# Create the 3D scene
base_name = os.path.splitext(__file__)[0]
s3d = Scene3D(display=False,
ren_size=(800, 800),
name=base_name,
background=black)
# create a python Grain object from the image data
orientation = Orientation.from_rodrigues(np.array([0.3889, -0.0885, 0.3268]))
grain = Grain(1, orientation)
grain_data = HST_read(im_file,
header_size=0,
autoparse_filename=True,
verbose=True)
grain.position = ndimage.measurements.center_of_mass(grain_data, grain_data)
print('grain position: %s' % str(grain.position))
grain.volume = ndimage.measurements.sum(grain_data) # label is 1.0 here
grain.add_vtk_mesh(grain_data, contour=False)
print('adding bounding box')
grain_bbox = box_3d(size=np.shape(grain_data), line_color=white)
print('adding grain with slip planes')
z_offsets = np.linspace(-50, 50, 6, endpoint=True)
print(z_offsets)
plane_origins = np.zeros((len(z_offsets), 3), dtype=float)
plane_origins[:, 2] = z_offsets
hkl_planes = [HklPlane(1, 1, 1)] * len(z_offsets)
grain_with_planes = grain_3d(grain, hkl_planes, plane_origins=plane_origins, show_normal=False, \
def load(file_path='experiment.txt'):
with open(file_path, 'r') as f:
dict_exp = json.load(f)
sample = Sample()
sample.set_name(dict_exp['Sample']['Name'])
sample.set_position(dict_exp['Sample']['Position'])
if 'Geometry' in dict_exp['Sample']:
sample_geo = ObjectGeometry()
sample_geo.set_type(dict_exp['Sample']['Geometry']['Type'])
sample.set_geometry(sample_geo)
if 'Material' in dict_exp['Sample']:
a, b, c = dict_exp['Sample']['Material']['Lengths']
alpha, beta, gamma = dict_exp['Sample']['Material']['Angles']
centering = dict_exp['Sample']['Material']['Centering']
symmetry = Symmetry.from_string(
dict_exp['Sample']['Material']['Symmetry'])
material = Lattice.from_parameters(a,
b,
c,
alpha,
beta,
gamma,
centering=centering,
symmetry=symmetry)
sample.set_material(material)
if 'Microstructure' in dict_exp['Sample']:
micro = Microstructure(
dict_exp['Sample']['Microstructure']['Name'])
for i in range(len(
dict_exp['Sample']['Microstructure']['Grains'])):
dict_grain = dict_exp['Sample']['Microstructure']['Grains'][i]
grain = Grain(
dict_grain['Id'],
Orientation.from_euler(
dict_grain['Orientation']['Euler Angles (degrees)']))
grain.position = np.array(dict_grain['Position'])
grain.volume = dict_grain['Volume']
micro.grains.append(grain)
sample.set_microstructure(micro)
exp = Experiment()
exp.set_sample(sample)
source = XraySource()
source.set_position(dict_exp['Source']['Position'])
if 'Min Energy (keV)' in dict_exp['Source']:
source.set_min_energy(dict_exp['Source']['Min Energy (keV)'])
if 'Max Energy (keV)' in dict_exp['Source']:
source.set_max_energy(dict_exp['Source']['Max Energy (keV)'])
exp.set_source(source)
for i in range(len(dict_exp['Detectors'])):
dict_det = dict_exp['Detectors'][i]
if dict_det['Class'] == 'Detector2d':
det = Detector2d(size=dict_det['Size (pixels)'])
det.ref_pos = dict_det['Reference Position (mm)']
if dict_det['Class'] == 'RegArrayDetector2d':
det = RegArrayDetector2d(size=dict_det['Size (pixels)'])
det.pixel_size = dict_det['Pixel Size (mm)']
det.ref_pos = dict_det['Reference Position (mm)']
if 'Binning' in dict_det:
det.set_binning(dict_det['Binning'])
det.u_dir = np.array(dict_det['u_dir'])
det.v_dir = np.array(dict_det['v_dir'])
det.w_dir = np.array(dict_det['w_dir'])
exp.add_detector(det)
return exp
