def test_jscode_inline_function():
x = symbols('x')
g = implemented_function('g', Lambda(x, 2*x))
assert jscode(g(x)) == "2*x"
g = implemented_function('g', Lambda(x, 2*x/Catalan))
assert jscode(g(x)) == "var Catalan = %s;\n2*x/Catalan" % Catalan.n()
A = IndexedBase('A')
i = Idx('i', symbols('n', integer=True))
g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
assert jscode(g(A[i]), assign_to=A[i]) == (
"for (var i=0; i<n; i++){\n"
" A[i] = (A[i] + 1)*(A[i] + 2)*A[i];\n"
"}"
)
def test_jscode_Pow():
g = implemented_function('g', Lambda(x, 2*x))
assert jscode(x**3) == "Math.pow(x, 3)"
assert jscode(x**(y**3)) == "Math.pow(x, Math.pow(y, 3))"
assert jscode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
"Math.pow(3.5*2*x, -x + Math.pow(y, x))/(Math.pow(x, 2) + y)"
assert jscode(x**-1.0) == '1/x'
def test_Trace():
assert isinstance(Trace(A), Trace)
assert not isinstance(Trace(A), MatrixExpr)
pytest.raises(ShapeError, lambda: Trace(C))
assert trace(eye(3)) == 3
assert trace(Matrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])) == 15
assert adjoint(Trace(A)) == trace(Adjoint(A))
assert conjugate(Trace(A)) == trace(Adjoint(A))
assert transpose(Trace(A)) == Trace(A)
A / Trace(A) # Make sure this is possible
# Some easy simplifications
assert trace(Identity(5)) == 5
assert trace(ZeroMatrix(5, 5)) == 0
assert trace(2 * A * B) == 2 * Trace(A * B)
assert trace(A.T) == trace(A)
i, j = symbols('i j')
F = FunctionMatrix(3, 3, Lambda((i, j), i + j))
assert trace(F) == (0 + 0) + (1 + 1) + (2 + 2)
pytest.raises(TypeError, lambda: Trace(S.One))
assert Trace(A).arg is A
assert str(trace(A)) == str(Trace(A).doit())
def test_Pow():
assert mcode(x**3) == "x.^3"
assert mcode(x**(y**3)) == "x.^(y.^3)"
assert mcode(x**Rational(2, 3)) == 'x.^(2/3)'
g = implemented_function('g', Lambda(x, 2 * x))
assert mcode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
"(3.5*2*x).^(-x + y.^x)./(x.^2 + y)"
def test_ccode_Pow():
assert ccode(x**3) == "pow(x, 3)"
assert ccode(x**(y**3)) == "pow(x, pow(y, 3))"
g = implemented_function('g', Lambda(x, 2 * x))
assert ccode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
"pow(3.5*2*x, -x + pow(y, x))/(pow(x, 2) + y)"
assert ccode(x**-1.0) == '1.0/x'
assert ccode(x**Rational(2, 3)) == 'pow(x, 2.0L/3.0L)'
_cond_cfunc = [(lambda base, exp: exp.is_integer, "dpowi"),
(lambda base, exp: not exp.is_integer, "pow")]
assert ccode(x**3, user_functions={'Pow': _cond_cfunc}) == 'dpowi(x, 3)'
assert ccode(x**3.2, user_functions={'Pow': _cond_cfunc}) == 'pow(x, 3.2)'
def test_RootSum():
r = RootSum(x**3 + x + 3, Lambda(y, log(y*z)))
assert mcode(r) == ("RootSum[Function[{x}, x^3 + x + 3], "
"Function[{y}, Log[y*z]]]")
def test_Lambda():
f1 = Lambda(x, x**2)
assert mcode(f1) == "Function[{x}, x^2]"
f2 = Lambda((x, y), x + 2*y)
assert mcode(f2) == "Function[{x, y}, x + 2*y]"
def _eval_derivative(self, x):
var, expr = self.fun.args
func = Lambda(var, expr.diff(x))
return self.new(self.poly, func, self.auto)
def __new__(cls, expr, func=None, x=None, auto=True, quadratic=False):
"""Construct a new ``RootSum`` instance carrying all roots of a polynomial. """
coeff, poly = cls._transform(expr, x)
if not poly.is_univariate:
raise MultivariatePolynomialError(
"only univariate polynomials are allowed")
if func is None:
func = Lambda(poly.gen, poly.gen)
else:
try:
is_func = func.is_Function
except AttributeError:
is_func = False
if is_func and 1 in func.nargs:
if not isinstance(func, Lambda):
func = Lambda(poly.gen, func(poly.gen))
else:
raise ValueError("expected a univariate function, got %s" %
func)
var, expr = func.variables[0], func.expr
if coeff is not S.One:
expr = expr.subs(var, coeff * var)
deg = poly.degree()
if not expr.has(var):
return deg * expr
if expr.is_Add:
add_const, expr = expr.as_independent(var)
else:
add_const = S.Zero
if expr.is_Mul:
mul_const, expr = expr.as_independent(var)
else:
mul_const = S.One
func = Lambda(var, expr)
rational = cls._is_func_rational(poly, func)
(_, factors), terms = poly.factor_list(), []
for poly, k in factors:
if poly.is_linear:
term = func(roots_linear(poly)[0])
elif quadratic and poly.is_quadratic:
term = sum(map(func, roots_quadratic(poly)))
else:
if not rational or not auto:
term = cls._new(poly, func, auto)
else:
term = cls._rational_case(poly, func)
terms.append(k * term)
return mul_const * Add(*terms) + deg * add_const
def assemble_partfrac_list(partial_list):
r"""Reassemble a full partial fraction decomposition
from a structured result obtained by the function ``apart_list``.
