amep.functions.Gaussian2d#

class amep.functions.Gaussian2d#

Bases: BaseFunction

Two-dimensional Gaussian.

__init__()#

Two-dimensional Gaussian function.

Return type:: None.

Examples

>>> import amep
>>> import numpy as np
>>> x = np.linspace(-10, 10, 500)
>>> y = np.linspace(-10, 10, 500)
>>> X,Y = np.meshgrid(x,y)
>>> g2d = amep.functions.Gaussian2d()
>>> Z = g2d.generate(
...     amep.utils.mesh_to_coords(X,Y),
...     p=[1,0,0,2,5,np.pi/3]
... ).reshape(X.shape)
>>> fig, axs = amep.plot.new(figsize=(3,3))
>>> amep.plot.field(axs, Z, X, Y)
>>> axs.set_xlabel(r'$x$')
>>> axs.set_ylabel(r'$y$')
>>> fig.savefig('./figures/functions/functions-Gaussian2d.png')

Methods

`__init__`()	Two-dimensional Gaussian function.
`f`(p, x)	2D Gaussian.
`fit`(xdata, ydata[, p0, sigma, maxit, verbose])	Fits the function self.f to the given data by using ODR (orthogonal distance regression).
`generate`(x[, p])	Returns the y values for given x values.

Attributes

`errors`	Returns the fit errors for each parameter as an array.
`keys`
`mean`
`name`
`nparams`
`output`
`params`	Returns an array of the optimal fit parameters.
`results`	Returns the dictionary of fit results including parameter names, parameter values, and fit errors.
`std`

property errors: ndarray#

Returns the fit errors for each parameter as an array.

Returns:: Fit errors.
Return type:: np.ndarray

f(p, x)#

2D Gaussian.

Parameters:

p (list) – Parameters.
x (np.ndarray) – x-values as 2d array of shape (N,2).

Returns:

1D array of shape (N,).

Return type:

np.ndarray

Examples

>>>

fit(xdata: ndarray, ydata: ndarray, p0: list | None = None, sigma: ndarray | None = None, maxit: int | None = None, verbose: bool = False) → None#

Fits the function self.f to the given data by using ODR (orthogonal distance regression).

Parameters:

xdata (np.ndarray) – x values.
ydata (np.ndarray) – y values.
p0 (list or None, optional) – List of initial values. The default is None.
sigma (np.ndarray or None, optional) – Absolute error for each data point. The default is None.
maxit (int, optional) – Maximum number of iterations. The default is 200.
verbose (bool, optional) – If True, the main results are printed. The default is False.

Return type:

None.

generate(x: ndarray, p: list | None = None) → ndarray#

Returns the y values for given x values.

Parameters:

x (np.ndarray) – x values.
p (list, optional) – List of parameters. The default is None.

Returns:

y – f(x)

Return type:

np.ndarray

property params: ndarray#

Returns an array of the optimal fit parameters.

Returns:: Fit parameter values.
Return type:: np.ndarray

property results: dict#

Returns the dictionary of fit results including parameter names, parameter values, and fit errors.

Returns:: Fit results.
Return type:: dict