def test_marker_placement_correct_beta():
dps = []
dp_cord_list = np.divide(generate_dp_cord_list(), 80)
max_r = np.max(dp_cord_list) + 0.1
for coords in dp_cord_list:
dp_sim = DiffractionSimulation(coordinates=coords,
intensities=np.ones_like(coords[:, 0]))
dps.append(dp_sim.as_signal(144, 0.025, max_r).data) # stores a numpy array of pattern
dp = pxm.ElectronDiffraction(np.array([dps[0:2], dps[2:]])) # now from a 2x2 array of patterns
dp.set_diffraction_calibration(2 * max_r / (144))
local_plotter(dp, dp_cord_list)
# This is human assessed, if you see this comment, you should check it
assert True
def create_library():
dps = []
half_side_length = 72
half_shape = (half_side_length, half_side_length)
num_orientations = 11
simulations = np.empty(num_orientations, dtype='object')
orientations = np.empty(num_orientations, dtype='object')
pixel_coords = np.empty(num_orientations, dtype='object')
intensities = np.empty(num_orientations, dtype='object')
# Creating the matchresults.
for alpha in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
coords = (np.random.rand(5, 2) -
0.5) * 2 # zero mean, range from -1 to +1
dp_sim = DiffractionSimulation(coordinates=coords,
intensities=np.ones_like(coords[:, 0]),
calibration=1 / half_side_length)
simulations[alpha] = dp_sim
orientations[alpha] = (alpha, alpha, alpha)
pixel_coords[alpha] = (dp_sim.calibrated_coordinates[:, :2] +
half_shape).astype(int)
intensities[alpha] = dp_sim.intensities
if alpha < 4:
dps.append(
dp_sim.as_signal(2 * half_side_length, 0.075,
1).data) # stores a numpy array of pattern
library = DiffractionLibrary()
library["Phase"] = {
'simulations': simulations,
'orientations': orientations,
'pixel_coords': pixel_coords,
'intensities': intensities,
}
dp = pxm.ElectronDiffraction([dps[0:2],
dps[2:]]) # now from a 2x2 array of patterns
return dp, library
dps, dp_sim_list = [], []
half_side_length = 72
library = dict()
half_shape = (half_side_length, half_side_length)
library["Phase"] = {}
# Creating the matchresults.
for alpha in [0, 1, 2, 3]:
coords = (np.random.rand(5, 2) - 0.5) * 2 #zero mean, range from -1 to +1
dp_sim = DiffractionSimulation(coordinates=coords,
intensities=np.ones_like(coords[:, 0]),
calibration=1 / half_side_length)
dp_sim_list.append(dp_sim) #stores the simulations
dps.append(dp_sim.as_signal(2 * half_side_length, 0.075,
1).data) #stores a numpy array of pattern
dp = pxm.ElectronDiffraction([dps[0:2],
dps[2:]]) #now from a 2x2 array of patterns
for alpha in np.arange(0, 10, 1):
rotation = (alpha, 0, 0)
if rotation[0] < 4:
library = create_library_entry(library, rotation,
dp_sim_list[rotation[0]])
else:
local_cords = np.random.rand(5, 2)
pat = DiffractionSimulation(coordinates=local_cords,
intensities=np.ones_like(local_cords[:,
0]))
library = create_library_entry(library, rotation, pat)