Image Drizzling Step

The operation of drizzling each input image needs to be performed twice during processing:

  • single drizzle step: this initial step drizzles each image onto the final output WCS as separate images

  • final drizzle step: this step produces the final combined image based on the cosmic-ray masks determined by AstroDrizzle

Interfaces to main drizzle functions.


Warren Hack



drizzlepac.adrizzle.buildDrizParamDict(configObj, single=True)[source]
drizzlepac.adrizzle.create_output(filename, arr)[source]
drizzlepac.adrizzle.do_driz(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon, expin, in_units, wt_scl, wcslin_pscale=1.0, uniqid=1, pixfrac=1.0, kernel='square', fillval='INDEF', stepsize=10, wcsmap=None)[source]

Core routine for performing ‘drizzle’ operation on a single input image All input values will be Python objects such as ndarrays, instead of filenames. File handling (input and output) will be performed by calling routine.

drizzlepac.adrizzle.drizFinal(imageObjectList, output_wcs, configObj, build=None, wcsmap=None, logfile=None, procSteps=None)[source]
drizzlepac.adrizzle.drizSeparate(imageObjectList, output_wcs, configObj, logfile=None, wcsmap=None, procSteps=None)[source]
drizzlepac.adrizzle.drizzle(input, outdata, wcsmap=None, editpars=False, configObj=None, **input_dict)[source]

Each input image gets drizzled onto a separate copy of the output frame. When stacked, these copies would correspond to the final combined product. As separate images, they allow for treatment of each input image separately in the undistorted, final WCS system. These images provide the information necessary for refining image registration for each of the input images. They also provide the images that will be succeedingly combined into a median image and then used for the subsequent blot and cosmic ray detection steps.

Aside from the input parameters, this step requires:

  • valid input images with SCI extensions

  • valid distortion coefficients tables

  • any optional secondary distortion correction images

  • numpy object (in memory) for static mask

This step produces:

  • singly drizzled science image (simple FITS format)

  • singly drizzled weight images (simple FITS format)

These images all have the same WCS based on the original input parameters and those provided for this step; specifically, output shape, pixel size, and orientation, if any have been specified at all.

Other Parameters:
driz_separatebool (Default = No)

This parameter specifies whether or not to drizzle each input image onto separate output images. The separate output images will all have the same WCS as the final combined output frame. These images are used to create the median image, needed for cosmic ray rejection.

driz_sep_kernel{‘square’, ‘point’, ‘turbo’, ‘gaussian’, ‘lanczos3’} (Default = ‘turbo’)

Used for the initial separate drizzling operation only, this parameter specifies the form of the kernel function used to distribute flux onto the separate output images. The current options are:

  • square: original classic drizzling kernel

  • point: this kernel is a point so each input pixel can only contribute to the single pixel that is closest to the output position. It is equivalent to the limit as pixfrac -> 0, and is very fast.

  • gaussian: this kernel is a circular gaussian with a FWHM equal to the value of pixfrac, measured in input pixels.

  • turbo: this is similar to kernel=”square” but the box is always the same shape and size on the output grid, and is always aligned with the X and Y axes. This may result in a significant speed increase.

  • lanczos3: a Lanczos style kernel, extending a radius of 3 pixels from the center of the detection. The Lanczos kernel is a damped and bounded form of the “sinc” interpolator, and is very effective for resampling single images when scale=pixfrac=1. It leads to less resolution loss than other kernels, and typically results in reduced correlated noise in outputs.


    While the 'gaussian' and 'lanczos3' kernels may produce reasonable results, we cannot guarantee that they will properly conserve flux; understand the effects of these kernels before using them.


    The 'lanczos3' kernel tends to result in much slower processing as compared to other kernel options. This option should never be used for pixfrac!=1.0, and is not recommended for scale != 1.0.

The default for this step is “turbo” since it is much faster than “square”, and it is quite satisfactory for the purposes of generating the median image. More information about the different kernels can be found in the help file for the drizzle task.

driz_sep_wt_sclfloat (Default = exptime)

This parameter specifies the weighting factor for input image. If driz_sep_wt_scl=exptime, then the scaling value will be set equal to the exposure time found in the image header. The use of the default value is recommended for producing optimal behavior for most scenarious. It is possible to set wt_scl=‘expsq’ for weighting by the square of the exposure time, which is optimal for read-noise dominated images.

driz_sep_pixfracfloat (Default = 1.0)

Fraction by which input pixels are “shrunk” before being drizzled onto the output image grid, given as a real number between 0 and 1. This specifies the size of the footprint, or “dropsize”, of a pixel in units of the input pixel size. If pixfrac is set to less than 0.001, the kernel parameter will be reset to ‘point’ for more efficient processing. In the step of drizzling each input image onto a separate output image, the default value of 1.0 is best in order to ensure that each output drizzled image is fully populated with pixels from the input image. For more information, see the help for the drizzle task.

driz_sep_fillvalint or INDEF (Default = INDEF)

Value to be assigned to output pixels that have zero weight, or that receive flux from any input pixels during drizzling. This parameter corresponds to the fillval parameter of the drizzle task. If the default of INDEF is used, and if the weight in both the input and output images for a given pixel are zero, then the output pixel will be set to the value it would have had if the input had a non-zero weight. Otherwise, if a numerical value is provided (e.g. 0), then these pixels will be set to that value.

