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()
#!/bin/bash
from datastore import InputDatastore
from calcsim import CalcSimWrapper
from numpy import *
from scipy.optimize import *
from expercomparison import ComparisonEngine
from itertools import product
ds = InputDatastore('../InputData', 'NiCu')
x = linspace(0, 25, num=100)
fedict = ds.edict_for_direction('forward')
redict = ds.edict_for_direction('reverse')
fexpr = ds.interpolated_experiment_dict(x, fedict)
rexpr = ds.interpolated_experiment_dict(x, redict)
diffusivity = ds.interpolated_diffusivity(1001, 973)
resistivity = ds.interpolated_resistivity(1001, 973)
cs = CalcSimWrapper()
ce = ComparisonEngine(cs)
initcond = ones(100)
initcond[50:] = 0
dt = 0.05
ndt = int(2 * 60 * 60 / 0.05)
dx = 25e-6 / 100
def make_objective(I, direction):
if direction == 'forward':
exprd = fexpr[I]
ebars_list_lower.append(cbounds[I][0])
ebars_list_upper.append(cbounds[I][2])
ebars = np.array([ebars_list_lower, ebars_list_upper])
plot_data = np.column_stack((I_plot, cvf_plot))
outfile = path.join(args.outputdir, 'cvfplot_combined.png')
print(I_plot)
print(ebars)
dmplots.plot_cvf_function_ebars(plot_data, ebars, 'combined', outfile)
#and politely output simulations
for direction in ('forward', 'reverse'):
for I in cresults.keys():
for idx, edge in ((0, 'lower'), (1, 'best'), (2, 'upper')):
outfile = path.join(args.outputdir, str.format('Comparison_I{}_{}_{}bound.png', I, direction, edge))
cvf = cbounds[I][1]
if idx == 0:
cvf -= cbounds[I][idx]
elif idx == 2:
cvf += cbounds[I][idx]
print('using cvf = ' + str(cvf))
edict = dstore.edict_for_direction(direction)
exper = dstore.interpolated_experiment_dict(x, edict)[I]
simd, lsq = quicksim(zaverage_rounded, cvf, I, exper, direction)
simd = np.column_stack((x, simd))
exper = np.column_stack((x, exper))
f = dmplots.plot_sim_fit(simd, exper, I, zaverage_rounded, cvf, direction)
c = FigureCanvasAgg(f)
c.print_figure(outfile)
help='File to output to')
aparser.add_argument('--z', type=float, required=True,
help='Effective valence')
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))
