def test_SetExpr_Intersection():
x, y, z, w = symbols("x y z w")
set1 = Interval(x, y)
set2 = Interval(w, z)
inter = Intersection(set1, set2)
se = SetExpr(inter)
assert exp(se).set == Intersection(ImageSet(Lambda(x, exp(x)), set1),
ImageSet(Lambda(x, exp(x)), set2))
assert cos(se).set == ImageSet(Lambda(x, cos(x)), inter)
def test_booleans():
""" test basic unions and intersections """
half = S.Half
p1, p2, p3, p4 = map(Point, [(0, 0), (1, 0), (5, 1), (0, 1)])
p5, p6, p7 = map(Point, [(3, 2), (1, -1), (0, 2)])
l1 = Line(Point(0,0), Point(1,1))
l2 = Line(Point(half, half), Point(5,5))
l3 = Line(p2, p3)
l4 = Line(p3, p4)
poly1 = Polygon(p1, p2, p3, p4)
poly2 = Polygon(p5, p6, p7)
poly3 = Polygon(p1, p2, p5)
assert Union(l1, l2).equals(l1)
assert Intersection(l1, l2).equals(l1)
assert Intersection(l1, l4) == FiniteSet(Point(1,1))
assert Intersection(Union(l1, l4), l3) == FiniteSet(Point(Rational(-1, 3), Rational(-1, 3)), Point(5, 1))
assert Intersection(l1, FiniteSet(Point(7,-7))) == EmptySet
assert Intersection(Circle(Point(0,0), 3), Line(p1,p2)) == FiniteSet(Point(-3,0), Point(3,0))
assert Intersection(l1, FiniteSet(p1)) == FiniteSet(p1)
assert Union(l1, FiniteSet(p1)) == l1
fs = FiniteSet(Point(Rational(1, 3), 1), Point(Rational(2, 3), 0), Point(Rational(9, 5), Rational(1, 5)), Point(Rational(7, 3), 1))
# test the intersection of polygons
assert Intersection(poly1, poly2) == fs
# make sure if we union polygons with subsets, the subsets go away
assert Union(poly1, poly2, fs) == Union(poly1, poly2)
# make sure that if we union with a FiniteSet that isn't a subset,
# that the points in the intersection stop being listed
assert Union(poly1, FiniteSet(Point(0,0), Point(3,5))) == Union(poly1, FiniteSet(Point(3,5)))
# intersect two polygons that share an edge
assert Intersection(poly1, poly3) == Union(FiniteSet(Point(Rational(3, 2), 1), Point(2, 1)), Segment(Point(0, 0), Point(1, 0)))
def _set_function(f, x):
from sympy.sets.sets import is_function_invertible_in_set
# If the function is invertible, intersect the maps of the sets.
if is_function_invertible_in_set(f, x):
return Intersection(imageset(f, arg) for arg in x.args)
else:
return ImageSet(Lambda(_x, f(_x)), x)
def test_CondSet_intersect():
input_conditionset = ConditionSet(x, x**2 > 4,
Interval(1, 4, False, False))
other_domain = Interval(0, 3, False, False)
output_conditionset = ConditionSet(x, x**2 > 4,
Interval(1, 3, False, False))
assert Intersection(input_conditionset,
other_domain) == output_conditionset
def test_booleans():
""" test basic unions and intersections """
assert Union(l1, l2).equals(l1)
assert Intersection(l1, l2).equals(l1)
assert Intersection(l1, l4) == FiniteSet(Point(1, 1))
assert Intersection(Union(l1, l4),
l3) == FiniteSet(Point(-1 / 3, -1 / 3), Point(5, 1))
assert Intersection(l1, FiniteSet(Point(7, -7))) == EmptySet()
assert Intersection(Circle(Point(0, 0), 3),
Line(p1, p2)) == FiniteSet(Point(-3, 0), Point(3, 0))
fs = FiniteSet(Point(1 / 3, 1), Point(2 / 3, 0), Point(9 / 5, 1 / 5),
Point(7 / 3, 1))
# test the intersection of polygons
assert Intersection(poly1, poly2) == fs
# make sure if we union polygons with subsets, the subsets go away
assert Union(poly1, poly2, fs) == Union(poly1, poly2)
# make sure that if we union with a FiniteSet that isn't a subset,
# that the points in the intersection stop being listed
assert Union(poly1, FiniteSet(Point(0, 0),
Point(3,
5))) == Union(poly1,
FiniteSet(Point(3, 5)))
# intersect two polygons that share an edge
assert Intersection(poly1, poly3) == Union(
FiniteSet(Point(3 / 2, 1), Point(2, 1)),
Segment(Point(0, 0), Point(1, 0)))
def are_concurrent(*lines):
"""Is a sequence of linear entities concurrent?
Two or more linear entities are concurrent if they all
intersect at a single point.
Parameters
==========
lines : a sequence of linear entities.
Returns
=======
True : if the set of linear entities intersect in one point
False : otherwise.
