def setup_default_output(self, conf=None, options=None):
"""
Provide default values to `ProblemDefinition.setup_output()`
from `conf.options` and `options`.
"""
conf = get_default(conf, self.conf)
if options and options.output_filename_trunk:
default_output_dir, of = op.split(options.output_filename_trunk)
default_trunk = io.get_trunk(of)
else:
default_trunk = None
default_output_dir = get_default_attr(conf.options,
'output_dir', None)
if options and options.output_format:
default_output_format = options.output_format
else:
default_output_format = get_default_attr(conf.options,
'output_format', None)
default_file_per_var = get_default_attr(conf.options,
'file_per_var', None)
default_float_format = get_default_attr(conf.options,
'float_format', None)
default_linearization = Struct(kind='strip')
self.setup_output(output_filename_trunk=default_trunk,
output_dir=default_output_dir,
file_per_var=default_file_per_var,
output_format=default_output_format,
float_format=default_float_format,
linearization=default_linearization)
def add_from_bc(self, bc, field):
"""
Create a new LCBC operator described by `bc`, and add it to the
container.
Parameters
----------
bc : LinearCombinationBC instance
The LCBC condition description.
field : Field instance
The field of the variable.
"""
region = bc.region
dofs, kind = bc.dofs
nmaster = field.get_dofs_in_region(region, merge=True)
if kind == 'rigid':
op = RigidOperator('%d_rigid' % len(self), nmaster, field, dofs,
self.eq_map.dof_names)
elif kind == 'no_penetration':
filename = get_default_attr(bc, 'filename', None)
op = NoPenetrationOperator('%d_no_penetration' % len(self),
nmaster,
region,
field,
dofs,
filename=filename)
elif kind == 'normal_direction':
filename = get_default_attr(bc, 'filename', None)
op = NormalDirectionOperator('%d_normal_direction' % len(self),
nmaster,
region,
field,
dofs,
filename=filename)
elif kind == 'integral_mean_value':
filename = get_default_attr(bc, 'filename', None)
op = IntegralMeanValueOperator('%d_integral_mean_value' %
len(self),
nmaster,
region,
field,
dofs,
filename=filename)
self.append(op)
def from_conf(conf, functions):
"""
Construct Material instance from configuration.
"""
kind = get_default_attr(conf, 'kind', 'time-dependent')
flags = get_default_attr(conf, 'flags', {})
function = get_default_attr(conf, 'function', None)
values = get_default_attr(conf, 'values', None)
if isinstance(function, basestr):
function = functions[function]
obj = Material(conf.name, kind, function, values, flags)
return obj
def from_conf(conf, functions):
"""
Construct Material instance from configuration.
"""
kind = get_default_attr(conf, 'kind', 'time-dependent')
flags = get_default_attr(conf, 'flags', {})
function = get_default_attr(conf, 'function', None)
values = get_default_attr(conf, 'values', None)
if isinstance(function, basestr):
function = functions[function]
obj = Material(conf.name, kind, function, values, flags)
return obj
def new_ulf_iteration(self, nls, vec, it, err, err0):
pb = self.problem
vec = self.make_full_vec(vec)
pb.equations.set_variables_from_state(vec)
upd_vars = get_default_attr(pb.conf.options, 'mesh_update_variables',
None)
for varname in upd_vars:
try:
state = pb.equations.variables[varname]
except IndexError:
msg = 'variable "%s" does not exist!' % varname
raise KeyError(msg)
nods = state.field.get_dofs_in_region(state.field.region, merge=True)
coors = pb.domain.get_mesh_coors().copy()
vs = state()
coors[nods, :] = coors[nods, :] + vs.reshape(len(nods),
state.n_components)
if pb.ts.step == 1 and it == 0:
state.field.save_mappings()
state.field.clear_mappings()
pb.set_mesh_coors(coors,
update_fields=False,
actual=True,
clear_all=False)
def new_ulf_iteration(self, nls, vec, it, err, err0):
pb = self.problem
vec = self.make_full_vec(vec)
pb.equations.set_variables_from_state(vec)
upd_vars = get_default_attr(pb.conf.options, 'mesh_update_variables', None)
for varname in upd_vars:
try:
state = pb.equations.variables[varname]
except IndexError:
msg = 'variable "%s" does not exist!' % varname
raise KeyError( msg )
nods = state.field.get_dofs_in_region(state.field.region, merge=True)
