"""
Classes of variables for equations/terms.
"""
from __future__ import print_function
from __future__ import absolute_import
import numpy as nm
from sfepy.base.base import (real_types, complex_types, assert_, get_default,
output, OneTypeList, Container, Struct,
iter_dict_of_lists)
from sfepy.base.timing import Timer
import sfepy.linalg as la
from sfepy.discrete.functions import Function
from sfepy.discrete.conditions import get_condition_value
from sfepy.discrete.integrals import Integral
from sfepy.discrete.common.dof_info import (DofInfo, EquationMap,
expand_nodes_to_equations,
is_active_bc)
from sfepy.discrete.fem.lcbc_operators import LCBCOperators
from sfepy.discrete.common.mappings import get_physical_qps
from sfepy.discrete.evaluate_variable import eval_real, eval_complex
import six
from six.moves import range
is_state = 0
is_virtual = 1
is_parameter = 2
is_field = 10
[docs]
def create_adof_conns(conn_info, var_indx=None, active_only=True,
regions_changed=None, verbose=True):
"""
Create active DOF connectivities for all variables referenced in
`conn_info`.
If a variable has not the equation mapping, a trivial mapping is assumed
and connectivity with all DOFs active is created.
DOF connectivity key is a tuple ``(primary variable name, region name,
type, trace_region)``.
Notes
-----
If `active_only` is False, the DOF connectivities contain all DOFs, with
the E(P)BC-constrained ones stored as `-1 - <DOF number>`, so that the full
connectivities can be reconstructed for the matrix graph creation.
"""
var_indx = get_default(var_indx, {})
if regions_changed is None:
recompute = lambda info: True
elif isinstance(regions_changed, str):
recompute = lambda info: info.region.name == regions_changed
else:
regions_changed = set(regions_changed)
recompute = lambda info: info.region in regions_changed
def _create(var, econn):
offset = var_indx.get(var.name, slice(0, 0)).start
if var.eq_map is None:
eq = nm.arange(var.n_dof, dtype=nm.int32)
else:
if isinstance(var, DGFieldVariable):
eq = nm.arange(var.n_dof, dtype=nm.int32)
else:
if active_only:
eq = var.eq_map.eq
else:
eq = nm.arange(var.n_dof, dtype=nm.int32)
eq[var.eq_map.eq_ebc] = -1 - (var.eq_map.eq_ebc + offset)
eq[var.eq_map.master] = eq[var.eq_map.slave]
adc = create_adof_conn(eq, econn, var.n_components, offset)
return adc
def _assign(adof_conns, dof_conn_type, region, var, field, trace_region):
key = (var.name, region.name, dof_conn_type, trace_region)
if not key in adof_conns:
econn = field.get_econn(dof_conn_type, region, trace_region)
if econn is None: return
adof_conns[key] = _create(var, econn)
if trace_region is not None:
key = (var.name, region.name, dof_conn_type, None)
if not key in adof_conns:
econn = field.get_econn(dof_conn_type, region,
trace_region=None)
adof_conns[key] = _create(var, econn)
if verbose:
output('setting up dof connectivities...')
timer = Timer(start=True)
adof_conns = {}
for key, ii, info in iter_dict_of_lists(conn_info, return_keys=True):
if not recompute(info): continue
if info.primary is not None:
var = info.primary
field = var.get_field()
region = info.get_region()
field.setup_extra_data(info)
mreg_name = info.get_region_name(can_trace=False)
mreg_name = None if region.name == mreg_name else mreg_name
dct = info.dof_conn_types[var.name]
_assign(adof_conns, dct, region, var, field, mreg_name)
if info.has_virtual and info.trace_region is None:
var = info.virtual
field = var.get_field()
field.setup_extra_data(info)
aux = var.get_primary()
var = aux if aux is not None else var
region = info.get_region(can_trace=False)
dct = info.dof_conn_types[var.name]
_assign(adof_conns, dct, region, var, field, None)
if verbose:
output('...done in %.2f s' % timer.stop())
return adof_conns
[docs]
def create_adof_conn(eq, conn, dpn, offset):
"""
Given a node connectivity, number of DOFs per node and equation mapping,
create the active dof connectivity.
Locally (in a connectivity row), the DOFs are stored DOF-by-DOF (u_0 in all
local nodes, u_1 in all local nodes, ...).
Globally (in a state vector), the DOFs are stored node-by-node (u_0, u_1,
..., u_X in node 0, u_0, u_1, ..., u_X in node 1, ...).
"""
if dpn == 1:
aux = nm.take(eq, conn)
adc = aux + nm.asarray(offset * (aux >= 0), dtype=nm.int32)
else:
n_el, n_ep = conn.shape
adc = nm.empty((n_el, n_ep * dpn), dtype=conn.dtype)
ii = 0
for idof in range(dpn):
aux = nm.take(eq, dpn * conn + idof)
adc[:, ii : ii + n_ep] = aux + nm.asarray(offset * (aux >= 0),
dtype=nm.int32)
ii += n_ep
return adc
[docs]
def expand_basis(basis, dpn):
"""
Expand basis for variables with several components (DOFs per node), in a
way compatible with :func:`create_adof_conn()`, according to `dpn`
(DOF-per-node count).
"""
n_c, n_bf = basis.shape[-2:]
ebasis = nm.zeros(basis.shape[:2] + (dpn, n_bf * dpn), dtype=nm.float64)
for ic in range(n_c):
for ir in range(dpn):
ebasis[..., n_c*ir+ic, ir*n_bf:(ir+1)*n_bf] = basis[..., ic, :]
return ebasis
[docs]
class Variables(Container):
"""
Container holding instances of Variable.
"""
[docs]
@staticmethod
def from_conf(conf, fields):
obj = Variables()
for key, val in six.iteritems(conf):
var = Variable.from_conf(key, val, fields)
obj[var.name] = var
obj.setup_dtype()
return obj
def __init__(self, variables=None):
Container.__init__(self, OneTypeList(Variable),
vec=None,
r_vec=None,
state=set(),
virtual=set(),
parameter=set(),
has_virtual_dcs=False,
has_lcbc=False,
has_lcbc_rhs=False,
has_eq_map=False,
ordered_state=[],
ordered_virtual=[])
if variables is not None:
for var in variables:
self[var.name] = var
self.setup_dtype()
self.adof_conns = {}
def __setitem__(self, ii, var):
Container.__setitem__(self, ii, var)
if var.is_state():
self.state.add(var.name)
elif var.is_virtual():
self.virtual.add(var.name)
elif var.is_parameter():
self.parameter.add(var.name)
var._variables = self
self.setup_ordering()
self.setup_dof_info()
[docs]
def setup_dtype(self):
"""
Setup data types of state variables - all have to be of the same
data type, one of nm.float64 or nm.complex128.
"""
dtypes = {nm.complex128 : 0, nm.float64 : 0}
for var in self.iter_state(ordered=False):
dtypes[var.dtype] += 1
if dtypes[nm.float64] and dtypes[nm.complex128]:
raise ValueError("All variables must have the same dtype!")
elif dtypes[nm.float64]:
self.dtype = nm.float64
elif dtypes[nm.complex128]:
self.dtype = nm.complex128
else:
self.dtype = None
[docs]
def link_duals(self):
"""
Link state variables with corresponding virtual variables,
and assign link to self to each variable instance.
