lsst.gauss2d.utils Namespace Reference

Functions
	covar_to_ellipse (sigma_x_sq, sigma_y_sq, cov_xy, degrees=False)
	gauss2dint (xdivsigma)

Function Documentation

covar_to_ellipse()

lsst.gauss2d.utils.covar_to_ellipse	(		sigma_x_sq,
			sigma_y_sq,
			cov_xy,
			degrees = False )

Convert covariance matrix terms to ellipse major axis, axis ratio and
position angle representation.

Parameters
----------
sigma_x_sq, sigma_y_sq : `float` or array-like
    x- and y-axis squared standard deviations of a 2-dimensional normal
    distribution (diagonal terms of its covariance matrix).
    Must be scalar or identical length array-likes.
cov_xy : `float` or array-like
    x-y covariance of a of a 2-dimensional normal distribution
    (off-diagonal term of its covariance matrix).
    Must be scalar or identical length array-likes.
degrees : `bool`
    Whether to return the position angle in degrees instead of radians.

Returns
-------
r_major, axrat, angle : `float` or array-like
    Converted major-axis radius, axis ratio and position angle
    (counter-clockwise from the +x axis) of the ellipse defined by
    each set of input covariance matrix terms.

Notes
-----
The eigenvalues from the determinant of a covariance matrix are:
|a-m b|
|b c-m|
det = (a-m)(c-m) - b^2 = ac - (a+c)m + m^2 - b^2 = m^2 - (a+c)m + (ac-b^2)
Solving:
m = ((a+c) +/- sqrt((a+c)^2 - 4(ac-b^2)))/2
...or equivalently:
m = ((a+c) +/- sqrt((a-c)^2 + 4b^2))/2

Unfortunately, the latter simplification is not as well-behaved
in floating point math, leading to square roots of negative numbers when
one of a or c is very close to zero.

The values from this function should match those from
`Ellipse.make_ellipse_major` to within rounding error, except in the
special case of sigma_x == sigma_y == 0, which returns a NaN axis ratio
here by default. This function mainly intended to be more convenient
(and possibly faster) for array-like inputs.

Definition at line 25 of file utils.py.

def covar_to_ellipse(sigma_x_sq, sigma_y_sq, cov_xy, degrees=False):
    """Convert covariance matrix terms to ellipse major axis, axis ratio and
    position angle representation.
 
    Parameters
    ----------
    sigma_x_sq, sigma_y_sq : `float` or array-like
        x- and y-axis squared standard deviations of a 2-dimensional normal
        distribution (diagonal terms of its covariance matrix).
        Must be scalar or identical length array-likes.
    cov_xy : `float` or array-like
        x-y covariance of a of a 2-dimensional normal distribution
        (off-diagonal term of its covariance matrix).
        Must be scalar or identical length array-likes.
    degrees : `bool`
        Whether to return the position angle in degrees instead of radians.
 
    Returns
    -------
    r_major, axrat, angle : `float` or array-like
        Converted major-axis radius, axis ratio and position angle
        (counter-clockwise from the +x axis) of the ellipse defined by
        each set of input covariance matrix terms.
 
    Notes
    -----
    The eigenvalues from the determinant of a covariance matrix are:
    |a-m b|
    |b c-m|
    det = (a-m)(c-m) - b^2 = ac - (a+c)m + m^2 - b^2 = m^2 - (a+c)m + (ac-b^2)
    Solving:
    m = ((a+c) +/- sqrt((a+c)^2 - 4(ac-b^2)))/2
    ...or equivalently:
    m = ((a+c) +/- sqrt((a-c)^2 + 4b^2))/2
 
    Unfortunately, the latter simplification is not as well-behaved
    in floating point math, leading to square roots of negative numbers when
    one of a or c is very close to zero.
 
    The values from this function should match those from
    `Ellipse.make_ellipse_major` to within rounding error, except in the
    special case of sigma_x == sigma_y == 0, which returns a NaN axis ratio
    here by default. This function mainly intended to be more convenient
    (and possibly faster) for array-like inputs.
    """
    apc = sigma_x_sq + sigma_y_sq
    x = apc/2
    pm = np.sqrt(apc**2 - 4*(sigma_x_sq*sigma_y_sq - cov_xy**2))/2
 
    r_major = x + pm
    axrat = np.sqrt((x - pm)/r_major)
    r_major = np.sqrt(r_major)
    angle = np.arctan2(2*cov_xy, sigma_x_sq - sigma_y_sq)/2
    return r_major, axrat, (np.degrees(angle) if degrees else angle)
 
 

gauss2dint()

lsst.gauss2d.utils.gauss2dint

(

xdivsigma

)

Return the fraction of the total surface integral of a 2D Gaussian
contained with a given multiple of its dispersion.

Parameters
----------
xdivsigma : `float`
    The multiple of the dispersion to integrate to.

Returns
-------
frac : `float`
    The fraction of the surface integral contained within an ellipse of
    size `xdivsigma`.

Notes
-----
This solution can be computed as follows:

https://www.wolframalpha.com/input/?i=
Integrate+2*pi*x*exp(-x%5E2%2F(2*s%5E2))%2F(s*sqrt(2*pi))+dx+from+0+to+r

Definition at line 81 of file utils.py.

def gauss2dint(xdivsigma):
    """Return the fraction of the total surface integral of a 2D Gaussian
    contained with a given multiple of its dispersion.
 
    Parameters
    ----------
    xdivsigma : `float`
        The multiple of the dispersion to integrate to.
 
    Returns
    -------
    frac : `float`
        The fraction of the surface integral contained within an ellipse of
        size `xdivsigma`.
 
    Notes
    -----
    This solution can be computed as follows:
 
    https://www.wolframalpha.com/input/?i=
    Integrate+2*pi*x*exp(-x%5E2%2F(2*s%5E2))%2F(s*sqrt(2*pi))+dx+from+0+to+r
    """
    return 1 - np.exp(-xdivsigma**2/2.)