coors = pb.domain.get_mesh_coors().copy()
vs = state()
coors[nods,:] = coors[nods,:] + vs.reshape(len(nods), state.n_components)
if pb.ts.step == 1 and it == 0:
state.field.save_mappings()
state.field.clear_mappings()
pb.set_mesh_coors(coors, update_fields=False, actual=True,
clear_all=False)
def load_coefs(filename):
coefs = Coefficients.from_file_hdf5(filename)
try:
options = import_file(coefs.filename).options
except:
options = None
if options is not None:
plot_info = options['plot_info']
tex_names = options['tex_names']
else:
plot_info = get_default_attr(coefs, 'plot_info', {})
tex_names = get_default_attr(coefs, 'tex_names', {})
return coefs, plot_info, tex_names
def load_coefs( filename ):
coefs = Coefficients.from_file_hdf5( filename )
try:
options = import_file( coefs.filename ).options
except:
options = None
if options is not None:
plot_info = options['plot_info']
tex_names = options['tex_names']
else:
plot_info = get_default_attr(coefs, 'plot_info', {})
tex_names = get_default_attr(coefs, 'tex_names', {})
return coefs, plot_info, tex_names
def test_solvers( self ):
from sfepy.base.base import IndexedStruct, get_default_attr
import os.path as op
solver_confs = self._list_linear_solvers( self.problem.solver_confs )
ok = True
tt = []
for solver_conf in solver_confs:
method = get_default_attr( solver_conf, 'method', '' )
precond = get_default_attr( solver_conf, 'precond', '' )
name = ' '.join( (solver_conf.name, solver_conf.kind,
method, precond) ).rstrip()
self.report( name )
self.report( 'matrix size:', self.problem.mtx_a.shape )
self.report( ' nnz:', self.problem.mtx_a.nnz )
status = IndexedStruct()
try:
self.problem.init_solvers( nls_status = status,
ls_conf = solver_conf )
state = self.problem.solve()
failed = status.condition != 0
## self.problem.mtx_a.save( 'mtx_laplace_cube',
## format='%d %d %.12e\n' )
except Exception, exc:
failed = True
status = None
ok = ok and ((not failed) or (solver_conf.kind in self.can_fail))
if status is not None:
for kv in status.time_stats.iteritems():
self.report( '%10s: %7.2f [s]' % kv )
self.report( 'condition: %d, err0: %.3e, err: %.3e'\
% (status.condition, status.err0, status.err) )
tt.append( [name, status.time_stats['solve'], status.err] )
aux = name.replace(' ', '_')
fname = op.join( self.options.out_dir,
op.split( self.conf.output_name )[1] ) % aux
self.problem.save_state( fname, state )
else:
self.report( 'solver failed:' )
self.report( exc )
tt.append( [name, 1e10, 1e10] )
def test_solvers(self):
from sfepy.base.base import IndexedStruct, get_default_attr
import os.path as op
solver_confs = self._list_linear_solvers(self.problem.solver_confs)
ok = True
tt = []
for solver_conf in solver_confs:
method = get_default_attr(solver_conf, 'method', '')
precond = get_default_attr(solver_conf, 'precond', '')
name = ' '.join((solver_conf.name, solver_conf.kind, method,
precond)).rstrip()
self.report(name)
self.report('matrix size:', self.problem.mtx_a.shape)
self.report(' nnz:', self.problem.mtx_a.nnz)
status = IndexedStruct()
try:
self.problem.init_solvers(nls_status=status,
ls_conf=solver_conf)
state = self.problem.solve()
failed = status.condition != 0
## self.problem.mtx_a.save( 'mtx_laplace_cube',
## format='%d %d %.12e\n' )
except Exception, exc:
failed = True
status = None
ok = ok and ((not failed) or (solver_conf.kind in self.can_fail))
if status is not None:
for kv in status.time_stats.iteritems():
self.report('%10s: %7.2f [s]' % kv)
self.report( 'condition: %d, err0: %.3e, err: %.3e'\
% (status.condition, status.err0, status.err) )
tt.append([name, status.time_stats['solve'], status.err])
aux = name.replace(' ', '_')
fname = op.join(self.options.out_dir,
op.split(self.conf.output_name)[1]) % aux
self.problem.save_state(fname, state)
else:
self.report('solver failed:')
self.report(exc)
tt.append([name, 1e10, 1e10])
def add_from_bc(self, bc, field):
"""
Create a new LCBC operator described by `bc`, and add it to the
container.
Parameters
----------
bc : LinearCombinationBC instance
The LCBC condition description.
field : Field instance
The field of the variable.
