# 3+1 Simon-Mars tensor in the $\delta=2$ Tomimatsu-Sato spacetime¶

This worksheet demonstrates a few capabilities of SageManifolds (version 1.0, as included in SageMath 7.5) in computations regarding the 3+1 decomposition of the Simon-Mars tensor in the $\delta=2$ Tomimatsu-Sato spacetime. The results obtained here are used in the article arXiv:1412.6542.

Click here to download the worksheet file (ipynb format). To run it, you must start SageMath with the Jupyter notebook, via the command sage -n jupyter

NB: a version of SageMath at least equal to 7.5 is required to run this worksheet:

In [1]:
version()

Out[1]:
'SageMath version 7.5.1, Release Date: 2017-01-15'

First we set up the notebook to display mathematical objects using LaTeX rendering:

In [2]:
%display latex


Since some computations are quite long, we ask for running them in parallel on 8 cores:

In [3]:
Parallelism().set(nproc=8)


## Tomimatsu-Sato spacetime

The Tomimatsu-Sato metric is an exact stationary and axisymmetric solution of the vacuum Einstein equation, which is asymptotically flat and has a non-zero angular momentum. It has been found in 1972 by A. Tomimatsu and H. Sato [Phys. Rev. Lett. 29, 1344 (1972)], as a solution of the Ernst equation. It is actually the member $\delta=2$ of a larger family of solutions parametrized by a positive integer $\delta$ and exhibited by Tomimatsu and Sato in 1973 [Prog. Theor. Phys. 50, 95 (1973)], the member $\delta=1$ being nothing but the Kerr metric. We refer to [Manko, Prog. Theor. Phys. 127, 1057 (2012)] for a discussion of the properties of this solution.

## Spacelike hypersurface

We consider some hypersurface $\Sigma$ of a spacelike foliation $(\Sigma_t)_{t\in\mathbb{R}}$ of $\delta=2$ Tomimatsu-Sato spacetime; we declare $\Sigma_t$ as a 3-dimensional manifold:

In [4]:
Sig = Manifold(3, 'Sigma', r'\Sigma', start_index=1)


On $\Sigma$, we consider the prolate spheroidal coordinates $(x,y,\phi)$, with $x\in(1,+\infty)$, $y\in(-1,1)$ and $\phi\in(0,2\pi)$ :

In [5]:
X.<r,y,ph> = Sig.chart(r'x:(1,+oo) y:(-1,1) ph:(0,2*pi):\phi')
print X ; X

Chart (Sigma, (x, y, ph))

Out[5]:

### Riemannian metric on $\Sigma$

The Tomimatsu-Sato metric depens on three parameters: the integer $\delta$, the real number $p\in[0,1]$, and the total mass $m$:

In [6]:
var('d, p, m')
assume(m>0)
assumptions()

Out[6]:

We set $\delta=2$ and choose a specific value for $p$, namely $p=1/5$:

In [7]:
d = 2
p = 1/5


Furthermore, without any loss of generality, we may set $m=1$ (this simply fixes some length scale):

In [8]:
m=1


The parameter $q$ is related to $p$ by $p^2+q^2=1$:

In [9]:
q = sqrt(1-p^2)


Some shortcut notations:

In [10]:
AA2 = (p^2*(x^2-1)^2+q^2*(1-y^2)^2)^2 \
- 4*p^2*q^2*(x^2-1)*(1-y^2)*(x^2-y^2)^2
BB2 = (p^2*x^4+2*p*x^3-2*p*x+q^2*y^4-1)^2 \
+ 4*q^2*y^2*(p*x^3-p*x*y^2-y^2+1)^2
CC2 = p^3*x*(1-x^2)*(2*(x^4-1)+(x^2+3)*(1-y^2)) \
+ p^2*(x^2-1)*((x^2-1)*(1-y^2)-4*x^2*(x^2-y^2)) \
+ q^2*(1-y^2)^3*(p*x+1)


The Riemannian metric $\gamma$ induced by the spacetime metric $g$ on $\Sigma$:

In [11]:
gam = Sig.riemannian_metric('gam', latex_name=r'\gamma')
gam[1,1] = m^2*BB2/(p^2*d^2*(x^2-1)*(x^2-y^2)^3)
gam[2,2] = m^2*BB2/(p^2*d^2*(y^2-1)*(-x^2+y^2)^3)
gam[3,3] = - m^2*(y^2-1)*(p^2*BB2^2*(x^2-1)
+ 4*q^2*d^2*CC2^2*(y^2-1))/(AA2*BB2*d^2)
gam.display()

Out[11]:

A view of the non-vanishing components of $\gamma$ w.r.t. coordinates $(x,y,\phi)$:

In [12]:
gam.display_comp()

Out[12]:

The expression of the metric determinant with respect to the default chart (coordinates $(x,y,\phi)$):

In [13]:
gam.determinant().expr()

Out[13]:

### Lapse function and shift vector

In [14]:
N2 = AA2/BB2 - 2*m*q*CC2*(y^2-1)/BB2*(2*m*q*CC2*(y^2-1)
/(BB2*(m^2*(y^2-1)*(p^2*BB2^2*(x^2-1)
+4*q^2*d^2*CC2^2*(y^2-1))/(AA2*BB2*d^2))))
N2.simplify_full()

Out[14]:
In [15]:
N = Sig.scalar_field(sqrt(N2.simplify_full()), name='N')
print N
N.display()

Scalar field N on the 3-dimensional differentiable manifold Sigma

Out[15]:

The coordinate expression of the scalar field $N$:

In [16]:
N.expr()

Out[16]:
In [17]:
b3 = 2*m*q*CC2*(y^2-1)/(BB2*(m^2*(y^2-1)*(p^2*BB2^2*(x^2-1)
+4*q^2*d^2*CC2^2*(y^2-1))/(AA2*BB2*d^2)))
b = Sig.vector_field('beta', latex_name=r'\beta')
b[3] = b3.simplify_full()
# unset components are zero
b.display_comp(only_nonzero=False)

Out[17]:

### Extrinsic curvature of $\Sigma$

We use the formula $$K_{ij} = \frac{1}{2N} \mathcal{L}_{\beta} \gamma_{ij},$$ which is valid for any stationary spacetime:

In [18]:
K = gam.lie_derivative(b) / (2*N)
K.set_name('K')
print K

Field of symmetric bilinear forms K on the 3-dimensional differentiable manifold Sigma


The component $K_{13} = K_{x\phi}$:

In [19]:
K[1,3]

Out[19]:

The type-(1,1) tensor $K^\sharp$ of components $K^i_{\ \, j} = \gamma^{ik} K_{kj}$:

In [20]:
Ku = K.up(gam, 0)
print Ku

Tensor field of type (1,1) on the 3-dimensional differentiable manifold Sigma


We may check that the hypersurface $\Sigma$ is maximal, i.e. that $K^k_{\ \, k} = 0$:

In [21]:
trK = Ku.trace()
trK

Out[21]:

### Connection and curvature

Let us call $D$ the Levi-Civita connection associated with $\gamma$:

In [22]:
D = gam.connection(name='D')
print D

Levi-Civita connection D associated with the Riemannian metric gam on the 3-dimensional differentiable manifold Sigma


The Ricci tensor associated with $\gamma$:

In [23]:
Ric = gam.ricci()
print Ric

Field of symmetric bilinear forms Ric(gam) on the 3-dimensional differentiable manifold Sigma


The scalar curvature $R = \gamma^{ij} R_{ij}$:

In [24]:
R = gam.ricci_scalar(name='R')
print R

Scalar field R on the 3-dimensional differentiable manifold Sigma


### Terms related to the extrinsic curvature

Let us first evaluate the term $K_{ij} K^{ij}$:

In [25]:
Kuu = Ku.up(gam, 1)
trKK = K['_ij']*Kuu['^ij']
print trKK

Scalar field on the 3-dimensional differentiable manifold Sigma


Then we compute the symmetric bilinear form $k_{ij} := K_{ik} K^k_{\ \, j}$:

