Example #1
0
def test_Function():
    assert mcode(f(x, y, z)) == "f[x, y, z]"
    assert mcode(sin(x)**cos(x)) == "Sin[x]^Cos[x]"
    assert mcode(conjugate(x)) == "Conjugate[x]"
    assert mcode(Max(x, y, z) * Min(y, z)) == "Max[x, y, z]*Min[y, z]"
    assert mcode(fresnelc(x)) == "FresnelC[x]"
    assert mcode(fresnels(x)) == "FresnelS[x]"
    assert mcode(gamma(x)) == "Gamma[x]"
    assert mcode(uppergamma(x, y)) == "Gamma[x, y]"
    assert mcode(polygamma(x, y)) == "PolyGamma[x, y]"
    assert mcode(loggamma(x)) == "LogGamma[x]"
    assert mcode(erf(x)) == "Erf[x]"
    assert mcode(erfc(x)) == "Erfc[x]"
    assert mcode(erfi(x)) == "Erfi[x]"
    assert mcode(erf2(x, y)) == "Erf[x, y]"
    assert mcode(expint(x, y)) == "ExpIntegralE[x, y]"
    assert mcode(erfcinv(x)) == "InverseErfc[x]"
    assert mcode(erfinv(x)) == "InverseErf[x]"
    assert mcode(erf2inv(x, y)) == "InverseErf[x, y]"
    assert mcode(Ei(x)) == "ExpIntegralEi[x]"
    assert mcode(Ci(x)) == "CosIntegral[x]"
    assert mcode(li(x)) == "LogIntegral[x]"
    assert mcode(Si(x)) == "SinIntegral[x]"
    assert mcode(Shi(x)) == "SinhIntegral[x]"
    assert mcode(Chi(x)) == "CoshIntegral[x]"
    assert mcode(beta(x, y)) == "Beta[x, y]"
    assert mcode(factorial(x)) == "Factorial[x]"
    assert mcode(factorial2(x)) == "Factorial2[x]"
    assert mcode(subfactorial(x)) == "Subfactorial[x]"
    assert mcode(FallingFactorial(x, y)) == "FactorialPower[x, y]"
    assert mcode(RisingFactorial(x, y)) == "Pochhammer[x, y]"
    assert mcode(catalan(x)) == "CatalanNumber[x]"
    assert mcode(harmonic(x)) == "HarmonicNumber[x]"
    assert mcode(harmonic(x, y)) == "HarmonicNumber[x, y]"
Example #2
0
def test_normal():
    Float.store()
    Float.setdps(20)
    N = Normal(0, 1)
    assert N.mean == 0
    assert N.variance == 1
    assert N.probability(-1, 1) == erf(1/sqrt(2))
    assert N.probability(-1, 0) == erf(1/sqrt(2))/2
    N = Normal(2, 4)
    assert N.mean == 2
    assert N.variance == 16
    assert N.confidence(1) == (-oo, oo)
    assert N.probability(1, 3) == erf(1/sqrt(32))
    for p in [0.1, 0.3, 0.7, 0.9, 0.995]:
        a, b = N.confidence(p)
        assert operator.abs(float(N.probability(a, b).evalf()) - p) < 1e-10
    Float.revert()
Example #3
0
def test_C99CodePrinter__precision():
    n = symbols('n', integer=True)
    f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
    f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
    f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
    assert f32_printer.doprint(sin(x+2.1)) == 'sinf(x + 2.1F)'
    assert f64_printer.doprint(sin(x+2.1)) == 'sin(x + 2.1000000000000001)'
    assert f80_printer.doprint(sin(x+Float('2.0'))) == 'sinl(x + 2.0L)'

    for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ['f', '', 'l']):
        def check(expr, ref):
            assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())
        check(Abs(n), 'abs(n)')
        check(Abs(x + 2.0), 'fabs{s}(x + 2.0{S})')
        check(sin(x + 4.0)**cos(x - 2.0), 'pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))')
        check(exp(x*8.0), 'exp{s}(8.0{S}*x)')
        check(exp2(x), 'exp2{s}(x)')
        check(expm1(x*4.0), 'expm1{s}(4.0{S}*x)')
        check(Mod(n, 2), '((n) % (2))')
        check(Mod(2*n + 3, 3*n + 5), '((2*n + 3) % (3*n + 5))')
        check(Mod(x + 2.0, 3.0), 'fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})')
        check(Mod(x, 2.0*x + 3.0), 'fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})')
        check(log(x/2), 'log{s}((1.0{S}/2.0{S})*x)')
        check(log10(3*x/2), 'log10{s}((3.0{S}/2.0{S})*x)')
        check(log2(x*8.0), 'log2{s}(8.0{S}*x)')
        check(log1p(x), 'log1p{s}(x)')
        check(2**x, 'pow{s}(2, x)')
        check(2.0**x, 'pow{s}(2.0{S}, x)')
        check(x**3, 'pow{s}(x, 3)')
        check(x**4.0, 'pow{s}(x, 4.0{S})')
        check(sqrt(3+x), 'sqrt{s}(x + 3)')
        check(Cbrt(x-2.0), 'cbrt{s}(x - 2.0{S})')
        check(hypot(x, y), 'hypot{s}(x, y)')
        check(sin(3.*x + 2.), 'sin{s}(3.0{S}*x + 2.0{S})')
        check(cos(3.*x - 1.), 'cos{s}(3.0{S}*x - 1.0{S})')
        check(tan(4.*y + 2.), 'tan{s}(4.0{S}*y + 2.0{S})')
        check(asin(3.*x + 2.), 'asin{s}(3.0{S}*x + 2.0{S})')
        check(acos(3.*x + 2.), 'acos{s}(3.0{S}*x + 2.0{S})')
        check(atan(3.*x + 2.), 'atan{s}(3.0{S}*x + 2.0{S})')
        check(atan2(3.*x, 2.*y), 'atan2{s}(3.0{S}*x, 2.0{S}*y)')

