amplitude = Parameter('amplitude', amplitude, min=0)
phase = Parameter('phase', phase)
period = Parameter('period', period, min=0, max=2 * np.pi)
return cls(amplitude, phase, period)
def fit_fun(self, x, *args, **kwargs):
# In the real case we would gust call the evaluation fn without reference to the BaseObj
return self.amplitude.raw_value * np.sin(
(x + self.phase.raw_value) / self.period.raw_value)
b = Wavey.from_params()
bb = Wavey.from_params(1.1, 0.1, 1.9 * np.pi)
f = Fitter()
f.initialize(b, b.fit_fun)
nx = 1E3
x_min = 0
x_max = 100
x = np.linspace(x_min, x_max, num=int(nx))
y1 = bb.fit_fun(x) + .75 * (0.5 - np.random.random(size=x.shape))
x2 = x + 20
y2 = bb.fit_fun(x2) + .75 * (0.5 - np.random.random(size=x2.shape))
d.easyCore.add_coordinate('x1', x)
d.easyCore.add_variable('y1', ['x1'], y1, auto_sigma=True)
d.easyCore.add_coordinate('x2', x2)
d.easyCore.add_variable('y2', ['x2'], y2, auto_sigma=True)
from easyCore.Fitting.Fitting import Fitter
import matplotlib.pyplot as plt
d = xr.Dataset()
b = BaseObj('line', m=Parameter('m', 1), c=Parameter('c', 1))
def fit_fun(x, *args, **kwargs):
# In the real case we would gust call the evaluation fn without reference to the BaseObj
return b.c.raw_value + b.m.raw_value * x
f = Fitter()
f.initialize(b, fit_fun)
nx = 1E3
x_min = 0
x_max = 100
x = np.linspace(x_min, x_max, num=int(nx))
y1 = 2 * x - 1 + 5 * (np.random.random(size=x.shape) - 0.5)
x2 = x + 20
y2 = 2 * x2 - 1 + 5 * (np.random.random(size=x2.shape) - 0.5)
d.easyCore.add_coordinate('x1', x)
d.easyCore.add_variable('y1', ['x1'], y1, auto_sigma=True)
d.easyCore.add_coordinate('x2', x2)
d.easyCore.add_variable('y2', ['x2'], y2, auto_sigma=True)
_defaults = [Parameter('m', 1), Parameter('c', 0)]
def __init__(self):
super().__init__(self.__class__.__name__, *self._defaults)
@property
def gradient(self):
return self.m.raw_value
@property
def intercept(self):
return self.c.raw_value
def fit_func(self, x: np.ndarray) -> np.ndarray:
return self.gradient * x + self.intercept
def __repr__(self):
return f'Line: m={self.m}, c={self.c}'
l = Line()
f = Fitter()
f.initialize(l, l.fit_func)
x = np.array([1, 2, 3])
y = np.array([2, 4, 6]) - 1
f_res = f.fit(x, y)
print(f_res.fit_report())
print(l)
