def plot_for(z, d):
cs = CalcSimWrapper()
ds = InputDatastore('../InputData', 'NiCu', 973)
ce = ComparisonEngine(cs)
D = ds.interpolated_diffusivity(10001)
R = ds.interpolated_resistivity(10001)
dx = 0.5 * 35e-8
ndx = 200
dt = 0.01
ndt = int(2 * 60 * 60 / 0.01)
init = ones(ndx)
init[ndx/2:] = 0
x = linspace(0, 25, 200)
ddict = ds.interpolated_experiment_dict(x)
for I in ddict.keys():
if I == 0:
dv = 1
else:
dv = d
r = cs.emigration_factor(z, I, 973)
mdl = cs.calc_simulation(D, R, init, ndt, dt, dx, r, dv)
ce = ComparisonEngine(cs)
lsq, shfit = ce.calibrate(mdl, ddict[I])
smdl = ce.shift_data(mdl)
plot(x, ddict[I], label=str.format('Exper. (I={} A/cm^2)', I/100/100))
plot(x, smdl, label=str.format('Model. (I={} A/cm^2)', I/100/100))
legend(loc=3)
show()
class CalcSimExecutor():
def __init__(self, dstore, T, ndt=defaults.simulation_tsteps, dt=defaults.simulation_dt):
assert isinstance(dstore, InputDatastore)
self.T = T
#first, get defaults
self.dx = defaults.simulation_dx
self.dt = dt
self.ndx = defaults.simulation_xsteps
self.ndt = ndt
#now we can set up the initial conditions
#x in micron
self.x = np.arange(0, self.ndx * self.dx, self.dx) * 1e6
self.init_cond = np.ones(self.ndx)
self.init_cond[(self.ndx // 2):] = 0
#D/R vectors
self.Dvector = dstore.interpolated_diffusivity(10001, T, precise=False).copy()
self.Rvector = dstore.interpolated_resistivity(10001, T).copy()
self.cs = CalcSimWrapper()
#now we're ready to fire on demand
def compute(self, z, cvf, I, direction):
"""
Actually runs the simulation that's been set up, with the supplied
parameters. I is supplied in A/cm^2
Returns sim results as a 2 column array of x, y
"""
if direction == 'forward':
pass
elif direction == 'reverse':
I = -I
else:
raise ValueError('Unknown direction ' + str(direction))
r = self.cs.emigration_factor(z, I * 100 * 100, self.T)
outy = self.cs.calc_simulation(self.Dvector, self.Rvector, self.init_cond,
self.ndt, self.dt, self.dx, r, cvf)
return np.column_stack((self.x, outy))
aparser.add_argument('--cvf', type=float, required=True,
help='Vacancy concentration factor')
aparser.add_argument('--direction', type=str, default='forward',
help='Direction of application of current')
args = aparser.parse_args()
accelcs = CalcSimWrapper()
dstore = InputDatastore(args.inputdata, args.dataprefix, 973, args.direction)
ce = ComparisonEngine(accelcs)
x = np.linspace(0, 25, num=100)
exper_data = dstore.interpolated_experiment_dict(x)[args.current]
diffusivity = dstore.interpolated_diffusivity(10001)
resistivity = dstore.interpolated_resistivity(10001)
init_cond = np.ones(100)
init_cond[50:] = 0
emigration_T = 973
dt = 0.05
ndt = int(2 * 60 * 60 / 0.05)
dx = 25e-6 / 100
r = accelcs.emigration_factor(args.z, args.current * 100 * 100, emigration_T)
simd = accelcs.calc_simulation(diffusivity, resistivity, init_cond, ndt, dt, dx, r, args.cvf)
lsq, shift = ce.calibrate(simd, exper_data)
shifted_simd = ce.shift_data(simd)
full_simd = np.column_stack((x, shifted_simd))
full_exper = np.column_stack((x, exper_data))
dmplots.plot_sim_fit(full_simd, full_exper, args.current, args.z, args.cvf, args.direction, args.output)
print('lsq = ' + str(lsq))
x = np.linspace(0, 25, num=100)
dstore = InputDatastore(args.inputdata, args.dataprefix)
edict = dstore.edict_for_direction(direction)
accelcs = CalcSimWrapper()
ce = ComparisonEngine(accelcs)
diffusivity = dstore.interpolated_diffusivity(10001, args.temperature, precise=True)
resistivity = dstore.interpolated_resistivity(10001, args.temperature)
init_cond = np.ones(100)
init_cond[50:] = 0
emigration_T = args.temperature
dt = 0.05
ndt = int(2 * 60 * 60 / 0.05)
dx = 25e-6 / 100
for I in result_stash.keys():
cvf_best = cvfrange[result_stash[I][zaverage_index, :].argmin()]
if direction == "forward":
exper_data = dstore.interpolated_experiment_dict(x, edict)[I]
r = accelcs.emigration_factor(zaverage_rounded, I * 100 * 100, emigration_T)
else:
exper_data = dstore.interpolated_experiment_dict(x, edict)[I]
r = accelcs.emigration_factor(zaverage_rounded, -I * 100 * 100, emigration_T)
simd = accelcs.calc_simulation(diffusivity, resistivity, init_cond, ndt, dt, dx, r, cvf_best)
lsq, shift = ce.calibrate(simd, exper_data)
shifted_simd = ce.shift_data(simd)
full_simd = np.column_stack((x, shifted_simd))
full_exper = np.column_stack((x, exper_data))
outfname = os.path.join(args.outputdir, str.format("SimExperComp_I{}_{}.png", I, direction))
f = dmplots.plot_sim_fit(full_simd, full_exper, I, zaverage_rounded, cvf_best, direction)
c = FigureCanvasAgg(f)
c.print_figure(outfname)
