def __init__(self, X, P, codomain = None, check = False):
r"""
Create the discrete probability space with probabilities on the
space X given by the dictionary P with values in the field
real_field.
EXAMPLES::
sage: S = [ i for i in range(16) ]
sage: P = {}
sage: for i in range(15): P[i] = 2^(-i-1)
sage: P[15] = 2^-16
sage: X = DiscreteProbabilitySpace(S,P)
sage: X.domain()
(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
sage: X.set()
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}
sage: X.entropy()
1.9997253418
A probability space can be defined on any list of elements.
EXAMPLES::
sage: AZ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
sage: S = [ AZ[i] for i in range(26) ]
sage: P = { 'A':1/2, 'B':1/4, 'C':1/4 }
sage: X = DiscreteProbabilitySpace(S,P)
sage: X
Discrete probability space defined by {'A': 1/2, 'C': 1/4, 'B': 1/4}
sage: X.entropy()
1.5
"""
if codomain is None:
codomain = RealField()
if not is_RealField(codomain) and not is_RationalField(codomain):
raise TypeError("Argument codomain (= %s) must be the reals or rationals" % codomain)
if check:
one = sum([ P[x] for x in P.keys() ])
if is_RationalField(codomain):
if not one == 1:
raise TypeError("Argument P (= %s) does not define a probability function")
else:
if not Abs(one-1) < 2^(-codomain.precision()+1):
raise TypeError("Argument P (= %s) does not define a probability function")
ProbabilitySpace_generic.__init__(self, X, codomain)
DiscreteRandomVariable.__init__(self, self, P, codomain, check)
def theta_by_cholesky(self, q_prec):
r"""
Uses the real Cholesky decomposition to compute (the `q`-expansion of) the
theta function of the quadratic form as a power series in `q` with terms
correct up to the power `q^{\text{q\_prec}}`. (So its error is `O(q^
{\text{q\_prec} + 1})`.)
REFERENCE:
From Cohen's "A Course in Computational Algebraic Number Theory" book,
p 102.
EXAMPLES::
## Check the sum of 4 squares form against Jacobi's formula
sage: Q = DiagonalQuadraticForm(ZZ, [1,1,1,1])
sage: Theta = Q.theta_by_cholesky(10)
sage: Theta
1 + 8*q + 24*q^2 + 32*q^3 + 24*q^4 + 48*q^5 + 96*q^6 + 64*q^7 + 24*q^8 + 104*q^9 + 144*q^10
sage: Expected = [1] + [8*sum([d for d in divisors(n) if d%4 != 0]) for n in range(1,11)]
sage: Expected
[1, 8, 24, 32, 24, 48, 96, 64, 24, 104, 144]
sage: Theta.list() == Expected
True
::
## Check the form x^2 + 3y^2 + 5z^2 + 7w^2 represents everything except 2 and 22.
sage: Q = DiagonalQuadraticForm(ZZ, [1,3,5,7])
sage: Theta = Q.theta_by_cholesky(50)
sage: Theta_list = Theta.list()
sage: [m for m in range(len(Theta_list)) if Theta_list[m] == 0]
[2, 22]
"""
## RAISE AN ERROR -- This routine is deprecated!
#raise NotImplementedError, "This routine is deprecated. Try theta_series(), which uses theta_by_pari()."
n = self.dim()
theta = [0 for i in range(q_prec+1)]
PS = PowerSeriesRing(ZZ, 'q')
bit_prec = 53 ## TO DO: Set this precision to reflect the appropriate roundoff
Cholesky = self.cholesky_decomposition(bit_prec) ## error estimate, to be confident through our desired q-precision.
Q = Cholesky ## <---- REDUNDANT!!!
R = RealField(bit_prec)
half = R(0.5)
## 1. Initialize
i = n - 1
T = [R(0) for j in range(n)]
U = [R(0) for j in range(n)]
T[i] = R(q_prec)
U[i] = 0
L = [0 for j in range (n)]
x = [0 for j in range (n)]
## 2. Compute bounds
#Z = sqrt(T[i] / Q[i,i]) ## IMPORTANT NOTE: sqrt preserves the precision of the real number it's given... which is not so good... =|
#L[i] = floor(Z - U[i]) ## Note: This is a Sage Integer
#x[i] = ceil(-Z - U[i]) - 1 ## Note: This is a Sage Integer too
done_flag = False
from_step4_flag = False
from_step3_flag = True ## We start by pretending this, since then we get to run through 2 and 3a once. =)
#double Q_val_double;
#unsigned long Q_val; // WARNING: Still need a good way of checking overflow for this value...
## Big loop which runs through all vectors
while not done_flag:
## Loop through until we get to i=1 (so we defined a vector x)
while from_step3_flag or from_step4_flag: ## IMPORTANT WARNING: This replaces a do...while loop, so it may have to be adjusted!
## Go to directly to step 3 if we're coming from step 4, otherwise perform step 2.
if from_step4_flag:
from_step4_flag = False
else:
## 2. Compute bounds
from_step3_flag = False
Z = sqrt(T[i] / Q[i,i])
L[i] = floor(Z - U[i])
x[i] = ceil(-Z - U[i]) - 1
## 3a. Main loop
## DIAGNOSTIC
#print
#print " L = ", L
#print " x = ", x
x[i] += 1
while (x[i] > L[i]):
## DIAGNOSTIC
#print " x = ", x
i += 1
x[i] += 1
## 3b. Main loop
if (i > 0):
from_step3_flag = True
## DIAGNOSTIC
#print " i = " + str(i)
#print " T[i] = " + str(T[i])
#print " Q[i,i] = " + str(Q[i,i])
#print " x[i] = " + str(x[i])
#print " U[i] = " + str(U[i])
#print " x[i] + U[i] = " + str(x[i] + U[i])
#print " T[i-1] = " + str(T[i-1])
T[i-1] = T[i] - Q[i,i] * (x[i] + U[i]) * (x[i] + U[i])
# DIAGNOSTIC
#print " T[i-1] = " + str(T[i-1])
#print
i += - 1
U[i] = 0
for j in range(i+1, n):
U[i] += Q[i,j] * x[j]
## 4. Solution found (This happens when i=0)
from_step4_flag = True
Q_val_double = q_prec - T[0] + Q[0,0] * (x[0] + U[0]) * (x[0] + U[0])
Q_val = floor(Q_val_double + half) ## Note: This rounds the value up, since the "round" function returns a float, but floor returns integer.
## DIAGNOSTIC
#print " Q_val_double = ", Q_val_double
#print " Q_val = ", Q_val
#raise RuntimeError
## OPTIONAL SAFETY CHECK:
eps = 0.000000001
if (abs(Q_val_double - Q_val) > eps):
raise RuntimeError("Oh No! We have a problem with the floating point precision... \n" \
+ " Q_val_double = " + str(Q_val_double) + "\n" \
+ " Q_val = " + str(Q_val) + "\n" \
+ " x = " + str(x) + "\n")
## DIAGNOSTIC
#print " The float value is " + str(Q_val_double)
#print " The associated long value is " + str(Q_val)
#print
if (Q_val <= q_prec):
theta[Q_val] += 2
## 5. Check if x = 0, for exit condition. =)
done_flag = True
for j in range(n):
if (x[j] != 0):
done_flag = False
## Set the value: theta[0] = 1
theta[0] = 1
## DIAGNOSTIC
#print "Leaving ComputeTheta \n"
## Return the series, truncated to the desired q-precision
return PS(theta)
def green_function(self, G,v, **kwds):
r"""
Evaluates the local Green's function at the place ``v`` for ``self`` with ``N`` terms of the series
or, in dimension 1, to within the specified error bound. Defaults to ``N=10`` if no kwds provided
Use ``v=0`` for the archimedean place. Must be over `\ZZ` or `\QQ`.
ALGORITHM:
See Exercise 5.29 and Figure 5.6 of ``The Arithmetic of Dynamics Systems``, Joseph H. Silverman, Springer, GTM 241, 2007.
INPUT:
- ``G`` - an endomorphism of self.codomain()
- ``v`` - non-negative integer. a place, use v=0 for the archimedean place
kwds:
- ``N`` - positive integer. number of terms of the series to use
- ``prec`` - positive integer, float point or p-adic precision, default: 100
- ``error_bound`` - a positive real number
OUTPUT:
- a real number
Examples::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,x*y]);
sage: Q=P(5,1)
sage: f.green_function(Q,0,N=30)
1.6460930159932946233759277576
::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,x*y]);
sage: Q=P(5,1)
sage: Q.green_function(f,0,N=200,prec=200)
1.6460930160038721802875250367738355497198064992657997569827
.. TODO::
error bounds for dimension > 1
"""
N = kwds.get('N', None) #Get number of iterates (if entered)
err = kwds.get('error_bound', None) #Get error bound (if entered)
prec = kwds.get('prec', 100) #Get precision (if entered)
R=RealField(prec)
if not (v == 0 or is_prime(v)):
raise ValueError("Invalid valuation (=%s) entered."%v)
if v == 0:
K = R
else:
K = Qp(v, prec)
#Coerce all polynomials in F into polynomials with coefficients in K
F=G.change_ring(K,False)
d = F.degree()
D=F.codomain().ambient_space().dimension_relative()
if err is not None:
if D!=1:
raise NotImplementedError("error bounds only for dimension 1")
err = R(err)
if not err>0:
raise ValueError, "Error bound (=%s) must be positive."%err
#if doing error estimates, compute needed number of iterates
res = F.resultant()
#compute maximum coefficient of polynomials of F
C = R(G.global_height(prec))
if v == 0:
log_fact = R(0)
for i in range(2*d+1):
log_fact += R(i+1).log()
B = max((R(res.abs()) - R(2*d).log() - (2*d-1)*C - log_fact).log().abs(), (C + R(d+1).log()).abs())
else:
B = max(R(res.abs()).log() - ((2*d-1)*C).abs(), C.abs())
N = R(B/(err*(d-1))).log(d).abs().ceil()
elif N is None:
N=10 #default is to do 10 iterations
#Coerce the coordinates into Q_v
self.normalize_coordinates()
if self.codomain().base_ring()==QQ:
self.clear_denominators()
P=self.change_ring(K,False)
#START GREEN FUNCTION CALCULATION
g = R(0)
for i in range(N+1):
m = -1
#compute the maximum absolute value of entries of a, and where it occurs
for n in range(D+1):
a_v = R(P[n].abs())
if a_v > m:
j = n
m = a_v
#add to Greens function
g += (1/R(d))**(i)*R(m).log()
#normalize coordinates and evaluate
P.scale_by(1/P[j])
P = F(P,False)
return g
def exponential_integral_1(x, n=0):
r"""
Returns the exponential integral `E_1(x)`. If the optional
argument `n` is given, computes list of the first
`n` values of the exponential integral
`E_1(x m)`.
The exponential integral `E_1(x)` is
.. math::
E_1(x) = \int_{x}^{\infty} e^{-t}/t dt
INPUT:
- ``x`` -- a positive real number
- ``n`` -- (default: 0) a nonnegative integer; if
nonzero, then return a list of values ``E_1(x*m)`` for m =
1,2,3,...,n. This is useful, e.g., when computing derivatives of
L-functions.
OUTPUT:
A real number if n is 0 (the default) or a list of reals if n > 0.
The precision is the same as the input, with a default of 53 bits
in case the input is exact.
EXAMPLES::
sage: exponential_integral_1(2)
0.0489005107080611
sage: exponential_integral_1(2,4) # abs tol 1e-18
[0.0489005107080611, 0.00377935240984891, 0.000360082452162659, 0.0000376656228439245]
sage: exponential_integral_1(40,5)
[1.03677326145166e-19, 2.22854325868847e-37, 6.33732515501151e-55, 2.02336191509997e-72, 6.88522610630764e-90]
sage: exponential_integral_1(0)
+Infinity
sage: r = exponential_integral_1(RealField(150)(1))
sage: r
0.21938393439552027367716377546012164903104729
sage: parent(r)
Real Field with 150 bits of precision
sage: exponential_integral_1(RealField(150)(100))
3.6835977616820321802351926205081189876552201e-46
TESTS:
The relative error for a single value should be less than 1 ulp::
sage: for prec in [20..1000]: # long time (22s on sage.math, 2013)
....: R = RealField(prec)
....: S = RealField(prec+64)
....: for t in range(8): # Try 8 values for each precision
....: a = R.random_element(-15,10).exp()
....: x = exponential_integral_1(a)
....: y = exponential_integral_1(S(a))
....: e = float(abs(S(x) - y)/x.ulp())
....: if e >= 1.0:
....: print "exponential_integral_1(%s) with precision %s has error of %s ulp"%(a, prec, e)
The absolute error for a vector should be less than `c 2^{-p}`, where
`p` is the precision in bits of `x` and `c = 2 max(1, exponential_integral_1(x))`::
sage: for prec in [20..128]: # long time (15s on sage.math, 2013)
....: R = RealField(prec)
....: S = RealField(prec+64)
....: a = R.random_element(-15,10).exp()
....: n = 2^ZZ.random_element(14)
....: x = exponential_integral_1(a, n)
....: y = exponential_integral_1(S(a), n)
....: c = RDF(2 * max(1.0, y[0]))
....: for i in range(n):
....: e = float(abs(S(x[i]) - y[i]) << prec)
....: if e >= c:
....: print "exponential_integral_1(%s, %s)[%s] with precision %s has error of %s >= %s"%(a, n, i, prec, e, c)
ALGORITHM: use the PARI C-library function ``eint1``.
REFERENCE:
- See Proposition 5.6.12 of Cohen's book "A Course in
Computational Algebraic Number Theory".
