Note: this code is not by any means production ready!
This is a demonstration of how to call a python step from DPPP. The data from DPPP is passed to python, where in this case stefcal is performed. The solutions are not yet saved.
The code is currently painfully slow, due to non-efficient reordering of the data.
Usage:: put the files dpstefcal.py, stefcal.py and DPPP.parset in a directory with a measurement set. Then just call DPPP.
Example output
MSReader
input MS: /data/scratch/dijkema/nikki/pydppp/tmp.MS
band 0
startchan: 0 (0)
nchan: 40 (0)
ncorrelations: 4
nbaselines: 946
ntimes: 105
time interval: 4.00556
DATA column: DATA
WEIGHT column: WEIGHT_SPECTRUM
autoweight: false
PythonStep pystep. class=DPStefcal
MSUpdater msout.
MS: /data/scratch/dijkema/nikki/pydppp/tmp.MS
datacolumn: DATA
weightcolumn WEIGHT_SPECTRUM
Compressed: no
flush: 0
Processing 105 time slots ...
0%....10....20....30....40....50....60....70....80....90....100%
Finishing processing ...
NaN/infinite data flagged in reader
===================================
Percentage of flagged visibilities detected per correlation:
[0,0,0,0] out of 3973200 visibilities [0%, 0%, 0%, 0%]
0 missing time slots were inserted
Time spent in getting data: 0.476607322693
Time spent in checking flags: 1.73473906517
Time spent in reordering data: 679.8027215
Time spent in actual stefcal: 11.7115616798
Total NDPPP time 694.62 real 692.83 user 0.67 system
0.1% MSReader
99.9% PythonStep pystep. class=DPStefcal
0.0% MSUpdater msout.