def _get_indexed_data(err_y=0.01, a=1.2, b=2.3, c=3.4):
_x_0 = np.linspace(start=-10, stop=10, num=101, endpoint=True)
_y_0 = quadratic_model(_x_0, a, b, c)
_y_jitter = np.random.normal(loc=0, scale=err_y, size=101)
_y_data = _y_0 + _y_jitter
return _y_data
def _get_xy_data(err_x=0.01, err_y=0.01, a=1.2, b=2.3, c=3.4):
_x_0 = np.linspace(start=-10, stop=10, num=25, endpoint=True)
_x_jitter = np.random.normal(loc=0, scale=err_x, size=25)
_x_data = _x_0 + _x_jitter
_y_0 = quadratic_model(_x_data, a, b, c)
_y_jitter = np.random.normal(loc=0, scale=err_y, size=25)
_y_data = _y_0 + _y_jitter
return np.array([_x_data, _y_data])
def quadratic_model_indexed_split_2(a, b, c):
_x = np.linspace(start=0, stop=10, num=51, endpoint=True)
return quadratic_model(_x, a, b, c)
def quadratic_model_indexed_split_1(a, b, c):
_x = np.linspace(start=-10, stop=0, num=50, endpoint=False)
return quadratic_model(_x, a, b, c)
def quadratic_model_indexed_all(a, b, c):
_x = np.linspace(start=-10, stop=10, num=101, endpoint=True)
return quadratic_model(_x, a, b, c)