        check(sinh(3.*x + 2.), 'sinh{s}(3.0{S}*x + 2.0{S})')
        check(cosh(3.*x - 1.), 'cosh{s}(3.0{S}*x - 1.0{S})')
        check(tanh(4.0*y + 2.), 'tanh{s}(4.0{S}*y + 2.0{S})')
        check(asinh(3.*x + 2.), 'asinh{s}(3.0{S}*x + 2.0{S})')
        check(acosh(3.*x + 2.), 'acosh{s}(3.0{S}*x + 2.0{S})')
        check(atanh(3.*x + 2.), 'atanh{s}(3.0{S}*x + 2.0{S})')
        check(erf(42.*x), 'erf{s}(42.0{S}*x)')
        check(erfc(42.*x), 'erfc{s}(42.0{S}*x)')
        check(gamma(x), 'tgamma{s}(x)')
        check(loggamma(x), 'lgamma{s}(x)')

        check(ceiling(x + 2.), "ceil{s}(x + 2.0{S})")
        check(floor(x + 2.), "floor{s}(x + 2.0{S})")
        check(fma(x, y, -z), 'fma{s}(x, y, -z)')
        check(Max(x, 8.0, x**4.0), 'fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))')
        check(Min(x, 2.0), 'fmin{s}(2.0{S}, x)')
Example #4
0
def test_C99CodePrinter__precision():
    n = symbols('n', integer=True)
    f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
    f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
    f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
    assert f32_printer.doprint(sin(x+2.1)) == 'sinf(x + 2.1F)'
    assert f64_printer.doprint(sin(x+2.1)) == 'sin(x + 2.1000000000000001)'
    assert f80_printer.doprint(sin(x+Float('2.0'))) == 'sinl(x + 2.0L)'

    for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ['f', '', 'l']):
        def check(expr, ref):
            assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())
        check(Abs(n), 'abs(n)')
        check(Abs(x + 2.0), 'fabs{s}(x + 2.0{S})')
        check(sin(x + 4.0)**cos(x - 2.0), 'pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))')
        check(exp(x*8.0), 'exp{s}(8.0{S}*x)')
        check(exp2(x), 'exp2{s}(x)')
        check(expm1(x*4.0), 'expm1{s}(4.0{S}*x)')
        check(Mod(n, 2), '((n) % (2))')
        check(Mod(2*n + 3, 3*n + 5), '((2*n + 3) % (3*n + 5))')
        check(Mod(x + 2.0, 3.0), 'fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})')
        check(Mod(x, 2.0*x + 3.0), 'fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})')
        check(log(x/2), 'log{s}((1.0{S}/2.0{S})*x)')
        check(log10(3*x/2), 'log10{s}((3.0{S}/2.0{S})*x)')
        check(log2(x*8.0), 'log2{s}(8.0{S}*x)')
        check(log1p(x), 'log1p{s}(x)')
        check(2**x, 'pow{s}(2, x)')
        check(2.0**x, 'pow{s}(2.0{S}, x)')
        check(x**3, 'pow{s}(x, 3)')
        check(x**4.0, 'pow{s}(x, 4.0{S})')
        check(sqrt(3+x), 'sqrt{s}(x + 3)')
        check(Cbrt(x-2.0), 'cbrt{s}(x - 2.0{S})')
        check(hypot(x, y), 'hypot{s}(x, y)')
        check(sin(3.*x + 2.), 'sin{s}(3.0{S}*x + 2.0{S})')
        check(cos(3.*x - 1.), 'cos{s}(3.0{S}*x - 1.0{S})')
        check(tan(4.*y + 2.), 'tan{s}(4.0{S}*y + 2.0{S})')
        check(asin(3.*x + 2.), 'asin{s}(3.0{S}*x + 2.0{S})')
        check(acos(3.*x + 2.), 'acos{s}(3.0{S}*x + 2.0{S})')
        check(atan(3.*x + 2.), 'atan{s}(3.0{S}*x + 2.0{S})')
        check(atan2(3.*x, 2.*y), 'atan2{s}(3.0{S}*x, 2.0{S}*y)')

        check(sinh(3.*x + 2.), 'sinh{s}(3.0{S}*x + 2.0{S})')
        check(cosh(3.*x - 1.), 'cosh{s}(3.0{S}*x - 1.0{S})')
        check(tanh(4.0*y + 2.), 'tanh{s}(4.0{S}*y + 2.0{S})')
        check(asinh(3.*x + 2.), 'asinh{s}(3.0{S}*x + 2.0{S})')
        check(acosh(3.*x + 2.), 'acosh{s}(3.0{S}*x + 2.0{S})')
        check(atanh(3.*x + 2.), 'atanh{s}(3.0{S}*x + 2.0{S})')
        check(erf(42.*x), 'erf{s}(42.0{S}*x)')
        check(erfc(42.*x), 'erfc{s}(42.0{S}*x)')
        check(gamma(x), 'tgamma{s}(x)')
        check(loggamma(x), 'lgamma{s}(x)')

        check(ceiling(x + 2.), "ceil{s}(x + 2.0{S})")
        check(floor(x + 2.), "floor{s}(x + 2.0{S})")
        check(fma(x, y, -z), 'fma{s}(x, y, -z)')
        check(Max(x, 8.0, x**4.0), 'fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))')
        check(Min(x, 2.0), 'fmin{s}(2.0{S}, x)')
Example #5
0
def test_normal():
    dps, mp.dps = mp.dps, 20

    N = Normal(0, 1)
    assert N.mean == 0
    assert N.variance == 1
    assert N.probability(-1, 1) == erf(1/sqrt(2))
    assert N.probability(-1, 0) == erf(1/sqrt(2))/2
    N = Normal(2, 4)
    assert N.mean == 2
    assert N.variance == 16
    assert N.confidence(1) == (-oo, oo)
    assert N.probability(1, 3) == erf(1/sqrt(32))
    assert N.pdf(1).evalf() == (exp(Rational(-1,32)) / (4*sqrt(2*pi))).evalf()
    for p in [0.1, 0.3, 0.7, 0.9, 0.995]:
        a, b = N.confidence(p)
        assert operator.abs(float(N.probability(a, b).evalf()) - p) < 1e-10

    N = Normal(0, 2/sqrt(2*pi))
    assert N.pdf(0) == Rational(1,2)
    mp.dps = dps
Example #6
0
def test_normal():
    dps, mp.dps = mp.dps, 20

