def padically_evaluate(self, shuffle=False):
if self.root() is None:
return SR(1)
r = ((1 - SR('q')**(-1))**(-1) * LocalZetaProcessor.padically_evaluate(
self, shuffle=shuffle)).factor()
return r if self.translation is None else r(t=self.translation *
SR('t')).factor()
def padically_evaluate(self, shuffle=False):
if self.root() is None:
return SR(1)
r = ((1 - SR('q')**(-1))**self.nvertices *
LocalZetaProcessor.padically_evaluate(self,
shuffle=shuffle)).factor()
return r
def taylor_processor_factored(new_ring, Phi, scalar, alpha, I, omega):
k = alpha.nrows() - 1
tau = SR.var('tau')
y = [SR('y%d' % i) for i in range(k + 1)]
R = PolynomialRing(QQ, len(y), y)
beta = [a * Phi for a in alpha]
ell = len(I)
def f(i):
if i == 0:
return QQ(scalar) * y[0] * exp(tau * omega[0])
elif i in I:
return tau / (1 - exp(tau * omega[i]))
else:
return 1 / (1 - y[i] * exp(tau * omega[i]))
H = [
f(i).series(tau, ell + 1).truncate().collect(tau) for i in range(k + 1)
]
for i in range(k + 1):
H[i] = [H[i].coefficient(tau, j) for j in range(ell + 1)]
r = []
# Get coefficient of tau^ell in prod(H)
for w in NonnegativeCompositions(ell, k + 1):
r = prod(
CyclotomicRationalFunction.from_split_expression(H[i][
w[i]], y, R).monomial_substitution(new_ring, beta)
for i in range(k + 1))
yield r
def build_vts_poly(ts, tcs, mpp_opt):
vts = [[t.subs(tc) for t in ts] for tc in tcs]
vts = vset(vts, idfun=repr)
#add 0 to the end of each vertex and identity elem to terms
vts = [vt + [SR(0)] for vt in vts]
ts = ts + [SR(0)] #the identity element is 0 in MPP
if mpp_opt == IeqMPP.opt_max_then_min:
def worker_(Q, ts, is_max_plus):
Q.put(IeqMPPGen.build_poly(vts, ts, is_max_plus))
Q = mp.Queue()
workers = [
mp.Process(target=worker_, args=(Q, ts, is_max_plus))
for is_max_plus in [True, False]
]
for w in workers:
w.start()
rs = []
for _ in workers:
rs.extend(Q.get())
else:
is_max_plus = mpp_opt == IeqMPP.opt_max_plus
rs = IeqMPPGen.build_poly(vts, ts, is_max_plus=is_max_plus)
return rs
def __init__(self, precision):
Parent.__init__(self)
self._precision = precision
self.register_coercion(MorphismToSPN(ZZ, self, self._precision))
self.register_coercion(MorphismToSPN(QQ, self, self._precision))
to_SR = Hom(self, SR, Sets())(lambda x: SR(x.sage()))
SR.register_coercion(to_SR)
def assertIsReduced(t, m_path, x_name, eps_name):
M = fuchsia.import_matrix_from_file(m_path)
x = SR.var(x_name)
eps = SR.var(eps_name)
pranks = fuchsia.singularities(M, x).values()
t.assertEqual(pranks, [0]*len(pranks))
t.assertTrue(eps not in (M/eps).simplify_rational().variables())
def test_normalize_3(t):
# Test with non-zero normalized eigenvalues
x = SR.var("x")
e = SR.var("epsilon")
M = matrix([[(1 - e) / x, 0], [0, (1 + e) / 3 / x]])
with t.assertRaises(FuchsiaError):
N, T = normalize(M, x, e)
def test_factorize_1(t):
x = SR.var("x")
e = SR.var("epsilon")
M = matrix([[1 / x, 0, 0], [0, 2 / x, 0], [0, 0, 3 / x]]) * e
M = transform(M, x, matrix([[1, 1, 0], [0, 1, 0], [1 + 2 * e, 0, e]]))
F, T = factorize(M, x, e)
F = F.simplify_rational()
for f in F.list():
t.assertEqual(limit_fixed(f, e, 0), 0)
def test_is_normalized_1(t):
x = SR.var("x")
e = SR.var("epsilon")
t.assertFalse(is_normalized(matrix([[1 / x / 2]]), x, e))
t.assertFalse(is_normalized(matrix([[-1 / x / 2]]), x, e))
t.assertTrue(is_normalized(matrix([[1 / x / 3]]), x, e))
t.assertFalse(is_normalized(matrix([[x]]), x, e))
t.assertFalse(is_normalized(matrix([[1 / x**2]]), x, e))
t.assertTrue (is_normalized( \
matrix([[(e+SR(1)/3)/x-SR(1)/2/(x-1)]]), x, e))
def test_normalize_1(t):