"""
if isinstance(x, Expression):
if x.is_trivial_zero():
from sage.rings.infinity import Infinity
return Infinity
else:
raise NotImplementedError(
"Use the symbolic exponential integral " +
"function: exp_integral_e1.")
elif not is_inexact(x): # x is exact and not an expression
if not x: # test if exact x == 0 quickly
from sage.rings.infinity import Infinity
return Infinity
# else x is not an exact 0
from sage.libs.pari.all import pari
# Figure out output precision
try:
prec = parent(x).precision()
except AttributeError:
prec = 53
R = RealField(prec)
if n <= 0:
# Add extra bits to the input.
# (experimentally verified -- Jeroen Demeyer)
inprec = prec + math.ceil(math.log(2 * prec))
x = RealField(inprec)(x)._pari_()
return R(x.eint1())
else:
# PARI's algorithm is less precise as n grows larger:
# add extra bits.
# (experimentally verified -- Jeroen Demeyer)
inprec = prec + 1 + math.ceil(1.4427 * math.log(n))
x = RealField(inprec)(x)._pari_()
return [R(z) for z in x.eint1(n)]
def bdd_height(K, height_bound, tolerance=1e-2, precision=53):
r"""
Compute all elements in the number field `K` which have relative
multiplicative height at most ``height_bound``.
The function can only be called for number fields `K` with positive unit
rank. An error will occur if `K` is `QQ` or an imaginary quadratic field.
This algorithm computes 2 lists: L containing elements x in `K` such that
H_k(x) <= B, and a list L' containing elements x in `K` that, due to
floating point issues,
may be slightly larger then the bound. This can be controlled
by lowering the tolerance.
In current implementation both lists (L,L') are merged and returned in
form of iterator.
ALGORITHM:
This is an implementation of the revised algorithm (Algorithm 4) in
[DK2013]_.
INPUT:
- ``height_bound`` -- real number
- ``tolerance`` -- (default: 0.01) a rational number in (0,1]
- ``precision`` -- (default: 53) positive integer
OUTPUT:
- an iterator of number field elements
EXAMPLES:
There are no elements of negative height::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^5 - x + 7)
sage: list(bdd_height(K,-3))
[]
The only nonzero elements of height 1 are the roots of unity::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(3)
sage: list(bdd_height(K,1))
[0, -1, 1]
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(36865)
sage: len(list(bdd_height(K,101))) # long time (4 s)
131
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^6 + 2)
sage: len(list(bdd_height(K,60))) # long time (5 s)
1899
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^4 - x^3 - 3*x^2 + x + 1)
sage: len(list(bdd_height(K,10)))
99
TESTS:
Check that :trac:`22771` is fixed::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<v> = NumberField(x^3 + x + 1)
sage: len(list(bdd_height(K,3)))
23
"""
# global values, used in internal function
B = height_bound
theta = tolerance
if B < 1:
return
embeddings = K.places(prec=precision)
O_K = K.ring_of_integers()
r1, r2 = K.signature()
r = r1 + r2 - 1
RF = RealField(precision)
lambda_gens_approx = {}
class_group_rep_norm_log_approx = []
unit_log_dict = {}
def rational_in(x, y):
r"""
Compute a rational number q, such that x<q<y using Archimedes' axiom
"""
z = y - x
if z == 0:
n = 1
else:
n = RR(1 / z).ceil() + 1
if RR(n * y).ceil() is n * y: # WHAT !?
m = n * y - 1
else:
m = RR(n * y).floor()
return m / n
def delta_approximation(x, delta):
r"""
Compute a rational number in range (x-delta, x+delta)
"""
return rational_in(x - delta, x + delta)
def vector_delta_approximation(v, delta):
r"""
Compute a rational vector w=(w1, ..., wn)
such that |vi-wi|<delta for all i in [1, n]
"""
return [delta_approximation(vi, delta) for vi in v]
def log_map(number):
r"""
Compute the image of an element of `K` under the logarithmic map.
"""
x = number
x_logs = []
for i in range(r1):
sigma = embeddings[i] # real embeddings
x_logs.append(sigma(x).abs().log())
for i in range(r1, r + 1):
tau = embeddings[i] # Complex embeddings
x_logs.append(2 * tau(x).abs().log())
return vector(x_logs)
def log_height_for_generators_approx(alpha, beta, Lambda):
r"""
Compute the rational approximation of logarithmic height function.
Return a lambda approximation h_K(alpha/beta)
"""
delta = Lambda / (r + 2)
norm_log = delta_approximation(
RR(O_K.ideal(alpha, beta).norm()).log(), delta)
log_ga = vector_delta_approximation(log_map(alpha), delta)
log_gb = vector_delta_approximation(log_map(beta), delta)
arch_sum = sum([max(log_ga[k], log_gb[k]) for k in range(r + 1)])
return (arch_sum - norm_log)
def packet_height(n, pair, u):
r"""
Compute the height of the element of `K` encoded by a given packet.
"""
gens = generator_lists[n]
i = pair[0]
j = pair[1]
Log_gi = lambda_gens_approx[gens[i]]
Log_gj = lambda_gens_approx[gens[j]]
Log_u_gi = vector(Log_gi) + unit_log_dict[u]
arch_sum = sum([max(Log_u_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_log_approx[n])
# Step 1
# Computes ideal class representative and their rational approx norm
t = theta / (3 * B)
delta_1 = t / (6 * r + 12)
class_group_reps = []
class_group_rep_norms = []
for c in K.class_group():
a = c.ideal()
a_norm = a.norm()
log_norm = RF(a_norm).log()
log_norm_approx = delta_approximation(log_norm, delta_1)
class_group_reps.append(a)
class_group_rep_norms.append(a_norm)
class_group_rep_norm_log_approx.append(log_norm_approx)
class_number = len(class_group_reps)
# Step 2
# Find generators for principal ideals of bounded norm
possible_norm_set = set([])
for n in range(class_number):
for m in range(1, (B + 1).ceil()):
possible_norm_set.add(m * class_group_rep_norms[n])
bdd_ideals = bdd_norm_pr_ideal_gens(K, possible_norm_set)
# Stores it in form of an dictionary and gives lambda(g)_approx for key g
for norm in possible_norm_set:
gens = bdd_ideals[norm]
for g in gens:
lambda_g_approx = vector_delta_approximation(log_map(g), delta_1)
lambda_gens_approx[g] = lambda_g_approx
# Step 3
# Find a list of all generators corresponding to each ideal a_l
generator_lists = []
for l in range(class_number):
this_ideal = class_group_reps[l]
this_ideal_norm = class_group_rep_norms[l]
gens = []
for i in range(1, (B + 1).ceil()):
for g in bdd_ideals[i * this_ideal_norm]:
if g in this_ideal:
gens.append(g)
generator_lists.append(gens)
# Step 4
# Finds all relevant pair and their height
gen_height_approx_dictionary = {}
relevant_pair_lists = []
for n in range(class_number):
relevant_pairs = []
gens = generator_lists[n]
l = len(gens)
for i in range(l):
for j in range(i + 1, l):
if K.ideal(gens[i], gens[j]) == class_group_reps[n]:
relevant_pairs.append([i, j])
gen_height_approx_dictionary[(
n, i, j)] = log_height_for_generators_approx(
gens[i], gens[j], t / 6)
relevant_pair_lists.append(relevant_pairs)
# Step 5
b = rational_in(t / 12 + RR(B).log(), t / 4 + RR(B).log())
maximum = 0
for n in range(class_number):
for p in relevant_pair_lists[n]:
maximum = max(maximum,
gen_height_approx_dictionary[(n, p[0], p[1])])
d_tilde = b + t / 6 + maximum
# Step 6
# computes fundamental units and their value under log map
fund_units = UnitGroup(K).fundamental_units()
fund_unit_logs = [log_map(fund_units[i]) for i in range(r)]
S = column_matrix(fund_unit_logs).delete_rows([r])
S_inverse = S.inverse()
S_norm = S.norm(Infinity)
S_inverse_norm = S_inverse.norm(Infinity)
upper_bound = (r**2) * max(S_norm, S_inverse_norm)
m = RR(upper_bound).ceil() + 1
# Step 7
# Variables needed for rational approximation
lambda_tilde = (t / 12) / (d_tilde * r * (1 + m))
delta_tilde = min(lambda_tilde / ((r**2) * ((m**2) + m * lambda_tilde)),
1 / (r**2))
M = d_tilde * (upper_bound + lambda_tilde * RR(r).sqrt())
M = RR(M).ceil()
d_tilde = RR(d_tilde)
delta_2 = min(delta_tilde, (t / 6) / (r * (r + 1) * M))
# Step 8, 9
# Computes relevant points in polytope
fund_unit_log_approx = [
vector_delta_approximation(fund_unit_logs[i], delta_2)
for i in range(r)
]
S_tilde = column_matrix(fund_unit_log_approx).delete_rows([r])
S_tilde_inverse = S_tilde.inverse()
U = integer_points_in_polytope(S_tilde_inverse, d_tilde)
# Step 10
# tilde suffixed list are used for computing second list (L_primed)
yield K(0)
U0 = []
U0_tilde = []
L0 = []
L0_tilde = []
# Step 11
# Computes unit height
unit_height_dict = {}
U_copy = copy(U)
inter_bound = b - (5 * t) / 12
for u in U:
u_log = sum([u[j] * vector(fund_unit_log_approx[j]) for j in range(r)])
unit_log_dict[u] = u_log
u_height = sum([max(u_log[k], 0) for k in range(r + 1)])
unit_height_dict[u] = u_height
if u_height < inter_bound:
U0.append(u)
if inter_bound <= u_height and u_height < b - (t / 12):
U0_tilde.append(u)
if u_height > t / 12 + d_tilde:
U_copy.remove(u)
U = U_copy
relevant_tuples = set(U0 + U0_tilde)
# Step 12
# check for relevant packets
for n in range(class_number):
for pair in relevant_pair_lists[n]:
i = pair[0]
j = pair[1]
u_height_bound = b + gen_height_approx_dictionary[(n, i,
j)] + t / 4
for u in U:
if unit_height_dict[u] < u_height_bound:
candidate_height = packet_height(n, pair, u)
if candidate_height <= b - 7 * t / 12:
L0.append([n, pair, u])
relevant_tuples.add(u)
elif candidate_height < b + t / 4:
L0_tilde.append([n, pair, u])
relevant_tuples.add(u)
# Step 13
# forms a dictionary of all_unit_tuples and their value
tuple_to_unit_dict = {}
for u in relevant_tuples:
unit = K.one()
for k in range(r):
unit *= fund_units[k]**u[k]
tuple_to_unit_dict[u] = unit
# Step 14
# Build all output numbers
roots_of_unity = K.roots_of_unity()
for u in U0 + U0_tilde:
for zeta in roots_of_unity:
yield zeta * tuple_to_unit_dict[u]
# Step 15
for p in L0 + L0_tilde:
gens = generator_lists[p[0]]
i = p[1][0]
j = p[1][1]
u = p[2]
c_p = tuple_to_unit_dict[u] * (gens[i] / gens[j])
for zeta in roots_of_unity:
yield zeta * c_p
yield zeta / c_p
def dynatomic_polynomial(self,period):
r"""
For a map `f:\mathbb{P}^1 \to \mathbb{P}^1` this function computes the dynatomic polynomial.
The dynatomic polynomial is the analog of the cyclotomic polynomial and its roots are the points of formal period `n`.
ALGORITHM:
For a positive integer `n`, let `[F_n,G_n]` be the coordinates of the `nth` iterate of `f`.
Then construct
.. MATH::
\Phi^{\ast}_n(f)(x,y) = \sum_{d \mid n} (yF_d(x,y) - xG_d(x,y))^{\mu(n/d)}
where `\mu` is the Moebius function.
For a pair `[m,n]`, let `f^m = [F_m,G_m]`. Compute
.. MATH::
\Phi^{\ast}_{m,n}(f)(x,y) = \Phi^{\ast}_n(f)(F_m,G_m)/\Phi^{\ast}_n(f)(F_{m-1},G_{m-1})
REFERENCES:
..
- B. Hutz. Efficient determination of rational preperiodic points for endomorphisms of projective space. arxiv:1210.6246, 2012.
- P. Morton and P. Patel. The Galois theory of periodic points of polynomial maps. Proc. London Math. Soc., 68 (1994), 225-263.
INPUT:
- ``period`` -- a positive integer or a list/tuple `[m,n]` where `m` is the preperiod and `n` is the period
OUTPUT:
- If possible, a two variable polynomial in the coordinate ring of ``self``.
Otherwise a fraction field element of the coordinate ring of ``self``
.. TODO::
Do the division when the base ring is p-adic or a function field so that the output is a polynomial.
