def evaluate_old(
n=60,
omega=56,
noise=0.,
nb_cl1=16,
sigma=np.deg2rad(13),
shift=np.deg2rad(40),
nb_tb1=8,
use_default=False,
weighted=True,
fibonacci=False,
simple_pol=False,
uniform_poliriser=False,
# single evaluation
sun_azi=None,
sun_ele=None,
# data parameters
tilting=True,
samples=1000,
show_plots=False,
show_structure=False,
verbose=False):
# default parameters
tau_L = 2.
c1 = .6
c2 = 4.
eps = np.finfo(float).eps
AA, BB, CC, DD, EE = T_L.dot(np.array([tau_L, 1.])) # sky parameters
T_T = np.linalg.pinv(T_L)
tau_L, c = T_T.dot(np.array([AA, BB, CC, DD, EE]))
tau_L /= c # turbidity correction
# Prez. et. al. Luminance function
def L(cchi, zz):
ii = zz < (np.pi / 2)
ff = np.zeros_like(zz)
if zz.ndim > 0:
ff[ii] = (1. + AA * np.exp(BB / (np.cos(zz[ii]) + eps)))
elif ii:
ff = (1. + AA * np.exp(BB / (np.cos(zz) + eps)))
pphi = (1. + CC * np.exp(DD * cchi) + EE * np.square(np.cos(cchi)))
return ff * pphi
if tilting:
angles = np.array([[0., 0.], [np.pi / 6, 0.], [np.pi / 6, np.pi / 4],
[np.pi / 6, 2 * np.pi / 4],
[np.pi / 6, 3 * np.pi / 4],
[np.pi / 6, 4 * np.pi / 4],
[np.pi / 6, 5 * np.pi / 4],
[np.pi / 6, 6 * np.pi / 4],
[np.pi / 6, 7 * np.pi / 4], [np.pi / 3, 0.],
[np.pi / 3, np.pi / 4], [np.pi / 3, 2 * np.pi / 4],
[np.pi / 3, 3 * np.pi / 4],
[np.pi / 3, 4 * np.pi / 4],
[np.pi / 3, 5 * np.pi / 4],
[np.pi / 3, 6 * np.pi / 4],
[np.pi / 3, 7 * np.pi / 4]]) # 17
if samples == 1000:
samples /= 2
else:
angles = np.array([[0., 0.]]) # 1
# generate the different sun positions
if sun_azi is not None or sun_ele is not None:
theta_s = sun_ele if type(sun_ele) is np.ndarray else np.array(
[sun_ele])
phi_s = sun_azi if type(sun_azi) is np.ndarray else np.array([sun_azi])
else:
theta_s, phi_s = fibonacci_sphere(samples=samples, fov=161)
phi_s = phi_s[theta_s <= np.pi / 2]
theta_s = theta_s[theta_s <= np.pi / 2]
samples = theta_s.size
# generate the properties of the sensor
try:
theta, phi, fit = angles_distribution(n, float(omega))
except ValueError:
theta = np.empty(0, dtype=np.float32)
phi = np.empty(0, dtype=np.float32)
fit = False
if not fit or n > 100 or fibonacci:
theta, phi = fibonacci_sphere(n, float(omega))
# theta, phi, fit = angles_distribution(n, omega)
# if not fit:
# print theta.shape, phi.shape
theta = (theta - np.pi) % (2 * np.pi) - np.pi
phi = (phi + np.pi) % (2 * np.pi) - np.pi
alpha = (phi + np.pi / 2) % (2 * np.pi) - np.pi
# computational model parameters
phi_cl1 = np.linspace(0., 4 * np.pi, nb_cl1,
endpoint=False) # CL1 preference angles
phi_tb1 = np.linspace(0., 2 * np.pi, nb_tb1,
endpoint=False) # TB1 preference angles
# initialise lists for the statistical data
d = np.zeros((samples, angles.shape[0]), dtype=np.float32)
t = np.zeros_like(d)
d_eff = np.zeros((samples, angles.shape[0]), dtype=np.float32)
a_ret = np.zeros_like(t)
tb1 = np.zeros((samples, angles.shape[0], nb_tb1), dtype=np.float32)
