def _eval_expand_log(self, deep=True, **hints): if deep: arg = self.args[0].expand(deep=deep, **hints) else: arg = self.args[0] if arg.is_Mul: expr = sympify(0) nonpos = sympify(1) for x in arg.args: if deep: x = x.expand(deep=deep, **hints) if x.is_positive: expr += self.func(x)._eval_expand_log(deep=deep, **hints) else: nonpos *= x return expr + log(nonpos) elif arg.is_Pow: if arg.exp.is_real:# and arg.base.is_positive: # This should only run when base.is_positive, but it breaks # nseries, so it will have to wait for the new assumptions system. # See the variable obj2 in log._eval_nseries. if deep: b = arg.base.expand(deep=deep, **hints) e = arg.exp.expand(deep=deep, **hints) else: b = arg.base e = arg.exp return e * self.func(b)._eval_expand_log(deep=deep,\ **hints) return self.func(arg)
def eval(cls, x, k): x = sympify(x) k = sympify(k) if x is S.NaN: return S.NaN elif k.is_Integer: if k is S.NaN: return S.NaN elif k is S.Zero: return S.One else: result = S.One if k.is_positive: if x is S.Infinity: return S.Infinity elif x is S.NegativeInfinity: if k.is_odd: return S.NegativeInfinity else: return S.Infinity else: return reduce(lambda r, i: r*(x-i), xrange(0, int(k)), 1) else: if x is S.Infinity: return S.Infinity elif x is S.NegativeInfinity: return S.Infinity else: return 1/reduce(lambda r, i: r*(x+i), xrange(1, abs(int(k))+1), 1)
def __new__(cls, center=None, hradius=None, vradius=None, eccentricity=None, **kwargs): hradius = sympify(hradius) vradius = sympify(vradius) eccentricity = sympify(eccentricity) if len(filter(None, (hradius, vradius, eccentricity))) != 2: raise ValueError, 'Exactly two arguments between "hradius", '\ '"vradius", and "eccentricity" must be not None."' if eccentricity is not None: if hradius is None: hradius = vradius / sqrt(1 - eccentricity**2) elif vradius is None: vradius = hradius * sqrt(1 - eccentricity**2) else: if hradius is None and vradius is None: raise ValueError("At least two arguments between hradius, " "vradius and eccentricity must not be none.") if center is None: center = Point(0, 0) if not isinstance(center, Point): raise TypeError("center must be a Point") if hradius == vradius: return Circle(center, hradius, **kwargs) return GeometryEntity.__new__(cls, center, hradius, vradius, **kwargs)
def _eval_subs(self, old, new): old = sympify(old) if old==self.func: arg = self.args[0] new = sympify(new) return new(arg._eval_subs(old, new)) return self
def __new__(cls, center, hradius, vradius, **kwargs): hradius = sympify(hradius) vradius = sympify(vradius) if not isinstance(center, Point): raise TypeError("center must be be a Point") if hradius == vradius: return Circle(center, hradius, **kwargs) return GeometryEntity.__new__(cls, center, hradius, vradius, **kwargs)
def eval(cls, arg, base=None): if base is not None: base = sympify(base) if arg.is_positive and arg.is_Integer and \ base.is_positive and base.is_Integer: base = int(base) arg = int(arg) n = multiplicity(base, arg) return S(n) + log(arg // base ** n) / log(base) if base is not S.Exp1: return cls(arg)/cls(base) else: return cls(arg) arg = sympify(arg) if arg.is_Number: if arg is S.Zero: return S.NegativeInfinity elif arg is S.One: return S.Zero elif arg is S.Infinity: return S.Infinity elif arg is S.NegativeInfinity: return S.Infinity elif arg is S.NaN: return S.NaN elif arg.is_negative: return S.Pi * S.ImaginaryUnit + cls(-arg) elif arg is S.Exp1: return S.One #this doesn't work due to caching: :( #elif arg.func is exp and arg.args[0].is_real: #using this one instead: elif arg.func is exp: return arg.args[0] #this