def test_minModelStep_stepExactFalse():
pList = [load_p('ladr_2d_p', modulepath)]
nList = [load_n('ladr_2d_n', modulepath)]
so = So()
so.name = pList[0].name = "ladr"
so.tnList = nList[0].tnList
so.systemStepControllerType = SplitOperator.Sequential_MinModelStep
so.systemStepExact = False
so.sList = [default_s]
opts.logLevel = 7
opts.verbose = True
opts.profile = True
opts.gatherArchive = True
nList[0].runCFL = 0.33
nList[0].linearSolver = default_n.LU
nList[0].multilevelLinearSolver = default_n.LU
ns = NumericalSolution.NS_base(so, pList, nList, so.sList, opts)
ns.calculateSolution('ladr_minModelStep_stepExactFalse')
assert ns.tCount + 1 == len(
so.tnList), "wrong number of archvie steps " + repr(ns.tCount)
assert ns.modelList[0].solver.solverList[
0].solveCalls == 34, "wrong number of steps " + repr(
ns.modelList[0].solver.solverList[0].solveCalls)
archiveTimes = []
for t in ns.ar[0].treeGlobal.iter('Time'):
archiveTimes.append(t.attrib['Value'])
archiveTimesCorrect = [
'0.0', '0.029516097303', '0.0516531702802', '0.0811692675832',
'0.10330634056', '0.125443413538', '0.154959510841', '0.177096583818',
'0.206612681121', '0.228749754098', '0.250886827075'
]
npt.assert_almost_equal(np.array(archiveTimes, 'd'),
np.array(archiveTimesCorrect, 'd'))
del ns
def test_minModelStep_stepExactTrue():
pList = [load_p('ladr_2d_p', modulepath)]
nList = [load_n('ladr_2d_n', modulepath)]
so = So()
so.name = pList[0].name = "ladr"
so.tnList = nList[0].tnList
so.systemStepControllerType = SplitOperator.Sequential_MinModelStep
so.systemStepExact = True
so.sList = [default_s]
opts.logLevel = 7
opts.verbose = True
opts.profile = True
opts.gatherArchive = True
nList[0].runCFL = 0.33
nList[0].linearSolver = default_n.LU
nList[0].multilevelLinearSolver = default_n.LU
ns = NumericalSolution.NS_base(so, pList, nList, so.sList, opts)
ns.calculateSolution('ladr_minModelStep_stepExactTrue')
assert ns.tCount + 1 == len(
so.tnList), "wrong number of archvie steps " + repr(ns.tCount)
assert ns.modelList[0].solver.solverList[
0].solveCalls == 40, "wrong number of steps " + repr(
ns.modelList[0].solver.solverList[0].solveCalls)
archiveTimes = []
for t in ns.ar[0].treeGlobal.iter('Time'):
archiveTimes.append(t.attrib['Value'])
archiveTimesCorrect = so.tnList
npt.assert_almost_equal(np.array(archiveTimes, 'd'),
np.array(archiveTimesCorrect, 'd'))
del ns
def test_minModelStep_stepExactTrue():
pList = [load_p('ladr_2d_p', modulepath)]
nList = [load_n('ladr_2d_n', modulepath)]
so = So()
so.name = pList[0].name = "ladr"
so.tnList = nList[0].tnList
so.systemStepControllerType = SplitOperator.Sequential_MinModelStep
so.systemStepExact=True
so.sList=[default_s]
opts.logLevel=7
opts.verbose=True
opts.profile=True
opts.gatherArchive=True
nList[0].runCFL=0.33
nList[0].linearSolver=default_n.LU
nList[0].multilevelLinearSolver=default_n.LU
ns = NumericalSolution.NS_base(so,pList,nList,so.sList,opts)
ns.calculateSolution('ladr_minModelStep_stepExactTrue')
assert ns.tCount + 1 == len(so.tnList), "wrong number of archvie steps "+`ns.tCount`
assert ns.modelList[0].solver.solverList[0].solveCalls == 40, "wrong number of steps "+`ns.modelList[0].solver.solverList[0].solveCalls`
archiveTimes=[]
for t in ns.ar[0].treeGlobal.iter('Time'):
archiveTimes.append(t.attrib['Value'])
archiveTimesCorrect = so.tnList
npt.assert_almost_equal(np.array(archiveTimes,'d'), np.array(archiveTimesCorrect,'d'))
del ns
def test_minModelStep_stepExactFalse():
pList = [load_p('ladr_2d_p', modulepath)]
nList = [load_n('ladr_2d_n', modulepath)]
so = So()
so.name = pList[0].name = "ladr"
so.tnList = nList[0].tnList
so.systemStepControllerType = SplitOperator.Sequential_MinModelStep
so.systemStepExact=False
so.sList=[default_s]
opts.logLevel=7
opts.verbose=True
opts.profile=True
opts.gatherArchive=True
nList[0].runCFL=0.33
nList[0].linearSolver=default_n.LU
nList[0].multilevelLinearSolver=default_n.LU
ns = NumericalSolution.NS_base(so,pList,nList,so.sList,opts)
ns.calculateSolution('ladr_minModelStep_stepExactFalse')
assert ns.tCount + 1 == len(so.tnList), "wrong number of archvie steps " +`ns.tCount`
assert ns.modelList[0].solver.solverList[0].solveCalls == 34, "wrong number of steps "+`ns.modelList[0].solver.solverList[0].solveCalls`
archiveTimes=[]
for t in ns.ar[0].treeGlobal.iter('Time'):
archiveTimes.append(t.attrib['Value'])
archiveTimesCorrect = ['0.0', '0.029516097303', '0.0516531702802', '0.0811692675832', '0.10330634056', '0.125443413538', '0.154959510841', '0.177096583818', '0.206612681121', '0.228749754098', '0.250886827075']
npt.assert_almost_equal(np.array(archiveTimes,'d'), np.array(archiveTimesCorrect,'d'))
del ns
def test_run(self):
from petsc4py import PETSc
pList = []
nList = []
so = load_so('nonlinear_waves_so',modulepath)
for (p,n) in so.pnList:
pList.append(load_p(p,modulepath))
nList.append(load_n(n,modulepath))
if pList[-1].name == None:
pList[-1].name = p
so.name = "nonlinear_waves"
if so.sList == []:
for i in range(len(so.pnList)):
s = default_s
so.sList.append(s)
Profiling.logLevel=7
Profiling.verbose=True
# PETSc solver configuration
OptDB = PETSc.Options()
with open(petsc_options) as f:
all = f.read().split()
i=0
while i < len(all):
if i < len(all)-1:
if all[i+1][0]!='-':
print "setting ", all[i].strip(), all[i+1]
OptDB.setValue(all[i].strip('-'),all[i+1])
i=i+2
else:
print "setting ", all[i].strip(), "True"
OptDB.setValue(all[i].strip('-'),True)
i=i+1
else:
print "setting ", all[i].strip(), "True"
OptDB.setValue(all[i].strip('-'),True)
i=i+1
so.tnList=[0.0,0.001,0.011]
#so.tnList=[0.0,0.001]+[0.001 + i*0.01 for i in range(1,int(round(0.03/0.01))+1)]
ns = NumericalSolution.NS_base(so,pList,nList,so.sList,opts)
ns.calculateSolution('nonlinear_waves')
non_linear_log = NumericResults.create_log('proteus.log')
text = non_linear_log.read()
#if text.find('CFL') != -1:
if text.find('Step Failed,') != -1:
a = "No convergence"
else:
a = "good"
if a == "No convergence":
print ("Convergence issue")
assert False
else:
assert True
