Functions
const char *	moo_get_license (void)
	Get the pipeline copyright and license.

cpl_image *	moo_compute_sigma_map (cpl_imagelist list, cpl_imagelist qlist, cpl_image *img)
	Compute image of sigma variation.

cpl_mask *	moo_kappa_sigma_clipping (cpl_image *sigma_img, int niter, double kappa, double cdiff, double maxfrac)
	Compute mask of rejected pixels using kappa sigma algorithm.

cpl_error_code	moo_barycenter_fit (cpl_bivector points, double center, double *width)
	Fit positions using weighted fluxes.

cpl_error_code	moo_gaussian_fit (cpl_bivector points, cpl_fit_mode fit_pars, double center, double width, double background, double *area)
	Fit the data with a gaussian.

double	moo_gaussian_eval (double x, double x0, double sigma, double offset, double area)
	Evaluate the gaussian at the given x position.

cpl_error_code	moo_gaussian_eval_inv (double y, double x0, double sigma, double offset, double area, double x1, double x2)
	Find the x positions of the gaussian at the given y position.

cpl_error_code	moo_find_threshold_limits (cpl_bivector points, double threshold, double xmin, double *xmax)
	Find threshold limits of a 1D signal.

moo_tcheby_polynomial *	moo_tcheby_polynomial_fit (cpl_bivector *data, int degree, double xmin, double xmax)
	Computes Tchebitchev transformation of data.

moo_tcheby2d_polynomial *	moo_tcheby2d_polynomial_fit (cpl_vector in_x, int xdegree, double xmin, double xmax, cpl_vector in_y, int ydegree, double ymin, double ymax, cpl_vector *in_l, double lmin, double lmax)
	Computes Tchebitchev transformation of data.

cpl_error_code	moo_tchebychev_fit (cpl_bivector data, int flag, int degree, double xmin, double xmax, double ymin, double ymax)
	Computes Tchebitchev transformation of data.

double	moo_vector_get_min (const cpl_vector v, int flags)
	Find minimum values in a vector using flags.

double	moo_vector_get_max (const cpl_vector v, int flags)
	Find maximum values in a vector using flags.

cpl_error_code	moo_interpolate_linear (cpl_bivector fout, cpl_vector fout_errs, int fout_qual, const cpl_bivector fref, const cpl_vector fref_errs, const int fref_qual)
	Linear interpolation of a 1d-function.

double	moo_vector_get_percentile (cpl_vector *v, double f)
	Get percentile of input vector.

cpl_vector *	moo_vector_filter_nan (cpl_vector *v)
	Create new vector with nan values filter.

cpl_bivector *	moo_bivector_filter_nan (cpl_bivector *v)
	Create new bi vector with nan values filter.

hdrl_image *	moo_image_collapse_median_create (hdrl_image *image)
	Collapse row of an image using a median and compute associate error.

double	moo_sky_distance (double alpha1, double delta1, double alpha2, double delta2)
	Compute sky distance (in rad)

cpl_error_code	moo_fit_mul (const cpl_vector vx, const cpl_vector vw, const cpl_vector vy, const cpl_vector vy_err, double c, double sig_c)
	This function computes the best-fit linear regression coefficient c1 of the model Y = c_1 X for the weighted datasets (x, y)

cpl_vector *	moo_savgol_filter (cpl_vector *v, int window_length, int polyorder)
	Apply a Savitzky-Golay filter to a vector.

cpl_vector *	moo_median_filter (cpl_vector *v, int winhsize)
	Apply a median filter to a vector.

cpl_image *	moo_imagelist_collapse_bitwiseor (cpl_imagelist list, hdrl_imagelist datalist)
	get the QUAL resulting in a bitwise OR operation on the QUAL list

double	moo_image_get_ron (cpl_image *diff, int llx, int lly, int urx, int ury, int nb_boxes, int box_hsize, double max_error_frac, int max_niter)
	compute ron in a diff image using boxes

cpl_error_code	moo_image_get_quartile (cpl_image image, double qmin, double *qmax)
	compute first and last quartile from an image

double	moo_vector_get_dersnr (const cpl_vector *ve)
	This function computes the signal to noise ratio DER_SNR following the definition set forth by the Spectral Container Working Group of ST-ECF, MAST and CADC.

int	moo_string_is_strictly_equal (const char a, const char b)
	This function compares to string to see if the two string are stricly equal.

cpl_size	moo_table_or_selected_sequal_string (cpl_table table, const char name, const char *string)
	Select from unselected table rows, by comparing column values with a constant.

cpl_size	moo_table_and_selected_sequal_string (cpl_table table, const char name, const char *string)
	Select from unselected table rows, by comparing column values with a constant.

