def test_diagonal_locations(self):
# tests the helper function `diagonal_locations` which identifies where
# the diagonal elements or blocks occur in the H matrix
H = Matrix(GF(2), [[0, 0, 0], [0, 0, 0], [0, 0, 0]])
index_one, index_B = diagonal_locations(H)
self.assertEqual(index_one, 3)
self.assertEqual(index_B, -1)
H = Matrix(GF(2), [[1, 0, 0], [0, 0, 0], [0, 0, 0]])
index_one, index_B = diagonal_locations(H)
self.assertEqual(index_one, 0)
self.assertEqual(index_B, -1)
H = Matrix(GF(2), [[0, 1, 0], [1, 0, 0], [0, 0, 0]])
index_one, index_B = diagonal_locations(H)
self.assertEqual(index_one, 3)
self.assertEqual(index_B, 0)
H = Matrix(GF(2), [[0, 1, 0], [1, 0, 0], [0, 0, 1]])
index_one, index_B = diagonal_locations(H)
self.assertEqual(index_one, 2)
self.assertEqual(index_B, 0)
H = Matrix(GF(2), [[1, 0, 0], [0, 0, 1], [0, 1, 0]])
index_one, index_B = diagonal_locations(H)
self.assertEqual(index_one, 0)
self.assertEqual(index_B, 1)
def attack(p, t, a, B):
"""
Solves the hidden number problem using an attack based on the shortest vector problem.
The hidden number problem is defined as finding y such that {x_i - t_i * y + a_i = 0 mod p}.
More information: Breitner J., Heninger N., "Biased Nonce Sense: Lattice Attacks against Weak ECDSA Signatures in Cryptocurrencies"
:param p: the modulus
:param t: the t_i values
:param a: the a_i values
:param B: a bound on the x values
:return: a tuple containing y, and a list of x values
"""
assert len(t) == len(a), "t and a lists should be of equal length."
m = len(t)
M = Matrix(QQ, m + 2, m + 2)
for i in range(m):
M[i, i] = p
M[m] = t + [B / QQ(p), 0]
M[m + 1] = list(map(lambda x: x - B // 2, a)) + [0, B]
L = M.LLL()
for row in L.rows():
y = (int(row[m] * p) // B) % p
if y != 0 and row[m + 1] == B:
return int(y), list(map(lambda x: int(x + B // 2), row[:m]))
def TwoPointsMinusInfinity(self, P, Q): # Creates a divisor class of P + Q - g*\infty
maxlower = self.lower
xP, yP = self.Rextraprec(P[0]), self.Rextraprec(P[1])
xQ, yQ = self.Rextraprec(Q[0]), self.Rextraprec(Q[1])
assert (self.f(xP) - yP**2).abs() <= (yP**2).abs() * self.almostzero
assert (self.f(xQ) - yQ**2).abs() <= (yQ**2).abs() * self.almostzero
WPQ = Matrix(self.Rextraprec, self.nZ, 3 * self.g + 7) # H0(3D0 - g*\infty)
Ev = Matrix(self.Rextraprec, 2, 3 * self.g + 7) # basis of H0(3D0 - g*\infty) evaluated at P and Q
Exps=[]
for n in range( 2 * self.g + 4):
Exps.append((n,0))
if n > self.g:
Exps.append(( n - self.g - 1, 1))
for j in range( 3 * self.g + 7):
u, v = Exps[j]
for i in range( self.nZ ):
x, y = self.Z[i]
WPQ[ i, j ] = x**u * y**v
Ev[0,j] = xP**u * yP**v
Ev[1,j] = xQ**u * yQ**v
K, upper, lower = Kernel(Ev, 2)
assert self.threshold_check(upper, lower), "upper = %s lower = %s" % (RealField(35)(upper), RealField(35)(lower))
maxlower = max(maxlower, lower);
#Returns H0(3*D0 - P - Q - g infty)
# note that [P + Q + g infty - D0] ~ P + Q - infty
return (WPQ * K).change_ring(self.R), maxlower
def delta_rank(f):
"""Returns the Gamma-rank of the function with LUT f.
The Gamma-rank is the rank of the 2^{2n} \times 2^{2n} binary
matrix M defined by
M[x][y] = 1 if and only if x + y \in \Delta,
where \Delta is defined as
\Delta = \{ (a, b), DDT_f[a][b] == 2 \} ~.
"""
n = int(log(len(f), 2))
dim = 2**(2 * n)
d = ddt(f)
gamma = [(a << n) | b
for a, b in itertools.product(xrange(1, 2**n), xrange(0, 2**n))
if d[a][b] == 2]
mat_content = []
for x in xrange(0, dim):
row = [0 for j in xrange(0, dim)]
for y in gamma:
row[oplus(x, y)] = 1
mat_content.append(row)
mat_gf2 = Matrix(GF(2), dim, dim, mat_content)
return mat_gf2.rank()
def extract_basis(v, N):
"""Returns a subset of v such that the span of these elements is at
least as big as v. In particular, if v is a vector space, it returns a
base.
"""
dim = rank_of_vector_set(v, N)
i = 0
basis = []
while i < len(v) and v[i] == 0:
i += 1
if i == len(v):
return []
basis.append(v[i])
if dim == 1:
return basis
i += 1
r = Matrix(GF(2), 1, N, [tobin(x, N) for x in basis]).rank()
while r < dim and i < len(v):
new_basis = basis + [v[i]]
new_r = Matrix(GF(2), len(new_basis), N,
[tobin(x, N) for x in new_basis]).rank()
if new_r == dim:
return new_basis
elif new_r > r:
basis = new_basis
r = new_r
i += 1
return []
def test_lll(self):
# do a quick verification of the basis from cryptopals
B = Matrix(
QQ,
[[-_sage_const_2, _sage_const_0, _sage_const_2, _sage_const_0],
[
_sage_const_1 / _sage_const_2, -_sage_const_1, _sage_const_0,
_sage_const_0
],
[
-_sage_const_1, _sage_const_0, -_sage_const_2,
_sage_const_1 / _sage_const_2
], [-_sage_const_1, _sage_const_1, _sage_const_1, _sage_const_2]])
ex = Matrix(QQ, [[
_sage_const_1 / _sage_const_2, -_sage_const_1, _sage_const_0,
_sage_const_0
],
[
-_sage_const_1, _sage_const_0, -_sage_const_2,
_sage_const_1 / _sage_const_2
],
[
-_sage_const_1 / _sage_const_2, _sage_const_0,
_sage_const_1, _sage_const_2
],
[
-_sage_const_3 / _sage_const_2, -_sage_const_1,
_sage_const_2, _sage_const_0
]])
re = matrix_utils.LLL(B)
assert re == ex
def get_shift_and_width_for_coset(self, n):
r"""
Compute the shift and width of coset nr. n.
"""
if self._shift.has_key(n) and self._width.has_key(n):
return self._shift[n], self._width[n]
if not is_squarefree(self._level):
raise NotImplementedError, "Only square-free levels implemented"
G = Gamma0(self._level)
A = self.coset_rep(n)
a = A[0, 0]
c = A[1, 0]
width = G.cusp_width(Cusp(a, c))
if self._verbose > 0:
print "width=", width
Amat = Matrix(ZZ, 2, 2, A.matrix().list())
h = -1
for j in range(width):
Th = Matrix(ZZ, 2, 2, [width, -j, 0, 1])
W = Amat * Th
if self._verbose > 1:
print "W=", W
if self.is_involution(W):
h = j
break
if h < 0:
raise ArithmeticError, "Could not find shift!"
self._shift[n] = h
self._width[n] = width
return h, width
def matrix(self):
r"""
Return the persistence matrix of self.
EXAMPLES::
sage: from pGroupCohomology import CohomologyRing
sage: CohomologyRing.doctest_setup() # reset, block web access, use temporary workspace
sage: H = CohomologyRing(8,3)
sage: H.make()
sage: B = H.bar_code('UpperCentralSeries')
sage: B.matrix()
[ 1/(-t + 1) -1/(t^2 - 1) 1]
[ 0 1/(t^2 - 2*t + 1) (-t - 1)/(t - 1)]
[ 0 0 1/(t^2 - 2*t + 1)]
"""
from sage.all import Matrix
M = Matrix(2 * self._length + 1, 2 * self._length + 1)
for X in self._L.items():
if not (isinstance(X[1], int)
or M.base_ring().has_coerce_map_from(X[1].parent())):
M = M * X[1].parent()(1)
M[X[0][0] + self._length, X[0][1] + self._length] = X[1]
return M
def PointMinusInfinity(self, P, sign = 0):
# Creates a divisor class of P- \inty_{(-1)**sign}
maxlower = self.lower
assert self.f.degree() == 2*self.g + 2;
sqrtan = (-1)**sign * self.Rextraprec( sqrt( self.Rdoubleextraprec(self.an)) );
xP, yP = self.Rextraprec(P[0]),self.Rextraprec(P[1])
assert (self.f(xP) - yP**2).abs() <= (yP**2).abs() * self.almostzero
if xP != 0 and self.a0 != 0:
x0 = self.Rextraprec(0);
else:
x0 = xP
while x0 == xP:
x0 = self.Rextraprec(ZZ.random_element())
y0 = self.Rextraprec( sqrt( self.Rdoubleextraprec( self.f( x0 )))) # P0 = (x0, y0)
WP1 = Matrix(self.Rextraprec, self.nZ, 3 * self.g + 6) # H0(3D0 - P0 - g \infty) = H0(3D0 - P0 - g(\infty_{+} + \infty_{-}))
EvP = Matrix(self.Rextraprec, 1, 3 * self.g + 6) # the functions of H0(3D0-P0-g \infty) at P
B = Matrix(self.Rextraprec, self.nZ, 3 * self.g + 5) # H0(3D0 - \infty_{sign} - P0 - g\infty)
# adds the functions x - x0, (x - x0)**1, ..., (x - x0)**(2g+2) to it
for j in xrange(2 * self.g + 2 ):
for i, (x, y) in enumerate( self.Z ):
WP1[ i, j] = B[ i, j] = (x - x0) ** (j + 1)
EvP[ 0, j] = (xP - x0) ** (j + 1)
# adds y - y0
for i, (x, y) in enumerate( self.Z ):
WP1[ i, 2 * self.g + 2 ] = B[ i, 2 * self.g + 2] = y - y0
EvP[ 0, 2 * self.g + 2] = yP - y0
# adds (x - x0) * y ... (x-x0)**(g+1)*y
for j in range(1, self.g + 2):
for i, (x,y) in enumerate( self.Z ):
WP1[ i, 2 * self.g + 2 + j ] = B[ i, 2 * self.g + 2 + j] = (x-x0)**j * y
EvP[ 0, 2 * self.g + 2 + j] = (xP - x0)**j * yP
# adds (x - x0)**(2g + 3) and y * (x - x0)**(g + 2)
for i,(x,y) in enumerate(self.Z):
WP1[ i, 3 * self.g + 4 ] = (x - x0)**( 2 * self.g + 3)
WP1[ i, 3 * self.g + 5 ] = y * (x - x0)**(self.g + 2)
B[ i, 3 * self.g + 4 ] = (x - x0)**( self.g + 2) * ( y - sqrtan * x**(self.g + 1) )
EvP[ 0, 3 * self.g + 4] = (xP - x0) ** ( 2 * self.g + 3)
EvP[ 0, 3 * self.g + 5] = yP * (xP - x0)**(self.g + 2)
# A = functions in H0(3D0-P0-g \infty) that vanish at P
# = H**0(3D0 - P0 - P -g \infty)
K, upper, lower = Kernel(EvP, EvP.ncols() - (3 * self.g + 5))
assert self.threshold_check(upper, lower), "upper = %s lower = %s" % (RealField(35)(upper), RealField(35)(lower))
maxlower = max(maxlower, lower);
A = WP1 * K
# A - B = P0 + P + g \infty - (\infty_{+} + P0 + g\infty) = P - \inty_{+}
res, lower = self.Sub(A,B)
maxlower = max(maxlower, lower);
return res.change_ring(self.R), maxlower;
def upper_bound_index_cusps_in_JG_torsion(G, d, bound=60):
"""
INPUT:
- G - a congruence subgroup
- d - integer, the size of the rational cuspidal subgroup
- bound (optional, default = 60) - the bound for the primes p up to which to use
the hecke matrix `T_p - <p> - p` for bounding the torsion subgroup
OUTPUT:
- an integer `i` such that `(\#J_G(\QQ)_{tors})/d` is a divisor of `i`.