shouldn't happen automatically (see the issue 252): #elif arg.is_Pow: # if arg.exp.is_Number or arg.exp.is_NumberSymbol or \ # arg.exp.is_number: # return arg.exp * self(arg.base) #elif arg.is_Mul and arg.is_real: # return C.Add(*[self(a) for a in arg]) elif not arg.is_Add: coeff = arg.as_coefficient(S.ImaginaryUnit) if coeff is not None: if coeff is S.Infinity: return S.Infinity elif coeff is S.NegativeInfinity: return S.Infinity elif coeff.is_Rational: if coeff.is_nonnegative: return S.Pi * S.ImaginaryUnit * S.Half + cls(coeff) else: return -S.Pi * S.ImaginaryUnit * S.Half + cls(-coeff)
def __new__(cls, *args, **kwargs): if isinstance(args[0], (tuple, list, set)): coords = tuple([sympify(x) for x in args[0]]) else: coords = tuple([sympify(x) for x in args]) if len(coords) != 2: raise NotImplementedError("Only two dimensional points currently supported") return GeometryEntity.__new__(cls, *coords)
def canonize(cls, arg, k = 0): k = sympify(k) if not k.is_Integer or k.is_negative: raise ValueError("Error: the second argument of DiracDelta must be \ a non-negative integer, %s given instead." %(k,)) arg = sympify(arg) if arg is S.NaN: return S.NaN if arg.is_positive or arg.is_negative: return S.Zero elif arg.is_zero: return S.Infinity
def taylor_term(n, x, *previous_terms): if n == 0: return 1 / sympify(x) elif n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) B = C.bernoulli(n+1) F = C.factorial(n+1) return (-1)**((n+1)//2) * 2**(n+1) * B/F * x**n
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.Infinity * S.ImaginaryUnit elif arg is S.NegativeInfinity: return S.NegativeInfinity * S.ImaginaryUnit elif arg is S.Zero: return S.Pi * S.ImaginaryUnit / 2 elif arg is S.One: return S.Zero elif arg is S.NegativeOne: return S.Pi * S.ImaginaryUnit else: cst_table = { S.Half: S.Pi / 3, -S.Half: 2 * S.Pi / 3, sqrt(2) / 2: S.Pi / 4, -sqrt(2) / 2: 3 * S.Pi / 4, 1 / sqrt(2): S.Pi / 4, -1 / sqrt(2): 3 * S.Pi / 4, sqrt(3) / 2: S.Pi / 6, -sqrt(3) / 2: 5 * S.Pi / 6, } if arg in cst_table: return cst_table[arg] * S.ImaginaryUnit
def __mul__(self, factor): """ Create a new point where each coordinate in this point is multiplied by factor. """ factor = sympify(factor) return Point( [x*factor for x in self] )
def __div__(self, divisor): """ Create a new point where each coordinate in this point is divided by factor. """ divisor = sympify(divisor) return Point( [x/divisor for x in self] )
def canonize(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.Infinity elif arg is S.NegativeInfinity: return S.NegativeInfinity elif arg is S.Zero: return S.Zero elif arg is S.One: return C.log(2**S.Half + 1) elif arg is S.NegativeOne: return C.log(2**S.Half - 1) elif arg.is_negative: return -cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return S.ImaginaryUnit * C.asin(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return -cls(-arg)
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.Zero elif arg is S.NegativeInfinity: return S.Zero elif arg is S.Zero: return S.Pi * S.ImaginaryUnit / 2 elif arg is S.One: return S.Infinity elif arg is S.NegativeOne: return S.NegativeInfinity elif arg.is_negative: return -cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return -S.ImaginaryUnit * C.acot(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return -cls(-arg)
def eval(cls, n): n = sympify(n) if n.is_Number: if n is S.Zero: return S.One elif n.is_Integer: if n.is_negative: return S.Zero else: n, result = n.p, 1 if n < 20: for i in range(2, n+1): result *= i else: N, bits = n, 0 while N != 0: if N & 1 == 1: bits += 1 N = N >> 1 result = cls._recursive(n)*2**(n-bits) return C.Integer(result) if n.is_integer: if n.is_negative: return S.Zero else: return C.gamma(n+1)
