Riemann matrices and endomorphism rings of algebraic Riemann surfaces#

This module provides a class, RiemannSurface, to model the Riemann surface determined by a plane algebraic curve over a subfield of the complex numbers.

A homology basis is derived from the edges of a Voronoi cell decomposition based on the branch locus. The pull-back of these edges to the Riemann surface provides a graph on it that contains a homology basis.

The class provides methods for computing the Riemann period matrix of the surface numerically, using a certified homotopy continuation method due to [Kr2016].

The class also provides facilities for computing the endomorphism ring of the period lattice numerically, by determining integer (near) solutions to the relevant approximate linear equations.

One can also calculate the Abel-Jacobi map on the Riemann surface, and there is basic functionality to interface with divisors of curves to facilitate this.

AUTHORS:

Alexandre Zotine, Nils Bruin (2017-06-10): initial version
Nils Bruin, Jeroen Sijsling (2018-01-05): algebraization, isomorphisms
Linden Disney-Hogg, Nils Bruin (2021-06-23): efficient integration
Linden Disney-Hogg, Nils Bruin (2022-09-07): Abel-Jacobi map

EXAMPLES:

We compute the Riemann matrix of a genus 3 curve:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: f = x^4-x^3*y+2*x^3+2*x^2*y+2*x^2-2*x*y^2+4*x*y-y^3+3*y^2+2*y+1
sage: S = RiemannSurface(f, prec=100)
sage: M = S.riemann_matrix()

We test the usual properties, i.e., that the period matrix is symmetric and that the imaginary part is positive definite:

sage: all(abs(a) < 1e-20 for a in (M-M.T).list())
True
sage: iM = Matrix(RDF,3,3,[a.imag_part() for a in M.list()])
sage: iM.is_positive_definite()
True

We compute the endomorphism ring and check it has \(\ZZ\)-rank 6:

sage: A = S.endomorphism_basis(80,8)
sage: len(A) == 6
True

In fact it is an order in a number field:

sage: T.<t> = QQ[]
sage: K.<a> = NumberField(t^6 - t^5 + 2*t^4 + 8*t^3 - t^2 - 5*t + 7)
sage: all(len(a.minpoly().roots(K)) == a.minpoly().degree() for a in A)
True

We can look at an extended example of the Abel-Jacobi functionality. We will demonstrate a particular half-canonical divisor on Klein’s Curve, known in the literature:

sage: f = x^3*y + y^3 + x
sage: S = RiemannSurface(f, integration_method='rigorous')
sage: BL = S.places_at_branch_locus(); BL
[Place (x, y, y^2),
 Place (x^7 + 27/4, y + 4/9*x^5, y^2 + 4/3*x^3),
 Place (x^7 + 27/4, y - 2/9*x^5, y^2 + 1/3*x^3)]

We can read off out the output of places_at_branch_locus to choose our divisor, and we can calculate the canonical divisor using curve functionality:

sage: P0 = 1*BL[0]
sage: from sage.schemes.curves.constructor import Curve
sage: C = Curve(f)
sage: F = C.function_field()
sage: K = (F(x).differential()).divisor() - F(f.derivative(y)).divisor()
sage: Pinf, Pinf_prime = C.places_at_infinity()
sage: if K-3*Pinf-1*Pinf_prime: Pinf, Pinf_prime = (Pinf_prime, Pinf);
sage: D = P0 + 2*Pinf - Pinf_prime

Note we could check using exact techniques that \(2D = K\):

sage: Z = K - 2*D
sage: (Z.degree() == 0, len(Z.basis_differential_space()) == S.genus, len(Z.basis_function_space()) == 1)
(True, True, True)

We can also check this using our Abel-Jacobi functions:

sage: avoid = C.places_at_infinity()
sage: Zeq, _ = S.strong_approximation(Z, avoid)
sage: Zlist = S.divisor_to_divisor_list(Zeq)
sage: AJ = S.abel_jacobi(Zlist)  # long time (1 second)
sage: S.reduce_over_period_lattice(AJ).norm() < 1e-10  # long time
True

REFERENCES:

The initial version of this code was developed alongside [BSZ2019].

exception sage.schemes.riemann_surfaces.riemann_surface.ConvergenceError#

Bases: ValueError

Error object suitable for raising and catching when Newton iteration fails.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import ConvergenceError
sage: raise ConvergenceError("test")
Traceback (most recent call last):
...
ConvergenceError: test
sage: isinstance(ConvergenceError(),ValueError)
True

class sage.schemes.riemann_surfaces.riemann_surface.RiemannSurface(f, prec=53, certification=True, differentials=None, integration_method='rigorous')#

Bases: object

Construct a Riemann Surface. This is specified by the zeroes of a bivariate polynomial with rational coefficients \(f(z,w) = 0\).

INPUT:

f – a bivariate polynomial with rational coefficients. The surface is interpreted as the covering space of the coordinate plane in the first variable.
prec – the desired precision of computations on the surface in bits (default: 53)
certification – a boolean (default: True) value indicating whether homotopy continuation is certified or not. Uncertified homotopy continuation can be faster.
differentials – (default: None). If specified, provides a list of polynomials \(h\) such that \(h/(df/dw) dz\) is a regular differential on the Riemann surface. This is taken as a basis of the regular differentials, so the genus is assumed to be equal to the length of this list. The results from the homology basis computation are checked against this value. Providing this parameter makes the computation independent from Singular. For a nonsingular plane curve of degree \(d\), an appropriate set is given by the monomials of degree up to \(d-3\).
integration_method – (default: 'rigorous'). String specifying the integration method to use when calculating the integrals of differentials. The options are 'heuristic' and 'rigorous', the latter of which is often the most efficient.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^3 + 1
sage: RiemannSurface(f)
Riemann surface defined by polynomial f = -z^3 + w^2 + 1 = 0, with 53 bits of precision

Another Riemann surface with 100 bits of precision:

sage: S = RiemannSurface(f, prec=100); S
Riemann surface defined by polynomial f = -z^3 + w^2 + 1 = 0, with 100 bits of precision
sage: S.riemann_matrix()^6 #abs tol 0.00000001
[1.0000000000000000000000000000 - 1.1832913578315177081175928479e-30*I]

We can also work with Riemann surfaces that are defined over fields with a complex embedding, but since the current interface for computing genus and regular differentials in Singular presently does not support extensions of QQ, we need to specify a description of the differentials ourselves. We give an example of a CM elliptic curve:

sage: Qt.<t> = QQ[]
sage: K.<a> = NumberField(t^2-t+3,embedding=CC(0.5+1.6*I))
sage: R.<x,y> = K[]
sage: f = y^2 + y - (x^3 + (1-a)*x^2 - (2+a)*x - 2)
sage: S = RiemannSurface(f, prec=100, differentials=[1])
sage: A = S.endomorphism_basis()
sage: len(A)
2
sage: all(len(T.minpoly().roots(K)) > 0 for T in A)
True

