def save_ebc(self, filename, force=True, default=0.0):
"""
Save essential boundary conditions as state variables.
"""
output('saving ebc...')
variables = self.get_variables(auto_create=True)
ebcs = Conditions.from_conf(self.conf.ebcs, self.domain.regions)
epbcs = Conditions.from_conf(self.conf.epbcs, self.domain.regions)
try:
variables.equation_mapping(ebcs, epbcs, self.ts, self.functions,
problem=self)
except:
output('cannot make equation mapping!')
raise
state = State(variables)
state.fill(default)
if force:
vals = dict_from_keys_init(variables.state)
for ii, key in enumerate(vals.iterkeys()):
vals[key] = ii + 1
state.apply_ebc(force_values=vals)
else:
state.apply_ebc()
out = state.create_output_dict(extend=True)
self.save_state(filename, out=out, fill_value=default)
output('...done')
def save_ebc(self, filename, force=True, default=0.0):
"""
Save essential boundary conditions as state variables.
"""
output("saving ebc...")
variables = self.get_variables(auto_create=True)
ebcs = Conditions.from_conf(self.conf.ebcs, self.domain.regions)
epbcs = Conditions.from_conf(self.conf.epbcs, self.domain.regions)
try:
variables.equation_mapping(ebcs, epbcs, self.ts, self.functions, problem=self)
except:
output("cannot make equation mapping!")
raise
state = State(variables)
state.fill(default)
if force:
vals = dict_from_keys_init(variables.state)
for ii, key in enumerate(vals.iterkeys()):
vals[key] = ii + 1
state.apply_ebc(force_values=vals)
else:
state.apply_ebc()
out = state.create_output_dict(extend=True)
self.save_state(filename, out=out, fill_value=default)
output("...done")
def __call__(self, problem=None, data=None):
from sfepy.base.base import select_by_names
from sfepy.discrete.variables import Variables
from sfepy.discrete.state import State
from sfepy.discrete.conditions import Conditions
problem = get_default(problem, self.problem)
conf_ebc = select_by_names(problem.conf.ebcs, self.ebcs)
conf_epbc = select_by_names(problem.conf.epbcs, self.epbcs)
ebcs = Conditions.from_conf(conf_ebc, problem.domain.regions)
epbcs = Conditions.from_conf(conf_epbc, problem.domain.regions)
conf_variables = select_by_names(problem.conf.variables, self.variable)
problem.set_variables(conf_variables)
variables = Variables.from_conf(conf_variables, problem.fields)
variables.equation_mapping(ebcs, epbcs, problem.ts, problem.functions)
state = State(variables)
state.fill(0.0)
state.apply_ebc()
corr_sol = CorrSolution(name=self.name,
state=state.get_parts())
self.save(corr_sol, problem, variables)
return corr_sol
def save_ebc(self, filename, ebcs=None, epbcs=None,
force=True, default=0.0):
"""
Save essential boundary conditions as state variables.
Parameters
----------
filename : str
The output file name.
ebcs : Conditions instance, optional
The essential (Dirichlet) boundary conditions. If not given,
`self.conf.ebcs` are used.
epbcs : Conditions instance, optional
The periodic boundary conditions. If not given, `self.conf.epbcs`
are used.
force : bool
If True, sequential nonzero values are forced to individual `ebcs`
so that the conditions are visible even when zero.
default : float
The default constant value of state vector.
"""
output('saving ebc...')
variables = self.get_variables(auto_create=True)
if ebcs is None:
ebcs = Conditions.from_conf(self.conf.ebcs, self.domain.regions)
if epbcs is None:
epbcs = Conditions.from_conf(self.conf.epbcs, self.domain.regions)
try:
variables.equation_mapping(ebcs, epbcs, self.ts, self.functions,
problem=self)
except:
output('cannot make equation mapping!')