EXAMPLES::
sage: from mdsage import *
sage: d = rational_cuspidal_classgroup(Gamma1(23)).cardinality()
sage: upper_bound_index_cusps_in_JG_torsion(Gamma1(23),d)
1
"""
N = G.level()
M = ModularSymbols(G)
Sint = cuspidal_integral_structure(M)
kill_mat = (M.star_involution().matrix().restrict(Sint) - 1)
kill = kill_mat.transpose().change_ring(ZZ).row_module()
for p in prime_range(3, bound):
if not N % p == 0:
kill += kill_torsion_coprime_to_q(
p, M).restrict(Sint).change_ring(ZZ).transpose().row_module()
if kill.matrix().is_square() and kill.matrix().determinant() == d:
#print p
break
kill_mat = kill.matrix().transpose()
#print N,"index of torsion in stuff killed",kill.matrix().determinant()/d
if kill.matrix().determinant() == d:
return 1
pm = integral_period_mapping(M)
period_images1 = [
sum([M.coordinate_vector(M([c, infinity])) for c in cusps]) * pm
for cusps in galois_orbits(G)
]
m = (Matrix(period_images1) * kill_mat).stack(kill_mat)
diag = m.change_ring(ZZ).echelon_form().diagonal()
#print diag,prod(diag)
assert prod(diag) == kill.matrix().determinant() / d
period_images2 = [
M.coordinate_vector(M([c, infinity])) * pm for c in G.cusps()
if c != Cusp(oo)
]
m = (Matrix(period_images2) * kill_mat).stack(kill_mat)
m, denom = m._clear_denom()
diag = (m.change_ring(ZZ).echelon_form() / denom).diagonal()
#print diag
#print prod(i.numerator() for i in diag),"if this is 1 then :)"
return prod(i.numerator() for i in diag)
def fractional_CRT_QQ(residues, ps_roots):
residues_ZZ = [ r.lift() for r in residues];
primes = [p for p, _ in ps_roots]
lift = CRT_list(residues_ZZ, primes);
N = prod(primes);
M = Matrix([[1 ,lift, N]]);
short_vector = Matrix(M.transpose().kernel().basis()).LLL()[0]
return -short_vector[0]/short_vector[1];
def __init__(self,M=None,genus_list=None):
#print genus_list
if M:
self.M = copy(M)
elif genus_list:
self.M = Matrix(StrataGraph.R,len(genus_list)+1,1,[-1]+genus_list)
else:
self.M = Matrix(StrataGraph.R,1,1,-1)
def __init__(self,
parent,
matrix,
inhomogeneous,
is_zero=lambda p: False,
relations=[]):
## Checking the input of matrix and vector
if (not SAGE_element.is_Matrix(matrix)):
matrix = Matrix(matrix)
if (not SAGE_element.is_Vector(inhomogeneous)):
inhomogeneous = vector(inhomogeneous)
if (isinstance(parent, Wrap_w_Sequence_Ring)):
parent = parent.base()
## Building the parent of the matrix and the vector
pmatrix = matrix.parent().base()
pinhom = inhomogeneous.parent().base()
## Setting the variables for the matrix, the parent and the vector
self.__parent = pushout(pmatrix, pinhom)
self.__matrix = matrix.change_ring(self.__parent)
self.__inhomogeneous = inhomogeneous.change_ring(self.__parent)
## Setting the parent for the solutions
if (not pushout(parent, self.__parent) == parent):
try:
self.__matrix = self.__matrix.change_ring(parent)
self.__inhomogeneous = self.__inhomogeneous.change_ring(parent)
self.__parent = parent
except:
raise TypeError(
"The parent for the solutions must be an extension of the parent for the input"
)
self.__solution_parent = parent
## Setting other variables
if (relations is None):
relations = []
self.__relations = [self.__parent(el) for el in relations]
try:
self.__gb = ideal(self.parent(), self.__relations).groebner_basis()
except AttributeError:
try:
self.__gb = [ideal(self.parent(), self.__relations).gen()]
except:
self.__gb = [0]
self.__is_zero = is_zero
## Creating the variables for the echelon form
self.__echelon = None
self.__transformation = None
## Creating the variables for the solutions
self.__solution = None
self.__syzygy = None
def from_nice_matrix(lists):
Mx = Matrix(StrataGraph.R, len(lists), len(lists[0]))
Mx[0, 0] = -1
Mx[0, :] = Matrix([lists[0]])
for v in range(1, len(lists)):
if lists[v][0] in ZZ:
Mx[v, 0] = lists[v][0]
continue
if lists[v][0].operator() == sage.symbolic.operators.add_vararg:
operands = lists[v][0].operands()
if len(operands) != 2:
raise Exception("Input error!")
genus = operands[1] #the genus
kappas = operands[0]
else:
kappas = lists[v][0]
genus = 0
if kappas.operator() == sage.symbolic.operators.mul_vararg:
for operand in kappas.operands():
Mx[v, 0] += StrataGraph._kappaSR_monom_to_X(operand)
Mx[v, 0] += genus
else:
Mx[v, 0] = StrataGraph._kappaSR_monom_to_X(kappas) + genus
X = StrataGraph.Xvar
for v in range(1, len(lists)):
for edge in range(1, len(lists[0])):
if lists[v][edge] in ZZ:
Mx[v, edge] = lists[v][edge]
else:
for operand in lists[v][edge].operands():
if operand in ZZ:
Mx[v, edge] += operand
elif operand.operator() is None:
#it is a ps or ps2
Mx[v, edge] += X
elif operand.operator() == operator.pow:
#it is ps^n or ps2^n
Mx[v, edge] += X * operand.operands()[1]
elif operand.operator(
) == sage.symbolic.operators.mul_vararg:
#it is a ps^n*ps2^m
op1, op2 = operand.operands()
if op1.operator() is None:
exp1 = 1
else:
exp1 = op1.operand()[1]
if op2.operator() == None:
exp2 = 1
else:
exp2 = op2.operand()[1]
if exp1 >= exp2:
Mx[v, edge] += exp1 * X + exp2 * X**2
else:
Mx[v, edge] += exp1 * X**2 + exp2 * X
return StrataGraph(Mx)
def __init__(self, M=None, genus_list=None):
#print genus_list
if M:
self.M = copy(M)
elif genus_list:
self.M = Matrix(StrataGraph.R,
len(genus_list) + 1, 1, [-1] + genus_list)
else:
self.M = Matrix(StrataGraph.R, 1, 1, -1)
def symmetrize_periods(Pa, Pb, tol=1e-4):
r"""Returns symmetric a- and b-periods `Pa_symm` and `Pb_symm`, as well as the
corresponding symplectic operator `Gamma` such that `Gamma [Pa \\ Pb] =
[Pa_symm \\ Pb_symm]`.
Parameters
----------
Pa : complex matrix
Pb : complex matrix
The a- and b-periods, respectively, of a genus `g` Riemann surface.
tol : double
(Default: 1e-4) Tolerance used to verify integrality of intermediate
matrices. Dependent on precision of period matrices.
Returns
-------
Gamma : integer matrix
The symplectic transformation operator.
Pa : complex matrix
Pb : complex matrix
Symmetric a- and b-periods, respectively, of a genus `g` Riemann surface.
Notes
-----
The algorithm described in Kalla, Klein actually operates on the transposes
of the a- and b-period matrices.
"""
# coerce from numpy, if necessary
if isinstance(Pa, numpy.ndarray):
Pa = Matrix(CDF, numpy.ascontiguousarray(Pa))
if isinstance(Pb, numpy.ndarray):
Pb = Matrix(CDF, numpy.ascontiguousarray(Pb))
# use the transposes of the period matrices and coerce to Sage matrices
Pa = Pa.T
Pb = Pb.T
# use above functions to obtain topological type matrix
g,g = Pa.dimensions()
R = involution_matrix(Pa, Pb, tol=tol)
S = integer_kernel_basis(R)
N1 = N1_matrix(Pa, Pb, S, tol=tol)
H,Q = symmetric_block_diagonalize(N1)
Gamma = symmetric_transformation_matrix(Pa, Pb, S, H, Q, tol=tol)
# compute the corresponding symmetric periods
stacked_periods = zero_matrix(CDF, 2*g, g)
stacked_periods[:g,:] = Pa
stacked_periods[g:,:] = Pb
stacked_symmetric_periods = Gamma*stacked_periods
Pa_symm = stacked_symmetric_periods[:g,:]
Pb_symm = stacked_symmetric_periods[g:,:]
# transpose results back
Pa_symm = Pa_symm.T
Pb_symm = Pb_symm.T
return Pa_symm, Pb_symm
def symmetrize_periods(Pa, Pb, tol=1e-4):
r"""Returns symmetric a- and b-periods `Pa_symm` and `Pb_symm`, as well as the
corresponding symplectic operator `Gamma` such that `Gamma [Pa \\ Pb] =
[Pa_symm \\ Pb_symm]`.
Parameters
----------
Pa : complex matrix
Pb : complex matrix
The a- and b-periods, respectively, of a genus `g` Riemann surface.
tol : double
(Default: 1e-4) Tolerance used to verify integrality of intermediate
matrices. Dependent on precision of period matrices.
Returns
-------
Gamma : integer matrix
The symplectic transformation operator.
Pa : complex matrix
Pb : complex matrix
Symmetric a- and b-periods, respectively, of a genus `g` Riemann surface.