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.One elif arg is S.NegativeInfinity: return S.NegativeOne elif arg is S.Zero: return S.Zero elif arg.is_negative: return -cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return S.ImaginaryUnit * C.tan(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return -cls(-arg) if isinstance(arg, asinh): x = arg.args[0] return x/sqrt(1+x**2) if isinstance(arg, acosh): x = arg.args[0] return sqrt(x-1) * sqrt(x+1) / x if isinstance(arg, atanh): return arg.args[0]
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.Infinity elif arg is S.NegativeInfinity: return S.Infinity elif arg is S.Zero: return S.One elif arg.is_negative: return cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return C.cos(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return cls(-arg) if arg.func == asinh: return sqrt(1 + arg.args[0] ** 2) if arg.func == acosh: return arg.args[0] if arg.func == atanh: return 1 / sqrt(1 - arg.args[0] ** 2)
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Infinity: return S.Infinity elif arg is S.NegativeInfinity: return S.NegativeInfinity elif arg is S.Zero: return S.Zero elif arg.is_negative: return -cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return S.ImaginaryUnit * C.sin(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return -cls(-arg) if arg.func == asinh: return arg.args[0] if arg.func == acosh: x = arg.args[0] return sqrt(x - 1) * sqrt(x + 1) if arg.func == atanh: x = arg.args[0] return x / sqrt(1 - x ** 2)
def taylor_term(n, x, *previous_terms): if n == 0: return S.Pi * S.ImaginaryUnit / 2 elif n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) return x ** n / n
def taylor_term(n, x, *previous_terms): if n == 0: return S.Pi / 2 # FIX THIS elif n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) return (-1)**((n+1)//2) * x**n / n
def _pi_coeff(arg, cycles=1): """ When arg is a Number times pi (e.g. 3*pi/2) then return the Number normalized to be in the range [0, 2], else None. When an even multiple of pi is encountered, if it is multiplying something with known parity then the multiple is returned as 0 otherwise as 2. Examples: >>> from sympy.functions.elementary.trigonometric import _pi_coeff as coeff >>> from sympy import pi >>> from sympy.abc import x, y >>> coeff(3*x*pi) 3*x >>> coeff(11*pi/7) 11/7 >>> coeff(-11*pi/7) 3/7 >>> coeff(4*pi) 0 >>> coeff(5*pi) 1 >>> coeff(5.0*pi) 1 >>> coeff(5.5*pi) 3/2 >>> coeff(2 + pi) """ arg = sympify(arg) if arg is S.Pi: return S.One elif not arg: return S.Zero elif arg.is_Mul: cx = arg.coeff(S.Pi) if cx: c, x = cx.as_coeff_Mul() # pi is not included as coeff if c.is_Float: # recast exact binary fractions to Rationals m = int(c*2) if Float(float(m)/2) == c: c = Rational(m, 2) if x is not S.One or not (c.is_Rational and c.q != 1): if x.is_integer: c2 = c % 2 if c2 == 1: return x elif not c2: if x.is_even is not None: # known parity return S.Zero return 2*x else: return c2*x return cx else: return Rational(c.p % (2*c.q), c.q)
def taylor_term(n, x, *previous_terms): if n<0: return S.Zero if n==0: return S.One x = sympify(x) if previous_terms: p = previous_terms[-1] if p is not None: return p * x / n return x**n/C.Factorial()(n)
def taylor_term(n, x, *previous_terms): # of log(1+x) if n<0: return S.Zero x = sympify(x) if n==0: return x if previous_terms: p = previous_terms[-1] if p is not None: return (-n) * p * x / (n+1) return (1-2*(n%2)) * x**(n+1)/(n+1)
