The muse_bias recipe



Combine several separate bias images into one master bias file.


This recipe combines several separate bias images into one master bias file. The master bias contains the combined pixel values, in adu, of the raw bias exposures, with respect to the image combination method used. Processing trims the raw data and records the overscan statistics, corrects the data levels using the overscan (if overscan is not “none”) and combines the exposures using input parameters. The read-out noise is computed for each quadrant of the raw input images and stored as QC parameter. The variance extension is filled with an initial value accordingly, before image combination. Further QC statistics are computed on the output master bias. Additionally, bad columns are searched for and marked in the data quality extension.



Create an object for the recipe muse_bias.

import cpl
muse_bias = cpl.Recipe("muse_bias")



IFU to handle. If set to 0, all IFUs are processed serially. If set to -1, all IFUs are processed in parallel. (int; default: 0) [default=0].


If this is “none”, stop when detecting discrepant overscan levels (see ovscsigma), for “offset” it assumes that the mean overscan level represents the real offset in the bias levels of the exposures involved, and adjusts the data accordingly; for “vpoly”, a polynomial is fit to the vertical overscan and subtracted from the whole quadrant. (str; default: ‘vpoly’) [default=”vpoly”].


This influences how values are rejected when computing overscan statistics. Either no rejection at all (“none”), rejection using the DCR algorithm (“dcr”), or rejection using an iterative constant fit (“fit”). (str; default: ‘dcr’) [default=”dcr”].


If the deviation of mean overscan levels between a raw input image and the reference image is higher than |ovscsigma x stdev|, stop the processing. If overscan=”vpoly”, this is used as sigma rejection level for the iterative polynomial fit (the level comparison is then done afterwards with |100 x stdev| to guard against incompatible settings). Has no effect for overscan=”offset”. (float; default: 30.0) [default=30.0].


The number of pixels of the overscan adjacent to the data section of the CCD that are ignored when computing statistics or fits. (int; default: 3) [default=3].


Type of image combination to use. (str; default: ‘sigclip’) [default=”sigclip”].


Number of minimum pixels to reject with minmax. (int; default: 1) [default=1].


Number of maximum pixels to reject with minmax. (int; default: 1) [default=1].


Number of pixels to keep with minmax. (int; default: 1) [default=1].


Low sigma for pixel rejection with sigclip. (float; default: 3.0) [default=3.0].


High sigma for pixel rejection with sigclip. (float; default: 3.0) [default=3.0].


Low sigma to find dark columns in the combined bias (float; default: 30.0) [default=30.0].


High sigma to find bright columns in the combined bias (float; default: 3.0) [default=3.0].


Merge output products from different IFUs into a common file. (bool; default: False) [default=False].

The following code snippet shows the default settings for the available parameters.

import cpl
muse_bias = cpl.Recipe("muse_bias")

muse_bias.param.nifu = 0
muse_bias.param.overscan = "vpoly"
muse_bias.param.ovscreject = "dcr"
muse_bias.param.ovscsigma = 30.0
muse_bias.param.ovscignore = 3
muse_bias.param.combine = "sigclip"
muse_bias.param.nlow = 1
muse_bias.param.nhigh = 1
muse_bias.param.nkeep = 1
muse_bias.param.lsigma = 3.0
muse_bias.param.hsigma = 3.0
muse_bias.param.losigmabadpix = 30.0
muse_bias.param.hisigmabadpix = 3.0
muse_bias.param.merge = False

You may also set or overwrite some or all parameters by the recipe parameter param, as shown in the following example:

import cpl
muse_bias = cpl.Recipe("muse_bias")
res = muse_bias( ..., param = {"nifu":0, "overscan":"vpoly"})

See also

cpl.Recipe for more information about the recipe object.

Bug reports

Please report any problems to Peter Weilbacher. Alternatively, you may send a report to the ESO User Support Department.