"""
region = bc.region
dofs, kind = bc.dofs
nmaster = field.get_dofs_in_region(region, merge=True)
if kind == 'rigid':
op = RigidOperator('%d_rigid' % len(self),
nmaster, field, dofs, self.eq_map.dof_names)
elif kind == 'no_penetration':
filename = get_default_attr(bc, 'filename', None)
op = NoPenetrationOperator('%d_no_penetration' % len(self),
nmaster, region, field, dofs,
filename=filename)
elif kind == 'normal_direction':
filename = get_default_attr(bc, 'filename', None)
op = NormalDirectionOperator('%d_normal_direction' % len(self),
nmaster, region, field, dofs,
filename=filename)
elif kind == 'integral_mean_value':
filename = get_default_attr(bc, 'filename', None)
op = IntegralMeanValueOperator('%d_integral_mean_value' % len(self),
nmaster, region, field, dofs,
filename=filename)
self.append(op)
def init_solvers(self, nls_status=None, ls_conf=None, nls_conf=None,
mtx=None, presolve=False):
"""Create and initialize solvers."""
ls_conf = get_default( ls_conf, self.ls_conf,
'you must set linear solver!' )
nls_conf = get_default( nls_conf, self.nls_conf,
'you must set nonlinear solver!' )
if presolve:
tt = time.clock()
if get_default_attr(ls_conf, 'needs_problem_instance', False):
extra_args = {'problem' : self}
else:
extra_args = {}
ls = Solver.any_from_conf(ls_conf, mtx=mtx, presolve=presolve,
**extra_args)
if presolve:
tt = time.clock() - tt
output('presolve: %.2f [s]' % tt)
if get_default_attr(nls_conf, 'needs_problem_instance', False):
extra_args = {'problem' : self}
else:
extra_args = {}
ev = self.get_evaluator()
if get_default_attr(self.conf.options, 'ulf', False):
self.nls_iter_hook = ev.new_ulf_iteration
nls = Solver.any_from_conf(nls_conf, fun=ev.eval_residual,
fun_grad=ev.eval_tangent_matrix,
lin_solver=ls, iter_hook=self.nls_iter_hook,
status=nls_status, **extra_args)
self.solvers = Struct( name = 'solvers', ls = ls, nls = nls )
def recover_micro_hook(micro_filename,
region,
macro,
naming_scheme='step_iel',
recovery_file_tag=''):
# Create a micro-problem instance.
required, other = get_standard_keywords()
required.remove('equations')
pb = ProblemDefinition.from_conf_file(micro_filename,
required=required,
other=other,
init_equations=False,
init_solvers=False)
coefs_filename = pb.conf.options.get_default_attr('coefs_filename',
'coefs')
output_dir = pb.conf.options.get_default_attr('output_dir', '.')
coefs_filename = op.join(output_dir, coefs_filename) + '.h5'
# Coefficients and correctors
coefs = Coefficients.from_file_hdf5(coefs_filename)
corrs = get_correctors_from_file(dump_names=coefs.dump_names)
recovery_hook = get_default_attr(pb.conf.options, 'recovery_hook', None)
if recovery_hook is not None:
recovery_hook = pb.conf.get_function(recovery_hook)
aux = max(pb.domain.shape.n_gr, 2)
format = get_print_info( aux, fill = '0' )[1] \
+ '_' + get_print_info( pb.domain.mesh.n_el, fill = '0' )[1]
for ig, ii, iel in region.iter_cells():
print 'ig: %d, ii: %d, iel: %d' % (ig, ii, iel)
local_macro = {}
for k, v in macro.iteritems():
local_macro[k] = v[ii, 0]
out = recovery_hook(pb, corrs, local_macro)
# save data
suffix = format % (ig, iel)
micro_name = pb.get_output_name(extra='recovered_'\
+ recovery_file_tag + suffix)
filename = op.join(output_dir, op.basename(micro_name))
fpv = pb.conf.options.get_default_attr('file_per_var', False)
pb.save_state(filename, out=out, file_per_var=fpv)
def recover_micro_hook( micro_filename, region, macro,
naming_scheme = 'step_iel',
recovery_file_tag='' ):
# Create a micro-problem instance.
required, other = get_standard_keywords()
required.remove( 'equations' )
pb = ProblemDefinition.from_conf_file(micro_filename,
required=required,
other=other,
init_equations=False,
init_solvers=False)
coefs_filename = pb.conf.options.get_default_attr('coefs_filename', 'coefs')
output_dir = pb.conf.options.get_default_attr('output_dir', '.')