# iterate through the different tilting angles
for j, (theta_t, phi_t) in enumerate(angles):
# transform relative coordinates
theta_s_, phi_s_ = tilt(theta_t, phi_t, theta=theta_s, phi=phi_s)
theta_, phi_ = tilt(theta_t, phi_t + np.pi, theta=theta, phi=phi)
_, alpha_ = tilt(theta_t, phi_t + np.pi, theta=np.pi / 2, phi=alpha)
for i, (e, a, e_org,
a_org) in enumerate(zip(theta_s_, phi_s_, theta_s, phi_s)):
# SKY INTEGRATION
gamma = np.arccos(
np.cos(theta_) * np.cos(e_org) +
np.sin(theta_) * np.sin(e_org) * np.cos(phi_ - a_org))
# Intensity
I_prez, I_00, I_90 = L(gamma, theta_), L(0., e_org), L(
np.pi / 2, np.absolute(e_org - np.pi / 2))
# influence of sky intensity
I = (1. / (I_prez + eps) - 1. /
(I_00 + eps)) * I_00 * I_90 / (I_00 - I_90 + eps)
chi = (4. / 9. - tau_L / 120.) * (np.pi - 2 * e_org)
Y_z = (4.0453 * tau_L -
4.9710) * np.tan(chi) - 0.2155 * tau_L + 2.4192
if uniform_poliriser:
Y = np.maximum(np.full_like(I_prez, Y_z), 0.)
else:
Y = np.maximum(Y_z * I_prez / (I_00 + eps), 0.) # Illumination
# Degree of Polarisation
M_p = np.exp(-(tau_L - c1) / (c2 + eps))
LP = np.square(np.sin(gamma)) / (1 + np.square(np.cos(gamma)))
if uniform_poliriser:
P = np.ones_like(LP)
elif simple_pol:
P = np.clip(2. / np.pi * M_p * LP, 0., 1.)
else:
P = np.clip(
2. / np.pi * M_p * LP * (theta_ * np.cos(theta_) +
(np.pi / 2 - theta_) * I), 0., 1.)
# Angle of polarisation
if uniform_poliriser:
A = np.full_like(P, a_org + np.pi)
else:
_, A = tilt(e_org, a_org + np.pi, theta_, phi_)
# create cloud disturbance
if noise > 0:
eta = np.absolute(np.random.randn(*P.shape)) < noise
if verbose:
print "Noise level: %.4f (%.2f %%)" % (
noise, 100. * eta.sum() / float(eta.size))
P[eta] = 0. # destroy the polarisation pattern
else:
eta = np.zeros(1)
# COMPUTATIONAL MODEL
# Input (POL) layer -- Photo-receptors
s_1 = 15. * (np.square(np.sin(A - alpha_)) +
np.square(np.cos(A - alpha_)) * np.square(1. - P))
s_2 = 15. * (np.square(np.cos(A - alpha_)) +
np.square(np.sin(A - alpha_)) * np.square(1. - P))
r_1, r_2 = np.sqrt(s_1), np.sqrt(s_2)
# r_1, r_2 = np.log(s_1 + 1.), np.log(s_2 + 1.)
r_pol = (r_1 - r_2) / (r_1 + r_2 + eps)
# Tilting (CL1) layer
d_cl1 = (
np.sin(shift - theta) * np.cos(theta_t) +
np.cos(shift - theta) * np.sin(theta_t) * np.cos(phi - phi_t))
gate = np.power(
np.exp(-np.square(d_cl1) / (2. * np.square(sigma))), 1)
w_cl1 = float(nb_cl1) / float(n) * np.sin(
alpha[:, np.newaxis] - phi_cl1[np.newaxis]) * gate[:,
np.newaxis]
r_cl1 = r_pol.dot(w_cl1)
# Output (TB1) layer
w_tb1 = float(nb_tb1) / float(
2 * nb_cl1) * np.cos(phi_tb1[np.newaxis] -
phi_cl1[:, np.newaxis])
r_tb1 = r_cl1.dot(w_tb1)
if use_default:
w = -float(nb_tb1) / (2. * float(n)) * np.sin(phi_tb1[
np.newaxis] - alpha[:, np.newaxis]) * gate[:, np.newaxis]
r_tb1 = r_pol.dot(w)
# decode response - FFT
R = r_tb1.dot(
np.exp(-np.arange(nb_tb1) * (0. + 1.j) * np.pi /
(float(nb_tb1) / 2.)))