Usually, when solving a PDE in the weak form, each state
variable has a corresponding virtual variable.
"""
for ii in self.state:
self[ii].dual_var_name = None
for ii in self.virtual:
vvar = self[ii]
try:
self[vvar.primary_var_name].dual_var_name = vvar.name
except IndexError:
pass
[docs]
def get_dual_names(self):
"""
Get names of pairs of dual variables.
Returns
-------
duals : dict
The dual names as virtual name : state name pairs.
"""
duals = {}
for name in self.virtual:
duals[name] = self[name].primary_var_name
return duals
[docs]
def setup_ordering(self):
"""
Setup ordering of variables.
"""
self.link_duals()
is_given = [self[name]._order is not None for name in self.state]
if any(is_given) and not all(is_given):
raise ValueError('either all or none of state variables have to'
' be created with a given order!')
if all(is_given):
aux = [self[name]._order for name in self.state]
orders = [(self[name]._order, name) for name in self.state]
else:
orders = [ii for ii in enumerate(self.state)]
if len(orders):
self.ordered_state = [name for order, name in sorted(orders)]
self.ordered_virtual = [var.dual_var_name
for var in self.iter_state(ordered=True)
if var.dual_var_name is not None]
[docs]
def has_virtuals(self):
return len(self.virtual) > 0
def _create_dof_info(self, ordered_vars, flag, active=False):
orders = {}
di = DofInfo(f'{flag}_dof_info')
for var_name in ordered_vars:
var = self[var_name]
order = var._order
if order in orders:
di.append_variable(var, active=active, shared=orders[order])
else:
di.append_variable(var, active=active)
if order is not None:
orders[order] = var_name
return di
[docs]
def setup_dof_info(self, make_virtual=False):
"""
Setup global DOF information.
"""
self.di = self._create_dof_info(self.ordered_state, 'state')
if make_virtual:
self.vdi = self._create_dof_info(self.ordered_virtual, 'virtual')
else:
self.vdi = self.di
[docs]
def setup_lcbc_operators(self, lcbcs, ts=None, functions=None):
"""
Prepare linear combination BC operator matrix and right-hand side
vector.
"""
from sfepy.discrete.common.region import are_disjoint
if lcbcs is None:
self.lcdi = self.adi
return
self.lcbcs = lcbcs
if (ts is None) or ((ts is not None) and (ts.step == 0)):
regs = []
var_names = []
for bcs in self.lcbcs:
for bc in bcs.iter_single():
vns = bc.get_var_names()
regs.append(bc.regions[0])
var_names.append(vns[0])
if bc.regions[1] is not None:
regs.append(bc.regions[1])
var_names.append(vns[1])
for i0 in range(len(regs) - 1):
for i1 in range(i0 + 1, len(regs)):
if ((var_names[i0] == var_names[i1])
and not are_disjoint(regs[i0], regs[i1])):
raise ValueError('regions %s and %s are not disjoint!'
% (regs[i0].name, regs[i1].name))
ops = LCBCOperators('lcbcs', self, functions=functions)
for bcs in self.lcbcs:
for bc in bcs.iter_single():
vns = bc.get_var_names()
dofs = [self[vn].dofs for vn in vns if vn is not None]
bc.canonize_dof_names(*dofs)
if not is_active_bc(bc, ts=ts, functions=functions):
continue
output('lcbc:', bc.name)
ops.add_from_bc(bc, ts)
aux = ops.make_global_operator(self.adi)
self.mtx_lcbc, self.vec_lcbc, self.lcdi = aux
self.has_lcbc = self.mtx_lcbc is not None
self.has_lcbc_rhs = self.vec_lcbc is not None
[docs]
def get_lcbc_operator(self):
if self.has_lcbc:
return self.mtx_lcbc
else:
raise ValueError('no LCBC defined!')
[docs]
def equation_mapping(self, ebcs, epbcs, ts, functions, problem=None,
active_only=True):
"""
Create the mapping of active DOFs from/to all DOFs for all state
variables.
Parameters
----------
ebcs : Conditions instance
The essential (Dirichlet) boundary conditions.
epbcs : Conditions instance
The periodic boundary conditions.
ts : TimeStepper instance
The time stepper.
functions : Functions instance
The user functions for boundary conditions.
problem : Problem instance, optional
The problem that can be passed to user functions as a context.
active_only : bool
If True, the active DOF info ``self.adi`` uses the reduced (active
DOFs only) numbering. Otherwise it is the same as ``self.di``.
Returns
-------
active_bcs : set
The set of boundary conditions active in the current time.
"""
self.ebcs = ebcs
self.epbcs = epbcs
##
# Assing EBC, PBC to variables and regions.
if ebcs is not None:
self.bc_of_vars = self.ebcs.group_by_variables()
else:
self.bc_of_vars = {}
if epbcs is not None:
self.bc_of_vars = self.epbcs.group_by_variables(self.bc_of_vars)
##
# List EBC nodes/dofs for each variable.
active_bcs = set()
for var_name in self.di.var_names:
var = self[var_name]
bcs = self.bc_of_vars.get(var.name, None)
var_di = self.di.get_info(var_name)
active = var.equation_mapping(bcs, var_di, ts, functions,
problem=problem)
active_bcs.update(active)
if self.has_virtual_dcs:
vvar = self[var.dual_var_name]
vvar_di = self.vdi.get_info(var_name)
active = vvar.equation_mapping(bcs, vvar_di, ts, functions,
problem=problem)
active_bcs.update(active)
self.adi = self._create_dof_info(self.ordered_state, 'active_state',
active=active_only)
if self.has_virtual_dcs:
self.avdi = self._create_dof_info(self.ordered_virtual,
'active_virtual',
active=active_only)
else:
self.avdi = self.adi
self.has_eq_map = True
return active_bcs
[docs]
def get_indx(self, var_name, reduced=False, allow_dual=False):
var = self[var_name]
if not var.is_state():
if allow_dual and var.is_virtual():
var_name = var.primary_var_name
else:
msg = '%s is not a state part' % var_name
raise IndexError(msg)
if reduced:
return self.adi.indx[var_name]
else:
return self.di.indx[var_name]
[docs]
def get_matrix_shape(self):
if not self.has_eq_map:
raise ValueError('call equation_mapping() first!')
return (self.avdi.n_dof_total, self.adi.n_dof_total)
[docs]
def setup_initial_conditions(self, ics, functions):
self.ics = ics
self.ic_of_vars = self.ics.group_by_variables()
for var_name in self.di.var_names:
var = self[var_name]
ics = self.ic_of_vars.get(var.name, None)
if ics is None: continue
var.setup_initial_conditions(ics, self.di, functions)
for var_name in self.parameter:
var = self[var_name]
if hasattr(var, 'special') and ('ic' in var.special):
setter, sargs, skwargs = var._get_setter('ic', functions)
var.set_data(setter(*sargs, **skwargs))
output('IC data of %s set by %s()' % (var.name, setter.name))
def _set_var_adof_conns(self, adof_conns):
for key, val in adof_conns.items():
if key[0] in self.names:
var = self[key[0]]
var.adof_conns[key] = val
var = var.get_dual()
if var is not None:
var.adof_conns[key] = val
[docs]
def set_adof_conns(self, adof_conns):
"""
Set all active DOF connectivities to `self` as well as relevant
sub-dicts to the individual variables.
