def phi_t(self, value): theta_t, phi_t = tilt(self._theta_c, self._phi_c - np.pi, theta=self._theta_t, phi=self._phi_t) theta_t, self._phi_t = tilt(self._theta_c, self._phi_c, theta=self.theta_t, phi=value)
def theta_t(self, value): theta_t, phi_t = tilt(self._theta_c, self._phi_c - np.pi, theta=self._theta_t, phi=self._phi_t) self._theta_t, phi_t = tilt(self._theta_c, self._phi_c, theta=value, phi=self.phi_t)
def visualise_angle_of_polarisation(sky): import matplotlib.pyplot as plt plt.figure("angle-of-polarisation", figsize=(4.5, 4.5)) ax = plt.subplot(111, polar=True) ax.set_theta_zero_location("N") ax.set_theta_direction(-1) theta_s, phi_s = tilt(sky.theta_t, sky.phi_t, theta=sky.theta_s, phi=sky.phi_s) print theta_s, phi_s ax.scatter(sky.phi, sky.theta, s=10, c=sky.AOP, marker='.', cmap='hsv', vmin=-np.pi, vmax=np.pi) ax.scatter(phi_s, theta_s, s=100, edgecolor='black', facecolor='yellow') # ax.scatter(sky.phi_t + np.pi, sky.theta_t, s=200, edgecolor='black', facecolor='greenyellow') ax.set_ylim([0, np.pi / 2]) ax.set_yticks([]) ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False)) ax.set_xticklabels([ r'$0^\circ$ (N)', r'$45^\circ$ (NE)', r'$90^\circ$ (E)', r'$135^\circ$ (SE)', r'$180^\circ$ (S)', r'$-135^\circ$ (SW)', r'$-90^\circ$ (W)', r'$-45^\circ$ (NW)' ]) plt.show()
def __call__(self, env, *args, **kwargs): """ :param env: the environment where the photorectors can percieve light :type env: Environment :param args: unlabeled arguments :type args: list :param kwargs: labeled arguments :type kwargs: dict :return: """ env.theta_t = self.theta_t env.phi_t = self.phi_t _, alpha = tilt(self.theta_t, self.phi_t + np.pi, theta=np.pi / 2, phi=self.mic_a) y, p, a = env(self.theta, self.phi, *args, **kwargs) # influence of the acceptance angle on the luminance and DOP # y = y.dot(self._rho_gaussian) # p = p.dot(self._rho_gaussian) # influence of the wavelength on the perceived light ry = spectrum_influence(y, self.rhabdom) s = ry * ((np.square(np.sin(a - alpha + self.mic_l)) + np.square( np.cos(a - alpha + self.mic_l)) * np.square(1. - p)) * self.mic_p + (1. - self.mic_p)) self.__r = np.sqrt(s) self._is_called = True return self.__r
def visualise(my_sky, y): import matplotlib.pyplot as plt plt.figure("Luminance", figsize=(4.5, 4.5)) ax = plt.subplot(111, polar=True) ax.set_theta_zero_location("N") ax.set_theta_direction(-1) theta_s, phi_s = tilt(my_sky.theta_t, my_sky.phi_t, theta=my_sky.theta_s, phi=my_sky.phi_s) ax.scatter(my_sky.phi, my_sky.theta, s=100, c=y, marker='.', cmap='coolwarm', vmin=-1, vmax=1) ax.scatter(phi_s, theta_s, s=100, edgecolor='black', facecolor='yellow') ax.scatter(my_sky.phi_t, my_sky.theta_t, s=50, edgecolor='black', facecolor='greenyellow') ax.set_ylim([0, np.pi / 2]) ax.set_yticks([]) ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False)) ax.set_xticklabels([ r'$0^\circ$ (N)', r'$45^\circ$ (NE)', r'$90^\circ$ (E)', r'$135^\circ$ (SE)', r'$180^\circ$ (S)', r'$-135^\circ$ (SW)', r'$-90^\circ$ (W)', r'$-45^\circ$ (NW)' ]) plt.show()