The 'heuristic' integration method uses the method integrate_vector defined in sage.numerical.gauss_legendre to compute integrals of differentials. As mentioned there, this works by iteratively doubling the number of nodes used in the quadrature, and uses a heuristic based on the rate at which the result is seemingly converging to estimate the error. The 'rigorous' method uses results from [Neu2018], and bounds the algebraic integrands on circular domains using Cauchy’s form of the remainder in Taylor approximation coupled to Fujiwara’s bound on polynomial roots (see Bruin-DisneyHogg-Gao, in preparation). Note this method of bounding on circular domains is also implemented in _compute_delta(). The net result of this bounding is that one can know (an upper bound on) the number of nodes required to achieve a certain error. This means that for any given integral, assuming that the same number of nodes is required by both methods in order to achieve the desired error (not necessarily true in practice), approximately half the number of integrand evaluations are required. When the required number of nodes is high, e.g. when the precision required is high, this can make the 'rigorous' method much faster. However, the 'rigorous' method does not benefit as much from the caching of the nodes method over multiple integrals. The result of this is that, for calls of matrix_of_integral_values() if the computation is ‘fast’, the heuristic method may outperform the rigorous method, but for slower computations the rigorous method can be much faster:

sage: f = z*w^3 + z^3 + w
sage: p = 53
sage: Sh = RiemannSurface(f, prec=p, integration_method='heuristic')
sage: Sr = RiemannSurface(f, prec=p, integration_method='rigorous')
sage: from sage.numerical.gauss_legendre import nodes
sage: import time
sage: nodes.cache.clear()
sage: ct = time.time()
sage: Rh = Sh.riemann_matrix()
sage: ct1 = time.time()-ct
sage: nodes.cache.clear()
sage: ct = time.time()
sage: Rr = Sr.riemann_matrix()
sage: ct2 = time.time()-ct
sage: ct2/ct1  # random
1.2429363969691192

Note that for the above curve, the branch points are evenly distributed, and hence the implicit assumptions in the heuristic method are more sensible, meaning that a higher precision is required to see the heuristic method being significantly slower than the rigorous method. For a worse conditioned curve, this effect is more pronounced:

sage: q = 1 / 10
sage: f = y^2 - (x^2 - 2*x + 1 + q^2) * (x^2 + 2*x + 1 + q^2)
sage: p = 500
sage: Sh = RiemannSurface(f, prec=p, integration_method='heuristic')
sage: Sr = RiemannSurface(f, prec=p, integration_method='rigorous')
sage: nodes.cache.clear()
sage: Rh = Sh.riemann_matrix()  # long time (8 seconds)
sage: nodes.cache.clear()
sage: Rr = Sr.riemann_matrix()  # long time (1 seconds)

This disparity in timings can get increasingly worse, and testing has shown that even for random quadrics the heuristic method can be as bad as 30 times slower.

abel_jacobi(divisor, verbose=False)#

Return the Abel-Jacobi map of divisor.

Return a representative of the Abel-Jacobi map of a divisor with basepoint self._basepoint.

INPUT:

divisor – list. A list with each entry a tuple of the form (v, P), where v is the valuation of the divisor at point P, P as per the input to _aj_based().
verbose – logical (default: False). Whether to report the progress of the computation, in terms of how many elements of the list divisor have been completed.

OUTPUT:

A vector of length self.genus.

EXAMPLES:

We can test that the Abel-Jacobi map between two branchpoints of a superelliptic curve of degree \(p\) is a \(p\)-torsion point in the Jacobian:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: p = 4
sage: S = RiemannSurface(y^p-x^4+1, prec=100)
sage: divisor = [(-1, (-1, 0)), (1, (1, 0))]
sage: AJ = S.abel_jacobi(divisor)  # long time (15 seconds)
sage: AJxp = [p*z for z in AJ]  # long time
sage: bool(S.reduce_over_period_lattice(AJxp).norm()<1e-7)  # long time
True

cohomology_basis(option=1)#

Compute the cohomology basis of this surface.

INPUT:

option – Presently, this routine uses Singular’s adjointIdeal and passes the option parameter on. Legal values are 1, 2, 3 ,4, where 1 is the default. See the Singular documentation for the meaning. The backend for this function may change, and support for this parameter may disappear.

OUTPUT:

This returns a list of polynomials \(g\) representing the holomorphic differentials \(g/(df/dw) dz\), where \(f(z,w)=0\) is the equation specifying the Riemann surface.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = z^3*w + w^3 + z
sage: S = RiemannSurface(f)
sage: S.cohomology_basis()
[1, w, z]

curve()#

Return the curve from which this Riemann surface is obtained.

Riemann surfaces explicitly obtained from a curve return that same object. For others, the curve is constructed and cached, so that an identical curve is returned upon subsequent calls.

OUTPUT:

Curve from which Riemann surface is obtained.

EXAMPLES:

sage: R.<x,y> = QQ[]
sage: C = Curve( y^3+x^3-1)
sage: S = C.riemann_surface()
sage: S.curve() is C
True

divisor_to_divisor_list(divisor, eps=None)#

Turn a divisor into a list for abel_jacobi().

Given divisor in Curve(self.f).function_field().divisor_group(), consisting of places above finite points in the base, return an equivalent divisor list suitable for input into abel_jacboi().

INPUT:

divisor – an element of Curve(self.f).function_field().divisor_group()
eps – real number (optional); tolerance used to determine whether a complex number is close enough to a root of a polynomial

OUTPUT:

A list with elements of the form (v, (z, w)) representing the finite places.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(y^2-x^3+1)
sage: D = sum(S.places_at_branch_locus())
sage: S.divisor_to_divisor_list(D)
[(1, (1.00000000000000, 0.000000000000000)),
 (1, (-0.500000000000000 - 0.866025403784439*I, 0.000000000000000)),
 (1, (-0.500000000000000 + 0.866025403784439*I, 0.000000000000000))]

Todo

Currently this method can only handle places above finite points in the base. It would be useful to extend this to allow for places at infinity.

downstairs_edges()#

Compute the edgeset of the Voronoi diagram.

OUTPUT:

A list of integer tuples corresponding to edges between vertices in the Voronoi diagram.

EXAMPLES:

Form a Riemann surface, one with a particularly simple branch locus:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 + z^3 - z^2
sage: S = RiemannSurface(f)

Compute the edges:

sage: S.downstairs_edges()
[(0, 1), (0, 5), (1, 4), (2, 3), (2, 4), (3, 5), (4, 5)]

This now gives an edgeset which one could use to form a graph.

