def _generate_patterns():
"""
Generates patterns for transcendental equations.
This is lazily calculated (called) in the tsolve() function and stored in
the patterns global variable.
"""
tmp1 = _f ** (_h-(_c*_g/_b))
tmp2 = (-_e*tmp1/_a)**(1/_d)
global _patterns
_patterns = [
(_a*(_b*_x+_c)**_d + _e ,
((-(_e/_a))**(1/_d)-_c)/_b),
(_b+_c*exp(_d*_x+_e) ,
(log(-_b/_c)-_e)/_d),
(_a*_x+_b+_c*exp(_d*_x+_e) ,
-_b/_a-LambertW(_c*_d*exp(_e-_b*_d/_a)/_a)/_d),
(_b+_c*_f**(_d*_x+_e) ,
(log(-_b/_c)-_e*log(_f))/_d/log(_f)),
(_a*_x+_b+_c*_f**(_d*_x+_e) ,
-_b/_a-LambertW(_c*_d*_f**(_e-_b*_d/_a)*log(_f)/_a)/_d/log(_f)),
(_b+_c*log(_d*_x+_e) ,
(exp(-_b/_c)-_e)/_d),
(_a*_x+_b+_c*log(_d*_x+_e) ,
-_e/_d+_c/_a*LambertW(_a/_c/_d*exp(-_b/_c+_a*_e/_c/_d))),
(_a*(_b*_x+_c)**_d + _e*_f**(_g*_x+_h) ,
-_c/_b-_d*LambertW(-tmp2*_g*log(_f)/_b/_d)/_g/log(_f))
]
def test_contraction_structure_Mul_and_Pow():
x = IndexedBase('x')
y = IndexedBase('y')
i, j, k = Idx('i'), Idx('j'), Idx('k')
i_ji = x[i]**(y[j]*x[i])
assert get_contraction_structure(i_ji) == {None: set([i_ji])}
ij_i = (x[i]*y[j])**(y[i])
assert get_contraction_structure(ij_i) == {None: set([ij_i])}
j_ij_i = x[j]*(x[i]*y[j])**(y[i])
assert get_contraction_structure(j_ij_i) == {(j,): set([j_ij_i])}
j_i_ji = x[j]*x[i]**(y[j]*x[i])
assert get_contraction_structure(j_i_ji) == {(j,): set([j_i_ji])}
ij_exp_kki = x[i]*y[j]*exp(y[i]*y[k, k])
result = get_contraction_structure(ij_exp_kki)
expected = {
(i,): set([ij_exp_kki]),
ij_exp_kki: [{
None: set([exp(y[i]*y[k, k])]),
exp(y[i]*y[k, k]): [{
None: set([y[i]*y[k, k]]),
y[i]*y[k, k]: [{(k,): set([y[k, k]])}]
}]}
]
}
assert result == expected
def test_positive():
x, y, z, w = symbols('x,y,z,w')
assert ask(Q.positive(x), Q.positive(x)) == True
assert ask(Q.positive(x), Q.negative(x)) == False
assert ask(Q.positive(x), Q.nonzero(x)) == None
assert ask(Q.positive(-x), Q.positive(x)) == False
assert ask(Q.positive(-x), Q.negative(x)) == True
assert ask(Q.positive(x+y), Q.positive(x) & Q.positive(y)) == True
assert ask(Q.positive(x+y), Q.positive(x) & Q.negative(y)) == None
assert ask(Q.positive(2*x), Q.positive(x)) == True
assumptions = Q.positive(x) & Q.negative(y) & Q.negative(z) & Q.positive(w)
assert ask(Q.positive(x*y*z)) == None
assert ask(Q.positive(x*y*z), assumptions) == True
assert ask(Q.positive(-x*y*z), assumptions) == False
assert ask(Q.positive(x**2), Q.positive(x)) == True
assert ask(Q.positive(x**2), Q.negative(x)) == True
#exponential
assert ask(Q.positive(exp(x)), Q.real(x)) == True
assert ask(Q.positive(x + exp(x)), Q.real(x)) == None
#absolute value
assert ask(Q.positive(Abs(x))) == None # Abs(0) = 0
assert ask(Q.positive(Abs(x)), Q.positive(x)) == True
def moveup2(s, x):
r = SubsSet()
for expr, var in s.iteritems():
r[expr.subs(x, exp(x))] = var
for var, expr in s.rewrites.iteritems():
r.rewrites[var] = s.rewrites[var].subs(x, exp(x))
return r
def moveup2(s, x):
r = SubsSet()
for expr, var in s.items():
r[expr.xreplace({x: exp(x)})] = var
for var, expr in s.rewrites.items():
r.rewrites[var] = s.rewrites[var].xreplace({x: exp(x)})
return r
def test_positive():
x, y, z, w = symbols('xyzw')
assert ask(x, Q.positive, Assume(x, Q.positive)) == True
assert ask(x, Q.positive, Assume(x, Q.negative)) == False
assert ask(x, Q.positive, Assume(x, Q.nonzero)) == None
assert ask(-x, Q.positive, Assume(x, Q.positive)) == False
assert ask(-x, Q.positive, Assume(x, Q.negative)) == True
assert ask(x+y, Q.positive, Assume(x, Q.positive) & \
Assume(y, Q.positive)) == True
assert ask(x+y, Q.positive, Assume(x, Q.positive) & \
Assume(y, Q.negative)) == None
assert ask(2*x, Q.positive, Assume(x, Q.positive)) == True
assumptions = Assume(x, Q.positive) & Assume(y, Q.negative) & \
Assume(z, Q.negative) & Assume(w, Q.positive)
assert ask(x*y*z, Q.positive) == None
assert ask(x*y*z, Q.positive, assumptions) == True
assert ask(-x*y*z, Q.positive, assumptions) == False
assert ask(x**2, Q.positive, Assume(x, Q.positive)) == True
assert ask(x**2, Q.positive, Assume(x, Q.negative)) == True
#exponential
assert ask(exp(x), Q.positive, Assume(x, Q.real)) == True
assert ask(x + exp(x), Q.positive, Assume(x, Q.real)) == None
#absolute value
assert ask(Abs(x), Q.positive) == None # Abs(0) = 0
assert ask(Abs(x), Q.positive, Assume(x, Q.positive)) == True
def _expr_big_minus(cls, a, z, n):
from sympy import I, pi, exp, sqrt, atan, sin
if n.is_even:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z)*sin(2*a*atan(sqrt(z)))
else:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z) \
*sin(2*a*atan(sqrt(z)) - 2*pi*a)
def mrv(e, x):
"""Returns a SubsSet of most rapidly varying (mrv) subexpressions of 'e',
and e rewritten in terms of these"""
e = powsimp(e, deep=True, combine='exp')
assert isinstance(e, Basic)
if not e.has(x):
return SubsSet(), e
elif e == x:
s = SubsSet()
return s, s[x]
elif e.is_Mul or e.is_Add:
i, d = e.as_independent(x) # throw away x-independent terms
if d.func != e.func:
s, expr = mrv(d, x)
return s, e.func(i, expr)
a, b = d.as_two_terms()
s1, e1 = mrv(a, x)
s2, e2 = mrv(b, x)
return mrv_max1(s1, s2, e.func(i, e1, e2), x)
elif e.is_Pow:
b, e = e.as_base_exp()
if e.has(x):
return mrv(exp(e * log(b)), x)
else:
s, expr = mrv(b, x)
return s, expr**e
elif e.func is log:
s, expr = mrv(e.args[0], x)
return s, log(expr)
elif e.func is exp:
# We know from the theory of this algorithm that exp(log(...)) may always
# be simplified here, and doing so is vital for termination.
if e.args[0].func is log:
return mrv(e.args[0].args[0], x)
if limitinf(e.args[0], x).is_unbounded:
s1 = SubsSet()
e1 = s1[e]
s2, e2 = mrv(e.args[0], x)
su = s1.union(s2)[0]
su.rewrites[e1] = exp(e2)
return mrv_max3(s1, e1, s2, exp(e2), su, e1, x)
else:
s, expr = mrv(e.args[0], x)
return s, exp(expr)
elif e.is_Function:
l = [mrv(a, x) for a in e.args]
l2 = [s for (s, _) in l if s != SubsSet()]
if len(l2) != 1:
# e.g. something like BesselJ(x, x)
raise NotImplementedError("MRV set computation for functions in"
" several variables not implemented.")