coefs_filename = op.join(output_dir, coefs_filename) + '.h5'
# Coefficients and correctors
coefs = Coefficients.from_file_hdf5( coefs_filename )
corrs = get_correctors_from_file( dump_names = coefs.dump_names )
recovery_hook = get_default_attr( pb.conf.options,
'recovery_hook', None )
if recovery_hook is not None:
recovery_hook = getattr( pb.conf.funmod, recovery_hook )
aux = max(pb.domain.shape.n_gr, 2)
format = get_print_info( aux, fill = '0' )[1] \
+ '_' + get_print_info( pb.domain.mesh.n_el, fill = '0' )[1]
for ig, ii, iel in region.iter_cells():
print 'ig: %d, ii: %d, iel: %d' % (ig, ii, iel)
local_macro = {}
for k, v in macro.iteritems():
local_macro[k] = v[ii,0]
out = recovery_hook( pb, corrs, local_macro )
# save data
suffix = format % (ig, iel)
micro_name = pb.get_output_name(extra='recovered_'\
+ recovery_file_tag + suffix)
filename = op.join(output_dir, op.basename(micro_name))
fpv = pb.conf.options.get_default_attr('file_per_var', False)
pb.save_state(filename, out=out,
file_per_var=fpv)
def from_conf(conf, init_fields=True, init_equations=True,
init_solvers=True):
if conf.options.get_default_attr('absolute_mesh_path', False):
conf_dir = None
else:
conf_dir = op.dirname(conf.funmod.__file__)
functions = Functions.from_conf(conf.functions)
mesh = Mesh.from_file(conf.filename_mesh, prefix_dir=conf_dir)
trans_mtx = conf.options.get_default_attr('mesh_coors_transform', None)
if trans_mtx is not None:
mesh.transform_coors(trans_mtx)
domain = Domain(mesh.name, mesh)
if get_default_attr(conf.options, 'ulf', False):
domain.mesh.coors_act = domain.mesh.coors.copy()
obj = ProblemDefinition('problem_from_conf', conf=conf,
functions=functions, domain=domain,
auto_conf=False, auto_solvers=False)
obj.set_regions(conf.regions, obj.functions)
obj.clear_equations()
if init_fields:
obj.set_fields( conf.fields )
if init_equations:
obj.set_equations(conf.equations, user={'ts' : obj.ts})
if init_solvers:
obj.set_solvers( conf.solvers, conf.options )
return obj
def generate_probes(filename_input,
filename_results,
options,
conf=None,
problem=None,
probes=None,
labels=None,
probe_hooks=None):
"""
Generate probe figures and data files.
"""
if conf is None:
required, other = get_standard_keywords()
conf = ProblemConf.from_file(filename_input, required, other)
opts = conf.options
if options.auto_dir:
output_dir = get_default_attr(opts, 'output_dir', '.')
filename_results = os.path.join(output_dir, filename_results)
output('results in: %s' % filename_results)
io = MeshIO.any_from_filename(filename_results)
all_data = io.read_data(options.step)
output('loaded:', all_data.keys())
if options.only_names is None:
data = all_data
else:
data = {}
for key, val in all_data.iteritems():
if key in options.only_names:
data[key] = val
if problem is None:
problem = ProblemDefinition.from_conf(conf,
init_equations=False,
init_solvers=False)
if probes is None:
gen_probes = conf.get_function(conf.options.gen_probes)
probes, labels = gen_probes(problem)
if probe_hooks is None:
probe_hooks = {None: conf.get_function(conf.options.probe_hook)}
if options.output_filename_trunk is None:
options.output_filename_trunk = problem.ofn_trunk
filename_template = options.output_filename_trunk \
+ ('_%%d.%s' % options.output_format)
if options.same_dir:
filename_template = os.path.join(os.path.dirname(filename_results),
filename_template)
output_dir = os.path.dirname(filename_results)
for ip, probe in enumerate(probes):
output(ip, probe.name)
probe.set_options(close_limit=options.close_limit)
for key, probe_hook in probe_hooks.iteritems():
out = probe_hook(data, probe, labels[ip], problem)
if out is None: continue
if isinstance(out, tuple):
fig, results = out
else:
fig = out
if key is not None:
filename = filename_template % (key, ip)
else:
filename = filename_template % ip
if fig is not None:
if isinstance(fig, dict):
for fig_name, fig_fig in fig.iteritems():
fig_filename = edit_filename(filename,
suffix='_' + fig_name)
fig_fig.savefig(fig_filename)
output('figure ->', os.path.normpath(fig_filename))
else:
fig.savefig(filename)
output('figure ->', os.path.normpath(filename))
if results is not None:
txt_filename = edit_filename(filename, new_ext='.txt')
fd = open(txt_filename, 'w')
fd.write('\n'.join(probe.report()) + '\n')
for key, res in results.iteritems():
pars, vals = res
fd.write('\n# %s\n' % key)
if vals.ndim == 1:
aux = nm.hstack((pars[:, None], vals[:, None]))
else:
aux = nm.hstack((pars[:, None], vals))
nm.savetxt(fd, aux)
fd.close()
output('data ->', os.path.normpath(txt_filename))
def generate_probes(filename_input, filename_results, options,
conf=None, problem=None, probes=None, labels=None,
probe_hooks=None):
"""Generate probe figures and data files."""
