def setUp(self):
DCST = 0.08e-10
self.model = smodel.Model()
A = smodel.Spec('A', self.model)
self.vsys = smodel.Volsys('vsys', self.model)
self.ssys = smodel.Surfsys('ssys', self.model)
self.diff = smodel.Diff("diff", self.vsys, A, DCST)
self.sdiff = smodel.Diff("diff", self.ssys, A, DCST)
if __name__ == "__main__":
self.mesh = meshio.importAbaqus('meshes/test_mesh.inp', 1e-7)[0]
else:
self.mesh = meshio.importAbaqus(
'parallel_std_string_bugfix_test/meshes/test_mesh.inp',
1e-7)[0]
self.tmcomp = sgeom.TmComp('comp', self.mesh, range(self.mesh.ntets))
self.tmcomp.addVolsys('vsys')
self.surf_tris = self.mesh.getSurfTris()
self.tmpatch = sgeom.TmPatch('patch',
self.mesh,
self.surf_tris,
icomp=self.tmcomp)
self.tmpatch.addSurfsys('ssys')
self.rng = srng.create('r123', 512)
self.rng.initialize(1000)
tet_hosts = gd.binTetsByAxis(self.mesh, steps.mpi.nhosts)
tri_hosts = gd.partitionTris(self.mesh, tet_hosts, self.surf_tris)
self.solver = solv.TetOpSplit(self.model, self.mesh, self.rng,
solv.EF_NONE, tet_hosts, tri_hosts)
self.solver.reset()
def testSetGetTriCount(self):
tet_hosts = gd.binTetsByAxis(self.mesh, steps.mpi.nhosts)
tri_hosts = gd.partitionTris(self.mesh, tet_hosts, self.surf_tris)
solver = solv.TetOpSplit(self.model, self.mesh, self.rng, solv.EF_NONE,
tet_hosts, tri_hosts)
for tri in self.surf_tris:
solver.setTriCount(tri, 'A', tri)
get_count = solver.getTriCount(tri, 'A')
self.assertEqual(get_count, tri)
def testDiffSel(self):
tet_hosts = gd.binTetsByAxis(self.mesh, steps.mpi.nhosts)
solver = solv.TetOpSplit(self.model, self.mesh, self.rng, solv.EF_NONE, tet_hosts)
solver.setCompCount('comp', 'A', 1)
if __name__ == "__main__":
print("Running...")
solver.run(0.001)
if __name__ == "__main__":
print("")
print("A:", solver.getCompCount("comp", "A"))
print("steps:", solver.getNSteps())
self.assertEqual(solver.getCompCount("comp", "A"), 1)
self.assertNotEqual(solver.getNSteps(), 0)
def host_assignment_by_axis(mesh, tri_set):
def tet_neighbs(tri):
return [tet for tet in mesh.getTriTetNeighb(tri) if tet != -1]
tet_hosts = gd.binTetsByAxis(mesh, steps.mpi.nhosts)
tri_hosts = {}
for part in consistent_neighbourhood_part(mesh, tri_set):
tets = set.union(*(set(tet_neighbs(tri)) for tri in part))
if not tets: continue
tet0 = tets.pop()
host = tet_hosts[tet0]
for tri in part:
tri_hosts[tri] = host
for tet in tets:
tet_hosts[tet] = host
return (tet_hosts, tri_hosts)
def get_solver(mdl, geom):
r = srng.create('r123', 1000)
tet_hosts = gd.binTetsByAxis(geom, steps.mpi.nhosts)
solver = ssolv.TetOpSplit(mdl, geom, r, ssolv.EF_NONE, tet_hosts)
return solver
def test_unbdiff2D_linesource_ring():
"Surface Diffusion - Unbounded, line source (Parallel TetOpSplit)"
m = gen_model()
g, patch_tris, partition_tris, patch_tris_n, inject_tris, tridists, triareas = gen_geom(
)
tet_hosts = gd.binTetsByAxis(g, steps.mpi.nhosts)
tri_hosts = gd.partitionTris(g, tet_hosts, partition_tris)
sim = solvmod.TetOpSplit(m, g, rng, False, tet_hosts, tri_hosts)
tpnts = np.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
#Create the big old data structure: iterations x time points x concentrations
res_count = np.zeros((NITER, ntpnts, patch_tris_n))
res_conc = np.zeros((NITER, ntpnts, patch_tris_n))
for j in range(NITER):
sim.reset()
for t in inject_tris:
sim.setTriCount(t, 'X', float(NINJECT) / len(inject_tris))
for i in range(ntpnts):
sim.run(tpnts[i])
for k in range(patch_tris_n):
res_count[j, i, k] = sim.getTriCount(patch_tris[k], 'X')
res_conc[j, i,
k] = 1e-12 * (sim.getTriCount(patch_tris[k], 'X') /
sim.getTriArea(patch_tris[k]))
itermeans_count = np.mean(res_count, axis=0)
itermeans_conc = np.mean(res_conc, axis=0)
########################################################################
tpnt_compare = [100, 150]
passed = True
max_err = 0.0
for t in tpnt_compare:
bin_n = 50
r_min = 0
r_max = 0
for i in tridists:
if (i > r_max): r_max = i
if (i < r_min): r_min = i
r_seg = (r_max - r_min) / bin_n
