + y_d + z_d)
+        return result
+    else:
+        integral_value = S.Zero
+        polynomials = decompose(expr)
+        for deg in polynomials:
+            poly_contribute = S.Zero
+            facet_count = 0
+            for i, facet in enumerate(facets):
+                hp = hp_params[i]
+                if hp[1].is_zero:
+                    continue
+                pi = polygon_integrate(facet, hp, i, facets, vertices, expr, deg)
+                poly_contribute += pi *\
+                    (hp[1] / norm(tuple(hp[0])))
+                facet_count += 1
+            poly_contribute /= (dim_length + deg)
+            integral_value += poly_contribute
+    return integral_value
+
+
+def main_integrate(expr, facets, hp_params, max_degree=None):
+    """Function to translate the problem of integrating univariate/bivariate
+    polynomials over a 2-Polytope to integrating over its boundary facets.
+    This is done using Generalized Stokes's Theorem and Euler's Theorem.
+
+    Parameters
+    ==========
+
+    expr :
+        The input polynomial.
+    facets :
+        Facets(Line Segments) of the 2-Polytope.
+    hp_params :
+        Hyperplane Parameters of the facets.
+    max_degree : optional
+        The maximum degree of any monomial of the input polynomial.
+
+    >>> from sympy.abc import x, y
+    >>> from sympy.integrals.intpoly import main_integrate,\
+    hyperplane_parameters
+    >>> from sympy import Point, Polygon
+    >>> triangle = Polygon(Point(0, 3), Point(5, 3), Point(1, 1))
+    >>> facets = triangle.sides
+    >>> hp_params = hyperplane_parameters(triangle)
+    >>> main_integrate(x**2 + y**2, facets, hp_params)
+    325/6
+    """
+    dims = (x, y)
+    dim_length = len(dims)
+    result = {}
+
+    if max_degree:
+        grad_terms = [[0, 0, 0, 0]] + gradient_terms(max_degree)
+
+        for facet_count, hp in enumerate(hp_params):
+            a, b = hp[0], hp[1]
+            x0 = facets[facet_count].points[0]
+
+            for i, monom in enumerate(grad_terms):
+                #  Every monomial is a tuple :
+                #  (term, x_degree, y_degree, value over boundary)
+                m, x_d, y_d, _ = monom
+                value = result.get(m, None)
+                degree = S.Zero
+                if b.is_zero:
+                    value_over_boundary = S.Zero
+                else:
+                    degree = x_d + y_d
+                    value_over_boundary = \
+                        integration_reduction_dynamic(facets, facet_count, a,
+                                                      b, m, degree, dims, x_d,
+                                                      y_d, max_degree, x0,
+                                                      grad_terms, i)
+                monom[3] = value_over_boundary
+                if value is not None:
+                    result[m] += value_over_boundary * \
+                                        (b / norm(a)) / (dim_length + degree)
+                else:
+                    result[m] = value_over_boundary * \
+                                (b / norm(a)) / (dim_length + degree)
+        return result
+    else:
+        if not isinstance(expr, list):
+            polynomials = decompose(expr)
+            return _polynomial_integrate(polynomials, facets, hp_params)
+        else:
+            return {e: _polynomial_integrate(decompose(e), facets, hp_params) for e in expr}
+
+
+def polygon_integrate(facet, hp_param, index, facets, vertices, expr, degree):
+    """Helper function to integrate the input uni/bi/trivariate polynomial
+    over a certain face of the 3-Polytope.
+
+    Parameters
+    ==========
+
+    facet :
+        Particular face of the 3-Polytope over which ``expr`` is integrated.
+    index :
+        The index of ``facet`` in ``facets``.
+    facets :
+        Faces of the 3-Polytope(expressed as indices of `vertices`).
+    vertices :
+        Vertices that constitute the facet.
+    expr :
+        The input polynomial.
+    degree :
+        Degree of ``expr``.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import polygon_integrate
+    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
+                 (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
+                 [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
+                 [3, 1, 0, 2], [0, 4, 6, 2]]
+    >>> facet = cube[1]
+    >>> facets = cube[1:]
+    >>> vertices = cube[0]
+    >>> polygon_integrate(facet, [(0, 1, 0), 5], 0, facets, vertices, 1, 0)
+    -25
+    """
+    expr = S(expr)
+    if expr.is_zero:
+        return S.Zero
+    result = S.Zero
+    x0 = vertices[facet[0]]
+    facet_len = len(facet)
+    for i, fac in enumerate(facet):
+        side = (vertices[fac], vertices[facet[(i + 1) % facet_len]])
+        result += distance_to_side(x0, side, hp_param[0]) *\
+            lineseg_integrate(facet, i, side, expr, degree)
+    if not expr.is_number:
+        expr = diff(expr, x) * x0[0] + diff(expr, y) * x0[1] +\
+            diff(expr, z) * x0[2]
+        result += polygon_integrate(facet, hp_param, index, facets, vertices,
+                                    expr, degree - 1)
+    result /= (degree + 2)
+    return result
+
+
+def distance_to_side(point, line_seg, A):
+    """Helper function to compute the signed distance between given 3D point
+    and a line segment.
+
+    Parameters
+    ==========
+
+    point : 3D Point
+    line_seg : Line Segment
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import distance_to_side
+    >>> point = (0, 0, 0)
+    >>> distance_to_side(point, [(0, 0, 1), (0, 1, 0)], (1, 0, 0))
+    -sqrt(2)/2
+    """
+    x1, x2 = line_seg
+    rev_normal = [-1 * S(i)/norm(A) for i in A]
+    vector = [x2[i] - x1[i] for i in range(0, 3)]
+    vector = [vector[i]/norm(vector) for i in range(0, 3)]
+
+    n_side = cross_product((0, 0, 0), rev_normal, vector)
+    vectorx0 = [line_seg[0][i] - point[i] for i in range(0, 3)]
+    dot_product = sum([vectorx0[i] * n_side[i] for i in range(0, 3)])
+
+    return dot_product
+
+
+def lineseg_integrate(polygon, index, line_seg, expr, degree):
+    """Helper function to compute the line integral of ``expr`` over ``line_seg``.
+
+    Parameters
+    ===========
+
+    polygon :
+        Face of a 3-Polytope.
+    index :
+        Index of line_seg in polygon.
+    line_seg :
+        Line Segment.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import lineseg_integrate
+    >>> polygon = [(0, 5, 0), (5, 5, 0), (5, 5, 5), (0, 5, 5)]
+    >>> line_seg = [(0, 5, 0), (5, 5, 0)]
+    >>> lineseg_integrate(polygon, 0, line_seg, 1, 0)
+    5
+    """
+    expr = _sympify(expr)
+    if expr.is_zero:
+        return S.Zero
+    result = S.Zero
+    x0 = line_seg[0]
+    distance = norm(tuple([line_seg[1][i] - line_seg[0][i] for i in
+                           range(3)]))
+    if isinstance(expr, Expr):
+        expr_dict = {x: line_seg[1][0],
+                     y: line_seg[1][1],
+                     z: line_seg[1][2]}
+        result += distance * expr.subs(expr_dict)
+    else:
+        result += distance * expr
+
+    expr = diff(expr, x) * x0[0] + diff(expr, y) * x0[1] +\
+        diff(expr, z) * x0[2]
+
+    result += lineseg_integrate(polygon, index, line_seg, expr, degree - 1)
+    result /= (degree + 1)
+    return result
+
+
+def integration_reduction(facets, index, a, b, expr, dims, degree):
+    """Helper method for main_integrate. Returns the value of the input
+    expression evaluated over the polytope facet referenced by a given index.
+
+    Parameters
+    ===========
+
+    facets :
+        List of facets of the polytope.
+    index :
+        Index referencing the facet to integrate the expression over.
+    a :
+        Hyperplane parameter denoting direction.
+    b :
+        Hyperplane parameter denoting distance.
+    expr :
+        The expression to integrate over the facet.
+    dims :
+        List of symbols denoting axes.
+    degree :
+        Degree of the homogeneous polynomial.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy.integrals.intpoly import integration_reduction,\
+    hyperplane_parameters
+    >>> from sympy import Point, Polygon
+    >>> triangle = Polygon(Point(0, 3), Point(5, 3), Point(1, 1))
+    >>> facets = triangle.sides
+    >>> a, b = hyperplane_parameters(triangle)[0]
+    >>> integration_reduction(facets, 0, a, b, 1, (x, y), 0)
+    5
+    """
+    expr = _sympify(expr)
+    if expr.is_zero:
+        return expr
+
+    value = S.Zero
+    x0 = facets[index].points[0]
+    m = len(facets)
+    gens = (x, y)
+
+    inner_product = diff(expr, gens[0]) * x0[0] + diff(expr, gens[1]) * x0[1]
+
+    if inner_product != 0:
+        value += integration_reduction(facets, index, a, b,
+                                       inner_product, dims, degree - 1)
+
+    value += left_integral2D(m, index, facets, x0, expr, gens)
+
+    return value/(len(dims) + degree - 1)
+
+
+def left_integral2D(m, index, facets, x0, expr, gens):
+    """Computes the left integral of Eq 10 in Chin et al.
+    For the 2D case, the integral is just an evaluation of the polynomial
+    at the intersection of two facets which is multiplied by the distance
+    between the first point of facet and that intersection.
+
+    Parameters
+    ==========
+
+    m :
+        No. of hyperplanes.
+    index :
+        Index of facet to find intersections with.
+    facets :
+        List of facets(Line Segments in 2D case).
+    x0 :
+        First point on facet referenced by index.
+    expr :
+        Input polynomial
+    gens :
+        Generators which generate the polynomial
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy.integrals.intpoly import left_integral2D
+    >>> from sympy import Point, Polygon
+    >>> triangle = Polygon(Point(0, 3), Point(5, 3), Point(1, 1))
+    >>> facets = triangle.sides
+    >>> left_integral2D(3, 0, facets, facets[0].points[0], 1, (x, y))
+    5
+    """
+    value = S.Zero
+    for j in range(m):
+        intersect = ()
+        if j in ((index - 1) % m, (index + 1) % m):
+            intersect = intersection(facets[index], facets[j], "segment2D")
+        if intersect:
+            distance_origin = norm(tuple(map(lambda x, y: x - y,
+                                             intersect, x0)))
+            if is_vertex(intersect):
+                if isinstance(expr, Expr):
+                    if len(gens) == 3:
+                        expr_dict = {gens[0]: intersect[0],
+                                     gens[1]: intersect[1],
+                                     gens[2]: intersect[2]}
+                    else:
+                        expr_dict = {gens[0]: intersect[0],
+                                     gens[1]: intersect[1]}
+                    value += distance_origin * expr.subs(expr_dict)
+                else:
+                    value += distance_origin * expr
+    return value
+
+
+def integration_reduction_dynamic(facets, index, a, b, expr, degree, dims,
+                                  x_index, y_index, max_index, x0,
+                                  monomial_values, monom_index, vertices=None,
+                                  hp_param=None):
+    """The same integration_reduction function which uses a dynamic
+    programming approach to compute terms by using the values of the integral
+    of previously computed terms.
+
+    Parameters
+    ==========
+
+    facets :
+        Facets of the Polytope.
+    index :
+        Index of facet to find intersections with.(Used in left_integral()).
+    a, b :
+        Hyperplane parameters.
+    expr :
+        Input monomial.
+    degree :
+        Total degree of ``expr``.
+    dims :
+        Tuple denoting axes variables.
+    x_index :
+        Exponent of 'x' in ``expr``.
+    y_index :
+        Exponent of 'y' in ``expr``.
+    max_index :
+        Maximum exponent of any monomial in ``monomial_values``.
+    x0 :
+        First point on ``facets[index]``.
+    monomial_values :
+        List of monomial values constituting the polynomial.
+    monom_index :
+        Index of monomial whose integration is being found.
+    vertices : optional
+        Coordinates of vertices constituting the 3-Polytope.
+    hp_param : optional
+        Hyperplane Parameter of the face of the facets[index].
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy.integrals.intpoly import (integration_reduction_dynamic, \
+            hyperplane_parameters)
+    >>> from sympy import Point, Polygon
+    >>> triangle = Polygon(Point(0, 3), Point(5, 3), Point(1, 1))
+    >>> facets = triangle.sides
+    >>> a, b = hyperplane_parameters(triangle)[0]
+    >>> x0 = facets[0].points[0]
+    >>> monomial_values = [[0, 0, 0, 0], [1, 0, 0, 5],\
+                           [y, 0, 1, 15], [x, 1, 0, None]]
+    >>> integration_reduction_dynamic(facets, 0, a, b, x, 1, (x, y), 1, 0, 1,\
+                                      x0, monomial_values, 3)
+    25/2
+    """
+    value = S.Zero
+    m = len(facets)
+
+    if expr == S.Zero:
+        return expr
+
+    if len(dims) == 2:
+        if not expr.is_number:
+            _, x_degree, y_degree, _ = monomial_values[monom_index]
+            x_index = monom_index - max_index + \
+                x_index - 2 if x_degree > 0 else 0
+            y_index = monom_index - 1 if y_degree > 0 else 0
+            x_value, y_value =\
+                monomial_values[x_index][3], monomial_values[y_index][3]
+
+            value += x_degree * x_value * x0[0] + y_degree * y_value * x0[1]
+
+        value += left_integral2D(m, index, facets, x0, expr, dims)
+    else:
+        # For 3D use case the max_index contains the z_degree of the term
+        z_index = max_index
+        if not expr.is_number:
+            x_degree, y_degree, z_degree = y_index,\
+                                           z_index - x_index - y_index, x_index
+            x_value = monomial_values[z_index - 1][y_index - 1][x_index][7]\
+                if x_degree > 0 else 0
+            y_value = monomial_values[z_index - 1][y_index][x_index][7]\
+                if y_degree > 0 else 0
+            z_value = monomial_values[z_index - 1][y_index][x_index - 1][7]\
+                if z_degree > 0 else 0
+
+            value += x_degree * x_value * x0[0] + y_degree * y_value * x0[1] \
+                + z_degree * z_value * x0[2]
+
+        value += left_integral3D(facets, index, expr,
+                                 vertices, hp_param, degree)
+    return value / (len(dims) + degree - 1)
+
+
+def left_integral3D(facets, index, expr, vertices, hp_param, degree):
+    """Computes the left integral of Eq 10 in Chin et al.
+
+    Explanation
+    ===========
+
+    For the 3D case, this is the sum of the integral values over constituting
+    line segments of the face (which is accessed by facets[index]) multiplied
+    by the distance between the first point of facet and that line segment.
+
+    Parameters
+    ==========
+
+    facets :
+        List of faces of the 3-Polytope.
+    index :
+        Index of face over which integral is to be calculated.
+    expr :
+        Input polynomial.
+    vertices :
+        List of vertices that constitute the 3-Polytope.
+    hp_param :
+        The hyperplane parameters of the face.
+    degree :
+        Degree of the ``expr``.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import left_integral3D
+    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
+                 (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
+                 [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
+                 [3, 1, 0, 2], [0, 4, 6, 2]]
+    >>> facets = cube[1:]
+    >>> vertices = cube[0]
+    >>> left_integral3D(facets, 3, 1, vertices, ([0, -1, 0], -5), 0)
+    -50
+    """
+    value = S.Zero
+    facet = facets[index]
+    x0 = vertices[facet[0]]
+    facet_len = len(facet)
+    for i, fac in enumerate(facet):
+        side = (vertices[fac], vertices[facet[(i + 1) % facet_len]])
+        value += distance_to_side(x0, side, hp_param[0]) * \
+            lineseg_integrate(facet, i, side, expr, degree)
+    return value
+
+
+def gradient_terms(binomial_power=0, no_of_gens=2):
+    """Returns a list of all the possible monomials between
+    0 and y**binomial_power for 2D case and z**binomial_power
+    for 3D case.
+
+    Parameters
+    ==========
+
+    binomial_power :
+        Power upto which terms are generated.
+    no_of_gens :
+        Denotes whether terms are being generated for 2D or 3D case.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import gradient_terms
+    >>> gradient_terms(2)
+    [[1, 0, 0, 0], [y, 0, 1, 0], [y**2, 0, 2, 0], [x, 1, 0, 0],
+    [x*y, 1, 1, 0], [x**2, 2, 0, 0]]
+    >>> gradient_terms(2, 3)
+    [[[[1, 0, 0, 0, 0, 0, 0, 0]]], [[[y, 0, 1, 0, 1, 0, 0, 0],
+    [z, 0, 0, 1, 1, 0, 1, 0]], [[x, 1, 0, 0, 1, 1, 0, 0]]],
+    [[[y**2, 0, 2, 0, 2, 0, 0, 0], [y*z, 0, 1, 1, 2, 0, 1, 0],
+    [z**2, 0, 0, 2, 2, 0, 2, 0]], [[x*y, 1, 1, 0, 2, 1, 0, 0],
+    [x*z, 1, 0, 1, 2, 1, 1, 0]], [[x**2, 2, 0, 0, 2, 2, 0, 0]]]]
+    """
+    if no_of_gens == 2:
+        count = 0
+        terms = [None] * int((binomial_power ** 2 + 3 * binomial_power + 2) / 2)
+        for x_count in range(0, binomial_power + 1):
+            for y_count in range(0, binomial_power - x_count + 1):
+                terms[count] = [x**x_count*y**y_count,
+                                x_count, y_count, 0]
+                count += 1
+    else:
+        terms = [[[[x ** x_count * y ** y_count *
+                    z ** (z_count - y_count - x_count),
+                    x_count, y_count, z_count - y_count - x_count,
+                    z_count, x_count, z_count - y_count - x_count, 0]
+                 for y_count in range(z_count - x_count, -1, -1)]
+                 for x_count in range(0, z_count + 1)]
+                 for z_count in range(0, binomial_power + 1)]
+    return terms
+
+
+def hyperplane_parameters(poly, vertices=None):
+    """A helper function to return the hyperplane parameters
+    of which the facets of the polytope are a part of.
+
+    Parameters
+    ==========
+
+    poly :
+        The input 2/3-Polytope.
+    vertices :
+        Vertex indices of 3-Polytope.
+
+    Examples
+    ========
+
+    >>> from sympy import Point, Polygon
+    >>> from sympy.integrals.intpoly import hyperplane_parameters
+    >>> hyperplane_parameters(Polygon(Point(0, 3), Point(5, 3), Point(1, 1)))
+    [((0, 1), 3), ((1, -2), -1), ((-2, -1), -3)]
+    >>> cube = [[(0, 0, 0), (0, 0, 5), (0, 5, 0), (0, 5, 5), (5, 0, 0),\
+                (5, 0, 5), (5, 5, 0), (5, 5, 5)],\
+                [2, 6, 7, 3], [3, 7, 5, 1], [7, 6, 4, 5], [1, 5, 4, 0],\
+                [3, 1, 0, 2], [0, 4, 6, 2]]
+    >>> hyperplane_parameters(cube[1:], cube[0])
+    [([0, -1, 0], -5), ([0, 0, -1], -5), ([-1, 0, 0], -5),
+    ([0, 1, 0], 0), ([1, 0, 0], 0), ([0, 0, 1], 0)]
+    """
+    if isinstance(poly, Polygon):
+        vertices = list(poly.vertices) + [poly.vertices[0]]  # Close the polygon
+        params = [None] * (len(vertices) - 1)
+
+        for i in range(len(vertices) - 1):
+            v1 = vertices[i]
+            v2 = vertices[i + 1]
+
+            a1 = v1[1] - v2[1]
+            a2 = v2[0] - v1[0]
+            b = v2[0] * v1[1] - v2[1] * v1[0]
+
+            factor = gcd_list([a1, a2, b])
+
+            b = S(b) / factor
+            a = (S(a1) / factor, S(a2) / factor)
+            params[i] = (a, b)
+    else:
+        params = [None] * len(poly)
+        for i, polygon in enumerate(poly):
+            v1, v2, v3 = [vertices[vertex] for vertex in polygon[:3]]
+            normal = cross_product(v1, v2, v3)
+            b = sum([normal[j] * v1[j] for j in range(0, 3)])
+            fac = gcd_list(normal)
+            if fac.is_zero:
+                fac = 1
+            normal = [j / fac for j in normal]
+            b = b / fac
+            params[i] = (normal, b)
+    return params
+
+
+def cross_product(v1, v2, v3):
+    """Returns the cross-product of vectors (v2 - v1) and (v3 - v1)
+    That is : (v2 - v1) X (v3 - v1)
+    """
+    v2 = [v2[j] - v1[j] for j in range(0, 3)]
+    v3 = [v3[j] - v1[j] for j in range(0, 3)]
+    return [v3[2] * v2[1] - v3[1] * v2[2],
+            v3[0] * v2[2] - v3[2] * v2[0],
+            v3[1] * v2[0] - v3[0] * v2[1]]
+
+
+def best_origin(a, b, lineseg, expr):
+    """Helper method for polytope_integrate. Currently not used in the main
+    algorithm.
+
+    Explanation
+    ===========
+
+    Returns a point on the lineseg whose vector inner product with the
+    divergence of `expr` yields an expression with the least maximum
+    total power.
+
+    Parameters
+    ==========
+
+    a :
+        Hyperplane parameter denoting direction.
+    b :
+        Hyperplane parameter denoting distance.
+    lineseg :
+        Line segment on which to find the origin.
+    expr :
+        The expression which determines the best point.
+
+    Algorithm(currently works only for 2D use case)
+    ===============================================
+
+    1 > Firstly, check for edge cases. Here that would refer to vertical
+        or horizontal lines.
+
+    2 > If input expression is a polynomial containing more than one generator
+        then find out the total power of each of the generators.
+
+        x**2 + 3 + x*y + x**4*y**5 ---> {x: 7, y: 6}
+
+        If expression is a constant value then pick the first boundary point
+        of the line segment.
+
+    3 > First check if a point exists on the line segment where the value of
+        the highest power generator becomes 0. If not check if the value of
+        the next highest becomes 0. If none becomes 0 within line segment
+        constraints then pick the first boundary point of the line segment.
+        Actually, any point lying on the segment can be picked as best origin
+        in the last case.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import best_origin
+    >>> from sympy.abc import x, y
+    >>> from sympy import Point, Segment2D
+    >>> l = Segment2D(Point(0, 3), Point(1, 1))
+    >>> expr = x**3*y**7
+    >>> best_origin((2, 1), 3, l, expr)
+    (0, 3.0)
+    """
+    a1, b1 = lineseg.points[0]
+
+    def x_axis_cut(ls):
+        """Returns the point where the input line segment
+        intersects the x-axis.
+
+        Parameters
+        ==========
+
+        ls :
+            Line segment
+        """
+        p, q = ls.points
+        if p.y.is_zero:
+            return tuple(p)
+        elif q.y.is_zero:
+            return tuple(q)
+        elif p.y/q.y < S.Zero:
+            return p.y * (p.x - q.x)/(q.y - p.y) + p.x, S.Zero
+        else:
+            return ()
+
+    def y_axis_cut(ls):
+        """Returns the point where the input line segment
+        intersects the y-axis.
+
+        Parameters
+        ==========
+
+        ls :
+            Line segment
+        """
+        p, q = ls.points
+        if p.x.is_zero:
+            return tuple(p)
+        elif q.x.is_zero:
+            return tuple(q)
+        elif p.x/q.x < S.Zero:
+            return S.Zero, p.x * (p.y - q.y)/(q.x - p.x) + p.y
+        else:
+            return ()
+
+    gens = (x, y)
+    power_gens = {}
+
+    for i in gens:
+        power_gens[i] = S.Zero
+
+    if len(gens) > 1:
+        # Special case for vertical and horizontal lines
+        if len(gens) == 2:
+            if a[0] == 0:
+                if y_axis_cut(lineseg):
+                    return S.Zero, b/a[1]
+                else:
+                    return a1, b1
+            elif a[1] == 0:
+                if x_axis_cut(lineseg):
+                    return b/a[0], S.Zero
+                else:
+                    return a1, b1
+
+        if isinstance(expr, Expr):  # Find the sum total of power of each
+            if expr.is_Add:         # generator and store in a dictionary.
+                for monomial in expr.args:
+                    if monomial.is_Pow:
+                        if monomial.args[0] in gens:
+                            power_gens[monomial.args[0]] += monomial.args[1]
+                    else:
+                        for univariate in monomial.args:
+                            term_type = len(univariate.args)
+                            if term_type == 0 and univariate in gens:
+                                power_gens[univariate] += 1
+                            elif term_type == 2 and univariate.args[0] in gens:
+                                power_gens[univariate.args[0]] +=\
+                                           univariate.args[1]
+            elif expr.is_Mul:
+                for term in expr.args:
+                    term_type = len(term.args)
+                    if term_type == 0 and term in gens:
+                        power_gens[term] += 1
+                    elif term_type == 2 and term.args[0] in gens:
+                        power_gens[term.args[0]] += term.args[1]
+            elif expr.is_Pow:
+                power_gens[expr.args[0]] = expr.args[1]
+            elif expr.is_Symbol:
+                power_gens[expr] += 1
+        else:  # If `expr` is a constant take first vertex of the line segment.
+            return a1, b1
+
+        #  TODO : This part is quite hacky. Should be made more robust with
+        #  TODO : respect to symbol names and scalable w.r.t higher dimensions.
+        power_gens = sorted(power_gens.items(), key=lambda k: str(k[0]))
+        if power_gens[0][1] >= power_gens[1][1]:
+            if y_axis_cut(lineseg):
+                x0 = (S.Zero, b / a[1])
+            elif x_axis_cut(lineseg):
+                x0 = (b / a[0], S.Zero)
+            else:
+                x0 = (a1, b1)
+        else:
+            if x_axis_cut(lineseg):
+                x0 = (b/a[0], S.Zero)
+            elif y_axis_cut(lineseg):
+                x0 = (S.Zero, b/a[1])
+            else:
+                x0 = (a1, b1)
+    else:
+        x0 = (b/a[0])
+    return x0
+
+
+def decompose(expr, separate=False):
+    """Decomposes an input polynomial into homogeneous ones of
+    smaller or equal degree.
+
+    Explanation
+    ===========
+
+    Returns a dictionary with keys as the degree of the smaller
+    constituting polynomials. Values are the constituting polynomials.
+
+    Parameters
+    ==========
+
+    expr : Expr
+        Polynomial(SymPy expression).
+    separate : bool
+        If True then simply return a list of the constituent monomials
+        If not then break up the polynomial into constituent homogeneous
+        polynomials.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy.integrals.intpoly import decompose
+    >>> decompose(x**2 + x*y + x + y + x**3*y**2 + y**5)
+    {1: x + y, 2: x**2 + x*y, 5: x**3*y**2 + y**5}
+    >>> decompose(x**2 + x*y + x + y + x**3*y**2 + y**5, True)
+    {x, x**2, y, y**5, x*y, x**3*y**2}
+    """
+    poly_dict = {}
+
+    if isinstance(expr, Expr) and not expr.is_number:
+        if expr.is_Symbol:
+            poly_dict[1] = expr
+        elif expr.is_Add:
+            symbols = expr.atoms(Symbol)
+            degrees = [(sum(degree_list(monom, *symbols)), monom)
+                       for monom in expr.args]
+            if separate:
+                return {monom[1] for monom in degrees}
+            else:
+                for monom in degrees:
+                    degree, term = monom
+                    if poly_dict.get(degree):
+                        poly_dict[degree] += term
+                    else:
+                        poly_dict[degree] = term
+        elif expr.is_Pow:
+            _, degree = expr.args
+            poly_dict[degree] = expr
+        else:  # Now expr can only be of `Mul` type
+            degree = 0
+            for term in expr.args:
+                term_type = len(term.args)
+                if term_type == 0 and term.is_Symbol:
+                    degree += 1
+                elif term_type == 2:
+                    degree += term.args[1]
+            poly_dict[degree] = expr
+    else:
+        poly_dict[0] = expr
+
+    if separate:
+        return set(poly_dict.values())
+    return poly_dict
+
+
+def point_sort(poly, normal=None, clockwise=True):
+    """Returns the same polygon with points sorted in clockwise or
+    anti-clockwise order.
+
+    Note that it's necessary for input points to be sorted in some order
+    (clockwise or anti-clockwise) for the integration algorithm to work.
+    As a convention algorithm has been implemented keeping clockwise
+    orientation in mind.
+
+    Parameters
+    ==========
+
+    poly:
+        2D or 3D Polygon.
+    normal : optional
+        The normal of the plane which the 3-Polytope is a part of.
+    clockwise : bool, optional
+        Returns points sorted in clockwise order if True and
+        anti-clockwise if False.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import point_sort
+    >>> from sympy import Point
+    >>> point_sort([Point(0, 0), Point(1, 0), Point(1, 1)])
+    [Point2D(1, 1), Point2D(1, 0), Point2D(0, 0)]
+    """
+    pts = poly.vertices if isinstance(poly, Polygon) else poly
+    n = len(pts)
+    if n < 2:
+        return list(pts)
+
+    order = S.One if clockwise else S.NegativeOne
+    dim = len(pts[0])
+    if dim == 2:
+        center = Point(sum((vertex.x for vertex in pts)) / n,
+                        sum((vertex.y for vertex in pts)) / n)
+    else:
+        center = Point(sum((vertex.x for vertex in pts)) / n,
+                        sum((vertex.y for vertex in pts)) / n,
+                        sum((vertex.z for vertex in pts)) / n)
+
+    def compare(a, b):
+        if a.x - center.x >= S.Zero and b.x - center.x < S.Zero:
+            return -order
+        elif a.x - center.x < 0 and b.x - center.x >= 0:
+            return order
+        elif a.x - center.x == 0 and b.x - center.x == 0:
+            if a.y - center.y >= 0 or b.y - center.y >= 0:
+                return -order if a.y > b.y else order
+            return -order if b.y > a.y else order
+
+        det = (a.x - center.x) * (b.y - center.y) -\
+              (b.x - center.x) * (a.y - center.y)
+        if det < 0:
+            return -order
+        elif det > 0:
+            return order
+
+        first = (a.x - center.x) * (a.x - center.x) +\
+                (a.y - center.y) * (a.y - center.y)
+        second = (b.x - center.x) * (b.x - center.x) +\
+                 (b.y - center.y) * (b.y - center.y)
+        return -order if first > second else order
+
+    def compare3d(a, b):
+        det = cross_product(center, a, b)
+        dot_product = sum([det[i] * normal[i] for i in range(0, 3)])
+        if dot_product < 0:
+            return -order
+        elif dot_product > 0:
+            return order
+
+    return sorted(pts, key=cmp_to_key(compare if dim==2 else compare3d))
+
+
+def norm(point):
+    """Returns the Euclidean norm of a point from origin.
+
+    Parameters
+    ==========
+
+    point:
+        This denotes a point in the dimension_al spac_e.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import norm
+    >>> from sympy import Point
+    >>> norm(Point(2, 7))
+    sqrt(53)
+    """
+    half = S.Half
+    if isinstance(point, (list, tuple)):
+        return sum([coord ** 2 for coord in point]) ** half
+    elif isinstance(point, Point):
+        if isinstance(point, Point2D):
+            return (point.x ** 2 + point.y ** 2) ** half
+        else:
+            return (point.x ** 2 + point.y ** 2 + point.z) ** half
+    elif isinstance(point, dict):
+        return sum(i**2 for i in point.values()) ** half
+
+
+def intersection(geom_1, geom_2, intersection_type):
+    """Returns intersection between geometric objects.
+
+    Explanation
+    ===========
+
+    Note that this function is meant for use in integration_reduction and
+    at that point in the calling function the lines denoted by the segments
+    surely intersect within segment boundaries. Coincident lines are taken
+    to be non-intersecting. Also, the hyperplane intersection for 2D case is
+    also implemented.
+
+    Parameters
+    ==========
+
+    geom_1, geom_2:
+        The input line segments.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.intpoly import intersection
+    >>> from sympy import Point, Segment2D
+    >>> l1 = Segment2D(Point(1, 1), Point(3, 5))
+    >>> l2 = Segment2D(Point(2, 0), Point(2, 5))
+    >>> intersection(l1, l2, "segment2D")
+    (2, 3)
+    >>> p1 = ((-1, 0), 0)
+    >>> p2 = ((0, 1), 1)
+    >>> intersection(p1, p2, "plane2D")
+    (0, 1)
+    """
+    if intersection_type[:-2] == "segment":
+        if intersection_type == "segment2D":
+            x1, y1 = geom_1.points[0]
+            x2, y2 = geom_1.points[1]
+            x3, y3 = geom_2.points[0]
+            x4, y4 = geom_2.points[1]
+        elif intersection_type == "segment3D":
+            x1, y1, z1 = geom_1.points[0]
+            x2, y2, z2 = geom_1.points[1]
+            x3, y3, z3 = geom_2.points[0]
+            x4, y4, z4 = geom_2.points[1]
+
+        denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+        if denom:
+            t1 = x1 * y2 - y1 * x2
+            t2 = x3 * y4 - x4 * y3
+            return (S(t1 * (x3 - x4) - t2 * (x1 - x2)) / denom,
+                    S(t1 * (y3 - y4) - t2 * (y1 - y2)) / denom)
+    if intersection_type[:-2] == "plane":
+        if intersection_type == "plane2D":  # Intersection of hyperplanes
+            a1x, a1y = geom_1[0]
+            a2x, a2y = geom_2[0]
+            b1, b2 = geom_1[1], geom_2[1]
+
+            denom = a1x * a2y - a2x * a1y
+            if denom:
+                return (S(b1 * a2y - b2 * a1y) / denom,
+                        S(b2 * a1x - b1 * a2x) / denom)
+
+
+def is_vertex(ent):
+    """If the input entity is a vertex return True.
+
+    Parameter
+    =========
+
+    ent :
+        Denotes a geometric entity representing a point.
+
+    Examples
+    ========
+
+    >>> from sympy import Point
+    >>> from sympy.integrals.intpoly import is_vertex
+    >>> is_vertex((2, 3))
+    True
+    >>> is_vertex((2, 3, 6))
+    True
+    >>> is_vertex(Point(2, 3))
+    True
+    """
+    if isinstance(ent, tuple):
+        if len(ent) in [2, 3]:
+            return True
+    elif isinstance(ent, Point):
+        return True
+    return False
+
+
+def plot_polytope(poly):
+    """Plots the 2D polytope using the functions written in plotting
+    module which in turn uses matplotlib backend.
+
+    Parameter
+    =========
+
+    poly:
+        Denotes a 2-Polytope.
+    """
+    from sympy.plotting.plot import Plot, List2DSeries
+
+    xl = [vertex.x for vertex in poly.vertices]
+    yl = [vertex.y for vertex in poly.vertices]
+
+    xl.append(poly.vertices[0].x)  # Closing the polygon
+    yl.append(poly.vertices[0].y)
+
+    l2ds = List2DSeries(xl, yl)
+    p = Plot(l2ds, axes='label_axes=True')
+    p.show()
+
+
+def plot_polynomial(expr):
+    """Plots the polynomial using the functions written in
+    plotting module which in turn uses matplotlib backend.
+
+    Parameter
+    =========
+
+    expr:
+        Denotes a polynomial(SymPy expression).
+    """
+    from sympy.plotting.plot import plot3d, plot
+    gens = expr.free_symbols
+    if len(gens) == 2:
+        plot3d(expr)
+    else:
+        plot(expr)

