The ImageFinding namespace is for functions related to finding and mapping images. More...

Functions
void	find_images_kist (LensHndl lens, PosType y_source, PosType r_source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, unsigned long *Nimagepoints, PosType initial_size, bool splitimages, short edge_refinement, bool verbose=false)
	Finds images given a source position and size. More...

void	find_images_microlens (LensHndl lens, double y_source, double r_source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, unsigned long *Nimagepoints, double initial_size, double mu_min, bool splitimages, short edge_refinement, bool verbose)
	Finds images given a source position and size. More...

void	find_images_microlens_exper (LensHndl lens, PosType y_source, PosType r_source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, unsigned long *Nimagepoints, PosType initial_size, PosType mu_min, bool splitimages, short edge_refinement, bool verbose)
	experimental version of find_image_microlens() More...

void	image_finder_kist (LensHndl lens, PosType y_source, PosType r_source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, unsigned long *Nimagepoints, short splitparities, short true_images)
	Finds images for a given source position and size. Not meant for high level user. More...

void	find_crit (LensHndl lens, GridHndl grid, std::vector< CriticalCurve > &crtcurve, int *Ncrits, double resolution, double invmag_min=0.0, bool verbose=false, bool test=false)
	Finds critical curves and caustics. More...

void	find_crit (Lens &lens, GridMap &gridmap, std::vector< CriticalCurve > &crtcurves, bool verbose=false)

CritType	find_pseudo (ImageInfo &pseudocurve, ImageInfo &negimage, PosType pseudolimit, LensHndl lens, GridHndl grid, PosType resolution, Kist< Point > &paritypoints, bool TEST=false)

void	find_contour (LensHndl lens, GridHndl grid, std::vector< CriticalCurve > &contour, int Ncrits, PosType resolution, bool orderingsuccess, bool ordercurve, bool dividecurves, double contour_value, LensingVariable contour_type, bool verbose=false)
	Finds iso kappa contours. More...

void	printCriticalCurves (std::string filename, const std::vector< ImageFinding::CriticalCurve > &critcurves)

PixelMap	mapCriticalCurves (const std::vector< ImageFinding::CriticalCurve > &critcurves, int Nx)
	Makes an image of the critical curves. The map will encompose all curves found. The pixel values are the caustic type + 1 ( 2=radial,3=tangential,4=pseudo ) More...

PixelMap	mapCausticCurves (const std::vector< ImageFinding::CriticalCurve > &critcurves, int Nx)
	Makes an image of the caustic curves. The map will encompose all curves found. The pixel values are the caustic type + 1 ( 2=radial,3=tangential,4=pseudo ) More...

void	map_images (LensHndl lens, Source source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, double xmax, double xmin, double initial_size, ExitCriterion criterion, bool FindCenter, bool divide_images)

void	map_images_fixedgrid (Source source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, double xmax, bool divide_images, bool find_borders)
	Find the images without any additional grid refinement or ray shooting. More...

void	map_imagesISOP (LensHndl lens, Source source, GridHndl grid, int Nimages, std::vector< ImageInfo > &imageinfo, double rmax, double res_min, double initial_size, ExitCriterion criterion, bool divide_images, bool int_on=true, bool verbos=false)
	Find images and refine them based on their surface brightness distribution. More...

short	find_peaks (LensHndl lens, GridHndl grid, PosType rEinsteinMin, PosType kappa_max, std::vector< ImageInfo > &imageinfo, int *Nimages)
	Refines the grid based on the convergence so that high density regions have high resolution. More...

Detailed Description

The ImageFinding namespace is for functions related to finding and mapping images.

Function Documentation

◆ find_contour()

void ImageFinding::find_contour	(	LensHndl	lens,
		GridHndl	grid,
		std::vector< CriticalCurve > &	crtcurve,
		int *	Ncrits,
		PosType	resolution,
		bool *	orderingsuccess,
		bool	ordercurve,
		bool	dividecurves,
		double	contour_value,
		LensingVariable	contour_type,
		bool	verbose = `false`
	)

Finds iso kappa contours.

