def _evalf_(self, x, **kwds):
"""
TESTS:
Check that :trac:`16587` is fixed::
sage: M = sgn(3/2, hold=True); M
sgn(3/2)
sage: M.n()
1
sage: h(x) = sgn(x)
sage: h(pi).numerical_approx()
1.00000000000000
"""
if hasattr(x,'sign'): # First check if x has a sign method
return x.sign()
if hasattr(x,'sgn'): # or a sgn method
return x.sgn()
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return ZZ(0)
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return ZZ(1)
else:
return ZZ(-1)
raise ValueError("Numeric evaluation of symbolic expression")
def _evalf_(self, x, **kwds):
"""
TESTS:
Check that :trac:`16587` is fixed::
sage: M = sgn(3/2, hold=True); M
sgn(3/2)
sage: M.n()
1
sage: h(x) = sgn(x)
sage: h(pi).numerical_approx()
1.00000000000000
"""
if hasattr(x, 'sign'): # First check if x has a sign method
return x.sign()
if hasattr(x, 'sgn'): # or a sgn method
return x.sgn()
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return ZZ(0)
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return ZZ(1)
else:
return ZZ(-1)
raise ValueError("Numeric evaluation of symbolic expression")
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: dirac_delta(1)
0
sage: dirac_delta(0)
dirac_delta(0)
sage: dirac_delta(x)
dirac_delta(x)
sage: dirac_delta(exp(-10000000000000000000))
0
Evaluation test::
sage: dirac_delta(x).subs(x=1)
0
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
else:
return 0
except StandardError: # x is symbolic
pass
return None
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: dirac_delta(1)
0
sage: dirac_delta(0)
dirac_delta(0)
sage: dirac_delta(x)
dirac_delta(x)
sage: dirac_delta(exp(-10000000000000000000))
0
Evaluation test::
sage: dirac_delta(x).subs(x=1)
0
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
else:
return 0
except Exception: # x is symbolic
pass
return None
def _is_numerically_zero_CIF(x):
from sage.rings.all import ComplexIntervalField
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0) and bool(approx_x.real() == 0):
return True
except:
return False
def _eval_(self, x):
"""
EXAMPLES::
sage: sgn(-1)
-1
sage: sgn(1)
1
sage: sgn(0)
0
sage: sgn(x)
sgn(x)
sage: sgn(-exp(-10000000000000000000))
-1
Evaluation test::
sage: sgn(x).subs(x=1)
1
sage: sgn(x).subs(x=0)
0
sage: sgn(x).subs(x=-1)
-1
More tests::
sage: sign(RR(2))
1
sage: sign(RDF(2))
1
sage: sign(AA(-2))
-1
sage: sign(AA(0))
0
"""
if hasattr(x, 'sign'): # First check if x has a sign method
return x.sign()
if hasattr(x, 'sgn'): # or a sgn method
return x.sgn()
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return ZZ(0)
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return ZZ(1)
else:
return ZZ(-1)
except StandardError: # x is symbolic
pass
return None
def _eval_(self, x):
"""
EXAMPLES::
sage: sgn(-1)
-1
sage: sgn(1)
1
sage: sgn(0)
0
sage: sgn(x)
sgn(x)
sage: sgn(-exp(-10000000000000000000))
-1
Evaluation test::
sage: sgn(x).subs(x=1)
1
sage: sgn(x).subs(x=0)
0
sage: sgn(x).subs(x=-1)
-1
More tests::
sage: sign(RR(2))
1
sage: sign(RDF(2))
1
sage: sign(AA(-2))
-1
sage: sign(AA(0))
0
"""
if hasattr(x,'sign'): # First check if x has a sign method
return x.sign()
if hasattr(x,'sgn'): # or a sgn method
return x.sgn()
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return ZZ(0)
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return ZZ(1)
else:
return ZZ(-1)
except Exception: # x is symbolic
pass
return None
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: heaviside(-1/2)
0
sage: heaviside(1)
1
sage: heaviside(0)
heaviside(0)
sage: heaviside(x)
heaviside(x)
sage: heaviside(exp(-1000000000000000000000))
1
Evaluation test::
sage: heaviside(x).subs(x=1)
1
sage: heaviside(x).subs(x=-1)
0
::
sage: ex = heaviside(x)+1
sage: t = loads(dumps(ex)); t
heaviside(x) + 1
sage: bool(t == ex)
True
sage: t.subs(x=1)
2
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
except Exception: # x is symbolic
pass
return None
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: heaviside(-1/2)
0
sage: heaviside(1)
1
sage: heaviside(0)
