def recip_ex(detector_size, pixel_size, calibrated_center, dist_sample,
ub_mat, wavelength, motors, i_stack, H_range, K_range, L_range):
# convert to Q space
q_values = recip.process_to_q(motors, detector_size, pixel_size,
calibrated_center, dist_sample,
wavelength, ub_mat)
# minimum and maximum values of the voxel
q_min = np.array([H_range[0], K_range[0], L_range[0]])
q_max = np.array([H_range[1], K_range[1], L_range[1]])
# no. of bins
dqn = np.array([40, 40, 1])
# process the grid values
(grid_data, grid_occu, std_err,
grid_out, bounds) = grid3d(q_values, i_stack, dqn[0], dqn[1],
dqn[2])
grid = np.mgrid[0:dqn[0], 0:dqn[1], 0:dqn[2]]
r = (q_max - q_min) / dqn
X = grid[0] * r[0] + q_min[0]
Y = grid[1] * r[1] + q_min[1]
Z = grid[2] * r[2] + q_min[2]
# creating a mask
_mask = grid_occu <= 10
grid_mask_data = ma.masked_array(grid_data, _mask)
grid_mask_std_err = ma.masked_array(std_err, _mask)
grid_mask_occu = ma.masked_array(grid_occu, _mask)
return X, Y, Z, grid_mask_data
def recip_ex(detector_size, pixel_size, calibrated_center, dist_sample, ub_mat,
wavelength, motors, i_stack, H_range, K_range, L_range):
# convert to Q space
q_values = recip.process_to_q(motors, detector_size, pixel_size,
calibrated_center, dist_sample, wavelength,
ub_mat)
# minimum and maximum values of the voxel
q_min = np.array([H_range[0], K_range[0], L_range[0]])
q_max = np.array([H_range[1], K_range[1], L_range[1]])
# no. of bins
dqn = np.array([40, 40, 1])
# process the grid values
(grid_data, grid_occu, std_err, grid_out,
bounds) = grid3d(q_values, i_stack, dqn[0], dqn[1], dqn[2])
grid = np.mgrid[0:dqn[0], 0:dqn[1], 0:dqn[2]]
r = (q_max - q_min) / dqn
X = grid[0] * r[0] + q_min[0]
Y = grid[1] * r[1] + q_min[1]
Z = grid[2] * r[2] + q_min[2]
# creating a mask
_mask = grid_occu <= 10
grid_mask_data = ma.masked_array(grid_data, _mask)
grid_mask_std_err = ma.masked_array(std_err, _mask)
grid_mask_occu = ma.masked_array(grid_occu, _mask)
return X, Y, Z, grid_mask_data
def test_process_to_q():
detector_size = (256, 256)
pixel_size = (0.0135 * 8, 0.0135 * 8)
calibrated_center = (256 / 2.0, 256 / 2.0)
dist_sample = 355.0
energy = 640 # ( in eV)
# HC_OVER_E to convert from Energy to wavelength (Lambda)
hc_over_e = 12398.4
wavelength = hc_over_e / energy # (Angstrom )
ub_mat = np.array([[-0.01231028454, 0.7405370482, 0.06323870032],
[0.4450897473, 0.04166852402, -0.9509449389],
[-0.7449130975, 0.01265920962, -0.5692399963]])
setting_angles = np.array([[40., 15., 30., 25., 10., 5.],
[90., 60., 0., 30., 10., 5.]])
# delta=40, theta=15, chi = 90, phi = 30, mu = 10.0, gamma=5.0
pdict = {}
pdict['setting_angles'] = setting_angles
pdict['detector_size'] = detector_size
pdict['pixel_size'] = pixel_size
pdict['calibrated_center'] = calibrated_center
pdict['dist_sample'] = dist_sample
pdict['wavelength'] = wavelength
pdict['ub'] = ub_mat
# ensure invalid entries for frame_mode actually fail
# todo test frame_modes 1, 2, and 3
# test that the values are coming back as expected for frame_mode=4
hkl = recip.process_to_q(**pdict)
# Known HKL values for the given six angles)
# each entry in list is (pixel_number, known hkl value)
known_hkl = [(32896, np.array([-0.15471196, 0.19673939, -0.11440936])),
(98432, np.array([0.10205953, 0.45624416, -0.27200778]))]
for pixel, kn_hkl in known_hkl:
npt.assert_array_almost_equal(hkl[pixel], kn_hkl, decimal=8)
# smoketest the frame_mode variable
pass_list = recip.process_to_q.frame_mode
pass_list.append(None)
for passes in pass_list:
recip.process_to_q(frame_mode=passes, **pdict)
def test_process_to_q():
detector_size = (256, 256)
pixel_size = (0.0135*8, 0.0135*8)
calibrated_center = (256/2.0, 256/2.0)
dist_sample = 355.0
energy = 640 # ( in eV)
# HC_OVER_E to convert from Energy to wavelength (Lambda)
hc_over_e = 12398.4
wavelength = hc_over_e / energy # (Angstrom )
ub_mat = np.array([[-0.01231028454, 0.7405370482, 0.06323870032],
[0.4450897473, 0.04166852402, -0.9509449389],
[-0.7449130975, 0.01265920962, -0.5692399963]])
setting_angles = np.array([[40., 15., 30., 25., 10., 5.],
[90., 60., 0., 30., 10., 5.]])
# delta=40, theta=15, chi = 90, phi = 30, mu = 10.0, gamma=5.0
pdict = {}
pdict['setting_angles'] = setting_angles
pdict['detector_size'] = detector_size
pdict['pixel_size'] = pixel_size
pdict['calibrated_center'] = calibrated_center
pdict['dist_sample'] = dist_sample
pdict['wavelength'] = wavelength
pdict['ub'] = ub_mat
# ensure invalid entries for frame_mode actually fail
# todo test frame_modes 1, 2, and 3
# test that the values are coming back as expected for frame_mode=4
hkl = recip.process_to_q(**pdict)
# Known HKL values for the given six angles)
# each entry in list is (pixel_number, known hkl value)
known_hkl = [(32896, np.array([-0.15471196, 0.19673939, -0.11440936])),
(98432, np.array([0.10205953, 0.45624416, -0.27200778]))]
for pixel, kn_hkl in known_hkl:
npt.assert_array_almost_equal(hkl[pixel], kn_hkl, decimal=8)
# smoketest the frame_mode variable
pass_list = recip.process_to_q.frame_mode
pass_list.append(None)
for passes in pass_list:
recip.process_to_q(frame_mode=passes, **pdict)
def _process_to_q_exception(param_dict, frame_mode):
with pytest.raises(KeyError):
recip.process_to_q(frame_mode=frame_mode, **param_dict)
def _process_to_q_exception(param_dict, frame_mode):
recip.process_to_q(frame_mode=frame_mode, **param_dict)
def _process_to_q_exception(param_dict, frame_mode):
recip.process_to_q(frame_mode=frame_mode, **param_dict)