def integrate(**kwargs):
times_cpu, times_wal = [], []
nrepeat = 21
ndrop = 7
for i in range(nrepeat):
tout, yout, info, rmsd_over_atol, sys, rmsd = integrate_rd(**kwargs)
times_cpu.append(info['time_cpu'])
times_wal.append(info['time_wall'])
for _ in range(ndrop):
times_cpu.pop(times_cpu.index(max(times_cpu)))
times_wal.pop(times_wal.index(max(times_wal)))
times_cpu = np.array(times_cpu)
times_wal = np.array(times_wal)
info['time_cpu'] = avg_cpu = np.average(times_cpu)
info['time_wall'] = avg_wal = np.average(times_wal)
info['dtime_cpu'] = np.sqrt(
np.sum((times_cpu - avg_cpu)**2 / (len(times_cpu) - 1)))
info['dtime_wall'] = np.sqrt(
np.sum((times_wal - avg_wal)**2 / (len(times_wal) - 1)))
info['tout'] = tout
# info['yout'] = yout
info['rmsd'] = rmsd
info['rmsd_over_atol'] = np.sqrt(np.sum(rmsd_over_atol**2))
for k in default_varied:
info[k] = kwargs[k]
return info
def _test_gaussian_diffusion(params, **kwargs):
g, ly, lt, r, nspecies, nstencil = params
tout, yout, info, ave_rmsd_over_atol, rd, rmsd = integrate_rd(
geom=g, logt=lt, logy=ly, N=128, random=r, nspecies=nspecies,
nstencil=nstencil, atol=1e-6, rtol=1e-6)
assert info['success']
for rmsd in ave_rmsd_over_atol:
if r:
forgiveness = 5 if nstencil < 7 else 50
# Randomized grid has lower convergence order.
for i in range(6):
if np.all(rmsd > tol[nstencil]*forgiveness):
rmsd = integrate_rd(
geom=g, logt=lt, logy=ly, N=128, random=r,
nspecies=nspecies, nstencil=nstencil, atol=1e-6,
rtol=1e-6)[3]
else:
break
assert np.all(rmsd < tol[nstencil]*forgiveness)
else:
assert np.all(rmsd < tol[nstencil])
def integrate(**kwargs):
tout, yout, info, rmsd_over_atol, rd, rmsd = integrate_rd(**kwargs)
info['rmsd'] = rmsd
info['rmsd_over_atol'] = rmsd_over_atol
return info
