def test_trigsimp2():
x, y = symbols('x,y')
assert trigsimp(cos(x)**2 * sin(y)**2 + cos(x)**2 * cos(y)**2 + sin(x)**2,
recursive=True) == 1
assert trigsimp(sin(x)**2 * sin(y)**2 + sin(x)**2 * cos(y)**2 + cos(x)**2,
recursive=True) == 1
assert trigsimp(Subs(x, x, sin(y)**2 + cos(y)**2)) == Subs(x, x, 1)
def test_complete_simple_double_pendulum():
q1, q2 = dynamicsymbols('q1 q2')
u1, u2 = dynamicsymbols('u1 u2')
m, l, g = symbols('m l g')
C = Body('C') # ceiling
PartP = Body('P', mass=m)
PartR = Body('R', mass=m)
J1 = PinJoint('J1', C, PartP, speeds=u1, coordinates=q1,
child_joint_pos=-l*PartP.x, parent_axis=C.z,
child_axis=PartP.z)
J2 = PinJoint('J2', PartP, PartR, speeds=u2, coordinates=q2,
child_joint_pos=-l*PartR.x, parent_axis=PartP.z,
child_axis=PartR.z)
PartP.apply_force(m*g*C.x)
PartR.apply_force(m*g*C.x)
method = JointsMethod(C, J1, J2)
method.form_eoms()
assert expand(method.mass_matrix_full) == Matrix([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 2*l**2*m*cos(q2) + 3*l**2*m, l**2*m*cos(q2) + l**2*m],
[0, 0, l**2*m*cos(q2) + l**2*m, l**2*m]])
assert trigsimp(method.forcing_full) == trigsimp(Matrix([[u1], [u2], [-g*l*m*(sin(q1 + q2) + sin(q1)) -
g*l*m*sin(q1) + l**2*m*(2*u1 + u2)*u2*sin(q2)],
[-g*l*m*sin(q1 + q2) - l**2*m*u1**2*sin(q2)]]))
def test_trigsimp_groebner():
from sympy.simplify.trigsimp import trigsimp_groebner
c = cos(x)
s = sin(x)
ex = (4 * s * c + 12 * s + 5 * c**3 + 21 * c**2 + 23 * c + 15) / (
-s * c**2 + 2 * s * c + 15 * s + 7 * c**3 + 31 * c**2 + 37 * c + 21)
resnum = (5 * s - 5 * c + 1)
resdenom = (8 * s - 6 * c)
results = [resnum / resdenom, (-resnum) / (-resdenom)]
assert trigsimp_groebner(ex) in results
assert trigsimp_groebner(s / c, hints=[tan]) == tan(x)
assert trigsimp_groebner(c * s) == c * s
assert trigsimp((-s + 1) / c + c / (-s + 1), method='groebner') == 2 / c
assert trigsimp((-s + 1) / c + c / (-s + 1),
method='groebner',
polynomial=True) == 2 / c
# Test quick=False works
assert trigsimp_groebner(ex, hints=[2]) in results
assert trigsimp_groebner(ex, hints=[int(2)]) in results
# test "I"
assert trigsimp_groebner(sin(I * x) / cos(I * x),
hints=[tanh]) == I * tanh(x)
# test hyperbolic / sums
assert trigsimp_groebner((tanh(x) + tanh(y)) / (1 + tanh(x) * tanh(y)),
hints=[(tanh, x, y)]) == tanh(x + y)
def test_quaternion_conversions():
q1 = Quaternion(1, 2, 3, 4)
assert q1.to_axis_angle() == ((2 * sqrt(29)/29,
3 * sqrt(29)/29,
4 * sqrt(29)/29),
2 * acos(sqrt(30)/30))
assert q1.to_rotation_matrix() == Matrix([[Rational(-2, 3), Rational(2, 15), Rational(11, 15)],
[Rational(2, 3), Rational(-1, 3), Rational(2, 3)],
[Rational(1, 3), Rational(14, 15), Rational(2, 15)]])
assert q1.to_rotation_matrix((1, 1, 1)) == Matrix([[Rational(-2, 3), Rational(2, 15), Rational(11, 15), Rational(4, 5)],
[Rational(2, 3), Rational(-1, 3), Rational(2, 3), S.Zero],
[Rational(1, 3), Rational(14, 15), Rational(2, 15), Rational(-2, 5)],
[S.Zero, S.Zero, S.Zero, S.One]])
theta = symbols("theta", real=True)
q2 = Quaternion(cos(theta/2), 0, 0, sin(theta/2))
assert trigsimp(q2.to_rotation_matrix()) == Matrix([
[cos(theta), -sin(theta), 0],
[sin(theta), cos(theta), 0],
[0, 0, 1]])
assert q2.to_axis_angle() == ((0, 0, sin(theta/2)/Abs(sin(theta/2))),
2*acos(cos(theta/2)))
assert trigsimp(q2.to_rotation_matrix((1, 1, 1))) == Matrix([
[cos(theta), -sin(theta), 0, sin(theta) - cos(theta) + 1],
[sin(theta), cos(theta), 0, -sin(theta) - cos(theta) + 1],
[0, 0, 1, 0],
[0, 0, 0, 1]])
def test_issue_4280():
a, x, y = symbols('a x y')
assert trigsimp(cos(x)**2 + cos(y)**2 * sin(x)**2 +
sin(y)**2 * sin(x)**2) == 1
assert trigsimp(a**2 * sin(x)**2 + a**2 * cos(y)**2 * cos(x)**2 +
a**2 * cos(x)**2 * sin(y)**2) == a**2
assert trigsimp(a**2 * cos(y)**2 * sin(x)**2 +
a**2 * sin(y)**2 * sin(x)**2) == a**2 * sin(x)**2
def test_issue_15129_trigsimp_methods():
t1 = Matrix([sin(Rational(1, 50)), cos(Rational(1, 50)), 0])
t2 = Matrix([sin(Rational(1, 25)), cos(Rational(1, 25)), 0])
t3 = Matrix([cos(Rational(1, 25)), sin(Rational(1, 25)), 0])
r1 = t1.dot(t2)
r2 = t1.dot(t3)
assert trigsimp(r1) == cos(Rational(1, 50))
assert trigsimp(r2) == sin(Rational(3, 50))
def test_issue_4661():
a, x, y = symbols('a x y')
eq = -4 * sin(x)**4 + 4 * cos(x)**4 - 8 * cos(x)**2
assert trigsimp(eq) == -4
n = sin(x)**6 + 4 * sin(x)**4 * cos(x)**2 + 5 * sin(x)**2 * cos(
x)**4 + 2 * cos(x)**6
d = -sin(x)**2 - 2 * cos(x)**2
assert simplify(n / d) == -1
assert trigsimp(-2 * cos(x)**2 + cos(x)**4 - sin(x)**4) == -1
eq = (-sin(x)**3 / 4) * cos(x) + (cos(x)**3 / 4) * sin(x) - sin(
2 * x) * cos(2 * x) / 8
assert trigsimp(eq) == 0
def test_trigsimp():
# issue 16736
s, c = sin(2 * x), cos(2 * x)
eq = Eq(s, c)
assert trigsimp(eq) == eq # no rearrangement of sides
