def _cholesky(self, hermitian=True):
"""Helper function of cholesky.
Without the error checks.
To be used privately.
Implements the Cholesky-Banachiewicz algorithm.
Returns L such that L*L.H == self if hermitian flag is True,
or L*L.T == self if hermitian is False.
"""
L = zeros(self.rows, self.rows)
if hermitian:
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / L[j, j])*expand_mul(self[i, j] -
sum(L[i, k]*L[j, k].conjugate() for k in range(j)))
Lii2 = expand_mul(self[i, i] -
sum(L[i, k]*L[i, k].conjugate() for k in range(i)))
if Lii2.is_positive is False:
raise ValueError("Matrix must be positive-definite")
L[i, i] = sqrt(Lii2)
else:
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / L[j, j])*(self[i, j] -
sum(L[i, k]*L[j, k] for k in range(j)))
L[i, i] = sqrt(self[i, i] -
sum(L[i, k]**2 for k in range(i)))
return self._new(L)
def _LDLdecomposition(self, hermitian=True):
"""Helper function of LDLdecomposition.
Without the error checks.
To be used privately.
Returns L and D such that L*D*L.H == self if hermitian flag is True,
or L*D*L.T == self if hermitian is False.
"""
# https://en.wikipedia.org/wiki/Cholesky_decomposition#LDL_decomposition_2
D = zeros(self.rows, self.rows)
L = eye(self.rows)
if hermitian:
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / D[j, j])*expand_mul(self[i, j] - sum(
L[i, k]*L[j, k].conjugate()*D[k, k] for k in range(j)))
D[i, i] = expand_mul(self[i, i] -
sum(L[i, k]*L[i, k].conjugate()*D[k, k] for k in range(i)))
if D[i, i].is_positive is False:
raise ValueError("Matrix must be positive-definite")
else:
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / D[j, j])*(self[i, j] - sum(
L[i, k]*L[j, k]*D[k, k] for k in range(j)))
D[i, i] = self[i, i] - sum(L[i, k]**2*D[k, k] for k in range(i))
return self._new(L), self._new(D)
def _bell_incomplete_poly(n, k, symbols):
r"""
The second kind of Bell polynomials (incomplete Bell polynomials).
Calculated by recurrence formula:
.. math:: B_{n,k}(x_1, x_2, \dotsc, x_{n-k+1}) =
\sum_{m=1}^{n-k+1}
\x_m \binom{n-1}{m-1} B_{n-m,k-1}(x_1, x_2, \dotsc, x_{n-m-k})
where
B_{0,0} = 1;
B_{n,0} = 0; for n>=1
B_{0,k} = 0; for k>=1
"""
if (n == 0) and (k == 0):
return S.One
elif (n == 0) or (k == 0):
return S.Zero
s = S.Zero
a = S.One
for m in xrange(1, n - k + 2):
s += a * bell._bell_incomplete_poly(
n - m, k - 1, symbols) * symbols[m - 1]
a = a * (n - m) / m
return expand_mul(s)
def test_issue_11230():
# a specific test that always failed
a, b, f, k, l, i = symbols('a b f k l i')
p = [a*b*f*k*l, a*i*k**2*l, f*i*k**2*l]
R, C = cse(p)
assert not any(i.is_Mul for a in C for i in a.args)
# random tests for the issue
from random import choice
from sympy.core.function import expand_mul
s = symbols('a:m')
# 35 Mul tests, none of which should ever fail
ex = [Mul(*[choice(s) for i in range(5)]) for i in range(7)]
for p in subsets(ex, 3):
p = list(p)
R, C = cse(p)
assert not any(i.is_Mul for a in C for i in a.args)
for ri in reversed(R):
for i in range(len(C)):
C[i] = C[i].subs(*ri)
assert p == C
# 35 Add tests, none of which should ever fail
ex = [Add(*[choice(s[:7]) for i in range(5)]) for i in range(7)]
for p in subsets(ex, 3):
p = list(p)
was = R, C = cse(p)
assert not any(i.is_Add for a in C for i in a.args)
for ri in reversed(R):
for i in range(len(C)):
C[i] = C[i].subs(*ri)
# use expand_mul to handle cases like this:
# p = [a + 2*b + 2*e, 2*b + c + 2*e, b + 2*c + 2*g]
# x0 = 2*(b + e) is identified giving a rebuilt p that
# is now `[a + 2*(b + e), c + 2*(b + e), b + 2*c + 2*g]`
assert p == [expand_mul(i) for i in C]
def valid(x):
# this is used to see if gen=x satisfies the
# relational by substituting it into the
# expanded form and testing against 0, e.g.
# if expr = x*(x + 1) < 2 then e = x*(x + 1) - 2
# and expanded_e = x**2 + x - 2; the test is
# whether a given value of x satisfies
# x**2 + x - 2 < 0
#
# expanded_e, expr and gen used from enclosing scope
v = expanded_e.subs(gen, expand_mul(x))
try:
r = expr.func(v, 0)
except TypeError:
r = S.false
if r in (S.true, S.false):
return r
if v.is_real is False:
return S.false
else:
v = v.n(2)
if v.is_comparable:
return expr.func(v, 0)
# not comparable or couldn't be evaluated
raise NotImplementedError(
'relationship did not evaluate: %s' % r)
def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
expr = expand_mul(self)
if not expr.is_Add:
return expr.as_leading_term(x)
infinite = [t for t in expr.args if t.is_infinite]
expr = expr.func(*[t.as_leading_term(x) for t in expr.args]).removeO()
if not expr:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif expr is S.NaN:
return old.func._from_args(infinite)
elif not expr.is_Add:
return expr
else:
plain = expr.func(*[s for s, _ in expr.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_simplify = rv.simplify()
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_simplify.free_symbols:
if rv_simplify.is_zero and plain.is_zero is not True:
return (expr - plain)._eval_as_leading_term(x)
return rv_simplify
return rv
def sqrtdenest(expr, max_iter=3):
"""Denests sqrts in an expression that contain other square roots
if possible, otherwise returns the expr unchanged. This is based on the
algorithms of [1].
Examples
========
>>> from sympy.simplify.sqrtdenest import sqrtdenest
>>> from sympy import sqrt
>>> sqrtdenest(sqrt(5 + 2 * sqrt(6)))
sqrt(2) + sqrt(3)
See Also
========
sympy.solvers.solvers.unrad
References
==========
[1] http://researcher.watson.ibm.com/researcher/files/us-fagin/symb85.pdf
[2] D. J. Jeffrey and A. D. Rich, 'Symplifying Square Roots of Square Roots
by Denesting' (available at http://www.cybertester.com/data/denest.pdf)
"""
expr = expand_mul(sympify(expr))
for i in range(max_iter):
z = _sqrtdenest0(expr)
if expr == z:
return expr
expr = z
return expr
def sqrtdenest(expr, max_iter=3):
"""Denests sqrts in an expression that contain other square roots
if possible, otherwise returns the expr unchanged. This is based on the
algorithms of [1].
Examples
========
>>> from sympy.simplify.sqrtdenest import sqrtdenest
>>> from sympy import sqrt
>>> sqrtdenest(sqrt(5 + 2 * sqrt(6)))
sqrt(2) + sqrt(3)
See Also
========
sympy.solvers.solvers.unrad
References
==========
[1] http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf
"""
expr = expand_mul(sympify(expr))
for i in range(max_iter):
z = _sqrtdenest0(expr)
if expr == z:
return expr
expr = z
return expr
def _bell_poly(n, prev):
s = 1
a = 1
for k in xrange(2, n + 1):
a = a * (n - k + 1) // (k - 1)
s += a * prev[k - 1]
return expand_mul(_sym * s)
def _LDLdecomposition(self):
"""Helper function of LDLdecomposition.
Without the error checks.
To be used privately.
"""
# https://en.wikipedia.org/wiki/Cholesky_decomposition#LDL_decomposition_2
D = zeros(self.rows, self.rows)
L = eye(self.rows)
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / D[j, j])*expand_mul(self[i, j] - sum(
L[i, k]*L[j, k].conjugate()*D[k, k] for k in range(j)))
D[i, i] = expand_mul(self[i, i] -
sum(L[i, k]*L[i, k].conjugate()*D[k, k] for k in range(i)))
if D[i, i].is_positive is False:
raise ValueError("Matrix must be positive-definite")
return self._new(L), self._new(D)
def _cholesky(self):
"""Helper function of cholesky.
Without the error checks.
To be used privately.
Implements the Cholesky-Banachiewicz algorithm.
"""
L = zeros(self.rows, self.rows)
for i in range(self.rows):
for j in range(i):
L[i, j] = (1 / L[j, j])*expand_mul(self[i, j] -
sum(L[i, k]*L[j, k].conjugate() for k in range(j)))
Lii2 = expand_mul(self[i, i] -
sum(L[i, k]*L[i, k].conjugate() for k in range(i)))
if Lii2.is_positive is False:
raise ValueError("Matrix must be positive-definite")
L[i, i] = sqrt(Lii2)
return self._new(L)
def convolution_fwht(a, b):
"""
Performs dyadic (*bitwise-XOR*) convolution using Fast Walsh Hadamard
Transform.
The convolution is automatically padded to the right with zeros, as the
*radix-2 FWHT* requires the number of sample points to be a power of 2.
Parameters
==========
a, b : iterables
The sequences for which convolution is performed.
Examples
========
>>> from sympy import symbols, S, I
>>> from sympy.discrete.convolutions import convolution_fwht
>>> u, v, x, y = symbols('u v x y')
>>> convolution_fwht([u, v], [x, y])
[u*x + v*y, u*y + v*x]
>>> convolution_fwht([2, 3], [4, 5])
[23, 22]
>>> convolution_fwht([2, 5 + 4*I, 7], [6*I, 7, 3 + 4*I])
[56 + 68*I, -10 + 30*I, 6 + 50*I, 48 + 32*I]
>>> convolution_fwht([S(33)/7, S(55)/6, S(7)/4], [S(2)/3, 5])
[2057/42, 1870/63, 7/6, 35/4]
References
==========
.. [1] https://www.radioeng.cz/fulltexts/2002/02_03_40_42.pdf
.. [2] https://en.wikipedia.org/wiki/Hadamard_transform
"""
if not a or not b:
return []
a, b = a[:], b[:]
n = max(len(a), len(b))
if n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
a, b = fwht(a), fwht(b)
a = [expand_mul(x*y) for x, y in zip(a, b)]
a = ifwht(a)
return a
def _combine_inverse(lhs, rhs):
"""
Returns lhs - rhs, but treats oo like a symbol so oo - oo
returns 0, instead of a nan.
"""
from sympy.core.function import expand_mul
from sympy.core.symbol import Dummy
inf = (S.Infinity, S.NegativeInfinity)
if lhs.has(*inf) or rhs.has(*inf):
oo = Dummy('oo')
reps = {
S.Infinity: oo,
S.NegativeInfinity: -oo}
ireps = dict([(v, k) for k, v in reps.items()])
eq = expand_mul(lhs.xreplace(reps) - rhs.xreplace(reps))
if eq.has(oo):
eq = eq.replace(
lambda x: x.is_Pow and x.base == oo,
lambda x: x.base)
return eq.xreplace(ireps)
else:
return expand_mul(lhs - rhs)
def eval(cls, p, q):
from sympy.simplify.simplify import nsimplify
if q.is_Number:
float = not q.is_Rational
pnew = expand_mul(p)
if pnew.is_Number:
float = float or not pnew.is_Rational
if not float:
return pnew % q
return Float(nsimplify(pnew) % nsimplify(q))
elif pnew.is_Add and pnew.args[0].is_Number:
r, p = pnew.as_two_terms()
p += Mod(r, q)
return Mod(p, q, evaluate=False)
def convolution_fft(a, b, dps=None):
"""
Performs linear convolution using Fast Fourier Transform.
Parameters
==========
a, b : iterables
The sequences for which convolution is performed.
dps : Integer
Specifies the number of decimal digits for precision.
Examples
========
>>> from sympy import S, I
>>> from sympy.discrete.convolutions import convolution_fft
>>> convolution_fft([2, 3], [4, 5])
[8, 22, 15]
>>> convolution_fft([2, 5], [6, 7, 3])
[12, 44, 41, 15]
>>> convolution_fft([1 + 2*I, 4 + 3*I], [S(5)/4, 6])
[5/4 + 5*I/2, 11 + 63*I/4, 24 + 18*I]
References
==========
.. [1] https://en.wikipedia.org/wiki/Convolution_theorem
.. [2] https://en.wikipedia.org/wiki/Discrete_Fourier_transform_(general%29
"""
a, b = a[:], b[:]
n = m = len(a) + len(b) - 1 # convolution size
if n > 0 and n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
a, b = fft(a, dps), fft(b, dps)
a = [expand_mul(x*y) for x, y in zip(a, b)]
a = ifft(a, dps)[:m]
return a
def _fourier_transform(seq, dps, inverse=False):
"""Utility function for the Discrete Fourier Transform"""
if not iterable(seq):
raise TypeError("Expected a sequence of numeric coefficients "
"for Fourier Transform")
a = [sympify(arg) for arg in seq]
if any(x.has(Symbol) for x in a):
raise ValueError("Expected non-symbolic coefficients")
n = len(a)
if n < 2:
return a
b = n.bit_length() - 1
if n&(n - 1): # not a power of 2
b += 1
n = 2**b
a += [S.Zero]*(n - len(a))
for i in range(1, n):
j = int(ibin(i, b, str=True)[::-1], 2)
if i < j:
a[i], a[j] = a[j], a[i]
ang = -2*pi/n if inverse else 2*pi/n
if dps is not None:
ang = ang.evalf(dps + 2)
w = [cos(ang*i) + I*sin(ang*i) for i in range(n // 2)]
h = 2
while h <= n:
hf, ut = h // 2, n // h
for i in range(0, n, h):
for j in range(hf):
u, v = a[i + j], expand_mul(a[i + j + hf]*w[ut * j])
a[i + j], a[i + j + hf] = u + v, u - v
h *= 2
if inverse:
a = [(x/n).evalf(dps) for x in a] if dps is not None \
else [x/n for x in a]
return a
def projection(self, pt):
"""Projection of any point on the plane
Will result in a 3D point.
Parameters
==========
Point or Point3D
Returns
=======
Point3D
Notes
=====
For the interaction between 2D and 3D points, you should convert the 2D
point to 3D by using this method. For example for finding the distance
between Point(1, 2) and Point3D(1, 2, 3) you can convert the 2D point
to Point3D(1, 2, 0) by projecting it to plane which is parallel to xy
plane.
Examples
========
>>> from sympy import Plane, Point, Point3D
>>> a = Plane(Point3D(1, 1, 1), normal_vector=(1, 1, 1))
>>> b = Point(1, 2)
>>> a.projection(b)
Point3D(1, 2, 0)
>>> c = Point3D(1, 1, 1)
>>> a.projection(c)
Point3D(4/3, 4/3, 4/3)
"""
x, y, z = map(Dummy, 'xyz')
k = expand_mul(self.equation(x, y, z))
a, b, c = [k.coeff(i) for i in (x, y, z)]
d = k.xreplace({x: pt.args[0], y: pt.args[1], z:0})
t = -d/(a**2 + b**2 + c**2)
if isinstance(pt, Point):
return Point3D(pt.x + t*a, pt.y + t*b, t*c)
if isinstance(pt, Point3D):
return Point3D(pt.x + t*a, pt.y + t*b, pt.z + t*c)
def _set_function(f, self):
from sympy.core.function import expand_mul
if not self:
return S.EmptySet
if not isinstance(f.expr, Expr):
return
if self.size == 1:
return FiniteSet(f(self[0]))
if f is S.IdentityFunction:
return self
x = f.variables[0]
expr = f.expr
# handle f that is linear in f's variable
if x not in expr.free_symbols or x in expr.diff(x).free_symbols:
return
if self.start.is_finite:
F = f(self.step*x + self.start) # for i in range(len(self))
else:
F = f(-self.step*x + self[-1])
F = expand_mul(F)
if F != expr:
return imageset(x, F, Range(self.size))
def trigsimp_old(expr, *, first=True, **opts):
"""
Reduces expression by using known trig identities.
Notes
=====
deep:
- Apply trigsimp inside all objects with arguments
recursive:
- Use common subexpression elimination (cse()) and apply
trigsimp recursively (this is quite expensive if the
expression is large)
method:
- Determine the method to use. Valid choices are 'matching' (default),
'groebner', 'combined', 'fu' and 'futrig'. If 'matching', simplify the
expression recursively by pattern matching. If 'groebner', apply an
experimental groebner basis algorithm. In this case further options
are forwarded to ``trigsimp_groebner``, please refer to its docstring.
If 'combined', first run the groebner basis algorithm with small
default parameters, then run the 'matching' algorithm. 'fu' runs the
collection of trigonometric transformations described by Fu, et al.
(see the `fu` docstring) while `futrig` runs a subset of Fu-transforms
that mimic the behavior of `trigsimp`.
compare:
- show input and output from `trigsimp` and `futrig` when different,
but returns the `trigsimp` value.