EXAMPLES::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,y^2])
sage: f.dynatomic_polynomial(2)
x^2 + x*y + 2*y^2
::
sage: P.<x,y>=ProjectiveSpace(ZZ,1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,x*y])
sage: f.dynatomic_polynomial(4)
2*x^12 + 18*x^10*y^2 + 57*x^8*y^4 + 79*x^6*y^6 + 48*x^4*y^8 + 12*x^2*y^10 + y^12
::
sage: P.<x,y>=ProjectiveSpace(CC,1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,3*x*y])
sage: f.dynatomic_polynomial(3)
13.0000000000000*x^6 + 117.000000000000*x^4*y^2 +
78.0000000000000*x^2*y^4 + y^6
::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^2-10/9*y^2,y^2])
sage: f.dynatomic_polynomial([2,1])
x^4*y^2 - 11/9*x^2*y^4 - 80/81*y^6
::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^2-29/16*y^2,y^2])
sage: f.dynatomic_polynomial([2,3])
x^12 - 95/8*x^10*y^2 + 13799/256*x^8*y^4 - 119953/1024*x^6*y^6 +
8198847/65536*x^4*y^8 - 31492431/524288*x^2*y^10 +
172692729/16777216*y^12
::
sage: P.<x,y>=ProjectiveSpace(ZZ,1)
sage: H=Hom(P,P)
sage: f=H([x^2-y^2,y^2])
sage: f.dynatomic_polynomial([1,2])
x^2 - x*y
::
sage: P.<x,y>=ProjectiveSpace(QQ,1)
sage: H=Hom(P,P)
sage: f=H([x^3-y^3,3*x*y^2])
sage: f.dynatomic_polynomial([0,4])==f.dynatomic_polynomial(4)
True
::
sage: P.<x,y,z>=ProjectiveSpace(QQ,2)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,x*y,z^2])
sage: f.dynatomic_polynomial(2)
Traceback (most recent call last):
...
TypeError: Does not make sense in dimension >1
::
#TODO: it would be nice to get this to actually be a polynomial
sage: P.<x,y>=ProjectiveSpace(Qp(5),1)
sage: H=Hom(P,P)
sage: f=H([x^2+y^2,y^2])
sage: f.dynatomic_polynomial(2)
(x^4*y + (2 + O(5^20))*x^2*y^3 - x*y^4 + (2 + O(5^20))*y^5)/(x^2*y -
x*y^2 + y^3)
::
sage: L.<t>=PolynomialRing(QQ)
sage: P.<x,y>=ProjectiveSpace(L,1)
sage: H=Hom(P,P)
sage: f=H([x^2+t*y^2,y^2])
sage: f.dynatomic_polynomial(2)
x^2 + x*y + (t + 1)*y^2
::
sage: K.<c>=PolynomialRing(ZZ)
sage: P.<x,y>=ProjectiveSpace(K,1)
sage: H=Hom(P,P)
sage: f=H([x^2+ c*y^2,y^2])
sage: f.dynatomic_polynomial([1,2])
x^2 - x*y + (c + 1)*y^2
"""
if self.domain() != self.codomain():
raise TypeError("Must have same domain and codomain to iterate")
from sage.schemes.projective.projective_space import is_ProjectiveSpace
if is_ProjectiveSpace(self.domain())==False:
raise NotImplementedError("Not implemented for subschemes")
if self.domain().dimension_relative()>1:
raise TypeError("Does not make sense in dimension >1")
if (isinstance(period,(list,tuple))==False):
period=[0,period]
try:
period[0]=ZZ(period[0])
period[1]=ZZ(period[1])
except TypeError:
raise TypeError("Period and preperiod must be integers")
if period[1]<=0:
raise AttributeError("Period must be at least 1")
if period[0]!=0:
m=period[0]
fm=self.nth_iterate_map(m)
fm1=self.nth_iterate_map(m-1)
n=period[1]
PHI=1;
x=self.domain().gen(0)
y=self.domain().gen(1)
F=self._polys
f=F
for d in range(1,n+1):
if n%d ==0:
PHI=PHI*((y*F[0]-x*F[1])**moebius(n/d))
if d !=n: #avoid extra iteration
F=[f[0](F[0],F[1]),f[1](F[0],F[1])]
if m!=0:
PHI=PHI(fm._polys)/PHI(fm1._polys)
else:
PHI=1;
x=self.domain().gen(0)
y=self.domain().gen(1)
F=self._polys
f=F
for d in range(1,period[1]+1):
if period[1]%d ==0:
PHI=PHI*((y*F[0]-x*F[1])**moebius(period[1]/d))
if d !=period[1]: #avoid extra iteration
F=[f[0](F[0],F[1]),f[1](F[0],F[1])]
from sage.rings.polynomial.polynomial_ring import PolynomialRing_general
from sage.rings.polynomial.multi_polynomial_ring_generic import MPolynomialRing_generic
if self.domain().base_ring()==RealField() or self.domain().base_ring()==ComplexField():
PHI=PHI.numerator()._maxima_().divide(PHI.denominator())[0].sage()
elif isinstance(self.domain().base_ring(),(PolynomialRing_general,MPolynomialRing_generic)):
from sage.rings.padics.generic_nodes import is_pAdicField, is_pAdicRing
from sage.rings.function_field.function_field import is_FunctionField
BR=self.domain().base_ring().base_ring()
if is_pAdicField(BR) or is_pAdicRing(BR) or is_FunctionField(BR):
raise NotImplementedError("Not implemented")
PHI=PHI.numerator()._maxima_().divide(PHI.denominator())[0].sage()
#do it again to divide out by denominators of coefficients
PHI=PHI.numerator()._maxima_().divide(PHI.denominator())[0].sage()
if PHI.denominator()==1:
PHI=self.coordinate_ring()(PHI)
return(PHI)
def homogenize(self, n, newvar='h'):
r"""
Return the homogenization of ``self``. If ``self.domain()`` is a subscheme, the domain of
the homogenized map is the projective embedding of ``self.domain()``
INPUT:
- ``newvar`` -- the name of the homogenization variable (only used when ``self.domain()`` is affine space)
- ``n`` -- the n-th projective embedding into projective space
OUTPUT:
- :class:`SchemMorphism_polynomial_projective_space`
EXAMPLES::
sage: A.<x,y>=AffineSpace(ZZ,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/x^5,y^2])
sage: f.homogenize(2,'z')
Scheme endomorphism of Projective Space of dimension 2 over Integer Ring
Defn: Defined on coordinates by sending (x : y : z) to
(x^2*z^5 - 2*z^7 : x^5*y^2 : x^5*z^2)
::
sage: A.<x,y>=AffineSpace(CC,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/(x*y),y^2-x])
sage: f.homogenize(0,'z')
Scheme endomorphism of Projective Space of dimension 2 over Complex
Field with 53 bits of precision
Defn: Defined on coordinates by sending (x : y : z) to
(x*y*z^2 : x^2*z^2 + (-2.00000000000000)*z^4 : x*y^3 - x^2*y*z)
::
sage: A.<x,y>=AffineSpace(ZZ,2)
sage: X=A.subscheme([x-y^2])
sage: H=Hom(X,X)
sage: f=H([9*y^2,3*y])
sage: f.homogenize(2)
Scheme endomorphism of Closed subscheme of Projective Space of dimension 2 over Integer Ring defined by:
-x1^2 + x0*x2
Defn: Defined on coordinates by sending (x0 : x1 : x2) to
(9*x0*x2 : 3*x1*x2 : x2^2)
::
sage: R.<t>=PolynomialRing(ZZ)
sage: A.<x,y>=AffineSpace(R,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/y,y^2-x])
sage: f.homogenize(0,'z')
Scheme endomorphism of Projective Space of dimension 2 over Univariate
Polynomial Ring in t over Integer Ring
Defn: Defined on coordinates by sending (x : y : z) to
(y*z^2 : x^2*z + (-2)*z^3 : y^3 - x*y*z)
"""
A = self.domain()
B = self.codomain()
N = A.ambient_space().dimension_relative()
NB = B.ambient_space().dimension_relative()
Vars = list(A.ambient_space().variable_names()) + [newvar]
S = PolynomialRing(A.base_ring(), Vars)
try:
l = lcm([self[i].denominator() for i in range(N)])
except Exception: #no lcm
l = prod([self[i].denominator() for i in range(N)])
from sage.rings.polynomial.polynomial_ring import PolynomialRing_general
from sage.rings.polynomial.multi_polynomial_ring_generic import MPolynomialRing_generic
if self.domain().base_ring() == RealField() or self.domain().base_ring(
) == ComplexField():
F = [
S(((self[i] * l).numerator())._maxima_().divide(
self[i].denominator())[0].sage()) for i in range(N)
]
elif isinstance(self.domain().base_ring(),
(PolynomialRing_general, MPolynomialRing_generic)):
F = [
S(((self[i] * l).numerator())._maxima_().divide(
self[i].denominator())[0].sage()) for i in range(N)
]
else:
F = [S(self[i] * l) for i in range(N)]
F.insert(n, S(l))
d = max([F[i].degree() for i in range(N + 1)])
F = [
F[i].homogenize(newvar) * S.gen(N)**(d - F[i].degree())
for i in range(N + 1)
]
from sage.schemes.affine.affine_space import is_AffineSpace
if is_AffineSpace(A) == True:
from sage.schemes.projective.projective_space import ProjectiveSpace
X = ProjectiveSpace(A.base_ring(), NB, Vars)
else:
X = A.projective_embedding(n).codomain()
phi = S.hom(X.ambient_space().gens(),
X.ambient_space().coordinate_ring())
F = [phi(f) for f in F]
H = Hom(X, X)
return (H(F))
def genfiles_mintides(integrator,
driver,
f,
ics,
initial,
final,
delta,
tolrel=1e-16,
tolabs=1e-16,
output=''):
r"""
Generate the needed files for the min_tides library.
INPUT:
- ``integrator`` -- the name of the integrator file.
- ``driver`` -- the name of the driver file.
- ``f`` -- the function that determines the differential equation.
- ``ics`` -- a list or tuple with the initial conditions.
- ``initial`` -- the initial time for the integration.
- ``final`` -- the final time for the integration.
- ``delta`` -- the step of the output.
- ``tolrel`` -- the relative tolerance.
- ``tolabs`` -- the absolute tolerance.
- ``output`` -- the name of the file that the compiled integrator will write to
This function creates two files, integrator and driver, that can be used
later with the min_tides library [TIDES]_.