    N = Normal(0, 1)
    assert N.mean == 0
    assert N.variance == 1
    assert N.probability(-1, 1) == erf(1 / sqrt(2))
    assert N.probability(-1, 0) == erf(1 / sqrt(2)) / 2
    N = Normal(2, 4)
    assert N.mean == 2
    assert N.variance == 16
    assert N.confidence(1) == (-oo, oo)
    assert N.probability(1, 3) == erf(1 / sqrt(32))
    assert N.pdf(1).evalf() == (exp(Rational(-1, 32)) /
                                (4 * sqrt(2 * pi))).evalf()
    for p in [0.1, 0.3, 0.7, 0.9, 0.995]:
        a, b = N.confidence(p)
        assert abs(float(N.probability(a, b).evalf()) - p) < 1e-10

    N = Normal(0, 2 / sqrt(2 * pi))
    assert N.pdf(0) == Rational(1, 2)
    mp.dps = dps
Example #7
0
    def cdf(s, x):
        """
        Return the cumulative density function as an expression in x

        Examples
        ========

            >>> from sympy.statistics import Normal
            >>> Normal(1, 2).cdf(0)
            -erf(sqrt(2)/4)/2 + 1/2
            >>> from sympy.abc import x
            >>> Normal(1, 2).cdf(x)
            erf(sqrt(2)*(x - 1)/4)/2 + 1/2

        """
        x = sympify(x)
        return (1 + erf((x - s.mu) / (s.sigma * sqrt(2)))) / 2
Example #8
0
    def cdf(s, x):
        """
        Return the cumulative density function as an expression in x

        Examples
        ========

            >>> from sympy.statistics import Normal
            >>> Normal(1, 2).cdf(0)
            -erf(sqrt(2)/4)/2 + 1/2
            >>> from sympy.abc import x
            >>> Normal(1, 2).cdf(x)
            erf(sqrt(2)*(x - 1)/4)/2 + 1/2

        """
        x = sympify(x)
        return (1+erf((x-s.mu)/(s.sigma*sqrt(2))))/2
Example #9
0
    def confidence(s, p):
        """Return a symmetric (p*100)% confidence interval. For example,
        p=0.95 gives a 95% confidence interval. Currently this function
        only handles numerical values except in the trivial case p=1.

        Examples usage:
            # One standard deviation
            >>> N = Normal(0, 1)
            >>> N.confidence(0.68)
            (-0.994457883209753, 0.994457883209753)
            >>> N.probability(*_).evalf()
            0.680000000000000

            # Two standard deviations
            >>> N = Normal(0, 1)
            >>> N.confidence(0.95)
            (-1.95996398454005, 1.95996398454005)
            >>> N.probability(*_).evalf()
            0.950000000000000
        """

        if p == 1:
            return (-oo, oo)

        assert p <= 1

        # In terms of n*sigma, we have n = sqrt(2)*ierf(p). The inverse
        # error function is not yet implemented in SymPy but can easily be
        # computed numerically

        from sympy.mpmath import mpf, erf
        from sympy.mpmath.calculus import secant

        p = mpf(p)
        # calculate y = ierf(p) by solving erf(y) - p = 0
        y = secant(lambda y: erf(y) - p, 0)
        t = Real(str(mpf(float(s.sigma)) * mpf(2)**0.5 * y))
        mu = s.mu.evalf()
        return (mu-t, mu+t)
Example #10
0
def test_functions_special():
    assert oclcode(erf(x)) == "erf(x)"
    assert oclcode(erfc(x)) == "erfc(x)"
    assert oclcode(gamma(x)) == "tgamma(x)"
    assert oclcode(loggamma(x)) == "lgamma(x)"
Example #11
0
def heurisch(f, x, rewrite=False, hints=None, mappings=None, retries=3):
    """
    Compute indefinite integral using heuristic Risch algorithm.

    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 toplevel
    '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":

    [1] http://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
    components
    """
    f = sympify(f)

    if not f.is_Add:
        indep, f = f.as_independent(x)
    else:
        indep = S.One

    if not f.has(x):
        return indep * f * x

    rewritables = {
        (sin, cos, cot): tan,
        (sinh, cosh, coth): tanh,
    }

    if rewrite:
        for candidates, rule in rewritables.iteritems():
            f = f.rewrite(candidates, rule)
    else:
        for candidates in rewritables.iterkeys():
            if f.has(*candidates):
                break
        else:
            rewrite = True

    terms = components(f, 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):
                if g.is_Function:
                    if g.func is exp:
                        M = g.args[0].match(a * x**2)

                        if M is not None:
                            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])) *
                                    erf(-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(-I * erf(I *
                                                   (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:
                                terms.add(acosh(sqrt(M[a] / M[b]) * x))
                            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):
        terms |= components(cancel(g.diff(x)), x)

    # TODO: caching is significant factor for why permutations work at all. Change this.
    V = _symbols('x', len(terms))

    mapping = dict(zip(terms, V))

    rev_mapping = {}

    for k, v in mapping.iteritems():
        rev_mapping[v] = k

    if mappings is None:
        # Pre-sort mapping in order of largest to smallest expressions (last is always x).
        def _sort_key(arg):
            return default_sort_key(arg[0].as_independent(x)[1])

        mapping = sorted(mapping.items(), key=_sort_key, reverse=True)
        mappings = permutations(mapping)

    def _substitute(expr):
        return expr.subs(mapping)

    for mapping in mappings:
        # TODO: optimize this by not generating permutations where mapping[-1] != x.
        if mapping[-1][0] != x:
            continue

        mapping = list(mapping)

        diffs = [_substitute(cancel(g.diff(x))) 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)

            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()
        else:
            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])
        else:
            return (S.One, p)

    special = {}

    for term in terms:
        if term.is_Function:
            if term.func is tan:
                special[1 + _substitute(term)**2] = False
            elif term.func is tanh:
                special[1 + _substitute(term)] = False
                special[1 - _substitute(term)] = False
            elif term.func is C.LambertW:
                special[_substitute(term)] = True

    F = _substitute(f)

