Source code for lenstronomy.ImSim.Numerics.point_source_rendering
from lenstronomy.Util import image_util
from lenstronomy.Util import kernel_util
import numpy as np
__all__ = ['PointSourceRendering']
[docs]class PointSourceRendering(object):
"""
numerics to compute the point source response on an image
"""
def __init__(self, pixel_grid, supersampling_factor, psf):
"""
:param pixel_grid: PixelGrid() instance
:param supersampling_factor: int, factor of supersampling of point source
:param psf: PSF() instance
"""
self._pixel_grid = pixel_grid
self._nx, self._ny = self._pixel_grid.num_pixel_axes
self._supersampling_factor = supersampling_factor
self._psf = psf
[docs] def point_source_rendering(self, ra_pos, dec_pos, amp):
"""
:param ra_pos: list of RA positions of point source(s)
:param dec_pos: list of DEC positions of point source(s)
:param amp: list of amplitudes of point source(s)
:return: 2d numpy array of size of the image with the point source(s) rendered
"""
subgrid = self._supersampling_factor
x_pos, y_pos = self._pixel_grid.map_coord2pix(ra_pos, dec_pos)
# translate coordinates to higher resolution grid
x_pos_subgird = x_pos * subgrid + (subgrid - 1) / 2.
y_pos_subgrid = y_pos * subgrid + (subgrid - 1) / 2.
kernel_point_source_subgrid = self._kernel_supersampled
# initialize grid with higher resolution
subgrid2d = np.zeros((self._nx*subgrid, self._ny*subgrid))
# add_layer2image
if len(x_pos) > len(amp):
raise ValueError('there are %s images appearing but only %s amplitudes provided!' % (len(x_pos), len(amp)))
for i in range(len(x_pos)):
subgrid2d = image_util.add_layer2image(subgrid2d, x_pos_subgird[i], y_pos_subgrid[i], amp[i] * kernel_point_source_subgrid)
# re-size grid to data resolution
grid2d = image_util.re_size(subgrid2d, factor=subgrid)
return grid2d*subgrid**2
@property
def _kernel_supersampled(self):
if not hasattr(self, '_kernel_supersampled_instance'):
self._kernel_supersampled_instance = self._psf.kernel_point_source_supersampled(self._supersampling_factor, updata_cache=False)
return self._kernel_supersampled_instance
[docs] def psf_error_map(self, ra_pos, dec_pos, amp, data, fix_psf_error_map=False):
"""
:param ra_pos: image positions of point sources
:param dec_pos: image positions of point sources
:param amp: amplitude of modeled point sources
:param data: 2d numpy array of the data
:param fix_psf_error_map: bool, if True, estimates the error based on the imput (modeled) amplitude, else uses the data to do so.
:return: 2d array of size of the image with error terms (sigma**2) expected from inaccuracies in the PSF modeling
"""
x_pos, y_pos = self._pixel_grid.map_coord2pix(ra_pos, dec_pos)
psf_kernel = self._psf.kernel_point_source
psf_error_map = self._psf.psf_error_map
error_map = np.zeros_like(data)
for i in range(len(x_pos)):
if fix_psf_error_map is True:
amp_estimated = amp
else:
amp_estimated = kernel_util.estimate_amp(data, x_pos[i], y_pos[i], psf_kernel)
error_map = image_util.add_layer2image(error_map, x_pos[i], y_pos[i], psf_error_map * (psf_kernel * amp_estimated) ** 2)
return error_map