See Also
========
sympy.geometry.util.intersection
Examples
========
>>> from sympy import Point, Line, Line3D
>>> p1, p2 = Point(0, 0), Point(3, 5)
>>> p3, p4 = Point(-2, -2), Point(0, 2)
>>> l1, l2, l3 = Line(p1, p2), Line(p1, p3), Line(p1, p4)
>>> Line.are_concurrent(l1, l2, l3)
True
>>> l4 = Line(p2, p3)
>>> Line.are_concurrent(l2, l3, l4)
False
"""
common_points = Intersection(*lines)
if common_points.is_FiniteSet and len(common_points) == 1:
return True
return False
def _solve_trig(f, symbol, domain):
""" Helper to solve trigonometric equations """
f = trigsimp(f)
f_original = f
f = f.rewrite(exp)
f = together(f)
g, h = fraction(f)
y = Dummy('y')
g, h = g.expand(), h.expand()
g, h = g.subs(exp(I*symbol), y), h.subs(exp(I*symbol), y)
if g.has(symbol) or h.has(symbol):
return ConditionSet(symbol, Eq(f, 0), S.Reals)
solns = solveset_complex(g, y) - solveset_complex(h, y)
if isinstance(solns, FiniteSet):
result = Union(*[invert_complex(exp(I*symbol), s, symbol)[1]
for s in solns])
return Intersection(result, domain)
elif solns is S.EmptySet:
return S.EmptySet
else:
return ConditionSet(symbol, Eq(f_original, 0), S.Reals)
def solveset(f, symbol=None, domain=S.Complexes):
"""Solves a given inequality or equation with set as output
Parameters
==========
f : Expr or a relational.
The target equation or inequality
symbol : Symbol
The variable for which the equation is solved
domain : Set
The domain over which the equation is solved
Returns
=======
Set
A set of values for `symbol` for which `f` is True or is equal to
zero. An `EmptySet` is returned if no solution is found.
A `ConditionSet` is returned as unsolved object if algorithms
to evaluatee complete solution are not yet implemented.
`solveset` claims to be complete in the solution set that it returns.
Raises
======
NotImplementedError
The algorithms to solve inequalities in complex domain are
not yet implemented.
ValueError
The input is not valid.
RuntimeError
It is a bug, please report to the github issue tracker.
`solveset` uses two underlying functions `solveset_real` and
`solveset_complex` to solve equations. They are the solvers for real and
complex domain respectively. `solveset` ignores the assumptions on the
variable being solved for and instead, uses the `domain` parameter to
decide which solver to use.
See Also
========
solveset_real: solver for real domain
solveset_complex: solver for complex domain
Examples
========
>>> from sympy import exp, Symbol, Eq, pprint, S, solveset
>>> from sympy.abc import x
* The default domain is complex. Not specifying a domain will lead to the
solving of the equation in the complex domain.
>>> pprint(solveset(exp(x) - 1, x), use_unicode=False)
{2*n*I*pi | n in Integers()}
* If you want to solve equation in real domain by the `solveset`
interface, then specify that the domain is real. Alternatively use
`solveset\_real`.
>>> x = Symbol('x')
>>> solveset(exp(x) - 1, x, S.Reals)
{0}
>>> solveset(Eq(exp(x), 1), x, S.Reals)
{0}
* Inequalities can be solved over the real domain only. Use of a complex
domain leads to a NotImplementedError.
>>> solveset(exp(x) > 1, x, S.Reals)
(0, oo)
"""
from sympy.solvers.inequalities import solve_univariate_inequality
if symbol is None:
free_symbols = f.free_symbols
if len(free_symbols) == 1:
symbol = free_symbols.pop()
else:
raise ValueError(
filldedent('''
The independent variable must be specified for a
multivariate equation.'''))
