def FowlerNordheim(V, params):
"""Simmons model tunnelling at V>phi
V=bias voltage, params=[A, phi, d]
A in m^2, phi barrier height in eV, d barrier width in angstrom
Simmons model as in
Simmons J. App. Phys. 34 6 1963
"""
return _SF.FowlerNordheim(V, *params)
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)
def WLfit(B, params):
"""Weak localisation
VRH(B, params):
B = mag. field, params=list of parameter values, s0, B1, B2
2D WL model as per
Wu PRL 98, 136801 (2007)
Porter PRB 86, 064423 (2012)
"""
return _SF.WLfit(B, *params)
def FluchsSondheimer(t, params):
"""Evaluate a Fluchs-Sondheumer model function for conductivity.
Args:
t (array): Thickness values
params (array): [mean-free-path, reflection co-efficient,sigma_0]
Returns:
Reduced Resistivity
Note:
Expression used from: G.N.Gould and L.A. Moraga, Thin Solid Films 10 (2), 1972 pp 327-330
"""
return _SF.FluchsSondheimer(t, *params)
def strijkers(V, params):
"""strijkers(V, params):
Args:
V (array): bias voltages
params (list): parameter values: omega, delta,P and Z
Note:
PCAR fitting Strijkers modified BTK model
BTK PRB 25 4515 1982, Strijkers PRB 63, 104510 2000
Only using 1 delta, not modified for proximity
"""
return _SF.Strijkers(V, *params)
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
# Make some data
T = linspace(200, 350, 101)
R = SF.modArrhenius(T, 1e6, 0.5, 1.5) * normal(
scale=0.00005, loc=1.0, size=len(T))
d = Data(T, R, setas="xy", column_headers=["T", "Rate"])
# Curve_fit on its own
d.curve_fit(SF.modArrhenius,
p0=[1e6, 0.5, 1.5],
result=True,
header="curve_fit")
d.setas = "xyy"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(SF.modArrhenius, x=0.2, y=0.5)
# lmfit using lmfit guesses
fit = SF.ModArrhenius()
p0 = [1e6, 0.5, 1.5]
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "x..y"
d.plot()
d.annotate_fit(SF.ModArrhenius, x=0.2, y=0.25, prefix="ModArrhenius")
d.title = "Modified Arrhenius Test Fit"
d.ylabel = "Rate"
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
# Make some data
V = linspace(-4, 4, 1001)
I = SF.simmons(V, 2500, 5.2, 15.0) + normal(size=len(V), scale=100e-9)
dI = ones_like(V) * 100e-9
d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
d.curve_fit(SF.simmons, p0=[2500, 5.2, 15.0], result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.simmons,
x=0.25,
y=0.25,
prefix="simmons",
fontdict={"size": "x-small", "color": "blue"},
)
d.setas = "xye"
fit = SF.Simmons()
p0 = [2500, 5.2, 15.0]
d.lmfit(SF.Simmons, p0=p0, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
# Make some data
V = linspace(-4, 4, 1001)
I = SF.simmons(V, 2500, 5.2, 15.0) + normal(size=len(V), scale=100e-9)
dI = ones_like(V) * 100e-9
d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
d.curve_fit(SF.simmons, p0=[2500, 5.2, 15.0], result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.simmons,
x=0.25,
y=0.25,
prefix="simmons",
fontdict={
"size": "x-small",
"color": "blue"
},
)
d.setas = "xye"
fit = SF.Simmons()
p0 = [2500, 5.2, 15.0]
d.lmfit(SF.Simmons, p0=p0, result=True, header="lmfit")
"""Example of Arrhenius Fit.""" from Stoner import Data import Stoner.Fit as SF from numpy import linspace from numpy.random import normal #Make some data T=linspace(200,350,101) R=SF.arrhenius(T+normal(size=len(T),scale=1.0,loc=1.0),1E6,0.5) d=Data(T,R,setas="xy",column_headers=["T","Rate"]) #Curve_fit on its own d.curve_fit(SF.arrhenius,p0=[1E6,0.5],result=True,header="curve_fit") d.setas="xyy" d.plot() d.annotate_fit(SF.arrhenius,x=200,y=0.04) # lmfit using lmfit guesses fit=SF.Arrhenius() p0=fit.guess(R,x=T) d.lmfit(fit,p0=p0,result=True,header="lmfit") d.setas="x..y" d.plot() d.annotate_fit(SF.Arrhenius,x=200,y=0.02,prefix="Arrhenius") d.title="Arrhenius Test Fit" d.ylabel="Rate" d.xlabel="Temperature (K)"