Notes
-----
The algorithm described in Kalla, Klein actually operates on the transposes
of the a- and b-period matrices.
"""
# coerce from numpy, if necessary
if isinstance(Pa, numpy.ndarray):
Pa = Matrix(CDF, numpy.ascontiguousarray(Pa))
if isinstance(Pb, numpy.ndarray):
Pb = Matrix(CDF, numpy.ascontiguousarray(Pb))
# use the transposes of the period matrices and coerce to Sage matrices
Pa = Pa.T
Pb = Pb.T
# use above functions to obtain topological type matrix
g, g = Pa.dimensions()
R = involution_matrix(Pa, Pb, tol=tol)
S = integer_kernel_basis(R)
N1 = N1_matrix(Pa, Pb, S, tol=tol)
H, Q = symmetric_block_diagonalize(N1)
Gamma = symmetric_transformation_matrix(Pa, Pb, S, H, Q, tol=tol)
# compute the corresponding symmetric periods
stacked_periods = zero_matrix(CDF, 2 * g, g)
stacked_periods[:g, :] = Pa
stacked_periods[g:, :] = Pb
stacked_symmetric_periods = Gamma * stacked_periods
Pa_symm = stacked_symmetric_periods[:g, :]
Pb_symm = stacked_symmetric_periods[g:, :]
# transpose results back
Pa_symm = Pa_symm.T
Pb_symm = Pb_symm.T
return Pa_symm, Pb_symm
def as_polynomial_in_E4_and_E6(self,insert_in_db=True):
r"""
If self is on the full modular group writes self as a polynomial in E_4 and E_6.
OUTPUT:
-''X'' -- vector (x_1,...,x_n)
with f = Sum_{i=0}^{k/6} x_(n-i) E_6^i * E_4^{k/4-i}
i.e. x_i is the coefficient of E_6^(k/6-i)*
"""
if(self.level() != 1):
raise NotImplementedError("Only implemented for SL(2,Z). Need more generators in general.")
if(self._as_polynomial_in_E4_and_E6 is not None and self._as_polynomial_in_E4_and_E6 != ''):
return self._as_polynomial_in_E4_and_E6
d = self._parent.dimension_modular_forms() # dimension of space of modular forms
k = self.weight()
K = self.base_ring()
l = list()
# for n in range(d+1):
# l.append(self._f.q_expansion(d+2)[n])
# v=vector(l) # (self._f.coefficients(d+1))
v = vector(self.coefficients(range(d),insert_in_db=insert_in_db))
d = dimension_modular_forms(1, k)
lv = len(v)
if(lv < d):
raise ArithmeticError("not enough Fourier coeffs")
e4 = EisensteinForms(1, 4).basis()[0].q_expansion(lv + 2)
e6 = EisensteinForms(1, 6).basis()[0].q_expansion(lv + 2)
m = Matrix(K, lv, d)
lima = floor(k / 6) # lima=k\6;
if((lima - (k / 2)) % 2 == 1):
lima = lima - 1
poldeg = lima
col = 0
monomials = dict()
while(lima >= 0):
deg6 = ZZ(lima)
deg4 = (ZZ((ZZ(k / 2) - 3 * lima) / 2))
e6p = (e6 ** deg6)
e4p = (e4 ** deg4)
monomials[col] = [deg4, deg6]
eis = e6p * e4p
for i in range(1, lv + 1):
m[i - 1, col] = eis.coefficients()[i - 1]
lima = lima - 2
col = col + 1
if (col != d):
raise ArithmeticError("bug dimension")
# return [m,v]
if self._verbose > 0:
wmf_logger.debug("m={0}".format(m, type(m)))
wmf_logger.debug("v={0}".format(v, type(v)))
try:
X = m.solve_right(v)
except:
return ""
self._as_polynomial_in_E4_and_E6 = [poldeg, monomials, X]
return [poldeg, monomials, X]
def modular_univariate(f, N, m, t, X, early_return=True):
"""
Computes small modular roots of a univariate polynomial.
More information: May A., "New RSA Vulnerabilities Using Lattice Reduction Methods (Section 3.2)"
:param f: the polynomial
:param N: the modulus
:param m: the amount of g shifts to use
:param t: the amount of h shifts to use
:param X: an approximate bound on the roots
:param early_return: try to return as early as possible (default: true)
:return: a generator generating small roots of the polynomial
"""
f = f.monic().change_ring(ZZ)
x = f.parent().gen()
d = f.degree()
B = Matrix(ZZ, d * m + t)
row = 0
logging.debug("Generating g shifts...")
for i in range(m):
for j in range(d):
g = x**j * N**(m - i) * f**i
for col in range(row + 1):
B[row, col] = g(x * X)[col]
row += 1
logging.debug("Generating h shifts...")
for i in range(t):
h = x**i * f**m
h = h(x * X)
for col in range(row + 1):
B[row, col] = h[col]
row += 1
logging.debug("Executing the LLL algorithm...")
B = B.LLL()
logging.debug("Reconstructing polynomials...")
for row in range(B.nrows()):
new_polynomial = 0
for col in range(B.ncols()):
new_polynomial += B[row, col] * x**col
if new_polynomial.is_constant():
continue
new_polynomial = new_polynomial(x / X).change_ring(ZZ)
for x0, _ in new_polynomial.roots():
yield int(x0)
if early_return:
# Assuming that the first "good" polynomial in the lattice doesn't provide roots, we return.
return
def tu_decomposition(s, v, verbose=False):
"""Using the knowledge that v is a subspace of Z_s of dimension n, a
TU-decomposition (as defined in [Perrin17]) of s is performed and T
and U are returned.
"""
N = int(log(len(s), 2))
# building B
basis = extract_basis(v[0], N)
t = len(basis)
basis = complete_basis(basis, N)
B = Matrix(GF(2), N, N, [tobin(x, N) for x in reversed(basis)])
if verbose:
print "B= (rank={})\n{}".format(B.rank(), B.str())
# building A
basis = complete_basis(extract_basis(v[1], N), N)
A = Matrix(GF(2), N, N, [tobin(x, N) for x in reversed(basis)])
if verbose:
print "A= (rank={})\n{}".format(A.rank(), A.str())
# building linear equivalent s_prime
s_prime = [
apply_bin_mat(s[apply_bin_mat(x, A.inverse())], B)
for x in xrange(0, 2**N)
]
# TU decomposition of s'
T, U = get_tu_open(s_prime, t)
if verbose:
print "T=["
for i in xrange(0, 2**(N - t)):
print " {} {}".format(T[i], is_permutation(T[i]))
print "]\nU=["
for i in xrange(0, 2**t):
print " {} {}".format(U[i], is_permutation(U[i]))
print "]"
return T, U
def convert_magma_matrix_to_sage(m, K):
try:
return Matrix(K, m.sage());
except:
pass
from re import sub
text = str(m)
text = sub('\[\s+', '[', text)
text = sub('\s+', ' ', text)
text = '['+text.replace(' ',', ').replace('\n',', ')+']'
return Matrix(K, sage_eval(text, locals={'r': K.gens()}))
def involution_matrix(Pa, Pb, tol=1e-4):
r"""Returns the transformation matrix `R` corresponding to the anti-holomorphic
involution on the periods of the Riemann surface.
Given an aritrary `2g x g` period matrix `[Pa, Pb]^T` of a genus `g`
Riemann surface `X` the action of the anti-holomorphic involution on `X` of
these periods is given by left-multiplication by a `2g x 2g` matrix `R`.
That is, .. math::
[\tau P_a^T, \tau P_b^T]^T = R [P_a^T, P_b^T]^T
Parameters
----------
Pa : complex matrix
Pb : complex matrix
The a- and b-periods, respectively, of a genus `g` Riemann surface.
tol : double
(Default: 1e-4) Tolerance used to veryify integrality of transformation
matrix. Dependent on precision of period matrices.
Returns
-------
R : complex matrix
The anti-holomorphic involution matrix.
Todo
----
For numerical stability, replace matrix inversion with linear system
solves.
"""
g, g = Pa.dimensions()
R_RDF = Matrix(RDF, 2 * g, 2 * g)
Ig = identity_matrix(RDF, g)
M = Im(Pb.T) * Re(Pa) - Im(Pa.T) * Re(Pb)
Minv = M.inverse()
R_RDF[:g, :g] = (2 * Re(Pb) * Minv * Im(Pa.T) + Ig).T
R_RDF[:g, g:] = -2 * Re(Pa) * Minv * Im(Pa.T)
R_RDF[g:, :g] = 2 * Re(Pb) * Minv * Im(Pb.T)
R_RDF[g:, g:] = -(2 * Re(Pb) * Minv * Im(Pa.T) + Ig)
R = R_RDF.round().change_ring(ZZ)
# sanity check: make sure that R_RDF is close to integral. we perform this
# test here since the matrix returned should be over ZZ
error = (R_RDF.round() - R_RDF).norm()
if error > tol:
raise ValueError(
"The anti-holomorphic involution matrix is not "
"integral. Try increasing the precision of the input "
"period matrices."
)
return R
def get_ccz_equivalent_permutation(l, v0, v1, verbose=False):
N = int(log(len(l), 2))
mask = sum(int(1 << i) for i in xrange(0, N))
L = v0 + v1
L = Matrix(GF(2), 2 * N, 2 * N, [tobin(x, 2 * N) for x in L]).transpose()
if verbose:
print "\n\nrank={}\n{}".format(L.rank(), L.str())
new_l = [[0 for b in xrange(0, 2**N)] for a in xrange(0, 2**N)]
for a, b in itertools.product(xrange(0, 2**N), xrange(0, 2**N)):
v = apply_bin_mat((a << N) | b, L)
a_prime, b_prime = v >> N, v & mask
new_l[a][b] = l[a_prime][b_prime]
return invert_lat(new_l)
def involution_matrix(Pa, Pb, tol=1e-4):
r"""Returns the transformation matrix `R` corresponding to the anti-holomorphic
involution on the periods of the Riemann surface.
Given an aritrary `2g x g` period matrix `[Pa, Pb]^T` of a genus `g`
Riemann surface `X` the action of the anti-holomorphic involution on `X` of
these periods is given by left-multiplication by a `2g x 2g` matrix `R`.
That is, .. math::
[\tau P_a^T, \tau P_b^T]^T = R [P_a^T, P_b^T]^T
Parameters
----------
Pa : complex matrix
Pb : complex matrix
The a- and b-periods, respectively, of a genus `g` Riemann surface.
tol : double
(Default: 1e-4) Tolerance used to veryify integrality of transformation
matrix. Dependent on precision of period matrices.
Returns
-------
R : complex matrix
The anti-holomorphic involution matrix.
Todo
----
For numerical stability, replace matrix inversion with linear system
solves.
