def translation_opencl(R_oriented, init_t=None):
"""
Params:
mat:
Returns:
modelmat: a 4x4 matrix
global state:
meanx,meany are the 'fixes' used to transform opencl.get_modelxyz()
into true model coordinates. The values of opencl.get_modelxyz() do
not reflect the correct value of modelmat. The modelmat includes a
correction by [-meanx, 0, -meany] that must be applied to modelxyz,
and passed as a parameter to opencl.computegridinds.
"""
assert R_oriented.dtype == np.float32
global modelmat
modelmat = np.array(R_oriented)
# Returns warped coordinates, and sincos values for the lattice
opencl.compute_lattice2(modelmat[:3,:4], LW)
global cx,cz,face
# If we don't have a good initialization for the model space translation,
# use the centroid of the surface points.
if init_t:
global face
X,Y,Z,face = np.rollaxis(opencl.get_modelxyz(),1)
cx,_,cz,_ = np.rollaxis(np.frombuffer(np.array(face).data,
dtype='i1').reshape(-1,2),1)
modelmat[:,3] -= np.round([X[cz!=0].mean()/LW,
0,
Z[cx!=0].mean()/LW,
0])*LW
opencl.compute_lattice2(modelmat[:3,:4], LW)
# Find the circular mean, using weights
def cmean(mxy,c):
x,y = mxy / c
a2 = np.arctan2(y,x) / (2*np.pi) * LW
if np.isnan(a2): a2 = 0
return a2, np.sqrt(x**2 + y**2), c
global meanx,meanz,cxyz_,qx2qz2, dmx, dmy, countx, county
cxyz_,qx2qz2 = opencl.reduce_lattice2()
meanx,dmx,countx = cmean(qx2qz2[:2],cxyz_[0])
meanz,dmy,county = cmean(qx2qz2[2:],cxyz_[2])
modelmat[:,3] -= np.array([meanx, 0, meanz, 0])
return modelmat
def translation_opencl(R_oriented, init_t=None):
"""
Params:
mat:
Returns:
modelmat: a 4x4 matrix
global state:
meanx,meany are the 'fixes' used to transform opencl.get_modelxyz()
into true model coordinates. The values of opencl.get_modelxyz() do
not reflect the correct value of modelmat. The modelmat includes a
correction by [-meanx, 0, -meany] that must be applied to modelxyz,
and passed as a parameter to opencl.computegridinds.
"""
assert R_oriented.dtype == np.float32
global modelmat
modelmat = np.array(R_oriented)
# Returns warped coordinates, and sincos values for the lattice
opencl.compute_lattice2(modelmat[:3, :4], LW)
global cx, cz, face
# If we don't have a good initialization for the model space translation,
# use the centroid of the surface points.
if init_t:
global face
X, Y, Z, face = np.rollaxis(opencl.get_modelxyz(), 1)
cx, _, cz, _ = np.rollaxis(
np.frombuffer(np.array(face).data, dtype='i1').reshape(-1, 2), 1)
modelmat[:, 3] -= np.round(
[X[cz != 0].mean() / LW, 0, Z[cx != 0].mean() / LW, 0]) * LW
opencl.compute_lattice2(modelmat[:3, :4], LW)
# Find the circular mean, using weights
def cmean(mxy, c):
x, y = mxy / c
a2 = np.arctan2(y, x) / (2 * np.pi) * LW
if np.isnan(a2): a2 = 0
return a2, np.sqrt(x**2 + y**2), c
global meanx, meanz, cxyz_, qx2qz2, dmx, dmy, countx, county
cxyz_, qx2qz2 = opencl.reduce_lattice2()
meanx, dmx, countx = cmean(qx2qz2[:2], cxyz_[0])
meanz, dmy, county = cmean(qx2qz2[2:], cxyz_[2])
modelmat[:, 3] -= np.array([meanx, 0, meanz, 0])
return modelmat
def carve(xfix=None, zfix=None, use_opencl=True):
global gridinds, inds, grid
gridmin = np.zeros((4,), "f")
gridmax = np.zeros((4,), "f")
gridmin[:3] = config.bounds[0]
gridmax[:3] = config.bounds[1]
global occ, vac
if xfix is None or zfix is None:
import lattice
xfix, zfix = lattice.meanx, lattice.meanz
if use_opencl:
opencl.compute_gridinds(xfix, zfix, config.LW, config.LH, gridmin, gridmax)
gridinds = opencl.get_gridinds()
if 0:
inds = gridinds[gridinds[:, 0, 3] != 0, :, :3]
if len(inds) == 0:
return None
bins = [np.arange(0, gridmax[i] - gridmin[i] + 1) for i in range(3)]
global occ, vac
occH, _ = np.histogramdd(inds[:, 0, :], bins)
vacH, _ = np.histogramdd(inds[:, 1, :], bins)
vac = vacH > 30
occ = occH > 30
return occ, vac
else:
shape = [gridmax[i] - gridmin[i] for i in range(3)]
occ = np.zeros(shape, "u1")
vac = np.zeros(shape, "u1")
speedup_cy.occvac(gridinds, occ, vac, gridmin.astype("i"), gridmax.astype("i"))
return occ.astype("bool"), vac.astype("bool")
else:
global X, Y, Z, XYZ
X, Y, Z, face = np.rollaxis(opencl.get_modelxyz(), 1)
XYZ = np.array((X, Y, Z)).transpose()
fix = np.array((xfix, 0, zfix))
cxyz = np.frombuffer(np.array(face).data, dtype="i1").reshape(-1, 4)[:, :3]
global cx, cy, cz
cx, cy, cz = np.rollaxis(cxyz, 1)
f1 = cxyz * 0.5
mod = np.array([config.LW, config.LH, config.LW])
gi = np.floor(-gridmin[:3] + (XYZ - fix) / mod + f1)
gi = np.array((gi, gi - cxyz))
gi = np.rollaxis(gi, 1)
def get_gridinds():
(L, T), (R, B) = opencl.rect
length = opencl.length
return (
gridinds[:length, :, :].reshape(T - B, R - L, 2, 3),
gridinds[length:, :, :].reshape(T - B, R - L, 2, 3),
)
gridinds = gi
grid = get_gridinds()
inds = gridinds[np.any(cxyz != 0, 1), :, :]
if len(inds) == 0:
return None
bins = [np.arange(0, gridmax[i] - gridmin[i] + 1) for i in range(3)]
occH, _ = np.histogramdd(inds[:, 0, :], bins)
vacH, _ = np.histogramdd(inds[:, 1, :], bins)
vac = vacH > 30
occ = occH > 30
return occ, vac
def carve(xfix=None, zfix=None, use_opencl=True):
global gridinds, inds, grid
gridmin = np.zeros((4, ), 'f')
gridmax = np.zeros((4, ), 'f')
gridmin[:3] = config.bounds[0]
gridmax[:3] = config.bounds[1]
global occ, vac
if xfix is None or zfix is None:
import lattice
xfix, zfix = lattice.meanx, lattice.meanz
if use_opencl:
opencl.compute_gridinds(xfix, zfix, config.LW, config.LH, gridmin,
gridmax)
gridinds = opencl.get_gridinds()
if 0:
inds = gridinds[gridinds[:, 0, 3] != 0, :, :3]
if len(inds) == 0:
return None
bins = [
np.arange(0, gridmax[i] - gridmin[i] + 1) for i in range(3)
]
global occ, vac
occH, _ = np.histogramdd(inds[:, 0, :], bins)
vacH, _ = np.histogramdd(inds[:, 1, :], bins)
vac = vacH > 30
occ = occH > 30
return occ, vac
else:
shape = [gridmax[i] - gridmin[i] for i in range(3)]
occ = np.zeros(shape, 'u1')
vac = np.zeros(shape, 'u1')
speedup_cy.occvac(gridinds, occ, vac, gridmin.astype('i'),
gridmax.astype('i'))
return occ.astype('bool'), vac.astype('bool')
else:
global X, Y, Z, XYZ
X, Y, Z, face = np.rollaxis(opencl.get_modelxyz(), 1)
XYZ = np.array((X, Y, Z)).transpose()
fix = np.array((xfix, 0, zfix))
cxyz = np.frombuffer(np.array(face).data, dtype='i1').reshape(-1,
4)[:, :3]
global cx, cy, cz
cx, cy, cz = np.rollaxis(cxyz, 1)
f1 = cxyz * 0.5
mod = np.array([config.LW, config.LH, config.LW])
gi = np.floor(-gridmin[:3] + (XYZ - fix) / mod + f1)
gi = np.array((gi, gi - cxyz))
gi = np.rollaxis(gi, 1)
def get_gridinds():
(L, T), (R, B) = opencl.rect
length = opencl.length
return (gridinds[:length, :, :].reshape(T - B, R - L, 2, 3),
gridinds[length:, :, :].reshape(T - B, R - L, 2, 3))
gridinds = gi
grid = get_gridinds()
inds = gridinds[np.any(cxyz != 0, 1), :, :]
if len(inds) == 0:
return None
bins = [np.arange(0, gridmax[i] - gridmin[i] + 1) for i in range(3)]
occH, _ = np.histogramdd(inds[:, 0, :], bins)
vacH, _ = np.histogramdd(inds[:, 1, :], bins)
vac = vacH > 30
occ = occH > 30
return occ, vac