bin_mins = np.zeros(bin_n + 1)
r_tris_binned = np.zeros(bin_n)
bin_areas = np.zeros(bin_n)
r = r_min
for b in range(bin_n + 1):
bin_mins[b] = r
if (b != bin_n): r_tris_binned[b] = r + r_seg / 2.0
r += r_seg
bin_counts = [None] * bin_n
for i in range(bin_n):
bin_counts[i] = []
for i in range((itermeans_count[t].size)):
i_r = tridists[i]
for b in range(bin_n):
if (i_r >= bin_mins[b] and i_r < bin_mins[b + 1]):
bin_counts[b].append(itermeans_count[t][i])
bin_areas[b] += sim.getTriArea(int(patch_tris[i]))
break
bin_concs = np.zeros(bin_n)
for c in range(bin_n):
for d in range(bin_counts[c].__len__()):
bin_concs[c] += bin_counts[c][d]
bin_concs[c] /= (bin_areas[c] * 1.0e12)
for i in range(bin_n):
if (r_tris_binned[i] > -10.0 and r_tris_binned[i] < -2.0) \
or (r_tris_binned[i] > 2.0 and r_tris_binned[i] < 10.0):
dist = r_tris_binned[i] * 1e-6
det_conc = 1e-6 * (NINJECT / (4 * np.sqrt(
(np.pi * DCST * tpnts[t])))) * (np.exp(
(-1.0 * (dist * dist)) / (4 * DCST * tpnts[t])))
steps_conc = bin_concs[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
def test_kisilevich():
"Reaction-diffusion - Degradation-diffusion (Parallel TetOpSplit)"
NITER = 50 # The number of iterations
DT = 0.1 # Sampling time-step
INT = 0.3 # Sim endtime
DCSTA = 400 * 1e-12
DCSTB = DCSTA
RCST = 100000.0e6
#NA0 = 100000 # 1000000 # Initial number of A molecules
NA0 = 1000
NB0 = NA0 # Initial number of B molecules
SAMPLE = 1686
# <1% fail with a tolerance of 7.5%
tolerance = 7.5 / 100
# create the array of tet indices to be found at random
tetidxs = numpy.zeros(SAMPLE, dtype='int')
# further create the array of tet barycentre distance to centre
tetrads = numpy.zeros(SAMPLE)
mdl = smod.Model()
A = smod.Spec('A', mdl)
B = smod.Spec('B', mdl)
volsys = smod.Volsys('vsys', mdl)
R1 = smod.Reac('R1', volsys, lhs=[A, B], rhs=[])
R1.setKcst(RCST)
D_a = smod.Diff('D_a', volsys, A)
D_a.setDcst(DCSTA)
D_b = smod.Diff('D_b', volsys, B)
D_b.setDcst(DCSTB)
mesh = meshio.loadMesh('validation_rd_mpi/meshes/brick_40_4_4_1686tets')[0]
VOLA = mesh.getMeshVolume() / 2.0
VOLB = VOLA
ntets = mesh.countTets()
acomptets = []
bcomptets = []
max = mesh.getBoundMax()
min = mesh.getBoundMax()
midz = 0.0
compatris = set()
compbtris = set()
for t in range(ntets):
barycz = mesh.getTetBarycenter(t)[0]
tris = mesh.getTetTriNeighb(t)
if barycz < midz:
acomptets.append(t)
compatris.add(tris[0])
compatris.add(tris[1])
compatris.add(tris[2])
compatris.add(tris[3])
else:
bcomptets.append(t)
compbtris.add(tris[0])
compbtris.add(tris[1])
compbtris.add(tris[2])
compbtris.add(tris[3])
dbset = compatris.intersection(compbtris)
dbtris = list(dbset)
compa = sgeom.TmComp('compa', mesh, acomptets)
compb = sgeom.TmComp('compb', mesh, bcomptets)
compa.addVolsys('vsys')
compb.addVolsys('vsys')
diffb = sgeom.DiffBoundary('diffb', mesh, dbtris)
# Now fill the array holding the tet indices to sample at random
assert (SAMPLE <= ntets)
numfilled = 0
while (numfilled < SAMPLE):
tetidxs[numfilled] = numfilled
numfilled += 1
# Now find the distance of the centre of the tets to the Z lower face
for i in range(SAMPLE):
baryc = mesh.getTetBarycenter(int(tetidxs[i]))
r = baryc[0]
tetrads[i] = r * 1.0e6
Atets = acomptets
Btets = bcomptets
rng = srng.create('r123', 512)
rng.initialize(1000)
tet_hosts = gd.binTetsByAxis(mesh, steps.mpi.nhosts)
sim = solvmod.TetOpSplit(mdl, mesh, rng, False, tet_hosts)
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
resA = numpy.zeros((NITER, ntpnts, SAMPLE))
resB = numpy.zeros((NITER, ntpnts, SAMPLE))
for i in range(0, NITER):
sim.reset()
sim.setDiffBoundaryDiffusionActive('diffb', 'A', True)
sim.setDiffBoundaryDiffusionActive('diffb', 'B', True)
sim.setCompCount('compa', 'A', NA0)
sim.setCompCount('compb', 'B', NB0)
for t in range(0, ntpnts):
sim.run(tpnts[t])
for k in range(SAMPLE):
resA[i, t, k] = sim.getTetCount(int(tetidxs[k]), 'A')
resB[i, t, k] = sim.getTetCount(int(tetidxs[k]), 'B')
itermeansA = numpy.mean(resA, axis=0)
itermeansB = numpy.mean(resB, axis=0)