Note

The numbering of the vertices is given by the Voronoi package.

downstairs_graph()#

Return the Voronoi decomposition as a planar graph.

The result of this routine can be useful to interpret the labelling of the vertices. See also upstairs_graph().

OUTPUT:

The Voronoi decomposition as a graph, with appropriate planar embedding.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^4 + 1
sage: S = RiemannSurface(f)
sage: S.downstairs_graph()
Graph on 11 vertices

edge_permutations()#

Compute the permutations of branches associated to each edge.

Over the vertices of the Voronoi decomposition around the branch locus, we label the fibres. By following along an edge, the lifts of the edge induce a permutation of that labelling.

OUTPUT:

A dictionary with as keys the edges of the Voronoi decomposition and as values the corresponding permutations.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 + z^2+1
sage: S = RiemannSurface(f)
sage: S.edge_permutations()
{(0, 2): (),
 (0, 4): (),
 (1, 2): (),
 (1, 3): (0,1),
 (1, 6): (),
 (2, 0): (),
 (2, 1): (),
 (2, 5): (0,1),
 (3, 1): (0,1),
 (3, 4): (),
 (4, 0): (),
 (4, 3): (),
 (5, 2): (0,1),
 (5, 7): (),
 (6, 1): (),
 (6, 7): (),
 (7, 5): (),
 (7, 6): ()}

endomorphism_basis(b=None, r=None)#

Numerically compute a \(\ZZ\)-basis for the endomorphism ring.

Let \(\left(I | M \right)\) be the normalized period matrix (\(M\) is the \(g\times g\) riemann_matrix()). We consider the system of matrix equations \(MA + C = (MB + D)M\) where \(A, B, C, D\) are \(g\times g\) integer matrices. We determine small integer (near) solutions using LLL reductions. These solutions are returned as \(2g \times 2g\) integer matrices obtained by stacking \(\left(D | B\right)\) on top of \(\left(C | A\right)\).

INPUT:

b – integer (default provided). The equation coefficients are scaled by \(2^b\) before rounding to integers.
r – integer (default: b/4). Solutions that have all coefficients smaller than \(2^r\) in absolute value are reported as actual solutions.

OUTPUT:

A list of \(2g \times 2g\) integer matrices that, for large enough r and b-r, generate the endomorphism ring.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(x^3 + y^3 + 1)
sage: B = S.endomorphism_basis(); B #random
[
[1 0]  [ 0 -1]
[0 1], [ 1  1]
]
sage: sorted([b.minpoly().disc() for b in B])
[-3, 1]

homology_basis()#

Compute the homology basis of the Riemann surface.

OUTPUT:

A list of paths \(L = [P_1, \dots, P_n]\). Each path \(P_i\) is of the form \((k, [p_1 ... p_m, p_1])\), where \(k\) is the number of times to traverse the path (if negative, to traverse it backwards), and the \(p_i\) are vertices of the upstairs graph.

EXAMPLES:

In this example, there are two paths that form the homology basis:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: g = w^2 - z^4 + 1
sage: S = RiemannSurface(g)
sage: S.homology_basis()  # random
[[(1, [(3, 1), (5, 0), (9, 0), (10, 0), (2, 0), (4, 0),
    (7, 1), (10, 1), (3, 1)])],
 [(1, [(8, 0), (6, 0), (7, 0), (10, 0), (2, 0), (4, 0),
    (7, 1), (10, 1), (9, 1), (8, 0)])]]

In order to check that the answer returned above is reasonable, we test some basic properties. We express the faces of the downstairs graph as ZZ-linear combinations of the edges and check that the projection of the homology basis upstairs projects down to independent linear combinations of an even number of faces:

sage: dg = S.downstairs_graph()
sage: edges = dg.edges(sort=True)
sage: E = ZZ^len(edges)
sage: edge_to_E = { e[:2]: E.gen(i) for i,e in enumerate(edges)}
sage: edge_to_E.update({ (e[1],e[0]): -E.gen(i) for i,e in enumerate(edges)})
sage: face_span = E.submodule([sum(edge_to_E[e] for e in f) for f in dg.faces()])
sage: def path_to_E(path):
....:     k,P = path
....:     return k*sum(edge_to_E[(P[i][0],P[i+1][0])] for i in range(len(P)-1))
sage: hom_basis = [sum(path_to_E(p) for p in loop) for loop in S.homology_basis()]
sage: face_span.submodule(hom_basis).rank()
2
sage: [sum(face_span.coordinate_vector(b))%2 for b in hom_basis]
[0, 0]

homomorphism_basis(other, b=None, r=None)#

Numerically compute a \(\ZZ\)-basis for module of homomorphisms to a given complex torus.

Given another complex torus (given as the analytic Jacobian of a Riemann surface), numerically compute a basis for the homomorphism module. The answer is returned as a list of 2g x 2g integer matrices T=(D, B; C, A) such that if the columns of (I|M1) generate the lattice defining the Jacobian of the Riemann surface and the columns of (I|M2) do this for the codomain, then approximately we have (I|M2)T=(D+M2C)(I|M1), i.e., up to a choice of basis for \(\CC^g\) as a complex vector space, we we realize (I|M1) as a sublattice of (I|M2).

INPUT:

b – integer (default provided). The equation coefficients are scaled by \(2^b\) before rounding to integers.
r – integer (default: b/4). Solutions that have all coefficients smaller than \(2^r\) in absolute value are reported as actual solutions.

OUTPUT:

A list of \(2g \times 2g\) integer matrices that, for large enough r and b-r, generate the homomorphism module.

EXAMPLES:

sage: S1 = EllipticCurve("11a1").riemann_surface()
sage: S2 = EllipticCurve("11a3").riemann_surface()
sage: [m.det() for m in S1.homomorphism_basis(S2)]
[5]

homotopy_continuation(edge)#

Perform homotopy continuation along an edge of the Voronoi diagram using Newton iteration.

INPUT:

edge – a tuple (z_start, z_end) indicating the straight line over which to perform the homotopy continutation

OUTPUT:

A list containing the initialised continuation data. Each entry in the list contains: the \(t\) values that entry corresponds to, a list of complex numbers corresponding to the points which are reached when continued along the edge when traversing along the direction of the edge, and a value epsilon giving the minimumdistance between the fibre values divided by 3. The ordering of these points indicates how they have been permuted due to the weaving of the curve.