Examples
========
>>> from sympy import trigsimp, sin, cos, log, cot
>>> from sympy.abc import x
>>> e = 2*sin(x)**2 + 2*cos(x)**2
>>> trigsimp(e, old=True)
2
>>> trigsimp(log(e), old=True)
log(2*sin(x)**2 + 2*cos(x)**2)
>>> trigsimp(log(e), deep=True, old=True)
log(2)
Using `method="groebner"` (or `"combined"`) can sometimes lead to a lot
more simplification:
>>> e = (-sin(x) + 1)/cos(x) + cos(x)/(-sin(x) + 1)
>>> trigsimp(e, old=True)
(1 - sin(x))/cos(x) + cos(x)/(1 - sin(x))
>>> trigsimp(e, method="groebner", old=True)
2/cos(x)
>>> trigsimp(1/cot(x)**2, compare=True, old=True)
futrig: tan(x)**2
cot(x)**(-2)
"""
old = expr
if first:
if not expr.has(*_trigs):
return expr
trigsyms = set().union(*[t.free_symbols for t in expr.atoms(*_trigs)])
if len(trigsyms) > 1:
from sympy.simplify.simplify import separatevars
d = separatevars(expr)
if d.is_Mul:
d = separatevars(d, dict=True) or d
if isinstance(d, dict):
expr = 1
for k, v in d.items():
# remove hollow factoring
was = v
v = expand_mul(v)
opts['first'] = False
vnew = trigsimp(v, **opts)
if vnew == v:
vnew = was
expr *= vnew
old = expr
else:
if d.is_Add:
for s in trigsyms:
r, e = expr.as_independent(s)
if r:
opts['first'] = False
expr = r + trigsimp(e, **opts)
if not expr.is_Add:
break
old = expr
recursive = opts.pop('recursive', False)
deep = opts.pop('deep', False)
method = opts.pop('method', 'matching')
def groebnersimp(ex, deep, **opts):
def traverse(e):
if e.is_Atom:
return e
args = [traverse(x) for x in e.args]
if e.is_Function or e.is_Pow:
args = [trigsimp_groebner(x, **opts) for x in args]
return e.func(*args)
if deep:
ex = traverse(ex)
return trigsimp_groebner(ex, **opts)
trigsimpfunc = {
'matching': (lambda x, d: _trigsimp(x, d)),
'groebner': (lambda x, d: groebnersimp(x, d, **opts)),
'combined': (lambda x, d: _trigsimp(
groebnersimp(x, d, polynomial=True, hints=[2, tan]), d))
}[method]
if recursive:
w, g = cse(expr)
g = trigsimpfunc(g[0], deep)
for sub in reversed(w):
g = g.subs(sub[0], sub[1])
g = trigsimpfunc(g, deep)
result = g
else:
result = trigsimpfunc(expr, deep)
if opts.get('compare', False):
f = futrig(old)
if f != result:
print('\tfutrig:', f)
return result
def _mexpand(expr):
return expand_mul(expand_multinomial(expr))
def simp_hyp(expr):
return factor_terms(expand_mul(expr)).rewrite(sin)
def eval(cls, arg, base=None):
from sympy import unpolarify
from sympy.calculus import AccumBounds
from sympy.sets.setexpr import SetExpr
from sympy.functions.elementary.complexes import Abs
arg = sympify(arg)
if base is not None:
base = sympify(base)
if base == 1:
if arg == 1:
return S.NaN
else:
return S.ComplexInfinity
try:
# handle extraction of powers of the base now
# or else expand_log in Mul would have to handle this
n = multiplicity(base, arg)
if n:
return n + log(arg / base**n) / log(base)
else:
return log(arg) / log(base)
except ValueError:
pass
if base is not S.Exp1:
return cls(arg) / cls(base)
else:
return cls(arg)
if arg.is_Number:
if arg.is_zero:
return S.ComplexInfinity
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_Rational and arg.p == 1:
return -cls(arg.q)
I = S.ImaginaryUnit
if isinstance(arg, exp) and arg.args[0].is_extended_real:
return arg.args[0]
elif isinstance(arg, exp) and arg.args[0].is_number:
r_, i_ = match_real_imag(arg.args[0])
if i_ and i_.is_comparable:
i_ %= 2 * S.Pi
if i_ > S.Pi:
i_ -= 2 * S.Pi
return r_ + expand_mul(i_ * I, deep=False)
elif isinstance(arg, exp_polar):
return unpolarify(arg.exp)
elif isinstance(arg, AccumBounds):
if arg.min.is_positive:
return AccumBounds(log(arg.min), log(arg.max))
else:
return
elif isinstance(arg, SetExpr):
return arg._eval_func(cls)
if arg.is_number:
if arg.is_negative:
return S.Pi * I + cls(-arg)
elif arg is S.ComplexInfinity:
return S.ComplexInfinity
elif arg is S.Exp1:
return S.One
if arg.is_zero:
return S.ComplexInfinity
# don't autoexpand Pow or Mul (see the issue 3351):
if not arg.is_Add:
coeff = arg.as_coefficient(I)
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 * I * S.Half + cls(coeff)
else:
return -S.Pi * I * S.Half + cls(-coeff)
if arg.is_number and arg.is_algebraic:
# Match arg = coeff*(r_ + i_*I) with coeff>0, r_ and i_ real.
coeff, arg_ = arg.as_independent(I, as_Add=False)
if coeff.is_negative:
coeff *= -1
arg_ *= -1
arg_ = expand_mul(arg_, deep=False)
r_, i_ = arg_.as_independent(I, as_Add=True)
i_ = i_.as_coefficient(I)
if coeff.is_real and i_ and i_.is_real and r_.is_real:
if r_.is_zero:
if i_.is_positive:
return S.Pi * I * S.Half + cls(coeff * i_)
elif i_.is_negative:
return -S.Pi * I * S.Half + cls(coeff * -i_)
else:
from sympy.simplify import ratsimp
# Check for arguments involving rational multiples of pi
t = (i_ / r_).cancel()
atan_table = {
# first quadrant only
sqrt(3):
S.Pi / 3,
1:
S.Pi / 4,
sqrt(5 - 2 * sqrt(5)):
S.Pi / 5,
sqrt(2) * sqrt(5 - sqrt(5)) / (1 + sqrt(5)):
S.Pi / 5,
sqrt(5 + 2 * sqrt(5)):
S.Pi * Rational(2, 5),
sqrt(2) * sqrt(sqrt(5) + 5) / (-1 + sqrt(5)):
S.Pi * Rational(2, 5),
sqrt(3) / 3:
S.Pi / 6,
sqrt(2) - 1:
S.Pi / 8,
sqrt(2 - sqrt(2)) / sqrt(sqrt(2) + 2):
S.Pi / 8,
sqrt(2) + 1:
S.Pi * Rational(3, 8),
sqrt(sqrt(2) + 2) / sqrt(2 - sqrt(2)):
S.Pi * Rational(3, 8),
sqrt(1 - 2 * sqrt(5) / 5):
S.Pi / 10,
(-sqrt(2) + sqrt(10)) / (2 * sqrt(sqrt(5) + 5)):
S.Pi / 10,
sqrt(1 + 2 * sqrt(5) / 5):
S.Pi * Rational(3, 10),
(sqrt(2) + sqrt(10)) / (2 * sqrt(5 - sqrt(5))):
S.Pi * Rational(3, 10),
2 - sqrt(3):
S.Pi / 12,
(-1 + sqrt(3)) / (1 + sqrt(3)):
S.Pi / 12,
2 + sqrt(3):
S.Pi * Rational(5, 12),
(1 + sqrt(3)) / (-1 + sqrt(3)):
S.Pi * Rational(5, 12)
}
if t in atan_table:
modulus = ratsimp(coeff * Abs(arg_))
if r_.is_positive:
return cls(modulus) + I * atan_table[t]
else:
return cls(modulus) + I * (atan_table[t] - S.Pi)
elif -t in atan_table:
modulus = ratsimp(coeff * Abs(arg_))
if r_.is_positive:
return cls(modulus) + I * (-atan_table[-t])
else:
return cls(modulus) + I * (S.Pi - atan_table[-t])
def _eval_expand_multinomial(self, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
b = self.base
e = self.exp
if b is None:
base = self.base
else:
base = b
if e is None:
exp = self.exp
else:
exp = e
if e is not None or b is not None:
result = Pow(base, exp)
if result.is_Pow:
base, exp = result.base, result.exp
else:
return result
else:
result = None
if exp.is_Rational and exp.p > 0 and base.is_Add:
if not exp.is_Integer:
n = Integer(exp.p // exp.q)
if not n:
return Pow(base, exp)
else:
radical, result = Pow(base, exp - n), []
expanded_base_n = Pow(base, n)
if expanded_base_n.is_Pow:
expanded_base_n = expanded_base_n._eval_expand_multinomial()
for term in Add.make_args(expanded_base_n):
result.append(term*radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for order in base.args:
if order.is_Order:
order_terms.append(order)
else:
other_terms.append(order)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
if n == 2:
return expand_multinomial(f**n, deep=False) + n*f*Add(*order_terms)
else:
g = expand_multinomial(f**(n - 1), deep=False)
return expand_mul(f*g, deep=False) + n*g*Add(*order_terms)
if base.is_number:
# Efficiently expand expressions of the form (a + b*I)**n
# where 'a' and 'b' are real numbers and 'n' is integer.
a, b = base.as_real_imag()
if a.is_Rational and b.is_Rational:
if not a.is_Integer:
if not b.is_Integer:
k = Pow(a.q * b.q, n)
a, b = a.p*b.q, a.q*b.p
else:
k = Pow(a.q, n)
a, b = a.p, a.q*b
elif not b.is_Integer:
k = Pow(b.q, n)
a, b = a*b.q, b.p
else:
k = 1
a, b, c, d = int(a), int(b), 1, 0
while n:
if n & 1:
c, d = a*c-b*d, b*c+a*d
n -= 1
a, b = a*a-b*b, 2*a*b
n //= 2
I = S.ImaginaryUnit
if k == 1:
return c + I*d
else:
return Integer(c)/k + I*d/k
p = other_terms
# (x+y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
# in this particular example:
# p = [x,y]; n = 3
# so now it's easy to get the correct result -- we get the
# coefficients first:
from sympy import multinomial_coefficients
expansion_dict = multinomial_coefficients(len(p), n)
# in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
# and now construct the expression.
# An elegant way would be to use Poly, but unfortunately it is
# slower than the direct method below, so it is commented out:
#b = {}
#for k in expansion_dict:
# b[k] = Integer(expansion_dict[k])
#return Poly(b, *p).as_expr()
from sympy.polys.polyutils import basic_from_dict
result = basic_from_dict(expansion_dict, *p)
return result
else:
if n == 2:
return Add(*[f*g for f in base.args for g in base.args])
else:
multi = (base**(n-1))._eval_expand_multinomial()
if multi.is_Add:
return Add(*[f*g for f in base.args for g in multi.args])
else:
return Add(*[f*multi for f in base.args])
elif exp.is_Rational and exp.p < 0 and base.is_Add and abs(exp.p) > exp.q:
return 1 / Pow(base, -exp)._eval_expand_multinomial()
elif exp.is_Add and base.is_Number:
# a + b a b
# n --> n n , where n, a, b are Numbers
coeff, tail = S.One, S.Zero
for term in exp.args:
if term.is_Number:
coeff *= Pow(base, term)
else:
tail += term
return coeff * Pow(base, tail)
else:
return result
def _eval_expand_multinomial(self, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
base, exp = self.args
result = self
if exp.is_Rational and exp.p > 0 and base.is_Add:
if not exp.is_Integer:
n = Integer(exp.p // exp.q)
if not n:
return result
else:
radical, result = self.func(base, exp - n), []
expanded_base_n = self.func(base, n)
if expanded_base_n.is_Pow:
expanded_base_n = \
expanded_base_n._eval_expand_multinomial()
for term in Add.make_args(expanded_base_n):
result.append(term * radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for b in base.args:
if b.is_Order:
order_terms.append(b)
else:
other_terms.append(b)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
o = Add(*order_terms)
if n == 2:
return expand_multinomial(f**n, deep=False) + n * f * o
else:
g = expand_multinomial(f**(n - 1), deep=False)
return expand_mul(f * g, deep=False) + n * g * o
if base.is_number:
# Efficiently expand expressions of the form (a + b*I)**n
# where 'a' and 'b' are real numbers and 'n' is integer.
a, b = base.as_real_imag()
if a.is_Rational and b.is_Rational:
if not a.is_Integer:
if not b.is_Integer:
k = self.func(a.q * b.q, n)
a, b = a.p * b.q, a.q * b.p
else:
k = self.func(a.q, n)
a, b = a.p, a.q * b
elif not b.is_Integer:
k = self.func(b.q, n)
a, b = a * b.q, b.p
else:
k = 1
a, b, c, d = int(a), int(b), 1, 0
while n:
if n & 1:
c, d = a * c - b * d, b * c + a * d
n -= 1
a, b = a * a - b * b, 2 * a * b
n //= 2
I = S.ImaginaryUnit
if k == 1:
return c + I * d
else:
return Integer(c) / k + I * d / k
p = other_terms
# (x+y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
# in this particular example:
# p = [x,y]; n = 3
# so now it's easy to get the correct result -- we get the
# coefficients first:
from sympy import multinomial_coefficients
from sympy.polys.polyutils import basic_from_dict
expansion_dict = multinomial_coefficients(len(p), n)
# in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
# and now construct the expression.
return basic_from_dict(expansion_dict, *p)
else:
if n == 2:
return Add(*[f * g for f in base.args for g in base.args])
else:
multi = (base**(n - 1))._eval_expand_multinomial()
if multi.is_Add:
return Add(
*[f * g for f in base.args for g in multi.args])
else:
# XXX can this ever happen if base was an Add?
return Add(*[f * multi for f in base.args])
elif (exp.is_Rational and exp.p < 0 and base.is_Add
and abs(exp.p) > exp.q):
return 1 / self.func(base, -exp)._eval_expand_multinomial()
elif exp.is_Add and base.is_Number:
# a + b a b
# n --> n n , where n, a, b are Numbers
coeff, tail = S.One, S.Zero
for term in exp.args:
if term.is_Number:
coeff *= self.func(base, term)
else:
tail += term
return coeff * self.func(base, tail)
else:
return result
def intersecting_product(a, b):
"""
Returns the intersecting product of given sequences.
The indices of each argument, considered as bit strings, correspond to
subsets of a finite set.
The intersecting product of given sequences is the sequence which
contains the sum of products of the elements of the given sequences
grouped by the *bitwise-AND* of the corresponding indices.
The sequence is automatically padded to the right with zeros, as the
definition of subset based on bitmasks (indices) requires the size of
sequence to be a power of 2.
Parameters
==========
a, b : iterables
The sequences for which intersecting product is to be obtained.
Examples
========
>>> from sympy import symbols, S, I, intersecting_product
>>> u, v, x, y, z = symbols('u v x y z')
>>> intersecting_product([u, v], [x, y])
[u*x + u*y + v*x, v*y]
>>> intersecting_product([u, v, x], [y, z])
[u*y + u*z + v*y + x*y + x*z, v*z, 0, 0]
>>> intersecting_product([1, S(2)/3], [3, 4 + 5*I])
[9 + 5*I, 8/3 + 10*I/3]
>>> intersecting_product([1, 3, S(5)/7], [7, 8])
[327/7, 24, 0, 0]
References
==========
.. [1] https://people.csail.mit.edu/rrw/presentations/subset-conv.pdf
"""
if not a or not b:
return []
a, b = a[:], b[:]
n = max(len(a), len(b))
if n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
a, b = mobius_transform(a, subset=False), mobius_transform(b, subset=False)
a = [expand_mul(x*y) for x, y in zip(a, b)]
a = inverse_mobius_transform(a, subset=False)
return a
def _eval_expand_multinomial(self, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
base, exp = self.args
result = self
if exp.is_Rational and exp.p > 0 and base.is_Add:
if not exp.is_Integer:
n = Integer(exp.p // exp.q)
if not n:
return result
else:
radical, result = self.func(base, exp - n), []
expanded_base_n = self.func(base, n)
if expanded_base_n.is_Pow:
expanded_base_n = \
expanded_base_n._eval_expand_multinomial()
for term in Add.make_args(expanded_base_n):
result.append(term*radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for b in base.args:
if b.is_Order:
order_terms.append(b)
else:
other_terms.append(b)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
o = Add(*order_terms)
if n == 2:
return expand_multinomial(f**n, deep=False) + n*f*o
else:
g = expand_multinomial(f**(n - 1), deep=False)
return expand_mul(f*g, deep=False) + n*g*o
if base.is_number:
# Efficiently expand expressions of the form (a + b*I)**n
# where 'a' and 'b' are real numbers and 'n' is integer.
a, b = base.as_real_imag()
if a.is_Rational and b.is_Rational:
if not a.is_Integer:
if not b.is_Integer:
k = self.func(a.q * b.q, n)
a, b = a.p*b.q, a.q*b.p
else:
k = self.func(a.q, n)
a, b = a.p, a.q*b
elif not b.is_Integer:
k = self.func(b.q, n)
a, b = a*b.q, b.p
else:
k = 1
a, b, c, d = int(a), int(b), 1, 0
while n:
if n & 1:
c, d = a*c - b*d, b*c + a*d
n -= 1
a, b = a*a - b*b, 2*a*b
n //= 2
I = S.ImaginaryUnit
if k == 1:
return c + I*d
else:
return Integer(c)/k + I*d/k
p = other_terms
# (x+y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
# in this particular example:
# p = [x,y]; n = 3
# so now it's easy to get the correct result -- we get the
# coefficients first:
from sympy import multinomial_coefficients
from sympy.polys.polyutils import basic_from_dict
expansion_dict = multinomial_coefficients(len(p), n)
