def q_from_geometry(
shape: Tuple[int, int],
geometry: Geometry,
reflection: bool,
alphai: float,
):
"""
Calculate the q vector for each pixel in an image
Parameters:
-----------
image: ndarray,
detector image
geometry: pyFAI Geometry
PONI, Pixel Size, Sample Det dist etc
alphai: scalar, deg
angle of incedence
out_range: list, optional
[[left, right],[lower, upper]] for the output image
res: list, optional
resolution of the output image
coord_sys: str
'qp_qz', 'qy_qz' or 'theta_alpha'
Returns
-------
qimage: ndarray
remeshed/warped image
xcrd: ndarray
x-coords of warped image
ycrd: ndarray
y-coords of warped image
"""
assert shape == geometry.detector.shape[-2:]
x = np.arange(shape[1])
y = np.arange(shape[0])
px_x, px_y = np.meshgrid(x, y)
r_z, r_y, r_x = geometry.calc_pos_zyx(d1=px_y, d2=px_x)
if reflection:
alphas = alpha(r_x, r_y, r_z) - alphai
phis = phi(r_x, r_y, r_z)
k_i = k_f = 2 * np.pi / geometry.wavelength * 1e-10
q_x = k_f * np.cos(alphas) * np.cos(phis) - k_i * np.cos(alphai)
q_y = k_f * np.cos(alphas) * np.sin(phis)
q_z = (k_f * np.sin(alphas) + k_i * np.sin(alphai))
q_r = np.sqrt(q_x**2 + q_y**2) * np.sign(q_y)
q_h = q_r
q_v = q_z
else:
alphas = alpha(r_x, r_y, r_z)
phis = phi(r_x, r_y, r_z)
q_x, q_y, q_z = q_from_angles(phis, alphas,
geometry.wavelength) * 1e-10
q_v = q_y
q_h = q_x
return np.dstack((q_h, q_v))
def remesh(image: np.ndarray,
geometry: Geometry,
reflection: bool,
alphai: float,
bins: Tuple[int, int] = None,
q_h_range: Tuple[float, float] = None,
q_v_range: Tuple[float, float] = None,
out_range=None,
res=None,
coord_sys='qp_qz'):
"""
Redraw the GI Image in (qp, qz) coordinates.
Parameters:
-----------
image: ndarray,
detector image
geometry: pyFAI Geometry
PONI, Pixel Size, Sample Det dist etc
alphai: scalar, deg
angle of incedence
out_range: list, optional
[[left, right],[lower, upper]] for the output image
res: list, optional
resolution of the output image
coord_sys: str
'qp_qz', 'qy_qz' or 'theta_alpha'
Returns
-------
qimage: ndarray
remeshed/warped image
xcrd: ndarray
x-coords of warped image
ycrd: ndarray
y-coords of warped image
"""
geometry = geometry.__deepcopy__()
geometry.setFit2D(geometry.getFit2D()["directDist"], geometry.getFit2D()["centerX"], len(image) - geometry.getFit2D()["centerY"])
assert image.shape == geometry.detector.shape
x = np.arange(image.shape[1])
y = np.arange(image.shape[0])
px_x, px_y = np.meshgrid(x, y)
r_z, r_y, r_x = geometry.calc_pos_zyx(d1=px_y, d2=px_x)
if reflection:
alphas = alpha(r_x, r_y, r_z) - alphai
phis = phi(r_x, r_y, r_z)
k_i = k_f = 2 * np.pi / geometry.wavelength * 1e-10
q_x = k_f * np.cos(alphas) * np.cos(phis) - k_i * np.cos(alphai)
q_y = k_f * np.cos(alphas) * np.sin(phis)
q_z = -(k_f * np.sin(alphas) + k_i * np.sin(alphai))
q_r = np.sqrt(q_x ** 2 + q_y ** 2) * np.sign(q_y)
q_h = q_r
q_v = -q_z
else:
alphas = alpha(r_x, r_y, r_z)
phis = phi(r_x, r_y, r_z)
q_x, q_y, q_z = q_from_angles(phis, alphas, geometry.wavelength) * 1e-10
q_v = -q_y # -q_y
q_h = q_x
if bins is None: bins = tuple(image.shape)
if q_h_range is None: q_h_range = (q_h.min(), q_h.max())
if q_v_range is None: q_v_range = (q_v.min(), q_v.max())
I, q_h, q_v, _, _ = splitBBox.histoBBox2d(weights=image,
pos0=q_h,
delta_pos0=np.ones_like(image) * (q_h_range[1] - q_h_range[0]) / bins[0],
pos1=q_v,
delta_pos1=np.ones_like(image) * (q_v_range[1] - q_v_range[0]) / bins[1],
bins=bins,
pos0Range=q_h_range,
pos1Range=q_v_range,
dummy=None,
delta_dummy=None,
allow_pos0_neg=True,
# mask=mask,
# dark=dark,
# flat=flat,
# solidangle=solidangle,
# polarization=polarization,
# normalization_factor=normalization_factor,
# chiDiscAtPi=self.chiDiscAtPi,
# empty=dummy if dummy is not None else self._empty
)
q_h, q_v = np.meshgrid(q_h, q_v)
return I, q_h, q_v
