This week I been working on a python program for calculating the wigner d matrix and displaying the real and imaginary parts. Below I have posted some of the images produced by this program:

This looks great when animated:

below is the python program for these diagrams.

from scipy.special import jv, legendre, sph_harm, jacobi

from scipy.misc import factorial, comb

from numpy import floor, sqrt, sin, cos, exp, power

from math import pi

def wignerd(j,m,n=0,approx_lim=10):

”’

Wigner “small d” matrix. (Euler z-y-z convention)

example:

j = 2

m = 1

n = 0

beta = linspace(0,pi,100)

wd210 = wignerd(j,m,n)(beta)

some conditions have to be met:

j >= 0

-j <= m <= j

-j <= n <= j

The approx_lim determines at what point

bessel functions are used. Default is when:

j > m+10

and

j > n+10

for integer l and n=0, we can use the spherical harmonics. If in

addition m=0, we can use the ordinary legendre polynomials.

”’

if (j < 0) or (abs(m) > j) or (abs(n) > j):

raise ValueError(“wignerd(j = {0}, m = {1}, n = {2}) value error.”.format(j,m,n) \

+ ” Valid range for parameters: j>=0, -j<=m,n<=j.”)

if (j > (m + approx_lim)) and (j > (n + approx_lim)):

#print ‘bessel (approximation)’

return lambda beta: jv(m-n, j*beta)

if (floor(j) == j) and (n == 0):

if m == 0:

#print ‘legendre (exact)’

return lambda beta: legendre(j)(cos(beta))

elif False:

#print ‘spherical harmonics (exact)’

a = sqrt(4.*pi / (2.*j + 1.))

return lambda beta: a * conjugate(sph_harm(m,j,beta,0.))

jmn_terms = {

j+n : (m-n,m-n),

j-n : (n-m,0.),

j+m : (n-m,0.),

j-m : (m-n,m-n),

}

k = min(jmn_terms)

a, lmb = jmn_terms[k]

b = 2.*j – 2.*k – a

if (a < 0) or (b < 0):

raise ValueError(“wignerd(j = {0}, m = {1}, n = {2}) value error.”.format(j,m,n) \

+ ” Encountered negative values in (a,b) = ({0},{1})”.format(a,b))

coeff = power(-1.,lmb) * sqrt(comb(2.*j-k,k+a)) * (1./sqrt(comb(k+b,b)))

#print ‘jacobi (exact)’

return lambda beta: coeff \

* power(sin(0.5*beta),a) \

* power(cos(0.5*beta),b) \

* jacobi(k,a,b)(cos(beta))

def wignerD(j,m,n=0,approx_lim=10):

”’

Wigner D-function. (Euler z-y-z convention)

This returns a function of 2 to 3 Euler angles:

(alpha, beta, gamma)

gamma defaults to zero and does not need to be

specified.

The approx_lim determines at what point

bessel functions are used. Default is when:

j > m+10

and

j > n+10

usage:

from numpy import linspace, meshgrid

a = linspace(0, 2*pi, 100)

b = linspace(0, pi, 100)

aa,bb = meshgrid(a,b)

j,m,n = 1,1,1

zz = wignerD(j,m,n)(aa,bb)

”’

return lambda alpha,beta,gamma=0: \

exp(-1j*m*alpha) \

* wignerd(j,m,n,approx_lim)(beta) \

* exp(-1j*n*gamma)

if __name__ == ‘__main__’:

”’

just a bunch of plots in (phi,theta) for

integer and half-integer j and where m and

n take values of [-j, -j+1, …, j-1, j]

Note that all indexes can be any real number

with the conditions:

j >= 0

-j <= m <= j

-j <= n <= j

”’

from matplotlib import pyplot, cm, rc

from numpy import linspace, arange, meshgrid, real, imag, arccos

rc(‘text’, usetex=False)

ext = [0.,2.*pi,0.,pi]

phi = linspace(ext[0],ext[1],200)

theta = linspace(ext[2],ext[3],200)

pphi,ttheta = meshgrid(phi,theta)

