fluchsSondheimer¶
- Stoner.analysis.fitting.models.e_transport.fluchsSondheimer(t, l, p, sigma_0)[source]¶
Evaluate a Fluchs-Sondheumer model function for conductivity.
- Parameters:
t (array) – Thickness values
l (float) – mean-free-path
p (float) – reflection co-efficient
sigma_0 (float) – intrinsic conductivity
- Returns:
Reduced Resistivity
Note
Expression used from: G.N.Gould and L.A. Moraga, Thin Solid Films 10 (2), 1972 pp 327-330
Example
"""Test Weak-localisation fitting.""" from numpy import logspace, ones_like, log10 from numpy.random import normal from Stoner import Data from Stoner.analysis.fitting.models.e_transport import ( fluchsSondheimer, FluchsSondheimer, ) B = logspace(log10(2), 2, 26) params = [12.5, 0.75, 1e3] G = fluchsSondheimer(B, *params) + normal(size=len(B), scale=5e-5) dG = ones_like(B) * 5e-5 d = Data( B, G, dG, setas="xye", column_headers=["Thickness (nm)", "Conductance", "dConductance"], ) d.curve_fit(fluchsSondheimer, p0=params, result=True, header="curve_fit") d.setas = "xye" d.lmfit(FluchsSondheimer, result=True, header="lmfit") d.setas = "xyeyy" d.plot(fmt=["r.", "b-", "g-"]) d.annotate_fit( fluchsSondheimer, x=0.2, y=0.6, fontdict={"size": "x-small", "color": "blue"}, ) d.annotate_fit( FluchsSondheimer, x=0.2, y=0.4, fontdict={"size": "x-small", "color": "green"}, prefix="FluchsSondheimer", ) d.title = "Fluchs-Sondheimer Fit"