"""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()
"""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 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(
