def _arc(p,q,s,**kwds):
#rewrite this to use polar_plot and get points to do filled triangles
from sage.misc.functional import det
from sage.plot.line import line
from sage.misc.functional import norm
from sage.symbolic.all import pi
from sage.plot.arc import arc
p,q,s = map( lambda x: vector(x), [p,q,s])
# to avoid running into division by 0 we set to be colinear vectors that are
# almost colinear
if abs(det(matrix([p-s,q-s])))<0.01:
return line((p,q),**kwds)
(cx,cy)=var('cx','cy')
equations=[
2*cx*(s[0]-p[0])+2*cy*(s[1]-p[1]) == s[0]**2+s[1]**2-p[0]**2-p[1]**2,
2*cx*(s[0]-q[0])+2*cy*(s[1]-q[1]) == s[0]**2+s[1]**2-q[0]**2-q[1]**2
]
c = vector( [solve( equations, (cx,cy), solution_dict=True )[0][i] for i in [cx,cy]] )
r = norm(p-c)
a_p, a_q, a_s = map(lambda x: atan2(x[1],x[0]), [p-c,q-c,s-c])
a_p, a_q = sorted([a_p,a_q])
if a_s < a_p or a_s > a_q:
return arc( c, r, angle=a_q, sector=(0,2*pi-a_q+a_p), **kwds)
return arc( c, r, angle=a_p, sector=(0,a_q-a_p), **kwds)
def _arc(p, q, s, **kwds):
#rewrite this to use polar_plot and get points to do filled triangles
from sage.misc.functional import det
from sage.plot.line import line
from sage.misc.functional import norm
from sage.symbolic.all import pi
from sage.plot.arc import arc
p, q, s = map(lambda x: vector(x), [p, q, s])
# to avoid running into division by 0 we set to be colinear vectors that are
# almost colinear
if abs(det(matrix([p - s, q - s]))) < 0.01:
return line((p, q), **kwds)
(cx, cy) = var('cx', 'cy')
equations = [
2 * cx * (s[0] - p[0]) + 2 * cy * (s[1] - p[1]) == s[0]**2 + s[1]**2 -
p[0]**2 - p[1]**2, 2 * cx * (s[0] - q[0]) + 2 * cy *
(s[1] - q[1]) == s[0]**2 + s[1]**2 - q[0]**2 - q[1]**2
]
c = vector([
solve(equations, (cx, cy), solution_dict=True)[0][i] for i in [cx, cy]
])
r = norm(p - c)
a_p, a_q, a_s = map(lambda x: atan2(x[1], x[0]), [p - c, q - c, s - c])
a_p, a_q = sorted([a_p, a_q])
if a_s < a_p or a_s > a_q:
return arc(c, r, angle=a_q, sector=(0, 2 * pi - a_q + a_p), **kwds)
return arc(c, r, angle=a_p, sector=(0, a_q - a_p), **kwds)
def refresh(vars):
"""
Return a list of replacement of given variables with fresh ones.
"""
if len(vars) == 0:
return []
elif len(vars) == 1:
vars = list(vars) * 2
return solve([x == x for x in vars], list(vars))[0]
def inverse(self, chartname1=None, chartname2=None):
r"""
Returns the inverse diffeomorphism.