"""
g,g = Pa.dimensions()
R_RDF = Matrix(RDF, 2*g, 2*g)
Ig = identity_matrix(RDF, g)
M = Im(Pb.T)*Re(Pa) - Im(Pa.T)*Re(Pb)
Minv = M.inverse()
R_RDF[:g,:g] = (2*Re(Pb)*Minv*Im(Pa.T) + Ig).T
R_RDF[:g,g:] = -2*Re(Pa)*Minv*Im(Pa.T)
R_RDF[g:,:g] = 2*Re(Pb)*Minv*Im(Pb.T)
R_RDF[g:,g:] = -(2*Re(Pb)*Minv*Im(Pa.T) + Ig)
R = R_RDF.round().change_ring(ZZ)
# sanity check: make sure that R_RDF is close to integral. we perform this
# test here since the matrix returned should be over ZZ
error = (R_RDF.round() - R_RDF).norm()
if error > tol:
raise ValueError("The anti-holomorphic involution matrix is not "
"integral. Try increasing the precision of the input "
"period matrices.")
return R
def attack(p, x1, y1, x2, y2):
"""
Recovers the a and b parameters from an elliptic curve when two points are known.
:param p: the prime of the curve base ring
:param x1: the x coordinate of the first point
:param y1: the y coordinate of the first point
:param x2: the x coordinate of the second point
:param y2: the y coordinate of the second point
:return: a tuple containing the a and b parameters of the elliptic curve
"""
m = Matrix(GF(p), [[x1, 1], [x2, 1]])
v = vector(GF(p), [y1**2 - x1**3, y2**2 - x2**3])
a, b = m.solve_right(v)
return int(a), int(b)
def __init__(self, parent, matrix, is_zero=lambda p: False, relations=[]):
## Checking the parent parameter
if (parent.is_field()):
parent = parent.base()
if (not (isUniPolynomial(parent) or isMPolynomial(parent))):
raise TypeError(
"The parent for this algorithm must be a polynomial ring.\n\t Got: %s"
% parent)
## Checking the matrix input
matrix = Matrix(parent, matrix)
super().__init__(parent, matrix, vector(parent,
matrix.ncols() * [0]), is_zero,
relations)
def _get_system_product(self, other, ncols, inhom=False):
r'''
Method to compute an ansatz system for the addition of a fixed size.
This method computes an ansatz system for the addition of two solutions to
``self`` and ``other`` respectively. Namely, if `f(x)` is a solution to
``self`` and `g(x)` is a solution to ``other``, then we can consider the module:
.. MATH::
M = \langle \partial^i(f)\partial^j(g)\ :\ i,j\in \mathbb{n}\rangle.
which (since `f(x)` and `g(x)` are annihilated by ``self`` and ``other``) is a finitely
generated module. Hence, the module generated by `f(x)+g(x)` is also finitely generated.
This method creates an ansatz system in the module `M` for computing the linear
relation between `h(x) = f(x)g(x)` and its derivatives.
INPUT:
* ``other``: the operator for the second operand.
* ``ncols``: size of the desired system (in number of columns)
* ``inhom``: if ``True``, the method return the ansazt matrix system together
with a vector representing the inhomogeneous term of the system.
OUTPUT:
The ansazt system for compute a linear relation with `\partial^{ncols}(f(x)g(x))`
and its previous derivatives within the module `M`.
'''
from ajpastor.misc.matrix import vector_derivative as der
## Controlling the input ncols
if (ncols < 0):
raise ValueError("The number of columns must be a natural number")
d_matrix = self._get_derivation_matrix_product(other)
## Base case: 0 column, only inhomogeneous term
if (ncols == 0):
v = self._get_vector_product(other)
system = Matrix(d_matrix.parent().base(), [[]])
else: # General case, we build the next column
aux_s, new_v = self._get_system_product(other, ncols - 1, True)
system = Matrix(aux_s.columns() + [new_v]).transpose()
v = der(d_matrix, new_v, self.derivate())
if (inhom):
return self._post_proc(system), v
else:
return self._post_proc(system)
def incidence_matrix_from_multiplicities(n, mu):
m = sum(mu.values())
A = Matrix(n, m)
j = 0
for support, multiplicity in mu.items():
if multiplicity < 0:
raise ValueError('invalid multiplicity')
if any(x not in range(n) for x in support):
raise ValueError('invalid support')
col = [1 if i in support else 0 for i in range(n)]
for _ in range(multiplicity):
A.set_column(j, col)
j += 1
return A
def _save(self, file, var_name='L'):
from sage.all import Matrix
HH = self.hyperplane_arrangement.parent()
A = Matrix(
map(lambda H: H.coefficients(),
self.hyperplane_arrangement.hyperplanes())).rows()
CR = tuple(map(lambda T: tuple(T), self.poset.cover_relations()))
FL = self.flat_labels
FL_tup = tuple([tuple([x, list(FL[x])]) for x in FL.keys()])
del FL
dict_builder = "FL = {x[0] : Set(x[1]) for x in FL_tup}\n"
with open(file, "w") as F:
F.write(
"from sage.all import HyperplaneArrangements, QQ, Poset, Set\n"
)
F.write("import hypigu as hi\n")
F.write("H = HyperplaneArrangements(QQ, {0})\n".format(
HH.variable_names()))
del HH
F.write("A = H({0})\n".format(A).replace("), ", "),\n"))
del A
F.write("CR = {0}\n".format(CR).replace("), ", "),\n"))
del CR
F.write(
"P = Poset([range({0}), CR], cover_relations=True)\n".format(
len(self.poset._elements)))
F.write("FL_tup = {0}\n".format(FL_tup).replace("), ", "),\n"))
F.write(dict_builder)
F.write("del FL_tup\n")
F.write(
"{0} = hi.LatticeOfFlats(A, poset=P, flat_labels=FL)\n".format(
var_name))
F.write("del H, A, CR, P, FL\n")
F.write("print('Loaded a lattice of flats. Variable name: {0}')".
format(var_name))
def FZ_matrix_pushforward_basis(self,r):
"""
Return the matrix of Faber-Zagier relations, using the "pushforward" basis, NOT the kappa monomial basis that the rest of the code uses.
:param r: The codimension.
:rtype: :class:`Matrix`
The columns correspond to the basis elements of the Strata algebra, and each row is a relation.
This matrix should be the same as Pixton's original ``tautrel.sage`` program after permuting columns. ::
sage: from strataalgebra import *
sage: s = StrataAlgebra(QQ,0,(1,2,3,4))
sage: s.FZ_matrix_pushforward_basis(1)
[ -9/4 -9/4 -9/4 -153/4 45/4 45/4 45/4 45/4]
[ 3/2 3/2 3/2 -33/2 -21/2 15/2 15/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 -21/2 15/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 15/2 -21/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 15/2 15/2 -21/2]
[ -15/4 21/4 21/4 -15/4 -21/4 -21/4 15/4 15/4]
[ 21/4 -15/4 21/4 -15/4 -21/4 15/4 -21/4 15/4]
[ 21/4 21/4 -15/4 -15/4 -21/4 15/4 15/4 -21/4]
[ 21/4 21/4 -15/4 -15/4 15/4 -21/4 -21/4 15/4]
[ 21/4 -15/4 21/4 -15/4 15/4 -21/4 15/4 -21/4]
[ -15/4 21/4 21/4 -15/4 15/4 15/4 -21/4 -21/4]
[ -15/4 -15/4 -15/4 -15/4 15/4 15/4 15/4 15/4]
.. SEEALSO ::
:meth:`~strataalgebra.StrataAlgebra.FZ_matrix`
"""
return Matrix(self.list_all_FZ(r))
def _lof_from_matroid(A=None, matroid=None):
from sage.all import Set, Matrix, Matroid, Poset
from functools import reduce
if A != None:
rows = list(map(lambda H: H.coefficients()[1:], A.hyperplanes()))
mat = Matrix(A.base_ring(), rows).transpose()
M = Matroid(mat)
n = len(A)
lbl_map = lambda S: S
else:
M = matroid.simplify()
n = len(M.groundset())
grd_list = list(M.groundset())
l_map = {x: grd_list.index(x) for x in grd_list}
lbl_map = lambda S: frozenset([l_map[x] for x in S])
L = M.lattice_of_flats()
rank_r = lambda L, r: list(
map(lbl_map, filter(lambda x: L.rank(x) == r, L._elements)))
rank_1 = [frozenset([k]) for k in range(n)]
ranks = reduce(lambda x, y: x + y,
[rank_r(L, r)
for r in range(2,
L.rank() + 1)], [L.bottom()] + rank_1)
P = Poset(L,
element_labels={x: ranks.index(lbl_map(x))
for x in L._elements})
adj_set = lambda S: Set([x + 1 for x in S])
label_dict = {i: adj_set(ranks[i]) for i in range(len(L))}
if A != None:
hyp_dict = {i: A[list(ranks[i])[0]] for i in range(1, n + 1)}
else:
hyp_dict = None
return [P, label_dict, hyp_dict]
def reduced_FZ_param_list(G,v,g,d,n):
X = StrataGraph.Xvar
params = FZ_param_list(n,tuple(range(1,d+1)))
graph_params = []
M = Matrix(StrataGraph.R,2,d+1)
M[0,0] = -1
for i in range(1,d+1):
M[0,i] = i
for p in params:
G_copy = StrataGraph(G.M)
M[1,0] = -g-1
for j in p[0]:
M[1,0] += X**j
for i in range(1,d+1):
M[1,i] = 1 + p[1][i-1][1][0]*X
G_p = StrataGraph(M)
G_copy.replace_vertex_with_graph(v,G_p)
graph_params.append([p,G_copy])
params_reduced = []
graphs_seen = []
for x in graph_params:
x[1].compute_invariant()
good = True
for GG in graphs_seen:
if graph_isomorphic(x[1],GG):
good = False
break
if good:
graphs_seen.append(x[1])
params_reduced.append(x[0])
return params_reduced
def FZ_matrix(self,r):
"""
Return the matrix of Faber-Zagier relations.
:param r: The codimension.
:rtype: :class:`Matrix`
The columns correspond to the basis elements of the Strata algebra, and each row is a relation.