def getdetc(t, x):
N = 1000 # The number to represent infinity in the exponential calculation
L = 20e-6
concA = 0.0
for n in range(N):
concA += ((1.0 / (2 * n + 1)) * math.exp(
(-(DCSTA / (20.0e-6)) * math.pow(
(2 * n + 1), 2) * math.pow(math.pi, 2) * t) / (4 * L)) *
math.sin(((2 * n + 1) * math.pi * x) / (2 * L)))
concA *= ((4 * NA0 / math.pi) / (VOLA * 6.022e26)) * 1.0e6
return concA
tpnt_compare = [1, 2]
passed = True
max_err = 0.0
for tidx in tpnt_compare:
NBINS = 10
radmax = 0.0
radmin = 10.0
for r in tetrads:
if (r > radmax): radmax = r
if (r < radmin): radmin = r
rsec = (radmax - radmin) / NBINS
binmins = numpy.zeros(NBINS + 1)
tetradsbinned = numpy.zeros(NBINS)
r = radmin
bin_vols = numpy.zeros(NBINS)
for b in range(NBINS + 1):
binmins[b] = r
if (b != NBINS): tetradsbinned[b] = r + rsec / 2.0
r += rsec
bin_countsA = [None] * NBINS
bin_countsB = [None] * NBINS
for i in range(NBINS):
bin_countsA[i] = []
bin_countsB[i] = []
filled = 0
for i in range(itermeansA[tidx].size):
irad = tetrads[i]
for b in range(NBINS):
if (irad >= binmins[b] and irad < binmins[b + 1]):
bin_countsA[b].append(itermeansA[tidx][i])
bin_vols[b] += sim.getTetVol(int(tetidxs[i]))
filled += 1.0
break
filled = 0
for i in range(itermeansB[tidx].size):
irad = tetrads[i]
for b in range(NBINS):
if (irad >= binmins[b] and irad < binmins[b + 1]):
bin_countsB[b].append(itermeansB[tidx][i])
filled += 1.0
break
bin_concsA = numpy.zeros(NBINS)
bin_concsB = numpy.zeros(NBINS)
for c in range(NBINS):
for d in range(bin_countsA[c].__len__()):
bin_concsA[c] += bin_countsA[c][d]
for d in range(bin_countsB[c].__len__()):
bin_concsB[c] += bin_countsB[c][d]
bin_concsA[c] /= (bin_vols[c])
bin_concsA[c] *= (1.0e-3 / 6.022e23) * 1.0e6
bin_concsB[c] /= (bin_vols[c])
bin_concsB[c] *= (1.0e-3 / 6.022e23) * 1.0e6
for i in range(NBINS):
rad = abs(tetradsbinned[i]) * 1.0e-6
if (tetradsbinned[i] < -5):
# compare A
det_conc = getdetc(tpnts[tidx], rad)
steps_conc = bin_concsA[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
if (tetradsbinned[i] > 5):
# compare B
det_conc = getdetc(tpnts[tidx], rad)
steps_conc = bin_concsB[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
def test_csd_clamped():
"Diffusion - Clamped (Parallel TetOpSplit)"
m = gen_model()
g = gen_geom()
# And fetch the total number of tets to make the data structures
ntets = g.countTets()
tet_hosts = gd.binTetsByAxis(g, steps.mpi.nhosts)
sim = solvmod.TetOpSplit(m, g, rng, False, tet_hosts)
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
#Create the big old data structure: iterations x time points x concentrations
res = numpy.zeros((NITER, ntpnts, SAMPLE))
# Find the tets connected to the bottom face
# First find all the tets with ONE face on a boundary
boundtets = []
# store the 0to3 index of the surface triangle for each of these boundary tets
bt_srftriidx = []
for i in range(ntets):
tettemp = g.getTetTetNeighb(i)
if (tettemp[0] == -1 or tettemp[1] == -1 or tettemp[2] == -1
or tettemp[3] == -1):
boundtets.append(i)
templist = []
if (tettemp[0] == -1):
templist.append(0)
if (tettemp[1] == -1):
templist.append(1)
if (tettemp[2] == -1):
templist.append(2)
if (tettemp[3] == -1):
templist.append(3)
bt_srftriidx.append(templist)
assert (boundtets.__len__() == bt_srftriidx.__len__())
minztets = []
boundminz = g.getBoundMin()[2] + 0.01e-06
num2s = 0
for i in range(boundtets.__len__()):
# get the boundary triangle
if (bt_srftriidx[i].__len__() == 2): num2s += 1
for btriidx in bt_srftriidx[i]:
zminboundtri = True
tribidx = g.getTetTriNeighb(boundtets[i])[btriidx]
tritemp = g.getTri(tribidx)
trizs = [0.0, 0.0, 0.0]
trizs[0] = g.getVertex(tritemp[0])[2]
trizs[1] = g.getVertex(tritemp[1])[2]
trizs[2] = g.getVertex(tritemp[2])[2]
for j in range(3):
if (trizs[j] > boundminz): zminboundtri = False
if (zminboundtri): minztets.append(boundtets[i])
nztets = minztets.__len__()
volztets = 0.0
for z in minztets:
volztets += g.getTetVol(z)
for j in range(NITER):
