class TestUserDefiniedModel(unittest.TestCase):
# mainly aimed at checking that the API does what it says it does
# and raises the right exceptions or warnings when things are not right
def setUp(self):
self.x = np.linspace(-10, 10, num=1000)
np.random.seed(1)
self.noise = 0.01*np.random.randn(*self.x.shape)
self.true_values = lambda: dict(amplitude=7.1, center=1.1, sigma=2.40)
self.guess = lambda: dict(amplitude=5, center=2, sigma=4)
# return a fresh copy
self.model = Model(gaussian, ['x'])
self.data = gaussian(x=self.x, **self.true_values()) + self.noise
def test_fit_with_keyword_params(self):
result = self.model.fit(self.data, x=self.x, **self.guess())
assert_results_close(result.values, self.true_values())
def test_fit_with_parameters_obj(self):
params = self.model.params()
for param_name, value in self.guess().items():
params[param_name].value = value
result = self.model.fit(self.data, params, x=self.x)
assert_results_close(result.values, self.true_values())
def test_missing_param_raises_error(self):
# using keyword argument parameters
guess_missing_sigma = self.guess()
del guess_missing_sigma['sigma']
f = lambda: self.model.fit(self.data, x=self.x, **guess_missing_sigma)
self.assertRaises(ValueError, f)
# using Parameters
params = self.model.params()
for param_name, value in guess_missing_sigma.items():
params[param_name].value = value
f = lambda: self.model.fit(self.data, params, x=self.x)
def test_extra_param_issues_warning(self):
# The function accepts extra params, Model will warn but not raise.
guess = self.guess()
guess['extra'] = 5
def flexible_func(x, amplitude, center, sigma, **kwargs):
return gaussian(x, amplitude, center, sigma)
flexible_model = Model(flexible_func, ['x'])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
flexible_model.fit(self.data, x=self.x, **guess)
self.assertTrue(len(w) == 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
def test_missing_independent_variable_raises_error(self):
f = lambda: self.model.fit(self.data, **self.guess())
self.assertRaises(KeyError, f)
def test_bounding(self):
guess = self.guess()
guess['center'] = Parameter(value=2, min=1.3)
true_values = self.true_values()
true_values['center'] = 1.3 # as close as it's allowed to get
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
def test_vary_false(self):
guess = self.guess()
guess['center'] = Parameter(value=1.3, vary=False)
true_values = self.true_values()
true_values['center'] = 1.3
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
def test_result_attributes(self):
# result.init_values
result = self.model.fit(self.data, x=self.x, **self.guess())
assert_results_close(result.values, self.true_values())
self.assertTrue(result.init_values == self.guess())
# result.init_params
params = self.model.params()
for param_name, value in self.guess().items():
params[param_name].value = value
self.assertTrue(result.init_params == params)
# result.best_fit
assert_allclose(result.best_fit, self.data, atol=self.noise.max())
# result.init_fit
init_fit = self.model.func(x=self.x, **self.guess())
assert_allclose(result.init_fit, init_fit)
# result.model
self.assertTrue(result.model is self.model)
# testing model addition...
def test_user_defined_gaussian_plus_constant(self):
data = self.data + 5.0
model = self.model + specified_models.Constant()
guess = self.guess()
guess['c'] = 10.1
true_values = self.true_values()
true_values['c'] = 5.0
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.01, atol=0.01)
def test_sum_of_two_gaussians(self):
# two user-defined gaussians
model1 = self.model
f2 = lambda x, amplitude_, center_, sigma_: gaussian(
x, amplitude_, center_, sigma_)
model2 = Model(f2, ['x'])
values1 = self.true_values()
values2 = self.true_values()
values2['sigma'] = 1.5
data = gaussian(x=self.x, **values1)
data += gaussian(x=self.x, **values2)
model = self.model + model2
values2 = {k + '_': v for k, v in values2.items()}
guess = {'sigma': Parameter(value=2, min=0), 'center': 1,
'amplitude': Parameter(value=3, min=0),
'sigma_': Parameter(value=1, min=0), 'center_': 1,
'amplitude_': Parameter(value=2.3)}
true_values = dict(list(values1.items()) + list(values2.items()))
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values)
# user-defined models with common parameter names
# cannot be added, and should raise
f = lambda: model1 + model1
self.assertRaises(NameError, f)
# two predefined_gaussians, using suffix to differentiate
model1 = specified_models.Gaussian(['x'])
model2 = specified_models.Gaussian(['x'], suffix='_')
model = model1 + model2
true_values = {'center': values1['center'],
'amplitude': values1['amplitude'],
'sigma': values1['sigma'],
'center_': values2['center_'],
'amplitude_': values2['amplitude_'],
'sigma_': values2['sigma_']}
guess = {'sigma': 2, 'center': 1, 'amplitude': 1,
'sigma_': 1, 'center_': 1, 'amplitude_': 1}
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values)
# without suffix, the names collide and Model should raise
model1 = specified_models.Gaussian(['x'])
model2 = specified_models.Gaussian(['x'])
f = lambda: model1 + model2
self.assertRaises(NameError, f)
chis = FHmodel.chi(phi1, phi2)
chi_errs = FHmodel.chiError(phi1, phi1_err, phi2, phi2_err)
#out = mod.fit(T,x=chis,A=-1,B=10,kws=(FHmodel.chi))
out = mod.fit(chis, x=T, dS=-1., dH=10, kws=(FHmodel.chi))
print(out.fit_report())
# extract fit params
dH = out.params["dH"].value
std_dH = out.params["dH"].stderr
dS = out.params["dS"].value
std_dS = out.params["dS"].stderr
# plot dH and dS fits
plt.plot(
1. / T,
mod.func(T, dS=dS, dH=dH),
"k--",
