apnormalize: Normalize 2D apertures by 1D functions

Package: ctioslit

Usage

apnormalize input output

Parameters

input: List of input images to be normalized.

output: List of output image names for the normalized input images. If no output name is given then the input name is used as a root with the extension ".norm" added.

apertures = "": Apertures to recenter, resize, trace, and normalize. This only applies to apertures read from the input or reference database. Any new apertures defined with the automatic finding algorithm or interactively are always selected. The syntax is a list comma separated ranges where a range can be a single aperture number, a hyphen separated range of aperture numbers, or a range with a step specified by "x<step>"; for example, "1,3-5,9-12x2".

references = "": List of reference images to be used to define apertures for the input images. When a reference image is given it supersedes apertures previously defined for the input image. The list may be null, "", or any number of images less than or equal to the list of input images. There are three special words which may be used in place of an image name. The word "last" refers to the last set of apertures written to the database. The word "OLD" requires that an entry exist and the word "NEW" requires that the entry not exist for each input image.

interactive = yes: Run this task interactively? If the task is not run interactively then all user queries are suppressed and interactive aperture editing and trace fitting are disabled.

find = yes: Find the spectra and define apertures automatically? In order for spectra to be found automatically there must be no apertures for the input image or reference image defined in the database.

recenter = yes: Recenter the apertures?

resize = yes: Resize the apertures?

edit = yes: Edit the apertures? The interactive parameter must also be yes.

trace = yes: Trace the apertures?

fittrace = yes: Interactively fit the traced positions by a function? The interactive parameter must also be yes.

normalize = yes: Normalize the aperture spectra by a one dimensional function?

fitspec = yes: Fit normalization spectrum interactively? The interactive parameter must also be yes.

line = INDEF, nsum = 1: The dispersion line (line or column perpendicular to the dispersion axis) and number of adjacent lines (half before and half after unless at the end of the image) used in finding, recentering, resizing, and editing operations. For tracing this is the starting line and the same number of lines are summed at each tracing point. A line of INDEF selects the middle of the image along the dispersion axis. A negative nsum selects a median rather than a sum except that tracing always uses a sum.

cennorm = no: Normalize to the aperture center rather than the mean?

threshold = 10.: All pixels in the normalization spectrum less than this value are replaced by this value.

The following parameters control the normalization spectrum extraction.

background = "none": Type of background subtraction. The choices are "none" for no background subtraction, "average" to average the background within the background regions, or "fit" to fit across the dispersion using the background within the background regions. Note that the "average" option does not do any medianing or bad pixel checking; it is faster than fitting however. Background subtraction also requires that the background fitting parameters are properly defined. For the "average" option only the background sample regions parameter is used.

weights = "none"

Type of extraction weighting. Note that if the clean parameter is set then the weights used are "variance" regardless of the weights specified by this parameter. The choices are:

"none": The pixels are summed without weights except for partial pixels at the ends.

"variance": The extraction is weighted by estimated variances of the pixels using a poisson noise model.

pfit = "fit1d" (fit1d|fit2d): Profile fitting algorithm to use with variance weighting or cleaning. When determining a profile the two dimensional spectrum is divided by an estimate of the one dimensional spectrum to form a normalized two dimensional spectrum profile. This profile is then smoothed by fitting one dimensional functions, "fit1d", along the lines or columns most closely corresponding to the dispersion axis or a special two dimensional function, "fit2d", described by Marsh (see approfile).

clean = no: Detect and replace deviant pixels?

skybox = 1: Box car smoothing length for sky background when using background subtraction. Since the background noise is often the limiting factor for good extraction one may box car smooth the sky to improve the statistics in smooth background regions at the expense of distorting the subtraction near spectral features. This is most appropriate when the sky regions are limited due to a small slit length.

saturation = INDEF: Saturation or nonlinearity level. During variance weighted extractions wavelength points having any pixels above this value are excluded from the profile determination.

readnoise = 0.: Read out noise in photons. This parameter defines the minimum noise sigma. It is defined in terms of photons (or electrons) and scales to the data values through the gain parameter. A image header keyword (case insensitive) may be specified to get the value from the image.

gain = 1: Detector gain or conversion factor between photons/electrons and data values. It is specified as the number of photons per data value. A image header keyword (case insensitive) may be specified to get the value from the image.

lsigma = 3., usigma = 3.: Lower and upper rejection thresholds, given as a number of times the estimated sigma of a pixel, for cleaning.

The following parameters are used to fit the normalization spectrum using the ICFIT routine.

function = "legendre": Fitting function for the normalization spectra. The choices are "legendre" polynomial, "chebyshev" polynomial, linear spline ("spline1"), and cubic spline ("spline3").

order = 1: Number of polynomial terms or number of spline pieces for the fitting function.

sample = "*": Sample regions for fitting points. Intervals are separated by "," and an interval may be one point or a range separated by ":".

naverage = 1: Number of points within a sample interval to be subaveraged or submedianed to form fitting points. Positive values are for averages and negative points for medians.

niterate = 0: Number of sigma clipping rejection iterations.

low_reject = 3. , high_reject = 3.: Lower and upper sigma clipping rejection threshold in units of sigma determined from the RMS sigma of the data to the fit.

grow = 0.: Growing radius for rejected points (in pixels). That is, any rejected point also rejects other points within this distance of the rejected point.