venv/lib/python3.10/site-packages/sympy/integrals/laplace.py

@@ -0,0 +1,1761 @@
+"""Laplace Transforms"""
+from sympy.core import S, pi, I
+from sympy.core.add import Add
+from sympy.core.cache import cacheit
+from sympy.core.function import (
+    AppliedUndef, Derivative, expand, expand_complex, expand_mul, expand_trig,
+    Lambda, WildFunction, diff)
+from sympy.core.mul import Mul, prod
+from sympy.core.relational import _canonical, Ge, Gt, Lt, Unequality, Eq
+from sympy.core.sorting import ordered
+from sympy.core.symbol import Dummy, symbols, Wild
+from sympy.functions.elementary.complexes import (
+    re, im, arg, Abs, polar_lift, periodic_argument)
+from sympy.functions.elementary.exponential import exp, log
+from sympy.functions.elementary.hyperbolic import cosh, coth, sinh, asinh
+from sympy.functions.elementary.miscellaneous import Max, Min, sqrt
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.elementary.trigonometric import cos, sin, atan
+from sympy.functions.special.bessel import besseli, besselj, besselk, bessely
+from sympy.functions.special.delta_functions import DiracDelta, Heaviside
+from sympy.functions.special.error_functions import erf, erfc, Ei
+from sympy.functions.special.gamma_functions import digamma, gamma, lowergamma
+from sympy.integrals import integrate, Integral
+from sympy.integrals.transforms import (
+    _simplify, IntegralTransform, IntegralTransformError)
+from sympy.logic.boolalg import to_cnf, conjuncts, disjuncts, Or, And
+from sympy.matrices.matrices import MatrixBase
+from sympy.polys.matrices.linsolve import _lin_eq2dict
+from sympy.polys.polyerrors import PolynomialError
+from sympy.polys.polyroots import roots
+from sympy.polys.polytools import Poly
+from sympy.polys.rationaltools import together
+from sympy.polys.rootoftools import RootSum
+from sympy.utilities.exceptions import (
+    sympy_deprecation_warning, SymPyDeprecationWarning, ignore_warnings)
+from sympy.utilities.misc import debug, debugf
+
+
+def _simplifyconds(expr, s, a):
+    r"""
+    Naively simplify some conditions occurring in ``expr``,
+    given that `\operatorname{Re}(s) > a`.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.laplace import _simplifyconds
+    >>> from sympy.abc import x
+    >>> from sympy import sympify as S
+    >>> _simplifyconds(abs(x**2) < 1, x, 1)
+    False
+    >>> _simplifyconds(abs(x**2) < 1, x, 2)
+    False
+    >>> _simplifyconds(abs(x**2) < 1, x, 0)
+    Abs(x**2) < 1
+    >>> _simplifyconds(abs(1/x**2) < 1, x, 1)
+    True
+    >>> _simplifyconds(S(1) < abs(x), x, 1)
+    True
+    >>> _simplifyconds(S(1) < abs(1/x), x, 1)
+    False
+
+    >>> from sympy import Ne
+    >>> _simplifyconds(Ne(1, x**3), x, 1)
+    True
+    >>> _simplifyconds(Ne(1, x**3), x, 2)
+    True
+    >>> _simplifyconds(Ne(1, x**3), x, 0)
+    Ne(1, x**3)
+    """
+
+    def power(ex):
+        if ex == s:
+            return 1
+        if ex.is_Pow and ex.base == s:
+            return ex.exp
+        return None
+
+    def bigger(ex1, ex2):
+        """ Return True only if |ex1| > |ex2|, False only if |ex1| < |ex2|.
+            Else return None. """
+        if ex1.has(s) and ex2.has(s):
+            return None
+        if isinstance(ex1, Abs):
+            ex1 = ex1.args[0]
+        if isinstance(ex2, Abs):
+            ex2 = ex2.args[0]
+        if ex1.has(s):
+            return bigger(1/ex2, 1/ex1)
+        n = power(ex2)
+        if n is None:
+            return None
+        try:
+            if n > 0 and (Abs(ex1) <= Abs(a)**n) == S.true:
+                return False
+            if n < 0 and (Abs(ex1) >= Abs(a)**n) == S.true:
+                return True
+        except TypeError:
+            pass
+
+    def replie(x, y):
+        """ simplify x < y """
+        if (not (x.is_positive or isinstance(x, Abs))
+                or not (y.is_positive or isinstance(y, Abs))):
+            return (x < y)
+        r = bigger(x, y)
+        if r is not None:
+            return not r
+        return (x < y)
+
+    def replue(x, y):
+        b = bigger(x, y)
+        if b in (True, False):
+            return True
+        return Unequality(x, y)
+
+    def repl(ex, *args):
+        if ex in (True, False):
+            return bool(ex)
+        return ex.replace(*args)
+    from sympy.simplify.radsimp import collect_abs
+    expr = collect_abs(expr)
+    expr = repl(expr, Lt, replie)
+    expr = repl(expr, Gt, lambda x, y: replie(y, x))
+    expr = repl(expr, Unequality, replue)
+    return S(expr)
+
+
+def expand_dirac_delta(expr):
+    """
+    Expand an expression involving DiractDelta to get it as a linear
+    combination of DiracDelta functions.
+    """
+    return _lin_eq2dict(expr, expr.atoms(DiracDelta))
+
+
+def _laplace_transform_integration(f, t, s_, simplify=True):
+    """ The backend function for doing Laplace transforms by integration.
+
+    This backend assumes that the frontend has already split sums
+    such that `f` is to an addition anymore.
+    """
+    s = Dummy('s')
+    debugf('[LT _l_t_i ] started with (%s, %s, %s)', (f, t, s))
+    debugf('[LT _l_t_i ]     and simplify=%s', (simplify, ))
+
+    if f.has(DiracDelta):
+        return None
+
+    F = integrate(f*exp(-s*t), (t, S.Zero, S.Infinity))
+    debugf('[LT _l_t_i ]     integrated: %s', (F, ))
+
+    if not F.has(Integral):
+        return _simplify(F.subs(s, s_), simplify), S.NegativeInfinity, S.true
+
+    if not F.is_Piecewise:
+        debug('[LT _l_t_i ]     not piecewise.')
+        return None
+
+    F, cond = F.args[0]
+    if F.has(Integral):
+        debug('[LT _l_t_i ]     integral in unexpected form.')
+        return None
+
+    def process_conds(conds):
+        """ Turn ``conds`` into a strip and auxiliary conditions. """
+        from sympy.solvers.inequalities import _solve_inequality
+        a = S.NegativeInfinity
+        aux = S.true
+        conds = conjuncts(to_cnf(conds))
+        p, q, w1, w2, w3, w4, w5 = symbols(
+            'p q w1 w2 w3 w4 w5', cls=Wild, exclude=[s])
+        patterns = (
+            p*Abs(arg((s + w3)*q)) < w2,
+            p*Abs(arg((s + w3)*q)) <= w2,
+            Abs(periodic_argument((s + w3)**p*q, w1)) < w2,
+            Abs(periodic_argument((s + w3)**p*q, w1)) <= w2,
+            Abs(periodic_argument((polar_lift(s + w3))**p*q, w1)) < w2,
+            Abs(periodic_argument((polar_lift(s + w3))**p*q, w1)) <= w2)
+        for c in conds:
+            a_ = S.Infinity
+            aux_ = []
+            for d in disjuncts(c):
+                if d.is_Relational and s in d.rhs.free_symbols:
+                    d = d.reversed
+                if d.is_Relational and isinstance(d, (Ge, Gt)):
+                    d = d.reversedsign
+                for pat in patterns:
+                    m = d.match(pat)
+                    if m:
+                        break
+                if m and m[q].is_positive and m[w2]/m[p] == pi/2:
+                    d = -re(s + m[w3]) < 0
+                m = d.match(p - cos(w1*Abs(arg(s*w5))*w2)*Abs(s**w3)**w4 < 0)
+                if not m:
+                    m = d.match(
+                        cos(p - Abs(periodic_argument(s**w1*w5, q))*w2) *
+                        Abs(s**w3)**w4 < 0)
+                if not m:
+                    m = d.match(
+                        p - cos(
+                            Abs(periodic_argument(polar_lift(s)**w1*w5, q))*w2
+                            )*Abs(s**w3)**w4 < 0)
+                if m and all(m[wild].is_positive for wild in [
+                        w1, w2, w3, w4, w5]):
+                    d = re(s) > m[p]
+                d_ = d.replace(
+                    re, lambda x: x.expand().as_real_imag()[0]).subs(re(s), t)
+                if (
+                        not d.is_Relational or d.rel_op in ('==', '!=')
+                        or d_.has(s) or not d_.has(t)):
+                    aux_ += [d]
+                    continue
+                soln = _solve_inequality(d_, t)
+                if not soln.is_Relational or soln.rel_op in ('==', '!='):
+                    aux_ += [d]
+                    continue
+                if soln.lts == t:
+                    debug('[LT _l_t_i ]     convergence not in half-plane.')
+                    return None
+                else:
+                    a_ = Min(soln.lts, a_)
+            if a_ is not S.Infinity:
+                a = Max(a_, a)
+            else:
+                aux = And(aux, Or(*aux_))
+        return a, aux.canonical if aux.is_Relational else aux
+
+    conds = [process_conds(c) for c in disjuncts(cond)]
+    conds2 = [x for x in conds if x[1] !=
+              S.false and x[0] is not S.NegativeInfinity]
+    if not conds2:
+        conds2 = [x for x in conds if x[1] != S.false]
+    conds = list(ordered(conds2))
+
+    def cnt(expr):
+        if expr in (True, False):
+            return 0
+        return expr.count_ops()
+    conds.sort(key=lambda x: (-x[0], cnt(x[1])))
+
+    if not conds:
+        debug('[LT _l_t_i ]     no convergence found.')
+        return None
+    a, aux = conds[0]  # XXX is [0] always the right one?
+
+    def sbs(expr):
+        return expr.subs(s, s_)
+    if simplify:
+        F = _simplifyconds(F, s, a)
+        aux = _simplifyconds(aux, s, a)
+    return _simplify(F.subs(s, s_), simplify), sbs(a), _canonical(sbs(aux))
+
+
+def _laplace_deep_collect(f, t):
+    """
+    This is an internal helper function that traverses through the epression
+    tree of `f(t)` and collects arguments. The purpose of it is that
+    anything like `f(w*t-1*t-c)` will be written as `f((w-1)*t-c)` such that
+    it can match `f(a*t+b)`.
+    """
+    func = f.func
+    args = list(f.args)
+    if len(f.args) == 0:
+        return f
+    else:
+        args = [_laplace_deep_collect(arg, t) for arg in args]
+        if func.is_Add:
+            return func(*args).collect(t)
+        else:
+            return func(*args)
+
+
+@cacheit
+def _laplace_build_rules():
+    """
+    This is an internal helper function that returns the table of Laplace
+    transform rules in terms of the time variable `t` and the frequency
+    variable `s`.  It is used by ``_laplace_apply_rules``.  Each entry is a
+    tuple containing:
+
+        (time domain pattern,
+         frequency-domain replacement,
+         condition for the rule to be applied,
+         convergence plane,
+         preparation function)
+
+    The preparation function is a function with one argument that is applied
+    to the expression before matching. For most rules it should be
+    ``_laplace_deep_collect``.
+    """
+    t = Dummy('t')
+    s = Dummy('s')
+    a = Wild('a', exclude=[t])
+    b = Wild('b', exclude=[t])
+    n = Wild('n', exclude=[t])
+    tau = Wild('tau', exclude=[t])
+    omega = Wild('omega', exclude=[t])
+    def dco(f): return _laplace_deep_collect(f, t)
+    debug('_laplace_build_rules is building rules')
+
+    laplace_transform_rules = [
+        (a, a/s,
+         S.true, S.Zero, dco),  # 4.2.1
+        (DiracDelta(a*t-b), exp(-s*b/a)/Abs(a),
+         Or(And(a > 0, b >= 0), And(a < 0, b <= 0)),
+         S.NegativeInfinity, dco),  # Not in Bateman54
+        (DiracDelta(a*t-b), S(0),
+         Or(And(a < 0, b >= 0), And(a > 0, b <= 0)),
+         S.NegativeInfinity, dco),  # Not in Bateman54
+        (Heaviside(a*t-b), exp(-s*b/a)/s,
+         And(a > 0, b > 0), S.Zero, dco),  # 4.4.1
+        (Heaviside(a*t-b), (1-exp(-s*b/a))/s,
+         And(a < 0, b < 0), S.Zero, dco),  # 4.4.1
+        (Heaviside(a*t-b), 1/s,
+         And(a > 0, b <= 0), S.Zero, dco),  # 4.4.1
+        (Heaviside(a*t-b), 0,
+         And(a < 0, b > 0), S.Zero, dco),  # 4.4.1
+        (t, 1/s**2,
+         S.true, S.Zero, dco),  # 4.2.3
+        (1/(a*t+b), -exp(-b/a*s)*Ei(-b/a*s)/a,
+         Abs(arg(b/a)) < pi, S.Zero, dco),  # 4.2.6
+        (1/sqrt(a*t+b), sqrt(a*pi/s)*exp(b/a*s)*erfc(sqrt(b/a*s))/a,
+         Abs(arg(b/a)) < pi, S.Zero, dco),  # 4.2.18
+        ((a*t+b)**(-S(3)/2),
+         2*b**(-S(1)/2)-2*(pi*s/a)**(S(1)/2)*exp(b/a*s) * erfc(sqrt(b/a*s))/a,
+         Abs(arg(b/a)) < pi, S.Zero, dco),  # 4.2.20
+        (sqrt(t)/(t+b), sqrt(pi/s)-pi*sqrt(b)*exp(b*s)*erfc(sqrt(b*s)),
+         Abs(arg(b)) < pi, S.Zero, dco),  # 4.2.22
+        (1/(a*sqrt(t) + t**(3/2)), pi*a**(S(1)/2)*exp(a*s)*erfc(sqrt(a*s)),
+         S.true, S.Zero, dco),  # Not in Bateman54
+        (t**n, gamma(n+1)/s**(n+1),
+         n > -1, S.Zero, dco),  # 4.3.1
+        ((a*t+b)**n, lowergamma(n+1, b/a*s)*exp(-b/a*s)/s**(n+1)/a,
+         And(n > -1, Abs(arg(b/a)) < pi), S.Zero, dco),  # 4.3.4
+        (t**n/(t+a), a**n*gamma(n+1)*lowergamma(-n, a*s),
+         And(n > -1, Abs(arg(a)) < pi), S.Zero, dco),  # 4.3.7
+        (exp(a*t-tau), exp(-tau)/(s-a),
+         S.true, re(a), dco),  # 4.5.1
+        (t*exp(a*t-tau), exp(-tau)/(s-a)**2,
+         S.true, re(a), dco),  # 4.5.2
+        (t**n*exp(a*t), gamma(n+1)/(s-a)**(n+1),
+         re(n) > -1, re(a), dco),  # 4.5.3
+        (exp(-a*t**2), sqrt(pi/4/a)*exp(s**2/4/a)*erfc(s/sqrt(4*a)),
+         re(a) > 0, S.Zero, dco),  # 4.5.21
+        (t*exp(-a*t**2),
+         1/(2*a)-2/sqrt(pi)/(4*a)**(S(3)/2)*s*erfc(s/sqrt(4*a)),
+         re(a) > 0, S.Zero, dco),  # 4.5.22
+        (exp(-a/t), 2*sqrt(a/s)*besselk(1, 2*sqrt(a*s)),
+         re(a) >= 0, S.Zero, dco),  # 4.5.25
+        (sqrt(t)*exp(-a/t),
+         S(1)/2*sqrt(pi/s**3)*(1+2*sqrt(a*s))*exp(-2*sqrt(a*s)),
+         re(a) >= 0, S.Zero, dco),  # 4.5.26
+        (exp(-a/t)/sqrt(t), sqrt(pi/s)*exp(-2*sqrt(a*s)),
+         re(a) >= 0, S.Zero, dco),  # 4.5.27
+        (exp(-a/t)/(t*sqrt(t)), sqrt(pi/a)*exp(-2*sqrt(a*s)),
+         re(a) > 0, S.Zero, dco),  # 4.5.28
+        (t**n*exp(-a/t), 2*(a/s)**((n+1)/2)*besselk(n+1, 2*sqrt(a*s)),
+         re(a) > 0, S.Zero, dco),  # 4.5.29
+        (exp(-2*sqrt(a*t)),
+         s**(-1)-sqrt(pi*a)*s**(-S(3)/2)*exp(a/s) * erfc(sqrt(a/s)),
+         Abs(arg(a)) < pi, S.Zero, dco),  # 4.5.31
+        (exp(-2*sqrt(a*t))/sqrt(t), (pi/s)**(S(1)/2)*exp(a/s)*erfc(sqrt(a/s)),
+         Abs(arg(a)) < pi, S.Zero, dco),  # 4.5.33
+        (log(a*t), -log(exp(S.EulerGamma)*s/a)/s,
+         a > 0, S.Zero, dco),  # 4.6.1
+        (log(1+a*t), -exp(s/a)/s*Ei(-s/a),
+         Abs(arg(a)) < pi, S.Zero, dco),  # 4.6.4
+        (log(a*t+b), (log(b)-exp(s/b/a)/s*a*Ei(-s/b))/s*a,
+         And(a > 0, Abs(arg(b)) < pi), S.Zero, dco),  # 4.6.5
+        (log(t)/sqrt(t), -sqrt(pi/s)*log(4*s*exp(S.EulerGamma)),
+         S.true, S.Zero, dco),  # 4.6.9
+        (t**n*log(t), gamma(n+1)*s**(-n-1)*(digamma(n+1)-log(s)),
+         re(n) > -1, S.Zero, dco),  # 4.6.11
+        (log(a*t)**2, (log(exp(S.EulerGamma)*s/a)**2+pi**2/6)/s,
+         a > 0, S.Zero, dco),  # 4.6.13
+        (sin(omega*t), omega/(s**2+omega**2),
+         S.true, Abs(im(omega)), dco),  # 4,7,1
+        (Abs(sin(omega*t)), omega/(s**2+omega**2)*coth(pi*s/2/omega),
+         omega > 0, S.Zero, dco),  # 4.7.2
+        (sin(omega*t)/t, atan(omega/s),
+         S.true, Abs(im(omega)), dco),  # 4.7.16
+        (sin(omega*t)**2/t, log(1+4*omega**2/s**2)/4,
+         S.true, 2*Abs(im(omega)), dco),  # 4.7.17
+        (sin(omega*t)**2/t**2,
+         omega*atan(2*omega/s)-s*log(1+4*omega**2/s**2)/4,
+         S.true, 2*Abs(im(omega)), dco),  # 4.7.20
+        (sin(2*sqrt(a*t)), sqrt(pi*a)/s/sqrt(s)*exp(-a/s),
+         S.true, S.Zero, dco),  # 4.7.32
+        (sin(2*sqrt(a*t))/t, pi*erf(sqrt(a/s)),
+         S.true, S.Zero, dco),  # 4.7.34
+        (cos(omega*t), s/(s**2+omega**2),
+         S.true, Abs(im(omega)), dco),  # 4.7.43
+        (cos(omega*t)**2, (s**2+2*omega**2)/(s**2+4*omega**2)/s,
+         S.true, 2*Abs(im(omega)), dco),  # 4.7.45
+        (sqrt(t)*cos(2*sqrt(a*t)), sqrt(pi)/2*s**(-S(5)/2)*(s-2*a)*exp(-a/s),
+         S.true, S.Zero, dco),  # 4.7.66
+        (cos(2*sqrt(a*t))/sqrt(t), sqrt(pi/s)*exp(-a/s),
+         S.true, S.Zero, dco),  # 4.7.67
+        (sin(a*t)*sin(b*t), 2*a*b*s/(s**2+(a+b)**2)/(s**2+(a-b)**2),
+         S.true, Abs(im(a))+Abs(im(b)), dco),  # 4.7.78
+        (cos(a*t)*sin(b*t), b*(s**2-a**2+b**2)/(s**2+(a+b)**2)/(s**2+(a-b)**2),
+         S.true, Abs(im(a))+Abs(im(b)), dco),  # 4.7.79
+        (cos(a*t)*cos(b*t), s*(s**2+a**2+b**2)/(s**2+(a+b)**2)/(s**2+(a-b)**2),
+         S.true, Abs(im(a))+Abs(im(b)), dco),  # 4.7.80
+        (sinh(a*t), a/(s**2-a**2),
+         S.true, Abs(re(a)), dco),  # 4.9.1
+        (cosh(a*t), s/(s**2-a**2),
+         S.true, Abs(re(a)), dco),  # 4.9.2
+        (sinh(a*t)**2, 2*a**2/(s**3-4*a**2*s),
+         S.true, 2*Abs(re(a)), dco),  # 4.9.3
+        (cosh(a*t)**2, (s**2-2*a**2)/(s**3-4*a**2*s),
+         S.true, 2*Abs(re(a)), dco),  # 4.9.4
+        (sinh(a*t)/t, log((s+a)/(s-a))/2,
+         S.true, Abs(re(a)), dco),  # 4.9.12
+        (t**n*sinh(a*t), gamma(n+1)/2*((s-a)**(-n-1)-(s+a)**(-n-1)),
+         n > -2, Abs(a), dco),  # 4.9.18
+        (t**n*cosh(a*t), gamma(n+1)/2*((s-a)**(-n-1)+(s+a)**(-n-1)),
+         n > -1, Abs(a), dco),  # 4.9.19
+        (sinh(2*sqrt(a*t)), sqrt(pi*a)/s/sqrt(s)*exp(a/s),
+         S.true, S.Zero, dco),  # 4.9.34
+        (cosh(2*sqrt(a*t)), 1/s+sqrt(pi*a)/s/sqrt(s)*exp(a/s)*erf(sqrt(a/s)),
+         S.true, S.Zero, dco),  # 4.9.35
+        (
+            sqrt(t)*sinh(2*sqrt(a*t)),
+            pi**(S(1)/2)*s**(-S(5)/2)*(s/2+a) *
+            exp(a/s)*erf(sqrt(a/s))-a**(S(1)/2)*s**(-2),
+            S.true, S.Zero, dco),  # 4.9.36
+        (sqrt(t)*cosh(2*sqrt(a*t)), pi**(S(1)/2)*s**(-S(5)/2)*(s/2+a)*exp(a/s),
+         S.true, S.Zero, dco),  # 4.9.37
+        (sinh(2*sqrt(a*t))/sqrt(t),
+         pi**(S(1)/2)*s**(-S(1)/2)*exp(a/s) * erf(sqrt(a/s)),
+            S.true, S.Zero, dco),  # 4.9.38
+        (cosh(2*sqrt(a*t))/sqrt(t), pi**(S(1)/2)*s**(-S(1)/2)*exp(a/s),
+         S.true, S.Zero, dco),  # 4.9.39
+        (sinh(sqrt(a*t))**2/sqrt(t), pi**(S(1)/2)/2*s**(-S(1)/2)*(exp(a/s)-1),
+         S.true, S.Zero, dco),  # 4.9.40
+        (cosh(sqrt(a*t))**2/sqrt(t), pi**(S(1)/2)/2*s**(-S(1)/2)*(exp(a/s)+1),
+         S.true, S.Zero, dco),  # 4.9.41
+        (erf(a*t), exp(s**2/(2*a)**2)*erfc(s/(2*a))/s,
+         4*Abs(arg(a)) < pi, S.Zero, dco),  # 4.12.2
+        (erf(sqrt(a*t)), sqrt(a)/sqrt(s+a)/s,
+         S.true, Max(S.Zero, -re(a)), dco),  # 4.12.4
+        (exp(a*t)*erf(sqrt(a*t)), sqrt(a)/sqrt(s)/(s-a),
+         S.true, Max(S.Zero, re(a)), dco),  # 4.12.5
+        (erf(sqrt(a/t)/2), (1-exp(-sqrt(a*s)))/s,
+         re(a) > 0, S.Zero, dco),  # 4.12.6
+        (erfc(sqrt(a*t)), (sqrt(s+a)-sqrt(a))/sqrt(s+a)/s,
+         S.true, -re(a), dco),  # 4.12.9
+        (exp(a*t)*erfc(sqrt(a*t)), 1/(s+sqrt(a*s)),
+         S.true, S.Zero, dco),  # 4.12.10
+        (erfc(sqrt(a/t)/2), exp(-sqrt(a*s))/s,
+         re(a) > 0, S.Zero, dco),  # 4.2.11
+        (besselj(n, a*t), a**n/(sqrt(s**2+a**2)*(s+sqrt(s**2+a**2))**n),
+         re(n) > -1, Abs(im(a)), dco),  # 4.14.1
+        (t**b*besselj(n, a*t),
+         2**n/sqrt(pi)*gamma(n+S.Half)*a**n*(s**2+a**2)**(-n-S.Half),
+         And(re(n) > -S.Half, Eq(b, n)), Abs(im(a)), dco),  # 4.14.7
+        (t**b*besselj(n, a*t),
+         2**(n+1)/sqrt(pi)*gamma(n+S(3)/2)*a**n*s*(s**2+a**2)**(-n-S(3)/2),
+         And(re(n) > -1, Eq(b, n+1)), Abs(im(a)), dco),  # 4.14.8
+        (besselj(0, 2*sqrt(a*t)), exp(-a/s)/s,
+         S.true, S.Zero, dco),  # 4.14.25
+        (t**(b)*besselj(n, 2*sqrt(a*t)), a**(n/2)*s**(-n-1)*exp(-a/s),
+         And(re(n) > -1, Eq(b, n*S.Half)), S.Zero, dco),  # 4.14.30
+        (besselj(0, a*sqrt(t**2+b*t)),
+         exp(b*s-b*sqrt(s**2+a**2))/sqrt(s**2+a**2),
+         Abs(arg(b)) < pi, Abs(im(a)), dco),  # 4.15.19
+        (besseli(n, a*t), a**n/(sqrt(s**2-a**2)*(s+sqrt(s**2-a**2))**n),
+         re(n) > -1, Abs(re(a)), dco),  # 4.16.1
+        (t**b*besseli(n, a*t),
+         2**n/sqrt(pi)*gamma(n+S.Half)*a**n*(s**2-a**2)**(-n-S.Half),
+         And(re(n) > -S.Half, Eq(b, n)), Abs(re(a)), dco),  # 4.16.6
+        (t**b*besseli(n, a*t),
+         2**(n+1)/sqrt(pi)*gamma(n+S(3)/2)*a**n*s*(s**2-a**2)**(-n-S(3)/2),
+         And(re(n) > -1, Eq(b, n+1)), Abs(re(a)), dco),  # 4.16.7
+        (t**(b)*besseli(n, 2*sqrt(a*t)), a**(n/2)*s**(-n-1)*exp(a/s),
+         And(re(n) > -1, Eq(b, n*S.Half)), S.Zero, dco),  # 4.16.18
+        (bessely(0, a*t), -2/pi*asinh(s/a)/sqrt(s**2+a**2),
+         S.true, Abs(im(a)), dco),  # 4.15.44
+        (besselk(0, a*t), log((s + sqrt(s**2-a**2))/a)/(sqrt(s**2-a**2)),
+         S.true, -re(a), dco)  # 4.16.23
+    ]
+    return laplace_transform_rules, t, s
+
+
+def _laplace_rule_timescale(f, t, s):
+    """
+    This function applies the time-scaling rule of the Laplace transform in
+    a straight-forward way. For example, if it gets ``(f(a*t), t, s)``, it will
+    compute ``LaplaceTransform(f(t)/a, t, s/a)`` if ``a>0``.
+    """
+
+    a = Wild('a', exclude=[t])
+    g = WildFunction('g', nargs=1)
+    ma1 = f.match(g)
+    if ma1:
+        arg = ma1[g].args[0].collect(t)
+        ma2 = arg.match(a*t)
+        if ma2 and ma2[a].is_positive and ma2[a] != 1:
+            debug('_laplace_apply_prog rules match:')
+            debugf('      f:    %s _ %s, %s )', (f, ma1, ma2))
+            debug('      rule: time scaling (4.1.4)')
+            r, pr, cr = _laplace_transform(1/ma2[a]*ma1[g].func(t),
+                                           t, s/ma2[a], simplify=False)
+            return (r, pr, cr)
+    return None
+
+
+def _laplace_rule_heaviside(f, t, s):
+    """
+    This function deals with time-shifted Heaviside step functions. If the time
+    shift is positive, it applies the time-shift rule of the Laplace transform.
+    For example, if it gets ``(Heaviside(t-a)*f(t), t, s)``, it will compute
+    ``exp(-a*s)*LaplaceTransform(f(t+a), t, s)``.
+
+    If the time shift is negative, the Heaviside function is simply removed
+    as it means nothing to the Laplace transform.
+
+    The function does not remove a factor ``Heaviside(t)``; this is done by
+    the simple rules.
+    """
+
+    a = Wild('a', exclude=[t])
+    y = Wild('y')
+    g = Wild('g')
+    ma1 = f.match(Heaviside(y)*g)
+    if ma1:
+        ma2 = ma1[y].match(t-a)
+        if ma2 and ma2[a].is_positive:
+            debug('_laplace_apply_prog_rules match:')
+            debugf('      f:    %s ( %s, %s )', (f, ma1, ma2))
+            debug('      rule: time shift (4.1.4)')
+            r, pr, cr = _laplace_transform(ma1[g].subs(t, t+ma2[a]), t, s,
+                                           simplify=False)
+            return (exp(-ma2[a]*s)*r, pr, cr)
+        if ma2 and ma2[a].is_negative:
+            debug('_laplace_apply_prog_rules match:')
+            debugf('      f:    %s ( %s, %s )', (f, ma1, ma2))
+            debug('      rule: Heaviside factor, negative time shift (4.1.4)')
+            r, pr, cr = _laplace_transform(ma1[g], t, s, simplify=False)
+            return (r, pr, cr)
+    return None
+
+
+def _laplace_rule_exp(f, t, s):
+    """
+    If this function finds a factor ``exp(a*t)``, it applies the
+    frequency-shift rule of the Laplace transform and adjusts the convergence
+    plane accordingly.  For example, if it gets ``(exp(-a*t)*f(t), t, s)``, it
+    will compute ``LaplaceTransform(f(t), t, s+a)``.
+    """
+
+    a = Wild('a', exclude=[t])
+    y = Wild('y')
+    z = Wild('z')
+    ma1 = f.match(exp(y)*z)
+    if ma1:
+        ma2 = ma1[y].collect(t).match(a*t)
+        if ma2:
+            debug('_laplace_apply_prog_rules match:')
+            debugf('      f:    %s ( %s, %s )', (f, ma1, ma2))
+            debug('      rule: multiply with exp (4.1.5)')
+            r, pr, cr = _laplace_transform(ma1[z], t, s-ma2[a],
+                                           simplify=False)
+            return (r, pr+re(ma2[a]), cr)
+    return None
+
+
+def _laplace_rule_delta(f, t, s):
+    """
+    If this function finds a factor ``DiracDelta(b*t-a)``, it applies the
+    masking property of the delta distribution. For example, if it gets
+    ``(DiracDelta(t-a)*f(t), t, s)``, it will return
+    ``(f(a)*exp(-a*s), -a, True)``.
+    """
+    # This rule is not in Bateman54
+
+    a = Wild('a', exclude=[t])
+    b = Wild('b', exclude=[t])
+
+    y = Wild('y')
+    z = Wild('z')
+    ma1 = f.match(DiracDelta(y)*z)
+    if ma1 and not ma1[z].has(DiracDelta):
+        ma2 = ma1[y].collect(t).match(b*t-a)
+        if ma2:
+            debug('_laplace_apply_prog_rules match:')
+            debugf('      f:    %s ( %s, %s )', (f, ma1, ma2))
+            debug('      rule: multiply with DiracDelta')
+            loc = ma2[a]/ma2[b]
+            if re(loc) >= 0 and im(loc) == 0:
+                r = exp(-ma2[a]/ma2[b]*s)*ma1[z].subs(t, ma2[a]/ma2[b])/ma2[b]
+                return (r, S.NegativeInfinity, S.true)
+            else:
+                return (0, S.NegativeInfinity, S.true)
+        if ma1[y].is_polynomial(t):
+            ro = roots(ma1[y], t)
+            if roots is not {} and set(ro.values()) == {1}:
+                slope = diff(ma1[y], t)
+                r = Add(
+                    *[exp(-x*s)*ma1[z].subs(t, s)/slope.subs(t, x)
+                      for x in list(ro.keys()) if im(x) == 0 and re(x) >= 0])
+                return (r, S.NegativeInfinity, S.true)
+    return None
+
+
+def _laplace_trig_split(fn):
+    """
+    Helper function for `_laplace_rule_trig`.  This function returns two terms
+    `f` and `g`.  `f` contains all product terms with sin, cos, sinh, cosh in
+    them; `g` contains everything else.
+    """
+    trigs = [S.One]
+    other = [S.One]
+    for term in Mul.make_args(fn):
+        if term.has(sin, cos, sinh, cosh, exp):
+            trigs.append(term)
+        else:
+            other.append(term)
+    f = Mul(*trigs)
+    g = Mul(*other)
+    return f, g
+
+
+def _laplace_trig_expsum(f, t):
+    """
+    Helper function for `_laplace_rule_trig`.  This function expects the `f`
+    from `_laplace_trig_split`.  It returns two lists `xm` and `xn`.  `xm` is
+    a list of dictionaries with keys `k` and `a` representing a function
+    `k*exp(a*t)`.  `xn` is a list of all terms that cannot be brought into
+    that form, which may happen, e.g., when a trigonometric function has
+    another function in its argument.
+    """
+    m = Wild('m')
+    p = Wild('p', exclude=[t])
+    xm = []
+    xn = []
+
+    x1 = f.rewrite(exp).expand()
+
+    for term in Add.make_args(x1):
+        if not term.has(t):
+            xm.append({'k': term, 'a': 0, re: 0, im: 0})
+            continue
+        term = term.powsimp(combine='exp')
+        if (r := term.match(p*exp(m))) is not None:
+            if (mp := r[m].as_poly(t)) is not None:
+                mc = mp.all_coeffs()
+                if len(mc) == 2:
+                    xm.append({
+                        'k': r[p]*exp(mc[1]), 'a': mc[0],
+                        re: re(mc[0]), im: im(mc[0])})
+                else:
+                    xn.append(term)
+            else:
+                xn.append(term)
+        else:
+            xn.append(term)
+    return xm, xn
+
+
+def _laplace_trig_ltex(xm, t, s):
+    """
+    Helper function for `_laplace_rule_trig`.  This function takes the list of
+    exponentials `xm` from `_laplace_trig_expsum` and simplifies complex
+    conjugate and real symmetric poles.  It returns the result as a sum and
+    the convergence plane.
+    """
+    results = []
+    planes = []
+
+    def _simpc(coeffs):
+        nc = coeffs.copy()
+        for k in range(len(nc)):
+            ri = nc[k].as_real_imag()
+            if ri[0].has(im):
+                nc[k] = nc[k].rewrite(cos)
+            else:
+                nc[k] = (ri[0] + I*ri[1]).rewrite(cos)
+        return nc
+
+    def _quadpole(t1, k1, k2, k3, s):
+        a, k0, a_r, a_i = t1['a'], t1['k'], t1[re], t1[im]
+        nc = [
+            k0 + k1 + k2 + k3,
+            a*(k0 + k1 - k2 - k3) - 2*I*a_i*k1 + 2*I*a_i*k2,
+            (
+                a**2*(-k0 - k1 - k2 - k3) +
+                a*(4*I*a_i*k0 + 4*I*a_i*k3) +
+                4*a_i**2*k0 + 4*a_i**2*k3),
+            (
+                a**3*(-k0 - k1 + k2 + k3) +
+                a**2*(4*I*a_i*k0 + 2*I*a_i*k1 - 2*I*a_i*k2 - 4*I*a_i*k3) +
+                a*(4*a_i**2*k0 - 4*a_i**2*k3))
+        ]
+        dc = [
+            S.One, S.Zero, 2*a_i**2 - 2*a_r**2,
+            S.Zero, a_i**4 + 2*a_i**2*a_r**2 + a_r**4]
+        n = Add(
+            *[x*s**y for x, y in zip(_simpc(nc), range(len(nc))[::-1])])
+        d = Add(
+            *[x*s**y for x, y in zip(dc, range(len(dc))[::-1])])
+        debugf('        quadpole: (%s) / (%s)', (n, d))
+        return n/d
+
+    def _ccpole(t1, k1, s):
+        a, k0, a_r, a_i = t1['a'], t1['k'], t1[re], t1[im]
+        nc = [k0 + k1, -a*k0 - a*k1 + 2*I*a_i*k0]
+        dc = [S.One, -2*a_r, a_i**2 + a_r**2]
+        n = Add(
+            *[x*s**y for x, y in zip(_simpc(nc), range(len(nc))[::-1])])
+        d = Add(
+            *[x*s**y for x, y in zip(dc, range(len(dc))[::-1])])
+        debugf('        ccpole: (%s) / (%s)', (n, d))
+        return n/d
+
+    def _rspole(t1, k2, s):
+        a, k0, a_r, a_i = t1['a'], t1['k'], t1[re], t1[im]
+        nc = [k0 + k2, a*k0 - a*k2 - 2*I*a_i*k0]
+        dc = [S.One, -2*I*a_i, -a_i**2 - a_r**2]
+        n = Add(
+            *[x*s**y for x, y in zip(_simpc(nc), range(len(nc))[::-1])])
+        d = Add(
+            *[x*s**y for x, y in zip(dc, range(len(dc))[::-1])])
+        debugf('        rspole: (%s) / (%s)', (n, d))
+        return n/d
+
+    def _sypole(t1, k3, s):
+        a, k0 = t1['a'], t1['k']
+        nc = [k0 + k3, a*(k0 - k3)]
+        dc = [S.One, S.Zero, -a**2]
+        n = Add(
+            *[x*s**y for x, y in zip(_simpc(nc), range(len(nc))[::-1])])
+        d = Add(
+            *[x*s**y for x, y in zip(dc, range(len(dc))[::-1])])
+        debugf('        sypole: (%s) / (%s)', (n, d))
+        return n/d
+
+    def _simplepole(t1, s):
+        a, k0 = t1['a'], t1['k']
+        n = k0
+        d = s - a
+        debugf('        simplepole: (%s) / (%s)', (n, d))
+        return n/d
+
+    while len(xm) > 0:
+        t1 = xm.pop()
+        i_imagsym = None
+        i_realsym = None
+        i_pointsym = None
+        # The following code checks all remaining poles. If t1 is a pole at
+        # a+b*I, then we check for a-b*I, -a+b*I, and -a-b*I, and
+        # assign the respective indices to i_imagsym, i_realsym, i_pointsym.
+        # -a-b*I / i_pointsym only applies if both a and b are != 0.
+        for i in range(len(xm)):
+            real_eq = t1[re] == xm[i][re]
+            realsym = t1[re] == -xm[i][re]
+            imag_eq = t1[im] == xm[i][im]
+            imagsym = t1[im] == -xm[i][im]
+            if realsym and imagsym and t1[re] != 0 and t1[im] != 0:
+                i_pointsym = i
+            elif realsym and imag_eq and t1[re] != 0:
+                i_realsym = i
+            elif real_eq and imagsym and t1[im] != 0:
+                i_imagsym = i
+
+        # The next part looks for four possible pole constellations:
+        # quad:   a+b*I, a-b*I, -a+b*I, -a-b*I
+        # cc:     a+b*I, a-b*I (a may be zero)
+        # quad:   a+b*I, -a+b*I (b may be zero)
+        # point:  a+b*I, -a-b*I (a!=0 and b!=0 is needed, but that has been
+        #                        asserted when finding i_pointsym above.)
+        # If none apply, then t1 is a simple pole.
+        if (
+                i_imagsym is not None and i_realsym is not None
+                and i_pointsym is not None):
+            results.append(
+                _quadpole(t1,
+                          xm[i_imagsym]['k'], xm[i_realsym]['k'],
+                          xm[i_pointsym]['k'], s))
+            planes.append(Abs(re(t1['a'])))
+            # The three additional poles have now been used; to pop them
+            # easily we have to do it from the back.
+            indices_to_pop = [i_imagsym, i_realsym, i_pointsym]
+            indices_to_pop.sort(reverse=True)
+            for i in indices_to_pop:
+                xm.pop(i)
+        elif i_imagsym is not None:
+            results.append(_ccpole(t1, xm[i_imagsym]['k'], s))
+            planes.append(t1[re])
+            xm.pop(i_imagsym)
+        elif i_realsym is not None:
+            results.append(_rspole(t1, xm[i_realsym]['k'], s))
+            planes.append(Abs(t1[re]))
+            xm.pop(i_realsym)
+        elif i_pointsym is not None:
+            results.append(_sypole(t1, xm[i_pointsym]['k'], s))
+            planes.append(Abs(t1[re]))
+            xm.pop(i_pointsym)
+        else:
+            results.append(_simplepole(t1, s))
+            planes.append(t1[re])
+
+    return Add(*results), Max(*planes)
+
+
+def _laplace_rule_trig(fn, t_, s, doit=True, **hints):
+    """
+    This rule covers trigonometric factors by splitting everything into a
+    sum of exponential functions and collecting complex conjugate poles and
+    real symmetric poles.
+    """
+    t = Dummy('t', real=True)
+
+    if not fn.has(sin, cos, sinh, cosh):
+        return None
+
+    debugf('_laplace_rule_trig: (%s, %s, %s)', (fn, t_, s))
+
+    f, g = _laplace_trig_split(fn.subs(t_, t))
+    debugf('    f = %s\n    g = %s', (f, g))
+
+    xm, xn = _laplace_trig_expsum(f, t)
+    debugf('    xm = %s\n    xn = %s', (xm, xn))
+
+    if len(xn) > 0:
+        # not implemented yet
+        debug('    --> xn is not empty; giving up.')
+        return None
+
+    if not g.has(t):
+        r, p = _laplace_trig_ltex(xm, t, s)
+        return g*r, p, S.true
+    else:
+        # Just transform `g` and make frequency-shifted copies
+        planes = []
+        results = []
+        G, G_plane, G_cond = _laplace_transform(g, t, s)
+        for x1 in xm:
+            results.append(x1['k']*G.subs(s, s-x1['a']))
+            planes.append(G_plane+re(x1['a']))
+    return Add(*results).subs(t, t_), Max(*planes), G_cond
+
+
+def _laplace_rule_diff(f, t, s, doit=True, **hints):
+    """
+    This function looks for derivatives in the time domain and replaces it
+    by factors of `s` and initial conditions in the frequency domain. For
+    example, if it gets ``(diff(f(t), t), t, s)``, it will compute
+    ``s*LaplaceTransform(f(t), t, s) - f(0)``.
+    """
+
+    a = Wild('a', exclude=[t])
+    n = Wild('n', exclude=[t])
+    g = WildFunction('g')
+    ma1 = f.match(a*Derivative(g, (t, n)))
+    if ma1 and ma1[n].is_integer:
+        m = [z.has(t) for z in ma1[g].args]
+        if sum(m) == 1:
+            debug('_laplace_apply_rules match:')
+            debugf('      f, n: %s, %s', (f, ma1[n]))
+            debug('      rule: time derivative (4.1.8)')
+            d = []
+            for k in range(ma1[n]):
+                if k == 0:
+                    y = ma1[g].subs(t, 0)
+                else:
+                    y = Derivative(ma1[g], (t, k)).subs(t, 0)
+                d.append(s**(ma1[n]-k-1)*y)
+            r, pr, cr = _laplace_transform(ma1[g], t, s, simplify=False)
+            return (ma1[a]*(s**ma1[n]*r - Add(*d)),  pr, cr)
+    return None
+
+
+def _laplace_rule_sdiff(f, t, s, doit=True, **hints):
+    """
+    This function looks for multiplications with polynoimials in `t` as they
+    correspond to differentiation in the frequency domain. For example, if it
+    gets ``(t*f(t), t, s)``, it will compute
+    ``-Derivative(LaplaceTransform(f(t), t, s), s)``.
+    """
+
+    if f.is_Mul:
+        pfac = [1]
+        ofac = [1]
+        for fac in Mul.make_args(f):
+            if fac.is_polynomial(t):
+                pfac.append(fac)
+            else:
+                ofac.append(fac)
+        if len(pfac) > 1:
+            pex = prod(pfac)
+            pc = Poly(pex, t).all_coeffs()
+            N = len(pc)
+            if N > 1:
+                debug('_laplace_apply_rules match:')
+                debugf('      f, n: %s, %s', (f, pfac))
+                debug('      rule: frequency derivative (4.1.6)')
+                oex = prod(ofac)
+                r_, p_, c_ = _laplace_transform(oex, t, s, simplify=False)
+                deri = [r_]
+                d1 = False
+                try:
+                    d1 = -diff(deri[-1], s)
+                except ValueError:
+                    d1 = False
+                if r_.has(LaplaceTransform):
+                    for k in range(N-1):
+                        deri.append((-1)**(k+1)*Derivative(r_, s, k+1))
+                else:
+                    if d1:
+                        deri.append(d1)
+                        for k in range(N-2):
+                            deri.append(-diff(deri[-1], s))
+                if d1:
+                    r = Add(*[pc[N-n-1]*deri[n] for n in range(N)])
+                    return (r, p_, c_)
+    return None
+
+
+def _laplace_expand(f, t, s, doit=True, **hints):
+    """
+    This function tries to expand its argument with successively stronger
+    methods: first it will expand on the top level, then it will expand any
+    multiplications in depth, then it will try all avilable expansion methods,
+    and finally it will try to expand trigonometric functions.
+
+    If it can expand, it will then compute the Laplace transform of the
+    expanded term.
+    """
+
+    if f.is_Add:
+        return None
+    r = expand(f, deep=False)
+    if r.is_Add:
+        return _laplace_transform(r, t, s, simplify=False)
+    r = expand_mul(f)
+    if r.is_Add:
+        return _laplace_transform(r, t, s, simplify=False)
+    r = expand(f)
+    if r.is_Add:
+        return _laplace_transform(r, t, s, simplify=False)
+    if r != f:
+        return _laplace_transform(r, t, s, simplify=False)
+    r = expand(expand_trig(f))
+    if r.is_Add:
+        return _laplace_transform(r, t, s, simplify=False)
+    return None
+
+
+def _laplace_apply_prog_rules(f, t, s):
+    """
+    This function applies all program rules and returns the result if one
+    of them gives a result.
+    """
+
+    prog_rules = [_laplace_rule_heaviside, _laplace_rule_delta,
+                  _laplace_rule_timescale, _laplace_rule_exp,
+                  _laplace_rule_trig,
+                  _laplace_rule_diff, _laplace_rule_sdiff]
+
+    for p_rule in prog_rules:
+        if (L := p_rule(f, t, s)) is not None:
+            return L
+    return None
+
+
+def _laplace_apply_simple_rules(f, t, s):
+    """
+    This function applies all simple rules and returns the result if one
+    of them gives a result.
+    """
+    simple_rules, t_, s_ = _laplace_build_rules()
+    prep_old = ''
+    prep_f = ''
+    for t_dom, s_dom, check, plane, prep in simple_rules:
+        if prep_old != prep:
+            prep_f = prep(f.subs({t: t_}))
+            prep_old = prep
+        ma = prep_f.match(t_dom)
+        if ma:
+            try:
+                c = check.xreplace(ma)
+            except TypeError:
+                # This may happen if the time function has imaginary
+                # numbers in it. Then we give up.
+                continue
+            if c == S.true:
+                debug('_laplace_apply_simple_rules match:')
+                debugf('      f:     %s', (f,))
+                debugf('      rule:  %s o---o %s', (t_dom, s_dom))
+                debugf('      match: %s', (ma, ))
+                return (s_dom.xreplace(ma).subs({s_: s}),
+                        plane.xreplace(ma), S.true)
+    return None
+
+
+def _laplace_transform(fn, t_, s_, simplify=True):
+    """
+    Front-end function of the Laplace transform. It tries to apply all known
+    rules recursively, and if everything else fails, it tries to integrate.
+    """
+    debugf('[LT _l_t] (%s, %s, %s)', (fn, t_, s_))
+
+    terms = Add.make_args(fn)
+    terms_s = []
+    planes = []
+    conditions = []
+    for ff in terms:
+        k, ft = ff.as_independent(t_, as_Add=False)
+        if (r := _laplace_apply_simple_rules(ft, t_, s_)) is not None:
+            pass
+        elif (r := _laplace_apply_prog_rules(ft, t_, s_)) is not None:
+            pass
+        elif (r := _laplace_expand(ft, t_, s_)) is not None:
+            pass
+        elif any(undef.has(t_) for undef in ft.atoms(AppliedUndef)):
+            # If there are undefined functions f(t) then integration is
+            # unlikely to do anything useful so we skip it and given an
+            # unevaluated LaplaceTransform.
+            r = (LaplaceTransform(ft, t_, s_), S.NegativeInfinity, True)
+        elif (r := _laplace_transform_integration(
+                ft, t_, s_, simplify=simplify)) is not None:
+            pass
+        else:
+            r = (LaplaceTransform(ft, t_, s_), S.NegativeInfinity, True)
+        (ri_, pi_, ci_) = r
+        terms_s.append(k*ri_)
+        planes.append(pi_)
+        conditions.append(ci_)
+
+    result = Add(*terms_s)
+    if simplify:
+        result = result.simplify(doit=False)
+    plane = Max(*planes)
+    condition = And(*conditions)
+
+    return result, plane, condition
+
+
+class LaplaceTransform(IntegralTransform):
+    """
+    Class representing unevaluated Laplace transforms.
+
+    For usage of this class, see the :class:`IntegralTransform` docstring.
+
+    For how to compute Laplace transforms, see the :func:`laplace_transform`
+    docstring.
+
+    If this is called with ``.doit()``, it returns the Laplace transform as an
+    expression. If it is called with ``.doit(noconds=False)``, it returns a
+    tuple containing the same expression, a convergence plane, and conditions.
+    """
+
+    _name = 'Laplace'
+
+    def _compute_transform(self, f, t, s, **hints):
+        _simplify = hints.get('simplify', False)
+        LT = _laplace_transform_integration(f, t, s, simplify=_simplify)
+        return LT
+
+    def _as_integral(self, f, t, s):
+        return Integral(f*exp(-s*t), (t, S.Zero, S.Infinity))
+
+    def _collapse_extra(self, extra):
+        conds = []
+        planes = []
+        for plane, cond in extra:
+            conds.append(cond)
+            planes.append(plane)
+        cond = And(*conds)
+        plane = Max(*planes)
+        if cond == S.false:
+            raise IntegralTransformError(
+                'Laplace', None, 'No combined convergence.')
+        return plane, cond
+
+    def doit(self, **hints):
+        """
+        Try to evaluate the transform in closed form.
+
+        Explanation
+        ===========
+
+        Standard hints are the following:
+        - ``noconds``:  if True, do not return convergence conditions. The
+        default setting is `True`.
+        - ``simplify``: if True, it simplifies the final result. The
+        default setting is `False`.
+        """
+        _noconds = hints.get('noconds', True)
+        _simplify = hints.get('simplify', False)
+
+        debugf('[LT doit] (%s, %s, %s)', (self.function,
+                                          self.function_variable,
+                                          self.transform_variable))
+
+        t_ = self.function_variable
+        s_ = self.transform_variable
+        fn = self.function
+
+        r = _laplace_transform(fn, t_, s_, simplify=_simplify)
+
+        if _noconds:
+            return r[0]
+        else:
+            return r
+
+
+def laplace_transform(f, t, s, legacy_matrix=True, **hints):
+    r"""
+    Compute the Laplace Transform `F(s)` of `f(t)`,
+
+    .. math :: F(s) = \int_{0^{-}}^\infty e^{-st} f(t) \mathrm{d}t.
+
+    Explanation
+    ===========
+
+    For all sensible functions, this converges absolutely in a
+    half-plane
+
+    .. math :: a < \operatorname{Re}(s)
+
+    This function returns ``(F, a, cond)`` where ``F`` is the Laplace
+    transform of ``f``, `a` is the half-plane of convergence, and `cond` are
+    auxiliary convergence conditions.
+
+    The implementation is rule-based, and if you are interested in which
+    rules are applied, and whether integration is attempted, you can switch
+    debug information on by setting ``sympy.SYMPY_DEBUG=True``. The numbers
+    of the rules in the debug information (and the code) refer to Bateman's
+    Tables of Integral Transforms [1].
+
+    The lower bound is `0-`, meaning that this bound should be approached
+    from the lower side. This is only necessary if distributions are involved.
+    At present, it is only done if `f(t)` contains ``DiracDelta``, in which
+    case the Laplace transform is computed implicitly as
+
+    .. math ::
+        F(s) = \lim_{\tau\to 0^{-}} \int_{\tau}^\infty e^{-st}
+        f(t) \mathrm{d}t
+
+    by applying rules.
+
+    If the Laplace transform cannot be fully computed in closed form, this
+    function returns expressions containing unevaluated
+    :class:`LaplaceTransform` objects.
+
+    For a description of possible hints, refer to the docstring of
+    :func:`sympy.integrals.transforms.IntegralTransform.doit`. If
+    ``noconds=True``, only `F` will be returned (i.e. not ``cond``, and also
+    not the plane ``a``).
+
+    .. deprecated:: 1.9
+        Legacy behavior for matrices where ``laplace_transform`` with
+        ``noconds=False`` (the default) returns a Matrix whose elements are
+        tuples. The behavior of ``laplace_transform`` for matrices will change
+        in a future release of SymPy to return a tuple of the transformed
+        Matrix and the convergence conditions for the matrix as a whole. Use
+        ``legacy_matrix=False`` to enable the new behavior.
+
+    Examples
+    ========
+
+    >>> from sympy import DiracDelta, exp, laplace_transform
+    >>> from sympy.abc import t, s, a
+    >>> laplace_transform(t**4, t, s)
+    (24/s**5, 0, True)
+    >>> laplace_transform(t**a, t, s)
+    (gamma(a + 1)/(s*s**a), 0, re(a) > -1)
+    >>> laplace_transform(DiracDelta(t)-a*exp(-a*t), t, s, simplify=True)
+    (s/(a + s), -re(a), True)
+
+    References
+    ==========
+
+    .. [1] Erdelyi, A. (ed.), Tables of Integral Transforms, Volume 1,
+           Bateman Manuscript Prooject, McGraw-Hill (1954), available:
+           https://resolver.caltech.edu/CaltechAUTHORS:20140123-101456353
+
+    See Also
+    ========
+
+    inverse_laplace_transform, mellin_transform, fourier_transform
+    hankel_transform, inverse_hankel_transform
+
+    """
+
+    _noconds = hints.get('noconds', False)
+    _simplify = hints.get('simplify', False)
+
+    if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):
+
+        conds = not hints.get('noconds', False)
+
+        if conds and legacy_matrix:
+            adt = 'deprecated-laplace-transform-matrix'
+            sympy_deprecation_warning(
+                """
+Calling laplace_transform() on a Matrix with noconds=False (the default) is
+deprecated. Either noconds=True or use legacy_matrix=False to get the new
+behavior.
+                """,
+                deprecated_since_version='1.9',
+                active_deprecations_target=adt,
+            )
+            # Temporarily disable the deprecation warning for non-Expr objects
+            # in Matrix
+            with ignore_warnings(SymPyDeprecationWarning):
+                return f.applyfunc(
+                    lambda fij: laplace_transform(fij, t, s, **hints))
+        else:
+            elements_trans = [laplace_transform(
+                fij, t, s, **hints) for fij in f]
+            if conds:
+                elements, avals, conditions = zip(*elements_trans)
+                f_laplace = type(f)(*f.shape, elements)
+                return f_laplace, Max(*avals), And(*conditions)
+            else:
+                return type(f)(*f.shape, elements_trans)
+
+    LT = LaplaceTransform(f, t, s).doit(noconds=False, simplify=_simplify)
+
+    if not _noconds:
+        return LT
+    else:
+        return LT[0]
+
+
+def _inverse_laplace_transform_integration(F, s, t_, plane, simplify=True):
+    """ The backend function for inverse Laplace transforms. """
+    from sympy.integrals.meijerint import meijerint_inversion, _get_coeff_exp
+    from sympy.integrals.transforms import inverse_mellin_transform
+
+    # There are two strategies we can try:
+    # 1) Use inverse mellin transform, related by a simple change of variables.
+    # 2) Use the inversion integral.
+
+    t = Dummy('t', real=True)
+
+    def pw_simp(*args):
+        """ Simplify a piecewise expression from hyperexpand. """
+        # XXX we break modularity here!
+        if len(args) != 3:
+            return Piecewise(*args)
+        arg = args[2].args[0].argument
+        coeff, exponent = _get_coeff_exp(arg, t)
+        e1 = args[0].args[0]
+        e2 = args[1].args[0]
+        return (
+            Heaviside(1/Abs(coeff) - t**exponent)*e1 +
+            Heaviside(t**exponent - 1/Abs(coeff))*e2)
+
+    if F.is_rational_function(s):
+        F = F.apart(s)
+
+    if F.is_Add:
+        f = Add(
+            *[_inverse_laplace_transform_integration(X, s, t, plane, simplify)
+              for X in F.args])
+        return _simplify(f.subs(t, t_), simplify), True
+
+    try:
+        f, cond = inverse_mellin_transform(F, s, exp(-t), (None, S.Infinity),
+                                           needeval=True, noconds=False)
+    except IntegralTransformError:
+        f = None
+    if f is None:
+        f = meijerint_inversion(F, s, t)
+        if f is None:
+            return None
+        if f.is_Piecewise:
+            f, cond = f.args[0]
+            if f.has(Integral):
+                return None
+        else:
+            cond = S.true
+        f = f.replace(Piecewise, pw_simp)
+
+    if f.is_Piecewise:
+        # many of the functions called below can't work with piecewise
+        # (b/c it has a bool in args)
+        return f.subs(t, t_), cond
+
+    u = Dummy('u')
+
+    def simp_heaviside(arg, H0=S.Half):
+        a = arg.subs(exp(-t), u)
+        if a.has(t):
+            return Heaviside(arg, H0)
+        from sympy.solvers.inequalities import _solve_inequality
+        rel = _solve_inequality(a > 0, u)
+        if rel.lts == u:
+            k = log(rel.gts)
+            return Heaviside(t + k, H0)
+        else:
+            k = log(rel.lts)
+            return Heaviside(-(t + k), H0)
+
+    f = f.replace(Heaviside, simp_heaviside)
+
+    def simp_exp(arg):
+        return expand_complex(exp(arg))
+
+    f = f.replace(exp, simp_exp)
+
+    # TODO it would be nice to fix cosh and sinh ... simplify messes these
+    #      exponentials up
+
+    return _simplify(f.subs(t, t_), simplify), cond
+
+
+def _complete_the_square_in_denom(f, s):
+    from sympy.simplify.radsimp import fraction
+    [n, d] = fraction(f)
+    if d.is_polynomial(s):
+        cf = d.as_poly(s).all_coeffs()
+        if len(cf) == 3:
+            a, b, c = cf
+            d = a*((s+b/(2*a))**2+c/a-(b/(2*a))**2)
+    return n/d
+
+
+@cacheit
+def _inverse_laplace_build_rules():
+    """
+    This is an internal helper function that returns the table of inverse
+    Laplace transform rules in terms of the time variable `t` and the
+    frequency variable `s`.  It is used by `_inverse_laplace_apply_rules`.
+    """
+    s = Dummy('s')
+    t = Dummy('t')
+    a = Wild('a', exclude=[s])
+    b = Wild('b', exclude=[s])
+    c = Wild('c', exclude=[s])
+
+    debug('_inverse_laplace_build_rules is building rules')
+
+    def _frac(f, s):
+        try:
+            return f.factor(s)
+        except PolynomialError:
+            return f
+
+    def same(f): return f
+    # This list is sorted according to the prep function needed.
+    _ILT_rules = [
+        (a/s, a, S.true, same, 1),
+        (b*(s+a)**(-c), t**(c-1)*exp(-a*t)/gamma(c), c > 0, same, 1),
+        (1/(s**2+a**2)**2, (sin(a*t) - a*t*cos(a*t))/(2*a**3),
+         S.true, same, 1),
+        # The next two rules must be there in that order. For the second
+        # one, the condition would be a != 0 or, respectively, to take the
+        # limit a -> 0 after the transform if a == 0. It is much simpler if
+        # the case a == 0 has its own rule.
+        (1/(s**b), t**(b - 1)/gamma(b), S.true, same, 1),
+        (1/(s*(s+a)**b), lowergamma(b, a*t)/(a**b*gamma(b)),
+         S.true, same, 1)
+    ]
+    return _ILT_rules, s, t
+
+
+def _inverse_laplace_apply_simple_rules(f, s, t):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    if f == 1:
+        debug('_inverse_laplace_apply_simple_rules match:')
+        debugf('      f:    %s', (1,))
+        debugf('      rule: 1 o---o DiracDelta(%s)', (t,))
+        return DiracDelta(t), S.true
+
+    _ILT_rules, s_, t_ = _inverse_laplace_build_rules()
+    _prep = ''
+    fsubs = f.subs({s: s_})
+
+    for s_dom, t_dom, check, prep, fac in _ILT_rules:
+        if _prep != (prep, fac):
+            _F = prep(fsubs*fac)
+            _prep = (prep, fac)
+        ma = _F.match(s_dom)
+        if ma:
+            try:
+                c = check.xreplace(ma)
+            except TypeError:
+                continue
+            if c == S.true:
+                debug('_inverse_laplace_apply_simple_rules match:')
+                debugf('      f:    %s', (f,))
+                debugf('      rule: %s o---o %s', (s_dom, t_dom))
+                debugf('      ma:   %s', (ma,))
+                return Heaviside(t)*t_dom.xreplace(ma).subs({t_: t}), S.true
+
+    return None
+
+
+def _inverse_laplace_time_shift(F, s, t, plane):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    a = Wild('a', exclude=[s])
+    g = Wild('g')
+
+    if not F.has(s):
+        return F*DiracDelta(t), S.true
+    ma1 = F.match(exp(a*s))
+    if ma1:
+        if ma1[a].is_negative:
+            debug('_inverse_laplace_time_shift match:')
+            debugf('      f:    %s', (F,))
+            debug('      rule: exp(-a*s) o---o DiracDelta(t-a)')
+            debugf('      ma:   %s', (ma1,))
+            return DiracDelta(t+ma1[a]), S.true
+        else:
+            debug('_inverse_laplace_time_shift match: negative time shift')
+            return InverseLaplaceTransform(F, s, t, plane), S.true
+
+    ma1 = F.match(exp(a*s)*g)
+    if ma1:
+        if ma1[a].is_negative:
+            debug('_inverse_laplace_time_shift match:')
+            debugf('      f:    %s', (F,))
+            debug('      rule: exp(-a*s)*F(s) o---o Heaviside(t-a)*f(t-a)')
+            debugf('      ma:   %s', (ma1,))
+            return _inverse_laplace_transform(ma1[g], s, t+ma1[a], plane)
+        else:
+            debug('_inverse_laplace_time_shift match: negative time shift')
+            return InverseLaplaceTransform(F, s, t, plane), S.true
+    return None
+
+
+def _inverse_laplace_time_diff(F, s, t, plane):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    n = Wild('n', exclude=[s])
+    g = Wild('g')
+
+    ma1 = F.match(s**n*g)
+    if ma1 and ma1[n].is_integer and ma1[n].is_positive:
+        debug('_inverse_laplace_time_diff match:')
+        debugf('      f:    %s', (F,))
+        debug('      rule: s**n*F(s) o---o diff(f(t), t, n)')
+        debugf('      ma:   %s', (ma1,))
+        r, c = _inverse_laplace_transform(ma1[g], s, t, plane)
+        r = r.replace(Heaviside(t), 1)
+        if r.has(InverseLaplaceTransform):
+            return diff(r, t, ma1[n]), c
+        else:
+            return Heaviside(t)*diff(r, t, ma1[n]), c
+    return None
+
+
+def _inverse_laplace_apply_prog_rules(F, s, t, plane):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    prog_rules = [_inverse_laplace_time_shift,
+                  _inverse_laplace_time_diff]
+
+    for p_rule in prog_rules:
+        if (r := p_rule(F, s, t, plane)) is not None:
+            return r
+    return None
+
+
+def _inverse_laplace_expand(fn, s, t, plane):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    if fn.is_Add:
+        return None
+    r = expand(fn, deep=False)
+    if r.is_Add:
+        return _inverse_laplace_transform(r, s, t, plane)
+    r = expand_mul(fn)
+    if r.is_Add:
+        return _inverse_laplace_transform(r, s, t, plane)
+    r = expand(fn)
+    if r.is_Add:
+        return _inverse_laplace_transform(r, s, t, plane)
+    if fn.is_rational_function(s):
+        r = fn.apart(s).doit()
+    if r.is_Add:
+        return _inverse_laplace_transform(r, s, t, plane)
+    return None
+
+
+def _inverse_laplace_rational(fn, s, t, plane, simplify):
+    """
+    Helper function for the class InverseLaplaceTransform.
+    """
+    debugf('[ILT _i_l_r] (%s, %s, %s)', (fn, s, t))
+    x_ = symbols('x_')
+    f = fn.apart(s)
+    terms = Add.make_args(f)
+    terms_t = []
+    conditions = [S.true]
+    for term in terms:
+        [n, d] = term.as_numer_denom()
+        dc = d.as_poly(s).all_coeffs()
+        dc_lead = dc[0]
+        dc = [x/dc_lead for x in dc]
+        nc = [x/dc_lead for x in n.as_poly(s).all_coeffs()]
+        if len(dc) == 1:
+            r = nc[0]*DiracDelta(t)
+            terms_t.append(r)
+        elif len(dc) == 2:
+            r = nc[0]*exp(-dc[1]*t)
+            terms_t.append(Heaviside(t)*r)
+        elif len(dc) == 3:
+            a = dc[1]/2
+            b = (dc[2]-a**2).factor()
+            if len(nc) == 1:
+                nc = [S.Zero] + nc
+            l, m = tuple(nc)
+            if b == 0:
+                r = (m*t+l*(1-a*t))*exp(-a*t)
+            else:
+                hyp = False
+                if b.is_negative:
+                    b = -b
+                    hyp = True
+                b2 = list(roots(x_**2-b, x_).keys())[0]
+                bs = sqrt(b).simplify()
+                if hyp:
+                    r = (
+                        l*exp(-a*t)*cosh(b2*t) + (m-a*l) /
+                        bs*exp(-a*t)*sinh(bs*t))
+                else:
+                    r = l*exp(-a*t)*cos(b2*t) + (m-a*l)/bs*exp(-a*t)*sin(bs*t)
+            terms_t.append(Heaviside(t)*r)
+        else:
+            ft, cond = _inverse_laplace_transform(
+                fn, s, t, plane, simplify=True, dorational=False)
+            terms_t.append(ft)
+            conditions.append(cond)
+
+    result = Add(*terms_t)
+    if simplify:
+        result = result.simplify(doit=False)
+    debugf('[ILT _i_l_r]   returns %s', (result,))
+    return result, And(*conditions)
+
+
+def _inverse_laplace_transform(
+        fn, s_, t_, plane, simplify=True, dorational=True):
+    """
+    Front-end function of the inverse Laplace transform. It tries to apply all
+    known rules recursively.  If everything else fails, it tries to integrate.
+    """
+    terms = Add.make_args(fn)
+    terms_t = []
+    conditions = []
+
+    debugf('[ILT _i_l_t] (%s, %s, %s)', (fn, s_, t_))
+
+    for term in terms:
+        k, f = term.as_independent(s_, as_Add=False)
+        if (
+                dorational and term.is_rational_function(s_) and
+                (
+                    r := _inverse_laplace_rational(
+                        f, s_, t_, plane, simplify)) is not None):
+            pass
+        elif (r := _inverse_laplace_apply_simple_rules(f, s_, t_)) is not None:
+            pass
+        elif (r := _inverse_laplace_expand(f, s_, t_, plane)) is not None:
+            pass
+        elif (
+                (r := _inverse_laplace_apply_prog_rules(f, s_, t_, plane))
+                is not None):
+            pass
+        elif any(undef.has(s_) for undef in f.atoms(AppliedUndef)):
+            # If there are undefined functions f(t) then integration is
+            # unlikely to do anything useful so we skip it and given an
+            # unevaluated LaplaceTransform.
+            r = (InverseLaplaceTransform(f, s_, t_, plane), S.true)
+        elif (
+                r := _inverse_laplace_transform_integration(
+                    f, s_, t_, plane, simplify=simplify)) is not None:
+            pass
+        else:
+            r = (InverseLaplaceTransform(f, s_, t_, plane), S.true)
+        (ri_, ci_) = r
+        terms_t.append(k*ri_)
+        conditions.append(ci_)
+
+    result = Add(*terms_t)
+    if simplify:
+        result = result.simplify(doit=False)
+    condition = And(*conditions)
+
+    return result, condition
+
+
+class InverseLaplaceTransform(IntegralTransform):
+    """
+    Class representing unevaluated inverse Laplace transforms.
+
+    For usage of this class, see the :class:`IntegralTransform` docstring.
+
+    For how to compute inverse Laplace transforms, see the
+    :func:`inverse_laplace_transform` docstring.
+    """
+
+    _name = 'Inverse Laplace'
+    _none_sentinel = Dummy('None')
+    _c = Dummy('c')
+
+    def __new__(cls, F, s, x, plane, **opts):
+        if plane is None:
+            plane = InverseLaplaceTransform._none_sentinel
+        return IntegralTransform.__new__(cls, F, s, x, plane, **opts)
+
+    @property
+    def fundamental_plane(self):
+        plane = self.args[3]
+        if plane is InverseLaplaceTransform._none_sentinel:
+            plane = None
+        return plane
+
+    def _compute_transform(self, F, s, t, **hints):
+        return _inverse_laplace_transform_integration(
+            F, s, t, self.fundamental_plane, **hints)
+
+    def _as_integral(self, F, s, t):
+        c = self.__class__._c
+        return (
+            Integral(exp(s*t)*F, (s, c - S.ImaginaryUnit*S.Infinity,
+                                  c + S.ImaginaryUnit*S.Infinity)) /
+            (2*S.Pi*S.ImaginaryUnit))
+
+    def doit(self, **hints):
+        """
+        Try to evaluate the transform in closed form.
+
+        Explanation
+        ===========
+
+        Standard hints are the following:
+        - ``noconds``:  if True, do not return convergence conditions. The
+        default setting is `True`.
+        - ``simplify``: if True, it simplifies the final result. The
+        default setting is `False`.
+        """
+        _noconds = hints.get('noconds', True)
+        _simplify = hints.get('simplify', False)
+
+        debugf('[ILT doit] (%s, %s, %s)', (self.function,
+                                           self.function_variable,
+                                           self.transform_variable))
+
+        s_ = self.function_variable
+        t_ = self.transform_variable
+        fn = self.function
+        plane = self.fundamental_plane
+
+        r = _inverse_laplace_transform(fn, s_, t_, plane, simplify=_simplify)
+
+        if _noconds:
+            return r[0]
+        else:
+            return r
+
+
+def inverse_laplace_transform(F, s, t, plane=None, **hints):
+    r"""
+    Compute the inverse Laplace transform of `F(s)`, defined as
+
+    .. math ::
+        f(t) = \frac{1}{2\pi i} \int_{c-i\infty}^{c+i\infty} e^{st}
+        F(s) \mathrm{d}s,
+
+    for `c` so large that `F(s)` has no singularites in the
+    half-plane `\operatorname{Re}(s) > c-\epsilon`.
+
+    Explanation
+    ===========
+
+    The plane can be specified by
+    argument ``plane``, but will be inferred if passed as None.
+
+    Under certain regularity conditions, this recovers `f(t)` from its
+    Laplace Transform `F(s)`, for non-negative `t`, and vice
+    versa.
+
+    If the integral cannot be computed in closed form, this function returns
+    an unevaluated :class:`InverseLaplaceTransform` object.
+
+    Note that this function will always assume `t` to be real,
+    regardless of the SymPy assumption on `t`.
+
+    For a description of possible hints, refer to the docstring of
+    :func:`sympy.integrals.transforms.IntegralTransform.doit`.
+
+    Examples
+    ========
+
+    >>> from sympy import inverse_laplace_transform, exp, Symbol
+    >>> from sympy.abc import s, t
+    >>> a = Symbol('a', positive=True)
+    >>> inverse_laplace_transform(exp(-a*s)/s, s, t)
+    Heaviside(-a + t)
+
+    See Also
+    ========
+
+    laplace_transform
+    hankel_transform, inverse_hankel_transform
+    """
+    if isinstance(F, MatrixBase) and hasattr(F, 'applyfunc'):
+        return F.applyfunc(
+            lambda Fij: inverse_laplace_transform(Fij, s, t, plane, **hints))
+    return InverseLaplaceTransform(F, s, t, plane).doit(**hints)
+
+
+def _fast_inverse_laplace(e, s, t):
+    """Fast inverse Laplace transform of rational function including RootSum"""
+    a, b, n = symbols('a, b, n', cls=Wild, exclude=[s])
+
+    def _ilt(e):
+        if not e.has(s):
+            return e
+        elif e.is_Add:
+            return _ilt_add(e)
+        elif e.is_Mul:
+            return _ilt_mul(e)
+        elif e.is_Pow:
+            return _ilt_pow(e)
+        elif isinstance(e, RootSum):
+            return _ilt_rootsum(e)
+        else:
+            raise NotImplementedError
+
+    def _ilt_add(e):
+        return e.func(*map(_ilt, e.args))
+
+    def _ilt_mul(e):
+        coeff, expr = e.as_independent(s)
+        if expr.is_Mul:
+            raise NotImplementedError
+        return coeff * _ilt(expr)
+
+    def _ilt_pow(e):
+        match = e.match((a*s + b)**n)
+        if match is not None:
+            nm, am, bm = match[n], match[a], match[b]
+            if nm.is_Integer and nm < 0:
+                return t**(-nm-1)*exp(-(bm/am)*t)/(am**-nm*gamma(-nm))
+            if nm == 1:
+                return exp(-(bm/am)*t) / am
+        raise NotImplementedError
+
+    def _ilt_rootsum(e):
+        expr = e.fun.expr
+        [variable] = e.fun.variables
+        return RootSum(e.poly, Lambda(variable, together(_ilt(expr))))
+
+    return _ilt(e)