heaviside(0)
sage: heaviside(x)
heaviside(x)
sage: heaviside(exp(-1000000000000000000000))
1
Evaluation test::
sage: heaviside(x).subs(x=1)
1
sage: heaviside(x).subs(x=-1)
0
::
sage: ex = heaviside(x)+1
sage: t = loads(dumps(ex)); t
heaviside(x) + 1
sage: bool(t == ex)
True
sage: t.subs(x=1)
2
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
except StandardError: # x is symbolic
pass
return None
def _evalf_(self, x, **kwds):
"""
TESTS::
sage: h(x) = dirac_delta(x)
sage: h(pi).numerical_approx()
0.000000000000000
"""
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
else:
return 0
raise ValueError("Numeric evaluation of symbolic expression")
def _evalf_(self, x, **kwds):
"""
TESTS::
sage: h(x) = dirac_delta(x)
sage: h(pi).numerical_approx()
0.000000000000000
"""
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return None
else:
return 0
raise ValueError("Numeric evaluation of symbolic expression")
def _evalf_(self, x, **kwds):
"""
TESTS::
sage: h(x) = unit_step(x)
sage: h(pi).numerical_approx()
1.00000000000000
"""
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
raise ValueError("Numeric evaluation of symbolic expression")
def _evalf_(self, m, n, **kwds):
"""
TESTS::
sage: h(x) = kronecker_delta(3,x)
sage: h(pi).numerical_approx()
0.000000000000000
"""
if bool(repr(m) > repr(n)):
return kronecker_delta(n, m)
x = m - n
approx_x = ComplexIntervalField()(x)
if approx_x.imag() == 0: # x is real
if approx_x.real() == 0: # x is zero
return 1
else:
return 0
return 0 # x is complex
def _evalf_(self, x, **kwds):
"""
TESTS::
sage: h(x) = unit_step(x)
sage: h(pi).numerical_approx()
1.00000000000000
"""
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
raise ValueError("Numeric evaluation of symbolic expression")
def _eval_(self, m, n):
"""
The Kronecker delta function.
EXAMPLES::
sage: kronecker_delta(1,2)
0
sage: kronecker_delta(1,1)
1
Kronecker delta is a symmetric function. We keep arguments sorted to
ensure that k_d(m, n) - k_d(n, m) cancels automatically::
sage: x,y=var('x,y')
sage: kronecker_delta(x, y)
kronecker_delta(x, y)
sage: kronecker_delta(y, x)
kronecker_delta(x, y)
sage: kronecker_delta(x,2*x)
kronecker_delta(2*x, x)
Evaluation test::
sage: kronecker_delta(1,x).subs(x=1)
1
"""
if bool(repr(m) > repr(n)):
return kronecker_delta(n, m)
x = m - n
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
else:
return 0
else:
return 0 # x is complex
except Exception: # x is symbolic
pass
return None
def _eval_(self, m, n):
"""
The Kronecker delta function.
EXAMPLES::
sage: kronecker_delta(1,2)
0
sage: kronecker_delta(1,1)
1
Kronecker delta is a symmetric function. We keep arguments sorted to
ensure that (k_d(m, n) - k_d(n, m) cancels automatically::
sage: x,y=var('x,y')
sage: kronecker_delta(x, y)
kronecker_delta(x, y)
sage: kronecker_delta(y, x)
kronecker_delta(x, y)
sage: kronecker_delta(x,2*x)
kronecker_delta(2*x, x)
Evaluation test::
sage: kronecker_delta(1,x).subs(x=1)
1
"""
if bool(repr(m) > repr(n)):
return kronecker_delta(n, m)
x = m - n
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
else:
return 0
else:
return 0 # x is complex
except StandardError: # x is symbolic
pass
return None
def _evalf_(self, m, n, **kwds):
"""
TESTS::
sage: h(x) = kronecker_delta(3,x)
sage: h(pi).numerical_approx()
0.000000000000000
"""
if bool(repr(m) > repr(n)):
return kronecker_delta(n, m)
x = m - n
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
else:
return 0
else:
return 0 # x is complex
raise ValueError("Numeric evaluation of symbolic expression")
def _evalf_(self, m, n, **kwds):
"""
TESTS::
sage: h(x) = kronecker_delta(3,x)
sage: h(pi).numerical_approx()
0.000000000000000
"""
if bool(repr(m) > repr(n)):
return kronecker_delta(n, m)
x = m - n
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
else:
return 0
else:
return 0 # x is complex
raise ValueError("Numeric evaluation of symbolic expression")
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: unit_step(-1)
0
sage: unit_step(1)
1
sage: unit_step(0)
1
sage: unit_step(x)
