def calc_q_range(lims, geometry, alphai, cen):
nanometer = 1000000000.0
sdd = geometry.get_dist() * nanometer
wavelen = geometry.get_wavelength() * nanometer
pixel1 = geometry.get_pixel1() * nanometer
pixel2 = geometry.get_pixel2() * nanometer
y = np.array([0, lims[1] - 1], dtype=np.float) * pixel1
z = np.array([0, lims[0] - 1], dtype=np.float) * pixel2
y, z = np.meshgrid(y, z)
y -= cen[0]
z -= cen[1]
tmp = np.sqrt(y ** 2 + sdd ** 2)
cos2theta = sdd / tmp
sin2theta = y / tmp
tmp = np.sqrt(z ** 2 + sdd ** 2)
cosalpha = sdd / tmp
sinalpha = z / tmp
k0 = 2.0 * np.pi / wavelen
qx = k0 * (cosalpha * cos2theta - np.cos(alphai))
qy = k0 * cosalpha * sin2theta
qz = k0 * (sinalpha + np.sin(alphai))
qp = np.sign(qy) * np.sqrt(qx ** 2 + qy ** 2)
q_range = [qp.min(), qp.max(), qz.min(), qz.max()]
return (
q_range, k0)
def calc_q_range(lims, geometry, alphai, cen):
nanometer = 1.0E+09
sdd = geometry.get_dist() * nanometer
wavelen = geometry.get_wavelength() * nanometer
pixel1 = geometry.get_pixel1() * nanometer
pixel2 = geometry.get_pixel2() * nanometer
# calculate q-range for the image
y = np.array([0, lims[1] - 1], dtype=np.float) * pixel1
z = np.array([0, lims[0] - 1], dtype=np.float) * pixel2
y, z = np.meshgrid(y, z)
y -= cen[0]
z -= cen[1]
# calculate angles
tmp = np.sqrt(y ** 2 + sdd ** 2)
cos2theta = sdd / tmp
sin2theta = y / tmp
tmp = np.sqrt(z ** 2 + y ** 2 + sdd ** 2)
cosalpha = sdd / tmp
sinalpha = z / tmp
k0 = 2. * np.pi / wavelen
# calculate q-values of each corner
qx = k0 * (cosalpha * cos2theta - np.cos(alphai))
qy = k0 * cosalpha * sin2theta
qz = k0 * (sinalpha + np.sin(alphai))
qp = np.sign(qy) * np.sqrt(qx ** 2 + qy ** 2)
q_range = [qp.min(), qp.max(), qz.min(), qz.max()]
return q_range, k0
def remeshqarray(image, filename, geometry, alphai):
shape = image.shape
center = np.zeros(2, dtype=np.float)
pixel = np.zeros(2, dtype=np.float)
# get calibrated parameters
nanometer = 1.0E+09
pixel[0] = geometry.get_pixel1() * nanometer
pixel[1] = geometry.get_pixel2() * nanometer
center[0] = geometry.get_poni2() * nanometer
center[1] = shape[0] * pixel[0] - geometry.get_poni1() * nanometer
# calculate q values
qrange, k0 = calc_q_range(image.shape, geometry, alphai, center)
# uniformly spaced q-values for remeshed image
nqz = image.shape[0]
dqz = (qrange[3] - qrange[2]) / (nqz - 1)
nqp = np.int((qrange[1] - qrange[0]) / dqz)
qvrt = np.linspace(qrange[2], qrange[3], nqz)
qpar = qrange[0] + np.arange(nqp) * dqz
qpar, qvrt = np.meshgrid(qpar, qvrt)
return np.rot90(qpar, 3), np.rot90(qvrt, 3)
def remesh(image, filename, geometry):
shape = image.shape
center = np.zeros(2, dtype=np.float)
pixel = np.zeros(2, dtype=np.float)
nanometer = 1000000000.0
sdd = geometry.get_dist() * nanometer
pixel[0] = geometry.get_pixel1() * nanometer
pixel[1] = geometry.get_pixel2() * nanometer
center[0] = geometry.get_poni2() * nanometer
center[1] = shape[0] * pixel[0] - geometry.get_poni1() * nanometer
print center
paras = loader.loadparas(filename)
if paras is None:
print 'Failed to read incident angle'
return (
np.rot90(image, 3), None, None)
if 'Sample Alpha Stage' in paras:
alphai = np.deg2rad(float(paras['Sample Alpha Stage']))
else:
if 'Alpha' in paras:
alphai = np.deg2rad(float(paras['Alpha']))