def taylor_term(n, x, *previous_terms): # of log(1+x) from sympy import powsimp if n<0: return S.Zero x = sympify(x) if n==0: return x if previous_terms: p = previous_terms[-1] if p is not None: return powsimp((-n) * p * x / (n+1), deep=True, combine='exp') return (1-2*(n%2)) * x**(n+1)/(n+1)
def canonize(cls, arg): arg = sympify(arg) if arg is S.NaN: return S.NaN elif arg.is_negative: return S.Zero elif arg.is_zero: return S.Half elif arg.is_positive: return S.One
def __new__(self, c, r, n, **kwargs): r = sympify(r) if not isinstance(c, Point): raise GeometryError("RegularPolygon.__new__ requires c to be a Point instance") if not isinstance(r, Basic): raise GeometryError("RegularPolygon.__new__ requires r to be a number or Basic instance") if n < 3: raise GeometryError("RegularPolygon.__new__ requires n >= 3") obj = GeometryEntity.__new__(self, c, r, n, **kwargs) return obj
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 1: return S.Zero else: x = sympify(x) if len(previous_terms) > 2: p = previous_terms[-2] return -p * x**2 / (n*(n-1)) else: return (-1)**(n//2)*x**(n)/C.factorial(n)
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) k = (n - 1) // 2 if len(previous_terms) > 2: return -previous_terms[-2] * x**2 * (n - 2) / (n * k) else: return 2 * (-1)**k * x**n / (n * C.Factorial(k) * sqrt(S.Pi))
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) a = 2**(n + 1) B = C.bernoulli(n + 1) F = C.Factorial(n + 1) return a * (a - 1) * B / F * x**n
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) a, b = ((n-1)//2), 2**(n+1) B = C.bernoulli(n+1) F = C.factorial(n+1) return (-1)**a * b*(b-1) * B/F * x**n
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) a = 2 ** (n + 1) B = C.bernoulli(n + 1) F = C.Factorial(n + 1) return a * (a - 1) * B / F * x ** n
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) a, b = ((n - 1) // 2), 2**(n + 1) B = C.bernoulli(n + 1) F = C.factorial(n + 1) return (-1)**a * b * (b - 1) * B / F * x**n
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) k = (n - 1)//2 if len(previous_terms) > 2: return -previous_terms[-2] * x**2 * (n-2)/(n*k) else: return 2*(-1)**k * x**n/(n*C.Factorial(k)*sqrt(S.Pi))
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) if len(previous_terms) >= 2 and n > 2: p = previous_terms[-2] return p * (n - 2)**2 / (n * (n - 1)) * x**2 else: k = (n - 1) // 2 R = C.RisingFactorial(S.Half, k) F = C.factorial(k) return R / F * x**n / n
def __new__(self, c, r, n, **kwargs): r = sympify(r) if not isinstance(c, Point): raise GeometryError( "RegularPolygon.__new__ requires c to be a Point instance") if not isinstance(r, Basic): raise GeometryError( "RegularPolygon.__new__ requires r to be a number or Basic instance" ) if n < 3: raise GeometryError("RegularPolygon.__new__ requires n >= 3") obj = GeometryEntity.__new__(self, c, r, n, **kwargs) return obj
def __add__(self, other): """ Create a new point where each coordinate in this point is increased by the corresponding coordinate in other. """ if isinstance(other, Point): if len(other) == len(self): return Point([simplify(a + b) for a, b in zip(self, other)]) else: raise TypeError( "Points must have the same number of dimensions") else: other = sympify(other) return Point([simplify(a + other) for a in self])
def _eval_args(cls, args): # If we are passed a QubitState or subclass, we just take its qubit # values directly. if len(args) == 1 and isinstance(args[0], QubitState): return args[0].qubit_values # Turn strings into tuple of strings if len(args) == 1 and isinstance(args[0], str): args = tuple(args[0]) args = sympify(args) # Validate input (must have 0 or 1 input) for element in args: if not (element == 1 or element == 0): raise ValueError("Qubit values must be 0 or 1, got: %r" % element) return args