TESTS::
sage: from sage.interfaces.tides import genfiles_mintides
sage: import os
sage: import shutil
sage: from sage.misc.temporary_file import tmp_dir
sage: tempdir = tmp_dir()
sage: intfile = os.path.join(tempdir, 'integrator.c')
sage: drfile = os.path.join(tempdir ,'driver.c')
sage: var('t,x,y,X,Y')
(t, x, y, X, Y)
sage: f(t,x,y,X,Y)=[X, Y, -x/(x^2+y^2)^(3/2), -y/(x^2+y^2)^(3/2)]
sage: genfiles_mintides(intfile, drfile, f, [1,0, 0, 0.2], 0, 10, 0.1, output = 'out')
sage: fileint = open(intfile)
sage: l = fileint.readlines()
sage: fileint.close()
sage: l[5]
' #include "minc_tides.h"\n'
sage: l[15]
' double XX[TT+1][MO+1];\n'
sage: l[25]
'\n'
sage: l[35]
'\t\tXX[1][i+1] = XX[3][i] / (i+1.0);\n'
sage: filedr = open(drfile)
sage: l = filedr.readlines()
sage: filedr.close()
sage: l[6]
' #include "minc_tides.h"\n'
sage: l[15]
' double tolrel, tolabs, tini, tend, dt;\n'
sage: l[25]
'\ttolrel = 9.9999999999999998e-17 ;\n'
sage: shutil.rmtree(tempdir)
Check that ticket :trac:`17179` is fixed (handle expressions like `\\pi`)::
sage: from sage.interfaces.tides import genfiles_mintides
sage: import os
sage: import shutil
sage: from sage.misc.temporary_file import tmp_dir
sage: tempdir = tmp_dir()
sage: intfile = os.path.join(tempdir, 'integrator.c')
sage: drfile = os.path.join(tempdir ,'driver.c')
sage: var('t,x,y,X,Y')
(t, x, y, X, Y)
sage: f(t,x,y,X,Y)=[X, Y, -x/(x^2+y^2)^(3/2), -y/(x^2+y^2)^(3/2)]
sage: genfiles_mintides(intfile, drfile, f, [pi, 0, 0, 0.2], 0, 10, 0.1, output = 'out')
sage: fileint = open(intfile)
sage: l = fileint.readlines()
sage: fileint.close()
sage: l[30]
'\t\tXX[8][i] = pow_mc_c(XX[7],-1.5000000000000000,XX[8], i);\n'
sage: filedr = open(drfile)
sage: l = filedr.readlines()
sage: filedr.close()
sage: l[18]
' \tv[0] = 3.1415926535897931 ; \n'
sage: shutil.rmtree(tempdir)
"""
RR = RealField()
l1, l2 = subexpressions_list(f)
remove_repeated(l1, l2)
remove_constants(l1, l2)
l0 = map(str, l1)
#generate the corresponding c lines
l3 = []
var = f[0].arguments()
lv = map(str, var)
for i in l2:
oper = i[0]
if oper in ["log", "exp", "sin", "cos"]:
a = i[1]
if a in var:
l3.append((oper, 'XX[{}]'.format(lv.index(str(a)))))
elif a in l1:
l3.append((oper, 'XX[{}]'.format(l0.index(str(a)) + len(var))))
else:
a = i[1]
b = i[2]
consta = False
constb = False
if str(a) in lv:
aa = 'XX[{}]'.format(lv.index(str(a)))
elif str(a) in l0:
aa = 'XX[{}]'.format(l0.index(str(a)) + len(var))
else:
consta = True
aa = RR(a).str(truncate=False)
if str(b) in lv:
bb = 'XX[{}]'.format(lv.index(str(b)))
elif str(b) in l0:
bb = 'XX[{}]'.format(l0.index(str(b)) + len(var))
else:
constb = True
bb = RR(b).str(truncate=False)
if consta:
oper += '_c'
if not oper == 'div':
bb, aa = aa, bb
elif constb:
oper += '_c'
l3.append((oper, aa, bb))
n = len(var)
res = []
for i in range(len(l3)):
el = l3[i]
string = "XX[{}][i] = ".format(i + n)
if el[0] == 'add':
string += el[1] + "[i] + " + el[2] + "[i];"
elif el[0] == 'add_c':
string += "(i==0)? {}+".format(
el[2]) + el[1] + "[0] : " + el[1] + "[i];"
elif el[0] == 'mul':
string += "mul_mc(" + el[1] + "," + el[2] + ",i);"
elif el[0] == 'mul_c':
string += el[2] + "*" + el[1] + "[i];"
elif el[0] == 'pow_c':
string += "pow_mc_c(" + el[1] + "," + el[
2] + ",XX[{}], i);".format(i + n)
elif el[0] == 'div':
string += "div_mc(" + el[2] + "," + el[1] + ",XX[{}], i);".format(
i + n)
elif el[0] == 'div_c':
string += "inv_mc(" + el[2] + "," + el[1] + ",XX[{}], i);".format(
i + n)
elif el[0] == 'log':
string += "log_mc(" + el[1] + ",XX[{}], i);".format(i + n)
elif el[0] == 'exp':
string += "exp_mc(" + el[1] + ",XX[{}], i);".format(i + n)
elif el[0] == 'sin':
string += "sin_mc(" + el[1] + ",XX[{}], i);".format(i + n + 1)
elif el[0] == 'cos':
string += "cos_mc(" + el[1] + ",XX[{}], i);".format(i + n - 1)
res.append(string)
l0 = lv + l0
indices = [l0.index(str(i(*var))) + n for i in f]
for i in range(1, n):
res.append("XX[{}][i+1] = XX[{}][i] / (i+1.0);".format(
i, indices[i - 1] - n))
code = res
outfile = open(integrator, 'a')
auxstring = """
/****************************************************************************
This file has been created by Sage for its use with TIDES
*****************************************************************************/
#include "minc_tides.h"
void mincseries(double t,double *v, double *p, double **XVAR,int ORDER, int MO)
{
int VAR,PAR,TT,i,j, inext;
"""
outfile.write(auxstring)
outfile.write("\tVAR = {};\n".format(n))
outfile.write("\tPAR = {};\n".format(0))
outfile.write("\tTT = {};\n".format(len(res)))
auxstring = """
double XX[TT+1][MO+1];
for(j=0; j<=TT; j++)
for(i=0; i<=ORDER; i++)
XX[j][i] = 0.e0;
XX[0][0] = t;
XX[0][1] = 1.e0;
for(i=1;i<=VAR;i++) {
XX[i][0] = v[i-1];
}
for(i=0;i<ORDER;i++) {
"""
outfile.write(auxstring)
outfile.writelines(["\t\t" + i + "\n" for i in code])
outfile.write('\t}\n')
outfile.write('\n')
outfile.write('\tfor(j=0; j<=VAR; j++)\n')
outfile.write('\t\tfor(i=0; i<=ORDER; i++)\n')
outfile.write('\t\t\tXVAR[i][j] = XX[j][i];\n')
outfile.write('}\n')
outfile.write('\n')
outfile = open(driver, 'a')
auxstring = """
/****************************************************************************
Driver file of the minc_tides program
This file has been automatically created by Sage
*****************************************************************************/
#include "minc_tides.h"
int main() {
int i, VARS, PARS;
VARS = %s ;
PARS = 1;
double tolrel, tolabs, tini, tend, dt;
double v[VARS], p[PARS];
""" % (n - 1)
outfile.write(auxstring)
for i in range(len(ics)):
outfile.write('\tv[{}] = {} ; \n'.format(
i,
RR(ics[i]).str(truncate=False)))
outfile.write('\ttini = {} ;\n'.format(RR(initial).str(truncate=False)))
outfile.write('\ttend = {} ;\n'.format(RR(final).str(truncate=False)))
outfile.write('\tdt = {} ;\n'.format(RR(delta).str(truncate=False)))
outfile.write('\ttolrel = {} ;\n'.format(RR(tolrel).str(truncate=False)))
outfile.write('\ttolabs = {} ;\n'.format(RR(tolabs).str(truncate=False)))
outfile.write('\textern char ofname[500];')
outfile.write('\tstrcpy(ofname, "' + output + '");\n')
outfile.write('\tminc_tides(v,VARS,p,PARS,tini,tend,dt,tolrel,tolabs);\n')
outfile.write('\treturn 0; \n }')
outfile.close()
def bessel_J(nu, z, algorithm="pari", prec=53):
r"""
Return value of the "J-Bessel function", or "Bessel function, 1st
kind", with index (or "order") nu and argument z.
::
Defn:
Maxima:
inf
==== - nu - 2 k nu + 2 k
\ (-1)^k 2 z
> -------------------------
/ k! Gamma(nu + k + 1)
====
k = 0
PARI:
inf
==== - 2k 2k
\ (-1)^k 2 z Gamma(nu + 1)
> ----------------------------
/ k! Gamma(nu + k + 1)
====
k = 0
Sometimes bessel_J(nu,z) is denoted J_nu(z) in the literature.
.. warning::
Inaccurate for small values of z.
EXAMPLES::
sage: bessel_J(2,1.1)
0.136564153956658
sage: bessel_J(0,1.1)
0.719622018527511
sage: bessel_J(0,1)
0.765197686557967
sage: bessel_J(0,0)
1.00000000000000
sage: bessel_J(0.1,0.1)
0.777264368097005
We check consistency of PARI and Maxima::
sage: n(bessel_J(3,10,"maxima"))
0.0583793793051...
sage: n(bessel_J(3,10,"pari"))
0.0583793793051868
sage: bessel_J(3,10,"scipy")
0.0583793793052...
Check whether the return value is real whenever the argument is real (#10251)::
sage: bessel_J(5, 1.5, algorithm='scipy') in RR
True
"""
if algorithm == "pari":
from sage.libs.pari.all import pari
try:
R = RealField(prec)
nu = R(nu)
z = R(z)
except TypeError:
C = ComplexField(prec)
nu = C(nu)
z = C(z)
K = C
if nu == 0:
nu = ZZ(0)
K = z.parent()
return K(pari(nu).besselj(z, precision=prec))
elif algorithm == "scipy":
if prec != 53:
raise ValueError, "for the scipy algorithm the precision must be 53"
import scipy.special
ans = str(scipy.special.jv(float(nu), complex(real(z), imag(z))))
ans = ans.replace("(", "")
ans = ans.replace(")", "")
ans = ans.replace("j", "*I")
ans = sage_eval(ans)
return real(ans) if z in RR else ans
elif algorithm == "maxima":
if prec != 53:
raise ValueError, "for the maxima algorithm the precision must be 53"
return maxima_function("bessel_j")(nu, z)
else:
raise ValueError, "unknown algorithm '%s'" % algorithm
def bessel_K(nu, z, algorithm="pari", prec=53):
r"""
Implements the "K-Bessel function", or "modified Bessel function,
2nd kind", with index (or "order") nu and argument z. Defn::
pi*(bessel_I(-nu, z) - bessel_I(nu, z))
----------------------------------------
2*sin(pi*nu)
if nu is not an integer and by taking a limit otherwise.
Sometimes bessel_K(nu,z) is denoted K_nu(z) in the literature. In
PARI, nu can be complex and z must be real and positive.
EXAMPLES::
sage: bessel_K(3,2,"scipy")
0.64738539094...
sage: bessel_K(3,2)
0.64738539094...
sage: bessel_K(1,1)
0.60190723019...
sage: bessel_K(1,1,"pari",10)
0.60
sage: bessel_K(1,1,"pari",100)
0.60190723019723457473754000154
TESTS::
sage: bessel_K(2,1.1, algorithm="maxima")
Traceback (most recent call last):
...
NotImplementedError: The K-Bessel function is only implemented for the pari and scipy algorithms
Check whether the return value is real whenever the argument is real (#10251)::
sage: bessel_K(5, 1.5, algorithm='scipy') in RR
True
"""
if algorithm == "scipy":
if prec != 53:
raise ValueError, "for the scipy algorithm the precision must be 53"
import scipy.special
ans = str(scipy.special.kv(float(nu), float(z)))
ans = ans.replace("(", "")
ans = ans.replace(")", "")
ans = ans.replace("j", "*I")
ans = sage_eval(ans)
return real(ans) if z in RR else ans
elif algorithm == 'pari':
from sage.libs.pari.all import pari
try:
R = RealField(prec)
nu = R(nu)
z = R(z)
except TypeError:
C = ComplexField(prec)
nu = C(nu)
z = C(z)
K = C
K = z.parent()
return K(pari(nu).besselk(z, precision=prec))
elif algorithm == 'maxima':
raise NotImplementedError, "The K-Bessel function is only implemented for the pari and scipy algorithms"
else:
raise ValueError, "unknown algorithm '%s'" % algorithm
def bessel_I(nu, z, algorithm="pari", prec=53):
r"""
Implements the "I-Bessel function", or "modified Bessel function,
1st kind", with index (or "order") nu and argument z.
INPUT:
- ``nu`` - a real (or complex, for pari) number
- ``z`` - a real (positive) algorithm - "pari" or
"maxima" or "scipy" prec - real precision (for PARI only)
DEFINITION::
Maxima:
inf
==== - nu - 2 k nu + 2 k
\ 2 z
> -------------------
/ k! Gamma(nu + k + 1)
====
k = 0
PARI:
inf
==== - 2 k 2 k
\ 2 z Gamma(nu + 1)
> -----------------------
/ k! Gamma(nu + k + 1)
====
k = 0
Sometimes ``bessel_I(nu,z)`` is denoted
``I_nu(z)`` in the literature.
.. warning::
In Maxima (the manual says) i0 is deprecated but
``bessel_i(0,*)`` is broken. (Was fixed in recent CVS patch
though.)
EXAMPLES::
sage: bessel_I(1,1,"pari",500)
0.565159103992485027207696027609863307328899621621092009480294489479255640964371134092664997766814410064677886055526302676857637684917179812041131208121
sage: bessel_I(1,1)
0.565159103992485
sage: bessel_I(2,1.1,"maxima")
0.16708949925104...
sage: bessel_I(0,1.1,"maxima")
1.32616018371265...
sage: bessel_I(0,1,"maxima")
1.2660658777520...
sage: bessel_I(1,1,"scipy")
0.565159103992...
Check whether the return value is real whenever the argument is real (#10251)::
sage: bessel_I(5, 1.5, algorithm='scipy') in RR
True
"""
if algorithm == "pari":
from sage.libs.pari.all import pari
try:
R = RealField(prec)
nu = R(nu)
z = R(z)
except TypeError:
C = ComplexField(prec)
nu = C(nu)
z = C(z)
K = C
K = z.parent()
return K(pari(nu).besseli(z, precision=prec))
elif algorithm == "scipy":
if prec != 53:
raise ValueError, "for the scipy algorithm the precision must be 53"
import scipy.special
ans = str(scipy.special.iv(float(nu), complex(real(z), imag(z))))
ans = ans.replace("(", "")
ans = ans.replace(")", "")
ans = ans.replace("j", "*I")
ans = sage_eval(ans)
return real(
ans) if z in RR else ans # Return real value when arg is real
elif algorithm == "maxima":
if prec != 53:
raise ValueError, "for the maxima algorithm the precision must be 53"
return sage_eval(maxima.eval("bessel_i(%s,%s)" %
(float(nu), float(z))))
else:
raise ValueError, "unknown algorithm '%s'" % algorithm
def covariant_z0(F, z0_cov=False, prec=53, emb=None, error_limit=0.000001):
r"""
Return the covariant and Julia invariant from Cremona-Stoll [CS2003]_.
In [CS2003]_ and [HS2018]_ the Julia invariant is denoted as `\Theta(F)`
or `R(F, z(F))`. Note that you may get faster convergence if you first move
`z_0(F)` to the fundamental domain before computing the true covariant
INPUT:
- ``F`` -- binary form of degree at least 3 with no multiple roots
- ``z0_cov`` -- boolean, compute only the `z_0` invariant. Otherwise, solve
the minimization problem
- ``prec``-- positive integer. precision to use in CC
- ``emb`` -- embedding into CC
- ``error_limit`` -- sets the error tolerance (default:0.000001)
OUTPUT: a complex number, a real number
EXAMPLES::
sage: from sage.rings.polynomial.binary_form_reduce import covariant_z0
sage: R.<x,y> = QQ[]
sage: F = 19*x^8 - 262*x^7*y + 1507*x^6*y^2 - 4784*x^5*y^3 + 9202*x^4*y^4\
....: - 10962*x^3*y^5 + 7844*x^2*y^6 - 3040*x*y^7 + 475*y^8
sage: covariant_z0(F, prec=80, z0_cov=True)
(1.3832330115323681438175 + 0.31233552177413614978744*I,
3358.4074848663492819259)
sage: F = -x^8 + 6*x^7*y - 7*x^6*y^2 - 12*x^5*y^3 + 27*x^4*y^4\
....: - 4*x^3*y^5 - 19*x^2*y^6 + 10*x*y^7 - 5*y^8
sage: covariant_z0(F, prec=80)
(0.64189877107807122203366 + 1.1852516565091601348355*I,
3134.5148284344627168276)
::
sage: R.<x,y> = QQ[]
sage: covariant_z0(x^3 + 2*x^2*y - 3*x*y^2, z0_cov=True)[0]
0.230769230769231 + 0.799408065031789*I
sage: -1/covariant_z0(-y^3 + 2*y^2*x + 3*y*x^2, z0_cov=True)[0]
0.230769230769231 + 0.799408065031789*I
::
sage: R.<x,y> = QQ[]
sage: covariant_z0(2*x^2*y - 3*x*y^2, z0_cov=True)[0]
0.750000000000000 + 1.29903810567666*I
sage: -1/covariant_z0(-x^3 - x^2*y + 2*x*y^2, z0_cov=True)[0] + 1
0.750000000000000 + 1.29903810567666*I
::
sage: R.<x,y> = QQ[]
sage: covariant_z0(x^2*y - x*y^2, prec=100)
(0.50000000000000000000000000003 + 0.86602540378443864676372317076*I,
1.5396007178390020386910634147)
TESTS::
sage: R.<x,y>=QQ[]
sage: covariant_z0(x^2 + 24*x*y + y^2)
Traceback (most recent call last):
...