"""
self.adof_conns = adof_conns
for var in self:
var.adof_conns = {}
self._set_var_adof_conns(self.adof_conns)
[docs]
def update_adof_conns(self, adof_conns):
"""
Update active DOF connectivities stored in `self` as well as relevant
sub-dicts to the individual variables.
"""
self.adof_conns.update(adof_conns)
for var in self:
var.set_default('adof_connfs', {})
self._set_var_adof_conns(self.adof_conns)
[docs]
def create_vec(self):
vec = nm.zeros((self.di.n_dof_total,), dtype=self.dtype)
return vec
[docs]
def create_reduced_vec(self):
vec = nm.zeros((self.adi.n_dof_total,), dtype=self.dtype)
return vec
[docs]
def check_vec_size(self, vec, reduced=False):
"""
Check whether the shape of the DOF vector corresponds to the
total number of DOFs of the state variables.
Parameters
----------
vec : array
The vector of DOF values.
reduced : bool
If True, the size of the DOF vector should be reduced,
i.e. without DOFs fixed by boundary conditions.
"""
if not reduced:
n_dof = self.di.get_n_dof_total()
if vec.size != n_dof:
msg = 'incompatible data size!' \
' (%d (variables) == %d (DOF vector))' \
% (n_dof, vec.size)
raise ValueError(msg)
else:
if self.has_lcbc:
n_dof = self.lcdi.get_n_dof_total()
else:
n_dof = self.adi.get_n_dof_total()
if vec.size != n_dof:
msg = 'incompatible data size!' \
' (%d (active variables) == %d (reduced DOF vector))' \
% (n_dof, vec.size)
raise ValueError(msg)
[docs]
def reduce_vec(self, vec, follow_epbc=False, svec=None):
"""
Get the reduced DOF vector, with EBC and PBC DOFs removed.
Notes
-----
If 'follow_epbc' is True, values of EPBC master dofs are not simply
thrown away, but added to the corresponding slave dofs, just like when
assembling. For vectors with state (unknown) variables it should be set
to False, for assembled vectors it should be set to True.
"""
if svec is None:
svec = nm.empty((self.adi.n_dof_total,), dtype=self.dtype)
for var in self.iter_state():
aindx = self.adi.indx[var.name]
svec[aindx] = var.get_reduced(vec, self.di.indx[var.name].start,
follow_epbc)
return svec
[docs]
def make_full_vec(self, svec, force_value=None, vec=None):
"""
Make a full DOF vector satisfying E(P)BCs from a reduced DOF
vector.
Parameters
----------
svec : array
The reduced DOF vector.
force_value : float, optional
Passing a `force_value` overrides the EBC values.
vec : array, optional
If given, the buffer for storing the result (zeroed).
Returns
-------
vec : array
The full DOF vector.
"""
self.check_vec_size(svec, reduced=True)
if self.has_lcbc:
if self.has_lcbc_rhs:
svec = self.mtx_lcbc * svec + self.vec_lcbc
else:
svec = self.mtx_lcbc * svec
if vec is None:
vec = self.create_vec()
for var in self.iter_state():
indx = self.di.indx[var.name]
aindx = self.adi.indx[var.name]
var.get_full(svec, aindx.start, force_value, vec, indx.start)
return vec
[docs]
def set_vec_part(self, vec, var_name, part, reduced=False):
self.check_vec_size(vec, reduced=reduced)
vec[self.get_indx(var_name, reduced)] = part
[docs]
def get_vec_part(self, vec, var_name, reduced=False):
self.check_vec_size(vec, reduced=reduced)
return vec[self.get_indx(var_name, reduced)]
[docs]
def invalidate_evaluate_caches(self, step=0):
for var in self.iter_state():
var.invalidate_evaluate_cache(step=step)
[docs]
def init_state(self, vec=None):
self.init_history()
if vec is None:
vec = self.create_vec()
for var in self.iter_state():
var.locked = False
self.set_data(vec)
for var in self.iter_state():
var.locked = True
self.vec = vec
[docs]
def fill_state(self, value):
"""
Fill the DOF vector with given value.
"""
if self.r_vec is not None:
self.r_vec.fill(value)
self.vec.fill(value)
self.invalidate_evaluate_caches(step=0)
[docs]
def apply_ebc(self, vec=None, force_values=None):
"""
Apply essential (Dirichlet) and periodic boundary conditions
to state all variables or the given vector `vec`.
"""
if vec is None:
vec = self.vec
self.invalidate_evaluate_caches(step=0)
for var in self.iter_state():
var.apply_ebc(vec, self.di.indx[var.name].start, force_values)
[docs]
def apply_ic(self, vec=None, force_values=None):
"""
Apply initial conditions to all state variables or the given
vector `vec`.
"""
if vec is None:
vec = self.vec
self.invalidate_evaluate_caches(step=0)
for var in self.iter_state():
var.apply_ic(vec, self.di.indx[var.name].start, force_values)
[docs]
def has_ebc(self, vec=None, force_values=None, verbose=False):
if vec is None:
vec = self.vec
ok = True
for var in self.iter_state():
_ok = self[var.name].has_ebc(vec=vec[self.di.indx[var.name]],
force_values=force_values)
ok = ok and _ok
if verbose:
output(f'variable {var.name} has E(P)BC:', _ok)
return ok
[docs]
def set_data(self, data, step=0, ignore_unknown=False,
preserve_caches=False):
"""
Set data (vectors of DOF values) of variables.
Parameters
----------
data : array
The state vector or dictionary of {variable_name : data vector}.
step : int, optional
The time history step, 0 (default) = current.
ignore_unknown : bool, optional
Ignore unknown variable names if `data` is a dict.
preserve_caches : bool
If True, do not invalidate evaluate caches of variables.
"""
if data is None: return
if isinstance(data, dict):
for key, val in six.iteritems(data):
try:
var = self[key]
except (ValueError, IndexError):
if ignore_unknown:
pass
else:
raise KeyError('unknown variable! (%s)' % key)
else:
var.set_data(val, step=step,
preserve_caches=preserve_caches)
elif isinstance(data, nm.ndarray):
self.check_vec_size(data)
for ii in self.state:
var = self[ii]
var.set_data(data, self.di.indx[var.name], step=step,
preserve_caches=preserve_caches)
else:
raise ValueError('unknown data class! (%s)' % data.__class__)
[docs]
def set_reduced_state(self, r_vec, preserve_caches=False):
"""
Set the reduced DOF vector, with EBC and PBC DOFs removed.
Parameters
----------
r_vec : array
The reduced DOF vector corresponding to the variables.
preserve_caches : bool
If True, do not invalidate evaluate caches of variables.
