本文整理汇总了Python中lmfit.Model.params方法的典型用法代码示例。如果您正苦于以下问题：Python Model.params方法的具体用法？Python Model.params怎么用？Python Model.params使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类lmfit.Model的用法示例。
在下文中一共展示了Model.params方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TestUserDefiniedModel
# 需要导入模块: from lmfit import Model [as 别名]
# 或者: from lmfit.Model import params [as 别名]
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):
#.........这里部分代码省略.........