sim.reset()
totset = 0
for k in minztets:
sim.setTetConc(k, 'X', CONC)
sim.setTetClamped(k, 'X', True)
totset += sim.getTetCount(k, 'X')
for i in range(ntpnts):
sim.run(tpnts[i])
for k in range(SAMPLE):
res[j, i, k] = sim.getTetCount(int(tetidxs[k]), 'X')
#print('{0} / {1}'.format(j + 1, NITER))
itermeans = numpy.mean(res, axis=0)
########################################################################
tpnt_compare = [3, 4]
passed = True
max_err = 0.0
for t in tpnt_compare:
NBINS = 10
radmax = 0.0
radmin = 11.0
for r in tetrads:
if (r > radmax): radmax = r
if (r < radmin): radmin = r
rsec = (radmax - radmin) / NBINS
binmins = numpy.zeros(NBINS + 1)
tetradsbinned = numpy.zeros(NBINS)
r = radmin
bin_vols = numpy.zeros(NBINS)
for b in range(NBINS + 1):
binmins[b] = r
if (b != NBINS): tetradsbinned[b] = r + rsec / 2.0
r += rsec
bin_counts = [None] * NBINS
for i in range(NBINS):
bin_counts[i] = []
filled = 0
for i in range(itermeans[t].size):
irad = tetrads[i]
for b in range(NBINS):
if (irad >= binmins[b] and irad < binmins[b + 1]):
bin_counts[b].append(itermeans[t][i])
bin_vols[b] += sim.getTetVol(int(tetidxs[i]))
filled += 1.0
break
bin_concs = numpy.zeros(NBINS)
for c in range(NBINS):
for d in range(bin_counts[c].__len__()):
bin_concs[c] += bin_counts[c][d]
bin_concs[c] /= (bin_vols[c])
bin_concs[c] *= (1.0e-3 / 6.022e23) * 1.0e6
for i in range(NBINS):
if (tetradsbinned[i] > 1 and tetradsbinned[i] < 4):
rad = tetradsbinned[i] * 1.0e-6
det_conc = (getConc(CONC * 6.022e26, DCST, rad, tpnts[t]) /
6.022e26) * 1.0e6
steps_conc = bin_concs[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
def run_sim():
# Set up and run the simulations once, before the tests
# analyze the results.
##################### First order irreversible #########################
global KCST_foi, N_foi, tolerance_foi
KCST_foi = 5 # The reaction constant
N_foi = 50 # Can set count or conc
NITER_foi = 100000 # The number of iterations
# Tolerance for the comparison:
# In test runs, with good code, < 1% will fail with a 1.5% tolerance
tolerance_foi = 2.0 / 100
####################### First order reversible #########################
global KCST_f_for, KCST_b_for, COUNT_for, tolerance_for
KCST_f_for = 10.0 # The reaction constant
KCST_b_for = 2.0
COUNT_for = 100000 # Can set count or conc
NITER_for = 10 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 1%
tolerance_for = 1.0 / 100
####################### Second order irreversible A2 ###################
global KCST_soA2, CONCA_soA2, tolerance_soA2
KCST_soA2 = 10.0e6 # The reaction constant
CONCA_soA2 = 10.0e-6
NITER_soA2 = 1000 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 2%
tolerance_soA2 = 3.0 / 100
####################### Second order irreversible AA ###################
global KCST_soAA, CONCA_soAA, CONCB_soAA, tolerance_soAA
KCST_soAA = 5.0e6 # The reaction constant
CONCA_soAA = 20.0e-6
CONCB_soAA = CONCA_soAA
NITER_soAA = 1000 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 1%
tolerance_soAA = 2.0 / 100
####################### Second order irreversible AB ###################
global KCST_soAB, CONCA_soAB, CONCB_soAB, tolerance_soAB
KCST_soAB = 5.0e6 # The reaction constant
CONCA_soAB = 1.0e-6
n_soAB = 2
CONCB_soAB = CONCA_soAB / n_soAB
NITER_soAB = 1000 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 1%
tolerance_soAB = 1.0 / 100
####################### Third order irreversible A3 ###################
global KCST_toA3, CONCA_toA3, tolerance_toA3
KCST_toA3 = 1.0e12 # The reaction constant
CONCA_toA3 = 10.0e-6
NITER_toA3 = 1000 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 1%
tolerance_toA3 = 3.0 / 100
####################### Third order irreversible A2B ###################
global KCST_toA2B, CONCA_toA2B, CONCB_toA2B, tolerance_toA2B
KCST_toA2B = 0.1e12 # The reaction constant
CONCA_toA2B = 30.0e-6
CONCB_toA2B = 20.0e-6
NITER_toA2B = 1000 # The number of iterations
# In test runs, with good code, <0.1% will fail with a tolerance of 1%
tolerance_toA2B = 1.0 / 100