# simplification of sides might result in
# an unevaluated Eq
changed = trigsimp(Eq(s + c, sqrt(2)))
assert isinstance(changed, Eq)
assert changed.subs(x, pi / 8) is S.true
# or an evaluated one
assert trigsimp(Eq(cos(x)**2 + sin(x)**2, 1)) is S.true
def test_trigsimp3():
x, y = symbols('x,y')
assert trigsimp(sin(x) / cos(x)) == tan(x)
assert trigsimp(sin(x)**2 / cos(x)**2) == tan(x)**2
assert trigsimp(sin(x)**3 / cos(x)**3) == tan(x)**3
assert trigsimp(sin(x)**10 / cos(x)**10) == tan(x)**10
assert trigsimp(cos(x) / sin(x)) == 1 / tan(x)
assert trigsimp(cos(x)**2 / sin(x)**2) == 1 / tan(x)**2
assert trigsimp(cos(x)**10 / sin(x)**10) == 1 / tan(x)**10
assert trigsimp(tan(x)) == trigsimp(sin(x) / cos(x))
def repl(nu, z):
if (nu % 1) == S(1)/2:
return simplify(trigsimp(unpolarify(
fro(nu, z0).rewrite(besselj).rewrite(jn).expand(
func=True)).subs(z0, z)))
elif nu.is_Integer and nu > 1:
return fro(nu, z).expand(func=True)
return fro(nu, z)
def test_issue_2827_trigsimp_methods():
measure1 = lambda expr: len(str(expr))
measure2 = lambda expr: -count_ops(expr)
# Return the most complicated result
expr = (x + 1) / (x + sin(x)**2 + cos(x)**2)
ans = Matrix([1])
M = Matrix([expr])
assert trigsimp(M, method='fu', measure=measure1) == ans
assert trigsimp(M, method='fu', measure=measure2) != ans
# all methods should work with Basic expressions even if they
# aren't Expr
M = Matrix.eye(1)
assert all(
trigsimp(M, method=m) == M for m in 'fu matching groebner old'.split())
# watch for E in exptrigsimp, not only exp()
eq = 1 / sqrt(E) + E
assert exptrigsimp(eq) == eq
def repl(nu, z):
if (nu % 1) == S(1)/2:
return exptrigsimp(trigsimp(unpolarify(
fro(nu, z0).rewrite(besselj).rewrite(jn).expand(
func=True)).subs(z0, z)))
elif nu.is_Integer and nu > 1:
return fro(nu, z).expand(func=True)
return fro(nu, z)
def test_issue_6811_fail():
# from doc/src/modules/physics/mechanics/examples.rst, the current `eq`
# at Line 576 (in different variables) was formerly the equivalent and
# shorter expression given below...it would be nice to get the short one
# back again
xp, y, x, z = symbols('xp, y, x, z')
eq = 4 * (-19 * sin(x) * y + 5 * sin(3 * x) * y + 15 * cos(2 * x) * z -
21 * z) * xp / (9 * cos(x) - 5 * cos(3 * x))
assert trigsimp(eq) == -2 * (2 * cos(x) * tan(x) * y + 3 * z) * xp / cos(x)
def test_Piecewise():
e1 = x * (x + y) - y * (x + y)
e2 = sin(x)**2 + cos(x)**2
e3 = expand((x + y) * y / x)
# s1 = simplify(e1)
s2 = simplify(e2)
# s3 = simplify(e3)
# trigsimp tries not to touch non-trig containing args
assert trigsimp(Piecewise((e1, e3 < e2), (e3, True))) == \
Piecewise((e1, e3 < s2), (e3, True))
def to_axis_angle(self):
"""Returns the axis and angle of rotation of a quaternion
Returns
=======
tuple
Tuple of (axis, angle)
Examples
========
>>> from sympy import Quaternion
>>> q = Quaternion(1, 1, 1, 1)
>>> (axis, angle) = q.to_axis_angle()
>>> axis
(sqrt(3)/3, sqrt(3)/3, sqrt(3)/3)
>>> angle
2*pi/3
"""
q = self
if q.a.is_negative:
q = q * -1
q = q.normalize()
angle = trigsimp(2 * acos(q.a))
# Since quaternion is normalised, q.a is less than 1.
s = sqrt(1 - q.a * q.a)
x = trigsimp(q.b / s)
y = trigsimp(q.c / s)
z = trigsimp(q.d / s)
v = (x, y, z)
t = (v, angle)
return t
def test_quaternion_rotation_iss1593():
"""
There was a sign mistake in the definition,
of the rotation matrix. This tests that particular sign mistake.
See issue 1593 for reference.
See wikipedia
https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Quaternion-derived_rotation_matrix
for the correct definition
"""
q = Quaternion(cos(phi / 2), sin(phi / 2), 0, 0)
assert (trigsimp(q.to_rotation_matrix()) == Matrix(
[[1, 0, 0], [0, cos(phi), -sin(phi)], [0, sin(phi),
cos(phi)]]))
def test_quaternion_construction():
q = Quaternion(w, x, y, z)
assert q + q == Quaternion(2*w, 2*x, 2*y, 2*z)
q2 = Quaternion.from_axis_angle((sqrt(3)/3, sqrt(3)/3, sqrt(3)/3),
pi*Rational(2, 3))
assert q2 == Quaternion(S.Half, S.Half,
S.Half, S.Half)
M = Matrix([[cos(phi), -sin(phi), 0], [sin(phi), cos(phi), 0], [0, 0, 1]])
q3 = trigsimp(Quaternion.from_rotation_matrix(M))
assert q3 == Quaternion(sqrt(2)*sqrt(cos(phi) + 1)/2, 0, 0, sqrt(2 - 2*cos(phi))*sign(sin(phi))/2)
nc = Symbol('nc', commutative=False)
raises(ValueError, lambda: Quaternion(w, x, nc, z))
def test_issue_3210():
eqs = (
sin(2) * cos(3) + sin(3) * cos(2),
-sin(2) * sin(3) + cos(2) * cos(3),
sin(2) * cos(3) - sin(3) * cos(2),
sin(2) * sin(3) + cos(2) * cos(3),
sin(2) * sin(3) + cos(2) * cos(3) + cos(2),
sinh(2) * cosh(3) + sinh(3) * cosh(2),
sinh(2) * sinh(3) + cosh(2) * cosh(3),
)
assert [trigsimp(e) for e in eqs] == [
sin(5),
cos(5),
-sin(1),
cos(1),
cos(1) + cos(2),
sinh(5),
cosh(5),
]
def __and__(self, other):
"""Dot product of two vectors.