venv/lib/python3.10/site-packages/sympy/integrals/manualintegrate.py

@@ -0,0 +1,2171 @@
+"""Integration method that emulates by-hand techniques.
+
+This module also provides functionality to get the steps used to evaluate a
+particular integral, in the ``integral_steps`` function. This will return
+nested ``Rule`` s representing the integration rules used.
+
+Each ``Rule`` class represents a (maybe parametrized) integration rule, e.g.
+``SinRule`` for integrating ``sin(x)`` and ``ReciprocalSqrtQuadraticRule``
+for integrating ``1/sqrt(a+b*x+c*x**2)``. The ``eval`` method returns the
+integration result.
+
+The ``manualintegrate`` function computes the integral by calling ``eval``
+on the rule returned by ``integral_steps``.
+
+The integrator can be extended with new heuristics and evaluation
+techniques. To do so, extend the ``Rule`` class, implement ``eval`` method,
+then write a function that accepts an ``IntegralInfo`` object and returns
+either a ``Rule`` instance or ``None``. If the new technique requires a new
+match, add the key and call to the antiderivative function to integral_steps.
+To enable simple substitutions, add the match to find_substitutions.
+
+"""
+
+from __future__ import annotations
+from typing import NamedTuple, Type, Callable, Sequence
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from collections import defaultdict
+from collections.abc import Mapping
+
+from sympy.core.add import Add
+from sympy.core.cache import cacheit
+from sympy.core.containers import Dict
+from sympy.core.expr import Expr
+from sympy.core.function import Derivative
+from sympy.core.logic import fuzzy_not
+from sympy.core.mul import Mul
+from sympy.core.numbers import Integer, Number, E
+from sympy.core.power import Pow
+from sympy.core.relational import Eq, Ne, Boolean
+from sympy.core.singleton import S
+from sympy.core.symbol import Dummy, Symbol, Wild
+from sympy.functions.elementary.complexes import Abs
+from sympy.functions.elementary.exponential import exp, log
+from sympy.functions.elementary.hyperbolic import (HyperbolicFunction, csch,
+    cosh, coth, sech, sinh, tanh, asinh)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.elementary.trigonometric import (TrigonometricFunction,
+    cos, sin, tan, cot, csc, sec, acos, asin, atan, acot, acsc, asec)
+from sympy.functions.special.delta_functions import Heaviside, DiracDelta
+from sympy.functions.special.error_functions import (erf, erfi, fresnelc,
+    fresnels, Ci, Chi, Si, Shi, Ei, li)
+from sympy.functions.special.gamma_functions import uppergamma
+from sympy.functions.special.elliptic_integrals import elliptic_e, elliptic_f
+from sympy.functions.special.polynomials import (chebyshevt, chebyshevu,
+    legendre, hermite, laguerre, assoc_laguerre, gegenbauer, jacobi,
+    OrthogonalPolynomial)
+from sympy.functions.special.zeta_functions import polylog
+from .integrals import Integral
+from sympy.logic.boolalg import And
+from sympy.ntheory.factor_ import primefactors
+from sympy.polys.polytools import degree, lcm_list, gcd_list, Poly
+from sympy.simplify.radsimp import fraction
+from sympy.simplify.simplify import simplify
+from sympy.solvers.solvers import solve
+from sympy.strategies.core import switch, do_one, null_safe, condition
+from sympy.utilities.iterables import iterable
+from sympy.utilities.misc import debug
+
+
+@dataclass
+class Rule(ABC):
+    integrand: Expr
+    variable: Symbol
+
+    @abstractmethod
+    def eval(self) -> Expr:
+        pass
+
+    @abstractmethod
+    def contains_dont_know(self) -> bool:
+        pass
+
+
+@dataclass
+class AtomicRule(Rule, ABC):
+    """A simple rule that does not depend on other rules"""
+    def contains_dont_know(self) -> bool:
+        return False
+
+
+@dataclass
+class ConstantRule(AtomicRule):
+    """integrate(a, x)  ->  a*x"""
+    def eval(self) -> Expr:
+        return self.integrand * self.variable
+
+
+@dataclass
+class ConstantTimesRule(Rule):
+    """integrate(a*f(x), x)  ->  a*integrate(f(x), x)"""
+    constant: Expr
+    other: Expr
+    substep: Rule
+
+    def eval(self) -> Expr:
+        return self.constant * self.substep.eval()
+
+    def contains_dont_know(self) -> bool:
+        return self.substep.contains_dont_know()
+
+
+@dataclass
+class PowerRule(AtomicRule):
+    """integrate(x**a, x)"""
+    base: Expr
+    exp: Expr
+
+    def eval(self) -> Expr:
+        return Piecewise(
+            ((self.base**(self.exp + 1))/(self.exp + 1), Ne(self.exp, -1)),
+            (log(self.base), True),
+        )
+
+
+@dataclass
+class NestedPowRule(AtomicRule):
+    """integrate((x**a)**b, x)"""
+    base: Expr
+    exp: Expr
+
+    def eval(self) -> Expr:
+        m = self.base * self.integrand
+        return Piecewise((m / (self.exp + 1), Ne(self.exp, -1)),
+                         (m * log(self.base), True))
+
+
+@dataclass
+class AddRule(Rule):
+    """integrate(f(x) + g(x), x) -> integrate(f(x), x) + integrate(g(x), x)"""
+    substeps: list[Rule]
+
+    def eval(self) -> Expr:
+        return Add(*(substep.eval() for substep in self.substeps))
+
+    def contains_dont_know(self) -> bool:
+        return any(substep.contains_dont_know() for substep in self.substeps)
+
+
+@dataclass
+class URule(Rule):
+    """integrate(f(g(x))*g'(x), x) -> integrate(f(u), u), u = g(x)"""
+    u_var: Symbol
+    u_func: Expr
+    substep: Rule
+
+    def eval(self) -> Expr:
+        result = self.substep.eval()
+        if self.u_func.is_Pow:
+            base, exp_ = self.u_func.as_base_exp()
+            if exp_ == -1:
+                # avoid needless -log(1/x) from substitution
+                result = result.subs(log(self.u_var), -log(base))
+        return result.subs(self.u_var, self.u_func)
+
+    def contains_dont_know(self) -> bool:
+        return self.substep.contains_dont_know()
+
+
+@dataclass
+class PartsRule(Rule):
+    """integrate(u(x)*v'(x), x) -> u(x)*v(x) - integrate(u'(x)*v(x), x)"""
+    u: Symbol
+    dv: Expr
+    v_step: Rule
+    second_step: Rule | None  # None when is a substep of CyclicPartsRule
+
+    def eval(self) -> Expr:
+        assert self.second_step is not None
+        v = self.v_step.eval()
+        return self.u * v - self.second_step.eval()
+
+    def contains_dont_know(self) -> bool:
+        return self.v_step.contains_dont_know() or (
+            self.second_step is not None and self.second_step.contains_dont_know())
+
+
+@dataclass
+class CyclicPartsRule(Rule):
+    """Apply PartsRule multiple times to integrate exp(x)*sin(x)"""
+    parts_rules: list[PartsRule]
+    coefficient: Expr
+
+    def eval(self) -> Expr:
+        result = []
+        sign = 1
+        for rule in self.parts_rules:
+            result.append(sign * rule.u * rule.v_step.eval())
+            sign *= -1
+        return Add(*result) / (1 - self.coefficient)
+
+    def contains_dont_know(self) -> bool:
+        return any(substep.contains_dont_know() for substep in self.parts_rules)
+
+
+@dataclass
+class TrigRule(AtomicRule, ABC):
+    pass
+
+
+@dataclass
+class SinRule(TrigRule):
+    """integrate(sin(x), x) -> -cos(x)"""
+    def eval(self) -> Expr:
+        return -cos(self.variable)
+
+
+@dataclass
+class CosRule(TrigRule):
+    """integrate(cos(x), x) -> sin(x)"""
+    def eval(self) -> Expr:
+        return sin(self.variable)
+
+
+@dataclass
+class SecTanRule(TrigRule):
+    """integrate(sec(x)*tan(x), x) -> sec(x)"""
+    def eval(self) -> Expr:
+        return sec(self.variable)
+
+
+@dataclass
+class CscCotRule(TrigRule):
+    """integrate(csc(x)*cot(x), x) -> -csc(x)"""
+    def eval(self) -> Expr:
+        return -csc(self.variable)
+
+
+@dataclass
+class Sec2Rule(TrigRule):
+    """integrate(sec(x)**2, x) -> tan(x)"""
+    def eval(self) -> Expr:
+        return tan(self.variable)
+
+
+@dataclass
+class Csc2Rule(TrigRule):
+    """integrate(csc(x)**2, x) -> -cot(x)"""
+    def eval(self) -> Expr:
+        return -cot(self.variable)
+
+
+@dataclass
+class HyperbolicRule(AtomicRule, ABC):
+    pass
+
+
+@dataclass
+class SinhRule(HyperbolicRule):
+    """integrate(sinh(x), x) -> cosh(x)"""
+    def eval(self) -> Expr:
+        return cosh(self.variable)
+
+
+@dataclass
+class CoshRule(HyperbolicRule):
+    """integrate(cosh(x), x) -> sinh(x)"""
+    def eval(self):
+        return sinh(self.variable)
+
+
+@dataclass
+class ExpRule(AtomicRule):
+    """integrate(a**x, x) -> a**x/ln(a)"""
+    base: Expr
+    exp: Expr
+
+    def eval(self) -> Expr:
+        return self.integrand / log(self.base)
+
+
+@dataclass
+class ReciprocalRule(AtomicRule):
+    """integrate(1/x, x) -> ln(x)"""
+    base: Expr
+
+    def eval(self) -> Expr:
+        return log(self.base)
+
+
+@dataclass
+class ArcsinRule(AtomicRule):
+    """integrate(1/sqrt(1-x**2), x) -> asin(x)"""
+    def eval(self) -> Expr:
+        return asin(self.variable)
+
+
+@dataclass
+class ArcsinhRule(AtomicRule):
+    """integrate(1/sqrt(1+x**2), x) -> asin(x)"""
+    def eval(self) -> Expr:
+        return asinh(self.variable)
+
+
+@dataclass
+class ReciprocalSqrtQuadraticRule(AtomicRule):
+    """integrate(1/sqrt(a+b*x+c*x**2), x) -> log(2*sqrt(c)*sqrt(a+b*x+c*x**2)+b+2*c*x)/sqrt(c)"""
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        a, b, c, x = self.a, self.b, self.c, self.variable
+        return log(2*sqrt(c)*sqrt(a+b*x+c*x**2)+b+2*c*x)/sqrt(c)
+
+
+@dataclass
+class SqrtQuadraticDenomRule(AtomicRule):
+    """integrate(poly(x)/sqrt(a+b*x+c*x**2), x)"""
+    a: Expr
+    b: Expr
+    c: Expr
+    coeffs: list[Expr]
+
+    def eval(self) -> Expr:
+        a, b, c, coeffs, x = self.a, self.b, self.c, self.coeffs.copy(), self.variable
+        # Integrate poly/sqrt(a+b*x+c*x**2) using recursion.
+        # coeffs are coefficients of the polynomial.
+        # Let I_n = x**n/sqrt(a+b*x+c*x**2), then
+        # I_n = A * x**(n-1)*sqrt(a+b*x+c*x**2) - B * I_{n-1} - C * I_{n-2}
+        # where A = 1/(n*c), B = (2*n-1)*b/(2*n*c), C = (n-1)*a/(n*c)
+        # See https://github.com/sympy/sympy/pull/23608 for proof.
+        result_coeffs = []
+        coeffs = coeffs.copy()
+        for i in range(len(coeffs)-2):
+            n = len(coeffs)-1-i
+            coeff = coeffs[i]/(c*n)
+            result_coeffs.append(coeff)
+            coeffs[i+1] -= (2*n-1)*b/2*coeff
+            coeffs[i+2] -= (n-1)*a*coeff
+        d, e = coeffs[-1], coeffs[-2]
+        s = sqrt(a+b*x+c*x**2)
+        constant = d-b*e/(2*c)
+        if constant == 0:
+            I0 = 0
+        else:
+            step = inverse_trig_rule(IntegralInfo(1/s, x), degenerate=False)
+            I0 = constant*step.eval()
+        return Add(*(result_coeffs[i]*x**(len(coeffs)-2-i)
+                     for i in range(len(result_coeffs))), e/c)*s + I0
+
+
+@dataclass
+class SqrtQuadraticRule(AtomicRule):
+    """integrate(sqrt(a+b*x+c*x**2), x)"""
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        step = sqrt_quadratic_rule(IntegralInfo(self.integrand, self.variable), degenerate=False)
+        return step.eval()
+
+
+@dataclass
+class AlternativeRule(Rule):
+    """Multiple ways to do integration."""
+    alternatives: list[Rule]
+
+    def eval(self) -> Expr:
+        return self.alternatives[0].eval()
+
+    def contains_dont_know(self) -> bool:
+        return any(substep.contains_dont_know() for substep in self.alternatives)
+
+
+@dataclass
+class DontKnowRule(Rule):
+    """Leave the integral as is."""
+    def eval(self) -> Expr:
+        return Integral(self.integrand, self.variable)
+
+    def contains_dont_know(self) -> bool:
+        return True
+
+
+@dataclass
+class DerivativeRule(AtomicRule):
+    """integrate(f'(x), x) -> f(x)"""
+    def eval(self) -> Expr:
+        assert isinstance(self.integrand, Derivative)
+        variable_count = list(self.integrand.variable_count)
+        for i, (var, count) in enumerate(variable_count):
+            if var == self.variable:
+                variable_count[i] = (var, count - 1)
+                break
+        return Derivative(self.integrand.expr, *variable_count)
+
+
+@dataclass
+class RewriteRule(Rule):
+    """Rewrite integrand to another form that is easier to handle."""
+    rewritten: Expr
+    substep: Rule
+
+    def eval(self) -> Expr:
+        return self.substep.eval()
+
+    def contains_dont_know(self) -> bool:
+        return self.substep.contains_dont_know()
+
+
+@dataclass
+class CompleteSquareRule(RewriteRule):
+    """Rewrite a+b*x+c*x**2 to a-b**2/(4*c) + c*(x+b/(2*c))**2"""
+    pass
+
+
+@dataclass
+class PiecewiseRule(Rule):
+    subfunctions: Sequence[tuple[Rule, bool | Boolean]]
+
+    def eval(self) -> Expr:
+        return Piecewise(*[(substep.eval(), cond)
+                           for substep, cond in self.subfunctions])
+
+    def contains_dont_know(self) -> bool:
+        return any(substep.contains_dont_know() for substep, _ in self.subfunctions)
+
+
+@dataclass
+class HeavisideRule(Rule):
+    harg: Expr
+    ibnd: Expr
+    substep: Rule
+
+    def eval(self) -> Expr:
+        # If we are integrating over x and the integrand has the form
+        #       Heaviside(m*x+b)*g(x) == Heaviside(harg)*g(symbol)
+        # then there needs to be continuity at -b/m == ibnd,
+        # so we subtract the appropriate term.
+        result = self.substep.eval()
+        return Heaviside(self.harg) * (result - result.subs(self.variable, self.ibnd))
+
+    def contains_dont_know(self) -> bool:
+        return self.substep.contains_dont_know()
+
+
+@dataclass
+class DiracDeltaRule(AtomicRule):
+    n: Expr
+    a: Expr
+    b: Expr
+
+    def eval(self) -> Expr:
+        n, a, b, x = self.n, self.a, self.b, self.variable
+        if n == 0:
+            return Heaviside(a+b*x)/b
+        return DiracDelta(a+b*x, n-1)/b
+
+
+@dataclass
+class TrigSubstitutionRule(Rule):
+    theta: Expr
+    func: Expr
+    rewritten: Expr
+    substep: Rule
+    restriction: bool | Boolean
+
+    def eval(self) -> Expr:
+        theta, func, x = self.theta, self.func, self.variable
+        func = func.subs(sec(theta), 1/cos(theta))
+        func = func.subs(csc(theta), 1/sin(theta))
+        func = func.subs(cot(theta), 1/tan(theta))
+
+        trig_function = list(func.find(TrigonometricFunction))
+        assert len(trig_function) == 1
+        trig_function = trig_function[0]
+        relation = solve(x - func, trig_function)
+        assert len(relation) == 1
+        numer, denom = fraction(relation[0])
+
+        if isinstance(trig_function, sin):
+            opposite = numer
+            hypotenuse = denom
+            adjacent = sqrt(denom**2 - numer**2)
+            inverse = asin(relation[0])
+        elif isinstance(trig_function, cos):
+            adjacent = numer
+            hypotenuse = denom
+            opposite = sqrt(denom**2 - numer**2)
+            inverse = acos(relation[0])
+        else:  # tan
+            opposite = numer
+            adjacent = denom
+            hypotenuse = sqrt(denom**2 + numer**2)
+            inverse = atan(relation[0])
+
+        substitution = [
+            (sin(theta), opposite/hypotenuse),
+            (cos(theta), adjacent/hypotenuse),
+            (tan(theta), opposite/adjacent),
+            (theta, inverse)
+        ]
+        return Piecewise(
+            (self.substep.eval().subs(substitution).trigsimp(), self.restriction)
+        )
+
+    def contains_dont_know(self) -> bool:
+        return self.substep.contains_dont_know()
+
+
+@dataclass
+class ArctanRule(AtomicRule):
+    """integrate(a/(b*x**2+c), x) -> a/b / sqrt(c/b) * atan(x/sqrt(c/b))"""
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        a, b, c, x = self.a, self.b, self.c, self.variable
+        return a/b / sqrt(c/b) * atan(x/sqrt(c/b))
+
+
+@dataclass
+class OrthogonalPolyRule(AtomicRule, ABC):
+    n: Expr
+
+
+@dataclass
+class JacobiRule(OrthogonalPolyRule):
+    a: Expr
+    b: Expr
+
+    def eval(self) -> Expr:
+        n, a, b, x = self.n, self.a, self.b, self.variable
+        return Piecewise(
+            (2*jacobi(n + 1, a - 1, b - 1, x)/(n + a + b), Ne(n + a + b, 0)),
+            (x, Eq(n, 0)),
+            ((a + b + 2)*x**2/4 + (a - b)*x/2, Eq(n, 1)))
+
+
+@dataclass
+class GegenbauerRule(OrthogonalPolyRule):
+    a: Expr
+
+    def eval(self) -> Expr:
+        n, a, x = self.n, self.a, self.variable
+        return Piecewise(
+            (gegenbauer(n + 1, a - 1, x)/(2*(a - 1)), Ne(a, 1)),
+            (chebyshevt(n + 1, x)/(n + 1), Ne(n, -1)),
+            (S.Zero, True))
+
+
+@dataclass
+class ChebyshevTRule(OrthogonalPolyRule):
+    def eval(self) -> Expr:
+        n, x = self.n, self.variable
+        return Piecewise(
+            ((chebyshevt(n + 1, x)/(n + 1) -
+              chebyshevt(n - 1, x)/(n - 1))/2, Ne(Abs(n), 1)),
+            (x**2/2, True))
+
+
+@dataclass
+class ChebyshevURule(OrthogonalPolyRule):
+    def eval(self) -> Expr:
+        n, x = self.n, self.variable
+        return Piecewise(
+            (chebyshevt(n + 1, x)/(n + 1), Ne(n, -1)),
+            (S.Zero, True))
+
+
+@dataclass
+class LegendreRule(OrthogonalPolyRule):
+    def eval(self) -> Expr:
+        n, x = self.n, self.variable
+        return(legendre(n + 1, x) - legendre(n - 1, x))/(2*n + 1)
+
+
+@dataclass
+class HermiteRule(OrthogonalPolyRule):
+    def eval(self) -> Expr:
+        n, x = self.n, self.variable
+        return hermite(n + 1, x)/(2*(n + 1))
+
+
+@dataclass
+class LaguerreRule(OrthogonalPolyRule):
+    def eval(self) -> Expr:
+        n, x = self.n, self.variable
+        return laguerre(n, x) - laguerre(n + 1, x)
+
+
+@dataclass
+class AssocLaguerreRule(OrthogonalPolyRule):
+    a: Expr
+
+    def eval(self) -> Expr:
+        return -assoc_laguerre(self.n + 1, self.a - 1, self.variable)
+
+
+@dataclass
+class IRule(AtomicRule, ABC):
+    a: Expr
+    b: Expr
+
+
+@dataclass
+class CiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return cos(b)*Ci(a*x) - sin(b)*Si(a*x)
+
+
+@dataclass
+class ChiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return cosh(b)*Chi(a*x) + sinh(b)*Shi(a*x)
+
+
+@dataclass
+class EiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return exp(b)*Ei(a*x)
+
+
+@dataclass
+class SiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return sin(b)*Ci(a*x) + cos(b)*Si(a*x)
+
+
+@dataclass
+class ShiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return sinh(b)*Chi(a*x) + cosh(b)*Shi(a*x)
+
+
+@dataclass
+class LiRule(IRule):
+    def eval(self) -> Expr:
+        a, b, x = self.a, self.b, self.variable
+        return li(a*x + b)/a
+
+
+@dataclass
+class ErfRule(AtomicRule):
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        a, b, c, x = self.a, self.b, self.c, self.variable
+        if a.is_extended_real:
+            return Piecewise(
+                (sqrt(S.Pi/(-a))/2 * exp(c - b**2/(4*a)) *
+                    erf((-2*a*x - b)/(2*sqrt(-a))), a < 0),
+                (sqrt(S.Pi/a)/2 * exp(c - b**2/(4*a)) *
+                    erfi((2*a*x + b)/(2*sqrt(a))), True))
+        return sqrt(S.Pi/a)/2 * exp(c - b**2/(4*a)) * \
+                erfi((2*a*x + b)/(2*sqrt(a)))
+
+
+@dataclass
+class FresnelCRule(AtomicRule):
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        a, b, c, x = self.a, self.b, self.c, self.variable
+        return sqrt(S.Pi/(2*a)) * (
+            cos(b**2/(4*a) - c)*fresnelc((2*a*x + b)/sqrt(2*a*S.Pi)) +
+            sin(b**2/(4*a) - c)*fresnels((2*a*x + b)/sqrt(2*a*S.Pi)))
+
+
+@dataclass
+class FresnelSRule(AtomicRule):
+    a: Expr
+    b: Expr
+    c: Expr
+
+    def eval(self) -> Expr:
+        a, b, c, x = self.a, self.b, self.c, self.variable
+        return sqrt(S.Pi/(2*a)) * (
+            cos(b**2/(4*a) - c)*fresnels((2*a*x + b)/sqrt(2*a*S.Pi)) -
+            sin(b**2/(4*a) - c)*fresnelc((2*a*x + b)/sqrt(2*a*S.Pi)))
+
+
+@dataclass
+class PolylogRule(AtomicRule):
+    a: Expr
+    b: Expr
+
+    def eval(self) -> Expr:
+        return polylog(self.b + 1, self.a * self.variable)
+
+
+@dataclass
+class UpperGammaRule(AtomicRule):
+    a: Expr
+    e: Expr
+
+    def eval(self) -> Expr:
+        a, e, x = self.a, self.e, self.variable
+        return x**e * (-a*x)**(-e) * uppergamma(e + 1, -a*x)/a
+
+
+@dataclass
+class EllipticFRule(AtomicRule):
+    a: Expr
+    d: Expr
+
+    def eval(self) -> Expr:
+        return elliptic_f(self.variable, self.d/self.a)/sqrt(self.a)
+
+
+@dataclass
+class EllipticERule(AtomicRule):
+    a: Expr
+    d: Expr
+
+    def eval(self) -> Expr:
+        return elliptic_e(self.variable, self.d/self.a)*sqrt(self.a)
+
+
+class IntegralInfo(NamedTuple):
+    integrand: Expr
+    symbol: Symbol
+
+
+def manual_diff(f, symbol):
+    """Derivative of f in form expected by find_substitutions
+
+    SymPy's derivatives for some trig functions (like cot) are not in a form
+    that works well with finding substitutions; this replaces the
+    derivatives for those particular forms with something that works better.
+
+    """
+    if f.args:
+        arg = f.args[0]
+        if isinstance(f, tan):
+            return arg.diff(symbol) * sec(arg)**2
+        elif isinstance(f, cot):
+            return -arg.diff(symbol) * csc(arg)**2
+        elif isinstance(f, sec):
+            return arg.diff(symbol) * sec(arg) * tan(arg)
+        elif isinstance(f, csc):
+            return -arg.diff(symbol) * csc(arg) * cot(arg)
+        elif isinstance(f, Add):
+            return sum([manual_diff(arg, symbol) for arg in f.args])
+        elif isinstance(f, Mul):
+            if len(f.args) == 2 and isinstance(f.args[0], Number):
+                return f.args[0] * manual_diff(f.args[1], symbol)
+    return f.diff(symbol)
+
+def manual_subs(expr, *args):
+    """
+    A wrapper for `expr.subs(*args)` with additional logic for substitution
+    of invertible functions.
+    """
+    if len(args) == 1:
+        sequence = args[0]
+        if isinstance(sequence, (Dict, Mapping)):
+            sequence = sequence.items()
+        elif not iterable(sequence):
+            raise ValueError("Expected an iterable of (old, new) pairs")
+    elif len(args) == 2:
+        sequence = [args]
+    else:
+        raise ValueError("subs accepts either 1 or 2 arguments")
+
+    new_subs = []
+    for old, new in sequence:
+        if isinstance(old, log):
+            # If log(x) = y, then exp(a*log(x)) = exp(a*y)
+            # that is, x**a = exp(a*y). Replace nontrivial powers of x
+            # before subs turns them into `exp(y)**a`, but
+            # do not replace x itself yet, to avoid `log(exp(y))`.
+            x0 = old.args[0]
+            expr = expr.replace(lambda x: x.is_Pow and x.base == x0,
+                lambda x: exp(x.exp*new))
+            new_subs.append((x0, exp(new)))
+
+    return expr.subs(list(sequence) + new_subs)
+
+# Method based on that on SIN, described in "Symbolic Integration: The
+# Stormy Decade"
+
+inverse_trig_functions = (atan, asin, acos, acot, acsc, asec)
+
+
+def find_substitutions(integrand, symbol, u_var):
+    results = []
+
+    def test_subterm(u, u_diff):
+        if u_diff == 0:
+            return False
+        substituted = integrand / u_diff
+        debug("substituted: {}, u: {}, u_var: {}".format(substituted, u, u_var))
+        substituted = manual_subs(substituted, u, u_var).cancel()
+
+        if substituted.has_free(symbol):
+            return False
+        # avoid increasing the degree of a rational function
+        if integrand.is_rational_function(symbol) and substituted.is_rational_function(u_var):
+            deg_before = max([degree(t, symbol) for t in integrand.as_numer_denom()])
+            deg_after = max([degree(t, u_var) for t in substituted.as_numer_denom()])
+            if deg_after > deg_before:
+                return False
+        return substituted.as_independent(u_var, as_Add=False)
+
+    def exp_subterms(term: Expr):
+        linear_coeffs = []
+        terms = []
+        n = Wild('n', properties=[lambda n: n.is_Integer])
+        for exp_ in term.find(exp):
+            arg = exp_.args[0]
+            if symbol not in arg.free_symbols:
+                continue
+            match = arg.match(n*symbol)
+            if match:
+                linear_coeffs.append(match[n])
+            else:
+                terms.append(exp_)
+        if linear_coeffs:
+            terms.append(exp(gcd_list(linear_coeffs)*symbol))
+        return terms
+
+    def possible_subterms(term):
+        if isinstance(term, (TrigonometricFunction, HyperbolicFunction,
+                             *inverse_trig_functions,
+                             exp, log, Heaviside)):
+            return [term.args[0]]
+        elif isinstance(term, (chebyshevt, chebyshevu,
+                        legendre, hermite, laguerre)):
+            return [term.args[1]]
+        elif isinstance(term, (gegenbauer, assoc_laguerre)):
+            return [term.args[2]]
+        elif isinstance(term, jacobi):
+            return [term.args[3]]
+        elif isinstance(term, Mul):
+            r = []
+            for u in term.args:
+                r.append(u)
+                r.extend(possible_subterms(u))
+            return r
+        elif isinstance(term, Pow):
+            r = [arg for arg in term.args if arg.has(symbol)]
+            if term.exp.is_Integer:
+                r.extend([term.base**d for d in primefactors(term.exp)
+                    if 1 < d < abs(term.args[1])])
+                if term.base.is_Add:
+                    r.extend([t for t in possible_subterms(term.base)
+                        if t.is_Pow])
+            return r
+        elif isinstance(term, Add):
+            r = []
+            for arg in term.args:
+                r.append(arg)
+                r.extend(possible_subterms(arg))
+            return r
+        return []
+
+    for u in list(dict.fromkeys(possible_subterms(integrand) + exp_subterms(integrand))):
+        if u == symbol:
+            continue
+        u_diff = manual_diff(u, symbol)
+        new_integrand = test_subterm(u, u_diff)
+        if new_integrand is not False:
+            constant, new_integrand = new_integrand
+            if new_integrand == integrand.subs(symbol, u_var):
+                continue
+            substitution = (u, constant, new_integrand)
+            if substitution not in results:
+                results.append(substitution)
+
+    return results
+
+def rewriter(condition, rewrite):
+    """Strategy that rewrites an integrand."""
+    def _rewriter(integral):
+        integrand, symbol = integral
+        debug("Integral: {} is rewritten with {} on symbol: {}".format(integrand, rewrite, symbol))
+        if condition(*integral):
+            rewritten = rewrite(*integral)
+            if rewritten != integrand:
+                substep = integral_steps(rewritten, symbol)
+                if not isinstance(substep, DontKnowRule) and substep:
+                    return RewriteRule(integrand, symbol, rewritten, substep)
+    return _rewriter
+
+def proxy_rewriter(condition, rewrite):
+    """Strategy that rewrites an integrand based on some other criteria."""
+    def _proxy_rewriter(criteria):
+        criteria, integral = criteria
+        integrand, symbol = integral
+        debug("Integral: {} is rewritten with {} on symbol: {} and criteria: {}".format(integrand, rewrite, symbol, criteria))
+        args = criteria + list(integral)
+        if condition(*args):
+            rewritten = rewrite(*args)
+            if rewritten != integrand:
+                return RewriteRule(integrand, symbol, rewritten, integral_steps(rewritten, symbol))
+    return _proxy_rewriter
+
+def multiplexer(conditions):
+    """Apply the rule that matches the condition, else None"""
+    def multiplexer_rl(expr):
+        for key, rule in conditions.items():
+            if key(expr):
+                return rule(expr)
+    return multiplexer_rl
+
+def alternatives(*rules):
+    """Strategy that makes an AlternativeRule out of multiple possible results."""
+    def _alternatives(integral):
+        alts = []
+        count = 0
+        debug("List of Alternative Rules")
+        for rule in rules:
+            count = count + 1
+            debug("Rule {}: {}".format(count, rule))
+
+            result = rule(integral)
+            if (result and not isinstance(result, DontKnowRule) and
+                result != integral and result not in alts):
+                alts.append(result)
+        if len(alts) == 1:
+            return alts[0]
+        elif alts:
+            doable = [rule for rule in alts if not rule.contains_dont_know()]
+            if doable:
+                return AlternativeRule(*integral, doable)
+            else:
+                return AlternativeRule(*integral, alts)
+    return _alternatives
+
+def constant_rule(integral):
+    return ConstantRule(*integral)
+
+def power_rule(integral):
+    integrand, symbol = integral
+    base, expt = integrand.as_base_exp()
+
+    if symbol not in expt.free_symbols and isinstance(base, Symbol):
+        if simplify(expt + 1) == 0:
+            return ReciprocalRule(integrand, symbol, base)
+        return PowerRule(integrand, symbol, base, expt)
+    elif symbol not in base.free_symbols and isinstance(expt, Symbol):
+        rule = ExpRule(integrand, symbol, base, expt)
+
+        if fuzzy_not(log(base).is_zero):
+            return rule
+        elif log(base).is_zero:
+            return ConstantRule(1, symbol)
+
+        return PiecewiseRule(integrand, symbol, [
+            (rule, Ne(log(base), 0)),
+            (ConstantRule(1, symbol), True)
+        ])
+
+def exp_rule(integral):
+    integrand, symbol = integral
+    if isinstance(integrand.args[0], Symbol):
+        return ExpRule(integrand, symbol, E, integrand.args[0])
+
+
+def orthogonal_poly_rule(integral):
+    orthogonal_poly_classes = {
+        jacobi: JacobiRule,
+        gegenbauer: GegenbauerRule,
+        chebyshevt: ChebyshevTRule,
+        chebyshevu: ChebyshevURule,
+        legendre: LegendreRule,
+        hermite: HermiteRule,
+        laguerre: LaguerreRule,
+        assoc_laguerre: AssocLaguerreRule
+        }
+    orthogonal_poly_var_index = {
+        jacobi: 3,
+        gegenbauer: 2,
+        assoc_laguerre: 2
+        }
+    integrand, symbol = integral
+    for klass in orthogonal_poly_classes:
+        if isinstance(integrand, klass):
+            var_index = orthogonal_poly_var_index.get(klass, 1)
+            if (integrand.args[var_index] is symbol and not
+                any(v.has(symbol) for v in integrand.args[:var_index])):
+                    return orthogonal_poly_classes[klass](integrand, symbol, *integrand.args[:var_index])
+
+
+_special_function_patterns: list[tuple[Type, Expr, Callable | None, tuple]] = []
+_wilds = []
+_symbol = Dummy('x')
+
+
+def special_function_rule(integral):
+    integrand, symbol = integral
+    if not _special_function_patterns:
+        a = Wild('a', exclude=[_symbol], properties=[lambda x: not x.is_zero])
+        b = Wild('b', exclude=[_symbol])
+        c = Wild('c', exclude=[_symbol])
+        d = Wild('d', exclude=[_symbol], properties=[lambda x: not x.is_zero])
+        e = Wild('e', exclude=[_symbol], properties=[
+            lambda x: not (x.is_nonnegative and x.is_integer)])
+        _wilds.extend((a, b, c, d, e))
+        # patterns consist of a SymPy class, a wildcard expr, an optional
+        # condition coded as a lambda (when Wild properties are not enough),
+        # followed by an applicable rule
+        linear_pattern = a*_symbol + b
+        quadratic_pattern = a*_symbol**2 + b*_symbol + c
+        _special_function_patterns.extend((
+            (Mul, exp(linear_pattern, evaluate=False)/_symbol, None, EiRule),
+            (Mul, cos(linear_pattern, evaluate=False)/_symbol, None, CiRule),
+            (Mul, cosh(linear_pattern, evaluate=False)/_symbol, None, ChiRule),
+            (Mul, sin(linear_pattern, evaluate=False)/_symbol, None, SiRule),
+            (Mul, sinh(linear_pattern, evaluate=False)/_symbol, None, ShiRule),
+            (Pow, 1/log(linear_pattern, evaluate=False), None, LiRule),
+            (exp, exp(quadratic_pattern, evaluate=False), None, ErfRule),
+            (sin, sin(quadratic_pattern, evaluate=False), None, FresnelSRule),
+            (cos, cos(quadratic_pattern, evaluate=False), None, FresnelCRule),
+            (Mul, _symbol**e*exp(a*_symbol, evaluate=False), None, UpperGammaRule),
+            (Mul, polylog(b, a*_symbol, evaluate=False)/_symbol, None, PolylogRule),
+            (Pow, 1/sqrt(a - d*sin(_symbol, evaluate=False)**2),
+                lambda a, d: a != d, EllipticFRule),
+            (Pow, sqrt(a - d*sin(_symbol, evaluate=False)**2),
+                lambda a, d: a != d, EllipticERule),
+        ))
+    _integrand = integrand.subs(symbol, _symbol)
+    for type_, pattern, constraint, rule in _special_function_patterns:
+        if isinstance(_integrand, type_):
+            match = _integrand.match(pattern)
+            if match:
+                wild_vals = tuple(match.get(w) for w in _wilds
+                                  if match.get(w) is not None)
+                if constraint is None or constraint(*wild_vals):
+                    return rule(integrand, symbol, *wild_vals)
+
+
+def _add_degenerate_step(generic_cond, generic_step: Rule, degenerate_step: Rule | None) -> Rule:
+    if degenerate_step is None:
+        return generic_step
+    if isinstance(generic_step, PiecewiseRule):
+        subfunctions = [(substep, (cond & generic_cond).simplify())
+                        for substep, cond in generic_step.subfunctions]
+    else:
+        subfunctions = [(generic_step, generic_cond)]
+    if isinstance(degenerate_step, PiecewiseRule):
+        subfunctions += degenerate_step.subfunctions
+    else:
+        subfunctions.append((degenerate_step, S.true))
+    return PiecewiseRule(generic_step.integrand, generic_step.variable, subfunctions)
+
+
+def nested_pow_rule(integral: IntegralInfo):
+    # nested (c*(a+b*x)**d)**e
+    integrand, x = integral
+
+    a_ = Wild('a', exclude=[x])
+    b_ = Wild('b', exclude=[x, 0])
+    pattern = a_+b_*x
+    generic_cond = S.true
+
+    class NoMatch(Exception):
+        pass
+
+    def _get_base_exp(expr: Expr) -> tuple[Expr, Expr]:
+        if not expr.has_free(x):
+            return S.One, S.Zero
+        if expr.is_Mul:
+            _, terms = expr.as_coeff_mul()
+            if not terms:
+                return S.One, S.Zero
+            results = [_get_base_exp(term) for term in terms]
+            bases = {b for b, _ in results}
+            bases.discard(S.One)
+            if len(bases) == 1:
+                return bases.pop(), Add(*(e for _, e in results))
+            raise NoMatch
+        if expr.is_Pow:
+            b, e = expr.base, expr.exp  # type: ignore
+            if e.has_free(x):
+                raise NoMatch
+            base_, sub_exp = _get_base_exp(b)
+            return base_, sub_exp * e
+        match = expr.match(pattern)
+        if match:
+            a, b = match[a_], match[b_]
+            base_ = x + a/b
+            nonlocal generic_cond
+            generic_cond = Ne(b, 0)
+            return base_, S.One
+        raise NoMatch
+
+    try:
+        base, exp_ = _get_base_exp(integrand)
+    except NoMatch:
+        return
+    if generic_cond is S.true:
+        degenerate_step = None
+    else:
+        # equivalent with subs(b, 0) but no need to find b
+        degenerate_step = ConstantRule(integrand.subs(x, 0), x)
+    generic_step = NestedPowRule(integrand, x, base, exp_)
+    return _add_degenerate_step(generic_cond, generic_step, degenerate_step)
+
+
+def inverse_trig_rule(integral: IntegralInfo, degenerate=True):
+    """
+    Set degenerate=False on recursive call where coefficient of quadratic term
+    is assumed non-zero.
+    """
+    integrand, symbol = integral
+    base, exp = integrand.as_base_exp()
+    a = Wild('a', exclude=[symbol])
+    b = Wild('b', exclude=[symbol])
+    c = Wild('c', exclude=[symbol, 0])
+    match = base.match(a + b*symbol + c*symbol**2)
+
+    if not match:
+        return
+
+    def make_inverse_trig(RuleClass, a, sign_a, c, sign_c, h) -> Rule:
+        u_var = Dummy("u")
+        rewritten = 1/sqrt(sign_a*a + sign_c*c*(symbol-h)**2)  # a>0, c>0
+        quadratic_base = sqrt(c/a)*(symbol-h)
+        constant = 1/sqrt(c)
+        u_func = None
+        if quadratic_base is not symbol:
+            u_func = quadratic_base
+            quadratic_base = u_var
+        standard_form = 1/sqrt(sign_a + sign_c*quadratic_base**2)
+        substep = RuleClass(standard_form, quadratic_base)
+        if constant != 1:
+            substep = ConstantTimesRule(constant*standard_form, symbol, constant, standard_form, substep)
+        if u_func is not None:
+            substep = URule(rewritten, symbol, u_var, u_func, substep)
+        if h != 0:
+            substep = CompleteSquareRule(integrand, symbol, rewritten, substep)
+        return substep
+
+    a, b, c = [match.get(i, S.Zero) for i in (a, b, c)]
+    generic_cond = Ne(c, 0)
+    if not degenerate or generic_cond is S.true:
+        degenerate_step = None
+    elif b.is_zero:
+        degenerate_step = ConstantRule(a ** exp, symbol)
+    else:
+        degenerate_step = sqrt_linear_rule(IntegralInfo((a + b * symbol) ** exp, symbol))
+
+    if simplify(2*exp + 1) == 0:
+        h, k = -b/(2*c), a - b**2/(4*c)  # rewrite base to k + c*(symbol-h)**2
+        non_square_cond = Ne(k, 0)
+        square_step = None
+        if non_square_cond is not S.true:
+            square_step = NestedPowRule(1/sqrt(c*(symbol-h)**2), symbol, symbol-h, S.NegativeOne)
+        if non_square_cond is S.false:
+            return square_step
+        generic_step = ReciprocalSqrtQuadraticRule(integrand, symbol, a, b, c)
+        step = _add_degenerate_step(non_square_cond, generic_step, square_step)
+        if k.is_real and c.is_real:
+            # list of ((rule, base_exp, a, sign_a, b, sign_b), condition)
+            rules = []
+            for args, cond in (  # don't apply ArccoshRule to x**2-1
+                ((ArcsinRule, k, 1, -c, -1, h), And(k > 0, c < 0)),  # 1-x**2
+                ((ArcsinhRule, k, 1, c, 1, h), And(k > 0, c > 0)),  # 1+x**2
+            ):
+                if cond is S.true:
+                    return make_inverse_trig(*args)
+                if cond is not S.false:
+                    rules.append((make_inverse_trig(*args), cond))
+            if rules:
+                if not k.is_positive:  # conditions are not thorough, need fall back rule
+                    rules.append((generic_step, S.true))
+                step = PiecewiseRule(integrand, symbol, rules)
+            else:
+                step = generic_step
+        return _add_degenerate_step(generic_cond, step, degenerate_step)
+    if exp == S.Half:
+        step = SqrtQuadraticRule(integrand, symbol, a, b, c)
+        return _add_degenerate_step(generic_cond, step, degenerate_step)
+
+
+def add_rule(integral):
+    integrand, symbol = integral
+    results = [integral_steps(g, symbol)
+              for g in integrand.as_ordered_terms()]
+    return None if None in results else AddRule(integrand, symbol, results)
+
+
+def mul_rule(integral: IntegralInfo):
+    integrand, symbol = integral
+
+    # Constant times function case
+    coeff, f = integrand.as_independent(symbol)
+    if coeff != 1:
+        next_step = integral_steps(f, symbol)
+        if next_step is not None:
+            return ConstantTimesRule(integrand, symbol, coeff, f, next_step)
+
+
+def _parts_rule(integrand, symbol) -> tuple[Expr, Expr, Expr, Expr, Rule] | None:
+    # LIATE rule:
+    # log, inverse trig, algebraic, trigonometric, exponential
+    def pull_out_algebraic(integrand):
+        integrand = integrand.cancel().together()
+        # iterating over Piecewise args would not work here
+        algebraic = ([] if isinstance(integrand, Piecewise) or not integrand.is_Mul
+            else [arg for arg in integrand.args if arg.is_algebraic_expr(symbol)])
+        if algebraic:
+            u = Mul(*algebraic)
+            dv = (integrand / u).cancel()
+            return u, dv
+
+    def pull_out_u(*functions) -> Callable[[Expr], tuple[Expr, Expr] | None]:
+        def pull_out_u_rl(integrand: Expr) -> tuple[Expr, Expr] | None:
+            if any(integrand.has(f) for f in functions):
+                args = [arg for arg in integrand.args
+                        if any(isinstance(arg, cls) for cls in functions)]
+                if args:
+                    u = Mul(*args)
+                    dv = integrand / u
+                    return u, dv
+            return None
+
+        return pull_out_u_rl
+
+    liate_rules = [pull_out_u(log), pull_out_u(*inverse_trig_functions),
+                   pull_out_algebraic, pull_out_u(sin, cos),
+                   pull_out_u(exp)]
+
+
+    dummy = Dummy("temporary")
+    # we can integrate log(x) and atan(x) by setting dv = 1
+    if isinstance(integrand, (log, *inverse_trig_functions)):
+        integrand = dummy * integrand
+
+    for index, rule in enumerate(liate_rules):
+        result = rule(integrand)
+
+        if result:
+            u, dv = result
+
+            # Don't pick u to be a constant if possible
+            if symbol not in u.free_symbols and not u.has(dummy):
+                return None
+
+            u = u.subs(dummy, 1)
+            dv = dv.subs(dummy, 1)
+
+            # Don't pick a non-polynomial algebraic to be differentiated
+            if rule == pull_out_algebraic and not u.is_polynomial(symbol):
+                return None
+            # Don't trade one logarithm for another
+            if isinstance(u, log):
+                rec_dv = 1/dv
+                if (rec_dv.is_polynomial(symbol) and
+                    degree(rec_dv, symbol) == 1):
+                    return None
+
+            # Can integrate a polynomial times OrthogonalPolynomial
+            if rule == pull_out_algebraic:
+                if dv.is_Derivative or dv.has(TrigonometricFunction) or \
+                        isinstance(dv, OrthogonalPolynomial):
+                    v_step = integral_steps(dv, symbol)
+                    if v_step.contains_dont_know():
+                        return None
+                    else:
+                        du = u.diff(symbol)
+                        v = v_step.eval()
+                        return u, dv, v, du, v_step
+
+            # make sure dv is amenable to integration
+            accept = False
+            if index < 2:  # log and inverse trig are usually worth trying
+                accept = True
+            elif (rule == pull_out_algebraic and dv.args and
+                all(isinstance(a, (sin, cos, exp))
+                for a in dv.args)):
+                    accept = True
+            else:
+                for lrule in liate_rules[index + 1:]:
+                    r = lrule(integrand)
+                    if r and r[0].subs(dummy, 1).equals(dv):
+                        accept = True
+                        break
+
+            if accept:
+                du = u.diff(symbol)
+                v_step = integral_steps(simplify(dv), symbol)
+                if not v_step.contains_dont_know():
+                    v = v_step.eval()
+                    return u, dv, v, du, v_step
+    return None
+
+
+def parts_rule(integral):
+    integrand, symbol = integral
+    constant, integrand = integrand.as_coeff_Mul()
+
+    result = _parts_rule(integrand, symbol)
+
+    steps = []
+    if result:
+        u, dv, v, du, v_step = result
+        debug("u : {}, dv : {}, v : {}, du : {}, v_step: {}".format(u, dv, v, du, v_step))
+        steps.append(result)
+
+        if isinstance(v, Integral):
+            return
+
+        # Set a limit on the number of times u can be used
+        if isinstance(u, (sin, cos, exp, sinh, cosh)):
+            cachekey = u.xreplace({symbol: _cache_dummy})
+            if _parts_u_cache[cachekey] > 2:
+                return
+            _parts_u_cache[cachekey] += 1
+
+        # Try cyclic integration by parts a few times
+        for _ in range(4):
+            debug("Cyclic integration {} with v: {}, du: {}, integrand: {}".format(_, v, du, integrand))
+            coefficient = ((v * du) / integrand).cancel()
+            if coefficient == 1:
+                break
+            if symbol not in coefficient.free_symbols:
+                rule = CyclicPartsRule(integrand, symbol,
+                    [PartsRule(None, None, u, dv, v_step, None)
+                     for (u, dv, v, du, v_step) in steps],
+                    (-1) ** len(steps) * coefficient)
+                if (constant != 1) and rule:
+                    rule = ConstantTimesRule(constant * integrand, symbol, constant, integrand, rule)
+                return rule
+
+            # _parts_rule is sensitive to constants, factor it out
+            next_constant, next_integrand = (v * du).as_coeff_Mul()
+            result = _parts_rule(next_integrand, symbol)
+
+            if result:
+                u, dv, v, du, v_step = result
+                u *= next_constant
+                du *= next_constant
+                steps.append((u, dv, v, du, v_step))
+            else:
+                break
+
+    def make_second_step(steps, integrand):
+        if steps:
+            u, dv, v, du, v_step = steps[0]
+            return PartsRule(integrand, symbol, u, dv, v_step, make_second_step(steps[1:], v * du))
+        return integral_steps(integrand, symbol)
+
+    if steps:
+        u, dv, v, du, v_step = steps[0]
+        rule = PartsRule(integrand, symbol, u, dv, v_step, make_second_step(steps[1:], v * du))
+        if (constant != 1) and rule:
+            rule = ConstantTimesRule(constant * integrand, symbol, constant, integrand, rule)
+        return rule
+
+
+def trig_rule(integral):
+    integrand, symbol = integral
+    if integrand == sin(symbol):
+        return SinRule(integrand, symbol)
+    if integrand == cos(symbol):
+        return CosRule(integrand, symbol)
+    if integrand == sec(symbol)**2:
+        return Sec2Rule(integrand, symbol)
+    if integrand == csc(symbol)**2:
+        return Csc2Rule(integrand, symbol)
+
+    if isinstance(integrand, tan):
+        rewritten = sin(*integrand.args) / cos(*integrand.args)
+    elif isinstance(integrand, cot):
+        rewritten = cos(*integrand.args) / sin(*integrand.args)
+    elif isinstance(integrand, sec):
+        arg = integrand.args[0]
+        rewritten = ((sec(arg)**2 + tan(arg) * sec(arg)) /
+                     (sec(arg) + tan(arg)))
+    elif isinstance(integrand, csc):
+        arg = integrand.args[0]
+        rewritten = ((csc(arg)**2 + cot(arg) * csc(arg)) /
+                     (csc(arg) + cot(arg)))
+    else:
+        return
+
+    return RewriteRule(integrand, symbol, rewritten, integral_steps(rewritten, symbol))
+
+def trig_product_rule(integral: IntegralInfo):
+    integrand, symbol = integral
+    if integrand == sec(symbol) * tan(symbol):
+        return SecTanRule(integrand, symbol)
+    if integrand == csc(symbol) * cot(symbol):
+        return CscCotRule(integrand, symbol)
+
+
+def quadratic_denom_rule(integral):
+    integrand, symbol = integral
+    a = Wild('a', exclude=[symbol])
+    b = Wild('b', exclude=[symbol])
+    c = Wild('c', exclude=[symbol])
+
+    match = integrand.match(a / (b * symbol ** 2 + c))
+
+    if match:
+        a, b, c = match[a], match[b], match[c]
+        general_rule = ArctanRule(integrand, symbol, a, b, c)
+        if b.is_extended_real and c.is_extended_real:
+            positive_cond = c/b > 0
+            if positive_cond is S.true:
+                return general_rule
+            coeff = a/(2*sqrt(-c)*sqrt(b))
+            constant = sqrt(-c/b)
+            r1 = 1/(symbol-constant)
+            r2 = 1/(symbol+constant)
+            log_steps = [ReciprocalRule(r1, symbol, symbol-constant),
+                         ConstantTimesRule(-r2, symbol, -1, r2, ReciprocalRule(r2, symbol, symbol+constant))]
+            rewritten = sub = r1 - r2
+            negative_step = AddRule(sub, symbol, log_steps)
+            if coeff != 1:
+                rewritten = Mul(coeff, sub, evaluate=False)
+                negative_step = ConstantTimesRule(rewritten, symbol, coeff, sub, negative_step)
+            negative_step = RewriteRule(integrand, symbol, rewritten, negative_step)
+            if positive_cond is S.false:
+                return negative_step
+            return PiecewiseRule(integrand, symbol, [(general_rule, positive_cond), (negative_step, S.true)])
+        return general_rule
+
+    d = Wild('d', exclude=[symbol])
+    match2 = integrand.match(a / (b * symbol ** 2 + c * symbol + d))
+    if match2:
+        b, c =  match2[b], match2[c]
+        if b.is_zero:
+            return
+        u = Dummy('u')
+        u_func = symbol + c/(2*b)
+        integrand2 = integrand.subs(symbol, u - c / (2*b))
+        next_step = integral_steps(integrand2, u)
+        if next_step:
+            return URule(integrand2, symbol, u, u_func, next_step)
+        else:
+            return
+    e = Wild('e', exclude=[symbol])
+    match3 = integrand.match((a* symbol + b) / (c * symbol ** 2 + d * symbol + e))
+    if match3:
+        a, b, c, d, e = match3[a], match3[b], match3[c], match3[d], match3[e]
+        if c.is_zero:
+            return
+        denominator = c * symbol**2 + d * symbol + e
+        const =  a/(2*c)
+        numer1 =  (2*c*symbol+d)
+        numer2 = - const*d + b
+        u = Dummy('u')
+        step1 = URule(integrand, symbol,
+                      u, denominator, integral_steps(u**(-1), u))
+        if const != 1:
+            step1 = ConstantTimesRule(const*numer1/denominator, symbol,
+                                      const, numer1/denominator, step1)
+        if numer2.is_zero:
+            return step1
+        step2 = integral_steps(numer2/denominator, symbol)
+        substeps = AddRule(integrand, symbol, [step1, step2])
+        rewriten = const*numer1/denominator+numer2/denominator
+        return RewriteRule(integrand, symbol, rewriten, substeps)
+
+    return
+
+
+def sqrt_linear_rule(integral: IntegralInfo):
+    """
+    Substitute common (a+b*x)**(1/n)
+    """
+    integrand, x = integral
+    a = Wild('a', exclude=[x])
+    b = Wild('b', exclude=[x, 0])
+    a0 = b0 = 0
+    bases, qs, bs = [], [], []
+    for pow_ in integrand.find(Pow):  # collect all (a+b*x)**(p/q)
+        base, exp_ = pow_.base, pow_.exp
+        if exp_.is_Integer or x not in base.free_symbols:  # skip 1/x and sqrt(2)
+            continue
+        if not exp_.is_Rational:  # exclude x**pi
+            return
+        match = base.match(a+b*x)
+        if not match:  # skip non-linear
+            continue  # for sqrt(x+sqrt(x)), although base is non-linear, we can still substitute sqrt(x)
+        a1, b1 = match[a], match[b]
+        if a0*b1 != a1*b0 or not (b0/b1).is_nonnegative:  # cannot transform sqrt(x) to sqrt(x+1) or sqrt(-x)
+            return
+        if b0 == 0 or (b0/b1 > 1) is S.true:  # choose the latter of sqrt(2*x) and sqrt(x) as representative
+            a0, b0 = a1, b1
+        bases.append(base)
+        bs.append(b1)
+        qs.append(exp_.q)
+    if b0 == 0:  # no such pattern found
+        return
+    q0: Integer = lcm_list(qs)
+    u_x = (a0 + b0*x)**(1/q0)
+    u = Dummy("u")
+    substituted = integrand.subs({base**(S.One/q): (b/b0)**(S.One/q)*u**(q0/q)
+                                  for base, b, q in zip(bases, bs, qs)}).subs(x, (u**q0-a0)/b0)
+    substep = integral_steps(substituted*u**(q0-1)*q0/b0, u)
+    if not substep.contains_dont_know():
+        step: Rule = URule(integrand, x, u, u_x, substep)
+        generic_cond = Ne(b0, 0)
+        if generic_cond is not S.true:  # possible degenerate case
+            simplified = integrand.subs({b: 0 for b in bs})
+            degenerate_step = integral_steps(simplified, x)
+            step = PiecewiseRule(integrand, x, [(step, generic_cond), (degenerate_step, S.true)])
+        return step
+
+
+def sqrt_quadratic_rule(integral: IntegralInfo, degenerate=True):
+    integrand, x = integral
+    a = Wild('a', exclude=[x])
+    b = Wild('b', exclude=[x])
+    c = Wild('c', exclude=[x, 0])
+    f = Wild('f')
+    n = Wild('n', properties=[lambda n: n.is_Integer and n.is_odd])
+    match = integrand.match(f*sqrt(a+b*x+c*x**2)**n)
+    if not match:
+        return
+    a, b, c, f, n = match[a], match[b], match[c], match[f], match[n]
+    f_poly = f.as_poly(x)
+    if f_poly is None:
+        return
+
+    generic_cond = Ne(c, 0)
+    if not degenerate or generic_cond is S.true:
+        degenerate_step = None
+    elif b.is_zero:
+        degenerate_step = integral_steps(f*sqrt(a)**n, x)
+    else:
+        degenerate_step = sqrt_linear_rule(IntegralInfo(f*sqrt(a+b*x)**n, x))
+
+    def sqrt_quadratic_denom_rule(numer_poly: Poly, integrand: Expr):
+        denom = sqrt(a+b*x+c*x**2)
+        deg = numer_poly.degree()
+        if deg <= 1:
+            # integrand == (d+e*x)/sqrt(a+b*x+c*x**2)
+            e, d = numer_poly.all_coeffs() if deg == 1 else (S.Zero, numer_poly.as_expr())
+            # rewrite numerator to A*(2*c*x+b) + B
+            A = e/(2*c)
+            B = d-A*b
+            pre_substitute = (2*c*x+b)/denom
+            constant_step: Rule | None = None
+            linear_step: Rule | None = None
+            if A != 0:
+                u = Dummy("u")
+                pow_rule = PowerRule(1/sqrt(u), u, u, -S.Half)
+                linear_step = URule(pre_substitute, x, u, a+b*x+c*x**2, pow_rule)
+                if A != 1:
+                    linear_step = ConstantTimesRule(A*pre_substitute, x, A, pre_substitute, linear_step)
+            if B != 0:
+                constant_step = inverse_trig_rule(IntegralInfo(1/denom, x), degenerate=False)
+                if B != 1:
+                    constant_step = ConstantTimesRule(B/denom, x, B, 1/denom, constant_step)  # type: ignore
+            if linear_step and constant_step:
+                add = Add(A*pre_substitute, B/denom, evaluate=False)
+                step: Rule | None = RewriteRule(integrand, x, add, AddRule(add, x, [linear_step, constant_step]))
+            else:
+                step = linear_step or constant_step
+        else:
+            coeffs = numer_poly.all_coeffs()
+            step = SqrtQuadraticDenomRule(integrand, x, a, b, c, coeffs)
+        return step
+
+    if n > 0:  # rewrite poly * sqrt(s)**(2*k-1) to poly*s**k / sqrt(s)
+        numer_poly = f_poly * (a+b*x+c*x**2)**((n+1)/2)
+        rewritten = numer_poly.as_expr()/sqrt(a+b*x+c*x**2)
+        substep = sqrt_quadratic_denom_rule(numer_poly, rewritten)
+        generic_step = RewriteRule(integrand, x, rewritten, substep)
+    elif n == -1:
+        generic_step = sqrt_quadratic_denom_rule(f_poly, integrand)
+    else:
+        return  # todo: handle n < -1 case
+    return _add_degenerate_step(generic_cond, generic_step, degenerate_step)
+
+
+def hyperbolic_rule(integral: tuple[Expr, Symbol]):
+    integrand, symbol = integral
+    if isinstance(integrand, HyperbolicFunction) and integrand.args[0] == symbol:
+        if integrand.func == sinh:
+            return SinhRule(integrand, symbol)
+        if integrand.func == cosh:
+            return CoshRule(integrand, symbol)
+        u = Dummy('u')
+        if integrand.func == tanh:
+            rewritten = sinh(symbol)/cosh(symbol)
+            return RewriteRule(integrand, symbol, rewritten,
+                   URule(rewritten, symbol, u, cosh(symbol), ReciprocalRule(1/u, u, u)))
+        if integrand.func == coth:
+            rewritten = cosh(symbol)/sinh(symbol)
+            return RewriteRule(integrand, symbol, rewritten,
+                   URule(rewritten, symbol, u, sinh(symbol), ReciprocalRule(1/u, u, u)))
+        else:
+            rewritten = integrand.rewrite(tanh)
+            if integrand.func == sech:
+                return RewriteRule(integrand, symbol, rewritten,
+                       URule(rewritten, symbol, u, tanh(symbol/2),
+                       ArctanRule(2/(u**2 + 1), u, S(2), S.One, S.One)))
+            if integrand.func == csch:
+                return RewriteRule(integrand, symbol, rewritten,
+                       URule(rewritten, symbol, u, tanh(symbol/2),
+                       ReciprocalRule(1/u, u, u)))
+
+@cacheit
+def make_wilds(symbol):
+    a = Wild('a', exclude=[symbol])
+    b = Wild('b', exclude=[symbol])
+    m = Wild('m', exclude=[symbol], properties=[lambda n: isinstance(n, Integer)])
+    n = Wild('n', exclude=[symbol], properties=[lambda n: isinstance(n, Integer)])
+
+    return a, b, m, n
+
+@cacheit
+def sincos_pattern(symbol):
+    a, b, m, n = make_wilds(symbol)
+    pattern = sin(a*symbol)**m * cos(b*symbol)**n
+
+    return pattern, a, b, m, n
+
+@cacheit
+def tansec_pattern(symbol):
+    a, b, m, n = make_wilds(symbol)
+    pattern = tan(a*symbol)**m * sec(b*symbol)**n
+
+    return pattern, a, b, m, n
+
+@cacheit
+def cotcsc_pattern(symbol):
+    a, b, m, n = make_wilds(symbol)
+    pattern = cot(a*symbol)**m * csc(b*symbol)**n
+
+    return pattern, a, b, m, n
+
+@cacheit
+def heaviside_pattern(symbol):
+    m = Wild('m', exclude=[symbol])
+    b = Wild('b', exclude=[symbol])
+    g = Wild('g')
+    pattern = Heaviside(m*symbol + b) * g
+
+    return pattern, m, b, g
+
+def uncurry(func):
+    def uncurry_rl(args):
+        return func(*args)
+    return uncurry_rl
+
+def trig_rewriter(rewrite):
+    def trig_rewriter_rl(args):
+        a, b, m, n, integrand, symbol = args
+        rewritten = rewrite(a, b, m, n, integrand, symbol)
+        if rewritten != integrand:
+            return RewriteRule(integrand, symbol, rewritten, integral_steps(rewritten, symbol))
+    return trig_rewriter_rl
+
+sincos_botheven_condition = uncurry(
+    lambda a, b, m, n, i, s: m.is_even and n.is_even and
+    m.is_nonnegative and n.is_nonnegative)
+
+sincos_botheven = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (((1 - cos(2*a*symbol)) / 2) ** (m / 2)) *
+                                    (((1 + cos(2*b*symbol)) / 2) ** (n / 2)) ))
+
+sincos_sinodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd and m >= 3)
+
+sincos_sinodd = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (1 - cos(a*symbol)**2)**((m - 1) / 2) *
+                                    sin(a*symbol) *
+                                    cos(b*symbol) ** n))
+
+sincos_cosodd_condition = uncurry(lambda a, b, m, n, i, s: n.is_odd and n >= 3)
+
+sincos_cosodd = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (1 - sin(b*symbol)**2)**((n - 1) / 2) *
+                                    cos(b*symbol) *
+                                    sin(a*symbol) ** m))
+
+tansec_seceven_condition = uncurry(lambda a, b, m, n, i, s: n.is_even and n >= 4)
+tansec_seceven = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (1 + tan(b*symbol)**2) ** (n/2 - 1) *
+                                    sec(b*symbol)**2 *
+                                    tan(a*symbol) ** m ))
+
+tansec_tanodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd)
+tansec_tanodd = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (sec(a*symbol)**2 - 1) ** ((m - 1) / 2) *
+                                     tan(a*symbol) *
+                                     sec(b*symbol) ** n ))
+
+tan_tansquared_condition = uncurry(lambda a, b, m, n, i, s: m == 2 and n == 0)
+tan_tansquared = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( sec(a*symbol)**2 - 1))
+
+cotcsc_csceven_condition = uncurry(lambda a, b, m, n, i, s: n.is_even and n >= 4)
+cotcsc_csceven = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (1 + cot(b*symbol)**2) ** (n/2 - 1) *
+                                    csc(b*symbol)**2 *
+                                    cot(a*symbol) ** m ))
+
+cotcsc_cotodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd)
+cotcsc_cotodd = trig_rewriter(
+    lambda a, b, m, n, i, symbol: ( (csc(a*symbol)**2 - 1) ** ((m - 1) / 2) *
+                                    cot(a*symbol) *
+                                    csc(b*symbol) ** n ))
+
+def trig_sincos_rule(integral):
+    integrand, symbol = integral
+
+    if any(integrand.has(f) for f in (sin, cos)):
+        pattern, a, b, m, n = sincos_pattern(symbol)
+        match = integrand.match(pattern)
+        if not match:
+            return
+
+        return multiplexer({
+            sincos_botheven_condition: sincos_botheven,
+            sincos_sinodd_condition: sincos_sinodd,
+            sincos_cosodd_condition: sincos_cosodd
+        })(tuple(
+            [match.get(i, S.Zero) for i in (a, b, m, n)] +
+            [integrand, symbol]))
+
+def trig_tansec_rule(integral):
+    integrand, symbol = integral
+
+    integrand = integrand.subs({
+        1 / cos(symbol): sec(symbol)
+    })
+
+    if any(integrand.has(f) for f in (tan, sec)):
+        pattern, a, b, m, n = tansec_pattern(symbol)
+        match = integrand.match(pattern)
+        if not match:
+            return
+
+        return multiplexer({
+            tansec_tanodd_condition: tansec_tanodd,
+            tansec_seceven_condition: tansec_seceven,
+            tan_tansquared_condition: tan_tansquared
+        })(tuple(
+            [match.get(i, S.Zero) for i in (a, b, m, n)] +
+            [integrand, symbol]))
+
+def trig_cotcsc_rule(integral):
+    integrand, symbol = integral
+    integrand = integrand.subs({
+        1 / sin(symbol): csc(symbol),
+        1 / tan(symbol): cot(symbol),
+        cos(symbol) / tan(symbol): cot(symbol)
+    })
+
+    if any(integrand.has(f) for f in (cot, csc)):
+        pattern, a, b, m, n = cotcsc_pattern(symbol)
+        match = integrand.match(pattern)
+        if not match:
+            return
+
+        return multiplexer({
+            cotcsc_cotodd_condition: cotcsc_cotodd,
+            cotcsc_csceven_condition: cotcsc_csceven
+        })(tuple(
+            [match.get(i, S.Zero) for i in (a, b, m, n)] +
+            [integrand, symbol]))
+
+def trig_sindouble_rule(integral):
+    integrand, symbol = integral
+    a = Wild('a', exclude=[sin(2*symbol)])
+    match = integrand.match(sin(2*symbol)*a)
+    if match:
+        sin_double = 2*sin(symbol)*cos(symbol)/sin(2*symbol)
+        return integral_steps(integrand * sin_double, symbol)
+
+def trig_powers_products_rule(integral):
+    return do_one(null_safe(trig_sincos_rule),
+                  null_safe(trig_tansec_rule),
+                  null_safe(trig_cotcsc_rule),
+                  null_safe(trig_sindouble_rule))(integral)
+
+def trig_substitution_rule(integral):
+    integrand, symbol = integral
+    A = Wild('a', exclude=[0, symbol])
+    B = Wild('b', exclude=[0, symbol])
+    theta = Dummy("theta")
+    target_pattern = A + B*symbol**2
+
+    matches = integrand.find(target_pattern)
+    for expr in matches:
+        match = expr.match(target_pattern)
+        a = match.get(A, S.Zero)
+        b = match.get(B, S.Zero)
+
+        a_positive = ((a.is_number and a > 0) or a.is_positive)
+        b_positive = ((b.is_number and b > 0) or b.is_positive)
+        a_negative = ((a.is_number and a < 0) or a.is_negative)
+        b_negative = ((b.is_number and b < 0) or b.is_negative)
+        x_func = None
+        if a_positive and b_positive:
+            # a**2 + b*x**2. Assume sec(theta) > 0, -pi/2 < theta < pi/2
+            x_func = (sqrt(a)/sqrt(b)) * tan(theta)
+            # Do not restrict the domain: tan(theta) takes on any real
+            # value on the interval -pi/2 < theta < pi/2 so x takes on
+            # any value
+            restriction = True
+        elif a_positive and b_negative:
+            # a**2 - b*x**2. Assume cos(theta) > 0, -pi/2 < theta < pi/2
+            constant = sqrt(a)/sqrt(-b)
+            x_func = constant * sin(theta)
+            restriction = And(symbol > -constant, symbol < constant)
+        elif a_negative and b_positive:
+            # b*x**2 - a**2. Assume sin(theta) > 0, 0 < theta < pi
+            constant = sqrt(-a)/sqrt(b)
+            x_func = constant * sec(theta)
+            restriction = And(symbol > -constant, symbol < constant)
+        if x_func:
+            # Manually simplify sqrt(trig(theta)**2) to trig(theta)
+            # Valid due to assumed domain restriction
+            substitutions = {}
+            for f in [sin, cos, tan,
+                      sec, csc, cot]:
+                substitutions[sqrt(f(theta)**2)] = f(theta)
+                substitutions[sqrt(f(theta)**(-2))] = 1/f(theta)
+
+            replaced = integrand.subs(symbol, x_func).trigsimp()
+            replaced = manual_subs(replaced, substitutions)
+            if not replaced.has(symbol):
+                replaced *= manual_diff(x_func, theta)
+                replaced = replaced.trigsimp()
+                secants = replaced.find(1/cos(theta))
+                if secants:
+                    replaced = replaced.xreplace({
+                        1/cos(theta): sec(theta)
+                    })
+
+                substep = integral_steps(replaced, theta)
+                if not substep.contains_dont_know():
+                    return TrigSubstitutionRule(integrand, symbol,
+                        theta, x_func, replaced, substep, restriction)
+
+def heaviside_rule(integral):
+    integrand, symbol = integral
+    pattern, m, b, g = heaviside_pattern(symbol)
+    match = integrand.match(pattern)
+    if match and 0 != match[g]:
+        # f = Heaviside(m*x + b)*g
+        substep = integral_steps(match[g], symbol)
+        m, b = match[m], match[b]
+        return HeavisideRule(integrand, symbol, m*symbol + b, -b/m, substep)
+
+
+def dirac_delta_rule(integral: IntegralInfo):
+    integrand, x = integral
+    if len(integrand.args) == 1:
+        n = S.Zero
+    else:
+        n = integrand.args[1]
+    if not n.is_Integer or n < 0:
+        return
+    a, b = Wild('a', exclude=[x]), Wild('b', exclude=[x, 0])
+    match = integrand.args[0].match(a+b*x)
+    if not match:
+        return
+    a, b = match[a], match[b]
+    generic_cond = Ne(b, 0)
+    if generic_cond is S.true:
+        degenerate_step = None
+    else:
+        degenerate_step = ConstantRule(DiracDelta(a, n), x)
+    generic_step = DiracDeltaRule(integrand, x, n, a, b)
+    return _add_degenerate_step(generic_cond, generic_step, degenerate_step)
+
+
+def substitution_rule(integral):
+    integrand, symbol = integral
+
+    u_var = Dummy("u")
+    substitutions = find_substitutions(integrand, symbol, u_var)
+    count = 0
+    if substitutions:
+        debug("List of Substitution Rules")
+        ways = []
+        for u_func, c, substituted in substitutions:
+            subrule = integral_steps(substituted, u_var)
+            count = count + 1
+            debug("Rule {}: {}".format(count, subrule))
+
+            if subrule.contains_dont_know():
+                continue
+
+            if simplify(c - 1) != 0:
+                _, denom = c.as_numer_denom()
+                if subrule:
+                    subrule = ConstantTimesRule(c * substituted, u_var, c, substituted, subrule)
+
+                if denom.free_symbols:
+                    piecewise = []
+                    could_be_zero = []
+
+                    if isinstance(denom, Mul):
+                        could_be_zero = denom.args
+                    else:
+                        could_be_zero.append(denom)
+
+                    for expr in could_be_zero:
+                        if not fuzzy_not(expr.is_zero):
+                            substep = integral_steps(manual_subs(integrand, expr, 0), symbol)
+
+                            if substep:
+                                piecewise.append((
+                                    substep,
+                                    Eq(expr, 0)
+                                ))
+                    piecewise.append((subrule, True))
+                    subrule = PiecewiseRule(substituted, symbol, piecewise)
+
+            ways.append(URule(integrand, symbol, u_var, u_func, subrule))
+
+        if len(ways) > 1:
+            return AlternativeRule(integrand, symbol, ways)
+        elif ways:
+            return ways[0]
+
+
+partial_fractions_rule = rewriter(
+    lambda integrand, symbol: integrand.is_rational_function(),
+    lambda integrand, symbol: integrand.apart(symbol))
+
+cancel_rule = rewriter(
+    # lambda integrand, symbol: integrand.is_algebraic_expr(),
+    # lambda integrand, symbol: isinstance(integrand, Mul),
+    lambda integrand, symbol: True,
+    lambda integrand, symbol: integrand.cancel())
+
+distribute_expand_rule = rewriter(
+    lambda integrand, symbol: (
+        all(arg.is_Pow or arg.is_polynomial(symbol) for arg in integrand.args)
+        or isinstance(integrand, Pow)
+        or isinstance(integrand, Mul)),
+    lambda integrand, symbol: integrand.expand())
+
+trig_expand_rule = rewriter(
+    # If there are trig functions with different arguments, expand them
+    lambda integrand, symbol: (
+        len({a.args[0] for a in integrand.atoms(TrigonometricFunction)}) > 1),
+    lambda integrand, symbol: integrand.expand(trig=True))
+
+def derivative_rule(integral):
+    integrand = integral[0]
+    diff_variables = integrand.variables
+    undifferentiated_function = integrand.expr
+    integrand_variables = undifferentiated_function.free_symbols
+
+    if integral.symbol in integrand_variables:
+        if integral.symbol in diff_variables:
+            return DerivativeRule(*integral)
+        else:
+            return DontKnowRule(integrand, integral.symbol)
+    else:
+        return ConstantRule(*integral)
+
+def rewrites_rule(integral):
+    integrand, symbol = integral
+
+    if integrand.match(1/cos(symbol)):
+        rewritten = integrand.subs(1/cos(symbol), sec(symbol))
+        return RewriteRule(integrand, symbol, rewritten, integral_steps(rewritten, symbol))
+
+def fallback_rule(integral):
+    return DontKnowRule(*integral)
+
+# Cache is used to break cyclic integrals.
+# Need to use the same dummy variable in cached expressions for them to match.
+# Also record "u" of integration by parts, to avoid infinite repetition.
+_integral_cache: dict[Expr, Expr | None] = {}
+_parts_u_cache: dict[Expr, int] = defaultdict(int)
+_cache_dummy = Dummy("z")
+
+def integral_steps(integrand, symbol, **options):
+    """Returns the steps needed to compute an integral.
+
+    Explanation
+    ===========
+
+    This function attempts to mirror what a student would do by hand as
+    closely as possible.
+
+    SymPy Gamma uses this to provide a step-by-step explanation of an
+    integral. The code it uses to format the results of this function can be
+    found at
+    https://github.com/sympy/sympy_gamma/blob/master/app/logic/intsteps.py.
+
+    Examples
+    ========
+
+    >>> from sympy import exp, sin
+    >>> from sympy.integrals.manualintegrate import integral_steps
+    >>> from sympy.abc import x
+    >>> print(repr(integral_steps(exp(x) / (1 + exp(2 * x)), x))) \
+    # doctest: +NORMALIZE_WHITESPACE
+    URule(integrand=exp(x)/(exp(2*x) + 1), variable=x, u_var=_u, u_func=exp(x),
+    substep=ArctanRule(integrand=1/(_u**2 + 1), variable=_u, a=1, b=1, c=1))
+    >>> print(repr(integral_steps(sin(x), x))) \
+    # doctest: +NORMALIZE_WHITESPACE
+    SinRule(integrand=sin(x), variable=x)
+    >>> print(repr(integral_steps((x**2 + 3)**2, x))) \
+    # doctest: +NORMALIZE_WHITESPACE
+    RewriteRule(integrand=(x**2 + 3)**2, variable=x, rewritten=x**4 + 6*x**2 + 9,
+    substep=AddRule(integrand=x**4 + 6*x**2 + 9, variable=x,
+    substeps=[PowerRule(integrand=x**4, variable=x, base=x, exp=4),
+    ConstantTimesRule(integrand=6*x**2, variable=x, constant=6, other=x**2,
+    substep=PowerRule(integrand=x**2, variable=x, base=x, exp=2)),
+    ConstantRule(integrand=9, variable=x)]))
+
+    Returns
+    =======
+
+    rule : Rule
+        The first step; most rules have substeps that must also be
+        considered. These substeps can be evaluated using ``manualintegrate``
+        to obtain a result.
+
+    """
+    cachekey = integrand.xreplace({symbol: _cache_dummy})
+    if cachekey in _integral_cache:
+        if _integral_cache[cachekey] is None:
+            # Stop this attempt, because it leads around in a loop
+            return DontKnowRule(integrand, symbol)
+        else:
+            # TODO: This is for future development, as currently
+            # _integral_cache gets no values other than None
+            return (_integral_cache[cachekey].xreplace(_cache_dummy, symbol),
+                symbol)
+    else:
+        _integral_cache[cachekey] = None
+
+    integral = IntegralInfo(integrand, symbol)
+
+    def key(integral):
+        integrand = integral.integrand
+
+        if symbol not in integrand.free_symbols:
+            return Number
+        for cls in (Symbol, TrigonometricFunction, OrthogonalPolynomial):
+            if isinstance(integrand, cls):
+                return cls
+        return type(integrand)
+
+    def integral_is_subclass(*klasses):
+        def _integral_is_subclass(integral):
+            k = key(integral)
+            return k and issubclass(k, klasses)
+        return _integral_is_subclass
+
+    result = do_one(
+        null_safe(special_function_rule),
+        null_safe(switch(key, {
+            Pow: do_one(null_safe(power_rule), null_safe(inverse_trig_rule),
+                        null_safe(sqrt_linear_rule),
+                        null_safe(quadratic_denom_rule)),
+            Symbol: power_rule,
+            exp: exp_rule,
+            Add: add_rule,
+            Mul: do_one(null_safe(mul_rule), null_safe(trig_product_rule),
+                        null_safe(heaviside_rule), null_safe(quadratic_denom_rule),
+                        null_safe(sqrt_linear_rule),
+                        null_safe(sqrt_quadratic_rule)),
+            Derivative: derivative_rule,
+            TrigonometricFunction: trig_rule,
+            Heaviside: heaviside_rule,
+            DiracDelta: dirac_delta_rule,
+            OrthogonalPolynomial: orthogonal_poly_rule,
+            Number: constant_rule
+        })),
+        do_one(
+            null_safe(trig_rule),
+            null_safe(hyperbolic_rule),
+            null_safe(alternatives(
+                rewrites_rule,
+                substitution_rule,
+                condition(
+                    integral_is_subclass(Mul, Pow),
+                    partial_fractions_rule),
+                condition(
+                    integral_is_subclass(Mul, Pow),
+                    cancel_rule),
+                condition(
+                    integral_is_subclass(Mul, log,
+                    *inverse_trig_functions),
+                    parts_rule),
+                condition(
+                    integral_is_subclass(Mul, Pow),
+                    distribute_expand_rule),
+                trig_powers_products_rule,
+                trig_expand_rule
+            )),
+            null_safe(condition(integral_is_subclass(Mul, Pow), nested_pow_rule)),
+            null_safe(trig_substitution_rule)
+        ),
+        fallback_rule)(integral)
+    del _integral_cache[cachekey]
+    return result
+
+
+def manualintegrate(f, var):
+    """manualintegrate(f, var)
+
+    Explanation
+    ===========
+
+    Compute indefinite integral of a single variable using an algorithm that
+    resembles what a student would do by hand.
+
+    Unlike :func:`~.integrate`, var can only be a single symbol.
+
+    Examples
+    ========
+
+    >>> from sympy import sin, cos, tan, exp, log, integrate
+    >>> from sympy.integrals.manualintegrate import manualintegrate
+    >>> from sympy.abc import x
+    >>> manualintegrate(1 / x, x)
+    log(x)
+    >>> integrate(1/x)
+    log(x)
+    >>> manualintegrate(log(x), x)
+    x*log(x) - x
+    >>> integrate(log(x))
+    x*log(x) - x
+    >>> manualintegrate(exp(x) / (1 + exp(2 * x)), x)
+    atan(exp(x))
+    >>> integrate(exp(x) / (1 + exp(2 * x)))
+    RootSum(4*_z**2 + 1, Lambda(_i, _i*log(2*_i + exp(x))))
+    >>> manualintegrate(cos(x)**4 * sin(x), x)
+    -cos(x)**5/5
+    >>> integrate(cos(x)**4 * sin(x), x)
+    -cos(x)**5/5
+    >>> manualintegrate(cos(x)**4 * sin(x)**3, x)
+    cos(x)**7/7 - cos(x)**5/5
+    >>> integrate(cos(x)**4 * sin(x)**3, x)
+    cos(x)**7/7 - cos(x)**5/5
+    >>> manualintegrate(tan(x), x)
+    -log(cos(x))
+    >>> integrate(tan(x), x)
+    -log(cos(x))
+
+    See Also
+    ========
+
+    sympy.integrals.integrals.integrate
+    sympy.integrals.integrals.Integral.doit
+    sympy.integrals.integrals.Integral
+    """
+    result = integral_steps(f, var).eval()
+    # Clear the cache of u-parts
+    _parts_u_cache.clear()
+    # If we got Piecewise with two parts, put generic first
+    if isinstance(result, Piecewise) and len(result.args) == 2:
+        cond = result.args[0][1]
+        if isinstance(cond, Eq) and result.args[1][1] == True:
+            result = result.func(
+                (result.args[1][0], Ne(*cond.args)),
+                (result.args[0][0], True))
+    return result