# Test with apparent singularities at 0 and oo, but not at 1.
x = SR.var("x")
M = matrix([[1 / x, 5 / (x - 1), 0, 6 / (x - 1)], [0, 2 / x, 0, 0],
[0, 0, 3 / x, 7 / (x - 1)], [6 / (x - 1), 0, 0, 1 / x]])
N, T = normalize(M, x, SR.var("epsilon"))
N = N.simplify_rational()
t.assertEqual(N, transform(M, x, T).simplify_rational())
for point, prank in singularities(N, x).iteritems():
R = matrix_c0(N, x, point, prank)
evlist = R.eigenvalues()
t.assertEqual(evlist, [0] * len(evlist))
def test_normalize_4(t):
# Test with non-zero normalized eigenvalues
x, e = SR.var("x eps")
M = matrix([[1 / x / 2, 0], [0, 0]])
with t.assertRaises(FuchsiaError):
N, T = normalize(M, x, e)
def symbolic_to_polynomial(f, vars):
# Rewrite a symbolic expression as a polynomial over SR in a given
# list of variables.
poly = f.polynomial(QQ)
allvars = poly.variables()
indices = []
for x in allvars:
try:
indices.append(vars.index(x))
except ValueError:
indices.append(None)
R = PolynomialRing(SR, len(vars), vars)
res = R.zero()
for coeff, alpha in zip(poly.coefficients(), poly.exponents()):
if type(alpha) == int:
# Once again, univariate polynomials receive special treatment
# in Sage.
alpha = [alpha]
term = R(coeff)
for i, e in enumerate(alpha):
if not e:
continue
if indices[i] is None:
term *= SR(allvars[i]**e)
else:
term *= R.gen(indices[i])**e
res += term
return res
def __init__(self,dof,name,shortname=None):
self.dof = int(dof)
self.name = name
if shortname == None :
self.shortname = name.replace(' ','_').replace('.','_')
else :
self.shortname = shortname.replace(' ','_').replace('.','_')
for i in range(1 ,1 +dof):
var('q'+str(i),domain=RR)
(alpha , a , d , theta) = SR.var('alpha,a,d,theta',domain=RR)
default_params_dh = [ alpha , a , d , theta ]
default_T_dh = matrix( \
[[ cos(theta), -sin(theta)*cos(alpha), sin(theta)*sin(alpha), a*cos(theta) ], \
[ sin(theta), cos(theta)*cos(alpha), -cos(theta)*sin(alpha), a*sin(theta) ], \
[ 0.0, sin(alpha), cos(alpha), d ]
,[ 0.0, 0.0, 0.0, 1.0] ] )
#g_a = SR.var('g_a',domain=RR)
g_a = 9.81
self.grav = matrix([[0.0],[0.0],[-g_a]])
self.params_dh = default_params_dh
self.T_dh = default_T_dh
self.links_dh = []
self.motors = []
self.Imzi = []
self._gensymbols()
def assertReductionWorks(t, filename):
M = import_matrix_from_file(filename)
x, eps = SR.var("x eps")
t.assertIn(x, M.variables())
M_pranks = singularities(M, x).values()
t.assertNotEqual(M_pranks, [0] * len(M_pranks))
#1 Fuchsify
m, t1 = simplify_by_factorization(M, x)
Mf, t2 = fuchsify(m, x)
Tf = t1 * t2
t.assertTrue((Mf - transform(M, x, Tf)).simplify_rational().is_zero())