if conf is None:
required, other = get_standard_keywords()
conf = ProblemConf.from_file( filename_input, required, other )
opts = conf.options
if options.auto_dir:
output_dir = get_default_attr( opts, 'output_dir', '.' )
filename_results = os.path.join(output_dir, filename_results)
output('results in: %s' % filename_results)
io = MeshIO.any_from_filename(filename_results)
all_data = io.read_data(options.step)
output('loaded:', all_data.keys())
if options.only_names is None:
data = all_data
else:
data = {}
for key, val in all_data.iteritems():
if key in options.only_names:
data[key] = val
if problem is None:
problem = ProblemDefinition.from_conf(conf,
init_equations=False,
init_solvers=False)
if probes is None:
gen_probes = getattr(conf.funmod, conf.options.gen_probes)
probes, labels = gen_probes(problem)
if probe_hooks is None:
probe_hooks = {None : getattr(conf.funmod, conf.options.probe_hook)}
if options.output_filename_trunk is None:
options.output_filename_trunk = problem.ofn_trunk
filename_template = options.output_filename_trunk \
+ ('_%%d.%s' % options.output_format)
if options.same_dir:
filename_template = os.path.join(os.path.dirname(filename_results),
filename_template)
output_dir = os.path.dirname(filename_results)
edit_pname = re.compile('[^a-zA-Z0-9-_.\[\]]').sub
for ip, probe in enumerate(probes):
output(ip, probe.name)
for key, probe_hook in probe_hooks.iteritems():
out = probe_hook(data, probe, labels[ip], problem)
if out is None: continue
if isinstance(out, tuple):
fig, results = out
else:
fig = out
if key is not None:
filename = filename_template % (key, ip)
else:
filename = filename_template % ip
if fig is not None:
fig.savefig(filename)
output('figure ->', os.path.normpath(filename))
if results is not None:
aux = os.path.splitext(filename)[0]
txt_filename = aux + '.txt'
fd = open(txt_filename, 'w')
fd.write('\n'.join(probe.report()) + '\n')
for key, res in results.iteritems():
pars, vals = res
fd.write('\n# %s\n' % key)
if vals.ndim == 1:
aux = nm.hstack((pars[:,None], vals[:,None]))
else:
aux = nm.hstack((pars[:,None], vals))
nm.savetxt(fd, aux)
fd.close()
output('data ->', os.path.normpath(txt_filename))
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 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
def get_ref_coors_general(field, coors, close_limit=0.1, get_cells_fun=None, cache=None, verbose=False):
"""
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.
close_limit : float, optional
The maximum limit distance of a point from the closest
element allowed for extrapolation.
get_cells_fun : callable, optional
If given, a function with signature ``get_cells_fun(coors, cmesh,
**kwargs)`` returning cells and offsets that potentially contain points
with the coordinates `coors`. When not given,
:func:`get_potential_cells()` is used.
cache : Struct, optional
To speed up a sequence of evaluations, the field mesh and other data
can be cached. 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 mesh 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, 4 is failure due to
non-convergence of the Newton iteration in tensor product cells. If
close_limit is 0, then status 5 indicates points outside of the field
domain that had no potential cells.
"""
ref_coors = get_default_attr(cache, "ref_coors", None)
if ref_coors is None:
extrapolate = close_limit > 0.0
get = get_potential_cells if get_cells_fun is None else get_cells_fun
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0],), dtype=nm.int32)
status = nm.empty((coors.shape[0],), dtype=nm.int32)
cmesh = get_default_attr(cache, "cmesh", None)
if cmesh is None:
tt = time.clock()
mesh = field.create_mesh(extra_nodes=False)
cmesh = mesh.cmesh
gels = create_geometry_elements()
cmesh.set_local_entities(gels)
cmesh.setup_entities()
if get_cells_fun is None:
centroids = cmesh.get_centroids(cmesh.tdim)
else:
centroids = None
output("cmesh setup: %f s" % (time.clock() - tt), verbose=verbose)
else:
centroids = cache.centroids
tt = time.clock()
potential_cells, offsets = get(coors, cmesh, centroids=centroids, extrapolate=extrapolate)
output("potential cells: %f s" % (time.clock() - tt), verbose=verbose)
ap = field.ap
ps = ap.interp.gel.interp.poly_spaces["v"]
mtx_i = ps.get_mtx_i()
ac = nm.ascontiguousarray
tt = time.clock()
crc.find_ref_coors(
ref_coors,
cells,
status,
ac(coors),
cmesh,
potential_cells,
offsets,
ps.geometry.coors,
ps.nodes,
mtx_i,
extrapolate,
close_limit,
1e-15,
100,
1e-8,
)
if extrapolate:
assert_(nm.all(status < 5))
output("ref. coordinates: %f s" % (time.clock() - tt), verbose=verbose)
else:
cells = cache.cells
status = cache.status
return ref_coors, cells, status
def get_ref_coors_convex(field, coors, close_limit=0.1, cache=None, verbose=False):
"""
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.