"""
self.vec[:] = self.make_full_vec(r_vec)
if self.has_lcbc:
self.r_vec = r_vec
if not preserve_caches:
self.invalidate_evaluate_caches(step=0)
[docs]
def get_reduced_state(self, follow_epbc=False, force=False):
"""
Get the reduced DOF vector, with EBC and PBC DOFs removed.
"""
if self.has_lcbc:
if self.r_vec is None:
if force:
r_vec = self.reduce_vec(self.vec, follow_epbc=follow_epbc)
# This just sets the correct vector size (wrong values)!
r_vec = self.mtx_lcbc.T * r_vec
else:
raise ValueError('Reduced state DOFs are not available!')
else:
r_vec = self.r_vec
else:
r_vec = self.reduce_vec(self.vec, follow_epbc=follow_epbc)
return r_vec
[docs]
def set_full_state(self, vec, force=False, preserve_caches=False):
"""
Set the full DOF vector (including EBC and PBC DOFs). If
`var_name` is given, set only the DOF sub-vector corresponding
to the given variable. If `force` is True, setting variables
with LCBC DOFs is allowed.
"""
if self.has_lcbc:
if not force:
raise ValueError('cannot set full DOF vector with LCBCs!')
else:
self.r_vec = None
self.vec[:] = vec
if not preserve_caches:
self.invalidate_evaluate_caches(step=0)
def __call__(self, var_name=None):
"""
Get the full DOF vector (including EBC and PBC DOFs). If
`var_name` is given, return only the DOF vector corresponding to
the given variable.
"""
if var_name is None:
out = self.vec
else:
out = self.vec[self.di.indx[var_name]]
return out
[docs]
def set_state(self, vec, reduced=False, force=False, preserve_caches=False,
apply_ebc=False):
preserve_caches = preserve_caches and (not apply_ebc)
if reduced:
self.set_reduced_state(vec, preserve_caches=preserve_caches)
if apply_ebc:
self.apply_ebc()
else:
if apply_ebc:
self.apply_ebc(vec)
self.set_full_state(vec, force=force,
preserve_caches=preserve_caches)
[docs]
def get_state(self, reduced=False, follow_epbc=False, force=False):
if reduced:
vec = self.get_reduced_state(follow_epbc=follow_epbc, force=force)
else:
vec = self()
return vec
[docs]
def set_state_parts(self, parts, vec=None, force=False):
"""
Set parts of the DOF vector corresponding to individual state
variables.
Parameters
----------
parts : dict
The dictionary of the DOF vector parts.
force : bool
If True, proceed even with LCBCs present.
"""
if self.has_lcbc and not force:
raise ValueError('cannot set full DOF vector with LCBCs!')
if vec is None:
vec = self.vec
self.invalidate_evaluate_caches(step=0)
else:
self.check_vec_size(vec, reduced=False)
for key, part in parts.items():
vec[self.di.indx[key]] = part
[docs]
def get_state_parts(self, vec=None):
"""
Return parts of a state vector corresponding to individual state
variables.
Parameters
----------
vec : array, optional
The state vector. If not given, then the data stored in the
variables are returned instead.
Returns
-------
out : dict
The dictionary of the state parts.
"""
if vec is None:
vec = self.vec
else:
self.check_vec_size(vec, reduced=False)
out = {}
for var in self.iter_state():
out[var.name] = vec[self.di.indx[var.name]]
return out
[docs]
def create_output(self, vec=None, fill_value=None, var_info=None,
extend=True, linearization=None):
"""
Creates an output dictionary with state variables data, that can be
passed as 'out' kwarg to :func:`Mesh.write()`.
Then the dictionary entries are formed by components of the
state vector corresponding to unknown variables according to
kind of linearization given by `linearization`.
"""
if vec is None:
vec = self.vec
di = self.di
if var_info is None:
self.check_vec_size(vec)
var_info = {}
for name in di.var_names:
var_info[name] = (False, name)
out = {}
for key, indx in six.iteritems(di.indx):
var = self[key]
if key not in list(var_info.keys()): continue
is_part, name = var_info[key]
if is_part:
aux = vec
else:
aux = vec[indx]
out.update(var.create_output(aux, key=name, extend=extend,
fill_value=fill_value,
linearization=linearization))
return out
[docs]
def iter_state(self, ordered=True):
if ordered:
for ii in self.ordered_state:
yield self[ii]
else:
for ii in self.state:
yield self[ii]
[docs]
def init_history(self):
for var in self.iter_state():
var.init_history()
[docs]
def time_update(self, ts, functions, verbose=True):
if verbose:
output('updating variables...')
for var in self:
var.time_update(ts, functions)
if verbose:
output('...done')
[docs]
def advance(self, ts):
for var in self.iter_state():
var.advance(ts)
[docs]
class Variable(Struct):
[docs]
@staticmethod
def from_conf(key, conf, fields):
aux = conf.kind.split()
if len(aux) == 2:
kind, family = aux
elif len(aux) == 3:
kind, family = aux[0], '_'.join(aux[1:])
else:
raise ValueError('variable kind is 2 or 3 words! (%s)' % conf.kind)
history = conf.get('history', None)
if history is not None:
try:
history = int(history)
assert_(history >= 0)
except (ValueError, TypeError):
raise ValueError('history must be integer >= 0! (got "%s")'
% history)
order = conf.get('order', None)
if order is not None:
order = int(order)
primary_var_name = conf.get('dual', None)
if primary_var_name is None:
if hasattr(conf, 'like'):
primary_var_name = get_default(conf.like, '(set-to-None)')
else:
primary_var_name = None
special = conf.get('special', None)
if family == 'field':
try:
fld = fields[conf.field]
except IndexError:
msg = 'field "%s" does not exist!' % conf.field
raise KeyError(msg)
if "DG" in fld.family_name:
obj = DGFieldVariable(conf.name, kind, fld, order, primary_var_name,
special=special, key=key, history=history)
else:
obj = FieldVariable(conf.name, kind, fld, order, primary_var_name,
special=special, key=key, history=history)
else:
raise ValueError('unknown variable family! (%s)' % family)
return obj
def __init__(self, name, kind, order=None, primary_var_name=None,
special=None, flags=None, **kwargs):
Struct.__init__(self, name=name, locked=False, **kwargs)
self.flags = set()
if flags is not None:
for flag in flags:
self.flags.add(flag)
self.indx = slice(None)
self.n_dof = None
self.step = 0
self.dt = 1.0
self.initial_condition = None
self.dual_var_name = None
self.eq_map = None
if self.is_virtual():
self.data = None
else:
self.data = []
self.data.append(None)
self._set_kind(kind, order, primary_var_name, special=special)
def _set_kind(self, kind, order, primary_var_name, special=None):
if kind == 'unknown':
self.flags.add(is_state)
self._order = order
self.dof_name = self.name
elif kind == 'test':
if primary_var_name == self.name:
raise ValueError('primary variable for %s cannot be %s!'