EXAMPLES:

We check that continued values along an edge correspond (up to the appropriate permutation) to what is stored. Note that the permutation was originally computed from this data:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = z^3*w + w^3 + z
sage: S = RiemannSurface(f)
sage: edge1 = sorted(S.edge_permutations())[0]
sage: sigma = S.edge_permutations()[edge1]
sage: edge = [S._vertices[i] for i in edge1]
sage: continued_values = S.homotopy_continuation(edge)[-1][1]
sage: stored_values = S.w_values(S._vertices[edge1[1]])
sage: all(abs(continued_values[i]-stored_values[sigma(i)]) < 1e-8 for i in range(3))
True

make_zw_interpolator(upstairs_edge, initial_continuation=None)#

Given a downstairs edge for which continuation data has been initialised, return a function that computes \(z(t), w(t)\) , where \(t\) in \([0,1]\) is a parametrization of the edge.

INPUT:

upstairs_edge – tuple ((z_start, sb), (z_end,)) giving the start and end values of the base coordinate along the straight-line path and the starting branch
initial_continuation – list (optional); output of homotopy_continuation initialising the continuation data

OUTPUT:

A tuple (g, d), where g is the function that computes the interpolation along the edge and d is the difference of the z-values of the end and start point.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^4 + 1
sage: S = RiemannSurface(f)
sage: _ = S.homology_basis()
sage: u_edge = [(0, 0), (1, 0)]
sage: d_edge = tuple(u[0] for u in u_edge)
sage: u_edge = [(S._vertices[i], j) for i, j in u_edge]
sage: initial_continuation = S._L[d_edge]
sage: g, d = S.make_zw_interpolator(u_edge, initial_continuation)
sage: all(f(*g(i*0.1)).abs() < 1e-13 for i in range(10))
True
sage: abs((g(1)[0]-g(0)[0]) - d) < 1e-13
True

Note

The interpolator returned by this method can effectively hang if either z_start or z_end are branchpoints. In these situations it is better to take a different approach rather than continue to use the interpolator.

matrix_of_integral_values(differentials, integration_method='heuristic')#

Compute the path integrals of the given differentials along the homology basis.

The returned answer has a row for each differential. If the Riemann surface is given by the equation \(f(z,w)=0\), then the differentials are encoded by polynomials g, signifying the differential \(g(z,w)/(df/dw) dz\).

INPUT:

differentials – a list of polynomials.
integration_method – (default: 'heuristic'). String specifying the integration method to use. The options are 'heuristic' and 'rigorous'.

OUTPUT:

A matrix, one row per differential, containing the values of the path integrals along the homology basis of the Riemann surface.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(x^3 + y^3 + 1)
sage: B = S.cohomology_basis()
sage: m = S.matrix_of_integral_values(B)
sage: parent(m)
Full MatrixSpace of 1 by 2 dense matrices over Complex Field with 53 bits of precision
sage: (m[0,0]/m[0,1]).algdep(3).degree() # curve is CM, so the period is quadratic
2

Note

If differentials is self.cohomology_basis(), the calculations of the integrals along the edges are written to \(self._integral_dict`\). This is as this data will be required when computing the Abel-Jacobi map, and so it is helpful to have is stored rather than recomputing.

monodromy_group()#

Compute local monodromy generators of the Riemann surface.

For each branch point, the local monodromy is encoded by a permutation. The permutations returned correspond to positively oriented loops around each branch point, with a fixed base point. This means the generators are properly conjugated to ensure that together they generate the global monodromy. The list has an entry for every finite point stored in self.branch_locus, plus an entry for the ramification above infinity.

OUTPUT:

A list of permutations, encoding the local monodromy at each branch point.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z, w> = QQ[]
sage: f = z^3*w + w^3 + z
sage: S = RiemannSurface(f)
sage: G = S.monodromy_group(); G
[(0,1,2), (0,1), (0,2), (1,2), (1,2), (1,2), (0,1), (0,2), (0,2)]

The permutations give the local monodromy generators for the branch points:

sage: list(zip(S.branch_locus + [unsigned_infinity], G)) #abs tol 0.0000001
[(0.000000000000000, (0,1,2)),
 (-1.31362670141929, (0,1)),
 (-0.819032851784253 - 1.02703471138023*I, (0,2)),
 (-0.819032851784253 + 1.02703471138023*I, (1,2)),
 (0.292309440469772 - 1.28069133740100*I, (1,2)),
 (0.292309440469772 + 1.28069133740100*I, (1,2)),
 (1.18353676202412 - 0.569961265016465*I, (0,1)),
 (1.18353676202412 + 0.569961265016465*I, (0,2)),
 (Infinity, (0,2))]

We can check the ramification by looking at the cycle lengths and verify it agrees with the Riemann-Hurwitz formula:

sage: 2*S.genus-2 == -2*S.degree + sum(e-1 for g in G for e in g.cycle_type())
True

period_matrix()#

Compute the period matrix of the surface.

OUTPUT:

A matrix of complex values.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = z^3*w + w^3 + z
sage: S = RiemannSurface(f, prec=30)
sage: M = S.period_matrix()

The results are highly arbitrary, so it is hard to check if the result produced is correct. The closely related riemann_matrix is somewhat easier to test.:

sage: parent(M)
Full MatrixSpace of 3 by 6 dense matrices over Complex Field with 30 bits of precision
sage: M.rank()
3

One can check that the two methods give similar answers:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: f = y^2 - x^3 + 1
sage: S = RiemannSurface(f, integration_method="rigorous")
sage: T = RiemannSurface(f, integration_method="heuristic")
sage: RM_S = S.riemann_matrix()
sage: RM_T = T.riemann_matrix()
sage: (RM_S-RM_T).norm() < 1e-10
True

places_at_branch_locus()#

Return the places above the branch locus.

Return a list of the of places above the branch locus. This must be done over the base ring, and so the places are given in terms of the factors of the discriminant. Currently, this method only works when self._R.base_ring() == QQ as for other rings, the function field for Curve(self.f) is not implemented. To go from these divisors to a divisor list, see divisor_to_divisor_list().

OUTPUT:

List of places of the functions field Curve(self.f).function_field().

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(25*(x^4+y^4+1) - 34*(x^2*y^2+x^2+y^2))
sage: S.places_at_branch_locus()
[Place (x - 2, (x - 2)*y, y^2 - 17/5, y^3 - 17/5*y),
 Place (x + 2, (x + 2)*y, y^2 - 17/5, y^3 - 17/5*y),
 Place (x - 1/2, (x - 1/2)*y, y^2 - 17/20, y^3 - 17/20*y),
 Place (x + 1/2, (x + 1/2)*y, y^2 - 17/20, y^3 - 17/20*y),
 Place (x^4 - 34/25*x^2 + 1, y, y^2, y^3),
 Place (x^4 - 34/25*x^2 + 1, (x^4 - 34/25*x^2 + 1)*y, y^2 - 34/25*x^2 - 34/25, y^3 + (-34/25*x^2 - 34/25)*y)]

plot_paths()#

Make a graphical representation of the integration paths.