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 and other data
can be cached. 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 mesh 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, 4 is failure due to
non-convergence of the Newton iteration in tensor product cells.
Notes
-----
Outline of the algorithm for finding xi such that X(xi) = P:
1. make inverse connectivity - for each vertex have cells it is in.
2. find the closest vertex V.
3. choose initial cell: i0 = first from cells incident to V.
4. while not P in C_i, change C_i towards P, check if P in new C_i.
"""
ref_coors = get_default_attr(cache, "ref_coors", None)
if ref_coors is None:
extrapolate = close_limit > 0.0
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0],), dtype=nm.int32)
status = nm.empty((coors.shape[0],), dtype=nm.int32)
cmesh = get_default_attr(cache, "cmesh", None)
if cmesh is None:
tt = time.clock()
mesh = field.create_mesh(extra_nodes=False)
cmesh = mesh.cmesh
gels = create_geometry_elements()
cmesh.set_local_entities(gels)
cmesh.setup_entities()
centroids = cmesh.get_centroids(cmesh.tdim)
if field.gel.name != "3_8":
normals0 = cmesh.get_facet_normals()
normals1 = None
else:
normals0 = cmesh.get_facet_normals(0)
normals1 = cmesh.get_facet_normals(1)
output("cmesh setup: %f s" % (time.clock() - tt), verbose=verbose)
else:
centroids = cache.centroids
normals0 = cache.normals0
normals1 = cache.normals1
kdtree = get_default_attr(cache, "kdtree", None)
if kdtree is None:
from scipy.spatial import cKDTree as KDTree
tt = time.clock()
kdtree = KDTree(cmesh.coors)
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)
ics = nm.asarray(ics, dtype=nm.int32)
ap = field.ap
ps = ap.interp.gel.interp.poly_spaces["v"]
mtx_i = ps.get_mtx_i()
ac = nm.ascontiguousarray
tt = time.clock()
crc.find_ref_coors_convex(
ref_coors,
cells,
status,
ac(coors),
cmesh,
centroids,
normals0,
normals1,
ics,
ps.geometry.coors,
ps.nodes,
mtx_i,
extrapolate,
close_limit,
1e-15,
100,
1e-8,
)
output("ref. coordinates: %f s" % (time.clock() - tt), verbose=verbose)
else:
cells = cache.cells
status = cache.status
return ref_coors, cells, status
def main():
parser = OptionParser(usage = usage, version = "%prog " + sfepy.__version__)
parser.add_option('-c', '--conf', metavar='"key : value, ..."',
action='store', dest='conf', type='string',
default=None, help= help['conf'])
parser.add_option('-O', '--options', metavar='"key : value, ..."',
action='store', dest='app_options', type='string',
default=None, help=help['options'])
parser.add_option('-d', '--define', metavar='"key : value, ..."',
action='store', dest='define_args', type='string',
default=None, help=help['define'])
parser.add_option( "-o", "", metavar = 'filename',
action = "store", dest = "output_filename_trunk",
default = None, help = help['filename'] )
parser.add_option( "", "--format", metavar = 'format',
action = "store", dest = "output_format",
default = None, help = help['output_format'] )
parser.add_option( "", "--log", metavar = 'file',
action = "store", dest = "log",
default = None, help = help['log'] )
parser.add_option( "-q", "--quiet",
action = "store_true", dest = "quiet",
default = False, help = help['quiet'] )
parser.add_option( "", "--save-ebc",
action = "store_true", dest = "save_ebc",
default = False, help = help['save_ebc'] )
parser.add_option( "", "--save-regions",
action = "store_true", dest = "save_regions",
default = False, help = help['save_regions'] )
parser.add_option( "", "--save-regions-as-groups",
action = "store_true", dest = "save_regions_as_groups",
default = False, help = help['save_regions_as_groups'] )
parser.add_option( "", "--save-field-meshes",
action = "store_true", dest = "save_field_meshes",
default = False, help = help['save_field_meshes'] )
parser.add_option( "", "--solve-not",
action = "store_true", dest = "solve_not",
default = False, help = help['solve_not'] )
parser.add_option( "", "--list", metavar = 'what',
action = "store", dest = "_list",
default = None, help = help['list'] )
options, args = parser.parse_args()
if (len( args ) == 1):
filename_in = args[0];
else:
if options._list == 'terms':
print_terms()
else:
parser.print_help(),
return
output.set_output(filename=options.log,
quiet=options.quiet,
combined=options.log is not None)
required, other = get_standard_keywords()
if options.solve_not:
required.remove( 'equations' )
required.remove( 'solver_[0-9]+|solvers' )
other.extend( ['equations'] )
conf = ProblemConf.from_file_and_options(filename_in, options,
required, other,
define_args=options.define_args)
opts = conf.options
output_prefix = get_default_attr( opts, 'output_prefix', 'sfepy:' )
app = SimpleApp( conf, options, output_prefix )
if hasattr( opts, 'parametric_hook' ): # Parametric study.