a_pred = (np.pi - np.arctan2(R.imag, R.real)) % (
2. * np.pi) - np.pi # sun azimuth (prediction)
tau_pred = np.absolute(R) # certainty of prediction
d[i, j] = np.absolute(
azidist(np.array([e, a]), np.array([0., a_pred])))
t[i, j] = tau_pred if weighted else 1.
a_ret[i, j] = a_pred
tb1[i, j] = r_tb1
# effective degree of polarisation
M = r_cl1.max() - r_cl1.min()
# M = t[i, j] * 2.
p = np.power(10, M / 2.)
d_eff[i, j] = np.mean((p - 1.) / (p + 1.))
if show_plots:
plt.figure("sensor-noise-%2d" %
(100. * eta.sum() / float(eta.size)),
figsize=(18, 4.5))
ax = plt.subplot(1, 12, 10)
plt.imshow(w_cl1, cmap="coolwarm", vmin=-1, vmax=1)
plt.xlabel("CBL", fontsize=16)
plt.xticks([0, 15], ["1", "16"])
plt.yticks([0, 59], ["1", "60"])
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 6, 6) # , sharey=ax)
plt.imshow(w_tb1, cmap="coolwarm", vmin=-1, vmax=1)
plt.xlabel("TB1", fontsize=16)
plt.xticks([0, 7], ["1", "8"])
plt.yticks([0, 15], ["1", "16"])
cbar = plt.colorbar(ticks=[-1, 0, 1])
cbar.ax.set_yticklabels([r'$\leq$ -1', r'0', r'$\geq$ 1'])
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 1, polar=True)
ax.scatter(phi,
theta,
s=150,
c=r_pol,
marker='o',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(a,
e,
s=100,
marker='o',
edgecolor='black',
facecolor='yellow')
ax.scatter(phi_t + np.pi,
theta_t,
s=200,
marker='o',
edgecolor='black',
facecolor='yellowgreen')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(40)])
ax.set_yticks([])
ax.set_xticks(
np.linspace(-3 * np.pi / 4,
5 * np.pi / 4,
8,
endpoint=False))
ax.set_title("POL Response", fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 2, polar=True)
ax.scatter(phi,
theta,
s=150,
c=r_pol * gate,
marker='o',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(a,
e,
s=100,
marker='o',
edgecolor='black',
facecolor='yellow')
ax.scatter(phi_t + np.pi,
theta_t,
s=200,
marker='o',
edgecolor='black',
facecolor='yellowgreen')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(40)])
ax.set_yticks([])
ax.set_xticks(
np.linspace(-3 * np.pi / 4,
5 * np.pi / 4,
8,
endpoint=False))
ax.set_title("Gated Response", fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 3, polar=True)
x = np.linspace(0, 2 * np.pi, 721)
# CBL
ax.fill_between(x,
np.full_like(x, np.deg2rad(60)),
np.full_like(x, np.deg2rad(90)),
facecolor="C1",
alpha=.5,
label="CBL")
ax.scatter(phi_cl1[:nb_cl1 / 2] - np.pi / 24,
np.full(nb_cl1 / 2, np.deg2rad(75)),
s=600,
c=r_cl1[:nb_cl1 / 2],
marker='o',
edgecolor='red',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(phi_cl1[nb_cl1 / 2:] + np.pi / 24,
np.full(nb_cl1 / 2, np.deg2rad(75)),
s=600,
c=r_cl1[nb_cl1 / 2:],
marker='o',
edgecolor='green',
cmap="coolwarm",
vmin=-1,
vmax=1)
for ii, pp in enumerate(phi_cl1[:nb_cl1 / 2] - np.pi / 24):