Finding

Parameters

lens	The lens model.
grid	The grid. It must be initialized.
crtcurve	Structure to hold critical curve. Must be pre-allocated with maxNcrit elements. Stored in critcurve[i].imagekist.
Ncrits	The number of critical curves found.
resolution	The target resolution that the critical curve is mapped on the image plane.
orderingsuccess	true if ordering was successful.
ordercurve	Order the curve so that it can be drawn or used to find the winding number.
dividecurves	Divide the critical curves into seporate curves by whether they are attached
contour_value	value at which the contour is wanted
contour_type	KAPPA, INVMAG or DELAYT

◆ find_crit() [1/2]

void ImageFinding::find_crit	(	Lens &	lens,
		GridMap &	gridmap,
		std::vector< CriticalCurve > &	critcurves,
		bool	verbose = `false`
	)

Parameters

lens	The lens model.
gridmap	The grid. It must be initialized.
critcurves	Structure to hold critical curve.

◆ find_crit() [2/2]

void ImageFinding::find_crit	(	LensHndl	lens,
		GridHndl	grid,
		std::vector< CriticalCurve > &	crtcurve,
		int *	Ncrits,
		double	resolution,
		double	invmag_min = `0.0`,
		bool	verbose = `false`,
		bool	TEST = `false`
	)

Finds critical curves and caustics.

Finding

The critical curve is found by refining the edges of regions of negative magnification. If there are no regions of negative magnification in the original grid the grid is refined around the point of highest kappa. If there are other points of high kappa that are not of interest one should be careful that the region of interest is not missed. For this reason critical curves that are smaller than the grid resolution (could be un-uniform) are not guaranteed to be found.

After the borders of the negative regions are found the code looks for radial and pseudo caustics within the island. It usually finds at least one, but if there are more than one per island some might be missed.

All the critical curve / caustic pairs are classified as radial, tangential or pseudo. small enough radial critical curve could be miss classified as a pseudo caustic.

Structure to hold critical curve. Must be pre-

make some figures

Parameters

lens	The lens model.
grid	The grid. It must be initialized.
crtcurve	Structure to hold critical curve.
Ncrits	The number of critical curves found.
resolution	The target resolution that the critical curve is mapped on the image plane.
invmag_min	finds regions with 1/magnification < invmag_min, set to zero for caustics

◆ find_images_kist()

void ImageFinding::find_images_kist	(	LensHndl	lens,
		PosType *	y_source,
		PosType	r_source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		unsigned long *	Nimagepoints,
		PosType	initial_size,
		bool	splitimages,
		short	edge_refinement,
		bool	verbose = `false`
	)

Finds images given a source position and size.

Finding

find_images_kist returns finite refined images in images[0...*Nimages-1].imagekist It starts with a large source and reduces down to the right size refining at each step. It should not miss any image larger than ~ munin*r_source linear size, but seems to do much better than that. It does nothing with the surface brightnesses.

The routine can follow three different strategies for refining each image controlled by edge_refinement.

edge_refinement

0 does not do edge refinement, Every pixel in every image is refined until the criterion is met. The image(s) are found again after each refinement which can make it slower.
1 uses refine_edge(). After an initial refinement of all the pixels in the image(s) the code switches to refining only the edges of the images. The images are found after each refinement.
2 uses refine_edge2() Same as 1, but the images are not found after each refinement. This can make the
any other number does no additional refinement after telescoping routine run much faster, but has the disadvantage that the number of images will not change during the final stage of refinement. This is the setting generally recommended.