Detailed Description

Function Documentation

◆ moo_barycenter_fit()

cpl_error_code moo_barycenter_fit	(	cpl_bivector *	points,
		double *	center,
		double *	width
	)

Fit positions using weighted fluxes.

Parameters

points	to fit (ordered ASC)
center	the fitted center
width	the width

Returns: the error code or CPL_ERROR_NONE

Possible cpl_error_code set in this function:

CPL_ERROR_NULL_INPUT if an input pointer is NULL
CPL_ERROR_ILLEGAL_INPUT if the size is equal to zero

Definition at line 254 of file moo_utils.c.

◆ moo_bivector_filter_nan()

cpl_bivector * moo_bivector_filter_nan ( cpl_bivector * v )

Create new bi vector with nan values filter.

Parameters

v	input bivector

Returns: new vector with no nan values

Definition at line 1343 of file moo_utils.c.

◆ moo_compute_sigma_map()

cpl_image * moo_compute_sigma_map	(	cpl_imagelist *	list,
		cpl_imagelist *	qlist,
		cpl_image *	img
	)

Compute image of sigma variation.

Parameters

list	list of images to compare to reference image
qlist	list of QUAL images to ignore badpixel in computation og sigma
img	reference image

Returns: the sigma map or NULL

Possible cpl_error_code set in this function:

CPL_ERROR_NULL_INPUT if an input pointer is NULL
CPL_ERROR_ILLEGAL_INPUT if the size is equal to zero

Definition at line 100 of file moo_utils.c.

References MOO_BADPIX_GOOD.

Referenced by moo_single_compute_noise_map().

◆ moo_find_threshold_limits()

cpl_error_code moo_find_threshold_limits	(	cpl_bivector *	points,
		double	threshold,
		double *	xmin,
		double *	xmax
	)

Find threshold limits of a 1D signal.

Parameters

points	data to analyse
threshold	the threshold
xmin	[OUT] minimum X position of the threshold
xmax	[OUT] maximum X position of the threshold

Returns: CPL_ERROR_NONE or the relevant cpl_error_code on error

Definition at line 423 of file moo_utils.c.

◆ moo_fit_mul()

cpl_error_code moo_fit_mul	(	const cpl_vector *	vx,
		const cpl_vector *	vw,
		const cpl_vector *	vy,
		const cpl_vector *	vy_err,
		double *	c,
		double *	sig_c
	)

This function computes the best-fit linear regression coefficient c1 of the model Y = c_1 X for the weighted datasets (x, y)

Parameters

vx
vw
vy
vy_err
c	fit result
sig_c	error on fit

Returns: cpl_error_code

Definition at line 1567 of file moo_utils.c.

◆ moo_gaussian_eval()

double moo_gaussian_eval	(	double	x,
		double	x0,
		double	sigma,
		double	offset,
		double	area
	)

Evaluate the gaussian at the given x position.

Parameters

x	x position
x0	center of gaussian.
sigma	width of gaussian
offset	background level of gaussian
area	area of gaussian

Returns: Y gaussian result for x

Definition at line 348 of file moo_utils.c.

◆ moo_gaussian_eval_inv()

cpl_error_code moo_gaussian_eval_inv	(	double	y,
		double	x0,
		double	sigma,
		double	offset,
		double	area,
		double *	x1,
		double *	x2
	)

Find the x positions of the gaussian at the given y position.

Parameters

y	y position
x0	center of gaussian.
sigma	width of gaussian
offset	background level of gaussian
area	area of gaussian
x1	[OUT] first x solution (minimum)
x2	[OUT] second x solution (maximum)

Returns: CPL_ERROR_NONE

Definition at line 373 of file moo_utils.c.

◆ moo_gaussian_fit()

cpl_error_code moo_gaussian_fit	(	cpl_bivector *	points,
		cpl_fit_mode	fit_pars,
		double *	center,
		double *	width,
		double *	background,
		double *	area
	)

Fit the data with a gaussian.