label=r"$\Delta H = %.3f \pm %.3f, \Delta S = %.3f \pm %.3f$" %
(dH, std_dH, dS, std_dS))
#plt.errorbar(1./T,chis,yerr=chi_errs,fmt="ro")
plt.errorbar(1. / T, chis, fmt="ro")
plt.ylabel(r"$\chi$")
plt.xlabel(r"1/T")
plt.legend(loc=0)
plt.savefig(os.path.join(outpath, "Chi_vs_T_%s.pdf" % fname_ext))
plt.show()
# plot expt. phi values for coexistence curve
plt.errorbar(phi1, T, fmt="ro", xerr=phi1_err, label=fname)
plt.errorbar(phi2, T, fmt="ro", xerr=phi2_err)
plt.ylim(273.15, 373.15)
class TestUserDefiniedModel(unittest.TestCase):
# mainly aimed at checking that the API does what it says it does
# and raises the right exceptions or warnings when things are not right
def setUp(self):
self.x = np.linspace(-10, 10, num=1000)
np.random.seed(1)
self.noise = 0.01 * np.random.randn(*self.x.shape)
self.true_values = lambda: dict(height=7, center=1, sigma=3)
self.guess = lambda: dict(height=5, center=2, sigma=4)
# return a fresh copy
self.model = Model(gaussian, ['x'])
self.data = gaussian(x=self.x, **self.true_values()) + self.noise
def test_fit_with_keyword_params(self):
result = self.model.fit(self.data, x=self.x, **self.guess())
assert_results_close(result.values, self.true_values())
def test_fit_with_parameters_obj(self):
params = self.model.params()
for param_name, value in self.guess().items():
params[param_name].value = value
result = self.model.fit(self.data, params, x=self.x)
assert_results_close(result.values, self.true_values())
def test_missing_param_raises_error(self):
# using keyword argument parameters
guess_missing_sigma = self.guess()
del guess_missing_sigma['sigma']
f = lambda: self.model.fit(self.data, x=self.x, **guess_missing_sigma)
self.assertRaises(ValueError, f)
# using Parameters
params = self.model.params()
for param_name, value in guess_missing_sigma.items():
params[param_name].value = value
f = lambda: self.model.fit(self.data, params, x=self.x)
def test_extra_param_issues_warning(self):
# The function accepts extra params, Model will warn but not raise.
guess = self.guess()
guess['extra'] = 5
def flexible_func(x, height, center, sigma, **kwargs):
return gaussian(x, height, center, sigma)
flexible_model = Model(flexible_func, ['x'])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
flexible_model.fit(self.data, x=self.x, **guess)
self.assertTrue(len(w) == 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
def test_missing_independent_variable_raises_error(self):
f = lambda: self.model.fit(self.data, **self.guess())
self.assertRaises(KeyError, f)
def test_bounding(self):
guess = self.guess()
guess['center'] = Parameter(value=2, min=1.3)
true_values = self.true_values()
true_values['center'] = 1.3 # as close as it's allowed to get
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
def test_vary_false(self):
guess = self.guess()
guess['center'] = Parameter(value=1.3, vary=False)
true_values = self.true_values()
true_values['center'] = 1.3
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
def test_result_attributes(self):
# result.init_values
result = self.model.fit(self.data, x=self.x, **self.guess())
assert_results_close(result.values, self.true_values())
self.assertTrue(result.init_values == self.guess())
# result.init_params
params = self.model.params()
for param_name, value in self.guess().items():
params[param_name].value = value
self.assertTrue(result.init_params == params)
# result.best_fit
assert_allclose(result.best_fit, self.data, atol=self.noise.max())
# result.init_fit
init_fit = self.model.func(x=self.x, **self.guess())
assert_allclose(result.init_fit, init_fit)
# result.model
self.assertTrue(result.model is self.model)
# testing model addition...
def test_user_defined_gaussian_plus_constant(self):
data = self.data + 5
model = self.model + specified_models.Constant()
guess = self.guess()
guess['c'] = 10
true_values = self.true_values()
true_values['c'] = 5
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values)
def test_sum_of_two_gaussians(self):
# two user-defined gaussians
model1 = self.model
f2 = lambda x, height_, center_, sigma_: gaussian(
x, height_, center_, sigma_)
model2 = Model(f2, ['x'])
values1 = self.true_values()
values2 = self.true_values()
values2['sigma'] = 1.5
values2['height'] = 4
data = gaussian(x=self.x, **values1)
data += gaussian(x=self.x, **values2)
model = self.model + model2
values2 = {k + '_': v for k, v in values2.items()}
guess = {
'sigma': Parameter(value=2, min=0),
'center': 1,
'height': 1,
'sigma_': Parameter(value=1, min=0),
'center_': 1,
'height_': 1
}
true_values = dict(list(values1.items()) + list(values2.items()))
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values)
# user-defined models with common parameter names
# cannot be added, and should raise
f = lambda: model1 + model1
self.assertRaises(NameError, f)
# two predefined_gaussians, using suffix to differentiate
model1 = specified_models.Gaussian(['x'])
model2 = specified_models.Gaussian(['x'], suffix='_')
model = model1 + model2
true_values = {
'center': values1['center'],
'height': values1['height'],
'sigma': values1['sigma'],
'center_': values2['center_'],
'height_': values2['height_'],
'sigma_': values2['sigma_']
}
guess = {
'sigma': 2,
'center': 1,
'height': 1,
'sigma_': 1,
'center_': 1,
'height_': 1
}
result = model.fit(data, x=self.x, **guess)
assert_results_close(result.values, true_values)
# without suffix, the names collide and Model should raise
model1 = specified_models.Gaussian(['x'])
model2 = specified_models.Gaussian(['x'])
f = lambda: model1 + model2
self.assertRaises(NameError, f)