# The maximum value of j to plot. Will plot real and imaginary

# distributions for j = 0, 0.5, … maxj

maxj = 2.0

for j in arange(0,maxj+.1,step=0.5):

fsize = (j*2+3,j*2+3)

title = ‘WignerD(j,m,n)(phi,theta)’

if j == 0:

fsize = (4,4)

else:

title += ‘, j = ‘+str(j)

figr = pyplot.figure(figsize=fsize)

figr.suptitle(r’Real Part of ‘+title)

figi = pyplot.figure(figsize=fsize)

figi.suptitle(r’Imaginary Part of ‘+title)

for fig in [figr,figi]:

fig.subplots_adjust(left=.1,bottom=.02,right=.98,top=.9,wspace=.02,hspace=.1)

if j == 0:

fig.subplots_adjust(left=.1,bottom=.1,right=.9,top=.9)

if j == 0.5:

fig.subplots_adjust(left=.2,top=.8)

if j == 1:

fig.subplots_adjust(left=.15,top=.85)

if j == 1.5:

fig.subplots_adjust(left=.15,top=.85)

if j == 2:

fig.subplots_adjust(top=.87)

if j != 0:

axtot = fig.add_subplot(1,1,1)

axtot.axesPatch.set_alpha(0.)

axtot.xaxis.set_ticks_position(‘top’)

axtot.xaxis.set_label_position(‘top’)

axtot.yaxis.set_ticks_position(‘left’)

axtot.spines[‘left’].set_position((‘outward’,10))

axtot.spines[‘top’].set_position((‘outward’,10))

axtot.spines[‘right’].set_visible(False)

axtot.spines[‘bottom’].set_visible(False)

axtot.set_xlim(-j-.5,j+.5)

axtot.set_ylim(-j-.5,j+.5)

axtot.xaxis.set_ticks(arange(-j,j+0.1,1))

axtot.yaxis.set_ticks(arange(-j,j+0.1,1))

axtot.set_xlabel(‘n’)

axtot.set_ylabel(‘m’)

nplts = 2*j+1

data_j=[]

data_zz=[]

for m in arange(-j,j+0.1,step=1):

for n in arange(-j,j+0.1,step=1):

print j,m,n

zz = wignerD(j,m,n)(pphi,ttheta)

data_j.append(j)

data_zz.append(zz)

i = n+j + nplts*(j-m)

for fig,data in zip((figr,figi), (real(zz),imag(zz))):

ax = fig.add_subplot(nplts, nplts, i+1, projection=’polar’)

plt = ax.pcolormesh(pphi,ttheta,data.copy(),

cmap=cm.jet,

#cmap=cm.RdYlBu_r,

vmin=-1., vmax=1.)

if j == 0:

ax.grid(True, alpha=0.5)

ax.set_title(r’j,m,n = (0,0,0)’, position=(0.5,1.1), size=12)

ax.set_xlabel(r’$\phi$’)

ax.set_ylabel(r’$\theta$’, rotation=’horizontal’, va=’bottom’)

ax.xaxis.set_ticks([0,.25*pi,.5*pi,.75*pi,pi,1.25*pi,1.5*pi,1.75*pi])

ax.xaxis.set_ticklabels([‘0′,r’$\frac{\pi}{4}$’,r’$\frac{\pi}{2}$’,r’$\frac{3 \pi}{4}$’,r’$\pi$’,r’$\frac{5 \pi}{4}$’,r’$\frac{3 \pi}{2}$’,r’$\frac{7 \pi}{4}$’], size=14)

ax.yaxis.set_ticks([0,.25*pi,.5*pi,.75*pi,pi])

ax.yaxis.set_ticklabels([‘0′,r’$\frac{\pi}{4}$’,r’$\frac{\pi}{2}$’,r’$\frac{3 \pi}{4}$’,r’$\pi$’], size=14)

else:

ax.xaxis.set_ticks([])

ax.yaxis.set_ticks([])

ax.set_xlim(ext[0],ext[1])

ax.set_ylim(ext[2],ext[3])

if j == 0:

fig.colorbar(plt, pad=0.07)

# uncomment the following if you want to save these to image files

#figr.savefig(‘wignerD_j’+str(j)+’_real.png’, dpi=150)

#figi.savefig(‘wignerD_j’+str(j)+’_imag.png’, dpi=150)

#pyplot.show()

#pl.plot(j_data,zz_data)

print ‘j_data=’,data_j

#print ‘zz_data=’, data_zz

**Related posts**

- The wigner d matrix
- Numerical work with sagemath 23: wigner reduced rotation matrix elements as limits of 6j symbols
- Review of wigner nj symbols
- Exact computation and asymptotic approximations of 6j symbols illustration of their semiclassical limits