def __call__(self, theta=None, phi=None, noise=0., eta=None, uniform_polariser=False): """ Call the sky instance to generate the sky cues. :param theta: array of points' elevation :type theta: np.ndarray :param phi: array of points' azimuth :type phi: np.ndarray :param noise: the noise level (sigma) :type noise: float :param eta: array of noise level in each point of interest :type eta: np.ndarray :param uniform_polariser: :type uniform_polariser: bool :return: Y, P, A """ # set default arguments theta = ((self.__theta if theta is None else theta) + np.pi) % (2 * np.pi) - np.pi phi = ( (self.__phi if phi is None else phi) + np.pi) % (2 * np.pi) - np.pi # save points of interest self.__theta = theta.copy() self.__phi = phi.copy() # transform points in the sky according to tilting parameters theta, phi = tilt(self.theta_t, self.phi_t + np.pi, theta=theta, phi=phi) theta_s, phi_s = self.theta_s, self.phi_s # SKY INTEGRATION gamma = np.arccos( np.cos(theta) * np.cos(theta_s) + np.sin(theta) * np.sin(theta_s) * np.cos(phi - phi_s)) # Intensity i_prez = self.L(gamma, theta) i_00 = self.L(0., theta_s) # the luminance (Cd/m^2) at the zenith point i_90 = self.L( np.pi / 2, np.absolute(theta_s - np.pi / 2)) # the luminance (Cd/m^2) on the horizon # influence of sky intensity i = (1. / (i_prez + eps) - 1. / (i_00 + eps)) * i_00 * i_90 / (i_00 - i_90 + eps) if uniform_polariser: y = np.maximum(np.full_like(i_prez, self.Y_z), 0.) else: y = np.maximum(self.Y_z * i_prez / (i_00 + eps), 0.) # Illumination # Degree of Polarisation lp = np.square(np.sin(gamma)) / (1 + np.square(np.cos(gamma))) if uniform_polariser: p = np.ones_like(lp) else: p = np.clip( 2. / np.pi * self.M_p * lp * (theta * np.cos(theta) + (np.pi / 2 - theta) * i), 0., 1.) # Angle of polarisation if uniform_polariser: a = np.full_like(p, phi_s + np.pi) else: _, a = tilt(theta_s, phi_s + np.pi, theta, phi) # create cloud disturbance if type(noise) is np.ndarray: if noise.size == p.size: # print "yeah!" eta = np.array(noise, dtype=bool) if self.verbose: print "Noise level: %.4f (%.2f %%)" % ( noise, 100. * eta.sum() / float(eta.size)) else: eta = np.zeros_like(theta, dtype=bool) eta[:noise.size] = noise elif noise > 0: eta = np.argsort(np.absolute( np.random.randn(*p.shape)))[:int(noise * p.shape[0])] elif eta is None: eta = np.zeros_like(theta, dtype=bool) p[eta] = 0. # destroy the polarisation pattern y[eta] = 0. # destroy the polarisation pattern self.__y = y self.__dop = p self.__aop = a self.__eta = eta self.__is_generated = True return y, p, a
def create_ephem_paths(): # sensor design n = 60 omega = 56 theta, phi, fit = angles_distribution(n, float(omega)) theta_t, phi_t = 0., 0. # ant-world noise = 0.0 ttau = .06 dx = 1e-02 # meters dt = 2. / 60. # min delta = timedelta(minutes=dt) routes = load_routes() flow = dx * np.ones(2) / np.sqrt(2) max_theta = 0. def encode(theta, phi, Y, P, A, theta_t=0., phi_t=0., d_phi=0., nb_tcl=8, sigma=np.deg2rad(13), shift=np.deg2rad(40)): alpha = (phi + np.pi / 2) % (2 * np.pi) - np.pi phi_tcl = np.linspace(0., 2 * np.pi, nb_tcl, endpoint=False) # TB1 preference angles phi_tcl = (phi_tcl + d_phi) % (2 * np.pi) # Input (POL) layer -- Photo-receptors s_1 = Y * (np.square(np.sin(A - alpha)) + np.square(np.cos(A - alpha)) * np.square(1. - P)) s_2 = Y * (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_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 = -float(nb_tcl) / (2. * float(n)) * np.sin(phi_tcl[np.newaxis] - alpha[:, np.newaxis]) * gate[:, np.newaxis] r_tcl = r_pol.dot(w) R = r_tcl.dot(np.exp(-np.arange(nb_tcl) * (0. + 1.j) * 2. * np.pi / float(nb_tcl))) res = np.clip(3.5 * (np.absolute(R) - .53), 0, 2) # certainty of prediction ele_pred = 26 * (1 - 2 * np.arcsin(1 - res) / np.pi) + 15 d_phi += np.deg2rad(9 + np.exp(.1 * (54 - ele_pred))) / (60. / float(dt)) return r_tcl, d_phi stats = { "max_alt": [], "noise": [], "opath": [], "ipath": [], "d_x": [], "d_c": [], "tau": [] } avg_time = timedelta(0.) terrain = z_terrain.copy() for enable_ephemeris in [False, True]: if enable_ephemeris: print "Foraging with the time compensation mechanism." else: print "Foraging without the time compensation mechanism." # stats d_x = [] # logarithmic distance d_c = [] tau = [] # tortuosity ri = 0 print "Routes: ", for route in routes[::2]: net = CX(noise=0., pontin=False) net.update = True # sun position cur = datetime(2018, 6, 21, 10, 0, 0) seville_obs.date = cur sun.compute(seville_obs) theta_s = np.array([np.pi / 2 - sun.alt]) phi_s = np.array([(sun.az + np.pi) % (2 * np.pi) - np.pi]) sun_azi = [] sun_ele = [] time = [] # outward route route.condition = Hybrid(tau_x=dx) oroute = route.reverse() x, y, yaw = [(x0, y0, yaw0) for x0, y0, _, yaw0 in oroute][0] opath = [[x, y, yaw]] v = np.zeros(2) tb1 = [] d_phi = 0. for _, _, _, yaw in oroute: theta_n, phi_n = tilt(theta_t, phi_t, theta, phi + yaw) sun_ele.append(theta_s[0]) sun_azi.append(phi_s[0]) time.append(cur) sky.theta_s, sky.phi_s = theta_s, phi_s Y, P, A = sky(theta_n, phi_n, noise=noise) if enable_ephemeris: r_tb1, d_phi = encode(theta, phi, Y, P, A, d_phi=d_phi) else: r_tb1, d_phi = encode(theta, phi, Y, P, A, d_phi=0.) yaw0 = yaw _, yaw = np.pi - decode_sph(r_tb1) + phi_s net(yaw, flow) yaw = (yaw + np.pi) % (2 * np.pi) - np.pi v = np.array([np.sin(yaw), np.cos(yaw)]) * route.dx opath.append([opath[-1][0] + v[0], opath[-1][1] + v[1], yaw]) tb1.append(net.tb1) cur += delta seville_obs.date = cur sun.compute(seville_obs) theta_s = np.array([np.pi / 2 - sun.alt]) phi_s = np.array([(sun.az + np.pi) % (2 * np.pi) - np.pi]) opath = np.array(opath) yaw -= phi_s # inward route ipath = [[opath[-1][0], opath[-1][1], opath[-1][2]]] L = 0. # straight distance to the nest C = 0. # distance towards the nest that the agent has covered SL = 0. TC = 0. tb1 = [] tau.append([]) d_x.append([]) d_c.append([]) while C < 15: theta_n, phi_n = tilt(theta_t, phi_t, theta, phi + yaw) sun_ele.append(theta_s[0]) sun_azi.append(phi_s[0]) time.append(cur) sky.theta_s, sky.phi_s = theta_s, phi_s Y, P, A = sky(theta_n, phi_n, noise=noise) if enable_ephemeris: r_tb1, d_phi = encode(theta, phi, Y, P, A, d_phi=d_phi) else: r_tb1, d_phi = encode(theta, phi, Y, P, A, d_phi=0.) _, yaw = np.pi - decode_sph(r_tb1) + phi_s motor = net(yaw, flow) yaw = (ipath[-1][2] + motor + np.pi) % (2 * np.pi) - np.pi v = np.array([np.sin(yaw), np.cos(yaw)]) * route.dx ipath.append([ipath[-1][0] + v[0], ipath[-1][1] + v[1], yaw]) tb1.append(net.tb1) L = np.sqrt(np.square(opath[0][0] - ipath[-1][0]) + np.square(opath[0][1] - ipath[-1][1])) C += route.dx d_x[-1].append(L) d_c[-1].append(C) tau[-1].append(L / C) if C <= route.dx: SL = L if TC == 0. and len(d_x[-1]) > 50 and d_x[-1][-1] > d_x[-1][-2]: TC = C cur += delta seville_obs.date = cur sun.compute(seville_obs) theta_s = np.array([np.pi / 2 - sun.alt]) phi_s = np.array([(sun.az + np.pi) % (2 * np.pi) - np.pi]) ipath = np.array(ipath) d_x[-1] = np.array(d_x[-1]) / SL * 100 d_c[-1] = np.array(d_c[-1]) / TC * 100 tau[-1] = np.array(tau[-1]) ri += 1 avg_time += cur - datetime(2018, 6, 21, 10, 0, 0) stats["max_alt"].append(0.) stats["noise"].append(noise) stats["opath"].append(opath) stats["ipath"].append(ipath) stats["d_x"].append(d_x[-1]) stats["d_c"].append(d_c[-1]) stats["tau"].append(tau[-1]) print ".", print "" print "average time:", avg_time / ri # 1:16:40 np.savez_compressed("data/pi-stats-ephem.npz", **stats)
def create_paths(noise_type="uniform"): global seville_obs, sun, dx # sensor design n = 60 omega = 56 theta, phi, fit = angles_distribution(n, float(omega)) theta_t, phi_t = 0., 0. # sun position seville_obs.date = datetime(2018, 6, 21, 9, 0, 0) sun.compute(seville_obs) theta_s = np.array([np.pi / 2 - sun.alt]) phi_s = np.array([(sun.az + np.pi) % (2 * np.pi) - np.pi]) # ant-world noise = 0.0 ttau = .06 dx = 1e-02 routes = load_routes() flow = dx * np.ones(2) / np.sqrt(2) max_theta = 0. stats = { "max_alt": [], "noise": [], "opath": [], "ipath": [], "d_x": [], "d_c": [], "tau": [] } for max_altitude in [.0, .1, .2, .3, .4, .5]: for ni, noise in enumerate([0.0, 0.2, 0.4, 0.6, 0.8, .97]): # stats d_x = [] # logarithmic distance d_c = [] tau = [] # tortuosity ri = 0 for route in routes[::2]: dx = route.dx net = CX(noise=0., pontin=False) net.update = True # outward route route.condition = Hybrid(tau_x=dx) oroute = route.reverse() x, y, yaw = [(x0, y0, yaw0) for x0, y0, _, yaw0 in oroute][0] opath = [[x, y, yaw]] v = np.zeros(2) tb1 = [] for _, _, _, yaw in oroute: theta_t, phi_t = get_3d_direction(opath[-1][0], opath[-1][1], yaw, tau=ttau) max_theta = max_theta if max_theta > np.absolute(theta_t) else np.absolute(theta_t) theta_n, phi_n = tilt(theta_t, phi_t, theta, phi + yaw) sky.theta_s, sky.phi_s = theta_s, phi_s Y, P, A = sky(theta_n, phi_n, noise=get_noise(theta_n, phi_n, noise, mode=noise_type)) r_tb1 = encode(theta, phi, Y, P, A) yaw0 = yaw _, yaw = np.pi - decode_sph(r_tb1) + phi_s net(yaw, flow) yaw = (yaw + np.pi) % (2 * np.pi) - np.pi v = np.array([np.sin(yaw), np.cos(yaw)]) * route.dx opath.append([opath[-1][0] + v[0], opath[-1][1] + v[1], yaw]) tb1.append(net.tb1) opath = np.array(opath) yaw -= phi_s # inward route ipath = [[opath[-1][0], opath[-1][1], opath[-1][2]]] L = 0. # straight distance to the nest C = 0. # distance towards the nest that the agent has covered SL = 0. TC = 0. tb1 = [] tau.append([]) d_x.append([]) d_c.append([]) while C < 15: theta_t, phi_t = get_3d_direction(ipath[-1][0], ipath[-1][1], yaw, tau=ttau) theta_n, phi_n = tilt(theta_t, phi_t, theta, phi + yaw) sky.theta_s, sky.phi_s = theta_s, phi_s Y, P, A = sky(theta_n, phi_n, noise=noise) r_tb1 = encode(theta, phi, Y, P, A) _, yaw = np.pi - decode_sph(r_tb1) + phi_s motor = net(yaw, flow) yaw = (ipath[-1][2] + motor + np.pi) % (2 * np.pi) - np.pi v = np.array([np.sin(yaw), np.cos(yaw)]) * route.dx ipath.append([ipath[-1][0] + v[0], ipath[-1][1] + v[1], yaw]) tb1.append(net.tb1) L = np.sqrt(np.square(opath[0][0] - ipath[-1][0]) + np.square(opath[0][1] - ipath[-1][1])) C += route.dx d_x[-1].append(L) d_c[-1].append(C) tau[-1].append(L / C) if C <= route.dx: SL = L if TC == 0. and len(d_x[-1]) > 50 and d_x[-1][-1] > d_x[-1][-2]: TC = C ipath = np.array(ipath) d_x[-1] = np.array(d_x[-1]) / SL * 100 d_c[-1] = np.array(d_c[-1]) / TC * 100 tau[-1] = np.array(tau[-1]) ri += 1 stats["max_alt"].append(max_altitude) stats["noise"].append(noise) stats["opath"].append(opath) stats["ipath"].append(ipath) stats["d_x"].append(d_x[-1]) stats["d_c"].append(d_c[-1]) stats["tau"].append(tau[-1]) np.savez_compressed("../data/pi-stats-%s.npz" % noise_type, **stats)
opath = [[x, y, yaw]] v = np.zeros(2) tb1 = [] # print np.rad2deg(phi_s) # plt.figure("yaws") for _, _, _, yaw in oroute: if mode == "uneven": theta_t, phi_t = get_3d_direction(opath[-1][0], opath[-1][1], yaw, tau=ttau) max_theta = max_theta if max_theta > np.absolute( theta_t) else np.absolute(theta_t) theta_n, phi_n = tilt(theta_t, phi_t, theta, phi + yaw) sky.theta_s, sky.phi_s = theta_s, phi_s eta = get_noise(theta, phi, noise, noise_type) Y, P, A = sky(theta_n, phi_n, noise=eta) r_tb1 = encode(theta, phi, Y, P, A) yaw0 = yaw _, yaw = np.pi - decode_sph(r_tb1) + phi_s # plt.plot(yaw0 % (2*np.pi), yaw % (2*np.pi), 'k.') # motor = net(r_tb1, flow) net(yaw, flow) yaw = (yaw + np.pi) % (2 * np.pi) - np.pi v = np.array([np.sin(yaw), np.cos(yaw)]) * route.dx
def skyfeatures(noise=0., simple_pol=False, samples=1000, verbose=False): # default parameters tau_L = 2. c1 = .6 c2 = 4. eps = np.finfo(float).eps AA, BB, CC, DD, EE = SensorObjective.T_L.dot(np.array( [tau_L, 1.])) # sky parameters T_T = np.linalg.pinv(SensorObjective.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 theta_s, phi_s = np.array([np.pi / 6]), np.array([np.pi]) theta, phi = fibonacci_sphere(samples, 180) phi = phi[theta <= np.pi / 2] theta = theta[theta <= np.pi / 2] samples = theta.size 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 # SKY INTEGRATION gamma = np.arccos( np.cos(theta) * np.cos(theta_s) + np.sin(theta) * np.sin(theta_s) * np.cos(phi - phi_s)) # Intensity I_prez, I_00, I_90 = L(gamma, theta), L(0., theta_s), L( np.pi / 2, np.absolute(theta_s - 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 * theta_s) Y_z = (4.0453 * tau_L - 4.9710) * np.tan(chi) - 0.2155 * tau_L + 2.4192 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 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 _, A = tilt(theta_s, phi_s + np.pi, theta, phi) # create cloud disturbance if type(noise) is np.ndarray: if noise.size == P.size: # print "yeah!" eta = np.array(noise, dtype=bool) else: eta = np.zeros_like(theta, dtype=bool) eta[:noise.size] = noise elif noise > 0: eta = np.argsort(np.absolute( np.random.randn(*P.shape)))[:int(noise * P.shape[0])] # eta = np.array(np.absolute(np.random.randn(*P.shape)) < noise, dtype=bool) if verbose: print "Noise level: %.4f (%.2f %%)" % (noise, 100. * eta.sum() / float(eta.size)) else: eta = np.zeros_like(theta, dtype=bool) P[eta] = 0. # destroy the polarisation pattern Y[eta] = 10. return Y, P, A, theta, phi
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