% (self.name, primary_var_name))
self.flags.add(is_virtual)
msg = 'test variable %s: related unknown missing' % self.name
self.primary_var_name = get_default(primary_var_name, None, msg)
self.dof_name = self.primary_var_name
elif kind == 'parameter':
self.flags.add(is_parameter)
msg = 'parameter variable %s: related unknown missing' % self.name
self.primary_var_name = get_default(primary_var_name, None, msg)
if self.primary_var_name == '(set-to-None)':
self.primary_var_name = None
self.dof_name = self.name
else:
self.dof_name = self.primary_var_name
if special is not None:
self.special = special
else:
raise NotImplementedError('unknown variable kind: %s' % kind)
self.kind = kind
def _setup_dofs(self, n_nod, n_components, val_shape):
"""
Setup number of DOFs and DOF names.
"""
self.n_nod = n_nod
self.n_components = n_components
self.val_shape = val_shape
self.n_dof = self.n_nod * self.n_components
self.dofs = [self.dof_name + ('.%d' % ii)
for ii in range(self.n_components)]
[docs]
def get_primary(self):
"""
Get the corresponding primary variable.
Returns
-------
var : Variable instance
The primary variable, or `self` for state
variables or if `primary_var_name` is None, or None if no other
variables are defined.
"""
if self.is_state():
var = self
elif self.primary_var_name is not None:
if ((self._variables is not None)
and (self.primary_var_name in self._variables.names)):
var = self._variables[self.primary_var_name]
else:
var = None
else:
var = self
return var
[docs]
def get_dual(self):
"""
Get the dual variable.
Returns
-------
var : Variable instance
The primary variable for non-state variables, or the dual
variable for state variables.
"""
if self.is_state():
if ((self._variables is not None)
and (self.dual_var_name in self._variables.names)):
var = self._variables[self.dual_var_name]
else:
var = None
else:
if ((self._variables is not None)
and (self.primary_var_name in self._variables.names)):
var = self._variables[self.primary_var_name]
else:
var = None
return var
[docs]
def is_state(self):
return is_state in self.flags
[docs]
def is_virtual(self):
return is_virtual in self.flags
[docs]
def is_parameter(self):
return is_parameter in self.flags
[docs]
def is_state_or_parameter(self):
return (is_state in self.flags) or (is_parameter in self.flags)
[docs]
def is_kind(self, kind):
return eval('self.is_%s()' % kind)
[docs]
def is_real(self):
return self.dtype in real_types
[docs]
def is_complex(self):
return self.dtype in complex_types
[docs]
def is_finite(self, step=0, derivative=None, dt=None):
return nm.isfinite(self(step=step, derivative=derivative, dt=dt)).all()
[docs]
def get_primary_name(self):
if self.is_state():
name = self.name
else:
name = self.primary_var_name
return name
[docs]
def init_history(self):
"""Initialize data of variables with history."""
if self.history is None: return
self.data = (self.history + 1) * [None]
self.step = 0
[docs]
def time_update(self, ts, functions):
"""Implemented in subclasses."""
pass
[docs]
def advance(self, ts):
"""
Advance in time the DOF state history. A copy of the DOF vector
is made to prevent history modification.
"""
if self.history is None: return
self.step = ts.step + 1
if self.history > 0:
# Copy the current step data to the history data, shift history,
# initialize if needed. The current step data are left intact.
# Note: self.data can point into Variables.vec.
for ii in range(1, self.history + 1):
if self.data[ii] is None:
self.data[ii] = self.data[ii-1].copy()
for ii in range(self.history, 0, -1):
self.data[ii][self.indx] = self.data[ii - 1][self.indx]
# Advance evaluate cache.
for step_cache in six.itervalues(self.evaluate_cache):
steps = sorted(step_cache.keys())
for step in steps:
if step is None:
# Special caches with possible custom advance()
# function.
for key, val in six.iteritems(step_cache[step]):
if hasattr(val, '__advance__'):
val.__advance__(ts, val)
elif -step < self.history:
step_cache[step-1] = step_cache[step]
if len(steps) and (steps[0] is not None):
step_cache.pop(steps[-1])
[docs]
def init_data(self, step=0):
"""
Initialize the dof vector data of time step `step` to zeros.
"""
if self.is_state_or_parameter():
self.set_constant(val=0.0, step=step)
[docs]
def set_constant(self, val=0.0, step=0):
"""
Set the variable dof vector data of time step `step` to a scalar `val`.
"""
data = nm.empty((self.n_dof,), dtype=self.dtype)
data.fill(val)
self.set_data(data, step=step)
[docs]
def set_data(self, data=None, indx=None, step=0,
preserve_caches=False):
"""
Set data (vector of DOF values) of the variable.
Parameters
----------
data : array
The vector of DOF values.
indx : int, optional
If given, `data[indx]` is used.
step : int, optional
The time history step, 0 (default) = current.
preserve_caches : bool
If True, do not invalidate evaluate caches of the variable.
"""
if not self.is_state_or_parameter():
raise ValueError(
'Only state or parameter variables can have values!'
)
if self.locked:
raise ValueError(
f'Variable {self.name} is locked! It can be set only using'
' the state manipulation functions of Variables.'
)
data = data.ravel()
if indx is None:
indx = slice(0, len(data))
else:
indx = slice(int(indx.start), int(indx.stop))
n_data_dof = indx.stop - indx.start
if self.n_dof != n_data_dof:
msg = 'incompatible data shape! (%d (variable) == %d (data))' \
% (self.n_dof, n_data_dof)
raise ValueError(msg)
elif (step > 0) or (-step >= len(self.data)):
raise ValueError('step %d out of range! ([%d, 0])'
% (step, -(len(self.data) - 1)))
else:
self.data[step] = data
self.indx = indx
if not preserve_caches:
self.invalidate_evaluate_cache(step=step)
def __call__(self, step=0, derivative=None, dt=None):
"""
Return vector of degrees of freedom of the variable.
Parameters
----------
step : int, default 0
The time step (0 means current, -1 previous, ...).
derivative : None or 'dt'
If not None, return time derivative of the DOF vector,
approximated by the backward finite difference.
Returns
-------
vec : array
The DOF vector. If `derivative` is None: a view of the data vector,
otherwise: required derivative of the DOF vector
at time step given by `step`.
Notes
-----
If the previous time step is requested in step 0, the step 0
DOF vector is returned instead.
"""
if derivative is None:
if (self.step == 0) and (step == -1):
data = self.data[0]
else:
data = self.data[-step]
if data is None:
raise ValueError('data of variable are not set! (%s, step %d)' \
% (self.name, step))
return data[self.indx]
else:
if self.history is None:
msg = 'set history type of variable %s to use derivatives!'\
% self.name
raise ValueError(msg)
dt = get_default(dt, self.dt)
return (self(step=step) - self(step=step-1)) / dt
[docs]
def get_initial_condition(self):
if self.initial_condition is None:
return 0.0
else:
return self.initial_condition
[docs]
class FieldVariable(Variable):
"""
A finite element field variable.
field .. field description of variable (borrowed)
"""
def __init__(self, name, kind, field, order=None, primary_var_name=None,
special=None, flags=None, history=None, **kwargs):
Variable.__init__(self, name, kind, order, primary_var_name,
special, flags, history=history, **kwargs)
self._set_field(field)
self.has_field = True
self.has_bc = True
self._variables = None
self.clear_evaluate_cache()
def _set_field(self, field):
"""
Set field of the variable.
Takes reference to a Field instance. Sets dtype according to
field.dtype. Sets `dim` attribute to spatial dimension.