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
from copy import copy
B = linspace(-0.01, 0.01, 100)
params = [1, 1.0e-11, 250]
G = SF.langevin(B, *params) + normal(size=len(B), scale=5e-3)
dG = ones_like(B) * 5e-3
d = Data(B,
G,
dG,
setas="xye",
column_headers=["Field $\\mu_0H (T)$", "Moment", "dM"])
func = lambda H, M_s, m: SF.langevin(H, M_s, m, 250)
d.curve_fit(func, p0=copy(params)[0:2], result=True, header="curve_fit")
d.setas = "xye"
fit = SF.Langevin()
p0 = fit.guess(G, x=B)
for p, v in zip(p0, params):
p0[p].set(v)
p0[p].max = v * 5
p0[p].min = 0
p0[p].vary = p != "T"
d.lmfit(fit, p0=p0, result=True, header="lmfit")
def PowerLaw(x, p):
"""Power Law Fitting Equation"""
return _SF.PowerLaw(x, *p)
"""Example of PowerLaw Fit.""" from Stoner import Data import Stoner.Fit as SF from numpy import linspace from numpy.random import normal # Make some data T = linspace(50, 500, 101) R = SF.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(SF.powerLaw, p0=[1, 0.5], result=True, header="curve_fit") d.setas = "xyy" d.plot(fmt=["r.", "b-"]) d.annotate_fit(SF.powerLaw, x=0.5, y=0.25) # lmfit using lmfit guesses fit = SF.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(SF.PowerLaw, x=0.5, y=0.05, prefix="PowerLaw") d.title = "Powerlaw Test Fit" d.ylabel = "Rate" d.xlabel = "Temperature (K)"
"""Example of nDimArrhenius Fit.""" from Stoner import Data import Stoner.Fit as SF from numpy import linspace from numpy.random import normal # Make some data T = linspace(50, 500, 101) R = SF.nDimArrhenius(T + normal(size=len(T), scale=5.0, loc=1.0), 1e6, 0.5, 2) d = Data(T, R, setas="xy", column_headers=["T", "Rate"]) # Curve_fit on its own d.curve_fit(SF.nDimArrhenius, p0=[1e6, 0.5, 2], result=True, header="curve_fit") d.setas = "xyy" d.plot(fmt=["r.", "b-"]) d.annotate_fit(SF.nDimArrhenius, x=0.25, y=0.3) # lmfit using lmfit guesses fit = SF.NDimArrhenius() 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(SF.NDimArrhenius, x=0.25, y=0.05, prefix="NDimArrhenius") d.title = "n-D Arrhenius Test Fit" d.ylabel = "Rate" d.xlabel = "Temperature (K)"
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
#Make some data
T = linspace(50, 500, 101)
R = SF.nDimArrhenius(T + normal(size=len(T), scale=5.0, loc=1.0), 1E6, 0.5, 2)
d = Data(T, R, setas="xy", column_headers=["T", "Rate"])
#Curve_fit on its own
d.curve_fit(SF.nDimArrhenius,
p0=[1E6, 0.5, 2],
result=True,
header="curve_fit")
d.setas = "xyy"
d.plot()
d.annotate_fit(SF.nDimArrhenius, x=150, y=6E5)
# lmfit using lmfit guesses
fit = SF.NDimArrhenius()
p0 = fit.guess(R, x=T)
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "x..y"
d.plot()
d.annotate_fit(SF.NDimArrhenius, x=150, y=3.5E5, prefix="NDimArrhenius")
d.title = "n-D Arrhenius Test Fit"
d.ylabel = "Rate"
d.xlabel = "Temperature (K)"
def TersoffHammann(V, params):
"""TersoffHamman model for tunnelling through STM tip
V=bias voltage, params=[A]
"""
return _SF.TersoffHammann(V, *params)
"""Example of Quadratic Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
import matplotlib.pyplot as plt
# Make some data
x = linspace(-10, 10, 101)
y = SF.quadratic(x + normal(size=len(x), scale=0.1), 4, -2, 11) * normal(
size=len(x), scale=0.05, loc=1.0)
s = y * 0.05
d = Data(x, y, s, setas="xye", column_headers=["X", "Y"])
d.plot(fmt="r.")