def taylor_term(n, x, *previous_terms): if n == 0: return S.Pi * S.ImaginaryUnit / 2 elif n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) if len(previous_terms) > 2: p = previous_terms[-2] return p * (n - 2)**2 / (k * (k - 1)) * x**2 else: k = (n - 1) // 2 R = C.RisingFactorial(S.Half, k) F = C.Factorial(k) return -R / F * S.ImaginaryUnit * x**n / n
def __new__(cls, *args, **kwargs): c, r = None, None if len(args) == 3 and isinstance(args[0], Point): from polygon import Triangle t = Triangle(args[0], args[1], args[2]) if t.area == 0: raise GeometryError( "Cannot construct a circle from three collinear points") c = t.circumcenter r = t.circumradius elif len(args) == 2: # Assume (center, radius) pair c = args[0] r = sympify(args[1]) if not (c is None or r is None): return GeometryEntity.__new__(cls, c, r, **kwargs) raise GeometryError("Circle.__new__ received unknown arguments")
def eval(cls, arg): arg = sympify(arg) if arg.is_Number: if arg is S.NaN: return S.NaN elif arg is S.Zero: return S.Zero elif arg is S.One: return S.Infinity elif arg is S.NegativeOne: return S.NegativeInfinity elif arg.is_negative: return -cls(-arg) else: i_coeff = arg.as_coefficient(S.ImaginaryUnit) if i_coeff is not None: return S.ImaginaryUnit * C.atan(i_coeff) else: coeff, terms = arg.as_coeff_terms() if coeff.is_negative: return -cls(-arg)
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
def heurisch_wrapper(f, x, rewrite=False, hints=None, mappings=None, retries=3, degree_offset=0, unnecessary_permutations=None, _try_heurisch=None): """ A wrapper around the heurisch integration algorithm. Explanation =========== This method takes the result from heurisch and checks for poles in the denominator. For each of these poles, the integral is reevaluated, and the final integration result is given in terms of a Piecewise. Examples ======== >>> from sympy import cos, symbols >>> from sympy.integrals.heurisch import heurisch, heurisch_wrapper >>> n, x = symbols('n x') >>> heurisch(cos(n*x), x) sin(n*x)/n >>> heurisch_wrapper(cos(n*x), x) Piecewise((sin(n*x)/n, Ne(n, 0)), (x, True)) See Also ======== heurisch """ from sympy.solvers.solvers import solve, denoms f = sympify(f) if not f.has_free(x): return f*x res = heurisch(f, x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations, _try_heurisch) if not isinstance(res, Basic): return res # We consider each denominator in the expression, and try to find # cases where one or more symbolic denominator might be zero. The # conditions for these cases are stored in the list slns. slns = [] for d in denoms(res): try: slns += solve(d, dict=True, exclude=(x,)) except NotImplementedError: pass if not slns: return res slns = list(uniq(slns)) # Remove the solutions corresponding to poles in the original expression. slns0 = [] for d in denoms(f): try: slns0 += solve(d, dict=True, exclude=(x,)) except NotImplementedError: pass slns = [s for s in slns if s not in slns0] if not slns: return res if len(slns) > 1: eqs = [] for sub_dict in slns: eqs.extend([Eq(key, value) for key, value in sub_dict.items()]) slns = solve(eqs, dict=True, exclude=(x,)) + slns # For each case listed in the list slns, we reevaluate the integral. pairs = [] for sub_dict in slns: expr = heurisch(f.subs(sub_dict), x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations, _try_heurisch) cond = And(*[Eq(key, value) for key, value in sub_dict.items()]) generic = Or(*[Ne(key, value) for key, value in sub_dict.items()]) if expr is None: expr = integrate(f.subs(sub_dict),x) pairs.append((expr, cond)) # If there is one condition, put the generic case first. Otherwise, # doing so may lead to longer Piecewise formulas if len(pairs) == 1: pairs = [(heurisch(f, x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations, _try_heurisch), generic), (pairs[0][0], True)] else: pairs.append((heurisch(f, x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations, _try_heurisch), True)) return Piecewise(*pairs)
def trim(f, *symbols, **flags): """Cancel common factors in a given formal rational expression. Given an arbitrary expression, map all functional components to temporary symbols, rewriting this expression to rational function form and perform cancelation of common factors. When given a rational function or a list of symbols discards all functional components, then this procedure is equivalent to cancel(). Note that this procedure can thread over composite objects like big operators, matrices, relational operators etc. It can be also called recursively (to change this behaviour unset 'recursive' flag). >>> from sympy import * >>> x,y = symbols('xy') >>> f = Function('f') >>> trim((f(x)**2+f(x))/f(x)) 1 + f(x) >>> trim((x**2+x)/x) 1 + x Recursively simplify expressions: >>> trim(sin((f(x)**2+f(x))/f(x))) sin(1 + f(x)) """ f = sympify(f) if isinstance(f, Relational): return Relational(trim(f.lhs, *symbols, **flags), trim(f.rhs, *symbols, **flags), f.rel_op) #elif isinstance(f, Matrix): # return f.applyfunc(lambda g: trim(g, *symbols, **flags)) else: recursive = flags.get('recursive', True) def is_functional(g): return not (g.is_Atom or g.is_number) \ and (not symbols or g.has(*symbols)) def components(g): result = set() if is_functional(g): if g.is_Add or g.is_Mul: args = [] for h in g.args: h, terms = components(h) result |= terms args.append(h) g = g.__class__(*args) elif g.is_Pow: if recursive: base = trim(g.base, *symbols, **flags) else: base = g.base if g.exp.is_Rational: if g.exp.is_Integer: if g.exp is S.NegativeOne: h, terms = components(base) return h**S.NegativeOne, terms else: h = base else: h = base**Rational(1, g.exp.q) g = base**g.exp else: if recursive: h = g = base**trim(g.exp, *symbols, **flags) else: h = g = base**g.exp if is_functional(h): result.add(h) else: if not recursive: result.add(g) else: g = g.__class__(*[trim(h, *symbols, **flags) for h in g.args]) if is_functional(g): result.add(g) return g, result if f.is_number or not f.has_any_symbols(*symbols): return f f = together(f.expand()) f, terms = components(f) if not terms: return Poly.cancel(f, *symbols) else: mapping, reverse = {}, {} for g in terms: mapping[g] = Temporary() reverse[mapping[g]] = g p, q = f.as_numer_denom() f = p.expand()/q.expand() if not symbols: symbols = tuple(f.atoms(Symbol)) symbols = tuple(mapping.values()) + symbols H = Poly.cancel(f.subs(mapping), *symbols) if not flags.get('extract', True): return H.subs(reverse) else: def extract(f): p = f.args[0] for q in f.args[1:]: p = gcd(p, q, *symbols) if p.is_number: return S.One, f return p, Add(*[quo(g, p, *symbols) for g in f.args]) P, Q = H.as_numer_denom() if P.is_Add: GP, P = extract(P) else: GP = S.One if Q.is_Add: GQ, Q = extract(Q) else: GQ = S.One return ((GP*P)/(GQ*Q)).subs(reverse)