####################### Second order irreversible 2D ###################
global COUNTA_so2d, COUNTB_so2d, CCST_so2d, tolerance_so2d
COUNTA_so2d = 100.0
n_so2d = 2.0
COUNTB_so2d = COUNTA_so2d / n_so2d
KCST_so2d = 10.0e10 # The reaction constant
AREA_so2d = 10.0e-12
NITER_so2d = 1000 # The number of iterations
# In tests fewer than 0.1% fail with tolerance of 2%
tolerance_so2d = 2.0 / 100
############################ Common parameters ########################
global VOL
DT = 0.1 # Sampling time-step
INT = 1.1 # Sim endtime
NITER_max = 100000
########################################################################
mdl = smod.Model()
volsys = smod.Volsys('vsys', mdl)
surfsys = smod.Surfsys('ssys', mdl)
# First order irreversible
A_foi = smod.Spec('A_foi', mdl)
A_foi_diff = smod.Diff('A_foi_diff', volsys, A_foi, 0.01e-12)
R1_foi = smod.Reac('R1_foi', volsys, lhs=[A_foi], rhs=[], kcst=KCST_foi)
# First order reversible
A_for = smod.Spec('A_for', mdl)
B_for = smod.Spec('B_for', mdl)
A_for_diff = smod.Diff('A_for_diff', volsys, A_for, 0.01e-12)
B_for_diff = smod.Diff('B_for_diff', volsys, B_for, 0.01e-12)
R1_for = smod.Reac('R1_for',
volsys,
lhs=[A_for],
rhs=[B_for],
kcst=KCST_f_for)
R2_for = smod.Reac('R2_for',
volsys,
lhs=[B_for],
rhs=[A_for],
kcst=KCST_b_for)
# Second order irreversible A2
A_soA2 = smod.Spec('A_soA2', mdl)
C_soA2 = smod.Spec('C_soA2', mdl)
A_soA2_diff = smod.Diff('A_soA2_diff', volsys, A_soA2, 1e-12)
R1_soA2 = smod.Reac('R1_soA2',
volsys,
lhs=[A_soA2, A_soA2],
rhs=[C_soA2],
kcst=KCST_soA2)
# Second order irreversible AA
A_soAA = smod.Spec('A_soAA', mdl)
B_soAA = smod.Spec('B_soAA', mdl)
C_soAA = smod.Spec('C_soAA', mdl)
A_soAA_diff = smod.Diff('A_soAA_diff', volsys, A_soAA, 0.2e-12)
B_soAA_diff = smod.Diff('B_soAA_diff', volsys, B_soAA, 0.2e-12)
R1_soAA = smod.Reac('R1_soAA',
volsys,
lhs=[A_soAA, B_soAA],
rhs=[C_soAA],
kcst=KCST_soAA)
# Second order irreversible AB
A_soAB = smod.Spec('A_soAB', mdl)
B_soAB = smod.Spec('B_soAB', mdl)
C_soAB = smod.Spec('C_soAB', mdl)
A_soAB_diff = smod.Diff('A_soAB_diff', volsys, A_soAB, 0.1e-12)
B_soAB_diff = smod.Diff('B_soAB_diff', volsys, B_soAB, 0.1e-12)
R1_soAB = smod.Reac('R1_soAB',
volsys,
lhs=[A_soAB, B_soAB],
rhs=[C_soAB],
kcst=KCST_soAB)
# Third order irreversible A3
A_toA3 = smod.Spec('A_toA3', mdl)
C_toA3 = smod.Spec('C_toA3', mdl)
A_soA3_diff = smod.Diff('A_soA3_diff', volsys, A_toA3, 0.2e-12)
R1_toA3 = smod.Reac('R1_toA3',
volsys,
lhs=[A_toA3, A_toA3, A_toA3],
rhs=[C_toA3],
kcst=KCST_toA3)
# Third order irreversible A2B
A_toA2B = smod.Spec('A_toA2B', mdl)
B_toA2B = smod.Spec('B_toA2B', mdl)
C_toA2B = smod.Spec('C_toA2B', mdl)
A_soA2B_diff = smod.Diff('A_soA2B_diff', volsys, A_toA2B, 0.1e-12)
B_soA2B_diff = smod.Diff('B_soA2B_diff', volsys, B_toA2B, 0.1e-12)
R1_toA3 = smod.Reac('R1_toA2B',
volsys,
lhs=[A_toA2B, A_toA2B, B_toA2B],
rhs=[C_toA2B],
kcst=KCST_toA2B)
# Second order irreversible 2D
A_so2d = smod.Spec('A_so2d', mdl)
B_so2d = smod.Spec('B_so2d', mdl)
C_so2d = smod.Spec('C_so2d', mdl)
A_so2d_diff = smod.Diff('A_so2d_diff', surfsys, A_so2d, 1.0e-12)
B_so2d_diff = smod.Diff('B_so2d_diff', surfsys, B_so2d, 1.0e-12)
SR1_so2d = smod.SReac('SR1_so2d',
surfsys,
slhs=[A_so2d, B_so2d],
srhs=[C_so2d],
kcst=KCST_so2d)
mesh = smeshio.importAbaqus(
'validation_rd_mpi/meshes/sphere_rad1_37tets.inp', 1e-6)[0]
VOL = mesh.getMeshVolume()
comp1 = sgeom.TmComp('comp1', mesh, range(mesh.ntets))
comp1.addVolsys('vsys')
patch_tris = mesh.getSurfTris()
patch1 = sgeom.TmPatch('patch1', mesh, patch_tris, comp1)
patch1.addSurfsys('ssys')
CCST_so2d = KCST_so2d / (6.02214179e23 * patch1.getArea())
rng = srng.create('r123', 512)
rng.initialize(100)
tet_hosts = gd.binTetsByAxis(mesh, steps.mpi.nhosts)
tri_hosts = gd.partitionTris(mesh, tet_hosts, patch_tris)
sim = ssolv.TetOpSplit(mdl, mesh, rng, ssolv.EF_NONE, tet_hosts, tri_hosts)
sim.reset()
global tpnts, ntpnts
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