elif not symbol.is_Symbol:
raise ValueError('A Symbol must be given, not type %s: %s' %
(type(symbol), symbol))
f = sympify(f)
if f is S.false:
return EmptySet()
if f is S.true:
return domain
if isinstance(f, Eq):
from sympy.core import Add
f = Add(f.lhs, -f.rhs, evaluate=False)
if f.is_Relational:
if not domain.is_subset(S.Reals):
raise NotImplementedError("Inequalities in the complex domain are "
"not supported. Try the real domain by"
"setting domain=S.Reals")
try:
result = solve_univariate_inequality(
f, symbol, relational=False).intersection(domain)
except NotImplementedError:
result = ConditionSet(symbol, f, domain)
return result
if isinstance(f, (Expr, Number)):
if domain is S.Reals:
return solveset_real(f, symbol)
elif domain is S.Complexes:
return solveset_complex(f, symbol)
elif domain.is_subset(S.Reals):
return Intersection(solveset_real(f, symbol), domain)
else:
return Intersection(solveset_complex(f, symbol), domain)
def _set_function(f, x):
from sympy.functions.elementary.miscellaneous import Min, Max
from sympy.solvers.solveset import solveset
from sympy.core.function import diff, Lambda
from sympy.series import limit
from sympy.calculus.singularities import singularities
from sympy.sets import Complement
# TODO: handle functions with infinitely many solutions (eg, sin, tan)
# TODO: handle multivariate functions
expr = f.expr
if len(expr.free_symbols) > 1 or len(f.variables) != 1:
return
var = f.variables[0]
if expr.is_Piecewise:
result = S.EmptySet
domain_set = x
for (p_expr, p_cond) in expr.args:
if p_cond is true:
intrvl = domain_set
else:
intrvl = p_cond.as_set()
intrvl = Intersection(domain_set, intrvl)
if p_expr.is_Number:
image = FiniteSet(p_expr)
else:
image = imageset(Lambda(var, p_expr), intrvl)
result = Union(result, image)
# remove the part which has been `imaged`
domain_set = Complement(domain_set, intrvl)
if domain_set.is_EmptySet:
break
return result
if not x.start.is_comparable or not x.end.is_comparable:
return
try:
sing = [i for i in singularities(expr, var)
if i.is_real and i in x]
except NotImplementedError:
return
if x.left_open:
_start = limit(expr, var, x.start, dir="+")
elif x.start not in sing:
_start = f(x.start)
if x.right_open:
_end = limit(expr, var, x.end, dir="-")
elif x.end not in sing:
_end = f(x.end)
if len(sing) == 0:
solns = list(solveset(diff(expr, var), var))
extr = [_start, _end] + [f(i) for i in solns
if i.is_real and i in x]
start, end = Min(*extr), Max(*extr)
left_open, right_open = False, False
if _start <= _end:
# the minimum or maximum value can occur simultaneously
# on both the edge of the interval and in some interior
# point
if start == _start and start not in solns:
left_open = x.left_open
if end == _end and end not in solns:
right_open = x.right_open
else:
if start == _end and start not in solns:
left_open = x.right_open
if end == _start and end not in solns:
right_open = x.left_open
return Interval(start, end, left_open, right_open)
else:
return imageset(f, Interval(x.start, sing[0],
x.left_open, True)) + \
Union(*[imageset(f, Interval(sing[i], sing[i + 1], True, True))
for i in range(0, len(sing) - 1)]) + \
imageset(f, Interval(sing[-1], x.end, True, x.right_open))
def solve_decomposition(f, symbol, domain):
"""
Function to solve equations via the principle of "Decomposition
and Rewriting".
Examples
========
>>> from sympy import exp, sin, Symbol, pprint, S
>>> from sympy.solvers.solveset import solve_decomposition as sd
>>> x = Symbol('x')
>>> f1 = exp(2*x) - 3*exp(x) + 2
>>> sd(f1, x, S.Reals)
{0, log(2)}
>>> f2 = sin(x)**2 + 2*sin(x) + 1
>>> pprint(sd(f2, x, S.Reals), use_unicode=False)
3*pi
{2*n*pi + ---- | n in Integers()}
2
>>> f3 = sin(x + 2)
>>> pprint(sd(f3, x, S.Reals), use_unicode=False)
{2*n*pi - 2 | n in Integers()} U {pi*(2*n + 1) - 2 | n in Integers()}
"""
from sympy.solvers.decompogen import decompogen
from sympy.calculus.util import function_range
# decompose the given function
g_s = decompogen(f, symbol)
# `y_s` represents the set of values for which the function `g` is to be
# solved.
# `solutions` represent the solutions of the equations `g = y_s` or
# `g = 0` depending on the type of `y_s`.
# As we are interested in solving the equation: f = 0
y_s = FiniteSet(0)
for g in g_s:
frange = function_range(g, symbol, domain)
y_s = Intersection(frange, y_s)
result = S.EmptySet
if isinstance(y_s, FiniteSet):
for y in y_s:
solutions = solveset(Eq(g, y), symbol, domain)
if not isinstance(solutions, ConditionSet):
result += solutions
else:
if isinstance(y_s, ImageSet):
iter_iset = (y_s, )
elif isinstance(y_s, Union):
iter_iset = y_s.args
for iset in iter_iset:
new_solutions = solveset(Eq(iset.lamda.expr, g), symbol,
domain)
dummy_var = tuple(iset.lamda.expr.free_symbols)[0]
base_set = iset.base_set
if isinstance(new_solutions, FiniteSet):
new_exprs = new_solutions
elif isinstance(new_solutions, Intersection):
if isinstance(new_solutions.args[1], FiniteSet):
new_exprs = new_solutions.args[1]
for new_expr in new_exprs:
result += ImageSet(Lambda(dummy_var, new_expr), base_set)
if result is S.EmptySet:
return ConditionSet(symbol, Eq(f, 0), domain)
y_s = result
return y_s
def _set_function(f, x): # noqa:F811
# If the function is invertible, intersect the maps of the sets.
if is_function_invertible_in_set(f, x):
return Intersection(*(imageset(f, arg) for arg in x.args))
else:
return ImageSet(Lambda(_x, f(_x)), x)