Notice that this matrix considers the kappa classes to be in the monomial basis. Thus, is different than the
output of Pixton's original ``taurel.sage`` program. ::
sage: from strataalgebra import *
sage: s = StrataAlgebra(QQ,0,(1,2,3,4))
sage: s.FZ_matrix(1)
[ -9/4 -9/4 -9/4 -153/4 45/4 45/4 45/4 45/4]
[ 3/2 3/2 3/2 -33/2 -21/2 15/2 15/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 -21/2 15/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 15/2 -21/2 15/2]
[ 3/2 3/2 3/2 -33/2 15/2 15/2 15/2 -21/2]
[ -15/4 21/4 21/4 -15/4 -21/4 -21/4 15/4 15/4]
[ 21/4 -15/4 21/4 -15/4 -21/4 15/4 -21/4 15/4]
[ 21/4 21/4 -15/4 -15/4 -21/4 15/4 15/4 -21/4]
[ 21/4 21/4 -15/4 -15/4 15/4 -21/4 -21/4 15/4]
[ 21/4 -15/4 21/4 -15/4 15/4 -21/4 15/4 -21/4]
[ -15/4 21/4 21/4 -15/4 15/4 15/4 -21/4 -21/4]
[ -15/4 -15/4 -15/4 -15/4 15/4 15/4 15/4 15/4]
.. SEEALSO ::
:meth:`~strataalgebra.StrataAlgebra.FZ_matrix_pushforward_basis`, :meth:`~strataalgebra.StrataAlgebraElement.in_kernel`
"""
return Matrix(self.list_all_FZ(r))*self._convert_kappa_basis(r)
def build_matrix(P, M, c=1000):
factors_M = factor(M)
rows = []
# add logarithms
for p in P:
row = []
for q, e in factors_M:
row.extend(Zmod(q**e)(p).generalised_log()
) # generalised_log() uses unit_gens() generators
row = [c * x
for x in row] # multiply logs by a large constant to help LLL
rows.append(row)
height = len(rows)
width = len(rows[0])
# add unit matrix
for i, row in enumerate(rows):
row.extend([1 if j == i else 0 for j in range(0, height)])
# add group orders
generators_M = [g for q, e in factors_M for g in Zmod(q**e).unit_gens()]
for i, g in enumerate(generators_M):
rows.append([
g.multiplicative_order() * c if j == i else 0
for j in range(0, width + height)
])
return Matrix(rows)
def random_matrix_eigenvalues(F,n):
r"""
Give a random matrix together with its eigenvalues.
"""
l=list()
M = MatrixSpace(F,n)
U = Matrix(F,n)
D = Matrix(F,n)
for i in xrange(n):
x = F.random_element()
l.append(x)
D[i,i]=x
# now we need a unitary matrix:
# make a random choice of vectors and use Gram-Schmidt to orthogonolize
U = random_unitary_matrix(F,n)
UT = U.transpose().conjugate()
A = U*D*UT
l.sort(cmp=my_abscmp)
return A,U,l
def modular_unit_lattice(G,return_ambient=True,ambient_degree_zero=True):
M=G.modular_symbols()
period_mapping=integral_period_mapping(M)
H1QQ=QQ**period_mapping.ncols()
cusps=G.cusps()
assert cusps[-1]==Cusp(infinity)
n=len(cusps)
D=ZZ**n
D0=D.submodule_with_basis([D.gen(i)-D.gen(n-1) for i in xrange(n-1)])
period_images=[M.coordinate_vector(M([c,infinity]))*period_mapping for c in cusps if not c.is_infinity()]
m=Matrix(period_images).transpose().augment(H1QQ.basis_matrix()).transpose()
mint=(m*m.denominator()).change_ring(ZZ)
kernel=mint.kernel()
F0=kernel.basis_matrix().change_ring(ZZ).transpose()[:n-1].transpose()
F0inD0=D0.submodule([D0.linear_combination_of_basis(i) for i in F0])
if return_ambient and ambient_degree_zero:
return F0inD0,D0
if return_ambient:
return F0inD0,D
return F0inD0
def get_T1(K, S, unit_first=True, verbose=False):
# Sx is a list of generators of K(S,2) with the unit first or last, assuming h(K)=1
u = -1 if K==QQ else K(K.unit_group().torsion_generator())
from KSp import IdealGenerator
Sx = [IdealGenerator(P) for P in S]
if unit_first:
Sx = [u] + Sx
else:
Sx = Sx + [u]
r = len(Sx)
N = prod(S,1)
# Initialize T1 to be empty and A to be a matric with 0 rows and r=#Sx columns
T1 = []
A = Matrix(GF(2),0,len(Sx))
primes = primes_iter(K,1)
p = primes.next()
# Repeat the following until A has full rank: take the next prime p
# from the iterator, skip if it divides N (=product over S), form the
# vector v, and keep p and append v to the bottom of A if it increases
# the rank:
while A.rank() < r:
p = primes.next()
while p.divides(N):
p = primes.next()
if verbose:
print("A={} with {} rows and {} cols".format(A,A.nrows(),A.ncols()))
v = vector(alphalist(p, Sx))
if verbose:
print("v={}".format(v))
A1 = A.stack(v)
if A1.rank() > A.rank():
A = A1
T1 = T1 + [p]
if verbose:
print("new A={} with {} rows and {} cols".format(A,A.nrows(),A.ncols()))
print("T1 increases to {}".format(T1))
# the last thing returned is a "decoder" which returns the
# discriminant Delta given the splitting beavious at the primes in
# T1:
B = A.inverse()
def decoder(alphalist):
e = list(B*vector(alphalist))
return prod([D**ZZ(ei) for D,ei in zip(Sx,e)], 1)
return T1, A, decoder
def NonCubicSet(K,S, verbose=False):
u = -1 if K==QQ else K(K.unit_group().torsion_generator())
from KSp import IdealGenerator
Sx = [u] + [IdealGenerator(P) for P in S]
r = len(Sx)
d123 = r + binomial(r,2) + binomial(r,3)
vecP = vec123(K,Sx)
A = Matrix(GF(2),0,d123)
N = prod(S,1)
primes = primes_iter(K,None)
T = []
while A.rank() < d123:
p = primes.next()
while p.divides(N):
p = primes.next()
v = vecP(p)
if verbose:
print("v={}".format(v))
A1 = A.stack(vector(v))
if A1.rank() > A.rank():
A = A1
T.append(p)
if verbose:
print("new A={} with {} rows and {} cols".format(A,A.nrows(),A.ncols()))
print("T increases to {}".format(T))
return T
def solve_vectors(n,d, verbose=False):
def w(i,j):
return 0 if i==j else (d[Set([i,j])] + d[Set([i])] + d[Set([j])])
W = Matrix(GF(2),[[w(i,j) for j in range(n)] for i in range(n)])
v = vector(GF(2),[d[Set([i])] for i in range(n)])
if verbose:
print("W = {}".format(W))
print("v = {}".format(v))
if W==0:
if v==0:
return 0
else:
return v, v
Wrows = [wr for wr in W.rows() if wr]
if v==0:
x = Wrows[0]
y = next(wr for wr in Wrows[1:] if wr!=x)
return x, y
z = W.row(v.support()[0])
y = next(wr for wr in Wrows if wr!=z)
return y+z, y
class StrataGraph(object):
R = PolynomialRing(ZZ,"X",1,order='lex')
Xvar = R.gen()
def __init__(self,M=None,genus_list=None):
#print genus_list
if M:
self.M = copy(M)
elif genus_list:
self.M = Matrix(StrataGraph.R,len(genus_list)+1,1,[-1]+genus_list)
else:
self.M = Matrix(StrataGraph.R,1,1,-1)
def __repr__(self):
"""
Drew added this.
"""
name = self.nice_name()
if name == None:
return repr(self.nice_matrix())
else:
return name
def num_vertices(self):
return self.M.nrows() - 1
def num_edges(self):
return self.M.ncols() - 1
def h1(self):
return self.M.ncols()-self.M.nrows()+1
def add_vertex(self,g):
self.M = self.M.stack(Matrix(1,self.M.ncols()))
self.M[-1,0] = g
def add_edge(self,i1,i2,marking=0):
self.M = self.M.augment(Matrix(self.M.nrows(),1))
self.M[0,-1] = marking
if i1 > 0:
self.M[i1,-1] += 1
if i2 > 0:
self.M[i2,-1] += 1
def del_vertex(self,i):
self.M = self.M[:i].stack(self.M[(i+1):])
def del_edge(self,i):
self.M = self.M[0:,:i].augment(self.M[0:,(i+1):])
def compute_degree_vec(self):
self.degree_vec = [sum(self.M[i,j][0] for j in range(1,self.M.ncols())) for i in range(1,self.M.nrows())]
def degree(self,i):
return self.degree_vec[i-1]
def split_vertex(self,i,row1,row2):
self.M = self.M.stack(Matrix(2,self.M.ncols(),row1+row2))
self.add_edge(self.M.nrows()-2, self.M.nrows()-1)
self.del_vertex(i)
def compute_parity_vec_original(self):
X = StrataGraph.Xvar
self.parity_vec = [(ZZ(1+self.M[i,0][0]+sum(self.M[i,k][1] for k in range(1,self.M.ncols()))+sum(self.M[i,k][2] for k in range(1,self.M.ncols()))+self.M[i,0].derivative(X).substitute(X=1)) % 2) for i in range(1,self.M.nrows())]
def compute_parity_vec(self):
X = StrataGraph.Xvar
self.parity_vec = [(ZZ(1+self.M[i,0][0]+sum(self.M[i,k][1] for k in range(1,self.M.ncols()))+sum(self.M[i,k][2] for k in range(1,self.M.ncols()))+self.last_parity_summand(i)) % 2) for i in range(1,self.M.nrows())]
def last_parity_summand(self,i):
return sum(( expon[0] * coef for expon, coef in self.M[i,0].dict().items() ))
def parity(self,i):
return self.parity_vec[i-1]
def replace_vertex_with_graph(self,i,G):
X = StrataGraph.Xvar
nv = self.num_vertices()
ne = self.num_edges()
#i should have degree d, there should be no classes near i, and G should have markings 1,...,d and genus equal to the genus of i
hedge_list = []
for k in range(1,self.M.ncols()):
for j in range(self.M[i,k]):
hedge_list.append(k)
self.del_vertex(i)
for j in range(G.num_edges() - len(hedge_list)):
self.add_edge(0,0)
for j in range(G.num_vertices()):
self.add_vertex(G.M[j+1,0])
col = ne+1
for k in range(1,G.M.ncols()):
if G.M[0,k] > 0:
mark = ZZ(G.M[0,k])
for j in range(G.num_vertices()):
if self.M[nv+j,hedge_list[mark-1]] == 0:
self.M[nv+j,hedge_list[mark-1]] = G.M[j+1,k]
elif G.M[j+1,k] != 0:
a = self.M[nv+j,hedge_list[mark-1]][1]
b = G.M[j+1,k][1]
self.M[nv+j,hedge_list[mark-1]] = 2 + max(a,b)*X + min(a,b)*X**2
else:
for j in range(G.num_vertices()):
self.M[nv+j,col] = G.M[j+1,k]
col += 1
def compute_invariant(self):
self.compute_parity_vec()
self.compute_degree_vec()
nr,nc = self.M.nrows(),self.M.ncols()
self.invariant = [[self.M[i,0], [], [], [[] for j in range(1,nr)]] for i in range(1,nr)]
for k in range(1,nc):
L = [i for i in range(1,nr) if self.M[i,k] != 0]
if len(L) == 1:
if self.M[0,k] != 0:
self.invariant[L[0]-1][2].append([self.M[0,k],self.M[L[0],k]])
else:
self.invariant[L[0]-1][1].append(self.M[L[0],k])
else:
self.invariant[L[0]-1][3][L[1]-1].append([self.M[L[0],k],self.M[L[1],k]])
self.invariant[L[1]-1][3][L[0]-1].append([self.M[L[1],k],self.M[L[0],k]])
for i in range(1,nr):
self.invariant[i-1][3] = [term for term in self.invariant[i-1][3] if len(term) > 0]
for term in self.invariant[i-1][3]:
term.sort()
self.invariant[i-1][3].sort()
self.invariant[i-1][2].sort()
self.invariant[i-1][1].sort()
vertex_invariants = [[i,self.invariant[i-1]] for i in range(1,nr)]
self.invariant.sort()
vertex_invariants.sort(key=lambda x: x[1])
self.vertex_groupings = []
for i in range(nr-1):
if i == 0 or vertex_invariants[i][1] != vertex_invariants[i-1][1]:
self.vertex_groupings.append([])
self.vertex_groupings[-1].append(vertex_invariants[i][0])
#Drew added this
self.invariant = tupleit(self.invariant)
self.hash = hash(self.invariant)
def __eq__(self,other):
"""
Drew added this.