Returns a scalar, the dot product of the two Vectors
Parameters
==========
other : Vector
The Vector which we are dotting with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dot
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = ReferenceFrame('N')
>>> dot(N.x, N.x)
1
>>> dot(N.x, N.y)
0
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
>>> dot(N.y, A.y)
cos(q1)
"""
from sympy.physics.vector.dyadic import Dyadic
if isinstance(other, Dyadic):
return NotImplemented
other = _check_vector(other)
out = S.Zero
for i, v1 in enumerate(self.args):
for j, v2 in enumerate(other.args):
out += ((v2[0].T) * (v2[1].dcm(v1[1])) * (v1[0]))[0]
if Vector.simp:
return trigsimp(sympify(out), recursive=True)
else:
return sympify(out)
def scalar_map(self, other):
"""
Returns a dictionary which expresses the coordinate variables
(base scalars) of this frame in terms of the variables of
otherframe.
Parameters
==========
otherframe : CoordSys3D
The other system to map the variables to.
Examples
========
>>> from sympy.vector import CoordSys3D
>>> from sympy import Symbol
>>> A = CoordSys3D('A')
>>> q = Symbol('q')
>>> B = A.orient_new_axis('B', q, A.k)
>>> A.scalar_map(B)
{A.x: B.x*cos(q) - B.y*sin(q), A.y: B.x*sin(q) + B.y*cos(q), A.z: B.z}
"""
relocated_scalars = []
origin_coords = tuple(self.position_wrt(other).to_matrix(other))
for i, x in enumerate(other.base_scalars()):
relocated_scalars.append(x - origin_coords[i])
vars_matrix = (self.rotation_matrix(other) *
Matrix(relocated_scalars))
mapping = {}
for i, x in enumerate(self.base_scalars()):
mapping[x] = trigsimp(vars_matrix[i])
return mapping
def express(expr, frame, frame2=None, variables=False):
"""
Global function for 'express' functionality.
Re-expresses a Vector, scalar(sympyfiable) or Dyadic in given frame.
Refer to the local methods of Vector and Dyadic for details.
If 'variables' is True, then the coordinate variables (CoordinateSym
instances) of other frames present in the vector/scalar field or
dyadic expression are also substituted in terms of the base scalars of
this frame.
Parameters
==========
expr : Vector/Dyadic/scalar(sympyfiable)
The expression to re-express in ReferenceFrame 'frame'
frame: ReferenceFrame
The reference frame to express expr in
frame2 : ReferenceFrame
The other frame required for re-expression(only for Dyadic expr)
variables : boolean
Specifies whether to substitute the coordinate variables present
in expr, in terms of those of frame
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, dynamicsymbols
>>> from sympy.physics.vector import init_vprinting
>>> init_vprinting(pretty_print=False)
>>> N = ReferenceFrame('N')
>>> q = dynamicsymbols('q')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> d = outer(N.x, N.x)
>>> from sympy.physics.vector import express
>>> express(d, B, N)
cos(q)*(B.x|N.x) - sin(q)*(B.y|N.x)
>>> express(B.x, N)
cos(q)*N.x + sin(q)*N.y
>>> express(N[0], B, variables=True)
B_x*cos(q) - B_y*sin(q)
"""
_check_frame(frame)
if expr == 0:
return expr
if isinstance(expr, Vector):
#Given expr is a Vector
if variables:
#If variables attribute is True, substitute
#the coordinate variables in the Vector
frame_list = [x[-1] for x in expr.args]
subs_dict = {}
for f in frame_list:
subs_dict.update(f.variable_map(frame))
expr = expr.subs(subs_dict)
#Re-express in this frame
outvec = Vector([])
for i, v in enumerate(expr.args):
if v[1] != frame:
temp = frame.dcm(v[1]) * v[0]
if Vector.simp:
temp = temp.applyfunc(lambda x: trigsimp(x, method='fu'))
outvec += Vector([(temp, frame)])
else:
outvec += Vector([v])
return outvec
if isinstance(expr, Dyadic):
if frame2 is None:
frame2 = frame
_check_frame(frame2)
ol = Dyadic(0)
for i, v in enumerate(expr.args):
ol += express(v[0], frame, variables=variables) * \
(express(v[1], frame, variables=variables) |
express(v[2], frame2, variables=variables))
return ol
else:
if variables:
#Given expr is a scalar field
frame_set = set()
expr = sympify(expr)
#Substitute all the coordinate variables
for x in expr.free_symbols:
if isinstance(x, CoordinateSym) and x.frame != frame:
frame_set.add(x.frame)
subs_dict = {}
for f in frame_set:
subs_dict.update(f.variable_map(frame))
return expr.subs(subs_dict)
return expr
def test_trigsimp_old():
x, y = symbols('x,y')
assert trigsimp(1 - sin(x)**2, old=True) == cos(x)**2
assert trigsimp(1 - cos(x)**2, old=True) == sin(x)**2
assert trigsimp(sin(x)**2 + cos(x)**2, old=True) == 1
assert trigsimp(1 + tan(x)**2, old=True) == 1 / cos(x)**2
assert trigsimp(1 / cos(x)**2 - 1, old=True) == tan(x)**2
assert trigsimp(1 / cos(x)**2 - tan(x)**2, old=True) == 1
assert trigsimp(1 + cot(x)**2, old=True) == 1 / sin(x)**2
assert trigsimp(1 / sin(x)**2 - cot(x)**2, old=True) == 1
assert trigsimp(5 * cos(x)**2 + 5 * sin(x)**2, old=True) == 5
assert trigsimp(sin(x) / cos(x), old=True) == tan(x)
assert trigsimp(2 * tan(x) * cos(x), old=True) == 2 * sin(x)
assert trigsimp(cot(x)**3 * sin(x)**3, old=True) == cos(x)**3
assert trigsimp(y * tan(x)**2 / sin(x)**2, old=True) == y / cos(x)**2
assert trigsimp(cot(x) / cos(x), old=True) == 1 / sin(x)
assert trigsimp(sin(x + y) + sin(x - y), old=True) == 2 * sin(x) * cos(y)
assert trigsimp(sin(x + y) - sin(x - y), old=True) == 2 * sin(y) * cos(x)
assert trigsimp(cos(x + y) + cos(x - y), old=True) == 2 * cos(x) * cos(y)
assert trigsimp(cos(x + y) - cos(x - y), old=True) == -2 * sin(x) * sin(y)
assert trigsimp(sinh(x + y) + sinh(x - y),
old=True) == 2 * sinh(x) * cosh(y)
assert trigsimp(sinh(x + y) - sinh(x - y),
old=True) == 2 * sinh(y) * cosh(x)
assert trigsimp(cosh(x + y) + cosh(x - y),
old=True) == 2 * cosh(x) * cosh(y)
assert trigsimp(cosh(x + y) - cosh(x - y),
old=True) == 2 * sinh(x) * sinh(y)
assert trigsimp(cos(0.12345)**2 + sin(0.12345)**2, old=True) == 1
assert trigsimp(sin(x) / cos(x), old=True, method='combined') == tan(x)
assert trigsimp(sin(x) / cos(x), old=True,
method='groebner') == sin(x) / cos(x)
assert trigsimp(sin(x) / cos(x), old=True, method='groebner',
hints=[tan]) == tan(x)
assert trigsimp(1 - sin(sin(x)**2 + cos(x)**2)**2, old=True,
deep=True) == cos(1)**2
def simplify(expr, ratio=1.7, measure=count_ops, fu=False):
"""
Simplifies the given expression.