INPUT:
- ``chartname1`` -- (default: None) string defining the chart in which
the computation of the inverse is performed; if none is provided, the
default chart of self.manifold1 will be used
- ``chartname2`` -- (default: None) string defining the chart in which
the computation of the inverse is performed; if none is provided, the
default chart of self.manifold2 will be used
OUTPUT:
- the inverse diffeomorphism
EXAMPLES:
The inverse of a rotation in the plane::
sage: m = Manifold(2, "plane")
sage: c_cart = Chart(m, 'x y', 'cart')
sage: # A pi/3 rotation around the origin:
sage: rot = Diffeomorphism(m, m, ((x - sqrt(3)*y)/2, (sqrt(3)*x + y)/2))
sage: p = Point(m,(1,2))
sage: q = rot(p)
sage: irot = rot.inverse()
sage: p1 = irot(q)
sage: p1 == p
True
"""
from sage.symbolic.ring import SR
from sage.symbolic.relation import solve
from utilities import simplify_chain
if self._inverse is not None:
return self._inverse
if chartname1 is None: chartname1 = self.manifold1.def_chart.name
if chartname2 is None: chartname2 = self.manifold2.def_chart.name
coord_map = self.coord_expression[(chartname1, chartname2)]
chart1 = self.manifold1.atlas[chartname1]
chart2 = self.manifold2.atlas[chartname2]
n1 = len(chart1.xx)
n2 = len(chart2.xx)
# New symbolic variables (different from chart2.xx to allow for a
# correct solution even when chart2 = chart1):
x2 = [ SR.var('xxxx' + str(i)) for i in range(n2) ]
equations = [x2[i] == coord_map.functions[i] for i in range(n2) ]
solutions = solve(equations, chart1.xx, solution_dict=True)
if len(solutions) == 0:
raise ValueError("No solution found")
if len(solutions) > 1:
raise ValueError("Non-unique solution found")
#!# This should be the Python 2.7 form:
# substitutions = {x2[i]: chart2.xx[i] for i in range(n2)}
#
# Here we use a form compatible with Python 2.6:
substitutions = dict([(x2[i], chart2.xx[i]) for i in range(n2)])
inv_functions = [solutions[0][chart1.xx[i]].subs(substitutions)
for i in range(n1)]
for i in range(n1):
x = inv_functions[i]
try:
inv_functions[i] = simplify_chain(x)
except AttributeError:
pass
self._inverse = Diffeomorphism(self.manifold2, self.manifold1,
inv_functions, chartname2, chartname1)
return self._inverse
def inverse(self, chart1=None, chart2=None):
r"""
Return the inverse diffeomorphism.
INPUT:
- ``chart1`` -- (default: None) chart in which the computation of the
inverse is performed if necessary; if none is provided, the default
chart of the start domain will be used
- ``chart2`` -- (default: None) chart in which the computation of the
inverse is performed if necessary; if none is provided, the default
chart of the arrival domain will be used
OUTPUT:
- the inverse diffeomorphism
EXAMPLES:
The inverse of a rotation in the Euclidean plane::
sage: m = Manifold(2, 'R^2', r'\RR^2')
sage: c_cart.<x,y> = m.chart('x y')
sage: # A pi/3 rotation around the origin:
sage: rot = Diffeomorphism(m, m, ((x - sqrt(3)*y)/2, (sqrt(3)*x + y)/2), name='R')
sage: rot.inverse()
diffeomorphism 'R^(-1)' on the 2-dimensional manifold 'R^2'
sage: rot.inverse().view()
R^(-1): R^2 --> R^2, (x, y) |--> (1/2*sqrt(3)*y + 1/2*x, -1/2*sqrt(3)*x + 1/2*y)
Checking that applying successively the diffeomorphism and its
inverse results in the identity::
sage: (a, b) = var('a b')
sage: p = Point(m, (a,b)) # a generic point on M
sage: q = rot(p)
sage: p1 = rot.inverse()(q)
sage: p1 == p
True
"""
from sage.symbolic.ring import SR
from sage.symbolic.relation import solve
from utilities import simplify_chain
if self._inverse is not None:
return self._inverse
if chart1 is None: chart1 = self.domain1.def_chart
if chart2 is None: chart2 = self.domain2.def_chart
coord_map = self.coord_expression[(chart1, chart2)]
n1 = len(chart1.xx)
n2 = len(chart2.xx)
# New symbolic variables (different from chart2.xx to allow for a
# correct solution even when chart2 = chart1):
x2 = [ SR.var('xxxx' + str(i)) for i in range(n2) ]
equations = [ x2[i] == coord_map.functions[i].express
for i in range(n2) ]
solutions = solve(equations, chart1.xx, solution_dict=True)
if len(solutions) == 0:
raise ValueError("No solution found")
if len(solutions) > 1:
raise ValueError("Non-unique solution found")
#!# This should be the Python 2.7 form:
# substitutions = {x2[i]: chart2.xx[i] for i in range(n2)}
#
# Here we use a form compatible with Python 2.6:
substitutions = dict([(x2[i], chart2.xx[i]) for i in range(n2)])
inv_functions = [solutions[0][chart1.xx[i]].subs(substitutions)
for i in range(n1)]
for i in range(n1):
x = inv_functions[i]
try:
inv_functions[i] = simplify_chain(x)
except AttributeError:
pass
if self.name is None:
name = None
else:
name = self.name + '^(-1)'
if self.latex_name is None:
latex_name = None
else:
latex_name = self.latex_name + r'^{-1}'
self._inverse = Diffeomorphism(self.domain2, self.domain1,
inv_functions, chart2, chart1,
name=name, latex_name=latex_name)
return self._inverse
def edges_intersection(P, i, cmatrix=None):
r"""Return the point in the plane where the edges adjacent to the input edge intersect.
