def s2c(p):
# if self.type != 'spherical':
# print 'EE: Huh? The coordinates are not spherical...'
# return
if p.shape[1] == 1:
print 'EE: Can\'t do much with a single coordinate...'
return
elif p.shape[1] == 2:
r = p[:,0]
theta = p[:,1]
# phi = zeros(self.p[:,3]
# rho =
z = r*cos(theta)
y = zeros(z.size)
x = r*sin(theta)
p[:] = vstack((x,y,z)).T
elif p.shape[1] == 3:
r = p[:,0]
theta = p[:,1]
phi = p[:,2]
rho = r*sin(theta)
z = r*cos(theta)
y = rho*sin(phi)
x = rho*cos(phi)
p = Coordinate(x=x,y=y,z=z)
return p
def s2c(p):
# if self.type != 'spherical':
# print 'EE: Huh? The coordinates are not spherical...'
# return
if p.shape[1] == 1:
print 'EE: Can\'t do much with a single coordinate...'
return
elif p.shape[1] == 2:
r = p[:, 0]
theta = p[:, 1]
# phi = zeros(self.p[:,3]
# rho =
z = r * cos(theta)
y = zeros(z.size)
x = r * sin(theta)
p[:] = vstack((x, y, z)).T
elif p.shape[1] == 3:
r = p[:, 0]
theta = p[:, 1]
phi = p[:, 2]
rho = r * sin(theta)
z = r * cos(theta)
y = rho * sin(phi)
x = rho * cos(phi)
p = Coordinate(x=x, y=y, z=z)
return p
def __new__(self, M=10, a=0.54, phi=0, normalised=True):
# Create the window
if phi == 0:
wc = a + (1 - a) * np.cos(2 * pi * np.linspace(-0.5, 0.5, M))
win = wc / sum(wc) # Normalised window
else:
n = np.linspace(-0.5, 0.5, M)
wc = a + (1 - a) * np.cos(2 * pi * n) # Window coefficients
m = np.arange(0, M) # Create M indeces from 0 to 1
aa = exp(-1j * 2 * pi * m * phi) # Steering vector
ws = dot(wc, aa) # Normalisation factor
win = aa * wc / ws # Steered and normalised window
w = np.Ndarray.__new__(self, win)
# axes=('M',),
# desc = 'Rectangular (phi=%d)'%phi)
# desc='Rectangular (phi=%d)'%phi,
# shape_desc=('M','1'))
return w
def getWindow(
image,
tx_coords, # x,y
tx_angle, # radians
img_coords, # x_min, x_max, y_min, y_max
width=0.8,
scale=1):
Nx, Ny = image.shape
N = Nx
img_width = img_coords[1] - img_coords[0]
img_length = img_coords[3] - img_coords[2]
x_mean = np.mean(img_coords[0:2])
y_mean = np.mean(img_coords[2:4])
dx = float(img_width) / Nx
dy = float(img_length) / Ny
image_window = np.ones(image.shape)
x_old = np.zeros(N)
x_new = np.zeros(N)
# tx_img_range = img_coords[2]
r = img_coords[2] + dy / 2
has_overflowed = False
for row in range(Ny):
row_data = image_window[:, row]
x_cut = r * np.sin(tx_angle / 2)
# x_cut_rel = x_cut / extent[0]
# w_idx = np.linspace(0,1,N)
x_old[:] = np.linspace(x_mean - (2 - width) * x_cut,
x_mean + (2 - width) * x_cut,
N) #*0.5/img_width + 0.5
x_new[:] = np.linspace(img_coords[0], img_coords[1],
Nx) #*0.5/img_width + 0.5
up = x_new[x_new > x_old[0]]
updown = up[up <= x_old[-1]]
# print(x_cut, updown.shape[0])
Nlower = Nx - up.shape[0]
Nhigher = Nx - updown.shape[0] - Nlower
transition = (0.5 + 0.5 * np.cos(
np.linspace(0, np.pi, int(Nx * width * x_cut /
(2 * img_width))))) * scale + (1 - scale)
# fn = None
# if row == 0:
# fn = pl.figure()
# ax = fn.add_subplot(121)
# ax.plot(transition)
Nt = transition.shape[0]
if N <= 2 * Nt:
w = np.ones(N)
has_overflowed = True
else:
w = np.hstack(
(2 - scale - transition, np.ones(N - 2 * Nt), transition))
w_new_center = np.interpolate1D(x_old,
w,
updown,
kind='linear',
fill_value=1 - scale)
w_new = np.hstack((np.ones(Nlower) * (1 - scale), w_new_center,
np.ones(Nhigher) * (1 - scale)))
image_window[:, row] = w_new
# if row == 0:
# ax = fn.add_subplot(122)
# ax.plot(w_new)
# fn.savefig('test2.eps')
r = r + dy
if has_overflowed:
print("WARNING: Not making a window")
return image_window