"""
self.is_surface = field.is_surface
self.field = field
self._setup_dofs(field.n_nod, field.n_components, field.val_shape)
self.flags.add(is_field)
self.dtype = field.dtype
self.dim = field.domain.shape.dim
def _get_setter(self, kind, functions, **kwargs):
"""
Get the setter function of the variable and its arguments depending in
the setter kind.
"""
if not (hasattr(self, 'special') and (kind in self.special)):
return
setter_name = self.special[kind]
setter = functions[setter_name]
region = self.field.region
nod_list = self.field.get_dofs_in_region(region)
nods = nm.unique(nod_list)
coors = self.field.get_coor(nods)
if kind == 'setter':
sargs = (kwargs.get('ts'), coors)
elif kind == 'ic':
sargs = (coors, )
skwargs = {'region' : region, 'variable' : self}
return setter, sargs, skwargs
[docs]
def get_field(self):
return self.field
[docs]
def get_mapping(self, region, integral, integration,
get_saved=False, return_key=False):
"""
Get the reference element mapping of the underlying field.
See Also
--------
sfepy.discrete.common.fields.Field.get_mapping
"""
if region is None:
region = self.field.region
out = self.field.get_mapping(region, integral, integration,
get_saved=get_saved,
return_key=return_key)
return out
[docs]
def get_dof_conn(self, region_name, dct, trace_region=None):
"""
Get active dof connectivity of a variable.
Notes
-----
The primary and dual variables must have the same Region.
"""
if self.is_virtual():
var = self.get_primary()
# No primary variable can occur in single term evaluations.
var_name = var.name if var is not None else self.name
else:
var_name = self.name
if trace_region is None:
mregion_name = None
else:
mregion = self.field.domain.regions[region_name]
region = mregion.get_mirror_region(trace_region)
region_name = region.name
mregion_name = mregion.name
key = (var_name, region_name, dct, mregion_name)
dc = self.adof_conns[key]
return dc
[docs]
def get_dof_info(self, active=False):
details = Struct(name='field_var_dof_details',
n_nod=self.n_nod,
dpn=self.n_components)
if active:
n_dof = self.n_adof
else:
n_dof = self.n_dof
return n_dof, details
[docs]
def time_update(self, ts, functions):
"""
Store time step, set variable data for variables with the setter
function.
"""
if ts is not None:
self.dt = ts.dt
if hasattr(self, 'special') and ('setter' in self.special):
setter, sargs, skwargs = self._get_setter('setter', functions,
ts=ts)
self.set_data(setter(*sargs, **skwargs))
output('data of %s set by %s()' % (self.name, setter.name))
[docs]
def set_from_qp(self, data_qp, integral, step=0):
"""
Set DOFs of variable using values in quadrature points
corresponding to the given integral.
"""
data_vertex = self.field.average_qp_to_vertices(data_qp, integral)
# Field nodes values.
data = self.field.interp_v_vals_to_n_vals(data_vertex)
data = data.ravel()
self.indx = slice(0, len(data))
self.data[step] = data
[docs]
def set_from_mesh_vertices(self, data):
"""
Set the variable using values at the mesh vertices.
"""
ndata = self.field.interp_v_vals_to_n_vals(data)
self.set_data(ndata)
[docs]
def set_from_function(self, fun, step=0):
"""
Set the variable data (the vector of DOF values) using a function of
space coordinates.
Parameters
----------
fun : callable
The function of coordinates returning DOF values of shape
`(n_coor, n_components)`.
step : int, optional
The time history step, 0 (default) = current.
"""
_, vv = self.field.set_dofs(fun, self.field.region, self.n_components)
self.set_data(vv.ravel(), step=step)
[docs]
def equation_mapping(self, bcs, var_di, ts, functions, problem=None,
warn=False):
"""
Create the mapping of active DOFs from/to all DOFs.
Sets n_adof.
Returns
-------
active_bcs : set
The set of boundary conditions active in the current time.
"""
self.eq_map = EquationMap('eq_map', self.dofs, var_di)
if var_di.shared_dofs_with is not None:
svar = self._variables[var_di.shared_dofs_with]
eq_map = self.eq_map
seq_map = svar.eq_map
eq_map.eq = seq_map.eq
eq_map.eq_ebc = seq_map.eq_ebc
eq_map.eqi = seq_map.eqi
eq_map.master = seq_map.master
eq_map.n_eq = seq_map.n_eq
eq_map.slave = seq_map.slave
eq_map.val_ebc = seq_map.val_ebc
self.n_adof = eq_map.n_eq
return {}
if bcs is not None:
bcs.canonize_dof_names(self.dofs)
bcs.sort()
active_bcs = self.eq_map.map_equations(bcs, self.field, ts, functions,
problem=problem, warn=warn)
self.n_adof = self.eq_map.n_eq
return active_bcs
[docs]
def setup_initial_conditions(self, ics, di, functions, warn=False):
"""
Setup of initial conditions.
"""
ics.canonize_dof_names(self.dofs)
ics.sort()
self.initial_condition = nm.zeros((di.n_dof[self.name],),
dtype=self.dtype)
for ic in ics:
region = ic.region
dofs, val = ic.dofs
if warn:
clean_msg = ('warning: ignoring nonexistent' \
' IC node (%s) in ' % self.name)
else:
clean_msg = None
nod_list = self.field.get_dofs_in_region(region)
if len(nod_list) == 0:
continue
fun = get_condition_value(val, functions, 'IC', ic.name)
if isinstance(fun, Function):
aux = fun
fun = lambda coors: aux(coors, ic=ic)
nods, vv = self.field.set_dofs(fun, region, len(dofs), clean_msg)
eq = expand_nodes_to_equations(nods, dofs, self.dofs)
self.initial_condition[eq] = nm.ravel(vv)
[docs]
def get_data_shape(self, integral, integration='cell', region_name=None):
"""
Get element data dimensions for given approximation.
Parameters
----------
integral : Integral instance
The integral describing used numerical quadrature.
integration : 'cell', 'facet', 'facet_extra', 'point' or 'custom'
The term integration mode.
region_name : str
The name of the region of the integral.
Returns
-------
data_shape : 5 ints
The `(n_el, n_qp, dim, n_en, n_comp)` for volume shape kind,
`(n_fa, n_qp, dim, n_fn, n_comp)` for surface shape kind and
`(n_nod, 0, 0, 1, n_comp)` for point shape kind.
Notes
-----
- `n_el`, `n_fa` = number of elements/facets
- `n_qp` = number of quadrature points per element/facet
- `dim` = spatial dimension
- `n_en`, `n_fn` = number of element/facet nodes
- `n_comp` = number of variable components in a point/node
- `n_nod` = number of element nodes
"""
aux = self.field.get_data_shape(integral, integration=integration,
region_name=region_name)
data_shape = aux + (self.n_components,)
return data_shape
[docs]
def clear_evaluate_cache(self):
"""
Clear current evaluate cache.
"""
self.evaluate_cache = {}
[docs]
def invalidate_evaluate_cache(self, step=0):
"""
Invalidate variable data in evaluate cache for time step given
by `step` (0 is current, -1 previous, ...).