d.polyfit(result=True, header="Polyfit")
d.setas = "x..y"
d.plot(fmt="m-", label="Polyfit")
d.text(
-9,
450,
"Polynominal co-efficients\n{}".format(
d["2nd-order polyfit coefficients"]),
fontdict={
"size": "x-small",
"color": "magenta"
},
)
d.setas = "xy"
d.curve_fit(SF.quadratic, result=True, header="Curve-fit")
def Linear(x, p):
"""Simple linear function"""
return _SF.Lineaer(x, *p)
def NDimArrhenius(x, p):
"""Arrhenius Equation without T dependendent prefactor"""
return _SF.NDimArrhehius(x, *p)
def Quadratic(x, p):
"""Simple Qudratic Function."""
return _SF.Quadratic(x, *p)
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
from copy import copy
B = linspace(-0.01, 0.01, 100)
params = [1, 1.0e-11, 250]
G = SF.langevin(B, *params) + normal(size=len(B), scale=5e-3)
dG = ones_like(B) * 5e-3
d = Data(B, G, dG, setas="xye", column_headers=["Field $\\mu_0H (T)$", "Moment", "dM"])
func = lambda H, M_s, m: SF.langevin(H, M_s, m, 250)
d.curve_fit(func, p0=copy(params)[0:2], result=True, header="curve_fit")
d.setas = "xye"
fit = SF.Langevin()
p0 = fit.guess(G, x=B)
for p, v in zip(p0, params):
p0[p].set(v)
p0[p].max = v * 5
p0[p].min = 0
p0[p].vary = p != "T"
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
# Make some data
V = linspace(-10, 10, 1000)
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],
):
"""Example of Arrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import logspace, log10
from numpy.random import normal
#Make some data
T = logspace(log10(200), log10(350), 51)
params = (1E6, 0.5, 150)
noise = 0.5
R = SF.vftEquation(T, *params) * normal(size=len(T), scale=noise, loc=1.0)
dR = SF.vftEquation(T, *params) * noise
d = Data(T, R, dR, setas="xye", column_headers=["T", "Rate"])
#Curve_fit on its own
d.curve_fit(SF.vftEquation, p0=params, result=True, header="curve_fit")
# lmfit using lmfit guesses
fit = SF.VFTEquation()
p0 = params
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "xyeyyy"
d.plot(fmt=["k+", "r-", "b-"])
d.yscale = "log"
d.ylim = (1E-43, 1)
d.annotate_fit(SF.vftEquation,
x=270,
y=1E-27,
fontdict={"size": "x-small"},
def ModArrhenius(x, p):
"""Arrhenius Equation with a variable T power dependent prefactor"""
return _SF.ModArrhehius(x, *p)
"""Example of Quadratic Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
import matplotlib.pyplot as plt
# Make some data
x = linspace(-10, 10, 101)
y = SF.quadratic(x + normal(size=len(x), scale=0.1), 4, -2, 11) * normal(
size=len(x), scale=0.05, loc=1.0
)
s = y * 0.05
d = Data(x, y, s, setas="xye", column_headers=["X", "Y"])
d.plot(fmt="r.")
