def gen_heatmap(self, phi, theta):
if self.counter%120 == 0:
lobe_values = lobe_values_x*np.sin(theta)*np.cos(phi) + lobe_values_y*np.sin(theta)*np.sin(phi) + lobe_values_z*np.cos(theta)
lobe_values = 0.5*lobe_amplitude*np.exp(lobe_sharpness*(lobe_values-1))
phi0 = np.pi
theta0 = np.pi/2.0
lobe_values0 = lobe_values_x*np.sin(theta0)*np.cos(phi0) + lobe_values_y*np.sin(theta0)*np.sin(phi0) + lobe_values_z*np.cos(theta0)
lobe_values += 0.75*lobe_amplitude*np.exp(lobe_sharpness*(lobe_values0-1))
min_lobe = np.min(lobe_values)
max_lobe = np.max(lobe_values)
lobe_values = (lobe_values-min_lobe)/(max_lobe-min_lobe)*255.0
h = lobe_values.astype(int)
h = np.ndarray.tolist(h.flatten())
h = [chr(n) for n in h]
h = ''.join(h)
msg = image_t()
msg.utime = self.counter*1e5/12.0
msg.width = 360
msg.height = 180
msg.row_stride = 360
msg.pixelformat = 1497715271
msg.size = len(h)
msg.data = h
msg.nmetadata = 0
#lc.publish("BF_HEATMAP_ONLINE", msg.encode())
#print 'elevation:', theta*180/np.pi, 'azimuth:', phi*180/np.pi
lc.publish("BF_HEATMAP", msg.encode())
self.counter += 1
def gen_heatmap(self, phi, theta):
if self.counter % 120 == 0:
lobe_values = (
lobe_values_x * np.sin(theta) * np.cos(phi)
+ lobe_values_y * np.sin(theta) * np.sin(phi)
+ lobe_values_z * np.cos(theta)
)
lobe_values = 0.5 * lobe_amplitude * np.exp(lobe_sharpness * (lobe_values - 1))
phi0 = np.pi
theta0 = np.pi / 2.0
lobe_values0 = (
lobe_values_x * np.sin(theta0) * np.cos(phi0)
+ lobe_values_y * np.sin(theta0) * np.sin(phi0)
+ lobe_values_z * np.cos(theta0)
)
lobe_values += 0.75 * lobe_amplitude * np.exp(lobe_sharpness * (lobe_values0 - 1))
min_lobe = np.min(lobe_values)
max_lobe = np.max(lobe_values)
lobe_values = (lobe_values - min_lobe) / (max_lobe - min_lobe) * 255.0
h = lobe_values.astype(int)
h = np.ndarray.tolist(h.flatten())
h = [chr(n) for n in h]
h = "".join(h)
msg = image_t()
msg.utime = self.counter * 1e5 / 12.0
msg.width = 360
msg.height = 180
msg.row_stride = 360
msg.pixelformat = 1497715271
msg.size = len(h)
msg.data = h
msg.nmetadata = 0
# lc.publish("BF_HEATMAP_ONLINE", msg.encode())
# print 'elevation:', theta*180/np.pi, 'azimuth:', phi*180/np.pi
lc.publish("BF_HEATMAP", msg.encode())
self.counter += 1
phi = np.arctan2(source_bf[1,0], source_bf[0,0]) #azimuth
if phi < 0:
phi = -phi
else:
phi = 2*np.pi - phi
# create the heatmap as a spherical gaussian centered at this azimuth and elevation, and publish in image_t format
lobe_values = lobe_values_x*np.sin(theta)*np.cos(phi) + lobe_values_y*np.sin(theta)*np.sin(phi) + lobe_values_z*np.cos(theta)
lobe_values = lobe_amplitude*np.exp(lobe_sharpness*(lobe_values-1))
min_lobe = np.min(lobe_values)
max_lobe = np.max(lobe_values)
lobe_values = (lobe_values-min_lobe)/(max_lobe-min_lobe)*255.0
h = lobe_values.astype(int)
h = np.ndarray.tolist(h.flatten())
h = [chr(n) for n in h]
h = ''.join(h)
msg = image_t()
msg.utime = curr_time
msg.width = 360
msg.height = 180
msg.row_stride = 360
msg.pixelformat = 1497715271
msg.size = len(h)
msg.data = h
msg.nmetadata = 0
lc.publish("BF_HEATMAP", msg.encode())
print 'elevation:', theta*180/np.pi, 'azimuth:', phi*180/np.pi
except KeyboardInterrupt:
pass
''' rejection sampling scheme for Von-Mises-Fisher '''