In [26]:
KK = K['_ik']*Ku['^k_j']
print KK

Tensor field of type (0,2) on the 3-dimensional differentiable manifold Sigma


We check that this tensor field is symmetric:

In [27]:
KK1 = KK.symmetrize()
KK == KK1

Out[27]:

Accordingly, we work with the explicitly symmetric version:

In [28]:
KK = KK1
print KK

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma


## Electric and magnetic parts of the Weyl tensor

The electric part is the bilinear form $E$ given by $$E_{ij} = R_{ij} + K K_{ij} - K_{ik} K^k_{\ \, j}$$

In [29]:
E = Ric + trK*K - KK
print E

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma


The magnetic part is the bilinear form $B$ defined by $$B_{ij} = \epsilon^k_{\ \, l i} D_k K^l_{\ \, j},$$

where $\epsilon^k_{\ \, l i}$ are the components of the type-(1,2) tensor $\epsilon^\sharp$, related to the Levi-Civita alternating tensor $\epsilon$ associated with $\gamma$ by $\epsilon^k_{\ \, l i} = \gamma^{km} \epsilon_{m l i}$. In SageManifolds, $\epsilon$ is obtained by the command volume_form() and $\epsilon^\sharp$ by the command volume_form(1) (1 = 1 index raised):

In [30]:
eps = gam.volume_form()
print eps

3-form eps_gam on the 3-dimensional differentiable manifold Sigma

In [31]:
epsu = gam.volume_form(1)
print epsu

Tensor field of type (1,2) on the 3-dimensional differentiable manifold Sigma

In [32]:
DKu = D(Ku)
B = epsu['^k_li']*DKu['^l_jk']
print B

Tensor field of type (0,2) on the 3-dimensional differentiable manifold Sigma


Let us check that $B$ is symmetric:

In [33]:
B1 = B.symmetrize()
B == B1

Out[33]:

Accordingly, we set

In [34]:
B = B1
B.set_name('B')
print B

Field of symmetric bilinear forms B on the 3-dimensional differentiable manifold Sigma


## 3+1 decomposition of the Simon-Mars tensor

We proceed according to the computation presented in arXiv:1412.6542.

Tensor $E^\sharp$ of components $E^i_ {\ \, j}$:

In [35]:
Eu = E.up(gam, 0)
print Eu

Tensor field of type (1,1) on the 3-dimensional differentiable manifold Sigma


Tensor $B^\sharp$ of components $B^i_{\ \, j}$:

In [36]:
Bu = B.up(gam, 0)
print Bu

Tensor field of type (1,1) on the 3-dimensional differentiable manifold Sigma


1-form $\beta^\flat$ of components $\beta_i$ and its exterior derivative:

In [37]:
bd = b.down(gam)
xdb = bd.exterior_derivative()
print xdb

2-form on the 3-dimensional differentiable manifold Sigma


Scalar square of shift $\beta_i \beta^i$:

In [38]:
b2 = bd(b)
print b2

Scalar field on the 3-dimensional differentiable manifold Sigma


Scalar $Y = E(\beta,\beta) = E_{ij} \beta^i \beta^j$:

In [39]:
Ebb = E(b,b)
Y = Ebb
print Y

Scalar field on the 3-dimensional differentiable manifold Sigma


Scalar $\bar Y = B(\beta,\beta) = B_{ij}\beta^i \beta^j$:

In [40]:
Bbb = B(b,b)
Y_bar = Bbb
print Y_bar

Scalar field B(beta,beta) on the 3-dimensional differentiable manifold Sigma


1-form of components $Eb_i = E_{ij} \beta^j$:

In [41]:
Eb = E.contract(b)
print Eb

1-form on the 3-dimensional differentiable manifold Sigma


Vector field of components $Eub^i = E^i_{\ \, j} \beta^j$:

In [42]:
Eub = Eu.contract(b)
print Eub

Vector field on the 3-dimensional differentiable manifold Sigma


1-form of components $Bb_i = B_{ij} \beta^j$:

In [43]:
Bb = B.contract(b)
print Bb

1-form on the 3-dimensional differentiable manifold Sigma


Vector field of components $Bub^i = B^i_{\ \, j} \beta^j$:

In [44]:
Bub = Bu.contract(b)
print Bub

Vector field on the 3-dimensional differentiable manifold Sigma


Vector field of components $Kub^i = K^i_{\ \, j} \beta^j$:

In [45]:
Kub = Ku.contract(b)
print Kub

Vector field on the 3-dimensional differentiable manifold Sigma

In [46]:
T = 2*b(N) - 2*K(b,b)
print T ; T.display()

Scalar field zero on the 3-dimensional differentiable manifold Sigma

Out[46]:
In [47]:
Db = D(b)  #  Db^i_j = D_j b^i
Dbu = Db.up(gam, 1)  # Dbu^{ij} = D^j b^i
bDb = b*Dbu  # bDb^{ijk} = b^i D^k b^j
T_bar = eps['_ijk']*bDb['^ikj']
print T_bar ; T_bar.display()

Scalar field zero on the 3-dimensional differentiable manifold Sigma

Out[47]:
In [48]:
epsb = eps.contract(b)
print epsb

2-form on the 3-dimensional differentiable manifold Sigma

In [49]:
epsB = eps['_ijl']*Bu['^l_k']
print epsB

Tensor field of type (0,3) on the 3-dimensional differentiable manifold Sigma

In [50]:
Z = 2*N*( D(N)  -K.contract(b)) + b.contract(xdb)
print Z

1-form on the 3-dimensional differentiable manifold Sigma

In [51]:
DNu = D(N).up(gam)
A = 2*(DNu - Ku.contract(b))*b + N*Dbu
Z_bar = eps['_ijk']*A['^kj']
print Z_bar

1-form on the 3-dimensional differentiable manifold Sigma

In [52]:
W = N*Eb + epsb.contract(Bub)
print W

1-form on the 3-dimensional differentiable manifold Sigma

In [53]:
W_bar = N*Bb - epsb.contract(Eub)
print W_bar

1-form on the 3-dimensional differentiable manifold Sigma

In [54]:
M = - 4*Eb(Kub - DNu) - 2*(epsB['_ij.']*Dbu['^ji'])(b)
print M ; M.display()

Scalar field zero on the 3-dimensional differentiable manifold Sigma

Out[54]:
In [55]:
M_bar = 2*(eps.contract(Eub))['_ij']*Dbu['^ji'] - 4*Bb(Kub - DNu)
print M_bar ; M_bar.display()

Scalar field zero on the 3-dimensional differentiable manifold Sigma

Out[55]:
In [56]:
F = (N^2 - b2)*gam + bd*bd
print F

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma

In [57]:
A = epsB['_ilk']*b['^l'] + epsB['_ikl']*b['^l'] \
+ Bu['^m_i']*epsb['_mk'] - 2*N*E
xdbE = xdb['_kl']*Eu['^k_i']
L = 2*N*epsB['_kli']*Dbu['^kl'] + 2*xdb['_ij']*Eub['^j'] \
+ 2*xdbE['_li']*b['^l'] + 2*A['_ik']*(Kub - DNu)['^k']
print L

1-form on the 3-dimensional differentiable manifold Sigma

In [58]:
N2pbb = N^2 + b2
V = N2pbb*E - 2*(b.contract(E)*bd).symmetrize() + Ebb*gam \
+ 2*N*(b.contract(epsB).symmetrize())
print V

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma

In [59]:
beps = b.contract(eps)
V_bar = N2pbb*B - 2*(b.contract(B)*bd).symmetrize() + Bbb*gam \
-2*N*(beps['_il']*Eu['^l_j']).symmetrize()
print V_bar

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma

In [60]:
F = (N^2 - b2)*gam + bd*bd
print F

Field of symmetric bilinear forms on the 3-dimensional differentiable manifold Sigma