ValueError: must be at least degree 3
sage: covariant_z0((x+y)^3, z0_cov=True)
Traceback (most recent call last):
...
ValueError: cannot have multiple roots for z0 invariant
sage: covariant_z0(x^3 + 3*x*y + y)
Traceback (most recent call last):
...
TypeError: must be a binary form
sage: covariant_z0(-2*x^2*y^3 + 3*x*y^4 + 127*y^5)
Traceback (most recent call last):
...
ValueError: cannot have a root with multiplicity >= 5/2
sage: covariant_z0((x^2+2*y^2)^2)
Traceback (most recent call last):
...
ValueError: must have at least 3 distinct roots
"""
R = F.parent()
d = ZZ(F.degree())
if R.ngens() != 2 or any([sum(t) != d for t in F.exponents()]):
raise TypeError('must be a binary form')
if d < 3:
raise ValueError('must be at least degree 3')
f = F.subs({R.gen(1): 1}).univariate_polynomial()
if f.degree() < d:
# we have a root at infinity
if f.constant_coefficient() != 0:
# invert so we find all roots!
mat = matrix(ZZ, 2, 2, [0, -1, 1, 0])
else:
t = 0
poly_ring = f.parent()
while f(t) == 0:
t += 1
mat = matrix(ZZ, 2, 2, [t, -1, 1, 0])
else:
mat = matrix(ZZ, 2, 2, [1, 0, 0, 1])
f = F(list(mat * vector(R.gens()))).subs({
R.gen(1): 1
}).univariate_polynomial()
# now we have a single variable polynomial with all the roots of F
K = ComplexField(prec=prec)
if f.base_ring() != K:
if emb == None:
f = f.change_ring(K)
else:
f = f.change_ring(emb)
roots = f.roots()
if (max([ex for p,ex in roots]) > 1)\
or (f.degree() < d-1):
if z0_cov:
raise ValueError('cannot have multiple roots for z0 invariant')
else:
# just need a starting point for Newton's method
f = f.lc() * prod([p for p, ex in f.factor()
]) # removes multiple roots
if f.degree() < 3:
raise ValueError('must have at least 3 distinct roots')
roots = f.roots()
roots = [p for p, ex in roots]
# finding quadratic Q_0, gives us our covariant, z_0
dF = f.derivative()
n = ZZ(f.degree())
PR = PolynomialRing(K, 'x,y')
x, y = PR.gens()
# finds Stoll and Cremona's Q_0
q = sum([(1/(dF(r).abs()**(2/(n-2)))) * ((x-(r*y)) * (x-(r.conjugate()*y)))\
for r in roots])
# this is Q_0 , always positive def as long as F has distinct roots
A = q.monomial_coefficient(x**2)
B = q.monomial_coefficient(x * y)
C = q.monomial_coefficient(y**2)
# need positive root
try:
z = ((-B + ((B**2) - (4 * A * C)).sqrt()) / (2 * A))
except ValueError:
raise ValueError("not enough precision")
if z.imag() < 0:
z = (-B - ((B**2) - (4 * A * C)).sqrt()) / (2 * A)
if z0_cov:
FM = f # for Julia's invariant
else:
# solve the minimization problem for 'true' covariant
CF = ComplexIntervalField(
prec=prec) # keeps trac of our precision error
RF = RealField(prec=prec)
z = CF(z)
FM = F(list(mat * vector(R.gens()))).subs({
R.gen(1): 1
}).univariate_polynomial()
from sage.rings.polynomial.complex_roots import complex_roots
L1 = complex_roots(FM, min_prec=prec)
L = []
err = z.diameter()
# making sure multiplicity isn't too large using convergence conditions in paper
for p, e in L1:
if e >= d / 2:
raise ValueError(
'cannot have a root with multiplicity >= %s/2' % d)
for _ in range(e):
L.append(p)
RCF = PolynomialRing(CF, 'u,t')
a = RCF(0)
c = RCF(0)
u, t = RCF.gens()
for l in L:
a += u**2 / ((t - l) * (t - l.conjugate()) + u**2)
c += (t - l.real()) / ((t - l) * (t - l.conjugate()) + u**2)
# Newton's Method, to find solutions. Error bound is less than diameter of our z
err = z.diameter()
zz = z.diameter()
g1 = a.numerator() - d / 2 * a.denominator()
g2 = c.numerator()
G = vector([g1, g2])
J = jacobian(G, [u, t])
v0 = vector([z.imag(), z.real()]) # z0 as starting point
# finds our correct z
while err <= zz:
NJ = J.subs({u: v0[0], t: v0[1]})
NJinv = NJ.inverse()
# inverse for CIF matrix seems to return fractions not CIF elements, fix them
if NJinv.base_ring() != CF:
NJinv = matrix(CF, 2, 2, [
CF(zw.numerator() / zw.denominator())
for zw in NJinv.list()
])
w = z
v0 = v0 - NJinv * G.subs({u: v0[0], t: v0[1]})
z = v0[1].constant_coefficient(
) + v0[0].constant_coefficient() * CF.gen(0)
err = z.diameter() # precision
zz = (w - z).abs() # difference in w and z
else:
if err > error_limit or err.is_NaN():
raise ValueError(
"accuracy of Newton's root not within tolerance(%s > %s), increase precision"
% (err, error_limit))
if z.imag() <= z.diameter():
raise ArithmeticError(
"Newton's method converged to z not in the upper half plane")
z = z.center()
# Julia's invariant
if FM.base_ring() != ComplexField(prec=prec):
FM = FM.change_ring(ComplexField(prec=prec))
tF = z.real()
uF = z.imag()
th = FM.lc().abs()**2
for r, ex in FM.roots():
for _ in range(ex):
th = th * ((((r - tF).abs())**2 + uF**2) / uF)
# undo shift and invert (if needed)
# since F \cdot m ~ m^(-1)\cdot z
# we apply m to z to undo m acting on F
l = mat * vector([z, 1])
return l[0] / l[1], th
def epsinv(F, target, prec=53, target_tol=0.001, z=None, emb=None):
"""
Compute a bound on the hyperbolic distance.
The true minimum will be within the computed bound.
It is computed as the inverse of epsilon_F from [HS2018]_.
INPUT:
- ``F`` -- binary form of degree at least 3 with no multiple roots
- ``target`` -- positive real number. The value we want to attain, i.e.,
the value we are taking the inverse of
- ``prec``-- positive integer. precision to use in CC
- ``target_tol`` -- positive real number. The tolerance with which we
attain the target value.
- ``z`` -- complex number. ``z_0`` covariant for F.
- ``emb`` -- embedding into CC
OUTPUT: a real number delta satisfying target + target_tol > eps_F(delta) > target.
EXAMPLES::
sage: from sage.rings.polynomial.binary_form_reduce import epsinv
sage: R.<x,y> = QQ[]
sage: epsinv(-2*x^3 + 2*x^2*y + 3*x*y^2 + 127*y^3, 31.5022020249597)
4.02520895942207
"""
def coshdelta(z):
#The cosh of the hyperbolic distance from z = t+uj to j
return (z.norm() + 1) / (2 * z.imag())
def RQ(delta):
# this is the quotient R(F_0,z)/R(F_0,z(F)) for a generic z
# at distance delta from j. See Lemma 4.2 in [HS2018].
cd = cosh(delta).n(prec=prec)
sd = sinh(delta).n(prec=prec)
return prod(
[cd + (cost * phi[0] + sint * phi[1]) * sd for phi in phis])
def epsF(delta):
pol = RQ(delta) #get R quotient in terms of z
S = PolynomialRing(C, 'v')
g = S([(i - d) * pol[i - d]
for i in range(2 * d + 1)]) # take derivative
drts = [
e for e in g.roots(ring=C, multiplicities=False)
if (e.norm() - 1).abs() < 0.1
]
# find min
return min([pol(r / r.abs()).real() for r in drts])
C = ComplexField(prec=prec)
if F.base_ring() != C:
if emb is None:
compF = F.change_ring(C)
else:
compF = F.change_ring(emb)
else:
compF = F
R = F.parent()
d = F.degree()
if z is None:
z, th = covariant_z0(F, prec=prec, emb=emb)
else: #need to do our own input checking
if R.ngens() != 2 or any([sum(t) != d for t in F.exponents()]):
raise TypeError('must be a binary form')
if d < 3:
raise ValueError('must be at least degree 3')
f = F.subs({R.gen(1): 1}).univariate_polynomial()
#now we have a single variable polynomial
x = f.parent().gen()
if (max([ex for p,ex in f.roots(ring=C)]) >= QQ(d)/2)\
or (f.degree() < QQ(d)/2):
raise ValueError('cannot have root with multiplicity >= deg(F)/2')
R = RealField(prec=prec)
PR = PolynomialRing(R, 't')
t = PR.gen(0)
# compute phi_1, ..., phi_k
# first find F_0 and its roots
# this change of variables on f moves z(f) to j, i.e. produces F_0
rts = f(z.imag() * t + z.real()).roots(ring=C)
phis = [] # stereographic projection of roots
for r, e in rts:
phis.extend(
[[2 * r.real() / (r.norm() + 1), (r.norm() - 1) / (r.norm() + 1)]])
if d != f.degree(): # include roots at infinity
phis.extend([(d - f.degree()) * [0, 1]])
# for writing RQ in terms of generic z to minimize
LC = LaurentSeriesRing(C, 'u', default_prec=2 * d + 2)
u = LC.gen(0)
cost = (u + u**(-1)) / 2
sint = (u - u**(-1)) / (2 * C.gen(0))
# first find an interval containing the desired value
# then use regula falsi on log eps_F
# d -> delta value in interval [0,1]
# v in value in interval [1,epsF(1)]
dl = R(0.0)
vl = R(1.0)
du = R(1.0)
vu = epsF(du)
while vu < target:
# compute the next value of epsF for delta = 2*delta
dl = du
vl = vu
du *= 2
vu = epsF(du)
# now dl < delta <= du
logt = target.log()
l2 = (vu.log() - logt).n(prec=prec)
l1 = (vl.log() - logt).n(prec=prec)
dn = (dl * l2 - du * l1) / (l2 - l1)
vn = epsF(dn)
dl = du
vl = vu
du = dn
vu = vn
while (du - dl).abs() >= target_tol or max(vl, vu) < target:
l2 = (vu.log() - logt).n(prec=prec)
l1 = (vl.log() - logt).n(prec=prec)
dn = (dl * l2 - du * l1) / (l2 - l1)
vn = epsF(dn)
dl = du
vl = vu
du = dn
vu = vn
return max(dl, du)
def extract(cls, obj):
"""
Takes an object extracted by the json parser and decodes the
special-formating dictionaries used to store special types.
"""
if isinstance(obj, dict) and 'data' in obj:
if len(obj) == 2 and '__ComplexList__' in obj:
return [complex(*v) for v in obj['data']]
elif len(obj) == 2 and '__QQList__' in obj:
return [QQ(tuple(v)) for v in obj['data']]
elif len(obj) == 3 and '__NFList__' in obj and 'base' in obj:
base = cls.extract(obj['base'])
return [cls._extract(base, c) for c in obj['data']]
elif len(obj) == 2 and '__IntDict__' in obj:
return {Integer(k): cls.extract(v) for k, v in obj['data']}
elif len(obj) == 3 and '__Vector__' in obj and 'base' in obj:
base = cls.extract(obj['base'])
return vector([cls._extract(base, v) for v in obj['data']])
elif len(obj) == 2 and '__Rational__' in obj:
return Rational(*obj['data'])
elif len(obj) == 3 and '__RealLiteral__' in obj and 'prec' in obj:
return LmfdbRealLiteral(RealField(obj['prec']), obj['data'])
elif len(obj) == 2 and '__complex__' in obj:
return complex(*obj['data'])
elif len(obj) == 3 and '__Complex__' in obj and 'prec' in obj:
return ComplexNumber(ComplexField(obj['prec']), *obj['data'])
elif len(obj) == 3 and '__NFElt__' in obj and 'parent' in obj:
return cls._extract(cls.extract(obj['parent']), obj['data'])
elif len(obj) == 3 and ('__NFRelative__' in obj or '__NFAbsolute__'
in obj) and 'vname' in obj:
poly = cls.extract(obj['data'])
return NumberField(poly, name=obj['vname'])
elif len(obj) == 2 and '__NFCyclotomic__' in obj:
return CyclotomicField(obj['data'])
elif len(obj) == 2 and '__IntegerRing__' in obj:
return ZZ
elif len(obj) == 2 and '__RationalField__' in obj:
return QQ
elif len(
obj) == 3 and '__RationalPoly__' in obj and 'vname' in obj:
return QQ[obj['vname']]([QQ(tuple(v)) for v in obj['data']])
elif len(
obj
) == 4 and '__Poly__' in obj and 'vname' in obj and 'base' in obj:
base = cls.extract(obj['base'])
return base[obj['vname']](
[cls._extract(base, c) for c in obj['data']])
elif len(
obj
) == 5 and '__PowerSeries__' in obj and 'vname' in obj and 'base' in obj and 'prec' in obj:
base = cls.extract(obj['base'])
prec = infinity if obj['prec'] == 'inf' else int(obj['prec'])
return base[[obj['vname']
]]([cls._extract(base, c) for c in obj['data']],
prec=prec)
elif len(obj) == 2 and '__date__' in obj:
return datetime.datetime.strptime(obj['data'],
"%Y-%m-%d").date()
elif len(obj) == 2 and '__time__' in obj:
return datetime.datetime.strptime(obj['data'],
"%H:%M:%S.%f").time()
elif len(obj) == 2 and '__datetime__' in obj:
return datetime.datetime.strptime(obj['data'],
"%Y-%m-%d %H:%M:%S.%f")
return obj
def bdd_height(K, height_bound, precision=53, LLL=False):
r"""
Computes all elements in the number number field `K` which have relative
multiplicative height at most ``height_bound``.