Simplification is not a well defined term and the exact strategies
this function tries can change in the future versions of SymPy. If
your algorithm relies on "simplification" (whatever it is), try to
determine what you need exactly - is it powsimp()?, radsimp()?,
together()?, logcombine()?, or something else? And use this particular
function directly, because those are well defined and thus your algorithm
will be robust.
Nonetheless, especially for interactive use, or when you don't know
anything about the structure of the expression, simplify() tries to apply
intelligent heuristics to make the input expression "simpler". For
example:
>>> from sympy import simplify, cos, sin
>>> from sympy.abc import x, y
>>> a = (x + x**2)/(x*sin(y)**2 + x*cos(y)**2)
>>> a
(x**2 + x)/(x*sin(y)**2 + x*cos(y)**2)
>>> simplify(a)
x + 1
Note that we could have obtained the same result by using specific
simplification functions:
>>> from sympy import trigsimp, cancel
>>> trigsimp(a)
(x**2 + x)/x
>>> cancel(_)
x + 1
In some cases, applying :func:`simplify` may actually result in some more
complicated expression. The default ``ratio=1.7`` prevents more extreme
cases: if (result length)/(input length) > ratio, then input is returned
unmodified. The ``measure`` parameter lets you specify the function used
to determine how complex an expression is. The function should take a
single argument as an expression and return a number such that if
expression ``a`` is more complex than expression ``b``, then
``measure(a) > measure(b)``. The default measure function is
:func:`count_ops`, which returns the total number of operations in the
expression.
For example, if ``ratio=1``, ``simplify`` output can't be longer
than input.
::
>>> from sympy import sqrt, simplify, count_ops, oo
>>> root = 1/(sqrt(2)+3)
Since ``simplify(root)`` would result in a slightly longer expression,
root is returned unchanged instead::
>>> simplify(root, ratio=1) == root
True
If ``ratio=oo``, simplify will be applied anyway::
>>> count_ops(simplify(root, ratio=oo)) > count_ops(root)
True
Note that the shortest expression is not necessary the simplest, so
setting ``ratio`` to 1 may not be a good idea.
Heuristically, the default value ``ratio=1.7`` seems like a reasonable
choice.
You can easily define your own measure function based on what you feel
should represent the "size" or "complexity" of the input expression. Note
that some choices, such as ``lambda expr: len(str(expr))`` may appear to be
good metrics, but have other problems (in this case, the measure function
may slow down simplify too much for very large expressions). If you don't
know what a good metric would be, the default, ``count_ops``, is a good
one.
For example:
>>> from sympy import symbols, log
>>> a, b = symbols('a b', positive=True)
>>> g = log(a) + log(b) + log(a)*log(1/b)
>>> h = simplify(g)
>>> h
log(a*b**(-log(a) + 1))
>>> count_ops(g)
8
>>> count_ops(h)
5
So you can see that ``h`` is simpler than ``g`` using the count_ops metric.
However, we may not like how ``simplify`` (in this case, using
``logcombine``) has created the ``b**(log(1/a) + 1)`` term. A simple way
to reduce this would be to give more weight to powers as operations in
``count_ops``. We can do this by using the ``visual=True`` option:
>>> print(count_ops(g, visual=True))
2*ADD + DIV + 4*LOG + MUL
>>> print(count_ops(h, visual=True))
2*LOG + MUL + POW + SUB
>>> from sympy import Symbol, S
>>> def my_measure(expr):
... POW = Symbol('POW')
... # Discourage powers by giving POW a weight of 10
... count = count_ops(expr, visual=True).subs(POW, 10)
... # Every other operation gets a weight of 1 (the default)
... count = count.replace(Symbol, type(S.One))
... return count
>>> my_measure(g)
8
>>> my_measure(h)
14
>>> 15./8 > 1.7 # 1.7 is the default ratio
True
>>> simplify(g, measure=my_measure)
-log(a)*log(b) + log(a) + log(b)
Note that because ``simplify()`` internally tries many different
simplification strategies and then compares them using the measure
function, we get a completely different result that is still different
from the input expression by doing this.
"""
expr = sympify(expr)
try:
return expr._eval_simplify(ratio=ratio, measure=measure)
except AttributeError:
pass
original_expr = expr = signsimp(expr)
from sympy.simplify.hyperexpand import hyperexpand
from sympy.functions.special.bessel import BesselBase
from sympy import Sum, Product
if not isinstance(expr, Basic) or not expr.args: # XXX: temporary hack
return expr
if not isinstance(expr, (Add, Mul, Pow, ExpBase)):
if isinstance(expr, Function) and hasattr(expr, "inverse"):
if len(expr.args) == 1 and len(expr.args[0].args) == 1 and \
isinstance(expr.args[0], expr.inverse(argindex=1)):
return simplify(expr.args[0].args[0], ratio=ratio,
measure=measure, fu=fu)
return expr.func(*[simplify(x, ratio=ratio, measure=measure, fu=fu)
for x in expr.args])
# TODO: Apply different strategies, considering expression pattern:
# is it a purely rational function? Is there any trigonometric function?...
# See also https://github.com/sympy/sympy/pull/185.
def shorter(*choices):
'''Return the choice that has the fewest ops. In case of a tie,
the expression listed first is selected.'''