Mf_pranks = singularities(Mf, x).values()
t.assertEqual(Mf_pranks, [0] * len(Mf_pranks))
#2 Normalize
t.assertFalse(is_normalized(Mf, x, eps))
m, t1 = simplify_by_factorization(Mf, x)
Mn, t2 = normalize(m, x, eps)
Tn = t1 * t2
t.assertTrue((Mn - transform(Mf, x, Tn)).simplify_rational().is_zero())
t.assertTrue(is_normalized(Mn, x, eps))
#3 Factorize
t.assertIn(eps, Mn.variables())
m, t1 = simplify_by_factorization(Mn, x)
Mc, t2 = factorize(m, x, eps, seed=3)
Tc = t1 * t2
t.assertTrue((Mc - transform(Mn, x, Tc)).simplify_rational().is_zero())
t.assertNotIn(eps, (Mc / eps).simplify_rational().variables())
def slope(self): # It was before named "coefficient"
"""
return the angular coefficient of line.
This is the coefficient a in the equation
y = ax + b
This is not the same as the coefficient a in self.equation
ax + by + c == 0
of the points.
OUTPUT:
a number
EXAMPLES::
sage: from yanntricks import *
sage: Segment(Point(0,0),Point(1,1)).slope
1
sage: Segment(Point(1,1),Point(0,0)).slope
1
sage: Segment(Point(1,2),Point(-1,8)).slope
-3
NOTE:
If the line is vertical, raise a ZeroDivisionError
"""
return SR(self.Dy)/self.Dx
def __init__(self, arg, mode=None):
if mode is None:
mode = 'O'
if mode not in ['O', 'K', 'BO', 'BK', 'TO', 'TK']:
raise ValueError("invalid mode")
self.translation = SR('q')**(arg.nrows() -
arg.ncols()) if 'T' in mode else None
self.R = arg
for x in mode[:-1]:
if x == 'B':
self.R = bullet_dual(self.R)
elif x == 'T':
self.R = self.R.transpose()
else:
raise ValueError
self.d = self.R.nrows()
self.e = self.R.ncols()
self.mode = mode[-1]
self.ring = self.R.base_ring()
self.ell = self.ring.ngens()
self.basis = [
evaluate_matrix(self.R, y) for y in identity_matrix(QQ, self.ell)
]
def test_import_export_mathematica(t):
a, b = SR.var("v1 v2")
M = matrix([[1, a, b], [a + b, Rational((2, 3)), a / b]])
fout = StringIO()
export_matrix_mathematica(fout, M)
MM = import_matrix_mathematica(StringIO(fout.getvalue()))
t.assertEqual(M, MM)
def place_list(self, mx, Mx, frac=1, mark_origin=True):
"""
return a tuple of
1. values that are all the integer multiple of
<frac>*self.numerical_value
between mx and Mx
2. the multiple of the basis unit.
Give <frac> as literal real. Recall that python evaluates 1/2 to 0. If you pass 0.5, it will be converted back to 1/2 for a nice display.
"""
try:
# If the user enters "0.5", it is converted to 1/2
frac = Rational(frac)
except TypeError:
pass
if frac == 0:
raise ValueError(
"frac is zero in AxesUnit.place_list(). Maybe you ignore that python evaluates 1/2 to 0 ? (writes literal 0.5 instead) \n Or are you trying to push me in an infinite loop ?"