"""
return graph_isomorphic(self, other)
def __hash__(self):
"""
Drew added this.
Note that it returns a value stored when "compute_invariant" is called.
"""
try:
return self.hash
except:
self.compute_invariant()
return self.hash
def codim(self):
codim = 0
for v in range(1, self.num_vertices()+1):
for expon, coef in self.M[v,0].dict().items():
codim += expon[0]*coef
for e in range(1, self.num_edges()+1):
for expon, coef in self.M[v,e].dict().items():
if expon[0] > 0:
codim += coef
for e in range(1, self.num_edges()+1):
if self.M[0,e] == 0:
codim +=1
return codim
def codim_undecorated(self):
codim = 0
for e in range(1, self.num_edges()+1):
if self.M[0,e] == 0:
codim +=1
return codim
print "not implemented!!!!!!"
return 1
def kappa_on_v(self,v):
"""
Drew added this.
"""
#print "kappa",self.M[v,0].dict()
for ex, coef in self.M[v,0].dict().items():
if ex[0] != 0:
yield ex[0], coef
def psi_no_loop_on_v(self,v):
for edge in range(1,self.num_edges()+1):
if self.M[v,edge][0] == 1:
psi_expon = self.M[v,edge][1]
if psi_expon > 0:
yield edge, psi_expon
def psi_loop_on_v(self,v):
for edge in range(1,self.num_edges()+1):
if self.M[v,edge][0] == 2:
psi_expon1 = self.M[v,edge][1]
psi_expon2 = self.M[v,edge][2]
if psi_expon1 > 0:
yield edge, psi_expon1, psi_expon2
def moduli_dim_v(self,v):
"""
Drew added this.
"""
return 3*self.M[v,0][0]-3 + sum(( self.M[v,j][0] for j in range(1, self.num_edges()+1 ) ))
def num_loops(self):
count = 0
for edge in range(1, self.num_edges()+1):
for v in range(1, self.num_vertices()+1):
if self.M[v,edge][0] == 2:
count+=1
break
return count
def num_undecorated_loops(self):
count = 0
for edge in range(1, self.num_edges()+1):
for v in range(1, self.num_vertices()+1):
if self.M[v,edge] == 2:
count+=1
break
return count
def num_full_edges(self):
count = 0
for edge in range(1, self.num_edges()+1):
if sum(( self.M[v,edge][0] for v in range(1, self.num_vertices()+1 ))) == 2:
count += 1
return count
def forget_kappas(self):
M = copy(self.M)
for v in range(1, self.num_vertices()+1):
M[v,0] = M[v,0][0]
return StrataGraph(M)
def forget_decorations(self):
M = Matrix(StrataGraph.R,[[self.M[r,c][0] for c in range(self.M.ncols())] for r in range(self.M.nrows())] )
Gnew = StrataGraph(M)
#Gnew.compute_invariant()
return Gnew
def is_loop(self,edge):
for v in range(1, self.num_vertices()+1):
if self.M[v,edge][0] == 2:
return True
if self.M[v,edge][0] == 1:
return False
raise Exception("Unexpected!")
ps_name = "ps"
ps2_name = "ps_"
def nice_matrix(self):
Mnice = Matrix(SR, self.M.nrows(), self.M.ncols())
for edge in range(1, self.num_edges()+1):
Mnice[0,edge] = self.M[0,edge]
for v in range(1, self.num_vertices()+1):
kappas = 1
for expon, coef in self.M[v,0].dict().items():
if expon[0]==0:
Mnice[v,0] += coef
else:
kappas *= var("ka{0}".format(expon[0]))**coef
if kappas != 1:
Mnice[v,0] += kappas
for edge in range(1, self.num_edges()+1):
psis = 1
for expon, coef in self.M[v,edge].dict().items():
if expon[0]==0:
Mnice[v,edge] += coef
elif expon[0]==1:
psis *= var(StrataGraph.ps_name)**coef
elif expon[0]==2:
psis *= var(StrataGraph.ps2_name)**coef
if psis != 1:
Mnice[v,edge] += psis
return Mnice
@staticmethod
def from_nice_matrix(lists):
Mx = Matrix(StrataGraph.R, len(lists), len(lists[0]))
Mx[0,0]=-1
Mx[0,:] = Matrix([lists[0]])
for v in range(1, len(lists)):
if lists[v][0] in ZZ:
Mx[v,0]=lists[v][0]
continue
if lists[v][0].operator() == sage.symbolic.operators.add_vararg:
operands = lists[v][0].operands()
if len(operands) != 2:
raise Exception("Input error!")
genus = operands[1] #the genus
kappas = operands[0]
else:
kappas = lists[v][0]
genus = 0
if kappas.operator() == sage.symbolic.operators.mul_vararg:
for operand in kappas.operands():
Mx[v,0] += StrataGraph._kappaSR_monom_to_X(operand)
Mx[v,0] += genus
else:
Mx[v,0] = StrataGraph._kappaSR_monom_to_X(kappas) + genus
X = StrataGraph.Xvar
for v in range(1, len(lists)):
for edge in range(1, len(lists[0])):
if lists[v][edge] in ZZ:
Mx[v,edge] = lists[v][edge]
else:
for operand in lists[v][edge].operands():
if operand in ZZ:
Mx[v,edge] += operand
elif operand.operator() is None:
#it is a ps or ps2
Mx[v,edge] += X
elif operand.operator() == operator.pow:
#it is ps^n or ps2^n
Mx[v,edge] += X * operand.operands()[1]
elif operand.operator() == sage.symbolic.operators.mul_vararg:
#it is a ps^n*ps2^m
op1,op2 = operand.operands()
if op1.operator() is None:
exp1 = 1
else:
exp1 = op1.operand()[1]
if op2.operator() == None:
exp2 = 1
else:
exp2 = op2.operand()[1]
if exp1 >= exp2:
Mx[v,edge] += exp1*X + exp2*X**2
else:
Mx[v,edge] += exp1*X**2 + exp2*X
return StrataGraph(Mx)
@staticmethod
def _kappaSR_monom_to_X(expr):
X = StrataGraph.Xvar
if expr in ZZ:
return expr
elif expr.operator() == None:
ka_subscript = Integer(str(expr)[2:])
return X ** ka_subscript
elif expr.operator() == operator.pow:
ops = expr.operands()
expon = ops[1]
ka = ops[0]
ka_subscript = Integer(str(ka)[2:])
return expon * X ** ka_subscript
def nice_name(self):
"""
:return:
"""
if self.num_vertices() == 1:
num_loops = self.num_loops()
if num_loops >1:
return None
elif num_loops == 1:
if self.codim() == 1:
return "D_irr"
else:
return None
#else, there are no loops
var_strs = []
for expon, coef in self.M[1,0].dict().items():
if expon[0] == 0 or coef == 0:
continue
if coef == 1:
var_strs.append("ka{0}".format(expon[0]))
else:
var_strs.append("ka{0}^{1}".format(expon[0],coef))
for he in range(1,self.num_edges()+1): #should only be half edges now
if self.M[1,he][1] > 1:
var_strs.append("ps{0}^{1}".format(self.M[0,he],self.M[1,he][1]))
elif self.M[1,he][1] == 1:
var_strs.append("ps{0}".format(self.M[0,he]))
if len(var_strs) > 0:
return "*".join(var_strs)
else:
return "one"
if self.num_vertices() == 2 and self.num_full_edges() == 1 and self.codim() == 1:
#it is a boundary divisor
v1_marks = [self.M[0,j] for j in range(1, self.num_edges()+1) if self.M[1,j] == 1 and self.M[0,j] != 0]
v1_marks.sort()
v2_marks = [self.M[0,j] for j in range(1, self.num_edges()+1) if self.M[2,j] == 1 and self.M[0,j] != 0]
v2_marks.sort()
if v1_marks < v2_marks:
g = self.M[1,0]
elif v1_marks == v2_marks:
if self.M[1,0] <= self.M[2,0]:
g = self.M[1,0]
else:
g = self.M[2,0]
else:
g = self.M[2,0]
#temp = v1_marks
v1_marks = v2_marks
#v2_marks = temp
if len(v1_marks) == 0:
return "Dg{0}".format(g)
else:
return "Dg{0}m".format(g) + "_".join([str(m) for m in v1_marks])
def invariant_generators(self):
"""
Wraps Singular's invariant_algebra_reynolds and invariant_ring
in finvar.lib, with help from Simon King and Martin Albrecht.
Computes generators for the polynomial ring
`F[x_1,\ldots,x_n]^G`, where G in GL(n,F) is a finite
matrix group.
In the "good characteristic" case the polynomials returned form a
minimal generating set for the algebra of G-invariant polynomials.
In the "bad" case, the polynomials returned are primary and
secondary invariants, forming a not necessarily minimal generating
set for the algebra of G-invariant polynomials.
