def test_serialization():
rd = load(JSON_PATH)
assert rd.n == 3
assert rd.stoich_active == [[0, 1]]
assert rd.stoich_prod == [[2]]
assert list(rd.k) == [0.3]
assert rd.D.tolist() == [0.1, 0.2, 0.3]
def test_chemistry():
sbstncs = mk_sn_dict_from_names('ABC', D=[.1, .2, .3])
r1 = Reaction({'A': 1, 'B': 1}, {'C': 1}, 0.3)
rsys = ReactionSystem([r1], sbstncs)
rd = ReactionDiffusion.from_ReactionSystem(rsys)
serialized_rd = load(JSON_PATH)
assert rd.stoich_active == serialized_rd.stoich_active
assert rd.stoich_prod == serialized_rd.stoich_prod
assert rd.stoich_inact == serialized_rd.stoich_inact
assert np.allclose(rd.k, serialized_rd.k)
assert np.allclose(rd.D, serialized_rd.D)
def test_f(log):
logy, logt, use_log2 = log
N = 1
rd = load(JSON_PATH, N=N, logy=logy, logt=logt, use_log2=use_log2)
y0 = np.array(C0)
t0 = 42.0
ref_f = _get_ref_f(rd, t0, y0, logy, logt, use_log2=use_log2)
fout = rd.alloc_fout()
y = rd.logb(y0) if logy else y0
t = rd.logb(t0) if logt else t0
rd.f(t, y, fout)
assert np.allclose(fout, ref_f)
def test_dump_load():
rd1 = ReactionDiffusion(2, [[0]], [[1]], [1])
f = tempfile.NamedTemporaryFile(delete=False)
try:
f.close()
dump(rd1, f.name)
rd2 = load(f.name)
assert rd1.n == rd2.n
assert rd1.k == rd2.k
assert rd1.stoich_active == rd2.stoich_active
assert rd1.stoich_prod == rd2.stoich_prod
finally:
os.unlink(f.name)
def main(tend=3.0, N=30, nt=30, plot=False, logy=False, logt=False,
savefig='None', verbose=False):
def mod1(x):
return x/(x**2+1)
# decay A->B is modulated with x
rd = load(
os.path.join(os.path.dirname(
__file__), 'four_species.json'),
N=N, modulation=[[mod1(x/3+0.1) for x in range(N)]],
modulated_rxns=[1], logt=logt, logy=logy)
y0 = np.array([1.3, 1e-4, 0.7, 1e-4])
# y0 = np.concatenate([y0/(i+1)*(0.25*i**2+1) for i in range(N)])
y0 = np.concatenate([y0 for i in range(N)])
t0 = 1e-10
tout = np.linspace(t0, tend, nt)
integr = run(rd, y0, tout)
if verbose:
pprint(integr.info)
cx = rd.x[:-1]+np.diff(rd.x)/2 # center x
if plot:
# Using matplotlib for 3D plots:
from mpl_toolkits.mplot3d import Axes3D
assert Axes3D # silence pyflakes
from matplotlib import cm
from matplotlib import pyplot as plt
from chemreac.util.plotting import save_and_or_show_plot
fig = plt.figure()
for i, l in enumerate('ABCD'):
ax = fig.add_subplot(3, 2, i+1, projection='3d')
T, X = np.meshgrid(cx, tout)
ax.plot_surface(T, X, integr.Cout[:, :, i], rstride=1, cstride=1,
cmap=cm.YlGnBu_r)
ax.set_xlabel('x / m')
ax.set_ylabel('time / s')
ax.set_zlabel(r'C / mol*m**-3')
ax.set_title(l)
ax.legend(loc='best')
ax = fig.add_subplot(3, 2, 5)
print(np.array(rd.modulation).shape)
ax.plot(cx, np.array(rd.modulation)[0, :])
save_and_or_show_plot(savefig=savefig)
def test_dense_jac_cmaj(log):
logy, logt, use_log2 = log
N = 1
rd = load(JSON_PATH, N=N, logy=logy, logt=logt, use_log2=use_log2)
y0 = np.array(C0)
t0 = 42.0
ref_J = _get_ref_J(rd, t0, y0, logy, logt, order='F', use_log2=use_log2)
Jout = np.empty_like(ref_J)
y = rd.logb(y0) if logy else y0
t = rd.logb(t0) if logt else t0
rd.dense_jac_cmaj(t, y, Jout)
assert np.allclose(Jout, ref_J)
def _test_integrate(params):
logy, logt, N, geom, use_log2 = params
rd = load(JSON_PATH, N=N, logy=logy, logt=logt, geom=geom, use_log2=use_log2)
assert rd.geom == geom
y0 = np.array(C0*N)
ref = np.genfromtxt(BLESSED_PATH)
ref_t = ref[:, 0]