parametric_hook = conf.get_function(opts.parametric_hook)
app.parametrize( parametric_hook )
app()
def get_ref_coors_general(field,
coors,
close_limit=0.1,
get_cells_fun=None,
cache=None,
verbose=False):
"""
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.
close_limit : float, optional
The maximum limit distance of a point from the closest
element allowed for extrapolation.
get_cells_fun : callable, optional
If given, a function with signature ``get_cells_fun(coors, cmesh,
**kwargs)`` returning cells and offsets that potentially contain points
with the coordinates `coors`. When not given,
:func:`get_potential_cells()` is used.
cache : Struct, optional
To speed up a sequence of evaluations, the field mesh and other data
can be cached. 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 mesh 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, 4 is failure due to
non-convergence of the Newton iteration in tensor product cells. If
close_limit is 0, then status 5 indicates points outside of the field
domain that had no potential cells.
"""
timer = Timer()
ref_coors = get_default_attr(cache, 'ref_coors', None)
if ref_coors is None:
extrapolate = close_limit > 0.0
get = get_potential_cells if get_cells_fun is None else get_cells_fun
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0], ), dtype=nm.int32)
status = nm.empty((coors.shape[0], ), dtype=nm.int32)
cmesh = get_default_attr(cache, 'cmesh', None)
if cmesh is None:
timer.start()
mesh = field.create_mesh(extra_nodes=False)
cmesh = mesh.cmesh
if get_cells_fun is None:
centroids = cmesh.get_centroids(cmesh.tdim)
else:
centroids = None
output('cmesh setup: %f s' % timer.stop(), verbose=verbose)
else:
centroids = cache.centroids
timer.start()
potential_cells, offsets = get(coors,
cmesh,
centroids=centroids,
extrapolate=extrapolate)
output('potential cells: %f s' % timer.stop(), verbose=verbose)
coors = nm.ascontiguousarray(coors)
ctx = field.create_basis_context()
eval_cmesh = get_default_attr(cache, 'eval_cmesh', None)
if eval_cmesh is None:
timer.start()
mesh = field.create_eval_mesh()
if mesh is None:
eval_cmesh = cmesh
else:
eval_cmesh = mesh.cmesh
output('eval_cmesh setup: %f s' % timer.stop(), verbose=verbose)
timer.start()
crc.find_ref_coors(ref_coors, cells, status, coors, eval_cmesh,
potential_cells, offsets, extrapolate, 1e-15,
close_limit, ctx)
if extrapolate:
assert_(nm.all(status < 5))
output('ref. coordinates: %f s' % timer.stop(), verbose=verbose)
else:
cells = cache.cells
status = cache.status
return ref_coors, cells, status
def get_ref_coors_convex(field,
coors,
close_limit=0.1,
cache=None,
verbose=False):
"""
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.
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 and other data
can be cached. 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 mesh 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, 4 is failure due to
non-convergence of the Newton iteration in tensor product cells.
Notes
-----
Outline of the algorithm for finding xi such that X(xi) = P:
1. make inverse connectivity - for each vertex have cells it is in.
2. find the closest vertex V.
3. choose initial cell: i0 = first from cells incident to V.
4. while not P in C_i, change C_i towards P, check if P in new C_i.
