diff --git a/CEP/Pipeline/recipes/sip/bin/long_baseline_pipeline.py b/CEP/Pipeline/recipes/sip/bin/long_baseline_pipeline.py
index 4faec44bbffe0d3f390a8da0121324e89fcf3ba3..6350c58fc485d30aa535633c32ef5d59d2d3668c 100644
--- a/CEP/Pipeline/recipes/sip/bin/long_baseline_pipeline.py
+++ b/CEP/Pipeline/recipes/sip/bin/long_baseline_pipeline.py
@@ -26,43 +26,38 @@ from lofar.parameterset import parameterset
class msss_imager_pipeline(control):
"""
- 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)
+ The Automatic MSSS long baselione pipeline is used to generate MSSS
+ measurement sets combining information of multiple subbands and or
+ observations into measurements sets. They are 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
+ The pipeline is able to generate measurementssets 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).
+ subbandgroups.
*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 measurmentsets are compiled from a number of so-called (time) slices. Each
+ slice comprises an observation of a field (an area on
the 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.
-
+
**This pipeline performs the following operations:**
- 1. Prepare Phase. Copy the preprocessed MS's from the different compute
+ 1. Long baseline . 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. Generate meta information feedback files based on dataproduct information
+ and parset/configuration data
**Per subband-group, the following output products will be delivered:**
- a. An image
- b. A source list
- c. (Calibration solutions and corrected visibilities)
-
+ a. An measurement set
"""
def __init__(self):
"""
@@ -165,8 +160,6 @@ class msss_imager_pipeline(control):
processed_ms_dir = self._long_baseline(input_mapfile,
target_mapfile, add_beam_tables)
-
-
# *********************************************************************
# (7) Get metadata
# create a parset with information that is available on the toplevel
@@ -205,8 +198,6 @@ class msss_imager_pipeline(control):
toplevel_meta_data_path=toplevel_meta_data_path,
product_type = "Correlated")
-
-
return 0
def _get_io_product_specs(self):
diff --git a/CEP/Pipeline/recipes/sip/master/long_baseline.py b/CEP/Pipeline/recipes/sip/master/long_baseline.py
index 637933d7f97b9ede209d6015b11d9febb7d66fde..a1456ac808aab50f6d9e9e54c6e7b0d5e22822cf 100644
--- a/CEP/Pipeline/recipes/sip/master/long_baseline.py
+++ b/CEP/Pipeline/recipes/sip/master/long_baseline.py
@@ -1,5 +1,5 @@
# LOFAR IMAGING PIPELINE
-# Prepare phase master
+# long basseline master
#
# 1. Create input files for individual nodes based on the input mapfile
# 2. Perform basic input parsing and input validation
@@ -7,7 +7,7 @@
# 4. validate performance
#
# Wouter Klijn
-# 2012
+# 2014
# klijn@astron.nl
# ------------------------------------------------------------------------------
from __future__ import with_statement
@@ -22,7 +22,7 @@ from lofarpipe.support.data_map import DataMap, MultiDataMap
class long_baseline(BaseRecipe, RemoteCommandRecipeMixIn):
"""
- Prepare phase master:
+ Long baseline master:
1. Validate input
2. Create mapfiles with input for work to be perform on the individual nodes
diff --git a/CEP/Pipeline/recipes/sip/nodes/long_baseline.py b/CEP/Pipeline/recipes/sip/nodes/long_baseline.py
index d897ed562aa25cd178bad8506103535c2fe98283..da84684044c6715d8967b3577e0bca6a3e83cd6e 100644
--- a/CEP/Pipeline/recipes/sip/nodes/long_baseline.py
+++ b/CEP/Pipeline/recipes/sip/nodes/long_baseline.py
@@ -1,7 +1,7 @@
# LOFAR IMAGING PIPELINE
# long_baseline node
# Wouter Klijn
-# 2012
+# 2014
# klijn@astron.nl
# -----------------------------------------------------------------------------
from __future__ import with_statement