Parameters

points	the data to fit
fit_pars	specifies which parameters participate in the fit (any other parameters will be held constant). Possible values are CPL_FIT_CENTROID, CPL_FIT_STDEV, CPL_FIT_AREA, CPL_FIT_OFFSET and any bitwise combination of these. As a shorthand for including all four parameters in the fit, use CPL_FIT_ALL.
center	[OUT] center of the fitted gaussian
width	[OUT] width of the fitted gaussian
background	[OUT] background of the fitted gaussian
area	[OUT] area of the fitted gaussian

Returns: CPL_ERROR_NONE or the relevant cpl_error_code on error

Definition at line 309 of file moo_utils.c.

◆ moo_get_license()

const char * moo_get_license ( void )

Get the pipeline copyright and license.

Returns: The copyright and license string

The function returns a pointer to the statically allocated license string. This string should not be modified using the returned pointer.

Definition at line 81 of file moo_utils.c.

◆ moo_image_collapse_median_create()

hdrl_image * moo_image_collapse_median_create ( hdrl_image * image )

Collapse row of an image using a median and compute associate error.

Parameters

image input image

Returns: image of median

Definition at line 1386 of file moo_utils.c.

◆ moo_image_get_quartile()

cpl_error_code moo_image_get_quartile	(	cpl_image *	image,
		double *	qmin,
		double *	qmax
	)

compute first and last quartile from an image

Parameters

image	input image
qmin	the first quartile
qmax	the last quartile

Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL or list size == 0

Definition at line 1906 of file moo_utils.c.

◆ moo_image_get_ron()

double moo_image_get_ron	(	cpl_image *	diff,
		int	llx,
		int	lly,
		int	urx,
		int	ury,
		int	nb_boxes,
		int	box_hsize,
		double	max_error_frac,
		int	max_niter
	)

compute ron in a diff image using boxes

Parameters

diff	input diff image
llx	lower left x coord
lly	lower left y coord
urx	upper right x coord
ury	upper right y coord
nb_boxes	number of boxes
box_hsize	half size of a box
max_error_frac	max error fraction on ron
max_niter	max number of iteration to find ron

Returns: the ron value

Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL or list size == 0

Definition at line 1863 of file moo_utils.c.

◆ moo_imagelist_collapse_bitwiseor()

cpl_image * moo_imagelist_collapse_bitwiseor	(	cpl_imagelist *	list,
		hdrl_imagelist *	datalist
	)

get the QUAL resulting in a bitwise OR operation on the QUAL list

Parameters

list	image list
datalist	the image data list

Returns: the QUAL collapse with bitwise OR

Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL or list size == 0

Definition at line 1791 of file moo_utils.c.

Referenced by moo_coadd().

◆ moo_interpolate_linear()

cpl_error_code moo_interpolate_linear	(	cpl_bivector *	fout,
		cpl_vector *	fout_errs,
		int *	fout_qual,
		const cpl_bivector *	fref,
		const cpl_vector *	fref_errs,
		const int *	fref_qual
	)

Linear interpolation of a 1d-function.

Parameters

fout	Preallocated with X-vector set, to hold interpolation in Y
fout_errs	Preallocated to hold interpolation in Y errs
fout_qual	Preallocated to hold interpolation in Y qual
fref	Reference 1d-function
fref_errs	Reference 1d-function errs
fref_qual	Reference 1d-function qual

Returns: CPL_ERROR_NONE or the relevant cpl_error_code

fref must have both its abscissa and ordinate defined. fout must have its abscissa defined and its ordinate allocated.

The linear interpolation will be done from the values in fref to the abscissa points in fout.

For each abscissa point in fout, fref must either have two neigboring abscissa points such that xref_i < xout_j < xref{i+1}, or a single identical abscissa point, such that xref_i == xout_j.

This is ensured by monotonely growing abscissa points in both fout and fref (and by min(xref) <= min(xout) and max(xout) < max(xref)).

However, for efficiency reasons (since fref can be very long) the monotonicity is only verified to the extent necessary to actually perform the interpolation.

This input requirement implies that extrapolation is not allowed.

Possible cpl_error_code set in this function:

CPL_ERROR_NULL_INPUT if an input pointer is NULL
CPL_ERROR_DATA_NOT_FOUND if fout has an endpoint which is out of range
CPL_ERROR_ILLEGAL_INPUT if the monotonicity requirement on the 2 input abscissa vectors is not met.

Definition at line 1160 of file moo_utils.c.

