lenstronomy.ImSim.Numerics package¶
Submodules¶
lenstronomy.ImSim.Numerics.adaptive_numerics module¶
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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
lenstronomy.ImSim.Numerics.convolution module¶
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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)
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convolution2d
(image)[source]¶ Parameters: image – 2d array (image) to be convolved Returns: fft convolution
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copy_transpose
()[source]¶ Returns: copy of the class with kernel set to the transpose of original one
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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
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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.
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convolution2d
(image)[source]¶ 2d convolution
Parameters: image – 2d numpy array, image to be convolved Returns: convolved image, 2d numpy array
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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
lenstronomy.ImSim.Numerics.grid module¶
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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
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coordinates_evaluate
¶ Returns: 1d array of all coordinates being evaluated to perform the image computation
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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)
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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
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coordinates_evaluate
¶ Returns: 1d array of all coordinates being evaluated to perform the image computation
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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)
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grid_points_spacing
¶ effective spacing between coordinate points, after supersampling :return: sqrt(pixel_area)/supersampling_factor
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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
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supersampling_factor
¶ Returns: factor (per axis) of super-sampling relative to a pixel
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lenstronomy.ImSim.Numerics.numba_convolution module¶
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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
lenstronomy.ImSim.Numerics.numerics module¶
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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()
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convolution_class
¶ Returns: convolution class (can be SubgridKernelConvolution, PixelKernelConvolution, MultiGaussianConvolution, …)
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coordinates_evaluate
¶ Returns: 1d array of all coordinates being evaluated to perform the image computation
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grid_class
¶ Returns: grid class (can be RegularGrid, AdaptiveGrid)
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grid_supersampling_factor
¶ Returns: supersampling factor set for higher resolution sub-pixel sampling of surface brightness
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lenstronomy.ImSim.Numerics.partial_image module¶
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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.
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array_from_partial
(partial_array)[source]¶ Parameters: partial_array – 1d array of the partial indexes Returns: full 1d array
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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
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index_array
¶ Returns: 2d array with indexes (integers) corresponding to the 1d array, -1 when masked
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num_partial
¶ Returns: number of indexes handled in the partial section
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lenstronomy.ImSim.Numerics.point_source_rendering module¶
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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
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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
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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
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