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."""
[docs]
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) / 2.0
y_pos_subgrid = y_pos * subgrid + (subgrid - 1) / 2.0
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
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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 input (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 * amp_estimated**2
)
return error_map