    P, Q = F.as_numer_denom()

    u_split = _splitter(denom)
    v_split = _splitter(Q)

    polys = list(v_split) + [u_split[0]] + special.keys()

    s = u_split[0] * Mul(*[k for k, v in special.iteritems() if v])
    polified = [p.as_poly(*V) for p in [s, P, Q]]

    if None in polified:
        return None

    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 = monomials(V, A + B - 1)
    else:
        monoms = monomials(V, A + B)

    poly_coeffs = _symbols('A', len(monoms))

    poly_part = Add(
        *[poly_coeffs[i] * monomial for i, monomial in enumerate(monoms)])

    reducibles = set()

    for poly in polys:
        if poly.has(*V):
            try:
                factorization = factor(poly, greedy=True)
            except PolynomialError:
                factorization = poly
            factorization = poly

            if factorization.is_Mul:
                reducibles |= set(factorization.args)
            else:
                reducibles.add(factorization)

    def _integrate(field=None):
        irreducibles = set()

        for poly in reducibles:
            for z in poly.atoms(Symbol):
                if z in V:
                    break
            else:
                continue

            irreducibles |= set(root_factors(poly, z, filter=field))

        log_coeffs, log_part = [], []
        B = _symbols('B', len(irreducibles))

        for i, poly in enumerate(irreducibles):
            if poly.has(*V):
                log_coeffs.append(B[i])
                log_part.append(log_coeffs[-1] * log(poly))

        coeffs = poly_coeffs + log_coeffs

        candidate = poly_part / poly_denom + Add(*log_part)

        h = F - _derivation(candidate) / denom

        numer = h.as_numer_denom()[0].expand(force=True)

        equations = defaultdict(lambda: S.Zero)

        for term in Add.make_args(numer):
            coeff, dependent = term.as_independent(*V)
            equations[dependent] += coeff

        solution = solve(equations.values(), *coeffs)

        return (solution, candidate, coeffs) if solution else None

    if not (F.atoms(Symbol) - set(V)):
        result = _integrate('Q')

        if result is None:
            result = _integrate()
    else:
        result = _integrate()

    if result is not None:
        (solution, candidate, coeffs) = result

        antideriv = candidate.subs(solution)

        for coeff in coeffs:
            if coeff not in solution:
                antideriv = antideriv.subs(coeff, S.Zero)

        antideriv = antideriv.subs(rev_mapping)
        antideriv = cancel(antideriv).expand(force=True)

        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)

            if result is not None:
                return indep * result

        return None
Example #12
0
File: risch.py Project: haz/sympy
def heurisch(f, x, **kwargs):
    """Compute indefinite integral using heuristic Risch algorithm.

       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 toplevel
       '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.risch import heurisch
       >>> from sympy.abc import x, y

       >>> heurisch(y*tan(x), x)
       y*log(1 + tan(x)**2)/2

       See Manuel Bronstein's "Poor Man's Integrator":

       [1] http://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.

    """
    f = sympify(f)

    if not f.is_Add:
        indep, f = f.as_independent(x)
    else:
        indep = S.One

    if not f.has(x):
        return indep * f * x

    rewritables = {
        (sin, cos, cot)     : tan,
        (sinh, cosh, coth)  : tanh,
    }

    rewrite = kwargs.pop('rewrite', False)

    if rewrite:
        for candidates, rule in rewritables.iteritems():
            f = f.rewrite(candidates, rule)
    else:
        for candidates in rewritables.iterkeys():
            if f.has(*candidates):
                break
        else:
            rewrite = True

    terms = components(f, x)

    hints = kwargs.get('hints', None)

    if hints is not None:
        if not hints:
            a = Wild('a', exclude=[x])
            b = Wild('b', exclude=[x])

            for g in set(terms):
                if g.is_Function:
                    if g.func is exp:
                        M = g.args[0].match(a*x**2)

                        if M is not None:
                            terms.add(erf(sqrt(-M[a])*x))

                        M = g.args[0].match(a*log(x)**2)

                        if M is not None:
                            if M[a].is_positive:
                                terms.add(-I*erf(I*(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:
                                terms.add(acosh(sqrt(M[a]/M[b])*x))
                            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):
        terms |= components(cancel(g.diff(x)), x)

    V = _symbols('x', len(terms))

    mapping = dict(zip(terms, V))

    rev_mapping = {}

    for k, v in mapping.iteritems():
        rev_mapping[v] = k

    def substitute(expr):
        return expr.subs(mapping)

    diffs = [ substitute(cancel(g.diff(x))) for g in terms ]

    denoms = [ g.as_numer_denom()[1] for g in diffs ]
    try:
        denom = reduce(lambda p, q: lcm(p, q, *V), denoms)
    except PolynomialError:
        # lcm can fail with this. See issue 1418.
        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()
        else:
            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])
        else:
            return (S.One, p)

    special = {}

    for term in terms:
        if term.is_Function:
            if term.func is tan:
                special[1 + substitute(term)**2] = False
            elif term.func is tanh:
                special[1 + substitute(term)] = False
                special[1 - substitute(term)] = False
            elif term.func is C.LambertW:
                special[substitute(term)] = True

    F = substitute(f)

    P, Q = F.as_numer_denom()

    u_split = splitter(denom)
    v_split = splitter(Q)

    polys = list(v_split) + [ u_split[0] ] + special.keys()

    s = u_split[0] * Mul(*[ k for k, v in special.iteritems() if v ])
    polified = [ p.as_poly(*V) for p in [s, P, Q] ]
    if None in polified:
        return
    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:
            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 = monomials(V, A + B - 1)
    else:
        monoms = monomials(V, A + B)

    poly_coeffs = _symbols('A', len(monoms))

    poly_part = Add(*[ poly_coeffs[i]*monomial
        for i, monomial in enumerate(monoms) ])

    reducibles = set()

    for poly in polys:
        if poly.has(*V):
            try:
                factorization = factor(poly, greedy=True)
            except PolynomialError:
                factorization = poly
            factorization = poly

            if factorization.is_Mul:
                reducibles |= set(factorization.args)
            else:
                reducibles.add(factorization)

    def integrate(field=None):
        irreducibles = set()

        for poly in reducibles:
            for z in poly.atoms(Symbol):
                if z in V:
                    break
            else:
                continue

            irreducibles |= set(root_factors(poly, z, filter=field))

        log_coeffs, log_part = [], []
        B = _symbols('B', len(irreducibles))

        for i, poly in enumerate(irreducibles):
            if poly.has(*V):
                log_coeffs.append(B[i])
                log_part.append(log_coeffs[-1] * log(poly))

