REFERENCES · SEE_ALSO
noisemodel -- Derive noise model parameters using pixel-to-pixel variations.
This task calculates and plots the mean as a function of the square-root of the variance within bins (of dimension `xsize' * `ysize') on the input image(s). A predicted relation, based on the noise model parameters, is then overplotted for comparison. If the chi-square clipping limits have finite values, then the uncertainty range corresponding to these limits, and based on the noise model, is overplotted as a pair of dashed curves. You can then interactively adjust the noise model coefficients until a satisfactory agreement is reached; the value of the normalized chi-square will aid in this effort. Output coefficients may optionally be written to the parameter file. You can also interactively adjust the X- and Y-size of the bins, and switch groups on GEIS-format images.
This task is most useful if the input image(s) are fairly featureless and span the full range of possible DN values; a graded set of flat fields, particularly if they include dark (non-illuminated) frames, is ideal. Since it is possible for the mean of some bins to be negative (if, for example, the bias correction is a bit too large), the plot scale is linear between -10 and 10, and logarithmic for values below and above, respectively. The distribution of points for a detector with no internal noise would have a slope of +1/2 (on the log part of the plot), because of photon shot noise, with a vertical position set by the detector `gain'. `readnoise' is evident if the distribution approaches zero slope at some non-zero sigma at low DNs, which is common for most detectors. The `readnoise' component of the model is generic, in that it can account for all additive components of the noise in a calibrated image, such as a noisey bias correction on a CCD frame. `scalenoise' is evident if the slope of the distribution is increasing beyond +1/2 (to a limit of +1) at high DNs, and results from noise that scales linearly with DN. Such an effect would occur if the image were corrected for flat field response with an imperfectly normalized flat field frame. First time users of this task should plot several limiting combinations of the model parameters.
The input images are specified by an IRAF filename template or a list. Different sections of the same image may be specified by including the image name in the input list multiple times---once with each desired section appended. The images may be of any size, dimension, and pixel data type, but must all be consistent with regard to GEIS format.
Deviant values are rejected in two stages: bad pixel rejection, and bad bin rejection. Deviant pixels within bins, i.e., those that differ from the mean within a bin by more than sigma * `ksigreject', may be excluded from the calculated mean and sigma for that bin. The `ksireject' parameter may be changed interactively; setting it to "INDEF" will turn off rejection of deviant pixels altogether. Deviant bins, i.e., those whose sigma differs from that predicted from the noise model by more than `cliplo' (or `cliphi', as appropriate) are excluded from the calculation of chi-square. Setting these parameters to "INDEF" will bypass bin rejection altogether. (These bins will also be excluded from the parameter fit, when it is implemented.)
The associated DQF files may be referenced for masking bad pixels in the input images. Note that the DQF files must be of type `integer', and MUST have the same rootname as the input images: this task will substitute the file extension specified by the `datextn' parameter for that specified by the `dqfextn' parameter. The user may specify which types of pixel errors are to be rejected by setting various flags in the `dqfpar' pset. For example, setting rsbit = yes will exclude all pixels that suffered Reed-Solomon errors from the calculation. The bit-codes are then stored in a 32-bit integer constant which is Boolean ANDed with the actual DQF values corresponding to the input values for each pixel.
This task lets most parameters to be changed interactively, and lets you replot, reset the plot limits, and show all of the current parameters in the text window. The interactive options unique to this task are listed below; many other options that are common to all IRAF tasks are discussed in the help file for `language.cursors'.
NOISEMODEL CURSOR KEYS ? Print help q Quit the task r Redraw the graph I Interrupt task immediately. Noise parameters are not saved. NOISEMODEL COLON COMMANDSColon commands may be abbreviated. They take one or two optional arguments. If no argument is given then the current value is printed.
:binsize [Xsize][Ysize] Show or set binsize for X-, Y-axis :clip [low][high] Show or set the lower, upper chi-square clipping limits :group [value] Show or change the current image group :gain [value] Show or set the detector gain :ksigma [value] Show or set the pixel rejection threshold :readnoise [value] Show or set the detector readnoise :scalenoise [value] Show or set the detector scalenoise :show Show the current parameters :xrange [low][high] Show or set the lower, upper X-axis bounds :yrange [low][high] Show or set the lower, upper Y-axis bounds :write Write model parameters the `noisepar' parameter file
- input [string]
- List or template of input image names to use in determining the noise model parameters.
- (group = INDEF) [integer, min=1, max=4]
- Initial input image group.
- (xbinsize = 10) [integer, min=1]
- Size of bin in X-direction for calculating means and sigmas. Note that the product of `xbinsize' * `ybinsize' must be at least 3.
- (ybinsize = 10) [integer, min=1]
- Size of bin in Y-direction for calculating means and sigmas. Note that the product of `xbinsize' * `ybinsize' must be at least 3.
- (ksigreject = INDEF) [real]
- Rejection threshold for bin arithmetic. Pixel values above and below sigma times this threshold will be excluded from the calculation of mean and sigma. Setting this parameter to INDEF will inhibit kappa-sigma rejection altogether.
- (cliplo = 3.) [real, min=0.]
- Rejection threshold for chi-square calculation. Deviations from the `noisemodel' below sigma times this threshold will be excluded from the calculation of chi-square. Setting this parameter to INDEF will inhibit chi-square rejection altogether for bins that lie below the noise model.
- (cliphi = 3.) [real, min=0.]
- Rejection threshold for chi-square calculation. Deviations from the noise model above sigma times this threshold will be excluded from the calculation of chi-square. Setting this parameter to INDEF will inhibit chi-square rejection altogether for bins that lie above the noise model.
- (device = "stdgraph") [string]
- Output graphics device, ordinarily the user terminal. You can send
plots directly to your default printer or plotter by setting this to
- (listout = no) [boolean]
- List the output instead of plotting? The output can be piped to an ASCII file for later use as input to other tasks (e.g., `stplot.igi').
- (usedqf = no) [boolean]
- Reference the Data Quality Files for each input image?
If this is set to "yes", data flaged as bad will be excluded from the calculation. Note that there must be a DQF file (of datatype `integer') for each input image.
- (dqfpar = "") [string]
- Pset name for DQF parameters. Edit this pset to select which pathologies (bit-flags) to apply.
- (noisepar = "") [string]
- P-set name for coefficients of terms used in the noise model.
- (cur = "") [string]
- Cursor input file. If a cursor file is not given then the standard graphics cursor is read. (Type "help cursor" for information about cursor input files.)
1. Generate a noise model plot without rejecting any pixels in the calculation, but clipping bins that differ from the noise model prediction by more than +/- 2 sigma. The image names are read from the ASCII file "imlist".
cl> noisemodel @imlist ksig=INDEF cliphi=2. cliplo=2.
2. Generate a noise model plot, using the DQF files to mask flagged pixels:
cl> noisemodel @imlist usedqf+ datextn="c0h" dqfextn="c1h"
When using small bins (less than, say, 10x10), or large numbers of large input images, this task may generate a larger plot buffer than can be accommodated on most laser printers. The screen plot should not be adversely affected, however.
This task is based upon a FORTRAN program written by K. Horne (STScI), and was enhanced and re-written in SPP as an STSDAS application by R.A. Shaw (STScI). The utility of this task, particularly in rejecting image transients, can be found in a paper entitled "Noise Model-Based Cosmic Ray Rejection" by R. Shaw & K. Horne (ASP Conf. Ser., 25, 311, 1992.)