raise
state = State(variables)
state.fill(default)
if force:
vals = dict_from_keys_init(variables.state)
for ii, key in enumerate(vals.iterkeys()):
vals[key] = ii + 1
state.apply_ebc(force_values=vals)
else:
state.apply_ebc()
out = state.create_output_dict(extend=True)
self.save_state(filename, out=out, fill_value=default)
output('...done')
def __call__(self, problem=None, data=None):
from sfepy.base.base import select_by_names
from sfepy.discrete.variables import Variables
from sfepy.discrete.state import State
from sfepy.discrete.conditions import Conditions
problem = get_default(problem, self.problem)
conf_ebc = select_by_names(problem.conf.ebcs, self.ebcs)
conf_epbc = select_by_names(problem.conf.epbcs, self.epbcs)
ebcs = Conditions.from_conf(conf_ebc, problem.domain.regions)
epbcs = Conditions.from_conf(conf_epbc, problem.domain.regions)
conf_variables = select_by_names(problem.conf.variables, self.variable)
problem.set_variables(conf_variables)
variables = Variables.from_conf(conf_variables, problem.fields)
variables.equation_mapping(ebcs, epbcs, problem.ts, problem.functions)
state = State(variables)
state.fill(0.0)
state.apply_ebc()
corr_sol = CorrSolution(name=self.name,
state=state.get_parts())
self.save(corr_sol, problem, variables)
return corr_sol
def solve(self, state0=None, nls_status=None, ls_conf=None, nls_conf=None, force_values=None, var_data=None):
"""Solve self.equations in current time step.
Parameters
----------
var_data : dict
A dictionary of {variable_name : data vector} used to initialize
parameter variables.
"""
solvers = self.get_solvers()
if solvers is None:
self.init_solvers(nls_status, ls_conf, nls_conf)
solvers = self.get_solvers()
if state0 is None:
state0 = State(self.equations.variables)
else:
if isinstance(state0, nm.ndarray):
state0 = State(self.equations.variables, vec=state0)
self.equations.set_data(var_data, ignore_unknown=True)
self.update_materials()
state0.apply_ebc(force_values=force_values)
vec0 = state0.get_reduced()
self.nls_status = get_default(nls_status, self.nls_status)
vec = solvers.nls(vec0, status=self.nls_status)
state = state0.copy(preserve_caches=True)
state.set_reduced(vec, preserve_caches=True)
return state
def __init__(self, conf, problem, **kwargs):
from sfepy.discrete.state import State
ScipyDirect.__init__(self, conf, **kwargs)
equations = problem.equations
aux_state = State(equations.variables)
conf.idxs = {}
for bk, bv in conf.blocks.iteritems():
aux_state.fill(0.0)
for jj in bv:
idx = equations.variables.di.indx[jj]
aux_state.vec[idx] = nm.nan
aux_state.apply_ebc()
vec0 = aux_state.get_reduced()
conf.idxs[bk] = nm.where(nm.isnan(vec0))[0]
def __init__(self, conf, **kwargs):
from sfepy.discrete.state import State
ScipyDirect.__init__(self, conf, **kwargs)
equations = self.problem.equations
aux_state = State(equations.variables)
conf.idxs = {}
for bk, bv in conf.blocks.iteritems():
aux_state.fill(0.0)
for jj in bv:
idx = equations.variables.di.indx[jj]
aux_state.vec[idx] = nm.nan
aux_state.apply_ebc()
vec0 = aux_state.get_reduced()
conf.idxs[bk] = nm.where(nm.isnan(vec0))[0]
def solve(self, state0=None, nls_status=None,
ls_conf=None, nls_conf=None, force_values=None,
var_data=None):
"""Solve self.equations in current time step.
Parameters
----------
var_data : dict
A dictionary of {variable_name : data vector} used to initialize
parameter variables.
