######################################################################
# Modelling S2
# ~~~~~~~~~~~~
#
model = GeologicalModel(bb[0,:],bb[1,:])
model.set_model_data(data)
s2 = model.create_and_add_fold_frame('s2',
nelements=10000,
buffer=0.5,
solver='lu',
damp=True)
viewer = LavaVuModelViewer(model)
viewer.add_scalar_field(s2[0],
cmap='prism')
viewer.add_isosurface(s2[0],
slices=[0,1])
viewer.add_data(s2[0])
viewer.rotate(rotation)
viewer.display()
######################################################################
# Modelling S1
# ~~~~~~~~~~~~
#
s1 = model.create_and_add_folded_fold_frame('s1',
limb_wl=4,
def loop2LoopStructural(thickness_file,orientation_file,contacts_file,bbox):
from LoopStructural import GeologicalModel
from LoopStructural.visualisation import LavaVuModelViewer
import lavavu
df = pd.read_csv(thickness_file)
thickness = {}
for f in df['formation'].unique():
thickness[f] = np.mean(df[df['formation']==f]['thickness'])
#display(thickness)
order = ['P__TKa_xs_k','P__TKo_stq','P__TKk_sf','P__TK_s',
'A_HAu_xsl_ci', 'A_HAd_kd', 'A_HAm_cib', 'A_FOj_xs_b',
'A_FO_xo_a', 'A_FO_od', 'A_FOu_bbo',
'A_FOp_bs', 'A_FOo_bbo', 'A_FOh_xs_f', 'A_FOr_b']
strat_val = {}
val = 0
for o in order:
if o in thickness:
strat_val[o] = val
val+=thickness[o]
#display(strat_val)
orientations = pd.read_csv(orientation_file)
contacts = pd.read_csv(contacts_file)
contacts['val'] = np.nan
for o in strat_val:
contacts.loc[contacts['formation']==o,'val'] = strat_val[o]
data = pd.concat([orientations,contacts],sort=False)
data['type'] = np.nan
for o in order:
data.loc[data['formation']==o,'type'] = 's0'
data
boundary_points = np.zeros((2,3))
boundary_points[0,0] = bbox[0]
boundary_points[0,1] = bbox[1]
boundary_points[0,2] = -20000
boundary_points[1,0] = bbox[2]
boundary_points[1,1] = bbox[3]
boundary_points[1,2] = 1200
model = GeologicalModel(boundary_points[0,:],boundary_points[1,:])
model.set_model_data(data)
strati = model.create_and_add_foliation('s0', #identifier in data frame
interpolatortype="FDI", #which interpolator to use
nelements=400000, # how many tetras/voxels
buffer=0.1, # how much to extend nterpolation around box
solver='external',
external=solve_pyamg
)
#viewer = LavaVuModelViewer()
viewer = LavaVuModelViewer(model)
viewer.add_data(strati['feature'])
viewer.add_isosurface(strati['feature'],
# nslices=10,
slices= strat_val.values(),
# voxet={'bounding_box':boundary_points,'nsteps':(100,100,50)},
paint_with=strati['feature'],
cmap='tab20'
)
#viewer.add_scalar_field(model.bounding_box,(100,100,100),
# 'scalar',
## norm=True,
# paint_with=strati['feature'],
# cmap='tab20')
viewer.add_scalar_field(strati['feature'])
viewer.set_viewer_rotation([-53.8190803527832, -17.1993350982666, -2.1576387882232666])
#viewer.save("fdi_surfaces.png")
viewer.interactive()
# It is recommended to visualise
# the vectorfield at a lower resolution than the mesh otherwise it can be
# difficult to see the vectors. You can use numpy stepping along the
# array: ``locations = mesh.barycentre[::20,:]`` which will sample every
# 20th sample in the numpy array.
#
viewer = LavaVuModelViewer(model,background="white")
# determine the number of unique surfaces in the model from
# the input data and then calculate isosurfaces for this
unique = np.unique(strati.interpolator.get_value_constraints()[:,3])
viewer.add_isosurface(strati,
slices=unique,
cmap='prism',
paint_with=strati)
viewer.add_section(strati,
axis='x',
value=0.,
boundary_points=model.bounding_box,
nsteps=np.array([30,30,30]),
cmap='prism')
viewer.add_scalar_field(strati,
cmap='prism')
viewer.add_model(cmap='tab20')
# Add the data addgrad/addvalue arguments are optional
viewer.add_data(strati,addgrad=True,addvalue=True, cmap='prism')
viewer.lv.rotate([-85.18760681152344, 42.93233871459961, 0.8641873002052307])
viewer.display()# to add an interactive display