ref_y = ref[:, 1:rd.n+1]
t0 = 3.0
tend = 5.0+t0
nt = 137
tout = np.linspace(t0, tend, nt+1)
assert np.allclose(tout-t0, ref_t)
_test_f_and_dense_jac_rmaj(rd, t0, rd.logb(y0) if logy else y0)
integr = run(rd, y0, tout, with_jacobian=True)
for i in range(N):
assert np.allclose(integr.Cout[:, i, :], ref_y, atol=1e-4)
def integrate_rd(t0=1e-7,
tend=.1,
doserate=15,
N=1000,
nt=512,
nstencil=3,
logy=False,
logt=False,
num_jacobian=False,
savefig='None',
verbose=False,
plot=False,
plot_jacobians=False):
null_conc = 1e-24
mu = 1.0 # linear attenuation
rho = 1.0 # kg/dm3
rd = load(os.path.join(os.path.dirname(__file__),
'aqueous_radiolysis.json'),
ReactionDiffusion,
N=N,
logy=logy,
logt=logt,
bin_k_factor=[[doserate * rho * exp(-mu * i / N)]
for i in range(N)],
nstencil=nstencil)
y0_by_name = json.load(
open(
os.path.join(os.path.dirname(__file__),
'aqueous_radiolysis.y0.json'), 'rt'))
# y0 with a H2 gradient
y0 = np.array([[
y0_by_name.get(k, null_conc) if k != 'H2' else 1e-3 / (i + 2)
for k in rd.substance_names
] for i in range(rd.N)])
tout = np.logspace(log(t0), log(tend), nt + 1, base=e)
integr = run(rd, y0, tout, with_jacobian=(not num_jacobian))
def integrate_rd(tend=10.0, N=1, nt=500, jac_spy=False,
linear_solver='default', logy=False, logt=False,
plot=False, savefig='None', verbose=False, graph=False,
**kwargs):
"""
Integrates the reaction system defined by
:download:`four_species.json <examples/four_species.json>`
"""
rd = load(os.path.join(os.path.dirname(
__file__), 'four_species.json'), N=N, x=N, logy=logy, logt=logt)
y0 = np.array([1.3, 1e-4, 0.7, 1e-4])
y0 = np.concatenate([y0/(i+1)*(0.25*i**2+1) for i in range(N)])
t0 = 1e-10
if linear_solver == 'default':
if rd.N == 1:
linear_solver = 'dense'
elif rd.N > 1:
linear_solver = 'banded'
if linear_solver not in ('dense', 'banded'):
raise NotImplementedError("dense or banded linear_solver")
import matplotlib.pyplot as plt
if jac_spy:
fout = np.empty(rd.n*rd.N)
rd.f(t0, y0, fout)
print(fout)
if linear_solver == 'dense':
jout = np.zeros((rd.n*rd.N, rd.n*rd.N), order='F')
rd.dense_jac_cmaj(t0, y0, jout)
coloured_spy(np.log(np.abs(jout)))
elif linear_solver == 'banded':
# note rd.n*3 needed in call from scipy.integrate.ode
jout = np.zeros((rd.n*2+1, rd.n*rd.N), order='F')
rd.banded_packed_jac_cmaj(t0, y0, jout)
coloured_spy(np.log(np.abs(jout)))
print(jout)
plt.show()
else:
tout = np.linspace(t0, tend, nt)
integr = run(rd, y0, tout, **kwargs)
if verbose:
print(integr.info)
if plot:
plt.figure(figsize=(6, 4))
for i, l in enumerate('ABCD'):
plt.plot(integr.tout, integr.Cout[:, 0, i], label=l)
plt.title("Time evolution of concentrations")
plt.legend()
save_and_or_show_plot(savefig=savefig)
plt.figure(figsize=(6, 10))
plot_jacobian(
rd,
np.log(integr.tout) if rd.logt else integr.tout,
np.log(integr.Cout) if rd.logy else integr.Cout,
'ABCD',
lintreshy=1e-10
)
plt.tight_layout()
if savefig != 'None':
base, ext = os.path.splitext(savefig)
savefig = base + '_jacobian' + ext
save_and_or_show_plot(savefig=savefig)
plt.figure(figsize=(6, 10))
plot_per_reaction_contribution(
integr,
'ABCD'
)
plt.tight_layout()
if savefig != 'None':
savefig = base + '_per_reaction' + ext
save_and_or_show_plot(savefig=savefig)
if graph:
print(rsys2graph(ReactionSystem.from_ReactionDiffusion(rd, 'ABCD'),
'four_species_graph.png', save='.'))
return integr