# in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
# and now construct the expression.
return basic_from_dict(expansion_dict, *p)
else:
if n == 2:
return Add(*[f*g for f in base.args for g in base.args])
else:
multi = (base**(n - 1))._eval_expand_multinomial()
if multi.is_Add:
return Add(*[f*g for f in base.args
for g in multi.args])
else:
# XXX can this ever happen if base was an Add?
return Add(*[f*multi for f in base.args])
elif (exp.is_Rational and exp.p < 0 and base.is_Add and
abs(exp.p) > exp.q):
return 1 / self.func(base, -exp)._eval_expand_multinomial()
elif exp.is_Add and base.is_Number:
# a + b a b
# n --> n n , where n, a, b are Numbers
coeff, tail = S.One, S.Zero
for term in exp.args:
if term.is_Number:
coeff *= self.func(base, term)
else:
tail += term
return coeff * self.func(base, tail)
else:
return result
def simp_pows(expr):
return simplify(powsimp(expand_mul(expr, deep=False), force=True)).replace(exp_polar, exp)
def test_asech():
x = Symbol('x')
assert unchanged(asech, -x)
# values at fixed points
assert asech(1) == 0
assert asech(-1) == pi * I
assert asech(0) is oo
assert asech(2) == I * pi / 3
assert asech(-2) == 2 * I * pi / 3
assert asech(nan) is nan
# at infinites
assert asech(oo) == I * pi / 2
assert asech(-oo) == I * pi / 2
assert asech(zoo) == I * AccumBounds(-pi / 2, pi / 2)
assert asech(I) == log(1 + sqrt(2)) - I * pi / 2
assert asech(-I) == log(1 + sqrt(2)) + I * pi / 2
assert asech(sqrt(2) - sqrt(6)) == 11 * I * pi / 12
assert asech(sqrt(2 - 2 / sqrt(5))) == I * pi / 10
assert asech(-sqrt(2 - 2 / sqrt(5))) == 9 * I * pi / 10
assert asech(2 / sqrt(2 + sqrt(2))) == I * pi / 8
assert asech(-2 / sqrt(2 + sqrt(2))) == 7 * I * pi / 8
assert asech(sqrt(5) - 1) == I * pi / 5
assert asech(1 - sqrt(5)) == 4 * I * pi / 5
assert asech(-sqrt(2 * (2 + sqrt(2)))) == 5 * I * pi / 8
# properties
# asech(x) == acosh(1/x)
assert asech(sqrt(2)) == acosh(1 / sqrt(2))
assert asech(2 / sqrt(3)) == acosh(sqrt(3) / 2)
assert asech(2 / sqrt(2 + sqrt(2))) == acosh(sqrt(2 + sqrt(2)) / 2)
assert asech(2) == acosh(S.Half)
# asech(x) == I*acos(1/x)
# (Note: the exact formula is asech(x) == +/- I*acos(1/x))
assert asech(-sqrt(2)) == I * acos(-1 / sqrt(2))
assert asech(-2 / sqrt(3)) == I * acos(-sqrt(3) / 2)
assert asech(-S(2)) == I * acos(Rational(-1, 2))
assert asech(-2 / sqrt(2)) == I * acos(-sqrt(2) / 2)
# sech(asech(x)) / x == 1
assert expand_mul(sech(asech(sqrt(6) - sqrt(2))) /
(sqrt(6) - sqrt(2))) == 1
assert expand_mul(sech(asech(sqrt(6) + sqrt(2))) /
(sqrt(6) + sqrt(2))) == 1
assert (sech(asech(sqrt(2 + 2 / sqrt(5)))) /
(sqrt(2 + 2 / sqrt(5)))).simplify() == 1
assert (sech(asech(-sqrt(2 + 2 / sqrt(5)))) /
(-sqrt(2 + 2 / sqrt(5)))).simplify() == 1
assert (sech(asech(sqrt(2 * (2 + sqrt(2))))) /
(sqrt(2 * (2 + sqrt(2))))).simplify() == 1
assert expand_mul(sech(asech(1 + sqrt(5))) / (1 + sqrt(5))) == 1
assert expand_mul(sech(asech(-1 - sqrt(5))) / (-1 - sqrt(5))) == 1
assert expand_mul(sech(asech(-sqrt(6) - sqrt(2))) /
(-sqrt(6) - sqrt(2))) == 1
# numerical evaluation
assert str(asech(5 * I).n(6)) == '0.19869 - 1.5708*I'
assert str(asech(-5 * I).n(6)) == '0.19869 + 1.5708*I'
def eval(cls, arg):
from sympy.simplify.simplify import signsimp
from sympy.core.function import expand_mul
if hasattr(arg, '_eval_Abs'):
obj = arg._eval_Abs()
if obj is not None:
return obj
if not isinstance(arg, Expr):
raise TypeError("Bad argument type for Abs(): %s" % type(arg))
# handle what we can
arg = signsimp(arg, evaluate=False)
if arg.is_Mul:
known = []
unk = []
for t in arg.args:
tnew = cls(t)
if isinstance(tnew, cls):
unk.append(tnew.args[0])
else:
known.append(tnew)
known = Mul(*known)
unk = cls(Mul(*unk), evaluate=False) if unk else S.One
return known*unk
if arg is S.NaN:
return S.NaN
if arg is S.ComplexInfinity:
return S.Infinity
if arg.is_Pow:
base, exponent = arg.as_base_exp()
if base.is_real:
if exponent.is_integer:
if exponent.is_even:
return arg
if base is S.NegativeOne:
return S.One
if isinstance(base, cls) and exponent is S.NegativeOne:
return arg
return Abs(base)**exponent
if base.is_nonnegative:
return base**re(exponent)
if base.is_negative:
return (-base)**re(exponent)*exp(-S.Pi*im(exponent))
return
elif not base.has(Symbol): # complex base
# express base**exponent as exp(exponent*log(base))
a, b = log(base).as_real_imag()
z = a + I*b
return exp(re(exponent*z))
if isinstance(arg, exp):
return exp(re(arg.args[0]))
if isinstance(arg, AppliedUndef):
return
if arg.is_Add and arg.has(S.Infinity, S.NegativeInfinity):
if any(a.is_infinite for a in arg.as_real_imag()):
return S.Infinity
if arg.is_zero:
return S.Zero
if arg.is_nonnegative:
return arg
if arg.is_nonpositive:
return -arg
if arg.is_imaginary:
arg2 = -S.ImaginaryUnit * arg
if arg2.is_nonnegative:
return arg2
# reject result if all new conjugates are just wrappers around
# an expression that was already in the arg
conj = signsimp(arg.conjugate(), evaluate=False)
new_conj = conj.atoms(conjugate) - arg.atoms(conjugate)
if new_conj and all(arg.has(i.args[0]) for i in new_conj):
return
if arg != conj and arg != -conj:
ignore = arg.atoms(Abs)
abs_free_arg = arg.xreplace({i: Dummy(real=True) for i in ignore})
unk = [a for a in abs_free_arg.free_symbols if a.is_real is None]
if not unk or not all(conj.has(conjugate(u)) for u in unk):
return sqrt(expand_mul(arg*conj))
def _minpoly_groebner(ex, x, cls):
"""
Computes the minimal polynomial of an algebraic number
using Groebner bases
Examples
========
>>> from sympy import minimal_polynomial, sqrt, Rational
>>> from sympy.abc import x
>>> minimal_polynomial(sqrt(2) + 3*Rational(1, 3), x, compose=False)
x**2 - 2*x - 1
"""
generator = numbered_symbols('a', cls=Dummy)
mapping, symbols = {}, {}
def update_mapping(ex, exp, base=None):
a = next(generator)
symbols[ex] = a
if base is not None:
mapping[ex] = a**exp + base
else:
mapping[ex] = exp.as_expr(a)
return a
def bottom_up_scan(ex):
"""
Transform a given algebraic expression *ex* into a multivariate
polynomial, by introducing fresh variables with defining equations.
Explanation
===========
The critical elements of the algebraic expression *ex* are root
extractions, instances of :py:class:`~.AlgebraicNumber`, and negative
powers.
When we encounter a root extraction or an :py:class:`~.AlgebraicNumber`
we replace this expression with a fresh variable ``a_i``, and record
the defining polynomial for ``a_i``. For example, if ``a_0**(1/3)``
occurs, we will replace it with ``a_1``, and record the new defining
polynomial ``a_1**3 - a_0``.
When we encounter a negative power we transform it into a positive
power by algebraically inverting the base. This means computing the
minimal polynomial in ``x`` for the base, inverting ``x`` modulo this
poly (which generates a new polynomial) and then substituting the
original base expression for ``x`` in this last polynomial.
We return the transformed expression, and we record the defining
equations for new symbols using the ``update_mapping()`` function.
"""
if ex.is_Atom:
if ex is S.ImaginaryUnit:
if ex not in mapping:
return update_mapping(ex, 2, 1)
else:
return symbols[ex]
elif ex.is_Rational:
return ex
elif ex.is_Add:
return Add(*[bottom_up_scan(g) for g in ex.args])
elif ex.is_Mul:
return Mul(*[bottom_up_scan(g) for g in ex.args])
elif ex.is_Pow:
if ex.exp.is_Rational:
if ex.exp < 0:
minpoly_base = _minpoly_groebner(ex.base, x, cls)
inverse = invert(x, minpoly_base).as_expr()
base_inv = inverse.subs(x, ex.base).expand()
if ex.exp == -1:
return bottom_up_scan(base_inv)
else:
ex = base_inv**(-ex.exp)
if not ex.exp.is_Integer:
base, exp = (ex.base**ex.exp.p).expand(), Rational(
1, ex.exp.q)
else:
base, exp = ex.base, ex.exp
base = bottom_up_scan(base)
expr = base**exp
if expr not in mapping:
if exp.is_Integer:
return expr.expand()
else:
return update_mapping(expr, 1 / exp, -base)
else:
return symbols[expr]
elif ex.is_AlgebraicNumber:
if ex not in mapping:
return update_mapping(ex, ex.minpoly_of_element())
else:
return symbols[ex]
raise NotAlgebraic("%s doesn't seem to be an algebraic number" % ex)
def simpler_inverse(ex):
"""
Returns True if it is more likely that the minimal polynomial
algorithm works better with the inverse
"""
if ex.is_Pow:
if (1 / ex.exp).is_integer and ex.exp < 0:
if ex.base.is_Add:
return True
if ex.is_Mul:
hit = True
for p in ex.args:
if p.is_Add:
return False
if p.is_Pow:
if p.base.is_Add and p.exp > 0:
return False
if hit:
return True
return False
inverted = False
ex = expand_multinomial(ex)
if ex.is_AlgebraicNumber:
return ex.minpoly_of_element().as_expr(x)
elif ex.is_Rational:
result = ex.q * x - ex.p
else:
inverted = simpler_inverse(ex)
if inverted:
ex = ex**-1
res = None
if ex.is_Pow and (1 / ex.exp).is_Integer:
n = 1 / ex.exp
res = _minimal_polynomial_sq(ex.base, n, x)
elif _is_sum_surds(ex):
res = _minimal_polynomial_sq(ex, S.One, x)
if res is not None:
result = res
if res is None:
bus = bottom_up_scan(ex)
F = [x - bus] + list(mapping.values())
G = groebner(F, list(symbols.values()) + [x], order='lex')
_, factors = factor_list(G[-1])
# by construction G[-1] has root `ex`
result = _choose_factor(factors, x, ex)
if inverted:
result = _invertx(result, x)
if result.coeff(x**degree(result, x)) < 0:
result = expand_mul(-result)
return result
def test_pinjoint_arbitrary_axis():
theta, omega = dynamicsymbols('theta_J, omega_J')
# When the bodies are attached though masscenters but axess are opposite.
N, A, P, C = _generate_body()
PinJoint('J', P, C, child_axis=-A.x)
assert (-A.x).angle_between(N.x) == 0
assert -A.x.express(N) == N.x
assert A.dcm(N) == Matrix([[-1, 0, 0], [0, -cos(theta), -sin(theta)],
[0, -sin(theta), cos(theta)]])
assert A.ang_vel_in(N) == omega * N.x
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
assert C.masscenter.pos_from(P.masscenter) == 0
assert C.masscenter.pos_from(P.masscenter).express(N).simplify() == 0
assert C.masscenter.vel(N) == 0
# When axes are different and parent joint is at masscenter but child joint
# is at a unit vector from child masscenter.
N, A, P, C = _generate_body()
PinJoint('J', P, C, child_axis=A.y, child_joint_pos=A.x)
assert A.y.angle_between(N.x) == 0 # Axis are aligned
assert A.y.express(N) == N.x
assert A.dcm(N) == Matrix([[0, -cos(theta), -sin(theta)], [1, 0, 0],
[0, -sin(theta), cos(theta)]])
assert A.ang_vel_in(N) == omega * N.x
assert A.ang_vel_in(N).express(A) == omega * A.y
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
angle = A.ang_vel_in(N).angle_between(A.y)
assert angle.xreplace({omega: 1}) == 0
assert C.masscenter.vel(N) == omega * A.z
assert C.masscenter.pos_from(P.masscenter) == -A.x
assert (C.masscenter.pos_from(
P.masscenter).express(N).simplify() == cos(theta) * N.y +
sin(theta) * N.z)
assert C.masscenter.vel(N).angle_between(A.x) == pi / 2
# Similar to previous case but wrt parent body
N, A, P, C = _generate_body()
PinJoint('J', P, C, parent_axis=N.y, parent_joint_pos=N.x)
assert N.y.angle_between(A.x) == 0 # Axis are aligned
assert N.y.express(A) == A.x
assert A.dcm(N) == Matrix([[0, 1, 0], [-cos(theta), 0,
sin(theta)],
[sin(theta), 0, cos(theta)]])
assert A.ang_vel_in(N) == omega * N.y
assert A.ang_vel_in(N).express(A) == omega * A.x
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
angle = A.ang_vel_in(N).angle_between(A.x)
assert angle.xreplace({omega: 1}) == 0
assert C.masscenter.vel(N).simplify() == -omega * N.z
assert C.masscenter.pos_from(P.masscenter) == N.x
# Both joint pos id defined but different axes
N, A, P, C = _generate_body()
PinJoint('J',
P,
C,
parent_joint_pos=N.x,
child_joint_pos=A.x,
child_axis=A.x + A.y)
assert expand_mul(N.x.angle_between(A.x + A.y)) == 0 # Axis are aligned
assert (A.x + A.y).express(N).simplify() == sqrt(2) * N.x
assert _simplify_matrix(A.dcm(N)) == Matrix(
[[sqrt(2) / 2, -sqrt(2) * cos(theta) / 2, -sqrt(2) * sin(theta) / 2],
[sqrt(2) / 2,
sqrt(2) * cos(theta) / 2,
sqrt(2) * sin(theta) / 2], [0, -sin(theta),
cos(theta)]])
assert A.ang_vel_in(N) == omega * N.x
assert (
A.ang_vel_in(N).express(A).simplify() == (omega * A.x + omega * A.y) /
sqrt(2))
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
angle = A.ang_vel_in(N).angle_between(A.x + A.y)
assert angle.xreplace({omega: 1}) == 0
assert C.masscenter.vel(N).simplify() == (omega * A.z) / sqrt(2)
assert C.masscenter.pos_from(P.masscenter) == N.x - A.x
assert (C.masscenter.pos_from(
P.masscenter).express(N).simplify() == (1 - sqrt(2) / 2) * N.x +
sqrt(2) * cos(theta) / 2 * N.y + sqrt(2) * sin(theta) / 2 * N.z)
assert (C.masscenter.vel(N).express(N).simplify() == -sqrt(2) * omega *
sin(theta) / 2 * N.y + sqrt(2) * omega * cos(theta) / 2 * N.z)
assert C.masscenter.vel(N).angle_between(A.x) == pi / 2
N, A, P, C = _generate_body()
PinJoint('J',
P,
C,
parent_joint_pos=N.x,
child_joint_pos=A.x,
child_axis=A.x + A.y - A.z)
assert expand_mul(N.x.angle_between(A.x + A.y - A.z)) == 0 # Axis aligned
assert (A.x + A.y - A.z).express(N).simplify() == sqrt(3) * N.x
assert _simplify_matrix(A.dcm(N)) == Matrix(
[[
sqrt(3) / 3, -sqrt(6) * sin(theta + pi / 4) / 3,
sqrt(6) * cos(theta + pi / 4) / 3
],
[
sqrt(3) / 3,
sqrt(6) * cos(theta + pi / 12) / 3,
sqrt(6) * sin(theta + pi / 12) / 3
],
[
-sqrt(3) / 3,
sqrt(6) * cos(theta + 5 * pi / 12) / 3,
sqrt(6) * sin(theta + 5 * pi / 12) / 3
]])
assert A.ang_vel_in(N) == omega * N.x
assert A.ang_vel_in(N).express(
A).simplify() == (omega * A.x + omega * A.y - omega * A.z) / sqrt(3)
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
angle = A.ang_vel_in(N).angle_between(A.x + A.y - A.z)
assert angle.xreplace({omega: 1}) == 0
assert C.masscenter.vel(N).simplify() == (omega * A.y +
omega * A.z) / sqrt(3)
assert C.masscenter.pos_from(P.masscenter) == N.x - A.x
assert (C.masscenter.pos_from(
P.masscenter).express(N).simplify() == (1 - sqrt(3) / 3) * N.x +
sqrt(6) * sin(theta + pi / 4) / 3 * N.y -
sqrt(6) * cos(theta + pi / 4) / 3 * N.z)
assert (C.masscenter.vel(N).express(N).simplify() == sqrt(6) * omega *
cos(theta + pi / 4) / 3 * N.y +
sqrt(6) * omega * sin(theta + pi / 4) / 3 * N.z)
assert C.masscenter.vel(N).angle_between(A.x) == pi / 2
N, A, P, C = _generate_body()
m, n = symbols('m n')
PinJoint('J',
P,
C,
parent_joint_pos=m * N.x,
child_joint_pos=n * A.x,
child_axis=A.x + A.y - A.z,
parent_axis=N.x - N.y + N.z)
angle = (N.x - N.y + N.z).angle_between(A.x + A.y - A.z)
assert expand_mul(angle) == 0 # Axis are aligned
assert ((
A.x - A.y +
A.z).express(N).simplify() == (-4 * cos(theta) / 3 - S(1) / 3) * N.x +
(S(1) / 3 - 4 * sin(theta + pi / 6) / 3) * N.y +
(4 * cos(theta + pi / 3) / 3 - S(1) / 3) * N.z)
assert _simplify_matrix(A.dcm(N)) == Matrix(
[[
S(1) / 3 - 2 * cos(theta) / 3,
-2 * sin(theta + pi / 6) / 3 - S(1) / 3,
2 * cos(theta + pi / 3) / 3 + S(1) / 3
],
[
2 * cos(theta + pi / 3) / 3 + S(1) / 3,
2 * cos(theta) / 3 - S(1) / 3,
2 * sin(theta + pi / 6) / 3 + S(1) / 3
],
[
-2 * sin(theta + pi / 6) / 3 - S(1) / 3,
2 * cos(theta + pi / 3) / 3 + S(1) / 3,
2 * cos(theta) / 3 - S(1) / 3
]])
assert A.ang_vel_in(N) == (omega * N.x - omega * N.y +
omega * N.z) / sqrt(3)
assert A.ang_vel_in(N).express(
A).simplify() == (omega * A.x + omega * A.y - omega * A.z) / sqrt(3)
assert A.ang_vel_in(N).magnitude() == sqrt(omega**2)
angle = A.ang_vel_in(N).angle_between(A.x + A.y - A.z)
assert angle.xreplace({omega: 1}) == 0
assert (
C.masscenter.vel(N).simplify() == (m * omega * N.y + m * omega * N.z +
n * omega * A.y + n * omega * A.z) /
sqrt(3))
assert C.masscenter.pos_from(P.masscenter) == m * N.x - n * A.x
assert (C.masscenter.pos_from(
P.masscenter).express(N).simplify() == (m + n *
(2 * cos(theta) - 1) / 3) * N.x
+ n * (2 * sin(theta + pi / 6) + 1) / 3 * N.y - n *
(2 * cos(theta + pi / 3) + 1) / 3 * N.z)
assert (C.masscenter.vel(N).express(N).simplify() == -2 * n * omega *
sin(theta) / 3 * N.x +
(sqrt(3) * m + 2 * n * cos(theta + pi / 6)) * omega / 3 * N.y +
(sqrt(3) * m + 2 * n * sin(theta + pi / 3)) * omega / 3 * N.z)
assert expand_mul(C.masscenter.vel(N).angle_between(m * N.x -
n * A.x)) == pi / 2
def minimal_polynomial(ex, x=None, **args):
"""
Computes the minimal polynomial of an algebraic number.