        coeffs = poly_coeffs + log_coeffs

        candidate = poly_part/poly_denom + Add(*log_part)

        h = F - derivation(candidate) / denom

        numer = h.as_numer_denom()[0].expand()

        equations = {}

        for term in Add.make_args(numer):
            coeff, dependent = term.as_independent(*V)

            if dependent in equations:
                equations[dependent] += coeff
            else:
                equations[dependent] = coeff

        solution = solve(equations.values(), *coeffs)

        if solution is not None:
            return (solution, candidate, coeffs)
        else:
            return None

    if not (F.atoms(Symbol) - set(V)):
        result = integrate('Q')

        if result is None:
            result = integrate()
    else:
        result = integrate()

    if result is not None:
        (solution, candidate, coeffs) = result

        antideriv = candidate.subs(solution)

        for coeff in coeffs:
            if coeff not in solution:
                antideriv = antideriv.subs(coeff, S.Zero)

        antideriv = antideriv.subs(rev_mapping)
        antideriv = cancel(antideriv).expand()

        if antideriv.is_Add:
            antideriv = antideriv.as_independent(x)[1]

        return indep * antideriv
    else:
        if not rewrite:
            result = heurisch(f, x, rewrite=True, **kwargs)

            if result is not None:
                return indep * result

        return None
Example #13
0
def heurisch(f, x, **kwargs):
    """Compute indefinite integral using heuristic Risch algorithm.

       This is a huristic approach to indefinite integration in finite
       terms using extened 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 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 toplevel
       'integrate' function in most cases,  as this procedure needs some
       preprocessing steps and otherwise may fail.

       Specificaion
       ============

         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 antiderivate

              - hints = None          --> no suggestions at all
              - hints = [ ]           --> try to figure out
              - hints = [f1, ..., fn] --> we know better

       Examples
       ========

       >>> from sympy import *
       >>> x,y = symbols('xy')

       >>> heurisch(y*tan(x), x)
       y*log(1 + tan(x)**2)/2

       See Manuel Bronstein's "Poor Man's Integrator":

       [1] http://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.

    """
    f = sympify(f)

    if not f.is_Add:
        indep, f = f.as_independent(x)
    else:
        indep = S.One

    if not f.has(x):
        return indep * f * x

    rewritables = {
        (sin, cos, cot): tan,
        (sinh, cosh, coth): tanh,
    }

    rewrite = kwargs.pop('rewrite', False)

    if rewrite:
        for candidates, rule in rewritables.iteritems():
            f = f.rewrite(candidates, rule)
    else:
        for candidates in rewritables.iterkeys():
            if f.has(*candidates):
                break
        else:
            rewrite = True

    terms = components(f, x)

    hints = kwargs.get('hints', None)

    if hints is not None:
        if not hints:
            a = Wild('a', exclude=[x])
            b = Wild('b', exclude=[x])

            for g in set(terms):
                if g.is_Function:
                    if g.func is exp:
                        M = g.args[0].match(a * x**2)

                        if M is not None:
                            terms.add(erf(sqrt(-M[a]) * x))
                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))
        else:
            terms |= set(hints)

    for g in set(terms):
        terms |= components(g.diff(x), x)

    V = _symbols('x', len(terms))

    mapping = dict(zip(terms, V))

    rev_mapping = {}

    for k, v in mapping.iteritems():
        rev_mapping[v] = k

    def substitute(expr):
        return expr.subs(mapping)

    diffs = [substitute(simplify(g.diff(x))) for g in terms]

    denoms = [g.as_numer_denom()[1] for g in diffs]
    denom = reduce(lambda p, q: lcm(p, q, V), denoms)

    numers = [Poly.cancel(denom * g, *V) 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_any_symbols(y):
                continue

            if derivation(p) is not S.Zero:
                c, q = p.as_poly(y).as_primitive()
                return deflation(c) * gcd(q, q.diff(y))
        else:
            return p

    def splitter(p):
        for y in V:
            if not p.has_any_symbols(y):
                continue

            if derivation(y) is not S.Zero:
                c, q = p.as_poly(y).as_primitive()

                q = q.as_basic()

                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(Poly.cancel((q, s), *V))

                return (c_split[0] * q_split[0] * s, c_split[1] * q_split[1])
        else:
            return (S.One, p)

    special = {}

    for term in terms:
        if term.is_Function:
            if term.func is tan:
                special[1 + substitute(term)**2] = False
            elif term.func is tanh:
                special[1 + substitute(term)] = False
                special[1 - substitute(term)] = False
            elif term.func is C.LambertW:
                special[substitute(term)] = True

    F = substitute(f)

    P, Q = F.as_numer_denom()

    u_split = splitter(denom)
    v_split = splitter(Q)

    polys = list(v_split) + [u_split[0]] + special.keys()

    s = u_split[0] * Mul(*[k for k, v in special.iteritems() if v])
    a, b, c = [p.as_poly(*V).degree for p in [s, P, Q]]

    poly_denom = s * v_split[0] * deflation(v_split[1])

    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:
            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 = monomials(V, A + B - 1)
    else:
        monoms = monomials(V, A + B)

    poly_coeffs = _symbols('A', len(monoms))

    poly_part = Add(
        *[poly_coeffs[i] * monomial for i, monomial in enumerate(monoms)])

    reducibles = set()

    for poly in polys:
        if poly.has(*V):
            try:
                factorization = factor(poly, *V)
            except PolynomialError:
                factorization = poly

            if factorization.is_Mul:
                reducibles |= set(factorization.args)
            else:
                reducibles.add(factorization)

    def integrate(field=None):
        irreducibles = set()

        for poly in reducibles:
            for z in poly.atoms(Symbol):
                if z in V:
                    break
            else:
                continue

            irreducibles |= set(root_factors(poly, z, domain=field))

        log_coeffs, log_part = [], []
        B = _symbols('B', len(irreducibles))

        for i, poly in enumerate(irreducibles):
            if poly.has(*V):
                log_coeffs.append(B[i])
                log_part.append(log_coeffs[-1] * log(poly))

        coeffs = poly_coeffs + log_coeffs

        candidate = poly_part / poly_denom + Add(*log_part)