"""
timer = Timer()
ref_coors = get_default_attr(cache, 'ref_coors', None)
if ref_coors is None:
extrapolate = close_limit > 0.0
ref_coors = nm.empty_like(coors)
cells = nm.empty((coors.shape[0], ), dtype=nm.int32)
status = nm.empty((coors.shape[0], ), dtype=nm.int32)
cmesh = get_default_attr(cache, 'cmesh', None)
if cmesh is None:
timer.start()
mesh = field.create_mesh(extra_nodes=False)
cmesh = mesh.cmesh
gels = create_geometry_elements()
cmesh.set_local_entities(gels)
cmesh.setup_entities()
centroids = cmesh.get_centroids(cmesh.tdim)
if field.gel.name != '3_8':
normals0 = cmesh.get_facet_normals()
normals1 = None
else:
normals0 = cmesh.get_facet_normals(0)
normals1 = cmesh.get_facet_normals(1)
output('cmesh setup: %f s' % timer.stop(), verbose=verbose)
else:
centroids = cache.centroids
normals0 = cache.normals0
normals1 = cache.normals1
kdtree = get_default_attr(cache, 'kdtree', None)
if kdtree is None:
from scipy.spatial import cKDTree as KDTree
timer.start()
kdtree = KDTree(cmesh.coors)
output('kdtree: %f s' % timer.stop(), verbose=verbose)
timer.start()
ics = kdtree.query(coors)[1]
output('kdtree query: %f s' % timer.stop(), verbose=verbose)
ics = nm.asarray(ics, dtype=nm.int32)
coors = nm.ascontiguousarray(coors)
ctx = field.create_basis_context()
timer.start()
crc.find_ref_coors_convex(ref_coors, cells, status, coors, cmesh,
centroids, normals0, normals1, ics,
extrapolate, 1e-15, close_limit, ctx)
output('ref. coordinates: %f s' % timer.stop(), verbose=verbose)
else:
cells = cache.cells
status = cache.status
return ref_coors, cells, status
def main():
parser = OptionParser(usage=usage, version='%prog ' + sfepy.__version__)
parser.add_option('-c', '--conf', metavar='"key : value, ..."',
action='store', dest='conf', type='string',
default=None, help= help['conf'])
parser.add_option('-O', '--options', metavar='"key : value, ..."',
action='store', dest='app_options', type='string',
default=None, help=help['options'])
parser.add_option('-d', '--define', metavar='"key : value, ..."',
action='store', dest='define_args', type='string',
default=None, help=help['define'])
parser.add_option('-o', '', metavar='filename',
action='store', dest='output_filename_trunk',
default=None, help=help['filename'])
parser.add_option('', '--format', metavar='format',
action='store', dest='output_format',
default=None, help=help['output_format'])
parser.add_option('', '--log', metavar='file',
action='store', dest='log',
default=None, help=help['log'])
parser.add_option('-q', '--quiet',
action='store_true', dest='quiet',
default=False, help=help['quiet'])
parser.add_option('', '--save-ebc',
action='store_true', dest='save_ebc',
default=False, help=help['save_ebc'])
parser.add_option('', '--save-ebc-nodes',
action='store_true', dest='save_ebc_nodes',
default=False, help=help['save_ebc_nodes'])
parser.add_option('', '--save-regions',
action='store_true', dest='save_regions',
default=False, help=help['save_regions'])
parser.add_option('', '--save-regions-as-groups',
action='store_true', dest='save_regions_as_groups',
default=False, help=help['save_regions_as_groups'])
parser.add_option('', '--save-field-meshes',
action='store_true', dest='save_field_meshes',
default=False, help=help['save_field_meshes'])
parser.add_option('', '--solve-not',
action='store_true', dest='solve_not',
default=False, help=help['solve_not'])
parser.add_option('', '--list', metavar='what',
action='store', dest='_list',
default=None, help=help['list'])
options, args = parser.parse_args()
if (len(args) == 1):
filename_in = args[0];
else:
if options._list == 'terms':
print_terms()
else:
parser.print_help(),
return
output.set_output(filename=options.log,
quiet=options.quiet,
combined=options.log is not None)
required, other = get_standard_keywords()
if options.solve_not:
required.remove('equations')
required.remove('solver_[0-9]+|solvers')
other.extend(['equations'])
conf = ProblemConf.from_file_and_options(filename_in, options,
required, other,
define_args=options.define_args)
opts = conf.options
output_prefix = get_default_attr(opts, 'output_prefix', 'sfepy:')
app = PDESolverApp(conf, options, output_prefix)
if hasattr(opts, 'parametric_hook'): # Parametric study.
parametric_hook = conf.get_function(opts.parametric_hook)
app.parametrize(parametric_hook)
app()