)
l = []
k = SR.var("TheTag")
for x in MultipleBetween(frac * self.numerical_value, mx, Mx,
mark_origin):
if self.latex_symbol == "":
l.append((x, "$" + latex(x) + "$"))
else:
pos = (x / self.numerical_value) * k
# This risks to be Sage-version dependent.
text = "$" + latex(pos).replace("TheTag",
self.latex_symbol) + "$"
l.append((x, text))
return l
def test_reduce_at_one_point_1(t):
x = SR.var("x")
M0 = matrix([[1 / x, 4, 0, 5], [0, 2 / x, 0, 0], [0, 0, 3 / x, 6],
[0, 0, 0, 4 / x]])
u = matrix([[0, Rational((3, 5)),
Rational((4, 5)), 0],
[Rational((5, 13)), 0, 0,
Rational((12, 13))]])
M1 = transform(M0, x, balance(u.transpose() * u, 0, 1, x))
M1 = M1.simplify_rational()
u = matrix([[8, 0, 15, 0]]) / 17
M2 = transform(M1, x, balance(u.transpose() * u, 0, 2, x))
M2 = M2.simplify_rational()
M2_sing = singularities(M2, x)
t.assertIn(0, M2_sing)
t.assertEqual(M2_sing[0], 2)
M3, T23 = reduce_at_one_point(M2, x, 0, 2)
M3 = M3.simplify_rational()
t.assertEqual(M3, transform(M2, x, T23).simplify_rational())
M3_sing = singularities(M3, x)
t.assertIn(0, M3_sing)
t.assertEqual(M3_sing[0], 1)
M4, T34 = reduce_at_one_point(M3, x, 0, 1)
M4 = M4.simplify_rational()
t.assertEqual(M4, transform(M3, x, T34).simplify_rational())
M4_sing = singularities(M4, x)
t.assertIn(0, M4_sing)
t.assertEqual(M4_sing[0], 0)
def get_rels_elems1(self, tcs):
if __debug__:
assert len(tcs) >= 1, tcs
aeterms = tcs[0].keys()
#Find rels among array elements
logger.debug('Find linear eqts over {} array elems'
.format(len(aeterms)))
aeterms = [SR(1)] + aeterms
from dig_polynomials import Eqt
solverE = Eqt(aeterms, tcs, self.xinfo)
logger.info('Select traces (note: |tcs|=|terms|={})'.format(len(aeterms)))
ntcs_extra = 200
solverE.tcs, solverE.tcs_extra = get_traces(tcs,len(aeterms),ntcs_extra=200)
solverE.do_refine=False
solverE._go()
from dig_refine import Refine
rf = Refine(solverE.sols)
rf.rfilter(tcs=solverE.tcs_extra)
ps = [p.p for p in rf.ps]
return ps
def sreduce(ps):
"""
Return the basis (e.g., a min subset of ps that implies ps)
of the set of eqts input ps using Groebner basis
Examples:
sage: var('a y b q k')
(a, y, b, q, k)
sage: rs = InvEqt.sreduce([a*y-b==0,q*y+k-x==0,a*x-a*k-b*q==0])
sage: assert set(rs) == set([a*y - b == 0, q*y + k - x == 0])
sage: rs = InvEqt.sreduce([x*y==6,y==2,x==3])
sage: assert set(rs) == set([x - 3 == 0, y - 2 == 0])
#Attribute error occurs when only 1 var, thus return as is
sage: rs = InvEqt.sreduce([x*x==4,x==2])
sage: assert set(rs) == set([x == 2, x^2 == 4])
"""
if __debug__:
assert all(p.operator() == operator.eq for p in ps), ps
try:
Q = PolynomialRing(QQ, get_vars(ps))
I = Q * ps
#ps_ = I.radical().groebner_basis()
ps = I.radical().interreduced_basis()
ps = [(SR(p) == 0) for p in ps]
except AttributeError:
pass
return ps
def Llenar_Vector_Funciones(nombreArchTxt):
"""Funcion que lee un archivo de texto, extrae las expresiones que estan despues de un signo '=' y las almacena en un vector columna"""
try:
# Se ubica a dos directorios anteriores porque esta funcion es llamada desde un programa en el directorio donde esta la funcion principal
archFun = open(f"{nombreArchTxt}.txt", "r")
except:
print("\nNo es posible abrir el archivo")
print(
"Crea un archivo de texto nuevo e ingresa las funciones ahí, guardalo con el formato 'txt' y vuelve a correr el programa\n"
)
sys.exit(1)
# Bucle que recorre el archivo para extraer lo que aparece despues del signo '=' en el archivo de texto
for linea in archFun:
indIn = linea.find("=")
funciones = linea[(indIn + 1):]
# Elimina las llaves que hay en la cadena
indLlave1 = funciones.find("[")
indLlave2 = funciones.find("]")
funciones = funciones[(indLlave1 + 1):indLlave2]
# Separa las funciones delimitadas por comas
funciones = funciones.split(',')
# Crea vector que contendra las expresiones que escriba el usuario en el archivo de texto
matFun = np.empty((len(funciones), 1), dtype=type(SR()))
contFun = 0
# Recorre la lista de funciones, intenta convertir a formato de funciones de sagemath y las almacena en el vector de funciones
for funcion in funciones:
try:
matFun[contFun, 0] = SR(funcion)
except:
print(
"\nNo ha sido posible convertir alguna de las expresiones en un formato compatible con el programa"
)
print(
"Revise la documentacion de SageMath para ver como se pueden ingresar las expresiones\n"
)
sys.exit(1)
contFun += 1
# Cierra el archivo
archFun.close()
# Regresa el vector columna con las expresiones leidas desde el archivo de texto
return matFun
def quadratic_L_function__exact(n, d):
r"""
Returns the exact value of a quadratic twist of the Riemann Zeta function
by `\chi_d(x) = \left(\frac{d}{x}\right)`.
The input `n` must be a critical value.
EXAMPLES::
sage: quadratic_L_function__exact(1, -4)
1/4*pi
sage: quadratic_L_function__exact(-4, -4)
5/2
sage: quadratic_L_function__exact(2, 1)
1/6*pi^2
TESTS::
sage: quadratic_L_function__exact(2, -4)
Traceback (most recent call last):
...
TypeError: n must be a critical value (i.e. odd > 0 or even <= 0)
REFERENCES:
- [Iwa1972]_, pp 16-17, Special values of `L(1-n, \chi)` and `L(n, \chi)`
- [IR1990]_
- [Was1997]_
"""
from sage.all import SR, sqrt
if n <= 0:
return QuadraticBernoulliNumber(1 - n, d) / (n - 1)
elif n >= 1:
# Compute the kind of critical values (p10)
if kronecker_symbol(fundamental_discriminant(d), -1) == 1:
delta = 0
else:
delta = 1
# Compute the positive special values (p17)
if ((n - delta) % 2 == 0):
f = abs(fundamental_discriminant(d))
if delta == 0:
GS = sqrt(f)
else:
GS = I * sqrt(f)
ans = SR(ZZ(-1)**(1 + (n - delta) / 2))
ans *= (2 * pi / f)**n
ans *= GS # Evaluate the Gauss sum here! =0
ans *= QQ.one() / (2 * I**delta)
ans *= QuadraticBernoulliNumber(n, d) / factorial(n)
return ans
else:
if delta == 0:
raise TypeError(
"n must be a critical value (i.e. even > 0 or odd < 0)")
if delta == 1:
raise TypeError(
"n must be a critical value (i.e. odd > 0 or even <= 0)")
def __init__(self, a, b):
self.x = SR(a)
self.y = SR(b)
ObjectGraph.__init__(self, self)
self.point = self.obj
self.add_option("PointSymbol=*")
self._advised_mark_angle = None
try:
