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# test of fitting one variable
# From Nick Schurch
import numpy
from numpy.testing import assert_allclose
import lmfit
def linear_chisq(params, x, data, errs=None):
"""Calculates chi-squared residuals for linear model."""
if not isinstance(params, lmfit.parameter.Parameters):
msg = "Params argument is not a lmfit parameter set"
raise TypeError(msg)
if "m" not in params.keys():
msg = "No slope parameter (m) defined in the model"
raise KeyError(msg)
if "c" not in params.keys():
msg = "No intercept parameter (c) defined in the model"
raise KeyError(msg)
model = params["m"]*x + params["c"]
residuals = (data-model)
if errs is not None:
residuals = residuals/errs
return residuals
def test_1var():
rands = [-0.21698284, 0.41900591, 0.02349374, -0.218552, -0.3513699,
0.33418304, 0.04226855, 0.213303, 0.45948731, 0.33587736]
x = numpy.arange(10)+1
y = numpy.arange(10)+1+rands
params = lmfit.Parameters()
params.add(name="m", value=1.0, vary=True)
params.add(name="c", value=0.0, vary=False)
out = lmfit.minimize(linear_chisq, params, args=(x, y))
assert_allclose(params['m'].value, 1.025, rtol=0.02, atol=0.02)
assert len(params) == 2
assert out.nvarys == 1
assert out.chisqr > 0.01
assert out.chisqr < 5.00
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