Parameters

y_source	contains the lens/es and source/sources position of source center
r_source	radius of source
grid	grid provided to routine
Nimages	number of images found
imageinfo	information on each image
Nimagepoints	number of points in final images
initial_size	Initial size of source for telescoping, 0 to start from the initial grid size.
splitimages	true each image is refined to target accuracy, otherwise all images are treated as one
edge_refinement	see comment
verbose	verbose

◆ find_images_microlens()

void ImageFinding::find_images_microlens	(	LensHndl	lens,
		double *	y_source,
		double	r_source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		unsigned long *	Nimagepoints,
		double	initial_size,
		double	mu_min,
		bool	splitimages,
		short	edge_refinement,
		bool	verbose
	)

Finds images given a source position and size.

Finding

ImageFinding::find_images_kist returns finite refined images in images[0...*Nimages-1].imagekist It starts with a large source and reduces down to the right size refining at each step. It should not miss any image larger than ~ munin*r_source linear size, but seems to do much better than that. It does nothing with the surface brightnesses.

The routine can follow three different strategies for refining each image controlled by edge_refinement.

edge_refinement

0 does not do edge refinement, Every pixel in every image is refined until the criterion is met. The image(s) are found again after each refinement which can make it slower.
1 uses refine_edge(). After an initial refinement of all the pixels in the image(s) the code switches to refining only the edges of the images. The images are found after each refinement.
2 uses refine_edge2() Same as 1, but the images are not found after each refinement. This can make the routine run much faster, but has the disadvantage that the number of images will not change during the final stage of refinement. This is the setting generally recommended.

When finding a finite sized source these quantities are generally not required and slow down the routine.

Parameters

y_source	contains the lens/es and source/sources position of source center
r_source	radius of source
grid	grid provided to routine
Nimages	number of images found
imageinfo	information on each image
Nimagepoints	number of points in final images
initial_size	Initial size of source for telescoping, 0 to start from the initial grid size.
splitimages	true each image is refined to target accuracy, otherwise all images are treated as one
edge_refinement	see comment
verbose	verbose

◆ find_images_microlens_exper()

void ImageFinding::find_images_microlens_exper	(	LensHndl	lens,
		PosType *	y_source,
		PosType	r_source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		unsigned long *	Nimagepoints,
		PosType	initial_size,
		PosType	mu_min,
		bool	splitimages,
		short	edge_refinement,
		bool	verbose
	)

experimental version of find_image_microlens()

Parameters

y_source	contains the lens/es and source/sources position of source center
r_source	radius of source
grid	grid provided to routine
Nimages	number of images found
imageinfo	information on each image
Nimagepoints	number of points in final images
initial_size	Initial size of source for telescoping, 0 to start from the initial grid size.
splitimages	TRUE each image is refined to target accuracy, otherwise all images are treated as one
edge_refinement	see comment
verbose	verbose

◆ find_peaks()

short ImageFinding::find_peaks	(	LensHndl	lens,
		GridHndl	grid,
		PosType	rEinsteinMin,
		PosType	kappa_max,
		std::vector< ImageInfo > &	imageinfo,
		int *	Nimages
	)

Refines the grid based on the convergence so that high density regions have high resolution.

Finding

No source is used in this process. The code acts as if all the mass is in SIS halos iteratively increasing the resolution and kappa threshhold until the desired resolution is found.

Parameters

lens	Lens model
grid	Grid to be refined. It must be initialized.
rEinsteinMin	the Einstein radius of the smallest lens that should be resolved, sets resolution target
kappa_max	highest kappa to be refined to, 1 or 2 is probably good enough
imageinfo	the image
Nimages	number of peaks

◆ find_pseudo()

CritType ImageFinding::find_pseudo	(	ImageInfo &	pseudocurve,
		ImageInfo &	negimage,
		PosType	pseudolimit,
		LensHndl	lens,
		GridHndl	grid,
		PosType	resolution,
		Kist< Point > &	paritypoints,
		bool	TEST = `false`
	)

remove all but the points below tmp_pseudolimit

◆ image_finder_kist()

void ImageFinding::image_finder_kist	(	LensHndl	lens,
		PosType *	y_source,
		PosType	r_source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		unsigned long *	Nimagepoints,
		short	splitparities,
		short	true_images
	)

Finds images for a given source position and size. Not meant for high level user.