This returns a two dimensional plot containing the branch points (in red) and the integration paths (obtained from the Voronoi cells of the branch points). The integration paths are plotted by plotting the points that have been computed for homotopy continuation, so the density gives an indication of where numerically sensitive features occur.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(y^2 - x^3 - x)
sage: S.plot_paths()
Graphics object consisting of 2 graphics primitives

plot_paths3d(thickness=0.01)#

Return the homology basis as a graph in 3-space.

The homology basis of the surface is constructed by taking the Voronoi cells around the branch points and taking the inverse image of the edges on the Riemann surface. If the surface is given by the equation \(f(z,w)\), the returned object gives the image of this graph in 3-space with coordinates \(\left(\operatorname{Re}(z), \operatorname{Im}(z), \operatorname{Im}(w)\right)\).

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(y^2-x^3-x)
sage: S.plot_paths3d()
Graphics3d Object

reduce_over_period_lattice(vector, method='ip', b=None, r=None, normalised=False)#

Reduce a vector over the period lattice.

Given a vector of length self.genus, this method returns a vector in the same orbit of the period lattice that is short. There are two possible methods, 'svp' which returns a certified shortest vector, but can be much slower for higher genus curves, and 'ip', which is faster but not guaranteed to return the shortest vector. In general the latter will perform well when the lattice basis vectors are of similar size.

INPUT:

vector – vector. A vector of length self.genus to reduce over the lattice.
method – string (default: 'ip'). String specifying the method to use to reduce the vector. THe options are 'ip' and 'svp'.
b – integer (default provided): as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.
r – integer (default: b/4). as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.
normalised – logical (default: False). Whether to use the period matrix with the differentials normalised s.t. the \(A\)-matrix is the identity.

OUTPUT:

Complex vector of length self.genus in the same orbit as vector in the lattice.

EXAMPLES:

We can check that the lattice basis vectors themselves are reduced to zero:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(y^2-x^5+1)
sage: epsilon = S._RR(2)^(-S._prec+1)
sage: for vector in S.period_matrix().columns():
....:     print(bool(S.reduce_over_period_lattice(vector).norm()<epsilon))
True
True
True
True

We can also check that the method 'svp' always gives a smaller norm than 'ip':

sage: for vector in S.period_matrix().columns():
....:     n1 = S.reduce_over_period_lattice(vector).norm()
....:     n2 = S.reduce_over_period_lattice(vector, method="svp").norm()
....:     print(bool(n2<=n1))
True
True
True
True

riemann_matrix()#

Compute the Riemann matrix.

OUTPUT:

A matrix of complex values.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = z^3*w + w^3 + z
sage: S = RiemannSurface(f, prec=60)
sage: M = S.riemann_matrix()

The Klein quartic has a Riemann matrix with values in a quadratic field:

sage: x = polygen(QQ)
sage: K.<a> = NumberField(x^2-x+2)
sage: all(len(m.algdep(6).roots(K)) > 0 for m in M.list())
True

rigorous_line_integral(upstairs_edge, differentials, bounding_data)#

Perform vectorized integration along a straight path.

Using the error bounds for Gauss-Legendre integration found in [Neu2018] and a method for bounding an algebraic integrand on a circular domains using Cauchy’s form of the remainder in Taylor approximation coupled to Fujiwara’s bound on polynomial roots (see Bruin-DisneyHogg-Gao, in preparation), this method calculates (semi-)rigorously the integral of a list of differentials along an edge of the upstairs graph.

INPUT:

upstairs_edge – tuple. Either a pair of integer tuples corresponding to an edge of the upstairs graph, or a tuple ((z_start, sb), (z_end, )) as in the input of make_zw_interpolator.
differentials – a list of polynomials; a polynomial \(g\) represents the differential \(g(z,w)/(df/dw) dz\) where \(f(z,w)=0\) is the equation defining the Riemann surface.
bounding_data – tuple containing the data required for bounding the integrands. This should be in the form of the output from _bounding_data().

OUTPUT:

A complex number, the value of the line integral.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^4 + 1
sage: S = RiemannSurface(f); S
Riemann surface defined by polynomial f = -z^4 + w^2 + 1 = 0, with 53 bits of precision

Since we make use of data from homotopy continuation, we need to compute the necessary data:

sage: _ = S.homology_basis()
sage: differentials = S.cohomology_basis()
sage: bounding_data = S._bounding_data(differentials)
sage: S.rigorous_line_integral([(0,0), (1,0)], differentials, bounding_data)  # abs tol 1e-10
(1.80277751848459e-16 - 0.352971844594760*I)

Note

Uses data that homology_basis initializes, and may give incorrect values if homology_basis() has not initialized them.

Note also that the data of the differentials is contained within bounding_data. It is, however, still advantageous to have this be a separate argument, as it lets the user supply a fast-callable version of the differentials, to significantly speed up execution of the integrand calls, and not have to re-calculate these fast-callables for every run of the function. This is also the benefit of representing the differentials as a polynomial over a known common denominator.

Todo

Note that bounding_data contains the information of the integrands, so one may want to check for consistency between bounding_data and differentials. If so one would not want to do so at the expense of speed.

Moreover, the current implementation bounds along a line by splitting it up into segments, each of which can be covered entirely by a single circle, and then placing inside that the ellipse required to bound as per [Neu2018]. This is reliably more efficient than the heuristic method, especially in poorly-conditioned cases where discriminant points are close together around the edges, but in the case where the branch locus is well separated, it can require slightly more nodes than necessary. One may want to include a method here to transition in this regime to an algorithm that covers the entire line with one ellipse, then bounds along that ellipse with multiple circles.

rosati_involution(R)#

Compute the Rosati involution of an endomorphism.

The endomorphism in question should be given by its homology representation with respect to the symplectic basis of the Jacobian.

INPUT:

R – integral matrix.

OUTPUT:

The result of applying the Rosati involution to R.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: A.<x,y> = QQ[]
sage: S = RiemannSurface(y^2 - (x^6 + 2*x^4 + 4*x^2 + 8), prec = 100)
sage: Rs = S.endomorphism_basis()
sage: S.rosati_involution(S.rosati_involution(Rs[1])) == Rs[1]
True

simple_vector_line_integral(upstairs_edge, differentials)#

Perform vectorized integration along a straight path.