This should be done, for example, prior to every nonlinear
solver iteration.
"""
for step_cache in six.itervalues(self.evaluate_cache):
for key in list(step_cache.keys()):
if key == step: # Given time step to clear.
step_cache.pop(key)
[docs]
def evaluate(self, mode='val',
region=None, integral=None, integration=None,
step=0, time_derivative=None, trace_region=None,
dt=None, bf=None):
"""
Evaluate various quantities related to the variable according to
`mode` in quadrature points defined by `integral`.
The evaluated data are cached in the variable instance in
`evaluate_cache` attribute.
Parameters
----------
mode : one of 'val', 'grad', 'div', 'cauchy_strain'
The evaluation mode.
region : Region instance, optional
The region where the evaluation occurs. If None, the
underlying field region is used.
integral : Integral instance, optional
The integral defining quadrature points in which the
evaluation occurs. If None, the first order volume integral
is created. Must not be None for surface integrations.
integration : 'cell', 'facet', 'facet_extra', or 'point'
The term integration type. If None, it is derived from
the region kind.
step : int, default 0
The time step (0 means current, -1 previous, ...).
time_derivative : None or 'dt'
If not None, return time derivative of the data,
approximated by the backward finite difference.
trace_region : None or str
If not None, evaluate of trace of the variable on a boundary
region.
dt : float, optional
The time step to be used if `derivative` is `'dt'`. If None,
the `dt` attribute of the variable is used.
bf : Base function, optional
The base function to be used in 'val' mode.
Returns
-------
out : array
The 4-dimensional array of shape
`(n_el, n_qp, n_row, n_col)` with the requested data,
where `n_row`, `n_col` depend on `mode`.
"""
if integration == 'custom':
msg = 'cannot use FieldVariable.evaluate() with custom integration!'
raise ValueError(msg)
step_cache = self.evaluate_cache.setdefault(mode, {})
cache = step_cache.setdefault(step, {})
field = self.field
if region is None:
region = field.region
if trace_region is not None:
mregion = region.get_mirror_region(trace_region)
trace_region = region.name
region = mregion
if (region is not field.region) and not region.is_empty:
assert_(field.region.contains(region))
if integral is None:
integral = Integral('aux_1', 1)
if integration is None:
integration = region.kind
geo, _, key = field.get_mapping(region, integral, integration,
return_key=True)
key += (time_derivative, trace_region)
dct = ('cell' if integration == 'facet_extra' else integration,
region.tdim)
if key in cache:
out = cache[key]
else:
vec = self(step=step, derivative=time_derivative, dt=dt)
conn = field.get_econn(dct, region, trace_region)
shape = self.get_data_shape(integral, integration, region.name)
if self.dtype == nm.float64:
out = eval_real(vec, conn, geo, mode, shape, bf)
else:
out = eval_complex(vec, conn, geo, mode, shape, bf)
cache[key] = out
return out
[docs]
def get_state_in_region(self, region, reshape=True, step=0):
"""
Get DOFs of the variable in the given region.
Parameters
----------
region : Region
The selected region.
reshape : bool
If True, reshape the DOF vector to a 2D array with the individual
components as columns. Otherwise a 1D DOF array of the form [all
DOFs in region node 0, all DOFs in region node 1, ...] is returned.
step : int, default 0
The time step (0 means current, -1 previous, ...).
Returns
-------
out : array
The selected DOFs.
"""
nods = self.field.get_dofs_in_region(region, merge=True)
eq = nm.empty((len(nods) * self.n_components,), dtype=nm.int32)
for idof in range(self.n_components):
eq[idof::self.n_components] = self.n_components * nods \
+ idof + self.indx.start
out = self.data[step][eq]
if reshape:
out.shape = (len(nods), self.n_components)
return out
[docs]
def apply_ebc(self, vec, offset=0, force_values=None):
"""
Apply essential (Dirichlet) and periodic boundary conditions to
vector `vec`, starting at `offset`.
"""
eq_map = self.eq_map
ii = offset + eq_map.eq_ebc
# EBC,
if force_values is None:
vec[ii] = eq_map.val_ebc
else:
if isinstance(force_values, dict):
vec[ii] = force_values[self.name]
else:
vec[ii] = force_values
# EPBC.
vec[offset+eq_map.master] = vec[offset+eq_map.slave]
[docs]
def apply_ic(self, vec, offset=0, force_values=None):
"""
Apply initial conditions conditions to vector `vec`, starting at
`offset`.
"""
ii = slice(offset, offset + self.n_dof)
if force_values is None:
vec[ii] = self.get_initial_condition()
else:
if isinstance(force_values, dict):
vec[ii] = force_values[self.name]
else:
vec[ii] = force_values
[docs]
def has_ebc(self, vec=None, force_values=None):
eq_map = self.eq_map
ii = eq_map.eq_ebc
if force_values is None:
if not nm.allclose(vec[ii], eq_map.val_ebc):
return False
else:
if isinstance(force_values, dict):
if not nm.allclose(vec[ii], force_values[self.name]):
return False
else:
if not nm.allclose(vec[ii], force_values):
return False
# EPBC.
if not nm.allclose(vec[eq_map.master], vec[eq_map.slave]):
return False
return True
[docs]
def get_reduced(self, vec, offset=0, follow_epbc=False):
"""
Get the reduced DOF vector, with EBC and PBC DOFs removed.
Notes
-----
The full vector starts in `vec` at `offset`. If 'follow_epbc' is True,
values of EPBC master DOFs are not simply thrown away, but added to the
corresponding slave DOFs, just like when assembling. For vectors with
state (unknown) variables it should be set to False, for assembled
vectors it should be set to True.
"""
eq_map = self.eq_map
ii = offset + eq_map.eqi
r_vec = vec[ii]
if follow_epbc:
master = offset + eq_map.master
slave = eq_map.eq[eq_map.slave]
ii = slave >= 0
la.assemble1d(r_vec, slave[ii], vec[master[ii]])
return r_vec
[docs]
def get_full(self, r_vec, r_offset=0, force_value=None,
vec=None, offset=0):
"""
Get the full DOF vector satisfying E(P)BCs from a reduced DOF
vector.
Notes
-----
The reduced vector starts in `r_vec` at `r_offset`.
Passing a `force_value` overrides the EBC values. Optionally,
`vec` argument can be provided to store the full vector (in
place) starting at `offset`.
"""
if vec is None:
vec = nm.empty(self.n_dof, dtype=r_vec.dtype)
else:
vec = vec[offset:offset+self.n_dof]
eq_map = self.eq_map
r_vec = r_vec[r_offset:r_offset+eq_map.n_eq]
# EBC.
vec[eq_map.eq_ebc] = get_default(force_value, eq_map.val_ebc)
# Reduced vector values.
vec[eq_map.eqi] = r_vec
# EPBC.
vec[eq_map.master] = vec[eq_map.slave]
unused_dofs = self.field.get('unused_dofs')
if unused_dofs is not None:
vec[:] = self.field.restore_substituted(vec)
return vec
[docs]
def create_output(self, vec=None, key=None, extend=True, fill_value=None,
linearization=None):
"""
Convert the DOF vector to a dictionary of output data usable by
Mesh.write().