def __new__(cls, expr, *symbols, **assumptions): expr = sympify(expr).expand() if expr is S.NaN: return S.NaN if symbols: symbols = map(sympify, symbols) else: symbols = list(expr.atoms(C.Symbol)) symbols.sort(Basic.compare) if expr.is_Order: new_symbols = list(expr.symbols) for s in symbols: if s not in new_symbols: new_symbols.append(s) if len(new_symbols) == len(expr.symbols): return expr symbols = new_symbols elif symbols: symbol_map = {} new_symbols = [] for s in symbols: if isinstance(s, C.Symbol): new_symbols.append(s) continue z = C.Symbol('z', dummy=True) x1, s1 = s.solve4linearsymbol(z) expr = expr.subs(x1, s1) symbol_map[z] = s new_symbols.append(z) if symbol_map: r = Order(expr, *new_symbols, **assumptions) expr = r.expr.subs(symbol_map) symbols = [] for s in r.symbols: if symbol_map.has_key(s): symbols.append(symbol_map[s]) else: symbols.append(s) else: if expr.is_Add: lst = expr.extract_leading_order(*symbols) expr = C.Add(*[f.expr for (e, f) in lst]) else: expr = expr.as_leading_term(*symbols) coeff, terms = expr.as_coeff_terms() if coeff is S.Zero: return coeff expr = C.Mul(*[t for t in terms if t.has(*symbols)]) elif expr is not S.Zero: expr = S.One if expr is S.Zero: return expr # create Order instance: obj = Basic.__new__(cls, expr, *symbols, **assumptions) return obj
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
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
def bspline_basis(d, knots, n, x, close=True): """The n-th B-spline at x of degree d with knots. B-Splines are piecewise polynomials of degree d [1]. They are defined on a set of knots, which is a sequence of integers or floats. The 0th degree splines have a value of one on a single interval: >>> from sympy import bspline_basis >>> from sympy.abc import x >>> d = 0 >>> knots = range(5) >>> bspline_basis(d, knots, 0, x) Piecewise((1, [0, 1]), (0, True)) For a given (d, knots) there are len(knots)-d-1 B-splines defined, that are indexed by n (starting at 0). Here is an example of a cubic B-spline: >>> bspline_basis(3, range(5), 0, x) Piecewise((x**3/6, [0, 1)), (2/3 - 2*x + 2*x**2 - x**3/2, [1, 2)), (-22/3 + 10*x - 4*x**2 + x**3/2, [2, 3)), (32/3 - 8*x + 2*x**2 - x**3/6, [3, 4]), (0, True)) By repeating knot points, you can introduce discontinuities in the B-splines and their derivatives: >>> d = 1 >>> knots = [0,0,2,3,4] >>> bspline_basis(d, knots, 0, x) Piecewise((1 - x/2, [0, 2]), (0, True)) It is quite time consuming to construct and evaluate B-splines. If you need to evaluate a B-splines many times, it is best to lambdify them first: >>> from sympy import lambdify >>> d = 3 >>> knots = range(10) >>> b0 = bspline_basis(d, knots, 0, x) >>> f = lambdify(x, b0) >>> y = f(0.5) [1] http://en.wikipedia.org/wiki/B-spline """ knots = [sympify(k) for k in knots] d = int(d) n = int(n) n_knots = len(knots) n_intervals = n_knots - 1 if n + d + 1 > n_intervals: raise ValueError('n+d+1 must not exceed len(knots)-1') if d == 0: result = Piecewise( (S.One, Interval(knots[n], knots[n + 1], False, True)), (0, True)) elif d > 0: denom = knots[n + d] - knots[n] if denom != S.Zero: A = (x - knots[n]) / denom b1 = bspline_basis(d - 1, knots, n, x, close=False) else: b1 = A = S.Zero denom = knots[n + d + 1] - knots[n + 1] if denom != S.Zero: B = (knots[n + d + 1] - x) / denom b2 = bspline_basis(d - 1, knots, n + 1, x, close=False) else: b2 = B = S.Zero result = _add_splines(A, b1, B, b2) else: raise ValueError('degree must be non-negative: %r' % n) if close: final_ec_pair = result.args[-2] final_cond = final_ec_pair.cond final_expr = final_ec_pair.expr new_args = final_cond.args[:3] + (False, ) new_ec_pair = ExprCondPair(final_expr, Interval(*new_args)) new_args = result.args[:-2] + (new_ec_pair, result.args[-1]) result = Piecewise(*new_args) return result