INPUT:

upstairs_edge – tuple. Either a pair of integer tuples corresponding to an edge of the upstairs graph, or a tuple ((z_start, sb), (z_end, )) as in the input of make_zw_interpolator.
differentials – a list of polynomials; a polynomial \(g\) represents the differential \(g(z,w)/(df/dw) dz\) where \(f(z,w)=0\) is the equation defining the Riemann surface.

OUTPUT:

A complex number, the value of the line integral.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^4 + 1
sage: S = RiemannSurface(f); S
Riemann surface defined by polynomial f = -z^4 + w^2 + 1 = 0, with 53 bits of precision

Since we make use of data from homotopy continuation, we need to compute the necessary data:

sage: M = S.riemann_matrix()
sage: differentials = S.cohomology_basis()
sage: S.simple_vector_line_integral([(0, 0), (1, 0)], differentials) #abs tol 0.00000001
(1.14590610929717e-16 - 0.352971844594760*I)

Note

Uses data that homology_basis() initializes, and may give incorrect values if homology_basis() has not initialized them. In practice it is more efficient to set differentials to a fast-callable version of differentials to speed up execution.

strong_approximation(divisor, S)#

Apply the method of strong approximation to a divisor.

As described in [Neu2018], apply the method of strong approximation to divisor with list of places to avoid S. Currently, this method only works when self._R.base_ring() == QQ as for other rings, the function field for Curve(self.f) is not implemented.

INPUT:

divisor – an element of Curve(self.f).function_field().divisor_group()
S – list of places to avoid

OUTPUT:

A tuple (D, B), where D is a new divisor, linearly equivalent to divisor, but not intersecting S, and B is a list of tuples (v, b) where b are the functions giving the linear equivalence, added with multiplicity v.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: S = RiemannSurface(y^2-x^3+1)
sage: avoid = Curve(S.f).places_at_infinity()
sage: D = 1*avoid[0]
sage: S.strong_approximation(D, avoid)
(- Place (x - 2, (x - 2)*y)
  + Place (x - 1, y)
  + Place (x^2 + x + 1, y),
 [(1, (1/(x - 2))*y)])

symplectic_automorphism_group(endo_basis=None, b=None, r=None)#

Numerically compute the symplectic automorphism group as a permutation group.

INPUT:

endo_basis (default: None) – a \(\ZZ\)-basis of the endomorphisms of self, as obtained from endomorphism_basis(). If you have already calculated this basis, it saves time to pass it via this keyword argument. Otherwise the method will calculate it.
b – integer (default provided): as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.
r – integer (default: b/4). as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.

OUTPUT:

The symplectic automorphism group of the Jacobian of the Riemann surface. The automorphism group of the Riemann surface itself can be recovered from this; if the curve is hyperelliptic, then it is identical, and if not, then one divides out by the central element corresponding to multiplication by -1.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: A.<x,y> = QQ[]
sage: S = RiemannSurface(y^2 - (x^6 + 2*x^4 + 4*x^2 + 8), prec = 100)
sage: G = S.symplectic_automorphism_group()
sage: G.as_permutation_group().is_isomorphic(DihedralGroup(4))
True

symplectic_isomorphisms(other=None, hom_basis=None, b=None, r=None)#

Numerically compute symplectic isomorphisms.

INPUT:

other (default: self) – the codomain, another Riemann surface.
hom_basis (default: None) – a \(\ZZ\)-basis of the homomorphisms from self to other, as obtained from homomorphism_basis(). If you have already calculated this basis, it saves time to pass it via this keyword argument. Otherwise the method will calculate it.
b – integer (default provided): as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.
r – integer (default: b/4). as for homomorphism_basis(), and used in its invocation if (re)calculating said basis.

OUTPUT:

This returns the combinations of the elements of homomorphism_basis() that correspond to symplectic isomorphisms between the Jacobians of self and other.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<x,y> = QQ[]
sage: f = y^2 - (x^6 + 2*x^4 + 4*x^2 + 8)
sage: X = RiemannSurface(f, prec=100)
sage: P = X.period_matrix()
sage: g = y^2 - (x^6 + x^4 + x^2 + 1)
sage: Y = RiemannSurface(g, prec=100)
sage: Q = Y.period_matrix()
sage: Rs = X.symplectic_isomorphisms(Y)
sage: Ts = X.tangent_representation_numerical(Rs, other = Y)
sage: test1 = all(((T*P - Q*R).norm() < 2^(-80)) for [T, R] in zip(Ts, Rs))
sage: test2 = all(det(R) == 1 for R in Rs)
sage: test1 and test2
True

tangent_representation_algebraic(Rs, other=None, epscomp=None)#

Compute the algebraic tangent representations corresponding to the homology representations in Rs.

The representations on homology Rs have to be given with respect to the symplectic homology basis of the Jacobian of self and other. Such matrices can for example be obtained via endomorphism_basis().

Let \(P\) and \(Q\) be the period matrices of self and other. Then for a homology representation \(R\), the corresponding tangential representation \(T\) satisfies \(T P = Q R\).

INPUT:

Rs – a set of matrices on homology to be converted to their tangent representations.
other (default: self) – the codomain, another Riemann surface.
epscomp – real number (default: 2^(-prec + 30)). Used to determine whether a complex number is close enough to a root of a polynomial.

OUTPUT:

The algebraic tangent representations of the matrices in Rs.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: A.<x,y> = QQ[]
sage: S = RiemannSurface(y^2 - (x^6 + 2*x^4 + 4*x^2 + 8), prec = 100)
sage: Rs = S.endomorphism_basis()
sage: Ts = S.tangent_representation_algebraic(Rs)
sage: Ts[0].base_ring().maximal_order().discriminant() == 8
True

tangent_representation_numerical(Rs, other=None)#

Compute the numerical tangent representations corresponding to the homology representations in Rs.

Let \(P\) and \(Q\) be the period matrices of self and other. Then for a homology representation \(R\), the corresponding tangential representation \(T\) satisfies \(T P = Q R\).

INPUT:

Rs – a set of matrices on homology to be converted to their tangent representations.
other (default: self) – the codomain, another Riemann surface.

OUTPUT:

The numerical tangent representations of the matrices in Rs.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: A.<x,y> = QQ[]
sage: S = RiemannSurface(y^2 - (x^6 + 2*x^4 + 4*x^2 + 8), prec = 100)
sage: P = S.period_matrix()
sage: Rs = S.endomorphism_basis()
sage: Ts = S.tangent_representation_numerical(Rs)
sage: all(((T*P - P*R).norm() < 2^(-80)) for [T, R] in zip(Ts, Rs))
True

upstairs_edges()#

Compute the edgeset of the lift of the downstairs graph onto the Riemann surface.