venv/lib/python3.10/site-packages/sympy/integrals/meijerint.py

@@ -0,0 +1,2190 @@
+"""
+Integrate functions by rewriting them as Meijer G-functions.
+
+There are three user-visible functions that can be used by other parts of the
+sympy library to solve various integration problems:
+
+- meijerint_indefinite
+- meijerint_definite
+- meijerint_inversion
+
+They can be used to compute, respectively, indefinite integrals, definite
+integrals over intervals of the real line, and inverse laplace-type integrals
+(from c-I*oo to c+I*oo). See the respective docstrings for details.
+
+The main references for this are:
+
+[L] Luke, Y. L. (1969), The Special Functions and Their Approximations,
+    Volume 1
+
+[R] Kelly B. Roach.  Meijer G Function Representations.
+    In: Proceedings of the 1997 International Symposium on Symbolic and
+    Algebraic Computation, pages 205-211, New York, 1997. ACM.
+
+[P] A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990).
+    Integrals and Series: More Special Functions, Vol. 3,.
+    Gordon and Breach Science Publisher
+"""
+
+from __future__ import annotations
+import itertools
+
+from sympy import SYMPY_DEBUG
+from sympy.core import S, Expr
+from sympy.core.add import Add
+from sympy.core.basic import Basic
+from sympy.core.cache import cacheit
+from sympy.core.containers import Tuple
+from sympy.core.exprtools import factor_terms
+from sympy.core.function import (expand, expand_mul, expand_power_base,
+                                 expand_trig, Function)
+from sympy.core.mul import Mul
+from sympy.core.numbers import ilcm, Rational, pi
+from sympy.core.relational import Eq, Ne, _canonical_coeff
+from sympy.core.sorting import default_sort_key, ordered
+from sympy.core.symbol import Dummy, symbols, Wild, Symbol
+from sympy.core.sympify import sympify
+from sympy.functions.combinatorial.factorials import factorial
+from sympy.functions.elementary.complexes import (re, im, arg, Abs, sign,
+        unpolarify, polarify, polar_lift, principal_branch, unbranched_argument,
+        periodic_argument)
+from sympy.functions.elementary.exponential import exp, exp_polar, log
+from sympy.functions.elementary.integers import ceiling
+from sympy.functions.elementary.hyperbolic import (cosh, sinh,
+        _rewrite_hyperbolics_as_exp, HyperbolicFunction)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.piecewise import Piecewise, piecewise_fold
+from sympy.functions.elementary.trigonometric import (cos, sin, sinc,
+        TrigonometricFunction)
+from sympy.functions.special.bessel import besselj, bessely, besseli, besselk
+from sympy.functions.special.delta_functions import DiracDelta, Heaviside
+from sympy.functions.special.elliptic_integrals import elliptic_k, elliptic_e
+from sympy.functions.special.error_functions import (erf, erfc, erfi, Ei,
+        expint, Si, Ci, Shi, Chi, fresnels, fresnelc)
+from sympy.functions.special.gamma_functions import gamma
+from sympy.functions.special.hyper import hyper, meijerg
+from sympy.functions.special.singularity_functions import SingularityFunction
+from .integrals import Integral
+from sympy.logic.boolalg import And, Or, BooleanAtom, Not, BooleanFunction
+from sympy.polys import cancel, factor
+from sympy.utilities.iterables import multiset_partitions
+from sympy.utilities.misc import debug as _debug
+from sympy.utilities.misc import debugf as _debugf
+
+# keep this at top for easy reference
+z = Dummy('z')
+
+
+def _has(res, *f):
+    # return True if res has f; in the case of Piecewise
+    # only return True if *all* pieces have f
+    res = piecewise_fold(res)
+    if getattr(res, 'is_Piecewise', False):
+        return all(_has(i, *f) for i in res.args)
+    return res.has(*f)
+
+
+def _create_lookup_table(table):
+    """ Add formulae for the function -> meijerg lookup table. """
+    def wild(n):
+        return Wild(n, exclude=[z])
+    p, q, a, b, c = list(map(wild, 'pqabc'))
+    n = Wild('n', properties=[lambda x: x.is_Integer and x > 0])
+    t = p*z**q
+
+    def add(formula, an, ap, bm, bq, arg=t, fac=S.One, cond=True, hint=True):
+        table.setdefault(_mytype(formula, z), []).append((formula,
+                                     [(fac, meijerg(an, ap, bm, bq, arg))], cond, hint))
+
+    def addi(formula, inst, cond, hint=True):
+        table.setdefault(
+            _mytype(formula, z), []).append((formula, inst, cond, hint))
+
+    def constant(a):
+        return [(a, meijerg([1], [], [], [0], z)),
+                (a, meijerg([], [1], [0], [], z))]
+    table[()] = [(a, constant(a), True, True)]
+
+    # [P], Section 8.
+    class IsNonPositiveInteger(Function):
+
+        @classmethod
+        def eval(cls, arg):
+            arg = unpolarify(arg)
+            if arg.is_Integer is True:
+                return arg <= 0
+
+    # Section 8.4.2
+    # TODO this needs more polar_lift (c/f entry for exp)
+    add(Heaviside(t - b)*(t - b)**(a - 1), [a], [], [], [0], t/b,
+        gamma(a)*b**(a - 1), And(b > 0))
+    add(Heaviside(b - t)*(b - t)**(a - 1), [], [a], [0], [], t/b,
+        gamma(a)*b**(a - 1), And(b > 0))
+    add(Heaviside(z - (b/p)**(1/q))*(t - b)**(a - 1), [a], [], [], [0], t/b,
+        gamma(a)*b**(a - 1), And(b > 0))
+    add(Heaviside((b/p)**(1/q) - z)*(b - t)**(a - 1), [], [a], [0], [], t/b,
+        gamma(a)*b**(a - 1), And(b > 0))
+    add((b + t)**(-a), [1 - a], [], [0], [], t/b, b**(-a)/gamma(a),
+        hint=Not(IsNonPositiveInteger(a)))
+    add(Abs(b - t)**(-a), [1 - a], [(1 - a)/2], [0], [(1 - a)/2], t/b,
+        2*sin(pi*a/2)*gamma(1 - a)*Abs(b)**(-a), re(a) < 1)
+    add((t**a - b**a)/(t - b), [0, a], [], [0, a], [], t/b,
+        b**(a - 1)*sin(a*pi)/pi)
+
+    # 12
+    def A1(r, sign, nu):
+        return pi**Rational(-1, 2)*(-sign*nu/2)**(1 - 2*r)
+
+    def tmpadd(r, sgn):
+        # XXX the a**2 is bad for matching
+        add((sqrt(a**2 + t) + sgn*a)**b/(a**2 + t)**r,
+            [(1 + b)/2, 1 - 2*r + b/2], [],
+            [(b - sgn*b)/2], [(b + sgn*b)/2], t/a**2,
+            a**(b - 2*r)*A1(r, sgn, b))
+    tmpadd(0, 1)
+    tmpadd(0, -1)
+    tmpadd(S.Half, 1)
+    tmpadd(S.Half, -1)
+
+    # 13
+    def tmpadd(r, sgn):
+        add((sqrt(a + p*z**q) + sgn*sqrt(p)*z**(q/2))**b/(a + p*z**q)**r,
+            [1 - r + sgn*b/2], [1 - r - sgn*b/2], [0, S.Half], [],
+            p*z**q/a, a**(b/2 - r)*A1(r, sgn, b))
+    tmpadd(0, 1)
+    tmpadd(0, -1)
+    tmpadd(S.Half, 1)
+    tmpadd(S.Half, -1)
+    # (those after look obscure)
+
+    # Section 8.4.3
+    add(exp(polar_lift(-1)*t), [], [], [0], [])
+
+    # TODO can do sin^n, sinh^n by expansion ... where?
+    # 8.4.4 (hyperbolic functions)
+    add(sinh(t), [], [1], [S.Half], [1, 0], t**2/4, pi**Rational(3, 2))
+    add(cosh(t), [], [S.Half], [0], [S.Half, S.Half], t**2/4, pi**Rational(3, 2))
+
+    # Section 8.4.5
+    # TODO can do t + a. but can also do by expansion... (XXX not really)
+    add(sin(t), [], [], [S.Half], [0], t**2/4, sqrt(pi))
+    add(cos(t), [], [], [0], [S.Half], t**2/4, sqrt(pi))
+
+    # Section 8.4.6 (sinc function)
+    add(sinc(t), [], [], [0], [Rational(-1, 2)], t**2/4, sqrt(pi)/2)
+
+    # Section 8.5.5
+    def make_log1(subs):
+        N = subs[n]
+        return [(S.NegativeOne**N*factorial(N),
+                 meijerg([], [1]*(N + 1), [0]*(N + 1), [], t))]
+
+    def make_log2(subs):
+        N = subs[n]
+        return [(factorial(N),
+                 meijerg([1]*(N + 1), [], [], [0]*(N + 1), t))]
+    # TODO these only hold for positive p, and can be made more general
+    #      but who uses log(x)*Heaviside(a-x) anyway ...
+    # TODO also it would be nice to derive them recursively ...
+    addi(log(t)**n*Heaviside(1 - t), make_log1, True)
+    addi(log(t)**n*Heaviside(t - 1), make_log2, True)
+
+    def make_log3(subs):
+        return make_log1(subs) + make_log2(subs)
+    addi(log(t)**n, make_log3, True)
+    addi(log(t + a),
+         constant(log(a)) + [(S.One, meijerg([1, 1], [], [1], [0], t/a))],
+         True)
+    addi(log(Abs(t - a)), constant(log(Abs(a))) +
+         [(pi, meijerg([1, 1], [S.Half], [1], [0, S.Half], t/a))],
+         True)
+    # TODO log(x)/(x+a) and log(x)/(x-1) can also be done. should they
+    #      be derivable?
+    # TODO further formulae in this section seem obscure
+
+    # Sections 8.4.9-10
+    # TODO
+
+    # Section 8.4.11
+    addi(Ei(t),
+         constant(-S.ImaginaryUnit*pi) + [(S.NegativeOne, meijerg([], [1], [0, 0], [],
+                  t*polar_lift(-1)))],
+         True)
+
+    # Section 8.4.12
+    add(Si(t), [1], [], [S.Half], [0, 0], t**2/4, sqrt(pi)/2)
+    add(Ci(t), [], [1], [0, 0], [S.Half], t**2/4, -sqrt(pi)/2)
+
+    # Section 8.4.13
+    add(Shi(t), [S.Half], [], [0], [Rational(-1, 2), Rational(-1, 2)], polar_lift(-1)*t**2/4,
+        t*sqrt(pi)/4)
+    add(Chi(t), [], [S.Half, 1], [0, 0], [S.Half, S.Half], t**2/4, -
+        pi**S('3/2')/2)
+
+    # generalized exponential integral
+    add(expint(a, t), [], [a], [a - 1, 0], [], t)
+
+    # Section 8.4.14
+    add(erf(t), [1], [], [S.Half], [0], t**2, 1/sqrt(pi))
+    # TODO exp(-x)*erf(I*x) does not work
+    add(erfc(t), [], [1], [0, S.Half], [], t**2, 1/sqrt(pi))
+    # This formula for erfi(z) yields a wrong(?) minus sign
+    #add(erfi(t), [1], [], [S.Half], [0], -t**2, I/sqrt(pi))
+    add(erfi(t), [S.Half], [], [0], [Rational(-1, 2)], -t**2, t/sqrt(pi))
+
+    # Fresnel Integrals
+    add(fresnels(t), [1], [], [Rational(3, 4)], [0, Rational(1, 4)], pi**2*t**4/16, S.Half)
+    add(fresnelc(t), [1], [], [Rational(1, 4)], [0, Rational(3, 4)], pi**2*t**4/16, S.Half)
+
+    ##### bessel-type functions #####
+    # Section 8.4.19
+    add(besselj(a, t), [], [], [a/2], [-a/2], t**2/4)
+
+    # all of the following are derivable
+    #add(sin(t)*besselj(a, t), [Rational(1, 4), Rational(3, 4)], [], [(1+a)/2],
+    #    [-a/2, a/2, (1-a)/2], t**2, 1/sqrt(2))
+    #add(cos(t)*besselj(a, t), [Rational(1, 4), Rational(3, 4)], [], [a/2],
+    #    [-a/2, (1+a)/2, (1-a)/2], t**2, 1/sqrt(2))
+    #add(besselj(a, t)**2, [S.Half], [], [a], [-a, 0], t**2, 1/sqrt(pi))
+    #add(besselj(a, t)*besselj(b, t), [0, S.Half], [], [(a + b)/2],
+    #    [-(a+b)/2, (a - b)/2, (b - a)/2], t**2, 1/sqrt(pi))
+
+    # Section 8.4.20
+    add(bessely(a, t), [], [-(a + 1)/2], [a/2, -a/2], [-(a + 1)/2], t**2/4)
+
+    # TODO all of the following should be derivable
+    #add(sin(t)*bessely(a, t), [Rational(1, 4), Rational(3, 4)], [(1 - a - 1)/2],
+    #    [(1 + a)/2, (1 - a)/2], [(1 - a - 1)/2, (1 - 1 - a)/2, (1 - 1 + a)/2],
+    #    t**2, 1/sqrt(2))
+    #add(cos(t)*bessely(a, t), [Rational(1, 4), Rational(3, 4)], [(0 - a - 1)/2],
+    #    [(0 + a)/2, (0 - a)/2], [(0 - a - 1)/2, (1 - 0 - a)/2, (1 - 0 + a)/2],
+    #    t**2, 1/sqrt(2))
+    #add(besselj(a, t)*bessely(b, t), [0, S.Half], [(a - b - 1)/2],
+    #    [(a + b)/2, (a - b)/2], [(a - b - 1)/2, -(a + b)/2, (b - a)/2],
+    #    t**2, 1/sqrt(pi))
+    #addi(bessely(a, t)**2,
+    #     [(2/sqrt(pi), meijerg([], [S.Half, S.Half - a], [0, a, -a],
+    #                           [S.Half - a], t**2)),
+    #      (1/sqrt(pi), meijerg([S.Half], [], [a], [-a, 0], t**2))],
+    #     True)
+    #addi(bessely(a, t)*bessely(b, t),
+    #     [(2/sqrt(pi), meijerg([], [0, S.Half, (1 - a - b)/2],
+    #                           [(a + b)/2, (a - b)/2, (b - a)/2, -(a + b)/2],
+    #                           [(1 - a - b)/2], t**2)),
+    #      (1/sqrt(pi), meijerg([0, S.Half], [], [(a + b)/2],
+    #                           [-(a + b)/2, (a - b)/2, (b - a)/2], t**2))],
+    #     True)
+
+    # Section 8.4.21 ?
+    # Section 8.4.22
+    add(besseli(a, t), [], [(1 + a)/2], [a/2], [-a/2, (1 + a)/2], t**2/4, pi)
+    # TODO many more formulas. should all be derivable
+
+    # Section 8.4.23
+    add(besselk(a, t), [], [], [a/2, -a/2], [], t**2/4, S.Half)
+    # TODO many more formulas. should all be derivable
+
+    # Complete elliptic integrals K(z) and E(z)
+    add(elliptic_k(t), [S.Half, S.Half], [], [0], [0], -t, S.Half)
+    add(elliptic_e(t), [S.Half, 3*S.Half], [], [0], [0], -t, Rational(-1, 2)/2)
+
+
+####################################################################
+# First some helper functions.
+####################################################################
+
+from sympy.utilities.timeutils import timethis
+timeit = timethis('meijerg')
+
+
+def _mytype(f: Basic, x: Symbol) -> tuple[type[Basic], ...]:
+    """ Create a hashable entity describing the type of f. """
+    def key(x: type[Basic]) -> tuple[int, int, str]:
+        return x.class_key()
+
+    if x not in f.free_symbols:
+        return ()
+    elif f.is_Function:
+        return type(f),
+    return tuple(sorted((t for a in f.args for t in _mytype(a, x)), key=key))
+
+
+class _CoeffExpValueError(ValueError):
+    """
+    Exception raised by _get_coeff_exp, for internal use only.
+    """
+    pass
+
+
+def _get_coeff_exp(expr, x):
+    """
+    When expr is known to be of the form c*x**b, with c and/or b possibly 1,
+    return c, b.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, a, b
+    >>> from sympy.integrals.meijerint import _get_coeff_exp
+    >>> _get_coeff_exp(a*x**b, x)
+    (a, b)
+    >>> _get_coeff_exp(x, x)
+    (1, 1)
+    >>> _get_coeff_exp(2*x, x)
+    (2, 1)
+    >>> _get_coeff_exp(x**3, x)
+    (1, 3)
+    """
+    from sympy.simplify import powsimp
+    (c, m) = expand_power_base(powsimp(expr)).as_coeff_mul(x)
+    if not m:
+        return c, S.Zero
+    [m] = m
+    if m.is_Pow:
+        if m.base != x:
+            raise _CoeffExpValueError('expr not of form a*x**b')
+        return c, m.exp
+    elif m == x:
+        return c, S.One
+    else:
+        raise _CoeffExpValueError('expr not of form a*x**b: %s' % expr)
+
+
+def _exponents(expr, x):
+    """
+    Find the exponents of ``x`` (not including zero) in ``expr``.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _exponents
+    >>> from sympy.abc import x, y
+    >>> from sympy import sin
+    >>> _exponents(x, x)
+    {1}
+    >>> _exponents(x**2, x)
+    {2}
+    >>> _exponents(x**2 + x, x)
+    {1, 2}
+    >>> _exponents(x**3*sin(x + x**y) + 1/x, x)
+    {-1, 1, 3, y}
+    """
+    def _exponents_(expr, x, res):
+        if expr == x:
+            res.update([1])
+            return
+        if expr.is_Pow and expr.base == x:
+            res.update([expr.exp])
+            return
+        for argument in expr.args:
+            _exponents_(argument, x, res)
+    res = set()
+    _exponents_(expr, x, res)
+    return res
+
+
+def _functions(expr, x):
+    """ Find the types of functions in expr, to estimate the complexity. """
+    return {e.func for e in expr.atoms(Function) if x in e.free_symbols}
+
+
+def _find_splitting_points(expr, x):
+    """
+    Find numbers a such that a linear substitution x -> x + a would
+    (hopefully) simplify expr.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _find_splitting_points as fsp
+    >>> from sympy import sin
+    >>> from sympy.abc import x
+    >>> fsp(x, x)
+    {0}
+    >>> fsp((x-1)**3, x)
+    {1}
+    >>> fsp(sin(x+3)*x, x)
+    {-3, 0}
+    """
+    p, q = [Wild(n, exclude=[x]) for n in 'pq']
+
+    def compute_innermost(expr, res):
+        if not isinstance(expr, Expr):
+            return
+        m = expr.match(p*x + q)
+        if m and m[p] != 0:
+            res.add(-m[q]/m[p])
+            return
+        if expr.is_Atom:
+            return
+        for argument in expr.args:
+            compute_innermost(argument, res)
+    innermost = set()
+    compute_innermost(expr, innermost)
+    return innermost
+
+
+def _split_mul(f, x):
+    """
+    Split expression ``f`` into fac, po, g, where fac is a constant factor,
+    po = x**s for some s independent of s, and g is "the rest".
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _split_mul
+    >>> from sympy import sin
+    >>> from sympy.abc import s, x
+    >>> _split_mul((3*x)**s*sin(x**2)*x, x)
+    (3**s, x*x**s, sin(x**2))
+    """
+    fac = S.One
+    po = S.One
+    g = S.One
+    f = expand_power_base(f)
+
+    args = Mul.make_args(f)
+    for a in args:
+        if a == x:
+            po *= x
+        elif x not in a.free_symbols:
+            fac *= a
+        else:
+            if a.is_Pow and x not in a.exp.free_symbols:
+                c, t = a.base.as_coeff_mul(x)
+                if t != (x,):
+                    c, t = expand_mul(a.base).as_coeff_mul(x)
+                if t == (x,):
+                    po *= x**a.exp
+                    fac *= unpolarify(polarify(c**a.exp, subs=False))
+                    continue
+            g *= a
+
+    return fac, po, g
+
+
+def _mul_args(f):
+    """
+    Return a list ``L`` such that ``Mul(*L) == f``.
+
+    If ``f`` is not a ``Mul`` or ``Pow``, ``L=[f]``.
+    If ``f=g**n`` for an integer ``n``, ``L=[g]*n``.
+    If ``f`` is a ``Mul``, ``L`` comes from applying ``_mul_args`` to all factors of ``f``.
+    """
+    args = Mul.make_args(f)
+    gs = []
+    for g in args:
+        if g.is_Pow and g.exp.is_Integer:
+            n = g.exp
+            base = g.base
+            if n < 0:
+                n = -n
+                base = 1/base
+            gs += [base]*n
+        else:
+            gs.append(g)
+    return gs
+
+
+def _mul_as_two_parts(f):
+    """
+    Find all the ways to split ``f`` into a product of two terms.
+    Return None on failure.
+
+    Explanation
+    ===========
+
+    Although the order is canonical from multiset_partitions, this is
+    not necessarily the best order to process the terms. For example,
+    if the case of len(gs) == 2 is removed and multiset is allowed to
+    sort the terms, some tests fail.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _mul_as_two_parts
+    >>> from sympy import sin, exp, ordered
+    >>> from sympy.abc import x
+    >>> list(ordered(_mul_as_two_parts(x*sin(x)*exp(x))))
+    [(x, exp(x)*sin(x)), (x*exp(x), sin(x)), (x*sin(x), exp(x))]
+    """
+
+    gs = _mul_args(f)
+    if len(gs) < 2:
+        return None
+    if len(gs) == 2:
+        return [tuple(gs)]
+    return [(Mul(*x), Mul(*y)) for (x, y) in multiset_partitions(gs, 2)]
+
+
+def _inflate_g(g, n):
+    """ Return C, h such that h is a G function of argument z**n and
+        g = C*h. """
+    # TODO should this be a method of meijerg?
+    # See: [L, page 150, equation (5)]
+    def inflate(params, n):
+        """ (a1, .., ak) -> (a1/n, (a1+1)/n, ..., (ak + n-1)/n) """
+        return [(a + i)/n for a, i in itertools.product(params, range(n))]
+    v = S(len(g.ap) - len(g.bq))
+    C = n**(1 + g.nu + v/2)
+    C /= (2*pi)**((n - 1)*g.delta)
+    return C, meijerg(inflate(g.an, n), inflate(g.aother, n),
+                      inflate(g.bm, n), inflate(g.bother, n),
+                      g.argument**n * n**(n*v))
+
+
+def _flip_g(g):
+    """ Turn the G function into one of inverse argument
+        (i.e. G(1/x) -> G'(x)) """
+    # See [L], section 5.2
+    def tr(l):
+        return [1 - a for a in l]
+    return meijerg(tr(g.bm), tr(g.bother), tr(g.an), tr(g.aother), 1/g.argument)
+
+
+def _inflate_fox_h(g, a):
+    r"""
+    Let d denote the integrand in the definition of the G function ``g``.
+    Consider the function H which is defined in the same way, but with
+    integrand d/Gamma(a*s) (contour conventions as usual).
+
+    If ``a`` is rational, the function H can be written as C*G, for a constant C
+    and a G-function G.
+
+    This function returns C, G.
+    """
+    if a < 0:
+        return _inflate_fox_h(_flip_g(g), -a)
+    p = S(a.p)
+    q = S(a.q)
+    # We use the substitution s->qs, i.e. inflate g by q. We are left with an
+    # extra factor of Gamma(p*s), for which we use Gauss' multiplication
+    # theorem.
+    D, g = _inflate_g(g, q)
+    z = g.argument
+    D /= (2*pi)**((1 - p)/2)*p**Rational(-1, 2)
+    z /= p**p
+    bs = [(n + 1)/p for n in range(p)]
+    return D, meijerg(g.an, g.aother, g.bm, list(g.bother) + bs, z)
+
+_dummies: dict[tuple[str, str], Dummy]  = {}
+
+
+def _dummy(name, token, expr, **kwargs):
+    """
+    Return a dummy. This will return the same dummy if the same token+name is
+    requested more than once, and it is not already in expr.
+    This is for being cache-friendly.
+    """
+    d = _dummy_(name, token, **kwargs)
+    if d in expr.free_symbols:
+        return Dummy(name, **kwargs)
+    return d
+
+
+def _dummy_(name, token, **kwargs):
+    """
+    Return a dummy associated to name and token. Same effect as declaring
+    it globally.
+    """
+    global _dummies
+    if not (name, token) in _dummies:
+        _dummies[(name, token)] = Dummy(name, **kwargs)
+    return _dummies[(name, token)]
+
+
+def _is_analytic(f, x):
+    """ Check if f(x), when expressed using G functions on the positive reals,
+        will in fact agree with the G functions almost everywhere """
+    return not any(x in expr.free_symbols for expr in f.atoms(Heaviside, Abs))
+
+
+def _condsimp(cond, first=True):
+    """
+    Do naive simplifications on ``cond``.
+
+    Explanation
+    ===========
+
+    Note that this routine is completely ad-hoc, simplification rules being
+    added as need arises rather than following any logical pattern.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _condsimp as simp
+    >>> from sympy import Or, Eq
+    >>> from sympy.abc import x, y
+    >>> simp(Or(x < y, Eq(x, y)))
+    x <= y
+    """
+    if first:
+        cond = cond.replace(lambda _: _.is_Relational, _canonical_coeff)
+        first = False
+    if not isinstance(cond, BooleanFunction):
+        return cond
+    p, q, r = symbols('p q r', cls=Wild)
+    # transforms tests use 0, 4, 5 and 11-14
+    # meijer tests use 0, 2, 11, 14
+    # joint_rv uses 6, 7
+    rules = [
+        (Or(p < q, Eq(p, q)), p <= q),  # 0
+        # The next two obviously are instances of a general pattern, but it is
+        # easier to spell out the few cases we care about.
+        (And(Abs(arg(p)) <= pi, Abs(arg(p) - 2*pi) <= pi),
+         Eq(arg(p) - pi, 0)),  # 1
+        (And(Abs(2*arg(p) + pi) <= pi, Abs(2*arg(p) - pi) <= pi),
+         Eq(arg(p), 0)), # 2
+        (And(Abs(2*arg(p) + pi) < pi, Abs(2*arg(p) - pi) <= pi),
+         S.false),  # 3
+        (And(Abs(arg(p) - pi/2) <= pi/2, Abs(arg(p) + pi/2) <= pi/2),
+         Eq(arg(p), 0)),  # 4
+        (And(Abs(arg(p) - pi/2) <= pi/2, Abs(arg(p) + pi/2) < pi/2),
+         S.false),  # 5
+        (And(Abs(arg(p**2/2 + 1)) < pi, Ne(Abs(arg(p**2/2 + 1)), pi)),
+         S.true),  # 6
+        (Or(Abs(arg(p**2/2 + 1)) < pi, Ne(1/(p**2/2 + 1), 0)),
+         S.true),  # 7
+        (And(Abs(unbranched_argument(p)) <= pi,
+           Abs(unbranched_argument(exp_polar(-2*pi*S.ImaginaryUnit)*p)) <= pi),
+         Eq(unbranched_argument(exp_polar(-S.ImaginaryUnit*pi)*p), 0)),  # 8
+        (And(Abs(unbranched_argument(p)) <= pi/2,
+           Abs(unbranched_argument(exp_polar(-pi*S.ImaginaryUnit)*p)) <= pi/2),
+         Eq(unbranched_argument(exp_polar(-S.ImaginaryUnit*pi/2)*p), 0)),  # 9
+        (Or(p <= q, And(p < q, r)), p <= q),  # 10
+        (Ne(p**2, 1) & (p**2 > 1), p**2 > 1),  # 11
+        (Ne(1/p, 1) & (cos(Abs(arg(p)))*Abs(p) > 1), Abs(p) > 1),  # 12
+        (Ne(p, 2) & (cos(Abs(arg(p)))*Abs(p) > 2), Abs(p) > 2),  # 13
+        ((Abs(arg(p)) < pi/2) & (cos(Abs(arg(p)))*sqrt(Abs(p**2)) > 1), p**2 > 1),  # 14
+    ]
+    cond = cond.func(*[_condsimp(_, first) for _ in cond.args])
+    change = True
+    while change:
+        change = False
+        for irule, (fro, to) in enumerate(rules):
+            if fro.func != cond.func:
+                continue
+            for n, arg1 in enumerate(cond.args):
+                if r in fro.args[0].free_symbols:
+                    m = arg1.match(fro.args[1])
+                    num = 1
+                else:
+                    num = 0
+                    m = arg1.match(fro.args[0])
+                if not m:
+                    continue
+                otherargs = [x.subs(m) for x in fro.args[:num] + fro.args[num + 1:]]
+                otherlist = [n]
+                for arg2 in otherargs:
+                    for k, arg3 in enumerate(cond.args):
+                        if k in otherlist:
+                            continue
+                        if arg2 == arg3:
+                            otherlist += [k]
+                            break
+                        if isinstance(arg3, And) and arg2.args[1] == r and \
+                                isinstance(arg2, And) and arg2.args[0] in arg3.args:
+                            otherlist += [k]
+                            break
+                        if isinstance(arg3, And) and arg2.args[0] == r and \
+                                isinstance(arg2, And) and arg2.args[1] in arg3.args:
+                            otherlist += [k]
+                            break
+                if len(otherlist) != len(otherargs) + 1:
+                    continue
+                newargs = [arg_ for (k, arg_) in enumerate(cond.args)
+                           if k not in otherlist] + [to.subs(m)]
+                if SYMPY_DEBUG:
+                    if irule not in (0, 2, 4, 5, 6, 7, 11, 12, 13, 14):
+                        print('used new rule:', irule)
+                cond = cond.func(*newargs)
+                change = True
+                break
+
+    # final tweak
+    def rel_touchup(rel):
+        if rel.rel_op != '==' or rel.rhs != 0:
+            return rel
+
+        # handle Eq(*, 0)
+        LHS = rel.lhs
+        m = LHS.match(arg(p)**q)
+        if not m:
+            m = LHS.match(unbranched_argument(polar_lift(p)**q))
+        if not m:
+            if isinstance(LHS, periodic_argument) and not LHS.args[0].is_polar \
+                    and LHS.args[1] is S.Infinity:
+                return (LHS.args[0] > 0)
+            return rel
+        return (m[p] > 0)
+    cond = cond.replace(lambda _: _.is_Relational, rel_touchup)
+    if SYMPY_DEBUG:
+        print('_condsimp: ', cond)
+    return cond
+
+def _eval_cond(cond):
+    """ Re-evaluate the conditions. """
+    if isinstance(cond, bool):
+        return cond
+    return _condsimp(cond.doit())
+
+####################################################################
+# Now the "backbone" functions to do actual integration.
+####################################################################
+
+
+def _my_principal_branch(expr, period, full_pb=False):
+    """ Bring expr nearer to its principal branch by removing superfluous
+        factors.
+        This function does *not* guarantee to yield the principal branch,
+        to avoid introducing opaque principal_branch() objects,
+        unless full_pb=True. """
+    res = principal_branch(expr, period)
+    if not full_pb:
+        res = res.replace(principal_branch, lambda x, y: x)
+    return res
+
+
+def _rewrite_saxena_1(fac, po, g, x):
+    """
+    Rewrite the integral fac*po*g dx, from zero to infinity, as
+    integral fac*G, where G has argument a*x. Note po=x**s.
+    Return fac, G.
+    """
+    _, s = _get_coeff_exp(po, x)
+    a, b = _get_coeff_exp(g.argument, x)
+    period = g.get_period()
+    a = _my_principal_branch(a, period)
+
+    # We substitute t = x**b.
+    C = fac/(Abs(b)*a**((s + 1)/b - 1))
+    # Absorb a factor of (at)**((1 + s)/b - 1).
+
+    def tr(l):
+        return [a + (1 + s)/b - 1 for a in l]
+    return C, meijerg(tr(g.an), tr(g.aother), tr(g.bm), tr(g.bother),
+                      a*x)
+
+
+def _check_antecedents_1(g, x, helper=False):
+    r"""
+    Return a condition under which the mellin transform of g exists.
+    Any power of x has already been absorbed into the G function,
+    so this is just $\int_0^\infty g\, dx$.
+
+    See [L, section 5.6.1]. (Note that s=1.)
+
+    If ``helper`` is True, only check if the MT exists at infinity, i.e. if
+    $\int_1^\infty g\, dx$ exists.
+    """
+    # NOTE if you update these conditions, please update the documentation as well
+    delta = g.delta
+    eta, _ = _get_coeff_exp(g.argument, x)
+    m, n, p, q = S([len(g.bm), len(g.an), len(g.ap), len(g.bq)])
+
+    if p > q:
+        def tr(l):
+            return [1 - x for x in l]
+        return _check_antecedents_1(meijerg(tr(g.bm), tr(g.bother),
+                                            tr(g.an), tr(g.aother), x/eta),
+                                    x)
+
+    tmp = [-re(b) < 1 for b in g.bm] + [1 < 1 - re(a) for a in g.an]
+    cond_3 = And(*tmp)
+
+    tmp += [-re(b) < 1 for b in g.bother]
+    tmp += [1 < 1 - re(a) for a in g.aother]
+    cond_3_star = And(*tmp)
+
+    cond_4 = (-re(g.nu) + (q + 1 - p)/2 > q - p)
+
+    def debug(*msg):
+        _debug(*msg)
+
+    def debugf(string, arg):
+        _debugf(string, arg)
+
+    debug('Checking antecedents for 1 function:')
+    debugf('  delta=%s, eta=%s, m=%s, n=%s, p=%s, q=%s',
+           (delta, eta, m, n, p, q))
+    debugf('  ap = %s, %s', (list(g.an), list(g.aother)))
+    debugf('  bq = %s, %s', (list(g.bm), list(g.bother)))
+    debugf('  cond_3=%s, cond_3*=%s, cond_4=%s', (cond_3, cond_3_star, cond_4))
+
+    conds = []
+
+    # case 1
+    case1 = []
+    tmp1 = [1 <= n, p < q, 1 <= m]
+    tmp2 = [1 <= p, 1 <= m, Eq(q, p + 1), Not(And(Eq(n, 0), Eq(m, p + 1)))]
+    tmp3 = [1 <= p, Eq(q, p)]
+    for k in range(ceiling(delta/2) + 1):
+        tmp3 += [Ne(Abs(unbranched_argument(eta)), (delta - 2*k)*pi)]
+    tmp = [delta > 0, Abs(unbranched_argument(eta)) < delta*pi]
+    extra = [Ne(eta, 0), cond_3]
+    if helper:
+        extra = []
+    for t in [tmp1, tmp2, tmp3]:
+        case1 += [And(*(t + tmp + extra))]
+    conds += case1
+    debug('  case 1:', case1)
+
+    # case 2
+    extra = [cond_3]
+    if helper:
+        extra = []
+    case2 = [And(Eq(n, 0), p + 1 <= m, m <= q,
+                 Abs(unbranched_argument(eta)) < delta*pi, *extra)]
+    conds += case2
+    debug('  case 2:', case2)
+
+    # case 3
+    extra = [cond_3, cond_4]
+    if helper:
+        extra = []
+    case3 = [And(p < q, 1 <= m, delta > 0, Eq(Abs(unbranched_argument(eta)), delta*pi),
+                 *extra)]
+    case3 += [And(p <= q - 2, Eq(delta, 0), Eq(Abs(unbranched_argument(eta)), 0), *extra)]
+    conds += case3
+    debug('  case 3:', case3)
+
+    # TODO altered cases 4-7
+
+    # extra case from wofram functions site:
+    # (reproduced verbatim from Prudnikov, section 2.24.2)
+    # https://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/01/
+    case_extra = []
+    case_extra += [Eq(p, q), Eq(delta, 0), Eq(unbranched_argument(eta), 0), Ne(eta, 0)]
+    if not helper:
+        case_extra += [cond_3]
+    s = []
+    for a, b in zip(g.ap, g.bq):
+        s += [b - a]
+    case_extra += [re(Add(*s)) < 0]
+    case_extra = And(*case_extra)
+    conds += [case_extra]
+    debug('  extra case:', [case_extra])
+
+    case_extra_2 = [And(delta > 0, Abs(unbranched_argument(eta)) < delta*pi)]
+    if not helper:
+        case_extra_2 += [cond_3]
+    case_extra_2 = And(*case_extra_2)
+    conds += [case_extra_2]
+    debug('  second extra case:', [case_extra_2])
+
+    # TODO This leaves only one case from the three listed by Prudnikov.
+    #      Investigate if these indeed cover everything; if so, remove the rest.
+
+    return Or(*conds)
+
+
+def _int0oo_1(g, x):
+    r"""
+    Evaluate $\int_0^\infty g\, dx$ using G functions,
+    assuming the necessary conditions are fulfilled.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import a, b, c, d, x, y
+    >>> from sympy import meijerg
+    >>> from sympy.integrals.meijerint import _int0oo_1
+    >>> _int0oo_1(meijerg([a], [b], [c], [d], x*y), x)
+    gamma(-a)*gamma(c + 1)/(y*gamma(-d)*gamma(b + 1))
+    """
+    from sympy.simplify import gammasimp
+    # See [L, section 5.6.1]. Note that s=1.
+    eta, _ = _get_coeff_exp(g.argument, x)
+    res = 1/eta
+    # XXX TODO we should reduce order first
+    for b in g.bm:
+        res *= gamma(b + 1)
+    for a in g.an:
+        res *= gamma(1 - a - 1)
+    for b in g.bother:
+        res /= gamma(1 - b - 1)
+    for a in g.aother:
+        res /= gamma(a + 1)
+    return gammasimp(unpolarify(res))
+
+
+def _rewrite_saxena(fac, po, g1, g2, x, full_pb=False):
+    """
+    Rewrite the integral ``fac*po*g1*g2`` from 0 to oo in terms of G
+    functions with argument ``c*x``.
+
+    Explanation
+    ===========
+
+    Return C, f1, f2 such that integral C f1 f2 from 0 to infinity equals
+    integral fac ``po``, ``g1``, ``g2`` from 0 to infinity.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _rewrite_saxena
+    >>> from sympy.abc import s, t, m
+    >>> from sympy import meijerg
+    >>> g1 = meijerg([], [], [0], [], s*t)
+    >>> g2 = meijerg([], [], [m/2], [-m/2], t**2/4)
+    >>> r = _rewrite_saxena(1, t**0, g1, g2, t)
+    >>> r[0]
+    s/(4*sqrt(pi))
+    >>> r[1]
+    meijerg(((), ()), ((-1/2, 0), ()), s**2*t/4)
+    >>> r[2]
+    meijerg(((), ()), ((m/2,), (-m/2,)), t/4)
+    """
+    def pb(g):
+        a, b = _get_coeff_exp(g.argument, x)
+        per = g.get_period()
+        return meijerg(g.an, g.aother, g.bm, g.bother,
+                       _my_principal_branch(a, per, full_pb)*x**b)
+
+    _, s = _get_coeff_exp(po, x)
+    _, b1 = _get_coeff_exp(g1.argument, x)
+    _, b2 = _get_coeff_exp(g2.argument, x)
+    if (b1 < 0) == True:
+        b1 = -b1
+        g1 = _flip_g(g1)
+    if (b2 < 0) == True:
+        b2 = -b2
+        g2 = _flip_g(g2)
+    if not b1.is_Rational or not b2.is_Rational:
+        return
+    m1, n1 = b1.p, b1.q
+    m2, n2 = b2.p, b2.q
+    tau = ilcm(m1*n2, m2*n1)
+    r1 = tau//(m1*n2)
+    r2 = tau//(m2*n1)
+
+    C1, g1 = _inflate_g(g1, r1)
+    C2, g2 = _inflate_g(g2, r2)
+    g1 = pb(g1)
+    g2 = pb(g2)
+
+    fac *= C1*C2
+    a1, b = _get_coeff_exp(g1.argument, x)
+    a2, _ = _get_coeff_exp(g2.argument, x)
+
+    # arbitrarily tack on the x**s part to g1
+    # TODO should we try both?
+    exp = (s + 1)/b - 1
+    fac = fac/(Abs(b) * a1**exp)
+
+    def tr(l):
+        return [a + exp for a in l]
+    g1 = meijerg(tr(g1.an), tr(g1.aother), tr(g1.bm), tr(g1.bother), a1*x)
+    g2 = meijerg(g2.an, g2.aother, g2.bm, g2.bother, a2*x)
+
+    from sympy.simplify import powdenest
+    return powdenest(fac, polar=True), g1, g2
+
+
+def _check_antecedents(g1, g2, x):
+    """ Return a condition under which the integral theorem applies. """
+    #  Yes, this is madness.
+    # XXX TODO this is a testing *nightmare*
+    # NOTE if you update these conditions, please update the documentation as well
+
+    # The following conditions are found in
+    # [P], Section 2.24.1
+    #
+    # They are also reproduced (verbatim!) at
+    # https://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/03/
+    #
+    # Note: k=l=r=alpha=1
+    sigma, _ = _get_coeff_exp(g1.argument, x)
+    omega, _ = _get_coeff_exp(g2.argument, x)
+    s, t, u, v = S([len(g1.bm), len(g1.an), len(g1.ap), len(g1.bq)])
+    m, n, p, q = S([len(g2.bm), len(g2.an), len(g2.ap), len(g2.bq)])
+    bstar = s + t - (u + v)/2
+    cstar = m + n - (p + q)/2
+    rho = g1.nu + (u - v)/2 + 1
+    mu = g2.nu + (p - q)/2 + 1
+    phi = q - p - (v - u)
+    eta = 1 - (v - u) - mu - rho
+    psi = (pi*(q - m - n) + Abs(unbranched_argument(omega)))/(q - p)
+    theta = (pi*(v - s - t) + Abs(unbranched_argument(sigma)))/(v - u)
+
+    _debug('Checking antecedents:')
+    _debugf('  sigma=%s, s=%s, t=%s, u=%s, v=%s, b*=%s, rho=%s',
+            (sigma, s, t, u, v, bstar, rho))
+    _debugf('  omega=%s, m=%s, n=%s, p=%s, q=%s, c*=%s, mu=%s,',
+            (omega, m, n, p, q, cstar, mu))
+    _debugf('  phi=%s, eta=%s, psi=%s, theta=%s', (phi, eta, psi, theta))
+
+    def _c1():
+        for g in [g1, g2]:
+            for i, j in itertools.product(g.an, g.bm):
+                diff = i - j
+                if diff.is_integer and diff.is_positive:
+                    return False
+        return True
+    c1 = _c1()
+    c2 = And(*[re(1 + i + j) > 0 for i in g1.bm for j in g2.bm])
+    c3 = And(*[re(1 + i + j) < 1 + 1 for i in g1.an for j in g2.an])
+    c4 = And(*[(p - q)*re(1 + i - 1) - re(mu) > Rational(-3, 2) for i in g1.an])
+    c5 = And(*[(p - q)*re(1 + i) - re(mu) > Rational(-3, 2) for i in g1.bm])
+    c6 = And(*[(u - v)*re(1 + i - 1) - re(rho) > Rational(-3, 2) for i in g2.an])
+    c7 = And(*[(u - v)*re(1 + i) - re(rho) > Rational(-3, 2) for i in g2.bm])
+    c8 = (Abs(phi) + 2*re((rho - 1)*(q - p) + (v - u)*(q - p) + (mu -
+          1)*(v - u)) > 0)
+    c9 = (Abs(phi) - 2*re((rho - 1)*(q - p) + (v - u)*(q - p) + (mu -
+          1)*(v - u)) > 0)
+    c10 = (Abs(unbranched_argument(sigma)) < bstar*pi)
+    c11 = Eq(Abs(unbranched_argument(sigma)), bstar*pi)
+    c12 = (Abs(unbranched_argument(omega)) < cstar*pi)
+    c13 = Eq(Abs(unbranched_argument(omega)), cstar*pi)
+
+    # The following condition is *not* implemented as stated on the wolfram
+    # function site. In the book of Prudnikov there is an additional part
+    # (the And involving re()). However, I only have this book in russian, and
+    # I don't read any russian. The following condition is what other people
+    # have told me it means.
+    # Worryingly, it is different from the condition implemented in REDUCE.
+    # The REDUCE implementation:
+    #   https://reduce-algebra.svn.sourceforge.net/svnroot/reduce-algebra/trunk/packages/defint/definta.red
+    #   (search for tst14)
+    # The Wolfram alpha version:
+    #   https://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/03/03/0014/
+    z0 = exp(-(bstar + cstar)*pi*S.ImaginaryUnit)
+    zos = unpolarify(z0*omega/sigma)
+    zso = unpolarify(z0*sigma/omega)
+    if zos == 1/zso:
+        c14 = And(Eq(phi, 0), bstar + cstar <= 1,
+                  Or(Ne(zos, 1), re(mu + rho + v - u) < 1,
+                     re(mu + rho + q - p) < 1))
+    else:
+        def _cond(z):
+            '''Returns True if abs(arg(1-z)) < pi, avoiding arg(0).
+
+            Explanation
+            ===========
+
+            If ``z`` is 1 then arg is NaN. This raises a
+            TypeError on `NaN < pi`. Previously this gave `False` so
+            this behavior has been hardcoded here but someone should
+            check if this NaN is more serious! This NaN is triggered by
+            test_meijerint() in test_meijerint.py:
+            `meijerint_definite(exp(x), x, 0, I)`
+            '''
+            return z != 1 and Abs(arg(1 - z)) < pi
+
+        c14 = And(Eq(phi, 0), bstar - 1 + cstar <= 0,
+                  Or(And(Ne(zos, 1), _cond(zos)),
+                     And(re(mu + rho + v - u) < 1, Eq(zos, 1))))
+
+        c14_alt = And(Eq(phi, 0), cstar - 1 + bstar <= 0,
+                  Or(And(Ne(zso, 1), _cond(zso)),
+                     And(re(mu + rho + q - p) < 1, Eq(zso, 1))))
+
+        # Since r=k=l=1, in our case there is c14_alt which is the same as calling
+        # us with (g1, g2) = (g2, g1). The conditions below enumerate all cases
+        # (i.e. we don't have to try arguments reversed by hand), and indeed try
+        # all symmetric cases. (i.e. whenever there is a condition involving c14,
+        # there is also a dual condition which is exactly what we would get when g1,
+        # g2 were interchanged, *but c14 was unaltered*).
+        # Hence the following seems correct:
+        c14 = Or(c14, c14_alt)
+
+    '''
+    When `c15` is NaN (e.g. from `psi` being NaN as happens during
+    'test_issue_4992' and/or `theta` is NaN as in 'test_issue_6253',
+    both in `test_integrals.py`) the comparison to 0 formerly gave False
+    whereas now an error is raised. To keep the old behavior, the value
+    of NaN is replaced with False but perhaps a closer look at this condition
+    should be made: XXX how should conditions leading to c15=NaN be handled?
+    '''
+    try:
+        lambda_c = (q - p)*Abs(omega)**(1/(q - p))*cos(psi) \
+            + (v - u)*Abs(sigma)**(1/(v - u))*cos(theta)
+        # the TypeError might be raised here, e.g. if lambda_c is NaN
+        if _eval_cond(lambda_c > 0) != False:
+            c15 = (lambda_c > 0)
+        else:
+            def lambda_s0(c1, c2):
+                return c1*(q - p)*Abs(omega)**(1/(q - p))*sin(psi) \
+                    + c2*(v - u)*Abs(sigma)**(1/(v - u))*sin(theta)
+            lambda_s = Piecewise(
+                ((lambda_s0(+1, +1)*lambda_s0(-1, -1)),
+                 And(Eq(unbranched_argument(sigma), 0), Eq(unbranched_argument(omega), 0))),
+                (lambda_s0(sign(unbranched_argument(omega)), +1)*lambda_s0(sign(unbranched_argument(omega)), -1),
+                 And(Eq(unbranched_argument(sigma), 0), Ne(unbranched_argument(omega), 0))),
+                (lambda_s0(+1, sign(unbranched_argument(sigma)))*lambda_s0(-1, sign(unbranched_argument(sigma))),
+                 And(Ne(unbranched_argument(sigma), 0), Eq(unbranched_argument(omega), 0))),
+                (lambda_s0(sign(unbranched_argument(omega)), sign(unbranched_argument(sigma))), True))
+            tmp = [lambda_c > 0,
+                   And(Eq(lambda_c, 0), Ne(lambda_s, 0), re(eta) > -1),
+                   And(Eq(lambda_c, 0), Eq(lambda_s, 0), re(eta) > 0)]
+            c15 = Or(*tmp)
+    except TypeError:
+        c15 = False
+    for cond, i in [(c1, 1), (c2, 2), (c3, 3), (c4, 4), (c5, 5), (c6, 6),
+                    (c7, 7), (c8, 8), (c9, 9), (c10, 10), (c11, 11),
+                    (c12, 12), (c13, 13), (c14, 14), (c15, 15)]:
+        _debugf('  c%s: %s', (i, cond))
+
+    # We will return Or(*conds)
+    conds = []
+
+    def pr(count):
+        _debugf('  case %s: %s', (count, conds[-1]))
+    conds += [And(m*n*s*t != 0, bstar.is_positive is True, cstar.is_positive is True, c1, c2, c3, c10,
+                  c12)]  # 1
+    pr(1)
+    conds += [And(Eq(u, v), Eq(bstar, 0), cstar.is_positive is True, sigma.is_positive is True, re(rho) < 1,
+                  c1, c2, c3, c12)]  # 2
+    pr(2)