res_m_foi = numpy.zeros([NITER_foi, ntpnts, 1])
res_std1_foi = numpy.zeros([ntpnts, 1])
res_std2_foi = numpy.zeros([ntpnts, 1])
res_m_for = numpy.zeros([NITER_for, ntpnts, 2])
res_m_soA2 = numpy.zeros([NITER_soA2, ntpnts, 2])
res_m_soAA = numpy.zeros([NITER_soAA, ntpnts, 3])
res_m_soAB = numpy.zeros([NITER_soAB, ntpnts, 3])
res_m_toA3 = numpy.zeros([NITER_toA3, ntpnts, 2])
res_m_toA2B = numpy.zeros([NITER_toA2B, ntpnts, 3])
res_m_so2d = numpy.zeros([NITER_so2d, ntpnts, 3])
for i in range(0, NITER_max):
sim.reset()
if i < NITER_foi:
sim.setCompCount('comp1', 'A_foi', N_foi)
if i < NITER_for:
sim.setCompCount('comp1', 'A_for', COUNT_for)
sim.setCompCount('comp1', 'B_for', 0.0)
if i < NITER_soA2:
sim.setCompConc('comp1', 'A_soA2', CONCA_soA2)
if i < NITER_soAA:
sim.setCompConc('comp1', 'A_soAA', CONCA_soAA)
sim.setCompConc('comp1', 'B_soAA', CONCB_soAA)
if i < NITER_soAB:
sim.setCompConc('comp1', 'A_soAB', CONCA_soAB)
sim.setCompConc('comp1', 'B_soAB', CONCB_soAB)
if i < NITER_toA3:
sim.setCompConc('comp1', 'A_toA3', CONCA_toA3)
if i < NITER_toA2B:
sim.setCompConc('comp1', 'A_toA2B', CONCA_toA2B)
sim.setCompConc('comp1', 'B_toA2B', CONCB_toA2B)
if i < NITER_so2d:
sim.setPatchCount('patch1', 'A_so2d', COUNTA_so2d)
sim.setPatchCount('patch1', 'B_so2d', COUNTB_so2d)
for t in range(0, ntpnts):
sim.run(tpnts[t])
if i < NITER_foi:
res_m_foi[i, t, 0] = sim.getCompCount('comp1', 'A_foi')
if i < NITER_for:
res_m_for[i, t, 0] = sim.getCompConc('comp1', 'A_for') * 1e6
res_m_for[i, t, 1] = sim.getCompConc('comp1', 'B_for') * 1e6
if i < NITER_soA2:
res_m_soA2[i, t, 0] = sim.getCompConc('comp1', 'A_soA2')
if i < NITER_soAA:
res_m_soAA[i, t, 0] = sim.getCompConc('comp1', 'A_soAA')
res_m_soAA[i, t, 1] = sim.getCompConc('comp1', 'B_soAA')
if i < NITER_soAB:
res_m_soAB[i, t, 0] = sim.getCompConc('comp1', 'A_soAB')
res_m_soAB[i, t, 1] = sim.getCompConc('comp1', 'B_soAB')
if i < NITER_toA3:
res_m_toA3[i, t, 0] = sim.getCompConc('comp1', 'A_toA3')
if i < NITER_toA2B:
res_m_toA2B[i, t, 0] = sim.getCompConc('comp1', 'A_toA2B')
res_m_toA2B[i, t, 1] = sim.getCompConc('comp1', 'B_toA2B')
res_m_toA2B[i, t, 2] = sim.getCompConc('comp1', 'C_toA2B')
if i < NITER_so2d:
res_m_so2d[i, t, 0] = sim.getPatchCount('patch1', 'A_so2d')
res_m_so2d[i, t, 1] = sim.getPatchCount('patch1', 'B_so2d')
global mean_res_foi, std_res_foi
mean_res_foi = numpy.mean(res_m_foi, 0)
std_res_foi = numpy.std(res_m_foi, 0)
global mean_res_for
mean_res_for = numpy.mean(res_m_for, 0)
global mean_res_soA2
mean_res_soA2 = numpy.mean(res_m_soA2, 0)
global mean_res_soAA
mean_res_soAA = numpy.mean(res_m_soAA, 0)
global mean_res_soAB
mean_res_soAB = numpy.mean(res_m_soAB, 0)
global mean_res_toA3
mean_res_toA3 = numpy.mean(res_m_toA3, 0)
global mean_res_toA2B
mean_res_toA2B = numpy.mean(res_m_toA2B, 0)
global mean_res_so2d
mean_res_so2d = numpy.mean(res_m_so2d, 0)
global ran_sim
ran_sim = True
def test_bounddiff():
"Diffusion - Bounded (Parallel TetOpSplit)"
m = gen_model()
g, area, a = gen_geom()
# And fetch the total number of tets to make the data structures
ntets = g.countTets()
tet_hosts = gd.binTetsByAxis(g, steps.mpi.nhosts)
sim = solvmod.TetOpSplit(m, g, rng, False, tet_hosts)
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
# Create the big old data structure: iterations x time points x concentrations
res = numpy.zeros((NITER, ntpnts, SAMPLE))
# Find the tets connected to the bottom face
# First find all the tets with ONE face on a boundary
boundtets = []
#store the 0to3 index of the surface triangle for each of these boundary tets
bt_srftriidx = []
for i in range(ntets):
tettemp = g.getTetTetNeighb(i)
if (tettemp[0] == -1 or tettemp[1] == -1 or tettemp[2] == -1
or tettemp[3] == -1):
boundtets.append(i)
templist = []
if (tettemp[0] == -1):
templist.append(0)
if (tettemp[1] == -1):
templist.append(1)
if (tettemp[2] == -1):
templist.append(2)
if (tettemp[3] == -1):
templist.append(3)
bt_srftriidx.append(templist)
