lenstronomy.ImSim.Numerics package

Submodules

lenstronomy.ImSim.Numerics.adaptive_numerics module

class lenstronomy.ImSim.Numerics.adaptive_numerics.AdaptiveConvolution(kernel_super, supersampling_factor, conv_supersample_pixels, supersampling_kernel_size=None, compute_pixels=None, nopython=True, cache=True, parallel=False)[source]

Bases: object

This class performs convolutions of a subset of pixels at higher supersampled resolution Goal: speed up relative to higher resolution FFT when only considering a (small) subset of pixels to be convolved on the higher resolution grid.

strategy: 1. lower resolution convolution over full image with FFT 2. subset of pixels with higher resolution Numba convolution (with smaller kernel) 3. the same subset of pixels with low resolution Numba convolution (with same kernel as step 2) adaptive solution is 1 + 2 - 3

convolve2d(image_high_res)[source]
Parameters

image_high_res – supersampled image/model to be convolved on a regular pixel grid

Returns

convolved and re-sized image

re_size_convolve(image_low_res, image_high_res)[source]
Parameters

image_high_res – supersampled image/model to be convolved on a regular pixel grid

Returns

convolved and re-sized image

lenstronomy.ImSim.Numerics.convolution module

class lenstronomy.ImSim.Numerics.convolution.FWHMGaussianConvolution(kernel, truncation=4)[source]

Bases: object

uses a two-dimensional Gaussian function with same FWHM of given kernel as approximation

convolution2d(image)[source]

2d convolution

Parameters

image – 2d numpy array, image to be convolved

Returns

convolved image, 2d numpy array

class lenstronomy.ImSim.Numerics.convolution.MGEConvolution(kernel, pixel_scale, order=1)[source]

Bases: object

approximates a 2d kernel with an azimuthal Multi-Gaussian expansion

convolution2d(image)[source]
Parameters

image

Returns

kernel_difference()[source]
Returns

difference between true kernel and MGE approximation

class lenstronomy.ImSim.Numerics.convolution.MultiGaussianConvolution(sigma_list, fraction_list, pixel_scale, supersampling_factor=1, supersampling_convolution=False, truncation=2)[source]

Bases: object

class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel.

convolution2d(image)[source]

2d convolution

Parameters

image – 2d numpy array, image to be convolved

Returns

convolved image, 2d numpy array

pixel_kernel(num_pix)[source]

computes a pixelized kernel from the MGE parameters

Parameters

num_pix – int, size of kernel (odd number per axis)

Returns

pixel kernel centered

re_size_convolve(image_low_res, image_high_res)[source]
Parameters

image_high_res – supersampled image/model to be convolved on a regular pixel grid

Returns

convolved and re-sized image

class lenstronomy.ImSim.Numerics.convolution.PixelKernelConvolution(kernel, convolution_type='fft_static')[source]

Bases: object

class to compute convolutions for a given pixelized kernel (fft, grid)

convolution2d(image)[source]
Parameters

image – 2d array (image) to be convolved

Returns

fft convolution

copy_transpose()[source]
Returns

copy of the class with kernel set to the transpose of original one

pixel_kernel(num_pix=None)[source]

access pixelated kernel

Parameters

num_pix – size of returned kernel (odd number per axis). If None, return the original kernel.

Returns

pixel kernel centered

re_size_convolve(image_low_res, image_high_res=None)[source]
Parameters

image_high_res – supersampled image/model to be convolved on a regular pixel grid

Returns

convolved and re-sized image

class lenstronomy.ImSim.Numerics.convolution.SubgridKernelConvolution(kernel_supersampled, supersampling_factor, supersampling_kernel_size=None, convolution_type='fft_static')[source]

Bases: object

class to compute the convolution on a supersampled grid with partial convolution computed on the regular grid

convolution2d(image)[source]
Parameters

image – 2d array (high resoluton image) to be convolved and re-sized

Returns

convolved image

re_size_convolve(image_low_res, image_high_res)[source]
Parameters

image_high_res – supersampled image/model to be convolved on a regular pixel grid

Returns

convolved and re-sized image

lenstronomy.ImSim.Numerics.grid module

class lenstronomy.ImSim.Numerics.grid.AdaptiveGrid(nx, ny, transform_pix2angle, ra_at_xy_0, dec_at_xy_0, supersampling_indexes, supersampling_factor, flux_evaluate_indexes=None)[source]

Bases: lenstronomy.Data.coord_transforms.Coordinates1D

manages a super-sampled grid on the partial image

property coordinates_evaluate
Returns

1d array of all coordinates being evaluated to perform the image computation

flux_array2image_low_high(flux_array, high_res_return=True)[source]
Parameters
  • flux_array – 1d array of low and high resolution flux values corresponding to the coordinates_evaluate order

  • high_res_return – bool, if True also returns the high resolution image (needs more computation and is only needed when convolution is performed on the supersampling level)