        h = together(F - derivation(candidate) / denom)

        numer = h.as_numer_denom()[0].expand()

        if not numer.is_Add:
            numer = [numer]

        equations = {}

        for term in numer.args:
            coeff, dependent = term.as_independent(*V)

            if dependent in equations:
                equations[dependent] += coeff
            else:
                equations[dependent] = coeff

        solution = solve(equations.values(), *coeffs)

        if solution is not None:
            return (solution, candidate, coeffs)
        else:
            return None

    if not (F.atoms(Symbol) - set(V)):
        result = integrate('Q')

        if result is None:
            result = integrate()
    else:
        result = integrate()

    if result is not None:
        (solution, candidate, coeffs) = result

        antideriv = candidate.subs(solution)

        for coeff in coeffs:
            if coeff not in solution:
                antideriv = antideriv.subs(coeff, S.Zero)

        antideriv = antideriv.subs(rev_mapping)
        antideriv = simplify(antideriv).expand()

        if antideriv.is_Add:
            antideriv = antideriv.as_independent(x)[1]

        return indep * antideriv
    else:
        if not rewrite:
            result = heurisch(f, x, rewrite=True, **kwargs)

            if result is not None:
                return indep * result

        return None
Example #14
0
def heurisch(f,
             x,
             rewrite=False,
             hints=None,
             mappings=None,
             retries=3,
             degree_offset=0,
             unnecessary_permutations=None):
    """
    Compute indefinite integral using heuristic Risch algorithm.

    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 toplevel
    '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":

    [1] http://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
    components
    """
    f = sympify(f)
    if x not in f.free_symbols:
        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)

    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):
                if g.is_Function:
                    if g.func is 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 g.func is 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:
                                terms.add(acosh(sqrt(M[a] / M[b]) * x))
                            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):
        terms |= components(cancel(g.diff(x)), x)

    # TODO: caching is significant factor for why permutations work at all. Change this.
    V = _symbols('x', len(terms))

    mapping = dict(list(zip(terms, V)))

    rev_mapping = {}

    if unnecessary_permutations is None:
        unnecessary_permutations = []
    for k, v in mapping.items():
        rev_mapping[v] = k

    if mappings is None:
        # Pre-sort mapping in order of largest to smallest expressions (last is always x).
        def _sort_key(arg):
            return default_sort_key(arg[0].as_independent(x)[1])

        #optimizing the number of permutations of mappping
        unnecessary_permutations = [(x, mapping[x])]
        del mapping[x]
        mapping = sorted(list(mapping.items()), key=_sort_key, reverse=True)
        mappings = permutations(mapping)

    def _substitute(expr):
        return expr.subs(mapping)

    for mapping in mappings:
        mapping = list(mapping)
        mapping = mapping + unnecessary_permutations
        diffs = [_substitute(cancel(g.diff(x))) 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()
        else:
            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])
        else:
            return (S.One, p)

    special = {}

    for term in terms:
        if term.is_Function:
            if term.func is tan:
                special[1 + _substitute(term)**2] = False
            elif term.func is tanh:
                special[1 + _substitute(term)] = False
                special[1 - _substitute(term)] = False
            elif term.func is C.LambertW:
                special[_substitute(term)] = True

    F = _substitute(f)

    P, Q = F.as_numer_denom()

    u_split = _splitter(denom)
    v_split = _splitter(Q)

    polys = 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

    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 = itermonomials(V, A + B - 1 + degree_offset)
    else:
        monoms = 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 polys:
        if poly.has(*V):
            try:
                factorization = factor(poly, greedy=True)
            except PolynomialError:
                factorization = poly
            factorization = poly

            if factorization.is_Mul:
                reducibles |= set(factorization.args)
            else:
                reducibles.add(factorization)

    def _integrate(field=None):
        irreducibles = set()

        for poly in reducibles:
            for z in poly.free_symbols:
                if z in V:
                    break
            else:
                continue

            irreducibles |= set(root_factors(poly, z, filter=field))

        log_coeffs, log_part = [], []
        B = _symbols('B', len(irreducibles))

        for i, poly in enumerate(irreducibles):
            if poly.has(*V):
                log_coeffs.append(B[i])
                log_part.append(log_coeffs[-1] * log(poly))

        coeffs = poly_coeffs + log_coeffs

        # 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 cancelation is slow
        # due to slow polynomial GCD algorithms. If this gets improved then
        # revise this code.
        candidate = poly_part / poly_denom + Add(*log_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(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(*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(coeffs, ground)
        ring = PolyRing(V, coeff_ring)

        numer = ring.from_expr(raw_numer)

        solution = solve_lin_sys(numer.coeffs(), coeff_ring)

        if solution is None:
            return None
        else:
            # If the ring is RR k.as_expr() will be 1.0*A
            solution = [(k.as_expr().as_coeff_Mul()[1], v.as_expr())
                        for k, v in solution.items()]
            return candidate.subs(solution).subs(
                list(zip(coeffs, [S.Zero] * len(coeffs))))

    if not (F.free_symbols - set(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(force=True)

        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
Example #15
0
def test_tensorflow_math():
    if not tf:
        skip("TensorFlow not installed")

    expr = Abs(x)
    assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
    _compare_tensorflow_scalar((x, ), expr)

    expr = sign(x)
    assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
    _compare_tensorflow_scalar((x, ), expr)

    expr = ceiling(x)
    assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = floor(x)
    assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = exp(x)
    assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = sqrt(x)
    assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = x**4
    assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = cos(x)
    assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = acos(x)
    assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
    _compare_tensorflow_scalar((x, ),
                               expr,
                               rng=lambda: random.uniform(0, 0.95))

    expr = sin(x)
    assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = asin(x)
    assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = tan(x)
    assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = atan(x)
    assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = atan2(y, x)
    assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
    _compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())

    expr = cosh(x)
    assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = acosh(x)
    assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = sinh(x)
    assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = asinh(x)
    assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = tanh(x)
    assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = atanh(x)
    assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
    _compare_tensorflow_scalar((x, ),
                               expr,
                               rng=lambda: random.uniform(-.5, .5))

    expr = erf(x)
    assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())