OUTPUT:

An edgeset between vertices (i, j), where i corresponds to the i-th point in the Voronoi diagram vertices, and j is the j-th w-value associated with that point.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 + z^3 - z^2
sage: S = RiemannSurface(f)
sage: edgeset = S.upstairs_edges()
sage: len(edgeset) == S.degree*len(S.downstairs_edges())
True
sage: {(v[0],w[0]) for v,w in edgeset} == set(S.downstairs_edges())
True

upstairs_graph()#

Return the graph of the upstairs edges.

This method can be useful for generating paths in the surface between points labelled by upstairs vertices, and verifying that a homology basis is likely computed correctly. See also downstairs_graph().

OUTPUT:

The homotopy-continued Voronoi decomposition as a graph, with appropriate 3D embedding.

EXAMPLES:

sage: R.<z,w> = QQ[]
sage: S = Curve(w^2-z^4+1).riemann_surface()
sage: G = S.upstairs_graph(); G
Graph on 22 vertices
sage: G.genus()
1
sage: G.is_connected()
True

w_values(z0)#

Return the points lying on the surface above z0.

INPUT:

z0 – (complex) a point in the complex z-plane.

OUTPUT:

A set of complex numbers corresponding to solutions of \(f(z_0,w) = 0\).

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface
sage: R.<z,w> = QQ[]
sage: f = w^2 - z^4 + 1
sage: S = RiemannSurface(f)

Find the w-values above the origin, i.e. the solutions of \(w^2 + 1 = 0\):

sage: S.w_values(0)  # abs tol 1e-14
[-1.00000000000000*I, 1.00000000000000*I]

Note that typically the method returns a list of length self.degree, but that at ramification points, this may no longer be true:

sage: S.w_values(1)  # abs tol 1e-14
[0.000000000000000]

class sage.schemes.riemann_surfaces.riemann_surface.RiemannSurfaceSum(L)#

Bases: RiemannSurface

Represent the disjoint union of finitely many Riemann surfaces.

Rudimentary class to represent disjoint unions of Riemann surfaces. Exists mainly (and this is the only functionality actually implemented) to represents direct products of the complex tori that arise as analytic Jacobians of Riemann surfaces.

INPUT:

L – list of RiemannSurface objects

EXAMPLES:

sage: _.<x> = QQ[]
sage: SC = HyperellipticCurve(x^6-2*x^4+3*x^2-7).riemann_surface(prec=60)
sage: S1 = HyperellipticCurve(x^3-2*x^2+3*x-7).riemann_surface(prec=60)
sage: S2 = HyperellipticCurve(1-2*x+3*x^2-7*x^3).riemann_surface(prec=60)
sage: len(SC.homomorphism_basis(S1+S2))
2

period_matrix()#

Return the period matrix of the surface.

This is just the diagonal block matrix constructed from the period matrices of the constituents.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface, RiemannSurfaceSum
sage: R.<x,y> = QQ[]
sage: S1 = RiemannSurface(y^2-x^3-x-1)
sage: S2 = RiemannSurface(y^2-x^3-x-5)
sage: S = RiemannSurfaceSum([S1,S2])
sage: S1S2 = S1.period_matrix().block_sum(S2.period_matrix())
sage: S.period_matrix() == S1S2[[0,1],[0,2,1,3]]
True

riemann_matrix()#

Return the normalized period matrix of the surface.

This is just the diagonal block matrix constructed from the Riemann matrices of the constituents.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface, RiemannSurfaceSum
sage: R.<x,y> = QQ[]
sage: S1 = RiemannSurface(y^2-x^3-x-1)
sage: S2 = RiemannSurface(y^2-x^3-x-5)
sage: S = RiemannSurfaceSum([S1,S2])
sage: S.riemann_matrix() == S1.riemann_matrix().block_sum(S2.riemann_matrix())
True

sage.schemes.riemann_surfaces.riemann_surface.bisect(L, t)#

Find position in a sorted list using bisection.

Given a list \(L = [(t_0,...),(t_1,...),...(t_n,...)]\) with increasing \(t_i\), find the index i such that \(t_i <= t < t_{i+1}\) using bisection. The rest of the tuple is available for whatever use required.

INPUT:

L – A list of tuples such that the first term of each tuple is a real number between 0 and 1. These real numbers must be increasing.
t – A real number between \(t_0\) and \(t_n\).

OUTPUT:

An integer i, giving the position in L where t would be in

EXAMPLES:

Form a list of the desired form, and pick a real number between 0 and 1:

sage: from sage.schemes.riemann_surfaces.riemann_surface import bisect
sage: L = [(0.0, 'a'), (0.3, 'b'), (0.7, 'c'), (0.8, 'd'), (0.9, 'e'), (1.0, 'f')]
sage: t = 0.5
sage: bisect(L,t)
1

Another example which demonstrates that if t is equal to one of the t_i, it returns that index:

sage: L = [(0.0, 'a'), (0.1, 'b'), (0.45, 'c'), (0.5, 'd'), (0.65, 'e'), (1.0, 'f')]
sage: t = 0.5
sage: bisect(L,t)
3

sage.schemes.riemann_surfaces.riemann_surface.differential_basis_baker(f)#

Compute a differential basis for a curve that is nonsingular outside (1:0:0),(0:1:0),(0:0:1)

Baker’s theorem tells us that if a curve has its singularities at the coordinate vertices and meets some further easily tested genericity criteria, then we can read off a basis for the regular differentials from the interior of the Newton polygon spanned by the monomials. While this theorem only applies to special plane curves it is worth implementing because the analysis is relatively cheap and it applies to a lot of commonly encountered curves (e.g., curves given by a hyperelliptic model). Other advantages include that we can do the computation over any exact base ring (the alternative Singular based method for computing the adjoint ideal requires the rationals), and that we can avoid being affected by subtle bugs in the Singular code.

None is returned when f does not describe a curve of the relevant type. If f is of the relevant type, but is of genus \(0\) then [] is returned (which are both False values, but they are not equal).

INPUT:

\(f\) – a bivariate polynomial

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import differential_basis_baker
sage: R.<x,y> = QQ[]
sage: f = x^3 + y^3 + x^5*y^5
sage: differential_basis_baker(f)
[y^2, x*y, x*y^2, x^2, x^2*y, x^2*y^2, x^2*y^3, x^3*y^2, x^3*y^3]
sage: f = y^2 - (x-3)^2*x
sage: differential_basis_baker(f) is None
True
sage: differential_basis_baker(x^2+y^2-1)
[]

sage.schemes.riemann_surfaces.riemann_surface.find_closest_element(item, lst)#

Return the index of the closest element of a list.

Given List and item, return the index of the element l of List which minimises (item-l).abs(). If there are multiple such elements, the first is returned.