+    conds += [And(Eq(p, q), Eq(cstar, 0), bstar.is_positive is True, omega.is_positive is True, re(mu) < 1,
+                  c1, c2, c3, c10)]  # 3
+    pr(3)
+    conds += [And(Eq(p, q), Eq(u, v), Eq(bstar, 0), Eq(cstar, 0),
+                  sigma.is_positive is True, omega.is_positive is True, re(mu) < 1, re(rho) < 1,
+                  Ne(sigma, omega), c1, c2, c3)]  # 4
+    pr(4)
+    conds += [And(Eq(p, q), Eq(u, v), Eq(bstar, 0), Eq(cstar, 0),
+                  sigma.is_positive is True, omega.is_positive is True, re(mu + rho) < 1,
+                  Ne(omega, sigma), c1, c2, c3)]  # 5
+    pr(5)
+    conds += [And(p > q, s.is_positive is True, bstar.is_positive is True, cstar >= 0,
+                  c1, c2, c3, c5, c10, c13)]  # 6
+    pr(6)
+    conds += [And(p < q, t.is_positive is True, bstar.is_positive is True, cstar >= 0,
+                  c1, c2, c3, c4, c10, c13)]  # 7
+    pr(7)
+    conds += [And(u > v, m.is_positive is True, cstar.is_positive is True, bstar >= 0,
+                  c1, c2, c3, c7, c11, c12)]  # 8
+    pr(8)
+    conds += [And(u < v, n.is_positive is True, cstar.is_positive is True, bstar >= 0,
+                  c1, c2, c3, c6, c11, c12)]  # 9
+    pr(9)
+    conds += [And(p > q, Eq(u, v), Eq(bstar, 0), cstar >= 0, sigma.is_positive is True,
+                  re(rho) < 1, c1, c2, c3, c5, c13)]  # 10
+    pr(10)
+    conds += [And(p < q, Eq(u, v), Eq(bstar, 0), cstar >= 0, sigma.is_positive is True,
+                  re(rho) < 1, c1, c2, c3, c4, c13)]  # 11
+    pr(11)
+    conds += [And(Eq(p, q), u > v, bstar >= 0, Eq(cstar, 0), omega.is_positive is True,
+                  re(mu) < 1, c1, c2, c3, c7, c11)]  # 12
+    pr(12)
+    conds += [And(Eq(p, q), u < v, bstar >= 0, Eq(cstar, 0), omega.is_positive is True,
+                  re(mu) < 1, c1, c2, c3, c6, c11)]  # 13
+    pr(13)
+    conds += [And(p < q, u > v, bstar >= 0, cstar >= 0,
+                  c1, c2, c3, c4, c7, c11, c13)]  # 14
+    pr(14)
+    conds += [And(p > q, u < v, bstar >= 0, cstar >= 0,
+                  c1, c2, c3, c5, c6, c11, c13)]  # 15
+    pr(15)
+    conds += [And(p > q, u > v, bstar >= 0, cstar >= 0,
+                  c1, c2, c3, c5, c7, c8, c11, c13, c14)]  # 16
+    pr(16)
+    conds += [And(p < q, u < v, bstar >= 0, cstar >= 0,
+                  c1, c2, c3, c4, c6, c9, c11, c13, c14)]  # 17
+    pr(17)
+    conds += [And(Eq(t, 0), s.is_positive is True, bstar.is_positive is True, phi.is_positive is True, c1, c2, c10)]  # 18
+    pr(18)
+    conds += [And(Eq(s, 0), t.is_positive is True, bstar.is_positive is True, phi.is_negative is True, c1, c3, c10)]  # 19
+    pr(19)
+    conds += [And(Eq(n, 0), m.is_positive is True, cstar.is_positive is True, phi.is_negative is True, c1, c2, c12)]  # 20
+    pr(20)
+    conds += [And(Eq(m, 0), n.is_positive is True, cstar.is_positive is True, phi.is_positive is True, c1, c3, c12)]  # 21
+    pr(21)
+    conds += [And(Eq(s*t, 0), bstar.is_positive is True, cstar.is_positive is True,
+                  c1, c2, c3, c10, c12)]  # 22
+    pr(22)
+    conds += [And(Eq(m*n, 0), bstar.is_positive is True, cstar.is_positive is True,
+                  c1, c2, c3, c10, c12)]  # 23
+    pr(23)
+
+    # The following case is from [Luke1969]. As far as I can tell, it is *not*
+    # covered by Prudnikov's.
+    # Let G1 and G2 be the two G-functions. Suppose the integral exists from
+    # 0 to a > 0 (this is easy the easy part), that G1 is exponential decay at
+    # infinity, and that the mellin transform of G2 exists.
+    # Then the integral exists.
+    mt1_exists = _check_antecedents_1(g1, x, helper=True)
+    mt2_exists = _check_antecedents_1(g2, x, helper=True)
+    conds += [And(mt2_exists, Eq(t, 0), u < s, bstar.is_positive is True, c10, c1, c2, c3)]
+    pr('E1')
+    conds += [And(mt2_exists, Eq(s, 0), v < t, bstar.is_positive is True, c10, c1, c2, c3)]
+    pr('E2')
+    conds += [And(mt1_exists, Eq(n, 0), p < m, cstar.is_positive is True, c12, c1, c2, c3)]
+    pr('E3')
+    conds += [And(mt1_exists, Eq(m, 0), q < n, cstar.is_positive is True, c12, c1, c2, c3)]
+    pr('E4')
+
+    # Let's short-circuit if this worked ...
+    # the rest is corner-cases and terrible to read.
+    r = Or(*conds)
+    if _eval_cond(r) != False:
+        return r
+
+    conds += [And(m + n > p, Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True, cstar.is_negative is True,
+                  Abs(unbranched_argument(omega)) < (m + n - p + 1)*pi,
+                  c1, c2, c10, c14, c15)]  # 24
+    pr(24)
+    conds += [And(m + n > q, Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True, cstar.is_negative is True,
+                  Abs(unbranched_argument(omega)) < (m + n - q + 1)*pi,
+                  c1, c3, c10, c14, c15)]  # 25
+    pr(25)
+    conds += [And(Eq(p, q - 1), Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True,
+                  cstar >= 0, cstar*pi < Abs(unbranched_argument(omega)),
+                  c1, c2, c10, c14, c15)]  # 26
+    pr(26)
+    conds += [And(Eq(p, q + 1), Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True,
+                  cstar >= 0, cstar*pi < Abs(unbranched_argument(omega)),
+                  c1, c3, c10, c14, c15)]  # 27
+    pr(27)
+    conds += [And(p < q - 1, Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True,
+                  cstar >= 0, cstar*pi < Abs(unbranched_argument(omega)),
+                  Abs(unbranched_argument(omega)) < (m + n - p + 1)*pi,
+                  c1, c2, c10, c14, c15)]  # 28
+    pr(28)
+    conds += [And(
+        p > q + 1, Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True, cstar >= 0,
+                  cstar*pi < Abs(unbranched_argument(omega)),
+                  Abs(unbranched_argument(omega)) < (m + n - q + 1)*pi,
+                  c1, c3, c10, c14, c15)]  # 29
+    pr(29)
+    conds += [And(Eq(n, 0), Eq(phi, 0), s + t > 0, m.is_positive is True, cstar.is_positive is True, bstar.is_negative is True,
+                  Abs(unbranched_argument(sigma)) < (s + t - u + 1)*pi,
+                  c1, c2, c12, c14, c15)]  # 30
+    pr(30)
+    conds += [And(Eq(m, 0), Eq(phi, 0), s + t > v, n.is_positive is True, cstar.is_positive is True, bstar.is_negative is True,
+                  Abs(unbranched_argument(sigma)) < (s + t - v + 1)*pi,
+                  c1, c3, c12, c14, c15)]  # 31
+    pr(31)
+    conds += [And(Eq(n, 0), Eq(phi, 0), Eq(u, v - 1), m.is_positive is True, cstar.is_positive is True,
+                  bstar >= 0, bstar*pi < Abs(unbranched_argument(sigma)),
+                  Abs(unbranched_argument(sigma)) < (bstar + 1)*pi,
+                  c1, c2, c12, c14, c15)]  # 32
+    pr(32)
+    conds += [And(Eq(m, 0), Eq(phi, 0), Eq(u, v + 1), n.is_positive is True, cstar.is_positive is True,
+                  bstar >= 0, bstar*pi < Abs(unbranched_argument(sigma)),
+                  Abs(unbranched_argument(sigma)) < (bstar + 1)*pi,
+                  c1, c3, c12, c14, c15)]  # 33
+    pr(33)
+    conds += [And(
+        Eq(n, 0), Eq(phi, 0), u < v - 1, m.is_positive is True, cstar.is_positive is True, bstar >= 0,
+        bstar*pi < Abs(unbranched_argument(sigma)),
+        Abs(unbranched_argument(sigma)) < (s + t - u + 1)*pi,
+        c1, c2, c12, c14, c15)]  # 34
+    pr(34)
+    conds += [And(
+        Eq(m, 0), Eq(phi, 0), u > v + 1, n.is_positive is True, cstar.is_positive is True, bstar >= 0,
+        bstar*pi < Abs(unbranched_argument(sigma)),
+        Abs(unbranched_argument(sigma)) < (s + t - v + 1)*pi,
+        c1, c3, c12, c14, c15)]  # 35
+    pr(35)
+
+    return Or(*conds)
+
+    # NOTE An alternative, but as far as I can tell weaker, set of conditions
+    #      can be found in [L, section 5.6.2].
+
+
+def _int0oo(g1, g2, x):
+    """
+    Express integral from zero to infinity g1*g2 using a G function,
+    assuming the necessary conditions are fulfilled.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import _int0oo
+    >>> from sympy.abc import s, t, m
+    >>> from sympy import meijerg, S
+    >>> g1 = meijerg([], [], [-S(1)/2, 0], [], s**2*t/4)
+    >>> g2 = meijerg([], [], [m/2], [-m/2], t/4)
+    >>> _int0oo(g1, g2, t)
+    4*meijerg(((1/2, 0), ()), ((m/2,), (-m/2,)), s**(-2))/s**2
+    """
+    # See: [L, section 5.6.2, equation (1)]
+    eta, _ = _get_coeff_exp(g1.argument, x)
+    omega, _ = _get_coeff_exp(g2.argument, x)
+
+    def neg(l):
+        return [-x for x in l]
+    a1 = neg(g1.bm) + list(g2.an)
+    a2 = list(g2.aother) + neg(g1.bother)
+    b1 = neg(g1.an) + list(g2.bm)
+    b2 = list(g2.bother) + neg(g1.aother)
+    return meijerg(a1, a2, b1, b2, omega/eta)/eta
+
+
+def _rewrite_inversion(fac, po, g, x):
+    """ Absorb ``po`` == x**s into g. """
+    _, s = _get_coeff_exp(po, x)
+    a, b = _get_coeff_exp(g.argument, x)
+
+    def tr(l):
+        return [t + s/b for t in l]
+    from sympy.simplify import powdenest
+    return (powdenest(fac/a**(s/b), polar=True),
+            meijerg(tr(g.an), tr(g.aother), tr(g.bm), tr(g.bother), g.argument))
+
+
+def _check_antecedents_inversion(g, x):
+    """ Check antecedents for the laplace inversion integral. """
+    _debug('Checking antecedents for inversion:')
+    z = g.argument
+    _, e = _get_coeff_exp(z, x)
+    if e < 0:
+        _debug('  Flipping G.')
+        # We want to assume that argument gets large as |x| -> oo
+        return _check_antecedents_inversion(_flip_g(g), x)
+
+    def statement_half(a, b, c, z, plus):
+        coeff, exponent = _get_coeff_exp(z, x)
+        a *= exponent
+        b *= coeff**c
+        c *= exponent
+        conds = []
+        wp = b*exp(S.ImaginaryUnit*re(c)*pi/2)
+        wm = b*exp(-S.ImaginaryUnit*re(c)*pi/2)
+        if plus:
+            w = wp
+        else:
+            w = wm
+        conds += [And(Or(Eq(b, 0), re(c) <= 0), re(a) <= -1)]
+        conds += [And(Ne(b, 0), Eq(im(c), 0), re(c) > 0, re(w) < 0)]
+        conds += [And(Ne(b, 0), Eq(im(c), 0), re(c) > 0, re(w) <= 0,
+                      re(a) <= -1)]
+        return Or(*conds)
+
+    def statement(a, b, c, z):
+        """ Provide a convergence statement for z**a * exp(b*z**c),
+             c/f sphinx docs. """
+        return And(statement_half(a, b, c, z, True),
+                   statement_half(a, b, c, z, False))
+
+    # Notations from [L], section 5.7-10
+    m, n, p, q = S([len(g.bm), len(g.an), len(g.ap), len(g.bq)])
+    tau = m + n - p
+    nu = q - m - n
+    rho = (tau - nu)/2
+    sigma = q - p
+    if sigma == 1:
+        epsilon = S.Half
+    elif sigma > 1:
+        epsilon = 1
+    else:
+        epsilon = S.NaN
+    theta = ((1 - sigma)/2 + Add(*g.bq) - Add(*g.ap))/sigma
+    delta = g.delta
+    _debugf('  m=%s, n=%s, p=%s, q=%s, tau=%s, nu=%s, rho=%s, sigma=%s',
+            (m, n, p, q, tau, nu, rho, sigma))
+    _debugf('  epsilon=%s, theta=%s, delta=%s', (epsilon, theta, delta))
+
+    # First check if the computation is valid.
+    if not (g.delta >= e/2 or (p >= 1 and p >= q)):
+        _debug('  Computation not valid for these parameters.')
+        return False
+
+    # Now check if the inversion integral exists.
+
+    # Test "condition A"
+    for a, b in itertools.product(g.an, g.bm):
+        if (a - b).is_integer and a > b:
+            _debug('  Not a valid G function.')
+            return False
+
+    # There are two cases. If p >= q, we can directly use a slater expansion
+    # like [L], 5.2 (11). Note in particular that the asymptotics of such an
+    # expansion even hold when some of the parameters differ by integers, i.e.
+    # the formula itself would not be valid! (b/c G functions are cts. in their
+    # parameters)
+    # When p < q, we need to use the theorems of [L], 5.10.
+
+    if p >= q:
+        _debug('  Using asymptotic Slater expansion.')
+        return And(*[statement(a - 1, 0, 0, z) for a in g.an])
+
+    def E(z):
+        return And(*[statement(a - 1, 0, 0, z) for a in g.an])
+
+    def H(z):
+        return statement(theta, -sigma, 1/sigma, z)
+
+    def Hp(z):
+        return statement_half(theta, -sigma, 1/sigma, z, True)
+
+    def Hm(z):
+        return statement_half(theta, -sigma, 1/sigma, z, False)
+
+    # [L], section 5.10
+    conds = []
+    # Theorem 1 -- p < q from test above
+    conds += [And(1 <= n, 1 <= m, rho*pi - delta >= pi/2, delta > 0,
+                  E(z*exp(S.ImaginaryUnit*pi*(nu + 1))))]
+    # Theorem 2, statements (2) and (3)
+    conds += [And(p + 1 <= m, m + 1 <= q, delta > 0, delta < pi/2, n == 0,
+                  (m - p + 1)*pi - delta >= pi/2,
+                  Hp(z*exp(S.ImaginaryUnit*pi*(q - m))),
+                  Hm(z*exp(-S.ImaginaryUnit*pi*(q - m))))]
+    # Theorem 2, statement (5)  -- p < q from test above
+    conds += [And(m == q, n == 0, delta > 0,
+                  (sigma + epsilon)*pi - delta >= pi/2, H(z))]
+    # Theorem 3, statements (6) and (7)
+    conds += [And(Or(And(p <= q - 2, 1 <= tau, tau <= sigma/2),
+                     And(p + 1 <= m + n, m + n <= (p + q)/2)),
+                  delta > 0, delta < pi/2, (tau + 1)*pi - delta >= pi/2,
+                  Hp(z*exp(S.ImaginaryUnit*pi*nu)),
+                  Hm(z*exp(-S.ImaginaryUnit*pi*nu)))]
+    # Theorem 4, statements (10) and (11)  -- p < q from test above
+    conds += [And(1 <= m, rho > 0, delta > 0, delta + rho*pi < pi/2,
+                  (tau + epsilon)*pi - delta >= pi/2,
+                  Hp(z*exp(S.ImaginaryUnit*pi*nu)),
+                  Hm(z*exp(-S.ImaginaryUnit*pi*nu)))]
+    # Trivial case
+    conds += [m == 0]
+
+    # TODO
+    # Theorem 5 is quite general
+    # Theorem 6 contains special cases for q=p+1
+
+    return Or(*conds)
+
+
+def _int_inversion(g, x, t):
+    """
+    Compute the laplace inversion integral, assuming the formula applies.
+    """
+    b, a = _get_coeff_exp(g.argument, x)
+    C, g = _inflate_fox_h(meijerg(g.an, g.aother, g.bm, g.bother, b/t**a), -a)
+    return C/t*g
+
+
+####################################################################
+# Finally, the real meat.
+####################################################################
+
+_lookup_table = None
+
+
+@cacheit
+@timeit
+def _rewrite_single(f, x, recursive=True):
+    """
+    Try to rewrite f as a sum of single G functions of the form
+    C*x**s*G(a*x**b), where b is a rational number and C is independent of x.
+    We guarantee that result.argument.as_coeff_mul(x) returns (a, (x**b,))
+    or (a, ()).
+    Returns a list of tuples (C, s, G) and a condition cond.
+    Returns None on failure.
+    """
+    from .transforms import (mellin_transform, inverse_mellin_transform,
+        IntegralTransformError, MellinTransformStripError)
+
+    global _lookup_table
+    if not _lookup_table:
+        _lookup_table = {}
+        _create_lookup_table(_lookup_table)
+
+    if isinstance(f, meijerg):
+        coeff, m = factor(f.argument, x).as_coeff_mul(x)
+        if len(m) > 1:
+            return None
+        m = m[0]
+        if m.is_Pow:
+            if m.base != x or not m.exp.is_Rational:
+                return None
+        elif m != x:
+            return None
+        return [(1, 0, meijerg(f.an, f.aother, f.bm, f.bother, coeff*m))], True
+
+    f_ = f
+    f = f.subs(x, z)
+    t = _mytype(f, z)
+    if t in _lookup_table:
+        l = _lookup_table[t]
+        for formula, terms, cond, hint in l:
+            subs = f.match(formula, old=True)
+            if subs:
+                subs_ = {}
+                for fro, to in subs.items():
+                    subs_[fro] = unpolarify(polarify(to, lift=True),
+                                            exponents_only=True)
+                subs = subs_
+                if not isinstance(hint, bool):
+                    hint = hint.subs(subs)
+                if hint == False:
+                    continue
+                if not isinstance(cond, (bool, BooleanAtom)):
+                    cond = unpolarify(cond.subs(subs))
+                if _eval_cond(cond) == False:
+                    continue
+                if not isinstance(terms, list):
+                    terms = terms(subs)
+                res = []
+                for fac, g in terms:
+                    r1 = _get_coeff_exp(unpolarify(fac.subs(subs).subs(z, x),
+                                                   exponents_only=True), x)
+                    try:
+                        g = g.subs(subs).subs(z, x)
+                    except ValueError:
+                        continue
+                    # NOTE these substitutions can in principle introduce oo,
+                    #      zoo and other absurdities. It shouldn't matter,
+                    #      but better be safe.
+                    if Tuple(*(r1 + (g,))).has(S.Infinity, S.ComplexInfinity, S.NegativeInfinity):
+                        continue
+                    g = meijerg(g.an, g.aother, g.bm, g.bother,
+                                unpolarify(g.argument, exponents_only=True))
+                    res.append(r1 + (g,))
+                if res:
+                    return res, cond
+
+    # try recursive mellin transform
+    if not recursive:
+        return None
+    _debug('Trying recursive Mellin transform method.')
+
+    def my_imt(F, s, x, strip):
+        """ Calling simplify() all the time is slow and not helpful, since
+            most of the time it only factors things in a way that has to be
+            un-done anyway. But sometimes it can remove apparent poles. """
+        # XXX should this be in inverse_mellin_transform?
+        try:
+            return inverse_mellin_transform(F, s, x, strip,
+                                            as_meijerg=True, needeval=True)
+        except MellinTransformStripError:
+            from sympy.simplify import simplify
+            return inverse_mellin_transform(
+                simplify(cancel(expand(F))), s, x, strip,
+                as_meijerg=True, needeval=True)
+    f = f_
+    s = _dummy('s', 'rewrite-single', f)
+    # to avoid infinite recursion, we have to force the two g functions case
+
+    def my_integrator(f, x):
+        r = _meijerint_definite_4(f, x, only_double=True)
+        if r is not None:
+            from sympy.simplify import hyperexpand
+            res, cond = r
+            res = _my_unpolarify(hyperexpand(res, rewrite='nonrepsmall'))
+            return Piecewise((res, cond),
+                             (Integral(f, (x, S.Zero, S.Infinity)), True))
+        return Integral(f, (x, S.Zero, S.Infinity))
+    try:
+        F, strip, _ = mellin_transform(f, x, s, integrator=my_integrator,
+                                       simplify=False, needeval=True)
+        g = my_imt(F, s, x, strip)
+    except IntegralTransformError:
+        g = None
+    if g is None:
+        # We try to find an expression by analytic continuation.
+        # (also if the dummy is already in the expression, there is no point in
+        #  putting in another one)
+        a = _dummy_('a', 'rewrite-single')
+        if a not in f.free_symbols and _is_analytic(f, x):
+            try:
+                F, strip, _ = mellin_transform(f.subs(x, a*x), x, s,
+                                               integrator=my_integrator,
+                                               needeval=True, simplify=False)
+                g = my_imt(F, s, x, strip).subs(a, 1)
+            except IntegralTransformError:
+                g = None
+    if g is None or g.has(S.Infinity, S.NaN, S.ComplexInfinity):
+        _debug('Recursive Mellin transform failed.')
+        return None
+    args = Add.make_args(g)
+    res = []
+    for f in args:
+        c, m = f.as_coeff_mul(x)
+        if len(m) > 1:
+            raise NotImplementedError('Unexpected form...')
+        g = m[0]
+        a, b = _get_coeff_exp(g.argument, x)
+        res += [(c, 0, meijerg(g.an, g.aother, g.bm, g.bother,
+                               unpolarify(polarify(
+                                   a, lift=True), exponents_only=True)
+                               *x**b))]
+    _debug('Recursive Mellin transform worked:', g)
+    return res, True
+
+
+def _rewrite1(f, x, recursive=True):
+    """
+    Try to rewrite ``f`` using a (sum of) single G functions with argument a*x**b.
+    Return fac, po, g such that f = fac*po*g, fac is independent of ``x``.
+    and po = x**s.
+    Here g is a result from _rewrite_single.
+    Return None on failure.
+    """
+    fac, po, g = _split_mul(f, x)
+    g = _rewrite_single(g, x, recursive)
+    if g:
+        return fac, po, g[0], g[1]
+
+
+def _rewrite2(f, x):
+    """
+    Try to rewrite ``f`` as a product of two G functions of arguments a*x**b.
+    Return fac, po, g1, g2 such that f = fac*po*g1*g2, where fac is
+    independent of x and po is x**s.
+    Here g1 and g2 are results of _rewrite_single.
+    Returns None on failure.
+    """
+    fac, po, g = _split_mul(f, x)
+    if any(_rewrite_single(expr, x, False) is None for expr in _mul_args(g)):
+        return None
+    l = _mul_as_two_parts(g)
+    if not l:
+        return None
+    l = list(ordered(l, [
+        lambda p: max(len(_exponents(p[0], x)), len(_exponents(p[1], x))),
+        lambda p: max(len(_functions(p[0], x)), len(_functions(p[1], x))),
+        lambda p: max(len(_find_splitting_points(p[0], x)),
+                      len(_find_splitting_points(p[1], x)))]))
+
+    for recursive, (fac1, fac2) in itertools.product((False, True), l):
+        g1 = _rewrite_single(fac1, x, recursive)
+        g2 = _rewrite_single(fac2, x, recursive)
+        if g1 and g2:
+            cond = And(g1[1], g2[1])
+            if cond != False:
+                return fac, po, g1[0], g2[0], cond
+
+
+def meijerint_indefinite(f, x):
+    """
+    Compute an indefinite integral of ``f`` by rewriting it as a G function.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import meijerint_indefinite
+    >>> from sympy import sin
+    >>> from sympy.abc import x
+    >>> meijerint_indefinite(sin(x), x)
+    -cos(x)
+    """
+    f = sympify(f)
+    results = []
+    for a in sorted(_find_splitting_points(f, x) | {S.Zero}, key=default_sort_key):
+        res = _meijerint_indefinite_1(f.subs(x, x + a), x)
+        if not res:
+            continue
+        res = res.subs(x, x - a)
+        if _has(res, hyper, meijerg):
+            results.append(res)
+        else:
+            return res
+    if f.has(HyperbolicFunction):
+        _debug('Try rewriting hyperbolics in terms of exp.')
+        rv = meijerint_indefinite(
+            _rewrite_hyperbolics_as_exp(f), x)
+        if rv:
+            if not isinstance(rv, list):
+                from sympy.simplify.radsimp import collect
+                return collect(factor_terms(rv), rv.atoms(exp))
+            results.extend(rv)
+    if results:
+        return next(ordered(results))
+
+
+def _meijerint_indefinite_1(f, x):
+    """ Helper that does not attempt any substitution. """
+    _debug('Trying to compute the indefinite integral of', f, 'wrt', x)
+    from sympy.simplify import hyperexpand, powdenest
+
+    gs = _rewrite1(f, x)
+    if gs is None:
+        # Note: the code that calls us will do expand() and try again
+        return None
+
+    fac, po, gl, cond = gs
+    _debug(' could rewrite:', gs)
+    res = S.Zero
+    for C, s, g in gl:
+        a, b = _get_coeff_exp(g.argument, x)
+        _, c = _get_coeff_exp(po, x)
+        c += s
+
+        # we do a substitution t=a*x**b, get integrand fac*t**rho*g
+        fac_ = fac * C / (b*a**((1 + c)/b))
+        rho = (c + 1)/b - 1
+
+        # we now use t**rho*G(params, t) = G(params + rho, t)
+        # [L, page 150, equation (4)]
+        # and integral G(params, t) dt = G(1, params+1, 0, t)
+        #   (or a similar expression with 1 and 0 exchanged ... pick the one
+        #    which yields a well-defined function)
+        # [R, section 5]
+        # (Note that this dummy will immediately go away again, so we
+        #  can safely pass S.One for ``expr``.)
+        t = _dummy('t', 'meijerint-indefinite', S.One)
+
+        def tr(p):
+            return [a + rho + 1 for a in p]
+        if any(b.is_integer and (b <= 0) == True for b in tr(g.bm)):
+            r = -meijerg(
+                tr(g.an), tr(g.aother) + [1], tr(g.bm) + [0], tr(g.bother), t)
+        else:
+            r = meijerg(
+                tr(g.an) + [1], tr(g.aother), tr(g.bm), tr(g.bother) + [0], t)
+        # The antiderivative is most often expected to be defined
+        # in the neighborhood of  x = 0.
+        if b.is_extended_nonnegative and not f.subs(x, 0).has(S.NaN, S.ComplexInfinity):
+            place = 0  # Assume we can expand at zero
+        else:
+            place = None
+        r = hyperexpand(r.subs(t, a*x**b), place=place)
+
+        # now substitute back
+        # Note: we really do want the powers of x to combine.
+        res += powdenest(fac_*r, polar=True)
+
+    def _clean(res):
+        """This multiplies out superfluous powers of x we created, and chops off
+        constants:
+
+            >> _clean(x*(exp(x)/x - 1/x) + 3)
+            exp(x)
+
+        cancel is used before mul_expand since it is possible for an
+        expression to have an additive constant that does not become isolated
+        with simple expansion. Such a situation was identified in issue 6369:
+
+        Examples
+        ========
+
+        >>> from sympy import sqrt, cancel
+        >>> from sympy.abc import x
+        >>> a = sqrt(2*x + 1)
+        >>> bad = (3*x*a**5 + 2*x - a**5 + 1)/a**2
+        >>> bad.expand().as_independent(x)[0]
+        0
+        >>> cancel(bad).expand().as_independent(x)[0]
+        1
+        """
+        res = expand_mul(cancel(res), deep=False)
+        return Add._from_args(res.as_coeff_add(x)[1])
+
+    res = piecewise_fold(res, evaluate=None)
+    if res.is_Piecewise:
+        newargs = []
+        for e, c in res.args:
+            e = _my_unpolarify(_clean(e))
+            newargs += [(e, c)]
+        res = Piecewise(*newargs, evaluate=False)
+    else:
+        res = _my_unpolarify(_clean(res))
+    return Piecewise((res, _my_unpolarify(cond)), (Integral(f, x), True))
+
+
+@timeit
+def meijerint_definite(f, x, a, b):
+    """
+    Integrate ``f`` over the interval [``a``, ``b``], by rewriting it as a product
+    of two G functions, or as a single G function.
+
+    Return res, cond, where cond are convergence conditions.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.meijerint import meijerint_definite
+    >>> from sympy import exp, oo
+    >>> from sympy.abc import x
+    >>> meijerint_definite(exp(-x**2), x, -oo, oo)
+    (sqrt(pi), True)
+
+    This function is implemented as a succession of functions
+    meijerint_definite, _meijerint_definite_2, _meijerint_definite_3,
+    _meijerint_definite_4. Each function in the list calls the next one
+    (presumably) several times. This means that calling meijerint_definite
+    can be very costly.
+    """
+    # This consists of three steps:
+    # 1) Change the integration limits to 0, oo
+    # 2) Rewrite in terms of G functions
+    # 3) Evaluate the integral
+    #
+    # There are usually several ways of doing this, and we want to try all.
+    # This function does (1), calls _meijerint_definite_2 for step (2).
+    _debugf('Integrating %s wrt %s from %s to %s.', (f, x, a, b))
+    f = sympify(f)
+    if f.has(DiracDelta):
+        _debug('Integrand has DiracDelta terms - giving up.')
+        return None
+
+    if f.has(SingularityFunction):
+        _debug('Integrand has Singularity Function terms - giving up.')
+        return None
+
+    f_, x_, a_, b_ = f, x, a, b
+
+    # Let's use a dummy in case any of the boundaries has x.
+    d = Dummy('x')
+    f = f.subs(x, d)
+    x = d
+
+    if a == b:
+        return (S.Zero, True)
+
+    results = []
+    if a is S.NegativeInfinity and b is not S.Infinity:
+        return meijerint_definite(f.subs(x, -x), x, -b, -a)
+
+    elif a is S.NegativeInfinity:
+        # Integrating -oo to oo. We need to find a place to split the integral.
+        _debug('  Integrating -oo to +oo.')
+        innermost = _find_splitting_points(f, x)
+        _debug('  Sensible splitting points:', innermost)
+        for c in sorted(innermost, key=default_sort_key, reverse=True) + [S.Zero]:
+            _debug('  Trying to split at', c)
+            if not c.is_extended_real:
+                _debug('  Non-real splitting point.')
+                continue
+            res1 = _meijerint_definite_2(f.subs(x, x + c), x)
+            if res1 is None:
+                _debug('  But could not compute first integral.')
+                continue
+            res2 = _meijerint_definite_2(f.subs(x, c - x), x)
+            if res2 is None:
+                _debug('  But could not compute second integral.')
+                continue
+            res1, cond1 = res1
+            res2, cond2 = res2
+            cond = _condsimp(And(cond1, cond2))
+            if cond == False:
+                _debug('  But combined condition is always false.')
+                continue
+            res = res1 + res2
+            return res, cond
+
+    elif a is S.Infinity:
+        res = meijerint_definite(f, x, b, S.Infinity)
+        return -res[0], res[1]
+
+    elif (a, b) == (S.Zero, S.Infinity):
+        # This is a common case - try it directly first.
+        res = _meijerint_definite_2(f, x)
+        if res:
+            if _has(res[0], meijerg):
+                results.append(res)
+            else:
+                return res
+
+    else:
+        if b is S.Infinity:
+            for split in _find_splitting_points(f, x):
+                if (a - split >= 0) == True:
+                    _debugf('Trying x -> x + %s', split)
+                    res = _meijerint_definite_2(f.subs(x, x + split)
+                                                *Heaviside(x + split - a), x)
+                    if res:
+                        if _has(res[0], meijerg):
+                            results.append(res)
+                        else:
+                            return res
+
+        f = f.subs(x, x + a)
+        b = b - a
+        a = 0
+        if b is not S.Infinity:
+            phi = exp(S.ImaginaryUnit*arg(b))
+            b = Abs(b)
+            f = f.subs(x, phi*x)
+            f *= Heaviside(b - x)*phi
+            b = S.Infinity
+
+        _debug('Changed limits to', a, b)
+        _debug('Changed function to', f)
+        res = _meijerint_definite_2(f, x)
+        if res:
+            if _has(res[0], meijerg):
+                results.append(res)
+            else:
+                return res
+    if f_.has(HyperbolicFunction):
+        _debug('Try rewriting hyperbolics in terms of exp.')
+        rv = meijerint_definite(
+            _rewrite_hyperbolics_as_exp(f_), x_, a_, b_)
+        if rv:
+            if not isinstance(rv, list):
+                from sympy.simplify.radsimp import collect
+                rv = (collect(factor_terms(rv[0]), rv[0].atoms(exp)),) + rv[1:]
+                return rv
+            results.extend(rv)
+    if results:
+        return next(ordered(results))
+
+
+def _guess_expansion(f, x):
+    """ Try to guess sensible rewritings for integrand f(x). """
+    res = [(f, 'original integrand')]
+
+    orig = res[-1][0]
+    saw = {orig}
+    expanded = expand_mul(orig)
+    if expanded not in saw:
+        res += [(expanded, 'expand_mul')]
+        saw.add(expanded)
+
+    expanded = expand(orig)
+    if expanded not in saw:
+        res += [(expanded, 'expand')]
+        saw.add(expanded)
+
+    if orig.has(TrigonometricFunction, HyperbolicFunction):
+        expanded = expand_mul(expand_trig(orig))
+        if expanded not in saw:
+            res += [(expanded, 'expand_trig, expand_mul')]
+            saw.add(expanded)
+
+    if orig.has(cos, sin):
+        from sympy.simplify.fu import sincos_to_sum
+        reduced = sincos_to_sum(orig)
+        if reduced not in saw:
+            res += [(reduced, 'trig power reduction')]
+            saw.add(reduced)
+
+    return res
+
+
+def _meijerint_definite_2(f, x):
+    """
+    Try to integrate f dx from zero to infinity.
+
+    The body of this function computes various 'simplifications'
+    f1, f2, ... of f (e.g. by calling expand_mul(), trigexpand()
+    - see _guess_expansion) and calls _meijerint_definite_3 with each of
+    these in succession.
+    If _meijerint_definite_3 succeeds with any of the simplified functions,
+    returns this result.
+    """
+    # This function does preparation for (2), calls
+    # _meijerint_definite_3 for (2) and (3) combined.
+
+    # use a positive dummy - we integrate from 0 to oo
+    # XXX if a nonnegative symbol is used there will be test failures
+    dummy = _dummy('x', 'meijerint-definite2', f, positive=True)
+    f = f.subs(x, dummy)
+    x = dummy
+
+    if f == 0:
+        return S.Zero, True
+
+    for g, explanation in _guess_expansion(f, x):
+        _debug('Trying', explanation)
+        res = _meijerint_definite_3(g, x)
+        if res:
+            return res
+
+
+def _meijerint_definite_3(f, x):
+    """
+    Try to integrate f dx from zero to infinity.
+
+    This function calls _meijerint_definite_4 to try to compute the
+    integral. If this fails, it tries using linearity.
+    """
+    res = _meijerint_definite_4(f, x)
+    if res and res[1] != False:
+        return res
+    if f.is_Add:
+        _debug('Expanding and evaluating all terms.')
+        ress = [_meijerint_definite_4(g, x) for g in f.args]
+        if all(r is not None for r in ress):
+            conds = []
+            res = S.Zero
+            for r, c in ress:
+                res += r
+                conds += [c]
+            c = And(*conds)
+            if c != False:
+                return res, c
+
+
+def _my_unpolarify(f):
+    return _eval_cond(unpolarify(f))
+
+
+@timeit
+def _meijerint_definite_4(f, x, only_double=False):
+    """
+    Try to integrate f dx from zero to infinity.
+
+    Explanation
+    ===========
+
+    This function tries to apply the integration theorems found in literature,
+    i.e. it tries to rewrite f as either one or a product of two G-functions.
+
+    The parameter ``only_double`` is used internally in the recursive algorithm
+    to disable trying to rewrite f as a single G-function.
+    """
+    from sympy.simplify import hyperexpand
+    # This function does (2) and (3)
+    _debug('Integrating', f)
+    # Try single G function.
+    if not only_double:
+        gs = _rewrite1(f, x, recursive=False)
+        if gs is not None:
+            fac, po, g, cond = gs
+            _debug('Could rewrite as single G function:', fac, po, g)
+            res = S.Zero
+            for C, s, f in g:
+                if C == 0:
+                    continue
+                C, f = _rewrite_saxena_1(fac*C, po*x**s, f, x)
+                res += C*_int0oo_1(f, x)
+                cond = And(cond, _check_antecedents_1(f, x))
+                if cond == False:
+                    break
+            cond = _my_unpolarify(cond)
+            if cond == False:
+                _debug('But cond is always False.')
+            else:
+                _debug('Result before branch substitutions is:', res)
+                return _my_unpolarify(hyperexpand(res)), cond
+
+    # Try two G functions.
+    gs = _rewrite2(f, x)
+    if gs is not None:
+        for full_pb in [False, True]:
+            fac, po, g1, g2, cond = gs
+            _debug('Could rewrite as two G functions:', fac, po, g1, g2)
+            res = S.Zero
+            for C1, s1, f1 in g1:
+                for C2, s2, f2 in g2:
+                    r = _rewrite_saxena(fac*C1*C2, po*x**(s1 + s2),
+                                        f1, f2, x, full_pb)
+                    if r is None:
+                        _debug('Non-rational exponents.')
+                        return
+                    C, f1_, f2_ = r
+                    _debug('Saxena subst for yielded:', C, f1_, f2_)
+                    cond = And(cond, _check_antecedents(f1_, f2_, x))
+                    if cond == False:
+                        break
+                    res += C*_int0oo(f1_, f2_, x)
+                else:
+                    continue
+                break
+            cond = _my_unpolarify(cond)
+            if cond == False:
+                _debugf('But cond is always False (full_pb=%s).', full_pb)
+            else:
+                _debugf('Result before branch substitutions is: %s', (res, ))
+                if only_double:
+                    return res, cond
+                return _my_unpolarify(hyperexpand(res)), cond
+
+
+def meijerint_inversion(f, x, t):
+    r"""
+    Compute the inverse laplace transform
+    $\int_{c+i\infty}^{c-i\infty} f(x) e^{tx}\, dx$,
+    for real c larger than the real part of all singularities of ``f``.
+
+    Note that ``t`` is always assumed real and positive.
+
+    Return None if the integral does not exist or could not be evaluated.
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, t
+    >>> from sympy.integrals.meijerint import meijerint_inversion
+    >>> meijerint_inversion(1/x, x, t)
+    Heaviside(t)
+    """
+    f_ = f
+    t_ = t
+    t = Dummy('t', polar=True)  # We don't want sqrt(t**2) = abs(t) etc
+    f = f.subs(t_, t)
+    _debug('Laplace-inverting', f)
+    if not _is_analytic(f, x):
+        _debug('But expression is not analytic.')
+        return None
+    # Exponentials correspond to shifts; we filter them out and then
+    # shift the result later.  If we are given an Add this will not
+    # work, but the calling code will take care of that.
+    shift = S.Zero
+
+    if f.is_Mul:
+        args = list(f.args)
+    elif isinstance(f, exp):
+        args = [f]
+    else:
+        args = None
+
+    if args:
+        newargs = []
+        exponentials = []
+        while args:
+            arg = args.pop()
+            if isinstance(arg, exp):
+                arg2 = expand(arg)
+                if arg2.is_Mul:
+                    args += arg2.args
+                    continue
+                try:
+                    a, b = _get_coeff_exp(arg.args[0], x)
+                except _CoeffExpValueError:
+                    b = 0
+                if b == 1:
+                    exponentials.append(a)
+                else:
+                    newargs.append(arg)
+            elif arg.is_Pow:
+                arg2 = expand(arg)
+                if arg2.is_Mul:
+                    args += arg2.args
+                    continue
+                if x not in arg.base.free_symbols:
+                    try:
+                        a, b = _get_coeff_exp(arg.exp, x)
+                    except _CoeffExpValueError:
+                        b = 0
+                    if b == 1:
+                        exponentials.append(a*log(arg.base))
+                newargs.append(arg)
+            else:
+                newargs.append(arg)
+        shift = Add(*exponentials)
+        f = Mul(*newargs)
+
+    if x not in f.free_symbols:
+        _debug('Expression consists of constant and exp shift:', f, shift)
+        cond = Eq(im(shift), 0)
+        if cond == False:
+            _debug('but shift is nonreal, cannot be a Laplace transform')
+            return None
+        res = f*DiracDelta(t + shift)
+        _debug('Result is a delta function, possibly conditional:', res, cond)
+        # cond is True or Eq
+        return Piecewise((res.subs(t, t_), cond))
+
+    gs = _rewrite1(f, x)
+    if gs is not None:
+        fac, po, g, cond = gs
+        _debug('Could rewrite as single G function:', fac, po, g)
+        res = S.Zero
+        for C, s, f in g:
+            C, f = _rewrite_inversion(fac*C, po*x**s, f, x)
+            res += C*_int_inversion(f, x, t)
+            cond = And(cond, _check_antecedents_inversion(f, x))
+            if cond == False:
+                break
+        cond = _my_unpolarify(cond)
+        if cond == False:
+            _debug('But cond is always False.')
+        else:
+            _debug('Result before branch substitution:', res)
+            from sympy.simplify import hyperexpand
+            res = _my_unpolarify(hyperexpand(res))
+            if not res.has(Heaviside):
+                res *= Heaviside(t)
+            res = res.subs(t, t + shift)
+            if not isinstance(cond, bool):
+                cond = cond.subs(t, t + shift)
+            from .transforms import InverseLaplaceTransform
+            return Piecewise((res.subs(t, t_), cond),
+                             (InverseLaplaceTransform(f_.subs(t, t_), x, t_, None), True))

venv/lib/python3.10/site-packages/sympy/integrals/meijerint_doc.py

@@ -0,0 +1,37 @@
+""" This module cooks up a docstring when imported. Its only purpose is to
+    be displayed in the sphinx documentation. """
+
+from __future__ import annotations
+from typing import Any
+
+from sympy.integrals.meijerint import _create_lookup_table
+from sympy.core.add import Add
+from sympy.core.basic import Basic
+from sympy.core.relational import Eq
+from sympy.core.symbol import Symbol
+from sympy.printing.latex import latex
+
+t: dict[tuple[type[Basic], ...], list[Any]] = {}
+_create_lookup_table(t)
+
+
+doc = ""
+for about, category in t.items():
+    if about == ():
+        doc += 'Elementary functions:\n\n'
+    else:
+        doc += 'Functions involving ' + ', '.join('`%s`' % latex(
+            list(category[0][0].atoms(func))[0]) for func in about) + ':\n\n'
+    for formula, gs, cond, hint in category:
+        if not isinstance(gs, list):
+            g = Symbol('\\text{generated}')
+        else:
+            g = Add(*[fac*f for (fac, f) in gs])
+        obj = Eq(formula, g)
+        if cond is True:
+            cond = ""
+        else:
+            cond = ',\\text{ if } %s' % latex(cond)
+        doc += ".. math::\n  %s%s\n\n" % (latex(obj), cond)
+
+__doc__ = doc

