def get_ref_coors(field,
coors,
strategy='kdtree',
close_limit=0.1,
cache=None):
"""
Get reference element coordinates and elements corresponding to given
physical coordinates.
Parameters
----------
field : Field instance
The field defining the approximation.
coors : array
The physical coordinates.
strategy : str, optional
The strategy for finding the elements that contain the
coordinates. Only 'kdtree' is supported for the moment.
close_limit : float, optional
The maximum limit distance of a point from the closest
element allowed for extrapolation.
cache : Struct, optional
To speed up a sequence of evaluations, the field mesh, the inverse
connectivity of the field mesh and the KDTree instance can be cached as
`cache.mesh`, `cache.offsets`, `cache.iconn` and
`cache.kdtree`. Optionally, the cache can also contain the reference
element coordinates as `cache.ref_coors`, `cache.cells` and
`cache.status`, if the evaluation occurs in the same coordinates
repeatedly. In that case the KDTree related data are ignored.
Returns
-------
ref_coors : array
The reference coordinates.
cells : array
The cell indices corresponding to the reference coordinates.
status : array
The status: 0 is success, 1 is extrapolation within `close_limit`, 2 is
extrapolation outside `close_limit`, 3 is failure.
"""
ref_coors = get_default_attr(cache, 'ref_coors', None)
if ref_coors is None:
mesh = get_default_attr(cache, 'mesh', None)
if mesh is None:
mesh = field.create_mesh(extra_nodes=False)
scoors = mesh.coors
output('reference field: %d vertices' % scoors.shape[0])
iconn = get_default_attr(cache, 'iconn', None)
if iconn is None:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
ii = nm.where(offsets[1:] == offsets[:-1])[0]
if len(ii):
raise ValueError('some vertices not in any element! (%s)' % ii)
else:
offsets = cache.offsets
if strategy == 'kdtree':
kdtree = get_default_attr(cache, 'kdtree', None)
if kdtree is None:
from scipy.spatial import cKDTree as KDTree
tt = time.clock()
kdtree = KDTree(scoors)
output('kdtree: %f s' % (time.clock() - tt))
tt = time.clock()
ics = kdtree.query(coors)[1]
output('kdtree query: %f s' % (time.clock() - tt))
tt = time.clock()
ics = nm.asarray(ics, dtype=nm.int32)
vertex_coorss, nodess, mtx_is = [], [], []
conns = []
for ig, ap in field.aps.iteritems():
ps = ap.interp.gel.interp.poly_spaces['v']
vertex_coorss.append(ps.geometry.coors)
nodess.append(ps.nodes)
mtx_is.append(ps.get_mtx_i())
conns.append(mesh.conns[ig].copy())
# Get reference element coordinates corresponding to
# destination coordinates.
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0], 2), dtype=nm.int32)
status = nm.empty((coors.shape[0], ), dtype=nm.int32)
find_ref_coors(ref_coors, cells, status, coors, ics, offsets,
iconn, scoors, conns, vertex_coorss, nodess, mtx_is,
1, close_limit, 1e-15, 100, 1e-8)
output('ref. coordinates: %f s' % (time.clock() - tt))
elif strategy == 'crawl':
raise NotImplementedError
else:
raise ValueError('unknown search strategy! (%s)' % strategy)
else:
ref_coors = cache.ref_coors
cells = cache.cells
status = cache.status
return ref_coors, cells, status
def __init__(self, name, mesh, share_mesh=True, n_point=None, **kwargs):
"""
Parameters
----------
name : str
The probe name, set automatically by the subclasses.
mesh : Mesh instance
The FE mesh where the variables to be probed are defined.
share_mesh : bool
Set to True to indicate that all the probes will work on the same
mesh. Certain data are then computed only for the first probe and
cached.
n_point : int
The (fixed) number of probe points, when positive. When non-positive,
the number of points is adaptively increased starting from -n_point,
until the neighboring point distance is less than the diameter of the
elements enclosing the points. When None, it is set to -10.
For additional parameters see the __init__() docstrings of the
subclasses.
Notes
-----
If the mesh contains vertices that are not contained in any element, we
shift coordinates of such vertices so that they never match in the
nearest node search.
"""
Struct.__init__(self, name=name, mesh=mesh, **kwargs)
self.set_n_point(n_point)
self.options = Struct()
self.set_options(close_limit=0.1, size_hint=None)
self.is_refined = False
tt = time.clock()
if share_mesh:
if Probe.cache.iconn is None:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
Probe.cache.iconn = iconn
Probe.cache.offsets = offsets
self.cache = Probe.cache
else:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
self.cache = Struct(name='probe_cache',
offsets=offsets, iconn=iconn, kdtree=None)
output('iconn: %f s' % (time.clock()-tt))
i_bad = nm.where(nm.diff(self.cache.offsets) == 0)[0]
if len(i_bad):
bbox = mesh.get_bounding_box()
mesh.coors[i_bad] = bbox[1] + bbox[1] - bbox[0]
output('warning: some vertices are not in any element!')
output('warning: vertex-based results will be wrong!')
tt = time.clock()
if share_mesh:
if Probe.cache.kdtree is None:
self.cache.kdtree = KDTree(mesh.coors)
else:
self.cache.kdtree = KDTree(mesh.coors)
output('kdtree: %f s' % (time.clock()-tt))
def __init__(self, name, mesh, share_mesh=True, n_point=None, **kwargs):
"""
Parameters
----------
name : str
The probe name, set automatically by the subclasses.
mesh : Mesh instance
The FE mesh where the variables to be probed are defined.
share_mesh : bool
Set to True to indicate that all the probes will work on the same
mesh. Certain data are then computed only for the first probe and
cached.
n_point : int
The (fixed) number of probe points, when positive. When non-positive,
the number of points is adaptively increased starting from -n_point,
until the neighboring point distance is less than the diameter of the
elements enclosing the points. When None, it is set to -10.