assert (boundtets.__len__() == bt_srftriidx.__len__())
minztets = []
boundminz = g.getBoundMin()[2] + 0.01e-06
num2s = 0
for i in range(boundtets.__len__()):
# get the boundary triangle
if (bt_srftriidx[i].__len__() == 2): num2s += 1
for btriidx in bt_srftriidx[i]:
zminboundtri = True
tribidx = g.getTetTriNeighb(boundtets[i])[btriidx]
tritemp = g.getTri(tribidx)
trizs = [0.0, 0.0, 0.0]
trizs[0] = g.getVertex(tritemp[0])[2]
trizs[1] = g.getVertex(tritemp[1])[2]
trizs[2] = g.getVertex(tritemp[2])[2]
for j in range(3):
if (trizs[j] > boundminz): zminboundtri = False
if (zminboundtri): minztets.append(boundtets[i])
nztets = minztets.__len__()
volztets = 0.0
for z in minztets:
volztets += g.getTetVol(z)
conc = NITER * 6.022e23 * 1.0e-3 / volztets
for j in range(NITER):
sim.reset()
tetcount = int((1.0 * NINJECT) / nztets)
totset = 0
for k in minztets:
sim.setTetCount(k, 'X', tetcount)
totset += tetcount
for i in range(ntpnts):
sim.run(tpnts[i])
for k in range(SAMPLE):
res[j, i, k] = sim.getTetCount(int(tetidxs[k]), 'X')
itermeans = numpy.mean(res, axis=0)
########################################################################
D = DCST
pi = math.pi
nmax = 1000
N = NINJECT
N = int((1.0 * NINJECT) / nztets) * nztets
def getprob(x, t):
if (x > a):
print('x out of bounds')
return
p = 0.0
for n in range(nmax):
if (n == 0): A = math.sqrt(1.0 / a)
else: A = math.sqrt(2.0 / a)
p += math.exp(-D * math.pow(
(n * pi / a), 2) * t) * A * math.cos(n * pi * x / a) * A * a
return p * N / a
tpnt_compare = [6, 8, 10]
passed = True
max_err = 0.0
for t in tpnt_compare:
NBINS = 5
radmax = 0.0
radmin = 11.0
for r in tetrads:
if (r > radmax): radmax = r
if (r < radmin): radmin = r
rsec = (radmax - radmin) / NBINS
binmins = numpy.zeros(NBINS + 1)
tetradsbinned = numpy.zeros(NBINS)
r = radmin
bin_vols = numpy.zeros(NBINS)
for b in range(NBINS + 1):
binmins[b] = r
if (b != NBINS): tetradsbinned[b] = r + rsec / 2.0
r += rsec
bin_counts = [None] * NBINS
for i in range(NBINS):
bin_counts[i] = []
filled = 0
for i in range(itermeans[t].size):
irad = tetrads[i]
for b in range(NBINS):
if (irad >= binmins[b] and irad < binmins[b + 1]):
bin_counts[b].append(itermeans[t][i])
bin_vols[b] += sim.getTetVol(int(tetidxs[i]))
filled += 1.0
break
bin_concs = numpy.zeros(NBINS)
for c in range(NBINS):
for d in range(bin_counts[c].__len__()):
bin_concs[c] += bin_counts[c][d]
bin_concs[c] /= (bin_vols[c])
bin_concs[c] *= (1.0e-3 / 6.022e23) * 1.0e6
for i in range(NBINS):
if (tetradsbinned[i] > 2 and tetradsbinned[i] < 8):
rad = tetradsbinned[i] * 1.0e-6
det_conc = (getprob(rad, tpnts[t]) / area) * (1.0 / 6.022e20)
steps_conc = bin_concs[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
k=lambda V:1.0e3*_b_h(V*1.0e3), vrange=Vrange)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Create ohmic current objects
OC_K = smodel.OhmicCurr('OC_K', ssys, chanstate=K_n4, g=K_G, erev=K_rev)
OC_Na = smodel.OhmicCurr('OC_Na', ssys, chanstate=Na_m3h1, g=Na_G, erev=Na_rev)
OC_L = smodel.OhmicCurr('OC_L', ssys, chanstate=Leak, g=L_G, erev=leak_rev)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # TETRAHEDRAL MESH # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
mesh = meshio.importAbaqus(meshfile_ab, 1e-6)[0]
tet_hosts = gd.binTetsByAxis(mesh, steps.mpi.nhosts)
tri_hosts = gd.partitionTris(mesh, tet_hosts, mesh.getSurfTris())
# # # # # # # # # # # # # # # MESH MANIPULATION # # # # # # # # # # # # # # # # #
# Find the vertices for the current clamp and store in a list
injverts = []
for i in range(mesh.nverts):
if ((mesh.getVertex(i)[2] < (mesh.getBoundMin()[2] + 0.1e-6))):
injverts.append(i)
if steps.mpi.rank == 0: print "Found ", injverts.__len__(), "I_inject vertices"
facetris = []
for i in range(mesh.ntris):
tri = mesh.getTri(i)
if ((tri[0] in injverts) and (tri[1] in injverts)
def test_unbdiff():
"Diffusion - Unbounded (Parallel TetOpSplit)"
m = gen_model()
g = gen_geom()
# Fetch the index of the centre tet
ctetidx = g.findTetByPoint([0.0, 0.0, 0.0])