s, ss = l2[0], SubsSet()
args = [ss.do_subs(x[1]) for x in l]
return s, e.func(*args)
elif e.is_Derivative:
raise NotImplementedError("MRV set computation for derviatives"
" not implemented yet.")
return mrv(e.args[0], x)
raise NotImplementedError(
"Don't know how to calculate the mrv of '%s'" % e)
def _expr_big(cls, a, z, n):
if n.is_even:
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n - 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))
else:
n -= 1
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))
def _expr_big(cls, a, z, n):
if n.is_even:
return ((sqrt(z) + 1)**(2*a)*exp(2*pi*I*n*a) +
(sqrt(z) - 1)**(2*a)*exp(2*pi*I*(n - 1)*a))/2
else:
n -= 1
return ((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) +
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))/2
def test_wavefunction():
Psi = {
0: (nu/pi)**(S(1)/4) * exp(-nu * x**2 /2),
1: (nu/pi)**(S(1)/4) * sqrt(2*nu) * x * exp(-nu * x**2 /2),
2: (nu/pi)**(S(1)/4) * (2 * nu * x**2 - 1)/sqrt(2) * exp(-nu * x**2 /2),
3: (nu/pi)**(S(1)/4) * sqrt(nu/3) * (2 * nu * x**3 - 3 * x) * exp(-nu * x**2 /2)
}
for n in Psi:
assert simplify(psi_n(n, x, m, omega) - Psi[n]) == 0
def test_exp():
A = MatrixSymbol('A', 2, 2)
B = MatrixSymbol('B', 2, 2)
expr1 = exp(A)*exp(B)
expr2 = exp(B)*exp(A)
assert expr1 != expr2
assert expr1 - expr2 != 0
assert not isinstance(expr1, exp)
assert not isinstance(expr2, exp)
def _expr_big(cls, a, z, n):
from sympy import I, pi, exp, sqrt, atan, sin
if n.is_even:
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n - 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))
else:
n -= 1
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))
def _expr_big(cls, a, z, n):
from sympy import I, pi, exp, sqrt, atan, cos
if n.is_even:
return ((sqrt(z) + 1)**(2*a)*exp(2*pi*I*n*a) +
(sqrt(z) - 1)**(2*a)*exp(2*pi*I*(n - 1)*a))/2
else:
n -= 1
return ((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) +
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))/2
def rs_exp(p, x, prec):
"""
Exponentiation of a series modulo ``O(x**prec)``
Examples
========
>>> from sympy.polys.domains import QQ
>>> from sympy.polys.rings import ring
>>> from sympy.polys.ring_series import rs_exp
>>> R, x = ring('x', QQ)
>>> rs_exp(x**2, x, 7)
1/6*x**6 + 1/2*x**4 + x**2 + 1
"""
if rs_is_puiseux(p, x):
return rs_puiseux(rs_exp, p, x, prec)
R = p.ring
c = _get_constant_term(p, x)
if c:
if R.domain is EX:
c_expr = c.as_expr()
const = exp(c_expr)
elif isinstance(c, PolyElement):
try:
c_expr = c.as_expr()
const = R(exp(c_expr))
except ValueError:
R = R.add_gens([exp(c_expr)])
p = p.set_ring(R)
x = x.set_ring(R)
c = c.set_ring(R)
const = R(exp(c_expr))
else:
try:
const = R(exp(c))
except ValueError:
raise DomainError("The given series can't be expanded in "
"this domain.")
p1 = p - c
# Makes use of sympy fuctions to evaluate the values of the cos/sin
# of the constant term.
return const*rs_exp(p1, x, prec)
if len(p) > 20:
return _exp1(p, x, prec)
one = R(1)
n = 1
k = 1
c = []
for k in range(prec):
c.append(one/n)
k += 1
n *= k
r = rs_series_from_list(p, c, x, prec)
return r
def solve_ODE_second_order(eq, f):
"""
solves many kinds of second order odes, different methods are used
depending on the form of the given equation. So far the constants
coefficients case and a special case are implemented.
"""
x = f.args[0]
f = f.func
#constant coefficients case: af''(x)+bf'(x)+cf(x)=0
a = Wild('a', exclude=[x])
b = Wild('b', exclude=[x])
c = Wild('c', exclude=[x])
r = eq.match(a*f(x).diff(x,x) + c*f(x))
if r:
return Symbol("C1")*C.sin(sqrt(r[c]/r[a])*x)+Symbol("C2")*C.cos(sqrt(r[c]/r[a])*x)
r = eq.match(a*f(x).diff(x,x) + b*diff(f(x),x) + c*f(x))
if r:
r1 = solve(r[a]*x**2 + r[b]*x + r[c], x)
if r1[0].is_real:
if len(r1) == 1:
return (Symbol("C1") + Symbol("C2")*x)*exp(r1[0]*x)
else:
return Symbol("C1")*exp(r1[0]*x) + Symbol("C2")*exp(r1[1]*x)
else:
r2 = abs((r1[0] - r1[1])/(2*S.ImaginaryUnit))
return (Symbol("C2")*C.cos(r2*x) + Symbol("C1")*C.sin(r2*x))*exp((r1[0] + r1[1])*x/2)
#other cases of the second order odes will be implemented here
#special equations, that we know how to solve
a = Wild('a')
t = x*C.exp(f(x))
tt = a*t.diff(x, x)/t
r = eq.match(tt.expand())
if r:
return -solve_ODE_1(f(x), x)
t = x*C.exp(-f(x))
tt = a*t.diff(x, x)/t
r = eq.match(tt.expand())
if r:
#check, that we've rewritten the equation correctly:
#assert ( r[a]*t.diff(x,2)/t ) == eq.subs(f, t)
return solve_ODE_1(f(x), x)
neq = eq*C.exp(f(x))/C.exp(-f(x))
r = neq.match(tt.expand())
if r:
#check, that we've rewritten the equation correctly:
#assert ( t.diff(x,2)*r[a]/t ).expand() == eq
return solve_ODE_1(f(x), x)
raise NotImplementedError("solve_ODE_second_order: cannot solve " + str(eq))
def test_rs_series():
x, a, b, c = symbols('x, a, b, c')
assert rs_series(a, a, 5).as_expr() == a
assert rs_series(sin(a), a, 5).as_expr() == (sin(a).series(a, 0,
5)).removeO()
assert rs_series(sin(a) + cos(a), a, 5).as_expr() == ((sin(a) +
cos(a)).series(a, 0, 5)).removeO()
assert rs_series(sin(a)*cos(a), a, 5).as_expr() == ((sin(a)*
cos(a)).series(a, 0, 5)).removeO()
p = (sin(a) - a)*(cos(a**2) + a**4/2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(p.series(a, 0,
10).removeO())
p = sin(a**2/2 + a/3) + cos(a/5)*sin(a/2)**3
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = sin(x**2 + a)*(cos(x**3 - 1) - a - a**2)
