Tasks in the Package¶
The Aperture Masking Interferometry (AMI) package currently consists of three tasks:
ami_analyze
: apply the LG algorithm to a NIRISS AMI exposureami_average
: average the results of LG processing for multiple exposuresami_normalize
: normalize the LG results for a science target using LG results from a reference target
The three tasks can be applied to an association of AMI exposures using the
pipeline module calwebb_ami3
.
CALWEBB_AMI3 Pipeline¶
Overview¶
The calwebb_ami3
pipeline module can be used to apply all 3 steps of AMI
processing to an association (ASN) of AMI exposures. The processing flow through
the pipeline is as follows:
- Apply the
ami_analyze
step to all products listed in the input association table. Output files will have a product type suffix ofami
. There will be oneami
product per input exposure. - Apply the
ami_average
step to combine the above results for both science target and reference target exposures, if both types exist in the ASN table. If the optional parametersave_averages
is set to true (see below), the results will be saved to output files with a product type suffix ofamiavg
. There will be oneamiavg
product for the science target and one for the reference target. - If reference target results exist, apply the
ami_normalize
step to the averaged science target result, using the averaged reference target result to do the normalization. The output file will have a product type suffix ofaminorm
.
Input¶
The only input to the calwebb_ami3
pipeline is the name of a json-formatted
association file. There is one optional parameter save_averages
. If set to
true, the results of the ami_average
step will be saved to files.
It is assumed that the
ASN file will define a single output product for the science target result,
containing a list of input member file names, for both science target and
reference target exposures. An example ASN file is shown below.
{"asn_rule": "NIRISS_AMI", "targname": "NGC-3603", "asn_pool": "jw00017_001_01_pool", "program": "00017",
"products": [
{"prodtype": "ami", "name": "jw87003-c1001_t001_niriss_f277w-nrm",
"members": [
{"exptype": "science", "expname": "test_targ14_cal.fits"},
{"exptype": "science", "expname": "test_targ15_cal.fits"},
{"exptype": "science", "expname": "test_targ16_cal.fits"},
{"exptype": "psf", "expname": "test_ref1_cal.fits"},
{"exptype": "psf", "expname": "test_ref2_cal.fits"},
{"exptype": "psf", "expname": "test_ref3_cal.fits"}]}],
"asn_type": "ami",
"asn_id": "c1001"}
Note that the exptype
attribute value for each input member is used to
indicate which files contain science target images and which contain reference
psf images.
AMI_Analyze¶
Overview¶
The ami_analyze
step applies the Lacour-Greenbaum (LG) image plane
modeling algorithm to a NIRISS AMI image.
The routine computes a number of parameters, including a model fit (and
residuals) to the image, fringe amplitudes and phases, and closure phases
and amplitudes.
The JWST AMI observing template allows for exposures to be obtained using
either full-frame (SUBARRAY=”FULL”) or subarray (SUBARRAY=”SUB80”) readouts.
When processing a full-frame exposure, the ami_analyze
step extracts
(on the fly) a region from the image corresponding to the size and location of
the SUB80 subarray, in order to keep the processing time to a reasonable level.
Inputs¶
The ami_analyze
step takes a single input image, in the form of a simple 2D
ImageModel. There are two optional parameters:
oversample
: specifies the oversampling factor to be used in the model fit (default value = 3)rotation
: specifies an initial guess, in degrees, for the rotation of the PSF in the input image (default value = 0.0)
Output¶
The ami_analyze
step produces a single output file, which contains the
following list of extensions:
FIT
: a 2-D image of the fitted modelRESID
: a 2-D image of the fit residualsCLOSURE_AMP
: table of closure amplitudesCLOSURE_PHA
: table of closure phasesFRINGE_AMP
: table of fringe amplitudesFRINGE_PHA
: table of fringe phasesPUPIL_PHA
: table of pupil phasesSOLNS
: table of fringe coefficients
AMI_Average¶
Overview¶
The ami_average
step averages the results of LG processing from the
ami_analyze
step for multiple exposures of a given target. It averages
all 8 components of the ami_analyze
output files for all input exposures.
Inputs¶
The only input to the ami_average
step is a list of input files to be
processed. These will presumably be output files from the ami_analyze
step.
The step has no other required or optional parameters, nor does it use any
reference files.
Output¶
The step produces a single output file, having the same format as the input files, where the data for the 8 file components are the average of each component from the list of input files.
AMI_Normalize¶
Overview¶
The ami_normalize
step provides normalization of LG processing results for
a science target using LG results of a reference target. The algorithm
subtracts the reference target closure phases from the science target closure
phases and divides the science target fringe amplitudes by the reference target
fringe amplitudes.
Inputs¶
The ami_normalize
step takes two input files: the first is the LG
processed results for a science target and the second is the LG processed
results for the reference target. There are no optional parameters and no
reference files are used.
Output¶
The output is a new LG product for the science target in which the closure phases and fringe amplitudes have been normalized using the reference target closure phases and fringe amplitudes. The remaining components of the science target data model are left unchanged.