def setUpClass(cls):
"===Exponential_decay_Jacobian==="
pnames = ['p1', 'p2', 'p3']
def func(var, x):
return var[0] * np.exp(-var[1] * x) + var[2]
def dfunc(var, x):
v = np.exp(-var[1] * x)
return np.array([-v, +var[0] * x * v, -np.ones(len(x))]).T
cls.function = func
cls.jacobian = dfunc
model = fit.Function(function=func, par_names=pnames, jacobian=dfunc)
pars = [10, 10, 10]
bounds = [(0, 20), (0, 20), (0, 20)]
vary = [True, True, True]
model.setup_parameters(values=pars, bounds=bounds, vary=vary)
cls._model = model
#-- Creating data to fit
xs = np.linspace(0, 4, 50)
ys = func([2.5, 1.3, 0.5], xs)
np.random.seed(3333)
err = 0.1 * np.random.normal(size=len(xs))
yn = ys + err
cls.x = xs
cls.y = yn
cls.err = np.abs(err)
def setUpClass(cls):
#-- setup the function
pnames = ['ampl', 'freq', 'phase']
function = lambda p, x: p[0] * np.sin(2 * np.pi * (p[1] * x + p[2]))
Dfunction = lambda p, x: p[0] * np.cos(2 * np.pi * (p[1] * x + p[2]))
fitFunc = fit.Function(function=function, par_names=pnames)
value = [1.5, 0.12, 3.14]
bounds = [[1.0, 2.0], [0.0, 0.25], [0.0, 6.28]]
vary = [True, True, True]
fitFunc.setup_parameters(values=value, bounds=bounds, vary=vary)
#-- create fake data
x = np.linspace(0, 10, 1000)
np.random.seed(1111)
y = fitFunc.evaluate(x) + np.random.normal(
loc=0.0, scale=0.1, size=1000)
#-- Change starting parameters
fitFunc.update_parameter(parameter='ampl', values=1.2)
fitFunc.update_parameter(parameter='freq', values=0.10)
fitFunc.update_parameter(parameter='phase', values=3.0)
cls.x = x
cls.y = y
cls.pnames = pnames
cls.value = value
cls.fitFunc = fitFunc
cls.dFunc = Dfunction
def setUp(self):
self.pnames1 = ['p1', 'p2']
self.function1 = lambda p, x: p[0] + p[1] * x
self.func1 = fit.Function(function=self.function1,
par_names=self.pnames1)
self.values1 = [2.5, 1.0]
self.func1.setup_parameters(values=self.values1)
self.pnames2 = ['p3', 'p4']
self.function2 = lambda p, x: p[0] * x**2 + p[1] * x**3
self.func2 = fit.Function(function=self.function2,
par_names=self.pnames2)
self.values2 = [-1.0, 0.5]
self.func2.setup_parameters(values=self.values2)
self.mexpr = lambda x: x[0] + x[1]
self.model = fit.Model(functions=[self.func1, self.func2],
expr=self.mexpr)
def setUp(self):
self.pnames = ['ampl', 'freq', 'phase']
self.function = lambda p, x: p[0] * np.sin(2 * np.pi *
(p[1] * x + p[2]))
self.Dfunction = lambda p, x: p[0] * np.cos(2 * np.pi *
(p[1] * x + p[2]))
self.model = fit.Function(function=self.function, par_names=self.pnames,\
jacobian=self.Dfunction)
self.value = [1.5, 0.12, 3.14]
self.bounds = [[1.0, 2.0], [-0.20, 0.20], [0.0, 6.24]]
self.vary = [True, True, True]
self.expression = ['<10', None, None]
self.model.setup_parameters(values=self.value, bounds=self.bounds, vary=self.vary,\
exprs=self.expression)