Examples
========
>>> from sympy import minimal_polynomial, sqrt
>>> from sympy.abc import x
>>> minimal_polynomial(sqrt(2), x)
x**2 - 2
>>> minimal_polynomial(sqrt(2) + sqrt(3), x)
x**4 - 10*x**2 + 1
"""
from sympy.polys.polytools import degree
from sympy.core.function import expand_mul
from sympy.simplify.simplify import _is_sum_surds
generator = numbered_symbols('a', cls=Dummy)
mapping, symbols, replace = {}, {}, []
ex = sympify(ex)
if x is not None:
x, cls = sympify(x), Poly
else:
x, cls = Dummy('x'), PurePoly
def update_mapping(ex, exp, base=None):
a = generator.next()
symbols[ex] = a
if base is not None:
mapping[ex] = a**exp + base
else:
mapping[ex] = exp.as_expr(a)
return a
def bottom_up_scan(ex):
if ex.is_Atom:
if ex is S.ImaginaryUnit:
if ex not in mapping:
return update_mapping(ex, 2, 1)
else:
return symbols[ex]
elif ex.is_Rational:
return ex
elif ex.is_Add:
return Add(*[bottom_up_scan(g) for g in ex.args])
elif ex.is_Mul:
return Mul(*[bottom_up_scan(g) for g in ex.args])
elif ex.is_Pow:
if ex.exp.is_Rational:
if ex.exp < 0 and ex.base.is_Add:
coeff, terms = ex.base.as_coeff_add()
elt, _ = primitive_element(terms, polys=True)
alg = ex.base - coeff
# XXX: turn this into eval()
inverse = invert(elt.gen + coeff, elt).as_expr()
base = inverse.subs(elt.gen, alg).expand()
if ex.exp == -1:
return bottom_up_scan(base)
else:
ex = base**(-ex.exp)
if not ex.exp.is_Integer:
base, exp = (ex.base**ex.exp.p).expand(), Rational(
1, ex.exp.q)
else:
base, exp = ex.base, ex.exp
base = bottom_up_scan(base)
expr = base**exp
if expr not in mapping:
return update_mapping(expr, 1 / exp, -base)
else:
return symbols[expr]
elif ex.is_AlgebraicNumber:
if ex.root not in mapping:
return update_mapping(ex.root, ex.minpoly)
else:
return symbols[ex.root]
raise NotAlgebraic("%s doesn't seem to be an algebraic number" % ex)
def simpler_inverse(ex):
"""
Returns True if it is more likely that the minimal polynomial
algorithm works better with the inverse
"""
if ex.is_Pow:
if (1 / ex.exp).is_integer and ex.exp < 0:
if ex.base.is_Add:
return True
if ex.is_Mul:
hit = True
a = []
for p in ex.args:
if p.is_Add:
return False
if p.is_Pow:
if p.base.is_Add and p.exp > 0:
return False
if hit:
return True
return False
polys = args.get('polys', False)
prec = args.pop('prec', 10)
inverted = False
if ex.is_AlgebraicNumber:
if not polys:
return ex.minpoly.as_expr(x)
else:
return ex.minpoly.replace(x)
elif ex.is_Rational:
result = ex.q * x - ex.p
else:
inverted = simpler_inverse(ex)
if inverted:
ex = ex**-1
res = None
if ex.is_Pow and (1 / ex.exp).is_Integer:
n = 1 / ex.exp
res = _minimal_polynomial_sq(ex.base, n, x, prec)
elif _is_sum_surds(ex):
res = _minimal_polynomial_sq(ex, S.One, x, prec)
if res is not None:
result = res
if res is None:
bus = bottom_up_scan(ex)
F = [x - bus] + mapping.values()
G = groebner(F, symbols.values() + [x], order='lex')
_, factors = factor_list(G[-1])
# by construction G[-1] has root `ex`
result = _choose_factor(factors, x, ex, prec)
if result is None:
raise NotImplementedError(
"multiple candidates for the minimal polynomial of %s" %
ex)
if inverted:
result = expand_mul(x**degree(result) * result.subs(x, 1 / x))
if result.coeff(x**degree(result)) < 0:
result = expand_mul(-result)
if polys:
return cls(result, x, field=True)
else:
return result
def minimal_polynomial(ex, x=None, **args):
"""
Computes the minimal polynomial of an algebraic number.
Examples
========
>>> from sympy import minimal_polynomial, sqrt
>>> from sympy.abc import x
>>> minimal_polynomial(sqrt(2), x)
x**2 - 2
>>> minimal_polynomial(sqrt(2) + sqrt(3), x)
x**4 - 10*x**2 + 1
"""
from sympy.polys.polytools import degree
from sympy.core.function import expand_mul
from sympy.simplify.simplify import _is_sum_surds
generator = numbered_symbols('a', cls=Dummy)
mapping, symbols, replace = {}, {}, []
ex = sympify(ex)
if x is not None:
x, cls = sympify(x), Poly
else:
x, cls = Dummy('x'), PurePoly
def update_mapping(ex, exp, base=None):
a = generator.next()
symbols[ex] = a
if base is not None:
mapping[ex] = a**exp + base
else:
mapping[ex] = exp.as_expr(a)
return a
def bottom_up_scan(ex):
if ex.is_Atom:
if ex is S.ImaginaryUnit:
if ex not in mapping:
return update_mapping(ex, 2, 1)
else:
return symbols[ex]
elif ex.is_Rational:
return ex
elif ex.is_Add:
return Add(*[ bottom_up_scan(g) for g in ex.args ])
elif ex.is_Mul:
return Mul(*[ bottom_up_scan(g) for g in ex.args ])
elif ex.is_Pow:
if ex.exp.is_Rational:
if ex.exp < 0 and ex.base.is_Add:
coeff, terms = ex.base.as_coeff_add()
elt, _ = primitive_element(terms, polys=True)
alg = ex.base - coeff
# XXX: turn this into eval()
inverse = invert(elt.gen + coeff, elt).as_expr()
base = inverse.subs(elt.gen, alg).expand()
if ex.exp == -1:
return bottom_up_scan(base)
else:
ex = base**(-ex.exp)
if not ex.exp.is_Integer:
base, exp = (
ex.base**ex.exp.p).expand(), Rational(1, ex.exp.q)
else:
base, exp = ex.base, ex.exp
base = bottom_up_scan(base)
expr = base**exp
if expr not in mapping:
return update_mapping(expr, 1/exp, -base)
else:
return symbols[expr]
elif ex.is_AlgebraicNumber:
if ex.root not in mapping:
return update_mapping(ex.root, ex.minpoly)
else:
return symbols[ex.root]
raise NotAlgebraic("%s doesn't seem to be an algebraic number" % ex)
def simpler_inverse(ex):
"""
Returns True if it is more likely that the minimal polynomial
algorithm works better with the inverse
"""
if ex.is_Pow:
if (1/ex.exp).is_integer and ex.exp < 0:
if ex.base.is_Add:
return True
if ex.is_Mul:
hit = True
a = []
for p in ex.args:
if p.is_Add:
return False
if p.is_Pow:
if p.base.is_Add and p.exp > 0:
return False
if hit:
return True
return False
polys = args.get('polys', False)
prec = args.pop('prec', 10)
inverted = False
if ex.is_AlgebraicNumber:
if not polys:
return ex.minpoly.as_expr(x)
else:
return ex.minpoly.replace(x)
elif ex.is_Rational:
result = ex.q*x - ex.p
else:
inverted = simpler_inverse(ex)
if inverted:
ex = ex**-1
res = None
if ex.is_Pow and (1/ex.exp).is_Integer:
n = 1/ex.exp
res = _minimal_polynomial_sq(ex.base, n, x, prec)
elif _is_sum_surds(ex):
res = _minimal_polynomial_sq(ex, S.One, x, prec)
if res is not None:
result = res
if res is None:
bus = bottom_up_scan(ex)
F = [x - bus] + mapping.values()
G = groebner(F, symbols.values() + [x], order='lex')
_, factors = factor_list(G[-1])
# by construction G[-1] has root `ex`
result = _choose_factor(factors, x, ex, prec)
if result is None:
raise NotImplementedError("multiple candidates for the minimal polynomial of %s" % ex)
if inverted:
result = expand_mul(x**degree(result)*result.subs(x, 1/x))
if result.coeff(x**degree(result)) < 0:
result = expand_mul(-result)
if polys:
return cls(result, x, field=True)
else:
return result
def _eval_expand_multinomial(self, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
b = self.base
e = self.exp
if b is None:
base = self.base
else:
base = b
if e is None:
exp = self.exp
else:
exp = e
if e is not None or b is not None:
result = Pow(base, exp)
if result.is_Pow:
base, exp = result.base, result.exp
else:
return result
else:
result = None
if exp.is_Rational and exp.p > 0 and base.is_Add:
if not exp.is_Integer:
n = Integer(exp.p // exp.q)
if not n:
return Pow(base, exp)
else:
radical, result = Pow(base, exp - n), []
expanded_base_n = Pow(base, n)
if expanded_base_n.is_Pow:
expanded_base_n = \
expanded_base_n._eval_expand_multinomial()
for term in Add.make_args(expanded_base_n):
result.append(term * radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for order in base.args:
if order.is_Order:
order_terms.append(order)
else:
other_terms.append(order)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
if n == 2:
return expand_multinomial(f**n, deep=False) + \
n*f*Add(*order_terms)
else:
g = expand_multinomial(f**(n - 1), deep=False)
return expand_mul(f*g, deep=False) + \
n*g*Add(*order_terms)
if base.is_number:
# Efficiently expand expressions of the form (a + b*I)**n
# where 'a' and 'b' are real numbers and 'n' is integer.
a, b = base.as_real_imag()
if a.is_Rational and b.is_Rational:
if not a.is_Integer:
if not b.is_Integer:
k = Pow(a.q * b.q, n)
a, b = a.p * b.q, a.q * b.p
else:
k = Pow(a.q, n)
a, b = a.p, a.q * b
elif not b.is_Integer:
k = Pow(b.q, n)
a, b = a * b.q, b.p
else:
k = 1
a, b, c, d = int(a), int(b), 1, 0
while n:
if n & 1:
c, d = a * c - b * d, b * c + a * d
n -= 1
a, b = a * a - b * b, 2 * a * b
n //= 2
I = S.ImaginaryUnit
if k == 1:
return c + I * d
else:
return Integer(c) / k + I * d / k
p = other_terms
# (x+y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
# in this particular example:
# p = [x,y]; n = 3
# so now it's easy to get the correct result -- we get the
# coefficients first:
from sympy import multinomial_coefficients
expansion_dict = multinomial_coefficients(len(p), n)
# in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
# and now construct the expression.
# An elegant way would be to use Poly, but unfortunately it is
# slower than the direct method below, so it is commented out:
#b = {}
#for k in expansion_dict:
# b[k] = Integer(expansion_dict[k])
#return Poly(b, *p).as_expr()
from sympy.polys.polyutils import basic_from_dict
result = basic_from_dict(expansion_dict, *p)
return result
else:
if n == 2:
return Add(*[f * g for f in base.args for g in base.args])
else:
multi = (base**(n - 1))._eval_expand_multinomial()
if multi.is_Add:
return Add(
*[f * g for f in base.args for g in multi.args])
else:
return Add(*[f * multi for f in base.args])
elif (exp.is_Rational and exp.p < 0 and base.is_Add
and abs(exp.p) > exp.q):
return 1 / Pow(base, -exp)._eval_expand_multinomial()
elif exp.is_Add and base.is_Number:
# a + b a b
# n --> n n , where n, a, b are Numbers
coeff, tail = S.One, S.Zero
for term in exp.args:
if term.is_Number:
coeff *= Pow(base, term)
else:
tail += term
return coeff * Pow(base, tail)
else:
return result
def _eval_nseries(self, x, n, logx):
# NOTE! This function is an important part of the gruntz algorithm
# for computing limits. It has to return a generalized power
# series with coefficients in C(log, log(x)). In more detail:
# It has to return an expression
# c_0*x**e_0 + c_1*x**e_1 + ... (finitely many terms)
# where e_i are numbers (not necessarily integers) and c_i are
# expressions involving only numbers, the log function, and log(x).
from sympy import powsimp, collect, exp, log, O, ceiling
b, e = self.args
if e.is_Integer:
if e > 0:
# positive integer powers are easy to expand, e.g.:
# sin(x)**4 = (x-x**3/3+...)**4 = ...
return expand_multinomial(self.func(
b._eval_nseries(x, n=n, logx=logx), e),
deep=False)
elif e is S.NegativeOne:
# this is also easy to expand using the formula:
# 1/(1 + x) = 1 - x + x**2 - x**3 ...
# so we need to rewrite base to the form "1+x"
nuse = n
cf = 1
try:
ord = b.as_leading_term(x)
cf = C.Order(ord, x).getn()
if cf and cf.is_Number:
nuse = n + 2 * ceiling(cf)
else:
cf = 1
except NotImplementedError:
pass
b_orig, prefactor = b, O(1, x)
while prefactor.is_Order:
nuse += 1
b = b_orig._eval_nseries(x, n=nuse, logx=logx)
prefactor = b.as_leading_term(x)
# express "rest" as: rest = 1 + k*x**l + ... + O(x**n)
rest = expand_mul((b - prefactor) / prefactor)
if rest.is_Order:
return 1 / prefactor + rest / prefactor + O(x**n, x)
k, l = rest.leadterm(x)
if l.is_Rational and l > 0:
pass
elif l.is_number and l > 0:
l = l.evalf()
elif l == 0:
k = k.simplify()
if k == 0:
# if prefactor == w**4 + x**2*w**4 + 2*x*w**4, we need to
# factor the w**4 out using collect:
return 1 / collect(prefactor, x)
else:
raise NotImplementedError()
else:
raise NotImplementedError()
if cf < 0:
cf = S.One / abs(cf)
try:
dn = C.Order(1 / prefactor, x).getn()
if dn and dn < 0:
pass
else:
dn = 0
except NotImplementedError:
dn = 0
terms = [1 / prefactor]
for m in xrange(1, ceiling((n - dn) / l * cf)):
new_term = terms[-1] * (-rest)
if new_term.is_Pow:
new_term = new_term._eval_expand_multinomial(
deep=False)
else:
new_term = expand_mul(new_term, deep=False)
terms.append(new_term)
terms.append(O(x**n, x))
return powsimp(Add(*terms), deep=True, combine='exp')
else:
# negative powers are rewritten to the cases above, for
# example:
# sin(x)**(-4) = 1/( sin(x)**4) = ...
# and expand the denominator:
nuse, denominator = n, O(1, x)
while denominator.is_Order:
denominator = (b**(-e))._eval_nseries(x, n=nuse, logx=logx)
nuse += 1
if 1 / denominator == self:
return self
# now we have a type 1/f(x), that we know how to expand
return (1 / denominator)._eval_nseries(x, n=n, logx=logx)
if e.has(Symbol):
return exp(e * log(b))._eval_nseries(x, n=n, logx=logx)
# see if the base is as simple as possible
bx = b
while bx.is_Pow and bx.exp.is_Rational:
bx = bx.base
if bx == x:
return self
# work for b(x)**e where e is not an Integer and does not contain x
# and hopefully has no other symbols
def e2int(e):
"""return the integer value (if possible) of e and a
flag indicating whether it is bounded or not."""