unit_step(x)
sage: unit_step(-exp(-10000000000000000000))
0
Evaluation test::
sage: unit_step(x).subs(x=1)
1
sage: unit_step(x).subs(x=0)
1
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
except Exception: # x is symbolic
pass
return None
def _eval_(self, x):
"""
INPUT:
- ``x`` - a real number or a symbolic expression
EXAMPLES::
sage: unit_step(-1)
0
sage: unit_step(1)
1
sage: unit_step(0)
1
sage: unit_step(x)
unit_step(x)
sage: unit_step(-exp(-10000000000000000000))
0
Evaluation test::
sage: unit_step(x).subs(x=1)
1
sage: unit_step(x).subs(x=0)
1
"""
try:
approx_x = ComplexIntervalField()(x)
if bool(approx_x.imag() == 0): # x is real
if bool(approx_x.real() == 0): # x is zero
return 1
# Now we have a non-zero real
if bool((approx_x**(0.5)).imag() == 0): # Check: x > 0
return 1
else:
return 0
except StandardError: # x is symbolic
pass
return None
def dimension__vector_valued(k, L, conjugate=False):
r"""
Compute the dimension of the space of weight `k` vector valued modular forms
for the Weil representation (or its conjugate) attached to the lattice `L`.
See [Borcherds, Borcherds - Reflection groups of Lorentzian lattices] for a proof
of the formula that we use here.
INPUT:
- `k` -- A half-integer.
- ``L`` -- An quadratic form.
- ``conjugate`` -- A boolean; If ``True``, then compute the dimension for
the conjugated Weil representation.
OUTPUT:
An integer.
TESTS::
sage: dimension__vector_valued(3, QuadraticForm(-matrix(2, [2, 1, 1, 2])))
1
sage: dimension__vector_valued(3, QuadraticForm(-matrix(2, [2, 0, 0, 2])))
1
sage: dimension__vector_valued(3, QuadraticForm(-matrix(2, [2, 0, 0, 4])))
1
"""
if 2 * k not in ZZ:
raise ValueError("Weight must be half-integral")
if k <= 0:
return 0
if k < 2:
raise NotImplementedError("Weight <2 is not implemented.")
if L.matrix().rank() != L.matrix().nrows():
raise ValueError(
"The lattice (={0}) must be non-degenerate.".format(L))
L_dimension = L.matrix().nrows()
if L_dimension % 2 != ZZ(2 * k) % 2:
return 0
plus_basis = ZZ(L_dimension + 2 * k) % 4 == 0
## The bilinear and the quadratic form attached to L
quadratic = lambda x: L(x) // 2
bilinear = lambda x, y: L(x + y) - L(x) - L(y)
## A dual basis for L
(elementary_divisors, dual_basis_pre, _) = L.matrix().smith_form()
elementary_divisors = elementary_divisors.diagonal()
dual_basis = map(operator.div, list(dual_basis_pre), elementary_divisors)
L_level = ZZ(lcm([b.denominator() for b in dual_basis]))
(elementary_divisors, _, discriminant_basis_pre) = (
L_level * matrix(dual_basis)).change_ring(ZZ).smith_form()
elementary_divisors = filter(lambda d: d not in ZZ,
(elementary_divisors / L_level).diagonal())
elementary_divisors_inv = map(ZZ, [ed**-1 for ed in elementary_divisors])
discriminant_basis = matrix(
map(operator.mul,
discriminant_basis_pre.inverse().rows()[:len(elementary_divisors)],
elementary_divisors)).transpose()
## This is a form over QQ, so that we cannot use an instance of QuadraticForm
discriminant_form = discriminant_basis.transpose() * L.matrix(
) * discriminant_basis
if conjugate:
discriminant_form = -discriminant_form
if prod(elementary_divisors_inv) > 100:
disc_den = discriminant_form.denominator()
disc_bilinear_pre = \
cython_lambda( ', '.join( ['int a{0}'.format(i) for i in range(discriminant_form.nrows())]
+ ['int b{0}'.format(i) for i in range(discriminant_form.nrows())] ),
' + '.join('{0} * a{1} * b{2}'.format(disc_den * discriminant_form[i,j], i, j)
for i in range(discriminant_form.nrows())
for j in range(discriminant_form.nrows())) )
disc_bilinear = lambda *a: disc_bilinear_pre(*a) / disc_den
else:
disc_bilinear = lambda *xy: vector(ZZ, xy[:discriminant_form.nrows(
)]) * discriminant_form * vector(ZZ, xy[discriminant_form.nrows():])
disc_quadratic = lambda *a: disc_bilinear(*(2 * a)) / 2
## red gives a normal form for elements in the discriminant group
red = lambda x: map(operator.mod, x, elementary_divisors_inv)
def is_singl(x):
y = red(map(operator.neg, x))
for (e, f) in zip(x, y):
if e < f:
return -1
elif e > f:
return 1
return 0
## singls and pairs are elements of the discriminant group that are, respectively,
## fixed and not fixed by negation.
singls = list()
pairs = list()
for x in mrange(elementary_divisors_inv):
si = is_singl(x)
if si == 0:
singls.append(x)
elif si == 1:
pairs.append(x)
if plus_basis:
subspace_dimension = len(singls + pairs)
else:
subspace_dimension = len(pairs)
## 200 bits are, by far, sufficient to distinguish 12-th roots of unity
## by increasing the precision by 4 for each additional dimension, we
## compensate, by far, the errors introduced by the QR decomposition,
## which are of the size of (absolute error) * dimension
CC = ComplexIntervalField(200 + subspace_dimension * 4)
zeta_order = ZZ(
lcm([8, 12] + map(lambda ed: 2 * ed, elementary_divisors_inv)))
zeta = CC(exp(2 * pi * I / zeta_order))
sqrt2 = CC(sqrt(2))
drt = CC(sqrt(L.det()))
Tmat = diagonal_matrix(CC, [
zeta**(zeta_order * disc_quadratic(*a))
for a in (singls + pairs if plus_basis else pairs)
])
if plus_basis:
Smat = zeta**(zeta_order / 8 * L_dimension) / drt \
* matrix( CC, [ [zeta**(-zeta_order * disc_bilinear(*(gamma + delta))) for delta in singls]
+ [sqrt2 * zeta**(-zeta_order * disc_bilinear(*(gamma + delta))) for delta in pairs]
for gamma in singls] \
+ [ [sqrt2 * zeta**(-zeta_order * disc_bilinear(*(gamma + delta))) for delta in singls]
+ [zeta**(-zeta_order * disc_bilinear(*(gamma + delta))) + zeta**(-zeta_order * disc_bilinear(*(gamma + map(operator.neg, delta)))) for delta in pairs]
for gamma in pairs] )
else:
Smat = zeta**(zeta_order / 8 * L_dimension) / drt \
* matrix( CC, [ [zeta**(-zeta_order * disc_bilinear(*(gamma + delta))) - zeta**(-zeta_order * disc_bilinear(*(gamma + map(operator.neg,delta)))) for delta in pairs]
for gamma in pairs ] )
STmat = Smat * Tmat
## This function overestimates the number of eigenvalues, if it is not correct
def eigenvalue_multiplicity(mat, ev):
mat = matrix(CC, mat - ev * identity_matrix(subspace_dimension))
return len(
filter(lambda row: all(e.contains_zero() for e in row),
_qr(mat).rows()))
rti = CC(exp(2 * pi * I / 8))
S_ev_multiplicity = [
eigenvalue_multiplicity(Smat, rti**n) for n in range(8)
]
## Together with the fact that eigenvalue_multiplicity overestimates the multiplicities
## this asserts that the computed multiplicities are correct
assert sum(S_ev_multiplicity) == subspace_dimension
rho = CC(exp(2 * pi * I / 12))
ST_ev_multiplicity = [
eigenvalue_multiplicity(STmat, rho**n) for n in range(12)
]
## Together with the fact that eigenvalue_multiplicity overestimates the multiplicities
## this asserts that the computed multiplicities are correct
assert sum(ST_ev_multiplicity) == subspace_dimension
T_evs = [
ZZ((zeta_order * disc_quadratic(*a)) % zeta_order) / zeta_order
for a in (singls + pairs if plus_basis else pairs)
]
return subspace_dimension * (1 + QQ(k) / 12) \
- ZZ(sum( (ST_ev_multiplicity[n] * ((-2 * k - n) % 12)) for n in range(12) )) / 12 \
- ZZ(sum( (S_ev_multiplicity[n] * ((2 * k + n) % 8)) for n in range(8) )) / 8 \
- sum(T_evs)