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = sin(a**2 - a/3 + 2)**5*exp(a**3 - a/2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(p.series(a, 0,
10).removeO())
p = sin(a + b + c)
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = tan(sin(a**2 + 4) + b + c)
assert expand(rs_series(p, a, 6).as_expr()) == expand(p.series(a, 0,
6).removeO())
p = a**QQ(2,5) + a**QQ(2,3) + a
r = rs_series(tan(p), a, 2)
assert r.as_expr() == a**QQ(9,5) + a**QQ(26,15) + a**QQ(22,15) + a**QQ(6,5)/3 + \
a + a**QQ(2,3) + a**QQ(2,5)
r = rs_series(exp(p), a, 1)
assert r.as_expr() == a**QQ(4,5)/2 + a**QQ(2,3) + a**QQ(2,5) + 1
r = rs_series(sin(p), a, 2)
assert r.as_expr() == -a**QQ(9,5)/2 - a**QQ(26,15)/2 - a**QQ(22,15)/2 - \
a**QQ(6,5)/6 + a + a**QQ(2,3) + a**QQ(2,5)
r = rs_series(cos(p), a, 2)
assert r.as_expr() == a**QQ(28,15)/6 - a**QQ(5,3) + a**QQ(8,5)/24 - a**QQ(7,5) - \
a**QQ(4,3)/2 - a**QQ(16,15) - a**QQ(4,5)/2 + 1
assert rs_series(sin(a)/7, a, 5).as_expr() == (sin(a)/7).series(a, 0,
5).removeO()
def test_functional_exponent():
t = standard_transformations + (convert_xor, function_exponentiation)
x = Symbol('x')
y = Symbol('y')
a = Symbol('a')
yfcn = Function('y')
assert parse_expr("sin^2(x)", transformations=t) == (sin(x))**2
assert parse_expr("sin^y(x)", transformations=t) == (sin(x))**y
assert parse_expr("exp^y(x)", transformations=t) == (exp(x))**y
assert parse_expr("E^y(x)", transformations=t) == exp(yfcn(x))
assert parse_expr("a^y(x)", transformations=t) == a**(yfcn(x))
def test_bounded():
x, y = symbols('x,y')
assert ask(Q.bounded(x)) == False
assert ask(Q.bounded(x), Q.bounded(x)) == True
assert ask(Q.bounded(x), Q.bounded(y)) == False
assert ask(Q.bounded(x), Q.complex(x)) == False
assert ask(Q.bounded(x+1)) == False
assert ask(Q.bounded(x+1), Q.bounded(x)) == True
assert ask(Q.bounded(x+y)) == None
assert ask(Q.bounded(x+y), Q.bounded(x)) == False
assert ask(Q.bounded(x+1), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(2*x)) == False
assert ask(Q.bounded(2*x), Q.bounded(x)) == True
assert ask(Q.bounded(x*y)) == None
assert ask(Q.bounded(x*y), Q.bounded(x)) == False
assert ask(Q.bounded(x*y), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(x**2)) == False
assert ask(Q.bounded(2**x)) == False
assert ask(Q.bounded(2**x), Q.bounded(x)) == True
assert ask(Q.bounded(x**x)) == False
assert ask(Q.bounded(Rational(1,2) ** x)) == None
assert ask(Q.bounded(Rational(1,2) ** x), Q.positive(x)) == True
assert ask(Q.bounded(Rational(1,2) ** x), Q.negative(x)) == False
assert ask(Q.bounded(sqrt(x))) == False
# sign function
assert ask(Q.bounded(sign(x))) == True
assert ask(Q.bounded(sign(x)), ~Q.bounded(x)) == True
# exponential functions
assert ask(Q.bounded(log(x))) == False
assert ask(Q.bounded(log(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(x))) == False
assert ask(Q.bounded(exp(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(2))) == True
# trigonometric functions
assert ask(Q.bounded(sin(x))) == True
assert ask(Q.bounded(sin(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(cos(x))) == True
assert ask(Q.bounded(cos(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(2*sin(x))) == True
assert ask(Q.bounded(sin(x)**2)) == True
assert ask(Q.bounded(cos(x)**2)) == True
assert ask(Q.bounded(cos(x) + sin(x))) == True
def test_bounded():
x, y = symbols('xy')
assert ask(x, Q.bounded) == False
assert ask(x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x, Q.bounded, Assume(y, Q.bounded)) == False
assert ask(x, Q.bounded, Assume(x, Q.complex)) == False
assert ask(x+1, Q.bounded) == False
assert ask(x+1, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x+y, Q.bounded) == None
assert ask(x+y, Q.bounded, Assume(x, Q.bounded)) == False
assert ask(x+1, Q.bounded, Assume(x, Q.bounded) & \
Assume(y, Q.bounded)) == True
assert ask(2*x, Q.bounded) == False
assert ask(2*x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x*y, Q.bounded) == None
assert ask(x*y, Q.bounded, Assume(x, Q.bounded)) == False
assert ask(x*y, Q.bounded, Assume(x, Q.bounded) & \
Assume(y, Q.bounded)) == True
assert ask(x**2, Q.bounded) == False
assert ask(2**x, Q.bounded) == False
assert ask(2**x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x**x, Q.bounded) == False
assert ask(Rational(1,2) ** x, Q.bounded) == True
assert ask(x ** Rational(1,2), Q.bounded) == False
# sign function
assert ask(sign(x), Q.bounded) == True
assert ask(sign(x), Q.bounded, Assume(x, Q.bounded, False)) == True
# exponential functions
assert ask(log(x), Q.bounded) == False
assert ask(log(x), Q.bounded, Assume(x, Q.bounded)) == True
assert ask(exp(x), Q.bounded) == False
assert ask(exp(x), Q.bounded, Assume(x, Q.bounded)) == True
assert ask(exp(2), Q.bounded) == True
# trigonometric functions
assert ask(sin(x), Q.bounded) == True
assert ask(sin(x), Q.bounded, Assume(x, Q.bounded, False)) == True
assert ask(cos(x), Q.bounded) == True
assert ask(cos(x), Q.bounded, Assume(x, Q.bounded, False)) == True
assert ask(2*sin(x), Q.bounded) == True
assert ask(sin(x)**2, Q.bounded) == True
assert ask(cos(x)**2, Q.bounded) == True
assert ask(cos(x) + sin(x), Q.bounded) == True
def _expr_big(cls, a, x, n):
sgn = -1
if n.is_odd:
sgn = 1
n -= 1
return 2**(2*a - 1)*(1 + sgn*I*sqrt(x - 1))**(1 - 2*a) \
*exp(-2*n*pi*I*a)
def _eval_evalf(self, prec):