image points are put into imageinfo[].imagekist imageinfo[].points and imageinfo[].Npoints are not changed

splitparities= 0 don't split attached negative and positive parity images = 1 do split parities NOTE: this is now obsolete = -1 doesn't slit into images at all , also does not find borders or change in_image markers true_images = 1 gives just the points that are in the image = 0 if there are not enough points in images this will include close points to be refined

side-effects : Will make in_image = true for all image points if splitparities == 0

< >

◆ map_images_fixedgrid()

void ImageFinding::map_images_fixedgrid	(	Source *	source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		double	xmax,
		bool	divide_images,
		bool	find_borders
	)

Find the images without any additional grid refinement or ray shooting.

Finding The images and surface brighnesses are found. This will be much faster than map_images(), but will miss images that are not resolved on the current grid.

The surface brightnesses on all of the grid are refreshed. The images centroids and gridranges are maintained.

Parameters

grid	Tree of grid points
Nimages	number of images found
imageinfo	information on each image
xmax	Maximum size of source on image plane. The entire source must be within this distance from source->getTheta()[]. Decreasing it will make the code run faster. Making xmax much bigger than the grid boundaries will check all points for surface brightness.
divide_images	if true will divide images.
find_borders	if true will find the inner and outer borders of each image

◆ map_imagesISOP()

void ImageFinding::map_imagesISOP	(	LensHndl	lens,
		Source *	source,
		GridHndl	grid,
		int *	Nimages,
		std::vector< ImageInfo > &	imageinfo,
		double	rmax,
		double	res_min,
		double	initial_size,
		ExitCriterion	criterion,
		bool	divide_images,
		bool	int_on = `true`,
		bool	verbose = `false`
	)

Find images and refine them based on their surface brightness distribution.

Finding Uses ImageFinding::find_images_kist() to initially find and refine images and then using IF_routines::IntegrateFluxInCell(). If cells cannot be integrated refiments are madebased on a surface brightness based criterion to refine the most important parts of the lens.

map_images is intended for mapping images of sources more complicated than simple circles.

Parameters

lens	model
grid	Tree of grid points
Nimages	number of images found
imageinfo	information on each image
rmax	Maximum size of source on souce plane. The entire source must be within this distance from source->getTheta()[]
res_min	requred resolution of image, typically the pixel size of the final image
initial_size	Initial size of source for telescoping, 0 to start from the initial grid size. If < 0 no telescoping is used and only the already existing points are used to to initiate the image finding.
criterion	see data type
divide_images	if true will divide images and apply the exit criterion to them separately.
int_on	if true the flux in each cell is integrated, if false the surface brightness at the center point of the cell is used

◆ mapCausticCurves()

PixelMap ImageFinding::mapCausticCurves	(	const std::vector< ImageFinding::CriticalCurve > &	critcurves,
		int	Nx
	)

Makes an image of the caustic curves. The map will encompose all curves found. The pixel values are the caustic type + 1 ( 2=radial,3=tangential,4=pseudo )

Parameters

critcurves	list of critical curves
Nx	number of pixels to each size

◆ mapCriticalCurves()

PixelMap ImageFinding::mapCriticalCurves	(	const std::vector< ImageFinding::CriticalCurve > &	critcurves,
		int	Nx
	)

Makes an image of the critical curves. The map will encompose all curves found. The pixel values are the caustic type + 1 ( 2=radial,3=tangential,4=pseudo )

Parameters

critcurves	list of critical curves
Nx	number of pixels to each size

Functions

Detailed Description

Function Documentation

◆ find_contour()

◆ find_crit() [1/2]

◆ find_crit() [2/2]

◆ find_images_kist()

◆ find_images_microlens()

◆ find_images_microlens_exper()

◆ find_peaks()

◆ find_pseudo()

◆ image_finder_kist()

◆ map_images_fixedgrid()

◆ map_imagesISOP()

◆ mapCausticCurves()

◆ mapCriticalCurves()