def test_smeared_reflectivity_fitter(self):
# test smeared reflectivity calculation with values generated from
# Motofit (quadrature precsion order = 13)
theoretical = np.loadtxt(os.path.join(path, 'smeared_theoretical.txt'))
qvals, rvals, dqvals = np.hsplit(theoretical, 3)
'''
the order of the quadrature precision used to create these smeared
values in Motofit was 13.
Do the same here
'''
params = curvefitter.to_parameters(self.coefs)
fitfunc = RFF(quad_order=13)
fitter = CurveFitter(fitfunc,
(qvals, rvals),
params,
fcn_kws={'dqvals': dqvals})
model = fitter.model(params)
assert_almost_equal(model, rvals)
class TestFitter(unittest.TestCase):
def setUp(self):
self.xdata = np.linspace(-4, 4, 100)
self.p0 = np.array([0., 1., 0.0, 1.])
self.bounds = [(-1, 1), (0, 2), (-1, 1.), (0.001, 2)]
self.params = curvefitter.to_parameters(self.p0 + 0.2,
bounds=self.bounds)
self.final_params = curvefitter.to_parameters(self.p0,
bounds=self.bounds)
self.ydata = gauss(self.xdata, self.final_params)
self.f = CurveFitter(gauss, (self.xdata, self.ydata), self.params)
def test_fitting(self):
# the simplest test - a really simple gauss curve with perfect data
res = self.f.fit()
assert_almost_equal(values(res.params), self.p0)
assert_almost_equal(res.chisqr, 0)
def test_NIST(self):
# Run all the NIST standard tests with leastsq
for model in Models:
try:
NIST_runner(model)
except Exception:
print(model)
raise
def test_model_returns_function(self):
ydata = gauss(self.xdata, self.final_params)
model = self.f.model(self.final_params)
assert_almost_equal(ydata, model)
def test_residuals(self):
resid = self.f.residuals(self.final_params)
assert_almost_equal(np.sum(resid**2), 0)
def test_cost(self):
resid = self.f.residuals(self.final_params)
assert_almost_equal(0, np.sum(resid**2))
def test_leastsq(self):
# test that a custom method can be used with scipy.optimize.minimize
res = self.f.fit()
assert_almost_equal(values(res.params), self.p0)
def test_resid_length(self):
# the residuals length should be equal to the data length
resid = self.f.residuals(self.params)
assert_equal(resid.size, self.f.dataset.y.size)
def test_scalar_minimize(self):
assert_equal(values(self.params), self.p0 + 0.2)
res = self.f.fit(method='differential_evolution')
assert_almost_equal(values(res.params), self.p0, 3)
def test_holding_parameter(self):
# holding parameters means that those parameters shouldn't change
# during a fit
self.params['p0'].vary = False
res = self.f.fit()
assert_almost_equal(self.p0[0] + 0.2, self.params['p0'].value)
assert_almost_equal(res.params['p0'].value, self.params['p0'].value)
def test_fit_returns_MinimizerResult(self):
self.params['p0'].vary = False
res = self.f.fit()
assert_(isinstance(res, MinimizerResult))
def test_costfun(self):
# test user defined costfun
res = self.f.fit('nelder')
def costfun(params, generative, y, e):
return np.sum((y - generative / e) ** 2)
g = CurveFitter(gauss,
(self.xdata, self.ydata),
self.params,
costfun=costfun)
res2 = g.fit('nelder')
assert_almost_equal(values(res.params), values(res2.params))
def test_args_kwds_are_used(self):
# check that user defined args and kwds make their way into the user
# function
a = [1., 2.]
x = np.linspace(0, 10, 11)
y = a[0] + 1 + 2 * a[1] * x
par = Parameters()
par.add('p0', 1.5)
par.add('p1', 2.5)
def fun(x, p, *args, **kwds):
assert_equal(args, a)
return args[0] + p['p0'] + p['p1'] * a[1] * x
g = CurveFitter(fun, (x, y), par, fcn_args=a)
res = g.fit()
assert_almost_equal(values(res.params), [1., 2.])
d = {'a': 1, 'b': 2}
def fun(x, p, *args, **kwds):
return kwds['a'] + p['p0'] + p['p1'] * kwds['b'] * x
g = CurveFitter(fun, (x, y), par, fcn_kws=d)
res = g.fit()
assert_almost_equal(values(res.params), [1., 2.])
class TestFitter(unittest.TestCase):
def setUp(self):
self.xdata = np.linspace(-4, 4, 100)
self.p0 = np.array([0., 1., 0.0, 1.])
self.bounds = [(-1, 1), (0, 2), (-1, 1.), (0.001, 2)]
self.params = curvefitter.to_parameters(self.p0 + 0.2, bounds=self.bounds)
self.final_params = curvefitter.to_parameters(self.p0, bounds=self.bounds)
self.ydata = gauss(self.xdata, self.final_params)
self.f = CurveFitter(gauss, (self.xdata, self.ydata), self.params)
def pvals(self, params):
return np.asfarray(list(params.valuesdict().values()))
def test_fitting(self):
# the simplest test - a really simple gauss curve with perfect data
res = self.f.fit()
assert_almost_equal(self.pvals(res.params), self.p0)
assert_almost_equal(res.chisqr, 0)
def test_NIST(self):
# Run all the NIST standard tests with leastsq
for model in Models.keys():
try:
NIST_runner(model)
except Exception:
print(model)
raise
def test_model_returns_function(self):
ydata = gauss(self.xdata, self.final_params)
model = self.f.model(self.final_params)
assert_almost_equal(ydata, model)
def test_residuals(self):
resid = self.f.residuals(self.final_params)
assert_almost_equal(np.sum(resid**2), 0)
def test_cost(self):
resid = self.f.residuals(self.final_params)
assert_almost_equal(0, np.sum(resid**2))
def test_leastsq(self):
# test that a custom method can be used with scipy.optimize.minimize
res = self.f.fit()
assert_almost_equal(self.pvals(res.params), self.p0)
def test_resid_length(self):
# the residuals length should be equal to the data length
resid = self.f.residuals(self.params)
assert_equal(resid.size, self.f.ydata.size)
def test_scalar_minimize(self):
assert_equal(self.pvals(self.params), self.p0 + 0.2)
res = self.f.fit(method='differential_evolution')
assert_almost_equal(self.pvals(res.params), self.p0, 3)
def test_holding_parameter(self):
# holding parameters means that those parameters shouldn't change
# during a fit
self.params['p0'].vary = False
res = self.f.fit()
assert_almost_equal(self.p0[0] + 0.2, self.params['p0'].value)
def test_fit_returns_MinimizerResult(self):
self.params['p0'].vary = False
res = self.f.fit()
assert_(isinstance(res, MinimizerResult))
def test_costfun(self):
# test user defined costfun
res = self.f.fit('nelder')
def costfun(params, generative, y, e):
return np.sum((y - generative / e) ** 2)
g = CurveFitter(gauss, (self.xdata, self.ydata), self.params, costfun=costfun)
res2 = g.fit('nelder')
assert_almost_equal(self.pvals(res.params), self.pvals(res2.params))