# The default code is insufficient for polar arguments.
# mpmath provides an optional argument "r", which evaluates
# G(z**(1/r)). I am not sure what its intended use is, but we hijack it
# here in the following way: to evaluate at a number z of |argument|
# less than (say) n*pi, we put r=1/n, compute z' = root(z, n)
# (carefully so as not to loose the branch information), and evaluate
# G(z'**(1/r)) = G(z'**n) = G(z).
from sympy.functions import exp_polar, ceiling
from sympy import Expr
import mpmath
z = self.argument
znum = self.argument._eval_evalf(prec)
if znum.has(exp_polar):
znum, branch = znum.as_coeff_mul(exp_polar)
if len(branch) != 1:
return
branch = branch[0].args[0]/I
else:
branch = S(0)
n = ceiling(abs(branch/S.Pi)) + 1
znum = znum**(S(1)/n)*exp(I*branch / n)
# Convert all args to mpf or mpc
try:
[z, r, ap, bq] = [arg._to_mpmath(prec)
for arg in [znum, 1/n, self.args[0], self.args[1]]]
except ValueError:
return
with mpmath.workprec(prec):
v = mpmath.meijerg(ap, bq, z, r)
return Expr._from_mpmath(v, prec)
def roots_cyclotomic(f, factor=False):
"""Compute roots of cyclotomic polynomials. """
L, U = _inv_totient_estimate(f.degree())
for n in xrange(L, U + 1):
g = cyclotomic_poly(n, f.gen, polys=True)
if f == g:
break
else: # pragma: no cover
raise RuntimeError("failed to find index of a cyclotomic polynomial")
roots = []
if not factor:
for k in xrange(1, n + 1):
if igcd(k, n) == 1:
roots.append(exp(2*k*S.Pi*I/n).expand(complex=True))
else:
g = Poly(f, extension=(-1)**Rational(1, n))
for h, _ in g.factor_list()[1]:
roots.append(-h.TC())
return sorted(roots, key=default_sort_key)
def roots_binomial(f):
"""Returns a list of roots of a binomial polynomial."""
n = f.degree()
a, b = f.nth(n), f.nth(0)
alpha = (-cancel(b/a))**Rational(1, n)
if alpha.is_number:
alpha = alpha.expand(complex=True)
roots, I = [], S.ImaginaryUnit
for k in xrange(n):
zeta = exp(2*k*S.Pi*I/n).expand(complex=True)
roots.append((alpha*zeta).expand(power_base=False))
if all([ r.is_number for r in roots ]):
reals, complexes = [], []
for root in roots:
if root.is_real:
reals.append(root)
else:
complexes.append(root)
roots = sorted(reals) + sorted(complexes, key=lambda r: (re(r), -im(r)))
return roots
def mrv(e, x):
"Returns a python set of most rapidly varying (mrv) subexpressions of 'e'"
assert isinstance(e, Basic)
if not e.has(x):
return set([])
elif e == x:
return set([x])
elif e.is_Mul:
a, b = e.as_two_terms()
return mrv_max(mrv(a,x), mrv(b,x), x)
elif e.is_Add:
a, b = e.as_two_terms()
return mrv_max(mrv(a,x), mrv(b,x), x)
elif e.is_Pow:
if e.exp.has(x):
return mrv(exp(e.exp * log(e.base)), x)
else:
return mrv(e.base, x)
elif e.func is log:
return mrv(e.args[0], x)
elif e.func is exp:
if limitinf(e.args[0], x) in [oo,-oo]:
return mrv_max(set([e]), mrv(e.args[0], x), x)
else:
return mrv(e.args[0], x)
elif e.is_Function:
if len(e.args) == 1:
return mrv(e.args[0], x)
#only functions of 1 argument currently implemented
raise NotImplementedError("Functions with more arguments: '%s'" % e)
raise NotImplementedError("Don't know how to calculate the mrv of '%s'" % e)
def psi_n(n, x, m, omega):
"""
Returns the wavefunction psi_{n} for the One-dimensional harmonic oscillator.
``n``
the "nodal" quantum number. Corresponds to the number of nodes in the
wavefunction. n >= 0
``x``
x coordinate
``m``
mass of the particle
``omega``
angular frequency of the oscillator
:Examples
========
>>> from sympy.physics.qho_1d import psi_n
>>> from sympy import var
>>> var("x m omega")
(x, m, omega)
>>> psi_n(0, x, m, omega)
(m*omega)**(1/4)*exp(-m*omega*x**2/(2*hbar))/(hbar**(1/4)*pi**(1/4))
"""
# sympify arguments
n, x, m, omega = map(S, [n, x, m, omega])
nu = m * omega / hbar
# normalization coefficient
C = (nu/pi)**(S(1)/4) * sqrt(1/(2**n*factorial(n)))
return C * exp(-nu* x**2 /2) * hermite(n, sqrt(nu)*x)
def test_Pow():
assert rust_code(1/x) == "x.recip()"
assert rust_code(x**-1) == rust_code(x**-1.0) == "x.recip()"
assert rust_code(sqrt(x)) == "x.sqrt()"
assert rust_code(x**S.Half) == rust_code(x**0.5) == "x.sqrt()"
assert rust_code(1/sqrt(x)) == "x.sqrt().recip()"
assert rust_code(x**-S.Half) == rust_code(x**-0.5) == "x.sqrt().recip()"
assert rust_code(1/pi) == "PI.recip()"
assert rust_code(pi**-1) == rust_code(pi**-1.0) == "PI.recip()"
assert rust_code(pi**-0.5) == "PI.sqrt().recip()"
assert rust_code(x**Rational(1, 3)) == "x.cbrt()"
assert rust_code(2**x) == "x.exp2()"
assert rust_code(exp(x)) == "x.exp()"
assert rust_code(x**3) == "x.powi(3)"
assert rust_code(x**(y**3)) == "x.powf(y.powi(3))"
assert rust_code(x**Rational(2, 3)) == "x.powf(2_f64/3.0)"
g = implemented_function('g', Lambda(x, 2*x))
assert rust_code(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
"(3.5*2*x).powf(-x + y.powf(x))/(x.powi(2) + y)"
_cond_cfunc = [(lambda base, exp: exp.is_integer, "dpowi", 1),
(lambda base, exp: not exp.is_integer, "pow", 1)]
assert rust_code(x**3, user_functions={'Pow': _cond_cfunc}) == 'x.dpowi(3)'
assert rust_code(x**3.2, user_functions={'Pow': _cond_cfunc}) == 'x.pow(3.2)'
def roots_binomial(f):
"""Returns a list of roots of a binomial polynomial."""