if not has_variety(choices):
return choices[0]
return min(choices, key=measure)
expr = bottom_up(expr, lambda w: w.normal())
expr = Mul(*powsimp(expr).as_content_primitive())
_e = cancel(expr)
expr1 = shorter(_e, _mexpand(_e).cancel()) # issue 6829
expr2 = shorter(together(expr, deep=True), together(expr1, deep=True))
if ratio is S.Infinity:
expr = expr2
else:
expr = shorter(expr2, expr1, expr)
if not isinstance(expr, Basic): # XXX: temporary hack
return expr
expr = factor_terms(expr, sign=False)
# hyperexpand automatically only works on hypergeometric terms
expr = hyperexpand(expr)
expr = piecewise_fold(expr)
if expr.has(BesselBase):
expr = besselsimp(expr)
if expr.has(TrigonometricFunction) and not fu or expr.has(
HyperbolicFunction):
expr = trigsimp(expr, deep=True)
if expr.has(log):
expr = shorter(expand_log(expr, deep=True), logcombine(expr))
if expr.has(CombinatorialFunction, gamma):
expr = combsimp(expr)
if expr.has(Sum):
expr = sum_simplify(expr)
if expr.has(Product):
expr = product_simplify(expr)
short = shorter(powsimp(expr, combine='exp', deep=True), powsimp(expr), expr)
short = shorter(short, factor_terms(short), expand_power_exp(expand_mul(short)))
if short.has(TrigonometricFunction, HyperbolicFunction, ExpBase):
short = exptrigsimp(short, simplify=False)
# get rid of hollow 2-arg Mul factorization
hollow_mul = Transform(
lambda x: Mul(*x.args),
lambda x:
x.is_Mul and
len(x.args) == 2 and
x.args[0].is_Number and
x.args[1].is_Add and
x.is_commutative)
expr = short.xreplace(hollow_mul)
numer, denom = expr.as_numer_denom()
if denom.is_Add:
n, d = fraction(radsimp(1/denom, symbolic=False, max_terms=1))
if n is not S.One:
expr = (numer*n).expand()/d
if expr.could_extract_minus_sign():
n, d = fraction(expr)
if d != 0:
expr = signsimp(-n/(-d))
if measure(expr) > ratio*measure(original_expr):
expr = original_expr
return expr
def test_issue_4373():
x = Symbol("x")
assert abs(trigsimp(2.0 * sin(x)**2 + 2.0 * cos(x)**2) - 2.0) < 1e-10
def test_trigsimp1a():
assert trigsimp(sin(2)**2 * cos(3) * exp(2) /
cos(2)**2) == tan(2)**2 * cos(3) * exp(2)
assert trigsimp(tan(2)**2 * cos(3) * exp(2) *
cos(2)**2) == sin(2)**2 * cos(3) * exp(2)
assert trigsimp(cot(2) * cos(3) * exp(2) *
sin(2)) == cos(3) * exp(2) * cos(2)
assert trigsimp(tan(2) * cos(3) * exp(2) /
sin(2)) == cos(3) * exp(2) / cos(2)
assert trigsimp(cot(2) * cos(3) * exp(2) /
cos(2)) == cos(3) * exp(2) / sin(2)
assert trigsimp(cot(2) * cos(3) * exp(2) * tan(2)) == cos(3) * exp(2)
assert trigsimp(sinh(2) * cos(3) * exp(2) /
cosh(2)) == tanh(2) * cos(3) * exp(2)
assert trigsimp(tanh(2) * cos(3) * exp(2) *
cosh(2)) == sinh(2) * cos(3) * exp(2)
assert trigsimp(coth(2) * cos(3) * exp(2) *
sinh(2)) == cosh(2) * cos(3) * exp(2)
assert trigsimp(tanh(2) * cos(3) * exp(2) /
sinh(2)) == cos(3) * exp(2) / cosh(2)
assert trigsimp(coth(2) * cos(3) * exp(2) /
cosh(2)) == cos(3) * exp(2) / sinh(2)
assert trigsimp(coth(2) * cos(3) * exp(2) * tanh(2)) == cos(3) * exp(2)
def simplify(expr, ratio=1.7, measure=count_ops, rational=False):
# type: (object, object, object, object) -> object
"""
Simplifies the given expression.
Simplification is not a well defined term and the exact strategies
this function tries can change in the future versions of SymPy. If
your algorithm relies on "simplification" (whatever it is), try to
determine what you need exactly - is it powsimp()?, radsimp()?,
together()?, logcombine()?, or something else? And use this particular
function directly, because those are well defined and thus your algorithm
will be robust.
Nonetheless, especially for interactive use, or when you don't know
anything about the structure of the expression, simplify() tries to apply
intelligent heuristics to make the input expression "simpler". For
example:
>>> from sympy import simplify, cos, sin
>>> from sympy.abc import x, y
>>> a = (x + x**2)/(x*sin(y)**2 + x*cos(y)**2)
>>> a
(x**2 + x)/(x*sin(y)**2 + x*cos(y)**2)
>>> simplify(a)
x + 1
Note that we could have obtained the same result by using specific
simplification functions:
>>> from sympy import trigsimp, cancel
>>> trigsimp(a)
(x**2 + x)/x
>>> cancel(_)
x + 1
In some cases, applying :func:`simplify` may actually result in some more
complicated expression. The default ``ratio=1.7`` prevents more extreme
cases: if (result length)/(input length) > ratio, then input is returned
unmodified. The ``measure`` parameter lets you specify the function used
to determine how complex an expression is. The function should take a
single argument as an expression and return a number such that if
expression ``a`` is more complex than expression ``b``, then
``measure(a) > measure(b)``. The default measure function is
:func:`count_ops`, which returns the total number of operations in the
expression.
For example, if ``ratio=1``, ``simplify`` output can't be longer
than input.
::
>>> from sympy import sqrt, simplify, count_ops, oo
>>> root = 1/(sqrt(2)+3)
Since ``simplify(root)`` would result in a slightly longer expression,
root is returned unchanged instead::
>>> simplify(root, ratio=1) == root
True
If ``ratio=oo``, simplify will be applied anyway::
>>> count_ops(simplify(root, ratio=oo)) > count_ops(root)
True
Note that the shortest expression is not necessary the simplest, so
setting ``ratio`` to 1 may not be a good idea.
Heuristically, the default value ``ratio=1.7`` seems like a reasonable
choice.
You can easily define your own measure function based on what you feel
should represent the "size" or "complexity" of the input expression. Note
that some choices, such as ``lambda expr: len(str(expr))`` may appear to be
good metrics, but have other problems (in this case, the measure function
may slow down simplify too much for very large expressions). If you don't
know what a good metric would be, the default, ``count_ops``, is a good
one.
For example:
>>> from sympy import symbols, log
>>> a, b = symbols('a b', positive=True)
>>> g = log(a) + log(b) + log(a)*log(1/b)
>>> h = simplify(g)
>>> h
log(a*b**(-log(a) + 1))
>>> count_ops(g)
8
>>> count_ops(h)
5
So you can see that ``h`` is simpler than ``g`` using the count_ops metric.
However, we may not like how ``simplify`` (in this case, using
``logcombine``) has created the ``b**(log(1/a) + 1)`` term. A simple way
to reduce this would be to give more weight to powers as operations in
``count_ops``. We can do this by using the ``visual=True`` option:
>>> print(count_ops(g, visual=True))
2*ADD + DIV + 4*LOG + MUL
>>> print(count_ops(h, visual=True))
2*LOG + MUL + POW + SUB
>>> from sympy import Symbol, S
>>> def my_measure(expr):
... POW = Symbol('POW')
... # Discourage powers by giving POW a weight of 10
... count = count_ops(expr, visual=True).subs(POW, 10)
... # Every other operation gets a weight of 1 (the default)
... count = count.replace(Symbol, type(S.One))
... return count
>>> my_measure(g)
8
>>> my_measure(h)
14
>>> 15./8 > 1.7 # 1.7 is the default ratio
True
>>> simplify(g, measure=my_measure)
-log(a)*log(b) + log(a) + log(b)
Note that because ``simplify()`` internally tries many different
simplification strategies and then compares them using the measure
function, we get a completely different result that is still different
from the input expression by doing this.
If rational=True, Floats will be recast as Rationals before simplification.
If rational=None, Floats will be recast as Rationals but the result will
be recast as Floats. If rational=False(default) then nothing will be done
to the Floats.
