model.py
def gauss2d(x, y, var, centre):
        """
        Define a 2-D Gaussian with variance var and mean centre.

        :param x: Scalar or array with x positions
        :param y: Scalar or array with y positions
        :param var: Variance of the distribution
        :param centre: Mean of the distribution
        :return:
        """
        return np.exp(-((x - centre[0]) ** 2 + (y - centre[1]) ** 2) / (2 * var)) / (2 * np.pi * var)

        Define a 2-D Gaussian with variance var and mean centre.

        :param x: Scalar or array with x positions
        :param y: Scalar or array with y positions
        :param var: Variance of the distribution
        :param centre: Mean of the distribution
        :return:
        
Define a 2-D Gaussian with variance var and mean centre.
Define a 2 - D Gaussian with variance var and mean centre .
def gauss2d(x, y, var, centre):
        return np.exp(-((x - centre[0]) ** 2 + (y - centre[1]) ** 2) / (2 * var)) / (2 * np.pi * var)
def gauss2d ( x , y , var , centre ) : return np . exp ( - ( ( x - centre [ 0 ] ) ** 2 + ( y - centre [ 1 ] ) ** 2 ) / ( 2 * var ) ) / ( 2 * np . pi * var )
Define a 2-D Gaussian with variance var and mean centre.
Define a 2 - D Gaussian with variance var and mean centre .
"""
        Define a 2-D Gaussian with variance var and mean centre.

        :param x: Scalar or array with x positions
        :param y: Scalar or array with y positions
        :param var: Variance of the distribution
        :param centre: Mean of the distribution
        :return:
        """
None
x
Scalar or array with x positions
Scalar or array with x positions
y
Scalar or array with y positions
Scalar or array with y positions
var
Variance of the distribution
Variance of the distribution
centre
Mean of the distribution
Mean of the distribution