def BDR(V, params):
"""BDR model tunnelling
V=bias voltage, params=[A, phi, dphi, d, mass]
A: in m^2, phi: average barrier height in eV, dphi: change in barrier height in eV,
d: barrier width in angstrom, mass: effective electron mass as a fraction of electron rest mass
See Brinkman et. al. J. Appl. Phys. 41 1915 (1970)
or Tuan Comm. in Phys. 16, 1, (2006)"""
return _SF.BDR(V, *params)
I = SF.bdr(V, 2.5, 3.2, 0.3, 15.0, 1.0) + normal(size=len(V), scale=1.0e-3)
dI = ones_like(V) * 1.0e-3
# Curve fit
d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
d.curve_fit(SF.bdr, p0=[2.5, 3.2, 0.3, 15.0, 1.0], result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.bdr, x=0.6, y=0.05, prefix="bdr", fontdict={"size": "x-small", "color": "blue"}
)
# lmfit
d.setas = "xy"
fit = SF.BDR(missing="drop")
p0 = fit.guess(I, x=V)
for p, v, mi, mx in zip(
["A", "phi", "dphi", "d", "mass"],
[2.500, 3.2, 0.3, 15.0, 1.0],
[0.100, 1.0, 0.05, 5.0, 0.5],
[10, 10.0, 2.0, 30.0, 5.0],
):
p0[p].value = v
p0[p].bounds = [mi, mx]
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
fit, x=0.2, y=0.05, prefix="BDR", fontdict={"size": "x-small", "color": "green"}
)
