just some scatter plot

class Source(object):
    def __init__(self, x=None, y=None, amp=None, ngroup=None):
        self.x = x
        self.y = y
        self.amp = amp
        self.ngroup = ngroup

import numpy as np
import matplotlib.pyplot as plt
import copy as Copy

ng0 = [(4,4)]
ng1 = [(3,3), (3,4), (4,5), (5,4), (5,3), (4,3)]
ng2 = [(2,3), (2,4), (2,5), (3,5), (4,6), (5,5),
       (6,5), (6,4), (6,3), (5,2), (4,2), (3,2)]
ng3 = []
ng4 = [(0,2), (0,3), (0,4), (0,5),
       (0,6), (1,6), (2,7), (3,7),
       (4,8), (5,7), (6,7), (7,6),
       (8,6), (8,5), (8,4), (8,3),
       (8,2), (7,1), (6,1), (5,0),
       (4,0), (3,0), (2,1), (1,1)]

pi = np.pi
twopi = 2.0 * np.pi

j = np.complex(0,1)

x = np.linspace(-4, 4, 9)
y = (np.sqrt(3.)/2.) * x
X, Y = np.meshgrid(x, y)
X[1::2] += 0.5

sources = []
for p in ng0:
    source = Source(X[p], Y[p], ngroup=0)
    sources.append(source)
for p in ng1:
    source = Source(X[p], Y[p], ngroup=1)
    sources.append(source)
for p in ng2:
    source = Source(X[p], Y[p], ngroup=2)
    sources.append(source)
for p in ng4:
    source = Source(X[p], Y[p], ngroup=4)
    sources.append(source)

antenna = Copy.deepcopy(sources)  # save for later

count = np.array([sum([s.ngroup == i for s in antenna])
                  for i in range(5)])

d = 0.190           # meters spacing estimated from photo with humans
clight = 2.9979E+08 # m/s
freq   = 1.6E+09    # (Hz)  1/s
lam = clight / freq
k0 = twopi / lam

nangles = 21
theta_deg = np.linspace(0, 10, nangles)
theta = (pi/180.) * theta_deg
sth = np.sin(theta)

E = np.zeros_like(theta, dtype=complex)

nst = 20
nstep = 2*nst + 1

a2 =  np.logspace(np.log10(0.15), np.log10(0.40), nstep)
a4 = -np.logspace(np.log10(0.10), np.log10(0.16), nstep)
a0 = 4./3.
a1 = 1.0
a3 = 0.0
amplitudes = np.zeros((nstep, nstep, 5))
amplitudes[...,2] = a2[None, :]
amplitudes[...,4] = a4[:, None]
amplitudes[...,0] = a0
amplitudes[...,1] = a1
amplitudes[...,3] = a3


amps = amplitudes.reshape(-1,5)

powers = ((np.abs(amps)**2) * count[None, :]).sum(axis=-1)

amps /= np.sqrt(powers)[:, None]   # equalize the total power

Ehs, Evs = [], []
Eh = np.zeros_like(theta, dtype=complex)
Ev = Eh.copy()
for amp in amps:
    for s in antenna:
        s.amp = amp[s.ngroup]
    Eh[:], Ev[:] = 0.0, 0.0
    for s in antenna:
        Eh += s.amp * np.exp(j*(d*s.x*sth)*k0)
        Ev += s.amp * np.exp(j*(d*s.y*sth)*k0)
    Ehs.append(Eh.copy())
    Evs.append(Ev.copy())

Ehs = np.array(Ehs).reshape(nstep, nstep, -1)
Evs = np.array(Evs).reshape(nstep, nstep, -1)

Ihs = np.abs(Ehs)**2
Ivs = np.abs(Evs)**2

Ipaper = 1.0 + 1.86*(theta_deg/10.)**2 - 1.82*(theta_deg/10.)**4

# ok rankem!

if 1 == 1:
    use = theta_deg < 9.5  # don't compare beyond

    Ihsflat = Ihs.reshape(-1, nangles)
    Ihsflatzero = Ihsflat[:,0]
    Ihsflatnorm = Ihsflat/Ihsflatzero[:, None]

    diffmax = []
    for Ih, Ihn in zip(Ihsflat, Ihsflatnorm):
        diffmax.append( (np.abs(Ihn - Ipaper)/Ipaper)[use].max() )
    diffmax = np.array(diffmax)

    consider = (diffmax < 0.05)  *  (Ihsflatzero > 1)

    Ihsfzc = Ihsflatzero[consider]
    dmc = diffmax[consider]
    ampsc = amps[consider]
    rankor = Ihsfzc.argsort()[::-1]
    ampscs = ampsc[rankor]




if 1 == 1:
    plt.figure()
    plt.scatter(Ihsfzc, dmc)
    plt.xscale('log')
    plt.yscale('log')
    plt.show()

    plt.figure()
    data = ampscs.T
    plt.subplot(2,2,1)
    for thing in data:
        plt.plot(thing)
    plt.subplot(2,2,2)
    plt.plot(data[2]/data[1])
    plt.plot(data[4]/data[1])
    plt.subplot(2,1,2)
    data = ampscs[:200].T
    plt.plot(data[2]/data[1])
    plt.plot(data[4]/data[1])

    plt.show()