EXAMPLES::
sage: F = GF(7); MS = MatrixSpace(F,2,2)
sage: gens = [MS([[0,1],[-1,0]]),MS([[1,1],[2,3]])]
sage: G = MatrixGroup(gens)
sage: G.invariant_generators()
[x1^7*x2 - x1*x2^7, x1^12 - 2*x1^9*x2^3 - x1^6*x2^6 + 2*x1^3*x2^9 + x2^12, x1^18 + 2*x1^15*x2^3 + 3*x1^12*x2^6 + 3*x1^6*x2^12 - 2*x1^3*x2^15 + x2^18]
sage: q = 4; a = 2
sage: MS = MatrixSpace(QQ, 2, 2)
sage: gen1 = [[1/a,(q-1)/a],[1/a, -1/a]]; gen2 = [[1,0],[0,-1]]; gen3 = [[-1,0],[0,1]]
sage: G = MatrixGroup([MS(gen1),MS(gen2),MS(gen3)])
sage: G.cardinality()
12
sage: G.invariant_generators()
[x1^2 + 3*x2^2, x1^6 + 15*x1^4*x2^2 + 15*x1^2*x2^4 + 33*x2^6]
sage: F = GF(5); MS = MatrixSpace(F,2,2)
sage: gens = [MS([[1,2],[-1,1]]),MS([[1,1],[-1,1]])]
sage: G = MatrixGroup(gens)
sage: G.invariant_generators() # long time (64s on sage.math, 2011)
[x1^20 + x1^16*x2^4 + x1^12*x2^8 + x1^8*x2^12 + x1^4*x2^16 + x2^20, x1^20*x2^4 + x1^16*x2^8 + x1^12*x2^12 + x1^8*x2^16 + x1^4*x2^20]
sage: F=CyclotomicField(8)
sage: z=F.gen()
sage: a=z+1/z
sage: b=z^2
sage: MS=MatrixSpace(F,2,2)
sage: g1=MS([[1/a,1/a],[1/a,-1/a]])
sage: g2=MS([[1,0],[0,b]])
sage: g3=MS([[b,0],[0,1]])
sage: G=MatrixGroup([g1,g2,g3])
sage: G.invariant_generators() # long time (12s on sage.math, 2011)
[x1^8 + 14*x1^4*x2^4 + x2^8,
x1^24 + 10626/1025*x1^20*x2^4 + 735471/1025*x1^16*x2^8 + 2704156/1025*x1^12*x2^12 + 735471/1025*x1^8*x2^16 + 10626/1025*x1^4*x2^20 + x2^24]
AUTHORS:
- David Joyner, Simon King and Martin Albrecht.
REFERENCES:
- Singular reference manual
- B. Sturmfels, "Algorithms in invariant theory", Springer-Verlag, 1993.
- S. King, "Minimal Generating Sets of non-modular invariant rings of
finite groups", arXiv:math.AC/0703035
"""
from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing
from sage.interfaces.singular import singular
gens = self.gens()
singular.LIB("finvar.lib")
n = self.degree() #len((gens[0].matrix()).rows())
F = self.base_ring()
q = F.characteristic()
## test if the field is admissible
if F.gen()==1: # we got the rationals or GF(prime)
FieldStr = str(F.characteristic())
elif hasattr(F,'polynomial'): # we got an algebraic extension
if len(F.gens())>1:
raise NotImplementedError, "can only deal with finite fields and (simple algebraic extensions of) the rationals"
FieldStr = '(%d,%s)'%(F.characteristic(),str(F.gen()))
else: # we have a transcendental extension
FieldStr = '(%d,%s)'%(F.characteristic(),','.join([str(p) for p in F.gens()]))
## Setting Singular's variable names
## We need to make sure that field generator and variables get different names.
if str(F.gen())[0]=='x':
VarStr = 'y'
else:
VarStr = 'x'
VarNames='('+','.join((VarStr+str(i+1) for i in range(n)))+')'
R=singular.ring(FieldStr,VarNames,'dp')
if hasattr(F,'polynomial') and F.gen()!=1: # we have to define minpoly
singular.eval('minpoly = '+str(F.polynomial()).replace('x',str(F.gen())))
A = [singular.matrix(n,n,str((x.matrix()).list())) for x in gens]
Lgens = ','.join((x.name() for x in A))
PR = PolynomialRing(F,n,[VarStr+str(i) for i in range(1,n+1)])
if q == 0 or (q > 0 and self.cardinality()%q != 0):
from sage.all import Integer, Matrix
try:
gapself = gap(self)
# test whether the backwards transformation works as well:
for i in range(self.ngens()):
if Matrix(gapself.gen(i+1),F) != self.gen(i).matrix():
raise ValueError
except (TypeError,ValueError):
gapself is None
if gapself is not None:
ReyName = 't'+singular._next_var_name()
singular.eval('matrix %s[%d][%d]'%(ReyName,self.cardinality(),n))
En = gapself.Enumerator()
for i in range(1,self.cardinality()+1):
M = Matrix(En[i],F)
D = [{} for foobar in range(self.degree())]
for x,y in M.dict().items():
D[x[0]][x[1]] = y
for row in range(self.degree()):
for t in D[row].items():
singular.eval('%s[%d,%d]=%s[%d,%d]+(%s)*var(%d)'%(ReyName,i,row+1,ReyName,i,row+1, repr(t[1]),t[0]+1))
foobar = singular(ReyName)
IRName = 't'+singular._next_var_name()
singular.eval('matrix %s = invariant_algebra_reynolds(%s)'%(IRName,ReyName))
else:
ReyName = 't'+singular._next_var_name()
singular.eval('list %s=group_reynolds((%s))'%(ReyName,Lgens))
IRName = 't'+singular._next_var_name()
singular.eval('matrix %s = invariant_algebra_reynolds(%s[1])'%(IRName,ReyName))
OUT = [singular.eval(IRName+'[1,%d]'%(j)) for j in range(1,1+singular('ncols('+IRName+')'))]
return [PR(gen) for gen in OUT]
if self.cardinality()%q == 0:
PName = 't'+singular._next_var_name()
SName = 't'+singular._next_var_name()
singular.eval('matrix %s,%s=invariant_ring(%s)'%(PName,SName,Lgens))
OUT = [singular.eval(PName+'[1,%d]'%(j)) for j in range(1,1+singular('ncols('+PName+')'))] + [singular.eval(SName+'[1,%d]'%(j)) for j in range(2,1+singular('ncols('+SName+')'))]
return [PR(gen) for gen in OUT]
def invariant_generators(self):
r"""
Return invariant ring generators.
Computes generators for the polynomial ring
`F[x_1,\ldots,x_n]^G`, where `G` in `GL(n,F)` is a finite matrix
group.
In the "good characteristic" case the polynomials returned
form a minimal generating set for the algebra of `G`-invariant
polynomials. In the "bad" case, the polynomials returned
are primary and secondary invariants, forming a not
necessarily minimal generating set for the algebra of
`G`-invariant polynomials.
ALGORITHM:
Wraps Singular's ``invariant_algebra_reynolds`` and ``invariant_ring``
in ``finvar.lib``.
EXAMPLES::
sage: F = GF(7); MS = MatrixSpace(F,2,2)
sage: gens = [MS([[0,1],[-1,0]]),MS([[1,1],[2,3]])]
sage: G = MatrixGroup(gens)
sage: G.invariant_generators()
[x1^7*x2 - x1*x2^7,
x1^12 - 2*x1^9*x2^3 - x1^6*x2^6 + 2*x1^3*x2^9 + x2^12,
x1^18 + 2*x1^15*x2^3 + 3*x1^12*x2^6 + 3*x1^6*x2^12 - 2*x1^3*x2^15 + x2^18]
sage: q = 4; a = 2
sage: MS = MatrixSpace(QQ, 2, 2)
sage: gen1 = [[1/a,(q-1)/a],[1/a, -1/a]]; gen2 = [[1,0],[0,-1]]; gen3 = [[-1,0],[0,1]]
sage: G = MatrixGroup([MS(gen1),MS(gen2),MS(gen3)])
sage: G.cardinality()
12
sage: G.invariant_generators()
[x1^2 + 3*x2^2, x1^6 + 15*x1^4*x2^2 + 15*x1^2*x2^4 + 33*x2^6]
sage: F = CyclotomicField(8)
sage: z = F.gen()
sage: a = z+1/z
sage: b = z^2
sage: MS = MatrixSpace(F,2,2)
sage: g1 = MS([[1/a, 1/a], [1/a, -1/a]])
sage: g2 = MS([[-b, 0], [0, b]])
sage: G=MatrixGroup([g1,g2])
sage: G.invariant_generators()
[x1^4 + 2*x1^2*x2^2 + x2^4,
x1^5*x2 - x1*x2^5,
x1^8 + 28/9*x1^6*x2^2 + 70/9*x1^4*x2^4 + 28/9*x1^2*x2^6 + x2^8]
AUTHORS:
- David Joyner, Simon King and Martin Albrecht.
REFERENCES:
- Singular reference manual
- [Stu1993]_
- S. King, "Minimal Generating Sets of non-modular invariant
rings of finite groups", :arxiv:`math/0703035`.
"""
from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing
from sage.interfaces.singular import singular
gens = self.gens()
singular.LIB("finvar.lib")
n = self.degree() #len((gens[0].matrix()).rows())
F = self.base_ring()
q = F.characteristic()
## test if the field is admissible
if F.gen()==1: # we got the rationals or GF(prime)
FieldStr = str(F.characteristic())
elif hasattr(F,'polynomial'): # we got an algebraic extension
if len(F.gens())>1:
raise NotImplementedError("can only deal with finite fields and (simple algebraic extensions of) the rationals")
FieldStr = '(%d,%s)'%(F.characteristic(),str(F.gen()))
else: # we have a transcendental extension
FieldStr = '(%d,%s)'%(F.characteristic(),','.join([str(p) for p in F.gens()]))
## Setting Singular's variable names
## We need to make sure that field generator and variables get different names.
if str(F.gen())[0]=='x':
VarStr = 'y'
else:
VarStr = 'x'
VarNames='('+','.join((VarStr+str(i+1) for i in range(n)))+')'
R=singular.ring(FieldStr,VarNames,'dp')
if hasattr(F,'polynomial') and F.gen()!=1: # we have to define minpoly
singular.eval('minpoly = '+str(F.polynomial()).replace('x',str(F.gen())))
A = [singular.matrix(n,n,str((x.matrix()).list())) for x in gens]
Lgens = ','.join((x.name() for x in A))
PR = PolynomialRing(F,n,[VarStr+str(i) for i in range(1,n+1)])
if q == 0 or (q > 0 and self.cardinality()%q != 0):
from sage.all import Integer, Matrix
try:
elements = [ g.matrix() for g in self.list() ]
except (TypeError,ValueError):
elements
if elements is not None:
ReyName = 't'+singular._next_var_name()
singular.eval('matrix %s[%d][%d]'%(ReyName,self.cardinality(),n))
for i in range(1,self.cardinality()+1):
M = Matrix(F, elements[i-1])
D = [{} for foobar in range(self.degree())]
for x,y in M.dict().items():
D[x[0]][x[1]] = y
for row in range(self.degree()):
for t in D[row].items():
singular.eval('%s[%d,%d]=%s[%d,%d]+(%s)*var(%d)'
%(ReyName,i,row+1,ReyName,i,row+1, repr(t[1]),t[0]+1))
foobar = singular(ReyName)
IRName = 't'+singular._next_var_name()
singular.eval('matrix %s = invariant_algebra_reynolds(%s)'%(IRName,ReyName))
else:
ReyName = 't'+singular._next_var_name()
singular.eval('list %s=group_reynolds((%s))'%(ReyName,Lgens))
IRName = 't'+singular._next_var_name()
singular.eval('matrix %s = invariant_algebra_reynolds(%s[1])'%(IRName,ReyName))
OUT = [singular.eval(IRName+'[1,%d]'%(j))
for j in range(1,1+singular('ncols('+IRName+')'))]
return [PR(gen) for gen in OUT]
if self.cardinality()%q == 0:
PName = 't'+singular._next_var_name()
SName = 't'+singular._next_var_name()
singular.eval('matrix %s,%s=invariant_ring(%s)'%(PName,SName,Lgens))
OUT = [
singular.eval(PName+'[1,%d]'%(j))
for j in range(1,1+singular('ncols('+PName+')'))
] + [
singular.eval(SName+'[1,%d]'%(j)) for j in range(2,1+singular('ncols('+SName+')'))
]
return [PR(gen) for gen in OUT]
def invariants_eps(FQM, TM, use_reduction = True, proof = False, debug = 0):
r"""
Computes the invariants of a direct summand in the decomposition for weight
one-half modular forms. Such a summand is of the form
\[
\mathbb{C}[(\mathbb{Z}/2N\mathbb{Z}, -x^2/4N)]^\epsilon \otimes \mathbb{C}[M],
\]
where $M$ is a given finite quadratic module and $\epsilon$ acts on the first
factor via $\mathfrak{e}_\mu \mapsto \mathfrak{e}_{-\mu}$.