INPUT:
``P`` - a polygon (Polyhedron in 2d).
``i`` - integer, index of edge in ``P.inequalities_list()``.
``cmatrix`` - (optional) if None, the constraints matrix corresponding to P is computed inside the function.
OUTPUT:
``p`` - coordinates of the intersection of the edges that are adjacent to i.
``neighbor_constraints`` - indices of the edges that are adjacent to it. The edges are indexed according to P.inequalities_list().
NOTES:
- This has been tested for P in QQ and RDF.
"""
from sage.symbolic.ring import SR
from sage.symbolic.relation import solve
if cmatrix is None:
cmatrix = vertex_connections(P)
got_QQ = True if P.base_ring() == QQ else False
#constraint_i = P.inequalities_list()[i]
neighbor_constraints = []
# vertices associated to the given edge i
vert_i = cmatrix[i]
for j, cj in enumerate(cmatrix):
if (vert_i[0] in cj or vert_i[1] in cj) and (j != i):
neighbor_constraints.append(j)
# first one
constr_1 = P.inequalities_list()[neighbor_constraints[0]]
# second one
constr_2 = P.inequalities_list()[neighbor_constraints[1]]
# write and solve the intersection of the two lines
x1 = SR.var('x1')
x2 = SR.var('x2')
eq1 = constr_1[1] * x1 + constr_1[2] * x2 == -constr_1[0]
eq2 = constr_2[1] * x1 + constr_2[2] * x2 == -constr_2[0]
p = solve([eq1, eq2], x1, x2)
p = [p[0][0].right_hand_side(), p[0][1].right_hand_side()]
if not got_QQ: # assuming RDF
# transform to RDF (because solve produces in general rational answers)
p = [RDF(pi) for pi in p]
return p, neighbor_constraints
def inverse(self, chartname1=None, chartname2=None):
r"""
Returns the inverse diffeomorphism.