    expr = loggamma(x)
    assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
    _compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
Example #16
0
def test_C99CodePrinter__precision():
    n = symbols("n", integer=True)
    f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
    f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
    f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
    assert f32_printer.doprint(sin(x + 2.1)) == "sinf(x + 2.1F)"
    assert f64_printer.doprint(sin(x + 2.1)) == "sin(x + 2.1000000000000001)"
    assert f80_printer.doprint(sin(x + Float("2.0"))) == "sinl(x + 2.0L)"

    for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ["f", "", "l"]):

        def check(expr, ref):
            assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())

        check(Abs(n), "abs(n)")
        check(Abs(x + 2.0), "fabs{s}(x + 2.0{S})")
        check(
            sin(x + 4.0) ** cos(x - 2.0),
            "pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))",
        )
        check(exp(x * 8.0), "exp{s}(8.0{S}*x)")
        check(exp2(x), "exp2{s}(x)")
        check(expm1(x * 4.0), "expm1{s}(4.0{S}*x)")
        check(Mod(n, 2), "((n) % (2))")
        check(Mod(2 * n + 3, 3 * n + 5), "((2*n + 3) % (3*n + 5))")
        check(Mod(x + 2.0, 3.0), "fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})")
        check(Mod(x, 2.0 * x + 3.0), "fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})")
        check(log(x / 2), "log{s}((1.0{S}/2.0{S})*x)")
        check(log10(3 * x / 2), "log10{s}((3.0{S}/2.0{S})*x)")
        check(log2(x * 8.0), "log2{s}(8.0{S}*x)")
        check(log1p(x), "log1p{s}(x)")
        check(2 ** x, "pow{s}(2, x)")
        check(2.0 ** x, "pow{s}(2.0{S}, x)")
        check(x ** 3, "pow{s}(x, 3)")
        check(x ** 4.0, "pow{s}(x, 4.0{S})")
        check(sqrt(3 + x), "sqrt{s}(x + 3)")
        check(Cbrt(x - 2.0), "cbrt{s}(x - 2.0{S})")
        check(hypot(x, y), "hypot{s}(x, y)")
        check(sin(3.0 * x + 2.0), "sin{s}(3.0{S}*x + 2.0{S})")
        check(cos(3.0 * x - 1.0), "cos{s}(3.0{S}*x - 1.0{S})")
        check(tan(4.0 * y + 2.0), "tan{s}(4.0{S}*y + 2.0{S})")
        check(asin(3.0 * x + 2.0), "asin{s}(3.0{S}*x + 2.0{S})")
        check(acos(3.0 * x + 2.0), "acos{s}(3.0{S}*x + 2.0{S})")
        check(atan(3.0 * x + 2.0), "atan{s}(3.0{S}*x + 2.0{S})")
        check(atan2(3.0 * x, 2.0 * y), "atan2{s}(3.0{S}*x, 2.0{S}*y)")

        check(sinh(3.0 * x + 2.0), "sinh{s}(3.0{S}*x + 2.0{S})")
        check(cosh(3.0 * x - 1.0), "cosh{s}(3.0{S}*x - 1.0{S})")
        check(tanh(4.0 * y + 2.0), "tanh{s}(4.0{S}*y + 2.0{S})")
        check(asinh(3.0 * x + 2.0), "asinh{s}(3.0{S}*x + 2.0{S})")
        check(acosh(3.0 * x + 2.0), "acosh{s}(3.0{S}*x + 2.0{S})")
        check(atanh(3.0 * x + 2.0), "atanh{s}(3.0{S}*x + 2.0{S})")
        check(erf(42.0 * x), "erf{s}(42.0{S}*x)")
        check(erfc(42.0 * x), "erfc{s}(42.0{S}*x)")
        check(gamma(x), "tgamma{s}(x)")
        check(loggamma(x), "lgamma{s}(x)")

        check(ceiling(x + 2.0), "ceil{s}(x + 2.0{S})")
        check(floor(x + 2.0), "floor{s}(x + 2.0{S})")
        check(fma(x, y, -z), "fma{s}(x, y, -z)")
        check(Max(x, 8.0, x ** 4.0), "fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))")
        check(Min(x, 2.0), "fmin{s}(2.0{S}, x)")
 def cdf(s, x):
     """Return the cumulative density function as an expression in x"""
     x = sympify(x)
     return (1 + erf((x - s.mu) / (s.sigma * sqrt(2)))) / 2
Example #18
0
import scipy
import matplotlib.pyplot as plt
from mpi4py import MPI
from mpi4py_fft import generate_xdmf
from shenfun import inner, div, grad, TestFunction, TrialFunction, Function, \
    HDF5File, ETDRK4, TensorProductSpace, VectorTensorProductSpace, Basis, Array

comm = MPI.COMM_WORLD

# Use sympy to set up initial condition
x = Symbol("x")
y = Symbol("y")

# Initial conditions
a = 0.0001
u0 = 0.5 * (1 - ((0.5 * (erf((x - 0.04) / a) + 1) - 0.5 * (erf(
    (x + 0.04) / a) + 1)) * (0.5 * (erf((y - 0.04) / a) + 1) - 0.5 * (erf(
        (y + 0.04) / a) + 1)))) + 0.5
v0 = 0.25 * (0.5 * (erf((x - 0.04) / a) + 1) - 0.5 * (erf(
    (x + 0.04) / a) + 1)) * (0.5 * (erf((y - 0.04) / a) + 1) - 0.5 * (erf(
        (y + 0.04) / a) + 1))

ul = lambdify((x, y), u0, modules=['numpy', {'erf': scipy.special.erf}])
vl = lambdify((x, y), v0, modules=['numpy', {'erf': scipy.special.erf}])

# Size of discretization
N = (200, 200)

K0 = Basis(N[0], 'F', dtype='D', domain=(-1., 1.))
K1 = Basis(N[1], 'F', dtype='d', domain=(-1., 1.))
T = TensorProductSpace(comm, (K0, K1))
Example #19
0
def heurisch(f, x, rewrite=False, hints=None, mappings=None, retries=3,
             degree_offset=0, unnecessary_permutations=None):
    """
    Compute indefinite integral using heuristic Risch algorithm.

