diff --git a/CEP/Pipeline/recipes/sip/bin/msss_imager_pipeline.py b/CEP/Pipeline/recipes/sip/bin/msss_imager_pipeline.py
index 954745b1416a0223ac766155bea79c1edd2ce420..76ac9b4d94c8bb538f527dbc6da9938a9ab4eb7f 100755
--- a/CEP/Pipeline/recipes/sip/bin/msss_imager_pipeline.py
+++ b/CEP/Pipeline/recipes/sip/bin/msss_imager_pipeline.py
@@ -20,43 +20,70 @@ from lofarpipe.support.utilities import patch_parset
class msss_imager_pipeline(control):
- """
- The MSSS imager pipeline can be used to generate MSSS images.
-
- MSSS images are compiled from a number of so-called slices. Each slice
+ """
+ The Automatic MSSS imager pipeline is used to generate MSSS images and find
+ sources in the generated images. Generated images and lists of found sources
+ are complemented with meta data and thus ready for consumption by the
+ Long Term Storage (LTA)
+
+ *subband groups*
+ The imager_pipeline is able to generate images on the frequency range of
+ LOFAR in parallel. Combining the frequency subbands together in so called
+ subbandgroups. Each subband group will result in an image and sourcelist,
+ (typically 8, because ten subband groups are combined).
+
+ *Time Slices*
+ MSSS images are compiled from a number of so-called (time) slices. Each slice
comprises a short (approx. 10 min) observation of a field (an area on the
- sky) containing 80 subbands. The number of slices will be different for LBA
- observations (typically 9) and HBA observations (typically 2), due to
- differences in sensitivity.
-
- One MSSS observation will produce a number of images (typically 8), one for
- each so-called subband-group (SBG). Each SBG consists of the same number
- of consecutive subbands (typically 10).
+ sky) containing typically 80 subbands. The number of slices will be
+ different for LBA observations (typically 9) and HBA observations
+ (typically 2), due to differences in sensitivity.
Each image will be compiled on a different cluster node to balance the
processing load. The input- and output- files and locations are determined
by the scheduler and specified in the parset-file.
+
+ *steps*
+ This pipeline performs the following operations:
+
+ 1. Prepare Phase: Copy the preprocessed MS's from the different compute
+ nodes to the nodes where the images will be compiled (the prepare phase).
+ Combine the subbands in subband groups, concattenate the timeslice in a
+ single large measurement set and perform flagging, RFI and bad station
+ exclusion.
+ 2. Create db: Generate a local sky model (LSM) from the global sky model
+ (GSM) for the sources that are in the field-of-view (FoV). The LSM
+ is stored as sourcedb.
+ In step 3 calibration of the measurement sets is performed on these
+ sources and in step 4 to create a mask for the awimager. The calibration
+ solution will be placed in an instrument table/db also created in this
+ step.
+ 3. BBS: Calibrate the measurement set with the sourcedb from the gsm.
+ In later iterations sourced found in the created images will be added
+ to this list. Resulting in a selfcalibration cycle.
+ 4. Awimager: The combined measurement sets are now imaged. The imaging
+ is performed using a mask: The sources in the sourcedb are used to create
+ an casa image masking known sources. Together with the measurement set
+ an image is created.
+ 5. Sourcefinding: The images created in step 4 are fed to pyBDSM to find and
+ describe sources. In multiple itterations substracting the found sources,
+ all sources are collectedin a sourcelist.
+ I. The sources found in step 5 are fed back into step 2. This allows the
+ Measurement sets to be calibrated with sources currently found in the
+ image. This loop will continue until convergence (3 times for the time
+ being).
+ 6. Finalize: Meta data with regards to the input, computations performed and
+ results are collected an added to the casa image. The images created are
+ converted from casa to HDF5 and copied to the correct output location.
+ 7. Export meta data: An outputfile with meta data is generated ready for
+ consumption by the LTA and/or the LOFAR framework
+
+ Per subband-group, the following output products will be delivered:
+ a. An image
+ b. A source list
+ c. (Calibration solutions and corrected visibilities)
- This pipeline will perform the following operations:
- - Copy the preprocessed MS's from the different compute nodes to the nodes
- where the images will be compiled (the prepare phase).
- - Flag the long baselines using DPPP
- - Concatenate the MS's of the different slices as one virtual MS for
- imaging.
- - Generate a local sky model (LSM) from the global sky model (GSM) for the
- sources that are in the field-of-view (FoV).
- - Repeat until convergence (3 times for the time being):
- - Per slice: solve and correct for phases using BBS with TEC enabled
- - Run the awimager.
- - Run the source finder (PyBDSM) and update the local sky model (LSM).
-
- Per subband-group, the following output products will be delivered:
- - Calibration solutions and corrected visibilities
- - An image
- - A source list
"""
-
-
def __init__(self):
control.__init__(self)
self.parset = parameterset()