venv/lib/python3.10/site-packages/sympy/integrals/prde.py

@@ -0,0 +1,1332 @@
+"""
+Algorithms for solving Parametric Risch Differential Equations.
+
+The methods used for solving Parametric Risch Differential Equations parallel
+those for solving Risch Differential Equations.  See the outline in the
+docstring of rde.py for more information.
+
+The Parametric Risch Differential Equation problem is, given f, g1, ..., gm in
+K(t), to determine if there exist y in K(t) and c1, ..., cm in Const(K) such
+that Dy + f*y == Sum(ci*gi, (i, 1, m)), and to find such y and ci if they exist.
+
+For the algorithms here G is a list of tuples of factions of the terms on the
+right hand side of the equation (i.e., gi in k(t)), and Q is a list of terms on
+the right hand side of the equation (i.e., qi in k[t]).  See the docstring of
+each function for more information.
+"""
+import itertools
+from functools import reduce
+
+from sympy.core import Dummy, ilcm, Add, Mul, Pow, S
+from sympy.integrals.rde import (order_at, order_at_oo, weak_normalizer,
+    bound_degree)
+from sympy.integrals.risch import (gcdex_diophantine, frac_in, derivation,
+    residue_reduce, splitfactor, residue_reduce_derivation, DecrementLevel,
+    recognize_log_derivative)
+from sympy.polys import Poly, lcm, cancel, sqf_list
+from sympy.polys.polymatrix import PolyMatrix as Matrix
+from sympy.solvers import solve
+
+zeros = Matrix.zeros
+eye = Matrix.eye
+
+
+def prde_normal_denom(fa, fd, G, DE):
+    """
+    Parametric Risch Differential Equation - Normal part of the denominator.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and f, g1, ..., gm in k(t) with f weakly
+    normalized with respect to t, return the tuple (a, b, G, h) such that
+    a, h in k[t], b in k<t>, G = [g1, ..., gm] in k(t)^m, and for any solution
+    c1, ..., cm in Const(k) and y in k(t) of Dy + f*y == Sum(ci*gi, (i, 1, m)),
+    q == y*h in k<t> satisfies a*Dq + b*q == Sum(ci*Gi, (i, 1, m)).
+    """
+    dn, ds = splitfactor(fd, DE)
+    Gas, Gds = list(zip(*G))
+    gd = reduce(lambda i, j: i.lcm(j), Gds, Poly(1, DE.t))
+    en, es = splitfactor(gd, DE)
+
+    p = dn.gcd(en)
+    h = en.gcd(en.diff(DE.t)).quo(p.gcd(p.diff(DE.t)))
+
+    a = dn*h
+    c = a*h
+
+    ba = a*fa - dn*derivation(h, DE)*fd
+    ba, bd = ba.cancel(fd, include=True)
+
+    G = [(c*A).cancel(D, include=True) for A, D in G]
+
+    return (a, (ba, bd), G, h)
+
+def real_imag(ba, bd, gen):
+    """
+    Helper function, to get the real and imaginary part of a rational function
+    evaluated at sqrt(-1) without actually evaluating it at sqrt(-1).
+
+    Explanation
+    ===========
+
+    Separates the even and odd power terms by checking the degree of terms wrt
+    mod 4. Returns a tuple (ba[0], ba[1], bd) where ba[0] is real part
+    of the numerator ba[1] is the imaginary part and bd is the denominator
+    of the rational function.
+    """
+    bd = bd.as_poly(gen).as_dict()
+    ba = ba.as_poly(gen).as_dict()
+    denom_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in bd.items()]
+    denom_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in bd.items()]
+    bd_real = sum(r for r in denom_real)
+    bd_imag = sum(r for r in denom_imag)
+    num_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in ba.items()]
+    num_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in ba.items()]
+    ba_real = sum(r for r in num_real)
+    ba_imag = sum(r for r in num_imag)
+    ba = ((ba_real*bd_real + ba_imag*bd_imag).as_poly(gen), (ba_imag*bd_real - ba_real*bd_imag).as_poly(gen))
+    bd = (bd_real*bd_real + bd_imag*bd_imag).as_poly(gen)
+    return (ba[0], ba[1], bd)
+
+
+def prde_special_denom(a, ba, bd, G, DE, case='auto'):
+    """
+    Parametric Risch Differential Equation - Special part of the denominator.
+
+    Explanation
+    ===========
+
+    Case is one of {'exp', 'tan', 'primitive'} for the hyperexponential,
+    hypertangent, and primitive cases, respectively.  For the hyperexponential
+    (resp. hypertangent) case, given a derivation D on k[t] and a in k[t],
+    b in k<t>, and g1, ..., gm in k(t) with Dt/t in k (resp. Dt/(t**2 + 1) in
+    k, sqrt(-1) not in k), a != 0, and gcd(a, t) == 1 (resp.
+    gcd(a, t**2 + 1) == 1), return the tuple (A, B, GG, h) such that A, B, h in
+    k[t], GG = [gg1, ..., ggm] in k(t)^m, and for any solution c1, ..., cm in
+    Const(k) and q in k<t> of a*Dq + b*q == Sum(ci*gi, (i, 1, m)), r == q*h in
+    k[t] satisfies A*Dr + B*r == Sum(ci*ggi, (i, 1, m)).
+
+    For case == 'primitive', k<t> == k[t], so it returns (a, b, G, 1) in this
+    case.
+    """
+    # TODO: Merge this with the very similar special_denom() in rde.py
+    if case == 'auto':
+        case = DE.case
+
+    if case == 'exp':
+        p = Poly(DE.t, DE.t)
+    elif case == 'tan':
+        p = Poly(DE.t**2 + 1, DE.t)
+    elif case in ('primitive', 'base'):
+        B = ba.quo(bd)
+        return (a, B, G, Poly(1, DE.t))
+    else:
+        raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
+            "'base'}, not %s." % case)
+
+    nb = order_at(ba, p, DE.t) - order_at(bd, p, DE.t)
+    nc = min([order_at(Ga, p, DE.t) - order_at(Gd, p, DE.t) for Ga, Gd in G])
+    n = min(0, nc - min(0, nb))
+    if not nb:
+        # Possible cancellation.
+        if case == 'exp':
+            dcoeff = DE.d.quo(Poly(DE.t, DE.t))
+            with DecrementLevel(DE):  # We are guaranteed to not have problems,
+                                      # because case != 'base'.
+                alphaa, alphad = frac_in(-ba.eval(0)/bd.eval(0)/a.eval(0), DE.t)
+                etaa, etad = frac_in(dcoeff, DE.t)
+                A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
+                if A is not None:
+                    Q, m, z = A
+                    if Q == 1:
+                        n = min(n, m)
+
+        elif case == 'tan':
+            dcoeff = DE.d.quo(Poly(DE.t**2 + 1, DE.t))
+            with DecrementLevel(DE):  # We are guaranteed to not have problems,
+                                      # because case != 'base'.
+                betaa, alphaa, alphad =  real_imag(ba, bd*a, DE.t)
+                betad = alphad
+                etaa, etad = frac_in(dcoeff, DE.t)
+                if recognize_log_derivative(Poly(2, DE.t)*betaa, betad, DE):
+                    A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
+                    B = parametric_log_deriv(betaa, betad, etaa, etad, DE)
+                    if A is not None and B is not None:
+                        Q, s, z = A
+                        # TODO: Add test
+                        if Q == 1:
+                            n = min(n, s/2)
+
+    N = max(0, -nb)
+    pN = p**N
+    pn = p**-n  # This is 1/h
+
+    A = a*pN
+    B = ba*pN.quo(bd) + Poly(n, DE.t)*a*derivation(p, DE).quo(p)*pN
+    G = [(Ga*pN*pn).cancel(Gd, include=True) for Ga, Gd in G]
+    h = pn
+
+    # (a*p**N, (b + n*a*Dp/p)*p**N, g1*p**(N - n), ..., gm*p**(N - n), p**-n)
+    return (A, B, G, h)
+
+
+def prde_linear_constraints(a, b, G, DE):
+    """
+    Parametric Risch Differential Equation - Generate linear constraints on the constants.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], a, b, in k[t] with gcd(a, b) == 1, and
+    G = [g1, ..., gm] in k(t)^m, return Q = [q1, ..., qm] in k[t]^m and a
+    matrix M with entries in k(t) such that for any solution c1, ..., cm in
+    Const(k) and p in k[t] of a*Dp + b*p == Sum(ci*gi, (i, 1, m)),
+    (c1, ..., cm) is a solution of Mx == 0, and p and the ci satisfy
+    a*Dp + b*p == Sum(ci*qi, (i, 1, m)).
+
+    Because M has entries in k(t), and because Matrix does not play well with
+    Poly, M will be a Matrix of Basic expressions.
+    """
+    m = len(G)
+
+    Gns, Gds = list(zip(*G))
+    d = reduce(lambda i, j: i.lcm(j), Gds)
+    d = Poly(d, field=True)
+    Q = [(ga*(d).quo(gd)).div(d) for ga, gd in G]
+
+    if not all(ri.is_zero for _, ri in Q):
+        N = max(ri.degree(DE.t) for _, ri in Q)
+        M = Matrix(N + 1, m, lambda i, j: Q[j][1].nth(i), DE.t)
+    else:
+        M = Matrix(0, m, [], DE.t)  # No constraints, return the empty matrix.
+
+    qs, _ = list(zip(*Q))
+    return (qs, M)
+
+def poly_linear_constraints(p, d):
+    """
+    Given p = [p1, ..., pm] in k[t]^m and d in k[t], return
+    q = [q1, ..., qm] in k[t]^m and a matrix M with entries in k such
+    that Sum(ci*pi, (i, 1, m)), for c1, ..., cm in k, is divisible
+    by d if and only if (c1, ..., cm) is a solution of Mx = 0, in
+    which case the quotient is Sum(ci*qi, (i, 1, m)).
+    """
+    m = len(p)
+    q, r = zip(*[pi.div(d) for pi in p])
+
+    if not all(ri.is_zero for ri in r):
+        n = max(ri.degree() for ri in r)
+        M = Matrix(n + 1, m, lambda i, j: r[j].nth(i), d.gens)
+    else:
+        M = Matrix(0, m, [], d.gens)  # No constraints.
+
+    return q, M
+
+def constant_system(A, u, DE):
+    """
+    Generate a system for the constant solutions.
+
+    Explanation
+    ===========
+
+    Given a differential field (K, D) with constant field C = Const(K), a Matrix
+    A, and a vector (Matrix) u with coefficients in K, returns the tuple
+    (B, v, s), where B is a Matrix with coefficients in C and v is a vector
+    (Matrix) such that either v has coefficients in C, in which case s is True
+    and the solutions in C of Ax == u are exactly all the solutions of Bx == v,
+    or v has a non-constant coefficient, in which case s is False Ax == u has no
+    constant solution.
+
+    This algorithm is used both in solving parametric problems and in
+    determining if an element a of K is a derivative of an element of K or the
+    logarithmic derivative of a K-radical using the structure theorem approach.
+
+    Because Poly does not play well with Matrix yet, this algorithm assumes that
+    all matrix entries are Basic expressions.
+    """
+    if not A:
+        return A, u
+    Au = A.row_join(u)
+    Au, _ = Au.rref()
+    # Warning: This will NOT return correct results if cancel() cannot reduce
+    # an identically zero expression to 0.  The danger is that we might
+    # incorrectly prove that an integral is nonelementary (such as
+    # risch_integrate(exp((sin(x)**2 + cos(x)**2 - 1)*x**2), x).
+    # But this is a limitation in computer algebra in general, and implicit
+    # in the correctness of the Risch Algorithm is the computability of the
+    # constant field (actually, this same correctness problem exists in any
+    # algorithm that uses rref()).
+    #
+    # We therefore limit ourselves to constant fields that are computable
+    # via the cancel() function, in order to prevent a speed bottleneck from
+    # calling some more complex simplification function (rational function
+    # coefficients will fall into this class).  Furthermore, (I believe) this
+    # problem will only crop up if the integral explicitly contains an
+    # expression in the constant field that is identically zero, but cannot
+    # be reduced to such by cancel().  Therefore, a careful user can avoid this
+    # problem entirely by being careful with the sorts of expressions that
+    # appear in his integrand in the variables other than the integration
+    # variable (the structure theorems should be able to completely decide these
+    # problems in the integration variable).
+
+    A, u = Au[:, :-1], Au[:, -1]
+
+    D = lambda x: derivation(x, DE, basic=True)
+
+    for j, i in itertools.product(range(A.cols), range(A.rows)):
+        if A[i, j].expr.has(*DE.T):
+            # This assumes that const(F(t0, ..., tn) == const(K) == F
+            Ri = A[i, :]
+            # Rm+1; m = A.rows
+            DAij = D(A[i, j])
+            Rm1 = Ri.applyfunc(lambda x: D(x) / DAij)
+            um1 = D(u[i]) / DAij
+
+            Aj = A[:, j]
+            A = A - Aj * Rm1
+            u = u - Aj * um1
+
+            A = A.col_join(Rm1)
+            u = u.col_join(Matrix([um1], u.gens))
+
+    return (A, u)
+
+
+def prde_spde(a, b, Q, n, DE):
+    """
+    Special Polynomial Differential Equation algorithm: Parametric Version.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], an integer n, and a, b, q1, ..., qm in k[t]
+    with deg(a) > 0 and gcd(a, b) == 1, return (A, B, Q, R, n1), with
+    Qq = [q1, ..., qm] and R = [r1, ..., rm], such that for any solution
+    c1, ..., cm in Const(k) and q in k[t] of degree at most n of
+    a*Dq + b*q == Sum(ci*gi, (i, 1, m)), p = (q - Sum(ci*ri, (i, 1, m)))/a has
+    degree at most n1 and satisfies A*Dp + B*p == Sum(ci*qi, (i, 1, m))
+    """
+    R, Z = list(zip(*[gcdex_diophantine(b, a, qi) for qi in Q]))
+
+    A = a
+    B = b + derivation(a, DE)
+    Qq = [zi - derivation(ri, DE) for ri, zi in zip(R, Z)]
+    R = list(R)
+    n1 = n - a.degree(DE.t)
+
+    return (A, B, Qq, R, n1)
+
+
+def prde_no_cancel_b_large(b, Q, n, DE):
+    """
+    Parametric Poly Risch Differential Equation - No cancellation: deg(b) large enough.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with
+    b != 0 and either D == d/dt or deg(b) > max(0, deg(D) - 1), returns
+    h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that
+    if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and
+    Dq + b*q == Sum(ci*qi, (i, 1, m)), then q = Sum(dj*hj, (j, 1, r)), where
+    d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0.
+    """
+    db = b.degree(DE.t)
+    m = len(Q)
+    H = [Poly(0, DE.t)]*m
+
+    for N, i in itertools.product(range(n, -1, -1), range(m)):  # [n, ..., 0]
+        si = Q[i].nth(N + db)/b.LC()
+        sitn = Poly(si*DE.t**N, DE.t)
+        H[i] = H[i] + sitn
+        Q[i] = Q[i] - derivation(sitn, DE) - b*sitn
+
+    if all(qi.is_zero for qi in Q):
+        dc = -1
+    else:
+        dc = max([qi.degree(DE.t) for qi in Q])
+    M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i), DE.t)
+    A, u = constant_system(M, zeros(dc + 1, 1, DE.t), DE)
+    c = eye(m, DE.t)
+    A = A.row_join(zeros(A.rows, m, DE.t)).col_join(c.row_join(-c))
+
+    return (H, A)
+
+
+def prde_no_cancel_b_small(b, Q, n, DE):
+    """
+    Parametric Poly Risch Differential Equation - No cancellation: deg(b) small enough.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with
+    deg(b) < deg(D) - 1 and either D == d/dt or deg(D) >= 2, returns
+    h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that
+    if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and
+    Dq + b*q == Sum(ci*qi, (i, 1, m)) then q = Sum(dj*hj, (j, 1, r)) where
+    d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0.
+    """
+    m = len(Q)
+    H = [Poly(0, DE.t)]*m
+
+    for N, i in itertools.product(range(n, 0, -1), range(m)):  # [n, ..., 1]
+        si = Q[i].nth(N + DE.d.degree(DE.t) - 1)/(N*DE.d.LC())
+        sitn = Poly(si*DE.t**N, DE.t)
+        H[i] = H[i] + sitn
+        Q[i] = Q[i] - derivation(sitn, DE) - b*sitn
+
+    if b.degree(DE.t) > 0:
+        for i in range(m):
+            si = Poly(Q[i].nth(b.degree(DE.t))/b.LC(), DE.t)
+            H[i] = H[i] + si
+            Q[i] = Q[i] - derivation(si, DE) - b*si
+        if all(qi.is_zero for qi in Q):
+            dc = -1
+        else:
+            dc = max([qi.degree(DE.t) for qi in Q])
+        M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i), DE.t)
+        A, u = constant_system(M, zeros(dc + 1, 1, DE.t), DE)
+        c = eye(m, DE.t)
+        A = A.row_join(zeros(A.rows, m, DE.t)).col_join(c.row_join(-c))
+        return (H, A)
+
+    # else: b is in k, deg(qi) < deg(Dt)
+
+    t = DE.t
+    if DE.case != 'base':
+        with DecrementLevel(DE):
+            t0 = DE.t  # k = k0(t0)
+            ba, bd = frac_in(b, t0, field=True)
+            Q0 = [frac_in(qi.TC(), t0, field=True) for qi in Q]
+            f, B = param_rischDE(ba, bd, Q0, DE)
+
+            # f = [f1, ..., fr] in k^r and B is a matrix with
+            # m + r columns and entries in Const(k) = Const(k0)
+            # such that Dy0 + b*y0 = Sum(ci*qi, (i, 1, m)) has
+            # a solution y0 in k with c1, ..., cm in Const(k)
+            # if and only y0 = Sum(dj*fj, (j, 1, r)) where
+            # d1, ..., dr ar in Const(k) and
+            # B*Matrix([c1, ..., cm, d1, ..., dr]) == 0.
+
+        # Transform fractions (fa, fd) in f into constant
+        # polynomials fa/fd in k[t].
+        # (Is there a better way?)
+        f = [Poly(fa.as_expr()/fd.as_expr(), t, field=True)
+             for fa, fd in f]
+        B = Matrix.from_Matrix(B.to_Matrix(), t)
+    else:
+        # Base case. Dy == 0 for all y in k and b == 0.
+        # Dy + b*y = Sum(ci*qi) is solvable if and only if
+        # Sum(ci*qi) == 0 in which case the solutions are
+        # y = d1*f1 for f1 = 1 and any d1 in Const(k) = k.
+
+        f = [Poly(1, t, field=True)]  # r = 1
+        B = Matrix([[qi.TC() for qi in Q] + [S.Zero]], DE.t)
+        # The condition for solvability is
+        # B*Matrix([c1, ..., cm, d1]) == 0
+        # There are no constraints on d1.
+
+    # Coefficients of t^j (j > 0) in Sum(ci*qi) must be zero.
+    d = max([qi.degree(DE.t) for qi in Q])
+    if d > 0:
+        M = Matrix(d, m, lambda i, j: Q[j].nth(i + 1), DE.t)
+        A, _ = constant_system(M, zeros(d, 1, DE.t), DE)
+    else:
+        # No constraints on the hj.
+        A = Matrix(0, m, [], DE.t)
+
+    # Solutions of the original equation are
+    #    y = Sum(dj*fj, (j, 1, r) + Sum(ei*hi, (i, 1, m)),
+    # where  ei == ci  (i = 1, ..., m),  when
+    # A*Matrix([c1, ..., cm]) == 0 and
+    # B*Matrix([c1, ..., cm, d1, ..., dr]) == 0
+
+    # Build combined constraint matrix with m + r + m columns.
+
+    r = len(f)
+    I = eye(m, DE.t)
+    A = A.row_join(zeros(A.rows, r + m, DE.t))
+    B = B.row_join(zeros(B.rows, m, DE.t))
+    C = I.row_join(zeros(m, r, DE.t)).row_join(-I)
+
+    return f + H, A.col_join(B).col_join(C)
+
+
+def prde_cancel_liouvillian(b, Q, n, DE):
+    """
+    Pg, 237.
+    """
+    H = []
+
+    # Why use DecrementLevel? Below line answers that:
+    # Assuming that we can solve such problems over 'k' (not k[t])
+    if DE.case == 'primitive':
+        with DecrementLevel(DE):
+            ba, bd = frac_in(b, DE.t, field=True)
+
+    for i in range(n, -1, -1):
+        if DE.case == 'exp': # this re-checking can be avoided
+            with DecrementLevel(DE):
+                ba, bd = frac_in(b + (i*(derivation(DE.t, DE)/DE.t)).as_poly(b.gens),
+                                DE.t, field=True)
+        with DecrementLevel(DE):
+            Qy = [frac_in(q.nth(i), DE.t, field=True) for q in Q]
+            fi, Ai = param_rischDE(ba, bd, Qy, DE)
+        fi = [Poly(fa.as_expr()/fd.as_expr(), DE.t, field=True)
+                for fa, fd in fi]
+        Ai = Ai.set_gens(DE.t)
+
+        ri = len(fi)
+
+        if i == n:
+            M = Ai
+        else:
+            M = Ai.col_join(M.row_join(zeros(M.rows, ri, DE.t)))
+
+        Fi, hi = [None]*ri, [None]*ri
+
+        # from eq. on top of p.238 (unnumbered)
+        for j in range(ri):
+            hji = fi[j] * (DE.t**i).as_poly(fi[j].gens)
+            hi[j] = hji
+            # building up Sum(djn*(D(fjn*t^n) - b*fjnt^n))
+            Fi[j] = -(derivation(hji, DE) - b*hji)
+
+        H += hi
+        # in the next loop instead of Q it has
+        # to be Q + Fi taking its place
+        Q = Q + Fi
+
+    return (H, M)
+
+
+def param_poly_rischDE(a, b, q, n, DE):
+    """Polynomial solutions of a parametric Risch differential equation.
+
+    Explanation
+    ===========
+
+    Given a derivation D in k[t], a, b in k[t] relatively prime, and q
+    = [q1, ..., qm] in k[t]^m, return h = [h1, ..., hr] in k[t]^r and
+    a matrix A with m + r columns and entries in Const(k) such that
+    a*Dp + b*p = Sum(ci*qi, (i, 1, m)) has a solution p of degree <= n
+    in k[t] with c1, ..., cm in Const(k) if and only if p = Sum(dj*hj,
+    (j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm,
+    d1, ..., dr) is a solution of Ax == 0.
+    """
+    m = len(q)
+    if n < 0:
+        # Only the trivial zero solution is possible.
+        # Find relations between the qi.
+        if all(qi.is_zero for qi in q):
+            return [], zeros(1, m, DE.t)  # No constraints.
+
+        N = max([qi.degree(DE.t) for qi in q])
+        M = Matrix(N + 1, m, lambda i, j: q[j].nth(i), DE.t)
+        A, _ = constant_system(M, zeros(M.rows, 1, DE.t), DE)
+
+        return [], A
+
+    if a.is_ground:
+        # Normalization: a = 1.
+        a = a.LC()
+        b, q = b.quo_ground(a), [qi.quo_ground(a) for qi in q]
+
+        if not b.is_zero and (DE.case == 'base' or
+                b.degree() > max(0, DE.d.degree() - 1)):
+            return prde_no_cancel_b_large(b, q, n, DE)
+
+        elif ((b.is_zero or b.degree() < DE.d.degree() - 1)
+                and (DE.case == 'base' or DE.d.degree() >= 2)):
+            return prde_no_cancel_b_small(b, q, n, DE)
+
+        elif (DE.d.degree() >= 2 and
+              b.degree() == DE.d.degree() - 1 and
+              n > -b.as_poly().LC()/DE.d.as_poly().LC()):
+            raise NotImplementedError("prde_no_cancel_b_equal() is "
+                "not yet implemented.")
+
+        else:
+            # Liouvillian cases
+            if DE.case in ('primitive', 'exp'):
+                return prde_cancel_liouvillian(b, q, n, DE)
+            else:
+                raise NotImplementedError("non-linear and hypertangent "
+                        "cases have not yet been implemented")
+
+    # else: deg(a) > 0
+
+    # Iterate SPDE as long as possible cumulating coefficient
+    # and terms for the recovery of original solutions.
+    alpha, beta = a.one, [a.zero]*m
+    while n >= 0:  # and a, b relatively prime
+        a, b, q, r, n = prde_spde(a, b, q, n, DE)
+        beta = [betai + alpha*ri for betai, ri in zip(beta, r)]
+        alpha *= a
+        # Solutions p of a*Dp + b*p = Sum(ci*qi) correspond to
+        # solutions alpha*p + Sum(ci*betai) of the initial equation.
+        d = a.gcd(b)
+        if not d.is_ground:
+            break
+
+    # a*Dp + b*p = Sum(ci*qi) may have a polynomial solution
+    # only if the sum is divisible by d.
+
+    qq, M = poly_linear_constraints(q, d)
+    # qq = [qq1, ..., qqm] where qqi = qi.quo(d).
+    # M is a matrix with m columns an entries in k.
+    # Sum(fi*qi, (i, 1, m)), where f1, ..., fm are elements of k, is
+    # divisible by d if and only if M*Matrix([f1, ..., fm]) == 0,
+    # in which case the quotient is Sum(fi*qqi).
+
+    A, _ = constant_system(M, zeros(M.rows, 1, DE.t), DE)
+    # A is a matrix with m columns and entries in Const(k).
+    # Sum(ci*qqi) is Sum(ci*qi).quo(d), and the remainder is zero
+    # for c1, ..., cm in Const(k) if and only if
+    # A*Matrix([c1, ...,cm]) == 0.
+
+    V = A.nullspace()
+    # V = [v1, ..., vu] where each vj is a column matrix with
+    # entries aj1, ..., ajm in Const(k).
+    # Sum(aji*qi) is divisible by d with exact quotient Sum(aji*qqi).
+    # Sum(ci*qi) is divisible by d if and only if ci = Sum(dj*aji)
+    # (i = 1, ..., m) for some d1, ..., du in Const(k).
+    # In that case, solutions of
+    #     a*Dp + b*p = Sum(ci*qi) = Sum(dj*Sum(aji*qi))
+    # are the same as those of
+    #     (a/d)*Dp + (b/d)*p = Sum(dj*rj)
+    # where rj = Sum(aji*qqi).
+
+    if not V:  # No non-trivial solution.
+        return [], eye(m, DE.t)  # Could return A, but this has
+                                 # the minimum number of rows.
+
+    Mqq = Matrix([qq])  # A single row.
+    r = [(Mqq*vj)[0] for vj in V]  # [r1, ..., ru]
+
+    # Solutions of (a/d)*Dp + (b/d)*p = Sum(dj*rj) correspond to
+    # solutions alpha*p + Sum(Sum(dj*aji)*betai) of the initial
+    # equation. These are equal to alpha*p + Sum(dj*fj) where
+    # fj = Sum(aji*betai).
+    Mbeta = Matrix([beta])
+    f = [(Mbeta*vj)[0] for vj in V]  # [f1, ..., fu]
+
+    #
+    # Solve the reduced equation recursively.
+    #
+    g, B = param_poly_rischDE(a.quo(d), b.quo(d), r, n, DE)
+
+    # g = [g1, ..., gv] in k[t]^v and and B is a matrix with u + v
+    # columns and entries in Const(k) such that
+    # (a/d)*Dp + (b/d)*p = Sum(dj*rj) has a solution p of degree <= n
+    # in k[t] if and only if p = Sum(ek*gk) where e1, ..., ev are in
+    # Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0.
+    # The solutions of the original equation are then
+    # Sum(dj*fj, (j, 1, u)) + alpha*Sum(ek*gk, (k, 1, v)).
+
+    # Collect solution components.
+    h = f + [alpha*gk for gk in g]
+
+    # Build combined relation matrix.
+    A = -eye(m, DE.t)
+    for vj in V:
+        A = A.row_join(vj)
+    A = A.row_join(zeros(m, len(g), DE.t))
+    A = A.col_join(zeros(B.rows, m, DE.t).row_join(B))
+
+    return h, A
+
+
+def param_rischDE(fa, fd, G, DE):
+    """
+    Solve a Parametric Risch Differential Equation: Dy + f*y == Sum(ci*Gi, (i, 1, m)).
+
+    Explanation
+    ===========
+
+    Given a derivation D in k(t), f in k(t), and G
+    = [G1, ..., Gm] in k(t)^m, return h = [h1, ..., hr] in k(t)^r and
+    a matrix A with m + r columns and entries in Const(k) such that
+    Dy + f*y = Sum(ci*Gi, (i, 1, m)) has a solution y
+    in k(t) with c1, ..., cm in Const(k) if and only if y = Sum(dj*hj,
+    (j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm,
+    d1, ..., dr) is a solution of Ax == 0.
+
+    Elements of k(t) are tuples (a, d) with a and d in k[t].
+    """
+    m = len(G)
+    q, (fa, fd) = weak_normalizer(fa, fd, DE)
+    # Solutions of the weakly normalized equation Dz + f*z = q*Sum(ci*Gi)
+    # correspond to solutions y = z/q of the original equation.
+    gamma = q
+    G = [(q*ga).cancel(gd, include=True) for ga, gd in G]
+
+    a, (ba, bd), G, hn = prde_normal_denom(fa, fd, G, DE)
+    # Solutions q in k<t> of  a*Dq + b*q = Sum(ci*Gi) correspond
+    # to solutions z = q/hn of the weakly normalized equation.
+    gamma *= hn
+
+    A, B, G, hs = prde_special_denom(a, ba, bd, G, DE)
+    # Solutions p in k[t] of  A*Dp + B*p = Sum(ci*Gi) correspond
+    # to solutions q = p/hs of the previous equation.
+    gamma *= hs
+
+    g = A.gcd(B)
+    a, b, g = A.quo(g), B.quo(g), [gia.cancel(gid*g, include=True) for
+        gia, gid in G]
+
+    # a*Dp + b*p = Sum(ci*gi)  may have a polynomial solution
+    # only if the sum is in k[t].
+
+    q, M = prde_linear_constraints(a, b, g, DE)
+
+    # q = [q1, ..., qm] where qi in k[t] is the polynomial component
+    # of the partial fraction expansion of gi.
+    # M is a matrix with m columns and entries in k.
+    # Sum(fi*gi, (i, 1, m)), where f1, ..., fm are elements of k,
+    # is a polynomial if and only if M*Matrix([f1, ..., fm]) == 0,
+    # in which case the sum is equal to Sum(fi*qi).
+
+    M, _ = constant_system(M, zeros(M.rows, 1, DE.t), DE)
+    # M is a matrix with m columns and entries in Const(k).
+    # Sum(ci*gi) is in k[t] for c1, ..., cm in Const(k)
+    # if and only if M*Matrix([c1, ..., cm]) == 0,
+    # in which case the sum is Sum(ci*qi).
+
+    ## Reduce number of constants at this point
+
+    V = M.nullspace()
+    # V = [v1, ..., vu] where each vj is a column matrix with
+    # entries aj1, ..., ajm in Const(k).
+    # Sum(aji*gi) is in k[t] and equal to Sum(aji*qi) (j = 1, ..., u).
+    # Sum(ci*gi) is in k[t] if and only is ci = Sum(dj*aji)
+    # (i = 1, ..., m) for some d1, ..., du in Const(k).
+    # In that case,
+    #     Sum(ci*gi) = Sum(ci*qi) = Sum(dj*Sum(aji*qi)) = Sum(dj*rj)
+    # where rj = Sum(aji*qi) (j = 1, ..., u) in k[t].
+
+    if not V:  # No non-trivial solution
+        return [], eye(m, DE.t)
+
+    Mq = Matrix([q])  # A single row.
+    r = [(Mq*vj)[0] for vj in V]  # [r1, ..., ru]
+
+    # Solutions of a*Dp + b*p = Sum(dj*rj) correspond to solutions
+    # y = p/gamma of the initial equation with ci = Sum(dj*aji).
+
+    try:
+        # We try n=5. At least for prde_spde, it will always
+        # terminate no matter what n is.
+        n = bound_degree(a, b, r, DE, parametric=True)
+    except NotImplementedError:
+        # A temporary bound is set. Eventually, it will be removed.
+        # the currently added test case takes large time
+        # even with n=5, and much longer with large n's.
+        n = 5
+
+    h, B = param_poly_rischDE(a, b, r, n, DE)
+
+    # h = [h1, ..., hv] in k[t]^v and and B is a matrix with u + v
+    # columns and entries in Const(k) such that
+    # a*Dp + b*p = Sum(dj*rj) has a solution p of degree <= n
+    # in k[t] if and only if p = Sum(ek*hk) where e1, ..., ev are in
+    # Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0.
+    # The solutions of the original equation for ci = Sum(dj*aji)
+    # (i = 1, ..., m) are then y = Sum(ek*hk, (k, 1, v))/gamma.
+
+    ## Build combined relation matrix with m + u + v columns.
+
+    A = -eye(m, DE.t)
+    for vj in V:
+        A = A.row_join(vj)
+    A = A.row_join(zeros(m, len(h), DE.t))
+    A = A.col_join(zeros(B.rows, m, DE.t).row_join(B))
+
+    ## Eliminate d1, ..., du.
+
+    W = A.nullspace()
+
+    # W = [w1, ..., wt] where each wl is a column matrix with
+    # entries blk (k = 1, ..., m + u + v) in Const(k).
+    # The vectors (bl1, ..., blm) generate the space of those
+    # constant families (c1, ..., cm) for which a solution of
+    # the equation Dy + f*y == Sum(ci*Gi) exists. They generate
+    # the space and form a basis except possibly when Dy + f*y == 0
+    # is solvable in k(t}. The corresponding solutions are
+    # y = Sum(blk'*hk, (k, 1, v))/gamma, where k' = k + m + u.
+
+    v = len(h)
+    shape = (len(W), m+v)
+    elements = [wl[:m] + wl[-v:] for wl in W] # excise dj's.
+    items = [e for row in elements for e in row]
+
+    # Need to set the shape in case W is empty
+    M = Matrix(*shape, items, DE.t)
+    N = M.nullspace()
+
+    # N = [n1, ..., ns] where the ni in Const(k)^(m + v) are column
+    # vectors generating the space of linear relations between
+    # c1, ..., cm, e1, ..., ev.
+
+    C = Matrix([ni[:] for ni in N], DE.t)  # rows n1, ..., ns.
+
+    return [hk.cancel(gamma, include=True) for hk in h], C
+
+
+def limited_integrate_reduce(fa, fd, G, DE):
+    """
+    Simpler version of step 1 & 2 for the limited integration problem.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k(t) and f, g1, ..., gn in k(t), return
+    (a, b, h, N, g, V) such that a, b, h in k[t], N is a non-negative integer,
+    g in k(t), V == [v1, ..., vm] in k(t)^m, and for any solution v in k(t),
+    c1, ..., cm in C of f == Dv + Sum(ci*wi, (i, 1, m)), p = v*h is in k<t>, and
+    p and the ci satisfy a*Dp + b*p == g + Sum(ci*vi, (i, 1, m)).  Furthermore,
+    if S1irr == Sirr, then p is in k[t], and if t is nonlinear or Liouvillian
+    over k, then deg(p) <= N.
+
+    So that the special part is always computed, this function calls the more
+    general prde_special_denom() automatically if it cannot determine that
+    S1irr == Sirr.  Furthermore, it will automatically call bound_degree() when
+    t is linear and non-Liouvillian, which for the transcendental case, implies
+    that Dt == a*t + b with for some a, b in k*.
+    """
+    dn, ds = splitfactor(fd, DE)
+    E = [splitfactor(gd, DE) for _, gd in G]
+    En, Es = list(zip(*E))
+    c = reduce(lambda i, j: i.lcm(j), (dn,) + En)  # lcm(dn, en1, ..., enm)
+    hn = c.gcd(c.diff(DE.t))
+    a = hn
+    b = -derivation(hn, DE)
+    N = 0
+
+    # These are the cases where we know that S1irr = Sirr, but there could be
+    # others, and this algorithm will need to be extended to handle them.
+    if DE.case in ('base', 'primitive', 'exp', 'tan'):
+        hs = reduce(lambda i, j: i.lcm(j), (ds,) + Es)  # lcm(ds, es1, ..., esm)
+        a = hn*hs
+        b -= (hn*derivation(hs, DE)).quo(hs)
+        mu = min(order_at_oo(fa, fd, DE.t), min([order_at_oo(ga, gd, DE.t) for
+            ga, gd in G]))
+        # So far, all the above are also nonlinear or Liouvillian, but if this
+        # changes, then this will need to be updated to call bound_degree()
+        # as per the docstring of this function (DE.case == 'other_linear').
+        N = hn.degree(DE.t) + hs.degree(DE.t) + max(0, 1 - DE.d.degree(DE.t) - mu)
+    else:
+        # TODO: implement this
+        raise NotImplementedError
+
+    V = [(-a*hn*ga).cancel(gd, include=True) for ga, gd in G]
+    return (a, b, a, N, (a*hn*fa).cancel(fd, include=True), V)
+
+
+def limited_integrate(fa, fd, G, DE):
+    """
+    Solves the limited integration problem:  f = Dv + Sum(ci*wi, (i, 1, n))
+    """
+    fa, fd = fa*Poly(1/fd.LC(), DE.t), fd.monic()
+    # interpreting limited integration problem as a
+    # parametric Risch DE problem
+    Fa = Poly(0, DE.t)
+    Fd = Poly(1, DE.t)
+    G = [(fa, fd)] + G
+    h, A = param_rischDE(Fa, Fd, G, DE)
+    V = A.nullspace()
+    V = [v for v in V if v[0] != 0]
+    if not V:
+        return None
+    else:
+        # we can take any vector from V, we take V[0]
+        c0 = V[0][0]
+        # v = [-1, c1, ..., cm, d1, ..., dr]
+        v = V[0]/(-c0)
+        r = len(h)
+        m = len(v) - r - 1
+        C = list(v[1: m + 1])
+        y = -sum([v[m + 1 + i]*h[i][0].as_expr()/h[i][1].as_expr() \
+                for i in range(r)])
+        y_num, y_den = y.as_numer_denom()
+        Ya, Yd = Poly(y_num, DE.t), Poly(y_den, DE.t)
+        Y = Ya*Poly(1/Yd.LC(), DE.t), Yd.monic()
+        return Y, C
+
+
+def parametric_log_deriv_heu(fa, fd, wa, wd, DE, c1=None):
+    """
+    Parametric logarithmic derivative heuristic.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], f in k(t), and a hyperexponential monomial
+    theta over k(t), raises either NotImplementedError, in which case the
+    heuristic failed, or returns None, in which case it has proven that no
+    solution exists, or returns a solution (n, m, v) of the equation
+    n*f == Dv/v + m*Dtheta/theta, with v in k(t)* and n, m in ZZ with n != 0.
+
+    If this heuristic fails, the structure theorem approach will need to be
+    used.
+
+    The argument w == Dtheta/theta
+    """
+    # TODO: finish writing this and write tests
+    c1 = c1 or Dummy('c1')
+
+    p, a = fa.div(fd)
+    q, b = wa.div(wd)
+
+    B = max(0, derivation(DE.t, DE).degree(DE.t) - 1)
+    C = max(p.degree(DE.t), q.degree(DE.t))
+
+    if q.degree(DE.t) > B:
+        eqs = [p.nth(i) - c1*q.nth(i) for i in range(B + 1, C + 1)]
+        s = solve(eqs, c1)
+        if not s or not s[c1].is_Rational:
+            # deg(q) > B, no solution for c.
+            return None
+
+        M, N = s[c1].as_numer_denom()
+        M_poly = M.as_poly(q.gens)
+        N_poly = N.as_poly(q.gens)
+
+        nfmwa = N_poly*fa*wd - M_poly*wa*fd
+        nfmwd = fd*wd
+        Qv = is_log_deriv_k_t_radical_in_field(nfmwa, nfmwd, DE, 'auto')
+        if Qv is None:
+            # (N*f - M*w) is not the logarithmic derivative of a k(t)-radical.
+            return None
+
+        Q, v = Qv
+
+        if Q.is_zero or v.is_zero:
+            return None
+
+        return (Q*N, Q*M, v)
+
+    if p.degree(DE.t) > B:
+        return None
+
+    c = lcm(fd.as_poly(DE.t).LC(), wd.as_poly(DE.t).LC())
+    l = fd.monic().lcm(wd.monic())*Poly(c, DE.t)
+    ln, ls = splitfactor(l, DE)
+    z = ls*ln.gcd(ln.diff(DE.t))
+
+    if not z.has(DE.t):
+        # TODO: We treat this as 'no solution', until the structure
+        # theorem version of parametric_log_deriv is implemented.
+        return None
+
+    u1, r1 = (fa*l.quo(fd)).div(z)  # (l*f).div(z)
+    u2, r2 = (wa*l.quo(wd)).div(z)  # (l*w).div(z)
+
+    eqs = [r1.nth(i) - c1*r2.nth(i) for i in range(z.degree(DE.t))]
+    s = solve(eqs, c1)
+    if not s or not s[c1].is_Rational:
+        # deg(q) <= B, no solution for c.
+        return None
+
+    M, N = s[c1].as_numer_denom()
+
+    nfmwa = N.as_poly(DE.t)*fa*wd - M.as_poly(DE.t)*wa*fd
+    nfmwd = fd*wd
+    Qv = is_log_deriv_k_t_radical_in_field(nfmwa, nfmwd, DE)
+    if Qv is None:
+        # (N*f - M*w) is not the logarithmic derivative of a k(t)-radical.
+        return None
+
+    Q, v = Qv
+
+    if Q.is_zero or v.is_zero:
+        return None
+
+    return (Q*N, Q*M, v)
+
+
+def parametric_log_deriv(fa, fd, wa, wd, DE):
+    # TODO: Write the full algorithm using the structure theorems.
+#    try:
+    A = parametric_log_deriv_heu(fa, fd, wa, wd, DE)
+#    except NotImplementedError:
+        # Heuristic failed, we have to use the full method.
+        # TODO: This could be implemented more efficiently.
+        # It isn't too worrisome, because the heuristic handles most difficult
+        # cases.
+    return A
+
+
+def is_deriv_k(fa, fd, DE):
+    r"""
+    Checks if Df/f is the derivative of an element of k(t).
+
+    Explanation
+    ===========
+
+    a in k(t) is the derivative of an element of k(t) if there exists b in k(t)
+    such that a = Db.  Either returns (ans, u), such that Df/f == Du, or None,
+    which means that Df/f is not the derivative of an element of k(t).  ans is
+    a list of tuples such that Add(*[i*j for i, j in ans]) == u.  This is useful
+    for seeing exactly which elements of k(t) produce u.
+
+    This function uses the structure theorem approach, which says that for any
+    f in K, Df/f is the derivative of a element of K if and only if there are ri
+    in QQ such that::
+
+            ---               ---       Dt
+            \    r  * Dt   +  \    r  *   i      Df
+            /     i     i     /     i   ---   =  --.
+            ---               ---        t        f
+         i in L            i in E         i
+               K/C(x)            K/C(x)
+
+
+    Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is
+    transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i
+    in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic
+    monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i
+    is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some
+    a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of
+    hyperexponential monomials of K over C(x)).  If K is an elementary extension
+    over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the
+    transcendence degree of K over C(x).  Furthermore, because Const_D(K) ==
+    Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and
+    deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x)
+    and L_K/C(x) are disjoint.
+
+    The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed
+    recursively using this same function.  Therefore, it is required to pass
+    them as indices to D (or T).  E_args are the arguments of the
+    hyperexponentials indexed by E_K (i.e., if i is in E_K, then T[i] ==
+    exp(E_args[i])).  This is needed to compute the final answer u such that
+    Df/f == Du.
+
+    log(f) will be the same as u up to a additive constant.  This is because
+    they will both behave the same as monomials. For example, both log(x) and
+    log(2*x) == log(x) + log(2) satisfy Dt == 1/x, because log(2) is constant.
+    Therefore, the term const is returned.  const is such that
+    log(const) + f == u.  This is calculated by dividing the arguments of one
+    logarithm from the other.  Therefore, it is necessary to pass the arguments
+    of the logarithmic terms in L_args.
+
+    To handle the case where we are given Df/f, not f, use is_deriv_k_in_field().
+
+    See also
+    ========
+    is_log_deriv_k_t_radical_in_field, is_log_deriv_k_t_radical
+
+    """
+    # Compute Df/f
+    dfa, dfd = (fd*derivation(fa, DE) - fa*derivation(fd, DE)), fd*fa
+    dfa, dfd = dfa.cancel(dfd, include=True)
+
+    # Our assumption here is that each monomial is recursively transcendental
+    if len(DE.exts) != len(DE.D):
+        if [i for i in DE.cases if i == 'tan'] or \
+                ({i for i in DE.cases if i == 'primitive'} -
+                        set(DE.indices('log'))):
+            raise NotImplementedError("Real version of the structure "
+                "theorems with hypertangent support is not yet implemented.")
+
+        # TODO: What should really be done in this case?
+        raise NotImplementedError("Nonelementary extensions not supported "
+            "in the structure theorems.")
+
+    E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')]
+    L_part = [DE.D[i].as_expr() for i in DE.indices('log')]
+
+    # The expression dfa/dfd might not be polynomial in any of its symbols so we
+    # use a Dummy as the generator for PolyMatrix.
+    dum = Dummy()
+    lhs = Matrix([E_part + L_part], dum)
+    rhs = Matrix([dfa.as_expr()/dfd.as_expr()], dum)
+
+    A, u = constant_system(lhs, rhs, DE)
+
+    u = u.to_Matrix()  # Poly to Expr
+
+    if not A or not all(derivation(i, DE, basic=True).is_zero for i in u):
+        # If the elements of u are not all constant
+        # Note: See comment in constant_system
+
+        # Also note: derivation(basic=True) calls cancel()
+        return None
+    else:
+        if not all(i.is_Rational for i in u):
+            raise NotImplementedError("Cannot work with non-rational "
+                "coefficients in this case.")
+        else:
+            terms = ([DE.extargs[i] for i in DE.indices('exp')] +
+                    [DE.T[i] for i in DE.indices('log')])
+            ans = list(zip(terms, u))
+            result = Add(*[Mul(i, j) for i, j in ans])
+            argterms = ([DE.T[i] for i in DE.indices('exp')] +
+                    [DE.extargs[i] for i in DE.indices('log')])
+            l = []
+            ld = []
+            for i, j in zip(argterms, u):
+                # We need to get around things like sqrt(x**2) != x
+                # and also sqrt(x**2 + 2*x + 1) != x + 1
+                # Issue 10798: i need not be a polynomial
+                i, d = i.as_numer_denom()
+                icoeff, iterms = sqf_list(i)
+                l.append(Mul(*([Pow(icoeff, j)] + [Pow(b, e*j) for b, e in iterms])))
+                dcoeff, dterms = sqf_list(d)
+                ld.append(Mul(*([Pow(dcoeff, j)] + [Pow(b, e*j) for b, e in dterms])))
+            const = cancel(fa.as_expr()/fd.as_expr()/Mul(*l)*Mul(*ld))
+
+            return (ans, result, const)
+
+
+def is_log_deriv_k_t_radical(fa, fd, DE, Df=True):
+    r"""
+    Checks if Df is the logarithmic derivative of a k(t)-radical.
+
+    Explanation
+    ===========
+
+    b in k(t) can be written as the logarithmic derivative of a k(t) radical if
+    there exist n in ZZ and u in k(t) with n, u != 0 such that n*b == Du/u.
+    Either returns (ans, u, n, const) or None, which means that Df cannot be
+    written as the logarithmic derivative of a k(t)-radical.  ans is a list of
+    tuples such that Mul(*[i**j for i, j in ans]) == u.  This is useful for
+    seeing exactly what elements of k(t) produce u.
+
+    This function uses the structure theorem approach, which says that for any
+    f in K, Df is the logarithmic derivative of a K-radical if and only if there
+    are ri in QQ such that::
+
+            ---               ---       Dt
+            \    r  * Dt   +  \    r  *   i
+            /     i     i     /     i   ---   =  Df.
+            ---               ---        t
+         i in L            i in E         i
+               K/C(x)            K/C(x)
+
+
+    Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is
+    transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i
+    in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic
+    monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i
+    is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some
+    a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of
+    hyperexponential monomials of K over C(x)).  If K is an elementary extension
+    over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the
+    transcendence degree of K over C(x).  Furthermore, because Const_D(K) ==
+    Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and
+    deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x)
+    and L_K/C(x) are disjoint.
+
+    The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed
+    recursively using this same function.  Therefore, it is required to pass
+    them as indices to D (or T).  L_args are the arguments of the logarithms
+    indexed by L_K (i.e., if i is in L_K, then T[i] == log(L_args[i])).  This is
+    needed to compute the final answer u such that n*f == Du/u.
+
+    exp(f) will be the same as u up to a multiplicative constant.  This is
+    because they will both behave the same as monomials.  For example, both
+    exp(x) and exp(x + 1) == E*exp(x) satisfy Dt == t. Therefore, the term const
+    is returned.  const is such that exp(const)*f == u.  This is calculated by
+    subtracting the arguments of one exponential from the other.  Therefore, it
+    is necessary to pass the arguments of the exponential terms in E_args.
+
+    To handle the case where we are given Df, not f, use
+    is_log_deriv_k_t_radical_in_field().
+
+    See also
+    ========
+
+    is_log_deriv_k_t_radical_in_field, is_deriv_k
+
+    """
+    if Df:
+        dfa, dfd = (fd*derivation(fa, DE) - fa*derivation(fd, DE)).cancel(fd**2,
+            include=True)
+    else:
+        dfa, dfd = fa, fd
+
+    # Our assumption here is that each monomial is recursively transcendental
+    if len(DE.exts) != len(DE.D):
+        if [i for i in DE.cases if i == 'tan'] or \
+                ({i for i in DE.cases if i == 'primitive'} -
+                        set(DE.indices('log'))):
+            raise NotImplementedError("Real version of the structure "
+                "theorems with hypertangent support is not yet implemented.")
+
+        # TODO: What should really be done in this case?
+        raise NotImplementedError("Nonelementary extensions not supported "
+            "in the structure theorems.")
+
+    E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')]
+    L_part = [DE.D[i].as_expr() for i in DE.indices('log')]
+
+    # The expression dfa/dfd might not be polynomial in any of its symbols so we
+    # use a Dummy as the generator for PolyMatrix.
+    dum = Dummy()
+    lhs = Matrix([E_part + L_part], dum)
+    rhs = Matrix([dfa.as_expr()/dfd.as_expr()], dum)
+
+    A, u = constant_system(lhs, rhs, DE)
+
+    u = u.to_Matrix()  # Poly to Expr
+
+    if not A or not all(derivation(i, DE, basic=True).is_zero for i in u):
+        # If the elements of u are not all constant
+        # Note: See comment in constant_system
+
+        # Also note: derivation(basic=True) calls cancel()
+        return None
+    else:
+        if not all(i.is_Rational for i in u):
+            # TODO: But maybe we can tell if they're not rational, like
+            # log(2)/log(3). Also, there should be an option to continue
+            # anyway, even if the result might potentially be wrong.
+            raise NotImplementedError("Cannot work with non-rational "
+                "coefficients in this case.")
+        else:
+            n = reduce(ilcm, [i.as_numer_denom()[1] for i in u])
+            u *= n
+            terms = ([DE.T[i] for i in DE.indices('exp')] +
+                    [DE.extargs[i] for i in DE.indices('log')])
+            ans = list(zip(terms, u))
+            result = Mul(*[Pow(i, j) for i, j in ans])
+
+            # exp(f) will be the same as result up to a multiplicative
+            # constant.  We now find the log of that constant.
+            argterms = ([DE.extargs[i] for i in DE.indices('exp')] +
+                    [DE.T[i] for i in DE.indices('log')])
+            const = cancel(fa.as_expr()/fd.as_expr() -
+                Add(*[Mul(i, j/n) for i, j in zip(argterms, u)]))
+
+            return (ans, result, n, const)
+
+
+def is_log_deriv_k_t_radical_in_field(fa, fd, DE, case='auto', z=None):
+    """
+    Checks if f can be written as the logarithmic derivative of a k(t)-radical.
+
+    Explanation
+    ===========
+
+    It differs from is_log_deriv_k_t_radical(fa, fd, DE, Df=False)
+    for any given fa, fd, DE in that it finds the solution in the
+    given field not in some (possibly unspecified extension) and
+    "in_field" with the function name is used to indicate that.
+
+    f in k(t) can be written as the logarithmic derivative of a k(t) radical if
+    there exist n in ZZ and u in k(t) with n, u != 0 such that n*f == Du/u.
+    Either returns (n, u) or None, which means that f cannot be written as the
+    logarithmic derivative of a k(t)-radical.
+
+    case is one of {'primitive', 'exp', 'tan', 'auto'} for the primitive,
+    hyperexponential, and hypertangent cases, respectively.  If case is 'auto',
+    it will attempt to determine the type of the derivation automatically.
+
+    See also
+    ========
+    is_log_deriv_k_t_radical, is_deriv_k
+
+    """
+    fa, fd = fa.cancel(fd, include=True)
+
+    # f must be simple
+    n, s = splitfactor(fd, DE)
+    if not s.is_one:
+        pass
+
+    z = z or Dummy('z')
+    H, b = residue_reduce(fa, fd, DE, z=z)
+    if not b:
+        # I will have to verify, but I believe that the answer should be
+        # None in this case. This should never happen for the
+        # functions given when solving the parametric logarithmic
+        # derivative problem when integration elementary functions (see
+        # Bronstein's book, page 255), so most likely this indicates a bug.
+        return None
+
+    roots = [(i, i.real_roots()) for i, _ in H]
+    if not all(len(j) == i.degree() and all(k.is_Rational for k in j) for
+               i, j in roots):
+        # If f is the logarithmic derivative of a k(t)-radical, then all the
+        # roots of the resultant must be rational numbers.
+        return None
+
+    # [(a, i), ...], where i*log(a) is a term in the log-part of the integral
+    # of f
+    respolys, residues = list(zip(*roots)) or [[], []]
+    # Note: this might be empty, but everything below should work find in that
+    # case (it should be the same as if it were [[1, 1]])
+    residueterms = [(H[j][1].subs(z, i), i) for j in range(len(H)) for
+        i in residues[j]]
+
+    # TODO: finish writing this and write tests
+
+    p = cancel(fa.as_expr()/fd.as_expr() - residue_reduce_derivation(H, DE, z))
+
+    p = p.as_poly(DE.t)
+    if p is None:
+        # f - Dg will be in k[t] if f is the logarithmic derivative of a k(t)-radical
+        return None
+
+    if p.degree(DE.t) >= max(1, DE.d.degree(DE.t)):
+        return None
+
+    if case == 'auto':
+        case = DE.case
+
+    if case == 'exp':
+        wa, wd = derivation(DE.t, DE).cancel(Poly(DE.t, DE.t), include=True)
+        with DecrementLevel(DE):
+            pa, pd = frac_in(p, DE.t, cancel=True)
+            wa, wd = frac_in((wa, wd), DE.t)
+            A = parametric_log_deriv(pa, pd, wa, wd, DE)
+        if A is None:
+            return None
+        n, e, u = A
+        u *= DE.t**e
+
+    elif case == 'primitive':
+        with DecrementLevel(DE):
+            pa, pd = frac_in(p, DE.t)
+            A = is_log_deriv_k_t_radical_in_field(pa, pd, DE, case='auto')
+        if A is None:
+            return None
+        n, u = A
+
+    elif case == 'base':
+        # TODO: we can use more efficient residue reduction from ratint()
+        if not fd.is_sqf or fa.degree() >= fd.degree():
+            # f is the logarithmic derivative in the base case if and only if
+            # f = fa/fd, fd is square-free, deg(fa) < deg(fd), and
+            # gcd(fa, fd) == 1.  The last condition is handled by cancel() above.
+            return None
+        # Note: if residueterms = [], returns (1, 1)
+        # f had better be 0 in that case.
+        n = reduce(ilcm, [i.as_numer_denom()[1] for _, i in residueterms], S.One)
+        u = Mul(*[Pow(i, j*n) for i, j in residueterms])
+        return (n, u)
+
+    elif case == 'tan':
+        raise NotImplementedError("The hypertangent case is "
+        "not yet implemented for is_log_deriv_k_t_radical_in_field()")
+
+    elif case in ('other_linear', 'other_nonlinear'):
+        # XXX: If these are supported by the structure theorems, change to NotImplementedError.
+        raise ValueError("The %s case is not supported in this function." % case)
+
+    else:
+        raise ValueError("case must be one of {'primitive', 'exp', 'tan', "
+        "'base', 'auto'}, not %s" % case)
+
+    common_denom = reduce(ilcm, [i.as_numer_denom()[1] for i in [j for _, j in
+        residueterms]] + [n], S.One)
+    residueterms = [(i, j*common_denom) for i, j in residueterms]
+    m = common_denom//n
+    if common_denom != n*m:  # Verify exact division
+        raise ValueError("Inexact division")
+    u = cancel(u**m*Mul(*[Pow(i, j) for i, j in residueterms]))
+
+    return (common_denom, u)

venv/lib/python3.10/site-packages/sympy/integrals/quadrature.py

@@ -0,0 +1,617 @@
+from sympy.core import S, Dummy, pi
+from sympy.functions.combinatorial.factorials import factorial
+from sympy.functions.elementary.trigonometric import sin, cos
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.special.gamma_functions import gamma
+from sympy.polys.orthopolys import (legendre_poly, laguerre_poly,
+                                    hermite_poly, jacobi_poly)
+from sympy.polys.rootoftools import RootOf
+
+
+def gauss_legendre(n, n_digits):
+    r"""
+    Computes the Gauss-Legendre quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The Gauss-Legendre quadrature approximates the integral:
+
+    .. math::
+        \int_{-1}^1 f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `P_n`
+    and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{2}{\left(1-x_i^2\right) \left(P'_n(x_i)\right)^2}
+
+    Parameters
+    ==========
+
+    n :
+        The order of quadrature.
+    n_digits :
+        Number of significant digits of the points and weights to return.
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_legendre
+    >>> x, w = gauss_legendre(3, 5)
+    >>> x
+    [-0.7746, 0, 0.7746]
+    >>> w
+    [0.55556, 0.88889, 0.55556]
+    >>> x, w = gauss_legendre(4, 5)
+    >>> x
+    [-0.86114, -0.33998, 0.33998, 0.86114]
+    >>> w
+    [0.34785, 0.65215, 0.65215, 0.34785]
+
+    See Also
+    ========
+
+    gauss_laguerre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gaussian_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/legendre_rule/legendre_rule.html
+    """
+    x = Dummy("x")
+    p = legendre_poly(n, x, polys=True)
+    pd = p.diff(x)
+    xi = []
+    w = []
+    for r in p.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append((2/((1-r**2) * pd.subs(x, r)**2)).n(n_digits))
+    return xi, w
+
+
+def gauss_laguerre(n, n_digits):
+    r"""
+    Computes the Gauss-Laguerre quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The Gauss-Laguerre quadrature approximates the integral:
+
+    .. math::
+        \int_0^{\infty} e^{-x} f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
+
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `L_n`
+    and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{x_i}{(n+1)^2 \left(L_{n+1}(x_i)\right)^2}
+
+    Parameters
+    ==========
+
+    n :
+        The order of quadrature.
+    n_digits :
+        Number of significant digits of the points and weights to return.
+
+    Returns
+    =======
+
+    (x, w) : The ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_laguerre
+    >>> x, w = gauss_laguerre(3, 5)
+    >>> x
+    [0.41577, 2.2943, 6.2899]
+    >>> w
+    [0.71109, 0.27852, 0.010389]
+    >>> x, w = gauss_laguerre(6, 5)
+    >>> x
+    [0.22285, 1.1889, 2.9927, 5.7751, 9.8375, 15.983]
+    >>> w
+    [0.45896, 0.417, 0.11337, 0.010399, 0.00026102, 8.9855e-7]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gauss%E2%80%93Laguerre_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/laguerre_rule/laguerre_rule.html
+    """
+    x = Dummy("x")
+    p = laguerre_poly(n, x, polys=True)
+    p1 = laguerre_poly(n+1, x, polys=True)
+    xi = []
+    w = []
+    for r in p.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append((r/((n+1)**2 * p1.subs(x, r)**2)).n(n_digits))
+    return xi, w
+
+
+def gauss_hermite(n, n_digits):
+    r"""
+    Computes the Gauss-Hermite quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The Gauss-Hermite quadrature approximates the integral:
+
+    .. math::
+        \int_{-\infty}^{\infty} e^{-x^2} f(x)\,dx \approx
+            \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `H_n`
+    and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{2^{n-1} n! \sqrt{\pi}}{n^2 \left(H_{n-1}(x_i)\right)^2}
+
+    Parameters
+    ==========
+
+    n :
+        The order of quadrature.
+    n_digits :
+        Number of significant digits of the points and weights to return.
+
+    Returns
+    =======
+
+    (x, w) : The ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_hermite
+    >>> x, w = gauss_hermite(3, 5)
+    >>> x
+    [-1.2247, 0, 1.2247]
+    >>> w
+    [0.29541, 1.1816, 0.29541]
+
+    >>> x, w = gauss_hermite(6, 5)
+    >>> x
+    [-2.3506, -1.3358, -0.43608, 0.43608, 1.3358, 2.3506]
+    >>> w
+    [0.00453, 0.15707, 0.72463, 0.72463, 0.15707, 0.00453]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_laguerre, gauss_gen_laguerre, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gauss-Hermite_Quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/hermite_rule/hermite_rule.html
+    .. [3] https://people.sc.fsu.edu/~jburkardt/cpp_src/gen_hermite_rule/gen_hermite_rule.html
+    """
+    x = Dummy("x")
+    p = hermite_poly(n, x, polys=True)
+    p1 = hermite_poly(n-1, x, polys=True)
+    xi = []
+    w = []
+    for r in p.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append(((2**(n-1) * factorial(n) * sqrt(pi)) /
+                 (n**2 * p1.subs(x, r)**2)).n(n_digits))
+    return xi, w
+
+
+def gauss_gen_laguerre(n, alpha, n_digits):
+    r"""
+    Computes the generalized Gauss-Laguerre quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The generalized Gauss-Laguerre quadrature approximates the integral:
+
+    .. math::
+        \int_{0}^\infty x^{\alpha} e^{-x} f(x)\,dx \approx
+            \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of
+    `L^{\alpha}_n` and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{\Gamma(\alpha+n)}
+                {n \Gamma(n) L^{\alpha}_{n-1}(x_i) L^{\alpha+1}_{n-1}(x_i)}
+
+    Parameters
+    ==========
+
+    n :
+        The order of quadrature.
+
+    alpha :
+        The exponent of the singularity, `\alpha > -1`.
+
+    n_digits :
+        Number of significant digits of the points and weights to return.
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy import S
+    >>> from sympy.integrals.quadrature import gauss_gen_laguerre
+    >>> x, w = gauss_gen_laguerre(3, -S.Half, 5)
+    >>> x
+    [0.19016, 1.7845, 5.5253]
+    >>> w
+    [1.4493, 0.31413, 0.00906]
+
+    >>> x, w = gauss_gen_laguerre(4, 3*S.Half, 5)
+    >>> x
+    [0.97851, 2.9904, 6.3193, 11.712]
+    >>> w
+    [0.53087, 0.67721, 0.11895, 0.0023152]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gauss%E2%80%93Laguerre_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/gen_laguerre_rule/gen_laguerre_rule.html
+    """
+    x = Dummy("x")
+    p = laguerre_poly(n, x, alpha=alpha, polys=True)
+    p1 = laguerre_poly(n-1, x, alpha=alpha, polys=True)
+    p2 = laguerre_poly(n-1, x, alpha=alpha+1, polys=True)
+    xi = []
+    w = []
+    for r in p.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append((gamma(alpha+n) /
+                 (n*gamma(n)*p1.subs(x, r)*p2.subs(x, r))).n(n_digits))
+    return xi, w
+
+
+def gauss_chebyshev_t(n, n_digits):
+    r"""
+    Computes the Gauss-Chebyshev quadrature [1]_ points and weights of
+    the first kind.
+
+    Explanation
+    ===========
+
+    The Gauss-Chebyshev quadrature of the first kind approximates the integral:
+
+    .. math::
+        \int_{-1}^{1} \frac{1}{\sqrt{1-x^2}} f(x)\,dx \approx
+            \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `T_n`
+    and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{\pi}{n}
+
+    Parameters
+    ==========
+
+    n :
+        The order of quadrature.
+
+    n_digits :
+        Number of significant digits of the points and weights to return.
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_chebyshev_t
+    >>> x, w = gauss_chebyshev_t(3, 5)
+    >>> x
+    [0.86602, 0, -0.86602]
+    >>> w
+    [1.0472, 1.0472, 1.0472]
+
+    >>> x, w = gauss_chebyshev_t(6, 5)
+    >>> x
+    [0.96593, 0.70711, 0.25882, -0.25882, -0.70711, -0.96593]
+    >>> w
+    [0.5236, 0.5236, 0.5236, 0.5236, 0.5236, 0.5236]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Chebyshev%E2%80%93Gauss_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/chebyshev1_rule/chebyshev1_rule.html
+    """
+    xi = []
+    w = []
+    for i in range(1, n+1):
+        xi.append((cos((2*i-S.One)/(2*n)*S.Pi)).n(n_digits))
+        w.append((S.Pi/n).n(n_digits))
+    return xi, w
+
+
+def gauss_chebyshev_u(n, n_digits):
+    r"""
+    Computes the Gauss-Chebyshev quadrature [1]_ points and weights of
+    the second kind.
+
+    Explanation
+    ===========
+
+    The Gauss-Chebyshev quadrature of the second kind approximates the
+    integral:
+
+    .. math::
+        \int_{-1}^{1} \sqrt{1-x^2} f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `U_n`
+    and the weights `w_i` are given by:
+
+    .. math::
+        w_i = \frac{\pi}{n+1} \sin^2 \left(\frac{i}{n+1}\pi\right)
+
+    Parameters
+    ==========
+
+    n : the order of quadrature
+
+    n_digits : number of significant digits of the points and weights to return
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_chebyshev_u
+    >>> x, w = gauss_chebyshev_u(3, 5)
+    >>> x
+    [0.70711, 0, -0.70711]
+    >>> w
+    [0.3927, 0.7854, 0.3927]
+
+    >>> x, w = gauss_chebyshev_u(6, 5)
+    >>> x
+    [0.90097, 0.62349, 0.22252, -0.22252, -0.62349, -0.90097]
+    >>> w
+    [0.084489, 0.27433, 0.42658, 0.42658, 0.27433, 0.084489]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre, gauss_chebyshev_t, gauss_jacobi, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Chebyshev%E2%80%93Gauss_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/chebyshev2_rule/chebyshev2_rule.html
+    """
+    xi = []
+    w = []
+    for i in range(1, n+1):
+        xi.append((cos(i/(n+S.One)*S.Pi)).n(n_digits))
+        w.append((S.Pi/(n+S.One)*sin(i*S.Pi/(n+S.One))**2).n(n_digits))
+    return xi, w
+
+
+def gauss_jacobi(n, alpha, beta, n_digits):
+    r"""
+    Computes the Gauss-Jacobi quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The Gauss-Jacobi quadrature of the first kind approximates the integral:
+
+    .. math::
+        \int_{-1}^1 (1-x)^\alpha (1+x)^\beta f(x)\,dx \approx
+            \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of
+    `P^{(\alpha,\beta)}_n` and the weights `w_i` are given by:
+
+    .. math::
+        w_i = -\frac{2n+\alpha+\beta+2}{n+\alpha+\beta+1}
+              \frac{\Gamma(n+\alpha+1)\Gamma(n+\beta+1)}
+              {\Gamma(n+\alpha+\beta+1)(n+1)!}
+              \frac{2^{\alpha+\beta}}{P'_n(x_i)
+              P^{(\alpha,\beta)}_{n+1}(x_i)}
+
+    Parameters
+    ==========
+
+    n : the order of quadrature
+
+    alpha : the first parameter of the Jacobi Polynomial, `\alpha > -1`
+
+    beta : the second parameter of the Jacobi Polynomial, `\beta > -1`
+
+    n_digits : number of significant digits of the points and weights to return
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy import S
+    >>> from sympy.integrals.quadrature import gauss_jacobi
+    >>> x, w = gauss_jacobi(3, S.Half, -S.Half, 5)
+    >>> x
+    [-0.90097, -0.22252, 0.62349]
+    >>> w
+    [1.7063, 1.0973, 0.33795]
+
+    >>> x, w = gauss_jacobi(6, 1, 1, 5)
+    >>> x
+    [-0.87174, -0.5917, -0.2093, 0.2093, 0.5917, 0.87174]
+    >>> w
+    [0.050584, 0.22169, 0.39439, 0.39439, 0.22169, 0.050584]
+
+    See Also
+    ========
+
+    gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre,
+    gauss_chebyshev_t, gauss_chebyshev_u, gauss_lobatto
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gauss%E2%80%93Jacobi_quadrature
+    .. [2] https://people.sc.fsu.edu/~jburkardt/cpp_src/jacobi_rule/jacobi_rule.html
+    .. [3] https://people.sc.fsu.edu/~jburkardt/cpp_src/gegenbauer_rule/gegenbauer_rule.html
+    """
+    x = Dummy("x")
+    p = jacobi_poly(n, alpha, beta, x, polys=True)
+    pd = p.diff(x)
+    pn = jacobi_poly(n+1, alpha, beta, x, polys=True)
+    xi = []
+    w = []
+    for r in p.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append((
+            - (2*n+alpha+beta+2) / (n+alpha+beta+S.One) *
+            (gamma(n+alpha+1)*gamma(n+beta+1)) /
+            (gamma(n+alpha+beta+S.One)*gamma(n+2)) *
+            2**(alpha+beta) / (pd.subs(x, r) * pn.subs(x, r))).n(n_digits))
+    return xi, w
+
+
+def gauss_lobatto(n, n_digits):
+    r"""
+    Computes the Gauss-Lobatto quadrature [1]_ points and weights.
+
+    Explanation
+    ===========
+
+    The Gauss-Lobatto quadrature approximates the integral:
+
+    .. math::
+        \int_{-1}^1 f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
+
+    The nodes `x_i` of an order `n` quadrature rule are the roots of `P'_(n-1)`
+    and the weights `w_i` are given by:
+
+    .. math::
+        &w_i = \frac{2}{n(n-1) \left[P_{n-1}(x_i)\right]^2},\quad x\neq\pm 1\\
+        &w_i = \frac{2}{n(n-1)},\quad x=\pm 1
+
+    Parameters
+    ==========
+
+    n : the order of quadrature
+
+    n_digits : number of significant digits of the points and weights to return
+
+    Returns
+    =======
+
+    (x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
+             The points `x_i` and weights `w_i` are returned as ``(x, w)``
+             tuple of lists.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.quadrature import gauss_lobatto
+    >>> x, w = gauss_lobatto(3, 5)
+    >>> x
+    [-1, 0, 1]
+    >>> w
+    [0.33333, 1.3333, 0.33333]
+    >>> x, w = gauss_lobatto(4, 5)
+    >>> x
+    [-1, -0.44721, 0.44721, 1]
+    >>> w
+    [0.16667, 0.83333, 0.83333, 0.16667]
+
+    See Also
+    ========
+
+    gauss_legendre,gauss_laguerre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi
+
+    References
+    ==========
+
+    .. [1] https://en.wikipedia.org/wiki/Gaussian_quadrature#Gauss.E2.80.93Lobatto_rules
+    .. [2] https://web.archive.org/web/20200118141346/http://people.math.sfu.ca/~cbm/aands/page_888.htm
+    """
+    x = Dummy("x")
+    p = legendre_poly(n-1, x, polys=True)
+    pd = p.diff(x)
+    xi = []
+    w = []
+    for r in pd.real_roots():
+        if isinstance(r, RootOf):
+            r = r.eval_rational(S.One/10**(n_digits+2))
+        xi.append(r.n(n_digits))
+        w.append((2/(n*(n-1) * p.subs(x, r)**2)).n(n_digits))
+
+    xi.insert(0, -1)
+    xi.append(1)
+    w.insert(0, (S(2)/(n*(n-1))).n(n_digits))
+    w.append((S(2)/(n*(n-1))).n(n_digits))
+    return xi, w