"""
expr = sympify(expr)
try:
return expr._eval_simplify(ratio=ratio, measure=measure)
except AttributeError:
pass
original_expr = expr = signsimp(expr)
from sympy.simplify.hyperexpand import hyperexpand
from sympy.functions.special.bessel import BesselBase
from sympy import Sum, Product
if not isinstance(expr, Basic) or not expr.args: # XXX: temporary hack
return expr
if not isinstance(expr, (Add, Mul, Pow, ExpBase)):
if isinstance(expr, Function) and hasattr(expr, "inverse"):
if len(expr.args) == 1 and len(expr.args[0].args) == 1 and \
isinstance(expr.args[0], expr.inverse(argindex=1)):
return simplify(expr.args[0].args[0], ratio=ratio,
measure=measure, rational=rational)
return expr.func(*[simplify(x, ratio=ratio, measure=measure, rational=rational)
for x in expr.args])
# TODO: Apply different strategies, considering expression pattern:
# is it a purely rational function? Is there any trigonometric function?...
# See also https://github.com/sympy/sympy/pull/185.
def shorter(*choices):
'''Return the choice that has the fewest ops. In case of a tie,
the expression listed first is selected.'''
if not has_variety(choices):
return choices[0]
return min(choices, key=measure)
# rationalize Floats
floats = False
if rational is not False and expr.has(Float):
floats = True
expr = nsimplify(expr, rational=True)
expr = bottom_up(expr, lambda w: w.normal())
expr = Mul(*powsimp(expr).as_content_primitive())
_e = cancel(expr)
expr1 = shorter(_e, _mexpand(_e).cancel()) # issue 6829
expr2 = shorter(together(expr, deep=True), together(expr1, deep=True))
if ratio is S.Infinity:
expr = expr2
else:
expr = shorter(expr2, expr1, expr)
if not isinstance(expr, Basic): # XXX: temporary hack
return expr
expr = factor_terms(expr, sign=False)
# hyperexpand automatically only works on hypergeometric terms
expr = hyperexpand(expr)
expr = piecewise_fold(expr)
if expr.has(BesselBase):
expr = besselsimp(expr)
if expr.has(TrigonometricFunction, HyperbolicFunction):
expr = trigsimp(expr, deep=True)
if expr.has(log):
expr = shorter(expand_log(expr, deep=True), logcombine(expr))
if expr.has(CombinatorialFunction, gamma):
# expression with gamma functions or non-integer arguments is
# automatically passed to gammasimp
expr = combsimp(expr)
if expr.has(Sum):
expr = sum_simplify(expr)
if expr.has(Product):
expr = product_simplify(expr)
short = shorter(powsimp(expr, combine='exp', deep=True), powsimp(expr), expr)
short = shorter(short, cancel(short))
short = shorter(short, factor_terms(short), expand_power_exp(expand_mul(short)))
if short.has(TrigonometricFunction, HyperbolicFunction, ExpBase):
short = exptrigsimp(short)
# get rid of hollow 2-arg Mul factorization
hollow_mul = Transform(
lambda x: Mul(*x.args),
lambda x:
x.is_Mul and
len(x.args) == 2 and
x.args[0].is_Number and
x.args[1].is_Add and
x.is_commutative)
expr = short.xreplace(hollow_mul)
numer, denom = expr.as_numer_denom()
if denom.is_Add:
n, d = fraction(radsimp(1/denom, symbolic=False, max_terms=1))
if n is not S.One:
expr = (numer*n).expand()/d
if expr.could_extract_minus_sign():
n, d = fraction(expr)
if d != 0:
expr = signsimp(-n/(-d))
if measure(expr) > ratio*measure(original_expr):
expr = original_expr
# restore floats
if floats and rational is None:
expr = nfloat(expr, exponent=False)
return expr
def checkodesol(ode, sol, func=None, order='auto', solve_for_func=True):
r"""
Substitutes ``sol`` into ``ode`` and checks that the result is ``0``.
This works when ``func`` is one function, like `f(x)` or a list of
functions like `[f(x), g(x)]` when `ode` is a system of ODEs. ``sol`` can
be a single solution or a list of solutions. Each solution may be an
:py:class:`~sympy.core.relational.Equality` that the solution satisfies,
e.g. ``Eq(f(x), C1), Eq(f(x) + C1, 0)``; or simply an
:py:class:`~sympy.core.expr.Expr`, e.g. ``f(x) - C1``. In most cases it
will not be necessary to explicitly identify the function, but if the
function cannot be inferred from the original equation it can be supplied
through the ``func`` argument.
If a sequence of solutions is passed, the same sort of container will be
used to return the result for each solution.
It tries the following methods, in order, until it finds zero equivalence:
1. Substitute the solution for `f` in the original equation. This only
works if ``ode`` is solved for `f`. It will attempt to solve it first
unless ``solve_for_func == False``.
2. Take `n` derivatives of the solution, where `n` is the order of
``ode``, and check to see if that is equal to the solution. This only
works on exact ODEs.
3. Take the 1st, 2nd, ..., `n`\th derivatives of the solution, each time
solving for the derivative of `f` of that order (this will always be
possible because `f` is a linear operator). Then back substitute each
derivative into ``ode`` in reverse order.
This function returns a tuple. The first item in the tuple is ``True`` if
the substitution results in ``0``, and ``False`` otherwise. The second
item in the tuple is what the substitution results in. It should always
be ``0`` if the first item is ``True``. Sometimes this function will
return ``False`` even when an expression is identically equal to ``0``.
This happens when :py:meth:`~sympy.simplify.simplify.simplify` does not
reduce the expression to ``0``. If an expression returned by this
function vanishes identically, then ``sol`` really is a solution to
the ``ode``.
If this function seems to hang, it is probably because of a hard
simplification.
To use this function to test, test the first item of the tuple.
Examples
========
>>> from sympy import (Eq, Function, checkodesol, symbols,
... Derivative, exp)
>>> x, C1, C2 = symbols('x,C1,C2')
>>> f, g = symbols('f g', cls=Function)
>>> checkodesol(f(x).diff(x), Eq(f(x), C1))
(True, 0)
>>> assert checkodesol(f(x).diff(x), C1)[0]
>>> assert not checkodesol(f(x).diff(x), x)[0]
>>> checkodesol(f(x).diff(x, 2), x**2)
(False, 2)
>>> eqs = [Eq(Derivative(f(x), x), f(x)), Eq(Derivative(g(x), x), g(x))]
>>> sol = [Eq(f(x), C1*exp(x)), Eq(g(x), C2*exp(x))]
>>> checkodesol(eqs, sol)
(True, [0, 0])
"""
if iterable(ode):
return checksysodesol(ode, sol, func=func)
if not isinstance(ode, Equality):
ode = Eq(ode, 0)
if func is None:
try:
_, func = _preprocess(ode.lhs)
except ValueError:
funcs = [s.atoms(AppliedUndef) for s in (
sol if is_sequence(sol, set) else [sol])]
funcs = set().union(*funcs)
if len(funcs) != 1:
raise ValueError(
'must pass func arg to checkodesol for this case.')