INPUT:
- ``chartname1`` -- (default: None) string defining the chart in which
the computation of the inverse is performed; if none is provided, the
default chart of self.manifold1 will be used
- ``chartname2`` -- (default: None) string defining the chart in which
the computation of the inverse is performed; if none is provided, the
default chart of self.manifold2 will be used
OUTPUT:
- the inverse diffeomorphism
EXAMPLES:
The inverse of a rotation in the plane::
sage: m = Manifold(2, "plane")
sage: c_cart = Chart(m, 'x y', 'cart')
sage: # A pi/3 rotation around the origin:
sage: rot = Diffeomorphism(m, m, ((x - sqrt(3)*y)/2, (sqrt(3)*x + y)/2))
sage: p = Point(m,(1,2))
sage: q = rot(p)
sage: irot = rot.inverse()
sage: p1 = irot(q)
sage: p1 == p
True
"""
from sage.symbolic.ring import SR
from sage.symbolic.relation import solve
from utilities import simplify_chain
if self._inverse is not None:
return self._inverse
if chartname1 is None: chartname1 = self.manifold1.def_chart.name
if chartname2 is None: chartname2 = self.manifold2.def_chart.name
coord_map = self.coord_expression[(chartname1, chartname2)]
chart1 = self.manifold1.atlas[chartname1]
chart2 = self.manifold2.atlas[chartname2]
n1 = len(chart1.xx)
n2 = len(chart2.xx)
# New symbolic variables (different from chart2.xx to allow for a
# correct solution even when chart2 = chart1):
x2 = [SR.var('xxxx' + str(i)) for i in range(n2)]
equations = [x2[i] == coord_map.functions[i] for i in range(n2)]
solutions = solve(equations, chart1.xx, solution_dict=True)
if len(solutions) == 0:
raise ValueError("No solution found")
if len(solutions) > 1:
raise ValueError("Non-unique solution found")
#!# This should be the Python 2.7 form:
# substitutions = {x2[i]: chart2.xx[i] for i in range(n2)}
#
# Here we use a form compatible with Python 2.6:
substitutions = dict([(x2[i], chart2.xx[i]) for i in range(n2)])
inv_functions = [
solutions[0][chart1.xx[i]].subs(substitutions) for i in range(n1)
]
for i in range(n1):
x = inv_functions[i]
try:
inv_functions[i] = simplify_chain(x)
except AttributeError:
pass
self._inverse = Diffeomorphism(self.manifold2, self.manifold1,
inv_functions, chartname2, chartname1)
return self._inverse
def _compute_init_vector(self, point, pt0, pr0, pth0, pph0, r_increase,
th_increase, verbose):
r"""
Computes the initial 4-momentum vector `p` from the constants of motion
"""
BLchart = self._spacetime.boyer_lindquist_coordinates()
basis = BLchart.frame().at(point)
r, th = BLchart(point)[1:3]
a, m = self._a, self._m
r2 = r**2
a2 = a**2
rho2 = r2 + (a * cos(th))**2
Delta = r2 - 2 * m * r + a2
if pt0 is None:
if (self._mu is not None and pr0 is not None and pth0 is not None
and pph0 is not None):
xxx = SR.var('xxx')
v = self._spacetime.tangent_space(point)(
(xxx, pr0, pth0, pph0), basis=basis)
muv2 = -self._spacetime.metric().at(point)(v, v)
muv2 = muv2.substitute(self._numerical_substitutions())
solutions = solve(muv2 == self._mu**2, xxx, solution_dict=True)
if verbose:
print("Solutions for p^t:")
pretty_print(solutions)
for sol in solutions:
if sol[xxx] > 0:
pt0 = sol[xxx]
break
else: # pt0 <= 0 might occur in the ergoregion
pt0 = solutions[0][xxx]
try:
pt0 = RR(pt0)
except TypeError: # pt0 contains some symbolic expression
pass
else:
if self._E is None:
raise ValueError("the constant E must be provided")
if self._L is None:
raise ValueError("the constant L must be provided")
E, L = self._E, self._L
pt0 = ((r2 + a2) / Delta * ((r2 + a2) * E - a * L) + a *
(L - a * E * sin(th)**2)) / rho2
if pph0 is None:
if self._E is None:
raise ValueError("the constant E must be provided")
if self._L is None:
raise ValueError("the constant L must be provided")
E, L = self._E, self._L
pph0 = (L / sin(th)**2 - a * E + a / Delta *
((r2 + a2) * E - a * L)) / rho2
if pr0 is None:
if self._E is None:
raise ValueError("the constant E must be provided")
if self._L is None:
raise ValueError("the constant L must be provided")
if self._mu is None:
raise ValueError("the constant mu must be provided")
if self._Q is None:
raise ValueError("the constant Q must be provided")
E, L, Q = self._E, self._L, self._Q
mu2 = self._mu**2
E2_mu2 = E**2 - mu2
pr0 = sqrt((E2_mu2) * r**4 + 2 * m * mu2 * r**3 +
(a2 * E2_mu2 - L**2 - Q) * r**2 + 2 * m *
(Q + (L - a * E)**2) * r - a2 * Q) / rho2
if not r_increase:
pr0 = -pr0
if pth0 is None:
if self._E is None:
raise ValueError("the constant E must be provided")
if self._L is None:
raise ValueError("the constant L must be provided")
if self._mu is None:
raise ValueError("the constant mu must be provided")
if self._Q is None:
raise ValueError("the constant Q must be provided")
E2 = self._E**2
L2 = self._L**2
mu2 = self._mu**2
Q = self._Q
pth0 = sqrt(Q + cos(th)**2 * (a2 *
(E2 - mu2) - L2 / sin(th)**2)) / rho2
if not th_increase:
pth0 = -pth0
return self._spacetime.tangent_space(point)((pt0, pr0, pth0, pph0),
basis=basis,
name='p')
def find(expressions, vars, conditions=None, solver=None):
"""
Generate assignments of values to variables
such that the values of the expressions are integral
and subject to the specified lower and upper bounds.
