CALACS

CALACS Python Wrapper

The calacs module contains a function calacs that calls the CALACS executable. Use this function to facilitate batch runs of CALACS.

Examples

>>> from acstools import calacs
>>> calacs.calacs(filename)

For help usage use exe_args=['--help']

Functions

calacs(input_file[, exec_path, time_stamps, ...])

Run the calacs.e executable as from the shell.

ACSCCD Python Wrapper

The acsccd module contains a function acsccd that calls the ACSCCD executable. Use this function to facilitate batch runs of ACSCCD.

Note

Calibration flags are controlled by primary header.

Warning

Do not use with SBC MAMA images.

Examples

>>> from acstools import acsccd
>>> acsccd.acsccd('*raw.fits')

For help usage use exe_args=['--help']

Functions

acsccd(input[, exec_path, time_stamps, ...])

Run the acsccd.e executable as from the shell.

ACSCTE Python Wrapper

The acscte module contains a function acscte that calls the ACSCTE executable. Use this function to facilitate batch runs of ACSCTE.

Only WFC full-frame and some 2K subarrays are currently supported. See ACS Data Handbook for more details.

Note

Calibration flags are controlled by primary header.

Examples

>>> from acstools import acscte
>>> acscte.acscte('*blv_tmp.fits')

For help usage use exe_args=['--help']

Functions

acscte(input[, exec_path, time_stamps, ...])

Run the acscte.e executable as from the shell.

ACSREJ Python Wrapper

This module contains a function acsrej() that calls the acsrej.e executable. Use this function to facilitate batch runs of ACSREJ.

Examples

>>> from acstools import acsrej
>>> acsrej.acsrej('*flt.fits', 'combined_image.fits')

For help usage use exe_args=['--help']

Functions

acsrej(input, output[, exec_path, ...])

Run the acsrej.e executable as from the shell.

ACS2D Python Wrapper

The acs2d module contains a function acs2d that calls the ACS2D executable. Use this function to facilitate batch runs of ACS2D.

Examples

>>> from acstools import acs2d
>>> acs2d.acs2d('*blv_tmp.fits')

For help usage use exe_args=['--help'].

Functions

acs2d(input[, exec_path, time_stamps, ...])

Run the acs2d.e executable as from the shell.

ACSSUM Python Wrapper

The acssum module contains a function acssum that calls the ACSSUM executable. Use this function to facilitate batch runs of ACSSUM.

Examples

>>> from acstools import acssum
>>> acssum.acssum('*flt.fits', 'combined_image.fits')

For help usage use exe_args=['--help']

Functions

acssum(input, output[, exec_path, ...])

Run the acssum.e executable as from the shell.

CTE Forward Model

This functionality is provided to simulate the CTE effects associated with readout of the WFC detectors. It is not part of the standard CALACS pipeline.

The acscteforwardmodel module contains a function acscteforwardmodel that calls the ACSCTE forward model executable. Use this function to facilitate batch runs of the forward model.

Only WFC full-frame and some 2K subarrays are currently supported. See ACS Data Handbook for more details.

For guidance on running the CTE forward model, see the Jupyter notebook ACS CTE Forward Model Example.

Note

Calibration flags are controlled by primary header.

Examples

>>> from acstools import acscteforwardmodel
>>> acscteforwardmodel.acscteforwardmodel('*blc_tmp.fits')

For help usage use exe_args=['--help']

Functions

acscteforwardmodel(input[, exec_path, ...])

Run the acscteforwardmodel.e executable as from the shell.

PIXVALUE in FITS File

CALACS uses HSTIO that utilizes PIXVALUE keyword to represent a data extension with constant value. However, this is not a standard FITS behavior and is not recognized by astropy.io.fits. While you should not encounter errors or warnings, constant value data extensions may exhibit unexpected behavior when reading, writing, or manipulating them with astropy.io.fits. Therefore, if issues such as these arise, we recommend use of stsci.tools.stpyfits, which is distributed as part of stsci_python, instead of astropy.io.fits when working with CALACS products. To use stpyfits in Python:

from stsci.tools import stpyfits as fits

calacs.e (C Program)

A detailed description of CALACS is available in the ACS Data Handbook. If you have questions not answered in the documentation, please contact STScI Help Desk.

Where to Find CALACS

CALACS is part of the HSTCAL package.

Usage

From the command line:

calacs.e jb1f89eaq_raw.fits [command line options]

Command Line Options

CALACS supports several command line options:

  • -t

    • Print verbose time stamps.

  • -s

    • Save temporary files.

  • -v

    • Turn on verbose output.

  • -d

    • Turn on debug output.

  • -q

    • Turn on quiet output.

  • -1

    • Turn off parallel processing for PCTECORR. Try this option if you encounter problems running CALACS with PCTECORR=PERFORM.

  • --nthreads <N>

    • Specify the number of threads used for PCTECORR. The default is ‘greedy’ and will use that specified by the system environment variable OMP_NUM_THREADS. If N > OMP_NUM_THREADS, OMP_NUM_THREADS will be used instead. The option -1 takes precedence.

  • --ctegen <1|2>

    • Specify which generation CTE correction to use, the default is 2. Gen 1 (officially deprecated) refers to the correction algorithm used in calacs version pre 9.2.0 in relation to ACS ISR 2010-03. Gen 2 refers to the new CTE correction algorithm implemented in calacs version 9.2.0 (HSTCAL 1.3.0) in relation to ACS ISR 2018-04.