In [61]:
R1 = (4*(V*Z - V_bar*Z_bar) + F*L).antisymmetrize(1,2)
print R1

Tensor field of type (0,3) on the 3-dimensional differentiable manifold Sigma

In [62]:
R2 = 4*(T*V - T_bar*V_bar - W*Z + W_bar*Z_bar) + M*F - N*bd*L
print R2

Tensor field of type (0,2) on the 3-dimensional differentiable manifold Sigma

In [63]:
R3 = (4*(W*Z - W_bar*Z_bar) + N*bd*L).antisymmetrize()
print R3

2-form on the 3-dimensional differentiable manifold Sigma

In [64]:
R2[3,1] == -2*R3[3,1]

Out[64]:
In [65]:
R2[3,2] == -2*R3[3,2]

Out[65]:
In [66]:
R4 = 4*(T*W - T_bar*W_bar) -4*(Y*Z - Y_bar*Z_bar) + N*M*bd - b2*L
print R4

1-form on the 3-dimensional differentiable manifold Sigma

In [67]:
epsE = eps['_ijl']*Eu['^l_k']
print epsE

Tensor field of type (0,3) on the 3-dimensional differentiable manifold Sigma

In [68]:
A = - epsE['_ilk']*b['^l'] - epsE['_ikl']*b['^l'] \
- Eu['^m_i']*epsb['_mk'] - 2*N*B
xdbB = xdb['_kl']*Bu['^k_i']
L_bar = - 2*N*epsE['_kli']*Dbu['^kl'] + 2*xdb['_ij']*Bub['^j'] \
+ 2*xdbB['_li']*b['^l'] + 2*A['_ik']*(Kub - DNu)['^k']
print L_bar

1-form on the 3-dimensional differentiable manifold Sigma

In [69]:
R1_bar = (4*(V*Z_bar + V_bar*Z) + F*L_bar).antisymmetrize(1,2)
print R1_bar

Tensor field of type (0,3) on the 3-dimensional differentiable manifold Sigma

In [70]:
R2_bar = 4*(T_bar*V + T*V_bar) - 4*(W*Z_bar + W_bar*Z) \
+ M_bar*F - N*bd*L_bar
print R2_bar

Tensor field of type (0,2) on the 3-dimensional differentiable manifold Sigma

In [71]:
R3_bar = (4*(W*Z_bar + W_bar*Z) + N*bd*L_bar).antisymmetrize()
print R3_bar

2-form on the 3-dimensional differentiable manifold Sigma

In [72]:
R4_bar = 4*(T_bar*W + T*W_bar - Y*Z_bar - Y_bar*Z) \
+ M_bar*N*bd - b2*L_bar
print R4_bar

1-form on the 3-dimensional differentiable manifold Sigma

In [73]:
R1u = R1.up(gam)
print R1u

Tensor field of type (3,0) on the 3-dimensional differentiable manifold Sigma

In [74]:
R2u = R2.up(gam)
print R2u

Tensor field of type (2,0) on the 3-dimensional differentiable manifold Sigma

In [75]:
R3u = R3.up(gam)
print R3u

Tensor field of type (2,0) on the 3-dimensional differentiable manifold Sigma

In [76]:
R4u = R4.up(gam)
print R4u

Vector field on the 3-dimensional differentiable manifold Sigma

In [77]:
R1_baru = R1_bar.up(gam)
print R1_baru

Tensor field of type (3,0) on the 3-dimensional differentiable manifold Sigma

In [78]:
R2_baru = R2_bar.up(gam)
print R2_baru

Tensor field of type (2,0) on the 3-dimensional differentiable manifold Sigma

In [79]:
R3_baru = R3_bar.up(gam)
print R3_baru

Tensor field of type (2,0) on the 3-dimensional differentiable manifold Sigma

In [80]:
R4_baru = R4_bar.up(gam)
print R4_baru

Vector field on the 3-dimensional differentiable manifold Sigma


### Simon-Mars scalars

In [81]:
S1 = 4*(R1['_ijk']*R1u['^ijk'] - R1_bar['_ijk']*R1_baru['^ijk'] \
- 2*(R2['_ij']*R2u['^ij'] - R2_bar['_ij']*R2_baru['^ij']) \
- R3['_ij']*R3u['^ij'] + R3_bar['_ij']*R3_baru['^ij'] \
+ 2*(R4['_i']*R4u['^i'] - R4_bar['_i']*R4_baru['^i']))
print S1

