lenstronomy.LensModel.Util package

Submodules

lenstronomy.LensModel.Util.epl_util module

min_approx(x1, x2, x3, y1, y2, y3)

Get the x-value of the minimum of the parabola through the points (x1,y1), …

Parameters:

• x1 – x-coordinate point 1

• x2 – x-coordinate point 2

• x3 – x-coordinate point 3

• y1 – y-coordinate point 1

• y2 – y-coordinate point 2

• y3 – y-coordinate point 3

Returns:

x-location of the minimum

rotmat(th)

Calculates the rotation matrix :param th: angle :return: rotation matrix.

cdot(a, b)

Calculates some complex dot-product that simplifies the math :param a: complex number :param b: complex number :return: dot-product.

ps(x, p)

A regularized power-law that gets rid of singularities, abs(x)**p*sign(x) :param x: x :param p: p :return:

cart_to_pol(x, y)

Convert from cartesian to polar :param x: x-coordinate :param y: y-coordinate :return: tuple of (r, theta)

pol_to_cart(r, th)

Convert from polar to cartesian :param r: r-coordinate :param th: theta- coordinate :return: tuple of (x,y)

pol_to_ell(r, theta, q)

Converts from polar to elliptical coordinates.

ell_to_pol(rell, theta, q)

Converts from elliptical to polar coordinates.

geomlinspace(a, b, N)

Constructs a geomspace from a to b, with a linspace prepended to it from 0 to a, with the same spacing as the geomspace would have at a.

solvequadeq(a, b, c)

Solves a quadratic equation.

Care is taken for the numerics, see also https://en.wikipedia.org/wiki/Loss_of_significance :param a: a :param b: b :param c: c :return: tuple of two solutions

brentq_nojit(f, xa, xb, xtol=2e-14, rtol=3.552713678800501e-15, maxiter=100, args=())

A numba-compatible implementation of brentq (largely copied from scipy.optimize.brentq).

Unfortunately, the scipy verison is not compatible with numba, hence this reimplementation :( :param f: function to optimize :param xa: left bound :param xb: right bound :param xtol: x-coord root tolerance :param rtol: x-coord relative tolerance :param maxiter: maximum num of iterations :param args: additional arguments to pass to function in the form f(x, args) :return:

brentq_inline(f, xa, xb, xtol=2e-14, rtol=3.552713678800501e-15, maxiter=100, args=())

A numba-compatible implementation of brentq (largely copied from scipy.optimize.brentq).

Unfortunately, the scipy verison is not compatible with numba, hence this reimplementation :( :param f: function to optimize :param xa: left bound :param xb: right bound :param xtol: x-coord root tolerance :param rtol: x-coord relative tolerance :param maxiter: maximum num of iterations :param args: additional arguments to pass to function in the form f(x, args) :return:

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