else:
return (
np.rot90(image, 3), None, None)
qrange, k0 = calc_q_range(image.shape, geometry, alphai, center)
nqz = image.shape[0]
dqz = (qrange[3] - qrange[2]) / (nqz - 1)
nqp = np.int((qrange[1] - qrange[0]) / dqz)
qvrt = np.linspace(qrange[2], qrange[3], nqz)
qpar = qrange[0] + np.arange(nqp) * dqz
qpar, qvrt = np.meshgrid(qpar, qvrt)
try:
center /= pixel
qimg = warp_image(image, qpar, qvrt, pixel, center, alphai, k0, sdd, 0)
return (
np.rot90(qimg, 3), qpar, qvrt)
except:
cosi = np.cos(alphai)
sini = np.sin(alphai)
sina = qvrt / k0 - sini
cosa2 = 1 - sina ** 2
cosa = np.sqrt(cosa2)
tana = sina / cosa
t1 = cosa2 + cosi ** 2 - (qpar / k0) ** 2
t2 = 2.0 * cosa * cosi
cost = t1 / t2
cost[t1 > t2] = 0
with np.errstate(divide='ignore'):
tant = np.sign(qpar) * np.sqrt(1.0 - cost ** 2) / cost
tant[cost == 0] = 0
map_x = (tant * sdd + center[0]) / pixel[0]
map_y = (tana * sdd + center[1]) / pixel[1]
m1 = t1 > t2
m2 = np.logical_or(map_x < 0, map_x > 1474)
mask = np.logical_or(m1, m2)
map_x[mask] = -1
qsize = map_x.size
qshape = map_x.shape
qimg = np.fromiter((image[(i, j)] for i, j in np.nditer([map_y.astype(int),
map_x.astype(int)])), dtype=np.float, count=qsize).reshape(qshape)
qimg[mask] = 0.0
return (
np.rot90(qimg, 3), qpar, qvrt)
return
def remesh(image, filename, geometry, alphai):
shape = image.shape
center = np.zeros(2, dtype=np.float)
pixel = np.zeros(2, dtype=np.float)
# get calibrated parameters
nanometer = 1.0E+09
sdd = geometry.get_dist() * nanometer
pixel[0] = geometry.get_pixel1() * nanometer
pixel[1] = geometry.get_pixel2() * nanometer
center[0] = geometry.get_poni2() * nanometer
center[1] = shape[0] * pixel[0] - geometry.get_poni1() * nanometer
# calculate q values
qrange, k0 = calc_q_range(image.shape, geometry, alphai, center)
# uniformly spaced q-values for remeshed image
nqz = image.shape[0]
dqz = (qrange[3] - qrange[2]) / (nqz - 1)
nqp = np.int((qrange[1] - qrange[0]) / dqz)
qvrt = np.linspace(qrange[2], qrange[3], nqz)
qpar = qrange[0] + np.arange(nqp) * dqz
qpar, qvrt = np.meshgrid(qpar, qvrt)
try:
qimg = warp_image(image, qpar, qvrt, pixel, center, alphai, k0, sdd, 0)
return np.rot90(qimg, 3), np.rot90(qpar, 3), np.rot90(qvrt, 3)
except Exception:
# find inverse map
cosi = np.cos(alphai)
sini = np.sin(alphai)
sina = (qvrt / k0) - sini
cosa2 = 1 - sina ** 2
cosa = np.sqrt(cosa2)
tana = sina / cosa
t1 = cosa2 + cosi ** 2 - (qpar / k0) ** 2
t2 = 2. * cosa * cosi
cost = t1 / t2
cost[t1 > t2] = 0
with np.errstate(divide='ignore'):
tant = np.sign(qpar) * np.sqrt(1. - cost ** 2) / cost
tant[cost == 0] = 0
# F : (qp,qz) --> (x,y)
map_x = (tant * sdd + center[0]) / pixel[0]
cost[cost < 0] = 1
with np.errstate(divide='ignore'):
map_y = (tana * sdd / cost + center[1]) / pixel[1]
# compute null space
nrow, ncol = image.shape
m1 = t1 > t2
m2 = np.logical_or(map_x < 0, map_x > ncol - 1)
m3 = np.logical_or(map_y < 0, map_y > nrow - 1)
mask = np.logical_or(np.logical_or(m1, m2), m3)
map_x[mask] = -1
map_y[mask] = -1
qsize = map_x.size
qshape = map_x.shape
qimg = np.fromiter((image[i, j] for i, j in np.nditer([map_y.astype(int),
map_x.astype(int)])), dtype=np.float,
count=qsize).reshape(qshape)
qimg[mask] = 0.
return np.rot90(qimg, 3), np.rot90(qpar, 3), np.rot90(qvrt, 3)