◆ moo_kappa_sigma_clipping()

cpl_mask * moo_kappa_sigma_clipping	(	cpl_image *	sigma_img,
		int	niter,
		double	kappa,
		double	cdiff,
		double	maxfrac
	)

Compute mask of rejected pixels using kappa sigma algorithm.

Parameters

sigma_img	image on which we used kappa sigma
niter	maximum number of iteration
kappa	multiple of sigma
cdiff	minimum relative change in sigma
maxfrac	maximum fraction of bad pixels allowed

Returns: mask of bad pixels

Possible cpl_error_code set in this function:

CPL_ERROR_NULL_INPUT if an input pointer is NULL
CPL_ERROR_ILLEGAL_INPUT if the size is equal to zero

Definition at line 163 of file moo_utils.c.

Referenced by moo_single_compute_noise_map().

◆ moo_median_filter()

cpl_vector * moo_median_filter	(	cpl_vector *	v,
		int	winhsize
	)

Apply a median filter to a vector.

Parameters

v	input vector
winhsize	window half size

Returns: the filtered vector

Definition at line 1730 of file moo_utils.c.

Referenced by moo_compute_resp().

◆ moo_savgol_filter()

cpl_vector * moo_savgol_filter	(	cpl_vector *	v,
		int	window_length,
		int	polyorder
	)

Apply a Savitzky-Golay filter to a vector.

Parameters

v	input vector
window_length	The length of the filter window (i.e. the number of coefficients). window_length must be a positive odd integer.
polyorder	The order of the polynomial used to fit the samples. polyorder must be less than window_length.

Returns: the filtered vector

Definition at line 1656 of file moo_utils.c.

◆ moo_sky_distance()

double moo_sky_distance	(	double	alpha1,
		double	delta1,
		double	alpha2,
		double	delta2
	)

Compute sky distance (in rad)

Parameters

alpha1	alpha corrdinates of position 1 in degree
delta1	delta corrdinates of position 1 in degree
alpha2	alpha corrdinates of position 2 in degree
delta2	delta corrdinates of position 2 in degree

Returns: result in rad

Definition at line 1417 of file moo_utils.c.

Referenced by moo_select_sky().

◆ moo_string_is_strictly_equal()

int moo_string_is_strictly_equal	(	const char *	a,
		const char *	b
	)

This function compares to string to see if the two string are stricly equal.

Parameters

a	first string to compare
b	second string to compare

Returns

1 if a===b else 0 Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL

Definition at line 2233 of file moo_utils.c.

Referenced by moo_table_or_selected_sequal_string().

◆ moo_table_and_selected_sequal_string()

cpl_size moo_table_and_selected_sequal_string	(	cpl_table *	table,
		const char *	name,
		const char *	string
	)

Select from unselected table rows, by comparing column values with a constant.

Parameters

table	Pointer to table
name	Column name
string	Reference value

Returns

Current number of selected rows, or a negative number in case of error. Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL

Definition at line 2317 of file moo_utils.c.

Referenced by moo_rebin().

◆ moo_table_or_selected_sequal_string()

cpl_size moo_table_or_selected_sequal_string	(	cpl_table *	table,
		const char *	name,
		const char *	string
	)

Select from unselected table rows, by comparing column values with a constant.

Parameters

table	Pointer to table
name	Column name
string	Reference value

Returns

Current number of selected rows, or a negative number in case of error. Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL

Definition at line 2285 of file moo_utils.c.

References moo_string_is_strictly_equal().

Referenced by moo_target_table_find_index(), moo_target_table_find_target(), and moo_target_table_merge().

◆ moo_tcheby2d_polynomial_fit()

moo_tcheby2d_polynomial * moo_tcheby2d_polynomial_fit	(	cpl_vector *	in_x,
		int	xdegree,
		double	xmin,
		double	xmax,
		cpl_vector *	in_y,
		int	ydegree,
		double	ymin,
		double	ymax,
		cpl_vector *	in_l,
		double	lmin,
		double	lmax
	)

Computes Tchebitchev transformation of data.

Parameters

in_x	X data between 1 - 4096
xdegree	degree of 2D polynomial for X
xmin	the minimum x value
xmax	the maximum x value
in_y	Y data
ydegree	degree of 2D polynomial for Y
ymin	the minimum y value
ymax	the maximum y value
in_l	L data
lmin	the minimum l value
lmax	the maximum l value

Returns: tchebitchev polynomial 2D solution

This function fit the input data using tchebitchev transformation.