ax.text(pp - np.pi / 20,
np.deg2rad(75),
"%d" % (ii + 1),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
for ii, pp in enumerate(phi_cl1[nb_cl1 / 2:] + np.pi / 24):
ax.text(pp + np.pi / 20,
np.deg2rad(75),
"%d" % (ii + 9),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
# TB1
ax.fill_between(x,
np.full_like(x, np.deg2rad(30)),
np.full_like(x, np.deg2rad(60)),
facecolor="C2",
alpha=.5,
label="TB1")
ax.scatter(phi_tb1,
np.full_like(phi_tb1, np.deg2rad(45)),
s=600,
c=r_tb1,
marker='o',
edgecolor='blue',
cmap="coolwarm",
vmin=-1,
vmax=1)
for ii, pp in enumerate(phi_tb1):
ax.text(pp,
np.deg2rad(35),
"%d" % (ii + 1),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
ax.arrow(pp,
np.deg2rad(35),
0,
np.deg2rad(10),
fc='k',
ec='k',
head_width=.1,
overhang=.3)
# Sun position
ax.scatter(a,
e,
s=500,
marker='o',
edgecolor='black',
facecolor='yellow')
# Decoded TB1
# ax.plot([0, a_pred], [0, e_pred], 'k--', lw=1)
ax.plot([0, a_pred], [0, np.pi / 2], 'k--', lw=1)
ax.arrow(a_pred,
0,
0,
np.deg2rad(20),
fc='k',
ec='k',
head_width=.3,
head_length=.2,
overhang=.3)
ax.legend(ncol=2, loc=(-.55, -.1), fontsize=16)
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.pi / 2])
ax.set_yticks([])
ax.set_xticks([])
ax.set_title("Sensor Response", fontsize=16)
plt.subplots_adjust(left=.02, bottom=.12, right=.98, top=.88)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
plt.show()
d_deg = np.rad2deg(d)
if show_structure:
plt.figure("sensor-structure", figsize=(4.5, 4.5))
ax = plt.subplot(111, polar=True)
ax.scatter(phi, theta, s=150, c="black", marker='o')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(50)])
ax.set_yticks([])
ax.set_xticks(
np.linspace(-3 * np.pi / 4, 5 * np.pi / 4, 8, endpoint=False))
ax.set_title("POL Response")
plt.show()
return d_deg, d_eff, t, a_ret, tb1
def evaluate_slow(
n=60,
omega=56,
noise=0.,
nb_cl1=8,
sigma_pol=np.deg2rad(13),
shift_pol=np.deg2rad(40),
nb_tb1=8,
sigma_sol=np.deg2rad(13),
shift_sol=np.deg2rad(40),
use_default=False,
weighted=True,
fibonacci=False,
uniform_polariser=False,
# single evaluation
sun_azi=None,
sun_ele=None,
# data parameters
tilting=True,
samples=1000,
show_plots=False,
show_structure=False,
verbose=False):
if tilting:
angles = np.array([[0., 0.], [np.pi / 6, 0.], [np.pi / 6, np.pi / 4],
[np.pi / 6, 2 * np.pi / 4],
[np.pi / 6, 3 * np.pi / 4],
[np.pi / 6, 4 * np.pi / 4],
[np.pi / 6, 5 * np.pi / 4],
[np.pi / 6, 6 * np.pi / 4],
[np.pi / 6, 7 * np.pi / 4], [np.pi / 3, 0.],
[np.pi / 3, np.pi / 4], [np.pi / 3, 2 * np.pi / 4],
[np.pi / 3, 3 * np.pi / 4],
[np.pi / 3, 4 * np.pi / 4],
[np.pi / 3, 5 * np.pi / 4],
[np.pi / 3, 6 * np.pi / 4],
[np.pi / 3, 7 * np.pi / 4]]) # 17
if samples == 1000:
samples /= 2
else:
angles = np.array([[0., 0.]]) # 1