INPUT:
- FQM: A given finite quadratic module, referred to as $M$ above
- TM: A cyclic module of the form as given abve (the first factor)
NOTE:
We do not check that TM is of the stated form. The function is an auxiliary function
usually only called by ``weight_one_half_dim``.
EXAMPLES:
NONE
"""
eps = True
if TM != None and FQM != None:
TMM = TM+FQM
elif TM != None:
TMM = TM
else:
TMM = FQM
eps = False
debug2 = 0
if debug > 1:
print "FQM = {0}, TM = {1}, TMM = {2}".format(FQM, TM, TMM)
debug2 = 1
if debug > 2:
debug2=debug
inv = invariants(TMM, use_reduction, proof=proof, debug=debug2)
if debug > 1: print inv
if type(inv) in [list,tuple]:
V = inv[1]
else:
V = inv
d = [0,0]
if V.dimension() != 0:
el = list()
M = Matrix(V.base_ring(), V.ambient_module().dimension())
if eps:
f = 1 if TMM.signature() % 4 == 0 else -1
for v in inv[0]:
#Get the coordinate of this isotropic element
vv = v.c_list()
#change the first coordinate to its negative (the eps-action)
vv[0] = -vv[0]
vv = TMM(vv,can_coords=True)
#since the isotropic elements are taken up to the action of +-1, we need to check
#if we have this element (vv) or its negative (-vv) in the list
#we append the index of the match, together with a sign to the list `el`,
#where the sign is -1 if -vv is in inv[0] and the signature is 2 mod 4
#(i.e. the std generator of the center acts as -1)
if inv[0].count(vv) > 0:
el.append((inv[0].index(vv),1))
else:
el.append((inv[0].index(-vv),f))
#We create the entries of the matrix M
#which acts as eps on the space spanned by the isotropic vectors (mod +-1)
for i in range(len(el)):
M[el[i][0],i] = el[i][1]
#import pdb; pdb.set_trace()
if debug > 1: print "M={0}, V={1}".format(M, V)
try:
KM = (M-M.parent().one()).kernel_on(V)
if debug > 1: print "KM for ev 1 = {0}".format(KM)
d[0] = KM.dimension()
KM = (M+M.parent().one()).kernel_on(V)
if debug > 1: print "KM for ev -1 = {0}".format(KM)
d[1] = KM.dimension()
except Exception as e:
raise RuntimeError("Error occured for {0}, {1}".format(FQM.jordan_decomposition().genus_symbol(), e), M, V)
else:
d = [V.dimension(), 0]
if debug > 1: print d
return d
def algo64(K, S, BB, T2=None, unit_first = True, verbose=False):
r = 1+len(S)
if T2 is None:
T2 = get_T2(QQ,S, unit_first)
if verbose:
print("T2 = {}".format(T2))
assert len(T2)==r*(r+1)//2
from KSp import IdealGenerator
Sx = [IdealGenerator(P) for P in S]
u = -1 if K==QQ else K(K.unit_group().torsion_generator())
Sx = [u] + Sx if unit_first else Sx+[u]
if verbose:
print("Basis for K(S,2): {}".format(Sx))
#BBdict = dict((k,BB(T2[k])) for k in T2)
ap = BB_trace(BB)
apdict = dict((k,ap(T2[k])) for k in T2)
t4 = BB_t4(BB)
t4dict = dict((k,t4(T2[k])) for k in T2)
if verbose:
print("apdict = {}".format(apdict))
print("t4dict = {}".format(t4dict))
v = vector(GF(2),[t4dict[Set([i])] for i in range(r)])
if verbose:
print("v = {}".format(v))
def w(i,j):
if i==j:
return 0
else:
return (t4dict[Set([i,j])] + t4dict[Set([i])] + t4dict[Set([j])])
# Note that W ignores the first coordinate
Wd = Matrix(GF(2),[[w(i,j) for j in range(1,r)] for i in range(1,r)])
vd = vector(v.list()[1:])
rkWd = Wd.rank()
if verbose:
print("W' = \n{} with rank {}".format(Wd,rkWd))
# Case 1: rank(W)=2
if rkWd==2:
# x' and y' are any two distinct nonzero rows of W
Wrows = [wr for wr in Wd.rows() if wr]
xd = Wrows[0]
yd = next(wr for wr in Wrows if wr!=xd)
zd = xd+yd
ud = vd - vector(xi*yi for xi,yi in zip(list(xd),list(yd)))
if verbose:
print("x' = {}".format(xd))
print("y' = {}".format(yd))
print("z' = {}".format(zd))
print("u' = {}".format(ud))
print("v' = {}".format(vd))
u = vector([1]+ud.list())
v = vector([0]+vd.list())
if t4dict[Set([0])]==1:
x = vector([1]+xd.list())
y = vector([1]+yd.list())
z = vector([1]+zd.list())
if verbose:
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))
print("u = {}".format(u))
print("v = {}".format(v))
else:
raise NotImplementedError("t_4(p_1) case not yet implemented in algo64")
return [vec_to_disc(Sx,vec) for vec in [u,x,y,z]]
# W==0
raise NotImplementedError("rank(W)=0 case not yet implemented in algo64")
def algo63(K, S, BB, T2=None, unit_first = True, verbose=False):
r = 1+len(S)
if T2 is None:
T2 = get_T2(QQ,S, unit_first)
if verbose:
print("T2 = {}".format(T2))
assert len(T2)==r*(r+1)//2
from KSp import IdealGenerator
Sx = [IdealGenerator(P) for P in S]
u = -1 if K==QQ else K(K.unit_group().torsion_generator())
Sx = [u] + Sx if unit_first else Sx+[u]
if verbose:
print("Basis for K(S,2): {}".format(Sx))
BBdict = dict((k,BB(T2[k])) for k in T2)
ap = BB_trace(BB)
apdict = dict((k,ap(T2[k])) for k in T2)
t2 = BB_t2(BB)
t2dict = dict((k,t2(T2[k])) for k in T2)
if verbose:
print("apdict = {}".format(apdict))
print("t2dict = {}".format(t2dict))
v = vector(GF(2),[t2dict[Set([i])] for i in range(r)])
if verbose:
print("v = {}".format(v))
def w(i,j):
if i==j:
return 0
else:
return (t2dict[Set([i,j])] + t2dict[Set([i])] + t2dict[Set([j])])
W = Matrix(GF(2),[[w(i,j) for j in range(r)] for i in range(r)])
rkW = W.rank()
if verbose:
print("W = \n{} with rank {}".format(W,rkW))
# Case 1: rank(W)=2
if rkW==2:
# x and y are any two distinct nonzero rows of W
Wrows = [wr for wr in W.rows() if wr]
x = Wrows[0]
y = next(wr for wr in Wrows if wr!=x)
z = x+y
u = v - vector(xi*yi for xi,yi in zip(list(x),list(y)))
if verbose:
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))
print("u = {}".format(u))
else: # W==0
# y=0, x=z
u = v
y = u-u # zero vector
if verbose:
print("u = {}".format(u))
print("y = {}".format(y))
t3dict = {}
for k in T2:
t = 1 if t2dict[k]==0 else -1
t3dict[k] = (BBdict[k](t)//8)%2
s = solve_vectors(r,t3dict)
if not s:
z = x = y # = 0
if verbose:
print("x and y are both zero, so class has >4 vertices")
else:
x, _ = s
z = x
if verbose:
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))
return [vec_to_disc(Sx,vec) for vec in [u,x,y,z]]
def wrapper(*args):
mat = Matrix(f(*args))
mat.set_immutable()
return mat
def invariants_eps(FQM, TM, use_reduction = True, proof = False, debug = 0):
r"""
Computes the invariants of a direct summand in the decomposition for weight
one-half modular forms. Such a summand is of the form
\[
\mathbb{C}[(\mathbb{Z}/2N\mathbb{Z}, -x^2/4N)]^\epsilon \otimes \mathbb{C}[M],
\]
where $M$ is a given finite quadratic module and $\epsilon$ acts on the first
factor via $\mathfrak{e}_\mu \mapsto \mathfrak{e}_{-\mu}$.
INPUT:
- FQM: A given finite quadratic module, referred to as $M$ above
- TM: A cyclic module of the form as given abve (the first factor)
NOTE:
We do not check that TM is of the stated form. The function is an auxiliary function
usually only called by ``weight_one_half_dim``.
EXAMPLES:
NONE
"""
eps = True
if TM != None and FQM != None:
TMM = TM+FQM
elif TM != None:
TMM = TM
else:
TMM = FQM
eps = False
if debug > 1: print "FQM = {0}, TM = {1}, TMM = {2}".format(FQM, TM, TMM)
if debug > 2:
debug2=debug
else:
debug2=0
inv = invariants(TMM, use_reduction, proof, debug2)
if debug > 1: print inv
if type(inv) in [list,tuple]:
V = inv[1]
else:
V = inv
d = [0,0]
if V.dimension() != 0:
el = list()
M = Matrix(V.base_ring(), V.ambient_module().dimension())
if eps:
f = 1 if TMM.signature() % 4 == 0 else -1
for v in inv[0]:
vv = v.c_list()
vv[0] = -vv[0]
vv = TMM(vv,can_coords=True)
if inv[0].count(vv) > 0:
el.append((inv[0].index(vv),1))
else:
el.append((inv[0].index(-vv),f))
for i in range(len(el)):
M[el[i][0],i] = el[i][1]
if debug > 1: print "M={0}, V={1}".format(M, V)
try:
KM = (M-M.parent().one()).kernel_on(V)
if debug > 1: print "KM for ev 1 = {0}".format(KM)
d[0] = KM.dimension()
KM = (M+M.parent().one()).kernel_on(V)
if debug > 1: print "KM for ev -1 = {0}".format(KM)
d[1] = KM.dimension()
except:
raise RuntimeError("Error occured for {0}".format(FQM.jordan_decomposition().genus_symbol()), M, V)
else:
d = [V.dimension(), 0]
if debug > 1: print d
return d