for all chunks:
load chunk from disk (buffer the io)
wait for any previous gridding operations to finish up
deep copy chunk
call the correct gridding method (single / duel / quad with corrections) to operate on the
in parallel start up the gridder
if last chunk then normalize else continue on to start buffering the next chunk
#end for chunks
#end for ms
compact and finalize grids
write out to disk (either png or FITS)
optionally stitch together using montage
'''
for ms_index,ms in enumerate(ms_names):
print "NOW IMAGING %s" % ms
data = data_set_loader.data_set_loader(ms,read_jones_terms=parser_args['do_jones_corrections'])
data.read_head()
'''
Create convolution filter
'''
filter_creation_timer = timer.timer()
lambda_min = np.min(data._chan_wavelengths)
#as per Synthesis Imaging II, pg 24. w is maximum at low elevations and when baseline and source vectors have same azimuth (ie. parallel):
w_max = data._maximum_baseline_length / lambda_min
print "The maximum w (measured in wavelengths) is estimated to be", w_max
with filter_creation_timer:
conv = convolution_filter.convolution_filter(parser_args['conv_sup'],
parser_args['conv_oversamp'],parser_args['conv'],
"1D_AA" if parser_args['wplanes'] <= 1 else ("2D_WPROJ" if parser_args['use_back_end'] == 'CPU' else "1D_WPROJ"),parser_args['wplanes'],
parser_args['npix_l'],parser_args['npix_m'],
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
'''
import argparse
import numpy as np
import pylab
import math
from pyrap.tables import *
from helpers import data_set_loader
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="A small utility to write out a set of jones matricies to a new casa table")
parser.add_argument("input_ms", help="Name of MS to modify", type=str)
parser.add_argument("no_direction", help="Name of model FITS image",type=int)
parser_args = vars(parser.parse_args())
data = data_set_loader.data_set_loader(parser_args['input_ms'])
data.read_head()
assert(data._no_polarization_correlations == 4)
#compute set of timestamp indicies:
casa_ms_table = table(parser_args['input_ms'],ack=False,readonly=False)
time_window_centre = casa_ms_table.getcol("TIME")
time_indicies = np.zeros(casa_ms_table.nrows(),dtype=np.intp)
timestamp_index = 0
last_timestamp_time = time_window_centre[0]
print "FOUND %d ROWS IN MAIN TABLE" % casa_ms_table.nrows()
for r in range(0,casa_ms_table.nrows()):
current_time = time_window_centre[r]
if current_time > last_timestamp_time:
timestamp_index+=1
last_timestamp_time = current_time