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
def _pi_coeff(arg, cycles=1): """ When arg is a Number times pi (e.g. 3*pi/2) then return the Number normalized to be in the range [0, 2], else None. When an even multiple of pi is encountered, if it is multiplying something with known parity then the multiple is returned as 0 otherwise as 2. Examples ======== >>> from sympy.functions.elementary.trigonometric import _pi_coeff as coeff >>> from sympy import pi >>> from sympy.abc import x, y >>> coeff(3*x*pi) 3*x >>> coeff(11*pi/7) 11/7 >>> coeff(-11*pi/7) 3/7 >>> coeff(4*pi) 0 >>> coeff(5*pi) 1 >>> coeff(5.0*pi) 1 >>> coeff(5.5*pi) 3/2 >>> coeff(2 + pi) """ arg = sympify(arg) if arg is S.Pi: return S.One elif not arg: return S.Zero elif arg.is_Mul: cx = arg.coeff(S.Pi) if cx: c, x = cx.as_coeff_Mul() # pi is not included as coeff if c.is_Float: # recast exact binary fractions to Rationals f = abs(c) % 1 if f != 0: p = -int(round(log(f, 2).evalf())) m = 2**p cm = c * m i = int(cm) if i == cm: c = C.Rational(i, m) cx = c * x else: c = C.Rational(int(c)) cx = c * x if x.is_integer: c2 = c % 2 if c2 == 1: return x elif not c2: if x.is_even is not None: # known parity return S.Zero return 2 * x else: return c2 * x return cx
def heurisch_wrapper(f, x, rewrite=False, hints=None, mappings=None, retries=3, degree_offset=0, unnecessary_permutations=None): """ A wrapper around the heurisch integration algorithm. This method takes the result from heurisch and checks for poles in the denominator. For each of these poles, the integral is reevaluated, and the final integration result is given in terms of a Piecewise. Examples ======== >>> from sympy.core import symbols >>> from sympy.functions import cos >>> from sympy.integrals.heurisch import heurisch, heurisch_wrapper >>> n, x = symbols('n x') >>> heurisch(cos(n*x), x) sin(n*x)/n >>> heurisch_wrapper(cos(n*x), x) Piecewise((x, n == 0), (sin(n*x)/n, True)) See Also ======== heurisch """ f = sympify(f) if x not in f.free_symbols: return f*x res = heurisch(f, x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations) if not isinstance(res, Basic): return res # We consider each denominator in the expression, and try to find # cases where one or more symbolic denominator might be zero. The # conditions for these cases are stored in the list slns. slns = [] for d in denoms(res): try: slns += solve(d, dict=True, exclude=(x,)) except NotImplementedError: pass if not slns: return res slns = list(uniq(slns)) # Remove the solutions corresponding to poles in the original expression. slns0 = [] for d in denoms(f): try: slns0 += solve(d, dict=True, exclude=(x,)) except NotImplementedError: pass slns = [s for s in slns if s not in slns0] if not slns: return res if len(slns) > 1: eqs = [] for sub_dict in slns: eqs.extend([Eq(key, value) for key, value in sub_dict.items()]) slns = solve(eqs, dict=True, exclude=(x,)) + slns # For each case listed in the list slns, we reevaluate the integral. pairs = [] for sub_dict in slns: expr = heurisch(f.subs(sub_dict), x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations) cond = And(*[Eq(key, value) for key, value in sub_dict.items()]) pairs.append((expr, cond)) pairs.append((heurisch(f, x, rewrite, hints, mappings, retries, degree_offset, unnecessary_permutations), True)) return Piecewise(*pairs)
def taylor_term(n, x, *previous_terms): if n < 0 or n % 2 == 0: return S.Zero else: x = sympify(x) return (-1)**((n - 1) // 2) * x**n / n
def sqrt(arg): arg = sympify(arg) return arg**S.Half