The algorithm requires arithmetic with floating point numbers;
``precision`` gives the user the option to set the precision for such
computations.
It might be helpful to work with an LLL-reduced system of fundamental
units, so the user has the option to perform an LLL reduction for the
fundamental units by setting ``LLL`` to True.
Certain computations may be faster assuming GRH, which may be done
globally by using the number_field(True/False) switch.
The function will only be called for number fields `K` with positive unit
rank. An error will occur if `K` is `QQ` or an imaginary quadratic field.
ALGORITHM:
This is an implementation of the main algorithm (Algorithm 3) in
[Doyle-Krumm].
INPUT:
- ``height_bound`` - real number
- ``precision`` - (default: 53) positive integer
- ``LLL`` - (default: False) boolean value
OUTPUT:
- an iterator of number field elements
.. WARNING::
In the current implementation, the output of the algorithm cannot be
guaranteed to be correct due to the necessity of floating point
computations. In some cases, the default 53-bit precision is
considerably lower than would be required for the algorithm to
generate correct output.
.. TODO::
Should implement a version of the algorithm that guarantees correct
output. See Algorithm 4 in [Doyle-Krumm] for details of an
implementation that takes precision issues into account.
EXAMPLES:
There are no elements of negative height::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^5 - x + 7)
sage: list(bdd_height(K,-3))
[]
The only nonzero elements of height 1 are the roots of unity::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(3)
sage: list(bdd_height(K,1))
[0, -1, 1]
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = QuadraticField(36865)
sage: len(list(bdd_height(K,101))) # long time (4 s)
131
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^3 - 197*x + 39)
sage: len(list(bdd_height(K, 200))) # long time (5 s)
451
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^6 + 2)
sage: len(list(bdd_height(K,60,precision=100))) # long time (5 s)
1899
::
sage: from sage.rings.number_field.bdd_height import bdd_height
sage: K.<g> = NumberField(x^4 - x^3 - 3*x^2 + x + 1)
sage: len(list(bdd_height(K,10,LLL=true)))
99
"""
B = height_bound
r1, r2 = K.signature()
r = r1 + r2 - 1
if B < 1:
return
yield K(0)
roots_of_unity = K.roots_of_unity()
if B == 1:
for zeta in roots_of_unity:
yield zeta
return
RF = RealField(precision)
embeddings = K.places(prec=precision)
logB = RF(B).log()
def log_map(number):
r"""
Computes the image of an element of `K` under the logarithmic map.
"""
x = number
x_logs = []
for i in xrange(r1):
sigma = embeddings[i]
x_logs.append(abs(sigma(x)).log())
for i in xrange(r1, r + 1):
tau = embeddings[i]
x_logs.append(2 * abs(tau(x)).log())
return vector(x_logs)
def log_height_for_generators(n, i, j):
r"""
Computes the logarithmic height of elements of the form `g_i/g_j`.
"""
gen_logs = generator_logs[n]
Log_gi = gen_logs[i]
Log_gj = gen_logs[j]
arch_sum = sum([max(Log_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
def packet_height(n, pair, u):
r"""
Computes the height of the element of `K` encoded by a given packet.
"""
gen_logs = generator_logs[n]
i = pair[0]
j = pair[1]
Log_gi = gen_logs[i]
Log_gj = gen_logs[j]
Log_u_gi = Log_gi + unit_log_dictionary[u]
arch_sum = sum([max(Log_u_gi[k], Log_gj[k]) for k in range(r + 1)])
return (arch_sum - class_group_rep_norm_logs[n])
class_group_reps = []
class_group_rep_norms = []
class_group_rep_norm_logs = []
for c in K.class_group():
a = c.ideal()
a_norm = a.norm()
class_group_reps.append(a)
class_group_rep_norms.append(a_norm)
class_group_rep_norm_logs.append(RF(a_norm).log())
class_number = len(class_group_reps)
# Get fundamental units and their images under the log map
fund_units = UnitGroup(K).fundamental_units()
fund_unit_logs = [log_map(fund_units[i]) for i in range(r)]
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError(
'Precision too low.') # QQ(log(0)) may occur if precision too low
# If LLL is set to True, find an LLL-reduced system of fundamental units
if LLL:
cut_fund_unit_logs = column_matrix(fund_unit_logs).delete_rows([r])
lll_fund_units = []
for c in pari(cut_fund_unit_logs).qflll().python().columns():
new_unit = 1
for i in xrange(r):
new_unit *= fund_units[i]**c[i]
lll_fund_units.append(new_unit)
fund_units = lll_fund_units
fund_unit_logs = [log_map(_) for _ in fund_units]
unit_prec_test = fund_unit_logs
try:
[l.change_ring(QQ) for l in unit_prec_test]
except ValueError:
raise ValueError('Precision too low.'
) # QQ(log(0)) may occur if precision too low
# Find generators for principal ideals of bounded norm
possible_norm_set = set([])
for n in xrange(class_number):
for m in xrange(1, B + 1):
possible_norm_set.add(m * class_group_rep_norms[n])
bdd_ideals = bdd_norm_pr_ideal_gens(K, possible_norm_set)
# Distribute the principal ideal generators
generator_lists = []
generator_logs = []
for n in xrange(class_number):
this_ideal = class_group_reps[n]
this_ideal_norm = class_group_rep_norms[n]
gens = []
gen_logs = []
for i in xrange(1, B + 1):
for g in bdd_ideals[i * this_ideal_norm]:
if g in this_ideal:
gens.append(g)
gen_logs.append(log_map(g))
generator_lists.append(gens)
generator_logs.append(gen_logs)
# Compute the lists of relevant pairs and corresponding heights
gen_height_dictionary = dict()
relevant_pair_lists = []
for n in xrange(class_number):
relevant_pairs = []
gens = generator_lists[n]
s = len(gens)
for i in xrange(s):
for j in xrange(i + 1, s):
if K.ideal(gens[i], gens[j]) == class_group_reps[n]:
relevant_pairs.append([i, j])
gen_height_dictionary[(n, i,
j)] = log_height_for_generators(
n, i, j)
relevant_pair_lists.append(relevant_pairs)
# Find the bound for units to be computed
gen_height_list = [
gen_height_dictionary[x] for x in gen_height_dictionary.keys()
]
if len(gen_height_list) == 0:
d = logB
else:
d = logB + max(gen_height_list)
# Create the matrix whose columns are the logs of the fundamental units
S = column_matrix(fund_unit_logs).delete_rows([r])
try:
T = S.inverse()
except ZeroDivisionError:
raise ValueError('Precision too low.')
# Find all integer lattice points in the unit polytope
U = integer_points_in_polytope(T, ceil(d))
U0 = []
L0 = []
# Compute unit heights
unit_height_dictionary = dict()
unit_log_dictionary = dict()
Ucopy = copy(U)
for u in U:
u_log = sum([u[j] * fund_unit_logs[j] for j in range(r)])
unit_log_dictionary[u] = u_log
u_height = sum([max(u_log[k], 0) for k in range(r + 1)])
unit_height_dictionary[u] = u_height
if u_height <= logB:
U0.append(u)
if u_height > d:
Ucopy.remove(u)
U = Ucopy
# Sort U by height
U = sorted(U, key=lambda u: unit_height_dictionary[u])
U_length = len(U)
all_unit_tuples = set(copy(U0))
# Check candidate heights
for n in xrange(class_number):
relevant_pairs = relevant_pair_lists[n]
for pair in relevant_pairs:
i = pair[0]
j = pair[1]
gen_height = gen_height_dictionary[(n, i, j)]
u_height_bound = logB + gen_height
for k in xrange(U_length):
u = U[k]
u_height = unit_height_dictionary[u]
if u_height <= u_height_bound:
candidate_height = packet_height(n, pair, u)
if candidate_height <= logB:
L0.append([n, pair, u])
all_unit_tuples.add(u)
else:
break
# Use previous data to build all necessary units
units_dictionary = dict()
for u in all_unit_tuples:
unit = K(1)
for j in xrange(r):
unit *= (fund_units[j])**(u[j])
units_dictionary[u] = unit
# Build all the output numbers
for u in U0:
unit = units_dictionary[u]
for zeta in roots_of_unity:
yield zeta * unit
for packet in L0:
n = packet[0]
pair = packet[1]
u = packet[2]
i = pair[0]
j = pair[1]
relevant_pairs = relevant_pair_lists[n]
gens = generator_lists[n]
unit = units_dictionary[u]
c = unit * gens[i] / gens[j]
for zeta in roots_of_unity:
yield zeta * c
yield zeta / c
def cholesky_decomposition(self, bit_prec=53):
"""
Give the Cholesky decomposition of this quadratic form `Q` as a real matrix
of precision ``bit_prec``.
RESTRICTIONS:
Q must be given as a QuadraticForm defined over `\ZZ`, `\QQ`, or some
real field. If it is over some real field, then an error is raised if
the precision given is not less than the defined precision of the real
field defining the quadratic form!
REFERENCE:
From Cohen's "A Course in Computational Algebraic Number Theory" book,
p 103.
INPUT:
``bit_prec`` -- a natural number (default 53).
OUTPUT:
an upper triangular real matrix of precision ``bit_prec``.
TO DO:
If we only care about working over the real double field (RDF), then we
can use the ``cholesky()`` method present for square matrices over that.
.. note::
There is a note in the original code reading
::
##/////////////////////////////////////////////////////////////////////////////////////////////////
##/// Finds the Cholesky decomposition of a quadratic form -- as an upper-triangular matrix!
##/// (It's assumed to be global, hence twice the form it refers to.) <-- Python revision asks: Is this true?!? =|
##/////////////////////////////////////////////////////////////////////////////////////////////////
EXAMPLES::
sage: Q = DiagonalQuadraticForm(ZZ, [1,1,1])
sage: Q.cholesky_decomposition()
[ 1.00000000000000 0.000000000000000 0.000000000000000]
[0.000000000000000 1.00000000000000 0.000000000000000]
[0.000000000000000 0.000000000000000 1.00000000000000]
::
sage: Q = QuadraticForm(QQ, 3, range(1,7)); Q
Quadratic form in 3 variables over Rational Field with coefficients:
[ 1 2 3 ]
[ * 4 5 ]
[ * * 6 ]
sage: Q.cholesky_decomposition()
[ 1.00000000000000 1.00000000000000 1.50000000000000]
[0.000000000000000 3.00000000000000 0.333333333333333]
[0.000000000000000 0.000000000000000 3.41666666666667]
"""
## Check that the precision passed is allowed.
if isinstance(self.base_ring(),
RealField_class) and (self.base_ring().prec() < bit_prec):
raise RuntimeError, "Oops! The precision requested is greater than that of the given quadratic form!"
## 1. Initialization
n = self.dim()
R = RealField(bit_prec)
MS = MatrixSpace(R, n, n)
Q = MS(R(0.5)) * MS(
self.matrix()
) ## Initialize the real symmetric matrix A with the matrix for Q(x) = x^t * A * x
## DIAGNOSTIC
#print "After 1: Q is \n" + str(Q)
## 2. Loop on i
for i in range(n):
for j in range(i + 1, n):
Q[j, i] = Q[i, j] ## Is this line redundant?
Q[i, j] = Q[i, j] / Q[i, i]
## 3. Main Loop
for k in range(i + 1, n):
for l in range(k, n):
Q[k, l] = Q[k, l] - Q[k, i] * Q[i, l]
## 4. Zero out the strictly lower-triangular entries
for i in range(n):
for j in range(i):
Q[i, j] = 0
return Q
def _coerce_map_from_(self, R):
r"""
There is a coercion from anything that has a coercion into the reals.
The way Sage works is that everything that should be
comparable with infinity can be coerced into the infinity
ring, so if you ever compare with infinity the comparison is
done there. If you don't have a coercion then you will get
undesirable answers from the fallback comparison (likely
memory location).
EXAMPLES::
sage: InfinityRing.has_coerce_map_from(int) # indirect doctest
True
sage: InfinityRing.has_coerce_map_from(AA)
True
sage: InfinityRing.has_coerce_map_from(RDF)
True
sage: InfinityRing.has_coerce_map_from(RIF)
True
As explained above, comparison works by coercing to the
infinity ring::
sage: cm = get_coercion_model()
sage: cm.explain(AA(3), oo, operator.lt)
Coercion on left operand via
Coercion map:
From: Algebraic Real Field
To: The Infinity Ring
Arithmetic performed after coercions.
