Source code for lenstronomy.Util.mask_util

import numpy as np
import lenstronomy.Util.util as util

from lenstronomy.Util.package_util import exporter
export, __all__ = exporter()


[docs]@export def mask_center_2d(center_x, center_y, r, x_grid, y_grid): """ :param center_x: x-coordinate of center position of circular mask :param center_y: y-coordinate of center position of circular mask :param r: radius of mask in pixel values :param x_grid: x-coordinate grid :param y_grid: y-coordinate grid :return: mask array of shape x_grid with =0 inside the radius and =1 outside """ x_shift = x_grid - center_x y_shift = y_grid - center_y R = np.sqrt(x_shift*x_shift + y_shift*y_shift) mask = np.empty_like(R) mask[R > r] = 1 mask[R <= r] = 0 return mask
[docs]@export def mask_azimuthal(x, y, center_x, center_y, r): """ :param x: x-coordinates (1d or 2d array numpy array) :param y: y-coordinates (1d or 2d array numpy array) :param center_x: center of azimuthal mask in x :param center_y: center of azimuthal mask in y :param r: radius of azimuthal mask :return: array with zeros outside r and ones inside azimuthal radius r """ x_shift = x - center_x y_shift = y - center_y R = np.sqrt(x_shift*x_shift + y_shift*y_shift) mask = np.empty_like(R) mask[R > r] = 0 mask[R <= r] = 1 return mask
[docs]@export def mask_ellipse(x, y, center_x, center_y, a, b, angle): """ :param x: x-coordinates of pixels :param y: y-coordinates of pixels :param center_x: center of mask :param center_y: center of mask :param a: major axis :param b: minor axis :param angle: angle of major axis :return: mask (list of zeros and ones) """ x_shift = x - center_x y_shift = y - center_y x_rot, y_rot = util.rotate(x_shift, y_shift, angle) r_ab = x_rot**2 / a**2 + y_rot**2 / b**2 mask = np.empty_like(r_ab) mask[r_ab > 1] = 0 mask[r_ab <= 1] = 1 return mask
[docs]@export def mask_half_moon(x, y, center_x, center_y, r_in, r_out, phi0=0, delta_phi=2*np.pi): """ :param x: :param y: :param center_x: :param center_y: :param r_in: :param r_out: :param phi0: :param delta_phi: :return: """ x_shift = x - center_x y_shift = y - center_y R = np.sqrt(x_shift*x_shift + y_shift*y_shift) phi = np.arctan2(x_shift, y_shift) phi_min = phi0 - delta_phi/2. phi_max = phi0 + delta_phi/2. mask = np.zeros_like(x) if phi_max > phi_min: mask[(R < r_out) & (R > r_in) & (phi > phi_min) & (phi < phi_max)] = 1 else: mask[(R < r_out) & (R > r_in) & (phi > phi_max)] = 1 mask[(R < r_out) & (R > r_in) & (phi < phi_min)] = 1 return mask