n = e.limit(x, 0)
unbounded = n.is_unbounded
if not unbounded:
# XXX was int or floor intended? int used to behave like floor
# so int(-Rational(1, 2)) returned -1 rather than int's 0
try:
n = int(n)
except TypeError:
#well, the n is something more complicated (like 1+log(2))
try:
n = int(n.evalf()) + 1 # XXX why is 1 being added?
except TypeError:
pass # hope that base allows this to be resolved
n = _sympify(n)
return n, unbounded
order = O(x**n, x)
ei, unbounded = e2int(e)
b0 = b.limit(x, 0)
if unbounded and (b0 is S.One or b0.has(Symbol)):
# XXX what order
if b0 is S.One:
resid = (b - 1)
if resid.is_positive:
return S.Infinity
elif resid.is_negative:
return S.Zero
raise ValueError('cannot determine sign of %s' % resid)
return b0**ei
if (b0 is S.Zero or b0.is_unbounded):
if unbounded is not False:
return b0**e # XXX what order
if not ei.is_number: # if not, how will we proceed?
raise ValueError('expecting numerical exponent but got %s' %
ei)
nuse = n - ei
if e.is_real and e.is_positive:
lt = b.as_leading_term(x)
# Try to correct nuse (= m) guess from:
# (lt + rest + O(x**m))**e =
# lt**e*(1 + rest/lt + O(x**m)/lt)**e =
# lt**e + ... + O(x**m)*lt**(e - 1) = ... + O(x**n)
try:
cf = C.Order(lt, x).getn()
nuse = ceiling(n - cf * (e - 1))
except NotImplementedError:
pass
bs = b._eval_nseries(x, n=nuse, logx=logx)
terms = bs.removeO()
if terms.is_Add:
bs = terms
lt = terms.as_leading_term(x)
# bs -> lt + rest -> lt*(1 + (bs/lt - 1))
return ((self.func(lt, e) * self.func(
(bs / lt).expand(), e).nseries(
x, n=nuse, logx=logx)).expand() + order)
if bs.is_Add:
from sympy import O
# So, bs + O() == terms
c = Dummy('c')
res = []
for arg in bs.args:
if arg.is_Order:
arg = c * arg.expr
res.append(arg)
bs = Add(*res)
rv = (bs**e).series(x).subs(c, O(1, x))
rv += order
return rv
rv = bs**e
if terms != bs:
rv += order
return rv
# either b0 is bounded but neither 1 nor 0 or e is unbounded
# b -> b0 + (b-b0) -> b0 * (1 + (b/b0-1))
o2 = order * (b0**-e)
z = (b / b0 - 1)
o = O(z, x)
#r = self._compute_oseries3(z, o2, self.taylor_term)
if o is S.Zero or o2 is S.Zero:
unbounded = True
else:
if o.expr.is_number:
e2 = log(o2.expr * x) / log(x)
else:
e2 = log(o2.expr) / log(o.expr)
n, unbounded = e2int(e2)
if unbounded:
# requested accuracy gives infinite series,
# order is probably non-polynomial e.g. O(exp(-1/x), x).
r = 1 + z
else:
l = []
g = None
for i in xrange(n + 2):
g = self._taylor_term(i, z, g)
g = g.nseries(x, n=n, logx=logx)
l.append(g)
r = Add(*l)
return expand_mul(r * b0**e) + order
def sincos_to_sum(expr):
if not expr.has(cos, sin):
return expr
else:
return TR8(expand_mul(TRpower(expr)))
def convolution_subset(a, b):
"""
Performs Subset Convolution of given sequences.
The indices of each argument, considered as bit strings, correspond to
subsets of a finite set.
The sequence is automatically padded to the right with zeros, as the
definition of subset based on bitmasks (indices) requires the size of
sequence to be a power of 2.
Parameters
==========
a, b : iterables
The sequences for which convolution is performed.
Examples
========
>>> from sympy import symbols, S
>>> from sympy.discrete.convolutions import convolution_subset
>>> u, v, x, y, z = symbols('u v x y z')
>>> convolution_subset([u, v], [x, y])
[u*x, u*y + v*x]
>>> convolution_subset([u, v, x], [y, z])
[u*y, u*z + v*y, x*y, x*z]
>>> convolution_subset([1, S(2)/3], [3, 4])
[3, 6]
>>> convolution_subset([1, 3, S(5)/7], [7])
[7, 21, 5, 0]
References
==========
.. [1] https://people.csail.mit.edu/rrw/presentations/subset-conv.pdf
"""
if not a or not b:
return []
if not iterable(a) or not iterable(b):
raise TypeError("Expected a sequence of coefficients for convolution")
a = [sympify(arg) for arg in a]
b = [sympify(arg) for arg in b]
n = max(len(a), len(b))
if n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
c = [S.Zero]*n
for mask in range(n):
smask = mask
while smask > 0:
c[mask] += expand_mul(a[smask] * b[mask^smask])
smask = (smask - 1)&mask
c[mask] += expand_mul(a[smask] * b[mask^smask])
return c
def periodicity(f, symbol, check=False):
"""
Tests the given function for periodicity in the given symbol.
Parameters
==========
f : :py:class:`~.Expr`.
The concerned function.
symbol : :py:class:`~.Symbol`
The variable for which the period is to be determined.
check : bool, optional
The flag to verify whether the value being returned is a period or not.
Returns
=======
period
The period of the function is returned.
``None`` is returned when the function is aperiodic or has a complex period.
The value of $0$ is returned as the period of a constant function.
Raises
======
NotImplementedError
The value of the period computed cannot be verified.
Notes
=====
Currently, we do not support functions with a complex period.
The period of functions having complex periodic values such
as ``exp``, ``sinh`` is evaluated to ``None``.
The value returned might not be the "fundamental" period of the given
function i.e. it may not be the smallest periodic value of the function.
The verification of the period through the ``check`` flag is not reliable
due to internal simplification of the given expression. Hence, it is set
to ``False`` by default.
Examples
========
>>> from sympy import periodicity, Symbol, sin, cos, tan, exp
>>> x = Symbol('x')
>>> f = sin(x) + sin(2*x) + sin(3*x)
>>> periodicity(f, x)
2*pi
>>> periodicity(sin(x)*cos(x), x)
pi
>>> periodicity(exp(tan(2*x) - 1), x)
pi/2
>>> periodicity(sin(4*x)**cos(2*x), x)
pi
>>> periodicity(exp(x), x)
"""
if symbol.kind is not NumberKind:
raise NotImplementedError("Cannot use symbol of kind %s" % symbol.kind)
temp = Dummy('x', real=True)
f = f.subs(symbol, temp)
symbol = temp
def _check(orig_f, period):
'''Return the checked period or raise an error.'''
new_f = orig_f.subs(symbol, symbol + period)
if new_f.equals(orig_f):
return period
else:
raise NotImplementedError(
filldedent('''
The period of the given function cannot be verified.
When `%s` was replaced with `%s + %s` in `%s`, the result
was `%s` which was not recognized as being the same as
the original function.
So either the period was wrong or the two forms were
not recognized as being equal.
Set check=False to obtain the value.''' %
(symbol, symbol, period, orig_f, new_f)))
orig_f = f
period = None
if isinstance(f, Relational):
f = f.lhs - f.rhs
f = f.simplify()
if symbol not in f.free_symbols:
return S.Zero
if isinstance(f, TrigonometricFunction):
try:
period = f.period(symbol)
except NotImplementedError:
pass
if isinstance(f, Abs):
arg = f.args[0]
if isinstance(arg, (sec, csc, cos)):
# all but tan and cot might have a
# a period that is half as large
# so recast as sin
arg = sin(arg.args[0])
period = periodicity(arg, symbol)
if period is not None and isinstance(arg, sin):
# the argument of Abs was a trigonometric other than
# cot or tan; test to see if the half-period
# is valid. Abs(arg) has behaviour equivalent to
# orig_f, so use that for test:
orig_f = Abs(arg)
try:
return _check(orig_f, period / 2)
except NotImplementedError as err:
if check:
raise NotImplementedError(err)
# else let new orig_f and period be
# checked below
if isinstance(f, exp) or (f.is_Pow and f.base == S.Exp1):
f = Pow(S.Exp1, expand_mul(f.exp))
if im(f) != 0:
period_real = periodicity(re(f), symbol)
period_imag = periodicity(im(f), symbol)
if period_real is not None and period_imag is not None:
period = lcim([period_real, period_imag])
if f.is_Pow and f.base != S.Exp1:
base, expo = f.args
base_has_sym = base.has(symbol)
expo_has_sym = expo.has(symbol)
if base_has_sym and not expo_has_sym:
period = periodicity(base, symbol)
elif expo_has_sym and not base_has_sym:
period = periodicity(expo, symbol)
else:
period = _periodicity(f.args, symbol)
elif f.is_Mul:
coeff, g = f.as_independent(symbol, as_Add=False)
if isinstance(g, TrigonometricFunction) or coeff is not S.One:
period = periodicity(g, symbol)
else:
period = _periodicity(g.args, symbol)
elif f.is_Add:
k, g = f.as_independent(symbol)
if k is not S.Zero:
return periodicity(g, symbol)
period = _periodicity(g.args, symbol)
elif isinstance(f, Mod):
a, n = f.args
if a == symbol:
period = n
elif isinstance(a, TrigonometricFunction):
period = periodicity(a, symbol)
#check if 'f' is linear in 'symbol'
elif (a.is_polynomial(symbol) and degree(a, symbol) == 1
and symbol not in n.free_symbols):
period = Abs(n / a.diff(symbol))
elif isinstance(f, Piecewise):
pass # not handling Piecewise yet as the return type is not favorable
elif period is None:
from sympy.solvers.decompogen import compogen, decompogen
g_s = decompogen(f, symbol)
num_of_gs = len(g_s)
if num_of_gs > 1:
for index, g in enumerate(reversed(g_s)):
start_index = num_of_gs - 1 - index
g = compogen(g_s[start_index:], symbol)
if g not in (orig_f, f): # Fix for issue 12620
period = periodicity(g, symbol)
if period is not None:
break
if period is not None:
if check:
return _check(orig_f, period)
return period
return None
def eval(cls, arg):
from sympy.simplify.simplify import signsimp
from sympy.core.function import expand_mul
from sympy.core.power import Pow
if hasattr(arg, '_eval_Abs'):
obj = arg._eval_Abs()
if obj is not None:
return obj
if not isinstance(arg, Expr):
raise TypeError("Bad argument type for Abs(): %s" % type(arg))
# handle what we can
arg = signsimp(arg, evaluate=False)
n, d = arg.as_numer_denom()
if d.free_symbols and not n.free_symbols:
return cls(n) / cls(d)
if arg.is_Mul:
known = []
unk = []
for t in arg.args:
if t.is_Pow and t.exp.is_integer and t.exp.is_negative:
bnew = cls(t.base)
if isinstance(bnew, cls):
unk.append(t)
else:
known.append(Pow(bnew, t.exp))
else:
tnew = cls(t)
if isinstance(tnew, cls):
unk.append(t)
else:
known.append(tnew)
known = Mul(*known)
unk = cls(Mul(*unk), evaluate=False) if unk else S.One
return known * unk
if arg is S.NaN:
return S.NaN
if arg is S.ComplexInfinity:
return S.Infinity
if arg.is_Pow:
base, exponent = arg.as_base_exp()
if base.is_extended_real:
if exponent.is_integer:
if exponent.is_even:
return arg
if base is S.NegativeOne:
return S.One
return Abs(base)**exponent
if base.is_extended_nonnegative:
return base**re(exponent)
if base.is_extended_negative:
return (-base)**re(exponent) * exp(-S.Pi * im(exponent))
return
elif not base.has(Symbol): # complex base
# express base**exponent as exp(exponent*log(base))
a, b = log(base).as_real_imag()
z = a + I * b
return exp(re(exponent * z))
if isinstance(arg, exp):
return exp(re(arg.args[0]))
if isinstance(arg, AppliedUndef):
return
if arg.is_Add and arg.has(S.Infinity, S.NegativeInfinity):
if any(a.is_infinite for a in arg.as_real_imag()):
return S.Infinity
if arg.is_zero:
return S.Zero
if arg.is_extended_nonnegative:
return arg
if arg.is_extended_nonpositive:
return -arg
if arg.is_imaginary:
arg2 = -S.ImaginaryUnit * arg
if arg2.is_extended_nonnegative:
return arg2
# reject result if all new conjugates are just wrappers around
# an expression that was already in the arg
conj = signsimp(arg.conjugate(), evaluate=False)
new_conj = conj.atoms(conjugate) - arg.atoms(conjugate)
if new_conj and all(arg.has(i.args[0]) for i in new_conj):
return
if arg != conj and arg != -conj:
ignore = arg.atoms(Abs)
abs_free_arg = arg.xreplace({i: Dummy(real=True) for i in ignore})
unk = [
a for a in abs_free_arg.free_symbols
if a.is_extended_real is None
]
if not unk or not all(conj.has(conjugate(u)) for u in unk):
return sqrt(expand_mul(arg * conj))
def _mexpand(expr):
return expand_mul(expand_multinomial(expr))
def solve_univariate_inequality(expr,
gen,
relational=True,
domain=S.Reals,
continuous=False):
"""Solves a real univariate inequality.
Parameters
==========
expr : Relational
The target inequality
gen : Symbol
The variable for which the inequality is solved
relational : bool
A Relational type output is expected or not
domain : Set
The domain over which the equation is solved
continuous: bool
True if expr is known to be continuous over the given domain
(and so continuous_domain() doesn't need to be called on it)
Raises
======
NotImplementedError
The solution of the inequality cannot be determined due to limitation
in `solvify`.
Notes
=====
Currently, we cannot solve all the inequalities due to limitations in
`solvify`. Also, the solution returned for trigonometric inequalities
are restricted in its periodic interval.
See Also
========
solvify: solver returning solveset solutions with solve's output API
Examples
========
>>> from sympy.solvers.inequalities import solve_univariate_inequality
>>> from sympy import Symbol, sin, Interval, S
>>> x = Symbol('x')
>>> solve_univariate_inequality(x**2 >= 4, x)
((2 <= x) & (x < oo)) | ((x <= -2) & (-oo < x))
>>> solve_univariate_inequality(x**2 >= 4, x, relational=False)
Union(Interval(-oo, -2), Interval(2, oo))
>>> domain = Interval(0, S.Infinity)
>>> solve_univariate_inequality(x**2 >= 4, x, False, domain)
Interval(2, oo)
>>> solve_univariate_inequality(sin(x) > 0, x, relational=False)
Interval.open(0, pi)
"""
from sympy import im
from sympy.calculus.util import (continuous_domain, periodicity,
function_range)
from sympy.solvers.solvers import denoms
from sympy.solvers.solveset import solvify, solveset
# This keeps the function independent of the assumptions about `gen`.
# `solveset` makes sure this function is called only when the domain is
# real.
_gen = gen
_domain = domain
if gen.is_extended_real is False:
rv = S.EmptySet
return rv if not relational else rv.as_relational(_gen)
elif gen.is_extended_real is None:
gen = Dummy('gen', extended_real=True)
try:
expr = expr.xreplace({_gen: gen})
except TypeError:
raise TypeError(
filldedent('''
When gen is real, the relational has a complex part
which leads to an invalid comparison like I < 0.