func = funcs.pop()
if not isinstance(func, AppliedUndef) or len(func.args) != 1:
raise ValueError(
"func must be a function of one variable, not %s" % func)
if is_sequence(sol, set):
return type(sol)([checkodesol(ode, i, order=order, solve_for_func=solve_for_func) for i in sol])
if not isinstance(sol, Equality):
sol = Eq(func, sol)
elif sol.rhs == func:
sol = sol.reversed
if order == 'auto':
order = ode_order(ode, func)
solved = sol.lhs == func and not sol.rhs.has(func)
if solve_for_func and not solved:
rhs = solve(sol, func)
if rhs:
eqs = [Eq(func, t) for t in rhs]
if len(rhs) == 1:
eqs = eqs[0]
return checkodesol(ode, eqs, order=order,
solve_for_func=False)
x = func.args[0]
# Handle series solutions here
if sol.has(Order):
assert sol.lhs == func
Oterm = sol.rhs.getO()
solrhs = sol.rhs.removeO()
Oexpr = Oterm.expr
assert isinstance(Oexpr, Pow)
sorder = Oexpr.exp
assert Oterm == Order(x**sorder)
odesubs = (ode.lhs-ode.rhs).subs(func, solrhs).doit().expand()
neworder = Order(x**(sorder - order))
odesubs = odesubs + neworder
assert odesubs.getO() == neworder
residual = odesubs.removeO()
return (residual == 0, residual)
s = True
testnum = 0
while s:
if testnum == 0:
# First pass, try substituting a solved solution directly into the
# ODE. This has the highest chance of succeeding.
ode_diff = ode.lhs - ode.rhs
if sol.lhs == func:
s = sub_func_doit(ode_diff, func, sol.rhs)
s = besselsimp(s)
else:
testnum += 1
continue
ss = simplify(s.rewrite(exp))
if ss:
# with the new numer_denom in power.py, if we do a simple
# expansion then testnum == 0 verifies all solutions.
s = ss.expand(force=True)
else:
s = 0
testnum += 1
elif testnum == 1:
# Second pass. If we cannot substitute f, try seeing if the nth
# derivative is equal, this will only work for odes that are exact,
# by definition.
s = simplify(
trigsimp(diff(sol.lhs, x, order) - diff(sol.rhs, x, order)) -
trigsimp(ode.lhs) + trigsimp(ode.rhs))
# s2 = simplify(
# diff(sol.lhs, x, order) - diff(sol.rhs, x, order) - \
# ode.lhs + ode.rhs)
testnum += 1
elif testnum == 2:
# Third pass. Try solving for df/dx and substituting that into the
# ODE. Thanks to Chris Smith for suggesting this method. Many of
# the comments below are his, too.
# The method:
# - Take each of 1..n derivatives of the solution.
# - Solve each nth derivative for d^(n)f/dx^(n)
# (the differential of that order)
# - Back substitute into the ODE in decreasing order
# (i.e., n, n-1, ...)
# - Check the result for zero equivalence
if sol.lhs == func and not sol.rhs.has(func):
diffsols = {0: sol.rhs}
elif sol.rhs == func and not sol.lhs.has(func):
diffsols = {0: sol.lhs}
else:
diffsols = {}
sol = sol.lhs - sol.rhs
for i in range(1, order + 1):
# Differentiation is a linear operator, so there should always
# be 1 solution. Nonetheless, we test just to make sure.
# We only need to solve once. After that, we automatically
# have the solution to the differential in the order we want.
if i == 1:
ds = sol.diff(x)
try:
sdf = solve(ds, func.diff(x, i))
if not sdf:
raise NotImplementedError
except NotImplementedError:
testnum += 1
break
else:
diffsols[i] = sdf[0]
else:
# This is what the solution says df/dx should be.
diffsols[i] = diffsols[i - 1].diff(x)
# Make sure the above didn't fail.
if testnum > 2:
continue
else:
# Substitute it into ODE to check for self consistency.
lhs, rhs = ode.lhs, ode.rhs
for i in range(order, -1, -1):
if i == 0 and 0 not in diffsols:
# We can only substitute f(x) if the solution was
# solved for f(x).
break
lhs = sub_func_doit(lhs, func.diff(x, i), diffsols[i])
rhs = sub_func_doit(rhs, func.diff(x, i), diffsols[i])
ode_or_bool = Eq(lhs, rhs)
ode_or_bool = simplify(ode_or_bool)
if isinstance(ode_or_bool, (bool, BooleanAtom)):
if ode_or_bool:
lhs = rhs = S.Zero
else:
lhs = ode_or_bool.lhs
rhs = ode_or_bool.rhs
# No sense in overworking simplify -- just prove that the
# numerator goes to zero
num = trigsimp((lhs - rhs).as_numer_denom()[0])
# since solutions are obtained using force=True we test
# using the same level of assumptions
## replace function with dummy so assumptions will work
_func = Dummy('func')
num = num.subs(func, _func)
## posify the expression
num, reps = posify(num)
s = simplify(num).xreplace(reps).xreplace({_func: func})
testnum += 1
else:
break
if not s:
return (True, s)
elif s is True: # The code above never was able to change s
raise NotImplementedError("Unable to test if " + str(sol) +
" is a solution to " + str(ode) + ".")
else:
return (False, s)
def test_hyperbolic_simp():
x, y = symbols('x,y')
assert trigsimp(sinh(x)**2 + 1) == cosh(x)**2
assert trigsimp(cosh(x)**2 - 1) == sinh(x)**2
assert trigsimp(cosh(x)**2 - sinh(x)**2) == 1
assert trigsimp(1 - tanh(x)**2) == 1 / cosh(x)**2
assert trigsimp(1 - 1 / cosh(x)**2) == tanh(x)**2
assert trigsimp(tanh(x)**2 + 1 / cosh(x)**2) == 1
assert trigsimp(coth(x)**2 - 1) == 1 / sinh(x)**2
assert trigsimp(1 / sinh(x)**2 + 1) == 1 / tanh(x)**2
assert trigsimp(coth(x)**2 - 1 / sinh(x)**2) == 1
assert trigsimp(5 * cosh(x)**2 - 5 * sinh(x)**2) == 5
assert trigsimp(5 * cosh(x / 2)**2 -
2 * sinh(x / 2)**2) == 3 * cosh(x) / 2 + Rational(7, 2)
assert trigsimp(sinh(x) / cosh(x)) == tanh(x)
assert trigsimp(tanh(x)) == trigsimp(sinh(x) / cosh(x))
assert trigsimp(cosh(x) / sinh(x)) == 1 / tanh(x)
assert trigsimp(2 * tanh(x) * cosh(x)) == 2 * sinh(x)
assert trigsimp(coth(x)**3 * sinh(x)**3) == cosh(x)**3
assert trigsimp(y * tanh(x)**2 / sinh(x)**2) == y / cosh(x)**2
assert trigsimp(coth(x) / cosh(x)) == 1 / sinh(x)
for a in (pi / 6 * I, pi / 4 * I, pi / 3 * I):
assert trigsimp(sinh(a) * cosh(x) + cosh(a) * sinh(x)) == sinh(x + a)
assert trigsimp(-sinh(a) * cosh(x) + cosh(a) * sinh(x)) == sinh(x - a)
e = 2 * cosh(x)**2 - 2 * sinh(x)**2
assert trigsimp(log(e)) == log(2)
# issue 19535:
assert trigsimp(sqrt(cosh(x)**2 - 1)) == sqrt(sinh(x)**2)
assert trigsimp(cosh(x)**2 * cosh(y)**2 - cosh(x)**2 * sinh(y)**2 -
sinh(x)**2,
recursive=True) == 1
assert trigsimp(sinh(x)**2 * sinh(y)**2 - sinh(x)**2 * cosh(y)**2 +
cosh(x)**2,
recursive=True) == 1
assert abs(trigsimp(2.0 * cosh(x)**2 - 2.0 * sinh(x)**2) - 2.0) < 1e-10
assert trigsimp(sinh(x)**2 / cosh(x)**2) == tanh(x)**2
assert trigsimp(sinh(x)**3 / cosh(x)**3) == tanh(x)**3
assert trigsimp(sinh(x)**10 / cosh(x)**10) == tanh(x)**10
assert trigsimp(cosh(x)**3 / sinh(x)**3) == 1 / tanh(x)**3
assert trigsimp(cosh(x) / sinh(x)) == 1 / tanh(x)
assert trigsimp(cosh(x)**2 / sinh(x)**2) == 1 / tanh(x)**2
assert trigsimp(cosh(x)**10 / sinh(x)**10) == 1 / tanh(x)**10
assert trigsimp(x * cosh(x) * tanh(x)) == x * sinh(x)
assert trigsimp(-sinh(x) + cosh(x) * tanh(x)) == 0
assert tan(x) != 1 / cot(x) # cot doesn't auto-simplify
assert trigsimp(tan(x) - 1 / cot(x)) == 0
assert trigsimp(3 * tanh(x)**7 - 2 / coth(x)**7) == tanh(x)**7
def _eval_trigsimp(self, **opts):
from sympy.simplify.trigsimp import trigsimp
return self.func(trigsimp(self.lhs, **opts),
trigsimp(self.rhs, **opts))
def orient_new(self, name, orienters, location=None,
vector_names=None, variable_names=None):
"""
Creates a new CoordSys3D oriented in the user-specified way
with respect to this system.