Assumes that the expressions and conditions are linear in the variables.
"""
expressions = dict(expressions)
conditions = set() if conditions is None else set(conditions)
vars = set(vars)
for c in set(conditions):
if verify(c):
conditions.discard(c)
elif verify(c.negation()):
return
if len(vars) > 0:
_, opt = min((Infinity if None in (u, l) else u - l, e)
for e, (l, u) in expressions.items()
if not is_constant(e))
_, x = min((abs(opt.coefficient(y)), y) for y in variables(opt))
s = symbol("__opt")
xsol = solve(s == opt, x)[0]
rest = vars - {x}
zero = [z == 0 for z in vars]
lp = MixedIntegerLinearProgram(maximization=False, solver=solver)
v = lp.new_variable(real=True)
w = lp.new_variable(integer=True)
lp.add_constraint(lp[1] == 1)
def makeLPExpression(e):
return sum(e.coefficient(y) * v[str(y)] for y in vars) \
+ e.subs(zero) * lp[1]
lpopt = makeLPExpression(opt)
def addCondition(c):
op = c.operator()
if op is operator.gt:
op = operator.ge
elif op is operator.lt:
op = operator.le
elif op not in [operator.eq, operator.ge, operator.le]:
return
lp.add_constraint(
op(makeLPExpression(c.lhs()), makeLPExpression(c.rhs())))
else:
x = None
delete = set()
for i, (e, (l, u)) in enumerate(expressions.items()):
if is_constant(e):
try:
integralize(e)
except TypeError:
return
if e < l or e > u:
return
delete.add(e)
continue
elif x is None:
return
lp.add_constraint(w[i] == makeLPExpression(e))
lp.set_min(w[i], l)
lp.set_max(w[i], u)
for e in delete:
del expressions[e]
if x is None:
yield ()
return
for c in conditions:
addCondition(c)
lp.set_objective(-lpopt)
try:
vmax = round(-lp.solve())
except MIPSolverException as ex:
if len(ex.args) == 0 or 'feasible' in ex.args[0]:
return
lp.set_objective(lpopt)
vnew = vmin = round(lp.solve())
while vmin <= vmax:
eq = xsol.subs(s == vmin)
g = find(make_expressions(
(e.subs(eq), l, u) for e, (l, u) in expressions.items()),
vars=rest,
conditions={c.subs(eq)
for c in conditions})
try:
while vnew == vmin:
sol = next(g)
t = (yield (eq.subs(sol), ) + sol)
while t is not None:
b, c = t
if b:
t = (yield find(expressions,
vars=vars,
conditions=conditions | {c}))
else:
if c in conditions or verify(c):
t = yield
continue
elif verify(c.negation()):
return
conditions.add(c)
addCondition(c)
lp.set_objective(-lpopt)
try:
vmax = round(-lp.solve())
except MIPSolverException:
return
lp.set_objective(lpopt)
vnew = round(lp.solve())
if vnew == vmin:
g.send((False, c.subs(eq)))
t = yield
vmin = vnew
except StopIteration:
vmin += 1
vnew = vmin
finally:
g.close()
def cluster_expansion(self, beta):
if beta == 0:
return dict()
coefficients=beta.monomial_coefficients()
if any ( x < 0 for x in coefficients.values() ):
alpha = [ -x for x in self.initial_cluster() ]
negative_part = dict( [(-alpha[x],-coefficients[x]) for x in
coefficients if coefficients[x] < 0 ] )
positive_part = sum( [ coefficients[x]*alpha[x] for x in
coefficients if coefficients[x] > 0 ] )
return dict( negative_part.items() +
self.cluster_expansion(positive_part).items() )
if self.is_affine():
if self.gamma().associated_coroot().scalar(beta) < 0:
shifted_expansion = self.cluster_expansion( self.tau_c()(beta) )
return dict( [ (self.tau_c_inverse()(x),shifted_expansion[x]) for x in
shifted_expansion ] )
elif self.gamma().associated_coroot().scalar(beta) > 0:
shifted_expansion = self.cluster_expansion( self.tau_c_inverse()(beta) )