venv/lib/python3.10/site-packages/sympy/integrals/rationaltools.py

@@ -0,0 +1,418 @@
+"""This module implements tools for integrating rational functions. """
+
+from sympy.core.function import Lambda
+from sympy.core.numbers import I
+from sympy.core.singleton import S
+from sympy.core.symbol import (Dummy, Symbol, symbols)
+from sympy.functions.elementary.exponential import log
+from sympy.functions.elementary.trigonometric import atan
+from sympy.polys.polyroots import roots
+from sympy.polys.polytools import cancel
+from sympy.polys.rootoftools import RootSum
+from sympy.polys import Poly, resultant, ZZ
+
+
+def ratint(f, x, **flags):
+    """
+    Performs indefinite integration of rational functions.
+
+    Explanation
+    ===========
+
+    Given a field :math:`K` and a rational function :math:`f = p/q`,
+    where :math:`p` and :math:`q` are polynomials in :math:`K[x]`,
+    returns a function :math:`g` such that :math:`f = g'`.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.rationaltools import ratint
+    >>> from sympy.abc import x
+
+    >>> ratint(36/(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2), x)
+    (12*x + 6)/(x**2 - 1) + 4*log(x - 2) - 4*log(x + 1)
+
+    References
+    ==========
+
+    .. [1] M. Bronstein, Symbolic Integration I: Transcendental
+       Functions, Second Edition, Springer-Verlag, 2005, pp. 35-70
+
+    See Also
+    ========
+
+    sympy.integrals.integrals.Integral.doit
+    sympy.integrals.rationaltools.ratint_logpart
+    sympy.integrals.rationaltools.ratint_ratpart
+
+    """
+    if isinstance(f, tuple):
+        p, q = f
+    else:
+        p, q = f.as_numer_denom()
+
+    p, q = Poly(p, x, composite=False, field=True), Poly(q, x, composite=False, field=True)
+
+    coeff, p, q = p.cancel(q)
+    poly, p = p.div(q)
+
+    result = poly.integrate(x).as_expr()
+
+    if p.is_zero:
+        return coeff*result
+
+    g, h = ratint_ratpart(p, q, x)
+
+    P, Q = h.as_numer_denom()
+
+    P = Poly(P, x)
+    Q = Poly(Q, x)
+
+    q, r = P.div(Q)
+
+    result += g + q.integrate(x).as_expr()
+
+    if not r.is_zero:
+        symbol = flags.get('symbol', 't')
+
+        if not isinstance(symbol, Symbol):
+            t = Dummy(symbol)
+        else:
+            t = symbol.as_dummy()
+
+        L = ratint_logpart(r, Q, x, t)
+
+        real = flags.get('real')
+
+        if real is None:
+            if isinstance(f, tuple):
+                p, q = f
+                atoms = p.atoms() | q.atoms()
+            else:
+                atoms = f.atoms()
+
+            for elt in atoms - {x}:
+                if not elt.is_extended_real:
+                    real = False
+                    break
+            else:
+                real = True
+
+        eps = S.Zero
+
+        if not real:
+            for h, q in L:
+                _, h = h.primitive()
+                eps += RootSum(
+                    q, Lambda(t, t*log(h.as_expr())), quadratic=True)
+        else:
+            for h, q in L:
+                _, h = h.primitive()
+                R = log_to_real(h, q, x, t)
+
+                if R is not None:
+                    eps += R
+                else:
+                    eps += RootSum(
+                        q, Lambda(t, t*log(h.as_expr())), quadratic=True)
+
+        result += eps
+
+    return coeff*result
+
+
+def ratint_ratpart(f, g, x):
+    """
+    Horowitz-Ostrogradsky algorithm.
+
+    Explanation
+    ===========
+
+    Given a field K and polynomials f and g in K[x], such that f and g
+    are coprime and deg(f) < deg(g), returns fractions A and B in K(x),
+    such that f/g = A' + B and B has square-free denominator.
+
+    Examples
+    ========
+
+        >>> from sympy.integrals.rationaltools import ratint_ratpart
+        >>> from sympy.abc import x, y
+        >>> from sympy import Poly
+        >>> ratint_ratpart(Poly(1, x, domain='ZZ'),
+        ... Poly(x + 1, x, domain='ZZ'), x)
+        (0, 1/(x + 1))
+        >>> ratint_ratpart(Poly(1, x, domain='EX'),
+        ... Poly(x**2 + y**2, x, domain='EX'), x)
+        (0, 1/(x**2 + y**2))
+        >>> ratint_ratpart(Poly(36, x, domain='ZZ'),
+        ... Poly(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2, x, domain='ZZ'), x)
+        ((12*x + 6)/(x**2 - 1), 12/(x**2 - x - 2))
+
+    See Also
+    ========
+
+    ratint, ratint_logpart
+    """
+    from sympy.solvers.solvers import solve
+
+    f = Poly(f, x)
+    g = Poly(g, x)
+
+    u, v, _ = g.cofactors(g.diff())
+
+    n = u.degree()
+    m = v.degree()
+
+    A_coeffs = [ Dummy('a' + str(n - i)) for i in range(0, n) ]
+    B_coeffs = [ Dummy('b' + str(m - i)) for i in range(0, m) ]
+
+    C_coeffs = A_coeffs + B_coeffs
+
+    A = Poly(A_coeffs, x, domain=ZZ[C_coeffs])
+    B = Poly(B_coeffs, x, domain=ZZ[C_coeffs])
+
+    H = f - A.diff()*v + A*(u.diff()*v).quo(u) - B*u
+
+    result = solve(H.coeffs(), C_coeffs)
+
+    A = A.as_expr().subs(result)
+    B = B.as_expr().subs(result)
+
+    rat_part = cancel(A/u.as_expr(), x)
+    log_part = cancel(B/v.as_expr(), x)
+
+    return rat_part, log_part
+
+
+def ratint_logpart(f, g, x, t=None):
+    r"""
+    Lazard-Rioboo-Trager algorithm.
+
+    Explanation
+    ===========
+
+    Given a field K and polynomials f and g in K[x], such that f and g
+    are coprime, deg(f) < deg(g) and g is square-free, returns a list
+    of tuples (s_i, q_i) of polynomials, for i = 1..n, such that s_i
+    in K[t, x] and q_i in K[t], and::
+
+                           ___    ___
+                 d  f   d  \  `   \  `
+                 -- - = --  )      )   a log(s_i(a, x))
+                 dx g   dx /__,   /__,
+                          i=1..n a | q_i(a) = 0
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.rationaltools import ratint_logpart
+    >>> from sympy.abc import x
+    >>> from sympy import Poly
+    >>> ratint_logpart(Poly(1, x, domain='ZZ'),
+    ... Poly(x**2 + x + 1, x, domain='ZZ'), x)
+    [(Poly(x + 3*_t/2 + 1/2, x, domain='QQ[_t]'),
+    ...Poly(3*_t**2 + 1, _t, domain='ZZ'))]
+    >>> ratint_logpart(Poly(12, x, domain='ZZ'),
+    ... Poly(x**2 - x - 2, x, domain='ZZ'), x)
+    [(Poly(x - 3*_t/8 - 1/2, x, domain='QQ[_t]'),
+    ...Poly(-_t**2 + 16, _t, domain='ZZ'))]
+
+    See Also
+    ========
+
+    ratint, ratint_ratpart
+    """
+    f, g = Poly(f, x), Poly(g, x)
+
+    t = t or Dummy('t')
+    a, b = g, f - g.diff()*Poly(t, x)
+
+    res, R = resultant(a, b, includePRS=True)
+    res = Poly(res, t, composite=False)
+
+    assert res, "BUG: resultant(%s, %s) cannot be zero" % (a, b)
+
+    R_map, H = {}, []
+
+    for r in R:
+        R_map[r.degree()] = r
+
+    def _include_sign(c, sqf):
+        if c.is_extended_real and (c < 0) == True:
+            h, k = sqf[0]
+            c_poly = c.as_poly(h.gens)
+            sqf[0] = h*c_poly, k
+
+    C, res_sqf = res.sqf_list()
+    _include_sign(C, res_sqf)
+
+    for q, i in res_sqf:
+        _, q = q.primitive()
+
+        if g.degree() == i:
+            H.append((g, q))
+        else:
+            h = R_map[i]
+            h_lc = Poly(h.LC(), t, field=True)
+
+            c, h_lc_sqf = h_lc.sqf_list(all=True)
+            _include_sign(c, h_lc_sqf)
+
+            for a, j in h_lc_sqf:
+                h = h.quo(Poly(a.gcd(q)**j, x))
+
+            inv, coeffs = h_lc.invert(q), [S.One]
+
+            for coeff in h.coeffs()[1:]:
+                coeff = coeff.as_poly(inv.gens)
+                T = (inv*coeff).rem(q)
+                coeffs.append(T.as_expr())
+
+            h = Poly(dict(list(zip(h.monoms(), coeffs))), x)
+
+            H.append((h, q))
+
+    return H
+
+
+def log_to_atan(f, g):
+    """
+    Convert complex logarithms to real arctangents.
+
+    Explanation
+    ===========
+
+    Given a real field K and polynomials f and g in K[x], with g != 0,
+    returns a sum h of arctangents of polynomials in K[x], such that:
+
+                   dh   d         f + I g
+                   -- = -- I log( ------- )
+                   dx   dx        f - I g
+
+    Examples
+    ========
+
+        >>> from sympy.integrals.rationaltools import log_to_atan
+        >>> from sympy.abc import x
+        >>> from sympy import Poly, sqrt, S
+        >>> log_to_atan(Poly(x, x, domain='ZZ'), Poly(1, x, domain='ZZ'))
+        2*atan(x)
+        >>> log_to_atan(Poly(x + S(1)/2, x, domain='QQ'),
+        ... Poly(sqrt(3)/2, x, domain='EX'))
+        2*atan(2*sqrt(3)*x/3 + sqrt(3)/3)
+
+    See Also
+    ========
+
+    log_to_real
+    """
+    if f.degree() < g.degree():
+        f, g = -g, f
+
+    f = f.to_field()
+    g = g.to_field()
+
+    p, q = f.div(g)
+
+    if q.is_zero:
+        return 2*atan(p.as_expr())
+    else:
+        s, t, h = g.gcdex(-f)
+        u = (f*s + g*t).quo(h)
+        A = 2*atan(u.as_expr())
+
+        return A + log_to_atan(s, t)
+
+
+def log_to_real(h, q, x, t):
+    r"""
+    Convert complex logarithms to real functions.
+
+    Explanation
+    ===========
+
+    Given real field K and polynomials h in K[t,x] and q in K[t],
+    returns real function f such that:
+                          ___
+                  df   d  \  `
+                  -- = --  )  a log(h(a, x))
+                  dx   dx /__,
+                         a | q(a) = 0
+
+    Examples
+    ========
+
+        >>> from sympy.integrals.rationaltools import log_to_real
+        >>> from sympy.abc import x, y
+        >>> from sympy import Poly, S
+        >>> log_to_real(Poly(x + 3*y/2 + S(1)/2, x, domain='QQ[y]'),
+        ... Poly(3*y**2 + 1, y, domain='ZZ'), x, y)
+        2*sqrt(3)*atan(2*sqrt(3)*x/3 + sqrt(3)/3)/3
+        >>> log_to_real(Poly(x**2 - 1, x, domain='ZZ'),
+        ... Poly(-2*y + 1, y, domain='ZZ'), x, y)
+        log(x**2 - 1)/2
+
+    See Also
+    ========
+
+    log_to_atan
+    """
+    from sympy.simplify.radsimp import collect
+    u, v = symbols('u,v', cls=Dummy)
+
+    H = h.as_expr().subs({t: u + I*v}).expand()
+    Q = q.as_expr().subs({t: u + I*v}).expand()
+
+    H_map = collect(H, I, evaluate=False)
+    Q_map = collect(Q, I, evaluate=False)
+
+    a, b = H_map.get(S.One, S.Zero), H_map.get(I, S.Zero)
+    c, d = Q_map.get(S.One, S.Zero), Q_map.get(I, S.Zero)
+
+    R = Poly(resultant(c, d, v), u)
+
+    R_u = roots(R, filter='R')
+
+    if len(R_u) != R.count_roots():
+        return None
+
+    result = S.Zero
+
+    for r_u in R_u.keys():
+        C = Poly(c.subs({u: r_u}), v)
+        R_v = roots(C, filter='R')
+
+        if len(R_v) != C.count_roots():
+            return None
+
+        R_v_paired = [] # take one from each pair of conjugate roots
+        for r_v in R_v:
+            if r_v not in R_v_paired and -r_v not in R_v_paired:
+                if r_v.is_negative or r_v.could_extract_minus_sign():
+                    R_v_paired.append(-r_v)
+                elif not r_v.is_zero:
+                    R_v_paired.append(r_v)
+
+        for r_v in R_v_paired:
+
+            D = d.subs({u: r_u, v: r_v})
+
+            if D.evalf(chop=True) != 0:
+                continue
+
+            A = Poly(a.subs({u: r_u, v: r_v}), x)
+            B = Poly(b.subs({u: r_u, v: r_v}), x)
+
+            AB = (A**2 + B**2).as_expr()
+
+            result += r_u*log(AB) + r_v*log_to_atan(A, B)
+
+    R_q = roots(q, filter='R')
+
+    if len(R_q) != q.count_roots():
+        return None
+
+    for r in R_q.keys():
+        result += r*log(h.as_expr().subs(t, r))
+
+    return result

venv/lib/python3.10/site-packages/sympy/integrals/rde.py

@@ -0,0 +1,800 @@
+"""
+Algorithms for solving the Risch differential equation.
+
+Given a differential field K of characteristic 0 that is a simple
+monomial extension of a base field k and f, g in K, the Risch
+Differential Equation problem is to decide if there exist y in K such
+that Dy + f*y == g and to find one if there are some.  If t is a
+monomial over k and the coefficients of f and g are in k(t), then y is
+in k(t), and the outline of the algorithm here is given as:
+
+1. Compute the normal part n of the denominator of y.  The problem is
+then reduced to finding y' in k<t>, where y == y'/n.
+2. Compute the special part s of the denominator of y.   The problem is
+then reduced to finding y'' in k[t], where y == y''/(n*s)
+3. Bound the degree of y''.
+4. Reduce the equation Dy + f*y == g to a similar equation with f, g in
+k[t].
+5. Find the solutions in k[t] of bounded degree of the reduced equation.
+
+See Chapter 6 of "Symbolic Integration I: Transcendental Functions" by
+Manuel Bronstein.  See also the docstring of risch.py.
+"""
+
+from operator import mul
+from functools import reduce
+
+from sympy.core import oo
+from sympy.core.symbol import Dummy
+
+from sympy.polys import Poly, gcd, ZZ, cancel
+
+from sympy.functions.elementary.complexes import (im, re)
+from sympy.functions.elementary.miscellaneous import sqrt
+
+from sympy.integrals.risch import (gcdex_diophantine, frac_in, derivation,
+    splitfactor, NonElementaryIntegralException, DecrementLevel, recognize_log_derivative)
+
+# TODO: Add messages to NonElementaryIntegralException errors
+
+
+def order_at(a, p, t):
+    """
+    Computes the order of a at p, with respect to t.
+
+    Explanation
+    ===========
+
+    For a, p in k[t], the order of a at p is defined as nu_p(a) = max({n
+    in Z+ such that p**n|a}), where a != 0.  If a == 0, nu_p(a) = +oo.
+
+    To compute the order at a rational function, a/b, use the fact that
+    nu_p(a/b) == nu_p(a) - nu_p(b).
+    """
+    if a.is_zero:
+        return oo
+    if p == Poly(t, t):
+        return a.as_poly(t).ET()[0][0]
+
+    # Uses binary search for calculating the power. power_list collects the tuples
+    # (p^k,k) where each k is some power of 2. After deciding the largest k
+    # such that k is power of 2 and p^k|a the loop iteratively calculates
+    # the actual power.
+    power_list = []
+    p1 = p
+    r = a.rem(p1)
+    tracks_power = 1
+    while r.is_zero:
+        power_list.append((p1,tracks_power))
+        p1 = p1*p1
+        tracks_power *= 2
+        r = a.rem(p1)
+    n = 0
+    product = Poly(1, t)
+    while len(power_list) != 0:
+        final = power_list.pop()
+        productf = product*final[0]
+        r = a.rem(productf)
+        if r.is_zero:
+            n += final[1]
+            product = productf
+    return n
+
+
+def order_at_oo(a, d, t):
+    """
+    Computes the order of a/d at oo (infinity), with respect to t.
+
+    For f in k(t), the order or f at oo is defined as deg(d) - deg(a), where
+    f == a/d.
+    """
+    if a.is_zero:
+        return oo
+    return d.degree(t) - a.degree(t)
+
+
+def weak_normalizer(a, d, DE, z=None):
+    """
+    Weak normalization.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and f == a/d in k(t), return q in k[t]
+    such that f - Dq/q is weakly normalized with respect to t.
+
+    f in k(t) is said to be "weakly normalized" with respect to t if
+    residue_p(f) is not a positive integer for any normal irreducible p
+    in k[t] such that f is in R_p (Definition 6.1.1).  If f has an
+    elementary integral, this is equivalent to no logarithm of
+    integral(f) whose argument depends on t has a positive integer
+    coefficient, where the arguments of the logarithms not in k(t) are
+    in k[t].
+
+    Returns (q, f - Dq/q)
+    """
+    z = z or Dummy('z')
+    dn, ds = splitfactor(d, DE)
+
+    # Compute d1, where dn == d1*d2**2*...*dn**n is a square-free
+    # factorization of d.
+    g = gcd(dn, dn.diff(DE.t))
+    d_sqf_part = dn.quo(g)
+    d1 = d_sqf_part.quo(gcd(d_sqf_part, g))
+
+    a1, b = gcdex_diophantine(d.quo(d1).as_poly(DE.t), d1.as_poly(DE.t),
+        a.as_poly(DE.t))
+    r = (a - Poly(z, DE.t)*derivation(d1, DE)).as_poly(DE.t).resultant(
+        d1.as_poly(DE.t))
+    r = Poly(r, z)
+
+    if not r.expr.has(z):
+        return (Poly(1, DE.t), (a, d))
+
+    N = [i for i in r.real_roots() if i in ZZ and i > 0]
+
+    q = reduce(mul, [gcd(a - Poly(n, DE.t)*derivation(d1, DE), d1) for n in N],
+        Poly(1, DE.t))
+
+    dq = derivation(q, DE)
+    sn = q*a - d*dq
+    sd = q*d
+    sn, sd = sn.cancel(sd, include=True)
+
+    return (q, (sn, sd))
+
+
+def normal_denom(fa, fd, ga, gd, DE):
+    """
+    Normal part of the denominator.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and f, g in k(t) with f weakly
+    normalized with respect to t, either raise NonElementaryIntegralException,
+    in which case the equation Dy + f*y == g has no solution in k(t), or the
+    quadruplet (a, b, c, h) such that a, h in k[t], b, c in k<t>, and for any
+    solution y in k(t) of Dy + f*y == g, q = y*h in k<t> satisfies
+    a*Dq + b*q == c.
+
+    This constitutes step 1 in the outline given in the rde.py docstring.
+    """
+    dn, ds = splitfactor(fd, DE)
+    en, es = splitfactor(gd, DE)
+
+    p = dn.gcd(en)
+    h = en.gcd(en.diff(DE.t)).quo(p.gcd(p.diff(DE.t)))
+
+    a = dn*h
+    c = a*h
+    if c.div(en)[1]:
+        # en does not divide dn*h**2
+        raise NonElementaryIntegralException
+    ca = c*ga
+    ca, cd = ca.cancel(gd, include=True)
+
+    ba = a*fa - dn*derivation(h, DE)*fd
+    ba, bd = ba.cancel(fd, include=True)
+
+    # (dn*h, dn*h*f - dn*Dh, dn*h**2*g, h)
+    return (a, (ba, bd), (ca, cd), h)
+
+
+def special_denom(a, ba, bd, ca, cd, DE, case='auto'):
+    """
+    Special part of the denominator.
+
+    Explanation
+    ===========
+
+    case is one of {'exp', 'tan', 'primitive'} for the hyperexponential,
+    hypertangent, and primitive cases, respectively.  For the
+    hyperexponential (resp. hypertangent) case, given a derivation D on
+    k[t] and a in k[t], b, c, in k<t> with Dt/t in k (resp. Dt/(t**2 + 1) in
+    k, sqrt(-1) not in k), a != 0, and gcd(a, t) == 1 (resp.
+    gcd(a, t**2 + 1) == 1), return the quadruplet (A, B, C, 1/h) such that
+    A, B, C, h in k[t] and for any solution q in k<t> of a*Dq + b*q == c,
+    r = qh in k[t] satisfies A*Dr + B*r == C.
+
+    For ``case == 'primitive'``, k<t> == k[t], so it returns (a, b, c, 1) in
+    this case.
+
+    This constitutes step 2 of the outline given in the rde.py docstring.
+    """
+    # TODO: finish writing this and write tests
+
+    if case == 'auto':
+        case = DE.case
+
+    if case == 'exp':
+        p = Poly(DE.t, DE.t)
+    elif case == 'tan':
+        p = Poly(DE.t**2 + 1, DE.t)
+    elif case in ('primitive', 'base'):
+        B = ba.to_field().quo(bd)
+        C = ca.to_field().quo(cd)
+        return (a, B, C, Poly(1, DE.t))
+    else:
+        raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
+            "'base'}, not %s." % case)
+
+    nb = order_at(ba, p, DE.t) - order_at(bd, p, DE.t)
+    nc = order_at(ca, p, DE.t) - order_at(cd, p, DE.t)
+
+    n = min(0, nc - min(0, nb))
+    if not nb:
+        # Possible cancellation.
+        from .prde import parametric_log_deriv
+        if case == 'exp':
+            dcoeff = DE.d.quo(Poly(DE.t, DE.t))
+            with DecrementLevel(DE):  # We are guaranteed to not have problems,
+                                      # because case != 'base'.
+                alphaa, alphad = frac_in(-ba.eval(0)/bd.eval(0)/a.eval(0), DE.t)
+                etaa, etad = frac_in(dcoeff, DE.t)
+                A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
+                if A is not None:
+                    Q, m, z = A
+                    if Q == 1:
+                        n = min(n, m)
+
+        elif case == 'tan':
+            dcoeff = DE.d.quo(Poly(DE.t**2+1, DE.t))
+            with DecrementLevel(DE):  # We are guaranteed to not have problems,
+                                      # because case != 'base'.
+                alphaa, alphad = frac_in(im(-ba.eval(sqrt(-1))/bd.eval(sqrt(-1))/a.eval(sqrt(-1))), DE.t)
+                betaa, betad = frac_in(re(-ba.eval(sqrt(-1))/bd.eval(sqrt(-1))/a.eval(sqrt(-1))), DE.t)
+                etaa, etad = frac_in(dcoeff, DE.t)
+
+                if recognize_log_derivative(Poly(2, DE.t)*betaa, betad, DE):
+                    A = parametric_log_deriv(alphaa*Poly(sqrt(-1), DE.t)*betad+alphad*betaa, alphad*betad, etaa, etad, DE)
+                    if A is not None:
+                       Q, m, z = A
+                       if Q == 1:
+                           n = min(n, m)
+    N = max(0, -nb, n - nc)
+    pN = p**N
+    pn = p**-n
+
+    A = a*pN
+    B = ba*pN.quo(bd) + Poly(n, DE.t)*a*derivation(p, DE).quo(p)*pN
+    C = (ca*pN*pn).quo(cd)
+    h = pn
+
+    # (a*p**N, (b + n*a*Dp/p)*p**N, c*p**(N - n), p**-n)
+    return (A, B, C, h)
+
+
+def bound_degree(a, b, cQ, DE, case='auto', parametric=False):
+    """
+    Bound on polynomial solutions.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and ``a``, ``b``, ``c`` in k[t] with ``a != 0``, return
+    n in ZZ such that deg(q) <= n for any solution q in k[t] of
+    a*Dq + b*q == c, when parametric=False, or deg(q) <= n for any solution
+    c1, ..., cm in Const(k) and q in k[t] of a*Dq + b*q == Sum(ci*gi, (i, 1, m))
+    when parametric=True.
+
+    For ``parametric=False``, ``cQ`` is ``c``, a ``Poly``; for ``parametric=True``, ``cQ`` is Q ==
+    [q1, ..., qm], a list of Polys.
+
+    This constitutes step 3 of the outline given in the rde.py docstring.
+    """
+    # TODO: finish writing this and write tests
+
+    if case == 'auto':
+        case = DE.case
+
+    da = a.degree(DE.t)
+    db = b.degree(DE.t)
+
+    # The parametric and regular cases are identical, except for this part
+    if parametric:
+        dc = max([i.degree(DE.t) for i in cQ])
+    else:
+        dc = cQ.degree(DE.t)
+
+    alpha = cancel(-b.as_poly(DE.t).LC().as_expr()/
+        a.as_poly(DE.t).LC().as_expr())
+
+    if case == 'base':
+        n = max(0, dc - max(db, da - 1))
+        if db == da - 1 and alpha.is_Integer:
+            n = max(0, alpha, dc - db)
+
+    elif case == 'primitive':
+        if db > da:
+            n = max(0, dc - db)
+        else:
+            n = max(0, dc - da + 1)
+
+        etaa, etad = frac_in(DE.d, DE.T[DE.level - 1])
+
+        t1 = DE.t
+        with DecrementLevel(DE):
+            alphaa, alphad = frac_in(alpha, DE.t)
+            if db == da - 1:
+                from .prde import limited_integrate
+                # if alpha == m*Dt + Dz for z in k and m in ZZ:
+                try:
+                    (za, zd), m = limited_integrate(alphaa, alphad, [(etaa, etad)],
+                        DE)
+                except NonElementaryIntegralException:
+                    pass
+                else:
+                    if len(m) != 1:
+                        raise ValueError("Length of m should be 1")
+                    n = max(n, m[0])
+
+            elif db == da:
+                # if alpha == Dz/z for z in k*:
+                    # beta = -lc(a*Dz + b*z)/(z*lc(a))
+                    # if beta == m*Dt + Dw for w in k and m in ZZ:
+                        # n = max(n, m)
+                from .prde import is_log_deriv_k_t_radical_in_field
+                A = is_log_deriv_k_t_radical_in_field(alphaa, alphad, DE)
+                if A is not None:
+                    aa, z = A
+                    if aa == 1:
+                        beta = -(a*derivation(z, DE).as_poly(t1) +
+                            b*z.as_poly(t1)).LC()/(z.as_expr()*a.LC())
+                        betaa, betad = frac_in(beta, DE.t)
+                        from .prde import limited_integrate
+                        try:
+                            (za, zd), m = limited_integrate(betaa, betad,
+                                [(etaa, etad)], DE)
+                        except NonElementaryIntegralException:
+                            pass
+                        else:
+                            if len(m) != 1:
+                                raise ValueError("Length of m should be 1")
+                            n = max(n, m[0].as_expr())
+
+    elif case == 'exp':
+        from .prde import parametric_log_deriv
+
+        n = max(0, dc - max(db, da))
+        if da == db:
+            etaa, etad = frac_in(DE.d.quo(Poly(DE.t, DE.t)), DE.T[DE.level - 1])
+            with DecrementLevel(DE):
+                alphaa, alphad = frac_in(alpha, DE.t)
+                A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
+                if A is not None:
+                    # if alpha == m*Dt/t + Dz/z for z in k* and m in ZZ:
+                        # n = max(n, m)
+                    a, m, z = A
+                    if a == 1:
+                        n = max(n, m)
+
+    elif case in ('tan', 'other_nonlinear'):
+        delta = DE.d.degree(DE.t)
+        lam = DE.d.LC()
+        alpha = cancel(alpha/lam)
+        n = max(0, dc - max(da + delta - 1, db))
+        if db == da + delta - 1 and alpha.is_Integer:
+            n = max(0, alpha, dc - db)
+
+    else:
+        raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
+            "'other_nonlinear', 'base'}, not %s." % case)
+
+    return n
+
+
+def spde(a, b, c, n, DE):
+    """
+    Rothstein's Special Polynomial Differential Equation algorithm.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], an integer n and ``a``,``b``,``c`` in k[t] with
+    ``a != 0``, either raise NonElementaryIntegralException, in which case the
+    equation a*Dq + b*q == c has no solution of degree at most ``n`` in
+    k[t], or return the tuple (B, C, m, alpha, beta) such that B, C,
+    alpha, beta in k[t], m in ZZ, and any solution q in k[t] of degree
+    at most n of a*Dq + b*q == c must be of the form
+    q == alpha*h + beta, where h in k[t], deg(h) <= m, and Dh + B*h == C.
+
+    This constitutes step 4 of the outline given in the rde.py docstring.
+    """
+    zero = Poly(0, DE.t)
+
+    alpha = Poly(1, DE.t)
+    beta = Poly(0, DE.t)
+
+    while True:
+        if c.is_zero:
+            return (zero, zero, 0, zero, beta)  # -1 is more to the point
+        if (n < 0) is True:
+            raise NonElementaryIntegralException
+
+        g = a.gcd(b)
+        if not c.rem(g).is_zero:  # g does not divide c
+            raise NonElementaryIntegralException
+
+        a, b, c = a.quo(g), b.quo(g), c.quo(g)
+
+        if a.degree(DE.t) == 0:
+            b = b.to_field().quo(a)
+            c = c.to_field().quo(a)
+            return (b, c, n, alpha, beta)
+
+        r, z = gcdex_diophantine(b, a, c)
+        b += derivation(a, DE)
+        c = z - derivation(r, DE)
+        n -= a.degree(DE.t)
+
+        beta += alpha * r
+        alpha *= a
+
+def no_cancel_b_large(b, c, n, DE):
+    """
+    Poly Risch Differential Equation - No cancellation: deg(b) large enough.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], ``n`` either an integer or +oo, and ``b``,``c``
+    in k[t] with ``b != 0`` and either D == d/dt or
+    deg(b) > max(0, deg(D) - 1), either raise NonElementaryIntegralException, in
+    which case the equation ``Dq + b*q == c`` has no solution of degree at
+    most n in k[t], or a solution q in k[t] of this equation with
+    ``deg(q) < n``.
+    """
+    q = Poly(0, DE.t)
+
+    while not c.is_zero:
+        m = c.degree(DE.t) - b.degree(DE.t)
+        if not 0 <= m <= n:  # n < 0 or m < 0 or m > n
+            raise NonElementaryIntegralException
+
+        p = Poly(c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC()*DE.t**m, DE.t,
+            expand=False)
+        q = q + p
+        n = m - 1
+        c = c - derivation(p, DE) - b*p
+
+    return q
+
+
+def no_cancel_b_small(b, c, n, DE):
+    """
+    Poly Risch Differential Equation - No cancellation: deg(b) small enough.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], ``n`` either an integer or +oo, and ``b``,``c``
+    in k[t] with deg(b) < deg(D) - 1 and either D == d/dt or
+    deg(D) >= 2, either raise NonElementaryIntegralException, in which case the
+    equation Dq + b*q == c has no solution of degree at most n in k[t],
+    or a solution q in k[t] of this equation with deg(q) <= n, or the
+    tuple (h, b0, c0) such that h in k[t], b0, c0, in k, and for any
+    solution q in k[t] of degree at most n of Dq + bq == c, y == q - h
+    is a solution in k of Dy + b0*y == c0.
+    """
+    q = Poly(0, DE.t)
+
+    while not c.is_zero:
+        if n == 0:
+            m = 0
+        else:
+            m = c.degree(DE.t) - DE.d.degree(DE.t) + 1
+
+        if not 0 <= m <= n:  # n < 0 or m < 0 or m > n
+            raise NonElementaryIntegralException
+
+        if m > 0:
+            p = Poly(c.as_poly(DE.t).LC()/(m*DE.d.as_poly(DE.t).LC())*DE.t**m,
+                DE.t, expand=False)
+        else:
+            if b.degree(DE.t) != c.degree(DE.t):
+                raise NonElementaryIntegralException
+            if b.degree(DE.t) == 0:
+                return (q, b.as_poly(DE.T[DE.level - 1]),
+                    c.as_poly(DE.T[DE.level - 1]))
+            p = Poly(c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC(), DE.t,
+                expand=False)
+
+        q = q + p
+        n = m - 1
+        c = c - derivation(p, DE) - b*p
+
+    return q
+
+
+# TODO: better name for this function
+def no_cancel_equal(b, c, n, DE):
+    """
+    Poly Risch Differential Equation - No cancellation: deg(b) == deg(D) - 1
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] with deg(D) >= 2, n either an integer
+    or +oo, and b, c in k[t] with deg(b) == deg(D) - 1, either raise
+    NonElementaryIntegralException, in which case the equation Dq + b*q == c has
+    no solution of degree at most n in k[t], or a solution q in k[t] of
+    this equation with deg(q) <= n, or the tuple (h, m, C) such that h
+    in k[t], m in ZZ, and C in k[t], and for any solution q in k[t] of
+    degree at most n of Dq + b*q == c, y == q - h is a solution in k[t]
+    of degree at most m of Dy + b*y == C.
+    """
+    q = Poly(0, DE.t)
+    lc = cancel(-b.as_poly(DE.t).LC()/DE.d.as_poly(DE.t).LC())
+    if lc.is_Integer and lc.is_positive:
+        M = lc
+    else:
+        M = -1
+
+    while not c.is_zero:
+        m = max(M, c.degree(DE.t) - DE.d.degree(DE.t) + 1)
+
+        if not 0 <= m <= n:  # n < 0 or m < 0 or m > n
+            raise NonElementaryIntegralException
+
+        u = cancel(m*DE.d.as_poly(DE.t).LC() + b.as_poly(DE.t).LC())
+        if u.is_zero:
+            return (q, m, c)
+        if m > 0:
+            p = Poly(c.as_poly(DE.t).LC()/u*DE.t**m, DE.t, expand=False)
+        else:
+            if c.degree(DE.t) != DE.d.degree(DE.t) - 1:
+                raise NonElementaryIntegralException
+            else:
+                p = c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC()
+
+        q = q + p
+        n = m - 1
+        c = c - derivation(p, DE) - b*p
+
+    return q
+
+
+def cancel_primitive(b, c, n, DE):
+    """
+    Poly Risch Differential Equation - Cancellation: Primitive case.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], n either an integer or +oo, ``b`` in k, and
+    ``c`` in k[t] with Dt in k and ``b != 0``, either raise
+    NonElementaryIntegralException, in which case the equation Dq + b*q == c
+    has no solution of degree at most n in k[t], or a solution q in k[t] of
+    this equation with deg(q) <= n.
+    """
+    # Delayed imports
+    from .prde import is_log_deriv_k_t_radical_in_field
+    with DecrementLevel(DE):
+        ba, bd = frac_in(b, DE.t)
+        A = is_log_deriv_k_t_radical_in_field(ba, bd, DE)
+        if A is not None:
+            n, z = A
+            if n == 1:  # b == Dz/z
+                raise NotImplementedError("is_deriv_in_field() is required to "
+                    " solve this problem.")
+                # if z*c == Dp for p in k[t] and deg(p) <= n:
+                #     return p/z
+                # else:
+                #     raise NonElementaryIntegralException
+
+    if c.is_zero:
+        return c  # return 0
+
+    if n < c.degree(DE.t):
+        raise NonElementaryIntegralException
+
+    q = Poly(0, DE.t)
+    while not c.is_zero:
+        m = c.degree(DE.t)
+        if n < m:
+            raise NonElementaryIntegralException
+        with DecrementLevel(DE):
+            a2a, a2d = frac_in(c.LC(), DE.t)
+            sa, sd = rischDE(ba, bd, a2a, a2d, DE)
+        stm = Poly(sa.as_expr()/sd.as_expr()*DE.t**m, DE.t, expand=False)
+        q += stm
+        n = m - 1
+        c -= b*stm + derivation(stm, DE)
+
+    return q
+
+
+def cancel_exp(b, c, n, DE):
+    """
+    Poly Risch Differential Equation - Cancellation: Hyperexponential case.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t], n either an integer or +oo, ``b`` in k, and
+    ``c`` in k[t] with Dt/t in k and ``b != 0``, either raise
+    NonElementaryIntegralException, in which case the equation Dq + b*q == c
+    has no solution of degree at most n in k[t], or a solution q in k[t] of
+    this equation with deg(q) <= n.
+    """
+    from .prde import parametric_log_deriv
+    eta = DE.d.quo(Poly(DE.t, DE.t)).as_expr()
+
+    with DecrementLevel(DE):
+        etaa, etad = frac_in(eta, DE.t)
+        ba, bd = frac_in(b, DE.t)
+        A = parametric_log_deriv(ba, bd, etaa, etad, DE)
+        if A is not None:
+            a, m, z = A
+            if a == 1:
+                raise NotImplementedError("is_deriv_in_field() is required to "
+                    "solve this problem.")
+                # if c*z*t**m == Dp for p in k<t> and q = p/(z*t**m) in k[t] and
+                # deg(q) <= n:
+                #     return q
+                # else:
+                #     raise NonElementaryIntegralException
+
+    if c.is_zero:
+        return c  # return 0
+
+    if n < c.degree(DE.t):
+        raise NonElementaryIntegralException
+
+    q = Poly(0, DE.t)
+    while not c.is_zero:
+        m = c.degree(DE.t)
+        if n < m:
+            raise NonElementaryIntegralException
+        # a1 = b + m*Dt/t
+        a1 = b.as_expr()
+        with DecrementLevel(DE):
+            # TODO: Write a dummy function that does this idiom
+            a1a, a1d = frac_in(a1, DE.t)
+            a1a = a1a*etad + etaa*a1d*Poly(m, DE.t)
+            a1d = a1d*etad
+
+            a2a, a2d = frac_in(c.LC(), DE.t)
+
+            sa, sd = rischDE(a1a, a1d, a2a, a2d, DE)
+        stm = Poly(sa.as_expr()/sd.as_expr()*DE.t**m, DE.t, expand=False)
+        q += stm
+        n = m - 1
+        c -= b*stm + derivation(stm, DE)  # deg(c) becomes smaller
+    return q
+
+
+def solve_poly_rde(b, cQ, n, DE, parametric=False):
+    """
+    Solve a Polynomial Risch Differential Equation with degree bound ``n``.
+
+    This constitutes step 4 of the outline given in the rde.py docstring.
+
+    For parametric=False, cQ is c, a Poly; for parametric=True, cQ is Q ==
+    [q1, ..., qm], a list of Polys.
+    """
+    # No cancellation
+    if not b.is_zero and (DE.case == 'base' or
+            b.degree(DE.t) > max(0, DE.d.degree(DE.t) - 1)):
+
+        if parametric:
+            # Delayed imports
+            from .prde import prde_no_cancel_b_large
+            return prde_no_cancel_b_large(b, cQ, n, DE)
+        return no_cancel_b_large(b, cQ, n, DE)
+
+    elif (b.is_zero or b.degree(DE.t) < DE.d.degree(DE.t) - 1) and \
+            (DE.case == 'base' or DE.d.degree(DE.t) >= 2):
+
+        if parametric:
+            from .prde import prde_no_cancel_b_small
+            return prde_no_cancel_b_small(b, cQ, n, DE)
+
+        R = no_cancel_b_small(b, cQ, n, DE)
+
+        if isinstance(R, Poly):
+            return R
+        else:
+            # XXX: Might k be a field? (pg. 209)
+            h, b0, c0 = R
+            with DecrementLevel(DE):
+                b0, c0 = b0.as_poly(DE.t), c0.as_poly(DE.t)
+                if b0 is None:  # See above comment
+                    raise ValueError("b0 should be a non-Null value")
+                if c0 is  None:
+                    raise ValueError("c0 should be a non-Null value")
+                y = solve_poly_rde(b0, c0, n, DE).as_poly(DE.t)
+            return h + y
+
+    elif DE.d.degree(DE.t) >= 2 and b.degree(DE.t) == DE.d.degree(DE.t) - 1 and \
+            n > -b.as_poly(DE.t).LC()/DE.d.as_poly(DE.t).LC():
+
+        # TODO: Is this check necessary, and if so, what should it do if it fails?
+        # b comes from the first element returned from spde()
+        if not b.as_poly(DE.t).LC().is_number:
+            raise TypeError("Result should be a number")
+
+        if parametric:
+            raise NotImplementedError("prde_no_cancel_b_equal() is not yet "
+                "implemented.")
+
+        R = no_cancel_equal(b, cQ, n, DE)
+
+        if isinstance(R, Poly):
+            return R
+        else:
+            h, m, C = R
+            # XXX: Or should it be rischDE()?
+            y = solve_poly_rde(b, C, m, DE)
+            return h + y
+
+    else:
+        # Cancellation
+        if b.is_zero:
+            raise NotImplementedError("Remaining cases for Poly (P)RDE are "
+            "not yet implemented (is_deriv_in_field() required).")
+        else:
+            if DE.case == 'exp':
+                if parametric:
+                    raise NotImplementedError("Parametric RDE cancellation "
+                        "hyperexponential case is not yet implemented.")
+                return cancel_exp(b, cQ, n, DE)
+
+            elif DE.case == 'primitive':
+                if parametric:
+                    raise NotImplementedError("Parametric RDE cancellation "
+                        "primitive case is not yet implemented.")
+                return cancel_primitive(b, cQ, n, DE)
+
+            else:
+                raise NotImplementedError("Other Poly (P)RDE cancellation "
+                    "cases are not yet implemented (%s)." % DE.case)
+
+        if parametric:
+            raise NotImplementedError("Remaining cases for Poly PRDE not yet "
+                "implemented.")
+        raise NotImplementedError("Remaining cases for Poly RDE not yet "
+            "implemented.")
+
+
+def rischDE(fa, fd, ga, gd, DE):
+    """
+    Solve a Risch Differential Equation: Dy + f*y == g.
+
+    Explanation
+    ===========
+
+    See the outline in the docstring of rde.py for more information
+    about the procedure used.  Either raise NonElementaryIntegralException, in
+    which case there is no solution y in the given differential field,
+    or return y in k(t) satisfying Dy + f*y == g, or raise
+    NotImplementedError, in which case, the algorithms necessary to
+    solve the given Risch Differential Equation have not yet been
+    implemented.
+    """
+    _, (fa, fd) = weak_normalizer(fa, fd, DE)
+    a, (ba, bd), (ca, cd), hn = normal_denom(fa, fd, ga, gd, DE)
+    A, B, C, hs = special_denom(a, ba, bd, ca, cd, DE)
+    try:
+        # Until this is fully implemented, use oo.  Note that this will almost
+        # certainly cause non-termination in spde() (unless A == 1), and
+        # *might* lead to non-termination in the next step for a nonelementary
+        # integral (I don't know for certain yet).  Fortunately, spde() is
+        # currently written recursively, so this will just give
+        # RuntimeError: maximum recursion depth exceeded.
+        n = bound_degree(A, B, C, DE)
+    except NotImplementedError:
+        # Useful for debugging:
+        # import warnings
+        # warnings.warn("rischDE: Proceeding with n = oo; may cause "
+        #     "non-termination.")
+        n = oo
+
+    B, C, m, alpha, beta = spde(A, B, C, n, DE)
+    if C.is_zero:
+        y = C
+    else:
+        y = solve_poly_rde(B, C, m, DE)
+
+    return (alpha*y + beta, hn*hs)