INPUT:

item – value to minimize the distance to over the list
lst – list to look for closest element in

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import find_closest_element
sage: i = 5
sage: l = list(range(10))
sage: i == find_closest_element(i, l)
True

Note that this method does no checks on the input, but will fail for inputs where the absolute value or subtraction do not make sense.

sage.schemes.riemann_surfaces.riemann_surface.integer_matrix_relations(M1, M2, b=None, r=None)#

Determine integer relations between complex matrices.

Given two square matrices with complex entries of size g, h respectively, numerically determine an (approximate) ZZ-basis for the 2g x 2h matrices with integer entries of the shape (D, B; C, A) such that B+M1*A=(D+M1*C)*M2. By considering real and imaginary parts separately we obtain \(2gh\) equations with real coefficients in \(4gh\) variables. We scale the coefficients by a constant \(2^b\) and round them to integers, in order to obtain an integer system of equations. Standard application of LLL allows us to determine near solutions.

The user can specify the parameter \(b\), but by default the system will choose a \(b\) based on the size of the coefficients and the precision with which they are given.

INPUT:

M1 – square complex valued matrix
M2 – square complex valued matrix of same size as M1
b – integer (default provided). The equation coefficients are scaled by \(2^b\) before rounding to integers.
r – integer (default: b/4). The vectors found by LLL that satisfy the scaled equations to within \(2^r\) are reported as solutions.

OUTPUT:

A list of 2g x 2h integer matrices that, for large enough \(r\), \(b-r\), generate the ZZ-module of relevant transformations.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import integer_matrix_relations
sage: M1=M2=matrix(CC,2,2,[sqrt(d) for d in [2,-3,-3,-6]])
sage: T=integer_matrix_relations(M1,M2)
sage: id=parent(M1)(1)
sage: M1t=[id.augment(M1) * t for t in T]
sage: [((m[:,:2]^(-1)*m)[:,2:]-M2).norm() < 1e-13 for m in M1t]
[True, True]

sage.schemes.riemann_surfaces.riemann_surface.numerical_inverse(C)#

Compute numerical inverse of a matrix via LU decomposition

INPUT:

C – A real or complex invertible square matrix

EXAMPLES:

sage: C = matrix(CC,3,3,[-4.5606e-31 + 1.2326e-31*I,
....: -0.21313 + 0.24166*I,
....: -3.4513e-31 + 0.16111*I,
....: -1.0175 + 9.8608e-32*I,
....: 0.30912 + 0.19962*I,
....: -4.9304e-32 + 0.39923*I,
....: 0.96793 - 3.4513e-31*I,
....: -0.091587 + 0.19276*I,
....: 3.9443e-31 + 0.38552*I])
sage: from sage.schemes.riemann_surfaces.riemann_surface import numerical_inverse
sage: 3e-16 < (C^-1*C-C^0).norm() < 1e-15
True
sage: (numerical_inverse(C)*C-C^0).norm() < 3e-16
True

sage.schemes.riemann_surfaces.riemann_surface.reparameterize_differential_minpoly(minpoly, z0)#

Rewrites a minimal polynomial to write is around \(z_0\).

Given a minimal polynomial \(m(z,g)\), where \(g\) corresponds to a differential on the surface (that is, it is represented as a rational function, and implicitly carries a factor \(dz\)), we rewrite the minpoly in terms of variables \(\bar{z}, \bar{g}\) s.t now \(\bar{z}=0 \Leftrightarrow z=z_0\).

INPUT:

minpoly – a polynomial in two variables, where the first variables
corresponds to the base coordinate on the Riemann surface
z0 – complex number or infinity; the point about which to reparameterize

OUTPUT:

A polynomial in two variables giving the reparameterize minimal polynomial.

EXAMPLES:

On the curve given by \(w^2 - z^3 + 1 = 0\), we have differential \(\frac{dz}{2w} = \frac{dz}{2\sqrt{z^3-1}}\) with minimal polynomial \(g^2(z^3-1) - 1/4=0\). We can make the substitution \(\bar{z}=z^{-1}\) to parameterise the differential about \(z=\infty\) as

\[`\frac{-\bar{z}^{-2} d\bar{z}}{2\sqrt{\bar{z}^{-3}-1}} = \frac{-d\bar{z}}{2\sqrt{\bar{z}(1-\bar{z}^3)}}`.\]

Hence the transformed differential should have minimal polynomial \(\bar{g}^2 \bar{z} (1 - \bar{z}^3) - 1/4 = 0\), and we can check this:

sage: from sage.schemes.riemann_surfaces.riemann_surface import RiemannSurface, reparameterize_differential_minpoly
sage: R.<z,w> = QQ[]
sage: S = RiemannSurface(w^2-z^3+1)
sage: minpoly = S._cohomology_basis_bounding_data[1][0][2]
sage: z0 = Infinity
sage: reparameterize_differential_minpoly(minpoly, z0)
-zbar^4*gbar^2 + zbar*gbar^2 - 1/4

We can further check that reparameterising about \(0\) is the identity operation:

sage: reparameterize_differential_minpoly(minpoly, 0)(*minpoly.parent().gens()) == minpoly
True

Note

As part of the routine, when reparameterising about infinity, a rational function is reduced and then the numerator is taken. Over an inexact ring this is numerically unstable, and so it is advisable to only reparameterize about infinity over an exact ring.

sage.schemes.riemann_surfaces.riemann_surface.voronoi_ghost(cpoints, n=6, CC=Complex Double Field)#

Convert a set of complex points to a list of real tuples \((x,y)\), and appends n points in a big circle around them.

The effect is that, with n >= 3, a Voronoi decomposition will have only finite cells around the original points. Furthermore, because the extra points are placed on a circle centered on the average of the given points, with a radius 3/2 times the largest distance between the center and the given points, these finite cells form a simply connected region.

INPUT:

cpoints – a list of complex numbers

OUTPUT:

A list of real tuples \((x,y)\) consisting of the original points and a set of points which surround them.

EXAMPLES:

sage: from sage.schemes.riemann_surfaces.riemann_surface import voronoi_ghost
sage: L = [1 + 1*I, 1 - 1*I, -1 + 1*I, -1 - 1*I]
sage: voronoi_ghost(L)  # abs tol 1e-6
[(1.0, 1.0),
 (1.0, -1.0),
 (-1.0, 1.0),
 (-1.0, -1.0),
 (2.121320343559643, 0.0),
 (1.0606601717798216, 1.8371173070873836),
 (-1.060660171779821, 1.8371173070873839),
 (-2.121320343559643, 2.59786816870648e-16),
 (-1.0606601717798223, -1.8371173070873832),
 (1.06066017177982, -1.8371173070873845)]