driz_sep_bitsint (Default = 0)

Integer sum of all the DQ bit values from the input image’s DQ array that should be considered ‘good’ when building the weighting mask. This can also be used to reset pixels to good if they had been flagged as cosmic rays during a previous run of AstroDrizzle, by adding the value 4096 for ACS and WFPC2 data. Please see the section on Selecting the Bits Parameter for a more detailed discussion.

driz_sep_wcsbool (Default = No)

Define custom WCS for seperate output images?

driz_sep_refimagestr (Default = ‘’)

Reference image from which a WCS solution can be obtained.

driz_sep_rotfloat (Default = INDEF)

Position Angle of output image’s Y-axis relative to North. A value of 0.0 would orient the final output image to be North up. The default of INDEF specifies that the images will not be rotated, but will instead be drizzled in the default orientation for the camera with the x and y axes of the drizzled image corresponding approximately to the detector axes. This conserves disk space, as these single drizzled images are only used in the intermediate step of creating a median image.

driz_sep_scalefloat (Default = INDEF)

Linear size of the output pixels in arcseconds/pixel for each separate drizzled image (used in creating the median for cosmic ray rejection). The default value of INDEF specifies that the undistorted pixel scale for the first input image will be used as the pixel scale for all the output images.

driz_sep_outnxint (Default = INDEF)

Size, in pixels, of the X axis in the output images that each input will be drizzled onto. If no value is specified, the smallest size that can accommodate the full dithered field will be used.

driz_sep_outnyint (Default = INDEF)

Size, in pixels, of the Y axis in the output images that each input will be drizzled onto. If no value is specified, the smallest size that can accommodate the full dithered field will be used.

driz_sep_rafloat (Default = INDEF)

Right ascension (in decimal degrees) specifying the center of the output image. If this value is not designated, the center will automatically be calculated based on the distribution of image dither positions.

driz_sep_decfloat (Default = INDEF)

Declination (in decimal degrees) specifying the center of the output image. If this value is not designated, the center will automatically be calculated based on the distribution of image dither positions.


These tasks are designed to work together seemlessly when run in the full AstroDrizzle interface. More advanced users may wish to create specialized scripts for their own datasets, making use of only a subset of the predefined AstroDrizzle tasks, or add additional processing, which may be usefull for their particular data. In these cases, individual access to the tasks is important.

Something to keep in mind is that the full AstroDrizzle interface will make backup copies of your original files and place them in the OrIg/ directory of your current working directory. If you are working with the stand alone interfaces, it is assumed that the user has already taken care of backing up their original datafiles as the input file with be directly altered.

There are two user interface function for this task, one to allow you to create seperately drizzled images of each image in your list and the other to create one single output drizzled image, which is the combination of all of them:

def drizSeparate(imageObjectList,output_wcs,configObj,wcsmap=wcs_functions.WCSMap)
def drizFinal(imageObjectList, output_wcs, configObj,build=None,wcsmap=wcs_functions.WCSMap)
if configObj[single_step]['driz_separate']:


Basic example of how to call static yourself from a Python command line, using the default parameters for the task.

>>> from drizzlepac import adrizzle

Apply logic for interpreting final_maskval value…

drizzlepac.adrizzle.mergeDQarray(maskname, dqarr)[source]

Merge static or CR mask with mask created from DQ array on-the-fly here., wcsmap=None)[source]

Interface for running wdrizzle from TEAL or Python command-line.

This code performs all file I/O to set up the use of the drizzle code for a single exposure to replicate the functionality of the original wdrizzle.

drizzlepac.adrizzle.run_driz(imageObjectList, output_wcs, paramDict, single, build, wcsmap=None)[source]

Perform drizzle operation on input to create output. The input parameters originally was a list of dictionaries, one for each input, that matches the primary parameters for an IRAF drizzle task.

This method would then loop over all the entries in the list and run drizzle for each entry.

Parameters required for input in paramDict:

build,single,units,wt_scl,pixfrac,kernel,fillval, rot,scale,xsh,ysh,blotnx,blotny,outnx,outny,data

drizzlepac.adrizzle.run_driz_chip(img, chip, output_wcs, outwcs, template, paramDict, single, doWrite, build, _versions, _numctx, _nplanes, _numchips, _outsci, _outwht, _outctx, _hdrlist, wcsmap)[source]

Perform the drizzle operation on a single chip. This is separated out from run_driz_img so as to keep together the entirety of the code which is inside the loop over chips. See the run_driz code for more documentation.

drizzlepac.adrizzle.run_driz_img(img, chiplist, output_wcs, outwcs, template, paramDict, single, num_in_prod, build, _versions, _numctx, _nplanes, chipIdxCopy, _outsci, _outwht, _outctx, _hdrlist, wcsmap)[source]

Perform the drizzle operation on a single image. This is separated out from run_driz() so as to keep together the entirety of the code which is inside the loop over images. See the run_driz() code for more documentation.

drizzlepac.adrizzle.updateInputDQArray(dqfile, dq_extn, chip, crmaskname, cr_bits_value)[source]