venv/lib/python3.10/site-packages/sympy/integrals/risch.py

@@ -0,0 +1,1858 @@
+"""
+The Risch Algorithm for transcendental function integration.
+
+The core algorithms for the Risch algorithm are here.  The subproblem
+algorithms are in the rde.py and prde.py files for the Risch
+Differential Equation solver and the parametric problems solvers,
+respectively.  All important information concerning the differential extension
+for an integrand is stored in a DifferentialExtension object, which in the code
+is usually called DE.  Throughout the code and Inside the DifferentialExtension
+object, the conventions/attribute names are that the base domain is QQ and each
+differential extension is x, t0, t1, ..., tn-1 = DE.t. DE.x is the variable of
+integration (Dx == 1), DE.D is a list of the derivatives of
+x, t1, t2, ..., tn-1 = t, DE.T is the list [x, t1, t2, ..., tn-1], DE.t is the
+outer-most variable of the differential extension at the given level (the level
+can be adjusted using DE.increment_level() and DE.decrement_level()),
+k is the field C(x, t0, ..., tn-2), where C is the constant field.  The
+numerator of a fraction is denoted by a and the denominator by
+d.  If the fraction is named f, fa == numer(f) and fd == denom(f).
+Fractions are returned as tuples (fa, fd).  DE.d and DE.t are used to
+represent the topmost derivation and extension variable, respectively.
+The docstring of a function signifies whether an argument is in k[t], in
+which case it will just return a Poly in t, or in k(t), in which case it
+will return the fraction (fa, fd). Other variable names probably come
+from the names used in Bronstein's book.
+"""
+from types import GeneratorType
+from functools import reduce
+
+from sympy.core.function import Lambda
+from sympy.core.mul import Mul
+from sympy.core.numbers import ilcm, I, oo
+from sympy.core.power import Pow
+from sympy.core.relational import Ne
+from sympy.core.singleton import S
+from sympy.core.sorting import ordered, default_sort_key
+from sympy.core.symbol import Dummy, Symbol
+from sympy.functions.elementary.exponential import log, exp
+from sympy.functions.elementary.hyperbolic import (cosh, coth, sinh,
+    tanh)
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.functions.elementary.trigonometric import (atan, sin, cos,
+    tan, acot, cot, asin, acos)
+from .integrals import integrate, Integral
+from .heurisch import _symbols
+from sympy.polys.polyerrors import DomainError, PolynomialError
+from sympy.polys.polytools import (real_roots, cancel, Poly, gcd,
+    reduced)
+from sympy.polys.rootoftools import RootSum
+from sympy.utilities.iterables import numbered_symbols
+
+
+def integer_powers(exprs):
+    """
+    Rewrites a list of expressions as integer multiples of each other.
+
+    Explanation
+    ===========
+
+    For example, if you have [x, x/2, x**2 + 1, 2*x/3], then you can rewrite
+    this as [(x/6) * 6, (x/6) * 3, (x**2 + 1) * 1, (x/6) * 4]. This is useful
+    in the Risch integration algorithm, where we must write exp(x) + exp(x/2)
+    as (exp(x/2))**2 + exp(x/2), but not as exp(x) + sqrt(exp(x)) (this is
+    because only the transcendental case is implemented and we therefore cannot
+    integrate algebraic extensions). The integer multiples returned by this
+    function for each term are the smallest possible (their content equals 1).
+
+    Returns a list of tuples where the first element is the base term and the
+    second element is a list of `(item, factor)` terms, where `factor` is the
+    integer multiplicative factor that must multiply the base term to obtain
+    the original item.
+
+    The easiest way to understand this is to look at an example:
+
+    >>> from sympy.abc import x
+    >>> from sympy.integrals.risch import integer_powers
+    >>> integer_powers([x, x/2, x**2 + 1, 2*x/3])
+    [(x/6, [(x, 6), (x/2, 3), (2*x/3, 4)]), (x**2 + 1, [(x**2 + 1, 1)])]
+
+    We can see how this relates to the example at the beginning of the
+    docstring.  It chose x/6 as the first base term.  Then, x can be written as
+    (x/2) * 2, so we get (0, 2), and so on. Now only element (x**2 + 1)
+    remains, and there are no other terms that can be written as a rational
+    multiple of that, so we get that it can be written as (x**2 + 1) * 1.
+
+    """
+    # Here is the strategy:
+
+    # First, go through each term and determine if it can be rewritten as a
+    # rational multiple of any of the terms gathered so far.
+    # cancel(a/b).is_Rational is sufficient for this.  If it is a multiple, we
+    # add its multiple to the dictionary.
+
+    terms = {}
+    for term in exprs:
+        for trm, trm_list in terms.items():
+            a = cancel(term/trm)
+            if a.is_Rational:
+                trm_list.append((term, a))
+                break
+        else:
+            terms[term] = [(term, S.One)]
+
+    # After we have done this, we have all the like terms together, so we just
+    # need to find a common denominator so that we can get the base term and
+    # integer multiples such that each term can be written as an integer
+    # multiple of the base term, and the content of the integers is 1.
+
+    newterms = {}
+    for term, term_list in terms.items():
+        common_denom = reduce(ilcm, [i.as_numer_denom()[1] for _, i in
+            term_list])
+        newterm = term/common_denom
+        newmults = [(i, j*common_denom) for i, j in term_list]
+        newterms[newterm] = newmults
+
+    return sorted(iter(newterms.items()), key=lambda item: item[0].sort_key())
+
+
+class DifferentialExtension:
+    """
+    A container for all the information relating to a differential extension.
+
+    Explanation
+    ===========
+
+    The attributes of this object are (see also the docstring of __init__):
+
+    - f: The original (Expr) integrand.
+    - x: The variable of integration.
+    - T: List of variables in the extension.
+    - D: List of derivations in the extension; corresponds to the elements of T.
+    - fa: Poly of the numerator of the integrand.
+    - fd: Poly of the denominator of the integrand.
+    - Tfuncs: Lambda() representations of each element of T (except for x).
+      For back-substitution after integration.
+    - backsubs: A (possibly empty) list of further substitutions to be made on
+      the final integral to make it look more like the integrand.
+    - exts:
+    - extargs:
+    - cases: List of string representations of the cases of T.
+    - t: The top level extension variable, as defined by the current level
+      (see level below).
+    - d: The top level extension derivation, as defined by the current
+      derivation (see level below).
+    - case: The string representation of the case of self.d.
+    (Note that self.T and self.D will always contain the complete extension,
+    regardless of the level.  Therefore, you should ALWAYS use DE.t and DE.d
+    instead of DE.T[-1] and DE.D[-1].  If you want to have a list of the
+    derivations or variables only up to the current level, use
+    DE.D[:len(DE.D) + DE.level + 1] and DE.T[:len(DE.T) + DE.level + 1].  Note
+    that, in particular, the derivation() function does this.)
+
+    The following are also attributes, but will probably not be useful other
+    than in internal use:
+    - newf: Expr form of fa/fd.
+    - level: The number (between -1 and -len(self.T)) such that
+      self.T[self.level] == self.t and self.D[self.level] == self.d.
+      Use the methods self.increment_level() and self.decrement_level() to change
+      the current level.
+    """
+    # __slots__ is defined mainly so we can iterate over all the attributes
+    # of the class easily (the memory use doesn't matter too much, since we
+    # only create one DifferentialExtension per integration).  Also, it's nice
+    # to have a safeguard when debugging.
+    __slots__ = ('f', 'x', 'T', 'D', 'fa', 'fd', 'Tfuncs', 'backsubs',
+        'exts', 'extargs', 'cases', 'case', 't', 'd', 'newf', 'level',
+        'ts', 'dummy')
+
+    def __init__(self, f=None, x=None, handle_first='log', dummy=False, extension=None, rewrite_complex=None):
+        """
+        Tries to build a transcendental extension tower from ``f`` with respect to ``x``.
+
+        Explanation
+        ===========
+
+        If it is successful, creates a DifferentialExtension object with, among
+        others, the attributes fa, fd, D, T, Tfuncs, and backsubs such that
+        fa and fd are Polys in T[-1] with rational coefficients in T[:-1],
+        fa/fd == f, and D[i] is a Poly in T[i] with rational coefficients in
+        T[:i] representing the derivative of T[i] for each i from 1 to len(T).
+        Tfuncs is a list of Lambda objects for back replacing the functions
+        after integrating.  Lambda() is only used (instead of lambda) to make
+        them easier to test and debug. Note that Tfuncs corresponds to the
+        elements of T, except for T[0] == x, but they should be back-substituted
+        in reverse order.  backsubs is a (possibly empty) back-substitution list
+        that should be applied on the completed integral to make it look more
+        like the original integrand.
+
+        If it is unsuccessful, it raises NotImplementedError.
+
+        You can also create an object by manually setting the attributes as a
+        dictionary to the extension keyword argument.  You must include at least
+        D.  Warning, any attribute that is not given will be set to None. The
+        attributes T, t, d, cases, case, x, and level are set automatically and
+        do not need to be given.  The functions in the Risch Algorithm will NOT
+        check to see if an attribute is None before using it.  This also does not
+        check to see if the extension is valid (non-algebraic) or even if it is
+        self-consistent.  Therefore, this should only be used for
+        testing/debugging purposes.
+        """
+        # XXX: If you need to debug this function, set the break point here
+
+        if extension:
+            if 'D' not in extension:
+                raise ValueError("At least the key D must be included with "
+                    "the extension flag to DifferentialExtension.")
+            for attr in extension:
+                setattr(self, attr, extension[attr])
+
+            self._auto_attrs()
+
+            return
+        elif f is None or x is None:
+            raise ValueError("Either both f and x or a manual extension must "
+            "be given.")
+
+        if handle_first not in ('log', 'exp'):
+            raise ValueError("handle_first must be 'log' or 'exp', not %s." %
+                str(handle_first))
+
+        # f will be the original function, self.f might change if we reset
+        # (e.g., we pull out a constant from an exponential)
+        self.f = f
+        self.x = x
+        # setting the default value 'dummy'
+        self.dummy = dummy
+        self.reset()
+        exp_new_extension, log_new_extension = True, True
+
+        # case of 'automatic' choosing
+        if rewrite_complex is None:
+            rewrite_complex = I in self.f.atoms()
+
+        if rewrite_complex:
+            rewritables = {
+                (sin, cos, cot, tan, sinh, cosh, coth, tanh): exp,
+                (asin, acos, acot, atan): log,
+            }
+            # rewrite the trigonometric components
+            for candidates, rule in rewritables.items():
+                self.newf = self.newf.rewrite(candidates, rule)
+            self.newf = cancel(self.newf)
+        else:
+            if any(i.has(x) for i in self.f.atoms(sin, cos, tan, atan, asin, acos)):
+                raise NotImplementedError("Trigonometric extensions are not "
+                "supported (yet!)")
+
+        exps = set()
+        pows = set()
+        numpows = set()
+        sympows = set()
+        logs = set()
+        symlogs = set()
+
+        while True:
+            if self.newf.is_rational_function(*self.T):
+                break
+
+            if not exp_new_extension and not log_new_extension:
+                # We couldn't find a new extension on the last pass, so I guess
+                # we can't do it.
+                raise NotImplementedError("Couldn't find an elementary "
+                    "transcendental extension for %s.  Try using a " % str(f) +
+                    "manual extension with the extension flag.")
+
+            exps, pows, numpows, sympows, log_new_extension = \
+                    self._rewrite_exps_pows(exps, pows, numpows, sympows, log_new_extension)
+
+            logs, symlogs = self._rewrite_logs(logs, symlogs)
+
+            if handle_first == 'exp' or not log_new_extension:
+                exp_new_extension = self._exp_part(exps)
+                if exp_new_extension is None:
+                    # reset and restart
+                    self.f = self.newf
+                    self.reset()
+                    exp_new_extension = True
+                    continue
+
+            if handle_first == 'log' or not exp_new_extension:
+                log_new_extension = self._log_part(logs)
+
+        self.fa, self.fd = frac_in(self.newf, self.t)
+        self._auto_attrs()
+
+        return
+
+    def __getattr__(self, attr):
+        # Avoid AttributeErrors when debugging
+        if attr not in self.__slots__:
+            raise AttributeError("%s has no attribute %s" % (repr(self), repr(attr)))
+        return None
+
+    def _rewrite_exps_pows(self, exps, pows, numpows,
+            sympows, log_new_extension):
+        """
+        Rewrite exps/pows for better processing.
+        """
+        from .prde import is_deriv_k
+
+        # Pre-preparsing.
+        #################
+        # Get all exp arguments, so we can avoid ahead of time doing
+        # something like t1 = exp(x), t2 = exp(x/2) == sqrt(t1).
+
+        # Things like sqrt(exp(x)) do not automatically simplify to
+        # exp(x/2), so they will be viewed as algebraic.  The easiest way
+        # to handle this is to convert all instances of exp(a)**Rational
+        # to exp(Rational*a) before doing anything else.  Note that the
+        # _exp_part code can generate terms of this form, so we do need to
+        # do this at each pass (or else modify it to not do that).
+
+        ratpows = [i for i in self.newf.atoms(Pow)
+                   if (isinstance(i.base, exp) and i.exp.is_Rational)]
+
+        ratpows_repl = [
+            (i, i.base.base**(i.exp*i.base.exp)) for i in ratpows]
+        self.backsubs += [(j, i) for i, j in ratpows_repl]
+        self.newf = self.newf.xreplace(dict(ratpows_repl))
+
+        # To make the process deterministic, the args are sorted
+        # so that functions with smaller op-counts are processed first.
+        # Ties are broken with the default_sort_key.
+
+        # XXX Although the method is deterministic no additional work
+        # has been done to guarantee that the simplest solution is
+        # returned and that it would be affected be using different
+        # variables. Though it is possible that this is the case
+        # one should know that it has not been done intentionally, so
+        # further improvements may be possible.
+
+        # TODO: This probably doesn't need to be completely recomputed at
+        # each pass.
+        exps = update_sets(exps, self.newf.atoms(exp),
+            lambda i: i.exp.is_rational_function(*self.T) and
+            i.exp.has(*self.T))
+        pows = update_sets(pows, self.newf.atoms(Pow),
+            lambda i: i.exp.is_rational_function(*self.T) and
+            i.exp.has(*self.T))
+        numpows = update_sets(numpows, set(pows),
+            lambda i: not i.base.has(*self.T))
+        sympows = update_sets(sympows, set(pows) - set(numpows),
+            lambda i: i.base.is_rational_function(*self.T) and
+            not i.exp.is_Integer)
+
+        # The easiest way to deal with non-base E powers is to convert them
+        # into base E, integrate, and then convert back.
+        for i in ordered(pows):
+            old = i
+            new = exp(i.exp*log(i.base))
+            # If exp is ever changed to automatically reduce exp(x*log(2))
+            # to 2**x, then this will break.  The solution is to not change
+            # exp to do that :)
+            if i in sympows:
+                if i.exp.is_Rational:
+                    raise NotImplementedError("Algebraic extensions are "
+                        "not supported (%s)." % str(i))
+                # We can add a**b only if log(a) in the extension, because
+                # a**b == exp(b*log(a)).
+                basea, based = frac_in(i.base, self.t)
+                A = is_deriv_k(basea, based, self)
+                if A is None:
+                    # Nonelementary monomial (so far)
+
+                    # TODO: Would there ever be any benefit from just
+                    # adding log(base) as a new monomial?
+                    # ANSWER: Yes, otherwise we can't integrate x**x (or
+                    # rather prove that it has no elementary integral)
+                    # without first manually rewriting it as exp(x*log(x))
+                    self.newf = self.newf.xreplace({old: new})
+                    self.backsubs += [(new, old)]
+                    log_new_extension = self._log_part([log(i.base)])
+                    exps = update_sets(exps, self.newf.atoms(exp), lambda i:
+                        i.exp.is_rational_function(*self.T) and i.exp.has(*self.T))
+                    continue
+                ans, u, const = A
+                newterm = exp(i.exp*(log(const) + u))
+                # Under the current implementation, exp kills terms
+                # only if they are of the form a*log(x), where a is a
+                # Number.  This case should have already been killed by the
+                # above tests.  Again, if this changes to kill more than
+                # that, this will break, which maybe is a sign that you
+                # shouldn't be changing that.  Actually, if anything, this
+                # auto-simplification should be removed.  See
+                # https://groups.google.com/group/sympy/browse_thread/thread/a61d48235f16867f
+
+                self.newf = self.newf.xreplace({i: newterm})
+
+            elif i not in numpows:
+                continue
+            else:
+                # i in numpows
+                newterm = new
+            # TODO: Just put it in self.Tfuncs
+            self.backsubs.append((new, old))
+            self.newf = self.newf.xreplace({old: newterm})
+            exps.append(newterm)
+
+        return exps, pows, numpows, sympows, log_new_extension
+
+    def _rewrite_logs(self, logs, symlogs):
+        """
+        Rewrite logs for better processing.
+        """
+        atoms = self.newf.atoms(log)
+        logs = update_sets(logs, atoms,
+            lambda i: i.args[0].is_rational_function(*self.T) and
+            i.args[0].has(*self.T))
+        symlogs = update_sets(symlogs, atoms,
+            lambda i: i.has(*self.T) and i.args[0].is_Pow and
+            i.args[0].base.is_rational_function(*self.T) and
+            not i.args[0].exp.is_Integer)
+
+        # We can handle things like log(x**y) by converting it to y*log(x)
+        # This will fix not only symbolic exponents of the argument, but any
+        # non-Integer exponent, like log(sqrt(x)).  The exponent can also
+        # depend on x, like log(x**x).
+        for i in ordered(symlogs):
+            # Unlike in the exponential case above, we do not ever
+            # potentially add new monomials (above we had to add log(a)).
+            # Therefore, there is no need to run any is_deriv functions
+            # here.  Just convert log(a**b) to b*log(a) and let
+            # log_new_extension() handle it from there.
+            lbase = log(i.args[0].base)
+            logs.append(lbase)
+            new = i.args[0].exp*lbase
+            self.newf = self.newf.xreplace({i: new})
+            self.backsubs.append((new, i))
+
+        # remove any duplicates
+        logs = sorted(set(logs), key=default_sort_key)
+
+        return logs, symlogs
+
+    def _auto_attrs(self):
+        """
+        Set attributes that are generated automatically.
+        """
+        if not self.T:
+            # i.e., when using the extension flag and T isn't given
+            self.T = [i.gen for i in self.D]
+        if not self.x:
+            self.x = self.T[0]
+        self.cases = [get_case(d, t) for d, t in zip(self.D, self.T)]
+        self.level = -1
+        self.t = self.T[self.level]
+        self.d = self.D[self.level]
+        self.case = self.cases[self.level]
+
+    def _exp_part(self, exps):
+        """
+        Try to build an exponential extension.
+
+        Returns
+        =======
+
+        Returns True if there was a new extension, False if there was no new
+        extension but it was able to rewrite the given exponentials in terms
+        of the existing extension, and None if the entire extension building
+        process should be restarted.  If the process fails because there is no
+        way around an algebraic extension (e.g., exp(log(x)/2)), it will raise
+        NotImplementedError.
+        """
+        from .prde import is_log_deriv_k_t_radical
+        new_extension = False
+        restart = False
+        expargs = [i.exp for i in exps]
+        ip = integer_powers(expargs)
+        for arg, others in ip:
+            # Minimize potential problems with algebraic substitution
+            others.sort(key=lambda i: i[1])
+
+            arga, argd = frac_in(arg, self.t)
+            A = is_log_deriv_k_t_radical(arga, argd, self)
+
+            if A is not None:
+                ans, u, n, const = A
+                # if n is 1 or -1, it's algebraic, but we can handle it
+                if n == -1:
+                    # This probably will never happen, because
+                    # Rational.as_numer_denom() returns the negative term in
+                    # the numerator.  But in case that changes, reduce it to
+                    # n == 1.
+                    n = 1
+                    u **= -1
+                    const *= -1
+                    ans = [(i, -j) for i, j in ans]
+
+                if n == 1:
+                    # Example: exp(x + x**2) over QQ(x, exp(x), exp(x**2))
+                    self.newf = self.newf.xreplace({exp(arg): exp(const)*Mul(*[
+                        u**power for u, power in ans])})
+                    self.newf = self.newf.xreplace({exp(p*exparg):
+                        exp(const*p) * Mul(*[u**power for u, power in ans])
+                        for exparg, p in others})
+                    # TODO: Add something to backsubs to put exp(const*p)
+                    # back together.
+
+                    continue
+
+                else:
+                    # Bad news: we have an algebraic radical.  But maybe we
+                    # could still avoid it by choosing a different extension.
+                    # For example, integer_powers() won't handle exp(x/2 + 1)
+                    # over QQ(x, exp(x)), but if we pull out the exp(1), it
+                    # will.  Or maybe we have exp(x + x**2/2), over
+                    # QQ(x, exp(x), exp(x**2)), which is exp(x)*sqrt(exp(x**2)),
+                    # but if we use QQ(x, exp(x), exp(x**2/2)), then they will
+                    # all work.
+                    #
+                    # So here is what we do: If there is a non-zero const, pull
+                    # it out and retry.  Also, if len(ans) > 1, then rewrite
+                    # exp(arg) as the product of exponentials from ans, and
+                    # retry that.  If const == 0 and len(ans) == 1, then we
+                    # assume that it would have been handled by either
+                    # integer_powers() or n == 1 above if it could be handled,
+                    # so we give up at that point.  For example, you can never
+                    # handle exp(log(x)/2) because it equals sqrt(x).
+
+                    if const or len(ans) > 1:
+                        rad = Mul(*[term**(power/n) for term, power in ans])
+                        self.newf = self.newf.xreplace({exp(p*exparg):
+                            exp(const*p)*rad for exparg, p in others})
+                        self.newf = self.newf.xreplace(dict(list(zip(reversed(self.T),
+                            reversed([f(self.x) for f in self.Tfuncs])))))
+                        restart = True
+                        break
+                    else:
+                        # TODO: give algebraic dependence in error string
+                        raise NotImplementedError("Cannot integrate over "
+                            "algebraic extensions.")
+
+            else:
+                arga, argd = frac_in(arg, self.t)
+                darga = (argd*derivation(Poly(arga, self.t), self) -
+                    arga*derivation(Poly(argd, self.t), self))
+                dargd = argd**2
+                darga, dargd = darga.cancel(dargd, include=True)
+                darg = darga.as_expr()/dargd.as_expr()
+                self.t = next(self.ts)
+                self.T.append(self.t)
+                self.extargs.append(arg)
+                self.exts.append('exp')
+                self.D.append(darg.as_poly(self.t, expand=False)*Poly(self.t,
+                    self.t, expand=False))
+                if self.dummy:
+                    i = Dummy("i")
+                else:
+                    i = Symbol('i')
+                self.Tfuncs += [Lambda(i, exp(arg.subs(self.x, i)))]
+                self.newf = self.newf.xreplace(
+                        {exp(exparg): self.t**p for exparg, p in others})
+                new_extension = True
+
+        if restart:
+            return None
+        return new_extension
+
+    def _log_part(self, logs):
+        """
+        Try to build a logarithmic extension.
+
+        Returns
+        =======
+
+        Returns True if there was a new extension and False if there was no new
+        extension but it was able to rewrite the given logarithms in terms
+        of the existing extension.  Unlike with exponential extensions, there
+        is no way that a logarithm is not transcendental over and cannot be
+        rewritten in terms of an already existing extension in a non-algebraic
+        way, so this function does not ever return None or raise
+        NotImplementedError.
+        """
+        from .prde import is_deriv_k
+        new_extension = False
+        logargs = [i.args[0] for i in logs]
+        for arg in ordered(logargs):
+            # The log case is easier, because whenever a logarithm is algebraic
+            # over the base field, it is of the form a1*t1 + ... an*tn + c,
+            # which is a polynomial, so we can just replace it with that.
+            # In other words, we don't have to worry about radicals.
+            arga, argd = frac_in(arg, self.t)
+            A = is_deriv_k(arga, argd, self)
+            if A is not None:
+                ans, u, const = A
+                newterm = log(const) + u
+                self.newf = self.newf.xreplace({log(arg): newterm})
+                continue
+
+            else:
+                arga, argd = frac_in(arg, self.t)
+                darga = (argd*derivation(Poly(arga, self.t), self) -
+                    arga*derivation(Poly(argd, self.t), self))
+                dargd = argd**2
+                darg = darga.as_expr()/dargd.as_expr()
+                self.t = next(self.ts)
+                self.T.append(self.t)
+                self.extargs.append(arg)
+                self.exts.append('log')
+                self.D.append(cancel(darg.as_expr()/arg).as_poly(self.t,
+                    expand=False))
+                if self.dummy:
+                    i = Dummy("i")
+                else:
+                    i = Symbol('i')
+                self.Tfuncs += [Lambda(i, log(arg.subs(self.x, i)))]
+                self.newf = self.newf.xreplace({log(arg): self.t})
+                new_extension = True
+
+        return new_extension
+
+    @property
+    def _important_attrs(self):
+        """
+        Returns some of the more important attributes of self.
+
+        Explanation
+        ===========
+
+        Used for testing and debugging purposes.
+
+        The attributes are (fa, fd, D, T, Tfuncs, backsubs,
+        exts, extargs).
+        """
+        return (self.fa, self.fd, self.D, self.T, self.Tfuncs,
+            self.backsubs, self.exts, self.extargs)
+
+    # NOTE: this printing doesn't follow the Python's standard
+    # eval(repr(DE)) == DE, where DE is the DifferentialExtension object,
+    # also this printing is supposed to contain all the important
+    # attributes of a DifferentialExtension object
+    def __repr__(self):
+        # no need to have GeneratorType object printed in it
+        r = [(attr, getattr(self, attr)) for attr in self.__slots__
+                if not isinstance(getattr(self, attr), GeneratorType)]
+        return self.__class__.__name__ + '(dict(%r))' % (r)
+
+    # fancy printing of DifferentialExtension object
+    def __str__(self):
+        return (self.__class__.__name__ + '({fa=%s, fd=%s, D=%s})' %
+                (self.fa, self.fd, self.D))
+
+    # should only be used for debugging purposes, internally
+    # f1 = f2 = log(x) at different places in code execution
+    # may return D1 != D2 as True, since 'level' or other attribute
+    # may differ
+    def __eq__(self, other):
+        for attr in self.__class__.__slots__:
+            d1, d2 = getattr(self, attr), getattr(other, attr)
+            if not (isinstance(d1, GeneratorType) or d1 == d2):
+                return False
+        return True
+
+    def reset(self):
+        """
+        Reset self to an initial state.  Used by __init__.
+        """
+        self.t = self.x
+        self.T = [self.x]
+        self.D = [Poly(1, self.x)]
+        self.level = -1
+        self.exts = [None]
+        self.extargs = [None]
+        if self.dummy:
+            self.ts = numbered_symbols('t', cls=Dummy)
+        else:
+            # For testing
+            self.ts = numbered_symbols('t')
+        # For various things that we change to make things work that we need to
+        # change back when we are done.
+        self.backsubs = []
+        self.Tfuncs = []
+        self.newf = self.f
+
+    def indices(self, extension):
+        """
+        Parameters
+        ==========
+
+        extension : str
+            Represents a valid extension type.
+
+        Returns
+        =======
+
+        list: A list of indices of 'exts' where extension of
+            type 'extension' is present.
+
+        Examples
+        ========
+
+        >>> from sympy.integrals.risch import DifferentialExtension
+        >>> from sympy import log, exp
+        >>> from sympy.abc import x
+        >>> DE = DifferentialExtension(log(x) + exp(x), x, handle_first='exp')
+        >>> DE.indices('log')
+        [2]
+        >>> DE.indices('exp')
+        [1]
+
+        """
+        return [i for i, ext in enumerate(self.exts) if ext == extension]
+
+    def increment_level(self):
+        """
+        Increment the level of self.
+
+        Explanation
+        ===========
+
+        This makes the working differential extension larger.  self.level is
+        given relative to the end of the list (-1, -2, etc.), so we do not need
+        do worry about it when building the extension.
+        """
+        if self.level >= -1:
+            raise ValueError("The level of the differential extension cannot "
+                "be incremented any further.")
+
+        self.level += 1
+        self.t = self.T[self.level]
+        self.d = self.D[self.level]
+        self.case = self.cases[self.level]
+        return None
+
+    def decrement_level(self):
+        """
+        Decrease the level of self.
+
+        Explanation
+        ===========
+
+        This makes the working differential extension smaller.  self.level is
+        given relative to the end of the list (-1, -2, etc.), so we do not need
+        do worry about it when building the extension.
+        """
+        if self.level <= -len(self.T):
+            raise ValueError("The level of the differential extension cannot "
+                "be decremented any further.")
+
+        self.level -= 1
+        self.t = self.T[self.level]
+        self.d = self.D[self.level]
+        self.case = self.cases[self.level]
+        return None
+
+
+def update_sets(seq, atoms, func):
+    s = set(seq)
+    s = atoms.intersection(s)
+    new = atoms - s
+    s.update(list(filter(func, new)))
+    return list(s)
+
+
+class DecrementLevel:
+    """
+    A context manager for decrementing the level of a DifferentialExtension.
+    """
+    __slots__ = ('DE',)
+
+    def __init__(self, DE):
+        self.DE = DE
+        return
+
+    def __enter__(self):
+        self.DE.decrement_level()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.DE.increment_level()
+
+
+class NonElementaryIntegralException(Exception):
+    """
+    Exception used by subroutines within the Risch algorithm to indicate to one
+    another that the function being integrated does not have an elementary
+    integral in the given differential field.
+    """
+    # TODO: Rewrite algorithms below to use this (?)
+
+    # TODO: Pass through information about why the integral was nonelementary,
+    # and store that in the resulting NonElementaryIntegral somehow.
+    pass
+
+
+def gcdex_diophantine(a, b, c):
+    """
+    Extended Euclidean Algorithm, Diophantine version.
+
+    Explanation
+    ===========
+
+    Given ``a``, ``b`` in K[x] and ``c`` in (a, b), the ideal generated by ``a`` and
+    ``b``, return (s, t) such that s*a + t*b == c and either s == 0 or s.degree()
+    < b.degree().
+    """
+    # Extended Euclidean Algorithm (Diophantine Version) pg. 13
+    # TODO: This should go in densetools.py.
+    # XXX: Bettter name?
+
+    s, g = a.half_gcdex(b)
+    s *= c.exquo(g)  # Inexact division means c is not in (a, b)
+    if s and s.degree() >= b.degree():
+        _, s = s.div(b)
+    t = (c - s*a).exquo(b)
+    return (s, t)
+
+
+def frac_in(f, t, *, cancel=False, **kwargs):
+    """
+    Returns the tuple (fa, fd), where fa and fd are Polys in t.
+
+    Explanation
+    ===========
+
+    This is a common idiom in the Risch Algorithm functions, so we abstract
+    it out here. ``f`` should be a basic expression, a Poly, or a tuple (fa, fd),
+    where fa and fd are either basic expressions or Polys, and f == fa/fd.
+    **kwargs are applied to Poly.
+    """
+    if isinstance(f, tuple):
+        fa, fd = f
+        f = fa.as_expr()/fd.as_expr()
+    fa, fd = f.as_expr().as_numer_denom()
+    fa, fd = fa.as_poly(t, **kwargs), fd.as_poly(t, **kwargs)
+    if cancel:
+        fa, fd = fa.cancel(fd, include=True)
+    if fa is None or fd is None:
+        raise ValueError("Could not turn %s into a fraction in %s." % (f, t))
+    return (fa, fd)
+
+
+def as_poly_1t(p, t, z):
+    """
+    (Hackish) way to convert an element ``p`` of K[t, 1/t] to K[t, z].
+
+    In other words, ``z == 1/t`` will be a dummy variable that Poly can handle
+    better.
+
+    See issue 5131.
+
+    Examples
+    ========
+
+    >>> from sympy import random_poly
+    >>> from sympy.integrals.risch import as_poly_1t
+    >>> from sympy.abc import x, z
+
+    >>> p1 = random_poly(x, 10, -10, 10)
+    >>> p2 = random_poly(x, 10, -10, 10)
+    >>> p = p1 + p2.subs(x, 1/x)
+    >>> as_poly_1t(p, x, z).as_expr().subs(z, 1/x) == p
+    True
+    """
+    # TODO: Use this on the final result.  That way, we can avoid answers like
+    # (...)*exp(-x).
+    pa, pd = frac_in(p, t, cancel=True)
+    if not pd.is_monomial:
+        # XXX: Is there a better Poly exception that we could raise here?
+        # Either way, if you see this (from the Risch Algorithm) it indicates
+        # a bug.
+        raise PolynomialError("%s is not an element of K[%s, 1/%s]." % (p, t, t))
+    d = pd.degree(t)
+    one_t_part = pa.slice(0, d + 1)
+    r = pd.degree() - pa.degree()
+    t_part = pa - one_t_part
+    try:
+        t_part = t_part.to_field().exquo(pd)
+    except DomainError as e:
+        # issue 4950
+        raise NotImplementedError(e)
+    # Compute the negative degree parts.
+    one_t_part = Poly.from_list(reversed(one_t_part.rep.rep), *one_t_part.gens,
+        domain=one_t_part.domain)
+    if 0 < r < oo:
+        one_t_part *= Poly(t**r, t)
+
+    one_t_part = one_t_part.replace(t, z)  # z will be 1/t
+    if pd.nth(d):
+        one_t_part *= Poly(1/pd.nth(d), z, expand=False)
+    ans = t_part.as_poly(t, z, expand=False) + one_t_part.as_poly(t, z,
+        expand=False)
+
+    return ans
+
+
+def derivation(p, DE, coefficientD=False, basic=False):
+    """
+    Computes Dp.
+
+    Explanation
+    ===========
+
+    Given the derivation D with D = d/dx and p is a polynomial in t over
+    K(x), return Dp.
+
+    If coefficientD is True, it computes the derivation kD
+    (kappaD), which is defined as kD(sum(ai*Xi**i, (i, 0, n))) ==
+    sum(Dai*Xi**i, (i, 1, n)) (Definition 3.2.2, page 80).  X in this case is
+    T[-1], so coefficientD computes the derivative just with respect to T[:-1],
+    with T[-1] treated as a constant.
+
+    If ``basic=True``, the returns a Basic expression.  Elements of D can still be
+    instances of Poly.
+    """
+    if basic:
+        r = 0
+    else:
+        r = Poly(0, DE.t)
+
+    t = DE.t
+    if coefficientD:
+        if DE.level <= -len(DE.T):
+            # 'base' case, the answer is 0.
+            return r
+        DE.decrement_level()
+
+    D = DE.D[:len(DE.D) + DE.level + 1]
+    T = DE.T[:len(DE.T) + DE.level + 1]
+
+    for d, v in zip(D, T):
+        pv = p.as_poly(v)
+        if pv is None or basic:
+            pv = p.as_expr()
+
+        if basic:
+            r += d.as_expr()*pv.diff(v)
+        else:
+            r += (d.as_expr()*pv.diff(v).as_expr()).as_poly(t)
+
+    if basic:
+        r = cancel(r)
+    if coefficientD:
+        DE.increment_level()
+
+    return r
+
+
+def get_case(d, t):
+    """
+    Returns the type of the derivation d.
+
+    Returns one of {'exp', 'tan', 'base', 'primitive', 'other_linear',
+    'other_nonlinear'}.
+    """
+    if not d.expr.has(t):
+        if d.is_one:
+            return 'base'
+        return 'primitive'
+    if d.rem(Poly(t, t)).is_zero:
+        return 'exp'
+    if d.rem(Poly(1 + t**2, t)).is_zero:
+        return 'tan'
+    if d.degree(t) > 1:
+        return 'other_nonlinear'
+    return 'other_linear'
+
+
+def splitfactor(p, DE, coefficientD=False, z=None):
+    """
+    Splitting factorization.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and ``p`` in k[t], return (p_n, p_s) in
+    k[t] x k[t] such that p = p_n*p_s, p_s is special, and each square
+    factor of p_n is normal.
+
+    Page. 100
+    """
+    kinv = [1/x for x in DE.T[:DE.level]]
+    if z:
+        kinv.append(z)
+
+    One = Poly(1, DE.t, domain=p.get_domain())
+    Dp = derivation(p, DE, coefficientD=coefficientD)
+    # XXX: Is this right?
+    if p.is_zero:
+        return (p, One)
+
+    if not p.expr.has(DE.t):
+        s = p.as_poly(*kinv).gcd(Dp.as_poly(*kinv)).as_poly(DE.t)
+        n = p.exquo(s)
+        return (n, s)
+
+    if not Dp.is_zero:
+        h = p.gcd(Dp).to_field()
+        g = p.gcd(p.diff(DE.t)).to_field()
+        s = h.exquo(g)
+
+        if s.degree(DE.t) == 0:
+            return (p, One)
+
+        q_split = splitfactor(p.exquo(s), DE, coefficientD=coefficientD)
+
+        return (q_split[0], q_split[1]*s)
+    else:
+        return (p, One)
+
+
+def splitfactor_sqf(p, DE, coefficientD=False, z=None, basic=False):
+    """
+    Splitting Square-free Factorization.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and ``p`` in k[t], returns (N1, ..., Nm)
+    and (S1, ..., Sm) in k[t]^m such that p =
+    (N1*N2**2*...*Nm**m)*(S1*S2**2*...*Sm**m) is a splitting
+    factorization of ``p`` and the Ni and Si are square-free and coprime.
+    """
+    # TODO: This algorithm appears to be faster in every case
+    # TODO: Verify this and splitfactor() for multiple extensions
+    kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level]
+    if z:
+        kkinv = [z]
+
+    S = []
+    N = []
+    p_sqf = p.sqf_list_include()
+    if p.is_zero:
+        return (((p, 1),), ())
+
+    for pi, i in p_sqf:
+        Si = pi.as_poly(*kkinv).gcd(derivation(pi, DE,
+            coefficientD=coefficientD,basic=basic).as_poly(*kkinv)).as_poly(DE.t)
+        pi = Poly(pi, DE.t)
+        Si = Poly(Si, DE.t)
+        Ni = pi.exquo(Si)
+        if not Si.is_one:
+            S.append((Si, i))
+        if not Ni.is_one:
+            N.append((Ni, i))
+
+    return (tuple(N), tuple(S))
+
+
+def canonical_representation(a, d, DE):
+    """
+    Canonical Representation.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k[t] and f = a/d in k(t), return (f_p, f_s,
+    f_n) in k[t] x k(t) x k(t) such that f = f_p + f_s + f_n is the
+    canonical representation of f (f_p is a polynomial, f_s is reduced
+    (has a special denominator), and f_n is simple (has a normal
+    denominator).
+    """
+    # Make d monic
+    l = Poly(1/d.LC(), DE.t)
+    a, d = a.mul(l), d.mul(l)
+
+    q, r = a.div(d)
+    dn, ds = splitfactor(d, DE)
+
+    b, c = gcdex_diophantine(dn.as_poly(DE.t), ds.as_poly(DE.t), r.as_poly(DE.t))
+    b, c = b.as_poly(DE.t), c.as_poly(DE.t)
+
+    return (q, (b, ds), (c, dn))
+
+
+def hermite_reduce(a, d, DE):
+    """
+    Hermite Reduction - Mack's Linear Version.
+
+    Given a derivation D on k(t) and f = a/d in k(t), returns g, h, r in
+    k(t) such that f = Dg + h + r, h is simple, and r is reduced.
+
+    """
+    # Make d monic
+    l = Poly(1/d.LC(), DE.t)
+    a, d = a.mul(l), d.mul(l)
+
+    fp, fs, fn = canonical_representation(a, d, DE)
+    a, d = fn
+    l = Poly(1/d.LC(), DE.t)
+    a, d = a.mul(l), d.mul(l)
+
+    ga = Poly(0, DE.t)
+    gd = Poly(1, DE.t)
+
+    dd = derivation(d, DE)
+    dm = gcd(d.to_field(), dd.to_field()).as_poly(DE.t)
+    ds, _ = d.div(dm)
+
+    while dm.degree(DE.t) > 0:
+
+        ddm = derivation(dm, DE)
+        dm2 = gcd(dm.to_field(), ddm.to_field())
+        dms, _ = dm.div(dm2)
+        ds_ddm = ds.mul(ddm)
+        ds_ddm_dm, _ = ds_ddm.div(dm)
+
+        b, c = gcdex_diophantine(-ds_ddm_dm.as_poly(DE.t),
+            dms.as_poly(DE.t), a.as_poly(DE.t))
+        b, c = b.as_poly(DE.t), c.as_poly(DE.t)
+
+        db = derivation(b, DE).as_poly(DE.t)
+        ds_dms, _ = ds.div(dms)
+        a = c.as_poly(DE.t) - db.mul(ds_dms).as_poly(DE.t)
+
+        ga = ga*dm + b*gd
+        gd = gd*dm
+        ga, gd = ga.cancel(gd, include=True)
+        dm = dm2
+
+    q, r = a.div(ds)
+    ga, gd = ga.cancel(gd, include=True)
+
+    r, d = r.cancel(ds, include=True)
+    rra = q*fs[1] + fp*fs[1] + fs[0]
+    rrd = fs[1]
+    rra, rrd = rra.cancel(rrd, include=True)
+
+    return ((ga, gd), (r, d), (rra, rrd))
+
+
+def polynomial_reduce(p, DE):
+    """
+    Polynomial Reduction.
+
+    Explanation
+    ===========
+
+    Given a derivation D on k(t) and p in k[t] where t is a nonlinear
+    monomial over k, return q, r in k[t] such that p = Dq  + r, and
+    deg(r) < deg_t(Dt).
+    """
+    q = Poly(0, DE.t)
+    while p.degree(DE.t) >= DE.d.degree(DE.t):
+        m = p.degree(DE.t) - DE.d.degree(DE.t) + 1
+        q0 = Poly(DE.t**m, DE.t).mul(Poly(p.as_poly(DE.t).LC()/
+            (m*DE.d.LC()), DE.t))
+        q += q0
+        p = p - derivation(q0, DE)
+
+    return (q, p)
+
+
+def laurent_series(a, d, F, n, DE):
+    """
+    Contribution of ``F`` to the full partial fraction decomposition of A/D.
+
+    Explanation
+    ===========
+
+    Given a field K of characteristic 0 and ``A``,``D``,``F`` in K[x] with D monic,
+    nonzero, coprime with A, and ``F`` the factor of multiplicity n in the square-
+    free factorization of D, return the principal parts of the Laurent series of
+    A/D at all the zeros of ``F``.
+    """
+    if F.degree()==0:
+        return 0
+    Z = _symbols('z', n)
+    z = Symbol('z')
+    Z.insert(0, z)
+    delta_a = Poly(0, DE.t)
+    delta_d = Poly(1, DE.t)
+
+    E = d.quo(F**n)
+    ha, hd = (a, E*Poly(z**n, DE.t))
+    dF = derivation(F,DE)
+    B, _ = gcdex_diophantine(E, F, Poly(1,DE.t))
+    C, _ = gcdex_diophantine(dF, F, Poly(1,DE.t))
+
+    # initialization
+    F_store = F
+    V, DE_D_list, H_list= [], [], []
+
+    for j in range(0, n):
+    # jth derivative of z would be substituted with dfnth/(j+1) where dfnth =(d^n)f/(dx)^n
+        F_store = derivation(F_store, DE)
+        v = (F_store.as_expr())/(j + 1)
+        V.append(v)
+        DE_D_list.append(Poly(Z[j + 1],Z[j]))
+
+    DE_new = DifferentialExtension(extension = {'D': DE_D_list}) #a differential indeterminate
+    for j in range(0, n):
+        zEha = Poly(z**(n + j), DE.t)*E**(j + 1)*ha
+        zEhd = hd
+        Pa, Pd = cancel((zEha, zEhd))[1], cancel((zEha, zEhd))[2]
+        Q = Pa.quo(Pd)
+        for i in range(0, j + 1):
+            Q = Q.subs(Z[i], V[i])
+        Dha = (hd*derivation(ha, DE, basic=True).as_poly(DE.t)
+             + ha*derivation(hd, DE, basic=True).as_poly(DE.t)
+             + hd*derivation(ha, DE_new, basic=True).as_poly(DE.t)
+             + ha*derivation(hd, DE_new, basic=True).as_poly(DE.t))
+        Dhd = Poly(j + 1, DE.t)*hd**2
+        ha, hd = Dha, Dhd
+
+        Ff, _ = F.div(gcd(F, Q))
+        F_stara, F_stard = frac_in(Ff, DE.t)
+        if F_stara.degree(DE.t) - F_stard.degree(DE.t) > 0:
+            QBC = Poly(Q, DE.t)*B**(1 + j)*C**(n + j)
+            H = QBC
+            H_list.append(H)
+            H = (QBC*F_stard).rem(F_stara)
+            alphas = real_roots(F_stara)
+            for alpha in list(alphas):
+                delta_a = delta_a*Poly((DE.t - alpha)**(n - j), DE.t) + Poly(H.eval(alpha), DE.t)
+                delta_d = delta_d*Poly((DE.t - alpha)**(n - j), DE.t)
+    return (delta_a, delta_d, H_list)
+
+
+def recognize_derivative(a, d, DE, z=None):
+    """
+    Compute the squarefree factorization of the denominator of f
+    and for each Di the polynomial H in K[x] (see Theorem 2.7.1), using the
+    LaurentSeries algorithm. Write Di = GiEi where Gj = gcd(Hn, Di) and
+    gcd(Ei,Hn) = 1. Since the residues of f at the roots of Gj are all 0, and
+    the residue of f at a root alpha of Ei is Hi(a) != 0, f is the derivative of a
+    rational function if and only if Ei = 1 for each i, which is equivalent to
+    Di | H[-1] for each i.
+    """
+    flag =True
+    a, d = a.cancel(d, include=True)
+    _, r = a.div(d)
+    Np, Sp = splitfactor_sqf(d, DE, coefficientD=True, z=z)
+
+    j = 1
+    for s, _ in Sp:
+        delta_a, delta_d, H = laurent_series(r, d, s, j, DE)
+        g = gcd(d, H[-1]).as_poly()
+        if g is not d:
+            flag = False
+            break
+        j = j + 1
+    return flag
+
+
+def recognize_log_derivative(a, d, DE, z=None):
+    """
+    There exists a v in K(x)* such that f = dv/v
+    where f a rational function if and only if f can be written as f = A/D
+    where D is squarefree,deg(A) < deg(D), gcd(A, D) = 1,
+    and all the roots of the Rothstein-Trager resultant are integers. In that case,
+    any of the Rothstein-Trager, Lazard-Rioboo-Trager or Czichowski algorithm
+    produces u in K(x) such that du/dx = uf.
+    """
+
+    z = z or Dummy('z')
+    a, d = a.cancel(d, include=True)
+    _, a = a.div(d)
+
+    pz = Poly(z, DE.t)
+    Dd = derivation(d, DE)
+    q = a - pz*Dd
+    r, _ = d.resultant(q, includePRS=True)
+    r = Poly(r, z)
+    Np, Sp = splitfactor_sqf(r, DE, coefficientD=True, z=z)
+
+    for s, _ in Sp:
+        # TODO also consider the complex roots which should
+        # turn the flag false
+        a = real_roots(s.as_poly(z))
+
+        if not all(j.is_Integer for j in a):
+            return False
+    return True
+
+def residue_reduce(a, d, DE, z=None, invert=True):
+    """
+    Lazard-Rioboo-Rothstein-Trager resultant reduction.
+
+    Explanation
+    ===========
+
+    Given a derivation ``D`` on k(t) and f in k(t) simple, return g
+    elementary over k(t) and a Boolean b in {True, False} such that f -
+    Dg in k[t] if b == True or f + h and f + h - Dg do not have an
+    elementary integral over k(t) for any h in k<t> (reduced) if b ==
+    False.
+
+    Returns (G, b), where G is a tuple of tuples of the form (s_i, S_i),
+    such that g = Add(*[RootSum(s_i, lambda z: z*log(S_i(z, t))) for
+    S_i, s_i in G]). f - Dg is the remaining integral, which is elementary
+    only if b == True, and hence the integral of f is elementary only if
+    b == True.
+
+    f - Dg is not calculated in this function because that would require
+    explicitly calculating the RootSum.  Use residue_reduce_derivation().
+    """
+    # TODO: Use log_to_atan() from rationaltools.py
+    # If r = residue_reduce(...), then the logarithmic part is given by:
+    # sum([RootSum(a[0].as_poly(z), lambda i: i*log(a[1].as_expr()).subs(z,
+    # i)).subs(t, log(x)) for a in r[0]])
+
+    z = z or Dummy('z')
+    a, d = a.cancel(d, include=True)
+    a, d = a.to_field().mul_ground(1/d.LC()), d.to_field().mul_ground(1/d.LC())
+    kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level]
+
+    if a.is_zero:
+        return ([], True)
+    _, a = a.div(d)
+
+    pz = Poly(z, DE.t)
+
+    Dd = derivation(d, DE)
+    q = a - pz*Dd
+
+    if Dd.degree(DE.t) <= d.degree(DE.t):
+        r, R = d.resultant(q, includePRS=True)
+    else:
+        r, R = q.resultant(d, includePRS=True)
+
+    R_map, H = {}, []
+    for i in R:
+        R_map[i.degree()] = i
+
+    r = Poly(r, z)
+    Np, Sp = splitfactor_sqf(r, DE, coefficientD=True, z=z)
+
+    for s, i in Sp:
+        if i == d.degree(DE.t):
+            s = Poly(s, z).monic()
+            H.append((s, d))
+        else:
+            h = R_map.get(i)
+            if h is None:
+                continue
+            h_lc = Poly(h.as_poly(DE.t).LC(), DE.t, field=True)
+
+            h_lc_sqf = h_lc.sqf_list_include(all=True)
+
+            for a, j in h_lc_sqf:
+                h = Poly(h, DE.t, field=True).exquo(Poly(gcd(a, s**j, *kkinv),
+                    DE.t))
+
+            s = Poly(s, z).monic()
+
+            if invert:
+                h_lc = Poly(h.as_poly(DE.t).LC(), DE.t, field=True, expand=False)
+                inv, coeffs = h_lc.as_poly(z, field=True).invert(s), [S.One]
+
+                for coeff in h.coeffs()[1:]:
+                    L = reduced(inv*coeff.as_poly(inv.gens), [s])[1]
+                    coeffs.append(L.as_expr())
+
+                h = Poly(dict(list(zip(h.monoms(), coeffs))), DE.t)
+
+            H.append((s, h))
+
+    b = not any(cancel(i.as_expr()).has(DE.t, z) for i, _ in Np)
+
+    return (H, b)
+
+
+def residue_reduce_to_basic(H, DE, z):
+    """
+    Converts the tuple returned by residue_reduce() into a Basic expression.
+    """
+    # TODO: check what Lambda does with RootOf
+    i = Dummy('i')
+    s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
+
+    return sum(RootSum(a[0].as_poly(z), Lambda(i, i*log(a[1].as_expr()).subs(
+        {z: i}).subs(s))) for a in H)
+
+
+def residue_reduce_derivation(H, DE, z):
+    """
+    Computes the derivation of an expression returned by residue_reduce().
+
+    In general, this is a rational function in t, so this returns an
+    as_expr() result.
+    """
+    # TODO: verify that this is correct for multiple extensions
+    i = Dummy('i')
+    return S(sum(RootSum(a[0].as_poly(z), Lambda(i, i*derivation(a[1],
+        DE).as_expr().subs(z, i)/a[1].as_expr().subs(z, i))) for a in H))
+
+
+def integrate_primitive_polynomial(p, DE):
+    """
+    Integration of primitive polynomials.
+
+    Explanation
+    ===========
+
+    Given a primitive monomial t over k, and ``p`` in k[t], return q in k[t],
+    r in k, and a bool b in {True, False} such that r = p - Dq is in k if b is
+    True, or r = p - Dq does not have an elementary integral over k(t) if b is
+    False.
+    """
+    Zero = Poly(0, DE.t)
+    q = Poly(0, DE.t)
+
+    if not p.expr.has(DE.t):
+        return (Zero, p, True)
+
+    from .prde import limited_integrate
+    while True:
+        if not p.expr.has(DE.t):
+            return (q, p, True)
+
+        Dta, Dtb = frac_in(DE.d, DE.T[DE.level - 1])
+
+        with DecrementLevel(DE):  # We had better be integrating the lowest extension (x)
+                                  # with ratint().
+            a = p.LC()
+            aa, ad = frac_in(a, DE.t)
+
+            try:
+                rv = limited_integrate(aa, ad, [(Dta, Dtb)], DE)
+                if rv is None:
+                    raise NonElementaryIntegralException
+                (ba, bd), c = rv
+            except NonElementaryIntegralException:
+                return (q, p, False)
+
+        m = p.degree(DE.t)
+        q0 = c[0].as_poly(DE.t)*Poly(DE.t**(m + 1)/(m + 1), DE.t) + \
+            (ba.as_expr()/bd.as_expr()).as_poly(DE.t)*Poly(DE.t**m, DE.t)
+
+        p = p - derivation(q0, DE)
+        q = q + q0
+
+
+def integrate_primitive(a, d, DE, z=None):
+    """
+    Integration of primitive functions.
+
+    Explanation
+    ===========
+
+    Given a primitive monomial t over k and f in k(t), return g elementary over
+    k(t), i in k(t), and b in {True, False} such that i = f - Dg is in k if b
+    is True or i = f - Dg does not have an elementary integral over k(t) if b
+    is False.
+
+    This function returns a Basic expression for the first argument.  If b is
+    True, the second argument is Basic expression in k to recursively integrate.
+    If b is False, the second argument is an unevaluated Integral, which has
+    been proven to be nonelementary.
+    """
+    # XXX: a and d must be canceled, or this might return incorrect results
+    z = z or Dummy("z")
+    s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
+
+    g1, h, r = hermite_reduce(a, d, DE)
+    g2, b = residue_reduce(h[0], h[1], DE, z=z)
+    if not b:
+        i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) -
+            g1[0]*derivation(g1[1], DE)).as_expr()/(g1[1]**2).as_expr() -
+            residue_reduce_derivation(g2, DE, z))
+        i = NonElementaryIntegral(cancel(i).subs(s), DE.x)
+        return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
+            residue_reduce_to_basic(g2, DE, z), i, b)
+
+    # h - Dg2 + r
+    p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
+        DE, z) + r[0].as_expr()/r[1].as_expr())
+    p = p.as_poly(DE.t)
+
+    q, i, b = integrate_primitive_polynomial(p, DE)
+
+    ret = ((g1[0].as_expr()/g1[1].as_expr() + q.as_expr()).subs(s) +
+        residue_reduce_to_basic(g2, DE, z))
+    if not b:
+        # TODO: This does not do the right thing when b is False
+        i = NonElementaryIntegral(cancel(i.as_expr()).subs(s), DE.x)
+    else:
+        i = cancel(i.as_expr())
+
+    return (ret, i, b)
+
+
+def integrate_hyperexponential_polynomial(p, DE, z):
+    """
+    Integration of hyperexponential polynomials.
+
+    Explanation
+    ===========
+
+    Given a hyperexponential monomial t over k and ``p`` in k[t, 1/t], return q in
+    k[t, 1/t] and a bool b in {True, False} such that p - Dq in k if b is True,
+    or p - Dq does not have an elementary integral over k(t) if b is False.
+    """
+    t1 = DE.t
+    dtt = DE.d.exquo(Poly(DE.t, DE.t))
+    qa = Poly(0, DE.t)
+    qd = Poly(1, DE.t)
+    b = True
+
+    if p.is_zero:
+        return(qa, qd, b)
+
+    from sympy.integrals.rde import rischDE
+
+    with DecrementLevel(DE):
+        for i in range(-p.degree(z), p.degree(t1) + 1):
+            if not i:
+                continue
+            elif i < 0:
+                # If you get AttributeError: 'NoneType' object has no attribute 'nth'
+                # then this should really not have expand=False
+                # But it shouldn't happen because p is already a Poly in t and z
+                a = p.as_poly(z, expand=False).nth(-i)
+            else:
+                # If you get AttributeError: 'NoneType' object has no attribute 'nth'
+                # then this should really not have expand=False
+                a = p.as_poly(t1, expand=False).nth(i)
+
+            aa, ad = frac_in(a, DE.t, field=True)
+            aa, ad = aa.cancel(ad, include=True)
+            iDt = Poly(i, t1)*dtt
+            iDta, iDtd = frac_in(iDt, DE.t, field=True)
+            try:
+                va, vd = rischDE(iDta, iDtd, Poly(aa, DE.t), Poly(ad, DE.t), DE)
+                va, vd = frac_in((va, vd), t1, cancel=True)
+            except NonElementaryIntegralException:
+                b = False
+            else:
+                qa = qa*vd + va*Poly(t1**i)*qd
+                qd *= vd
+
+    return (qa, qd, b)
+
+
+def integrate_hyperexponential(a, d, DE, z=None, conds='piecewise'):
+    """
+    Integration of hyperexponential functions.
+
+    Explanation
+    ===========
+
+    Given a hyperexponential monomial t over k and f in k(t), return g
+    elementary over k(t), i in k(t), and a bool b in {True, False} such that
+    i = f - Dg is in k if b is True or i = f - Dg does not have an elementary
+    integral over k(t) if b is False.
+
+    This function returns a Basic expression for the first argument.  If b is
+    True, the second argument is Basic expression in k to recursively integrate.
+    If b is False, the second argument is an unevaluated Integral, which has
+    been proven to be nonelementary.
+    """
+    # XXX: a and d must be canceled, or this might return incorrect results
+    z = z or Dummy("z")
+    s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
+
+    g1, h, r = hermite_reduce(a, d, DE)
+    g2, b = residue_reduce(h[0], h[1], DE, z=z)
+    if not b:
+        i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) -
+            g1[0]*derivation(g1[1], DE)).as_expr()/(g1[1]**2).as_expr() -
+            residue_reduce_derivation(g2, DE, z))
+        i = NonElementaryIntegral(cancel(i.subs(s)), DE.x)
+        return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
+            residue_reduce_to_basic(g2, DE, z), i, b)
+
+    # p should be a polynomial in t and 1/t, because Sirr == k[t, 1/t]
+    # h - Dg2 + r
+    p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
+        DE, z) + r[0].as_expr()/r[1].as_expr())
+    pp = as_poly_1t(p, DE.t, z)
+
+    qa, qd, b = integrate_hyperexponential_polynomial(pp, DE, z)
+
+    i = pp.nth(0, 0)
+
+    ret = ((g1[0].as_expr()/g1[1].as_expr()).subs(s) \
+        + residue_reduce_to_basic(g2, DE, z))
+
+    qas = qa.as_expr().subs(s)
+    qds = qd.as_expr().subs(s)
+    if conds == 'piecewise' and DE.x not in qds.free_symbols:
+        # We have to be careful if the exponent is S.Zero!
+
+        # XXX: Does qd = 0 always necessarily correspond to the exponential
+        # equaling 1?
+        ret += Piecewise(
+                (qas/qds, Ne(qds, 0)),
+                (integrate((p - i).subs(DE.t, 1).subs(s), DE.x), True)
+            )
+    else:
+        ret += qas/qds
+
+    if not b:
+        i = p - (qd*derivation(qa, DE) - qa*derivation(qd, DE)).as_expr()/\
+            (qd**2).as_expr()
+        i = NonElementaryIntegral(cancel(i).subs(s), DE.x)
+    return (ret, i, b)
+
+
+def integrate_hypertangent_polynomial(p, DE):
+    """
+    Integration of hypertangent polynomials.
+
+    Explanation
+    ===========
+
+    Given a differential field k such that sqrt(-1) is not in k, a
+    hypertangent monomial t over k, and p in k[t], return q in k[t] and
+    c in k such that p - Dq - c*D(t**2 + 1)/(t**1 + 1) is in k and p -
+    Dq does not have an elementary integral over k(t) if Dc != 0.
+    """
+    # XXX: Make sure that sqrt(-1) is not in k.
+    q, r = polynomial_reduce(p, DE)
+    a = DE.d.exquo(Poly(DE.t**2 + 1, DE.t))
+    c = Poly(r.nth(1)/(2*a.as_expr()), DE.t)
+    return (q, c)
+
+
+def integrate_nonlinear_no_specials(a, d, DE, z=None):
+    """
+    Integration of nonlinear monomials with no specials.
+
+    Explanation
+    ===========
+
+    Given a nonlinear monomial t over k such that Sirr ({p in k[t] | p is
+    special, monic, and irreducible}) is empty, and f in k(t), returns g
+    elementary over k(t) and a Boolean b in {True, False} such that f - Dg is
+    in k if b == True, or f - Dg does not have an elementary integral over k(t)
+    if b == False.
+
+    This function is applicable to all nonlinear extensions, but in the case
+    where it returns b == False, it will only have proven that the integral of
+    f - Dg is nonelementary if Sirr is empty.
+
+    This function returns a Basic expression.
+    """
+    # TODO: Integral from k?
+    # TODO: split out nonelementary integral
+    # XXX: a and d must be canceled, or this might not return correct results
+    z = z or Dummy("z")
+    s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
+
+    g1, h, r = hermite_reduce(a, d, DE)
+    g2, b = residue_reduce(h[0], h[1], DE, z=z)
+    if not b:
+        return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
+            residue_reduce_to_basic(g2, DE, z), b)
+
+    # Because f has no specials, this should be a polynomial in t, or else
+    # there is a bug.
+    p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
+        DE, z).as_expr() + r[0].as_expr()/r[1].as_expr()).as_poly(DE.t)
+    q1, q2 = polynomial_reduce(p, DE)
+
+    if q2.expr.has(DE.t):
+        b = False
+    else:
+        b = True
+
+    ret = (cancel(g1[0].as_expr()/g1[1].as_expr() + q1.as_expr()).subs(s) +
+        residue_reduce_to_basic(g2, DE, z))
+    return (ret, b)
+
+
+class NonElementaryIntegral(Integral):
+    """
+    Represents a nonelementary Integral.
+
+    Explanation
+    ===========
+
+    If the result of integrate() is an instance of this class, it is
+    guaranteed to be nonelementary.  Note that integrate() by default will try
+    to find any closed-form solution, even in terms of special functions which
+    may themselves not be elementary.  To make integrate() only give
+    elementary solutions, or, in the cases where it can prove the integral to
+    be nonelementary, instances of this class, use integrate(risch=True).
+    In this case, integrate() may raise NotImplementedError if it cannot make
+    such a determination.
+
+    integrate() uses the deterministic Risch algorithm to integrate elementary
+    functions or prove that they have no elementary integral.  In some cases,
+    this algorithm can split an integral into an elementary and nonelementary
+    part, so that the result of integrate will be the sum of an elementary
+    expression and a NonElementaryIntegral.
+
+    Examples
+    ========
+
+    >>> from sympy import integrate, exp, log, Integral
+    >>> from sympy.abc import x
+
+    >>> a = integrate(exp(-x**2), x, risch=True)
+    >>> print(a)
+    Integral(exp(-x**2), x)
+    >>> type(a)
+    <class 'sympy.integrals.risch.NonElementaryIntegral'>
+
+    >>> expr = (2*log(x)**2 - log(x) - x**2)/(log(x)**3 - x**2*log(x))
+    >>> b = integrate(expr, x, risch=True)
+    >>> print(b)
+    -log(-x + log(x))/2 + log(x + log(x))/2 + Integral(1/log(x), x)
+    >>> type(b.atoms(Integral).pop())
+    <class 'sympy.integrals.risch.NonElementaryIntegral'>
+
+    """
+    # TODO: This is useful in and of itself, because isinstance(result,
+    # NonElementaryIntegral) will tell if the integral has been proven to be
+    # elementary. But should we do more?  Perhaps a no-op .doit() if
+    # elementary=True?  Or maybe some information on why the integral is
+    # nonelementary.
+    pass
+
+
+def risch_integrate(f, x, extension=None, handle_first='log',
+                    separate_integral=False, rewrite_complex=None,
+                    conds='piecewise'):
+    r"""
+    The Risch Integration Algorithm.
+
+    Explanation
+    ===========
+
+    Only transcendental functions are supported.  Currently, only exponentials
+    and logarithms are supported, but support for trigonometric functions is
+    forthcoming.
+
+    If this function returns an unevaluated Integral in the result, it means
+    that it has proven that integral to be nonelementary.  Any errors will
+    result in raising NotImplementedError.  The unevaluated Integral will be
+    an instance of NonElementaryIntegral, a subclass of Integral.
+
+    handle_first may be either 'exp' or 'log'.  This changes the order in
+    which the extension is built, and may result in a different (but
+    equivalent) solution (for an example of this, see issue 5109).  It is also
+    possible that the integral may be computed with one but not the other,
+    because not all cases have been implemented yet.  It defaults to 'log' so
+    that the outer extension is exponential when possible, because more of the
+    exponential case has been implemented.
+
+    If ``separate_integral`` is ``True``, the result is returned as a tuple (ans, i),
+    where the integral is ans + i, ans is elementary, and i is either a
+    NonElementaryIntegral or 0.  This useful if you want to try further
+    integrating the NonElementaryIntegral part using other algorithms to
+    possibly get a solution in terms of special functions.  It is False by
+    default.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.risch import risch_integrate
+    >>> from sympy import exp, log, pprint
+    >>> from sympy.abc import x
+
+    First, we try integrating exp(-x**2). Except for a constant factor of
+    2/sqrt(pi), this is the famous error function.
+
+    >>> pprint(risch_integrate(exp(-x**2), x))
+      /
+     |
+     |    2
+     |  -x
+     | e    dx
+     |
+    /
+
+    The unevaluated Integral in the result means that risch_integrate() has
+    proven that exp(-x**2) does not have an elementary anti-derivative.
+
+    In many cases, risch_integrate() can split out the elementary
+    anti-derivative part from the nonelementary anti-derivative part.
+    For example,
+
+    >>> pprint(risch_integrate((2*log(x)**2 - log(x) - x**2)/(log(x)**3 -
+    ... x**2*log(x)), x))
+                                             /
+                                            |
+      log(-x + log(x))   log(x + log(x))    |   1
+    - ---------------- + --------------- +  | ------ dx
+             2                  2           | log(x)
+                                            |
+                                           /
+
+    This means that it has proven that the integral of 1/log(x) is
+    nonelementary.  This function is also known as the logarithmic integral,
+    and is often denoted as Li(x).
+
+    risch_integrate() currently only accepts purely transcendental functions
+    with exponentials and logarithms, though note that this can include
+    nested exponentials and logarithms, as well as exponentials with bases
+    other than E.
+
+    >>> pprint(risch_integrate(exp(x)*exp(exp(x)), x))
+     / x\
+     \e /
+    e
+    >>> pprint(risch_integrate(exp(exp(x)), x))
+      /
+     |
+     |  / x\
+     |  \e /
+     | e     dx
+     |
+    /
+
+    >>> pprint(risch_integrate(x*x**x*log(x) + x**x + x*x**x, x))
+       x
+    x*x
+    >>> pprint(risch_integrate(x**x, x))
+      /
+     |
+     |  x
+     | x  dx
+     |
+    /
+
+    >>> pprint(risch_integrate(-1/(x*log(x)*log(log(x))**2), x))
+         1
+    -----------
+    log(log(x))
+
+    """
+    f = S(f)
+
+    DE = extension or DifferentialExtension(f, x, handle_first=handle_first,
+            dummy=True, rewrite_complex=rewrite_complex)
+    fa, fd = DE.fa, DE.fd
+
+    result = S.Zero
+    for case in reversed(DE.cases):
+        if not fa.expr.has(DE.t) and not fd.expr.has(DE.t) and not case == 'base':
+            DE.decrement_level()
+            fa, fd = frac_in((fa, fd), DE.t)
+            continue
+
+        fa, fd = fa.cancel(fd, include=True)
+        if case == 'exp':
+            ans, i, b = integrate_hyperexponential(fa, fd, DE, conds=conds)
+        elif case == 'primitive':
+            ans, i, b = integrate_primitive(fa, fd, DE)
+        elif case == 'base':
+            # XXX: We can't call ratint() directly here because it doesn't
+            # handle polynomials correctly.
+            ans = integrate(fa.as_expr()/fd.as_expr(), DE.x, risch=False)
+            b = False
+            i = S.Zero
+        else:
+            raise NotImplementedError("Only exponential and logarithmic "
+            "extensions are currently supported.")
+
+        result += ans
+        if b:
+            DE.decrement_level()
+            fa, fd = frac_in(i, DE.t)
+        else:
+            result = result.subs(DE.backsubs)
+            if not i.is_zero:
+                i = NonElementaryIntegral(i.function.subs(DE.backsubs),i.limits)
+            if not separate_integral:
+                result += i
+                return result
+            else:
+
+                if isinstance(i, NonElementaryIntegral):
+                    return (result, i)
+                else:
+                    return (result, 0)

venv/lib/python3.10/site-packages/sympy/integrals/singularityfunctions.py

@@ -0,0 +1,63 @@
+from sympy.functions import SingularityFunction, DiracDelta
+from sympy.integrals import integrate
+
+
+def singularityintegrate(f, x):
+    """
+    This function handles the indefinite integrations of Singularity functions.
+    The ``integrate`` function calls this function internally whenever an
+    instance of SingularityFunction is passed as argument.
+
+    Explanation
+    ===========
+
+    The idea for integration is the following:
+
+    - If we are dealing with a SingularityFunction expression,
+      i.e. ``SingularityFunction(x, a, n)``, we just return
+      ``SingularityFunction(x, a, n + 1)/(n + 1)`` if ``n >= 0`` and
+      ``SingularityFunction(x, a, n + 1)`` if ``n < 0``.
+
+    - If the node is a multiplication or power node having a
+      SingularityFunction term we rewrite the whole expression in terms of
+      Heaviside and DiracDelta and then integrate the output. Lastly, we
+      rewrite the output of integration back in terms of SingularityFunction.
+
+    - If none of the above case arises, we return None.
+
+    Examples
+    ========
+
+    >>> from sympy.integrals.singularityfunctions import singularityintegrate
+    >>> from sympy import SingularityFunction, symbols, Function
+    >>> x, a, n, y = symbols('x a n y')
+    >>> f = Function('f')
+    >>> singularityintegrate(SingularityFunction(x, a, 3), x)
+    SingularityFunction(x, a, 4)/4
+    >>> singularityintegrate(5*SingularityFunction(x, 5, -2), x)
+    5*SingularityFunction(x, 5, -1)
+    >>> singularityintegrate(6*SingularityFunction(x, 5, -1), x)
+    6*SingularityFunction(x, 5, 0)
+    >>> singularityintegrate(x*SingularityFunction(x, 0, -1), x)
+    0
+    >>> singularityintegrate(SingularityFunction(x, 1, -1) * f(x), x)
+    f(1)*SingularityFunction(x, 1, 0)
+
+    """
+
+    if not f.has(SingularityFunction):
+        return None
+
+    if isinstance(f, SingularityFunction):
+        x, a, n = f.args
+        if n.is_positive or n.is_zero:
+            return SingularityFunction(x, a, n + 1)/(n + 1)
+        elif n in (-1, -2):
+            return SingularityFunction(x, a, n + 1)
+
+    if f.is_Mul or f.is_Pow:
+
+        expr = f.rewrite(DiracDelta)
+        expr = integrate(expr, x)
+        return expr.rewrite(SingularityFunction)
+    return None