# generate the different sun positions
if sun_azi is not None or sun_ele is not None:
theta_s = sun_ele if type(sun_ele) is np.ndarray else np.array(
[sun_ele])
phi_s = sun_azi if type(sun_azi) is np.ndarray else np.array([sun_azi])
else:
theta_s, phi_s = fibonacci_sphere(samples=samples, fov=161)
phi_s = phi_s[theta_s <= np.pi / 2]
theta_s = theta_s[theta_s <= np.pi / 2]
samples = theta_s.size
# generate the properties of the sensor
try:
theta, phi, fit = angles_distribution(n, float(omega))
except ValueError:
theta = np.empty(0, dtype=np.float32)
phi = np.empty(0, dtype=np.float32)
fit = False
if not fit or n > 100 or fibonacci:
theta, phi = fibonacci_sphere(n, float(omega))
# theta, phi, fit = angles_distribution(n, omega)
# if not fit:
# print theta.shape, phi.shape
theta = (theta - np.pi) % (2 * np.pi) - np.pi
phi = (phi + np.pi) % (2 * np.pi) - np.pi
alpha = (phi + np.pi / 2) % (2 * np.pi) - np.pi
# computational model parameters
phi_cl1 = np.linspace(0., 2 * np.pi, nb_cl1,
endpoint=False) # CL1 preference angles
phi_tb1 = np.linspace(0., 2 * np.pi, nb_tb1,
endpoint=False) # TB1 preference angles
# initialise lists for the statistical data
d = np.zeros((samples, angles.shape[0]), dtype=np.float32)
t = np.zeros_like(d)
d_eff = np.zeros((samples, angles.shape[0]), dtype=np.float32)
a_ret = np.zeros_like(t)
tb1 = np.zeros((samples, angles.shape[0], nb_tb1), dtype=np.float32)
# iterate through the different tilting angles
for j, (theta_t, phi_t) in enumerate(angles):
# transform relative coordinates
theta_s_, phi_s_ = tilt(theta_t, phi_t, theta=theta_s, phi=phi_s)
_, alpha_ = tilt(theta_t, phi_t + np.pi, theta=np.pi / 2, phi=alpha)
for i, (e, a, e_org,
a_org) in enumerate(zip(theta_s_, phi_s_, theta_s, phi_s)):
sky = Sky(theta_s=e_org, phi_s=a_org, theta_t=theta_t, phi_t=phi_t)
sky.verbose = verbose
# COMPUTATIONAL MODEL
# Input (POL) layer -- Photo-receptors
dra = POLCompassDRA(n=n, omega=omega)
dra.theta_t = theta_t
dra.phi_t = phi_t
r_pol = dra(sky, noise=noise, uniform_polariser=uniform_polariser)
r_sol = dra.r_po
# Tilting (SOL) layer
d_pol = (np.sin(shift_pol - theta) * np.cos(theta_t) +
np.cos(shift_pol - theta) * np.sin(theta_t) *
np.cos(phi - phi_t))
gate_pol = np.power(
np.exp(-np.square(d_pol) / (2. * np.square(sigma_pol))), 1)
z_pol = -float(nb_cl1) / float(n)
w_cl1_pol = z_pol * np.sin(phi_cl1[
np.newaxis] - alpha[:, np.newaxis]) * gate_pol[:, np.newaxis]
d_sol = (np.sin(shift_sol - theta) * np.cos(theta_t) +
np.cos(shift_sol - theta) * np.sin(theta_t) *
np.cos(phi - phi_t))
gate_sol = np.power(
np.exp(-np.square(d_sol) / (2. * np.square(sigma_sol))), 1)
z_sol = float(nb_cl1) / float(n)
w_cl1_sol = z_sol * np.sin(phi_cl1[
np.newaxis] - alpha[:, np.newaxis]) * gate_sol[:, np.newaxis]
o = 1. / 64.