ax = abs(numerical_approx(self.x))
if ax < 0.00001 and ax > 0:
self.x = 0
ay = abs(numerical_approx(self.y))
if ay < 0.00001 and ay > 0:
self.y = 0
except TypeError:
pass
def padically_evaluate_regular(self, datum):
T = datum.toric_datum
if not T.is_regular():
raise ValueError('Can only processed regular toric data')
M = Set(range(T.length()))
q = SR.var('q')
alpha = {}
for I in Subsets(M):
F = [T.initials[i] for i in I]
V = SubvarietyOfTorus(F, torus_dim=T.ambient_dim)
alpha[I] = V.count()
def cat(u, v):
return vector(list(u) + list(v))
for I in Subsets(M):
cnt = sum((-1)**len(J) * alpha[I + J] for J in Subsets(M - I))
if not cnt:
continue
P = DirectProductOfPolyhedra(T.polyhedron,
StrictlyPositiveOrthant(len(I)))
it = iter(identity_matrix(ZZ, len(I)).rows())
ieqs = []
for i in M:
ieqs.append(
cat(
vector(ZZ, (0, )),
cat(
vector(ZZ, T.initials[i].exponents()[0]) -
vector(ZZ, T.lhs[i].exponents()[0]),
next(it) if i in I else zero_vector(ZZ, len(I)))))
if not ieqs:
ieqs = [vector(ZZ, (T.ambient_dim + len(I) + 1) * [0])]
Q = Polyhedron(ieqs=ieqs,
base_ring=QQ,
ambient_dim=T.ambient_dim + len(I))
foo, ring = symbolic_to_ratfun(
cnt * (q - 1)**len(I) / q**(T.ambient_dim),
[var('t'), var('q')])
corr_cnt = CyclotomicRationalFunction.from_laurent_polynomial(
foo, ring)
Phi = matrix([
cat(T.integrand[0], zero_vector(ZZ, len(I))),
cat(T.integrand[1], vector(ZZ,
len(I) * [-1]))
]).transpose()
sm = RationalSet([P.intersection(Q)]).generating_function()
for z in sm.monomial_substitution(QQ['t', 'q'], Phi):
yield corr_cnt * z
def test_fuchsify_by_blocks_05(test):
x, eps = SR.var("x eps")
m = matrix([
[eps / x, 0, 0],
[0, 2 * eps / x, -eps / x],
[x**2, 0, 3 * eps / x],
])
b = [(0, 1), (1, 2)]
test.assert_fuchsify_by_blocks_works(m, b, x, eps)
def test_fuchsify_by_blocks_03(test):
x, eps = SR.var("x eps")
m = matrix([
[eps / x, 0, 0],
[1 / x**2, 2 * eps / x, 0],
[1 / x**2, 2 / x**2, 3 * eps / x],
])
b = [(0, 1), (1, 1), (2, 1)]
test.assert_fuchsify_by_blocks_works(m, b, x, eps)
def _gen_rbt_Si( rbt ):
var('a theta alpha d',domain=RR)
D_exp2trig = { e**(SR.wild(0)) : e**SR.wild(0).real_part() * ( cos(SR.wild(0).imag_part()) + I*sin(SR.wild(0).imag_part()) ) }
M = matrix(SR,[[1,0,0,a],[0,cos(alpha),-sin(alpha),0],[0,sin(alpha),cos(alpha),d],[0,0,0,1]])
P = matrix([[0,-1,0,0],[1,0,0,0],[0,0,0,0],[0,0,0,0]])
ePtM = (exp(P*theta)*M).expand().subs(D_exp2trig).simplify_rational()
restore('a theta alpha d')
if bool( ePtM != rbt.T_dh ):
raise Exception('_gen_rbt_Si: interlink transformation does not follows implemented DH formulation')
S = ( inverse_T(M) * P * M ).expand().simplify_trig()
dof = rbt.dof
Si = range(dof+1)
for l in range(dof): Si[l+1] = se3unskew( S.subs(LoP_to_D(zip(rbt.params_dh , rbt.links_dh[l]))) )
return Si
def count(self):
for i in ([0, 1, 2] if common.symbolic else [0]):
try:
return SR(self._count_general(level=i)).expand()
except CountException:
logger.debug('count (level=%d) failed: %s' % (i, self))
raise CountException(
'Failed to count number of rational points. Try switching to symbolic mode.'