Result lives in The Infinity Ring
The Infinity Ring
The symbolic ring does not coerce to the infinity ring, so
symbolic comparisons with infinities all happen in the
symbolic ring::
sage: SR.has_coerce_map_from(InfinityRing)
True
sage: InfinityRing.has_coerce_map_from(SR)
False
Complex numbers do not coerce into the infinity ring (what
would `i \infty` coerce to?). This is fine since they can not
be compared, so we do not have to enforce consistency when
comparing with infinity either::
sage: InfinityRing.has_coerce_map_from(CDF)
False
sage: InfinityRing.has_coerce_map_from(CC)
False
sage: CC(0, oo) < CC(1) # does not coerce to infinity ring
True
"""
from sage.rings.real_mpfr import mpfr_prec_min, RealField
if RealField(mpfr_prec_min()).has_coerce_map_from(R):
return True
from sage.rings.real_mpfi import RealIntervalField_class
if isinstance(R, RealIntervalField_class):
return True
try:
from sage.rings.real_arb import RealBallField
if isinstance(R, RealBallField):
return True
except ImportError:
pass
return False
def genfiles_mpfr(integrator,
driver,
f,
ics,
initial,
final,
delta,
parameters=None,
parameter_values=None,
dig=20,
tolrel=1e-16,
tolabs=1e-16,
output=''):
r"""
Generate the needed files for the mpfr module of the tides library.
INPUT:
- ``integrator`` -- the name of the integrator file.
- ``driver`` -- the name of the driver file.
- ``f`` -- the function that determines the differential equation.
- ``ics`` -- a list or tuple with the initial conditions.
- ``initial`` -- the initial time for the integration.
- ``final`` -- the final time for the integration.
- ``delta`` -- the step of the output.
- ``parameters`` -- the variables inside the function that should be treated
as parameters.
- ``parameter_values`` -- the values of the parameters for the particular
initial value problem.
- ``dig`` -- the number of digits of precision that will be used in the integration
- ``tolrel`` -- the relative tolerance.
- ``tolabs`` -- the absolute tolerance.
- ``output`` -- the name of the file that the compiled integrator will write to
This function creates two files, integrator and driver, that can be used
later with the tides library ([TIDES]_).
TESTS::
sage: from tempfile import mkdtemp
sage: from sage.interfaces.tides import genfiles_mpfr
sage: import os
sage: import shutil
sage: from sage.misc.temporary_file import tmp_dir
sage: tempdir = tmp_dir()
sage: intfile = os.path.join(tempdir, 'integrator.c')
sage: drfile = os.path.join(tempdir ,'driver.c')
sage: var('t,x,y,X,Y')
(t, x, y, X, Y)
sage: f(t,x,y,X,Y)=[X, Y, -x/(x^2+y^2)^(3/2), -y/(x^2+y^2)^(3/2)]
sage: genfiles_mpfr(intfile, drfile, f, [1,0, 0, 0.2], 0, 10, 0.1, output = 'out', dig = 50)
sage: fileint = open(intfile)
sage: l = fileint.readlines()
sage: fileint.close()
sage: l[5]
' #include "mp_tides.h"\n'
sage: l[15]
'\tstatic int PARAMETERS = 0;\n'
sage: l[25]
'\t\tmpfrts_var_t(itd, link[5], var[3], i);\n'
sage: l[30]
'\t\tmpfrts_pow_t_c(itd, link[2], "-1.500000000000000000000000000000000000000000000000000", link[3], i);\n'
sage: l[35]
'\n'
sage: l[36]
' }\n'
sage: l[37]
' write_mp_solution();\n'
sage: filedr = open(drfile)
sage: l = filedr.readlines()
sage: filedr.close()
sage: l[6]
' #include "mpfr.h"\n'
sage: l[16]
' int nfun = 0;\n'
sage: l[26]
'\tmpfr_set_str(v[2], "0.0000000000000000000000000000000000000000000000000000", 10, TIDES_RND);\n'
sage: l[30]
'\tmpfr_init2(tolabs, TIDES_PREC); \n'
sage: l[34]
'\tmpfr_init2(tini, TIDES_PREC); \n'
sage: l[40]
'\tmp_tides_delta(function_iteration, NULL, nvar, npar, nfun, v, p, tini, dt, nipt, tolrel, tolabs, NULL, fd);\n'
sage: shutil.rmtree(tempdir)
Check that ticket :trac:`17179` is fixed (handle expressions like `\\pi`)::
sage: from sage.interfaces.tides import genfiles_mpfr
sage: import os
sage: import shutil
sage: from sage.misc.temporary_file import tmp_dir
sage: tempdir = tmp_dir()
sage: intfile = os.path.join(tempdir, 'integrator.c')
sage: drfile = os.path.join(tempdir ,'driver.c')
sage: var('t,x,y,X,Y')
(t, x, y, X, Y)
sage: f(t,x,y,X,Y)=[X, Y, -x/(x^2+y^2)^(3/2), -y/(x^2+y^2)^(3/2)]
sage: genfiles_mpfr(intfile, drfile, f, [pi, 0, 0, 0.2], 0, 10, 0.1, output = 'out', dig = 50)
sage: fileint = open(intfile)
sage: l = fileint.readlines()
sage: fileint.close()
sage: l[30]
'\t\tmpfrts_pow_t_c(itd, link[2], "-1.500000000000000000000000000000000000000000000000000", link[3], i);\n'
sage: filedr = open(drfile)
sage: l = filedr.readlines()
sage: filedr.close()
sage: l[24]
'\tmpfr_set_str(v[0], "3.141592653589793238462643383279502884197169399375101", 10, TIDES_RND);\n'
sage: shutil.rmtree(tempdir)
"""
if parameters == None:
parameters = []
if parameter_values == None:
parameter_values = []
RR = RealField(ceil(dig * 3.3219))
l1, l2 = subexpressions_list(f, parameters)
remove_repeated(l1, l2)
remove_constants(l1, l2)
l3 = []
var = f[0].arguments()
l0 = map(str, l1)
lv = map(str, var)
lp = map(str, parameters)
for i in l2:
oper = i[0]
if oper in ["log", "exp", "sin", "cos", "atan", "asin", "acos"]:
a = i[1]
if str(a) in lv:
l3.append((oper, 'var[{}]'.format(lv.index(str(a)))))
elif str(a) in lp:
l3.append((oper, 'par[{}]'.format(lp.index(str(a)))))
else:
l3.append((oper, 'link[{}]'.format(l0.index(str(a)))))
else:
a = i[1]
b = i[2]
sa = str(a)
sb = str(b)
consta = False
constb = False
if sa in lv:
aa = 'var[{}]'.format(lv.index(sa))
elif sa in l0:
aa = 'link[{}]'.format(l0.index(sa))
elif sa in lp:
aa = 'par[{}]'.format(lp.index(sa))
else:
consta = True
aa = RR(a).str(truncate=False)
if sb in lv:
bb = 'var[{}]'.format(lv.index(sb))
elif sb in l0:
bb = 'link[{}]'.format(l0.index(sb))
elif sb in lp:
bb = 'par[{}]'.format(lp.index(sb))
else:
constb = True
bb = RR(b).str(truncate=False)
if consta:
oper += '_c'
if not oper == 'div':
bb, aa = aa, bb
elif constb:
oper += '_c'
l3.append((oper, aa, bb))
n = len(var)
code = []
l0 = lv + l0
indices = [l0.index(str(i(*var))) + n for i in f]
for i in range(1, n):
aux = indices[i - 1] - n
if aux < n:
code.append('mpfrts_var_t(itd, var[{}], var[{}], i);'.format(
aux, i))
else:
code.append('mpfrts_var_t(itd, link[{}], var[{}], i);'.format(
aux - n, i))
for i in range(len(l3)):
el = l3[i]
string = "mpfrts_"
if el[0] == 'add':
string += 'add_t(itd, ' + el[1] + ', ' + el[
2] + ', link[{}], i);'.format(i)
elif el[0] == 'add_c':
string += 'add_t_c(itd, "' + el[2] + '", ' + el[
1] + ', link[{}], i);'.format(i)
elif el[0] == 'mul':
string += 'mul_t(itd, ' + el[1] + ', ' + el[
2] + ', link[{}], i);'.format(i)
elif el[0] == 'mul_c':
string += 'mul_t_c(itd, "' + el[2] + '", ' + el[
1] + ', link[{}], i);'.format(i)
elif el[0] == 'pow_c':
string += 'pow_t_c(itd, ' + el[1] + ', "' + el[
2] + '", link[{}], i);'.format(i)
elif el[0] == 'div':
string += 'div_t(itd, ' + el[2] + ', ' + el[
1] + ', link[{}], i);'.format(i)
elif el[0] == 'div_c':
string += 'div_t_cv(itd, "' + el[2] + '", ' + el[
1] + ', link[{}], i);'.format(i)
elif el[0] == 'log':
string += 'log_t(itd, ' + el[1] + ', link[{}], i);'.format(i)
elif el[0] == 'exp':
string += 'exp_t(itd, ' + el[1] + ', link[{}], i);'.format(i)
elif el[0] == 'sin':
string += 'sin_t(itd, ' + el[
1] + ', link[{}], link[{}], i);'.format(i + 1, i)
elif el[0] == 'cos':
string += 'cos_t(itd, ' + el[
1] + ', link[{}], link[{}], i);'.format(i - 1, i)
elif el[0] == 'atan':
indarg = l0.index(str(1 + l2[i][1]**2)) - n
string += 'atan_t(itd, ' + el[
1] + ', link[{}], link[{}], i);'.format(indarg, i)
elif el[0] == 'asin':
indarg = l0.index(str(sqrt(1 - l2[i][1]**2))) - n
string += 'asin_t(itd, ' + el[
1] + ', link[{}], link[{}], i);'.format(indarg, i)
elif el[0] == 'acos':
indarg = l0.index(str(-sqrt(1 - l2[i][1]**2))) - n
string += 'acos_t(itd, ' + el[
1] + ', link[{}], link[{}], i);'.format(indarg, i)
code.append(string)
VAR = n - 1
PAR = len(parameters)
TT = len(code) + 1 - VAR
outfile = open(integrator, 'a')
auxstring = """
/****************************************************************************
This file has been created by Sage for its use with TIDES
*****************************************************************************/
#include "mp_tides.h"
long function_iteration(iteration_data *itd, mpfr_t t, mpfr_t v[], mpfr_t p[], int ORDER, mpfr_t *cvfd)
{
int i;
int NCONST = 0;
mpfr_t ct[0];
"""
outfile.write(auxstring)
outfile.write("\n\tstatic int VARIABLES = {};\n".format(VAR))
outfile.write("\tstatic int PARAMETERS = {};\n".format(PAR))
outfile.write("\tstatic int LINKS = {};\n".format(TT))
outfile.write('\tstatic int FUNCTIONS = 0;\n')
outfile.write('\tstatic int POS_FUNCTIONS[1] = {0};\n')
outfile.write('\n\tinitialize_mp_case();\n')
outfile.write('\n\tfor(i=0; i<=ORDER; i++) {\n')
for i in code:
outfile.write('\t\t' + i + '\n')
auxstring = """
}
write_mp_solution();
clear_vpl();
clear_cts();
return NUM_COLUMNS;
}
"""
outfile.write(auxstring)
outfile.close()
npar = len(parameter_values)
outfile = open(driver, 'a')
auxstring = """
/****************************************************************************
Driver file of the mp_tides program
This file has been created automatically by Sage
*****************************************************************************/
#include "mpfr.h"
#include "mp_tides.h"
long function_iteration(iteration_data *itd, mpfr_t t, mpfr_t v[], mpfr_t p[], int ORDER, mpfr_t *cvfd);
int main() {
int i;
int nfun = 0;
"""
outfile.write(auxstring)
outfile.write('\tset_precision_digits({});'.format(dig))
outfile.write('\n\tint npar = {};\n'.format(npar))
outfile.write('\tmpfr_t p[npar];\n')
outfile.write('\tfor(i=0; i<npar; i++) mpfr_init2(p[i], TIDES_PREC);\n')
for i in range(npar):
outfile.write('\tmpfr_set_str(p[{}], "{}", 10, TIDES_RND);\n'.format(
i,
RR(parameter_values[i]).str(truncate=False)))
outfile.write('\tint nvar = {};\n\tmpfr_t v[nvar];\n'.format(VAR))
outfile.write('\tfor(i=0; i<nvar; i++) mpfr_init2(v[i], TIDES_PREC);\n')
for i in range(len(ics)):
outfile.write('\tmpfr_set_str(v[{}], "{}", 10, TIDES_RND);\n'.format(
i,
RR(ics[i]).str(truncate=False)))
outfile.write('\tmpfr_t tolrel, tolabs;\n')
outfile.write('\tmpfr_init2(tolrel, TIDES_PREC); \n')
outfile.write('\tmpfr_init2(tolabs, TIDES_PREC); \n')
outfile.write('\tmpfr_set_str(tolrel, "{}", 10, TIDES_RND);\n'.format(
RR(tolrel).str(truncate=False)))
outfile.write('\tmpfr_set_str(tolabs, "{}", 10, TIDES_RND);\n'.format(
RR(tolabs).str(truncate=False)))
outfile.write('\tmpfr_t tini, dt; \n')
outfile.write('\tmpfr_init2(tini, TIDES_PREC); \n')
outfile.write('\tmpfr_init2(dt, TIDES_PREC); \n')
outfile.write('\tmpfr_set_str(tini, "{}", 10, TIDES_RND);;\n'.format(
RR(initial).str(truncate=False)))
outfile.write('\tmpfr_set_str(dt, "{}", 10, TIDES_RND);\n'.format(
RR(delta).str(truncate=False)))
outfile.write('\tint nipt = {};\n'.format(floor(
(final - initial) / delta)))
outfile.write('\tFILE* fd = fopen("' + output + '", "w");\n')
outfile.write(
'\tmp_tides_delta(function_iteration, NULL, nvar, npar, nfun, v, p, tini, dt, nipt, tolrel, tolabs, NULL, fd);\n'
)
outfile.write('\tfclose(fd);\n\treturn 0;\n}')
outfile.close()
def homogenize(self,n,newvar='h'):
r"""
Return the homogenization of ``self``. If ``self.domain()`` is a subscheme, the domain of
the homogenized map is the projective embedding of ``self.domain()``. The domain and codomain
can be homogenized at different coordinates: ``n[0]`` for the domain and ``n[1]`` for the codomain.