d.polyfit(result=True, header="Polyfit")
d.setas = "x..y"
d.plot(fmt="m-", label="Polyfit")
d.text(
-9,
450,
"Polynominal co-efficients\n{}".format(d["2nd-order polyfit coefficients"]),
fontdict={"size": "x-small", "color": "magenta"},
)
d.setas = "xy"
d.curve_fit(SF.quadratic, result=True, header="Curve-fit")
d.setas = "x...y"
d.plot(fmt="b-", label="curve-fit")
d.annotate_fit(
"""Example of Arrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ceil, log10, abs
from numpy.random import normal
# Make some data
T = linspace(200, 350, 101)
R = SF.arrhenius(T + normal(size=len(T), scale=3.0, loc=0.0), 1e6, 0.5)
E = 10 ** ceil(log10(abs(R - SF.arrhenius(T, 1e6, 0.5))))
d = Data(T, R, E, setas="xye", column_headers=["T", "Rate"])
# Curve_fit on its own
d.curve_fit(SF.arrhenius, p0=(1e6, 0.5), result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.arrhenius,
x=0.5,
y=0.5,
mode="eng",
fontdict={"size": "x-small", "color": "blue"},
)
# lmfit using lmfit guesses
fit = SF.Arrhenius()
d.setas = "xye"
d.lmfit(fit, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
B = linspace(2, 100, 26)
params = [12.5, 0.75, 1e3]
G = SF.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(SF.fluchsSondheimer, p0=params, result=True, header="curve_fit")
d.setas = "xye"
d.lmfit(SF.FluchsSondheimer, p0=params, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
d.annotate_fit(SF.fluchsSondheimer,
x=0.2,
y=0.6,
fontdict={
"size": "x-small",
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from Stoner.plot.formats import TexEngFormatter
from numpy import linspace, ones_like
from numpy.random import normal
from copy import copy
B = linspace(1e3, 5e4, 51)
params = [2.2, 1e5, 2e2]
G = SF.kittelEquation(B, *params) + normal(size=len(B), scale=5e7)
dG = ones_like(B) * 5e7
d = Data(
B,
G,
dG,
setas="xye",
column_headers=["Field $Oe$", r"$\nu (Hz)$", r"\delta $\nu (Hz)$"],
)
d.curve_fit(SF.kittelEquation, p0=copy(params), result=True, header="curve_fit")
fit = SF.KittelEquation()
p0 = fit.guess(G, x=B)
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from Stoner.plot.formats import TexEngFormatter
from numpy import linspace, ones_like
from numpy.random import normal
from copy import copy
B = linspace(0, 5E4, 51)
params = [2.2, 1E5, 2E2]
G = SF.kittelEquation(B, *params) + normal(size=len(B), scale=5E7)
dG = ones_like(B) * 5E7
d = Data(B,
G,
dG,
setas="xye",
column_headers=["Field $Oe$", r"$\nu (Hz)$", r"\delta $\nu (Hz)$"])
d.curve_fit(SF.kittelEquation,
p0=copy(params),
result=True,
header="curve_fit")
fit = SF.KittelEquation()
p0 = fit.guess(G, x=B)
d.lmfit(fit, p0=p0, result=True, header="lmfit")
d.setas = "xyeyy"
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
# Make some data
V = linspace(-4, 4, 101)
I = SF.simmons(V, 2500, 3.2, 15.0) + normal(size=len(V), scale=5e-7)
dI = ones_like(V) * 500e-9
p0 = p0 = [2500, 3, 10.0]
d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
d.curve_fit(SF.simmons, p0=p0, result=True, header="curve_fit", maxfev=2000)