return dict( [ (self.tau_c()(x),shifted_expansion[x]) for x in
shifted_expansion ] )
else:
###
# Assumptions
#
# Two cases are possible for vectors in the interior of the cone
# according to how many tubes there are:
# 1) If there is only one tube then its extremal rays are linearly
# independent, therefore a point is in the interior of the cone
# if and only if it is a linear combination of all the extremal
# rays with strictly positive coefficients. In this case solve()
# should produce only one solution.
# 2) If there are two or three tubes then the extreme rays are
# linearly dependent. A vector is in the interior of the cone if
# and only if it can be written as a strictly positive linear
# combination of all the rays of at least one tube. In this case
# solve() should return at least two solutions.
#
# If a vector is on one face of the cone than it can be written
# uniquely as linear combination of the rays of that face (they
# are linearly independent). solve() should return only one
# solution no matter how many tubes there are.
rays = flatten([ t[0] for t in self.affine_tubes() ])
system = matrix( map( vector, rays ) ).transpose()
x = vector( var ( ['x%d'%i for i in range(len(rays))] ) )
eqs = [ (system*x)[i] == vector(beta)[i] for i in
range(self._n)]
ieqs = [ y >= 0 for y in x ]
solutions = solve( eqs+ieqs, x, solution_dict=True )
if not solutions:
# we are outside the cone
shifted_expansion = self.cluster_expansion( self.tau_c()(beta) )
return dict( [ (self.tau_c_inverse()(v),shifted_expansion[v]) for v in
shifted_expansion ] )
if len(solutions) > 1 or all( v > 0 for v in solutions[0].values() ):
# we are in the interior of the cone
raise ValueError("Vectors in the interior of the cone do "
"not have a cluster expansion")
# we are on the boundary of the cone
solution_dict=dict( [(rays[i],solutions[0][x[i]]) for i in range(len(rays)) ] )
tube_bases = [ t[0] for t in self.affine_tubes() ]
connected_components = []
index = 0
for t in tube_bases:
component = []
for a in t:
if solution_dict[a] == 0:
if component:
connected_components.append( component )
component = []
else:
component.append( (a,solution_dict[a]) )
if component:
if connected_components:
connected_components[index] = ( component +
connected_components[index] )
else:
connected_components.append( component )
index = len(connected_components)
expansion = dict()
while connected_components:
component = connected_components.pop()
c = min( [ a[1] for a in component] )
expansion[sum( [a[0] for a in component])] = c
component = [ (a[0],a[1]-c) for a in component ]
new_component = []
for a in component:
if a[1] == 0:
if new_component:
connected_components.append( new_component )
new_component = []
else:
new_component.append( a )
if new_component:
connected_components.append( new_component )
return expansion
if self.is_finite():
shifted_expansion = self.cluster_expansion( self.tau_c()(beta) )
return dict( [ (self.tau_c_inverse()(x),shifted_expansion[x]) for x
in shifted_expansion ] )
def cluster_expansion(self, beta):
if beta == 0:
return dict()
coefficients = beta.monomial_coefficients()
if any(x < 0 for x in coefficients.values()):
alpha = [-x for x in self.initial_cluster()]
negative_part = dict([(-alpha[x], -coefficients[x])
for x in coefficients
if coefficients[x] < 0])
positive_part = sum([
coefficients[x] * alpha[x] for x in coefficients
if coefficients[x] > 0
])
