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))
def basic_test(): 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 = cs.optimum_dt(dx, D, 1) ndt = cs.num_sim_steps(dt, 2 * 60 * 60) init = np.ones(ndx) init[ndx/2:] = 0 res = cs.calc_simulation(D, R, init, ndt, dt, dx, 0, 1) res = cs.calc_simulation(D, R, init, ndt, dt, dx, 0, 1) x = np.linspace(0, dx * ndx, num=ndx) plot(x, res) show()
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()
from calcsim import CalcSimWrapper from datastore import InputDatastore import numpy as np from pylab import * ds = InputDatastore('../InputData', 'NiCu', 973) cs = CalcSimWrapper() D = ds.interpolated_diffusivity(10001) R = ds.interpolated_resistivity(10001) dx = 0.5*35e-8 ndx = 200 dt = cs.optimum_dt(dx, D, 1) ndt = cs.num_sim_steps(dt, 2 * 60 * 60) init = np.ones(ndx) init[ndx/2:] = 0 res = cs.calc_simulation(D, R, init, ndt, dt, dx, 0, 1) x = np.linspace(0, dx * ndx, num=ndx) plot(x, res) show()
aparser.add_argument('--inputdata', metavar='DIR', type=str, required=True, help='Directory containing input data') aparser.add_argument('--dataprefix', metavar='PREFIX', type=str, default='NiCu', help='Prefix to data files') aparser.add_argument('--output', metavar='FILE', type=str, required=True, 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
help="Limits of the effective valence search (up to but not including ZMAX)", ) aparser.add_argument( "--cvflim", metavar=("CVFMIN", "CVFMAX", "CVFSTEP"), nargs=3, type=float, required=True, help="Limits of the vacancy concentration multiplier search (up to but not including CVFSTEP)", ) aparser.add_argument("--resume", action="store_true", default=False, help="Resume using .csv files in outputdir") args = aparser.parse_args() dstore = InputDatastore(args.inputdata, args.dataprefix) accelcs = CalcSimWrapper() zrange = np.arange(args.zlim[0], args.zlim[1], args.zlim[2]) cvfrange = np.arange(args.cvflim[0], args.cvflim[1], args.cvflim[2]) fresults = dict() rresults = dict() np.savetxt(path.join(args.outputdir, "zrange.csv"), zrange, delimiter=",") np.savetxt(path.join(args.outputdir, "cvfrange.csv"), cvfrange, delimiter=",") if args.resume: files = [ path.join(args.outputdir, f) for f in os.listdir(args.outputdir) if path.isfile(path.join(args.outputdir, f)) ] forwardfiles = [f for f in files if f.endswith("forward.csv")]