Please refer to the documentation of the orienter classes
for more information about the orientation procedure.
Parameters
==========
name : str
The name of the new CoordSys3D instance.
orienters : iterable/Orienter
An Orienter or an iterable of Orienters for orienting the
new coordinate system.
If an Orienter is provided, it is applied to get the new
system.
If an iterable is provided, the orienters will be applied
in the order in which they appear in the iterable.
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSys3D
>>> from sympy import symbols
>>> q0, q1, q2, q3 = symbols('q0 q1 q2 q3')
>>> N = CoordSys3D('N')
Using an AxisOrienter
>>> from sympy.vector import AxisOrienter
>>> axis_orienter = AxisOrienter(q1, N.i + 2 * N.j)
>>> A = N.orient_new('A', (axis_orienter, ))
Using a BodyOrienter
>>> from sympy.vector import BodyOrienter
>>> body_orienter = BodyOrienter(q1, q2, q3, '123')
>>> B = N.orient_new('B', (body_orienter, ))
Using a SpaceOrienter
>>> from sympy.vector import SpaceOrienter
>>> space_orienter = SpaceOrienter(q1, q2, q3, '312')
>>> C = N.orient_new('C', (space_orienter, ))
Using a QuaternionOrienter
>>> from sympy.vector import QuaternionOrienter
>>> q_orienter = QuaternionOrienter(q0, q1, q2, q3)
>>> D = N.orient_new('D', (q_orienter, ))
"""
if variable_names is None:
variable_names = self._variable_names
if vector_names is None:
vector_names = self._vector_names
if isinstance(orienters, Orienter):
if isinstance(orienters, AxisOrienter):
final_matrix = orienters.rotation_matrix(self)
else:
final_matrix = orienters.rotation_matrix()
# TODO: trigsimp is needed here so that the matrix becomes
# canonical (scalar_map also calls trigsimp; without this, you can
# end up with the same CoordinateSystem that compares differently
# due to a differently formatted matrix). However, this is
# probably not so good for performance.
final_matrix = trigsimp(final_matrix)
else:
final_matrix = Matrix(eye(3))
for orienter in orienters:
if isinstance(orienter, AxisOrienter):
final_matrix *= orienter.rotation_matrix(self)
else:
final_matrix *= orienter.rotation_matrix()
return CoordSys3D(name, rotation_matrix=final_matrix,
vector_names=vector_names,
variable_names=variable_names,
location=location,
parent=self)
def norm(self):
"""Returns the norm of the quaternion."""
q = self
# trigsimp is used to simplify sin(x)^2 + cos(x)^2 (these terms
# arise when from_axis_angle is used).
return sqrt(trigsimp(q.a**2 + q.b**2 + q.c**2 + q.d**2))
def test_trigsimp_noncommutative():
x, y = symbols('x,y')
A, B = symbols('A,B', commutative=False)
assert trigsimp(A - A * sin(x)**2) == A * cos(x)**2
assert trigsimp(A - A * cos(x)**2) == A * sin(x)**2
assert trigsimp(A * sin(x)**2 + A * cos(x)**2) == A
assert trigsimp(A + A * tan(x)**2) == A / cos(x)**2
assert trigsimp(A / cos(x)**2 - A) == A * tan(x)**2
assert trigsimp(A / cos(x)**2 - A * tan(x)**2) == A
assert trigsimp(A + A * cot(x)**2) == A / sin(x)**2
assert trigsimp(A / sin(x)**2 - A) == A / tan(x)**2
assert trigsimp(A / sin(x)**2 - A * cot(x)**2) == A
assert trigsimp(y * A * cos(x)**2 + y * A * sin(x)**2) == y * A
assert trigsimp(A * sin(x) / cos(x)) == A * tan(x)
assert trigsimp(A * tan(x) * cos(x)) == A * sin(x)
assert trigsimp(A * cot(x)**3 * sin(x)**3) == A * cos(x)**3
assert trigsimp(y * A * tan(x)**2 / sin(x)**2) == y * A / cos(x)**2
assert trigsimp(A * cot(x) / cos(x)) == A / sin(x)
assert trigsimp(A * sin(x + y) + A * sin(x - y)) == 2 * A * sin(x) * cos(y)
assert trigsimp(A * sin(x + y) - A * sin(x - y)) == 2 * A * sin(y) * cos(x)
assert trigsimp(A * cos(x + y) + A * cos(x - y)) == 2 * A * cos(x) * cos(y)
assert trigsimp(A * cos(x + y) -
A * cos(x - y)) == -2 * A * sin(x) * sin(y)
assert trigsimp(A * sinh(x + y) +
A * sinh(x - y)) == 2 * A * sinh(x) * cosh(y)
assert trigsimp(A * sinh(x + y) -
A * sinh(x - y)) == 2 * A * sinh(y) * cosh(x)
assert trigsimp(A * cosh(x + y) +
A * cosh(x - y)) == 2 * A * cosh(x) * cosh(y)
assert trigsimp(A * cosh(x + y) -
A * cosh(x - y)) == 2 * A * sinh(x) * sinh(y)
assert trigsimp(A * cos(0.12345)**2 + A * sin(0.12345)**2) == 1.0 * A