return dict(negative_part.items() +
self.cluster_expansion(positive_part).items())
if self.is_affine():
if self.gamma().associated_coroot().scalar(beta) < 0:
shifted_expansion = self.cluster_expansion(self.tau_c()(beta))
return dict([(self.tau_c_inverse()(x), shifted_expansion[x])
for x in shifted_expansion])
elif self.gamma().associated_coroot().scalar(beta) > 0:
shifted_expansion = self.cluster_expansion(
self.tau_c_inverse()(beta))
return dict([(self.tau_c()(x), shifted_expansion[x])
for x in shifted_expansion])
else:
###
# Assumptions
#
# Two cases are possible for vectors in the interior of the cone
# according to how many tubes there are:
# 1) If there is only one tube then its extremal rays are linearly
# independent, therefore a point is in the interior of the cone
# if and only if it is a linear combination of all the extremal
# rays with strictly positive coefficients. In this case solve()
# should produce only one solution.
# 2) If there are two or three tubes then the extreme rays are
# linearly dependent. A vector is in the interior of the cone if
# and only if it can be written as a strictly positive linear
# combination of all the rays of at least one tube. In this case
# solve() should return at least two solutions.
#
# If a vector is on one face of the cone than it can be written
# uniquely as linear combination of the rays of that face (they
# are linearly independent). solve() should return only one
# solution no matter how many tubes there are.
rays = flatten([t[0] for t in self.affine_tubes()])
system = matrix(map(vector, rays)).transpose()
x = vector(var(['x%d' % i for i in range(len(rays))]))
eqs = [(system * x)[i] == vector(beta)[i]
for i in range(self._n)]
ieqs = [y >= 0 for y in x]
solutions = solve(eqs + ieqs, x, solution_dict=True)
if not solutions:
# we are outside the cone
shifted_expansion = self.cluster_expansion(
self.tau_c()(beta))
return dict([(self.tau_c_inverse()(v),
shifted_expansion[v])
for v in shifted_expansion])
if len(solutions) > 1 or all(v > 0
for v in solutions[0].values()):
# we are in the interior of the cone
raise ValueError("Vectors in the interior of the cone do "
"not have a cluster expansion")
# we are on the boundary of the cone
solution_dict = dict([(rays[i], solutions[0][x[i]])
for i in range(len(rays))])
tube_bases = [t[0] for t in self.affine_tubes()]
connected_components = []
index = 0
for t in tube_bases:
component = []
for a in t:
if solution_dict[a] == 0:
if component:
connected_components.append(component)
component = []
else:
component.append((a, solution_dict[a]))
if component:
if connected_components:
connected_components[index] = (
component + connected_components[index])
else:
connected_components.append(component)
index = len(connected_components)
expansion = dict()
while connected_components:
component = connected_components.pop()
c = min([a[1] for a in component])
expansion[sum([a[0] for a in component])] = c
component = [(a[0], a[1] - c) for a in component]
new_component = []
for a in component:
if a[1] == 0:
if new_component:
connected_components.append(new_component)
new_component = []
else:
new_component.append(a)
if new_component:
connected_components.append(new_component)
return expansion
if self.is_finite():
shifted_expansion = self.cluster_expansion(self.tau_c()(beta))
return dict([(self.tau_c_inverse()(x), shifted_expansion[x])
for x in shifted_expansion])
