def test_basteman_parent(p):
from scipy.special import binom
from math import log
N = 32
a = 27
lmbd = [(i+p+1)*log(a) for i in range(N)]
bp = bateman_parent(lmbd, 1)
for i, v in enumerate(bp, 1):
assert abs(v - _yi1(i, p, a, binom)) < 1e-14
def test_basteman_parent(p):
from scipy.special import binom
from math import log
N = 32
a = 27
lmbd = [(i + p + 1) * log(a) for i in range(N)]
bp = bateman_parent(lmbd, 1)
for i, v in enumerate(bp, 1):
assert abs(v - _yi1(i, p, a, binom)) < 1e-14
def integrate_rd(N=64, geom='f', nspecies=1, nstencil=3,
D=2e-3, t0=3.0, tend=7., x0=0.0, xend=1.0, center=None,
nt=42, logt=False, logy=False, logx=False,
random=False, p=0, a=0.2,
linterpol=False, rinterpol=False, ilu_limit=5.0,
n_jac_diags=-1, num_jacobian=False,
method='bdf', integrator='cvode', iter_type='undecided',
linear_solver='default',
atol=1e-8, rtol=1e-10,
efield=False, random_seed=42, mobility=0.01,
plot=False, savefig='None', verbose=False, yscale='linear',
vline_limit=100, use_log2=False, Dexpr='[D]*nspecies', check_conserv=False
): # remember: anayltic_N_scaling.main kwargs
# Example:
# python3 analytic_diffusion.py --plot --Dexpr "D*np.exp(10*(x[:-1]+np.diff(x)/2))"
if t0 == 0.0:
raise ValueError("t0==0 => Dirac delta function C0 profile.")
if random_seed:
np.random.seed(random_seed)
# decay = (nspecies > 1)
# n = 2 if decay else 1
center = float(center or x0)
tout = np.linspace(t0, tend, nt)
assert geom in 'fcs'
analytic = {
'f': flat_analytic,
'c': cylindrical_analytic,
's': spherical_analytic
}[geom]
# Setup the grid
logx0 = math.log(x0) if logx else None
logxend = math.log(xend) if logx else None
if logx and use_log2:
logx0 /= math.log(2)
logxend /= math.log(2)
_x0 = logx0 if logx else x0
_xend = logxend if logx else xend
x = np.linspace(_x0, _xend, N+1)
if random:
x += (np.random.random(N+1)-0.5)*(_xend-_x0)/(N+2)
def _k(si):
return (si+p)*math.log(a+1)
k = [_k(i+1) for i in range(nspecies-1)]
rd = ReactionDiffusion(
nspecies,
[[i] for i in range(nspecies-1)],
[[i+1] for i in range(nspecies-1)],
k,
N,
D=eval(Dexpr),
z_chg=[1]*nspecies,
mobility=[mobility]*nspecies,
x=x,
geom=geom,
logy=logy,
logt=logt,
logx=logx,
nstencil=nstencil,
lrefl=not linterpol,
rrefl=not rinterpol,
ilu_limit=ilu_limit,
n_jac_diags=n_jac_diags,
use_log2=use_log2
)
if efield:
if geom != 'f':
raise ValueError("Only analytic sol. for flat drift implemented.")
rd.efield = _efield_cb(rd.xcenters)
# Calc initial conditions / analytic reference values
t = tout.copy().reshape((nt, 1))
yref = analytic(rd.xcenters, t, D, center, x0, xend,
-mobility if efield else 0, logy, logx, use_log2).reshape(nt, N, 1)
if nspecies > 1:
from batemaneq import bateman_parent
bateman_out = np.array(bateman_parent(k, tout)).T
terminal = (1 - np.sum(bateman_out, axis=1)).reshape((nt, 1))
bateman_out = np.concatenate((bateman_out, terminal), axis=1).reshape(
(nt, 1, nspecies))
if logy:
yref = yref + rd.logb(bateman_out)
else:
yref = yref * bateman_out
# Run the integration
integr = run(rd, yref[0, ...], tout, atol=atol, rtol=rtol,
with_jacobian=(not num_jacobian), method=method,
iter_type=iter_type, linear_solver=linear_solver,
C0_is_log=logy, integrator=integrator)
info = integr.info
if logy:
def lin_err(i, j):
linref = rd.expb(yref[i, :, j])
linerr = rd.expb(integr.yout[i, :, j])-linref
linatol = np.average(yref[i, :, j])
linrtol = linatol
return linerr/(linrtol*np.abs(linref)+linatol)
if logy:
rmsd = np.sum(lin_err(slice(None), slice(None))**2 / N, axis=1)**0.5
else:
rmsd = np.sum((yref-integr.yout)**2 / N, axis=1)**0.5
ave_rmsd_over_atol = np.average(rmsd, axis=0)/atol
if verbose:
# Print statistics
from pprint import pprint
pprint(info)
pprint(ave_rmsd_over_atol)
# Plot results
if plot:
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 10))
# colors: (0.5, 0.5, 0.5), (0.5, 0.5, 1), ...
base_colors = list(product([.5, 1], repeat=3))[1:-1]
def color(ci, ti):
return np.array(base_colors[ci % len(base_colors)])*tout[ti]/tend
for ti in range(nt):
plt.subplot(4, 1, 1)
for si in range(nspecies):
plt.plot(rd.xcenters, integr.Cout[ti, :, si], c=color(si, ti),
label=None if ti < nt - 1 else rd.substance_names[si])
plt.subplot(4, 1, 2)
for si in range(nspecies):
plt.plot(rd.xcenters, rd.expb(yref[ti, :, si]) if logy
else yref[ti, :, si], c=color(si, ti))
plt.subplot(4, 1, 3)
if logy:
for si in range(nspecies):
plt.plot(rd.xcenters, lin_err(ti, si)/atol,
c=color(si, ti))
else:
for si in range(nspecies):
plt.plot(
rd.xcenters,
(yref[ti, :, si] - integr.yout[ti, :, si])/atol,
c=color(si, ti))
if N < vline_limit:
for idx in range(1, 4):
plt.subplot(4, 1, idx)
for bi in range(N):
plt.axvline(rd.x[bi], color='gray')
plt.subplot(4, 1, 1)
plt.title('Simulation (N={})'.format(rd.N))
plt.xlabel('x / m')
plt.ylabel('C / M')
plt.gca().set_yscale(yscale)
plt.legend()
plt.subplot(4, 1, 2)
plt.title('Analytic solution')
plt.gca().set_yscale(yscale)
plt.subplot(4, 1, 3)
plt.title('Linear rel. error / Abs. tol. (={})'.format(atol))
plt.subplot(4, 1, 4)
plt.title('RMS error vs. time'.format(atol))
tspan = [tout[0], tout[-1]]
for si in range(nspecies):
plt.plot(tout, rmsd[:, si] / atol, c=color(si, -1))
plt.plot(tspan, [ave_rmsd_over_atol[si]]*2,
c=color(si, -1), ls='--')
plt.xlabel('Time / s')
plt.ylabel(r'$\sqrt{\langle E^2 \rangle} / atol$')
plt.tight_layout()
save_and_or_show_plot(savefig=savefig)
if check_conserv:
tot_amount = np.zeros(tout.size)
for ti in range(tout.size):
for si in range(nspecies):
tot_amount[ti] += rd.integrated_conc(integr.yout[ti, :, si])
if plot:
plt.plot(tout, tot_amount)
plt.show()
assert np.allclose(tot_amount[0], tot_amount[1:])
return tout, integr.yout, info, ave_rmsd_over_atol, rd, rmsd