Parameters
----------
vec : array, optional
An alternative DOF vector to be used instead of the variable
DOF vector.
key : str, optional
The key to be used in the output dictionary instead of the
variable name.
extend : bool
Extend the DOF values to cover the whole domain.
fill_value : float or complex
The value used to fill the missing DOF values if `extend` is True.
linearization : Struct or None
The linearization configuration for higher order approximations.
"""
linearization = get_default(linearization, Struct(kind='strip'))
if vec is None:
vec = self()
key = get_default(key, self.name)
aux = nm.reshape(vec,
(self.n_dof // self.n_components, self.n_components))
out = self.field.create_output(aux, self.name, dof_names=self.dofs,
key=key, extend=extend,
fill_value=fill_value,
linearization=linearization)
return out
[docs]
def get_element_diameters(self, cells, mode, square=False):
"""Get diameters of selected elements."""
field = self.field
domain = field.domain
cells = nm.array(cells)
diameters = nm.empty((cells.shape[0],), dtype=nm.float64)
integral = Integral('i_tmp', 1)
vg, _ = field.get_mapping(field.region, integral, 'cell')
diameters = domain.get_element_diameters(cells, vg.volume, mode,
square=square)
return diameters
[docs]
def save_as_mesh(self, filename):
"""
Save the field mesh and the variable values into a file for
visualization. Only the vertex values are stored.
"""
mesh = self.field.create_mesh(extra_nodes=False)
vec = self()
n_dof, dpn = self.n_dof, self.n_components
aux = nm.reshape(vec, (n_dof // dpn, dpn))
ext = self.field.extend_dofs(aux, 0.0)
out = {}
if self.field.approx_order != 0:
out[self.name] = Struct(name='output_data',
mode='vertex', data=ext,
var_name=self.name, dofs=self.dofs)
else:
ext.shape = (ext.shape[0], 1, ext.shape[1], 1)
out[self.name] = Struct(name='output_data',
mode='cell', data=ext,
var_name=self.name, dofs=self.dofs)
mesh.write(filename, io='auto', out=out)
[docs]
def has_same_mesh(self, other):
"""
Returns
-------
flag : int
The flag can be either 'different' (different meshes), 'deformed'
(slightly deformed same mesh), or 'same' (same).
"""
f1 = self.field
f2 = other.field
c1 = f1.get_coor()
c2 = f2.get_coor()
if c1.shape != c2.shape:
flag = 'different'
else:
eps = 10.0 * nm.finfo(nm.float64).eps
if nm.allclose(c1, c2, rtol=eps, atol=0.0):
flag = 'same'
elif nm.allclose(c1, c2, rtol=0.1, atol=0.0):
flag = 'deformed'
else:
flag = 'different'
return flag
[docs]
def get_interp_coors(self, strategy='interpolation', interp_term=None):
"""
Get the physical coordinates to interpolate into, based on the strategy
used.
"""
if strategy == 'interpolation':
coors = self.field.get_coor()
elif strategy == 'projection':
region = self.field.region
integral = Integral(term=interp_term)
coors = get_physical_qps(region, integral)
else:
raise ValueError('unknown interpolation strategy! (%s)' % strategy)
return coors
[docs]
def evaluate_at(self, coors, mode='val', strategy='general',
close_limit=0.1, get_cells_fun=None,
cache=None, ret_cells=False,
ret_status=False, ret_ref_coors=False, verbose=False):
"""
Evaluate the variable in the given physical coordinates. Convenience
wrapper around :func:`Field.evaluate_at()
<sfepy.discrete.common.fields.Field.evaluate_at()>`, see its
docstring for more details.
"""
source_vals = self().reshape((self.n_nod, self.n_components))
out = self.field.evaluate_at(coors, source_vals,
mode=mode,
strategy=strategy,
close_limit=close_limit,
get_cells_fun=get_cells_fun,
cache=cache,
ret_cells=ret_cells,
ret_status=ret_status,
ret_ref_coors=ret_ref_coors,
verbose=verbose)
return out
[docs]
def set_from_other(self, other, strategy='projection', close_limit=0.1):
"""
Set the variable using another variable. Undefined values (e.g. outside
the other mesh) are set to numpy.nan, or extrapolated.
Parameters
----------
strategy : 'projection' or 'interpolation'
The strategy to set the values: the L^2 orthogonal projection (not
implemented!), or a direct interpolation to the nodes (nodal
elements only!)
Notes
-----
If the other variable uses the same field mesh, the coefficients are
set directly.
"""
flag_same_mesh = self.has_same_mesh(other)
if flag_same_mesh == 'same':
self.set_data(other())
return
if strategy == 'interpolation':
coors = self.get_interp_coors(strategy)
elif strategy == 'projection':
## interp_term = Term() # TODO
## coors = self.get_interp_coors(strategy, interp_term)
pass
else:
raise ValueError('unknown interpolation strategy! (%s)' % strategy)
vals = other.evaluate_at(coors, strategy='general',
close_limit=close_limit)
if strategy == 'interpolation':
self.set_data(vals)
elif strategy == 'projection':
raise NotImplementedError('unsupported strategy! (%s)' % strategy)
else:
raise ValueError('unknown interpolation strategy! (%s)' % strategy)
[docs]
class DGFieldVariable(FieldVariable):
"""
Fieald variable specificaly intended for use with DGFields, bypasses
application of EBC and EPBC as this is done in DGField.
Is instance checked in create_adof_conns.
"""
def __init__(self, name, kind, field, order=None, primary_var_name=None,
special=None, flags=None, history=None, **kwargs):
FieldVariable.__init__(self, name, kind, field, order=order,
primary_var_name=primary_var_name,
special=special, flags=flags,
history=history, **kwargs)
from sfepy.discrete.dg.fields import DGField
if isinstance(field, DGField):
pass
else:
raise ValueError("Attempted to use DGFieldVariable with non DGField!")
[docs]
def apply_ebc(self, vec, offset=0, force_values=None):
pass
[docs]
def get_full(self, r_vec, r_offset=0, force_value=None,
vec=None, offset=0):
"""
Get the full DOF vector satisfying E(P)BCs from a reduced DOF
vector.
Notes
-----
The reduced vector starts in `r_vec` at `r_offset`.
Passing a `force_value` overrides the EBC values. Optionally,
`vec` argument can be provided to store the full vector (in
place) starting at `offset`.
"""
if vec is None:
vec = nm.empty(self.n_dof, dtype=r_vec.dtype)
else:
vec = vec[offset:offset+self.n_dof]
eq_map = self.eq_map
r_vec = r_vec[r_offset:r_offset+eq_map.n_eq]
# overide to hotfix second application of EBCs
# # EBC.
# vec[eq_map.eq_ebc] = get_default(force_value, eq_map.val_ebc)
# Reduced vector values, for DG this is full vector as eq_map.eq
# contains all dofs, cf. create_adof_conns
vec[eq_map.eqi] = r_vec
# EPBC.
# vec[eq_map.master] = vec[eq_map.slave]
unused_dofs = self.field.get('unused_dofs')
if unused_dofs is not None:
vec[:] = self.field.restore_substituted(vec)
return vec