add_comment("Solve the equation")
add_eq(f.as_expr(), 0)
n = f.degree()
a, b = f.nth(n), f.nth(0)
add_comment("Rewrite this equation as")
add_eq(f.gen**n, -b/a)
alpha = (-cancel(b/a))**Rational(1, n)
if alpha.is_number:
alpha = alpha.expand(complex=True)
roots, I = [], S.ImaginaryUnit
add_comment("We have the following roots")
for k in xrange(n):
zeta = exp(2*k*S.Pi*I/n).expand(complex=True)
add_eq(f.gen, Mul(alpha, zeta, evaluate=False))
roots.append((alpha*zeta).expand(power_base=False))
roots = sorted(roots, key=default_sort_key)
return roots
def test_rs_series():
x, a, b, c = symbols('x, a, b, c')
assert rs_series(a, a, 5).as_expr() == a
assert rs_series(sin(1/a), a, 5).as_expr() == sin(1/a)
assert rs_series(sin(a), a, 5).as_expr() == (sin(a).series(a, 0,
5)).removeO()
assert rs_series(sin(a) + cos(a), a, 5).as_expr() == ((sin(a) +
cos(a)).series(a, 0, 5)).removeO()
assert rs_series(sin(a)*cos(a), a, 5).as_expr() == ((sin(a)*
cos(a)).series(a, 0, 5)).removeO()
p = (sin(a) - a)*(cos(a**2) + a**4/2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(p.series(a, 0,
10).removeO())
p = sin(a**2/2 + a/3) + cos(a/5)*sin(a/2)**3
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = sin(x**2 + a)*(cos(x**3 - 1) - a - a**2)
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = sin(a**2 - a/3 + 2)**5*exp(a**3 - a/2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(p.series(a, 0,
10).removeO())
p = sin(a + b + c)
assert expand(rs_series(p, a, 5).as_expr()) == expand(p.series(a, 0,
5).removeO())
p = tan(sin(a**2 + 4) + b + c)
assert expand(rs_series(p, a, 6).as_expr()) == expand(p.series(a, 0,
6).removeO())
def roots_cyclotomic(f, factor=False):
"""Compute roots of cyclotomic polynomials. """
L, U = _inv_totient_estimate(f.degree())
for n in range(L, U + 1):
g = cyclotomic_poly(n, f.gen, polys=True)
if f == g:
break
else: # pragma: no cover
raise RuntimeError("failed to find index of a cyclotomic polynomial")
roots = []
if not factor:
# get the indices in the right order so the computed
# roots will be sorted
h = n//2
ks = [i for i in range(1, n + 1) if igcd(i, n) == 1]
ks.sort(key=lambda x: (x, -1) if x <= h else (abs(x - n), 1))
d = 2*I*pi/n
for k in reversed(ks):
roots.append(exp(k*d).expand(complex=True))
else:
g = Poly(f, extension=root(-1, n))
for h, _ in ordered(g.factor_list()[1]):
roots.append(-h.TC())
return roots
def parse_term(expr):
"""Parses expression expr and outputs tuple (sexpr, rat_expo,
sym_expo, deriv)
where:
- sexpr is the base expression
- rat_expo is the rational exponent that sexpr is raised to
- sym_expo is the symbolic exponent that sexpr is raised to
- deriv contains the derivatives the the expression
for example, the output of x would be (x, 1, None, None)
the output of 2**x would be (2, 1, x, None)
"""
rat_expo, sym_expo = S.One, None
sexpr, deriv = expr, None
if expr.is_Pow:
if isinstance(expr.base, Derivative):
sexpr, deriv = parse_derivative(expr.base)
else:
sexpr = expr.base
if expr.exp.is_Number:
rat_expo = expr.exp
else:
coeff, tail = expr.exp.as_coeff_Mul()
if coeff.is_Number:
rat_expo, sym_expo = coeff, tail
else:
sym_expo = expr.exp
elif isinstance(expr, exp):
arg = expr.args[0]
if arg.is_Rational:
sexpr, rat_expo = S.Exp1, arg
elif arg.is_Mul:
coeff, tail = arg.as_coeff_Mul(rational=True)
sexpr, rat_expo = exp(tail), coeff
elif isinstance(expr, Derivative):
sexpr, deriv = parse_derivative(expr)
return sexpr, rat_expo, sym_expo, deriv
def _eval_evalf(self, prec):
# The default code is insufficient for polar arguments.
# mpmath provides an optional argument "r", which evaluates
# G(z**(1/r)). I am not sure what its intended use is, but we hijack it
# here in the following way: to evaluate at a number z of |argument|
# less than (say) n*pi, we put r=1/n, compute z' = root(z, n)
# (carefully so as not to loose the branch information), and evaluate
# G(z'**(1/r)) = G(z'**n) = G(z).
from sympy.functions import exp_polar, ceiling
from sympy import mpmath, Expr
z = self.argument
znum = self.argument._eval_evalf(prec)
if znum.has(exp_polar):
znum, branch = znum.as_coeff_mul(exp_polar)
if len(branch) != 1:
return
branch = branch[0].args[0]/I
else:
branch = S(0)
n = ceiling(abs(branch/S.Pi)) + 1
znum = znum**(S(1)/n)*exp(I*branch / n)
#print znum, branch, n
# Convert all args to mpf or mpc
try:
[z, r, ap, bq] = [arg._to_mpmath(prec)
for arg in [znum, 1/n, self.args[0], self.args[1]]]
except ValueError:
return
# Set mpmath precision and apply. Make sure precision is restored
# afterwards
orig = mpmath.mp.prec
try:
mpmath.mp.prec = prec
v = mpmath.meijerg(ap, bq, z, r)
#print ap, bq, z, r, v
finally:
mpmath.mp.prec = orig
return Expr._from_mpmath(v, prec)
def mrv(e, x):
"Returns a python set of most rapidly varying (mrv) subexpressions of 'e'"
e = powsimp(e, deep=True, combine='exp')
assert isinstance(e, Basic)
if not e.has(x):
return set([])
elif e == x:
return set([x])
elif e.is_Mul or e.is_Add:
while 1:
i, d = e.as_independent(x) # throw away x-independent terms
if d.func != e.func and (d.is_Add or d.is_Mul):
e = d
continue
break
if d.func != e.func:
return mrv(d, x)
a, b = d.as_two_terms()
return mrv_max(mrv(a, x), mrv(b, x), x)
elif e.is_Pow:
b, e = e.as_base_exp()
if e.has(x):
return mrv(exp(e * log(b)), x)
else:
return mrv(b, x)
elif e.func is log:
return mrv(e.args[0], x)
elif e.func is exp:
if limitinf(e.args[0], x).is_unbounded:
return mrv_max(set([e]), mrv(e.args[0], x), x)
else:
return mrv(e.args[0], x)
elif e.is_Function:
return reduce(lambda a, b: mrv_max(a, b, x),
[mrv(a, x) for a in e.args])
elif e.is_Derivative:
return mrv(e.args[0], x)
raise NotImplementedError("Don't know how to calculate the mrv of '%s'" %
e)
def rewrite(e,Omega,x,wsym):
"""e(x) ... the function
Omega ... the mrv set
wsym ... the symbol which is going to be used for w
Returns the rewritten e in terms of w and log(w). See test_rewrite1()
for examples and correct results.
"""
assert isinstance(Omega, set)
assert len(Omega)!=0
#all items in Omega must be exponentials
for t in Omega: assert t.func is exp
def cmpfunc(a,b):
return -cmp(len(mrv(a,x)), len(mrv(b,x)))
#sort Omega (mrv set) from the most complicated to the simplest ones
#the complexity of "a" from Omega: the length of the mrv set of "a"
Omega = list(Omega)
Omega.sort(cmp=cmpfunc)
g=Omega[-1] #g is going to be the "w" - the simplest one in the mrv set
sig = (sign(g.args[0], x) == 1)
if sig: wsym=1/wsym #if g goes to oo, substitute 1/w
#O2 is a list, which results by rewriting each item in Omega using "w"
O2=[]
for f in Omega:
c=mrv_leadterm(f.args[0]/g.args[0], x)
#the c is a constant, because both f and g are from Omega:
assert c[1] == 0
O2.append(exp((f.args[0]-c[0]*g.args[0]).expand())*wsym**c[0])
#Remember that Omega contains subexpressions of "e". So now we find
#them in "e" and substitute them for our rewriting, stored in O2
f=e
for a,b in zip(Omega,O2):
f=f.subs(a,b)
#finally compute the logarithm of w (logw).