f_pol, f_sol = .5 * np.power(2 * theta_t / np.pi, o), .5 * (
1 - np.power(2 * theta_t / np.pi, o))
r_cl1_pol = r_pol.dot(w_cl1_pol)
r_cl1_sol = r_sol.dot(w_cl1_sol)
r_cl1 = f_pol * r_cl1_pol + f_sol * r_cl1_sol
# r_cl1 = r_cl1_sol
# Output (TCL) layer
# w_tb1 = np.eye(nb_tb1)
w_tb1 = float(nb_tb1) / float(nb_cl1) * np.cos(
phi_tb1[np.newaxis] - phi_cl1[:, np.newaxis])
r_tb1 = r_cl1.dot(w_tb1)
if use_default:
w = -float(nb_tb1) / (2. * float(n)) * np.sin(
phi_tb1[np.newaxis] -
alpha[:, np.newaxis]) * gate_pol[:, np.newaxis]
r_tb1 = r_pol.dot(w)
# decode response - FFT
R = r_tb1.dot(
np.exp(-np.arange(nb_tb1) * (0. + 1.j) * np.pi /
(float(nb_tb1) / 2.)))
a_pred = (np.pi - np.arctan2(R.imag, R.real)) % (
2. * np.pi) - np.pi # sun azimuth (prediction)
tau_pred = np.maximum(np.absolute(R), 0) # certainty of prediction
d[i, j] = np.absolute(
azidist(np.array([e, a]), np.array([0., a_pred])))
t[i, j] = tau_pred if weighted else 1.
a_ret[i, j] = a_pred
tb1[i, j] = r_tb1
# effective degree of polarisation
M = r_cl1.max() - r_cl1.min()
# M = t[i, j] * 2.
p = np.power(10, M / 2.)
d_eff[i, j] = np.mean((p - 1.) / (p + 1.))
if show_plots:
plt.figure("sensor-noise-%2d" %
(100. * sky.eta.sum() / float(sky.eta.size)),
figsize=(18, 4.5))
ax = plt.subplot(1, 12, 10)
plt.imshow(w_cl1_pol, cmap="coolwarm", vmin=-1, vmax=1)
plt.xlabel("CBL", fontsize=16)
plt.xticks([0, 15], ["1", "16"])
plt.yticks([0, 59], ["1", "60"])
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 6, 6) # , sharey=ax)
plt.imshow(w_tb1, cmap="coolwarm", vmin=-1, vmax=1)
plt.xlabel("TB1", fontsize=16)
plt.xticks([0, 7], ["1", "8"])
plt.yticks([0, 15], ["1", "16"])
cbar = plt.colorbar(ticks=[-1, 0, 1])
cbar.ax.set_yticklabels([r'$\leq$ -1', r'0', r'$\geq$ 1'])
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 1, polar=True)
ax.scatter(phi,
theta,
s=150,
c=r_pol,
marker='o',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(a,
e,
s=100,
marker='o',
edgecolor='black',
facecolor='yellow')
ax.scatter(phi_t + np.pi,
theta_t,
s=200,
marker='o',
edgecolor='black',
facecolor='yellowgreen')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(40)])
ax.set_yticks([])
ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False))
ax.set_xticklabels([
r'$0^\circ$', r'$45^\circ$', r'$90^\circ$', r'$135^\circ$',
r'$180^\circ$', r'$-135^\circ$', r'$-90^\circ$',
r'$-45^\circ$'
])
ax.set_title("POL Response", fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 2, polar=True)
ax.scatter(phi,
theta,
s=150,
c=r_pol * gate_pol,
marker='o',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(a,
e,
s=100,
marker='o',
edgecolor='black',
facecolor='yellow')
ax.scatter(phi_t + np.pi,
theta_t,
s=200,
marker='o',
edgecolor='black',
facecolor='yellowgreen')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(40)])
ax.set_yticks([])
ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False))
ax.set_xticklabels([
r'$0^\circ$', r'$45^\circ$', r'$90^\circ$', r'$135^\circ$',
r'$180^\circ$', r'$-135^\circ$', r'$-90^\circ$',
r'$-45^\circ$'
])
ax.set_title("Gated Response", fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
ax = plt.subplot(1, 4, 3, polar=True)
x = np.linspace(0, 2 * np.pi, 721)
# CBL
ax.fill_between(x,
np.full_like(x, np.deg2rad(60)),
np.full_like(x, np.deg2rad(90)),
facecolor="C1",
alpha=.5,
label="CBL")
ax.scatter(phi_cl1[:nb_cl1 / 2] - np.pi / 24,
np.full(nb_cl1 / 2, np.deg2rad(75)),
s=600,
c=r_cl1[:nb_cl1 / 2],
marker='o',
edgecolor='red',
cmap="coolwarm",
vmin=-1,
vmax=1)
ax.scatter(phi_cl1[nb_cl1 / 2:] + np.pi / 24,
np.full(nb_cl1 / 2, np.deg2rad(75)),
s=600,
c=r_cl1[nb_cl1 / 2:],
marker='o',
edgecolor='green',
cmap="coolwarm",
vmin=-1,
vmax=1)
for ii, pp in enumerate(phi_cl1[:nb_cl1 / 2] - np.pi / 24):
ax.text(pp - np.pi / 20,
np.deg2rad(75),
"%d" % (ii + 1),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
for ii, pp in enumerate(phi_cl1[nb_cl1 / 2:] + np.pi / 24):
ax.text(pp + np.pi / 20,
np.deg2rad(75),
"%d" % (ii + 9),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
# TB1
ax.fill_between(x,
np.full_like(x, np.deg2rad(30)),
np.full_like(x, np.deg2rad(60)),
facecolor="C2",
alpha=.5,
label="TB1")
ax.scatter(phi_tb1,
np.full_like(phi_tb1, np.deg2rad(45)),
s=600,
c=r_tb1,
marker='o',
edgecolor='blue',
cmap="coolwarm",
vmin=-1,
vmax=1)
for ii, pp in enumerate(phi_tb1):
ax.text(pp,
np.deg2rad(35),
"%d" % (ii + 1),
ha="center",
va="center",
size=10,
bbox=dict(boxstyle="circle", fc="w", ec="k"))
ax.arrow(pp,
np.deg2rad(35),
0,
np.deg2rad(10),
fc='k',
ec='k',
head_width=.1,
overhang=.3)
# Sun position
ax.scatter(a,
e,
s=500,
marker='o',
edgecolor='black',
facecolor='yellow')
# Decoded TB1
# ax.plot([0, a_pred], [0, e_pred], 'k--', lw=1)
ax.plot([0, a_pred], [0, np.pi / 2], 'k--', lw=1)
ax.arrow(a_pred,
0,
0,
np.deg2rad(20),
fc='k',
ec='k',
head_width=.3,
head_length=.2,
overhang=.3)
ax.legend(ncol=2, loc=(-.55, -.1), fontsize=16)
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.pi / 2])
ax.set_yticks([])
ax.set_xticks([])
ax.set_title("Sensor Response", fontsize=16)
plt.subplots_adjust(left=.02, bottom=.12, right=.98, top=.88)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.tick_params(axis='both', which='minor', labelsize=16)
plt.show()
d_deg = np.rad2deg(d)
if show_structure:
plt.figure("sensor-structure", figsize=(4.5, 4.5))
ax = plt.subplot(111, polar=True)
ax.scatter(phi, theta, s=150, c="black", marker='o')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_ylim([0, np.deg2rad(50)])
ax.set_yticks([])
ax.set_xticks(
np.linspace(-3 * np.pi / 4, 5 * np.pi / 4, 8, endpoint=False))
ax.set_title("POL Response")
plt.show()
return d_deg, d_eff, t, a_ret, tb1