'''))
rv = None
if expr is S.true:
rv = domain
elif expr is S.false:
rv = S.EmptySet
else:
e = expr.lhs - expr.rhs
period = periodicity(e, gen)
if period == S.Zero:
e = expand_mul(e)
const = expr.func(e, 0)
if const is S.true:
rv = domain
elif const is S.false:
rv = S.EmptySet
elif period is not None:
frange = function_range(e, gen, domain)
rel = expr.rel_op
if rel == '<' or rel == '<=':
if expr.func(frange.sup, 0):
rv = domain
elif not expr.func(frange.inf, 0):
rv = S.EmptySet
elif rel == '>' or rel == '>=':
if expr.func(frange.inf, 0):
rv = domain
elif not expr.func(frange.sup, 0):
rv = S.EmptySet
inf, sup = domain.inf, domain.sup
if sup - inf is S.Infinity:
domain = Interval(0, period, False, True)
if rv is None:
n, d = e.as_numer_denom()
try:
if gen not in n.free_symbols and len(e.free_symbols) > 1:
raise ValueError
# this might raise ValueError on its own
# or it might give None...
solns = solvify(e, gen, domain)
if solns is None:
# in which case we raise ValueError
raise ValueError
except (ValueError, NotImplementedError):
# replace gen with generic x since it's
# univariate anyway
raise NotImplementedError(
filldedent('''
The inequality, %s, cannot be solved using
solve_univariate_inequality.
''' % expr.subs(gen, Symbol('x'))))
expanded_e = expand_mul(e)
def valid(x):
# this is used to see if gen=x satisfies the
# relational by substituting it into the
# expanded form and testing against 0, e.g.
# if expr = x*(x + 1) < 2 then e = x*(x + 1) - 2
# and expanded_e = x**2 + x - 2; the test is
# whether a given value of x satisfies
# x**2 + x - 2 < 0
#
# expanded_e, expr and gen used from enclosing scope
v = expanded_e.subs(gen, expand_mul(x))
try:
r = expr.func(v, 0)
except TypeError:
r = S.false
if r in (S.true, S.false):
return r
if v.is_extended_real is False:
return S.false
else:
v = v.n(2)
if v.is_comparable:
return expr.func(v, 0)
# not comparable or couldn't be evaluated
raise NotImplementedError(
'relationship did not evaluate: %s' % r)
singularities = []
for d in denoms(expr, gen):
singularities.extend(solvify(d, gen, domain))
if not continuous:
domain = continuous_domain(expanded_e, gen, domain)
include_x = '=' in expr.rel_op and expr.rel_op != '!='
try:
discontinuities = set(domain.boundary -
FiniteSet(domain.inf, domain.sup))
# remove points that are not between inf and sup of domain
critical_points = FiniteSet(
*(solns + singularities +
list(discontinuities))).intersection(
Interval(domain.inf, domain.sup, domain.inf
not in domain, domain.sup not in domain))
if all(r.is_number for r in critical_points):
reals = _nsort(critical_points, separated=True)[0]
else:
sifted = sift(critical_points,
lambda x: x.is_extended_real)
if sifted[None]:
# there were some roots that weren't known
# to be real
raise NotImplementedError
try:
reals = sifted[True]
if len(reals) > 1:
reals = list(sorted(reals))
except TypeError:
raise NotImplementedError
except NotImplementedError:
raise NotImplementedError(
'sorting of these roots is not supported')
# If expr contains imaginary coefficients, only take real
# values of x for which the imaginary part is 0
make_real = S.Reals
if im(expanded_e) != S.Zero:
check = True
im_sol = FiniteSet()
try:
a = solveset(im(expanded_e), gen, domain)
if not isinstance(a, Interval):
for z in a:
if z not in singularities and valid(
z) and z.is_extended_real:
im_sol += FiniteSet(z)
else:
start, end = a.inf, a.sup
for z in _nsort(critical_points + FiniteSet(end)):
valid_start = valid(start)
if start != end:
valid_z = valid(z)
pt = _pt(start, z)
if pt not in singularities and pt.is_extended_real and valid(
pt):
if valid_start and valid_z:
im_sol += Interval(start, z)
elif valid_start:
im_sol += Interval.Ropen(start, z)
elif valid_z:
im_sol += Interval.Lopen(start, z)
else:
im_sol += Interval.open(start, z)
start = z
for s in singularities:
im_sol -= FiniteSet(s)
except (TypeError):
im_sol = S.Reals
check = False
if isinstance(im_sol, EmptySet):
raise ValueError(
filldedent('''
%s contains imaginary parts which cannot be
made 0 for any value of %s satisfying the
inequality, leading to relations like I < 0.
''' % (expr.subs(gen, _gen), _gen)))
make_real = make_real.intersect(im_sol)
sol_sets = [S.EmptySet]
start = domain.inf
if valid(start) and start.is_finite:
sol_sets.append(FiniteSet(start))
for x in reals:
end = x
if valid(_pt(start, end)):
sol_sets.append(Interval(start, end, True, True))
if x in singularities:
singularities.remove(x)
else:
if x in discontinuities:
discontinuities.remove(x)
_valid = valid(x)
else: # it's a solution
_valid = include_x
if _valid:
sol_sets.append(FiniteSet(x))
start = end
end = domain.sup
if valid(end) and end.is_finite:
sol_sets.append(FiniteSet(end))
if valid(_pt(start, end)):
sol_sets.append(Interval.open(start, end))
if im(expanded_e) != S.Zero and check:
rv = (make_real).intersect(_domain)
else:
rv = Intersection((Union(*sol_sets)), make_real,
_domain).subs(gen, _gen)
return rv if not relational else rv.as_relational(_gen)
def _is_zero_after_expand_mul(x):
"""Tests by expand_mul only, suitable for polynomials and rational
functions."""
return expand_mul(x) == 0
def intersecting_product(a, b):
"""
Returns the intersecting product of given sequences.
The indices of each argument, considered as bit strings, correspond to
subsets of a finite set.
The intersecting product of given sequences is the sequence which
contains the sum of products of the elements of the given sequences
grouped by the *bitwise-AND* of the corresponding indices.
The sequence is automatically padded to the right with zeros, as the
definition of subset based on bitmasks (indices) requires the size of
sequence to be a power of 2.
Parameters
==========
a, b : iterables
The sequences for which intersecting product is to be obtained.
Examples
========
>>> from sympy import symbols, S, I, intersecting_product
>>> u, v, x, y, z = symbols('u v x y z')
>>> intersecting_product([u, v], [x, y])
[u*x + u*y + v*x, v*y]
>>> intersecting_product([u, v, x], [y, z])
[u*y + u*z + v*y + x*y + x*z, v*z, 0, 0]
>>> intersecting_product([1, S(2)/3], [3, 4 + 5*I])
[9 + 5*I, 8/3 + 10*I/3]
>>> intersecting_product([1, 3, S(5)/7], [7, 8])
[327/7, 24, 0, 0]
References
==========
.. [1] https://people.csail.mit.edu/rrw/presentations/subset-conv.pdf
"""
if not a or not b:
return []
a, b = a[:], b[:]
n = max(len(a), len(b))
if n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
a, b = mobius_transform(a, subset=False), mobius_transform(b, subset=False)
a = [expand_mul(x*y) for x, y in zip(a, b)]
a = inverse_mobius_transform(a, subset=False)
return a
def minimal_polynomial(ex, x=None, compose=True, polys=False, domain=None):
"""
Computes the minimal polynomial of an algebraic element.
Parameters
==========
ex : Expr
Element or expression whose minimal polynomial is to be calculated.
x : Symbol, optional
Independent variable of the minimal polynomial
compose : boolean, optional (default=True)
Method to use for computing minimal polynomial. If ``compose=True``
(default) then ``_minpoly_compose`` is used, if ``compose=False`` then
groebner bases are used.
polys : boolean, optional (default=False)
If ``True`` returns a ``Poly`` object else an ``Expr`` object.
domain : Domain, optional
Ground domain
Notes
=====
By default ``compose=True``, the minimal polynomial of the subexpressions of ``ex``
are computed, then the arithmetic operations on them are performed using the resultant
and factorization.
If ``compose=False``, a bottom-up algorithm is used with ``groebner``.
The default algorithm stalls less frequently.
If no ground domain is given, it will be generated automatically from the expression.
Examples
========
>>> from sympy import minimal_polynomial, sqrt, solve, QQ
>>> from sympy.abc import x, y
>>> minimal_polynomial(sqrt(2), x)
x**2 - 2
>>> minimal_polynomial(sqrt(2), x, domain=QQ.algebraic_field(sqrt(2)))
x - sqrt(2)
>>> minimal_polynomial(sqrt(2) + sqrt(3), x)
x**4 - 10*x**2 + 1
>>> minimal_polynomial(solve(x**3 + x + 3)[0], x)
x**3 + x + 3
>>> minimal_polynomial(sqrt(y), x)
x**2 - y
"""
ex = sympify(ex)
if ex.is_number:
# not sure if it's always needed but try it for numbers (issue 8354)
ex = _mexpand(ex, recursive=True)
for expr in preorder_traversal(ex):
if expr.is_AlgebraicNumber:
compose = False
break
if x is not None:
x, cls = sympify(x), Poly
else:
x, cls = Dummy('x'), PurePoly
if not domain:
if ex.free_symbols:
domain = FractionField(QQ, list(ex.free_symbols))
else:
domain = QQ
if hasattr(domain, 'symbols') and x in domain.symbols:
raise GeneratorsError("the variable %s is an element of the ground "
"domain %s" % (x, domain))
if compose:
result = _minpoly_compose(ex, x, domain)
result = result.primitive()[1]
c = result.coeff(x**degree(result, x))
if c.is_negative:
result = expand_mul(-result)
return cls(result, x, field=True) if polys else result.collect(x)
if not domain.is_QQ:
raise NotImplementedError("groebner method only works for QQ")
result = _minpoly_groebner(ex, x, cls)
return cls(result, x, field=True) if polys else result.collect(x)
def convolution_subset(a, b):
"""
Performs Subset Convolution of given sequences.
The indices of each argument, considered as bit strings, correspond to
subsets of a finite set.
The sequence is automatically padded to the right with zeros, as the
definition of subset based on bitmasks (indices) requires the size of
sequence to be a power of 2.
Parameters
==========
a, b : iterables
The sequences for which convolution is performed.
Examples
========
>>> from sympy import symbols, S, I
>>> from sympy.discrete.convolutions import convolution_subset
>>> u, v, x, y, z = symbols('u v x y z')
>>> convolution_subset([u, v], [x, y])
[u*x, u*y + v*x]
>>> convolution_subset([u, v, x], [y, z])
[u*y, u*z + v*y, x*y, x*z]
>>> convolution_subset([1, S(2)/3], [3, 4])
[3, 6]
>>> convolution_subset([1, 3, S(5)/7], [7])
[7, 21, 5, 0]
References
==========
.. [1] https://people.csail.mit.edu/rrw/presentations/subset-conv.pdf
"""
if not a or not b:
return []
if not iterable(a) or not iterable(b):
raise TypeError("Expected a sequence of coefficients for convolution")
a = [sympify(arg) for arg in a]
b = [sympify(arg) for arg in b]
n = max(len(a), len(b))
if n&(n - 1): # not a power of 2
n = 2**n.bit_length()
# padding with zeros
a += [S.Zero]*(n - len(a))
b += [S.Zero]*(n - len(b))
c = [S.Zero]*n
for mask in range(n):
smask = mask
while smask > 0:
c[mask] += expand_mul(a[smask] * b[mask^smask])
smask = (smask - 1)&mask
c[mask] += expand_mul(a[smask] * b[mask^smask])
return c
def _linear_neq_order1_type4(match_):
r"""
System of n first-order nonconstant-coefficient linear non-homogeneous differential equations
.. math::
X' = A(t) X + f(t)
where $X$ is the vector of $n$ dependent variables, $t$ is the dependent variable, $X'$
is the first order differential of $X$ with respect to $t$ and $A(t)$ is a $n \times n$
coefficient matrix.
Let us define $B$ as antiderivative of coefficient matrix $A$:
.. math::
B(t) = \int A(t) dt
If the system of ODEs defined above is such that its antiderivative $B(t)$ commutes with
$A(t)$ itself, then, the solution of the above system is given as:
.. math::
X = e^{B(t)} ( \int e^{-B(t)} f(t) \,dt + C)
where $C$ is the vector of constants.
"""
# Some parts of code is repeated, this needs to be taken care of
# The constant vector obtained here can be done so in the match
# function itself.
eq = match_['eq']
func = match_['func']
fc = match_['func_coeff']
b = match_['rhs']
n = len(eq)
t = list(list(eq[0].atoms(Derivative))[0].atoms(Symbol))[0]
constants = numbered_symbols(prefix='C', cls=Symbol, start=1)
# This needs to be modified in future so that fc is only of type Matrix
M = -fc if type(fc) is Matrix else Matrix(n, n,
lambda i, j: -fc[i, func[j], 0])
Cvect = Matrix(list(next(constants) for _ in range(n)))
# The code in if block will be removed when it is made sure
# that the code works without the statements in if block.
if "commutative_antiderivative" not in match_:
B, is_commuting = _is_commutative_anti_derivative(M, t)
# This course is subject to change
if not is_commuting:
return None
else:
B = match_['commutative_antiderivative']
sol_vector = B.exp() * ((
(-B).exp() * b).applyfunc(lambda x: Integral(x, t)) + Cvect)
sol_vector = [
collect(expand_mul(s), ordered(sol_vector.atoms(exp)), exact=True)
for s in sol_vector
]
sol_dict = [Eq(func[i], sol_vector[i]) for i in range(n)]
return sol_dict
def test_acsch():
x = Symbol('x')
assert unchanged(acsch, x)
assert acsch(-x) == -acsch(x)
# values at fixed points
assert acsch(1) == log(1 + sqrt(2))
assert acsch(-1) == -log(1 + sqrt(2))
assert acsch(0) is zoo
assert acsch(2) == log((1 + sqrt(5)) / 2)
assert acsch(-2) == -log((1 + sqrt(5)) / 2)
assert acsch(I) == -I * pi / 2
assert acsch(-I) == I * pi / 2
assert acsch(-I * (sqrt(6) + sqrt(2))) == I * pi / 12
assert acsch(I * (sqrt(2) + sqrt(6))) == -I * pi / 12
assert acsch(-I * (1 + sqrt(5))) == I * pi / 10
assert acsch(I * (1 + sqrt(5))) == -I * pi / 10
assert acsch(-I * 2 / sqrt(2 - sqrt(2))) == I * pi / 8
assert acsch(I * 2 / sqrt(2 - sqrt(2))) == -I * pi / 8
assert acsch(-I * 2) == I * pi / 6
assert acsch(I * 2) == -I * pi / 6
assert acsch(-I * sqrt(2 + 2 / sqrt(5))) == I * pi / 5
assert acsch(I * sqrt(2 + 2 / sqrt(5))) == -I * pi / 5
assert acsch(-I * sqrt(2)) == I * pi / 4
assert acsch(I * sqrt(2)) == -I * pi / 4
assert acsch(-I * (sqrt(5) - 1)) == 3 * I * pi / 10
assert acsch(I * (sqrt(5) - 1)) == -3 * I * pi / 10
assert acsch(-I * 2 / sqrt(3)) == I * pi / 3
assert acsch(I * 2 / sqrt(3)) == -I * pi / 3
assert acsch(-I * 2 / sqrt(2 + sqrt(2))) == 3 * I * pi / 8
assert acsch(I * 2 / sqrt(2 + sqrt(2))) == -3 * I * pi / 8
assert acsch(-I * sqrt(2 - 2 / sqrt(5))) == 2 * I * pi / 5
assert acsch(I * sqrt(2 - 2 / sqrt(5))) == -2 * I * pi / 5
assert acsch(-I * (sqrt(6) - sqrt(2))) == 5 * I * pi / 12
assert acsch(I * (sqrt(6) - sqrt(2))) == -5 * I * pi / 12
assert acsch(nan) is nan
# properties
# acsch(x) == asinh(1/x)
assert acsch(-I * sqrt(2)) == asinh(I / sqrt(2))
assert acsch(-I * 2 / sqrt(3)) == asinh(I * sqrt(3) / 2)
# acsch(x) == -I*asin(I/x)
assert acsch(-I * sqrt(2)) == -I * asin(-1 / sqrt(2))
assert acsch(-I * 2 / sqrt(3)) == -I * asin(-sqrt(3) / 2)
# csch(acsch(x)) / x == 1
assert expand_mul(
csch(acsch(-I * (sqrt(6) + sqrt(2)))) / (-I *
(sqrt(6) + sqrt(2)))) == 1
assert expand_mul(csch(acsch(I * (1 + sqrt(5)))) / (I *
(1 + sqrt(5)))) == 1
assert (csch(acsch(I * sqrt(2 - 2 / sqrt(5)))) /
(I * sqrt(2 - 2 / sqrt(5)))).simplify() == 1
assert (csch(acsch(-I * sqrt(2 - 2 / sqrt(5)))) /
(-I * sqrt(2 - 2 / sqrt(5)))).simplify() == 1
# numerical evaluation
assert str(acsch(5 * I + 1).n(6)) == '0.0391819 - 0.193363*I'
assert str(acsch(-5 * I + 1).n(6)) == '0.0391819 + 0.193363*I'
def linodesolve(A, t, b=None, B=None, type="auto", doit=False):
r"""
System of n equations linear first-order differential equations
Explanation
===========
This solver solves the system of ODEs of the follwing form:
.. math::
X'(t) = A(t) X(t) + b(t)
Here, $A(t)$ is the coefficient matrix, $X(t)$ is the vector of n independent variables,
$b(t)$ is the non-homogeneous term and $X'(t)$ is the derivative of $X(t)$
Depending on the properties of $A(t)$ and $b(t)$, this solver evaluates the solution
differently.