# And fetch the total number of tets to make the data structures
ntets = g.countTets()
tet_hosts = gd.binTetsByAxis(g, steps.mpi.nhosts)
sim = solvmod.TetOpSplit(m, g, rng, False, tet_hosts)
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
# Create the big old data structure: iterations x time points x concentrations
res = numpy.zeros((NITER, ntpnts, SAMPLE))
for j in range(NITER):
sim.reset()
sim.setTetCount(ctetidx, 'X', NINJECT)
for i in range(ntpnts):
sim.run(tpnts[i])
for k in range(SAMPLE):
res[j, i, k] = sim.getTetCount(int(tetidxs[k]), 'X')
itermeans = numpy.mean(res, axis = 0)
tpnt_compare = [10, 15, 20]
passed = True
max_err = 0.0
for t in tpnt_compare:
bin_n = 20
r_max = tetrads.max()
r_min = 0.0
r_seg = (r_max-r_min)/bin_n
bin_mins = numpy.zeros(bin_n+1)
r_tets_binned = numpy.zeros(bin_n)
bin_vols = numpy.zeros(bin_n)
r = r_min
for b in range(bin_n + 1):
bin_mins[b] = r
if (b!=bin_n): r_tets_binned[b] = r +r_seg/2.0
r+=r_seg
bin_counts = [None]*bin_n
for i in range(bin_n): bin_counts[i] = []
for i in range((itermeans[t].size)):
i_r = tetrads[i]
for b in range(bin_n):
if(i_r>=bin_mins[b] and i_r<bin_mins[b+1]):
bin_counts[b].append(itermeans[t][i])
bin_vols[b]+=sim.getTetVol(int(tetidxs[i]))
break
bin_concs = numpy.zeros(bin_n)
for c in range(bin_n):
for d in range(bin_counts[c].__len__()):
bin_concs[c] += bin_counts[c][d]
bin_concs[c]/=(bin_vols[c]*1.0e18)
for i in range(bin_n):
if (r_tets_binned[i] > 2.0 and r_tets_binned[i] < 6.0):
rad = r_tets_binned[i]*1.0e-6
det_conc = 1e-18*((NINJECT/(math.pow((4*math.pi*DCST*tpnts[t]),1.5)))*(math.exp((-1.0*(rad*rad))/(4*DCST*tpnts[t]))))
steps_conc = bin_concs[i]
assert tol_funcs.tolerable(det_conc, steps_conc, tolerance)
def test_masteq_diff():
"Reaction-diffusion - Production and second order degradation (Parallel TetOpSplit)"
### NOW A+B-> B, 0->A (see Erban and Chapman, 2009)
########################################################################
SCALE = 1.0
KCST_f = 100e6 * SCALE # The reaction constant, degradation
KCST_b = (20.0e-10 * SCALE) # The reaction constant, production
DCST_A = 20e-12
DCST_B = 20e-12
B0 = 1 # The number of B moleucles
DT = 0.1 # Sampling time-step
INT = 50000.1 # Sim endtime
filename = 'cube_1_1_1_73tets.inp'
# A tolerance of 7.5% will fail <1% of the time
tolerance = 7.5 / 100
########################################################################
mdl = smod.Model()
A = smod.Spec('A', mdl)
B = smod.Spec('B', mdl)
volsys = smod.Volsys('vsys', mdl)
diffA = smod.Diff('diffA', volsys, A)
diffA.setDcst(DCST_A)
diffB = smod.Diff('diffB', volsys, B)
diffB.setDcst(DCST_B)
# Production
R1 = smod.Reac('R1', volsys, lhs=[A, B], rhs=[B], kcst=KCST_f)
R2 = smod.Reac('R2', volsys, lhs=[], rhs=[A], kcst=KCST_b)
geom = meshio.loadMesh('validation_rd_mpi/meshes/' + filename)[0]
comp1 = sgeom.TmComp('comp1', geom, range(geom.ntets))
comp1.addVolsys('vsys')
rng = srng.create('r123', 512)
rng.initialize(1000)
tet_hosts = gd.binTetsByAxis(geom, steps.mpi.nhosts)
sim = solvmod.TetOpSplit(mdl, geom, rng, False, tet_hosts)
sim.reset()
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
res = numpy.zeros([ntpnts])
res_std1 = numpy.zeros([ntpnts])
res_std2 = numpy.zeros([ntpnts])
sim.reset()
sim.setCompCount('comp1', 'A', 0)
sim.setCompCount('comp1', 'B', B0)
b_time = time.time()
for t in range(0, ntpnts):
sim.run(tpnts[t])
res[t] = sim.getCompCount('comp1', 'A')
def fact(x):
return (1 if x == 0 else x * fact(x - 1))
# Do cumulative count, but not comparing them all.
# Don't get over 50 (I hope)
steps_n_res = numpy.zeros(50)
for r in res:
steps_n_res[int(r)] += 1
for s in range(50):
steps_n_res[s] = steps_n_res[s] / ntpnts
k1 = KCST_f / 6.022e23
k2 = KCST_b * 6.022e23
v = comp1.getVol() * 1.0e3 # litres
for m in range(5, 11):
analy = (1.0 / fact(m)) * math.pow(
(k2 * v * v) / (B0 * k1), m) * math.exp(-((k2 * v * v) /
(k1 * B0)))
assert tol_funcs.tolerable(steps_n_res[m], analy, tolerance)