)
def test_transform_2(t):
# transform(transform(M, x, I), x, I^-1) == M
x = SR.var("x")
M = randpolym(x, 2)
T = randpolym(x, 2)
invT = T.inverse()
M1 = transform(M, x, T)
M2 = transform(M1, x, invT)
t.assertEqual(M2.simplify_rational(), M)
def get_background_graphic(self, **bdry_options):
r"""
Return a graphic object that makes the model easier to visualize.
For the hyperboloid model, the background object is the hyperboloid
itself.
EXAMPLES::
sage: H = HyperbolicPlane().HM().get_background_graphic()
"""
from sage.plot.plot3d.all import plot3d
from sage.all import SR
hyperboloid_opacity = bdry_options.get('hyperboloid_opacity', .1)
z_height = bdry_options.get('z_height', 7.0)
x_max = sqrt((z_height ** 2 - 1) / 2.0)
x = SR.var('x')
y = SR.var('y')
return plot3d((1 + x ** 2 + y ** 2).sqrt(),
(x, -x_max, x_max), (y,-x_max, x_max),
opacity=hyperboloid_opacity, **bdry_options)
def elliptic_cm_form(E, n, prec, aplist_only=False, anlist_only=False):
"""
Return q-expansion of the CM modular form associated to the n-th
power of the Grossencharacter associated to the elliptic curve E.
INPUT:
- E -- CM elliptic curve
- n -- positive integer
- prec -- positive integer
- aplist_only -- return list only of ap for p prime
- anlist_only -- return list only of an
OUTPUT:
- power series with integer coefficients
EXAMPLES::
sage: from psage.modform.rational.special import elliptic_cm_form
sage: f = CuspForms(121,4).newforms(names='a')[0]; f
q + 8*q^3 - 8*q^4 + 18*q^5 + O(q^6)
sage: E = EllipticCurve('121b')
sage: elliptic_cm_form(E, 3, 7)
q + 8*q^3 - 8*q^4 + 18*q^5 + O(q^7)
sage: g = elliptic_cm_form(E, 3, 100)
sage: g == f.q_expansion(100)
True
"""
if not E.has_cm():
raise ValueError, "E must have CM"
n = ZZ(n)
if n <= 0:
raise ValueError, "n must be positive"
prec = ZZ(prec)
if prec <= 0:
return []
elif prec <= 1:
return [ZZ(0)]
elif prec <= 2:
return [ZZ(0), ZZ(1)]
# Derive formula for sum of n-th powers of roots
a,p,T = SR.var('a,p,T')
roots = (T**2 - a*T + p).roots(multiplicities=False)
s = sum(alpha**n for alpha in roots).simplify_full()
# Create fast callable expression from formula
g = fast_callable(s.polynomial(ZZ))
# Compute aplist for the curve
v = E.aplist(prec)
# Use aplist to compute ap values for the CM form attached to n-th
# power of Grossencharacter.
P = prime_range(prec)
if aplist_only:
# case when we only want the a_p (maybe for computing an
# L-series via Euler product)
return [g(ap,p) for ap,p in zip(v,P)]
# Default cause where we want all a_n
anlist = [ZZ(0),ZZ(1)] + [None]*(prec-2)
for ap,p in zip(v,P):
anlist[p] = g(ap,p)
# Fill in the prime power a_{p^r} for r >= 2.
N = E.conductor()
for p in P:
prm2 = 1
prm1 = p
pr = p*p
pn = p**n
e = 1 if N%p else 0
while pr < prec:
anlist[pr] = anlist[prm1] * anlist[p]
if e:
anlist[pr] -= pn * anlist[prm2]
prm2 = prm1
prm1 = pr
pr *= p
# fill in a_n with n divisible by at least 2 primes
extend_multiplicatively_generic(anlist)
if anlist_only:
return anlist
f = Integer_list_to_polynomial(anlist, 'q')
return ZZ[['q']](f, prec=prec)