d.setas = "xyey"
d.plot(fmt=["r,", "b-"], capsize=1)
d.annotate_fit(
SF.simmons,
x=0.25,
y=0.25,
prefix="simmons",
fontdict={
"size": "x-small",
"color": "blue"
},
)
d.setas = "xye"
fit = SF.Simmons()
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
from copy import deepcopy
T = linspace(4.2, 300, 101)
params = [265, 65, 1.0, 5]
params2 = deepcopy(params)
G = SF.blochGrueneisen(T, *params) + normal(size=len(T), scale=5E-5)
dG = ones_like(T) * 5E-5
d = Data(T,
G,
dG,
setas="xye",
column_headers=["Temperature (K)", "Resistivity", "dR"])
d.curve_fit(SF.blochGrueneisen, p0=params, result=True, header="curve_fit")
d.setas = "xy"
d.lmfit(SF.BlochGrueneisen, p0=params2, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
d.annotate_fit(SF.blochGrueneisen, x=20, y=65.05, fontdict={"size": "x-small"})
d.annotate_fit(SF.BlochGrueneisen,
x=100,
y=65,
fontdict={"size": "x-small"},
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
B = linspace(2, 100, 26)
params = [12.5, 0.75, 1e3]
G = SF.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(SF.fluchsSondheimer, p0=params, result=True, header="curve_fit")
d.setas = "xye"
d.lmfit(SF.FluchsSondheimer, p0=params, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
d.annotate_fit(
SF.fluchsSondheimer, x=0.2, y=0.6, fontdict={"size": "x-small", "color": "blue"}
)
d.annotate_fit(
SF.FluchsSondheimer,
"""Example of Arrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ceil, log10, abs as np_abs
from numpy.random import normal
# Make some data
T = linspace(200, 350, 101)
R = SF.arrhenius(T + normal(size=len(T), scale=3.0, loc=0.0), 1e6, 0.5)
E = 10 ** ceil(log10(np_abs(R - SF.arrhenius(T, 1e6, 0.5))))
d = Data(T, R, E, setas="xye", column_headers=["T", "Rate"])
# Curve_fit on its own
d.curve_fit(SF.arrhenius, p0=(1e6, 0.5), result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.arrhenius,
x=0.5,
y=0.5,
mode="eng",
fontdict={"size": "x-small", "color": "blue"},
)
# lmfit using lmfit guesses
fit = SF.Arrhenius()
d.setas = "xye"
d.lmfit(fit, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
from copy import copy
B = linspace(-8, 8, 201)
params = [1e-3, 2.0, 0.25, 1.4]
G = SF.wlfit(B, *params) + normal(size=len(B), scale=5e-7)
dG = ones_like(B) * 5e-7
d = Data(
B,
G,
dG,
setas="xye",
column_headers=["Field $\\mu_0H (T)$", "Conductance", "dConductance"],
)
d.curve_fit(SF.wlfit, p0=copy(params), result=True, header="curve_fit")
d.setas = "xye"
d.lmfit(SF.WLfit, p0=copy(params), result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
d.annotate_fit(SF.wlfit, x=0.05, y=0.75, fontdict={"size": "x-small", "color": "blue"})
d.annotate_fit(
SF.WLfit,
x=0.05,
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace,ones_like
from numpy.random import normal
#Make some data
V=linspace(-4,4,1000)
I=SF.fowlerNordheim(V,2500,3.2,15.0)+normal(size=len(V),scale=10E-6)
dI=ones_like(V)*10E-6
d=Data(V,I,dI,setas="xye",column_headers=["Bias","Current","Noise"])