Examples
========
This example is taken from Bronstein's original paper:
>>> from diofant.abc import x, y
>>> f = 36 / (x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2)
>>> pfd = apart_list(f)
>>> pfd
(1,
Poly(0, x, domain='ZZ'),
[(Poly(_w - 2, _w, domain='ZZ'), Lambda(_a, 4), Lambda(_a, x - _a), 1),
(Poly(_w**2 - 1, _w, domain='ZZ'), Lambda(_a, -3*_a - 6), Lambda(_a, x - _a), 2),
(Poly(_w + 1, _w, domain='ZZ'), Lambda(_a, -4), Lambda(_a, x - _a), 1)])
>>> assemble_partfrac_list(pfd)
-4/(x + 1) - 3/(x + 1)**2 - 9/(x - 1)**2 + 4/(x - 2)
If we happen to know some roots we can provide them easily inside the structure:
>>> pfd = apart_list(2/(x**2-2))
>>> pfd
(1,
Poly(0, x, domain='ZZ'),
[(Poly(_w**2 - 2, _w, domain='ZZ'),
Lambda(_a, _a/2), Lambda(_a, x - _a),
1)])
>>> pfda = assemble_partfrac_list(pfd)
>>> pfda
RootSum(_w**2 - 2, Lambda(_a, _a/(x - _a)))/2
>>> pfda.doit()
-sqrt(2)/(2*(x + sqrt(2))) + sqrt(2)/(2*(x - sqrt(2)))
>>> from diofant import Dummy, Poly, Lambda, sqrt
>>> a = Dummy("a")
>>> pfd = (1, Poly(0, x, domain='ZZ'), [([sqrt(2),-sqrt(2)], Lambda(a, a/2), Lambda(a, -a + x), 1)])
>>> assemble_partfrac_list(pfd)
-sqrt(2)/(2*(x + sqrt(2))) + sqrt(2)/(2*(x - sqrt(2)))
See also
========
apart, apart_list
"""
# Common factor
common = partial_list[0]
# Polynomial part
polypart = partial_list[1]
pfd = polypart.as_expr()
# Rational parts
for r, nf, df, ex in partial_list[2]:
if isinstance(r, Poly):
# Assemble in case the roots are given implicitly by a polynomials
an, nu = nf.variables, nf.expr
ad, de = df.variables, df.expr
# Hack to make dummies equal because Lambda created new Dummies
de = de.subs(ad[0], an[0])
func = Lambda(an, nu / de**ex)
pfd += RootSum(r, func, auto=False, quadratic=False)
else:
# Assemble in case the roots are given explicitely by a list of algebraic numbers
for root in r:
pfd += nf(root) / df(root)**ex
return common * pfd
def apart_list_full_decomposition(P, Q, dummygen):
"""
Bronstein's full partial fraction decomposition algorithm.
Given a univariate rational function ``f``, performing only GCD
operations over the algebraic closure of the initial ground domain
of definition, compute full partial fraction decomposition with
fractions having linear denominators.
Note that no factorization of the initial denominator of ``f`` is
performed. The final decomposition is formed in terms of a sum of
:class:`RootSum` instances.
References
==========
.. [1] [Bronstein93]_
"""
f, x, U = P / Q, P.gen, []
u = Function('u')(x)
a = Dummy('a')
partial = []
for d, n in Q.sqf_list_include(all=True):
b = d.as_expr()
U += [u.diff(x, n - 1)]
h = cancel(f * b**n) / u**n
H, subs = [h], []
for j in range(1, n):
H += [H[-1].diff(x) / j]
for j in range(1, n + 1):
subs += [(U[j - 1], b.diff(x, j) / j)]
for j in range(0, n):
P, Q = cancel(H[j]).as_numer_denom()
for i in range(0, j + 1):
P = P.subs(*subs[j - i])
Q = Q.subs(*subs[0])
P = Poly(P, x)
Q = Poly(Q, x)
G = P.gcd(d)
D = d.quo(G)
B, g = Q.half_gcdex(D)
b = (P * B.quo(g)).rem(D)
Dw = D.subs(x, next(dummygen))
numer = Lambda(a, b.as_expr().subs(x, a))
denom = Lambda(a, (x - a))
exponent = n - j
partial.append((Dw, numer, denom, exponent))
return partial