logw=g.args[0]
if sig: logw=-logw #log(w)->log(1/w)=-log(w)
return f, logw
def ccfunc(μ,dists):
x = Symbol('x')
t = Symbol('t')
func = -t*μ
# func = exp(-t*μ)
if "uniform" in dists:
for interval, num in dists["uniform"].items():
func += num*log(integrate(exp(t*x)/(2*interval),(x,-interval,interval)))
# func *= integrate(exp(t*x)/(2*interval),(x,-interval,interval))**num
if "normal" in dists:
# func += log(integrate(exp(t*x)/(sqrt(2*pi*dists["normal"]))*exp(-(x**2)/(2*dists["normal"])),(x,-oo,oo)))
func += (t**2)*dists["normal"]/2
# func *= exp((t**2)*dists["normal"]/2)
# print(func)
def numccfunc(numtarr):
return np.array([func.subs([(t,numt)]).evalf() for numt in numtarr],dtype=np.float64)
# return func.subs([(t,numtarr)]).evalf()
def diffccfunc(numtarr):
return np.array([diff(func,t).subs([(t,numt)]).evalf() for numt in numtarr],dtype=np.float64)
# return diff(func,t).subs([(t,numtarr)]).evalf()
def funwithjac(numtarr):
return numccfunc(numtarr),diffccfunc(numtarr)
return numccfunc, diffccfunc, funwithjac
def test_rs_series():
x, a, b, c = symbols('x, a, b, c')
assert rs_series(a, a, 5).as_expr() == a
assert rs_series(sin(1 / a), a, 5).as_expr() == sin(1 / a)
assert rs_series(sin(a), a, 5).as_expr() == (sin(a).series(a, 0,
5)).removeO()
assert rs_series(sin(a) + cos(a), a,
5).as_expr() == ((sin(a) + cos(a)).series(a, 0,
5)).removeO()
assert rs_series(sin(a) * cos(a), a,
5).as_expr() == ((sin(a) * cos(a)).series(a, 0,
5)).removeO()
p = (sin(a) - a) * (cos(a**2) + a**4 / 2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(
p.series(a, 0, 10).removeO())
p = sin(a**2 / 2 + a / 3) + cos(a / 5) * sin(a / 2)**3
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = sin(x**2 + a) * (cos(x**3 - 1) - a - a**2)
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = sin(a**2 - a / 3 + 2)**5 * exp(a**3 - a / 2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(
p.series(a, 0, 10).removeO())
p = sin(a + b + c)
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = tan(sin(a**2 + 4) + b + c)
assert expand(rs_series(p, a, 6).as_expr()) == expand(
p.series(a, 0, 6).removeO())
def test_Matrices():
assert maple_code(Matrix(1, 1,
[10])) == "Matrix([[10]], storage = rectangular)"
A = Matrix([[1, sin(x / 2), abs(x)], [0, 1, pi], [0, exp(1), ceiling(x)]])
expected = ("Matrix("
"[[1, sin((1/2)*x), abs(x)],"
" [0, 1, Pi],"
" [0, exp(1), ceil(x)]], "
"storage = rectangular)")
assert maple_code(A) == expected
# row and columns
assert maple_code(A[:,
0]) == "Matrix([[1], [0], [0]], storage = rectangular)"
assert (maple_code(A[0, :]) ==
"Matrix([[1, sin((1/2)*x), abs(x)]], storage = rectangular)")
assert (maple_code(Matrix(
[[x, x - y,
-y]])) == "Matrix([[x, x - y, -y]], storage = rectangular)")
# empty matrices
assert maple_code(Matrix(0, 0, [])) == "Matrix([], storage = rectangular)"
assert maple_code(Matrix(0, 3, [])) == "Matrix([], storage = rectangular)"
def test_sympy_parser():
x = Symbol('x')
inputs = {
'2*x': 2 * x,
'3.00': Float(3),
'22/7': Rational(22, 7),
'2+3j': 2 + 3*I,
'exp(x)': exp(x),
'x!': factorial(x),
'3.[3]': Rational(10, 3),
'10!': 3628800,
'-(2)': -Integer(2),
'[-1, -2, 3]': [Integer(-1), Integer(-2), Integer(3)],
'Symbol("x").free_symbols': x.free_symbols,
"S('S(3).n(n=3)')": 3.00,
'factorint(12, visual=True)': Mul(
Pow(2, 2, evaluate=False),
Pow(3, 1, evaluate=False),
evaluate=False),
'Limit(sin(x), x, 0, dir="-")': Limit(sin(x), x, 0, dir='-'),
}
for text, result in inputs.items():
assert parse_expr(text) == result
def _expr_big_minus(cls, a, x, n):
if a.is_integer:
return cls._expr_small_minus(a, x)
return (1 + x)**a * exp(2 * n * pi * I * a)
def _expr_big(cls, a, x, n):
if a.is_integer:
return cls._expr_small(a, x)
return (x - 1)**a * exp((2 * n - 1) * pi * I * a)
def pde_1st_linear_constant_coeff_homogeneous(eq, func, order, match, solvefun):
r"""
Solves a first order linear homogeneous
partial differential equation with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{df(x,y)}{dx} + b \frac{df(x,y)}{dy} + c f(x,y) = 0
where `a`, `b` and `c` are constants.
The general solution is of the form::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*ux + b*uy + c*u
>>> pprint(genform)
d d
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y)
dx dy
>>> pprint(pdsolve(genform))
-c*(a*x + b*y)
---------------
2 2
a + b
f(x, y) = F(-a*y + b*x)*e
Examples
========
>>> from sympy.solvers.pde import (
... pde_1st_linear_constant_coeff_homogeneous)
>>> from sympy import pdsolve
>>> from sympy import Function, diff, pprint
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y))
f(x, y) == F(x - y)*exp(-x/2 - y/2)
>>> pprint(pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y)))
x y
- - - -
2 2
f(x, y) = F(x - y)*e
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
return Eq(f(x,y), exp(-S(d)/(b**2 + c**2)*(b*x + c*y))*solvefun(c*x - b*y))
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 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)')
def rewrite(e, Omega, x, wsym):
"""e(x) ... the function
Omega ... the mrv set
wsym ... the symbol which is going to be used for w
Returns the rewritten e in terms of w and log(w). See test_rewrite1()
for examples and correct results.
"""
from sympy import ilcm
if not isinstance(Omega, SubsSet):
raise TypeError("Omega should be an instance of SubsSet")
if len(Omega) == 0:
raise ValueError("Length can not be 0")
#all items in Omega must be exponentials
for t in Omega.keys():
if not t.func is exp:
raise ValueError("Value should be exp")
rewrites = Omega.rewrites
Omega = list(Omega.items())
nodes = build_expression_tree(Omega, rewrites)
Omega.sort(key=lambda x: nodes[x[1]].ht(), reverse=True)
# make sure we know the sign of each exp() term; after the loop,
# g is going to be the "w" - the simplest one in the mrv set
for g, _ in Omega:
sig = sign(g.args[0], x)
if sig != 1 and sig != -1:
raise NotImplementedError('Result depends on the sign of %s' % sig)
if sig == 1:
wsym = 1 / wsym # if g goes to oo, substitute 1/w
#O2 is a list, which results by rewriting each item in Omega using "w"
O2 = []
denominators = []
for f, var in Omega:
c = limitinf(f.args[0] / g.args[0], x)
if c.is_Rational:
denominators.append(c.q)
arg = f.args[0]
if var in rewrites:
if not rewrites[var].func is exp:
raise ValueError("Value should be exp")
arg = rewrites[var].args[0]
O2.append((var, exp((arg - c * g.args[0]).expand()) * wsym**c))
#Remember that Omega contains subexpressions of "e". So now we find
#them in "e" and substitute them for our rewriting, stored in O2
# the following powsimp is necessary to automatically combine exponentials,
# so that the .subs() below succeeds:
# TODO this should not be necessary
f = powsimp(e, deep=True, combine='exp')
for a, b in O2:
f = f.subs(a, b)
for _, var in Omega:
assert not f.has(var)
#finally compute the logarithm of w (logw).