Definition at line 667 of file moo_utils.c.

◆ moo_tcheby_polynomial_fit()

moo_tcheby_polynomial * moo_tcheby_polynomial_fit	(	cpl_bivector *	data,
		int	degree,
		double	xmin,
		double	xmax
	)

Computes Tchebitchev transformation of data.

Parameters

data	bivector containing data to fit
degree	degree used for polynomial fit
xmin	Minimum range along X axis
xmax	Maximum range along X axis

Returns: the tchebychev polynomial

This function fit the input data using tchebitchev transformation. The result replace the original values.

Definition at line 573 of file moo_utils.c.

◆ moo_tchebychev_fit()

cpl_error_code moo_tchebychev_fit	(	cpl_bivector *	data,
		int *	flag,
		int	degree,
		double	xmin,
		double	xmax,
		double	ymin,
		double	ymax
	)

Computes Tchebitchev transformation of data.

Parameters

data	bivector containing data to fit
flag	array of flag (same size of data)
degree	degree used for polynomial fit
xmin	minimum value used in x part of data
xmax	maximum value used in x part of data
ymin	minmum value used in y part of data
ymax	maximum value used in y part of data

Returns: CPL_ERROR_NONE or the relevant cpl_error_code on error

This function fit the input data using tchebitchev transformation. The result replace the original values.

Definition at line 880 of file moo_utils.c.

◆ moo_vector_filter_nan()

cpl_vector * moo_vector_filter_nan ( cpl_vector * v )

Create new vector with nan values filter.

Parameters

v	input vector

Returns: new vector with no nan values

Definition at line 1309 of file moo_utils.c.

◆ moo_vector_get_dersnr()

double moo_vector_get_dersnr ( const cpl_vector * ve )

This function computes the signal to noise ratio DER_SNR following the definition set forth by the Spectral Container Working Group of ST-ECF, MAST and CADC.

Parameters

ve	input flux

Returns

the estimated signal-to-noise ratio Possible error code :

CPL_ERROR_NULL_INPUT if an input pointer is NULL or list size == 0

Definition at line 2189 of file moo_utils.c.

Referenced by moo_ext_single_compute_snr().

◆ moo_vector_get_max()

double moo_vector_get_max	(	const cpl_vector *	v,
		int *	flags
	)

Find maximum values in a vector using flags.

Parameters

v	the vector to use
flags	array of flags (same size of v)

Returns: the maximum value of the vector

Definition at line 1003 of file moo_utils.c.

◆ moo_vector_get_min()

double moo_vector_get_min	(	const cpl_vector *	v,
		int *	flags
	)

Find minimum values in a vector using flags.

Parameters

v	the vector to use
flags	array of flag (same size of v)

Returns: the minimum value of the vector

Definition at line 979 of file moo_utils.c.

◆ moo_vector_get_percentile()

double moo_vector_get_percentile	(	cpl_vector *	v,
		double	f
	)

Get percentile of input vector.

Parameters

v	input vector
f	Fraction between 0 and 1

Returns: the quantile value

Definition at line 1263 of file moo_utils.c.

Functions

Detailed Description

Function Documentation

◆ moo_barycenter_fit()

◆ moo_bivector_filter_nan()

◆ moo_compute_sigma_map()

◆ moo_find_threshold_limits()

◆ moo_fit_mul()

◆ moo_gaussian_eval()

◆ moo_gaussian_eval_inv()

◆ moo_gaussian_fit()

◆ moo_get_license()

◆ moo_image_collapse_median_create()

◆ moo_image_get_quartile()

◆ moo_image_get_ron()

◆ moo_imagelist_collapse_bitwiseor()

◆ moo_interpolate_linear()

◆ moo_kappa_sigma_clipping()

◆ moo_median_filter()

◆ moo_savgol_filter()

◆ moo_sky_distance()

◆ moo_string_is_strictly_equal()

◆ moo_table_and_selected_sequal_string()

◆ moo_table_or_selected_sequal_string()

◆ moo_tcheby2d_polynomial_fit()

◆ moo_tcheby_polynomial_fit()

◆ moo_tchebychev_fit()

◆ moo_vector_filter_nan()

◆ moo_vector_get_dersnr()

◆ moo_vector_get_max()

◆ moo_vector_get_min()

◆ moo_vector_get_percentile()