.help apnoise Sep96 noao.twodspec.apextract
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NAME
apnoise -- Compute and examine noise characteristics of spectra
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USAGE
apnoise input dmin dmax nbins
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PARAMETERS
.ls input
List of input spectra to examine.
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.ls apertures = ""
Apertures to recenter, resize, trace, and extract.  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".
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.ls 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.
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.ls dmin, dmax, nbins
The noise sigma is computed in a set of bins over the specified
range of image data numbers.
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.ls 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.
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.ls 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.
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.ls recenter = yes
Recenter the apertures?
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.ls resize = yes
Resize the apertures?
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.ls edit = yes
Edit the apertures?  The \fIinteractive\fR parameter must also be yes.
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.ls trace = yes
Trace the apertures?
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.ls fittrace = yes
Interactively fit the traced positions by a function?  The \fIinteractive\fR
parameter must also be yes.
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.ls 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 positive nsum sums the lines and a negative value takes the median.
However, for tracing only sums are allowed and the absolute value
is used.
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.ls threshold = 10.
Division threshold.  If a pixel in the two dimensional normalization spectrum
is less than this value then a flat field value of 1 is output.
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The following parameters control the profile and spectrum fitting.
.ls 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.
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.ls 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 \fBapprofile\fR).
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.ls clean = no
Detect and replace deviant pixels?
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.ls 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.
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.ls saturation = INDEF
Saturation or nonlinearity level.  During variance weighted extractions
wavelength points having any pixels above this value are excluded from the
profile determination.
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.ls 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.
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.ls 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.
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.ls lsigma = 3., usigma = 3.
Lower and upper rejection thresholds, given as a number of times the
estimated sigma of a pixel, for cleaning.
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ADDITIONAL PARAMETERS
I/O parameters and the default dispersion axis are taken from the
package parameters, the default aperture parameters from
\fBapdefault\fR, automatic aperture finding parameters from
\fBapfind\fR, recentering parameters from \fBaprecenter\fR, resizing
parameters from \fBapresize\fR, parameters used for centering and
editing the apertures from \fBapedit\fR, and tracing parameters from
\fBaptrace\fR.
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CURSOR COMMANDS
The following cursor keys and colon commands are available during the
display of the noise sigmas and noise model.  See \fBapedit\fR for
the commands for that mode.

.nf
?  Print command help
q  Quit
r  Redraw	
w  Window the graph (see :/help)
I  Interupt immediately

:gain <value>		Check or set the gain model parameter
:readnoise <value>	Check or set the read noise model parameter

Also see the CURSOR MODE commands (:.help) and the windowing commands
(:/help).
.fi
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DESCRIPTION
\fBApnoise\fR computes the noise sigma as a function of data value
using the same profile model used for weighted extraction and
cosmic ray cleanning.  In particular, the residuals used in computing the
noise sigma are the same as those during cleanning.  By looking
at the noise sigma as a function of data value as compared to that
predicted by the noise model based on the read out noise and gain
parameters one can then better refine these values for proper
rejection of cosmic rays without rejection of valid data.
So this task can be used to check or deduce these values and also
to adjust them to include additional sources of error such as
flat field noise and, especially, an additional source of noise due
to the accuracy of the profile modeling.

The first part of this task follows the standard model of allowing
one to define apertures by finding, recentering, editing, and
tracing.  If one has previously defined apertures then these
steps can be skipped.  Once the apertures are defined the apertures
are internally extracted using the profile modeling (see \fBapprofile\fR)
with the optional background subtraction, cleanning, and choices of
profile fitting algorithm, "fit1d" or "fit2d".  But rather than
outputing the extracted spectrum as in \fBapsum\fR or \fBapall\fR
or various functions of the data and profile model as in \fBapfit\fR,
\fBapnormalize\fR, or \fBapflatten\fR, the task computes the
residuals for all points in all apertures (essentially the same
as the difference output of \fBapfit\fR) and determines the
sigma (population corrected RMS) as a function of model data value
in the specified bins.  The bins are defined by a minimum and
maximum data value (found using \fBminmax\fR, \fBimplot\fR, or
\fBimexamine\fR) and the number of bins.

The noise sigma values, with their estimated uncertainties, are then
plotted as a function of data numer.  A curve representing the specified
read out noise and gain is also plotted.  The user then has the
option of varying these two parameters with colon commands.  The
aim of this is to find a noise model which either represents the
measure noise sigmas or at least exceeds them so that only valid
outliers such as cosmic rays will be rejected during cleanning.
The interactive graphical mode only has this function.  The other
keys and colon commands are the standard ones for redrawing, windowing,
and quitting.
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EXAMPLES
1.  To check that the read noise and gain parameters are reasonable for
cleaning \fBapnoise\fR is run.  In this case it is assumed that the
apertures have already been defined and traced.

.nf
	cl> minmax lsobj
	    lsobj  -2.058870315551758  490.3247375488282
	cl> apnoise lsobj 0 500 50 rece- resi- edit- trace-
	    A graph of the noise sigma for data between 0 and 500
	    data numbers is given with a line showing the
	    expected value for the current read noise and gain.
	    The read noise and gain may be varied if desired.
	    Exit with 'q'
.fi
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REVISIONS
.ls APNOISE 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".
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SEE ALSO
apbackground, approfile, apvariance, apfit, icfit, minmax,
apdefault, apfind, aprecenter, apresize, apedit, aptrace, apsum
.endhelp