"""
solvers = self.get_solvers()
if solvers is None:
self.init_solvers(nls_status, ls_conf, nls_conf)
solvers = self.get_solvers()
if state0 is None:
state0 = State(self.equations.variables)
else:
if isinstance(state0, nm.ndarray):
state0 = State(self.equations.variables, vec=state0)
self.equations.set_data(var_data, ignore_unknown=True)
self.update_materials()
state0.apply_ebc(force_values=force_values)
vec0 = state0.get_reduced()
self.nls_status = get_default(nls_status, self.nls_status)
vec = solvers.nls(vec0, status=self.nls_status)
state = state0.copy(preserve_caches=True)
state.set_reduced(vec, preserve_caches=True)
return state
def init_subproblems(self, conf, **kwargs):
from sfepy.discrete.state import State
from sfepy.discrete import Problem
from sfepy.base.conf import ProblemConf, get_standard_keywords
from scipy.spatial import cKDTree as KDTree
# init subproblems
problem = self.context
pb_vars = problem.get_variables()
# get "master" DofInfo and last index
pb_adi_indx = problem.equations.variables.adi.indx
self.adi_indx = pb_adi_indx.copy()
last_indx = -1
for ii in six.itervalues(self.adi_indx):
last_indx = nm.max([last_indx, ii.stop])
# coupling variables
self.cvars_to_pb = {}
for jj in conf.coupling_variables:
self.cvars_to_pb[jj] = [None, None]
if jj in pb_vars.names:
if pb_vars[jj].dual_var_name is not None:
self.cvars_to_pb[jj][0] = -1
else:
self.cvars_to_pb[jj][1] = -1
# init subproblems
self.subpb = []
required, other = get_standard_keywords()
master_prefix = output.get_output_prefix()
for ii, ifname in enumerate(conf.others):
sub_prefix = master_prefix[:-1] + '-sub%d:' % (ii + 1)
output.set_output_prefix(sub_prefix)
kwargs['master_problem'] = problem
confi = ProblemConf.from_file(ifname,
required,
other,
define_args=kwargs)
pbi = Problem.from_conf(confi, init_equations=True)
sti = State(pbi.equations.variables)
pbi.equations.set_data(None, ignore_unknown=True)
pbi.time_update()
pbi.update_materials()
sti.apply_ebc()
pbi_vars = pbi.get_variables()
output.set_output_prefix(master_prefix)
self.subpb.append([pbi, sti, None])
# append "slave" DofInfo
for jj in pbi_vars.names:
if not (pbi_vars[jj].is_state()):
continue
didx = pbi.equations.variables.adi.indx[jj]
ndof = didx.stop - didx.start
if jj in self.adi_indx:
if ndof != \
(self.adi_indx[jj].stop - self.adi_indx[jj].start):
raise ValueError('DOFs do not match!')
else:
self.adi_indx.update(
{jj: slice(last_indx, last_indx + ndof, None)})
last_indx += ndof
for jj in conf.coupling_variables:
if jj in pbi_vars.names:
if pbi_vars[jj].dual_var_name is not None:
self.cvars_to_pb[jj][0] = ii
else:
self.cvars_to_pb[jj][1] = ii
self.subpb.append([problem, None, None])
self.cvars_to_pb_map = {}
for varname, pbs in six.iteritems(self.cvars_to_pb):
# match field nodes
coors = []
for ii in pbs:
pbi = self.subpb[ii][0]
pbi_vars = pbi.get_variables()
fcoors = pbi_vars[varname].field.coors
dc = nm.abs(nm.max(fcoors, axis=0)\
- nm.min(fcoors, axis=0))
ax = nm.where(dc > 1e-9)[0]
coors.append(fcoors[:, ax])
if len(coors[0]) != len(coors[1]):
raise ValueError('number of nodes does not match!')
kdtree = KDTree(coors[0])
map_12 = kdtree.query(coors[1])[1]
pbi1 = self.subpb[pbs[0]][0]
pbi1_vars = pbi1.get_variables()
eq_map_1 = pbi1_vars[varname].eq_map
pbi2 = self.subpb[pbs[1]][0]
pbi2_vars = pbi2.get_variables()
eq_map_2 = pbi2_vars[varname].eq_map
