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import numpy as np
from lmfit.models import ConstantModel, RectangleModel, StepModel
def get_data():
np.random.seed(2021)
x = np.linspace(0, 10, 201)
dat = np.ones_like(x)
dat[:48] = 0.0
dat[48:77] = np.arange(77-48)/(77.0-48)
dat = dat + 5e-2*np.random.randn(len(x))
dat = 110.2 * dat + 12.0
return x, dat
def test_stepmodel_linear():
x, y = get_data()
stepmod = StepModel(form='linear')
const = ConstantModel()
pars = stepmod.guess(y, x)
pars = pars + const.make_params(c=3*y.min())
mod = stepmod + const
out = mod.fit(y, pars, x=x)
assert out.nfev > 5
assert out.nvarys == 4
assert out.chisqr > 1
assert out.params['c'].value > 3
assert out.params['center'].value > 1
assert out.params['center'].value < 4
assert out.params['sigma'].value > 0.5
assert out.params['sigma'].value < 3.5
assert out.params['amplitude'].value > 50
def test_stepmodel_erf():
x, y = get_data()
stepmod = StepModel(form='linear')
const = ConstantModel()
pars = stepmod.guess(y, x)
pars = pars + const.make_params(c=3*y.min())
mod = stepmod + const
out = mod.fit(y, pars, x=x)
assert out.nfev > 5
assert out.nvarys == 4
assert out.chisqr > 1
assert out.params['c'].value > 3
assert out.params['center'].value > 1
assert out.params['center'].value < 4
assert out.params['amplitude'].value > 50
assert out.params['sigma'].value > 0.2
assert out.params['sigma'].value < 1.5
def test_stepmodel_stepdown():
x = np.linspace(0, 50, 201)
y = np.ones_like(x)
y[129:] = 0.0
y[109:129] = 1.0 - np.arange(20)/20
y = y + 5e-2*np.random.randn(len(x))
stepmod = StepModel(form='linear')
pars = stepmod.guess(y, x)
out = stepmod.fit(y, pars, x=x)
assert out.nfev > 10
assert out.nvarys == 3
assert out.chisqr > 0.2
assert out.chisqr < 5.0
assert out.params['center'].value > 28
assert out.params['center'].value < 32
assert out.params['amplitude'].value > 0.5
assert out.params['sigma'].value < -2.0
assert out.params['sigma'].value > -8.0
def test_rectangle():
x = np.linspace(0, 50, 201)
y = np.ones_like(x) * 2.5
y[:33] = 0.0
y[162:] = 0.0
y[33:50] = 2.5*np.arange(50-33)/(50-33)
y[155:162] = 2.5 * (1 - np.arange(162-155)/(162-155))
y = y + 5e-2*np.random.randn(len(x))
stepmod = RectangleModel(form='linear')
pars = stepmod.guess(y, x)
out = stepmod.fit(y, pars, x=x)
assert out.nfev > 10
assert out.nvarys == 5
assert out.chisqr > 0.2
assert out.chisqr < 5.0
assert out.params['center1'].value > 8
assert out.params['center1'].value < 14
assert out.params['amplitude'].value > 2.0
assert out.params['amplitude'].value < 10.0
assert out.params['sigma1'].value > 1.0
assert out.params['sigma1'].value < 5.0
assert out.params['sigma2'].value > 0.3
assert out.params['sigma2'].value < 2.5
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