# On a grid.
if 1:
#x_vals[j] = 10.0 + 10.0*(j%23)
#y_vals[j] = 10.0 + 10.0*math.floor(float(j)/23.0)
x_vals[j] = 20.0 + 20.0 * (j % 10)
y_vals[j] = 20.0 + 20.0 * math.floor(float(j) / 10.0)
z_off = -0.5 + float(j) / float(num_objects - 1)
#z_off = -0.4 + 0.8 * float(j)/float(num_objects - 1)
#z_off = 0.0
z_vals[j] = z_off * 1000.0
# Generate objects.
objects = PSF.PSF(x_vals, y_vals, z_vals, h_vals)
# Draw the image.
image = dg.drawGaussians([x_size, y_size], objects, background=50, res=5)
#image = dg.drawGaussians([x_size, y_size], objects, background = 0, res = 5)
#image[0:(x_size/2),:] += 50
# Add poisson noise and baseline.
image = numpy.random.poisson(image) + 100.0
# Save the image.
dax_data.addFrame(image)
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):
# On a grid.
if 1:
#x_vals[j] = 10.0 + 10.0*(j%23)
#y_vals[j] = 10.0 + 10.0*math.floor(float(j)/23.0)
x_vals[j] = 20.0 + 20.0*(j%10)
y_vals[j] = 20.0 + 20.0*math.floor(float(j)/10.0)
z_off = -0.5 + float(j)/float(num_objects - 1)
#z_off = -0.4 + 0.8 * float(j)/float(num_objects - 1)
#z_off = 0.0
z_vals[j] = z_off * 1000.0
# Generate objects.
objects = PSF.PSF(x_vals, y_vals, z_vals, h_vals)
# Draw the image.
image = dg.drawGaussians([x_size, y_size], objects, background = 50, res = 5)
#image = dg.drawGaussians([x_size, y_size], objects, background = 0, res = 5)
#image[0:(x_size/2),:] += 50
# Add poisson noise and baseline.
image = numpy.random.poisson(image) + 100.0
# Save the image.
dax_data.addFrame(image)
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):