Scalar field on the 3-dimensional differentiable manifold Sigma

In [82]:
S1E = S1.expr()

In [83]:
S2 = 4*(R1['_ijk']*R1_baru['^ijk'] + R1_bar['_ijk']*R1u['^ijk'] \
- 2*(R2['_ij']*R2_baru['^ij'] + R2_bar['_ij']*R2u['^ij']) \
- R3['_ij']*R3_baru['^ij'] - R3_bar['_ij']*R3u['^ij'] \
+ 2*(R4['_i']*R4_baru['^i'] + R4_bar['_i']*R4u['^i']))
print S2

Scalar field on the 3-dimensional differentiable manifold Sigma

In [84]:
S2E = S2.expr()

In [85]:
lS1E = log(S1E,10).simplify_full()

In [86]:
lS2E = log(S2E,10).simplify_full()


Simon-Mars scalars expressed in terms of the coordinates $X=-1/x,y$:

In [87]:
var('X')
S1EX = S1E.subs(x=-1/X).simplify_full()
S2EX = S2E.subs(x=-1/X).simplify_full()


Definition of the ergoregion:

In [88]:
g00 = - AA2/BB2
g00X = g00.subs(x=-1/X).simplify_full()

In [89]:
ergXy = implicit_plot(g00X, (X,-1,0), (y,-1,1), plot_points=200,
fill=False, linewidth=1, color='black',
axes_labels=(r"$X\,\left[M^{-1}\right]$",
r"$y\,\left[M\right]$"),
fontsize=14)


Due to the very high degree of the polynomials involved in the expression of the Simon-Mars scalars, the floating-point precision of Sage's contour_plot function (53 bits) is not sufficient. Taking avantage that Sage is open-source, we modify the function to allow for an arbitrary precision. First, we define a sampling function with a floating-point precision specified by the user (argument precis):

In [90]:
def array_precisXy(fXy, Xmin, Xmax, ymin, ymax, np, precis, tronc):
RP = RealField(precis)
Xmin = RP(Xmin)
Xmax = RP(Xmax)
ymin = RP(ymin)
ymax = RP(ymax)
dX = (Xmax - Xmin) / RP(np-1)
dy = (ymax - ymin) / RP(np-1)
resu = []
for i in range(np):
list_y = []
yy = ymin + dy * RP(i)
fyy = fXy.subs(y=yy)
for j in range(np):
XX = Xmin + dX * RP(j)
fyyXX = fyy.subs(X = XX)
val = RP(log(abs(fyyXX) + 1e-20, 10))
if val < -tronc:
val = -tronc
elif val > tronc:
val = tronc
list_y.append(val)
resu.append(list_y)
return resu


Then we redefine contour_plot so that it uses the sampling function with a floating-point precision of 200 bits:

In [91]:
from sage.misc.decorators import options, suboptions

@suboptions('colorbar', orientation='vertical', format=None,
spacing=None)
@suboptions('label', fontsize=9, colors='blue', inline=None,
inline_spacing=3, fmt="%1.2f")
@options(plot_points=100, fill=True, contours=None, linewidths=None,
linestyles=None, labels=False, frame=True, axes=False,
colorbar=False, legend_label=None, aspect_ratio=1)
def contour_plot_precisXy(f, xrange, yrange, **options):
from sage.plot.all import Graphics
from sage.plot.misc import setup_for_eval_on_grid
from sage.plot.contour_plot import ContourPlot

np = options['plot_points']
precis = 200  # floating-point precision = 200 bits
tronc = 10
xy_data_array = array_precisXy(f, xrange[0], xrange[1],
yrange[0], yrange[1], np, precis,
tronc)

g = Graphics()