    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 toplevel
    '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":

    [1] http://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
    components
    """
    f = sympify(f)
    if x not in f.free_symbols:
        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)

    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):
                if g.is_Function:
                    if g.func is 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 g.func is 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:
                                terms.add(acosh(sqrt(M[a]/M[b])*x))
                            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):
        terms |= components(cancel(g.diff(x)), x)

    # TODO: caching is significant factor for why permutations work at all. Change this.
    V = _symbols('x', len(terms))

    mapping = dict(list(zip(terms, V)))

    rev_mapping = {}

    if unnecessary_permutations is None:
        unnecessary_permutations = []
    for k, v in mapping.items():
        rev_mapping[v] = k

    if mappings is None:
        # Pre-sort mapping in order of largest to smallest expressions (last is always x).
        def _sort_key(arg):
            return default_sort_key(arg[0].as_independent(x)[1])
        #optimizing the number of permutations of mappping
        unnecessary_permutations = [(x, mapping[x])]
        del mapping[x]
        mapping = sorted(list(mapping.items()), key=_sort_key, reverse=True)
        mappings = permutations(mapping)

    def _substitute(expr):
        return expr.subs(mapping)

    for mapping in mappings:
        mapping = list(mapping)
        mapping = mapping + unnecessary_permutations
        diffs = [ _substitute(cancel(g.diff(x))) 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()
        else:
            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])
        else:
            return (S.One, p)

    special = {}

    for term in terms:
        if term.is_Function:
            if term.func is tan:
                special[1 + _substitute(term)**2] = False
            elif term.func is tanh:
                special[1 + _substitute(term)] = False
                special[1 - _substitute(term)] = False
            elif term.func is C.LambertW:
                special[_substitute(term)] = True

    F = _substitute(f)

    P, Q = F.as_numer_denom()

    u_split = _splitter(denom)
    v_split = _splitter(Q)

    polys = 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

    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 = itermonomials(V, A + B - 1 + degree_offset)
    else:
        monoms = 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 polys:
        if poly.has(*V):
            try:
                factorization = factor(poly, greedy=True)
            except PolynomialError:
                factorization = poly
            factorization = poly

            if factorization.is_Mul:
                reducibles |= set(factorization.args)
            else:
                reducibles.add(factorization)

    def _integrate(field=None):
        irreducibles = set()

        for poly in reducibles:
            for z in poly.atoms(Symbol):
                if z in V:
                    break
            else:
                continue

            irreducibles |= set(root_factors(poly, z, filter=field))

        log_coeffs, log_part = [], []
        B = _symbols('B', len(irreducibles))

        for i, poly in enumerate(irreducibles):
            if poly.has(*V):
                log_coeffs.append(B[i])
                log_part.append(log_coeffs[-1] * log(poly))

        coeffs = poly_coeffs + log_coeffs

        # 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 cancelation is slow
        # due to slow polynomial GCD algorithms. If this gets improved then
        # revise this code.
        candidate = poly_part/poly_denom + Add(*log_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(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(*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(coeffs, ground)
        ring = PolyRing(V, coeff_ring)

        numer = ring.from_expr(raw_numer)

        solution = solve_lin_sys(numer.coeffs(), coeff_ring)

        if solution is None:
            return None
        else:
            solution = [ (k.as_expr(), v.as_expr()) for k, v in solution.items() ]
            return candidate.subs(solution).subs(list(zip(coeffs, [S.Zero]*len(coeffs))))

    if not (F.atoms(Symbol) - set(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(force=True)

        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
Example #20
0
 def cdf(s, x):
     """Return the cumulative density function as an expression in x"""
     x = Basic.sympify(x)
     return (1+erf((x-s.mu)/(s.sigma*sqrt(2))))/2
Example #21
0
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] http://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)

    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:
                                terms.add(acosh(sqrt(M[a]/M[b])*x))
                            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)

    dcache = DiffCache(x)

    for g in set(terms):  # using copy of terms
        terms |= components(dcache.get_diff(g), x)

    # 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
Example #22
0
def test_torch_math():
    if not torch:
        skip("Torch not installed")

    ma = torch.tensor([[1, 2, -3, -4]])

    expr = Abs(x)
    assert torch_code(expr) == "torch.abs(x)"
    f = lambdify(x, expr, 'torch')
    y = f(ma)
    c = torch.abs(ma)
    assert (y == c).all()

    expr = sign(x)
    assert torch_code(expr) == "torch.sign(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.randint(0, 10))

    expr = ceiling(x)
    assert torch_code(expr) == "torch.ceil(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = floor(x)
    assert torch_code(expr) == "torch.floor(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = exp(x)
    assert torch_code(expr) == "torch.exp(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    # expr = sqrt(x)
    # assert torch_code(expr) == "torch.sqrt(x)"
    # _compare_torch_scalar((x,), expr, rng=lambda: random.random())

    # expr = x ** 4
    # assert torch_code(expr) == "torch.pow(x, 4)"
    # _compare_torch_scalar((x,), expr, rng=lambda: random.random())

    expr = cos(x)
    assert torch_code(expr) == "torch.cos(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = acos(x)
    assert torch_code(expr) == "torch.acos(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(0, 0.95))

    expr = sin(x)
    assert torch_code(expr) == "torch.sin(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = asin(x)
    assert torch_code(expr) == "torch.asin(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = tan(x)
    assert torch_code(expr) == "torch.tan(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = atan(x)
    assert torch_code(expr) == "torch.atan(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    # expr = atan2(y, x)
    # assert torch_code(expr) == "torch.atan2(y, x)"
    # _compare_torch_scalar((y, x), expr, rng=lambda: random.random())

    expr = cosh(x)
    assert torch_code(expr) == "torch.cosh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = acosh(x)
    assert torch_code(expr) == "torch.acosh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = sinh(x)
    assert torch_code(expr) == "torch.sinh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = asinh(x)
    assert torch_code(expr) == "torch.asinh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = tanh(x)
    assert torch_code(expr) == "torch.tanh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))

    expr = atanh(x)
    assert torch_code(expr) == "torch.atanh(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(-.5, .5))

    expr = erf(x)
    assert torch_code(expr) == "torch.erf(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())

    expr = loggamma(x)
    assert torch_code(expr) == "torch.lgamma(x)"
    _compare_torch_scalar((x, ), expr, rng=lambda: random.random())