d.curve_fit(SF.fowlerNordheim,p0=[2500,3.2,15.0],result=True,header="curve_fit")
d.setas="xyey"
d.plot(fmt=["r.","b-"])
d.annotate_fit(SF.fowlerNordheim,x=0,y=10,prefix="fowlerNordheim",fontdict={"size":"x-small"})
d.setas="xye"
fit=SF.FowlerNordheim()
p0=[2500,5.2,15.0]
p0=fit.guess(I,x=V)
for p,v,mi,mx in zip(["A","phi","d"],[2500,3.2,15.0],[100,1,5],[1E4,20.0,30.0]):
p0[p].value=v
p0[p].bounds=[mi,mx]
d.lmfit(SF.FowlerNordheim,p0=p0,result=True,header="lmfit")
d.setas="x...y"
d.plot()
d.annotate_fit(fit,x=-3,y=-60,prefix="FowlerNordheim",fontdict={"size":"x-small"})
d.ylabel="Current"
"""Example of nDimArrhenius Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
# Make some data
V = linspace(-4, 4, 1000)
I = SF.fowlerNordheim(V, 2500, 3.2, 15.0) + normal(size=len(V), scale=1e-6)
dI = ones_like(V) * 10e-6
d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
d.curve_fit(SF.fowlerNordheim, p0=[2500, 3.2, 15.0], result=True, header="curve_fit")
d.setas = "xyey"
d.plot(fmt=["r.", "b-"])
d.annotate_fit(
SF.fowlerNordheim,
x=0.2,
y=0.6,
prefix="fowlerNordheim",
fontdict={"size": "x-small", "color": "blue"},
)
d.setas = "xye"
fit = SF.FowlerNordheim()
p0 = [2500, 5.2, 15.0]
p0 = fit.guess(I, x=V)
for p, v, mi, mx in zip(
["A", "phi", "d"], [2500, 3.2, 15.0], [100, 1, 5], [1e4, 20.0, 30.0]
):
"""Example of nDimArrhenius Fit.""" from Stoner import Data import Stoner.Fit as SF from numpy import linspace from numpy.random import normal # Make some data T = linspace(200, 350, 101) R = SF.modArrhenius(T, 1e6, 0.5, 1.5) * normal(scale=0.00005, loc=1.0, size=len(T)) d = Data(T, R, setas="xy", column_headers=["T", "Rate"]) # Curve_fit on its own d.curve_fit(SF.modArrhenius, p0=[1e6, 0.5, 1.5], result=True, header="curve_fit") d.setas = "xyy" d.plot(fmt=["r.", "b-"]) d.annotate_fit(SF.modArrhenius, x=0.2, y=0.5) # lmfit using lmfit guesses fit = SF.ModArrhenius() p0 = [1e6, 0.5, 1.5] d.lmfit(fit, p0=p0, result=True, header="lmfit") d.setas = "x..y" d.plot() d.annotate_fit(SF.ModArrhenius, x=0.2, y=0.25, prefix="ModArrhenius") d.title = "Modified Arrhenius Test Fit" d.ylabel = "Rate" d.xlabel = "Temperature (K)"
"""Test Weak-localisation fitting."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace, ones_like
from numpy.random import normal
from copy import deepcopy
T = linspace(4.2, 300, 101)
params = [265, 65, 1.0, 5]
params2 = deepcopy(params)
G = SF.blochGrueneisen(T, *params) + normal(size=len(T), scale=5e-5)
dG = ones_like(T) * 5e-5
d = Data(T, G, dG, setas="xye", column_headers=["Temperature (K)", "Resistivity", "dR"])
d.curve_fit(SF.blochGrueneisen, p0=params, result=True, header="curve_fit")
d.setas = "xy"
d.lmfit(SF.BlochGrueneisen, p0=params2, result=True, header="lmfit")
d.setas = "xyeyy"
d.plot(fmt=["r.", "b-", "g-"])
d.annotate_fit(SF.blochGrueneisen, x=0.65, y=0.35, fontdict={"size": "x-small"})
d.annotate_fit(
SF.BlochGrueneisen,
x=0.65,
y=0.05,
fontdict={"size": "x-small"},
prefix="BlochGrueneisen",
)
d.title = "Bloch-Grueneisen Fit"