nden

'''
Definitions:

SIP forward transformation:
'''
foc_X = (px_x - crpix1) + Forward_SIP_X((px_x - crpix1), (px_y - crpix2))
foc_Y = (px_y - crpix2) + Forward_SIP_Y((px_x - crpix1), (px_y - crpix2))

# SIP inverse transformation:

nden

import numpy as np
from astropy.modeling.fitting import (_validate_model, _fitter_to_model_params, Fitter, _convert_input)
from astropy.modeling.optimizers import *

def chi_line(measured_vals, updated_model, x_sigma, y_sigma, x):
    """
    Chi^2 statistic for fitting a straight line with uncertainties in x and y.

    Parameters
    ----------

nden

from astropy.modeling.core import Model
from astropy.modeling.parameters import Parameter



class MyModel1(Model):
    param_names = ['a']

    def __init__(self, a):
        self._a = Parameter('a', a, self, 1)

nden

from astropy.modeling import models, fitting
import numpy as np
from scipy.optimize import leastsq

def gaussian2d(p, x, y):
    amplitude, x_mean, y_mean, x_stddev, y_stddev, theta = p[:]
    a = 0.5 * ((np.cos(theta) / x_stddev) ** 2 + (np.sin(theta) / y_stddev) ** 2)
    b = 0.5 * (np.cos(theta) * np.sin(theta) * (1. / x_stddev ** 2 - 1. / y_stddev ** 2))
    c = 0.5 * ((np.sin(theta) / x_stddev) ** 2 + (np.cos(theta) / y_stddev) ** 2)
    return amplitude * np.exp(-a * (x - x_mean) ** 2 - b * (x - x_mean) * (y - y_mean) - c * (y - y_mean) ** 2)

nden

from astropy.models.fitting import Fitter
import numpy as np
from scipy import optimize

class SLSQPFitter(Fitter):
    def __init__(self):
        super().__init__(optimizer=SLSQP, statistic=leastsquare)
                             
    def errorfunc(self, fps, *args):
        model = args[0]

nden

This page lists features to be added to astropy.models. It was compiled from suggestions in the initial astropy.models PR#493. Feel free to add to this list. Please make a note if you are interested in working on any of them. This list is not prioritized. 

* Add spline models and fitters.

* Write a general `Model.inverse()` method.

  This is intended to be used in models for which an analytical inverse is not available.

* Add a `CompositeModel.simplify` method, similar to AST.