  • --pctetab <filename>

    • Override the PCTETAB reference file specified in the image header.

Parallel Processing with OpenMP

By default, CALACS will attempt to perform PCTECORR using all available CPUs on your machine. You can set the maximum number of CPUs available for CALACS by either using the command line option –nthreads <N> or by setting the OMP_NUM_THREADS environmental variable.

In tcsh:

setenv OMP_NUM_THREADS 2

In bash:

export OMP_NUM_THREADS=2

Batch CALACS

The recommended method for running CALACS in batch mode is to use Python and the acstools package.

For example:

from acstools import calacs
import glob

for fits in glob.iglob('j*_raw.fits'):
    calacs.calacs(fits)

CALACS Processing Steps

Please see Chapter 3 of the ACS Data Handbook for a more thorough description of the steps within CALACS.

Bias Image Subtraction (BIASCORR)

Bias correction is done by subtracting the BIASFILE in units of DN, removing the low-order quasi-static structure of the bias, including the bias gradient (post-SM4).

Unit Conversion to Electrons

The image is multiplied by the gain, converting it to ELECTRONS.

Bias Level Correction (BLEVCORR)

BLEVCORR is performed after BIASCORR. Calculations are done in ELECTRONS.

For post-SM4 full-frame WFC exposures, it also includes:

Sink Pixel Flagging (SINKCORR)

SINKCORR flags sink pixels and the adjacent affected pixels with the value 1024 in the DQ array of WFC images using the SNKCFILE. Only performed on images taken after January 2015.

Pixel-Based CTE Correction (PCTECORR)

For all full-frame WFC exposures, pixel-based CTE correction (ACS ISR 2018-04) is applied during ACSCTE that occurs after ACSCCD, specifically, after BLEVCORR.

Because the CTE correction is applied before DARKCORR and FLSHCORR, it is necessary to use a CTE-corrected dark (DRKCFILE) if the PCTECORR step is enabled.

Parameters characterizing the CTE correction are stored in a reference table, PCTETAB.

Note

CALACS 8.2 and later uses a slightly different PCTETAB format, where the COL_SCALE extension does not include overscan columns.

Required Keywords

Running CALACS with pixel-based CTE correction requires the following header keywords:

  • PCTECORR

    • By default, set to PERFORM for all full-frame WFC exposures, except BIAS.

  • PCTETAB

    • Reference table containing CTE correction parameters. By default, it should be in the jref directory and have the suffix _cte.fits.

  • DRKCFILE

    • Similar to DARKFILE but with CTE correction performed. By default, it should be in the jref directory and have the suffix _dkc.fits. This is necessary because PCTECORR is done before DARKCORR.

Optional Keywords

You may adjust some CTE correction algorithm parameters by changing the following keywords in the RAW image header. The default values are picked for optimal results in a typical WFC full-frame exposure. Changing these values is not recommended unless you know what you are doing.

  • FIXROCR

    • Account for and correct readout cosmic ray over-subtraction.

      • 0 - Off: do not correct

      • 1 - On: correct

  • PCTENPAR

    • Number of parallel transfer iterations.

  • PCTENSMD

    • Read noise mitigation mode:

      • 0 - No mitigation

      • 1 - Perform noise smoothing

      • 2 - No noise smoothing

  • PCTERNOI

    • Read noise amplitude in ELECTRONS.

  • PCTETLEN

    • Maximum length of CTE trail.

  • PCTENFOR

    • Number of iterations used for forward CTE model.

Dark Current Subtraction (DARKCORR)

Dark correction uses DARKFILE if PCTECORR=OMIT, otherwise it uses DRKCFILE (CTE-corrected dark reference file).

The dark image is scaled by DARKTIME, which is the sum of EXPTIME, FLASHDUR, and for WFC, a commanding overhead based on the observing mode, which is listed in the CCDTAB. Any image with non-zero EXPTIME is assumed to not be a BIAS.

Post-Flash Correction (FLSHCORR)

Post-flash correction is performed after DARKCORR in the ACS2D step. When FLSHCORR=PERFORM, it scales FLSHFILE by FLASHDUR for correction.

Flat-Field Correction (FLATCORR)

PFLTFILE is used for flat-field correction, which is a combination of the pixel-to-pixel flats and low-order flats. This corrects for pixel-to-pixel and large-scale sensitivity gradients across the detector by dividing the data with the flat-field image.

Photometry Keywords (PHOTCORR)

The PHOTCORR step is performed using tables of precomputed values (IMPHTTAB). The correct table for a given image must be specified in the IMPHTTAB header keyword in order for CALACS to perform the PHOTCORR step. By default, it should be in the jref directory and have the suffix _imp.fits. Each DETECTOR uses a different table.

CALACS Output

Using RAW as input:

  • flt.fits: Also called FLT.

  • flc.fits: Similar to FLT, except with pixel-based CTE correction applied.

  • Temporary files: blv_tmp.fits (BLV_TMP), blc_tmp.fits (BLC_TMP)

Using ASN as input with ACSREJ:

  • crj.fits: Also called CRJ.

  • crc.fits: Similar to CRJ, except with pixel-based CTE correction applied.

  • Temporary files: crj_tmp.fits (CRJ_TMP), crc_tmp.fits (CRC_TMP)