dpn = eq_map_2.dpn
nnd = map_12.shape[0]
map_12_nd = nm.zeros((nnd * dpn, ), dtype=nm.int32)
if dpn > 1:
for ii in range(dpn):
map_12_nd[ii::dpn] = map_12 * dpn + ii
else:
map_12_nd = map_12
idx = nm.where(eq_map_2.eq >= 0)[0]
self.cvars_to_pb_map[varname] = eq_map_1.eq[map_12[idx]]
def __call__(self,
rhs,
x0=None,
conf=None,
eps_a=None,
eps_r=None,
i_max=None,
mtx=None,
status=None,
**kwargs):
from sfepy.discrete.state import State
equations = self.context.equations
aux_state = State(equations.variables)
mtxi = {}
for bk, bv in six.iteritems(conf.blocks):
aux_state.fill(0.0)
for jj in bv:
idx = equations.variables.di.indx[jj]
aux_state.vec[idx] = nm.nan
aux_state.apply_ebc()
vec0 = aux_state.get_reduced()
mtxi[bk] = nm.where(nm.isnan(vec0))[0]
mtxslc_s = {}
mtxslc_f = {}
nn = {}
for ik, iv in six.iteritems(mtxi):
ptr = 0
nn[ik] = len(iv)
mtxslc_s[ik] = []
mtxslc_f[ik] = []
while ptr < nn[ik]:
idx0 = iv[ptr:]
idxrange = nm.arange(idx0[0], idx0[0] + len(idx0))
aux = nm.where(idx0 == idxrange)[0]
mtxslc_s[ik].append(slice(ptr + aux[0], ptr + aux[-1] + 1))
mtxslc_f[ik].append(slice(idx0[aux][0], idx0[aux][-1] + 1))
ptr += aux[-1] + 1
mtxs = {}
rhss = {}
ress = {}
get_sub = mtx._get_submatrix
for ir in six.iterkeys(mtxi):
rhss[ir] = nm.zeros((nn[ir], ), dtype=nm.float64)
ress[ir] = nm.zeros((nn[ir], ), dtype=nm.float64)
for jr, idxr in enumerate(mtxslc_f[ir]):
rhss[ir][mtxslc_s[ir][jr]] = rhs[idxr]
for ic in six.iterkeys(mtxi):
mtxid = '%s%s' % (ir, ic)
mtxs[mtxid] = nm.zeros((nn[ir], nn[ic]), dtype=nm.float64)
for jr, idxr in enumerate(mtxslc_f[ir]):
for jc, idxc in enumerate(mtxslc_f[ic]):
iir = mtxslc_s[ir][jr]
iic = mtxslc_s[ic][jc]
mtxs[mtxid][iir, iic] = get_sub(idxr, idxc).todense()
self.orig_conf.function(ress, mtxs, rhss, nn)
res = nm.zeros_like(rhs)
for ir in six.iterkeys(mtxi):
for jr, idxr in enumerate(mtxslc_f[ir]):
res[idxr] = ress[ir][mtxslc_s[ir][jr]]
return res
def test_ls_reuse(self):
import numpy as nm
from sfepy.solvers import Solver
from sfepy.discrete.state import State
self.problem.init_solvers(ls_conf=self.problem.solver_confs['d00'])
nls = self.problem.get_nls()
state0 = State(self.problem.equations.variables)
state0.apply_ebc()
vec0 = state0.get_reduced()
self.problem.update_materials()
rhs = nls.fun(vec0)
mtx = nls.fun_grad(vec0)
ok = True
for name in ['i12', 'i01']:
solver_conf = self.problem.solver_confs[name]
method = solver_conf.get('method', '')
precond = solver_conf.get('precond', '')
name = ' '.join((solver_conf.name, solver_conf.kind, method,
precond)).rstrip()
self.report(name)
try:
ls = Solver.any_from_conf(solver_conf)
except:
self.report('skipped!')
continue
conf = ls.conf.copy()
conf.force_reuse = True
sol00 = ls(rhs, mtx=mtx, conf=conf)
digest00 = ls.mtx_digest
sol0 = ls(rhs, mtx=mtx)
digest0 = ls.mtx_digest
sol1 = ls(rhs, mtx=2 * mtx, conf=conf)
digest1 = ls.mtx_digest
sol2 = ls(rhs, mtx=2 * mtx)
digest2 = ls.mtx_digest
ls(rhs, mtx=2 * mtx)
digest3 = ls.mtx_digest
_ok = digest00 != digest0
self.report(digest00, '!=', digest0, ':', _ok)
ok = ok and _ok
_ok = digest0 == digest1
self.report(digest0, '==', digest1, ':', _ok)
ok = ok and _ok
_ok = digest1 != digest2
self.report(digest1, '!=', digest2, ':', _ok)
ok = ok and _ok
_ok = digest2[1] == digest3[1]
self.report(digest2[1], '==', digest3[1], ':', _ok)
ok = ok and _ok
_ok = nm.allclose(sol00, sol0, atol=1e-12, rtol=0.0)