logw = g.args[0]
if sig == 1:
logw = -logw # log(w)->log(1/w)=-log(w)
# Some parts of sympy have difficulty computing series expansions with
# non-integral exponents. The following heuristic improves the situation:
exponent = reduce(ilcm, denominators, 1)
f = f.subs(wsym, wsym**exponent)
logw /= exponent
return f, logw
def moveup(l, x):
return [e.xreplace({x: exp(x)}) for e in l]
def test_rcode_constants_mathh():
p=rcode(exp(1))
assert rcode(exp(1)) == "exp(1)"
assert rcode(pi) == "pi"
assert rcode(oo) == "Inf"
assert rcode(-oo) == "-Inf"
def test_ccode_constants_mathh():
assert ccode(exp(1)) == "M_E"
assert ccode(pi) == "M_PI"
assert ccode(oo) == "HUGE_VAL"
assert ccode(-oo) == "-HUGE_VAL"
def _expr_big_minus(cls, a, z, n):
if n.is_even:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z)*sin(2*a*atan(sqrt(z)))
else:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z) \
*sin(2*a*atan(sqrt(z)) - 2*pi*a)
def pdf(s, x):
"""Return the probability density function as an expression in x"""
x = sympify(x)
return 1 / (s.sigma * sqrt(2 * pi)) * exp(-(x - s.mu)**2 /
(2 * s.sigma**2))
def _expr_big_minus(cls, a, x, n):
sgn = 1
if n.is_odd:
sgn = -1
return sgn * 2**(2 * a - 1) * (sqrt(1 + x) + sgn)**(1 - 2 * a) * exp(
-2 * pi * I * a * n)
def sym_ter(x, y):
return 500 * exp(-(((x - 500)**2) +
((y - 500)**2)) / 40000) # gaussian "mountain"
def mrv(e, x):
"""Returns a SubsSet of most rapidly varying (mrv) subexpressions of 'e',
and e rewritten in terms of these"""
e = powsimp(e, deep=True, combine='exp')
if not isinstance(e, Basic):
raise TypeError("e should be an instance of Basic")
if not e.has(x):
return SubsSet(), e
elif e == x:
s = SubsSet()
return s, s[x]
elif e.is_Mul or e.is_Add:
i, d = e.as_independent(x) # throw away x-independent terms
if d.func != e.func:
s, expr = mrv(d, x)
return s, e.func(i, expr)
a, b = d.as_two_terms()
s1, e1 = mrv(a, x)
s2, e2 = mrv(b, x)
return mrv_max1(s1, s2, e.func(i, e1, e2), x)
elif e.is_Pow:
b, e = e.as_base_exp()
if b == 1:
return SubsSet(), b
if e.has(x):
return mrv(exp(e * log(b)), x)
else:
s, expr = mrv(b, x)
return s, expr**e
elif e.func is log:
s, expr = mrv(e.args[0], x)
return s, log(expr)
elif e.func is exp:
# We know from the theory of this algorithm that exp(log(...)) may always
# be simplified here, and doing so is vital for termination.
if e.args[0].func is log:
return mrv(e.args[0].args[0], x)
# if a product has an infinite factor the result will be
# infinite if there is no zero, otherwise NaN; here, we
# consider the result infinite if any factor is infinite
li = limitinf(e.args[0], x)
if any(_.is_infinite for _ in Mul.make_args(li)):
s1 = SubsSet()
e1 = s1[e]
s2, e2 = mrv(e.args[0], x)
su = s1.union(s2)[0]
su.rewrites[e1] = exp(e2)
return mrv_max3(s1, e1, s2, exp(e2), su, e1, x)
else:
s, expr = mrv(e.args[0], x)
return s, exp(expr)
elif e.is_Function:
l = [mrv(a, x) for a in e.args]
l2 = [s for (s, _) in l if s != SubsSet()]
if len(l2) != 1:
# e.g. something like BesselJ(x, x)
raise NotImplementedError("MRV set computation for functions in"
" several variables not implemented.")
s, ss = l2[0], SubsSet()
args = [ss.do_subs(x[1]) for x in l]
return s, e.func(*args)
elif e.is_Derivative:
raise NotImplementedError("MRV set computation for derviatives"
" not implemented yet.")
return mrv(e.args[0], x)
raise NotImplementedError("Don't know how to calculate the mrv of '%s'" %
e)
def moveup(l, x):
return [e.subs(x,exp(x)) for e in l]
def test_exp():
R, x = ring('x', QQ)
p = x + x**4
for h in [10, 30]:
q = rs_series_inversion(1 + p, x, h) - 1
p1 = rs_exp(q, x, h)
q1 = rs_log(p1, x, h)
assert q1 == q
p1 = rs_exp(p, x, 30)
assert p1.coeff(x**29) == QQ(74274246775059676726972369,
353670479749588078181744640000)
prec = 21
p = rs_log(1 + x, x, prec)
p1 = rs_exp(p, x, prec)
assert p1 == x + 1
# Constant term in series
a = symbols('a')
R, x, y = ring('x, y', QQ[exp(a), a])
assert rs_exp(x + a, x, 5) == exp(a)*x**4/24 + exp(a)*x**3/6 + \
exp(a)*x**2/2 + exp(a)*x + exp(a)
assert rs_exp(x + x**2*y + a, x, 5) == exp(a)*x**4*y**2/2 + \
exp(a)*x**4*y/2 + exp(a)*x**4/24 + exp(a)*x**3*y + \
exp(a)*x**3/6 + exp(a)*x**2*y + exp(a)*x**2/2 + exp(a)*x + exp(a)
R, x, y = ring('x, y', EX)
assert rs_exp(x + a, x, 5) == EX(exp(a)/24)*x**4 + EX(exp(a)/6)*x**3 + \
EX(exp(a)/2)*x**2 + EX(exp(a))*x + EX(exp(a))
assert rs_exp(x + x**2*y + a, x, 5) == EX(exp(a)/2)*x**4*y**2 + \
EX(exp(a)/2)*x**4*y + EX(exp(a)/24)*x**4 + EX(exp(a))*x**3*y + \
EX(exp(a)/6)*x**3 + EX(exp(a))*x**2*y + EX(exp(a)/2)*x**2 + \
EX(exp(a))*x + EX(exp(a))
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 test_rs_series():
x, a, b, c = symbols('x, a, b, c')
assert rs_series(a, a, 5).as_expr() == a
assert rs_series(sin(a), a, 5).as_expr() == (sin(a).series(a, 0,
5)).removeO()
assert rs_series(sin(a) + cos(a), a,
5).as_expr() == ((sin(a) + cos(a)).series(a, 0,
5)).removeO()
assert rs_series(sin(a) * cos(a), a,
5).as_expr() == ((sin(a) * cos(a)).series(a, 0,
5)).removeO()
p = (sin(a) - a) * (cos(a**2) + a**4 / 2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(
p.series(a, 0, 10).removeO())
p = sin(a**2 / 2 + a / 3) + cos(a / 5) * sin(a / 2)**3
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = sin(x**2 + a) * (cos(x**3 - 1) - a - a**2)
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = sin(a**2 - a / 3 + 2)**5 * exp(a**3 - a / 2)
assert expand(rs_series(p, a, 10).as_expr()) == expand(
p.series(a, 0, 10).removeO())
p = sin(a + b + c)
assert expand(rs_series(p, a, 5).as_expr()) == expand(
p.series(a, 0, 5).removeO())
p = tan(sin(a**2 + 4) + b + c)
assert expand(rs_series(p, a, 6).as_expr()) == expand(
p.series(a, 0, 6).removeO())
p = a**QQ(2, 5) + a**QQ(2, 3) + a
r = rs_series(tan(p), a, 2)
assert r.as_expr() == a**QQ(9,5) + a**QQ(26,15) + a**QQ(22,15) + a**QQ(6,5)/3 + \
a + a**QQ(2,3) + a**QQ(2,5)
r = rs_series(exp(p), a, 1)
assert r.as_expr() == a**QQ(4, 5) / 2 + a**QQ(2, 3) + a**QQ(2, 5) + 1