# Reset aspect_ratio to 'automatic' in case scale is 'semilog[xy]'.
# Otherwise matplotlib complains.
scale = options.get('scale', None)
if isinstance(scale, (list, tuple)):
scale = scale[0]
if scale == 'semilogy' or scale == 'semilogx':
options['aspect_ratio'] = 'automatic'

g._set_extra_kwds(Graphics._extract_kwds_for_show(options,
ignore=['xmin', 'xmax']))
yrange, options))
return g


Then we are able to draw the contour plot of the two Simon-Mars scalars, in terms of the coordinates $(X,y)$ (Figure 11 of arXiv:1412.6542):

In [92]:
c1Xy = contour_plot_precisXy(S1EX, (-1,0), (-1,1),
plot_points=200,
fill=False, cmap='hsv',
linewidths=1,
contours=(-10,-9,-8,-7,-6,-5,-4,-3,
-2,-1,0,1,2,3,4,5,6,7,8),
colorbar=True,
colorbar_spacing='uniform',
colorbar_format='%1.f',
axes_labels=(r"$X\,\left[M^{-1}\right]$",
r"$y\,\left[M\right]$"),
fontsize=14)

In [93]:
S1TSXy = c1Xy+ergXy
show(S1TSXy)

In [94]:
c2Xy = contour_plot_precisXy(S2EX, (-1,0), (-1,1),
plot_points=200,
fill=False, cmap='hsv',
linewidths=1,
contours=(-10,-9,-8,-7,-6,-5,-4,-3,-2,
-1,0,1,2,3,4,5,6,7,8,9,10),
colorbar=True,
colorbar_spacing='uniform',
colorbar_format='%1.f',
axes_labels=(r"$X\,\left[M^{-1}\right]$",
r"$y\,\left[M\right]$"),
fontsize=14)

In [95]:
S2TSXy = c2Xy + ergXy
show(S2TSXy)


Let us do the same in terms of the Weyl-Lewis-Papapetrou cylindrical coordinates $(\rho,z)$, which are related to the prolate spheroidal coordinates $(x,y)$ by $$\rho = \sqrt{(x^2-1)(1-y^2)} \quad\mbox{and}\quad z=xy .$$

For simplicity, we denote $\rho$ by $r$:

In [96]:
var('r z')

Out[96]:
In [97]:
S1Erz = S1E.subs(x=1/2*(sqrt(r^2+(z+1)^2)+sqrt(r^2+(z-1)^2)),
y=1/2*(sqrt(r^2+(z+1)^2)-sqrt(r^2+(z-1)^2)))
S1Erz = S1Erz.simplify_full()

In [98]:
S2Erz = S2E.subs(x=1/2*(sqrt(r^2+(z+1)^2)+sqrt(r^2+(z-1)^2)),
y=1/2*(sqrt(r^2+(z+1)^2)-sqrt(r^2+(z-1)^2)))
S2Erz = S2Erz.simplify_full()

In [99]:
def tab_precis(fz, zz, rmin, rmax, np, precis, tronc):
RP = RealField(precis)
rmin = RP(rmin)
rmax = RP(rmax)
zz = RP(zz)
dr = (rmax - rmin) / RP(np-1)
resu = []
fzz = fz.subs(z=zz)
for i in range(np):
rr = rmin + dr * RP(i)
val = RP(log(abs(fzz.subs(r = rr)), 10))
if val < -tronc:
val = -tronc
elif val > tronc:
val = tronc
resu.append((rr, zz, val))
return resu


### 3D plots¶

We also a viewer for 3D plots (use 'threejs' or 'jmol' for interactive 3D graphics):

In [100]:
viewer3D = 'threejs' # must be 'threejs', jmol', 'tachyon' or None (default)

In [101]:
gg = Graphics()
rmin = 0.1
rmax = 3
zmin = -2
zmax = 2
npr = 200
npz = npr
precis = 200 # 200-bits floating-point precision
tronc = 5
dz = (zmax-zmin) / (npz-1)
for i in range(npz):
zz = zmin + i*dz
gg += line3d(tab_precis(S1Erz, zz, rmin, rmax,
npr, precis, tronc))
show(gg, viewer=viewer3D, axes_labels=['rho', 'z', 'S_1'],
aspect_ratio=[1,1,0.3])