self.report('sol00 == sol0:', _ok)
ok = ok and _ok
_ok = nm.allclose(sol0, sol1, atol=1e-12, rtol=0.0)
self.report('sol0 == sol1:', _ok)
ok = ok and _ok
_ok = nm.allclose(sol0, 2 * sol2, atol=1e-12, rtol=0.0)
self.report('sol0 == 2 * sol2:', _ok)
ok = ok and _ok
return ok
def __init__(self, conf, problem, **kwargs):
from sfepy.discrete.state import State
from sfepy.discrete import Problem
from sfepy.base.conf import ProblemConf, get_standard_keywords
from scipy.spatial import cKDTree as KDTree
ScipyDirect.__init__(self, conf, **kwargs)
# init subproblems
pb_vars = problem.get_variables()
# get "master" DofInfo and last index
pb_adi_indx = problem.equations.variables.adi.indx
self.adi_indx = pb_adi_indx.copy()
last_indx = -1
for ii in self.adi_indx.itervalues():
last_indx = nm.max([last_indx, ii.stop])
# coupling variables
self.cvars_to_pb = {}
for jj in conf.coupling_variables:
self.cvars_to_pb[jj] = [None, None]
if jj in pb_vars.names:
if pb_vars[jj].dual_var_name is not None:
self.cvars_to_pb[jj][0] = -1
else:
self.cvars_to_pb[jj][1] = -1
# init subproblems
self.subpb = []
required, other = get_standard_keywords()
master_prefix = output.get_output_prefix()
for ii, ifname in enumerate(conf.others):
sub_prefix = master_prefix[:-1] + '-sub%d:' % (ii + 1)
output.set_output_prefix(sub_prefix)
kwargs['master_problem'] = problem
confi = ProblemConf.from_file(ifname, required, other,
define_args=kwargs)
pbi = Problem.from_conf(confi, init_equations=True)
sti = State(pbi.equations.variables)
pbi.equations.set_data(None, ignore_unknown=True)
pbi.time_update()
pbi.update_materials()
sti.apply_ebc()
pbi_vars = pbi.get_variables()
output.set_output_prefix(master_prefix)
self.subpb.append([pbi, sti, None])
# append "slave" DofInfo
for jj in pbi_vars.names:
if not(pbi_vars[jj].is_state()):
continue
didx = pbi.equations.variables.adi.indx[jj]
ndof = didx.stop - didx.start
if jj in self.adi_indx:
if ndof != \
(self.adi_indx[jj].stop - self.adi_indx[jj].start):
raise ValueError('DOFs do not match!')
else:
self.adi_indx.update({
jj: slice(last_indx, last_indx + ndof, None)})
last_indx += ndof
for jj in conf.coupling_variables:
if jj in pbi_vars.names:
if pbi_vars[jj].dual_var_name is not None:
self.cvars_to_pb[jj][0] = ii
else:
self.cvars_to_pb[jj][1] = ii
self.subpb.append([problem, None, None])
self.cvars_to_pb_map = {}
for varname, pbs in self.cvars_to_pb.iteritems():
# match field nodes
coors = []
for ii in pbs:
pbi = self.subpb[ii][0]
pbi_vars = pbi.get_variables()
fcoors = pbi_vars[varname].field.coors
dc = nm.abs(nm.max(fcoors, axis=0)\
- nm.min(fcoors, axis=0))
ax = nm.where(dc > 1e-9)[0]
coors.append(fcoors[:,ax])
if len(coors[0]) != len(coors[1]):
raise ValueError('number of nodes does not match!')
kdtree = KDTree(coors[0])
map_12 = kdtree.query(coors[1])[1]
pbi1 = self.subpb[pbs[0]][0]
pbi1_vars = pbi1.get_variables()
eq_map_1 = pbi1_vars[varname].eq_map
pbi2 = self.subpb[pbs[1]][0]
pbi2_vars = pbi2.get_variables()
eq_map_2 = pbi2_vars[varname].eq_map
dpn = eq_map_2.dpn
nnd = map_12.shape[0]
map_12_nd = nm.zeros((nnd * dpn,), dtype=nm.int32)
if dpn > 1:
for ii in range(dpn):
map_12_nd[ii::dpn] = map_12 * dpn + ii
else:
map_12_nd = map_12
idx = nm.where(eq_map_2.eq >= 0)[0]
self.cvars_to_pb_map[varname] = eq_map_1.eq[map_12[idx]]
def __call__(self, rhs, x0=None, conf=None, eps_a=None, eps_r=None,
