def add_gaussian_holes(array, nholes=100, rad_max=30, rad_min=10,
return_info=False, hole_level=None):
ylim, xlim = array.shape
yy, xx = np.mgrid[-ylim/2:ylim/2, -xlim/2:xlim/2]
xcenters = np.random.random_integers(-(xlim/2-rad_max),
high=xlim/2-rad_max, size=nholes)
ycenters = np.random.random_integers(-(ylim/2-rad_max),
high=ylim/2-rad_max, size=nholes)
radii = np.random.random_integers(rad_min, rad_max, size=nholes)
if hole_level is None:
# Choose random hole bkg levels
bkgs = np.random.uniform(0.5, 0.75, size=nholes)
else:
bkgs = np.array([hole_level] * nholes, dtype=np.int)
for x, y, radius, amp in zip(xcenters, ycenters, radii, bkgs):
gauss = Gaussian2D.evaluate(yy, xx,
amp, x, y, radius, radius, 0.0)
array -= gauss
if return_info:
return array, np.vstack([ycenters+ylim/2, xcenters+xlim/2, radii])
return array
def GaussianGalaxy(cell_name,
center,
sigmax,
sigmay,
angle,
num_squares,
square_size=1.0,
layer=1):
# We're going to create a brightness profile by randomly dithering
# num_squares squares of size square_size
# Create a Cell and add the box
cell = gds.core.Cell(cell_name)
for n in range(num_squares):
# Here we use rejection sampling to generate the 2D Gaussian Profile
# This is slower, but more general.
Reject = True
while Reject:
x0 = -3.0 * sigmax + 6.0 * sigmax * rand()
y0 = -3.0 * sigmay + 6.0 * sigmay * rand()
if Gaussian2D.evaluate(x0, y0, 1.0, 0.0, 0.0, sigmax, sigmay,
0.0) > rand():
Reject = False
x = center[0] + x0 * cos(angle) + y0 * sin(angle)
y = center[1] - x0 * sin(angle) + y0 * cos(angle)
x = round(x / square_size) * square_size
y = round(y / square_size) * square_size
pixel = gds.shapes.Rectangle(
(x - square_size / 2.0, y - square_size / 2.0),
(x + square_size / 2.0, y + square_size / 2.0),
layer=layer)
cell.add(pixel)
return cell
def add_gaussian_peaks(array, nholes=100, rad_max=30, rad_min=10):
ylim, xlim = array.shape
yy, xx = np.mgrid[-ylim/2:ylim/2, -xlim/2:xlim/2]
xcenters = np.random.random_integers(-(xlim/2-rad_max),
high=xlim/2-rad_max, size=nholes)
ycenters = np.random.random_integers(-(ylim/2-rad_max),
high=ylim/2-rad_max, size=nholes)
radii = np.random.random_integers(rad_min, rad_max, size=nholes)
for x, y, radius in zip(xcenters, ycenters, radii):
amp = np.random.uniform(0.5, 0.75)
gauss = Gaussian2D.evaluate(yy, xx,
amp, x, y, radius, radius, 0.0)
array += gauss
return array