INPUT:
- ``newvar`` -- the name of the homogenization variable (only used when ``self.domain()`` is affine space)
- ``n`` -- a tuple of nonnegative integers. If ``n`` is an integer, then the two values of
the tuple are assumed to be the same.
OUTPUT:
- :class:`SchemMorphism_polynomial_projective_space`
EXAMPLES::
sage: A.<x,y>=AffineSpace(ZZ,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/x^5,y^2])
sage: f.homogenize(2,'z')
Scheme endomorphism of Projective Space of dimension 2 over Integer Ring
Defn: Defined on coordinates by sending (x : y : z) to
(x^2*z^5 - 2*z^7 : x^5*y^2 : x^5*z^2)
::
sage: A.<x,y>=AffineSpace(CC,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/(x*y),y^2-x])
sage: f.homogenize((2,0),'z')
Scheme endomorphism of Projective Space of dimension 2 over Complex
Field with 53 bits of precision
Defn: Defined on coordinates by sending (x : y : z) to
(x*y*z^2 : x^2*z^2 + (-2.00000000000000)*z^4 : x*y^3 - x^2*y*z)
::
sage: A.<x,y>=AffineSpace(ZZ,2)
sage: X=A.subscheme([x-y^2])
sage: H=Hom(X,X)
sage: f=H([9*y^2,3*y])
sage: f.homogenize(2)
Scheme endomorphism of Closed subscheme of Projective Space of dimension 2 over Integer Ring defined by:
-x1^2 + x0*x2
Defn: Defined on coordinates by sending (x0 : x1 : x2) to
(9*x0*x2 : 3*x1*x2 : x2^2)
::
sage: R.<t>=PolynomialRing(ZZ)
sage: A.<x,y>=AffineSpace(R,2)
sage: H=Hom(A,A)
sage: f=H([(x^2-2)/y,y^2-x])
sage: f.homogenize((2,0),'z')
Scheme endomorphism of Projective Space of dimension 2 over Univariate
Polynomial Ring in t over Integer Ring
Defn: Defined on coordinates by sending (x : y : z) to
(y*z^2 : x^2*z + (-2)*z^3 : y^3 - x*y*z)
::
sage: A.<x>=AffineSpace(QQ,1)
sage: H=End(A)
sage: f=H([x^2-1])
sage: f.homogenize((1,0),'y')
Scheme endomorphism of Projective Space of dimension 1 over Rational
Field
Defn: Defined on coordinates by sending (x : y) to
(y^2 : x^2 - y^2)
"""
A=self.domain()
B=self.codomain()
N=A.ambient_space().dimension_relative()
NB=B.ambient_space().dimension_relative()
#it is possible to homogenize the domain and codomain at different coordinates
if isinstance(n,(tuple,list)):
ind=tuple(n)
else:
ind=(n,n)
#homogenize the domain
Vars=list(A.ambient_space().variable_names())
Vars.insert(ind[0],newvar)
S=PolynomialRing(A.base_ring(),Vars)
#find the denominators if a rational function
try:
l=lcm([self[i].denominator() for i in range(N)])
except Exception: #no lcm
l=prod([self[i].denominator() for i in range(N)])
from sage.rings.polynomial.polynomial_ring import PolynomialRing_general
from sage.rings.polynomial.multi_polynomial_ring_generic import MPolynomialRing_generic
if self.domain().base_ring()==RealField() or self.domain().base_ring()==ComplexField():
F=[S(((self[i]*l).numerator())._maxima_().divide(self[i].denominator())[0].sage()) for i in range(N)]
elif isinstance(self.domain().base_ring(),(PolynomialRing_general,MPolynomialRing_generic)):
F=[S(((self[i]*l).numerator())._maxima_().divide(self[i].denominator())[0].sage()) for i in range(N)]
else:
F=[S(self[i]*l) for i in range(N)]
#homogenize the codomain
F.insert(ind[1],S(l))
d=max([F[i].degree() for i in range(N+1)])
F=[F[i].homogenize(newvar)*S.gen(N)**(d-F[i].degree()) for i in range(N+1)]
from sage.schemes.affine.affine_space import is_AffineSpace
if is_AffineSpace(A)==True:
from sage.schemes.projective.projective_space import ProjectiveSpace
X=ProjectiveSpace(A.base_ring(),NB,Vars)
else:
X=A.projective_embedding(ind[1]).codomain()
phi=S.hom(X.ambient_space().gens(),X.ambient_space().coordinate_ring())
F=[phi(f) for f in F]
H=Hom(X,X)
return(H(F))
def exponential_integral_1(x, n=0):
r"""
Returns the exponential integral `E_1(x)`. If the optional
argument `n` is given, computes list of the first
`n` values of the exponential integral
`E_1(x m)`.
The exponential integral `E_1(x)` is
.. math::
E_1(x) = \int_{x}^{\infty} e^{-t}/t dt
INPUT:
- ``x`` -- a positive real number
- ``n`` -- (default: 0) a nonnegative integer; if
nonzero, then return a list of values ``E_1(x*m)`` for m =
1,2,3,...,n. This is useful, e.g., when computing derivatives of
L-functions.
OUTPUT:
A real number if n is 0 (the default) or a list of reals if n > 0.
The precision is the same as the input, with a default of 53 bits
in case the input is exact.
EXAMPLES::
sage: exponential_integral_1(2)
0.0489005107080611
sage: exponential_integral_1(2,4) # abs tol 1e-18
[0.0489005107080611, 0.00377935240984891, 0.000360082452162659, 0.0000376656228439245]
sage: exponential_integral_1(40,5)
[1.03677326145166e-19, 2.22854325868847e-37, 6.33732515501151e-55, 2.02336191509997e-72, 6.88522610630764e-90]
sage: exponential_integral_1(0)
+Infinity
sage: r = exponential_integral_1(RealField(150)(1))
sage: r
0.21938393439552027367716377546012164903104729
sage: parent(r)
Real Field with 150 bits of precision
sage: exponential_integral_1(RealField(150)(100))
3.6835977616820321802351926205081189876552201e-46
TESTS:
The relative error for a single value should be less than 1 ulp::
sage: for prec in [20..1000]: # long time (22s on sage.math, 2013)
....: R = RealField(prec)
....: S = RealField(prec+64)
....: for t in range(8): # Try 8 values for each precision
....: a = R.random_element(-15,10).exp()
....: x = exponential_integral_1(a)
....: y = exponential_integral_1(S(a))
....: e = float(abs(S(x) - y)/x.ulp())
....: if e >= 1.0:
....: print "exponential_integral_1(%s) with precision %s has error of %s ulp"%(a, prec, e)
The absolute error for a vector should be less than `c 2^{-p}`, where
`p` is the precision in bits of `x` and `c = 2 max(1, exponential_integral_1(x))`::
sage: for prec in [20..128]: # long time (15s on sage.math, 2013)
....: R = RealField(prec)
....: S = RealField(prec+64)
....: a = R.random_element(-15,10).exp()
....: n = 2^ZZ.random_element(14)
....: x = exponential_integral_1(a, n)
....: y = exponential_integral_1(S(a), n)
....: c = RDF(2 * max(1.0, y[0]))
....: for i in range(n):
....: e = float(abs(S(x[i]) - y[i]) << prec)
....: if e >= c:
....: print "exponential_integral_1(%s, %s)[%s] with precision %s has error of %s >= %s"%(a, n, i, prec, e, c)
ALGORITHM: use the PARI C-library function ``eint1``.
REFERENCE:
- See Proposition 5.6.12 of Cohen's book "A Course in
Computational Algebraic Number Theory".
"""
if isinstance(x, Expression):
if x.is_trivial_zero():
from sage.rings.infinity import Infinity
return Infinity
else:
raise NotImplementedError("Use the symbolic exponential integral " +
"function: exp_integral_e1.")
elif not is_inexact(x): # x is exact and not an expression
if not x: # test if exact x == 0 quickly
from sage.rings.infinity import Infinity
return Infinity
# else x is not an exact 0
from sage.libs.pari.all import pari
# Figure out output precision
try:
prec = parent(x).precision()
except AttributeError:
prec = 53
R = RealField(prec)
if n <= 0:
# Add extra bits to the input.
# (experimentally verified -- Jeroen Demeyer)
inprec = prec + math.ceil(math.log(2*prec))
x = RealField(inprec)(x)._pari_()
return R(x.eint1())
else:
# PARI's algorithm is less precise as n grows larger:
# add extra bits.
# (experimentally verified -- Jeroen Demeyer)
inprec = prec + 1 + math.ceil(1.4427 * math.log(n))
x = RealField(inprec)(x)._pari_()
return [R(z) for z in x.eint1(n)]
def _sage_(self):
r"""
Convert a maple expression back to a Sage expression.
This currently does not implement a parser for the Maple output language,
therefore only very simple expressions will convert successfully.
REFERENCE:
https://www.asc.tuwien.ac.at/compmath/download/Monagan_Maple_Programming.pdf
EXAMPLES::
sage: m = maple('x^2 + 5*y') # optional - maple
sage: m.sage() # optional - maple
x^2 + 5*y
sage: m._sage_() # optional - maple
x^2 + 5*y
sage: m = maple('sin(sqrt(1-x^2)) * (1 - cos(1/x))^2') # optional - maple
sage: m.sage() # optional - maple
(cos(1/x) - 1)^2*sin(sqrt(-x^2 + 1))
Some matrices can be converted back::
sage: m = matrix(2, 2, [1, 2, x, 3]) # optional - maple
sage: mm = maple(m) # optional - maple
sage: mm.sage() == m # optional - maple
True
Some vectors can be converted back::
sage: m = vector([1, x, 2, 3]) # optional - maple
sage: mm = maple(m) # optional - maple
sage: mm.sage() == m # optional - maple
True
Integers and rationals are converted as such::
sage: maple(33).sage().parent() # optional - maple
Integer Ring
sage: maple(191/5).sage().parent() # optional - maple
Rational Field
Sets, lists, sequences::
sage: maple("[4,5,6]").sage() # optional - maple
[4, 5, 6]
sage: maple({14,33,6}).sage() # optional - maple
{6, 14, 33}
sage: maple("seq(i**2,i=1..5)").sage() # optional - maple
(1, 4, 9, 16, 25)
Strings::
sage: maple('"banane"').sage() # optional - maple
'"banane"'
Floats::
sage: Z3 = maple('evalf(Zeta(3))') # optional - maple
sage: Z3.sage().parent() # optional - maple
Real Field with 53 bits of precision
sage: sq5 = maple('evalf(sqrt(5),100)') # optional - maple
sage: sq5 = sq5.sage(); sq5 # optional - maple
2.23606797749978969640...
sage: sq5.parent() # optional - maple
Real Field with 332 bits of precision
Functions are not yet converted back correctly::
sage: maple(hypergeometric([3,4],[5],x)) # optional - maple
hypergeom([3, 4],[5],x)
sage: _.sage() # known bug # optional - maple
hypergeometric((3, 4), (5,), x)
"""
from sage.matrix.constructor import matrix
from sage.modules.free_module_element import vector
from sage.rings.integer_ring import ZZ
# The next few lines are a very crude excuse for a maple "parser"
maple_type = repr(self.whattype())
result = repr(self)
result = result.replace("Pi", "pi")
if maple_type == 'symbol': # pi
pass # left to symbolic ring
elif maple_type == 'string': # "banane"
return result
elif maple_type == 'exprseq': # 2, 2
n = self.parent()(f"[{self._name}]").nops()._sage_()
return tuple(self[i] for i in range(1, n + 1))
elif maple_type == 'set': # {1, 2}
n = self.nops()._sage_()
return set(self.op(i)._sage_() for i in range(1, n + 1))
elif maple_type == 'list': # [1, 2]
n = self.nops()._sage_()
return [self.op(i)._sage_() for i in range(1, n + 1)]
elif maple_type == "Matrix": # Matrix(2, 2, [[1,2],[3,4]])
mn = self.op(1)
m = mn[1]._sage_()
n = mn[2]._sage_()
coeffs = [
self[i + 1, j + 1]._sage_() for i in range(m) for j in range(n)
]
return matrix(m, n, coeffs)
elif maple_type[:6] == "Vector": # Vector[row](3, [4,5,6])
n = self.op(1)._sage_()
return vector([self[i + 1]._sage_() for i in range(n)])
elif maple_type == 'integer':
return ZZ(result)
elif maple_type == 'fraction':
return self.op(1)._sage_() / self.op(2)._sage_()
elif maple_type == "function":
pass # TODO : here one should translate back function names
elif maple_type == "float":
from sage.rings.real_mpfr import RealField
mantissa = len(repr(self.op(1)))
prec = max(53, (mantissa * 13301) // 4004)
R = RealField(prec)
return R(result)
elif maple_type == '`=`': # (1, 1) = 2
return (self.op(1)._sage_() == self.op(2)._sage())
try:
from sage.symbolic.all import SR
return SR(result)
except Exception:
raise NotImplementedError("Unable to parse Maple output: %s" %
result)