When $A(t)$ is constant coefficient matrix and $b(t)$ is zero vector i.e. system is homogeneous,
the solution is:
.. math::
X(t) = \exp(A t) C
Here, $C$ is a vector of constants and $A$ is the constant coefficient matrix.
When $A(t)$ is constant coefficient matrix and $b(t)$ is non-zero i.e. system is non-homogeneous,
the solution is:
.. math::
X(t) = e^{A t} ( \int e^{- A t} b \,dt + C)
When $A(t)$ is coefficient matrix such that its commutative with its antiderivative $B(t)$ and
$b(t)$ is a zero vector i.e. system is homogeneous, the solution is:
.. math::
X(t) = \exp(B(t)) C
When $A(t)$ is commutative with its antiderivative $B(t)$ and $b(t)$ is non-zero i.e. system is
non-homogeneous, the solution is:
.. math::
X(t) = e^{B(t)} ( \int e^{-B(t)} b(t) \,dt + C)
The final solution is the general solution for all the four equations since a constant coefficient
matrix is always commutative with its antidervative.
Parameters
==========
A : Matrix
Coefficient matrix of the system of linear first order ODEs.
t : Symbol
Independent variable in the system of ODEs.
b : Matrix or None
Non-homogeneous term in the system of ODEs. If None is passed,
a homogeneous system of ODEs is assumed.
B : Matrix or None
Antiderivative of the coefficient matrix. If the antiderivative
is not passed and the solution requires the term, then the solver
would compute it internally.
type : String
Type of the system of ODEs passed. Depending on the type, the
solution is evaluated. The type values allowed and the corresponding
system it solves are: "type1" for constant coefficient homogeneous
"type2" for constant coefficient non-homogeneous, "type3" for non-constant
coefficient homogeneous and "type4" for non-constant coefficient non-homogeneous.
The default value is "auto" which will let the solver decide the correct type of
the system passed.
doit : Boolean
Evaluate the solution if True, default value is False
Examples
========
To solve the system of ODEs using this function directly, several things must be
done in the right order. Wrong inputs to the function will lead to incorrect results.
>>> from sympy import symbols, Function, Eq
>>> from sympy.solvers.ode.systems import canonical_odes, linear_ode_to_matrix, linodesolve, linodesolve_type
>>> from sympy.solvers.ode.subscheck import checkodesol
>>> f, g = symbols("f, g", cls=Function)
>>> x, a = symbols("x, a")
>>> funcs = [f(x), g(x)]
>>> eqs = [Eq(f(x).diff(x) - f(x), a*g(x) + 1), Eq(g(x).diff(x) + g(x), a*f(x))]
Here, it is important to note that before we derive the coefficient matrix, it is
important to get the system of ODEs into the desired form. For that we will use
:obj:`sympy.solvers.ode.systems.canonical_odes()`.
>>> eqs = canonical_odes(eqs, funcs, x)
>>> eqs
[[Eq(Derivative(f(x), x), a*g(x) + f(x) + 1), Eq(Derivative(g(x), x), a*f(x) - g(x))]]
Now, we will use :obj:`sympy.solvers.ode.systems.linear_ode_to_matrix()` to get the coefficient matrix and the
non-homogeneous term if it is there.
>>> eqs = eqs[0]
>>> (A1, A0), b = linear_ode_to_matrix(eqs, funcs, x, 1)
>>> A = A0
We have the coefficient matrices and the non-homogeneous term ready. Now, we can use
:obj:`sympy.solvers.ode.systems.linodesolve_type()` to get the information for the system of ODEs
to finally pass it to the solver.
>>> system_info = linodesolve_type(A, x, b=b)
>>> sol_vector = linodesolve(A, x, b=b, B=system_info['antiderivative'], type=system_info['type'])
Now, we can prove if the solution is correct or not by using :obj:`sympy.solvers.ode.checkodesol()`
>>> sol = [Eq(f, s) for f, s in zip(funcs, sol_vector)]
>>> checkodesol(eqs, sol)
(True, [0, 0])
We can also use the doit method to evaluate the solutions passed by the function.
>>> sol_vector_evaluated = linodesolve(A, x, b=b, type="type2", doit=True)
Now, we will look at a system of ODEs which is non-constant.
>>> eqs = [Eq(f(x).diff(x), f(x) + x*g(x)), Eq(g(x).diff(x), -x*f(x) + g(x))]
The system defined above is already in the desired form, so we don't have to convert it.
>>> (A1, A0), b = linear_ode_to_matrix(eqs, funcs, x, 1)
>>> A = A0
A user can also pass the commutative antiderivative required for type3 and type4 system of ODEs.
Passing an incorrect one will lead to incorrect results. If the coefficient matrix is not commutative
with its antiderivative, then :obj:`sympy.solvers.ode.systems.linodesolve_type()` raises a NotImplementedError.
If it does have a commutative antiderivative, then the function just returns the information about the system.
>>> system_info = linodesolve_type(A, x, b=b)
Now, we can pass the antiderivative as an argument to get the solution. If the system information is not
passed, then the solver will compute the required arguments internally.
>>> sol_vector = linodesolve(A, x, b=b)
Once again, we can verify the solution obtained.
>>> sol = [Eq(f, s) for f, s in zip(funcs, sol_vector)]
>>> checkodesol(eqs, sol)
(True, [0, 0])
Returns
=======
List
Raises
======
ValueError
This error is raised when the coefficient matrix, non-homogeneous term
or the antiderivative, if passed, aren't a matrix or
don't have correct dimensions
NonSquareMatrixError
When the coefficient matrix or its antiderivative, if passed isn't a square
matrix
NotImplementedError
If the coefficient matrix doesn't have a commutative antiderivative
See Also
========
linear_ode_to_matrix: Coefficient matrix computation function
canonical_odes: System of ODEs representation change
linodesolve_type: Getting information about systems of ODEs to pass in this solver
"""
if not isinstance(A, MatrixBase):
raise ValueError(
filldedent('''\
The coefficients of the system of ODEs should be of type Matrix
'''))
if not A.is_square:
raise NonSquareMatrixError(
filldedent('''\
The coefficient matrix must be a square
'''))
if b is not None:
if not isinstance(b, MatrixBase):
raise ValueError(
filldedent('''\
The non-homogeneous terms of the system of ODEs should be of type Matrix
'''))
if A.rows != b.rows:
raise ValueError(
filldedent('''\
The system of ODEs should have the same number of non-homogeneous terms and the number of
equations
'''))
if B is not None:
if not isinstance(B, MatrixBase):
raise ValueError(
filldedent('''\
The antiderivative of coefficients of the system of ODEs should be of type Matrix
'''))
if not B.is_square:
raise NonSquareMatrixError(
filldedent('''\
The antiderivative of the coefficient matrix must be a square
'''))
if A.rows != B.rows:
raise ValueError(
filldedent('''\
The coefficient matrix and its antiderivative should have same dimensions
'''))
if not any(type == "type{}".format(i)
for i in range(1, 5)) and not type == "auto":
raise ValueError(
filldedent('''\
The input type should be a valid one
'''))
n = A.rows
# constants = numbered_symbols(prefix='C', cls=Dummy, start=const_idx+1)
Cvect = Matrix(list(Dummy() for _ in range(n)))
if (type == "type2" or type == "type4") and b is None:
b = zeros(n, 1)
if type == "auto":
system_info = linodesolve_type(A, t, b=b)
type = system_info["type"]
B = system_info["antiderivative"]
if type == "type1" or type == "type2":
P, J = matrix_exp_jordan_form(A, t)
P = simplify(P)
if type == "type1":
sol_vector = P * (J * Cvect)
else:
sol_vector = P * J * (
(J.inv() * P.inv() * b).applyfunc(lambda x: Integral(x, t)) +
Cvect)
else:
if B is None:
B, _ = _is_commutative_anti_derivative(A, t)
if type == "type3":
sol_vector = B.exp() * Cvect
else:
sol_vector = B.exp() * ((
(-B).exp() * b).applyfunc(lambda x: Integral(x, t)) + Cvect)
gens = sol_vector.atoms(exp)
if type != "type1":
sol_vector = [expand_mul(s) for s in sol_vector]
sol_vector = [collect(s, ordered(gens), exact=True) for s in sol_vector]
if doit:
sol_vector = [s.doit() for s in sol_vector]
return sol_vector
def solve_univariate_inequality(expr, gen, relational=True, domain=S.Reals, continuous=False):
"""Solves a real univariate inequality.
Parameters
==========
expr : Relational
The target inequality
gen : Symbol
The variable for which the inequality is solved
relational : bool
A Relational type output is expected or not
domain : Set
The domain over which the equation is solved
continuous: bool
True if expr is known to be continuous over the given domain
(and so continuous_domain() doesn't need to be called on it)
Raises
======
NotImplementedError
The solution of the inequality cannot be determined due to limitation
in `solvify`.
Notes
=====
Currently, we cannot solve all the inequalities due to limitations in
`solvify`. Also, the solution returned for trigonometric inequalities
are restricted in its periodic interval.
See Also
========
solvify: solver returning solveset solutions with solve's output API
Examples
========
>>> from sympy.solvers.inequalities import solve_univariate_inequality
>>> from sympy import Symbol, sin, Interval, S
>>> x = Symbol('x')
>>> solve_univariate_inequality(x**2 >= 4, x)
((2 <= x) & (x < oo)) | ((x <= -2) & (-oo < x))
>>> solve_univariate_inequality(x**2 >= 4, x, relational=False)
Union(Interval(-oo, -2), Interval(2, oo))
>>> domain = Interval(0, S.Infinity)
>>> solve_univariate_inequality(x**2 >= 4, x, False, domain)
Interval(2, oo)
>>> solve_univariate_inequality(sin(x) > 0, x, relational=False)
Interval.open(0, pi)
"""
from sympy import im
from sympy.calculus.util import (continuous_domain, periodicity,
function_range)
from sympy.solvers.solvers import denoms
from sympy.solvers.solveset import solveset_real, solvify, solveset
from sympy.solvers.solvers import solve
# This keeps the function independent of the assumptions about `gen`.
# `solveset` makes sure this function is called only when the domain is
# real.
_gen = gen
_domain = domain
if gen.is_real is False:
rv = S.EmptySet
return rv if not relational else rv.as_relational(_gen)
elif gen.is_real is None:
gen = Dummy('gen', real=True)
try:
expr = expr.xreplace({_gen: gen})
except TypeError:
raise TypeError(filldedent('''
When gen is real, the relational has a complex part
which leads to an invalid comparison like I < 0.
'''))
rv = None
if expr is S.true:
rv = domain
elif expr is S.false:
rv = S.EmptySet
else:
e = expr.lhs - expr.rhs
period = periodicity(e, gen)
if period is not None:
frange = function_range(e, gen, domain)
rel = expr.rel_op
if rel == '<' or rel == '<=':
if expr.func(frange.sup, 0):
rv = domain
elif not expr.func(frange.inf, 0):
rv = S.EmptySet
elif rel == '>' or rel == '>=':
if expr.func(frange.inf, 0):
rv = domain
elif not expr.func(frange.sup, 0):
rv = S.EmptySet
inf, sup = domain.inf, domain.sup
if sup - inf is S.Infinity:
domain = Interval(0, period, False, True)
if rv is None:
n, d = e.as_numer_denom()
try:
if gen not in n.free_symbols and len(e.free_symbols) > 1:
raise ValueError
# this might raise ValueError on its own
# or it might give None...
solns = solvify(e, gen, domain)
if solns is None:
# in which case we raise ValueError
raise ValueError
except (ValueError, NotImplementedError):
raise NotImplementedError(filldedent('''
The inequality cannot be solved using solve_univariate_inequality.
'''))
expanded_e = expand_mul(e)
def valid(x):
# this is used to see if gen=x satisfies the
# relational by substituting it into the
# expanded form and testing against 0, e.g.
# if expr = x*(x + 1) < 2 then e = x*(x + 1) - 2
# and expanded_e = x**2 + x - 2; the test is
# whether a given value of x satisfies
# x**2 + x - 2 < 0
#
# expanded_e, expr and gen used from enclosing scope
v = expanded_e.subs(gen, x)
try:
r = expr.func(v, 0)
except TypeError:
r = S.false
if r in (S.true, S.false):
return r
if v.is_real is False:
return S.false
else:
v = v.n(2)
if v.is_comparable:
return expr.func(v, 0)
# not comparable or couldn't be evaluated
raise NotImplementedError(
'relationship did not evaluate: %s' % r)
singularities = []
for d in denoms(expr, gen):
singularities.extend(solvify(d, gen, domain))
if not continuous:
domain = continuous_domain(e, gen, domain)
include_x = '=' in expr.rel_op and expr.rel_op != '!='
try:
discontinuities = set(domain.boundary -
FiniteSet(domain.inf, domain.sup))
# remove points that are not between inf and sup of domain
critical_points = FiniteSet(*(solns + singularities + list(
discontinuities))).intersection(
Interval(domain.inf, domain.sup,
domain.inf not in domain, domain.sup not in domain))
if all(r.is_number for r in critical_points):
reals = _nsort(critical_points, separated=True)[0]
else:
from sympy.utilities.iterables import sift
sifted = sift(critical_points, lambda x: x.is_real)
if sifted[None]:
# there were some roots that weren't known
# to be real
raise NotImplementedError
try:
reals = sifted[True]
if len(reals) > 1:
reals = list(sorted(reals))
except TypeError:
raise NotImplementedError
except NotImplementedError:
raise NotImplementedError('sorting of these roots is not supported')
#If expr contains imaginary coefficients
#Only real values of x for which the imaginary part is 0 are taken
make_real = S.Reals
if im(expanded_e) != S.Zero:
check = True
im_sol = FiniteSet()
try:
a = solveset(im(expanded_e), gen, domain)
if not isinstance(a, Interval):
for z in a:
if z not in singularities and valid(z) and z.is_real:
im_sol += FiniteSet(z)
else:
start, end = a.inf, a.sup
for z in _nsort(critical_points + FiniteSet(end)):
valid_start = valid(start)
if start != end:
valid_z = valid(z)
pt = _pt(start, z)
if pt not in singularities and pt.is_real and valid(pt):
if valid_start and valid_z:
im_sol += Interval(start, z)
elif valid_start:
im_sol += Interval.Ropen(start, z)
elif valid_z:
im_sol += Interval.Lopen(start, z)
else:
im_sol += Interval.open(start, z)
start = z
for s in singularities:
im_sol -= FiniteSet(s)
except (TypeError):
im_sol = S.Reals
check = False
if isinstance(im_sol, EmptySet):
raise ValueError(filldedent('''
%s contains imaginary parts which cannot be made 0 for any value of %s
satisfying the inequality, leading to relations like I < 0.