Additional parameters

I/O parameters and the default dispersion axis are taken from the package parameters, the default aperture parameters from apdefault, automatic aperture finding parameters from apfind, recentering parameters from aprecenter, resizing parameters from apresize, parameters used for centering and editing the apertures from apedit, and tracing parameters from aptrace.

Description

For each image in the input image list the two dimensional spectra defined by a set of apertures are normalized by a one dimensional normalization function derived by extracting and smoothing the spectrum by fitting a function with the icfit procedure. The value of the fitting function at each point along the dispersion, divided by the aperture width to form a mean or scaled to the same mean as the center pixel of the aperture depending on the cennorm parameter, is divided into the two dimensional input aperture. All points outside the apertures are set to unity.

The purpose of this task is to remove a general shape from the aperture spectra. If low order (order = 1 for instance) functions are used then only the amplitudes of the spectra are affected, shifting each aperture to approximately unit intensity per pixel. If high order functions are used only the small spatial scale variations are preserved. This is useful for making flat field images with the spectral signature of the continuum source removed or for producing two dimensional normalized spectra similar to the task onedspec.continuum. For flat fields this algorithm retains the profile shape which may be useful for removing the profile response in short slit data. However, often one does not want the profile of the flat fielded observation to be modified in which case the task apflatten should be used.

The normalization spectrum is first extracted in the same way as is the one dimensional extraction in apsum or apall. In particular the same parameters for selecting weighting and cleaning are available. After extraction the spectrum is fit using the icfit routine. This may be done interactively or noninteractively depending on the interactive parameter. The default fitting parameters are part of this task. The goal of the fitting depends on the application. One may be trying to simply continuum normalize, in which case one wants to iteratively reject and grow the rejected points to exclude the lines and fit the continuum with a moderate order function (see continuum for more discussion). If one wants to simply normalize all spectra to a common flux, say to remove a blaze function in echelle data, then an order of 1 will normalize by a constant. For flat field and profile correction of small slits one wants to fit the large scale shape of the spectrum but not fit the small bumps and wiggles due to sensitivity variations and fringing.

The smoothed extracted spectrum represents the total flux within the aperture. There are two choices for scaling to a normalization per pixel. One is to divide by the aperture width, thus computing an average flux normalization. In this case the peak of the spectrum will be greater than unity. This is done when cennorm = no. When this parameter has the value yes then the mean of the normalization spectrum is scaled to the mean of the aperture center, computed by linearly interpolating the two pixels about the traced center. This will give values near one for the pixels at the center of the aperture in the final output image.

Before division of each pixel by the appropriate dispersion point in the normalization spectrum, all pixels below the value specified by the threshold parameter in the normalization spectrum are replaced by the threshold value. This suppresses division by very small numbers. Finally, the pixels within the aperture are divided by the normalization function and the pixels outside the apertures are set to 1.

The remainder of this description covers the basic steps defining the apertures to be used. These steps and parameter are much the same as in any of the other apextract tasks.

Aperture definitions may be inherited from those of other images by specifying a reference image with the references parameter. Images in the reference list are matched with those in the input list in order. If the reference image list is shorter than the number of input images, the last reference image is used for all remaining input images. Thus, a single reference image may be given for all the input images or different reference images may be given for each input image. The special reference name "last" may be used to select the last set apertures used in any of the apextract tasks.

If an aperture reference image is not specified or no apertures are found for the specified reference image, previously defined apertures for the input image are sought in the aperture database. Note that reference apertures supersede apertures for the input image. If no apertures are defined they may be created automatically, the find option, or interactively in the aperture editor, if the interactive and edit options are set.

The functions performed by the task are selected by a set of flag parameters. The functions are an automatic spectrum finding and aperture defining algorithm (see apfind) which is ignored if apertures are already defined, automatic recentering and resizing algorithms (see aprecenter and apresize), an interactive aperture editing function (see apedit), a spectrum position tracing and trace function fit (see aptrace), and the main function of this task, the one dimensional normalization of the aperture profiles.

Each function selection will produce a query for each input spectrum if the interactive parameter is set. The queries are answered by "yes", "no", "YES", or "NO", where the upper case responses suppress the query for following images. There are other queries associated with tracing which first ask whether the operation is to be done interactively and, if yes, lead to queries for each aperture. If the interactive parameter is not set then aperture editing, interactive trace fitting, and interactive spectrum shape fitting are ignored.

Examples

To make a flat field image which leaves the total counts of the object images approximately unchanged from a quartz echelle or slitlet image:

cl> apnormalize qtz001,qtz002 flat001,flat002
Yes find
No resize
No edit
Yes trace
Yes trace interactively
NO
Yes flatten
Yes fit interactively

Revisions

APNORMALIZE V2.11: The "apertures" parameter can be used to select apertures for resizing, recentering, tracing, and extraction. This parameter name was previously used for selecting apertures in the recentering algorithm. The new parameter name for this is now "aprecenter".