powerLaw¶
- Stoner.analysis.fitting.models.generic.powerLaw(x, A, k)[source]¶
Power Law Fitting Equation.
- Parameters:
x (array) – Input data
A (float) – Prefactor
k (float) – Power
- Returns:
Power law.
\(p=Ax^k\)
Example
"""Example of PowerLaw Fit.""" from numpy import linspace from numpy.random import normal from Stoner import Data from Stoner.analysis.fitting.models.generic import PowerLaw, powerLaw # Make some data T = linspace(50, 500, 101) R = powerLaw(T, 1e-2, 0.6666666) * normal(size=len(T), scale=0.1, loc=1.0) d = Data(T, R, setas="xy", column_headers=["T", "Rate"]) # Curve_fit on its own d.curve_fit(powerLaw, p0=[1, 0.5], result=True, header="curve_fit") d.setas = "xyy" d.plot(fmt=["r.", "b-"]) d.annotate_fit(powerLaw, x=0.5, y=0.25) # lmfit using lmfit guesses fit = PowerLaw() p0 = fit.guess(R, x=T) d.lmfit(fit, p0=p0, result=True, header="lmfit") d.setas = "x..y" d.plot(fmt="g-") d.annotate_fit(PowerLaw, x=0.5, y=0.05, prefix="PowerLaw") d.title = "Powerlaw Test Fit" d.ylabel = "Rate" d.xlabel = "Temperature (K)"