''' % (expr.subs(gen, _gen), _gen)))
make_real = make_real.intersect(im_sol)
empty = sol_sets = [S.EmptySet]
start = domain.inf
if valid(start) and start.is_finite:
sol_sets.append(FiniteSet(start))
for x in reals:
end = x
if valid(_pt(start, end)):
sol_sets.append(Interval(start, end, True, True))
if x in singularities:
singularities.remove(x)
else:
if x in discontinuities:
discontinuities.remove(x)
_valid = valid(x)
else: # it's a solution
_valid = include_x
if _valid:
sol_sets.append(FiniteSet(x))
start = end
end = domain.sup
if valid(end) and end.is_finite:
sol_sets.append(FiniteSet(end))
if valid(_pt(start, end)):
sol_sets.append(Interval.open(start, end))
if im(expanded_e) != S.Zero and check:
rv = (make_real).intersect(_domain)
else:
rv = Intersection(
(Union(*sol_sets)), make_real, _domain).subs(gen, _gen)
return rv if not relational else rv.as_relational(_gen)
def eval(cls, arg):
from sympy.simplify.simplify import signsimp
from sympy.core.function import expand_mul
if hasattr(arg, '_eval_Abs'):
obj = arg._eval_Abs()
if obj is not None:
return obj
if not isinstance(arg, Expr):
raise TypeError("Bad argument type for Abs(): %s" % type(arg))
# handle what we can
arg = signsimp(arg, evaluate=False)
if arg.is_Mul:
known = []
unk = []
for t in arg.args:
tnew = cls(t)
if tnew.func is cls:
unk.append(tnew.args[0])
else:
known.append(tnew)
known = Mul(*known)
unk = cls(Mul(*unk), evaluate=False) if unk else S.One
return known * unk
if arg is S.NaN:
return S.NaN
if arg.is_Pow:
base, exponent = arg.as_base_exp()
if base.is_real:
if exponent.is_integer:
if exponent.is_even:
return arg
if base is S.NegativeOne:
return S.One
if base.func is cls and exponent is S.NegativeOne:
return arg
return Abs(base)**exponent
if base.is_positive == True:
return base**re(exponent)
return (-base)**re(exponent) * exp(-S.Pi * im(exponent))
if isinstance(arg, exp):
return exp(re(arg.args[0]))
if isinstance(arg, AppliedUndef):
return
if arg.is_Add and arg.has(S.Infinity, S.NegativeInfinity):
if any(a.is_infinite for a in arg.as_real_imag()):
return S.Infinity
if arg.is_zero:
return S.Zero
if arg.is_nonnegative:
return arg
if arg.is_nonpositive:
return -arg
if arg.is_imaginary:
arg2 = -S.ImaginaryUnit * arg
if arg2.is_nonnegative:
return arg2
# reject result if all new conjugates are just wrappers around
# an expression that was already in the arg
conj = arg.conjugate()
new_conj = conj.atoms(conjugate) - arg.atoms(conjugate)
if new_conj and all(arg.has(i.args[0]) for i in new_conj):
return
if arg != conj and arg != -conj:
ignore = arg.atoms(Abs)
abs_free_arg = arg.xreplace(
dict([(i, Dummy(real=True)) for i in ignore]))
unk = [a for a in abs_free_arg.free_symbols if a.is_real is None]
if not unk or not all(conj.has(conjugate(u)) for u in unk):
return sqrt(expand_mul(arg * conj))
def _eval_nseries(self, x, n, logx):
# NOTE! This function is an important part of the gruntz algorithm
# for computing limits. It has to return a generalized power
# series with coefficients in C(log, log(x)). In more detail:
# It has to return an expression
# c_0*x**e_0 + c_1*x**e_1 + ... (finitely many terms)
# where e_i are numbers (not necessarily integers) and c_i are
# expressions involving only numbers, the log function, and log(x).
from sympy import powsimp, collect, exp, log, O, ceiling
b, e = self.args
if e.is_Integer:
if e > 0:
# positive integer powers are easy to expand, e.g.:
# sin(x)**4 = (x-x**3/3+...)**4 = ...
return expand_multinomial(self.func(b._eval_nseries(x, n=n,
logx=logx), e), deep=False)
elif e is S.NegativeOne:
# this is also easy to expand using the formula:
# 1/(1 + x) = 1 - x + x**2 - x**3 ...
# so we need to rewrite base to the form "1+x"
nuse = n
cf = 1
try:
ord = b.as_leading_term(x)
cf = C.Order(ord, x).getn()
if cf and cf.is_Number:
nuse = n + 2*ceiling(cf)
else:
cf = 1
except NotImplementedError:
pass
b_orig, prefactor = b, O(1, x)
while prefactor.is_Order:
nuse += 1
b = b_orig._eval_nseries(x, n=nuse, logx=logx)
prefactor = b.as_leading_term(x)
# express "rest" as: rest = 1 + k*x**l + ... + O(x**n)
rest = expand_mul((b - prefactor)/prefactor)
if rest.is_Order:
return 1/prefactor + rest/prefactor + O(x**n, x)
k, l = rest.leadterm(x)
if l.is_Rational and l > 0:
pass
elif l.is_number and l > 0:
l = l.evalf()
elif l == 0:
k = k.simplify()
if k == 0:
# if prefactor == w**4 + x**2*w**4 + 2*x*w**4, we need to
# factor the w**4 out using collect:
return 1/collect(prefactor, x)
else:
raise NotImplementedError()
else:
raise NotImplementedError()
if cf < 0:
cf = S.One/abs(cf)
try:
dn = C.Order(1/prefactor, x).getn()
if dn and dn < 0:
pass
else:
dn = 0
except NotImplementedError:
dn = 0
terms = [1/prefactor]
for m in xrange(1, ceiling((n - dn)/l*cf)):
new_term = terms[-1]*(-rest)
if new_term.is_Pow:
new_term = new_term._eval_expand_multinomial(
deep=False)
else:
new_term = expand_mul(new_term, deep=False)
terms.append(new_term)
terms.append(O(x**n, x))
return powsimp(Add(*terms), deep=True, combine='exp')
else:
# negative powers are rewritten to the cases above, for
# example:
# sin(x)**(-4) = 1/( sin(x)**4) = ...
# and expand the denominator:
nuse, denominator = n, O(1, x)
while denominator.is_Order:
denominator = (b**(-e))._eval_nseries(x, n=nuse, logx=logx)
nuse += 1
if 1/denominator == self:
return self
# now we have a type 1/f(x), that we know how to expand
return (1/denominator)._eval_nseries(x, n=n, logx=logx)
if e.has(Symbol):
return exp(e*log(b))._eval_nseries(x, n=n, logx=logx)
# see if the base is as simple as possible
bx = b
while bx.is_Pow and bx.exp.is_Rational:
bx = bx.base
if bx == x:
return self
# work for b(x)**e where e is not an Integer and does not contain x
# and hopefully has no other symbols
def e2int(e):
"""return the integer value (if possible) of e and a
flag indicating whether it is bounded or not."""
n = e.limit(x, 0)
unbounded = n.is_unbounded
if not unbounded:
# XXX was int or floor intended? int used to behave like floor
# so int(-Rational(1, 2)) returned -1 rather than int's 0
try:
n = int(n)
except TypeError:
#well, the n is something more complicated (like 1+log(2))
try:
n = int(n.evalf()) + 1 # XXX why is 1 being added?
except TypeError:
pass # hope that base allows this to be resolved
n = _sympify(n)
return n, unbounded
order = O(x**n, x)
ei, unbounded = e2int(e)
b0 = b.limit(x, 0)
if unbounded and (b0 is S.One or b0.has(Symbol)):
# XXX what order
if b0 is S.One:
resid = (b - 1)
if resid.is_positive:
return S.Infinity
elif resid.is_negative:
return S.Zero
raise ValueError('cannot determine sign of %s' % resid)
return b0**ei
if (b0 is S.Zero or b0.is_unbounded):
if unbounded is not False:
return b0**e # XXX what order
if not ei.is_number: # if not, how will we proceed?
raise ValueError(
'expecting numerical exponent but got %s' % ei)
nuse = n - ei
if e.is_real and e.is_positive:
lt = b.as_leading_term(x)
# Try to correct nuse (= m) guess from:
# (lt + rest + O(x**m))**e =
# lt**e*(1 + rest/lt + O(x**m)/lt)**e =
# lt**e + ... + O(x**m)*lt**(e - 1) = ... + O(x**n)
try:
cf = C.Order(lt, x).getn()
nuse = ceiling(n - cf*(e - 1))
except NotImplementedError:
pass
bs = b._eval_nseries(x, n=nuse, logx=logx)
terms = bs.removeO()
if terms.is_Add:
bs = terms
lt = terms.as_leading_term(x)
# bs -> lt + rest -> lt*(1 + (bs/lt - 1))
return ((self.func(lt, e) * self.func((bs/lt).expand(), e).nseries(
x, n=nuse, logx=logx)).expand() + order)
if bs.is_Add:
from sympy import O
# So, bs + O() == terms
c = Dummy('c')
res = []
for arg in bs.args:
if arg.is_Order:
arg = c*arg.expr
res.append(arg)
bs = Add(*res)
rv = (bs**e).series(x).subs(c, O(1, x))
rv += order
return rv
rv = bs**e
if terms != bs:
rv += order
return rv
# either b0 is bounded but neither 1 nor 0 or e is unbounded
# b -> b0 + (b-b0) -> b0 * (1 + (b/b0-1))
o2 = order*(b0**-e)
z = (b/b0 - 1)
o = O(z, x)
#r = self._compute_oseries3(z, o2, self.taylor_term)
if o is S.Zero or o2 is S.Zero:
unbounded = True
else:
if o.expr.is_number:
e2 = log(o2.expr*x)/log(x)
else:
e2 = log(o2.expr)/log(o.expr)
n, unbounded = e2int(e2)
if unbounded:
# requested accuracy gives infinite series,
# order is probably non-polynomial e.g. O(exp(-1/x), x).
r = 1 + z
else:
l = []
g = None
for i in xrange(n + 2):
g = self._taylor_term(i, z, g)
g = g.nseries(x, n=n, logx=logx)
l.append(g)
r = Add(*l)
return expand_mul(r*b0**e) + order
def radsimp(expr, symbolic=True, max_terms=4):
r"""
Rationalize the denominator by removing square roots.
Explanation
===========
The expression returned from radsimp must be used with caution
since if the denominator contains symbols, it will be possible to make
substitutions that violate the assumptions of the simplification process:
that for a denominator matching a + b*sqrt(c), a != +/-b*sqrt(c). (If
there are no symbols, this assumptions is made valid by collecting terms
of sqrt(c) so the match variable ``a`` does not contain ``sqrt(c)``.) If
you do not want the simplification to occur for symbolic denominators, set
``symbolic`` to False.
If there are more than ``max_terms`` radical terms then the expression is
returned unchanged.
Examples
========
>>> from sympy import radsimp, sqrt, Symbol, pprint
>>> from sympy import factor_terms, fraction, signsimp
>>> from sympy.simplify.radsimp import collect_sqrt
>>> from sympy.abc import a, b, c
>>> radsimp(1/(2 + sqrt(2)))
(2 - sqrt(2))/2
>>> x,y = map(Symbol, 'xy')
>>> e = ((2 + 2*sqrt(2))*x + (2 + sqrt(8))*y)/(2 + sqrt(2))
>>> radsimp(e)
sqrt(2)*(x + y)
No simplification beyond removal of the gcd is done. One might
want to polish the result a little, however, by collecting
square root terms:
>>> r2 = sqrt(2)
>>> r5 = sqrt(5)
>>> ans = radsimp(1/(y*r2 + x*r2 + a*r5 + b*r5)); pprint(ans)
___ ___ ___ ___
\/ 5 *a + \/ 5 *b - \/ 2 *x - \/ 2 *y
------------------------------------------
2 2 2 2
5*a + 10*a*b + 5*b - 2*x - 4*x*y - 2*y
>>> n, d = fraction(ans)
>>> pprint(factor_terms(signsimp(collect_sqrt(n))/d, radical=True))
___ ___
\/ 5 *(a + b) - \/ 2 *(x + y)
------------------------------------------
2 2 2 2
5*a + 10*a*b + 5*b - 2*x - 4*x*y - 2*y
If radicals in the denominator cannot be removed or there is no denominator,
the original expression will be returned.
>>> radsimp(sqrt(2)*x + sqrt(2))
sqrt(2)*x + sqrt(2)
Results with symbols will not always be valid for all substitutions:
>>> eq = 1/(a + b*sqrt(c))
>>> eq.subs(a, b*sqrt(c))
1/(2*b*sqrt(c))
>>> radsimp(eq).subs(a, b*sqrt(c))
nan
If ``symbolic=False``, symbolic denominators will not be transformed (but
numeric denominators will still be processed):
>>> radsimp(eq, symbolic=False)
1/(a + b*sqrt(c))
"""
from sympy.simplify.simplify import signsimp
syms = symbols("a:d A:D")
def _num(rterms):
# return the multiplier that will simplify the expression described
# by rterms [(sqrt arg, coeff), ... ]
a, b, c, d, A, B, C, D = syms
if len(rterms) == 2:
reps = dict(list(zip([A, a, B, b],
[j for i in rterms for j in i])))
return (sqrt(A) * a - sqrt(B) * b).xreplace(reps)
if len(rterms) == 3:
reps = dict(
list(zip([A, a, B, b, C, c], [j for i in rterms for j in i])))
return ((sqrt(A) * a + sqrt(B) * b - sqrt(C) * c) *
(2 * sqrt(A) * sqrt(B) * a * b - A * a**2 - B * b**2 +
C * c**2)).xreplace(reps)
elif len(rterms) == 4:
reps = dict(
list(
zip([A, a, B, b, C, c, D, d],
[j for i in rterms for j in i])))
return (
(sqrt(A) * a + sqrt(B) * b - sqrt(C) * c - sqrt(D) * d) *
(2 * sqrt(A) * sqrt(B) * a * b - A * a**2 - B * b**2 -
2 * sqrt(C) * sqrt(D) * c * d + C * c**2 + D * d**2) *
(-8 * sqrt(A) * sqrt(B) * sqrt(C) * sqrt(D) * a * b * c * d +
A**2 * a**4 - 2 * A * B * a**2 * b**2 -
2 * A * C * a**2 * c**2 - 2 * A * D * a**2 * d**2 +
B**2 * b**4 - 2 * B * C * b**2 * c**2 -
2 * B * D * b**2 * d**2 + C**2 * c**4 -
2 * C * D * c**2 * d**2 + D**2 * d**4)).xreplace(reps)
elif len(rterms) == 1:
return sqrt(rterms[0][0])
else:
raise NotImplementedError
def ispow2(d, log2=False):
if not d.is_Pow:
return False
e = d.exp
if e.is_Rational and e.q == 2 or symbolic and denom(e) == 2:
return True
if log2:
q = 1
if e.is_Rational:
q = e.q
elif symbolic:
d = denom(e)
if d.is_Integer:
q = d
if q != 1 and log(q, 2).is_Integer:
return True
return False
def handle(expr):
# Handle first reduces to the case
# expr = 1/d, where d is an add, or d is base**p/2.
# We do this by recursively calling handle on each piece.
from sympy.simplify.simplify import nsimplify
n, d = fraction(expr)
if expr.is_Atom or (d.is_Atom and n.is_Atom):
return expr
elif not n.is_Atom:
n = n.func(*[handle(a) for a in n.args])
return _unevaluated_Mul(n, handle(1 / d))
elif n is not S.One:
return _unevaluated_Mul(n, handle(1 / d))
elif d.is_Mul:
return _unevaluated_Mul(*[handle(1 / d) for d in d.args])
# By this step, expr is 1/d, and d is not a mul.
if not symbolic and d.free_symbols:
return expr
if ispow2(d):
d2 = sqrtdenest(sqrt(d.base))**numer(d.exp)
if d2 != d:
return handle(1 / d2)
elif d.is_Pow and (d.exp.is_integer or d.base.is_positive):
# (1/d**i) = (1/d)**i
return handle(1 / d.base)**d.exp
if not (d.is_Add or ispow2(d)):
return 1 / d.func(*[handle(a) for a in d.args])
# handle 1/d treating d as an Add (though it may not be)
keep = True # keep changes that are made
# flatten it and collect radicals after checking for special
# conditions
d = _mexpand(d)
# did it change?
if d.is_Atom:
return 1 / d
# is it a number that might be handled easily?
if d.is_number:
_d = nsimplify(d)
if _d.is_Number and _d.equals(d):
return 1 / _d
while True:
# collect similar terms
collected = defaultdict(list)
for m in Add.make_args(d): # d might have become non-Add
p2 = []
other = []
for i in Mul.make_args(m):
if ispow2(i, log2=True):
p2.append(i.base if i.exp is S.Half else i.base**(
2 * i.exp))
elif i is S.ImaginaryUnit:
p2.append(S.NegativeOne)
else:
other.append(i)
collected[tuple(ordered(p2))].append(Mul(*other))
rterms = list(ordered(list(collected.items())))
rterms = [(Mul(*i), Add(*j)) for i, j in rterms]
nrad = len(rterms) - (1 if rterms[0][0] is S.One else 0)
if nrad < 1:
break
elif nrad > max_terms:
# there may have been invalid operations leading to this point
# so don't keep changes, e.g. this expression is troublesome
# in collecting terms so as not to raise the issue of 2834:
# r = sqrt(sqrt(5) + 5)
# eq = 1/(sqrt(5)*r + 2*sqrt(5)*sqrt(-sqrt(5) + 5) + 5*r)
keep = False
break
if len(rterms) > 4:
# in general, only 4 terms can be removed with repeated squaring
# but other considerations can guide selection of radical terms
# so that radicals are removed
if all(x.is_Integer and (y**2).is_Rational for x, y in rterms):
nd, d = rad_rationalize(
S.One,
Add._from_args([sqrt(x) * y for x, y in rterms]))
n *= nd
else:
# is there anything else that might be attempted?
keep = False
break
from sympy.simplify.powsimp import powsimp, powdenest
num = powsimp(_num(rterms))
n *= num
d *= num
d = powdenest(_mexpand(d), force=symbolic)
if d.has(S.Zero, nan, zoo):
return expr
if d.is_Atom:
break
if not keep:
return expr
return _unevaluated_Mul(n, 1 / d)
coeff, expr = expr.as_coeff_Add()
expr = expr.normal()
old = fraction(expr)
n, d = fraction(handle(expr))
if old != (n, d):
if not d.is_Atom:
was = (n, d)
n = signsimp(n, evaluate=False)
d = signsimp(d, evaluate=False)
u = Factors(_unevaluated_Mul(n, 1 / d))
u = _unevaluated_Mul(*[k**v for k, v in u.factors.items()])
n, d = fraction(u)
if old == (n, d):
n, d = was
n = expand_mul(n)
if d.is_Number or d.is_Add:
n2, d2 = fraction(gcd_terms(_unevaluated_Mul(n, 1 / d)))
if d2.is_Number or (d2.count_ops() <= d.count_ops()):
n, d = [signsimp(i) for i in (n2, d2)]
if n.is_Mul and n.args[0].is_Number:
n = n.func(*n.args)
return coeff + _unevaluated_Mul(n, 1 / d)