For additional parameters see the __init__() docstrings of the
subclasses.
Notes
-----
If the mesh contains vertices that are not contained in any element, we
shift coordinates of such vertices so that they never match in the
nearest node search.
"""
Struct.__init__(self, name=name, mesh=mesh, **kwargs)
self.set_n_point(n_point)
self.options = Struct(close_limit=0.1, size_hint=None)
self.is_refined = False
tt = time.clock()
if share_mesh:
if Probe.cache.iconn is None:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
Probe.cache.iconn = iconn
Probe.cache.offsets = offsets
self.cache = Probe.cache
else:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
self.cache = Struct(name='probe_cache',
offsets=offsets, iconn=iconn, kdtree=None)
output('iconn: %f s' % (time.clock()-tt))
i_bad = nm.where(nm.diff(self.cache.offsets) == 0)[0]
if len(i_bad):
bbox = mesh.get_bounding_box()
mesh.coors[i_bad] = bbox[1] + bbox[1] - bbox[0]
output('warning: some vertices are not in any element!')
output('warning: vertex-based results will be wrong!')
tt = time.clock()
if share_mesh:
if Probe.cache.kdtree is None:
self.cache.kdtree = KDTree(mesh.coors)
else:
self.cache.kdtree = KDTree(mesh.coors)
output('kdtree: %f s' % (time.clock()-tt))
def get_ref_coors(field, coors, strategy='kdtree', close_limit=0.1, cache=None,
verbose=True):
"""
Get reference element coordinates and elements corresponding to given
physical coordinates.
Parameters
----------
field : Field instance
The field defining the approximation.
coors : array
The physical coordinates.
strategy : str, optional
The strategy for finding the elements that contain the
coordinates. Only 'kdtree' is supported for the moment.
close_limit : float, optional
The maximum limit distance of a point from the closest
element allowed for extrapolation.
cache : Struct, optional
To speed up a sequence of evaluations, the field mesh, the inverse
connectivity of the field mesh and the KDTree instance can be cached as
`cache.mesh`, `cache.offsets`, `cache.iconn` and
`cache.kdtree`. Optionally, the cache can also contain the reference
element coordinates as `cache.ref_coors`, `cache.cells` and
`cache.status`, if the evaluation occurs in the same coordinates
repeatedly. In that case the KDTree related data are ignored.
verbose : bool
If False, reduce verbosity.
Returns
-------
ref_coors : array
The reference coordinates.
cells : array
The cell indices corresponding to the reference coordinates.
status : array
The status: 0 is success, 1 is extrapolation within `close_limit`, 2 is
extrapolation outside `close_limit`, 3 is failure.
"""
ref_coors = get_default_attr(cache, 'ref_coors', None)
if ref_coors is None:
mesh = get_default_attr(cache, 'mesh', None)
if mesh is None:
mesh = field.create_mesh(extra_nodes=False)
scoors = mesh.coors
output('reference field: %d vertices' % scoors.shape[0],
verbose=verbose)
iconn = get_default_attr(cache, 'iconn', None)
if iconn is None:
offsets, iconn = make_inverse_connectivity(mesh.conns,
mesh.n_nod,
ret_offsets=True)
ii = nm.where(offsets[1:] == offsets[:-1])[0]
if len(ii):
raise ValueError('some vertices not in any element! (%s)'
% ii)
else:
offsets = cache.offsets
if strategy == 'kdtree':
kdtree = get_default_attr(cache, 'kdtree', None)
if kdtree is None:
from scipy.spatial import cKDTree as KDTree
tt = time.clock()
kdtree = KDTree(scoors)
output('kdtree: %f s' % (time.clock()-tt), verbose=verbose)
tt = time.clock()
ics = kdtree.query(coors)[1]
output('kdtree query: %f s' % (time.clock()-tt), verbose=verbose)
tt = time.clock()
ics = nm.asarray(ics, dtype=nm.int32)
vertex_coorss, nodess, mtx_is = [], [], []
conns = []
for ig, ap in field.aps.iteritems():
ps = ap.interp.gel.interp.poly_spaces['v']
vertex_coorss.append(ps.geometry.coors)
nodess.append(ps.nodes)
mtx_is.append(ps.get_mtx_i())
conns.append(mesh.conns[ig].copy())
# Get reference element coordinates corresponding to
# destination coordinates.
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0], 2), dtype=nm.int32)
status = nm.empty((coors.shape[0],), dtype=nm.int32)
find_ref_coors(ref_coors, cells, status, coors,
ics, offsets, iconn,
scoors, conns,
vertex_coorss, nodess, mtx_is,
1, close_limit, 1e-15, 100, 1e-8)
output('ref. coordinates: %f s' % (time.clock()-tt),
verbose=verbose)
elif strategy == 'crawl':
raise NotImplementedError
else:
raise ValueError('unknown search strategy! (%s)' % strategy)
else:
ref_coors = cache.ref_coors
cells = cache.cells
status = cache.status
return ref_coors, cells, status