r = rs_series(sin(p), a, 2)
assert r.as_expr() == -a**QQ(9,5)/2 - a**QQ(26,15)/2 - a**QQ(22,15)/2 - \
a**QQ(6,5)/6 + a + a**QQ(2,3) + a**QQ(2,5)
r = rs_series(cos(p), a, 2)
assert r.as_expr() == a**QQ(28,15)/6 - a**QQ(5,3) + a**QQ(8,5)/24 - a**QQ(7,5) - \
a**QQ(4,3)/2 - a**QQ(16,15) - a**QQ(4,5)/2 + 1
assert rs_series(sin(a) / 7, a,
5).as_expr() == (sin(a) / 7).series(a, 0, 5).removeO()
assert rs_series(log(1 + x), x, 5).as_expr() == -x**4/4 + x**3/3 - \
x**2/2 + x
assert rs_series(log(1 + 4*x), x, 5).as_expr() == -64*x**4 + 64*x**3/3 - \
8*x**2 + 4*x
assert rs_series(log(1 + x + x**2), x, 10).as_expr() == -2*x**9/9 + \
x**8/8 + x**7/7 - x**6/3 + x**5/5 + x**4/4 - 2*x**3/3 + \
x**2/2 + x
assert rs_series(log(1 + x*a**2), x, 7).as_expr() == -x**6*a**12/6 + \
x**5*a**10/5 - x**4*a**8/4 + x**3*a**6/3 - \
x**2*a**4/2 + x*a**2
def test_get_math_macros():
macros = get_math_macros()
assert macros[exp(1)] == 'M_E'
assert macros[1 / Sqrt(2)] == 'M_SQRT1_2'
def test_sympy_parser():
x = Symbol('x')
inputs = {
'2*x':
2 * x,
'3.00':
Float(3),
'22/7':
Rational(22, 7),
'2+3j':
2 + 3 * I,
'exp(x)':
exp(x),
'x!':
factorial(x),
'x!!':
factorial2(x),
'(x + 1)! - 1':
factorial(x + 1) - 1,
'3.[3]':
Rational(10, 3),
'.0[3]':
Rational(1, 30),
'3.2[3]':
Rational(97, 30),
'1.3[12]':
Rational(433, 330),
'1 + 3.[3]':
Rational(13, 3),
'1 + .0[3]':
Rational(31, 30),
'1 + 3.2[3]':
Rational(127, 30),
'.[0011]':
Rational(1, 909),
'0.1[00102] + 1':
Rational(366697, 333330),
'1.[0191]':
Rational(10190, 9999),
'10!':
3628800,
'-(2)':
-Integer(2),
'[-1, -2, 3]': [Integer(-1), Integer(-2),
Integer(3)],
'Symbol("x").free_symbols':
x.free_symbols,
"S('S(3).n(n=3)')":
3.00,
'factorint(12, visual=True)':
Mul(Pow(2, 2, evaluate=False),
Pow(3, 1, evaluate=False),
evaluate=False),
'Limit(sin(x), x, 0, dir="-")':
Limit(sin(x), x, 0, dir='-'),
}
for text, result in inputs.items():
assert parse_expr(text) == result
raises(TypeError, lambda: parse_expr('x', standard_transformations))
raises(TypeError, lambda: parse_expr('x', transformations=lambda x, y: 1))
raises(TypeError,
lambda: parse_expr('x', transformations=(lambda x, y: 1,
)))
raises(TypeError, lambda: parse_expr('x', transformations=((), )))
raises(TypeError, lambda: parse_expr('x', {}, [], []))
raises(TypeError, lambda: parse_expr('x', [], [], {}))
raises(TypeError, lambda: parse_expr('x', [], [], {}))
def test_tensorflow_math():
if not tf:
skip("TensorFlow not installed")
expr = Abs(x)
assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = sign(x)
assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = ceiling(x)
assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = floor(x)
assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = exp(x)
assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = sqrt(x)
assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = x**4
assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = cos(x)
assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acos(x)
assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(0, 0.95))
expr = sin(x)
assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = asin(x)
assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = tan(x)
assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan(x)
assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan2(y, x)
assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
_compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())
expr = cosh(x)
assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acosh(x)
assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = sinh(x)
assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = asinh(x)
assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = tanh(x)
assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = atanh(x)
assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(-.5, .5))
expr = erf(x)
assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = loggamma(x)
assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
def pde_1st_linear_constant_coeff(eq, func, order, match, solvefun):
r"""
Solves a first order linear partial differential equation
with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{df(x,y)}{dx} + b \frac{df(x,y)}{dy} + c f(x,y) = G(x,y)
where `a`, `b` and `c` are constants and `G(x, y)` can be an arbitrary
function in `x` and `y`.
The general solution of the PDE is::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> G = Function('G')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*u + b*ux + c*uy - G(x,y)
>>> pprint(genform)
d d
a*f(x, y) + b*--(f(x, y)) + c*--(f(x, y)) - G(x, y)
dx dy
>>> pprint(pdsolve(genform, hint='1st_linear_constant_coeff_Integral'))
// b*x + c*y \
|| / |
|| | |
|| | a*xi |
|| | ------- |
|| | 2 2 |
|| | /b*xi + c*eta -b*eta + c*xi\ b + c |
|| | G|------------, -------------|*e d(xi)|
|| | | 2 2 2 2 | |
|| | \ b + c b + c / |
|| | |
|| / |
|| |
f(x, y) = ||F(eta) + -------------------------------------------------------|*
|| 2 2 |
\\ b + c /
<BLANKLINE>
\|
||
||
||
||
||
||
||
||
-a*xi ||
-------||
2 2||
b + c ||
e ||
||
/|eta=-b*y + c*x, xi=b*x + c*y
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, diff, pprint, exp
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> eq = -2*f(x,y).diff(x) + 4*f(x,y).diff(y) + 5*f(x,y) - exp(x + 3*y)
>>> pdsolve(eq)
f(x, y) == (F(4*x + 2*y) + exp(x/2 + 4*y)/15)*exp(x/2 - y)
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
xi, eta = symbols("xi eta")
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
e = -match[match['e']]
expterm = exp(-S(d)/(b**2 + c**2)*xi)
functerm = solvefun(eta)
solvedict = solve((b*x + c*y - xi, c*x - b*y - eta), x, y)
# Integral should remain as it is in terms of xi,
# doit() should be done in _handle_Integral.
genterm = (1/S(b**2 + c**2))*C.Integral(
(1/expterm*e).subs(solvedict), (xi, b*x + c*y))
return Eq(f(x,y), Subs(expterm*(functerm + genterm),
(eta, xi), (c*x - b*y, b*x + c*y)))