Returns

2d array, 2d array, corresponding to (partial) images in low and high resolution (to be convolved)

class lenstronomy.ImSim.Numerics.grid.RegularGrid(nx, ny, transform_pix2angle, ra_at_xy_0, dec_at_xy_0, supersampling_factor=1, flux_evaluate_indexes=None)[source]

Bases: lenstronomy.Data.coord_transforms.Coordinates1D

manages a super-sampled grid on the partial image

property coordinates_evaluate
Returns

1d array of all coordinates being evaluated to perform the image computation

flux_array2image_low_high(flux_array, **kwargs)[source]
Parameters

flux_array – 1d array of low and high resolution flux values corresponding to the coordinates_evaluate order

Returns

2d array, 2d array, corresponding to (partial) images in low and high resolution (to be convolved)

property grid_points_spacing

effective spacing between coordinate points, after supersampling :return: sqrt(pixel_area)/supersampling_factor

property num_grid_points_axes

effective number of points along each axes, after supersampling :return: number of pixels per axis, nx*supersampling_factor ny*supersampling_factor

property supersampling_factor
Returns

factor (per axis) of super-sampling relative to a pixel

lenstronomy.ImSim.Numerics.numba_convolution module

class lenstronomy.ImSim.Numerics.numba_convolution.NumbaConvolution(kernel, conv_pixels, compute_pixels=None, nopython=True, cache=True, parallel=False, memory_raise=True)[source]

Bases: object

class to convolve explicit pixels only

the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens

convolve2d(image)[source]

2d convolution

Parameters

image – 2d numpy array, image to be convolved

Returns

convolved image, 2d numpy array

lenstronomy.ImSim.Numerics.numerics module

class lenstronomy.ImSim.Numerics.numerics.Numerics(pixel_grid, psf, supersampling_factor=1, compute_mode='regular', supersampling_convolution=False, supersampling_kernel_size=5, flux_evaluate_indexes=None, supersampled_indexes=None, compute_indexes=None, point_source_supersampling_factor=1, convolution_kernel_size=None, convolution_type='fft_static', truncation=4)[source]

Bases: lenstronomy.ImSim.Numerics.point_source_rendering.PointSourceRendering

this classes manages the numerical options and computations of an image. The class has two main functions, re_size_convolve() and coordinates_evaluate()

property convolution_class
Returns

convolution class (can be SubgridKernelConvolution, PixelKernelConvolution, MultiGaussianConvolution, …)

property coordinates_evaluate
Returns

1d array of all coordinates being evaluated to perform the image computation

property grid_class
Returns

grid class (can be RegularGrid, AdaptiveGrid)

property grid_supersampling_factor
Returns

supersampling factor set for higher resolution sub-pixel sampling of surface brightness

re_size_convolve(flux_array, unconvolved=False)[source]
Parameters
  • flux_array – 1d array, flux values corresponding to coordinates_evaluate

  • unconvolved – boolean, if True, does not apply a convolution

Returns

convolved image on regular pixel grid, 2d array

lenstronomy.ImSim.Numerics.partial_image module

class lenstronomy.ImSim.Numerics.partial_image.PartialImage(partial_read_bools)[source]

Bases: object

class to deal with the use of partial slicing of a 2d data array, to be used for various computations where only a subset of pixels need to be know.

array_from_partial(partial_array)[source]
Parameters

partial_array – 1d array of the partial indexes

Returns

full 1d array

image_from_partial(partial_array)[source]
Parameters

partial_array – 1d array corresponding to the indexes of the partial read

Returns

full image with zeros elsewhere

property index_array
Returns

2d array with indexes (integers) corresponding to the 1d array, -1 when masked

property num_partial
Returns

number of indexes handled in the partial section

partial_array(image)[source]
Parameters

image – 2d array

Returns

1d array of partial list

lenstronomy.ImSim.Numerics.point_source_rendering module

class lenstronomy.ImSim.Numerics.point_source_rendering.PointSourceRendering(pixel_grid, supersampling_factor, psf)[source]

Bases: object

numerics to compute the point source response on an image

point_source_rendering(ra_pos, dec_pos, amp)[source]
Parameters
  • ra_pos – list of RA positions of point source(s)

  • dec_pos – list of DEC positions of point source(s)

  • amp – list of amplitudes of point source(s)

Returns

2d numpy array of size of the image with the point source(s) rendered

psf_error_map(ra_pos, dec_pos, amp, data, fix_psf_error_map=False)[source]
Parameters
  • ra_pos – image positions of point sources

  • dec_pos – image positions of point sources

  • amp – amplitude of modeled point sources

  • data – 2d numpy array of the data

  • fix_psf_error_map – bool, if True, estimates the error based on the imput (modeled) amplitude, else uses the data to do so.

Returns

2d array of size of the image with error terms (sigma**2) expected from inaccuracies in the PSF modeling

Module contents