def image_test_rings():
rings = 10
mod = 50
detector = detector_factory("Titan")
sigma = detector.pixel1 * 4
shape = detector.max_shape
ai = AzimuthalIntegrator(detector=detector)
ai.setFit2D(1000, 1000, 1000)
r = ai.rArray(shape)
r_max = r.max()
chi = ai.chiArray(shape)
img = numpy.zeros(shape)
modulation = (1 + numpy.sin(5 * r + chi * mod))
for radius in numpy.linspace(0, r_max, rings):
img += numpy.exp(-(r - radius) ** 2 / (2 * (sigma * sigma)))
return img * modulation
def image_test_rings():
rings = 10
mod = 50
detector = detector_factory("Titan")
sigma = detector.pixel1 * 4
shape = detector.max_shape
ai = AzimuthalIntegrator(detector=detector)
ai.setFit2D(1000, 1000, 1000)
r = ai.rArray(shape)
r_max = r.max()
chi = ai.chiArray(shape)
img = numpy.zeros(shape)
modulation = (1 + numpy.sin(5 * r + chi * mod))
for radius in numpy.linspace(0, r_max, rings):
img += numpy.exp(-(r - radius)**2 / (2 * (sigma * sigma)))
return img * modulation
def image_test_rings():
"Creating a test image containing gaussian spots on several rings"
rings = 10
mod = 50
detector = detector_factory("Titan")
sigma = detector.pixel1 * 4
shape = detector.max_shape
ai = AzimuthalIntegrator(detector=detector)
ai.setFit2D(1000, 1000, 1000)
r = ai.rArray(shape)
r_max = r.max()
chi = ai.chiArray(shape)
img = numpy.zeros(shape)
modulation = (1 + numpy.sin(5 * r + chi * mod))
for radius in numpy.linspace(0, r_max, rings):
img += numpy.exp(-(r - radius) ** 2 / (2 * (sigma * sigma)))
img *= modulation
img = add_noise(img, 0.0)
return img
def image_test_rings():
"Creating a test image containing gaussian spots on several rings"
rings = 10
mod = 50
detector = detector_factory("Titan")
sigma = detector.pixel1 * 4
shape = detector.max_shape
ai = AzimuthalIntegrator(detector=detector)
ai.setFit2D(1000, 1000, 1000)
r = ai.rArray(shape)
r_max = r.max()
chi = ai.chiArray(shape)
img = numpy.zeros(shape)
modulation = (1 + numpy.sin(5 * r + chi * mod))
for radius in numpy.linspace(0, r_max, rings):
img += numpy.exp(-(r - radius)**2 / (2 * (sigma * sigma)))
img *= modulation
img = add_noise(img, 0.0)
return img