i_max=None, mtx=None, status=None, **kwargs):
from sfepy.discrete.state import State
equations = self.context.equations
aux_state = State(equations.variables)
mtxi = {}
for bk, bv in six.iteritems(conf.blocks):
aux_state.fill(0.0)
for jj in bv:
idx = equations.variables.di.indx[jj]
aux_state.vec[idx] = nm.nan
aux_state.apply_ebc()
vec0 = aux_state.get_reduced()
mtxi[bk] = nm.where(nm.isnan(vec0))[0]
mtxslc_s = {}
mtxslc_f = {}
nn = {}
for ik, iv in six.iteritems(mtxi):
ptr = 0
nn[ik] = len(iv)
mtxslc_s[ik] = []
mtxslc_f[ik] = []
while ptr < nn[ik]:
idx0 = iv[ptr:]
idxrange = nm.arange(idx0[0], idx0[0] + len(idx0))
aux = nm.where(idx0 == idxrange)[0]
mtxslc_s[ik].append(slice(ptr + aux[0], ptr + aux[-1] + 1))
mtxslc_f[ik].append(slice(idx0[aux][0], idx0[aux][-1] + 1))
ptr += aux[-1] + 1
mtxs = {}
rhss = {}
ress = {}
get_sub = mtx._get_submatrix
for ir in six.iterkeys(mtxi):
rhss[ir] = nm.zeros((nn[ir],), dtype=nm.float64)
ress[ir] = nm.zeros((nn[ir],), dtype=nm.float64)
for jr, idxr in enumerate(mtxslc_f[ir]):
rhss[ir][mtxslc_s[ir][jr]] = rhs[idxr]
for ic in six.iterkeys(mtxi):
mtxid = '%s%s' % (ir, ic)
mtxs[mtxid] = nm.zeros((nn[ir], nn[ic]), dtype=nm.float64)
for jr, idxr in enumerate(mtxslc_f[ir]):
for jc, idxc in enumerate(mtxslc_f[ic]):
iir = mtxslc_s[ir][jr]
iic = mtxslc_s[ic][jc]
mtxs[mtxid][iir, iic] = get_sub(idxr, idxc).todense()
self.orig_conf.function(ress, mtxs, rhss, nn)
res = nm.zeros_like(rhs)
for ir in six.iterkeys(mtxi):
for jr, idxr in enumerate(mtxslc_f[ir]):
res[idxr] = ress[ir][mtxslc_s[ir][jr]]
return res
def test_ls_reuse(self):
import numpy as nm
from sfepy.solvers import Solver
from sfepy.discrete.state import State
self.problem.init_solvers(ls_conf=self.problem.solver_confs['d00'])
nls = self.problem.get_nls()
state0 = State(self.problem.equations.variables)
state0.apply_ebc()
vec0 = state0.get_reduced()
self.problem.update_materials()
rhs = nls.fun(vec0)
mtx = nls.fun_grad(vec0)
ok = True
for name in ['i12', 'i01']:
solver_conf = self.problem.solver_confs[name]
method = solver_conf.get('method', '')
precond = solver_conf.get('precond', '')
name = ' '.join((solver_conf.name, solver_conf.kind,
method, precond)).rstrip()
self.report(name)
try:
ls = Solver.any_from_conf(solver_conf)
except:
self.report('skipped!')
continue
conf = ls.conf.copy()
conf.force_reuse = True
sol00 = ls(rhs, mtx=mtx, conf=conf)
digest00 = ls.mtx_digest
sol0 = ls(rhs, mtx=mtx)
digest0 = ls.mtx_digest
sol1 = ls(rhs, mtx=2*mtx, conf=conf)
digest1 = ls.mtx_digest
sol2 = ls(rhs, mtx=2*mtx)
digest2 = ls.mtx_digest
ls(rhs, mtx=2*mtx)
digest3 = ls.mtx_digest
_ok = digest00 != digest0
self.report(digest00, '!=', digest0, ':', _ok); ok = ok and _ok
_ok = digest0 == digest1
self.report(digest0, '==', digest1, ':', _ok); ok = ok and _ok
_ok = digest1 != digest2
self.report(digest1, '!=', digest2, ':', _ok); ok = ok and _ok
_ok = digest2[1] == digest3[1]
self.report(digest2[1], '==', digest3[1], ':', _ok); ok = ok and _ok
_ok = nm.allclose(sol00, sol0, atol=1e-12, rtol=0.0)
self.report('sol00 == sol0:', _ok); ok = ok and _ok
_ok = nm.allclose(sol0, sol1, atol=1e-12, rtol=0.0)
self.report('sol0 == sol1:', _ok); ok = ok and _ok
_ok = nm.allclose(sol0, 2 * sol2, atol=1e-12, rtol=0.0)
self.report('sol0 == 2 * sol2:', _ok); ok = ok and _ok
return ok
def create_state(self):
return State(self.equations.variables)
